diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..30180eb --- /dev/null +++ b/.gitignore @@ -0,0 +1,125 @@ +**/.DS_Store +.vscode + + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/en/_build/ +docs/zh_cn/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + +data/ +data +.vscode +.idea +.DS_Store + +# custom +*.pkl +*.pkl.json +*.log.json +docs/modelzoo_statistics.md +mmdet/.mim +work_dirs/ +exps/ +exps + +# Pytorch +*.pth +*.py~ +*.sh~ diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..b4dd6e2 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Multimedia Computing Group, Nanjing University + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..45d7071 --- /dev/null +++ b/Makefile @@ -0,0 +1,24 @@ +adamixer-r50: + CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 PORT=29502 ./tools/dist_train.sh \ + configs/adamixer/adamixer_r50_1x_coco.py \ + 8 + +adamixer-r50-3x: + CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 PORT=29501 ./tools/dist_train.sh \ + configs/adamixer/adamixer_r50_300_query_crop_mstrain_480-800_3x_coco.py \ + 8 + +adamixer-r101-3x: + CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 PORT=29501 ./tools/dist_train.sh \ + configs/adamixer/adamixer_r101_300_query_crop_mstrain_480-800_3x_coco.py \ + 8 + +adamixer-dx101-3x: + CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 PORT=29501 ./tools/dist_train.sh \ + configs/adamixer/adamixer_dx101_300_query_crop_mstrain_480-800_3x_coco.py \ + 8 + +adamixer-swin_s-3x: + CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 PORT=29501 ./tools/dist_train.sh \ + configs/adamixer/adamixer_swin_s_300_query_crop_mstrain_480-800_3x_coco.py \ + 8 diff --git a/README.md b/README.md new file mode 100644 index 0000000..5cbbec1 --- /dev/null +++ b/README.md @@ -0,0 +1,267 @@ +# AdaMixer: A Fast-Converging Query-Based Object Detector [arxiv](https://arxiv.org/abs/2203.16507) + +> [**AdaMixer: A Fast-Converging Query-Based Object Detector**](https://arxiv.org/abs/2203.16507)
+> _accept to CVPR 2022 as an oral presentation_
+> [Ziteng Gao](https://sebgao.github.io), [Limin Wang](http://wanglimin.github.io/), Bing Han, Sheng Guo
Nanjing University, MYbank Ant Group + +[[slides]](adamixer_cvpr_2022_keynote.pdf) +[[arxiv]](https://arxiv.org/abs/2203.16507) + +## 📰 News +[2022.4.4] The code is available now. + +[2022.3.31] Code will be released in a few days (not too long). Pre-trained models will take some time to grant the permission of Ant Group to be available online. Please stay tuned or *watch this repo* for quick information. + +## ✨ Highlights +### 🆕 MLP-Mixer for Object Detection +To our best knowledge, we are the first to introduce the MLP-Mixer for Object detection. The MLP-Mixer is used in the DETR-like decoder in an adaptive and query-wise manner to enrich the adaptibility to varying objects across images. + +### ⚡️ Fast Converging DETR-like Architecture +AdaMixer enjoys fast convergence speed and reach up to 45.0 AP on COCO val within 12 epochs with only the architectural design improvement. Our method is compatible with other training improvements, like [multiple predictions from a query](https://github.com/megvii-research/AnchorDETR) and [denosing training](https://github.com/FengLi-ust/DN-DETR), which are expected to improve AdaMixer further (we have not tried yet). + +### 🧱 Simple Architecture, NO extra attentional encoders or FPNs required +Our AdaMixer does not hunger for extra attention encoders or explicit feature pyramid networks. Instead, we improve the query decoder in DETR-like detectors to keep the architecture as simple, efficient, and strong as possible. + + + + + +## ➡️ Guide to Our Code +Our code structure follows the MMDetection framework. To get started, please refer to mmdetection doc [get_started.md](docs/get_started.md) for installation. + +Our AdaMixer config file lies in [configs/adamixer](configs/adamixer) folder. You can start training our detectors with make targets in [Makefile](Makefile). + +The code of a AdaMixer decoder stage is in +[mmdet/models/roi_heads/bbox_heads/adamixer_decoder_stage.py](mmdet/models/roi_heads/bbox_heads/adamixer_decoder_stage.py). +The code of the 3D feature space sampling is in [mmdet/models/roi_heads/bbox_heads/sampling_3d_operator.py](mmdet/models/roi_heads/bbox_heads/sampling_3d_operator.py). +The code of the adaptive mixing process is in [mmdet/models/roi_heads/bbox_heads/adaptive_mixing_operator.py](mmdet/models/roi_heads/bbox_heads/adaptive_mixing_operator.py). + + +__NOTE:__ +1. Please use `mmcv_full==1.3.3` and `pytorch>=1.5.0` for correct reproduction ([#4](/../../issues/4), [#12](/../../issues/12)).~~Please make sure `init_weight` methods in `AdaptiveSamplingMixing` and `AdaptiveMixing` are called for correct initializations *AND* the initialized weights are not overrided by other methods (some MMCV versions may incur repeated initializations).~~ +2. We notice ~0.3 AP (42.7 AP reported in the paper) noise for AdaMixer w/ R50 with 1x training settings. + +## 🧪 Main Results +| detector | backbone | APval | APtest | +| :-------: | :------: | :---: | :----: | +| AdaMixer | R50 | 47.0 | 47.2 | +| AdaMixer | R101 | 48.0 | 48.1 | +| AdaMixer | X101-DCN| 49.5 | 49.3 | +| AdaMixer | Swin-S | 51.3 | 51.3 | + + +## ✏️ Citation +If you find AdaMixer useful in your research, please cite us using the following entry: +``` +@inproceedings{adamixer22cvpr, + author = {Ziteng Gao and + Limin Wang and + Bing Han and + Sheng Guo}, + title = {AdaMixer: A Fast-Converging Query-Based Object Detector}, + booktitle = {{CVPR}}, + year = {2022} +} +``` + + +## 👍 Acknowledgement +Thanks to [Zhan Tong](https://github.com/yztongzhan) and Zihua Xiong for their help. + + + + + + + + + + + + + + + + + + + + + + + + +## Original MMDetection README.md +_The following begins the original mmdetection README.md file_ +
+ +
+ +**News**: We released the technical report on [ArXiv](https://arxiv.org/abs/1906.07155). + +Documentation: https://mmdetection.readthedocs.io/ + +## Introduction + +English | [简体中文](README_zh-CN.md) + +MMDetection is an open source object detection toolbox based on PyTorch. It is +a part of the [OpenMMLab](https://openmmlab.com/) project. + +The master branch works with **PyTorch 1.3+**. +The old v1.x branch works with PyTorch 1.1 to 1.4, but v2.0 is strongly recommended for faster speed, higher performance, better design and more friendly usage. + +![demo image](resources/coco_test_12510.jpg) + +### Major features + +- **Modular Design** + + We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules. + +- **Support of multiple frameworks out of box** + + The toolbox directly supports popular and contemporary detection frameworks, *e.g.* Faster RCNN, Mask RCNN, RetinaNet, etc. + +- **High efficiency** + + All basic bbox and mask operations run on GPUs. The training speed is faster than or comparable to other codebases, including [Detectron2](https://github.com/facebookresearch/detectron2), [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) and [SimpleDet](https://github.com/TuSimple/simpledet). + +- **State of the art** + + The toolbox stems from the codebase developed by the *MMDet* team, who won [COCO Detection Challenge](http://cocodataset.org/#detection-leaderboard) in 2018, and we keep pushing it forward. + +Apart from MMDetection, we also released a library [mmcv](https://github.com/open-mmlab/mmcv) for computer vision research, which is heavily depended on by this toolbox. + +## License + +The mmdetection project is released under the [Apache 2.0 license](https://github.com/open-mmlab/mmdetection/blob/master/LICENSE). + +## Changelog + +v2.12.0 was released in 01/05/2021. +Please refer to [changelog.md](docs/changelog.md) for details and release history. +A comparison between v1.x and v2.0 codebases can be found in [compatibility.md](docs/compatibility.md). + +## Benchmark and model zoo + +Results and models are available in the [model zoo](docs/model_zoo.md). + +Supported backbones: + +- [x] ResNet (CVPR'2016) +- [x] ResNeXt (CVPR'2017) +- [x] VGG (ICLR'2015) +- [x] HRNet (CVPR'2019) +- [x] RegNet (CVPR'2020) +- [x] Res2Net (TPAMI'2020) +- [x] ResNeSt (ArXiv'2020) + +Supported methods: + +- [x] [RPN (NeurIPS'2015)](configs/rpn) +- [x] [Fast R-CNN (ICCV'2015)](configs/fast_rcnn) +- [x] [Faster R-CNN (NeurIPS'2015)](configs/faster_rcnn) +- [x] [Mask R-CNN (ICCV'2017)](configs/mask_rcnn) +- [x] [Cascade R-CNN (CVPR'2018)](configs/cascade_rcnn) +- [x] [Cascade Mask R-CNN (CVPR'2018)](configs/cascade_rcnn) +- [x] [SSD (ECCV'2016)](configs/ssd) +- [x] [RetinaNet (ICCV'2017)](configs/retinanet) +- [x] [GHM (AAAI'2019)](configs/ghm) +- [x] [Mask Scoring R-CNN (CVPR'2019)](configs/ms_rcnn) +- [x] [Double-Head R-CNN (CVPR'2020)](configs/double_heads) +- [x] [Hybrid Task Cascade (CVPR'2019)](configs/htc) +- [x] [Libra R-CNN (CVPR'2019)](configs/libra_rcnn) +- [x] [Guided Anchoring (CVPR'2019)](configs/guided_anchoring) +- [x] [FCOS (ICCV'2019)](configs/fcos) +- [x] [RepPoints (ICCV'2019)](configs/reppoints) +- [x] [Foveabox (TIP'2020)](configs/foveabox) +- [x] [FreeAnchor (NeurIPS'2019)](configs/free_anchor) +- [x] [NAS-FPN (CVPR'2019)](configs/nas_fpn) +- [x] [ATSS (CVPR'2020)](configs/atss) +- [x] [FSAF (CVPR'2019)](configs/fsaf) +- [x] [PAFPN (CVPR'2018)](configs/pafpn) +- [x] [Dynamic R-CNN (ECCV'2020)](configs/dynamic_rcnn) +- [x] [PointRend (CVPR'2020)](configs/point_rend) +- [x] [CARAFE (ICCV'2019)](configs/carafe/README.md) +- [x] [DCNv2 (CVPR'2019)](configs/dcn/README.md) +- [x] [Group Normalization (ECCV'2018)](configs/gn/README.md) +- [x] [Weight Standardization (ArXiv'2019)](configs/gn+ws/README.md) +- [x] [OHEM (CVPR'2016)](configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py) +- [x] [Soft-NMS (ICCV'2017)](configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py) +- [x] [Generalized Attention (ICCV'2019)](configs/empirical_attention/README.md) +- [x] [GCNet (ICCVW'2019)](configs/gcnet/README.md) +- [x] [Mixed Precision (FP16) Training (ArXiv'2017)](configs/fp16/README.md) +- [x] [InstaBoost (ICCV'2019)](configs/instaboost/README.md) +- [x] [GRoIE (ICPR'2020)](configs/groie/README.md) +- [x] [DetectoRS (ArXix'2020)](configs/detectors/README.md) +- [x] [Generalized Focal Loss (NeurIPS'2020)](configs/gfl/README.md) +- [x] [CornerNet (ECCV'2018)](configs/cornernet/README.md) +- [x] [Side-Aware Boundary Localization (ECCV'2020)](configs/sabl/README.md) +- [x] [YOLOv3 (ArXiv'2018)](configs/yolo/README.md) +- [x] [PAA (ECCV'2020)](configs/paa/README.md) +- [x] [YOLACT (ICCV'2019)](configs/yolact/README.md) +- [x] [CentripetalNet (CVPR'2020)](configs/centripetalnet/README.md) +- [x] [VFNet (ArXix'2020)](configs/vfnet/README.md) +- [x] [DETR (ECCV'2020)](configs/detr/README.md) +- [x] [Deformable DETR (ICLR'2021)](configs/deformable_detr/README.md) +- [x] [CascadeRPN (NeurIPS'2019)](configs/cascade_rpn/README.md) +- [x] [SCNet (AAAI'2021)](configs/scnet/README.md) +- [x] [AutoAssign (ArXix'2020)](configs/autoassign/README.md) +- [x] [YOLOF (CVPR'2021)](configs/yolof/README.md) + + +Some other methods are also supported in [projects using MMDetection](./docs/projects.md). + +## Installation + +Please refer to [get_started.md](docs/get_started.md) for installation. + +## Getting Started + +Please see [get_started.md](docs/get_started.md) for the basic usage of MMDetection. +We provide [colab tutorial](demo/MMDet_Tutorial.ipynb), and full guidance for quick run [with existing dataset](docs/1_exist_data_model.md) and [with new dataset](docs/2_new_data_model.md) for beginners. +There are also tutorials for [finetuning models](docs/tutorials/finetune.md), [adding new dataset](docs/tutorials/new_dataset.md), [designing data pipeline](docs/tutorials/data_pipeline.md), [customizing models](docs/tutorials/customize_models.md), [customizing runtime settings](docs/tutorials/customize_runtime.md) and [useful tools](docs/useful_tools.md). + +Please refer to [FAQ](docs/faq.md) for frequently asked questions. + +## Contributing + +We appreciate all contributions to improve MMDetection. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline. + +## Acknowledgement + +MMDetection is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. +We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new detectors. + +## Citation + +If you use this toolbox or benchmark in your research, please cite this project. + +``` +@article{mmdetection, + title = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark}, + author = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and + Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and + Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and + Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and + Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong + and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua}, + journal= {arXiv preprint arXiv:1906.07155}, + year={2019} +} +``` + +## Projects in OpenMMLab + +- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision. +- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark. +- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark. +- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection. +- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark. +- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark. +- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark. +- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark. +- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox. +- [MMOCR](https://github.com/open-mmlab/mmocr): A Comprehensive Toolbox for Text Detection, Recognition and Understanding. +- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox. diff --git a/README_zh-CN.md b/README_zh-CN.md new file mode 100644 index 0000000..5e60cca --- /dev/null +++ b/README_zh-CN.md @@ -0,0 +1,190 @@ +
+ +
+ +**新闻**: 我们在 [ArXiv](https://arxiv.org/abs/1906.07155) 上公开了技术报告。 + +文档: https://mmdetection.readthedocs.io/ + +## 简介 + +[English](README.md) | 简体中文 + +MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [OpenMMLab](https://openmmlab.com/) 项目的一部分。 + +主分支代码目前支持 PyTorch 1.3 以上的版本。 + +v1.x 的历史版本支持 PyTorch 1.1 到 1.4，但是我们强烈建议用户使用新的 2.x 的版本，新的版本速度更快，性能更高，有更优雅的代码设计，对用户使用也更加友好。 + +![demo image](resources/coco_test_12510.jpg) + +### 主要特性 + +- **模块化设计** + + MMDetection 将检测框架解耦成不同的模块组件，通过组合不同的模块组件，用户可以便捷地构建自定义的检测模型 + +- **丰富的即插即用的算法和模型** + + MMDetection 支持了众多主流的和最新的检测算法，例如 Faster R-CNN，Mask R-CNN，RetinaNet 等。 + +- **速度快** + + 基本的框和 mask 操作都实现了 GPU 版本，训练速度比其他代码库更快或者相当，包括 [Detectron2](https://github.com/facebookresearch/detectron2), [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) 和 [SimpleDet](https://github.com/TuSimple/simpledet)。 + +- **性能高** + + MMDetection 这个算法库源自于 COCO 2018 目标检测竞赛的冠军团队 *MMDet* 团队开发的代码，我们在之后持续进行了改进和提升。 + +除了 MMDetection 之外，我们还开源了计算机视觉基础库 [MMCV](https://github.com/open-mmlab/mmcv)，MMCV 是 MMDetection 的主要依赖。 + +## 开源许可证 + +该项目采用 [Apache 2.0 开源许可证](LICENSE)。 + +## 更新日志 + +最新的月度版本 v2.12.0 在 2021.05.01 发布。 +如果想了解更多版本更新细节和历史信息，请阅读[更新日志](docs/changelog.md)。 +在[兼容性说明文档](docs/compatibility.md)中我们提供了 1.x 和 2.0 版本的详细比较。 + +## 基准测试和模型库 + +测试结果和模型可以在[模型库](docs/model_zoo.md)中找到。 + +已支持的骨干网络： + +- [x] ResNet (CVPR'2016) +- [x] ResNeXt (CVPR'2017) +- [x] VGG (ICLR'2015) +- [x] HRNet (CVPR'2019) +- [x] RegNet (CVPR'2020) +- [x] Res2Net (TPAMI'2020) +- [x] ResNeSt (ArXiv'2020) + +已支持的算法： + +- [x] [RPN (NeurIPS'2015)](configs/rpn) +- [x] [Fast R-CNN (ICCV'2015)](configs/fast_rcnn) +- [x] [Faster R-CNN (NeurIPS'2015)](configs/faster_rcnn) +- [x] [Mask R-CNN (ICCV'2017)](configs/mask_rcnn) +- [x] [Cascade R-CNN (CVPR'2018)](configs/cascade_rcnn) +- [x] [Cascade Mask R-CNN (CVPR'2018)](configs/cascade_rcnn) +- [x] [SSD (ECCV'2016)](configs/ssd) +- [x] [RetinaNet (ICCV'2017)](configs/retinanet) +- [x] [GHM (AAAI'2019)](configs/ghm) +- [x] [Mask Scoring R-CNN (CVPR'2019)](configs/ms_rcnn) +- [x] [Double-Head R-CNN (CVPR'2020)](configs/double_heads) +- [x] [Hybrid Task Cascade (CVPR'2019)](configs/htc) +- [x] [Libra R-CNN (CVPR'2019)](configs/libra_rcnn) +- [x] [Guided Anchoring (CVPR'2019)](configs/guided_anchoring) +- [x] [FCOS (ICCV'2019)](configs/fcos) +- [x] [RepPoints (ICCV'2019)](configs/reppoints) +- [x] [Foveabox (TIP'2020)](configs/foveabox) +- [x] [FreeAnchor (NeurIPS'2019)](configs/free_anchor) +- [x] [NAS-FPN (CVPR'2019)](configs/nas_fpn) +- [x] [ATSS (CVPR'2020)](configs/atss) +- [x] [FSAF (CVPR'2019)](configs/fsaf) +- [x] [PAFPN (CVPR'2018)](configs/pafpn) +- [x] [Dynamic R-CNN (ECCV'2020)](configs/dynamic_rcnn) +- [x] [PointRend (CVPR'2020)](configs/point_rend) +- [x] [CARAFE (ICCV'2019)](configs/carafe/README.md) +- [x] [DCNv2 (CVPR'2019)](configs/dcn/README.md) +- [x] [Group Normalization (ECCV'2018)](configs/gn/README.md) +- [x] [Weight Standardization (ArXiv'2019)](configs/gn+ws/README.md) +- [x] [OHEM (CVPR'2016)](configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py) +- [x] [Soft-NMS (ICCV'2017)](configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py) +- [x] [Generalized Attention (ICCV'2019)](configs/empirical_attention/README.md) +- [x] [GCNet (ICCVW'2019)](configs/gcnet/README.md) +- [x] [Mixed Precision (FP16) Training (ArXiv'2017)](configs/fp16/README.md) +- [x] [InstaBoost (ICCV'2019)](configs/instaboost/README.md) +- [x] [GRoIE (ICPR'2020)](configs/groie/README.md) +- [x] [DetectoRS (ArXix'2020)](configs/detectors/README.md) +- [x] [Generalized Focal Loss (NeurIPS'2020)](configs/gfl/README.md) +- [x] [CornerNet (ECCV'2018)](configs/cornernet/README.md) +- [x] [Side-Aware Boundary Localization (ECCV'2020)](configs/sabl/README.md) +- [x] [YOLOv3 (ArXiv'2018)](configs/yolo/README.md) +- [x] [PAA (ECCV'2020)](configs/paa/README.md) +- [x] [YOLACT (ICCV'2019)](configs/yolact/README.md) +- [x] [CentripetalNet (CVPR'2020)](configs/centripetalnet/README.md) +- [x] [VFNet (ArXix'2020)](configs/vfnet/README.md) +- [x] [DETR (ECCV'2020)](configs/detr/README.md) +- [x] [Deformable DETR (ICLR'2021)](configs/deformable_detr/README.md) +- [x] [CascadeRPN (NeurIPS'2019)](configs/cascade_rpn/README.md) +- [x] [SCNet (AAAI'2021)](configs/scnet/README.md) +- [x] [AutoAssign (ArXix'2020)](configs/autoassign/README.md) +- [x] [YOLOF (CVPR'2021)](configs/yolof/README.md) + +我们在[基于 MMDetection 的项目](./docs/projects.md)中列举了一些其他的支持的算法。 + +## 安装 + +请参考[快速入门文档](docs/get_started.md)进行安装。 + +## 快速入门 + +请参考[快速入门文档](docs/get_started.md)学习 MMDetection 的基本使用。 +我们提供了 [colab 教程](demo/MMDet_Tutorial.ipynb)，也为新手提供了完整的运行教程，分别针对[已有数据集](docs/1_exist_data_model.md)和[新数据集](docs/2_new_data_model.md) 完整的使用指南 + +我们也提供了一些进阶教程，内容覆盖了 [finetune 模型](docs/tutorials/finetune.md)，[增加新数据集支持](docs/tutorials/new_dataset.md)，[设计新的数据预处理流程](docs/tutorials/data_pipeline.md)，[增加自定义模型](ocs/tutorials/customize_models.md)，[增加自定义的运行时配置](docs/tutorials/customize_runtime.md)，[常用工具和脚本](docs/useful_tools.md)。 + +如果遇到问题，请参考 [FAQ 页面](docs/faq.md)。 + +## 贡献指南 + +我们感谢所有的贡献者为改进和提升 MMDetection 所作出的努力。请参考[贡献指南](.github/CONTRIBUTING.md)来了解参与项目贡献的相关指引。 + +## 致谢 + +MMDetection 是一款由来自不同高校和企业的研发人员共同参与贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。 我们希望这个工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现已有算法并开发自己的新模型，从而不断为开源社区提供贡献。 + +## 引用 + +如果你在研究中使用了本项目的代码或者性能基准，请参考如下 bibtex 引用 MMDetection。 + +``` +@article{mmdetection, + title = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark}, + author = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and + Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and + Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and + Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and + Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong + and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua}, + journal= {arXiv preprint arXiv:1906.07155}, + year={2019} +} +``` + +## OpenMMLab 的其他项目 + +- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库 +- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱 +- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱 +- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台 +- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱 +- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱 +- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台 +- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱 +- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱 +- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包 +- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱 + +## 欢迎加入 OpenMMLab 社区 + +扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，加入 OpenMMLab 团队的 [官方交流 QQ 群](https://jq.qq.com/?_wv=1027&k=aCvMxdr3) + +
+ +
+ +我们会在 OpenMMLab 社区为大家 + +- 📢 分享 AI 框架的前沿核心技术 +- 💻 解读 PyTorch 常用模块源码 +- 📰 发布 OpenMMLab 的相关新闻 +- 🚀 介绍 OpenMMLab 开发的前沿算法 +- 🏃 获取更高效的问题答疑和意见反馈 +- 🔥 提供与各行各业开发者充分交流的平台 + +干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬 diff --git a/configs/_base_/datasets/cityscapes_detection.py b/configs/_base_/datasets/cityscapes_detection.py new file mode 100644 index 0000000..e341b59 --- /dev/null +++ b/configs/_base_/datasets/cityscapes_detection.py @@ -0,0 +1,56 @@ +# dataset settings +dataset_type = 'CityscapesDataset' +data_root = 'data/cityscapes/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', img_scale=[(2048, 800), (2048, 1024)], keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(2048, 1024), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=1, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=8, + dataset=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/instancesonly_filtered_gtFine_train.json', + img_prefix=data_root + 'leftImg8bit/train/', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/instancesonly_filtered_gtFine_val.json', + img_prefix=data_root + 'leftImg8bit/val/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/instancesonly_filtered_gtFine_test.json', + img_prefix=data_root + 'leftImg8bit/test/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='bbox') diff --git a/configs/_base_/datasets/cityscapes_instance.py b/configs/_base_/datasets/cityscapes_instance.py new file mode 100644 index 0000000..4e3c34e --- /dev/null +++ b/configs/_base_/datasets/cityscapes_instance.py @@ -0,0 +1,56 @@ +# dataset settings +dataset_type = 'CityscapesDataset' +data_root = 'data/cityscapes/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', img_scale=[(2048, 800), (2048, 1024)], keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(2048, 1024), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=1, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=8, + dataset=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/instancesonly_filtered_gtFine_train.json', + img_prefix=data_root + 'leftImg8bit/train/', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/instancesonly_filtered_gtFine_val.json', + img_prefix=data_root + 'leftImg8bit/val/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/instancesonly_filtered_gtFine_test.json', + img_prefix=data_root + 'leftImg8bit/test/', + pipeline=test_pipeline)) +evaluation = dict(metric=['bbox', 'segm']) diff --git a/configs/_base_/datasets/coco_detection.py b/configs/_base_/datasets/coco_detection.py new file mode 100644 index 0000000..2b1f382 --- /dev/null +++ b/configs/_base_/datasets/coco_detection.py @@ -0,0 +1,55 @@ +# dataset settings +dataset_type = 'CocoDataset' +data_root = '/home/fangyi/data/COCO/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), +# test=dict( +# type=dataset_type, +# ann_file=data_root + 'annotations/image_info_test-dev2017.json', +# img_prefix=data_root + 'test2017/', +# pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline) +) +evaluation = dict(interval=1, metric='bbox') diff --git a/configs/_base_/datasets/coco_detection_tiny.py b/configs/_base_/datasets/coco_detection_tiny.py new file mode 100644 index 0000000..bfb064d --- /dev/null +++ b/configs/_base_/datasets/coco_detection_tiny.py @@ -0,0 +1,55 @@ +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(800, 480), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(800, 480), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + _delete_=True, + type='RepeatDataset', + times=2, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017_subset_9929.json', + # ann_file=data_root + 'annotations/instances_train2017_subset_99.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline) + ), + val=dict( + type=dataset_type, + # ann_file=data_root + 'annotations/instances_val2017.json', + ann_file=data_root + 'annotations/instances_train2017_subset_99.json', + img_prefix=data_root + 'train2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='bbox') diff --git a/configs/_base_/datasets/coco_instance.py b/configs/_base_/datasets/coco_instance.py new file mode 100644 index 0000000..9901a85 --- /dev/null +++ b/configs/_base_/datasets/coco_instance.py @@ -0,0 +1,49 @@ +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(metric=['bbox', 'segm']) diff --git a/configs/_base_/datasets/coco_instance_semantic.py b/configs/_base_/datasets/coco_instance_semantic.py new file mode 100644 index 0000000..6c8bf07 --- /dev/null +++ b/configs/_base_/datasets/coco_instance_semantic.py @@ -0,0 +1,54 @@ +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='SegRescale', scale_factor=1 / 8), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + seg_prefix=data_root + 'stuffthingmaps/train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(metric=['bbox', 'segm']) diff --git a/configs/_base_/datasets/deepfashion.py b/configs/_base_/datasets/deepfashion.py new file mode 100644 index 0000000..308b4b2 --- /dev/null +++ b/configs/_base_/datasets/deepfashion.py @@ -0,0 +1,53 @@ +# dataset settings +dataset_type = 'DeepFashionDataset' +data_root = 'data/DeepFashion/In-shop/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(750, 1101), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(750, 1101), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + imgs_per_gpu=2, + workers_per_gpu=1, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/DeepFashion_segmentation_query.json', + img_prefix=data_root + 'Img/', + pipeline=train_pipeline, + data_root=data_root), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/DeepFashion_segmentation_query.json', + img_prefix=data_root + 'Img/', + pipeline=test_pipeline, + data_root=data_root), + test=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/DeepFashion_segmentation_gallery.json', + img_prefix=data_root + 'Img/', + pipeline=test_pipeline, + data_root=data_root)) +evaluation = dict(interval=5, metric=['bbox', 'segm']) diff --git a/configs/_base_/datasets/lvis_v0.5_instance.py b/configs/_base_/datasets/lvis_v0.5_instance.py new file mode 100644 index 0000000..207e005 --- /dev/null +++ b/configs/_base_/datasets/lvis_v0.5_instance.py @@ -0,0 +1,24 @@ +# dataset settings +_base_ = 'coco_instance.py' +dataset_type = 'LVISV05Dataset' +data_root = 'data/lvis_v0.5/' +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + _delete_=True, + type='ClassBalancedDataset', + oversample_thr=1e-3, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v0.5_train.json', + img_prefix=data_root + 'train2017/')), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v0.5_val.json', + img_prefix=data_root + 'val2017/'), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v0.5_val.json', + img_prefix=data_root + 'val2017/')) +evaluation = dict(metric=['bbox', 'segm']) diff --git a/configs/_base_/datasets/lvis_v1_instance.py b/configs/_base_/datasets/lvis_v1_instance.py new file mode 100644 index 0000000..be791ed --- /dev/null +++ b/configs/_base_/datasets/lvis_v1_instance.py @@ -0,0 +1,24 @@ +# dataset settings +_base_ = 'coco_instance.py' +dataset_type = 'LVISV1Dataset' +data_root = 'data/lvis_v1/' +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + _delete_=True, + type='ClassBalancedDataset', + oversample_thr=1e-3, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v1_train.json', + img_prefix=data_root)), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v1_val.json', + img_prefix=data_root), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v1_val.json', + img_prefix=data_root)) +evaluation = dict(metric=['bbox', 'segm']) diff --git a/configs/_base_/datasets/voc0712.py b/configs/_base_/datasets/voc0712.py new file mode 100644 index 0000000..ae09acd --- /dev/null +++ b/configs/_base_/datasets/voc0712.py @@ -0,0 +1,55 @@ +# dataset settings +dataset_type = 'VOCDataset' +data_root = 'data/VOCdevkit/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1000, 600), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1000, 600), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=3, + dataset=dict( + type=dataset_type, + ann_file=[ + data_root + 'VOC2007/ImageSets/Main/trainval.txt', + data_root + 'VOC2012/ImageSets/Main/trainval.txt' + ], + img_prefix=[data_root + 'VOC2007/', data_root + 'VOC2012/'], + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt', + img_prefix=data_root + 'VOC2007/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt', + img_prefix=data_root + 'VOC2007/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='mAP') diff --git a/configs/_base_/datasets/wider_face.py b/configs/_base_/datasets/wider_face.py new file mode 100644 index 0000000..d1d649b --- /dev/null +++ b/configs/_base_/datasets/wider_face.py @@ -0,0 +1,63 @@ +# dataset settings +dataset_type = 'WIDERFaceDataset' +data_root = 'data/WIDERFace/' +img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 4)), + dict( + type='MinIoURandomCrop', + min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), + min_crop_size=0.3), + dict(type='Resize', img_scale=(300, 300), keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(300, 300), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=60, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=2, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'train.txt', + img_prefix=data_root + 'WIDER_train/', + min_size=17, + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + 'val.txt', + img_prefix=data_root + 'WIDER_val/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'val.txt', + img_prefix=data_root + 'WIDER_val/', + pipeline=test_pipeline)) diff --git a/configs/_base_/default_runtime.py b/configs/_base_/default_runtime.py new file mode 100644 index 0000000..55097c5 --- /dev/null +++ b/configs/_base_/default_runtime.py @@ -0,0 +1,16 @@ +checkpoint_config = dict(interval=1) +# yapf:disable +log_config = dict( + interval=50, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ]) +# yapf:enable +custom_hooks = [dict(type='NumClassCheckHook')] + +dist_params = dict(backend='nccl') +log_level = 'INFO' +load_from = None +resume_from = None +workflow = [('train', 1)] diff --git a/configs/_base_/models/cascade_mask_rcnn_r50_fpn.py b/configs/_base_/models/cascade_mask_rcnn_r50_fpn.py new file mode 100644 index 0000000..9ef6673 --- /dev/null +++ b/configs/_base_/models/cascade_mask_rcnn_r50_fpn.py @@ -0,0 +1,196 @@ +# model settings +model = dict( + type='CascadeRCNN', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + roi_head=dict( + type='CascadeRoIHead', + num_stages=3, + stage_loss_weights=[1, 0.5, 0.25], + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=[ + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) + ], + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=[ + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.6, + neg_iou_thr=0.6, + min_pos_iou=0.6, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.7, + min_pos_iou=0.7, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False) + ]), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5))) diff --git a/configs/_base_/models/cascade_rcnn_r50_fpn.py b/configs/_base_/models/cascade_rcnn_r50_fpn.py new file mode 100644 index 0000000..cde2a96 --- /dev/null +++ b/configs/_base_/models/cascade_rcnn_r50_fpn.py @@ -0,0 +1,179 @@ +# model settings +model = dict( + type='CascadeRCNN', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + roi_head=dict( + type='CascadeRoIHead', + num_stages=3, + stage_loss_weights=[1, 0.5, 0.25], + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=[ + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) + ]), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=[ + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.6, + neg_iou_thr=0.6, + min_pos_iou=0.6, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.7, + min_pos_iou=0.7, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False) + ]), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100))) diff --git a/configs/_base_/models/fast_rcnn_r50_fpn.py b/configs/_base_/models/fast_rcnn_r50_fpn.py new file mode 100644 index 0000000..1099165 --- /dev/null +++ b/configs/_base_/models/fast_rcnn_r50_fpn.py @@ -0,0 +1,62 @@ +# model settings +model = dict( + type='FastRCNN', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False)), + test_cfg=dict( + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100))) diff --git a/configs/_base_/models/faster_rcnn_r50_caffe_c4.py b/configs/_base_/models/faster_rcnn_r50_caffe_c4.py new file mode 100644 index 0000000..6e18f71 --- /dev/null +++ b/configs/_base_/models/faster_rcnn_r50_caffe_c4.py @@ -0,0 +1,112 @@ +# model settings +norm_cfg = dict(type='BN', requires_grad=False) +model = dict( + type='FasterRCNN', + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=3, + strides=(1, 2, 2), + dilations=(1, 1, 1), + out_indices=(2, ), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=True, + style='caffe'), + rpn_head=dict( + type='RPNHead', + in_channels=1024, + feat_channels=1024, + anchor_generator=dict( + type='AnchorGenerator', + scales=[2, 4, 8, 16, 32], + ratios=[0.5, 1.0, 2.0], + strides=[16]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + shared_head=dict( + type='ResLayer', + depth=50, + stage=3, + stride=2, + dilation=1, + style='caffe', + norm_cfg=norm_cfg, + norm_eval=True), + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=1024, + featmap_strides=[16]), + bbox_head=dict( + type='BBoxHead', + with_avg_pool=True, + roi_feat_size=7, + in_channels=2048, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=12000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=6000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100))) diff --git a/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py b/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py new file mode 100644 index 0000000..5089f0e --- /dev/null +++ b/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py @@ -0,0 +1,103 @@ +# model settings +norm_cfg = dict(type='BN', requires_grad=False) +model = dict( + type='FasterRCNN', + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + strides=(1, 2, 2, 1), + dilations=(1, 1, 1, 2), + out_indices=(3, ), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=True, + style='caffe'), + rpn_head=dict( + type='RPNHead', + in_channels=2048, + feat_channels=2048, + anchor_generator=dict( + type='AnchorGenerator', + scales=[2, 4, 8, 16, 32], + ratios=[0.5, 1.0, 2.0], + strides=[16]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=2048, + featmap_strides=[16]), + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=2048, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=12000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms=dict(type='nms', iou_threshold=0.7), + nms_pre=6000, + max_per_img=1000, + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100))) diff --git a/configs/_base_/models/faster_rcnn_r50_fpn.py b/configs/_base_/models/faster_rcnn_r50_fpn.py new file mode 100644 index 0000000..c67137e --- /dev/null +++ b/configs/_base_/models/faster_rcnn_r50_fpn.py @@ -0,0 +1,108 @@ +# model settings +model = dict( + type='FasterRCNN', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100) + # soft-nms is also supported for rcnn testing + # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05) + )) diff --git a/configs/_base_/models/mask_rcnn_r50_caffe_c4.py b/configs/_base_/models/mask_rcnn_r50_caffe_c4.py new file mode 100644 index 0000000..eaae134 --- /dev/null +++ b/configs/_base_/models/mask_rcnn_r50_caffe_c4.py @@ -0,0 +1,123 @@ +# model settings +norm_cfg = dict(type='BN', requires_grad=False) +model = dict( + type='MaskRCNN', + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=3, + strides=(1, 2, 2), + dilations=(1, 1, 1), + out_indices=(2, ), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=True, + style='caffe'), + rpn_head=dict( + type='RPNHead', + in_channels=1024, + feat_channels=1024, + anchor_generator=dict( + type='AnchorGenerator', + scales=[2, 4, 8, 16, 32], + ratios=[0.5, 1.0, 2.0], + strides=[16]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + shared_head=dict( + type='ResLayer', + depth=50, + stage=3, + stride=2, + dilation=1, + style='caffe', + norm_cfg=norm_cfg, + norm_eval=True), + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=1024, + featmap_strides=[16]), + bbox_head=dict( + type='BBoxHead', + with_avg_pool=True, + roi_feat_size=7, + in_channels=2048, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + mask_roi_extractor=None, + mask_head=dict( + type='FCNMaskHead', + num_convs=0, + in_channels=2048, + conv_out_channels=256, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=12000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=14, + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=6000, + nms=dict(type='nms', iou_threshold=0.7), + max_per_img=1000, + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5))) diff --git a/configs/_base_/models/mask_rcnn_r50_fpn.py b/configs/_base_/models/mask_rcnn_r50_fpn.py new file mode 100644 index 0000000..6fc7908 --- /dev/null +++ b/configs/_base_/models/mask_rcnn_r50_fpn.py @@ -0,0 +1,120 @@ +# model settings +model = dict( + type='MaskRCNN', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5))) diff --git a/configs/_base_/models/retinanet_r50_fpn.py b/configs/_base_/models/retinanet_r50_fpn.py new file mode 100644 index 0000000..f3b97b3 --- /dev/null +++ b/configs/_base_/models/retinanet_r50_fpn.py @@ -0,0 +1,60 @@ +# model settings +model = dict( + type='RetinaNet', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_input', + num_outs=5), + bbox_head=dict( + type='RetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) diff --git a/configs/_base_/models/rpn_r50_caffe_c4.py b/configs/_base_/models/rpn_r50_caffe_c4.py new file mode 100644 index 0000000..9c32a55 --- /dev/null +++ b/configs/_base_/models/rpn_r50_caffe_c4.py @@ -0,0 +1,56 @@ +# model settings +model = dict( + type='RPN', + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=3, + strides=(1, 2, 2), + dilations=(1, 1, 1), + out_indices=(2, ), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe'), + neck=None, + rpn_head=dict( + type='RPNHead', + in_channels=1024, + feat_channels=1024, + anchor_generator=dict( + type='AnchorGenerator', + scales=[2, 4, 8, 16, 32], + ratios=[0.5, 1.0, 2.0], + strides=[16]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=12000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0))) diff --git a/configs/_base_/models/rpn_r50_fpn.py b/configs/_base_/models/rpn_r50_fpn.py new file mode 100644 index 0000000..b9b7618 --- /dev/null +++ b/configs/_base_/models/rpn_r50_fpn.py @@ -0,0 +1,58 @@ +# model settings +model = dict( + type='RPN', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0))) diff --git a/configs/_base_/models/ssd300.py b/configs/_base_/models/ssd300.py new file mode 100644 index 0000000..ef5cd72 --- /dev/null +++ b/configs/_base_/models/ssd300.py @@ -0,0 +1,51 @@ +# model settings +input_size = 300 +model = dict( + type='SingleStageDetector', + pretrained='open-mmlab://vgg16_caffe', + backbone=dict( + type='SSDVGG', + input_size=input_size, + depth=16, + with_last_pool=False, + ceil_mode=True, + out_indices=(3, 4), + out_feature_indices=(22, 34), + l2_norm_scale=20), + neck=None, + bbox_head=dict( + type='SSDHead', + in_channels=(512, 1024, 512, 256, 256, 256), + num_classes=80, + anchor_generator=dict( + type='SSDAnchorGenerator', + scale_major=False, + input_size=input_size, + basesize_ratio_range=(0.15, 0.9), + strides=[8, 16, 32, 64, 100, 300], + ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2])), + # model training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0., + ignore_iof_thr=-1, + gt_max_assign_all=False), + smoothl1_beta=1., + allowed_border=-1, + pos_weight=-1, + neg_pos_ratio=3, + debug=False), + test_cfg=dict( + nms_pre=1000, + nms=dict(type='nms', iou_threshold=0.45), + min_bbox_size=0, + score_thr=0.02, + max_per_img=200)) +cudnn_benchmark = True diff --git a/configs/_base_/schedules/schedule_1x.py b/configs/_base_/schedules/schedule_1x.py new file mode 100644 index 0000000..13b3783 --- /dev/null +++ b/configs/_base_/schedules/schedule_1x.py @@ -0,0 +1,11 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[8, 11]) +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/configs/_base_/schedules/schedule_20e.py b/configs/_base_/schedules/schedule_20e.py new file mode 100644 index 0000000..00e8590 --- /dev/null +++ b/configs/_base_/schedules/schedule_20e.py @@ -0,0 +1,11 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/_base_/schedules/schedule_2x.py b/configs/_base_/schedules/schedule_2x.py new file mode 100644 index 0000000..69dc9ee --- /dev/null +++ b/configs/_base_/schedules/schedule_2x.py @@ -0,0 +1,11 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/adamixer/README.md b/configs/adamixer/README.md new file mode 100644 index 0000000..e69de29 diff --git a/configs/adamixer/adamixer_Qrecycle_r50_1x_coco.py b/configs/adamixer/adamixer_Qrecycle_r50_1x_coco.py new file mode 100644 index 0000000..167d70d --- /dev/null +++ b/configs/adamixer/adamixer_Qrecycle_r50_1x_coco.py @@ -0,0 +1,214 @@ +def __get_debug(): + import os + return 'C_DEBUG' in os.environ + + +debug = __get_debug() + +log_interval = 100 + + +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +work_dir_prefix = 'work_dirs/adamixer_mmdet' + +IMAGE_SCALE = (1333, 800) + +# dataset settings +dataset_type = 'CocoDataset' +data_root = '/home/fangyi/data/COCO/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=IMAGE_SCALE, keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=IMAGE_SCALE, + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), +) +evaluation = dict(interval=1, metric='bbox') + + +num_stages = 6 +num_query = 100 +QUERY_DIM = 256 +FEAT_DIM = 256 +FF_DIM = 2048 + +# P_in for spatial mixing in the paper. +in_points_list = [32, ] * num_stages + +# P_out for spatial mixing in the paper. Also named as `out_points` in this codebase. +out_patterns_list = [128, ] * num_stages + +# G for the mixer grouping in the paper. Please distinguishe it from num_heads in MHSA in this codebase. +n_group_list = [4, ] * num_stages + +model = dict( + type='QueryBased', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='ChannelMapping', + in_channels=[256, 512, 1024, 2048], + out_channels=FEAT_DIM, + start_level=0, + add_extra_convs='on_output', + num_outs=4), + rpn_head=dict( + type='InitialQueryGenerator', + num_query=num_query, + content_dim=QUERY_DIM), + roi_head=dict( + type='AdaMixerDecoder_Qrecycle', + featmap_strides=[4, 8, 16, 32], + num_stages=num_stages, + stage_loss_weights=[1] * num_stages, + content_dim=QUERY_DIM, + recycle=[0, 5], + start_q=[0, 0, 1, 2, 4, 7], + end_q=[1, 2, 4, 7, 12, 20], + #start_q=[0, 1, 1, 2, 3, 5], + #end_q=[1, 2, 3, 5, 8, 13], + #start_q=[0, 1, 2, 2, 3, 4], + #end_q=[1, 2, 3, 4, 6, 9], + #start_q=[0, 1, 2, 3, 3, 4], + #end_q=[1, 2, 3, 4, 5, 7], + #start_q=[0, 1, 2, 3, 4, 4], + #end_q=[1, 2, 3, 4, 5, 6], + bbox_head=[ + dict( + type='AdaMixerDecoderStage', + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=1, + feedforward_channels=FF_DIM, + content_dim=QUERY_DIM, + feat_channels=FEAT_DIM, + dropout=0.0, + in_points=in_points_list[stage_idx], + out_points=out_patterns_list[stage_idx], + n_groups=n_group_list[stage_idx], + ffn_act_cfg=dict(type='ReLU', inplace=True), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + # NOTE: The following argument is a placeholder to hack the code. No real effects for decoding or updating bounding boxes. + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=False, + target_means=[0., 0., 0., 0.], + target_stds=[0.5, 0.5, 1., 1.])) for stage_idx in range(num_stages) + ]), + # training and testing settings + train_cfg=dict( + rpn=None, + rcnn=[ + dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0), + iou_cost=dict(type='IoUCost', iou_mode='giou', + weight=2.0)), + sampler=dict(type='PseudoSampler'), + pos_weight=1) for _ in range(num_stages) + ]), + test_cfg=dict(rpn=None, rcnn=dict(max_per_img=num_query))) + +# optimizer +optimizer = dict( + _delete_=True, + type='AdamW', + lr=0.000025, + weight_decay=0.0001, +) + +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=1.0, norm_type=2), +) + +# learning policy +lr_config = dict( + policy='step', + step=[8, 11], + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001 +) +runner = dict(type='EpochBasedRunner', max_epochs=12) + + +def __date(): + import datetime + return datetime.datetime.now().strftime('%m%d_%H%M') + + +log_config = dict( + interval=log_interval, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ] +) + +postfix = '_' + __date() + +find_unused_parameters = True + + +resume_from = None diff --git a/configs/adamixer/adamixer_Qrecycle_r50_1x_coco_optimize.py b/configs/adamixer/adamixer_Qrecycle_r50_1x_coco_optimize.py new file mode 100644 index 0000000..4ede382 --- /dev/null +++ b/configs/adamixer/adamixer_Qrecycle_r50_1x_coco_optimize.py @@ -0,0 +1,214 @@ +def __get_debug(): + import os + return 'C_DEBUG' in os.environ + + +debug = __get_debug() + +log_interval = 100 + + +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +work_dir_prefix = 'work_dirs/adamixer_mmdet' + +IMAGE_SCALE = (1333, 800) + +# dataset settings +dataset_type = 'CocoDataset' +data_root = '/home/fangyi/data/COCO/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=IMAGE_SCALE, keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=IMAGE_SCALE, + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), +) +evaluation = dict(interval=1, metric='bbox') + + +num_stages = 4 +num_query = 50 +QUERY_DIM = 256 +FEAT_DIM = 256 +FF_DIM = 2048 + +# P_in for spatial mixing in the paper. +in_points_list = [32, ] * num_stages + +# P_out for spatial mixing in the paper. Also named as `out_points` in this codebase. +out_patterns_list = [128, ] * num_stages + +# G for the mixer grouping in the paper. Please distinguishe it from num_heads in MHSA in this codebase. +n_group_list = [4, ] * num_stages + +model = dict( + type='QueryBased', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='ChannelMapping', + in_channels=[256, 512, 1024, 2048], + out_channels=FEAT_DIM, + start_level=0, + add_extra_convs='on_output', + num_outs=4), + rpn_head=dict( + type='InitialQueryGenerator', + num_query=num_query, + content_dim=QUERY_DIM), + roi_head=dict( + type='AdaMixerDecoder_Qrecycle_optimize', + featmap_strides=[4, 8, 16, 32], + num_stages=num_stages, + stage_loss_weights=[1] * num_stages, + content_dim=QUERY_DIM, + recycle=[0, 5], + start_q=[0, 0, 1, 2, 4, 7], + end_q=[1, 2, 4, 7, 12, 20], + #start_q=[0, 1, 1, 2, 3, 5], + #end_q=[1, 2, 3, 5, 8, 13], + #start_q=[0, 1, 2, 2, 3, 4], + #end_q=[1, 2, 3, 4, 6, 9], + #start_q=[0, 1, 2, 3, 3, 4], + #end_q=[1, 2, 3, 4, 5, 7], + #start_q=[0, 1, 2, 3, 4, 4], + #end_q=[1, 2, 3, 4, 5, 6], + bbox_head=[ + dict( + type='AdaMixerDecoderStage', + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=1, + feedforward_channels=FF_DIM, + content_dim=QUERY_DIM, + feat_channels=FEAT_DIM, + dropout=0.0, + in_points=in_points_list[stage_idx], + out_points=out_patterns_list[stage_idx], + n_groups=n_group_list[stage_idx], + ffn_act_cfg=dict(type='ReLU', inplace=True), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + # NOTE: The following argument is a placeholder to hack the code. No real effects for decoding or updating bounding boxes. + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=False, + target_means=[0., 0., 0., 0.], + target_stds=[0.5, 0.5, 1., 1.])) for stage_idx in range(num_stages) + ]), + # training and testing settings + train_cfg=dict( + rpn=None, + rcnn=[ + dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0), + iou_cost=dict(type='IoUCost', iou_mode='giou', + weight=2.0)), + sampler=dict(type='PseudoSampler'), + pos_weight=1) for _ in range(num_stages) + ]), + test_cfg=dict(rpn=None, rcnn=dict(max_per_img=num_query))) + +# optimizer +optimizer = dict( + _delete_=True, + type='AdamW', + lr=0.000025, + weight_decay=0.0001, +) + +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=1.0, norm_type=2), +) + +# learning policy +lr_config = dict( + policy='step', + step=[8, 11], + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001 +) +runner = dict(type='EpochBasedRunner', max_epochs=12) + + +def __date(): + import datetime + return datetime.datetime.now().strftime('%m%d_%H%M') + + +log_config = dict( + interval=log_interval, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ] +) + +postfix = '_' + __date() + +find_unused_parameters = True + + +resume_from = None diff --git a/configs/adamixer/adamixer_aq_r50_1x_coco_recycle12.py b/configs/adamixer/adamixer_aq_r50_1x_coco_recycle12.py new file mode 100644 index 0000000..e523609 --- /dev/null +++ b/configs/adamixer/adamixer_aq_r50_1x_coco_recycle12.py @@ -0,0 +1,204 @@ +def __get_debug(): + import os + return 'C_DEBUG' in os.environ + + +debug = __get_debug() + +log_interval = 100 + + +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +work_dir_prefix = 'work_dirs/adamixer_mmdet' + +IMAGE_SCALE = (1333, 800) + +# dataset settings +dataset_type = 'CocoDataset' +data_root = '/home/fangyi/data/COCO/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=IMAGE_SCALE, keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=IMAGE_SCALE, + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), +) +evaluation = dict(interval=1, metric='bbox') + + +num_stages = 6 +num_query = 100 +QUERY_DIM = 256 +FEAT_DIM = 256 +FF_DIM = 2048 + +# P_in for spatial mixing in the paper. +in_points_list = [32, ] * num_stages + +# P_out for spatial mixing in the paper. Also named as `out_points` in this codebase. +out_patterns_list = [128, ] * num_stages + +# G for the mixer grouping in the paper. Please distinguishe it from num_heads in MHSA in this codebase. +n_group_list = [4, ] * num_stages + +model = dict( + type='QueryBased', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='ChannelMapping', + in_channels=[256, 512, 1024, 2048], + out_channels=FEAT_DIM, + start_level=0, + add_extra_convs='on_output', + num_outs=4), + rpn_head=dict( + type='InitialQueryGenerator', + num_query=num_query, + content_dim=QUERY_DIM), + roi_head=dict( + type='AdaMixerDecoder_aq', + featmap_strides=[4, 8, 16, 32], + num_stages=num_stages, + stage_loss_weights=[1] * num_stages, + content_dim=QUERY_DIM, + recycle=[1, 2], + bbox_head=[ + dict( + type='AdaMixerDecoderStage', + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=1, + feedforward_channels=FF_DIM, + content_dim=QUERY_DIM, + feat_channels=FEAT_DIM, + dropout=0.0, + in_points=in_points_list[stage_idx], + out_points=out_patterns_list[stage_idx], + n_groups=n_group_list[stage_idx], + ffn_act_cfg=dict(type='ReLU', inplace=True), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + # NOTE: The following argument is a placeholder to hack the code. No real effects for decoding or updating bounding boxes. + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=False, + target_means=[0., 0., 0., 0.], + target_stds=[0.5, 0.5, 1., 1.])) for stage_idx in range(num_stages) + ]), + # training and testing settings + train_cfg=dict( + rpn=None, + rcnn=[ + dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0), + iou_cost=dict(type='IoUCost', iou_mode='giou', + weight=2.0)), + sampler=dict(type='PseudoSampler'), + pos_weight=1) for _ in range(num_stages) + ]), + test_cfg=dict(rpn=None, rcnn=dict(max_per_img=800))) + +# optimizer +optimizer = dict( + _delete_=True, + type='AdamW', + lr=0.000025, + weight_decay=0.0001, +) + +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=1.0, norm_type=2), +) + +# learning policy +lr_config = dict( + policy='step', + step=[8, 11], + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001 +) +runner = dict(type='EpochBasedRunner', max_epochs=12) + + +def __date(): + import datetime + return datetime.datetime.now().strftime('%m%d_%H%M') + + +log_config = dict( + interval=log_interval, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ] +) + +postfix = '_' + __date() + +find_unused_parameters = True + + +resume_from = None diff --git a/configs/adamixer/adamixer_aq_r50_1x_coco_recycle13.py b/configs/adamixer/adamixer_aq_r50_1x_coco_recycle13.py new file mode 100644 index 0000000..2473850 --- /dev/null +++ b/configs/adamixer/adamixer_aq_r50_1x_coco_recycle13.py @@ -0,0 +1,204 @@ +def __get_debug(): + import os + return 'C_DEBUG' in os.environ + + +debug = __get_debug() + +log_interval = 100 + + +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +work_dir_prefix = 'work_dirs/adamixer_mmdet' + +IMAGE_SCALE = (1333, 800) + +# dataset settings +dataset_type = 'CocoDataset' +data_root = '/home/fangyi/data/COCO/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=IMAGE_SCALE, keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=IMAGE_SCALE, + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), +) +evaluation = dict(interval=1, metric='bbox') + + +num_stages = 6 +num_query = 100 +QUERY_DIM = 256 +FEAT_DIM = 256 +FF_DIM = 2048 + +# P_in for spatial mixing in the paper. +in_points_list = [32, ] * num_stages + +# P_out for spatial mixing in the paper. Also named as `out_points` in this codebase. +out_patterns_list = [128, ] * num_stages + +# G for the mixer grouping in the paper. Please distinguishe it from num_heads in MHSA in this codebase. +n_group_list = [4, ] * num_stages + +model = dict( + type='QueryBased', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='ChannelMapping', + in_channels=[256, 512, 1024, 2048], + out_channels=FEAT_DIM, + start_level=0, + add_extra_convs='on_output', + num_outs=4), + rpn_head=dict( + type='InitialQueryGenerator', + num_query=num_query, + content_dim=QUERY_DIM), + roi_head=dict( + type='AdaMixerDecoder_aq', + featmap_strides=[4, 8, 16, 32], + num_stages=num_stages, + stage_loss_weights=[1] * num_stages, + content_dim=QUERY_DIM, + recycle=[1, 3], + bbox_head=[ + dict( + type='AdaMixerDecoderStage', + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=1, + feedforward_channels=FF_DIM, + content_dim=QUERY_DIM, + feat_channels=FEAT_DIM, + dropout=0.0, + in_points=in_points_list[stage_idx], + out_points=out_patterns_list[stage_idx], + n_groups=n_group_list[stage_idx], + ffn_act_cfg=dict(type='ReLU', inplace=True), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + # NOTE: The following argument is a placeholder to hack the code. No real effects for decoding or updating bounding boxes. + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=False, + target_means=[0., 0., 0., 0.], + target_stds=[0.5, 0.5, 1., 1.])) for stage_idx in range(num_stages) + ]), + # training and testing settings + train_cfg=dict( + rpn=None, + rcnn=[ + dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0), + iou_cost=dict(type='IoUCost', iou_mode='giou', + weight=2.0)), + sampler=dict(type='PseudoSampler'), + pos_weight=1) for _ in range(num_stages) + ]), + test_cfg=dict(rpn=None, rcnn=dict(max_per_img=800))) + +# optimizer +optimizer = dict( + _delete_=True, + type='AdamW', + lr=0.000025, + weight_decay=0.0001, +) + +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=1.0, norm_type=2), +) + +# learning policy +lr_config = dict( + policy='step', + step=[8, 11], + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001 +) +runner = dict(type='EpochBasedRunner', max_epochs=12) + + +def __date(): + import datetime + return datetime.datetime.now().strftime('%m%d_%H%M') + + +log_config = dict( + interval=log_interval, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ] +) + +postfix = '_' + __date() + +find_unused_parameters = True + + +resume_from = None diff --git a/configs/adamixer/adamixer_aq_r50_1x_coco_recycle14.py b/configs/adamixer/adamixer_aq_r50_1x_coco_recycle14.py new file mode 100644 index 0000000..c1d6a31 --- /dev/null +++ b/configs/adamixer/adamixer_aq_r50_1x_coco_recycle14.py @@ -0,0 +1,204 @@ +def __get_debug(): + import os + return 'C_DEBUG' in os.environ + + +debug = __get_debug() + +log_interval = 100 + + +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +work_dir_prefix = 'work_dirs/adamixer_mmdet' + +IMAGE_SCALE = (1333, 800) + +# dataset settings +dataset_type = 'CocoDataset' +data_root = '/home/fangyi/data/COCO/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=IMAGE_SCALE, keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=IMAGE_SCALE, + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), +) +evaluation = dict(interval=1, metric='bbox') + + +num_stages = 6 +num_query = 100 +QUERY_DIM = 256 +FEAT_DIM = 256 +FF_DIM = 2048 + +# P_in for spatial mixing in the paper. +in_points_list = [32, ] * num_stages + +# P_out for spatial mixing in the paper. Also named as `out_points` in this codebase. +out_patterns_list = [128, ] * num_stages + +# G for the mixer grouping in the paper. Please distinguishe it from num_heads in MHSA in this codebase. +n_group_list = [4, ] * num_stages + +model = dict( + type='QueryBased', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='ChannelMapping', + in_channels=[256, 512, 1024, 2048], + out_channels=FEAT_DIM, + start_level=0, + add_extra_convs='on_output', + num_outs=4), + rpn_head=dict( + type='InitialQueryGenerator', + num_query=num_query, + content_dim=QUERY_DIM), + roi_head=dict( + type='AdaMixerDecoder_aq', + featmap_strides=[4, 8, 16, 32], + num_stages=num_stages, + stage_loss_weights=[1] * num_stages, + content_dim=QUERY_DIM, + recycle=[1, 4], + bbox_head=[ + dict( + type='AdaMixerDecoderStage', + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=1, + feedforward_channels=FF_DIM, + content_dim=QUERY_DIM, + feat_channels=FEAT_DIM, + dropout=0.0, + in_points=in_points_list[stage_idx], + out_points=out_patterns_list[stage_idx], + n_groups=n_group_list[stage_idx], + ffn_act_cfg=dict(type='ReLU', inplace=True), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + # NOTE: The following argument is a placeholder to hack the code. No real effects for decoding or updating bounding boxes. + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=False, + target_means=[0., 0., 0., 0.], + target_stds=[0.5, 0.5, 1., 1.])) for stage_idx in range(num_stages) + ]), + # training and testing settings + train_cfg=dict( + rpn=None, + rcnn=[ + dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0), + iou_cost=dict(type='IoUCost', iou_mode='giou', + weight=2.0)), + sampler=dict(type='PseudoSampler'), + pos_weight=1) for _ in range(num_stages) + ]), + test_cfg=dict(rpn=None, rcnn=dict(max_per_img=800))) + +# optimizer +optimizer = dict( + _delete_=True, + type='AdamW', + lr=0.000025, + weight_decay=0.0001, +) + +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=1.0, norm_type=2), +) + +# learning policy +lr_config = dict( + policy='step', + step=[8, 11], + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001 +) +runner = dict(type='EpochBasedRunner', max_epochs=12) + + +def __date(): + import datetime + return datetime.datetime.now().strftime('%m%d_%H%M') + + +log_config = dict( + interval=log_interval, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ] +) + +postfix = '_' + __date() + +find_unused_parameters = True + + +resume_from = None diff --git a/configs/adamixer/adamixer_aq_r50_1x_coco_recycle15.py b/configs/adamixer/adamixer_aq_r50_1x_coco_recycle15.py new file mode 100644 index 0000000..bdf35af --- /dev/null +++ b/configs/adamixer/adamixer_aq_r50_1x_coco_recycle15.py @@ -0,0 +1,204 @@ +def __get_debug(): + import os + return 'C_DEBUG' in os.environ + + +debug = __get_debug() + +log_interval = 100 + + +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +work_dir_prefix = 'work_dirs/adamixer_mmdet' + +IMAGE_SCALE = (1333, 800) + +# dataset settings +dataset_type = 'CocoDataset' +data_root = '/home/fangyi/data/COCO/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=IMAGE_SCALE, keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=IMAGE_SCALE, + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), +) +evaluation = dict(interval=1, metric='bbox') + + +num_stages = 6 +num_query = 100 +QUERY_DIM = 256 +FEAT_DIM = 256 +FF_DIM = 2048 + +# P_in for spatial mixing in the paper. +in_points_list = [32, ] * num_stages + +# P_out for spatial mixing in the paper. Also named as `out_points` in this codebase. +out_patterns_list = [128, ] * num_stages + +# G for the mixer grouping in the paper. Please distinguishe it from num_heads in MHSA in this codebase. +n_group_list = [4, ] * num_stages + +model = dict( + type='QueryBased', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='ChannelMapping', + in_channels=[256, 512, 1024, 2048], + out_channels=FEAT_DIM, + start_level=0, + add_extra_convs='on_output', + num_outs=4), + rpn_head=dict( + type='InitialQueryGenerator', + num_query=num_query, + content_dim=QUERY_DIM), + roi_head=dict( + type='AdaMixerDecoder_aq', + featmap_strides=[4, 8, 16, 32], + num_stages=num_stages, + stage_loss_weights=[1] * num_stages, + content_dim=QUERY_DIM, + recycle=[1, 5], + bbox_head=[ + dict( + type='AdaMixerDecoderStage', + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=1, + feedforward_channels=FF_DIM, + content_dim=QUERY_DIM, + feat_channels=FEAT_DIM, + dropout=0.0, + in_points=in_points_list[stage_idx], + out_points=out_patterns_list[stage_idx], + n_groups=n_group_list[stage_idx], + ffn_act_cfg=dict(type='ReLU', inplace=True), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + # NOTE: The following argument is a placeholder to hack the code. No real effects for decoding or updating bounding boxes. + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=False, + target_means=[0., 0., 0., 0.], + target_stds=[0.5, 0.5, 1., 1.])) for stage_idx in range(num_stages) + ]), + # training and testing settings + train_cfg=dict( + rpn=None, + rcnn=[ + dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0), + iou_cost=dict(type='IoUCost', iou_mode='giou', + weight=2.0)), + sampler=dict(type='PseudoSampler'), + pos_weight=1) for _ in range(num_stages) + ]), + test_cfg=dict(rpn=None, rcnn=dict(max_per_img=1600))) + +# optimizer +optimizer = dict( + _delete_=True, + type='AdamW', + lr=0.000025, + weight_decay=0.0001, +) + +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=1.0, norm_type=2), +) + +# learning policy +lr_config = dict( + policy='step', + step=[8, 11], + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001 +) +runner = dict(type='EpochBasedRunner', max_epochs=12) + + +def __date(): + import datetime + return datetime.datetime.now().strftime('%m%d_%H%M') + + +log_config = dict( + interval=log_interval, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ] +) + +postfix = '_' + __date() + +find_unused_parameters = True + + +resume_from = None diff --git a/configs/adamixer/adamixer_aq_r50_1x_coco_recycle25.py b/configs/adamixer/adamixer_aq_r50_1x_coco_recycle25.py new file mode 100644 index 0000000..3b8dfc5 --- /dev/null +++ b/configs/adamixer/adamixer_aq_r50_1x_coco_recycle25.py @@ -0,0 +1,204 @@ +def __get_debug(): + import os + return 'C_DEBUG' in os.environ + + +debug = __get_debug() + +log_interval = 100 + + +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +work_dir_prefix = 'work_dirs/adamixer_mmdet' + +IMAGE_SCALE = (1333, 800) + +# dataset settings +dataset_type = 'CocoDataset' +data_root = '/home/fangyi/data/COCO/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=IMAGE_SCALE, keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=IMAGE_SCALE, + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), +) +evaluation = dict(interval=1, metric='bbox') + + +num_stages = 6 +num_query = 100 +QUERY_DIM = 256 +FEAT_DIM = 256 +FF_DIM = 2048 + +# P_in for spatial mixing in the paper. +in_points_list = [32, ] * num_stages + +# P_out for spatial mixing in the paper. Also named as `out_points` in this codebase. +out_patterns_list = [128, ] * num_stages + +# G for the mixer grouping in the paper. Please distinguishe it from num_heads in MHSA in this codebase. +n_group_list = [4, ] * num_stages + +model = dict( + type='QueryBased', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='ChannelMapping', + in_channels=[256, 512, 1024, 2048], + out_channels=FEAT_DIM, + start_level=0, + add_extra_convs='on_output', + num_outs=4), + rpn_head=dict( + type='InitialQueryGenerator', + num_query=num_query, + content_dim=QUERY_DIM), + roi_head=dict( + type='AdaMixerDecoder_aq', + featmap_strides=[4, 8, 16, 32], + num_stages=num_stages, + stage_loss_weights=[1] * num_stages, + content_dim=QUERY_DIM, + recycle=[2, 5], + bbox_head=[ + dict( + type='AdaMixerDecoderStage', + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=1, + feedforward_channels=FF_DIM, + content_dim=QUERY_DIM, + feat_channels=FEAT_DIM, + dropout=0.0, + in_points=in_points_list[stage_idx], + out_points=out_patterns_list[stage_idx], + n_groups=n_group_list[stage_idx], + ffn_act_cfg=dict(type='ReLU', inplace=True), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + # NOTE: The following argument is a placeholder to hack the code. No real effects for decoding or updating bounding boxes. + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=False, + target_means=[0., 0., 0., 0.], + target_stds=[0.5, 0.5, 1., 1.])) for stage_idx in range(num_stages) + ]), + # training and testing settings + train_cfg=dict( + rpn=None, + rcnn=[ + dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0), + iou_cost=dict(type='IoUCost', iou_mode='giou', + weight=2.0)), + sampler=dict(type='PseudoSampler'), + pos_weight=1) for _ in range(num_stages) + ]), + test_cfg=dict(rpn=None, rcnn=dict(max_per_img=800))) + +# optimizer +optimizer = dict( + _delete_=True, + type='AdamW', + lr=0.000025, + weight_decay=0.0001, +) + +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=1.0, norm_type=2), +) + +# learning policy +lr_config = dict( + policy='step', + step=[8, 11], + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001 +) +runner = dict(type='EpochBasedRunner', max_epochs=12) + + +def __date(): + import datetime + return datetime.datetime.now().strftime('%m%d_%H%M') + + +log_config = dict( + interval=log_interval, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ] +) + +postfix = '_' + __date() + +find_unused_parameters = True + + +resume_from = None diff --git a/configs/adamixer/adamixer_aq_r50_1x_coco_recycle35.py b/configs/adamixer/adamixer_aq_r50_1x_coco_recycle35.py new file mode 100644 index 0000000..53316a4 --- /dev/null +++ b/configs/adamixer/adamixer_aq_r50_1x_coco_recycle35.py @@ -0,0 +1,204 @@ +def __get_debug(): + import os + return 'C_DEBUG' in os.environ + + +debug = __get_debug() + +log_interval = 100 + + +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +work_dir_prefix = 'work_dirs/adamixer_mmdet' + +IMAGE_SCALE = (1333, 800) + +# dataset settings +dataset_type = 'CocoDataset' +data_root = '/home/fangyi/data/COCO/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=IMAGE_SCALE, keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=IMAGE_SCALE, + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), +) +evaluation = dict(interval=1, metric='bbox') + + +num_stages = 6 +num_query = 100 +QUERY_DIM = 256 +FEAT_DIM = 256 +FF_DIM = 2048 + +# P_in for spatial mixing in the paper. +in_points_list = [32, ] * num_stages + +# P_out for spatial mixing in the paper. Also named as `out_points` in this codebase. +out_patterns_list = [128, ] * num_stages + +# G for the mixer grouping in the paper. Please distinguishe it from num_heads in MHSA in this codebase. +n_group_list = [4, ] * num_stages + +model = dict( + type='QueryBased', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='ChannelMapping', + in_channels=[256, 512, 1024, 2048], + out_channels=FEAT_DIM, + start_level=0, + add_extra_convs='on_output', + num_outs=4), + rpn_head=dict( + type='InitialQueryGenerator', + num_query=num_query, + content_dim=QUERY_DIM), + roi_head=dict( + type='AdaMixerDecoder_aq', + featmap_strides=[4, 8, 16, 32], + num_stages=num_stages, + stage_loss_weights=[1] * num_stages, + content_dim=QUERY_DIM, + recycle=[3, 5], + bbox_head=[ + dict( + type='AdaMixerDecoderStage', + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=1, + feedforward_channels=FF_DIM, + content_dim=QUERY_DIM, + feat_channels=FEAT_DIM, + dropout=0.0, + in_points=in_points_list[stage_idx], + out_points=out_patterns_list[stage_idx], + n_groups=n_group_list[stage_idx], + ffn_act_cfg=dict(type='ReLU', inplace=True), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + # NOTE: The following argument is a placeholder to hack the code. No real effects for decoding or updating bounding boxes. + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=False, + target_means=[0., 0., 0., 0.], + target_stds=[0.5, 0.5, 1., 1.])) for stage_idx in range(num_stages) + ]), + # training and testing settings + train_cfg=dict( + rpn=None, + rcnn=[ + dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0), + iou_cost=dict(type='IoUCost', iou_mode='giou', + weight=2.0)), + sampler=dict(type='PseudoSampler'), + pos_weight=1) for _ in range(num_stages) + ]), + test_cfg=dict(rpn=None, rcnn=dict(max_per_img=num_query))) + +# optimizer +optimizer = dict( + _delete_=True, + type='AdamW', + lr=0.000025, + weight_decay=0.0001, +) + +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=1.0, norm_type=2), +) + +# learning policy +lr_config = dict( + policy='step', + step=[8, 11], + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001 +) +runner = dict(type='EpochBasedRunner', max_epochs=12) + + +def __date(): + import datetime + return datetime.datetime.now().strftime('%m%d_%H%M') + + +log_config = dict( + interval=log_interval, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ] +) + +postfix = '_' + __date() + +find_unused_parameters = True + + +resume_from = None diff --git a/configs/adamixer/adamixer_aq_r50_1x_coco_recycle45.py b/configs/adamixer/adamixer_aq_r50_1x_coco_recycle45.py new file mode 100644 index 0000000..425634a --- /dev/null +++ b/configs/adamixer/adamixer_aq_r50_1x_coco_recycle45.py @@ -0,0 +1,204 @@ +def __get_debug(): + import os + return 'C_DEBUG' in os.environ + + +debug = __get_debug() + +log_interval = 100 + + +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +work_dir_prefix = 'work_dirs/adamixer_mmdet' + +IMAGE_SCALE = (1333, 800) + +# dataset settings +dataset_type = 'CocoDataset' +data_root = '/home/fangyi/data/COCO/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=IMAGE_SCALE, keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=IMAGE_SCALE, + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), +) +evaluation = dict(interval=1, metric='bbox') + + +num_stages = 6 +num_query = 100 +QUERY_DIM = 256 +FEAT_DIM = 256 +FF_DIM = 2048 + +# P_in for spatial mixing in the paper. +in_points_list = [32, ] * num_stages + +# P_out for spatial mixing in the paper. Also named as `out_points` in this codebase. +out_patterns_list = [128, ] * num_stages + +# G for the mixer grouping in the paper. Please distinguishe it from num_heads in MHSA in this codebase. +n_group_list = [4, ] * num_stages + +model = dict( + type='QueryBased', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='ChannelMapping', + in_channels=[256, 512, 1024, 2048], + out_channels=FEAT_DIM, + start_level=0, + add_extra_convs='on_output', + num_outs=4), + rpn_head=dict( + type='InitialQueryGenerator', + num_query=num_query, + content_dim=QUERY_DIM), + roi_head=dict( + type='AdaMixerDecoder_aq', + featmap_strides=[4, 8, 16, 32], + num_stages=num_stages, + stage_loss_weights=[1] * num_stages, + content_dim=QUERY_DIM, + recycle=[4, 5], + bbox_head=[ + dict( + type='AdaMixerDecoderStage', + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=1, + feedforward_channels=FF_DIM, + content_dim=QUERY_DIM, + feat_channels=FEAT_DIM, + dropout=0.0, + in_points=in_points_list[stage_idx], + out_points=out_patterns_list[stage_idx], + n_groups=n_group_list[stage_idx], + ffn_act_cfg=dict(type='ReLU', inplace=True), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + # NOTE: The following argument is a placeholder to hack the code. No real effects for decoding or updating bounding boxes. + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=False, + target_means=[0., 0., 0., 0.], + target_stds=[0.5, 0.5, 1., 1.])) for stage_idx in range(num_stages) + ]), + # training and testing settings + train_cfg=dict( + rpn=None, + rcnn=[ + dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0), + iou_cost=dict(type='IoUCost', iou_mode='giou', + weight=2.0)), + sampler=dict(type='PseudoSampler'), + pos_weight=1) for _ in range(num_stages) + ]), + test_cfg=dict(rpn=None, rcnn=dict(max_per_img=200))) + +# optimizer +optimizer = dict( + _delete_=True, + type='AdamW', + lr=0.000025, + weight_decay=0.0001, +) + +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=1.0, norm_type=2), +) + +# learning policy +lr_config = dict( + policy='step', + step=[8, 11], + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001 +) +runner = dict(type='EpochBasedRunner', max_epochs=12) + + +def __date(): + import datetime + return datetime.datetime.now().strftime('%m%d_%H%M') + + +log_config = dict( + interval=log_interval, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ] +) + +postfix = '_' + __date() + +find_unused_parameters = True + + +resume_from = None diff --git a/configs/adamixer/adamixer_aql_r50_1x_coco.py b/configs/adamixer/adamixer_aql_r50_1x_coco.py new file mode 100644 index 0000000..4b20986 --- /dev/null +++ b/configs/adamixer/adamixer_aql_r50_1x_coco.py @@ -0,0 +1,204 @@ +def __get_debug(): + import os + return 'C_DEBUG' in os.environ + + +debug = __get_debug() + +log_interval = 100 + + +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +work_dir_prefix = 'work_dirs/adamixer_mmdet' + +IMAGE_SCALE = (1333, 800) + +# dataset settings +dataset_type = 'CocoDataset' +data_root = '/home/fangyi/data/COCO/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=IMAGE_SCALE, keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=IMAGE_SCALE, + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), +) +evaluation = dict(interval=1, metric='bbox') + + +num_stages = 6 +num_query = 100 +QUERY_DIM = 256 +FEAT_DIM = 256 +FF_DIM = 2048 + +# P_in for spatial mixing in the paper. +in_points_list = [32, ] * num_stages + +# P_out for spatial mixing in the paper. Also named as `out_points` in this codebase. +out_patterns_list = [128, ] * num_stages + +# G for the mixer grouping in the paper. Please distinguishe it from num_heads in MHSA in this codebase. +n_group_list = [4, ] * num_stages + +model = dict( + type='QueryBased', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='ChannelMapping', + in_channels=[256, 512, 1024, 2048], + out_channels=FEAT_DIM, + start_level=0, + add_extra_convs='on_output', + num_outs=4), + rpn_head=dict( + type='InitialQueryGenerator', + num_query=num_query, + content_dim=QUERY_DIM), + roi_head=dict( + type='AdaMixerDecoder_aql', + featmap_strides=[4, 8, 16, 32], + num_stages=num_stages, + stage_loss_weights=[1] * num_stages, + content_dim=QUERY_DIM, + recycle=[0, 5], + bbox_head=[ + dict( + type='AdaMixerDecoderStage', + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=1, + feedforward_channels=FF_DIM, + content_dim=QUERY_DIM, + feat_channels=FEAT_DIM, + dropout=0.0, + in_points=in_points_list[stage_idx], + out_points=out_patterns_list[stage_idx], + n_groups=n_group_list[stage_idx], + ffn_act_cfg=dict(type='ReLU', inplace=True), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + # NOTE: The following argument is a placeholder to hack the code. No real effects for decoding or updating bounding boxes. + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=False, + target_means=[0., 0., 0., 0.], + target_stds=[0.5, 0.5, 1., 1.])) for stage_idx in range(num_stages) + ]), + # training and testing settings + train_cfg=dict( + rpn=None, + rcnn=[ + dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0), + iou_cost=dict(type='IoUCost', iou_mode='giou', + weight=2.0)), + sampler=dict(type='PseudoSampler'), + pos_weight=1) for _ in range(num_stages) + ]), + test_cfg=dict(rpn=None, rcnn=dict(max_per_img=num_query))) + +# optimizer +optimizer = dict( + _delete_=True, + type='AdamW', + lr=0.000025, + weight_decay=0.0001, +) + +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=1.0, norm_type=2), +) + +# learning policy +lr_config = dict( + policy='step', + step=[8, 11], + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001 +) +runner = dict(type='EpochBasedRunner', max_epochs=12) + + +def __date(): + import datetime + return datetime.datetime.now().strftime('%m%d_%H%M') + + +log_config = dict( + interval=log_interval, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ] +) + +postfix = '_' + __date() + +find_unused_parameters = True + + +resume_from = None diff --git a/configs/adamixer/adamixer_caq_r50_1x_coco.py b/configs/adamixer/adamixer_caq_r50_1x_coco.py new file mode 100644 index 0000000..6be53c7 --- /dev/null +++ b/configs/adamixer/adamixer_caq_r50_1x_coco.py @@ -0,0 +1,204 @@ +def __get_debug(): + import os + return 'C_DEBUG' in os.environ + + +debug = __get_debug() + +log_interval = 100 + + +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +work_dir_prefix = 'work_dirs/adamixer_mmdet' + +IMAGE_SCALE = (1333, 800) + +# dataset settings +dataset_type = 'CocoDataset' +data_root = '/home/fangyi/data/COCO/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=IMAGE_SCALE, keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=IMAGE_SCALE, + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), +) +evaluation = dict(interval=1, metric='bbox') + + +num_stages = 6 +num_query = 100 +QUERY_DIM = 256 +FEAT_DIM = 256 +FF_DIM = 2048 + +# P_in for spatial mixing in the paper. +in_points_list = [32, ] * num_stages + +# P_out for spatial mixing in the paper. Also named as `out_points` in this codebase. +out_patterns_list = [128, ] * num_stages + +# G for the mixer grouping in the paper. Please distinguishe it from num_heads in MHSA in this codebase. +n_group_list = [4, ] * num_stages + +model = dict( + type='QueryBased', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='ChannelMapping', + in_channels=[256, 512, 1024, 2048], + out_channels=FEAT_DIM, + start_level=0, + add_extra_convs='on_output', + num_outs=4), + rpn_head=dict( + type='InitialQueryGenerator', + num_query=num_query, + content_dim=QUERY_DIM), + roi_head=dict( + type='AdaMixerDecoder_caq', + featmap_strides=[4, 8, 16, 32], + num_stages=num_stages, + stage_loss_weights=[1] * num_stages, + content_dim=QUERY_DIM, + recycle=[0, 4], + bbox_head=[ + dict( + type='AdaMixerDecoderStage', + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=1, + feedforward_channels=FF_DIM, + content_dim=QUERY_DIM, + feat_channels=FEAT_DIM, + dropout=0.0, + in_points=in_points_list[stage_idx], + out_points=out_patterns_list[stage_idx], + n_groups=n_group_list[stage_idx], + ffn_act_cfg=dict(type='ReLU', inplace=True), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + # NOTE: The following argument is a placeholder to hack the code. No real effects for decoding or updating bounding boxes. + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=False, + target_means=[0., 0., 0., 0.], + target_stds=[0.5, 0.5, 1., 1.])) for stage_idx in range(num_stages) + ]), + # training and testing settings + train_cfg=dict( + rpn=None, + rcnn=[ + dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0), + iou_cost=dict(type='IoUCost', iou_mode='giou', + weight=2.0)), + sampler=dict(type='PseudoSampler'), + pos_weight=1) for _ in range(num_stages) + ]), + test_cfg=dict(rpn=None, rcnn=dict(max_per_img=500))) + +# optimizer +optimizer = dict( + _delete_=True, + type='AdamW', + lr=0.000025, + weight_decay=0.0001, +) + +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=1.0, norm_type=2), +) + +# learning policy +lr_config = dict( + policy='step', + step=[8, 11], + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001 +) +runner = dict(type='EpochBasedRunner', max_epochs=12) + + +def __date(): + import datetime + return datetime.datetime.now().strftime('%m%d_%H%M') + + +log_config = dict( + interval=log_interval, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ] +) + +postfix = '_' + __date() + +find_unused_parameters = True + + +resume_from = None diff --git a/configs/adamixer/adamixer_dx101_300_query_crop_mstrain_480-800_3x_coco.py b/configs/adamixer/adamixer_dx101_300_query_crop_mstrain_480-800_3x_coco.py new file mode 100644 index 0000000..a25cd4b --- /dev/null +++ b/configs/adamixer/adamixer_dx101_300_query_crop_mstrain_480-800_3x_coco.py @@ -0,0 +1,16 @@ +_base_ = './adamixer_r50_300_query_crop_mstrain_480-800_3x_coco.py' + +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/adamixer/adamixer_r101_300_query_crop_mstrain_480-800_3x_coco.py b/configs/adamixer/adamixer_r101_300_query_crop_mstrain_480-800_3x_coco.py new file mode 100644 index 0000000..a1ca8b0 --- /dev/null +++ b/configs/adamixer/adamixer_r101_300_query_crop_mstrain_480-800_3x_coco.py @@ -0,0 +1,3 @@ +_base_ = './adamixer_r50_300_query_crop_mstrain_480-800_3x_coco.py' + +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/adamixer/adamixer_r101_mstrain_480-800_3x_coco.py b/configs/adamixer/adamixer_r101_mstrain_480-800_3x_coco.py new file mode 100644 index 0000000..15073c9 --- /dev/null +++ b/configs/adamixer/adamixer_r101_mstrain_480-800_3x_coco.py @@ -0,0 +1,3 @@ +_base_ = './adamixer_r50_mstrain_480-800_3x_coco.py' + +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/adamixer/adamixer_r50_1x_coco.py b/configs/adamixer/adamixer_r50_1x_coco.py new file mode 100644 index 0000000..33b720c --- /dev/null +++ b/configs/adamixer/adamixer_r50_1x_coco.py @@ -0,0 +1,203 @@ +def __get_debug(): + import os + return 'C_DEBUG' in os.environ + + +debug = __get_debug() + +log_interval = 100 + + +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +work_dir_prefix = 'work_dirs/adamixer_mmdet' + +IMAGE_SCALE = (1333, 800) + +# dataset settings +dataset_type = 'CocoDataset' +data_root = '/home/fangyi/data/COCO/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=IMAGE_SCALE, keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=IMAGE_SCALE, + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), +) +evaluation = dict(interval=1, metric='bbox') + + +num_stages = 7 +num_query = 100 +QUERY_DIM = 256 +FEAT_DIM = 256 +FF_DIM = 2048 + +# P_in for spatial mixing in the paper. +in_points_list = [32, ] * num_stages + +# P_out for spatial mixing in the paper. Also named as `out_points` in this codebase. +out_patterns_list = [128, ] * num_stages + +# G for the mixer grouping in the paper. Please distinguishe it from num_heads in MHSA in this codebase. +n_group_list = [4, ] * num_stages + +model = dict( + type='QueryBased', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='ChannelMapping', + in_channels=[256, 512, 1024, 2048], + out_channels=FEAT_DIM, + start_level=0, + add_extra_convs='on_output', + num_outs=4), + rpn_head=dict( + type='InitialQueryGenerator', + num_query=num_query, + content_dim=QUERY_DIM), + roi_head=dict( + type='AdaMixerDecoder', + featmap_strides=[4, 8, 16, 32], + num_stages=num_stages, + stage_loss_weights=[1] * num_stages, + content_dim=QUERY_DIM, + bbox_head=[ + dict( + type='AdaMixerDecoderStage', + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=1, + feedforward_channels=FF_DIM, + content_dim=QUERY_DIM, + feat_channels=FEAT_DIM, + dropout=0.0, + in_points=in_points_list[stage_idx], + out_points=out_patterns_list[stage_idx], + n_groups=n_group_list[stage_idx], + ffn_act_cfg=dict(type='ReLU', inplace=True), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + # NOTE: The following argument is a placeholder to hack the code. No real effects for decoding or updating bounding boxes. + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=False, + target_means=[0., 0., 0., 0.], + target_stds=[0.5, 0.5, 1., 1.])) for stage_idx in range(num_stages) + ]), + # training and testing settings + train_cfg=dict( + rpn=None, + rcnn=[ + dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0), + iou_cost=dict(type='IoUCost', iou_mode='giou', + weight=2.0)), + sampler=dict(type='PseudoSampler'), + pos_weight=1) for _ in range(num_stages) + ]), + test_cfg=dict(rpn=None, rcnn=dict(max_per_img=num_query))) + +# optimizer +optimizer = dict( + _delete_=True, + type='AdamW', + lr=0.000025, + weight_decay=0.0001, +) + +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=1.0, norm_type=2), +) + +# learning policy +lr_config = dict( + policy='step', + step=[8, 11], + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001 +) +runner = dict(type='EpochBasedRunner', max_epochs=12) + + +def __date(): + import datetime + return datetime.datetime.now().strftime('%m%d_%H%M') + + +log_config = dict( + interval=log_interval, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ] +) + +postfix = '_' + __date() + +find_unused_parameters = True + + +resume_from = None diff --git a/configs/adamixer/adamixer_r50_1x_coco_300query.py b/configs/adamixer/adamixer_r50_1x_coco_300query.py new file mode 100644 index 0000000..6ef41b6 --- /dev/null +++ b/configs/adamixer/adamixer_r50_1x_coco_300query.py @@ -0,0 +1,6 @@ +_base_ = './adamixer_r50_1x_coco.py' +num_query = 300 +model = dict( + rpn_head=dict(num_query=num_query), + test_cfg=dict( + _delete_=True, rpn=None, rcnn=dict(max_per_img=num_query))) diff --git a/configs/adamixer/adamixer_r50_1x_coco_500query.py b/configs/adamixer/adamixer_r50_1x_coco_500query.py new file mode 100644 index 0000000..ebef9a1 --- /dev/null +++ b/configs/adamixer/adamixer_r50_1x_coco_500query.py @@ -0,0 +1,6 @@ +_base_ = './adamixer_r50_1x_coco.py' +num_query = 500 +model = dict( + rpn_head=dict(num_query=num_query), + test_cfg=dict( + _delete_=True, rpn=None, rcnn=dict(max_per_img=num_query))) diff --git a/configs/adamixer/adamixer_r50_300_query_crop_mstrain_480-800_3x_coco.py b/configs/adamixer/adamixer_r50_300_query_crop_mstrain_480-800_3x_coco.py new file mode 100644 index 0000000..6637a6b --- /dev/null +++ b/configs/adamixer/adamixer_r50_300_query_crop_mstrain_480-800_3x_coco.py @@ -0,0 +1,55 @@ +_base_ = './adamixer_r50_mstrain_480-800_3x_coco.py' +num_query = 300 +model = dict( + rpn_head=dict(num_query=num_query), + test_cfg=dict( + _delete_=True, rpn=None, rcnn=dict(max_per_img=num_query))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +# augmentation strategy originates from DETR. +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='AutoAugment', + policies=[[ + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict( + type='Resize', + img_scale=[(400, 1333), (500, 1333), (600, 1333)], + multiscale_mode='value', + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ]]), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +data = dict(train=dict(pipeline=train_pipeline)) + +lr_config = dict(policy='step', step=[24, 33]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/adamixer/adamixer_r50_mstrain_480-800_3x_coco.py b/configs/adamixer/adamixer_r50_mstrain_480-800_3x_coco.py new file mode 100644 index 0000000..92ba491 --- /dev/null +++ b/configs/adamixer/adamixer_r50_mstrain_480-800_3x_coco.py @@ -0,0 +1,23 @@ +_base_ = './adamixer_r50_1x_coco.py' + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +min_values = (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, value) for value in min_values], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] + +data = dict(train=dict(pipeline=train_pipeline)) +lr_config = dict(policy='step', step=[27, 33]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/adamixer/adamixer_stodepth2_r50_1x_coco.py b/configs/adamixer/adamixer_stodepth2_r50_1x_coco.py new file mode 100644 index 0000000..2f29dd1 --- /dev/null +++ b/configs/adamixer/adamixer_stodepth2_r50_1x_coco.py @@ -0,0 +1,204 @@ +def __get_debug(): + import os + return 'C_DEBUG' in os.environ + + +debug = __get_debug() + +log_interval = 100 + + +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +work_dir_prefix = 'work_dirs/adamixer_mmdet' + +IMAGE_SCALE = (1333, 800) + +# dataset settings +dataset_type = 'CocoDataset' +data_root = '/home/fangyi/data/COCO/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=IMAGE_SCALE, keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=IMAGE_SCALE, + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), +) +evaluation = dict(interval=1, metric='bbox') + + +num_stages = 6 +num_query = 100 +QUERY_DIM = 256 +FEAT_DIM = 256 +FF_DIM = 2048 + +# P_in for spatial mixing in the paper. +in_points_list = [32, ] * num_stages + +# P_out for spatial mixing in the paper. Also named as `out_points` in this codebase. +out_patterns_list = [128, ] * num_stages + +# G for the mixer grouping in the paper. Please distinguishe it from num_heads in MHSA in this codebase. +n_group_list = [4, ] * num_stages + +model = dict( + type='QueryBased', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='ChannelMapping', + in_channels=[256, 512, 1024, 2048], + out_channels=FEAT_DIM, + start_level=0, + add_extra_convs='on_output', + num_outs=4), + rpn_head=dict( + type='InitialQueryGenerator', + num_query=num_query, + content_dim=QUERY_DIM), + roi_head=dict( + type='AdaMixerDecoder_stodepth', + featmap_strides=[4, 8, 16, 32], + num_stages=num_stages, + stage_loss_weights=[1] * num_stages, + content_dim=QUERY_DIM, + Pl=[0.9, 0.9, 0.9, 0.9, 0.9, 0.9], + bbox_head=[ + dict( + type='AdaMixerDecoderStage', + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=1, + feedforward_channels=FF_DIM, + content_dim=QUERY_DIM, + feat_channels=FEAT_DIM, + dropout=0.0, + in_points=in_points_list[stage_idx], + out_points=out_patterns_list[stage_idx], + n_groups=n_group_list[stage_idx], + ffn_act_cfg=dict(type='ReLU', inplace=True), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + # NOTE: The following argument is a placeholder to hack the code. No real effects for decoding or updating bounding boxes. + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=False, + target_means=[0., 0., 0., 0.], + target_stds=[0.5, 0.5, 1., 1.])) for stage_idx in range(num_stages) + ]), + # training and testing settings + train_cfg=dict( + rpn=None, + rcnn=[ + dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0), + iou_cost=dict(type='IoUCost', iou_mode='giou', + weight=2.0)), + sampler=dict(type='PseudoSampler'), + pos_weight=1) for _ in range(num_stages) + ]), + test_cfg=dict(rpn=None, rcnn=dict(max_per_img=num_query))) + +# optimizer +optimizer = dict( + _delete_=True, + type='AdamW', + lr=0.000025, + weight_decay=0.0001, +) + +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=1.0, norm_type=2), +) + +# learning policy +lr_config = dict( + policy='step', + step=[8, 11], + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001 +) +runner = dict(type='EpochBasedRunner', max_epochs=12) + + +def __date(): + import datetime + return datetime.datetime.now().strftime('%m%d_%H%M') + + +log_config = dict( + interval=log_interval, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ] +) + +postfix = '_' + __date() + +find_unused_parameters = True + + +resume_from = None diff --git a/configs/adamixer/adamixer_stodepth_r50_1x_coco.py b/configs/adamixer/adamixer_stodepth_r50_1x_coco.py new file mode 100644 index 0000000..8d443e3 --- /dev/null +++ b/configs/adamixer/adamixer_stodepth_r50_1x_coco.py @@ -0,0 +1,204 @@ +def __get_debug(): + import os + return 'C_DEBUG' in os.environ + + +debug = __get_debug() + +log_interval = 100 + + +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +work_dir_prefix = 'work_dirs/adamixer_mmdet' + +IMAGE_SCALE = (1333, 800) + +# dataset settings +dataset_type = 'CocoDataset' +data_root = '/home/fangyi/data/COCO/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=IMAGE_SCALE, keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=IMAGE_SCALE, + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), +) +evaluation = dict(interval=1, metric='bbox') + + +num_stages = 6 +num_query = 100 +QUERY_DIM = 256 +FEAT_DIM = 256 +FF_DIM = 2048 + +# P_in for spatial mixing in the paper. +in_points_list = [32, ] * num_stages + +# P_out for spatial mixing in the paper. Also named as `out_points` in this codebase. +out_patterns_list = [128, ] * num_stages + +# G for the mixer grouping in the paper. Please distinguishe it from num_heads in MHSA in this codebase. +n_group_list = [4, ] * num_stages + +model = dict( + type='QueryBased', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='ChannelMapping', + in_channels=[256, 512, 1024, 2048], + out_channels=FEAT_DIM, + start_level=0, + add_extra_convs='on_output', + num_outs=4), + rpn_head=dict( + type='InitialQueryGenerator', + num_query=num_query, + content_dim=QUERY_DIM), + roi_head=dict( + type='AdaMixerDecoder_stodepth', + featmap_strides=[4, 8, 16, 32], + num_stages=num_stages, + stage_loss_weights=[1] * num_stages, + content_dim=QUERY_DIM, + Pl=[1, 0.9, 0.8, 0.7, 0.6, 0.5], + bbox_head=[ + dict( + type='AdaMixerDecoderStage', + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=1, + feedforward_channels=FF_DIM, + content_dim=QUERY_DIM, + feat_channels=FEAT_DIM, + dropout=0.0, + in_points=in_points_list[stage_idx], + out_points=out_patterns_list[stage_idx], + n_groups=n_group_list[stage_idx], + ffn_act_cfg=dict(type='ReLU', inplace=True), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + # NOTE: The following argument is a placeholder to hack the code. No real effects for decoding or updating bounding boxes. + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=False, + target_means=[0., 0., 0., 0.], + target_stds=[0.5, 0.5, 1., 1.])) for stage_idx in range(num_stages) + ]), + # training and testing settings + train_cfg=dict( + rpn=None, + rcnn=[ + dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0), + iou_cost=dict(type='IoUCost', iou_mode='giou', + weight=2.0)), + sampler=dict(type='PseudoSampler'), + pos_weight=1) for _ in range(num_stages) + ]), + test_cfg=dict(rpn=None, rcnn=dict(max_per_img=num_query))) + +# optimizer +optimizer = dict( + _delete_=True, + type='AdamW', + lr=0.000025, + weight_decay=0.0001, +) + +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=1.0, norm_type=2), +) + +# learning policy +lr_config = dict( + policy='step', + step=[8, 11], + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001 +) +runner = dict(type='EpochBasedRunner', max_epochs=12) + + +def __date(): + import datetime + return datetime.datetime.now().strftime('%m%d_%H%M') + + +log_config = dict( + interval=log_interval, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ] +) + +postfix = '_' + __date() + +find_unused_parameters = True + + +resume_from = None diff --git a/configs/adamixer/adamixer_swin_s_300_query_crop_mstrain_480-800_3x_coco.py b/configs/adamixer/adamixer_swin_s_300_query_crop_mstrain_480-800_3x_coco.py new file mode 100644 index 0000000..83d2680 --- /dev/null +++ b/configs/adamixer/adamixer_swin_s_300_query_crop_mstrain_480-800_3x_coco.py @@ -0,0 +1,42 @@ +_base_ = './adamixer_r50_300_query_crop_mstrain_480-800_3x_coco.py' +pretrained = './swin_small_patch4_window7_224.pth' +model = dict( + pretrained=None, + backbone=dict( + _delete_=True, + type='SwinTransformer', + embed_dims=96, + depths=[2, 2, 18, 2], + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.2, + patch_norm=True, + out_indices=(0, 1, 2, 3), + with_cp=False, + convert_weights=True, + init_cfg=dict(type='Pretrained', checkpoint=pretrained), + ), + neck=dict(in_channels=[96, 192, 384, 768]) +) + +optimizer = dict( + _delete_=True, + type='AdamW', + lr=0.000025, + weight_decay=0.0001, + paramwise_cfg=dict( + custom_keys={ + # Swin-related settings + 'absolute_pos_embed': dict(decay_mult=0.), + 'relative_position_bias_table': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.) + } + ) +) + +lr_config = dict(warmup_iters=1000) diff --git a/configs/albu_example/README.md b/configs/albu_example/README.md new file mode 100644 index 0000000..99b3d2b --- /dev/null +++ b/configs/albu_example/README.md @@ -0,0 +1,19 @@ +# Albu Example + + + +``` +@article{2018arXiv180906839B, + author = {A. Buslaev, A. Parinov, E. Khvedchenya, V.~I. Iglovikov and A.~A. Kalinin}, + title = "{Albumentations: fast and flexible image augmentations}", + journal = {ArXiv e-prints}, + eprint = {1809.06839}, + year = 2018 +} +``` + +## Results and Models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| +| R-50 | pytorch | 1x | 4.4 | 16.6 | 38.0 | 34.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/albu_example/mask_rcnn_r50_fpn_albu_1x_coco/mask_rcnn_r50_fpn_albu_1x_coco_20200208-ab203bcd.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/albu_example/mask_rcnn_r50_fpn_albu_1x_coco/mask_rcnn_r50_fpn_albu_1x_coco_20200208_225520.log.json) | diff --git a/configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py b/configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py new file mode 100644 index 0000000..b3f879a --- /dev/null +++ b/configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py @@ -0,0 +1,73 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +albu_train_transforms = [ + dict( + type='ShiftScaleRotate', + shift_limit=0.0625, + scale_limit=0.0, + rotate_limit=0, + interpolation=1, + p=0.5), + dict( + type='RandomBrightnessContrast', + brightness_limit=[0.1, 0.3], + contrast_limit=[0.1, 0.3], + p=0.2), + dict( + type='OneOf', + transforms=[ + dict( + type='RGBShift', + r_shift_limit=10, + g_shift_limit=10, + b_shift_limit=10, + p=1.0), + dict( + type='HueSaturationValue', + hue_shift_limit=20, + sat_shift_limit=30, + val_shift_limit=20, + p=1.0) + ], + p=0.1), + dict(type='JpegCompression', quality_lower=85, quality_upper=95, p=0.2), + dict(type='ChannelShuffle', p=0.1), + dict( + type='OneOf', + transforms=[ + dict(type='Blur', blur_limit=3, p=1.0), + dict(type='MedianBlur', blur_limit=3, p=1.0) + ], + p=0.1), +] +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='Pad', size_divisor=32), + dict( + type='Albu', + transforms=albu_train_transforms, + bbox_params=dict( + type='BboxParams', + format='pascal_voc', + label_fields=['gt_labels'], + min_visibility=0.0, + filter_lost_elements=True), + keymap={ + 'img': 'image', + 'gt_masks': 'masks', + 'gt_bboxes': 'bboxes' + }, + update_pad_shape=False, + skip_img_without_anno=True), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'], + meta_keys=('filename', 'ori_shape', 'img_shape', 'img_norm_cfg', + 'pad_shape', 'scale_factor')) +] +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/configs/atss/README.md b/configs/atss/README.md new file mode 100644 index 0000000..a2dc322 --- /dev/null +++ b/configs/atss/README.md @@ -0,0 +1,21 @@ +# Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection + +## Introduction + + + +```latex +@article{zhang2019bridging, + title = {Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection}, + author = {Zhang, Shifeng and Chi, Cheng and Yao, Yongqiang and Lei, Zhen and Li, Stan Z.}, + journal = {arXiv preprint arXiv:1912.02424}, + year = {2019} +} +``` + +## Results and Models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50 | pytorch | 1x | 3.7 | 19.7 | 39.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/atss/atss_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209_102539.log.json) | +| R-101 | pytorch | 1x | 5.6 | 12.3 | 41.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/atss/atss_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.log.json) | diff --git a/configs/atss/atss_r101_fpn_1x_coco.py b/configs/atss/atss_r101_fpn_1x_coco.py new file mode 100644 index 0000000..695779a --- /dev/null +++ b/configs/atss/atss_r101_fpn_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = './atss_r50_fpn_1x_coco.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(depth=101), +) diff --git a/configs/atss/atss_r50_fpn_1x_coco.py b/configs/atss/atss_r50_fpn_1x_coco.py new file mode 100644 index 0000000..cfd70ed --- /dev/null +++ b/configs/atss/atss_r50_fpn_1x_coco.py @@ -0,0 +1,62 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='ATSS', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5), + bbox_head=dict( + type='ATSSHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=2.0), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), + # training and testing settings + train_cfg=dict( + assigner=dict(type='ATSSAssigner', topk=9), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/autoassign/README.md b/configs/autoassign/README.md new file mode 100644 index 0000000..c0dba82 --- /dev/null +++ b/configs/autoassign/README.md @@ -0,0 +1,25 @@ +# AutoAssign: Differentiable Label Assignment for Dense Object Detection + +## Introduction + + + +``` +@article{zhu2020autoassign, + title={AutoAssign: Differentiable Label Assignment for Dense Object Detection}, + author={Zhu, Benjin and Wang, Jianfeng and Jiang, Zhengkai and Zong, Fuhang and Liu, Songtao and Li, Zeming and Sun, Jian}, + journal={arXiv preprint arXiv:2007.03496}, + year={2020} +} +``` + +## Results and Models + +| Backbone | Style | Lr schd | Mem (GB) | box AP | Config | Download | +|:---------:|:-------:|:-------:|:--------:|:------:|:------:|:--------:| +| R-50 | pytorch | 1x | 4.08 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/autoassign/auto_assign_r50_fpn_1x_coco/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/autoassign/auto_assign_r50_fpn_1x_coco/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.log.json) | + +**Note**: + +1. We find that the performance is unstable with 1x setting and may fluctuate by about 0.3 mAP. mAP 40.3 ~ 40.6 is acceptable. Such fluctuation can also be found in the original implementation. +2. You can get a more stable results ~ mAP 40.6 with a schedule total 13 epoch, and learning rate is divided by 10 at 10th and 13th epoch. diff --git a/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py b/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py new file mode 100644 index 0000000..c87d235 --- /dev/null +++ b/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py @@ -0,0 +1,84 @@ +# We follow the original implementation which +# adopts the Caffe pre-trained backbone. +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='AutoAssign', + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs=True, + extra_convs_on_inputs=True, + num_outs=5, + relu_before_extra_convs=True, + init_cfg=dict(type='Caffe2Xavier', layer='Conv2d')), + bbox_head=dict( + type='AutoAssignHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + strides=[8, 16, 32, 64, 128], + loss_bbox=dict(type='GIoULoss', loss_weight=5.0)), + train_cfg=None, + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) +img_norm_cfg = dict( + mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(lr=0.01, paramwise_cfg=dict(norm_decay_mult=0.)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=1.0 / 1000, + step=[8, 11]) +total_epochs = 12 diff --git a/configs/carafe/README.md b/configs/carafe/README.md new file mode 100644 index 0000000..ce3b862 --- /dev/null +++ b/configs/carafe/README.md @@ -0,0 +1,32 @@ +# CARAFE: Content-Aware ReAssembly of FEatures + +## Introduction + + + +We provide config files to reproduce the object detection & instance segmentation results in the ICCV 2019 Oral paper for [CARAFE: Content-Aware ReAssembly of FEatures](https://arxiv.org/abs/1905.02188). + +``` +@inproceedings{Wang_2019_ICCV, + title = {CARAFE: Content-Aware ReAssembly of FEatures}, + author = {Wang, Jiaqi and Chen, Kai and Xu, Rui and Liu, Ziwei and Loy, Chen Change and Lin, Dahua}, + booktitle = {The IEEE International Conference on Computer Vision (ICCV)}, + month = {October}, + year = {2019} +} +``` + +## Results and Models + +The results on COCO 2017 val is shown in the below table. + +| Method | Backbone | Style | Lr schd | Test Proposal Num | Inf time (fps) | Box AP | Mask AP | Config | Download | +|:--------------------:|:--------:|:-------:|:-------:|:-----------------:|:--------------:|:------:|:-------:|:------:|:--------:| +| Faster R-CNN w/ CARAFE | R-50-FPN | pytorch | 1x | 1000 | 16.5 | 38.6 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/carafe/faster_rcnn_r50_fpn_carafe_1x_coco/faster_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.386_20200504_175733-385a75b7.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/carafe/faster_rcnn_r50_fpn_carafe_1x_coco/faster_rcnn_r50_fpn_carafe_1x_coco_20200504_175733.log.json) | +| - | - | - | - | 2000 | | | | | +| Mask R-CNN w/ CARAFE | R-50-FPN | pytorch | 1x | 1000 | 14.0 | 39.3 | 35.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/carafe/mask_rcnn_r50_fpn_carafe_1x_coco/mask_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.393__segm_mAP-0.358_20200503_135957-8687f195.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/carafe/mask_rcnn_r50_fpn_carafe_1x_coco/mask_rcnn_r50_fpn_carafe_1x_coco_20200503_135957.log.json) | +| - | - | - | - | 2000 | | | | | + +## Implementation + +The CUDA implementation of CARAFE can be find at https://github.com/myownskyW7/CARAFE. diff --git a/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py b/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py new file mode 100644 index 0000000..dedac3f --- /dev/null +++ b/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py @@ -0,0 +1,50 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + neck=dict( + type='FPN_CARAFE', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5, + start_level=0, + end_level=-1, + norm_cfg=None, + act_cfg=None, + order=('conv', 'norm', 'act'), + upsample_cfg=dict( + type='carafe', + up_kernel=5, + up_group=1, + encoder_kernel=3, + encoder_dilation=1, + compressed_channels=64))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=64), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=64), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py b/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py new file mode 100644 index 0000000..668c023 --- /dev/null +++ b/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py @@ -0,0 +1,60 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + neck=dict( + type='FPN_CARAFE', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5, + start_level=0, + end_level=-1, + norm_cfg=None, + act_cfg=None, + order=('conv', 'norm', 'act'), + upsample_cfg=dict( + type='carafe', + up_kernel=5, + up_group=1, + encoder_kernel=3, + encoder_dilation=1, + compressed_channels=64)), + roi_head=dict( + mask_head=dict( + upsample_cfg=dict( + type='carafe', + scale_factor=2, + up_kernel=5, + up_group=1, + encoder_kernel=3, + encoder_dilation=1, + compressed_channels=64)))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=64), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=64), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/cascade_rcnn/README.md b/configs/cascade_rcnn/README.md new file mode 100644 index 0000000..4b1b4c8 --- /dev/null +++ b/configs/cascade_rcnn/README.md @@ -0,0 +1,55 @@ +# Cascade R-CNN: High Quality Object Detection and Instance Segmentation + +## Introduction + + + +```latex +@article{Cai_2019, + title={Cascade R-CNN: High Quality Object Detection and Instance Segmentation}, + ISSN={1939-3539}, + url={http://dx.doi.org/10.1109/tpami.2019.2956516}, + DOI={10.1109/tpami.2019.2956516}, + journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, + publisher={Institute of Electrical and Electronics Engineers (IEEE)}, + author={Cai, Zhaowei and Vasconcelos, Nuno}, + year={2019}, + pages={1–1} +} +``` + +## Results and models + +### Cascade R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: |:------:|:--------:| +| R-50-FPN | caffe | 1x | 4.2 | | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco/cascade_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.404_20200504_174853-b857be87.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco/cascade_rcnn_r50_caffe_fpn_1x_coco_20200504_174853.log.json) | +| R-50-FPN | pytorch | 1x | 4.4 | 16.1 | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316_214748.log.json) | +| R-50-FPN | pytorch | 20e | - | - | 41.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco/cascade_rcnn_r50_fpn_20e_coco_bbox_mAP-0.41_20200504_175131-e9872a90.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco/cascade_rcnn_r50_fpn_20e_coco_20200504_175131.log.json) | +| R-101-FPN | caffe | 1x | 6.2 | | 42.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco/cascade_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.423_20200504_175649-cab8dbd5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco/cascade_rcnn_r101_caffe_fpn_1x_coco_20200504_175649.log.json) | +| R-101-FPN | pytorch | 1x | 6.4 | 13.5 | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317-0b6a2fbf.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317_101744.log.json) | +| R-101-FPN | pytorch | 20e | - | - | 42.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco/cascade_rcnn_r101_fpn_20e_coco_bbox_mAP-0.425_20200504_231812-5057dcc5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco/cascade_rcnn_r101_fpn_20e_coco_20200504_231812.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.6 | 10.9 | 43.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316-95c2deb6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316_055608.log.json) | +| X-101-32x4d-FPN | pytorch | 20e | 7.6 | | 43.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco/cascade_rcnn_x101_32x4d_fpn_20e_coco_20200906_134608-9ae0a720.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco/cascade_rcnn_x101_32x4d_fpn_20e_coco_20200906_134608.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.7 | | 44.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco/cascade_rcnn_x101_64x4d_fpn_1x_coco_20200515_075702-43ce6a30.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco/cascade_rcnn_x101_64x4d_fpn_1x_coco_20200515_075702.log.json) | +| X-101-64x4d-FPN | pytorch | 20e | 10.7 | | 44.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357-051557b1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357.log.json)| + +### Cascade Mask R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +| R-50-FPN | caffe | 1x | 5.9 | | 41.2 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco/cascade_mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.412__segm_mAP-0.36_20200504_174659-5004b251.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco/cascade_mask_rcnn_r50_caffe_fpn_1x_coco_20200504_174659.log.json) | +| R-50-FPN | pytorch | 1x | 6.0 | 11.2 | 41.2 | 35.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203_170449.log.json) | +| R-50-FPN | pytorch | 20e | - | - | 41.9 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_bbox_mAP-0.419__segm_mAP-0.365_20200504_174711-4af8e66e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_20200504_174711.log.json)| +| R-101-FPN | caffe | 1x | 7.8 | | 43.2 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco/cascade_mask_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.432__segm_mAP-0.376_20200504_174813-5c1e9599.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco/cascade_mask_rcnn_r101_caffe_fpn_1x_coco_20200504_174813.log.json)| +| R-101-FPN | pytorch | 1x | 7.9 | 9.8 | 42.9 | 37.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203-befdf6ee.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203_092521.log.json) | +| R-101-FPN | pytorch | 20e | - | - | 43.4 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_bbox_mAP-0.434__segm_mAP-0.378_20200504_174836-005947da.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_20200504_174836.log.json)| +| X-101-32x4d-FPN | pytorch | 1x | 9.2 | 8.6 | 44.3 | 38.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201-0f411b1f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201_052416.log.json) | +| X-101-32x4d-FPN | pytorch | 20e | 9.2 | - | 45.0 | 39.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco_20200528_083917-ed1f4751.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco_20200528_083917.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 12.2 | 6.7 | 45.3 | 39.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203-9a2db89d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203_044059.log.json) | +| X-101-64x4d-FPN | pytorch | 20e | 12.2 | | 45.6 |39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033-bdb5126a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033.log.json)| + +**Notes:** + +- The `20e` schedule in Cascade (Mask) R-CNN indicates decreasing the lr at 16 and 19 epochs, with a total of 20 epochs. diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..f42165d --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 0000000..9212dda --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py new file mode 100644 index 0000000..d069f8c --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py @@ -0,0 +1,2 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..b371ed7 --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,38 @@ +_base_ = ['./cascade_mask_rcnn_r50_fpn_1x_coco.py'] + +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe')) + +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..49ab539 --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/cascade_mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py new file mode 100644 index 0000000..1296dc4 --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/cascade_mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_20e.py', '../_base_/default_runtime.py' +] diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..d05eb50 --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py new file mode 100644 index 0000000..0cfc7d7 --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py @@ -0,0 +1,13 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..33629ee --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py new file mode 100644 index 0000000..e64c22c --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py @@ -0,0 +1,13 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..8e8b830 --- /dev/null +++ b/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './cascade_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 0000000..6666651 --- /dev/null +++ b/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './cascade_rcnn_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py new file mode 100644 index 0000000..9cb3581 --- /dev/null +++ b/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py @@ -0,0 +1,2 @@ +_base_ = './cascade_rcnn_r50_fpn_20e_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..c576c74 --- /dev/null +++ b/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,38 @@ +_base_ = './cascade_rcnn_r50_fpn_1x_coco.py' + +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe')) + +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..87e21fb --- /dev/null +++ b/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/cascade_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] diff --git a/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py new file mode 100644 index 0000000..6f886e1 --- /dev/null +++ b/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py @@ -0,0 +1,4 @@ +_base_ = './cascade_rcnn_r50_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..1fbe6ce --- /dev/null +++ b/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './cascade_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py new file mode 100644 index 0000000..1afeeef --- /dev/null +++ b/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py @@ -0,0 +1,13 @@ +_base_ = './cascade_rcnn_r50_fpn_20e_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..b249bfa --- /dev/null +++ b/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './cascade_rcnn_r50_fpn_1x_coco.py' +model = dict( + type='CascadeRCNN', + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py new file mode 100644 index 0000000..500b48c --- /dev/null +++ b/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py @@ -0,0 +1,14 @@ +_base_ = './cascade_rcnn_r50_fpn_20e_coco.py' +model = dict( + type='CascadeRCNN', + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/cascade_rpn/README.md b/configs/cascade_rpn/README.md new file mode 100644 index 0000000..aa7782c --- /dev/null +++ b/configs/cascade_rpn/README.md @@ -0,0 +1,29 @@ +# Cascade RPN + + + +We provide the code for reproducing experiment results of [Cascade RPN](https://arxiv.org/abs/1909.06720). + +``` +@inproceedings{vu2019cascade, + title={Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution}, + author={Vu, Thang and Jang, Hyunjun and Pham, Trung X and Yoo, Chang D}, + booktitle={Conference on Neural Information Processing Systems (NeurIPS)}, + year={2019} +} +``` + +## Benchmark + +### Region proposal performance + +| Method | Backbone | Style | Mem (GB) | Train time (s/iter) | Inf time (fps) | AR 1000 | Download | +|:------:|:--------:|:-----:|:--------:|:-------------------:|:--------------:|:-------:|:--------------------------------------:| +| CRPN | R-50-FPN | caffe | - | - | - | 72.0 | [model](https://drive.google.com/file/d/1qxVdOnCgK-ee7_z0x6mvAir_glMu2Ihi/view?usp=sharing) | + +### Detection performance + +| Method | Proposal | Backbone | Style | Schedule | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download | +|:-------------:|:-----------:|:--------:|:-------:|:--------:|:--------:|:-------------------:|:--------------:|:------:|:--------------------------------------------:| +| Fast R-CNN | Cascade RPN | R-50-FPN | caffe | 1x | - | - | - | 39.9 | [model](https://drive.google.com/file/d/1NmbnuY5VHi8I9FE8xnp5uNvh2i-t-6_L/view?usp=sharing) | +| Faster R-CNN | Cascade RPN | R-50-FPN | caffe | 1x | - | - | - | 40.4 | [model](https://drive.google.com/file/d/1dS3Q66qXMJpcuuQgDNkLp669E5w1UMuZ/view?usp=sharing) | diff --git a/configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py b/configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..68c57df --- /dev/null +++ b/configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,75 @@ +_base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe'), + roi_head=dict( + bbox_head=dict( + bbox_coder=dict(target_stds=[0.04, 0.04, 0.08, 0.08]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.5), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rcnn=dict( + assigner=dict( + pos_iou_thr=0.65, neg_iou_thr=0.65, min_pos_iou=0.65), + sampler=dict(num=256))), + test_cfg=dict(rcnn=dict(score_thr=1e-3))) +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=300), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=300), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['proposals']), + dict( + type='ToDataContainer', + fields=[dict(key='proposals', stack=False)]), + dict(type='Collect', keys=['img', 'proposals']), + ]) +] +data = dict( + train=dict( + proposal_file=data_root + + 'proposals/crpn_r50_caffe_fpn_1x_train2017.pkl', + pipeline=train_pipeline), + val=dict( + proposal_file=data_root + + 'proposals/crpn_r50_caffe_fpn_1x_val2017.pkl', + pipeline=test_pipeline), + test=dict( + proposal_file=data_root + + 'proposals/crpn_r50_caffe_fpn_1x_val2017.pkl', + pipeline=test_pipeline)) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py b/configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..bad86e6 --- /dev/null +++ b/configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,92 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py' +rpn_weight = 0.7 +model = dict( + rpn_head=dict( + _delete_=True, + type='CascadeRPNHead', + num_stages=2, + stages=[ + dict( + type='StageCascadeRPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[1.0], + strides=[4, 8, 16, 32, 64]), + adapt_cfg=dict(type='dilation', dilation=3), + bridged_feature=True, + sampling=False, + with_cls=False, + reg_decoded_bbox=True, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=(.0, .0, .0, .0), + target_stds=(0.1, 0.1, 0.5, 0.5)), + loss_bbox=dict( + type='IoULoss', linear=True, + loss_weight=10.0 * rpn_weight)), + dict( + type='StageCascadeRPNHead', + in_channels=256, + feat_channels=256, + adapt_cfg=dict(type='offset'), + bridged_feature=False, + sampling=True, + with_cls=True, + reg_decoded_bbox=True, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=(.0, .0, .0, .0), + target_stds=(0.05, 0.05, 0.1, 0.1)), + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0 * rpn_weight), + loss_bbox=dict( + type='IoULoss', linear=True, + loss_weight=10.0 * rpn_weight)) + ]), + roi_head=dict( + bbox_head=dict( + bbox_coder=dict(target_stds=[0.04, 0.04, 0.08, 0.08]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.5), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=[ + dict( + assigner=dict( + type='RegionAssigner', center_ratio=0.2, ignore_ratio=0.5), + allowed_border=-1, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.7, + min_pos_iou=0.3, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False) + ], + rpn_proposal=dict(max_per_img=300, nms=dict(iou_threshold=0.8)), + rcnn=dict( + assigner=dict( + pos_iou_thr=0.65, neg_iou_thr=0.65, min_pos_iou=0.65), + sampler=dict(type='RandomSampler', num=256))), + test_cfg=dict( + rpn=dict(max_per_img=300, nms=dict(iou_threshold=0.8)), + rcnn=dict(score_thr=1e-3))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/cascade_rpn/crpn_r50_caffe_fpn_1x_coco.py b/configs/cascade_rpn/crpn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..5562e69 --- /dev/null +++ b/configs/cascade_rpn/crpn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,77 @@ +_base_ = '../rpn/rpn_r50_caffe_fpn_1x_coco.py' +model = dict( + rpn_head=dict( + _delete_=True, + type='CascadeRPNHead', + num_stages=2, + stages=[ + dict( + type='StageCascadeRPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[1.0], + strides=[4, 8, 16, 32, 64]), + adapt_cfg=dict(type='dilation', dilation=3), + bridged_feature=True, + sampling=False, + with_cls=False, + reg_decoded_bbox=True, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=(.0, .0, .0, .0), + target_stds=(0.1, 0.1, 0.5, 0.5)), + loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0)), + dict( + type='StageCascadeRPNHead', + in_channels=256, + feat_channels=256, + adapt_cfg=dict(type='offset'), + bridged_feature=False, + sampling=True, + with_cls=True, + reg_decoded_bbox=True, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=(.0, .0, .0, .0), + target_stds=(0.05, 0.05, 0.1, 0.1)), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, + loss_weight=1.0), + loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0)) + ]), + train_cfg=dict(rpn=[ + dict( + assigner=dict( + type='RegionAssigner', center_ratio=0.2, ignore_ratio=0.5), + allowed_border=-1, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.7, + min_pos_iou=0.3, + ignore_iof_thr=-1, + iou_calculator=dict(type='BboxOverlaps2D')), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False) + ]), + test_cfg=dict( + rpn=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.8), + min_bbox_size=0))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/centripetalnet/README.md b/configs/centripetalnet/README.md new file mode 100644 index 0000000..bc9a4b1 --- /dev/null +++ b/configs/centripetalnet/README.md @@ -0,0 +1,26 @@ +# CentripetalNet + +## Introduction + + + +```latex +@InProceedings{Dong_2020_CVPR, +author = {Dong, Zhiwei and Li, Guoxuan and Liao, Yue and Wang, Fei and Ren, Pengju and Qian, Chen}, +title = {CentripetalNet: Pursuing High-Quality Keypoint Pairs for Object Detection}, +booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, +month = {June}, +year = {2020} +} +``` + +## Results and models + +| Backbone | Batch Size | Step/Total Epochs | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :--------: |:----------------: | :------: | :------------: | :----: | :------: | :--------: | +| HourglassNet-104 | [16 x 6](./centripetalnet_hourglass104_mstest_16x6_210e_coco.py) | 190/210 | 16.7 | 3.7 | 44.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804.log.json) | + +Note: + +- TTA setting is single-scale and `flip=True`. +- The model we released is the best checkpoint rather than the latest checkpoint (box AP 44.8 vs 44.6 in our experiment). diff --git a/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py b/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py new file mode 100644 index 0000000..e9c5def --- /dev/null +++ b/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py @@ -0,0 +1,105 @@ +_base_ = [ + '../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py' +] + +# model settings +model = dict( + type='CornerNet', + backbone=dict( + type='HourglassNet', + downsample_times=5, + num_stacks=2, + stage_channels=[256, 256, 384, 384, 384, 512], + stage_blocks=[2, 2, 2, 2, 2, 4], + norm_cfg=dict(type='BN', requires_grad=True)), + neck=None, + bbox_head=dict( + type='CentripetalHead', + num_classes=80, + in_channels=256, + num_feat_levels=2, + corner_emb_channels=0, + loss_heatmap=dict( + type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1), + loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1), + loss_guiding_shift=dict( + type='SmoothL1Loss', beta=1.0, loss_weight=0.05), + loss_centripetal_shift=dict( + type='SmoothL1Loss', beta=1.0, loss_weight=1)), + # training and testing settings + train_cfg=None, + test_cfg=dict( + corner_topk=100, + local_maximum_kernel=3, + distance_threshold=0.5, + score_thr=0.05, + max_per_img=100, + nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian'))) +# data settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='RandomCenterCropPad', + crop_size=(511, 511), + ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3), + test_mode=False, + test_pad_mode=None, + **img_norm_cfg), + dict(type='Resize', img_scale=(511, 511), keep_ratio=False), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict( + type='MultiScaleFlipAug', + scale_factor=1.0, + flip=True, + transforms=[ + dict(type='Resize'), + dict( + type='RandomCenterCropPad', + crop_size=None, + ratios=None, + border=None, + test_mode=True, + test_pad_mode=['logical_or', 127], + **img_norm_cfg), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict( + type='Collect', + keys=['img'], + meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', + 'scale_factor', 'flip', 'img_norm_cfg', 'border')), + ]) +] +data = dict( + samples_per_gpu=6, + workers_per_gpu=3, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='Adam', lr=0.0005) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=1.0 / 3, + step=[190]) +runner = dict(type='EpochBasedRunner', max_epochs=210) diff --git a/configs/cityscapes/README.md b/configs/cityscapes/README.md new file mode 100644 index 0000000..10707fb --- /dev/null +++ b/configs/cityscapes/README.md @@ -0,0 +1,33 @@ +# Cityscapes Dataset + + + +``` +@inproceedings{Cordts2016Cityscapes, + title={The Cityscapes Dataset for Semantic Urban Scene Understanding}, + author={Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt}, + booktitle={Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + year={2016} +} +``` + +## Common settings + +- All baselines were trained using 8 GPU with a batch size of 8 (1 images per GPU) using the [linear scaling rule](https://arxiv.org/abs/1706.02677) to scale the learning rate. +- All models were trained on `cityscapes_train`, and tested on `cityscapes_val`. +- 1x training schedule indicates 64 epochs which corresponds to slightly less than the 24k iterations reported in the original schedule from the [Mask R-CNN paper](https://arxiv.org/abs/1703.06870) +- COCO pre-trained weights are used to initialize. +- A conversion [script](../../tools/dataset_converters/cityscapes.py) is provided to convert Cityscapes into COCO format. Please refer to [install.md](../../docs/1_exist_data_model.md#prepare-datasets) for details. +- `CityscapesDataset` implemented three evaluation methods. `bbox` and `segm` are standard COCO bbox/mask AP. `cityscapes` is the cityscapes dataset official evaluation, which may be slightly higher than COCO. + +### Faster R-CNN + +| Backbone | Style | Lr schd | Scale | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :---: | :------: | :------------: | :----: | :------: | :--------: | +| R-50-FPN | pytorch | 1x | 800-1024 | 5.2 | - | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes_20200502-829424c0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes_20200502_114915.log.json) | + +### Mask R-CNN + +| Backbone | Style | Lr schd | Scale | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------: | :------------: | :----: | :-----: | :------: | :------: | +| R-50-FPN | pytorch | 1x | 800-1024 | 5.3 | - | 40.9 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes/mask_rcnn_r50_fpn_1x_cityscapes_20201211_133733-d2858245.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes/mask_rcnn_r50_fpn_1x_cityscapes_20201211_133733.log.json) | diff --git a/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py b/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py new file mode 100644 index 0000000..5b17451 --- /dev/null +++ b/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/cityscapes_detection.py', + '../_base_/default_runtime.py' +] +model = dict( + pretrained=None, + roi_head=dict( + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=8, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)))) +# optimizer +# lr is set for a batch size of 8 +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + # [7] yields higher performance than [6] + step=[7]) +runner = dict( + type='EpochBasedRunner', max_epochs=8) # actual epoch = 8 * 8 = 64 +log_config = dict(interval=100) +# For better, more stable performance initialize from COCO +load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth' # noqa diff --git a/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py b/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py new file mode 100644 index 0000000..0a4d7ca --- /dev/null +++ b/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py @@ -0,0 +1,46 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/cityscapes_instance.py', '../_base_/default_runtime.py' +] +model = dict( + pretrained=None, + roi_head=dict( + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=8, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=8, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)))) +# optimizer +# lr is set for a batch size of 8 +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + # [7] yields higher performance than [6] + step=[7]) +runner = dict( + type='EpochBasedRunner', max_epochs=8) # actual epoch = 8 * 8 = 64 +log_config = dict(interval=100) +# For better, more stable performance initialize from COCO +load_from = 'https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth' # noqa diff --git a/configs/cornernet/README.md b/configs/cornernet/README.md new file mode 100644 index 0000000..302e332 --- /dev/null +++ b/configs/cornernet/README.md @@ -0,0 +1,33 @@ +# CornerNet + +## Introduction + + + +```latex +@inproceedings{law2018cornernet, + title={Cornernet: Detecting objects as paired keypoints}, + author={Law, Hei and Deng, Jia}, + booktitle={15th European Conference on Computer Vision, ECCV 2018}, + pages={765--781}, + year={2018}, + organization={Springer Verlag} +} +``` + +## Results and models + +| Backbone | Batch Size | Step/Total Epochs | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :--------: |:----------------: | :------: | :------------: | :----: | :------: | :--------: | +| HourglassNet-104 | [10 x 5](./cornernet_hourglass104_mstest_10x5_210e_coco.py) | 180/210 | 13.9 | 4.2 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720-5fefbf1c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720.log.json) | +| HourglassNet-104 | [8 x 6](./cornernet_hourglass104_mstest_8x6_210e_coco.py) | 180/210 | 15.9 | 4.2 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618-79b44c30.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618.log.json) | +| HourglassNet-104 | [32 x 3](./cornernet_hourglass104_mstest_32x3_210e_coco.py) | 180/210 | 9.5 | 3.9 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110-1efaea91.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110.log.json) | + +Note: + +- TTA setting is single-scale and `flip=True`. +- Experiments with `images_per_gpu=6` are conducted on Tesla V100-SXM2-32GB, `images_per_gpu=3` are conducted on GeForce GTX 1080 Ti. +- Here are the descriptions of each experiment setting: + - 10 x 5: 10 GPUs with 5 images per gpu. This is the same setting as that reported in the original paper. + - 8 x 6: 8 GPUs with 6 images per gpu. The total batchsize is similar to paper and only need 1 node to train. + - 32 x 3: 32 GPUs with 3 images per gpu. The default setting for 1080TI and need 4 nodes to train. diff --git a/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py b/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py new file mode 100644 index 0000000..89f3876 --- /dev/null +++ b/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py @@ -0,0 +1,105 @@ +_base_ = [ + '../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py' +] + +# model settings +model = dict( + type='CornerNet', + backbone=dict( + type='HourglassNet', + downsample_times=5, + num_stacks=2, + stage_channels=[256, 256, 384, 384, 384, 512], + stage_blocks=[2, 2, 2, 2, 2, 4], + norm_cfg=dict(type='BN', requires_grad=True)), + neck=None, + bbox_head=dict( + type='CornerHead', + num_classes=80, + in_channels=256, + num_feat_levels=2, + corner_emb_channels=1, + loss_heatmap=dict( + type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1), + loss_embedding=dict( + type='AssociativeEmbeddingLoss', + pull_weight=0.10, + push_weight=0.10), + loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1)), + # training and testing settings + train_cfg=None, + test_cfg=dict( + corner_topk=100, + local_maximum_kernel=3, + distance_threshold=0.5, + score_thr=0.05, + max_per_img=100, + nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian'))) +# data settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='RandomCenterCropPad', + crop_size=(511, 511), + ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3), + test_mode=False, + test_pad_mode=None, + **img_norm_cfg), + dict(type='Resize', img_scale=(511, 511), keep_ratio=False), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict( + type='MultiScaleFlipAug', + scale_factor=1.0, + flip=True, + transforms=[ + dict(type='Resize'), + dict( + type='RandomCenterCropPad', + crop_size=None, + ratios=None, + border=None, + test_mode=True, + test_pad_mode=['logical_or', 127], + **img_norm_cfg), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict( + type='Collect', + keys=['img'], + meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', + 'scale_factor', 'flip', 'img_norm_cfg', 'border')), + ]) +] +data = dict( + samples_per_gpu=5, + workers_per_gpu=3, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='Adam', lr=0.0005) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=1.0 / 3, + step=[180]) +runner = dict(type='EpochBasedRunner', max_epochs=210) diff --git a/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py b/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py new file mode 100644 index 0000000..873d598 --- /dev/null +++ b/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py @@ -0,0 +1,105 @@ +_base_ = [ + '../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py' +] + +# model settings +model = dict( + type='CornerNet', + backbone=dict( + type='HourglassNet', + downsample_times=5, + num_stacks=2, + stage_channels=[256, 256, 384, 384, 384, 512], + stage_blocks=[2, 2, 2, 2, 2, 4], + norm_cfg=dict(type='BN', requires_grad=True)), + neck=None, + bbox_head=dict( + type='CornerHead', + num_classes=80, + in_channels=256, + num_feat_levels=2, + corner_emb_channels=1, + loss_heatmap=dict( + type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1), + loss_embedding=dict( + type='AssociativeEmbeddingLoss', + pull_weight=0.10, + push_weight=0.10), + loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1)), + # training and testing settings + train_cfg=None, + test_cfg=dict( + corner_topk=100, + local_maximum_kernel=3, + distance_threshold=0.5, + score_thr=0.05, + max_per_img=100, + nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian'))) +# data settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='RandomCenterCropPad', + crop_size=(511, 511), + ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3), + test_mode=False, + test_pad_mode=None, + **img_norm_cfg), + dict(type='Resize', img_scale=(511, 511), keep_ratio=False), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict( + type='MultiScaleFlipAug', + scale_factor=1.0, + flip=True, + transforms=[ + dict(type='Resize'), + dict( + type='RandomCenterCropPad', + crop_size=None, + ratios=None, + border=None, + test_mode=True, + test_pad_mode=['logical_or', 127], + **img_norm_cfg), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict( + type='Collect', + keys=['img'], + meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', + 'scale_factor', 'flip', 'img_norm_cfg', 'border')), + ]) +] +data = dict( + samples_per_gpu=3, + workers_per_gpu=3, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='Adam', lr=0.0005) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=1.0 / 3, + step=[180]) +runner = dict(type='EpochBasedRunner', max_epochs=210) diff --git a/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py b/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py new file mode 100644 index 0000000..ef749cc --- /dev/null +++ b/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py @@ -0,0 +1,105 @@ +_base_ = [ + '../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py' +] + +# model settings +model = dict( + type='CornerNet', + backbone=dict( + type='HourglassNet', + downsample_times=5, + num_stacks=2, + stage_channels=[256, 256, 384, 384, 384, 512], + stage_blocks=[2, 2, 2, 2, 2, 4], + norm_cfg=dict(type='BN', requires_grad=True)), + neck=None, + bbox_head=dict( + type='CornerHead', + num_classes=80, + in_channels=256, + num_feat_levels=2, + corner_emb_channels=1, + loss_heatmap=dict( + type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1), + loss_embedding=dict( + type='AssociativeEmbeddingLoss', + pull_weight=0.10, + push_weight=0.10), + loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1)), + # training and testing settings + train_cfg=None, + test_cfg=dict( + corner_topk=100, + local_maximum_kernel=3, + distance_threshold=0.5, + score_thr=0.05, + max_per_img=100, + nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian'))) +# data settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='RandomCenterCropPad', + crop_size=(511, 511), + ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3), + test_mode=False, + test_pad_mode=None, + **img_norm_cfg), + dict(type='Resize', img_scale=(511, 511), keep_ratio=False), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict( + type='MultiScaleFlipAug', + scale_factor=1.0, + flip=True, + transforms=[ + dict(type='Resize'), + dict( + type='RandomCenterCropPad', + crop_size=None, + ratios=None, + border=None, + test_mode=True, + test_pad_mode=['logical_or', 127], + **img_norm_cfg), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict( + type='Collect', + keys=['img'], + meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', + 'scale_factor', 'flip', 'img_norm_cfg', 'border')), + ]) +] +data = dict( + samples_per_gpu=6, + workers_per_gpu=3, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='Adam', lr=0.0005) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=1.0 / 3, + step=[180]) +runner = dict(type='EpochBasedRunner', max_epochs=210) diff --git a/configs/dcn/README.md b/configs/dcn/README.md new file mode 100644 index 0000000..ebcf89d --- /dev/null +++ b/configs/dcn/README.md @@ -0,0 +1,52 @@ +# Deformable Convolutional Networks + +## Introduction + + + +```none +@inproceedings{dai2017deformable, + title={Deformable Convolutional Networks}, + author={Dai, Jifeng and Qi, Haozhi and Xiong, Yuwen and Li, Yi and Zhang, Guodong and Hu, Han and Wei, Yichen}, + booktitle={Proceedings of the IEEE international conference on computer vision}, + year={2017} +} +``` + + + +``` +@article{zhu2018deformable, + title={Deformable ConvNets v2: More Deformable, Better Results}, + author={Zhu, Xizhou and Hu, Han and Lin, Stephen and Dai, Jifeng}, + journal={arXiv preprint arXiv:1811.11168}, + year={2018} +} +``` + +## Results and Models + +| Backbone | Model | Style | Conv | Pool | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:----------------:|:------------:|:-------:|:-------------:|:------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| +| R-50-FPN | Faster | pytorch | dconv(c3-c5) | - | 1x | 4.0 | 17.8 | 41.3 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-d68aed1e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130_212941.log.json) | +| R-50-FPN | Faster | pytorch | mdconv(c3-c5) | - | 1x | 4.1 | 17.6 | 41.4 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200130-d099253b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200130_222144.log.json) | +| *R-50-FPN (dg=4) | Faster | pytorch | mdconv(c3-c5) | - | 1x | 4.2 | 17.4 | 41.5 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco_20200130-01262257.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco_20200130_222058.log.json) | +| R-50-FPN | Faster | pytorch | - | dpool | 1x | 5.0 | 17.2 | 38.9 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dpool_1x_coco/faster_rcnn_r50_fpn_dpool_1x_coco_20200307-90d3c01d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dpool_1x_coco/faster_rcnn_r50_fpn_dpool_1x_coco_20200307_203250.log.json) | +| R-50-FPN | Faster | pytorch | - | mdpool | 1x | 5.8 | 16.6 | 38.7 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco/faster_rcnn_r50_fpn_mdpool_1x_coco_20200307-c0df27ff.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco/faster_rcnn_r50_fpn_mdpool_1x_coco_20200307_203304.log.json) | +| R-101-FPN | Faster | pytorch | dconv(c3-c5) | - | 1x | 6.0 | 12.5 | 42.7 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-1377f13d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203_230019.log.json) | +| X-101-32x4d-FPN | Faster | pytorch | dconv(c3-c5) | - | 1x | 7.3 | 10.0 | 44.5 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco_20200203-4f85c69c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco_20200203_001325.log.json) | +| R-50-FPN | Mask | pytorch | dconv(c3-c5) | - | 1x | 4.5 | 15.4 | 41.8 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200203-4d9ad43b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200203_061339.log.json) | +| R-50-FPN | Mask | pytorch | mdconv(c3-c5) | - | 1x | 4.5 | 15.1 | 41.5 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200203-ad97591f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200203_063443.log.json) | +| R-101-FPN | Mask | pytorch | dconv(c3-c5) | - | 1x | 6.5 | 11.7 | 43.5 | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200216-a71f5bce.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200216_191601.log.json) | +| R-50-FPN | Cascade | pytorch | dconv(c3-c5) | - | 1x | 4.5 | 14.6 | 43.8 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-2f1fca44.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130_220843.log.json) | +| R-101-FPN | Cascade | pytorch | dconv(c3-c5) | - | 1x | 6.4 | 11.0 | 45.0 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-3b2f0594.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203_224829.log.json) | +| R-50-FPN | Cascade Mask | pytorch | dconv(c3-c5) | - | 1x | 6.0 | 10.0 | 44.4 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200202-42e767a2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200202_010309.log.json) | +| R-101-FPN | Cascade Mask | pytorch | dconv(c3-c5) | - | 1x | 8.0 | 8.6 | 45.8 | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200204-df0c5f10.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200204_134006.log.json) | +| X-101-32x4d-FPN | Cascade Mask | pytorch | dconv(c3-c5) | - | 1x | 9.2 | | 47.3 | 41.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco-e75f90c8.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco-20200606_183737.log.json) | + +**Notes:** + +- `dconv` and `mdconv` denote (modulated) deformable convolution, `c3-c5` means adding dconv in resnet stage 3 to 5. `dpool` and `mdpool` denote (modulated) deformable roi pooling. +- The dcn ops are modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch, which should be more memory efficient and slightly faster. +- (*) For R-50-FPN (dg=4), dg is short for deformable_group. This model is trained and tested on Amazon EC2 p3dn.24xlarge instance. +- **Memory, Train/Inf time is outdated.** diff --git a/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..081b998 --- /dev/null +++ b/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..3b3683a --- /dev/null +++ b/configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..daaa472 --- /dev/null +++ b/configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..a01df33 --- /dev/null +++ b/configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..aa664bd --- /dev/null +++ b/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..f5fee7e --- /dev/null +++ b/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..8787088 --- /dev/null +++ b/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py b/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py new file mode 100644 index 0000000..1b695f0 --- /dev/null +++ b/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py @@ -0,0 +1,12 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + _delete_=True, + type='DeformRoIPoolPack', + output_size=7, + output_channels=256), + out_channels=256, + featmap_strides=[4, 8, 16, 32]))) diff --git a/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py b/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..d1bcf3c --- /dev/null +++ b/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py b/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py new file mode 100644 index 0000000..d0ab89c --- /dev/null +++ b/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCNv2', deform_groups=4, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py b/configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py new file mode 100644 index 0000000..ad7b034 --- /dev/null +++ b/configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py @@ -0,0 +1,12 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + _delete_=True, + type='ModulatedDeformRoIPoolPack', + output_size=7, + output_channels=256), + out_channels=256, + featmap_strides=[4, 8, 16, 32]))) diff --git a/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..8357766 --- /dev/null +++ b/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,15 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..cb34002 --- /dev/null +++ b/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..ababe58 --- /dev/null +++ b/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py b/configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..5ca2a67 --- /dev/null +++ b/configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/deepfashion/README.md b/configs/deepfashion/README.md new file mode 100644 index 0000000..68e57e4 --- /dev/null +++ b/configs/deepfashion/README.md @@ -0,0 +1,56 @@ +# DeepFashion + + + +[MMFashion](https://github.com/open-mmlab/mmfashion) develops "fashion parsing and segmentation" module +based on the dataset +[DeepFashion-Inshop](https://drive.google.com/drive/folders/0B7EVK8r0v71pVDZFQXRsMDZCX1E?usp=sharing). +Its annotation follows COCO style. +To use it, you need to first download the data. Note that we only use "img_highres" in this task. +The file tree should be like this: + +```sh +mmdetection +├── mmdet +├── tools +├── configs +├── data +│ ├── DeepFashion +│ │ ├── In-shop +│ │ ├── Anno +│ │ │   ├── segmentation +│ │ │   | ├── DeepFashion_segmentation_train.json +│ │ │   | ├── DeepFashion_segmentation_query.json +│ │ │   | ├── DeepFashion_segmentation_gallery.json +│ │ │   ├── list_bbox_inshop.txt +│ │ │   ├── list_description_inshop.json +│ │ │   ├── list_item_inshop.txt +│ │ │   └── list_landmarks_inshop.txt +│ │ ├── Eval +│ │ │ └── list_eval_partition.txt +│ │ ├── Img +│ │ │ ├── img +│ │ │ │ ├──XXX.jpg +│ │ │ ├── img_highres +│ │ │ └── ├──XXX.jpg + +``` + +After that you can train the Mask RCNN r50 on DeepFashion-In-shop dataset by launching training with the `mask_rcnn_r50_fpn_1x.py` config +or creating your own config file. + +``` +@inproceedings{liuLQWTcvpr16DeepFashion, + author = {Liu, Ziwei and Luo, Ping and Qiu, Shi and Wang, Xiaogang and Tang, Xiaoou}, + title = {DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations}, + booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + month = {June}, + year = {2016} +} +``` + +## Model Zoo + +| Backbone | Model type | Dataset | bbox detection Average Precision | segmentation Average Precision | Config | Download (Google) | +| :---------: | :----------: | :-----------------: | :--------------------------------: | :----------------------------: | :---------:| :-------------------------: | +| ResNet50 | Mask RCNN | DeepFashion-In-shop | 0.599 | 0.584 |[config](https://github.com/open-mmlab/mmdetection/blob/master/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py)| [model](https://drive.google.com/open?id=1q6zF7J6Gb-FFgM87oIORIt6uBozaXp5r) | [log](https://drive.google.com/file/d/1qTK4Dr4FFLa9fkdI6UVko408gkrfTRLP/view?usp=sharing) | diff --git a/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py b/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py new file mode 100644 index 0000000..c4e8638 --- /dev/null +++ b/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py @@ -0,0 +1,10 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/deepfashion.py', '../_base_/schedules/schedule_1x.py', + '../_base_/default_runtime.py' +] +model = dict( + roi_head=dict( + bbox_head=dict(num_classes=15), mask_head=dict(num_classes=15))) +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=15) diff --git a/configs/deformable_detr/README.md b/configs/deformable_detr/README.md new file mode 100644 index 0000000..fe68002 --- /dev/null +++ b/configs/deformable_detr/README.md @@ -0,0 +1,31 @@ +# Deformable DETR + +## Introduction + + + +We provide the config files for Deformable DETR: [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159). + +``` +@inproceedings{ +zhu2021deformable, +title={Deformable DETR: Deformable Transformers for End-to-End Object Detection}, +author={Xizhou Zhu and Weijie Su and Lewei Lu and Bin Li and Xiaogang Wang and Jifeng Dai}, +booktitle={International Conference on Learning Representations}, +year={2021}, +url={https://openreview.net/forum?id=gZ9hCDWe6ke} +} +``` + +## Results and Models + +| Backbone | Model | Lr schd | box AP | Config | Download | +|:------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50 | Deformable DETR |50e | 44.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_r50_16x2_50e_coco/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_r50_16x2_50e_coco/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.log.json) | +| R-50 | + iterative bounding box refinement |50e | 46.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco/deformable_detr_refine_r50_16x2_50e_coco_20210419_220503-5f5dff21.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco/deformable_detr_refine_r50_16x2_50e_coco_20210419_220503-5f5dff21.log.json) | +| R-50 | ++ two-stage Deformable DETR |50e | 46.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco/deformable_detr_twostage_refine_r50_16x2_50e_coco_20210419_220613-9d28ab72.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco/deformable_detr_twostage_refine_r50_16x2_50e_coco_20210419_220613-9d28ab72.log.json) | + +# NOTE + +1. All models are trained with batch size 32. +2. The performance is unstable. `Deformable DETR` and `iterative bounding box refinement` may fluctuate about 0.3 mAP. `two-stage Deformable DETR` may fluctuate about 0.2 mAP. diff --git a/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py b/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py new file mode 100644 index 0000000..546f827 --- /dev/null +++ b/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py @@ -0,0 +1,172 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py' +] +model = dict( + type='DeformableDETR', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='pytorch'), + neck=dict( + type='ChannelMapper', + in_channels=[512, 1024, 2048], + kernel_size=1, + out_channels=256, + act_cfg=None, + norm_cfg=dict(type='GN', num_groups=32), + num_outs=4), + bbox_head=dict( + type='DeformableDETRHead', + num_query=300, + num_classes=80, + in_channels=2048, + sync_cls_avg_factor=True, + as_two_stage=False, + transformer=dict( + type='DeformableDetrTransformer', + encoder=dict( + type='DetrTransformerEncoder', + num_layers=6, + transformerlayers=dict( + type='BaseTransformerLayer', + attn_cfgs=dict( + type='MultiScaleDeformableAttention', embed_dims=256), + feedforward_channels=1024, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'ffn', 'norm'))), + decoder=dict( + type='DeformableDetrTransformerDecoder', + num_layers=6, + return_intermediate=True, + transformerlayers=dict( + type='DetrTransformerDecoderLayer', + attn_cfgs=[ + dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1), + dict( + type='MultiScaleDeformableAttention', + embed_dims=256) + ], + feedforward_channels=1024, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'cross_attn', 'norm', + 'ffn', 'norm')))), + positional_encoding=dict( + type='SinePositionalEncoding', + num_feats=128, + normalize=True, + offset=-0.5), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'), + iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))), + test_cfg=dict(max_per_img=100)) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different +# from the default setting in mmdet. +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='AutoAugment', + policies=[ + [ + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict( + type='Resize', + # The radio of all image in train dataset < 7 + # follow the original impl + img_scale=[(400, 4200), (500, 4200), (600, 4200)], + multiscale_mode='value', + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ] + ]), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=1), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +# test_pipeline, NOTE the Pad's size_divisor is different from the default +# setting (size_divisor=32). While there is little effect on the performance +# whether we use the default setting or use size_divisor=1. +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=1), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(filter_empty_gt=False, pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict( + type='AdamW', + lr=2e-4, + weight_decay=0.0001, + paramwise_cfg=dict( + custom_keys={ + 'backbone': dict(lr_mult=0.1), + 'sampling_offsets': dict(lr_mult=0.1), + 'reference_points': dict(lr_mult=0.1) + })) +optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2)) +# learning policy +lr_config = dict(policy='step', step=[40]) +runner = dict(type='EpochBasedRunner', max_epochs=50) diff --git a/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py b/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py new file mode 100644 index 0000000..01f13df --- /dev/null +++ b/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py @@ -0,0 +1,2 @@ +_base_ = 'deformable_detr_r50_16x2_50e_coco.py' +model = dict(bbox_head=dict(with_box_refine=True)) diff --git a/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py b/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py new file mode 100644 index 0000000..2aa840d --- /dev/null +++ b/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py @@ -0,0 +1,2 @@ +_base_ = 'deformable_detr_refine_r50_16x2_50e_coco.py' +model = dict(bbox_head=dict(as_two_stage=True)) diff --git a/configs/detectors/README.md b/configs/detectors/README.md new file mode 100644 index 0000000..a2c6d42 --- /dev/null +++ b/configs/detectors/README.md @@ -0,0 +1,58 @@ +# DetectoRS + +## Introduction + + + +We provide the config files for [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://arxiv.org/pdf/2006.02334.pdf). + +```BibTeX +@article{qiao2020detectors, + title={DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution}, + author={Qiao, Siyuan and Chen, Liang-Chieh and Yuille, Alan}, + journal={arXiv preprint arXiv:2006.02334}, + year={2020} +} +``` + +## Dataset + +DetectoRS requires COCO and [COCO-stuff](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip) dataset for training. You need to download and extract it in the COCO dataset path. +The directory should be like this. + +```none +mmdetection +├── mmdet +├── tools +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ ├── train2017 +│ │ ├── val2017 +│ │ ├── test2017 +| | ├── stuffthingmaps +``` + +## Results and Models + +DetectoRS includes two major components: + +- Recursive Feature Pyramid (RFP). +- Switchable Atrous Convolution (SAC). + +They can be used independently. +Combining them together results in DetectoRS. +The results on COCO 2017 val are shown in the below table. + +| Method | Detector | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:------:|:--------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| +| RFP | Cascade + ResNet-50 | 1x | 7.5 | - | 44.8 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_rfp_1x_coco/cascade_rcnn_r50_rfp_1x_coco-8cf51bfd.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_rfp_1x_coco/cascade_rcnn_r50_rfp_1x_coco_20200624_104126.log.json) | +| SAC | Cascade + ResNet-50 | 1x | 5.6 | - | 45.0| | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/cascade_rcnn_r50_sac_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_sac_1x_coco/cascade_rcnn_r50_sac_1x_coco-24bfda62.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_sac_1x_coco/cascade_rcnn_r50_sac_1x_coco_20200624_104402.log.json) | +| DetectoRS | Cascade + ResNet-50 | 1x | 9.9 | - | 47.4 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_cascade_rcnn_r50_1x_coco/detectors_cascade_rcnn_r50_1x_coco-32a10ba0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_cascade_rcnn_r50_1x_coco/detectors_cascade_rcnn_r50_1x_coco_20200706_001203.log.json) | +| RFP | HTC + ResNet-50 | 1x | 11.2 | - | 46.6 | 40.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/htc_r50_rfp_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_rfp_1x_coco/htc_r50_rfp_1x_coco-8ff87c51.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_rfp_1x_coco/htc_r50_rfp_1x_coco_20200624_103053.log.json) | +| SAC | HTC + ResNet-50 | 1x | 9.3 | - | 46.4 | 40.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/htc_r50_sac_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_sac_1x_coco/htc_r50_sac_1x_coco-bfa60c54.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_sac_1x_coco/htc_r50_sac_1x_coco_20200624_103111.log.json) | +| DetectoRS | HTC + ResNet-50 | 1x | 13.6 | - | 49.1 | 42.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_htc_r50_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r50_1x_coco/detectors_htc_r50_1x_coco-329b1453.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r50_1x_coco/detectors_htc_r50_1x_coco_20200624_103659.log.json) | + +*Note*: This is a re-implementation based on MMDetection-V2. +The original implementation is based on MMDetection-V1. diff --git a/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py b/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py new file mode 100644 index 0000000..4430d8a --- /dev/null +++ b/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py @@ -0,0 +1,28 @@ +_base_ = [ + '../_base_/models/cascade_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + backbone=dict( + type='DetectoRS_ResNet', + conv_cfg=dict(type='ConvAWS'), + output_img=True), + neck=dict( + type='RFP', + rfp_steps=2, + aspp_out_channels=64, + aspp_dilations=(1, 3, 6, 1), + rfp_backbone=dict( + rfp_inplanes=256, + type='DetectoRS_ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + conv_cfg=dict(type='ConvAWS'), + pretrained='torchvision://resnet50', + style='pytorch'))) diff --git a/configs/detectors/cascade_rcnn_r50_sac_1x_coco.py b/configs/detectors/cascade_rcnn_r50_sac_1x_coco.py new file mode 100644 index 0000000..ccd9319 --- /dev/null +++ b/configs/detectors/cascade_rcnn_r50_sac_1x_coco.py @@ -0,0 +1,12 @@ +_base_ = [ + '../_base_/models/cascade_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + backbone=dict( + type='DetectoRS_ResNet', + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True))) diff --git a/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py b/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py new file mode 100644 index 0000000..f760404 --- /dev/null +++ b/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py @@ -0,0 +1,32 @@ +_base_ = [ + '../_base_/models/cascade_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + backbone=dict( + type='DetectoRS_ResNet', + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True), + output_img=True), + neck=dict( + type='RFP', + rfp_steps=2, + aspp_out_channels=64, + aspp_dilations=(1, 3, 6, 1), + rfp_backbone=dict( + rfp_inplanes=256, + type='DetectoRS_ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True), + pretrained='torchvision://resnet50', + style='pytorch'))) diff --git a/configs/detectors/detectors_htc_r50_1x_coco.py b/configs/detectors/detectors_htc_r50_1x_coco.py new file mode 100644 index 0000000..0d2fc4f --- /dev/null +++ b/configs/detectors/detectors_htc_r50_1x_coco.py @@ -0,0 +1,28 @@ +_base_ = '../htc/htc_r50_fpn_1x_coco.py' + +model = dict( + backbone=dict( + type='DetectoRS_ResNet', + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True), + output_img=True), + neck=dict( + type='RFP', + rfp_steps=2, + aspp_out_channels=64, + aspp_dilations=(1, 3, 6, 1), + rfp_backbone=dict( + rfp_inplanes=256, + type='DetectoRS_ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True), + pretrained='torchvision://resnet50', + style='pytorch'))) diff --git a/configs/detectors/htc_r50_rfp_1x_coco.py b/configs/detectors/htc_r50_rfp_1x_coco.py new file mode 100644 index 0000000..496104e --- /dev/null +++ b/configs/detectors/htc_r50_rfp_1x_coco.py @@ -0,0 +1,24 @@ +_base_ = '../htc/htc_r50_fpn_1x_coco.py' + +model = dict( + backbone=dict( + type='DetectoRS_ResNet', + conv_cfg=dict(type='ConvAWS'), + output_img=True), + neck=dict( + type='RFP', + rfp_steps=2, + aspp_out_channels=64, + aspp_dilations=(1, 3, 6, 1), + rfp_backbone=dict( + rfp_inplanes=256, + type='DetectoRS_ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + conv_cfg=dict(type='ConvAWS'), + pretrained='torchvision://resnet50', + style='pytorch'))) diff --git a/configs/detectors/htc_r50_sac_1x_coco.py b/configs/detectors/htc_r50_sac_1x_coco.py new file mode 100644 index 0000000..72d4db9 --- /dev/null +++ b/configs/detectors/htc_r50_sac_1x_coco.py @@ -0,0 +1,8 @@ +_base_ = '../htc/htc_r50_fpn_1x_coco.py' + +model = dict( + backbone=dict( + type='DetectoRS_ResNet', + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True))) diff --git a/configs/detr/README.md b/configs/detr/README.md new file mode 100644 index 0000000..c5444b4 --- /dev/null +++ b/configs/detr/README.md @@ -0,0 +1,27 @@ +# DETR + +## Introduction + + + +We provide the config files for DETR: [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872). + +```BibTeX +@inproceedings{detr, + author = {Nicolas Carion and + Francisco Massa and + Gabriel Synnaeve and + Nicolas Usunier and + Alexander Kirillov and + Sergey Zagoruyko}, + title = {End-to-End Object Detection with Transformers}, + booktitle = {ECCV}, + year = {2020} +} +``` + +## Results and Models + +| Backbone | Model | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:------:|:--------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50 | DETR |150e |7.9| | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detr/detr_r50_8x2_150e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835.log.json) | diff --git a/configs/detr/detr_r50_8x2_150e_coco.py b/configs/detr/detr_r50_8x2_150e_coco.py new file mode 100644 index 0000000..45f6414 --- /dev/null +++ b/configs/detr/detr_r50_8x2_150e_coco.py @@ -0,0 +1,150 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py' +] +model = dict( + type='DETR', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(3, ), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='pytorch'), + bbox_head=dict( + type='DETRHead', + num_classes=80, + in_channels=2048, + transformer=dict( + type='Transformer', + encoder=dict( + type='DetrTransformerEncoder', + num_layers=6, + transformerlayers=dict( + type='BaseTransformerLayer', + attn_cfgs=[ + dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1) + ], + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'ffn', 'norm'))), + decoder=dict( + type='DetrTransformerDecoder', + return_intermediate=True, + num_layers=6, + transformerlayers=dict( + type='DetrTransformerDecoderLayer', + attn_cfgs=dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1), + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'cross_attn', 'norm', + 'ffn', 'norm')), + )), + positional_encoding=dict( + type='SinePositionalEncoding', num_feats=128, normalize=True), + loss_cls=dict( + type='CrossEntropyLoss', + bg_cls_weight=0.1, + use_sigmoid=False, + loss_weight=1.0, + class_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='ClassificationCost', weight=1.), + reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'), + iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))), + test_cfg=dict(max_per_img=100)) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different +# from the default setting in mmdet. +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='AutoAugment', + policies=[[ + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict( + type='Resize', + img_scale=[(400, 1333), (500, 1333), (600, 1333)], + multiscale_mode='value', + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ]]), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=1), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +# test_pipeline, NOTE the Pad's size_divisor is different from the default +# setting (size_divisor=32). While there is little effect on the performance +# whether we use the default setting or use size_divisor=1. +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=1), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict( + type='AdamW', + lr=0.0001, + weight_decay=0.0001, + paramwise_cfg=dict( + custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)})) +optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2)) +# learning policy +lr_config = dict(policy='step', step=[100]) +runner = dict(type='EpochBasedRunner', max_epochs=150) diff --git a/configs/double_heads/README.md b/configs/double_heads/README.md new file mode 100644 index 0000000..b7cf4c0 --- /dev/null +++ b/configs/double_heads/README.md @@ -0,0 +1,22 @@ +# Rethinking Classification and Localization for Object Detection + +## Introduction + + + +```latex +@article{wu2019rethinking, + title={Rethinking Classification and Localization for Object Detection}, + author={Yue Wu and Yinpeng Chen and Lu Yuan and Zicheng Liu and Lijuan Wang and Hongzhi Li and Yun Fu}, + year={2019}, + eprint={1904.06493}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + +## Results and models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| R-50-FPN | pytorch | 1x | 6.8 | 9.5 | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/double_heads/dh_faster_rcnn_r50_fpn_1x_coco/dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/double_heads/dh_faster_rcnn_r50_fpn_1x_coco/dh_faster_rcnn_r50_fpn_1x_coco_20200130_220238.log.json) | diff --git a/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py b/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..9b8118b --- /dev/null +++ b/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,23 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + type='DoubleHeadRoIHead', + reg_roi_scale_factor=1.3, + bbox_head=dict( + _delete_=True, + type='DoubleConvFCBBoxHead', + num_convs=4, + num_fcs=2, + in_channels=256, + conv_out_channels=1024, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=2.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=2.0)))) diff --git a/configs/dynamic_rcnn/README.md b/configs/dynamic_rcnn/README.md new file mode 100644 index 0000000..9c4c0e4 --- /dev/null +++ b/configs/dynamic_rcnn/README.md @@ -0,0 +1,20 @@ +# Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training + +## Introduction + + + +``` +@article{DynamicRCNN, + author = {Hongkai Zhang and Hong Chang and Bingpeng Ma and Naiyan Wang and Xilin Chen}, + title = {Dynamic {R-CNN}: Towards High Quality Object Detection via Dynamic Training}, + journal = {arXiv preprint arXiv:2004.06002}, + year = {2020} +} +``` + +## Results and Models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50 | pytorch | 1x | 3.8 | | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x-62a3f276.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x_20200618_095048.log.json) | diff --git a/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py b/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..f2deb99 --- /dev/null +++ b/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,28 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + type='DynamicRoIHead', + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + train_cfg=dict( + rpn_proposal=dict(nms=dict(iou_threshold=0.85)), + rcnn=dict( + dynamic_rcnn=dict( + iou_topk=75, + beta_topk=10, + update_iter_interval=100, + initial_iou=0.4, + initial_beta=1.0))), + test_cfg=dict(rpn=dict(nms=dict(iou_threshold=0.85)))) diff --git a/configs/empirical_attention/README.md b/configs/empirical_attention/README.md new file mode 100644 index 0000000..380acd0 --- /dev/null +++ b/configs/empirical_attention/README.md @@ -0,0 +1,23 @@ +# An Empirical Study of Spatial Attention Mechanisms in Deep Networks + +## Introduction + + + +```latex +@article{zhu2019empirical, + title={An Empirical Study of Spatial Attention Mechanisms in Deep Networks}, + author={Zhu, Xizhou and Cheng, Dazhi and Zhang, Zheng and Lin, Stephen and Dai, Jifeng}, + journal={arXiv preprint arXiv:1904.05873}, + year={2019} +} +``` + +## Results and Models + +| Backbone | Attention Component | DCN | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:---------:|:-------------------:|:----:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50 | 1111 | N | 1x | 8.0 | 13.8 | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130_210344.log.json) | +| R-50 | 0010 | N | 1x | 4.2 | 18.4 | 39.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco/faster_rcnn_r50_fpn_attention_0010_1x_coco_20200130-7cb0c14d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco/faster_rcnn_r50_fpn_attention_0010_1x_coco_20200130_210125.log.json) | +| R-50 | 1111 | Y | 1x | 8.0 | 12.7 | 42.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco_20200130-8b2523a6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco_20200130_204442.log.json) | +| R-50 | 0010 | Y | 1x | 4.2 | 17.1 | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco_20200130-1a2e831d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco_20200130_210410.log.json) | diff --git a/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py new file mode 100644 index 0000000..a544e3a --- /dev/null +++ b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict(plugins=[ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + stages=(False, False, True, True), + position='after_conv2') + ])) diff --git a/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py new file mode 100644 index 0000000..bbefd27 --- /dev/null +++ b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + plugins=[ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + stages=(False, False, True, True), + position='after_conv2') + ], + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py new file mode 100644 index 0000000..13a4645 --- /dev/null +++ b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict(plugins=[ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='1111', + kv_stride=2), + stages=(False, False, True, True), + position='after_conv2') + ])) diff --git a/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py new file mode 100644 index 0000000..b1f26c0 --- /dev/null +++ b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + plugins=[ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='1111', + kv_stride=2), + stages=(False, False, True, True), + position='after_conv2') + ], + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/fast_rcnn/README.md b/configs/fast_rcnn/README.md new file mode 100644 index 0000000..3be862a --- /dev/null +++ b/configs/fast_rcnn/README.md @@ -0,0 +1,16 @@ +# Fast R-CNN + +## Introduction + + + +```latex +@inproceedings{girshick2015fast, + title={Fast r-cnn}, + author={Girshick, Ross}, + booktitle={Proceedings of the IEEE international conference on computer vision}, + year={2015} +} +``` + +## Results and models diff --git a/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py b/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..6db24b1 --- /dev/null +++ b/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './fast_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py b/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 0000000..9a76b39 --- /dev/null +++ b/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './fast_rcnn_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py b/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py new file mode 100644 index 0000000..c9d5b4b --- /dev/null +++ b/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py @@ -0,0 +1,2 @@ +_base_ = './fast_rcnn_r50_fpn_2x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py b/configs/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..178deb6 --- /dev/null +++ b/configs/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,45 @@ +_base_ = './fast_rcnn_r50_fpn_1x_coco.py' + +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + norm_cfg=dict(type='BN', requires_grad=False), style='caffe')) + +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=2000), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=None), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['proposals']), + dict( + type='ToDataContainer', + fields=[dict(key='proposals', stack=False)]), + dict(type='Collect', keys=['img', 'proposals']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py b/configs/fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..d2f080e --- /dev/null +++ b/configs/fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,52 @@ +_base_ = [ + '../_base_/models/fast_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=2000), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=None), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['proposals']), + dict( + type='ToDataContainer', + fields=[dict(key='proposals', stack=False)]), + dict(type='Collect', keys=['img', 'proposals']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_train2017.pkl', + pipeline=train_pipeline), + val=dict( + proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_val2017.pkl', + pipeline=test_pipeline), + test=dict( + proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_val2017.pkl', + pipeline=test_pipeline)) diff --git a/configs/fast_rcnn/fast_rcnn_r50_fpn_2x_coco.py b/configs/fast_rcnn/fast_rcnn_r50_fpn_2x_coco.py new file mode 100644 index 0000000..228e856 --- /dev/null +++ b/configs/fast_rcnn/fast_rcnn_r50_fpn_2x_coco.py @@ -0,0 +1,5 @@ +_base_ = './fast_rcnn_r50_fpn_1x_coco.py' + +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/faster_rcnn/README.md b/configs/faster_rcnn/README.md new file mode 100644 index 0000000..74330bc --- /dev/null +++ b/configs/faster_rcnn/README.md @@ -0,0 +1,61 @@ +# Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks + +## Introduction + + + +```latex +@article{Ren_2017, + title={Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks}, + journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, + publisher={Institute of Electrical and Electronics Engineers (IEEE)}, + author={Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian}, + year={2017}, + month={Jun}, +} +``` + +## Results and models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| R-50-DC5 | caffe | 1x | - | - | 37.2 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco/faster_rcnn_r50_caffe_dc5_1x_coco_20201030_151909-531f0f43.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco/faster_rcnn_r50_caffe_dc5_1x_coco_20201030_151909.log.json) | +| R-50-FPN | caffe | 1x | 3.8 | | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.378_20200504_180032-c5925ee5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_20200504_180032.log.json) | +| R-50-FPN | pytorch | 1x | 4.0 | 21.4 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +| R-50-FPN | pytorch | 2x | - | - | 38.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_20200504_210434.log.json) | +| R-101-FPN | caffe | 1x | 5.7 | | 39.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.398_20200504_180057-b269e9dd.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_20200504_180057.log.json) | +| R-101-FPN | pytorch | 1x | 6.0 | 15.6 | 39.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130-f513f705.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130_204655.log.json) | +| R-101-FPN | pytorch | 2x | - | - | 39.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_bbox_mAP-0.398_20200504_210455-1d2dac9c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_20200504_210455.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.2 | 13.8 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203-cff10310.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203_000520.log.json) | +| X-101-32x4d-FPN | pytorch | 2x | - | - | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.412_20200506_041400-64a12c0b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_20200506_041400.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.3 | 9.4 | 42.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204_134340.log.json) | +| X-101-64x4d-FPN | pytorch | 2x | - | - | 41.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033-5961fa95.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033.log.json) | + +## Different regression loss + +We trained with R-50-FPN pytorch style backbone for 1x schedule. + +| Backbone | Loss type | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-------: | :------: | :------------: | :----: | :------: | :--------: | +| R-50-FPN | L1Loss | 4.0 | 21.4 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +| R-50-FPN | IoULoss | | | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco-fdd207f3.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco_20200506_095954.log.json) | +| R-50-FPN | GIoULoss | | | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco-0eada910.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco_20200505_161120.log.json) | +| R-50-FPN | BoundedIoULoss | | | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco-98ad993b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco_20200505_160738.log.json) | + +## Pre-trained Models + +We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks. + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| [R-50-DC5](./faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py) | caffe | 1x | - | | 37.4 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851-b33d21b9.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851.log.json) +| [R-50-DC5](./faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py) | caffe | 3x | - | | 38.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107-34a53b2c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107.log.json) +| [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py) | caffe | 2x | 4.3 | | 39.7 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_bbox_mAP-0.397_20200504_231813-10b2de58.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_20200504_231813.log.json) +| [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py) | caffe | 3x | 4.3 | | 40.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20200504_163323.log.json) + +We further finetune some pre-trained models on the COCO subsets, which only contain only a few of the 80 categories. + +| Backbone | Style | Class name | Pre-traind model | Mem (GB) | box AP | Config | Download | +| ------------------------------------------------------------ | ----- | ------------------ | ------------------------------------------------------------ | -------- | ------ | ------------------------------------------------------------ | ------------------------------------------------------------ | +| [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py) | caffe | person | [R-50-FPN-Caffe-3x](./faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py) | 3.7 | 55.8 | [config](./faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco-person/faster_rcnn_r50_fpn_1x_coco-person_20201216_175929-d022e227.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco-person/faster_rcnn_r50_fpn_1x_coco-person_20201216_175929.log.json) | +| [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py) | caffe | person-bicycle-car | [R-50-FPN-Caffe-3x](./faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py) | 3.7 | 44.1 | [config](./faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco-person-bicycle-car/faster_rcnn_r50_fpn_1x_coco-person-bicycle-car_20201216_173117-6eda6d92.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco-person-bicycle-car/faster_rcnn_r50_fpn_1x_coco-person-bicycle-car_20201216_173117.log.json) | diff --git a/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..95c7238 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './faster_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 0000000..d2edab1 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py b/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py new file mode 100644 index 0000000..9367a3c --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py @@ -0,0 +1,2 @@ +_base_ = './faster_rcnn_r50_fpn_2x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco.py new file mode 100644 index 0000000..92344a1 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_caffe_c4.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py new file mode 100644 index 0000000..ee2010c --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py @@ -0,0 +1,37 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_caffe_dc5.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py new file mode 100644 index 0000000..14eaef2 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py @@ -0,0 +1,42 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_caffe_dc5.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py new file mode 100644 index 0000000..403747f --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py @@ -0,0 +1,4 @@ +_base_ = './faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py' +# learning policy +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..762c72b --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,37 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe')) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py new file mode 100644 index 0000000..23d7285 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py @@ -0,0 +1,9 @@ +_base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' +model = dict(roi_head=dict(bbox_head=dict(num_classes=3))) +classes = ('person', 'bicycle', 'car') +data = dict( + train=dict(classes=classes), + val=dict(classes=classes), + test=dict(classes=classes)) + +load_from = 'http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth' # noqa diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py new file mode 100644 index 0000000..b0164c7 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py @@ -0,0 +1,9 @@ +_base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' +model = dict(roi_head=dict(bbox_head=dict(num_classes=1))) +classes = ('person', ) +data = dict( + train=dict(classes=classes), + val=dict(classes=classes), + test=dict(classes=classes)) + +load_from = 'http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth' # noqa diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py new file mode 100644 index 0000000..4b87b2c --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py @@ -0,0 +1,42 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe')) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py new file mode 100644 index 0000000..df58973 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..a0ba54d --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py @@ -0,0 +1,4 @@ +_base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' +# learning policy +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py new file mode 100644 index 0000000..74dca24 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py @@ -0,0 +1,15 @@ +_base_ = 'faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' + +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[60000, 80000]) + +# Runner type +runner = dict(_delete_=True, type='IterBasedRunner', max_iters=90000) + +checkpoint_config = dict(interval=10000) +evaluation = dict(interval=10000, metric='bbox') diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..009bd93 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py new file mode 100644 index 0000000..e77a7fa --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py new file mode 100644 index 0000000..648081f --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + bbox_head=dict( + reg_decoded_bbox=True, + loss_bbox=dict(type='BoundedIoULoss', loss_weight=10.0)))) diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py new file mode 100644 index 0000000..5556c49 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + bbox_head=dict( + reg_decoded_bbox=True, + loss_bbox=dict(type='GIoULoss', loss_weight=10.0)))) diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py new file mode 100644 index 0000000..ddf663e --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + bbox_head=dict( + reg_decoded_bbox=True, + loss_bbox=dict(type='IoULoss', loss_weight=10.0)))) diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py new file mode 100644 index 0000000..f897e7c --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict(train_cfg=dict(rcnn=dict(sampler=dict(type='OHEMSampler')))) diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py new file mode 100644 index 0000000..759ae3a --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py @@ -0,0 +1,12 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + test_cfg=dict( + rcnn=dict( + score_thr=0.05, + nms=dict(type='soft_nms', iou_threshold=0.5), + max_per_img=100))) diff --git a/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..c536fcc --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py new file mode 100644 index 0000000..9276092 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py @@ -0,0 +1,13 @@ +_base_ = './faster_rcnn_r50_fpn_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..b588b4e --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py new file mode 100644 index 0000000..e87d21a --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py @@ -0,0 +1,13 @@ +_base_ = './faster_rcnn_r50_fpn_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/fcos/README.md b/configs/fcos/README.md new file mode 100644 index 0000000..ae5470a --- /dev/null +++ b/configs/fcos/README.md @@ -0,0 +1,35 @@ +# FCOS: Fully Convolutional One-Stage Object Detection + +## Introduction + + + +```latex +@article{tian2019fcos, + title={FCOS: Fully Convolutional One-Stage Object Detection}, + author={Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong}, + journal={arXiv preprint arXiv:1904.01355}, + year={2019} +} +``` + +## Results and Models + +| Backbone | Style | GN | MS train | Tricks | DCN | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:---------:|:-------:|:-------:|:--------:|:-------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50 | caffe | Y | N | N | N | 1x | 3.6 | 22.7 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco/fcos_r50_caffe_fpn_gn-head_1x_coco-821213aa.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco/20201227_180009.log.json) | +| R-50 | caffe | Y | N | Y | N | 1x | 3.7 | - | 38.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco-0a0d75a8.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco/20210105_135818.log.json)| +| R-50 | caffe | Y | N | Y | Y | 1x | 3.8 | - | 42.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco-ae4d8b3d.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco/20210105_224556.log.json)| +| R-101 | caffe | Y | N | N | N | 1x | 5.5 | 17.3 | 39.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco/fcos_r101_caffe_fpn_gn-head_1x_coco-0e37b982.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco/20210103_155046.log.json) | + +| Backbone | Style | GN | MS train | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:---------:|:-------:|:-------:|:--------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50 | caffe | Y | Y | 2x | 2.6 | 22.9 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco-d92ceeea.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco/20201227_161900.log.json) | +| R-101 | caffe | Y | Y | 2x | 5.5 | 17.3 | 40.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco-511424d6.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco/20210103_155046.log.json) | +| X-101 | pytorch | Y | Y | 2x | 10.0 | 9.7 | 42.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco-ede514a8.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco/20210114_133041.log.json) | + +**Notes:** + +- The X-101 backbone is X-101-64x4d. +- Tricks means setting `norm_on_bbox`, `centerness_on_reg`, `center_sampling` as `True`. +- DCN means using `DCNv2` in both backbone and head. diff --git a/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py b/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py new file mode 100644 index 0000000..c25561e --- /dev/null +++ b/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py @@ -0,0 +1,51 @@ +_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py' + +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + bbox_head=dict( + norm_on_bbox=True, + centerness_on_reg=True, + dcn_on_last_conv=False, + center_sampling=True, + conv_bias=True, + loss_bbox=dict(type='GIoULoss', loss_weight=1.0)), + # training and testing settings + test_cfg=dict(nms=dict(type='nms', iou_threshold=0.6))) + +# dataset settings +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +optimizer_config = dict(_delete_=True, grad_clip=None) + +lr_config = dict(warmup='linear') diff --git a/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py b/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py new file mode 100644 index 0000000..72b90f8 --- /dev/null +++ b/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py @@ -0,0 +1,54 @@ +_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py' + +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True)), + bbox_head=dict( + norm_on_bbox=True, + centerness_on_reg=True, + dcn_on_last_conv=True, + center_sampling=True, + conv_bias=True, + loss_bbox=dict(type='GIoULoss', loss_weight=1.0)), + # training and testing settings + test_cfg=dict(nms=dict(type='nms', iou_threshold=0.6))) + +# dataset settings +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +optimizer_config = dict(_delete_=True, grad_clip=None) + +lr_config = dict(warmup='linear') diff --git a/configs/fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py b/configs/fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py new file mode 100644 index 0000000..9f502e7 --- /dev/null +++ b/configs/fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' +model = dict(bbox_head=dict(center_sampling=True, center_sample_radius=1.5)) diff --git a/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py b/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py new file mode 100644 index 0000000..6c38266 --- /dev/null +++ b/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py b/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py new file mode 100644 index 0000000..81f61c6 --- /dev/null +++ b/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py @@ -0,0 +1,44 @@ +_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron/resnet101_caffe', + backbone=dict(depth=101)) +img_norm_cfg = dict( + mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py b/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py new file mode 100644 index 0000000..6e12411 --- /dev/null +++ b/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py @@ -0,0 +1,105 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# model settings +model = dict( + type='FCOS', + pretrained='open-mmlab://detectron/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs=True, + extra_convs_on_inputs=False, # use P5 + num_outs=5, + relu_before_extra_convs=True), + bbox_head=dict( + type='FCOSHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + strides=[8, 16, 32, 64, 128], + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='IoULoss', loss_weight=1.0), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) +img_norm_cfg = dict( + mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict( + lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.)) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='constant', + warmup_iters=500, + warmup_ratio=1.0 / 3, + step=[8, 11]) +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py b/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py new file mode 100644 index 0000000..2816b16 --- /dev/null +++ b/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py @@ -0,0 +1,4 @@ +# TODO: Remove this config after benchmarking all related configs +_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py' + +data = dict(samples_per_gpu=4, workers_per_gpu=4) diff --git a/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py b/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py new file mode 100644 index 0000000..497d03f --- /dev/null +++ b/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py @@ -0,0 +1,39 @@ +_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' +img_norm_cfg = dict( + mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/fcos/fcos_r50_torch_fpn_gn-head_4x4_1x_coco.py b/configs/fcos/fcos_r50_torch_fpn_gn-head_4x4_1x_coco.py new file mode 100644 index 0000000..edf6126 --- /dev/null +++ b/configs/fcos/fcos_r50_torch_fpn_gn-head_4x4_1x_coco.py @@ -0,0 +1,17 @@ +# TODO: Remove this config after benchmarking all related configs +_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py' + +data = dict(samples_per_gpu=4, workers_per_gpu=4) + +model = dict( + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), +) diff --git a/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py b/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py new file mode 100644 index 0000000..fc576f6 --- /dev/null +++ b/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py @@ -0,0 +1,59 @@ +_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch')) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict( + lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.)) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/foveabox/README.md b/configs/foveabox/README.md new file mode 100644 index 0000000..47f0f94 --- /dev/null +++ b/configs/foveabox/README.md @@ -0,0 +1,41 @@ +# FoveaBox: Beyond Anchor-based Object Detector + + + +FoveaBox is an accurate, flexible and completely anchor-free object detection system for object detection framework, as presented in our paper [https://arxiv.org/abs/1904.03797](https://arxiv.org/abs/1904.03797): +Different from previous anchor-based methods, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. + +## Main Results + +### Results on R50/101-FPN + +| Backbone | Style | align | ms-train| Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:---------:|:-------:|:-------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50 | pytorch | N | N | 1x | 5.6 | 24.1 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219-ee4d5303.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219_223025.log.json) | +| R-50 | pytorch | N | N | 2x | 5.6 | - | 37.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203-2df792b1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203_112043.log.json) | +| R-50 | pytorch | Y | N | 2x | 8.1 | 19.4 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203_134252.log.json) | +| R-50 | pytorch | Y | Y | 2x | 8.1 | 18.3 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205-85ce26cb.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205_112557.log.json) | +| R-101 | pytorch | N | N | 1x | 9.2 | 17.4 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219-05e38f1c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219_011740.log.json) | +| R-101 | pytorch | N | N | 2x | 11.7 | - | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208-02320ea4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208_202059.log.json) | +| R-101 | pytorch | Y | N | 2x | 11.7 | 14.7 | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208-c39a027a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208_203337.log.json) | +| R-101 | pytorch | Y | Y | 2x | 11.7 | 14.7 | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208-649c5eb6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208_202124.log.json) | + +[1] *1x and 2x mean the model is trained for 12 and 24 epochs, respectively.* \ +[2] *Align means utilizing deformable convolution to align the cls branch.* \ +[3] *All results are obtained with a single model and without any test time data augmentation.*\ +[4] *We use 4 GPUs for training.* + +Any pull requests or issues are welcome. + +## Citations + +Please consider citing our paper in your publications if the project helps your research. BibTeX reference is as follows. + +```latex +@article{kong2019foveabox, + title={FoveaBox: Beyond Anchor-based Object Detector}, + author={Kong, Tao and Sun, Fuchun and Liu, Huaping and Jiang, Yuning and Shi, Jianbo}, + journal={arXiv preprint arXiv:1904.03797}, + year={2019} +} +``` diff --git a/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py b/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py new file mode 100644 index 0000000..30dca04 --- /dev/null +++ b/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py @@ -0,0 +1,10 @@ +_base_ = './fovea_r50_fpn_4x4_1x_coco.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(depth=101), + bbox_head=dict( + with_deform=True, + norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py b/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py new file mode 100644 index 0000000..a02a814 --- /dev/null +++ b/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py @@ -0,0 +1,27 @@ +_base_ = './fovea_r50_fpn_4x4_1x_coco.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(depth=101), + bbox_head=dict( + with_deform=True, + norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict(train=dict(pipeline=train_pipeline)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py b/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py new file mode 100644 index 0000000..e7265bc --- /dev/null +++ b/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py @@ -0,0 +1,10 @@ +_base_ = './fovea_r50_fpn_4x4_1x_coco.py' +model = dict( + bbox_head=dict( + with_deform=True, + norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py b/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py new file mode 100644 index 0000000..8fc39be --- /dev/null +++ b/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py @@ -0,0 +1,25 @@ +_base_ = './fovea_r50_fpn_4x4_1x_coco.py' +model = dict( + bbox_head=dict( + with_deform=True, + norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict(train=dict(pipeline=train_pipeline)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py b/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py new file mode 100644 index 0000000..907bede --- /dev/null +++ b/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './fovea_r50_fpn_4x4_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py b/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py new file mode 100644 index 0000000..9296393 --- /dev/null +++ b/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py @@ -0,0 +1,2 @@ +_base_ = './fovea_r50_fpn_4x4_2x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py b/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py new file mode 100644 index 0000000..fd39257 --- /dev/null +++ b/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py @@ -0,0 +1,52 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# model settings +model = dict( + type='FOVEA', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + num_outs=5, + add_extra_convs='on_input'), + bbox_head=dict( + type='FoveaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + strides=[8, 16, 32, 64, 128], + base_edge_list=[16, 32, 64, 128, 256], + scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)), + sigma=0.4, + with_deform=False, + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=1.50, + alpha=0.4, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)), + # training and testing settings + train_cfg=dict(), + test_cfg=dict( + nms_pre=1000, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) +data = dict(samples_per_gpu=4, workers_per_gpu=4) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py b/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py new file mode 100644 index 0000000..68ce4d2 --- /dev/null +++ b/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './fovea_r50_fpn_4x4_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/fp16/README.md b/configs/fp16/README.md new file mode 100644 index 0000000..23d6eaf --- /dev/null +++ b/configs/fp16/README.md @@ -0,0 +1,22 @@ +# Mixed Precision Training + +## Introduction + + + +```latex +@article{micikevicius2017mixed, + title={Mixed precision training}, + author={Micikevicius, Paulius and Narang, Sharan and Alben, Jonah and Diamos, Gregory and Elsen, Erich and Garcia, David and Ginsburg, Boris and Houston, Michael and Kuchaiev, Oleksii and Venkatesh, Ganesh and others}, + journal={arXiv preprint arXiv:1710.03740}, + year={2017} +} +``` + +## Results and Models + +| Architecture | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:------------:|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| +| Faster R-CNN | R-50 | pytorch | 1x | 3.4 | 28.8 | 37.5 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/faster_rcnn_r50_fpn_fp16_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204-d4dc1471.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204_143530.log.json) | +| Mask R-CNN | R-50 | pytorch | 1x | 3.6 | 24.1 | 38.1 | 34.7 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_1x_coco/mask_rcnn_r50_fpn_fp16_1x_coco_20200205-59faf7e4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_1x_coco/mask_rcnn_r50_fpn_fp16_1x_coco_20200205_130539.log.json) | +| Retinanet | R-50 | pytorch | 1x | 2.8 | 31.6 | 36.4 | |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/fp16/retinanet_r50_fpn_fp16_1x_coco/retinanet_r50_fpn_fp16_1x_coco_20200702-0dbfb212.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fp16/retinanet_r50_fpn_fp16_1x_coco/retinanet_r50_fpn_fp16_1x_coco_20200702_020127.log.json) | diff --git a/configs/fp16/faster_rcnn_r50_fpn_fp16_1x_coco.py b/configs/fp16/faster_rcnn_r50_fpn_fp16_1x_coco.py new file mode 100644 index 0000000..78fa5b6 --- /dev/null +++ b/configs/fp16/faster_rcnn_r50_fpn_fp16_1x_coco.py @@ -0,0 +1,3 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +# fp16 settings +fp16 = dict(loss_scale=512.) diff --git a/configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py b/configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py new file mode 100644 index 0000000..f506ea8 --- /dev/null +++ b/configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py @@ -0,0 +1,3 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +# fp16 settings +fp16 = dict(loss_scale=512.) diff --git a/configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py b/configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py new file mode 100644 index 0000000..519c4db --- /dev/null +++ b/configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py @@ -0,0 +1,3 @@ +_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' +# fp16 settings +fp16 = dict(loss_scale=512.) diff --git a/configs/fpg/README.md b/configs/fpg/README.md new file mode 100644 index 0000000..61c5296 --- /dev/null +++ b/configs/fpg/README.md @@ -0,0 +1,29 @@ +# Feature Pyramid Grids + +## Introduction + +```latex +@article{chen2020feature, + title={Feature pyramid grids}, + author={Chen, Kai and Cao, Yuhang and Loy, Chen Change and Lin, Dahua and Feichtenhofer, Christoph}, + journal={arXiv preprint arXiv:2004.03580}, + year={2020} +} +``` + +## Results and Models + +We benchmark the new training schedule (crop training, large batch, unfrozen BN, 50 epochs) introduced in NAS-FPN. +All backbones are Resnet-50 in pytorch style. + +| Method | Neck | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:------------:|:-----------:|:-------:|:--------:|:--------------:|:------:|:-------:|:-------:|:--------:| +| Faster R-CNN | FPG | 50e | 20.0 | - | 42.2 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py) |[model](http://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg_crop640_50e_coco/faster_rcnn_r50_fpg_crop640_50e_coco-76220505.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg_crop640_50e_coco/20210218_223520.log.json) | +| Faster R-CNN | FPG-chn128 | 50e | 11.9 | - | 41.2 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py) |[model](http://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco/faster_rcnn_r50_fpg-chn128_crop640_50e_coco-24257de9.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco/20210218_221412.log.json) | +| Mask R-CNN | FPG | 50e | 23.2 | - | 42.7 | 37.8 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py) |[model](http://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg_crop640_50e_coco/mask_rcnn_r50_fpg_crop640_50e_coco-c5860453.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg_crop640_50e_coco/20210222_205447.log.json) | +| Mask R-CNN | FPG-chn128 | 50e | 15.3 | - | 41.7 | 36.9 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py) |[model](http://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco/mask_rcnn_r50_fpg-chn128_crop640_50e_coco-5c6ea10d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco/20210223_025039.log.json) | +| RetinaNet | FPG | 50e | 20.8 | - | 40.5 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py) |[model](http://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg_crop640_50e_coco/retinanet_r50_fpg_crop640_50e_coco-46fdd1c6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg_crop640_50e_coco/20210225_143957.log.json) | +| RetinaNet | FPG-chn128 | 50e | 19.9 | - | 40.3 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py) |[model](http://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco/retinanet_r50_fpg-chn128_crop640_50e_coco-5cf33c76.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco/20210225_184328.log.json) | + +**Note**: Chn128 means to decrease the number of channels of features and convs from 256 (default) to 128 in +Neck and BBox Head, which can greatly decrease memory consumption without sacrificing much precision. diff --git a/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py b/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py new file mode 100644 index 0000000..4535034 --- /dev/null +++ b/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py @@ -0,0 +1,9 @@ +_base_ = 'faster_rcnn_r50_fpg_crop640_50e_coco.py' + +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + neck=dict(out_channels=128, inter_channels=128), + rpn_head=dict(in_channels=128), + roi_head=dict( + bbox_roi_extractor=dict(out_channels=128), + bbox_head=dict(in_channels=128))) diff --git a/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py b/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py new file mode 100644 index 0000000..3ab2a2c --- /dev/null +++ b/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py @@ -0,0 +1,48 @@ +_base_ = 'faster_rcnn_r50_fpn_crop640_50e_coco.py' + +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + neck=dict( + type='FPG', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + inter_channels=256, + num_outs=5, + stack_times=9, + paths=['bu'] * 9, + same_down_trans=None, + same_up_trans=dict( + type='conv', + kernel_size=3, + stride=2, + padding=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + across_lateral_trans=dict( + type='conv', + kernel_size=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + across_down_trans=dict( + type='interpolation_conv', + mode='nearest', + kernel_size=3, + norm_cfg=norm_cfg, + order=('act', 'conv', 'norm'), + inplace=False), + across_up_trans=None, + across_skip_trans=dict( + type='conv', + kernel_size=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + output_trans=dict( + type='last_conv', + kernel_size=3, + order=('act', 'conv', 'norm'), + inplace=False), + norm_cfg=norm_cfg, + skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()])) diff --git a/configs/fpg/faster_rcnn_r50_fpn_crop640_50e_coco.py b/configs/fpg/faster_rcnn_r50_fpn_crop640_50e_coco.py new file mode 100644 index 0000000..95f4e91 --- /dev/null +++ b/configs/fpg/faster_rcnn_r50_fpn_crop640_50e_coco.py @@ -0,0 +1,68 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + backbone=dict(norm_cfg=norm_cfg, norm_eval=False), + neck=dict(norm_cfg=norm_cfg), + roi_head=dict(bbox_head=dict(norm_cfg=norm_cfg))) +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=(640, 640), + ratio_range=(0.8, 1.2), + keep_ratio=True), + dict(type='RandomCrop', crop_size=(640, 640)), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=(640, 640)), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(640, 640), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=64), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=4, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# learning policy +optimizer = dict( + type='SGD', + lr=0.08, + momentum=0.9, + weight_decay=0.0001, + paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True)) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=0.1, + step=[30, 40]) +# runtime settings +runner = dict(max_epochs=50) +evaluation = dict(interval=2) diff --git a/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py b/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py new file mode 100644 index 0000000..baa4a5a --- /dev/null +++ b/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py @@ -0,0 +1,10 @@ +_base_ = 'mask_rcnn_r50_fpg_crop640_50e_coco.py' + +model = dict( + neck=dict(out_channels=128, inter_channels=128), + rpn_head=dict(in_channels=128), + roi_head=dict( + bbox_roi_extractor=dict(out_channels=128), + bbox_head=dict(in_channels=128), + mask_roi_extractor=dict(out_channels=128), + mask_head=dict(in_channels=128))) diff --git a/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py b/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py new file mode 100644 index 0000000..3c9ea27 --- /dev/null +++ b/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py @@ -0,0 +1,48 @@ +_base_ = 'mask_rcnn_r50_fpn_crop640_50e_coco.py' + +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + neck=dict( + type='FPG', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + inter_channels=256, + num_outs=5, + stack_times=9, + paths=['bu'] * 9, + same_down_trans=None, + same_up_trans=dict( + type='conv', + kernel_size=3, + stride=2, + padding=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + across_lateral_trans=dict( + type='conv', + kernel_size=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + across_down_trans=dict( + type='interpolation_conv', + mode='nearest', + kernel_size=3, + norm_cfg=norm_cfg, + order=('act', 'conv', 'norm'), + inplace=False), + across_up_trans=None, + across_skip_trans=dict( + type='conv', + kernel_size=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + output_trans=dict( + type='last_conv', + kernel_size=3, + order=('act', 'conv', 'norm'), + inplace=False), + norm_cfg=norm_cfg, + skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()])) diff --git a/configs/fpg/mask_rcnn_r50_fpn_crop640_50e_coco.py b/configs/fpg/mask_rcnn_r50_fpn_crop640_50e_coco.py new file mode 100644 index 0000000..8dfdbb4 --- /dev/null +++ b/configs/fpg/mask_rcnn_r50_fpn_crop640_50e_coco.py @@ -0,0 +1,74 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + backbone=dict(norm_cfg=norm_cfg, norm_eval=False), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + norm_cfg=norm_cfg, + num_outs=5), + roi_head=dict( + bbox_head=dict(norm_cfg=norm_cfg), mask_head=dict(norm_cfg=norm_cfg))) +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=(640, 640), + ratio_range=(0.8, 1.2), + keep_ratio=True), + dict(type='RandomCrop', crop_size=(640, 640)), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=(640, 640)), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(640, 640), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=64), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=4, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# learning policy +optimizer = dict( + type='SGD', + lr=0.08, + momentum=0.9, + weight_decay=0.0001, + paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True)) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=0.1, + step=[30, 40]) +# runtime settings +runner = dict(max_epochs=50) +evaluation = dict(interval=2) diff --git a/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py b/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py new file mode 100644 index 0000000..9a6cf7e --- /dev/null +++ b/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py @@ -0,0 +1,5 @@ +_base_ = 'retinanet_r50_fpg_crop640_50e_coco.py' + +model = dict( + neck=dict(out_channels=128, inter_channels=128), + bbox_head=dict(in_channels=128)) diff --git a/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py b/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py new file mode 100644 index 0000000..504ed5e --- /dev/null +++ b/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py @@ -0,0 +1,53 @@ +_base_ = '../nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py' + +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + neck=dict( + _delete_=True, + type='FPG', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + inter_channels=256, + num_outs=5, + add_extra_convs=True, + start_level=1, + stack_times=9, + paths=['bu'] * 9, + same_down_trans=None, + same_up_trans=dict( + type='conv', + kernel_size=3, + stride=2, + padding=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + across_lateral_trans=dict( + type='conv', + kernel_size=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + across_down_trans=dict( + type='interpolation_conv', + mode='nearest', + kernel_size=3, + norm_cfg=norm_cfg, + order=('act', 'conv', 'norm'), + inplace=False), + across_up_trans=None, + across_skip_trans=dict( + type='conv', + kernel_size=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + output_trans=dict( + type='last_conv', + kernel_size=3, + order=('act', 'conv', 'norm'), + inplace=False), + norm_cfg=norm_cfg, + skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()])) + +evaluation = dict(interval=2) diff --git a/configs/free_anchor/README.md b/configs/free_anchor/README.md new file mode 100644 index 0000000..c2ba545 --- /dev/null +++ b/configs/free_anchor/README.md @@ -0,0 +1,27 @@ +# FreeAnchor: Learning to Match Anchors for Visual Object Detection + +## Introduction + + + +```latex +@inproceedings{zhang2019freeanchor, + title = {{FreeAnchor}: Learning to Match Anchors for Visual Object Detection}, + author = {Zhang, Xiaosong and Wan, Fang and Liu, Chang and Ji, Rongrong and Ye, Qixiang}, + booktitle = {Neural Information Processing Systems}, + year = {2019} +} +``` + +## Results and Models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:--------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50 | pytorch | 1x | 4.9 | 18.4 | 38.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130_095625.log.json) | +| R-101 | pytorch | 1x | 6.8 | 14.9 | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco/retinanet_free_anchor_r101_fpn_1x_coco_20200130-358324e6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco/retinanet_free_anchor_r101_fpn_1x_coco_20200130_100723.log.json) | +| X-101-32x4d | pytorch | 1x | 8.1 | 11.1 | 41.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco/retinanet_free_anchor_x101_32x4d_fpn_1x_coco_20200130-d4846968.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco/retinanet_free_anchor_x101_32x4d_fpn_1x_coco_20200130_095627.log.json) | + +**Notes:** + +- We use 8 GPUs with 2 images/GPU. +- For more settings and models, please refer to the [official repo](https://github.com/zhangxiaosong18/FreeAnchor). diff --git a/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py b/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py new file mode 100644 index 0000000..9917d5c --- /dev/null +++ b/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py b/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py new file mode 100644 index 0000000..28f983c --- /dev/null +++ b/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py @@ -0,0 +1,22 @@ +_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' +model = dict( + bbox_head=dict( + _delete_=True, + type='FreeAnchorRetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=0.75))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py b/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..e2640c0 --- /dev/null +++ b/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,12 @@ +_base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch')) diff --git a/configs/fsaf/README.md b/configs/fsaf/README.md new file mode 100644 index 0000000..d6291dd --- /dev/null +++ b/configs/fsaf/README.md @@ -0,0 +1,45 @@ +# Feature Selective Anchor-Free Module for Single-Shot Object Detection + + + +FSAF is an anchor-free method published in CVPR2019 ([https://arxiv.org/pdf/1903.00621.pdf](https://arxiv.org/pdf/1903.00621.pdf)). +Actually it is equivalent to the anchor-based method with only one anchor at each feature map position in each FPN level. +And this is how we implemented it. +Only the anchor-free branch is released for its better compatibility with the current framework and less computational budget. + +In the original paper, feature maps within the central 0.2-0.5 area of a gt box are tagged as ignored. However, +it is empirically found that a hard threshold (0.2-0.2) gives a further gain on the performance. (see the table below) + +## Main Results + +### Results on R50/R101/X101-FPN + +| Backbone | ignore range | ms-train| Lr schd |Train Mem (GB)| Train time (s/iter) | Inf time (fps) | box AP | Config | Download | +|:----------:| :-------: |:-------:|:-------:|:------------:|:---------------:|:--------------:|:-------------:|:------:|:--------:| +| R-50 | 0.2-0.5 | N | 1x | 3.15 | 0.43 | 12.3 | 36.0 (35.9) | | [model](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco_20200715-b555b0e0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco_20200715_094657.log.json) | +| R-50 | 0.2-0.2 | N | 1x | 3.15 | 0.43 | 13.0 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco-94ccc51f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco_20200428_072327.log.json)| +| R-101 | 0.2-0.2 | N | 1x | 5.08 | 0.58 | 10.8 | 39.3 (37.9) | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco-9e71098f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco_20200428_160348.log.json)| +| X-101 | 0.2-0.2 | N | 1x | 9.38 | 1.23 | 5.6 | 42.4 (41.0) | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco-e3f6e6fd.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco_20200428_160424.log.json)| + +**Notes:** + +- *1x means the model is trained for 12 epochs.* +- *AP values in the brackets represent those reported in the original paper.* +- *All results are obtained with a single model and single-scale test.* +- *X-101 backbone represents ResNext-101-64x4d.* +- *All pretrained backbones use pytorch style.* +- *All models are trained on 8 Titan-XP gpus and tested on a single gpu.* + +## Citations + +BibTeX reference is as follows. + +```latex +@inproceedings{zhu2019feature, + title={Feature Selective Anchor-Free Module for Single-Shot Object Detection}, + author={Zhu, Chenchen and He, Yihui and Savvides, Marios}, + booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, + pages={840--849}, + year={2019} +} +``` diff --git a/configs/fsaf/fsaf_r101_fpn_1x_coco.py b/configs/fsaf/fsaf_r101_fpn_1x_coco.py new file mode 100644 index 0000000..95a7ae2 --- /dev/null +++ b/configs/fsaf/fsaf_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './fsaf_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/fsaf/fsaf_r50_fpn_1x_coco.py b/configs/fsaf/fsaf_r50_fpn_1x_coco.py new file mode 100644 index 0000000..67f3ec1 --- /dev/null +++ b/configs/fsaf/fsaf_r50_fpn_1x_coco.py @@ -0,0 +1,48 @@ +_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' +# model settings +model = dict( + type='FSAF', + bbox_head=dict( + type='FSAFHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + reg_decoded_bbox=True, + # Only anchor-free branch is implemented. The anchor generator only + # generates 1 anchor at each feature point, as a substitute of the + # grid of features. + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=1, + scales_per_octave=1, + ratios=[1.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict(_delete_=True, type='TBLRBBoxCoder', normalizer=4.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0, + reduction='none'), + loss_bbox=dict( + _delete_=True, + type='IoULoss', + eps=1e-6, + loss_weight=1.0, + reduction='none')), + # training and testing settings + train_cfg=dict( + assigner=dict( + _delete_=True, + type='CenterRegionAssigner', + pos_scale=0.2, + neg_scale=0.2, + min_pos_iof=0.01), + allowed_border=-1, + pos_weight=-1, + debug=False)) +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=10, norm_type=2)) diff --git a/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py b/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..b966f24 --- /dev/null +++ b/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './fsaf_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/gcnet/README.md b/configs/gcnet/README.md new file mode 100644 index 0000000..a1d3eca --- /dev/null +++ b/configs/gcnet/README.md @@ -0,0 +1,59 @@ +# GCNet for Object Detection + +By [Yue Cao](http://yue-cao.me), [Jiarui Xu](http://jerryxu.net), [Stephen Lin](https://scholar.google.com/citations?user=c3PYmxUAAAAJ&hl=en), Fangyun Wei, [Han Hu](https://sites.google.com/site/hanhushomepage/). + +We provide config files to reproduce the results in the paper for +["GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond"](https://arxiv.org/abs/1904.11492) on COCO object detection. + +## Introduction + + + +**GCNet** is initially described in [arxiv](https://arxiv.org/abs/1904.11492). Via absorbing advantages of Non-Local Networks (NLNet) and Squeeze-Excitation Networks (SENet), GCNet provides a simple, fast and effective approach for global context modeling, which generally outperforms both NLNet and SENet on major benchmarks for various recognition tasks. + +## Citing GCNet + +```latex +@article{cao2019GCNet, + title={GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond}, + author={Cao, Yue and Xu, Jiarui and Lin, Stephen and Wei, Fangyun and Hu, Han}, + journal={arXiv preprint arXiv:1904.11492}, + year={2019} +} +``` + +## Results and models + +The results on COCO 2017val are shown in the below table. + +| Backbone | Model | Context | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------: | :--------------: | :------------: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +| R-50-FPN | Mask | GC(c3-c5, r16) | 1x | 5.0 | | 39.7 | 35.9 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915-187da160.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915.log.json) | +| R-50-FPN | Mask | GC(c3-c5, r4) | 1x | 5.1 | 15.0 | 39.9 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204-17235656.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204_024626.log.json) | +| R-101-FPN | Mask | GC(c3-c5, r16) | 1x | 7.6 | 11.4 | 41.3 | 37.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205-e58ae947.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205_192835.log.json) | +| R-101-FPN | Mask | GC(c3-c5, r4) | 1x | 7.8 | 11.6 | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206-af22dc9d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206_112128.log.json) | + +| Backbone | Model | Context | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------: | :--------------: | :------------: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :-------: | +| R-50-FPN | Mask | - | 1x | 4.4 | 16.6 | 38.4 | 34.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202-bb3eb55c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202_214122.log.json) | +| R-50-FPN | Mask | GC(c3-c5, r16) | 1x | 5.0 | 15.5 | 40.4 | 36.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202_174907.log.json) | +| R-50-FPN | Mask | GC(c3-c5, r4) | 1x | 5.1 | 15.1 | 40.7 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202_085547.log.json) | +| R-101-FPN | Mask | - | 1x | 6.4 | 13.3 | 40.5 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210-81658c8a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210_220422.log.json) | +| R-101-FPN | Mask | GC(c3-c5, r16) | 1x | 7.6 | 12.0 | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207-945e77ca.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207_015330.log.json) | +| R-101-FPN | Mask | GC(c3-c5, r4) | 1x | 7.8 | 11.8 | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206_142508.log.json) | +| X-101-FPN | Mask | - | 1x | 7.6 | 11.3 | 42.4 | 37.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211-7584841c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211_054326.log.json) | +| X-101-FPN | Mask | GC(c3-c5, r16) | 1x | 8.8 | 9.8 | 43.5 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-cbed3d2c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211_164715.log.json) | +| X-101-FPN | Mask | GC(c3-c5, r4) | 1x | 9.0 | 9.7 | 43.9 | 39.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212-68164964.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212_070942.log.json) | +| X-101-FPN | Cascade Mask | - | 1x | 9.2 | 8.4 | 44.7 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310-d5ad2a5e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310_115217.log.json) | +| X-101-FPN | Cascade Mask | GC(c3-c5, r16) | 1x | 10.3 | 7.7 | 46.2 | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-10bf2463.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211_184154.log.json) | +| X-101-FPN | Cascade Mask | GC(c3-c5, r4) | 1x | 10.6 | | 46.4 | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653-ed035291.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653.log.json) | +| X-101-FPN | DCN Cascade Mask | - | 1x | | | 44.9 | 38.9 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20200516_182249-680fc3f2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20200516_182249.log.json)| +| X-101-FPN | DCN Cascade Mask | GC(c3-c5, r16) | 1x | | | 44.6 | |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20200516_015634-08f56b56.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20200516_015634.log.json) | +| X-101-FPN | DCN Cascade Mask | GC(c3-c5, r4) | 1x | | | 45.7 | 39.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20200518_041145-24cabcfd.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20200518_041145.log.json) | + +**Notes:** + +- The `SyncBN` is added in the backbone for all models in **Table 2**. +- `GC` denotes Global Context (GC) block is inserted after 1x1 conv of backbone. +- `DCN` denotes replace 3x3 conv with 3x3 Deformable Convolution in `c3-c5` stages of backbone. +- `r4` and `r16` denote ratio 4 and ratio 16 in GC block respectively. diff --git a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py new file mode 100644 index 0000000..5118895 --- /dev/null +++ b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) diff --git a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..464aef7 --- /dev/null +++ b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = '../dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) diff --git a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..fa4b6f1 --- /dev/null +++ b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..b76e3e6 --- /dev/null +++ b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 4), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..50883ff --- /dev/null +++ b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..31fdd07 --- /dev/null +++ b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 4), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..ad6ad47 --- /dev/null +++ b/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py @@ -0,0 +1,8 @@ +_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict(plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..29f9167 --- /dev/null +++ b/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py @@ -0,0 +1,8 @@ +_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict(plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 4), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py b/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py new file mode 100644 index 0000000..6e1c5d0 --- /dev/null +++ b/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) diff --git a/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..781dba7 --- /dev/null +++ b/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..32972de --- /dev/null +++ b/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 4), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..d299b69 --- /dev/null +++ b/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py @@ -0,0 +1,8 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict(plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..5ac908e --- /dev/null +++ b/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py @@ -0,0 +1,8 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict(plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 4), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py b/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py new file mode 100644 index 0000000..0308a56 --- /dev/null +++ b/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) diff --git a/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..e04780c --- /dev/null +++ b/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..980f819 --- /dev/null +++ b/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 4), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py b/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py new file mode 100644 index 0000000..f0c96e5 --- /dev/null +++ b/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) diff --git a/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..7fb8e82 --- /dev/null +++ b/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..b1ddbee --- /dev/null +++ b/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 4), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gfl/README.md b/configs/gfl/README.md new file mode 100644 index 0000000..a71b948 --- /dev/null +++ b/configs/gfl/README.md @@ -0,0 +1,32 @@ +# Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection + +## Introduction + + + +We provide config files to reproduce the object detection results in the paper [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://arxiv.org/abs/2006.04388) + +```latex +@article{li2020generalized, + title={Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection}, + author={Li, Xiang and Wang, Wenhai and Wu, Lijun and Chen, Shuo and Hu, Xiaolin and Li, Jun and Tang, Jinhui and Yang, Jian}, + journal={arXiv preprint arXiv:2006.04388}, + year={2020} +} +``` + +## Results and Models + +| Backbone | Style | Lr schd | Multi-scale Training| Inf time (fps) | box AP | Config | Download | +|:-----------------:|:-------:|:-------:|:-------------------:|:--------------:|:------:|:------:|:--------:| +| R-50 | pytorch | 1x | No | 19.5 | 40.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244.log.json) | +| R-50 | pytorch | 2x | Yes | 19.5 | 42.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802-37bb1edc.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802.log.json) | +| R-101 | pytorch | 2x | Yes | 14.7 | 44.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126.log.json) | +| R-101-dcnv2 | pytorch | 2x | Yes | 12.9 | 47.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002.log.json) | +| X-101-32x4d | pytorch | 2x | Yes | 12.1 | 45.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002-50c1ffdb.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002.log.json) | +| X-101-32x4d-dcnv2 | pytorch | 2x | Yes | 10.7 | 48.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002-14a2bf25.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002.log.json) | + +[1] *1x and 2x mean the model is trained for 90K and 180K iterations, respectively.* \ +[2] *All results are obtained with a single model and without any test time data augmentation such as multi-scale, flipping and etc..* \ +[3] *`dcnv2` denotes deformable convolutional networks v2.* \ +[4] *FPS is tested with a single GeForce RTX 2080Ti GPU, using a batch size of 1.* diff --git a/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py b/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py new file mode 100644 index 0000000..eab622b --- /dev/null +++ b/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py @@ -0,0 +1,14 @@ +_base_ = './gfl_r50_fpn_mstrain_2x_coco.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict( + type='ResNet', + depth=101, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True), + norm_eval=True, + style='pytorch')) diff --git a/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py b/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py new file mode 100644 index 0000000..c972d0c --- /dev/null +++ b/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py @@ -0,0 +1,12 @@ +_base_ = './gfl_r50_fpn_mstrain_2x_coco.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict( + type='ResNet', + depth=101, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch')) diff --git a/configs/gfl/gfl_r50_fpn_1x_coco.py b/configs/gfl/gfl_r50_fpn_1x_coco.py new file mode 100644 index 0000000..29fb077 --- /dev/null +++ b/configs/gfl/gfl_r50_fpn_1x_coco.py @@ -0,0 +1,57 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='GFL', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5), + bbox_head=dict( + type='GFLHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128]), + loss_cls=dict( + type='QualityFocalLoss', + use_sigmoid=True, + beta=2.0, + loss_weight=1.0), + loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25), + reg_max=16, + loss_bbox=dict(type='GIoULoss', loss_weight=2.0)), + # training and testing settings + train_cfg=dict( + assigner=dict(type='ATSSAssigner', topk=9), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py b/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py new file mode 100644 index 0000000..b8be601 --- /dev/null +++ b/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py @@ -0,0 +1,22 @@ +_base_ = './gfl_r50_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) +# multi-scale training +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 480), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py b/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py new file mode 100644 index 0000000..a2370e2 --- /dev/null +++ b/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py @@ -0,0 +1,17 @@ +_base_ = './gfl_r50_fpn_mstrain_2x_coco.py' +model = dict( + type='GFL', + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, False, True, True), + norm_eval=True, + style='pytorch')) diff --git a/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py b/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py new file mode 100644 index 0000000..4e00a05 --- /dev/null +++ b/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py @@ -0,0 +1,15 @@ +_base_ = './gfl_r50_fpn_mstrain_2x_coco.py' +model = dict( + type='GFL', + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch')) diff --git a/configs/ghm/README.md b/configs/ghm/README.md new file mode 100644 index 0000000..5f16594 --- /dev/null +++ b/configs/ghm/README.md @@ -0,0 +1,23 @@ +# Gradient Harmonized Single-stage Detector + +## Introduction + + + +``` +@inproceedings{li2019gradient, + title={Gradient Harmonized Single-stage Detector}, + author={Li, Buyu and Liu, Yu and Wang, Xiaogang}, + booktitle={AAAI Conference on Artificial Intelligence}, + year={2019} +} +``` + +## Results and Models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| R-50-FPN | pytorch | 1x | 4.0 | 3.3 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130-a437fda3.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130_004213.log.json) | +| R-101-FPN | pytorch | 1x | 6.0 | 4.4 | 39.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r101_fpn_1x_coco/retinanet_ghm_r101_fpn_1x_coco_20200130-c148ee8f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r101_fpn_1x_coco/retinanet_ghm_r101_fpn_1x_coco_20200130_145259.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.2 | 5.1 | 40.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco/retinanet_ghm_x101_32x4d_fpn_1x_coco_20200131-e4333bd0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco/retinanet_ghm_x101_32x4d_fpn_1x_coco_20200131_113653.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.3 | 5.2 | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco/retinanet_ghm_x101_64x4d_fpn_1x_coco_20200131-dd381cef.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco/retinanet_ghm_x101_64x4d_fpn_1x_coco_20200131_113723.log.json) | diff --git a/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py b/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py new file mode 100644 index 0000000..18f899a --- /dev/null +++ b/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './retinanet_ghm_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py b/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py new file mode 100644 index 0000000..61b9751 --- /dev/null +++ b/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py @@ -0,0 +1,19 @@ +_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' +model = dict( + bbox_head=dict( + loss_cls=dict( + _delete_=True, + type='GHMC', + bins=30, + momentum=0.75, + use_sigmoid=True, + loss_weight=1.0), + loss_bbox=dict( + _delete_=True, + type='GHMR', + mu=0.02, + bins=10, + momentum=0.7, + loss_weight=10.0))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py b/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..a89fc13 --- /dev/null +++ b/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './retinanet_ghm_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py b/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..88013f5 --- /dev/null +++ b/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './retinanet_ghm_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/gn+ws/README.md b/configs/gn+ws/README.md new file mode 100644 index 0000000..8c7a234 --- /dev/null +++ b/configs/gn+ws/README.md @@ -0,0 +1,44 @@ +# Weight Standardization + +## Introduction + + + +``` +@article{weightstandardization, + author = {Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Yuille}, + title = {Weight Standardization}, + journal = {arXiv preprint arXiv:1903.10520}, + year = {2019}, +} +``` + +## Results and Models + +Faster R-CNN + +| Backbone | Style | Normalization | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:---------:|:-------:|:-------------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| +| R-50-FPN | pytorch | GN+WS | 1x | 5.9 | 11.7 | 39.7 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130_210936.log.json) | +| R-101-FPN | pytorch | GN+WS | 1x | 8.9 | 9.0 | 41.7 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco/faster_rcnn_r101_fpn_gn_ws-all_1x_coco_20200205-a93b0d75.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco/faster_rcnn_r101_fpn_gn_ws-all_1x_coco_20200205_232146.log.json) | +| X-50-32x4d-FPN | pytorch | GN+WS | 1x | 7.0 | 10.3 | 40.7 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco_20200203-839c5d9d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco_20200203_220113.log.json) | +| X-101-32x4d-FPN | pytorch | GN+WS | 1x | 10.8 | 7.6 | 42.1 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco_20200212-27da1bc2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco_20200212_195302.log.json) | + +Mask R-CNN + +| Backbone | Style | Normalization | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:---------:|:-------:|:-------------:|:---------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| +| R-50-FPN | pytorch | GN+WS | 2x | 7.3 | 10.5 | 40.6 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco/mask_rcnn_r50_fpn_gn_ws-all_2x_coco_20200226-16acb762.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco/mask_rcnn_r50_fpn_gn_ws-all_2x_coco_20200226_062128.log.json) | +| R-101-FPN | pytorch | GN+WS | 2x | 10.3 | 8.6 | 42.0 | 37.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco/mask_rcnn_r101_fpn_gn_ws-all_2x_coco_20200212-ea357cd9.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco/mask_rcnn_r101_fpn_gn_ws-all_2x_coco_20200212_213627.log.json) | +| X-50-32x4d-FPN | pytorch | GN+WS | 2x | 8.4 | 9.3 | 41.1 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco_20200216-649fdb6f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco_20200216_201500.log.json) | +| X-101-32x4d-FPN | pytorch | GN+WS | 2x | 12.2 | 7.1 | 42.1 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco_20200319-33fb95b5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco_20200319_104101.log.json) | +| R-50-FPN | pytorch | GN+WS | 20-23-24e | 7.3 | - | 41.1 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213-487d1283.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213_035123.log.json) | +| R-101-FPN | pytorch | GN+WS | 20-23-24e | 10.3 | - | 43.1 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco_20200213-57b5a50f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco_20200213_130142.log.json) | +| X-50-32x4d-FPN | pytorch | GN+WS | 20-23-24e | 8.4 | - | 42.1 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200226-969bcb2c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200226_093732.log.json) | +| X-101-32x4d-FPN | pytorch | GN+WS | 20-23-24e | 12.2 | - | 42.7 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200316-e6cd35ef.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200316_013741.log.json) | + +Note: + +- GN+WS requires about 5% more memory than GN, and it is only 5% slower than GN. +- In the paper, a 20-23-24e lr schedule is used instead of 2x. +- The X-50-GN and X-101-GN pretrained models are also shared by the authors. diff --git a/configs/gn+ws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py b/configs/gn+ws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py new file mode 100644 index 0000000..a5f6bd2 --- /dev/null +++ b/configs/gn+ws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py @@ -0,0 +1,3 @@ +_base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py' +model = dict( + pretrained='open-mmlab://jhu/resnet101_gn_ws', backbone=dict(depth=101)) diff --git a/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py b/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py new file mode 100644 index 0000000..497267b --- /dev/null +++ b/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +conv_cfg = dict(type='ConvWS') +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='open-mmlab://jhu/resnet50_gn_ws', + backbone=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), + neck=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), + roi_head=dict( + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + conv_out_channels=256, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg))) diff --git a/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py b/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py new file mode 100644 index 0000000..061ca69 --- /dev/null +++ b/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py' +conv_cfg = dict(type='ConvWS') +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='open-mmlab://jhu/resnext101_32x4d_gn_ws', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch', + conv_cfg=conv_cfg, + norm_cfg=norm_cfg)) diff --git a/configs/gn+ws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py b/configs/gn+ws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py new file mode 100644 index 0000000..1268980 --- /dev/null +++ b/configs/gn+ws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py' +conv_cfg = dict(type='ConvWS') +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='open-mmlab://jhu/resnext50_32x4d_gn_ws', + backbone=dict( + type='ResNeXt', + depth=50, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch', + conv_cfg=conv_cfg, + norm_cfg=norm_cfg)) diff --git a/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py b/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py new file mode 100644 index 0000000..a790d93 --- /dev/null +++ b/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py' +# learning policy +lr_config = dict(step=[20, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py b/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py new file mode 100644 index 0000000..4be6817 --- /dev/null +++ b/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py @@ -0,0 +1,3 @@ +_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' +model = dict( + pretrained='open-mmlab://jhu/resnet101_gn_ws', backbone=dict(depth=101)) diff --git a/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py b/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py new file mode 100644 index 0000000..5516808 --- /dev/null +++ b/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' +# learning policy +lr_config = dict(step=[20, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py b/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py new file mode 100644 index 0000000..b83e7b5 --- /dev/null +++ b/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py @@ -0,0 +1,17 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +conv_cfg = dict(type='ConvWS') +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='open-mmlab://jhu/resnet50_gn_ws', + backbone=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), + neck=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), + roi_head=dict( + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + conv_out_channels=256, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg), + mask_head=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg))) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py b/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py new file mode 100644 index 0000000..cfa14c9 --- /dev/null +++ b/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py' +# learning policy +lr_config = dict(step=[20, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py b/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py new file mode 100644 index 0000000..dbe8877 --- /dev/null +++ b/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py @@ -0,0 +1,17 @@ +_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' +# model settings +conv_cfg = dict(type='ConvWS') +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='open-mmlab://jhu/resnext101_32x4d_gn_ws', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch', + conv_cfg=conv_cfg, + norm_cfg=norm_cfg)) diff --git a/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py b/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py new file mode 100644 index 0000000..79ce0ad --- /dev/null +++ b/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py' +# learning policy +lr_config = dict(step=[20, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py b/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py new file mode 100644 index 0000000..9bbc86e --- /dev/null +++ b/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py @@ -0,0 +1,17 @@ +_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' +# model settings +conv_cfg = dict(type='ConvWS') +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='open-mmlab://jhu/resnext50_32x4d_gn_ws', + backbone=dict( + type='ResNeXt', + depth=50, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch', + conv_cfg=conv_cfg, + norm_cfg=norm_cfg)) diff --git a/configs/gn/README.md b/configs/gn/README.md new file mode 100644 index 0000000..27f5014 --- /dev/null +++ b/configs/gn/README.md @@ -0,0 +1,31 @@ +# Group Normalization + +## Introduction + + + +```latex +@inproceedings{wu2018group, + title={Group Normalization}, + author={Wu, Yuxin and He, Kaiming}, + booktitle={Proceedings of the European Conference on Computer Vision (ECCV)}, + year={2018} +} +``` + +## Results and Models + +| Backbone | model | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:-------------:|:----------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| +| R-50-FPN (d) | Mask R-CNN | 2x | 7.1 | 11.0 | 40.2 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206_050355.log.json) | +| R-50-FPN (d) | Mask R-CNN | 3x | 7.1 | - | 40.5 | 36.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_3x_coco/mask_rcnn_r50_fpn_gn-all_3x_coco_20200214-8b23b1e5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_3x_coco/mask_rcnn_r50_fpn_gn-all_3x_coco_20200214_063512.log.json) | +| R-101-FPN (d) | Mask R-CNN | 2x | 9.9 | 9.0 | 41.9 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_2x_coco/mask_rcnn_r101_fpn_gn-all_2x_coco_20200205-d96b1b50.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_2x_coco/mask_rcnn_r101_fpn_gn-all_2x_coco_20200205_234402.log.json) | +| R-101-FPN (d) | Mask R-CNN | 3x | 9.9 | | 42.1 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_3x_coco/mask_rcnn_r101_fpn_gn-all_3x_coco_20200513_181609-0df864f4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_3x_coco/mask_rcnn_r101_fpn_gn-all_3x_coco_20200513_181609.log.json) | +| R-50-FPN (c) | Mask R-CNN | 2x | 7.1 | 10.9 | 40.0 | 36.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco_20200207-20d3e849.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco_20200207_225832.log.json) | +| R-50-FPN (c) | Mask R-CNN | 3x | 7.1 | - | 40.1 | 36.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco_20200225-542aefbc.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco_20200225_235135.log.json) | + +**Notes:** + +- (d) means pretrained model converted from Detectron, and (c) means the contributed model pretrained by [@thangvubk](https://github.com/thangvubk). +- The `3x` schedule is epoch [28, 34, 36]. +- **Memory, Train/Inf time is outdated.** diff --git a/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py b/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py new file mode 100644 index 0000000..0fcc558 --- /dev/null +++ b/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py @@ -0,0 +1,3 @@ +_base_ = './mask_rcnn_r50_fpn_gn-all_2x_coco.py' +model = dict( + pretrained='open-mmlab://detectron/resnet101_gn', backbone=dict(depth=101)) diff --git a/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py b/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py new file mode 100644 index 0000000..12a9d17 --- /dev/null +++ b/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py @@ -0,0 +1,5 @@ +_base_ = './mask_rcnn_r101_fpn_gn-all_2x_coco.py' + +# learning policy +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py b/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py new file mode 100644 index 0000000..9c85d26 --- /dev/null +++ b/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py @@ -0,0 +1,46 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='open-mmlab://detectron/resnet50_gn', + backbone=dict(norm_cfg=norm_cfg), + neck=dict(norm_cfg=norm_cfg), + roi_head=dict( + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + conv_out_channels=256, + norm_cfg=norm_cfg), + mask_head=dict(norm_cfg=norm_cfg))) +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py b/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py new file mode 100644 index 0000000..f917719 --- /dev/null +++ b/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py @@ -0,0 +1,5 @@ +_base_ = './mask_rcnn_r50_fpn_gn-all_2x_coco.py' + +# learning policy +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py b/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py new file mode 100644 index 0000000..89caaaf --- /dev/null +++ b/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py @@ -0,0 +1,15 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='open-mmlab://contrib/resnet50_gn', + backbone=dict(norm_cfg=norm_cfg), + neck=dict(norm_cfg=norm_cfg), + roi_head=dict( + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + conv_out_channels=256, + norm_cfg=norm_cfg), + mask_head=dict(norm_cfg=norm_cfg))) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py b/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py new file mode 100644 index 0000000..66834f0 --- /dev/null +++ b/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py @@ -0,0 +1,5 @@ +_base_ = './mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py' + +# learning policy +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/grid_rcnn/README.md b/configs/grid_rcnn/README.md new file mode 100644 index 0000000..3fba57b --- /dev/null +++ b/configs/grid_rcnn/README.md @@ -0,0 +1,35 @@ +# Grid R-CNN + +## Introduction + + + +```latex +@inproceedings{lu2019grid, + title={Grid r-cnn}, + author={Lu, Xin and Li, Buyu and Yue, Yuxin and Li, Quanquan and Yan, Junjie}, + booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, + year={2019} +} + +@article{lu2019grid, + title={Grid R-CNN Plus: Faster and Better}, + author={Lu, Xin and Li, Buyu and Yue, Yuxin and Li, Quanquan and Yan, Junjie}, + journal={arXiv preprint arXiv:1906.05688}, + year={2019} +} +``` + +## Results and Models + +| Backbone | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:-----------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50 | 2x | 5.1 | 15.0 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco/grid_rcnn_r50_fpn_gn-head_2x_coco_20200130-6cca8223.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco/grid_rcnn_r50_fpn_gn-head_2x_coco_20200130_221140.log.json) | +| R-101 | 2x | 7.0 | 12.6 | 41.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco/grid_rcnn_r101_fpn_gn-head_2x_coco_20200309-d6eca030.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco/grid_rcnn_r101_fpn_gn-head_2x_coco_20200309_164224.log.json) | +| X-101-32x4d | 2x | 8.3 | 10.8 | 42.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco_20200130-d8f0e3ff.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco_20200130_215413.log.json) | +| X-101-64x4d | 2x | 11.3 | 7.7 | 43.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco_20200204-ec76a754.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco_20200204_080641.log.json) | + +**Notes:** + +- All models are trained with 8 GPUs instead of 32 GPUs in the original paper. +- The warming up lasts for 1 epoch and `2x` here indicates 25 epochs. diff --git a/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py b/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py new file mode 100644 index 0000000..cf8b648 --- /dev/null +++ b/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py @@ -0,0 +1,3 @@ +_base_ = './grid_rcnn_r50_fpn_gn-head_2x_coco.py' + +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py b/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py new file mode 100644 index 0000000..4aa00ec --- /dev/null +++ b/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = ['grid_rcnn_r50_fpn_gn-head_2x_coco.py'] +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[8, 11]) +checkpoint_config = dict(interval=1) +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py b/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py new file mode 100644 index 0000000..6ed5bcb --- /dev/null +++ b/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py @@ -0,0 +1,131 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py' +] +# model settings +model = dict( + type='GridRCNN', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + roi_head=dict( + type='GridRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared2FCBBoxHead', + with_reg=False, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False), + grid_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + grid_head=dict( + type='GridHead', + grid_points=9, + num_convs=8, + in_channels=256, + point_feat_channels=64, + norm_cfg=dict(type='GN', num_groups=36), + loss_grid=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=15))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_radius=1, + pos_weight=-1, + max_num_grid=192, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.03, + nms=dict(type='nms', iou_threshold=0.3), + max_per_img=100))) +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=3665, + warmup_ratio=1.0 / 80, + step=[17, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=25) diff --git a/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py b/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py new file mode 100644 index 0000000..14c1eb2 --- /dev/null +++ b/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py @@ -0,0 +1,23 @@ +_base_ = './grid_rcnn_r50_fpn_gn-head_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch')) +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=3665, + warmup_ratio=1.0 / 80, + step=[17, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=25) diff --git a/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py b/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py new file mode 100644 index 0000000..2fdc53c --- /dev/null +++ b/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py @@ -0,0 +1,12 @@ +_base_ = './grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch')) diff --git a/configs/groie/README.md b/configs/groie/README.md new file mode 100644 index 0000000..7016b36 --- /dev/null +++ b/configs/groie/README.md @@ -0,0 +1,65 @@ +# GRoIE + +## A novel Region of Interest Extraction Layer for Instance Segmentation + +By Leonardo Rossi, Akbar Karimi and Andrea Prati from +[IMPLab](http://implab.ce.unipr.it/). + +We provide configs to reproduce the results in the paper for +"*A novel Region of Interest Extraction Layer for Instance Segmentation*" +on COCO object detection. + +## Introduction + + + +This paper is motivated by the need to overcome to the limitations of existing +RoI extractors which select only one (the best) layer from FPN. + +Our intuition is that all the layers of FPN retain useful information. + +Therefore, the proposed layer (called Generic RoI Extractor - **GRoIE**) +introduces non-local building blocks and attention mechanisms to boost the +performance. + +## Results and models + +The results on COCO 2017 minival (5k images) are shown in the below table. +You can find +[here](https://drive.google.com/drive/folders/19ssstbq_h0Z1cgxHmJYFO8s1arf3QJbT) +the trained models. + +### Application of GRoIE to different architectures + +| Backbone | Method | Lr schd | box AP | mask AP | Config | Download| +| :-------: | :--------------: | :-----: | :----: | :-----: | :-------:| :--------:| +| R-50-FPN | Faster Original | 1x | 37.4 | | [config](../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +| R-50-FPN | + GRoIE | 1x | 38.3 | | [config](./faster_rcnn_r50_fpn_groie_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/groie/faster_rcnn_r50_fpn_groie_1x_coco/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715-66ee9516.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/groie/faster_rcnn_r50_fpn_groie_1x_coco/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715.log.json) | +| R-50-FPN | Grid R-CNN | 1x | 39.1 | | [config](./grid_rcnn_r50_fpn_gn-head_1x_coco.py)| [model](http://download.openmmlab.com/mmdetection/v2.0/groie/grid_rcnn_r50_fpn_gn-head_1x_coco/grid_rcnn_r50_fpn_gn-head_1x_coco_20200605_202059-64f00ee8.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/groie/grid_rcnn_r50_fpn_gn-head_1x_coco/grid_rcnn_r50_fpn_gn-head_1x_coco_20200605_202059.log.json) | +| R-50-FPN | + GRoIE | 1x | | | [config](./grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py)|| +| R-50-FPN | Mask R-CNN | 1x | 38.2 | 34.7 | [config](../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py)| [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json) | +| R-50-FPN | + GRoIE | 1x | 39.0 | 36.0 | [config](./mask_rcnn_r50_fpn_groie_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_groie_1x_coco/mask_rcnn_r50_fpn_groie_1x_coco_20200604_211715-50d90c74.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_groie_1x_coco/mask_rcnn_r50_fpn_groie_1x_coco_20200604_211715.log.json) | +| R-50-FPN | GC-Net | 1x | 40.7 | 36.5 | [config](../gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202_085547.log.json) | +| R-50-FPN | + GRoIE | 1x | 41.0 | 37.8 | [config](./mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py) |[model](http://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth) | +| R-101-FPN | GC-Net | 1x | 42.2 | 37.8 | [config](../gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206_142508.log.json) | +| R-101-FPN | + GRoIE | 1x | | | [config](./mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py)| [model](http://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507-8daae01c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507.log.json) | + +## Citation + +If you use this work or benchmark in your research, please cite this project. + +```latex +@misc{rossi2020novel, + title={A novel Region of Interest Extraction Layer for Instance Segmentation}, + author={Leonardo Rossi and Akbar Karimi and Andrea Prati}, + year={2020}, + eprint={2004.13665}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + +## Contact + +The implementation of GROI is currently maintained by +[Leonardo Rossi](https://github.com/hachreak/). diff --git a/configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py b/configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py new file mode 100644 index 0000000..0fc528b --- /dev/null +++ b/configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py @@ -0,0 +1,25 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +# model settings +model = dict( + roi_head=dict( + bbox_roi_extractor=dict( + type='GenericRoIExtractor', + aggregation='sum', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)))) diff --git a/configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py b/configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py new file mode 100644 index 0000000..8e4b4ab --- /dev/null +++ b/configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py @@ -0,0 +1,45 @@ +_base_ = '../grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py' +# model settings +model = dict( + roi_head=dict( + bbox_roi_extractor=dict( + type='GenericRoIExtractor', + aggregation='sum', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)), + grid_roi_extractor=dict( + type='GenericRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)))) diff --git a/configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py b/configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py new file mode 100644 index 0000000..8b83722 --- /dev/null +++ b/configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py @@ -0,0 +1,45 @@ +_base_ = '../gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py' +# model settings +model = dict( + roi_head=dict( + bbox_roi_extractor=dict( + type='GenericRoIExtractor', + aggregation='sum', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)), + mask_roi_extractor=dict( + type='GenericRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)))) diff --git a/configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py b/configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py new file mode 100644 index 0000000..81dfb48 --- /dev/null +++ b/configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py @@ -0,0 +1,45 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +# model settings +model = dict( + roi_head=dict( + bbox_roi_extractor=dict( + type='GenericRoIExtractor', + aggregation='sum', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)), + mask_roi_extractor=dict( + type='GenericRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)))) diff --git a/configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py b/configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py new file mode 100644 index 0000000..852c5ca --- /dev/null +++ b/configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py @@ -0,0 +1,45 @@ +_base_ = '../gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py' +# model settings +model = dict( + roi_head=dict( + bbox_roi_extractor=dict( + type='GenericRoIExtractor', + aggregation='sum', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)), + mask_roi_extractor=dict( + type='GenericRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)))) diff --git a/configs/guided_anchoring/README.md b/configs/guided_anchoring/README.md new file mode 100644 index 0000000..b17cab1 --- /dev/null +++ b/configs/guided_anchoring/README.md @@ -0,0 +1,49 @@ +# Region Proposal by Guided Anchoring + +## Introduction + + + +We provide config files to reproduce the results in the CVPR 2019 paper for [Region Proposal by Guided Anchoring](https://arxiv.org/abs/1901.03278). + +```latex +@inproceedings{wang2019region, + title={Region Proposal by Guided Anchoring}, + author={Jiaqi Wang and Kai Chen and Shuo Yang and Chen Change Loy and Dahua Lin}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + year={2019} +} +``` + +## Results and Models + +The results on COCO 2017 val is shown in the below table. (results on test-dev are usually slightly higher than val). + +| Method | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | AR 1000 | Config | Download | +| :----: | :-------------: | :-----: | :-----: | :------: | :------------: | :-----: | :------: | :--------: | +| GA-RPN | R-50-FPN | caffe | 1x | 5.3 | 15.8 | 68.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco/ga_rpn_r50_caffe_fpn_1x_coco_20200531-899008a6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco/ga_rpn_r50_caffe_fpn_1x_coco_20200531_011819.log.json) | +| GA-RPN | R-101-FPN | caffe | 1x | 7.3 | 13.0 | 69.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco/ga_rpn_r101_caffe_fpn_1x_coco_20200531-ca9ba8fb.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco/ga_rpn_r101_caffe_fpn_1x_coco_20200531_011812.log.json) | +| GA-RPN | X-101-32x4d-FPN | pytorch | 1x | 8.5 | 10.0 | 70.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco/ga_rpn_x101_32x4d_fpn_1x_coco_20200220-c28d1b18.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco/ga_rpn_x101_32x4d_fpn_1x_coco_20200220_221326.log.json) | +| GA-RPN | X-101-64x4d-FPN | pytorch | 1x | 7.1 | 7.5 | 71.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco/ga_rpn_x101_64x4d_fpn_1x_coco_20200225-3c6e1aa2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco/ga_rpn_x101_64x4d_fpn_1x_coco_20200225_152704.log.json) | + +| Method | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :------------: | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| GA-Faster RCNN | R-50-FPN | caffe | 1x | 5.5 | | 39.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718-a11ccfe6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718.log.json) | +| GA-Faster RCNN | R-101-FPN | caffe | 1x | 7.5 | | 41.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco/ga_faster_r101_caffe_fpn_1x_coco_bbox_mAP-0.415_20200505_115528-fb82e499.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco/ga_faster_r101_caffe_fpn_1x_coco_20200505_115528.log.json) | +| GA-Faster RCNN | X-101-32x4d-FPN | pytorch | 1x | 8.7 | 9.7 | 43.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco/ga_faster_x101_32x4d_fpn_1x_coco_20200215-1ded9da3.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco/ga_faster_x101_32x4d_fpn_1x_coco_20200215_184547.log.json) | +| GA-Faster RCNN | X-101-64x4d-FPN | pytorch | 1x | 11.8 | 7.3 | 43.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco/ga_faster_x101_64x4d_fpn_1x_coco_20200215-0fa7bde7.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco/ga_faster_x101_64x4d_fpn_1x_coco_20200215_104455.log.json) | +| GA-RetinaNet | R-50-FPN | caffe | 1x | 3.5 | 16.8 | 36.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco/ga_retinanet_r50_caffe_fpn_1x_coco_20201020-39581c6f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco/ga_retinanet_r50_caffe_fpn_1x_coco_20201020_225450.log.json) | +| GA-RetinaNet | R-101-FPN | caffe | 1x | 5.5 | 12.9 | 39.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco/ga_retinanet_r101_caffe_fpn_1x_coco_20200531-6266453c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco/ga_retinanet_r101_caffe_fpn_1x_coco_20200531_012847.log.json) | +| GA-RetinaNet | X-101-32x4d-FPN | pytorch | 1x | 6.9 | 10.6 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco/ga_retinanet_x101_32x4d_fpn_1x_coco_20200219-40c56caa.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco/ga_retinanet_x101_32x4d_fpn_1x_coco_20200219_223025.log.json) | +| GA-RetinaNet | X-101-64x4d-FPN | pytorch | 1x | 9.9 | 7.7 | 41.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco/ga_retinanet_x101_64x4d_fpn_1x_coco_20200226-ef9f7f1f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco/ga_retinanet_x101_64x4d_fpn_1x_coco_20200226_221123.log.json) | + +- In the Guided Anchoring paper, `score_thr` is set to 0.001 in Fast/Faster RCNN and 0.05 in RetinaNet for both baselines and Guided Anchoring. + +- Performance on COCO test-dev benchmark are shown as follows. + +| Method | Backbone | Style | Lr schd | Aug Train | Score thr | AP | AP_50 | AP_75 | AP_small | AP_medium | AP_large | Download | +| :------------: | :-------: | :---: | :-----: | :-------: | :-------: | :---: | :---: | :---: | :------: | :-------: | :------: | :------: | +| GA-Faster RCNN | R-101-FPN | caffe | 1x | F | 0.05 | | | | | | | | +| GA-Faster RCNN | R-101-FPN | caffe | 1x | F | 0.001 | | | | | | | | +| GA-RetinaNet | R-101-FPN | caffe | 1x | F | 0.05 | | | | | | | | +| GA-RetinaNet | R-101-FPN | caffe | 2x | T | 0.05 | | | | | | | | diff --git a/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..e15bc29 --- /dev/null +++ b/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,63 @@ +_base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe'), + roi_head=dict( + bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))), + # model training and testing settings + train_cfg=dict( + rcnn=dict( + assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6), + sampler=dict(num=256))), + test_cfg=dict(rcnn=dict(score_thr=1e-3))) +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=300), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=None), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img', 'proposals']), + ]) +] +data = dict( + train=dict( + proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_train2017.pkl', + pipeline=train_pipeline), + val=dict( + proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_val2017.pkl', + pipeline=test_pipeline), + test=dict( + proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_val2017.pkl', + pipeline=test_pipeline)) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..f438a47 --- /dev/null +++ b/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './ga_faster_r50_caffe_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..b0add92 --- /dev/null +++ b/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,65 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + rpn_head=dict( + _delete_=True, + type='GARPNHead', + in_channels=256, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=8, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[8], + strides=[4, 8, 16, 32, 64]), + anchor_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.14, 0.14]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.11, 0.11]), + loc_filter_thr=0.01, + loss_loc=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), + roi_head=dict( + bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + center_ratio=0.2, + ignore_ratio=0.5), + rpn_proposal=dict(nms_post=1000, max_per_img=300), + rcnn=dict( + assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6), + sampler=dict(type='RandomSampler', num=256))), + test_cfg=dict( + rpn=dict(nms_post=1000, max_per_img=300), rcnn=dict(score_thr=1e-3))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/guided_anchoring/ga_faster_r50_fpn_1x_coco.py b/configs/guided_anchoring/ga_faster_r50_fpn_1x_coco.py new file mode 100644 index 0000000..e3d8238 --- /dev/null +++ b/configs/guided_anchoring/ga_faster_r50_fpn_1x_coco.py @@ -0,0 +1,65 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + rpn_head=dict( + _delete_=True, + type='GARPNHead', + in_channels=256, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=8, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[8], + strides=[4, 8, 16, 32, 64]), + anchor_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.14, 0.14]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.11, 0.11]), + loc_filter_thr=0.01, + loss_loc=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), + roi_head=dict( + bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + center_ratio=0.2, + ignore_ratio=0.5), + rpn_proposal=dict(nms_post=1000, max_per_img=300), + rcnn=dict( + assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6), + sampler=dict(type='RandomSampler', num=256))), + test_cfg=dict( + rpn=dict(nms_post=1000, max_per_img=300), rcnn=dict(score_thr=1e-3))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..c9a035f --- /dev/null +++ b/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './ga_faster_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..87bbfdc --- /dev/null +++ b/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './ga_faster_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..0048965 --- /dev/null +++ b/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './ga_retinanet_r50_caffe_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_mstrain_2x.py b/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_mstrain_2x.py new file mode 100644 index 0000000..85fa2f5 --- /dev/null +++ b/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_mstrain_2x.py @@ -0,0 +1,167 @@ +_base_ = '../_base_/default_runtime.py' + +# model settings +model = dict( + type='RetinaNet', + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict( + type='ResNet', + depth=101, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs=True, + num_outs=5), + bbox_head=dict( + type='GARetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128]), + anchor_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loc_filter_thr=0.01, + loss_loc=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=0.04, loss_weight=1.0))) +# training and testing settings +train_cfg = dict( + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.4, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + center_ratio=0.2, + ignore_ratio=0.5, + debug=False) +test_cfg = dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100) +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 480), (1333, 960)], + keep_ratio=True, + multiscale_mode='range'), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='bbox') +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=1.0 / 3, + step=[16, 22]) +checkpoint_config = dict(interval=1) +# yapf:disable +log_config = dict( + interval=50, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ]) +# yapf:enable +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..3351201 --- /dev/null +++ b/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,62 @@ +_base_ = '../retinanet/retinanet_r50_caffe_fpn_1x_coco.py' +model = dict( + bbox_head=dict( + _delete_=True, + type='GARetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128]), + anchor_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loc_filter_thr=0.01, + loss_loc=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=0.04, loss_weight=1.0)), + # training and testing settings + train_cfg=dict( + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.4, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + assigner=dict(neg_iou_thr=0.5, min_pos_iou=0.0), + center_ratio=0.2, + ignore_ratio=0.5)) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py b/configs/guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py new file mode 100644 index 0000000..7694723 --- /dev/null +++ b/configs/guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py @@ -0,0 +1,62 @@ +_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' +model = dict( + bbox_head=dict( + _delete_=True, + type='GARetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128]), + anchor_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loc_filter_thr=0.01, + loss_loc=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=0.04, loss_weight=1.0)), + # training and testing settings + train_cfg=dict( + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.4, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + assigner=dict(neg_iou_thr=0.5, min_pos_iou=0.0), + center_ratio=0.2, + ignore_ratio=0.5)) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..18daadd --- /dev/null +++ b/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './ga_retinanet_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..1b18c2b --- /dev/null +++ b/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './ga_retinanet_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..8d15476 --- /dev/null +++ b/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = './ga_rpn_r50_caffe_fpn_1x_coco.py' +# model settings +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..7830894 --- /dev/null +++ b/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,58 @@ +_base_ = '../rpn/rpn_r50_caffe_fpn_1x_coco.py' +model = dict( + rpn_head=dict( + _delete_=True, + type='GARPNHead', + in_channels=256, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=8, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[8], + strides=[4, 8, 16, 32, 64]), + anchor_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.14, 0.14]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.11, 0.11]), + loc_filter_thr=0.01, + loss_loc=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + rpn=dict( + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + center_ratio=0.2, + ignore_ratio=0.5)), + test_cfg=dict(rpn=dict(nms_post=1000))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/guided_anchoring/ga_rpn_r50_fpn_1x_coco.py b/configs/guided_anchoring/ga_rpn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..27ab3e7 --- /dev/null +++ b/configs/guided_anchoring/ga_rpn_r50_fpn_1x_coco.py @@ -0,0 +1,58 @@ +_base_ = '../rpn/rpn_r50_fpn_1x_coco.py' +model = dict( + rpn_head=dict( + _delete_=True, + type='GARPNHead', + in_channels=256, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=8, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[8], + strides=[4, 8, 16, 32, 64]), + anchor_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.14, 0.14]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.11, 0.11]), + loc_filter_thr=0.01, + loss_loc=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + rpn=dict( + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + center_ratio=0.2, + ignore_ratio=0.5)), + test_cfg=dict(rpn=dict(nms_post=1000))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..1e0fe49 --- /dev/null +++ b/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './ga_rpn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..bf66b6b --- /dev/null +++ b/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './ga_rpn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/hrnet/README.md b/configs/hrnet/README.md new file mode 100644 index 0000000..472f8ad --- /dev/null +++ b/configs/hrnet/README.md @@ -0,0 +1,88 @@ +# High-resolution networks (HRNets) for object detection + +## Introduction + + + +```latex +@inproceedings{SunXLW19, + title={Deep High-Resolution Representation Learning for Human Pose Estimation}, + author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang}, + booktitle={CVPR}, + year={2019} +} + +@article{SunZJCXLMWLW19, + title={High-Resolution Representations for Labeling Pixels and Regions}, + author={Ke Sun and Yang Zhao and Borui Jiang and Tianheng Cheng and Bin Xiao + and Dong Liu and Yadong Mu and Xinggang Wang and Wenyu Liu and Jingdong Wang}, + journal = {CoRR}, + volume = {abs/1904.04514}, + year={2019} +} +``` + +## Results and Models + +### Faster R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :-------------:|:------:| :------:| :--------:| +| HRNetV2p-W18 | pytorch | 1x | 6.6 | 13.4 | 36.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco/faster_rcnn_hrnetv2p_w18_1x_coco_20200130-56651a6d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco/faster_rcnn_hrnetv2p_w18_1x_coco_20200130_211246.log.json) | +| HRNetV2p-W18 | pytorch | 2x | 6.6 | | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco/faster_rcnn_hrnetv2p_w18_2x_coco_20200702_085731-a4ec0611.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco/faster_rcnn_hrnetv2p_w18_2x_coco_20200702_085731.log.json) | +| HRNetV2p-W32 | pytorch | 1x | 9.0 | 12.4 | 40.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco/faster_rcnn_hrnetv2p_w32_1x_coco_20200130-6e286425.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco/faster_rcnn_hrnetv2p_w32_1x_coco_20200130_204442.log.json) | +| HRNetV2p-W32 | pytorch | 2x | 9.0 | | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco/faster_rcnn_hrnetv2p_w32_2x_coco_20200529_015927-976a9c15.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco/faster_rcnn_hrnetv2p_w32_2x_coco_20200529_015927.log.json) | +| HRNetV2p-W40 | pytorch | 1x | 10.4 | 10.5 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco/faster_rcnn_hrnetv2p_w40_1x_coco_20200210-95c1f5ce.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco/faster_rcnn_hrnetv2p_w40_1x_coco_20200210_125315.log.json) | +| HRNetV2p-W40 | pytorch | 2x | 10.4 | | 42.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco/faster_rcnn_hrnetv2p_w40_2x_coco_20200512_161033-0f236ef4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco/faster_rcnn_hrnetv2p_w40_2x_coco_20200512_161033.log.json) | + +### Mask R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :-------------:|:------:| :------:|:------:|:--------:| +| HRNetV2p-W18 | pytorch | 1x | 7.0 | 11.7 | 37.7 | 34.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco/mask_rcnn_hrnetv2p_w18_1x_coco_20200205-1c3d78ed.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco/mask_rcnn_hrnetv2p_w18_1x_coco_20200205_232523.log.json) | +| HRNetV2p-W18 | pytorch | 2x | 7.0 | - | 39.8 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco/mask_rcnn_hrnetv2p_w18_2x_coco_20200212-b3c825b1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco/mask_rcnn_hrnetv2p_w18_2x_coco_20200212_134222.log.json) | +| HRNetV2p-W32 | pytorch | 1x | 9.4 | 11.3 | 41.2 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco/mask_rcnn_hrnetv2p_w32_1x_coco_20200207-b29f616e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco/mask_rcnn_hrnetv2p_w32_1x_coco_20200207_055017.log.json) | +| HRNetV2p-W32 | pytorch | 2x | 9.4 | - | 42.5 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco/mask_rcnn_hrnetv2p_w32_2x_coco_20200213-45b75b4d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco/mask_rcnn_hrnetv2p_w32_2x_coco_20200213_150518.log.json) | +| HRNetV2p-W40 | pytorch | 1x | 10.9 | | 42.1 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco/mask_rcnn_hrnetv2p_w40_1x_coco_20200511_015646-66738b35.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco/mask_rcnn_hrnetv2p_w40_1x_coco_20200511_015646.log.json) | +| HRNetV2p-W40 | pytorch | 2x | 10.9 | | 42.8 | 38.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco/mask_rcnn_hrnetv2p_w40_2x_coco_20200512_163732-aed5e4ab.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco/mask_rcnn_hrnetv2p_w40_2x_coco_20200512_163732.log.json) | + +### Cascade R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :-------------:|:------:| :------: | :--------: | +| HRNetV2p-W18 | pytorch | 20e | 7.0 | 11.0 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco/cascade_rcnn_hrnetv2p_w18_20e_coco_20200210-434be9d7.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco/cascade_rcnn_hrnetv2p_w18_20e_coco_20200210_105632.log.json) | +| HRNetV2p-W32 | pytorch | 20e | 9.4 | 11.0 | 43.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco/cascade_rcnn_hrnetv2p_w32_20e_coco_20200208-928455a4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco/cascade_rcnn_hrnetv2p_w32_20e_coco_20200208_160511.log.json) | +| HRNetV2p-W40 | pytorch | 20e | 10.8 | | 43.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco/cascade_rcnn_hrnetv2p_w40_20e_coco_20200512_161112-75e47b04.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco/cascade_rcnn_hrnetv2p_w40_20e_coco_20200512_161112.log.json) | + +### Cascade Mask R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :-------------:|:------:| :------:|:------:|:--------:| +| HRNetV2p-W18 | pytorch | 20e | 8.5 | 8.5 |41.6 |36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20200210-b543cd2b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20200210_093149.log.json) | +| HRNetV2p-W32 | pytorch | 20e | | 8.3 |44.3 |38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco/cascade_mask_rcnn_hrnetv2p_w32_20e_coco_20200512_154043-39d9cf7b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco/cascade_mask_rcnn_hrnetv2p_w32_20e_coco_20200512_154043.log.json) | +| HRNetV2p-W40 | pytorch | 20e | 12.5 | |45.1 |39.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco/cascade_mask_rcnn_hrnetv2p_w40_20e_coco_20200527_204922-969c4610.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco/cascade_mask_rcnn_hrnetv2p_w40_20e_coco_20200527_204922.log.json) | + +### Hybrid Task Cascade (HTC) + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :-------------:|:------:| :------:|:------:|:--------:| +| HRNetV2p-W18 | pytorch | 20e | 10.8 | 4.7 | 42.8 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w18_20e_coco/htc_hrnetv2p_w18_20e_coco_20200210-b266988c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w18_20e_coco/htc_hrnetv2p_w18_20e_coco_20200210_182735.log.json) | +| HRNetV2p-W32 | pytorch | 20e | 13.1 | 4.9 | 45.4 | 39.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w32_20e_coco/htc_hrnetv2p_w32_20e_coco_20200207-7639fa12.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w32_20e_coco/htc_hrnetv2p_w32_20e_coco_20200207_193153.log.json) | +| HRNetV2p-W40 | pytorch | 20e | 14.6 | | 46.4 | 40.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w40_20e_coco/htc_hrnetv2p_w40_20e_coco_20200529_183411-417c4d5b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w40_20e_coco/htc_hrnetv2p_w40_20e_coco_20200529_183411.log.json) | + +### FCOS + +| Backbone | Style | GN | MS train | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:---------:|:-------:|:-------:|:--------:|:-------:|:------:|:------:|:------:|:------:|:--------:| +|HRNetV2p-W18| pytorch | Y | N | 1x | 13.0 | 12.9 | 35.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco_20201212_100710-4ad151de.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco_20201212_100710.log.json) | +|HRNetV2p-W18| pytorch | Y | N | 2x | 13.0 | - | 38.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco_20201212_101110-5c575fa5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco_20201212_101110.log.json) | +|HRNetV2p-W32| pytorch | Y | N | 1x | 17.5 | 12.9 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco_20201211_134730-cb8055c0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco_20201211_134730.log.json) | +|HRNetV2p-W32| pytorch | Y | N | 2x | 17.5 | - | 40.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco_20201212_112133-77b6b9bb.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco_20201212_112133.log.json) | +|HRNetV2p-W18| pytorch | Y | Y | 2x | 13.0 | 12.9 | 38.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco_20201212_111651-441e9d9f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco_20201212_111651.log.json) | +|HRNetV2p-W32| pytorch | Y | Y | 2x | 17.5 | 12.4 | 41.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco_20201212_090846-b6f2b49f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco_20201212_090846.log.json) | +|HRNetV2p-W48| pytorch | Y | Y | 2x | 20.3 | 10.8 | 42.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco_20201212_124752-f22d2ce5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco_20201212_124752.log.json) | + +**Note:** + +- The `28e` schedule in HTC indicates decreasing the lr at 24 and 27 epochs, with a total of 28 epochs. +- HRNetV2 ImageNet pretrained models are in [HRNets for Image Classification](https://github.com/HRNet/HRNet-Image-Classification). diff --git a/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py new file mode 100644 index 0000000..e8df265 --- /dev/null +++ b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py @@ -0,0 +1,10 @@ +_base_ = './cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py' +# model settings +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18', + backbone=dict( + extra=dict( + stage2=dict(num_channels=(18, 36)), + stage3=dict(num_channels=(18, 36, 72)), + stage4=dict(num_channels=(18, 36, 72, 144)))), + neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py new file mode 100644 index 0000000..d410f23 --- /dev/null +++ b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py @@ -0,0 +1,39 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w32', + backbone=dict( + _delete_=True, + type='HRNet', + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256)))), + neck=dict( + _delete_=True, + type='HRFPN', + in_channels=[32, 64, 128, 256], + out_channels=256)) +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py new file mode 100644 index 0000000..29b1469 --- /dev/null +++ b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py @@ -0,0 +1,11 @@ +_base_ = './cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py' +# model settings +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w40', + backbone=dict( + type='HRNet', + extra=dict( + stage2=dict(num_channels=(40, 80)), + stage3=dict(num_channels=(40, 80, 160)), + stage4=dict(num_channels=(40, 80, 160, 320)))), + neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py b/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py new file mode 100644 index 0000000..9585a4f --- /dev/null +++ b/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py @@ -0,0 +1,10 @@ +_base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py' +# model settings +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18', + backbone=dict( + extra=dict( + stage2=dict(num_channels=(18, 36)), + stage3=dict(num_channels=(18, 36, 72)), + stage4=dict(num_channels=(18, 36, 72, 144)))), + neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py b/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py new file mode 100644 index 0000000..ec1bb76 --- /dev/null +++ b/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py @@ -0,0 +1,39 @@ +_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w32', + backbone=dict( + _delete_=True, + type='HRNet', + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256)))), + neck=dict( + _delete_=True, + type='HRFPN', + in_channels=[32, 64, 128, 256], + out_channels=256)) +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py b/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py new file mode 100644 index 0000000..bd43e47 --- /dev/null +++ b/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py @@ -0,0 +1,11 @@ +_base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py' +# model settings +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w40', + backbone=dict( + type='HRNet', + extra=dict( + stage2=dict(num_channels=(40, 80)), + stage3=dict(num_channels=(40, 80, 160)), + stage4=dict(num_channels=(40, 80, 160, 320)))), + neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py b/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py new file mode 100644 index 0000000..9907bcb --- /dev/null +++ b/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py @@ -0,0 +1,10 @@ +_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py' +# model settings +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18', + backbone=dict( + extra=dict( + stage2=dict(num_channels=(18, 36)), + stage3=dict(num_channels=(18, 36, 72)), + stage4=dict(num_channels=(18, 36, 72, 144)))), + neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py b/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py new file mode 100644 index 0000000..a4b987a --- /dev/null +++ b/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py @@ -0,0 +1,5 @@ +_base_ = './faster_rcnn_hrnetv2p_w18_1x_coco.py' + +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py b/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py new file mode 100644 index 0000000..190e81c --- /dev/null +++ b/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py @@ -0,0 +1,36 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w32', + backbone=dict( + _delete_=True, + type='HRNet', + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256)))), + neck=dict( + _delete_=True, + type='HRFPN', + in_channels=[32, 64, 128, 256], + out_channels=256)) diff --git a/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py b/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py new file mode 100644 index 0000000..63c8717 --- /dev/null +++ b/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py b/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py new file mode 100644 index 0000000..d0fd9fa --- /dev/null +++ b/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py @@ -0,0 +1,10 @@ +_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w40', + backbone=dict( + type='HRNet', + extra=dict( + stage2=dict(num_channels=(40, 80)), + stage3=dict(num_channels=(40, 80, 160)), + stage4=dict(num_channels=(40, 80, 160, 320)))), + neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py b/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py new file mode 100644 index 0000000..585cc2c --- /dev/null +++ b/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './faster_rcnn_hrnetv2p_w40_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py new file mode 100644 index 0000000..20bffb9 --- /dev/null +++ b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py @@ -0,0 +1,9 @@ +_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18', + backbone=dict( + extra=dict( + stage2=dict(num_channels=(18, 36)), + stage3=dict(num_channels=(18, 36, 72)), + stage4=dict(num_channels=(18, 36, 72, 144)))), + neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py new file mode 100644 index 0000000..3497595 --- /dev/null +++ b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py new file mode 100644 index 0000000..b845128 --- /dev/null +++ b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py @@ -0,0 +1,9 @@ +_base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18', + backbone=dict( + extra=dict( + stage2=dict(num_channels=(18, 36)), + stage3=dict(num_channels=(18, 36, 72)), + stage4=dict(num_channels=(18, 36, 72, 144)))), + neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py new file mode 100644 index 0000000..98f1cb7 --- /dev/null +++ b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py @@ -0,0 +1,69 @@ +_base_ = '../fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w32', + backbone=dict( + _delete_=True, + type='HRNet', + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256)))), + neck=dict( + _delete_=True, + type='HRFPN', + in_channels=[32, 64, 128, 256], + out_channels=256, + stride=2, + num_outs=5)) +img_norm_cfg = dict( + mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py new file mode 100644 index 0000000..7b38130 --- /dev/null +++ b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py new file mode 100644 index 0000000..482f887 --- /dev/null +++ b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py @@ -0,0 +1,39 @@ +_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py' +img_norm_cfg = dict( + mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py b/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py new file mode 100644 index 0000000..452b0fe --- /dev/null +++ b/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py @@ -0,0 +1,10 @@ +_base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w40', + backbone=dict( + type='HRNet', + extra=dict( + stage2=dict(num_channels=(40, 80)), + stage3=dict(num_channels=(40, 80, 160)), + stage4=dict(num_channels=(40, 80, 160, 320)))), + neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py b/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py new file mode 100644 index 0000000..391636f --- /dev/null +++ b/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py @@ -0,0 +1,9 @@ +_base_ = './htc_hrnetv2p_w32_20e_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18', + backbone=dict( + extra=dict( + stage2=dict(num_channels=(18, 36)), + stage3=dict(num_channels=(18, 36, 72)), + stage4=dict(num_channels=(18, 36, 72, 144)))), + neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py b/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py new file mode 100644 index 0000000..aee7808 --- /dev/null +++ b/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py @@ -0,0 +1,36 @@ +_base_ = '../htc/htc_r50_fpn_20e_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w32', + backbone=dict( + _delete_=True, + type='HRNet', + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256)))), + neck=dict( + _delete_=True, + type='HRFPN', + in_channels=[32, 64, 128, 256], + out_channels=256)) diff --git a/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py b/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py new file mode 100644 index 0000000..abf6fb5 --- /dev/null +++ b/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py @@ -0,0 +1,10 @@ +_base_ = './htc_hrnetv2p_w32_20e_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w40', + backbone=dict( + type='HRNet', + extra=dict( + stage2=dict(num_channels=(40, 80)), + stage3=dict(num_channels=(40, 80, 160)), + stage4=dict(num_channels=(40, 80, 160, 320)))), + neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/configs/hrnet/htc_hrnetv2p_w40_28e_coco.py b/configs/hrnet/htc_hrnetv2p_w40_28e_coco.py new file mode 100644 index 0000000..7067e8b --- /dev/null +++ b/configs/hrnet/htc_hrnetv2p_w40_28e_coco.py @@ -0,0 +1,4 @@ +_base_ = './htc_hrnetv2p_w40_20e_coco.py' +# learning policy +lr_config = dict(step=[24, 27]) +runner = dict(type='EpochBasedRunner', max_epochs=28) diff --git a/configs/hrnet/htc_x101_64x4d_fpn_16x1_28e_coco.py b/configs/hrnet/htc_x101_64x4d_fpn_16x1_28e_coco.py new file mode 100644 index 0000000..815f285 --- /dev/null +++ b/configs/hrnet/htc_x101_64x4d_fpn_16x1_28e_coco.py @@ -0,0 +1,4 @@ +_base_ = '../htc/htc_x101_64x4d_fpn_16x1_20e_coco.py' +# learning policy +lr_config = dict(step=[24, 27]) +runner = dict(type='EpochBasedRunner', max_epochs=28) diff --git a/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py b/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py new file mode 100644 index 0000000..82a5f46 --- /dev/null +++ b/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py @@ -0,0 +1,9 @@ +_base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18', + backbone=dict( + extra=dict( + stage2=dict(num_channels=(18, 36)), + stage3=dict(num_channels=(18, 36, 72)), + stage4=dict(num_channels=(18, 36, 72, 144)))), + neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py b/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py new file mode 100644 index 0000000..ca62682 --- /dev/null +++ b/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py b/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py new file mode 100644 index 0000000..f533af6 --- /dev/null +++ b/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py @@ -0,0 +1,36 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w32', + backbone=dict( + _delete_=True, + type='HRNet', + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256)))), + neck=dict( + _delete_=True, + type='HRFPN', + in_channels=[32, 64, 128, 256], + out_channels=256)) diff --git a/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py b/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py new file mode 100644 index 0000000..63d5d13 --- /dev/null +++ b/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py b/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py new file mode 100644 index 0000000..5b10c16 --- /dev/null +++ b/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py @@ -0,0 +1,10 @@ +_base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w40', + backbone=dict( + type='HRNet', + extra=dict( + stage2=dict(num_channels=(40, 80)), + stage3=dict(num_channels=(40, 80, 160)), + stage4=dict(num_channels=(40, 80, 160, 320)))), + neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py b/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py new file mode 100644 index 0000000..3a2a510 --- /dev/null +++ b/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_hrnetv2p_w40_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/htc/README.md b/configs/htc/README.md new file mode 100644 index 0000000..2cf9e77 --- /dev/null +++ b/configs/htc/README.md @@ -0,0 +1,57 @@ +# Hybrid Task Cascade for Instance Segmentation + +## Introduction + + + +We provide config files to reproduce the results in the CVPR 2019 paper for [Hybrid Task Cascade](https://arxiv.org/abs/1901.07518). + +```latex +@inproceedings{chen2019hybrid, + title={Hybrid task cascade for instance segmentation}, + author={Chen, Kai and Pang, Jiangmiao and Wang, Jiaqi and Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and Liu, Ziwei and Shi, Jianping and Ouyang, Wanli and Chen Change Loy and Dahua Lin}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + year={2019} +} +``` + +## Dataset + +HTC requires COCO and [COCO-stuff](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip) dataset for training. You need to download and extract it in the COCO dataset path. +The directory should be like this. + +```none +mmdetection +├── mmdet +├── tools +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ ├── train2017 +│ │ ├── val2017 +│ │ ├── test2017 +| | ├── stuffthingmaps +``` + +## Results and Models + +The results on COCO 2017val are shown in the below table. (results on test-dev are usually slightly higher than val) + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| +| R-50-FPN | pytorch | 1x | 8.2 | 5.8 | 42.3 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_1x_coco/htc_r50_fpn_1x_coco_20200317-7332cf16.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_1x_coco/htc_r50_fpn_1x_coco_20200317_070435.log.json) | +| R-50-FPN | pytorch | 20e | 8.2 | - | 43.3 | 38.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_r50_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_20e_coco/htc_r50_fpn_20e_coco_20200319-fe28c577.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_20e_coco/htc_r50_fpn_20e_coco_20200319_070313.log.json) | +| R-101-FPN | pytorch | 20e | 10.2 | 5.5 | 44.8 | 39.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_r101_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_r101_fpn_20e_coco/htc_r101_fpn_20e_coco_20200317-9b41b48f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_r101_fpn_20e_coco/htc_r101_fpn_20e_coco_20200317_153107.log.json) | +| X-101-32x4d-FPN | pytorch |20e| 11.4 | 5.0 | 46.1 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_32x4d_fpn_16x1_20e_coco/htc_x101_32x4d_fpn_16x1_20e_coco_20200318-de97ae01.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_32x4d_fpn_16x1_20e_coco/htc_x101_32x4d_fpn_16x1_20e_coco_20200318_034519.log.json) | +| X-101-64x4d-FPN | pytorch |20e| 14.5 | 4.4 | 47.0 | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_16x1_20e_coco/htc_x101_64x4d_fpn_16x1_20e_coco_20200318-b181fd7a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_16x1_20e_coco/htc_x101_64x4d_fpn_16x1_20e_coco_20200318_081711.log.json) | + +- In the HTC paper and COCO 2018 Challenge, `score_thr` is set to 0.001 for both baselines and HTC. +- We use 8 GPUs with 2 images/GPU for R-50 and R-101 models, and 16 GPUs with 1 image/GPU for X-101 models. + If you would like to train X-101 HTC with 8 GPUs, you need to change the lr from 0.02 to 0.01. + +We also provide a powerful HTC with DCN and multi-scale training model. No testing augmentation is used. + +| Backbone | Style | DCN | training scales | Lr schd | box AP | mask AP | Config | Download | +|:----------------:|:-------:|:-----:|:---------------:|:-------:|:------:|:-------:|:------:|:--------:| +| X-101-64x4d-FPN | pytorch | c3-c5 | 400~1400 | 20e | 50.4 | 43.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco_20200312-946fd751.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco_20200312_203410.log.json) | diff --git a/configs/htc/htc_r101_fpn_20e_coco.py b/configs/htc/htc_r101_fpn_20e_coco.py new file mode 100644 index 0000000..de3d5b7 --- /dev/null +++ b/configs/htc/htc_r101_fpn_20e_coco.py @@ -0,0 +1,5 @@ +_base_ = './htc_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/htc/htc_r50_fpn_1x_coco.py b/configs/htc/htc_r50_fpn_1x_coco.py new file mode 100644 index 0000000..929cf46 --- /dev/null +++ b/configs/htc/htc_r50_fpn_1x_coco.py @@ -0,0 +1,56 @@ +_base_ = './htc_without_semantic_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + semantic_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[8]), + semantic_head=dict( + type='FusedSemanticHead', + num_ins=5, + fusion_level=1, + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=183, + ignore_label=255, + loss_weight=0.2))) +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='SegRescale', scale_factor=1 / 8), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict( + seg_prefix=data_root + 'stuffthingmaps/train2017/', + pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/htc/htc_r50_fpn_20e_coco.py b/configs/htc/htc_r50_fpn_20e_coco.py new file mode 100644 index 0000000..7d2e011 --- /dev/null +++ b/configs/htc/htc_r50_fpn_20e_coco.py @@ -0,0 +1,4 @@ +_base_ = './htc_r50_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py b/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py new file mode 100644 index 0000000..d028d98 --- /dev/null +++ b/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py @@ -0,0 +1,236 @@ +_base_ = [ + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# model settings +model = dict( + type='HybridTaskCascade', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + roi_head=dict( + type='HybridTaskCascadeRoIHead', + interleaved=True, + mask_info_flow=True, + num_stages=3, + stage_loss_weights=[1, 0.5, 0.25], + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=[ + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) + ], + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=[ + dict( + type='HTCMaskHead', + with_conv_res=False, + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)), + dict( + type='HTCMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)), + dict( + type='HTCMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)) + ]), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=[ + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.6, + neg_iou_thr=0.6, + min_pos_iou=0.6, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.7, + min_pos_iou=0.7, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False) + ]), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.001, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline)) diff --git a/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py b/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py new file mode 100644 index 0000000..b9e5524 --- /dev/null +++ b/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py @@ -0,0 +1,18 @@ +_base_ = './htc_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch')) +data = dict(samples_per_gpu=1, workers_per_gpu=1) +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py b/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py new file mode 100644 index 0000000..b140f75 --- /dev/null +++ b/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py @@ -0,0 +1,18 @@ +_base_ = './htc_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch')) +data = dict(samples_per_gpu=1, workers_per_gpu=1) +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py b/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py new file mode 100644 index 0000000..da89e09 --- /dev/null +++ b/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py @@ -0,0 +1,42 @@ +_base_ = './htc_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) +# dataset settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True), + dict( + type='Resize', + img_scale=[(1600, 400), (1600, 1400)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='SegRescale', scale_factor=1 / 8), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']), +] +data = dict( + samples_per_gpu=1, workers_per_gpu=1, train=dict(pipeline=train_pipeline)) +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/instaboost/README.md b/configs/instaboost/README.md new file mode 100644 index 0000000..4755073 --- /dev/null +++ b/configs/instaboost/README.md @@ -0,0 +1,44 @@ +# InstaBoost for MMDetection + + + +Configs in this directory is the implementation for ICCV2019 paper "InstaBoost: Boosting Instance Segmentation Via Probability Map Guided Copy-Pasting" and provided by the authors of the paper. InstaBoost is a data augmentation method for object detection and instance segmentation. The paper has been released on [`arXiv`](https://arxiv.org/abs/1908.07801). + +```latex +@inproceedings{fang2019instaboost, + title={Instaboost: Boosting instance segmentation via probability map guided copy-pasting}, + author={Fang, Hao-Shu and Sun, Jianhua and Wang, Runzhong and Gou, Minghao and Li, Yong-Lu and Lu, Cewu}, + booktitle={Proceedings of the IEEE International Conference on Computer Vision}, + pages={682--691}, + year={2019} +} +``` + +## Usage + +### Requirements + +You need to install `instaboostfast` before using it. + +```shell +pip install instaboostfast +``` + +The code and more details can be found [here](https://github.com/GothicAi/Instaboost). + +### Integration with MMDetection + +InstaBoost have been already integrated in the data pipeline, thus all you need is to add or change **InstaBoost** configurations after **LoadImageFromFile**. We have provided examples like [this](mask_rcnn_r50_fpn_instaboost_4x#L121). You can refer to [`InstaBoostConfig`](https://github.com/GothicAi/InstaBoost-pypi#instaboostconfig) for more details. + +## Results and Models + +- All models were trained on `coco_2017_train` and tested on `coco_2017_val` for conveinience of evaluation and comparison. In the paper, the results are obtained from `test-dev`. +- To balance accuracy and training time when using InstaBoost, models released in this page are all trained for 48 Epochs. Other training and testing configs strictly follow the original framework. +- For results and models in MMDetection V1.x, please refer to [Instaboost](https://github.com/GothicAi/Instaboost). + +| Network | Backbone | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :--------: | :-----: | :------: | :------------: | :------:| :-----: | :------: | :-----------------: | +| Mask R-CNN | R-50-FPN | 4x | 4.4 | 17.5 | 40.6 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-d025f83a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307_223635.log.json) | +| Mask R-CNN | R-101-FPN | 4x | 6.4 | | 42.5 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco/mask_rcnn_r101_fpn_instaboost_4x_coco_20200703_235738-f23f3a5f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco/mask_rcnn_r101_fpn_instaboost_4x_coco_20200703_235738.log.json) | +| Mask R-CNN | X-101-64x4d-FPN | 4x | 10.7 | | 44.7 | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947-8ed58c1b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947.log.json) | +| Cascade R-CNN | R-101-FPN | 4x | 6.0 | 12.0 | 43.7 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-c19d98d9.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307_223646.log.json) | diff --git a/configs/instaboost/cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py b/configs/instaboost/cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py new file mode 100644 index 0000000..723ab02 --- /dev/null +++ b/configs/instaboost/cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py @@ -0,0 +1,3 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py' + +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py b/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py new file mode 100644 index 0000000..a89a81f --- /dev/null +++ b/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py @@ -0,0 +1,28 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='InstaBoost', + action_candidate=('normal', 'horizontal', 'skip'), + action_prob=(1, 0, 0), + scale=(0.8, 1.2), + dx=15, + dy=15, + theta=(-1, 1), + color_prob=0.5, + hflag=False, + aug_ratio=0.5), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(pipeline=train_pipeline)) +# learning policy +lr_config = dict(step=[32, 44]) +runner = dict(type='EpochBasedRunner', max_epochs=48) diff --git a/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py b/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py new file mode 100644 index 0000000..7cf5f30 --- /dev/null +++ b/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py @@ -0,0 +1,13 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py b/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py new file mode 100644 index 0000000..c281947 --- /dev/null +++ b/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py @@ -0,0 +1,2 @@ +_base_ = './mask_rcnn_r50_fpn_instaboost_4x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py b/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py new file mode 100644 index 0000000..55ca62b --- /dev/null +++ b/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py @@ -0,0 +1,28 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='InstaBoost', + action_candidate=('normal', 'horizontal', 'skip'), + action_prob=(1, 0, 0), + scale=(0.8, 1.2), + dx=15, + dy=15, + theta=(-1, 1), + color_prob=0.5, + hflag=False, + aug_ratio=0.5), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(pipeline=train_pipeline)) +# learning policy +lr_config = dict(step=[32, 44]) +runner = dict(type='EpochBasedRunner', max_epochs=48) diff --git a/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py b/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py new file mode 100644 index 0000000..0acd088 --- /dev/null +++ b/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py @@ -0,0 +1,13 @@ +_base_ = './mask_rcnn_r50_fpn_instaboost_4x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/kaihu3.zip b/configs/kaihu3.zip new file mode 100644 index 0000000..4f964ea Binary files /dev/null and b/configs/kaihu3.zip differ diff --git a/configs/kaihu3/adamixer_r101_300_query_crop_mstrain_480-800_3x_coco_7stage_fangyi.py b/configs/kaihu3/adamixer_r101_300_query_crop_mstrain_480-800_3x_coco_7stage_fangyi.py new file mode 100644 index 0000000..75fe94e --- /dev/null +++ b/configs/kaihu3/adamixer_r101_300_query_crop_mstrain_480-800_3x_coco_7stage_fangyi.py @@ -0,0 +1,3 @@ +_base_ = './adamixer_r50_300_query_crop_mstrain_480-800_3x_coco_7stage_fangyi.py' + +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/kaihu3/adamixer_r50_1x_coco_7stage_fangyi.py b/configs/kaihu3/adamixer_r50_1x_coco_7stage_fangyi.py new file mode 100644 index 0000000..68d41cb --- /dev/null +++ b/configs/kaihu3/adamixer_r50_1x_coco_7stage_fangyi.py @@ -0,0 +1,203 @@ +def __get_debug(): + import os + return 'C_DEBUG' in os.environ + + +debug = __get_debug() + +log_interval = 100 + + +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +work_dir_prefix = 'work_dirs/adamixer_mmdet' + +IMAGE_SCALE = (1333, 800) + +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=IMAGE_SCALE, keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=IMAGE_SCALE, + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/image_info_test-dev2017.json', + img_prefix=data_root + 'test2017/', + pipeline=test_pipeline), +) +evaluation = dict(interval=1, metric='bbox') + + +num_stages = 7 +num_query = 100 +QUERY_DIM = 256 +FEAT_DIM = 256 +FF_DIM = 2048 + +# P_in for spatial mixing in the paper. +in_points_list = [32, ] * num_stages + +# P_out for spatial mixing in the paper. Also named as `out_points` in this codebase. +out_patterns_list = [128, ] * num_stages + +# G for the mixer grouping in the paper. Please distinguishe it from num_heads in MHSA in this codebase. +n_group_list = [4, ] * num_stages + +model = dict( + type='QueryBased', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='ChannelMapping', + in_channels=[256, 512, 1024, 2048], + out_channels=FEAT_DIM, + start_level=0, + add_extra_convs='on_output', + num_outs=4), + rpn_head=dict( + type='InitialQueryGenerator', + num_query=num_query, + content_dim=QUERY_DIM), + roi_head=dict( + type='AdaMixerDecoder', + featmap_strides=[4, 8, 16, 32], + num_stages=num_stages, + stage_loss_weights=[1] * num_stages, + content_dim=QUERY_DIM, + bbox_head=[ + dict( + type='AdaMixerDecoderStage', + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=1, + feedforward_channels=FF_DIM, + content_dim=QUERY_DIM, + feat_channels=FEAT_DIM, + dropout=0.0, + in_points=in_points_list[stage_idx], + out_points=out_patterns_list[stage_idx], + n_groups=n_group_list[stage_idx], + ffn_act_cfg=dict(type='ReLU', inplace=True), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + # NOTE: The following argument is a placeholder to hack the code. No real effects for decoding or updating bounding boxes. + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=False, + target_means=[0., 0., 0., 0.], + target_stds=[0.5, 0.5, 1., 1.])) for stage_idx in range(num_stages) + ]), + # training and testing settings + train_cfg=dict( + rpn=None, + rcnn=[ + dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0), + iou_cost=dict(type='IoUCost', iou_mode='giou', + weight=2.0)), + sampler=dict(type='PseudoSampler'), + pos_weight=1) for _ in range(num_stages) + ]), + test_cfg=dict(rpn=None, rcnn=dict(max_per_img=num_query))) + +# optimizer +optimizer = dict( + _delete_=True, + type='AdamW', + lr=0.000025, + weight_decay=0.0001, +) + +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=1.0, norm_type=2), +) + +# learning policy +lr_config = dict( + policy='step', + step=[8, 11], + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001 +) +runner = dict(type='EpochBasedRunner', max_epochs=12) + + +def __date(): + import datetime + return datetime.datetime.now().strftime('%m%d_%H%M') + + +log_config = dict( + interval=log_interval, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ] +) + +postfix = '_' + __date() + +find_unused_parameters = True + + +resume_from = None diff --git a/configs/kaihu3/adamixer_r50_300_query_crop_mstrain_480-800_3x_coco_7stage_fangyi.py b/configs/kaihu3/adamixer_r50_300_query_crop_mstrain_480-800_3x_coco_7stage_fangyi.py new file mode 100644 index 0000000..2d0ecfb --- /dev/null +++ b/configs/kaihu3/adamixer_r50_300_query_crop_mstrain_480-800_3x_coco_7stage_fangyi.py @@ -0,0 +1,55 @@ +_base_ = './adamixer_r50_mstrain_480-800_3x_coco_7stage_fangyi.py' +num_query = 300 +model = dict( + rpn_head=dict(num_query=num_query), + test_cfg=dict( + _delete_=True, rpn=None, rcnn=dict(max_per_img=num_query))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +# augmentation strategy originates from DETR. +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='AutoAugment', + policies=[[ + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict( + type='Resize', + img_scale=[(400, 1333), (500, 1333), (600, 1333)], + multiscale_mode='value', + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ]]), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +data = dict(train=dict(pipeline=train_pipeline)) + +lr_config = dict(policy='step', step=[24, 33]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/kaihu3/adamixer_r50_mstrain_480-800_3x_coco_7stage_fangyi.py b/configs/kaihu3/adamixer_r50_mstrain_480-800_3x_coco_7stage_fangyi.py new file mode 100644 index 0000000..a1b107e --- /dev/null +++ b/configs/kaihu3/adamixer_r50_mstrain_480-800_3x_coco_7stage_fangyi.py @@ -0,0 +1,23 @@ +_base_ = './adamixer_r50_1x_coco_7stage_fangyi.py' + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +min_values = (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, value) for value in min_values], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] + +data = dict(train=dict(pipeline=train_pipeline)) +lr_config = dict(policy='step', step=[27, 33]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/ld/README.md b/configs/ld/README.md new file mode 100644 index 0000000..0177f1e --- /dev/null +++ b/configs/ld/README.md @@ -0,0 +1,31 @@ +# Localization Distillation for Object Detection + +## Introduction + + + +```latex +@Article{zheng2021LD, + title={Localization Distillation for Object Detection}, + author= {Zhaohui Zheng, Rongguang Ye, Ping Wang, Jun Wang, Dongwei Ren, Wangmeng Zuo}, + journal={arXiv:2102.12252}, + year={2021} +} +``` + +### GFocalV1 with LD + +| Teacher | Student | Training schedule | Mini-batch size | AP (val) | AP50 (val) | AP75 (val) | Config | +| :-------: | :-----: | :---------------: | :-------------: | :------: | :--------: | :--------: | :--------------: | +| -- | R-18 | 1x | 6 | 35.8 | 53.1 | 38.2 | | +| R-101 | R-18 | 1x | 6 | 36.5 | 52.9 | 39.3 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py) | +| -- | R-34 | 1x | 6 | 38.9 | 56.6 | 42.2 | | +| R-101 | R-34 | 1x | 6 | 39.8 | 56.6 | 43.1 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py) | +| -- | R-50 | 1x | 6 | 40.1 | 58.2 | 43.1 | | +| R-101 | R-50 | 1x | 6 | 41.1 | 58.7 | 44.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py) | +| -- | R-101 | 2x | 6 | 44.6 | 62.9 | 48.4 | | +| R-101-DCN | R-101 | 2x | 6 | 45.4 | 63.1 | 49.5 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_1x.py) | + +## Note + +- Meaning of Config name: ld_r18(student model)_gflv1(based on gflv1)_r101(teacher model)_fpn(neck)_coco(dataset)_1x(12 epoch).py diff --git a/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py b/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py new file mode 100644 index 0000000..37c66a9 --- /dev/null +++ b/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py @@ -0,0 +1,43 @@ +_base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py'] +teacher_ckpt = 'http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth' # noqa +model = dict( + pretrained='torchvision://resnet101', + teacher_config='configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py', + teacher_ckpt=teacher_ckpt, + backbone=dict( + type='ResNet', + depth=101, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5)) + +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) +# multi-scale training +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 480), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py b/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py new file mode 100644 index 0000000..7b8ce4a --- /dev/null +++ b/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py @@ -0,0 +1,62 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +teacher_ckpt = 'http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth' # noqa +model = dict( + type='KnowledgeDistillationSingleStageDetector', + pretrained='torchvision://resnet18', + teacher_config='configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py', + teacher_ckpt=teacher_ckpt, + backbone=dict( + type='ResNet', + depth=18, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[64, 128, 256, 512], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5), + bbox_head=dict( + type='LDHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128]), + loss_cls=dict( + type='QualityFocalLoss', + use_sigmoid=True, + beta=2.0, + loss_weight=1.0), + loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25), + loss_ld=dict( + type='KnowledgeDistillationKLDivLoss', loss_weight=0.25, T=10), + reg_max=16, + loss_bbox=dict(type='GIoULoss', loss_weight=2.0)), + # training and testing settings + train_cfg=dict( + assigner=dict(type='ATSSAssigner', topk=9), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) + +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py b/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py new file mode 100644 index 0000000..905651d --- /dev/null +++ b/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py @@ -0,0 +1,19 @@ +_base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py'] +model = dict( + pretrained='torchvision://resnet34', + backbone=dict( + type='ResNet', + depth=34, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[64, 128, 256, 512], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5)) diff --git a/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py b/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py new file mode 100644 index 0000000..923c626 --- /dev/null +++ b/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py @@ -0,0 +1,19 @@ +_base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py'] +model = dict( + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5)) diff --git a/configs/legacy_1.x/README.md b/configs/legacy_1.x/README.md new file mode 100644 index 0000000..38a2a0e --- /dev/null +++ b/configs/legacy_1.x/README.md @@ -0,0 +1,53 @@ +# Legacy Configs in MMDetection V1.x + + + +Configs in this directory implement the legacy configs used by MMDetection V1.x and its model zoos. + +To help users convert their models from V1.x to MMDetection V2.0, we provide v1.x configs to inference the converted v1.x models. +Due to the BC-breaking changes in MMDetection V2.0 from MMDetection V1.x, running inference with the same model weights in these two version will produce different results. The difference will cause within 1% AP absolute difference as can be found in the following table. + +## Usage + +To upgrade the model version, the users need to do the following steps. + +### 1. Convert model weights + +There are three main difference in the model weights between V1.x and V2.0 codebases. + +1. Since the class order in all the detector's classification branch is reordered, all the legacy model weights need to go through the conversion process. +2. The regression and segmentation head no longer contain the background channel. Weights in these background channels should be removed to fix in the current codebase. +3. For two-stage detectors, their wegihts need to be upgraded since MMDetection V2.0 refactors all the two-stage detectors with `RoIHead`. + +The users can do the same modification as mentioned above for the self-implemented +detectors. We provide a scripts `tools/model_converters/upgrade_model_version.py` to convert the model weights in the V1.x model zoo. + +```bash +python tools/model_converters/upgrade_model_version.py ${OLD_MODEL_PATH} ${NEW_MODEL_PATH} --num-classes ${NUM_CLASSES} + +``` + +- OLD_MODEL_PATH: the path to load the model weights in 1.x version. +- NEW_MODEL_PATH: the path to save the converted model weights in 2.0 version. +- NUM_CLASSES: number of classes of the original model weights. Usually it is 81 for COCO dataset, 21 for VOC dataset. + The number of classes in V2.0 models should be equal to that in V1.x models - 1. + +### 2. Use configs with legacy settings + +After converting the model weights, checkout to the v1.2 release to find the corresponding config file that uses the legacy settings. +The V1.x models usually need these three legacy modules: `LegacyAnchorGenerator`, `LegacyDeltaXYWHBBoxCoder`, and `RoIAlign(align=False)`. +For models using ResNet Caffe backbones, they also need to change the pretrain name and the corresponding `img_norm_cfg`. +An example is in [`retinanet_r50_caffe_fpn_1x_coco_v1.py`](retinanet_r50_caffe_fpn_1x_coco_v1.py) +Then use the config to test the model weights. For most models, the obtained results should be close to that in V1.x. +We provide configs of some common structures in this directory. + +## Performance + +The performance change after converting the models in this directory are listed as the following. +| Method | Style | Lr schd | V1.x box AP | V1.x mask AP | V2.0 box AP | V2.0 mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------:| :-----: |:------:| :-----: | :-------: |:------------------------------------------------------------------------------------------------------------------------------: | +| Mask R-CNN R-50-FPN | pytorch | 1x | 37.3 | 34.2 | 36.8 | 33.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/mask_rcnn_r50_fpn_1x_coco_v1.py) | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_1x_20181010-069fa190.pth)| +| RetinaNet R-50-FPN | caffe | 1x | 35.8 | - | 35.4 | - | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/retinanet_r50_caffe_1x_coco_v1.py) | +| RetinaNet R-50-FPN | pytorch | 1x | 35.6 |-|35.2| -| [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py) | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r50_fpn_1x_20181125-7b0c2548.pth) | +| Cascade Mask R-CNN R-50-FPN | pytorch | 1x | 41.2 | 35.7 |40.8| 35.6| [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py) | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r50_fpn_1x_20181123-88b170c9.pth) | +| SSD300-VGG16 | caffe | 120e | 25.7 |-|25.4|-| [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/ssd300_coco_v1.py) | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ssd300_coco_vgg16_caffe_120e_20181221-84d7110b.pth) | diff --git a/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py b/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py new file mode 100644 index 0000000..5899444 --- /dev/null +++ b/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py @@ -0,0 +1,79 @@ +_base_ = [ + '../_base_/models/cascade_mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='CascadeRCNN', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + anchor_generator=dict(type='LegacyAnchorGenerator', center_offset=0.5), + bbox_coder=dict( + type='LegacyDeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0])), + roi_head=dict( + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', + output_size=7, + sampling_ratio=2, + aligned=False)), + bbox_head=[ + dict( + type='Shared2FCBBoxHead', + reg_class_agnostic=True, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='LegacyDeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2])), + dict( + type='Shared2FCBBoxHead', + reg_class_agnostic=True, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='LegacyDeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1])), + dict( + type='Shared2FCBBoxHead', + reg_class_agnostic=True, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='LegacyDeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067])), + ], + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', + output_size=14, + sampling_ratio=2, + aligned=False)))) +dist_params = dict(backend='nccl', port=29515) diff --git a/configs/legacy_1.x/faster_rcnn_r50_fpn_1x_coco_v1.py b/configs/legacy_1.x/faster_rcnn_r50_fpn_1x_coco_v1.py new file mode 100644 index 0000000..fb2f2d1 --- /dev/null +++ b/configs/legacy_1.x/faster_rcnn_r50_fpn_1x_coco_v1.py @@ -0,0 +1,37 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + type='FasterRCNN', + pretrained='torchvision://resnet50', + rpn_head=dict( + type='RPNHead', + anchor_generator=dict( + type='LegacyAnchorGenerator', + center_offset=0.5, + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', + output_size=7, + sampling_ratio=2, + aligned=False), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn_proposal=dict(max_per_img=2000), + rcnn=dict(assigner=dict(match_low_quality=True)))) diff --git a/configs/legacy_1.x/mask_rcnn_r50_fpn_1x_coco_v1.py b/configs/legacy_1.x/mask_rcnn_r50_fpn_1x_coco_v1.py new file mode 100644 index 0000000..04581bb --- /dev/null +++ b/configs/legacy_1.x/mask_rcnn_r50_fpn_1x_coco_v1.py @@ -0,0 +1,34 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + rpn_head=dict( + anchor_generator=dict(type='LegacyAnchorGenerator', center_offset=0.5), + bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + roi_head=dict( + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', + output_size=7, + sampling_ratio=2, + aligned=False)), + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', + output_size=14, + sampling_ratio=2, + aligned=False)), + bbox_head=dict( + bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + + # model training and testing settings + train_cfg=dict( + rpn_proposal=dict(max_per_img=2000), + rcnn=dict(assigner=dict(match_low_quality=True)))) diff --git a/configs/legacy_1.x/retinanet_r50_caffe_fpn_1x_coco_v1.py b/configs/legacy_1.x/retinanet_r50_caffe_fpn_1x_coco_v1.py new file mode 100644 index 0000000..ef9392f --- /dev/null +++ b/configs/legacy_1.x/retinanet_r50_caffe_fpn_1x_coco_v1.py @@ -0,0 +1,37 @@ +_base_ = './retinanet_r50_fpn_1x_coco_v1.py' +model = dict( + pretrained='open-mmlab://detectron/resnet50_caffe', + backbone=dict( + norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe')) +# use caffe img_norm +img_norm_cfg = dict( + mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py b/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py new file mode 100644 index 0000000..6198b97 --- /dev/null +++ b/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py @@ -0,0 +1,17 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + bbox_head=dict( + type='RetinaHead', + anchor_generator=dict( + type='LegacyAnchorGenerator', + center_offset=0.5, + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'), + loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0))) diff --git a/configs/legacy_1.x/ssd300_coco_v1.py b/configs/legacy_1.x/ssd300_coco_v1.py new file mode 100644 index 0000000..b194e76 --- /dev/null +++ b/configs/legacy_1.x/ssd300_coco_v1.py @@ -0,0 +1,79 @@ +_base_ = [ + '../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] +# model settings +input_size = 300 +model = dict( + bbox_head=dict( + type='SSDHead', + anchor_generator=dict( + type='LegacySSDAnchorGenerator', + scale_major=False, + input_size=input_size, + basesize_ratio_range=(0.15, 0.9), + strides=[8, 16, 32, 64, 100, 300], + ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]), + bbox_coder=dict( + type='LegacyDeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]))) +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 4)), + dict( + type='MinIoURandomCrop', + min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), + min_crop_size=0.3), + dict(type='Resize', img_scale=(300, 300), keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(300, 300), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=3, + train=dict( + _delete_=True, + type='RepeatDataset', + times=5, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4) +optimizer_config = dict(_delete_=True) +dist_params = dict(backend='nccl', port=29555) diff --git a/configs/libra_rcnn/README.md b/configs/libra_rcnn/README.md new file mode 100644 index 0000000..49bf9e0 --- /dev/null +++ b/configs/libra_rcnn/README.md @@ -0,0 +1,28 @@ +# Libra R-CNN: Towards Balanced Learning for Object Detection + +## Introduction + + + +We provide config files to reproduce the results in the CVPR 2019 paper [Libra R-CNN](https://arxiv.org/pdf/1904.02701.pdf). + +``` +@inproceedings{pang2019libra, + title={Libra R-CNN: Towards Balanced Learning for Object Detection}, + author={Pang, Jiangmiao and Chen, Kai and Shi, Jianping and Feng, Huajun and Ouyang, Wanli and Dahua Lin}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + year={2019} +} +``` + +## Results and models + +The results on COCO 2017val are shown in the below table. (results on test-dev are usually slightly higher than val) + +| Architecture | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:------------:|:---------------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| Faster R-CNN | R-50-FPN | pytorch | 1x | 4.6 | 19.0 | 38.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco/libra_faster_rcnn_r50_fpn_1x_coco_20200130-3afee3a9.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco/libra_faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +| Fast R-CNN | R-50-FPN | pytorch | 1x | | | | | +| Faster R-CNN | R-101-FPN | pytorch | 1x | 6.5 | 14.4 | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco/libra_faster_rcnn_r101_fpn_1x_coco_20200203-8dba6a5a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco/libra_faster_rcnn_r101_fpn_1x_coco_20200203_001405.log.json) | +| Faster R-CNN | X-101-64x4d-FPN | pytorch | 1x | 10.8 | 8.5 | 42.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco/libra_faster_rcnn_x101_64x4d_fpn_1x_coco_20200315-3a7d0488.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco/libra_faster_rcnn_x101_64x4d_fpn_1x_coco_20200315_231625.log.json) | +| RetinaNet | R-50-FPN | pytorch | 1x | 4.2 | 17.7 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_retinanet_r50_fpn_1x_coco/libra_retinanet_r50_fpn_1x_coco_20200205-804d94ce.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_retinanet_r50_fpn_1x_coco/libra_retinanet_r50_fpn_1x_coco_20200205_112757.log.json) | diff --git a/configs/libra_rcnn/libra_fast_rcnn_r50_fpn_1x_coco.py b/configs/libra_rcnn/libra_fast_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..efbedc8 --- /dev/null +++ b/configs/libra_rcnn/libra_fast_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,50 @@ +_base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py' +# model settings +model = dict( + neck=[ + dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + dict( + type='BFP', + in_channels=256, + num_levels=5, + refine_level=2, + refine_type='non_local') + ], + roi_head=dict( + bbox_head=dict( + loss_bbox=dict( + _delete_=True, + type='BalancedL1Loss', + alpha=0.5, + gamma=1.5, + beta=1.0, + loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rcnn=dict( + sampler=dict( + _delete_=True, + type='CombinedSampler', + num=512, + pos_fraction=0.25, + add_gt_as_proposals=True, + pos_sampler=dict(type='InstanceBalancedPosSampler'), + neg_sampler=dict( + type='IoUBalancedNegSampler', + floor_thr=-1, + floor_fraction=0, + num_bins=3))))) +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +data = dict( + train=dict(proposal_file=data_root + + 'libra_proposals/rpn_r50_fpn_1x_train2017.pkl'), + val=dict(proposal_file=data_root + + 'libra_proposals/rpn_r50_fpn_1x_val2017.pkl'), + test=dict(proposal_file=data_root + + 'libra_proposals/rpn_r50_fpn_1x_val2017.pkl')) diff --git a/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py b/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 0000000..8e36c9b --- /dev/null +++ b/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './libra_faster_rcnn_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py b/configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..89a0d7b --- /dev/null +++ b/configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,41 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +# model settings +model = dict( + neck=[ + dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + dict( + type='BFP', + in_channels=256, + num_levels=5, + refine_level=2, + refine_type='non_local') + ], + roi_head=dict( + bbox_head=dict( + loss_bbox=dict( + _delete_=True, + type='BalancedL1Loss', + alpha=0.5, + gamma=1.5, + beta=1.0, + loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict(sampler=dict(neg_pos_ub=5), allowed_border=-1), + rcnn=dict( + sampler=dict( + _delete_=True, + type='CombinedSampler', + num=512, + pos_fraction=0.25, + add_gt_as_proposals=True, + pos_sampler=dict(type='InstanceBalancedPosSampler'), + neg_sampler=dict( + type='IoUBalancedNegSampler', + floor_thr=-1, + floor_fraction=0, + num_bins=3))))) diff --git a/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..e945532 --- /dev/null +++ b/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './libra_faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py b/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py new file mode 100644 index 0000000..be27420 --- /dev/null +++ b/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py @@ -0,0 +1,26 @@ +_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' +# model settings +model = dict( + neck=[ + dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_input', + num_outs=5), + dict( + type='BFP', + in_channels=256, + num_levels=5, + refine_level=1, + refine_type='non_local') + ], + bbox_head=dict( + loss_bbox=dict( + _delete_=True, + type='BalancedL1Loss', + alpha=0.5, + gamma=1.5, + beta=0.11, + loss_weight=1.0))) diff --git a/configs/lvis/README.md b/configs/lvis/README.md new file mode 100644 index 0000000..a40d3bf --- /dev/null +++ b/configs/lvis/README.md @@ -0,0 +1,44 @@ +# LVIS dataset + +## Introduction + + + +```latex +@inproceedings{gupta2019lvis, + title={{LVIS}: A Dataset for Large Vocabulary Instance Segmentation}, + author={Gupta, Agrim and Dollar, Piotr and Girshick, Ross}, + booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition}, + year={2019} +} +``` + +## Common Setting + +* Please follow [install guide](../../docs/install.md#install-mmdetection) to install open-mmlab forked cocoapi first. +* Run following scripts to install our forked lvis-api. + + ```shell + pip install git+https://github.com/lvis-dataset/lvis-api.git + ``` + +* All experiments use oversample strategy [here](../../docs/tutorials/new_dataset.md#class-balanced-dataset) with oversample threshold `1e-3`. +* The size of LVIS v0.5 is half of COCO, so schedule `2x` in LVIS is roughly the same iterations as `1x` in COCO. + +## Results and models of LVIS v0.5 + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: |:--------: | +| R-50-FPN | pytorch | 2x | - | - | 26.1 | 25.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis-dbd06831.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_20200531_160435.log.json) | +| R-101-FPN | pytorch | 2x | - | - | 27.1 | 27.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis-54582ee2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_20200601_134748.log.json) | +| X-101-32x4d-FPN | pytorch | 2x | - | - | 26.7 | 26.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis-3cf55ea2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_20200531_221749.log.json) | +| X-101-64x4d-FPN | pytorch | 2x | - | - | 26.4 | 26.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis-1c99a5ad.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_20200601_194651.log.json) | + +## Results and models of LVIS v1 + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +| R-50-FPN | pytorch | 1x | 9.1 | - | 22.5 | 21.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1-aa78ac3d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1-20200829_061305.log.json) | +| R-101-FPN | pytorch | 1x | 10.8 | - | 24.6 | 23.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1-ec55ce32.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1-20200829_070959.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 11.8 | - | 26.7 | 25.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-ebbc5c81.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-20200829_071317.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 14.6 | - | 27.2 | 25.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-43d9edfe.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-20200830_060206.log.json) | diff --git a/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py b/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py new file mode 100644 index 0000000..1881865 --- /dev/null +++ b/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py @@ -0,0 +1,2 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py new file mode 100644 index 0000000..2d2816c --- /dev/null +++ b/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py @@ -0,0 +1,2 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py b/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py new file mode 100644 index 0000000..92ddb52 --- /dev/null +++ b/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py @@ -0,0 +1,31 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/lvis_v1_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + roi_head=dict( + bbox_head=dict(num_classes=1203), mask_head=dict(num_classes=1203)), + test_cfg=dict( + rcnn=dict( + score_thr=0.0001, + # LVIS allows up to 300 + max_per_img=300))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(dataset=dict(pipeline=train_pipeline))) diff --git a/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py new file mode 100644 index 0000000..d53c5dc --- /dev/null +++ b/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py @@ -0,0 +1,31 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/lvis_v0.5_instance.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] +model = dict( + roi_head=dict( + bbox_head=dict(num_classes=1230), mask_head=dict(num_classes=1230)), + test_cfg=dict( + rcnn=dict( + score_thr=0.0001, + # LVIS allows up to 300 + max_per_img=300))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(dataset=dict(pipeline=train_pipeline))) diff --git a/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py b/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py new file mode 100644 index 0000000..5abcc2e --- /dev/null +++ b/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py @@ -0,0 +1,13 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py new file mode 100644 index 0000000..439c39a --- /dev/null +++ b/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py @@ -0,0 +1,13 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py b/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py new file mode 100644 index 0000000..f77adba --- /dev/null +++ b/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py @@ -0,0 +1,13 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py new file mode 100644 index 0000000..2136255 --- /dev/null +++ b/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py @@ -0,0 +1,13 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/mask_rcnn/README.md b/configs/mask_rcnn/README.md new file mode 100644 index 0000000..87e4d06 --- /dev/null +++ b/configs/mask_rcnn/README.md @@ -0,0 +1,43 @@ +# Mask R-CNN + +## Introduction + + + +```latex +@article{He_2017, + title={Mask R-CNN}, + journal={2017 IEEE International Conference on Computer Vision (ICCV)}, + publisher={IEEE}, + author={He, Kaiming and Gkioxari, Georgia and Dollar, Piotr and Girshick, Ross}, + year={2017}, + month={Oct} +} +``` + +## Results and models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +| R-50-FPN | caffe | 1x | 4.3 | | 38.0 | 34.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco/mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.38__segm_mAP-0.344_20200504_231812-0ebd1859.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco/mask_rcnn_r50_caffe_fpn_1x_coco_20200504_231812.log.json) | +| R-50-FPN | pytorch | 1x | 4.4 | 16.1 | 38.2 | 34.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json) | +| R-50-FPN | pytorch | 2x | - | - | 39.2 | 35.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/mask_rcnn_r50_fpn_2x_coco_bbox_mAP-0.392__segm_mAP-0.354_20200505_003907-3e542a40.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/mask_rcnn_r50_fpn_2x_coco_20200505_003907.log.json) | +| R-101-FPN | caffe | 1x | | | 40.4 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco/mask_rcnn_r101_caffe_fpn_1x_coco_20200601_095758-805e06c1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco/mask_rcnn_r101_caffe_fpn_1x_coco_20200601_095758.log.json)| +| R-101-FPN | pytorch | 1x | 6.4 | 13.5 | 40.0 | 36.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204_144809.log.json) | +| R-101-FPN | pytorch | 2x | - | - | 40.8 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_2x_coco/mask_rcnn_r101_fpn_2x_coco_bbox_mAP-0.408__segm_mAP-0.366_20200505_071027-14b391c7.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_2x_coco/mask_rcnn_r101_fpn_2x_coco_20200505_071027.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.6 | 11.3 | 41.9 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205-478d0b67.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205_034906.log.json) | +| X-101-32x4d-FPN | pytorch | 2x | - | - | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco/mask_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.422__segm_mAP-0.378_20200506_004702-faef898c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco/mask_rcnn_x101_32x4d_fpn_2x_coco_20200506_004702.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.7 | 8.0 | 42.8 | 38.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco/mask_rcnn_x101_64x4d_fpn_1x_coco_20200201-9352eb0d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco/mask_rcnn_x101_64x4d_fpn_1x_coco_20200201_124310.log.json) | +| X-101-64x4d-FPN | pytorch | 2x | - | - | 42.7 | 38.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco/mask_rcnn_x101_64x4d_fpn_2x_coco_20200509_224208-39d6f70c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco/mask_rcnn_x101_64x4d_fpn_2x_coco_20200509_224208.log.json)| +| X-101-32x8d-FPN | pytorch | 1x | - | - | 42.8 | 38.3 | | + +## Pre-trained Models + +We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks. + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +| [R-50-FPN](./mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py) | caffe | 2x | 4.3 | | 40.3 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco_bbox_mAP-0.403__segm_mAP-0.365_20200504_231822-a75c98ce.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco_20200504_231822.log.json) +| [R-50-FPN](./mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py) | caffe | 3x | 4.3 | | 40.8 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_20200504_163245.log.json) +| [X-101-32x8d-FPN](./mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py) | pytorch | 1x | - | | 43.6 | 39.0 | +| [X-101-32x8d-FPN](./mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py) | pytorch | 3x | - | | 44.0 | 39.3 | diff --git a/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..230181c --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 0000000..db02d9b --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './mask_rcnn_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py b/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py new file mode 100644 index 0000000..c8cb2d8 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py @@ -0,0 +1,2 @@ +_base_ = './mask_rcnn_r50_fpn_2x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_c4_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_c4_1x_coco.py new file mode 100644 index 0000000..a44c018 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_c4_1x_coco.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_caffe_c4.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..0471fe8 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,36 @@ +_base_ = './mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe')) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py new file mode 100644 index 0000000..5d6215d --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py @@ -0,0 +1,45 @@ +_base_ = './mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe')) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py new file mode 100644 index 0000000..4f7150c --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..1b48a21 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py' +# learning policy +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py new file mode 100644 index 0000000..86c5b13 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py @@ -0,0 +1,41 @@ +_base_ = './mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe')) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py new file mode 100644 index 0000000..431e5ab --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py @@ -0,0 +1,57 @@ +_base_ = './mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnet50_caffe_bgr', + backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'), + rpn_head=dict( + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + roi_head=dict( + bbox_roi_extractor=dict( + roi_layer=dict( + type='RoIAlign', + output_size=7, + sampling_ratio=2, + aligned=False)), + bbox_head=dict( + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), + mask_roi_extractor=dict( + roi_layer=dict( + type='RoIAlign', + output_size=14, + sampling_ratio=2, + aligned=False)))) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..6a6c924 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] diff --git a/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py new file mode 100644 index 0000000..932b1f9 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] diff --git a/configs/mask_rcnn/mask_rcnn_r50_fpn_poly_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_fpn_poly_1x_coco.py new file mode 100644 index 0000000..9eb6d57 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_fpn_poly_1x_coco.py @@ -0,0 +1,23 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..d0016d1 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py new file mode 100644 index 0000000..d4189c6 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py @@ -0,0 +1,13 @@ +_base_ = './mask_rcnn_r101_fpn_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py new file mode 100644 index 0000000..ee034b7 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py @@ -0,0 +1,63 @@ +_base_ = './mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnext101_32x8d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=8, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + style='pytorch')) + +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], + std=[57.375, 57.120, 58.395], + to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py new file mode 100644 index 0000000..1c12432 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py @@ -0,0 +1,58 @@ +_base_ = './mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnext101_32x8d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=8, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + style='pytorch')) + +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], + std=[57.375, 57.120, 58.395], + to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..93b7d51 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,61 @@ +_base_ = './mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnext101_32x8d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=8, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + style='pytorch')) + +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], + std=[57.375, 57.120, 58.395], + to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) + +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..31e5943 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py new file mode 100644 index 0000000..9ba92c5 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py @@ -0,0 +1,13 @@ +_base_ = './mask_rcnn_x101_32x4d_fpn_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/ms_rcnn/README.md b/configs/ms_rcnn/README.md new file mode 100644 index 0000000..76f5af3 --- /dev/null +++ b/configs/ms_rcnn/README.md @@ -0,0 +1,26 @@ +# Mask Scoring R-CNN + +## Introduction + + + +``` +@inproceedings{huang2019msrcnn, + title={Mask Scoring R-CNN}, + author={Zhaojin Huang and Lichao Huang and Yongchao Gong and Chang Huang and Xinggang Wang}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + year={2019}, +} +``` + +## Results and Models + +| Backbone | style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:-------------:|:----------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| +| R-50-FPN | caffe | 1x | 4.5 | | 38.2 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848-61c9355e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848.log.json) | +| R-50-FPN | caffe | 2x | - | - | 38.8 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco/ms_rcnn_r50_caffe_fpn_2x_coco_bbox_mAP-0.388__segm_mAP-0.363_20200506_004738-ee87b137.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco/ms_rcnn_r50_caffe_fpn_2x_coco_20200506_004738.log.json) | +| R-101-FPN | caffe | 1x | 6.5 | | 40.4 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco/ms_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.404__segm_mAP-0.376_20200506_004755-b9b12a37.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco/ms_rcnn_r101_caffe_fpn_1x_coco_20200506_004755.log.json) | +| R-101-FPN | caffe | 2x | - | - | 41.1 | 38.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco/ms_rcnn_r101_caffe_fpn_2x_coco_bbox_mAP-0.411__segm_mAP-0.381_20200506_011134-5f3cc74f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco/ms_rcnn_r101_caffe_fpn_2x_coco_20200506_011134.log.json) | +| R-X101-32x4d | pytorch | 2x | 7.9 | 11.0 | 41.8 | 38.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco/ms_rcnn_x101_32x4d_fpn_1x_coco_20200206-81fd1740.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco/ms_rcnn_x101_32x4d_fpn_1x_coco_20200206_100113.log.json) | +| R-X101-64x4d | pytorch | 1x | 11.0 | 8.0 | 43.0 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco/ms_rcnn_x101_64x4d_fpn_1x_coco_20200206-86ba88d2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco/ms_rcnn_x101_64x4d_fpn_1x_coco_20200206_091744.log.json) | +| R-X101-64x4d | pytorch | 2x | 11.0 | 8.0 | 42.6 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco/ms_rcnn_x101_64x4d_fpn_2x_coco_20200308-02a445e2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco/ms_rcnn_x101_64x4d_fpn_2x_coco_20200308_012247.log.json) | diff --git a/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py b/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..3bd33c4 --- /dev/null +++ b/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py b/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py new file mode 100644 index 0000000..202bcce --- /dev/null +++ b/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './ms_rcnn_r101_caffe_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py b/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..5845125 --- /dev/null +++ b/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + type='MaskScoringRCNN', + roi_head=dict( + type='MaskScoringRoIHead', + mask_iou_head=dict( + type='MaskIoUHead', + num_convs=4, + num_fcs=2, + roi_feat_size=14, + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + num_classes=80)), + # model training and testing settings + train_cfg=dict(rcnn=dict(mask_thr_binary=0.5))) diff --git a/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py b/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py new file mode 100644 index 0000000..008a70a --- /dev/null +++ b/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/ms_rcnn/ms_rcnn_r50_fpn_1x_coco.py b/configs/ms_rcnn/ms_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..0a163ce --- /dev/null +++ b/configs/ms_rcnn/ms_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + type='MaskScoringRCNN', + roi_head=dict( + type='MaskScoringRoIHead', + mask_iou_head=dict( + type='MaskIoUHead', + num_convs=4, + num_fcs=2, + roi_feat_size=14, + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + num_classes=80)), + # model training and testing settings + train_cfg=dict(rcnn=dict(mask_thr_binary=0.5))) diff --git a/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..4a78a25 --- /dev/null +++ b/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './ms_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..61a0cef --- /dev/null +++ b/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './ms_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py b/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py new file mode 100644 index 0000000..54c605b --- /dev/null +++ b/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './ms_rcnn_x101_64x4d_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/nas_fcos/README.md b/configs/nas_fcos/README.md new file mode 100644 index 0000000..84abfe0 --- /dev/null +++ b/configs/nas_fcos/README.md @@ -0,0 +1,25 @@ +# NAS-FCOS: Fast Neural Architecture Search for Object Detection + +## Introduction + + + +```latex +@article{wang2019fcos, + title={Nas-fcos: Fast neural architecture search for object detection}, + author={Wang, Ning and Gao, Yang and Chen, Hao and Wang, Peng and Tian, Zhi and Shen, Chunhua}, + journal={arXiv preprint arXiv:1906.04423}, + year={2019} +} +``` + +## Results and Models + +| Head | Backbone | Style | GN-head | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:---------:|:---------:|:-------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| NAS-FCOSHead | R-50 | caffe | Y | 1x | | | 39.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520-1bdba3ce.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520.log.json) | +| FCOSHead | R-50 | caffe | Y | 1x | | | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200521-7fdcbce0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200521.log.json) | + +**Notes:** + +- To be consistent with the author's implementation, we use 4 GPUs with 4 images/GPU. diff --git a/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py b/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py new file mode 100644 index 0000000..1910312 --- /dev/null +++ b/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py @@ -0,0 +1,98 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + type='NASFCOS', + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False, eps=0), + style='caffe'), + neck=dict( + type='NASFCOS_FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs=True, + num_outs=5, + norm_cfg=dict(type='BN'), + conv_cfg=dict(type='DCNv2', deform_groups=2)), + bbox_head=dict( + type='FCOSHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + strides=[8, 16, 32, 64, 128], + norm_cfg=dict(type='GN', num_groups=32), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='IoULoss', loss_weight=1.0), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) + +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + samples_per_gpu=4, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) + +optimizer = dict( + lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.)) diff --git a/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py b/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py new file mode 100644 index 0000000..ef81123 --- /dev/null +++ b/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py @@ -0,0 +1,97 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + type='NASFCOS', + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False, eps=0), + style='caffe'), + neck=dict( + type='NASFCOS_FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs=True, + num_outs=5, + norm_cfg=dict(type='BN'), + conv_cfg=dict(type='DCNv2', deform_groups=2)), + bbox_head=dict( + type='NASFCOSHead', + num_classes=80, + in_channels=256, + feat_channels=256, + strides=[8, 16, 32, 64, 128], + norm_cfg=dict(type='GN', num_groups=32), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='IoULoss', loss_weight=1.0), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) + +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + samples_per_gpu=4, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) + +optimizer = dict( + lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.)) diff --git a/configs/nas_fpn/README.md b/configs/nas_fpn/README.md new file mode 100644 index 0000000..6a52ead --- /dev/null +++ b/configs/nas_fpn/README.md @@ -0,0 +1,26 @@ +# NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection + +## Introduction + + + +```latex +@inproceedings{ghiasi2019fpn, + title={Nas-fpn: Learning scalable feature pyramid architecture for object detection}, + author={Ghiasi, Golnaz and Lin, Tsung-Yi and Le, Quoc V}, + booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, + pages={7036--7045}, + year={2019} +} +``` + +## Results and Models + +We benchmark the new training schedule (crop training, large batch, unfrozen BN, 50 epochs) introduced in NAS-FPN. RetinaNet is used in the paper. + +| Backbone | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:-----------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50-FPN | 50e | 12.9 | 22.9 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco-9b953d76.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco_20200529_095329.log.json) | +| R-50-NASFPN | 50e | 13.2 | 23.0 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco_20200528_230008.log.json) | + +**Note**: We find that it is unstable to train NAS-FPN and there is a small chance that results can be 3% mAP lower. diff --git a/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py b/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py new file mode 100644 index 0000000..d4c7c98 --- /dev/null +++ b/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py @@ -0,0 +1,80 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py' +] +cudnn_benchmark = True +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch'), + neck=dict( + relu_before_extra_convs=True, + no_norm_on_lateral=True, + norm_cfg=norm_cfg), + bbox_head=dict(type='RetinaSepBNHead', num_ins=5, norm_cfg=norm_cfg), + # training and testing settings + train_cfg=dict(assigner=dict(neg_iou_thr=0.5))) +# dataset settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=(640, 640), + ratio_range=(0.8, 1.2), + keep_ratio=True), + dict(type='RandomCrop', crop_size=(640, 640)), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=(640, 640)), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(640, 640), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=64), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=4, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict( + type='SGD', + lr=0.08, + momentum=0.9, + weight_decay=0.0001, + paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True)) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=0.1, + step=[30, 40]) +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=50) diff --git a/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py b/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py new file mode 100644 index 0000000..8a2ef26 --- /dev/null +++ b/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py @@ -0,0 +1,79 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py' +] +cudnn_benchmark = True +# model settings +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + type='RetinaNet', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch'), + neck=dict(type='NASFPN', stack_times=7, norm_cfg=norm_cfg), + bbox_head=dict(type='RetinaSepBNHead', num_ins=5, norm_cfg=norm_cfg), + # training and testing settings + train_cfg=dict(assigner=dict(neg_iou_thr=0.5))) +# dataset settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=(640, 640), + ratio_range=(0.8, 1.2), + keep_ratio=True), + dict(type='RandomCrop', crop_size=(640, 640)), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=(640, 640)), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(640, 640), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=128), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=4, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict( + type='SGD', + lr=0.08, + momentum=0.9, + weight_decay=0.0001, + paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True)) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=0.1, + step=[30, 40]) +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=50) diff --git a/configs/paa/README.md b/configs/paa/README.md new file mode 100644 index 0000000..82c19f3 --- /dev/null +++ b/configs/paa/README.md @@ -0,0 +1,35 @@ +# Probabilistic Anchor Assignment with IoU Prediction for Object Detection + + + +```latex +@inproceedings{paa-eccv2020, + title={Probabilistic Anchor Assignment with IoU Prediction for Object Detection}, + author={Kim, Kang and Lee, Hee Seok}, + booktitle = {ECCV}, + year={2020} +} +``` + +## Results and Models + +We provide config files to reproduce the object detection results in the +ECCV 2020 paper for Probabilistic Anchor Assignment with IoU +Prediction for Object Detection. + +| Backbone | Lr schd | Mem (GB) | Score voting | box AP | Config | Download | +|:-----------:|:-------:|:--------:|:------------:|:------:|:------:|:--------:| +| R-50-FPN | 12e | 3.7 | True | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.log.json) | +| R-50-FPN | 12e | 3.7 | False | 40.2 | - | +| R-50-FPN | 18e | 3.7 | True | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_1.5x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1.5x_coco/paa_r50_fpn_1.5x_coco_20200823-805d6078.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1.5x_coco/paa_r50_fpn_1.5x_coco_20200823-805d6078.log.json) | +| R-50-FPN | 18e | 3.7 | False | 41.2 | - | +| R-50-FPN | 24e | 3.7 | True | 41.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_2x_coco/paa_r50_fpn_2x_coco_20200821-c98bfc4e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_2x_coco/paa_r50_fpn_2x_coco_20200821-c98bfc4e.log.json) | +| R-50-FPN | 36e | 3.7 | True | 43.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_mstrain_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_mstrain_3x_coco/paa_r50_fpn_mstrain_3x_coco_20210121_145722-06a6880b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_mstrain_3x_coco/paa_r50_fpn_mstrain_3x_coco_20210121_145722.log.json) | +| R-101-FPN | 12e | 6.2 | True | 42.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.log.json) | +| R-101-FPN | 12e | 6.2 | False | 42.4 | - | +| R-101-FPN | 24e | 6.2 | True | 43.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r101_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_2x_coco/paa_r101_fpn_2x_coco_20200821-6829f96b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_2x_coco/paa_r101_fpn_2x_coco_20200821-6829f96b.log.json) | +| R-101-FPN | 36e | 6.2 | True | 45.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r101_fpn_mstrain_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_mstrain_3x_coco/paa_r101_fpn_mstrain_3x_coco_20210122_084202-83250d22.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_mstrain_3x_coco/paa_r101_fpn_mstrain_3x_coco_20210122_084202.log.json) | + +**Note**: + +1. We find that the performance is unstable with 1x setting and may fluctuate by about 0.2 mAP. We report the best results. diff --git a/configs/paa/paa_r101_fpn_1x_coco.py b/configs/paa/paa_r101_fpn_1x_coco.py new file mode 100644 index 0000000..9d2b1a6 --- /dev/null +++ b/configs/paa/paa_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './paa_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/paa/paa_r101_fpn_2x_coco.py b/configs/paa/paa_r101_fpn_2x_coco.py new file mode 100644 index 0000000..641ef76 --- /dev/null +++ b/configs/paa/paa_r101_fpn_2x_coco.py @@ -0,0 +1,3 @@ +_base_ = './paa_r101_fpn_1x_coco.py' +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/paa/paa_r101_fpn_mstrain_3x_coco.py b/configs/paa/paa_r101_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..6f23df7 --- /dev/null +++ b/configs/paa/paa_r101_fpn_mstrain_3x_coco.py @@ -0,0 +1,2 @@ +_base_ = './paa_r50_fpn_mstrain_3x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/paa/paa_r50_fpn_1.5x_coco.py b/configs/paa/paa_r50_fpn_1.5x_coco.py new file mode 100644 index 0000000..aabce4a --- /dev/null +++ b/configs/paa/paa_r50_fpn_1.5x_coco.py @@ -0,0 +1,3 @@ +_base_ = './paa_r50_fpn_1x_coco.py' +lr_config = dict(step=[12, 16]) +runner = dict(type='EpochBasedRunner', max_epochs=18) diff --git a/configs/paa/paa_r50_fpn_1x_coco.py b/configs/paa/paa_r50_fpn_1x_coco.py new file mode 100644 index 0000000..cd84410 --- /dev/null +++ b/configs/paa/paa_r50_fpn_1x_coco.py @@ -0,0 +1,70 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='PAA', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5), + bbox_head=dict( + type='PAAHead', + reg_decoded_bbox=True, + score_voting=True, + topk=9, + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=1.3), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.1, + neg_iou_thr=0.1, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/paa/paa_r50_fpn_2x_coco.py b/configs/paa/paa_r50_fpn_2x_coco.py new file mode 100644 index 0000000..663d2c0 --- /dev/null +++ b/configs/paa/paa_r50_fpn_2x_coco.py @@ -0,0 +1,3 @@ +_base_ = './paa_r50_fpn_1x_coco.py' +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/paa/paa_r50_fpn_mstrain_3x_coco.py b/configs/paa/paa_r50_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..91fa28c --- /dev/null +++ b/configs/paa/paa_r50_fpn_mstrain_3x_coco.py @@ -0,0 +1,20 @@ +_base_ = './paa_r50_fpn_1x_coco.py' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict(train=dict(pipeline=train_pipeline)) +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/pafpn/README.md b/configs/pafpn/README.md new file mode 100644 index 0000000..3ddd451 --- /dev/null +++ b/configs/pafpn/README.md @@ -0,0 +1,26 @@ +# Path Aggregation Network for Instance Segmentation + +## Introduction + + + +``` +@inproceedings{liu2018path, + author = {Shu Liu and + Lu Qi and + Haifang Qin and + Jianping Shi and + Jiaya Jia}, + title = {Path Aggregation Network for Instance Segmentation}, + booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + year = {2018} +} +``` + +## Results and Models + +## Results and Models + +| Backbone | style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:-------------:|:----------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| +| R-50-FPN | pytorch | 1x | 4.0 | 17.2 | 37.5 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/pafpn/faster_rcnn_r50_pafpn_1x_coco/faster_rcnn_r50_pafpn_1x_coco_bbox_mAP-0.375_20200503_105836-b7b4b9bd.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pafpn/faster_rcnn_r50_pafpn_1x_coco/faster_rcnn_r50_pafpn_1x_coco_20200503_105836.log.json) | diff --git a/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py b/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py new file mode 100644 index 0000000..b2fdef9 --- /dev/null +++ b/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py @@ -0,0 +1,8 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' + +model = dict( + neck=dict( + type='PAFPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5)) diff --git a/configs/pascal_voc/README.md b/configs/pascal_voc/README.md new file mode 100644 index 0000000..e7a7ee8 --- /dev/null +++ b/configs/pascal_voc/README.md @@ -0,0 +1,23 @@ +# PASCAL VOC Dataset + + + +``` +@Article{Everingham10, + author = "Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.", + title = "The Pascal Visual Object Classes (VOC) Challenge", + journal = "International Journal of Computer Vision", + volume = "88", + year = "2010", + number = "2", + month = jun, + pages = "303--338", +} +``` + +## Results and Models + +| Architecture | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:------------:|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| Faster R-CNN | R-50 | pytorch | 1x | 2.6 | - | 79.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712/faster_rcnn_r50_fpn_1x_voc0712_20200624-c9895d40.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712/20200623_015208.log.json) | +| Retinanet | R-50 | pytorch | 1x | 2.1 | - | 77.3 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/pascal_voc/retinanet_r50_fpn_1x_voc0712/retinanet_r50_fpn_1x_voc0712_20200617-47cbdd0e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pascal_voc/retinanet_r50_fpn_1x_voc0712/retinanet_r50_fpn_1x_voc0712_20200616_014642.log.json) | diff --git a/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py b/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py new file mode 100644 index 0000000..7866ace --- /dev/null +++ b/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py @@ -0,0 +1,14 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py', + '../_base_/default_runtime.py' +] +model = dict(roi_head=dict(bbox_head=dict(num_classes=20))) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +# actual epoch = 3 * 3 = 9 +lr_config = dict(policy='step', step=[3]) +# runtime settings +runner = dict( + type='EpochBasedRunner', max_epochs=4) # actual epoch = 4 * 3 = 12 diff --git a/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py b/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py new file mode 100644 index 0000000..12eee2c --- /dev/null +++ b/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py @@ -0,0 +1,75 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py', + '../_base_/default_runtime.py' +] +model = dict(roi_head=dict(bbox_head=dict(num_classes=20))) + +CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', + 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', + 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor') + +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/VOCdevkit/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1000, 600), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1000, 600), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=3, + dataset=dict( + type=dataset_type, + ann_file='data/voc0712_trainval.json', + img_prefix='data/VOCdevkit', + pipeline=train_pipeline, + classes=CLASSES)), + val=dict( + type=dataset_type, + ann_file='data/voc07_test.json', + img_prefix='data/VOCdevkit', + pipeline=test_pipeline, + classes=CLASSES), + test=dict( + type=dataset_type, + ann_file='data/voc07_test.json', + img_prefix='data/VOCdevkit', + pipeline=test_pipeline, + classes=CLASSES)) +evaluation = dict(interval=1, metric='bbox') + +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +# actual epoch = 3 * 3 = 9 +lr_config = dict(policy='step', step=[3]) +# runtime settings +runner = dict( + type='EpochBasedRunner', max_epochs=4) # actual epoch = 4 * 3 = 12 diff --git a/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py b/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py new file mode 100644 index 0000000..b4b050d --- /dev/null +++ b/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py @@ -0,0 +1,14 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', '../_base_/datasets/voc0712.py', + '../_base_/default_runtime.py' +] +model = dict(bbox_head=dict(num_classes=20)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +# actual epoch = 3 * 3 = 9 +lr_config = dict(policy='step', step=[3]) +# runtime settings +runner = dict( + type='EpochBasedRunner', max_epochs=4) # actual epoch = 4 * 3 = 12 diff --git a/configs/pascal_voc/ssd300_voc0712.py b/configs/pascal_voc/ssd300_voc0712.py new file mode 100644 index 0000000..271ebe3 --- /dev/null +++ b/configs/pascal_voc/ssd300_voc0712.py @@ -0,0 +1,69 @@ +_base_ = [ + '../_base_/models/ssd300.py', '../_base_/datasets/voc0712.py', + '../_base_/default_runtime.py' +] +model = dict( + bbox_head=dict( + num_classes=20, anchor_generator=dict(basesize_ratio_range=(0.2, + 0.9)))) +# dataset settings +dataset_type = 'VOCDataset' +data_root = 'data/VOCdevkit/' +img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 4)), + dict( + type='MinIoURandomCrop', + min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), + min_crop_size=0.3), + dict(type='Resize', img_scale=(300, 300), keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(300, 300), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=3, + train=dict( + type='RepeatDataset', times=10, dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=1e-3, momentum=0.9, weight_decay=5e-4) +optimizer_config = dict() +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[16, 20]) +checkpoint_config = dict(interval=1) +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/pascal_voc/ssd512_voc0712.py b/configs/pascal_voc/ssd512_voc0712.py new file mode 100644 index 0000000..365a65f --- /dev/null +++ b/configs/pascal_voc/ssd512_voc0712.py @@ -0,0 +1,53 @@ +_base_ = 'ssd300_voc0712.py' +input_size = 512 +model = dict( + backbone=dict(input_size=input_size), + bbox_head=dict( + in_channels=(512, 1024, 512, 256, 256, 256, 256), + anchor_generator=dict( + input_size=input_size, + strides=[8, 16, 32, 64, 128, 256, 512], + basesize_ratio_range=(0.15, 0.9), + ratios=([2], [2, 3], [2, 3], [2, 3], [2, 3], [2], [2])))) +img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 4)), + dict( + type='MinIoURandomCrop', + min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), + min_crop_size=0.3), + dict(type='Resize', img_scale=(512, 512), keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(512, 512), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/pisa/README.md b/configs/pisa/README.md new file mode 100644 index 0000000..69a1a3f --- /dev/null +++ b/configs/pisa/README.md @@ -0,0 +1,40 @@ +# Prime Sample Attention in Object Detection + +## Introduction + + + +```latex +@inproceedings{cao2019prime, + title={Prime sample attention in object detection}, + author={Cao, Yuhang and Chen, Kai and Loy, Chen Change and Lin, Dahua}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + year={2020} +} +``` + +## Results and models + +| PISA | Network | Backbone | Lr schd | box AP | mask AP | Config | Download | +|:----:|:-------:|:-------------------:|:-------:|:------:|:-------:|:------:|:--------:| +| × | Faster R-CNN | R-50-FPN | 1x | 36.4 | | - | +| √ | Faster R-CNN | R-50-FPN | 1x | 38.4 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco_20200506_185619.log.json) | +| × | Faster R-CNN | X101-32x4d-FPN | 1x | 40.1 | | - | +| √ | Faster R-CNN | X101-32x4d-FPN | 1x | 41.9 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco-e4accec4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco_20200505_181503.log.json) | +| × | Mask R-CNN | R-50-FPN | 1x | 37.3 | 34.2 | - | +| √ | Mask R-CNN | R-50-FPN | 1x | 39.1 | 35.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_mask_rcnn_r50_fpn_1x_coco/pisa_mask_rcnn_r50_fpn_1x_coco-dfcedba6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_mask_rcnn_r50_fpn_1x_coco/pisa_mask_rcnn_r50_fpn_1x_coco_20200508_150500.log.json) | +| × | Mask R-CNN | X101-32x4d-FPN | 1x | 41.1 | 37.1 | - | +| √ | Mask R-CNN | X101-32x4d-FPN | 1x | | | | +| × | RetinaNet | R-50-FPN | 1x | 35.6 | | - | +| √ | RetinaNet | R-50-FPN | 1x | 36.9 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_r50_fpn_1x_coco/pisa_retinanet_r50_fpn_1x_coco-76409952.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_r50_fpn_1x_coco/pisa_retinanet_r50_fpn_1x_coco_20200504_014311.log.json) | +| × | RetinaNet | X101-32x4d-FPN | 1x | 39.0 | | - | +| √ | RetinaNet | X101-32x4d-FPN | 1x | 40.7 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco/pisa_retinanet_x101_32x4d_fpn_1x_coco-a0c13c73.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco/pisa_retinanet_x101_32x4d_fpn_1x_coco_20200505_001404.log.json) | +| × | SSD300 | VGG16 | 1x | 25.6 | | - | +| √ | SSD300 | VGG16 | 1x | 27.6 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_ssd300_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd300_coco/pisa_ssd300_coco-710e3ac9.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd300_coco/pisa_ssd300_coco_20200504_144325.log.json) | +| × | SSD300 | VGG16 | 1x | 29.3 | | - | +| √ | SSD300 | VGG16 | 1x | 31.8 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_ssd512_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd512_coco/pisa_ssd512_coco-247addee.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd512_coco/pisa_ssd512_coco_20200508_131030.log.json) | + +**Notes:** + +- In the original paper, all models are trained and tested on mmdet v1.x, thus results may not be exactly the same with this release on v2.0. +- It is noted PISA only modifies the training pipeline so the inference time remains the same with the baseline. diff --git a/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py b/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..71e65b0 --- /dev/null +++ b/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,30 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' + +model = dict( + roi_head=dict( + type='PISARoIHead', + bbox_head=dict( + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + train_cfg=dict( + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + sampler=dict( + type='ScoreHLRSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True, + k=0.5, + bias=0.), + isr=dict(k=2, bias=0), + carl=dict(k=1, bias=0.2))), + test_cfg=dict( + rpn=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0))) diff --git a/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..16edd99 --- /dev/null +++ b/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,30 @@ +_base_ = '../faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py' + +model = dict( + roi_head=dict( + type='PISARoIHead', + bbox_head=dict( + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + train_cfg=dict( + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + sampler=dict( + type='ScoreHLRSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True, + k=0.5, + bias=0.), + isr=dict(k=2, bias=0), + carl=dict(k=1, bias=0.2))), + test_cfg=dict( + rpn=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0))) diff --git a/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py b/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..047a293 --- /dev/null +++ b/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,30 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' + +model = dict( + roi_head=dict( + type='PISARoIHead', + bbox_head=dict( + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + train_cfg=dict( + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + sampler=dict( + type='ScoreHLRSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True, + k=0.5, + bias=0.), + isr=dict(k=2, bias=0), + carl=dict(k=1, bias=0.2))), + test_cfg=dict( + rpn=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0))) diff --git a/configs/pisa/pisa_mask_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/pisa/pisa_mask_rcnn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..2186a8f --- /dev/null +++ b/configs/pisa/pisa_mask_rcnn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,30 @@ +_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py' + +model = dict( + roi_head=dict( + type='PISARoIHead', + bbox_head=dict( + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + train_cfg=dict( + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + sampler=dict( + type='ScoreHLRSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True, + k=0.5, + bias=0.), + isr=dict(k=2, bias=0), + carl=dict(k=1, bias=0.2))), + test_cfg=dict( + rpn=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0))) diff --git a/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py b/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py new file mode 100644 index 0000000..70f89e2 --- /dev/null +++ b/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' + +model = dict( + bbox_head=dict( + type='PISARetinaHead', + loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)), + train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2))) diff --git a/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py b/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..b97b672 --- /dev/null +++ b/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = '../retinanet/retinanet_x101_32x4d_fpn_1x_coco.py' + +model = dict( + bbox_head=dict( + type='PISARetinaHead', + loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)), + train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2))) diff --git a/configs/pisa/pisa_ssd300_coco.py b/configs/pisa/pisa_ssd300_coco.py new file mode 100644 index 0000000..b5cc006 --- /dev/null +++ b/configs/pisa/pisa_ssd300_coco.py @@ -0,0 +1,8 @@ +_base_ = '../ssd/ssd300_coco.py' + +model = dict( + bbox_head=dict(type='PISASSDHead'), + train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2))) + +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/pisa/pisa_ssd512_coco.py b/configs/pisa/pisa_ssd512_coco.py new file mode 100644 index 0000000..3219d6d --- /dev/null +++ b/configs/pisa/pisa_ssd512_coco.py @@ -0,0 +1,8 @@ +_base_ = '../ssd/ssd512_coco.py' + +model = dict( + bbox_head=dict(type='PISASSDHead'), + train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2))) + +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/point_rend/README.md b/configs/point_rend/README.md new file mode 100644 index 0000000..998e51e --- /dev/null +++ b/configs/point_rend/README.md @@ -0,0 +1,23 @@ +# PointRend + +## Introduction + + + +```latex +@InProceedings{kirillov2019pointrend, + title={{PointRend}: Image Segmentation as Rendering}, + author={Alexander Kirillov and Yuxin Wu and Kaiming He and Ross Girshick}, + journal={ArXiv:1912.08193}, + year={2019} +} +``` + +## Results and models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +| R-50-FPN | caffe | 1x | 4.6 | | 38.4 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco/point_rend_r50_caffe_fpn_mstrain_1x_coco-1bcb5fb4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco/point_rend_r50_caffe_fpn_mstrain_1x_coco_20200612_161407.log.json) | +| R-50-FPN | caffe | 3x | 4.6 | | 41.0 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco/point_rend_r50_caffe_fpn_mstrain_3x_coco-e0ebb6b7.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco/point_rend_r50_caffe_fpn_mstrain_3x_coco_20200614_002632.log.json) | + +Note: All models are trained with multi-scale, the input image shorter side is randomly scaled to one of (640, 672, 704, 736, 768, 800). diff --git a/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py b/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py new file mode 100644 index 0000000..0c0e563 --- /dev/null +++ b/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py @@ -0,0 +1,44 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' +# model settings +model = dict( + type='PointRend', + roi_head=dict( + type='PointRendRoIHead', + mask_roi_extractor=dict( + type='GenericRoIExtractor', + aggregation='concat', + roi_layer=dict( + _delete_=True, type='SimpleRoIAlign', output_size=14), + out_channels=256, + featmap_strides=[4]), + mask_head=dict( + _delete_=True, + type='CoarseMaskHead', + num_fcs=2, + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)), + point_head=dict( + type='MaskPointHead', + num_fcs=3, + in_channels=256, + fc_channels=256, + num_classes=80, + coarse_pred_each_layer=True, + loss_point=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rcnn=dict( + mask_size=7, + num_points=14 * 14, + oversample_ratio=3, + importance_sample_ratio=0.75)), + test_cfg=dict( + rcnn=dict( + subdivision_steps=5, + subdivision_num_points=28 * 28, + scale_factor=2))) diff --git a/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py b/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..169278e --- /dev/null +++ b/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py @@ -0,0 +1,4 @@ +_base_ = './point_rend_r50_caffe_fpn_mstrain_1x_coco.py' +# learning policy +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/regnet/README.md b/configs/regnet/README.md new file mode 100644 index 0000000..0ccd407 --- /dev/null +++ b/configs/regnet/README.md @@ -0,0 +1,96 @@ +# Designing Network Design Spaces + +## Introduction + +[BACKBONE] + +We implement RegNetX and RegNetY models in detection systems and provide their first results on Mask R-CNN, Faster R-CNN and RetinaNet. + +The pre-trained modles are converted from [model zoo of pycls](https://github.com/facebookresearch/pycls/blob/master/MODEL_ZOO.md). + +```latex +@article{radosavovic2020designing, + title={Designing Network Design Spaces}, + author={Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Dollár}, + year={2020}, + eprint={2003.13678}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + +## Usage + +To use a regnet model, there are two steps to do: + +1. Convert the model to ResNet-style supported by MMDetection +2. Modify backbone and neck in config accordingly + +### Convert model + +We already prepare models of FLOPs from 400M to 12G in our model zoo. + +For more general usage, we also provide script `regnet2mmdet.py` in the tools directory to convert the key of models pretrained by [pycls](https://github.com/facebookresearch/pycls/) to +ResNet-style checkpoints used in MMDetection. + +```bash +python -u tools/model_converters/regnet2mmdet.py ${PRETRAIN_PATH} ${STORE_PATH} +``` + +This script convert model from `PRETRAIN_PATH` and store the converted model in `STORE_PATH`. + +### Modify config + +The users can modify the config's `depth` of backbone and corresponding keys in `arch` according to the configs in the [pycls model zoo](https://github.com/facebookresearch/pycls/blob/master/MODEL_ZOO.md). +The parameter `in_channels` in FPN can be found in the Figure 15 & 16 of the paper (`wi` in the legend). +This directory already provides some configs with their performance, using RegNetX from 800MF to 12GF level. +For other pre-trained models or self-implemented regnet models, the users are responsible to check these parameters by themselves. + +**Note**: Although Fig. 15 & 16 also provide `w0`, `wa`, `wm`, `group_w`, and `bot_mul` for `arch`, they are quantized thus inaccurate, using them sometimes produces different backbone that does not match the key in the pre-trained model. + +## Results + +### Mask R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :---------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +| [R-50-FPN](../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py)| pytorch | 1x | 4.4 | 12.0 | 38.2 | 34.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json) | +|[RegNetX-3.2GF-FPN](./mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py)| pytorch | 1x |5.0 ||40.3|36.6|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141.log.json) | +|[RegNetX-4.0GF-FPN](./mask_rcnn_regnetx-4GF_fpn_1x_coco.py)| pytorch | 1x |5.5||41.5|37.4|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco/mask_rcnn_regnetx-4GF_fpn_1x_coco_20200517_180217-32e9c92d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco/mask_rcnn_regnetx-4GF_fpn_1x_coco_20200517_180217.log.json) | +| [R-101-FPN](../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py)| pytorch | 1x | 6.4 | 10.3 | 40.0 | 36.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204_144809.log.json) | +|[RegNetX-6.4GF-FPN](./mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py)| pytorch | 1x |6.1 ||41.0|37.1|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco/mask_rcnn_regnetx-6.4GF_fpn_1x_coco_20200517_180439-3a7aae83.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco/mask_rcnn_regnetx-6.4GF_fpn_1x_coco_20200517_180439.log.json) | +| [X-101-32x4d-FPN](../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py) | pytorch | 1x | 7.6 | 9.4 | 41.9 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205-478d0b67.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205_034906.log.json) | +|[RegNetX-8.0GF-FPN](./mask_rcnn_regnetx-8GF_fpn_1x_coco.py)| pytorch | 1x |6.4 ||41.7|37.5|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco/mask_rcnn_regnetx-8GF_fpn_1x_coco_20200517_180515-09daa87e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco/mask_rcnn_regnetx-8GF_fpn_1x_coco_20200517_180515.log.json) | +|[RegNetX-12GF-FPN](./mask_rcnn_regnetx-12GF_fpn_1x_coco.py)| pytorch | 1x |7.4 ||42.2|38|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco/mask_rcnn_regnetx-12GF_fpn_1x_coco_20200517_180552-b538bd8b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco/mask_rcnn_regnetx-12GF_fpn_1x_coco_20200517_180552.log.json) | +|[RegNetX-3.2GF-FPN-DCN-C3-C5](./mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py)| pytorch | 1x |5.0 ||40.3|36.6|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco_20200520_172726-75f40794.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco_20200520_172726.log.json) | + +### Faster R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :---------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| [R-50-FPN](../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py)| pytorch | 1x | 4.0 | 18.2 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +|[RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py)| pytorch | 1x | 4.5||39.9|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco/faster_rcnn_regnetx-3.2GF_fpn_1x_coco_20200517_175927-126fd9bf.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco/faster_rcnn_regnetx-3.2GF_fpn_1x_coco_20200517_175927.log.json) | +|[RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py)| pytorch | 2x | 4.5||41.1|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco/faster_rcnn_regnetx-3.2GF_fpn_2x_coco_20200520_223955-e2081918.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco/faster_rcnn_regnetx-3.2GF_fpn_2x_coco_20200520_223955.log.json) | + +### RetinaNet + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :---------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| [R-50-FPN](../retinanet/retinanet_r50_fpn_1x_coco.py) | pytorch | 1x | 3.8 | 16.6 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130_002941.log.json) | +|[RegNetX-800MF-FPN](./retinanet_regnetx-800MF_fpn_1x_coco.py)| pytorch | 1x |2.5||35.6|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-800MF_fpn_1x_coco/retinanet_regnetx-800MF_fpn_1x_coco_20200517_191403-f6f91d10.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-800MF_fpn_1x_coco/retinanet_regnetx-800MF_fpn_1x_coco_20200517_191403.log.json) | +|[RegNetX-1.6GF-FPN](./retinanet_regnetx-1.6GF_fpn_1x_coco.py)| pytorch | 1x |3.3||37.3|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco/retinanet_regnetx-1.6GF_fpn_1x_coco_20200517_191403-37009a9d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco/retinanet_regnetx-1.6GF_fpn_1x_coco_20200517_191403.log.json) | +|[RegNetX-3.2GF-FPN](./retinanet_regnetx-3.2GF_fpn_1x_coco.py)| pytorch | 1x |4.2 ||39.1|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco/retinanet_regnetx-3.2GF_fpn_1x_coco_20200520_163141-cb1509e8.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco/retinanet_regnetx-3.2GF_fpn_1x_coco_20200520_163141.log.json) | + +### Pre-trained models + +We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks. + +| Method | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-----: | :-----: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +|Faster RCNN |[RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py)| pytorch | 3x |5.0 ||42.2|-|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200520_224253-bf85ae3e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200520_224253.log.json) | +|Mask RCNN |[RegNetX-3.2GF-FPN](./mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py)| pytorch | 3x |5.0 ||43.1|38.7|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221-99879813.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221.log.json) | + +### Notice + +1. The models are trained using a different weight decay, i.e., `weight_decay=5e-5` according to the setting in ImageNet training. This brings improvement of at least 0.7 AP absolute but does not improve the model using ResNet-50. +2. RetinaNets using RegNets are trained with learning rate 0.02 with gradient clip. We find that using learning rate 0.02 could improve the results by at least 0.7 AP absolute and gradient clip is necessary to stabilize the training. However, this does not improve the performance of ResNet-50-FPN RetinaNet. diff --git a/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py new file mode 100644 index 0000000..4fc61a3 --- /dev/null +++ b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py @@ -0,0 +1,56 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + pretrained='open-mmlab://regnetx_3.2gf', + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_3.2gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[96, 192, 432, 1008], + out_channels=256, + num_outs=5)) +img_norm_cfg = dict( + # The mean and std are used in PyCls when training RegNets + mean=[103.53, 116.28, 123.675], + std=[57.375, 57.12, 58.395], + to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) diff --git a/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py new file mode 100644 index 0000000..612490b --- /dev/null +++ b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py @@ -0,0 +1,3 @@ +_base_ = './faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py' +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..e73a098 --- /dev/null +++ b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py @@ -0,0 +1,63 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + pretrained='open-mmlab://regnetx_3.2gf', + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_3.2gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[96, 192, 432, 1008], + out_channels=256, + num_outs=5)) +img_norm_cfg = dict( + # The mean and std are used in PyCls when training RegNets + mean=[103.53, 116.28, 123.675], + std=[57.375, 57.12, 58.395], + to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py new file mode 100644 index 0000000..104d6d4 --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://regnetx_12gf', + backbone=dict( + type='RegNet', + arch='regnetx_12gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[224, 448, 896, 2240], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py new file mode 100644 index 0000000..19168b5 --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py @@ -0,0 +1,57 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + pretrained='open-mmlab://regnetx_3.2gf', + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_3.2gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[96, 192, 432, 1008], + out_channels=256, + num_outs=5)) +img_norm_cfg = dict( + # The mean and std are used in PyCls when training RegNets + mean=[103.53, 116.28, 123.675], + std=[57.375, 57.12, 58.395], + to_rgb=False) +train_pipeline = [ + # Images are converted to float32 directly after loading in PyCls + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) diff --git a/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..dd5153e --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = 'mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://regnetx_3.2gf', + backbone=dict( + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..e4107e7 --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py @@ -0,0 +1,65 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + pretrained='open-mmlab://regnetx_3.2gf', + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_3.2gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[96, 192, 432, 1008], + out_channels=256, + num_outs=5)) +img_norm_cfg = dict( + # The mean and std are used in PyCls when training RegNets + mean=[103.53, 116.28, 123.675], + std=[57.375, 57.12, 58.395], + to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py new file mode 100644 index 0000000..8830ef0 --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://regnetx_4.0gf', + backbone=dict( + type='RegNet', + arch='regnetx_4.0gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[80, 240, 560, 1360], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py new file mode 100644 index 0000000..7569ef3 --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://regnetx_6.4gf', + backbone=dict( + type='RegNet', + arch='regnetx_6.4gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[168, 392, 784, 1624], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py new file mode 100644 index 0000000..b589026 --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://regnetx_8.0gf', + backbone=dict( + type='RegNet', + arch='regnetx_8.0gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[80, 240, 720, 1920], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py b/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py new file mode 100644 index 0000000..4f2beb8 --- /dev/null +++ b/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://regnetx_1.6gf', + backbone=dict( + type='RegNet', + arch='regnetx_1.6gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[72, 168, 408, 912], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py b/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py new file mode 100644 index 0000000..8f483a1 --- /dev/null +++ b/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py @@ -0,0 +1,58 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + pretrained='open-mmlab://regnetx_3.2gf', + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_3.2gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[96, 192, 432, 1008], + out_channels=256, + num_outs=5)) +img_norm_cfg = dict( + # The mean and std are used in PyCls when training RegNets + mean=[103.53, 116.28, 123.675], + std=[57.375, 57.12, 58.395], + to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py b/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py new file mode 100644 index 0000000..fe1d659 --- /dev/null +++ b/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://regnetx_800mf', + backbone=dict( + type='RegNet', + arch='regnetx_800mf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[64, 128, 288, 672], + out_channels=256, + num_outs=5)) diff --git a/configs/reppoints/README.md b/configs/reppoints/README.md new file mode 100644 index 0000000..10d6792 --- /dev/null +++ b/configs/reppoints/README.md @@ -0,0 +1,54 @@ +# RepPoints: Point Set Representation for Object Detection + +By [Ze Yang](https://yangze.tech/), [Shaohui Liu](http://b1ueber2y.me/), and [Han Hu](https://ancientmooner.github.io/). + +We provide code support and configuration files to reproduce the results in the paper for +["RepPoints: Point Set Representation for Object Detection"](https://arxiv.org/abs/1904.11490) on COCO object detection. + +## Introduction + + + +**RepPoints**, initially described in [arXiv](https://arxiv.org/abs/1904.11490), is a new representation method for visual objects, on which visual understanding tasks are typically centered. Visual object representation, aiming at both geometric description and appearance feature extraction, is conventionally achieved by `bounding box + RoIPool (RoIAlign)`. The bounding box representation is convenient to use; however, it provides only a rectangular localization of objects that lacks geometric precision and may consequently degrade feature quality. Our new representation, RepPoints, models objects by a `point set` instead of a `bounding box`, which learns to adaptively position themselves over an object in a manner that circumscribes the object’s `spatial extent` and enables `semantically aligned feature extraction`. This richer and more flexible representation maintains the convenience of bounding boxes while facilitating various visual understanding applications. This repo demonstrated the effectiveness of RepPoints for COCO object detection. + +Another feature of this repo is the demonstration of an `anchor-free detector`, which can be as effective as state-of-the-art anchor-based detection methods. The anchor-free detector can utilize either `bounding box` or `RepPoints` as the basic object representation. + +
+ +

Learning RepPoints in Object Detection.

+
+ +## Citing RepPoints + +``` +@inproceedings{yang2019reppoints, + title={RepPoints: Point Set Representation for Object Detection}, + author={Yang, Ze and Liu, Shaohui and Hu, Han and Wang, Liwei and Lin, Stephen}, + booktitle={The IEEE International Conference on Computer Vision (ICCV)}, + month={Oct}, + year={2019} +} +``` + +## Results and models + +The results on COCO 2017val are shown in the table below. + +| Method | Backbone | GN | Anchor | convert func | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:---------:|:-------------:|:---:|:------:|:------------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| BBox | R-50-FPN | Y | single | - | 1x | 3.9 | 15.9 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco_20200329-c98bfa96.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco_20200329_145916.log.json) | +| BBox | R-50-FPN | Y | none | - | 1x | 3.9 | 15.4 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+Bhead_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco_20200330-00f73d58.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco_20200330_233609.log.json) | +| RepPoints | R-50-FPN | N | none | moment | 1x | 3.3 | 18.5 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_1x_coco/reppoints_moment_r50_fpn_1x_coco_20200330-b73db8d1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_1x_coco/reppoints_moment_r50_fpn_1x_coco_20200330_233609.log.json) | +| RepPoints | R-50-FPN | Y | none | moment | 1x | 3.9 | 17.5 | 38.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco_20200329-4b38409a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco_20200329_145952.log.json) | +| RepPoints | R-50-FPN | Y | none | moment | 2x | 3.9 | - | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco_20200329-91babaa2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco_20200329_150020.log.json) | +| RepPoints | R-101-FPN | Y | none | moment | 2x | 5.8 | 13.7 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco_20200329-4fbc7310.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco_20200329_132205.log.json) | +| RepPoints | R-101-FPN-DCN | Y | none | moment | 2x | 5.9 | 12.1 | 42.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-3309fbf2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329_132134.log.json) | +| RepPoints | X-101-FPN-DCN | Y | none | moment | 2x | 7.1 | 9.3 | 44.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-f87da1ea.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329_132201.log.json) | + +**Notes:** + +- `R-xx`, `X-xx` denote the ResNet and ResNeXt architectures, respectively. +- `DCN` denotes replacing 3x3 conv with the 3x3 deformable convolution in `c3-c5` stages of backbone. +- `none` in the `anchor` column means 2-d `center point` (x,y) is used to represent the initial object hypothesis. `single` denotes one 4-d anchor box (x,y,w,h) with IoU based label assign criterion is adopted. +- `moment`, `partial MinMax`, `MinMax` in the `convert func` column are three functions to convert a point set to a pseudo box. +- Note the results here are slightly different from those reported in the paper, due to framework change. While the original paper uses an [MXNet](https://mxnet.apache.org/) implementation, we re-implement the method in [PyTorch](https://pytorch.org/) based on mmdetection. diff --git a/configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py b/configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py new file mode 100644 index 0000000..b24c8db --- /dev/null +++ b/configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py' +model = dict(bbox_head=dict(transform_method='minmax', use_grid_points=True)) diff --git a/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py b/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py new file mode 100644 index 0000000..8d5013d --- /dev/null +++ b/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py' +model = dict( + bbox_head=dict(transform_method='minmax', use_grid_points=True), + # training and testing settings + train_cfg=dict( + init=dict( + assigner=dict( + _delete_=True, + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1)))) diff --git a/configs/reppoints/reppoints.png b/configs/reppoints/reppoints.png new file mode 100644 index 0000000..a9306d9 Binary files /dev/null and b/configs/reppoints/reppoints.png differ diff --git a/configs/reppoints/reppoints_minmax_r50_fpn_gn-neck+head_1x_coco.py b/configs/reppoints/reppoints_minmax_r50_fpn_gn-neck+head_1x_coco.py new file mode 100644 index 0000000..0f56a46 --- /dev/null +++ b/configs/reppoints/reppoints_minmax_r50_fpn_gn-neck+head_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py' +model = dict(bbox_head=dict(transform_method='minmax')) diff --git a/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py b/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py new file mode 100644 index 0000000..241754c --- /dev/null +++ b/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py @@ -0,0 +1,7 @@ +_base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict( + depth=101, + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py b/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py new file mode 100644 index 0000000..19efa0d --- /dev/null +++ b/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py @@ -0,0 +1,2 @@ +_base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py b/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py new file mode 100644 index 0000000..8df2a8f --- /dev/null +++ b/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py @@ -0,0 +1,67 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='RepPointsDetector', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_input', + num_outs=5), + bbox_head=dict( + type='RepPointsHead', + num_classes=80, + in_channels=256, + feat_channels=256, + point_feat_channels=256, + stacked_convs=3, + num_points=9, + gradient_mul=0.1, + point_strides=[8, 16, 32, 64, 128], + point_base_scale=4, + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox_init=dict(type='SmoothL1Loss', beta=0.11, loss_weight=0.5), + loss_bbox_refine=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0), + transform_method='moment'), + # training and testing settings + train_cfg=dict( + init=dict( + assigner=dict(type='PointAssigner', scale=4, pos_num=1), + allowed_border=-1, + pos_weight=-1, + debug=False), + refine=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False)), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) +optimizer = dict(lr=0.01) diff --git a/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py b/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py new file mode 100644 index 0000000..337f167 --- /dev/null +++ b/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './reppoints_moment_r50_fpn_1x_coco.py' +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict(neck=dict(norm_cfg=norm_cfg), bbox_head=dict(norm_cfg=norm_cfg)) +optimizer = dict(lr=0.01) diff --git a/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py b/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py new file mode 100644 index 0000000..feca44a --- /dev/null +++ b/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py @@ -0,0 +1,3 @@ +_base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py' +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py b/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py new file mode 100644 index 0000000..c33019d --- /dev/null +++ b/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py @@ -0,0 +1,15 @@ +_base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/reppoints/reppoints_partial_minmax_r50_fpn_gn-neck+head_1x_coco.py b/configs/reppoints/reppoints_partial_minmax_r50_fpn_gn-neck+head_1x_coco.py new file mode 100644 index 0000000..9a63bd0 --- /dev/null +++ b/configs/reppoints/reppoints_partial_minmax_r50_fpn_gn-neck+head_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py' +model = dict(bbox_head=dict(transform_method='partial_minmax')) diff --git a/configs/res2net/README.md b/configs/res2net/README.md new file mode 100644 index 0000000..9bcc238 --- /dev/null +++ b/configs/res2net/README.md @@ -0,0 +1,65 @@ +# Res2Net for object detection and instance segmentation + +## Introduction + + + +We propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. + +| Backbone |Params. | GFLOPs | top-1 err. | top-5 err. | +| :-------------: |:----: | :-----: | :--------: | :--------: | +| ResNet-101 |44.6 M | 7.8 | 22.63 | 6.44 | +| ResNeXt-101-64x4d |83.5M | 15.5 | 20.40 | - | +| HRNetV2p-W48 | 77.5M | 16.1 | 20.70 | 5.50 | +| Res2Net-101 | 45.2M | 8.3 | 18.77 | 4.64 | + +Compared with other backbone networks, Res2Net requires fewer parameters and FLOPs. + +**Note:** + +- GFLOPs for classification are calculated with image size (224x224). + +```latex +@article{gao2019res2net, + title={Res2Net: A New Multi-scale Backbone Architecture}, + author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip}, + journal={IEEE TPAMI}, + year={2020}, + doi={10.1109/TPAMI.2019.2938758}, +} +``` + +## Results and Models + +### Faster R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +|R2-101-FPN | pytorch | 2x | 7.4 | - | 43.0 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/res2net/faster_rcnn_r2_101_fpn_2x_coco/faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/res2net/faster_rcnn_r2_101_fpn_2x_coco/faster_rcnn_r2_101_fpn_2x_coco_20200514_231734.log.json) | + +### Mask R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +|R2-101-FPN | pytorch | 2x | 7.9 | - | 43.6 | 38.7 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/res2net/mask_rcnn_r2_101_fpn_2x_coco/mask_rcnn_r2_101_fpn_2x_coco-17f061e8.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/res2net/mask_rcnn_r2_101_fpn_2x_coco/mask_rcnn_r2_101_fpn_2x_coco_20200515_002413.log.json) | + +### Cascade R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +|R2-101-FPN | pytorch | 20e | 7.8 | - | 45.7 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_rcnn_r2_101_fpn_20e_coco/cascade_rcnn_r2_101_fpn_20e_coco-f4b7b7db.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_rcnn_r2_101_fpn_20e_coco/cascade_rcnn_r2_101_fpn_20e_coco_20200515_091644.log.json) | + +### Cascade Mask R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +R2-101-FPN | pytorch | 20e | 9.5 | - | 46.4 | 40.0 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco-8a7b41e1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco_20200515_091645.log.json) | + +### Hybrid Task Cascade (HTC) + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +| R2-101-FPN | pytorch | 20e | - | - | 47.5 | 41.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/htc_r2_101_fpn_20e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/res2net/htc_r2_101_fpn_20e_coco/htc_r2_101_fpn_20e_coco-3a8d2112.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/res2net/htc_r2_101_fpn_20e_coco/htc_r2_101_fpn_20e_coco_20200515_150029.log.json) | + +- Res2Net ImageNet pretrained models are in [Res2Net-PretrainedModels](https://github.com/Res2Net/Res2Net-PretrainedModels). +- More applications of Res2Net are in [Res2Net-Github](https://github.com/Res2Net/). diff --git a/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py b/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py new file mode 100644 index 0000000..50df4e2 --- /dev/null +++ b/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py @@ -0,0 +1,4 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py' +model = dict( + pretrained='open-mmlab://res2net101_v1d_26w_4s', + backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) diff --git a/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py b/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py new file mode 100644 index 0000000..1cac759 --- /dev/null +++ b/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py @@ -0,0 +1,4 @@ +_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py' +model = dict( + pretrained='open-mmlab://res2net101_v1d_26w_4s', + backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) diff --git a/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py b/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py new file mode 100644 index 0000000..85004e0 --- /dev/null +++ b/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py' +model = dict( + pretrained='open-mmlab://res2net101_v1d_26w_4s', + backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) diff --git a/configs/res2net/htc_r2_101_fpn_20e_coco.py b/configs/res2net/htc_r2_101_fpn_20e_coco.py new file mode 100644 index 0000000..3c4cc75 --- /dev/null +++ b/configs/res2net/htc_r2_101_fpn_20e_coco.py @@ -0,0 +1,7 @@ +_base_ = '../htc/htc_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://res2net101_v1d_26w_4s', + backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py b/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py new file mode 100644 index 0000000..a620188 --- /dev/null +++ b/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py' +model = dict( + pretrained='open-mmlab://res2net101_v1d_26w_4s', + backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) diff --git a/configs/resnest/README.md b/configs/resnest/README.md new file mode 100644 index 0000000..d34d1c2 --- /dev/null +++ b/configs/resnest/README.md @@ -0,0 +1,44 @@ +# ResNeSt: Split-Attention Networks + +## Introduction + +[BACKBONE] + +```latex +@article{zhang2020resnest, +title={ResNeSt: Split-Attention Networks}, +author={Zhang, Hang and Wu, Chongruo and Zhang, Zhongyue and Zhu, Yi and Zhang, Zhi and Lin, Haibin and Sun, Yue and He, Tong and Muller, Jonas and Manmatha, R. and Li, Mu and Smola, Alexander}, +journal={arXiv preprint arXiv:2004.08955}, +year={2020} +} +``` + +## Results and Models + +### Faster R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +|S-50-FPN | pytorch | 1x | 4.8 | - | 42.0 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20200926_125502-20289c16.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20200926_125502.log.json) | +|S-101-FPN | pytorch | 1x | 7.1 | - | 44.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201006_021058-421517f1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20201006_021058.log.json) | + +### Mask R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +|S-50-FPN | pytorch | 1x | 5.5 | - | 42.6 | 38.1 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20200926_125503-8a2c3d47.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20200926_125503.log.json) | +|S-101-FPN | pytorch | 1x | 7.8 | - | 45.2 | 40.2 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_215831-af60cdf9.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20201005_215831.log.json) | + +### Cascade R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +|S-50-FPN | pytorch | 1x | - | - | 44.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201122_213640-763cc7b5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20201005_113242.log.json) | +|S-101-FPN | pytorch | 1x | 8.4 | - | 46.8 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201005_113242-b9459f8f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20201122_213640.log.json) | + +### Cascade Mask R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +|S-50-FPN | pytorch | 1x | - | - | 45.4 | 39.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201122_104428-99eca4c7.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20201122_104428.log.json) | +|S-101-FPN | pytorch | 1x | 10.5 | - | 47.7 | 41.4 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_113243-42607475.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20201005_113243.log.json) | diff --git a/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py b/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py new file mode 100644 index 0000000..3995603 --- /dev/null +++ b/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnest101', + backbone=dict(stem_channels=128, depth=101)) diff --git a/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py b/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py new file mode 100644 index 0000000..f2cf444 --- /dev/null +++ b/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py @@ -0,0 +1,118 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py' +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + pretrained='open-mmlab://resnest50', + backbone=dict( + type='ResNeSt', + stem_channels=64, + depth=50, + radix=2, + reduction_factor=4, + avg_down_stride=True, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch'), + roi_head=dict( + bbox_head=[ + dict( + type='Shared4Conv1FCBBoxHead', + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + norm_cfg=norm_cfg, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared4Conv1FCBBoxHead', + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + norm_cfg=norm_cfg, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared4Conv1FCBBoxHead', + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + norm_cfg=norm_cfg, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) + ], + mask_head=dict(norm_cfg=norm_cfg))) +# # use ResNeSt img_norm +img_norm_cfg = dict( + mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py b/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py new file mode 100644 index 0000000..53964a3 --- /dev/null +++ b/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnest101', + backbone=dict(stem_channels=128, depth=101)) diff --git a/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py b/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py new file mode 100644 index 0000000..78a154b --- /dev/null +++ b/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py @@ -0,0 +1,116 @@ +_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py' +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + pretrained='open-mmlab://resnest50', + backbone=dict( + type='ResNeSt', + stem_channels=64, + depth=50, + radix=2, + reduction_factor=4, + avg_down_stride=True, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch'), + roi_head=dict( + bbox_head=[ + dict( + type='Shared4Conv1FCBBoxHead', + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + norm_cfg=norm_cfg, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared4Conv1FCBBoxHead', + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + norm_cfg=norm_cfg, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared4Conv1FCBBoxHead', + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + norm_cfg=norm_cfg, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) + ], )) +# # use ResNeSt img_norm +img_norm_cfg = dict( + mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=False, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py b/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py new file mode 100644 index 0000000..1915ab1 --- /dev/null +++ b/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnest101', + backbone=dict(stem_channels=128, depth=101)) diff --git a/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py b/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py new file mode 100644 index 0000000..422fbca --- /dev/null +++ b/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py @@ -0,0 +1,62 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + pretrained='open-mmlab://resnest50', + backbone=dict( + type='ResNeSt', + stem_channels=64, + depth=50, + radix=2, + reduction_factor=4, + avg_down_stride=True, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch'), + roi_head=dict( + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + conv_out_channels=256, + norm_cfg=norm_cfg))) +# # use ResNeSt img_norm +img_norm_cfg = dict( + mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=False, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py b/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py new file mode 100644 index 0000000..89e077d --- /dev/null +++ b/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnest101', + backbone=dict(stem_channels=128, depth=101)) diff --git a/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py b/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py new file mode 100644 index 0000000..29f21fd --- /dev/null +++ b/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py @@ -0,0 +1,64 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + pretrained='open-mmlab://resnest50', + backbone=dict( + type='ResNeSt', + stem_channels=64, + depth=50, + radix=2, + reduction_factor=4, + avg_down_stride=True, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch'), + roi_head=dict( + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + conv_out_channels=256, + norm_cfg=norm_cfg), + mask_head=dict(norm_cfg=norm_cfg))) +# # use ResNeSt img_norm +img_norm_cfg = dict( + mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/retinanet/README.md b/configs/retinanet/README.md new file mode 100644 index 0000000..e963300 --- /dev/null +++ b/configs/retinanet/README.md @@ -0,0 +1,29 @@ +# Focal Loss for Dense Object Detection + +## Introduction + + + +```latex +@inproceedings{lin2017focal, + title={Focal loss for dense object detection}, + author={Lin, Tsung-Yi and Goyal, Priya and Girshick, Ross and He, Kaiming and Doll{\'a}r, Piotr}, + booktitle={Proceedings of the IEEE international conference on computer vision}, + year={2017} +} +``` + +## Results and models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| R-50-FPN | caffe | 1x | 3.5 | 18.6 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531-f11027c5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531_012518.log.json) | +| R-50-FPN | pytorch | 1x | 3.8 | 19.0 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130_002941.log.json) | +| R-50-FPN | pytorch | 2x | - | - | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131_114738.log.json) | +| R-101-FPN | caffe | 1x | 5.5 | 14.7 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531-b428fa0f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531_012536.log.json) | +| R-101-FPN | pytorch | 1x | 5.7 | 15.0 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130-7a93545f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130_003055.log.json) | +| R-101-FPN | pytorch | 2x | - | - | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131-5560aee8.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131_114859.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.0 | 12.1 | 39.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130-5c8b7ec4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130_003004.log.json) | +| X-101-32x4d-FPN | pytorch | 2x | - | - | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131-237fc5e1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131_114812.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.0 | 8.7 | 41.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130-366f5af1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130_003008.log.json) | +| X-101-64x4d-FPN | pytorch | 2x | - | - | 40.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131-bca068ab.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131_114833.log.json) | diff --git a/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py b/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..21d227b --- /dev/null +++ b/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './retinanet_r50_caffe_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/retinanet/retinanet_r101_fpn_1x_coco.py b/configs/retinanet/retinanet_r101_fpn_1x_coco.py new file mode 100644 index 0000000..1e6f463 --- /dev/null +++ b/configs/retinanet/retinanet_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './retinanet_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/retinanet/retinanet_r101_fpn_2x_coco.py b/configs/retinanet/retinanet_r101_fpn_2x_coco.py new file mode 100644 index 0000000..c12088a --- /dev/null +++ b/configs/retinanet/retinanet_r101_fpn_2x_coco.py @@ -0,0 +1,2 @@ +_base_ = './retinanet_r50_fpn_2x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py b/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..028c1a3 --- /dev/null +++ b/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,37 @@ +_base_ = './retinanet_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe')) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py b/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py new file mode 100644 index 0000000..f2a0dec --- /dev/null +++ b/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py @@ -0,0 +1,42 @@ +_base_ = './retinanet_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe')) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_2x_coco.py b/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_2x_coco.py new file mode 100644 index 0000000..eea9690 --- /dev/null +++ b/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_3x_coco.py b/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..8057650 --- /dev/null +++ b/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_3x_coco.py @@ -0,0 +1,4 @@ +_base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py' +# learning policy +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/retinanet/retinanet_r50_fpn_1x_coco.py b/configs/retinanet/retinanet_r50_fpn_1x_coco.py new file mode 100644 index 0000000..04bd696 --- /dev/null +++ b/configs/retinanet/retinanet_r50_fpn_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/retinanet/retinanet_r50_fpn_2x_coco.py b/configs/retinanet/retinanet_r50_fpn_2x_coco.py new file mode 100644 index 0000000..927915f --- /dev/null +++ b/configs/retinanet/retinanet_r50_fpn_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './retinanet_r50_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py b/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..9927f8f --- /dev/null +++ b/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './retinanet_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py b/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py new file mode 100644 index 0000000..cd78b6d --- /dev/null +++ b/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py @@ -0,0 +1,13 @@ +_base_ = './retinanet_r50_fpn_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py b/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..cc40f26 --- /dev/null +++ b/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './retinanet_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py b/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py new file mode 100644 index 0000000..eac05a6 --- /dev/null +++ b/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py @@ -0,0 +1,13 @@ +_base_ = './retinanet_r50_fpn_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/rpn/README.md b/configs/rpn/README.md new file mode 100644 index 0000000..44bf80e --- /dev/null +++ b/configs/rpn/README.md @@ -0,0 +1,29 @@ +# Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks + +## Introduction + + + +```latex +@inproceedings{ren2015faster, + title={Faster r-cnn: Towards real-time object detection with region proposal networks}, + author={Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian}, + booktitle={Advances in neural information processing systems}, + year={2015} +} +``` + +## Results and models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | AR1000 | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| R-50-FPN | caffe | 1x | 3.5 | 22.6 | 58.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_caffe_fpn_1x_coco/rpn_r50_caffe_fpn_1x_coco_20200531-5b903a37.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_caffe_fpn_1x_coco/rpn_r50_caffe_fpn_1x_coco_20200531_012334.log.json) | +| R-50-FPN | pytorch | 1x | 3.8 | 22.3 | 58.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_1x_coco/rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_1x_coco/rpn_r50_fpn_1x_coco_20200218_151240.log.json) | +| R-50-FPN | pytorch | 2x | - | - | 58.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r50_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_2x_coco/rpn_r50_fpn_2x_coco_20200131-0728c9b3.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_2x_coco/rpn_r50_fpn_2x_coco_20200131_190631.log.json) | +| R-101-FPN | caffe | 1x | 5.4 | 17.3 | 60.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_caffe_fpn_1x_coco/rpn_r101_caffe_fpn_1x_coco_20200531-0629a2e2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_caffe_fpn_1x_coco/rpn_r101_caffe_fpn_1x_coco_20200531_012345.log.json) | +| R-101-FPN | pytorch | 1x | 5.8 | 16.5 | 59.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_1x_coco/rpn_r101_fpn_1x_coco_20200131-2ace2249.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_1x_coco/rpn_r101_fpn_1x_coco_20200131_191000.log.json) | +| R-101-FPN | pytorch | 2x | - | - | 60.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r101_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_2x_coco/rpn_r101_fpn_2x_coco_20200131-24e3db1a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_2x_coco/rpn_r101_fpn_2x_coco_20200131_191106.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.0 | 13.0 | 60.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_1x_coco/rpn_x101_32x4d_fpn_1x_coco_20200219-b02646c6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_1x_coco/rpn_x101_32x4d_fpn_1x_coco_20200219_012037.log.json) | +| X-101-32x4d-FPN | pytorch | 2x | - | - | 61.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_2x_coco/rpn_x101_32x4d_fpn_2x_coco_20200208-d22bd0bb.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_2x_coco/rpn_x101_32x4d_fpn_2x_coco_20200208_200752.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.1 | 9.1 | 61.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_1x_coco/rpn_x101_64x4d_fpn_1x_coco_20200208-cde6f7dd.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_1x_coco/rpn_x101_64x4d_fpn_1x_coco_20200208_200752.log.json) | +| X-101-64x4d-FPN | pytorch | 2x | - | - | 61.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_2x_coco/rpn_x101_64x4d_fpn_2x_coco_20200208-c65f524f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_2x_coco/rpn_x101_64x4d_fpn_2x_coco_20200208_200752.log.json) | diff --git a/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py b/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..e616fdf --- /dev/null +++ b/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './rpn_r50_caffe_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/rpn/rpn_r101_fpn_1x_coco.py b/configs/rpn/rpn_r101_fpn_1x_coco.py new file mode 100644 index 0000000..b2af611 --- /dev/null +++ b/configs/rpn/rpn_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './rpn_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/rpn/rpn_r101_fpn_2x_coco.py b/configs/rpn/rpn_r101_fpn_2x_coco.py new file mode 100644 index 0000000..6908d30 --- /dev/null +++ b/configs/rpn/rpn_r101_fpn_2x_coco.py @@ -0,0 +1,2 @@ +_base_ = './rpn_r50_fpn_2x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/rpn/rpn_r50_caffe_c4_1x_coco.py b/configs/rpn/rpn_r50_caffe_c4_1x_coco.py new file mode 100644 index 0000000..6da0ee9 --- /dev/null +++ b/configs/rpn/rpn_r50_caffe_c4_1x_coco.py @@ -0,0 +1,38 @@ +_base_ = [ + '../_base_/models/rpn_r50_caffe_c4.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# dataset settings +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_label=False), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='proposal_fast') diff --git a/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py b/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..398f3c1 --- /dev/null +++ b/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,37 @@ +_base_ = './rpn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe')) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_label=False), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/rpn/rpn_r50_fpn_1x_coco.py b/configs/rpn/rpn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..26f95a3 --- /dev/null +++ b/configs/rpn/rpn_r50_fpn_1x_coco.py @@ -0,0 +1,18 @@ +_base_ = [ + '../_base_/models/rpn_r50_fpn.py', '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_label=False), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes']), +] +data = dict(train=dict(pipeline=train_pipeline)) +evaluation = dict(interval=1, metric='proposal_fast') diff --git a/configs/rpn/rpn_r50_fpn_2x_coco.py b/configs/rpn/rpn_r50_fpn_2x_coco.py new file mode 100644 index 0000000..2f264bf --- /dev/null +++ b/configs/rpn/rpn_r50_fpn_2x_coco.py @@ -0,0 +1,5 @@ +_base_ = './rpn_r50_fpn_1x_coco.py' + +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py b/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..83bd700 --- /dev/null +++ b/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './rpn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py b/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py new file mode 100644 index 0000000..979afb9 --- /dev/null +++ b/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py @@ -0,0 +1,13 @@ +_base_ = './rpn_r50_fpn_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py b/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..bb7f0a6 --- /dev/null +++ b/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './rpn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py b/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py new file mode 100644 index 0000000..8c766f0 --- /dev/null +++ b/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py @@ -0,0 +1,13 @@ +_base_ = './rpn_r50_fpn_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/sabl/README.md b/configs/sabl/README.md new file mode 100644 index 0000000..bb612a5 --- /dev/null +++ b/configs/sabl/README.md @@ -0,0 +1,37 @@ +# Side-Aware Boundary Localization for More Precise Object Detection + +## Introduction + + + +We provide config files to reproduce the object detection results in the ECCV 2020 Spotlight paper for [Side-Aware Boundary Localization for More Precise Object Detection](https://arxiv.org/abs/1912.04260). + +```latex +@inproceedings{Wang_2020_ECCV, + title = {Side-Aware Boundary Localization for More Precise Object Detection}, + author = {Jiaqi Wang and Wenwei Zhang and Yuhang Cao and Kai Chen and Jiangmiao Pang and Tao Gong and Jianping Shi and Chen Change Loy and Dahua Lin}, + booktitle = {ECCV}, + year = {2020} +} +``` + +## Results and Models + +The results on COCO 2017 val is shown in the below table. (results on test-dev are usually slightly higher than val). +Single-scale testing (1333x800) is adopted in all results. + +| Method | Backbone | Lr schd | ms-train | box AP | Config | Download | +| :----------------: | :-------: | :-----: | :------: | :----: | :----------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| SABL Faster R-CNN | R-50-FPN | 1x | N | 39.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r50_fpn_1x_coco/sabl_faster_rcnn_r50_fpn_1x_coco-e867595b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r50_fpn_1x_coco/20200830_130324.log.json) | +| SABL Faster R-CNN | R-101-FPN | 1x | N | 41.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r101_fpn_1x_coco/sabl_faster_rcnn_r101_fpn_1x_coco-f804c6c1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r101_fpn_1x_coco/20200830_183949.log.json) | +| SABL Cascade R-CNN | R-50-FPN | 1x | N | 41.6 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco/sabl_cascade_rcnn_r50_fpn_1x_coco-e1748e5e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco/20200831_033726.log.json) | +| SABL Cascade R-CNN | R-101-FPN | 1x | N | 43.0 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco/sabl_cascade_rcnn_r101_fpn_1x_coco-2b83e87c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco/20200831_141745.log.json) | + +| Method | Backbone | GN | Lr schd | ms-train | box AP | Config | Download | +| :------------: | :-------: | :---: | :-----: | :---------: | :----: | :---------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| SABL RetinaNet | R-50-FPN | N | 1x | N | 37.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_1x_coco/sabl_retinanet_r50_fpn_1x_coco-6c54fd4f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_1x_coco/20200830_053451.log.json) | +| SABL RetinaNet | R-50-FPN | Y | 1x | N | 38.8 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_gn_1x_coco/sabl_retinanet_r50_fpn_gn_1x_coco-e16dfcf1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_gn_1x_coco/20200831_141955.log.json) | +| SABL RetinaNet | R-101-FPN | N | 1x | N | 39.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_1x_coco/sabl_retinanet_r101_fpn_1x_coco-42026904.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_1x_coco/20200831_034256.log.json) | +| SABL RetinaNet | R-101-FPN | Y | 1x | N | 40.5 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_1x_coco/sabl_retinanet_r101_fpn_gn_1x_coco-40a893e8.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_1x_coco/20200830_201422.log.json) | +| SABL RetinaNet | R-101-FPN | Y | 2x | Y (640~800) | 42.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco-1e63382c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco/20200830_144807.log.json) | +| SABL RetinaNet | R-101-FPN | Y | 2x | Y (480~960) | 43.6 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco-5342f857.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco/20200830_164537.log.json) | diff --git a/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py b/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 0000000..0322006 --- /dev/null +++ b/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,88 @@ +_base_ = [ + '../_base_/models/cascade_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# model settings +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(depth=101), + roi_head=dict(bbox_head=[ + dict( + type='SABLHead', + num_classes=80, + cls_in_channels=256, + reg_in_channels=256, + roi_feat_size=7, + reg_feat_up_ratio=2, + reg_pre_kernel=3, + reg_post_kernel=3, + reg_pre_num=2, + reg_post_num=1, + cls_out_channels=1024, + reg_offset_out_channels=256, + reg_cls_out_channels=256, + num_cls_fcs=1, + num_reg_fcs=0, + reg_class_agnostic=True, + norm_cfg=None, + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, + loss_weight=1.0)), + dict( + type='SABLHead', + num_classes=80, + cls_in_channels=256, + reg_in_channels=256, + roi_feat_size=7, + reg_feat_up_ratio=2, + reg_pre_kernel=3, + reg_post_kernel=3, + reg_pre_num=2, + reg_post_num=1, + cls_out_channels=1024, + reg_offset_out_channels=256, + reg_cls_out_channels=256, + num_cls_fcs=1, + num_reg_fcs=0, + reg_class_agnostic=True, + norm_cfg=None, + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.5), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, + loss_weight=1.0)), + dict( + type='SABLHead', + num_classes=80, + cls_in_channels=256, + reg_in_channels=256, + roi_feat_size=7, + reg_feat_up_ratio=2, + reg_pre_kernel=3, + reg_post_kernel=3, + reg_pre_num=2, + reg_post_num=1, + cls_out_channels=1024, + reg_offset_out_channels=256, + reg_cls_out_channels=256, + num_cls_fcs=1, + num_reg_fcs=0, + reg_class_agnostic=True, + norm_cfg=None, + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.3), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, loss_weight=1.0)) + ])) diff --git a/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py b/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..4b28a59 --- /dev/null +++ b/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,86 @@ +_base_ = [ + '../_base_/models/cascade_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# model settings +model = dict( + roi_head=dict(bbox_head=[ + dict( + type='SABLHead', + num_classes=80, + cls_in_channels=256, + reg_in_channels=256, + roi_feat_size=7, + reg_feat_up_ratio=2, + reg_pre_kernel=3, + reg_post_kernel=3, + reg_pre_num=2, + reg_post_num=1, + cls_out_channels=1024, + reg_offset_out_channels=256, + reg_cls_out_channels=256, + num_cls_fcs=1, + num_reg_fcs=0, + reg_class_agnostic=True, + norm_cfg=None, + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, + loss_weight=1.0)), + dict( + type='SABLHead', + num_classes=80, + cls_in_channels=256, + reg_in_channels=256, + roi_feat_size=7, + reg_feat_up_ratio=2, + reg_pre_kernel=3, + reg_post_kernel=3, + reg_pre_num=2, + reg_post_num=1, + cls_out_channels=1024, + reg_offset_out_channels=256, + reg_cls_out_channels=256, + num_cls_fcs=1, + num_reg_fcs=0, + reg_class_agnostic=True, + norm_cfg=None, + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.5), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, + loss_weight=1.0)), + dict( + type='SABLHead', + num_classes=80, + cls_in_channels=256, + reg_in_channels=256, + roi_feat_size=7, + reg_feat_up_ratio=2, + reg_pre_kernel=3, + reg_post_kernel=3, + reg_pre_num=2, + reg_post_num=1, + cls_out_channels=1024, + reg_offset_out_channels=256, + reg_cls_out_channels=256, + num_cls_fcs=1, + num_reg_fcs=0, + reg_class_agnostic=True, + norm_cfg=None, + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.3), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, loss_weight=1.0)) + ])) diff --git a/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py b/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 0000000..4c797ca --- /dev/null +++ b/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,36 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(depth=101), + roi_head=dict( + bbox_head=dict( + _delete_=True, + type='SABLHead', + num_classes=80, + cls_in_channels=256, + reg_in_channels=256, + roi_feat_size=7, + reg_feat_up_ratio=2, + reg_pre_kernel=3, + reg_post_kernel=3, + reg_pre_num=2, + reg_post_num=1, + cls_out_channels=1024, + reg_offset_out_channels=256, + reg_cls_out_channels=256, + num_cls_fcs=1, + num_reg_fcs=0, + reg_class_agnostic=True, + norm_cfg=None, + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, + loss_weight=1.0)))) diff --git a/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py b/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..732c7ba --- /dev/null +++ b/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,34 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + roi_head=dict( + bbox_head=dict( + _delete_=True, + type='SABLHead', + num_classes=80, + cls_in_channels=256, + reg_in_channels=256, + roi_feat_size=7, + reg_feat_up_ratio=2, + reg_pre_kernel=3, + reg_post_kernel=3, + reg_pre_num=2, + reg_post_num=1, + cls_out_channels=1024, + reg_offset_out_channels=256, + reg_cls_out_channels=256, + num_cls_fcs=1, + num_reg_fcs=0, + reg_class_agnostic=True, + norm_cfg=None, + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, + loss_weight=1.0)))) diff --git a/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py b/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py new file mode 100644 index 0000000..ed3a96c --- /dev/null +++ b/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py @@ -0,0 +1,52 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# model settings +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(depth=101), + bbox_head=dict( + _delete_=True, + type='SABLRetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5), + loss_bbox_reg=dict( + type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py b/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py new file mode 100644 index 0000000..ec78263 --- /dev/null +++ b/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py @@ -0,0 +1,54 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# model settings +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(depth=101), + bbox_head=dict( + _delete_=True, + type='SABLRetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128]), + norm_cfg=norm_cfg, + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5), + loss_bbox_reg=dict( + type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py b/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py new file mode 100644 index 0000000..2a47c60 --- /dev/null +++ b/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py @@ -0,0 +1,71 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] +# model settings +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(depth=101), + bbox_head=dict( + _delete_=True, + type='SABLRetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128]), + norm_cfg=norm_cfg, + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5), + loss_bbox_reg=dict( + type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False)) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 480), (1333, 960)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict(train=dict(pipeline=train_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py b/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py new file mode 100644 index 0000000..f26062f --- /dev/null +++ b/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py @@ -0,0 +1,71 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] +# model settings +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(depth=101), + bbox_head=dict( + _delete_=True, + type='SABLRetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128]), + norm_cfg=norm_cfg, + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5), + loss_bbox_reg=dict( + type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False)) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict(train=dict(pipeline=train_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py b/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py new file mode 100644 index 0000000..6fe6bd6 --- /dev/null +++ b/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py @@ -0,0 +1,50 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# model settings +model = dict( + bbox_head=dict( + _delete_=True, + type='SABLRetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5), + loss_bbox_reg=dict( + type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py b/configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py new file mode 100644 index 0000000..6acf080 --- /dev/null +++ b/configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py @@ -0,0 +1,52 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# model settings +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + bbox_head=dict( + _delete_=True, + type='SABLRetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128]), + norm_cfg=norm_cfg, + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5), + loss_bbox_reg=dict( + type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/scnet/README.md b/configs/scnet/README.md new file mode 100644 index 0000000..3757be1 --- /dev/null +++ b/configs/scnet/README.md @@ -0,0 +1,51 @@ +# SCNet + +## Introduction + + + +We provide the code for reproducing experiment results of [SCNet](https://arxiv.org/abs/2012.10150). + +``` +@inproceedings{vu2019cascade, + title={SCNet: Training Inference Sample Consistency for Instance Segmentation}, + author={Vu, Thang and Haeyong, Kang and Yoo, Chang D}, + booktitle={AAAI}, + year={2021} +} +``` + +## Dataset + +SCNet requires COCO and [COCO-stuff](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip) dataset for training. You need to download and extract it in the COCO dataset path. +The directory should be like this. + +```none +mmdetection +├── mmdet +├── tools +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ ├── train2017 +│ │ ├── val2017 +│ │ ├── test2017 +| | ├── stuffthingmaps +``` + +## Results and Models + +The results on COCO 2017val are shown in the below table. (results on test-dev are usually slightly higher than val) + +| Backbone | Style | Lr schd | Mem (GB) | Inf speed (fps) | box AP | mask AP | TTA box AP | TTA mask AP | Config | Download | +|:---------------:|:-------:|:-------:|:--------:|:---------------:|:------:|:-------:|:----------:|:-----------:|:------:|:------------:| +| R-50-FPN | pytorch | 1x | 7.0 | 6.2 | 43.5 | 39.2 | 44.8 | 40.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_r50_fpn_1x_coco.py) | [model](https://drive.google.com/file/d/1K5_8-P0EC43WZFtoO3q9_JE-df8pEc7J/view?usp=sharing) \| [log](https://drive.google.com/file/d/1ZFS6QhFfxlOnDYPiGpSDP_Fzgb7iDGN3/view?usp=sharing) | +| R-50-FPN | pytorch | 20e | 7.0 | 6.2 | 44.5 | 40.0 | 45.8 | 41.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_r50_fpn_20e_coco.py) | [model](https://drive.google.com/file/d/15VGLCt5-IO5TbzB4Kw6ZyoF6QH0Q511A/view?usp=sharing) \| [log](https://drive.google.com/file/d/1-LnkOXN8n5ojQW34H0qZ625cgrnWpqSX/view?usp=sharing) | +| R-101-FPN | pytorch | 20e | 8.9 | 5.8 | 45.8 | 40.9 | 47.3 | 42.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_r101_fpn_20e_coco.py) | [model](https://drive.google.com/file/d/1aeCGHsOBdfIqVBnBPp0JUE_RSIau3583/view?usp=sharing) \| [log](https://drive.google.com/file/d/1iRx-9GRgTaIDsz-we3DGwFVH22nbvCLa/view?usp=sharing) | +| X-101-64x4d-FPN | pytorch | 20e | 13.2 | 4.9 | 47.5 | 42.3 | 48.9 | 44.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py) | [model](https://drive.google.com/file/d/1YjgutUKz4TTPpqSWGKUTkZJ8_X-kyCfY/view?usp=sharing) \| [log](https://drive.google.com/file/d/1OsfQJ8gwtqIQ61k358yxY21sCvbUcRjs/view?usp=sharing) | + +### Notes + +- Training hyper-parameters are identical to those of [HTC](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc). +- TTA means Test Time Augmentation, which applies horizonal flip and multi-scale testing. Refer to [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_r50_fpn_1x_coco.py). diff --git a/configs/scnet/scnet_r101_fpn_20e_coco.py b/configs/scnet/scnet_r101_fpn_20e_coco.py new file mode 100644 index 0000000..cef0668 --- /dev/null +++ b/configs/scnet/scnet_r101_fpn_20e_coco.py @@ -0,0 +1,2 @@ +_base_ = './scnet_r50_fpn_20e_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/scnet/scnet_r50_fpn_1x_coco.py b/configs/scnet/scnet_r50_fpn_1x_coco.py new file mode 100644 index 0000000..e4215a6 --- /dev/null +++ b/configs/scnet/scnet_r50_fpn_1x_coco.py @@ -0,0 +1,136 @@ +_base_ = '../htc/htc_r50_fpn_1x_coco.py' +# model settings +model = dict( + type='SCNet', + roi_head=dict( + _delete_=True, + type='SCNetRoIHead', + num_stages=3, + stage_loss_weights=[1, 0.5, 0.25], + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=[ + dict( + type='SCNetBBoxHead', + num_shared_fcs=2, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='SCNetBBoxHead', + num_shared_fcs=2, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='SCNetBBoxHead', + num_shared_fcs=2, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) + ], + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=dict( + type='SCNetMaskHead', + num_convs=12, + in_channels=256, + conv_out_channels=256, + num_classes=80, + conv_to_res=True, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)), + semantic_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[8]), + semantic_head=dict( + type='SCNetSemanticHead', + num_ins=5, + fusion_level=1, + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=183, + ignore_label=255, + loss_weight=0.2, + conv_to_res=True), + glbctx_head=dict( + type='GlobalContextHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_weight=3.0, + conv_to_res=True), + feat_relay_head=dict( + type='FeatureRelayHead', + in_channels=1024, + out_conv_channels=256, + roi_feat_size=7, + scale_factor=2))) + +# uncomment below code to enable test time augmentations +# img_norm_cfg = dict( +# mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# test_pipeline = [ +# dict(type='LoadImageFromFile'), +# dict( +# type='MultiScaleFlipAug', +# img_scale=[(600, 900), (800, 1200), (1000, 1500), (1200, 1800), +# (1400, 2100)], +# flip=True, +# transforms=[ +# dict(type='Resize', keep_ratio=True), +# dict(type='RandomFlip', flip_ratio=0.5), +# dict(type='Normalize', **img_norm_cfg), +# dict(type='Pad', size_divisor=32), +# dict(type='ImageToTensor', keys=['img']), +# dict(type='Collect', keys=['img']), +# ]) +# ] +# data = dict( +# val=dict(pipeline=test_pipeline), +# test=dict(pipeline=test_pipeline)) diff --git a/configs/scnet/scnet_r50_fpn_20e_coco.py b/configs/scnet/scnet_r50_fpn_20e_coco.py new file mode 100644 index 0000000..3b121a6 --- /dev/null +++ b/configs/scnet/scnet_r50_fpn_20e_coco.py @@ -0,0 +1,4 @@ +_base_ = './scnet_r50_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py b/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py new file mode 100644 index 0000000..a0ff32b --- /dev/null +++ b/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py @@ -0,0 +1,14 @@ +_base_ = './scnet_r50_fpn_20e_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch')) diff --git a/configs/scnet/scnet_x101_64x4d_fpn_8x1_20e_coco.py b/configs/scnet/scnet_x101_64x4d_fpn_8x1_20e_coco.py new file mode 100644 index 0000000..9f3ce6d --- /dev/null +++ b/configs/scnet/scnet_x101_64x4d_fpn_8x1_20e_coco.py @@ -0,0 +1,3 @@ +_base_ = './scnet_x101_64x4d_fpn_20e_coco.py' +data = dict(samples_per_gpu=1, workers_per_gpu=1) +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/scratch/README.md b/configs/scratch/README.md new file mode 100644 index 0000000..4d01cf4 --- /dev/null +++ b/configs/scratch/README.md @@ -0,0 +1,25 @@ +# Rethinking ImageNet Pre-training + +## Introduction + + + +```latex +@article{he2018rethinking, + title={Rethinking imagenet pre-training}, + author={He, Kaiming and Girshick, Ross and Doll{\'a}r, Piotr}, + journal={arXiv preprint arXiv:1811.08883}, + year={2018} +} +``` + +## Results and Models + +| Model | Backbone | Style | Lr schd | box AP | mask AP | Config | Download | +|:------------:|:---------:|:-------:|:-------:|:------:|:-------:|:------:|:--------:| +| Faster R-CNN | R-50-FPN | pytorch | 6x | 40.7 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_faster_rcnn_r50_fpn_gn_6x_bbox_mAP-0.407_20200201_193013-90813d01.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_faster_rcnn_r50_fpn_gn_6x_20200201_193013.log.json) | +| Mask R-CNN | R-50-FPN | pytorch | 6x | 41.2 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_mask_rcnn_r50_fpn_gn_6x_bbox_mAP-0.412__segm_mAP-0.374_20200201_193051-1e190a40.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_mask_rcnn_r50_fpn_gn_6x_20200201_193051.log.json) | + +Note: + +- The above models are trained with 16 GPUs. diff --git a/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py b/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py new file mode 100644 index 0000000..636f3f6 --- /dev/null +++ b/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained=None, + backbone=dict( + frozen_stages=-1, zero_init_residual=False, norm_cfg=norm_cfg), + neck=dict(norm_cfg=norm_cfg), + roi_head=dict( + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + conv_out_channels=256, + norm_cfg=norm_cfg))) +# optimizer +optimizer = dict(paramwise_cfg=dict(norm_decay_mult=0)) +optimizer_config = dict(_delete_=True, grad_clip=None) +# learning policy +lr_config = dict(warmup_ratio=0.1, step=[65, 71]) +runner = dict(type='EpochBasedRunner', max_epochs=73) diff --git a/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py b/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py new file mode 100644 index 0000000..6277a97 --- /dev/null +++ b/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py @@ -0,0 +1,23 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained=None, + backbone=dict( + frozen_stages=-1, zero_init_residual=False, norm_cfg=norm_cfg), + neck=dict(norm_cfg=norm_cfg), + roi_head=dict( + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + conv_out_channels=256, + norm_cfg=norm_cfg), + mask_head=dict(norm_cfg=norm_cfg))) +# optimizer +optimizer = dict(paramwise_cfg=dict(norm_decay_mult=0)) +optimizer_config = dict(_delete_=True, grad_clip=None) +# learning policy +lr_config = dict(warmup_ratio=0.1, step=[65, 71]) +runner = dict(type='EpochBasedRunner', max_epochs=73) diff --git a/configs/sparse_rcnn/README.md b/configs/sparse_rcnn/README.md new file mode 100644 index 0000000..bd5f157 --- /dev/null +++ b/configs/sparse_rcnn/README.md @@ -0,0 +1,28 @@ +# Sparse R-CNN: End-to-End Object Detection with Learnable Proposals + +## Introduction + + + +``` +@article{peize2020sparse, + title = {{SparseR-CNN}: End-to-End Object Detection with Learnable Proposals}, + author = {Peize Sun and Rufeng Zhang and Yi Jiang and Tao Kong and Chenfeng Xu and Wei Zhan and Masayoshi Tomizuka and Lei Li and Zehuan Yuan and Changhu Wang and Ping Luo}, + journal = {arXiv preprint arXiv:2011.12450}, + year = {2020} +} +``` + +## Results and Models + +| Model | Backbone | Style | Lr schd | Number of Proposals |Multi-Scale| RandomCrop | box AP | Config | Download | +|:------------:|:---------:|:-------:|:-------:|:-------: |:-------: |:---------:|:------:|:------:|:--------:| +| Sparse R-CNN | R-50-FPN | pytorch | 1x | 100 | False | False | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco/sparse_rcnn_r50_fpn_1x_coco_20201222_214453-dc79b137.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco/sparse_rcnn_r50_fpn_1x_coco_20201222_214453-dc79b137.log.json) | +| Sparse R-CNN | R-50-FPN | pytorch | 3x | 100 | True | False | 42.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco_20201218_154234-7bc5c054.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco_20201218_154234-7bc5c054.log.json) | +| Sparse R-CNN | R-50-FPN | pytorch | 3x | 300 | True | True | 45.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20201223_024605-9fe92701.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20201223_024605-9fe92701.log.json) | +| Sparse R-CNN | R-101-FPN | pytorch | 3x | 100 | True | False | 44.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco_20201223_121552-6c46c9d6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco_20201223_121552-6c46c9d6.log.json) | +| Sparse R-CNN | R-101-FPN | pytorch | 3x | 300 | True | True | 46.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20201223_023452-c23c3564.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20201223_023452-c23c3564.log.json) | + +### Notes + +We observe about 0.3 AP noise especially when using ResNet-101 as the backbone. diff --git a/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py b/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py new file mode 100644 index 0000000..e7a94db --- /dev/null +++ b/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py @@ -0,0 +1,3 @@ +_base_ = './sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py' + +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py b/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py new file mode 100644 index 0000000..0439fc1 --- /dev/null +++ b/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py @@ -0,0 +1,3 @@ +_base_ = './sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py' + +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py b/configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..512eca6 --- /dev/null +++ b/configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,95 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +num_stages = 6 +num_proposals = 100 +model = dict( + type='SparseRCNN', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=0, + add_extra_convs='on_input', + num_outs=4), + rpn_head=dict( + type='EmbeddingRPNHead', + num_proposals=num_proposals, + proposal_feature_channel=256), + roi_head=dict( + type='SparseRoIHead', + num_stages=num_stages, + stage_loss_weights=[1] * num_stages, + proposal_feature_channel=256, + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=[ + dict( + type='DIIHead', + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=3, + feedforward_channels=2048, + in_channels=256, + dropout=0.0, + ffn_act_cfg=dict(type='ReLU', inplace=True), + dynamic_conv_cfg=dict( + type='DynamicConv', + in_channels=256, + feat_channels=64, + out_channels=256, + input_feat_shape=7, + act_cfg=dict(type='ReLU', inplace=True), + norm_cfg=dict(type='LN')), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=False, + target_means=[0., 0., 0., 0.], + target_stds=[0.5, 0.5, 1., 1.])) for _ in range(num_stages) + ]), + # training and testing settings + train_cfg=dict( + rpn=None, + rcnn=[ + dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0), + iou_cost=dict(type='IoUCost', iou_mode='giou', + weight=2.0)), + sampler=dict(type='PseudoSampler'), + pos_weight=1) for _ in range(num_stages) + ]), + test_cfg=dict(rpn=None, rcnn=dict(max_per_img=num_proposals))) + +# optimizer +optimizer = dict(_delete_=True, type='AdamW', lr=0.000025, weight_decay=0.0001) +optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=1, norm_type=2)) +# learning policy +lr_config = dict(policy='step', step=[8, 11]) +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/configs/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py b/configs/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py new file mode 100644 index 0000000..36f1d62 --- /dev/null +++ b/configs/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py @@ -0,0 +1,52 @@ +_base_ = './sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py' +num_proposals = 300 +model = dict( + rpn_head=dict(num_proposals=num_proposals), + test_cfg=dict( + _delete_=True, rpn=None, rcnn=dict(max_per_img=num_proposals))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +# augmentation strategy originates from DETR. +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='AutoAugment', + policies=[[ + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict( + type='Resize', + img_scale=[(400, 1333), (500, 1333), (600, 1333)], + multiscale_mode='value', + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ]]), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/configs/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py b/configs/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py new file mode 100644 index 0000000..2fa2a80 --- /dev/null +++ b/configs/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py @@ -0,0 +1,23 @@ +_base_ = './sparse_rcnn_r50_fpn_1x_coco.py' + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +min_values = (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, value) for value in min_values], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] + +data = dict(train=dict(pipeline=train_pipeline)) +lr_config = dict(policy='step', step=[27, 33]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/ssd/README.md b/configs/ssd/README.md new file mode 100644 index 0000000..bef916d --- /dev/null +++ b/configs/ssd/README.md @@ -0,0 +1,21 @@ +# SSD: Single Shot MultiBox Detector + +## Introduction + + + +```latex +@article{Liu_2016, + title={SSD: Single Shot MultiBox Detector}, + journal={ECCV}, + author={Liu, Wei and Anguelov, Dragomir and Erhan, Dumitru and Szegedy, Christian and Reed, Scott and Fu, Cheng-Yang and Berg, Alexander C.}, + year={2016}, +} +``` + +## Results and models + +| Backbone | Size | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :------: | :---: | :---: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| VGG16 | 300 | caffe | 120e | 10.2 | 43.7 | 25.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd/ssd300_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ssd/ssd300_coco/ssd300_coco_20200307-a92d2092.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ssd/ssd300_coco/ssd300_coco_20200307_174216.log.json) | +| VGG16 | 512 | caffe | 120e | 9.3 | 30.7 | 29.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd/ssd512_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/ssd/ssd512_coco/ssd512_coco_20200308-038c5591.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ssd/ssd512_coco/ssd512_coco_20200308_134447.log.json) | diff --git a/configs/ssd/ssd300_coco.py b/configs/ssd/ssd300_coco.py new file mode 100644 index 0000000..75c5e4e --- /dev/null +++ b/configs/ssd/ssd300_coco.py @@ -0,0 +1,62 @@ +_base_ = [ + '../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 4)), + dict( + type='MinIoURandomCrop', + min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), + min_crop_size=0.3), + dict(type='Resize', img_scale=(300, 300), keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(300, 300), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=3, + train=dict( + _delete_=True, + type='RepeatDataset', + times=5, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4) +optimizer_config = dict(_delete_=True) diff --git a/configs/ssd/ssd512_coco.py b/configs/ssd/ssd512_coco.py new file mode 100644 index 0000000..44d2920 --- /dev/null +++ b/configs/ssd/ssd512_coco.py @@ -0,0 +1,71 @@ +_base_ = 'ssd300_coco.py' +input_size = 512 +model = dict( + backbone=dict(input_size=input_size), + bbox_head=dict( + in_channels=(512, 1024, 512, 256, 256, 256, 256), + anchor_generator=dict( + type='SSDAnchorGenerator', + scale_major=False, + input_size=input_size, + basesize_ratio_range=(0.1, 0.9), + strides=[8, 16, 32, 64, 128, 256, 512], + ratios=[[2], [2, 3], [2, 3], [2, 3], [2, 3], [2], [2]]))) +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 4)), + dict( + type='MinIoURandomCrop', + min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), + min_crop_size=0.3), + dict(type='Resize', img_scale=(512, 512), keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(512, 512), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=3, + train=dict( + _delete_=True, + type='RepeatDataset', + times=5, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4) +optimizer_config = dict(_delete_=True) diff --git a/configs/tridentnet/README.md b/configs/tridentnet/README.md new file mode 100644 index 0000000..b6263f2 --- /dev/null +++ b/configs/tridentnet/README.md @@ -0,0 +1,28 @@ +# Scale-Aware Trident Networks for Object Detection + +## Introduction + + + +``` +@InProceedings{li2019scale, + title={Scale-Aware Trident Networks for Object Detection}, + author={Li, Yanghao and Chen, Yuntao and Wang, Naiyan and Zhang, Zhaoxiang}, + journal={The International Conference on Computer Vision (ICCV)}, + year={2019} +} +``` + +## Results and models + +We reports the test results using only one branch for inference. + +| Backbone | Style | mstrain | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download | +| :-------------: | :-----: | :-----: | :-----: | :------: | :------------: | :----: | :------: | +| R-50 | caffe | N | 1x | | | 37.7 |[model](https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_1x_coco/tridentnet_r50_caffe_1x_coco_20201230_141838-2ec0b530.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_1x_coco/tridentnet_r50_caffe_1x_coco_20201230_141838.log.json) | +| R-50 | caffe | Y | 1x | | | 37.6 |[model](https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco/tridentnet_r50_caffe_mstrain_1x_coco_20201230_141839-6ce55ccb.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco/tridentnet_r50_caffe_mstrain_1x_coco_20201230_141839.log.json) | +| R-50 | caffe | Y | 3x | | | 40.3 |[model](https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco/tridentnet_r50_caffe_mstrain_3x_coco_20201130_100539-46d227ba.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco/tridentnet_r50_caffe_mstrain_3x_coco_20201130_100539.log.json) | + +**Note** + +Similar to [Detectron2](https://github.com/facebookresearch/detectron2/tree/master/projects/TridentNet), we haven't implemented the Scale-aware Training Scheme in section 4.2 of the paper. diff --git a/configs/tridentnet/tridentnet_r50_caffe_1x_coco.py b/configs/tridentnet/tridentnet_r50_caffe_1x_coco.py new file mode 100644 index 0000000..a6a668c --- /dev/null +++ b/configs/tridentnet/tridentnet_r50_caffe_1x_coco.py @@ -0,0 +1,53 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_caffe_c4.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + type='TridentFasterRCNN', + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + type='TridentResNet', + trident_dilations=(1, 2, 3), + num_branch=3, + test_branch_idx=1), + roi_head=dict(type='TridentRoIHead', num_branch=3, test_branch_idx=1), + train_cfg=dict( + rpn_proposal=dict(max_per_img=500), + rcnn=dict( + sampler=dict(num=128, pos_fraction=0.5, + add_gt_as_proposals=False)))) + +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco.py b/configs/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco.py new file mode 100644 index 0000000..c73d9ea --- /dev/null +++ b/configs/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco.py @@ -0,0 +1,22 @@ +_base_ = 'tridentnet_r50_caffe_1x_coco.py' + +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] + +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/configs/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco.py b/configs/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco.py new file mode 100644 index 0000000..0f40282 --- /dev/null +++ b/configs/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco.py @@ -0,0 +1,4 @@ +_base_ = 'tridentnet_r50_caffe_mstrain_1x_coco.py' + +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/vfnet/README.md b/configs/vfnet/README.md new file mode 100644 index 0000000..363f1b9 --- /dev/null +++ b/configs/vfnet/README.md @@ -0,0 +1,43 @@ +# VarifocalNet: An IoU-aware Dense Object Detector + +## Introduction + + + +**VarifocalNet (VFNet)** learns to predict the IoU-aware classification score which mixes the object presence confidence and localization accuracy together as the detection score for a bounding box. The learning is supervised by the proposed Varifocal Loss (VFL), based on a new star-shaped bounding box feature representation (the features at nine yellow sampling points). Given the new representation, the object localization accuracy is further improved by refining the initially regressed bounding box. The full paper is available at: [https://arxiv.org/abs/2008.13367](https://arxiv.org/abs/2008.13367). + +
+ +

Learning to Predict the IoU-aware Classification Score.

+
+ +## Citing VarifocalNet + +```latex +@article{zhang2020varifocalnet, + title={VarifocalNet: An IoU-aware Dense Object Detector}, + author={Zhang, Haoyang and Wang, Ying and Dayoub, Feras and S{\"u}nderhauf, Niko}, + journal={arXiv preprint arXiv:2008.13367}, + year={2020} +} +``` + +## Results and Models + +| Backbone | Style | DCN | MS train | Lr schd |Inf time (fps) | box AP (val) | box AP (test-dev) | Config | Download | +|:------------:|:---------:|:-------:|:--------:|:-------:|:-------------:|:------------:|:-----------------:|:------:|:--------:| +| R-50 | pytorch | N | N | 1x | - | 41.6 | 41.6 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r50_fpn_1x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_1x_coco/vfnet_r50_fpn_1x_coco_20201027-38db6f58.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_1x_coco/vfnet_r50_fpn_1x_coco.json)| +| R-50 | pytorch | N | Y | 2x | - | 44.5 | 44.8 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r50_fpn_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mstrain_2x_coco/vfnet_r50_fpn_mstrain_2x_coco_20201027-7cc75bd2.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mstrain_2x_coco/vfnet_r50_fpn_mstrain_2x_coco.json)| +| R-50 | pytorch | Y | Y | 2x | - | 47.8 | 48.0 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-6879c318.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.json)| +| R-101 | pytorch | N | N | 1x | - | 43.0 | 43.6 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r101_fpn_1x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_1x_coco/vfnet_r101_fpn_1x_coco_20201027pth-c831ece7.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_1x_coco/vfnet_r101_fpn_1x_coco.json)| +| R-101 | pytorch | N | Y | 2x | - | 46.2 | 46.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mstrain_2x_coco/vfnet_r101_fpn_mstrain_2x_coco_20201027pth-4a5d53f1.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mstrain_2x_coco/vfnet_r101_fpn_mstrain_2x_coco.json)| +| R-101 | pytorch | Y | Y | 2x | - | 49.0 | 49.2 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-7729adb5.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.json)| +| X-101-32x4d | pytorch | Y | Y | 2x | - | 49.7 | 50.0 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-d300a6fc.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.json)| +| X-101-64x4d | pytorch | Y | Y | 2x | - | 50.4 | 50.8 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-b5f6da5e.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.json)| + +**Notes:** + +- The MS-train scale range is 1333x[480:960] (`range` mode) and the inference scale keeps 1333x800. +- DCN means using `DCNv2` in both backbone and head. +- Inference time will be updated soon. +- More results and pre-trained models can be found in [VarifocalNet-Github](https://github.com/hyz-xmaster/VarifocalNet) diff --git a/configs/vfnet/vfnet_r101_fpn_1x_coco.py b/configs/vfnet/vfnet_r101_fpn_1x_coco.py new file mode 100644 index 0000000..0952131 --- /dev/null +++ b/configs/vfnet/vfnet_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './vfnet_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/vfnet/vfnet_r101_fpn_2x_coco.py b/configs/vfnet/vfnet_r101_fpn_2x_coco.py new file mode 100644 index 0000000..334657d --- /dev/null +++ b/configs/vfnet/vfnet_r101_fpn_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './vfnet_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py b/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py new file mode 100644 index 0000000..f8ef6ec --- /dev/null +++ b/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py @@ -0,0 +1,14 @@ +_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict( + type='ResNet', + depth=101, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py b/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py new file mode 100644 index 0000000..be7f075 --- /dev/null +++ b/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py @@ -0,0 +1,2 @@ +_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/vfnet/vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py b/configs/vfnet/vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py new file mode 100644 index 0000000..8da3122 --- /dev/null +++ b/configs/vfnet/vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py @@ -0,0 +1,16 @@ +_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py' +model = dict( + pretrained='open-mmlab://res2net101_v1d_26w_4s', + backbone=dict( + type='Res2Net', + depth=101, + scales=4, + base_width=26, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/vfnet/vfnet_r2_101_fpn_mstrain_2x_coco.py b/configs/vfnet/vfnet_r2_101_fpn_mstrain_2x_coco.py new file mode 100644 index 0000000..2bcf779 --- /dev/null +++ b/configs/vfnet/vfnet_r2_101_fpn_mstrain_2x_coco.py @@ -0,0 +1,14 @@ +_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py' +model = dict( + pretrained='open-mmlab://res2net101_v1d_26w_4s', + backbone=dict( + type='Res2Net', + depth=101, + scales=4, + base_width=26, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch')) diff --git a/configs/vfnet/vfnet_r50_fpn_1x_coco.py b/configs/vfnet/vfnet_r50_fpn_1x_coco.py new file mode 100644 index 0000000..76566bd --- /dev/null +++ b/configs/vfnet/vfnet_r50_fpn_1x_coco.py @@ -0,0 +1,108 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# model settings +model = dict( + type='VFNet', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs=True, + extra_convs_on_inputs=False, # use P5 + num_outs=5, + relu_before_extra_convs=True), + bbox_head=dict( + type='VFNetHead', + num_classes=80, + in_channels=256, + stacked_convs=3, + feat_channels=256, + strides=[8, 16, 32, 64, 128], + center_sampling=False, + dcn_on_last_conv=False, + use_atss=True, + use_vfl=True, + loss_cls=dict( + type='VarifocalLoss', + use_sigmoid=True, + alpha=0.75, + gamma=2.0, + iou_weighted=True, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=1.5), + loss_bbox_refine=dict(type='GIoULoss', loss_weight=2.0)), + # training and testing settings + train_cfg=dict( + assigner=dict(type='ATSSAssigner', topk=9), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) + +# data setting +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) + +# optimizer +optimizer = dict( + lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.)) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.1, + step=[8, 11]) +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/configs/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py b/configs/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py new file mode 100644 index 0000000..24d2093 --- /dev/null +++ b/configs/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py @@ -0,0 +1,6 @@ +_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True)), + bbox_head=dict(dcn_on_last_conv=True)) diff --git a/configs/vfnet/vfnet_r50_fpn_mstrain_2x_coco.py b/configs/vfnet/vfnet_r50_fpn_mstrain_2x_coco.py new file mode 100644 index 0000000..6078bb9 --- /dev/null +++ b/configs/vfnet/vfnet_r50_fpn_mstrain_2x_coco.py @@ -0,0 +1,39 @@ +_base_ = './vfnet_r50_fpn_1x_coco.py' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 480), (1333, 960)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py b/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py new file mode 100644 index 0000000..ebeef6f --- /dev/null +++ b/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py @@ -0,0 +1,16 @@ +_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/vfnet/vfnet_x101_32x4d_fpn_mstrain_2x_coco.py b/configs/vfnet/vfnet_x101_32x4d_fpn_mstrain_2x_coco.py new file mode 100644 index 0000000..5ed2650 --- /dev/null +++ b/configs/vfnet/vfnet_x101_32x4d_fpn_mstrain_2x_coco.py @@ -0,0 +1,14 @@ +_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch')) diff --git a/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py b/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py new file mode 100644 index 0000000..2e19078 --- /dev/null +++ b/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py @@ -0,0 +1,16 @@ +_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/vfnet/vfnet_x101_64x4d_fpn_mstrain_2x_coco.py b/configs/vfnet/vfnet_x101_64x4d_fpn_mstrain_2x_coco.py new file mode 100644 index 0000000..4329b34 --- /dev/null +++ b/configs/vfnet/vfnet_x101_64x4d_fpn_mstrain_2x_coco.py @@ -0,0 +1,14 @@ +_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch')) diff --git a/configs/wider_face/README.md b/configs/wider_face/README.md new file mode 100644 index 0000000..b8fe474 --- /dev/null +++ b/configs/wider_face/README.md @@ -0,0 +1,43 @@ +# WIDER Face Dataset + + + +To use the WIDER Face dataset you need to download it +and extract to the `data/WIDERFace` folder. Annotation in the VOC format +can be found in this [repo](https://github.com/sovrasov/wider-face-pascal-voc-annotations.git). +You should move the annotation files from `WIDER_train_annotations` and `WIDER_val_annotations` folders +to the `Annotation` folders inside the corresponding directories `WIDER_train` and `WIDER_val`. +Also annotation lists `val.txt` and `train.txt` should be copied to `data/WIDERFace` from `WIDER_train_annotations` and `WIDER_val_annotations`. +The directory should be like this: + +``` +mmdetection +├── mmdet +├── tools +├── configs +├── data +│ ├── WIDERFace +│ │ ├── WIDER_train +│ | │ ├──0--Parade +│ | │ ├── ... +│ | │ ├── Annotations +│ │ ├── WIDER_val +│ | │ ├──0--Parade +│ | │ ├── ... +│ | │ ├── Annotations +│ │ ├── val.txt +│ │ ├── train.txt + +``` + +After that you can train the SSD300 on WIDER by launching training with the `ssd300_wider_face.py` config or +create your own config based on the presented one. + +``` +@inproceedings{yang2016wider, + Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou}, + Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + Title = {WIDER FACE: A Face Detection Benchmark}, + Year = {2016} +} +``` diff --git a/configs/wider_face/ssd300_wider_face.py b/configs/wider_face/ssd300_wider_face.py new file mode 100644 index 0000000..5a3eb38 --- /dev/null +++ b/configs/wider_face/ssd300_wider_face.py @@ -0,0 +1,18 @@ +_base_ = [ + '../_base_/models/ssd300.py', '../_base_/datasets/wider_face.py', + '../_base_/default_runtime.py' +] +model = dict(bbox_head=dict(num_classes=1)) +# optimizer +optimizer = dict(type='SGD', lr=0.012, momentum=0.9, weight_decay=5e-4) +optimizer_config = dict() +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=0.001, + step=[16, 20]) +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=24) +log_config = dict(interval=1) diff --git a/configs/yolact/README.md b/configs/yolact/README.md new file mode 100644 index 0000000..da3559b --- /dev/null +++ b/configs/yolact/README.md @@ -0,0 +1,71 @@ +# **Y**ou **O**nly **L**ook **A**t **C**oefficien**T**s + + + +``` + ██╗ ██╗ ██████╗ ██╗ █████╗ ██████╗████████╗ + ╚██╗ ██╔╝██╔═══██╗██║ ██╔══██╗██╔════╝╚══██╔══╝ + ╚████╔╝ ██║ ██║██║ ███████║██║ ██║ + ╚██╔╝ ██║ ██║██║ ██╔══██║██║ ██║ + ██║ ╚██████╔╝███████╗██║ ██║╚██████╗ ██║ + ╚═╝ ╚═════╝ ╚══════╝╚═╝ ╚═╝ ╚═════╝ ╚═╝ +``` + +A simple, fully convolutional model for real-time instance segmentation. This is the code for our paper: + +- [YOLACT: Real-time Instance Segmentation](https://arxiv.org/abs/1904.02689) + + +For a real-time demo, check out our ICCV video: +[![IMAGE ALT TEXT HERE](https://img.youtube.com/vi/0pMfmo8qfpQ/0.jpg)](https://www.youtube.com/watch?v=0pMfmo8qfpQ) + +## Evaluation + +Here are our YOLACT models along with their FPS on a Titan Xp and mAP on COCO's `val`: + +| Image Size | GPU x BS | Backbone | *FPS | mAP | Weights | Configs | Download | +|:----------:|:--------:|:-------------:|:-----:|:----:|:-------:|:------:|:--------:| +| 550 | 1x8 | Resnet50-FPN | 42.5 | 29.0 | | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolact/yolact_r50_1x8_coco.py) |[model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/yolact/yolact_r50_1x8_coco_20200908-f38d58df.pth) | +| 550 | 8x8 | Resnet50-FPN | 42.5 | 28.4 | | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolact/yolact_r50_8x8_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/yolact/yolact_r50_8x8_coco_20200908-ca34f5db.pth) | +| 550 | 1x8 | Resnet101-FPN | 33.5 | 30.4 | | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolact/yolact_r101_1x8_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/yolact/yolact_r101_1x8_coco_20200908-4cbe9101.pth) | + +*Note: The FPS is evaluated by the [original implementation](https://github.com/dbolya/yolact). When calculating FPS, only the model inference time is taken into account. Data loading and post-processing operations such as converting masks to RLE code, generating COCO JSON results, image rendering are not included. + +## Training + +All the aforementioned models are trained with a single GPU. It typically takes ~12GB VRAM when using resnet-101 as the backbone. If you want to try multiple GPUs training, you may have to modify the configuration files accordingly, such as adjusting the training schedule and freezing batch norm. + +```Shell +# Trains using the resnet-101 backbone with a batch size of 8 on a single GPU. +./tools/dist_train.sh configs/yolact/yolact_r101.py 1 +``` + +## Testing + +Please refer to [mmdetection/docs/getting_started.md](https://github.com/open-mmlab/mmdetection/blob/master/docs/getting_started.md#inference-with-pretrained-models). + +## Citation + +If you use YOLACT or this code base in your work, please cite + +```latex +@inproceedings{yolact-iccv2019, + author = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee}, + title = {YOLACT: {Real-time} Instance Segmentation}, + booktitle = {ICCV}, + year = {2019}, +} +``` + + diff --git a/configs/yolact/yolact_r101_1x8_coco.py b/configs/yolact/yolact_r101_1x8_coco.py new file mode 100644 index 0000000..2864b59 --- /dev/null +++ b/configs/yolact/yolact_r101_1x8_coco.py @@ -0,0 +1,3 @@ +_base_ = './yolact_r50_1x8_coco.py' + +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/yolact/yolact_r50_1x8_coco.py b/configs/yolact/yolact_r50_1x8_coco.py new file mode 100644 index 0000000..d0e5ace --- /dev/null +++ b/configs/yolact/yolact_r50_1x8_coco.py @@ -0,0 +1,160 @@ +_base_ = '../_base_/default_runtime.py' + +# model settings +img_size = 550 +model = dict( + type='YOLACT', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=-1, # do not freeze stem + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=False, # update the statistics of bn + zero_init_residual=False, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_input', + num_outs=5, + upsample_cfg=dict(mode='bilinear')), + bbox_head=dict( + type='YOLACTHead', + num_classes=80, + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=3, + scales_per_octave=1, + base_sizes=[8, 16, 32, 64, 128], + ratios=[0.5, 1.0, 2.0], + strides=[550.0 / x for x in [69, 35, 18, 9, 5]], + centers=[(550 * 0.5 / x, 550 * 0.5 / x) + for x in [69, 35, 18, 9, 5]]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + reduction='none', + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.5), + num_head_convs=1, + num_protos=32, + use_ohem=True), + mask_head=dict( + type='YOLACTProtonet', + in_channels=256, + num_protos=32, + num_classes=80, + max_masks_to_train=100, + loss_mask_weight=6.125), + segm_head=dict( + type='YOLACTSegmHead', + num_classes=80, + in_channels=256, + loss_segm=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0., + ignore_iof_thr=-1, + gt_max_assign_all=False), + # smoothl1_beta=1., + allowed_border=-1, + pos_weight=-1, + neg_pos_ratio=3, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + iou_thr=0.5, + top_k=200, + max_per_img=100)) +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.68, 116.78, 103.94], std=[58.40, 57.12, 57.38], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='FilterAnnotations', min_gt_bbox_wh=(4.0, 4.0)), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 4)), + dict( + type='MinIoURandomCrop', + min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), + min_crop_size=0.3), + dict(type='Resize', img_scale=(img_size, img_size), keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(img_size, img_size), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=4, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=1e-3, momentum=0.9, weight_decay=5e-4) +optimizer_config = dict() +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.1, + step=[20, 42, 49, 52]) +runner = dict(type='EpochBasedRunner', max_epochs=55) +cudnn_benchmark = True +evaluation = dict(metric=['bbox', 'segm']) diff --git a/configs/yolact/yolact_r50_8x8_coco.py b/configs/yolact/yolact_r50_8x8_coco.py new file mode 100644 index 0000000..b3adcb7 --- /dev/null +++ b/configs/yolact/yolact_r50_8x8_coco.py @@ -0,0 +1,11 @@ +_base_ = 'yolact_r50_1x8_coco.py' + +optimizer = dict(type='SGD', lr=8e-3, momentum=0.9, weight_decay=5e-4) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=0.1, + step=[20, 42, 49, 52]) diff --git a/configs/yolo/README.md b/configs/yolo/README.md new file mode 100644 index 0000000..a2e7a2b --- /dev/null +++ b/configs/yolo/README.md @@ -0,0 +1,28 @@ +# YOLOv3 + +## Introduction + + + +```latex +@misc{redmon2018yolov3, + title={YOLOv3: An Incremental Improvement}, + author={Joseph Redmon and Ali Farhadi}, + year={2018}, + eprint={1804.02767}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + +## Results and Models + +| Backbone | Scale | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| DarkNet-53 | 320 | 273e | 2.7 | 63.9 | 27.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_320_273e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_320_273e_coco/yolov3_d53_320_273e_coco-421362b6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_320_273e_coco/yolov3_d53_320_273e_coco-20200819_172101.log.json) | +| DarkNet-53 | 416 | 273e | 3.8 | 61.2 | 30.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_mstrain-416_273e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-416_273e_coco/yolov3_d53_mstrain-416_273e_coco-2b60fcd9.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-416_273e_coco/yolov3_d53_mstrain-416_273e_coco-20200819_173424.log.json) | +| DarkNet-53 | 608 | 273e | 7.1 | 48.1 | 33.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-608_273e_coco/yolov3_d53_mstrain-608_273e_coco-139f5633.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-608_273e_coco/yolov3_d53_mstrain-608_273e_coco-20200819_170820.log.json) | + +## Credit + +This implementation originates from the project of Haoyu Wu(@wuhy08) at Western Digital. diff --git a/configs/yolo/yolov3_d53_320_273e_coco.py b/configs/yolo/yolov3_d53_320_273e_coco.py new file mode 100644 index 0000000..87359f6 --- /dev/null +++ b/configs/yolo/yolov3_d53_320_273e_coco.py @@ -0,0 +1,42 @@ +_base_ = './yolov3_d53_mstrain-608_273e_coco.py' +# dataset settings +img_norm_cfg = dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='PhotoMetricDistortion'), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 2)), + dict( + type='MinIoURandomCrop', + min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9), + min_crop_size=0.3), + dict(type='Resize', img_scale=(320, 320), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(320, 320), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/yolo/yolov3_d53_mstrain-416_273e_coco.py b/configs/yolo/yolov3_d53_mstrain-416_273e_coco.py new file mode 100644 index 0000000..d029b5c --- /dev/null +++ b/configs/yolo/yolov3_d53_mstrain-416_273e_coco.py @@ -0,0 +1,42 @@ +_base_ = './yolov3_d53_mstrain-608_273e_coco.py' +# dataset settings +img_norm_cfg = dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='PhotoMetricDistortion'), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 2)), + dict( + type='MinIoURandomCrop', + min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9), + min_crop_size=0.3), + dict(type='Resize', img_scale=[(320, 320), (416, 416)], keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(416, 416), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py b/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py new file mode 100644 index 0000000..9c65305 --- /dev/null +++ b/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py @@ -0,0 +1,124 @@ +_base_ = '../_base_/default_runtime.py' +# model settings +model = dict( + type='YOLOV3', + pretrained='open-mmlab://darknet53', + backbone=dict(type='Darknet', depth=53, out_indices=(3, 4, 5)), + neck=dict( + type='YOLOV3Neck', + num_scales=3, + in_channels=[1024, 512, 256], + out_channels=[512, 256, 128]), + bbox_head=dict( + type='YOLOV3Head', + num_classes=80, + in_channels=[512, 256, 128], + out_channels=[1024, 512, 256], + anchor_generator=dict( + type='YOLOAnchorGenerator', + base_sizes=[[(116, 90), (156, 198), (373, 326)], + [(30, 61), (62, 45), (59, 119)], + [(10, 13), (16, 30), (33, 23)]], + strides=[32, 16, 8]), + bbox_coder=dict(type='YOLOBBoxCoder'), + featmap_strides=[32, 16, 8], + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0, + reduction='sum'), + loss_conf=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0, + reduction='sum'), + loss_xy=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=2.0, + reduction='sum'), + loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum')), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='GridAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0)), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + conf_thr=0.005, + nms=dict(type='nms', iou_threshold=0.45), + max_per_img=100)) +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='PhotoMetricDistortion'), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 2)), + dict( + type='MinIoURandomCrop', + min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9), + min_crop_size=0.3), + dict(type='Resize', img_scale=[(320, 320), (608, 608)], keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(608, 608), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=4, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0005) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=2000, # same as burn-in in darknet + warmup_ratio=0.1, + step=[218, 246]) +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=273) +evaluation = dict(interval=1, metric=['bbox']) diff --git a/configs/yolof/README.md b/configs/yolof/README.md new file mode 100644 index 0000000..4eb9a4b --- /dev/null +++ b/configs/yolof/README.md @@ -0,0 +1,25 @@ +# You Only Look One-level Feature + +## Introduction + + + +``` +@inproceedings{chen2021you, + title={You Only Look One-level Feature}, + author={Chen, Qiang and Wang, Yingming and Yang, Tong and Zhang, Xiangyu and Cheng, Jian and Sun, Jian}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + year={2021} +} +``` + +## Results and Models + +| Backbone | Style | Epoch | Lr schd | Mem (GB) | box AP | Config | Download | +|:---------:|:-------:|:-------:|:-------:|:--------:|:------:|:------:|:--------:| +| R-50-C5 | caffe | Y | 1x | 8.3 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolof/yolof_r50_c5_8x8_1x_coco.py) |[model](http://download.openmmlab.com/mmdetection/v2.0/yolof/yolof_r50_c5_8x8_1x_coco/yolof_r50_c5_8x8_1x_coco_20210425_024427-8e864411.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/yolof/yolof_r50_c5_8x8_1x_coco/yolof_r50_c5_8x8_1x_coco_20210425_024427.log.json) | + +**Note**: + +1. We find that the performance is unstable and may fluctuate by about 0.3 mAP. mAP 37.4 ~ 37.7 is acceptable in YOLOF_R_50_C5_1x. Such fluctuation can also be found in the [original implementation](https://github.com/chensnathan/YOLOF). +2. In addition to instability issues, sometimes there are large loss fluctuations and NAN, so there may still be problems with this project, which will be improved subsequently. diff --git a/configs/yolof/yolof_r50_c5_8x8_1x_coco.py b/configs/yolof/yolof_r50_c5_8x8_1x_coco.py new file mode 100644 index 0000000..e7b31a1 --- /dev/null +++ b/configs/yolof/yolof_r50_c5_8x8_1x_coco.py @@ -0,0 +1,103 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='YOLOF', + pretrained='open-mmlab://detectron/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(3, ), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe'), + neck=dict( + type='DilatedEncoder', + in_channels=2048, + out_channels=512, + block_mid_channels=128, + num_residual_blocks=4), + bbox_head=dict( + type='YOLOFHead', + num_classes=80, + in_channels=512, + reg_decoded_bbox=True, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[1, 2, 4, 8, 16], + strides=[32]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1., 1., 1., 1.], + add_ctr_clamp=True, + ctr_clamp=32), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=1.0)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='UniformAssigner', pos_ignore_thr=0.15, neg_ignore_thr=0.7), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) +# optimizer +optimizer = dict( + type='SGD', + lr=0.12, + momentum=0.9, + weight_decay=0.0001, + paramwise_cfg=dict( + norm_decay_mult=0., custom_keys={'backbone': dict(lr_mult=1. / 3)})) +lr_config = dict(warmup_iters=1500, warmup_ratio=0.00066667) + +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='RandomShift', shift_ratio=0.5, max_shift_px=32), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=8, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/yolof/yolof_r50_c5_8x8_iter-1x_coco.py b/configs/yolof/yolof_r50_c5_8x8_iter-1x_coco.py new file mode 100644 index 0000000..c95c02d --- /dev/null +++ b/configs/yolof/yolof_r50_c5_8x8_iter-1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './yolof_r50_c5_8x8_1x_coco.py' + +# We implemented the iter-based config according to the source code. +# COCO dataset has 117266 images after filtering. We use 8 gpu and +# 8 batch size training, so 22500 is equivalent to +# 22500/(117266/(8x8))=12.3 epoch, 15000 is equivalent to 8.2 epoch, +# 20000 is equivalent to 10.9 epoch. Due to lr(0.12) is large, +# the iter-based and epoch-based setting have about 0.2 difference on +# the mAP evaluation value. +lr_config = dict(step=[15000, 20000]) +runner = dict(_delete_=True, type='IterBasedRunner', max_iters=22500) +checkpoint_config = dict(interval=2500) +evaluation = dict(interval=4500) +log_config = dict(interval=20) diff --git a/demo.py b/demo.py new file mode 100644 index 0000000..8a0e08e --- /dev/null +++ b/demo.py @@ -0,0 +1,21 @@ +from PIL import Image +from mmdet.apis import init_detector, inference_detector, show_result_pyplot +import sys +import os.path +name = sys.argv[1] +thr = sys.argv[2] +config_file = './configs/adamixer/adamixer_r50_1x_coco.py' +checkpoint_file = name + +if name == 'none': + checkpoint_file = None + + +model = init_detector(config_file, checkpoint_file, device='cuda:0') + +# test a single image and show the results +img = 'data/coco/val2017/000000057597.jpg' + +result = inference_detector(model, img) +show_result_pyplot(model, img, result, score_thr=float(thr), + out_file='demo/result.jpg') diff --git a/demo/MMDet_Tutorial.ipynb b/demo/MMDet_Tutorial.ipynb new file mode 100644 index 0000000..80af165 --- /dev/null +++ b/demo/MMDet_Tutorial.ipynb @@ -0,0 +1,1656 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "tJxJHruNLb7Y" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aGYwt_UjIrqp" + }, + "source": [ + "# MMDetection Tutorial\n", + "\n", + "Welcome to MMDetection! This is the official colab tutorial for using MMDetection. In this tutorial, you will learn\n", + "- Perform inference with a MMDet detector.\n", + "- Train a new detector with a new dataset.\n", + "\n", + "Let's start!\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Wi4LPmsR66sy", + "outputId": "8eb8aadf-1c70-42dd-9105-1a3dad85c504" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nvcc: NVIDIA (R) Cuda compiler driver\n", + "Copyright (c) 2005-2019 NVIDIA Corporation\n", + "Built on Sun_Jul_28_19:07:16_PDT_2019\n", + "Cuda compilation tools, release 10.1, V10.1.243\n", + "gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n", + "Copyright (C) 2017 Free Software Foundation, Inc.\n", + "This is free software; see the source for copying conditions. There is NO\n", + "warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n", + "\n" + ] + } + ], + "source": [ + "# Check nvcc version\n", + "!nvcc -V\n", + "# Check GCC version\n", + "!gcc --version" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gkGnB9WyHSXB", + "outputId": "f1360573-c24a-4a8f-98cd-cc654c1d7d05" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n", + "Collecting torch==1.5.1+cu101\n", + "\u001b[?25l Downloading https://download.pytorch.org/whl/cu101/torch-1.5.1%2Bcu101-cp36-cp36m-linux_x86_64.whl (704.4MB)\n", + "\u001b[K |████████████████████████████████| 704.4MB 26kB/s \n", + "\u001b[?25hCollecting torchvision==0.6.1+cu101\n", + "\u001b[?25l Downloading https://download.pytorch.org/whl/cu101/torchvision-0.6.1%2Bcu101-cp36-cp36m-linux_x86_64.whl 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"source": [ + "# install dependencies: (use cu101 because colab has CUDA 10.1)\n", + "!pip install -U torch==1.5.1+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html\n", + "\n", + "# install mmcv-full thus we could use CUDA operators\n", + "!pip install mmcv-full\n", + "\n", + "# Install mmdetection\n", + "!rm -rf mmdetection\n", + "!git clone https://github.com/open-mmlab/mmdetection.git\n", + "%cd mmdetection\n", + "\n", + "!pip install -e .\n", + "\n", + "# install Pillow 7.0.0 back in order to avoid bug in colab\n", + "!pip install Pillow==7.0.0" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6hD0mmMixT0p", + "outputId": "5316598c-233a-4140-db12-64d3a0df216b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.5.1+cu101 True\n", + "2.9.0\n", + "10.1\n", + "GCC 7.5\n" + ] + } + ], + "source": [ + "# Check Pytorch installation\n", + "import torch, torchvision\n", + "print(torch.__version__, torch.cuda.is_available())\n", + "\n", + "# Check MMDetection installation\n", + "import mmdet\n", + "print(mmdet.__version__)\n", + "\n", + "# Check mmcv installation\n", + "from mmcv.ops import get_compiling_cuda_version, get_compiler_version\n", + "print(get_compiling_cuda_version())\n", + "print(get_compiler_version())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gi9zw03oM4CH" + }, + "source": [ + "## Perform inference with a MMDet detector\n", + "MMDetection already provides high level APIs to do inference and training." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "j4doHX4exvS1", + "outputId": "688ef595-5742-4210-90d0-b841044a7892" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2021-02-20 03:03:09-- http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth\n", + "Resolving download.openmmlab.com (download.openmmlab.com)... 47.252.96.35\n", + "Connecting to download.openmmlab.com (download.openmmlab.com)|47.252.96.35|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 177867103 (170M) [application/octet-stream]\n", + "Saving to: ‘checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth’\n", + "\n", + "checkpoints/mask_rc 100%[===================>] 169.63M 8.44MB/s in 21s \n", + "\n", + "2021-02-20 03:03:32 (8.19 MB/s) - ‘checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth’ saved [177867103/177867103]\n", + "\n" + ] + } + ], + "source": [ + "!mkdir checkpoints\n", + "!wget -c http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth \\\n", + " -O checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "8M5KUnX7Np3h" + }, + "outputs": [], + "source": [ + "from mmdet.apis import inference_detector, init_detector, show_result_pyplot\n", + "\n", + "# Choose to use a config and initialize the detector\n", + "config = 'configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py'\n", + "# Setup a checkpoint file to load\n", + "checkpoint = 'checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth'\n", + "# initialize the detector\n", + "model = init_detector(config, checkpoint, device='cuda:0')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Wi6DRpsQPEmV", + "outputId": "8ea1de7e-d20f-44cf-9967-24578d51ff16" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/content/mmdetection/mmdet/datasets/utils.py:66: UserWarning: \"ImageToTensor\" pipeline is replaced by \"DefaultFormatBundle\" for batch inference. It is recommended to manually replace it in the test data pipeline in your config file.\n", + " 'data pipeline in your config file.', UserWarning)\n" + ] + } + ], + "source": [ + "# Use the detector to do inference\n", + "img = 'demo/demo.jpg'\n", + "result = inference_detector(model, img)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 576 + }, + "id": "UsJU5D-QPX8L", + "outputId": "04df7cef-6393-4147-da43-ab89f3b29a56" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/content/mmdetection/mmdet/apis/inference.py:205: UserWarning: \"block\" will be deprecated in v2.9.0,Please use \"wait_time\"\n", + " warnings.warn('\"block\" will be deprecated in v2.9.0,'\n", + "/content/mmdetection/mmdet/apis/inference.py:207: UserWarning: \"fig_size\" are deprecated and takes no effect.\n", + " warnings.warn('\"fig_size\" are deprecated and takes no effect.')\n", + "/content/mmdetection/mmdet/core/visualization/image.py:75: UserWarning: \"font_scale\" will be deprecated in v2.9.0,Please use \"font_size\"\n", + " warnings.warn('\"font_scale\" will be deprecated in v2.9.0,'\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "# Let's plot the result\n", + "show_result_pyplot(model, img, result, score_thr=0.3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7GrWIJywLV-V" + }, + "source": [ + "## Train a detector on customized dataset\n", + "\n", + "To train a new detector, there are usually three things to do:\n", + "1. Support a new dataset\n", + "2. Modify the config\n", + "3. Train a new detector\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E73y5Lru-wBx" + }, + "source": [ + "### Support a new dataset\n", + "\n", + "There are three ways to support a new dataset in MMDetection: \n", + " 1. reorganize the dataset into COCO format.\n", + " 2. reorganize the dataset into a middle format.\n", + " 3. implement a new dataset.\n", + "\n", + "Usually we recommend to use the first two methods which are usually easier than the third.\n", + "\n", + "In this tutorial, we gives an example that converting the data into the format of existing datasets like COCO, VOC, etc. Other methods and more advanced usages can be found in the [doc](https://mmdetection.readthedocs.io/en/latest/tutorials/new_dataset.html#).\n", + "\n", + "Firstly, let's download a tiny dataset obtained from [KITTI](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d). We select the first 75 images and their annotations from the 3D object detection dataset (it is the same dataset as the 2D object detection dataset but has 3D annotations). We convert the original images from PNG to JPEG format with 80% quality to reduce the size of dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rHnw5Q_nARXq", + "outputId": "a61e0685-6441-4ff2-994a-15da68e507fe" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2021-02-20 03:04:04-- https://download.openmmlab.com/mmdetection/data/kitti_tiny.zip\n", + "Resolving download.openmmlab.com (download.openmmlab.com)... 47.252.96.35\n", + "Connecting to download.openmmlab.com (download.openmmlab.com)|47.252.96.35|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 6918271 (6.6M) [application/zip]\n", + "Saving to: ‘kitti_tiny.zip’\n", + "\n", + "kitti_tiny.zip 100%[===================>] 6.60M 8.44MB/s in 0.8s \n", + "\n", + "2021-02-20 03:04:06 (8.44 MB/s) - ‘kitti_tiny.zip’ saved [6918271/6918271]\n", + "\n" + ] + } + ], + "source": [ + "# download, decompress the data\n", + "!wget https://download.openmmlab.com/mmdetection/data/kitti_tiny.zip\n", + "!unzip kitti_tiny.zip > /dev/null" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Wuwxw1oZRtVZ", + "outputId": "7f88e82a-0825-4c9e-e584-bd43589feeaf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading package lists...\n", + "Building dependency tree...\n", + "Reading state information...\n", + "The following NEW packages will be installed:\n", + " tree\n", + "0 upgraded, 1 newly installed, 0 to remove and 10 not upgraded.\n", + "Need to get 40.7 kB of archives.\n", + "After this operation, 105 kB of additional disk space will be used.\n", + "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 tree amd64 1.7.0-5 [40.7 kB]\n", + "Fetched 40.7 kB in 0s (165 kB/s)\n", + "Selecting previously unselected package tree.\n", + "(Reading database ... 146442 files and directories currently installed.)\n", + "Preparing to unpack .../tree_1.7.0-5_amd64.deb ...\n", + "Unpacking tree (1.7.0-5) ...\n", + "Setting up tree (1.7.0-5) ...\n", + "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n", + "kitti_tiny\n", + "├── training\n", + "│   ├── image_2\n", + "│   │   ├── 000000.jpeg\n", + "│   │   ├── 000001.jpeg\n", + "│   │   ├── 000002.jpeg\n", + "│   │   ├── 000003.jpeg\n", + "│   │   ├── 000004.jpeg\n", + "│   │   ├── 000005.jpeg\n", + "│   │   ├── 000006.jpeg\n", + "│   │   ├── 000007.jpeg\n", + "│   │   ├── 000008.jpeg\n", + "│   │   ├── 000009.jpeg\n", + "│   │   ├── 000010.jpeg\n", + "│   │   ├── 000011.jpeg\n", + "│   │   ├── 000012.jpeg\n", + "│   │   ├── 000013.jpeg\n", + "│   │   ├── 000014.jpeg\n", + "│   │   ├── 000015.jpeg\n", + "│   │   ├── 000016.jpeg\n", + "│   │   ├── 000017.jpeg\n", + "│   │   ├── 000018.jpeg\n", + "│   │   ├── 000019.jpeg\n", + "│   │   ├── 000020.jpeg\n", + "│   │   ├── 000021.jpeg\n", + "│   │   ├── 000022.jpeg\n", + "│   │   ├── 000023.jpeg\n", + "│   │   ├── 000024.jpeg\n", + "│   │   ├── 000025.jpeg\n", + "│   │   ├── 000026.jpeg\n", + "│   │   ├── 000027.jpeg\n", + "│   │   ├── 000028.jpeg\n", + "│   │   ├── 000029.jpeg\n", + "│   │   ├── 000030.jpeg\n", + "│   │   ├── 000031.jpeg\n", + "│   │   ├── 000032.jpeg\n", + "│   │   ├── 000033.jpeg\n", + "│   │   ├── 000034.jpeg\n", + "│   │   ├── 000035.jpeg\n", + "│   │   ├── 000036.jpeg\n", + "│   │   ├── 000037.jpeg\n", + "│   │   ├── 000038.jpeg\n", + "│   │   ├── 000039.jpeg\n", + "│   │   ├── 000040.jpeg\n", + "│   │   ├── 000041.jpeg\n", + "│   │   ├── 000042.jpeg\n", + "│   │   ├── 000043.jpeg\n", + "│   │   ├── 000044.jpeg\n", + "│   │   ├── 000045.jpeg\n", + "│   │   ├── 000046.jpeg\n", + "│   │   ├── 000047.jpeg\n", + "│   │   ├── 000048.jpeg\n", + "│   │   ├── 000049.jpeg\n", + "│   │   ├── 000050.jpeg\n", + "│   │   ├── 000051.jpeg\n", + "│   │   ├── 000052.jpeg\n", + "│   │   ├── 000053.jpeg\n", + "│   │   ├── 000054.jpeg\n", + "│   │   ├── 000055.jpeg\n", + "│   │   ├── 000056.jpeg\n", + "│   │   ├── 000057.jpeg\n", + "│   │   ├── 000058.jpeg\n", + "│   │   ├── 000059.jpeg\n", + "│   │   ├── 000060.jpeg\n", + "│   │   ├── 000061.jpeg\n", + "│   │   ├── 000062.jpeg\n", + "│   │   ├── 000063.jpeg\n", + "│   │   ├── 000064.jpeg\n", + "│   │   ├── 000065.jpeg\n", + "│   │   ├── 000066.jpeg\n", + "│   │   ├── 000067.jpeg\n", + "│   │   ├── 000068.jpeg\n", + "│   │   ├── 000069.jpeg\n", + "│   │   ├── 000070.jpeg\n", + "│   │   ├── 000071.jpeg\n", + "│   │   ├── 000072.jpeg\n", + "│   │   ├── 000073.jpeg\n", + "│   │   └── 000074.jpeg\n", + "│   └── label_2\n", + "│   ├── 000000.txt\n", + "│   ├── 000001.txt\n", + "│   ├── 000002.txt\n", + "│   ├── 000003.txt\n", + "│   ├── 000004.txt\n", + "│   ├── 000005.txt\n", + "│   ├── 000006.txt\n", + "│   ├── 000007.txt\n", + "│   ├── 000008.txt\n", + "│   ├── 000009.txt\n", + "│   ├── 000010.txt\n", + "│   ├── 000011.txt\n", + "│   ├── 000012.txt\n", + "│   ├── 000013.txt\n", + "│   ├── 000014.txt\n", + "│   ├── 000015.txt\n", + "│   ├── 000016.txt\n", + "│   ├── 000017.txt\n", + "│   ├── 000018.txt\n", + "│   ├── 000019.txt\n", + "│   ├── 000020.txt\n", + "│   ├── 000021.txt\n", + "│   ├── 000022.txt\n", + "│   ├── 000023.txt\n", + "│   ├── 000024.txt\n", + "│   ├── 000025.txt\n", + "│   ├── 000026.txt\n", + "│   ├── 000027.txt\n", + "│   ├── 000028.txt\n", + "│   ├── 000029.txt\n", + "│   ├── 000030.txt\n", + "│   ├── 000031.txt\n", + "│   ├── 000032.txt\n", + "│   ├── 000033.txt\n", + "│   ├── 000034.txt\n", + "│   ├── 000035.txt\n", + "│   ├── 000036.txt\n", + "│   ├── 000037.txt\n", + "│   ├── 000038.txt\n", + "│   ├── 000039.txt\n", + "│   ├── 000040.txt\n", + "│   ├── 000041.txt\n", + "│   ├── 000042.txt\n", + "│   ├── 000043.txt\n", + "│   ├── 000044.txt\n", + "│   ├── 000045.txt\n", + "│   ├── 000046.txt\n", + "│   ├── 000047.txt\n", + "│   ├── 000048.txt\n", + "│   ├── 000049.txt\n", + "│   ├── 000050.txt\n", + "│   ├── 000051.txt\n", + "│   ├── 000052.txt\n", + "│   ├── 000053.txt\n", + "│   ├── 000054.txt\n", + "│   ├── 000055.txt\n", + "│   ├── 000056.txt\n", + "│   ├── 000057.txt\n", + "│   ├── 000058.txt\n", + "│   ├── 000059.txt\n", + "│   ├── 000060.txt\n", + "│   ├── 000061.txt\n", + "│   ├── 000062.txt\n", + "│   ├── 000063.txt\n", + "│   ├── 000064.txt\n", + "│   ├── 000065.txt\n", + "│   ├── 000066.txt\n", + "│   ├── 000067.txt\n", + "│   ├── 000068.txt\n", + "│   ├── 000069.txt\n", + "│   ├── 000070.txt\n", + "│   ├── 000071.txt\n", + "│   ├── 000072.txt\n", + "│   ├── 000073.txt\n", + "│   └── 000074.txt\n", + "├── train.txt\n", + "└── val.txt\n", + "\n", + "3 directories, 152 files\n" + ] + } + ], + "source": [ + "# Check the directory structure of the tiny data\n", + "\n", + "# Install tree first\n", + "!apt-get -q install tree\n", + "!tree kitti_tiny" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 304 + }, + "id": "YnQQqzOWzE91", + "outputId": "455b3e61-0463-4dc5-e21f-17dd204938fb" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "# Let's take a look at the dataset image\n", + "import mmcv\n", + "import matplotlib.pyplot as plt\n", + "\n", + "img = mmcv.imread('kitti_tiny/training/image_2/000073.jpeg')\n", + "plt.figure(figsize=(15, 10))\n", + "plt.imshow(mmcv.bgr2rgb(img))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PMZvtSIl71qi" + }, + "source": [ + "After downloading the data, we need to implement a function to convert the kitti annotation format into the middle format. In this tutorial we choose to convert them in **`load_annotations`** function in a newly implemented **`KittiTinyDataset`**.\n", + "\n", + "Let's take a loot at the annotation txt file.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "n7rwalnPd6e1", + "outputId": "539d4183-cae3-4485-f894-772c334b613f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pedestrian 0.00 0 -0.20 712.40 143.00 810.73 307.92 1.89 0.48 1.20 1.84 1.47 8.41 0.01\n" + ] + } + ], + "source": [ + "# Check the label of a single image\n", + "!cat kitti_tiny/training/label_2/000000.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QA1pFg-FeO3l" + }, + "source": [ + "According to the KITTI's documentation, the first column indicates the class of the object, and the 5th to 8th columns indicates the bboxes. We need to read annotations of each image and convert them into middle format MMDetection accept is as below:\n", + "\n", + "```python\n", + "[\n", + " {\n", + " 'filename': 'a.jpg',\n", + " 'width': 1280,\n", + " 'height': 720,\n", + " 'ann': {\n", + " 'bboxes': (n, 4),\n", + " 'labels': (n, ),\n", + " 'bboxes_ignore': (k, 4), (optional field)\n", + " 'labels_ignore': (k, 4) (optional field)\n", + " }\n", + " },\n", + " ...\n", + "]\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "GdSaB2ad0EdX" + }, + "outputs": [], + "source": [ + "import copy\n", + "import os.path as osp\n", + "\n", + "import mmcv\n", + "import numpy as np\n", + "\n", + "from mmdet.datasets.builder import DATASETS\n", + "from mmdet.datasets.custom import CustomDataset\n", + "\n", + "@DATASETS.register_module()\n", + "class KittiTinyDataset(CustomDataset):\n", + "\n", + " CLASSES = ('Car', 'Pedestrian', 'Cyclist')\n", + "\n", + " def load_annotations(self, ann_file):\n", + " cat2label = {k: i for i, k in enumerate(self.CLASSES)}\n", + " # load image list from file\n", + " image_list = mmcv.list_from_file(self.ann_file)\n", + " \n", + " data_infos = []\n", + " # convert annotations to middle format\n", + " for image_id in image_list:\n", + " filename = f'{self.img_prefix}/{image_id}.jpeg'\n", + " image = mmcv.imread(filename)\n", + " height, width = image.shape[:2]\n", + " \n", + " data_info = dict(filename=f'{image_id}.jpeg', width=width, height=height)\n", + " \n", + " # load annotations\n", + " label_prefix = self.img_prefix.replace('image_2', 'label_2')\n", + " lines = mmcv.list_from_file(osp.join(label_prefix, f'{image_id}.txt'))\n", + " \n", + " content = [line.strip().split(' ') for line in lines]\n", + " bbox_names = [x[0] for x in content]\n", + " bboxes = [[float(info) for info in x[4:8]] for x in content]\n", + " \n", + " gt_bboxes = []\n", + " gt_labels = []\n", + " gt_bboxes_ignore = []\n", + " gt_labels_ignore = []\n", + " \n", + " # filter 'DontCare'\n", + " for bbox_name, bbox in zip(bbox_names, bboxes):\n", + " if bbox_name in cat2label:\n", + " gt_labels.append(cat2label[bbox_name])\n", + " gt_bboxes.append(bbox)\n", + " else:\n", + " gt_labels_ignore.append(-1)\n", + " gt_bboxes_ignore.append(bbox)\n", + "\n", + " data_anno = dict(\n", + " bboxes=np.array(gt_bboxes, dtype=np.float32).reshape(-1, 4),\n", + " labels=np.array(gt_labels, dtype=np.long),\n", + " bboxes_ignore=np.array(gt_bboxes_ignore,\n", + " dtype=np.float32).reshape(-1, 4),\n", + " labels_ignore=np.array(gt_labels_ignore, dtype=np.long))\n", + "\n", + " data_info.update(ann=data_anno)\n", + " data_infos.append(data_info)\n", + "\n", + " return data_infos" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PwqJOpBe-bMj" + }, + "source": [ + "### Modify the config\n", + "\n", + "In the next step, we need to modify the config for the training.\n", + "To accelerate the process, we finetune a detector using a pre-trained detector." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "hamZrlnH-YDD" + }, + "outputs": [], + "source": [ + "from mmcv import Config\n", + "cfg = Config.fromfile('./configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HntziLGq-92Z" + }, + "source": [ + "Given a config that trains a Faster R-CNN on COCO dataset, we need to modify some values to use it for training Faster R-CNN on KITTI dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pUbwD8uV0PR8", + "outputId": "43e76fd7-c74b-4ac8-c8b5-4d2cc94e610b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Config:\n", + "model = dict(\n", + " type='FasterRCNN',\n", + " pretrained='open-mmlab://detectron2/resnet50_caffe',\n", + " backbone=dict(\n", + " type='ResNet',\n", + " depth=50,\n", + " num_stages=4,\n", + " out_indices=(0, 1, 2, 3),\n", + " frozen_stages=1,\n", + " norm_cfg=dict(type='BN', requires_grad=False),\n", + " norm_eval=True,\n", + " style='caffe'),\n", + " neck=dict(\n", + " type='FPN',\n", + " in_channels=[256, 512, 1024, 2048],\n", + " out_channels=256,\n", + " num_outs=5),\n", + " rpn_head=dict(\n", + " type='RPNHead',\n", + " in_channels=256,\n", + " feat_channels=256,\n", + " anchor_generator=dict(\n", + " type='AnchorGenerator',\n", + " scales=[8],\n", + " ratios=[0.5, 1.0, 2.0],\n", + " strides=[4, 8, 16, 32, 64]),\n", + " bbox_coder=dict(\n", + " type='DeltaXYWHBBoxCoder',\n", + " target_means=[0.0, 0.0, 0.0, 0.0],\n", + " target_stds=[1.0, 1.0, 1.0, 1.0]),\n", + " loss_cls=dict(\n", + " type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n", + " loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n", + " roi_head=dict(\n", + " type='StandardRoIHead',\n", + " bbox_roi_extractor=dict(\n", + " type='SingleRoIExtractor',\n", + " roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n", + " out_channels=256,\n", + " featmap_strides=[4, 8, 16, 32]),\n", + " bbox_head=dict(\n", + " type='Shared2FCBBoxHead',\n", + " in_channels=256,\n", + " fc_out_channels=1024,\n", + " roi_feat_size=7,\n", + " num_classes=3,\n", + " bbox_coder=dict(\n", + " type='DeltaXYWHBBoxCoder',\n", + " target_means=[0.0, 0.0, 0.0, 0.0],\n", + " target_stds=[0.1, 0.1, 0.2, 0.2]),\n", + " reg_class_agnostic=False,\n", + " loss_cls=dict(\n", + " type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n", + " loss_bbox=dict(type='L1Loss', loss_weight=1.0))),\n", + " train_cfg=dict(\n", + " rpn=dict(\n", + " assigner=dict(\n", + " type='MaxIoUAssigner',\n", + " pos_iou_thr=0.7,\n", + " neg_iou_thr=0.3,\n", + " min_pos_iou=0.3,\n", + " match_low_quality=True,\n", + " ignore_iof_thr=-1),\n", + " sampler=dict(\n", + " type='RandomSampler',\n", + " num=256,\n", + " pos_fraction=0.5,\n", + " neg_pos_ub=-1,\n", + " add_gt_as_proposals=False),\n", + " allowed_border=-1,\n", + " pos_weight=-1,\n", + " debug=False),\n", + " rpn_proposal=dict(\n", + " nms_across_levels=False,\n", + " nms_pre=2000,\n", + " nms_post=1000,\n", + " max_num=1000,\n", + " nms_thr=0.7,\n", + " min_bbox_size=0),\n", + " rcnn=dict(\n", + " assigner=dict(\n", + " type='MaxIoUAssigner',\n", + " pos_iou_thr=0.5,\n", + " neg_iou_thr=0.5,\n", + " min_pos_iou=0.5,\n", + " match_low_quality=False,\n", + " ignore_iof_thr=-1),\n", + " sampler=dict(\n", + " type='RandomSampler',\n", + " num=512,\n", + " pos_fraction=0.25,\n", + " neg_pos_ub=-1,\n", + " add_gt_as_proposals=True),\n", + " pos_weight=-1,\n", + " debug=False)),\n", + " test_cfg=dict(\n", + " rpn=dict(\n", + " nms_across_levels=False,\n", + " nms_pre=1000,\n", + " nms_post=1000,\n", + " max_num=1000,\n", + " nms_thr=0.7,\n", + " min_bbox_size=0),\n", + " rcnn=dict(\n", + " score_thr=0.05,\n", + " nms=dict(type='nms', iou_threshold=0.5),\n", + " max_per_img=100)))\n", + "dataset_type = 'KittiTinyDataset'\n", + "data_root = 'kitti_tiny/'\n", + "img_norm_cfg = dict(\n", + " mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)\n", + "train_pipeline = [\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='LoadAnnotations', with_bbox=True),\n", + " dict(\n", + " type='Resize',\n", + " img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),\n", + " (1333, 768), (1333, 800)],\n", + " multiscale_mode='value',\n", + " keep_ratio=True),\n", + " dict(type='RandomFlip', flip_ratio=0.5),\n", + " dict(\n", + " type='Normalize',\n", + " mean=[103.53, 116.28, 123.675],\n", + " std=[1.0, 1.0, 1.0],\n", + " to_rgb=False),\n", + " dict(type='Pad', size_divisor=32),\n", + " dict(type='DefaultFormatBundle'),\n", + " dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n", + "]\n", + "test_pipeline = [\n", + " dict(type='LoadImageFromFile'),\n", + " dict(\n", + " type='MultiScaleFlipAug',\n", + " img_scale=(1333, 800),\n", + " flip=False,\n", + " transforms=[\n", + " dict(type='Resize', keep_ratio=True),\n", + " dict(type='RandomFlip'),\n", + " dict(\n", + " type='Normalize',\n", + " mean=[103.53, 116.28, 123.675],\n", + " std=[1.0, 1.0, 1.0],\n", + " to_rgb=False),\n", + " dict(type='Pad', size_divisor=32),\n", + " dict(type='ImageToTensor', keys=['img']),\n", + " dict(type='Collect', keys=['img'])\n", + " ])\n", + "]\n", + "data = dict(\n", + " samples_per_gpu=2,\n", + " workers_per_gpu=2,\n", + " train=dict(\n", + " type='KittiTinyDataset',\n", + " ann_file='train.txt',\n", + " img_prefix='training/image_2',\n", + " pipeline=[\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='LoadAnnotations', with_bbox=True),\n", + " dict(\n", + " type='Resize',\n", + " img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),\n", + " (1333, 768), (1333, 800)],\n", + " multiscale_mode='value',\n", + " keep_ratio=True),\n", + " dict(type='RandomFlip', flip_ratio=0.5),\n", + " dict(\n", + " type='Normalize',\n", + " mean=[103.53, 116.28, 123.675],\n", + " std=[1.0, 1.0, 1.0],\n", + " to_rgb=False),\n", + " dict(type='Pad', size_divisor=32),\n", + " dict(type='DefaultFormatBundle'),\n", + " dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n", + " ],\n", + " data_root='kitti_tiny/'),\n", + " val=dict(\n", + " type='KittiTinyDataset',\n", + " ann_file='val.txt',\n", + " img_prefix='training/image_2',\n", + " pipeline=[\n", + " dict(type='LoadImageFromFile'),\n", + " dict(\n", + " type='MultiScaleFlipAug',\n", + " img_scale=(1333, 800),\n", + " flip=False,\n", + " transforms=[\n", + " dict(type='Resize', keep_ratio=True),\n", + " dict(type='RandomFlip'),\n", + " dict(\n", + " type='Normalize',\n", + " mean=[103.53, 116.28, 123.675],\n", + " std=[1.0, 1.0, 1.0],\n", + " to_rgb=False),\n", + " dict(type='Pad', size_divisor=32),\n", + " dict(type='ImageToTensor', keys=['img']),\n", + " dict(type='Collect', keys=['img'])\n", + " ])\n", + " ],\n", + " data_root='kitti_tiny/'),\n", + " test=dict(\n", + " type='KittiTinyDataset',\n", + " ann_file='train.txt',\n", + " img_prefix='training/image_2',\n", + " pipeline=[\n", + " dict(type='LoadImageFromFile'),\n", + " dict(\n", + " type='MultiScaleFlipAug',\n", + " img_scale=(1333, 800),\n", + " flip=False,\n", + " transforms=[\n", + " dict(type='Resize', keep_ratio=True),\n", + " dict(type='RandomFlip'),\n", + " dict(\n", + " type='Normalize',\n", + " mean=[103.53, 116.28, 123.675],\n", + " std=[1.0, 1.0, 1.0],\n", + " to_rgb=False),\n", + " dict(type='Pad', size_divisor=32),\n", + " dict(type='ImageToTensor', keys=['img']),\n", + " dict(type='Collect', keys=['img'])\n", + " ])\n", + " ],\n", + " data_root='kitti_tiny/'))\n", + "evaluation = dict(interval=12, metric='mAP')\n", + "optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001)\n", + "optimizer_config = dict(grad_clip=None)\n", + "lr_config = dict(\n", + " policy='step',\n", + " warmup=None,\n", + " warmup_iters=500,\n", + " warmup_ratio=0.001,\n", + " step=[8, 11])\n", + "runner = dict(type='EpochBasedRunner', max_epochs=12)\n", + "checkpoint_config = dict(interval=12)\n", + "log_config = dict(interval=10, hooks=[dict(type='TextLoggerHook')])\n", + "custom_hooks = [dict(type='NumClassCheckHook')]\n", + "dist_params = dict(backend='nccl')\n", + "log_level = 'INFO'\n", + "load_from = 'checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth'\n", + "resume_from = None\n", + "workflow = [('train', 1)]\n", + "work_dir = './tutorial_exps'\n", + "seed = 0\n", + "gpu_ids = range(0, 1)\n", + "\n" + ] + } + ], + "source": [ + "from mmdet.apis import set_random_seed\n", + "\n", + "# Modify dataset type and path\n", + "cfg.dataset_type = 'KittiTinyDataset'\n", + "cfg.data_root = 'kitti_tiny/'\n", + "\n", + "cfg.data.test.type = 'KittiTinyDataset'\n", + "cfg.data.test.data_root = 'kitti_tiny/'\n", + "cfg.data.test.ann_file = 'train.txt'\n", + "cfg.data.test.img_prefix = 'training/image_2'\n", + "\n", + "cfg.data.train.type = 'KittiTinyDataset'\n", + "cfg.data.train.data_root = 'kitti_tiny/'\n", + "cfg.data.train.ann_file = 'train.txt'\n", + "cfg.data.train.img_prefix = 'training/image_2'\n", + "\n", + "cfg.data.val.type = 'KittiTinyDataset'\n", + "cfg.data.val.data_root = 'kitti_tiny/'\n", + "cfg.data.val.ann_file = 'val.txt'\n", + "cfg.data.val.img_prefix = 'training/image_2'\n", + "\n", + "# modify num classes of the model in box head\n", + "cfg.model.roi_head.bbox_head.num_classes = 3\n", + "# We can still use the pre-trained Mask RCNN model though we do not need to\n", + "# use the mask branch\n", + "cfg.load_from = 'checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth'\n", + "\n", + "# Set up working dir to save files and logs.\n", + "cfg.work_dir = './tutorial_exps'\n", + "\n", + "# The original learning rate (LR) is set for 8-GPU training.\n", + "# We divide it by 8 since we only use one GPU.\n", + "cfg.optimizer.lr = 0.02 / 8\n", + "cfg.lr_config.warmup = None\n", + "cfg.log_config.interval = 10\n", + "\n", + "# Change the evaluation metric since we use customized dataset.\n", + "cfg.evaluation.metric = 'mAP'\n", + "# We can set the evaluation interval to reduce the evaluation times\n", + "cfg.evaluation.interval = 12\n", + "# We can set the checkpoint saving interval to reduce the storage cost\n", + "cfg.checkpoint_config.interval = 12\n", + "\n", + "# Set seed thus the results are more reproducible\n", + "cfg.seed = 0\n", + "set_random_seed(0, deterministic=False)\n", + "cfg.gpu_ids = range(1)\n", + "\n", + "\n", + "# We can initialize the logger for training and have a look\n", + "# at the final config used for training\n", + "print(f'Config:\\n{cfg.pretty_text}')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "111W_oZV_3wa" + }, + "source": [ + "### Train a new detector\n", + "\n", + "Finally, lets initialize the dataset and detector, then train a new detector!" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "c3018c8715924d2b83d817cc6c448a2d", + "aca1c388eeca4c87b5b6306302630303", + "b9b75e2d894e467289cb83070b8bb998", + "767c8f4fbc924027885851365ceb6292", + "1489fe29d91748cab449718d687f4ee1", + "bf1e5d0665a141ac9c2085062ba77801", + "171ea927699a474084c49f8874942ae8", + "7189ce8a6634410a9e633832e8151070" + ] + }, + "id": "7WBWHu010PN3", + "outputId": "fac18cba-16a3-491e-b3ae-1ab99d71c325" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/content/mmdetection/mmdet/datasets/custom.py:155: UserWarning: CustomDataset does not support filtering empty gt images.\n", + " 'CustomDataset does not support filtering empty gt images.')\n", + "2021-02-20 03:04:44,198 - mmdet - INFO - load model from: open-mmlab://detectron2/resnet50_caffe\n", + "Downloading: \"https://download.openmmlab.com/pretrain/third_party/resnet50_msra-5891d200.pth\" to /root/.cache/torch/checkpoints/resnet50_msra-5891d200.pth\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c3018c8715924d2b83d817cc6c448a2d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=94284731.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-02-20 03:04:57,872 - mmdet - WARNING - The model and loaded state dict do not match exactly\n", + "\n", + "unexpected key in source state_dict: conv1.bias\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-02-20 03:04:58,180 - mmdet - INFO - load checkpoint from checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth\n", + "2021-02-20 03:04:58,313 - mmdet - WARNING - The model and loaded state dict do not match exactly\n", + "\n", + "size mismatch for roi_head.bbox_head.fc_cls.weight: copying a param with shape torch.Size([81, 1024]) from checkpoint, the shape in current model is torch.Size([4, 1024]).\n", + "size mismatch for roi_head.bbox_head.fc_cls.bias: copying a param with shape torch.Size([81]) from checkpoint, the shape in current model is torch.Size([4]).\n", + "size mismatch for roi_head.bbox_head.fc_reg.weight: copying a param with shape torch.Size([320, 1024]) from checkpoint, the shape in current model is torch.Size([12, 1024]).\n", + "size mismatch for roi_head.bbox_head.fc_reg.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([12]).\n", + "unexpected key in source state_dict: roi_head.mask_head.convs.0.conv.weight, roi_head.mask_head.convs.0.conv.bias, roi_head.mask_head.convs.1.conv.weight, roi_head.mask_head.convs.1.conv.bias, roi_head.mask_head.convs.2.conv.weight, roi_head.mask_head.convs.2.conv.bias, roi_head.mask_head.convs.3.conv.weight, roi_head.mask_head.convs.3.conv.bias, roi_head.mask_head.upsample.weight, roi_head.mask_head.upsample.bias, roi_head.mask_head.conv_logits.weight, roi_head.mask_head.conv_logits.bias\n", + "\n", + "2021-02-20 03:04:58,316 - mmdet - INFO - Start running, host: root@f0e5be20007b, work_dir: /content/mmdetection/tutorial_exps\n", + "2021-02-20 03:04:58,317 - mmdet - INFO - workflow: [('train', 1)], max: 12 epochs\n", + "2021-02-20 03:05:03,791 - mmdet - INFO - Epoch [1][10/25]\tlr: 2.500e-03, eta: 0:02:34, time: 0.531, data_time: 0.222, memory: 2133, loss_rpn_cls: 0.0286, loss_rpn_bbox: 0.0177, loss_cls: 0.5962, acc: 80.5273, loss_bbox: 0.3859, loss: 1.0284\n", + "2021-02-20 03:05:06,998 - mmdet - INFO - Epoch [1][20/25]\tlr: 2.500e-03, eta: 0:01:59, time: 0.321, data_time: 0.021, memory: 2133, loss_rpn_cls: 0.0214, loss_rpn_bbox: 0.0122, loss_cls: 0.1736, acc: 94.0332, loss_bbox: 0.3017, loss: 0.5089\n", + "2021-02-20 03:05:13,968 - mmdet - INFO - Epoch [2][10/25]\tlr: 2.500e-03, eta: 0:01:44, time: 0.530, data_time: 0.221, memory: 2133, loss_rpn_cls: 0.0183, loss_rpn_bbox: 0.0148, loss_cls: 0.1515, acc: 94.8535, loss_bbox: 0.2882, loss: 0.4728\n", + "2021-02-20 03:05:17,195 - mmdet - INFO - Epoch [2][20/25]\tlr: 2.500e-03, eta: 0:01:36, time: 0.323, data_time: 0.021, memory: 2133, loss_rpn_cls: 0.0115, loss_rpn_bbox: 0.0129, loss_cls: 0.1297, acc: 95.3516, loss_bbox: 0.1971, loss: 0.3512\n", + "2021-02-20 03:05:24,202 - mmdet - INFO - Epoch [3][10/25]\tlr: 2.500e-03, eta: 0:01:29, time: 0.533, data_time: 0.221, memory: 2133, loss_rpn_cls: 0.0075, loss_rpn_bbox: 0.0107, loss_cls: 0.0982, acc: 96.3672, loss_bbox: 0.1558, loss: 0.2722\n", + "2021-02-20 03:05:27,479 - mmdet - INFO - Epoch [3][20/25]\tlr: 2.500e-03, eta: 0:01:24, time: 0.327, data_time: 0.021, memory: 2133, loss_rpn_cls: 0.0071, loss_rpn_bbox: 0.0145, loss_cls: 0.1456, acc: 94.5801, loss_bbox: 0.2525, loss: 0.4197\n", + "2021-02-20 03:05:34,565 - mmdet - INFO - Epoch [4][10/25]\tlr: 2.500e-03, eta: 0:01:18, time: 0.538, data_time: 0.222, memory: 2133, loss_rpn_cls: 0.0082, loss_rpn_bbox: 0.0143, loss_cls: 0.1099, acc: 95.8789, loss_bbox: 0.2154, loss: 0.3477\n", + "2021-02-20 03:05:37,889 - mmdet - INFO - Epoch [4][20/25]\tlr: 2.500e-03, eta: 0:01:14, time: 0.332, data_time: 0.021, memory: 2133, loss_rpn_cls: 0.0056, loss_rpn_bbox: 0.0124, loss_cls: 0.1216, acc: 95.4492, loss_bbox: 0.2074, loss: 0.3470\n", + "2021-02-20 03:05:45,023 - mmdet - INFO - Epoch [5][10/25]\tlr: 2.500e-03, eta: 0:01:08, time: 0.544, data_time: 0.221, memory: 2133, loss_rpn_cls: 0.0034, loss_rpn_bbox: 0.0104, loss_cls: 0.1065, acc: 95.8496, loss_bbox: 0.2072, loss: 0.3275\n", + "2021-02-20 03:05:48,367 - mmdet - INFO - Epoch [5][20/25]\tlr: 2.500e-03, eta: 0:01:04, time: 0.334, data_time: 0.021, memory: 2133, loss_rpn_cls: 0.0043, loss_rpn_bbox: 0.0109, loss_cls: 0.0918, acc: 96.7285, loss_bbox: 0.1882, loss: 0.2952\n", + "2021-02-20 03:05:55,575 - mmdet - INFO - Epoch [6][10/25]\tlr: 2.500e-03, eta: 0:00:59, time: 0.548, data_time: 0.222, memory: 2133, loss_rpn_cls: 0.0028, loss_rpn_bbox: 0.0085, loss_cls: 0.0843, acc: 97.1582, loss_bbox: 0.1765, loss: 0.2721\n", + "2021-02-20 03:05:58,963 - mmdet - INFO - Epoch [6][20/25]\tlr: 2.500e-03, eta: 0:00:55, time: 0.339, data_time: 0.022, memory: 2133, loss_rpn_cls: 0.0037, loss_rpn_bbox: 0.0105, loss_cls: 0.0833, acc: 96.8359, loss_bbox: 0.1700, loss: 0.2675\n", + "2021-02-20 03:06:06,144 - mmdet - INFO - Epoch [7][10/25]\tlr: 2.500e-03, eta: 0:00:50, time: 0.545, data_time: 0.221, memory: 2133, loss_rpn_cls: 0.0030, loss_rpn_bbox: 0.0095, loss_cls: 0.0806, acc: 96.9238, loss_bbox: 0.1642, loss: 0.2573\n", + "2021-02-20 03:06:09,550 - mmdet - INFO - Epoch [7][20/25]\tlr: 2.500e-03, eta: 0:00:46, time: 0.340, data_time: 0.022, memory: 2133, loss_rpn_cls: 0.0019, loss_rpn_bbox: 0.0115, loss_cls: 0.0867, acc: 96.6602, loss_bbox: 0.1727, loss: 0.2728\n", + "2021-02-20 03:06:16,846 - mmdet - INFO - Epoch [8][10/25]\tlr: 2.500e-03, eta: 0:00:41, time: 0.553, data_time: 0.223, memory: 2133, loss_rpn_cls: 0.0021, loss_rpn_bbox: 0.0087, loss_cls: 0.0701, acc: 96.9141, loss_bbox: 0.1364, loss: 0.2174\n", + "2021-02-20 03:06:20,318 - mmdet - INFO - Epoch [8][20/25]\tlr: 2.500e-03, eta: 0:00:37, time: 0.347, data_time: 0.022, memory: 2133, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0083, loss_cls: 0.0689, acc: 97.3926, loss_bbox: 0.1634, loss: 0.2414\n", + "2021-02-20 03:06:27,654 - mmdet - INFO - Epoch [9][10/25]\tlr: 2.500e-04, eta: 0:00:32, time: 0.555, data_time: 0.221, memory: 2133, loss_rpn_cls: 0.0034, loss_rpn_bbox: 0.0080, loss_cls: 0.0632, acc: 97.5488, loss_bbox: 0.1285, loss: 0.2031\n", + "2021-02-20 03:06:31,136 - mmdet - INFO - Epoch [9][20/25]\tlr: 2.500e-04, eta: 0:00:28, time: 0.348, data_time: 0.022, memory: 2133, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0065, loss_cls: 0.0539, acc: 97.9004, loss_bbox: 0.1013, loss: 0.1625\n", + "2021-02-20 03:06:38,476 - mmdet - INFO - Epoch [10][10/25]\tlr: 2.500e-04, eta: 0:00:23, time: 0.554, data_time: 0.221, memory: 2133, loss_rpn_cls: 0.0026, loss_rpn_bbox: 0.0082, loss_cls: 0.0621, acc: 97.6172, loss_bbox: 0.1304, loss: 0.2033\n", + "2021-02-20 03:06:41,997 - mmdet - INFO - Epoch [10][20/25]\tlr: 2.500e-04, eta: 0:00:19, time: 0.352, data_time: 0.022, memory: 2133, loss_rpn_cls: 0.0011, loss_rpn_bbox: 0.0059, loss_cls: 0.0596, acc: 97.8223, loss_bbox: 0.1199, loss: 0.1866\n", + "2021-02-20 03:06:49,368 - mmdet - INFO - Epoch [11][10/25]\tlr: 2.500e-04, eta: 0:00:14, time: 0.557, data_time: 0.221, memory: 2133, loss_rpn_cls: 0.0036, loss_rpn_bbox: 0.0064, loss_cls: 0.0631, acc: 97.5000, loss_bbox: 0.1242, loss: 0.1973\n", + "2021-02-20 03:06:52,881 - mmdet - INFO - Epoch [11][20/25]\tlr: 2.500e-04, eta: 0:00:10, time: 0.351, data_time: 0.022, memory: 2133, loss_rpn_cls: 0.0016, loss_rpn_bbox: 0.0072, loss_cls: 0.0570, acc: 97.9199, loss_bbox: 0.1263, loss: 0.1921\n", + "2021-02-20 03:07:00,207 - mmdet - INFO - Epoch [12][10/25]\tlr: 2.500e-05, eta: 0:00:05, time: 0.554, data_time: 0.222, memory: 2134, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0063, loss_cls: 0.0606, acc: 97.6953, loss_bbox: 0.1232, loss: 0.1910\n", + "2021-02-20 03:07:03,655 - mmdet - INFO - Epoch [12][20/25]\tlr: 2.500e-05, eta: 0:00:01, time: 0.345, data_time: 0.022, memory: 2134, loss_rpn_cls: 0.0010, loss_rpn_bbox: 0.0056, loss_cls: 0.0486, acc: 98.1641, loss_bbox: 0.0882, loss: 0.1433\n", + "2021-02-20 03:07:05,260 - mmdet - INFO - Saving checkpoint at 12 epochs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 25/25, 11.4 task/s, elapsed: 2s, ETA: 0s\n", + "---------------iou_thr: 0.5---------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-02-20 03:07:08,400 - mmdet - INFO - \n", + "+------------+-----+------+--------+-------+\n", + "| class | gts | dets | recall | ap |\n", + "+------------+-----+------+--------+-------+\n", + "| Car | 62 | 131 | 0.968 | 0.879 |\n", + "| Pedestrian | 13 | 58 | 0.846 | 0.747 |\n", + "| Cyclist | 7 | 67 | 0.429 | 0.037 |\n", + "+------------+-----+------+--------+-------+\n", + "| mAP | | | | 0.555 |\n", + "+------------+-----+------+--------+-------+\n", + "2021-02-20 03:07:08,403 - mmdet - INFO - Epoch(val) [12][25]\tAP50: 0.5550, mAP: 0.5545\n" + ] + } + ], + "source": [ + "from mmdet.datasets import build_dataset\n", + "from mmdet.models import build_detector\n", + "from mmdet.apis import train_detector\n", + "\n", + "\n", + "# Build dataset\n", + "datasets = [build_dataset(cfg.data.train)]\n", + "\n", + "# Build the detector\n", + "model = build_detector(\n", + " cfg.model, train_cfg=cfg.get('train_cfg'), test_cfg=cfg.get('test_cfg'))\n", + "# Add an attribute for visualization convenience\n", + "model.CLASSES = datasets[0].CLASSES\n", + "\n", + "# Create work_dir\n", + "mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))\n", + "train_detector(model, datasets, cfg, distributed=False, validate=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_vYQF5K2NqqI" + }, + "source": [ + "### Understand the log\n", + "From the log, we can have a basic understanding the training process and know how well the detector is trained.\n", + "\n", + "Firstly, the ResNet-50 backbone pre-trained on ImageNet is loaded, this is a common practice since training from scratch is more cost. The log shows that all the weights of the ResNet-50 backbone are loaded except the `conv1.bias`, which has been merged into `conv.weights`.\n", + "\n", + "Second, since the dataset we are using is small, we loaded a Mask R-CNN model and finetune it for detection. Because the detector we actually using is Faster R-CNN, the weights in mask branch, e.g. `roi_head.mask_head`, are `unexpected key in source state_dict` and not loaded.\n", + "The original Mask R-CNN is trained on COCO dataset which contains 80 classes but KITTI Tiny dataset only have 3 classes. Therefore, the last FC layer of the pre-trained Mask R-CNN for classification has different weight shape and is not used.\n", + "\n", + "Third, after training, the detector is evaluated by the default VOC-style evaluation. The results show that the detector achieves 54.1 mAP on the val dataset,\n", + " not bad!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MfQ-yspZLuuI" + }, + "source": [ + "## Test the trained detector\n", + "\n", + "After finetuning the detector, let's visualize the prediction results!" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 578 + }, + "id": "_MuZurfGLq0p", + "outputId": "b4a77811-d159-4213-d8cb-b73f5b5b6d1c" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/content/mmdetection/mmdet/datasets/utils.py:66: UserWarning: \"ImageToTensor\" pipeline is replaced by \"DefaultFormatBundle\" for batch inference. It is recommended to manually replace it in the test data pipeline in your config file.\n", + " 'data pipeline in your config file.', UserWarning)\n", + "/content/mmdetection/mmdet/apis/inference.py:205: UserWarning: \"block\" will be deprecated in v2.9.0,Please use \"wait_time\"\n", + " warnings.warn('\"block\" will be deprecated in v2.9.0,'\n", + "/content/mmdetection/mmdet/apis/inference.py:207: UserWarning: \"fig_size\" are deprecated and takes no effect.\n", + " warnings.warn('\"fig_size\" are deprecated and takes no effect.')\n", + "/content/mmdetection/mmdet/core/visualization/image.py:75: UserWarning: \"font_scale\" will be deprecated in v2.9.0,Please use \"font_size\"\n", + " warnings.warn('\"font_scale\" will be deprecated in v2.9.0,'\n" + ] + }, + { + "data": { + "image/png": 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imageio +except ImportError: + imageio = None + + +def parse_args(): + parser = argparse.ArgumentParser(description='Create GIF for demo') + parser.add_argument( + 'image_dir', + help='directory where result ' + 'images save path generated by ‘analyze_results.py’') + parser.add_argument( + '--out', + type=str, + default='result.gif', + help='gif path where will be saved') + args = parser.parse_args() + return args + + +def _generate_batch_data(sampler, batch_size): + batch = [] + for idx in sampler: + batch.append(idx) + if len(batch) == batch_size: + yield batch + batch = [] + if len(batch) > 0: + yield batch + + +def create_gif(frames, gif_name, duration=2): + """Create gif through imageio. + + Args: + frames (list[ndarray]): Image frames + gif_name (str): Saved gif name + duration (int): Display interval (s), + Default: 2 + """ + if imageio is None: + raise RuntimeError('imageio is not installed,' + 'Please use “pip install imageio” to install') + imageio.mimsave(gif_name, frames, 'GIF', duration=duration) + + +def create_frame_by_matplotlib(image_dir, + nrows=1, + fig_size=(300, 300), + font_size=15): + """Create gif frame image through matplotlib. + + Args: + image_dir (str): Root directory of result images + nrows (int): Number of rows displayed, Default: 1 + fig_size (tuple): Figure size of the pyplot figure. + Default: (300, 300) + font_size (int): Font size of texts. Default: 15 + + Returns: + list[ndarray]: image frames + """ + + result_dir_names = os.listdir(image_dir) + assert len(result_dir_names) == 2 + # Longer length has higher priority + result_dir_names.reverse() + + images_list = [] + for dir_names in result_dir_names: + images_list.append(mmcv.scandir(osp.join(image_dir, dir_names))) + + frames = [] + for paths in _generate_batch_data(zip(*images_list), nrows): + + fig, axes = plt.subplots(nrows=nrows, ncols=2) + fig.suptitle('Good/bad case selected according ' + 'to the COCO mAP of the single image') + + det_patch = mpatches.Patch(color='salmon', label='prediction') + gt_patch = mpatches.Patch(color='royalblue', label='ground truth') + # bbox_to_anchor may need to be finetuned + plt.legend( + handles=[det_patch, gt_patch], + bbox_to_anchor=(1, -0.18), + loc='lower right', + borderaxespad=0.) + + if nrows == 1: + axes = [axes] + + dpi = fig.get_dpi() + # set fig size and margin + fig.set_size_inches( + (fig_size[0] * 2 + fig_size[0] // 20) / dpi, + (fig_size[1] * nrows + fig_size[1] // 3) / dpi, + ) + + fig.tight_layout() + # set subplot margin + plt.subplots_adjust( + hspace=.05, + wspace=0.05, + left=0.02, + right=0.98, + bottom=0.02, + top=0.98) + + for i, (path_tuple, ax_tuple) in enumerate(zip(paths, axes)): + image_path_left = osp.join( + osp.join(image_dir, result_dir_names[0], path_tuple[0])) + image_path_right = osp.join( + osp.join(image_dir, result_dir_names[1], path_tuple[1])) + image_left = mmcv.imread(image_path_left) + image_left = mmcv.rgb2bgr(image_left) + image_right = mmcv.imread(image_path_right) + image_right = mmcv.rgb2bgr(image_right) + + if i == 0: + ax_tuple[0].set_title( + result_dir_names[0], fontdict={'size': font_size}) + ax_tuple[1].set_title( + result_dir_names[1], fontdict={'size': font_size}) + ax_tuple[0].imshow( + image_left, extent=(0, *fig_size, 0), interpolation='bilinear') + ax_tuple[0].axis('off') + ax_tuple[1].imshow( + image_right, + extent=(0, *fig_size, 0), + interpolation='bilinear') + ax_tuple[1].axis('off') + + canvas = fig.canvas + s, (width, height) = canvas.print_to_buffer() + buffer = np.frombuffer(s, dtype='uint8') + img_rgba = buffer.reshape(height, width, 4) + rgb, alpha = np.split(img_rgba, [3], axis=2) + img = rgb.astype('uint8') + + frames.append(img) + + return frames + + +def main(): + args = parse_args() + frames = create_frame_by_matplotlib(args.image_dir) + create_gif(frames, args.out) + + +if __name__ == '__main__': + main() diff --git a/demo/image_demo.py b/demo/image_demo.py new file mode 100644 index 0000000..95de4fd --- /dev/null +++ b/demo/image_demo.py @@ -0,0 +1,49 @@ +import asyncio +from argparse import ArgumentParser + +from mmdet.apis import (async_inference_detector, inference_detector, + init_detector, show_result_pyplot) + + +def parse_args(): + parser = ArgumentParser() + parser.add_argument('img', help='Image file') + parser.add_argument('config', help='Config file') + parser.add_argument('checkpoint', help='Checkpoint file') + parser.add_argument( + '--device', default='cuda:0', help='Device used for inference') + parser.add_argument( + '--score-thr', type=float, default=0.3, help='bbox score threshold') + parser.add_argument( + '--async-test', + action='store_true', + help='whether to set async options for async inference.') + args = parser.parse_args() + return args + + +def main(args): + # build the model from a config file and a checkpoint file + model = init_detector(args.config, args.checkpoint, device=args.device) + # test a single image + result = inference_detector(model, args.img) + # show the results + show_result_pyplot(model, args.img, result, score_thr=args.score_thr) + + +async def async_main(args): + # build the model from a config file and a checkpoint file + model = init_detector(args.config, args.checkpoint, device=args.device) + # test a single image + tasks = asyncio.create_task(async_inference_detector(model, args.img)) + result = await asyncio.gather(tasks) + # show the results + show_result_pyplot(model, args.img, result[0], score_thr=args.score_thr) + + +if __name__ == '__main__': + args = parse_args() + if args.async_test: + asyncio.run(async_main(args)) + else: + main(args) diff --git a/demo/inference_demo.ipynb b/demo/inference_demo.ipynb new file mode 100644 index 0000000..4dab8c2 --- /dev/null +++ b/demo/inference_demo.ipynb @@ -0,0 +1,100 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from mmdet.apis import init_detector, inference_detector, show_result_pyplot\n", + "import mmcv" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "config_file = '../configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'\n", + "# download the checkpoint from model zoo and put it in `checkpoints/`\n", + "# url: http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth\n", + "checkpoint_file = '../checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# build the model from a config file and a checkpoint file\n", + "model = init_detector(config_file, checkpoint_file, device='cuda:0')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# test a single image\n", + "img = 'demo.jpg'\n", + "result = inference_detector(model, img)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# show the results\n", + "show_result_pyplot(model, img, result)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "metadata": { + "collapsed": false + }, + "source": [] + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/demo/video_demo.py b/demo/video_demo.py new file mode 100644 index 0000000..661130b --- /dev/null +++ b/demo/video_demo.py @@ -0,0 +1,60 @@ +import argparse + +import cv2 +import mmcv + +from mmdet.apis import inference_detector, init_detector + + +def parse_args(): + parser = argparse.ArgumentParser(description='MMDetection video demo') + parser.add_argument('video', help='Video file') + parser.add_argument('config', help='Config file') + parser.add_argument('checkpoint', help='Checkpoint file') + parser.add_argument( + '--device', default='cuda:0', help='Device used for inference') + parser.add_argument( + '--score-thr', type=float, default=0.3, help='Bbox score threshold') + parser.add_argument('--out', type=str, help='Output video file') + parser.add_argument('--show', action='store_true', help='Show video') + parser.add_argument( + '--wait-time', + type=float, + default=1, + help='The interval of show (s), 0 is block') + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + assert args.out or args.show, \ + ('Please specify at least one operation (save/show the ' + 'video) with the argument "--out" or "--show"') + + model = init_detector(args.config, args.checkpoint, device=args.device) + + video_reader = mmcv.VideoReader(args.video) + video_writer = None + if args.out: + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + video_writer = cv2.VideoWriter( + args.out, fourcc, video_reader.fps, + (video_reader.width, video_reader.height)) + + for frame in mmcv.track_iter_progress(video_reader): + result = inference_detector(model, frame) + frame = model.show_result(frame, result, score_thr=args.score_thr) + if args.show: + cv2.namedWindow('video', 0) + mmcv.imshow(frame, 'video', args.wait_time) + if args.out: + video_writer.write(frame) + + if video_writer: + video_writer.release() + cv2.destroyAllWindows() + + +if __name__ == '__main__': + main() diff --git a/demo/webcam_demo.py b/demo/webcam_demo.py new file mode 100644 index 0000000..5bded14 --- /dev/null +++ b/demo/webcam_demo.py @@ -0,0 +1,46 @@ +import argparse + +import cv2 +import torch + +from mmdet.apis import inference_detector, init_detector + + +def parse_args(): + parser = argparse.ArgumentParser(description='MMDetection webcam demo') + parser.add_argument('config', help='test config file path') + parser.add_argument('checkpoint', help='checkpoint file') + parser.add_argument( + '--device', type=str, default='cuda:0', help='CPU/CUDA device option') + parser.add_argument( + '--camera-id', type=int, default=0, help='camera device id') + parser.add_argument( + '--score-thr', type=float, default=0.5, help='bbox score threshold') + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + + device = torch.device(args.device) + + model = init_detector(args.config, args.checkpoint, device=device) + + camera = cv2.VideoCapture(args.camera_id) + + print('Press "Esc", "q" or "Q" to exit.') + while True: + ret_val, img = camera.read() + result = inference_detector(model, img) + + ch = cv2.waitKey(1) + if ch == 27 or ch == ord('q') or ch == ord('Q'): + break + + model.show_result( + img, result, score_thr=args.score_thr, wait_time=1, show=True) + + +if __name__ == '__main__': + main() diff --git a/docker/Dockerfile b/docker/Dockerfile new file mode 100644 index 0000000..81e458f --- /dev/null +++ b/docker/Dockerfile @@ -0,0 +1,24 @@ +ARG PYTORCH="1.6.0" +ARG CUDA="10.1" +ARG CUDNN="7" + +FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel + +ENV TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0+PTX" +ENV TORCH_NVCC_FLAGS="-Xfatbin -compress-all" +ENV CMAKE_PREFIX_PATH="$(dirname $(which conda))/../" + +RUN apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + +# Install MMCV +RUN pip install mmcv-full==latest+torch1.6.0+cu101 -f https://openmmlab.oss-accelerate.aliyuncs.com/mmcv/dist/index.html + +# Install MMDetection +RUN conda clean --all +RUN git clone https://github.com/open-mmlab/mmdetection.git /mmdetection +WORKDIR /mmdetection +ENV FORCE_CUDA="1" +RUN pip install -r requirements/build.txt +RUN pip install --no-cache-dir -e . diff --git a/docker/serve/Dockerfile b/docker/serve/Dockerfile new file mode 100644 index 0000000..9d356c5 --- /dev/null +++ b/docker/serve/Dockerfile @@ -0,0 +1,47 @@ +ARG PYTORCH="1.6.0" +ARG CUDA="10.1" +ARG CUDNN="7" +FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel + +ARG MMCV="1.2.7" +ARG MMDET="2.12.0" + +ENV PYTHONUNBUFFERED TRUE + +RUN apt-get update && \ + DEBIAN_FRONTEND=noninteractive apt-get install --no-install-recommends -y \ + ca-certificates \ + g++ \ + openjdk-11-jre-headless \ + # MMDet Requirements + ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 \ + && rm -rf /var/lib/apt/lists/* + +ENV PATH="/opt/conda/bin:$PATH" +RUN export FORCE_CUDA=1 + +# TORCHSEVER +RUN pip install torchserve torch-model-archiver + +# MMLAB +RUN pip install mmcv-full==${MMCV} -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html +RUN pip install mmdet==${MMDET} + +RUN useradd -m model-server \ + && mkdir -p /home/model-server/tmp + +COPY entrypoint.sh /usr/local/bin/entrypoint.sh + +RUN chmod +x /usr/local/bin/entrypoint.sh \ + && chown -R model-server /home/model-server + +COPY config.properties /home/model-server/config.properties +RUN mkdir /home/model-server/model-store && chown -R model-server /home/model-server/model-store + +EXPOSE 8080 8081 8082 + +USER model-server +WORKDIR /home/model-server +ENV TEMP=/home/model-server/tmp +ENTRYPOINT ["/usr/local/bin/entrypoint.sh"] +CMD ["serve"] diff --git a/docker/serve/config.properties b/docker/serve/config.properties new file mode 100644 index 0000000..efb9c47 --- /dev/null +++ b/docker/serve/config.properties @@ -0,0 +1,5 @@ +inference_address=http://0.0.0.0:8080 +management_address=http://0.0.0.0:8081 +metrics_address=http://0.0.0.0:8082 +model_store=/home/model-server/model-store +load_models=all diff --git a/docker/serve/entrypoint.sh b/docker/serve/entrypoint.sh new file mode 100644 index 0000000..41ba00b --- /dev/null +++ b/docker/serve/entrypoint.sh @@ -0,0 +1,12 @@ +#!/bin/bash +set -e + +if [[ "$1" = "serve" ]]; then + shift 1 + torchserve --start --ts-config /home/model-server/config.properties +else + eval "$@" +fi + +# prevent docker exit +tail -f /dev/null diff --git a/docs/1_exist_data_model.md b/docs/1_exist_data_model.md new file mode 100644 index 0000000..652dedc --- /dev/null +++ b/docs/1_exist_data_model.md @@ -0,0 +1,569 @@ +# 1: Inference and train with existing models and standard datasets + +MMDetection provides hundreds of existing and existing detection models in [Model Zoo](https://mmdetection.readthedocs.io/en/latest/model_zoo.html)), and supports multiple standard datasets, including Pascal VOC, COCO, CityScapes, LVIS, etc. This note will show how to perform common tasks on these existing models and standard datasets, including: + +- Use existing models to inference on given images. +- Test existing models on standard datasets. +- Train predefined models on standard datasets. + +## Inference with existing models + +By inference, we mean using trained models to detect objects on images. In MMDetection, a model is defined by a configuration file and existing model parameters are save in a checkpoint file. + +To start with, we recommend [Faster RCNN](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn) with this [configuration file](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) and this [checkpoint file](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth). It is recommended to download the checkpoint file to `checkpoints` directory. + +### High-level APIs for inference + +MMDetection provide high-level Python APIs for inference on images. Here is an example of building the model and inference on given images or videos. + +```python +from mmdet.apis import init_detector, inference_detector +import mmcv + +# Specify the path to model config and checkpoint file +config_file = 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth' + +# build the model from a config file and a checkpoint file +model = init_detector(config_file, checkpoint_file, device='cuda:0') + +# test a single image and show the results +img = 'test.jpg' # or img = mmcv.imread(img), which will only load it once +result = inference_detector(model, img) +# visualize the results in a new window +model.show_result(img, result) +# or save the visualization results to image files +model.show_result(img, result, out_file='result.jpg') + +# test a video and show the results +video = mmcv.VideoReader('video.mp4') +for frame in video: + result = inference_detector(model, frame) + model.show_result(frame, result, wait_time=1) +``` + +A notebook demo can be found in [demo/inference_demo.ipynb](https://github.com/open-mmlab/mmdetection/blob/master/demo/inference_demo.ipynb). + +Note: `inference_detector` only supports single-image inference for now. + +### Asynchronous interface - supported for Python 3.7+ + +For Python 3.7+, MMDetection also supports async interfaces. +By utilizing CUDA streams, it allows not to block CPU on GPU bound inference code and enables better CPU/GPU utilization for single-threaded application. Inference can be done concurrently either between different input data samples or between different models of some inference pipeline. + +See `tests/async_benchmark.py` to compare the speed of synchronous and asynchronous interfaces. + +```python +import asyncio +import torch +from mmdet.apis import init_detector, async_inference_detector +from mmdet.utils.contextmanagers import concurrent + +async def main(): + config_file = 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' + checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth' + device = 'cuda:0' + model = init_detector(config_file, checkpoint=checkpoint_file, device=device) + + # queue is used for concurrent inference of multiple images + streamqueue = asyncio.Queue() + # queue size defines concurrency level + streamqueue_size = 3 + + for _ in range(streamqueue_size): + streamqueue.put_nowait(torch.cuda.Stream(device=device)) + + # test a single image and show the results + img = 'test.jpg' # or img = mmcv.imread(img), which will only load it once + + async with concurrent(streamqueue): + result = await async_inference_detector(model, img) + + # visualize the results in a new window + model.show_result(img, result) + # or save the visualization results to image files + model.show_result(img, result, out_file='result.jpg') + + +asyncio.run(main()) + +``` + +### Demos + +We also provide three demo scripts, implemented with high-level APIs and supporting functionality codes. +Source codes are available [here](https://github.com/open-mmlab/mmdetection/tree/master/demo). + +#### Image demo + +This script performs inference on a single image. + +```shell +python demo/image_demo.py \ + ${IMAGE_FILE} \ + ${CONFIG_FILE} \ + ${CHECKPOINT_FILE} \ + [--device ${GPU_ID}] \ + [--score-thr ${SCORE_THR}] +``` + +Examples: + +```shell +python demo/image_demo.py demo/demo.jpg \ + configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \ + checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \ + --device cpu +``` + +#### Webcam demo + +This is a live demo from a webcam. + +```shell +python demo/webcam_demo.py \ + ${CONFIG_FILE} \ + ${CHECKPOINT_FILE} \ + [--device ${GPU_ID}] \ + [--camera-id ${CAMERA-ID}] \ + [--score-thr ${SCORE_THR}] +``` + +Examples: + +```shell +python demo/webcam_demo.py \ + configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \ + checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth +``` + +#### Video demo + +This script performs inference on a video. + +```shell +python demo/video_demo.py \ + ${VIDEO_FILE} \ + ${CONFIG_FILE} \ + ${CHECKPOINT_FILE} \ + [--device ${GPU_ID}] \ + [--score-thr ${SCORE_THR}] \ + [--out ${OUT_FILE}] \ + [--show] \ + [--wait-time ${WAIT_TIME}] +``` + +Examples: + +```shell +python demo/video_demo.py demo/demo.mp4 \ + configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \ + checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \ + --out result.mp4 +``` + +## Test existing models on standard datasets + +To evaluate a model's accuracy, one usually tests the model on some standard datasets. +MMDetection supports multiple public datasets including COCO, Pascal VOC, CityScapes, and [more](https://github.com/open-mmlab/mmdetection/tree/master/configs/_base_/datasets). +This section will show how to test existing models on supported datasets. + +### Prepare datasets + +Public datasets like [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/index.html) or mirror and [COCO](https://cocodataset.org/#download) are available from official websites or mirrors. Note: In the detection task, Pascal VOC 2012 is an extension of Pascal VOC 2007 without overlap, and we usually use them together. +It is recommended to download and extract the dataset somewhere outside the project directory and symlink the dataset root to `$MMDETECTION/data` as below. +If your folder structure is different, you may need to change the corresponding paths in config files. + +```plain +mmdetection +├── mmdet +├── tools +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ ├── train2017 +│ │ ├── val2017 +│ │ ├── test2017 +│ ├── cityscapes +│ │ ├── annotations +│ │ ├── leftImg8bit +│ │ │ ├── train +│ │ │ ├── val +│ │ ├── gtFine +│ │ │ ├── train +│ │ │ ├── val +│ ├── VOCdevkit +│ │ ├── VOC2007 +│ │ ├── VOC2012 +``` + +Some models require additional [COCO-stuff](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip) datasets, such as HTC, DetectoRS and SCNet, you can download and unzip then move to the coco folder. The directory should be like this. + +```plain +mmdetection +├── data +│ ├── coco +│ │ ├── annotations +│ │ ├── train2017 +│ │ ├── val2017 +│ │ ├── test2017 +│ │ ├── stuffthingmaps +``` + +The [cityscapes](https://www.cityscapes-dataset.com/) annotations need to be converted into the coco format using `tools/dataset_converters/cityscapes.py`: + +```shell +pip install cityscapesscripts + +python tools/dataset_converters/cityscapes.py \ + ./data/cityscapes \ + --nproc 8 \ + --out-dir ./data/cityscapes/annotations +``` + +TODO: CHANGE TO THE NEW PATH + +### Test existing models + +We provide testing scripts for evaluating an existing model on the whole dataset (COCO, PASCAL VOC, Cityscapes, etc.). +The following testing environments are supported: + +- single GPU +- single node multiple GPUs +- multiple nodes + +Choose the proper script to perform testing depending on the testing environment. + +```shell +# single-gpu testing +python tools/test.py \ + ${CONFIG_FILE} \ + ${CHECKPOINT_FILE} \ + [--out ${RESULT_FILE}] \ + [--eval ${EVAL_METRICS}] \ + [--show] + +# multi-gpu testing +bash tools/dist_test.sh \ + ${CONFIG_FILE} \ + ${CHECKPOINT_FILE} \ + ${GPU_NUM} \ + [--out ${RESULT_FILE}] \ + [--eval ${EVAL_METRICS}] +``` + +`tools/dist_test.sh` also supports multi-node testing, but relies on PyTorch's [launch utility](https://pytorch.org/docs/stable/distributed.html#launch-utility). + +Optional arguments: + +- `RESULT_FILE`: Filename of the output results in pickle format. If not specified, the results will not be saved to a file. +- `EVAL_METRICS`: Items to be evaluated on the results. Allowed values depend on the dataset, e.g., `proposal_fast`, `proposal`, `bbox`, `segm` are available for COCO, `mAP`, `recall` for PASCAL VOC. Cityscapes could be evaluated by `cityscapes` as well as all COCO metrics. +- `--show`: If specified, detection results will be plotted on the images and shown in a new window. It is only applicable to single GPU testing and used for debugging and visualization. Please make sure that GUI is available in your environment. Otherwise, you may encounter an error like `cannot connect to X server`. +- `--show-dir`: If specified, detection results will be plotted on the images and saved to the specified directory. It is only applicable to single GPU testing and used for debugging and visualization. You do NOT need a GUI available in your environment for using this option. +- `--show-score-thr`: If specified, detections with scores below this threshold will be removed. +- `--cfg-options`: if specified, the key-value pair optional cfg will be merged into config file +- `--eval-options`: if specified, the key-value pair optional eval cfg will be kwargs for dataset.evaluate() function, it's only for evaluation + +### Examples + +Assume that you have already downloaded the checkpoints to the directory `checkpoints/`. + +1. Test Faster R-CNN and visualize the results. Press any key for the next image. + Config and checkpoint files are available [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn). + + ```shell + python tools/test.py \ + configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \ + checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \ + --show + ``` + +2. Test Faster R-CNN and save the painted images for future visualization. + Config and checkpoint files are available [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn). + + ```shell + python tools/test.py \ + configs/faster_rcnn/faster_rcnn_r50_fpn_1x.py \ + checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \ + --show-dir faster_rcnn_r50_fpn_1x_results + ``` + +3. Test Faster R-CNN on PASCAL VOC (without saving the test results) and evaluate the mAP. + Config and checkpoint files are available [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc). + + ```shell + python tools/test.py \ + configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc.py \ + checkpoints/faster_rcnn_r50_fpn_1x_voc0712_20200624-c9895d40.pth \ + --eval mAP + ``` + +4. Test Mask R-CNN with 8 GPUs, and evaluate the bbox and mask AP. + Config and checkpoint files are available [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn). + + ```shell + ./tools/dist_test.sh \ + configs/mask_rcnn_r50_fpn_1x_coco.py \ + checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \ + 8 \ + --out results.pkl \ + --eval bbox segm + ``` + +5. Test Mask R-CNN with 8 GPUs, and evaluate the **classwise** bbox and mask AP. + Config and checkpoint files are available [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn). + + ```shell + ./tools/dist_test.sh \ + configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py \ + checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \ + 8 \ + --out results.pkl \ + --eval bbox segm \ + --options "classwise=True" + ``` + +6. Test Mask R-CNN on COCO test-dev with 8 GPUs, and generate JSON files for submitting to the official evaluation server. + Config and checkpoint files are available [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn). + + ```shell + ./tools/dist_test.sh \ + configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py \ + checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \ + 8 \ + --format-only \ + --options "jsonfile_prefix=./mask_rcnn_test-dev_results" + ``` + + This command generates two JSON files `mask_rcnn_test-dev_results.bbox.json` and `mask_rcnn_test-dev_results.segm.json`. + +7. Test Mask R-CNN on Cityscapes test with 8 GPUs, and generate txt and png files for submitting to the official evaluation server. + Config and checkpoint files are available [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/cityscapes). + + ```shell + ./tools/dist_test.sh \ + configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py \ + checkpoints/mask_rcnn_r50_fpn_1x_cityscapes_20200227-afe51d5a.pth \ + 8 \ + --format-only \ + --options "txtfile_prefix=./mask_rcnn_cityscapes_test_results" + ``` + + The generated png and txt would be under `./mask_rcnn_cityscapes_test_results` directory. + +### Test without Ground Truth Annotations + +MMDetection supports to test models without ground-truth annotations using `CocoDataset`. If your dataset format is not in COCO format, please convert them to COCO format. For example, if your dataset format is VOC, you can directly convert it to COCO format by the [script in tools.](https://github.com/open-mmlab/mmdetection/tree/master/tools/dataset_converters/pascal_voc.py) + +```shell +# single-gpu testing +python tools/test.py \ + ${CONFIG_FILE} \ + ${CHECKPOINT_FILE} \ + --format-only \ + --options ${JSONFILE_PREFIX} \ + [--show] + +# multi-gpu testing +bash tools/dist_test.sh \ + ${CONFIG_FILE} \ + ${CHECKPOINT_FILE} \ + ${GPU_NUM} \ + --format-only \ + --options ${JSONFILE_PREFIX} \ + [--show] +``` + +Assuming that the checkpoints in the [model zoo](https://mmdetection.readthedocs.io/en/latest/modelzoo_statistics.html) have been downloaded to the directory `checkpoints/`, we can test Mask R-CNN on COCO test-dev with 8 GPUs, and generate JSON files using the following command. + +```sh +./tools/dist_test.sh \ + configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py \ + checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \ + 8 \ + -format-only \ + --options "jsonfile_prefix=./mask_rcnn_test-dev_results" +``` + +This command generates two JSON files `mask_rcnn_test-dev_results.bbox.json` and `mask_rcnn_test-dev_results.segm.json`. + +### Batch Inference + +MMDetection supports inference with a single image or batched images in test mode. By default, we use single-image inference and you can use batch inference by modifying `samples_per_gpu` in the config of test data. You can do that either by modifying the config as below. + +```shell +data = dict(train=dict(...), val=dict(...), test=dict(samples_per_gpu=2, ...)) +``` + +Or you can set it through `--cfg-options` as `--cfg-options data.test.samples_per_gpu=2` + +### Deprecated ImageToTensor + +In test mode, `ImageToTensor` pipeline is deprecated, it's replaced by `DefaultFormatBundle` that recommended to manually replace it in the test data pipeline in your config file. examples: + +```python +# use ImageToTensor (deprecated) +pipelines = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', mean=[0, 0, 0], std=[1, 1, 1]), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) + ] + +# manually replace ImageToTensor to DefaultFormatBundle (recommended) +pipelines = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', mean=[0, 0, 0], std=[1, 1, 1]), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img']), + ]) + ] +``` + +## Train predefined models on standard datasets + +MMDetection also provides out-of-the-box tools for training detection models. +This section will show how to train _predefined_ models (under [configs](https://github.com/open-mmlab/mmdetection/tree/master/configs)) on standard datasets i.e. COCO. + +**Important**: The default learning rate in config files is for 8 GPUs and 2 img/gpu (batch size = 8\*2 = 16). +According to the [linear scaling rule](https://arxiv.org/abs/1706.02677), you need to set the learning rate proportional to the batch size if you use different GPUs or images per GPU, e.g., `lr=0.01` for 4 GPUs \* 2 imgs/gpu and `lr=0.08` for 16 GPUs \* 4 imgs/gpu. + +### Prepare datasets + +Training requires preparing datasets too. See section [Prepare datasets](#prepare-datasets) above for details. + +**Note**: +Currently, the config files under `configs/cityscapes` use COCO pretrained weights to initialize. +You could download the existing models in advance if the network connection is unavailable or slow. Otherwise, it would cause errors at the beginning of training. + +### Training on a single GPU + +We provide `tools/train.py` to launch training jobs on a single GPU. +The basic usage is as follows. + +```shell +python tools/train.py \ + ${CONFIG_FILE} \ + [optional arguments] +``` + +During training, log files and checkpoints will be saved to the working directory, which is specified by `work_dir` in the config file or via CLI argument `--work-dir`. + +By default, the model is evaluated on the validation set every epoch, the evaluation interval can be specified in the config file as shown below. + +```python +# evaluate the model every 12 epoch. +evaluation = dict(interval=12) +``` + +This tool accepts several optional arguments, including: + +- `--no-validate` (**not suggested**): Disable evaluation during training. +- `--work-dir ${WORK_DIR}`: Override the working directory. +- `--resume-from ${CHECKPOINT_FILE}`: Resume from a previous checkpoint file. +- `--options 'Key=value'`: Overrides other settings in the used config. + +**Note**: + +Difference between `resume-from` and `load-from`: + +`resume-from` loads both the model weights and optimizer status, and the epoch is also inherited from the specified checkpoint. It is usually used for resuming the training process that is interrupted accidentally. +`load-from` only loads the model weights and the training epoch starts from 0. It is usually used for finetuning. + +### Training on multiple GPUs + +We provide `tools/dist_train.sh` to launch training on multiple GPUs. +The basic usage is as follows. + +```shell +bash ./tools/dist_train.sh \ + ${CONFIG_FILE} \ + ${GPU_NUM} \ + [optional arguments] +``` + +Optional arguments remain the same as stated [above](#train-with-a-single-GPU). + +#### Launch multiple jobs simultaneously + +If you would like to launch multiple jobs on a single machine, e.g., 2 jobs of 4-GPU training on a machine with 8 GPUs, +you need to specify different ports (29500 by default) for each job to avoid communication conflict. + +If you use `dist_train.sh` to launch training jobs, you can set the port in commands. + +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 ./tools/dist_train.sh ${CONFIG_FILE} 4 +CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 ./tools/dist_train.sh ${CONFIG_FILE} 4 +``` + +### Training on multiple nodes + +MMDetection relies on `torch.distributed` package for distributed training. +Thus, as a basic usage, one can launch distributed training via PyTorch's [launch utility](https://pytorch.org/docs/stable/distributed.html#launch-utility). + +### Manage jobs with Slurm + +[Slurm](https://slurm.schedmd.com/) is a good job scheduling system for computing clusters. +On a cluster managed by Slurm, you can use `slurm_train.sh` to spawn training jobs. It supports both single-node and multi-node training. + +The basic usage is as follows. + +```shell +[GPUS=${GPUS}] ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR} +``` + +Below is an example of using 16 GPUs to train Mask R-CNN on a Slurm partition named _dev_, and set the work-dir to some shared file systems. + +```shell +GPUS=16 ./tools/slurm_train.sh dev mask_r50_1x configs/mask_rcnn_r50_fpn_1x_coco.py /nfs/xxxx/mask_rcnn_r50_fpn_1x +``` + +You can check [the source code](https://github.com/open-mmlab/mmdetection/blob/master/tools/slurm_train.sh) to review full arguments and environment variables. + +When using Slurm, the port option need to be set in one of the following ways: + +1. Set the port through `--options`. This is more recommended since it does not change the original configs. + + ```shell + CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR} --options 'dist_params.port=29500' + CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py ${WORK_DIR} --options 'dist_params.port=29501' + ``` + +2. Modify the config files to set different communication ports. + + In `config1.py`, set + + ```python + dist_params = dict(backend='nccl', port=29500) + ``` + + In `config2.py`, set + + ```python + dist_params = dict(backend='nccl', port=29501) + ``` + + Then you can launch two jobs with `config1.py` and `config2.py`. + + ```shell + CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR} + CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py ${WORK_DIR} + ``` diff --git a/docs/2_new_data_model.md b/docs/2_new_data_model.md new file mode 100644 index 0000000..a9736e7 --- /dev/null +++ b/docs/2_new_data_model.md @@ -0,0 +1,263 @@ +# 2: Train with customized datasets + +In this note, you will know how to inference, test, and train predefined models with customized datasets. We use the [balloon dataset](https://github.com/matterport/Mask_RCNN/tree/master/samples/balloon) as an example to describe the whole process. + +The basic steps are as below: + +1. Prepare the customized dataset +2. Prepare a config +3. Train, test, inference models on the customized dataset. + +## Prepare the customized dataset + +There are three ways to support a new dataset in MMDetection: + +1. reorganize the dataset into COCO format. +2. reorganize the dataset into a middle format. +3. implement a new dataset. + +Usually we recommend to use the first two methods which are usually easier than the third. + +In this note, we give an example for converting the data into COCO format. + +**Note**: MMDetection only supports evaluating mask AP of dataset in COCO format for now. +So for instance segmentation task users should convert the data into coco format. + +### COCO annotation format + +The necessary keys of COCO format for instance segmentation is as below, for the complete details, please refer [here](https://cocodataset.org/#format-data). + +```json +{ + "images": [image], + "annotations": [annotation], + "categories": [category] +} + + +image = { + "id": int, + "width": int, + "height": int, + "file_name": str, +} + +annotation = { + "id": int, + "image_id": int, + "category_id": int, + "segmentation": RLE or [polygon], + "area": float, + "bbox": [x,y,width,height], + "iscrowd": 0 or 1, +} + +categories = [{ + "id": int, + "name": str, + "supercategory": str, +}] +``` + +Assume we use the balloon dataset. +After downloading the data, we need to implement a function to convert the annotation format into the COCO format. Then we can use implemented COCODataset to load the data and perform training and evaluation. + +If you take a look at the dataset, you will find the dataset format is as below: + +```json +{'base64_img_data': '', + 'file_attributes': {}, + 'filename': '34020010494_e5cb88e1c4_k.jpg', + 'fileref': '', + 'regions': {'0': {'region_attributes': {}, + 'shape_attributes': {'all_points_x': [1020, + 1000, + 994, + 1003, + 1023, + 1050, + 1089, + 1134, + 1190, + 1265, + 1321, + 1361, + 1403, + 1428, + 1442, + 1445, + 1441, + 1427, + 1400, + 1361, + 1316, + 1269, + 1228, + 1198, + 1207, + 1210, + 1190, + 1177, + 1172, + 1174, + 1170, + 1153, + 1127, + 1104, + 1061, + 1032, + 1020], + 'all_points_y': [963, + 899, + 841, + 787, + 738, + 700, + 663, + 638, + 621, + 619, + 643, + 672, + 720, + 765, + 800, + 860, + 896, + 942, + 990, + 1035, + 1079, + 1112, + 1129, + 1134, + 1144, + 1153, + 1166, + 1166, + 1150, + 1136, + 1129, + 1122, + 1112, + 1084, + 1037, + 989, + 963], + 'name': 'polygon'}}}, + 'size': 1115004} +``` + +The annotation is a JSON file where each key indicates an image's all annotations. +The code to convert the balloon dataset into coco format is as below. + +```python +import os.path as osp + +def convert_balloon_to_coco(ann_file, out_file, image_prefix): + data_infos = mmcv.load(ann_file) + + annotations = [] + images = [] + obj_count = 0 + for idx, v in enumerate(mmcv.track_iter_progress(data_infos.values())): + filename = v['filename'] + img_path = osp.join(image_prefix, filename) + height, width = mmcv.imread(img_path).shape[:2] + + images.append(dict( + id=idx, + file_name=filename, + height=height, + width=width)) + + bboxes = [] + labels = [] + masks = [] + for _, obj in v['regions'].items(): + assert not obj['region_attributes'] + obj = obj['shape_attributes'] + px = obj['all_points_x'] + py = obj['all_points_y'] + poly = [(x + 0.5, y + 0.5) for x, y in zip(px, py)] + poly = [p for x in poly for p in x] + + x_min, y_min, x_max, y_max = ( + min(px), min(py), max(px), max(py)) + + + data_anno = dict( + image_id=idx, + id=obj_count, + category_id=0, + bbox=[x_min, y_min, x_max - x_min, y_max - y_min], + area=(x_max - x_min) * (y_max - y_min), + segmentation=[poly], + iscrowd=0) + annotations.append(data_anno) + obj_count += 1 + + coco_format_json = dict( + images=images, + annotations=annotations, + categories=[{'id':0, 'name': 'balloon'}]) + mmcv.dump(coco_format_json, out_file) + +``` + +Using the function above, users can successfully convert the annotation file into json format, then we can use `CocoDataset` to train and evaluate the model. + +## Prepare a config + +The second step is to prepare a config thus the dataset could be successfully loaded. Assume that we want to use Mask R-CNN with FPN, the config to train the detector on balloon dataset is as below. Assume the config is under directory `configs/balloon/` and named as `mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon.py`, the config is as below. + +```python +# The new config inherits a base config to highlight the necessary modification +_base_ = 'mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py' + +# We also need to change the num_classes in head to match the dataset's annotation +model = dict( + roi_head=dict( + bbox_head=dict(num_classes=1), + mask_head=dict(num_classes=1))) + +# Modify dataset related settings +dataset_type = 'COCODataset' +classes = ('balloon',) +data = dict( + train=dict( + img_prefix='balloon/train/', + classes=classes, + ann_file='balloon/train/annotation_coco.json'), + val=dict( + img_prefix='balloon/val/', + classes=classes, + ann_file='balloon/val/annotation_coco.json'), + test=dict( + img_prefix='balloon/val/', + classes=classes, + ann_file='balloon/val/annotation_coco.json')) + +# We can use the pre-trained Mask RCNN model to obtain higher performance +load_from = 'checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth' +``` + +## Train a new model + +To train a model with the new config, you can simply run + +```shell +python tools/train.py configs/balloon/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon.py +``` + +For more detailed usages, please refer to the [Case 1](1_exist_data_model.md). + +## Test and inference + +To test the trained model, you can simply run + +```shell +python tools/test.py configs/balloon/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon.py work_dirs/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon.py/latest.pth --eval bbox segm +``` + +For more detailed usages, please refer to the [Case 1](1_exist_data_model.md). diff --git a/docs/3_exist_data_new_model.md b/docs/3_exist_data_new_model.md new file mode 100644 index 0000000..9049564 --- /dev/null +++ b/docs/3_exist_data_new_model.md @@ -0,0 +1,275 @@ +# 3: Train with customized models and standard datasets + +In this note, you will know how to train, test and inference your own customized models under standard datasets. We use the cityscapes dataset to train a customized Cascade Mask R-CNN R50 model as an example to demonstrate the whole process, which using [`AugFPN`](https://github.com/Gus-Guo/AugFPN) to replace the defalut `FPN` as neck, and add `Rotate` or `Translate` as training-time auto augmentation. + +The basic steps are as below: + +1. Prepare the standard dataset +2. Prepare your own customized model +3. Prepare a config +4. Train, test, and inference models on the standard dataset. + +## Prepare the standard dataset + +In this note, as we use the standard cityscapes dataset as an example. + +It is recommended to symlink the dataset root to `$MMDETECTION/data`. +If your folder structure is different, you may need to change the corresponding paths in config files. + +```none +mmdetection +├── mmdet +├── tools +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ ├── train2017 +│ │ ├── val2017 +│ │ ├── test2017 +│ ├── cityscapes +│ │ ├── annotations +│ │ ├── leftImg8bit +│ │ │ ├── train +│ │ │ ├── val +│ │ ├── gtFine +│ │ │ ├── train +│ │ │ ├── val +│ ├── VOCdevkit +│ │ ├── VOC2007 +│ │ ├── VOC2012 + +``` + +The cityscapes annotations have to be converted into the coco format using `tools/dataset_converters/cityscapes.py`: + +```shell +pip install cityscapesscripts +python tools/dataset_converters/cityscapes.py ./data/cityscapes --nproc 8 --out-dir ./data/cityscapes/annotations +``` + +Currently the config files in `cityscapes` use COCO pre-trained weights to initialize. +You could download the pre-trained models in advance if network is unavailable or slow, otherwise it would cause errors at the beginning of training. + +## Prepare your own customized model + +The second step is to use your own module or training setting. Assume that we want to implement a new neck called `AugFPN` to replace with the default `FPN` under the existing detector Cascade Mask R-CNN R50. The following implements`AugFPN` under MMDetection. + +### 1. Define a new neck (e.g. AugFPN) + +Firstly create a new file `mmdet/models/necks/augfpn.py`. + +```python +from ..builder import NECKS + +@NECKS.register_module() +class AugFPN(nn.Module): + + def __init__(self, + in_channels, + out_channels, + num_outs, + start_level=0, + end_level=-1, + add_extra_convs=False): + pass + + def forward(self, inputs): + # implementation is ignored + pass +``` + +### 2. Import the module + +You can either add the following line to `mmdet/models/necks/__init__.py`, + +```python +from .augfpn import AugFPN +``` + +or alternatively add + +```python +custom_imports = dict( + imports=['mmdet.models.necks.augfpn.py'], + allow_failed_imports=False) +``` + +to the config file and avoid modifying the original code. + +### 3. Modify the config file + +```python +neck=dict( + type='AugFPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5) +``` + +For more detailed usages about customize your own models (e.g. implement a new backbone, head, loss, etc) and runtime training settings (e.g. define a new optimizer, use gradient clip, customize training schedules and hooks, etc), please refer to the guideline [Customize Models](tutorials/customize_models.md) and [Customize Runtime Settings](tutorials/customize_runtime.md) respectively. + +## Prepare a config + +The third step is to prepare a config for your own training setting. Assume that we want to add `AugFPN` and `Rotate` or `Translate` augmentation to existing Cascade Mask R-CNN R50 to train the cityscapes dataset, and assume the config is under directory `configs/cityscapes/` and named as `cascade_mask_rcnn_r50_augfpn_autoaug_10e_cityscapes.py`, the config is as below. + +```python +# The new config inherits the base configs to highlight the necessary modification +_base_ = [ + '../_base_/models/cascade_mask_rcnn_r50_fpn.py', + '../_base_/datasets/cityscapes_instance.py', '../_base_/default_runtime.py' +] + +model = dict( + # set None to avoid loading ImageNet pretrained backbone, + # instead here we set `load_from` to load from COCO pretrained detectors. + pretrained=None, + # replace neck from defaultly `FPN` to our new implemented module `AugFPN` + neck=dict( + type='AugFPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + # We also need to change the num_classes in head from 80 to 8, to match the + # cityscapes dataset's annotation. This modification involves `bbox_head` and `mask_head`. + roi_head=dict( + bbox_head=[ + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + # change the number of classes from defaultly COCO to cityscapes + num_classes=8, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + # change the number of classes from defaultly COCO to cityscapes + num_classes=8, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + # change the number of classes from defaultly COCO to cityscapes + num_classes=8, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) + ], + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + # change the number of classes from defaultly COCO to cityscapes + num_classes=8, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)))) + +# over-write `train_pipeline` for new added `AutoAugment` training setting +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='AutoAugment', + policies=[ + [dict( + type='Rotate', + level=5, + img_fill_val=(124, 116, 104), + prob=0.5, + scale=1) + ], + [dict(type='Rotate', level=7, img_fill_val=(124, 116, 104)), + dict( + type='Translate', + level=5, + prob=0.5, + img_fill_val=(124, 116, 104)) + ], + ]), + dict( + type='Resize', img_scale=[(2048, 800), (2048, 1024)], keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] + +# set batch_size per gpu, and set new training pipeline +data = dict( + samples_per_gpu=1, + workers_per_gpu=3, + # over-write `pipeline` with new training pipeline setting + train=dict(dataset=dict(pipeline=train_pipeline))) + +# Set optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# Set customized learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[8]) +total_epochs = 10 + +# We can use the COCO pretrained Cascade Mask R-CNN R50 model for more stable performance initialization +load_from = 'http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth' +``` + +## Train a new model + +To train a model with the new config, you can simply run + +```shell +python tools/train.py configs/cityscapes/cascade_mask_rcnn_r50_augfpn_autoaug_10e_cityscapes.py +``` + +For more detailed usages, please refer to the [Case 1](1_exist_data_model.md). + +## Test and inference + +To test the trained model, you can simply run + +```shell +python tools/test.py configs/cityscapes/cascade_mask_rcnn_r50_augfpn_autoaug_10e_cityscapes.py work_dirs/cascade_mask_rcnn_r50_augfpn_autoaug_10e_cityscapes.py/latest.pth --eval bbox segm +``` + +For more detailed usages, please refer to the [Case 1](1_exist_data_model.md). diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..d4bb2cb --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/api.rst b/docs/api.rst new file mode 100644 index 0000000..0440630 --- /dev/null +++ b/docs/api.rst @@ -0,0 +1,101 @@ +API Reference +================= + +mmdet.apis +-------------- +.. automodule:: mmdet.apis + :members: + +mmdet.core +-------------- + +anchor +^^^^^^^^^^ +.. automodule:: mmdet.core.anchor + :members: + +bbox +^^^^^^^^^^ +.. automodule:: mmdet.core.bbox + :members: + +export +^^^^^^^^^^ +.. automodule:: mmdet.core.export + :members: + +mask +^^^^^^^^^^ +.. automodule:: mmdet.core.mask + :members: + +evaluation +^^^^^^^^^^ +.. automodule:: mmdet.core.evaluation + :members: + +post_processing +^^^^^^^^^^^^^^^ +.. automodule:: mmdet.core.post_processing + :members: + +optimizer +^^^^^^^^^^ +.. automodule:: mmdet.core.optimizer + :members: + +utils +^^^^^^^^^^ +.. automodule:: mmdet.core.utils + :members: + +mmdet.datasets +-------------- + +datasets +^^^^^^^^^^ +.. automodule:: mmdet.datasets + :members: + +pipelines +^^^^^^^^^^ +.. automodule:: mmdet.datasets.pipelines + :members: + +mmdet.models +-------------- + +detectors +^^^^^^^^^^ +.. automodule:: mmdet.models.detectors + :members: + +backbones +^^^^^^^^^^ +.. automodule:: mmdet.models.backbones + :members: + +necks +^^^^^^^^^^^^ +.. automodule:: mmdet.models.necks + :members: + +dense_heads +^^^^^^^^^^^^ +.. automodule:: mmdet.models.dense_heads + :members: + +roi_heads +^^^^^^^^^^ +.. automodule:: mmdet.models.roi_heads + :members: + +losses +^^^^^^^^^^ +.. automodule:: mmdet.models.losses + :members: + +utils +^^^^^^^^^^ +.. automodule:: mmdet.models.utils + :members: diff --git a/docs/changelog.md b/docs/changelog.md new file mode 100644 index 0000000..f174f4e --- /dev/null +++ b/docs/changelog.md @@ -0,0 +1,846 @@ +## Changelog + +### v2.12.0 (01/5/2021) + +#### Highlights + +- Support new methods: [AutoAssign](https://arxiv.org/abs/2007.03496), [YOLOF](https://arxiv.org/abs/2103.09460), and [Deformable DETR](https://arxiv.org/abs/2010.04159) +- Stable support of exporting models to ONNX with batched images and dynamic shape (#5039) + +#### Backwards Incompatible Changes + +MMDetection is going through big refactoring for more general and convenient usages during the releases from v2.12.0 to v2.15.0 (maybe longer). +In v2.12.0 MMDetection inevitably brings some BC-breakings, including the MMCV dependency, model initialization, model registry, and mask AP evaluation. + +- MMCV version. MMDetection v2.12.0 relies on the newest features in MMCV 1.3.3, including `BaseModule` for unified parameter initialization, model registry, and the CUDA operator `MultiScaleDeformableAttn` for [Deformable DETR](https://arxiv.org/abs/2010.04159). Note that MMCV 1.3.2 already contains all the features used by MMDet but has known issues. Therefore, we recommend users skip MMCV v1.3.2 and use v1.3.2, though v1.3.2 might work for most cases. +- Unified model initialization (#4750). To unify the parameter initialization in OpenMMLab projects, MMCV supports `BaseModule` that accepts `init_cfg` to allow the modules' parameters initialized in a flexible and unified manner. Now the users need to explicitly call `model.init_weights()` in the training script to initialize the model (as in [here](https://github.com/open-mmlab/mmdetection/blob/master/tools/train.py#L162), previously this was handled by the detector. The models in MMDetection have been re-benchmarked to ensure accuracy based on PR #4750. **The downstream projects should update their code accordingly to use MMDetection v2.12.0**. +- Unified model registry (#5059). To easily use backbones implemented in other OpenMMLab projects, MMDetection migrates to inherit the model registry created in MMCV (#760). In this way, as long as the backbone is supported in an OpenMMLab project and that project also uses the registry in MMCV, users can use that backbone in MMDetection by simply modifying the config without copying the code of that backbone into MMDetection. +- Mask AP evaluation (#4898). Previous versions calculate the areas of masks through the bounding boxes when calculating the mask AP of small, medium, and large instances. To indeed use the areas of masks, we pop the key `bbox` during mask AP calculation. This change does not affect the overall mask AP evaluation and aligns the mask AP of similar models in other projects like Detectron2. + +#### New Features + +- Support paper [AutoAssign: Differentiable Label Assignment for Dense Object Detection](https://arxiv.org/abs/2007.03496) (#4295) +- Support paper [You Only Look One-level Feature](https://arxiv.org/abs/2103.09460) (#4295) +- Support paper [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) (#4778) +- Support calculating IoU with FP16 tensor in `bbox_overlaps` to save memory and keep speed (#4889) +- Add `__repr__` in custom dataset to count the number of instances (#4756) +- Add windows support by updating requirements.txt (#5052) +- Stable support of exporting models to ONNX with batched images and dynamic shape, including SSD, FSAF,FCOS, YOLOv3, RetinaNet, Faster R-CNN, and Mask R-CNN (#5039) + +#### Improvements + +- Use MMCV `MODEL_REGISTRY` (#5059) +- Unified parameter initialization for more flexible usage (#4750) +- Rename variable names and fix docstring in anchor head (#4883) +- Support training with empty GT in Cascade RPN (#4928) +- Add more details of usage of `test_robustness` in documentation (#4917) +- Changing to use `pycocotools` instead of `mmpycocotools` to fully support Detectron2 and MMDetection in one environment (#4939) +- Update torch serve dockerfile to support dockers of more versions (#4954) +- Add check for training with single class dataset (#4973) +- Refactor transformer and DETR Head (#4763) +- Update FPG model zoo (#5079) +- More accurate mask AP of small/medium/large instances (#4898) + +#### Bug Fixes + +- Fix bug in mean_ap.py when calculating mAP by 11 points (#4875) +- Fix error when key `meta` is not in old checkpoints (#4936) +- Fix hanging bug when training with empty GT in VFNet, GFL, and FCOS by changing the place of `reduce_mean` (#4923, #4978, #5058) +- Fix asyncronized inference error and provide related demo (#4941) +- Fix IoU losses dimensionality unmatch error (#4982) +- Fix torch.randperm whtn using PyTorch 1.8 (#5014) +- Fix empty bbox error in `mask_head` when using CARAFE (#5062) +- Fix `supplement_mask` bug when there are zero-size RoIs (#5065) +- Fix testing with empty rois in RoI Heads (#5081) + +### v2.11.0 (01/4/2021) + +**Highlights** + +- Support new method: [Localization Distillation for Object Detection](https://arxiv.org/pdf/2102.12252.pdf) +- Support Pytorch2ONNX with batch inference and dynamic shape + +**New Features** + +- Support [Localization Distillation for Object Detection](https://arxiv.org/pdf/2102.12252.pdf) (#4758) +- Support Pytorch2ONNX with batch inference and dynamic shape for Faster-RCNN and mainstream one-stage detectors (#4796) + +**Improvements** + +- Support batch inference in head of RetinaNet (#4699) +- Add batch dimension in second stage of Faster-RCNN (#4785) +- Support batch inference in bbox coder (#4721) +- Add check for `ann_ids` in `COCODataset` to ensure it is unique (#4789) +- support for showing the FPN results (#4716) +- support dynamic shape for grid_anchor (#4684) +- Move pycocotools version check to when it is used (#4880) + +**Bug Fixes** + +- Fix a bug of TridentNet when doing the batch inference (#4717) +- Fix a bug of Pytorch2ONNX in FASF (#4735) +- Fix a bug when show the image with float type (#4732) + +### v2.10.0 (01/03/2021) + +#### Highlights + +- Support new methods: [FPG](https://arxiv.org/abs/2004.03580) +- Support ONNX2TensorRT for SSD, FSAF, FCOS, YOLOv3, and Faster R-CNN. + +#### New Features + +- Support ONNX2TensorRT for SSD, FSAF, FCOS, YOLOv3, and Faster R-CNN (#4569) +- Support [Feature Pyramid Grids (FPG)](https://arxiv.org/abs/2004.03580) (#4645) +- Support video demo (#4420) +- Add seed option for sampler (#4665) +- Support to customize type of runner (#4570, #4669) +- Support synchronizing BN buffer in `EvalHook` (#4582) +- Add script for GIF demo (#4573) + +#### Bug Fixes + +- Fix ConfigDict AttributeError and add Colab link (#4643) +- Avoid crash in empty gt training of GFL head (#4631) +- Fix `iou_thrs` bug in RPN evaluation (#4581) +- Fix syntax error of config when upgrading model version (#4584) + +#### Improvements + +- Refactor unit test file structures (#4600) +- Refactor nms config (#4636) +- Get loading pipeline by checking the class directly rather than through config strings (#4619) +- Add doctests for mask target generation and mask structures (#4614) +- Use deep copy when copying pipeline arguments (#4621) +- Update documentations (#4642, #4650, #4620, #4630) +- Remove redundant code calling `import_modules_from_strings` (#4601) +- Clean deprecated FP16 API (#4571) +- Check whether `CLASSES` is correctly initialized in the intialization of `XMLDataset` (#4555) +- Support batch inference in the inference API (#4462, #4526) +- Clean deprecated warning and fix 'meta' error (#4695) + +### v2.9.0 (01/02/2021) + +#### Highlights + +- Support new methods: [SCNet](https://arxiv.org/abs/2012.10150), [Sparse R-CNN](https://arxiv.org/abs/2011.12450) +- Move `train_cfg` and `test_cfg` into model in configs +- Support to visualize results based on prediction quality + +#### New Features + +- Support [SCNet](https://arxiv.org/abs/2012.10150) (#4356) +- Support [Sparse R-CNN](https://arxiv.org/abs/2011.12450) (#4219) +- Support evaluate mAP by multiple IoUs (#4398) +- Support concatenate dataset for testing (#4452) +- Support to visualize results based on prediction quality (#4441) +- Add ONNX simplify option to Pytorch2ONNX script (#4468) +- Add hook for checking compatibility of class numbers in heads and datasets (#4508) + +#### Bug Fixes + +- Fix CPU inference bug of Cascade RPN (#4410) +- Fix NMS error of CornerNet when there is no prediction box (#4409) +- Fix TypeError in CornerNet inference (#4411) +- Fix bug of PAA when training with background images (#4391) +- Fix the error that the window data is not destroyed when `out_file is not None` and `show==False` (#4442) +- Fix order of NMS `score_factor` that will decrease the performance of YOLOv3 (#4473) +- Fix bug in HTC TTA when the number of detection boxes is 0 (#4516) +- Fix resize error in mask data structures (#4520) + +#### Improvements + +- Allow to customize classes in LVIS dataset (#4382) +- Add tutorials for building new models with existing datasets (#4396) +- Add CPU compatibility information in documentation (#4405) +- Add documentation of deprecated `ImageToTensor` for batch inference (#4408) +- Add more details in documentation for customizing dataset (#4430) +- Switch `imshow_det_bboxes` visualization backend from OpenCV to Matplotlib (#4389) +- Deprecate `ImageToTensor` in `image_demo.py` (#4400) +- Move train_cfg/test_cfg into model (#4347, #4489) +- Update docstring for `reg_decoded_bbox` option in bbox heads (#4467) +- Update dataset information in documentation (#4525) +- Release pre-trained R50 and R101 PAA detectors with multi-scale 3x training schedules (#4495) +- Add guidance for speed benchmark (#4537) + +### v2.8.0 (04/01/2021) + +#### Highlights + +- Support new methods: [Cascade RPN](https://arxiv.org/abs/1909.06720), [TridentNet](https://arxiv.org/abs/1901.01892) + +#### New Features + +- Support [Cascade RPN](https://arxiv.org/abs/1909.06720) (#1900) +- Support [TridentNet](https://arxiv.org/abs/1901.01892) (#3313) + +#### Bug Fixes + +- Fix bug of show result in async_benchmark (#4367) +- Fix scale factor in MaskTestMixin (#4366) +- Fix but when returning indices in `multiclass_nms` (#4362) +- Fix bug of empirical attention in resnext backbone error (#4300) +- Fix bug of `img_norm_cfg` in FCOS-HRNet models with updated performance and models (#4250) +- Fix invalid checkpoint and log in Mask R-CNN models on Cityscapes dataset (#4287) +- Fix bug in distributed sampler when dataset is too small (#4257) +- Fix bug of 'PAFPN has no attribute extra_convs_on_inputs' (#4235) + +#### Improvements + +- Update model url from aws to aliyun (#4349) +- Update ATSS for PyTorch 1.6+ (#4359) +- Update script to install ruby in pre-commit installation (#4360) +- Delete deprecated `mmdet.ops` (#4325) +- Refactor hungarian assigner for more general usage in Sparse R-CNN (#4259) +- Handle scipy import in DETR to reduce package dependencies (#4339) +- Update documentation of usages for config options after MMCV (1.2.3) supports overriding list in config (#4326) +- Update pre-train models of faster rcnn trained on COCO subsets (#4307) +- Avoid zero or too small value for beta in Dynamic R-CNN (#4303) +- Add doccumentation for Pytorch2ONNX (#4271) +- Add deprecated warning FPN arguments (#4264) +- Support returning indices of kept bboxes when using nms (#4251) +- Update type and device requirements when creating tensors `GFLHead` (#4210) +- Update device requirements when creating tensors in `CrossEntropyLoss` (#4224) + +### v2.7.0 (30/11/2020) + +- Support new method: [DETR](https://arxiv.org/abs/2005.12872), [ResNest](https://arxiv.org/abs/2004.08955), Faster R-CNN DC5. +- Support YOLO, Mask R-CNN, and Cascade R-CNN models exportable to ONNX. + +#### New Features + +- Support [DETR](https://arxiv.org/abs/2005.12872) (#4201, #4206) +- Support to link the best checkpoint in training (#3773) +- Support to override config through options in inference.py (#4175) +- Support YOLO, Mask R-CNN, and Cascade R-CNN models exportable to ONNX (#4087, #4083) +- Support [ResNeSt](https://arxiv.org/abs/2004.08955) backbone (#2959) +- Support unclip border bbox regression (#4076) +- Add tpfp func in evaluating AP (#4069) +- Support mixed precision training of SSD detector with other backbones (#4081) +- Add Faster R-CNN DC5 models (#4043) + +#### Bug Fixes + +- Fix bug of `gpu_id` in distributed training mode (#4163) +- Support Albumentations with version higher than 0.5 (#4032) +- Fix num_classes bug in faster rcnn config (#4088) +- Update code in docs/2_new_data_model.md (#4041) + +#### Improvements + +- Ensure DCN offset to have similar type as features in VFNet (#4198) +- Add config links in README files of models (#4190) +- Add tutorials for loss conventions (#3818) +- Add solution to installation issues in 30-series GPUs (#4176) +- Update docker version in get_started.md (#4145) +- Add model statistics and polish some titles in configs README (#4140) +- Clamp neg probability in FreeAnchor (#4082) +- Speed up expanding large images (#4089) +- Fix Pytorch 1.7 incompatibility issues (#4103) +- Update trouble shooting page to resolve segmentation fault (#4055) +- Update aLRP-Loss in project page (#4078) +- Clean duplicated `reduce_mean` function (#4056) +- Refactor Q&A (#4045) + +### v2.6.0 (1/11/2020) + +- Support new method: [VarifocalNet](https://arxiv.org/abs/2008.13367). +- Refactored documentation with more tutorials. + +#### New Features + +- Support GIoU calculation in `BboxOverlaps2D`, and re-implement `giou_loss` using `bbox_overlaps` (#3936) +- Support random sampling in CPU mode (#3948) +- Support VarifocalNet (#3666, #4024) + +#### Bug Fixes + +- Fix SABL validating bug in Cascade R-CNN (#3913) +- Avoid division by zero in PAA head when num_pos=0 (#3938) +- Fix temporary directory bug of multi-node testing error (#4034, #4017) +- Fix `--show-dir` option in test script (#4025) +- Fix GA-RetinaNet r50 model url (#3983) +- Update code in docs and fix broken urls (#3947) + +#### Improvements + +- Refactor pytorch2onnx API into `mmdet.core.export` and use `generate_inputs_and_wrap_model` for pytorch2onnx (#3857, #3912) +- Update RPN upgrade scripts for v2.5.0 compatibility (#3986) +- Use mmcv `tensor2imgs` (#4010) +- Update test robustness (#4000) +- Update trouble shooting page (#3994) +- Accelerate PAA training speed (#3985) +- Support batch_size > 1 in validation (#3966) +- Use RoIAlign implemented in MMCV for inference in CPU mode (#3930) +- Documentation refactoring (#4031) + +### v2.5.0 (5/10/2020) + +#### Highlights + +- Support new methods: [YOLACT](https://arxiv.org/abs/1904.02689), [CentripetalNet](https://arxiv.org/abs/2003.09119). +- Add more documentations for easier and more clear usage. + +#### Backwards Incompatible Changes + +**FP16 related methods are imported from mmcv instead of mmdet. (#3766, #3822)** +Mixed precision training utils in `mmdet.core.fp16` are moved to `mmcv.runner`, including `force_fp32`, `auto_fp16`, `wrap_fp16_model`, and `Fp16OptimizerHook`. A deprecation warning will be raised if users attempt to import those methods from `mmdet.core.fp16`, and will be finally removed in V2.10.0. + +**[0, N-1] represents foreground classes and N indicates background classes for all models. (#3221)** +Before v2.5.0, the background label for RPN is 0, and N for other heads. Now the behavior is consistent for all models. Thus `self.background_labels` in `dense_heads` is removed and all heads use `self.num_classes` to indicate the class index of background labels. +This change has no effect on the pre-trained models in the v2.x model zoo, but will affect the training of all models with RPN heads. Two-stage detectors whose RPN head uses softmax will be affected because the order of categories is changed. + +**Only call `get_subset_by_classes` when `test_mode=True` and `self.filter_empty_gt=True` (#3695)** +Function `get_subset_by_classes` in dataset is refactored and only filters out images when `test_mode=True` and `self.filter_empty_gt=True`. + In the original implementation, `get_subset_by_classes` is not related to the flag `self.filter_empty_gt` and will only be called when the classes is set during initialization no matter `test_mode` is `True` or `False`. This brings ambiguous behavior and potential bugs in many cases. After v2.5.0, if `filter_empty_gt=False`, no matter whether the classes are specified in a dataset, the dataset will use all the images in the annotations. If `filter_empty_gt=True` and `test_mode=True`, no matter whether the classes are specified, the dataset will call ``get_subset_by_classes` to check the images and filter out images containing no GT boxes. Therefore, the users should be responsible for the data filtering/cleaning process for the test dataset. + +#### New Features + +- Test time augmentation for single stage detectors (#3844, #3638) +- Support to show the name of experiments during training (#3764) +- Add `Shear`, `Rotate`, `Translate` Augmentation (#3656, #3619, #3687) +- Add image-only transformations including `Constrast`, `Equalize`, `Color`, and `Brightness`. (#3643) +- Support [YOLACT](https://arxiv.org/abs/1904.02689) (#3456) +- Support [CentripetalNet](https://arxiv.org/abs/2003.09119) (#3390) +- Support PyTorch 1.6 in docker (#3905) + +#### Bug Fixes + +- Fix the bug of training ATSS when there is no ground truth boxes (#3702) +- Fix the bug of using Focal Loss when there is `num_pos` is 0 (#3702) +- Fix the label index mapping in dataset browser (#3708) +- Fix Mask R-CNN training stuck problem when ther is no positive rois (#3713) +- Fix the bug of `self.rpn_head.test_cfg` in `RPNTestMixin` by using `self.rpn_head` in rpn head (#3808) +- Fix deprecated `Conv2d` from mmcv.ops (#3791) +- Fix device bug in RepPoints (#3836) +- Fix SABL validating bug (#3849) +- Use `https://download.openmmlab.com/mmcv/dist/index.html` for installing MMCV (#3840) +- Fix nonzero in NMS for PyTorch 1.6.0 (#3867) +- Fix the API change bug of PAA (#3883) +- Fix typo in bbox_flip (#3886) +- Fix cv2 import error of ligGL.so.1 in Dockerfile (#3891) + +#### Improvements + +- Change to use `mmcv.utils.collect_env` for collecting environment information to avoid duplicate codes (#3779) +- Update checkpoint file names to v2.0 models in documentation (#3795) +- Update tutorials for changing runtime settings (#3778), modifing loss (#3777) +- Improve the function of `simple_test_bboxes` in SABL (#3853) +- Convert mask to bool before using it as img's index for robustness and speedup (#3870) +- Improve documentation of modules and dataset customization (#3821) + +### v2.4.0 (5/9/2020) + +**Highlights** + +- Fix lots of issues/bugs and reorganize the trouble shooting page +- Support new methods [SABL](https://arxiv.org/abs/1912.04260), [YOLOv3](https://arxiv.org/abs/1804.02767), and [PAA Assign](https://arxiv.org/abs/2007.08103) +- Support Batch Inference +- Start to publish `mmdet` package to PyPI since v2.3.0 +- Switch model zoo to download.openmmlab.com + +**Backwards Incompatible Changes** + +- Support Batch Inference (#3564, #3686, #3705): Since v2.4.0, MMDetection could inference model with multiple images in a single GPU. + This change influences all the test APIs in MMDetection and downstream codebases. To help the users migrate their code, we use `replace_ImageToTensor` (#3686) to convert legacy test data pipelines during dataset initialization. +- Support RandomFlip with horizontal/vertical/diagonal direction (#3608): Since v2.4.0, MMDetection supports horizontal/vertical/diagonal flip in the data augmentation. This influences bounding box, mask, and image transformations in data augmentation process and the process that will map those data back to the original format. +- Migrate to use `mmlvis` and `mmpycocotools` for COCO and LVIS dataset (#3727). The APIs are fully compatible with the original `lvis` and `pycocotools`. Users need to uninstall the existing pycocotools and lvis packages in their environment first and install `mmlvis` & `mmpycocotools`. + +**Bug Fixes** + +- Fix default mean/std for onnx (#3491) +- Fix coco evaluation and add metric items (#3497) +- Fix typo for install.md (#3516) +- Fix atss when sampler per gpu is 1 (#3528) +- Fix import of fuse_conv_bn (#3529) +- Fix bug of gaussian_target, update unittest of heatmap (#3543) +- Fixed VOC2012 evaluate (#3553) +- Fix scale factor bug of rescale (#3566) +- Fix with_xxx_attributes in base detector (#3567) +- Fix boxes scaling when number is 0 (#3575) +- Fix rfp check when neck config is a list (#3591) +- Fix import of fuse conv bn in benchmark.py (#3606) +- Fix webcam demo (#3634) +- Fix typo and itemize issues in tutorial (#3658) +- Fix error in distributed training when some levels of FPN are not assigned with bounding boxes (#3670) +- Fix the width and height orders of stride in valid flag generation (#3685) +- Fix weight initialization bug in Res2Net DCN (#3714) +- Fix bug in OHEMSampler (#3677) + +**New Features** + +- Support Cutout augmentation (#3521) +- Support evaluation on multiple datasets through ConcatDataset (#3522) +- Support [PAA assign](https://arxiv.org/abs/2007.08103) #(3547) +- Support eval metric with pickle results (#3607) +- Support [YOLOv3](https://arxiv.org/abs/1804.02767) (#3083) +- Support [SABL](https://arxiv.org/abs/1912.04260) (#3603) +- Support to publish to Pypi in github-action (#3510) +- Support custom imports (#3641) + +**Improvements** + +- Refactor common issues in documentation (#3530) +- Add pytorch 1.6 to CI config (#3532) +- Add config to runner meta (#3534) +- Add eval-option flag for testing (#3537) +- Add init_eval to evaluation hook (#3550) +- Add include_bkg in ClassBalancedDataset (#3577) +- Using config's loading in inference_detector (#3611) +- Add ATSS ResNet-101 models in model zoo (#3639) +- Update urls to download.openmmlab.com (#3665) +- Support non-mask training for CocoDataset (#3711) + +### v2.3.0 (5/8/2020) + +**Highlights** + +- The CUDA/C++ operators have been moved to `mmcv.ops`. For backward compatibility `mmdet.ops` is kept as warppers of `mmcv.ops`. +- Support new methods [CornerNet](https://arxiv.org/abs/1808.01244), [DIOU](https://arxiv.org/abs/1911.08287)/[CIOU](https://arxiv.org/abs/2005.03572) loss, and new dataset: [LVIS V1](https://arxiv.org/abs/1908.03195) +- Provide more detailed colab training tutorials and more complete documentation. +- Support to convert RetinaNet from Pytorch to ONNX. + +**Bug Fixes** + +- Fix the model initialization bug of DetectoRS (#3187) +- Fix the bug of module names in NASFCOSHead (#3205) +- Fix the filename bug in publish_model.py (#3237) +- Fix the dimensionality bug when `inside_flags.any()` is `False` in dense heads (#3242) +- Fix the bug of forgetting to pass flip directions in `MultiScaleFlipAug` (#3262) +- Fixed the bug caused by default value of `stem_channels` (#3333) +- Fix the bug of model checkpoint loading for CPU inference (#3318, #3316) +- Fix topk bug when box number is smaller than the expected topk number in ATSSAssigner (#3361) +- Fix the gt priority bug in center_region_assigner.py (#3208) +- Fix NaN issue of iou calculation in iou_loss.py (#3394) +- Fix the bug that `iou_thrs` is not actually used during evaluation in coco.py (#3407) +- Fix test-time augmentation of RepPoints (#3435) +- Fix runtimeError caused by incontiguous tensor in Res2Net+DCN (#3412) + +**New Features** + +- Support [CornerNet](https://arxiv.org/abs/1808.01244) (#3036) +- Support [DIOU](https://arxiv.org/abs/1911.08287)/[CIOU](https://arxiv.org/abs/2005.03572) loss (#3151) +- Support [LVIS V1](https://arxiv.org/abs/1908.03195) dataset (#) +- Support customized hooks in training (#3395) +- Support fp16 training of generalized focal loss (#3410) +- Support to convert RetinaNet from Pytorch to ONNX (#3075) + +**Improvements** + +- Support to process ignore boxes in ATSS assigner (#3082) +- Allow to crop images without ground truth in `RandomCrop` (#3153) +- Enable the the `Accuracy` module to set threshold (#3155) +- Refactoring unit tests (#3206) +- Unify the training settings of `to_float32` and `norm_cfg` in RegNets configs (#3210) +- Add colab training tutorials for beginners (#3213, #3273) +- Move CUDA/C++ operators into `mmcv.ops` and keep `mmdet.ops` as warppers for backward compatibility (#3232)(#3457) +- Update installation scripts in documentation (#3290) and dockerfile (#3320) +- Support to set image resize backend (#3392) +- Remove git hash in version file (#3466) +- Check mmcv version to force version compatibility (#3460) + +### v2.2.0 (1/7/2020) + +**Highlights** + +- Support new methods: [DetectoRS](https://arxiv.org/abs/2006.02334), [PointRend](https://arxiv.org/abs/1912.08193), [Generalized Focal Loss](https://arxiv.org/abs/2006.04388), [Dynamic R-CNN](https://arxiv.org/abs/2004.06002) + +**Bug Fixes** + +- Fix FreeAnchor when no gt in image (#3176) +- Clean up deprecated usage of `register_module()` (#3092, #3161) +- Fix pretrain bug in NAS FCOS (#3145) +- Fix `num_classes` in SSD (#3142) +- Fix FCOS warmup (#3119) +- Fix `rstrip` in `tools/publish_model.py` +- Fix `flip_ratio` default value in RandomFLip pipeline (#3106) +- Fix cityscapes eval with ms_rcnn (#3112) +- Fix RPN softmax (#3056) +- Fix filename of LVIS@v0.5 (#2998) +- Fix nan loss by filtering out-of-frame gt_bboxes in COCO (#2999) +- Fix bug in FSAF (#3018) +- Add FocalLoss `num_classes` check (#2964) +- Fix PISA Loss when there are no gts (#2992) +- Avoid nan in `iou_calculator` (#2975) +- Prevent possible bugs in loading and transforms caused by shallow copy (#2967) + +**New Features** + +- Add DetectoRS (#3064) +- Support Generalize Focal Loss (#3097) +- Support PointRend (#2752) +- Support Dynamic R-CNN (#3040) +- Add DeepFashion dataset (#2968) +- Implement FCOS training tricks (#2935) +- Use BaseDenseHead as base class for anchor-base heads (#2963) +- Add `with_cp` for BasicBlock (#2891) +- Add `stem_channels` argument for ResNet (#2954) + +**Improvements** + +- Add anchor free base head (#2867) +- Migrate to github action (#3137) +- Add docstring for datasets, pipelines, core modules and methods (#3130, #3125, #3120) +- Add VOC benchmark (#3060) +- Add `concat` mode in GRoI (#3098) +- Remove cmd arg `autorescale-lr` (#3080) +- Use `len(data['img_metas'])` to indicate `num_samples` (#3073, #3053) +- Switch to EpochBasedRunner (#2976) + +### v2.1.0 (8/6/2020) + +**Highlights** + +- Support new backbones: [RegNetX](https://arxiv.org/abs/2003.13678), [Res2Net](https://arxiv.org/abs/1904.01169) +- Support new methods: [NASFCOS](https://arxiv.org/abs/1906.04423), [PISA](https://arxiv.org/abs/1904.04821), [GRoIE](https://arxiv.org/abs/2004.13665) +- Support new dataset: [LVIS](https://arxiv.org/abs/1908.03195) + +**Bug Fixes** + +- Change the CLI argument `--validate` to `--no-validate` to enable validation after training epochs by default. (#2651) +- Add missing cython to docker file (#2713) +- Fix bug in nms cpu implementation (#2754) +- Fix bug when showing mask results (#2763) +- Fix gcc requirement (#2806) +- Fix bug in async test (#2820) +- Fix mask encoding-decoding bugs in test API (#2824) +- Fix bug in test time augmentation (#2858, #2921, #2944) +- Fix a typo in comment of apis/train (#2877) +- Fix the bug of returning None when no gt bboxes are in the original image in `RandomCrop`. Fix the bug that misses to handle `gt_bboxes_ignore`, `gt_label_ignore`, and `gt_masks_ignore` in `RandomCrop`, `MinIoURandomCrop` and `Expand` modules. (#2810) +- Fix bug of `base_channels` of regnet (#2917) +- Fix the bug of logger when loading pre-trained weights in base detector (#2936) + +**New Features** + +- Add IoU models (#2666) +- Add colab demo for inference +- Support class agnostic nms (#2553) +- Add benchmark gathering scripts for development only (#2676) +- Add mmdet-based project links (#2736, #2767, #2895) +- Add config dump in training (#2779) +- Add ClassBalancedDataset (#2721) +- Add res2net backbone (#2237) +- Support RegNetX models (#2710) +- Use `mmcv.FileClient` to support different storage backends (#2712) +- Add ClassBalancedDataset (#2721) +- Code Release: Prime Sample Attention in Object Detection (CVPR 2020) (#2626) +- Implement NASFCOS (#2682) +- Add class weight in CrossEntropyLoss (#2797) +- Support LVIS dataset (#2088) +- Support GRoIE (#2584) + +**Improvements** + +- Allow different x and y strides in anchor heads. (#2629) +- Make FSAF loss more robust to no gt (#2680) +- Compute pure inference time instead (#2657) and update inference speed (#2730) +- Avoided the possibility that a patch with 0 area is cropped. (#2704) +- Add warnings when deprecated `imgs_per_gpu` is used. (#2700) +- Add a mask rcnn example for config (#2645) +- Update model zoo (#2762, #2866, #2876, #2879, #2831) +- Add `ori_filename` to img_metas and use it in test show-dir (#2612) +- Use `img_fields` to handle multiple images during image transform (#2800) +- Add upsample_cfg support in FPN (#2787) +- Add `['img']` as default `img_fields` for back compatibility (#2809) +- Rename the pretrained model from `open-mmlab://resnet50_caffe` and `open-mmlab://resnet50_caffe_bgr` to `open-mmlab://detectron/resnet50_caffe` and `open-mmlab://detectron2/resnet50_caffe`. (#2832) +- Added sleep(2) in test.py to reduce hanging problem (#2847) +- Support `c10::half` in CARAFE (#2890) +- Improve documentations (#2918, #2714) +- Use optimizer constructor in mmcv and clean the original implementation in `mmdet.core.optimizer` (#2947) + +### v2.0.0 (6/5/2020) + +In this release, we made lots of major refactoring and modifications. + +1. **Faster speed**. We optimize the training and inference speed for common models, achieving up to 30% speedup for training and 25% for inference. Please refer to [model zoo](model_zoo.md#comparison-with-detectron2) for details. + +2. **Higher performance**. We change some default hyperparameters with no additional cost, which leads to a gain of performance for most models. Please refer to [compatibility](compatibility.md#training-hyperparameters) for details. + +3. **More documentation and tutorials**. We add a bunch of documentation and tutorials to help users get started more smoothly. Read it [here](https://mmdetection.readthedocs.io/en/latest/). + +4. **Support PyTorch 1.5**. The support for 1.1 and 1.2 is dropped, and we switch to some new APIs. + +5. **Better configuration system**. Inheritance is supported to reduce the redundancy of configs. + +6. **Better modular design**. Towards the goal of simplicity and flexibility, we simplify some encapsulation while add more other configurable modules like BBoxCoder, IoUCalculator, OptimizerConstructor, RoIHead. Target computation is also included in heads and the call hierarchy is simpler. + +7. Support new methods: [FSAF](https://arxiv.org/abs/1903.00621) and PAFPN (part of [PAFPN](https://arxiv.org/abs/1803.01534)). + +**Breaking Changes** +Models training with MMDetection 1.x are not fully compatible with 2.0, please refer to the [compatibility doc](compatibility.md) for the details and how to migrate to the new version. + +**Improvements** + +- Unify cuda and cpp API for custom ops. (#2277) +- New config files with inheritance. (#2216) +- Encapsulate the second stage into RoI heads. (#1999) +- Refactor GCNet/EmpericalAttention into plugins. (#2345) +- Set low quality match as an option in IoU-based bbox assigners. (#2375) +- Change the codebase's coordinate system. (#2380) +- Refactor the category order in heads. 0 means the first positive class instead of background now. (#2374) +- Add bbox sampler and assigner registry. (#2419) +- Speed up the inference of RPN. (#2420) +- Add `train_cfg` and `test_cfg` as class members in all anchor heads. (#2422) +- Merge target computation methods into heads. (#2429) +- Add bbox coder to support different bbox encoding and losses. (#2480) +- Unify the API for regression loss. (#2156) +- Refactor Anchor Generator. (#2474) +- Make `lr` an optional argument for optimizers. (#2509) +- Migrate to modules and methods in MMCV. (#2502, #2511, #2569, #2572) +- Support PyTorch 1.5. (#2524) +- Drop the support for Python 3.5 and use F-string in the codebase. (#2531) + +**Bug Fixes** + +- Fix the scale factors for resized images without keep the aspect ratio. (#2039) +- Check if max_num > 0 before slicing in NMS. (#2486) +- Fix Deformable RoIPool when there is no instance. (#2490) +- Fix the default value of assigned labels. (#2536) +- Fix the evaluation of Cityscapes. (#2578) + +**New Features** + +- Add deep_stem and avg_down option to ResNet, i.e., support ResNetV1d. (#2252) +- Add L1 loss. (#2376) +- Support both polygon and bitmap for instance masks. (#2353, #2540) +- Support CPU mode for inference. (#2385) +- Add optimizer constructor for complicated configuration of optimizers. (#2397, #2488) +- Implement PAFPN. (#2392) +- Support empty tensor input for some modules. (#2280) +- Support for custom dataset classes without overriding it. (#2408, #2443) +- Support to train subsets of coco dataset. (#2340) +- Add iou_calculator to potentially support more IoU calculation methods. (2405) +- Support class wise mean AP (was removed in the last version). (#2459) +- Add option to save the testing result images. (#2414) +- Support MomentumUpdaterHook. (#2571) +- Add a demo to inference a single image. (#2605) + +### v1.1.0 (24/2/2020) + +**Highlights** + +- Dataset evaluation is rewritten with a unified api, which is used by both evaluation hooks and test scripts. +- Support new methods: [CARAFE](https://arxiv.org/abs/1905.02188). + +**Breaking Changes** + +- The new MMDDP inherits from the official DDP, thus the `__init__` api is changed to be the same as official DDP. +- The `mask_head` field in HTC config files is modified. +- The evaluation and testing script is updated. +- In all transforms, instance masks are stored as a numpy array shaped (n, h, w) instead of a list of (h, w) arrays, where n is the number of instances. + +**Bug Fixes** + +- Fix IOU assigners when ignore_iof_thr > 0 and there is no pred boxes. (#2135) +- Fix mAP evaluation when there are no ignored boxes. (#2116) +- Fix the empty RoI input for Deformable RoI Pooling. (#2099) +- Fix the dataset settings for multiple workflows. (#2103) +- Fix the warning related to `torch.uint8` in PyTorch 1.4. (#2105) +- Fix the inference demo on devices other than gpu:0. (#2098) +- Fix Dockerfile. (#2097) +- Fix the bug that `pad_val` is unused in Pad transform. (#2093) +- Fix the albumentation transform when there is no ground truth bbox. (#2032) + +**Improvements** + +- Use torch instead of numpy for random sampling. (#2094) +- Migrate to the new MMDDP implementation in MMCV v0.3. (#2090) +- Add meta information in logs. (#2086) +- Rewrite Soft NMS with pytorch extension and remove cython as a dependency. (#2056) +- Rewrite dataset evaluation. (#2042, #2087, #2114, #2128) +- Use numpy array for masks in transforms. (#2030) + +**New Features** + +- Implement "CARAFE: Content-Aware ReAssembly of FEatures". (#1583) +- Add `worker_init_fn()` in data_loader when seed is set. (#2066, #2111) +- Add logging utils. (#2035) + +### v1.0.0 (30/1/2020) + +This release mainly improves the code quality and add more docstrings. + +**Highlights** + +- Documentation is online now: https://mmdetection.readthedocs.io. +- Support new models: [ATSS](https://arxiv.org/abs/1912.02424). +- DCN is now available with the api `build_conv_layer` and `ConvModule` like the normal conv layer. +- A tool to collect environment information is available for trouble shooting. + +**Bug Fixes** + +- Fix the incompatibility of the latest numpy and pycocotools. (#2024) +- Fix the case when distributed package is unavailable, e.g., on Windows. (#1985) +- Fix the dimension issue for `refine_bboxes()`. (#1962) +- Fix the typo when `seg_prefix` is a list. (#1906) +- Add segmentation map cropping to RandomCrop. (#1880) +- Fix the return value of `ga_shape_target_single()`. (#1853) +- Fix the loaded shape of empty proposals. (#1819) +- Fix the mask data type when using albumentation. (#1818) + +**Improvements** + +- Enhance AssignResult and SamplingResult. (#1995) +- Add ability to overwrite existing module in Registry. (#1982) +- Reorganize requirements and make albumentations and imagecorruptions optional. (#1969) +- Check NaN in `SSDHead`. (#1935) +- Encapsulate the DCN in ResNe(X)t into a ConvModule & Conv_layers. (#1894) +- Refactoring for mAP evaluation and support multiprocessing and logging. (#1889) +- Init the root logger before constructing Runner to log more information. (#1865) +- Split `SegResizeFlipPadRescale` into different existing transforms. (#1852) +- Move `init_dist()` to MMCV. (#1851) +- Documentation and docstring improvements. (#1971, #1938, #1869, #1838) +- Fix the color of the same class for mask visualization. (#1834) +- Remove the option `keep_all_stages` in HTC and Cascade R-CNN. (#1806) + +**New Features** + +- Add two test-time options `crop_mask` and `rle_mask_encode` for mask heads. (#2013) +- Support loading grayscale images as single channel. (#1975) +- Implement "Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection". (#1872) +- Add sphinx generated docs. (#1859, #1864) +- Add GN support for flops computation. (#1850) +- Collect env info for trouble shooting. (#1812) + +### v1.0rc1 (13/12/2019) + +The RC1 release mainly focuses on improving the user experience, and fixing bugs. + +**Highlights** + +- Support new models: [FoveaBox](https://arxiv.org/abs/1904.03797), [RepPoints](https://arxiv.org/abs/1904.11490) and [FreeAnchor](https://arxiv.org/abs/1909.02466). +- Add a Dockerfile. +- Add a jupyter notebook demo and a webcam demo. +- Setup the code style and CI. +- Add lots of docstrings and unit tests. +- Fix lots of bugs. + +**Breaking Changes** + +- There was a bug for computing COCO-style mAP w.r.t different scales (AP_s, AP_m, AP_l), introduced by #621. (#1679) + +**Bug Fixes** + +- Fix a sampling interval bug in Libra R-CNN. (#1800) +- Fix the learning rate in SSD300 WIDER FACE. (#1781) +- Fix the scaling issue when `keep_ratio=False`. (#1730) +- Fix typos. (#1721, #1492, #1242, #1108, #1107) +- Fix the shuffle argument in `build_dataloader`. (#1693) +- Clip the proposal when computing mask targets. (#1688) +- Fix the "index out of range" bug for samplers in some corner cases. (#1610, #1404) +- Fix the NMS issue on devices other than GPU:0. (#1603) +- Fix SSD Head and GHM Loss on CPU. (#1578) +- Fix the OOM error when there are too many gt bboxes. (#1575) +- Fix the wrong keyword argument `nms_cfg` in HTC. (#1573) +- Process masks and semantic segmentation in Expand and MinIoUCrop transforms. (#1550, #1361) +- Fix a scale bug in the Non Local op. (#1528) +- Fix a bug in transforms when `gt_bboxes_ignore` is None. (#1498) +- Fix a bug when `img_prefix` is None. (#1497) +- Pass the device argument to `grid_anchors` and `valid_flags`. (#1478) +- Fix the data pipeline for test_robustness. (#1476) +- Fix the argument type of deformable pooling. (#1390) +- Fix the coco_eval when there are only two classes. (#1376) +- Fix a bug in Modulated DeformableConv when deformable_group>1. (#1359) +- Fix the mask cropping in RandomCrop. (#1333) +- Fix zero outputs in DeformConv when not running on cuda:0. (#1326) +- Fix the type issue in Expand. (#1288) +- Fix the inference API. (#1255) +- Fix the inplace operation in Expand. (#1249) +- Fix the from-scratch training config. (#1196) +- Fix inplace add in RoIExtractor which cause an error in PyTorch 1.2. (#1160) +- Fix FCOS when input images has no positive sample. (#1136) +- Fix recursive imports. (#1099) + +**Improvements** + +- Print the config file and mmdet version in the log. (#1721) +- Lint the code before compiling in travis CI. (#1715) +- Add a probability argument for the `Expand` transform. (#1651) +- Update the PyTorch and CUDA version in the docker file. (#1615) +- Raise a warning when specifying `--validate` in non-distributed training. (#1624, #1651) +- Beautify the mAP printing. (#1614) +- Add pre-commit hook. (#1536) +- Add the argument `in_channels` to backbones. (#1475) +- Add lots of docstrings and unit tests, thanks to [@Erotemic](https://github.com/Erotemic). (#1603, #1517, #1506, #1505, #1491, #1479, #1477, #1475, #1474) +- Add support for multi-node distributed test when there is no shared storage. (#1399) +- Optimize Dockerfile to reduce the image size. (#1306) +- Update new results of HRNet. (#1284, #1182) +- Add an argument `no_norm_on_lateral` in FPN. (#1240) +- Test the compiling in CI. (#1235) +- Move docs to a separate folder. (#1233) +- Add a jupyter notebook demo. (#1158) +- Support different type of dataset for training. (#1133) +- Use int64_t instead of long in cuda kernels. (#1131) +- Support unsquare RoIs for bbox and mask heads. (#1128) +- Manually add type promotion to make compatible to PyTorch 1.2. (#1114) +- Allowing validation dataset for computing validation loss. (#1093) +- Use `.scalar_type()` instead of `.type()` to suppress some warnings. (#1070) + +**New Features** + +- Add an option `--with_ap` to compute the AP for each class. (#1549) +- Implement "FreeAnchor: Learning to Match Anchors for Visual Object Detection". (#1391) +- Support [Albumentations](https://github.com/albumentations-team/albumentations) for augmentations in the data pipeline. (#1354) +- Implement "FoveaBox: Beyond Anchor-based Object Detector". (#1339) +- Support horizontal and vertical flipping. (#1273, #1115) +- Implement "RepPoints: Point Set Representation for Object Detection". (#1265) +- Add test-time augmentation to HTC and Cascade R-CNN. (#1251) +- Add a COCO result analysis tool. (#1228) +- Add Dockerfile. (#1168) +- Add a webcam demo. (#1155, #1150) +- Add FLOPs counter. (#1127) +- Allow arbitrary layer order for ConvModule. (#1078) + +### v1.0rc0 (27/07/2019) + +- Implement lots of new methods and components (Mixed Precision Training, HTC, Libra R-CNN, Guided Anchoring, Empirical Attention, Mask Scoring R-CNN, Grid R-CNN (Plus), GHM, GCNet, FCOS, HRNet, Weight Standardization, etc.). Thank all collaborators! +- Support two additional datasets: WIDER FACE and Cityscapes. +- Refactoring for loss APIs and make it more flexible to adopt different losses and related hyper-parameters. +- Speed up multi-gpu testing. +- Integrate all compiling and installing in a single script. + +### v0.6.0 (14/04/2019) + +- Up to 30% speedup compared to the model zoo. +- Support both PyTorch stable and nightly version. +- Replace NMS and SigmoidFocalLoss with Pytorch CUDA extensions. + +### v0.6rc0(06/02/2019) + +- Migrate to PyTorch 1.0. + +### v0.5.7 (06/02/2019) + +- Add support for Deformable ConvNet v2. (Many thanks to the authors and [@chengdazhi](https://github.com/chengdazhi)) +- This is the last release based on PyTorch 0.4.1. + +### v0.5.6 (17/01/2019) + +- Add support for Group Normalization. +- Unify RPNHead and single stage heads (RetinaHead, SSDHead) with AnchorHead. + +### v0.5.5 (22/12/2018) + +- Add SSD for COCO and PASCAL VOC. +- Add ResNeXt backbones and detection models. +- Refactoring for Samplers/Assigners and add OHEM. +- Add VOC dataset and evaluation scripts. + +### v0.5.4 (27/11/2018) + +- Add SingleStageDetector and RetinaNet. + +### v0.5.3 (26/11/2018) + +- Add Cascade R-CNN and Cascade Mask R-CNN. +- Add support for Soft-NMS in config files. + +### v0.5.2 (21/10/2018) + +- Add support for custom datasets. +- Add a script to convert PASCAL VOC annotations to the expected format. + +### v0.5.1 (20/10/2018) + +- Add BBoxAssigner and BBoxSampler, the `train_cfg` field in config files are restructured. +- `ConvFCRoIHead` / `SharedFCRoIHead` are renamed to `ConvFCBBoxHead` / `SharedFCBBoxHead` for consistency. diff --git a/docs/compatibility.md b/docs/compatibility.md new file mode 100644 index 0000000..c0bc211 --- /dev/null +++ b/docs/compatibility.md @@ -0,0 +1,114 @@ +# Compatibility of MMDetection 2.x + +## MMDetection 2.12.0 + +MMDetection is going through big refactoring for more general and convenient usages during the releases from v2.12.0 to v2.15.0 (maybe longer). +In v2.12.0 MMDetection inevitably brings some BC-breakings, including the MMCV dependency, model initialization, model registry, and mask AP evaluation. + +### MMCV Version + +MMDetection v2.12.0 relies on the newest features in MMCV 1.3.3, including `BaseModule` for unified parameter initialization, model registry, and the CUDA operator `MultiScaleDeformableAttn` for [Deformable DETR](https://arxiv.org/abs/2010.04159). Note that MMCV 1.3.2 already contains all the features used by MMDet but has known issues. Therefore, we recommend users to skip MMCV v1.3.2 and use v1.3.2, though v1.3.2 might work for most of the cases. + +### Unified model initialization + +To unify the parameter initialization in OpenMMLab projects, MMCV supports `BaseModule` that accepts `init_cfg` to allow the modules' parameters initialized in a flexible and unified manner. Now the users need to explicitly call `model.init_weights()` in the training script to initialize the model (as in [here](https://github.com/open-mmlab/mmdetection/blob/master/tools/train.py#L162), previously this was handled by the detector. **The downstream projects must update their model initialization accordingly to use MMDetection v2.12.0**. Please refer to PR #4750 for details. + +### Unified model registry + +To easily use backbones implemented in other OpenMMLab projects, MMDetection v2.12.0 inherits the model registry created in MMCV (#760). In this way, as long as the backbone is supported in an OpenMMLab project and that project also uses the registry in MMCV, users can use that backbone in MMDetection by simply modifying the config without copying the code of that backbone into MMDetection. Please refer to PR #5059 for more details. + +### Mask AP evaluation + +Before [PR 4898](https://github.com/open-mmlab/mmdetection/pull/4898) and V2.12.0, the mask AP of small, medium, and large instances is calculated based on the bounding box area rather than the real mask area. This leads to higher `APs` and `APm` but lower `APl` but will not affect the overall mask AP. [PR 4898](https://github.com/open-mmlab/mmdetection/pull/4898) change it to use mask areas by deleting `bbox` in mask AP calculation. +The new calculation does not affect the overall mask AP evaluation and is consistent with [Detectron2](https://github.com/facebookresearch/detectron2/). + +## Compatibility with MMDetection 1.x + +MMDetection 2.0 goes through a big refactoring and addresses many legacy issues. It is not compatible with the 1.x version, i.e., running inference with the same model weights in these two versions will produce different results. Thus, MMDetection 2.0 re-benchmarks all the models and provides their links and logs in the model zoo. + +The major differences are in four folds: coordinate system, codebase conventions, training hyperparameters, and modular design. + +### Coordinate System + +The new coordinate system is consistent with [Detectron2](https://github.com/facebookresearch/detectron2/) and treats the center of the most left-top pixel as (0, 0) rather than the left-top corner of that pixel. +Accordingly, the system interprets the coordinates in COCO bounding box and segmentation annotations as coordinates in range `[0, width]` or `[0, height]`. +This modification affects all the computation related to the bbox and pixel selection, +which is more natural and accurate. + +- The height and width of a box with corners (x1, y1) and (x2, y2) in the new coordinate system is computed as `width = x2 - x1` and `height = y2 - y1`. + In MMDetection 1.x and previous version, a "+ 1" was added both height and width. + This modification are in three folds: + + 1. Box transformation and encoding/decoding in regression. + 2. IoU calculation. This affects the matching process between ground truth and bounding box and the NMS process. The effect to compatibility is very negligible, though. + 3. The corners of bounding box is in float type and no longer quantized. This should provide more accurate bounding box results. This also makes the bounding box and RoIs not required to have minimum size of 1, whose effect is small, though. + +- The anchors are center-aligned to feature grid points and in float type. + In MMDetection 1.x and previous version, the anchors are in `int` type and not center-aligned. + This affects the anchor generation in RPN and all the anchor-based methods. + +- ROIAlign is better aligned with the image coordinate system. The new implementation is adopted from [Detectron2](https://github.com/facebookresearch/detectron2/tree/master/detectron2/layers/csrc/ROIAlign). + The RoIs are shifted by half a pixel by default when they are used to cropping RoI features, compared to MMDetection 1.x. + The old behavior is still available by setting `aligned=False` instead of `aligned=True`. + +- Mask cropping and pasting are more accurate. + + 1. We use the new RoIAlign to crop mask targets. In MMDetection 1.x, the bounding box is quantized before it is used to crop mask target, and the crop process is implemented by numpy. In new implementation, the bounding box for crop is not quantized and sent to RoIAlign. This implementation accelerates the training speed by a large margin (~0.1s per iter, ~2 hour when training Mask R50 for 1x schedule) and should be more accurate. + + 2. In MMDetection 2.0, the "`paste_mask()`" function is different and should be more accurate than those in previous versions. This change follows the modification in [Detectron2](https://github.com/facebookresearch/detectron2/blob/master/detectron2/structures/masks.py) and can improve mask AP on COCO by ~0.5% absolute. + +### Codebase Conventions + +- MMDetection 2.0 changes the order of class labels to reduce unused parameters in regression and mask branch more naturally (without +1 and -1). + This effect all the classification layers of the model to have a different ordering of class labels. The final layers of regression branch and mask head no longer keep K+1 channels for K categories, and their class orders are consistent with the classification branch. + + - In MMDetection 2.0, label "K" means background, and labels [0, K-1] correspond to the K = num_categories object categories. + + - In MMDetection 1.x and previous version, label "0" means background, and labels [1, K] correspond to the K categories. + + - **Note**: The class order of softmax RPN is still the same as that in 1.x in versions<=2.4.0 while sigmoid RPN is not affected. The class orders in all heads are unified since MMDetection v2.5.0. + +- Low quality matching in R-CNN is not used. In MMDetection 1.x and previous versions, the `max_iou_assigner` will match low quality boxes for each ground truth box in both RPN and R-CNN training. We observe this sometimes does not assign the most perfect GT box to some bounding boxes, + thus MMDetection 2.0 do not allow low quality matching by default in R-CNN training in the new system. This sometimes may slightly improve the box AP (~0.1% absolute). + +- Separate scale factors for width and height. In MMDetection 1.x and previous versions, the scale factor is a single float in mode `keep_ratio=True`. This is slightly inaccurate because the scale factors for width and height have slight difference. MMDetection 2.0 adopts separate scale factors for width and height, the improvement on AP ~0.1% absolute. + +- Configs name conventions are changed. MMDetection V2.0 adopts the new name convention to maintain the gradually growing model zoo as the following: + + ```shell + [model]_(model setting)_[backbone]_[neck]_(norm setting)_(misc)_(gpu x batch)_[schedule]_[dataset].py, + ``` + + where the (`misc`) includes DCN and GCBlock, etc. More details are illustrated in the [documentation for config](config.md) + +- MMDetection V2.0 uses new ResNet Caffe backbones to reduce warnings when loading pre-trained models. Most of the new backbones' weights are the same as the former ones but do not have `conv.bias`, except that they use a different `img_norm_cfg`. Thus, the new backbone will not cause warning of unexpected keys. + +### Training Hyperparameters + +The change in training hyperparameters does not affect +model-level compatibility but slightly improves the performance. The major ones are: + +- The number of proposals after nms is changed from 2000 to 1000 by setting `nms_post=1000` and `max_num=1000`. + This slightly improves both mask AP and bbox AP by ~0.2% absolute. + +- The default box regression losses for Mask R-CNN, Faster R-CNN and RetinaNet are changed from smooth L1 Loss to L1 loss. This leads to an overall improvement in box AP (~0.6% absolute). However, using L1-loss for other methods such as Cascade R-CNN and HTC does not improve the performance, so we keep the original settings for these methods. + +- The sample num of RoIAlign layer is set to be 0 for simplicity. This leads to slightly improvement on mask AP (~0.2% absolute). + +- The default setting does not use gradient clipping anymore during training for faster training speed. This does not degrade performance of the most of models. For some models such as RepPoints we keep using gradient clipping to stabilize the training process and to obtain better performance. + +- The default warmup ratio is changed from 1/3 to 0.001 for a more smooth warming up process since the gradient clipping is usually not used. The effect is found negligible during our re-benchmarking, though. + +### Upgrade Models from 1.x to 2.0 + +To convert the models trained by MMDetection V1.x to MMDetection V2.0, the users can use the script `tools/model_converters/upgrade_model_version.py` to convert +their models. The converted models can be run in MMDetection V2.0 with slightly dropped performance (less than 1% AP absolute). +Details can be found in `configs/legacy`. + +## pycocotools compatibility + +`mmpycocotools` is the OpenMMlab's folk of official `pycocotools`, which works for both MMDetection and Detectron2. +Before [PR 4939](https://github.com/open-mmlab/mmdetection/pull/4939), since `pycocotools` and `mmpycocotool` have the same package name, if users already installed `pyccocotools` (installed Detectron2 first under the same environment), then the setup of MMDetection will skip installing `mmpycocotool`. Thus MMDetection fails due to the missing `mmpycocotools`. +If MMDetection is installed before Detectron2, they could work under the same environment. +[PR 4939](https://github.com/open-mmlab/mmdetection/pull/4939) deprecates mmpycocotools in favor of official pycocotools. +Users may install MMDetection and Detectron2 under the same environment after [PR 4939](https://github.com/open-mmlab/mmdetection/pull/4939), no matter what the installation order is. diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 0000000..1c60d9c --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,90 @@ +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import subprocess +import sys + +sys.path.insert(0, os.path.abspath('..')) + +# -- Project information ----------------------------------------------------- + +project = 'MMDetection' +copyright = '2018-2020, OpenMMLab' +author = 'MMDetection Authors' +version_file = '../mmdet/version.py' + + +def get_version(): + with open(version_file, 'r') as f: + exec(compile(f.read(), version_file, 'exec')) + return locals()['__version__'] + + +# The full version, including alpha/beta/rc tags +release = get_version() + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.napoleon', + 'sphinx.ext.viewcode', + 'recommonmark', + 'sphinx_markdown_tables', +] + +autodoc_mock_imports = [ + 'matplotlib', 'pycocotools', 'terminaltables', 'mmdet.version', 'mmcv.ops' +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# +source_suffix = { + '.rst': 'restructuredtext', + '.md': 'markdown', +} + +# The master toctree document. +master_doc = 'index' + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'sphinx_rtd_theme' + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] + + +def builder_inited_handler(app): + subprocess.run(['./stat.py']) + + +def setup(app): + app.connect('builder-inited', builder_inited_handler) diff --git a/docs/conventions.md b/docs/conventions.md new file mode 100644 index 0000000..86e8cb7 --- /dev/null +++ b/docs/conventions.md @@ -0,0 +1,31 @@ +# Conventions + +Please check the following conventions if you would like to modify MMDetection as your own project. + +## Loss + +In MMDetection, a `dict` containing losses and metrics will be returned by `model(**data)`. + +For example, in bbox head, + +```python +class BBoxHead(nn.Module): + ... + def loss(self, ...): + losses = dict() + # classification loss + losses['loss_cls'] = self.loss_cls(...) + # classification accuracy + losses['acc'] = accuracy(...) + # bbox regression loss + losses['loss_bbox'] = self.loss_bbox(...) + return losses +``` + +`bbox_head.loss()` will be called during model forward. +The returned dict contains `'loss_bbox'`, `'loss_cls'`, `'acc'` . +Only `'loss_bbox'`, `'loss_cls'` will be used during back propagation, +`'acc'` will only be used as a metric to monitor training process. + +By default, only values whose keys contain `'loss'` will be back propagated. +This behavior could be changed by modifying `BaseDetector.train_step()`. diff --git a/docs/faq.md b/docs/faq.md new file mode 100644 index 0000000..e0ef6af --- /dev/null +++ b/docs/faq.md @@ -0,0 +1,82 @@ +We list some common troubles faced by many users and their corresponding solutions here. Feel free to enrich the list if you find any frequent issues and have ways to help others to solve them. If the contents here do not cover your issue, please create an issue using the [provided templates](https://github.com/open-mmlab/mmdetection/blob/master/.github/ISSUE_TEMPLATE/error-report.md) and make sure you fill in all required information in the template. + +## MMCV Installation + +- Compatibility issue between MMCV and MMDetection; "ConvWS is already registered in conv layer"; "AssertionError: MMCV==xxx is used but incompatible. Please install mmcv>=xxx, <=xxx." + + Please install the correct version of MMCV for the version of your MMDetection following the [installation instruction](https://mmdetection.readthedocs.io/en/latest/get_started.html#installation). + +- "No module named 'mmcv.ops'"; "No module named 'mmcv._ext'". + + 1. Uninstall existing mmcv in the environment using `pip uninstall mmcv`. + 2. Install mmcv-full following the [installation instruction](https://mmcv.readthedocs.io/en/latest/#installation). + +## PyTorch/CUDA Environment + +- "RTX 30 series card fails when building MMCV or MMDet" + + 1. Temporary work-around: do `MMCV_WITH_OPS=1 MMCV_CUDA_ARGS='-gencode=arch=compute_80,code=sm_80' pip install -e .`. + The common issue is `nvcc fatal : Unsupported gpu architecture 'compute_86'`. This means that the compiler should optimize for sm_86, i.e., nvidia 30 series card, but such optimizations have not been supported by CUDA toolkit 11.0. + This work-around modifies the compile flag by adding `MMCV_CUDA_ARGS='-gencode=arch=compute_80,code=sm_80'`, which tells `nvcc` to optimize for **sm_80**, i.e., Nvidia A100. Although A100 is different from the 30 series card, they use similar ampere architecture. This may hurt the performance but it works. + 2. PyTorch developers have updated that the default compiler flags should be fixed by [pytorch/pytorch#47585](https://github.com/pytorch/pytorch/pull/47585). So using PyTorch-nightly may also be able to solve the problem, though we have not tested it yet. + +- "invalid device function" or "no kernel image is available for execution". + + 1. Check if your cuda runtime version (under `/usr/local/`), `nvcc --version` and `conda list cudatoolkit` version match. + 2. Run `python mmdet/utils/collect_env.py` to check whether PyTorch, torchvision, and MMCV are built for the correct GPU architecture. + You may need to set `TORCH_CUDA_ARCH_LIST` to reinstall MMCV. + The GPU arch table could be found [here](https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#gpu-feature-list), + i.e. run `TORCH_CUDA_ARCH_LIST=7.0 pip install mmcv-full` to build MMCV for Volta GPUs. + The compatibility issue could happen when using old GPUS, e.g., Tesla K80 (3.7) on colab. + 3. Check whether the running environment is the same as that when mmcv/mmdet has compiled. + For example, you may compile mmcv using CUDA 10.0 but run it on CUDA 9.0 environments. + +- "undefined symbol" or "cannot open xxx.so". + + 1. If those symbols are CUDA/C++ symbols (e.g., libcudart.so or GLIBCXX), check whether the CUDA/GCC runtimes are the same as those used for compiling mmcv, + i.e. run `python mmdet/utils/collect_env.py` to see if `"MMCV Compiler"`/`"MMCV CUDA Compiler"` is the same as `"GCC"`/`"CUDA_HOME"`. + 2. If those symbols are PyTorch symbols (e.g., symbols containing caffe, aten, and TH), check whether the PyTorch version is the same as that used for compiling mmcv. + 3. Run `python mmdet/utils/collect_env.py` to check whether PyTorch, torchvision, and MMCV are built by and running on the same environment. + +- setuptools.sandbox.UnpickleableException: DistutilsSetupError("each element of 'ext_modules' option must be an Extension instance or 2-tuple") + + 1. If you are using miniconda rather than anaconda, check whether Cython is installed as indicated in [#3379](https://github.com/open-mmlab/mmdetection/issues/3379). + You need to manually install Cython first and then run command `pip install -r requirements.txt`. + 2. You may also need to check the compatibility between the `setuptools`, `Cython`, and `PyTorch` in your environment. + +- "Segmentation fault". + 1. Check you GCC version and use GCC 5.4. This usually caused by the incompatibility between PyTorch and the environment (e.g., GCC < 4.9 for PyTorch). We also recommand the users to avoid using GCC 5.5 because many feedbacks report that GCC 5.5 will cause "segmentation fault" and simply changing it to GCC 5.4 could solve the problem. + + 2. Check whether PyTorch is correctly installed and could use CUDA op, e.g. type the following command in your terminal. + + ```shell + python -c 'import torch; print(torch.cuda.is_available())' + ``` + + And see whether they could correctly output results. + + 3. If Pytorch is correctly installed, check whether MMCV is correctly installed. + + ```shell + python -c 'import mmcv; import mmcv.ops' + ``` + + If MMCV is correctly installed, then there will be no issue of the above two commands. + + 4. If MMCV and Pytorch is correctly installed, you man use `ipdb`, `pdb` to set breakpoints or directly add 'print' in mmdetection code and see which part leads the segmentation fault. + +## Training + +- "Loss goes Nan" + 1. Check if the dataset annotations are valid: zero-size bounding boxes will cause the regression loss to be Nan due to the commonly used transformation for box regression. Some small size (width or height are smaller than 1) boxes will also cause this problem after data augmentation (e.g., instaboost). So check the data and try to filter out those zero-size boxes and skip some risky augmentations on the small-size boxes when you face the problem. + 2. Reduce the learning rate: the learning rate might be too large due to some reasons, e.g., change of batch size. You can rescale them to the value that could stably train the model. + 3. Extend the warmup iterations: some models are sensitive to the learning rate at the start of the training. You can extend the warmup iterations, e.g., change the `warmup_iters` from 500 to 1000 or 2000. + 4. Add gradient clipping: some models requires gradient clipping to stablize the training process. You can add gradient clippint to avoid gradients that are too large. +- ’GPU out of memory" + 1. There are some scenarios when there are large amount of ground truth boxes, which may cause OOM during target assignment. You can set `gpu_assign_thr=N` in the config of assigner thus the assigner will calculate box overlaps through CPU when there are more than N GT boxes. + 2. Set `with_cp=True` in the backbone. This uses the sublinear strategy in PyTorch to reduce GPU memory cost in the backbone. + 3. Try mixed precision training using following the examples in `config/fp16`. The `loss_scale` might need further tuning for different models. + +- "RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one" + 1. This error indicates that your module has parameters that were not used in producing loss. This phenomenon may be caused by running different branches in your code in DDP mode. + 2. You can set ` find_unused_parameters = True` in the config to solve the above problems or find those unused parameters manually. diff --git a/docs/get_started.md b/docs/get_started.md new file mode 100644 index 0000000..8f3f7cb --- /dev/null +++ b/docs/get_started.md @@ -0,0 +1,220 @@ +## Prerequisites + +- Linux or macOS (Windows is in experimental support) +- Python 3.6+ +- PyTorch 1.3+ +- CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible) +- GCC 5+ +- [MMCV](https://mmcv.readthedocs.io/en/latest/#installation) + +The compatible MMDetection and MMCV versions are as below. Please install the correct version of MMCV to avoid installation issues. + +| MMDetection version | MMCV version | +|:-------------------:|:-------------------:| +| master | mmcv-full>=1.3.3, <1.4.0 | +| 2.12.0 | mmcv-full>=1.3.3, <1.4.0 | +| 2.11.0 | mmcv-full>=1.2.4, <1.4.0 | +| 2.10.0 | mmcv-full>=1.2.4, <1.4.0 | +| 2.9.0 | mmcv-full>=1.2.4, <1.4.0 | +| 2.8.0 | mmcv-full>=1.2.4, <1.4.0 | +| 2.7.0 | mmcv-full>=1.1.5, <1.4.0 | +| 2.6.0 | mmcv-full>=1.1.5, <1.4.0 | +| 2.5.0 | mmcv-full>=1.1.5, <1.4.0 | +| 2.4.0 | mmcv-full>=1.1.1, <1.4.0 | +| 2.3.0 | mmcv-full==1.0.5 | +| 2.3.0rc0 | mmcv-full>=1.0.2 | +| 2.2.1 | mmcv==0.6.2 | +| 2.2.0 | mmcv==0.6.2 | +| 2.1.0 | mmcv>=0.5.9, <=0.6.1| +| 2.0.0 | mmcv>=0.5.1, <=0.5.8| + +Note: You need to run `pip uninstall mmcv` first if you have mmcv installed. +If mmcv and mmcv-full are both installed, there will be `ModuleNotFoundError`. + +## Installation + +0. You can simply install mmdetection with the following commands: + `pip install mmdet` + +1. Create a conda virtual environment and activate it. + + ```shell + conda create -n open-mmlab python=3.7 -y + conda activate open-mmlab + ``` + +2. Install PyTorch and torchvision following the [official instructions](https://pytorch.org/), e.g., + + ```shell + conda install pytorch torchvision -c pytorch + ``` + + Note: Make sure that your compilation CUDA version and runtime CUDA version match. + You can check the supported CUDA version for precompiled packages on the [PyTorch website](https://pytorch.org/). + + `E.g.1` If you have CUDA 10.1 installed under `/usr/local/cuda` and would like to install + PyTorch 1.5, you need to install the prebuilt PyTorch with CUDA 10.1. + + ```shell + conda install pytorch cudatoolkit=10.1 torchvision -c pytorch + ``` + + `E.g. 2` If you have CUDA 9.2 installed under `/usr/local/cuda` and would like to install + PyTorch 1.3.1., you need to install the prebuilt PyTorch with CUDA 9.2. + + ```shell + conda install pytorch=1.3.1 cudatoolkit=9.2 torchvision=0.4.2 -c pytorch + ``` + + If you build PyTorch from source instead of installing the prebuilt pacakge, + you can use more CUDA versions such as 9.0. + +3. Install mmcv-full, we recommend you to install the pre-build package as below. + + ```shell + pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html + ``` + + Please replace `{cu_version}` and `{torch_version}` in the url to your desired one. For example, to install the latest `mmcv-full` with `CUDA 11` and `PyTorch 1.7.0`, use the following command: + + ```shell + pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html + ``` + + See [here](https://github.com/open-mmlab/mmcv#install-with-pip) for different versions of MMCV compatible to different PyTorch and CUDA versions. + Optionally you can choose to compile mmcv from source by the following command + + ```shell + git clone https://github.com/open-mmlab/mmcv.git + cd mmcv + MMCV_WITH_OPS=1 pip install -e . # package mmcv-full will be installed after this step + cd .. + ``` + + Or directly run + + ```shell + pip install mmcv-full + ``` + +4. Clone the MMDetection repository. + + ```shell + git clone https://github.com/open-mmlab/mmdetection.git + cd mmdetection + ``` + +5. Install build requirements and then install MMDetection. + + ```shell + pip install -r requirements/build.txt + pip install -v -e . # or "python setup.py develop" + ``` + +Note: + +a. Following the above instructions, MMDetection is installed on `dev` mode +, any local modifications made to the code will take effect without the need to reinstall it. + +b. If you would like to use `opencv-python-headless` instead of `opencv +-python`, +you can install it before installing MMCV. + +c. Some dependencies are optional. Simply running `pip install -v -e .` will + only install the minimum runtime requirements. To use optional dependencies like `albumentations` and `imagecorruptions` either install them manually with `pip install -r requirements/optional.txt` or specify desired extras when calling `pip` (e.g. `pip install -v -e .[optional]`). Valid keys for the extras field are: `all`, `tests`, `build`, and `optional`. + +### Install with CPU only + +The code can be built for CPU only environment (where CUDA isn't available). + +In CPU mode you can run the demo/webcam_demo.py for example. +However some functionality is gone in this mode: + +- Deformable Convolution +- Modulated Deformable Convolution +- ROI pooling +- Deformable ROI pooling +- CARAFE: Content-Aware ReAssembly of FEatures +- SyncBatchNorm +- CrissCrossAttention: Criss-Cross Attention +- MaskedConv2d +- Temporal Interlace Shift +- nms_cuda +- sigmoid_focal_loss_cuda +- bbox_overlaps + +So if you try to run inference with a model containing above ops you will get an error. The following table lists the related methods that cannot inference on CPU due to dependency on these operators + +| Operator | Model | +| :-----------------------------------------------------: | :----------------------------------------------------------: | +| Deformable Convolution/Modulated Deformable Convolution | DCN、Guided Anchoring、RepPoints、CentripetalNet、VFNet、CascadeRPN、NAS-FCOS、DetectoRS | +| MaskedConv2d | Guided Anchoring | +| CARAFE | CARAFE | +| SyncBatchNorm | ResNeSt | + +**Notice**: MMDetection does not support training with CPU for now. + +### Another option: Docker Image + +We provide a [Dockerfile](https://github.com/open-mmlab/mmdetection/blob/master/docker/Dockerfile) to build an image. Ensure that you are using [docker version](https://docs.docker.com/engine/install/) >=19.03. + +```shell +# build an image with PyTorch 1.6, CUDA 10.1 +docker build -t mmdetection docker/ +``` + +Run it with + +```shell +docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmdetection/data mmdetection +``` + +### A from-scratch setup script + +Assuming that you already have CUDA 10.1 installed, here is a full script for setting up MMDetection with conda. + +```shell +conda create -n open-mmlab python=3.7 -y +conda activate open-mmlab + +conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch -y + +# install the latest mmcv +pip install mmcv-full==latest+torch1.6.0+cu101 -f https://download.openmmlab.com/mmcv/dist/index.html + +# install mmdetection +git clone https://github.com/open-mmlab/mmdetection.git +cd mmdetection +pip install -r requirements/build.txt +pip install -v -e . +``` + +### Developing with multiple MMDetection versions + +The train and test scripts already modify the `PYTHONPATH` to ensure the script use the MMDetection in the current directory. + +To use the default MMDetection installed in the environment rather than that you are working with, you can remove the following line in those scripts + +```shell +PYTHONPATH="$(dirname $0)/..":$PYTHONPATH +``` + +## Verification + +To verify whether MMDetection and the required environment are installed correctly, we can run sample Python code to initialize a detector and run inference a demo image: + +```python +from mmdet.apis import init_detector, inference_detector + +config_file = 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +# download the checkpoint from model zoo and put it in `checkpoints/` +# url: http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth +checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth' +device = 'cuda:0' +# init a detector +model = init_detector(config_file, checkpoint_file, device=device) +# inference the demo image +inference_detector(model, 'demo/demo.jpg') +``` + +The above code is supposed to run successfully upon you finish the installation. diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 0000000..5b30e24 --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,50 @@ +Welcome to MMDetection's documentation! +======================================= + +.. toctree:: + :maxdepth: 2 + :caption: Get Started + + get_started.md + modelzoo_statistics.md + model_zoo.md + +.. toctree:: + :maxdepth: 2 + :caption: Quick Run + + 1_exist_data_model.md + 2_new_data_model.md + +.. toctree:: + :maxdepth: 2 + :caption: Tutorials + + tutorials/index.rst + +.. toctree:: + :maxdepth: 2 + :caption: Useful Tools and Scripts + + useful_tools.md + +.. toctree:: + :maxdepth: 2 + :caption: Notes + + conventions.md + compatibility.md + projects.md + changelog.md + faq.md + +.. toctree:: + :caption: API Reference + + api.rst + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`search` diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000..922152e --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/model_zoo.md b/docs/model_zoo.md new file mode 100644 index 0000000..00f0fc7 --- /dev/null +++ b/docs/model_zoo.md @@ -0,0 +1,294 @@ +# Benchmark and Model Zoo + +## Mirror sites + +We use AWS as the main site to host our model zoo, and maintain a mirror on aliyun. +You can replace `https://s3.ap-northeast-2.amazonaws.com/open-mmlab` with `https://open-mmlab.oss-cn-beijing.aliyuncs.com` in model urls. + +## Common settings + +- All models were trained on `coco_2017_train`, and tested on the `coco_2017_val`. +- We use distributed training. +- All pytorch-style pretrained backbones on ImageNet are from PyTorch model zoo, caffe-style pretrained backbones are converted from the newly released model from detectron2. +- For fair comparison with other codebases, we report the GPU memory as the maximum value of `torch.cuda.max_memory_allocated()` for all 8 GPUs. Note that this value is usually less than what `nvidia-smi` shows. +- We report the inference time as the total time of network forwarding and post-processing, excluding the data loading time. Results are obtained with the script [benchmark.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/benchmark.py) which computes the average time on 2000 images. + +## Baselines + +### RPN + +Please refer to [RPN](https://github.com/open-mmlab/mmdetection/blob/master/configs/rpn) for details. + +### Faster R-CNN + +Please refer to [Faster R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn) for details. + +### Mask R-CNN + +Please refer to [Mask R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn) for details. + +### Fast R-CNN (with pre-computed proposals) + +Please refer to [Fast R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/fast_rcnn) for details. + +### RetinaNet + +Please refer to [RetinaNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/retinanet) for details. + +### Cascade R-CNN and Cascade Mask R-CNN + +Please refer to [Cascade R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/cascade_rcnn) for details. + +### Hybrid Task Cascade (HTC) + +Please refer to [HTC](https://github.com/open-mmlab/mmdetection/blob/master/configs/htc) for details. + +### SSD + +Please refer to [SSD](https://github.com/open-mmlab/mmdetection/blob/master/configs/ssd) for details. + +### Group Normalization (GN) + +Please refer to [Group Normalization](https://github.com/open-mmlab/mmdetection/blob/master/configs/gn) for details. + +### Weight Standardization + +Please refer to [Weight Standardization](https://github.com/open-mmlab/mmdetection/blob/master/configs/gn+ws) for details. + +### Deformable Convolution v2 + +Please refer to [Deformable Convolutional Networks](https://github.com/open-mmlab/mmdetection/blob/master/configs/dcn) for details. + +### CARAFE: Content-Aware ReAssembly of FEatures + +Please refer to [CARAFE](https://github.com/open-mmlab/mmdetection/blob/master/configs/carafe) for details. + +### Instaboost + +Please refer to [Instaboost](https://github.com/open-mmlab/mmdetection/blob/master/configs/instaboost) for details. + +### Libra R-CNN + +Please refer to [Libra R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/libra_rcnn) for details. + +### Guided Anchoring + +Please refer to [Guided Anchoring](https://github.com/open-mmlab/mmdetection/blob/master/configs/guided_anchoring) for details. + +### FCOS + +Please refer to [FCOS](https://github.com/open-mmlab/mmdetection/blob/master/configs/fcos) for details. + +### FoveaBox + +Please refer to [FoveaBox](https://github.com/open-mmlab/mmdetection/blob/master/configs/foveabox) for details. + +### RepPoints + +Please refer to [RepPoints](https://github.com/open-mmlab/mmdetection/blob/master/configs/reppoints) for details. + +### FreeAnchor + +Please refer to [FreeAnchor](https://github.com/open-mmlab/mmdetection/blob/master/configs/free_anchor) for details. + +### Grid R-CNN (plus) + +Please refer to [Grid R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/grid_rcnn) for details. + +### GHM + +Please refer to [GHM](https://github.com/open-mmlab/mmdetection/blob/master/configs/ghm) for details. + +### GCNet + +Please refer to [GCNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/gcnet) for details. + +### HRNet + +Please refer to [HRNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/hrnet) for details. + +### Mask Scoring R-CNN + +Please refer to [Mask Scoring R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/ms_rcnn) for details. + +### Train from Scratch + +Please refer to [Rethinking ImageNet Pre-training](https://github.com/open-mmlab/mmdetection/blob/master/configs/scratch) for details. + +### NAS-FPN + +Please refer to [NAS-FPN](https://github.com/open-mmlab/mmdetection/blob/master/configs/nas_fpn) for details. + +### ATSS + +Please refer to [ATSS](https://github.com/open-mmlab/mmdetection/blob/master/configs/atss) for details. + +### FSAF + +Please refer to [FSAF](https://github.com/open-mmlab/mmdetection/blob/master/configs/fsaf) for details. + +### RegNetX + +Please refer to [RegNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/regnet) for details. + +### Res2Net + +Please refer to [Res2Net](https://github.com/open-mmlab/mmdetection/blob/master/configs/res2net) for details. + +### GRoIE + +Please refer to [GRoIE](https://github.com/open-mmlab/mmdetection/blob/master/configs/groie) for details. + +### Dynamic R-CNN + +Please refer to [Dynamic R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/dynamic_rcnn) for details. + +### PointRend + +Please refer to [PointRend](https://github.com/open-mmlab/mmdetection/blob/master/configs/point_rend) for details. + +### DetectoRS + +Please refer to [DetectoRS](https://github.com/open-mmlab/mmdetection/blob/master/configs/detectors) for details. + +### Generalized Focal Loss + +Please refer to [Generalized Focal Loss](https://github.com/open-mmlab/mmdetection/blob/master/configs/gfl) for details. + +### CornerNet + +Please refer to [CornerNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/cornernet) for details. + +### YOLOv3 + +Please refer to [YOLOv3](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolo) for details. + +### PAA + +Please refer to [PAA](https://github.com/open-mmlab/mmdetection/blob/master/configs/paa) for details. + +### SABL + +Please refer to [SABL](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl) for details. + +### CentripetalNet + +Please refer to [CentripetalNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/centripetalnet) for details. + +### ResNeSt + +Please refer to [ResNeSt](https://github.com/open-mmlab/mmdetection/blob/master/configs/resnest) for details. + +### DETR + +Please refer to [DETR](https://github.com/open-mmlab/mmdetection/blob/master/configs/detr) for details. + +### Deformable DETR + +Please refer to [Deformable DETR](https://github.com/open-mmlab/mmdetection/blob/master/configs/deformable_detr) for details. + +### AutoAssign + +Please refer to [AutoAssign](https://github.com/open-mmlab/mmdetection/blob/master/configs/autoassign) for details. + +### YOLOF + +Please refer to [YOLOF](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolof) for details. + +### Other datasets + +We also benchmark some methods on [PASCAL VOC](https://github.com/open-mmlab/mmdetection/blob/master/configs/pascal_voc), [Cityscapes](https://github.com/open-mmlab/mmdetection/blob/master/configs/cityscapes) and [WIDER FACE](https://github.com/open-mmlab/mmdetection/blob/master/configs/wider_face). + +### Pre-trained Models + +We also train [Faster R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn) and [Mask R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn) using ResNet-50 and [RegNetX-3.2G](https://github.com/open-mmlab/mmdetection/blob/master/configs/regnet) with multi-scale training and longer schedules. These models serve as strong pre-trained models for downstream tasks for convenience. + +## Speed benchmark + +### Training Speed benchmark + +We provide [analyze_logs.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/analyze_logs.py) to get average time of iteration in training. You can find examples in [Log Analysis](https://mmdetection.readthedocs.io/en/latest/useful_tools.html#log-analysis). + +We compare the training speed of Mask R-CNN with some other popular frameworks (The data is copied from [detectron2](https://github.com/facebookresearch/detectron2/blob/master/docs/notes/benchmarks.md)). +For mmdetection, we benchmark with [mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py), which should have the same setting with [mask_rcnn_R_50_FPN_noaug_1x.yaml](https://github.com/facebookresearch/detectron2/blob/master/configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml) of detectron2. +We also provide the [checkpoint](http://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug_compare_20200518-10127928.pth) and [training log](http://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug_20200518_105755.log.json) for reference. The throughput is computed as the average throughput in iterations 100-500 to skip GPU warmup time. + +| Implementation | Throughput (img/s) | +|----------------------|--------------------| +| [Detectron2](https://github.com/facebookresearch/detectron2) | 62 | +| [MMDetection](https://github.com/open-mmlab/mmdetection) | 61 | +| [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/) | 53 | +| [tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) | 50 | +| [simpledet](https://github.com/TuSimple/simpledet/) | 39 | +| [Detectron](https://github.com/facebookresearch/Detectron) | 19 | +| [matterport/Mask_RCNN](https://github.com/matterport/Mask_RCNN/) | 14 | + +### Inference Speed Benchmark + +We provide [benchmark.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/benchmark.py) to benchmark the inference latency. +The script benchmarkes the model with 2000 images and calculates the average time ignoring first 5 times. You can change the output log interval (defaults: 50) by setting `LOG-INTERVAL`. + +```shell +python toools/benchmark.py ${CONFIG} ${CHECKPOINT} [--log-interval $[LOG-INTERVAL]] [--fuse-conv-bn] +``` + +The latency of all models in our model zoo is benchmarked without setting `fuse-conv-bn`, you can get a lower latency by setting it. + +## Comparison with Detectron2 + +We compare mmdetection with [Detectron2](https://github.com/facebookresearch/detectron2.git) in terms of speed and performance. +We use the commit id [185c27e](https://github.com/facebookresearch/detectron2/tree/185c27e4b4d2d4c68b5627b3765420c6d7f5a659)(30/4/2020) of detectron. +For fair comparison, we install and run both frameworks on the same machine. + +### Hardware + +- 8 NVIDIA Tesla V100 (32G) GPUs +- Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz + +### Software environment + +- Python 3.7 +- PyTorch 1.4 +- CUDA 10.1 +- CUDNN 7.6.03 +- NCCL 2.4.08 + +### Performance + +| Type | Lr schd | Detectron2 | mmdetection | Download | +|--------------|---------|-------------|-------------|-------------| +| [Faster R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py) | 1x | [37.9](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml) | 38.0 | [model](http://download.openmmlab.com/mmdetection/v2.0/benchmark/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-5324cff8.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/benchmark/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco_20200429_234554.log.json) | +| [Mask R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py) | 1x | [38.6 & 35.2](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml) | 38.8 & 35.4 | [model](http://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco-dbecf295.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco_20200430_054239.log.json) | +| [Retinanet](https://github.com/open-mmlab/mmdetection/blob/master/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py) | 1x | [36.5](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml) | 37.0 | [model](http://download.openmmlab.com/mmdetection/v2.0/benchmark/retinanet_r50_caffe_fpn_mstrain_1x_coco/retinanet_r50_caffe_fpn_mstrain_1x_coco-586977a0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/benchmark/retinanet_r50_caffe_fpn_mstrain_1x_coco/retinanet_r50_caffe_fpn_mstrain_1x_coco_20200430_014748.log.json) | + +### Training Speed + +The training speed is measure with s/iter. The lower, the better. + +| Type | Detectron2 | mmdetection | +|--------------|------------|-------------| +| Faster R-CNN | 0.210 | 0.216 | +| Mask R-CNN | 0.261 | 0.265 | +| Retinanet | 0.200 | 0.205 | + +### Inference Speed + +The inference speed is measured with fps (img/s) on a single GPU, the higher, the better. +To be consistent with Detectron2, we report the pure inference speed (without the time of data loading). +For Mask R-CNN, we exclude the time of RLE encoding in post-processing. +We also include the officially reported speed in the parentheses, which is slightly higher +than the results tested on our server due to differences of hardwares. + +| Type | Detectron2 | mmdetection | +|--------------|-------------|-------------| +| Faster R-CNN | 25.6 (26.3) | 22.2 | +| Mask R-CNN | 22.5 (23.3) | 19.6 | +| Retinanet | 17.8 (18.2) | 20.6 | + +### Training memory + +| Type | Detectron2 | mmdetection | +|--------------|------------|-------------| +| Faster R-CNN | 3.0 | 3.8 | +| Mask R-CNN | 3.4 | 3.9 | +| Retinanet | 3.9 | 3.4 | diff --git a/docs/projects.md b/docs/projects.md new file mode 100644 index 0000000..110e1df --- /dev/null +++ b/docs/projects.md @@ -0,0 +1,46 @@ +# Projects based on MMDetection + +There are many projects built upon MMDetection. +We list some of them as examples of how to extend MMDetection for your own projects. +Pull requests are also welcomed. + +## Projects as an extension + +Some projects extend the boundary of MMDetection for deployment or other research fields. +They reveal the potential of what MMDetection can do. We list several of them as below. + +- [OTEDetection](https://github.com/opencv/mmdetection): OpenVINO training extensions for object detection. +- [MMDetection3d](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection. + +## Projects of papers + +There are also projects released with papers. +Some of the papers are published in top-tier conferences (CVPR, ICCV, and ECCV), the others are also highly influential. +To make this list also a reference for the community to develop and compare new object detection algorithms, we list them following the time order of top-tier conferences. +Methods already supported and maintained by MMDetection are not listed. + +- Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax, CVPR2020. [[paper]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Overcoming_Classifier_Imbalance_for_Long-Tail_Object_Detection_With_Balanced_Group_CVPR_2020_paper.pdf)[[github]](https://github.com/FishYuLi/BalancedGroupSoftmax) +- Coherent Reconstruction of Multiple Humans from a Single Image, CVPR2020. [[paper]](https://jiangwenpl.github.io/multiperson/)[[github]](https://github.com/JiangWenPL/multiperson) +- Look-into-Object: Self-supervised Structure Modeling for Object Recognition, CVPR 2020. [[paper]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_Look-Into-Object_Self-Supervised_Structure_Modeling_for_Object_Recognition_CVPR_2020_paper.pdf)[[github]](https://github.com/JDAI-CV/LIO) +- Video Panoptic Segmentation, CVPR2020. [[paper]](https://arxiv.org/abs/2006.11339)[[github]](https://github.com/mcahny/vps) +- D2Det: Towards High Quality Object Detection and Instance Segmentation, CVPR2020. [[paper]](http://openaccess.thecvf.com/content_CVPR_2020/html/Cao_D2Det_Towards_High_Quality_Object_Detection_and_Instance_Segmentation_CVPR_2020_paper.html)[[github]](https://github.com/JialeCao001/D2Det) +- CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection, CVPR2020. [[paper]](https://arxiv.org/abs/2003.09119)[[github]](https://github.com/KiveeDong/CentripetalNet) +- Learning a Unified Sample Weighting Network for Object Detection, CVPR 2020. [[paper]](http://openaccess.thecvf.com/content_CVPR_2020/html/Cai_Learning_a_Unified_Sample_Weighting_Network_for_Object_Detection_CVPR_2020_paper.html)[[github]](https://github.com/caiqi/sample-weighting-network) +- Scale-equalizing Pyramid Convolution for Object Detection, CVPR2020. [[paper]](https://arxiv.org/abs/2005.03101) [[github]](https://github.com/jshilong/SEPC) +- Revisiting the Sibling Head in Object Detector, CVPR2020. [[paper]](https://arxiv.org/abs/2003.07540)[[github]](https://github.com/Sense-X/TSD) +- PolarMask: Single Shot Instance Segmentation with Polar Representation, CVPR2020. [[paper]](https://arxiv.org/abs/1909.13226)[[github]](https://github.com/xieenze/PolarMask) +- Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection, CVPR2020. [[paper]](https://arxiv.org/abs/2003.11818)[[github]](https://github.com/ggjy/HitDet.pytorch) +- ZeroQ: A Novel Zero Shot Quantization Framework, CVPR2020. [[paper]](https://arxiv.org/abs/2001.00281)[[github]](https://github.com/amirgholami/ZeroQ) +- CBNet: A Novel Composite Backbone Network Architecture for Object Detection, AAAI2020. [[paper]](https://aaai.org/Papers/AAAI/2020GB/AAAI-LiuY.1833.pdf)[[github]](https://github.com/VDIGPKU/CBNet) +- RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation, AAAI2020. [[paper]](https://arxiv.org/abs/1912.05070)[[github]](https://github.com/wangsr126/RDSNet) +- Training-Time-Friendly Network for Real-Time Object Detection, AAAI2020. [[paper]](https://arxiv.org/abs/1909.00700)[[github]](https://github.com/ZJULearning/ttfnet) +- Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution, NeurIPS 2019. [[paper]](https://arxiv.org/abs/1909.06720)[[github]](https://github.com/thangvubk/Cascade-RPN) +- Reasoning R-CNN: Unifying Adaptive Global Reasoning into Large-scale Object Detection, CVPR2019. [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Xu_Reasoning-RCNN_Unifying_Adaptive_Global_Reasoning_Into_Large-Scale_Object_Detection_CVPR_2019_paper.pdf)[[github]](https://github.com/chanyn/Reasoning-RCNN) +- Learning RoI Transformer for Oriented Object Detection in Aerial Images, CVPR2019. [[paper]](https://arxiv.org/abs/1812.00155)[[github]](https://github.com/dingjiansw101/AerialDetection) +- SOLO: Segmenting Objects by Locations. [[paper]](https://arxiv.org/abs/1912.04488)[[github]](https://github.com/WXinlong/SOLO) +- SOLOv2: Dynamic, Faster and Stronger. [[paper]](https://arxiv.org/abs/2003.10152)[[github]](https://github.com/WXinlong/SOLO) +- Dense Peppoints: Representing Visual Objects with Dense Point Sets. [[paper]](https://arxiv.org/abs/1912.11473)[[github]](https://github.com/justimyhxu/Dense-RepPoints) +- IterDet: Iterative Scheme for Object Detection in Crowded Environments. [[paper]](https://arxiv.org/abs/2005.05708)[[github]](https://github.com/saic-vul/iterdet) +- Cross-Iteration Batch Normalization. [[paper]](https://arxiv.org/abs/2002.05712)[[github]](https://github.com/Howal/Cross-iterationBatchNorm) +- Pedestrian Detection: The Elephant In The Room. [[paper]](https://arxiv.org/abs/2003.08799)[[github]](https://github.com/hasanirtiza/Pedestron) +- A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection, NeurIPS2020 [[paper]](https://arxiv.org/abs/2009.13592)[[github]](https://github.com/kemaloksuz/aLRPLoss) diff --git a/docs/robustness_benchmarking.md b/docs/robustness_benchmarking.md new file mode 100644 index 0000000..5be16df --- /dev/null +++ b/docs/robustness_benchmarking.md @@ -0,0 +1,110 @@ +# Corruption Benchmarking + +## Introduction + +We provide tools to test object detection and instance segmentation models on the image corruption benchmark defined in [Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming](https://arxiv.org/abs/1907.07484). +This page provides basic tutorials how to use the benchmark. + +```latex +@article{michaelis2019winter, + title={Benchmarking Robustness in Object Detection: + Autonomous Driving when Winter is Coming}, + author={Michaelis, Claudio and Mitzkus, Benjamin and + Geirhos, Robert and Rusak, Evgenia and + Bringmann, Oliver and Ecker, Alexander S. and + Bethge, Matthias and Brendel, Wieland}, + journal={arXiv:1907.07484}, + year={2019} +} +``` + +![image corruption example](../resources/corruptions_sev_3.png) + +## About the benchmark + +To submit results to the benchmark please visit the [benchmark homepage](https://github.com/bethgelab/robust-detection-benchmark) + +The benchmark is modelled after the [imagenet-c benchmark](https://github.com/hendrycks/robustness) which was originally +published in [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://arxiv.org/abs/1903.12261) (ICLR 2019) by Dan Hendrycks and Thomas Dietterich. + +The image corruption functions are included in this library but can be installed separately using: + +```shell +pip install imagecorruptions +``` + +Compared to imagenet-c a few changes had to be made to handle images of arbitrary size and greyscale images. +We also modfied the 'motion blur' and 'snow' corruptions to remove dependency from a linux specific library, +which would have to be installed separately otherwise. For details please refer to the [imagecorruptions repository](https://github.com/bethgelab/imagecorruptions). + +## Inference with pretrained models + +We provide a testing script to evaluate a models performance on any combination of the corruptions provided in the benchmark. + +### Test a dataset + +- [x] single GPU testing +- [ ] multiple GPU testing +- [ ] visualize detection results + +You can use the following commands to test a models performance under the 15 corruptions used in the benchmark. + +```shell +# single-gpu testing +python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] +``` + +Alternatively different group of corruptions can be selected. + +```shell +# noise +python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions noise + +# blur +python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions blur + +# wetaher +python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions weather + +# digital +python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions digital +``` + +Or a costom set of corruptions e.g.: + +```shell +# gaussian noise, zoom blur and snow +python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions gaussian_noise zoom_blur snow +``` + +Finally the corruption severities to evaluate can be chosen. +Severity 0 corresponds to clean data and the effect increases from 1 to 5. + +```shell +# severity 1 +python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --severities 1 + +# severities 0,2,4 +python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --severities 0 2 4 +``` + +## Results for modelzoo models + +The results on COCO 2017val are shown in the below table. + +Model | Backbone | Style | Lr schd | box AP clean | box AP corr. | box % | mask AP clean | mask AP corr. | mask % | +:-----:|:---------:|:-------:|:-------:|:------------:|:------------:|:-----:|:-------------:|:-------------:|:------:| +Faster R-CNN | R-50-FPN | pytorch | 1x | 36.3 | 18.2 | 50.2 | - | - | - | +Faster R-CNN | R-101-FPN | pytorch | 1x | 38.5 | 20.9 | 54.2 | - | - | - | +Faster R-CNN | X-101-32x4d-FPN | pytorch |1x | 40.1 | 22.3 | 55.5 | - | - | - | +Faster R-CNN | X-101-64x4d-FPN | pytorch |1x | 41.3 | 23.4 | 56.6 | - | - | - | +Faster R-CNN | R-50-FPN-DCN | pytorch | 1x | 40.0 | 22.4 | 56.1 | - | - | - | +Faster R-CNN | X-101-32x4d-FPN-DCN | pytorch | 1x | 43.4 | 26.7 | 61.6 | - | - | - | +Mask R-CNN | R-50-FPN | pytorch | 1x | 37.3 | 18.7 | 50.1 | 34.2 | 16.8 | 49.1 | +Mask R-CNN | R-50-FPN-DCN | pytorch | 1x | 41.1 | 23.3 | 56.7 | 37.2 | 20.7 | 55.7 | +Cascade R-CNN | R-50-FPN | pytorch | 1x | 40.4 | 20.1 | 49.7 | - | - | - | +Cascade Mask R-CNN | R-50-FPN | pytorch | 1x| 41.2 | 20.7 | 50.2 | 35.7 | 17.6 | 49.3 | +RetinaNet | R-50-FPN | pytorch | 1x | 35.6 | 17.8 | 50.1 | - | - | - | +Hybrid Task Cascade | X-101-64x4d-FPN-DCN | pytorch | 1x | 50.6 | 32.7 | 64.7 | 43.8 | 28.1 | 64.0 | + +Results may vary slightly due to the stochastic application of the corruptions. diff --git a/docs/stat.py b/docs/stat.py new file mode 100755 index 0000000..9625c62 --- /dev/null +++ b/docs/stat.py @@ -0,0 +1,64 @@ +#!/usr/bin/env python +import functools as func +import glob +import os.path as osp +import re + +import numpy as np + +url_prefix = 'https://github.com/open-mmlab/mmdetection/blob/master/' + +files = sorted(glob.glob('../configs/*/README.md')) + +stats = [] +titles = [] +num_ckpts = 0 + +for f in files: + url = osp.dirname(f.replace('../', url_prefix)) + + with open(f, 'r') as content_file: + content = content_file.read() + + title = content.split('\n')[0].replace('# ', '').strip() + ckpts = set(x.lower().strip() + for x in re.findall(r'\[model\]\((https?.*)\)', content)) + + if len(ckpts) == 0: + continue + + _papertype = [x for x in re.findall(r'\[([A-Z]+)\]', content)] + assert len(_papertype) > 0 + papertype = _papertype[0] + + paper = set([(papertype, title)]) + + titles.append(title) + num_ckpts += len(ckpts) + + statsmsg = f""" +\t* [{papertype}] [{title}]({url}) ({len(ckpts)} ckpts) +""" + stats.append((paper, ckpts, statsmsg)) + +allpapers = func.reduce(lambda a, b: a.union(b), [p for p, _, _ in stats]) +msglist = '\n'.join(x for _, _, x in stats) + +papertypes, papercounts = np.unique([t for t, _ in allpapers], + return_counts=True) +countstr = '\n'.join( + [f' - {t}: {c}' for t, c in zip(papertypes, papercounts)]) + +modelzoo = f""" +# Model Zoo Statistics + +* Number of papers: {len(set(titles))} +{countstr} + +* Number of checkpoints: {num_ckpts} + +{msglist} +""" + +with open('modelzoo_statistics.md', 'w') as f: + f.write(modelzoo) diff --git a/docs/tutorials/config.md b/docs/tutorials/config.md new file mode 100644 index 0000000..f354d49 --- /dev/null +++ b/docs/tutorials/config.md @@ -0,0 +1,532 @@ +# Tutorial 1: Learn about Configs + +We incorporate modular and inheritance design into our config system, which is convenient to conduct various experiments. +If you wish to inspect the config file, you may run `python tools/misc/print_config.py /PATH/TO/CONFIG` to see the complete config. + +## Modify config through script arguments + +When submitting jobs using "tools/train.py" or "tools/test.py", you may specify `--cfg-options` to in-place modify the config. + +- Update config keys of dict chains. + + The config options can be specified following the order of the dict keys in the original config. + For example, `--cfg-options model.backbone.norm_eval=False` changes the all BN modules in model backbones to `train` mode. + +- Update keys inside a list of configs. + + Some config dicts are composed as a list in your config. For example, the training pipeline `data.train.pipeline` is normally a list + e.g. `[dict(type='LoadImageFromFile'), ...]`. If you want to change `'LoadImageFromFile'` to `'LoadImageFromWebcam'` in the pipeline, + you may specify `--cfg-options data.train.pipeline.0.type=LoadImageFromWebcam`. + +- Update values of list/tuples. + + If the value to be updated is a list or a tuple. For example, the config file normally sets `workflow=[('train', 1)]`. If you want to + change this key, you may specify `--cfg-options workflow="[(train,1),(val,1)]"`. Note that the quotation mark \" is necessary to + support list/tuple data types, and that **NO** white space is allowed inside the quotation marks in the specified value. + +## Config File Structure + +There are 4 basic component types under `config/_base_`, dataset, model, schedule, default_runtime. +Many methods could be easily constructed with one of each like Faster R-CNN, Mask R-CNN, Cascade R-CNN, RPN, SSD. +The configs that are composed by components from `_base_` are called _primitive_. + +For all configs under the same folder, it is recommended to have only **one** _primitive_ config. All other configs should inherit from the _primitive_ config. In this way, the maximum of inheritance level is 3. + +For easy understanding, we recommend contributors to inherit from existing methods. +For example, if some modification is made base on Faster R-CNN, user may first inherit the basic Faster R-CNN structure by specifying `_base_ = ../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py`, then modify the necessary fields in the config files. + +If you are building an entirely new method that does not share the structure with any of the existing methods, you may create a folder `xxx_rcnn` under `configs`, + +Please refer to [mmcv](https://mmcv.readthedocs.io/en/latest/utils.html#config) for detailed documentation. + +## Config Name Style + +We follow the below style to name config files. Contributors are advised to follow the same style. + +``` +{model}_[model setting]_{backbone}_{neck}_[norm setting]_[misc]_[gpu x batch_per_gpu]_{schedule}_{dataset} +``` + +`{xxx}` is required field and `[yyy]` is optional. + +- `{model}`: model type like `faster_rcnn`, `mask_rcnn`, etc. +- `[model setting]`: specific setting for some model, like `without_semantic` for `htc`, `moment` for `reppoints`, etc. +- `{backbone}`: backbone type like `r50` (ResNet-50), `x101` (ResNeXt-101). +- `{neck}`: neck type like `fpn`, `pafpn`, `nasfpn`, `c4`. +- `[norm_setting]`: `bn` (Batch Normalization) is used unless specified, other norm layer type could be `gn` (Group Normalization), `syncbn` (Synchronized Batch Normalization). + `gn-head`/`gn-neck` indicates GN is applied in head/neck only, while `gn-all` means GN is applied in the entire model, e.g. backbone, neck, head. +- `[misc]`: miscellaneous setting/plugins of model, e.g. `dconv`, `gcb`, `attention`, `albu`, `mstrain`. +- `[gpu x batch_per_gpu]`: GPUs and samples per GPU, `8x2` is used by default. +- `{schedule}`: training schedule, options are `1x`, `2x`, `20e`, etc. + `1x` and `2x` means 12 epochs and 24 epochs respectively. + `20e` is adopted in cascade models, which denotes 20 epochs. + For `1x`/`2x`, initial learning rate decays by a factor of 10 at the 8/16th and 11/22th epochs. + For `20e`, initial learning rate decays by a factor of 10 at the 16th and 19th epochs. +- `{dataset}`: dataset like `coco`, `cityscapes`, `voc_0712`, `wider_face`. + +## Deprecated train_cfg/test_cfg + +The `train_cfg` and `test_cfg` are deprecated in config file, please specify them in the model config. The original config structure is as below. + +```python +# deprecated +model = dict( + type=..., + ... +) +train_cfg=dict(...) +test_cfg=dict(...) +``` + +The migration example is as below. + +```python +# recommended +model = dict( + type=..., + ... + train_cfg=dict(...), + test_cfg=dict(...), +) +``` + +## An Example of Mask R-CNN + +To help the users have a basic idea of a complete config and the modules in a modern detection system, +we make brief comments on the config of Mask R-CNN using ResNet50 and FPN as the following. +For more detailed usage and the corresponding alternative for each modules, please refer to the API documentation. + +```python +model = dict( + type='MaskRCNN', # The name of detector + pretrained= + 'torchvision://resnet50', # The ImageNet pretrained backbone to be loaded + backbone=dict( # The config of backbone + type='ResNet', # The type of the backbone, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/backbones/resnet.py#L288 for more details. + depth=50, # The depth of backbone, usually it is 50 or 101 for ResNet and ResNext backbones. + num_stages=4, # Number of stages of the backbone. + out_indices=(0, 1, 2, 3), # The index of output feature maps produced in each stages + frozen_stages=1, # The weights in the first 1 stage are fronzen + norm_cfg=dict( # The config of normalization layers. + type='BN', # Type of norm layer, usually it is BN or GN + requires_grad=True), # Whether to train the gamma and beta in BN + norm_eval=True, # Whether to freeze the statistics in BN + style='pytorch'), # The style of backbone, 'pytorch' means that stride 2 layers are in 3x3 conv, 'caffe' means stride 2 layers are in 1x1 convs. + neck=dict( + type='FPN', # The neck of detector is FPN. We also support 'NASFPN', 'PAFPN', etc. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/necks/fpn.py#L10 for more details. + in_channels=[256, 512, 1024, 2048], # The input channels, this is consistent with the output channels of backbone + out_channels=256, # The output channels of each level of the pyramid feature map + num_outs=5), # The number of output scales + rpn_head=dict( + type='RPNHead', # The type of RPN head is 'RPNHead', we also support 'GARPNHead', etc. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/dense_heads/rpn_head.py#L12 for more details. + in_channels=256, # The input channels of each input feature map, this is consistent with the output channels of neck + feat_channels=256, # Feature channels of convolutional layers in the head. + anchor_generator=dict( # The config of anchor generator + type='AnchorGenerator', # Most of methods use AnchorGenerator, SSD Detectors uses `SSDAnchorGenerator`. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/anchor/anchor_generator.py#L10 for more details + scales=[8], # Basic scale of the anchor, the area of the anchor in one position of a feature map will be scale * base_sizes + ratios=[0.5, 1.0, 2.0], # The ratio between height and width. + strides=[4, 8, 16, 32, 64]), # The strides of the anchor generator. This is consistent with the FPN feature strides. The strides will be taken as base_sizes if base_sizes is not set. + bbox_coder=dict( # Config of box coder to encode and decode the boxes during training and testing + type='DeltaXYWHBBoxCoder', # Type of box coder. 'DeltaXYWHBBoxCoder' is applied for most of methods. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py#L9 for more details. + target_means=[0.0, 0.0, 0.0, 0.0], # The target means used to encode and decode boxes + target_stds=[1.0, 1.0, 1.0, 1.0]), # The standard variance used to encode and decode boxes + loss_cls=dict( # Config of loss function for the classification branch + type='CrossEntropyLoss', # Type of loss for classification branch, we also support FocalLoss etc. + use_sigmoid=True, # RPN usually perform two-class classification, so it usually uses sigmoid function. + loss_weight=1.0), # Loss weight of the classification branch. + loss_bbox=dict( # Config of loss function for the regression branch. + type='L1Loss', # Type of loss, we also support many IoU Losses and smooth L1-loss, etc. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/losses/smooth_l1_loss.py#L56 for implementation. + loss_weight=1.0)), # Loss weight of the regression branch. + roi_head=dict( # RoIHead encapsulates the second stage of two-stage/cascade detectors. + type='StandardRoIHead', # Type of the RoI head. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/standard_roi_head.py#L10 for implementation. + bbox_roi_extractor=dict( # RoI feature extractor for bbox regression. + type='SingleRoIExtractor', # Type of the RoI feature extractor, most of methods uses SingleRoIExtractor. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/roi_extractors/single_level.py#L10 for details. + roi_layer=dict( # Config of RoI Layer + type='RoIAlign', # Type of RoI Layer, DeformRoIPoolingPack and ModulatedDeformRoIPoolingPack are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/ops/roi_align/roi_align.py#L79 for details. + output_size=7, # The output size of feature maps. + sampling_ratio=0), # Sampling ratio when extracting the RoI features. 0 means adaptive ratio. + out_channels=256, # output channels of the extracted feature. + featmap_strides=[4, 8, 16, 32]), # Strides of multi-scale feature maps. It should be consistent to the architecture of the backbone. + bbox_head=dict( # Config of box head in the RoIHead. + type='Shared2FCBBoxHead', # Type of the bbox head, Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py#L177 for implementation details. + in_channels=256, # Input channels for bbox head. This is consistent with the out_channels in roi_extractor + fc_out_channels=1024, # Output feature channels of FC layers. + roi_feat_size=7, # Size of RoI features + num_classes=80, # Number of classes for classification + bbox_coder=dict( # Box coder used in the second stage. + type='DeltaXYWHBBoxCoder', # Type of box coder. 'DeltaXYWHBBoxCoder' is applied for most of methods. + target_means=[0.0, 0.0, 0.0, 0.0], # Means used to encode and decode box + target_stds=[0.1, 0.1, 0.2, 0.2]), # Standard variance for encoding and decoding. It is smaller since the boxes are more accurate. [0.1, 0.1, 0.2, 0.2] is a conventional setting. + reg_class_agnostic=False, # Whether the regression is class agnostic. + loss_cls=dict( # Config of loss function for the classification branch + type='CrossEntropyLoss', # Type of loss for classification branch, we also support FocalLoss etc. + use_sigmoid=False, # Whether to use sigmoid. + loss_weight=1.0), # Loss weight of the classification branch. + loss_bbox=dict( # Config of loss function for the regression branch. + type='L1Loss', # Type of loss, we also support many IoU Losses and smooth L1-loss, etc. + loss_weight=1.0)), # Loss weight of the regression branch. + mask_roi_extractor=dict( # RoI feature extractor for bbox regression. + type='SingleRoIExtractor', # Type of the RoI feature extractor, most of methods uses SingleRoIExtractor. + roi_layer=dict( # Config of RoI Layer that extracts features for instance segmentation + type='RoIAlign', # Type of RoI Layer, DeformRoIPoolingPack and ModulatedDeformRoIPoolingPack are also supported + output_size=14, # The output size of feature maps. + sampling_ratio=0), # Sampling ratio when extracting the RoI features. + out_channels=256, # Output channels of the extracted feature. + featmap_strides=[4, 8, 16, 32]), # Strides of multi-scale feature maps. + mask_head=dict( # Mask prediction head + type='FCNMaskHead', # Type of mask head, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py#L21 for implementation details. + num_convs=4, # Number of convolutional layers in mask head. + in_channels=256, # Input channels, should be consistent with the output channels of mask roi extractor. + conv_out_channels=256, # Output channels of the convolutional layer. + num_classes=80, # Number of class to be segmented. + loss_mask=dict( # Config of loss function for the mask branch. + type='CrossEntropyLoss', # Type of loss used for segmentation + use_mask=True, # Whether to only train the mask in the correct class. + loss_weight=1.0)))) # Loss weight of mask branch. + train_cfg = dict( # Config of training hyperparameters for rpn and rcnn + rpn=dict( # Training config of rpn + assigner=dict( # Config of assigner + type='MaxIoUAssigner', # Type of assigner, MaxIoUAssigner is used for many common detectors. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details. + pos_iou_thr=0.7, # IoU >= threshold 0.7 will be taken as positive samples + neg_iou_thr=0.3, # IoU < threshold 0.3 will be taken as negative samples + min_pos_iou=0.3, # The minimal IoU threshold to take boxes as positive samples + match_low_quality=True, # Whether to match the boxes under low quality (see API doc for more details). + ignore_iof_thr=-1), # IoF threshold for ignoring bboxes + sampler=dict( # Config of positive/negative sampler + type='RandomSampler', # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details. + num=256, # Number of samples + pos_fraction=0.5, # The ratio of positive samples in the total samples. + neg_pos_ub=-1, # The upper bound of negative samples based on the number of positive samples. + add_gt_as_proposals=False), # Whether add GT as proposals after sampling. + allowed_border=-1, # The border allowed after padding for valid anchors. + pos_weight=-1, # The weight of positive samples during training. + debug=False), # Whether to set the debug mode + rpn_proposal=dict( # The config to generate proposals during training + nms_across_levels=False, # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels. + nms_pre=2000, # The number of boxes before NMS + nms_post=1000, # The number of boxes to be kept by NMS, Only work in `GARPNHead`. + max_per_img=1000, # The number of boxes to be kept after NMS. + nms=dict( # Config of nms + type='nms', #Type of nms + iou_threshold=0.7 # NMS threshold + ), + min_bbox_size=0), # The allowed minimal box size + rcnn=dict( # The config for the roi heads. + assigner=dict( # Config of assigner for second stage, this is different for that in rpn + type='MaxIoUAssigner', # Type of assigner, MaxIoUAssigner is used for all roi_heads for now. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details. + pos_iou_thr=0.5, # IoU >= threshold 0.5 will be taken as positive samples + neg_iou_thr=0.5, # IoU >= threshold 0.5 will be taken as positive samples + min_pos_iou=0.5, # The minimal IoU threshold to take boxes as positive samples + match_low_quality=False, # Whether to match the boxes under low quality (see API doc for more details). + ignore_iof_thr=-1), # IoF threshold for ignoring bboxes + sampler=dict( + type='RandomSampler', # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details. + num=512, # Number of samples + pos_fraction=0.25, # The ratio of positive samples in the total samples. + neg_pos_ub=-1, # The upper bound of negative samples based on the number of positive samples. + add_gt_as_proposals=True + ), # Whether add GT as proposals after sampling. + mask_size=28, # Size of mask + pos_weight=-1, # The weight of positive samples during training. + debug=False)) # Whether to set the debug mode + test_cfg = dict( # Config for testing hyperparameters for rpn and rcnn + rpn=dict( # The config to generate proposals during testing + nms_across_levels=False, # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels. + nms_pre=1000, # The number of boxes before NMS + nms_post=1000, # The number of boxes to be kept by NMS, Only work in `GARPNHead`. + max_per_img=1000, # The number of boxes to be kept after NMS. + nms=dict( # Config of nms + type='nms', #Type of nms + iou_threshold=0.7 # NMS threshold + ), + min_bbox_size=0), # The allowed minimal box size + rcnn=dict( # The config for the roi heads. + score_thr=0.05, # Threshold to filter out boxes + nms=dict( # Config of nms in the second stage + type='nms', # Type of nms + iou_thr=0.5), # NMS threshold + max_per_img=100, # Max number of detections of each image + mask_thr_binary=0.5)) # Threshold of mask prediction +dataset_type = 'CocoDataset' # Dataset type, this will be used to define the dataset +data_root = 'data/coco/' # Root path of data +img_norm_cfg = dict( # Image normalization config to normalize the input images + mean=[123.675, 116.28, 103.53], # Mean values used to pre-training the pre-trained backbone models + std=[58.395, 57.12, 57.375], # Standard variance used to pre-training the pre-trained backbone models + to_rgb=True +) # The channel orders of image used to pre-training the pre-trained backbone models +train_pipeline = [ # Training pipeline + dict(type='LoadImageFromFile'), # First pipeline to load images from file path + dict( + type='LoadAnnotations', # Second pipeline to load annotations for current image + with_bbox=True, # Whether to use bounding box, True for detection + with_mask=True, # Whether to use instance mask, True for instance segmentation + poly2mask=False), # Whether to convert the polygon mask to instance mask, set False for acceleration and to save memory + dict( + type='Resize', # Augmentation pipeline that resize the images and their annotations + img_scale=(1333, 800), # The largest scale of image + keep_ratio=True + ), # whether to keep the ratio between height and width. + dict( + type='RandomFlip', # Augmentation pipeline that flip the images and their annotations + flip_ratio=0.5), # The ratio or probability to flip + dict( + type='Normalize', # Augmentation pipeline that normalize the input images + mean=[123.675, 116.28, 103.53], # These keys are the same of img_norm_cfg since the + std=[58.395, 57.12, 57.375], # keys of img_norm_cfg are used here as arguments + to_rgb=True), + dict( + type='Pad', # Padding config + size_divisor=32), # The number the padded images should be divisible + dict(type='DefaultFormatBundle'), # Default format bundle to gather data in the pipeline + dict( + type='Collect', # Pipeline that decides which keys in the data should be passed to the detector + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), # First pipeline to load images from file path + dict( + type='MultiScaleFlipAug', # An encapsulation that encapsulates the testing augmentations + img_scale=(1333, 800), # Decides the largest scale for testing, used for the Resize pipeline + flip=False, # Whether to flip images during testing + transforms=[ + dict(type='Resize', # Use resize augmentation + keep_ratio=True), # Whether to keep the ratio between height and width, the img_scale set here will be suppressed by the img_scale set above. + dict(type='RandomFlip'), # Thought RandomFlip is added in pipeline, it is not used because flip=False + dict( + type='Normalize', # Normalization config, the values are from img_norm_cfg + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict( + type='Pad', # Padding config to pad images divisable by 32. + size_divisor=32), + dict( + type='ImageToTensor', # convert image to tensor + keys=['img']), + dict( + type='Collect', # Collect pipeline that collect necessary keys for testing. + keys=['img']) + ]) +] +data = dict( + samples_per_gpu=2, # Batch size of a single GPU + workers_per_gpu=2, # Worker to pre-fetch data for each single GPU + train=dict( # Train dataset config + type='CocoDataset', # Type of dataset, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/coco.py#L19 for details. + ann_file='data/coco/annotations/instances_train2017.json', # Path of annotation file + img_prefix='data/coco/train2017/', # Prefix of image path + pipeline=[ # pipeline, this is passed by the train_pipeline created before. + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']) + ]), + val=dict( # Validation dataset config + type='CocoDataset', + ann_file='data/coco/annotations/instances_val2017.json', + img_prefix='data/coco/val2017/', + pipeline=[ # Pipeline is passed by test_pipeline created before + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) + ]), + test=dict( # Test dataset config, modify the ann_file for test-dev/test submission + type='CocoDataset', + ann_file='data/coco/annotations/instances_val2017.json', + img_prefix='data/coco/val2017/', + pipeline=[ # Pipeline is passed by test_pipeline created before + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) + ], + samples_per_gpu=2 # Batch size of a single GPU used in testing + )) +evaluation = dict( # The config to build the evaluation hook, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/evaluation/eval_hooks.py#L7 for more details. + interval=1, # Evaluation interval + metric=['bbox', 'segm']) # Metrics used during evaluation +optimizer = dict( # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch + type='SGD', # Type of optimizers, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/optimizer/default_constructor.py#L13 for more details + lr=0.02, # Learning rate of optimizers, see detail usages of the parameters in the documentaion of PyTorch + momentum=0.9, # Momentum + weight_decay=0.0001) # Weight decay of SGD +optimizer_config = dict( # Config used to build the optimizer hook, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/optimizer.py#L8 for implementation details. + grad_clip=None) # Most of the methods do not use gradient clip +lr_config = dict( # Learning rate scheduler config used to register LrUpdater hook + policy='step', # The policy of scheduler, also support CosineAnnealing, Cyclic, etc. Refer to details of supported LrUpdater from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py#L9. + warmup='linear', # The warmup policy, also support `exp` and `constant`. + warmup_iters=500, # The number of iterations for warmup + warmup_ratio= + 0.001, # The ratio of the starting learning rate used for warmup + step=[8, 11]) # Steps to decay the learning rate +runner = dict(type='EpochBasedRunner', max_epochs=12) # Runner that runs the workflow in total max_epochs +checkpoint_config = dict( # Config to set the checkpoint hook, Refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py for implementation. + interval=1) # The save interval is 1 +log_config = dict( # config to register logger hook + interval=50, # Interval to print the log + hooks=[ + # dict(type='TensorboardLoggerHook') # The Tensorboard logger is also supported + dict(type='TextLoggerHook') + ]) # The logger used to record the training process. +dist_params = dict(backend='nccl') # Parameters to setup distributed training, the port can also be set. +log_level = 'INFO' # The level of logging. +load_from = None # load models as a pre-trained model from a given path. This will not resume training. +resume_from = None # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved. +workflow = [('train', 1)] # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once. The workflow trains the model by 12 epochs according to the total_epochs. +work_dir = 'work_dir' # Directory to save the model checkpoints and logs for the current experiments. +``` + +## FAQ + +### Ignore some fields in the base configs + +Sometimes, you may set `_delete_=True` to ignore some of fields in base configs. +You may refer to [mmcv](https://mmcv.readthedocs.io/en/latest/utils.html#inherit-from-base-config-with-ignored-fields) for simple inllustration. + +In MMDetection, for example, to change the backbone of Mask R-CNN with the following config. + +```python +model = dict( + type='MaskRCNN', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict(...), + rpn_head=dict(...), + roi_head=dict(...)) +``` + +`ResNet` and `HRNet` use different keywords to construct. + +```python +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w32', + backbone=dict( + _delete_=True, + type='HRNet', + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256)))), + neck=dict(...)) +``` + +The `_delete_=True` would replace all old keys in `backbone` field with new keys. + +### Use intermediate variables in configs + +Some intermediate variables are used in the configs files, like `train_pipeline`/`test_pipeline` in datasets. +It's worth noting that when modifying intermediate variables in the children configs, user need to pass the intermediate variables into corresponding fields again. +For example, we would like to use multi scale strategy to train a Mask R-CNN. `train_pipeline`/`test_pipeline` are intermediate variable we would like modify. + +```python +_base_ = './mask_rcnn_r50_fpn_1x_coco.py' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode="value", + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +``` + +We first define the new `train_pipeline`/`test_pipeline` and pass them into `data`. diff --git a/docs/tutorials/customize_dataset.md b/docs/tutorials/customize_dataset.md new file mode 100644 index 0000000..d1e956d --- /dev/null +++ b/docs/tutorials/customize_dataset.md @@ -0,0 +1,487 @@ +# Tutorial 2: Customize Datasets + +## Support new data format + +To support a new data format, you can either convert them to existing formats (COCO format or PASCAL format) or directly convert them to the middle format. You could also choose to convert them offline (before training by a script) or online (implement a new dataset and do the conversion at training). In MMDetection, we recommend to convert the data into COCO formats and do the conversion offline, thus you only need to modify the config's data annotation paths and classes after the conversion of your data. + +### Reorganize new data formats to existing format + +The simplest way is to convert your dataset to existing dataset formats (COCO or PASCAL VOC). + +The annotation json files in COCO format has the following necessary keys: + +```python +'images': [ + { + 'file_name': 'COCO_val2014_000000001268.jpg', + 'height': 427, + 'width': 640, + 'id': 1268 + }, + ... +], + +'annotations': [ + { + 'segmentation': [[192.81, + 247.09, + ... + 219.03, + 249.06]], # if you have mask labels + 'area': 1035.749, + 'iscrowd': 0, + 'image_id': 1268, + 'bbox': [192.81, 224.8, 74.73, 33.43], + 'category_id': 16, + 'id': 42986 + }, + ... +], + +'categories': [ + {'id': 0, 'name': 'car'}, + ] +``` + +There are three necessary keys in the json file: + +- `images`: contains a list of images with their informations like `file_name`, `height`, `width`, and `id`. +- `annotations`: contains the list of instance annotations. +- `categories`: contains the list of categories names and their ID. + +After the data pre-processing, there are two steps for users to train the customized new dataset with existing format (e.g. COCO format): + +1. Modify the config file for using the customized dataset. +2. Check the annotations of the customized dataset. + +Here we give an example to show the above two steps, which uses a customized dataset of 5 classes with COCO format to train an existing Cascade MaskRCNN R50 FPN detector. + +#### 1. Modify the config file for using the customized dataset + +There are two aspects involved in the modification of config file: + +1. The `data` field. Specifically, you need to explicitly add the `classes` fields in `data.train`, `data.val` and `data.test`. +2. The `num_classes` field in the `model` part. Explicitly over-write all the `num_classes` from default value (e.g. 80 in COCO) to your classes number. + +In `configs/my_custom_config.py`: + +```python + +# the new config inherits the base configs to highlight the necessary modification +_base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py' + +# 1. dataset settings +dataset_type = 'CocoDataset' +classes = ('a', 'b', 'c', 'd', 'e') +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + # explicitly add your class names to the field `classes` + classes=classes, + ann_file='path/to/your/train/annotation_data', + img_prefix='path/to/your/train/image_data'), + val=dict( + type=dataset_type, + # explicitly add your class names to the field `classes` + classes=classes, + ann_file='path/to/your/val/annotation_data', + img_prefix='path/to/your/val/image_data'), + test=dict( + type=dataset_type, + # explicitly add your class names to the field `classes` + classes=classes, + ann_file='path/to/your/test/annotation_data', + img_prefix='path/to/your/test/image_data')) + +# 2. model settings + +# explicitly over-write all the `num_classes` field from default 80 to 5. +model = dict( + roi_head=dict( + bbox_head=[ + dict( + type='Shared2FCBBoxHead', + # explicitly over-write all the `num_classes` field from default 80 to 5. + num_classes=5), + dict( + type='Shared2FCBBoxHead', + # explicitly over-write all the `num_classes` field from default 80 to 5. + num_classes=5), + dict( + type='Shared2FCBBoxHead', + # explicitly over-write all the `num_classes` field from default 80 to 5. + num_classes=5)], + # explicitly over-write all the `num_classes` field from default 80 to 5. + mask_head=dict(num_classes=5))) +``` + +#### 2. Check the annotations of the customized dataset + +Assuming your customized dataset is COCO format, make sure you have the correct annotations in the customized dataset: + +1. The length for `categories` field in annotations should exactly equal the tuple length of `classes` fields in your config, meaning the number of classes (e.g. 5 in this example). +2. The `classes` fields in your config file should have exactly the same elements and the same order with the `name` in `categories` of annotations. MMDetection automatically maps the uncontinuous `id` in `categories` to the continuous label indices, so the string order of `name` in `categories` field affects the order of label indices. Meanwhile, the string order of `classes` in config affects the label text during visualization of predicted bounding boxes. +3. The `category_id` in `annotations` field should be valid, i.e., all values in `category_id` should belong to `id` in `categories`. + +Here is a valid example of annotations: + +```python + +'annotations': [ + { + 'segmentation': [[192.81, + 247.09, + ... + 219.03, + 249.06]], # if you have mask labels + 'area': 1035.749, + 'iscrowd': 0, + 'image_id': 1268, + 'bbox': [192.81, 224.8, 74.73, 33.43], + 'category_id': 16, + 'id': 42986 + }, + ... +], + +# MMDetection automatically maps the uncontinuous `id` to the continuous label indices. +'categories': [ + {'id': 1, 'name': 'a'}, {'id': 3, 'name': 'b'}, {'id': 4, 'name': 'c'}, {'id': 16, 'name': 'd'}, {'id': 17, 'name': 'e'}, + ] +``` + +We use this way to support CityScapes dataset. The script is in [cityscapes.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/dataset_converters/cityscapes.py) and we also provide the finetuning [configs](https://github.com/open-mmlab/mmdetection/blob/master/configs/cityscapes). + +**Note** + +1. For instance segmentation datasets, **MMDetection only supports evaluating mask AP of dataset in COCO format for now**. +2. It is recommanded to convert the data offline before training, thus you can still use `CocoDataset` and only need to modify the path of annotations and the training classes. + +### Reorganize new data format to middle format + +It is also fine if you do not want to convert the annotation format to COCO or PASCAL format. +Actually, we define a simple annotation format and all existing datasets are +processed to be compatible with it, either online or offline. + +The annotation of a dataset is a list of dict, each dict corresponds to an image. +There are 3 field `filename` (relative path), `width`, `height` for testing, +and an additional field `ann` for training. `ann` is also a dict containing at least 2 fields: +`bboxes` and `labels`, both of which are numpy arrays. Some datasets may provide +annotations like crowd/difficult/ignored bboxes, we use `bboxes_ignore` and `labels_ignore` +to cover them. + +Here is an example. + +```python + +[ + { + 'filename': 'a.jpg', + 'width': 1280, + 'height': 720, + 'ann': { + 'bboxes': (n, 4), + 'labels': (n, ), + 'bboxes_ignore': (k, 4), + 'labels_ignore': (k, ) (optional field) + } + }, + ... +] +``` + +There are two ways to work with custom datasets. + +- online conversion + + You can write a new Dataset class inherited from `CustomDataset`, and overwrite two methods + `load_annotations(self, ann_file)` and `get_ann_info(self, idx)`, + like [CocoDataset](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/coco.py) and [VOCDataset](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/voc.py). + +- offline conversion + + You can convert the annotation format to the expected format above and save it to + a pickle or json file, like [pascal_voc.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/dataset_converters/pascal_voc.py). + Then you can simply use `CustomDataset`. + +### An example of customized dataset + +Assume the annotation is in a new format in text files. +The bounding boxes annotations are stored in text file `annotation.txt` as the following + +``` +# +000001.jpg +1280 720 +2 +10 20 40 60 1 +20 40 50 60 2 +# +000002.jpg +1280 720 +3 +50 20 40 60 2 +20 40 30 45 2 +30 40 50 60 3 +``` + +We can create a new dataset in `mmdet/datasets/my_dataset.py` to load the data. + +```python +import mmcv +import numpy as np + +from .builder import DATASETS +from .custom import CustomDataset + + +@DATASETS.register_module() +class MyDataset(CustomDataset): + + CLASSES = ('person', 'bicycle', 'car', 'motorcycle') + + def load_annotations(self, ann_file): + ann_list = mmcv.list_from_file(ann_file) + + data_infos = [] + for i, ann_line in enumerate(ann_list): + if ann_line != '#': + continue + + img_shape = ann_list[i + 2].split(' ') + width = int(img_shape[0]) + height = int(img_shape[1]) + bbox_number = int(ann_list[i + 3]) + + anns = ann_line.split(' ') + bboxes = [] + labels = [] + for anns in ann_list[i + 4:i + 4 + bbox_number]: + bboxes.append([float(ann) for ann in anns[:4]]) + labels.append(int(anns[4])) + + data_infos.append( + dict( + filename=ann_list[i + 1], + width=width, + height=height, + ann=dict( + bboxes=np.array(bboxes).astype(np.float32), + labels=np.array(labels).astype(np.int64)) + )) + + return data_infos + + def get_ann_info(self, idx): + return self.data_infos[idx]['ann'] + +``` + +Then in the config, to use `MyDataset` you can modify the config as the following + +```python +dataset_A_train = dict( + type='MyDataset', + ann_file = 'image_list.txt', + pipeline=train_pipeline +) +``` + +## Customize datasets by dataset wrappers + +MMDetection also supports many dataset wrappers to mix the dataset or modify the dataset distribution for training. +Currently it supports to three dataset wrappers as below: + +- `RepeatDataset`: simply repeat the whole dataset. +- `ClassBalancedDataset`: repeat dataset in a class balanced manner. +- `ConcatDataset`: concat datasets. + +### Repeat dataset + +We use `RepeatDataset` as wrapper to repeat the dataset. For example, suppose the original dataset is `Dataset_A`, to repeat it, the config looks like the following + +```python +dataset_A_train = dict( + type='RepeatDataset', + times=N, + dataset=dict( # This is the original config of Dataset_A + type='Dataset_A', + ... + pipeline=train_pipeline + ) + ) +``` + +### Class balanced dataset + +We use `ClassBalancedDataset` as wrapper to repeat the dataset based on category +frequency. The dataset to repeat needs to instantiate function `self.get_cat_ids(idx)` +to support `ClassBalancedDataset`. +For example, to repeat `Dataset_A` with `oversample_thr=1e-3`, the config looks like the following + +```python +dataset_A_train = dict( + type='ClassBalancedDataset', + oversample_thr=1e-3, + dataset=dict( # This is the original config of Dataset_A + type='Dataset_A', + ... + pipeline=train_pipeline + ) + ) +``` + +You may refer to [source code](../../mmdet/datasets/dataset_wrappers.py) for details. + +### Concatenate dataset + +There are three ways to concatenate the dataset. + +1. If the datasets you want to concatenate are in the same type with different annotation files, you can concatenate the dataset configs like the following. + + ```python + dataset_A_train = dict( + type='Dataset_A', + ann_file = ['anno_file_1', 'anno_file_2'], + pipeline=train_pipeline + ) + ``` + + If the concatenated dataset is used for test or evaluation, this manner supports to evaluate each dataset separately. To test the concatenated datasets as a whole, you can set `separate_eval=False` as below. + + ```python + dataset_A_train = dict( + type='Dataset_A', + ann_file = ['anno_file_1', 'anno_file_2'], + separate_eval=False, + pipeline=train_pipeline + ) + ``` + +2. In case the dataset you want to concatenate is different, you can concatenate the dataset configs like the following. + + ```python + dataset_A_train = dict() + dataset_B_train = dict() + + data = dict( + imgs_per_gpu=2, + workers_per_gpu=2, + train = [ + dataset_A_train, + dataset_B_train + ], + val = dataset_A_val, + test = dataset_A_test + ) + ``` + + If the concatenated dataset is used for test or evaluation, this manner also supports to evaluate each dataset separately. + +3. We also support to define `ConcatDataset` explicitly as the following. + + ```python + dataset_A_val = dict() + dataset_B_val = dict() + + data = dict( + imgs_per_gpu=2, + workers_per_gpu=2, + train=dataset_A_train, + val=dict( + type='ConcatDataset', + datasets=[dataset_A_val, dataset_B_val], + separate_eval=False)) + ``` + + This manner allows users to evaluate all the datasets as a single one by setting `separate_eval=False`. + +**Note:** + +1. The option `separate_eval=False` assumes the datasets use `self.data_infos` during evaluation. Therefore, COCO datasets do not support this behavior since COCO datasets do not fully rely on `self.data_infos` for evaluation. Combining different types of datasets and evaluating them as a whole is not tested thus is not suggested. +2. Evaluating `ClassBalancedDataset` and `RepeatDataset` is not supported thus evaluating concatenated datasets of these types is also not supported. + +A more complex example that repeats `Dataset_A` and `Dataset_B` by N and M times, respectively, and then concatenates the repeated datasets is as the following. + +```python +dataset_A_train = dict( + type='RepeatDataset', + times=N, + dataset=dict( + type='Dataset_A', + ... + pipeline=train_pipeline + ) +) +dataset_A_val = dict( + ... + pipeline=test_pipeline +) +dataset_A_test = dict( + ... + pipeline=test_pipeline +) +dataset_B_train = dict( + type='RepeatDataset', + times=M, + dataset=dict( + type='Dataset_B', + ... + pipeline=train_pipeline + ) +) +data = dict( + imgs_per_gpu=2, + workers_per_gpu=2, + train = [ + dataset_A_train, + dataset_B_train + ], + val = dataset_A_val, + test = dataset_A_test +) + +``` + +## Modify Dataset Classes + +With existing dataset types, we can modify the class names of them to train subset of the annotations. +For example, if you want to train only three classes of the current dataset, +you can modify the classes of dataset. +The dataset will filter out the ground truth boxes of other classes automatically. + +```python +classes = ('person', 'bicycle', 'car') +data = dict( + train=dict(classes=classes), + val=dict(classes=classes), + test=dict(classes=classes)) +``` + +MMDetection V2.0 also supports to read the classes from a file, which is common in real applications. +For example, assume the `classes.txt` contains the name of classes as the following. + +``` +person +bicycle +car +``` + +Users can set the classes as a file path, the dataset will load it and convert it to a list automatically. + +```python +classes = 'path/to/classes.txt' +data = dict( + train=dict(classes=classes), + val=dict(classes=classes), + test=dict(classes=classes)) +``` + +**Note**: + +- Before MMDetection v2.5.0, the dataset will filter out the empty GT images automatically if the classes are set and there is no way to disable that through config. This is an undesirable behavior and introduces confusion because if the classes are not set, the dataset only filter the empty GT images when `filter_empty_gt=True` and `test_mode=False`. After MMDetection v2.5.0, we decouple the image filtering process and the classes modification, i.e., the dataset will only filter empty GT images when `filter_empty_gt=True` and `test_mode=False`, no matter whether the classes are set. Thus, setting the classes only influences the annotations of classes used for training and users could decide whether to filter empty GT images by themselves. +- Since the middle format only has box labels and does not contain the class names, when using `CustomDataset`, users cannot filter out the empty GT images through configs but only do this offline. +- Please remember to modify the `num_classes` in the head when specifying `classes` in dataset. We implemented [NumClassCheckHook](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/utils.py) to check whether the numbers are consistent since v2.9.0(after PR#4508). +- The features for setting dataset classes and dataset filtering will be refactored to be more user-friendly in the future (depends on the progress). diff --git a/docs/tutorials/customize_losses.md b/docs/tutorials/customize_losses.md new file mode 100644 index 0000000..c3e1ddd --- /dev/null +++ b/docs/tutorials/customize_losses.md @@ -0,0 +1,105 @@ +# Tutorial 6: Customize Losses + +MMDetection provides users with different loss functions. But the default configuration may be not applicable for different datasets or models, so users may want to modify a specific loss to adapt the new situation. + +This tutorial first elaborate the computation pipeline of losses, then give some instructions about how to modify each step. The modification can be categorized as tweaking and weighting. + +## Computation pipeline of a loss + +Given the input prediction and target, as well as the weights, a loss function maps the input tensor to the final loss scalar. The mapping can be divided into four steps: + +1. Get **element-wise** or sample-wise loss by the loss kernel function. + +2. Weighting the loss with a weight tensor **element-wisely**. + +3. Reduce the loss tensor to a **scalar**. + +4. Weighting the loss with a **scalar**. + +## Tweaking loss + +Tweaking a loss is more related with step 1, 3, 4, and most modifications can be specified in the config. +Here we take [Focal Loss (FL)](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/losses/focal_loss.py) as an example. +The following code sniper are the construction method and config of FL respectively, they are actually one to one correspondence. + +```python +@LOSSES.register_module() +class FocalLoss(nn.Module): + + def __init__(self, + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + reduction='mean', + loss_weight=1.0): +``` + +```python +loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0) +``` + +### Tweaking hyper-parameters (step 1) + +`gamma` and `beta` are two hyper-parameters in the Focal Loss. Say if we want to change the value of `gamma` to be 1.5 and `alpha` to be 0.5, then we can specify them in the config as follows: + +```python +loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=1.5, + alpha=0.5, + loss_weight=1.0) +``` + +### Tweaking the way of reduction (step 3) + +The default way of reduction is `mean` for FL. Say if we want to change the reduction from `mean` to `sum`, we can specify it in the config as follows: + +```python +loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0, + reduction='sum') +``` + +### Tweaking loss weight (step 4) + +The loss weight here is a scalar which controls the weight of different losses in multi-task learning, e.g. classification loss and regression loss. Say if we want to change to loss weight of classification loss to be 0.5, we can specify it in the config as follows: + +```python +loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=0.5) +``` + +## Weighting loss (step 2) + +Weighting loss means we re-weight the loss element-wisely. To be more specific, we multiply the loss tensor with a weight tensor which has the same shape. As a result, different entries of the loss can be scaled differently, and so called element-wisely. +The loss weight varies across different models and highly context related, but overall there are two kinds of loss weights, `label_weights` for classification loss and `bbox_weights` for bbox regression loss. You can find them in the `get_target` method of the corresponding head. Here we take [ATSSHead](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/dense_heads/atss_head.py#L530) as an example, which inherit [AnchorHead](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/dense_heads/anchor_head.py) but overwrite its `get_targets` method which yields different `label_weights` and `bbox_weights`. + +``` +class ATSSHead(AnchorHead): + + ... + + def get_targets(self, + anchor_list, + valid_flag_list, + gt_bboxes_list, + img_metas, + gt_bboxes_ignore_list=None, + gt_labels_list=None, + label_channels=1, + unmap_outputs=True): +``` diff --git a/docs/tutorials/customize_models.md b/docs/tutorials/customize_models.md new file mode 100644 index 0000000..81c3912 --- /dev/null +++ b/docs/tutorials/customize_models.md @@ -0,0 +1,363 @@ +# Tutorial 4: Customize Models + +We basically categorize model components into 5 types. + +- backbone: usually an FCN network to extract feature maps, e.g., ResNet, MobileNet. +- neck: the component between backbones and heads, e.g., FPN, PAFPN. +- head: the component for specific tasks, e.g., bbox prediction and mask prediction. +- roi extractor: the part for extracting RoI features from feature maps, e.g., RoI Align. +- loss: the component in head for calculating losses, e.g., FocalLoss, L1Loss, and GHMLoss. + +## Develop new components + +### Add a new backbone + +Here we show how to develop new components with an example of MobileNet. + +#### 1. Define a new backbone (e.g. MobileNet) + +Create a new file `mmdet/models/backbones/mobilenet.py`. + +```python +import torch.nn as nn + +from ..builder import BACKBONES + + +@BACKBONES.register_module() +class MobileNet(nn.Module): + + def __init__(self, arg1, arg2): + pass + + def forward(self, x): # should return a tuple + pass +``` + +#### 2. Import the module + +You can either add the following line to `mmdet/models/backbones/__init__.py` + +```python +from .mobilenet import MobileNet +``` + +or alternatively add + +```python +custom_imports = dict( + imports=['mmdet.models.backbones.mobilenet'], + allow_failed_imports=False) +``` + +to the config file to avoid modifying the original code. + +#### 3. Use the backbone in your config file + +```python +model = dict( + ... + backbone=dict( + type='MobileNet', + arg1=xxx, + arg2=xxx), + ... +``` + +### Add new necks + +#### 1. Define a neck (e.g. PAFPN) + +Create a new file `mmdet/models/necks/pafpn.py`. + +```python +from ..builder import NECKS + +@NECKS.register_module() +class PAFPN(nn.Module): + + def __init__(self, + in_channels, + out_channels, + num_outs, + start_level=0, + end_level=-1, + add_extra_convs=False): + pass + + def forward(self, inputs): + # implementation is ignored + pass +``` + +#### 2. Import the module + +You can either add the following line to `mmdet/models/necks/__init__.py`, + +```python +from .pafpn import PAFPN +``` + +or alternatively add + +```python +custom_imports = dict( + imports=['mmdet.models.necks.pafpn.py'], + allow_failed_imports=False) +``` + +to the config file and avoid modifying the original code. + +#### 3. Modify the config file + +```python +neck=dict( + type='PAFPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5) +``` + +### Add new heads + +Here we show how to develop a new head with the example of [Double Head R-CNN](https://arxiv.org/abs/1904.06493) as the following. + +First, add a new bbox head in `mmdet/models/roi_heads/bbox_heads/double_bbox_head.py`. +Double Head R-CNN implements a new bbox head for object detection. +To implement a bbox head, basically we need to implement three functions of the new module as the following. + +```python +from mmdet.models.builder import HEADS +from .bbox_head import BBoxHead + +@HEADS.register_module() +class DoubleConvFCBBoxHead(BBoxHead): + r"""Bbox head used in Double-Head R-CNN + + /-> cls + /-> shared convs -> + \-> reg + roi features + /-> cls + \-> shared fc -> + \-> reg + """ # noqa: W605 + + def __init__(self, + num_convs=0, + num_fcs=0, + conv_out_channels=1024, + fc_out_channels=1024, + conv_cfg=None, + norm_cfg=dict(type='BN'), + **kwargs): + kwargs.setdefault('with_avg_pool', True) + super(DoubleConvFCBBoxHead, self).__init__(**kwargs) + + + def forward(self, x_cls, x_reg): + +``` + +Second, implement a new RoI Head if it is necessary. We plan to inherit the new `DoubleHeadRoIHead` from `StandardRoIHead`. We can find that a `StandardRoIHead` already implements the following functions. + +```python +import torch + +from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler +from ..builder import HEADS, build_head, build_roi_extractor +from .base_roi_head import BaseRoIHead +from .test_mixins import BBoxTestMixin, MaskTestMixin + + +@HEADS.register_module() +class StandardRoIHead(BaseRoIHead, BBoxTestMixin, MaskTestMixin): + """Simplest base roi head including one bbox head and one mask head. + """ + + def init_assigner_sampler(self): + + def init_bbox_head(self, bbox_roi_extractor, bbox_head): + + def init_mask_head(self, mask_roi_extractor, mask_head): + + + def forward_dummy(self, x, proposals): + + + def forward_train(self, + x, + img_metas, + proposal_list, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + gt_masks=None): + + def _bbox_forward(self, x, rois): + + def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels, + img_metas): + + def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks, + img_metas): + + def _mask_forward(self, x, rois=None, pos_inds=None, bbox_feats=None): + + + def simple_test(self, + x, + proposal_list, + img_metas, + proposals=None, + rescale=False): + """Test without augmentation.""" + +``` + +Double Head's modification is mainly in the bbox_forward logic, and it inherits other logics from the `StandardRoIHead`. +In the `mmdet/models/roi_heads/double_roi_head.py`, we implement the new RoI Head as the following: + +```python +from ..builder import HEADS +from .standard_roi_head import StandardRoIHead + + +@HEADS.register_module() +class DoubleHeadRoIHead(StandardRoIHead): + """RoI head for Double Head RCNN + + https://arxiv.org/abs/1904.06493 + """ + + def __init__(self, reg_roi_scale_factor, **kwargs): + super(DoubleHeadRoIHead, self).__init__(**kwargs) + self.reg_roi_scale_factor = reg_roi_scale_factor + + def _bbox_forward(self, x, rois): + bbox_cls_feats = self.bbox_roi_extractor( + x[:self.bbox_roi_extractor.num_inputs], rois) + bbox_reg_feats = self.bbox_roi_extractor( + x[:self.bbox_roi_extractor.num_inputs], + rois, + roi_scale_factor=self.reg_roi_scale_factor) + if self.with_shared_head: + bbox_cls_feats = self.shared_head(bbox_cls_feats) + bbox_reg_feats = self.shared_head(bbox_reg_feats) + cls_score, bbox_pred = self.bbox_head(bbox_cls_feats, bbox_reg_feats) + + bbox_results = dict( + cls_score=cls_score, + bbox_pred=bbox_pred, + bbox_feats=bbox_cls_feats) + return bbox_results +``` + +Last, the users need to add the module in +`mmdet/models/bbox_heads/__init__.py` and `mmdet/models/roi_heads/__init__.py` thus the corresponding registry could find and load them. + +Alternatively, the users can add + +```python +custom_imports=dict( + imports=['mmdet.models.roi_heads.double_roi_head', 'mmdet.models.bbox_heads.double_bbox_head']) +``` + +to the config file and achieve the same goal. + +The config file of Double Head R-CNN is as the following + +```python +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + type='DoubleHeadRoIHead', + reg_roi_scale_factor=1.3, + bbox_head=dict( + _delete_=True, + type='DoubleConvFCBBoxHead', + num_convs=4, + num_fcs=2, + in_channels=256, + conv_out_channels=1024, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=2.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=2.0)))) + +``` + +Since MMDetection 2.0, the config system supports to inherit configs such that the users can focus on the modification. +The Double Head R-CNN mainly uses a new DoubleHeadRoIHead and a new +`DoubleConvFCBBoxHead`, the arguments are set according to the `__init__` function of each module. + +### Add new loss + +Assume you want to add a new loss as `MyLoss`, for bounding box regression. +To add a new loss function, the users need implement it in `mmdet/models/losses/my_loss.py`. +The decorator `weighted_loss` enable the loss to be weighted for each element. + +```python +import torch +import torch.nn as nn + +from ..builder import LOSSES +from .utils import weighted_loss + +@weighted_loss +def my_loss(pred, target): + assert pred.size() == target.size() and target.numel() > 0 + loss = torch.abs(pred - target) + return loss + +@LOSSES.register_module() +class MyLoss(nn.Module): + + def __init__(self, reduction='mean', loss_weight=1.0): + super(MyLoss, self).__init__() + self.reduction = reduction + self.loss_weight = loss_weight + + def forward(self, + pred, + target, + weight=None, + avg_factor=None, + reduction_override=None): + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + loss_bbox = self.loss_weight * my_loss( + pred, target, weight, reduction=reduction, avg_factor=avg_factor) + return loss_bbox +``` + +Then the users need to add it in the `mmdet/models/losses/__init__.py`. + +```python +from .my_loss import MyLoss, my_loss + +``` + +Alternatively, you can add + +```python +custom_imports=dict( + imports=['mmdet.models.losses.my_loss']) +``` + +to the config file and achieve the same goal. + +To use it, modify the `loss_xxx` field. +Since MyLoss is for regression, you need to modify the `loss_bbox` field in the head. + +```python +loss_bbox=dict(type='MyLoss', loss_weight=1.0)) +``` diff --git a/docs/tutorials/customize_runtime.md b/docs/tutorials/customize_runtime.md new file mode 100644 index 0000000..616ce50 --- /dev/null +++ b/docs/tutorials/customize_runtime.md @@ -0,0 +1,323 @@ +# Tutorial 5: Customize Runtime Settings + +## Customize optimization settings + +### Customize optimizer supported by Pytorch + +We already support to use all the optimizers implemented by PyTorch, and the only modification is to change the `optimizer` field of config files. +For example, if you want to use `ADAM` (note that the performance could drop a lot), the modification could be as the following. + +```python +optimizer = dict(type='Adam', lr=0.0003, weight_decay=0.0001) +``` + +To modify the learning rate of the model, the users only need to modify the `lr` in the config of optimizer. The users can directly set arguments following the [API doc](https://pytorch.org/docs/stable/optim.html?highlight=optim#module-torch.optim) of PyTorch. + +### Customize self-implemented optimizer + +#### 1. Define a new optimizer + +A customized optimizer could be defined as following. + +Assume you want to add a optimizer named `MyOptimizer`, which has arguments `a`, `b`, and `c`. +You need to create a new directory named `mmdet/core/optimizer`. +And then implement the new optimizer in a file, e.g., in `mmdet/core/optimizer/my_optimizer.py`: + +```python +from .registry import OPTIMIZERS +from torch.optim import Optimizer + + +@OPTIMIZERS.register_module() +class MyOptimizer(Optimizer): + + def __init__(self, a, b, c) + +``` + +#### 2. Add the optimizer to registry + +To find the above module defined above, this module should be imported into the main namespace at first. There are two options to achieve it. + +- Modify `mmdet/core/optimizer/__init__.py` to import it. + + The newly defined module should be imported in `mmdet/core/optimizer/__init__.py` so that the registry will + find the new module and add it: + +```python +from .my_optimizer import MyOptimizer +``` + +- Use `custom_imports` in the config to manually import it + +```python +custom_imports = dict(imports=['mmdet.core.optimizer.my_optimizer'], allow_failed_imports=False) +``` + +The module `mmdet.core.optimizer.my_optimizer` will be imported at the beginning of the program and the class `MyOptimizer` is then automatically registered. +Note that only the package containing the class `MyOptimizer` should be imported. +`mmdet.core.optimizer.my_optimizer.MyOptimizer` **cannot** be imported directly. + +Actually users can use a totally different file directory structure using this importing method, as long as the module root can be located in `PYTHONPATH`. + +#### 3. Specify the optimizer in the config file + +Then you can use `MyOptimizer` in `optimizer` field of config files. +In the configs, the optimizers are defined by the field `optimizer` like the following: + +```python +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +``` + +To use your own optimizer, the field can be changed to + +```python +optimizer = dict(type='MyOptimizer', a=a_value, b=b_value, c=c_value) +``` + +### Customize optimizer constructor + +Some models may have some parameter-specific settings for optimization, e.g. weight decay for BatchNorm layers. +The users can do those fine-grained parameter tuning through customizing optimizer constructor. + +```python +from mmcv.utils import build_from_cfg + +from mmcv.runner.optimizer import OPTIMIZER_BUILDERS, OPTIMIZERS +from mmdet.utils import get_root_logger +from .my_optimizer import MyOptimizer + + +@OPTIMIZER_BUILDERS.register_module() +class MyOptimizerConstructor(object): + + def __init__(self, optimizer_cfg, paramwise_cfg=None): + + def __call__(self, model): + + return my_optimizer + +``` + +The default optimizer constructor is implemented [here](https://github.com/open-mmlab/mmcv/blob/9ecd6b0d5ff9d2172c49a182eaa669e9f27bb8e7/mmcv/runner/optimizer/default_constructor.py#L11), which could also serve as a template for new optimizer constructor. + +### Additional settings + +Tricks not implemented by the optimizer should be implemented through optimizer constructor (e.g., set parameter-wise learning rates) or hooks. We list some common settings that could stabilize the training or accelerate the training. Feel free to create PR, issue for more settings. + +- __Use gradient clip to stabilize training__: + Some models need gradient clip to clip the gradients to stabilize the training process. An example is as below: + + ```python + optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) + ``` + + If your config inherits the base config which already sets the `optimizer_config`, you might need `_delete_=True` to overide the unnecessary settings. See the [config documenetation](https://mmdetection.readthedocs.io/en/latest/config.html) for more details. + +- __Use momentum schedule to accelerate model convergence__: + We support momentum scheduler to modify model's momentum according to learning rate, which could make the model converge in a faster way. + Momentum scheduler is usually used with LR scheduler, for example, the following config is used in 3D detection to accelerate convergence. + For more details, please refer to the implementation of [CyclicLrUpdater](https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/lr_updater.py#L327) and [CyclicMomentumUpdater](https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/momentum_updater.py#L130). + + ```python + lr_config = dict( + policy='cyclic', + target_ratio=(10, 1e-4), + cyclic_times=1, + step_ratio_up=0.4, + ) + momentum_config = dict( + policy='cyclic', + target_ratio=(0.85 / 0.95, 1), + cyclic_times=1, + step_ratio_up=0.4, + ) + ``` + +## Customize training schedules + +By default we use step learning rate with 1x schedule, this calls [`StepLRHook`](https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/lr_updater.py#L153) in MMCV. +We support many other learning rate schedule [here](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py), such as `CosineAnnealing` and `Poly` schedule. Here are some examples + +- Poly schedule: + + ```python + lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) + ``` + +- ConsineAnnealing schedule: + + ```python + lr_config = dict( + policy='CosineAnnealing', + warmup='linear', + warmup_iters=1000, + warmup_ratio=1.0 / 10, + min_lr_ratio=1e-5) + ``` + +## Customize workflow + +Workflow is a list of (phase, epochs) to specify the running order and epochs. +By default it is set to be + +```python +workflow = [('train', 1)] +``` + +which means running 1 epoch for training. +Sometimes user may want to check some metrics (e.g. loss, accuracy) about the model on the validate set. +In such case, we can set the workflow as + +```python +[('train', 1), ('val', 1)] +``` + +so that 1 epoch for training and 1 epoch for validation will be run iteratively. + +**Note**: + +1. The parameters of model will not be updated during val epoch. +2. Keyword `total_epochs` in the config only controls the number of training epochs and will not affect the validation workflow. +3. Workflows `[('train', 1), ('val', 1)]` and `[('train', 1)]` will not change the behavior of `EvalHook` because `EvalHook` is called by `after_train_epoch` and validation workflow only affect hooks that are called through `after_val_epoch`. Therefore, the only difference between `[('train', 1), ('val', 1)]` and `[('train', 1)]` is that the runner will calculate losses on validation set after each training epoch. + +## Customize hooks + +### Customize self-implemented hooks + +#### 1. Implement a new hook + +There are some occasions when the users might need to implement a new hook. MMDetection supports customized hooks in training (#3395) since v2.3.0. Thus the users could implement a hook directly in mmdet or their mmdet-based codebases and use the hook by only modifying the config in training. +Before v2.3.0, the users need to modify the code to get the hook registered before training starts. +Here we give an example of creating a new hook in mmdet and using it in training. + +```python +from mmcv.runner import HOOKS, Hook + + +@HOOKS.register_module() +class MyHook(Hook): + + def __init__(self, a, b): + pass + + def before_run(self, runner): + pass + + def after_run(self, runner): + pass + + def before_epoch(self, runner): + pass + + def after_epoch(self, runner): + pass + + def before_iter(self, runner): + pass + + def after_iter(self, runner): + pass +``` + +Depending on the functionality of the hook, the users need to specify what the hook will do at each stage of the training in `before_run`, `after_run`, `before_epoch`, `after_epoch`, `before_iter`, and `after_iter`. + +#### 2. Register the new hook + +Then we need to make `MyHook` imported. Assuming the file is in `mmdet/core/utils/my_hook.py` there are two ways to do that: + +- Modify `mmdet/core/utils/__init__.py` to import it. + + The newly defined module should be imported in `mmdet/core/utils/__init__.py` so that the registry will + find the new module and add it: + +```python +from .my_hook import MyHook +``` + +- Use `custom_imports` in the config to manually import it + +```python +custom_imports = dict(imports=['mmdet.core.utils.my_hook'], allow_failed_imports=False) +``` + +#### 3. Modify the config + +```python +custom_hooks = [ + dict(type='MyHook', a=a_value, b=b_value) +] +``` + +You can also set the priority of the hook by adding key `priority` to `'NORMAL'` or `'HIGHEST'` as below + +```python +custom_hooks = [ + dict(type='MyHook', a=a_value, b=b_value, priority='NORMAL') +] +``` + +By default the hook's priority is set as `NORMAL` during registration. + +### Use hooks implemented in MMCV + +If the hook is already implemented in MMCV, you can directly modify the config to use the hook as below + +#### 4. Example: `NumClassCheckHook` + +We implement a customized hook named [NumClassCheckHook](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/utils.py) to check whether the `num_classes` in head matches the length of `CLASSSES` in `dataset`. + +We set it in [default_runtime.py](https://github.com/open-mmlab/mmdetection/blob/master/configs/_base_/default_runtime.py). + +```python +custom_hooks = [dict(type='NumClassCheckHook')] +``` + +### Modify default runtime hooks + +There are some common hooks that are not registerd through `custom_hooks`, they are + +- log_config +- checkpoint_config +- evaluation +- lr_config +- optimizer_config +- momentum_config + +In those hooks, only the logger hook has the `VERY_LOW` priority, others' priority are `NORMAL`. +The above-mentioned tutorials already covers how to modify `optimizer_config`, `momentum_config`, and `lr_config`. +Here we reveals how what we can do with `log_config`, `checkpoint_config`, and `evaluation`. + +#### Checkpoint config + +The MMCV runner will use `checkpoint_config` to initialize [`CheckpointHook`](https://github.com/open-mmlab/mmcv/blob/9ecd6b0d5ff9d2172c49a182eaa669e9f27bb8e7/mmcv/runner/hooks/checkpoint.py#L9). + +```python +checkpoint_config = dict(interval=1) +``` + +The users could set `max_keep_ckpts` to only save only small number of checkpoints or decide whether to store state dict of optimizer by `save_optimizer`. More details of the arguments are [here](https://mmcv.readthedocs.io/en/latest/api.html#mmcv.runner.CheckpointHook) + +#### Log config + +The `log_config` wraps multiple logger hooks and enables to set intervals. Now MMCV supports `WandbLoggerHook`, `MlflowLoggerHook`, and `TensorboardLoggerHook`. +The detail usages can be found in the [doc](https://mmcv.readthedocs.io/en/latest/api.html#mmcv.runner.LoggerHook). + +```python +log_config = dict( + interval=50, + hooks=[ + dict(type='TextLoggerHook'), + dict(type='TensorboardLoggerHook') + ]) +``` + +#### Evaluation config + +The config of `evaluation` will be used to initialize the [`EvalHook`](https://github.com/open-mmlab/mmdetection/blob/7a404a2c000620d52156774a5025070d9e00d918/mmdet/core/evaluation/eval_hooks.py#L8). +Except the key `interval`, other arguments such as `metric` will be passed to the `dataset.evaluate()` + +```python +evaluation = dict(interval=1, metric='bbox') +``` diff --git a/docs/tutorials/data_pipeline.md b/docs/tutorials/data_pipeline.md new file mode 100644 index 0000000..7ea5665 --- /dev/null +++ b/docs/tutorials/data_pipeline.md @@ -0,0 +1,184 @@ +# Tutorial 3: Customize Data Pipelines + +## Design of Data pipelines + +Following typical conventions, we use `Dataset` and `DataLoader` for data loading +with multiple workers. `Dataset` returns a dict of data items corresponding +the arguments of models' forward method. +Since the data in object detection may not be the same size (image size, gt bbox size, etc.), +we introduce a new `DataContainer` type in MMCV to help collect and distribute +data of different size. +See [here](https://github.com/open-mmlab/mmcv/blob/master/mmcv/parallel/data_container.py) for more details. + +The data preparation pipeline and the dataset is decomposed. Usually a dataset +defines how to process the annotations and a data pipeline defines all the steps to prepare a data dict. +A pipeline consists of a sequence of operations. Each operation takes a dict as input and also output a dict for the next transform. + +We present a classical pipeline in the following figure. The blue blocks are pipeline operations. With the pipeline going on, each operator can add new keys (marked as green) to the result dict or update the existing keys (marked as orange). +![pipeline figure](../../resources/data_pipeline.png) + +The operations are categorized into data loading, pre-processing, formatting and test-time augmentation. + +Here is a pipeline example for Faster R-CNN. + +```python +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +``` + +For each operation, we list the related dict fields that are added/updated/removed. + +### Data loading + +`LoadImageFromFile` + +- add: img, img_shape, ori_shape + +`LoadAnnotations` + +- add: gt_bboxes, gt_bboxes_ignore, gt_labels, gt_masks, gt_semantic_seg, bbox_fields, mask_fields + +`LoadProposals` + +- add: proposals + +### Pre-processing + +`Resize` + +- add: scale, scale_idx, pad_shape, scale_factor, keep_ratio +- update: img, img_shape, *bbox_fields, *mask_fields, *seg_fields + +`RandomFlip` + +- add: flip +- update: img, *bbox_fields, *mask_fields, *seg_fields + +`Pad` + +- add: pad_fixed_size, pad_size_divisor +- update: img, pad_shape, *mask_fields, *seg_fields + +`RandomCrop` + +- update: img, pad_shape, gt_bboxes, gt_labels, gt_masks, *bbox_fields + +`Normalize` + +- add: img_norm_cfg +- update: img + +`SegRescale` + +- update: gt_semantic_seg + +`PhotoMetricDistortion` + +- update: img + +`Expand` + +- update: img, gt_bboxes + +`MinIoURandomCrop` + +- update: img, gt_bboxes, gt_labels + +`Corrupt` + +- update: img + +### Formatting + +`ToTensor` + +- update: specified by `keys`. + +`ImageToTensor` + +- update: specified by `keys`. + +`Transpose` + +- update: specified by `keys`. + +`ToDataContainer` + +- update: specified by `fields`. + +`DefaultFormatBundle` + +- update: img, proposals, gt_bboxes, gt_bboxes_ignore, gt_labels, gt_masks, gt_semantic_seg + +`Collect` + +- add: img_meta (the keys of img_meta is specified by `meta_keys`) +- remove: all other keys except for those specified by `keys` + +### Test time augmentation + +`MultiScaleFlipAug` + +## Extend and use custom pipelines + +1. Write a new pipeline in any file, e.g., `my_pipeline.py`. It takes a dict as input and return a dict. + + ```python + from mmdet.datasets import PIPELINES + + @PIPELINES.register_module() + class MyTransform: + + def __call__(self, results): + results['dummy'] = True + return results + ``` + +2. Import the new class. + + ```python + from .my_pipeline import MyTransform + ``` + +3. Use it in config files. + + ```python + img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='MyTransform'), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), + ] + ``` diff --git a/docs/tutorials/finetune.md b/docs/tutorials/finetune.md new file mode 100644 index 0000000..95cb1d5 --- /dev/null +++ b/docs/tutorials/finetune.md @@ -0,0 +1,89 @@ +# Tutorial 7: Finetuning Models + +Detectors pre-trained on the COCO dataset can serve as a good pre-trained model for other datasets, e.g., CityScapes and KITTI Dataset. +This tutorial provides instruction for users to use the models provided in the [Model Zoo](../model_zoo.md) for other datasets to obtain better performance. + +There are two steps to finetune a model on a new dataset. + +- Add support for the new dataset following [Tutorial 2: Customize Datasets](customize_dataset.md). +- Modify the configs as will be discussed in this tutorial. + +Take the finetuning process on Cityscapes Dataset as an example, the users need to modify five parts in the config. + +## Inherit base configs + +To release the burden and reduce bugs in writing the whole configs, MMDetection V2.0 support inheriting configs from multiple existing configs. To finetune a Mask RCNN model, the new config needs to inherit +`_base_/models/mask_rcnn_r50_fpn.py` to build the basic structure of the model. To use the Cityscapes Dataset, the new config can also simply inherit `_base_/datasets/cityscapes_instance.py`. For runtime settings such as training schedules, the new config needs to inherit `_base_/default_runtime.py`. This configs are in the `configs` directory and the users can also choose to write the whole contents rather than use inheritance. + +```python +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/cityscapes_instance.py', '../_base_/default_runtime.py' +] +``` + +## Modify head + +Then the new config needs to modify the head according to the class numbers of the new datasets. By only changing `num_classes` in the roi_head, the weights of the pre-trained models are mostly reused except the final prediction head. + +```python +model = dict( + pretrained=None, + roi_head=dict( + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=8, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=8, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)))) +``` + +## Modify dataset + +The users may also need to prepare the dataset and write the configs about dataset. MMDetection V2.0 already support VOC, WIDER FACE, COCO and Cityscapes Dataset. + +## Modify training schedule + +The finetuning hyperparameters vary from the default schedule. It usually requires smaller learning rate and less training epochs + +```python +# optimizer +# lr is set for a batch size of 8 +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + # [7] yields higher performance than [6] + step=[7]) +total_epochs = 8 # actual epoch = 8 * 8 = 64 +log_config = dict(interval=100) +``` + +## Use pre-trained model + +To use the pre-trained model, the new config add the link of pre-trained models in the `load_from`. The users might need to download the model weights before training to avoid the download time during training. + +```python +load_from = 'http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth' # noqa + +``` diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst new file mode 100644 index 0000000..659a5cb --- /dev/null +++ b/docs/tutorials/index.rst @@ -0,0 +1,12 @@ +.. toctree:: + :maxdepth: 2 + + config.md + customize_dataset.md + data_pipeline.md + customize_models.md + customize_runtime.md + customize_losses.md + finetune.md + pytorch2onnx.md + onnx2tensorrt.md diff --git a/docs/tutorials/onnx2tensorrt.md b/docs/tutorials/onnx2tensorrt.md new file mode 100644 index 0000000..455f2de --- /dev/null +++ b/docs/tutorials/onnx2tensorrt.md @@ -0,0 +1,93 @@ +# Tutorial 9: ONNX to TensorRT (Experimental) + + + +- [Tutorial 9: ONNX to TensorRT (Experimental)](#tutorial-9-onnx-to-tensorrt-experimental) + - [How to convert models from ONNX to TensorRT](#how-to-convert-models-from-onnx-to-tensorrt) + - [Prerequisite](#prerequisite) + - [Usage](#usage) + - [List of supported models convertable to TensorRT](#list-of-supported-models-convertable-to-tensorrt) + - [Reminders](#reminders) + - [FAQs](#faqs) + + + +## How to convert models from ONNX to TensorRT + +### Prerequisite + +1. Please refer to [get_started.md](https://mmdetection.readthedocs.io/en/latest/get_started.html) for installation of MMCV and MMDetection from source. +2. Please refer to [ONNXRuntime in mmcv](https://mmcv.readthedocs.io/en/latest/onnxruntime_op.html) and [TensorRT plugin in mmcv](https://github.com/open-mmlab/mmcv/blob/master/docs/tensorrt_plugin.md) to install `mmcv-full` with ONNXRuntime custom ops and TensorRT plugins. +3. Use our tool [pytorch2onnx](https://mmdetection.readthedocs.io/en/latest/tutorials/pytorch2onnx.html) to convert the model from PyTorch to ONNX. + +### Usage + +```bash +python tools/deployment/onnx2tensorrt.py \ + ${MODEL} \ + --trt-file ${TRT_FILE} \ + --input-img ${INPUT_IMAGE_PATH} \ + --shape ${IMAGE_SHAPE} \ + --mean ${IMAGE_MEAN} \ + --std ${IMAGE_STD} \ + --dataset ${DATASET_NAME} \ + --workspace-size {WORKSPACE_SIZE} \ + --show \ + --verify \ +``` + +Description of all arguments: + +- `model` : The path of an ONNX model file. +- `--trt-file`: The Path of output TensorRT engine file. If not specified, it will be set to `tmp.trt`. +- `--input-img` : The path of an input image for tracing and conversion. By default, it will be set to `demo/demo.jpg`. +- `--shape`: The height and width of model input. If not specified, it will be set to `400 600`. +- `--mean` : Three mean values for the input image. If not specified, it will be set to `123.675 116.28 103.53`. +- `--std` : Three std values for the input image. If not specified, it will be set to `58.395 57.12 57.375`. +- `--dataset` : The dataset name for the input model. If not specified, it will be set to `coco`. +- `--workspace-size` : The required GPU workspace size in GiB to build TensorRT engine. If not specified, it will be set to `1` GiB. +- `--show`: Determines whether to show the outputs of the model. If not specified, it will be set to `False`. +- `--verify`: Determines whether to verify the correctness of models between ONNXRuntime and TensorRT. If not specified, it will be set to `False`. +- `--to-rgb`: Determines whether to convert the input image to RGB mode. If not specified, it will be set to `True`. +- `--verbose`: Determines whether to print logging messages. It's useful for debugging. If not specified, it will be set to `False`. + +Example: + +```bash +python tools/deployment/onnx2tensorrt.py \ + checkpoints/retinanet_r50_fpn_1x_coco.onnx \ + --trt-file checkpoints/retinanet_r50_fpn_1x_coco.trt \ + --input-img demo/demo.jpg \ + --shape 400 600 \ + --mean 123.675 116.28 103.53 \ + --std 58.395 57.12 57.375 \ + --show \ + --verify \ +``` + +## List of supported models convertable to TensorRT + +The table below lists the models that are guaranteed to be convertable to TensorRT. + +| Model | Config | Status | +| :----------: | :--------------------------------------------------: | :----: | +| SSD | `configs/ssd/ssd300_coco.py` | Y | +| FSAF | `configs/fsaf/fsaf_r50_fpn_1x_coco.py` | Y | +| FCOS | `configs/fcos/fcos_r50_caffe_fpn_4x4_1x_coco.py` | Y | +| YOLOv3 | `configs/yolo/yolov3_d53_mstrain-608_273e_coco.py` | Y | +| RetinaNet | `configs/retinanet/retinanet_r50_fpn_1x_coco.py` | Y | +| Faster R-CNN | `configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py` | Y | +| Mask R-CNN | `configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py` | Y | + +Notes: + +- *All models above are tested with Pytorch==1.6.0 and TensorRT-7.2.1.6.Ubuntu-16.04.x86_64-gnu.cuda-10.2.cudnn8.0* + +## Reminders + +- If you meet any problem with the listed models above, please create an issue and it would be taken care of soon. For models not included in the list, we may not provide much help here due to the limited resources. Please try to dig a little deeper and debug by yourself. +- Because this feature is experimental and may change fast, please always try with the latest `mmcv` and `mmdetecion`. + +## FAQs + +- None diff --git a/docs/tutorials/pytorch2onnx.md b/docs/tutorials/pytorch2onnx.md new file mode 100644 index 0000000..0b99345 --- /dev/null +++ b/docs/tutorials/pytorch2onnx.md @@ -0,0 +1,251 @@ +# Tutorial 8: Pytorch to ONNX (Experimental) + + + +- [Tutorial 8: Pytorch to ONNX (Experimental)](#tutorial-8-pytorch-to-onnx-experimental) + - [How to convert models from Pytorch to ONNX](#how-to-convert-models-from-pytorch-to-onnx) + - [Prerequisite](#prerequisite) + - [Usage](#usage) + - [Description of all arguments](#description-of-all-arguments) + - [How to evaluate ONNX models with ONNX Runtime](#how-to-evaluate-onnx-models-with-onnx-runtime) + - [Prerequisite](#prerequisite-1) + - [Usage](#usage-1) + - [Description of all arguments](#description-of-all-arguments-1) + - [Results and Models](#results-and-models) + - [List of supported models exportable to ONNX](#list-of-supported-models-exportable-to-onnx) + - [The Parameters of Non-Maximum Suppression in ONNX Export](#the-parameters-of-non-maximum-suppression-in-onnx-export) + - [Reminders](#reminders) + - [FAQs](#faqs) + + + +## How to convert models from Pytorch to ONNX + +### Prerequisite + +1. Please refer to [get_started.md](../get_started.md) for installation of MMCV and MMDetection. +2. Install onnx and onnxruntime + + ```shell + pip install onnx onnxruntime + ``` + +### Usage + +```bash +python tools/deployment/pytorch2onnx.py \ + ${CONFIG_FILE} \ + ${CHECKPOINT_FILE} \ + --output-file ${OUTPUT_FILE} \ + --input-img ${INPUT_IMAGE_PATH} \ + --shape ${IMAGE_SHAPE} \ + --mean ${IMAGE_MEAN} \ + --std ${IMAGE_STD} \ + --dataset ${DATASET_NAME} \ + --test-img ${TEST_IMAGE_PATH} \ + --opset-version ${OPSET_VERSION} \ + --cfg-options ${CFG_OPTIONS} + --dynamic-export \ + --show \ + --verify \ + --simplify \ +``` + +### Description of all arguments + +- `config` : The path of a model config file. +- `checkpoint` : The path of a model checkpoint file. +- `--output-file`: The path of output ONNX model. If not specified, it will be set to `tmp.onnx`. +- `--input-img`: The path of an input image for tracing and conversion. By default, it will be set to `tests/data/color.jpg`. +- `--shape`: The height and width of input tensor to the model. If not specified, it will be set to `800 1216`. +- `--mean` : Three mean values for the input image. If not specified, it will be set to `123.675 116.28 103.53`. +- `--std` : Three std values for the input image. If not specified, it will be set to `58.395 57.12 57.375`. +- `--dataset` : The dataset name for the input model. If not specified, it will be set to `coco`. +- `--test-img` : The path of an image to verify the exported ONNX model. By default, it will be set to `None`, meaning it will use `--input-img` for verification. +- `--opset-version` : The opset version of ONNX. If not specified, it will be set to `11`. +- `--dynamic-export`: Determines whether to export ONNX model with dynamic input and output shapes. If not specified, it will be set to `False`. +- `--show`: Determines whether to print the architecture of the exported model and whether to show detection outputs when `--verify` is set to `True`. If not specified, it will be set to `False`. +- `--verify`: Determines whether to verify the correctness of an exported model. If not specified, it will be set to `False`. +- `--simplify`: Determines whether to simplify the exported ONNX model. If not specified, it will be set to `False`. +- `--cfg-options`: Override some settings in the used config file, the key-value pair in `xxx=yyy` format will be merged into config file. + +Example: + +```bash +python tools/deployment/pytorch2onnx.py \ + configs/yolo/yolov3_d53_mstrain-608_273e_coco.py \ + checkpoints/yolo/yolov3_d53_mstrain-608_273e_coco.pth \ + --output-file checkpoints/yolo/yolov3_d53_mstrain-608_273e_coco.onnx \ + --input-img demo/demo.jpg \ + --test-img tests/data/color.jpg \ + --shape 608 608 \ + --mean 0 0 0 \ + --std 255 255 255 \ + --show \ + --verify \ + --dynamic-export \ + --cfg-options \ + model.test_cfg.nms_pre=200 \ + model.test_cfg.max_per_img=200 \ + model.test_cfg.deploy_nms_pre=300 \ +``` + +## How to evaluate ONNX models with ONNX Runtime + +We prepare a tool `tools/deplopyment/test.py` to evaluate ONNX models with ONNX Runtime backend. + +### Prerequisite + +- Install onnx and onnxruntime-gpu + + ```shell + pip install onnx onnxruntime-gpu + ``` + +### Usage + +```bash +python tools/deployment/test.py \ + ${CONFIG_FILE} \ + ${ONNX_FILE} \ + --out ${OUTPUT_FILE} \ + --format-only ${FORMAT_ONLY} \ + --eval ${EVALUATION_METRICS} \ + --show-dir ${SHOW_DIRECTORY} \ + ----show-score-thr ${SHOW_SCORE_THRESHOLD} \ + ----cfg-options ${CFG_OPTIONS} \ + ----eval-options ${EVALUATION_OPTIONS} \ +``` + +### Description of all arguments + +- `config`: The path of a model config file. +- `model`: The path of a ONNX model file. +- `--out`: The path of output result file in pickle format. +- `--format-only` : Format the output results without perform evaluation. It is useful when you want to format the result to a specific format and submit it to the test server. If not specified, it will be set to `False`. +- `--eval`: Evaluation metrics, which depends on the dataset, e.g., "bbox", "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC. +- `--show-dir`: Directory where painted images will be saved +- `--show-score-thr`: Score threshold. Default is set to `0.3`. +- `--cfg-options`: Override some settings in the used config file, the key-value pair in `xxx=yyy` format will be merged into config file. +- `--eval-options`: Custom options for evaluation, the key-value pair in `xxx=yyy` format will be kwargs for `dataset.evaluate()` function + +### Results and Models + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelConfigMetricPyTorchONNX Runtime
FCOSconfigs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.pyBox AP36.636.5
FSAFconfigs/fsaf/fsaf_r50_fpn_1x_coco.pyBox AP36.036.0
RetinaNetconfigs/retinanet/retinanet_r50_fpn_1x_coco.pyBox AP36.536.4
SSDconfigs/ssd/ssd300_coco.pyBox AP25.625.6
YOLOv3configs/yolo/yolov3_d53_mstrain-608_273e_coco.pyBox AP33.533.5
Faster R-CNNconfigs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.pyBox AP37.437.4
Mask R-CNNconfigs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.pyBox AP38.238.1
Mask AP34.733.7
+ +Notes: + +- All ONNX models are evaluated with dynamic shape on coco dataset and images are preprocessed according to the original config file. + +- Mask AP of Mask R-CNN drops by 1% for ONNXRuntime. The main reason is that the predicted masks are directly interpolated to original image in PyTorch, while they are at first interpolated to the preprocessed input image of the model and then to original image in ONNXRuntime. + +## List of supported models exportable to ONNX + +The table below lists the models that are guaranteed to be exportable to ONNX and runnable in ONNX Runtime. + +| Model | Config | Dynamic Shape | Batch Inference | Note | +| :----------: | :------------------------------------------------------: | :-----------: | :-------------: | :---: | +| FCOS | `configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py` | Y | Y | | +| FSAF | `configs/fsaf/fsaf_r50_fpn_1x_coco.py` | Y | Y | | +| RetinaNet | `configs/retinanet/retinanet_r50_fpn_1x_coco.py` | Y | Y | | +| SSD | `configs/ssd/ssd300_coco.py` | Y | Y | | +| YOLOv3 | `configs/yolo/yolov3_d53_mstrain-608_273e_coco.py` | Y | Y | | +| Faster R-CNN | `configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py` | Y | Y | | +| Mask R-CNN | `configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py` | Y | Y | | + +Notes: + +- *All models above are tested with Pytorch==1.6.0 and onnxruntime==1.5.1* + +- If the deployed backend platform is TensorRT, please add environment variables before running the file: + + ```bash + export ONNX_BACKEND=MMCVTensorRT + ``` + +- If you want to use the `--dynamic-export` parameter in the TensorRT backend to export ONNX, please remove the `--simplify` parameter, and vice versa. + +## The Parameters of Non-Maximum Suppression in ONNX Export + +In the process of exporting the ONNX model, we set some parameters for the NMS op to control the number of output bounding boxes. The following will introduce the parameter setting of the NMS op in the supported models. You can set these parameters through `--cfg-options`. + +- `nms_pre`: The number of boxes before NMS. The default setting is `1000`. + +- `deploy_nms_pre`: The number of boxes before NMS when exporting to ONNX model. The default setting is `0`. + +- `max_per_img`: The number of boxes to be kept after NMS. The default setting is `100`. + +- `max_output_boxes_per_class`: Maximum number of output boxes per class of NMS. The default setting is `200`. + +## Reminders + +- When the input model has custom op such as `RoIAlign` and if you want to verify the exported ONNX model, you may have to build `mmcv` with [ONNXRuntime](https://mmcv.readthedocs.io/en/latest/onnxruntime_op.html) from source. +- `mmcv.onnx.simplify` feature is based on [onnx-simplifier](https://github.com/daquexian/onnx-simplifier). If you want to try it, please refer to [onnx in `mmcv`](https://mmcv.readthedocs.io/en/latest/onnx.html) and [onnxruntime op in `mmcv`](https://mmcv.readthedocs.io/en/latest/onnxruntime_op.html) for more information. +- If you meet any problem with the listed models above, please create an issue and it would be taken care of soon. For models not included in the list, please try to dig a little deeper and debug a little bit more and hopefully solve them by yourself. +- Because this feature is experimental and may change fast, please always try with the latest `mmcv` and `mmdetecion`. + +## FAQs + +- None diff --git a/docs/useful_tools.md b/docs/useful_tools.md new file mode 100644 index 0000000..25fcf6a --- /dev/null +++ b/docs/useful_tools.md @@ -0,0 +1,384 @@ +Apart from training/testing scripts, We provide lots of useful tools under the + `tools/` directory. + +## Log Analysis + +`tools/analysis_tools/analyze_logs.py` plots loss/mAP curves given a training + log file. Run `pip install seaborn` first to install the dependency. + + ```shell +python tools/analysis_tools/analyze_logs.py plot_curve [--keys ${KEYS}] [--title ${TITLE}] [--legend ${LEGEND}] [--backend ${BACKEND}] [--style ${STYLE}] [--out ${OUT_FILE}] + ``` + +![loss curve image](../resources/loss_curve.png) + +Examples: + +- Plot the classification loss of some run. + + ```shell + python tools/analysis_tools/analyze_logs.py plot_curve log.json --keys loss_cls --legend loss_cls + ``` + +- Plot the classification and regression loss of some run, and save the figure to a pdf. + + ```shell + python tools/analysis_tools/analyze_logs.py plot_curve log.json --keys loss_cls loss_bbox --out losses.pdf + ``` + +- Compare the bbox mAP of two runs in the same figure. + + ```shell + python tools/analysis_tools/analyze_logs.py plot_curve log1.json log2.json --keys bbox_mAP --legend run1 run2 + ``` + +- Compute the average training speed. + + ```shell + python tools/analysis_tools/analyze_logs.py cal_train_time log.json [--include-outliers] + ``` + + The output is expected to be like the following. + + ```text + -----Analyze train time of work_dirs/some_exp/20190611_192040.log.json----- + slowest epoch 11, average time is 1.2024 + fastest epoch 1, average time is 1.1909 + time std over epochs is 0.0028 + average iter time: 1.1959 s/iter + ``` + +## Result Analysis + +`tools/analysis_tools/analyze_results.py` calculates single image mAP and saves or shows the topk images with the highest and lowest scores based on prediction results. + +**Usage** + +```shell +python tools/analysis_tools/analyze_results.py \ + ${CONFIG} \ + ${PREDICTION_PATH} \ + ${SHOW_DIR} \ + [--show] \ + [--wait-time ${WAIT_TIME}] \ + [--topk ${TOPK}] \ + [--show-score-thr ${SHOW_SCORE_THR}] \ + [--cfg-options ${CFG_OPTIONS}] +``` + +Description of all arguments: + +- `config` : The path of a model config file. +- `prediction_path`: Output result file in pickle format from `tools/test.py` +- `show_dir`: Directory where painted GT and detection images will be saved +- `--show`：Determines whether to show painted images, If not specified, it will be set to `False` +- `--wait-time`: The interval of show (s), 0 is block +- `--topk`: The number of saved images that have the highest and lowest `topk` scores after sorting. If not specified, it will be set to `20`. +- `--show-score-thr`: Show score threshold. If not specified, it will be set to `0`. +- `--cfg-options`: If specified, the key-value pair optional cfg will be merged into config file + +**Examples**: + +Assume that you have got result file in pickle format from `tools/test.py` in the path './result.pkl'. + +1. Test Faster R-CNN and visualize the results, save images to the directory `results/` + +```shell +python tools/analysis_tools/analyze_results.py \ + configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \ + result.pkl \ + results \ + --show +``` + +2. Test Faster R-CNN and specified topk to 50, save images to the directory `results/` + +```shell +python tools/analysis_tools/analyze_results.py \ + configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \ + result.pkl \ + results \ + --topk 50 +``` + +3. If you want to filter the low score prediction results, you can specify the `show-score-thr` parameter + +```shell +python tools/analysis_tools/analyze_results.py \ + configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \ + result.pkl \ + results \ + --show-score-thr 0.3 +``` + +## Visualization + +### Visualize Datasets + +`tools/misc/browse_dataset.py` helps the user to browse a detection dataset (both + images and bounding box annotations) visually, or save the image to a + designated directory. + +```shell +python tools/misc/browse_dataset.py ${CONFIG} [-h] [--skip-type ${SKIP_TYPE[SKIP_TYPE...]}] [--output-dir ${OUTPUT_DIR}] [--not-show] [--show-interval ${SHOW_INTERVAL}] +``` + +### Visualize Models + +First, convert the model to ONNX as described +[here](#convert-mmdetection-model-to-onnx-experimental). +Note that currently only RetinaNet is supported, support for other models + will be coming in later versions. +The converted model could be visualized by tools like [Netron](https://github.com/lutzroeder/netron). + +### Visualize Predictions + +If you need a lightweight GUI for visualizing the detection results, you can refer [DetVisGUI project](https://github.com/Chien-Hung/DetVisGUI/tree/mmdetection). + +## Error Analysis + +`tools/analysis_tools/coco_error_analysis.py` analyzes COCO results per category and by + different criterion. It can also make a plot to provide useful information. + +```shell +python tools/analysis_tools/coco_error_analysis.py ${RESULT} ${OUT_DIR} [-h] [--ann ${ANN}] [--types ${TYPES[TYPES...]}] +``` + +Example: + +Assume that you have got [Mask R-CNN checkpoint file](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) in the path 'checkpoint'. For other checkpoints, please refer to our [model zoo](./model_zoo.md). You can use the following command to get the results bbox and segmentation json file. + +```shell +# out: results.bbox.json and results.segm.json +python tools/test.py \ + configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py \ + checkpoint/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \ + --format-only \ + --options "jsonfile_prefix=./results" +``` + +1. Get COCO bbox error results per category , save analyze result images to the directory `results/` + +```shell +python tools/analysis_tools/coco_error_analysis.py \ + results.bbox.json \ + results \ + --ann=data/coco/annotations/instances_val2017.json \ +``` + +2. Get COCO segmentation error results per category , save analyze result images to the directory `results/` + +```shell +python tools/analysis_tools/coco_error_analysis.py \ + results.segm.json \ + results \ + --ann=data/coco/annotations/instances_val2017.json \ + --types='segm' +``` + +## Model Serving + +In order to serve an `MMDetection` model with [`TorchServe`](https://pytorch.org/serve/), you can follow the steps: + +### 1. Convert model from MMDetection to TorchServe + +```shell +python tools/deployment/mmdet2torchserve.py ${CONFIG_FILE} ${CHECKPOINT_FILE} \ +--output-folder ${MODEL_STORE} \ +--model-name ${MODEL_NAME} +``` + +***Note**: ${MODEL_STORE} needs to be an absolute path to a folder. + +### 2. Build `mmdet-serve` docker image + +```shell +docker build -t mmdet-serve:latest docker/serve/ +``` + +### 3. Run `mmdet-serve` + +Check the official docs for [running TorchServe with docker](https://github.com/pytorch/serve/blob/master/docker/README.md#running-torchserve-in-a-production-docker-environment). + +In order to run in GPU, you need to install [nvidia-docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). You can omit the `--gpus` argument in order to run in CPU. + +Example: + +```shell +docker run --rm \ +--cpus 8 \ +--gpus device=0 \ +-p8080:8080 -p8081:8081 -p8082:8082 \ +--mount type=bind,source=$MODEL_STORE,target=/home/model-server/model-store \ +mmdet-serve:latest +``` + +[Read the docs](https://github.com/pytorch/serve/blob/072f5d088cce9bb64b2a18af065886c9b01b317b/docs/rest_api.md) about the Inference (8080), Management (8081) and Metrics (8082) APis + +### 4. Test deployment + +```shell +curl -O curl -O https://raw.githubusercontent.com/pytorch/serve/master/docs/images/3dogs.jpg +curl http://127.0.0.1:8080/predictions/${MODEL_NAME} -T 3dogs.jpg +``` + +You should obtain a respose similar to: + +```json +[ + { + "dog": [ + 402.9117736816406, + 124.19664001464844, + 571.7910766601562, + 292.6463623046875 + ], + "score": 0.9561963081359863 + }, + { + "dog": [ + 293.90057373046875, + 196.2908477783203, + 417.4869079589844, + 286.2522277832031 + ], + "score": 0.9179860353469849 + }, + { + "dog": [ + 202.178466796875, + 86.3709487915039, + 311.9863586425781, + 276.28411865234375 + ], + "score": 0.8933767080307007 + } +] +``` + +## Model Complexity + +`tools/analysis_tools/get_flops.py` is a script adapted from [flops-counter.pytorch](https://github.com/sovrasov/flops-counter.pytorch) to compute the FLOPs and params of a given model. + +```shell +python tools/analysis_tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}] +``` + +You will get the results like this. + +```text +============================== +Input shape: (3, 1280, 800) +Flops: 239.32 GFLOPs +Params: 37.74 M +============================== +``` + +**Note**: This tool is still experimental and we do not guarantee that the + number is absolutely correct. You may well use the result for simple + comparisons, but double check it before you adopt it in technical reports or papers. + +1. FLOPs are related to the input shape while parameters are not. The default + input shape is (1, 3, 1280, 800). +2. Some operators are not counted into FLOPs like GN and custom operators. Refer to [`mmcv.cnn.get_model_complexity_info()`](https://github.com/open-mmlab/mmcv/blob/master/mmcv/cnn/utils/flops_counter.py) for details. +3. The FLOPs of two-stage detectors is dependent on the number of proposals. + +## Model conversion + +### MMDetection model to ONNX (experimental) + +We provide a script to convert model to [ONNX](https://github.com/onnx/onnx) format. We also support comparing the output results between Pytorch and ONNX model for verification. + +```shell +python tools/deployment/pytorch2onnx.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --output_file ${ONNX_FILE} [--shape ${INPUT_SHAPE} --verify] +``` + +**Note**: This tool is still experimental. Some customized operators are not supported for now. For a detailed description of the usage and the list of supported models, please refer to [pytorch2onnx](tutorials/pytorch2onnx.md). + +### MMDetection 1.x model to MMDetection 2.x + +`tools/model_converters/upgrade_model_version.py` upgrades a previous MMDetection checkpoint + to the new version. Note that this script is not guaranteed to work as some + breaking changes are introduced in the new version. It is recommended to + directly use the new checkpoints. + +```shell +python tools/model_converters/upgrade_model_version.py ${IN_FILE} ${OUT_FILE} [-h] [--num-classes NUM_CLASSES] +``` + +### RegNet model to MMDetection + +`tools/model_converters/regnet2mmdet.py` convert keys in pycls pretrained RegNet models to + MMDetection style. + +```shell +python tools/model_converters/regnet2mmdet.py ${SRC} ${DST} [-h] +``` + +### Detectron ResNet to Pytorch + +`tools/model_converters/detectron2pytorch.py` converts keys in the original detectron pretrained + ResNet models to PyTorch style. + +```shell +python tools/model_converters/detectron2pytorch.py ${SRC} ${DST} ${DEPTH} [-h] +``` + +### Prepare a model for publishing + +`tools/model_converters/publish_model.py` helps users to prepare their model for publishing. + +Before you upload a model to AWS, you may want to + +1. convert model weights to CPU tensors +2. delete the optimizer states and +3. compute the hash of the checkpoint file and append the hash id to the + filename. + +```shell +python tools/model_converters/publish_model.py ${INPUT_FILENAME} ${OUTPUT_FILENAME} +``` + +E.g., + +```shell +python tools/model_converters/publish_model.py work_dirs/faster_rcnn/latest.pth faster_rcnn_r50_fpn_1x_20190801.pth +``` + +The final output filename will be `faster_rcnn_r50_fpn_1x_20190801-{hash id}.pth`. + +## Dataset Conversion + +`tools/data_converters/` contains tools to convert the Cityscapes dataset + and Pascal VOC dataset to the COCO format. + +```shell +python tools/dataset_converters/cityscapes.py ${CITYSCAPES_PATH} [-h] [--img-dir ${IMG_DIR}] [--gt-dir ${GT_DIR}] [-o ${OUT_DIR}] [--nproc ${NPROC}] +python tools/dataset_converters/pascal_voc.py ${DEVKIT_PATH} [-h] [-o ${OUT_DIR}] +``` + +## Robust Detection Benchmark + +`tools/analysis_tools/test_robustness.py` and`tools/analysis_tools/robustness_eval.py` helps users to evaluate model robustness. The core idea comes from [Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming](https://arxiv.org/abs/1907.07484). For more information how to evaluate models on corrupted images and results for a set of standard models please refer to [robustness_benchmarking.md](robustness_benchmarking.md). + +## Miscellaneous + +### Evaluating a metric + +`tools/analysis_tools/eval_metric.py` evaluates certain metrics of a pkl result file + according to a config file. + +```shell +python tools/analysis_tools/eval_metric.py ${CONFIG} ${PKL_RESULTS} [-h] [--format-only] [--eval ${EVAL[EVAL ...]}] + [--cfg-options ${CFG_OPTIONS [CFG_OPTIONS ...]}] + [--eval-options ${EVAL_OPTIONS [EVAL_OPTIONS ...]}] +``` + +### Print the entire config + +`tools/misc/print_config.py` prints the whole config verbatim, expanding all its + imports. + +```shell +python tools/misc/print_config.py ${CONFIG} [-h] [--options ${OPTIONS [OPTIONS...]}] +``` diff --git a/mmdet/__init__.py b/mmdet/__init__.py new file mode 100644 index 0000000..bb2117e --- /dev/null +++ b/mmdet/__init__.py @@ -0,0 +1,28 @@ +import mmcv + +from .version import __version__, short_version + + +def digit_version(version_str): + digit_version = [] + for x in version_str.split('.'): + if x.isdigit(): + digit_version.append(int(x)) + elif x.find('rc') != -1: + patch_version = x.split('rc') + digit_version.append(int(patch_version[0]) - 1) + digit_version.append(int(patch_version[1])) + return digit_version + + +mmcv_minimum_version = '1.3.2' +mmcv_maximum_version = '1.4.0' +mmcv_version = digit_version(mmcv.__version__) + + +assert (mmcv_version >= digit_version(mmcv_minimum_version) + and mmcv_version <= digit_version(mmcv_maximum_version)), \ + f'MMCV=={mmcv.__version__} is used but incompatible. ' \ + f'Please install mmcv>={mmcv_minimum_version}, <={mmcv_maximum_version}.' + +__all__ = ['__version__', 'short_version'] diff --git a/mmdet/__pycache__/__init__.cpython-37.pyc b/mmdet/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..d552653 Binary files /dev/null and b/mmdet/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/__pycache__/version.cpython-37.pyc b/mmdet/__pycache__/version.cpython-37.pyc new file mode 100644 index 0000000..b3960d9 Binary files /dev/null and b/mmdet/__pycache__/version.cpython-37.pyc differ diff --git a/mmdet/apis/__init__.py b/mmdet/apis/__init__.py new file mode 100644 index 0000000..1d8035b --- /dev/null +++ b/mmdet/apis/__init__.py @@ -0,0 +1,10 @@ +from .inference import (async_inference_detector, inference_detector, + init_detector, show_result_pyplot) +from .test import multi_gpu_test, single_gpu_test +from .train import get_root_logger, set_random_seed, train_detector + +__all__ = [ + 'get_root_logger', 'set_random_seed', 'train_detector', 'init_detector', + 'async_inference_detector', 'inference_detector', 'show_result_pyplot', + 'multi_gpu_test', 'single_gpu_test' +] diff --git a/mmdet/apis/__pycache__/__init__.cpython-37.pyc b/mmdet/apis/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..a632c78 Binary files /dev/null and b/mmdet/apis/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/apis/__pycache__/inference.cpython-37.pyc b/mmdet/apis/__pycache__/inference.cpython-37.pyc new file mode 100644 index 0000000..0428051 Binary files /dev/null and b/mmdet/apis/__pycache__/inference.cpython-37.pyc differ diff --git a/mmdet/apis/__pycache__/test.cpython-37.pyc b/mmdet/apis/__pycache__/test.cpython-37.pyc new file mode 100644 index 0000000..17a80f7 Binary files /dev/null and b/mmdet/apis/__pycache__/test.cpython-37.pyc differ diff --git a/mmdet/apis/__pycache__/train.cpython-37.pyc b/mmdet/apis/__pycache__/train.cpython-37.pyc new file mode 100644 index 0000000..7019937 Binary files /dev/null and b/mmdet/apis/__pycache__/train.cpython-37.pyc differ diff --git a/mmdet/apis/inference.py b/mmdet/apis/inference.py new file mode 100644 index 0000000..e9afd58 --- /dev/null +++ b/mmdet/apis/inference.py @@ -0,0 +1,248 @@ +import warnings + +import mmcv +import numpy as np +import torch +from mmcv.ops import RoIPool +from mmcv.parallel import collate, scatter +from mmcv.runner import load_checkpoint + +from mmdet.core import get_classes +from mmdet.datasets import replace_ImageToTensor +from mmdet.datasets.pipelines import Compose +from mmdet.models import build_detector + + +def init_detector(config, checkpoint=None, device='cuda:0', cfg_options=None): + """Initialize a detector from config file. + + Args: + config (str or :obj:`mmcv.Config`): Config file path or the config + object. + checkpoint (str, optional): Checkpoint path. If left as None, the model + will not load any weights. + cfg_options (dict): Options to override some settings in the used + config. + + Returns: + nn.Module: The constructed detector. + """ + if isinstance(config, str): + config = mmcv.Config.fromfile(config) + elif not isinstance(config, mmcv.Config): + raise TypeError('config must be a filename or Config object, ' + f'but got {type(config)}') + if cfg_options is not None: + config.merge_from_dict(cfg_options) + config.model.pretrained = None + config.model.train_cfg = None + model = build_detector(config.model, test_cfg=config.get('test_cfg')) + if checkpoint is not None: + map_loc = 'cpu' if device == 'cpu' else None + checkpoint = load_checkpoint(model, checkpoint, map_location=map_loc) + if 'CLASSES' in checkpoint.get('meta', {}): + model.CLASSES = checkpoint['meta']['CLASSES'] + else: + warnings.simplefilter('once') + warnings.warn('Class names are not saved in the checkpoint\'s ' + 'meta data, use COCO classes by default.') + model.CLASSES = get_classes('coco') + else: + warnings.simplefilter('once') + warnings.warn('Class names are not saved in the checkpoint\'s ' + 'meta data, use COCO classes by default.') + model.CLASSES = get_classes('coco') + model.init_weights() + model.cfg = config # save the config in the model for convenience + model.to(device) + model.eval() + return model + + +class LoadImage(object): + """Deprecated. + + A simple pipeline to load image. + """ + + def __call__(self, results): + """Call function to load images into results. + + Args: + results (dict): A result dict contains the file name + of the image to be read. + Returns: + dict: ``results`` will be returned containing loaded image. + """ + warnings.simplefilter('once') + warnings.warn('`LoadImage` is deprecated and will be removed in ' + 'future releases. You may use `LoadImageFromWebcam` ' + 'from `mmdet.datasets.pipelines.` instead.') + if isinstance(results['img'], str): + results['filename'] = results['img'] + results['ori_filename'] = results['img'] + else: + results['filename'] = None + results['ori_filename'] = None + img = mmcv.imread(results['img']) + results['img'] = img + results['img_fields'] = ['img'] + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + return results + + +def inference_detector(model, imgs): + """Inference image(s) with the detector. + + Args: + model (nn.Module): The loaded detector. + imgs (str/ndarray or list[str/ndarray] or tuple[str/ndarray]): + Either image files or loaded images. + + Returns: + If imgs is a list or tuple, the same length list type results + will be returned, otherwise return the detection results directly. + """ + + if isinstance(imgs, (list, tuple)): + is_batch = True + else: + imgs = [imgs] + is_batch = False + + cfg = model.cfg + device = next(model.parameters()).device # model device + + if isinstance(imgs[0], np.ndarray): + cfg = cfg.copy() + # set loading pipeline type + cfg.data.test.pipeline[0].type = 'LoadImageFromWebcam' + + cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline) + test_pipeline = Compose(cfg.data.test.pipeline) + + datas = [] + for img in imgs: + # prepare data + if isinstance(img, np.ndarray): + # directly add img + data = dict(img=img) + else: + # add information into dict + data = dict(img_info=dict(filename=img), img_prefix=None) + # build the data pipeline + data = test_pipeline(data) + datas.append(data) + + data = collate(datas, samples_per_gpu=len(imgs)) + # just get the actual data from DataContainer + data['img_metas'] = [img_metas.data[0] for img_metas in data['img_metas']] + data['img'] = [img.data[0] for img in data['img']] + if next(model.parameters()).is_cuda: + # scatter to specified GPU + data = scatter(data, [device])[0] + else: + for m in model.modules(): + assert not isinstance( + m, RoIPool + ), 'CPU inference with RoIPool is not supported currently.' + + # forward the model + with torch.no_grad(): + results = model(return_loss=False, rescale=True, **data) + + if not is_batch: + return results[0] + else: + return results + + +async def async_inference_detector(model, imgs): + """Async inference image(s) with the detector. + + Args: + model (nn.Module): The loaded detector. + img (str | ndarray): Either image files or loaded images. + + Returns: + Awaitable detection results. + """ + if not isinstance(imgs, (list, tuple)): + imgs = [imgs] + + cfg = model.cfg + device = next(model.parameters()).device # model device + + if isinstance(imgs[0], np.ndarray): + cfg = cfg.copy() + # set loading pipeline type + cfg.data.test.pipeline[0].type = 'LoadImageFromWebcam' + + cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline) + test_pipeline = Compose(cfg.data.test.pipeline) + + datas = [] + for img in imgs: + # prepare data + if isinstance(img, np.ndarray): + # directly add img + data = dict(img=img) + else: + # add information into dict + data = dict(img_info=dict(filename=img), img_prefix=None) + # build the data pipeline + data = test_pipeline(data) + datas.append(data) + + data = collate(datas, samples_per_gpu=len(imgs)) + # just get the actual data from DataContainer + data['img_metas'] = [img_metas.data[0] for img_metas in data['img_metas']] + data['img'] = [img.data[0] for img in data['img']] + if next(model.parameters()).is_cuda: + # scatter to specified GPU + data = scatter(data, [device])[0] + else: + for m in model.modules(): + assert not isinstance( + m, RoIPool + ), 'CPU inference with RoIPool is not supported currently.' + + # We don't restore `torch.is_grad_enabled()` value during concurrent + # inference since execution can overlap + torch.set_grad_enabled(False) + results = await model.aforward_test(rescale=True, **data) + return results + + +def show_result_pyplot(model, + img, + result, + score_thr=0.3, + title='result', + wait_time=0, + out_file=None): + """Visualize the detection results on the image. + + Args: + model (nn.Module): The loaded detector. + img (str or np.ndarray): Image filename or loaded image. + result (tuple[list] or list): The detection result, can be either + (bbox, segm) or just bbox. + score_thr (float): The threshold to visualize the bboxes and masks. + title (str): Title of the pyplot figure. + wait_time (float): Value of waitKey param. + Default: 0. + """ + if hasattr(model, 'module'): + model = model.module + model.show_result( + img, + result, + score_thr=score_thr, + show=True, + wait_time=wait_time, + win_name=title, + out_file=out_file, + bbox_color=(72, 101, 241), + text_color=(72, 101, 241)) diff --git a/mmdet/apis/test.py b/mmdet/apis/test.py new file mode 100644 index 0000000..d8d7cc0 --- /dev/null +++ b/mmdet/apis/test.py @@ -0,0 +1,190 @@ +import os.path as osp +import pickle +import shutil +import tempfile +import time + +import mmcv +import torch +import torch.distributed as dist +from mmcv.image import tensor2imgs +from mmcv.runner import get_dist_info + +from mmdet.core import encode_mask_results +import cccu + +def single_gpu_test(model, + data_loader, + show=False, + out_dir=None, + show_score_thr=0.3): + model.eval() + results = [] + dataset = data_loader.dataset + prog_bar = mmcv.ProgressBar(len(dataset)) + for i, data in enumerate(data_loader): + with torch.no_grad(): + result = model(return_loss=False, rescale=True, **data) + + batch_size = len(result) + if show or out_dir: + if batch_size == 1 and isinstance(data['img'][0], torch.Tensor): + img_tensor = data['img'][0] + else: + img_tensor = data['img'][0].data[0] + img_metas = data['img_metas'][0].data[0] + imgs = tensor2imgs(img_tensor, **img_metas[0]['img_norm_cfg']) + assert len(imgs) == len(img_metas) + + for i, (img, img_meta) in enumerate(zip(imgs, img_metas)): + h, w, _ = img_meta['img_shape'] + img_show = img[:h, :w, :] + + ori_h, ori_w = img_meta['ori_shape'][:-1] + img_show = mmcv.imresize(img_show, (ori_w, ori_h)) + + if out_dir: + out_file = osp.join(out_dir, img_meta['ori_filename']) + else: + out_file = None + + model.module.show_result( + img_show, + result[i], + show=show, + out_file=out_file, + score_thr=show_score_thr) + + # encode mask results + if isinstance(result[0], tuple): + result = [(bbox_results, encode_mask_results(mask_results)) + for bbox_results, mask_results in result] + results.extend(result) + + for _ in range(batch_size): + prog_bar.update() + return results + + +def multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False): + """Test model with multiple gpus. + + This method tests model with multiple gpus and collects the results + under two different modes: gpu and cpu modes. By setting 'gpu_collect=True' + it encodes results to gpu tensors and use gpu communication for results + collection. On cpu mode it saves the results on different gpus to 'tmpdir' + and collects them by the rank 0 worker. + + Args: + model (nn.Module): Model to be tested. + data_loader (nn.Dataloader): Pytorch data loader. + tmpdir (str): Path of directory to save the temporary results from + different gpus under cpu mode. + gpu_collect (bool): Option to use either gpu or cpu to collect results. + + Returns: + list: The prediction results. + """ + model.eval() + results = [] + dataset = data_loader.dataset + rank, world_size = get_dist_info() + if rank == 0: + prog_bar = mmcv.ProgressBar(len(dataset)) + time.sleep(2) # This line can prevent deadlock problem in some cases. + for i, data in enumerate(data_loader): + with torch.no_grad(): + result = model(return_loss=False, rescale=True, **data) + # encode mask results + if isinstance(result[0], tuple): + result = [(bbox_results, encode_mask_results(mask_results)) + for bbox_results, mask_results in result] + results.extend(result) + + if rank == 0: + batch_size = len(result) + for _ in range(batch_size * world_size): + prog_bar.update() + + # collect results from all ranks + if gpu_collect: + results = collect_results_gpu(results, len(dataset)) + else: + results = collect_results_cpu(results, len(dataset), tmpdir) + return results + + +def collect_results_cpu(result_part, size, tmpdir=None): + rank, world_size = get_dist_info() + # create a tmp dir if it is not specified + if tmpdir is None: + MAX_LEN = 512 + # 32 is whitespace + dir_tensor = torch.full((MAX_LEN, ), + 32, + dtype=torch.uint8, + device='cuda') + if rank == 0: + mmcv.mkdir_or_exist('.dist_test') + tmpdir = tempfile.mkdtemp(dir='.dist_test') + tmpdir = torch.tensor( + bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda') + dir_tensor[:len(tmpdir)] = tmpdir + dist.broadcast(dir_tensor, 0) + tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip() + else: + mmcv.mkdir_or_exist(tmpdir) + # dump the part result to the dir + mmcv.dump(result_part, osp.join(tmpdir, f'part_{rank}.pkl')) + dist.barrier() + # collect all parts + if rank != 0: + return None + else: + # load results of all parts from tmp dir + part_list = [] + for i in range(world_size): + part_file = osp.join(tmpdir, f'part_{i}.pkl') + part_list.append(mmcv.load(part_file)) + # sort the results + ordered_results = [] + for res in zip(*part_list): + ordered_results.extend(list(res)) + # the dataloader may pad some samples + ordered_results = ordered_results[:size] + # remove tmp dir + shutil.rmtree(tmpdir) + return ordered_results + + +def collect_results_gpu(result_part, size): + rank, world_size = get_dist_info() + # dump result part to tensor with pickle + part_tensor = torch.tensor( + bytearray(pickle.dumps(result_part)), dtype=torch.uint8, device='cuda') + # gather all result part tensor shape + shape_tensor = torch.tensor(part_tensor.shape, device='cuda') + shape_list = [shape_tensor.clone() for _ in range(world_size)] + dist.all_gather(shape_list, shape_tensor) + # padding result part tensor to max length + shape_max = torch.tensor(shape_list).max() + part_send = torch.zeros(shape_max, dtype=torch.uint8, device='cuda') + part_send[:shape_tensor[0]] = part_tensor + part_recv_list = [ + part_tensor.new_zeros(shape_max) for _ in range(world_size) + ] + # gather all result part + dist.all_gather(part_recv_list, part_send) + + if rank == 0: + part_list = [] + for recv, shape in zip(part_recv_list, shape_list): + part_list.append( + pickle.loads(recv[:shape[0]].cpu().numpy().tobytes())) + # sort the results + ordered_results = [] + for res in zip(*part_list): + ordered_results.extend(list(res)) + # the dataloader may pad some samples + ordered_results = ordered_results[:size] + return ordered_results diff --git a/mmdet/apis/train.py b/mmdet/apis/train.py new file mode 100644 index 0000000..82c20bf --- /dev/null +++ b/mmdet/apis/train.py @@ -0,0 +1,170 @@ +import random +import warnings + +import numpy as np +import torch +from mmcv.parallel import MMDataParallel, MMDistributedDataParallel +from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner, + Fp16OptimizerHook, OptimizerHook, build_optimizer, + build_runner) +from mmcv.utils import build_from_cfg + +from mmdet.core import DistEvalHook, EvalHook +from mmdet.datasets import (build_dataloader, build_dataset, + replace_ImageToTensor) +from mmdet.utils import get_root_logger + + +def set_random_seed(seed, deterministic=False): + """Set random seed. + + Args: + seed (int): Seed to be used. + deterministic (bool): Whether to set the deterministic option for + CUDNN backend, i.e., set `torch.backends.cudnn.deterministic` + to True and `torch.backends.cudnn.benchmark` to False. + Default: False. + """ + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + if deterministic: + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + +def train_detector(model, + dataset, + cfg, + distributed=False, + validate=False, + timestamp=None, + meta=None): + logger = get_root_logger(cfg.log_level) + + # prepare data loaders + dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset] + if 'imgs_per_gpu' in cfg.data: + logger.warning('"imgs_per_gpu" is deprecated in MMDet V2.0. ' + 'Please use "samples_per_gpu" instead') + if 'samples_per_gpu' in cfg.data: + logger.warning( + f'Got "imgs_per_gpu"={cfg.data.imgs_per_gpu} and ' + f'"samples_per_gpu"={cfg.data.samples_per_gpu}, "imgs_per_gpu"' + f'={cfg.data.imgs_per_gpu} is used in this experiments') + else: + logger.warning( + 'Automatically set "samples_per_gpu"="imgs_per_gpu"=' + f'{cfg.data.imgs_per_gpu} in this experiments') + cfg.data.samples_per_gpu = cfg.data.imgs_per_gpu + + data_loaders = [ + build_dataloader( + ds, + cfg.data.samples_per_gpu, + cfg.data.workers_per_gpu, + # cfg.gpus will be ignored if distributed + len(cfg.gpu_ids), + dist=distributed, + seed=cfg.seed) for ds in dataset + ] + + # put model on gpus + if distributed: + find_unused_parameters = cfg.get('find_unused_parameters', False) + # Sets the `find_unused_parameters` parameter in + # torch.nn.parallel.DistributedDataParallel + model = MMDistributedDataParallel( + model.cuda(), + device_ids=[torch.cuda.current_device()], + broadcast_buffers=False, + find_unused_parameters=find_unused_parameters) + else: + model = MMDataParallel( + model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids) + + # build runner + optimizer = build_optimizer(model, cfg.optimizer) + + if 'runner' not in cfg: + cfg.runner = { + 'type': 'EpochBasedRunner', + 'max_epochs': cfg.total_epochs + } + warnings.warn( + 'config is now expected to have a `runner` section, ' + 'please set `runner` in your config.', UserWarning) + else: + if 'total_epochs' in cfg: + assert cfg.total_epochs == cfg.runner.max_epochs + + runner = build_runner( + cfg.runner, + default_args=dict( + model=model, + optimizer=optimizer, + work_dir=cfg.work_dir, + logger=logger, + meta=meta)) + + # an ugly workaround to make .log and .log.json filenames the same + runner.timestamp = timestamp + + # fp16 setting + fp16_cfg = cfg.get('fp16', None) + if fp16_cfg is not None: + optimizer_config = Fp16OptimizerHook( + **cfg.optimizer_config, **fp16_cfg, distributed=distributed) + elif distributed and 'type' not in cfg.optimizer_config: + optimizer_config = OptimizerHook(**cfg.optimizer_config) + else: + optimizer_config = cfg.optimizer_config + + # register hooks + runner.register_training_hooks(cfg.lr_config, optimizer_config, + cfg.checkpoint_config, cfg.log_config, + cfg.get('momentum_config', None)) + if distributed: + if isinstance(runner, EpochBasedRunner): + runner.register_hook(DistSamplerSeedHook()) + + # register eval hooks + if validate: + # Support batch_size > 1 in validation + val_samples_per_gpu = cfg.data.val.pop('samples_per_gpu', 1) + if val_samples_per_gpu > 1: + # Replace 'ImageToTensor' to 'DefaultFormatBundle' + cfg.data.val.pipeline = replace_ImageToTensor( + cfg.data.val.pipeline) + val_dataset = build_dataset(cfg.data.val, dict(test_mode=True)) + val_dataloader = build_dataloader( + val_dataset, + samples_per_gpu=val_samples_per_gpu, + workers_per_gpu=cfg.data.workers_per_gpu, + dist=distributed, + shuffle=False) + eval_cfg = cfg.get('evaluation', {}) + eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner' + eval_hook = DistEvalHook if distributed else EvalHook + runner.register_hook(eval_hook(val_dataloader, **eval_cfg)) + + # user-defined hooks + if cfg.get('custom_hooks', None): + custom_hooks = cfg.custom_hooks + assert isinstance(custom_hooks, list), \ + f'custom_hooks expect list type, but got {type(custom_hooks)}' + for hook_cfg in cfg.custom_hooks: + assert isinstance(hook_cfg, dict), \ + 'Each item in custom_hooks expects dict type, but got ' \ + f'{type(hook_cfg)}' + hook_cfg = hook_cfg.copy() + priority = hook_cfg.pop('priority', 'NORMAL') + hook = build_from_cfg(hook_cfg, HOOKS) + runner.register_hook(hook, priority=priority) + + if cfg.resume_from: + runner.resume(cfg.resume_from) + elif cfg.load_from: + runner.load_checkpoint(cfg.load_from) + runner.run(data_loaders, cfg.workflow) diff --git a/mmdet/core/__init__.py b/mmdet/core/__init__.py new file mode 100644 index 0000000..00a54e2 --- /dev/null +++ b/mmdet/core/__init__.py @@ -0,0 +1,6 @@ +from .anchor import * # noqa: F401, F403 +from .bbox import * # noqa: F401, F403 +from .evaluation import * # noqa: F401, F403 +from .mask import * # noqa: F401, F403 +from .post_processing import * # noqa: F401, F403 +from .utils import * # noqa: F401, F403 diff --git a/mmdet/core/__pycache__/__init__.cpython-37.pyc b/mmdet/core/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..d8d7d6d Binary files /dev/null and b/mmdet/core/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/core/anchor/__init__.py b/mmdet/core/anchor/__init__.py new file mode 100644 index 0000000..5838ff3 --- /dev/null +++ b/mmdet/core/anchor/__init__.py @@ -0,0 +1,11 @@ +from .anchor_generator import (AnchorGenerator, LegacyAnchorGenerator, + YOLOAnchorGenerator) +from .builder import ANCHOR_GENERATORS, build_anchor_generator +from .point_generator import PointGenerator +from .utils import anchor_inside_flags, calc_region, images_to_levels + +__all__ = [ + 'AnchorGenerator', 'LegacyAnchorGenerator', 'anchor_inside_flags', + 'PointGenerator', 'images_to_levels', 'calc_region', + 'build_anchor_generator', 'ANCHOR_GENERATORS', 'YOLOAnchorGenerator' +] diff --git a/mmdet/core/anchor/__pycache__/__init__.cpython-37.pyc b/mmdet/core/anchor/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..da4f35b Binary files /dev/null and b/mmdet/core/anchor/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/core/anchor/__pycache__/anchor_generator.cpython-37.pyc b/mmdet/core/anchor/__pycache__/anchor_generator.cpython-37.pyc new file mode 100644 index 0000000..f1b32bb Binary files /dev/null and b/mmdet/core/anchor/__pycache__/anchor_generator.cpython-37.pyc differ diff --git a/mmdet/core/anchor/__pycache__/builder.cpython-37.pyc b/mmdet/core/anchor/__pycache__/builder.cpython-37.pyc new file mode 100644 index 0000000..d140a3f Binary files /dev/null and b/mmdet/core/anchor/__pycache__/builder.cpython-37.pyc differ diff --git a/mmdet/core/anchor/__pycache__/point_generator.cpython-37.pyc b/mmdet/core/anchor/__pycache__/point_generator.cpython-37.pyc new file mode 100644 index 0000000..9304ae0 Binary files /dev/null and b/mmdet/core/anchor/__pycache__/point_generator.cpython-37.pyc differ diff --git a/mmdet/core/anchor/__pycache__/utils.cpython-37.pyc b/mmdet/core/anchor/__pycache__/utils.cpython-37.pyc new file mode 100644 index 0000000..0385147 Binary files /dev/null and b/mmdet/core/anchor/__pycache__/utils.cpython-37.pyc differ diff --git a/mmdet/core/anchor/anchor_generator.py b/mmdet/core/anchor/anchor_generator.py new file mode 100644 index 0000000..388d260 --- /dev/null +++ b/mmdet/core/anchor/anchor_generator.py @@ -0,0 +1,727 @@ +import mmcv +import numpy as np +import torch +from torch.nn.modules.utils import _pair + +from .builder import ANCHOR_GENERATORS + + +@ANCHOR_GENERATORS.register_module() +class AnchorGenerator(object): + """Standard anchor generator for 2D anchor-based detectors. + + Args: + strides (list[int] | list[tuple[int, int]]): Strides of anchors + in multiple feature levels in order (w, h). + ratios (list[float]): The list of ratios between the height and width + of anchors in a single level. + scales (list[int] | None): Anchor scales for anchors in a single level. + It cannot be set at the same time if `octave_base_scale` and + `scales_per_octave` are set. + base_sizes (list[int] | None): The basic sizes + of anchors in multiple levels. + If None is given, strides will be used as base_sizes. + (If strides are non square, the shortest stride is taken.) + scale_major (bool): Whether to multiply scales first when generating + base anchors. If true, the anchors in the same row will have the + same scales. By default it is True in V2.0 + octave_base_scale (int): The base scale of octave. + scales_per_octave (int): Number of scales for each octave. + `octave_base_scale` and `scales_per_octave` are usually used in + retinanet and the `scales` should be None when they are set. + centers (list[tuple[float, float]] | None): The centers of the anchor + relative to the feature grid center in multiple feature levels. + By default it is set to be None and not used. If a list of tuple of + float is given, they will be used to shift the centers of anchors. + center_offset (float): The offset of center in proportion to anchors' + width and height. By default it is 0 in V2.0. + + Examples: + >>> from mmdet.core import AnchorGenerator + >>> self = AnchorGenerator([16], [1.], [1.], [9]) + >>> all_anchors = self.grid_anchors([(2, 2)], device='cpu') + >>> print(all_anchors) + [tensor([[-4.5000, -4.5000, 4.5000, 4.5000], + [11.5000, -4.5000, 20.5000, 4.5000], + [-4.5000, 11.5000, 4.5000, 20.5000], + [11.5000, 11.5000, 20.5000, 20.5000]])] + >>> self = AnchorGenerator([16, 32], [1.], [1.], [9, 18]) + >>> all_anchors = self.grid_anchors([(2, 2), (1, 1)], device='cpu') + >>> print(all_anchors) + [tensor([[-4.5000, -4.5000, 4.5000, 4.5000], + [11.5000, -4.5000, 20.5000, 4.5000], + [-4.5000, 11.5000, 4.5000, 20.5000], + [11.5000, 11.5000, 20.5000, 20.5000]]), \ + tensor([[-9., -9., 9., 9.]])] + """ + + def __init__(self, + strides, + ratios, + scales=None, + base_sizes=None, + scale_major=True, + octave_base_scale=None, + scales_per_octave=None, + centers=None, + center_offset=0.): + # check center and center_offset + if center_offset != 0: + assert centers is None, 'center cannot be set when center_offset' \ + f'!=0, {centers} is given.' + if not (0 <= center_offset <= 1): + raise ValueError('center_offset should be in range [0, 1], ' + f'{center_offset} is given.') + if centers is not None: + assert len(centers) == len(strides), \ + 'The number of strides should be the same as centers, got ' \ + f'{strides} and {centers}' + + # calculate base sizes of anchors + self.strides = [_pair(stride) for stride in strides] + self.base_sizes = [min(stride) for stride in self.strides + ] if base_sizes is None else base_sizes + assert len(self.base_sizes) == len(self.strides), \ + 'The number of strides should be the same as base sizes, got ' \ + f'{self.strides} and {self.base_sizes}' + + # calculate scales of anchors + assert ((octave_base_scale is not None + and scales_per_octave is not None) ^ (scales is not None)), \ + 'scales and octave_base_scale with scales_per_octave cannot' \ + ' be set at the same time' + if scales is not None: + self.scales = torch.Tensor(scales) + elif octave_base_scale is not None and scales_per_octave is not None: + octave_scales = np.array( + [2**(i / scales_per_octave) for i in range(scales_per_octave)]) + scales = octave_scales * octave_base_scale + self.scales = torch.Tensor(scales) + else: + raise ValueError('Either scales or octave_base_scale with ' + 'scales_per_octave should be set') + + self.octave_base_scale = octave_base_scale + self.scales_per_octave = scales_per_octave + self.ratios = torch.Tensor(ratios) + self.scale_major = scale_major + self.centers = centers + self.center_offset = center_offset + self.base_anchors = self.gen_base_anchors() + + @property + def num_base_anchors(self): + """list[int]: total number of base anchors in a feature grid""" + return [base_anchors.size(0) for base_anchors in self.base_anchors] + + @property + def num_levels(self): + """int: number of feature levels that the generator will be applied""" + return len(self.strides) + + def gen_base_anchors(self): + """Generate base anchors. + + Returns: + list(torch.Tensor): Base anchors of a feature grid in multiple \ + feature levels. + """ + multi_level_base_anchors = [] + for i, base_size in enumerate(self.base_sizes): + center = None + if self.centers is not None: + center = self.centers[i] + multi_level_base_anchors.append( + self.gen_single_level_base_anchors( + base_size, + scales=self.scales, + ratios=self.ratios, + center=center)) + return multi_level_base_anchors + + def gen_single_level_base_anchors(self, + base_size, + scales, + ratios, + center=None): + """Generate base anchors of a single level. + + Args: + base_size (int | float): Basic size of an anchor. + scales (torch.Tensor): Scales of the anchor. + ratios (torch.Tensor): The ratio between between the height + and width of anchors in a single level. + center (tuple[float], optional): The center of the base anchor + related to a single feature grid. Defaults to None. + + Returns: + torch.Tensor: Anchors in a single-level feature maps. + """ + w = base_size + h = base_size + if center is None: + x_center = self.center_offset * w + y_center = self.center_offset * h + else: + x_center, y_center = center + + h_ratios = torch.sqrt(ratios) + w_ratios = 1 / h_ratios + if self.scale_major: + ws = (w * w_ratios[:, None] * scales[None, :]).view(-1) + hs = (h * h_ratios[:, None] * scales[None, :]).view(-1) + else: + ws = (w * scales[:, None] * w_ratios[None, :]).view(-1) + hs = (h * scales[:, None] * h_ratios[None, :]).view(-1) + + # use float anchor and the anchor's center is aligned with the + # pixel center + base_anchors = [ + x_center - 0.5 * ws, y_center - 0.5 * hs, x_center + 0.5 * ws, + y_center + 0.5 * hs + ] + base_anchors = torch.stack(base_anchors, dim=-1) + + return base_anchors + + def _meshgrid(self, x, y, row_major=True): + """Generate mesh grid of x and y. + + Args: + x (torch.Tensor): Grids of x dimension. + y (torch.Tensor): Grids of y dimension. + row_major (bool, optional): Whether to return y grids first. + Defaults to True. + + Returns: + tuple[torch.Tensor]: The mesh grids of x and y. + """ + # use shape instead of len to keep tracing while exporting to onnx + xx = x.repeat(y.shape[0]) + yy = y.view(-1, 1).repeat(1, x.shape[0]).view(-1) + if row_major: + return xx, yy + else: + return yy, xx + + def grid_anchors(self, featmap_sizes, device='cuda'): + """Generate grid anchors in multiple feature levels. + + Args: + featmap_sizes (list[tuple]): List of feature map sizes in + multiple feature levels. + device (str): Device where the anchors will be put on. + + Return: + list[torch.Tensor]: Anchors in multiple feature levels. \ + The sizes of each tensor should be [N, 4], where \ + N = width * height * num_base_anchors, width and height \ + are the sizes of the corresponding feature level, \ + num_base_anchors is the number of anchors for that level. + """ + assert self.num_levels == len(featmap_sizes) + multi_level_anchors = [] + for i in range(self.num_levels): + anchors = self.single_level_grid_anchors( + self.base_anchors[i].to(device), + featmap_sizes[i], + self.strides[i], + device=device) + multi_level_anchors.append(anchors) + return multi_level_anchors + + def single_level_grid_anchors(self, + base_anchors, + featmap_size, + stride=(16, 16), + device='cuda'): + """Generate grid anchors of a single level. + + Note: + This function is usually called by method ``self.grid_anchors``. + + Args: + base_anchors (torch.Tensor): The base anchors of a feature grid. + featmap_size (tuple[int]): Size of the feature maps. + stride (tuple[int], optional): Stride of the feature map in order + (w, h). Defaults to (16, 16). + device (str, optional): Device the tensor will be put on. + Defaults to 'cuda'. + + Returns: + torch.Tensor: Anchors in the overall feature maps. + """ + # keep as Tensor, so that we can covert to ONNX correctly + feat_h, feat_w = featmap_size + shift_x = torch.arange(0, feat_w, device=device) * stride[0] + shift_y = torch.arange(0, feat_h, device=device) * stride[1] + + shift_xx, shift_yy = self._meshgrid(shift_x, shift_y) + shifts = torch.stack([shift_xx, shift_yy, shift_xx, shift_yy], dim=-1) + shifts = shifts.type_as(base_anchors) + # first feat_w elements correspond to the first row of shifts + # add A anchors (1, A, 4) to K shifts (K, 1, 4) to get + # shifted anchors (K, A, 4), reshape to (K*A, 4) + + all_anchors = base_anchors[None, :, :] + shifts[:, None, :] + all_anchors = all_anchors.view(-1, 4) + # first A rows correspond to A anchors of (0, 0) in feature map, + # then (0, 1), (0, 2), ... + return all_anchors + + def valid_flags(self, featmap_sizes, pad_shape, device='cuda'): + """Generate valid flags of anchors in multiple feature levels. + + Args: + featmap_sizes (list(tuple)): List of feature map sizes in + multiple feature levels. + pad_shape (tuple): The padded shape of the image. + device (str): Device where the anchors will be put on. + + Return: + list(torch.Tensor): Valid flags of anchors in multiple levels. + """ + assert self.num_levels == len(featmap_sizes) + multi_level_flags = [] + for i in range(self.num_levels): + anchor_stride = self.strides[i] + feat_h, feat_w = featmap_sizes[i] + h, w = pad_shape[:2] + valid_feat_h = min(int(np.ceil(h / anchor_stride[1])), feat_h) + valid_feat_w = min(int(np.ceil(w / anchor_stride[0])), feat_w) + flags = self.single_level_valid_flags((feat_h, feat_w), + (valid_feat_h, valid_feat_w), + self.num_base_anchors[i], + device=device) + multi_level_flags.append(flags) + return multi_level_flags + + def single_level_valid_flags(self, + featmap_size, + valid_size, + num_base_anchors, + device='cuda'): + """Generate the valid flags of anchor in a single feature map. + + Args: + featmap_size (tuple[int]): The size of feature maps. + valid_size (tuple[int]): The valid size of the feature maps. + num_base_anchors (int): The number of base anchors. + device (str, optional): Device where the flags will be put on. + Defaults to 'cuda'. + + Returns: + torch.Tensor: The valid flags of each anchor in a single level \ + feature map. + """ + feat_h, feat_w = featmap_size + valid_h, valid_w = valid_size + assert valid_h <= feat_h and valid_w <= feat_w + valid_x = torch.zeros(feat_w, dtype=torch.bool, device=device) + valid_y = torch.zeros(feat_h, dtype=torch.bool, device=device) + valid_x[:valid_w] = 1 + valid_y[:valid_h] = 1 + valid_xx, valid_yy = self._meshgrid(valid_x, valid_y) + valid = valid_xx & valid_yy + valid = valid[:, None].expand(valid.size(0), + num_base_anchors).contiguous().view(-1) + return valid + + def __repr__(self): + """str: a string that describes the module""" + indent_str = ' ' + repr_str = self.__class__.__name__ + '(\n' + repr_str += f'{indent_str}strides={self.strides},\n' + repr_str += f'{indent_str}ratios={self.ratios},\n' + repr_str += f'{indent_str}scales={self.scales},\n' + repr_str += f'{indent_str}base_sizes={self.base_sizes},\n' + repr_str += f'{indent_str}scale_major={self.scale_major},\n' + repr_str += f'{indent_str}octave_base_scale=' + repr_str += f'{self.octave_base_scale},\n' + repr_str += f'{indent_str}scales_per_octave=' + repr_str += f'{self.scales_per_octave},\n' + repr_str += f'{indent_str}num_levels={self.num_levels}\n' + repr_str += f'{indent_str}centers={self.centers},\n' + repr_str += f'{indent_str}center_offset={self.center_offset})' + return repr_str + + +@ANCHOR_GENERATORS.register_module() +class SSDAnchorGenerator(AnchorGenerator): + """Anchor generator for SSD. + + Args: + strides (list[int] | list[tuple[int, int]]): Strides of anchors + in multiple feature levels. + ratios (list[float]): The list of ratios between the height and width + of anchors in a single level. + basesize_ratio_range (tuple(float)): Ratio range of anchors. + input_size (int): Size of feature map, 300 for SSD300, + 512 for SSD512. + scale_major (bool): Whether to multiply scales first when generating + base anchors. If true, the anchors in the same row will have the + same scales. It is always set to be False in SSD. + """ + + def __init__(self, + strides, + ratios, + basesize_ratio_range, + input_size=300, + scale_major=True): + assert len(strides) == len(ratios) + assert mmcv.is_tuple_of(basesize_ratio_range, float) + + self.strides = [_pair(stride) for stride in strides] + self.input_size = input_size + self.centers = [(stride[0] / 2., stride[1] / 2.) + for stride in self.strides] + self.basesize_ratio_range = basesize_ratio_range + + # calculate anchor ratios and sizes + min_ratio, max_ratio = basesize_ratio_range + min_ratio = int(min_ratio * 100) + max_ratio = int(max_ratio * 100) + step = int(np.floor(max_ratio - min_ratio) / (self.num_levels - 2)) + min_sizes = [] + max_sizes = [] + for ratio in range(int(min_ratio), int(max_ratio) + 1, step): + min_sizes.append(int(self.input_size * ratio / 100)) + max_sizes.append(int(self.input_size * (ratio + step) / 100)) + if self.input_size == 300: + if basesize_ratio_range[0] == 0.15: # SSD300 COCO + min_sizes.insert(0, int(self.input_size * 7 / 100)) + max_sizes.insert(0, int(self.input_size * 15 / 100)) + elif basesize_ratio_range[0] == 0.2: # SSD300 VOC + min_sizes.insert(0, int(self.input_size * 10 / 100)) + max_sizes.insert(0, int(self.input_size * 20 / 100)) + else: + raise ValueError( + 'basesize_ratio_range[0] should be either 0.15' + 'or 0.2 when input_size is 300, got ' + f'{basesize_ratio_range[0]}.') + elif self.input_size == 512: + if basesize_ratio_range[0] == 0.1: # SSD512 COCO + min_sizes.insert(0, int(self.input_size * 4 / 100)) + max_sizes.insert(0, int(self.input_size * 10 / 100)) + elif basesize_ratio_range[0] == 0.15: # SSD512 VOC + min_sizes.insert(0, int(self.input_size * 7 / 100)) + max_sizes.insert(0, int(self.input_size * 15 / 100)) + else: + raise ValueError('basesize_ratio_range[0] should be either 0.1' + 'or 0.15 when input_size is 512, got' + f' {basesize_ratio_range[0]}.') + else: + raise ValueError('Only support 300 or 512 in SSDAnchorGenerator' + f', got {self.input_size}.') + + anchor_ratios = [] + anchor_scales = [] + for k in range(len(self.strides)): + scales = [1., np.sqrt(max_sizes[k] / min_sizes[k])] + anchor_ratio = [1.] + for r in ratios[k]: + anchor_ratio += [1 / r, r] # 4 or 6 ratio + anchor_ratios.append(torch.Tensor(anchor_ratio)) + anchor_scales.append(torch.Tensor(scales)) + + self.base_sizes = min_sizes + self.scales = anchor_scales + self.ratios = anchor_ratios + self.scale_major = scale_major + self.center_offset = 0 + self.base_anchors = self.gen_base_anchors() + + def gen_base_anchors(self): + """Generate base anchors. + + Returns: + list(torch.Tensor): Base anchors of a feature grid in multiple \ + feature levels. + """ + multi_level_base_anchors = [] + for i, base_size in enumerate(self.base_sizes): + base_anchors = self.gen_single_level_base_anchors( + base_size, + scales=self.scales[i], + ratios=self.ratios[i], + center=self.centers[i]) + indices = list(range(len(self.ratios[i]))) + indices.insert(1, len(indices)) + base_anchors = torch.index_select(base_anchors, 0, + torch.LongTensor(indices)) + multi_level_base_anchors.append(base_anchors) + return multi_level_base_anchors + + def __repr__(self): + """str: a string that describes the module""" + indent_str = ' ' + repr_str = self.__class__.__name__ + '(\n' + repr_str += f'{indent_str}strides={self.strides},\n' + repr_str += f'{indent_str}scales={self.scales},\n' + repr_str += f'{indent_str}scale_major={self.scale_major},\n' + repr_str += f'{indent_str}input_size={self.input_size},\n' + repr_str += f'{indent_str}scales={self.scales},\n' + repr_str += f'{indent_str}ratios={self.ratios},\n' + repr_str += f'{indent_str}num_levels={self.num_levels},\n' + repr_str += f'{indent_str}base_sizes={self.base_sizes},\n' + repr_str += f'{indent_str}basesize_ratio_range=' + repr_str += f'{self.basesize_ratio_range})' + return repr_str + + +@ANCHOR_GENERATORS.register_module() +class LegacyAnchorGenerator(AnchorGenerator): + """Legacy anchor generator used in MMDetection V1.x. + + Note: + Difference to the V2.0 anchor generator: + + 1. The center offset of V1.x anchors are set to be 0.5 rather than 0. + 2. The width/height are minused by 1 when calculating the anchors' \ + centers and corners to meet the V1.x coordinate system. + 3. The anchors' corners are quantized. + + Args: + strides (list[int] | list[tuple[int]]): Strides of anchors + in multiple feature levels. + ratios (list[float]): The list of ratios between the height and width + of anchors in a single level. + scales (list[int] | None): Anchor scales for anchors in a single level. + It cannot be set at the same time if `octave_base_scale` and + `scales_per_octave` are set. + base_sizes (list[int]): The basic sizes of anchors in multiple levels. + If None is given, strides will be used to generate base_sizes. + scale_major (bool): Whether to multiply scales first when generating + base anchors. If true, the anchors in the same row will have the + same scales. By default it is True in V2.0 + octave_base_scale (int): The base scale of octave. + scales_per_octave (int): Number of scales for each octave. + `octave_base_scale` and `scales_per_octave` are usually used in + retinanet and the `scales` should be None when they are set. + centers (list[tuple[float, float]] | None): The centers of the anchor + relative to the feature grid center in multiple feature levels. + By default it is set to be None and not used. It a list of float + is given, this list will be used to shift the centers of anchors. + center_offset (float): The offset of center in propotion to anchors' + width and height. By default it is 0.5 in V2.0 but it should be 0.5 + in v1.x models. + + Examples: + >>> from mmdet.core import LegacyAnchorGenerator + >>> self = LegacyAnchorGenerator( + >>> [16], [1.], [1.], [9], center_offset=0.5) + >>> all_anchors = self.grid_anchors(((2, 2),), device='cpu') + >>> print(all_anchors) + [tensor([[ 0., 0., 8., 8.], + [16., 0., 24., 8.], + [ 0., 16., 8., 24.], + [16., 16., 24., 24.]])] + """ + + def gen_single_level_base_anchors(self, + base_size, + scales, + ratios, + center=None): + """Generate base anchors of a single level. + + Note: + The width/height of anchors are minused by 1 when calculating \ + the centers and corners to meet the V1.x coordinate system. + + Args: + base_size (int | float): Basic size of an anchor. + scales (torch.Tensor): Scales of the anchor. + ratios (torch.Tensor): The ratio between between the height. + and width of anchors in a single level. + center (tuple[float], optional): The center of the base anchor + related to a single feature grid. Defaults to None. + + Returns: + torch.Tensor: Anchors in a single-level feature map. + """ + w = base_size + h = base_size + if center is None: + x_center = self.center_offset * (w - 1) + y_center = self.center_offset * (h - 1) + else: + x_center, y_center = center + + h_ratios = torch.sqrt(ratios) + w_ratios = 1 / h_ratios + if self.scale_major: + ws = (w * w_ratios[:, None] * scales[None, :]).view(-1) + hs = (h * h_ratios[:, None] * scales[None, :]).view(-1) + else: + ws = (w * scales[:, None] * w_ratios[None, :]).view(-1) + hs = (h * scales[:, None] * h_ratios[None, :]).view(-1) + + # use float anchor and the anchor's center is aligned with the + # pixel center + base_anchors = [ + x_center - 0.5 * (ws - 1), y_center - 0.5 * (hs - 1), + x_center + 0.5 * (ws - 1), y_center + 0.5 * (hs - 1) + ] + base_anchors = torch.stack(base_anchors, dim=-1).round() + + return base_anchors + + +@ANCHOR_GENERATORS.register_module() +class LegacySSDAnchorGenerator(SSDAnchorGenerator, LegacyAnchorGenerator): + """Legacy anchor generator used in MMDetection V1.x. + + The difference between `LegacySSDAnchorGenerator` and `SSDAnchorGenerator` + can be found in `LegacyAnchorGenerator`. + """ + + def __init__(self, + strides, + ratios, + basesize_ratio_range, + input_size=300, + scale_major=True): + super(LegacySSDAnchorGenerator, + self).__init__(strides, ratios, basesize_ratio_range, input_size, + scale_major) + self.centers = [((stride - 1) / 2., (stride - 1) / 2.) + for stride in strides] + self.base_anchors = self.gen_base_anchors() + + +@ANCHOR_GENERATORS.register_module() +class YOLOAnchorGenerator(AnchorGenerator): + """Anchor generator for YOLO. + + Args: + strides (list[int] | list[tuple[int, int]]): Strides of anchors + in multiple feature levels. + base_sizes (list[list[tuple[int, int]]]): The basic sizes + of anchors in multiple levels. + """ + + def __init__(self, strides, base_sizes): + self.strides = [_pair(stride) for stride in strides] + self.centers = [(stride[0] / 2., stride[1] / 2.) + for stride in self.strides] + self.base_sizes = [] + num_anchor_per_level = len(base_sizes[0]) + for base_sizes_per_level in base_sizes: + assert num_anchor_per_level == len(base_sizes_per_level) + self.base_sizes.append( + [_pair(base_size) for base_size in base_sizes_per_level]) + self.base_anchors = self.gen_base_anchors() + + @property + def num_levels(self): + """int: number of feature levels that the generator will be applied""" + return len(self.base_sizes) + + def gen_base_anchors(self): + """Generate base anchors. + + Returns: + list(torch.Tensor): Base anchors of a feature grid in multiple \ + feature levels. + """ + multi_level_base_anchors = [] + for i, base_sizes_per_level in enumerate(self.base_sizes): + center = None + if self.centers is not None: + center = self.centers[i] + multi_level_base_anchors.append( + self.gen_single_level_base_anchors(base_sizes_per_level, + center)) + return multi_level_base_anchors + + def gen_single_level_base_anchors(self, base_sizes_per_level, center=None): + """Generate base anchors of a single level. + + Args: + base_sizes_per_level (list[tuple[int, int]]): Basic sizes of + anchors. + center (tuple[float], optional): The center of the base anchor + related to a single feature grid. Defaults to None. + + Returns: + torch.Tensor: Anchors in a single-level feature maps. + """ + x_center, y_center = center + base_anchors = [] + for base_size in base_sizes_per_level: + w, h = base_size + + # use float anchor and the anchor's center is aligned with the + # pixel center + base_anchor = torch.Tensor([ + x_center - 0.5 * w, y_center - 0.5 * h, x_center + 0.5 * w, + y_center + 0.5 * h + ]) + base_anchors.append(base_anchor) + base_anchors = torch.stack(base_anchors, dim=0) + + return base_anchors + + def responsible_flags(self, featmap_sizes, gt_bboxes, device='cuda'): + """Generate responsible anchor flags of grid cells in multiple scales. + + Args: + featmap_sizes (list(tuple)): List of feature map sizes in multiple + feature levels. + gt_bboxes (Tensor): Ground truth boxes, shape (n, 4). + device (str): Device where the anchors will be put on. + + Return: + list(torch.Tensor): responsible flags of anchors in multiple level + """ + assert self.num_levels == len(featmap_sizes) + multi_level_responsible_flags = [] + for i in range(self.num_levels): + anchor_stride = self.strides[i] + flags = self.single_level_responsible_flags( + featmap_sizes[i], + gt_bboxes, + anchor_stride, + self.num_base_anchors[i], + device=device) + multi_level_responsible_flags.append(flags) + return multi_level_responsible_flags + + def single_level_responsible_flags(self, + featmap_size, + gt_bboxes, + stride, + num_base_anchors, + device='cuda'): + """Generate the responsible flags of anchor in a single feature map. + + Args: + featmap_size (tuple[int]): The size of feature maps. + gt_bboxes (Tensor): Ground truth boxes, shape (n, 4). + stride (tuple(int)): stride of current level + num_base_anchors (int): The number of base anchors. + device (str, optional): Device where the flags will be put on. + Defaults to 'cuda'. + + Returns: + torch.Tensor: The valid flags of each anchor in a single level \ + feature map. + """ + feat_h, feat_w = featmap_size + gt_bboxes_cx = ((gt_bboxes[:, 0] + gt_bboxes[:, 2]) * 0.5).to(device) + gt_bboxes_cy = ((gt_bboxes[:, 1] + gt_bboxes[:, 3]) * 0.5).to(device) + gt_bboxes_grid_x = torch.floor(gt_bboxes_cx / stride[0]).long() + gt_bboxes_grid_y = torch.floor(gt_bboxes_cy / stride[1]).long() + + # row major indexing + gt_bboxes_grid_idx = gt_bboxes_grid_y * feat_w + gt_bboxes_grid_x + + responsible_grid = torch.zeros( + feat_h * feat_w, dtype=torch.uint8, device=device) + responsible_grid[gt_bboxes_grid_idx] = 1 + + responsible_grid = responsible_grid[:, None].expand( + responsible_grid.size(0), num_base_anchors).contiguous().view(-1) + return responsible_grid diff --git a/mmdet/core/anchor/builder.py b/mmdet/core/anchor/builder.py new file mode 100644 index 0000000..d79b448 --- /dev/null +++ b/mmdet/core/anchor/builder.py @@ -0,0 +1,7 @@ +from mmcv.utils import Registry, build_from_cfg + +ANCHOR_GENERATORS = Registry('Anchor generator') + + +def build_anchor_generator(cfg, default_args=None): + return build_from_cfg(cfg, ANCHOR_GENERATORS, default_args) diff --git a/mmdet/core/anchor/point_generator.py b/mmdet/core/anchor/point_generator.py new file mode 100644 index 0000000..e6fbd98 --- /dev/null +++ b/mmdet/core/anchor/point_generator.py @@ -0,0 +1,37 @@ +import torch + +from .builder import ANCHOR_GENERATORS + + +@ANCHOR_GENERATORS.register_module() +class PointGenerator(object): + + def _meshgrid(self, x, y, row_major=True): + xx = x.repeat(len(y)) + yy = y.view(-1, 1).repeat(1, len(x)).view(-1) + if row_major: + return xx, yy + else: + return yy, xx + + def grid_points(self, featmap_size, stride=16, device='cuda'): + feat_h, feat_w = featmap_size + shift_x = torch.arange(0., feat_w, device=device) * stride + shift_y = torch.arange(0., feat_h, device=device) * stride + shift_xx, shift_yy = self._meshgrid(shift_x, shift_y) + stride = shift_x.new_full((shift_xx.shape[0], ), stride) + shifts = torch.stack([shift_xx, shift_yy, stride], dim=-1) + all_points = shifts.to(device) + return all_points + + def valid_flags(self, featmap_size, valid_size, device='cuda'): + feat_h, feat_w = featmap_size + valid_h, valid_w = valid_size + assert valid_h <= feat_h and valid_w <= feat_w + valid_x = torch.zeros(feat_w, dtype=torch.bool, device=device) + valid_y = torch.zeros(feat_h, dtype=torch.bool, device=device) + valid_x[:valid_w] = 1 + valid_y[:valid_h] = 1 + valid_xx, valid_yy = self._meshgrid(valid_x, valid_y) + valid = valid_xx & valid_yy + return valid diff --git a/mmdet/core/anchor/utils.py b/mmdet/core/anchor/utils.py new file mode 100644 index 0000000..ab9b53f --- /dev/null +++ b/mmdet/core/anchor/utils.py @@ -0,0 +1,71 @@ +import torch + + +def images_to_levels(target, num_levels): + """Convert targets by image to targets by feature level. + + [target_img0, target_img1] -> [target_level0, target_level1, ...] + """ + target = torch.stack(target, 0) + level_targets = [] + start = 0 + for n in num_levels: + end = start + n + # level_targets.append(target[:, start:end].squeeze(0)) + level_targets.append(target[:, start:end]) + start = end + return level_targets + + +def anchor_inside_flags(flat_anchors, + valid_flags, + img_shape, + allowed_border=0): + """Check whether the anchors are inside the border. + + Args: + flat_anchors (torch.Tensor): Flatten anchors, shape (n, 4). + valid_flags (torch.Tensor): An existing valid flags of anchors. + img_shape (tuple(int)): Shape of current image. + allowed_border (int, optional): The border to allow the valid anchor. + Defaults to 0. + + Returns: + torch.Tensor: Flags indicating whether the anchors are inside a \ + valid range. + """ + img_h, img_w = img_shape[:2] + if allowed_border >= 0: + inside_flags = valid_flags & \ + (flat_anchors[:, 0] >= -allowed_border) & \ + (flat_anchors[:, 1] >= -allowed_border) & \ + (flat_anchors[:, 2] < img_w + allowed_border) & \ + (flat_anchors[:, 3] < img_h + allowed_border) + else: + inside_flags = valid_flags + return inside_flags + + +def calc_region(bbox, ratio, featmap_size=None): + """Calculate a proportional bbox region. + + The bbox center are fixed and the new h' and w' is h * ratio and w * ratio. + + Args: + bbox (Tensor): Bboxes to calculate regions, shape (n, 4). + ratio (float): Ratio of the output region. + featmap_size (tuple): Feature map size used for clipping the boundary. + + Returns: + tuple: x1, y1, x2, y2 + """ + x1 = torch.round((1 - ratio) * bbox[0] + ratio * bbox[2]).long() + y1 = torch.round((1 - ratio) * bbox[1] + ratio * bbox[3]).long() + x2 = torch.round(ratio * bbox[0] + (1 - ratio) * bbox[2]).long() + y2 = torch.round(ratio * bbox[1] + (1 - ratio) * bbox[3]).long() + if featmap_size is not None: + x1 = x1.clamp(min=0, max=featmap_size[1]) + y1 = y1.clamp(min=0, max=featmap_size[0]) + x2 = x2.clamp(min=0, max=featmap_size[1]) + y2 = y2.clamp(min=0, max=featmap_size[0]) + return (x1, y1, x2, y2) diff --git a/mmdet/core/bbox/__init__.py b/mmdet/core/bbox/__init__.py new file mode 100644 index 0000000..a353729 --- /dev/null +++ b/mmdet/core/bbox/__init__.py @@ -0,0 +1,27 @@ +from .assigners import (AssignResult, BaseAssigner, CenterRegionAssigner, + MaxIoUAssigner, RegionAssigner) +from .builder import build_assigner, build_bbox_coder, build_sampler +from .coder import (BaseBBoxCoder, DeltaXYWHBBoxCoder, PseudoBBoxCoder, + TBLRBBoxCoder) +from .iou_calculators import BboxOverlaps2D, bbox_overlaps +from .samplers import (BaseSampler, CombinedSampler, + InstanceBalancedPosSampler, IoUBalancedNegSampler, + OHEMSampler, PseudoSampler, RandomSampler, + SamplingResult, ScoreHLRSampler) +from .transforms import (bbox2distance, bbox2result, bbox2roi, + bbox_cxcywh_to_xyxy, bbox_flip, bbox_mapping, + bbox_mapping_back, bbox_rescale, bbox_xyxy_to_cxcywh, + distance2bbox, roi2bbox) + +__all__ = [ + 'bbox_overlaps', 'BboxOverlaps2D', 'BaseAssigner', 'MaxIoUAssigner', + 'AssignResult', 'BaseSampler', 'PseudoSampler', 'RandomSampler', + 'InstanceBalancedPosSampler', 'IoUBalancedNegSampler', 'CombinedSampler', + 'OHEMSampler', 'SamplingResult', 'ScoreHLRSampler', 'build_assigner', + 'build_sampler', 'bbox_flip', 'bbox_mapping', 'bbox_mapping_back', + 'bbox2roi', 'roi2bbox', 'bbox2result', 'distance2bbox', 'bbox2distance', + 'build_bbox_coder', 'BaseBBoxCoder', 'PseudoBBoxCoder', + 'DeltaXYWHBBoxCoder', 'TBLRBBoxCoder', 'CenterRegionAssigner', + 'bbox_rescale', 'bbox_cxcywh_to_xyxy', 'bbox_xyxy_to_cxcywh', + 'RegionAssigner' +] diff --git a/mmdet/core/bbox/__pycache__/__init__.cpython-37.pyc b/mmdet/core/bbox/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..ae04adf Binary files /dev/null and b/mmdet/core/bbox/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/core/bbox/__pycache__/builder.cpython-37.pyc b/mmdet/core/bbox/__pycache__/builder.cpython-37.pyc new file mode 100644 index 0000000..9abc8fc Binary files /dev/null and b/mmdet/core/bbox/__pycache__/builder.cpython-37.pyc differ diff --git a/mmdet/core/bbox/__pycache__/transforms.cpython-37.pyc b/mmdet/core/bbox/__pycache__/transforms.cpython-37.pyc new file mode 100644 index 0000000..defb6c7 Binary files /dev/null and b/mmdet/core/bbox/__pycache__/transforms.cpython-37.pyc differ diff --git a/mmdet/core/bbox/assigners/__init__.py b/mmdet/core/bbox/assigners/__init__.py new file mode 100644 index 0000000..891e623 --- /dev/null +++ b/mmdet/core/bbox/assigners/__init__.py @@ -0,0 +1,17 @@ +from .approx_max_iou_assigner import ApproxMaxIoUAssigner +from .assign_result import AssignResult +from .atss_assigner import ATSSAssigner +from .base_assigner import BaseAssigner +from .center_region_assigner import CenterRegionAssigner +from .grid_assigner import GridAssigner +from .hungarian_assigner import HungarianAssigner +from .max_iou_assigner import MaxIoUAssigner +from .point_assigner import PointAssigner +from .region_assigner import RegionAssigner +from .uniform_assigner import UniformAssigner + +__all__ = [ + 'BaseAssigner', 'MaxIoUAssigner', 'ApproxMaxIoUAssigner', 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/dev/null +++ b/mmdet/core/bbox/assigners/approx_max_iou_assigner.py @@ -0,0 +1,145 @@ +import torch + +from ..builder import BBOX_ASSIGNERS +from ..iou_calculators import build_iou_calculator +from .max_iou_assigner import MaxIoUAssigner + + +@BBOX_ASSIGNERS.register_module() +class ApproxMaxIoUAssigner(MaxIoUAssigner): + """Assign a corresponding gt bbox or background to each bbox. + + Each proposals will be assigned with an integer indicating the ground truth + index. (semi-positive index: gt label (0-based), -1: background) + + - -1: negative sample, no assigned gt + - semi-positive integer: positive sample, index (0-based) of assigned gt + + Args: + pos_iou_thr (float): IoU threshold for positive bboxes. + neg_iou_thr (float or tuple): IoU threshold for negative bboxes. + min_pos_iou (float): Minimum iou for a bbox to be considered as a + positive bbox. Positive samples can have smaller IoU than + pos_iou_thr due to the 4th step (assign max IoU sample to each gt). + gt_max_assign_all (bool): Whether to assign all bboxes with the same + highest overlap with some gt to that gt. + ignore_iof_thr (float): IoF threshold for ignoring bboxes (if + `gt_bboxes_ignore` is specified). Negative values mean not + ignoring any bboxes. + ignore_wrt_candidates (bool): Whether to compute the iof between + `bboxes` and `gt_bboxes_ignore`, or the contrary. + match_low_quality (bool): Whether to allow quality matches. This is + usually allowed for RPN and single stage detectors, but not allowed + in the second stage. + gpu_assign_thr (int): The upper bound of the number of GT for GPU + assign. When the number of gt is above this threshold, will assign + on CPU device. Negative values mean not assign on CPU. + """ + + def __init__(self, + pos_iou_thr, + neg_iou_thr, + min_pos_iou=.0, + gt_max_assign_all=True, + ignore_iof_thr=-1, + ignore_wrt_candidates=True, + match_low_quality=True, + gpu_assign_thr=-1, + iou_calculator=dict(type='BboxOverlaps2D')): + self.pos_iou_thr = pos_iou_thr + self.neg_iou_thr = neg_iou_thr + self.min_pos_iou = min_pos_iou + self.gt_max_assign_all = gt_max_assign_all + self.ignore_iof_thr = ignore_iof_thr + self.ignore_wrt_candidates = ignore_wrt_candidates + self.gpu_assign_thr = gpu_assign_thr + self.match_low_quality = match_low_quality + self.iou_calculator = build_iou_calculator(iou_calculator) + + def assign(self, + approxs, + squares, + approxs_per_octave, + gt_bboxes, + gt_bboxes_ignore=None, + gt_labels=None): + """Assign gt to approxs. + + This method assign a gt bbox to each group of approxs (bboxes), + each group of approxs is represent by a base approx (bbox) and + will be assigned with -1, or a semi-positive number. + background_label (-1) means negative sample, + semi-positive number is the index (0-based) of assigned gt. + The assignment is done in following steps, the order matters. + + 1. assign every bbox to background_label (-1) + 2. use the max IoU of each group of approxs to assign + 2. assign proposals whose iou with all gts < neg_iou_thr to background + 3. for each bbox, if the iou with its nearest gt >= pos_iou_thr, + assign it to that bbox + 4. for each gt bbox, assign its nearest proposals (may be more than + one) to itself + + Args: + approxs (Tensor): Bounding boxes to be assigned, + shape(approxs_per_octave*n, 4). + squares (Tensor): Base Bounding boxes to be assigned, + shape(n, 4). + approxs_per_octave (int): number of approxs per octave + gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4). + gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are + labelled as `ignored`, e.g., crowd boxes in COCO. + gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ). + + Returns: + :obj:`AssignResult`: The assign result. + """ + num_squares = squares.size(0) + num_gts = gt_bboxes.size(0) + + if num_squares == 0 or num_gts == 0: + # No predictions and/or truth, return empty assignment + overlaps = approxs.new(num_gts, num_squares) + assign_result = self.assign_wrt_overlaps(overlaps, gt_labels) + return assign_result + + # re-organize anchors by approxs_per_octave x num_squares + approxs = torch.transpose( + approxs.view(num_squares, approxs_per_octave, 4), 0, + 1).contiguous().view(-1, 4) + assign_on_cpu = True if (self.gpu_assign_thr > 0) and ( + num_gts > self.gpu_assign_thr) else False + # compute overlap and assign gt on CPU when number of GT is large + if assign_on_cpu: + device = approxs.device + approxs = approxs.cpu() + gt_bboxes = gt_bboxes.cpu() + if gt_bboxes_ignore is not None: + gt_bboxes_ignore = gt_bboxes_ignore.cpu() + if gt_labels is not None: + gt_labels = gt_labels.cpu() + all_overlaps = self.iou_calculator(approxs, gt_bboxes) + + overlaps, _ = all_overlaps.view(approxs_per_octave, num_squares, + num_gts).max(dim=0) + overlaps = torch.transpose(overlaps, 0, 1) + + if (self.ignore_iof_thr > 0 and gt_bboxes_ignore is not None + and gt_bboxes_ignore.numel() > 0 and squares.numel() > 0): + if self.ignore_wrt_candidates: + ignore_overlaps = self.iou_calculator( + squares, gt_bboxes_ignore, mode='iof') + ignore_max_overlaps, _ = ignore_overlaps.max(dim=1) + else: + ignore_overlaps = self.iou_calculator( + gt_bboxes_ignore, squares, mode='iof') + ignore_max_overlaps, _ = ignore_overlaps.max(dim=0) + overlaps[:, ignore_max_overlaps > self.ignore_iof_thr] = -1 + + assign_result = self.assign_wrt_overlaps(overlaps, gt_labels) + if assign_on_cpu: + assign_result.gt_inds = assign_result.gt_inds.to(device) + assign_result.max_overlaps = assign_result.max_overlaps.to(device) + if assign_result.labels is not None: + assign_result.labels = assign_result.labels.to(device) + return assign_result diff --git a/mmdet/core/bbox/assigners/assign_result.py b/mmdet/core/bbox/assigners/assign_result.py new file mode 100644 index 0000000..4639fbd --- /dev/null +++ b/mmdet/core/bbox/assigners/assign_result.py @@ -0,0 +1,204 @@ +import torch + +from mmdet.utils import util_mixins + + +class AssignResult(util_mixins.NiceRepr): + """Stores assignments between predicted and truth boxes. + + Attributes: + num_gts (int): the number of truth boxes considered when computing this + assignment + + gt_inds (LongTensor): for each predicted box indicates the 1-based + index of the assigned truth box. 0 means unassigned and -1 means + ignore. + + max_overlaps (FloatTensor): the iou between the predicted box and its + assigned truth box. + + labels (None | LongTensor): If specified, for each predicted box + indicates the category label of the assigned truth box. + + Example: + >>> # An assign result between 4 predicted boxes and 9 true boxes + >>> # where only two boxes were assigned. + >>> num_gts = 9 + >>> max_overlaps = torch.LongTensor([0, .5, .9, 0]) + >>> gt_inds = torch.LongTensor([-1, 1, 2, 0]) + >>> labels = torch.LongTensor([0, 3, 4, 0]) + >>> self = AssignResult(num_gts, gt_inds, max_overlaps, labels) + >>> print(str(self)) # xdoctest: +IGNORE_WANT + + >>> # Force addition of gt labels (when adding gt as proposals) + >>> new_labels = torch.LongTensor([3, 4, 5]) + >>> self.add_gt_(new_labels) + >>> print(str(self)) # xdoctest: +IGNORE_WANT + + """ + + def __init__(self, num_gts, gt_inds, max_overlaps, labels=None): + self.num_gts = num_gts + self.gt_inds = gt_inds + self.max_overlaps = max_overlaps + self.labels = labels + # Interface for possible user-defined properties + self._extra_properties = {} + + @property + def num_preds(self): + """int: the number of predictions in this assignment""" + return len(self.gt_inds) + + def set_extra_property(self, key, value): + """Set user-defined new property.""" + assert key not in self.info + self._extra_properties[key] = value + + def get_extra_property(self, key): + """Get user-defined property.""" + return self._extra_properties.get(key, None) + + @property + def info(self): + """dict: a dictionary of info about the object""" + basic_info = { + 'num_gts': self.num_gts, + 'num_preds': self.num_preds, + 'gt_inds': self.gt_inds, + 'max_overlaps': self.max_overlaps, + 'labels': self.labels, + } + basic_info.update(self._extra_properties) + return basic_info + + def __nice__(self): + """str: a "nice" summary string describing this assign result""" + parts = [] + parts.append(f'num_gts={self.num_gts!r}') + if self.gt_inds is None: + parts.append(f'gt_inds={self.gt_inds!r}') + else: + parts.append(f'gt_inds.shape={tuple(self.gt_inds.shape)!r}') + if self.max_overlaps is None: + parts.append(f'max_overlaps={self.max_overlaps!r}') + else: + parts.append('max_overlaps.shape=' + f'{tuple(self.max_overlaps.shape)!r}') + if self.labels is None: + parts.append(f'labels={self.labels!r}') + else: + parts.append(f'labels.shape={tuple(self.labels.shape)!r}') + return ', '.join(parts) + + @classmethod + def random(cls, **kwargs): + """Create random AssignResult for tests or debugging. + + Args: + num_preds: number of predicted boxes + num_gts: number of true boxes + p_ignore (float): probability of a predicted box assinged to an + ignored truth + p_assigned (float): probability of a predicted box not being + assigned + p_use_label (float | bool): with labels or not + rng (None | int | numpy.random.RandomState): seed or state + + Returns: + :obj:`AssignResult`: Randomly generated assign results. + + Example: + >>> from mmdet.core.bbox.assigners.assign_result import * # NOQA + >>> self = AssignResult.random() + >>> print(self.info) + """ + from mmdet.core.bbox import demodata + rng = demodata.ensure_rng(kwargs.get('rng', None)) + + num_gts = kwargs.get('num_gts', None) + num_preds = kwargs.get('num_preds', None) + p_ignore = kwargs.get('p_ignore', 0.3) + p_assigned = kwargs.get('p_assigned', 0.7) + p_use_label = kwargs.get('p_use_label', 0.5) + num_classes = kwargs.get('p_use_label', 3) + + if num_gts is None: + num_gts = rng.randint(0, 8) + if num_preds is None: + num_preds = rng.randint(0, 16) + + if num_gts == 0: + max_overlaps = torch.zeros(num_preds, dtype=torch.float32) + gt_inds = torch.zeros(num_preds, dtype=torch.int64) + if p_use_label is True or p_use_label < rng.rand(): + labels = torch.zeros(num_preds, dtype=torch.int64) + else: + labels = None + else: + import numpy as np + # Create an overlap for each predicted box + max_overlaps = torch.from_numpy(rng.rand(num_preds)) + + # Construct gt_inds for each predicted box + is_assigned = torch.from_numpy(rng.rand(num_preds) < p_assigned) + # maximum number of assignments constraints + n_assigned = min(num_preds, min(num_gts, is_assigned.sum())) + + assigned_idxs = np.where(is_assigned)[0] + rng.shuffle(assigned_idxs) + assigned_idxs = assigned_idxs[0:n_assigned] + assigned_idxs.sort() + + is_assigned[:] = 0 + is_assigned[assigned_idxs] = True + + is_ignore = torch.from_numpy( + rng.rand(num_preds) < p_ignore) & is_assigned + + gt_inds = torch.zeros(num_preds, dtype=torch.int64) + + true_idxs = np.arange(num_gts) + rng.shuffle(true_idxs) + true_idxs = torch.from_numpy(true_idxs) + gt_inds[is_assigned] = true_idxs[:n_assigned] + + gt_inds = torch.from_numpy( + rng.randint(1, num_gts + 1, size=num_preds)) + gt_inds[is_ignore] = -1 + gt_inds[~is_assigned] = 0 + max_overlaps[~is_assigned] = 0 + + if p_use_label is True or p_use_label < rng.rand(): + if num_classes == 0: + labels = torch.zeros(num_preds, dtype=torch.int64) + else: + labels = torch.from_numpy( + # remind that we set FG labels to [0, num_class-1] + # since mmdet v2.0 + # BG cat_id: num_class + rng.randint(0, num_classes, size=num_preds)) + labels[~is_assigned] = 0 + else: + labels = None + + self = cls(num_gts, gt_inds, max_overlaps, labels) + return self + + def add_gt_(self, gt_labels): + """Add ground truth as assigned results. + + Args: + gt_labels (torch.Tensor): Labels of gt boxes + """ + self_inds = torch.arange( + 1, len(gt_labels) + 1, dtype=torch.long, device=gt_labels.device) + self.gt_inds = torch.cat([self_inds, self.gt_inds]) + + self.max_overlaps = torch.cat( + [self.max_overlaps.new_ones(len(gt_labels)), self.max_overlaps]) + + if self.labels is not None: + self.labels = torch.cat([gt_labels, self.labels]) diff --git a/mmdet/core/bbox/assigners/atss_assigner.py b/mmdet/core/bbox/assigners/atss_assigner.py new file mode 100644 index 0000000..d4fe9d0 --- /dev/null +++ b/mmdet/core/bbox/assigners/atss_assigner.py @@ -0,0 +1,178 @@ +import torch + +from ..builder import BBOX_ASSIGNERS +from ..iou_calculators import build_iou_calculator +from .assign_result import AssignResult +from .base_assigner import BaseAssigner + + +@BBOX_ASSIGNERS.register_module() +class ATSSAssigner(BaseAssigner): + """Assign a corresponding gt bbox or background to each bbox. + + Each proposals will be assigned with `0` or a positive integer + indicating the ground truth index. + + - 0: negative sample, no assigned gt + - positive integer: positive sample, index (1-based) of assigned gt + + Args: + topk (float): number of bbox selected in each level + """ + + def __init__(self, + topk, + iou_calculator=dict(type='BboxOverlaps2D'), + ignore_iof_thr=-1): + self.topk = topk + self.iou_calculator = build_iou_calculator(iou_calculator) + self.ignore_iof_thr = ignore_iof_thr + + # https://github.com/sfzhang15/ATSS/blob/master/atss_core/modeling/rpn/atss/loss.py + + def assign(self, + bboxes, + num_level_bboxes, + gt_bboxes, + gt_bboxes_ignore=None, + gt_labels=None): + """Assign gt to bboxes. + + The assignment is done in following steps + + 1. compute iou between all bbox (bbox of all pyramid levels) and gt + 2. compute center distance between all bbox and gt + 3. on each pyramid level, for each gt, select k bbox whose center + are closest to the gt center, so we total select k*l bbox as + candidates for each gt + 4. get corresponding iou for the these candidates, and compute the + mean and std, set mean + std as the iou threshold + 5. select these candidates whose iou are greater than or equal to + the threshold as positive + 6. limit the positive sample's center in gt + + + Args: + bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4). + num_level_bboxes (List): num of bboxes in each level + gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4). + gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are + labelled as `ignored`, e.g., crowd boxes in COCO. + gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ). + + Returns: + :obj:`AssignResult`: The assign result. + """ + INF = 100000000 + bboxes = bboxes[:, :4] + num_gt, num_bboxes = gt_bboxes.size(0), bboxes.size(0) + + # compute iou between all bbox and gt + overlaps = self.iou_calculator(bboxes, gt_bboxes) + + # assign 0 by default + assigned_gt_inds = overlaps.new_full((num_bboxes, ), + 0, + dtype=torch.long) + + if num_gt == 0 or num_bboxes == 0: + # No ground truth or boxes, return empty assignment + max_overlaps = overlaps.new_zeros((num_bboxes, )) + if num_gt == 0: + # No truth, assign everything to background + assigned_gt_inds[:] = 0 + if gt_labels is None: + assigned_labels = None + else: + assigned_labels = overlaps.new_full((num_bboxes, ), + -1, + dtype=torch.long) + return AssignResult( + num_gt, assigned_gt_inds, max_overlaps, labels=assigned_labels) + + # compute center distance between all bbox and gt + gt_cx = (gt_bboxes[:, 0] + gt_bboxes[:, 2]) / 2.0 + gt_cy = (gt_bboxes[:, 1] + gt_bboxes[:, 3]) / 2.0 + gt_points = torch.stack((gt_cx, gt_cy), dim=1) + + bboxes_cx = (bboxes[:, 0] + bboxes[:, 2]) / 2.0 + bboxes_cy = (bboxes[:, 1] + bboxes[:, 3]) / 2.0 + bboxes_points = torch.stack((bboxes_cx, bboxes_cy), dim=1) + + distances = (bboxes_points[:, None, :] - + gt_points[None, :, :]).pow(2).sum(-1).sqrt() + + if (self.ignore_iof_thr > 0 and gt_bboxes_ignore is not None + and gt_bboxes_ignore.numel() > 0 and bboxes.numel() > 0): + ignore_overlaps = self.iou_calculator( + bboxes, gt_bboxes_ignore, mode='iof') + ignore_max_overlaps, _ = ignore_overlaps.max(dim=1) + ignore_idxs = ignore_max_overlaps > self.ignore_iof_thr + distances[ignore_idxs, :] = INF + assigned_gt_inds[ignore_idxs] = -1 + + # Selecting candidates based on the center distance + candidate_idxs = [] + start_idx = 0 + for level, bboxes_per_level in enumerate(num_level_bboxes): + # on each pyramid level, for each gt, + # select k bbox whose center are closest to the gt center + end_idx = start_idx + bboxes_per_level + distances_per_level = distances[start_idx:end_idx, :] + selectable_k = min(self.topk, bboxes_per_level) + _, topk_idxs_per_level = distances_per_level.topk( + selectable_k, dim=0, largest=False) + candidate_idxs.append(topk_idxs_per_level + start_idx) + start_idx = end_idx + candidate_idxs = torch.cat(candidate_idxs, dim=0) + + # get corresponding iou for the these candidates, and compute the + # mean and std, set mean + std as the iou threshold + candidate_overlaps = overlaps[candidate_idxs, torch.arange(num_gt)] + overlaps_mean_per_gt = candidate_overlaps.mean(0) + overlaps_std_per_gt = candidate_overlaps.std(0) + overlaps_thr_per_gt = overlaps_mean_per_gt + overlaps_std_per_gt + + is_pos = candidate_overlaps >= overlaps_thr_per_gt[None, :] + + # limit the positive sample's center in gt + for gt_idx in range(num_gt): + candidate_idxs[:, gt_idx] += gt_idx * num_bboxes + ep_bboxes_cx = bboxes_cx.view(1, -1).expand( + num_gt, num_bboxes).contiguous().view(-1) + ep_bboxes_cy = bboxes_cy.view(1, -1).expand( + num_gt, num_bboxes).contiguous().view(-1) + candidate_idxs = candidate_idxs.view(-1) + + # calculate the left, top, right, bottom distance between positive + # bbox center and gt side + l_ = ep_bboxes_cx[candidate_idxs].view(-1, num_gt) - gt_bboxes[:, 0] + t_ = ep_bboxes_cy[candidate_idxs].view(-1, num_gt) - gt_bboxes[:, 1] + r_ = gt_bboxes[:, 2] - ep_bboxes_cx[candidate_idxs].view(-1, num_gt) + b_ = gt_bboxes[:, 3] - ep_bboxes_cy[candidate_idxs].view(-1, num_gt) + is_in_gts = torch.stack([l_, t_, r_, b_], dim=1).min(dim=1)[0] > 0.01 + is_pos = is_pos & is_in_gts + + # if an anchor box is assigned to multiple gts, + # the one with the highest IoU will be selected. + overlaps_inf = torch.full_like(overlaps, + -INF).t().contiguous().view(-1) + index = candidate_idxs.view(-1)[is_pos.view(-1)] + overlaps_inf[index] = overlaps.t().contiguous().view(-1)[index] + overlaps_inf = overlaps_inf.view(num_gt, -1).t() + + max_overlaps, argmax_overlaps = overlaps_inf.max(dim=1) + assigned_gt_inds[ + max_overlaps != -INF] = argmax_overlaps[max_overlaps != -INF] + 1 + + if gt_labels is not None: + assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1) + pos_inds = torch.nonzero( + assigned_gt_inds > 0, as_tuple=False).squeeze() + if pos_inds.numel() > 0: + assigned_labels[pos_inds] = gt_labels[ + assigned_gt_inds[pos_inds] - 1] + else: + assigned_labels = None + return AssignResult( + num_gt, assigned_gt_inds, max_overlaps, labels=assigned_labels) diff --git a/mmdet/core/bbox/assigners/base_assigner.py b/mmdet/core/bbox/assigners/base_assigner.py new file mode 100644 index 0000000..1ff0160 --- /dev/null +++ b/mmdet/core/bbox/assigners/base_assigner.py @@ -0,0 +1,9 @@ +from abc import ABCMeta, abstractmethod + + +class BaseAssigner(metaclass=ABCMeta): + """Base assigner that assigns boxes to ground truth boxes.""" + + @abstractmethod + def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None): + """Assign boxes to either a ground truth boxes or a negative boxes.""" diff --git a/mmdet/core/bbox/assigners/center_region_assigner.py b/mmdet/core/bbox/assigners/center_region_assigner.py new file mode 100644 index 0000000..488e3b6 --- /dev/null +++ b/mmdet/core/bbox/assigners/center_region_assigner.py @@ -0,0 +1,335 @@ +import torch + +from ..builder import BBOX_ASSIGNERS +from ..iou_calculators import build_iou_calculator +from .assign_result import AssignResult +from .base_assigner import BaseAssigner + + +def scale_boxes(bboxes, scale): + """Expand an array of boxes by a given scale. + + Args: + bboxes (Tensor): Shape (m, 4) + scale (float): The scale factor of bboxes + + Returns: + (Tensor): Shape (m, 4). Scaled bboxes + """ + assert bboxes.size(1) == 4 + w_half = (bboxes[:, 2] - bboxes[:, 0]) * .5 + h_half = (bboxes[:, 3] - bboxes[:, 1]) * .5 + x_c = (bboxes[:, 2] + bboxes[:, 0]) * .5 + y_c = (bboxes[:, 3] + bboxes[:, 1]) * .5 + + w_half *= scale + h_half *= scale + + boxes_scaled = torch.zeros_like(bboxes) + boxes_scaled[:, 0] = x_c - w_half + boxes_scaled[:, 2] = x_c + w_half + boxes_scaled[:, 1] = y_c - h_half + boxes_scaled[:, 3] = y_c + h_half + return boxes_scaled + + +def is_located_in(points, bboxes): + """Are points located in bboxes. + + Args: + points (Tensor): Points, shape: (m, 2). + bboxes (Tensor): Bounding boxes, shape: (n, 4). + + Return: + Tensor: Flags indicating if points are located in bboxes, shape: (m, n). + """ + assert points.size(1) == 2 + assert bboxes.size(1) == 4 + return (points[:, 0].unsqueeze(1) > bboxes[:, 0].unsqueeze(0)) & \ + (points[:, 0].unsqueeze(1) < bboxes[:, 2].unsqueeze(0)) & \ + (points[:, 1].unsqueeze(1) > bboxes[:, 1].unsqueeze(0)) & \ + (points[:, 1].unsqueeze(1) < bboxes[:, 3].unsqueeze(0)) + + +def bboxes_area(bboxes): + """Compute the area of an array of bboxes. + + Args: + bboxes (Tensor): The coordinates ox bboxes. Shape: (m, 4) + + Returns: + Tensor: Area of the bboxes. Shape: (m, ) + """ + assert bboxes.size(1) == 4 + w = (bboxes[:, 2] - bboxes[:, 0]) + h = (bboxes[:, 3] - bboxes[:, 1]) + areas = w * h + return areas + + +@BBOX_ASSIGNERS.register_module() +class CenterRegionAssigner(BaseAssigner): + """Assign pixels at the center region of a bbox as positive. + + Each proposals will be assigned with `-1`, `0`, or a positive integer + indicating the ground truth index. + - -1: negative samples + - semi-positive numbers: positive sample, index (0-based) of assigned gt + + Args: + pos_scale (float): Threshold within which pixels are + labelled as positive. + neg_scale (float): Threshold above which pixels are + labelled as positive. + min_pos_iof (float): Minimum iof of a pixel with a gt to be + labelled as positive. Default: 1e-2 + ignore_gt_scale (float): Threshold within which the pixels + are ignored when the gt is labelled as shadowed. Default: 0.5 + foreground_dominate (bool): If True, the bbox will be assigned as + positive when a gt's kernel region overlaps with another's shadowed + (ignored) region, otherwise it is set as ignored. Default to False. + """ + + def __init__(self, + pos_scale, + neg_scale, + min_pos_iof=1e-2, + ignore_gt_scale=0.5, + foreground_dominate=False, + iou_calculator=dict(type='BboxOverlaps2D')): + self.pos_scale = pos_scale + self.neg_scale = neg_scale + self.min_pos_iof = min_pos_iof + self.ignore_gt_scale = ignore_gt_scale + self.foreground_dominate = foreground_dominate + self.iou_calculator = build_iou_calculator(iou_calculator) + + def get_gt_priorities(self, gt_bboxes): + """Get gt priorities according to their areas. + + Smaller gt has higher priority. + + Args: + gt_bboxes (Tensor): Ground truth boxes, shape (k, 4). + + Returns: + Tensor: The priority of gts so that gts with larger priority is \ + more likely to be assigned. Shape (k, ) + """ + gt_areas = bboxes_area(gt_bboxes) + # Rank all gt bbox areas. Smaller objects has larger priority + _, sort_idx = gt_areas.sort(descending=True) + sort_idx = sort_idx.argsort() + return sort_idx + + def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None): + """Assign gt to bboxes. + + This method assigns gts to every bbox (proposal/anchor), each bbox \ + will be assigned with -1, or a semi-positive number. -1 means \ + negative sample, semi-positive number is the index (0-based) of \ + assigned gt. + + Args: + bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4). + gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4). + gt_bboxes_ignore (tensor, optional): Ground truth bboxes that are + labelled as `ignored`, e.g., crowd boxes in COCO. + gt_labels (tensor, optional): Label of gt_bboxes, shape (num_gts,). + + Returns: + :obj:`AssignResult`: The assigned result. Note that \ + shadowed_labels of shape (N, 2) is also added as an \ + `assign_result` attribute. `shadowed_labels` is a tensor \ + composed of N pairs of anchor_ind, class_label], where N \ + is the number of anchors that lie in the outer region of a \ + gt, anchor_ind is the shadowed anchor index and class_label \ + is the shadowed class label. + + Example: + >>> self = CenterRegionAssigner(0.2, 0.2) + >>> bboxes = torch.Tensor([[0, 0, 10, 10], [10, 10, 20, 20]]) + >>> gt_bboxes = torch.Tensor([[0, 0, 10, 10]]) + >>> assign_result = self.assign(bboxes, gt_bboxes) + >>> expected_gt_inds = torch.LongTensor([1, 0]) + >>> assert torch.all(assign_result.gt_inds == expected_gt_inds) + """ + # There are in total 5 steps in the pixel assignment + # 1. Find core (the center region, say inner 0.2) + # and shadow (the relatively ourter part, say inner 0.2-0.5) + # regions of every gt. + # 2. Find all prior bboxes that lie in gt_core and gt_shadow regions + # 3. Assign prior bboxes in gt_core with a one-hot id of the gt in + # the image. + # 3.1. For overlapping objects, the prior bboxes in gt_core is + # assigned with the object with smallest area + # 4. Assign prior bboxes with class label according to its gt id. + # 4.1. Assign -1 to prior bboxes lying in shadowed gts + # 4.2. Assign positive prior boxes with the corresponding label + # 5. Find pixels lying in the shadow of an object and assign them with + # background label, but set the loss weight of its corresponding + # gt to zero. + assert bboxes.size(1) == 4, 'bboxes must have size of 4' + # 1. Find core positive and shadow region of every gt + gt_core = scale_boxes(gt_bboxes, self.pos_scale) + gt_shadow = scale_boxes(gt_bboxes, self.neg_scale) + + # 2. Find prior bboxes that lie in gt_core and gt_shadow regions + bbox_centers = (bboxes[:, 2:4] + bboxes[:, 0:2]) / 2 + # The center points lie within the gt boxes + is_bbox_in_gt = is_located_in(bbox_centers, gt_bboxes) + # Only calculate bbox and gt_core IoF. This enables small prior bboxes + # to match large gts + bbox_and_gt_core_overlaps = self.iou_calculator( + bboxes, gt_core, mode='iof') + # The center point of effective priors should be within the gt box + is_bbox_in_gt_core = is_bbox_in_gt & ( + bbox_and_gt_core_overlaps > self.min_pos_iof) # shape (n, k) + + is_bbox_in_gt_shadow = ( + self.iou_calculator(bboxes, gt_shadow, mode='iof') > + self.min_pos_iof) + # Rule out center effective positive pixels + is_bbox_in_gt_shadow &= (~is_bbox_in_gt_core) + + num_gts, num_bboxes = gt_bboxes.size(0), bboxes.size(0) + if num_gts == 0 or num_bboxes == 0: + # If no gts exist, assign all pixels to negative + assigned_gt_ids = \ + is_bbox_in_gt_core.new_zeros((num_bboxes,), + dtype=torch.long) + pixels_in_gt_shadow = assigned_gt_ids.new_empty((0, 2)) + else: + # Step 3: assign a one-hot gt id to each pixel, and smaller objects + # have high priority to assign the pixel. + sort_idx = self.get_gt_priorities(gt_bboxes) + assigned_gt_ids, pixels_in_gt_shadow = \ + self.assign_one_hot_gt_indices(is_bbox_in_gt_core, + is_bbox_in_gt_shadow, + gt_priority=sort_idx) + + if gt_bboxes_ignore is not None and gt_bboxes_ignore.numel() > 0: + # No ground truth or boxes, return empty assignment + gt_bboxes_ignore = scale_boxes( + gt_bboxes_ignore, scale=self.ignore_gt_scale) + is_bbox_in_ignored_gts = is_located_in(bbox_centers, + gt_bboxes_ignore) + is_bbox_in_ignored_gts = is_bbox_in_ignored_gts.any(dim=1) + assigned_gt_ids[is_bbox_in_ignored_gts] = -1 + + # 4. Assign prior bboxes with class label according to its gt id. + assigned_labels = None + shadowed_pixel_labels = None + if gt_labels is not None: + # Default assigned label is the background (-1) + assigned_labels = assigned_gt_ids.new_full((num_bboxes, ), -1) + pos_inds = torch.nonzero( + assigned_gt_ids > 0, as_tuple=False).squeeze() + if pos_inds.numel() > 0: + assigned_labels[pos_inds] = gt_labels[assigned_gt_ids[pos_inds] + - 1] + # 5. Find pixels lying in the shadow of an object + shadowed_pixel_labels = pixels_in_gt_shadow.clone() + if pixels_in_gt_shadow.numel() > 0: + pixel_idx, gt_idx =\ + pixels_in_gt_shadow[:, 0], pixels_in_gt_shadow[:, 1] + assert (assigned_gt_ids[pixel_idx] != gt_idx).all(), \ + 'Some pixels are dually assigned to ignore and gt!' + shadowed_pixel_labels[:, 1] = gt_labels[gt_idx - 1] + override = ( + assigned_labels[pixel_idx] == shadowed_pixel_labels[:, 1]) + if self.foreground_dominate: + # When a pixel is both positive and shadowed, set it as pos + shadowed_pixel_labels = shadowed_pixel_labels[~override] + else: + # When a pixel is both pos and shadowed, set it as shadowed + assigned_labels[pixel_idx[override]] = -1 + assigned_gt_ids[pixel_idx[override]] = 0 + + assign_result = AssignResult( + num_gts, assigned_gt_ids, None, labels=assigned_labels) + # Add shadowed_labels as assign_result property. Shape: (num_shadow, 2) + assign_result.set_extra_property('shadowed_labels', + shadowed_pixel_labels) + return assign_result + + def assign_one_hot_gt_indices(self, + is_bbox_in_gt_core, + is_bbox_in_gt_shadow, + gt_priority=None): + """Assign only one gt index to each prior box. + + Gts with large gt_priority are more likely to be assigned. + + Args: + is_bbox_in_gt_core (Tensor): Bool tensor indicating the bbox center + is in the core area of a gt (e.g. 0-0.2). + Shape: (num_prior, num_gt). + is_bbox_in_gt_shadow (Tensor): Bool tensor indicating the bbox + center is in the shadowed area of a gt (e.g. 0.2-0.5). + Shape: (num_prior, num_gt). + gt_priority (Tensor): Priorities of gts. The gt with a higher + priority is more likely to be assigned to the bbox when the bbox + match with multiple gts. Shape: (num_gt, ). + + Returns: + tuple: Returns (assigned_gt_inds, shadowed_gt_inds). + + - assigned_gt_inds: The assigned gt index of each prior bbox \ + (i.e. index from 1 to num_gts). Shape: (num_prior, ). + - shadowed_gt_inds: shadowed gt indices. It is a tensor of \ + shape (num_ignore, 2) with first column being the \ + shadowed prior bbox indices and the second column the \ + shadowed gt indices (1-based). + """ + num_bboxes, num_gts = is_bbox_in_gt_core.shape + + if gt_priority is None: + gt_priority = torch.arange( + num_gts, device=is_bbox_in_gt_core.device) + assert gt_priority.size(0) == num_gts + # The bigger gt_priority, the more preferable to be assigned + # The assigned inds are by default 0 (background) + assigned_gt_inds = is_bbox_in_gt_core.new_zeros((num_bboxes, ), + dtype=torch.long) + # Shadowed bboxes are assigned to be background. But the corresponding + # label is ignored during loss calculation, which is done through + # shadowed_gt_inds + shadowed_gt_inds = torch.nonzero(is_bbox_in_gt_shadow, as_tuple=False) + if is_bbox_in_gt_core.sum() == 0: # No gt match + shadowed_gt_inds[:, 1] += 1 # 1-based. For consistency issue + return assigned_gt_inds, shadowed_gt_inds + + # The priority of each prior box and gt pair. If one prior box is + # matched bo multiple gts. Only the pair with the highest priority + # is saved + pair_priority = is_bbox_in_gt_core.new_full((num_bboxes, num_gts), + -1, + dtype=torch.long) + + # Each bbox could match with multiple gts. + # The following codes deal with this situation + # Matched bboxes (to any gt). Shape: (num_pos_anchor, ) + inds_of_match = torch.any(is_bbox_in_gt_core, dim=1) + # The matched gt index of each positive bbox. Length >= num_pos_anchor + # , since one bbox could match multiple gts + matched_bbox_gt_inds = torch.nonzero( + is_bbox_in_gt_core, as_tuple=False)[:, 1] + # Assign priority to each bbox-gt pair. + pair_priority[is_bbox_in_gt_core] = gt_priority[matched_bbox_gt_inds] + _, argmax_priority = pair_priority[inds_of_match].max(dim=1) + assigned_gt_inds[inds_of_match] = argmax_priority + 1 # 1-based + # Zero-out the assigned anchor box to filter the shadowed gt indices + is_bbox_in_gt_core[inds_of_match, argmax_priority] = 0 + # Concat the shadowed indices due to overlapping with that out side of + # effective scale. shape: (total_num_ignore, 2) + shadowed_gt_inds = torch.cat( + (shadowed_gt_inds, torch.nonzero( + is_bbox_in_gt_core, as_tuple=False)), + dim=0) + # `is_bbox_in_gt_core` should be changed back to keep arguments intact. + is_bbox_in_gt_core[inds_of_match, argmax_priority] = 1 + # 1-based shadowed gt indices, to be consistent with `assigned_gt_inds` + if shadowed_gt_inds.numel() > 0: + shadowed_gt_inds[:, 1] += 1 + return assigned_gt_inds, shadowed_gt_inds diff --git a/mmdet/core/bbox/assigners/grid_assigner.py b/mmdet/core/bbox/assigners/grid_assigner.py new file mode 100644 index 0000000..7390ea6 --- /dev/null +++ b/mmdet/core/bbox/assigners/grid_assigner.py @@ -0,0 +1,155 @@ +import torch + +from ..builder import BBOX_ASSIGNERS +from ..iou_calculators import build_iou_calculator +from .assign_result import AssignResult +from .base_assigner import BaseAssigner + + +@BBOX_ASSIGNERS.register_module() +class GridAssigner(BaseAssigner): + """Assign a corresponding gt bbox or background to each bbox. + + Each proposals will be assigned with `-1`, `0`, or a positive integer + indicating the ground truth index. + + - -1: don't care + - 0: negative sample, no assigned gt + - positive integer: positive sample, index (1-based) of assigned gt + + Args: + pos_iou_thr (float): IoU threshold for positive bboxes. + neg_iou_thr (float or tuple): IoU threshold for negative bboxes. + min_pos_iou (float): Minimum iou for a bbox to be considered as a + positive bbox. Positive samples can have smaller IoU than + pos_iou_thr due to the 4th step (assign max IoU sample to each gt). + gt_max_assign_all (bool): Whether to assign all bboxes with the same + highest overlap with some gt to that gt. + """ + + def __init__(self, + pos_iou_thr, + neg_iou_thr, + min_pos_iou=.0, + gt_max_assign_all=True, + iou_calculator=dict(type='BboxOverlaps2D')): + self.pos_iou_thr = pos_iou_thr + self.neg_iou_thr = neg_iou_thr + self.min_pos_iou = min_pos_iou + self.gt_max_assign_all = gt_max_assign_all + self.iou_calculator = build_iou_calculator(iou_calculator) + + def assign(self, bboxes, box_responsible_flags, gt_bboxes, gt_labels=None): + """Assign gt to bboxes. The process is very much like the max iou + assigner, except that positive samples are constrained within the cell + that the gt boxes fell in. + + This method assign a gt bbox to every bbox (proposal/anchor), each bbox + will be assigned with -1, 0, or a positive number. -1 means don't care, + 0 means negative sample, positive number is the index (1-based) of + assigned gt. + The assignment is done in following steps, the order matters. + + 1. assign every bbox to -1 + 2. assign proposals whose iou with all gts <= neg_iou_thr to 0 + 3. for each bbox within a cell, if the iou with its nearest gt > + pos_iou_thr and the center of that gt falls inside the cell, + assign it to that bbox + 4. for each gt bbox, assign its nearest proposals within the cell the + gt bbox falls in to itself. + + Args: + bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4). + box_responsible_flags (Tensor): flag to indicate whether box is + responsible for prediction, shape(n, ) + gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4). + gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ). + + Returns: + :obj:`AssignResult`: The assign result. + """ + num_gts, num_bboxes = gt_bboxes.size(0), bboxes.size(0) + + # compute iou between all gt and bboxes + overlaps = self.iou_calculator(gt_bboxes, bboxes) + + # 1. assign -1 by default + assigned_gt_inds = overlaps.new_full((num_bboxes, ), + -1, + dtype=torch.long) + + if num_gts == 0 or num_bboxes == 0: + # No ground truth or boxes, return empty assignment + max_overlaps = overlaps.new_zeros((num_bboxes, )) + if num_gts == 0: + # No truth, assign everything to background + assigned_gt_inds[:] = 0 + if gt_labels is None: + assigned_labels = None + else: + assigned_labels = overlaps.new_full((num_bboxes, ), + -1, + dtype=torch.long) + return AssignResult( + num_gts, + assigned_gt_inds, + max_overlaps, + labels=assigned_labels) + + # 2. assign negative: below + # for each anchor, which gt best overlaps with it + # for each anchor, the max iou of all gts + # shape of max_overlaps == argmax_overlaps == num_bboxes + max_overlaps, argmax_overlaps = overlaps.max(dim=0) + + if isinstance(self.neg_iou_thr, float): + assigned_gt_inds[(max_overlaps >= 0) + & (max_overlaps <= self.neg_iou_thr)] = 0 + elif isinstance(self.neg_iou_thr, (tuple, list)): + assert len(self.neg_iou_thr) == 2 + assigned_gt_inds[(max_overlaps > self.neg_iou_thr[0]) + & (max_overlaps <= self.neg_iou_thr[1])] = 0 + + # 3. assign positive: falls into responsible cell and above + # positive IOU threshold, the order matters. + # the prior condition of comparision is to filter out all + # unrelated anchors, i.e. not box_responsible_flags + overlaps[:, ~box_responsible_flags.type(torch.bool)] = -1. + + # calculate max_overlaps again, but this time we only consider IOUs + # for anchors responsible for prediction + max_overlaps, argmax_overlaps = overlaps.max(dim=0) + + # for each gt, which anchor best overlaps with it + # for each gt, the max iou of all proposals + # shape of gt_max_overlaps == gt_argmax_overlaps == num_gts + gt_max_overlaps, gt_argmax_overlaps = overlaps.max(dim=1) + + pos_inds = (max_overlaps > + self.pos_iou_thr) & box_responsible_flags.type(torch.bool) + assigned_gt_inds[pos_inds] = argmax_overlaps[pos_inds] + 1 + + # 4. assign positive to max overlapped anchors within responsible cell + for i in range(num_gts): + if gt_max_overlaps[i] > self.min_pos_iou: + if self.gt_max_assign_all: + max_iou_inds = (overlaps[i, :] == gt_max_overlaps[i]) & \ + box_responsible_flags.type(torch.bool) + assigned_gt_inds[max_iou_inds] = i + 1 + elif box_responsible_flags[gt_argmax_overlaps[i]]: + assigned_gt_inds[gt_argmax_overlaps[i]] = i + 1 + + # assign labels of positive anchors + if gt_labels is not None: + assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1) + pos_inds = torch.nonzero( + assigned_gt_inds > 0, as_tuple=False).squeeze() + if pos_inds.numel() > 0: + assigned_labels[pos_inds] = gt_labels[ + assigned_gt_inds[pos_inds] - 1] + + else: + assigned_labels = None + + return AssignResult( + num_gts, assigned_gt_inds, max_overlaps, labels=assigned_labels) diff --git a/mmdet/core/bbox/assigners/hungarian_assigner.py b/mmdet/core/bbox/assigners/hungarian_assigner.py new file mode 100644 index 0000000..e10cc14 --- /dev/null +++ b/mmdet/core/bbox/assigners/hungarian_assigner.py @@ -0,0 +1,145 @@ +import torch + +from ..builder import BBOX_ASSIGNERS +from ..match_costs import build_match_cost +from ..transforms import bbox_cxcywh_to_xyxy +from .assign_result import AssignResult +from .base_assigner import BaseAssigner + +try: + from scipy.optimize import linear_sum_assignment +except ImportError: + linear_sum_assignment = None + + +@BBOX_ASSIGNERS.register_module() +class HungarianAssigner(BaseAssigner): + """Computes one-to-one matching between predictions and ground truth. + + This class computes an assignment between the targets and the predictions + based on the costs. The costs are weighted sum of three components: + classification cost, regression L1 cost and regression iou cost. The + targets don't include the no_object, so generally there are more + predictions than targets. After the one-to-one matching, the un-matched + are treated as backgrounds. Thus each query prediction will be assigned + with `0` or a positive integer indicating the ground truth index: + + - 0: negative sample, no assigned gt + - positive integer: positive sample, index (1-based) of assigned gt + + Args: + cls_weight (int | float, optional): The scale factor for classification + cost. Default 1.0. + bbox_weight (int | float, optional): The scale factor for regression + L1 cost. Default 1.0. + iou_weight (int | float, optional): The scale factor for regression + iou cost. Default 1.0. + iou_calculator (dict | optional): The config for the iou calculation. + Default type `BboxOverlaps2D`. + iou_mode (str | optional): "iou" (intersection over union), "iof" + (intersection over foreground), or "giou" (generalized + intersection over union). Default "giou". + """ + + def __init__(self, + cls_cost=dict(type='ClassificationCost', weight=1.), + reg_cost=dict(type='BBoxL1Cost', weight=1.0), + iou_cost=dict(type='IoUCost', iou_mode='giou', weight=1.0)): + self.cls_cost = build_match_cost(cls_cost) + self.reg_cost = build_match_cost(reg_cost) + self.iou_cost = build_match_cost(iou_cost) + + def assign(self, + bbox_pred, + cls_pred, + gt_bboxes, + gt_labels, + img_meta, + gt_bboxes_ignore=None, + eps=1e-7): + """Computes one-to-one matching based on the weighted costs. + + This method assign each query prediction to a ground truth or + background. The `assigned_gt_inds` with -1 means don't care, + 0 means negative sample, and positive number is the index (1-based) + of assigned gt. + The assignment is done in the following steps, the order matters. + + 1. assign every prediction to -1 + 2. compute the weighted costs + 3. do Hungarian matching on CPU based on the costs + 4. assign all to 0 (background) first, then for each matched pair + between predictions and gts, treat this prediction as foreground + and assign the corresponding gt index (plus 1) to it. + + Args: + bbox_pred (Tensor): Predicted boxes with normalized coordinates + (cx, cy, w, h), which are all in range [0, 1]. Shape + [num_query, 4]. + cls_pred (Tensor): Predicted classification logits, shape + [num_query, num_class]. + gt_bboxes (Tensor): Ground truth boxes with unnormalized + coordinates (x1, y1, x2, y2). Shape [num_gt, 4]. + gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,). + img_meta (dict): Meta information for current image. + gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are + labelled as `ignored`. Default None. + eps (int | float, optional): A value added to the denominator for + numerical stability. Default 1e-7. + + Returns: + :obj:`AssignResult`: The assigned result. + """ + assert gt_bboxes_ignore is None, \ + 'Only case when gt_bboxes_ignore is None is supported.' + num_gts, num_bboxes = gt_bboxes.size(0), bbox_pred.size(0) + + # 1. assign -1 by default + assigned_gt_inds = bbox_pred.new_full((num_bboxes, ), + -1, + dtype=torch.long) + assigned_labels = bbox_pred.new_full((num_bboxes, ), + -1, + dtype=torch.long) + if num_gts == 0 or num_bboxes == 0: + # No ground truth or boxes, return empty assignment + if num_gts == 0: + # No ground truth, assign all to background + assigned_gt_inds[:] = 0 + return AssignResult( + num_gts, assigned_gt_inds, None, labels=assigned_labels) + img_h, img_w, _ = img_meta['img_shape'] + factor = gt_bboxes.new_tensor([img_w, img_h, img_w, + img_h]).unsqueeze(0) + + # 2. compute the weighted costs + # classification and bboxcost. + cls_cost = self.cls_cost(cls_pred, gt_labels) + # regression L1 cost + normalize_gt_bboxes = gt_bboxes / factor + reg_cost = self.reg_cost(bbox_pred, normalize_gt_bboxes) + # regression iou cost, defaultly giou is used in official DETR. + bboxes = bbox_cxcywh_to_xyxy(bbox_pred) * factor + iou_cost = self.iou_cost(bboxes, gt_bboxes) + # weighted sum of above three costs + cost = cls_cost + reg_cost + iou_cost + + # 3. do Hungarian matching on CPU using linear_sum_assignment + cost = cost.detach().cpu() + if linear_sum_assignment is None: + raise ImportError('Please run "pip install scipy" ' + 'to install scipy first.') + matched_row_inds, matched_col_inds = linear_sum_assignment(cost) + matched_row_inds = torch.from_numpy(matched_row_inds).to( + bbox_pred.device) + matched_col_inds = torch.from_numpy(matched_col_inds).to( + bbox_pred.device) + + # 4. assign backgrounds and foregrounds + # assign all indices to backgrounds first + assigned_gt_inds[:] = 0 + # assign foregrounds based on matching results + assigned_gt_inds[matched_row_inds] = matched_col_inds + 1 + assigned_labels[matched_row_inds] = gt_labels[matched_col_inds] + return AssignResult( + num_gts, assigned_gt_inds, None, labels=assigned_labels) diff --git a/mmdet/core/bbox/assigners/max_iou_assigner.py b/mmdet/core/bbox/assigners/max_iou_assigner.py new file mode 100644 index 0000000..5cf4c4b --- /dev/null +++ b/mmdet/core/bbox/assigners/max_iou_assigner.py @@ -0,0 +1,212 @@ +import torch + +from ..builder import BBOX_ASSIGNERS +from ..iou_calculators import build_iou_calculator +from .assign_result import AssignResult +from .base_assigner import BaseAssigner + + +@BBOX_ASSIGNERS.register_module() +class MaxIoUAssigner(BaseAssigner): + """Assign a corresponding gt bbox or background to each bbox. + + Each proposals will be assigned with `-1`, or a semi-positive integer + indicating the ground truth index. + + - -1: negative sample, no assigned gt + - semi-positive integer: positive sample, index (0-based) of assigned gt + + Args: + pos_iou_thr (float): IoU threshold for positive bboxes. + neg_iou_thr (float or tuple): IoU threshold for negative bboxes. + min_pos_iou (float): Minimum iou for a bbox to be considered as a + positive bbox. Positive samples can have smaller IoU than + pos_iou_thr due to the 4th step (assign max IoU sample to each gt). + gt_max_assign_all (bool): Whether to assign all bboxes with the same + highest overlap with some gt to that gt. + ignore_iof_thr (float): IoF threshold for ignoring bboxes (if + `gt_bboxes_ignore` is specified). Negative values mean not + ignoring any bboxes. + ignore_wrt_candidates (bool): Whether to compute the iof between + `bboxes` and `gt_bboxes_ignore`, or the contrary. + match_low_quality (bool): Whether to allow low quality matches. This is + usually allowed for RPN and single stage detectors, but not allowed + in the second stage. Details are demonstrated in Step 4. + gpu_assign_thr (int): The upper bound of the number of GT for GPU + assign. When the number of gt is above this threshold, will assign + on CPU device. Negative values mean not assign on CPU. + """ + + def __init__(self, + pos_iou_thr, + neg_iou_thr, + min_pos_iou=.0, + gt_max_assign_all=True, + ignore_iof_thr=-1, + ignore_wrt_candidates=True, + match_low_quality=True, + gpu_assign_thr=-1, + iou_calculator=dict(type='BboxOverlaps2D')): + self.pos_iou_thr = pos_iou_thr + self.neg_iou_thr = neg_iou_thr + self.min_pos_iou = min_pos_iou + self.gt_max_assign_all = gt_max_assign_all + self.ignore_iof_thr = ignore_iof_thr + self.ignore_wrt_candidates = ignore_wrt_candidates + self.gpu_assign_thr = gpu_assign_thr + self.match_low_quality = match_low_quality + self.iou_calculator = build_iou_calculator(iou_calculator) + + def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None): + """Assign gt to bboxes. + + This method assign a gt bbox to every bbox (proposal/anchor), each bbox + will be assigned with -1, or a semi-positive number. -1 means negative + sample, semi-positive number is the index (0-based) of assigned gt. + The assignment is done in following steps, the order matters. + + 1. assign every bbox to the background + 2. assign proposals whose iou with all gts < neg_iou_thr to 0 + 3. for each bbox, if the iou with its nearest gt >= pos_iou_thr, + assign it to that bbox + 4. for each gt bbox, assign its nearest proposals (may be more than + one) to itself + + Args: + bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4). + gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4). + gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are + labelled as `ignored`, e.g., crowd boxes in COCO. + gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ). + + Returns: + :obj:`AssignResult`: The assign result. + + Example: + >>> self = MaxIoUAssigner(0.5, 0.5) + >>> bboxes = torch.Tensor([[0, 0, 10, 10], [10, 10, 20, 20]]) + >>> gt_bboxes = torch.Tensor([[0, 0, 10, 9]]) + >>> assign_result = self.assign(bboxes, gt_bboxes) + >>> expected_gt_inds = torch.LongTensor([1, 0]) + >>> assert torch.all(assign_result.gt_inds == expected_gt_inds) + """ + assign_on_cpu = True if (self.gpu_assign_thr > 0) and ( + gt_bboxes.shape[0] > self.gpu_assign_thr) else False + # compute overlap and assign gt on CPU when number of GT is large + if assign_on_cpu: + device = bboxes.device + bboxes = bboxes.cpu() + gt_bboxes = gt_bboxes.cpu() + if gt_bboxes_ignore is not None: + gt_bboxes_ignore = gt_bboxes_ignore.cpu() + if gt_labels is not None: + gt_labels = gt_labels.cpu() + + overlaps = self.iou_calculator(gt_bboxes, bboxes) + + if (self.ignore_iof_thr > 0 and gt_bboxes_ignore is not None + and gt_bboxes_ignore.numel() > 0 and bboxes.numel() > 0): + if self.ignore_wrt_candidates: + ignore_overlaps = self.iou_calculator( + bboxes, gt_bboxes_ignore, mode='iof') + ignore_max_overlaps, _ = ignore_overlaps.max(dim=1) + else: + ignore_overlaps = self.iou_calculator( + gt_bboxes_ignore, bboxes, mode='iof') + ignore_max_overlaps, _ = ignore_overlaps.max(dim=0) + overlaps[:, ignore_max_overlaps > self.ignore_iof_thr] = -1 + + assign_result = self.assign_wrt_overlaps(overlaps, gt_labels) + if assign_on_cpu: + assign_result.gt_inds = assign_result.gt_inds.to(device) + assign_result.max_overlaps = assign_result.max_overlaps.to(device) + if assign_result.labels is not None: + assign_result.labels = assign_result.labels.to(device) + return assign_result + + def assign_wrt_overlaps(self, overlaps, gt_labels=None): + """Assign w.r.t. the overlaps of bboxes with gts. + + Args: + overlaps (Tensor): Overlaps between k gt_bboxes and n bboxes, + shape(k, n). + gt_labels (Tensor, optional): Labels of k gt_bboxes, shape (k, ). + + Returns: + :obj:`AssignResult`: The assign result. + """ + num_gts, num_bboxes = overlaps.size(0), overlaps.size(1) + + # 1. assign -1 by default + assigned_gt_inds = overlaps.new_full((num_bboxes, ), + -1, + dtype=torch.long) + + if num_gts == 0 or num_bboxes == 0: + # No ground truth or boxes, return empty assignment + max_overlaps = overlaps.new_zeros((num_bboxes, )) + if num_gts == 0: + # No truth, assign everything to background + assigned_gt_inds[:] = 0 + if gt_labels is None: + assigned_labels = None + else: + assigned_labels = overlaps.new_full((num_bboxes, ), + -1, + dtype=torch.long) + return AssignResult( + num_gts, + assigned_gt_inds, + max_overlaps, + labels=assigned_labels) + + # for each anchor, which gt best overlaps with it + # for each anchor, the max iou of all gts + max_overlaps, argmax_overlaps = overlaps.max(dim=0) + # for each gt, which anchor best overlaps with it + # for each gt, the max iou of all proposals + gt_max_overlaps, gt_argmax_overlaps = overlaps.max(dim=1) + + # 2. assign negative: below + # the negative inds are set to be 0 + if isinstance(self.neg_iou_thr, float): + assigned_gt_inds[(max_overlaps >= 0) + & (max_overlaps < self.neg_iou_thr)] = 0 + elif isinstance(self.neg_iou_thr, tuple): + assert len(self.neg_iou_thr) == 2 + assigned_gt_inds[(max_overlaps >= self.neg_iou_thr[0]) + & (max_overlaps < self.neg_iou_thr[1])] = 0 + + # 3. assign positive: above positive IoU threshold + pos_inds = max_overlaps >= self.pos_iou_thr + assigned_gt_inds[pos_inds] = argmax_overlaps[pos_inds] + 1 + + if self.match_low_quality: + # Low-quality matching will overwrite the assigned_gt_inds assigned + # in Step 3. Thus, the assigned gt might not be the best one for + # prediction. + # For example, if bbox A has 0.9 and 0.8 iou with GT bbox 1 & 2, + # bbox 1 will be assigned as the best target for bbox A in step 3. + # However, if GT bbox 2's gt_argmax_overlaps = A, bbox A's + # assigned_gt_inds will be overwritten to be bbox B. + # This might be the reason that it is not used in ROI Heads. + for i in range(num_gts): + if gt_max_overlaps[i] >= self.min_pos_iou: + if self.gt_max_assign_all: + max_iou_inds = overlaps[i, :] == gt_max_overlaps[i] + assigned_gt_inds[max_iou_inds] = i + 1 + else: + assigned_gt_inds[gt_argmax_overlaps[i]] = i + 1 + + if gt_labels is not None: + assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1) + pos_inds = torch.nonzero( + assigned_gt_inds > 0, as_tuple=False).squeeze() + if pos_inds.numel() > 0: + assigned_labels[pos_inds] = gt_labels[ + assigned_gt_inds[pos_inds] - 1] + else: + assigned_labels = None + + return AssignResult( + num_gts, assigned_gt_inds, max_overlaps, labels=assigned_labels) diff --git a/mmdet/core/bbox/assigners/point_assigner.py b/mmdet/core/bbox/assigners/point_assigner.py new file mode 100644 index 0000000..fb8f5e4 --- /dev/null +++ b/mmdet/core/bbox/assigners/point_assigner.py @@ -0,0 +1,133 @@ +import torch + +from ..builder import BBOX_ASSIGNERS +from .assign_result import AssignResult +from .base_assigner import BaseAssigner + + +@BBOX_ASSIGNERS.register_module() +class PointAssigner(BaseAssigner): + """Assign a corresponding gt bbox or background to each point. + + Each proposals will be assigned with `0`, or a positive integer + indicating the ground truth index. + + - 0: negative sample, no assigned gt + - positive integer: positive sample, index (1-based) of assigned gt + """ + + def __init__(self, scale=4, pos_num=3): + self.scale = scale + self.pos_num = pos_num + + def assign(self, points, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None): + """Assign gt to points. + + This method assign a gt bbox to every points set, each points set + will be assigned with the background_label (-1), or a label number. + -1 is background, and semi-positive number is the index (0-based) of + assigned gt. + The assignment is done in following steps, the order matters. + + 1. assign every points to the background_label (-1) + 2. A point is assigned to some gt bbox if + (i) the point is within the k closest points to the gt bbox + (ii) the distance between this point and the gt is smaller than + other gt bboxes + + Args: + points (Tensor): points to be assigned, shape(n, 3) while last + dimension stands for (x, y, stride). + gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4). + gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are + labelled as `ignored`, e.g., crowd boxes in COCO. + NOTE: currently unused. + gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ). + + Returns: + :obj:`AssignResult`: The assign result. + """ + num_points = points.shape[0] + num_gts = gt_bboxes.shape[0] + + if num_gts == 0 or num_points == 0: + # If no truth assign everything to the background + assigned_gt_inds = points.new_full((num_points, ), + 0, + dtype=torch.long) + if gt_labels is None: + assigned_labels = None + else: + assigned_labels = points.new_full((num_points, ), + -1, + dtype=torch.long) + return AssignResult( + num_gts, assigned_gt_inds, None, labels=assigned_labels) + + points_xy = points[:, :2] + points_stride = points[:, 2] + points_lvl = torch.log2( + points_stride).int() # [3...,4...,5...,6...,7...] + lvl_min, lvl_max = points_lvl.min(), points_lvl.max() + + # assign gt box + gt_bboxes_xy = (gt_bboxes[:, :2] + gt_bboxes[:, 2:]) / 2 + gt_bboxes_wh = (gt_bboxes[:, 2:] - gt_bboxes[:, :2]).clamp(min=1e-6) + scale = self.scale + gt_bboxes_lvl = ((torch.log2(gt_bboxes_wh[:, 0] / scale) + + torch.log2(gt_bboxes_wh[:, 1] / scale)) / 2).int() + gt_bboxes_lvl = torch.clamp(gt_bboxes_lvl, min=lvl_min, max=lvl_max) + + # stores the assigned gt index of each point + assigned_gt_inds = points.new_zeros((num_points, ), dtype=torch.long) + # stores the assigned gt dist (to this point) of each point + assigned_gt_dist = points.new_full((num_points, ), float('inf')) + points_range = torch.arange(points.shape[0]) + + for idx in range(num_gts): + gt_lvl = gt_bboxes_lvl[idx] + # get the index of points in this level + lvl_idx = gt_lvl == points_lvl + points_index = points_range[lvl_idx] + # get the points in this level + lvl_points = points_xy[lvl_idx, :] + # get the center point of gt + gt_point = gt_bboxes_xy[[idx], :] + # get width and height of gt + gt_wh = gt_bboxes_wh[[idx], :] + # compute the distance between gt center and + # all points in this level + points_gt_dist = ((lvl_points - gt_point) / gt_wh).norm(dim=1) + # find the nearest k points to gt center in this level + min_dist, min_dist_index = torch.topk( + points_gt_dist, self.pos_num, largest=False) + # the index of nearest k points to gt center in this level + min_dist_points_index = points_index[min_dist_index] + # The less_than_recorded_index stores the index + # of min_dist that is less then the assigned_gt_dist. Where + # assigned_gt_dist stores the dist from previous assigned gt + # (if exist) to each point. + less_than_recorded_index = min_dist < assigned_gt_dist[ + min_dist_points_index] + # The min_dist_points_index stores the index of points satisfy: + # (1) it is k nearest to current gt center in this level. + # (2) it is closer to current gt center than other gt center. + min_dist_points_index = min_dist_points_index[ + less_than_recorded_index] + # assign the result + assigned_gt_inds[min_dist_points_index] = idx + 1 + assigned_gt_dist[min_dist_points_index] = min_dist[ + less_than_recorded_index] + + if gt_labels is not None: + assigned_labels = assigned_gt_inds.new_full((num_points, ), -1) + pos_inds = torch.nonzero( + assigned_gt_inds > 0, as_tuple=False).squeeze() + if pos_inds.numel() > 0: + assigned_labels[pos_inds] = gt_labels[ + assigned_gt_inds[pos_inds] - 1] + else: + assigned_labels = None + + return AssignResult( + num_gts, assigned_gt_inds, None, labels=assigned_labels) diff --git a/mmdet/core/bbox/assigners/region_assigner.py b/mmdet/core/bbox/assigners/region_assigner.py new file mode 100644 index 0000000..2e8464b --- /dev/null +++ b/mmdet/core/bbox/assigners/region_assigner.py @@ -0,0 +1,221 @@ +import torch + +from mmdet.core import anchor_inside_flags +from ..builder import BBOX_ASSIGNERS +from .assign_result import AssignResult +from .base_assigner import BaseAssigner + + +def calc_region(bbox, ratio, stride, featmap_size=None): + """Calculate region of the box defined by the ratio, the ratio is from the + center of the box to every edge.""" + # project bbox on the feature + f_bbox = bbox / stride + x1 = torch.round((1 - ratio) * f_bbox[0] + ratio * f_bbox[2]) + y1 = torch.round((1 - ratio) * f_bbox[1] + ratio * f_bbox[3]) + x2 = torch.round(ratio * f_bbox[0] + (1 - ratio) * f_bbox[2]) + y2 = torch.round(ratio * f_bbox[1] + (1 - ratio) * f_bbox[3]) + if featmap_size is not None: + x1 = x1.clamp(min=0, max=featmap_size[1]) + y1 = y1.clamp(min=0, max=featmap_size[0]) + x2 = x2.clamp(min=0, max=featmap_size[1]) + y2 = y2.clamp(min=0, max=featmap_size[0]) + return (x1, y1, x2, y2) + + +def anchor_ctr_inside_region_flags(anchors, stride, region): + """Get the flag indicate whether anchor centers are inside regions.""" + x1, y1, x2, y2 = region + f_anchors = anchors / stride + x = (f_anchors[:, 0] + f_anchors[:, 2]) * 0.5 + y = (f_anchors[:, 1] + f_anchors[:, 3]) * 0.5 + flags = (x >= x1) & (x <= x2) & (y >= y1) & (y <= y2) + return flags + + +@BBOX_ASSIGNERS.register_module() +class RegionAssigner(BaseAssigner): + """Assign a corresponding gt bbox or background to each bbox. + + Each proposals will be assigned with `-1`, `0`, or a positive integer + indicating the ground truth index. + + - -1: don't care + - 0: negative sample, no assigned gt + - positive integer: positive sample, index (1-based) of assigned gt + + Args: + center_ratio: ratio of the region in the center of the bbox to + define positive sample. + ignore_ratio: ratio of the region to define ignore samples. + """ + + def __init__(self, center_ratio=0.2, ignore_ratio=0.5): + self.center_ratio = center_ratio + self.ignore_ratio = ignore_ratio + + def assign(self, + mlvl_anchors, + mlvl_valid_flags, + gt_bboxes, + img_meta, + featmap_sizes, + anchor_scale, + anchor_strides, + gt_bboxes_ignore=None, + gt_labels=None, + allowed_border=0): + """Assign gt to anchors. + + This method assign a gt bbox to every bbox (proposal/anchor), each bbox + will be assigned with -1, 0, or a positive number. -1 means don't care, + 0 means negative sample, positive number is the index (1-based) of + assigned gt. + The assignment is done in following steps, the order matters. + + 1. Assign every anchor to 0 (negative) + For each gt_bboxes: + 2. Compute ignore flags based on ignore_region then + assign -1 to anchors w.r.t. ignore flags + 3. Compute pos flags based on center_region then + assign gt_bboxes to anchors w.r.t. pos flags + 4. Compute ignore flags based on adjacent anchor lvl then + assign -1 to anchors w.r.t. ignore flags + 5. Assign anchor outside of image to -1 + + Args: + mlvl_anchors (list[Tensor]): Multi level anchors. + mlvl_valid_flags (list[Tensor]): Multi level valid flags. + gt_bboxes (Tensor): Ground truth bboxes of image + img_meta (dict): Meta info of image. + featmap_sizes (list[Tensor]): Feature mapsize each level + anchor_scale (int): Scale of the anchor. + anchor_strides (list[int]): Stride of the anchor. + gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4). + gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are + labelled as `ignored`, e.g., crowd boxes in COCO. + gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ). + allowed_border (int, optional): The border to allow the valid + anchor. Defaults to 0. + + Returns: + :obj:`AssignResult`: The assign result. + """ + if gt_bboxes_ignore is not None: + raise NotImplementedError + + num_gts = gt_bboxes.shape[0] + num_bboxes = sum(x.shape[0] for x in mlvl_anchors) + + if num_gts == 0 or num_bboxes == 0: + # No ground truth or boxes, return empty assignment + max_overlaps = gt_bboxes.new_zeros((num_bboxes, )) + assigned_gt_inds = gt_bboxes.new_zeros((num_bboxes, ), + dtype=torch.long) + if gt_labels is None: + assigned_labels = None + else: + assigned_labels = gt_bboxes.new_full((num_bboxes, ), + -1, + dtype=torch.long) + return AssignResult( + num_gts, + assigned_gt_inds, + max_overlaps, + labels=assigned_labels) + + num_lvls = len(mlvl_anchors) + r1 = (1 - self.center_ratio) / 2 + r2 = (1 - self.ignore_ratio) / 2 + + scale = torch.sqrt((gt_bboxes[:, 2] - gt_bboxes[:, 0]) * + (gt_bboxes[:, 3] - gt_bboxes[:, 1])) + min_anchor_size = scale.new_full( + (1, ), float(anchor_scale * anchor_strides[0])) + target_lvls = torch.floor( + torch.log2(scale) - torch.log2(min_anchor_size) + 0.5) + target_lvls = target_lvls.clamp(min=0, max=num_lvls - 1).long() + + # 1. assign 0 (negative) by default + mlvl_assigned_gt_inds = [] + mlvl_ignore_flags = [] + for lvl in range(num_lvls): + h, w = featmap_sizes[lvl] + assert h * w == mlvl_anchors[lvl].shape[0] + assigned_gt_inds = gt_bboxes.new_full((h * w, ), + 0, + dtype=torch.long) + ignore_flags = torch.zeros_like(assigned_gt_inds) + mlvl_assigned_gt_inds.append(assigned_gt_inds) + mlvl_ignore_flags.append(ignore_flags) + + for gt_id in range(num_gts): + lvl = target_lvls[gt_id].item() + featmap_size = featmap_sizes[lvl] + stride = anchor_strides[lvl] + anchors = mlvl_anchors[lvl] + gt_bbox = gt_bboxes[gt_id, :4] + + # Compute regions + ignore_region = calc_region(gt_bbox, r2, stride, featmap_size) + ctr_region = calc_region(gt_bbox, r1, stride, featmap_size) + + # 2. Assign -1 to ignore flags + ignore_flags = anchor_ctr_inside_region_flags( + anchors, stride, ignore_region) + mlvl_assigned_gt_inds[lvl][ignore_flags] = -1 + + # 3. Assign gt_bboxes to pos flags + pos_flags = anchor_ctr_inside_region_flags(anchors, stride, + ctr_region) + mlvl_assigned_gt_inds[lvl][pos_flags] = gt_id + 1 + + # 4. Assign -1 to ignore adjacent lvl + if lvl > 0: + d_lvl = lvl - 1 + d_anchors = mlvl_anchors[d_lvl] + d_featmap_size = featmap_sizes[d_lvl] + d_stride = anchor_strides[d_lvl] + d_ignore_region = calc_region(gt_bbox, r2, d_stride, + d_featmap_size) + ignore_flags = anchor_ctr_inside_region_flags( + d_anchors, d_stride, d_ignore_region) + mlvl_ignore_flags[d_lvl][ignore_flags] = 1 + if lvl < num_lvls - 1: + u_lvl = lvl + 1 + u_anchors = mlvl_anchors[u_lvl] + u_featmap_size = featmap_sizes[u_lvl] + u_stride = anchor_strides[u_lvl] + u_ignore_region = calc_region(gt_bbox, r2, u_stride, + u_featmap_size) + ignore_flags = anchor_ctr_inside_region_flags( + u_anchors, u_stride, u_ignore_region) + mlvl_ignore_flags[u_lvl][ignore_flags] = 1 + + # 4. (cont.) Assign -1 to ignore adjacent lvl + for lvl in range(num_lvls): + ignore_flags = mlvl_ignore_flags[lvl] + mlvl_assigned_gt_inds[lvl][ignore_flags] = -1 + + # 5. Assign -1 to anchor outside of image + flat_assigned_gt_inds = torch.cat(mlvl_assigned_gt_inds) + flat_anchors = torch.cat(mlvl_anchors) + flat_valid_flags = torch.cat(mlvl_valid_flags) + assert (flat_assigned_gt_inds.shape[0] == flat_anchors.shape[0] == + flat_valid_flags.shape[0]) + inside_flags = anchor_inside_flags(flat_anchors, flat_valid_flags, + img_meta['img_shape'], + allowed_border) + outside_flags = ~inside_flags + flat_assigned_gt_inds[outside_flags] = -1 + + if gt_labels is not None: + assigned_labels = torch.zeros_like(flat_assigned_gt_inds) + pos_flags = assigned_gt_inds > 0 + assigned_labels[pos_flags] = gt_labels[ + flat_assigned_gt_inds[pos_flags] - 1] + else: + assigned_labels = None + + return AssignResult( + num_gts, flat_assigned_gt_inds, None, labels=assigned_labels) diff --git a/mmdet/core/bbox/assigners/uniform_assigner.py b/mmdet/core/bbox/assigners/uniform_assigner.py new file mode 100644 index 0000000..1d606de --- /dev/null +++ b/mmdet/core/bbox/assigners/uniform_assigner.py @@ -0,0 +1,134 @@ +import torch + +from ..builder import BBOX_ASSIGNERS +from ..iou_calculators import build_iou_calculator +from ..transforms import bbox_xyxy_to_cxcywh +from .assign_result import AssignResult +from .base_assigner import BaseAssigner + + +@BBOX_ASSIGNERS.register_module() +class UniformAssigner(BaseAssigner): + """Uniform Matching between the anchors and gt boxes, which can achieve + balance in positive anchors, and gt_bboxes_ignore was not considered for + now. + + Args: + pos_ignore_thr (float): the threshold to ignore positive anchors + neg_ignore_thr (float): the threshold to ignore negative anchors + match_times(int): Number of positive anchors for each gt box. + Default 4. + iou_calculator (dict): iou_calculator config + """ + + def __init__(self, + pos_ignore_thr, + neg_ignore_thr, + match_times=4, + iou_calculator=dict(type='BboxOverlaps2D')): + self.match_times = match_times + self.pos_ignore_thr = pos_ignore_thr + self.neg_ignore_thr = neg_ignore_thr + self.iou_calculator = build_iou_calculator(iou_calculator) + + def assign(self, + bbox_pred, + anchor, + gt_bboxes, + gt_bboxes_ignore=None, + gt_labels=None): + num_gts, num_bboxes = gt_bboxes.size(0), bbox_pred.size(0) + + # 1. assign -1 by default + assigned_gt_inds = bbox_pred.new_full((num_bboxes, ), + 0, + dtype=torch.long) + assigned_labels = bbox_pred.new_full((num_bboxes, ), + -1, + dtype=torch.long) + if num_gts == 0 or num_bboxes == 0: + # No ground truth or boxes, return empty assignment + if num_gts == 0: + # No ground truth, assign all to background + assigned_gt_inds[:] = 0 + assign_result = AssignResult( + num_gts, assigned_gt_inds, None, labels=assigned_labels) + assign_result.set_extra_property( + 'pos_idx', bbox_pred.new_empty(0, dtype=torch.bool)) + assign_result.set_extra_property('pos_predicted_boxes', + bbox_pred.new_empty((0, 4))) + assign_result.set_extra_property('target_boxes', + bbox_pred.new_empty((0, 4))) + return assign_result + + # 2. Compute the L1 cost between boxes + # Note that we use anchors and predict boxes both + cost_bbox = torch.cdist( + bbox_xyxy_to_cxcywh(bbox_pred), + bbox_xyxy_to_cxcywh(gt_bboxes), + p=1) + cost_bbox_anchors = torch.cdist( + bbox_xyxy_to_cxcywh(anchor), bbox_xyxy_to_cxcywh(gt_bboxes), p=1) + + # We found that topk function has different results in cpu and + # cuda mode. In order to ensure consistency with the source code, + # we also use cpu mode. + # TODO: Check whether the performance of cpu and cuda are the same. + C = cost_bbox.cpu() + C1 = cost_bbox_anchors.cpu() + + # self.match_times x n + index = torch.topk( + C, # c=b,n,x c[i]=n,x + k=self.match_times, + dim=0, + largest=False)[1] + + # self.match_times x n + index1 = torch.topk(C1, k=self.match_times, dim=0, largest=False)[1] + # (self.match_times*2) x n + indexes = torch.cat((index, index1), + dim=1).reshape(-1).to(bbox_pred.device) + + pred_overlaps = self.iou_calculator(bbox_pred, gt_bboxes) + anchor_overlaps = self.iou_calculator(anchor, gt_bboxes) + pred_max_overlaps, _ = pred_overlaps.max(dim=1) + anchor_max_overlaps, _ = anchor_overlaps.max(dim=0) + + # 3. Compute the ignore indexes use gt_bboxes and predict boxes + ignore_idx = pred_max_overlaps > self.neg_ignore_thr + assigned_gt_inds[ignore_idx] = -1 + + # 4. Compute the ignore indexes of positive sample use anchors + # and predict boxes + pos_gt_index = torch.arange( + 0, C1.size(1), + device=bbox_pred.device).repeat(self.match_times * 2) + pos_ious = anchor_overlaps[indexes, pos_gt_index] + pos_ignore_idx = pos_ious < self.pos_ignore_thr + + pos_gt_index_with_ignore = pos_gt_index + 1 + pos_gt_index_with_ignore[pos_ignore_idx] = -1 + assigned_gt_inds[indexes] = pos_gt_index_with_ignore + + if gt_labels is not None: + assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1) + pos_inds = torch.nonzero( + assigned_gt_inds > 0, as_tuple=False).squeeze() + if pos_inds.numel() > 0: + assigned_labels[pos_inds] = gt_labels[ + assigned_gt_inds[pos_inds] - 1] + else: + assigned_labels = None + + assign_result = AssignResult( + num_gts, + assigned_gt_inds, + anchor_max_overlaps, + labels=assigned_labels) + assign_result.set_extra_property('pos_idx', ~pos_ignore_idx) + assign_result.set_extra_property('pos_predicted_boxes', + bbox_pred[indexes]) + assign_result.set_extra_property('target_boxes', + gt_bboxes[pos_gt_index]) + return assign_result diff --git a/mmdet/core/bbox/builder.py b/mmdet/core/bbox/builder.py new file mode 100644 index 0000000..682683b --- /dev/null +++ b/mmdet/core/bbox/builder.py @@ -0,0 +1,20 @@ +from mmcv.utils import Registry, build_from_cfg + +BBOX_ASSIGNERS = Registry('bbox_assigner') +BBOX_SAMPLERS = Registry('bbox_sampler') +BBOX_CODERS = Registry('bbox_coder') + + +def build_assigner(cfg, **default_args): + """Builder of box assigner.""" + return build_from_cfg(cfg, BBOX_ASSIGNERS, default_args) + + +def build_sampler(cfg, **default_args): + """Builder of box sampler.""" + return build_from_cfg(cfg, BBOX_SAMPLERS, default_args) + + +def build_bbox_coder(cfg, **default_args): + """Builder of box coder.""" + return build_from_cfg(cfg, BBOX_CODERS, default_args) diff --git a/mmdet/core/bbox/coder/__init__.py b/mmdet/core/bbox/coder/__init__.py new file mode 100644 index 0000000..ae455ba --- /dev/null +++ b/mmdet/core/bbox/coder/__init__.py @@ -0,0 +1,13 @@ +from .base_bbox_coder import BaseBBoxCoder +from .bucketing_bbox_coder import BucketingBBoxCoder +from .delta_xywh_bbox_coder import DeltaXYWHBBoxCoder +from .legacy_delta_xywh_bbox_coder import LegacyDeltaXYWHBBoxCoder +from .pseudo_bbox_coder import PseudoBBoxCoder +from .tblr_bbox_coder import TBLRBBoxCoder +from .yolo_bbox_coder import YOLOBBoxCoder + +__all__ = [ + 'BaseBBoxCoder', 'PseudoBBoxCoder', 'DeltaXYWHBBoxCoder', + 'LegacyDeltaXYWHBBoxCoder', 'TBLRBBoxCoder', 'YOLOBBoxCoder', + 'BucketingBBoxCoder' +] diff --git a/mmdet/core/bbox/coder/__pycache__/__init__.cpython-37.pyc b/mmdet/core/bbox/coder/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..e3b1bdb Binary files /dev/null and b/mmdet/core/bbox/coder/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/core/bbox/coder/__pycache__/base_bbox_coder.cpython-37.pyc 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b/mmdet/core/bbox/coder/__pycache__/yolo_bbox_coder.cpython-37.pyc differ diff --git a/mmdet/core/bbox/coder/base_bbox_coder.py b/mmdet/core/bbox/coder/base_bbox_coder.py new file mode 100644 index 0000000..cf0b34c --- /dev/null +++ b/mmdet/core/bbox/coder/base_bbox_coder.py @@ -0,0 +1,17 @@ +from abc import ABCMeta, abstractmethod + + +class BaseBBoxCoder(metaclass=ABCMeta): + """Base bounding box coder.""" + + def __init__(self, **kwargs): + pass + + @abstractmethod + def encode(self, bboxes, gt_bboxes): + """Encode deltas between bboxes and ground truth boxes.""" + + @abstractmethod + def decode(self, bboxes, bboxes_pred): + """Decode the predicted bboxes according to prediction and base + boxes.""" diff --git a/mmdet/core/bbox/coder/bucketing_bbox_coder.py b/mmdet/core/bbox/coder/bucketing_bbox_coder.py new file mode 100644 index 0000000..92d24b4 --- /dev/null +++ b/mmdet/core/bbox/coder/bucketing_bbox_coder.py @@ -0,0 +1,350 @@ +import mmcv +import numpy as np +import torch +import torch.nn.functional as F + +from ..builder import BBOX_CODERS +from ..transforms import bbox_rescale +from .base_bbox_coder import BaseBBoxCoder + + +@BBOX_CODERS.register_module() +class BucketingBBoxCoder(BaseBBoxCoder): + """Bucketing BBox Coder for Side-Aware Boundary Localization (SABL). + + Boundary Localization with Bucketing and Bucketing Guided Rescoring + are implemented here. + + Please refer to https://arxiv.org/abs/1912.04260 for more details. + + Args: + num_buckets (int): Number of buckets. + scale_factor (int): Scale factor of proposals to generate buckets. + offset_topk (int): Topk buckets are used to generate + bucket fine regression targets. Defaults to 2. + offset_upperbound (float): Offset upperbound to generate + bucket fine regression targets. + To avoid too large offset displacements. Defaults to 1.0. + cls_ignore_neighbor (bool): Ignore second nearest bucket or Not. + Defaults to True. + clip_border (bool, optional): Whether clip the objects outside the + border of the image. Defaults to True. + """ + + def __init__(self, + num_buckets, + scale_factor, + offset_topk=2, + offset_upperbound=1.0, + cls_ignore_neighbor=True, + clip_border=True): + super(BucketingBBoxCoder, self).__init__() + self.num_buckets = num_buckets + self.scale_factor = scale_factor + self.offset_topk = offset_topk + self.offset_upperbound = offset_upperbound + self.cls_ignore_neighbor = cls_ignore_neighbor + self.clip_border = clip_border + + def encode(self, bboxes, gt_bboxes): + """Get bucketing estimation and fine regression targets during + training. + + Args: + bboxes (torch.Tensor): source boxes, e.g., object proposals. + gt_bboxes (torch.Tensor): target of the transformation, e.g., + ground truth boxes. + + Returns: + encoded_bboxes(tuple[Tensor]): bucketing estimation + and fine regression targets and weights + """ + + assert bboxes.size(0) == gt_bboxes.size(0) + assert bboxes.size(-1) == gt_bboxes.size(-1) == 4 + encoded_bboxes = bbox2bucket(bboxes, gt_bboxes, self.num_buckets, + self.scale_factor, self.offset_topk, + self.offset_upperbound, + self.cls_ignore_neighbor) + return encoded_bboxes + + def decode(self, bboxes, pred_bboxes, max_shape=None): + """Apply transformation `pred_bboxes` to `boxes`. + Args: + boxes (torch.Tensor): Basic boxes. + pred_bboxes (torch.Tensor): Predictions for bucketing estimation + and fine regression + max_shape (tuple[int], optional): Maximum shape of boxes. + Defaults to None. + + Returns: + torch.Tensor: Decoded boxes. + """ + assert len(pred_bboxes) == 2 + cls_preds, offset_preds = pred_bboxes + assert cls_preds.size(0) == bboxes.size(0) and offset_preds.size( + 0) == bboxes.size(0) + decoded_bboxes = bucket2bbox(bboxes, cls_preds, offset_preds, + self.num_buckets, self.scale_factor, + max_shape, self.clip_border) + + return decoded_bboxes + + +@mmcv.jit(coderize=True) +def generat_buckets(proposals, num_buckets, scale_factor=1.0): + """Generate buckets w.r.t bucket number and scale factor of proposals. + + Args: + proposals (Tensor): Shape (n, 4) + num_buckets (int): Number of buckets. + scale_factor (float): Scale factor to rescale proposals. + + Returns: + tuple[Tensor]: (bucket_w, bucket_h, l_buckets, r_buckets, + t_buckets, d_buckets) + + - bucket_w: Width of buckets on x-axis. Shape (n, ). + - bucket_h: Height of buckets on y-axis. Shape (n, ). + - l_buckets: Left buckets. Shape (n, ceil(side_num/2)). + - r_buckets: Right buckets. Shape (n, ceil(side_num/2)). + - t_buckets: Top buckets. Shape (n, ceil(side_num/2)). + - d_buckets: Down buckets. Shape (n, ceil(side_num/2)). + """ + proposals = bbox_rescale(proposals, scale_factor) + + # number of buckets in each side + side_num = int(np.ceil(num_buckets / 2.0)) + pw = proposals[..., 2] - proposals[..., 0] + ph = proposals[..., 3] - proposals[..., 1] + px1 = proposals[..., 0] + py1 = proposals[..., 1] + px2 = proposals[..., 2] + py2 = proposals[..., 3] + + bucket_w = pw / num_buckets + bucket_h = ph / num_buckets + + # left buckets + l_buckets = px1[:, None] + (0.5 + torch.arange( + 0, side_num).to(proposals).float())[None, :] * bucket_w[:, None] + # right buckets + r_buckets = px2[:, None] - (0.5 + torch.arange( + 0, side_num).to(proposals).float())[None, :] * bucket_w[:, None] + # top buckets + t_buckets = py1[:, None] + (0.5 + torch.arange( + 0, side_num).to(proposals).float())[None, :] * bucket_h[:, None] + # down buckets + d_buckets = py2[:, None] - (0.5 + torch.arange( + 0, side_num).to(proposals).float())[None, :] * bucket_h[:, None] + return bucket_w, bucket_h, l_buckets, r_buckets, t_buckets, d_buckets + + +@mmcv.jit(coderize=True) +def bbox2bucket(proposals, + gt, + num_buckets, + scale_factor, + offset_topk=2, + offset_upperbound=1.0, + cls_ignore_neighbor=True): + """Generate buckets estimation and fine regression targets. + + Args: + proposals (Tensor): Shape (n, 4) + gt (Tensor): Shape (n, 4) + num_buckets (int): Number of buckets. + scale_factor (float): Scale factor to rescale proposals. + offset_topk (int): Topk buckets are used to generate + bucket fine regression targets. Defaults to 2. + offset_upperbound (float): Offset allowance to generate + bucket fine regression targets. + To avoid too large offset displacements. Defaults to 1.0. + cls_ignore_neighbor (bool): Ignore second nearest bucket or Not. + Defaults to True. + + Returns: + tuple[Tensor]: (offsets, offsets_weights, bucket_labels, cls_weights). + + - offsets: Fine regression targets. \ + Shape (n, num_buckets*2). + - offsets_weights: Fine regression weights. \ + Shape (n, num_buckets*2). + - bucket_labels: Bucketing estimation labels. \ + Shape (n, num_buckets*2). + - cls_weights: Bucketing estimation weights. \ + Shape (n, num_buckets*2). + """ + assert proposals.size() == gt.size() + + # generate buckets + proposals = proposals.float() + gt = gt.float() + (bucket_w, bucket_h, l_buckets, r_buckets, t_buckets, + d_buckets) = generat_buckets(proposals, num_buckets, scale_factor) + + gx1 = gt[..., 0] + gy1 = gt[..., 1] + gx2 = gt[..., 2] + gy2 = gt[..., 3] + + # generate offset targets and weights + # offsets from buckets to gts + l_offsets = (l_buckets - gx1[:, None]) / bucket_w[:, None] + r_offsets = (r_buckets - gx2[:, None]) / bucket_w[:, None] + t_offsets = (t_buckets - gy1[:, None]) / bucket_h[:, None] + d_offsets = (d_buckets - gy2[:, None]) / bucket_h[:, None] + + # select top-k nearset buckets + l_topk, l_label = l_offsets.abs().topk( + offset_topk, dim=1, largest=False, sorted=True) + r_topk, r_label = r_offsets.abs().topk( + offset_topk, dim=1, largest=False, sorted=True) + t_topk, t_label = t_offsets.abs().topk( + offset_topk, dim=1, largest=False, sorted=True) + d_topk, d_label = d_offsets.abs().topk( + offset_topk, dim=1, largest=False, sorted=True) + + offset_l_weights = l_offsets.new_zeros(l_offsets.size()) + offset_r_weights = r_offsets.new_zeros(r_offsets.size()) + offset_t_weights = t_offsets.new_zeros(t_offsets.size()) + offset_d_weights = d_offsets.new_zeros(d_offsets.size()) + inds = torch.arange(0, proposals.size(0)).to(proposals).long() + + # generate offset weights of top-k nearset buckets + for k in range(offset_topk): + if k >= 1: + offset_l_weights[inds, l_label[:, + k]] = (l_topk[:, k] < + offset_upperbound).float() + offset_r_weights[inds, r_label[:, + k]] = (r_topk[:, k] < + offset_upperbound).float() + offset_t_weights[inds, t_label[:, + k]] = (t_topk[:, k] < + offset_upperbound).float() + offset_d_weights[inds, d_label[:, + k]] = (d_topk[:, k] < + offset_upperbound).float() + else: + offset_l_weights[inds, l_label[:, k]] = 1.0 + offset_r_weights[inds, r_label[:, k]] = 1.0 + offset_t_weights[inds, t_label[:, k]] = 1.0 + offset_d_weights[inds, d_label[:, k]] = 1.0 + + offsets = torch.cat([l_offsets, r_offsets, t_offsets, d_offsets], dim=-1) + offsets_weights = torch.cat([ + offset_l_weights, offset_r_weights, offset_t_weights, offset_d_weights + ], + dim=-1) + + # generate bucket labels and weight + side_num = int(np.ceil(num_buckets / 2.0)) + labels = torch.stack( + [l_label[:, 0], r_label[:, 0], t_label[:, 0], d_label[:, 0]], dim=-1) + + batch_size = labels.size(0) + bucket_labels = F.one_hot(labels.view(-1), side_num).view(batch_size, + -1).float() + bucket_cls_l_weights = (l_offsets.abs() < 1).float() + bucket_cls_r_weights = (r_offsets.abs() < 1).float() + bucket_cls_t_weights = (t_offsets.abs() < 1).float() + bucket_cls_d_weights = (d_offsets.abs() < 1).float() + bucket_cls_weights = torch.cat([ + bucket_cls_l_weights, bucket_cls_r_weights, bucket_cls_t_weights, + bucket_cls_d_weights + ], + dim=-1) + # ignore second nearest buckets for cls if necessary + if cls_ignore_neighbor: + bucket_cls_weights = (~((bucket_cls_weights == 1) & + (bucket_labels == 0))).float() + else: + bucket_cls_weights[:] = 1.0 + return offsets, offsets_weights, bucket_labels, bucket_cls_weights + + +@mmcv.jit(coderize=True) +def bucket2bbox(proposals, + cls_preds, + offset_preds, + num_buckets, + scale_factor=1.0, + max_shape=None, + clip_border=True): + """Apply bucketing estimation (cls preds) and fine regression (offset + preds) to generate det bboxes. + + Args: + proposals (Tensor): Boxes to be transformed. Shape (n, 4) + cls_preds (Tensor): bucketing estimation. Shape (n, num_buckets*2). + offset_preds (Tensor): fine regression. Shape (n, num_buckets*2). + num_buckets (int): Number of buckets. + scale_factor (float): Scale factor to rescale proposals. + max_shape (tuple[int, int]): Maximum bounds for boxes. specifies (H, W) + clip_border (bool, optional): Whether clip the objects outside the + border of the image. Defaults to True. + + Returns: + tuple[Tensor]: (bboxes, loc_confidence). + + - bboxes: predicted bboxes. Shape (n, 4) + - loc_confidence: localization confidence of predicted bboxes. + Shape (n,). + """ + + side_num = int(np.ceil(num_buckets / 2.0)) + cls_preds = cls_preds.view(-1, side_num) + offset_preds = offset_preds.view(-1, side_num) + + scores = F.softmax(cls_preds, dim=1) + score_topk, score_label = scores.topk(2, dim=1, largest=True, sorted=True) + + rescaled_proposals = bbox_rescale(proposals, scale_factor) + + pw = rescaled_proposals[..., 2] - rescaled_proposals[..., 0] + ph = rescaled_proposals[..., 3] - rescaled_proposals[..., 1] + px1 = rescaled_proposals[..., 0] + py1 = rescaled_proposals[..., 1] + px2 = rescaled_proposals[..., 2] + py2 = rescaled_proposals[..., 3] + + bucket_w = pw / num_buckets + bucket_h = ph / num_buckets + + score_inds_l = score_label[0::4, 0] + score_inds_r = score_label[1::4, 0] + score_inds_t = score_label[2::4, 0] + score_inds_d = score_label[3::4, 0] + l_buckets = px1 + (0.5 + score_inds_l.float()) * bucket_w + r_buckets = px2 - (0.5 + score_inds_r.float()) * bucket_w + t_buckets = py1 + (0.5 + score_inds_t.float()) * bucket_h + d_buckets = py2 - (0.5 + score_inds_d.float()) * bucket_h + + offsets = offset_preds.view(-1, 4, side_num) + inds = torch.arange(proposals.size(0)).to(proposals).long() + l_offsets = offsets[:, 0, :][inds, score_inds_l] + r_offsets = offsets[:, 1, :][inds, score_inds_r] + t_offsets = offsets[:, 2, :][inds, score_inds_t] + d_offsets = offsets[:, 3, :][inds, score_inds_d] + + x1 = l_buckets - l_offsets * bucket_w + x2 = r_buckets - r_offsets * bucket_w + y1 = t_buckets - t_offsets * bucket_h + y2 = d_buckets - d_offsets * bucket_h + + if clip_border and max_shape is not None: + x1 = x1.clamp(min=0, max=max_shape[1] - 1) + y1 = y1.clamp(min=0, max=max_shape[0] - 1) + x2 = x2.clamp(min=0, max=max_shape[1] - 1) + y2 = y2.clamp(min=0, max=max_shape[0] - 1) + bboxes = torch.cat([x1[:, None], y1[:, None], x2[:, None], y2[:, None]], + dim=-1) + + # bucketing guided rescoring + loc_confidence = score_topk[:, 0] + top2_neighbor_inds = (score_label[:, 0] - score_label[:, 1]).abs() == 1 + loc_confidence += score_topk[:, 1] * top2_neighbor_inds.float() + loc_confidence = loc_confidence.view(-1, 4).mean(dim=1) + + return bboxes, loc_confidence diff --git a/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py b/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py new file mode 100644 index 0000000..98d3090 --- /dev/null +++ b/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py @@ -0,0 +1,271 @@ +import mmcv +import numpy as np +import torch + +from ..builder import BBOX_CODERS +from .base_bbox_coder import BaseBBoxCoder + + +@BBOX_CODERS.register_module() +class DeltaXYWHBBoxCoder(BaseBBoxCoder): + """Delta XYWH BBox coder. + + Following the practice in `R-CNN `_, + this coder encodes bbox (x1, y1, x2, y2) into delta (dx, dy, dw, dh) and + decodes delta (dx, dy, dw, dh) back to original bbox (x1, y1, x2, y2). + + Args: + target_means (Sequence[float]): Denormalizing means of target for + delta coordinates + target_stds (Sequence[float]): Denormalizing standard deviation of + target for delta coordinates + clip_border (bool, optional): Whether clip the objects outside the + border of the image. Defaults to True. + add_ctr_clamp (bool): Whether to add center clamp, when added, the + predicted box is clamped is its center is too far away from + the original anchor's center. Only used by YOLOF. Default False. + ctr_clamp (int): the maximum pixel shift to clamp. Only used by YOLOF. + Default 32. + """ + + def __init__(self, + target_means=(0., 0., 0., 0.), + target_stds=(1., 1., 1., 1.), + clip_border=True, + add_ctr_clamp=False, + ctr_clamp=32): + super(BaseBBoxCoder, self).__init__() + self.means = target_means + self.stds = target_stds + self.clip_border = clip_border + self.add_ctr_clamp = add_ctr_clamp + self.ctr_clamp = ctr_clamp + + def encode(self, bboxes, gt_bboxes): + """Get box regression transformation deltas that can be used to + transform the ``bboxes`` into the ``gt_bboxes``. + + Args: + bboxes (torch.Tensor): Source boxes, e.g., object proposals. + gt_bboxes (torch.Tensor): Target of the transformation, e.g., + ground-truth boxes. + + Returns: + torch.Tensor: Box transformation deltas + """ + + assert bboxes.size(0) == gt_bboxes.size(0) + assert bboxes.size(-1) == gt_bboxes.size(-1) == 4 + encoded_bboxes = bbox2delta(bboxes, gt_bboxes, self.means, self.stds) + return encoded_bboxes + + def decode(self, + bboxes, + pred_bboxes, + max_shape=None, + wh_ratio_clip=16 / 1000): + """Apply transformation `pred_bboxes` to `boxes`. + + Args: + bboxes (torch.Tensor): Basic boxes. Shape (B, N, 4) or (N, 4) + pred_bboxes (Tensor): Encoded offsets with respect to each roi. + Has shape (B, N, num_classes * 4) or (B, N, 4) or + (N, num_classes * 4) or (N, 4). Note N = num_anchors * W * H + when rois is a grid of anchors.Offset encoding follows [1]_. + max_shape (Sequence[int] or torch.Tensor or Sequence[ + Sequence[int]],optional): Maximum bounds for boxes, specifies + (H, W, C) or (H, W). If bboxes shape is (B, N, 4), then + the max_shape should be a Sequence[Sequence[int]] + and the length of max_shape should also be B. + wh_ratio_clip (float, optional): The allowed ratio between + width and height. + + Returns: + torch.Tensor: Decoded boxes. + """ + + assert pred_bboxes.size(0) == bboxes.size(0) + if pred_bboxes.ndim == 3: + assert pred_bboxes.size(1) == bboxes.size(1) + decoded_bboxes = delta2bbox(bboxes, pred_bboxes, self.means, self.stds, + max_shape, wh_ratio_clip, self.clip_border, + self.add_ctr_clamp, self.ctr_clamp) + + return decoded_bboxes + + +@mmcv.jit(coderize=True) +def bbox2delta(proposals, gt, means=(0., 0., 0., 0.), stds=(1., 1., 1., 1.)): + """Compute deltas of proposals w.r.t. gt. + + We usually compute the deltas of x, y, w, h of proposals w.r.t ground + truth bboxes to get regression target. + This is the inverse function of :func:`delta2bbox`. + + Args: + proposals (Tensor): Boxes to be transformed, shape (N, ..., 4) + gt (Tensor): Gt bboxes to be used as base, shape (N, ..., 4) + means (Sequence[float]): Denormalizing means for delta coordinates + stds (Sequence[float]): Denormalizing standard deviation for delta + coordinates + + Returns: + Tensor: deltas with shape (N, 4), where columns represent dx, dy, + dw, dh. + """ + assert proposals.size() == gt.size() + + proposals = proposals.float() + gt = gt.float() + px = (proposals[..., 0] + proposals[..., 2]) * 0.5 + py = (proposals[..., 1] + proposals[..., 3]) * 0.5 + pw = proposals[..., 2] - proposals[..., 0] + ph = proposals[..., 3] - proposals[..., 1] + + gx = (gt[..., 0] + gt[..., 2]) * 0.5 + gy = (gt[..., 1] + gt[..., 3]) * 0.5 + gw = gt[..., 2] - gt[..., 0] + gh = gt[..., 3] - gt[..., 1] + + dx = (gx - px) / pw + dy = (gy - py) / ph + dw = torch.log(gw / pw) + dh = torch.log(gh / ph) + deltas = torch.stack([dx, dy, dw, dh], dim=-1) + + means = deltas.new_tensor(means).unsqueeze(0) + stds = deltas.new_tensor(stds).unsqueeze(0) + deltas = deltas.sub_(means).div_(stds) + + return deltas + + +@mmcv.jit(coderize=True) +def delta2bbox(rois, + deltas, + means=(0., 0., 0., 0.), + stds=(1., 1., 1., 1.), + max_shape=None, + wh_ratio_clip=16 / 1000, + clip_border=True, + add_ctr_clamp=False, + ctr_clamp=32): + """Apply deltas to shift/scale base boxes. + + Typically the rois are anchor or proposed bounding boxes and the deltas are + network outputs used to shift/scale those boxes. + This is the inverse function of :func:`bbox2delta`. + + Args: + rois (Tensor): Boxes to be transformed. Has shape (N, 4) or (B, N, 4) + deltas (Tensor): Encoded offsets with respect to each roi. + Has shape (B, N, num_classes * 4) or (B, N, 4) or + (N, num_classes * 4) or (N, 4). Note N = num_anchors * W * H + when rois is a grid of anchors.Offset encoding follows [1]_. + means (Sequence[float]): Denormalizing means for delta coordinates + stds (Sequence[float]): Denormalizing standard deviation for delta + coordinates + max_shape (Sequence[int] or torch.Tensor or Sequence[ + Sequence[int]],optional): Maximum bounds for boxes, specifies + (H, W, C) or (H, W). If rois shape is (B, N, 4), then + the max_shape should be a Sequence[Sequence[int]] + and the length of max_shape should also be B. + wh_ratio_clip (float): Maximum aspect ratio for boxes. + clip_border (bool, optional): Whether clip the objects outside the + border of the image. Defaults to True. + add_ctr_clamp (bool): Whether to add center clamp, when added, the + predicted box is clamped is its center is too far away from + the original anchor's center. Only used by YOLOF. Default False. + ctr_clamp (int): the maximum pixel shift to clamp. Only used by YOLOF. + Default 32. + + Returns: + Tensor: Boxes with shape (B, N, num_classes * 4) or (B, N, 4) or + (N, num_classes * 4) or (N, 4), where 4 represent + tl_x, tl_y, br_x, br_y. + + References: + .. [1] https://arxiv.org/abs/1311.2524 + + Example: + >>> rois = torch.Tensor([[ 0., 0., 1., 1.], + >>> [ 0., 0., 1., 1.], + >>> [ 0., 0., 1., 1.], + >>> [ 5., 5., 5., 5.]]) + >>> deltas = torch.Tensor([[ 0., 0., 0., 0.], + >>> [ 1., 1., 1., 1.], + >>> [ 0., 0., 2., -1.], + >>> [ 0.7, -1.9, -0.5, 0.3]]) + >>> delta2bbox(rois, deltas, max_shape=(32, 32, 3)) + tensor([[0.0000, 0.0000, 1.0000, 1.0000], + [0.1409, 0.1409, 2.8591, 2.8591], + [0.0000, 0.3161, 4.1945, 0.6839], + [5.0000, 5.0000, 5.0000, 5.0000]]) + """ + means = deltas.new_tensor(means).view(1, + -1).repeat(1, + deltas.size(-1) // 4) + stds = deltas.new_tensor(stds).view(1, -1).repeat(1, deltas.size(-1) // 4) + denorm_deltas = deltas * stds + means + dx = denorm_deltas[..., 0::4] + dy = denorm_deltas[..., 1::4] + dw = denorm_deltas[..., 2::4] + dh = denorm_deltas[..., 3::4] + + x1, y1 = rois[..., 0], rois[..., 1] + x2, y2 = rois[..., 2], rois[..., 3] + # Compute center of each roi + px = ((x1 + x2) * 0.5).unsqueeze(-1).expand_as(dx) + py = ((y1 + y2) * 0.5).unsqueeze(-1).expand_as(dy) + # Compute width/height of each roi + pw = (x2 - x1).unsqueeze(-1).expand_as(dw) + ph = (y2 - y1).unsqueeze(-1).expand_as(dh) + + dx_width = pw * dx + dy_height = ph * dy + + max_ratio = np.abs(np.log(wh_ratio_clip)) + if add_ctr_clamp: + dx_width = torch.clamp(dx_width, max=ctr_clamp, min=-ctr_clamp) + dy_height = torch.clamp(dy_height, max=ctr_clamp, min=-ctr_clamp) + dw = torch.clamp(dw, max=max_ratio) + dh = torch.clamp(dh, max=max_ratio) + else: + dw = dw.clamp(min=-max_ratio, max=max_ratio) + dh = dh.clamp(min=-max_ratio, max=max_ratio) + # Use exp(network energy) to enlarge/shrink each roi + gw = pw * dw.exp() + gh = ph * dh.exp() + # Use network energy to shift the center of each roi + gx = px + dx_width + gy = py + dy_height + # Convert center-xy/width/height to top-left, bottom-right + x1 = gx - gw * 0.5 + y1 = gy - gh * 0.5 + x2 = gx + gw * 0.5 + y2 = gy + gh * 0.5 + + bboxes = torch.stack([x1, y1, x2, y2], dim=-1).view(deltas.size()) + + if clip_border and max_shape is not None: + # clip bboxes with dynamic `min` and `max` for onnx + if torch.onnx.is_in_onnx_export(): + from mmdet.core.export import dynamic_clip_for_onnx + x1, y1, x2, y2 = dynamic_clip_for_onnx(x1, y1, x2, y2, max_shape) + bboxes = torch.stack([x1, y1, x2, y2], dim=-1).view(deltas.size()) + return bboxes + if not isinstance(max_shape, torch.Tensor): + max_shape = x1.new_tensor(max_shape) + max_shape = max_shape[..., :2].type_as(x1) + if max_shape.ndim == 2: + assert bboxes.ndim == 3 + assert max_shape.size(0) == bboxes.size(0) + + min_xy = x1.new_tensor(0) + max_xy = torch.cat( + [max_shape] * (deltas.size(-1) // 2), + dim=-1).flip(-1).unsqueeze(-2) + bboxes = torch.where(bboxes < min_xy, min_xy, bboxes) + bboxes = torch.where(bboxes > max_xy, max_xy, bboxes) + + return bboxes diff --git a/mmdet/core/bbox/coder/legacy_delta_xywh_bbox_coder.py b/mmdet/core/bbox/coder/legacy_delta_xywh_bbox_coder.py new file mode 100644 index 0000000..190309f --- /dev/null +++ b/mmdet/core/bbox/coder/legacy_delta_xywh_bbox_coder.py @@ -0,0 +1,215 @@ +import mmcv +import numpy as np +import torch + +from ..builder import BBOX_CODERS +from .base_bbox_coder import BaseBBoxCoder + + +@BBOX_CODERS.register_module() +class LegacyDeltaXYWHBBoxCoder(BaseBBoxCoder): + """Legacy Delta XYWH BBox coder used in MMDet V1.x. + + Following the practice in R-CNN [1]_, this coder encodes bbox (x1, y1, x2, + y2) into delta (dx, dy, dw, dh) and decodes delta (dx, dy, dw, dh) + back to original bbox (x1, y1, x2, y2). + + Note: + The main difference between :class`LegacyDeltaXYWHBBoxCoder` and + :class:`DeltaXYWHBBoxCoder` is whether ``+ 1`` is used during width and + height calculation. We suggest to only use this coder when testing with + MMDet V1.x models. + + References: + .. [1] https://arxiv.org/abs/1311.2524 + + Args: + target_means (Sequence[float]): denormalizing means of target for + delta coordinates + target_stds (Sequence[float]): denormalizing standard deviation of + target for delta coordinates + """ + + def __init__(self, + target_means=(0., 0., 0., 0.), + target_stds=(1., 1., 1., 1.)): + super(BaseBBoxCoder, self).__init__() + self.means = target_means + self.stds = target_stds + + def encode(self, bboxes, gt_bboxes): + """Get box regression transformation deltas that can be used to + transform the ``bboxes`` into the ``gt_bboxes``. + + Args: + bboxes (torch.Tensor): source boxes, e.g., object proposals. + gt_bboxes (torch.Tensor): target of the transformation, e.g., + ground-truth boxes. + + Returns: + torch.Tensor: Box transformation deltas + """ + assert bboxes.size(0) == gt_bboxes.size(0) + assert bboxes.size(-1) == gt_bboxes.size(-1) == 4 + encoded_bboxes = legacy_bbox2delta(bboxes, gt_bboxes, self.means, + self.stds) + return encoded_bboxes + + def decode(self, + bboxes, + pred_bboxes, + max_shape=None, + wh_ratio_clip=16 / 1000): + """Apply transformation `pred_bboxes` to `boxes`. + + Args: + boxes (torch.Tensor): Basic boxes. + pred_bboxes (torch.Tensor): Encoded boxes with shape + max_shape (tuple[int], optional): Maximum shape of boxes. + Defaults to None. + wh_ratio_clip (float, optional): The allowed ratio between + width and height. + + Returns: + torch.Tensor: Decoded boxes. + """ + assert pred_bboxes.size(0) == bboxes.size(0) + decoded_bboxes = legacy_delta2bbox(bboxes, pred_bboxes, self.means, + self.stds, max_shape, wh_ratio_clip) + + return decoded_bboxes + + +@mmcv.jit(coderize=True) +def legacy_bbox2delta(proposals, + gt, + means=(0., 0., 0., 0.), + stds=(1., 1., 1., 1.)): + """Compute deltas of proposals w.r.t. gt in the MMDet V1.x manner. + + We usually compute the deltas of x, y, w, h of proposals w.r.t ground + truth bboxes to get regression target. + This is the inverse function of `delta2bbox()` + + Args: + proposals (Tensor): Boxes to be transformed, shape (N, ..., 4) + gt (Tensor): Gt bboxes to be used as base, shape (N, ..., 4) + means (Sequence[float]): Denormalizing means for delta coordinates + stds (Sequence[float]): Denormalizing standard deviation for delta + coordinates + + Returns: + Tensor: deltas with shape (N, 4), where columns represent dx, dy, + dw, dh. + """ + assert proposals.size() == gt.size() + + proposals = proposals.float() + gt = gt.float() + px = (proposals[..., 0] + proposals[..., 2]) * 0.5 + py = (proposals[..., 1] + proposals[..., 3]) * 0.5 + pw = proposals[..., 2] - proposals[..., 0] + 1.0 + ph = proposals[..., 3] - proposals[..., 1] + 1.0 + + gx = (gt[..., 0] + gt[..., 2]) * 0.5 + gy = (gt[..., 1] + gt[..., 3]) * 0.5 + gw = gt[..., 2] - gt[..., 0] + 1.0 + gh = gt[..., 3] - gt[..., 1] + 1.0 + + dx = (gx - px) / pw + dy = (gy - py) / ph + dw = torch.log(gw / pw) + dh = torch.log(gh / ph) + deltas = torch.stack([dx, dy, dw, dh], dim=-1) + + means = deltas.new_tensor(means).unsqueeze(0) + stds = deltas.new_tensor(stds).unsqueeze(0) + deltas = deltas.sub_(means).div_(stds) + + return deltas + + +@mmcv.jit(coderize=True) +def legacy_delta2bbox(rois, + deltas, + means=(0., 0., 0., 0.), + stds=(1., 1., 1., 1.), + max_shape=None, + wh_ratio_clip=16 / 1000): + """Apply deltas to shift/scale base boxes in the MMDet V1.x manner. + + Typically the rois are anchor or proposed bounding boxes and the deltas are + network outputs used to shift/scale those boxes. + This is the inverse function of `bbox2delta()` + + Args: + rois (Tensor): Boxes to be transformed. Has shape (N, 4) + deltas (Tensor): Encoded offsets with respect to each roi. + Has shape (N, 4 * num_classes). Note N = num_anchors * W * H when + rois is a grid of anchors. Offset encoding follows [1]_. + means (Sequence[float]): Denormalizing means for delta coordinates + stds (Sequence[float]): Denormalizing standard deviation for delta + coordinates + max_shape (tuple[int, int]): Maximum bounds for boxes. specifies (H, W) + wh_ratio_clip (float): Maximum aspect ratio for boxes. + + Returns: + Tensor: Boxes with shape (N, 4), where columns represent + tl_x, tl_y, br_x, br_y. + + References: + .. [1] https://arxiv.org/abs/1311.2524 + + Example: + >>> rois = torch.Tensor([[ 0., 0., 1., 1.], + >>> [ 0., 0., 1., 1.], + >>> [ 0., 0., 1., 1.], + >>> [ 5., 5., 5., 5.]]) + >>> deltas = torch.Tensor([[ 0., 0., 0., 0.], + >>> [ 1., 1., 1., 1.], + >>> [ 0., 0., 2., -1.], + >>> [ 0.7, -1.9, -0.5, 0.3]]) + >>> legacy_delta2bbox(rois, deltas, max_shape=(32, 32)) + tensor([[0.0000, 0.0000, 1.5000, 1.5000], + [0.0000, 0.0000, 5.2183, 5.2183], + [0.0000, 0.1321, 7.8891, 0.8679], + [5.3967, 2.4251, 6.0033, 3.7749]]) + """ + means = deltas.new_tensor(means).repeat(1, deltas.size(1) // 4) + stds = deltas.new_tensor(stds).repeat(1, deltas.size(1) // 4) + denorm_deltas = deltas * stds + means + dx = denorm_deltas[:, 0::4] + dy = denorm_deltas[:, 1::4] + dw = denorm_deltas[:, 2::4] + dh = denorm_deltas[:, 3::4] + max_ratio = np.abs(np.log(wh_ratio_clip)) + dw = dw.clamp(min=-max_ratio, max=max_ratio) + dh = dh.clamp(min=-max_ratio, max=max_ratio) + # Compute center of each roi + px = ((rois[:, 0] + rois[:, 2]) * 0.5).unsqueeze(1).expand_as(dx) + py = ((rois[:, 1] + rois[:, 3]) * 0.5).unsqueeze(1).expand_as(dy) + # Compute width/height of each roi + pw = (rois[:, 2] - rois[:, 0] + 1.0).unsqueeze(1).expand_as(dw) + ph = (rois[:, 3] - rois[:, 1] + 1.0).unsqueeze(1).expand_as(dh) + # Use exp(network energy) to enlarge/shrink each roi + gw = pw * dw.exp() + gh = ph * dh.exp() + # Use network energy to shift the center of each roi + gx = px + pw * dx + gy = py + ph * dy + # Convert center-xy/width/height to top-left, bottom-right + + # The true legacy box coder should +- 0.5 here. + # However, current implementation improves the performance when testing + # the models trained in MMDetection 1.X (~0.5 bbox AP, 0.2 mask AP) + x1 = gx - gw * 0.5 + y1 = gy - gh * 0.5 + x2 = gx + gw * 0.5 + y2 = gy + gh * 0.5 + if max_shape is not None: + x1 = x1.clamp(min=0, max=max_shape[1] - 1) + y1 = y1.clamp(min=0, max=max_shape[0] - 1) + x2 = x2.clamp(min=0, max=max_shape[1] - 1) + y2 = y2.clamp(min=0, max=max_shape[0] - 1) + bboxes = torch.stack([x1, y1, x2, y2], dim=-1).view_as(deltas) + return bboxes diff --git a/mmdet/core/bbox/coder/pseudo_bbox_coder.py b/mmdet/core/bbox/coder/pseudo_bbox_coder.py new file mode 100644 index 0000000..1c8346f --- /dev/null +++ b/mmdet/core/bbox/coder/pseudo_bbox_coder.py @@ -0,0 +1,18 @@ +from ..builder import BBOX_CODERS +from .base_bbox_coder import BaseBBoxCoder + + +@BBOX_CODERS.register_module() +class PseudoBBoxCoder(BaseBBoxCoder): + """Pseudo bounding box coder.""" + + def __init__(self, **kwargs): + super(BaseBBoxCoder, self).__init__(**kwargs) + + def encode(self, bboxes, gt_bboxes): + """torch.Tensor: return the given ``bboxes``""" + return gt_bboxes + + def decode(self, bboxes, pred_bboxes): + """torch.Tensor: return the given ``pred_bboxes``""" + return pred_bboxes diff --git a/mmdet/core/bbox/coder/tblr_bbox_coder.py b/mmdet/core/bbox/coder/tblr_bbox_coder.py new file mode 100644 index 0000000..c45c616 --- /dev/null +++ b/mmdet/core/bbox/coder/tblr_bbox_coder.py @@ -0,0 +1,205 @@ +import mmcv +import torch + +from ..builder import BBOX_CODERS +from .base_bbox_coder import BaseBBoxCoder + + +@BBOX_CODERS.register_module() +class TBLRBBoxCoder(BaseBBoxCoder): + """TBLR BBox coder. + + Following the practice in `FSAF `_, + this coder encodes gt bboxes (x1, y1, x2, y2) into (top, bottom, left, + right) and decode it back to the original. + + Args: + normalizer (list | float): Normalization factor to be + divided with when coding the coordinates. If it is a list, it should + have length of 4 indicating normalization factor in tblr dims. + Otherwise it is a unified float factor for all dims. Default: 4.0 + clip_border (bool, optional): Whether clip the objects outside the + border of the image. Defaults to True. + """ + + def __init__(self, normalizer=4.0, clip_border=True): + super(BaseBBoxCoder, self).__init__() + self.normalizer = normalizer + self.clip_border = clip_border + + def encode(self, bboxes, gt_bboxes): + """Get box regression transformation deltas that can be used to + transform the ``bboxes`` into the ``gt_bboxes`` in the (top, left, + bottom, right) order. + + Args: + bboxes (torch.Tensor): source boxes, e.g., object proposals. + gt_bboxes (torch.Tensor): target of the transformation, e.g., + ground truth boxes. + + Returns: + torch.Tensor: Box transformation deltas + """ + assert bboxes.size(0) == gt_bboxes.size(0) + assert bboxes.size(-1) == gt_bboxes.size(-1) == 4 + encoded_bboxes = bboxes2tblr( + bboxes, gt_bboxes, normalizer=self.normalizer) + return encoded_bboxes + + def decode(self, bboxes, pred_bboxes, max_shape=None): + """Apply transformation `pred_bboxes` to `boxes`. + + Args: + bboxes (torch.Tensor): Basic boxes.Shape (B, N, 4) or (N, 4) + pred_bboxes (torch.Tensor): Encoded boxes with shape + (B, N, 4) or (N, 4) + max_shape (Sequence[int] or torch.Tensor or Sequence[ + Sequence[int]],optional): Maximum bounds for boxes, specifies + (H, W, C) or (H, W). If bboxes shape is (B, N, 4), then + the max_shape should be a Sequence[Sequence[int]] + and the length of max_shape should also be B. + + Returns: + torch.Tensor: Decoded boxes. + """ + decoded_bboxes = tblr2bboxes( + bboxes, + pred_bboxes, + normalizer=self.normalizer, + max_shape=max_shape, + clip_border=self.clip_border) + + return decoded_bboxes + + +@mmcv.jit(coderize=True) +def bboxes2tblr(priors, gts, normalizer=4.0, normalize_by_wh=True): + """Encode ground truth boxes to tblr coordinate. + + It first convert the gt coordinate to tblr format, + (top, bottom, left, right), relative to prior box centers. + The tblr coordinate may be normalized by the side length of prior bboxes + if `normalize_by_wh` is specified as True, and it is then normalized by + the `normalizer` factor. + + Args: + priors (Tensor): Prior boxes in point form + Shape: (num_proposals,4). + gts (Tensor): Coords of ground truth for each prior in point-form + Shape: (num_proposals, 4). + normalizer (Sequence[float] | float): normalization parameter of + encoded boxes. If it is a list, it has to have length = 4. + Default: 4.0 + normalize_by_wh (bool): Whether to normalize tblr coordinate by the + side length (wh) of prior bboxes. + + Return: + encoded boxes (Tensor), Shape: (num_proposals, 4) + """ + + # dist b/t match center and prior's center + if not isinstance(normalizer, float): + normalizer = torch.tensor(normalizer, device=priors.device) + assert len(normalizer) == 4, 'Normalizer must have length = 4' + assert priors.size(0) == gts.size(0) + prior_centers = (priors[:, 0:2] + priors[:, 2:4]) / 2 + xmin, ymin, xmax, ymax = gts.split(1, dim=1) + top = prior_centers[:, 1].unsqueeze(1) - ymin + bottom = ymax - prior_centers[:, 1].unsqueeze(1) + left = prior_centers[:, 0].unsqueeze(1) - xmin + right = xmax - prior_centers[:, 0].unsqueeze(1) + loc = torch.cat((top, bottom, left, right), dim=1) + if normalize_by_wh: + # Normalize tblr by anchor width and height + wh = priors[:, 2:4] - priors[:, 0:2] + w, h = torch.split(wh, 1, dim=1) + loc[:, :2] /= h # tb is normalized by h + loc[:, 2:] /= w # lr is normalized by w + # Normalize tblr by the given normalization factor + return loc / normalizer + + +@mmcv.jit(coderize=True) +def tblr2bboxes(priors, + tblr, + normalizer=4.0, + normalize_by_wh=True, + max_shape=None, + clip_border=True): + """Decode tblr outputs to prediction boxes. + + The process includes 3 steps: 1) De-normalize tblr coordinates by + multiplying it with `normalizer`; 2) De-normalize tblr coordinates by the + prior bbox width and height if `normalize_by_wh` is `True`; 3) Convert + tblr (top, bottom, left, right) pair relative to the center of priors back + to (xmin, ymin, xmax, ymax) coordinate. + + Args: + priors (Tensor): Prior boxes in point form (x0, y0, x1, y1) + Shape: (N,4) or (B, N, 4). + tblr (Tensor): Coords of network output in tblr form + Shape: (N, 4) or (B, N, 4). + normalizer (Sequence[float] | float): Normalization parameter of + encoded boxes. By list, it represents the normalization factors at + tblr dims. By float, it is the unified normalization factor at all + dims. Default: 4.0 + normalize_by_wh (bool): Whether the tblr coordinates have been + normalized by the side length (wh) of prior bboxes. + max_shape (Sequence[int] or torch.Tensor or Sequence[ + Sequence[int]],optional): Maximum bounds for boxes, specifies + (H, W, C) or (H, W). If priors shape is (B, N, 4), then + the max_shape should be a Sequence[Sequence[int]] + and the length of max_shape should also be B. + clip_border (bool, optional): Whether clip the objects outside the + border of the image. Defaults to True. + + Return: + encoded boxes (Tensor): Boxes with shape (N, 4) or (B, N, 4) + """ + if not isinstance(normalizer, float): + normalizer = torch.tensor(normalizer, device=priors.device) + assert len(normalizer) == 4, 'Normalizer must have length = 4' + assert priors.size(0) == tblr.size(0) + if priors.ndim == 3: + assert priors.size(1) == tblr.size(1) + + loc_decode = tblr * normalizer + prior_centers = (priors[..., 0:2] + priors[..., 2:4]) / 2 + if normalize_by_wh: + wh = priors[..., 2:4] - priors[..., 0:2] + w, h = torch.split(wh, 1, dim=-1) + # Inplace operation with slice would failed for exporting to ONNX + th = h * loc_decode[..., :2] # tb + tw = w * loc_decode[..., 2:] # lr + loc_decode = torch.cat([th, tw], dim=-1) + # Cannot be exported using onnx when loc_decode.split(1, dim=-1) + top, bottom, left, right = loc_decode.split((1, 1, 1, 1), dim=-1) + xmin = prior_centers[..., 0].unsqueeze(-1) - left + xmax = prior_centers[..., 0].unsqueeze(-1) + right + ymin = prior_centers[..., 1].unsqueeze(-1) - top + ymax = prior_centers[..., 1].unsqueeze(-1) + bottom + + bboxes = torch.cat((xmin, ymin, xmax, ymax), dim=-1) + + if clip_border and max_shape is not None: + # clip bboxes with dynamic `min` and `max` for onnx + if torch.onnx.is_in_onnx_export(): + from mmdet.core.export import dynamic_clip_for_onnx + xmin, ymin, xmax, ymax = dynamic_clip_for_onnx( + xmin, ymin, xmax, ymax, max_shape) + bboxes = torch.cat([xmin, ymin, xmax, ymax], dim=-1) + return bboxes + if not isinstance(max_shape, torch.Tensor): + max_shape = priors.new_tensor(max_shape) + max_shape = max_shape[..., :2].type_as(priors) + if max_shape.ndim == 2: + assert bboxes.ndim == 3 + assert max_shape.size(0) == bboxes.size(0) + + min_xy = priors.new_tensor(0) + max_xy = torch.cat([max_shape, max_shape], + dim=-1).flip(-1).unsqueeze(-2) + bboxes = torch.where(bboxes < min_xy, min_xy, bboxes) + bboxes = torch.where(bboxes > max_xy, max_xy, bboxes) + + return bboxes diff --git a/mmdet/core/bbox/coder/yolo_bbox_coder.py b/mmdet/core/bbox/coder/yolo_bbox_coder.py new file mode 100644 index 0000000..d6d0e82 --- /dev/null +++ b/mmdet/core/bbox/coder/yolo_bbox_coder.py @@ -0,0 +1,89 @@ +import mmcv +import torch + +from ..builder import BBOX_CODERS +from .base_bbox_coder import BaseBBoxCoder + + +@BBOX_CODERS.register_module() +class YOLOBBoxCoder(BaseBBoxCoder): + """YOLO BBox coder. + + Following `YOLO `_, this coder divide + image into grids, and encode bbox (x1, y1, x2, y2) into (cx, cy, dw, dh). + cx, cy in [0., 1.], denotes relative center position w.r.t the center of + bboxes. dw, dh are the same as :obj:`DeltaXYWHBBoxCoder`. + + Args: + eps (float): Min value of cx, cy when encoding. + """ + + def __init__(self, eps=1e-6): + super(BaseBBoxCoder, self).__init__() + self.eps = eps + + @mmcv.jit(coderize=True) + def encode(self, bboxes, gt_bboxes, stride): + """Get box regression transformation deltas that can be used to + transform the ``bboxes`` into the ``gt_bboxes``. + + Args: + bboxes (torch.Tensor): Source boxes, e.g., anchors. + gt_bboxes (torch.Tensor): Target of the transformation, e.g., + ground-truth boxes. + stride (torch.Tensor | int): Stride of bboxes. + + Returns: + torch.Tensor: Box transformation deltas + """ + + assert bboxes.size(0) == gt_bboxes.size(0) + assert bboxes.size(-1) == gt_bboxes.size(-1) == 4 + x_center_gt = (gt_bboxes[..., 0] + gt_bboxes[..., 2]) * 0.5 + y_center_gt = (gt_bboxes[..., 1] + gt_bboxes[..., 3]) * 0.5 + w_gt = gt_bboxes[..., 2] - gt_bboxes[..., 0] + h_gt = gt_bboxes[..., 3] - gt_bboxes[..., 1] + x_center = (bboxes[..., 0] + bboxes[..., 2]) * 0.5 + y_center = (bboxes[..., 1] + bboxes[..., 3]) * 0.5 + w = bboxes[..., 2] - bboxes[..., 0] + h = bboxes[..., 3] - bboxes[..., 1] + w_target = torch.log((w_gt / w).clamp(min=self.eps)) + h_target = torch.log((h_gt / h).clamp(min=self.eps)) + x_center_target = ((x_center_gt - x_center) / stride + 0.5).clamp( + self.eps, 1 - self.eps) + y_center_target = ((y_center_gt - y_center) / stride + 0.5).clamp( + self.eps, 1 - self.eps) + encoded_bboxes = torch.stack( + [x_center_target, y_center_target, w_target, h_target], dim=-1) + return encoded_bboxes + + @mmcv.jit(coderize=True) + def decode(self, bboxes, pred_bboxes, stride): + """Apply transformation `pred_bboxes` to `boxes`. + + Args: + boxes (torch.Tensor): Basic boxes, e.g. anchors. + pred_bboxes (torch.Tensor): Encoded boxes with shape + stride (torch.Tensor | int): Strides of bboxes. + + Returns: + torch.Tensor: Decoded boxes. + """ + assert pred_bboxes.size(0) == bboxes.size(0) + assert pred_bboxes.size(-1) == bboxes.size(-1) == 4 + x_center = (bboxes[..., 0] + bboxes[..., 2]) * 0.5 + y_center = (bboxes[..., 1] + bboxes[..., 3]) * 0.5 + w = bboxes[..., 2] - bboxes[..., 0] + h = bboxes[..., 3] - bboxes[..., 1] + # Get outputs x, y + x_center_pred = (pred_bboxes[..., 0] - 0.5) * stride + x_center + y_center_pred = (pred_bboxes[..., 1] - 0.5) * stride + y_center + w_pred = torch.exp(pred_bboxes[..., 2]) * w + h_pred = torch.exp(pred_bboxes[..., 3]) * h + + decoded_bboxes = torch.stack( + (x_center_pred - w_pred / 2, y_center_pred - h_pred / 2, + x_center_pred + w_pred / 2, y_center_pred + h_pred / 2), + dim=-1) + + return decoded_bboxes diff --git a/mmdet/core/bbox/demodata.py b/mmdet/core/bbox/demodata.py new file mode 100644 index 0000000..feecb69 --- /dev/null +++ b/mmdet/core/bbox/demodata.py @@ -0,0 +1,41 @@ +import numpy as np +import torch + +from mmdet.utils.util_random import ensure_rng + + +def random_boxes(num=1, scale=1, rng=None): + """Simple version of ``kwimage.Boxes.random`` + + Returns: + Tensor: shape (n, 4) in x1, y1, x2, y2 format. + + References: + https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390 + + Example: + >>> num = 3 + >>> scale = 512 + >>> rng = 0 + >>> boxes = random_boxes(num, scale, rng) + >>> print(boxes) + tensor([[280.9925, 278.9802, 308.6148, 366.1769], + [216.9113, 330.6978, 224.0446, 456.5878], + [405.3632, 196.3221, 493.3953, 270.7942]]) + """ + rng = ensure_rng(rng) + + tlbr = rng.rand(num, 4).astype(np.float32) + + tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2]) + tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3]) + br_x = np.maximum(tlbr[:, 0], tlbr[:, 2]) + br_y = np.maximum(tlbr[:, 1], tlbr[:, 3]) + + tlbr[:, 0] = tl_x * scale + tlbr[:, 1] = tl_y * scale + tlbr[:, 2] = br_x * scale + tlbr[:, 3] = br_y * scale + + boxes = torch.from_numpy(tlbr) + return boxes diff --git a/mmdet/core/bbox/iou_calculators/__init__.py b/mmdet/core/bbox/iou_calculators/__init__.py new file mode 100644 index 0000000..e71369a --- /dev/null +++ b/mmdet/core/bbox/iou_calculators/__init__.py @@ -0,0 +1,4 @@ +from .builder import build_iou_calculator +from .iou2d_calculator import BboxOverlaps2D, bbox_overlaps + +__all__ = ['build_iou_calculator', 'BboxOverlaps2D', 'bbox_overlaps'] diff --git a/mmdet/core/bbox/iou_calculators/__pycache__/__init__.cpython-37.pyc b/mmdet/core/bbox/iou_calculators/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..a42b033 Binary files /dev/null and b/mmdet/core/bbox/iou_calculators/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/core/bbox/iou_calculators/__pycache__/builder.cpython-37.pyc b/mmdet/core/bbox/iou_calculators/__pycache__/builder.cpython-37.pyc new file mode 100644 index 0000000..7555900 Binary files /dev/null and b/mmdet/core/bbox/iou_calculators/__pycache__/builder.cpython-37.pyc differ diff --git a/mmdet/core/bbox/iou_calculators/__pycache__/iou2d_calculator.cpython-37.pyc b/mmdet/core/bbox/iou_calculators/__pycache__/iou2d_calculator.cpython-37.pyc new file mode 100644 index 0000000..3d1a055 Binary files /dev/null and b/mmdet/core/bbox/iou_calculators/__pycache__/iou2d_calculator.cpython-37.pyc differ diff --git a/mmdet/core/bbox/iou_calculators/builder.py b/mmdet/core/bbox/iou_calculators/builder.py new file mode 100644 index 0000000..09094d7 --- /dev/null +++ b/mmdet/core/bbox/iou_calculators/builder.py @@ -0,0 +1,8 @@ +from mmcv.utils import Registry, build_from_cfg + +IOU_CALCULATORS = Registry('IoU calculator') + + +def build_iou_calculator(cfg, default_args=None): + """Builder of IoU calculator.""" + return build_from_cfg(cfg, IOU_CALCULATORS, default_args) diff --git a/mmdet/core/bbox/iou_calculators/iou2d_calculator.py b/mmdet/core/bbox/iou_calculators/iou2d_calculator.py new file mode 100644 index 0000000..c1fba10 --- /dev/null +++ b/mmdet/core/bbox/iou_calculators/iou2d_calculator.py @@ -0,0 +1,260 @@ +import torch + +from .builder import IOU_CALCULATORS + + +def cast_tensor_type(x, scale=1., dtype=None): + if dtype == 'fp16': + # scale is for preventing overflows + x = (x / scale).half() + return x + + +def fp16_clamp(x, min=None, max=None): + if not x.is_cuda and x.dtype == torch.float16: + # clamp for cpu float16, tensor fp16 has no clamp implementation + return x.float().clamp(min, max).half() + + return x.clamp(min, max) + + +@IOU_CALCULATORS.register_module() +class BboxOverlaps2D(object): + """2D Overlaps (e.g. IoUs, GIoUs) Calculator.""" + + def __init__(self, scale=1., dtype=None): + self.scale = scale + self.dtype = dtype + + def __call__(self, bboxes1, bboxes2, mode='iou', is_aligned=False): + """Calculate IoU between 2D bboxes. + + Args: + bboxes1 (Tensor): bboxes have shape (m, 4) in + format, or shape (m, 5) in format. + bboxes2 (Tensor): bboxes have shape (m, 4) in + format, shape (m, 5) in format, or be + empty. If ``is_aligned `` is ``True``, then m and n must be + equal. + mode (str): "iou" (intersection over union), "iof" (intersection + over foreground), or "giou" (generalized intersection over + union). + is_aligned (bool, optional): If True, then m and n must be equal. + Default False. + + Returns: + Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,) + """ + assert bboxes1.size(-1) in [0, 4, 5] + assert bboxes2.size(-1) in [0, 4, 5] + if bboxes2.size(-1) == 5: + bboxes2 = bboxes2[..., :4] + if bboxes1.size(-1) == 5: + bboxes1 = bboxes1[..., :4] + + if self.dtype == 'fp16': + # change tensor type to save cpu and cuda memory and keep speed + bboxes1 = cast_tensor_type(bboxes1, self.scale, self.dtype) + bboxes2 = cast_tensor_type(bboxes2, self.scale, self.dtype) + overlaps = bbox_overlaps(bboxes1, bboxes2, mode, is_aligned) + if not overlaps.is_cuda and overlaps.dtype == torch.float16: + # resume cpu float32 + overlaps = overlaps.float() + return overlaps + + return bbox_overlaps(bboxes1, bboxes2, mode, is_aligned) + + def __repr__(self): + """str: a string describing the module""" + repr_str = self.__class__.__name__ + f'(' \ + f'scale={self.scale}, dtype={self.dtype})' + return repr_str + + +def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False, eps=1e-6): + """Calculate overlap between two set of bboxes. + + FP16 Contributed by https://github.com/open-mmlab/mmdetection/pull/4889 + Note: + Assume bboxes1 is M x 4, bboxes2 is N x 4, when mode is 'iou', + there are some new generated variable when calculating IOU + using bbox_overlaps function: + + 1) is_aligned is False + area1: M x 1 + area2: N x 1 + lt: M x N x 2 + rb: M x N x 2 + wh: M x N x 2 + overlap: M x N x 1 + union: M x N x 1 + ious: M x N x 1 + + Total memory: + S = (9 x N x M + N + M) * 4 Byte, + + When using FP16, we can reduce: + R = (9 x N x M + N + M) * 4 / 2 Byte + R large than (N + M) * 4 * 2 is always true when N and M >= 1. + Obviously, N + M <= N * M < 3 * N * M, when N >=2 and M >=2, + N + 1 < 3 * N, when N or M is 1. + + Given M = 40 (ground truth), N = 400000 (three anchor boxes + in per grid, FPN, R-CNNs), + R = 275 MB (one times) + + A special case (dense detection), M = 512 (ground truth), + R = 3516 MB = 3.43 GB + + When the batch size is B, reduce: + B x R + + Therefore, CUDA memory runs out frequently. + + Experiments on GeForce RTX 2080Ti (11019 MiB): + + | dtype | M | N | Use | Real | Ideal | + |:----:|:----:|:----:|:----:|:----:|:----:| + | FP32 | 512 | 400000 | 8020 MiB | -- | -- | + | FP16 | 512 | 400000 | 4504 MiB | 3516 MiB | 3516 MiB | + | FP32 | 40 | 400000 | 1540 MiB | -- | -- | + | FP16 | 40 | 400000 | 1264 MiB | 276MiB | 275 MiB | + + 2) is_aligned is True + area1: N x 1 + area2: N x 1 + lt: N x 2 + rb: N x 2 + wh: N x 2 + overlap: N x 1 + union: N x 1 + ious: N x 1 + + Total memory: + S = 11 x N * 4 Byte + + When using FP16, we can reduce: + R = 11 x N * 4 / 2 Byte + + So do the 'giou' (large than 'iou'). + + Time-wise, FP16 is generally faster than FP32. + + When gpu_assign_thr is not -1, it takes more time on cpu + but not reduce memory. + There, we can reduce half the memory and keep the speed. + + If ``is_aligned `` is ``False``, then calculate the overlaps between each + bbox of bboxes1 and bboxes2, otherwise the overlaps between each aligned + pair of bboxes1 and bboxes2. + + Args: + bboxes1 (Tensor): shape (B, m, 4) in format or empty. + bboxes2 (Tensor): shape (B, n, 4) in format or empty. + B indicates the batch dim, in shape (B1, B2, ..., Bn). + If ``is_aligned `` is ``True``, then m and n must be equal. + mode (str): "iou" (intersection over union), "iof" (intersection over + foreground) or "giou" (generalized intersection over union). + Default "iou". + is_aligned (bool, optional): If True, then m and n must be equal. + Default False. + eps (float, optional): A value added to the denominator for numerical + stability. Default 1e-6. + + Returns: + Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,) + + Example: + >>> bboxes1 = torch.FloatTensor([ + >>> [0, 0, 10, 10], + >>> [10, 10, 20, 20], + >>> [32, 32, 38, 42], + >>> ]) + >>> bboxes2 = torch.FloatTensor([ + >>> [0, 0, 10, 20], + >>> [0, 10, 10, 19], + >>> [10, 10, 20, 20], + >>> ]) + >>> overlaps = bbox_overlaps(bboxes1, bboxes2) + >>> assert overlaps.shape == (3, 3) + >>> overlaps = bbox_overlaps(bboxes1, bboxes2, is_aligned=True) + >>> assert overlaps.shape == (3, ) + + Example: + >>> empty = torch.empty(0, 4) + >>> nonempty = torch.FloatTensor([[0, 0, 10, 9]]) + >>> assert tuple(bbox_overlaps(empty, nonempty).shape) == (0, 1) + >>> assert tuple(bbox_overlaps(nonempty, empty).shape) == (1, 0) + >>> assert tuple(bbox_overlaps(empty, empty).shape) == (0, 0) + """ + + assert mode in ['iou', 'iof', 'giou'], f'Unsupported mode {mode}' + # Either the boxes are empty or the length of boxes' last dimension is 4 + assert (bboxes1.size(-1) == 4 or bboxes1.size(0) == 0) + assert (bboxes2.size(-1) == 4 or bboxes2.size(0) == 0) + + # Batch dim must be the same + # Batch dim: (B1, B2, ... Bn) + assert bboxes1.shape[:-2] == bboxes2.shape[:-2] + batch_shape = bboxes1.shape[:-2] + + rows = bboxes1.size(-2) + cols = bboxes2.size(-2) + if is_aligned: + assert rows == cols + + if rows * cols == 0: + if is_aligned: + return bboxes1.new(batch_shape + (rows, )) + else: + return bboxes1.new(batch_shape + (rows, cols)) + + area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * ( + bboxes1[..., 3] - bboxes1[..., 1]) + area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * ( + bboxes2[..., 3] - bboxes2[..., 1]) + + if is_aligned: + lt = torch.max(bboxes1[..., :2], bboxes2[..., :2]) # [B, rows, 2] + rb = torch.min(bboxes1[..., 2:], bboxes2[..., 2:]) # [B, rows, 2] + + wh = fp16_clamp(rb - lt, min=0) + overlap = wh[..., 0] * wh[..., 1] + + if mode in ['iou', 'giou']: + union = area1 + area2 - overlap + else: + union = area1 + if mode == 'giou': + enclosed_lt = torch.min(bboxes1[..., :2], bboxes2[..., :2]) + enclosed_rb = torch.max(bboxes1[..., 2:], bboxes2[..., 2:]) + else: + lt = torch.max(bboxes1[..., :, None, :2], + bboxes2[..., None, :, :2]) # [B, rows, cols, 2] + rb = torch.min(bboxes1[..., :, None, 2:], + bboxes2[..., None, :, 2:]) # [B, rows, cols, 2] + + wh = fp16_clamp(rb - lt, min=0) + overlap = wh[..., 0] * wh[..., 1] + + if mode in ['iou', 'giou']: + union = area1[..., None] + area2[..., None, :] - overlap + else: + union = area1[..., None] + if mode == 'giou': + enclosed_lt = torch.min(bboxes1[..., :, None, :2], + bboxes2[..., None, :, :2]) + enclosed_rb = torch.max(bboxes1[..., :, None, 2:], + bboxes2[..., None, :, 2:]) + + eps = union.new_tensor([eps]) + union = torch.max(union, eps) + ious = overlap / union + if mode in ['iou', 'iof']: + return ious + # calculate gious + enclose_wh = fp16_clamp(enclosed_rb - enclosed_lt, min=0) + enclose_area = enclose_wh[..., 0] * enclose_wh[..., 1] + enclose_area = torch.max(enclose_area, eps) + gious = ious - (enclose_area - union) / enclose_area + return gious diff --git a/mmdet/core/bbox/match_costs/__init__.py b/mmdet/core/bbox/match_costs/__init__.py new file mode 100644 index 0000000..add5e0d --- /dev/null +++ b/mmdet/core/bbox/match_costs/__init__.py @@ -0,0 +1,7 @@ +from .builder import build_match_cost +from .match_cost import BBoxL1Cost, ClassificationCost, FocalLossCost, IoUCost + +__all__ = [ + 'build_match_cost', 'ClassificationCost', 'BBoxL1Cost', 'IoUCost', + 'FocalLossCost' +] diff --git a/mmdet/core/bbox/match_costs/__pycache__/__init__.cpython-37.pyc b/mmdet/core/bbox/match_costs/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..cc4ce87 Binary files /dev/null and b/mmdet/core/bbox/match_costs/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/core/bbox/match_costs/__pycache__/builder.cpython-37.pyc b/mmdet/core/bbox/match_costs/__pycache__/builder.cpython-37.pyc new file mode 100644 index 0000000..01e7397 Binary files /dev/null and b/mmdet/core/bbox/match_costs/__pycache__/builder.cpython-37.pyc differ diff --git a/mmdet/core/bbox/match_costs/__pycache__/match_cost.cpython-37.pyc b/mmdet/core/bbox/match_costs/__pycache__/match_cost.cpython-37.pyc new file mode 100644 index 0000000..b49cb6a Binary files /dev/null and b/mmdet/core/bbox/match_costs/__pycache__/match_cost.cpython-37.pyc differ diff --git a/mmdet/core/bbox/match_costs/builder.py b/mmdet/core/bbox/match_costs/builder.py new file mode 100644 index 0000000..6894017 --- /dev/null +++ b/mmdet/core/bbox/match_costs/builder.py @@ -0,0 +1,8 @@ +from mmcv.utils import Registry, build_from_cfg + +MATCH_COST = Registry('Match Cost') + + +def build_match_cost(cfg, default_args=None): + """Builder of IoU calculator.""" + return build_from_cfg(cfg, MATCH_COST, default_args) diff --git a/mmdet/core/bbox/match_costs/match_cost.py b/mmdet/core/bbox/match_costs/match_cost.py new file mode 100644 index 0000000..3886973 --- /dev/null +++ b/mmdet/core/bbox/match_costs/match_cost.py @@ -0,0 +1,184 @@ +import torch + +from mmdet.core.bbox.iou_calculators import bbox_overlaps +from mmdet.core.bbox.transforms import bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh +from .builder import MATCH_COST + + +@MATCH_COST.register_module() +class BBoxL1Cost(object): + """BBoxL1Cost. + + Args: + weight (int | float, optional): loss_weight + box_format (str, optional): 'xyxy' for DETR, 'xywh' for Sparse_RCNN + + Examples: + >>> from mmdet.core.bbox.match_costs.match_cost import BBoxL1Cost + >>> import torch + >>> self = BBoxL1Cost() + >>> bbox_pred = torch.rand(1, 4) + >>> gt_bboxes= torch.FloatTensor([[0, 0, 2, 4], [1, 2, 3, 4]]) + >>> factor = torch.tensor([10, 8, 10, 8]) + >>> self(bbox_pred, gt_bboxes, factor) + tensor([[1.6172, 1.6422]]) + """ + + def __init__(self, weight=1., box_format='xyxy'): + self.weight = weight + assert box_format in ['xyxy', 'xywh'] + self.box_format = box_format + + def __call__(self, bbox_pred, gt_bboxes): + """ + Args: + bbox_pred (Tensor): Predicted boxes with normalized coordinates + (cx, cy, w, h), which are all in range [0, 1]. Shape + [num_query, 4]. + gt_bboxes (Tensor): Ground truth boxes with normalized + coordinates (x1, y1, x2, y2). Shape [num_gt, 4]. + + Returns: + torch.Tensor: bbox_cost value with weight + """ + if self.box_format == 'xywh': + gt_bboxes = bbox_xyxy_to_cxcywh(gt_bboxes) + elif self.box_format == 'xyxy': + bbox_pred = bbox_cxcywh_to_xyxy(bbox_pred) + bbox_cost = torch.cdist(bbox_pred, gt_bboxes, p=1) + return bbox_cost * self.weight + + +@MATCH_COST.register_module() +class FocalLossCost(object): + """FocalLossCost. + + Args: + weight (int | float, optional): loss_weight + alpha (int | float, optional): focal_loss alpha + gamma (int | float, optional): focal_loss gamma + eps (float, optional): default 1e-12 + + Examples: + >>> from mmdet.core.bbox.match_costs.match_cost import FocalLossCost + >>> import torch + >>> self = FocalLossCost() + >>> cls_pred = torch.rand(4, 3) + >>> gt_labels = torch.tensor([0, 1, 2]) + >>> factor = torch.tensor([10, 8, 10, 8]) + >>> self(cls_pred, gt_labels) + tensor([[-0.3236, -0.3364, -0.2699], + [-0.3439, -0.3209, -0.4807], + [-0.4099, -0.3795, -0.2929], + [-0.1950, -0.1207, -0.2626]]) + """ + + def __init__(self, weight=1., alpha=0.25, gamma=2, eps=1e-12): + self.weight = weight + self.alpha = alpha + self.gamma = gamma + self.eps = eps + + def __call__(self, cls_pred, gt_labels): + """ + Args: + cls_pred (Tensor): Predicted classification logits, shape + [num_query, num_class]. + gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,). + + Returns: + torch.Tensor: cls_cost value with weight + """ + cls_pred = cls_pred.sigmoid() + neg_cost = -(1 - cls_pred + self.eps).log() * ( + 1 - self.alpha) * cls_pred.pow(self.gamma) + pos_cost = -(cls_pred + self.eps).log() * self.alpha * ( + 1 - cls_pred).pow(self.gamma) + cls_cost = pos_cost[:, gt_labels] - neg_cost[:, gt_labels] + return cls_cost * self.weight + + +@MATCH_COST.register_module() +class ClassificationCost(object): + """ClsSoftmaxCost. + + Args: + weight (int | float, optional): loss_weight + + Examples: + >>> from mmdet.core.bbox.match_costs.match_cost import \ + ... ClassificationCost + >>> import torch + >>> self = ClassificationCost() + >>> cls_pred = torch.rand(4, 3) + >>> gt_labels = torch.tensor([0, 1, 2]) + >>> factor = torch.tensor([10, 8, 10, 8]) + >>> self(cls_pred, gt_labels) + tensor([[-0.3430, -0.3525, -0.3045], + [-0.3077, -0.2931, -0.3992], + [-0.3664, -0.3455, -0.2881], + [-0.3343, -0.2701, -0.3956]]) + """ + + def __init__(self, weight=1.): + self.weight = weight + + def __call__(self, cls_pred, gt_labels): + """ + Args: + cls_pred (Tensor): Predicted classification logits, shape + [num_query, num_class]. + gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,). + + Returns: + torch.Tensor: cls_cost value with weight + """ + # Following the official DETR repo, contrary to the loss that + # NLL is used, we approximate it in 1 - cls_score[gt_label]. + # The 1 is a constant that doesn't change the matching, + # so it can be omitted. + cls_score = cls_pred.softmax(-1) + cls_cost = -cls_score[:, gt_labels] + return cls_cost * self.weight + + +@MATCH_COST.register_module() +class IoUCost(object): + """IoUCost. + + Args: + iou_mode (str, optional): iou mode such as 'iou' | 'giou' + weight (int | float, optional): loss weight + + Examples: + >>> from mmdet.core.bbox.match_costs.match_cost import IoUCost + >>> import torch + >>> self = IoUCost() + >>> bboxes = torch.FloatTensor([[1,1, 2, 2], [2, 2, 3, 4]]) + >>> gt_bboxes = torch.FloatTensor([[0, 0, 2, 4], [1, 2, 3, 4]]) + >>> self(bboxes, gt_bboxes) + tensor([[-0.1250, 0.1667], + [ 0.1667, -0.5000]]) + """ + + def __init__(self, iou_mode='giou', weight=1.): + self.weight = weight + self.iou_mode = iou_mode + + def __call__(self, bboxes, gt_bboxes): + """ + Args: + bboxes (Tensor): Predicted boxes with unnormalized coordinates + (x1, y1, x2, y2). Shape [num_query, 4]. + gt_bboxes (Tensor): Ground truth boxes with unnormalized + coordinates (x1, y1, x2, y2). Shape [num_gt, 4]. + + Returns: + torch.Tensor: iou_cost value with weight + """ + # overlaps: [num_bboxes, num_gt] + overlaps = bbox_overlaps( + bboxes, gt_bboxes, mode=self.iou_mode, is_aligned=False) + # The 1 is a constant that doesn't change the matching, so omitted. + iou_cost = -overlaps + return iou_cost * self.weight diff --git a/mmdet/core/bbox/samplers/__init__.py b/mmdet/core/bbox/samplers/__init__.py new file mode 100644 index 0000000..0b06303 --- /dev/null +++ b/mmdet/core/bbox/samplers/__init__.py @@ -0,0 +1,15 @@ +from .base_sampler import BaseSampler +from .combined_sampler import CombinedSampler +from .instance_balanced_pos_sampler import InstanceBalancedPosSampler +from .iou_balanced_neg_sampler import IoUBalancedNegSampler +from .ohem_sampler import OHEMSampler +from .pseudo_sampler import PseudoSampler +from .random_sampler import RandomSampler +from .sampling_result import SamplingResult +from .score_hlr_sampler import ScoreHLRSampler + +__all__ = [ + 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a/mmdet/core/bbox/samplers/__pycache__/score_hlr_sampler.cpython-37.pyc b/mmdet/core/bbox/samplers/__pycache__/score_hlr_sampler.cpython-37.pyc new file mode 100644 index 0000000..373a1ae Binary files /dev/null and b/mmdet/core/bbox/samplers/__pycache__/score_hlr_sampler.cpython-37.pyc differ diff --git a/mmdet/core/bbox/samplers/base_sampler.py b/mmdet/core/bbox/samplers/base_sampler.py new file mode 100644 index 0000000..9ea35de --- /dev/null +++ b/mmdet/core/bbox/samplers/base_sampler.py @@ -0,0 +1,101 @@ +from abc import ABCMeta, abstractmethod + +import torch + +from .sampling_result import SamplingResult + + +class BaseSampler(metaclass=ABCMeta): + """Base class of samplers.""" + + def __init__(self, + num, + pos_fraction, + neg_pos_ub=-1, + add_gt_as_proposals=True, + **kwargs): + self.num = num + self.pos_fraction = pos_fraction + self.neg_pos_ub = neg_pos_ub + self.add_gt_as_proposals = add_gt_as_proposals + self.pos_sampler = self + self.neg_sampler = self + + @abstractmethod + def _sample_pos(self, assign_result, num_expected, **kwargs): + """Sample positive samples.""" + pass + + @abstractmethod + def _sample_neg(self, assign_result, num_expected, **kwargs): + """Sample negative samples.""" + pass + + def sample(self, + assign_result, + bboxes, + gt_bboxes, + gt_labels=None, + **kwargs): + """Sample positive and negative bboxes. + + This is a simple implementation of bbox sampling given candidates, + assigning results and ground truth bboxes. + + Args: + assign_result (:obj:`AssignResult`): Bbox assigning results. + bboxes (Tensor): Boxes to be sampled from. + gt_bboxes (Tensor): Ground truth bboxes. + gt_labels (Tensor, optional): Class labels of ground truth bboxes. + + Returns: + :obj:`SamplingResult`: Sampling result. + + Example: + >>> from mmdet.core.bbox import RandomSampler + >>> from mmdet.core.bbox import AssignResult + >>> from mmdet.core.bbox.demodata import ensure_rng, random_boxes + >>> rng = ensure_rng(None) + >>> assign_result = AssignResult.random(rng=rng) + >>> bboxes = random_boxes(assign_result.num_preds, rng=rng) + >>> gt_bboxes = random_boxes(assign_result.num_gts, rng=rng) + >>> gt_labels = None + >>> self = RandomSampler(num=32, pos_fraction=0.5, neg_pos_ub=-1, + >>> add_gt_as_proposals=False) + >>> self = self.sample(assign_result, bboxes, gt_bboxes, gt_labels) + """ + if len(bboxes.shape) < 2: + bboxes = bboxes[None, :] + + bboxes = bboxes[:, :4] + + gt_flags = bboxes.new_zeros((bboxes.shape[0], ), dtype=torch.uint8) + if self.add_gt_as_proposals and len(gt_bboxes) > 0: + if gt_labels is None: + raise ValueError( + 'gt_labels must be given when add_gt_as_proposals is True') + bboxes = torch.cat([gt_bboxes, bboxes], dim=0) + assign_result.add_gt_(gt_labels) + gt_ones = bboxes.new_ones(gt_bboxes.shape[0], dtype=torch.uint8) + gt_flags = torch.cat([gt_ones, gt_flags]) + + num_expected_pos = int(self.num * self.pos_fraction) + pos_inds = self.pos_sampler._sample_pos( + assign_result, num_expected_pos, bboxes=bboxes, **kwargs) + # We found that sampled indices have duplicated items occasionally. + # (may be a bug of PyTorch) + pos_inds = pos_inds.unique() + num_sampled_pos = pos_inds.numel() + num_expected_neg = self.num - num_sampled_pos + if self.neg_pos_ub >= 0: + _pos = max(1, num_sampled_pos) + neg_upper_bound = int(self.neg_pos_ub * _pos) + if num_expected_neg > neg_upper_bound: + num_expected_neg = neg_upper_bound + neg_inds = self.neg_sampler._sample_neg( + assign_result, num_expected_neg, bboxes=bboxes, **kwargs) + neg_inds = neg_inds.unique() + + sampling_result = SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes, + assign_result, gt_flags) + return sampling_result diff --git a/mmdet/core/bbox/samplers/combined_sampler.py b/mmdet/core/bbox/samplers/combined_sampler.py new file mode 100644 index 0000000..564729f --- /dev/null +++ b/mmdet/core/bbox/samplers/combined_sampler.py @@ -0,0 +1,20 @@ +from ..builder import BBOX_SAMPLERS, build_sampler +from .base_sampler import BaseSampler + + +@BBOX_SAMPLERS.register_module() +class CombinedSampler(BaseSampler): + """A sampler that combines positive sampler and negative sampler.""" + + def __init__(self, pos_sampler, neg_sampler, **kwargs): + super(CombinedSampler, self).__init__(**kwargs) + self.pos_sampler = build_sampler(pos_sampler, **kwargs) + self.neg_sampler = build_sampler(neg_sampler, **kwargs) + + def _sample_pos(self, **kwargs): + """Sample positive samples.""" + raise NotImplementedError + + def _sample_neg(self, **kwargs): + """Sample negative samples.""" + raise NotImplementedError diff --git a/mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py b/mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py new file mode 100644 index 0000000..c735298 --- /dev/null +++ b/mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py @@ -0,0 +1,55 @@ +import numpy as np +import torch + +from ..builder import BBOX_SAMPLERS +from .random_sampler import RandomSampler + + +@BBOX_SAMPLERS.register_module() +class InstanceBalancedPosSampler(RandomSampler): + """Instance balanced sampler that samples equal number of positive samples + for each instance.""" + + def _sample_pos(self, assign_result, num_expected, **kwargs): + """Sample positive boxes. + + Args: + assign_result (:obj:`AssignResult`): The assigned results of boxes. + num_expected (int): The number of expected positive samples + + Returns: + Tensor or ndarray: sampled indices. + """ + pos_inds = torch.nonzero(assign_result.gt_inds > 0, as_tuple=False) + if pos_inds.numel() != 0: + pos_inds = pos_inds.squeeze(1) + if pos_inds.numel() <= num_expected: + return pos_inds + else: + unique_gt_inds = assign_result.gt_inds[pos_inds].unique() + num_gts = len(unique_gt_inds) + num_per_gt = int(round(num_expected / float(num_gts)) + 1) + sampled_inds = [] + for i in unique_gt_inds: + inds = torch.nonzero( + assign_result.gt_inds == i.item(), as_tuple=False) + if inds.numel() != 0: + inds = inds.squeeze(1) + else: + continue + if len(inds) > num_per_gt: + inds = self.random_choice(inds, num_per_gt) + sampled_inds.append(inds) + sampled_inds = torch.cat(sampled_inds) + if len(sampled_inds) < num_expected: + num_extra = num_expected - len(sampled_inds) + extra_inds = np.array( + list(set(pos_inds.cpu()) - set(sampled_inds.cpu()))) + if len(extra_inds) > num_extra: + extra_inds = self.random_choice(extra_inds, num_extra) + extra_inds = torch.from_numpy(extra_inds).to( + assign_result.gt_inds.device).long() + sampled_inds = torch.cat([sampled_inds, extra_inds]) + elif len(sampled_inds) > num_expected: + sampled_inds = self.random_choice(sampled_inds, num_expected) + return sampled_inds diff --git a/mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py b/mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py new file mode 100644 index 0000000..f275e43 --- /dev/null +++ b/mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py @@ -0,0 +1,157 @@ +import numpy as np +import torch + +from ..builder import BBOX_SAMPLERS +from .random_sampler import RandomSampler + + +@BBOX_SAMPLERS.register_module() +class IoUBalancedNegSampler(RandomSampler): + """IoU Balanced Sampling. + + arXiv: https://arxiv.org/pdf/1904.02701.pdf (CVPR 2019) + + Sampling proposals according to their IoU. `floor_fraction` of needed RoIs + are sampled from proposals whose IoU are lower than `floor_thr` randomly. + The others are sampled from proposals whose IoU are higher than + `floor_thr`. These proposals are sampled from some bins evenly, which are + split by `num_bins` via IoU evenly. + + Args: + num (int): number of proposals. + pos_fraction (float): fraction of positive proposals. + floor_thr (float): threshold (minimum) IoU for IoU balanced sampling, + set to -1 if all using IoU balanced sampling. + floor_fraction (float): sampling fraction of proposals under floor_thr. + num_bins (int): number of bins in IoU balanced sampling. + """ + + def __init__(self, + num, + pos_fraction, + floor_thr=-1, + floor_fraction=0, + num_bins=3, + **kwargs): + super(IoUBalancedNegSampler, self).__init__(num, pos_fraction, + **kwargs) + assert floor_thr >= 0 or floor_thr == -1 + assert 0 <= floor_fraction <= 1 + assert num_bins >= 1 + + self.floor_thr = floor_thr + self.floor_fraction = floor_fraction + self.num_bins = num_bins + + def sample_via_interval(self, max_overlaps, full_set, num_expected): + """Sample according to the iou interval. + + Args: + max_overlaps (torch.Tensor): IoU between bounding boxes and ground + truth boxes. + full_set (set(int)): A full set of indices of boxes。 + num_expected (int): Number of expected samples。 + + Returns: + np.ndarray: Indices of samples + """ + max_iou = max_overlaps.max() + iou_interval = (max_iou - self.floor_thr) / self.num_bins + per_num_expected = int(num_expected / self.num_bins) + + sampled_inds = [] + for i in range(self.num_bins): + start_iou = self.floor_thr + i * iou_interval + end_iou = self.floor_thr + (i + 1) * iou_interval + tmp_set = set( + np.where( + np.logical_and(max_overlaps >= start_iou, + max_overlaps < end_iou))[0]) + tmp_inds = list(tmp_set & full_set) + if len(tmp_inds) > per_num_expected: + tmp_sampled_set = self.random_choice(tmp_inds, + per_num_expected) + else: + tmp_sampled_set = np.array(tmp_inds, dtype=np.int) + sampled_inds.append(tmp_sampled_set) + + sampled_inds = np.concatenate(sampled_inds) + if len(sampled_inds) < num_expected: + num_extra = num_expected - len(sampled_inds) + extra_inds = np.array(list(full_set - set(sampled_inds))) + if len(extra_inds) > num_extra: + extra_inds = self.random_choice(extra_inds, num_extra) + sampled_inds = np.concatenate([sampled_inds, extra_inds]) + + return sampled_inds + + def _sample_neg(self, assign_result, num_expected, **kwargs): + """Sample negative boxes. + + Args: + assign_result (:obj:`AssignResult`): The assigned results of boxes. + num_expected (int): The number of expected negative samples + + Returns: + Tensor or ndarray: sampled indices. + """ + neg_inds = torch.nonzero(assign_result.gt_inds == 0, as_tuple=False) + if neg_inds.numel() != 0: + neg_inds = neg_inds.squeeze(1) + if len(neg_inds) <= num_expected: + return neg_inds + else: + max_overlaps = assign_result.max_overlaps.cpu().numpy() + # balance sampling for negative samples + neg_set = set(neg_inds.cpu().numpy()) + + if self.floor_thr > 0: + floor_set = set( + np.where( + np.logical_and(max_overlaps >= 0, + max_overlaps < self.floor_thr))[0]) + iou_sampling_set = set( + np.where(max_overlaps >= self.floor_thr)[0]) + elif self.floor_thr == 0: + floor_set = set(np.where(max_overlaps == 0)[0]) + iou_sampling_set = set( + np.where(max_overlaps > self.floor_thr)[0]) + else: + floor_set = set() + iou_sampling_set = set( + np.where(max_overlaps > self.floor_thr)[0]) + # for sampling interval calculation + self.floor_thr = 0 + + floor_neg_inds = list(floor_set & neg_set) + iou_sampling_neg_inds = list(iou_sampling_set & neg_set) + num_expected_iou_sampling = int(num_expected * + (1 - self.floor_fraction)) + if len(iou_sampling_neg_inds) > num_expected_iou_sampling: + if self.num_bins >= 2: + iou_sampled_inds = self.sample_via_interval( + max_overlaps, set(iou_sampling_neg_inds), + num_expected_iou_sampling) + else: + iou_sampled_inds = self.random_choice( + iou_sampling_neg_inds, num_expected_iou_sampling) + else: + iou_sampled_inds = np.array( + iou_sampling_neg_inds, dtype=np.int) + num_expected_floor = num_expected - len(iou_sampled_inds) + if len(floor_neg_inds) > num_expected_floor: + sampled_floor_inds = self.random_choice( + floor_neg_inds, num_expected_floor) + else: + sampled_floor_inds = np.array(floor_neg_inds, dtype=np.int) + sampled_inds = np.concatenate( + (sampled_floor_inds, iou_sampled_inds)) + if len(sampled_inds) < num_expected: + num_extra = num_expected - len(sampled_inds) + extra_inds = np.array(list(neg_set - set(sampled_inds))) + if len(extra_inds) > num_extra: + extra_inds = self.random_choice(extra_inds, num_extra) + sampled_inds = np.concatenate((sampled_inds, extra_inds)) + sampled_inds = torch.from_numpy(sampled_inds).long().to( + assign_result.gt_inds.device) + return sampled_inds diff --git a/mmdet/core/bbox/samplers/ohem_sampler.py b/mmdet/core/bbox/samplers/ohem_sampler.py new file mode 100644 index 0000000..8b99f60 --- /dev/null +++ b/mmdet/core/bbox/samplers/ohem_sampler.py @@ -0,0 +1,107 @@ +import torch + +from ..builder import BBOX_SAMPLERS +from ..transforms import bbox2roi +from .base_sampler import BaseSampler + + +@BBOX_SAMPLERS.register_module() +class OHEMSampler(BaseSampler): + r"""Online Hard Example Mining Sampler described in `Training Region-based + Object Detectors with Online Hard Example Mining + `_. + """ + + def __init__(self, + num, + pos_fraction, + context, + neg_pos_ub=-1, + add_gt_as_proposals=True, + **kwargs): + super(OHEMSampler, self).__init__(num, pos_fraction, neg_pos_ub, + add_gt_as_proposals) + self.context = context + if not hasattr(self.context, 'num_stages'): + self.bbox_head = self.context.bbox_head + else: + self.bbox_head = self.context.bbox_head[self.context.current_stage] + + def hard_mining(self, inds, num_expected, bboxes, labels, feats): + with torch.no_grad(): + rois = bbox2roi([bboxes]) + if not hasattr(self.context, 'num_stages'): + bbox_results = self.context._bbox_forward(feats, rois) + else: + bbox_results = self.context._bbox_forward( + self.context.current_stage, feats, rois) + cls_score = bbox_results['cls_score'] + loss = self.bbox_head.loss( + cls_score=cls_score, + bbox_pred=None, + rois=rois, + labels=labels, + label_weights=cls_score.new_ones(cls_score.size(0)), + bbox_targets=None, + bbox_weights=None, + reduction_override='none')['loss_cls'] + _, topk_loss_inds = loss.topk(num_expected) + return inds[topk_loss_inds] + + def _sample_pos(self, + assign_result, + num_expected, + bboxes=None, + feats=None, + **kwargs): + """Sample positive boxes. + + Args: + assign_result (:obj:`AssignResult`): Assigned results + num_expected (int): Number of expected positive samples + bboxes (torch.Tensor, optional): Boxes. Defaults to None. + feats (list[torch.Tensor], optional): Multi-level features. + Defaults to None. + + Returns: + torch.Tensor: Indices of positive samples + """ + # Sample some hard positive samples + pos_inds = torch.nonzero(assign_result.gt_inds > 0, as_tuple=False) + if pos_inds.numel() != 0: + pos_inds = pos_inds.squeeze(1) + if pos_inds.numel() <= num_expected: + return pos_inds + else: + return self.hard_mining(pos_inds, num_expected, bboxes[pos_inds], + assign_result.labels[pos_inds], feats) + + def _sample_neg(self, + assign_result, + num_expected, + bboxes=None, + feats=None, + **kwargs): + """Sample negative boxes. + + Args: + assign_result (:obj:`AssignResult`): Assigned results + num_expected (int): Number of expected negative samples + bboxes (torch.Tensor, optional): Boxes. Defaults to None. + feats (list[torch.Tensor], optional): Multi-level features. + Defaults to None. + + Returns: + torch.Tensor: Indices of negative samples + """ + # Sample some hard negative samples + neg_inds = torch.nonzero(assign_result.gt_inds == 0, as_tuple=False) + if neg_inds.numel() != 0: + neg_inds = neg_inds.squeeze(1) + if len(neg_inds) <= num_expected: + return neg_inds + else: + neg_labels = assign_result.labels.new_empty( + neg_inds.size(0)).fill_(self.bbox_head.num_classes) + return self.hard_mining(neg_inds, num_expected, bboxes[neg_inds], + neg_labels, feats) diff --git a/mmdet/core/bbox/samplers/pseudo_sampler.py b/mmdet/core/bbox/samplers/pseudo_sampler.py new file mode 100644 index 0000000..2bd81ab --- /dev/null +++ b/mmdet/core/bbox/samplers/pseudo_sampler.py @@ -0,0 +1,41 @@ +import torch + +from ..builder import BBOX_SAMPLERS +from .base_sampler import BaseSampler +from .sampling_result import SamplingResult + + +@BBOX_SAMPLERS.register_module() +class PseudoSampler(BaseSampler): + """A pseudo sampler that does not do sampling actually.""" + + def __init__(self, **kwargs): + pass + + def _sample_pos(self, **kwargs): + """Sample positive samples.""" + raise NotImplementedError + + def _sample_neg(self, **kwargs): + """Sample negative samples.""" + raise NotImplementedError + + def sample(self, assign_result, bboxes, gt_bboxes, **kwargs): + """Directly returns the positive and negative indices of samples. + + Args: + assign_result (:obj:`AssignResult`): Assigned results + bboxes (torch.Tensor): Bounding boxes + gt_bboxes (torch.Tensor): Ground truth boxes + + Returns: + :obj:`SamplingResult`: sampler results + """ + pos_inds = torch.nonzero( + assign_result.gt_inds > 0, as_tuple=False).squeeze(-1).unique() + neg_inds = torch.nonzero( + assign_result.gt_inds == 0, as_tuple=False).squeeze(-1).unique() + gt_flags = bboxes.new_zeros(bboxes.shape[0], dtype=torch.uint8) + sampling_result = SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes, + assign_result, gt_flags) + return sampling_result diff --git a/mmdet/core/bbox/samplers/random_sampler.py b/mmdet/core/bbox/samplers/random_sampler.py new file mode 100644 index 0000000..c23a7a1 --- /dev/null +++ b/mmdet/core/bbox/samplers/random_sampler.py @@ -0,0 +1,81 @@ +import torch + +from ..builder import BBOX_SAMPLERS +from .base_sampler import BaseSampler + + +@BBOX_SAMPLERS.register_module() +class RandomSampler(BaseSampler): + """Random sampler. + + Args: + num (int): Number of samples + pos_fraction (float): Fraction of positive samples + neg_pos_up (int, optional): Upper bound number of negative and + positive samples. Defaults to -1. + add_gt_as_proposals (bool, optional): Whether to add ground truth + boxes as proposals. Defaults to True. + """ + + def __init__(self, + num, + pos_fraction, + neg_pos_ub=-1, + add_gt_as_proposals=True, + **kwargs): + from mmdet.core.bbox import demodata + super(RandomSampler, self).__init__(num, pos_fraction, neg_pos_ub, + add_gt_as_proposals) + self.rng = demodata.ensure_rng(kwargs.get('rng', None)) + + def random_choice(self, gallery, num): + """Random select some elements from the gallery. + + If `gallery` is a Tensor, the returned indices will be a Tensor; + If `gallery` is a ndarray or list, the returned indices will be a + ndarray. + + Args: + gallery (Tensor | ndarray | list): indices pool. + num (int): expected sample num. + + Returns: + Tensor or ndarray: sampled indices. + """ + assert len(gallery) >= num + + is_tensor = isinstance(gallery, torch.Tensor) + if not is_tensor: + if torch.cuda.is_available(): + device = torch.cuda.current_device() + else: + device = 'cpu' + gallery = torch.tensor(gallery, dtype=torch.long, device=device) + # This is a temporary fix. We can revert the following code + # when PyTorch fixes the abnormal return of torch.randperm. + # See: https://github.com/open-mmlab/mmdetection/pull/5014 + perm = torch.randperm(gallery.numel())[:num].to(device=gallery.device) + rand_inds = gallery[perm] + if not is_tensor: + rand_inds = rand_inds.cpu().numpy() + return rand_inds + + def _sample_pos(self, assign_result, num_expected, **kwargs): + """Randomly sample some positive samples.""" + pos_inds = torch.nonzero(assign_result.gt_inds > 0, as_tuple=False) + if pos_inds.numel() != 0: + pos_inds = pos_inds.squeeze(1) + if pos_inds.numel() <= num_expected: + return pos_inds + else: + return self.random_choice(pos_inds, num_expected) + + def _sample_neg(self, assign_result, num_expected, **kwargs): + """Randomly sample some negative samples.""" + neg_inds = torch.nonzero(assign_result.gt_inds == 0, as_tuple=False) + if neg_inds.numel() != 0: + neg_inds = neg_inds.squeeze(1) + if len(neg_inds) <= num_expected: + return neg_inds + else: + return self.random_choice(neg_inds, num_expected) diff --git a/mmdet/core/bbox/samplers/sampling_result.py b/mmdet/core/bbox/samplers/sampling_result.py new file mode 100644 index 0000000..419a8e3 --- /dev/null +++ b/mmdet/core/bbox/samplers/sampling_result.py @@ -0,0 +1,152 @@ +import torch + +from mmdet.utils import util_mixins + + +class SamplingResult(util_mixins.NiceRepr): + """Bbox sampling result. + + Example: + >>> # xdoctest: +IGNORE_WANT + >>> from mmdet.core.bbox.samplers.sampling_result import * # NOQA + >>> self = SamplingResult.random(rng=10) + >>> print(f'self = {self}') + self = + """ + + def __init__(self, pos_inds, neg_inds, bboxes, gt_bboxes, assign_result, + gt_flags): + self.pos_inds = pos_inds + self.neg_inds = neg_inds + self.pos_bboxes = bboxes[pos_inds] + self.neg_bboxes = bboxes[neg_inds] + self.pos_is_gt = gt_flags[pos_inds] + + self.num_gts = gt_bboxes.shape[0] + self.pos_assigned_gt_inds = assign_result.gt_inds[pos_inds] - 1 + + if gt_bboxes.numel() == 0: + # hack for index error case + assert self.pos_assigned_gt_inds.numel() == 0 + self.pos_gt_bboxes = torch.empty_like(gt_bboxes).view(-1, 4) + else: + if len(gt_bboxes.shape) < 2: + gt_bboxes = gt_bboxes.view(-1, 4) + + self.pos_gt_bboxes = gt_bboxes[self.pos_assigned_gt_inds, :] + + if assign_result.labels is not None: + self.pos_gt_labels = assign_result.labels[pos_inds] + else: + self.pos_gt_labels = None + + @property + def bboxes(self): + """torch.Tensor: concatenated positive and negative boxes""" + return torch.cat([self.pos_bboxes, self.neg_bboxes]) + + def to(self, device): + """Change the device of the data inplace. + + Example: + >>> self = SamplingResult.random() + >>> print(f'self = {self.to(None)}') + >>> # xdoctest: +REQUIRES(--gpu) + >>> print(f'self = {self.to(0)}') + """ + _dict = self.__dict__ + for key, value in _dict.items(): + if isinstance(value, torch.Tensor): + _dict[key] = value.to(device) + return self + + def __nice__(self): + data = self.info.copy() + data['pos_bboxes'] = data.pop('pos_bboxes').shape + data['neg_bboxes'] = data.pop('neg_bboxes').shape + parts = [f"'{k}': {v!r}" for k, v in sorted(data.items())] + body = ' ' + ',\n '.join(parts) + return '{\n' + body + '\n}' + + @property + def info(self): + """Returns a dictionary of info about the object.""" + return { + 'pos_inds': self.pos_inds, + 'neg_inds': self.neg_inds, + 'pos_bboxes': self.pos_bboxes, + 'neg_bboxes': self.neg_bboxes, + 'pos_is_gt': self.pos_is_gt, + 'num_gts': self.num_gts, + 'pos_assigned_gt_inds': self.pos_assigned_gt_inds, + } + + @classmethod + def random(cls, rng=None, **kwargs): + """ + Args: + rng (None | int | numpy.random.RandomState): seed or state. + kwargs (keyword arguments): + - num_preds: number of predicted boxes + - num_gts: number of true boxes + - p_ignore (float): probability of a predicted box assinged to \ + an ignored truth. + - p_assigned (float): probability of a predicted box not being \ + assigned. + - p_use_label (float | bool): with labels or not. + + Returns: + :obj:`SamplingResult`: Randomly generated sampling result. + + Example: + >>> from mmdet.core.bbox.samplers.sampling_result import * # NOQA + >>> self = SamplingResult.random() + >>> print(self.__dict__) + """ + from mmdet.core.bbox.samplers.random_sampler import RandomSampler + from mmdet.core.bbox.assigners.assign_result import AssignResult + from mmdet.core.bbox import demodata + rng = demodata.ensure_rng(rng) + + # make probabalistic? + num = 32 + pos_fraction = 0.5 + neg_pos_ub = -1 + + assign_result = AssignResult.random(rng=rng, **kwargs) + + # Note we could just compute an assignment + bboxes = demodata.random_boxes(assign_result.num_preds, rng=rng) + gt_bboxes = demodata.random_boxes(assign_result.num_gts, rng=rng) + + if rng.rand() > 0.2: + # sometimes algorithms squeeze their data, be robust to that + gt_bboxes = gt_bboxes.squeeze() + bboxes = bboxes.squeeze() + + if assign_result.labels is None: + gt_labels = None + else: + gt_labels = None # todo + + if gt_labels is None: + add_gt_as_proposals = False + else: + add_gt_as_proposals = True # make probabalistic? + + sampler = RandomSampler( + num, + pos_fraction, + neg_pos_ub=neg_pos_ub, + add_gt_as_proposals=add_gt_as_proposals, + rng=rng) + self = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels) + return self diff --git a/mmdet/core/bbox/samplers/score_hlr_sampler.py b/mmdet/core/bbox/samplers/score_hlr_sampler.py new file mode 100644 index 0000000..11d46b9 --- /dev/null +++ b/mmdet/core/bbox/samplers/score_hlr_sampler.py @@ -0,0 +1,264 @@ +import torch +from mmcv.ops import nms_match + +from ..builder import BBOX_SAMPLERS +from ..transforms import bbox2roi +from .base_sampler import BaseSampler +from .sampling_result import SamplingResult + + +@BBOX_SAMPLERS.register_module() +class ScoreHLRSampler(BaseSampler): + r"""Importance-based Sample Reweighting (ISR_N), described in `Prime Sample + Attention in Object Detection `_. + + Score hierarchical local rank (HLR) differentiates with RandomSampler in + negative part. It firstly computes Score-HLR in a two-step way, + then linearly maps score hlr to the loss weights. + + Args: + num (int): Total number of sampled RoIs. + pos_fraction (float): Fraction of positive samples. + context (:class:`BaseRoIHead`): RoI head that the sampler belongs to. + neg_pos_ub (int): Upper bound of the ratio of num negative to num + positive, -1 means no upper bound. + add_gt_as_proposals (bool): Whether to add ground truth as proposals. + k (float): Power of the non-linear mapping. + bias (float): Shift of the non-linear mapping. + score_thr (float): Minimum score that a negative sample is to be + considered as valid bbox. + """ + + def __init__(self, + num, + pos_fraction, + context, + neg_pos_ub=-1, + add_gt_as_proposals=True, + k=0.5, + bias=0, + score_thr=0.05, + iou_thr=0.5, + **kwargs): + super().__init__(num, pos_fraction, neg_pos_ub, add_gt_as_proposals) + self.k = k + self.bias = bias + self.score_thr = score_thr + self.iou_thr = iou_thr + self.context = context + # context of cascade detectors is a list, so distinguish them here. + if not hasattr(context, 'num_stages'): + self.bbox_roi_extractor = context.bbox_roi_extractor + self.bbox_head = context.bbox_head + self.with_shared_head = context.with_shared_head + if self.with_shared_head: + self.shared_head = context.shared_head + else: + self.bbox_roi_extractor = context.bbox_roi_extractor[ + context.current_stage] + self.bbox_head = context.bbox_head[context.current_stage] + + @staticmethod + def random_choice(gallery, num): + """Randomly select some elements from the gallery. + + If `gallery` is a Tensor, the returned indices will be a Tensor; + If `gallery` is a ndarray or list, the returned indices will be a + ndarray. + + Args: + gallery (Tensor | ndarray | list): indices pool. + num (int): expected sample num. + + Returns: + Tensor or ndarray: sampled indices. + """ + assert len(gallery) >= num + + is_tensor = isinstance(gallery, torch.Tensor) + if not is_tensor: + if torch.cuda.is_available(): + device = torch.cuda.current_device() + else: + device = 'cpu' + gallery = torch.tensor(gallery, dtype=torch.long, device=device) + perm = torch.randperm(gallery.numel(), device=gallery.device)[:num] + rand_inds = gallery[perm] + if not is_tensor: + rand_inds = rand_inds.cpu().numpy() + return rand_inds + + def _sample_pos(self, assign_result, num_expected, **kwargs): + """Randomly sample some positive samples.""" + pos_inds = torch.nonzero(assign_result.gt_inds > 0).flatten() + if pos_inds.numel() <= num_expected: + return pos_inds + else: + return self.random_choice(pos_inds, num_expected) + + def _sample_neg(self, + assign_result, + num_expected, + bboxes, + feats=None, + img_meta=None, + **kwargs): + """Sample negative samples. + + Score-HLR sampler is done in the following steps: + 1. Take the maximum positive score prediction of each negative samples + as s_i. + 2. Filter out negative samples whose s_i <= score_thr, the left samples + are called valid samples. + 3. Use NMS-Match to divide valid samples into different groups, + samples in the same group will greatly overlap with each other + 4. Rank the matched samples in two-steps to get Score-HLR. + (1) In the same group, rank samples with their scores. + (2) In the same score rank across different groups, + rank samples with their scores again. + 5. Linearly map Score-HLR to the final label weights. + + Args: + assign_result (:obj:`AssignResult`): result of assigner. + num_expected (int): Expected number of samples. + bboxes (Tensor): bbox to be sampled. + feats (Tensor): Features come from FPN. + img_meta (dict): Meta information dictionary. + """ + neg_inds = torch.nonzero(assign_result.gt_inds == 0).flatten() + num_neg = neg_inds.size(0) + if num_neg == 0: + return neg_inds, None + with torch.no_grad(): + neg_bboxes = bboxes[neg_inds] + neg_rois = bbox2roi([neg_bboxes]) + bbox_result = self.context._bbox_forward(feats, neg_rois) + cls_score, bbox_pred = bbox_result['cls_score'], bbox_result[ + 'bbox_pred'] + + ori_loss = self.bbox_head.loss( + cls_score=cls_score, + bbox_pred=None, + rois=None, + labels=neg_inds.new_full((num_neg, ), + self.bbox_head.num_classes), + label_weights=cls_score.new_ones(num_neg), + bbox_targets=None, + bbox_weights=None, + reduction_override='none')['loss_cls'] + + # filter out samples with the max score lower than score_thr + max_score, argmax_score = cls_score.softmax(-1)[:, :-1].max(-1) + valid_inds = (max_score > self.score_thr).nonzero().view(-1) + invalid_inds = (max_score <= self.score_thr).nonzero().view(-1) + num_valid = valid_inds.size(0) + num_invalid = invalid_inds.size(0) + + num_expected = min(num_neg, num_expected) + num_hlr = min(num_valid, num_expected) + num_rand = num_expected - num_hlr + if num_valid > 0: + valid_rois = neg_rois[valid_inds] + valid_max_score = max_score[valid_inds] + valid_argmax_score = argmax_score[valid_inds] + valid_bbox_pred = bbox_pred[valid_inds] + + # valid_bbox_pred shape: [num_valid, #num_classes, 4] + valid_bbox_pred = valid_bbox_pred.view( + valid_bbox_pred.size(0), -1, 4) + selected_bbox_pred = valid_bbox_pred[range(num_valid), + valid_argmax_score] + pred_bboxes = self.bbox_head.bbox_coder.decode( + valid_rois[:, 1:], selected_bbox_pred) + pred_bboxes_with_score = torch.cat( + [pred_bboxes, valid_max_score[:, None]], -1) + group = nms_match(pred_bboxes_with_score, self.iou_thr) + + # imp: importance + imp = cls_score.new_zeros(num_valid) + for g in group: + g_score = valid_max_score[g] + # g_score has already sorted + rank = g_score.new_tensor(range(g_score.size(0))) + imp[g] = num_valid - rank + g_score + _, imp_rank_inds = imp.sort(descending=True) + _, imp_rank = imp_rank_inds.sort() + hlr_inds = imp_rank_inds[:num_expected] + + if num_rand > 0: + rand_inds = torch.randperm(num_invalid)[:num_rand] + select_inds = torch.cat( + [valid_inds[hlr_inds], invalid_inds[rand_inds]]) + else: + select_inds = valid_inds[hlr_inds] + + neg_label_weights = cls_score.new_ones(num_expected) + + up_bound = max(num_expected, num_valid) + imp_weights = (up_bound - + imp_rank[hlr_inds].float()) / up_bound + neg_label_weights[:num_hlr] = imp_weights + neg_label_weights[num_hlr:] = imp_weights.min() + neg_label_weights = (self.bias + + (1 - self.bias) * neg_label_weights).pow( + self.k) + ori_selected_loss = ori_loss[select_inds] + new_loss = ori_selected_loss * neg_label_weights + norm_ratio = ori_selected_loss.sum() / new_loss.sum() + neg_label_weights *= norm_ratio + else: + neg_label_weights = cls_score.new_ones(num_expected) + select_inds = torch.randperm(num_neg)[:num_expected] + + return neg_inds[select_inds], neg_label_weights + + def sample(self, + assign_result, + bboxes, + gt_bboxes, + gt_labels=None, + img_meta=None, + **kwargs): + """Sample positive and negative bboxes. + + This is a simple implementation of bbox sampling given candidates, + assigning results and ground truth bboxes. + + Args: + assign_result (:obj:`AssignResult`): Bbox assigning results. + bboxes (Tensor): Boxes to be sampled from. + gt_bboxes (Tensor): Ground truth bboxes. + gt_labels (Tensor, optional): Class labels of ground truth bboxes. + + Returns: + tuple[:obj:`SamplingResult`, Tensor]: Sampling result and negetive + label weights. + """ + bboxes = bboxes[:, :4] + + gt_flags = bboxes.new_zeros((bboxes.shape[0], ), dtype=torch.uint8) + if self.add_gt_as_proposals: + bboxes = torch.cat([gt_bboxes, bboxes], dim=0) + assign_result.add_gt_(gt_labels) + gt_ones = bboxes.new_ones(gt_bboxes.shape[0], dtype=torch.uint8) + gt_flags = torch.cat([gt_ones, gt_flags]) + + num_expected_pos = int(self.num * self.pos_fraction) + pos_inds = self.pos_sampler._sample_pos( + assign_result, num_expected_pos, bboxes=bboxes, **kwargs) + num_sampled_pos = pos_inds.numel() + num_expected_neg = self.num - num_sampled_pos + if self.neg_pos_ub >= 0: + _pos = max(1, num_sampled_pos) + neg_upper_bound = int(self.neg_pos_ub * _pos) + if num_expected_neg > neg_upper_bound: + num_expected_neg = neg_upper_bound + neg_inds, neg_label_weights = self.neg_sampler._sample_neg( + assign_result, + num_expected_neg, + bboxes, + img_meta=img_meta, + **kwargs) + + return SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes, + assign_result, gt_flags), neg_label_weights diff --git a/mmdet/core/bbox/transforms.py b/mmdet/core/bbox/transforms.py new file mode 100644 index 0000000..fb141f4 --- /dev/null +++ b/mmdet/core/bbox/transforms.py @@ -0,0 +1,246 @@ +import numpy as np +import torch + + +def bbox_flip(bboxes, img_shape, direction='horizontal'): + """Flip bboxes horizontally or vertically. + + Args: + bboxes (Tensor): Shape (..., 4*k) + img_shape (tuple): Image shape. + direction (str): Flip direction, options are "horizontal", "vertical", + "diagonal". Default: "horizontal" + + Returns: + Tensor: Flipped bboxes. + """ + assert bboxes.shape[-1] % 4 == 0 + assert direction in ['horizontal', 'vertical', 'diagonal'] + flipped = bboxes.clone() + if direction == 'horizontal': + flipped[..., 0::4] = img_shape[1] - bboxes[..., 2::4] + flipped[..., 2::4] = img_shape[1] - bboxes[..., 0::4] + elif direction == 'vertical': + flipped[..., 1::4] = img_shape[0] - bboxes[..., 3::4] + flipped[..., 3::4] = img_shape[0] - bboxes[..., 1::4] + else: + flipped[..., 0::4] = img_shape[1] - bboxes[..., 2::4] + flipped[..., 1::4] = img_shape[0] - bboxes[..., 3::4] + flipped[..., 2::4] = img_shape[1] - bboxes[..., 0::4] + flipped[..., 3::4] = img_shape[0] - bboxes[..., 1::4] + return flipped + + +def bbox_mapping(bboxes, + img_shape, + scale_factor, + flip, + flip_direction='horizontal'): + """Map bboxes from the original image scale to testing scale.""" + new_bboxes = bboxes * bboxes.new_tensor(scale_factor) + if flip: + new_bboxes = bbox_flip(new_bboxes, img_shape, flip_direction) + return new_bboxes + + +def bbox_mapping_back(bboxes, + img_shape, + scale_factor, + flip, + flip_direction='horizontal'): + """Map bboxes from testing scale to original image scale.""" + new_bboxes = bbox_flip(bboxes, img_shape, + flip_direction) if flip else bboxes + new_bboxes = new_bboxes.view(-1, 4) / new_bboxes.new_tensor(scale_factor) + return new_bboxes.view(bboxes.shape) + + +def bbox2roi(bbox_list): + """Convert a list of bboxes to roi format. + + Args: + bbox_list (list[Tensor]): a list of bboxes corresponding to a batch + of images. + + Returns: + Tensor: shape (n, 5), [batch_ind, x1, y1, x2, y2] + """ + rois_list = [] + for img_id, bboxes in enumerate(bbox_list): + if bboxes.size(0) > 0: + img_inds = bboxes.new_full((bboxes.size(0), 1), img_id) + rois = torch.cat([img_inds, bboxes[:, :4]], dim=-1) + else: + rois = bboxes.new_zeros((0, 5)) + rois_list.append(rois) + rois = torch.cat(rois_list, 0) + return rois + + +def roi2bbox(rois): + """Convert rois to bounding box format. + + Args: + rois (torch.Tensor): RoIs with the shape (n, 5) where the first + column indicates batch id of each RoI. + + Returns: + list[torch.Tensor]: Converted boxes of corresponding rois. + """ + bbox_list = [] + img_ids = torch.unique(rois[:, 0].cpu(), sorted=True) + for img_id in img_ids: + inds = (rois[:, 0] == img_id.item()) + bbox = rois[inds, 1:] + bbox_list.append(bbox) + return bbox_list + + +def bbox2result(bboxes, labels, num_classes): + """Convert detection results to a list of numpy arrays. + + Args: + bboxes (torch.Tensor | np.ndarray): shape (n, 5) + labels (torch.Tensor | np.ndarray): shape (n, ) + num_classes (int): class number, including background class + + Returns: + list(ndarray): bbox results of each class + """ + if bboxes.shape[0] == 0: + return [np.zeros((0, 5), dtype=np.float32) for i in range(num_classes)] + else: + if isinstance(bboxes, torch.Tensor): + bboxes = bboxes.detach().cpu().numpy() + labels = labels.detach().cpu().numpy() + return [bboxes[labels == i, :] for i in range(num_classes)] + + +def distance2bbox(points, distance, max_shape=None): + """Decode distance prediction to bounding box. + + Args: + points (Tensor): Shape (B, N, 2) or (N, 2). + distance (Tensor): Distance from the given point to 4 + boundaries (left, top, right, bottom). Shape (B, N, 4) or (N, 4) + max_shape (Sequence[int] or torch.Tensor or Sequence[ + Sequence[int]],optional): Maximum bounds for boxes, specifies + (H, W, C) or (H, W). If priors shape is (B, N, 4), then + the max_shape should be a Sequence[Sequence[int]] + and the length of max_shape should also be B. + + Returns: + Tensor: Boxes with shape (N, 4) or (B, N, 4) + """ + x1 = points[..., 0] - distance[..., 0] + y1 = points[..., 1] - distance[..., 1] + x2 = points[..., 0] + distance[..., 2] + y2 = points[..., 1] + distance[..., 3] + + bboxes = torch.stack([x1, y1, x2, y2], -1) + + if max_shape is not None: + # clip bboxes with dynamic `min` and `max` for onnx + if torch.onnx.is_in_onnx_export(): + from mmdet.core.export import dynamic_clip_for_onnx + x1, y1, x2, y2 = dynamic_clip_for_onnx(x1, y1, x2, y2, max_shape) + bboxes = torch.stack([x1, y1, x2, y2], dim=-1) + return bboxes + if not isinstance(max_shape, torch.Tensor): + max_shape = x1.new_tensor(max_shape) + max_shape = max_shape[..., :2].type_as(x1) + if max_shape.ndim == 2: + assert bboxes.ndim == 3 + assert max_shape.size(0) == bboxes.size(0) + + min_xy = x1.new_tensor(0) + max_xy = torch.cat([max_shape, max_shape], + dim=-1).flip(-1).unsqueeze(-2) + bboxes = torch.where(bboxes < min_xy, min_xy, bboxes) + bboxes = torch.where(bboxes > max_xy, max_xy, bboxes) + + return bboxes + + +def bbox2distance(points, bbox, max_dis=None, eps=0.1): + """Decode bounding box based on distances. + + Args: + points (Tensor): Shape (n, 2), [x, y]. + bbox (Tensor): Shape (n, 4), "xyxy" format + max_dis (float): Upper bound of the distance. + eps (float): a small value to ensure target < max_dis, instead <= + + Returns: + Tensor: Decoded distances. + """ + left = points[:, 0] - bbox[:, 0] + top = points[:, 1] - bbox[:, 1] + right = bbox[:, 2] - points[:, 0] + bottom = bbox[:, 3] - points[:, 1] + if max_dis is not None: + left = left.clamp(min=0, max=max_dis - eps) + top = top.clamp(min=0, max=max_dis - eps) + right = right.clamp(min=0, max=max_dis - eps) + bottom = bottom.clamp(min=0, max=max_dis - eps) + return torch.stack([left, top, right, bottom], -1) + + +def bbox_rescale(bboxes, scale_factor=1.0): + """Rescale bounding box w.r.t. scale_factor. + + Args: + bboxes (Tensor): Shape (n, 4) for bboxes or (n, 5) for rois + scale_factor (float): rescale factor + + Returns: + Tensor: Rescaled bboxes. + """ + if bboxes.size(1) == 5: + bboxes_ = bboxes[:, 1:] + inds_ = bboxes[:, 0] + else: + bboxes_ = bboxes + cx = (bboxes_[:, 0] + bboxes_[:, 2]) * 0.5 + cy = (bboxes_[:, 1] + bboxes_[:, 3]) * 0.5 + w = bboxes_[:, 2] - bboxes_[:, 0] + h = bboxes_[:, 3] - bboxes_[:, 1] + w = w * scale_factor + h = h * scale_factor + x1 = cx - 0.5 * w + x2 = cx + 0.5 * w + y1 = cy - 0.5 * h + y2 = cy + 0.5 * h + if bboxes.size(1) == 5: + rescaled_bboxes = torch.stack([inds_, x1, y1, x2, y2], dim=-1) + else: + rescaled_bboxes = torch.stack([x1, y1, x2, y2], dim=-1) + return rescaled_bboxes + + +def bbox_cxcywh_to_xyxy(bbox): + """Convert bbox coordinates from (cx, cy, w, h) to (x1, y1, x2, y2). + + Args: + bbox (Tensor): Shape (n, 4) for bboxes. + + Returns: + Tensor: Converted bboxes. + """ + cx, cy, w, h = bbox.split((1, 1, 1, 1), dim=-1) + bbox_new = [(cx - 0.5 * w), (cy - 0.5 * h), (cx + 0.5 * w), (cy + 0.5 * h)] + return torch.cat(bbox_new, dim=-1) + + +def bbox_xyxy_to_cxcywh(bbox): + """Convert bbox coordinates from (x1, y1, x2, y2) to (cx, cy, w, h). + + Args: + bbox (Tensor): Shape (n, 4) for bboxes. + + Returns: + Tensor: Converted bboxes. + """ + x1, y1, x2, y2 = bbox.split((1, 1, 1, 1), dim=-1) + bbox_new = [(x1 + x2) / 2, (y1 + y2) / 2, (x2 - x1), (y2 - y1)] + return torch.cat(bbox_new, dim=-1) diff --git a/mmdet/core/evaluation/__init__.py b/mmdet/core/evaluation/__init__.py new file mode 100644 index 0000000..d11ef15 --- /dev/null +++ b/mmdet/core/evaluation/__init__.py @@ -0,0 +1,15 @@ +from .class_names import (cityscapes_classes, coco_classes, dataset_aliases, + get_classes, imagenet_det_classes, + imagenet_vid_classes, voc_classes) +from .eval_hooks import DistEvalHook, EvalHook +from .mean_ap import average_precision, eval_map, print_map_summary +from .recall import (eval_recalls, plot_iou_recall, plot_num_recall, + print_recall_summary) + +__all__ = [ + 'voc_classes', 'imagenet_det_classes', 'imagenet_vid_classes', + 'coco_classes', 'cityscapes_classes', 'dataset_aliases', 'get_classes', + 'DistEvalHook', 'EvalHook', 'average_precision', 'eval_map', + 'print_map_summary', 'eval_recalls', 'print_recall_summary', + 'plot_num_recall', 'plot_iou_recall' +] diff --git a/mmdet/core/evaluation/__pycache__/__init__.cpython-37.pyc b/mmdet/core/evaluation/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..4568519 Binary files /dev/null and b/mmdet/core/evaluation/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/core/evaluation/__pycache__/bbox_overlaps.cpython-37.pyc b/mmdet/core/evaluation/__pycache__/bbox_overlaps.cpython-37.pyc new file mode 100644 index 0000000..f666cc8 Binary files /dev/null and b/mmdet/core/evaluation/__pycache__/bbox_overlaps.cpython-37.pyc differ diff --git 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/dev/null and b/mmdet/core/evaluation/__pycache__/recall.cpython-37.pyc differ diff --git a/mmdet/core/evaluation/bbox_overlaps.py b/mmdet/core/evaluation/bbox_overlaps.py new file mode 100644 index 0000000..93559ea --- /dev/null +++ b/mmdet/core/evaluation/bbox_overlaps.py @@ -0,0 +1,48 @@ +import numpy as np + + +def bbox_overlaps(bboxes1, bboxes2, mode='iou', eps=1e-6): + """Calculate the ious between each bbox of bboxes1 and bboxes2. + + Args: + bboxes1(ndarray): shape (n, 4) + bboxes2(ndarray): shape (k, 4) + mode(str): iou (intersection over union) or iof (intersection + over foreground) + + Returns: + ious(ndarray): shape (n, k) + """ + + assert mode in ['iou', 'iof'] + + bboxes1 = bboxes1.astype(np.float32) + bboxes2 = bboxes2.astype(np.float32) + rows = bboxes1.shape[0] + cols = bboxes2.shape[0] + ious = np.zeros((rows, cols), dtype=np.float32) + if rows * cols == 0: + return ious + exchange = False + if bboxes1.shape[0] > bboxes2.shape[0]: + bboxes1, bboxes2 = bboxes2, bboxes1 + ious = np.zeros((cols, rows), dtype=np.float32) + exchange = True + area1 = (bboxes1[:, 2] - bboxes1[:, 0]) * (bboxes1[:, 3] - bboxes1[:, 1]) + area2 = (bboxes2[:, 2] - bboxes2[:, 0]) * (bboxes2[:, 3] - bboxes2[:, 1]) + for i in range(bboxes1.shape[0]): + x_start = np.maximum(bboxes1[i, 0], bboxes2[:, 0]) + y_start = np.maximum(bboxes1[i, 1], bboxes2[:, 1]) + x_end = np.minimum(bboxes1[i, 2], bboxes2[:, 2]) + y_end = np.minimum(bboxes1[i, 3], bboxes2[:, 3]) + overlap = np.maximum(x_end - x_start, 0) * np.maximum( + y_end - y_start, 0) + if mode == 'iou': + union = area1[i] + area2 - overlap + else: + union = area1[i] if not exchange else area2 + union = np.maximum(union, eps) + ious[i, :] = overlap / union + if exchange: + ious = ious.T + return ious diff --git a/mmdet/core/evaluation/class_names.py b/mmdet/core/evaluation/class_names.py new file mode 100644 index 0000000..c2487c2 --- /dev/null +++ b/mmdet/core/evaluation/class_names.py @@ -0,0 +1,116 @@ +import mmcv + + +def wider_face_classes(): + return ['face'] + + +def voc_classes(): + return [ + 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', + 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', + 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor' + ] + + +def imagenet_det_classes(): + return [ + 'accordion', 'airplane', 'ant', 'antelope', 'apple', 'armadillo', + 'artichoke', 'axe', 'baby_bed', 'backpack', 'bagel', 'balance_beam', + 'banana', 'band_aid', 'banjo', 'baseball', 'basketball', 'bathing_cap', + 'beaker', 'bear', 'bee', 'bell_pepper', 'bench', 'bicycle', 'binder', + 'bird', 'bookshelf', 'bow_tie', 'bow', 'bowl', 'brassiere', 'burrito', + 'bus', 'butterfly', 'camel', 'can_opener', 'car', 'cart', 'cattle', + 'cello', 'centipede', 'chain_saw', 'chair', 'chime', 'cocktail_shaker', + 'coffee_maker', 'computer_keyboard', 'computer_mouse', 'corkscrew', + 'cream', 'croquet_ball', 'crutch', 'cucumber', 'cup_or_mug', 'diaper', + 'digital_clock', 'dishwasher', 'dog', 'domestic_cat', 'dragonfly', + 'drum', 'dumbbell', 'electric_fan', 'elephant', 'face_powder', 'fig', + 'filing_cabinet', 'flower_pot', 'flute', 'fox', 'french_horn', 'frog', + 'frying_pan', 'giant_panda', 'goldfish', 'golf_ball', 'golfcart', + 'guacamole', 'guitar', 'hair_dryer', 'hair_spray', 'hamburger', + 'hammer', 'hamster', 'harmonica', 'harp', 'hat_with_a_wide_brim', + 'head_cabbage', 'helmet', 'hippopotamus', 'horizontal_bar', 'horse', + 'hotdog', 'iPod', 'isopod', 'jellyfish', 'koala_bear', 'ladle', + 'ladybug', 'lamp', 'laptop', 'lemon', 'lion', 'lipstick', 'lizard', + 'lobster', 'maillot', 'maraca', 'microphone', 'microwave', 'milk_can', + 'miniskirt', 'monkey', 'motorcycle', 'mushroom', 'nail', 'neck_brace', + 'oboe', 'orange', 'otter', 'pencil_box', 'pencil_sharpener', 'perfume', + 'person', 'piano', 'pineapple', 'ping-pong_ball', 'pitcher', 'pizza', + 'plastic_bag', 'plate_rack', 'pomegranate', 'popsicle', 'porcupine', + 'power_drill', 'pretzel', 'printer', 'puck', 'punching_bag', 'purse', + 'rabbit', 'racket', 'ray', 'red_panda', 'refrigerator', + 'remote_control', 'rubber_eraser', 'rugby_ball', 'ruler', + 'salt_or_pepper_shaker', 'saxophone', 'scorpion', 'screwdriver', + 'seal', 'sheep', 'ski', 'skunk', 'snail', 'snake', 'snowmobile', + 'snowplow', 'soap_dispenser', 'soccer_ball', 'sofa', 'spatula', + 'squirrel', 'starfish', 'stethoscope', 'stove', 'strainer', + 'strawberry', 'stretcher', 'sunglasses', 'swimming_trunks', 'swine', + 'syringe', 'table', 'tape_player', 'tennis_ball', 'tick', 'tie', + 'tiger', 'toaster', 'traffic_light', 'train', 'trombone', 'trumpet', + 'turtle', 'tv_or_monitor', 'unicycle', 'vacuum', 'violin', + 'volleyball', 'waffle_iron', 'washer', 'water_bottle', 'watercraft', + 'whale', 'wine_bottle', 'zebra' + ] + + +def imagenet_vid_classes(): + return [ + 'airplane', 'antelope', 'bear', 'bicycle', 'bird', 'bus', 'car', + 'cattle', 'dog', 'domestic_cat', 'elephant', 'fox', 'giant_panda', + 'hamster', 'horse', 'lion', 'lizard', 'monkey', 'motorcycle', 'rabbit', + 'red_panda', 'sheep', 'snake', 'squirrel', 'tiger', 'train', 'turtle', + 'watercraft', 'whale', 'zebra' + ] + + +def coco_classes(): + return [ + 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', + 'truck', 'boat', 'traffic_light', 'fire_hydrant', 'stop_sign', + 'parking_meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', + 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', + 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', + 'sports_ball', 'kite', 'baseball_bat', 'baseball_glove', 'skateboard', + 'surfboard', 'tennis_racket', 'bottle', 'wine_glass', 'cup', 'fork', + 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', + 'broccoli', 'carrot', 'hot_dog', 'pizza', 'donut', 'cake', 'chair', + 'couch', 'potted_plant', 'bed', 'dining_table', 'toilet', 'tv', + 'laptop', 'mouse', 'remote', 'keyboard', 'cell_phone', 'microwave', + 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', + 'scissors', 'teddy_bear', 'hair_drier', 'toothbrush' + ] + + +def cityscapes_classes(): + return [ + 'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', + 'bicycle' + ] + + +dataset_aliases = { + 'voc': ['voc', 'pascal_voc', 'voc07', 'voc12'], + 'imagenet_det': ['det', 'imagenet_det', 'ilsvrc_det'], + 'imagenet_vid': ['vid', 'imagenet_vid', 'ilsvrc_vid'], + 'coco': ['coco', 'mscoco', 'ms_coco'], + 'wider_face': ['WIDERFaceDataset', 'wider_face', 'WIDERFace'], + 'cityscapes': ['cityscapes'] +} + + +def get_classes(dataset): + """Get class names of a dataset.""" + alias2name = {} + for name, aliases in dataset_aliases.items(): + for alias in aliases: + alias2name[alias] = name + + if mmcv.is_str(dataset): + if dataset in alias2name: + labels = eval(alias2name[dataset] + '_classes()') + else: + raise ValueError(f'Unrecognized dataset: {dataset}') + else: + raise TypeError(f'dataset must a str, but got {type(dataset)}') + return labels diff --git a/mmdet/core/evaluation/eval_hooks.py b/mmdet/core/evaluation/eval_hooks.py new file mode 100644 index 0000000..6fb932e --- /dev/null +++ b/mmdet/core/evaluation/eval_hooks.py @@ -0,0 +1,303 @@ +import os.path as osp +import warnings +from math import inf + +import mmcv +import torch.distributed as dist +from mmcv.runner import Hook +from torch.nn.modules.batchnorm import _BatchNorm +from torch.utils.data import DataLoader + +from mmdet.utils import get_root_logger + + +class EvalHook(Hook): + """Evaluation hook. + + Notes: + If new arguments are added for EvalHook, tools/test.py, + tools/analysis_tools/eval_metric.py may be effected. + + Attributes: + dataloader (DataLoader): A PyTorch dataloader. + start (int, optional): Evaluation starting epoch. It enables evaluation + before the training starts if ``start`` <= the resuming epoch. + If None, whether to evaluate is merely decided by ``interval``. + Default: None. + interval (int): Evaluation interval (by epochs). Default: 1. + save_best (str, optional): If a metric is specified, it would measure + the best checkpoint during evaluation. The information about best + checkpoint would be save in best.json. + Options are the evaluation metrics to the test dataset. e.g., + ``bbox_mAP``, ``segm_mAP`` for bbox detection and instance + segmentation. ``AR@100`` for proposal recall. If ``save_best`` is + ``auto``, the first key will be used. The interval of + ``CheckpointHook`` should device EvalHook. Default: None. + rule (str, optional): Comparison rule for best score. If set to None, + it will infer a reasonable rule. Keys such as 'mAP' or 'AR' will + be inferred by 'greater' rule. Keys contain 'loss' will be inferred + by 'less' rule. Options are 'greater', 'less'. Default: None. + **eval_kwargs: Evaluation arguments fed into the evaluate function of + the dataset. + """ + + rule_map = {'greater': lambda x, y: x > y, 'less': lambda x, y: x < y} + init_value_map = {'greater': -inf, 'less': inf} + greater_keys = ['mAP', 'AR'] + less_keys = ['loss'] + + def __init__(self, + dataloader, + start=None, + interval=1, + by_epoch=True, + save_best=None, + rule=None, + **eval_kwargs): + if not isinstance(dataloader, DataLoader): + raise TypeError('dataloader must be a pytorch DataLoader, but got' + f' {type(dataloader)}') + if not interval > 0: + raise ValueError(f'interval must be positive, but got {interval}') + if start is not None and start < 0: + warnings.warn( + f'The evaluation start epoch {start} is smaller than 0, ' + f'use 0 instead', UserWarning) + start = 0 + self.dataloader = dataloader + self.interval = interval + self.by_epoch = by_epoch + self.start = start + assert isinstance(save_best, str) or save_best is None + self.save_best = save_best + self.eval_kwargs = eval_kwargs + self.initial_epoch_flag = True + + self.logger = get_root_logger() + + if self.save_best is not None: + self._init_rule(rule, self.save_best) + + def _init_rule(self, rule, key_indicator): + """Initialize rule, key_indicator, comparison_func, and best score. + + Args: + rule (str | None): Comparison rule for best score. + key_indicator (str | None): Key indicator to determine the + comparison rule. + """ + if rule not in self.rule_map and rule is not None: + raise KeyError(f'rule must be greater, less or None, ' + f'but got {rule}.') + + if rule is None: + if key_indicator != 'auto': + if any(key in key_indicator for key in self.greater_keys): + rule = 'greater' + elif any(key in key_indicator for key in self.less_keys): + rule = 'less' + else: + raise ValueError(f'Cannot infer the rule for key ' + f'{key_indicator}, thus a specific rule ' + f'must be specified.') + self.rule = rule + self.key_indicator = key_indicator + if self.rule is not None: + self.compare_func = self.rule_map[self.rule] + + def before_run(self, runner): + if self.save_best is not None: + if runner.meta is None: + warnings.warn('runner.meta is None. Creating a empty one.') + runner.meta = dict() + runner.meta.setdefault('hook_msgs', dict()) + + def before_train_epoch(self, runner): + """Evaluate the model only at the start of training.""" + if not self.initial_epoch_flag: + return + if self.start is not None and runner.epoch >= self.start: + self.after_train_epoch(runner) + self.initial_epoch_flag = False + + def evaluation_flag(self, runner): + """Judge whether to perform_evaluation after this epoch. + + Returns: + bool: The flag indicating whether to perform evaluation. + """ + if self.start is None: + if not self.every_n_epochs(runner, self.interval): + # No evaluation during the interval epochs. + return False + elif (runner.epoch + 1) < self.start: + # No evaluation if start is larger than the current epoch. + return False + else: + # Evaluation only at epochs 3, 5, 7... if start==3 and interval==2 + if (runner.epoch + 1 - self.start) % self.interval: + return False + return True + + def after_train_epoch(self, runner): + if not self.by_epoch or not self.evaluation_flag(runner): + return + from mmdet.apis import single_gpu_test + results = single_gpu_test(runner.model, self.dataloader, show=False) + key_score = self.evaluate(runner, results) + if self.save_best: + self.save_best_checkpoint(runner, key_score) + + def after_train_iter(self, runner): + if self.by_epoch or not self.every_n_iters(runner, self.interval): + return + from mmdet.apis import single_gpu_test + results = single_gpu_test(runner.model, self.dataloader, show=False) + key_score = self.evaluate(runner, results) + if self.save_best: + self.save_best_checkpoint(runner, key_score) + + def save_best_checkpoint(self, runner, key_score): + best_score = runner.meta['hook_msgs'].get( + 'best_score', self.init_value_map[self.rule]) + if self.compare_func(key_score, best_score): + best_score = key_score + runner.meta['hook_msgs']['best_score'] = best_score + last_ckpt = runner.meta['hook_msgs']['last_ckpt'] + runner.meta['hook_msgs']['best_ckpt'] = last_ckpt + mmcv.symlink( + last_ckpt, + osp.join(runner.work_dir, f'best_{self.key_indicator}.pth')) + time_stamp = runner.epoch + 1 if self.by_epoch else runner.iter + 1 + self.logger.info(f'Now best checkpoint is epoch_{time_stamp}.pth.' + f'Best {self.key_indicator} is {best_score:0.4f}') + + def evaluate(self, runner, results): + eval_res = self.dataloader.dataset.evaluate( + results, logger=runner.logger, **self.eval_kwargs) + for name, val in eval_res.items(): + runner.log_buffer.output[name] = val + runner.log_buffer.ready = True + if self.save_best is not None: + if self.key_indicator == 'auto': + # infer from eval_results + self._init_rule(self.rule, list(eval_res.keys())[0]) + return eval_res[self.key_indicator] + else: + return None + + +class DistEvalHook(EvalHook): + """Distributed evaluation hook. + + Notes: + If new arguments are added, tools/test.py may be effected. + + Attributes: + dataloader (DataLoader): A PyTorch dataloader. + start (int, optional): Evaluation starting epoch. It enables evaluation + before the training starts if ``start`` <= the resuming epoch. + If None, whether to evaluate is merely decided by ``interval``. + Default: None. + interval (int): Evaluation interval (by epochs). Default: 1. + tmpdir (str | None): Temporary directory to save the results of all + processes. Default: None. + gpu_collect (bool): Whether to use gpu or cpu to collect results. + Default: False. + save_best (str, optional): If a metric is specified, it would measure + the best checkpoint during evaluation. The information about best + checkpoint would be save in best.json. + Options are the evaluation metrics to the test dataset. e.g., + ``bbox_mAP``, ``segm_mAP`` for bbox detection and instance + segmentation. ``AR@100`` for proposal recall. If ``save_best`` is + ``auto``, the first key will be used. The interval of + ``CheckpointHook`` should device EvalHook. Default: None. + rule (str | None): Comparison rule for best score. If set to None, + it will infer a reasonable rule. Default: 'None'. + broadcast_bn_buffer (bool): Whether to broadcast the + buffer(running_mean and running_var) of rank 0 to other rank + before evaluation. Default: True. + **eval_kwargs: Evaluation arguments fed into the evaluate function of + the dataset. + """ + + def __init__(self, + dataloader, + start=None, + interval=1, + by_epoch=True, + tmpdir=None, + gpu_collect=False, + save_best=None, + rule=None, + broadcast_bn_buffer=True, + **eval_kwargs): + super().__init__( + dataloader, + start=start, + interval=interval, + by_epoch=by_epoch, + save_best=save_best, + rule=rule, + **eval_kwargs) + self.broadcast_bn_buffer = broadcast_bn_buffer + self.tmpdir = tmpdir + self.gpu_collect = gpu_collect + + def _broadcast_bn_buffer(self, runner): + # Synchronization of BatchNorm's buffer (running_mean + # and running_var) is not supported in the DDP of pytorch, + # which may cause the inconsistent performance of models in + # different ranks, so we broadcast BatchNorm's buffers + # of rank 0 to other ranks to avoid this. + if self.broadcast_bn_buffer: + model = runner.model + for name, module in model.named_modules(): + if isinstance(module, + _BatchNorm) and module.track_running_stats: + dist.broadcast(module.running_var, 0) + dist.broadcast(module.running_mean, 0) + + def after_train_epoch(self, runner): + if not self.by_epoch or not self.evaluation_flag(runner): + return + + if self.broadcast_bn_buffer: + self._broadcast_bn_buffer(runner) + + from mmdet.apis import multi_gpu_test + tmpdir = self.tmpdir + if tmpdir is None: + tmpdir = osp.join(runner.work_dir, '.eval_hook') + results = multi_gpu_test( + runner.model, + self.dataloader, + tmpdir=tmpdir, + gpu_collect=self.gpu_collect) + if runner.rank == 0: + print('\n') + key_score = self.evaluate(runner, results) + if self.save_best: + self.save_best_checkpoint(runner, key_score) + + def after_train_iter(self, runner): + if self.by_epoch or not self.every_n_iters(runner, self.interval): + return + + if self.broadcast_bn_buffer: + self._broadcast_bn_buffer(runner) + + from mmdet.apis import multi_gpu_test + tmpdir = self.tmpdir + if tmpdir is None: + tmpdir = osp.join(runner.work_dir, '.eval_hook') + results = multi_gpu_test( + runner.model, + self.dataloader, + tmpdir=tmpdir, + gpu_collect=self.gpu_collect) + if runner.rank == 0: + print('\n') + key_score = self.evaluate(runner, results) + if self.save_best: + self.save_best_checkpoint(runner, key_score) diff --git a/mmdet/core/evaluation/mean_ap.py b/mmdet/core/evaluation/mean_ap.py new file mode 100644 index 0000000..1d653a3 --- /dev/null +++ b/mmdet/core/evaluation/mean_ap.py @@ -0,0 +1,469 @@ +from multiprocessing import Pool + +import mmcv +import numpy as np +from mmcv.utils import print_log +from terminaltables import AsciiTable + +from .bbox_overlaps import bbox_overlaps +from .class_names import get_classes + + +def average_precision(recalls, precisions, mode='area'): + """Calculate average precision (for single or multiple scales). + + Args: + recalls (ndarray): shape (num_scales, num_dets) or (num_dets, ) + precisions (ndarray): shape (num_scales, num_dets) or (num_dets, ) + mode (str): 'area' or '11points', 'area' means calculating the area + under precision-recall curve, '11points' means calculating + the average precision of recalls at [0, 0.1, ..., 1] + + Returns: + float or ndarray: calculated average precision + """ + no_scale = False + if recalls.ndim == 1: + no_scale = True + recalls = recalls[np.newaxis, :] + precisions = precisions[np.newaxis, :] + assert recalls.shape == precisions.shape and recalls.ndim == 2 + num_scales = recalls.shape[0] + ap = np.zeros(num_scales, dtype=np.float32) + if mode == 'area': + zeros = np.zeros((num_scales, 1), dtype=recalls.dtype) + ones = np.ones((num_scales, 1), dtype=recalls.dtype) + mrec = np.hstack((zeros, recalls, ones)) + mpre = np.hstack((zeros, precisions, zeros)) + for i in range(mpre.shape[1] - 1, 0, -1): + mpre[:, i - 1] = np.maximum(mpre[:, i - 1], mpre[:, i]) + for i in range(num_scales): + ind = np.where(mrec[i, 1:] != mrec[i, :-1])[0] + ap[i] = np.sum( + (mrec[i, ind + 1] - mrec[i, ind]) * mpre[i, ind + 1]) + elif mode == '11points': + for i in range(num_scales): + for thr in np.arange(0, 1 + 1e-3, 0.1): + precs = precisions[i, recalls[i, :] >= thr] + prec = precs.max() if precs.size > 0 else 0 + ap[i] += prec + ap /= 11 + else: + raise ValueError( + 'Unrecognized mode, only "area" and "11points" are supported') + if no_scale: + ap = ap[0] + return ap + + +def tpfp_imagenet(det_bboxes, + gt_bboxes, + gt_bboxes_ignore=None, + default_iou_thr=0.5, + area_ranges=None): + """Check if detected bboxes are true positive or false positive. + + Args: + det_bbox (ndarray): Detected bboxes of this image, of shape (m, 5). + gt_bboxes (ndarray): GT bboxes of this image, of shape (n, 4). + gt_bboxes_ignore (ndarray): Ignored gt bboxes of this image, + of shape (k, 4). Default: None + default_iou_thr (float): IoU threshold to be considered as matched for + medium and large bboxes (small ones have special rules). + Default: 0.5. + area_ranges (list[tuple] | None): Range of bbox areas to be evaluated, + in the format [(min1, max1), (min2, max2), ...]. Default: None. + + Returns: + tuple[np.ndarray]: (tp, fp) whose elements are 0 and 1. The shape of + each array is (num_scales, m). + """ + # an indicator of ignored gts + gt_ignore_inds = np.concatenate( + (np.zeros(gt_bboxes.shape[0], dtype=np.bool), + np.ones(gt_bboxes_ignore.shape[0], dtype=np.bool))) + # stack gt_bboxes and gt_bboxes_ignore for convenience + gt_bboxes = np.vstack((gt_bboxes, gt_bboxes_ignore)) + + num_dets = det_bboxes.shape[0] + num_gts = gt_bboxes.shape[0] + if area_ranges is None: + area_ranges = [(None, None)] + num_scales = len(area_ranges) + # tp and fp are of shape (num_scales, num_gts), each row is tp or fp + # of a certain scale. + tp = np.zeros((num_scales, num_dets), dtype=np.float32) + fp = np.zeros((num_scales, num_dets), dtype=np.float32) + if gt_bboxes.shape[0] == 0: + if area_ranges == [(None, None)]: + fp[...] = 1 + else: + det_areas = (det_bboxes[:, 2] - det_bboxes[:, 0]) * ( + det_bboxes[:, 3] - det_bboxes[:, 1]) + for i, (min_area, max_area) in enumerate(area_ranges): + fp[i, (det_areas >= min_area) & (det_areas < max_area)] = 1 + return tp, fp + ious = bbox_overlaps(det_bboxes, gt_bboxes - 1) + gt_w = gt_bboxes[:, 2] - gt_bboxes[:, 0] + gt_h = gt_bboxes[:, 3] - gt_bboxes[:, 1] + iou_thrs = np.minimum((gt_w * gt_h) / ((gt_w + 10.0) * (gt_h + 10.0)), + default_iou_thr) + # sort all detections by scores in descending order + sort_inds = np.argsort(-det_bboxes[:, -1]) + for k, (min_area, max_area) in enumerate(area_ranges): + gt_covered = np.zeros(num_gts, dtype=bool) + # if no area range is specified, gt_area_ignore is all False + if min_area is None: + gt_area_ignore = np.zeros_like(gt_ignore_inds, dtype=bool) + else: + gt_areas = gt_w * gt_h + gt_area_ignore = (gt_areas < min_area) | (gt_areas >= max_area) + for i in sort_inds: + max_iou = -1 + matched_gt = -1 + # find best overlapped available gt + for j in range(num_gts): + # different from PASCAL VOC: allow finding other gts if the + # best overlapped ones are already matched by other det bboxes + if gt_covered[j]: + continue + elif ious[i, j] >= iou_thrs[j] and ious[i, j] > max_iou: + max_iou = ious[i, j] + matched_gt = j + # there are 4 cases for a det bbox: + # 1. it matches a gt, tp = 1, fp = 0 + # 2. it matches an ignored gt, tp = 0, fp = 0 + # 3. it matches no gt and within area range, tp = 0, fp = 1 + # 4. it matches no gt but is beyond area range, tp = 0, fp = 0 + if matched_gt >= 0: + gt_covered[matched_gt] = 1 + if not (gt_ignore_inds[matched_gt] + or gt_area_ignore[matched_gt]): + tp[k, i] = 1 + elif min_area is None: + fp[k, i] = 1 + else: + bbox = det_bboxes[i, :4] + area = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1]) + if area >= min_area and area < max_area: + fp[k, i] = 1 + return tp, fp + + +def tpfp_default(det_bboxes, + gt_bboxes, + gt_bboxes_ignore=None, + iou_thr=0.5, + area_ranges=None): + """Check if detected bboxes are true positive or false positive. + + Args: + det_bbox (ndarray): Detected bboxes of this image, of shape (m, 5). + gt_bboxes (ndarray): GT bboxes of this image, of shape (n, 4). + gt_bboxes_ignore (ndarray): Ignored gt bboxes of this image, + of shape (k, 4). Default: None + iou_thr (float): IoU threshold to be considered as matched. + Default: 0.5. + area_ranges (list[tuple] | None): Range of bbox areas to be evaluated, + in the format [(min1, max1), (min2, max2), ...]. Default: None. + + Returns: + tuple[np.ndarray]: (tp, fp) whose elements are 0 and 1. The shape of + each array is (num_scales, m). + """ + # an indicator of ignored gts + gt_ignore_inds = np.concatenate( + (np.zeros(gt_bboxes.shape[0], dtype=np.bool), + np.ones(gt_bboxes_ignore.shape[0], dtype=np.bool))) + # stack gt_bboxes and gt_bboxes_ignore for convenience + gt_bboxes = np.vstack((gt_bboxes, gt_bboxes_ignore)) + + num_dets = det_bboxes.shape[0] + num_gts = gt_bboxes.shape[0] + if area_ranges is None: + area_ranges = [(None, None)] + num_scales = len(area_ranges) + # tp and fp are of shape (num_scales, num_gts), each row is tp or fp of + # a certain scale + tp = np.zeros((num_scales, num_dets), dtype=np.float32) + fp = np.zeros((num_scales, num_dets), dtype=np.float32) + + # if there is no gt bboxes in this image, then all det bboxes + # within area range are false positives + if gt_bboxes.shape[0] == 0: + if area_ranges == [(None, None)]: + fp[...] = 1 + else: + det_areas = (det_bboxes[:, 2] - det_bboxes[:, 0]) * ( + det_bboxes[:, 3] - det_bboxes[:, 1]) + for i, (min_area, max_area) in enumerate(area_ranges): + fp[i, (det_areas >= min_area) & (det_areas < max_area)] = 1 + return tp, fp + + ious = bbox_overlaps(det_bboxes, gt_bboxes) + # for each det, the max iou with all gts + ious_max = ious.max(axis=1) + # for each det, which gt overlaps most with it + ious_argmax = ious.argmax(axis=1) + # sort all dets in descending order by scores + sort_inds = np.argsort(-det_bboxes[:, -1]) + for k, (min_area, max_area) in enumerate(area_ranges): + gt_covered = np.zeros(num_gts, dtype=bool) + # if no area range is specified, gt_area_ignore is all False + if min_area is None: + gt_area_ignore = np.zeros_like(gt_ignore_inds, dtype=bool) + else: + gt_areas = (gt_bboxes[:, 2] - gt_bboxes[:, 0]) * ( + gt_bboxes[:, 3] - gt_bboxes[:, 1]) + gt_area_ignore = (gt_areas < min_area) | (gt_areas >= max_area) + for i in sort_inds: + if ious_max[i] >= iou_thr: + matched_gt = ious_argmax[i] + if not (gt_ignore_inds[matched_gt] + or gt_area_ignore[matched_gt]): + if not gt_covered[matched_gt]: + gt_covered[matched_gt] = True + tp[k, i] = 1 + else: + fp[k, i] = 1 + # otherwise ignore this detected bbox, tp = 0, fp = 0 + elif min_area is None: + fp[k, i] = 1 + else: + bbox = det_bboxes[i, :4] + area = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1]) + if area >= min_area and area < max_area: + fp[k, i] = 1 + return tp, fp + + +def get_cls_results(det_results, annotations, class_id): + """Get det results and gt information of a certain class. + + Args: + det_results (list[list]): Same as `eval_map()`. + annotations (list[dict]): Same as `eval_map()`. + class_id (int): ID of a specific class. + + Returns: + tuple[list[np.ndarray]]: detected bboxes, gt bboxes, ignored gt bboxes + """ + cls_dets = [img_res[class_id] for img_res in det_results] + cls_gts = [] + cls_gts_ignore = [] + for ann in annotations: + gt_inds = ann['labels'] == class_id + cls_gts.append(ann['bboxes'][gt_inds, :]) + + if ann.get('labels_ignore', None) is not None: + ignore_inds = ann['labels_ignore'] == class_id + cls_gts_ignore.append(ann['bboxes_ignore'][ignore_inds, :]) + else: + cls_gts_ignore.append(np.empty((0, 4), dtype=np.float32)) + + return cls_dets, cls_gts, cls_gts_ignore + + +def eval_map(det_results, + annotations, + scale_ranges=None, + iou_thr=0.5, + dataset=None, + logger=None, + tpfp_fn=None, + nproc=4): + """Evaluate mAP of a dataset. + + Args: + det_results (list[list]): [[cls1_det, cls2_det, ...], ...]. + The outer list indicates images, and the inner list indicates + per-class detected bboxes. + annotations (list[dict]): Ground truth annotations where each item of + the list indicates an image. Keys of annotations are: + + - `bboxes`: numpy array of shape (n, 4) + - `labels`: numpy array of shape (n, ) + - `bboxes_ignore` (optional): numpy array of shape (k, 4) + - `labels_ignore` (optional): numpy array of shape (k, ) + scale_ranges (list[tuple] | None): Range of scales to be evaluated, + in the format [(min1, max1), (min2, max2), ...]. A range of + (32, 64) means the area range between (32**2, 64**2). + Default: None. + iou_thr (float): IoU threshold to be considered as matched. + Default: 0.5. + dataset (list[str] | str | None): Dataset name or dataset classes, + there are minor differences in metrics for different datsets, e.g. + "voc07", "imagenet_det", etc. Default: None. + logger (logging.Logger | str | None): The way to print the mAP + summary. See `mmcv.utils.print_log()` for details. Default: None. + tpfp_fn (callable | None): The function used to determine true/ + false positives. If None, :func:`tpfp_default` is used as default + unless dataset is 'det' or 'vid' (:func:`tpfp_imagenet` in this + case). If it is given as a function, then this function is used + to evaluate tp & fp. Default None. + nproc (int): Processes used for computing TP and FP. + Default: 4. + + Returns: + tuple: (mAP, [dict, dict, ...]) + """ + assert len(det_results) == len(annotations) + + num_imgs = len(det_results) + num_scales = len(scale_ranges) if scale_ranges is not None else 1 + num_classes = len(det_results[0]) # positive class num + area_ranges = ([(rg[0]**2, rg[1]**2) for rg in scale_ranges] + if scale_ranges is not None else None) + + pool = Pool(nproc) + eval_results = [] + for i in range(num_classes): + # get gt and det bboxes of this class + cls_dets, cls_gts, cls_gts_ignore = get_cls_results( + det_results, annotations, i) + # choose proper function according to datasets to compute tp and fp + if tpfp_fn is None: + if dataset in ['det', 'vid']: + tpfp_fn = tpfp_imagenet + else: + tpfp_fn = tpfp_default + if not callable(tpfp_fn): + raise ValueError( + f'tpfp_fn has to be a function or None, but got {tpfp_fn}') + + # compute tp and fp for each image with multiple processes + tpfp = pool.starmap( + tpfp_fn, + zip(cls_dets, cls_gts, cls_gts_ignore, + [iou_thr for _ in range(num_imgs)], + [area_ranges for _ in range(num_imgs)])) + tp, fp = tuple(zip(*tpfp)) + # calculate gt number of each scale + # ignored gts or gts beyond the specific scale are not counted + num_gts = np.zeros(num_scales, dtype=int) + for j, bbox in enumerate(cls_gts): + if area_ranges is None: + num_gts[0] += bbox.shape[0] + else: + gt_areas = (bbox[:, 2] - bbox[:, 0]) * ( + bbox[:, 3] - bbox[:, 1]) + for k, (min_area, max_area) in enumerate(area_ranges): + num_gts[k] += np.sum((gt_areas >= min_area) + & (gt_areas < max_area)) + # sort all det bboxes by score, also sort tp and fp + cls_dets = np.vstack(cls_dets) + num_dets = cls_dets.shape[0] + sort_inds = np.argsort(-cls_dets[:, -1]) + tp = np.hstack(tp)[:, sort_inds] + fp = np.hstack(fp)[:, sort_inds] + # calculate recall and precision with tp and fp + tp = np.cumsum(tp, axis=1) + fp = np.cumsum(fp, axis=1) + eps = np.finfo(np.float32).eps + recalls = tp / np.maximum(num_gts[:, np.newaxis], eps) + precisions = tp / np.maximum((tp + fp), eps) + # calculate AP + if scale_ranges is None: + recalls = recalls[0, :] + precisions = precisions[0, :] + num_gts = num_gts.item() + mode = 'area' if dataset != 'voc07' else '11points' + ap = average_precision(recalls, precisions, mode) + eval_results.append({ + 'num_gts': num_gts, + 'num_dets': num_dets, + 'recall': recalls, + 'precision': precisions, + 'ap': ap + }) + pool.close() + if scale_ranges is not None: + # shape (num_classes, num_scales) + all_ap = np.vstack([cls_result['ap'] for cls_result in eval_results]) + all_num_gts = np.vstack( + [cls_result['num_gts'] for cls_result in eval_results]) + mean_ap = [] + for i in range(num_scales): + if np.any(all_num_gts[:, i] > 0): + mean_ap.append(all_ap[all_num_gts[:, i] > 0, i].mean()) + else: + mean_ap.append(0.0) + else: + aps = [] + for cls_result in eval_results: + if cls_result['num_gts'] > 0: + aps.append(cls_result['ap']) + mean_ap = np.array(aps).mean().item() if aps else 0.0 + + print_map_summary( + mean_ap, eval_results, dataset, area_ranges, logger=logger) + + return mean_ap, eval_results + + +def print_map_summary(mean_ap, + results, + dataset=None, + scale_ranges=None, + logger=None): + """Print mAP and results of each class. + + A table will be printed to show the gts/dets/recall/AP of each class and + the mAP. + + Args: + mean_ap (float): Calculated from `eval_map()`. + results (list[dict]): Calculated from `eval_map()`. + dataset (list[str] | str | None): Dataset name or dataset classes. + scale_ranges (list[tuple] | None): Range of scales to be evaluated. + logger (logging.Logger | str | None): The way to print the mAP + summary. See `mmcv.utils.print_log()` for details. Default: None. + """ + + if logger == 'silent': + return + + if isinstance(results[0]['ap'], np.ndarray): + num_scales = len(results[0]['ap']) + else: + num_scales = 1 + + if scale_ranges is not None: + assert len(scale_ranges) == num_scales + + num_classes = len(results) + + recalls = np.zeros((num_scales, num_classes), dtype=np.float32) + aps = np.zeros((num_scales, num_classes), dtype=np.float32) + num_gts = np.zeros((num_scales, num_classes), dtype=int) + for i, cls_result in enumerate(results): + if cls_result['recall'].size > 0: + recalls[:, i] = np.array(cls_result['recall'], ndmin=2)[:, -1] + aps[:, i] = cls_result['ap'] + num_gts[:, i] = cls_result['num_gts'] + + if dataset is None: + label_names = [str(i) for i in range(num_classes)] + elif mmcv.is_str(dataset): + label_names = get_classes(dataset) + else: + label_names = dataset + + if not isinstance(mean_ap, list): + mean_ap = [mean_ap] + + header = ['class', 'gts', 'dets', 'recall', 'ap'] + for i in range(num_scales): + if scale_ranges is not None: + print_log(f'Scale range {scale_ranges[i]}', logger=logger) + table_data = [header] + for j in range(num_classes): + row_data = [ + label_names[j], num_gts[i, j], results[j]['num_dets'], + f'{recalls[i, j]:.3f}', f'{aps[i, j]:.3f}' + ] + table_data.append(row_data) + table_data.append(['mAP', '', '', '', f'{mean_ap[i]:.3f}']) + table = AsciiTable(table_data) + table.inner_footing_row_border = True + print_log('\n' + table.table, logger=logger) diff --git a/mmdet/core/evaluation/recall.py b/mmdet/core/evaluation/recall.py new file mode 100644 index 0000000..23ec744 --- /dev/null +++ b/mmdet/core/evaluation/recall.py @@ -0,0 +1,189 @@ +from collections.abc import Sequence + +import numpy as np +from mmcv.utils import print_log +from terminaltables import AsciiTable + +from .bbox_overlaps import bbox_overlaps + + +def _recalls(all_ious, proposal_nums, thrs): + + img_num = all_ious.shape[0] + total_gt_num = sum([ious.shape[0] for ious in all_ious]) + + _ious = np.zeros((proposal_nums.size, total_gt_num), dtype=np.float32) + for k, proposal_num in enumerate(proposal_nums): + tmp_ious = np.zeros(0) + for i in range(img_num): + ious = all_ious[i][:, :proposal_num].copy() + gt_ious = np.zeros((ious.shape[0])) + if ious.size == 0: + tmp_ious = np.hstack((tmp_ious, gt_ious)) + continue + for j in range(ious.shape[0]): + gt_max_overlaps = ious.argmax(axis=1) + max_ious = ious[np.arange(0, ious.shape[0]), gt_max_overlaps] + gt_idx = max_ious.argmax() + gt_ious[j] = max_ious[gt_idx] + box_idx = gt_max_overlaps[gt_idx] + ious[gt_idx, :] = -1 + ious[:, box_idx] = -1 + tmp_ious = np.hstack((tmp_ious, gt_ious)) + _ious[k, :] = tmp_ious + + _ious = np.fliplr(np.sort(_ious, axis=1)) + recalls = np.zeros((proposal_nums.size, thrs.size)) + for i, thr in enumerate(thrs): + recalls[:, i] = (_ious >= thr).sum(axis=1) / float(total_gt_num) + + return recalls + + +def set_recall_param(proposal_nums, iou_thrs): + """Check proposal_nums and iou_thrs and set correct format.""" + if isinstance(proposal_nums, Sequence): + _proposal_nums = np.array(proposal_nums) + elif isinstance(proposal_nums, int): + _proposal_nums = np.array([proposal_nums]) + else: + _proposal_nums = proposal_nums + + if iou_thrs is None: + _iou_thrs = np.array([0.5]) + elif isinstance(iou_thrs, Sequence): + _iou_thrs = np.array(iou_thrs) + elif isinstance(iou_thrs, float): + _iou_thrs = np.array([iou_thrs]) + else: + _iou_thrs = iou_thrs + + return _proposal_nums, _iou_thrs + + +def eval_recalls(gts, + proposals, + proposal_nums=None, + iou_thrs=0.5, + logger=None): + """Calculate recalls. + + Args: + gts (list[ndarray]): a list of arrays of shape (n, 4) + proposals (list[ndarray]): a list of arrays of shape (k, 4) or (k, 5) + proposal_nums (int | Sequence[int]): Top N proposals to be evaluated. + iou_thrs (float | Sequence[float]): IoU thresholds. Default: 0.5. + logger (logging.Logger | str | None): The way to print the recall + summary. See `mmcv.utils.print_log()` for details. Default: None. + + Returns: + ndarray: recalls of different ious and proposal nums + """ + + img_num = len(gts) + assert img_num == len(proposals) + + proposal_nums, iou_thrs = set_recall_param(proposal_nums, iou_thrs) + + all_ious = [] + for i in range(img_num): + if proposals[i].ndim == 2 and proposals[i].shape[1] == 5: + scores = proposals[i][:, 4] + sort_idx = np.argsort(scores)[::-1] + img_proposal = proposals[i][sort_idx, :] + else: + img_proposal = proposals[i] + prop_num = min(img_proposal.shape[0], proposal_nums[-1]) + if gts[i] is None or gts[i].shape[0] == 0: + ious = np.zeros((0, img_proposal.shape[0]), dtype=np.float32) + else: + ious = bbox_overlaps(gts[i], img_proposal[:prop_num, :4]) + all_ious.append(ious) + all_ious = np.array(all_ious) + recalls = _recalls(all_ious, proposal_nums, iou_thrs) + + print_recall_summary(recalls, proposal_nums, iou_thrs, logger=logger) + return recalls + + +def print_recall_summary(recalls, + proposal_nums, + iou_thrs, + row_idxs=None, + col_idxs=None, + logger=None): + """Print recalls in a table. + + Args: + recalls (ndarray): calculated from `bbox_recalls` + proposal_nums (ndarray or list): top N proposals + iou_thrs (ndarray or list): iou thresholds + row_idxs (ndarray): which rows(proposal nums) to print + col_idxs (ndarray): which cols(iou thresholds) to print + logger (logging.Logger | str | None): The way to print the recall + summary. See `mmcv.utils.print_log()` for details. Default: None. + """ + proposal_nums = np.array(proposal_nums, dtype=np.int32) + iou_thrs = np.array(iou_thrs) + if row_idxs is None: + row_idxs = np.arange(proposal_nums.size) + if col_idxs is None: + col_idxs = np.arange(iou_thrs.size) + row_header = [''] + iou_thrs[col_idxs].tolist() + table_data = [row_header] + for i, num in enumerate(proposal_nums[row_idxs]): + row = [f'{val:.3f}' for val in recalls[row_idxs[i], col_idxs].tolist()] + row.insert(0, num) + table_data.append(row) + table = AsciiTable(table_data) + print_log('\n' + table.table, logger=logger) + + +def plot_num_recall(recalls, proposal_nums): + """Plot Proposal_num-Recalls curve. + + Args: + recalls(ndarray or list): shape (k,) + proposal_nums(ndarray or list): same shape as `recalls` + """ + if isinstance(proposal_nums, np.ndarray): + _proposal_nums = proposal_nums.tolist() + else: + _proposal_nums = proposal_nums + if isinstance(recalls, np.ndarray): + _recalls = recalls.tolist() + else: + _recalls = recalls + + import matplotlib.pyplot as plt + f = plt.figure() + plt.plot([0] + _proposal_nums, [0] + _recalls) + plt.xlabel('Proposal num') + plt.ylabel('Recall') + plt.axis([0, proposal_nums.max(), 0, 1]) + f.show() + + +def plot_iou_recall(recalls, iou_thrs): + """Plot IoU-Recalls curve. + + Args: + recalls(ndarray or list): shape (k,) + iou_thrs(ndarray or list): same shape as `recalls` + """ + if isinstance(iou_thrs, np.ndarray): + _iou_thrs = iou_thrs.tolist() + else: + _iou_thrs = iou_thrs + if isinstance(recalls, np.ndarray): + _recalls = recalls.tolist() + else: + _recalls = recalls + + import matplotlib.pyplot as plt + f = plt.figure() + plt.plot(_iou_thrs + [1.0], _recalls + [0.]) + plt.xlabel('IoU') + plt.ylabel('Recall') + plt.axis([iou_thrs.min(), 1, 0, 1]) + f.show() diff --git a/mmdet/core/export/__init__.py b/mmdet/core/export/__init__.py new file mode 100644 index 0000000..9168561 --- /dev/null +++ b/mmdet/core/export/__init__.py @@ -0,0 +1,11 @@ +from .onnx_helper import (add_dummy_nms_for_onnx, dynamic_clip_for_onnx, + get_k_for_topk) +from .pytorch2onnx import (build_model_from_cfg, + generate_inputs_and_wrap_model, + preprocess_example_input) + +__all__ = [ + 'build_model_from_cfg', 'generate_inputs_and_wrap_model', + 'preprocess_example_input', 'get_k_for_topk', 'add_dummy_nms_for_onnx', + 'dynamic_clip_for_onnx' +] diff --git a/mmdet/core/export/model_wrappers.py b/mmdet/core/export/model_wrappers.py new file mode 100644 index 0000000..e9988ba --- /dev/null +++ b/mmdet/core/export/model_wrappers.py @@ -0,0 +1,106 @@ +import os.path as osp +import warnings + +import numpy as np +import onnxruntime as ort +import torch + +from mmdet.core import bbox2result +from mmdet.models import BaseDetector + + +class ONNXRuntimeDetector(BaseDetector): + """Wrapper for detector's inference with ONNXRuntime.""" + + def __init__(self, onnx_file, class_names, device_id): + super(ONNXRuntimeDetector, self).__init__() + # get the custom op path + ort_custom_op_path = '' + try: + from mmcv.ops import get_onnxruntime_op_path + ort_custom_op_path = get_onnxruntime_op_path() + except (ImportError, ModuleNotFoundError): + warnings.warn('If input model has custom op from mmcv, \ + you may have to build mmcv with ONNXRuntime from source.') + session_options = ort.SessionOptions() + # register custom op for onnxruntime + if osp.exists(ort_custom_op_path): + session_options.register_custom_ops_library(ort_custom_op_path) + sess = ort.InferenceSession(onnx_file, session_options) + providers = ['CPUExecutionProvider'] + options = [{}] + is_cuda_available = ort.get_device() == 'GPU' + if is_cuda_available: + providers.insert(0, 'CUDAExecutionProvider') + options.insert(0, {'device_id': device_id}) + + sess.set_providers(providers, options) + + self.sess = sess + self.CLASSES = class_names + self.device_id = device_id + self.io_binding = sess.io_binding() + self.output_names = [_.name for _ in sess.get_outputs()] + self.is_cuda_available = is_cuda_available + + def simple_test(self, img, img_metas, **kwargs): + raise NotImplementedError('This method is not implemented.') + + def aug_test(self, imgs, img_metas, **kwargs): + raise NotImplementedError('This method is not implemented.') + + def extract_feat(self, imgs): + raise NotImplementedError('This method is not implemented.') + + def forward_test(self, imgs, img_metas, **kwargs): + input_data = imgs[0] + img_metas = img_metas[0] + batch_size = input_data.shape[0] + # set io binding for inputs/outputs + device_type = 'cuda' if self.is_cuda_available else 'cpu' + if not self.is_cuda_available: + input_data = input_data.cpu() + self.io_binding.bind_input( + name='input', + device_type=device_type, + device_id=self.device_id, + element_type=np.float32, + shape=input_data.shape, + buffer_ptr=input_data.data_ptr()) + + for name in self.output_names: + self.io_binding.bind_output(name) + # run session to get outputs + self.sess.run_with_iobinding(self.io_binding) + ort_outputs = self.io_binding.copy_outputs_to_cpu() + batch_dets, batch_labels = ort_outputs[:2] + batch_masks = ort_outputs[2] if len(ort_outputs) == 3 else None + + results = [] + for i in range(batch_size): + scale_factor = img_metas[i]['scale_factor'] + dets, labels = batch_dets[i], batch_labels[i] + dets[:, :4] /= scale_factor + dets_results = bbox2result(dets, labels, len(self.CLASSES)) + if batch_masks is not None: + masks = batch_masks[i] + img_h, img_w = img_metas[i]['img_shape'][:2] + ori_h, ori_w = img_metas[i]['ori_shape'][:2] + masks = masks[:, :img_h, :img_w] + mask_dtype = masks.dtype + masks = masks.astype(np.float32) + masks = torch.from_numpy(masks) + masks = torch.nn.functional.interpolate( + masks.unsqueeze(0), size=(ori_h, ori_w)) + masks = masks.squeeze(0).detach().numpy() + # convert mask to range(0,1) + if mask_dtype != np.bool: + masks /= 255 + masks = masks >= 0.5 + segms_results = [[] for _ in range(len(self.CLASSES))] + for j in range(len(dets)): + segms_results[labels[j]].append(masks[j]) + results.append((dets_results, segms_results)) + else: + results.append(dets_results) + return results diff --git a/mmdet/core/export/onnx_helper.py b/mmdet/core/export/onnx_helper.py new file mode 100644 index 0000000..c701a19 --- /dev/null +++ b/mmdet/core/export/onnx_helper.py @@ -0,0 +1,222 @@ +import os + +import torch + + +def dynamic_clip_for_onnx(x1, y1, x2, y2, max_shape): + """Clip boxes dynamically for onnx. + + Since torch.clamp cannot have dynamic `min` and `max`, we scale the + boxes by 1/max_shape and clamp in the range [0, 1]. + + Args: + x1 (Tensor): The x1 for bounding boxes. + y1 (Tensor): The y1 for bounding boxes. + x2 (Tensor): The x2 for bounding boxes. + y2 (Tensor): The y2 for bounding boxes. + max_shape (Tensor or torch.Size): The (H,W) of original image. + Returns: + tuple(Tensor): The clipped x1, y1, x2, y2. + """ + assert isinstance( + max_shape, + torch.Tensor), '`max_shape` should be tensor of (h,w) for onnx' + + # scale by 1/max_shape + x1 = x1 / max_shape[1] + y1 = y1 / max_shape[0] + x2 = x2 / max_shape[1] + y2 = y2 / max_shape[0] + + # clamp [0, 1] + x1 = torch.clamp(x1, 0, 1) + y1 = torch.clamp(y1, 0, 1) + x2 = torch.clamp(x2, 0, 1) + y2 = torch.clamp(y2, 0, 1) + + # scale back + x1 = x1 * max_shape[1] + y1 = y1 * max_shape[0] + x2 = x2 * max_shape[1] + y2 = y2 * max_shape[0] + return x1, y1, x2, y2 + + +def get_k_for_topk(k, size): + """Get k of TopK for onnx exporting. + + The K of TopK in TensorRT should not be a Tensor, while in ONNX Runtime + it could be a Tensor.Due to dynamic shape feature, we have to decide + whether to do TopK and what K it should be while exporting to ONNX. + If returned K is less than zero, it means we do not have to do + TopK operation. + + Args: + k (int or Tensor): The set k value for nms from config file. + size (Tensor or torch.Size): The number of elements of \ + TopK's input tensor + Returns: + tuple: (int or Tensor): The final K for TopK. + """ + ret_k = -1 + if k <= 0 or size <= 0: + return ret_k + if torch.onnx.is_in_onnx_export(): + is_trt_backend = os.environ.get('ONNX_BACKEND') == 'MMCVTensorRT' + if is_trt_backend: + # TensorRT does not support dynamic K with TopK op + if 0 < k < size: + ret_k = k + else: + # Always keep topk op for dynamic input in onnx for ONNX Runtime + ret_k = torch.where(k < size, k, size) + elif k < size: + ret_k = k + else: + # ret_k is -1 + pass + return ret_k + + +def add_dummy_nms_for_onnx(boxes, + scores, + max_output_boxes_per_class=1000, + iou_threshold=0.5, + score_threshold=0.05, + pre_top_k=-1, + after_top_k=-1, + labels=None): + """Create a dummy onnx::NonMaxSuppression op while exporting to ONNX. + + This function helps exporting to onnx with batch and multiclass NMS op. + It only supports class-agnostic detection results. That is, the scores + is of shape (N, num_bboxes, num_classes) and the boxes is of shape + (N, num_boxes, 4). + + Args: + boxes (Tensor): The bounding boxes of shape [N, num_boxes, 4] + scores (Tensor): The detection scores of shape + [N, num_boxes, num_classes] + max_output_boxes_per_class (int): Maximum number of output + boxes per class of nms. Defaults to 1000. + iou_threshold (float): IOU threshold of nms. Defaults to 0.5 + score_threshold (float): score threshold of nms. + Defaults to 0.05. + pre_top_k (bool): Number of top K boxes to keep before nms. + Defaults to -1. + after_top_k (int): Number of top K boxes to keep after nms. + Defaults to -1. + labels (Tensor, optional): It not None, explicit labels would be used. + Otherwise, labels would be automatically generated using + num_classed. Defaults to None. + + Returns: + tuple[Tensor, Tensor]: dets of shape [N, num_det, 5] and class labels + of shape [N, num_det]. + """ + max_output_boxes_per_class = torch.LongTensor([max_output_boxes_per_class]) + iou_threshold = torch.tensor([iou_threshold], dtype=torch.float32) + score_threshold = torch.tensor([score_threshold], dtype=torch.float32) + batch_size = scores.shape[0] + num_class = scores.shape[2] + + nms_pre = torch.tensor(pre_top_k, device=scores.device, dtype=torch.long) + nms_pre = get_k_for_topk(nms_pre, boxes.shape[1]) + + if nms_pre > 0: + max_scores, _ = scores.max(-1) + _, topk_inds = max_scores.topk(nms_pre) + batch_inds = torch.arange(batch_size).view( + -1, 1).expand_as(topk_inds).long() + # Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501 + transformed_inds = boxes.shape[1] * batch_inds + topk_inds + boxes = boxes.reshape(-1, 4)[transformed_inds, :].reshape( + batch_size, -1, 4) + scores = scores.reshape(-1, num_class)[transformed_inds, :].reshape( + batch_size, -1, num_class) + if labels is not None: + labels = labels.reshape(-1, 1)[transformed_inds].reshape( + batch_size, -1) + + scores = scores.permute(0, 2, 1) + num_box = boxes.shape[1] + # turn off tracing to create a dummy output of nms + state = torch._C._get_tracing_state() + # dummy indices of nms's output + num_fake_det = 2 + batch_inds = torch.randint(batch_size, (num_fake_det, 1)) + cls_inds = torch.randint(num_class, (num_fake_det, 1)) + box_inds = torch.randint(num_box, (num_fake_det, 1)) + indices = torch.cat([batch_inds, cls_inds, box_inds], dim=1) + output = indices + setattr(DymmyONNXNMSop, 'output', output) + + # open tracing + torch._C._set_tracing_state(state) + selected_indices = DymmyONNXNMSop.apply(boxes, scores, + max_output_boxes_per_class, + iou_threshold, score_threshold) + + batch_inds, cls_inds = selected_indices[:, 0], selected_indices[:, 1] + box_inds = selected_indices[:, 2] + if labels is None: + labels = torch.arange(num_class, dtype=torch.long).to(scores.device) + labels = labels.view(1, num_class, 1).expand_as(scores) + scores = scores.reshape(-1, 1) + boxes = boxes.reshape(batch_size, -1).repeat(1, num_class).reshape(-1, 4) + pos_inds = (num_class * batch_inds + cls_inds) * num_box + box_inds + mask = scores.new_zeros(scores.shape) + # Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501 + # PyTorch style code: mask[batch_inds, box_inds] += 1 + mask[pos_inds, :] += 1 + scores = scores * mask + boxes = boxes * mask + + scores = scores.reshape(batch_size, -1) + boxes = boxes.reshape(batch_size, -1, 4) + labels = labels.reshape(batch_size, -1) + + nms_after = torch.tensor( + after_top_k, device=scores.device, dtype=torch.long) + nms_after = get_k_for_topk(nms_after, num_box * num_class) + + if nms_after > 0: + _, topk_inds = scores.topk(nms_after) + batch_inds = torch.arange(batch_size).view(-1, 1).expand_as(topk_inds) + # Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501 + transformed_inds = scores.shape[1] * batch_inds + topk_inds + scores = scores.reshape(-1, 1)[transformed_inds, :].reshape( + batch_size, -1) + boxes = boxes.reshape(-1, 4)[transformed_inds, :].reshape( + batch_size, -1, 4) + labels = labels.reshape(-1, 1)[transformed_inds, :].reshape( + batch_size, -1) + + scores = scores.unsqueeze(2) + dets = torch.cat([boxes, scores], dim=2) + return dets, labels + + +class DymmyONNXNMSop(torch.autograd.Function): + """DymmyONNXNMSop. + + This class is only for creating onnx::NonMaxSuppression. + """ + + @staticmethod + def forward(ctx, boxes, scores, max_output_boxes_per_class, iou_threshold, + score_threshold): + + return DymmyONNXNMSop.output + + @staticmethod + def symbolic(g, boxes, scores, max_output_boxes_per_class, iou_threshold, + score_threshold): + return g.op( + 'NonMaxSuppression', + boxes, + scores, + max_output_boxes_per_class, + iou_threshold, + score_threshold, + outputs=1) diff --git a/mmdet/core/export/pytorch2onnx.py b/mmdet/core/export/pytorch2onnx.py new file mode 100644 index 0000000..809a817 --- /dev/null +++ b/mmdet/core/export/pytorch2onnx.py @@ -0,0 +1,154 @@ +from functools import partial + +import mmcv +import numpy as np +import torch +from mmcv.runner import load_checkpoint + + +def generate_inputs_and_wrap_model(config_path, + checkpoint_path, + input_config, + cfg_options=None): + """Prepare sample input and wrap model for ONNX export. + + The ONNX export API only accept args, and all inputs should be + torch.Tensor or corresponding types (such as tuple of tensor). + So we should call this function before exporting. This function will: + + 1. generate corresponding inputs which are used to execute the model. + 2. Wrap the model's forward function. + + For example, the MMDet models' forward function has a parameter + ``return_loss:bool``. As we want to set it as False while export API + supports neither bool type or kwargs. So we have to replace the forward + like: ``model.forward = partial(model.forward, return_loss=False)`` + + Args: + config_path (str): the OpenMMLab config for the model we want to + export to ONNX + checkpoint_path (str): Path to the corresponding checkpoint + input_config (dict): the exactly data in this dict depends on the + framework. For MMSeg, we can just declare the input shape, + and generate the dummy data accordingly. However, for MMDet, + we may pass the real img path, or the NMS will return None + as there is no legal bbox. + + Returns: + tuple: (model, tensor_data) wrapped model which can be called by \ + model(*tensor_data) and a list of inputs which are used to execute \ + the model while exporting. + """ + + model = build_model_from_cfg( + config_path, checkpoint_path, cfg_options=cfg_options) + one_img, one_meta = preprocess_example_input(input_config) + tensor_data = [one_img] + model.forward = partial( + model.forward, img_metas=[[one_meta]], return_loss=False) + + # pytorch has some bug in pytorch1.3, we have to fix it + # by replacing these existing op + opset_version = 11 + # put the import within the function thus it will not cause import error + # when not using this function + try: + from mmcv.onnx.symbolic import register_extra_symbolics + except ModuleNotFoundError: + raise NotImplementedError('please update mmcv to version>=v1.0.4') + register_extra_symbolics(opset_version) + + return model, tensor_data + + +def build_model_from_cfg(config_path, checkpoint_path, cfg_options=None): + """Build a model from config and load the given checkpoint. + + Args: + config_path (str): the OpenMMLab config for the model we want to + export to ONNX + checkpoint_path (str): Path to the corresponding checkpoint + + Returns: + torch.nn.Module: the built model + """ + from mmdet.models import build_detector + + cfg = mmcv.Config.fromfile(config_path) + if cfg_options is not None: + cfg.merge_from_dict(cfg_options) + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) + # set cudnn_benchmark + if cfg.get('cudnn_benchmark', False): + torch.backends.cudnn.benchmark = True + cfg.model.pretrained = None + cfg.data.test.test_mode = True + + # build the model + cfg.model.train_cfg = None + model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg')) + load_checkpoint(model, checkpoint_path, map_location='cpu') + model.cpu().eval() + return model + + +def preprocess_example_input(input_config): + """Prepare an example input image for ``generate_inputs_and_wrap_model``. + + Args: + input_config (dict): customized config describing the example input. + + Returns: + tuple: (one_img, one_meta), tensor of the example input image and \ + meta information for the example input image. + + Examples: + >>> from mmdet.core.export import preprocess_example_input + >>> input_config = { + >>> 'input_shape': (1,3,224,224), + >>> 'input_path': 'demo/demo.jpg', + >>> 'normalize_cfg': { + >>> 'mean': (123.675, 116.28, 103.53), + >>> 'std': (58.395, 57.12, 57.375) + >>> } + >>> } + >>> one_img, one_meta = preprocess_example_input(input_config) + >>> print(one_img.shape) + torch.Size([1, 3, 224, 224]) + >>> print(one_meta) + {'img_shape': (224, 224, 3), + 'ori_shape': (224, 224, 3), + 'pad_shape': (224, 224, 3), + 'filename': '.png', + 'scale_factor': 1.0, + 'flip': False} + """ + input_path = input_config['input_path'] + input_shape = input_config['input_shape'] + one_img = mmcv.imread(input_path) + one_img = mmcv.imresize(one_img, input_shape[2:][::-1]) + show_img = one_img.copy() + if 'normalize_cfg' in input_config.keys(): + normalize_cfg = input_config['normalize_cfg'] + mean = np.array(normalize_cfg['mean'], dtype=np.float32) + std = np.array(normalize_cfg['std'], dtype=np.float32) + to_rgb = normalize_cfg.get('to_rgb', True) + one_img = mmcv.imnormalize(one_img, mean, std, to_rgb=to_rgb) + one_img = one_img.transpose(2, 0, 1) + one_img = torch.from_numpy(one_img).unsqueeze(0).float().requires_grad_( + True) + (_, C, H, W) = input_shape + one_meta = { + 'img_shape': (H, W, C), + 'ori_shape': (H, W, C), + 'pad_shape': (H, W, C), + 'filename': '.png', + 'scale_factor': 1.0, + 'flip': False, + 'show_img': show_img, + } + + return one_img, one_meta diff --git a/mmdet/core/mask/__init__.py b/mmdet/core/mask/__init__.py new file mode 100644 index 0000000..ab1e88b --- /dev/null +++ b/mmdet/core/mask/__init__.py @@ -0,0 +1,8 @@ +from .mask_target import mask_target +from .structures import BaseInstanceMasks, BitmapMasks, PolygonMasks +from .utils import encode_mask_results, split_combined_polys + +__all__ = [ + 'split_combined_polys', 'mask_target', 'BaseInstanceMasks', 'BitmapMasks', + 'PolygonMasks', 'encode_mask_results' +] diff --git a/mmdet/core/mask/__pycache__/__init__.cpython-37.pyc b/mmdet/core/mask/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..3c2404b Binary files /dev/null and b/mmdet/core/mask/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/core/mask/__pycache__/mask_target.cpython-37.pyc b/mmdet/core/mask/__pycache__/mask_target.cpython-37.pyc new file mode 100644 index 0000000..36b6a30 Binary files /dev/null and b/mmdet/core/mask/__pycache__/mask_target.cpython-37.pyc differ diff --git a/mmdet/core/mask/__pycache__/structures.cpython-37.pyc b/mmdet/core/mask/__pycache__/structures.cpython-37.pyc new file mode 100644 index 0000000..e3fb378 Binary files /dev/null and b/mmdet/core/mask/__pycache__/structures.cpython-37.pyc differ diff --git a/mmdet/core/mask/__pycache__/utils.cpython-37.pyc b/mmdet/core/mask/__pycache__/utils.cpython-37.pyc new file mode 100644 index 0000000..491fd58 Binary files /dev/null and b/mmdet/core/mask/__pycache__/utils.cpython-37.pyc differ diff --git a/mmdet/core/mask/mask_target.py b/mmdet/core/mask/mask_target.py new file mode 100644 index 0000000..15d26a8 --- /dev/null +++ b/mmdet/core/mask/mask_target.py @@ -0,0 +1,122 @@ +import numpy as np +import torch +from torch.nn.modules.utils import _pair + + +def mask_target(pos_proposals_list, pos_assigned_gt_inds_list, gt_masks_list, + cfg): + """Compute mask target for positive proposals in multiple images. + + Args: + pos_proposals_list (list[Tensor]): Positive proposals in multiple + images. + pos_assigned_gt_inds_list (list[Tensor]): Assigned GT indices for each + positive proposals. + gt_masks_list (list[:obj:`BaseInstanceMasks`]): Ground truth masks of + each image. + cfg (dict): Config dict that specifies the mask size. + + Returns: + list[Tensor]: Mask target of each image. + + Example: + >>> import mmcv + >>> import mmdet + >>> from mmdet.core.mask import BitmapMasks + >>> from mmdet.core.mask.mask_target import * + >>> H, W = 17, 18 + >>> cfg = mmcv.Config({'mask_size': (13, 14)}) + >>> rng = np.random.RandomState(0) + >>> # Positive proposals (tl_x, tl_y, br_x, br_y) for each image + >>> pos_proposals_list = [ + >>> torch.Tensor([ + >>> [ 7.2425, 5.5929, 13.9414, 14.9541], + >>> [ 7.3241, 3.6170, 16.3850, 15.3102], + >>> ]), + >>> torch.Tensor([ + >>> [ 4.8448, 6.4010, 7.0314, 9.7681], + >>> [ 5.9790, 2.6989, 7.4416, 4.8580], + >>> [ 0.0000, 0.0000, 0.1398, 9.8232], + >>> ]), + >>> ] + >>> # Corresponding class index for each proposal for each image + >>> pos_assigned_gt_inds_list = [ + >>> torch.LongTensor([7, 0]), + >>> torch.LongTensor([5, 4, 1]), + >>> ] + >>> # Ground truth mask for each true object for each image + >>> gt_masks_list = [ + >>> BitmapMasks(rng.rand(8, H, W), height=H, width=W), + >>> BitmapMasks(rng.rand(6, H, W), height=H, width=W), + >>> ] + >>> mask_targets = mask_target( + >>> pos_proposals_list, pos_assigned_gt_inds_list, + >>> gt_masks_list, cfg) + >>> assert mask_targets.shape == (5,) + cfg['mask_size'] + """ + cfg_list = [cfg for _ in range(len(pos_proposals_list))] + mask_targets = map(mask_target_single, pos_proposals_list, + pos_assigned_gt_inds_list, gt_masks_list, cfg_list) + mask_targets = list(mask_targets) + if len(mask_targets) > 0: + mask_targets = torch.cat(mask_targets) + return mask_targets + + +def mask_target_single(pos_proposals, pos_assigned_gt_inds, gt_masks, cfg): + """Compute mask target for each positive proposal in the image. + + Args: + pos_proposals (Tensor): Positive proposals. + pos_assigned_gt_inds (Tensor): Assigned GT inds of positive proposals. + gt_masks (:obj:`BaseInstanceMasks`): GT masks in the format of Bitmap + or Polygon. + cfg (dict): Config dict that indicate the mask size. + + Returns: + Tensor: Mask target of each positive proposals in the image. + + Example: + >>> import mmcv + >>> import mmdet + >>> from mmdet.core.mask import BitmapMasks + >>> from mmdet.core.mask.mask_target import * # NOQA + >>> H, W = 32, 32 + >>> cfg = mmcv.Config({'mask_size': (7, 11)}) + >>> rng = np.random.RandomState(0) + >>> # Masks for each ground truth box (relative to the image) + >>> gt_masks_data = rng.rand(3, H, W) + >>> gt_masks = BitmapMasks(gt_masks_data, height=H, width=W) + >>> # Predicted positive boxes in one image + >>> pos_proposals = torch.FloatTensor([ + >>> [ 16.2, 5.5, 19.9, 20.9], + >>> [ 17.3, 13.6, 19.3, 19.3], + >>> [ 14.8, 16.4, 17.0, 23.7], + >>> [ 0.0, 0.0, 16.0, 16.0], + >>> [ 4.0, 0.0, 20.0, 16.0], + >>> ]) + >>> # For each predicted proposal, its assignment to a gt mask + >>> pos_assigned_gt_inds = torch.LongTensor([0, 1, 2, 1, 1]) + >>> mask_targets = mask_target_single( + >>> pos_proposals, pos_assigned_gt_inds, gt_masks, cfg) + >>> assert mask_targets.shape == (5,) + cfg['mask_size'] + """ + device = pos_proposals.device + mask_size = _pair(cfg.mask_size) + num_pos = pos_proposals.size(0) + if num_pos > 0: + proposals_np = pos_proposals.cpu().numpy() + maxh, maxw = gt_masks.height, gt_masks.width + proposals_np[:, [0, 2]] = np.clip(proposals_np[:, [0, 2]], 0, maxw) + proposals_np[:, [1, 3]] = np.clip(proposals_np[:, [1, 3]], 0, maxh) + pos_assigned_gt_inds = pos_assigned_gt_inds.cpu().numpy() + + mask_targets = gt_masks.crop_and_resize( + proposals_np, mask_size, device=device, + inds=pos_assigned_gt_inds).to_ndarray() + + mask_targets = torch.from_numpy(mask_targets).float().to(device) + else: + mask_targets = pos_proposals.new_zeros((0, ) + mask_size) + + return mask_targets diff --git a/mmdet/core/mask/structures.py b/mmdet/core/mask/structures.py new file mode 100644 index 0000000..d9ec577 --- /dev/null +++ b/mmdet/core/mask/structures.py @@ -0,0 +1,1024 @@ +from abc import ABCMeta, abstractmethod + +import cv2 +import mmcv +import numpy as np +import pycocotools.mask as maskUtils +import torch +from mmcv.ops.roi_align import roi_align + + +class BaseInstanceMasks(metaclass=ABCMeta): + """Base class for instance masks.""" + + @abstractmethod + def rescale(self, scale, interpolation='nearest'): + """Rescale masks as large as possible while keeping the aspect ratio. + For details can refer to `mmcv.imrescale`. + + Args: + scale (tuple[int]): The maximum size (h, w) of rescaled mask. + interpolation (str): Same as :func:`mmcv.imrescale`. + + Returns: + BaseInstanceMasks: The rescaled masks. + """ + + @abstractmethod + def resize(self, out_shape, interpolation='nearest'): + """Resize masks to the given out_shape. + + Args: + out_shape: Target (h, w) of resized mask. + interpolation (str): See :func:`mmcv.imresize`. + + Returns: + BaseInstanceMasks: The resized masks. + """ + + @abstractmethod + def flip(self, flip_direction='horizontal'): + """Flip masks alone the given direction. + + Args: + flip_direction (str): Either 'horizontal' or 'vertical'. + + Returns: + BaseInstanceMasks: The flipped masks. + """ + + @abstractmethod + def pad(self, out_shape, pad_val): + """Pad masks to the given size of (h, w). + + Args: + out_shape (tuple[int]): Target (h, w) of padded mask. + pad_val (int): The padded value. + + Returns: + BaseInstanceMasks: The padded masks. + """ + + @abstractmethod + def crop(self, bbox): + """Crop each mask by the given bbox. + + Args: + bbox (ndarray): Bbox in format [x1, y1, x2, y2], shape (4, ). + + Return: + BaseInstanceMasks: The cropped masks. + """ + + @abstractmethod + def crop_and_resize(self, + bboxes, + out_shape, + inds, + device, + interpolation='bilinear'): + """Crop and resize masks by the given bboxes. + + This function is mainly used in mask targets computation. + It firstly align mask to bboxes by assigned_inds, then crop mask by the + assigned bbox and resize to the size of (mask_h, mask_w) + + Args: + bboxes (Tensor): Bboxes in format [x1, y1, x2, y2], shape (N, 4) + out_shape (tuple[int]): Target (h, w) of resized mask + inds (ndarray): Indexes to assign masks to each bbox, + shape (N,) and values should be between [0, num_masks - 1]. + device (str): Device of bboxes + interpolation (str): See `mmcv.imresize` + + Return: + BaseInstanceMasks: the cropped and resized masks. + """ + + @abstractmethod + def expand(self, expanded_h, expanded_w, top, left): + """see :class:`Expand`.""" + + @property + @abstractmethod + def areas(self): + """ndarray: areas of each instance.""" + + @abstractmethod + def to_ndarray(self): + """Convert masks to the format of ndarray. + + Return: + ndarray: Converted masks in the format of ndarray. + """ + + @abstractmethod + def to_tensor(self, dtype, device): + """Convert masks to the format of Tensor. + + Args: + dtype (str): Dtype of converted mask. + device (torch.device): Device of converted masks. + + Returns: + Tensor: Converted masks in the format of Tensor. + """ + + @abstractmethod + def translate(self, + out_shape, + offset, + direction='horizontal', + fill_val=0, + interpolation='bilinear'): + """Translate the masks. + + Args: + out_shape (tuple[int]): Shape for output mask, format (h, w). + offset (int | float): The offset for translate. + direction (str): The translate direction, either "horizontal" + or "vertical". + fill_val (int | float): Border value. Default 0. + interpolation (str): Same as :func:`mmcv.imtranslate`. + + Returns: + Translated masks. + """ + + def shear(self, + out_shape, + magnitude, + direction='horizontal', + border_value=0, + interpolation='bilinear'): + """Shear the masks. + + Args: + out_shape (tuple[int]): Shape for output mask, format (h, w). + magnitude (int | float): The magnitude used for shear. + direction (str): The shear direction, either "horizontal" + or "vertical". + border_value (int | tuple[int]): Value used in case of a + constant border. Default 0. + interpolation (str): Same as in :func:`mmcv.imshear`. + + Returns: + ndarray: Sheared masks. + """ + + @abstractmethod + def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0): + """Rotate the masks. + + Args: + out_shape (tuple[int]): Shape for output mask, format (h, w). + angle (int | float): Rotation angle in degrees. Positive values + mean counter-clockwise rotation. + center (tuple[float], optional): Center point (w, h) of the + rotation in source image. If not specified, the center of + the image will be used. + scale (int | float): Isotropic scale factor. + fill_val (int | float): Border value. Default 0 for masks. + + Returns: + Rotated masks. + """ + + +class BitmapMasks(BaseInstanceMasks): + """This class represents masks in the form of bitmaps. + + Args: + masks (ndarray): ndarray of masks in shape (N, H, W), where N is + the number of objects. + height (int): height of masks + width (int): width of masks + + Example: + >>> from mmdet.core.mask.structures import * # NOQA + >>> num_masks, H, W = 3, 32, 32 + >>> rng = np.random.RandomState(0) + >>> masks = (rng.rand(num_masks, H, W) > 0.1).astype(np.int) + >>> self = BitmapMasks(masks, height=H, width=W) + + >>> # demo crop_and_resize + >>> num_boxes = 5 + >>> bboxes = np.array([[0, 0, 30, 10.0]] * num_boxes) + >>> out_shape = (14, 14) + >>> inds = torch.randint(0, len(self), size=(num_boxes,)) + >>> device = 'cpu' + >>> interpolation = 'bilinear' + >>> new = self.crop_and_resize( + ... bboxes, out_shape, inds, device, interpolation) + >>> assert len(new) == num_boxes + >>> assert new.height, new.width == out_shape + """ + + def __init__(self, masks, height, width): + self.height = height + self.width = width + if len(masks) == 0: + self.masks = np.empty((0, self.height, self.width), dtype=np.uint8) + else: + assert isinstance(masks, (list, np.ndarray)) + if isinstance(masks, list): + assert isinstance(masks[0], np.ndarray) + assert masks[0].ndim == 2 # (H, W) + else: + assert masks.ndim == 3 # (N, H, W) + + self.masks = np.stack(masks).reshape(-1, height, width) + assert self.masks.shape[1] == self.height + assert self.masks.shape[2] == self.width + + def __getitem__(self, index): + """Index the BitmapMask. + + Args: + index (int | ndarray): Indices in the format of integer or ndarray. + + Returns: + :obj:`BitmapMasks`: Indexed bitmap masks. + """ + masks = self.masks[index].reshape(-1, self.height, self.width) + return BitmapMasks(masks, self.height, self.width) + + def __iter__(self): + return iter(self.masks) + + def __repr__(self): + s = self.__class__.__name__ + '(' + s += f'num_masks={len(self.masks)}, ' + s += f'height={self.height}, ' + s += f'width={self.width})' + return s + + def __len__(self): + """Number of masks.""" + return len(self.masks) + + def rescale(self, scale, interpolation='nearest'): + """See :func:`BaseInstanceMasks.rescale`.""" + if len(self.masks) == 0: + new_w, new_h = mmcv.rescale_size((self.width, self.height), scale) + rescaled_masks = np.empty((0, new_h, new_w), dtype=np.uint8) + else: + rescaled_masks = np.stack([ + mmcv.imrescale(mask, scale, interpolation=interpolation) + for mask in self.masks + ]) + height, width = rescaled_masks.shape[1:] + return BitmapMasks(rescaled_masks, height, width) + + def resize(self, out_shape, interpolation='nearest'): + """See :func:`BaseInstanceMasks.resize`.""" + if len(self.masks) == 0: + resized_masks = np.empty((0, *out_shape), dtype=np.uint8) + else: + resized_masks = np.stack([ + mmcv.imresize( + mask, out_shape[::-1], interpolation=interpolation) + for mask in self.masks + ]) + return BitmapMasks(resized_masks, *out_shape) + + def flip(self, flip_direction='horizontal'): + """See :func:`BaseInstanceMasks.flip`.""" + assert flip_direction in ('horizontal', 'vertical', 'diagonal') + + if len(self.masks) == 0: + flipped_masks = self.masks + else: + flipped_masks = np.stack([ + mmcv.imflip(mask, direction=flip_direction) + for mask in self.masks + ]) + return BitmapMasks(flipped_masks, self.height, self.width) + + def pad(self, out_shape, pad_val=0): + """See :func:`BaseInstanceMasks.pad`.""" + if len(self.masks) == 0: + padded_masks = np.empty((0, *out_shape), dtype=np.uint8) + else: + padded_masks = np.stack([ + mmcv.impad(mask, shape=out_shape, pad_val=pad_val) + for mask in self.masks + ]) + return BitmapMasks(padded_masks, *out_shape) + + def crop(self, bbox): + """See :func:`BaseInstanceMasks.crop`.""" + assert isinstance(bbox, np.ndarray) + assert bbox.ndim == 1 + + # clip the boundary + bbox = bbox.copy() + bbox[0::2] = np.clip(bbox[0::2], 0, self.width) + bbox[1::2] = np.clip(bbox[1::2], 0, self.height) + x1, y1, x2, y2 = bbox + w = np.maximum(x2 - x1, 1) + h = np.maximum(y2 - y1, 1) + + if len(self.masks) == 0: + cropped_masks = np.empty((0, h, w), dtype=np.uint8) + else: + cropped_masks = self.masks[:, y1:y1 + h, x1:x1 + w] + return BitmapMasks(cropped_masks, h, w) + + def crop_and_resize(self, + bboxes, + out_shape, + inds, + device='cpu', + interpolation='bilinear'): + """See :func:`BaseInstanceMasks.crop_and_resize`.""" + if len(self.masks) == 0: + empty_masks = np.empty((0, *out_shape), dtype=np.uint8) + return BitmapMasks(empty_masks, *out_shape) + + # convert bboxes to tensor + if isinstance(bboxes, np.ndarray): + bboxes = torch.from_numpy(bboxes).to(device=device) + if isinstance(inds, np.ndarray): + inds = torch.from_numpy(inds).to(device=device) + + num_bbox = bboxes.shape[0] + fake_inds = torch.arange( + num_bbox, device=device).to(dtype=bboxes.dtype)[:, None] + rois = torch.cat([fake_inds, bboxes], dim=1) # Nx5 + rois = rois.to(device=device) + if num_bbox > 0: + gt_masks_th = torch.from_numpy(self.masks).to(device).index_select( + 0, inds).to(dtype=rois.dtype) + targets = roi_align(gt_masks_th[:, None, :, :], rois, out_shape, + 1.0, 0, 'avg', True).squeeze(1) + resized_masks = (targets >= 0.5).cpu().numpy() + else: + resized_masks = [] + return BitmapMasks(resized_masks, *out_shape) + + def expand(self, expanded_h, expanded_w, top, left): + """See :func:`BaseInstanceMasks.expand`.""" + if len(self.masks) == 0: + expanded_mask = np.empty((0, expanded_h, expanded_w), + dtype=np.uint8) + else: + expanded_mask = np.zeros((len(self), expanded_h, expanded_w), + dtype=np.uint8) + expanded_mask[:, top:top + self.height, + left:left + self.width] = self.masks + return BitmapMasks(expanded_mask, expanded_h, expanded_w) + + def translate(self, + out_shape, + offset, + direction='horizontal', + fill_val=0, + interpolation='bilinear'): + """Translate the BitmapMasks. + + Args: + out_shape (tuple[int]): Shape for output mask, format (h, w). + offset (int | float): The offset for translate. + direction (str): The translate direction, either "horizontal" + or "vertical". + fill_val (int | float): Border value. Default 0 for masks. + interpolation (str): Same as :func:`mmcv.imtranslate`. + + Returns: + BitmapMasks: Translated BitmapMasks. + + Example: + >>> from mmdet.core.mask.structures import BitmapMasks + >>> self = BitmapMasks.random(dtype=np.uint8) + >>> out_shape = (32, 32) + >>> offset = 4 + >>> direction = 'horizontal' + >>> fill_val = 0 + >>> interpolation = 'bilinear' + >>> # Note, There seem to be issues when: + >>> # * out_shape is different than self's shape + >>> # * the mask dtype is not supported by cv2.AffineWarp + >>> new = self.translate(out_shape, offset, direction, fill_val, + >>> interpolation) + >>> assert len(new) == len(self) + >>> assert new.height, new.width == out_shape + """ + if len(self.masks) == 0: + translated_masks = np.empty((0, *out_shape), dtype=np.uint8) + else: + translated_masks = mmcv.imtranslate( + self.masks.transpose((1, 2, 0)), + offset, + direction, + border_value=fill_val, + interpolation=interpolation) + if translated_masks.ndim == 2: + translated_masks = translated_masks[:, :, None] + translated_masks = translated_masks.transpose( + (2, 0, 1)).astype(self.masks.dtype) + return BitmapMasks(translated_masks, *out_shape) + + def shear(self, + out_shape, + magnitude, + direction='horizontal', + border_value=0, + interpolation='bilinear'): + """Shear the BitmapMasks. + + Args: + out_shape (tuple[int]): Shape for output mask, format (h, w). + magnitude (int | float): The magnitude used for shear. + direction (str): The shear direction, either "horizontal" + or "vertical". + border_value (int | tuple[int]): Value used in case of a + constant border. + interpolation (str): Same as in :func:`mmcv.imshear`. + + Returns: + BitmapMasks: The sheared masks. + """ + if len(self.masks) == 0: + sheared_masks = np.empty((0, *out_shape), dtype=np.uint8) + else: + sheared_masks = mmcv.imshear( + self.masks.transpose((1, 2, 0)), + magnitude, + direction, + border_value=border_value, + interpolation=interpolation) + if sheared_masks.ndim == 2: + sheared_masks = sheared_masks[:, :, None] + sheared_masks = sheared_masks.transpose( + (2, 0, 1)).astype(self.masks.dtype) + return BitmapMasks(sheared_masks, *out_shape) + + def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0): + """Rotate the BitmapMasks. + + Args: + out_shape (tuple[int]): Shape for output mask, format (h, w). + angle (int | float): Rotation angle in degrees. Positive values + mean counter-clockwise rotation. + center (tuple[float], optional): Center point (w, h) of the + rotation in source image. If not specified, the center of + the image will be used. + scale (int | float): Isotropic scale factor. + fill_val (int | float): Border value. Default 0 for masks. + + Returns: + BitmapMasks: Rotated BitmapMasks. + """ + if len(self.masks) == 0: + rotated_masks = np.empty((0, *out_shape), dtype=self.masks.dtype) + else: + rotated_masks = mmcv.imrotate( + self.masks.transpose((1, 2, 0)), + angle, + center=center, + scale=scale, + border_value=fill_val) + if rotated_masks.ndim == 2: + # case when only one mask, (h, w) + rotated_masks = rotated_masks[:, :, None] # (h, w, 1) + rotated_masks = rotated_masks.transpose( + (2, 0, 1)).astype(self.masks.dtype) + return BitmapMasks(rotated_masks, *out_shape) + + @property + def areas(self): + """See :py:attr:`BaseInstanceMasks.areas`.""" + return self.masks.sum((1, 2)) + + def to_ndarray(self): + """See :func:`BaseInstanceMasks.to_ndarray`.""" + return self.masks + + def to_tensor(self, dtype, device): + """See :func:`BaseInstanceMasks.to_tensor`.""" + return torch.tensor(self.masks, dtype=dtype, device=device) + + @classmethod + def random(cls, + num_masks=3, + height=32, + width=32, + dtype=np.uint8, + rng=None): + """Generate random bitmap masks for demo / testing purposes. + + Example: + >>> from mmdet.core.mask.structures import BitmapMasks + >>> self = BitmapMasks.random() + >>> print('self = {}'.format(self)) + self = BitmapMasks(num_masks=3, height=32, width=32) + """ + from mmdet.utils.util_random import ensure_rng + rng = ensure_rng(rng) + masks = (rng.rand(num_masks, height, width) > 0.1).astype(dtype) + self = cls(masks, height=height, width=width) + return self + + +class PolygonMasks(BaseInstanceMasks): + """This class represents masks in the form of polygons. + + Polygons is a list of three levels. The first level of the list + corresponds to objects, the second level to the polys that compose the + object, the third level to the poly coordinates + + Args: + masks (list[list[ndarray]]): The first level of the list + corresponds to objects, the second level to the polys that + compose the object, the third level to the poly coordinates + height (int): height of masks + width (int): width of masks + + Example: + >>> from mmdet.core.mask.structures import * # NOQA + >>> masks = [ + >>> [ np.array([0, 0, 10, 0, 10, 10., 0, 10, 0, 0]) ] + >>> ] + >>> height, width = 16, 16 + >>> self = PolygonMasks(masks, height, width) + + >>> # demo translate + >>> new = self.translate((16, 16), 4., direction='horizontal') + >>> assert np.all(new.masks[0][0][1::2] == masks[0][0][1::2]) + >>> assert np.all(new.masks[0][0][0::2] == masks[0][0][0::2] + 4) + + >>> # demo crop_and_resize + >>> num_boxes = 3 + >>> bboxes = np.array([[0, 0, 30, 10.0]] * num_boxes) + >>> out_shape = (16, 16) + >>> inds = torch.randint(0, len(self), size=(num_boxes,)) + >>> device = 'cpu' + >>> interpolation = 'bilinear' + >>> new = self.crop_and_resize( + ... bboxes, out_shape, inds, device, interpolation) + >>> assert len(new) == num_boxes + >>> assert new.height, new.width == out_shape + """ + + def __init__(self, masks, height, width): + assert isinstance(masks, list) + if len(masks) > 0: + assert isinstance(masks[0], list) + assert isinstance(masks[0][0], np.ndarray) + + self.height = height + self.width = width + self.masks = masks + + def __getitem__(self, index): + """Index the polygon masks. + + Args: + index (ndarray | List): The indices. + + Returns: + :obj:`PolygonMasks`: The indexed polygon masks. + """ + if isinstance(index, np.ndarray): + index = index.tolist() + if isinstance(index, list): + masks = [self.masks[i] for i in index] + else: + try: + masks = self.masks[index] + except Exception: + raise ValueError( + f'Unsupported input of type {type(index)} for indexing!') + if len(masks) and isinstance(masks[0], np.ndarray): + masks = [masks] # ensure a list of three levels + return PolygonMasks(masks, self.height, self.width) + + def __iter__(self): + return iter(self.masks) + + def __repr__(self): + s = self.__class__.__name__ + '(' + s += f'num_masks={len(self.masks)}, ' + s += f'height={self.height}, ' + s += f'width={self.width})' + return s + + def __len__(self): + """Number of masks.""" + return len(self.masks) + + def rescale(self, scale, interpolation=None): + """see :func:`BaseInstanceMasks.rescale`""" + new_w, new_h = mmcv.rescale_size((self.width, self.height), scale) + if len(self.masks) == 0: + rescaled_masks = PolygonMasks([], new_h, new_w) + else: + rescaled_masks = self.resize((new_h, new_w)) + return rescaled_masks + + def resize(self, out_shape, interpolation=None): + """see :func:`BaseInstanceMasks.resize`""" + if len(self.masks) == 0: + resized_masks = PolygonMasks([], *out_shape) + else: + h_scale = out_shape[0] / self.height + w_scale = out_shape[1] / self.width + resized_masks = [] + for poly_per_obj in self.masks: + resized_poly = [] + for p in poly_per_obj: + p = p.copy() + p[0::2] *= w_scale + p[1::2] *= h_scale + resized_poly.append(p) + resized_masks.append(resized_poly) + resized_masks = PolygonMasks(resized_masks, *out_shape) + return resized_masks + + def flip(self, flip_direction='horizontal'): + """see :func:`BaseInstanceMasks.flip`""" + assert flip_direction in ('horizontal', 'vertical', 'diagonal') + if len(self.masks) == 0: + flipped_masks = PolygonMasks([], self.height, self.width) + else: + flipped_masks = [] + for poly_per_obj in self.masks: + flipped_poly_per_obj = [] + for p in poly_per_obj: + p = p.copy() + if flip_direction == 'horizontal': + p[0::2] = self.width - p[0::2] + elif flip_direction == 'vertical': + p[1::2] = self.height - p[1::2] + else: + p[0::2] = self.width - p[0::2] + p[1::2] = self.height - p[1::2] + flipped_poly_per_obj.append(p) + flipped_masks.append(flipped_poly_per_obj) + flipped_masks = PolygonMasks(flipped_masks, self.height, + self.width) + return flipped_masks + + def crop(self, bbox): + """see :func:`BaseInstanceMasks.crop`""" + assert isinstance(bbox, np.ndarray) + assert bbox.ndim == 1 + + # clip the boundary + bbox = bbox.copy() + bbox[0::2] = np.clip(bbox[0::2], 0, self.width) + bbox[1::2] = np.clip(bbox[1::2], 0, self.height) + x1, y1, x2, y2 = bbox + w = np.maximum(x2 - x1, 1) + h = np.maximum(y2 - y1, 1) + + if len(self.masks) == 0: + cropped_masks = PolygonMasks([], h, w) + else: + cropped_masks = [] + for poly_per_obj in self.masks: + cropped_poly_per_obj = [] + for p in poly_per_obj: + # pycocotools will clip the boundary + p = p.copy() + p[0::2] -= bbox[0] + p[1::2] -= bbox[1] + cropped_poly_per_obj.append(p) + cropped_masks.append(cropped_poly_per_obj) + cropped_masks = PolygonMasks(cropped_masks, h, w) + return cropped_masks + + def pad(self, out_shape, pad_val=0): + """padding has no effect on polygons`""" + return PolygonMasks(self.masks, *out_shape) + + def expand(self, *args, **kwargs): + """TODO: Add expand for polygon""" + raise NotImplementedError + + def crop_and_resize(self, + bboxes, + out_shape, + inds, + device='cpu', + interpolation='bilinear'): + """see :func:`BaseInstanceMasks.crop_and_resize`""" + out_h, out_w = out_shape + if len(self.masks) == 0: + return PolygonMasks([], out_h, out_w) + + resized_masks = [] + for i in range(len(bboxes)): + mask = self.masks[inds[i]] + bbox = bboxes[i, :] + x1, y1, x2, y2 = bbox + w = np.maximum(x2 - x1, 1) + h = np.maximum(y2 - y1, 1) + h_scale = out_h / max(h, 0.1) # avoid too large scale + w_scale = out_w / max(w, 0.1) + + resized_mask = [] + for p in mask: + p = p.copy() + # crop + # pycocotools will clip the boundary + p[0::2] -= bbox[0] + p[1::2] -= bbox[1] + + # resize + p[0::2] *= w_scale + p[1::2] *= h_scale + resized_mask.append(p) + resized_masks.append(resized_mask) + return PolygonMasks(resized_masks, *out_shape) + + def translate(self, + out_shape, + offset, + direction='horizontal', + fill_val=None, + interpolation=None): + """Translate the PolygonMasks. + + Example: + >>> self = PolygonMasks.random(dtype=np.int) + >>> out_shape = (self.height, self.width) + >>> new = self.translate(out_shape, 4., direction='horizontal') + >>> assert np.all(new.masks[0][0][1::2] == self.masks[0][0][1::2]) + >>> assert np.all(new.masks[0][0][0::2] == self.masks[0][0][0::2] + 4) # noqa: E501 + """ + assert fill_val is None or fill_val == 0, 'Here fill_val is not '\ + f'used, and defaultly should be None or 0. got {fill_val}.' + if len(self.masks) == 0: + translated_masks = PolygonMasks([], *out_shape) + else: + translated_masks = [] + for poly_per_obj in self.masks: + translated_poly_per_obj = [] + for p in poly_per_obj: + p = p.copy() + if direction == 'horizontal': + p[0::2] = np.clip(p[0::2] + offset, 0, out_shape[1]) + elif direction == 'vertical': + p[1::2] = np.clip(p[1::2] + offset, 0, out_shape[0]) + translated_poly_per_obj.append(p) + translated_masks.append(translated_poly_per_obj) + translated_masks = PolygonMasks(translated_masks, *out_shape) + return translated_masks + + def shear(self, + out_shape, + magnitude, + direction='horizontal', + border_value=0, + interpolation='bilinear'): + """See :func:`BaseInstanceMasks.shear`.""" + if len(self.masks) == 0: + sheared_masks = PolygonMasks([], *out_shape) + else: + sheared_masks = [] + if direction == 'horizontal': + shear_matrix = np.stack([[1, magnitude], + [0, 1]]).astype(np.float32) + elif direction == 'vertical': + shear_matrix = np.stack([[1, 0], [magnitude, + 1]]).astype(np.float32) + for poly_per_obj in self.masks: + sheared_poly = [] + for p in poly_per_obj: + p = np.stack([p[0::2], p[1::2]], axis=0) # [2, n] + new_coords = np.matmul(shear_matrix, p) # [2, n] + new_coords[0, :] = np.clip(new_coords[0, :], 0, + out_shape[1]) + new_coords[1, :] = np.clip(new_coords[1, :], 0, + out_shape[0]) + sheared_poly.append( + new_coords.transpose((1, 0)).reshape(-1)) + sheared_masks.append(sheared_poly) + sheared_masks = PolygonMasks(sheared_masks, *out_shape) + return sheared_masks + + def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0): + """See :func:`BaseInstanceMasks.rotate`.""" + if len(self.masks) == 0: + rotated_masks = PolygonMasks([], *out_shape) + else: + rotated_masks = [] + rotate_matrix = cv2.getRotationMatrix2D(center, -angle, scale) + for poly_per_obj in self.masks: + rotated_poly = [] + for p in poly_per_obj: + p = p.copy() + coords = np.stack([p[0::2], p[1::2]], axis=1) # [n, 2] + # pad 1 to convert from format [x, y] to homogeneous + # coordinates format [x, y, 1] + coords = np.concatenate( + (coords, np.ones((coords.shape[0], 1), coords.dtype)), + axis=1) # [n, 3] + rotated_coords = np.matmul( + rotate_matrix[None, :, :], + coords[:, :, None])[..., 0] # [n, 2, 1] -> [n, 2] + rotated_coords[:, 0] = np.clip(rotated_coords[:, 0], 0, + out_shape[1]) + rotated_coords[:, 1] = np.clip(rotated_coords[:, 1], 0, + out_shape[0]) + rotated_poly.append(rotated_coords.reshape(-1)) + rotated_masks.append(rotated_poly) + rotated_masks = PolygonMasks(rotated_masks, *out_shape) + return rotated_masks + + def to_bitmap(self): + """convert polygon masks to bitmap masks.""" + bitmap_masks = self.to_ndarray() + return BitmapMasks(bitmap_masks, self.height, self.width) + + @property + def areas(self): + """Compute areas of masks. + + This func is modified from `detectron2 + `_. + The function only works with Polygons using the shoelace formula. + + Return: + ndarray: areas of each instance + """ # noqa: W501 + area = [] + for polygons_per_obj in self.masks: + area_per_obj = 0 + for p in polygons_per_obj: + area_per_obj += self._polygon_area(p[0::2], p[1::2]) + area.append(area_per_obj) + return np.asarray(area) + + def _polygon_area(self, x, y): + """Compute the area of a component of a polygon. + + Using the shoelace formula: + https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates + + Args: + x (ndarray): x coordinates of the component + y (ndarray): y coordinates of the component + + Return: + float: the are of the component + """ # noqa: 501 + return 0.5 * np.abs( + np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1))) + + def to_ndarray(self): + """Convert masks to the format of ndarray.""" + if len(self.masks) == 0: + return np.empty((0, self.height, self.width), dtype=np.uint8) + bitmap_masks = [] + for poly_per_obj in self.masks: + bitmap_masks.append( + polygon_to_bitmap(poly_per_obj, self.height, self.width)) + return np.stack(bitmap_masks) + + def to_tensor(self, dtype, device): + """See :func:`BaseInstanceMasks.to_tensor`.""" + if len(self.masks) == 0: + return torch.empty((0, self.height, self.width), + dtype=dtype, + device=device) + ndarray_masks = self.to_ndarray() + return torch.tensor(ndarray_masks, dtype=dtype, device=device) + + @classmethod + def random(cls, + num_masks=3, + height=32, + width=32, + n_verts=5, + dtype=np.float32, + rng=None): + """Generate random polygon masks for demo / testing purposes. + + Adapted from [1]_ + + References: + .. [1] https://gitlab.kitware.com/computer-vision/kwimage/-/blob/928cae35ca8/kwimage/structs/polygon.py#L379 # noqa: E501 + + Example: + >>> from mmdet.core.mask.structures import PolygonMasks + >>> self = PolygonMasks.random() + >>> print('self = {}'.format(self)) + """ + from mmdet.utils.util_random import ensure_rng + rng = ensure_rng(rng) + + def _gen_polygon(n, irregularity, spikeyness): + """Creates the polygon by sampling points on a circle around the + centre. Random noise is added by varying the angular spacing + between sequential points, and by varying the radial distance of + each point from the centre. + + Based on original code by Mike Ounsworth + + Args: + n (int): number of vertices + irregularity (float): [0,1] indicating how much variance there + is in the angular spacing of vertices. [0,1] will map to + [0, 2pi/numberOfVerts] + spikeyness (float): [0,1] indicating how much variance there is + in each vertex from the circle of radius aveRadius. [0,1] + will map to [0, aveRadius] + + Returns: + a list of vertices, in CCW order. + """ + from scipy.stats import truncnorm + # Generate around the unit circle + cx, cy = (0.0, 0.0) + radius = 1 + + tau = np.pi * 2 + + irregularity = np.clip(irregularity, 0, 1) * 2 * np.pi / n + spikeyness = np.clip(spikeyness, 1e-9, 1) + + # generate n angle steps + lower = (tau / n) - irregularity + upper = (tau / n) + irregularity + angle_steps = rng.uniform(lower, upper, n) + + # normalize the steps so that point 0 and point n+1 are the same + k = angle_steps.sum() / (2 * np.pi) + angles = (angle_steps / k).cumsum() + rng.uniform(0, tau) + + # Convert high and low values to be wrt the standard normal range + # https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.truncnorm.html + low = 0 + high = 2 * radius + mean = radius + std = spikeyness + a = (low - mean) / std + b = (high - mean) / std + tnorm = truncnorm(a=a, b=b, loc=mean, scale=std) + + # now generate the points + radii = tnorm.rvs(n, random_state=rng) + x_pts = cx + radii * np.cos(angles) + y_pts = cy + radii * np.sin(angles) + + points = np.hstack([x_pts[:, None], y_pts[:, None]]) + + # Scale to 0-1 space + points = points - points.min(axis=0) + points = points / points.max(axis=0) + + # Randomly place within 0-1 space + points = points * (rng.rand() * .8 + .2) + min_pt = points.min(axis=0) + max_pt = points.max(axis=0) + + high = (1 - max_pt) + low = (0 - min_pt) + offset = (rng.rand(2) * (high - low)) + low + points = points + offset + return points + + def _order_vertices(verts): + """ + References: + https://stackoverflow.com/questions/1709283/how-can-i-sort-a-coordinate-list-for-a-rectangle-counterclockwise + """ + mlat = verts.T[0].sum() / len(verts) + mlng = verts.T[1].sum() / len(verts) + + tau = np.pi * 2 + angle = (np.arctan2(mlat - verts.T[0], verts.T[1] - mlng) + + tau) % tau + sortx = angle.argsort() + verts = verts.take(sortx, axis=0) + return verts + + # Generate a random exterior for each requested mask + masks = [] + for _ in range(num_masks): + exterior = _order_vertices(_gen_polygon(n_verts, 0.9, 0.9)) + exterior = (exterior * [(width, height)]).astype(dtype) + masks.append([exterior.ravel()]) + + self = cls(masks, height, width) + return self + + +def polygon_to_bitmap(polygons, height, width): + """Convert masks from the form of polygons to bitmaps. + + Args: + polygons (list[ndarray]): masks in polygon representation + height (int): mask height + width (int): mask width + + Return: + ndarray: the converted masks in bitmap representation + """ + rles = maskUtils.frPyObjects(polygons, height, width) + rle = maskUtils.merge(rles) + bitmap_mask = maskUtils.decode(rle).astype(np.bool) + return bitmap_mask diff --git a/mmdet/core/mask/utils.py b/mmdet/core/mask/utils.py new file mode 100644 index 0000000..c882082 --- /dev/null +++ b/mmdet/core/mask/utils.py @@ -0,0 +1,63 @@ +import mmcv +import numpy as np +import pycocotools.mask as mask_util + + +def split_combined_polys(polys, poly_lens, polys_per_mask): + """Split the combined 1-D polys into masks. + + A mask is represented as a list of polys, and a poly is represented as + a 1-D array. In dataset, all masks are concatenated into a single 1-D + tensor. Here we need to split the tensor into original representations. + + Args: + polys (list): a list (length = image num) of 1-D tensors + poly_lens (list): a list (length = image num) of poly length + polys_per_mask (list): a list (length = image num) of poly number + of each mask + + Returns: + list: a list (length = image num) of list (length = mask num) of \ + list (length = poly num) of numpy array. + """ + mask_polys_list = [] + for img_id in range(len(polys)): + polys_single = polys[img_id] + polys_lens_single = poly_lens[img_id].tolist() + polys_per_mask_single = polys_per_mask[img_id].tolist() + + split_polys = mmcv.slice_list(polys_single, polys_lens_single) + mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single) + mask_polys_list.append(mask_polys) + return mask_polys_list + + +# TODO: move this function to more proper place +def encode_mask_results(mask_results): + """Encode bitmap mask to RLE code. + + Args: + mask_results (list | tuple[list]): bitmap mask results. + In mask scoring rcnn, mask_results is a tuple of (segm_results, + segm_cls_score). + + Returns: + list | tuple: RLE encoded mask. + """ + if isinstance(mask_results, tuple): # mask scoring + cls_segms, cls_mask_scores = mask_results + else: + cls_segms = mask_results + num_classes = len(cls_segms) + encoded_mask_results = [[] for _ in range(num_classes)] + for i in range(len(cls_segms)): + for cls_segm in cls_segms[i]: + encoded_mask_results[i].append( + mask_util.encode( + np.array( + cls_segm[:, :, np.newaxis], order='F', + dtype='uint8'))[0]) # encoded with RLE + if isinstance(mask_results, tuple): + return encoded_mask_results, cls_mask_scores + else: + return encoded_mask_results diff --git a/mmdet/core/post_processing/__init__.py b/mmdet/core/post_processing/__init__.py new file mode 100644 index 0000000..880b3f0 --- /dev/null +++ b/mmdet/core/post_processing/__init__.py @@ -0,0 +1,8 @@ +from .bbox_nms import fast_nms, multiclass_nms +from .merge_augs import (merge_aug_bboxes, merge_aug_masks, + merge_aug_proposals, merge_aug_scores) + +__all__ = [ + 'multiclass_nms', 'merge_aug_proposals', 'merge_aug_bboxes', + 'merge_aug_scores', 'merge_aug_masks', 'fast_nms' +] diff --git a/mmdet/core/post_processing/__pycache__/__init__.cpython-37.pyc b/mmdet/core/post_processing/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..7de0f23 Binary files /dev/null and b/mmdet/core/post_processing/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/core/post_processing/__pycache__/bbox_nms.cpython-37.pyc b/mmdet/core/post_processing/__pycache__/bbox_nms.cpython-37.pyc new file mode 100644 index 0000000..d42c8de Binary files /dev/null and b/mmdet/core/post_processing/__pycache__/bbox_nms.cpython-37.pyc differ diff --git a/mmdet/core/post_processing/__pycache__/merge_augs.cpython-37.pyc b/mmdet/core/post_processing/__pycache__/merge_augs.cpython-37.pyc new file mode 100644 index 0000000..15e062d Binary files /dev/null and b/mmdet/core/post_processing/__pycache__/merge_augs.cpython-37.pyc differ diff --git a/mmdet/core/post_processing/bbox_nms.py b/mmdet/core/post_processing/bbox_nms.py new file mode 100644 index 0000000..966d3a6 --- /dev/null +++ b/mmdet/core/post_processing/bbox_nms.py @@ -0,0 +1,168 @@ +import torch +from mmcv.ops.nms import batched_nms + +from mmdet.core.bbox.iou_calculators import bbox_overlaps + + +def multiclass_nms(multi_bboxes, + multi_scores, + score_thr, + nms_cfg, + max_num=-1, + score_factors=None, + return_inds=False): + """NMS for multi-class bboxes. + + Args: + multi_bboxes (Tensor): shape (n, #class*4) or (n, 4) + multi_scores (Tensor): shape (n, #class), where the last column + contains scores of the background class, but this will be ignored. + score_thr (float): bbox threshold, bboxes with scores lower than it + will not be considered. + nms_thr (float): NMS IoU threshold + max_num (int, optional): if there are more than max_num bboxes after + NMS, only top max_num will be kept. Default to -1. + score_factors (Tensor, optional): The factors multiplied to scores + before applying NMS. Default to None. + return_inds (bool, optional): Whether return the indices of kept + bboxes. Default to False. + + Returns: + tuple: (bboxes, labels, indices (optional)), tensors of shape (k, 5), + (k), and (k). Labels are 0-based. + """ + num_classes = multi_scores.size(1) - 1 + # exclude background category + if multi_bboxes.shape[1] > 4: + bboxes = multi_bboxes.view(multi_scores.size(0), -1, 4) + else: + bboxes = multi_bboxes[:, None].expand( + multi_scores.size(0), num_classes, 4) + + scores = multi_scores[:, :-1] + + labels = torch.arange(num_classes, dtype=torch.long) + labels = labels.view(1, -1).expand_as(scores) + + bboxes = bboxes.reshape(-1, 4) + scores = scores.reshape(-1) + labels = labels.reshape(-1) + + if not torch.onnx.is_in_onnx_export(): + # NonZero not supported in TensorRT + # remove low scoring boxes + valid_mask = scores > score_thr + # multiply score_factor after threshold to preserve more bboxes, improve + # mAP by 1% for YOLOv3 + if score_factors is not None: + # expand the shape to match original shape of score + score_factors = score_factors.view(-1, 1).expand( + multi_scores.size(0), num_classes) + score_factors = score_factors.reshape(-1) + scores = scores * score_factors + + if not torch.onnx.is_in_onnx_export(): + # NonZero not supported in TensorRT + inds = valid_mask.nonzero(as_tuple=False).squeeze(1) + bboxes, scores, labels = bboxes[inds], scores[inds], labels[inds] + else: + # TensorRT NMS plugin has invalid output filled with -1 + # add dummy data to make detection output correct. + bboxes = torch.cat([bboxes, bboxes.new_zeros(1, 4)], dim=0) + scores = torch.cat([scores, scores.new_zeros(1)], dim=0) + labels = torch.cat([labels, labels.new_zeros(1)], dim=0) + + if bboxes.numel() == 0: + if torch.onnx.is_in_onnx_export(): + raise RuntimeError('[ONNX Error] Can not record NMS ' + 'as it has not been executed this time') + if return_inds: + return bboxes, labels, inds + else: + return bboxes, labels + + dets, keep = batched_nms(bboxes, scores, labels, nms_cfg) + + if max_num > 0: + dets = dets[:max_num] + keep = keep[:max_num] + + if return_inds: + return dets, labels[keep], keep + else: + return dets, labels[keep] + + +def fast_nms(multi_bboxes, + multi_scores, + multi_coeffs, + score_thr, + iou_thr, + top_k, + max_num=-1): + """Fast NMS in `YOLACT `_. + + Fast NMS allows already-removed detections to suppress other detections so + that every instance can be decided to be kept or discarded in parallel, + which is not possible in traditional NMS. This relaxation allows us to + implement Fast NMS entirely in standard GPU-accelerated matrix operations. + + Args: + multi_bboxes (Tensor): shape (n, #class*4) or (n, 4) + multi_scores (Tensor): shape (n, #class+1), where the last column + contains scores of the background class, but this will be ignored. + multi_coeffs (Tensor): shape (n, #class*coeffs_dim). + score_thr (float): bbox threshold, bboxes with scores lower than it + will not be considered. + iou_thr (float): IoU threshold to be considered as conflicted. + top_k (int): if there are more than top_k bboxes before NMS, + only top top_k will be kept. + max_num (int): if there are more than max_num bboxes after NMS, + only top max_num will be kept. If -1, keep all the bboxes. + Default: -1. + + Returns: + tuple: (bboxes, labels, coefficients), tensors of shape (k, 5), (k, 1), + and (k, coeffs_dim). Labels are 0-based. + """ + + scores = multi_scores[:, :-1].t() # [#class, n] + scores, idx = scores.sort(1, descending=True) + + idx = idx[:, :top_k].contiguous() + scores = scores[:, :top_k] # [#class, topk] + num_classes, num_dets = idx.size() + boxes = multi_bboxes[idx.view(-1), :].view(num_classes, num_dets, 4) + coeffs = multi_coeffs[idx.view(-1), :].view(num_classes, num_dets, -1) + + iou = bbox_overlaps(boxes, boxes) # [#class, topk, topk] + iou.triu_(diagonal=1) + iou_max, _ = iou.max(dim=1) + + # Now just filter out the ones higher than the threshold + keep = iou_max <= iou_thr + + # Second thresholding introduces 0.2 mAP gain at negligible time cost + keep *= scores > score_thr + + # Assign each kept detection to its corresponding class + classes = torch.arange( + num_classes, device=boxes.device)[:, None].expand_as(keep) + classes = classes[keep] + + boxes = boxes[keep] + coeffs = coeffs[keep] + scores = scores[keep] + + # Only keep the top max_num highest scores across all classes + scores, idx = scores.sort(0, descending=True) + if max_num > 0: + idx = idx[:max_num] + scores = scores[:max_num] + + classes = classes[idx] + boxes = boxes[idx] + coeffs = coeffs[idx] + + cls_dets = torch.cat([boxes, scores[:, None]], dim=1) + return cls_dets, classes, coeffs diff --git a/mmdet/core/post_processing/merge_augs.py b/mmdet/core/post_processing/merge_augs.py new file mode 100644 index 0000000..dbcf79d --- /dev/null +++ b/mmdet/core/post_processing/merge_augs.py @@ -0,0 +1,150 @@ +import copy +import warnings + +import numpy as np +import torch +from mmcv import ConfigDict +from mmcv.ops import nms + +from ..bbox import bbox_mapping_back + + +def merge_aug_proposals(aug_proposals, img_metas, cfg): + """Merge augmented proposals (multiscale, flip, etc.) + + Args: + aug_proposals (list[Tensor]): proposals from different testing + schemes, shape (n, 5). Note that they are not rescaled to the + original image size. + + img_metas (list[dict]): list of image info dict where each dict has: + 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmdet/datasets/pipelines/formatting.py:Collect`. + + cfg (dict): rpn test config. + + Returns: + Tensor: shape (n, 4), proposals corresponding to original image scale. + """ + + cfg = copy.deepcopy(cfg) + + # deprecate arguments warning + if 'nms' not in cfg or 'max_num' in cfg or 'nms_thr' in cfg: + warnings.warn( + 'In rpn_proposal or test_cfg, ' + 'nms_thr has been moved to a dict named nms as ' + 'iou_threshold, max_num has been renamed as max_per_img, ' + 'name of original arguments and the way to specify ' + 'iou_threshold of NMS will be deprecated.') + if 'nms' not in cfg: + cfg.nms = ConfigDict(dict(type='nms', iou_threshold=cfg.nms_thr)) + if 'max_num' in cfg: + if 'max_per_img' in cfg: + assert cfg.max_num == cfg.max_per_img, f'You set max_num and ' \ + f'max_per_img at the same time, but get {cfg.max_num} ' \ + f'and {cfg.max_per_img} respectively' \ + f'Please delete max_num which will be deprecated.' + else: + cfg.max_per_img = cfg.max_num + if 'nms_thr' in cfg: + assert cfg.nms.iou_threshold == cfg.nms_thr, f'You set ' \ + f'iou_threshold in nms and ' \ + f'nms_thr at the same time, but get ' \ + f'{cfg.nms.iou_threshold} and {cfg.nms_thr}' \ + f' respectively. Please delete the nms_thr ' \ + f'which will be deprecated.' + + recovered_proposals = [] + for proposals, img_info in zip(aug_proposals, img_metas): + img_shape = img_info['img_shape'] + scale_factor = img_info['scale_factor'] + flip = img_info['flip'] + flip_direction = img_info['flip_direction'] + _proposals = proposals.clone() + _proposals[:, :4] = bbox_mapping_back(_proposals[:, :4], img_shape, + scale_factor, flip, + flip_direction) + recovered_proposals.append(_proposals) + aug_proposals = torch.cat(recovered_proposals, dim=0) + merged_proposals, _ = nms(aug_proposals[:, :4].contiguous(), + aug_proposals[:, -1].contiguous(), + cfg.nms.iou_threshold) + scores = merged_proposals[:, 4] + _, order = scores.sort(0, descending=True) + num = min(cfg.max_per_img, merged_proposals.shape[0]) + order = order[:num] + merged_proposals = merged_proposals[order, :] + return merged_proposals + + +def merge_aug_bboxes(aug_bboxes, aug_scores, img_metas, rcnn_test_cfg): + """Merge augmented detection bboxes and scores. + + Args: + aug_bboxes (list[Tensor]): shape (n, 4*#class) + aug_scores (list[Tensor] or None): shape (n, #class) + img_shapes (list[Tensor]): shape (3, ). + rcnn_test_cfg (dict): rcnn test config. + + Returns: + tuple: (bboxes, scores) + """ + recovered_bboxes = [] + for bboxes, img_info in zip(aug_bboxes, img_metas): + img_shape = img_info[0]['img_shape'] + scale_factor = img_info[0]['scale_factor'] + flip = img_info[0]['flip'] + flip_direction = img_info[0]['flip_direction'] + bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip, + flip_direction) + recovered_bboxes.append(bboxes) + bboxes = torch.stack(recovered_bboxes).mean(dim=0) + if aug_scores is None: + return bboxes + else: + scores = torch.stack(aug_scores).mean(dim=0) + return bboxes, scores + + +def merge_aug_scores(aug_scores): + """Merge augmented bbox scores.""" + if isinstance(aug_scores[0], torch.Tensor): + return torch.mean(torch.stack(aug_scores), dim=0) + else: + return np.mean(aug_scores, axis=0) + + +def merge_aug_masks(aug_masks, img_metas, rcnn_test_cfg, weights=None): + """Merge augmented mask prediction. + + Args: + aug_masks (list[ndarray]): shape (n, #class, h, w) + img_shapes (list[ndarray]): shape (3, ). + rcnn_test_cfg (dict): rcnn test config. + + Returns: + tuple: (bboxes, scores) + """ + recovered_masks = [] + for mask, img_info in zip(aug_masks, img_metas): + flip = img_info[0]['flip'] + flip_direction = img_info[0]['flip_direction'] + if flip: + if flip_direction == 'horizontal': + mask = mask[:, :, :, ::-1] + elif flip_direction == 'vertical': + mask = mask[:, :, ::-1, :] + else: + raise ValueError( + f"Invalid flipping direction '{flip_direction}'") + recovered_masks.append(mask) + + if weights is None: + merged_masks = np.mean(recovered_masks, axis=0) + else: + merged_masks = np.average( + np.array(recovered_masks), axis=0, weights=np.array(weights)) + return merged_masks diff --git a/mmdet/core/utils/__init__.py b/mmdet/core/utils/__init__.py new file mode 100644 index 0000000..5c51dac --- /dev/null +++ b/mmdet/core/utils/__init__.py @@ -0,0 +1,7 @@ +from .dist_utils import DistOptimizerHook, allreduce_grads, reduce_mean +from .misc import mask2ndarray, multi_apply, unmap + +__all__ = [ + 'allreduce_grads', 'DistOptimizerHook', 'reduce_mean', 'multi_apply', + 'unmap', 'mask2ndarray' +] diff --git a/mmdet/core/utils/__pycache__/__init__.cpython-37.pyc b/mmdet/core/utils/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..cb7f5f2 Binary files /dev/null and b/mmdet/core/utils/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/core/utils/__pycache__/dist_utils.cpython-37.pyc b/mmdet/core/utils/__pycache__/dist_utils.cpython-37.pyc new file mode 100644 index 0000000..d5c8ad8 Binary files /dev/null and b/mmdet/core/utils/__pycache__/dist_utils.cpython-37.pyc differ diff --git a/mmdet/core/utils/__pycache__/misc.cpython-37.pyc b/mmdet/core/utils/__pycache__/misc.cpython-37.pyc new file mode 100644 index 0000000..82bbf58 Binary files /dev/null and b/mmdet/core/utils/__pycache__/misc.cpython-37.pyc differ diff --git a/mmdet/core/utils/dist_utils.py b/mmdet/core/utils/dist_utils.py new file mode 100644 index 0000000..5fe7775 --- /dev/null +++ b/mmdet/core/utils/dist_utils.py @@ -0,0 +1,69 @@ +import warnings +from collections import OrderedDict + +import torch.distributed as dist +from mmcv.runner import OptimizerHook +from torch._utils import (_flatten_dense_tensors, _take_tensors, + _unflatten_dense_tensors) + + +def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1): + if bucket_size_mb > 0: + bucket_size_bytes = bucket_size_mb * 1024 * 1024 + buckets = _take_tensors(tensors, bucket_size_bytes) + else: + buckets = OrderedDict() + for tensor in tensors: + tp = tensor.type() + if tp not in buckets: + buckets[tp] = [] + buckets[tp].append(tensor) + buckets = buckets.values() + + for bucket in buckets: + flat_tensors = _flatten_dense_tensors(bucket) + dist.all_reduce(flat_tensors) + flat_tensors.div_(world_size) + for tensor, synced in zip( + bucket, _unflatten_dense_tensors(flat_tensors, bucket)): + tensor.copy_(synced) + + +def allreduce_grads(params, coalesce=True, bucket_size_mb=-1): + """Allreduce gradients. + + Args: + params (list[torch.Parameters]): List of parameters of a model + coalesce (bool, optional): Whether allreduce parameters as a whole. + Defaults to True. + bucket_size_mb (int, optional): Size of bucket, the unit is MB. + Defaults to -1. + """ + grads = [ + param.grad.data for param in params + if param.requires_grad and param.grad is not None + ] + world_size = dist.get_world_size() + if coalesce: + _allreduce_coalesced(grads, world_size, bucket_size_mb) + else: + for tensor in grads: + dist.all_reduce(tensor.div_(world_size)) + + +class DistOptimizerHook(OptimizerHook): + """Deprecated optimizer hook for distributed training.""" + + def __init__(self, *args, **kwargs): + warnings.warn('"DistOptimizerHook" is deprecated, please switch to' + '"mmcv.runner.OptimizerHook".') + super().__init__(*args, **kwargs) + + +def reduce_mean(tensor): + """"Obtain the mean of tensor on different GPUs.""" + if not (dist.is_available() and dist.is_initialized()): + return tensor + tensor = tensor.clone() + dist.all_reduce(tensor.div_(dist.get_world_size()), op=dist.ReduceOp.SUM) + return tensor diff --git a/mmdet/core/utils/misc.py b/mmdet/core/utils/misc.py new file mode 100644 index 0000000..3e22c7b --- /dev/null +++ b/mmdet/core/utils/misc.py @@ -0,0 +1,61 @@ +from functools import partial + +import numpy as np +import torch +from six.moves import map, zip + +from ..mask.structures import BitmapMasks, PolygonMasks + + +def multi_apply(func, *args, **kwargs): + """Apply function to a list of arguments. + + Note: + This function applies the ``func`` to multiple inputs and + map the multiple outputs of the ``func`` into different + list. Each list contains the same type of outputs corresponding + to different inputs. + + Args: + func (Function): A function that will be applied to a list of + arguments + + Returns: + tuple(list): A tuple containing multiple list, each list contains \ + a kind of returned results by the function + """ + pfunc = partial(func, **kwargs) if kwargs else func + map_results = map(pfunc, *args) + return tuple(map(list, zip(*map_results))) + + +def unmap(data, count, inds, fill=0): + """Unmap a subset of item (data) back to the original set of items (of size + count)""" + if data.dim() == 1: + ret = data.new_full((count, ), fill) + ret[inds.type(torch.bool)] = data + else: + new_size = (count, ) + data.size()[1:] + ret = data.new_full(new_size, fill) + ret[inds.type(torch.bool), :] = data + return ret + + +def mask2ndarray(mask): + """Convert Mask to ndarray.. + + Args: + mask (:obj:`BitmapMasks` or :obj:`PolygonMasks` or + torch.Tensor or np.ndarray): The mask to be converted. + + Returns: + np.ndarray: Ndarray mask of shape (n, h, w) that has been converted + """ + if isinstance(mask, (BitmapMasks, PolygonMasks)): + mask = mask.to_ndarray() + elif isinstance(mask, torch.Tensor): + mask = mask.detach().cpu().numpy() + elif not isinstance(mask, np.ndarray): + raise TypeError(f'Unsupported {type(mask)} data type') + return mask diff --git a/mmdet/core/visualization/__init__.py b/mmdet/core/visualization/__init__.py new file mode 100644 index 0000000..4ff995c --- /dev/null +++ b/mmdet/core/visualization/__init__.py @@ -0,0 +1,4 @@ +from .image import (color_val_matplotlib, imshow_det_bboxes, + imshow_gt_det_bboxes) + +__all__ = ['imshow_det_bboxes', 'imshow_gt_det_bboxes', 'color_val_matplotlib'] diff --git a/mmdet/core/visualization/__pycache__/__init__.cpython-37.pyc b/mmdet/core/visualization/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..4c743fb Binary files /dev/null and b/mmdet/core/visualization/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/core/visualization/__pycache__/image.cpython-37.pyc b/mmdet/core/visualization/__pycache__/image.cpython-37.pyc new file mode 100644 index 0000000..1796177 Binary files /dev/null and b/mmdet/core/visualization/__pycache__/image.cpython-37.pyc differ diff --git a/mmdet/core/visualization/image.py b/mmdet/core/visualization/image.py new file mode 100644 index 0000000..562c621 --- /dev/null +++ b/mmdet/core/visualization/image.py @@ -0,0 +1,312 @@ +import matplotlib.pyplot as plt +import mmcv +import numpy as np +import pycocotools.mask as mask_util +from matplotlib.collections import PatchCollection +from matplotlib.patches import Polygon + +from ..utils import mask2ndarray + +EPS = 1e-2 + + +def color_val_matplotlib(color): + """Convert various input in BGR order to normalized RGB matplotlib color + tuples, + + Args: + color (:obj:`Color`/str/tuple/int/ndarray): Color inputs + + Returns: + tuple[float]: A tuple of 3 normalized floats indicating RGB channels. + """ + color = mmcv.color_val(color) + color = [color / 255 for color in color[::-1]] + return tuple(color) + + +def imshow_det_bboxes(img, + bboxes, + labels, + segms=None, + class_names=None, + score_thr=0, + bbox_color='green', + text_color='green', + mask_color=None, + thickness=2, + font_size=13, + win_name='', + show=True, + wait_time=0, + out_file=None): + """Draw bboxes and class labels (with scores) on an image. + + Args: + img (str or ndarray): The image to be displayed. + bboxes (ndarray): Bounding boxes (with scores), shaped (n, 4) or + (n, 5). + labels (ndarray): Labels of bboxes. + segms (ndarray or None): Masks, shaped (n,h,w) or None + class_names (list[str]): Names of each classes. + score_thr (float): Minimum score of bboxes to be shown. Default: 0 + bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines. + The tuple of color should be in BGR order. Default: 'green' + text_color (str or tuple(int) or :obj:`Color`):Color of texts. + The tuple of color should be in BGR order. Default: 'green' + mask_color (str or tuple(int) or :obj:`Color`, optional): + Color of masks. The tuple of color should be in BGR order. + Default: None + thickness (int): Thickness of lines. Default: 2 + font_size (int): Font size of texts. Default: 13 + show (bool): Whether to show the image. Default: True + win_name (str): The window name. Default: '' + wait_time (float): Value of waitKey param. Default: 0. + out_file (str, optional): The filename to write the image. + Default: None + + Returns: + ndarray: The image with bboxes drawn on it. + """ + assert bboxes.ndim == 2, \ + f' bboxes ndim should be 2, but its ndim is {bboxes.ndim}.' + assert labels.ndim == 1, \ + f' labels ndim should be 1, but its ndim is {labels.ndim}.' + assert bboxes.shape[0] == labels.shape[0], \ + 'bboxes.shape[0] and labels.shape[0] should have the same length.' + assert bboxes.shape[1] == 4 or bboxes.shape[1] == 5, \ + f' bboxes.shape[1] should be 4 or 5, but its {bboxes.shape[1]}.' + img = mmcv.imread(img).astype(np.uint8) + UP_FACTOR = 1 + # img = mmcv.imrescale(img, UP_FACTOR) + + if score_thr > 0: + assert bboxes.shape[1] == 5 + scores = bboxes[:, -1] + inds = scores > score_thr + bboxes = bboxes[inds, :] + labels = labels[inds] + if segms is not None: + segms = segms[inds, ...] + + mask_colors = [] + if labels.shape[0] > 0: + if mask_color is None: + # random color + np.random.seed(42) + mask_colors = [ + np.random.randint(0, 256, (1, 3), dtype=np.uint8) + for _ in range(max(labels) + 1) + ] + else: + # specify color + mask_colors = [ + np.array(mmcv.color_val(mask_color)[::-1], dtype=np.uint8) + ] * ( + max(labels) + 1) + + bbox_color = color_val_matplotlib(bbox_color) + text_color = color_val_matplotlib(text_color) + + img = mmcv.bgr2rgb(img) + width, height = img.shape[1], img.shape[0] + img = np.ascontiguousarray(img) + + fig = plt.figure(win_name, frameon=False, dpi=50) + plt.title(win_name) + canvas = fig.canvas + dpi = fig.get_dpi() + # add a small EPS to avoid precision lost due to matplotlib's truncation + # (https://github.com/matplotlib/matplotlib/issues/15363) + fig.set_size_inches((width + EPS) / dpi, (height + EPS) / dpi) + + # remove white edges by set subplot margin + plt.subplots_adjust(left=0, right=1, bottom=0, top=1) + ax = plt.gca() + ax.axis('off') + + polygons = [] + color = [] + alpha = [] + for i, (bbox, label) in enumerate(zip(bboxes, labels)): + bbox_int = bbox.astype(np.int32) * UP_FACTOR + poly = [[bbox_int[0], bbox_int[1]], [bbox_int[0], bbox_int[3]], + [bbox_int[2], bbox_int[3]], [bbox_int[2], bbox_int[1]]] + np_poly = np.array(poly).reshape((4, 2)) + polygons.append(Polygon(np_poly)) + + bbox_color = color_val_matplotlib(np.random.choice(['green', 'red', 'cyan', 'yellow'])) + # bbox_color = color_val_matplotlib(['green', 'red', 'cyan', 'yellow'][i % 4]) + + color.append(bbox_color) + alpha.append(bbox[-1]) + label_text = class_names[ + label] if class_names is not None else f'class {label}' + if len(bbox) > 4: + label_text += f'|{bbox[-1]:.02f}' + ax.text( + bbox_int[0], + bbox_int[1], + f'{label_text}', + bbox={ + 'facecolor': 'black', + 'alpha': 0.8, + 'pad': 0.7, + 'edgecolor': 'none' + }, + color=text_color, + fontsize=font_size, + verticalalignment='top', + horizontalalignment='left',) + if segms is not None: + color_mask = mask_colors[labels[i]] + mask = segms[i].astype(bool) + img[mask] = img[mask] * 0.5 + color_mask * 0.5 + + plt.imshow(img) + + p = PatchCollection( + polygons, facecolor='none', edgecolors=color, linewidths=thickness, + ) + ax.add_collection(p) + + stream, _ = canvas.print_to_buffer() + buffer = np.frombuffer(stream, dtype='uint8') + img_rgba = buffer.reshape(height, width, 4) + rgb, alpha = np.split(img_rgba, [3], axis=2) + img = rgb.astype('uint8') + img = mmcv.rgb2bgr(img) + + if show: + # We do not use cv2 for display because in some cases, opencv will + # conflict with Qt, it will output a warning: Current thread + # is not the object's thread. You can refer to + # https://github.com/opencv/opencv-python/issues/46 for details + if wait_time == 0: + plt.show() + else: + plt.show(block=False) + plt.pause(wait_time) + if out_file is not None: + mmcv.imwrite(img, out_file) + + plt.close() + + return img + + +def imshow_gt_det_bboxes(img, + annotation, + result, + class_names=None, + score_thr=0, + gt_bbox_color=(255, 102, 61), + gt_text_color=(255, 102, 61), + gt_mask_color=(255, 102, 61), + det_bbox_color=(72, 101, 241), + det_text_color=(72, 101, 241), + det_mask_color=(72, 101, 241), + thickness=2, + font_size=13, + win_name='', + show=True, + wait_time=0, + out_file=None): + """General visualization GT and result function. + + Args: + img (str or ndarray): The image to be displayed.) + annotation (dict): Ground truth annotations where contain keys of + 'gt_bboxes' and 'gt_labels' or 'gt_masks' + result (tuple[list] or list): The detection result, can be either + (bbox, segm) or just bbox. + class_names (list[str]): Names of each classes. + score_thr (float): Minimum score of bboxes to be shown. Default: 0 + gt_bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines. + The tuple of color should be in BGR order. Default: (255, 102, 61) + gt_text_color (str or tuple(int) or :obj:`Color`):Color of texts. + The tuple of color should be in BGR order. Default: (255, 102, 61) + gt_mask_color (str or tuple(int) or :obj:`Color`, optional): + Color of masks. The tuple of color should be in BGR order. + Default: (255, 102, 61) + det_bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines. + The tuple of color should be in BGR order. Default: (72, 101, 241) + det_text_color (str or tuple(int) or :obj:`Color`):Color of texts. + The tuple of color should be in BGR order. Default: (72, 101, 241) + det_mask_color (str or tuple(int) or :obj:`Color`, optional): + Color of masks. The tuple of color should be in BGR order. + Default: (72, 101, 241) + thickness (int): Thickness of lines. Default: 2 + font_size (int): Font size of texts. Default: 13 + win_name (str): The window name. Default: '' + show (bool): Whether to show the image. Default: True + wait_time (float): Value of waitKey param. Default: 0. + out_file (str, optional): The filename to write the image. + Default: None + + Returns: + ndarray: The image with bboxes or masks drawn on it. + """ + assert 'gt_bboxes' in annotation + assert 'gt_labels' in annotation + assert isinstance( + result, + (tuple, list)), f'Expected tuple or list, but get {type(result)}' + + gt_masks = annotation.get('gt_masks', None) + if gt_masks is not None: + gt_masks = mask2ndarray(gt_masks) + + img = mmcv.imread(img) + + img = imshow_det_bboxes( + img, + annotation['gt_bboxes'], + annotation['gt_labels'], + gt_masks, + class_names=class_names, + bbox_color=gt_bbox_color, + text_color=gt_text_color, + mask_color=gt_mask_color, + thickness=thickness, + font_size=font_size, + win_name=win_name, + show=False) + + if isinstance(result, tuple): + bbox_result, segm_result = result + if isinstance(segm_result, tuple): + segm_result = segm_result[0] # ms rcnn + else: + bbox_result, segm_result = result, None + + bboxes = np.vstack(bbox_result) + labels = [ + np.full(bbox.shape[0], i, dtype=np.int32) + for i, bbox in enumerate(bbox_result) + ] + labels = np.concatenate(labels) + + segms = None + if segm_result is not None and len(labels) > 0: # non empty + segms = mmcv.concat_list(segm_result) + segms = mask_util.decode(segms) + segms = segms.transpose(2, 0, 1) + + img = imshow_det_bboxes( + img, + bboxes, + labels, + segms=segms, + class_names=class_names, + score_thr=score_thr, + bbox_color=det_bbox_color, + text_color=det_text_color, + mask_color=det_mask_color, + thickness=thickness, + font_size=font_size, + win_name=win_name, + show=show, + wait_time=wait_time, + out_file=out_file) + return img diff --git a/mmdet/datasets/__init__.py b/mmdet/datasets/__init__.py new file mode 100644 index 0000000..9b18b30 --- /dev/null +++ b/mmdet/datasets/__init__.py @@ -0,0 +1,24 @@ +from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset +from .cityscapes import CityscapesDataset +from .coco import CocoDataset +from .custom import CustomDataset +from .dataset_wrappers import (ClassBalancedDataset, ConcatDataset, + RepeatDataset) +from .deepfashion import DeepFashionDataset +from .lvis import LVISDataset, LVISV1Dataset, LVISV05Dataset +from .samplers import DistributedGroupSampler, DistributedSampler, GroupSampler +from .utils import (NumClassCheckHook, get_loading_pipeline, + replace_ImageToTensor) +from .voc import VOCDataset +from .wider_face import WIDERFaceDataset +from .xml_style import XMLDataset + +__all__ = [ + 'CustomDataset', 'XMLDataset', 'CocoDataset', 'DeepFashionDataset', + 'VOCDataset', 'CityscapesDataset', 'LVISDataset', 'LVISV05Dataset', + 'LVISV1Dataset', 'GroupSampler', 'DistributedGroupSampler', + 'DistributedSampler', 'build_dataloader', 'ConcatDataset', 'RepeatDataset', + 'ClassBalancedDataset', 'WIDERFaceDataset', 'DATASETS', 'PIPELINES', + 'build_dataset', 'replace_ImageToTensor', 'get_loading_pipeline', + 'NumClassCheckHook' +] diff --git 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/dev/null +++ b/mmdet/datasets/api_wrappers/coco_api.py @@ -0,0 +1,46 @@ +# This file add snake case alias for coco api + +import warnings + +import pycocotools +from pycocotools.coco import COCO as _COCO +from pycocotools.cocoeval import COCOeval as _COCOeval + + +class COCO(_COCO): + """This class is almost the same as official pycocotools package. + + It implements some snake case function aliases. So that the COCO class has + the same interface as LVIS class. + """ + + def __init__(self, annotation_file=None): + if getattr(pycocotools, '__version__', '0') >= '12.0.2': + warnings.warn( + 'mmpycocotools is deprecated. Please install official pycocotools by "pip install pycocotools"', # noqa: E501 + UserWarning) + super().__init__(annotation_file=annotation_file) + self.img_ann_map = self.imgToAnns + self.cat_img_map = self.catToImgs + + def get_ann_ids(self, img_ids=[], cat_ids=[], area_rng=[], iscrowd=None): + return self.getAnnIds(img_ids, cat_ids, area_rng, iscrowd) + + def get_cat_ids(self, cat_names=[], sup_names=[], cat_ids=[]): + return self.getCatIds(cat_names, sup_names, cat_ids) + + def get_img_ids(self, img_ids=[], cat_ids=[]): + return self.getImgIds(img_ids, cat_ids) + + def load_anns(self, ids): + return self.loadAnns(ids) + + def load_cats(self, ids): + return self.loadCats(ids) + + def load_imgs(self, ids): + return self.loadImgs(ids) + + +# just for the ease of import +COCOeval = _COCOeval diff --git a/mmdet/datasets/builder.py b/mmdet/datasets/builder.py new file mode 100644 index 0000000..c9466a5 --- /dev/null +++ b/mmdet/datasets/builder.py @@ -0,0 +1,143 @@ +import copy +import platform +import random +from functools import partial + +import numpy as np +from mmcv.parallel import collate +from mmcv.runner import get_dist_info +from mmcv.utils import Registry, build_from_cfg +from torch.utils.data import DataLoader + +from .samplers import DistributedGroupSampler, DistributedSampler, GroupSampler + +if platform.system() != 'Windows': + # https://github.com/pytorch/pytorch/issues/973 + import resource + rlimit = resource.getrlimit(resource.RLIMIT_NOFILE) + hard_limit = rlimit[1] + soft_limit = min(4096, hard_limit) + resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit)) + +DATASETS = Registry('dataset') +PIPELINES = Registry('pipeline') + + +def _concat_dataset(cfg, default_args=None): + from .dataset_wrappers import ConcatDataset + ann_files = cfg['ann_file'] + img_prefixes = cfg.get('img_prefix', None) + seg_prefixes = cfg.get('seg_prefix', None) + proposal_files = cfg.get('proposal_file', None) + separate_eval = cfg.get('separate_eval', True) + + datasets = [] + num_dset = len(ann_files) + for i in range(num_dset): + data_cfg = copy.deepcopy(cfg) + # pop 'separate_eval' since it is not a valid key for common datasets. + if 'separate_eval' in data_cfg: + data_cfg.pop('separate_eval') + data_cfg['ann_file'] = ann_files[i] + if isinstance(img_prefixes, (list, tuple)): + data_cfg['img_prefix'] = img_prefixes[i] + if isinstance(seg_prefixes, (list, tuple)): + data_cfg['seg_prefix'] = seg_prefixes[i] + if isinstance(proposal_files, (list, tuple)): + data_cfg['proposal_file'] = proposal_files[i] + datasets.append(build_dataset(data_cfg, default_args)) + + return ConcatDataset(datasets, separate_eval) + + +def build_dataset(cfg, default_args=None): + from .dataset_wrappers import (ConcatDataset, RepeatDataset, + ClassBalancedDataset) + if isinstance(cfg, (list, tuple)): + dataset = ConcatDataset([build_dataset(c, default_args) for c in cfg]) + elif cfg['type'] == 'ConcatDataset': + dataset = ConcatDataset( + [build_dataset(c, default_args) for c in cfg['datasets']], + cfg.get('separate_eval', True)) + elif cfg['type'] == 'RepeatDataset': + dataset = RepeatDataset( + build_dataset(cfg['dataset'], default_args), cfg['times']) + elif cfg['type'] == 'ClassBalancedDataset': + dataset = ClassBalancedDataset( + build_dataset(cfg['dataset'], default_args), cfg['oversample_thr']) + elif isinstance(cfg.get('ann_file'), (list, tuple)): + dataset = _concat_dataset(cfg, default_args) + else: + dataset = build_from_cfg(cfg, DATASETS, default_args) + + return dataset + + +def build_dataloader(dataset, + samples_per_gpu, + workers_per_gpu, + num_gpus=1, + dist=True, + shuffle=True, + seed=None, + **kwargs): + """Build PyTorch DataLoader. + + In distributed training, each GPU/process has a dataloader. + In non-distributed training, there is only one dataloader for all GPUs. + + Args: + dataset (Dataset): A PyTorch dataset. + samples_per_gpu (int): Number of training samples on each GPU, i.e., + batch size of each GPU. + workers_per_gpu (int): How many subprocesses to use for data loading + for each GPU. + num_gpus (int): Number of GPUs. Only used in non-distributed training. + dist (bool): Distributed training/test or not. Default: True. + shuffle (bool): Whether to shuffle the data at every epoch. + Default: True. + kwargs: any keyword argument to be used to initialize DataLoader + + Returns: + DataLoader: A PyTorch dataloader. + """ + rank, world_size = get_dist_info() + if dist: + # DistributedGroupSampler will definitely shuffle the data to satisfy + # that images on each GPU are in the same group + if shuffle: + sampler = DistributedGroupSampler( + dataset, samples_per_gpu, world_size, rank, seed=seed) + else: + sampler = DistributedSampler( + dataset, world_size, rank, shuffle=False, seed=seed) + batch_size = samples_per_gpu + num_workers = workers_per_gpu + else: + sampler = GroupSampler(dataset, samples_per_gpu) if shuffle else None + batch_size = num_gpus * samples_per_gpu + num_workers = num_gpus * workers_per_gpu + + init_fn = partial( + worker_init_fn, num_workers=num_workers, rank=rank, + seed=seed) if seed is not None else None + + data_loader = DataLoader( + dataset, + batch_size=batch_size, + sampler=sampler, + num_workers=num_workers, + collate_fn=partial(collate, samples_per_gpu=samples_per_gpu), + pin_memory=False, + worker_init_fn=init_fn, + **kwargs) + + return data_loader + + +def worker_init_fn(worker_id, num_workers, rank, seed): + # The seed of each worker equals to + # num_worker * rank + worker_id + user_seed + worker_seed = num_workers * rank + worker_id + seed + np.random.seed(worker_seed) + random.seed(worker_seed) diff --git a/mmdet/datasets/cityscapes.py b/mmdet/datasets/cityscapes.py new file mode 100644 index 0000000..71eead8 --- /dev/null +++ b/mmdet/datasets/cityscapes.py @@ -0,0 +1,334 @@ +# Modified from https://github.com/facebookresearch/detectron2/blob/master/detectron2/data/datasets/cityscapes.py # noqa +# and https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa + +import glob +import os +import os.path as osp +import tempfile +from collections import OrderedDict + +import mmcv +import numpy as np +import pycocotools.mask as maskUtils +from mmcv.utils import print_log + +from .builder import DATASETS +from .coco import CocoDataset + + +@DATASETS.register_module() +class CityscapesDataset(CocoDataset): + + CLASSES = ('person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', + 'bicycle') + + def _filter_imgs(self, min_size=32): + """Filter images too small or without ground truths.""" + valid_inds = [] + # obtain images that contain annotation + ids_with_ann = set(_['image_id'] for _ in self.coco.anns.values()) + # obtain images that contain annotations of the required categories + ids_in_cat = set() + for i, class_id in enumerate(self.cat_ids): + ids_in_cat |= set(self.coco.cat_img_map[class_id]) + # merge the image id sets of the two conditions and use the merged set + # to filter out images if self.filter_empty_gt=True + ids_in_cat &= ids_with_ann + + valid_img_ids = [] + for i, img_info in enumerate(self.data_infos): + img_id = img_info['id'] + ann_ids = self.coco.getAnnIds(imgIds=[img_id]) + ann_info = self.coco.loadAnns(ann_ids) + all_iscrowd = all([_['iscrowd'] for _ in ann_info]) + if self.filter_empty_gt and (self.img_ids[i] not in ids_in_cat + or all_iscrowd): + continue + if min(img_info['width'], img_info['height']) >= min_size: + valid_inds.append(i) + valid_img_ids.append(img_id) + self.img_ids = valid_img_ids + return valid_inds + + def _parse_ann_info(self, img_info, ann_info): + """Parse bbox and mask annotation. + + Args: + img_info (dict): Image info of an image. + ann_info (list[dict]): Annotation info of an image. + + Returns: + dict: A dict containing the following keys: bboxes, \ + bboxes_ignore, labels, masks, seg_map. \ + "masks" are already decoded into binary masks. + """ + gt_bboxes = [] + gt_labels = [] + gt_bboxes_ignore = [] + gt_masks_ann = [] + + for i, ann in enumerate(ann_info): + if ann.get('ignore', False): + continue + x1, y1, w, h = ann['bbox'] + if ann['area'] <= 0 or w < 1 or h < 1: + continue + if ann['category_id'] not in self.cat_ids: + continue + bbox = [x1, y1, x1 + w, y1 + h] + if ann.get('iscrowd', False): + gt_bboxes_ignore.append(bbox) + else: + gt_bboxes.append(bbox) + gt_labels.append(self.cat2label[ann['category_id']]) + gt_masks_ann.append(ann['segmentation']) + + if gt_bboxes: + gt_bboxes = np.array(gt_bboxes, dtype=np.float32) + gt_labels = np.array(gt_labels, dtype=np.int64) + else: + gt_bboxes = np.zeros((0, 4), dtype=np.float32) + gt_labels = np.array([], dtype=np.int64) + + if gt_bboxes_ignore: + gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32) + else: + gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32) + + ann = dict( + bboxes=gt_bboxes, + labels=gt_labels, + bboxes_ignore=gt_bboxes_ignore, + masks=gt_masks_ann, + seg_map=img_info['segm_file']) + + return ann + + def results2txt(self, results, outfile_prefix): + """Dump the detection results to a txt file. + + Args: + results (list[list | tuple]): Testing results of the + dataset. + outfile_prefix (str): The filename prefix of the json files. + If the prefix is "somepath/xxx", + the txt files will be named "somepath/xxx.txt". + + Returns: + list[str]: Result txt files which contains corresponding \ + instance segmentation images. + """ + try: + import cityscapesscripts.helpers.labels as CSLabels + except ImportError: + raise ImportError('Please run "pip install citscapesscripts" to ' + 'install cityscapesscripts first.') + result_files = [] + os.makedirs(outfile_prefix, exist_ok=True) + prog_bar = mmcv.ProgressBar(len(self)) + for idx in range(len(self)): + result = results[idx] + filename = self.data_infos[idx]['filename'] + basename = osp.splitext(osp.basename(filename))[0] + pred_txt = osp.join(outfile_prefix, basename + '_pred.txt') + + bbox_result, segm_result = result + bboxes = np.vstack(bbox_result) + # segm results + if isinstance(segm_result, tuple): + # Some detectors use different scores for bbox and mask, + # like Mask Scoring R-CNN. Score of segm will be used instead + # of bbox score. + segms = mmcv.concat_list(segm_result[0]) + mask_score = segm_result[1] + else: + # use bbox score for mask score + segms = mmcv.concat_list(segm_result) + mask_score = [bbox[-1] for bbox in bboxes] + labels = [ + np.full(bbox.shape[0], i, dtype=np.int32) + for i, bbox in enumerate(bbox_result) + ] + labels = np.concatenate(labels) + + assert len(bboxes) == len(segms) == len(labels) + num_instances = len(bboxes) + prog_bar.update() + with open(pred_txt, 'w') as fout: + for i in range(num_instances): + pred_class = labels[i] + classes = self.CLASSES[pred_class] + class_id = CSLabels.name2label[classes].id + score = mask_score[i] + mask = maskUtils.decode(segms[i]).astype(np.uint8) + png_filename = osp.join(outfile_prefix, + basename + f'_{i}_{classes}.png') + mmcv.imwrite(mask, png_filename) + fout.write(f'{osp.basename(png_filename)} {class_id} ' + f'{score}\n') + result_files.append(pred_txt) + + return result_files + + def format_results(self, results, txtfile_prefix=None): + """Format the results to txt (standard format for Cityscapes + evaluation). + + Args: + results (list): Testing results of the dataset. + txtfile_prefix (str | None): The prefix of txt files. It includes + the file path and the prefix of filename, e.g., "a/b/prefix". + If not specified, a temp file will be created. Default: None. + + Returns: + tuple: (result_files, tmp_dir), result_files is a dict containing \ + the json filepaths, tmp_dir is the temporal directory created \ + for saving txt/png files when txtfile_prefix is not specified. + """ + assert isinstance(results, list), 'results must be a list' + assert len(results) == len(self), ( + 'The length of results is not equal to the dataset len: {} != {}'. + format(len(results), len(self))) + + assert isinstance(results, list), 'results must be a list' + assert len(results) == len(self), ( + 'The length of results is not equal to the dataset len: {} != {}'. + format(len(results), len(self))) + + if txtfile_prefix is None: + tmp_dir = tempfile.TemporaryDirectory() + txtfile_prefix = osp.join(tmp_dir.name, 'results') + else: + tmp_dir = None + result_files = self.results2txt(results, txtfile_prefix) + + return result_files, tmp_dir + + def evaluate(self, + results, + metric='bbox', + logger=None, + outfile_prefix=None, + classwise=False, + proposal_nums=(100, 300, 1000), + iou_thrs=np.arange(0.5, 0.96, 0.05)): + """Evaluation in Cityscapes/COCO protocol. + + Args: + results (list[list | tuple]): Testing results of the dataset. + metric (str | list[str]): Metrics to be evaluated. Options are + 'bbox', 'segm', 'proposal', 'proposal_fast'. + logger (logging.Logger | str | None): Logger used for printing + related information during evaluation. Default: None. + outfile_prefix (str | None): The prefix of output file. It includes + the file path and the prefix of filename, e.g., "a/b/prefix". + If results are evaluated with COCO protocol, it would be the + prefix of output json file. For example, the metric is 'bbox' + and 'segm', then json files would be "a/b/prefix.bbox.json" and + "a/b/prefix.segm.json". + If results are evaluated with cityscapes protocol, it would be + the prefix of output txt/png files. The output files would be + png images under folder "a/b/prefix/xxx/" and the file name of + images would be written into a txt file + "a/b/prefix/xxx_pred.txt", where "xxx" is the video name of + cityscapes. If not specified, a temp file will be created. + Default: None. + classwise (bool): Whether to evaluating the AP for each class. + proposal_nums (Sequence[int]): Proposal number used for evaluating + recalls, such as recall@100, recall@1000. + Default: (100, 300, 1000). + iou_thrs (Sequence[float]): IoU threshold used for evaluating + recalls. If set to a list, the average recall of all IoUs will + also be computed. Default: 0.5. + + Returns: + dict[str, float]: COCO style evaluation metric or cityscapes mAP \ + and AP@50. + """ + eval_results = dict() + + metrics = metric.copy() if isinstance(metric, list) else [metric] + + if 'cityscapes' in metrics: + eval_results.update( + self._evaluate_cityscapes(results, outfile_prefix, logger)) + metrics.remove('cityscapes') + + # left metrics are all coco metric + if len(metrics) > 0: + # create CocoDataset with CityscapesDataset annotation + self_coco = CocoDataset(self.ann_file, self.pipeline.transforms, + None, self.data_root, self.img_prefix, + self.seg_prefix, self.proposal_file, + self.test_mode, self.filter_empty_gt) + # TODO: remove this in the future + # reload annotations of correct class + self_coco.CLASSES = self.CLASSES + self_coco.data_infos = self_coco.load_annotations(self.ann_file) + eval_results.update( + self_coco.evaluate(results, metrics, logger, outfile_prefix, + classwise, proposal_nums, iou_thrs)) + + return eval_results + + def _evaluate_cityscapes(self, results, txtfile_prefix, logger): + """Evaluation in Cityscapes protocol. + + Args: + results (list): Testing results of the dataset. + txtfile_prefix (str | None): The prefix of output txt file + logger (logging.Logger | str | None): Logger used for printing + related information during evaluation. Default: None. + + Returns: + dict[str: float]: Cityscapes evaluation results, contains 'mAP' \ + and 'AP@50'. + """ + + try: + import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as CSEval # noqa + except ImportError: + raise ImportError('Please run "pip install citscapesscripts" to ' + 'install cityscapesscripts first.') + msg = 'Evaluating in Cityscapes style' + if logger is None: + msg = '\n' + msg + print_log(msg, logger=logger) + + result_files, tmp_dir = self.format_results(results, txtfile_prefix) + + if tmp_dir is None: + result_dir = osp.join(txtfile_prefix, 'results') + else: + result_dir = osp.join(tmp_dir.name, 'results') + + eval_results = OrderedDict() + print_log(f'Evaluating results under {result_dir} ...', logger=logger) + + # set global states in cityscapes evaluation API + CSEval.args.cityscapesPath = os.path.join(self.img_prefix, '../..') + CSEval.args.predictionPath = os.path.abspath(result_dir) + CSEval.args.predictionWalk = None + CSEval.args.JSONOutput = False + CSEval.args.colorized = False + CSEval.args.gtInstancesFile = os.path.join(result_dir, + 'gtInstances.json') + CSEval.args.groundTruthSearch = os.path.join( + self.img_prefix.replace('leftImg8bit', 'gtFine'), + '*/*_gtFine_instanceIds.png') + + groundTruthImgList = glob.glob(CSEval.args.groundTruthSearch) + assert len(groundTruthImgList), 'Cannot find ground truth images' \ + f' in {CSEval.args.groundTruthSearch}.' + predictionImgList = [] + for gt in groundTruthImgList: + predictionImgList.append(CSEval.getPrediction(gt, CSEval.args)) + CSEval_results = CSEval.evaluateImgLists(predictionImgList, + groundTruthImgList, + CSEval.args)['averages'] + + eval_results['mAP'] = CSEval_results['allAp'] + eval_results['AP@50'] = CSEval_results['allAp50%'] + if tmp_dir is not None: + tmp_dir.cleanup() + return eval_results diff --git a/mmdet/datasets/coco.py b/mmdet/datasets/coco.py new file mode 100644 index 0000000..21bba21 --- /dev/null +++ b/mmdet/datasets/coco.py @@ -0,0 +1,560 @@ +import itertools +import logging +import os.path as osp +import tempfile +import warnings +from collections import OrderedDict + +import mmcv +import numpy as np +from mmcv.utils import print_log +from terminaltables import AsciiTable + +from mmdet.core import eval_recalls +from .api_wrappers import COCO, COCOeval +from .builder import DATASETS +from .custom import CustomDataset + + +@DATASETS.register_module() +class CocoDataset(CustomDataset): + + CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', + 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', + 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', + 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', + 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', + 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', + 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', + 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', + 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', + 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', + 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', + 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', + 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', + 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush') + + def load_annotations(self, ann_file): + """Load annotation from COCO style annotation file. + + Args: + ann_file (str): Path of annotation file. + + Returns: + list[dict]: Annotation info from COCO api. + """ + + self.coco = COCO(ann_file) + self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES) + self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)} + self.img_ids = self.coco.get_img_ids() + data_infos = [] + total_ann_ids = [] + for i in self.img_ids: + info = self.coco.load_imgs([i])[0] + info['filename'] = info['file_name'] + data_infos.append(info) + ann_ids = self.coco.get_ann_ids(img_ids=[i]) + total_ann_ids.extend(ann_ids) + assert len(set(total_ann_ids)) == len( + total_ann_ids), f"Annotation ids in '{ann_file}' are not unique!" + return data_infos + + def get_ann_info(self, idx): + """Get COCO annotation by index. + + Args: + idx (int): Index of data. + + Returns: + dict: Annotation info of specified index. + """ + + img_id = self.data_infos[idx]['id'] + ann_ids = self.coco.get_ann_ids(img_ids=[img_id]) + ann_info = self.coco.load_anns(ann_ids) + return self._parse_ann_info(self.data_infos[idx], ann_info) + + def get_cat_ids(self, idx): + """Get COCO category ids by index. + + Args: + idx (int): Index of data. + + Returns: + list[int]: All categories in the image of specified index. + """ + + img_id = self.data_infos[idx]['id'] + ann_ids = self.coco.get_ann_ids(img_ids=[img_id]) + ann_info = self.coco.load_anns(ann_ids) + return [ann['category_id'] for ann in ann_info] + + def _filter_imgs(self, min_size=32): + """Filter images too small or without ground truths.""" + valid_inds = [] + # obtain images that contain annotation + ids_with_ann = set(_['image_id'] for _ in self.coco.anns.values()) + # obtain images that contain annotations of the required categories + ids_in_cat = set() + for i, class_id in enumerate(self.cat_ids): + ids_in_cat |= set(self.coco.cat_img_map[class_id]) + # merge the image id sets of the two conditions and use the merged set + # to filter out images if self.filter_empty_gt=True + ids_in_cat &= ids_with_ann + + valid_img_ids = [] + for i, img_info in enumerate(self.data_infos): + img_id = self.img_ids[i] + if self.filter_empty_gt and img_id not in ids_in_cat: + continue + if min(img_info['width'], img_info['height']) >= min_size: + valid_inds.append(i) + valid_img_ids.append(img_id) + self.img_ids = valid_img_ids + return valid_inds + + def _parse_ann_info(self, img_info, ann_info): + """Parse bbox and mask annotation. + + Args: + ann_info (list[dict]): Annotation info of an image. + with_mask (bool): Whether to parse mask annotations. + + Returns: + dict: A dict containing the following keys: bboxes, bboxes_ignore,\ + labels, masks, seg_map. "masks" are raw annotations and not \ + decoded into binary masks. + """ + gt_bboxes = [] + gt_labels = [] + gt_bboxes_ignore = [] + gt_masks_ann = [] + for i, ann in enumerate(ann_info): + if ann.get('ignore', False): + continue + x1, y1, w, h = ann['bbox'] + inter_w = max(0, min(x1 + w, img_info['width']) - max(x1, 0)) + inter_h = max(0, min(y1 + h, img_info['height']) - max(y1, 0)) + if inter_w * inter_h == 0: + continue + if ann['area'] <= 0 or w < 1 or h < 1: + continue + if ann['category_id'] not in self.cat_ids: + continue + bbox = [x1, y1, x1 + w, y1 + h] + if ann.get('iscrowd', False): + gt_bboxes_ignore.append(bbox) + else: + gt_bboxes.append(bbox) + gt_labels.append(self.cat2label[ann['category_id']]) + gt_masks_ann.append(ann.get('segmentation', None)) + + if gt_bboxes: + gt_bboxes = np.array(gt_bboxes, dtype=np.float32) + gt_labels = np.array(gt_labels, dtype=np.int64) + else: + gt_bboxes = np.zeros((0, 4), dtype=np.float32) + gt_labels = np.array([], dtype=np.int64) + + if gt_bboxes_ignore: + gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32) + else: + gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32) + + seg_map = img_info['filename'].replace('jpg', 'png') + + ann = dict( + bboxes=gt_bboxes, + labels=gt_labels, + bboxes_ignore=gt_bboxes_ignore, + masks=gt_masks_ann, + seg_map=seg_map) + + return ann + + def xyxy2xywh(self, bbox): + """Convert ``xyxy`` style bounding boxes to ``xywh`` style for COCO + evaluation. + + Args: + bbox (numpy.ndarray): The bounding boxes, shape (4, ), in + ``xyxy`` order. + + Returns: + list[float]: The converted bounding boxes, in ``xywh`` order. + """ + + _bbox = bbox.tolist() + return [ + _bbox[0], + _bbox[1], + _bbox[2] - _bbox[0], + _bbox[3] - _bbox[1], + ] + + def _proposal2json(self, results): + """Convert proposal results to COCO json style.""" + json_results = [] + for idx in range(len(self)): + img_id = self.img_ids[idx] + bboxes = results[idx] + for i in range(bboxes.shape[0]): + data = dict() + data['image_id'] = img_id + data['bbox'] = self.xyxy2xywh(bboxes[i]) + data['score'] = float(bboxes[i][4]) + data['category_id'] = 1 + json_results.append(data) + return json_results + + def _det2json(self, results): + """Convert detection results to COCO json style.""" + json_results = [] + for idx in range(len(self)): + img_id = self.img_ids[idx] + result = results[idx] + for label in range(len(result)): + bboxes = result[label] + for i in range(bboxes.shape[0]): + data = dict() + data['image_id'] = img_id + data['bbox'] = self.xyxy2xywh(bboxes[i]) + try: + data['fangyi_label'] = int(bboxes[i][5]) + data['fangyi_query'] = int(bboxes[i][6]) + except: + a = 1 + data['score'] = float(bboxes[i][4]) + data['category_id'] = self.cat_ids[label] + json_results.append(data) + return json_results + + def _segm2json(self, results): + """Convert instance segmentation results to COCO json style.""" + bbox_json_results = [] + segm_json_results = [] + for idx in range(len(self)): + img_id = self.img_ids[idx] + det, seg = results[idx] + for label in range(len(det)): + # bbox results + bboxes = det[label] + for i in range(bboxes.shape[0]): + data = dict() + data['image_id'] = img_id + data['bbox'] = self.xyxy2xywh(bboxes[i]) + data['score'] = float(bboxes[i][4]) + data['category_id'] = self.cat_ids[label] + bbox_json_results.append(data) + + # segm results + # some detectors use different scores for bbox and mask + if isinstance(seg, tuple): + segms = seg[0][label] + mask_score = seg[1][label] + else: + segms = seg[label] + mask_score = [bbox[4] for bbox in bboxes] + for i in range(bboxes.shape[0]): + data = dict() + data['image_id'] = img_id + data['bbox'] = self.xyxy2xywh(bboxes[i]) + data['score'] = float(mask_score[i]) + data['category_id'] = self.cat_ids[label] + if isinstance(segms[i]['counts'], bytes): + segms[i]['counts'] = segms[i]['counts'].decode() + data['segmentation'] = segms[i] + segm_json_results.append(data) + return bbox_json_results, segm_json_results + + def results2json(self, results, outfile_prefix): + """Dump the detection results to a COCO style json file. + + There are 3 types of results: proposals, bbox predictions, mask + predictions, and they have different data types. This method will + automatically recognize the type, and dump them to json files. + + Args: + results (list[list | tuple | ndarray]): Testing results of the + dataset. + outfile_prefix (str): The filename prefix of the json files. If the + prefix is "somepath/xxx", the json files will be named + "somepath/xxx.bbox.json", "somepath/xxx.segm.json", + "somepath/xxx.proposal.json". + + Returns: + dict[str: str]: Possible keys are "bbox", "segm", "proposal", and \ + values are corresponding filenames. + """ + result_files = dict() + if isinstance(results[0], list): + json_results = self._det2json(results) + result_files['bbox'] = f'{outfile_prefix}.bbox.json' + result_files['proposal'] = f'{outfile_prefix}.bbox.json' + mmcv.dump(json_results, result_files['bbox']) + elif isinstance(results[0], tuple): + json_results = self._segm2json(results) + result_files['bbox'] = f'{outfile_prefix}.bbox.json' + result_files['proposal'] = f'{outfile_prefix}.bbox.json' + result_files['segm'] = f'{outfile_prefix}.segm.json' + mmcv.dump(json_results[0], result_files['bbox']) + mmcv.dump(json_results[1], result_files['segm']) + elif isinstance(results[0], np.ndarray): + json_results = self._proposal2json(results) + result_files['proposal'] = f'{outfile_prefix}.proposal.json' + mmcv.dump(json_results, result_files['proposal']) + else: + raise TypeError('invalid type of results') + return result_files + + def fast_eval_recall(self, results, proposal_nums, iou_thrs, logger=None): + gt_bboxes = [] + for i in range(len(self.img_ids)): + ann_ids = self.coco.get_ann_ids(img_ids=self.img_ids[i]) + ann_info = self.coco.load_anns(ann_ids) + if len(ann_info) == 0: + gt_bboxes.append(np.zeros((0, 4))) + continue + bboxes = [] + for ann in ann_info: + if ann.get('ignore', False) or ann['iscrowd']: + continue + x1, y1, w, h = ann['bbox'] + bboxes.append([x1, y1, x1 + w, y1 + h]) + bboxes = np.array(bboxes, dtype=np.float32) + if bboxes.shape[0] == 0: + bboxes = np.zeros((0, 4)) + gt_bboxes.append(bboxes) + + recalls = eval_recalls( + gt_bboxes, results, proposal_nums, iou_thrs, logger=logger) + ar = recalls.mean(axis=1) + return ar + + def format_results(self, results, jsonfile_prefix=None, **kwargs): + """Format the results to json (standard format for COCO evaluation). + + Args: + results (list[tuple | numpy.ndarray]): Testing results of the + dataset. + jsonfile_prefix (str | None): The prefix of json files. It includes + the file path and the prefix of filename, e.g., "a/b/prefix". + If not specified, a temp file will be created. Default: None. + + Returns: + tuple: (result_files, tmp_dir), result_files is a dict containing \ + the json filepaths, tmp_dir is the temporal directory created \ + for saving json files when jsonfile_prefix is not specified. + """ + assert isinstance(results, list), 'results must be a list' + assert len(results) == len(self), ( + 'The length of results is not equal to the dataset len: {} != {}'. + format(len(results), len(self))) + + if jsonfile_prefix is None: + tmp_dir = tempfile.TemporaryDirectory() + jsonfile_prefix = osp.join(tmp_dir.name, 'results') + else: + tmp_dir = None + result_files = self.results2json(results, jsonfile_prefix) + return result_files, tmp_dir + + def evaluate(self, + results, + metric='bbox', + logger=None, + jsonfile_prefix=None, + classwise=False, + proposal_nums=(100, 300, 1000), + iou_thrs=None, + metric_items=None): + """Evaluation in COCO protocol. + + Args: + results (list[list | tuple]): Testing results of the dataset. + metric (str | list[str]): Metrics to be evaluated. Options are + 'bbox', 'segm', 'proposal', 'proposal_fast'. + logger (logging.Logger | str | None): Logger used for printing + related information during evaluation. Default: None. + jsonfile_prefix (str | None): The prefix of json files. It includes + the file path and the prefix of filename, e.g., "a/b/prefix". + If not specified, a temp file will be created. Default: None. + classwise (bool): Whether to evaluating the AP for each class. + proposal_nums (Sequence[int]): Proposal number used for evaluating + recalls, such as recall@100, recall@1000. + Default: (100, 300, 1000). + iou_thrs (Sequence[float], optional): IoU threshold used for + evaluating recalls/mAPs. If set to a list, the average of all + IoUs will also be computed. If not specified, [0.50, 0.55, + 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95] will be used. + Default: None. + metric_items (list[str] | str, optional): Metric items that will + be returned. If not specified, ``['AR@100', 'AR@300', + 'AR@1000', 'AR_s@1000', 'AR_m@1000', 'AR_l@1000' ]`` will be + used when ``metric=='proposal'``, ``['mAP', 'mAP_50', 'mAP_75', + 'mAP_s', 'mAP_m', 'mAP_l']`` will be used when + ``metric=='bbox' or metric=='segm'``. + + Returns: + dict[str, float]: COCO style evaluation metric. + """ + + metrics = metric if isinstance(metric, list) else [metric] + allowed_metrics = ['bbox', 'segm', 'proposal', 'proposal_fast'] + for metric in metrics: + if metric not in allowed_metrics: + raise KeyError(f'metric {metric} is not supported') + if iou_thrs is None: + iou_thrs = np.linspace( + .5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True) + if metric_items is not None: + if not isinstance(metric_items, list): + metric_items = [metric_items] + + result_files, tmp_dir = self.format_results(results, jsonfile_prefix) + + eval_results = OrderedDict() + cocoGt = self.coco + for metric in metrics: + msg = f'Evaluating {metric}...' + if logger is None: + msg = '\n' + msg + print_log(msg, logger=logger) + + if metric == 'proposal_fast': + ar = self.fast_eval_recall( + results, proposal_nums, iou_thrs, logger='silent') + log_msg = [] + for i, num in enumerate(proposal_nums): + eval_results[f'AR@{num}'] = ar[i] + log_msg.append(f'\nAR@{num}\t{ar[i]:.4f}') + log_msg = ''.join(log_msg) + print_log(log_msg, logger=logger) + continue + + iou_type = 'bbox' if metric == 'proposal' else metric + if metric not in result_files: + raise KeyError(f'{metric} is not in results') + try: + predictions = mmcv.load(result_files[metric]) + if iou_type == 'segm': + # Refer to https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/coco.py#L331 # noqa + # When evaluating mask AP, if the results contain bbox, + # cocoapi will use the box area instead of the mask area + # for calculating the instance area. Though the overall AP + # is not affected, this leads to different + # small/medium/large mask AP results. + for x in predictions: + x.pop('bbox') + warnings.simplefilter('once') + warnings.warn( + 'The key "bbox" is deleted for more accurate mask AP ' + 'of small/medium/large instances since v2.12.0. This ' + 'does not change the overall mAP calculation.', + UserWarning) + cocoDt = cocoGt.loadRes(predictions) + except IndexError: + print_log( + 'The testing results of the whole dataset is empty.', + logger=logger, + level=logging.ERROR) + break + + cocoEval = COCOeval(cocoGt, cocoDt, iou_type) + cocoEval.params.catIds = self.cat_ids + cocoEval.params.imgIds = self.img_ids + cocoEval.params.maxDets = list(proposal_nums) + cocoEval.params.iouThrs = iou_thrs + # mapping of cocoEval.stats + coco_metric_names = { + 'mAP': 0, + 'mAP_50': 1, + 'mAP_75': 2, + 'mAP_s': 3, + 'mAP_m': 4, + 'mAP_l': 5, + 'AR@100': 6, + 'AR@300': 7, + 'AR@1000': 8, + 'AR_s@1000': 9, + 'AR_m@1000': 10, + 'AR_l@1000': 11 + } + if metric_items is not None: + for metric_item in metric_items: + if metric_item not in coco_metric_names: + raise KeyError( + f'metric item {metric_item} is not supported') + + if metric == 'proposal': + cocoEval.params.useCats = 0 + cocoEval.evaluate() + cocoEval.accumulate() + cocoEval.summarize() + if metric_items is None: + metric_items = [ + 'AR@100', 'AR@300', 'AR@1000', 'AR_s@1000', + 'AR_m@1000', 'AR_l@1000' + ] + + for item in metric_items: + val = float( + f'{cocoEval.stats[coco_metric_names[item]]:.3f}') + eval_results[item] = val + else: + cocoEval.evaluate() + cocoEval.accumulate() + cocoEval.summarize() + if classwise: # Compute per-category AP + # Compute per-category AP + # from https://github.com/facebookresearch/detectron2/ + precisions = cocoEval.eval['precision'] + # precision: (iou, recall, cls, area range, max dets) + assert len(self.cat_ids) == precisions.shape[2] + + results_per_category = [] + for idx, catId in enumerate(self.cat_ids): + # area range index 0: all area ranges + # max dets index -1: typically 100 per image + nm = self.coco.loadCats(catId)[0] + precision = precisions[:, :, idx, 0, -1] + precision = precision[precision > -1] + if precision.size: + ap = np.mean(precision) + else: + ap = float('nan') + results_per_category.append( + (f'{nm["name"]}', f'{float(ap):0.3f}')) + + num_columns = min(6, len(results_per_category) * 2) + results_flatten = list( + itertools.chain(*results_per_category)) + headers = ['category', 'AP'] * (num_columns // 2) + results_2d = itertools.zip_longest(*[ + results_flatten[i::num_columns] + for i in range(num_columns) + ]) + table_data = [headers] + table_data += [result for result in results_2d] + table = AsciiTable(table_data) + print_log('\n' + table.table, logger=logger) + + if metric_items is None: + metric_items = [ + 'mAP', 'mAP_50', 'mAP_75', 'mAP_s', 'mAP_m', 'mAP_l' + ] + + for metric_item in metric_items: + key = f'{metric}_{metric_item}' + val = float( + f'{cocoEval.stats[coco_metric_names[metric_item]]:.3f}' + ) + eval_results[key] = val + ap = cocoEval.stats[:6] + eval_results[f'{metric}_mAP_copypaste'] = ( + f'{ap[0]:.3f} {ap[1]:.3f} {ap[2]:.3f} {ap[3]:.3f} ' + f'{ap[4]:.3f} {ap[5]:.3f}') + if tmp_dir is not None: + tmp_dir.cleanup() + return eval_results diff --git a/mmdet/datasets/custom.py b/mmdet/datasets/custom.py new file mode 100644 index 0000000..2942ecc --- /dev/null +++ b/mmdet/datasets/custom.py @@ -0,0 +1,361 @@ +import os.path as osp +import warnings +from collections import OrderedDict + +import mmcv +import numpy as np +from mmcv.utils import print_log +from terminaltables import AsciiTable +from torch.utils.data import Dataset + +from mmdet.core import eval_map, eval_recalls +from .builder import DATASETS +from .pipelines import Compose + + +@DATASETS.register_module() +class CustomDataset(Dataset): + """Custom dataset for detection. + + The annotation format is shown as follows. The `ann` field is optional for + testing. + + .. code-block:: none + + [ + { + 'filename': 'a.jpg', + 'width': 1280, + 'height': 720, + 'ann': { + 'bboxes': (n, 4) in (x1, y1, x2, y2) order. + 'labels': (n, ), + 'bboxes_ignore': (k, 4), (optional field) + 'labels_ignore': (k, 4) (optional field) + } + }, + ... + ] + + Args: + ann_file (str): Annotation file path. + pipeline (list[dict]): Processing pipeline. + classes (str | Sequence[str], optional): Specify classes to load. + If is None, ``cls.CLASSES`` will be used. Default: None. + data_root (str, optional): Data root for ``ann_file``, + ``img_prefix``, ``seg_prefix``, ``proposal_file`` if specified. + test_mode (bool, optional): If set True, annotation will not be loaded. + filter_empty_gt (bool, optional): If set true, images without bounding + boxes of the dataset's classes will be filtered out. This option + only works when `test_mode=False`, i.e., we never filter images + during tests. + """ + + CLASSES = None + + def __init__(self, + ann_file, + pipeline, + classes=None, + data_root=None, + img_prefix='', + seg_prefix=None, + proposal_file=None, + test_mode=False, + filter_empty_gt=True): + self.ann_file = ann_file + self.data_root = data_root + self.img_prefix = img_prefix + self.seg_prefix = seg_prefix + self.proposal_file = proposal_file + self.test_mode = test_mode + self.filter_empty_gt = filter_empty_gt + self.CLASSES = self.get_classes(classes) + + # join paths if data_root is specified + if self.data_root is not None: + if not osp.isabs(self.ann_file): + self.ann_file = osp.join(self.data_root, self.ann_file) + if not (self.img_prefix is None or osp.isabs(self.img_prefix)): + self.img_prefix = osp.join(self.data_root, self.img_prefix) + if not (self.seg_prefix is None or osp.isabs(self.seg_prefix)): + self.seg_prefix = osp.join(self.data_root, self.seg_prefix) + if not (self.proposal_file is None + or osp.isabs(self.proposal_file)): + self.proposal_file = osp.join(self.data_root, + self.proposal_file) + # load annotations (and proposals) + self.data_infos = self.load_annotations(self.ann_file) + + if self.proposal_file is not None: + self.proposals = self.load_proposals(self.proposal_file) + else: + self.proposals = None + + # filter images too small and containing no annotations + if not test_mode: + valid_inds = self._filter_imgs() + self.data_infos = [self.data_infos[i] for i in valid_inds] + if self.proposals is not None: + self.proposals = [self.proposals[i] for i in valid_inds] + # set group flag for the sampler + self._set_group_flag() + + # processing pipeline + self.pipeline = Compose(pipeline) + + def __len__(self): + """Total number of samples of data.""" + return len(self.data_infos) + + def load_annotations(self, ann_file): + """Load annotation from annotation file.""" + return mmcv.load(ann_file) + + def load_proposals(self, proposal_file): + """Load proposal from proposal file.""" + return mmcv.load(proposal_file) + + def get_ann_info(self, idx): + """Get annotation by index. + + Args: + idx (int): Index of data. + + Returns: + dict: Annotation info of specified index. + """ + + return self.data_infos[idx]['ann'] + + def get_cat_ids(self, idx): + """Get category ids by index. + + Args: + idx (int): Index of data. + + Returns: + list[int]: All categories in the image of specified index. + """ + + return self.data_infos[idx]['ann']['labels'].astype(np.int).tolist() + + def pre_pipeline(self, results): + """Prepare results dict for pipeline.""" + results['img_prefix'] = self.img_prefix + results['seg_prefix'] = self.seg_prefix + results['proposal_file'] = self.proposal_file + results['bbox_fields'] = [] + results['mask_fields'] = [] + results['seg_fields'] = [] + + def _filter_imgs(self, min_size=32): + """Filter images too small.""" + if self.filter_empty_gt: + warnings.warn( + 'CustomDataset does not support filtering empty gt images.') + valid_inds = [] + for i, img_info in enumerate(self.data_infos): + if min(img_info['width'], img_info['height']) >= min_size: + valid_inds.append(i) + return valid_inds + + def _set_group_flag(self): + """Set flag according to image aspect ratio. + + Images with aspect ratio greater than 1 will be set as group 1, + otherwise group 0. + """ + self.flag = np.zeros(len(self), dtype=np.uint8) + for i in range(len(self)): + img_info = self.data_infos[i] + if img_info['width'] / img_info['height'] > 1: + self.flag[i] = 1 + + def _rand_another(self, idx): + """Get another random index from the same group as the given index.""" + pool = np.where(self.flag == self.flag[idx])[0] + return np.random.choice(pool) + + def __getitem__(self, idx): + """Get training/test data after pipeline. + + Args: + idx (int): Index of data. + + Returns: + dict: Training/test data (with annotation if `test_mode` is set \ + True). + """ + + if self.test_mode: + return self.prepare_test_img(idx) + while True: + data = self.prepare_train_img(idx) + if data is None: + idx = self._rand_another(idx) + continue + return data + + def prepare_train_img(self, idx): + """Get training data and annotations after pipeline. + + Args: + idx (int): Index of data. + + Returns: + dict: Training data and annotation after pipeline with new keys \ + introduced by pipeline. + """ + + img_info = self.data_infos[idx] + ann_info = self.get_ann_info(idx) + results = dict(img_info=img_info, ann_info=ann_info) + if self.proposals is not None: + results['proposals'] = self.proposals[idx] + self.pre_pipeline(results) + return self.pipeline(results) + + def prepare_test_img(self, idx): + """Get testing data after pipeline. + + Args: + idx (int): Index of data. + + Returns: + dict: Testing data after pipeline with new keys introduced by \ + pipeline. + """ + + img_info = self.data_infos[idx] + results = dict(img_info=img_info) + if self.proposals is not None: + results['proposals'] = self.proposals[idx] + self.pre_pipeline(results) + return self.pipeline(results) + + @classmethod + def get_classes(cls, classes=None): + """Get class names of current dataset. + + Args: + classes (Sequence[str] | str | None): If classes is None, use + default CLASSES defined by builtin dataset. If classes is a + string, take it as a file name. The file contains the name of + classes where each line contains one class name. If classes is + a tuple or list, override the CLASSES defined by the dataset. + + Returns: + tuple[str] or list[str]: Names of categories of the dataset. + """ + if classes is None: + return cls.CLASSES + + if isinstance(classes, str): + # take it as a file path + class_names = mmcv.list_from_file(classes) + elif isinstance(classes, (tuple, list)): + class_names = classes + else: + raise ValueError(f'Unsupported type {type(classes)} of classes.') + + return class_names + + def format_results(self, results, **kwargs): + """Place holder to format result to dataset specific output.""" + + def evaluate(self, + results, + metric='mAP', + logger=None, + proposal_nums=(100, 300, 1000), + iou_thr=0.5, + scale_ranges=None): + """Evaluate the dataset. + + Args: + results (list): Testing results of the dataset. + metric (str | list[str]): Metrics to be evaluated. + logger (logging.Logger | None | str): Logger used for printing + related information during evaluation. Default: None. + proposal_nums (Sequence[int]): Proposal number used for evaluating + recalls, such as recall@100, recall@1000. + Default: (100, 300, 1000). + iou_thr (float | list[float]): IoU threshold. Default: 0.5. + scale_ranges (list[tuple] | None): Scale ranges for evaluating mAP. + Default: None. + """ + + if not isinstance(metric, str): + assert len(metric) == 1 + metric = metric[0] + allowed_metrics = ['mAP', 'recall'] + if metric not in allowed_metrics: + raise KeyError(f'metric {metric} is not supported') + annotations = [self.get_ann_info(i) for i in range(len(self))] + eval_results = OrderedDict() + iou_thrs = [iou_thr] if isinstance(iou_thr, float) else iou_thr + if metric == 'mAP': + assert isinstance(iou_thrs, list) + mean_aps = [] + for iou_thr in iou_thrs: + print_log(f'\n{"-" * 15}iou_thr: {iou_thr}{"-" * 15}') + mean_ap, _ = eval_map( + results, + annotations, + scale_ranges=scale_ranges, + iou_thr=iou_thr, + dataset=self.CLASSES, + logger=logger) + mean_aps.append(mean_ap) + eval_results[f'AP{int(iou_thr * 100):02d}'] = round(mean_ap, 3) + eval_results['mAP'] = sum(mean_aps) / len(mean_aps) + elif metric == 'recall': + gt_bboxes = [ann['bboxes'] for ann in annotations] + recalls = eval_recalls( + gt_bboxes, results, proposal_nums, iou_thr, logger=logger) + for i, num in enumerate(proposal_nums): + for j, iou in enumerate(iou_thrs): + eval_results[f'recall@{num}@{iou}'] = recalls[i, j] + if recalls.shape[1] > 1: + ar = recalls.mean(axis=1) + for i, num in enumerate(proposal_nums): + eval_results[f'AR@{num}'] = ar[i] + return eval_results + + def __repr__(self): + """Print the number of instance number.""" + dataset_type = 'Test' if self.test_mode else 'Train' + result = (f'\n{self.__class__.__name__} {dataset_type} dataset ' + f'with number of images {len(self)}, ' + f'and instance counts: \n') + if self.CLASSES is None: + result += 'Category names are not provided. \n' + return result + instance_count = np.zeros(len(self.CLASSES) + 1).astype(int) + # count the instance number in each image + for idx in range(len(self)): + label = self.get_ann_info(idx)['labels'] + unique, counts = np.unique(label, return_counts=True) + if len(unique) > 0: + # add the occurrence number to each class + instance_count[unique] += counts + else: + # background is the last index + instance_count[-1] += 1 + # create a table with category count + table_data = [['category', 'count'] * 5] + row_data = [] + for cls, count in enumerate(instance_count): + if cls < len(self.CLASSES): + row_data += [f'{cls} [{self.CLASSES[cls]}]', f'{count}'] + else: + # add the background number + row_data += ['-1 background', f'{count}'] + if len(row_data) == 10: + table_data.append(row_data) + row_data = [] + + table = AsciiTable(table_data) + result += table.table + return result diff --git a/mmdet/datasets/dataset_wrappers.py b/mmdet/datasets/dataset_wrappers.py new file mode 100644 index 0000000..55ad5cb --- /dev/null +++ b/mmdet/datasets/dataset_wrappers.py @@ -0,0 +1,282 @@ +import bisect +import math +from collections import defaultdict + +import numpy as np +from mmcv.utils import print_log +from torch.utils.data.dataset import ConcatDataset as _ConcatDataset + +from .builder import DATASETS +from .coco import CocoDataset + + +@DATASETS.register_module() +class ConcatDataset(_ConcatDataset): + """A wrapper of concatenated dataset. + + Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but + concat the group flag for image aspect ratio. + + Args: + datasets (list[:obj:`Dataset`]): A list of datasets. + separate_eval (bool): Whether to evaluate the results + separately if it is used as validation dataset. + Defaults to True. + """ + + def __init__(self, datasets, separate_eval=True): + super(ConcatDataset, self).__init__(datasets) + self.CLASSES = datasets[0].CLASSES + self.separate_eval = separate_eval + if not separate_eval: + if any([isinstance(ds, CocoDataset) for ds in datasets]): + raise NotImplementedError( + 'Evaluating concatenated CocoDataset as a whole is not' + ' supported! Please set "separate_eval=True"') + elif len(set([type(ds) for ds in datasets])) != 1: + raise NotImplementedError( + 'All the datasets should have same types') + + if hasattr(datasets[0], 'flag'): + flags = [] + for i in range(0, len(datasets)): + flags.append(datasets[i].flag) + self.flag = np.concatenate(flags) + + def get_cat_ids(self, idx): + """Get category ids of concatenated dataset by index. + + Args: + idx (int): Index of data. + + Returns: + list[int]: All categories in the image of specified index. + """ + + if idx < 0: + if -idx > len(self): + raise ValueError( + 'absolute value of index should not exceed dataset length') + idx = len(self) + idx + dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx) + if dataset_idx == 0: + sample_idx = idx + else: + sample_idx = idx - self.cumulative_sizes[dataset_idx - 1] + return self.datasets[dataset_idx].get_cat_ids(sample_idx) + + def evaluate(self, results, logger=None, **kwargs): + """Evaluate the results. + + Args: + results (list[list | tuple]): Testing results of the dataset. + logger (logging.Logger | str | None): Logger used for printing + related information during evaluation. Default: None. + + Returns: + dict[str: float]: AP results of the total dataset or each separate + dataset if `self.separate_eval=True`. + """ + assert len(results) == self.cumulative_sizes[-1], \ + ('Dataset and results have different sizes: ' + f'{self.cumulative_sizes[-1]} v.s. {len(results)}') + + # Check whether all the datasets support evaluation + for dataset in self.datasets: + assert hasattr(dataset, 'evaluate'), \ + f'{type(dataset)} does not implement evaluate function' + + if self.separate_eval: + dataset_idx = -1 + total_eval_results = dict() + for size, dataset in zip(self.cumulative_sizes, self.datasets): + start_idx = 0 if dataset_idx == -1 else \ + self.cumulative_sizes[dataset_idx] + end_idx = self.cumulative_sizes[dataset_idx + 1] + + results_per_dataset = results[start_idx:end_idx] + print_log( + f'\nEvaluateing {dataset.ann_file} with ' + f'{len(results_per_dataset)} images now', + logger=logger) + + eval_results_per_dataset = dataset.evaluate( + results_per_dataset, logger=logger, **kwargs) + dataset_idx += 1 + for k, v in eval_results_per_dataset.items(): + total_eval_results.update({f'{dataset_idx}_{k}': v}) + + return total_eval_results + elif any([isinstance(ds, CocoDataset) for ds in self.datasets]): + raise NotImplementedError( + 'Evaluating concatenated CocoDataset as a whole is not' + ' supported! Please set "separate_eval=True"') + elif len(set([type(ds) for ds in self.datasets])) != 1: + raise NotImplementedError( + 'All the datasets should have same types') + else: + original_data_infos = self.datasets[0].data_infos + self.datasets[0].data_infos = sum( + [dataset.data_infos for dataset in self.datasets], []) + eval_results = self.datasets[0].evaluate( + results, logger=logger, **kwargs) + self.datasets[0].data_infos = original_data_infos + return eval_results + + +@DATASETS.register_module() +class RepeatDataset(object): + """A wrapper of repeated dataset. + + The length of repeated dataset will be `times` larger than the original + dataset. This is useful when the data loading time is long but the dataset + is small. Using RepeatDataset can reduce the data loading time between + epochs. + + Args: + dataset (:obj:`Dataset`): The dataset to be repeated. + times (int): Repeat times. + """ + + def __init__(self, dataset, times): + self.dataset = dataset + self.times = times + self.CLASSES = dataset.CLASSES + if hasattr(self.dataset, 'flag'): + self.flag = np.tile(self.dataset.flag, times) + + self._ori_len = len(self.dataset) + + def __getitem__(self, idx): + return self.dataset[idx % self._ori_len] + + def get_cat_ids(self, idx): + """Get category ids of repeat dataset by index. + + Args: + idx (int): Index of data. + + Returns: + list[int]: All categories in the image of specified index. + """ + + return self.dataset.get_cat_ids(idx % self._ori_len) + + def __len__(self): + """Length after repetition.""" + return self.times * self._ori_len + + +# Modified from https://github.com/facebookresearch/detectron2/blob/41d475b75a230221e21d9cac5d69655e3415e3a4/detectron2/data/samplers/distributed_sampler.py#L57 # noqa +@DATASETS.register_module() +class ClassBalancedDataset(object): + """A wrapper of repeated dataset with repeat factor. + + Suitable for training on class imbalanced datasets like LVIS. Following + the sampling strategy in the `paper `_, + in each epoch, an image may appear multiple times based on its + "repeat factor". + The repeat factor for an image is a function of the frequency the rarest + category labeled in that image. The "frequency of category c" in [0, 1] + is defined by the fraction of images in the training set (without repeats) + in which category c appears. + The dataset needs to instantiate :func:`self.get_cat_ids` to support + ClassBalancedDataset. + + The repeat factor is computed as followed. + + 1. For each category c, compute the fraction # of images + that contain it: :math:`f(c)` + 2. For each category c, compute the category-level repeat factor: + :math:`r(c) = max(1, sqrt(t/f(c)))` + 3. For each image I, compute the image-level repeat factor: + :math:`r(I) = max_{c in I} r(c)` + + Args: + dataset (:obj:`CustomDataset`): The dataset to be repeated. + oversample_thr (float): frequency threshold below which data is + repeated. For categories with ``f_c >= oversample_thr``, there is + no oversampling. For categories with ``f_c < oversample_thr``, the + degree of oversampling following the square-root inverse frequency + heuristic above. + filter_empty_gt (bool, optional): If set true, images without bounding + boxes will not be oversampled. Otherwise, they will be categorized + as the pure background class and involved into the oversampling. + Default: True. + """ + + def __init__(self, dataset, oversample_thr, filter_empty_gt=True): + self.dataset = dataset + self.oversample_thr = oversample_thr + self.filter_empty_gt = filter_empty_gt + self.CLASSES = dataset.CLASSES + + repeat_factors = self._get_repeat_factors(dataset, oversample_thr) + repeat_indices = [] + for dataset_idx, repeat_factor in enumerate(repeat_factors): + repeat_indices.extend([dataset_idx] * math.ceil(repeat_factor)) + self.repeat_indices = repeat_indices + + flags = [] + if hasattr(self.dataset, 'flag'): + for flag, repeat_factor in zip(self.dataset.flag, repeat_factors): + flags.extend([flag] * int(math.ceil(repeat_factor))) + assert len(flags) == len(repeat_indices) + self.flag = np.asarray(flags, dtype=np.uint8) + + def _get_repeat_factors(self, dataset, repeat_thr): + """Get repeat factor for each images in the dataset. + + Args: + dataset (:obj:`CustomDataset`): The dataset + repeat_thr (float): The threshold of frequency. If an image + contains the categories whose frequency below the threshold, + it would be repeated. + + Returns: + list[float]: The repeat factors for each images in the dataset. + """ + + # 1. For each category c, compute the fraction # of images + # that contain it: f(c) + category_freq = defaultdict(int) + num_images = len(dataset) + for idx in range(num_images): + cat_ids = set(self.dataset.get_cat_ids(idx)) + if len(cat_ids) == 0 and not self.filter_empty_gt: + cat_ids = set([len(self.CLASSES)]) + for cat_id in cat_ids: + category_freq[cat_id] += 1 + for k, v in category_freq.items(): + category_freq[k] = v / num_images + + # 2. For each category c, compute the category-level repeat factor: + # r(c) = max(1, sqrt(t/f(c))) + category_repeat = { + cat_id: max(1.0, math.sqrt(repeat_thr / cat_freq)) + for cat_id, cat_freq in category_freq.items() + } + + # 3. For each image I, compute the image-level repeat factor: + # r(I) = max_{c in I} r(c) + repeat_factors = [] + for idx in range(num_images): + cat_ids = set(self.dataset.get_cat_ids(idx)) + if len(cat_ids) == 0 and not self.filter_empty_gt: + cat_ids = set([len(self.CLASSES)]) + repeat_factor = 1 + if len(cat_ids) > 0: + repeat_factor = max( + {category_repeat[cat_id] + for cat_id in cat_ids}) + repeat_factors.append(repeat_factor) + + return repeat_factors + + def __getitem__(self, idx): + ori_index = self.repeat_indices[idx] + return self.dataset[ori_index] + + def __len__(self): + """Length after repetition.""" + return len(self.repeat_indices) diff --git a/mmdet/datasets/deepfashion.py b/mmdet/datasets/deepfashion.py new file mode 100644 index 0000000..1125376 --- /dev/null +++ b/mmdet/datasets/deepfashion.py @@ -0,0 +1,10 @@ +from .builder import DATASETS +from .coco import CocoDataset + + +@DATASETS.register_module() +class DeepFashionDataset(CocoDataset): + + CLASSES = ('top', 'skirt', 'leggings', 'dress', 'outer', 'pants', 'bag', + 'neckwear', 'headwear', 'eyeglass', 'belt', 'footwear', 'hair', + 'skin', 'face') diff --git a/mmdet/datasets/lvis.py b/mmdet/datasets/lvis.py new file mode 100644 index 0000000..dac8e2e --- /dev/null +++ b/mmdet/datasets/lvis.py @@ -0,0 +1,737 @@ +import itertools +import logging +import os.path as osp +import tempfile +import warnings +from collections import OrderedDict + +import numpy as np +from mmcv.utils import print_log +from terminaltables import AsciiTable + +from .builder import DATASETS +from .coco import CocoDataset + + +@DATASETS.register_module() +class LVISV05Dataset(CocoDataset): + + CLASSES = ( + 'acorn', 'aerosol_can', 'air_conditioner', 'airplane', 'alarm_clock', + 'alcohol', 'alligator', 'almond', 'ambulance', 'amplifier', 'anklet', + 'antenna', 'apple', 'apple_juice', 'applesauce', 'apricot', 'apron', + 'aquarium', 'armband', 'armchair', 'armoire', 'armor', 'artichoke', + 'trash_can', 'ashtray', 'asparagus', 'atomizer', 'avocado', 'award', + 'awning', 'ax', 'baby_buggy', 'basketball_backboard', 'backpack', + 'handbag', 'suitcase', 'bagel', 'bagpipe', 'baguet', 'bait', 'ball', + 'ballet_skirt', 'balloon', 'bamboo', 'banana', 'Band_Aid', 'bandage', + 'bandanna', 'banjo', 'banner', 'barbell', 'barge', 'barrel', + 'barrette', 'barrow', 'baseball_base', 'baseball', 'baseball_bat', + 'baseball_cap', 'baseball_glove', 'basket', 'basketball_hoop', + 'basketball', 'bass_horn', 'bat_(animal)', 'bath_mat', 'bath_towel', + 'bathrobe', 'bathtub', 'batter_(food)', 'battery', 'beachball', 'bead', + 'beaker', 'bean_curd', 'beanbag', 'beanie', 'bear', 'bed', + 'bedspread', 'cow', 'beef_(food)', 'beeper', 'beer_bottle', 'beer_can', + 'beetle', 'bell', 'bell_pepper', 'belt', 'belt_buckle', 'bench', + 'beret', 'bib', 'Bible', 'bicycle', 'visor', 'binder', 'binoculars', + 'bird', 'birdfeeder', 'birdbath', 'birdcage', 'birdhouse', + 'birthday_cake', 'birthday_card', 'biscuit_(bread)', 'pirate_flag', + 'black_sheep', 'blackboard', 'blanket', 'blazer', 'blender', 'blimp', + 'blinker', 'blueberry', 'boar', 'gameboard', 'boat', 'bobbin', + 'bobby_pin', 'boiled_egg', 'bolo_tie', 'deadbolt', 'bolt', 'bonnet', + 'book', 'book_bag', 'bookcase', 'booklet', 'bookmark', + 'boom_microphone', 'boot', 'bottle', 'bottle_opener', 'bouquet', + 'bow_(weapon)', 'bow_(decorative_ribbons)', 'bow-tie', 'bowl', + 'pipe_bowl', 'bowler_hat', 'bowling_ball', 'bowling_pin', + 'boxing_glove', 'suspenders', 'bracelet', 'brass_plaque', 'brassiere', + 'bread-bin', 'breechcloth', 'bridal_gown', 'briefcase', + 'bristle_brush', 'broccoli', 'broach', 'broom', 'brownie', + 'brussels_sprouts', 'bubble_gum', 'bucket', 'horse_buggy', 'bull', + 'bulldog', 'bulldozer', 'bullet_train', 'bulletin_board', + 'bulletproof_vest', 'bullhorn', 'corned_beef', 'bun', 'bunk_bed', + 'buoy', 'burrito', 'bus_(vehicle)', 'business_card', 'butcher_knife', + 'butter', 'butterfly', 'button', 'cab_(taxi)', 'cabana', 'cabin_car', + 'cabinet', 'locker', 'cake', 'calculator', 'calendar', 'calf', + 'camcorder', 'camel', 'camera', 'camera_lens', 'camper_(vehicle)', + 'can', 'can_opener', 'candelabrum', 'candle', 'candle_holder', + 'candy_bar', 'candy_cane', 'walking_cane', 'canister', 'cannon', + 'canoe', 'cantaloup', 'canteen', 'cap_(headwear)', 'bottle_cap', + 'cape', 'cappuccino', 'car_(automobile)', 'railcar_(part_of_a_train)', + 'elevator_car', 'car_battery', 'identity_card', 'card', 'cardigan', + 'cargo_ship', 'carnation', 'horse_carriage', 'carrot', 'tote_bag', + 'cart', 'carton', 'cash_register', 'casserole', 'cassette', 'cast', + 'cat', 'cauliflower', 'caviar', 'cayenne_(spice)', 'CD_player', + 'celery', 'cellular_telephone', 'chain_mail', 'chair', 'chaise_longue', + 'champagne', 'chandelier', 'chap', 'checkbook', 'checkerboard', + 'cherry', 'chessboard', 'chest_of_drawers_(furniture)', + 'chicken_(animal)', 'chicken_wire', 'chickpea', 'Chihuahua', + 'chili_(vegetable)', 'chime', 'chinaware', 'crisp_(potato_chip)', + 'poker_chip', 'chocolate_bar', 'chocolate_cake', 'chocolate_milk', + 'chocolate_mousse', 'choker', 'chopping_board', 'chopstick', + 'Christmas_tree', 'slide', 'cider', 'cigar_box', 'cigarette', + 'cigarette_case', 'cistern', 'clarinet', 'clasp', 'cleansing_agent', + 'clementine', 'clip', 'clipboard', 'clock', 'clock_tower', + 'clothes_hamper', 'clothespin', 'clutch_bag', 'coaster', 'coat', + 'coat_hanger', 'coatrack', 'cock', 'coconut', 'coffee_filter', + 'coffee_maker', 'coffee_table', 'coffeepot', 'coil', 'coin', + 'colander', 'coleslaw', 'coloring_material', 'combination_lock', + 'pacifier', 'comic_book', 'computer_keyboard', 'concrete_mixer', + 'cone', 'control', 'convertible_(automobile)', 'sofa_bed', 'cookie', + 'cookie_jar', 'cooking_utensil', 'cooler_(for_food)', + 'cork_(bottle_plug)', 'corkboard', 'corkscrew', 'edible_corn', + 'cornbread', 'cornet', 'cornice', 'cornmeal', 'corset', + 'romaine_lettuce', 'costume', 'cougar', 'coverall', 'cowbell', + 'cowboy_hat', 'crab_(animal)', 'cracker', 'crape', 'crate', 'crayon', + 'cream_pitcher', 'credit_card', 'crescent_roll', 'crib', 'crock_pot', + 'crossbar', 'crouton', 'crow', 'crown', 'crucifix', 'cruise_ship', + 'police_cruiser', 'crumb', 'crutch', 'cub_(animal)', 'cube', + 'cucumber', 'cufflink', 'cup', 'trophy_cup', 'cupcake', 'hair_curler', + 'curling_iron', 'curtain', 'cushion', 'custard', 'cutting_tool', + 'cylinder', 'cymbal', 'dachshund', 'dagger', 'dartboard', + 'date_(fruit)', 'deck_chair', 'deer', 'dental_floss', 'desk', + 'detergent', 'diaper', 'diary', 'die', 'dinghy', 'dining_table', 'tux', + 'dish', 'dish_antenna', 'dishrag', 'dishtowel', 'dishwasher', + 'dishwasher_detergent', 'diskette', 'dispenser', 'Dixie_cup', 'dog', + 'dog_collar', 'doll', 'dollar', 'dolphin', 'domestic_ass', 'eye_mask', + 'doorbell', 'doorknob', 'doormat', 'doughnut', 'dove', 'dragonfly', + 'drawer', 'underdrawers', 'dress', 'dress_hat', 'dress_suit', + 'dresser', 'drill', 'drinking_fountain', 'drone', 'dropper', + 'drum_(musical_instrument)', 'drumstick', 'duck', 'duckling', + 'duct_tape', 'duffel_bag', 'dumbbell', 'dumpster', 'dustpan', + 'Dutch_oven', 'eagle', 'earphone', 'earplug', 'earring', 'easel', + 'eclair', 'eel', 'egg', 'egg_roll', 'egg_yolk', 'eggbeater', + 'eggplant', 'electric_chair', 'refrigerator', 'elephant', 'elk', + 'envelope', 'eraser', 'escargot', 'eyepatch', 'falcon', 'fan', + 'faucet', 'fedora', 'ferret', 'Ferris_wheel', 'ferry', 'fig_(fruit)', + 'fighter_jet', 'figurine', 'file_cabinet', 'file_(tool)', 'fire_alarm', + 'fire_engine', 'fire_extinguisher', 'fire_hose', 'fireplace', + 'fireplug', 'fish', 'fish_(food)', 'fishbowl', 'fishing_boat', + 'fishing_rod', 'flag', 'flagpole', 'flamingo', 'flannel', 'flash', + 'flashlight', 'fleece', 'flip-flop_(sandal)', 'flipper_(footwear)', + 'flower_arrangement', 'flute_glass', 'foal', 'folding_chair', + 'food_processor', 'football_(American)', 'football_helmet', + 'footstool', 'fork', 'forklift', 'freight_car', 'French_toast', + 'freshener', 'frisbee', 'frog', 'fruit_juice', 'fruit_salad', + 'frying_pan', 'fudge', 'funnel', 'futon', 'gag', 'garbage', + 'garbage_truck', 'garden_hose', 'gargle', 'gargoyle', 'garlic', + 'gasmask', 'gazelle', 'gelatin', 'gemstone', 'giant_panda', + 'gift_wrap', 'ginger', 'giraffe', 'cincture', + 'glass_(drink_container)', 'globe', 'glove', 'goat', 'goggles', + 'goldfish', 'golf_club', 'golfcart', 'gondola_(boat)', 'goose', + 'gorilla', 'gourd', 'surgical_gown', 'grape', 'grasshopper', 'grater', + 'gravestone', 'gravy_boat', 'green_bean', 'green_onion', 'griddle', + 'grillroom', 'grinder_(tool)', 'grits', 'grizzly', 'grocery_bag', + 'guacamole', 'guitar', 'gull', 'gun', 'hair_spray', 'hairbrush', + 'hairnet', 'hairpin', 'ham', 'hamburger', 'hammer', 'hammock', + 'hamper', 'hamster', 'hair_dryer', 'hand_glass', 'hand_towel', + 'handcart', 'handcuff', 'handkerchief', 'handle', 'handsaw', + 'hardback_book', 'harmonium', 'hat', 'hatbox', 'hatch', 'veil', + 'headband', 'headboard', 'headlight', 'headscarf', 'headset', + 'headstall_(for_horses)', 'hearing_aid', 'heart', 'heater', + 'helicopter', 'helmet', 'heron', 'highchair', 'hinge', 'hippopotamus', + 'hockey_stick', 'hog', 'home_plate_(baseball)', 'honey', 'fume_hood', + 'hook', 'horse', 'hose', 'hot-air_balloon', 'hotplate', 'hot_sauce', + 'hourglass', 'houseboat', 'hummingbird', 'hummus', 'polar_bear', + 'icecream', 'popsicle', 'ice_maker', 'ice_pack', 'ice_skate', + 'ice_tea', 'igniter', 'incense', 'inhaler', 'iPod', + 'iron_(for_clothing)', 'ironing_board', 'jacket', 'jam', 'jean', + 'jeep', 'jelly_bean', 'jersey', 'jet_plane', 'jewelry', 'joystick', + 'jumpsuit', 'kayak', 'keg', 'kennel', 'kettle', 'key', 'keycard', + 'kilt', 'kimono', 'kitchen_sink', 'kitchen_table', 'kite', 'kitten', + 'kiwi_fruit', 'knee_pad', 'knife', 'knight_(chess_piece)', + 'knitting_needle', 'knob', 'knocker_(on_a_door)', 'koala', 'lab_coat', + 'ladder', 'ladle', 'ladybug', 'lamb_(animal)', 'lamb-chop', 'lamp', + 'lamppost', 'lampshade', 'lantern', 'lanyard', 'laptop_computer', + 'lasagna', 'latch', 'lawn_mower', 'leather', 'legging_(clothing)', + 'Lego', 'lemon', 'lemonade', 'lettuce', 'license_plate', 'life_buoy', + 'life_jacket', 'lightbulb', 'lightning_rod', 'lime', 'limousine', + 'linen_paper', 'lion', 'lip_balm', 'lipstick', 'liquor', 'lizard', + 'Loafer_(type_of_shoe)', 'log', 'lollipop', 'lotion', + 'speaker_(stero_equipment)', 'loveseat', 'machine_gun', 'magazine', + 'magnet', 'mail_slot', 'mailbox_(at_home)', 'mallet', 'mammoth', + 'mandarin_orange', 'manger', 'manhole', 'map', 'marker', 'martini', + 'mascot', 'mashed_potato', 'masher', 'mask', 'mast', + 'mat_(gym_equipment)', 'matchbox', 'mattress', 'measuring_cup', + 'measuring_stick', 'meatball', 'medicine', 'melon', 'microphone', + 'microscope', 'microwave_oven', 'milestone', 'milk', 'minivan', + 'mint_candy', 'mirror', 'mitten', 'mixer_(kitchen_tool)', 'money', + 'monitor_(computer_equipment) computer_monitor', 'monkey', 'motor', + 'motor_scooter', 'motor_vehicle', 'motorboat', 'motorcycle', + 'mound_(baseball)', 'mouse_(animal_rodent)', + 'mouse_(computer_equipment)', 'mousepad', 'muffin', 'mug', 'mushroom', + 'music_stool', 'musical_instrument', 'nailfile', 'nameplate', 'napkin', + 'neckerchief', 'necklace', 'necktie', 'needle', 'nest', 'newsstand', + 'nightshirt', 'nosebag_(for_animals)', 'noseband_(for_animals)', + 'notebook', 'notepad', 'nut', 'nutcracker', 'oar', 'octopus_(food)', + 'octopus_(animal)', 'oil_lamp', 'olive_oil', 'omelet', 'onion', + 'orange_(fruit)', 'orange_juice', 'oregano', 'ostrich', 'ottoman', + 'overalls_(clothing)', 'owl', 'packet', 'inkpad', 'pad', 'paddle', + 'padlock', 'paintbox', 'paintbrush', 'painting', 'pajamas', 'palette', + 'pan_(for_cooking)', 'pan_(metal_container)', 'pancake', 'pantyhose', + 'papaya', 'paperclip', 'paper_plate', 'paper_towel', 'paperback_book', + 'paperweight', 'parachute', 'parakeet', 'parasail_(sports)', + 'parchment', 'parka', 'parking_meter', 'parrot', + 'passenger_car_(part_of_a_train)', 'passenger_ship', 'passport', + 'pastry', 'patty_(food)', 'pea_(food)', 'peach', 'peanut_butter', + 'pear', 'peeler_(tool_for_fruit_and_vegetables)', 'pegboard', + 'pelican', 'pen', 'pencil', 'pencil_box', 'pencil_sharpener', + 'pendulum', 'penguin', 'pennant', 'penny_(coin)', 'pepper', + 'pepper_mill', 'perfume', 'persimmon', 'baby', 'pet', 'petfood', + 'pew_(church_bench)', 'phonebook', 'phonograph_record', 'piano', + 'pickle', 'pickup_truck', 'pie', 'pigeon', 'piggy_bank', 'pillow', + 'pin_(non_jewelry)', 'pineapple', 'pinecone', 'ping-pong_ball', + 'pinwheel', 'tobacco_pipe', 'pipe', 'pistol', 'pita_(bread)', + 'pitcher_(vessel_for_liquid)', 'pitchfork', 'pizza', 'place_mat', + 'plate', 'platter', 'playing_card', 'playpen', 'pliers', + 'plow_(farm_equipment)', 'pocket_watch', 'pocketknife', + 'poker_(fire_stirring_tool)', 'pole', 'police_van', 'polo_shirt', + 'poncho', 'pony', 'pool_table', 'pop_(soda)', 'portrait', + 'postbox_(public)', 'postcard', 'poster', 'pot', 'flowerpot', 'potato', + 'potholder', 'pottery', 'pouch', 'power_shovel', 'prawn', 'printer', + 'projectile_(weapon)', 'projector', 'propeller', 'prune', 'pudding', + 'puffer_(fish)', 'puffin', 'pug-dog', 'pumpkin', 'puncher', 'puppet', + 'puppy', 'quesadilla', 'quiche', 'quilt', 'rabbit', 'race_car', + 'racket', 'radar', 'radiator', 'radio_receiver', 'radish', 'raft', + 'rag_doll', 'raincoat', 'ram_(animal)', 'raspberry', 'rat', + 'razorblade', 'reamer_(juicer)', 'rearview_mirror', 'receipt', + 'recliner', 'record_player', 'red_cabbage', 'reflector', + 'remote_control', 'rhinoceros', 'rib_(food)', 'rifle', 'ring', + 'river_boat', 'road_map', 'robe', 'rocking_chair', 'roller_skate', + 'Rollerblade', 'rolling_pin', 'root_beer', + 'router_(computer_equipment)', 'rubber_band', 'runner_(carpet)', + 'plastic_bag', 'saddle_(on_an_animal)', 'saddle_blanket', 'saddlebag', + 'safety_pin', 'sail', 'salad', 'salad_plate', 'salami', + 'salmon_(fish)', 'salmon_(food)', 'salsa', 'saltshaker', + 'sandal_(type_of_shoe)', 'sandwich', 'satchel', 'saucepan', 'saucer', + 'sausage', 'sawhorse', 'saxophone', 'scale_(measuring_instrument)', + 'scarecrow', 'scarf', 'school_bus', 'scissors', 'scoreboard', + 'scrambled_eggs', 'scraper', 'scratcher', 'screwdriver', + 'scrubbing_brush', 'sculpture', 'seabird', 'seahorse', 'seaplane', + 'seashell', 'seedling', 'serving_dish', 'sewing_machine', 'shaker', + 'shampoo', 'shark', 'sharpener', 'Sharpie', 'shaver_(electric)', + 'shaving_cream', 'shawl', 'shears', 'sheep', 'shepherd_dog', + 'sherbert', 'shield', 'shirt', 'shoe', 'shopping_bag', 'shopping_cart', + 'short_pants', 'shot_glass', 'shoulder_bag', 'shovel', 'shower_head', + 'shower_curtain', 'shredder_(for_paper)', 'sieve', 'signboard', 'silo', + 'sink', 'skateboard', 'skewer', 'ski', 'ski_boot', 'ski_parka', + 'ski_pole', 'skirt', 'sled', 'sleeping_bag', 'sling_(bandage)', + 'slipper_(footwear)', 'smoothie', 'snake', 'snowboard', 'snowman', + 'snowmobile', 'soap', 'soccer_ball', 'sock', 'soda_fountain', + 'carbonated_water', 'sofa', 'softball', 'solar_array', 'sombrero', + 'soup', 'soup_bowl', 'soupspoon', 'sour_cream', 'soya_milk', + 'space_shuttle', 'sparkler_(fireworks)', 'spatula', 'spear', + 'spectacles', 'spice_rack', 'spider', 'sponge', 'spoon', 'sportswear', + 'spotlight', 'squirrel', 'stapler_(stapling_machine)', 'starfish', + 'statue_(sculpture)', 'steak_(food)', 'steak_knife', + 'steamer_(kitchen_appliance)', 'steering_wheel', 'stencil', + 'stepladder', 'step_stool', 'stereo_(sound_system)', 'stew', 'stirrer', + 'stirrup', 'stockings_(leg_wear)', 'stool', 'stop_sign', 'brake_light', + 'stove', 'strainer', 'strap', 'straw_(for_drinking)', 'strawberry', + 'street_sign', 'streetlight', 'string_cheese', 'stylus', 'subwoofer', + 'sugar_bowl', 'sugarcane_(plant)', 'suit_(clothing)', 'sunflower', + 'sunglasses', 'sunhat', 'sunscreen', 'surfboard', 'sushi', 'mop', + 'sweat_pants', 'sweatband', 'sweater', 'sweatshirt', 'sweet_potato', + 'swimsuit', 'sword', 'syringe', 'Tabasco_sauce', 'table-tennis_table', + 'table', 'table_lamp', 'tablecloth', 'tachometer', 'taco', 'tag', + 'taillight', 'tambourine', 'army_tank', 'tank_(storage_vessel)', + 'tank_top_(clothing)', 'tape_(sticky_cloth_or_paper)', 'tape_measure', + 'tapestry', 'tarp', 'tartan', 'tassel', 'tea_bag', 'teacup', + 'teakettle', 'teapot', 'teddy_bear', 'telephone', 'telephone_booth', + 'telephone_pole', 'telephoto_lens', 'television_camera', + 'television_set', 'tennis_ball', 'tennis_racket', 'tequila', + 'thermometer', 'thermos_bottle', 'thermostat', 'thimble', 'thread', + 'thumbtack', 'tiara', 'tiger', 'tights_(clothing)', 'timer', 'tinfoil', + 'tinsel', 'tissue_paper', 'toast_(food)', 'toaster', 'toaster_oven', + 'toilet', 'toilet_tissue', 'tomato', 'tongs', 'toolbox', 'toothbrush', + 'toothpaste', 'toothpick', 'cover', 'tortilla', 'tow_truck', 'towel', + 'towel_rack', 'toy', 'tractor_(farm_equipment)', 'traffic_light', + 'dirt_bike', 'trailer_truck', 'train_(railroad_vehicle)', 'trampoline', + 'tray', 'tree_house', 'trench_coat', 'triangle_(musical_instrument)', + 'tricycle', 'tripod', 'trousers', 'truck', 'truffle_(chocolate)', + 'trunk', 'vat', 'turban', 'turkey_(bird)', 'turkey_(food)', 'turnip', + 'turtle', 'turtleneck_(clothing)', 'typewriter', 'umbrella', + 'underwear', 'unicycle', 'urinal', 'urn', 'vacuum_cleaner', 'valve', + 'vase', 'vending_machine', 'vent', 'videotape', 'vinegar', 'violin', + 'vodka', 'volleyball', 'vulture', 'waffle', 'waffle_iron', 'wagon', + 'wagon_wheel', 'walking_stick', 'wall_clock', 'wall_socket', 'wallet', + 'walrus', 'wardrobe', 'wasabi', 'automatic_washer', 'watch', + 'water_bottle', 'water_cooler', 'water_faucet', 'water_filter', + 'water_heater', 'water_jug', 'water_gun', 'water_scooter', 'water_ski', + 'water_tower', 'watering_can', 'watermelon', 'weathervane', 'webcam', + 'wedding_cake', 'wedding_ring', 'wet_suit', 'wheel', 'wheelchair', + 'whipped_cream', 'whiskey', 'whistle', 'wick', 'wig', 'wind_chime', + 'windmill', 'window_box_(for_plants)', 'windshield_wiper', 'windsock', + 'wine_bottle', 'wine_bucket', 'wineglass', 'wing_chair', + 'blinder_(for_horses)', 'wok', 'wolf', 'wooden_spoon', 'wreath', + 'wrench', 'wristband', 'wristlet', 'yacht', 'yak', 'yogurt', + 'yoke_(animal_equipment)', 'zebra', 'zucchini') + + def load_annotations(self, ann_file): + """Load annotation from lvis style annotation file. + + Args: + ann_file (str): Path of annotation file. + + Returns: + list[dict]: Annotation info from LVIS api. + """ + + try: + import lvis + if getattr(lvis, '__version__', '0') >= '10.5.3': + warnings.warn( + 'mmlvis is deprecated, please install official lvis-api by "pip install git+https://github.com/lvis-dataset/lvis-api.git"', # noqa: E501 + UserWarning) + from lvis import LVIS + except ImportError: + raise ImportError( + 'Package lvis is not installed. Please run "pip install git+https://github.com/lvis-dataset/lvis-api.git".' # noqa: E501 + ) + self.coco = LVIS(ann_file) + self.cat_ids = self.coco.get_cat_ids() + self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)} + self.img_ids = self.coco.get_img_ids() + data_infos = [] + for i in self.img_ids: + info = self.coco.load_imgs([i])[0] + if info['file_name'].startswith('COCO'): + # Convert form the COCO 2014 file naming convention of + # COCO_[train/val/test]2014_000000000000.jpg to the 2017 + # naming convention of 000000000000.jpg + # (LVIS v1 will fix this naming issue) + info['filename'] = info['file_name'][-16:] + else: + info['filename'] = info['file_name'] + data_infos.append(info) + return data_infos + + def evaluate(self, + results, + metric='bbox', + logger=None, + jsonfile_prefix=None, + classwise=False, + proposal_nums=(100, 300, 1000), + iou_thrs=np.arange(0.5, 0.96, 0.05)): + """Evaluation in LVIS protocol. + + Args: + results (list[list | tuple]): Testing results of the dataset. + metric (str | list[str]): Metrics to be evaluated. Options are + 'bbox', 'segm', 'proposal', 'proposal_fast'. + logger (logging.Logger | str | None): Logger used for printing + related information during evaluation. Default: None. + jsonfile_prefix (str | None): + classwise (bool): Whether to evaluating the AP for each class. + proposal_nums (Sequence[int]): Proposal number used for evaluating + recalls, such as recall@100, recall@1000. + Default: (100, 300, 1000). + iou_thrs (Sequence[float]): IoU threshold used for evaluating + recalls. If set to a list, the average recall of all IoUs will + also be computed. Default: 0.5. + + Returns: + dict[str, float]: LVIS style metrics. + """ + + try: + import lvis + if getattr(lvis, '__version__', '0') >= '10.5.3': + warnings.warn( + 'mmlvis is deprecated, please install official lvis-api by "pip install git+https://github.com/lvis-dataset/lvis-api.git"', # noqa: E501 + UserWarning) + from lvis import LVISResults, LVISEval + except ImportError: + raise ImportError( + 'Package lvis is not installed. Please run "pip install git+https://github.com/lvis-dataset/lvis-api.git".' # noqa: E501 + ) + assert isinstance(results, list), 'results must be a list' + assert len(results) == len(self), ( + 'The length of results is not equal to the dataset len: {} != {}'. + format(len(results), len(self))) + + metrics = metric if isinstance(metric, list) else [metric] + allowed_metrics = ['bbox', 'segm', 'proposal', 'proposal_fast'] + for metric in metrics: + if metric not in allowed_metrics: + raise KeyError('metric {} is not supported'.format(metric)) + + if jsonfile_prefix is None: + tmp_dir = tempfile.TemporaryDirectory() + jsonfile_prefix = osp.join(tmp_dir.name, 'results') + else: + tmp_dir = None + result_files = self.results2json(results, jsonfile_prefix) + + eval_results = OrderedDict() + # get original api + lvis_gt = self.coco + for metric in metrics: + msg = 'Evaluating {}...'.format(metric) + if logger is None: + msg = '\n' + msg + print_log(msg, logger=logger) + + if metric == 'proposal_fast': + ar = self.fast_eval_recall( + results, proposal_nums, iou_thrs, logger='silent') + log_msg = [] + for i, num in enumerate(proposal_nums): + eval_results['AR@{}'.format(num)] = ar[i] + log_msg.append('\nAR@{}\t{:.4f}'.format(num, ar[i])) + log_msg = ''.join(log_msg) + print_log(log_msg, logger=logger) + continue + + if metric not in result_files: + raise KeyError('{} is not in results'.format(metric)) + try: + lvis_dt = LVISResults(lvis_gt, result_files[metric]) + except IndexError: + print_log( + 'The testing results of the whole dataset is empty.', + logger=logger, + level=logging.ERROR) + break + + iou_type = 'bbox' if metric == 'proposal' else metric + lvis_eval = LVISEval(lvis_gt, lvis_dt, iou_type) + lvis_eval.params.imgIds = self.img_ids + if metric == 'proposal': + lvis_eval.params.useCats = 0 + lvis_eval.params.maxDets = list(proposal_nums) + lvis_eval.evaluate() + lvis_eval.accumulate() + lvis_eval.summarize() + for k, v in lvis_eval.get_results().items(): + if k.startswith('AR'): + val = float('{:.3f}'.format(float(v))) + eval_results[k] = val + else: + lvis_eval.evaluate() + lvis_eval.accumulate() + lvis_eval.summarize() + lvis_results = lvis_eval.get_results() + if classwise: # Compute per-category AP + # Compute per-category AP + # from https://github.com/facebookresearch/detectron2/ + precisions = lvis_eval.eval['precision'] + # precision: (iou, recall, cls, area range, max dets) + assert len(self.cat_ids) == precisions.shape[2] + + results_per_category = [] + for idx, catId in enumerate(self.cat_ids): + # area range index 0: all area ranges + # max dets index -1: typically 100 per image + nm = self.coco.load_cats(catId)[0] + precision = precisions[:, :, idx, 0, -1] + precision = precision[precision > -1] + if precision.size: + ap = np.mean(precision) + else: + ap = float('nan') + results_per_category.append( + (f'{nm["name"]}', f'{float(ap):0.3f}')) + + num_columns = min(6, len(results_per_category) * 2) + results_flatten = list( + itertools.chain(*results_per_category)) + headers = ['category', 'AP'] * (num_columns // 2) + results_2d = itertools.zip_longest(*[ + results_flatten[i::num_columns] + for i in range(num_columns) + ]) + table_data = [headers] + table_data += [result for result in results_2d] + table = AsciiTable(table_data) + print_log('\n' + table.table, logger=logger) + + for k, v in lvis_results.items(): + if k.startswith('AP'): + key = '{}_{}'.format(metric, k) + val = float('{:.3f}'.format(float(v))) + eval_results[key] = val + ap_summary = ' '.join([ + '{}:{:.3f}'.format(k, float(v)) + for k, v in lvis_results.items() if k.startswith('AP') + ]) + eval_results['{}_mAP_copypaste'.format(metric)] = ap_summary + lvis_eval.print_results() + if tmp_dir is not None: + tmp_dir.cleanup() + return eval_results + + +LVISDataset = LVISV05Dataset +DATASETS.register_module(name='LVISDataset', module=LVISDataset) + + +@DATASETS.register_module() +class LVISV1Dataset(LVISDataset): + + CLASSES = ( + 'aerosol_can', 'air_conditioner', 'airplane', 'alarm_clock', 'alcohol', + 'alligator', 'almond', 'ambulance', 'amplifier', 'anklet', 'antenna', + 'apple', 'applesauce', 'apricot', 'apron', 'aquarium', + 'arctic_(type_of_shoe)', 'armband', 'armchair', 'armoire', 'armor', + 'artichoke', 'trash_can', 'ashtray', 'asparagus', 'atomizer', + 'avocado', 'award', 'awning', 'ax', 'baboon', 'baby_buggy', + 'basketball_backboard', 'backpack', 'handbag', 'suitcase', 'bagel', + 'bagpipe', 'baguet', 'bait', 'ball', 'ballet_skirt', 'balloon', + 'bamboo', 'banana', 'Band_Aid', 'bandage', 'bandanna', 'banjo', + 'banner', 'barbell', 'barge', 'barrel', 'barrette', 'barrow', + 'baseball_base', 'baseball', 'baseball_bat', 'baseball_cap', + 'baseball_glove', 'basket', 'basketball', 'bass_horn', 'bat_(animal)', + 'bath_mat', 'bath_towel', 'bathrobe', 'bathtub', 'batter_(food)', + 'battery', 'beachball', 'bead', 'bean_curd', 'beanbag', 'beanie', + 'bear', 'bed', 'bedpan', 'bedspread', 'cow', 'beef_(food)', 'beeper', + 'beer_bottle', 'beer_can', 'beetle', 'bell', 'bell_pepper', 'belt', + 'belt_buckle', 'bench', 'beret', 'bib', 'Bible', 'bicycle', 'visor', + 'billboard', 'binder', 'binoculars', 'bird', 'birdfeeder', 'birdbath', + 'birdcage', 'birdhouse', 'birthday_cake', 'birthday_card', + 'pirate_flag', 'black_sheep', 'blackberry', 'blackboard', 'blanket', + 'blazer', 'blender', 'blimp', 'blinker', 'blouse', 'blueberry', + 'gameboard', 'boat', 'bob', 'bobbin', 'bobby_pin', 'boiled_egg', + 'bolo_tie', 'deadbolt', 'bolt', 'bonnet', 'book', 'bookcase', + 'booklet', 'bookmark', 'boom_microphone', 'boot', 'bottle', + 'bottle_opener', 'bouquet', 'bow_(weapon)', 'bow_(decorative_ribbons)', + 'bow-tie', 'bowl', 'pipe_bowl', 'bowler_hat', 'bowling_ball', 'box', + 'boxing_glove', 'suspenders', 'bracelet', 'brass_plaque', 'brassiere', + 'bread-bin', 'bread', 'breechcloth', 'bridal_gown', 'briefcase', + 'broccoli', 'broach', 'broom', 'brownie', 'brussels_sprouts', + 'bubble_gum', 'bucket', 'horse_buggy', 'bull', 'bulldog', 'bulldozer', + 'bullet_train', 'bulletin_board', 'bulletproof_vest', 'bullhorn', + 'bun', 'bunk_bed', 'buoy', 'burrito', 'bus_(vehicle)', 'business_card', + 'butter', 'butterfly', 'button', 'cab_(taxi)', 'cabana', 'cabin_car', + 'cabinet', 'locker', 'cake', 'calculator', 'calendar', 'calf', + 'camcorder', 'camel', 'camera', 'camera_lens', 'camper_(vehicle)', + 'can', 'can_opener', 'candle', 'candle_holder', 'candy_bar', + 'candy_cane', 'walking_cane', 'canister', 'canoe', 'cantaloup', + 'canteen', 'cap_(headwear)', 'bottle_cap', 'cape', 'cappuccino', + 'car_(automobile)', 'railcar_(part_of_a_train)', 'elevator_car', + 'car_battery', 'identity_card', 'card', 'cardigan', 'cargo_ship', + 'carnation', 'horse_carriage', 'carrot', 'tote_bag', 'cart', 'carton', + 'cash_register', 'casserole', 'cassette', 'cast', 'cat', 'cauliflower', + 'cayenne_(spice)', 'CD_player', 'celery', 'cellular_telephone', + 'chain_mail', 'chair', 'chaise_longue', 'chalice', 'chandelier', + 'chap', 'checkbook', 'checkerboard', 'cherry', 'chessboard', + 'chicken_(animal)', 'chickpea', 'chili_(vegetable)', 'chime', + 'chinaware', 'crisp_(potato_chip)', 'poker_chip', 'chocolate_bar', + 'chocolate_cake', 'chocolate_milk', 'chocolate_mousse', 'choker', + 'chopping_board', 'chopstick', 'Christmas_tree', 'slide', 'cider', + 'cigar_box', 'cigarette', 'cigarette_case', 'cistern', 'clarinet', + 'clasp', 'cleansing_agent', 'cleat_(for_securing_rope)', 'clementine', + 'clip', 'clipboard', 'clippers_(for_plants)', 'cloak', 'clock', + 'clock_tower', 'clothes_hamper', 'clothespin', 'clutch_bag', 'coaster', + 'coat', 'coat_hanger', 'coatrack', 'cock', 'cockroach', + 'cocoa_(beverage)', 'coconut', 'coffee_maker', 'coffee_table', + 'coffeepot', 'coil', 'coin', 'colander', 'coleslaw', + 'coloring_material', 'combination_lock', 'pacifier', 'comic_book', + 'compass', 'computer_keyboard', 'condiment', 'cone', 'control', + 'convertible_(automobile)', 'sofa_bed', 'cooker', 'cookie', + 'cooking_utensil', 'cooler_(for_food)', 'cork_(bottle_plug)', + 'corkboard', 'corkscrew', 'edible_corn', 'cornbread', 'cornet', + 'cornice', 'cornmeal', 'corset', 'costume', 'cougar', 'coverall', + 'cowbell', 'cowboy_hat', 'crab_(animal)', 'crabmeat', 'cracker', + 'crape', 'crate', 'crayon', 'cream_pitcher', 'crescent_roll', 'crib', + 'crock_pot', 'crossbar', 'crouton', 'crow', 'crowbar', 'crown', + 'crucifix', 'cruise_ship', 'police_cruiser', 'crumb', 'crutch', + 'cub_(animal)', 'cube', 'cucumber', 'cufflink', 'cup', 'trophy_cup', + 'cupboard', 'cupcake', 'hair_curler', 'curling_iron', 'curtain', + 'cushion', 'cylinder', 'cymbal', 'dagger', 'dalmatian', 'dartboard', + 'date_(fruit)', 'deck_chair', 'deer', 'dental_floss', 'desk', + 'detergent', 'diaper', 'diary', 'die', 'dinghy', 'dining_table', 'tux', + 'dish', 'dish_antenna', 'dishrag', 'dishtowel', 'dishwasher', + 'dishwasher_detergent', 'dispenser', 'diving_board', 'Dixie_cup', + 'dog', 'dog_collar', 'doll', 'dollar', 'dollhouse', 'dolphin', + 'domestic_ass', 'doorknob', 'doormat', 'doughnut', 'dove', 'dragonfly', + 'drawer', 'underdrawers', 'dress', 'dress_hat', 'dress_suit', + 'dresser', 'drill', 'drone', 'dropper', 'drum_(musical_instrument)', + 'drumstick', 'duck', 'duckling', 'duct_tape', 'duffel_bag', 'dumbbell', + 'dumpster', 'dustpan', 'eagle', 'earphone', 'earplug', 'earring', + 'easel', 'eclair', 'eel', 'egg', 'egg_roll', 'egg_yolk', 'eggbeater', + 'eggplant', 'electric_chair', 'refrigerator', 'elephant', 'elk', + 'envelope', 'eraser', 'escargot', 'eyepatch', 'falcon', 'fan', + 'faucet', 'fedora', 'ferret', 'Ferris_wheel', 'ferry', 'fig_(fruit)', + 'fighter_jet', 'figurine', 'file_cabinet', 'file_(tool)', 'fire_alarm', + 'fire_engine', 'fire_extinguisher', 'fire_hose', 'fireplace', + 'fireplug', 'first-aid_kit', 'fish', 'fish_(food)', 'fishbowl', + 'fishing_rod', 'flag', 'flagpole', 'flamingo', 'flannel', 'flap', + 'flash', 'flashlight', 'fleece', 'flip-flop_(sandal)', + 'flipper_(footwear)', 'flower_arrangement', 'flute_glass', 'foal', + 'folding_chair', 'food_processor', 'football_(American)', + 'football_helmet', 'footstool', 'fork', 'forklift', 'freight_car', + 'French_toast', 'freshener', 'frisbee', 'frog', 'fruit_juice', + 'frying_pan', 'fudge', 'funnel', 'futon', 'gag', 'garbage', + 'garbage_truck', 'garden_hose', 'gargle', 'gargoyle', 'garlic', + 'gasmask', 'gazelle', 'gelatin', 'gemstone', 'generator', + 'giant_panda', 'gift_wrap', 'ginger', 'giraffe', 'cincture', + 'glass_(drink_container)', 'globe', 'glove', 'goat', 'goggles', + 'goldfish', 'golf_club', 'golfcart', 'gondola_(boat)', 'goose', + 'gorilla', 'gourd', 'grape', 'grater', 'gravestone', 'gravy_boat', + 'green_bean', 'green_onion', 'griddle', 'grill', 'grits', 'grizzly', + 'grocery_bag', 'guitar', 'gull', 'gun', 'hairbrush', 'hairnet', + 'hairpin', 'halter_top', 'ham', 'hamburger', 'hammer', 'hammock', + 'hamper', 'hamster', 'hair_dryer', 'hand_glass', 'hand_towel', + 'handcart', 'handcuff', 'handkerchief', 'handle', 'handsaw', + 'hardback_book', 'harmonium', 'hat', 'hatbox', 'veil', 'headband', + 'headboard', 'headlight', 'headscarf', 'headset', + 'headstall_(for_horses)', 'heart', 'heater', 'helicopter', 'helmet', + 'heron', 'highchair', 'hinge', 'hippopotamus', 'hockey_stick', 'hog', + 'home_plate_(baseball)', 'honey', 'fume_hood', 'hook', 'hookah', + 'hornet', 'horse', 'hose', 'hot-air_balloon', 'hotplate', 'hot_sauce', + 'hourglass', 'houseboat', 'hummingbird', 'hummus', 'polar_bear', + 'icecream', 'popsicle', 'ice_maker', 'ice_pack', 'ice_skate', + 'igniter', 'inhaler', 'iPod', 'iron_(for_clothing)', 'ironing_board', + 'jacket', 'jam', 'jar', 'jean', 'jeep', 'jelly_bean', 'jersey', + 'jet_plane', 'jewel', 'jewelry', 'joystick', 'jumpsuit', 'kayak', + 'keg', 'kennel', 'kettle', 'key', 'keycard', 'kilt', 'kimono', + 'kitchen_sink', 'kitchen_table', 'kite', 'kitten', 'kiwi_fruit', + 'knee_pad', 'knife', 'knitting_needle', 'knob', 'knocker_(on_a_door)', + 'koala', 'lab_coat', 'ladder', 'ladle', 'ladybug', 'lamb_(animal)', + 'lamb-chop', 'lamp', 'lamppost', 'lampshade', 'lantern', 'lanyard', + 'laptop_computer', 'lasagna', 'latch', 'lawn_mower', 'leather', + 'legging_(clothing)', 'Lego', 'legume', 'lemon', 'lemonade', 'lettuce', + 'license_plate', 'life_buoy', 'life_jacket', 'lightbulb', + 'lightning_rod', 'lime', 'limousine', 'lion', 'lip_balm', 'liquor', + 'lizard', 'log', 'lollipop', 'speaker_(stero_equipment)', 'loveseat', + 'machine_gun', 'magazine', 'magnet', 'mail_slot', 'mailbox_(at_home)', + 'mallard', 'mallet', 'mammoth', 'manatee', 'mandarin_orange', 'manger', + 'manhole', 'map', 'marker', 'martini', 'mascot', 'mashed_potato', + 'masher', 'mask', 'mast', 'mat_(gym_equipment)', 'matchbox', + 'mattress', 'measuring_cup', 'measuring_stick', 'meatball', 'medicine', + 'melon', 'microphone', 'microscope', 'microwave_oven', 'milestone', + 'milk', 'milk_can', 'milkshake', 'minivan', 'mint_candy', 'mirror', + 'mitten', 'mixer_(kitchen_tool)', 'money', + 'monitor_(computer_equipment) computer_monitor', 'monkey', 'motor', + 'motor_scooter', 'motor_vehicle', 'motorcycle', 'mound_(baseball)', + 'mouse_(computer_equipment)', 'mousepad', 'muffin', 'mug', 'mushroom', + 'music_stool', 'musical_instrument', 'nailfile', 'napkin', + 'neckerchief', 'necklace', 'necktie', 'needle', 'nest', 'newspaper', + 'newsstand', 'nightshirt', 'nosebag_(for_animals)', + 'noseband_(for_animals)', 'notebook', 'notepad', 'nut', 'nutcracker', + 'oar', 'octopus_(food)', 'octopus_(animal)', 'oil_lamp', 'olive_oil', + 'omelet', 'onion', 'orange_(fruit)', 'orange_juice', 'ostrich', + 'ottoman', 'oven', 'overalls_(clothing)', 'owl', 'packet', 'inkpad', + 'pad', 'paddle', 'padlock', 'paintbrush', 'painting', 'pajamas', + 'palette', 'pan_(for_cooking)', 'pan_(metal_container)', 'pancake', + 'pantyhose', 'papaya', 'paper_plate', 'paper_towel', 'paperback_book', + 'paperweight', 'parachute', 'parakeet', 'parasail_(sports)', 'parasol', + 'parchment', 'parka', 'parking_meter', 'parrot', + 'passenger_car_(part_of_a_train)', 'passenger_ship', 'passport', + 'pastry', 'patty_(food)', 'pea_(food)', 'peach', 'peanut_butter', + 'pear', 'peeler_(tool_for_fruit_and_vegetables)', 'wooden_leg', + 'pegboard', 'pelican', 'pen', 'pencil', 'pencil_box', + 'pencil_sharpener', 'pendulum', 'penguin', 'pennant', 'penny_(coin)', + 'pepper', 'pepper_mill', 'perfume', 'persimmon', 'person', 'pet', + 'pew_(church_bench)', 'phonebook', 'phonograph_record', 'piano', + 'pickle', 'pickup_truck', 'pie', 'pigeon', 'piggy_bank', 'pillow', + 'pin_(non_jewelry)', 'pineapple', 'pinecone', 'ping-pong_ball', + 'pinwheel', 'tobacco_pipe', 'pipe', 'pistol', 'pita_(bread)', + 'pitcher_(vessel_for_liquid)', 'pitchfork', 'pizza', 'place_mat', + 'plate', 'platter', 'playpen', 'pliers', 'plow_(farm_equipment)', + 'plume', 'pocket_watch', 'pocketknife', 'poker_(fire_stirring_tool)', + 'pole', 'polo_shirt', 'poncho', 'pony', 'pool_table', 'pop_(soda)', + 'postbox_(public)', 'postcard', 'poster', 'pot', 'flowerpot', 'potato', + 'potholder', 'pottery', 'pouch', 'power_shovel', 'prawn', 'pretzel', + 'printer', 'projectile_(weapon)', 'projector', 'propeller', 'prune', + 'pudding', 'puffer_(fish)', 'puffin', 'pug-dog', 'pumpkin', 'puncher', + 'puppet', 'puppy', 'quesadilla', 'quiche', 'quilt', 'rabbit', + 'race_car', 'racket', 'radar', 'radiator', 'radio_receiver', 'radish', + 'raft', 'rag_doll', 'raincoat', 'ram_(animal)', 'raspberry', 'rat', + 'razorblade', 'reamer_(juicer)', 'rearview_mirror', 'receipt', + 'recliner', 'record_player', 'reflector', 'remote_control', + 'rhinoceros', 'rib_(food)', 'rifle', 'ring', 'river_boat', 'road_map', + 'robe', 'rocking_chair', 'rodent', 'roller_skate', 'Rollerblade', + 'rolling_pin', 'root_beer', 'router_(computer_equipment)', + 'rubber_band', 'runner_(carpet)', 'plastic_bag', + 'saddle_(on_an_animal)', 'saddle_blanket', 'saddlebag', 'safety_pin', + 'sail', 'salad', 'salad_plate', 'salami', 'salmon_(fish)', + 'salmon_(food)', 'salsa', 'saltshaker', 'sandal_(type_of_shoe)', + 'sandwich', 'satchel', 'saucepan', 'saucer', 'sausage', 'sawhorse', + 'saxophone', 'scale_(measuring_instrument)', 'scarecrow', 'scarf', + 'school_bus', 'scissors', 'scoreboard', 'scraper', 'screwdriver', + 'scrubbing_brush', 'sculpture', 'seabird', 'seahorse', 'seaplane', + 'seashell', 'sewing_machine', 'shaker', 'shampoo', 'shark', + 'sharpener', 'Sharpie', 'shaver_(electric)', 'shaving_cream', 'shawl', + 'shears', 'sheep', 'shepherd_dog', 'sherbert', 'shield', 'shirt', + 'shoe', 'shopping_bag', 'shopping_cart', 'short_pants', 'shot_glass', + 'shoulder_bag', 'shovel', 'shower_head', 'shower_cap', + 'shower_curtain', 'shredder_(for_paper)', 'signboard', 'silo', 'sink', + 'skateboard', 'skewer', 'ski', 'ski_boot', 'ski_parka', 'ski_pole', + 'skirt', 'skullcap', 'sled', 'sleeping_bag', 'sling_(bandage)', + 'slipper_(footwear)', 'smoothie', 'snake', 'snowboard', 'snowman', + 'snowmobile', 'soap', 'soccer_ball', 'sock', 'sofa', 'softball', + 'solar_array', 'sombrero', 'soup', 'soup_bowl', 'soupspoon', + 'sour_cream', 'soya_milk', 'space_shuttle', 'sparkler_(fireworks)', + 'spatula', 'spear', 'spectacles', 'spice_rack', 'spider', 'crawfish', + 'sponge', 'spoon', 'sportswear', 'spotlight', 'squid_(food)', + 'squirrel', 'stagecoach', 'stapler_(stapling_machine)', 'starfish', + 'statue_(sculpture)', 'steak_(food)', 'steak_knife', 'steering_wheel', + 'stepladder', 'step_stool', 'stereo_(sound_system)', 'stew', 'stirrer', + 'stirrup', 'stool', 'stop_sign', 'brake_light', 'stove', 'strainer', + 'strap', 'straw_(for_drinking)', 'strawberry', 'street_sign', + 'streetlight', 'string_cheese', 'stylus', 'subwoofer', 'sugar_bowl', + 'sugarcane_(plant)', 'suit_(clothing)', 'sunflower', 'sunglasses', + 'sunhat', 'surfboard', 'sushi', 'mop', 'sweat_pants', 'sweatband', + 'sweater', 'sweatshirt', 'sweet_potato', 'swimsuit', 'sword', + 'syringe', 'Tabasco_sauce', 'table-tennis_table', 'table', + 'table_lamp', 'tablecloth', 'tachometer', 'taco', 'tag', 'taillight', + 'tambourine', 'army_tank', 'tank_(storage_vessel)', + 'tank_top_(clothing)', 'tape_(sticky_cloth_or_paper)', 'tape_measure', + 'tapestry', 'tarp', 'tartan', 'tassel', 'tea_bag', 'teacup', + 'teakettle', 'teapot', 'teddy_bear', 'telephone', 'telephone_booth', + 'telephone_pole', 'telephoto_lens', 'television_camera', + 'television_set', 'tennis_ball', 'tennis_racket', 'tequila', + 'thermometer', 'thermos_bottle', 'thermostat', 'thimble', 'thread', + 'thumbtack', 'tiara', 'tiger', 'tights_(clothing)', 'timer', 'tinfoil', + 'tinsel', 'tissue_paper', 'toast_(food)', 'toaster', 'toaster_oven', + 'toilet', 'toilet_tissue', 'tomato', 'tongs', 'toolbox', 'toothbrush', + 'toothpaste', 'toothpick', 'cover', 'tortilla', 'tow_truck', 'towel', + 'towel_rack', 'toy', 'tractor_(farm_equipment)', 'traffic_light', + 'dirt_bike', 'trailer_truck', 'train_(railroad_vehicle)', 'trampoline', + 'tray', 'trench_coat', 'triangle_(musical_instrument)', 'tricycle', + 'tripod', 'trousers', 'truck', 'truffle_(chocolate)', 'trunk', 'vat', + 'turban', 'turkey_(food)', 'turnip', 'turtle', 'turtleneck_(clothing)', + 'typewriter', 'umbrella', 'underwear', 'unicycle', 'urinal', 'urn', + 'vacuum_cleaner', 'vase', 'vending_machine', 'vent', 'vest', + 'videotape', 'vinegar', 'violin', 'vodka', 'volleyball', 'vulture', + 'waffle', 'waffle_iron', 'wagon', 'wagon_wheel', 'walking_stick', + 'wall_clock', 'wall_socket', 'wallet', 'walrus', 'wardrobe', + 'washbasin', 'automatic_washer', 'watch', 'water_bottle', + 'water_cooler', 'water_faucet', 'water_heater', 'water_jug', + 'water_gun', 'water_scooter', 'water_ski', 'water_tower', + 'watering_can', 'watermelon', 'weathervane', 'webcam', 'wedding_cake', + 'wedding_ring', 'wet_suit', 'wheel', 'wheelchair', 'whipped_cream', + 'whistle', 'wig', 'wind_chime', 'windmill', 'window_box_(for_plants)', + 'windshield_wiper', 'windsock', 'wine_bottle', 'wine_bucket', + 'wineglass', 'blinder_(for_horses)', 'wok', 'wolf', 'wooden_spoon', + 'wreath', 'wrench', 'wristband', 'wristlet', 'yacht', 'yogurt', + 'yoke_(animal_equipment)', 'zebra', 'zucchini') + + def load_annotations(self, ann_file): + try: + import lvis + if getattr(lvis, '__version__', '0') >= '10.5.3': + warnings.warn( + 'mmlvis is deprecated, please install official lvis-api by "pip install git+https://github.com/lvis-dataset/lvis-api.git"', # noqa: E501 + UserWarning) + from lvis import LVIS + except ImportError: + raise ImportError( + 'Package lvis is not installed. Please run "pip install git+https://github.com/lvis-dataset/lvis-api.git".' # noqa: E501 + ) + self.coco = LVIS(ann_file) + self.cat_ids = self.coco.get_cat_ids() + self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)} + self.img_ids = self.coco.get_img_ids() + data_infos = [] + for i in self.img_ids: + info = self.coco.load_imgs([i])[0] + # coco_url is used in LVISv1 instead of file_name + # e.g. http://images.cocodataset.org/train2017/000000391895.jpg + # train/val split in specified in url + info['filename'] = info['coco_url'].replace( + 'http://images.cocodataset.org/', '') + data_infos.append(info) + return data_infos diff --git a/mmdet/datasets/pipelines/__init__.py b/mmdet/datasets/pipelines/__init__.py new file mode 100644 index 0000000..9559969 --- /dev/null +++ b/mmdet/datasets/pipelines/__init__.py @@ -0,0 +1,26 @@ +from .auto_augment import (AutoAugment, BrightnessTransform, ColorTransform, + ContrastTransform, EqualizeTransform, Rotate, Shear, + Translate) +from .compose import Compose +from .formating import (Collect, DefaultFormatBundle, ImageToTensor, + ToDataContainer, ToTensor, Transpose, to_tensor) +from .instaboost import InstaBoost +from .loading import (LoadAnnotations, LoadImageFromFile, LoadImageFromWebcam, + LoadMultiChannelImageFromFiles, LoadProposals) +from .test_time_aug import MultiScaleFlipAug +from .transforms import (Albu, CutOut, Expand, MinIoURandomCrop, Normalize, + Pad, PhotoMetricDistortion, RandomCenterCropPad, + RandomCrop, RandomFlip, RandomShift, Resize, + SegRescale) + +__all__ = [ + 'Compose', 'to_tensor', 'ToTensor', 'ImageToTensor', 'ToDataContainer', + 'Transpose', 'Collect', 'DefaultFormatBundle', 'LoadAnnotations', + 'LoadImageFromFile', 'LoadImageFromWebcam', + 'LoadMultiChannelImageFromFiles', 'LoadProposals', 'MultiScaleFlipAug', + 'Resize', 'RandomFlip', 'Pad', 'RandomCrop', 'Normalize', 'SegRescale', + 'MinIoURandomCrop', 'Expand', 'PhotoMetricDistortion', 'Albu', + 'InstaBoost', 'RandomCenterCropPad', 'AutoAugment', 'CutOut', 'Shear', + 'Rotate', 'ColorTransform', 'EqualizeTransform', 'BrightnessTransform', + 'ContrastTransform', 'Translate', 'RandomShift' +] diff --git a/mmdet/datasets/pipelines/__pycache__/__init__.cpython-37.pyc b/mmdet/datasets/pipelines/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..4dc058e Binary files /dev/null and b/mmdet/datasets/pipelines/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/datasets/pipelines/__pycache__/auto_augment.cpython-37.pyc b/mmdet/datasets/pipelines/__pycache__/auto_augment.cpython-37.pyc new file mode 100644 index 0000000..57d22a6 Binary files /dev/null and b/mmdet/datasets/pipelines/__pycache__/auto_augment.cpython-37.pyc differ diff --git a/mmdet/datasets/pipelines/__pycache__/compose.cpython-37.pyc b/mmdet/datasets/pipelines/__pycache__/compose.cpython-37.pyc new file mode 100644 index 0000000..b1f2b1c Binary files /dev/null and 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+ + +def enhance_level_to_value(level, a=1.8, b=0.1): + """Map from level to values.""" + return (level / _MAX_LEVEL) * a + b + + +def random_negative(value, random_negative_prob): + """Randomly negate value based on random_negative_prob.""" + return -value if np.random.rand() < random_negative_prob else value + + +def bbox2fields(): + """The key correspondence from bboxes to labels, masks and + segmentations.""" + bbox2label = { + 'gt_bboxes': 'gt_labels', + 'gt_bboxes_ignore': 'gt_labels_ignore' + } + bbox2mask = { + 'gt_bboxes': 'gt_masks', + 'gt_bboxes_ignore': 'gt_masks_ignore' + } + bbox2seg = { + 'gt_bboxes': 'gt_semantic_seg', + } + return bbox2label, bbox2mask, bbox2seg + + +@PIPELINES.register_module() +class AutoAugment(object): + """Auto augmentation. + + This data augmentation is proposed in `Learning Data Augmentation + Strategies for Object Detection `_. + + TODO: Implement 'Shear', 'Sharpness' and 'Rotate' transforms + + Args: + policies (list[list[dict]]): The policies of auto augmentation. Each + policy in ``policies`` is a specific augmentation policy, and is + composed by several augmentations (dict). When AutoAugment is + called, a random policy in ``policies`` will be selected to + augment images. + + Examples: + >>> replace = (104, 116, 124) + >>> policies = [ + >>> [ + >>> dict(type='Sharpness', prob=0.0, level=8), + >>> dict( + >>> type='Shear', + >>> prob=0.4, + >>> level=0, + >>> replace=replace, + >>> axis='x') + >>> ], + >>> [ + >>> dict( + >>> type='Rotate', + >>> prob=0.6, + >>> level=10, + >>> replace=replace), + >>> dict(type='Color', prob=1.0, level=6) + >>> ] + >>> ] + >>> augmentation = AutoAugment(policies) + >>> img = np.ones(100, 100, 3) + >>> gt_bboxes = np.ones(10, 4) + >>> results = dict(img=img, gt_bboxes=gt_bboxes) + >>> results = augmentation(results) + """ + + def __init__(self, policies): + assert isinstance(policies, list) and len(policies) > 0, \ + 'Policies must be a non-empty list.' + for policy in policies: + assert isinstance(policy, list) and len(policy) > 0, \ + 'Each policy in policies must be a non-empty list.' + for augment in policy: + assert isinstance(augment, dict) and 'type' in augment, \ + 'Each specific augmentation must be a dict with key' \ + ' "type".' + + self.policies = copy.deepcopy(policies) + self.transforms = [Compose(policy) for policy in self.policies] + + def __call__(self, results): + transform = np.random.choice(self.transforms) + return transform(results) + + def __repr__(self): + return f'{self.__class__.__name__}(policies={self.policies})' + + +@PIPELINES.register_module() +class Shear(object): + """Apply Shear Transformation to image (and its corresponding bbox, mask, + segmentation). + + Args: + level (int | float): The level should be in range [0,_MAX_LEVEL]. + img_fill_val (int | float | tuple): The filled values for image border. + If float, the same fill value will be used for all the three + channels of image. If tuple, the should be 3 elements. + seg_ignore_label (int): The fill value used for segmentation map. + Note this value must equals ``ignore_label`` in ``semantic_head`` + of the corresponding config. Default 255. + prob (float): The probability for performing Shear and should be in + range [0, 1]. + direction (str): The direction for shear, either "horizontal" + or "vertical". + max_shear_magnitude (float): The maximum magnitude for Shear + transformation. + random_negative_prob (float): The probability that turns the + offset negative. Should be in range [0,1] + interpolation (str): Same as in :func:`mmcv.imshear`. + """ + + def __init__(self, + level, + img_fill_val=128, + seg_ignore_label=255, + prob=0.5, + direction='horizontal', + max_shear_magnitude=0.3, + random_negative_prob=0.5, + interpolation='bilinear'): + assert isinstance(level, (int, float)), 'The level must be type ' \ + f'int or float, got {type(level)}.' + assert 0 <= level <= _MAX_LEVEL, 'The level should be in range ' \ + f'[0,{_MAX_LEVEL}], got {level}.' + if isinstance(img_fill_val, (float, int)): + img_fill_val = tuple([float(img_fill_val)] * 3) + elif isinstance(img_fill_val, tuple): + assert len(img_fill_val) == 3, 'img_fill_val as tuple must ' \ + f'have 3 elements. got {len(img_fill_val)}.' + img_fill_val = tuple([float(val) for val in img_fill_val]) + else: + raise ValueError( + 'img_fill_val must be float or tuple with 3 elements.') + assert np.all([0 <= val <= 255 for val in img_fill_val]), 'all ' \ + 'elements of img_fill_val should between range [0,255].' \ + f'got {img_fill_val}.' + assert 0 <= prob <= 1.0, 'The probability of shear should be in ' \ + f'range [0,1]. got {prob}.' + assert direction in ('horizontal', 'vertical'), 'direction must ' \ + f'in be either "horizontal" or "vertical". got {direction}.' + assert isinstance(max_shear_magnitude, float), 'max_shear_magnitude ' \ + f'should be type float. got {type(max_shear_magnitude)}.' + assert 0. <= max_shear_magnitude <= 1., 'Defaultly ' \ + 'max_shear_magnitude should be in range [0,1]. ' \ + f'got {max_shear_magnitude}.' + self.level = level + self.magnitude = level_to_value(level, max_shear_magnitude) + self.img_fill_val = img_fill_val + self.seg_ignore_label = seg_ignore_label + self.prob = prob + self.direction = direction + self.max_shear_magnitude = max_shear_magnitude + self.random_negative_prob = random_negative_prob + self.interpolation = interpolation + + def _shear_img(self, + results, + magnitude, + direction='horizontal', + interpolation='bilinear'): + """Shear the image. + + Args: + results (dict): Result dict from loading pipeline. + magnitude (int | float): The magnitude used for shear. + direction (str): The direction for shear, either "horizontal" + or "vertical". + interpolation (str): Same as in :func:`mmcv.imshear`. + """ + for key in results.get('img_fields', ['img']): + img = results[key] + img_sheared = mmcv.imshear( + img, + magnitude, + direction, + border_value=self.img_fill_val, + interpolation=interpolation) + results[key] = img_sheared.astype(img.dtype) + + def _shear_bboxes(self, results, magnitude): + """Shear the bboxes.""" + h, w, c = results['img_shape'] + if self.direction == 'horizontal': + shear_matrix = np.stack([[1, magnitude], + [0, 1]]).astype(np.float32) # [2, 2] + else: + shear_matrix = np.stack([[1, 0], [magnitude, + 1]]).astype(np.float32) + for key in results.get('bbox_fields', []): + min_x, min_y, max_x, max_y = np.split( + results[key], results[key].shape[-1], axis=-1) + coordinates = np.stack([[min_x, min_y], [max_x, min_y], + [min_x, max_y], + [max_x, max_y]]) # [4, 2, nb_box, 1] + coordinates = coordinates[..., 0].transpose( + (2, 1, 0)).astype(np.float32) # [nb_box, 2, 4] + new_coords = np.matmul(shear_matrix[None, :, :], + coordinates) # [nb_box, 2, 4] + min_x = np.min(new_coords[:, 0, :], axis=-1) + min_y = np.min(new_coords[:, 1, :], axis=-1) + max_x = np.max(new_coords[:, 0, :], axis=-1) + max_y = np.max(new_coords[:, 1, :], axis=-1) + min_x = np.clip(min_x, a_min=0, a_max=w) + min_y = np.clip(min_y, a_min=0, a_max=h) + max_x = np.clip(max_x, a_min=min_x, a_max=w) + max_y = np.clip(max_y, a_min=min_y, a_max=h) + results[key] = np.stack([min_x, min_y, max_x, max_y], + axis=-1).astype(results[key].dtype) + + def _shear_masks(self, + results, + magnitude, + direction='horizontal', + fill_val=0, + interpolation='bilinear'): + """Shear the masks.""" + h, w, c = results['img_shape'] + for key in results.get('mask_fields', []): + masks = results[key] + results[key] = masks.shear((h, w), + magnitude, + direction, + border_value=fill_val, + interpolation=interpolation) + + def _shear_seg(self, + results, + magnitude, + direction='horizontal', + fill_val=255, + interpolation='bilinear'): + """Shear the segmentation maps.""" + for key in results.get('seg_fields', []): + seg = results[key] + results[key] = mmcv.imshear( + seg, + magnitude, + direction, + border_value=fill_val, + interpolation=interpolation).astype(seg.dtype) + + def _filter_invalid(self, results, min_bbox_size=0): + """Filter bboxes and corresponding masks too small after shear + augmentation.""" + bbox2label, bbox2mask, _ = bbox2fields() + for key in results.get('bbox_fields', []): + bbox_w = results[key][:, 2] - results[key][:, 0] + bbox_h = results[key][:, 3] - results[key][:, 1] + valid_inds = (bbox_w > min_bbox_size) & (bbox_h > min_bbox_size) + valid_inds = np.nonzero(valid_inds)[0] + results[key] = results[key][valid_inds] + # label fields. e.g. gt_labels and gt_labels_ignore + label_key = bbox2label.get(key) + if label_key in results: + results[label_key] = results[label_key][valid_inds] + # mask fields, e.g. gt_masks and gt_masks_ignore + mask_key = bbox2mask.get(key) + if mask_key in results: + results[mask_key] = results[mask_key][valid_inds] + + def __call__(self, results): + """Call function to shear images, bounding boxes, masks and semantic + segmentation maps. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Sheared results. + """ + if np.random.rand() > self.prob: + return results + magnitude = random_negative(self.magnitude, self.random_negative_prob) + self._shear_img(results, magnitude, self.direction, self.interpolation) + self._shear_bboxes(results, magnitude) + # fill_val set to 0 for background of mask. + self._shear_masks( + results, + magnitude, + self.direction, + fill_val=0, + interpolation=self.interpolation) + self._shear_seg( + results, + magnitude, + self.direction, + fill_val=self.seg_ignore_label, + interpolation=self.interpolation) + self._filter_invalid(results) + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(level={self.level}, ' + repr_str += f'img_fill_val={self.img_fill_val}, ' + repr_str += f'seg_ignore_label={self.seg_ignore_label}, ' + repr_str += f'prob={self.prob}, ' + repr_str += f'direction={self.direction}, ' + repr_str += f'max_shear_magnitude={self.max_shear_magnitude}, ' + repr_str += f'random_negative_prob={self.random_negative_prob}, ' + repr_str += f'interpolation={self.interpolation})' + return repr_str + + +@PIPELINES.register_module() +class Rotate(object): + """Apply Rotate Transformation to image (and its corresponding bbox, mask, + segmentation). + + Args: + level (int | float): The level should be in range (0,_MAX_LEVEL]. + scale (int | float): Isotropic scale factor. Same in + ``mmcv.imrotate``. + center (int | float | tuple[float]): Center point (w, h) of the + rotation in the source image. If None, the center of the + image will be used. Same in ``mmcv.imrotate``. + img_fill_val (int | float | tuple): The fill value for image border. + If float, the same value will be used for all the three + channels of image. If tuple, the should be 3 elements (e.g. + equals the number of channels for image). + seg_ignore_label (int): The fill value used for segmentation map. + Note this value must equals ``ignore_label`` in ``semantic_head`` + of the corresponding config. Default 255. + prob (float): The probability for perform transformation and + should be in range 0 to 1. + max_rotate_angle (int | float): The maximum angles for rotate + transformation. + random_negative_prob (float): The probability that turns the + offset negative. + """ + + def __init__(self, + level, + scale=1, + center=None, + img_fill_val=128, + seg_ignore_label=255, + prob=0.5, + max_rotate_angle=30, + random_negative_prob=0.5): + assert isinstance(level, (int, float)), \ + f'The level must be type int or float. got {type(level)}.' + assert 0 <= level <= _MAX_LEVEL, \ + f'The level should be in range (0,{_MAX_LEVEL}]. got {level}.' + assert isinstance(scale, (int, float)), \ + f'The scale must be type int or float. got type {type(scale)}.' + if isinstance(center, (int, float)): + center = (center, center) + elif isinstance(center, tuple): + assert len(center) == 2, 'center with type tuple must have '\ + f'2 elements. got {len(center)} elements.' + else: + assert center is None, 'center must be None or type int, '\ + f'float or tuple, got type {type(center)}.' + if isinstance(img_fill_val, (float, int)): + img_fill_val = tuple([float(img_fill_val)] * 3) + elif isinstance(img_fill_val, tuple): + assert len(img_fill_val) == 3, 'img_fill_val as tuple must '\ + f'have 3 elements. got {len(img_fill_val)}.' + img_fill_val = tuple([float(val) for val in img_fill_val]) + else: + raise ValueError( + 'img_fill_val must be float or tuple with 3 elements.') + assert np.all([0 <= val <= 255 for val in img_fill_val]), \ + 'all elements of img_fill_val should between range [0,255]. '\ + f'got {img_fill_val}.' + assert 0 <= prob <= 1.0, 'The probability should be in range [0,1]. '\ + 'got {prob}.' + assert isinstance(max_rotate_angle, (int, float)), 'max_rotate_angle '\ + f'should be type int or float. got type {type(max_rotate_angle)}.' + self.level = level + self.scale = scale + # Rotation angle in degrees. Positive values mean + # clockwise rotation. + self.angle = level_to_value(level, max_rotate_angle) + self.center = center + self.img_fill_val = img_fill_val + self.seg_ignore_label = seg_ignore_label + self.prob = prob + self.max_rotate_angle = max_rotate_angle + self.random_negative_prob = random_negative_prob + + def _rotate_img(self, results, angle, center=None, scale=1.0): + """Rotate the image. + + Args: + results (dict): Result dict from loading pipeline. + angle (float): Rotation angle in degrees, positive values + mean clockwise rotation. Same in ``mmcv.imrotate``. + center (tuple[float], optional): Center point (w, h) of the + rotation. Same in ``mmcv.imrotate``. + scale (int | float): Isotropic scale factor. Same in + ``mmcv.imrotate``. + """ + for key in results.get('img_fields', ['img']): + img = results[key].copy() + img_rotated = mmcv.imrotate( + img, angle, center, scale, border_value=self.img_fill_val) + results[key] = img_rotated.astype(img.dtype) + + def _rotate_bboxes(self, results, rotate_matrix): + """Rotate the bboxes.""" + h, w, c = results['img_shape'] + for key in results.get('bbox_fields', []): + min_x, min_y, max_x, max_y = np.split( + results[key], results[key].shape[-1], axis=-1) + coordinates = np.stack([[min_x, min_y], [max_x, min_y], + [min_x, max_y], + [max_x, max_y]]) # [4, 2, nb_bbox, 1] + # pad 1 to convert from format [x, y] to homogeneous + # coordinates format [x, y, 1] + coordinates = np.concatenate( + (coordinates, + np.ones((4, 1, coordinates.shape[2], 1), coordinates.dtype)), + axis=1) # [4, 3, nb_bbox, 1] + coordinates = coordinates.transpose( + (2, 0, 1, 3)) # [nb_bbox, 4, 3, 1] + rotated_coords = np.matmul(rotate_matrix, + coordinates) # [nb_bbox, 4, 2, 1] + rotated_coords = rotated_coords[..., 0] # [nb_bbox, 4, 2] + min_x, min_y = np.min( + rotated_coords[:, :, 0], axis=1), np.min( + rotated_coords[:, :, 1], axis=1) + max_x, max_y = np.max( + rotated_coords[:, :, 0], axis=1), np.max( + rotated_coords[:, :, 1], axis=1) + min_x, min_y = np.clip( + min_x, a_min=0, a_max=w), np.clip( + min_y, a_min=0, a_max=h) + max_x, max_y = np.clip( + max_x, a_min=min_x, a_max=w), np.clip( + max_y, a_min=min_y, a_max=h) + results[key] = np.stack([min_x, min_y, max_x, max_y], + axis=-1).astype(results[key].dtype) + + def _rotate_masks(self, + results, + angle, + center=None, + scale=1.0, + fill_val=0): + """Rotate the masks.""" + h, w, c = results['img_shape'] + for key in results.get('mask_fields', []): + masks = results[key] + results[key] = masks.rotate((h, w), angle, center, scale, fill_val) + + def _rotate_seg(self, + results, + angle, + center=None, + scale=1.0, + fill_val=255): + """Rotate the segmentation map.""" + for key in results.get('seg_fields', []): + seg = results[key].copy() + results[key] = mmcv.imrotate( + seg, angle, center, scale, + border_value=fill_val).astype(seg.dtype) + + def _filter_invalid(self, results, min_bbox_size=0): + """Filter bboxes and corresponding masks too small after rotate + augmentation.""" + bbox2label, bbox2mask, _ = bbox2fields() + for key in results.get('bbox_fields', []): + bbox_w = results[key][:, 2] - results[key][:, 0] + bbox_h = results[key][:, 3] - results[key][:, 1] + valid_inds = (bbox_w > min_bbox_size) & (bbox_h > min_bbox_size) + valid_inds = np.nonzero(valid_inds)[0] + results[key] = results[key][valid_inds] + # label fields. e.g. gt_labels and gt_labels_ignore + label_key = bbox2label.get(key) + if label_key in results: + results[label_key] = results[label_key][valid_inds] + # mask fields, e.g. gt_masks and gt_masks_ignore + mask_key = bbox2mask.get(key) + if mask_key in results: + results[mask_key] = results[mask_key][valid_inds] + + def __call__(self, results): + """Call function to rotate images, bounding boxes, masks and semantic + segmentation maps. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Rotated results. + """ + if np.random.rand() > self.prob: + return results + h, w = results['img'].shape[:2] + center = self.center + if center is None: + center = ((w - 1) * 0.5, (h - 1) * 0.5) + angle = random_negative(self.angle, self.random_negative_prob) + self._rotate_img(results, angle, center, self.scale) + rotate_matrix = cv2.getRotationMatrix2D(center, -angle, self.scale) + self._rotate_bboxes(results, rotate_matrix) + self._rotate_masks(results, angle, center, self.scale, fill_val=0) + self._rotate_seg( + results, angle, center, self.scale, fill_val=self.seg_ignore_label) + self._filter_invalid(results) + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(level={self.level}, ' + repr_str += f'scale={self.scale}, ' + repr_str += f'center={self.center}, ' + repr_str += f'img_fill_val={self.img_fill_val}, ' + repr_str += f'seg_ignore_label={self.seg_ignore_label}, ' + repr_str += f'prob={self.prob}, ' + repr_str += f'max_rotate_angle={self.max_rotate_angle}, ' + repr_str += f'random_negative_prob={self.random_negative_prob})' + return repr_str + + +@PIPELINES.register_module() +class Translate(object): + """Translate the images, bboxes, masks and segmentation maps horizontally + or vertically. + + Args: + level (int | float): The level for Translate and should be in + range [0,_MAX_LEVEL]. + prob (float): The probability for performing translation and + should be in range [0, 1]. + img_fill_val (int | float | tuple): The filled value for image + border. If float, the same fill value will be used for all + the three channels of image. If tuple, the should be 3 + elements (e.g. equals the number of channels for image). + seg_ignore_label (int): The fill value used for segmentation map. + Note this value must equals ``ignore_label`` in ``semantic_head`` + of the corresponding config. Default 255. + direction (str): The translate direction, either "horizontal" + or "vertical". + max_translate_offset (int | float): The maximum pixel's offset for + Translate. + random_negative_prob (float): The probability that turns the + offset negative. + min_size (int | float): The minimum pixel for filtering + invalid bboxes after the translation. + """ + + def __init__(self, + level, + prob=0.5, + img_fill_val=128, + seg_ignore_label=255, + direction='horizontal', + max_translate_offset=250., + random_negative_prob=0.5, + min_size=0): + assert isinstance(level, (int, float)), \ + 'The level must be type int or float.' + assert 0 <= level <= _MAX_LEVEL, \ + 'The level used for calculating Translate\'s offset should be ' \ + 'in range [0,_MAX_LEVEL]' + assert 0 <= prob <= 1.0, \ + 'The probability of translation should be in range [0, 1].' + if isinstance(img_fill_val, (float, int)): + img_fill_val = tuple([float(img_fill_val)] * 3) + elif isinstance(img_fill_val, tuple): + assert len(img_fill_val) == 3, \ + 'img_fill_val as tuple must have 3 elements.' + img_fill_val = tuple([float(val) for val in img_fill_val]) + else: + raise ValueError('img_fill_val must be type float or tuple.') + assert np.all([0 <= val <= 255 for val in img_fill_val]), \ + 'all elements of img_fill_val should between range [0,255].' + assert direction in ('horizontal', 'vertical'), \ + 'direction should be "horizontal" or "vertical".' + assert isinstance(max_translate_offset, (int, float)), \ + 'The max_translate_offset must be type int or float.' + # the offset used for translation + self.offset = int(level_to_value(level, max_translate_offset)) + self.level = level + self.prob = prob + self.img_fill_val = img_fill_val + self.seg_ignore_label = seg_ignore_label + self.direction = direction + self.max_translate_offset = max_translate_offset + self.random_negative_prob = random_negative_prob + self.min_size = min_size + + def _translate_img(self, results, offset, direction='horizontal'): + """Translate the image. + + Args: + results (dict): Result dict from loading pipeline. + offset (int | float): The offset for translate. + direction (str): The translate direction, either "horizontal" + or "vertical". + """ + for key in results.get('img_fields', ['img']): + img = results[key].copy() + results[key] = mmcv.imtranslate( + img, offset, direction, self.img_fill_val).astype(img.dtype) + + def _translate_bboxes(self, results, offset): + """Shift bboxes horizontally or vertically, according to offset.""" + h, w, c = results['img_shape'] + for key in results.get('bbox_fields', []): + min_x, min_y, max_x, max_y = np.split( + results[key], results[key].shape[-1], axis=-1) + if self.direction == 'horizontal': + min_x = np.maximum(0, min_x + offset) + max_x = np.minimum(w, max_x + offset) + elif self.direction == 'vertical': + min_y = np.maximum(0, min_y + offset) + max_y = np.minimum(h, max_y + offset) + + # the boxes translated outside of image will be filtered along with + # the corresponding masks, by invoking ``_filter_invalid``. + results[key] = np.concatenate([min_x, min_y, max_x, max_y], + axis=-1) + + def _translate_masks(self, + results, + offset, + direction='horizontal', + fill_val=0): + """Translate masks horizontally or vertically.""" + h, w, c = results['img_shape'] + for key in results.get('mask_fields', []): + masks = results[key] + results[key] = masks.translate((h, w), offset, direction, fill_val) + + def _translate_seg(self, + results, + offset, + direction='horizontal', + fill_val=255): + """Translate segmentation maps horizontally or vertically.""" + for key in results.get('seg_fields', []): + seg = results[key].copy() + results[key] = mmcv.imtranslate(seg, offset, direction, + fill_val).astype(seg.dtype) + + def _filter_invalid(self, results, min_size=0): + """Filter bboxes and masks too small or translated out of image.""" + bbox2label, bbox2mask, _ = bbox2fields() + for key in results.get('bbox_fields', []): + bbox_w = results[key][:, 2] - results[key][:, 0] + bbox_h = results[key][:, 3] - results[key][:, 1] + valid_inds = (bbox_w > min_size) & (bbox_h > min_size) + valid_inds = np.nonzero(valid_inds)[0] + results[key] = results[key][valid_inds] + # label fields. e.g. gt_labels and gt_labels_ignore + label_key = bbox2label.get(key) + if label_key in results: + results[label_key] = results[label_key][valid_inds] + # mask fields, e.g. gt_masks and gt_masks_ignore + mask_key = bbox2mask.get(key) + if mask_key in results: + results[mask_key] = results[mask_key][valid_inds] + return results + + def __call__(self, results): + """Call function to translate images, bounding boxes, masks and + semantic segmentation maps. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Translated results. + """ + if np.random.rand() > self.prob: + return results + offset = random_negative(self.offset, self.random_negative_prob) + self._translate_img(results, offset, self.direction) + self._translate_bboxes(results, offset) + # fill_val defaultly 0 for BitmapMasks and None for PolygonMasks. + self._translate_masks(results, offset, self.direction) + # fill_val set to ``seg_ignore_label`` for the ignored value + # of segmentation map. + self._translate_seg( + results, offset, self.direction, fill_val=self.seg_ignore_label) + self._filter_invalid(results, min_size=self.min_size) + return results + + +@PIPELINES.register_module() +class ColorTransform(object): + """Apply Color transformation to image. The bboxes, masks, and + segmentations are not modified. + + Args: + level (int | float): Should be in range [0,_MAX_LEVEL]. + prob (float): The probability for performing Color transformation. + """ + + def __init__(self, level, prob=0.5): + assert isinstance(level, (int, float)), \ + 'The level must be type int or float.' + assert 0 <= level <= _MAX_LEVEL, \ + 'The level should be in range [0,_MAX_LEVEL].' + assert 0 <= prob <= 1.0, \ + 'The probability should be in range [0,1].' + self.level = level + self.prob = prob + self.factor = enhance_level_to_value(level) + + def _adjust_color_img(self, results, factor=1.0): + """Apply Color transformation to image.""" + for key in results.get('img_fields', ['img']): + # NOTE defaultly the image should be BGR format + img = results[key] + results[key] = mmcv.adjust_color(img, factor).astype(img.dtype) + + def __call__(self, results): + """Call function for Color transformation. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Colored results. + """ + if np.random.rand() > self.prob: + return results + self._adjust_color_img(results, self.factor) + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(level={self.level}, ' + repr_str += f'prob={self.prob})' + return repr_str + + +@PIPELINES.register_module() +class EqualizeTransform(object): + """Apply Equalize transformation to image. The bboxes, masks and + segmentations are not modified. + + Args: + prob (float): The probability for performing Equalize transformation. + """ + + def __init__(self, prob=0.5): + assert 0 <= prob <= 1.0, \ + 'The probability should be in range [0,1].' + self.prob = prob + + def _imequalize(self, results): + """Equalizes the histogram of one image.""" + for key in results.get('img_fields', ['img']): + img = results[key] + results[key] = mmcv.imequalize(img).astype(img.dtype) + + def __call__(self, results): + """Call function for Equalize transformation. + + Args: + results (dict): Results dict from loading pipeline. + + Returns: + dict: Results after the transformation. + """ + if np.random.rand() > self.prob: + return results + self._imequalize(results) + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(prob={self.prob})' + + +@PIPELINES.register_module() +class BrightnessTransform(object): + """Apply Brightness transformation to image. The bboxes, masks and + segmentations are not modified. + + Args: + level (int | float): Should be in range [0,_MAX_LEVEL]. + prob (float): The probability for performing Brightness transformation. + """ + + def __init__(self, level, prob=0.5): + assert isinstance(level, (int, float)), \ + 'The level must be type int or float.' + assert 0 <= level <= _MAX_LEVEL, \ + 'The level should be in range [0,_MAX_LEVEL].' + assert 0 <= prob <= 1.0, \ + 'The probability should be in range [0,1].' + self.level = level + self.prob = prob + self.factor = enhance_level_to_value(level) + + def _adjust_brightness_img(self, results, factor=1.0): + """Adjust the brightness of image.""" + for key in results.get('img_fields', ['img']): + img = results[key] + results[key] = mmcv.adjust_brightness(img, + factor).astype(img.dtype) + + def __call__(self, results): + """Call function for Brightness transformation. + + Args: + results (dict): Results dict from loading pipeline. + + Returns: + dict: Results after the transformation. + """ + if np.random.rand() > self.prob: + return results + self._adjust_brightness_img(results, self.factor) + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(level={self.level}, ' + repr_str += f'prob={self.prob})' + return repr_str + + +@PIPELINES.register_module() +class ContrastTransform(object): + """Apply Contrast transformation to image. The bboxes, masks and + segmentations are not modified. + + Args: + level (int | float): Should be in range [0,_MAX_LEVEL]. + prob (float): The probability for performing Contrast transformation. + """ + + def __init__(self, level, prob=0.5): + assert isinstance(level, (int, float)), \ + 'The level must be type int or float.' + assert 0 <= level <= _MAX_LEVEL, \ + 'The level should be in range [0,_MAX_LEVEL].' + assert 0 <= prob <= 1.0, \ + 'The probability should be in range [0,1].' + self.level = level + self.prob = prob + self.factor = enhance_level_to_value(level) + + def _adjust_contrast_img(self, results, factor=1.0): + """Adjust the image contrast.""" + for key in results.get('img_fields', ['img']): + img = results[key] + results[key] = mmcv.adjust_contrast(img, factor).astype(img.dtype) + + def __call__(self, results): + """Call function for Contrast transformation. + + Args: + results (dict): Results dict from loading pipeline. + + Returns: + dict: Results after the transformation. + """ + if np.random.rand() > self.prob: + return results + self._adjust_contrast_img(results, self.factor) + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(level={self.level}, ' + repr_str += f'prob={self.prob})' + return repr_str diff --git a/mmdet/datasets/pipelines/compose.py b/mmdet/datasets/pipelines/compose.py new file mode 100644 index 0000000..ca48f1c --- /dev/null +++ b/mmdet/datasets/pipelines/compose.py @@ -0,0 +1,51 @@ +import collections + +from mmcv.utils import build_from_cfg + +from ..builder import PIPELINES + + +@PIPELINES.register_module() +class Compose(object): + """Compose multiple transforms sequentially. + + Args: + transforms (Sequence[dict | callable]): Sequence of transform object or + config dict to be composed. + """ + + def __init__(self, transforms): + assert isinstance(transforms, collections.abc.Sequence) + self.transforms = [] + for transform in transforms: + if isinstance(transform, dict): + transform = build_from_cfg(transform, PIPELINES) + self.transforms.append(transform) + elif callable(transform): + self.transforms.append(transform) + else: + raise TypeError('transform must be callable or a dict') + + def __call__(self, data): + """Call function to apply transforms sequentially. + + Args: + data (dict): A result dict contains the data to transform. + + Returns: + dict: Transformed data. + """ + + for t in self.transforms: + data = t(data) + if data is None: + return None + return data + + def __repr__(self): + format_string = self.__class__.__name__ + '(' + for t in self.transforms: + format_string += '\n' + format_string += f' {t}' + format_string += '\n)' + return format_string diff --git a/mmdet/datasets/pipelines/formating.py b/mmdet/datasets/pipelines/formating.py new file mode 100644 index 0000000..5781341 --- /dev/null +++ b/mmdet/datasets/pipelines/formating.py @@ -0,0 +1,364 @@ +from collections.abc import Sequence + +import mmcv +import numpy as np +import torch +from mmcv.parallel import DataContainer as DC + +from ..builder import PIPELINES + + +def to_tensor(data): + """Convert objects of various python types to :obj:`torch.Tensor`. + + Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`, + :class:`Sequence`, :class:`int` and :class:`float`. + + Args: + data (torch.Tensor | numpy.ndarray | Sequence | int | float): Data to + be converted. + """ + + if isinstance(data, torch.Tensor): + return data + elif isinstance(data, np.ndarray): + return torch.from_numpy(data) + elif isinstance(data, Sequence) and not mmcv.is_str(data): + return torch.tensor(data) + elif isinstance(data, int): + return torch.LongTensor([data]) + elif isinstance(data, float): + return torch.FloatTensor([data]) + else: + raise TypeError(f'type {type(data)} cannot be converted to tensor.') + + +@PIPELINES.register_module() +class ToTensor(object): + """Convert some results to :obj:`torch.Tensor` by given keys. + + Args: + keys (Sequence[str]): Keys that need to be converted to Tensor. + """ + + def __init__(self, keys): + self.keys = keys + + def __call__(self, results): + """Call function to convert data in results to :obj:`torch.Tensor`. + + Args: + results (dict): Result dict contains the data to convert. + + Returns: + dict: The result dict contains the data converted + to :obj:`torch.Tensor`. + """ + for key in self.keys: + results[key] = to_tensor(results[key]) + return results + + def __repr__(self): + return self.__class__.__name__ + f'(keys={self.keys})' + + +@PIPELINES.register_module() +class ImageToTensor(object): + """Convert image to :obj:`torch.Tensor` by given keys. + + The dimension order of input image is (H, W, C). The pipeline will convert + it to (C, H, W). If only 2 dimension (H, W) is given, the output would be + (1, H, W). + + Args: + keys (Sequence[str]): Key of images to be converted to Tensor. + """ + + def __init__(self, keys): + self.keys = keys + + def __call__(self, results): + """Call function to convert image in results to :obj:`torch.Tensor` and + transpose the channel order. + + Args: + results (dict): Result dict contains the image data to convert. + + Returns: + dict: The result dict contains the image converted + to :obj:`torch.Tensor` and transposed to (C, H, W) order. + """ + for key in self.keys: + img = results[key] + if len(img.shape) < 3: + img = np.expand_dims(img, -1) + results[key] = to_tensor(img.transpose(2, 0, 1)) + return results + + def __repr__(self): + return self.__class__.__name__ + f'(keys={self.keys})' + + +@PIPELINES.register_module() +class Transpose(object): + """Transpose some results by given keys. + + Args: + keys (Sequence[str]): Keys of results to be transposed. + order (Sequence[int]): Order of transpose. + """ + + def __init__(self, keys, order): + self.keys = keys + self.order = order + + def __call__(self, results): + """Call function to transpose the channel order of data in results. + + Args: + results (dict): Result dict contains the data to transpose. + + Returns: + dict: The result dict contains the data transposed to \ + ``self.order``. + """ + for key in self.keys: + results[key] = results[key].transpose(self.order) + return results + + def __repr__(self): + return self.__class__.__name__ + \ + f'(keys={self.keys}, order={self.order})' + + +@PIPELINES.register_module() +class ToDataContainer(object): + """Convert results to :obj:`mmcv.DataContainer` by given fields. + + Args: + fields (Sequence[dict]): Each field is a dict like + ``dict(key='xxx', **kwargs)``. The ``key`` in result will + be converted to :obj:`mmcv.DataContainer` with ``**kwargs``. + Default: ``(dict(key='img', stack=True), dict(key='gt_bboxes'), + dict(key='gt_labels'))``. + """ + + def __init__(self, + fields=(dict(key='img', stack=True), dict(key='gt_bboxes'), + dict(key='gt_labels'))): + self.fields = fields + + def __call__(self, results): + """Call function to convert data in results to + :obj:`mmcv.DataContainer`. + + Args: + results (dict): Result dict contains the data to convert. + + Returns: + dict: The result dict contains the data converted to \ + :obj:`mmcv.DataContainer`. + """ + + for field in self.fields: + field = field.copy() + key = field.pop('key') + results[key] = DC(results[key], **field) + return results + + def __repr__(self): + return self.__class__.__name__ + f'(fields={self.fields})' + + +@PIPELINES.register_module() +class DefaultFormatBundle(object): + """Default formatting bundle. + + It simplifies the pipeline of formatting common fields, including "img", + "proposals", "gt_bboxes", "gt_labels", "gt_masks" and "gt_semantic_seg". + These fields are formatted as follows. + + - img: (1)transpose, (2)to tensor, (3)to DataContainer (stack=True) + - proposals: (1)to tensor, (2)to DataContainer + - gt_bboxes: (1)to tensor, (2)to DataContainer + - gt_bboxes_ignore: (1)to tensor, (2)to DataContainer + - gt_labels: (1)to tensor, (2)to DataContainer + - gt_masks: (1)to tensor, (2)to DataContainer (cpu_only=True) + - gt_semantic_seg: (1)unsqueeze dim-0 (2)to tensor, \ + (3)to DataContainer (stack=True) + """ + + def __call__(self, results): + """Call function to transform and format common fields in results. + + Args: + results (dict): Result dict contains the data to convert. + + Returns: + dict: The result dict contains the data that is formatted with \ + default bundle. + """ + + if 'img' in results: + img = results['img'] + # add default meta keys + results = self._add_default_meta_keys(results) + if len(img.shape) < 3: + img = np.expand_dims(img, -1) + img = np.ascontiguousarray(img.transpose(2, 0, 1)) + results['img'] = DC(to_tensor(img), stack=True) + for key in ['proposals', 'gt_bboxes', 'gt_bboxes_ignore', 'gt_labels']: + if key not in results: + continue + results[key] = DC(to_tensor(results[key])) + if 'gt_masks' in results: + results['gt_masks'] = DC(results['gt_masks'], cpu_only=True) + if 'gt_semantic_seg' in results: + results['gt_semantic_seg'] = DC( + to_tensor(results['gt_semantic_seg'][None, ...]), stack=True) + return results + + def _add_default_meta_keys(self, results): + """Add default meta keys. + + We set default meta keys including `pad_shape`, `scale_factor` and + `img_norm_cfg` to avoid the case where no `Resize`, `Normalize` and + `Pad` are implemented during the whole pipeline. + + Args: + results (dict): Result dict contains the data to convert. + + Returns: + results (dict): Updated result dict contains the data to convert. + """ + img = results['img'] + results.setdefault('pad_shape', img.shape) + results.setdefault('scale_factor', 1.0) + num_channels = 1 if len(img.shape) < 3 else img.shape[2] + results.setdefault( + 'img_norm_cfg', + dict( + mean=np.zeros(num_channels, dtype=np.float32), + std=np.ones(num_channels, dtype=np.float32), + to_rgb=False)) + return results + + def __repr__(self): + return self.__class__.__name__ + + +@PIPELINES.register_module() +class Collect(object): + """Collect data from the loader relevant to the specific task. + + This is usually the last stage of the data loader pipeline. Typically keys + is set to some subset of "img", "proposals", "gt_bboxes", + "gt_bboxes_ignore", "gt_labels", and/or "gt_masks". + + The "img_meta" item is always populated. The contents of the "img_meta" + dictionary depends on "meta_keys". By default this includes: + + - "img_shape": shape of the image input to the network as a tuple \ + (h, w, c). Note that images may be zero padded on the \ + bottom/right if the batch tensor is larger than this shape. + + - "scale_factor": a float indicating the preprocessing scale + + - "flip": a boolean indicating if image flip transform was used + + - "filename": path to the image file + + - "ori_shape": original shape of the image as a tuple (h, w, c) + + - "pad_shape": image shape after padding + + - "img_norm_cfg": a dict of normalization information: + + - mean - per channel mean subtraction + - std - per channel std divisor + - to_rgb - bool indicating if bgr was converted to rgb + + Args: + keys (Sequence[str]): Keys of results to be collected in ``data``. + meta_keys (Sequence[str], optional): Meta keys to be converted to + ``mmcv.DataContainer`` and collected in ``data[img_metas]``. + Default: ``('filename', 'ori_filename', 'ori_shape', 'img_shape', + 'pad_shape', 'scale_factor', 'flip', 'flip_direction', + 'img_norm_cfg')`` + """ + + def __init__(self, + keys, + meta_keys=('filename', 'ori_filename', 'ori_shape', + 'img_shape', 'pad_shape', 'scale_factor', 'flip', + 'flip_direction', 'img_norm_cfg')): + self.keys = keys + self.meta_keys = meta_keys + + def __call__(self, results): + """Call function to collect keys in results. The keys in ``meta_keys`` + will be converted to :obj:mmcv.DataContainer. + + Args: + results (dict): Result dict contains the data to collect. + + Returns: + dict: The result dict contains the following keys + + - keys in``self.keys`` + - ``img_metas`` + """ + + data = {} + img_meta = {} + for key in self.meta_keys: + img_meta[key] = results[key] + data['img_metas'] = DC(img_meta, cpu_only=True) + for key in self.keys: + data[key] = results[key] + return data + + def __repr__(self): + return self.__class__.__name__ + \ + f'(keys={self.keys}, meta_keys={self.meta_keys})' + + +@PIPELINES.register_module() +class WrapFieldsToLists(object): + """Wrap fields of the data dictionary into lists for evaluation. + + This class can be used as a last step of a test or validation + pipeline for single image evaluation or inference. + + Example: + >>> test_pipeline = [ + >>> dict(type='LoadImageFromFile'), + >>> dict(type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + >>> dict(type='Pad', size_divisor=32), + >>> dict(type='ImageToTensor', keys=['img']), + >>> dict(type='Collect', keys=['img']), + >>> dict(type='WrapFieldsToLists') + >>> ] + """ + + def __call__(self, results): + """Call function to wrap fields into lists. + + Args: + results (dict): Result dict contains the data to wrap. + + Returns: + dict: The result dict where value of ``self.keys`` are wrapped \ + into list. + """ + + # Wrap dict fields into lists + for key, val in results.items(): + results[key] = [val] + return results + + def __repr__(self): + return f'{self.__class__.__name__}()' diff --git a/mmdet/datasets/pipelines/instaboost.py b/mmdet/datasets/pipelines/instaboost.py new file mode 100644 index 0000000..38b6819 --- /dev/null +++ b/mmdet/datasets/pipelines/instaboost.py @@ -0,0 +1,98 @@ +import numpy as np + +from ..builder import PIPELINES + + +@PIPELINES.register_module() +class InstaBoost(object): + r"""Data augmentation method in `InstaBoost: Boosting Instance + Segmentation Via Probability Map Guided Copy-Pasting + `_. + + Refer to https://github.com/GothicAi/Instaboost for implementation details. + """ + + def __init__(self, + action_candidate=('normal', 'horizontal', 'skip'), + action_prob=(1, 0, 0), + scale=(0.8, 1.2), + dx=15, + dy=15, + theta=(-1, 1), + color_prob=0.5, + hflag=False, + aug_ratio=0.5): + try: + import instaboostfast as instaboost + except ImportError: + raise ImportError( + 'Please run "pip install instaboostfast" ' + 'to install instaboostfast first for instaboost augmentation.') + self.cfg = instaboost.InstaBoostConfig(action_candidate, action_prob, + scale, dx, dy, theta, + color_prob, hflag) + self.aug_ratio = aug_ratio + + def _load_anns(self, results): + labels = results['ann_info']['labels'] + masks = results['ann_info']['masks'] + bboxes = results['ann_info']['bboxes'] + n = len(labels) + + anns = [] + for i in range(n): + label = labels[i] + bbox = bboxes[i] + mask = masks[i] + x1, y1, x2, y2 = bbox + # assert (x2 - x1) >= 1 and (y2 - y1) >= 1 + bbox = [x1, y1, x2 - x1, y2 - y1] + anns.append({ + 'category_id': label, + 'segmentation': mask, + 'bbox': bbox + }) + + return anns + + def _parse_anns(self, results, anns, img): + gt_bboxes = [] + gt_labels = [] + gt_masks_ann = [] + for ann in anns: + x1, y1, w, h = ann['bbox'] + # TODO: more essential bug need to be fixed in instaboost + if w <= 0 or h <= 0: + continue + bbox = [x1, y1, x1 + w, y1 + h] + gt_bboxes.append(bbox) + gt_labels.append(ann['category_id']) + gt_masks_ann.append(ann['segmentation']) + gt_bboxes = np.array(gt_bboxes, dtype=np.float32) + gt_labels = np.array(gt_labels, dtype=np.int64) + results['ann_info']['labels'] = gt_labels + results['ann_info']['bboxes'] = gt_bboxes + results['ann_info']['masks'] = gt_masks_ann + results['img'] = img + return results + + def __call__(self, results): + img = results['img'] + orig_type = img.dtype + anns = self._load_anns(results) + if np.random.choice([0, 1], p=[1 - self.aug_ratio, self.aug_ratio]): + try: + import instaboostfast as instaboost + except ImportError: + raise ImportError('Please run "pip install instaboostfast" ' + 'to install instaboostfast first.') + anns, img = instaboost.get_new_data( + anns, img.astype(np.uint8), self.cfg, background=None) + + results = self._parse_anns(results, anns, img.astype(orig_type)) + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(cfg={self.cfg}, aug_ratio={self.aug_ratio})' + return repr_str diff --git a/mmdet/datasets/pipelines/loading.py b/mmdet/datasets/pipelines/loading.py new file mode 100644 index 0000000..6922594 --- /dev/null +++ b/mmdet/datasets/pipelines/loading.py @@ -0,0 +1,458 @@ +import os.path as osp + +import mmcv +import numpy as np +import pycocotools.mask as maskUtils + +from mmdet.core import BitmapMasks, PolygonMasks +from ..builder import PIPELINES + + +@PIPELINES.register_module() +class LoadImageFromFile(object): + """Load an image from file. + + Required keys are "img_prefix" and "img_info" (a dict that must contain the + key "filename"). Added or updated keys are "filename", "img", "img_shape", + "ori_shape" (same as `img_shape`), "pad_shape" (same as `img_shape`), + "scale_factor" (1.0) and "img_norm_cfg" (means=0 and stds=1). + + Args: + to_float32 (bool): Whether to convert the loaded image to a float32 + numpy array. If set to False, the loaded image is an uint8 array. + Defaults to False. + color_type (str): The flag argument for :func:`mmcv.imfrombytes`. + Defaults to 'color'. + file_client_args (dict): Arguments to instantiate a FileClient. + See :class:`mmcv.fileio.FileClient` for details. + Defaults to ``dict(backend='disk')``. + """ + + def __init__(self, + to_float32=False, + color_type='color', + file_client_args=dict(backend='disk')): + self.to_float32 = to_float32 + self.color_type = color_type + self.file_client_args = file_client_args.copy() + self.file_client = None + + def __call__(self, results): + """Call functions to load image and get image meta information. + + Args: + results (dict): Result dict from :obj:`mmdet.CustomDataset`. + + Returns: + dict: The dict contains loaded image and meta information. + """ + + if self.file_client is None: + self.file_client = mmcv.FileClient(**self.file_client_args) + + if results['img_prefix'] is not None: + filename = osp.join(results['img_prefix'], + results['img_info']['filename']) + else: + filename = results['img_info']['filename'] + + img_bytes = self.file_client.get(filename) + img = mmcv.imfrombytes(img_bytes, flag=self.color_type) + if self.to_float32: + img = img.astype(np.float32) + + results['filename'] = filename + results['ori_filename'] = results['img_info']['filename'] + results['img'] = img + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + results['img_fields'] = ['img'] + return results + + def __repr__(self): + repr_str = (f'{self.__class__.__name__}(' + f'to_float32={self.to_float32}, ' + f"color_type='{self.color_type}', " + f'file_client_args={self.file_client_args})') + return repr_str + + +@PIPELINES.register_module() +class LoadImageFromWebcam(LoadImageFromFile): + """Load an image from webcam. + + Similar with :obj:`LoadImageFromFile`, but the image read from webcam is in + ``results['img']``. + """ + + def __call__(self, results): + """Call functions to add image meta information. + + Args: + results (dict): Result dict with Webcam read image in + ``results['img']``. + + Returns: + dict: The dict contains loaded image and meta information. + """ + + img = results['img'] + if self.to_float32: + img = img.astype(np.float32) + + results['filename'] = None + results['ori_filename'] = None + results['img'] = img + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + results['img_fields'] = ['img'] + return results + + +@PIPELINES.register_module() +class LoadMultiChannelImageFromFiles(object): + """Load multi-channel images from a list of separate channel files. + + Required keys are "img_prefix" and "img_info" (a dict that must contain the + key "filename", which is expected to be a list of filenames). + Added or updated keys are "filename", "img", "img_shape", + "ori_shape" (same as `img_shape`), "pad_shape" (same as `img_shape`), + "scale_factor" (1.0) and "img_norm_cfg" (means=0 and stds=1). + + Args: + to_float32 (bool): Whether to convert the loaded image to a float32 + numpy array. If set to False, the loaded image is an uint8 array. + Defaults to False. + color_type (str): The flag argument for :func:`mmcv.imfrombytes`. + Defaults to 'color'. + file_client_args (dict): Arguments to instantiate a FileClient. + See :class:`mmcv.fileio.FileClient` for details. + Defaults to ``dict(backend='disk')``. + """ + + def __init__(self, + to_float32=False, + color_type='unchanged', + file_client_args=dict(backend='disk')): + self.to_float32 = to_float32 + self.color_type = color_type + self.file_client_args = file_client_args.copy() + self.file_client = None + + def __call__(self, results): + """Call functions to load multiple images and get images meta + information. + + Args: + results (dict): Result dict from :obj:`mmdet.CustomDataset`. + + Returns: + dict: The dict contains loaded images and meta information. + """ + + if self.file_client is None: + self.file_client = mmcv.FileClient(**self.file_client_args) + + if results['img_prefix'] is not None: + filename = [ + osp.join(results['img_prefix'], fname) + for fname in results['img_info']['filename'] + ] + else: + filename = results['img_info']['filename'] + + img = [] + for name in filename: + img_bytes = self.file_client.get(name) + img.append(mmcv.imfrombytes(img_bytes, flag=self.color_type)) + img = np.stack(img, axis=-1) + if self.to_float32: + img = img.astype(np.float32) + + results['filename'] = filename + results['ori_filename'] = results['img_info']['filename'] + results['img'] = img + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + # Set initial values for default meta_keys + results['pad_shape'] = img.shape + results['scale_factor'] = 1.0 + num_channels = 1 if len(img.shape) < 3 else img.shape[2] + results['img_norm_cfg'] = dict( + mean=np.zeros(num_channels, dtype=np.float32), + std=np.ones(num_channels, dtype=np.float32), + to_rgb=False) + return results + + def __repr__(self): + repr_str = (f'{self.__class__.__name__}(' + f'to_float32={self.to_float32}, ' + f"color_type='{self.color_type}', " + f'file_client_args={self.file_client_args})') + return repr_str + + +@PIPELINES.register_module() +class LoadAnnotations(object): + """Load mutiple types of annotations. + + Args: + with_bbox (bool): Whether to parse and load the bbox annotation. + Default: True. + with_label (bool): Whether to parse and load the label annotation. + Default: True. + with_mask (bool): Whether to parse and load the mask annotation. + Default: False. + with_seg (bool): Whether to parse and load the semantic segmentation + annotation. Default: False. + poly2mask (bool): Whether to convert the instance masks from polygons + to bitmaps. Default: True. + file_client_args (dict): Arguments to instantiate a FileClient. + See :class:`mmcv.fileio.FileClient` for details. + Defaults to ``dict(backend='disk')``. + """ + + def __init__(self, + with_bbox=True, + with_label=True, + with_mask=False, + with_seg=False, + poly2mask=True, + file_client_args=dict(backend='disk')): + self.with_bbox = with_bbox + self.with_label = with_label + self.with_mask = with_mask + self.with_seg = with_seg + self.poly2mask = poly2mask + self.file_client_args = file_client_args.copy() + self.file_client = None + + def _load_bboxes(self, results): + """Private function to load bounding box annotations. + + Args: + results (dict): Result dict from :obj:`mmdet.CustomDataset`. + + Returns: + dict: The dict contains loaded bounding box annotations. + """ + + ann_info = results['ann_info'] + results['gt_bboxes'] = ann_info['bboxes'].copy() + + gt_bboxes_ignore = ann_info.get('bboxes_ignore', None) + if gt_bboxes_ignore is not None: + results['gt_bboxes_ignore'] = gt_bboxes_ignore.copy() + results['bbox_fields'].append('gt_bboxes_ignore') + results['bbox_fields'].append('gt_bboxes') + return results + + def _load_labels(self, results): + """Private function to load label annotations. + + Args: + results (dict): Result dict from :obj:`mmdet.CustomDataset`. + + Returns: + dict: The dict contains loaded label annotations. + """ + + results['gt_labels'] = results['ann_info']['labels'].copy() + return results + + def _poly2mask(self, mask_ann, img_h, img_w): + """Private function to convert masks represented with polygon to + bitmaps. + + Args: + mask_ann (list | dict): Polygon mask annotation input. + img_h (int): The height of output mask. + img_w (int): The width of output mask. + + Returns: + numpy.ndarray: The decode bitmap mask of shape (img_h, img_w). + """ + + if isinstance(mask_ann, list): + # polygon -- a single object might consist of multiple parts + # we merge all parts into one mask rle code + rles = maskUtils.frPyObjects(mask_ann, img_h, img_w) + rle = maskUtils.merge(rles) + elif isinstance(mask_ann['counts'], list): + # uncompressed RLE + rle = maskUtils.frPyObjects(mask_ann, img_h, img_w) + else: + # rle + rle = mask_ann + mask = maskUtils.decode(rle) + return mask + + def process_polygons(self, polygons): + """Convert polygons to list of ndarray and filter invalid polygons. + + Args: + polygons (list[list]): Polygons of one instance. + + Returns: + list[numpy.ndarray]: Processed polygons. + """ + + polygons = [np.array(p) for p in polygons] + valid_polygons = [] + for polygon in polygons: + if len(polygon) % 2 == 0 and len(polygon) >= 6: + valid_polygons.append(polygon) + return valid_polygons + + def _load_masks(self, results): + """Private function to load mask annotations. + + Args: + results (dict): Result dict from :obj:`mmdet.CustomDataset`. + + Returns: + dict: The dict contains loaded mask annotations. + If ``self.poly2mask`` is set ``True``, `gt_mask` will contain + :obj:`PolygonMasks`. Otherwise, :obj:`BitmapMasks` is used. + """ + + h, w = results['img_info']['height'], results['img_info']['width'] + gt_masks = results['ann_info']['masks'] + if self.poly2mask: + gt_masks = BitmapMasks( + [self._poly2mask(mask, h, w) for mask in gt_masks], h, w) + else: + gt_masks = PolygonMasks( + [self.process_polygons(polygons) for polygons in gt_masks], h, + w) + results['gt_masks'] = gt_masks + results['mask_fields'].append('gt_masks') + return results + + def _load_semantic_seg(self, results): + """Private function to load semantic segmentation annotations. + + Args: + results (dict): Result dict from :obj:`dataset`. + + Returns: + dict: The dict contains loaded semantic segmentation annotations. + """ + + if self.file_client is None: + self.file_client = mmcv.FileClient(**self.file_client_args) + + filename = osp.join(results['seg_prefix'], + results['ann_info']['seg_map']) + img_bytes = self.file_client.get(filename) + results['gt_semantic_seg'] = mmcv.imfrombytes( + img_bytes, flag='unchanged').squeeze() + results['seg_fields'].append('gt_semantic_seg') + return results + + def __call__(self, results): + """Call function to load multiple types annotations. + + Args: + results (dict): Result dict from :obj:`mmdet.CustomDataset`. + + Returns: + dict: The dict contains loaded bounding box, label, mask and + semantic segmentation annotations. + """ + + if self.with_bbox: + results = self._load_bboxes(results) + if results is None: + return None + if self.with_label: + results = self._load_labels(results) + if self.with_mask: + results = self._load_masks(results) + if self.with_seg: + results = self._load_semantic_seg(results) + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(with_bbox={self.with_bbox}, ' + repr_str += f'with_label={self.with_label}, ' + repr_str += f'with_mask={self.with_mask}, ' + repr_str += f'with_seg={self.with_seg}, ' + repr_str += f'poly2mask={self.poly2mask}, ' + repr_str += f'poly2mask={self.file_client_args})' + return repr_str + + +@PIPELINES.register_module() +class LoadProposals(object): + """Load proposal pipeline. + + Required key is "proposals". Updated keys are "proposals", "bbox_fields". + + Args: + num_max_proposals (int, optional): Maximum number of proposals to load. + If not specified, all proposals will be loaded. + """ + + def __init__(self, num_max_proposals=None): + self.num_max_proposals = num_max_proposals + + def __call__(self, results): + """Call function to load proposals from file. + + Args: + results (dict): Result dict from :obj:`mmdet.CustomDataset`. + + Returns: + dict: The dict contains loaded proposal annotations. + """ + + proposals = results['proposals'] + if proposals.shape[1] not in (4, 5): + raise AssertionError( + 'proposals should have shapes (n, 4) or (n, 5), ' + f'but found {proposals.shape}') + proposals = proposals[:, :4] + + if self.num_max_proposals is not None: + proposals = proposals[:self.num_max_proposals] + + if len(proposals) == 0: + proposals = np.array([[0, 0, 0, 0]], dtype=np.float32) + results['proposals'] = proposals + results['bbox_fields'].append('proposals') + return results + + def __repr__(self): + return self.__class__.__name__ + \ + f'(num_max_proposals={self.num_max_proposals})' + + +@PIPELINES.register_module() +class FilterAnnotations(object): + """Filter invalid annotations. + + Args: + min_gt_bbox_wh (tuple[int]): Minimum width and height of ground truth + boxes. + """ + + def __init__(self, min_gt_bbox_wh): + # TODO: add more filter options + self.min_gt_bbox_wh = min_gt_bbox_wh + + def __call__(self, results): + assert 'gt_bboxes' in results + gt_bboxes = results['gt_bboxes'] + w = gt_bboxes[:, 2] - gt_bboxes[:, 0] + h = gt_bboxes[:, 3] - gt_bboxes[:, 1] + keep = (w > self.min_gt_bbox_wh[0]) & (h > self.min_gt_bbox_wh[1]) + if not keep.any(): + return None + else: + keys = ('gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg') + for key in keys: + if key in results: + results[key] = results[key][keep] + return results diff --git a/mmdet/datasets/pipelines/test_time_aug.py b/mmdet/datasets/pipelines/test_time_aug.py new file mode 100644 index 0000000..b6226e0 --- /dev/null +++ b/mmdet/datasets/pipelines/test_time_aug.py @@ -0,0 +1,119 @@ +import warnings + +import mmcv + +from ..builder import PIPELINES +from .compose import Compose + + +@PIPELINES.register_module() +class MultiScaleFlipAug(object): + """Test-time augmentation with multiple scales and flipping. + + An example configuration is as followed: + + .. code-block:: + + img_scale=[(1333, 400), (1333, 800)], + flip=True, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ] + + After MultiScaleFLipAug with above configuration, the results are wrapped + into lists of the same length as followed: + + .. code-block:: + + dict( + img=[...], + img_shape=[...], + scale=[(1333, 400), (1333, 400), (1333, 800), (1333, 800)] + flip=[False, True, False, True] + ... + ) + + Args: + transforms (list[dict]): Transforms to apply in each augmentation. + img_scale (tuple | list[tuple] | None): Images scales for resizing. + scale_factor (float | list[float] | None): Scale factors for resizing. + flip (bool): Whether apply flip augmentation. Default: False. + flip_direction (str | list[str]): Flip augmentation directions, + options are "horizontal" and "vertical". If flip_direction is list, + multiple flip augmentations will be applied. + It has no effect when flip == False. Default: "horizontal". + """ + + def __init__(self, + transforms, + img_scale=None, + scale_factor=None, + flip=False, + flip_direction='horizontal'): + self.transforms = Compose(transforms) + assert (img_scale is None) ^ (scale_factor is None), ( + 'Must have but only one variable can be setted') + if img_scale is not None: + self.img_scale = img_scale if isinstance(img_scale, + list) else [img_scale] + self.scale_key = 'scale' + assert mmcv.is_list_of(self.img_scale, tuple) + else: + self.img_scale = scale_factor if isinstance( + scale_factor, list) else [scale_factor] + self.scale_key = 'scale_factor' + + self.flip = flip + self.flip_direction = flip_direction if isinstance( + flip_direction, list) else [flip_direction] + assert mmcv.is_list_of(self.flip_direction, str) + if not self.flip and self.flip_direction != ['horizontal']: + warnings.warn( + 'flip_direction has no effect when flip is set to False') + if (self.flip + and not any([t['type'] == 'RandomFlip' for t in transforms])): + warnings.warn( + 'flip has no effect when RandomFlip is not in transforms') + + def __call__(self, results): + """Call function to apply test time augment transforms on results. + + Args: + results (dict): Result dict contains the data to transform. + + Returns: + dict[str: list]: The augmented data, where each value is wrapped + into a list. + """ + + aug_data = [] + flip_args = [(False, None)] + if self.flip: + flip_args += [(True, direction) + for direction in self.flip_direction] + for scale in self.img_scale: + for flip, direction in flip_args: + _results = results.copy() + _results[self.scale_key] = scale + _results['flip'] = flip + _results['flip_direction'] = direction + data = self.transforms(_results) + aug_data.append(data) + # list of dict to dict of list + aug_data_dict = {key: [] for key in aug_data[0]} + for data in aug_data: + for key, val in data.items(): + aug_data_dict[key].append(val) + return aug_data_dict + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(transforms={self.transforms}, ' + repr_str += f'img_scale={self.img_scale}, flip={self.flip}, ' + repr_str += f'flip_direction={self.flip_direction})' + return repr_str diff --git a/mmdet/datasets/pipelines/transforms.py b/mmdet/datasets/pipelines/transforms.py new file mode 100644 index 0000000..c777b31 --- /dev/null +++ b/mmdet/datasets/pipelines/transforms.py @@ -0,0 +1,1901 @@ +import copy +import inspect + +import mmcv +import numpy as np +from numpy import random + +from mmdet.core import PolygonMasks +from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps +from ..builder import PIPELINES + +try: + from imagecorruptions import corrupt +except ImportError: + corrupt = None + +try: + import albumentations + from albumentations import Compose +except ImportError: + albumentations = None + Compose = None + + +@PIPELINES.register_module() +class Resize(object): + """Resize images & bbox & mask. + + This transform resizes the input image to some scale. Bboxes and masks are + then resized with the same scale factor. If the input dict contains the key + "scale", then the scale in the input dict is used, otherwise the specified + scale in the init method is used. If the input dict contains the key + "scale_factor" (if MultiScaleFlipAug does not give img_scale but + scale_factor), the actual scale will be computed by image shape and + scale_factor. + + `img_scale` can either be a tuple (single-scale) or a list of tuple + (multi-scale). There are 3 multiscale modes: + + - ``ratio_range is not None``: randomly sample a ratio from the ratio \ + range and multiply it with the image scale. + - ``ratio_range is None`` and ``multiscale_mode == "range"``: randomly \ + sample a scale from the multiscale range. + - ``ratio_range is None`` and ``multiscale_mode == "value"``: randomly \ + sample a scale from multiple scales. + + Args: + img_scale (tuple or list[tuple]): Images scales for resizing. + multiscale_mode (str): Either "range" or "value". + ratio_range (tuple[float]): (min_ratio, max_ratio) + keep_ratio (bool): Whether to keep the aspect ratio when resizing the + image. + bbox_clip_border (bool, optional): Whether clip the objects outside + the border of the image. Defaults to True. + backend (str): Image resize backend, choices are 'cv2' and 'pillow'. + These two backends generates slightly different results. Defaults + to 'cv2'. + override (bool, optional): Whether to override `scale` and + `scale_factor` so as to call resize twice. Default False. If True, + after the first resizing, the existed `scale` and `scale_factor` + will be ignored so the second resizing can be allowed. + This option is a work-around for multiple times of resize in DETR. + Defaults to False. + """ + + def __init__(self, + img_scale=None, + multiscale_mode='range', + ratio_range=None, + keep_ratio=True, + bbox_clip_border=True, + backend='cv2', + override=False): + if img_scale is None: + self.img_scale = None + else: + if isinstance(img_scale, list): + self.img_scale = img_scale + else: + self.img_scale = [img_scale] + assert mmcv.is_list_of(self.img_scale, tuple) + + if ratio_range is not None: + # mode 1: given a scale and a range of image ratio + assert len(self.img_scale) == 1 + else: + # mode 2: given multiple scales or a range of scales + assert multiscale_mode in ['value', 'range'] + + self.backend = backend + self.multiscale_mode = multiscale_mode + self.ratio_range = ratio_range + self.keep_ratio = keep_ratio + # TODO: refactor the override option in Resize + self.override = override + self.bbox_clip_border = bbox_clip_border + + @staticmethod + def random_select(img_scales): + """Randomly select an img_scale from given candidates. + + Args: + img_scales (list[tuple]): Images scales for selection. + + Returns: + (tuple, int): Returns a tuple ``(img_scale, scale_dix)``, \ + where ``img_scale`` is the selected image scale and \ + ``scale_idx`` is the selected index in the given candidates. + """ + + assert mmcv.is_list_of(img_scales, tuple) + scale_idx = np.random.randint(len(img_scales)) + img_scale = img_scales[scale_idx] + return img_scale, scale_idx + + @staticmethod + def random_sample(img_scales): + """Randomly sample an img_scale when ``multiscale_mode=='range'``. + + Args: + img_scales (list[tuple]): Images scale range for sampling. + There must be two tuples in img_scales, which specify the lower + and upper bound of image scales. + + Returns: + (tuple, None): Returns a tuple ``(img_scale, None)``, where \ + ``img_scale`` is sampled scale and None is just a placeholder \ + to be consistent with :func:`random_select`. + """ + + assert mmcv.is_list_of(img_scales, tuple) and len(img_scales) == 2 + img_scale_long = [max(s) for s in img_scales] + img_scale_short = [min(s) for s in img_scales] + long_edge = np.random.randint( + min(img_scale_long), + max(img_scale_long) + 1) + short_edge = np.random.randint( + min(img_scale_short), + max(img_scale_short) + 1) + img_scale = (long_edge, short_edge) + return img_scale, None + + @staticmethod + def random_sample_ratio(img_scale, ratio_range): + """Randomly sample an img_scale when ``ratio_range`` is specified. + + A ratio will be randomly sampled from the range specified by + ``ratio_range``. Then it would be multiplied with ``img_scale`` to + generate sampled scale. + + Args: + img_scale (tuple): Images scale base to multiply with ratio. + ratio_range (tuple[float]): The minimum and maximum ratio to scale + the ``img_scale``. + + Returns: + (tuple, None): Returns a tuple ``(scale, None)``, where \ + ``scale`` is sampled ratio multiplied with ``img_scale`` and \ + None is just a placeholder to be consistent with \ + :func:`random_select`. + """ + + assert isinstance(img_scale, tuple) and len(img_scale) == 2 + min_ratio, max_ratio = ratio_range + assert min_ratio <= max_ratio + ratio = np.random.random_sample() * (max_ratio - min_ratio) + min_ratio + scale = int(img_scale[0] * ratio), int(img_scale[1] * ratio) + return scale, None + + def _random_scale(self, results): + """Randomly sample an img_scale according to ``ratio_range`` and + ``multiscale_mode``. + + If ``ratio_range`` is specified, a ratio will be sampled and be + multiplied with ``img_scale``. + If multiple scales are specified by ``img_scale``, a scale will be + sampled according to ``multiscale_mode``. + Otherwise, single scale will be used. + + Args: + results (dict): Result dict from :obj:`dataset`. + + Returns: + dict: Two new keys 'scale` and 'scale_idx` are added into \ + ``results``, which would be used by subsequent pipelines. + """ + + if self.ratio_range is not None: + scale, scale_idx = self.random_sample_ratio( + self.img_scale[0], self.ratio_range) + elif len(self.img_scale) == 1: + scale, scale_idx = self.img_scale[0], 0 + elif self.multiscale_mode == 'range': + scale, scale_idx = self.random_sample(self.img_scale) + elif self.multiscale_mode == 'value': + scale, scale_idx = self.random_select(self.img_scale) + else: + raise NotImplementedError + + results['scale'] = scale + results['scale_idx'] = scale_idx + + def _resize_img(self, results): + """Resize images with ``results['scale']``.""" + for key in results.get('img_fields', ['img']): + if self.keep_ratio: + img, scale_factor = mmcv.imrescale( + results[key], + results['scale'], + return_scale=True, + backend=self.backend) + # the w_scale and h_scale has minor difference + # a real fix should be done in the mmcv.imrescale in the future + new_h, new_w = img.shape[:2] + h, w = results[key].shape[:2] + w_scale = new_w / w + h_scale = new_h / h + else: + img, w_scale, h_scale = mmcv.imresize( + results[key], + results['scale'], + return_scale=True, + backend=self.backend) + results[key] = img + + scale_factor = np.array([w_scale, h_scale, w_scale, h_scale], + dtype=np.float32) + results['img_shape'] = img.shape + # in case that there is no padding + results['pad_shape'] = img.shape + results['scale_factor'] = scale_factor + results['keep_ratio'] = self.keep_ratio + + def _resize_bboxes(self, results): + """Resize bounding boxes with ``results['scale_factor']``.""" + for key in results.get('bbox_fields', []): + bboxes = results[key] * results['scale_factor'] + if self.bbox_clip_border: + img_shape = results['img_shape'] + bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1]) + bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0]) + results[key] = bboxes + + def _resize_masks(self, results): + """Resize masks with ``results['scale']``""" + for key in results.get('mask_fields', []): + if results[key] is None: + continue + if self.keep_ratio: + results[key] = results[key].rescale(results['scale']) + else: + results[key] = results[key].resize(results['img_shape'][:2]) + + def _resize_seg(self, results): + """Resize semantic segmentation map with ``results['scale']``.""" + for key in results.get('seg_fields', []): + if self.keep_ratio: + gt_seg = mmcv.imrescale( + results[key], + results['scale'], + interpolation='nearest', + backend=self.backend) + else: + gt_seg = mmcv.imresize( + results[key], + results['scale'], + interpolation='nearest', + backend=self.backend) + results['gt_semantic_seg'] = gt_seg + + def __call__(self, results): + """Call function to resize images, bounding boxes, masks, semantic + segmentation map. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Resized results, 'img_shape', 'pad_shape', 'scale_factor', \ + 'keep_ratio' keys are added into result dict. + """ + + if 'scale' not in results: + if 'scale_factor' in results: + img_shape = results['img'].shape[:2] + scale_factor = results['scale_factor'] + assert isinstance(scale_factor, float) + results['scale'] = tuple( + [int(x * scale_factor) for x in img_shape][::-1]) + else: + self._random_scale(results) + else: + if not self.override: + assert 'scale_factor' not in results, ( + 'scale and scale_factor cannot be both set.') + else: + results.pop('scale') + if 'scale_factor' in results: + results.pop('scale_factor') + self._random_scale(results) + + self._resize_img(results) + self._resize_bboxes(results) + self._resize_masks(results) + self._resize_seg(results) + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(img_scale={self.img_scale}, ' + repr_str += f'multiscale_mode={self.multiscale_mode}, ' + repr_str += f'ratio_range={self.ratio_range}, ' + repr_str += f'keep_ratio={self.keep_ratio}, ' + repr_str += f'bbox_clip_border={self.bbox_clip_border})' + return repr_str + + +@PIPELINES.register_module() +class RandomFlip(object): + """Flip the image & bbox & mask. + + If the input dict contains the key "flip", then the flag will be used, + otherwise it will be randomly decided by a ratio specified in the init + method. + + When random flip is enabled, ``flip_ratio``/``direction`` can either be a + float/string or tuple of float/string. There are 3 flip modes: + + - ``flip_ratio`` is float, ``direction`` is string: the image will be + ``direction``ly flipped with probability of ``flip_ratio`` . + E.g., ``flip_ratio=0.5``, ``direction='horizontal'``, + then image will be horizontally flipped with probability of 0.5. + - ``flip_ratio`` is float, ``direction`` is list of string: the image wil + be ``direction[i]``ly flipped with probability of + ``flip_ratio/len(direction)``. + E.g., ``flip_ratio=0.5``, ``direction=['horizontal', 'vertical']``, + then image will be horizontally flipped with probability of 0.25, + vertically with probability of 0.25. + - ``flip_ratio`` is list of float, ``direction`` is list of string: + given ``len(flip_ratio) == len(direction)``, the image wil + be ``direction[i]``ly flipped with probability of ``flip_ratio[i]``. + E.g., ``flip_ratio=[0.3, 0.5]``, ``direction=['horizontal', + 'vertical']``, then image will be horizontally flipped with probability + of 0.3, vertically with probability of 0.5 + + Args: + flip_ratio (float | list[float], optional): The flipping probability. + Default: None. + direction(str | list[str], optional): The flipping direction. Options + are 'horizontal', 'vertical', 'diagonal'. Default: 'horizontal'. + If input is a list, the length must equal ``flip_ratio``. Each + element in ``flip_ratio`` indicates the flip probability of + corresponding direction. + """ + + def __init__(self, flip_ratio=None, direction='horizontal'): + if isinstance(flip_ratio, list): + assert mmcv.is_list_of(flip_ratio, float) + assert 0 <= sum(flip_ratio) <= 1 + elif isinstance(flip_ratio, float): + assert 0 <= flip_ratio <= 1 + elif flip_ratio is None: + pass + else: + raise ValueError('flip_ratios must be None, float, ' + 'or list of float') + self.flip_ratio = flip_ratio + + valid_directions = ['horizontal', 'vertical', 'diagonal'] + if isinstance(direction, str): + assert direction in valid_directions + elif isinstance(direction, list): + assert mmcv.is_list_of(direction, str) + assert set(direction).issubset(set(valid_directions)) + else: + raise ValueError('direction must be either str or list of str') + self.direction = direction + + if isinstance(flip_ratio, list): + assert len(self.flip_ratio) == len(self.direction) + + def bbox_flip(self, bboxes, img_shape, direction): + """Flip bboxes horizontally. + + Args: + bboxes (numpy.ndarray): Bounding boxes, shape (..., 4*k) + img_shape (tuple[int]): Image shape (height, width) + direction (str): Flip direction. Options are 'horizontal', + 'vertical'. + + Returns: + numpy.ndarray: Flipped bounding boxes. + """ + + assert bboxes.shape[-1] % 4 == 0 + flipped = bboxes.copy() + if direction == 'horizontal': + w = img_shape[1] + flipped[..., 0::4] = w - bboxes[..., 2::4] + flipped[..., 2::4] = w - bboxes[..., 0::4] + elif direction == 'vertical': + h = img_shape[0] + flipped[..., 1::4] = h - bboxes[..., 3::4] + flipped[..., 3::4] = h - bboxes[..., 1::4] + elif direction == 'diagonal': + w = img_shape[1] + h = img_shape[0] + flipped[..., 0::4] = w - bboxes[..., 2::4] + flipped[..., 1::4] = h - bboxes[..., 3::4] + flipped[..., 2::4] = w - bboxes[..., 0::4] + flipped[..., 3::4] = h - bboxes[..., 1::4] + else: + raise ValueError(f"Invalid flipping direction '{direction}'") + return flipped + + def __call__(self, results): + """Call function to flip bounding boxes, masks, semantic segmentation + maps. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Flipped results, 'flip', 'flip_direction' keys are added \ + into result dict. + """ + + if 'flip' not in results: + if isinstance(self.direction, list): + # None means non-flip + direction_list = self.direction + [None] + else: + # None means non-flip + direction_list = [self.direction, None] + + if isinstance(self.flip_ratio, list): + non_flip_ratio = 1 - sum(self.flip_ratio) + flip_ratio_list = self.flip_ratio + [non_flip_ratio] + else: + non_flip_ratio = 1 - self.flip_ratio + # exclude non-flip + single_ratio = self.flip_ratio / (len(direction_list) - 1) + flip_ratio_list = [single_ratio] * (len(direction_list) - + 1) + [non_flip_ratio] + + cur_dir = np.random.choice(direction_list, p=flip_ratio_list) + + results['flip'] = cur_dir is not None + if 'flip_direction' not in results: + results['flip_direction'] = cur_dir + if results['flip']: + # flip image + for key in results.get('img_fields', ['img']): + results[key] = mmcv.imflip( + results[key], direction=results['flip_direction']) + # flip bboxes + for key in results.get('bbox_fields', []): + results[key] = self.bbox_flip(results[key], + results['img_shape'], + results['flip_direction']) + # flip masks + for key in results.get('mask_fields', []): + results[key] = results[key].flip(results['flip_direction']) + + # flip segs + for key in results.get('seg_fields', []): + results[key] = mmcv.imflip( + results[key], direction=results['flip_direction']) + return results + + def __repr__(self): + return self.__class__.__name__ + f'(flip_ratio={self.flip_ratio})' + + +@PIPELINES.register_module() +class RandomShift(object): + """Shift the image and box given shift pixels and probability. + + Args: + shift_ratio (float): Probability of shifts. Default 0.5. + max_shift_px (int): The max pixels for shifting. Default 32. + filter_thr_px (int): The width and height threshold for filtering. + The bbox and the rest of the targets below the width and + height threshold will be filtered. Default 1. + """ + + def __init__(self, shift_ratio=0.5, max_shift_px=32, filter_thr_px=1): + assert 0 <= shift_ratio <= 1 + assert max_shift_px >= 0 + self.shift_ratio = shift_ratio + self.max_shift_px = max_shift_px + self.filter_thr_px = int(filter_thr_px) + # The key correspondence from bboxes to labels. + self.bbox2label = { + 'gt_bboxes': 'gt_labels', + 'gt_bboxes_ignore': 'gt_labels_ignore' + } + + def __call__(self, results): + """Call function to random shift images, bounding boxes. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Shift results. + """ + if random.random() < self.shift_ratio: + img_shape = results['img'].shape[:2] + + random_shift_x = random.randint(-self.max_shift_px, + self.max_shift_px) + random_shift_y = random.randint(-self.max_shift_px, + self.max_shift_px) + new_x = max(0, random_shift_x) + orig_x = max(0, -random_shift_x) + new_y = max(0, random_shift_y) + orig_y = max(0, -random_shift_y) + + # TODO: support mask and semantic segmentation maps. + for key in results.get('bbox_fields', []): + bboxes = results[key].copy() + bboxes[..., 0::2] += random_shift_x + bboxes[..., 1::2] += random_shift_y + + # clip border + bboxes[..., 0::2] = np.clip(bboxes[..., 0::2], 0, img_shape[1]) + bboxes[..., 1::2] = np.clip(bboxes[..., 1::2], 0, img_shape[0]) + + # remove invalid bboxes + bbox_w = bboxes[..., 2] - bboxes[..., 0] + bbox_h = bboxes[..., 3] - bboxes[..., 1] + valid_inds = (bbox_w > self.filter_thr_px) & ( + bbox_h > self.filter_thr_px) + # If the shift does not contain any gt-bbox area, skip this + # image. + if key == 'gt_bboxes' and not valid_inds.any(): + return results + bboxes = bboxes[valid_inds] + results[key] = bboxes + + # label fields. e.g. gt_labels and gt_labels_ignore + label_key = self.bbox2label.get(key) + if label_key in results: + results[label_key] = results[label_key][valid_inds] + + for key in results.get('img_fields', ['img']): + img = results[key] + new_img = np.zeros_like(img) + img_h, img_w = img.shape[:2] + new_h = img_h - np.abs(random_shift_y) + new_w = img_w - np.abs(random_shift_x) + new_img[new_y:new_y + new_h, new_x:new_x + new_w] \ + = img[orig_y:orig_y + new_h, orig_x:orig_x + new_w] + results[key] = new_img + + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(max_shift_px={self.max_shift_px}, ' + return repr_str + + +@PIPELINES.register_module() +class Pad(object): + """Pad the image & mask. + + There are two padding modes: (1) pad to a fixed size and (2) pad to the + minimum size that is divisible by some number. + Added keys are "pad_shape", "pad_fixed_size", "pad_size_divisor", + + Args: + size (tuple, optional): Fixed padding size. + size_divisor (int, optional): The divisor of padded size. + pad_val (float, optional): Padding value, 0 by default. + """ + + def __init__(self, size=None, size_divisor=None, pad_val=0): + self.size = size + self.size_divisor = size_divisor + self.pad_val = pad_val + # only one of size and size_divisor should be valid + assert size is not None or size_divisor is not None + assert size is None or size_divisor is None + + def _pad_img(self, results): + """Pad images according to ``self.size``.""" + for key in results.get('img_fields', ['img']): + if self.size is not None: + padded_img = mmcv.impad( + results[key], shape=self.size, pad_val=self.pad_val) + elif self.size_divisor is not None: + padded_img = mmcv.impad_to_multiple( + results[key], self.size_divisor, pad_val=self.pad_val) + results[key] = padded_img + results['pad_shape'] = padded_img.shape + results['pad_fixed_size'] = self.size + results['pad_size_divisor'] = self.size_divisor + + def _pad_masks(self, results): + """Pad masks according to ``results['pad_shape']``.""" + pad_shape = results['pad_shape'][:2] + for key in results.get('mask_fields', []): + results[key] = results[key].pad(pad_shape, pad_val=self.pad_val) + + def _pad_seg(self, results): + """Pad semantic segmentation map according to + ``results['pad_shape']``.""" + for key in results.get('seg_fields', []): + results[key] = mmcv.impad( + results[key], shape=results['pad_shape'][:2]) + + def __call__(self, results): + """Call function to pad images, masks, semantic segmentation maps. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Updated result dict. + """ + self._pad_img(results) + self._pad_masks(results) + self._pad_seg(results) + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(size={self.size}, ' + repr_str += f'size_divisor={self.size_divisor}, ' + repr_str += f'pad_val={self.pad_val})' + return repr_str + + +@PIPELINES.register_module() +class Normalize(object): + """Normalize the image. + + Added key is "img_norm_cfg". + + Args: + mean (sequence): Mean values of 3 channels. + std (sequence): Std values of 3 channels. + to_rgb (bool): Whether to convert the image from BGR to RGB, + default is true. + """ + + def __init__(self, mean, std, to_rgb=True): + self.mean = np.array(mean, dtype=np.float32) + self.std = np.array(std, dtype=np.float32) + self.to_rgb = to_rgb + + def __call__(self, results): + """Call function to normalize images. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Normalized results, 'img_norm_cfg' key is added into + result dict. + """ + for key in results.get('img_fields', ['img']): + results[key] = mmcv.imnormalize(results[key], self.mean, self.std, + self.to_rgb) + results['img_norm_cfg'] = dict( + mean=self.mean, std=self.std, to_rgb=self.to_rgb) + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(mean={self.mean}, std={self.std}, to_rgb={self.to_rgb})' + return repr_str + + +@PIPELINES.register_module() +class RandomCrop(object): + """Random crop the image & bboxes & masks. + + The absolute `crop_size` is sampled based on `crop_type` and `image_size`, + then the cropped results are generated. + + Args: + crop_size (tuple): The relative ratio or absolute pixels of + height and width. + crop_type (str, optional): one of "relative_range", "relative", + "absolute", "absolute_range". "relative" randomly crops + (h * crop_size[0], w * crop_size[1]) part from an input of size + (h, w). "relative_range" uniformly samples relative crop size from + range [crop_size[0], 1] and [crop_size[1], 1] for height and width + respectively. "absolute" crops from an input with absolute size + (crop_size[0], crop_size[1]). "absolute_range" uniformly samples + crop_h in range [crop_size[0], min(h, crop_size[1])] and crop_w + in range [crop_size[0], min(w, crop_size[1])]. Default "absolute". + allow_negative_crop (bool, optional): Whether to allow a crop that does + not contain any bbox area. Default False. + bbox_clip_border (bool, optional): Whether clip the objects outside + the border of the image. Defaults to True. + + Note: + - If the image is smaller than the absolute crop size, return the + original image. + - The keys for bboxes, labels and masks must be aligned. That is, + `gt_bboxes` corresponds to `gt_labels` and `gt_masks`, and + `gt_bboxes_ignore` corresponds to `gt_labels_ignore` and + `gt_masks_ignore`. + - If the crop does not contain any gt-bbox region and + `allow_negative_crop` is set to False, skip this image. + """ + + def __init__(self, + crop_size, + crop_type='absolute', + allow_negative_crop=False, + bbox_clip_border=True): + if crop_type not in [ + 'relative_range', 'relative', 'absolute', 'absolute_range' + ]: + raise ValueError(f'Invalid crop_type {crop_type}.') + if crop_type in ['absolute', 'absolute_range']: + assert crop_size[0] > 0 and crop_size[1] > 0 + assert isinstance(crop_size[0], int) and isinstance( + crop_size[1], int) + else: + assert 0 < crop_size[0] <= 1 and 0 < crop_size[1] <= 1 + self.crop_size = crop_size + self.crop_type = crop_type + self.allow_negative_crop = allow_negative_crop + self.bbox_clip_border = bbox_clip_border + # The key correspondence from bboxes to labels and masks. + self.bbox2label = { + 'gt_bboxes': 'gt_labels', + 'gt_bboxes_ignore': 'gt_labels_ignore' + } + self.bbox2mask = { + 'gt_bboxes': 'gt_masks', + 'gt_bboxes_ignore': 'gt_masks_ignore' + } + + def _crop_data(self, results, crop_size, allow_negative_crop): + """Function to randomly crop images, bounding boxes, masks, semantic + segmentation maps. + + Args: + results (dict): Result dict from loading pipeline. + crop_size (tuple): Expected absolute size after cropping, (h, w). + allow_negative_crop (bool): Whether to allow a crop that does not + contain any bbox area. Default to False. + + Returns: + dict: Randomly cropped results, 'img_shape' key in result dict is + updated according to crop size. + """ + assert crop_size[0] > 0 and crop_size[1] > 0 + for key in results.get('img_fields', ['img']): + img = results[key] + margin_h = max(img.shape[0] - crop_size[0], 0) + margin_w = max(img.shape[1] - crop_size[1], 0) + offset_h = np.random.randint(0, margin_h + 1) + offset_w = np.random.randint(0, margin_w + 1) + crop_y1, crop_y2 = offset_h, offset_h + crop_size[0] + crop_x1, crop_x2 = offset_w, offset_w + crop_size[1] + + # crop the image + img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...] + img_shape = img.shape + results[key] = img + results['img_shape'] = img_shape + + # crop bboxes accordingly and clip to the image boundary + for key in results.get('bbox_fields', []): + # e.g. gt_bboxes and gt_bboxes_ignore + bbox_offset = np.array([offset_w, offset_h, offset_w, offset_h], + dtype=np.float32) + bboxes = results[key] - bbox_offset + if self.bbox_clip_border: + bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1]) + bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0]) + valid_inds = (bboxes[:, 2] > bboxes[:, 0]) & ( + bboxes[:, 3] > bboxes[:, 1]) + # If the crop does not contain any gt-bbox area and + # allow_negative_crop is False, skip this image. + if (key == 'gt_bboxes' and not valid_inds.any() + and not allow_negative_crop): + return None + results[key] = bboxes[valid_inds, :] + # label fields. e.g. gt_labels and gt_labels_ignore + label_key = self.bbox2label.get(key) + if label_key in results: + results[label_key] = results[label_key][valid_inds] + + # mask fields, e.g. gt_masks and gt_masks_ignore + mask_key = self.bbox2mask.get(key) + if mask_key in results: + results[mask_key] = results[mask_key][ + valid_inds.nonzero()[0]].crop( + np.asarray([crop_x1, crop_y1, crop_x2, crop_y2])) + + # crop semantic seg + for key in results.get('seg_fields', []): + results[key] = results[key][crop_y1:crop_y2, crop_x1:crop_x2] + + return results + + def _get_crop_size(self, image_size): + """Randomly generates the absolute crop size based on `crop_type` and + `image_size`. + + Args: + image_size (tuple): (h, w). + + Returns: + crop_size (tuple): (crop_h, crop_w) in absolute pixels. + """ + h, w = image_size + if self.crop_type == 'absolute': + return (min(self.crop_size[0], h), min(self.crop_size[1], w)) + elif self.crop_type == 'absolute_range': + assert self.crop_size[0] <= self.crop_size[1] + crop_h = np.random.randint( + min(h, self.crop_size[0]), + min(h, self.crop_size[1]) + 1) + crop_w = np.random.randint( + min(w, self.crop_size[0]), + min(w, self.crop_size[1]) + 1) + return crop_h, crop_w + elif self.crop_type == 'relative': + crop_h, crop_w = self.crop_size + return int(h * crop_h + 0.5), int(w * crop_w + 0.5) + elif self.crop_type == 'relative_range': + crop_size = np.asarray(self.crop_size, dtype=np.float32) + crop_h, crop_w = crop_size + np.random.rand(2) * (1 - crop_size) + return int(h * crop_h + 0.5), int(w * crop_w + 0.5) + + def __call__(self, results): + """Call function to randomly crop images, bounding boxes, masks, + semantic segmentation maps. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Randomly cropped results, 'img_shape' key in result dict is + updated according to crop size. + """ + image_size = results['img'].shape[:2] + crop_size = self._get_crop_size(image_size) + results = self._crop_data(results, crop_size, self.allow_negative_crop) + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(crop_size={self.crop_size}, ' + repr_str += f'crop_type={self.crop_type}, ' + repr_str += f'allow_negative_crop={self.allow_negative_crop}, ' + repr_str += f'bbox_clip_border={self.bbox_clip_border})' + return repr_str + + +@PIPELINES.register_module() +class SegRescale(object): + """Rescale semantic segmentation maps. + + Args: + scale_factor (float): The scale factor of the final output. + backend (str): Image rescale backend, choices are 'cv2' and 'pillow'. + These two backends generates slightly different results. Defaults + to 'cv2'. + """ + + def __init__(self, scale_factor=1, backend='cv2'): + self.scale_factor = scale_factor + self.backend = backend + + def __call__(self, results): + """Call function to scale the semantic segmentation map. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Result dict with semantic segmentation map scaled. + """ + + for key in results.get('seg_fields', []): + if self.scale_factor != 1: + results[key] = mmcv.imrescale( + results[key], + self.scale_factor, + interpolation='nearest', + backend=self.backend) + return results + + def __repr__(self): + return self.__class__.__name__ + f'(scale_factor={self.scale_factor})' + + +@PIPELINES.register_module() +class PhotoMetricDistortion(object): + """Apply photometric distortion to image sequentially, every transformation + is applied with a probability of 0.5. The position of random contrast is in + second or second to last. + + 1. random brightness + 2. random contrast (mode 0) + 3. convert color from BGR to HSV + 4. random saturation + 5. random hue + 6. convert color from HSV to BGR + 7. random contrast (mode 1) + 8. randomly swap channels + + Args: + brightness_delta (int): delta of brightness. + contrast_range (tuple): range of contrast. + saturation_range (tuple): range of saturation. + hue_delta (int): delta of hue. + """ + + def __init__(self, + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18): + self.brightness_delta = brightness_delta + self.contrast_lower, self.contrast_upper = contrast_range + self.saturation_lower, self.saturation_upper = saturation_range + self.hue_delta = hue_delta + + def __call__(self, results): + """Call function to perform photometric distortion on images. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Result dict with images distorted. + """ + + if 'img_fields' in results: + assert results['img_fields'] == ['img'], \ + 'Only single img_fields is allowed' + img = results['img'] + assert img.dtype == np.float32, \ + 'PhotoMetricDistortion needs the input image of dtype np.float32,'\ + ' please set "to_float32=True" in "LoadImageFromFile" pipeline' + # random brightness + if random.randint(2): + delta = random.uniform(-self.brightness_delta, + self.brightness_delta) + img += delta + + # mode == 0 --> do random contrast first + # mode == 1 --> do random contrast last + mode = random.randint(2) + if mode == 1: + if random.randint(2): + alpha = random.uniform(self.contrast_lower, + self.contrast_upper) + img *= alpha + + # convert color from BGR to HSV + img = mmcv.bgr2hsv(img) + + # random saturation + if random.randint(2): + img[..., 1] *= random.uniform(self.saturation_lower, + self.saturation_upper) + + # random hue + if random.randint(2): + img[..., 0] += random.uniform(-self.hue_delta, self.hue_delta) + img[..., 0][img[..., 0] > 360] -= 360 + img[..., 0][img[..., 0] < 0] += 360 + + # convert color from HSV to BGR + img = mmcv.hsv2bgr(img) + + # random contrast + if mode == 0: + if random.randint(2): + alpha = random.uniform(self.contrast_lower, + self.contrast_upper) + img *= alpha + + # randomly swap channels + if random.randint(2): + img = img[..., random.permutation(3)] + + results['img'] = img + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(\nbrightness_delta={self.brightness_delta},\n' + repr_str += 'contrast_range=' + repr_str += f'{(self.contrast_lower, self.contrast_upper)},\n' + repr_str += 'saturation_range=' + repr_str += f'{(self.saturation_lower, self.saturation_upper)},\n' + repr_str += f'hue_delta={self.hue_delta})' + return repr_str + + +@PIPELINES.register_module() +class Expand(object): + """Random expand the image & bboxes. + + Randomly place the original image on a canvas of 'ratio' x original image + size filled with mean values. The ratio is in the range of ratio_range. + + Args: + mean (tuple): mean value of dataset. + to_rgb (bool): if need to convert the order of mean to align with RGB. + ratio_range (tuple): range of expand ratio. + prob (float): probability of applying this transformation + """ + + def __init__(self, + mean=(0, 0, 0), + to_rgb=True, + ratio_range=(1, 4), + seg_ignore_label=None, + prob=0.5): + self.to_rgb = to_rgb + self.ratio_range = ratio_range + if to_rgb: + self.mean = mean[::-1] + else: + self.mean = mean + self.min_ratio, self.max_ratio = ratio_range + self.seg_ignore_label = seg_ignore_label + self.prob = prob + + def __call__(self, results): + """Call function to expand images, bounding boxes. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Result dict with images, bounding boxes expanded + """ + + if random.uniform(0, 1) > self.prob: + return results + + if 'img_fields' in results: + assert results['img_fields'] == ['img'], \ + 'Only single img_fields is allowed' + img = results['img'] + + h, w, c = img.shape + ratio = random.uniform(self.min_ratio, self.max_ratio) + # speedup expand when meets large image + if np.all(self.mean == self.mean[0]): + expand_img = np.empty((int(h * ratio), int(w * ratio), c), + img.dtype) + expand_img.fill(self.mean[0]) + else: + expand_img = np.full((int(h * ratio), int(w * ratio), c), + self.mean, + dtype=img.dtype) + left = int(random.uniform(0, w * ratio - w)) + top = int(random.uniform(0, h * ratio - h)) + expand_img[top:top + h, left:left + w] = img + + results['img'] = expand_img + # expand bboxes + for key in results.get('bbox_fields', []): + results[key] = results[key] + np.tile( + (left, top), 2).astype(results[key].dtype) + + # expand masks + for key in results.get('mask_fields', []): + results[key] = results[key].expand( + int(h * ratio), int(w * ratio), top, left) + + # expand segs + for key in results.get('seg_fields', []): + gt_seg = results[key] + expand_gt_seg = np.full((int(h * ratio), int(w * ratio)), + self.seg_ignore_label, + dtype=gt_seg.dtype) + expand_gt_seg[top:top + h, left:left + w] = gt_seg + results[key] = expand_gt_seg + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(mean={self.mean}, to_rgb={self.to_rgb}, ' + repr_str += f'ratio_range={self.ratio_range}, ' + repr_str += f'seg_ignore_label={self.seg_ignore_label})' + return repr_str + + +@PIPELINES.register_module() +class MinIoURandomCrop(object): + """Random crop the image & bboxes, the cropped patches have minimum IoU + requirement with original image & bboxes, the IoU threshold is randomly + selected from min_ious. + + Args: + min_ious (tuple): minimum IoU threshold for all intersections with + bounding boxes + min_crop_size (float): minimum crop's size (i.e. h,w := a*h, a*w, + where a >= min_crop_size). + bbox_clip_border (bool, optional): Whether clip the objects outside + the border of the image. Defaults to True. + + Note: + The keys for bboxes, labels and masks should be paired. That is, \ + `gt_bboxes` corresponds to `gt_labels` and `gt_masks`, and \ + `gt_bboxes_ignore` to `gt_labels_ignore` and `gt_masks_ignore`. + """ + + def __init__(self, + min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), + min_crop_size=0.3, + bbox_clip_border=True): + # 1: return ori img + self.min_ious = min_ious + self.sample_mode = (1, *min_ious, 0) + self.min_crop_size = min_crop_size + self.bbox_clip_border = bbox_clip_border + self.bbox2label = { + 'gt_bboxes': 'gt_labels', + 'gt_bboxes_ignore': 'gt_labels_ignore' + } + self.bbox2mask = { + 'gt_bboxes': 'gt_masks', + 'gt_bboxes_ignore': 'gt_masks_ignore' + } + + def __call__(self, results): + """Call function to crop images and bounding boxes with minimum IoU + constraint. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Result dict with images and bounding boxes cropped, \ + 'img_shape' key is updated. + """ + + if 'img_fields' in results: + assert results['img_fields'] == ['img'], \ + 'Only single img_fields is allowed' + img = results['img'] + assert 'bbox_fields' in results + boxes = [results[key] for key in results['bbox_fields']] + boxes = np.concatenate(boxes, 0) + h, w, c = img.shape + while True: + mode = random.choice(self.sample_mode) + self.mode = mode + if mode == 1: + return results + + min_iou = mode + for i in range(50): + new_w = random.uniform(self.min_crop_size * w, w) + new_h = random.uniform(self.min_crop_size * h, h) + + # h / w in [0.5, 2] + if new_h / new_w < 0.5 or new_h / new_w > 2: + continue + + left = random.uniform(w - new_w) + top = random.uniform(h - new_h) + + patch = np.array( + (int(left), int(top), int(left + new_w), int(top + new_h))) + # Line or point crop is not allowed + if patch[2] == patch[0] or patch[3] == patch[1]: + continue + overlaps = bbox_overlaps( + patch.reshape(-1, 4), boxes.reshape(-1, 4)).reshape(-1) + if len(overlaps) > 0 and overlaps.min() < min_iou: + continue + + # center of boxes should inside the crop img + # only adjust boxes and instance masks when the gt is not empty + if len(overlaps) > 0: + # adjust boxes + def is_center_of_bboxes_in_patch(boxes, patch): + center = (boxes[:, :2] + boxes[:, 2:]) / 2 + mask = ((center[:, 0] > patch[0]) * + (center[:, 1] > patch[1]) * + (center[:, 0] < patch[2]) * + (center[:, 1] < patch[3])) + return mask + + mask = is_center_of_bboxes_in_patch(boxes, patch) + if not mask.any(): + continue + for key in results.get('bbox_fields', []): + boxes = results[key].copy() + mask = is_center_of_bboxes_in_patch(boxes, patch) + boxes = boxes[mask] + if self.bbox_clip_border: + boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:]) + boxes[:, :2] = boxes[:, :2].clip(min=patch[:2]) + boxes -= np.tile(patch[:2], 2) + + results[key] = boxes + # labels + label_key = self.bbox2label.get(key) + if label_key in results: + results[label_key] = results[label_key][mask] + + # mask fields + mask_key = self.bbox2mask.get(key) + if mask_key in results: + results[mask_key] = results[mask_key][ + mask.nonzero()[0]].crop(patch) + # adjust the img no matter whether the gt is empty before crop + img = img[patch[1]:patch[3], patch[0]:patch[2]] + results['img'] = img + results['img_shape'] = img.shape + + # seg fields + for key in results.get('seg_fields', []): + results[key] = results[key][patch[1]:patch[3], + patch[0]:patch[2]] + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(min_ious={self.min_ious}, ' + repr_str += f'min_crop_size={self.min_crop_size}, ' + repr_str += f'bbox_clip_border={self.bbox_clip_border})' + return repr_str + + +@PIPELINES.register_module() +class Corrupt(object): + """Corruption augmentation. + + Corruption transforms implemented based on + `imagecorruptions `_. + + Args: + corruption (str): Corruption name. + severity (int, optional): The severity of corruption. Default: 1. + """ + + def __init__(self, corruption, severity=1): + self.corruption = corruption + self.severity = severity + + def __call__(self, results): + """Call function to corrupt image. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Result dict with images corrupted. + """ + + if corrupt is None: + raise RuntimeError('imagecorruptions is not installed') + if 'img_fields' in results: + assert results['img_fields'] == ['img'], \ + 'Only single img_fields is allowed' + results['img'] = corrupt( + results['img'].astype(np.uint8), + corruption_name=self.corruption, + severity=self.severity) + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(corruption={self.corruption}, ' + repr_str += f'severity={self.severity})' + return repr_str + + +@PIPELINES.register_module() +class Albu(object): + """Albumentation augmentation. + + Adds custom transformations from Albumentations library. + Please, visit `https://albumentations.readthedocs.io` + to get more information. + + An example of ``transforms`` is as followed: + + .. code-block:: + + [ + dict( + type='ShiftScaleRotate', + shift_limit=0.0625, + scale_limit=0.0, + rotate_limit=0, + interpolation=1, + p=0.5), + dict( + type='RandomBrightnessContrast', + brightness_limit=[0.1, 0.3], + contrast_limit=[0.1, 0.3], + p=0.2), + dict(type='ChannelShuffle', p=0.1), + dict( + type='OneOf', + transforms=[ + dict(type='Blur', blur_limit=3, p=1.0), + dict(type='MedianBlur', blur_limit=3, p=1.0) + ], + p=0.1), + ] + + Args: + transforms (list[dict]): A list of albu transformations + bbox_params (dict): Bbox_params for albumentation `Compose` + keymap (dict): Contains {'input key':'albumentation-style key'} + skip_img_without_anno (bool): Whether to skip the image if no ann left + after aug + """ + + def __init__(self, + transforms, + bbox_params=None, + keymap=None, + update_pad_shape=False, + skip_img_without_anno=False): + if Compose is None: + raise RuntimeError('albumentations is not installed') + + # Args will be modified later, copying it will be safer + transforms = copy.deepcopy(transforms) + if bbox_params is not None: + bbox_params = copy.deepcopy(bbox_params) + if keymap is not None: + keymap = copy.deepcopy(keymap) + self.transforms = transforms + self.filter_lost_elements = False + self.update_pad_shape = update_pad_shape + self.skip_img_without_anno = skip_img_without_anno + + # A simple workaround to remove masks without boxes + if (isinstance(bbox_params, dict) and 'label_fields' in bbox_params + and 'filter_lost_elements' in bbox_params): + self.filter_lost_elements = True + self.origin_label_fields = bbox_params['label_fields'] + bbox_params['label_fields'] = ['idx_mapper'] + del bbox_params['filter_lost_elements'] + + self.bbox_params = ( + self.albu_builder(bbox_params) if bbox_params else None) + self.aug = Compose([self.albu_builder(t) for t in self.transforms], + bbox_params=self.bbox_params) + + if not keymap: + self.keymap_to_albu = { + 'img': 'image', + 'gt_masks': 'masks', + 'gt_bboxes': 'bboxes' + } + else: + self.keymap_to_albu = keymap + self.keymap_back = {v: k for k, v in self.keymap_to_albu.items()} + + def albu_builder(self, cfg): + """Import a module from albumentations. + + It inherits some of :func:`build_from_cfg` logic. + + Args: + cfg (dict): Config dict. It should at least contain the key "type". + + Returns: + obj: The constructed object. + """ + + assert isinstance(cfg, dict) and 'type' in cfg + args = cfg.copy() + + obj_type = args.pop('type') + if mmcv.is_str(obj_type): + if albumentations is None: + raise RuntimeError('albumentations is not installed') + obj_cls = getattr(albumentations, obj_type) + elif inspect.isclass(obj_type): + obj_cls = obj_type + else: + raise TypeError( + f'type must be a str or valid type, but got {type(obj_type)}') + + if 'transforms' in args: + args['transforms'] = [ + self.albu_builder(transform) + for transform in args['transforms'] + ] + + return obj_cls(**args) + + @staticmethod + def mapper(d, keymap): + """Dictionary mapper. Renames keys according to keymap provided. + + Args: + d (dict): old dict + keymap (dict): {'old_key':'new_key'} + Returns: + dict: new dict. + """ + + updated_dict = {} + for k, v in zip(d.keys(), d.values()): + new_k = keymap.get(k, k) + updated_dict[new_k] = d[k] + return updated_dict + + def __call__(self, results): + # dict to albumentations format + results = self.mapper(results, self.keymap_to_albu) + # TODO: add bbox_fields + if 'bboxes' in results: + # to list of boxes + if isinstance(results['bboxes'], np.ndarray): + results['bboxes'] = [x for x in results['bboxes']] + # add pseudo-field for filtration + if self.filter_lost_elements: + results['idx_mapper'] = np.arange(len(results['bboxes'])) + + # TODO: Support mask structure in albu + if 'masks' in results: + if isinstance(results['masks'], PolygonMasks): + raise NotImplementedError( + 'Albu only supports BitMap masks now') + ori_masks = results['masks'] + if albumentations.__version__ < '0.5': + results['masks'] = results['masks'].masks + else: + results['masks'] = [mask for mask in results['masks'].masks] + + results = self.aug(**results) + + if 'bboxes' in results: + if isinstance(results['bboxes'], list): + results['bboxes'] = np.array( + results['bboxes'], dtype=np.float32) + results['bboxes'] = results['bboxes'].reshape(-1, 4) + + # filter label_fields + if self.filter_lost_elements: + + for label in self.origin_label_fields: + results[label] = np.array( + [results[label][i] for i in results['idx_mapper']]) + if 'masks' in results: + results['masks'] = np.array( + [results['masks'][i] for i in results['idx_mapper']]) + results['masks'] = ori_masks.__class__( + results['masks'], results['image'].shape[0], + results['image'].shape[1]) + + if (not len(results['idx_mapper']) + and self.skip_img_without_anno): + return None + + if 'gt_labels' in results: + if isinstance(results['gt_labels'], list): + results['gt_labels'] = np.array(results['gt_labels']) + results['gt_labels'] = results['gt_labels'].astype(np.int64) + + # back to the original format + results = self.mapper(results, self.keymap_back) + + # update final shape + if self.update_pad_shape: + results['pad_shape'] = results['img'].shape + + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + f'(transforms={self.transforms})' + return repr_str + + +@PIPELINES.register_module() +class RandomCenterCropPad(object): + """Random center crop and random around padding for CornerNet. + + This operation generates randomly cropped image from the original image and + pads it simultaneously. Different from :class:`RandomCrop`, the output + shape may not equal to ``crop_size`` strictly. We choose a random value + from ``ratios`` and the output shape could be larger or smaller than + ``crop_size``. The padding operation is also different from :class:`Pad`, + here we use around padding instead of right-bottom padding. + + The relation between output image (padding image) and original image: + + .. code:: text + + output image + + +----------------------------+ + | padded area | + +------|----------------------------|----------+ + | | cropped area | | + | | +---------------+ | | + | | | . center | | | original image + | | | range | | | + | | +---------------+ | | + +------|----------------------------|----------+ + | padded area | + +----------------------------+ + + There are 5 main areas in the figure: + + - output image: output image of this operation, also called padding + image in following instruction. + - original image: input image of this operation. + - padded area: non-intersect area of output image and original image. + - cropped area: the overlap of output image and original image. + - center range: a smaller area where random center chosen from. + center range is computed by ``border`` and original image's shape + to avoid our random center is too close to original image's border. + + Also this operation act differently in train and test mode, the summary + pipeline is listed below. + + Train pipeline: + + 1. Choose a ``random_ratio`` from ``ratios``, the shape of padding image + will be ``random_ratio * crop_size``. + 2. Choose a ``random_center`` in center range. + 3. Generate padding image with center matches the ``random_center``. + 4. Initialize the padding image with pixel value equals to ``mean``. + 5. Copy the cropped area to padding image. + 6. Refine annotations. + + Test pipeline: + + 1. Compute output shape according to ``test_pad_mode``. + 2. Generate padding image with center matches the original image + center. + 3. Initialize the padding image with pixel value equals to ``mean``. + 4. Copy the ``cropped area`` to padding image. + + Args: + crop_size (tuple | None): expected size after crop, final size will + computed according to ratio. Requires (h, w) in train mode, and + None in test mode. + ratios (tuple): random select a ratio from tuple and crop image to + (crop_size[0] * ratio) * (crop_size[1] * ratio). + Only available in train mode. + border (int): max distance from center select area to image border. + Only available in train mode. + mean (sequence): Mean values of 3 channels. + std (sequence): Std values of 3 channels. + to_rgb (bool): Whether to convert the image from BGR to RGB. + test_mode (bool): whether involve random variables in transform. + In train mode, crop_size is fixed, center coords and ratio is + random selected from predefined lists. In test mode, crop_size + is image's original shape, center coords and ratio is fixed. + test_pad_mode (tuple): padding method and padding shape value, only + available in test mode. Default is using 'logical_or' with + 127 as padding shape value. + + - 'logical_or': final_shape = input_shape | padding_shape_value + - 'size_divisor': final_shape = int( + ceil(input_shape / padding_shape_value) * padding_shape_value) + bbox_clip_border (bool, optional): Whether clip the objects outside + the border of the image. Defaults to True. + """ + + def __init__(self, + crop_size=None, + ratios=(0.9, 1.0, 1.1), + border=128, + mean=None, + std=None, + to_rgb=None, + test_mode=False, + test_pad_mode=('logical_or', 127), + bbox_clip_border=True): + if test_mode: + assert crop_size is None, 'crop_size must be None in test mode' + assert ratios is None, 'ratios must be None in test mode' + assert border is None, 'border must be None in test mode' + assert isinstance(test_pad_mode, (list, tuple)) + assert test_pad_mode[0] in ['logical_or', 'size_divisor'] + else: + assert isinstance(crop_size, (list, tuple)) + assert crop_size[0] > 0 and crop_size[1] > 0, ( + 'crop_size must > 0 in train mode') + assert isinstance(ratios, (list, tuple)) + assert test_pad_mode is None, ( + 'test_pad_mode must be None in train mode') + + self.crop_size = crop_size + self.ratios = ratios + self.border = border + # We do not set default value to mean, std and to_rgb because these + # hyper-parameters are easy to forget but could affect the performance. + # Please use the same setting as Normalize for performance assurance. + assert mean is not None and std is not None and to_rgb is not None + self.to_rgb = to_rgb + self.input_mean = mean + self.input_std = std + if to_rgb: + self.mean = mean[::-1] + self.std = std[::-1] + else: + self.mean = mean + self.std = std + self.test_mode = test_mode + self.test_pad_mode = test_pad_mode + self.bbox_clip_border = bbox_clip_border + + def _get_border(self, border, size): + """Get final border for the target size. + + This function generates a ``final_border`` according to image's shape. + The area between ``final_border`` and ``size - final_border`` is the + ``center range``. We randomly choose center from the ``center range`` + to avoid our random center is too close to original image's border. + Also ``center range`` should be larger than 0. + + Args: + border (int): The initial border, default is 128. + size (int): The width or height of original image. + Returns: + int: The final border. + """ + k = 2 * border / size + i = pow(2, np.ceil(np.log2(np.ceil(k))) + (k == int(k))) + return border // i + + def _filter_boxes(self, patch, boxes): + """Check whether the center of each box is in the patch. + + Args: + patch (list[int]): The cropped area, [left, top, right, bottom]. + boxes (numpy array, (N x 4)): Ground truth boxes. + + Returns: + mask (numpy array, (N,)): Each box is inside or outside the patch. + """ + center = (boxes[:, :2] + boxes[:, 2:]) / 2 + mask = (center[:, 0] > patch[0]) * (center[:, 1] > patch[1]) * ( + center[:, 0] < patch[2]) * ( + center[:, 1] < patch[3]) + return mask + + def _crop_image_and_paste(self, image, center, size): + """Crop image with a given center and size, then paste the cropped + image to a blank image with two centers align. + + This function is equivalent to generating a blank image with ``size`` + as its shape. Then cover it on the original image with two centers ( + the center of blank image and the random center of original image) + aligned. The overlap area is paste from the original image and the + outside area is filled with ``mean pixel``. + + Args: + image (np array, H x W x C): Original image. + center (list[int]): Target crop center coord. + size (list[int]): Target crop size. [target_h, target_w] + + Returns: + cropped_img (np array, target_h x target_w x C): Cropped image. + border (np array, 4): The distance of four border of + ``cropped_img`` to the original image area, [top, bottom, + left, right] + patch (list[int]): The cropped area, [left, top, right, bottom]. + """ + center_y, center_x = center + target_h, target_w = size + img_h, img_w, img_c = image.shape + + x0 = max(0, center_x - target_w // 2) + x1 = min(center_x + target_w // 2, img_w) + y0 = max(0, center_y - target_h // 2) + y1 = min(center_y + target_h // 2, img_h) + patch = np.array((int(x0), int(y0), int(x1), int(y1))) + + left, right = center_x - x0, x1 - center_x + top, bottom = center_y - y0, y1 - center_y + + cropped_center_y, cropped_center_x = target_h // 2, target_w // 2 + cropped_img = np.zeros((target_h, target_w, img_c), dtype=image.dtype) + for i in range(img_c): + cropped_img[:, :, i] += self.mean[i] + y_slice = slice(cropped_center_y - top, cropped_center_y + bottom) + x_slice = slice(cropped_center_x - left, cropped_center_x + right) + cropped_img[y_slice, x_slice, :] = image[y0:y1, x0:x1, :] + + border = np.array([ + cropped_center_y - top, cropped_center_y + bottom, + cropped_center_x - left, cropped_center_x + right + ], + dtype=np.float32) + + return cropped_img, border, patch + + def _train_aug(self, results): + """Random crop and around padding the original image. + + Args: + results (dict): Image infomations in the augment pipeline. + + Returns: + results (dict): The updated dict. + """ + img = results['img'] + h, w, c = img.shape + boxes = results['gt_bboxes'] + while True: + scale = random.choice(self.ratios) + new_h = int(self.crop_size[0] * scale) + new_w = int(self.crop_size[1] * scale) + h_border = self._get_border(self.border, h) + w_border = self._get_border(self.border, w) + + for i in range(50): + center_x = random.randint(low=w_border, high=w - w_border) + center_y = random.randint(low=h_border, high=h - h_border) + + cropped_img, border, patch = self._crop_image_and_paste( + img, [center_y, center_x], [new_h, new_w]) + + mask = self._filter_boxes(patch, boxes) + # if image do not have valid bbox, any crop patch is valid. + if not mask.any() and len(boxes) > 0: + continue + + results['img'] = cropped_img + results['img_shape'] = cropped_img.shape + results['pad_shape'] = cropped_img.shape + + x0, y0, x1, y1 = patch + + left_w, top_h = center_x - x0, center_y - y0 + cropped_center_x, cropped_center_y = new_w // 2, new_h // 2 + + # crop bboxes accordingly and clip to the image boundary + for key in results.get('bbox_fields', []): + mask = self._filter_boxes(patch, results[key]) + bboxes = results[key][mask] + bboxes[:, 0:4:2] += cropped_center_x - left_w - x0 + bboxes[:, 1:4:2] += cropped_center_y - top_h - y0 + if self.bbox_clip_border: + bboxes[:, 0:4:2] = np.clip(bboxes[:, 0:4:2], 0, new_w) + bboxes[:, 1:4:2] = np.clip(bboxes[:, 1:4:2], 0, new_h) + keep = (bboxes[:, 2] > bboxes[:, 0]) & ( + bboxes[:, 3] > bboxes[:, 1]) + bboxes = bboxes[keep] + results[key] = bboxes + if key in ['gt_bboxes']: + if 'gt_labels' in results: + labels = results['gt_labels'][mask] + labels = labels[keep] + results['gt_labels'] = labels + if 'gt_masks' in results: + raise NotImplementedError( + 'RandomCenterCropPad only supports bbox.') + + # crop semantic seg + for key in results.get('seg_fields', []): + raise NotImplementedError( + 'RandomCenterCropPad only supports bbox.') + return results + + def _test_aug(self, results): + """Around padding the original image without cropping. + + The padding mode and value are from ``test_pad_mode``. + + Args: + results (dict): Image infomations in the augment pipeline. + + Returns: + results (dict): The updated dict. + """ + img = results['img'] + h, w, c = img.shape + results['img_shape'] = img.shape + if self.test_pad_mode[0] in ['logical_or']: + target_h = h | self.test_pad_mode[1] + target_w = w | self.test_pad_mode[1] + elif self.test_pad_mode[0] in ['size_divisor']: + divisor = self.test_pad_mode[1] + target_h = int(np.ceil(h / divisor)) * divisor + target_w = int(np.ceil(w / divisor)) * divisor + else: + raise NotImplementedError( + 'RandomCenterCropPad only support two testing pad mode:' + 'logical-or and size_divisor.') + + cropped_img, border, _ = self._crop_image_and_paste( + img, [h // 2, w // 2], [target_h, target_w]) + results['img'] = cropped_img + results['pad_shape'] = cropped_img.shape + results['border'] = border + return results + + def __call__(self, results): + img = results['img'] + assert img.dtype == np.float32, ( + 'RandomCenterCropPad needs the input image of dtype np.float32,' + ' please set "to_float32=True" in "LoadImageFromFile" pipeline') + h, w, c = img.shape + assert c == len(self.mean) + if self.test_mode: + return self._test_aug(results) + else: + return self._train_aug(results) + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(crop_size={self.crop_size}, ' + repr_str += f'ratios={self.ratios}, ' + repr_str += f'border={self.border}, ' + repr_str += f'mean={self.input_mean}, ' + repr_str += f'std={self.input_std}, ' + repr_str += f'to_rgb={self.to_rgb}, ' + repr_str += f'test_mode={self.test_mode}, ' + repr_str += f'test_pad_mode={self.test_pad_mode}, ' + repr_str += f'bbox_clip_border={self.bbox_clip_border})' + return repr_str + + +@PIPELINES.register_module() +class CutOut(object): + """CutOut operation. + + Randomly drop some regions of image used in + `Cutout `_. + + Args: + n_holes (int | tuple[int, int]): Number of regions to be dropped. + If it is given as a list, number of holes will be randomly + selected from the closed interval [`n_holes[0]`, `n_holes[1]`]. + cutout_shape (tuple[int, int] | list[tuple[int, int]]): The candidate + shape of dropped regions. It can be `tuple[int, int]` to use a + fixed cutout shape, or `list[tuple[int, int]]` to randomly choose + shape from the list. + cutout_ratio (tuple[float, float] | list[tuple[float, float]]): The + candidate ratio of dropped regions. It can be `tuple[float, float]` + to use a fixed ratio or `list[tuple[float, float]]` to randomly + choose ratio from the list. Please note that `cutout_shape` + and `cutout_ratio` cannot be both given at the same time. + fill_in (tuple[float, float, float] | tuple[int, int, int]): The value + of pixel to fill in the dropped regions. Default: (0, 0, 0). + """ + + def __init__(self, + n_holes, + cutout_shape=None, + cutout_ratio=None, + fill_in=(0, 0, 0)): + + assert (cutout_shape is None) ^ (cutout_ratio is None), \ + 'Either cutout_shape or cutout_ratio should be specified.' + assert (isinstance(cutout_shape, (list, tuple)) + or isinstance(cutout_ratio, (list, tuple))) + if isinstance(n_holes, tuple): + assert len(n_holes) == 2 and 0 <= n_holes[0] < n_holes[1] + else: + n_holes = (n_holes, n_holes) + self.n_holes = n_holes + self.fill_in = fill_in + self.with_ratio = cutout_ratio is not None + self.candidates = cutout_ratio if self.with_ratio else cutout_shape + if not isinstance(self.candidates, list): + self.candidates = [self.candidates] + + def __call__(self, results): + """Call function to drop some regions of image.""" + h, w, c = results['img'].shape + n_holes = np.random.randint(self.n_holes[0], self.n_holes[1] + 1) + for _ in range(n_holes): + x1 = np.random.randint(0, w) + y1 = np.random.randint(0, h) + index = np.random.randint(0, len(self.candidates)) + if not self.with_ratio: + cutout_w, cutout_h = self.candidates[index] + else: + cutout_w = int(self.candidates[index][0] * w) + cutout_h = int(self.candidates[index][1] * h) + + x2 = np.clip(x1 + cutout_w, 0, w) + y2 = np.clip(y1 + cutout_h, 0, h) + results['img'][y1:y2, x1:x2, :] = self.fill_in + + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(n_holes={self.n_holes}, ' + repr_str += (f'cutout_ratio={self.candidates}, ' if self.with_ratio + else f'cutout_shape={self.candidates}, ') + repr_str += f'fill_in={self.fill_in})' + return repr_str diff --git a/mmdet/datasets/samplers/__init__.py b/mmdet/datasets/samplers/__init__.py new file mode 100644 index 0000000..2596aeb --- /dev/null +++ b/mmdet/datasets/samplers/__init__.py @@ -0,0 +1,4 @@ +from .distributed_sampler import DistributedSampler +from .group_sampler import DistributedGroupSampler, GroupSampler + +__all__ = ['DistributedSampler', 'DistributedGroupSampler', 'GroupSampler'] diff --git a/mmdet/datasets/samplers/__pycache__/__init__.cpython-37.pyc b/mmdet/datasets/samplers/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..14520ab Binary files /dev/null and b/mmdet/datasets/samplers/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/datasets/samplers/__pycache__/distributed_sampler.cpython-37.pyc b/mmdet/datasets/samplers/__pycache__/distributed_sampler.cpython-37.pyc new file mode 100644 index 0000000..48c38cd Binary files /dev/null and b/mmdet/datasets/samplers/__pycache__/distributed_sampler.cpython-37.pyc differ diff --git a/mmdet/datasets/samplers/__pycache__/group_sampler.cpython-37.pyc b/mmdet/datasets/samplers/__pycache__/group_sampler.cpython-37.pyc new file mode 100644 index 0000000..49a4381 Binary files /dev/null and b/mmdet/datasets/samplers/__pycache__/group_sampler.cpython-37.pyc differ diff --git a/mmdet/datasets/samplers/distributed_sampler.py b/mmdet/datasets/samplers/distributed_sampler.py new file mode 100644 index 0000000..cc61019 --- /dev/null +++ b/mmdet/datasets/samplers/distributed_sampler.py @@ -0,0 +1,39 @@ +import math + +import torch +from torch.utils.data import DistributedSampler as _DistributedSampler + + +class DistributedSampler(_DistributedSampler): + + def __init__(self, + dataset, + num_replicas=None, + rank=None, + shuffle=True, + seed=0): + super().__init__( + dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle) + # for the compatibility from PyTorch 1.3+ + self.seed = seed if seed is not None else 0 + + def __iter__(self): + # deterministically shuffle based on epoch + if self.shuffle: + g = torch.Generator() + g.manual_seed(self.epoch + self.seed) + indices = torch.randperm(len(self.dataset), generator=g).tolist() + else: + indices = torch.arange(len(self.dataset)).tolist() + + # add extra samples to make it evenly divisible + # in case that indices is shorter than half of total_size + indices = (indices * + math.ceil(self.total_size / len(indices)))[:self.total_size] + assert len(indices) == self.total_size + + # subsample + indices = indices[self.rank:self.total_size:self.num_replicas] + assert len(indices) == self.num_samples + + return iter(indices) diff --git a/mmdet/datasets/samplers/group_sampler.py b/mmdet/datasets/samplers/group_sampler.py new file mode 100644 index 0000000..f88cf34 --- /dev/null +++ b/mmdet/datasets/samplers/group_sampler.py @@ -0,0 +1,148 @@ +from __future__ import division +import math + +import numpy as np +import torch +from mmcv.runner import get_dist_info +from torch.utils.data import Sampler + + +class GroupSampler(Sampler): + + def __init__(self, dataset, samples_per_gpu=1): + assert hasattr(dataset, 'flag') + self.dataset = dataset + self.samples_per_gpu = samples_per_gpu + self.flag = dataset.flag.astype(np.int64) + self.group_sizes = np.bincount(self.flag) + self.num_samples = 0 + for i, size in enumerate(self.group_sizes): + self.num_samples += int(np.ceil( + size / self.samples_per_gpu)) * self.samples_per_gpu + + def __iter__(self): + indices = [] + for i, size in enumerate(self.group_sizes): + if size == 0: + continue + indice = np.where(self.flag == i)[0] + assert len(indice) == size + np.random.shuffle(indice) + num_extra = int(np.ceil(size / self.samples_per_gpu) + ) * self.samples_per_gpu - len(indice) + indice = np.concatenate( + [indice, np.random.choice(indice, num_extra)]) + indices.append(indice) + indices = np.concatenate(indices) + indices = [ + indices[i * self.samples_per_gpu:(i + 1) * self.samples_per_gpu] + for i in np.random.permutation( + range(len(indices) // self.samples_per_gpu)) + ] + indices = np.concatenate(indices) + indices = indices.astype(np.int64).tolist() + assert len(indices) == self.num_samples + return iter(indices) + + def __len__(self): + return self.num_samples + + +class DistributedGroupSampler(Sampler): + """Sampler that restricts data loading to a subset of the dataset. + + It is especially useful in conjunction with + :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each + process can pass a DistributedSampler instance as a DataLoader sampler, + and load a subset of the original dataset that is exclusive to it. + + .. note:: + Dataset is assumed to be of constant size. + + Arguments: + dataset: Dataset used for sampling. + num_replicas (optional): Number of processes participating in + distributed training. + rank (optional): Rank of the current process within num_replicas. + seed (int, optional): random seed used to shuffle the sampler if + ``shuffle=True``. This number should be identical across all + processes in the distributed group. Default: 0. + """ + + def __init__(self, + dataset, + samples_per_gpu=1, + num_replicas=None, + rank=None, + seed=0): + _rank, _num_replicas = get_dist_info() + if num_replicas is None: + num_replicas = _num_replicas + if rank is None: + rank = _rank + self.dataset = dataset + self.samples_per_gpu = samples_per_gpu + self.num_replicas = num_replicas + self.rank = rank + self.epoch = 0 + self.seed = seed if seed is not None else 0 + + assert hasattr(self.dataset, 'flag') + self.flag = self.dataset.flag + self.group_sizes = np.bincount(self.flag) + + self.num_samples = 0 + for i, j in enumerate(self.group_sizes): + self.num_samples += int( + math.ceil(self.group_sizes[i] * 1.0 / self.samples_per_gpu / + self.num_replicas)) * self.samples_per_gpu + self.total_size = self.num_samples * self.num_replicas + + def __iter__(self): + # deterministically shuffle based on epoch + g = torch.Generator() + g.manual_seed(self.epoch + self.seed) + + indices = [] + for i, size in enumerate(self.group_sizes): + if size > 0: + indice = np.where(self.flag == i)[0] + assert len(indice) == size + # add .numpy() to avoid bug when selecting indice in parrots. + # TODO: check whether torch.randperm() can be replaced by + # numpy.random.permutation(). + indice = indice[list( + torch.randperm(int(size), generator=g).numpy())].tolist() + extra = int( + math.ceil( + size * 1.0 / self.samples_per_gpu / self.num_replicas) + ) * self.samples_per_gpu * self.num_replicas - len(indice) + # pad indice + tmp = indice.copy() + for _ in range(extra // size): + indice.extend(tmp) + indice.extend(tmp[:extra % size]) + indices.extend(indice) + + assert len(indices) == self.total_size + + indices = [ + indices[j] for i in list( + torch.randperm( + len(indices) // self.samples_per_gpu, generator=g)) + for j in range(i * self.samples_per_gpu, (i + 1) * + self.samples_per_gpu) + ] + + # subsample + offset = self.num_samples * self.rank + indices = indices[offset:offset + self.num_samples] + assert len(indices) == self.num_samples + + return iter(indices) + + def __len__(self): + return self.num_samples + + def set_epoch(self, epoch): + self.epoch = epoch diff --git a/mmdet/datasets/utils.py b/mmdet/datasets/utils.py new file mode 100644 index 0000000..4eb6423 --- /dev/null +++ b/mmdet/datasets/utils.py @@ -0,0 +1,163 @@ +import copy +import warnings + +from mmcv.cnn import VGG +from mmcv.runner.hooks import HOOKS, Hook + +from mmdet.datasets.builder import PIPELINES +from mmdet.datasets.pipelines import LoadAnnotations, LoadImageFromFile +from mmdet.models.dense_heads import GARPNHead, RPNHead +from mmdet.models.roi_heads.mask_heads import FusedSemanticHead + + +def replace_ImageToTensor(pipelines): + """Replace the ImageToTensor transform in a data pipeline to + DefaultFormatBundle, which is normally useful in batch inference. + + Args: + pipelines (list[dict]): Data pipeline configs. + + Returns: + list: The new pipeline list with all ImageToTensor replaced by + DefaultFormatBundle. + + Examples: + >>> pipelines = [ + ... dict(type='LoadImageFromFile'), + ... dict( + ... type='MultiScaleFlipAug', + ... img_scale=(1333, 800), + ... flip=False, + ... transforms=[ + ... dict(type='Resize', keep_ratio=True), + ... dict(type='RandomFlip'), + ... dict(type='Normalize', mean=[0, 0, 0], std=[1, 1, 1]), + ... dict(type='Pad', size_divisor=32), + ... dict(type='ImageToTensor', keys=['img']), + ... dict(type='Collect', keys=['img']), + ... ]) + ... ] + >>> expected_pipelines = [ + ... dict(type='LoadImageFromFile'), + ... dict( + ... type='MultiScaleFlipAug', + ... img_scale=(1333, 800), + ... flip=False, + ... transforms=[ + ... dict(type='Resize', keep_ratio=True), + ... dict(type='RandomFlip'), + ... dict(type='Normalize', mean=[0, 0, 0], std=[1, 1, 1]), + ... dict(type='Pad', size_divisor=32), + ... dict(type='DefaultFormatBundle'), + ... dict(type='Collect', keys=['img']), + ... ]) + ... ] + >>> assert expected_pipelines == replace_ImageToTensor(pipelines) + """ + pipelines = copy.deepcopy(pipelines) + for i, pipeline in enumerate(pipelines): + if pipeline['type'] == 'MultiScaleFlipAug': + assert 'transforms' in pipeline + pipeline['transforms'] = replace_ImageToTensor( + pipeline['transforms']) + elif pipeline['type'] == 'ImageToTensor': + warnings.warn( + '"ImageToTensor" pipeline is replaced by ' + '"DefaultFormatBundle" for batch inference. It is ' + 'recommended to manually replace it in the test ' + 'data pipeline in your config file.', UserWarning) + pipelines[i] = {'type': 'DefaultFormatBundle'} + return pipelines + + +def get_loading_pipeline(pipeline): + """Only keep loading image and annotations related configuration. + + Args: + pipeline (list[dict]): Data pipeline configs. + + Returns: + list[dict]: The new pipeline list with only keep + loading image and annotations related configuration. + + Examples: + >>> pipelines = [ + ... dict(type='LoadImageFromFile'), + ... dict(type='LoadAnnotations', with_bbox=True), + ... dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + ... dict(type='RandomFlip', flip_ratio=0.5), + ... dict(type='Normalize', **img_norm_cfg), + ... dict(type='Pad', size_divisor=32), + ... dict(type='DefaultFormatBundle'), + ... dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) + ... ] + >>> expected_pipelines = [ + ... dict(type='LoadImageFromFile'), + ... dict(type='LoadAnnotations', with_bbox=True) + ... ] + >>> assert expected_pipelines ==\ + ... get_loading_pipeline(pipelines) + """ + loading_pipeline_cfg = [] + for cfg in pipeline: + obj_cls = PIPELINES.get(cfg['type']) + # TODO：use more elegant way to distinguish loading modules + if obj_cls is not None and obj_cls in (LoadImageFromFile, + LoadAnnotations): + loading_pipeline_cfg.append(cfg) + assert len(loading_pipeline_cfg) == 2, \ + 'The data pipeline in your config file must include ' \ + 'loading image and annotations related pipeline.' + return loading_pipeline_cfg + + +@HOOKS.register_module() +class NumClassCheckHook(Hook): + + def _check_head(self, runner): + """Check whether the `num_classes` in head matches the length of + `CLASSSES` in `dataset`. + + Args: + runner (obj:`EpochBasedRunner`): Epoch based Runner. + """ + model = runner.model + dataset = runner.data_loader.dataset + if dataset.CLASSES is None: + runner.logger.warning( + f'Please set `CLASSES` ' + f'in the {dataset.__class__.__name__} and' + f'check if it is consistent with the `num_classes` ' + f'of head') + else: + assert type(dataset.CLASSES) is not str, \ + (f'`CLASSES` in {dataset.__class__.__name__}' + f'should be a tuple of str.' + f'Add comma if number of classes is 1 as ' + f'CLASSES = ({dataset.CLASSES},)') + for name, module in model.named_modules(): + if hasattr(module, 'num_classes') and not isinstance( + module, (RPNHead, VGG, FusedSemanticHead, GARPNHead)): + assert module.num_classes == len(dataset.CLASSES), \ + (f'The `num_classes` ({module.num_classes}) in ' + f'{module.__class__.__name__} of ' + f'{model.__class__.__name__} does not matches ' + f'the length of `CLASSES` ' + f'{len(dataset.CLASSES)}) in ' + f'{dataset.__class__.__name__}') + + def before_train_epoch(self, runner): + """Check whether the training dataset is compatible with head. + + Args: + runner (obj:`EpochBasedRunner`): Epoch based Runner. + """ + self._check_head(runner) + + def before_val_epoch(self, runner): + """Check whether the dataset in val epoch is compatible with head. + + Args: + runner (obj:`EpochBasedRunner`): Epoch based Runner. + """ + self._check_head(runner) diff --git a/mmdet/datasets/voc.py b/mmdet/datasets/voc.py new file mode 100644 index 0000000..abd4cb8 --- /dev/null +++ b/mmdet/datasets/voc.py @@ -0,0 +1,93 @@ +from collections import OrderedDict + +from mmcv.utils import print_log + +from mmdet.core import eval_map, eval_recalls +from .builder import DATASETS +from .xml_style import XMLDataset + + +@DATASETS.register_module() +class VOCDataset(XMLDataset): + + CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', + 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', + 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', + 'tvmonitor') + + def __init__(self, **kwargs): + super(VOCDataset, self).__init__(**kwargs) + if 'VOC2007' in self.img_prefix: + self.year = 2007 + elif 'VOC2012' in self.img_prefix: + self.year = 2012 + else: + raise ValueError('Cannot infer dataset year from img_prefix') + + def evaluate(self, + results, + metric='mAP', + logger=None, + proposal_nums=(100, 300, 1000), + iou_thr=0.5, + scale_ranges=None): + """Evaluate in VOC protocol. + + Args: + results (list[list | tuple]): Testing results of the dataset. + metric (str | list[str]): Metrics to be evaluated. Options are + 'mAP', 'recall'. + logger (logging.Logger | str, optional): Logger used for printing + related information during evaluation. Default: None. + proposal_nums (Sequence[int]): Proposal number used for evaluating + recalls, such as recall@100, recall@1000. + Default: (100, 300, 1000). + iou_thr (float | list[float]): IoU threshold. Default: 0.5. + scale_ranges (list[tuple], optional): Scale ranges for evaluating + mAP. If not specified, all bounding boxes would be included in + evaluation. Default: None. + + Returns: + dict[str, float]: AP/recall metrics. + """ + + if not isinstance(metric, str): + assert len(metric) == 1 + metric = metric[0] + allowed_metrics = ['mAP', 'recall'] + if metric not in allowed_metrics: + raise KeyError(f'metric {metric} is not supported') + annotations = [self.get_ann_info(i) for i in range(len(self))] + eval_results = OrderedDict() + iou_thrs = [iou_thr] if isinstance(iou_thr, float) else iou_thr + if metric == 'mAP': + assert isinstance(iou_thrs, list) + if self.year == 2007: + ds_name = 'voc07' + else: + ds_name = self.CLASSES + mean_aps = [] + for iou_thr in iou_thrs: + print_log(f'\n{"-" * 15}iou_thr: {iou_thr}{"-" * 15}') + mean_ap, _ = eval_map( + results, + annotations, + scale_ranges=None, + iou_thr=iou_thr, + dataset=ds_name, + logger=logger) + mean_aps.append(mean_ap) + eval_results[f'AP{int(iou_thr * 100):02d}'] = round(mean_ap, 3) + eval_results['mAP'] = sum(mean_aps) / len(mean_aps) + elif metric == 'recall': + gt_bboxes = [ann['bboxes'] for ann in annotations] + recalls = eval_recalls( + gt_bboxes, results, proposal_nums, iou_thr, logger=logger) + for i, num in enumerate(proposal_nums): + for j, iou in enumerate(iou_thr): + eval_results[f'recall@{num}@{iou}'] = recalls[i, j] + if recalls.shape[1] > 1: + ar = recalls.mean(axis=1) + for i, num in enumerate(proposal_nums): + eval_results[f'AR@{num}'] = ar[i] + return eval_results diff --git a/mmdet/datasets/wider_face.py b/mmdet/datasets/wider_face.py new file mode 100644 index 0000000..3a13907 --- /dev/null +++ b/mmdet/datasets/wider_face.py @@ -0,0 +1,51 @@ +import os.path as osp +import xml.etree.ElementTree as ET + +import mmcv + +from .builder import DATASETS +from .xml_style import XMLDataset + + +@DATASETS.register_module() +class WIDERFaceDataset(XMLDataset): + """Reader for the WIDER Face dataset in PASCAL VOC format. + + Conversion scripts can be found in + https://github.com/sovrasov/wider-face-pascal-voc-annotations + """ + CLASSES = ('face', ) + + def __init__(self, **kwargs): + super(WIDERFaceDataset, self).__init__(**kwargs) + + def load_annotations(self, ann_file): + """Load annotation from WIDERFace XML style annotation file. + + Args: + ann_file (str): Path of XML file. + + Returns: + list[dict]: Annotation info from XML file. + """ + + data_infos = [] + img_ids = mmcv.list_from_file(ann_file) + for img_id in img_ids: + filename = f'{img_id}.jpg' + xml_path = osp.join(self.img_prefix, 'Annotations', + f'{img_id}.xml') + tree = ET.parse(xml_path) + root = tree.getroot() + size = root.find('size') + width = int(size.find('width').text) + height = int(size.find('height').text) + folder = root.find('folder').text + data_infos.append( + dict( + id=img_id, + filename=osp.join(folder, filename), + width=width, + height=height)) + + return data_infos diff --git a/mmdet/datasets/xml_style.py b/mmdet/datasets/xml_style.py new file mode 100644 index 0000000..7106948 --- /dev/null +++ b/mmdet/datasets/xml_style.py @@ -0,0 +1,170 @@ +import os.path as osp +import xml.etree.ElementTree as ET + +import mmcv +import numpy as np +from PIL import Image + +from .builder import DATASETS +from .custom import CustomDataset + + +@DATASETS.register_module() +class XMLDataset(CustomDataset): + """XML dataset for detection. + + Args: + min_size (int | float, optional): The minimum size of bounding + boxes in the images. If the size of a bounding box is less than + ``min_size``, it would be add to ignored field. + """ + + def __init__(self, min_size=None, **kwargs): + assert self.CLASSES or kwargs.get( + 'classes', None), 'CLASSES in `XMLDataset` can not be None.' + super(XMLDataset, self).__init__(**kwargs) + self.cat2label = {cat: i for i, cat in enumerate(self.CLASSES)} + self.min_size = min_size + + def load_annotations(self, ann_file): + """Load annotation from XML style ann_file. + + Args: + ann_file (str): Path of XML file. + + Returns: + list[dict]: Annotation info from XML file. + """ + + data_infos = [] + img_ids = mmcv.list_from_file(ann_file) + for img_id in img_ids: + filename = f'JPEGImages/{img_id}.jpg' + xml_path = osp.join(self.img_prefix, 'Annotations', + f'{img_id}.xml') + tree = ET.parse(xml_path) + root = tree.getroot() + size = root.find('size') + if size is not None: + width = int(size.find('width').text) + height = int(size.find('height').text) + else: + img_path = osp.join(self.img_prefix, 'JPEGImages', + '{}.jpg'.format(img_id)) + img = Image.open(img_path) + width, height = img.size + data_infos.append( + dict(id=img_id, filename=filename, width=width, height=height)) + + return data_infos + + def _filter_imgs(self, min_size=32): + """Filter images too small or without annotation.""" + valid_inds = [] + for i, img_info in enumerate(self.data_infos): + if min(img_info['width'], img_info['height']) < min_size: + continue + if self.filter_empty_gt: + img_id = img_info['id'] + xml_path = osp.join(self.img_prefix, 'Annotations', + f'{img_id}.xml') + tree = ET.parse(xml_path) + root = tree.getroot() + for obj in root.findall('object'): + name = obj.find('name').text + if name in self.CLASSES: + valid_inds.append(i) + break + else: + valid_inds.append(i) + return valid_inds + + def get_ann_info(self, idx): + """Get annotation from XML file by index. + + Args: + idx (int): Index of data. + + Returns: + dict: Annotation info of specified index. + """ + + img_id = self.data_infos[idx]['id'] + xml_path = osp.join(self.img_prefix, 'Annotations', f'{img_id}.xml') + tree = ET.parse(xml_path) + root = tree.getroot() + bboxes = [] + labels = [] + bboxes_ignore = [] + labels_ignore = [] + for obj in root.findall('object'): + name = obj.find('name').text + if name not in self.CLASSES: + continue + label = self.cat2label[name] + difficult = obj.find('difficult') + difficult = 0 if difficult is None else int(difficult.text) + bnd_box = obj.find('bndbox') + # TODO: check whether it is necessary to use int + # Coordinates may be float type + bbox = [ + int(float(bnd_box.find('xmin').text)), + int(float(bnd_box.find('ymin').text)), + int(float(bnd_box.find('xmax').text)), + int(float(bnd_box.find('ymax').text)) + ] + ignore = False + if self.min_size: + assert not self.test_mode + w = bbox[2] - bbox[0] + h = bbox[3] - bbox[1] + if w < self.min_size or h < self.min_size: + ignore = True + if difficult or ignore: + bboxes_ignore.append(bbox) + labels_ignore.append(label) + else: + bboxes.append(bbox) + labels.append(label) + if not bboxes: + bboxes = np.zeros((0, 4)) + labels = np.zeros((0, )) + else: + bboxes = np.array(bboxes, ndmin=2) - 1 + labels = np.array(labels) + if not bboxes_ignore: + bboxes_ignore = np.zeros((0, 4)) + labels_ignore = np.zeros((0, )) + else: + bboxes_ignore = np.array(bboxes_ignore, ndmin=2) - 1 + labels_ignore = np.array(labels_ignore) + ann = dict( + bboxes=bboxes.astype(np.float32), + labels=labels.astype(np.int64), + bboxes_ignore=bboxes_ignore.astype(np.float32), + labels_ignore=labels_ignore.astype(np.int64)) + return ann + + def get_cat_ids(self, idx): + """Get category ids in XML file by index. + + Args: + idx (int): Index of data. + + Returns: + list[int]: All categories in the image of specified index. + """ + + cat_ids = [] + img_id = self.data_infos[idx]['id'] + xml_path = osp.join(self.img_prefix, 'Annotations', f'{img_id}.xml') + tree = ET.parse(xml_path) + root = tree.getroot() + for obj in root.findall('object'): + name = obj.find('name').text + if name not in self.CLASSES: + continue + label = self.cat2label[name] + cat_ids.append(label) + + return cat_ids diff --git a/mmdet/models/__init__.py b/mmdet/models/__init__.py new file mode 100644 index 0000000..44ac998 --- /dev/null +++ b/mmdet/models/__init__.py @@ -0,0 +1,16 @@ +from .backbones import * # noqa: F401,F403 +from .builder import (BACKBONES, DETECTORS, HEADS, LOSSES, NECKS, + ROI_EXTRACTORS, SHARED_HEADS, build_backbone, + build_detector, build_head, build_loss, build_neck, + build_roi_extractor, build_shared_head) +from .dense_heads import * # noqa: F401,F403 +from .detectors import * # noqa: F401,F403 +from .losses import * # noqa: F401,F403 +from .necks import * # noqa: F401,F403 +from .roi_heads import * # noqa: F401,F403 + +__all__ = [ + 'BACKBONES', 'NECKS', 'ROI_EXTRACTORS', 'SHARED_HEADS', 'HEADS', 'LOSSES', + 'DETECTORS', 'build_backbone', 'build_neck', 'build_roi_extractor', + 'build_shared_head', 'build_head', 'build_loss', 'build_detector' +] diff --git a/mmdet/models/__pycache__/__init__.cpython-37.pyc b/mmdet/models/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..15b2a1e Binary files /dev/null and b/mmdet/models/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/models/__pycache__/builder.cpython-37.pyc b/mmdet/models/__pycache__/builder.cpython-37.pyc new file mode 100644 index 0000000..d108a68 Binary files /dev/null and b/mmdet/models/__pycache__/builder.cpython-37.pyc differ diff --git a/mmdet/models/backbones/__init__.py b/mmdet/models/backbones/__init__.py new file mode 100644 index 0000000..aec48f6 --- /dev/null +++ b/mmdet/models/backbones/__init__.py @@ -0,0 +1,19 @@ +from .darknet import Darknet +from .detectors_resnet import DetectoRS_ResNet +from .detectors_resnext import DetectoRS_ResNeXt +from .hourglass import HourglassNet +from .hrnet import HRNet +from .regnet import RegNet +from .res2net import Res2Net 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Each ResBlock consists of two + ConvModules and the input is added to the final output. Each ConvModule is + composed of Conv, BN, and LeakyReLU. In YoloV3 paper, the first convLayer + has half of the number of the filters as much as the second convLayer. The + first convLayer has filter size of 1x1 and the second one has the filter + size of 3x3. + + Args: + in_channels (int): The input channels. Must be even. + conv_cfg (dict): Config dict for convolution layer. Default: None. + norm_cfg (dict): Dictionary to construct and config norm layer. + Default: dict(type='BN', requires_grad=True) + act_cfg (dict): Config dict for activation layer. + Default: dict(type='LeakyReLU', negative_slope=0.1). + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + in_channels, + conv_cfg=None, + norm_cfg=dict(type='BN', requires_grad=True), + act_cfg=dict(type='LeakyReLU', negative_slope=0.1), + init_cfg=None): + super(ResBlock, self).__init__(init_cfg) + assert in_channels % 2 == 0 # ensure the in_channels is even + half_in_channels = in_channels // 2 + + # shortcut + cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg) + + self.conv1 = ConvModule(in_channels, half_in_channels, 1, **cfg) + self.conv2 = ConvModule( + half_in_channels, in_channels, 3, padding=1, **cfg) + + def forward(self, x): + residual = x + out = self.conv1(x) + out = self.conv2(out) + out = out + residual + + return out + + +@BACKBONES.register_module() +class Darknet(BaseModule): + """Darknet backbone. + + Args: + depth (int): Depth of Darknet. Currently only support 53. + out_indices (Sequence[int]): Output from which stages. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. Default: -1. + conv_cfg (dict): Config dict for convolution layer. Default: None. + norm_cfg (dict): Dictionary to construct and config norm layer. + Default: dict(type='BN', requires_grad=True) + act_cfg (dict): Config dict for activation layer. + Default: dict(type='LeakyReLU', negative_slope=0.1). + norm_eval (bool): Whether to set norm layers to eval mode, namely, + freeze running stats (mean and var). Note: Effect on Batch Norm + and its variants only. + pretrained (str, optional): model pretrained path. Default: None + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + + Example: + >>> from mmdet.models import Darknet + >>> import torch + >>> self = Darknet(depth=53) + >>> self.eval() + >>> inputs = torch.rand(1, 3, 416, 416) + >>> level_outputs = self.forward(inputs) + >>> for level_out in level_outputs: + ... print(tuple(level_out.shape)) + ... + (1, 256, 52, 52) + (1, 512, 26, 26) + (1, 1024, 13, 13) + """ + + # Dict(depth: (layers, channels)) + arch_settings = { + 53: ((1, 2, 8, 8, 4), ((32, 64), (64, 128), (128, 256), (256, 512), + (512, 1024))) + } + + def __init__(self, + depth=53, + out_indices=(3, 4, 5), + frozen_stages=-1, + conv_cfg=None, + norm_cfg=dict(type='BN', requires_grad=True), + act_cfg=dict(type='LeakyReLU', negative_slope=0.1), + norm_eval=True, + pretrained=None, + init_cfg=None): + super(Darknet, self).__init__(init_cfg) + if depth not in self.arch_settings: + raise KeyError(f'invalid depth {depth} for darknet') + + self.depth = depth + self.out_indices = out_indices + self.frozen_stages = frozen_stages + self.layers, self.channels = self.arch_settings[depth] + + cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg) + + self.conv1 = ConvModule(3, 32, 3, padding=1, **cfg) + + self.cr_blocks = ['conv1'] + for i, n_layers in enumerate(self.layers): + layer_name = f'conv_res_block{i + 1}' + in_c, out_c = self.channels[i] + self.add_module( + layer_name, + self.make_conv_res_block(in_c, out_c, n_layers, **cfg)) + self.cr_blocks.append(layer_name) + + self.norm_eval = norm_eval + + assert not (init_cfg and pretrained), \ + 'init_cfg and pretrained cannot be setting at the same time' + if isinstance(pretrained, str): + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' + 'please use "init_cfg" instead') + self.init_cfg = dict(type='Pretrained', checkpoint=pretrained) + elif pretrained is None: + if init_cfg is None: + self.init_cfg = [ + dict(type='Kaiming', layer='Conv2d'), + dict( + type='Constant', + val=1, + layer=['_BatchNorm', 'GroupNorm']) + ] + else: + raise TypeError('pretrained must be a str or None') + + def forward(self, x): + outs = [] + for i, layer_name in enumerate(self.cr_blocks): + cr_block = getattr(self, layer_name) + x = cr_block(x) + if i in self.out_indices: + outs.append(x) + + return tuple(outs) + + def _freeze_stages(self): + if self.frozen_stages >= 0: + for i in range(self.frozen_stages): + m = getattr(self, self.cr_blocks[i]) + m.eval() + for param in m.parameters(): + param.requires_grad = False + + def train(self, mode=True): + super(Darknet, self).train(mode) + self._freeze_stages() + if mode and self.norm_eval: + for m in self.modules(): + if isinstance(m, _BatchNorm): + m.eval() + + @staticmethod + def make_conv_res_block(in_channels, + out_channels, + res_repeat, + conv_cfg=None, + norm_cfg=dict(type='BN', requires_grad=True), + act_cfg=dict(type='LeakyReLU', + negative_slope=0.1)): + """In Darknet backbone, ConvLayer is usually followed by ResBlock. This + function will make that. The Conv layers always have 3x3 filters with + stride=2. The number of the filters in Conv layer is the same as the + out channels of the ResBlock. + + Args: + in_channels (int): The number of input channels. + out_channels (int): The number of output channels. + res_repeat (int): The number of ResBlocks. + conv_cfg (dict): Config dict for convolution layer. Default: None. + norm_cfg (dict): Dictionary to construct and config norm layer. + Default: dict(type='BN', requires_grad=True) + act_cfg (dict): Config dict for activation layer. + Default: dict(type='LeakyReLU', negative_slope=0.1). + """ + + cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg) + + model = nn.Sequential() + model.add_module( + 'conv', + ConvModule( + in_channels, out_channels, 3, stride=2, padding=1, **cfg)) + for idx in range(res_repeat): + model.add_module('res{}'.format(idx), + ResBlock(out_channels, **cfg)) + return model diff --git a/mmdet/models/backbones/detectors_resnet.py b/mmdet/models/backbones/detectors_resnet.py new file mode 100644 index 0000000..b4afc4c --- /dev/null +++ b/mmdet/models/backbones/detectors_resnet.py @@ -0,0 +1,343 @@ +import torch.nn as nn +import torch.utils.checkpoint as cp +from mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init, + kaiming_init) +from mmcv.runner import Sequential, load_checkpoint +from torch.nn.modules.batchnorm import _BatchNorm + +from mmdet.utils import get_root_logger +from ..builder import BACKBONES +from .resnet import BasicBlock +from .resnet import Bottleneck as _Bottleneck +from .resnet import ResNet + + +class Bottleneck(_Bottleneck): + r"""Bottleneck for the ResNet backbone in `DetectoRS + `_. + + This bottleneck allows the users to specify whether to use + SAC (Switchable Atrous Convolution) and RFP (Recursive Feature Pyramid). + + Args: + inplanes (int): The number of input channels. + planes (int): The number of output channels before expansion. + rfp_inplanes (int, optional): The number of channels from RFP. + Default: None. If specified, an additional conv layer will be + added for ``rfp_feat``. Otherwise, the structure is the same as + base class. + sac (dict, optional): Dictionary to construct SAC. Default: None. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + expansion = 4 + + def __init__(self, + inplanes, + planes, + rfp_inplanes=None, + sac=None, + init_cfg=None, + **kwargs): + super(Bottleneck, self).__init__( + inplanes, planes, init_cfg=init_cfg, **kwargs) + + assert sac is None or isinstance(sac, dict) + self.sac = sac + self.with_sac = sac is not None + if self.with_sac: + self.conv2 = build_conv_layer( + self.sac, + planes, + planes, + kernel_size=3, + stride=self.conv2_stride, + padding=self.dilation, + dilation=self.dilation, + bias=False) + + self.rfp_inplanes = rfp_inplanes + if self.rfp_inplanes: + self.rfp_conv = build_conv_layer( + None, + self.rfp_inplanes, + planes * self.expansion, + 1, + stride=1, + bias=True) + if init_cfg is None: + self.init_cfg = dict( + type='Constant', val=0, override=dict(name='rfp_conv')) + + def rfp_forward(self, x, rfp_feat): + """The forward function that also takes the RFP features as input.""" + + def _inner_forward(x): + identity = x + + out = self.conv1(x) + out = self.norm1(out) + out = self.relu(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv1_plugin_names) + + out = self.conv2(out) + out = self.norm2(out) + out = self.relu(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv2_plugin_names) + + out = self.conv3(out) + out = self.norm3(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv3_plugin_names) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + + return out + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(_inner_forward, x) + else: + out = _inner_forward(x) + + if self.rfp_inplanes: + rfp_feat = self.rfp_conv(rfp_feat) + out = out + rfp_feat + + out = self.relu(out) + + return out + + +class ResLayer(Sequential): + """ResLayer to build ResNet style backbone for RPF in detectoRS. + + The difference between this module and base class is that we pass + ``rfp_inplanes`` to the first block. + + Args: + block (nn.Module): block used to build ResLayer. + inplanes (int): inplanes of block. + planes (int): planes of block. + num_blocks (int): number of blocks. + stride (int): stride of the first block. Default: 1 + avg_down (bool): Use AvgPool instead of stride conv when + downsampling in the bottleneck. Default: False + conv_cfg (dict): dictionary to construct and config conv layer. + Default: None + norm_cfg (dict): dictionary to construct and config norm layer. + Default: dict(type='BN') + downsample_first (bool): Downsample at the first block or last block. + False for Hourglass, True for ResNet. Default: True + rfp_inplanes (int, optional): The number of channels from RFP. + Default: None. If specified, an additional conv layer will be + added for ``rfp_feat``. Otherwise, the structure is the same as + base class. + """ + + def __init__(self, + block, + inplanes, + planes, + num_blocks, + stride=1, + avg_down=False, + conv_cfg=None, + norm_cfg=dict(type='BN'), + downsample_first=True, + rfp_inplanes=None, + **kwargs): + self.block = block + assert downsample_first, f'downsample_first={downsample_first} is ' \ + 'not supported in DetectoRS' + + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + downsample = [] + conv_stride = stride + if avg_down and stride != 1: + conv_stride = 1 + downsample.append( + nn.AvgPool2d( + kernel_size=stride, + stride=stride, + ceil_mode=True, + count_include_pad=False)) + downsample.extend([ + build_conv_layer( + conv_cfg, + inplanes, + planes * block.expansion, + kernel_size=1, + stride=conv_stride, + bias=False), + build_norm_layer(norm_cfg, planes * block.expansion)[1] + ]) + downsample = nn.Sequential(*downsample) + + layers = [] + layers.append( + block( + inplanes=inplanes, + planes=planes, + stride=stride, + downsample=downsample, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + rfp_inplanes=rfp_inplanes, + **kwargs)) + inplanes = planes * block.expansion + for _ in range(1, num_blocks): + layers.append( + block( + inplanes=inplanes, + planes=planes, + stride=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + **kwargs)) + + super(ResLayer, self).__init__(*layers) + + +@BACKBONES.register_module() +class DetectoRS_ResNet(ResNet): + """ResNet backbone for DetectoRS. + + Args: + sac (dict, optional): Dictionary to construct SAC (Switchable Atrous + Convolution). Default: None. + stage_with_sac (list): Which stage to use sac. Default: (False, False, + False, False). + rfp_inplanes (int, optional): The number of channels from RFP. + Default: None. If specified, an additional conv layer will be + added for ``rfp_feat``. Otherwise, the structure is the same as + base class. + output_img (bool): If ``True``, the input image will be inserted into + the starting position of output. Default: False. + """ + + arch_settings = { + 50: (Bottleneck, (3, 4, 6, 3)), + 101: (Bottleneck, (3, 4, 23, 3)), + 152: (Bottleneck, (3, 8, 36, 3)) + } + + def __init__(self, + sac=None, + stage_with_sac=(False, False, False, False), + rfp_inplanes=None, + output_img=False, + pretrained=None, + init_cfg=None, + **kwargs): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + self.pretrained = pretrained + self.sac = sac + self.stage_with_sac = stage_with_sac + self.rfp_inplanes = rfp_inplanes + self.output_img = output_img + super(DetectoRS_ResNet, self).__init__(**kwargs) + + self.inplanes = self.stem_channels + self.res_layers = [] + for i, num_blocks in enumerate(self.stage_blocks): + stride = self.strides[i] + dilation = self.dilations[i] + dcn = self.dcn if self.stage_with_dcn[i] else None + sac = self.sac if self.stage_with_sac[i] else None + if self.plugins is not None: + stage_plugins = self.make_stage_plugins(self.plugins, i) + else: + stage_plugins = None + planes = self.base_channels * 2**i + res_layer = self.make_res_layer( + block=self.block, + inplanes=self.inplanes, + planes=planes, + num_blocks=num_blocks, + stride=stride, + dilation=dilation, + style=self.style, + avg_down=self.avg_down, + with_cp=self.with_cp, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + dcn=dcn, + sac=sac, + rfp_inplanes=rfp_inplanes if i > 0 else None, + plugins=stage_plugins) + self.inplanes = planes * self.block.expansion + layer_name = f'layer{i + 1}' + self.add_module(layer_name, res_layer) + self.res_layers.append(layer_name) + + self._freeze_stages() + + # In order to be properly initialized by RFP + def init_weights(self): + # Calling this method will cause parameter initialization exception + # super(DetectoRS_ResNet, self).init_weights() + + if isinstance(self.pretrained, str): + logger = get_root_logger() + load_checkpoint(self, self.pretrained, strict=False, logger=logger) + elif self.pretrained is None: + for m in self.modules(): + if isinstance(m, nn.Conv2d): + kaiming_init(m) + elif isinstance(m, (_BatchNorm, nn.GroupNorm)): + constant_init(m, 1) + + if self.dcn is not None: + for m in self.modules(): + if isinstance(m, Bottleneck) and hasattr( + m.conv2, 'conv_offset'): + constant_init(m.conv2.conv_offset, 0) + + if self.zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + constant_init(m.norm3, 0) + elif isinstance(m, BasicBlock): + constant_init(m.norm2, 0) + else: + raise TypeError('pretrained must be a str or None') + + def make_res_layer(self, **kwargs): + """Pack all blocks in a stage into a ``ResLayer`` for DetectoRS.""" + return ResLayer(**kwargs) + + def forward(self, x): + """Forward function.""" + outs = list(super(DetectoRS_ResNet, self).forward(x)) + if self.output_img: + outs.insert(0, x) + return tuple(outs) + + def rfp_forward(self, x, rfp_feats): + """Forward function for RFP.""" + if self.deep_stem: + x = self.stem(x) + else: + x = self.conv1(x) + x = self.norm1(x) + x = self.relu(x) + x = self.maxpool(x) + outs = [] + for i, layer_name in enumerate(self.res_layers): + res_layer = getattr(self, layer_name) + rfp_feat = rfp_feats[i] if i > 0 else None + for layer in res_layer: + x = layer.rfp_forward(x, rfp_feat) + if i in self.out_indices: + outs.append(x) + return tuple(outs) diff --git a/mmdet/models/backbones/detectors_resnext.py b/mmdet/models/backbones/detectors_resnext.py new file mode 100644 index 0000000..57d032f --- /dev/null +++ b/mmdet/models/backbones/detectors_resnext.py @@ -0,0 +1,122 @@ +import math + +from mmcv.cnn import build_conv_layer, build_norm_layer + +from ..builder import BACKBONES +from .detectors_resnet import Bottleneck as _Bottleneck +from .detectors_resnet import DetectoRS_ResNet + + +class Bottleneck(_Bottleneck): + expansion = 4 + + def __init__(self, + inplanes, + planes, + groups=1, + base_width=4, + base_channels=64, + **kwargs): + """Bottleneck block for ResNeXt. + + If style is "pytorch", the stride-two layer is the 3x3 conv layer, if + it is "caffe", the stride-two layer is the first 1x1 conv layer. + """ + super(Bottleneck, self).__init__(inplanes, planes, **kwargs) + + if groups == 1: + width = self.planes + else: + width = math.floor(self.planes * + (base_width / base_channels)) * groups + + self.norm1_name, norm1 = build_norm_layer( + self.norm_cfg, width, postfix=1) + self.norm2_name, norm2 = build_norm_layer( + self.norm_cfg, width, postfix=2) + self.norm3_name, norm3 = build_norm_layer( + self.norm_cfg, self.planes * self.expansion, postfix=3) + + self.conv1 = build_conv_layer( + self.conv_cfg, + self.inplanes, + width, + kernel_size=1, + stride=self.conv1_stride, + bias=False) + self.add_module(self.norm1_name, norm1) + fallback_on_stride = False + self.with_modulated_dcn = False + if self.with_dcn: + fallback_on_stride = self.dcn.pop('fallback_on_stride', False) + if self.with_sac: + self.conv2 = build_conv_layer( + self.sac, + width, + width, + kernel_size=3, + stride=self.conv2_stride, + padding=self.dilation, + dilation=self.dilation, + groups=groups, + bias=False) + elif not self.with_dcn or fallback_on_stride: + self.conv2 = build_conv_layer( + self.conv_cfg, + width, + width, + kernel_size=3, + stride=self.conv2_stride, + padding=self.dilation, + dilation=self.dilation, + groups=groups, + bias=False) + else: + assert self.conv_cfg is None, 'conv_cfg must be None for DCN' + self.conv2 = build_conv_layer( + self.dcn, + width, + width, + kernel_size=3, + stride=self.conv2_stride, + padding=self.dilation, + dilation=self.dilation, + groups=groups, + bias=False) + + self.add_module(self.norm2_name, norm2) + self.conv3 = build_conv_layer( + self.conv_cfg, + width, + self.planes * self.expansion, + kernel_size=1, + bias=False) + self.add_module(self.norm3_name, norm3) + + +@BACKBONES.register_module() +class DetectoRS_ResNeXt(DetectoRS_ResNet): + """ResNeXt backbone for DetectoRS. + + Args: + groups (int): The number of groups in ResNeXt. + base_width (int): The base width of ResNeXt. + """ + + arch_settings = { + 50: (Bottleneck, (3, 4, 6, 3)), + 101: (Bottleneck, (3, 4, 23, 3)), + 152: (Bottleneck, (3, 8, 36, 3)) + } + + def __init__(self, groups=1, base_width=4, **kwargs): + self.groups = groups + self.base_width = base_width + super(DetectoRS_ResNeXt, self).__init__(**kwargs) + + def make_res_layer(self, **kwargs): + return super().make_res_layer( + groups=self.groups, + base_width=self.base_width, + base_channels=self.base_channels, + **kwargs) diff --git a/mmdet/models/backbones/hourglass.py b/mmdet/models/backbones/hourglass.py new file mode 100644 index 0000000..ea210f5 --- /dev/null +++ b/mmdet/models/backbones/hourglass.py @@ -0,0 +1,202 @@ +import torch.nn as nn +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule + +from ..builder import BACKBONES +from ..utils import ResLayer +from .resnet import BasicBlock + + +class HourglassModule(BaseModule): + """Hourglass Module for HourglassNet backbone. + + Generate module recursively and use BasicBlock as the base unit. + + Args: + depth (int): Depth of current HourglassModule. + stage_channels (list[int]): Feature channels of sub-modules in current + and follow-up HourglassModule. + stage_blocks (list[int]): Number of sub-modules stacked in current and + follow-up HourglassModule. + norm_cfg (dict): Dictionary to construct and config norm layer. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + depth, + stage_channels, + stage_blocks, + norm_cfg=dict(type='BN', requires_grad=True), + init_cfg=None): + super(HourglassModule, self).__init__(init_cfg) + + self.depth = depth + + cur_block = stage_blocks[0] + next_block = stage_blocks[1] + + cur_channel = stage_channels[0] + next_channel = stage_channels[1] + + self.up1 = ResLayer( + BasicBlock, cur_channel, cur_channel, cur_block, norm_cfg=norm_cfg) + + self.low1 = ResLayer( + BasicBlock, + cur_channel, + next_channel, + cur_block, + stride=2, + norm_cfg=norm_cfg) + + if self.depth > 1: + self.low2 = HourglassModule(depth - 1, stage_channels[1:], + stage_blocks[1:]) + else: + self.low2 = ResLayer( + BasicBlock, + next_channel, + next_channel, + next_block, + norm_cfg=norm_cfg) + + self.low3 = ResLayer( + BasicBlock, + next_channel, + cur_channel, + cur_block, + norm_cfg=norm_cfg, + downsample_first=False) + + self.up2 = nn.Upsample(scale_factor=2) + + def forward(self, x): + """Forward function.""" + up1 = self.up1(x) + low1 = self.low1(x) + low2 = self.low2(low1) + low3 = self.low3(low2) + up2 = self.up2(low3) + return up1 + up2 + + +@BACKBONES.register_module() +class HourglassNet(BaseModule): + """HourglassNet backbone. + + Stacked Hourglass Networks for Human Pose Estimation. + More details can be found in the `paper + `_ . + + Args: + downsample_times (int): Downsample times in a HourglassModule. + num_stacks (int): Number of HourglassModule modules stacked, + 1 for Hourglass-52, 2 for Hourglass-104. + stage_channels (list[int]): Feature channel of each sub-module in a + HourglassModule. + stage_blocks (list[int]): Number of sub-modules stacked in a + HourglassModule. + feat_channel (int): Feature channel of conv after a HourglassModule. + norm_cfg (dict): Dictionary to construct and config norm layer. + pretrained (str, optional): model pretrained path. Default: None + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + + Example: + >>> from mmdet.models import HourglassNet + >>> import torch + >>> self = HourglassNet() + >>> self.eval() + >>> inputs = torch.rand(1, 3, 511, 511) + >>> level_outputs = self.forward(inputs) + >>> for level_output in level_outputs: + ... print(tuple(level_output.shape)) + (1, 256, 128, 128) + (1, 256, 128, 128) + """ + + def __init__(self, + downsample_times=5, + num_stacks=2, + stage_channels=(256, 256, 384, 384, 384, 512), + stage_blocks=(2, 2, 2, 2, 2, 4), + feat_channel=256, + norm_cfg=dict(type='BN', requires_grad=True), + pretrained=None, + init_cfg=None): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + super(HourglassNet, self).__init__(init_cfg) + + self.num_stacks = num_stacks + assert self.num_stacks >= 1 + assert len(stage_channels) == len(stage_blocks) + assert len(stage_channels) > downsample_times + + cur_channel = stage_channels[0] + + self.stem = nn.Sequential( + ConvModule(3, 128, 7, padding=3, stride=2, norm_cfg=norm_cfg), + ResLayer(BasicBlock, 128, 256, 1, stride=2, norm_cfg=norm_cfg)) + + self.hourglass_modules = nn.ModuleList([ + HourglassModule(downsample_times, stage_channels, stage_blocks) + for _ in range(num_stacks) + ]) + + self.inters = ResLayer( + BasicBlock, + cur_channel, + cur_channel, + num_stacks - 1, + norm_cfg=norm_cfg) + + self.conv1x1s = nn.ModuleList([ + ConvModule( + cur_channel, cur_channel, 1, norm_cfg=norm_cfg, act_cfg=None) + for _ in range(num_stacks - 1) + ]) + + self.out_convs = nn.ModuleList([ + ConvModule( + cur_channel, feat_channel, 3, padding=1, norm_cfg=norm_cfg) + for _ in range(num_stacks) + ]) + + self.remap_convs = nn.ModuleList([ + ConvModule( + feat_channel, cur_channel, 1, norm_cfg=norm_cfg, act_cfg=None) + for _ in range(num_stacks - 1) + ]) + + self.relu = nn.ReLU(inplace=True) + + def init_weights(self): + """Init module weights.""" + # Training Centripetal Model needs to reset parameters for Conv2d + super(HourglassNet, self).init_weights() + for m in self.modules(): + if isinstance(m, nn.Conv2d): + m.reset_parameters() + + def forward(self, x): + """Forward function.""" + inter_feat = self.stem(x) + out_feats = [] + + for ind in range(self.num_stacks): + single_hourglass = self.hourglass_modules[ind] + out_conv = self.out_convs[ind] + + hourglass_feat = single_hourglass(inter_feat) + out_feat = out_conv(hourglass_feat) + out_feats.append(out_feat) + + if ind < self.num_stacks - 1: + inter_feat = self.conv1x1s[ind]( + inter_feat) + self.remap_convs[ind]( + out_feat) + inter_feat = self.inters[ind](self.relu(inter_feat)) + + return out_feats diff --git a/mmdet/models/backbones/hrnet.py b/mmdet/models/backbones/hrnet.py new file mode 100644 index 0000000..292403e --- /dev/null +++ b/mmdet/models/backbones/hrnet.py @@ -0,0 +1,564 @@ +import warnings + +import torch.nn as nn +from mmcv.cnn import build_conv_layer, build_norm_layer +from mmcv.runner import BaseModule, ModuleList, Sequential +from torch.nn.modules.batchnorm import _BatchNorm + +from ..builder import BACKBONES +from .resnet import BasicBlock, Bottleneck + + +class HRModule(BaseModule): + """High-Resolution Module for HRNet. + + In this module, every branch has 4 BasicBlocks/Bottlenecks. Fusion/Exchange + is in this module. + """ + + def __init__(self, + num_branches, + blocks, + num_blocks, + in_channels, + num_channels, + multiscale_output=True, + with_cp=False, + conv_cfg=None, + norm_cfg=dict(type='BN'), + block_init_cfg=None, + init_cfg=None): + super(HRModule, self).__init__(init_cfg) + self.block_init_cfg = block_init_cfg + self._check_branches(num_branches, num_blocks, in_channels, + num_channels) + + self.in_channels = in_channels + self.num_branches = num_branches + + self.multiscale_output = multiscale_output + self.norm_cfg = norm_cfg + self.conv_cfg = conv_cfg + self.with_cp = with_cp + self.branches = self._make_branches(num_branches, blocks, num_blocks, + num_channels) + self.fuse_layers = self._make_fuse_layers() + self.relu = nn.ReLU(inplace=False) + + def _check_branches(self, num_branches, num_blocks, in_channels, + num_channels): + if num_branches != len(num_blocks): + error_msg = f'NUM_BRANCHES({num_branches}) ' \ + f'!= NUM_BLOCKS({len(num_blocks)})' + raise ValueError(error_msg) + + if num_branches != len(num_channels): + error_msg = f'NUM_BRANCHES({num_branches}) ' \ + f'!= NUM_CHANNELS({len(num_channels)})' + raise ValueError(error_msg) + + if num_branches != len(in_channels): + error_msg = f'NUM_BRANCHES({num_branches}) ' \ + f'!= NUM_INCHANNELS({len(in_channels)})' + raise ValueError(error_msg) + + def _make_one_branch(self, + branch_index, + block, + num_blocks, + num_channels, + stride=1): + downsample = None + if stride != 1 or \ + self.in_channels[branch_index] != \ + num_channels[branch_index] * block.expansion: + downsample = nn.Sequential( + build_conv_layer( + self.conv_cfg, + self.in_channels[branch_index], + num_channels[branch_index] * block.expansion, + kernel_size=1, + stride=stride, + bias=False), + build_norm_layer(self.norm_cfg, num_channels[branch_index] * + block.expansion)[1]) + + layers = [] + layers.append( + block( + self.in_channels[branch_index], + num_channels[branch_index], + stride, + downsample=downsample, + with_cp=self.with_cp, + norm_cfg=self.norm_cfg, + conv_cfg=self.conv_cfg, + init_cfg=self.block_init_cfg)) + self.in_channels[branch_index] = \ + num_channels[branch_index] * block.expansion + for i in range(1, num_blocks[branch_index]): + layers.append( + block( + self.in_channels[branch_index], + num_channels[branch_index], + with_cp=self.with_cp, + norm_cfg=self.norm_cfg, + conv_cfg=self.conv_cfg, + init_cfg=self.block_init_cfg)) + + return Sequential(*layers) + + def _make_branches(self, num_branches, block, num_blocks, num_channels): + branches = [] + + for i in range(num_branches): + branches.append( + self._make_one_branch(i, block, num_blocks, num_channels)) + + return ModuleList(branches) + + def _make_fuse_layers(self): + if self.num_branches == 1: + return None + + num_branches = self.num_branches + in_channels = self.in_channels + fuse_layers = [] + num_out_branches = num_branches if self.multiscale_output else 1 + for i in range(num_out_branches): + fuse_layer = [] + for j in range(num_branches): + if j > i: + fuse_layer.append( + nn.Sequential( + build_conv_layer( + self.conv_cfg, + in_channels[j], + in_channels[i], + kernel_size=1, + stride=1, + padding=0, + bias=False), + build_norm_layer(self.norm_cfg, in_channels[i])[1], + nn.Upsample( + scale_factor=2**(j - i), mode='nearest'))) + elif j == i: + fuse_layer.append(None) + else: + conv_downsamples = [] + for k in range(i - j): + if k == i - j - 1: + conv_downsamples.append( + nn.Sequential( + build_conv_layer( + self.conv_cfg, + in_channels[j], + in_channels[i], + kernel_size=3, + stride=2, + padding=1, + bias=False), + build_norm_layer(self.norm_cfg, + in_channels[i])[1])) + else: + conv_downsamples.append( + nn.Sequential( + build_conv_layer( + self.conv_cfg, + in_channels[j], + in_channels[j], + kernel_size=3, + stride=2, + padding=1, + bias=False), + build_norm_layer(self.norm_cfg, + in_channels[j])[1], + nn.ReLU(inplace=False))) + fuse_layer.append(nn.Sequential(*conv_downsamples)) + fuse_layers.append(nn.ModuleList(fuse_layer)) + + return nn.ModuleList(fuse_layers) + + def forward(self, x): + """Forward function.""" + if self.num_branches == 1: + return [self.branches[0](x[0])] + + for i in range(self.num_branches): + x[i] = self.branches[i](x[i]) + + x_fuse = [] + for i in range(len(self.fuse_layers)): + y = 0 + for j in range(self.num_branches): + if i == j: + y += x[j] + else: + y += self.fuse_layers[i][j](x[j]) + x_fuse.append(self.relu(y)) + return x_fuse + + +@BACKBONES.register_module() +class HRNet(BaseModule): + """HRNet backbone. + + High-Resolution Representations for Labeling Pixels and Regions + arXiv: https://arxiv.org/abs/1904.04514 + + Args: + extra (dict): detailed configuration for each stage of HRNet. + in_channels (int): Number of input image channels. Default: 3. + conv_cfg (dict): dictionary to construct and config conv layer. + norm_cfg (dict): dictionary to construct and config norm layer. + norm_eval (bool): Whether to set norm layers to eval mode, namely, + freeze running stats (mean and var). Note: Effect on Batch Norm + and its variants only. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. + zero_init_residual (bool): whether to use zero init for last norm layer + in resblocks to let them behave as identity. + pretrained (str, optional): model pretrained path. Default: None + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + + Example: + >>> from mmdet.models import HRNet + >>> import torch + >>> extra = dict( + >>> stage1=dict( + >>> num_modules=1, + >>> num_branches=1, + >>> block='BOTTLENECK', + >>> num_blocks=(4, ), + >>> num_channels=(64, )), + >>> stage2=dict( + >>> num_modules=1, + >>> num_branches=2, + >>> block='BASIC', + >>> num_blocks=(4, 4), + >>> num_channels=(32, 64)), + >>> stage3=dict( + >>> num_modules=4, + >>> num_branches=3, + >>> block='BASIC', + >>> num_blocks=(4, 4, 4), + >>> num_channels=(32, 64, 128)), + >>> stage4=dict( + >>> num_modules=3, + >>> num_branches=4, + >>> block='BASIC', + >>> num_blocks=(4, 4, 4, 4), + >>> num_channels=(32, 64, 128, 256))) + >>> self = HRNet(extra, in_channels=1) + >>> self.eval() + >>> inputs = torch.rand(1, 1, 32, 32) + >>> level_outputs = self.forward(inputs) + >>> for level_out in level_outputs: + ... print(tuple(level_out.shape)) + (1, 32, 8, 8) + (1, 64, 4, 4) + (1, 128, 2, 2) + (1, 256, 1, 1) + """ + + blocks_dict = {'BASIC': BasicBlock, 'BOTTLENECK': Bottleneck} + + def __init__(self, + extra, + in_channels=3, + conv_cfg=None, + norm_cfg=dict(type='BN'), + norm_eval=True, + with_cp=False, + zero_init_residual=False, + pretrained=None, + init_cfg=None): + super(HRNet, self).__init__(init_cfg) + + self.pretrained = pretrained + assert not (init_cfg and pretrained), \ + 'init_cfg and pretrained cannot be setting at the same time' + if isinstance(pretrained, str): + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' + 'please use "init_cfg" instead') + self.init_cfg = dict(type='Pretrained', checkpoint=pretrained) + elif pretrained is None: + if init_cfg is None: + self.init_cfg = [ + dict(type='Kaiming', layer='Conv2d'), + dict( + type='Constant', + val=1, + layer=['_BatchNorm', 'GroupNorm']) + ] + else: + raise TypeError('pretrained must be a str or None') + + self.extra = extra + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.norm_eval = norm_eval + self.with_cp = with_cp + self.zero_init_residual = zero_init_residual + + # stem net + self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, 64, postfix=1) + self.norm2_name, norm2 = build_norm_layer(self.norm_cfg, 64, postfix=2) + + self.conv1 = build_conv_layer( + self.conv_cfg, + in_channels, + 64, + kernel_size=3, + stride=2, + padding=1, + bias=False) + + self.add_module(self.norm1_name, norm1) + self.conv2 = build_conv_layer( + self.conv_cfg, + 64, + 64, + kernel_size=3, + stride=2, + padding=1, + bias=False) + + self.add_module(self.norm2_name, norm2) + self.relu = nn.ReLU(inplace=True) + + # stage 1 + self.stage1_cfg = self.extra['stage1'] + num_channels = self.stage1_cfg['num_channels'][0] + block_type = self.stage1_cfg['block'] + num_blocks = self.stage1_cfg['num_blocks'][0] + + block = self.blocks_dict[block_type] + stage1_out_channels = num_channels * block.expansion + self.layer1 = self._make_layer(block, 64, num_channels, num_blocks) + + # stage 2 + self.stage2_cfg = self.extra['stage2'] + num_channels = self.stage2_cfg['num_channels'] + block_type = self.stage2_cfg['block'] + + block = self.blocks_dict[block_type] + num_channels = [channel * block.expansion for channel in num_channels] + self.transition1 = self._make_transition_layer([stage1_out_channels], + num_channels) + self.stage2, pre_stage_channels = self._make_stage( + self.stage2_cfg, num_channels) + + # stage 3 + self.stage3_cfg = self.extra['stage3'] + num_channels = self.stage3_cfg['num_channels'] + block_type = self.stage3_cfg['block'] + + block = self.blocks_dict[block_type] + num_channels = [channel * block.expansion for channel in num_channels] + self.transition2 = self._make_transition_layer(pre_stage_channels, + num_channels) + self.stage3, pre_stage_channels = self._make_stage( + self.stage3_cfg, num_channels) + + # stage 4 + self.stage4_cfg = self.extra['stage4'] + num_channels = self.stage4_cfg['num_channels'] + block_type = self.stage4_cfg['block'] + + block = self.blocks_dict[block_type] + num_channels = [channel * block.expansion for channel in num_channels] + self.transition3 = self._make_transition_layer(pre_stage_channels, + num_channels) + self.stage4, pre_stage_channels = self._make_stage( + self.stage4_cfg, num_channels) + + @property + def norm1(self): + """nn.Module: the normalization layer named "norm1" """ + return getattr(self, self.norm1_name) + + @property + def norm2(self): + """nn.Module: the normalization layer named "norm2" """ + return getattr(self, self.norm2_name) + + def _make_transition_layer(self, num_channels_pre_layer, + num_channels_cur_layer): + num_branches_cur = len(num_channels_cur_layer) + num_branches_pre = len(num_channels_pre_layer) + + transition_layers = [] + for i in range(num_branches_cur): + if i < num_branches_pre: + if num_channels_cur_layer[i] != num_channels_pre_layer[i]: + transition_layers.append( + nn.Sequential( + build_conv_layer( + self.conv_cfg, + num_channels_pre_layer[i], + num_channels_cur_layer[i], + kernel_size=3, + stride=1, + padding=1, + bias=False), + build_norm_layer(self.norm_cfg, + num_channels_cur_layer[i])[1], + nn.ReLU(inplace=True))) + else: + transition_layers.append(None) + else: + conv_downsamples = [] + for j in range(i + 1 - num_branches_pre): + in_channels = num_channels_pre_layer[-1] + out_channels = num_channels_cur_layer[i] \ + if j == i - num_branches_pre else in_channels + conv_downsamples.append( + nn.Sequential( + build_conv_layer( + self.conv_cfg, + in_channels, + out_channels, + kernel_size=3, + stride=2, + padding=1, + bias=False), + build_norm_layer(self.norm_cfg, out_channels)[1], + nn.ReLU(inplace=True))) + transition_layers.append(nn.Sequential(*conv_downsamples)) + + return nn.ModuleList(transition_layers) + + def _make_layer(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + downsample = nn.Sequential( + build_conv_layer( + self.conv_cfg, + inplanes, + planes * block.expansion, + kernel_size=1, + stride=stride, + bias=False), + build_norm_layer(self.norm_cfg, planes * block.expansion)[1]) + + layers = [] + block_init_cfg = None + if self.pretrained is None and not hasattr( + self, 'init_cfg') and self.zero_init_residual: + if block is BasicBlock: + block_init_cfg = dict( + type='Constant', val=0, override=dict(name='norm2')) + elif block is Bottleneck: + block_init_cfg = dict( + type='Constant', val=0, override=dict(name='norm3')) + layers.append( + block( + inplanes, + planes, + stride, + downsample=downsample, + with_cp=self.with_cp, + norm_cfg=self.norm_cfg, + conv_cfg=self.conv_cfg, + init_cfg=block_init_cfg, + )) + inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append( + block( + inplanes, + planes, + with_cp=self.with_cp, + norm_cfg=self.norm_cfg, + conv_cfg=self.conv_cfg, + init_cfg=block_init_cfg)) + + return Sequential(*layers) + + def _make_stage(self, layer_config, in_channels, multiscale_output=True): + num_modules = layer_config['num_modules'] + num_branches = layer_config['num_branches'] + num_blocks = layer_config['num_blocks'] + num_channels = layer_config['num_channels'] + block = self.blocks_dict[layer_config['block']] + + hr_modules = [] + block_init_cfg = None + if self.pretrained is None and not hasattr( + self, 'init_cfg') and self.zero_init_residual: + if block is BasicBlock: + block_init_cfg = dict( + type='Constant', val=0, override=dict(name='norm2')) + elif block is Bottleneck: + block_init_cfg = dict( + type='Constant', val=0, override=dict(name='norm3')) + + for i in range(num_modules): + # multi_scale_output is only used for the last module + if not multiscale_output and i == num_modules - 1: + reset_multiscale_output = False + else: + reset_multiscale_output = True + + hr_modules.append( + HRModule( + num_branches, + block, + num_blocks, + in_channels, + num_channels, + reset_multiscale_output, + with_cp=self.with_cp, + norm_cfg=self.norm_cfg, + conv_cfg=self.conv_cfg, + block_init_cfg=block_init_cfg)) + + return Sequential(*hr_modules), in_channels + + def forward(self, x): + """Forward function.""" + x = self.conv1(x) + x = self.norm1(x) + x = self.relu(x) + x = self.conv2(x) + x = self.norm2(x) + x = self.relu(x) + x = self.layer1(x) + + x_list = [] + for i in range(self.stage2_cfg['num_branches']): + if self.transition1[i] is not None: + x_list.append(self.transition1[i](x)) + else: + x_list.append(x) + y_list = self.stage2(x_list) + + x_list = [] + for i in range(self.stage3_cfg['num_branches']): + if self.transition2[i] is not None: + x_list.append(self.transition2[i](y_list[-1])) + else: + x_list.append(y_list[i]) + y_list = self.stage3(x_list) + + x_list = [] + for i in range(self.stage4_cfg['num_branches']): + if self.transition3[i] is not None: + x_list.append(self.transition3[i](y_list[-1])) + else: + x_list.append(y_list[i]) + y_list = self.stage4(x_list) + + return y_list + + def train(self, mode=True): + """Convert the model into training mode will keeping the normalization + layer freezed.""" + super(HRNet, self).train(mode) + if mode and self.norm_eval: + for m in self.modules(): + # trick: eval have effect on BatchNorm only + if isinstance(m, _BatchNorm): + m.eval() diff --git a/mmdet/models/backbones/regnet.py b/mmdet/models/backbones/regnet.py new file mode 100644 index 0000000..cc77cd8 --- /dev/null +++ b/mmdet/models/backbones/regnet.py @@ -0,0 +1,355 @@ +import warnings + +import numpy as np +import torch.nn as nn +from mmcv.cnn import build_conv_layer, build_norm_layer + +from ..builder import BACKBONES +from .resnet import ResNet +from .resnext import Bottleneck + + +@BACKBONES.register_module() +class RegNet(ResNet): + """RegNet backbone. + + More details can be found in `paper `_ . + + Args: + arch (dict): The parameter of RegNets. + + - w0 (int): initial width + - wa (float): slope of width + - wm (float): quantization parameter to quantize the width + - depth (int): depth of the backbone + - group_w (int): width of group + - bot_mul (float): bottleneck ratio, i.e. expansion of bottleneck. + strides (Sequence[int]): Strides of the first block of each stage. + base_channels (int): Base channels after stem layer. + in_channels (int): Number of input image channels. Default: 3. + dilations (Sequence[int]): Dilation of each stage. + out_indices (Sequence[int]): Output from which stages. + style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two + layer is the 3x3 conv layer, otherwise the stride-two layer is + the first 1x1 conv layer. + frozen_stages (int): Stages to be frozen (all param fixed). -1 means + not freezing any parameters. + norm_cfg (dict): dictionary to construct and config norm layer. + norm_eval (bool): Whether to set norm layers to eval mode, namely, + freeze running stats (mean and var). Note: Effect on Batch Norm + and its variants only. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. + zero_init_residual (bool): whether to use zero init for last norm layer + in resblocks to let them behave as identity. + pretrained (str, optional): model pretrained path. Default: None + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + + Example: + >>> from mmdet.models import RegNet + >>> import torch + >>> self = RegNet( + arch=dict( + w0=88, + wa=26.31, + wm=2.25, + group_w=48, + depth=25, + bot_mul=1.0)) + >>> self.eval() + >>> inputs = torch.rand(1, 3, 32, 32) + >>> level_outputs = self.forward(inputs) + >>> for level_out in level_outputs: + ... print(tuple(level_out.shape)) + (1, 96, 8, 8) + (1, 192, 4, 4) + (1, 432, 2, 2) + (1, 1008, 1, 1) + """ + arch_settings = { + 'regnetx_400mf': + dict(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22, bot_mul=1.0), + 'regnetx_800mf': + dict(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16, bot_mul=1.0), + 'regnetx_1.6gf': + dict(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18, bot_mul=1.0), + 'regnetx_3.2gf': + dict(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25, bot_mul=1.0), + 'regnetx_4.0gf': + dict(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23, bot_mul=1.0), + 'regnetx_6.4gf': + dict(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17, bot_mul=1.0), + 'regnetx_8.0gf': + dict(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23, bot_mul=1.0), + 'regnetx_12gf': + dict(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, bot_mul=1.0), + } + + def __init__(self, + arch, + in_channels=3, + stem_channels=32, + base_channels=32, + strides=(2, 2, 2, 2), + dilations=(1, 1, 1, 1), + out_indices=(0, 1, 2, 3), + style='pytorch', + deep_stem=False, + avg_down=False, + frozen_stages=-1, + conv_cfg=None, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + dcn=None, + stage_with_dcn=(False, False, False, False), + plugins=None, + with_cp=False, + zero_init_residual=True, + pretrained=None, + init_cfg=None): + super(ResNet, self).__init__(init_cfg) + + # Generate RegNet parameters first + if isinstance(arch, str): + assert arch in self.arch_settings, \ + f'"arch": "{arch}" is not one of the' \ + ' arch_settings' + arch = self.arch_settings[arch] + elif not isinstance(arch, dict): + raise ValueError('Expect "arch" to be either a string ' + f'or a dict, got {type(arch)}') + + widths, num_stages = self.generate_regnet( + arch['w0'], + arch['wa'], + arch['wm'], + arch['depth'], + ) + # Convert to per stage format + stage_widths, stage_blocks = self.get_stages_from_blocks(widths) + # Generate group widths and bot muls + group_widths = [arch['group_w'] for _ in range(num_stages)] + self.bottleneck_ratio = [arch['bot_mul'] for _ in range(num_stages)] + # Adjust the compatibility of stage_widths and group_widths + stage_widths, group_widths = self.adjust_width_group( + stage_widths, self.bottleneck_ratio, group_widths) + + # Group params by stage + self.stage_widths = stage_widths + self.group_widths = group_widths + self.depth = sum(stage_blocks) + self.stem_channels = stem_channels + self.base_channels = base_channels + self.num_stages = num_stages + assert num_stages >= 1 and num_stages <= 4 + self.strides = strides + self.dilations = dilations + assert len(strides) == len(dilations) == num_stages + self.out_indices = out_indices + assert max(out_indices) < num_stages + self.style = style + self.deep_stem = deep_stem + self.avg_down = avg_down + self.frozen_stages = frozen_stages + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.with_cp = with_cp + self.norm_eval = norm_eval + self.dcn = dcn + self.stage_with_dcn = stage_with_dcn + if dcn is not None: + assert len(stage_with_dcn) == num_stages + self.plugins = plugins + self.zero_init_residual = zero_init_residual + self.block = Bottleneck + expansion_bak = self.block.expansion + self.block.expansion = 1 + self.stage_blocks = stage_blocks[:num_stages] + + self._make_stem_layer(in_channels, stem_channels) + + block_init_cfg = None + assert not (init_cfg and pretrained), \ + 'init_cfg and pretrained cannot be setting at the same time' + if isinstance(pretrained, str): + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' + 'please use "init_cfg" instead') + self.init_cfg = dict(type='Pretrained', checkpoint=pretrained) + elif pretrained is None: + if init_cfg is None: + self.init_cfg = [ + dict(type='Kaiming', layer='Conv2d'), + dict( + type='Constant', + val=1, + layer=['_BatchNorm', 'GroupNorm']) + ] + if self.zero_init_residual: + block_init_cfg = dict( + type='Constant', val=0, override=dict(name='norm3')) + else: + raise TypeError('pretrained must be a str or None') + + self.inplanes = stem_channels + self.res_layers = [] + for i, num_blocks in enumerate(self.stage_blocks): + stride = self.strides[i] + dilation = self.dilations[i] + group_width = self.group_widths[i] + width = int(round(self.stage_widths[i] * self.bottleneck_ratio[i])) + stage_groups = width // group_width + + dcn = self.dcn if self.stage_with_dcn[i] else None + if self.plugins is not None: + stage_plugins = self.make_stage_plugins(self.plugins, i) + else: + stage_plugins = None + + res_layer = self.make_res_layer( + block=self.block, + inplanes=self.inplanes, + planes=self.stage_widths[i], + num_blocks=num_blocks, + stride=stride, + dilation=dilation, + style=self.style, + avg_down=self.avg_down, + with_cp=self.with_cp, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + dcn=dcn, + plugins=stage_plugins, + groups=stage_groups, + base_width=group_width, + base_channels=self.stage_widths[i], + init_cfg=block_init_cfg) + self.inplanes = self.stage_widths[i] + layer_name = f'layer{i + 1}' + self.add_module(layer_name, res_layer) + self.res_layers.append(layer_name) + + self._freeze_stages() + + self.feat_dim = stage_widths[-1] + self.block.expansion = expansion_bak + + def _make_stem_layer(self, in_channels, base_channels): + self.conv1 = build_conv_layer( + self.conv_cfg, + in_channels, + base_channels, + kernel_size=3, + stride=2, + padding=1, + bias=False) + self.norm1_name, norm1 = build_norm_layer( + self.norm_cfg, base_channels, postfix=1) + self.add_module(self.norm1_name, norm1) + self.relu = nn.ReLU(inplace=True) + + def generate_regnet(self, + initial_width, + width_slope, + width_parameter, + depth, + divisor=8): + """Generates per block width from RegNet parameters. + + Args: + initial_width ([int]): Initial width of the backbone + width_slope ([float]): Slope of the quantized linear function + width_parameter ([int]): Parameter used to quantize the width. + depth ([int]): Depth of the backbone. + divisor (int, optional): The divisor of channels. Defaults to 8. + + Returns: + list, int: return a list of widths of each stage and the number \ + of stages + """ + assert width_slope >= 0 + assert initial_width > 0 + assert width_parameter > 1 + assert initial_width % divisor == 0 + widths_cont = np.arange(depth) * width_slope + initial_width + ks = np.round( + np.log(widths_cont / initial_width) / np.log(width_parameter)) + widths = initial_width * np.power(width_parameter, ks) + widths = np.round(np.divide(widths, divisor)) * divisor + num_stages = len(np.unique(widths)) + widths, widths_cont = widths.astype(int).tolist(), widths_cont.tolist() + return widths, num_stages + + @staticmethod + def quantize_float(number, divisor): + """Converts a float to closest non-zero int divisible by divisor. + + Args: + number (int): Original number to be quantized. + divisor (int): Divisor used to quantize the number. + + Returns: + int: quantized number that is divisible by devisor. + """ + return int(round(number / divisor) * divisor) + + def adjust_width_group(self, widths, bottleneck_ratio, groups): + """Adjusts the compatibility of widths and groups. + + Args: + widths (list[int]): Width of each stage. + bottleneck_ratio (float): Bottleneck ratio. + groups (int): number of groups in each stage + + Returns: + tuple(list): The adjusted widths and groups of each stage. + """ + bottleneck_width = [ + int(w * b) for w, b in zip(widths, bottleneck_ratio) + ] + groups = [min(g, w_bot) for g, w_bot in zip(groups, bottleneck_width)] + bottleneck_width = [ + self.quantize_float(w_bot, g) + for w_bot, g in zip(bottleneck_width, groups) + ] + widths = [ + int(w_bot / b) + for w_bot, b in zip(bottleneck_width, bottleneck_ratio) + ] + return widths, groups + + def get_stages_from_blocks(self, widths): + """Gets widths/stage_blocks of network at each stage. + + Args: + widths (list[int]): Width in each stage. + + Returns: + tuple(list): width and depth of each stage + """ + width_diff = [ + width != width_prev + for width, width_prev in zip(widths + [0], [0] + widths) + ] + stage_widths = [ + width for width, diff in zip(widths, width_diff[:-1]) if diff + ] + stage_blocks = np.diff([ + depth for depth, diff in zip(range(len(width_diff)), width_diff) + if diff + ]).tolist() + return stage_widths, stage_blocks + + def forward(self, x): + """Forward function.""" + x = self.conv1(x) + x = self.norm1(x) + x = self.relu(x) + + outs = [] + for i, layer_name in enumerate(self.res_layers): + res_layer = getattr(self, layer_name) + x = res_layer(x) + if i in self.out_indices: + outs.append(x) + return tuple(outs) diff --git a/mmdet/models/backbones/res2net.py b/mmdet/models/backbones/res2net.py new file mode 100644 index 0000000..84951f0 --- /dev/null +++ b/mmdet/models/backbones/res2net.py @@ -0,0 +1,326 @@ +import math + +import torch +import torch.nn as nn +import torch.utils.checkpoint as cp +from mmcv.cnn import build_conv_layer, build_norm_layer +from mmcv.runner import Sequential + +from ..builder import BACKBONES +from .resnet import Bottleneck as _Bottleneck +from .resnet import ResNet + + +class Bottle2neck(_Bottleneck): + expansion = 4 + + def __init__(self, + inplanes, + planes, + scales=4, + base_width=26, + base_channels=64, + stage_type='normal', + **kwargs): + """Bottle2neck block for Res2Net. + + If style is "pytorch", the stride-two layer is the 3x3 conv layer, if + it is "caffe", the stride-two layer is the first 1x1 conv layer. + """ + super(Bottle2neck, self).__init__(inplanes, planes, **kwargs) + assert scales > 1, 'Res2Net degenerates to ResNet when scales = 1.' + width = int(math.floor(self.planes * (base_width / base_channels))) + + self.norm1_name, norm1 = build_norm_layer( + self.norm_cfg, width * scales, postfix=1) + self.norm3_name, norm3 = build_norm_layer( + self.norm_cfg, self.planes * self.expansion, postfix=3) + + self.conv1 = build_conv_layer( + self.conv_cfg, + self.inplanes, + width * scales, + kernel_size=1, + stride=self.conv1_stride, + bias=False) + self.add_module(self.norm1_name, norm1) + + if stage_type == 'stage' and self.conv2_stride != 1: + self.pool = nn.AvgPool2d( + kernel_size=3, stride=self.conv2_stride, padding=1) + convs = [] + bns = [] + + fallback_on_stride = False + if self.with_dcn: + fallback_on_stride = self.dcn.pop('fallback_on_stride', False) + if not self.with_dcn or fallback_on_stride: + for i in range(scales - 1): + convs.append( + build_conv_layer( + self.conv_cfg, + width, + width, + kernel_size=3, + stride=self.conv2_stride, + padding=self.dilation, + dilation=self.dilation, + bias=False)) + bns.append( + build_norm_layer(self.norm_cfg, width, postfix=i + 1)[1]) + self.convs = nn.ModuleList(convs) + self.bns = nn.ModuleList(bns) + else: + assert self.conv_cfg is None, 'conv_cfg must be None for DCN' + for i in range(scales - 1): + convs.append( + build_conv_layer( + self.dcn, + width, + width, + kernel_size=3, + stride=self.conv2_stride, + padding=self.dilation, + dilation=self.dilation, + bias=False)) + bns.append( + build_norm_layer(self.norm_cfg, width, postfix=i + 1)[1]) + self.convs = nn.ModuleList(convs) + self.bns = nn.ModuleList(bns) + + self.conv3 = build_conv_layer( + self.conv_cfg, + width * scales, + self.planes * self.expansion, + kernel_size=1, + bias=False) + self.add_module(self.norm3_name, norm3) + + self.stage_type = stage_type + self.scales = scales + self.width = width + delattr(self, 'conv2') + delattr(self, self.norm2_name) + + def forward(self, x): + """Forward function.""" + + def _inner_forward(x): + identity = x + + out = self.conv1(x) + out = self.norm1(out) + out = self.relu(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv1_plugin_names) + + spx = torch.split(out, self.width, 1) + sp = self.convs[0](spx[0].contiguous()) + sp = self.relu(self.bns[0](sp)) + out = sp + for i in range(1, self.scales - 1): + if self.stage_type == 'stage': + sp = spx[i] + else: + sp = sp + spx[i] + sp = self.convs[i](sp.contiguous()) + sp = self.relu(self.bns[i](sp)) + out = torch.cat((out, sp), 1) + + if self.stage_type == 'normal' or self.conv2_stride == 1: + out = torch.cat((out, spx[self.scales - 1]), 1) + elif self.stage_type == 'stage': + out = torch.cat((out, self.pool(spx[self.scales - 1])), 1) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv2_plugin_names) + + out = self.conv3(out) + out = self.norm3(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv3_plugin_names) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + + return out + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(_inner_forward, x) + else: + out = _inner_forward(x) + + out = self.relu(out) + + return out + + +class Res2Layer(Sequential): + """Res2Layer to build Res2Net style backbone. + + Args: + block (nn.Module): block used to build ResLayer. + inplanes (int): inplanes of block. + planes (int): planes of block. + num_blocks (int): number of blocks. + stride (int): stride of the first block. Default: 1 + avg_down (bool): Use AvgPool instead of stride conv when + downsampling in the bottle2neck. Default: False + conv_cfg (dict): dictionary to construct and config conv layer. + Default: None + norm_cfg (dict): dictionary to construct and config norm layer. + Default: dict(type='BN') + scales (int): Scales used in Res2Net. Default: 4 + base_width (int): Basic width of each scale. Default: 26 + """ + + def __init__(self, + block, + inplanes, + planes, + num_blocks, + stride=1, + avg_down=True, + conv_cfg=None, + norm_cfg=dict(type='BN'), + scales=4, + base_width=26, + **kwargs): + self.block = block + + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.AvgPool2d( + kernel_size=stride, + stride=stride, + ceil_mode=True, + count_include_pad=False), + build_conv_layer( + conv_cfg, + inplanes, + planes * block.expansion, + kernel_size=1, + stride=1, + bias=False), + build_norm_layer(norm_cfg, planes * block.expansion)[1], + ) + + layers = [] + layers.append( + block( + inplanes=inplanes, + planes=planes, + stride=stride, + downsample=downsample, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + scales=scales, + base_width=base_width, + stage_type='stage', + **kwargs)) + inplanes = planes * block.expansion + for i in range(1, num_blocks): + layers.append( + block( + inplanes=inplanes, + planes=planes, + stride=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + scales=scales, + base_width=base_width, + **kwargs)) + super(Res2Layer, self).__init__(*layers) + + +@BACKBONES.register_module() +class Res2Net(ResNet): + """Res2Net backbone. + + Args: + scales (int): Scales used in Res2Net. Default: 4 + base_width (int): Basic width of each scale. Default: 26 + depth (int): Depth of res2net, from {50, 101, 152}. + in_channels (int): Number of input image channels. Default: 3. + num_stages (int): Res2net stages. Default: 4. + strides (Sequence[int]): Strides of the first block of each stage. + dilations (Sequence[int]): Dilation of each stage. + out_indices (Sequence[int]): Output from which stages. + style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two + layer is the 3x3 conv layer, otherwise the stride-two layer is + the first 1x1 conv layer. + deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv + avg_down (bool): Use AvgPool instead of stride conv when + downsampling in the bottle2neck. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. + norm_cfg (dict): Dictionary to construct and config norm layer. + norm_eval (bool): Whether to set norm layers to eval mode, namely, + freeze running stats (mean and var). Note: Effect on Batch Norm + and its variants only. + plugins (list[dict]): List of plugins for stages, each dict contains: + + - cfg (dict, required): Cfg dict to build plugin. + - position (str, required): Position inside block to insert + plugin, options are 'after_conv1', 'after_conv2', 'after_conv3'. + - stages (tuple[bool], optional): Stages to apply plugin, length + should be same as 'num_stages'. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. + zero_init_residual (bool): Whether to use zero init for last norm layer + in resblocks to let them behave as identity. + pretrained (str, optional): model pretrained path. Default: None + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + + Example: + >>> from mmdet.models import Res2Net + >>> import torch + >>> self = Res2Net(depth=50, scales=4, base_width=26) + >>> self.eval() + >>> inputs = torch.rand(1, 3, 32, 32) + >>> level_outputs = self.forward(inputs) + >>> for level_out in level_outputs: + ... print(tuple(level_out.shape)) + (1, 256, 8, 8) + (1, 512, 4, 4) + (1, 1024, 2, 2) + (1, 2048, 1, 1) + """ + + arch_settings = { + 50: (Bottle2neck, (3, 4, 6, 3)), + 101: (Bottle2neck, (3, 4, 23, 3)), + 152: (Bottle2neck, (3, 8, 36, 3)) + } + + def __init__(self, + scales=4, + base_width=26, + style='pytorch', + deep_stem=True, + avg_down=True, + pretrained=None, + init_cfg=None, + **kwargs): + self.scales = scales + self.base_width = base_width + super(Res2Net, self).__init__( + style='pytorch', + deep_stem=True, + avg_down=True, + pretrained=pretrained, + init_cfg=init_cfg, + **kwargs) + + def make_res_layer(self, **kwargs): + return Res2Layer( + scales=self.scales, + base_width=self.base_width, + base_channels=self.base_channels, + **kwargs) diff --git a/mmdet/models/backbones/resnest.py b/mmdet/models/backbones/resnest.py new file mode 100644 index 0000000..0fd65ae --- /dev/null +++ b/mmdet/models/backbones/resnest.py @@ -0,0 +1,321 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as cp +from mmcv.cnn import build_conv_layer, build_norm_layer +from mmcv.runner import BaseModule + +from ..builder import BACKBONES +from ..utils import ResLayer +from .resnet import Bottleneck as _Bottleneck +from .resnet import ResNetV1d + + +class RSoftmax(nn.Module): + """Radix Softmax module in ``SplitAttentionConv2d``. + + Args: + radix (int): Radix of input. + groups (int): Groups of input. + """ + + def __init__(self, radix, groups): + super().__init__() + self.radix = radix + self.groups = groups + + def forward(self, x): + batch = x.size(0) + if self.radix > 1: + x = x.view(batch, self.groups, self.radix, -1).transpose(1, 2) + x = F.softmax(x, dim=1) + x = x.reshape(batch, -1) + else: + x = torch.sigmoid(x) + return x + + +class SplitAttentionConv2d(BaseModule): + """Split-Attention Conv2d in ResNeSt. + + Args: + in_channels (int): Number of channels in the input feature map. + channels (int): Number of intermediate channels. + kernel_size (int | tuple[int]): Size of the convolution kernel. + stride (int | tuple[int]): Stride of the convolution. + padding (int | tuple[int]): Zero-padding added to both sides of + dilation (int | tuple[int]): Spacing between kernel elements. + groups (int): Number of blocked connections from input channels to + output channels. + groups (int): Same as nn.Conv2d. + radix (int): Radix of SpltAtConv2d. Default: 2 + reduction_factor (int): Reduction factor of inter_channels. Default: 4. + conv_cfg (dict): Config dict for convolution layer. Default: None, + which means using conv2d. + norm_cfg (dict): Config dict for normalization layer. Default: None. + dcn (dict): Config dict for DCN. Default: None. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + in_channels, + channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + radix=2, + reduction_factor=4, + conv_cfg=None, + norm_cfg=dict(type='BN'), + dcn=None, + init_cfg=None): + super(SplitAttentionConv2d, self).__init__(init_cfg) + inter_channels = max(in_channels * radix // reduction_factor, 32) + self.radix = radix + self.groups = groups + self.channels = channels + self.with_dcn = dcn is not None + self.dcn = dcn + fallback_on_stride = False + if self.with_dcn: + fallback_on_stride = self.dcn.pop('fallback_on_stride', False) + if self.with_dcn and not fallback_on_stride: + assert conv_cfg is None, 'conv_cfg must be None for DCN' + conv_cfg = dcn + self.conv = build_conv_layer( + conv_cfg, + in_channels, + channels * radix, + kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups * radix, + bias=False) + # To be consistent with original implementation, starting from 0 + self.norm0_name, norm0 = build_norm_layer( + norm_cfg, channels * radix, postfix=0) + self.add_module(self.norm0_name, norm0) + self.relu = nn.ReLU(inplace=True) + self.fc1 = build_conv_layer( + None, channels, inter_channels, 1, groups=self.groups) + self.norm1_name, norm1 = build_norm_layer( + norm_cfg, inter_channels, postfix=1) + self.add_module(self.norm1_name, norm1) + self.fc2 = build_conv_layer( + None, inter_channels, channels * radix, 1, groups=self.groups) + self.rsoftmax = RSoftmax(radix, groups) + + @property + def norm0(self): + """nn.Module: the normalization layer named "norm0" """ + return getattr(self, self.norm0_name) + + @property + def norm1(self): + """nn.Module: the normalization layer named "norm1" """ + return getattr(self, self.norm1_name) + + def forward(self, x): + x = self.conv(x) + x = self.norm0(x) + x = self.relu(x) + + batch, rchannel = x.shape[:2] + batch = x.size(0) + if self.radix > 1: + splits = x.view(batch, self.radix, -1, *x.shape[2:]) + gap = splits.sum(dim=1) + else: + gap = x + gap = F.adaptive_avg_pool2d(gap, 1) + gap = self.fc1(gap) + + gap = self.norm1(gap) + gap = self.relu(gap) + + atten = self.fc2(gap) + atten = self.rsoftmax(atten).view(batch, -1, 1, 1) + + if self.radix > 1: + attens = atten.view(batch, self.radix, -1, *atten.shape[2:]) + out = torch.sum(attens * splits, dim=1) + else: + out = atten * x + return out.contiguous() + + +class Bottleneck(_Bottleneck): + """Bottleneck block for ResNeSt. + + Args: + inplane (int): Input planes of this block. + planes (int): Middle planes of this block. + groups (int): Groups of conv2. + base_width (int): Base of width in terms of base channels. Default: 4. + base_channels (int): Base of channels for calculating width. + Default: 64. + radix (int): Radix of SpltAtConv2d. Default: 2 + reduction_factor (int): Reduction factor of inter_channels in + SplitAttentionConv2d. Default: 4. + avg_down_stride (bool): Whether to use average pool for stride in + Bottleneck. Default: True. + kwargs (dict): Key word arguments for base class. + """ + expansion = 4 + + def __init__(self, + inplanes, + planes, + groups=1, + base_width=4, + base_channels=64, + radix=2, + reduction_factor=4, + avg_down_stride=True, + **kwargs): + """Bottleneck block for ResNeSt.""" + super(Bottleneck, self).__init__(inplanes, planes, **kwargs) + + if groups == 1: + width = self.planes + else: + width = math.floor(self.planes * + (base_width / base_channels)) * groups + + self.avg_down_stride = avg_down_stride and self.conv2_stride > 1 + + self.norm1_name, norm1 = build_norm_layer( + self.norm_cfg, width, postfix=1) + self.norm3_name, norm3 = build_norm_layer( + self.norm_cfg, self.planes * self.expansion, postfix=3) + + self.conv1 = build_conv_layer( + self.conv_cfg, + self.inplanes, + width, + kernel_size=1, + stride=self.conv1_stride, + bias=False) + self.add_module(self.norm1_name, norm1) + self.with_modulated_dcn = False + self.conv2 = SplitAttentionConv2d( + width, + width, + kernel_size=3, + stride=1 if self.avg_down_stride else self.conv2_stride, + padding=self.dilation, + dilation=self.dilation, + groups=groups, + radix=radix, + reduction_factor=reduction_factor, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + dcn=self.dcn) + delattr(self, self.norm2_name) + + if self.avg_down_stride: + self.avd_layer = nn.AvgPool2d(3, self.conv2_stride, padding=1) + + self.conv3 = build_conv_layer( + self.conv_cfg, + width, + self.planes * self.expansion, + kernel_size=1, + bias=False) + self.add_module(self.norm3_name, norm3) + + def forward(self, x): + + def _inner_forward(x): + identity = x + + out = self.conv1(x) + out = self.norm1(out) + out = self.relu(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv1_plugin_names) + + out = self.conv2(out) + + if self.avg_down_stride: + out = self.avd_layer(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv2_plugin_names) + + out = self.conv3(out) + out = self.norm3(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv3_plugin_names) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + + return out + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(_inner_forward, x) + else: + out = _inner_forward(x) + + out = self.relu(out) + + return out + + +@BACKBONES.register_module() +class ResNeSt(ResNetV1d): + """ResNeSt backbone. + + Args: + groups (int): Number of groups of Bottleneck. Default: 1 + base_width (int): Base width of Bottleneck. Default: 4 + radix (int): Radix of SplitAttentionConv2d. Default: 2 + reduction_factor (int): Reduction factor of inter_channels in + SplitAttentionConv2d. Default: 4. + avg_down_stride (bool): Whether to use average pool for stride in + Bottleneck. Default: True. + kwargs (dict): Keyword arguments for ResNet. + """ + + arch_settings = { + 50: (Bottleneck, (3, 4, 6, 3)), + 101: (Bottleneck, (3, 4, 23, 3)), + 152: (Bottleneck, (3, 8, 36, 3)), + 200: (Bottleneck, (3, 24, 36, 3)) + } + + def __init__(self, + groups=1, + base_width=4, + radix=2, + reduction_factor=4, + avg_down_stride=True, + **kwargs): + self.groups = groups + self.base_width = base_width + self.radix = radix + self.reduction_factor = reduction_factor + self.avg_down_stride = avg_down_stride + super(ResNeSt, self).__init__(**kwargs) + + def make_res_layer(self, **kwargs): + """Pack all blocks in a stage into a ``ResLayer``.""" + return ResLayer( + groups=self.groups, + base_width=self.base_width, + base_channels=self.base_channels, + radix=self.radix, + reduction_factor=self.reduction_factor, + avg_down_stride=self.avg_down_stride, + **kwargs) diff --git a/mmdet/models/backbones/resnet.py b/mmdet/models/backbones/resnet.py new file mode 100644 index 0000000..ce05f91 --- /dev/null +++ b/mmdet/models/backbones/resnet.py @@ -0,0 +1,674 @@ +import warnings + +import torch.nn as nn +import torch.utils.checkpoint as cp +from mmcv.cnn import build_conv_layer, build_norm_layer, build_plugin_layer +from mmcv.runner import BaseModule +from torch.nn.modules.batchnorm import _BatchNorm + +from ..builder import BACKBONES +from ..utils import ResLayer + + +class BasicBlock(BaseModule): + expansion = 1 + + def __init__(self, + inplanes, + planes, + stride=1, + dilation=1, + downsample=None, + style='pytorch', + with_cp=False, + conv_cfg=None, + norm_cfg=dict(type='BN'), + dcn=None, + plugins=None, + init_cfg=None): + super(BasicBlock, self).__init__(init_cfg) + assert dcn is None, 'Not implemented yet.' + assert plugins is None, 'Not implemented yet.' + + self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1) + self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2) + + self.conv1 = build_conv_layer( + conv_cfg, + inplanes, + planes, + 3, + stride=stride, + padding=dilation, + dilation=dilation, + bias=False) + self.add_module(self.norm1_name, norm1) + self.conv2 = build_conv_layer( + conv_cfg, planes, planes, 3, padding=1, bias=False) + self.add_module(self.norm2_name, norm2) + + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + self.dilation = dilation + self.with_cp = with_cp + + @property + def norm1(self): + """nn.Module: normalization layer after the first convolution layer""" + return getattr(self, self.norm1_name) + + @property + def norm2(self): + """nn.Module: normalization layer after the second convolution layer""" + return getattr(self, self.norm2_name) + + def forward(self, x): + """Forward function.""" + + def _inner_forward(x): + identity = x + + out = self.conv1(x) + out = self.norm1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.norm2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + + return out + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(_inner_forward, x) + else: + out = _inner_forward(x) + + out = self.relu(out) + + return out + + +class Bottleneck(BaseModule): + expansion = 4 + + def __init__(self, + inplanes, + planes, + stride=1, + dilation=1, + downsample=None, + style='pytorch', + with_cp=False, + conv_cfg=None, + norm_cfg=dict(type='BN'), + dcn=None, + plugins=None, + init_cfg=None): + """Bottleneck block for ResNet. + + If style is "pytorch", the stride-two layer is the 3x3 conv layer, if + it is "caffe", the stride-two layer is the first 1x1 conv layer. + """ + super(Bottleneck, self).__init__(init_cfg) + assert style in ['pytorch', 'caffe'] + assert dcn is None or isinstance(dcn, dict) + assert plugins is None or isinstance(plugins, list) + if plugins is not None: + allowed_position = ['after_conv1', 'after_conv2', 'after_conv3'] + assert all(p['position'] in allowed_position for p in plugins) + + self.inplanes = inplanes + self.planes = planes + self.stride = stride + self.dilation = dilation + self.style = style + self.with_cp = with_cp + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.dcn = dcn + self.with_dcn = dcn is not None + self.plugins = plugins + self.with_plugins = plugins is not None + + if self.with_plugins: + # collect plugins for conv1/conv2/conv3 + self.after_conv1_plugins = [ + plugin['cfg'] for plugin in plugins + if plugin['position'] == 'after_conv1' + ] + self.after_conv2_plugins = [ + plugin['cfg'] for plugin in plugins + if plugin['position'] == 'after_conv2' + ] + self.after_conv3_plugins = [ + plugin['cfg'] for plugin in plugins + if plugin['position'] == 'after_conv3' + ] + + if self.style == 'pytorch': + self.conv1_stride = 1 + self.conv2_stride = stride + else: + self.conv1_stride = stride + self.conv2_stride = 1 + + self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1) + self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2) + self.norm3_name, norm3 = build_norm_layer( + norm_cfg, planes * self.expansion, postfix=3) + + self.conv1 = build_conv_layer( + conv_cfg, + inplanes, + planes, + kernel_size=1, + stride=self.conv1_stride, + bias=False) + self.add_module(self.norm1_name, norm1) + fallback_on_stride = False + if self.with_dcn: + fallback_on_stride = dcn.pop('fallback_on_stride', False) + if not self.with_dcn or fallback_on_stride: + self.conv2 = build_conv_layer( + conv_cfg, + planes, + planes, + kernel_size=3, + stride=self.conv2_stride, + padding=dilation, + dilation=dilation, + bias=False) + else: + assert self.conv_cfg is None, 'conv_cfg must be None for DCN' + self.conv2 = build_conv_layer( + dcn, + planes, + planes, + kernel_size=3, + stride=self.conv2_stride, + padding=dilation, + dilation=dilation, + bias=False) + + self.add_module(self.norm2_name, norm2) + self.conv3 = build_conv_layer( + conv_cfg, + planes, + planes * self.expansion, + kernel_size=1, + bias=False) + self.add_module(self.norm3_name, norm3) + + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + + if self.with_plugins: + self.after_conv1_plugin_names = self.make_block_plugins( + planes, self.after_conv1_plugins) + self.after_conv2_plugin_names = self.make_block_plugins( + planes, self.after_conv2_plugins) + self.after_conv3_plugin_names = self.make_block_plugins( + planes * self.expansion, self.after_conv3_plugins) + + def make_block_plugins(self, in_channels, plugins): + """make plugins for block. + + Args: + in_channels (int): Input channels of plugin. + plugins (list[dict]): List of plugins cfg to build. + + Returns: + list[str]: List of the names of plugin. + """ + assert isinstance(plugins, list) + plugin_names = [] + for plugin in plugins: + plugin = plugin.copy() + name, layer = build_plugin_layer( + plugin, + in_channels=in_channels, + postfix=plugin.pop('postfix', '')) + assert not hasattr(self, name), f'duplicate plugin {name}' + self.add_module(name, layer) + plugin_names.append(name) + return plugin_names + + def forward_plugin(self, x, plugin_names): + out = x + for name in plugin_names: + out = getattr(self, name)(x) + return out + + @property + def norm1(self): + """nn.Module: normalization layer after the first convolution layer""" + return getattr(self, self.norm1_name) + + @property + def norm2(self): + """nn.Module: normalization layer after the second convolution layer""" + return getattr(self, self.norm2_name) + + @property + def norm3(self): + """nn.Module: normalization layer after the third convolution layer""" + return getattr(self, self.norm3_name) + + def forward(self, x): + """Forward function.""" + + def _inner_forward(x): + identity = x + out = self.conv1(x) + out = self.norm1(out) + out = self.relu(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv1_plugin_names) + + out = self.conv2(out) + out = self.norm2(out) + out = self.relu(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv2_plugin_names) + + out = self.conv3(out) + out = self.norm3(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv3_plugin_names) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + + return out + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(_inner_forward, x) + else: + out = _inner_forward(x) + + out = self.relu(out) + + return out + + +@BACKBONES.register_module() +class ResNet(BaseModule): + """ResNet backbone. + + Args: + depth (int): Depth of resnet, from {18, 34, 50, 101, 152}. + stem_channels (int | None): Number of stem channels. If not specified, + it will be the same as `base_channels`. Default: None. + base_channels (int): Number of base channels of res layer. Default: 64. + in_channels (int): Number of input image channels. Default: 3. + num_stages (int): Resnet stages. Default: 4. + strides (Sequence[int]): Strides of the first block of each stage. + dilations (Sequence[int]): Dilation of each stage. + out_indices (Sequence[int]): Output from which stages. + style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two + layer is the 3x3 conv layer, otherwise the stride-two layer is + the first 1x1 conv layer. + deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv + avg_down (bool): Use AvgPool instead of stride conv when + downsampling in the bottleneck. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. + norm_cfg (dict): Dictionary to construct and config norm layer. + norm_eval (bool): Whether to set norm layers to eval mode, namely, + freeze running stats (mean and var). Note: Effect on Batch Norm + and its variants only. + plugins (list[dict]): List of plugins for stages, each dict contains: + + - cfg (dict, required): Cfg dict to build plugin. + - position (str, required): Position inside block to insert + plugin, options are 'after_conv1', 'after_conv2', 'after_conv3'. + - stages (tuple[bool], optional): Stages to apply plugin, length + should be same as 'num_stages'. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. + zero_init_residual (bool): Whether to use zero init for last norm layer + in resblocks to let them behave as identity. + pretrained (str, optional): model pretrained path. Default: None + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + + Example: + >>> from mmdet.models import ResNet + >>> import torch + >>> self = ResNet(depth=18) + >>> self.eval() + >>> inputs = torch.rand(1, 3, 32, 32) + >>> level_outputs = self.forward(inputs) + >>> for level_out in level_outputs: + ... print(tuple(level_out.shape)) + (1, 64, 8, 8) + (1, 128, 4, 4) + (1, 256, 2, 2) + (1, 512, 1, 1) + """ + + arch_settings = { + 18: (BasicBlock, (2, 2, 2, 2)), + 34: (BasicBlock, (3, 4, 6, 3)), + 50: (Bottleneck, (3, 4, 6, 3)), + 101: (Bottleneck, (3, 4, 23, 3)), + 152: (Bottleneck, (3, 8, 36, 3)) + } + + def __init__(self, + depth, + in_channels=3, + stem_channels=None, + base_channels=64, + num_stages=4, + strides=(1, 2, 2, 2), + dilations=(1, 1, 1, 1), + out_indices=(0, 1, 2, 3), + style='pytorch', + deep_stem=False, + avg_down=False, + frozen_stages=-1, + conv_cfg=None, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + dcn=None, + stage_with_dcn=(False, False, False, False), + plugins=None, + with_cp=False, + zero_init_residual=True, + pretrained=None, + init_cfg=None): + super(ResNet, self).__init__(init_cfg) + self.zero_init_residual = zero_init_residual + if depth not in self.arch_settings: + raise KeyError(f'invalid depth {depth} for resnet') + + block_init_cfg = None + assert not (init_cfg and pretrained), \ + 'init_cfg and pretrained cannot be setting at the same time' + if isinstance(pretrained, str): + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' + 'please use "init_cfg" instead') + self.init_cfg = dict(type='Pretrained', checkpoint=pretrained) + elif pretrained is None: + if init_cfg is None: + self.init_cfg = [ + dict(type='Kaiming', layer='Conv2d'), + dict( + type='Constant', + val=1, + layer=['_BatchNorm', 'GroupNorm']) + ] + block = self.arch_settings[depth][0] + if self.zero_init_residual: + if block is BasicBlock: + block_init_cfg = dict( + type='Constant', + val=0, + override=dict(name='norm2')) + elif block is Bottleneck: + block_init_cfg = dict( + type='Constant', + val=0, + override=dict(name='norm3')) + else: + raise TypeError('pretrained must be a str or None') + + self.depth = depth + if stem_channels is None: + stem_channels = base_channels + self.stem_channels = stem_channels + self.base_channels = base_channels + self.num_stages = num_stages + assert num_stages >= 1 and num_stages <= 4 + self.strides = strides + self.dilations = dilations + assert len(strides) == len(dilations) == num_stages + self.out_indices = out_indices + assert max(out_indices) < num_stages + self.style = style + self.deep_stem = deep_stem + self.avg_down = avg_down + self.frozen_stages = frozen_stages + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.with_cp = with_cp + self.norm_eval = norm_eval + self.dcn = dcn + self.stage_with_dcn = stage_with_dcn + if dcn is not None: + assert len(stage_with_dcn) == num_stages + self.plugins = plugins + self.block, stage_blocks = self.arch_settings[depth] + self.stage_blocks = stage_blocks[:num_stages] + self.inplanes = stem_channels + + self._make_stem_layer(in_channels, stem_channels) + + self.res_layers = [] + for i, num_blocks in enumerate(self.stage_blocks): + stride = strides[i] + dilation = dilations[i] + dcn = self.dcn if self.stage_with_dcn[i] else None + if plugins is not None: + stage_plugins = self.make_stage_plugins(plugins, i) + else: + stage_plugins = None + planes = base_channels * 2**i + res_layer = self.make_res_layer( + block=self.block, + inplanes=self.inplanes, + planes=planes, + num_blocks=num_blocks, + stride=stride, + dilation=dilation, + style=self.style, + avg_down=self.avg_down, + with_cp=with_cp, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + dcn=dcn, + plugins=stage_plugins, + init_cfg=block_init_cfg) + self.inplanes = planes * self.block.expansion + layer_name = f'layer{i + 1}' + self.add_module(layer_name, res_layer) + self.res_layers.append(layer_name) + + self._freeze_stages() + + self.feat_dim = self.block.expansion * base_channels * 2**( + len(self.stage_blocks) - 1) + + def make_stage_plugins(self, plugins, stage_idx): + """Make plugins for ResNet ``stage_idx`` th stage. + + Currently we support to insert ``context_block``, + ``empirical_attention_block``, ``nonlocal_block`` into the backbone + like ResNet/ResNeXt. They could be inserted after conv1/conv2/conv3 of + Bottleneck. + + An example of plugins format could be: + + Examples: + >>> plugins=[ + ... dict(cfg=dict(type='xxx', arg1='xxx'), + ... stages=(False, True, True, True), + ... position='after_conv2'), + ... dict(cfg=dict(type='yyy'), + ... stages=(True, True, True, True), + ... position='after_conv3'), + ... dict(cfg=dict(type='zzz', postfix='1'), + ... stages=(True, True, True, True), + ... position='after_conv3'), + ... dict(cfg=dict(type='zzz', postfix='2'), + ... stages=(True, True, True, True), + ... position='after_conv3') + ... ] + >>> self = ResNet(depth=18) + >>> stage_plugins = self.make_stage_plugins(plugins, 0) + >>> assert len(stage_plugins) == 3 + + Suppose ``stage_idx=0``, the structure of blocks in the stage would be: + + .. code-block:: none + + conv1-> conv2->conv3->yyy->zzz1->zzz2 + + Suppose 'stage_idx=1', the structure of blocks in the stage would be: + + .. code-block:: none + + conv1-> conv2->xxx->conv3->yyy->zzz1->zzz2 + + If stages is missing, the plugin would be applied to all stages. + + Args: + plugins (list[dict]): List of plugins cfg to build. The postfix is + required if multiple same type plugins are inserted. + stage_idx (int): Index of stage to build + + Returns: + list[dict]: Plugins for current stage + """ + stage_plugins = [] + for plugin in plugins: + plugin = plugin.copy() + stages = plugin.pop('stages', None) + assert stages is None or len(stages) == self.num_stages + # whether to insert plugin into current stage + if stages is None or stages[stage_idx]: + stage_plugins.append(plugin) + + return stage_plugins + + def make_res_layer(self, **kwargs): + """Pack all blocks in a stage into a ``ResLayer``.""" + return ResLayer(**kwargs) + + @property + def norm1(self): + """nn.Module: the normalization layer named "norm1" """ + return getattr(self, self.norm1_name) + + def _make_stem_layer(self, in_channels, stem_channels): + if self.deep_stem: + self.stem = nn.Sequential( + build_conv_layer( + self.conv_cfg, + in_channels, + stem_channels // 2, + kernel_size=3, + stride=2, + padding=1, + bias=False), + build_norm_layer(self.norm_cfg, stem_channels // 2)[1], + nn.ReLU(inplace=True), + build_conv_layer( + self.conv_cfg, + stem_channels // 2, + stem_channels // 2, + kernel_size=3, + stride=1, + padding=1, + bias=False), + build_norm_layer(self.norm_cfg, stem_channels // 2)[1], + nn.ReLU(inplace=True), + build_conv_layer( + self.conv_cfg, + stem_channels // 2, + stem_channels, + kernel_size=3, + stride=1, + padding=1, + bias=False), + build_norm_layer(self.norm_cfg, stem_channels)[1], + nn.ReLU(inplace=True)) + else: + self.conv1 = build_conv_layer( + self.conv_cfg, + in_channels, + stem_channels, + kernel_size=7, + stride=2, + padding=3, + bias=False) + self.norm1_name, norm1 = build_norm_layer( + self.norm_cfg, stem_channels, postfix=1) + self.add_module(self.norm1_name, norm1) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + def _freeze_stages(self): + if self.frozen_stages >= 0: + if self.deep_stem: + self.stem.eval() + for param in self.stem.parameters(): + param.requires_grad = False + else: + self.norm1.eval() + for m in [self.conv1, self.norm1]: + for param in m.parameters(): + param.requires_grad = False + + for i in range(1, self.frozen_stages + 1): + m = getattr(self, f'layer{i}') + m.eval() + for param in m.parameters(): + param.requires_grad = False + + def forward(self, x): + """Forward function.""" + if self.deep_stem: + x = self.stem(x) + else: + x = self.conv1(x) + x = self.norm1(x) + x = self.relu(x) + x = self.maxpool(x) + outs = [] + max_i = max(self.out_indices) + for i, layer_name in enumerate(self.res_layers): + if i > max_i: + break + res_layer = getattr(self, layer_name) + x = res_layer(x) + if i in self.out_indices: + outs.append(x) + return tuple(outs) + + def train(self, mode=True): + """Convert the model into training mode while keep normalization layer + freezed.""" + super(ResNet, self).train(mode) + self._freeze_stages() + if mode and self.norm_eval: + for m in self.modules(): + # trick: eval have effect on BatchNorm only + if isinstance(m, _BatchNorm): + m.eval() + + +@BACKBONES.register_module() +class ResNetV1d(ResNet): + r"""ResNetV1d variant described in `Bag of Tricks + `_. + + Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in + the input stem with three 3x3 convs. And in the downsampling block, a 2x2 + avg_pool with stride 2 is added before conv, whose stride is changed to 1. + """ + + def __init__(self, **kwargs): + super(ResNetV1d, self).__init__( + deep_stem=True, avg_down=True, **kwargs) diff --git a/mmdet/models/backbones/resnext.py b/mmdet/models/backbones/resnext.py new file mode 100644 index 0000000..6dbcbd5 --- /dev/null +++ b/mmdet/models/backbones/resnext.py @@ -0,0 +1,153 @@ +import math + +from mmcv.cnn import build_conv_layer, build_norm_layer + +from ..builder import BACKBONES +from ..utils import ResLayer +from .resnet import Bottleneck as _Bottleneck +from .resnet import ResNet + + +class Bottleneck(_Bottleneck): + expansion = 4 + + def __init__(self, + inplanes, + planes, + groups=1, + base_width=4, + base_channels=64, + **kwargs): + """Bottleneck block for ResNeXt. + + If style is "pytorch", the stride-two layer is the 3x3 conv layer, if + it is "caffe", the stride-two layer is the first 1x1 conv layer. + """ + super(Bottleneck, self).__init__(inplanes, planes, **kwargs) + + if groups == 1: + width = self.planes + else: + width = math.floor(self.planes * + (base_width / base_channels)) * groups + + self.norm1_name, norm1 = build_norm_layer( + self.norm_cfg, width, postfix=1) + self.norm2_name, norm2 = build_norm_layer( + self.norm_cfg, width, postfix=2) + self.norm3_name, norm3 = build_norm_layer( + self.norm_cfg, self.planes * self.expansion, postfix=3) + + self.conv1 = build_conv_layer( + self.conv_cfg, + self.inplanes, + width, + kernel_size=1, + stride=self.conv1_stride, + bias=False) + self.add_module(self.norm1_name, norm1) + fallback_on_stride = False + self.with_modulated_dcn = False + if self.with_dcn: + fallback_on_stride = self.dcn.pop('fallback_on_stride', False) + if not self.with_dcn or fallback_on_stride: + self.conv2 = build_conv_layer( + self.conv_cfg, + width, + width, + kernel_size=3, + stride=self.conv2_stride, + padding=self.dilation, + dilation=self.dilation, + groups=groups, + bias=False) + else: + assert self.conv_cfg is None, 'conv_cfg must be None for DCN' + self.conv2 = build_conv_layer( + self.dcn, + width, + width, + kernel_size=3, + stride=self.conv2_stride, + padding=self.dilation, + dilation=self.dilation, + groups=groups, + bias=False) + + self.add_module(self.norm2_name, norm2) + self.conv3 = build_conv_layer( + self.conv_cfg, + width, + self.planes * self.expansion, + kernel_size=1, + bias=False) + self.add_module(self.norm3_name, norm3) + + if self.with_plugins: + self._del_block_plugins(self.after_conv1_plugin_names + + self.after_conv2_plugin_names + + self.after_conv3_plugin_names) + self.after_conv1_plugin_names = self.make_block_plugins( + width, self.after_conv1_plugins) + self.after_conv2_plugin_names = self.make_block_plugins( + width, self.after_conv2_plugins) + self.after_conv3_plugin_names = self.make_block_plugins( + self.planes * self.expansion, self.after_conv3_plugins) + + def _del_block_plugins(self, plugin_names): + """delete plugins for block if exist. + + Args: + plugin_names (list[str]): List of plugins name to delete. + """ + assert isinstance(plugin_names, list) + for plugin_name in plugin_names: + del self._modules[plugin_name] + + +@BACKBONES.register_module() +class ResNeXt(ResNet): + """ResNeXt backbone. + + Args: + depth (int): Depth of resnet, from {18, 34, 50, 101, 152}. + in_channels (int): Number of input image channels. Default: 3. + num_stages (int): Resnet stages. Default: 4. + groups (int): Group of resnext. + base_width (int): Base width of resnext. + strides (Sequence[int]): Strides of the first block of each stage. + dilations (Sequence[int]): Dilation of each stage. + out_indices (Sequence[int]): Output from which stages. + style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two + layer is the 3x3 conv layer, otherwise the stride-two layer is + the first 1x1 conv layer. + frozen_stages (int): Stages to be frozen (all param fixed). -1 means + not freezing any parameters. + norm_cfg (dict): dictionary to construct and config norm layer. + norm_eval (bool): Whether to set norm layers to eval mode, namely, + freeze running stats (mean and var). Note: Effect on Batch Norm + and its variants only. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. + zero_init_residual (bool): whether to use zero init for last norm layer + in resblocks to let them behave as identity. + """ + + arch_settings = { + 50: (Bottleneck, (3, 4, 6, 3)), + 101: (Bottleneck, (3, 4, 23, 3)), + 152: (Bottleneck, (3, 8, 36, 3)) + } + + def __init__(self, groups=1, base_width=4, **kwargs): + self.groups = groups + self.base_width = base_width + super(ResNeXt, self).__init__(**kwargs) + + def make_res_layer(self, **kwargs): + """Pack all blocks in a stage into a ``ResLayer``""" + return ResLayer( + groups=self.groups, + base_width=self.base_width, + base_channels=self.base_channels, + **kwargs) diff --git a/mmdet/models/backbones/ssd_vgg.py b/mmdet/models/backbones/ssd_vgg.py new file mode 100644 index 0000000..40d4988 --- /dev/null +++ b/mmdet/models/backbones/ssd_vgg.py @@ -0,0 +1,179 @@ +import warnings + +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import VGG +from mmcv.runner import BaseModule, Sequential + +from ..builder import BACKBONES + + +@BACKBONES.register_module() +class SSDVGG(VGG, BaseModule): + """VGG Backbone network for single-shot-detection. + + Args: + input_size (int): width and height of input, from {300, 512}. + depth (int): Depth of vgg, from {11, 13, 16, 19}. + out_indices (Sequence[int]): Output from which stages. + pretrained (str, optional): model pretrained path. Default: None + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + + Example: + >>> self = SSDVGG(input_size=300, depth=11) + >>> self.eval() + >>> inputs = torch.rand(1, 3, 300, 300) + >>> level_outputs = self.forward(inputs) + >>> for level_out in level_outputs: + ... print(tuple(level_out.shape)) + (1, 1024, 19, 19) + (1, 512, 10, 10) + (1, 256, 5, 5) + (1, 256, 3, 3) + (1, 256, 1, 1) + """ + extra_setting = { + 300: (256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256), + 512: (256, 'S', 512, 128, 'S', 256, 128, 'S', 256, 128, 'S', 256, 128), + } + + def __init__(self, + input_size, + depth, + with_last_pool=False, + ceil_mode=True, + out_indices=(3, 4), + out_feature_indices=(22, 34), + l2_norm_scale=20., + pretrained=None, + init_cfg=None): + # TODO: in_channels for mmcv.VGG + super(SSDVGG, self).__init__( + depth, + with_last_pool=with_last_pool, + ceil_mode=ceil_mode, + out_indices=out_indices) + assert input_size in (300, 512) + self.input_size = input_size + + self.features.add_module( + str(len(self.features)), + nn.MaxPool2d(kernel_size=3, stride=1, padding=1)) + self.features.add_module( + str(len(self.features)), + nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6)) + self.features.add_module( + str(len(self.features)), nn.ReLU(inplace=True)) + self.features.add_module( + str(len(self.features)), nn.Conv2d(1024, 1024, kernel_size=1)) + self.features.add_module( + str(len(self.features)), nn.ReLU(inplace=True)) + self.out_feature_indices = out_feature_indices + + self.inplanes = 1024 + self.extra = self._make_extra_layers(self.extra_setting[input_size]) + self.l2_norm = L2Norm( + self.features[out_feature_indices[0] - 1].out_channels, + l2_norm_scale) + + assert not (init_cfg and pretrained), \ + 'init_cfg and pretrained cannot be setting at the same time' + if isinstance(pretrained, str): + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' + 'please use "init_cfg" instead') + self.init_cfg = [dict(type='Pretrained', checkpoint=pretrained)] + elif pretrained is None: + if init_cfg is None: + self.init_cfg = [ + dict(type='Kaiming', layer='Conv2d'), + dict(type='Constant', val=1, layer='BatchNorm2d'), + dict(type='Normal', std=0.01, layer='Linear'), + ] + else: + raise TypeError('pretrained must be a str or None') + + if init_cfg is None: + self.init_cfg += [ + dict( + type='Xavier', + distribution='uniform', + override=dict(name='extra')), + dict( + type='Constant', + val=self.l2_norm.scale, + override=dict(name='l2_norm')) + ] + + def init_weights(self, pretrained=None): + super(VGG, self).init_weights() + + def forward(self, x): + """Forward function.""" + outs = [] + for i, layer in enumerate(self.features): + x = layer(x) + if i in self.out_feature_indices: + outs.append(x) + for i, layer in enumerate(self.extra): + x = F.relu(layer(x), inplace=True) + if i % 2 == 1: + outs.append(x) + outs[0] = self.l2_norm(outs[0]) + if len(outs) == 1: + return outs[0] + else: + return tuple(outs) + + def _make_extra_layers(self, outplanes): + layers = [] + kernel_sizes = (1, 3) + num_layers = 0 + outplane = None + for i in range(len(outplanes)): + if self.inplanes == 'S': + self.inplanes = outplane + continue + k = kernel_sizes[num_layers % 2] + if outplanes[i] == 'S': + outplane = outplanes[i + 1] + conv = nn.Conv2d( + self.inplanes, outplane, k, stride=2, padding=1) + else: + outplane = outplanes[i] + conv = nn.Conv2d( + self.inplanes, outplane, k, stride=1, padding=0) + layers.append(conv) + self.inplanes = outplanes[i] + num_layers += 1 + if self.input_size == 512: + layers.append(nn.Conv2d(self.inplanes, 256, 4, padding=1)) + + return Sequential(*layers) + + +class L2Norm(nn.Module): + + def __init__(self, n_dims, scale=20., eps=1e-10): + """L2 normalization layer. + + Args: + n_dims (int): Number of dimensions to be normalized + scale (float, optional): Defaults to 20.. + eps (float, optional): Used to avoid division by zero. + Defaults to 1e-10. + """ + super(L2Norm, self).__init__() + self.n_dims = n_dims + self.weight = nn.Parameter(torch.Tensor(self.n_dims)) + self.eps = eps + self.scale = scale + + def forward(self, x): + """Forward function.""" + # normalization layer convert to FP32 in FP16 training + x_float = x.float() + norm = x_float.pow(2).sum(1, keepdim=True).sqrt() + self.eps + return (self.weight[None, :, None, None].float().expand_as(x_float) * + x_float / norm).type_as(x) diff --git a/mmdet/models/backbones/swin.py b/mmdet/models/backbones/swin.py new file mode 100644 index 0000000..7bead6e --- /dev/null +++ b/mmdet/models/backbones/swin.py @@ -0,0 +1,836 @@ +import warnings +from collections import OrderedDict +from copy import deepcopy +from itertools import repeat +import collections.abc + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as cp +from mmcv.cnn import build_norm_layer, constant_init +from mmcv.cnn.bricks.transformer import FFN +from mmcv.runner import BaseModule, ModuleList, _load_checkpoint + +from ...utils import get_root_logger +from ..builder import BACKBONES +from ..utils.transformer import PatchEmbed, PatchMerging + + +def _ntuple(n): + + def parse(x): + if isinstance(x, collections.abc.Iterable): + return x + return tuple(repeat(x, n)) + + return parse + + +to_1tuple = _ntuple(1) +to_2tuple = _ntuple(2) +to_3tuple = _ntuple(3) +to_4tuple = _ntuple(4) +to_ntuple = _ntuple + + +def swin_converter(ckpt): + + new_ckpt = OrderedDict() + + def correct_unfold_reduction_order(x): + out_channel, in_channel = x.shape + x = x.reshape(out_channel, 4, in_channel // 4) + x = x[:, [0, 2, 1, 3], :].transpose(1, + 2).reshape(out_channel, in_channel) + return x + + def correct_unfold_norm_order(x): + in_channel = x.shape[0] + x = x.reshape(4, in_channel // 4) + x = x[[0, 2, 1, 3], :].transpose(0, 1).reshape(in_channel) + return x + + for k, v in ckpt.items(): + if k.startswith('head'): + continue + elif k.startswith('layers'): + new_v = v + if 'attn.' in k: + new_k = k.replace('attn.', 'attn.w_msa.') + elif 'mlp.' in k: + if 'mlp.fc1.' in k: + new_k = k.replace('mlp.fc1.', 'ffn.layers.0.0.') + elif 'mlp.fc2.' in k: + new_k = k.replace('mlp.fc2.', 'ffn.layers.1.') + else: + new_k = k.replace('mlp.', 'ffn.') + elif 'downsample' in k: + new_k = k + if 'reduction.' in k: + new_v = correct_unfold_reduction_order(v) + elif 'norm.' in k: + new_v = correct_unfold_norm_order(v) + else: + new_k = k + new_k = new_k.replace('layers', 'stages', 1) + elif k.startswith('patch_embed'): + new_v = v + if 'proj' in k: + new_k = k.replace('proj', 'projection') + else: + new_k = k + else: + new_v = v + new_k = k + + new_ckpt['backbone.' + new_k] = new_v + + return new_ckpt + + +class WindowMSA(BaseModule): + """Window based multi-head self-attention (W-MSA) module with relative + position bias. + + Args: + embed_dims (int): Number of input channels. + num_heads (int): Number of attention heads. + window_size (tuple[int]): The height and width of the window. + qkv_bias (bool, optional): If True, add a learnable bias to q, k, v. + Default: True. + qk_scale (float | None, optional): Override default qk scale of + head_dim ** -0.5 if set. Default: None. + attn_drop_rate (float, optional): Dropout ratio of attention weight. + Default: 0.0 + proj_drop_rate (float, optional): Dropout ratio of output. Default: 0. + init_cfg (dict | None, optional): The Config for initialization. + Default: None. + """ + + def __init__(self, + embed_dims, + num_heads, + window_size, + qkv_bias=True, + qk_scale=None, + attn_drop_rate=0., + proj_drop_rate=0., + init_cfg=None): + + super().__init__() + self.embed_dims = embed_dims + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_embed_dims = embed_dims // num_heads + self.scale = qk_scale or head_embed_dims**-0.5 + self.init_cfg = init_cfg + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), + num_heads)) # 2*Wh-1 * 2*Ww-1, nH + + # About 2x faster than original impl + Wh, Ww = self.window_size + rel_index_coords = self.double_step_seq(2 * Ww - 1, Wh, 1, Ww) + rel_position_index = rel_index_coords + rel_index_coords.T + rel_position_index = rel_position_index.flip(1).contiguous() + self.register_buffer('relative_position_index', rel_position_index) + + self.qkv = nn.Linear(embed_dims, embed_dims * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop_rate) + self.proj = nn.Linear(embed_dims, embed_dims) + self.proj_drop = nn.Dropout(proj_drop_rate) + + self.softmax = nn.Softmax(dim=-1) + + def init_weights(self): + nn.init.trunc_normal_(self.relative_position_bias_table, std=0.02) + + def forward(self, x, mask=None): + """ + Args: + + x (tensor): input features with shape of (num_windows*B, N, C) + mask (tensor | None, Optional): mask with shape of (num_windows, + Wh*Ww, Wh*Ww), value should be between (-inf, 0]. + """ + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, + C // self.num_heads).permute(2, 0, 3, 1, 4) + # make torchscript happy (cannot use tensor as tuple) + q, k, v = qkv[0], qkv[1], qkv[2] + + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + + relative_position_bias = self.relative_position_bias_table[ + self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], + self.window_size[0] * self.window_size[1], + -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute( + 2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B // nW, nW, self.num_heads, N, + N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + @staticmethod + def double_step_seq(step1, len1, step2, len2): + seq1 = torch.arange(0, step1 * len1, step1) + seq2 = torch.arange(0, step2 * len2, step2) + return (seq1[:, None] + seq2[None, :]).reshape(1, -1) + + +class ShiftWindowMSA(BaseModule): + """Shifted Window Multihead Self-Attention Module. + + Args: + embed_dims (int): Number of input channels. + num_heads (int): Number of attention heads. + window_size (int): The height and width of the window. + shift_size (int, optional): The shift step of each window towards + right-bottom. If zero, act as regular window-msa. Defaults to 0. + qkv_bias (bool, optional): If True, add a learnable bias to q, k, v. + Default: True + qk_scale (float | None, optional): Override default qk scale of + head_dim ** -0.5 if set. Defaults: None. + attn_drop_rate (float, optional): Dropout ratio of attention weight. + Defaults: 0. + proj_drop_rate (float, optional): Dropout ratio of output. + Defaults: 0. + dropout_layer (dict, optional): The dropout_layer used before output. + Defaults: dict(type='DropPath', drop_prob=0.). + init_cfg (dict, optional): The extra config for initialization. + Default: None. + """ + + def __init__(self, + embed_dims, + num_heads, + window_size, + shift_size=0, + qkv_bias=True, + qk_scale=None, + attn_drop_rate=0, + proj_drop_rate=0, + dropout_layer=dict(type='DropPath', drop_prob=0.), + init_cfg=None): + super().__init__(init_cfg) + + self.window_size = window_size + self.shift_size = shift_size + assert 0 <= self.shift_size < self.window_size + + self.w_msa = WindowMSA( + embed_dims=embed_dims, + num_heads=num_heads, + window_size=to_2tuple(window_size), + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop_rate=attn_drop_rate, + proj_drop_rate=proj_drop_rate, + init_cfg=None) + + # self.drop = build_dropout(dropout_layer) + self.drop = nn.Identity() + + def forward(self, query, hw_shape): + B, L, C = query.shape + H, W = hw_shape + assert L == H * W, 'input feature has wrong size' + query = query.view(B, H, W, C) + + # pad feature maps to multiples of window size + pad_r = (self.window_size - W % self.window_size) % self.window_size + pad_b = (self.window_size - H % self.window_size) % self.window_size + query = F.pad(query, (0, 0, 0, pad_r, 0, pad_b)) + H_pad, W_pad = query.shape[1], query.shape[2] + + # cyclic shift + if self.shift_size > 0: + shifted_query = torch.roll( + query, + shifts=(-self.shift_size, -self.shift_size), + dims=(1, 2)) + + # calculate attention mask for SW-MSA + img_mask = torch.zeros((1, H_pad, W_pad, 1), device=query.device) + h_slices = (slice(0, -self.window_size), + slice(-self.window_size, + -self.shift_size), slice(-self.shift_size, None)) + w_slices = (slice(0, -self.window_size), + slice(-self.window_size, + -self.shift_size), slice(-self.shift_size, None)) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + # nW, window_size, window_size, 1 + mask_windows = self.window_partition(img_mask) + mask_windows = mask_windows.view( + -1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, + float(-100.0)).masked_fill( + attn_mask == 0, float(0.0)) + else: + shifted_query = query + attn_mask = None + + # nW*B, window_size, window_size, C + query_windows = self.window_partition(shifted_query) + # nW*B, window_size*window_size, C + query_windows = query_windows.view(-1, self.window_size**2, C) + + # W-MSA/SW-MSA (nW*B, window_size*window_size, C) + attn_windows = self.w_msa(query_windows, mask=attn_mask) + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, + self.window_size, C) + + # B H' W' C + shifted_x = self.window_reverse(attn_windows, H_pad, W_pad) + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll( + shifted_x, + shifts=(self.shift_size, self.shift_size), + dims=(1, 2)) + else: + x = shifted_x + + if pad_r > 0 or pad_b: + x = x[:, :H, :W, :].contiguous() + + x = x.view(B, H * W, C) + + x = self.drop(x) + return x + + def window_reverse(self, windows, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + window_size = self.window_size + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, + window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + def window_partition(self, x): + """ + Args: + x: (B, H, W, C) + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + window_size = self.window_size + x = x.view(B, H // window_size, window_size, W // window_size, + window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous() + windows = windows.view(-1, window_size, window_size, C) + return windows + + +class SwinBlock(BaseModule): + """" + Args: + embed_dims (int): The feature dimension. + num_heads (int): Parallel attention heads. + feedforward_channels (int): The hidden dimension for FFNs. + window_size (int, optional): The local window scale. Default: 7. + shift (bool, optional): whether to shift window or not. Default False. + qkv_bias (bool, optional): enable bias for qkv if True. Default: True. + qk_scale (float | None, optional): Override default qk scale of + head_dim ** -0.5 if set. Default: None. + drop_rate (float, optional): Dropout rate. Default: 0. + attn_drop_rate (float, optional): Attention dropout rate. Default: 0. + drop_path_rate (float, optional): Stochastic depth rate. Default: 0. + act_cfg (dict, optional): The config dict of activation function. + Default: dict(type='GELU'). + norm_cfg (dict, optional): The config dict of normalization. + Default: dict(type='LN'). + with_cp (bool, optional): Use checkpoint or not. Using checkpoint + will save some memory while slowing down the training speed. + Default: False. + init_cfg (dict | list | None, optional): The init config. + Default: None. + """ + + def __init__(self, + embed_dims, + num_heads, + feedforward_channels, + window_size=7, + shift=False, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0., + act_cfg=dict(type='GELU'), + norm_cfg=dict(type='LN'), + with_cp=False, + init_cfg=None): + + super(SwinBlock, self).__init__() + + self.init_cfg = init_cfg + self.with_cp = with_cp + + self.norm1 = build_norm_layer(norm_cfg, embed_dims)[1] + self.attn = ShiftWindowMSA( + embed_dims=embed_dims, + num_heads=num_heads, + window_size=window_size, + shift_size=window_size // 2 if shift else 0, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop_rate=attn_drop_rate, + proj_drop_rate=drop_rate, + dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate), + init_cfg=None) + + self.norm2 = build_norm_layer(norm_cfg, embed_dims)[1] + self.ffn = FFN( + embed_dims=embed_dims, + feedforward_channels=feedforward_channels, + num_fcs=2, + ffn_drop=drop_rate, + dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate), + act_cfg=act_cfg, + add_identity=True, + init_cfg=None) + + def forward(self, x, hw_shape): + + def _inner_forward(x): + identity = x + x = self.norm1(x) + x = self.attn(x, hw_shape) + + x = x + identity + + identity = x + x = self.norm2(x) + x = self.ffn(x, identity=identity) + + return x + + if self.with_cp and x.requires_grad: + x = cp.checkpoint(_inner_forward, x) + else: + x = _inner_forward(x) + + return x + + +class SwinBlockSequence(BaseModule): + """Implements one stage in Swin Transformer. + + Args: + embed_dims (int): The feature dimension. + num_heads (int): Parallel attention heads. + feedforward_channels (int): The hidden dimension for FFNs. + depth (int): The number of blocks in this stage. + window_size (int, optional): The local window scale. Default: 7. + qkv_bias (bool, optional): enable bias for qkv if True. Default: True. + qk_scale (float | None, optional): Override default qk scale of + head_dim ** -0.5 if set. Default: None. + drop_rate (float, optional): Dropout rate. Default: 0. + attn_drop_rate (float, optional): Attention dropout rate. Default: 0. + drop_path_rate (float | list[float], optional): Stochastic depth + rate. Default: 0. + downsample (BaseModule | None, optional): The downsample operation + module. Default: None. + act_cfg (dict, optional): The config dict of activation function. + Default: dict(type='GELU'). + norm_cfg (dict, optional): The config dict of normalization. + Default: dict(type='LN'). + with_cp (bool, optional): Use checkpoint or not. Using checkpoint + will save some memory while slowing down the training speed. + Default: False. + init_cfg (dict | list | None, optional): The init config. + Default: None. + """ + + def __init__(self, + embed_dims, + num_heads, + feedforward_channels, + depth, + window_size=7, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0., + downsample=None, + act_cfg=dict(type='GELU'), + norm_cfg=dict(type='LN'), + with_cp=False, + init_cfg=None): + super().__init__(init_cfg=init_cfg) + + if isinstance(drop_path_rate, list): + drop_path_rates = drop_path_rate + assert len(drop_path_rates) == depth + else: + drop_path_rates = [deepcopy(drop_path_rate) for _ in range(depth)] + + self.blocks = ModuleList() + for i in range(depth): + block = SwinBlock( + embed_dims=embed_dims, + num_heads=num_heads, + feedforward_channels=feedforward_channels, + window_size=window_size, + shift=False if i % 2 == 0 else True, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop_rate=drop_rate, + attn_drop_rate=attn_drop_rate, + drop_path_rate=drop_path_rates[i], + act_cfg=act_cfg, + norm_cfg=norm_cfg, + with_cp=with_cp, + init_cfg=None) + self.blocks.append(block) + + self.downsample = downsample + + def forward(self, x, hw_shape): + for block in self.blocks: + x = block(x, hw_shape) + + if self.downsample: + x_down, down_hw_shape = self.downsample(x, hw_shape) + return x_down, down_hw_shape, x, hw_shape + else: + return x, hw_shape, x, hw_shape + + +@BACKBONES.register_module() +class SwinTransformer(BaseModule): + """ Swin Transformer + A PyTorch implement of : `Swin Transformer: + Hierarchical Vision Transformer using Shifted Windows` - + https://arxiv.org/abs/2103.14030 + + Inspiration from + https://github.com/microsoft/Swin-Transformer + + Args: + pretrain_img_size (int | tuple[int]): The size of input image when + pretrain. Defaults: 224. + in_channels (int): The num of input channels. + Defaults: 3. + embed_dims (int): The feature dimension. Default: 96. + patch_size (int | tuple[int]): Patch size. Default: 4. + window_size (int): Window size. Default: 7. + mlp_ratio (int): Ratio of mlp hidden dim to embedding dim. + Default: 4. + depths (tuple[int]): Depths of each Swin Transformer stage. + Default: (2, 2, 6, 2). + num_heads (tuple[int]): Parallel attention heads of each Swin + Transformer stage. Default: (3, 6, 12, 24). + strides (tuple[int]): The patch merging or patch embedding stride of + each Swin Transformer stage. (In swin, we set kernel size equal to + stride.) Default: (4, 2, 2, 2). + out_indices (tuple[int]): Output from which stages. + Default: (0, 1, 2, 3). + qkv_bias (bool, optional): If True, add a learnable bias to query, key, + value. Default: True + qk_scale (float | None, optional): Override default qk scale of + head_dim ** -0.5 if set. Default: None. + patch_norm (bool): If add a norm layer for patch embed and patch + merging. Default: True. + drop_rate (float): Dropout rate. Defaults: 0. + attn_drop_rate (float): Attention dropout rate. Default: 0. + drop_path_rate (float): Stochastic depth rate. Defaults: 0.1. + use_abs_pos_embed (bool): If True, add absolute position embedding to + the patch embedding. Defaults: False. + act_cfg (dict): Config dict for activation layer. + Default: dict(type='LN'). + norm_cfg (dict): Config dict for normalization layer at + output of backone. Defaults: dict(type='LN'). + with_cp (bool, optional): Use checkpoint or not. Using checkpoint + will save some memory while slowing down the training speed. + Default: False. + pretrained (str, optional): model pretrained path. Default: None. + convert_weights (bool): The flag indicates whether the + pre-trained model is from the original repo. We may need + to convert some keys to make it compatible. + Default: False. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. + init_cfg (dict, optional): The Config for initialization. + Defaults to None. + """ + + def __init__(self, + pretrain_img_size=224, + in_channels=3, + embed_dims=96, + patch_size=4, + window_size=7, + mlp_ratio=4, + depths=(2, 2, 6, 2), + num_heads=(3, 6, 12, 24), + strides=(4, 2, 2, 2), + out_indices=(0, 1, 2, 3), + qkv_bias=True, + qk_scale=None, + patch_norm=True, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.1, + use_abs_pos_embed=False, + act_cfg=dict(type='GELU'), + norm_cfg=dict(type='LN'), + with_cp=False, + pretrained=None, + convert_weights=False, + frozen_stages=-1, + init_cfg=None): + self.convert_weights = convert_weights + self.frozen_stages = frozen_stages + if isinstance(pretrain_img_size, int): + pretrain_img_size = to_2tuple(pretrain_img_size) + elif isinstance(pretrain_img_size, tuple): + if len(pretrain_img_size) == 1: + pretrain_img_size = to_2tuple(pretrain_img_size[0]) + assert len(pretrain_img_size) == 2, \ + f'The size of image should have length 1 or 2, ' \ + f'but got {len(pretrain_img_size)}' + + assert not (init_cfg and pretrained), \ + 'init_cfg and pretrained cannot be specified at the same time' + if isinstance(pretrained, str): + warnings.warn('DeprecationWarning: pretrained is deprecated, ' + 'please use "init_cfg" instead') + self.init_cfg = dict(type='Pretrained', checkpoint=pretrained) + elif pretrained is None: + self.init_cfg = init_cfg + else: + raise TypeError('pretrained must be a str or None') + + super(SwinTransformer, self).__init__(init_cfg=init_cfg) + + num_layers = len(depths) + self.out_indices = out_indices + self.use_abs_pos_embed = use_abs_pos_embed + + assert strides[0] == patch_size, 'Use non-overlapping patch embed.' + + self.patch_embed = PatchEmbed( + in_channels=in_channels, + embed_dims=embed_dims, + conv_type='Conv2d', + kernel_size=patch_size, + stride=strides[0], + norm_cfg=norm_cfg if patch_norm else None, + init_cfg=None) + + if self.use_abs_pos_embed: + patch_row = pretrain_img_size[0] // patch_size + patch_col = pretrain_img_size[1] // patch_size + num_patches = patch_row * patch_col + self.absolute_pos_embed = nn.Parameter( + torch.zeros((1, num_patches, embed_dims))) + + self.drop_after_pos = nn.Dropout(p=drop_rate) + + # set stochastic depth decay rule + total_depth = sum(depths) + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, total_depth) + ] + + self.stages = ModuleList() + in_channels = embed_dims + for i in range(num_layers): + if i < num_layers - 1: + downsample = PatchMerging( + in_channels=in_channels, + out_channels=2 * in_channels, + stride=strides[i + 1], + norm_cfg=norm_cfg if patch_norm else None, + init_cfg=None) + else: + downsample = None + + stage = SwinBlockSequence( + embed_dims=in_channels, + num_heads=num_heads[i], + feedforward_channels=mlp_ratio * in_channels, + depth=depths[i], + window_size=window_size, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop_rate=drop_rate, + attn_drop_rate=attn_drop_rate, + drop_path_rate=dpr[sum(depths[:i]):sum(depths[:i + 1])], + downsample=downsample, + act_cfg=act_cfg, + norm_cfg=norm_cfg, + with_cp=with_cp, + init_cfg=None) + self.stages.append(stage) + if downsample: + in_channels = downsample.out_channels + + self.num_features = [int(embed_dims * 2**i) for i in range(num_layers)] + # Add a norm layer for each output + for i in out_indices: + layer = build_norm_layer(norm_cfg, self.num_features[i])[1] + layer_name = f'norm{i}' + self.add_module(layer_name, layer) + + def train(self, mode=True): + """Convert the model into training mode while keep layers freezed.""" + super(SwinTransformer, self).train(mode) + self._freeze_stages() + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + if self.use_abs_pos_embed: + self.absolute_pos_embed.requires_grad = False + self.drop_after_pos.eval() + + for i in range(1, self.frozen_stages + 1): + + if (i - 1) in self.out_indices: + norm_layer = getattr(self, f'norm{i-1}') + norm_layer.eval() + for param in norm_layer.parameters(): + param.requires_grad = False + + m = self.stages[i - 1] + m.eval() + for param in m.parameters(): + param.requires_grad = False + + def init_weights(self): + logger = get_root_logger() + if self.init_cfg is None: + logger.warn(f'No pre-trained weights for ' + f'{self.__class__.__name__}, ' + f'training start from scratch') + if self.use_abs_pos_embed: + nn.init.trunc_normal_(self.absolute_pos_embed, std=0.02) + for m in self.modules(): + if isinstance(m, nn.Linear): + # trunc_normal_init(m, std=.02, bias=0.) + nn.init.trunc_normal_(m, mean=0., std=.02) + elif isinstance(m, nn.LayerNorm): + constant_init(m.bias, 0) + constant_init(m.weight, 1.0) + else: + assert 'checkpoint' in self.init_cfg, f'Only support ' \ + f'specify `Pretrained` in ' \ + f'`init_cfg` in ' \ + f'{self.__class__.__name__} ' + ckpt = _load_checkpoint( + self.init_cfg.checkpoint, logger=logger, map_location='cpu') + if 'state_dict' in ckpt: + _state_dict = ckpt['state_dict'] + elif 'model' in ckpt: + _state_dict = ckpt['model'] + else: + _state_dict = ckpt + if self.convert_weights: + # supported loading weight from original repo, + _state_dict = swin_converter(_state_dict) + + state_dict = OrderedDict() + for k, v in _state_dict.items(): + if k.startswith('backbone.'): + state_dict[k[9:]] = v + + # strip prefix of state_dict + if list(state_dict.keys())[0].startswith('module.'): + state_dict = {k[7:]: v for k, v in state_dict.items()} + + # reshape absolute position embedding + if state_dict.get('absolute_pos_embed') is not None: + absolute_pos_embed = state_dict['absolute_pos_embed'] + N1, L, C1 = absolute_pos_embed.size() + N2, C2, H, W = self.absolute_pos_embed.size() + if N1 != N2 or C1 != C2 or L != H * W: + logger.warning('Error in loading absolute_pos_embed, pass') + else: + state_dict['absolute_pos_embed'] = absolute_pos_embed.view( + N2, H, W, C2).permute(0, 3, 1, 2).contiguous() + + # interpolate position bias table if needed + relative_position_bias_table_keys = [ + k for k in state_dict.keys() + if 'relative_position_bias_table' in k + ] + for table_key in relative_position_bias_table_keys: + table_pretrained = state_dict[table_key] + table_current = self.state_dict()[table_key] + L1, nH1 = table_pretrained.size() + L2, nH2 = table_current.size() + if nH1 != nH2: + logger.warning(f'Error in loading {table_key}, pass') + elif L1 != L2: + S1 = int(L1**0.5) + S2 = int(L2**0.5) + table_pretrained_resized = F.interpolate( + table_pretrained.permute(1, 0).reshape(1, nH1, S1, S1), + size=(S2, S2), + mode='bicubic') + state_dict[table_key] = table_pretrained_resized.view( + nH2, L2).permute(1, 0).contiguous() + + # load state_dict + self.load_state_dict(state_dict, False) + + def forward(self, x): + x, hw_shape = self.patch_embed(x) + + if self.use_abs_pos_embed: + x = x + self.absolute_pos_embed + x = self.drop_after_pos(x) + + outs = [] + for i, stage in enumerate(self.stages): + x, hw_shape, out, out_hw_shape = stage(x, hw_shape) + if i in self.out_indices: + norm_layer = getattr(self, f'norm{i}') + out = norm_layer(out) + out = out.view(-1, *out_hw_shape, + self.num_features[i]).permute(0, 3, 1, + 2).contiguous() + outs.append(out) + + return outs diff --git a/mmdet/models/backbones/trident_resnet.py b/mmdet/models/backbones/trident_resnet.py new file mode 100644 index 0000000..44d8c96 --- /dev/null +++ b/mmdet/models/backbones/trident_resnet.py @@ -0,0 +1,297 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as cp +from mmcv.cnn import build_conv_layer, build_norm_layer +from mmcv.runner import BaseModule +from torch.nn.modules.utils import _pair + +from mmdet.models.backbones.resnet import Bottleneck, ResNet +from mmdet.models.builder import BACKBONES + + +class TridentConv(BaseModule): + """Trident Convolution Module. + + Args: + in_channels (int): Number of channels in input. + out_channels (int): Number of channels in output. + kernel_size (int): Size of convolution kernel. + stride (int, optional): Convolution stride. Default: 1. + trident_dilations (tuple[int, int, int], optional): Dilations of + different trident branch. Default: (1, 2, 3). + test_branch_idx (int, optional): In inference, all 3 branches will + be used if `test_branch_idx==-1`, otherwise only branch with + index `test_branch_idx` will be used. Default: 1. + bias (bool, optional): Whether to use bias in convolution or not. + Default: False. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + trident_dilations=(1, 2, 3), + test_branch_idx=1, + bias=False, + init_cfg=None): + super(TridentConv, self).__init__(init_cfg) + self.num_branch = len(trident_dilations) + self.with_bias = bias + self.test_branch_idx = test_branch_idx + self.stride = _pair(stride) + self.kernel_size = _pair(kernel_size) + self.paddings = _pair(trident_dilations) + self.dilations = trident_dilations + self.in_channels = in_channels + self.out_channels = out_channels + self.bias = bias + + self.weight = nn.Parameter( + torch.Tensor(out_channels, in_channels, *self.kernel_size)) + if bias: + self.bias = nn.Parameter(torch.Tensor(out_channels)) + else: + self.bias = None + + def extra_repr(self): + tmpstr = f'in_channels={self.in_channels}' + tmpstr += f', out_channels={self.out_channels}' + tmpstr += f', kernel_size={self.kernel_size}' + tmpstr += f', num_branch={self.num_branch}' + tmpstr += f', test_branch_idx={self.test_branch_idx}' + tmpstr += f', stride={self.stride}' + tmpstr += f', paddings={self.paddings}' + tmpstr += f', dilations={self.dilations}' + tmpstr += f', bias={self.bias}' + return tmpstr + + def forward(self, inputs): + if self.training or self.test_branch_idx == -1: + outputs = [ + F.conv2d(input, self.weight, self.bias, self.stride, padding, + dilation) for input, dilation, padding in zip( + inputs, self.dilations, self.paddings) + ] + else: + assert len(inputs) == 1 + outputs = [ + F.conv2d(inputs[0], self.weight, self.bias, self.stride, + self.paddings[self.test_branch_idx], + self.dilations[self.test_branch_idx]) + ] + + return outputs + + +# Since TridentNet is defined over ResNet50 and ResNet101, here we +# only support TridentBottleneckBlock. +class TridentBottleneck(Bottleneck): + """BottleBlock for TridentResNet. + + Args: + trident_dilations (tuple[int, int, int]): Dilations of different + trident branch. + test_branch_idx (int): In inference, all 3 branches will be used + if `test_branch_idx==-1`, otherwise only branch with index + `test_branch_idx` will be used. + concat_output (bool): Whether to concat the output list to a Tensor. + `True` only in the last Block. + """ + + def __init__(self, trident_dilations, test_branch_idx, concat_output, + **kwargs): + + super(TridentBottleneck, self).__init__(**kwargs) + self.trident_dilations = trident_dilations + self.num_branch = len(trident_dilations) + self.concat_output = concat_output + self.test_branch_idx = test_branch_idx + self.conv2 = TridentConv( + self.planes, + self.planes, + kernel_size=3, + stride=self.conv2_stride, + bias=False, + trident_dilations=self.trident_dilations, + test_branch_idx=test_branch_idx, + init_cfg=dict( + type='Kaiming', + distribution='uniform', + mode='fan_in', + override=dict(name='conv2'))) + + def forward(self, x): + + def _inner_forward(x): + num_branch = ( + self.num_branch + if self.training or self.test_branch_idx == -1 else 1) + identity = x + if not isinstance(x, list): + x = (x, ) * num_branch + identity = x + if self.downsample is not None: + identity = [self.downsample(b) for b in x] + + out = [self.conv1(b) for b in x] + out = [self.norm1(b) for b in out] + out = [self.relu(b) for b in out] + + if self.with_plugins: + for k in range(len(out)): + out[k] = self.forward_plugin(out[k], + self.after_conv1_plugin_names) + + out = self.conv2(out) + out = [self.norm2(b) for b in out] + out = [self.relu(b) for b in out] + if self.with_plugins: + for k in range(len(out)): + out[k] = self.forward_plugin(out[k], + self.after_conv2_plugin_names) + + out = [self.conv3(b) for b in out] + out = [self.norm3(b) for b in out] + + if self.with_plugins: + for k in range(len(out)): + out[k] = self.forward_plugin(out[k], + self.after_conv3_plugin_names) + + out = [ + out_b + identity_b for out_b, identity_b in zip(out, identity) + ] + return out + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(_inner_forward, x) + else: + out = _inner_forward(x) + + out = [self.relu(b) for b in out] + if self.concat_output: + out = torch.cat(out, dim=0) + return out + + +def make_trident_res_layer(block, + inplanes, + planes, + num_blocks, + stride=1, + trident_dilations=(1, 2, 3), + style='pytorch', + with_cp=False, + conv_cfg=None, + norm_cfg=dict(type='BN'), + dcn=None, + plugins=None, + test_branch_idx=-1): + """Build Trident Res Layers.""" + + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + downsample = [] + conv_stride = stride + downsample.extend([ + build_conv_layer( + conv_cfg, + inplanes, + planes * block.expansion, + kernel_size=1, + stride=conv_stride, + bias=False), + build_norm_layer(norm_cfg, planes * block.expansion)[1] + ]) + downsample = nn.Sequential(*downsample) + + layers = [] + for i in range(num_blocks): + layers.append( + block( + inplanes=inplanes, + planes=planes, + stride=stride if i == 0 else 1, + trident_dilations=trident_dilations, + downsample=downsample if i == 0 else None, + style=style, + with_cp=with_cp, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + dcn=dcn, + plugins=plugins, + test_branch_idx=test_branch_idx, + concat_output=True if i == num_blocks - 1 else False)) + inplanes = planes * block.expansion + return nn.Sequential(*layers) + + +@BACKBONES.register_module() +class TridentResNet(ResNet): + """The stem layer, stage 1 and stage 2 in Trident ResNet are identical to + ResNet, while in stage 3, Trident BottleBlock is utilized to replace the + normal BottleBlock to yield trident output. Different branch shares the + convolution weight but uses different dilations to achieve multi-scale + output. + + / stage3(b0) \ + x - stem - stage1 - stage2 - stage3(b1) - output + \ stage3(b2) / + + Args: + depth (int): Depth of resnet, from {50, 101, 152}. + num_branch (int): Number of branches in TridentNet. + test_branch_idx (int): In inference, all 3 branches will be used + if `test_branch_idx==-1`, otherwise only branch with index + `test_branch_idx` will be used. + trident_dilations (tuple[int]): Dilations of different trident branch. + len(trident_dilations) should be equal to num_branch. + """ # noqa + + def __init__(self, depth, num_branch, test_branch_idx, trident_dilations, + **kwargs): + + assert num_branch == len(trident_dilations) + assert depth in (50, 101, 152) + super(TridentResNet, self).__init__(depth, **kwargs) + assert self.num_stages == 3 + self.test_branch_idx = test_branch_idx + self.num_branch = num_branch + + last_stage_idx = self.num_stages - 1 + stride = self.strides[last_stage_idx] + dilation = trident_dilations + dcn = self.dcn if self.stage_with_dcn[last_stage_idx] else None + if self.plugins is not None: + stage_plugins = self.make_stage_plugins(self.plugins, + last_stage_idx) + else: + stage_plugins = None + planes = self.base_channels * 2**last_stage_idx + res_layer = make_trident_res_layer( + TridentBottleneck, + inplanes=(self.block.expansion * self.base_channels * + 2**(last_stage_idx - 1)), + planes=planes, + num_blocks=self.stage_blocks[last_stage_idx], + stride=stride, + trident_dilations=dilation, + style=self.style, + with_cp=self.with_cp, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + dcn=dcn, + plugins=stage_plugins, + test_branch_idx=self.test_branch_idx) + + layer_name = f'layer{last_stage_idx + 1}' + + self.__setattr__(layer_name, res_layer) + self.res_layers.pop(last_stage_idx) + self.res_layers.insert(last_stage_idx, layer_name) + + self._freeze_stages() diff --git a/mmdet/models/builder.py b/mmdet/models/builder.py new file mode 100644 index 0000000..85dc256 --- /dev/null +++ b/mmdet/models/builder.py @@ -0,0 +1,58 @@ +import warnings + +from mmcv.cnn import MODELS as MMCV_MODELS +from mmcv.utils import Registry + +MODELS = Registry('models', parent=MMCV_MODELS) + +BACKBONES = MODELS +NECKS = MODELS +ROI_EXTRACTORS = MODELS +SHARED_HEADS = MODELS +HEADS = MODELS +LOSSES = MODELS +DETECTORS = MODELS + + +def build_backbone(cfg): + """Build backbone.""" + return BACKBONES.build(cfg) + + +def build_neck(cfg): + """Build neck.""" + return NECKS.build(cfg) + + +def build_roi_extractor(cfg): + """Build roi extractor.""" + return ROI_EXTRACTORS.build(cfg) + + +def build_shared_head(cfg): + """Build shared head.""" + return SHARED_HEADS.build(cfg) + + +def build_head(cfg): + """Build head.""" + return HEADS.build(cfg) + + +def build_loss(cfg): + """Build loss.""" + return LOSSES.build(cfg) + + +def build_detector(cfg, train_cfg=None, test_cfg=None): + """Build detector.""" + if train_cfg is not None or test_cfg is not None: + warnings.warn( + 'train_cfg and test_cfg is deprecated, ' + 'please specify them in model', UserWarning) + assert cfg.get('train_cfg') is None or train_cfg is None, \ + 'train_cfg specified in both outer field and model field ' + assert cfg.get('test_cfg') is None or test_cfg is None, \ + 'test_cfg specified in both outer field and model field ' + return DETECTORS.build( + cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg)) diff --git a/mmdet/models/dense_heads/__init__.py b/mmdet/models/dense_heads/__init__.py new file mode 100644 index 0000000..9ee0d24 --- /dev/null +++ b/mmdet/models/dense_heads/__init__.py @@ -0,0 +1,45 @@ +from .anchor_free_head import AnchorFreeHead +from .anchor_head import AnchorHead +from .atss_head import ATSSHead +from .autoassign_head import AutoAssignHead +from .cascade_rpn_head import CascadeRPNHead, StageCascadeRPNHead +from .centripetal_head import CentripetalHead +from .corner_head import CornerHead +from .deformable_detr_head import DeformableDETRHead +from .detr_head import DETRHead +from .embedding_rpn_head import EmbeddingRPNHead +from .fcos_head import FCOSHead +from .fovea_head import FoveaHead +from .free_anchor_retina_head import FreeAnchorRetinaHead +from .fsaf_head import FSAFHead +from .ga_retina_head import GARetinaHead +from .ga_rpn_head import GARPNHead +from .gfl_head import GFLHead +from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead +from .ld_head import LDHead +from .nasfcos_head import NASFCOSHead +from .paa_head import PAAHead +from .pisa_retinanet_head import PISARetinaHead +from .pisa_ssd_head import PISASSDHead +from .reppoints_head import RepPointsHead +from .retina_head import RetinaHead +from .retina_sepbn_head import RetinaSepBNHead +from .rpn_head import RPNHead +from .sabl_retina_head import SABLRetinaHead +from .ssd_head import SSDHead +from .vfnet_head import VFNetHead +from .yolact_head import YOLACTHead, YOLACTProtonet, YOLACTSegmHead +from .yolo_head import YOLOV3Head +from .yolof_head import YOLOFHead +from .query_generator import InitialQueryGenerator +__all__ = [ + 'AnchorFreeHead', 'AnchorHead', 'GuidedAnchorHead', 'FeatureAdaption', + 'RPNHead', 'GARPNHead', 'RetinaHead', 'RetinaSepBNHead', 'GARetinaHead', + 'SSDHead', 'FCOSHead', 'RepPointsHead', 'FoveaHead', + 'FreeAnchorRetinaHead', 'ATSSHead', 'FSAFHead', 'NASFCOSHead', + 'PISARetinaHead', 'PISASSDHead', 'GFLHead', 'CornerHead', 'YOLACTHead', + 'YOLACTSegmHead', 'YOLACTProtonet', 'YOLOV3Head', 'PAAHead', + 'SABLRetinaHead', 'CentripetalHead', 'VFNetHead', 'StageCascadeRPNHead', + 'CascadeRPNHead', 'EmbeddingRPNHead', 'LDHead', 'CascadeRPNHead', + 'AutoAssignHead', 'DETRHead', 'YOLOFHead', 'DeformableDETRHead' +] diff --git a/mmdet/models/dense_heads/__pycache__/__init__.cpython-37.pyc b/mmdet/models/dense_heads/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..b67d0e3 Binary files /dev/null and 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Number of hidden channels. Used in child classes. + stacked_convs (int): Number of stacking convs of the head. + strides (tuple): Downsample factor of each feature map. + dcn_on_last_conv (bool): If true, use dcn in the last layer of + towers. Default: False. + conv_bias (bool | str): If specified as `auto`, it will be decided by + the norm_cfg. Bias of conv will be set as True if `norm_cfg` is + None, otherwise False. Default: "auto". + loss_cls (dict): Config of classification loss. + loss_bbox (dict): Config of localization loss. + conv_cfg (dict): Config dict for convolution layer. Default: None. + norm_cfg (dict): Config dict for normalization layer. Default: None. + train_cfg (dict): Training config of anchor head. + test_cfg (dict): Testing config of anchor head. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ # noqa: W605 + + _version = 1 + + def __init__(self, + num_classes, + in_channels, + feat_channels=256, + stacked_convs=4, + strides=(4, 8, 16, 32, 64), + dcn_on_last_conv=False, + conv_bias='auto', + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='IoULoss', loss_weight=1.0), + conv_cfg=None, + norm_cfg=None, + train_cfg=None, + test_cfg=None, + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=dict( + type='Normal', + name='conv_cls', + std=0.01, + bias_prob=0.01))): + super(AnchorFreeHead, self).__init__(init_cfg) + self.num_classes = num_classes + self.cls_out_channels = num_classes + self.in_channels = in_channels + self.feat_channels = feat_channels + self.stacked_convs = stacked_convs + self.strides = strides + self.dcn_on_last_conv = dcn_on_last_conv + assert conv_bias == 'auto' or isinstance(conv_bias, bool) + self.conv_bias = conv_bias + self.loss_cls = build_loss(loss_cls) + self.loss_bbox = build_loss(loss_bbox) + self.train_cfg = train_cfg + self.test_cfg = test_cfg + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.fp16_enabled = False + + self._init_layers() + + def _init_layers(self): + """Initialize layers of the head.""" + self._init_cls_convs() + self._init_reg_convs() + self._init_predictor() + + def _init_cls_convs(self): + """Initialize classification conv layers of the head.""" + self.cls_convs = nn.ModuleList() + for i in range(self.stacked_convs): + chn = self.in_channels if i == 0 else self.feat_channels + if self.dcn_on_last_conv and i == self.stacked_convs - 1: + conv_cfg = dict(type='DCNv2') + else: + conv_cfg = self.conv_cfg + self.cls_convs.append( + ConvModule( + chn, + self.feat_channels, + 3, + stride=1, + padding=1, + conv_cfg=conv_cfg, + norm_cfg=self.norm_cfg, + bias=self.conv_bias)) + + def _init_reg_convs(self): + """Initialize bbox regression conv layers of the head.""" + self.reg_convs = nn.ModuleList() + for i in range(self.stacked_convs): + chn = self.in_channels if i == 0 else self.feat_channels + if self.dcn_on_last_conv and i == self.stacked_convs - 1: + conv_cfg = dict(type='DCNv2') + else: + conv_cfg = self.conv_cfg + self.reg_convs.append( + ConvModule( + chn, + self.feat_channels, + 3, + stride=1, + padding=1, + conv_cfg=conv_cfg, + norm_cfg=self.norm_cfg, + bias=self.conv_bias)) + + def _init_predictor(self): + """Initialize predictor layers of the head.""" + self.conv_cls = nn.Conv2d( + self.feat_channels, self.cls_out_channels*int(os.environ['A']), 3, padding=1) + self.conv_reg = nn.Conv2d( + self.feat_channels, 4*int(os.environ['A']), 3, padding=1) + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs): + """Hack some keys of the model state dict so that can load checkpoints + of previous version.""" + version = local_metadata.get('version', None) + if version is None: + # the key is different in early versions + # for example, 'fcos_cls' become 'conv_cls' now + bbox_head_keys = [ + k for k in state_dict.keys() if k.startswith(prefix) + ] + ori_predictor_keys = [] + new_predictor_keys = [] + # e.g. 'fcos_cls' or 'fcos_reg' + for key in bbox_head_keys: + ori_predictor_keys.append(key) + key = key.split('.') + conv_name = None + if key[1].endswith('cls'): + conv_name = 'conv_cls' + elif key[1].endswith('reg'): + conv_name = 'conv_reg' + elif key[1].endswith('centerness'): + conv_name = 'conv_centerness' + else: + assert NotImplementedError + if conv_name is not None: + key[1] = conv_name + new_predictor_keys.append('.'.join(key)) + else: + ori_predictor_keys.pop(-1) + for i in range(len(new_predictor_keys)): + state_dict[new_predictor_keys[i]] = state_dict.pop( + ori_predictor_keys[i]) + super()._load_from_state_dict(state_dict, prefix, local_metadata, + strict, missing_keys, unexpected_keys, + error_msgs) + + def forward(self, feats): + """Forward features from the upstream network. + + Args: + feats (tuple[Tensor]): Features from the upstream network, each is + a 4D-tensor. + + Returns: + tuple: Usually contain classification scores and bbox predictions. + cls_scores (list[Tensor]): Box scores for each scale level, + each is a 4D-tensor, the channel number is + num_points * num_classes. + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level, each is a 4D-tensor, the channel number is + num_points * 4. + """ + return multi_apply(self.forward_single, feats)[:2] + + def forward_single(self, x): + """Forward features of a single scale level. + + Args: + x (Tensor): FPN feature maps of the specified stride. + + Returns: + tuple: Scores for each class, bbox predictions, features + after classification and regression conv layers, some + models needs these features like FCOS. + """ + cls_feat = x + reg_feat = x + + for cls_layer in self.cls_convs: + cls_feat = cls_layer(cls_feat) + cls_score = self.conv_cls(cls_feat)[:, :self.num_classes, :, :] + + for reg_layer in self.reg_convs: + reg_feat = reg_layer(reg_feat) + bbox_pred = self.conv_reg(reg_feat)[:, :4, :, :] + return cls_score, bbox_pred, cls_feat, reg_feat + + @abstractmethod + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) + def loss(self, + cls_scores, + bbox_preds, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute loss of the head. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level, + each is a 4D-tensor, the channel number is + num_points * num_classes. + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level, each is a 4D-tensor, the channel number is + num_points * 4. + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + """ + + raise NotImplementedError + + @abstractmethod + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) + def get_bboxes(self, + cls_scores, + bbox_preds, + img_metas, + cfg=None, + rescale=None): + """Transform network output for a batch into bbox predictions. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (N, num_points * num_classes, H, W) + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_points * 4, H, W) + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + cfg (mmcv.Config): Test / postprocessing configuration, + if None, test_cfg would be used + rescale (bool): If True, return boxes in original image space + """ + + raise NotImplementedError + + @abstractmethod + def get_targets(self, points, gt_bboxes_list, gt_labels_list): + """Compute regression, classification and centerness targets for points + in multiple images. + + Args: + points (list[Tensor]): Points of each fpn level, each has shape + (num_points, 2). + gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image, + each has shape (num_gt, 4). + gt_labels_list (list[Tensor]): Ground truth labels of each box, + each has shape (num_gt,). + """ + raise NotImplementedError + + def _get_points_single(self, + featmap_size, + stride, + dtype, + device, + flatten=False): + """Get points of a single scale level.""" + h, w = featmap_size + # First create Range with the defaut dtype, than convert to + # target `dtype` for onnx exporting. + x_range = torch.arange(w, device=device).to(dtype) + y_range = torch.arange(h, device=device).to(dtype) + y, x = torch.meshgrid(y_range, x_range) + if flatten: + y = y.flatten() + x = x.flatten() + return y, x + + def get_points(self, featmap_sizes, dtype, device, flatten=False): + """Get points according to feature map sizes. + + Args: + featmap_sizes (list[tuple]): Multi-level feature map sizes. + dtype (torch.dtype): Type of points. + device (torch.device): Device of points. + + Returns: + tuple: points of each image. + """ + mlvl_points = [] + for i in range(len(featmap_sizes)): + mlvl_points.append( + self._get_points_single(featmap_sizes[i], self.strides[i], + dtype, device, flatten)) + return mlvl_points + + def aug_test(self, feats, img_metas, rescale=False): + """Test function with test time augmentation. + + Args: + feats (list[Tensor]): the outer list indicates test-time + augmentations and inner Tensor should have a shape NxCxHxW, + which contains features for all images in the batch. + img_metas (list[list[dict]]): the outer list indicates test-time + augs (multiscale, flip, etc.) and the inner list indicates + images in a batch. each dict has image information. + rescale (bool, optional): Whether to rescale the results. + Defaults to False. + + Returns: + list[ndarray]: bbox results of each class + """ + return self.aug_test_bboxes(feats, img_metas, rescale=rescale) diff --git a/mmdet/models/dense_heads/anchor_head.py b/mmdet/models/dense_heads/anchor_head.py new file mode 100644 index 0000000..bbe3154 --- /dev/null +++ b/mmdet/models/dense_heads/anchor_head.py @@ -0,0 +1,741 @@ +import torch +import torch.nn as nn +from mmcv.runner import force_fp32 + +from mmdet.core import (anchor_inside_flags, build_anchor_generator, + build_assigner, build_bbox_coder, build_sampler, + images_to_levels, multi_apply, multiclass_nms, unmap) +from ..builder import HEADS, build_loss +from .base_dense_head import BaseDenseHead +from .dense_test_mixins import BBoxTestMixin + + +@HEADS.register_module() +class AnchorHead(BaseDenseHead, BBoxTestMixin): + """Anchor-based head (RPN, RetinaNet, SSD, etc.). + + Args: + num_classes (int): Number of categories excluding the background + category. + in_channels (int): Number of channels in the input feature map. + feat_channels (int): Number of hidden channels. Used in child classes. + anchor_generator (dict): Config dict for anchor generator + bbox_coder (dict): Config of bounding box coder. + reg_decoded_bbox (bool): If true, the regression loss would be + applied directly on decoded bounding boxes, converting both + the predicted boxes and regression targets to absolute + coordinates format. Default False. It should be `True` when + using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head. + loss_cls (dict): Config of classification loss. + loss_bbox (dict): Config of localization loss. + train_cfg (dict): Training config of anchor head. + test_cfg (dict): Testing config of anchor head. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ # noqa: W605 + + def __init__(self, + num_classes, + in_channels, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8, 16, 32], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=True, + target_means=(.0, .0, .0, .0), + target_stds=(1.0, 1.0, 1.0, 1.0)), + reg_decoded_bbox=False, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0), + loss_bbox=dict( + type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0), + train_cfg=None, + test_cfg=None, + init_cfg=dict(type='Normal', layers='Conv2d', std=0.01)): + super(AnchorHead, self).__init__(init_cfg) + self.in_channels = in_channels + self.num_classes = num_classes + self.feat_channels = feat_channels + self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False) + # TODO better way to determine whether sample or not + self.sampling = loss_cls['type'] not in [ + 'FocalLoss', 'GHMC', 'QualityFocalLoss' + ] + if self.use_sigmoid_cls: + self.cls_out_channels = num_classes + else: + self.cls_out_channels = num_classes + 1 + + if self.cls_out_channels <= 0: + raise ValueError(f'num_classes={num_classes} is too small') + self.reg_decoded_bbox = reg_decoded_bbox + + self.bbox_coder = build_bbox_coder(bbox_coder) + self.loss_cls = build_loss(loss_cls) + self.loss_bbox = build_loss(loss_bbox) + self.train_cfg = train_cfg + self.test_cfg = test_cfg + if self.train_cfg: + self.assigner = build_assigner(self.train_cfg.assigner) + # use PseudoSampler when sampling is False + if self.sampling and hasattr(self.train_cfg, 'sampler'): + sampler_cfg = self.train_cfg.sampler + else: + sampler_cfg = dict(type='PseudoSampler') + self.sampler = build_sampler(sampler_cfg, context=self) + self.fp16_enabled = False + + self.anchor_generator = build_anchor_generator(anchor_generator) + # usually the numbers of anchors for each level are the same + # except SSD detectors + self.num_anchors = self.anchor_generator.num_base_anchors[0] + self._init_layers() + + def _init_layers(self): + """Initialize layers of the head.""" + self.conv_cls = nn.Conv2d(self.in_channels, + self.num_anchors * self.cls_out_channels, 1) + self.conv_reg = nn.Conv2d(self.in_channels, self.num_anchors * 4, 1) + + def forward_single(self, x): + """Forward feature of a single scale level. + + Args: + x (Tensor): Features of a single scale level. + + Returns: + tuple: + cls_score (Tensor): Cls scores for a single scale level \ + the channels number is num_anchors * num_classes. + bbox_pred (Tensor): Box energies / deltas for a single scale \ + level, the channels number is num_anchors * 4. + """ + cls_score = self.conv_cls(x) + bbox_pred = self.conv_reg(x) + return cls_score, bbox_pred + + def forward(self, feats): + """Forward features from the upstream network. + + Args: + feats (tuple[Tensor]): Features from the upstream network, each is + a 4D-tensor. + + Returns: + tuple: A tuple of classification scores and bbox prediction. + + - cls_scores (list[Tensor]): Classification scores for all \ + scale levels, each is a 4D-tensor, the channels number \ + is num_anchors * num_classes. + - bbox_preds (list[Tensor]): Box energies / deltas for all \ + scale levels, each is a 4D-tensor, the channels number \ + is num_anchors * 4. + """ + return multi_apply(self.forward_single, feats) + + def get_anchors(self, featmap_sizes, img_metas, device='cuda'): + """Get anchors according to feature map sizes. + + Args: + featmap_sizes (list[tuple]): Multi-level feature map sizes. + img_metas (list[dict]): Image meta info. + device (torch.device | str): Device for returned tensors + + Returns: + tuple: + anchor_list (list[Tensor]): Anchors of each image. + valid_flag_list (list[Tensor]): Valid flags of each image. + """ + num_imgs = len(img_metas) + + # since feature map sizes of all images are the same, we only compute + # anchors for one time + multi_level_anchors = self.anchor_generator.grid_anchors( + featmap_sizes, device) + anchor_list = [multi_level_anchors for _ in range(num_imgs)] + + # for each image, we compute valid flags of multi level anchors + valid_flag_list = [] + for img_id, img_meta in enumerate(img_metas): + multi_level_flags = self.anchor_generator.valid_flags( + featmap_sizes, img_meta['pad_shape'], device) + valid_flag_list.append(multi_level_flags) + + return anchor_list, valid_flag_list + + def _get_targets_single(self, + flat_anchors, + valid_flags, + gt_bboxes, + gt_bboxes_ignore, + gt_labels, + img_meta, + label_channels=1, + unmap_outputs=True): + """Compute regression and classification targets for anchors in a + single image. + + Args: + flat_anchors (Tensor): Multi-level anchors of the image, which are + concatenated into a single tensor of shape (num_anchors ,4) + valid_flags (Tensor): Multi level valid flags of the image, + which are concatenated into a single tensor of + shape (num_anchors,). + gt_bboxes (Tensor): Ground truth bboxes of the image, + shape (num_gts, 4). + gt_bboxes_ignore (Tensor): Ground truth bboxes to be + ignored, shape (num_ignored_gts, 4). + img_meta (dict): Meta info of the image. + gt_labels (Tensor): Ground truth labels of each box, + shape (num_gts,). + label_channels (int): Channel of label. + unmap_outputs (bool): Whether to map outputs back to the original + set of anchors. + + Returns: + tuple: + labels_list (list[Tensor]): Labels of each level + label_weights_list (list[Tensor]): Label weights of each level + bbox_targets_list (list[Tensor]): BBox targets of each level + bbox_weights_list (list[Tensor]): BBox weights of each level + num_total_pos (int): Number of positive samples in all images + num_total_neg (int): Number of negative samples in all images + """ + inside_flags = anchor_inside_flags(flat_anchors, valid_flags, + img_meta['img_shape'][:2], + self.train_cfg.allowed_border) + if not inside_flags.any(): + return (None, ) * 7 + # assign gt and sample anchors + anchors = flat_anchors[inside_flags, :] + + assign_result = self.assigner.assign( + anchors, gt_bboxes, gt_bboxes_ignore, + None if self.sampling else gt_labels) + sampling_result = self.sampler.sample(assign_result, anchors, + gt_bboxes) + + num_valid_anchors = anchors.shape[0] + bbox_targets = torch.zeros_like(anchors) + bbox_weights = torch.zeros_like(anchors) + labels = anchors.new_full((num_valid_anchors, ), + self.num_classes, + dtype=torch.long) + label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float) + + pos_inds = sampling_result.pos_inds + neg_inds = sampling_result.neg_inds + if len(pos_inds) > 0: + if not self.reg_decoded_bbox: + pos_bbox_targets = self.bbox_coder.encode( + sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes) + else: + pos_bbox_targets = sampling_result.pos_gt_bboxes + bbox_targets[pos_inds, :] = pos_bbox_targets + bbox_weights[pos_inds, :] = 1.0 + if gt_labels is None: + # Only rpn gives gt_labels as None + # Foreground is the first class since v2.5.0 + labels[pos_inds] = 0 + else: + labels[pos_inds] = gt_labels[ + sampling_result.pos_assigned_gt_inds] + if self.train_cfg.pos_weight <= 0: + label_weights[pos_inds] = 1.0 + else: + label_weights[pos_inds] = self.train_cfg.pos_weight + if len(neg_inds) > 0: + label_weights[neg_inds] = 1.0 + + # map up to original set of anchors + if unmap_outputs: + num_total_anchors = flat_anchors.size(0) + labels = unmap( + labels, num_total_anchors, inside_flags, + fill=self.num_classes) # fill bg label + label_weights = unmap(label_weights, num_total_anchors, + inside_flags) + bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags) + bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags) + + return (labels, label_weights, bbox_targets, bbox_weights, pos_inds, + neg_inds, sampling_result) + + def get_targets(self, + anchor_list, + valid_flag_list, + gt_bboxes_list, + img_metas, + gt_bboxes_ignore_list=None, + gt_labels_list=None, + label_channels=1, + unmap_outputs=True, + return_sampling_results=False): + """Compute regression and classification targets for anchors in + multiple images. + + Args: + anchor_list (list[list[Tensor]]): Multi level anchors of each + image. The outer list indicates images, and the inner list + corresponds to feature levels of the image. Each element of + the inner list is a tensor of shape (num_anchors, 4). + valid_flag_list (list[list[Tensor]]): Multi level valid flags of + each image. The outer list indicates images, and the inner list + corresponds to feature levels of the image. Each element of + the inner list is a tensor of shape (num_anchors, ) + gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image. + img_metas (list[dict]): Meta info of each image. + gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be + ignored. + gt_labels_list (list[Tensor]): Ground truth labels of each box. + label_channels (int): Channel of label. + unmap_outputs (bool): Whether to map outputs back to the original + set of anchors. + + Returns: + tuple: Usually returns a tuple containing learning targets. + + - labels_list (list[Tensor]): Labels of each level. + - label_weights_list (list[Tensor]): Label weights of each \ + level. + - bbox_targets_list (list[Tensor]): BBox targets of each level. + - bbox_weights_list (list[Tensor]): BBox weights of each level. + - num_total_pos (int): Number of positive samples in all \ + images. + - num_total_neg (int): Number of negative samples in all \ + images. + additional_returns: This function enables user-defined returns from + `self._get_targets_single`. These returns are currently refined + to properties at each feature map (i.e. having HxW dimension). + The results will be concatenated after the end + """ + num_imgs = len(img_metas) + assert len(anchor_list) == len(valid_flag_list) == num_imgs + + # anchor number of multi levels + num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] + # concat all level anchors to a single tensor + concat_anchor_list = [] + concat_valid_flag_list = [] + for i in range(num_imgs): + assert len(anchor_list[i]) == len(valid_flag_list[i]) + concat_anchor_list.append(torch.cat(anchor_list[i])) + concat_valid_flag_list.append(torch.cat(valid_flag_list[i])) + + # compute targets for each image + if gt_bboxes_ignore_list is None: + gt_bboxes_ignore_list = [None for _ in range(num_imgs)] + if gt_labels_list is None: + gt_labels_list = [None for _ in range(num_imgs)] + results = multi_apply( + self._get_targets_single, + concat_anchor_list, + concat_valid_flag_list, + gt_bboxes_list, + gt_bboxes_ignore_list, + gt_labels_list, + img_metas, + label_channels=label_channels, + unmap_outputs=unmap_outputs) + (all_labels, all_label_weights, all_bbox_targets, all_bbox_weights, + pos_inds_list, neg_inds_list, sampling_results_list) = results[:7] + rest_results = list(results[7:]) # user-added return values + # no valid anchors + if any([labels is None for labels in all_labels]): + return None + # sampled anchors of all images + num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list]) + num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list]) + # split targets to a list w.r.t. multiple levels + labels_list = images_to_levels(all_labels, num_level_anchors) + label_weights_list = images_to_levels(all_label_weights, + num_level_anchors) + bbox_targets_list = images_to_levels(all_bbox_targets, + num_level_anchors) + bbox_weights_list = images_to_levels(all_bbox_weights, + num_level_anchors) + res = (labels_list, label_weights_list, bbox_targets_list, + bbox_weights_list, num_total_pos, num_total_neg) + if return_sampling_results: + res = res + (sampling_results_list, ) + for i, r in enumerate(rest_results): # user-added return values + rest_results[i] = images_to_levels(r, num_level_anchors) + + return res + tuple(rest_results) + + def loss_single(self, cls_score, bbox_pred, anchors, labels, label_weights, + bbox_targets, bbox_weights, num_total_samples): + """Compute loss of a single scale level. + + Args: + cls_score (Tensor): Box scores for each scale level + Has shape (N, num_anchors * num_classes, H, W). + bbox_pred (Tensor): Box energies / deltas for each scale + level with shape (N, num_anchors * 4, H, W). + anchors (Tensor): Box reference for each scale level with shape + (N, num_total_anchors, 4). + labels (Tensor): Labels of each anchors with shape + (N, num_total_anchors). + label_weights (Tensor): Label weights of each anchor with shape + (N, num_total_anchors) + bbox_targets (Tensor): BBox regression targets of each anchor wight + shape (N, num_total_anchors, 4). + bbox_weights (Tensor): BBox regression loss weights of each anchor + with shape (N, num_total_anchors, 4). + num_total_samples (int): If sampling, num total samples equal to + the number of total anchors; Otherwise, it is the number of + positive anchors. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + # classification loss + labels = labels.reshape(-1) + label_weights = label_weights.reshape(-1) + cls_score = cls_score.permute(0, 2, 3, + 1).reshape(-1, self.cls_out_channels) + loss_cls = self.loss_cls( + cls_score, labels, label_weights, avg_factor=num_total_samples) + # regression loss + bbox_targets = bbox_targets.reshape(-1, 4) + bbox_weights = bbox_weights.reshape(-1, 4) + bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4) + if self.reg_decoded_bbox: + # When the regression loss (e.g. `IouLoss`, `GIouLoss`) + # is applied directly on the decoded bounding boxes, it + # decodes the already encoded coordinates to absolute format. + anchors = anchors.reshape(-1, 4) + bbox_pred = self.bbox_coder.decode(anchors, bbox_pred) + loss_bbox = self.loss_bbox( + bbox_pred, + bbox_targets, + bbox_weights, + avg_factor=num_total_samples) + return loss_cls, loss_bbox + + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) + def loss(self, + cls_scores, + bbox_preds, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute losses of the head. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (N, num_anchors * num_classes, H, W) + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_anchors * 4, H, W) + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. Default: None + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + assert len(featmap_sizes) == self.anchor_generator.num_levels + + device = cls_scores[0].device + + anchor_list, valid_flag_list = self.get_anchors( + featmap_sizes, img_metas, device=device) + label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 + cls_reg_targets = self.get_targets( + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + gt_labels_list=gt_labels, + label_channels=label_channels) + if cls_reg_targets is None: + return None + (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, + num_total_pos, num_total_neg) = cls_reg_targets + num_total_samples = ( + num_total_pos + num_total_neg if self.sampling else num_total_pos) + + # anchor number of multi levels + num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] + # concat all level anchors and flags to a single tensor + concat_anchor_list = [] + for i in range(len(anchor_list)): + concat_anchor_list.append(torch.cat(anchor_list[i])) + all_anchor_list = images_to_levels(concat_anchor_list, + num_level_anchors) + + losses_cls, losses_bbox = multi_apply( + self.loss_single, + cls_scores, + bbox_preds, + all_anchor_list, + labels_list, + label_weights_list, + bbox_targets_list, + bbox_weights_list, + num_total_samples=num_total_samples) + return dict(loss_cls=losses_cls, loss_bbox=losses_bbox) + + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) + def get_bboxes(self, + cls_scores, + bbox_preds, + img_metas, + cfg=None, + rescale=False, + with_nms=True): + """Transform network output for a batch into bbox predictions. + + Args: + cls_scores (list[Tensor]): Box scores for each level in the + feature pyramid, has shape + (N, num_anchors * num_classes, H, W). + bbox_preds (list[Tensor]): Box energies / deltas for each + level in the feature pyramid, has shape + (N, num_anchors * 4, H, W). + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + cfg (mmcv.Config | None): Test / postprocessing configuration, + if None, test_cfg would be used + rescale (bool): If True, return boxes in original image space. + Default: False. + with_nms (bool): If True, do nms before return boxes. + Default: True. + + Returns: + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is an (n, 5) tensor, where 5 represent + (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. + The shape of the second tensor in the tuple is (n,), and + each element represents the class label of the corresponding + box. + + Example: + >>> import mmcv + >>> self = AnchorHead( + >>> num_classes=9, + >>> in_channels=1, + >>> anchor_generator=dict( + >>> type='AnchorGenerator', + >>> scales=[8], + >>> ratios=[0.5, 1.0, 2.0], + >>> strides=[4,])) + >>> img_metas = [{'img_shape': (32, 32, 3), 'scale_factor': 1}] + >>> cfg = mmcv.Config(dict( + >>> score_thr=0.00, + >>> nms=dict(type='nms', iou_thr=1.0), + >>> max_per_img=10)) + >>> feat = torch.rand(1, 1, 3, 3) + >>> cls_score, bbox_pred = self.forward_single(feat) + >>> # note the input lists are over different levels, not images + >>> cls_scores, bbox_preds = [cls_score], [bbox_pred] + >>> result_list = self.get_bboxes(cls_scores, bbox_preds, + >>> img_metas, cfg) + >>> det_bboxes, det_labels = result_list[0] + >>> assert len(result_list) == 1 + >>> assert det_bboxes.shape[1] == 5 + >>> assert len(det_bboxes) == len(det_labels) == cfg.max_per_img + """ + assert len(cls_scores) == len(bbox_preds) + num_levels = len(cls_scores) + + device = cls_scores[0].device + featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)] + mlvl_anchors = self.anchor_generator.grid_anchors( + featmap_sizes, device=device) + + mlvl_cls_scores = [cls_scores[i].detach() for i in range(num_levels)] + mlvl_bbox_preds = [bbox_preds[i].detach() for i in range(num_levels)] + + if torch.onnx.is_in_onnx_export(): + assert len( + img_metas + ) == 1, 'Only support one input image while in exporting to ONNX' + img_shapes = img_metas[0]['img_shape_for_onnx'] + else: + img_shapes = [ + img_metas[i]['img_shape'] + for i in range(cls_scores[0].shape[0]) + ] + scale_factors = [ + img_metas[i]['scale_factor'] for i in range(cls_scores[0].shape[0]) + ] + + if with_nms: + # some heads don't support with_nms argument + result_list = self._get_bboxes(mlvl_cls_scores, mlvl_bbox_preds, + mlvl_anchors, img_shapes, + scale_factors, cfg, rescale) + else: + result_list = self._get_bboxes(mlvl_cls_scores, mlvl_bbox_preds, + mlvl_anchors, img_shapes, + scale_factors, cfg, rescale, + with_nms) + return result_list + + def _get_bboxes(self, + mlvl_cls_scores, + mlvl_bbox_preds, + mlvl_anchors, + img_shapes, + scale_factors, + cfg, + rescale=False, + with_nms=True): + """Transform outputs for a batch item into bbox predictions. + + Args: + mlvl_cls_scores (list[Tensor]): Each element in the list is + the scores of bboxes of single level in the feature pyramid, + has shape (N, num_anchors * num_classes, H, W). + mlvl_bbox_preds (list[Tensor]): Each element in the list is the + bboxes predictions of single level in the feature pyramid, + has shape (N, num_anchors * 4, H, W). + mlvl_anchors (list[Tensor]): Each element in the list is + the anchors of single level in feature pyramid, has shape + (num_anchors, 4). + img_shapes (list[tuple[int]]): Each tuple in the list represent + the shape(height, width, 3) of single image in the batch. + scale_factors (list[ndarray]): Scale factor of the batch + image arange as list[(w_scale, h_scale, w_scale, h_scale)]. + cfg (mmcv.Config): Test / postprocessing configuration, + if None, test_cfg would be used. + rescale (bool): If True, return boxes in original image space. + Default: False. + with_nms (bool): If True, do nms before return boxes. + Default: True. + + Returns: + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is an (n, 5) tensor, where 5 represent + (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. + The shape of the second tensor in the tuple is (n,), and + each element represents the class label of the corresponding + box. + """ + cfg = self.test_cfg if cfg is None else cfg + assert len(mlvl_cls_scores) == len(mlvl_bbox_preds) == len( + mlvl_anchors) + batch_size = mlvl_cls_scores[0].shape[0] + # convert to tensor to keep tracing + nms_pre_tensor = torch.tensor( + cfg.get('nms_pre', -1), + device=mlvl_cls_scores[0].device, + dtype=torch.long) + + mlvl_bboxes = [] + mlvl_scores = [] + for cls_score, bbox_pred, anchors in zip(mlvl_cls_scores, + mlvl_bbox_preds, + mlvl_anchors): + assert cls_score.size()[-2:] == bbox_pred.size()[-2:] + cls_score = cls_score.permute(0, 2, 3, + 1).reshape(batch_size, -1, + self.cls_out_channels) + if self.use_sigmoid_cls: + scores = cls_score.sigmoid() + else: + scores = cls_score.softmax(-1) + bbox_pred = bbox_pred.permute(0, 2, 3, + 1).reshape(batch_size, -1, 4) + anchors = anchors.expand_as(bbox_pred) + # Always keep topk op for dynamic input in onnx + from mmdet.core.export import get_k_for_topk + nms_pre = get_k_for_topk(nms_pre_tensor, bbox_pred.shape[1]) + if nms_pre > 0: + # Get maximum scores for foreground classes. + if self.use_sigmoid_cls: + max_scores, _ = scores.max(-1) + else: + # remind that we set FG labels to [0, num_class-1] + # since mmdet v2.0 + # BG cat_id: num_class + max_scores, _ = scores[..., :-1].max(-1) + + _, topk_inds = max_scores.topk(nms_pre) + batch_inds = torch.arange(batch_size).view( + -1, 1).expand_as(topk_inds) + anchors = anchors[batch_inds, topk_inds, :] + bbox_pred = bbox_pred[batch_inds, topk_inds, :] + scores = scores[batch_inds, topk_inds, :] + + bboxes = self.bbox_coder.decode( + anchors, bbox_pred, max_shape=img_shapes) + mlvl_bboxes.append(bboxes) + mlvl_scores.append(scores) + + batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1) + if rescale: + batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor( + scale_factors).unsqueeze(1) + batch_mlvl_scores = torch.cat(mlvl_scores, dim=1) + + # Replace multiclass_nms with ONNX::NonMaxSuppression in deployment + if torch.onnx.is_in_onnx_export() and with_nms: + from mmdet.core.export import add_dummy_nms_for_onnx + # ignore background class + if not self.use_sigmoid_cls: + num_classes = batch_mlvl_scores.shape[2] - 1 + batch_mlvl_scores = batch_mlvl_scores[..., :num_classes] + max_output_boxes_per_class = cfg.nms.get( + 'max_output_boxes_per_class', 200) + iou_threshold = cfg.nms.get('iou_threshold', 0.5) + score_threshold = cfg.score_thr + nms_pre = cfg.get('deploy_nms_pre', -1) + return add_dummy_nms_for_onnx(batch_mlvl_bboxes, batch_mlvl_scores, + max_output_boxes_per_class, + iou_threshold, score_threshold, + nms_pre, cfg.max_per_img) + if self.use_sigmoid_cls: + # Add a dummy background class to the backend when using sigmoid + # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 + # BG cat_id: num_class + padding = batch_mlvl_scores.new_zeros(batch_size, + batch_mlvl_scores.shape[1], + 1) + batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1) + + if with_nms: + det_results = [] + for (mlvl_bboxes, mlvl_scores) in zip(batch_mlvl_bboxes, + batch_mlvl_scores): + det_bbox, det_label = multiclass_nms(mlvl_bboxes, mlvl_scores, + cfg.score_thr, cfg.nms, + cfg.max_per_img) + det_results.append(tuple([det_bbox, det_label])) + else: + det_results = [ + tuple(mlvl_bs) + for mlvl_bs in zip(batch_mlvl_bboxes, batch_mlvl_scores) + ] + return det_results + + def aug_test(self, feats, img_metas, rescale=False): + """Test function with test time augmentation. + + Args: + feats (list[Tensor]): the outer list indicates test-time + augmentations and inner Tensor should have a shape NxCxHxW, + which contains features for all images in the batch. + img_metas (list[list[dict]]): the outer list indicates test-time + augs (multiscale, flip, etc.) and the inner list indicates + images in a batch. each dict has image information. + rescale (bool, optional): Whether to rescale the results. + Defaults to False. + + Returns: + list[ndarray]: bbox results of each class + """ + return self.aug_test_bboxes(feats, img_metas, rescale=rescale) diff --git a/mmdet/models/dense_heads/atss_head.py b/mmdet/models/dense_heads/atss_head.py new file mode 100644 index 0000000..17dd395 --- /dev/null +++ b/mmdet/models/dense_heads/atss_head.py @@ -0,0 +1,684 @@ +import torch +import torch.nn as nn +from mmcv.cnn import ConvModule, Scale +from mmcv.runner import force_fp32 + +from mmdet.core import (anchor_inside_flags, build_assigner, build_sampler, + images_to_levels, multi_apply, multiclass_nms, + reduce_mean, unmap) +from ..builder import HEADS, build_loss +from .anchor_head import AnchorHead + + +@HEADS.register_module() +class ATSSHead(AnchorHead): + """Bridging the Gap Between Anchor-based and Anchor-free Detection via + Adaptive Training Sample Selection. + + ATSS head structure is similar with FCOS, however ATSS use anchor boxes + and assign label by Adaptive Training Sample Selection instead max-iou. + + https://arxiv.org/abs/1912.02424 + """ + + def __init__(self, + num_classes, + in_channels, + stacked_convs=4, + conv_cfg=None, + norm_cfg=dict(type='GN', num_groups=32, requires_grad=True), + loss_centerness=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0), + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=dict( + type='Normal', + name='atss_cls', + std=0.01, + bias_prob=0.01)), + **kwargs): + self.stacked_convs = stacked_convs + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + super(ATSSHead, self).__init__( + num_classes, in_channels, init_cfg=init_cfg, **kwargs) + + self.sampling = False + if self.train_cfg: + self.assigner = build_assigner(self.train_cfg.assigner) + # SSD sampling=False so use PseudoSampler + sampler_cfg = dict(type='PseudoSampler') + self.sampler = build_sampler(sampler_cfg, context=self) + self.loss_centerness = build_loss(loss_centerness) + + def _init_layers(self): + """Initialize layers of the head.""" + self.relu = nn.ReLU(inplace=True) + self.cls_convs = nn.ModuleList() + self.reg_convs = nn.ModuleList() + for i in range(self.stacked_convs): + chn = self.in_channels if i == 0 else self.feat_channels + self.cls_convs.append( + ConvModule( + chn, + self.feat_channels, + 3, + stride=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + self.reg_convs.append( + ConvModule( + chn, + self.feat_channels, + 3, + stride=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + self.atss_cls = nn.Conv2d( + self.feat_channels, + self.num_anchors * self.cls_out_channels, + 3, + padding=1) + self.atss_reg = nn.Conv2d( + self.feat_channels, self.num_anchors * 4, 3, padding=1) + self.atss_centerness = nn.Conv2d( + self.feat_channels, self.num_anchors * 1, 3, padding=1) + self.scales = nn.ModuleList( + [Scale(1.0) for _ in self.anchor_generator.strides]) + + def forward(self, feats): + """Forward features from the upstream network. + + Args: + feats (tuple[Tensor]): Features from the upstream network, each is + a 4D-tensor. + + Returns: + tuple: Usually a tuple of classification scores and bbox prediction + cls_scores (list[Tensor]): Classification scores for all scale + levels, each is a 4D-tensor, the channels number is + num_anchors * num_classes. + bbox_preds (list[Tensor]): Box energies / deltas for all scale + levels, each is a 4D-tensor, the channels number is + num_anchors * 4. + """ + return multi_apply(self.forward_single, feats, self.scales) + + def forward_single(self, x, scale): + """Forward feature of a single scale level. + + Args: + x (Tensor): Features of a single scale level. + scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize + the bbox prediction. + + Returns: + tuple: + cls_score (Tensor): Cls scores for a single scale level + the channels number is num_anchors * num_classes. + bbox_pred (Tensor): Box energies / deltas for a single scale + level, the channels number is num_anchors * 4. + centerness (Tensor): Centerness for a single scale level, the + channel number is (N, num_anchors * 1, H, W). + """ + cls_feat = x + reg_feat = x + for cls_conv in self.cls_convs: + cls_feat = cls_conv(cls_feat) + for reg_conv in self.reg_convs: + reg_feat = reg_conv(reg_feat) + cls_score = self.atss_cls(cls_feat) + # we just follow atss, not apply exp in bbox_pred + bbox_pred = scale(self.atss_reg(reg_feat)).float() + centerness = self.atss_centerness(reg_feat) + return cls_score, bbox_pred, centerness + + def loss_single(self, anchors, cls_score, bbox_pred, centerness, labels, + label_weights, bbox_targets, num_total_samples): + """Compute loss of a single scale level. + + Args: + cls_score (Tensor): Box scores for each scale level + Has shape (N, num_anchors * num_classes, H, W). + bbox_pred (Tensor): Box energies / deltas for each scale + level with shape (N, num_anchors * 4, H, W). + anchors (Tensor): Box reference for each scale level with shape + (N, num_total_anchors, 4). + labels (Tensor): Labels of each anchors with shape + (N, num_total_anchors). + label_weights (Tensor): Label weights of each anchor with shape + (N, num_total_anchors) + bbox_targets (Tensor): BBox regression targets of each anchor wight + shape (N, num_total_anchors, 4). + num_total_samples (int): Number os positive samples that is + reduced over all GPUs. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + + anchors = anchors.reshape(-1, 4) + cls_score = cls_score.permute(0, 2, 3, 1).reshape( + -1, self.cls_out_channels).contiguous() + bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4) + centerness = centerness.permute(0, 2, 3, 1).reshape(-1) + bbox_targets = bbox_targets.reshape(-1, 4) + labels = labels.reshape(-1) + label_weights = label_weights.reshape(-1) + + # classification loss + loss_cls = self.loss_cls( + cls_score, labels, label_weights, avg_factor=num_total_samples) + + # FG cat_id: [0, num_classes -1], BG cat_id: num_classes + bg_class_ind = self.num_classes + pos_inds = ((labels >= 0) + & (labels < bg_class_ind)).nonzero().squeeze(1) + + if len(pos_inds) > 0: + pos_bbox_targets = bbox_targets[pos_inds] + pos_bbox_pred = bbox_pred[pos_inds] + pos_anchors = anchors[pos_inds] + pos_centerness = centerness[pos_inds] + + centerness_targets = self.centerness_target( + pos_anchors, pos_bbox_targets) + pos_decode_bbox_pred = self.bbox_coder.decode( + pos_anchors, pos_bbox_pred) + pos_decode_bbox_targets = self.bbox_coder.decode( + pos_anchors, pos_bbox_targets) + + # regression loss + loss_bbox = self.loss_bbox( + pos_decode_bbox_pred, + pos_decode_bbox_targets, + weight=centerness_targets, + avg_factor=1.0) + + # centerness loss + loss_centerness = self.loss_centerness( + pos_centerness, + centerness_targets, + avg_factor=num_total_samples) + + else: + loss_bbox = bbox_pred.sum() * 0 + loss_centerness = centerness.sum() * 0 + centerness_targets = bbox_targets.new_tensor(0.) + + return loss_cls, loss_bbox, loss_centerness, centerness_targets.sum() + + @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'centernesses')) + def loss(self, + cls_scores, + bbox_preds, + centernesses, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute losses of the head. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (N, num_anchors * num_classes, H, W) + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_anchors * 4, H, W) + centernesses (list[Tensor]): Centerness for each scale + level with shape (N, num_anchors * 1, H, W) + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (list[Tensor] | None): specify which bounding + boxes can be ignored when computing the loss. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + assert len(featmap_sizes) == self.anchor_generator.num_levels + + device = cls_scores[0].device + anchor_list, valid_flag_list = self.get_anchors( + featmap_sizes, img_metas, device=device) + label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 + + cls_reg_targets = self.get_targets( + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + gt_labels_list=gt_labels, + label_channels=label_channels) + if cls_reg_targets is None: + return None + + (anchor_list, labels_list, label_weights_list, bbox_targets_list, + bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets + + num_total_samples = reduce_mean( + torch.tensor(num_total_pos, dtype=torch.float, + device=device)).item() + num_total_samples = max(num_total_samples, 1.0) + + losses_cls, losses_bbox, loss_centerness,\ + bbox_avg_factor = multi_apply( + self.loss_single, + anchor_list, + cls_scores, + bbox_preds, + centernesses, + labels_list, + label_weights_list, + bbox_targets_list, + num_total_samples=num_total_samples) + + bbox_avg_factor = sum(bbox_avg_factor) + bbox_avg_factor = reduce_mean(bbox_avg_factor).clamp_(min=1).item() + losses_bbox = list(map(lambda x: x / bbox_avg_factor, losses_bbox)) + return dict( + loss_cls=losses_cls, + loss_bbox=losses_bbox, + loss_centerness=loss_centerness) + + def centerness_target(self, anchors, bbox_targets): + # only calculate pos centerness targets, otherwise there may be nan + gts = self.bbox_coder.decode(anchors, bbox_targets) + anchors_cx = (anchors[:, 2] + anchors[:, 0]) / 2 + anchors_cy = (anchors[:, 3] + anchors[:, 1]) / 2 + l_ = anchors_cx - gts[:, 0] + t_ = anchors_cy - gts[:, 1] + r_ = gts[:, 2] - anchors_cx + b_ = gts[:, 3] - anchors_cy + + left_right = torch.stack([l_, r_], dim=1) + top_bottom = torch.stack([t_, b_], dim=1) + centerness = torch.sqrt( + (left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) * + (top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0])) + assert not torch.isnan(centerness).any() + return centerness + + @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'centernesses')) + def get_bboxes(self, + cls_scores, + bbox_preds, + centernesses, + img_metas, + cfg=None, + rescale=False, + with_nms=True): + """Transform network output for a batch into bbox predictions. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + with shape (N, num_anchors * num_classes, H, W). + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_anchors * 4, H, W). + centernesses (list[Tensor]): Centerness for each scale level with + shape (N, num_anchors * 1, H, W). + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + cfg (mmcv.Config | None): Test / postprocessing configuration, + if None, test_cfg would be used. Default: None. + rescale (bool): If True, return boxes in original image space. + Default: False. + with_nms (bool): If True, do nms before return boxes. + Default: True. + + Returns: + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is an (n, 5) tensor, where 5 represent + (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. + The shape of the second tensor in the tuple is (n,), and + each element represents the class label of the corresponding + box. + """ + cfg = self.test_cfg if cfg is None else cfg + assert len(cls_scores) == len(bbox_preds) + num_levels = len(cls_scores) + device = cls_scores[0].device + featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)] + mlvl_anchors = self.anchor_generator.grid_anchors( + featmap_sizes, device=device) + + cls_score_list = [cls_scores[i].detach() for i in range(num_levels)] + bbox_pred_list = [bbox_preds[i].detach() for i in range(num_levels)] + centerness_pred_list = [ + centernesses[i].detach() for i in range(num_levels) + ] + img_shapes = [ + img_metas[i]['img_shape'] for i in range(cls_scores[0].shape[0]) + ] + scale_factors = [ + img_metas[i]['scale_factor'] for i in range(cls_scores[0].shape[0]) + ] + result_list = self._get_bboxes(cls_score_list, bbox_pred_list, + centerness_pred_list, mlvl_anchors, + img_shapes, scale_factors, cfg, rescale, + with_nms) + return result_list + + def _get_bboxes(self, + cls_scores, + bbox_preds, + centernesses, + mlvl_anchors, + img_shapes, + scale_factors, + cfg, + rescale=False, + with_nms=True): + """Transform outputs for a single batch item into labeled boxes. + + Args: + cls_scores (list[Tensor]): Box scores for a single scale level + with shape (N, num_anchors * num_classes, H, W). + bbox_preds (list[Tensor]): Box energies / deltas for a single + scale level with shape (N, num_anchors * 4, H, W). + centernesses (list[Tensor]): Centerness for a single scale level + with shape (N, num_anchors * 1, H, W). + mlvl_anchors (list[Tensor]): Box reference for a single scale level + with shape (num_total_anchors, 4). + img_shapes (list[tuple[int]]): Shape of the input image, + list[(height, width, 3)]. + scale_factors (list[ndarray]): Scale factor of the image arrange as + (w_scale, h_scale, w_scale, h_scale). + cfg (mmcv.Config | None): Test / postprocessing configuration, + if None, test_cfg would be used. + rescale (bool): If True, return boxes in original image space. + Default: False. + with_nms (bool): If True, do nms before return boxes. + Default: True. + + Returns: + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is an (n, 5) tensor, where 5 represent + (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. + The shape of the second tensor in the tuple is (n,), and + each element represents the class label of the corresponding + box. + """ + assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors) + device = cls_scores[0].device + batch_size = cls_scores[0].shape[0] + # convert to tensor to keep tracing + nms_pre_tensor = torch.tensor( + cfg.get('nms_pre', -1), device=device, dtype=torch.long) + mlvl_bboxes = [] + mlvl_scores = [] + mlvl_centerness = [] + for cls_score, bbox_pred, centerness, anchors in zip( + cls_scores, bbox_preds, centernesses, mlvl_anchors): + assert cls_score.size()[-2:] == bbox_pred.size()[-2:] + scores = cls_score.permute(0, 2, 3, 1).reshape( + batch_size, -1, self.cls_out_channels).sigmoid() + centerness = centerness.permute(0, 2, 3, + 1).reshape(batch_size, + -1).sigmoid() + bbox_pred = bbox_pred.permute(0, 2, 3, + 1).reshape(batch_size, -1, 4) + + # Always keep topk op for dynamic input in onnx + if nms_pre_tensor > 0 and (torch.onnx.is_in_onnx_export() + or scores.shape[-2] > nms_pre_tensor): + from torch import _shape_as_tensor + # keep shape as tensor and get k + num_anchor = _shape_as_tensor(scores)[-2].to(device) + nms_pre = torch.where(nms_pre_tensor < num_anchor, + nms_pre_tensor, num_anchor) + + max_scores, _ = (scores * centerness[..., None]).max(-1) + _, topk_inds = max_scores.topk(nms_pre) + anchors = anchors[topk_inds, :] + batch_inds = torch.arange(batch_size).view( + -1, 1).expand_as(topk_inds).long() + bbox_pred = bbox_pred[batch_inds, topk_inds, :] + scores = scores[batch_inds, topk_inds, :] + centerness = centerness[batch_inds, topk_inds] + else: + anchors = anchors.expand_as(bbox_pred) + + bboxes = self.bbox_coder.decode( + anchors, bbox_pred, max_shape=img_shapes) + mlvl_bboxes.append(bboxes) + mlvl_scores.append(scores) + mlvl_centerness.append(centerness) + + batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1) + if rescale: + batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor( + scale_factors).unsqueeze(1) + batch_mlvl_scores = torch.cat(mlvl_scores, dim=1) + batch_mlvl_centerness = torch.cat(mlvl_centerness, dim=1) + + # Set max number of box to be feed into nms in deployment + deploy_nms_pre = cfg.get('deploy_nms_pre', -1) + if deploy_nms_pre > 0 and torch.onnx.is_in_onnx_export(): + batch_mlvl_scores, _ = ( + batch_mlvl_scores * + batch_mlvl_centerness.unsqueeze(2).expand_as(batch_mlvl_scores) + ).max(-1) + _, topk_inds = batch_mlvl_scores.topk(deploy_nms_pre) + batch_inds = torch.arange(batch_size).view(-1, + 1).expand_as(topk_inds) + batch_mlvl_scores = batch_mlvl_scores[batch_inds, topk_inds, :] + batch_mlvl_bboxes = batch_mlvl_bboxes[batch_inds, topk_inds, :] + batch_mlvl_centerness = batch_mlvl_centerness[batch_inds, + topk_inds] + # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 + # BG cat_id: num_class + padding = batch_mlvl_scores.new_zeros(batch_size, + batch_mlvl_scores.shape[1], 1) + batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1) + + if with_nms: + det_results = [] + for (mlvl_bboxes, mlvl_scores, + mlvl_centerness) in zip(batch_mlvl_bboxes, batch_mlvl_scores, + batch_mlvl_centerness): + det_bbox, det_label = multiclass_nms( + mlvl_bboxes, + mlvl_scores, + cfg.score_thr, + cfg.nms, + cfg.max_per_img, + score_factors=mlvl_centerness) + det_results.append(tuple([det_bbox, det_label])) + else: + det_results = [ + tuple(mlvl_bs) + for mlvl_bs in zip(batch_mlvl_bboxes, batch_mlvl_scores, + batch_mlvl_centerness) + ] + return det_results + + def get_targets(self, + anchor_list, + valid_flag_list, + gt_bboxes_list, + img_metas, + gt_bboxes_ignore_list=None, + gt_labels_list=None, + label_channels=1, + unmap_outputs=True): + """Get targets for ATSS head. + + This method is almost the same as `AnchorHead.get_targets()`. Besides + returning the targets as the parent method does, it also returns the + anchors as the first element of the returned tuple. + """ + num_imgs = len(img_metas) + assert len(anchor_list) == len(valid_flag_list) == num_imgs + + # anchor number of multi levels + num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] + num_level_anchors_list = [num_level_anchors] * num_imgs + + # concat all level anchors and flags to a single tensor + for i in range(num_imgs): + assert len(anchor_list[i]) == len(valid_flag_list[i]) + anchor_list[i] = torch.cat(anchor_list[i]) + valid_flag_list[i] = torch.cat(valid_flag_list[i]) + + # compute targets for each image + if gt_bboxes_ignore_list is None: + gt_bboxes_ignore_list = [None for _ in range(num_imgs)] + if gt_labels_list is None: + gt_labels_list = [None for _ in range(num_imgs)] + (all_anchors, all_labels, all_label_weights, all_bbox_targets, + all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply( + self._get_target_single, + anchor_list, + valid_flag_list, + num_level_anchors_list, + gt_bboxes_list, + gt_bboxes_ignore_list, + gt_labels_list, + img_metas, + label_channels=label_channels, + unmap_outputs=unmap_outputs) + # no valid anchors + if any([labels is None for labels in all_labels]): + return None + # sampled anchors of all images + num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list]) + num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list]) + # split targets to a list w.r.t. multiple levels + anchors_list = images_to_levels(all_anchors, num_level_anchors) + labels_list = images_to_levels(all_labels, num_level_anchors) + label_weights_list = images_to_levels(all_label_weights, + num_level_anchors) + bbox_targets_list = images_to_levels(all_bbox_targets, + num_level_anchors) + bbox_weights_list = images_to_levels(all_bbox_weights, + num_level_anchors) + return (anchors_list, labels_list, label_weights_list, + bbox_targets_list, bbox_weights_list, num_total_pos, + num_total_neg) + + def _get_target_single(self, + flat_anchors, + valid_flags, + num_level_anchors, + gt_bboxes, + gt_bboxes_ignore, + gt_labels, + img_meta, + label_channels=1, + unmap_outputs=True): + """Compute regression, classification targets for anchors in a single + image. + + Args: + flat_anchors (Tensor): Multi-level anchors of the image, which are + concatenated into a single tensor of shape (num_anchors ,4) + valid_flags (Tensor): Multi level valid flags of the image, + which are concatenated into a single tensor of + shape (num_anchors,). + num_level_anchors Tensor): Number of anchors of each scale level. + gt_bboxes (Tensor): Ground truth bboxes of the image, + shape (num_gts, 4). + gt_bboxes_ignore (Tensor): Ground truth bboxes to be + ignored, shape (num_ignored_gts, 4). + gt_labels (Tensor): Ground truth labels of each box, + shape (num_gts,). + img_meta (dict): Meta info of the image. + label_channels (int): Channel of label. + unmap_outputs (bool): Whether to map outputs back to the original + set of anchors. + + Returns: + tuple: N is the number of total anchors in the image. + labels (Tensor): Labels of all anchors in the image with shape + (N,). + label_weights (Tensor): Label weights of all anchor in the + image with shape (N,). + bbox_targets (Tensor): BBox targets of all anchors in the + image with shape (N, 4). + bbox_weights (Tensor): BBox weights of all anchors in the + image with shape (N, 4) + pos_inds (Tensor): Indices of positive anchor with shape + (num_pos,). + neg_inds (Tensor): Indices of negative anchor with shape + (num_neg,). + """ + inside_flags = anchor_inside_flags(flat_anchors, valid_flags, + img_meta['img_shape'][:2], + self.train_cfg.allowed_border) + if not inside_flags.any(): + return (None, ) * 7 + # assign gt and sample anchors + anchors = flat_anchors[inside_flags, :] + + num_level_anchors_inside = self.get_num_level_anchors_inside( + num_level_anchors, inside_flags) + assign_result = self.assigner.assign(anchors, num_level_anchors_inside, + gt_bboxes, gt_bboxes_ignore, + gt_labels) + + sampling_result = self.sampler.sample(assign_result, anchors, + gt_bboxes) + + num_valid_anchors = anchors.shape[0] + bbox_targets = torch.zeros_like(anchors) + bbox_weights = torch.zeros_like(anchors) + labels = anchors.new_full((num_valid_anchors, ), + self.num_classes, + dtype=torch.long) + label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float) + + pos_inds = sampling_result.pos_inds + neg_inds = sampling_result.neg_inds + if len(pos_inds) > 0: + if hasattr(self, 'bbox_coder'): + pos_bbox_targets = self.bbox_coder.encode( + sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes) + else: + # used in VFNetHead + pos_bbox_targets = sampling_result.pos_gt_bboxes + bbox_targets[pos_inds, :] = pos_bbox_targets + bbox_weights[pos_inds, :] = 1.0 + if gt_labels is None: + # Only rpn gives gt_labels as None + # Foreground is the first class since v2.5.0 + labels[pos_inds] = 0 + else: + labels[pos_inds] = gt_labels[ + sampling_result.pos_assigned_gt_inds] + if self.train_cfg.pos_weight <= 0: + label_weights[pos_inds] = 1.0 + else: + label_weights[pos_inds] = self.train_cfg.pos_weight + if len(neg_inds) > 0: + label_weights[neg_inds] = 1.0 + + # map up to original set of anchors + if unmap_outputs: + num_total_anchors = flat_anchors.size(0) + anchors = unmap(anchors, num_total_anchors, inside_flags) + labels = unmap( + labels, num_total_anchors, inside_flags, fill=self.num_classes) + label_weights = unmap(label_weights, num_total_anchors, + inside_flags) + bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags) + bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags) + + return (anchors, labels, label_weights, bbox_targets, bbox_weights, + pos_inds, neg_inds) + + def get_num_level_anchors_inside(self, num_level_anchors, inside_flags): + split_inside_flags = torch.split(inside_flags, num_level_anchors) + num_level_anchors_inside = [ + int(flags.sum()) for flags in split_inside_flags + ] + return num_level_anchors_inside diff --git a/mmdet/models/dense_heads/autoassign_head.py b/mmdet/models/dense_heads/autoassign_head.py new file mode 100644 index 0000000..c2fd42a --- /dev/null +++ b/mmdet/models/dense_heads/autoassign_head.py @@ -0,0 +1,517 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import bias_init_with_prob, normal_init +from mmcv.runner import force_fp32 + +from mmdet.core import distance2bbox, multi_apply +from mmdet.core.bbox import bbox_overlaps +from mmdet.models import HEADS +from mmdet.models.dense_heads.atss_head import reduce_mean +from mmdet.models.dense_heads.fcos_head import FCOSHead +from mmdet.models.dense_heads.paa_head import levels_to_images + +EPS = 1e-12 + + +class CenterPrior(nn.Module): + """Center Weighting module to adjust the category-specific prior + distributions. + + Args: + force_topk (bool): When no point falls into gt_bbox, forcibly + select the k points closest to the center to calculate + the center prior. Defaults to False. + topk (int): The number of points used to calculate the + center prior when no point falls in gt_bbox. Only work when + force_topk if True. Defaults to 9. + num_classes (int): The class number of dataset. Defaults to 80. + strides (tuple[int]): The stride of each input feature map. Defaults + to (8, 16, 32, 64, 128). + """ + + def __init__(self, + force_topk=False, + topk=9, + num_classes=80, + strides=(8, 16, 32, 64, 128)): + super(CenterPrior, self).__init__() + self.mean = nn.Parameter(torch.zeros(num_classes, 2)) + self.sigma = nn.Parameter(torch.ones(num_classes, 2)) + self.strides = strides + self.force_topk = force_topk + self.topk = topk + + def forward(self, anchor_points_list, gt_bboxes, labels, + inside_gt_bbox_mask): + """Get the center prior of each point on the feature map for each + instance. + + Args: + anchor_points_list (list[Tensor]): list of coordinate + of points on feature map. Each with shape + (num_points, 2). + gt_bboxes (Tensor): The gt_bboxes with shape of + (num_gt, 4). + labels (Tensor): The gt_labels with shape of (num_gt). + inside_gt_bbox_mask (Tensor): Tensor of bool type, + with shape of (num_points, num_gt), each + value is used to mark whether this point falls + within a certain gt. + + Returns: + tuple(Tensor): + + - center_prior_weights(Tensor): Float tensor with shape \ + of (num_points, num_gt). Each value represents \ + the center weighting coefficient. + - inside_gt_bbox_mask (Tensor): Tensor of bool type, \ + with shape of (num_points, num_gt), each \ + value is used to mark whether this point falls \ + within a certain gt or is the topk nearest points for \ + a specific gt_bbox. + """ + inside_gt_bbox_mask = inside_gt_bbox_mask.clone() + num_gts = len(labels) + num_points = sum([len(item) for item in anchor_points_list]) + if num_gts == 0: + return gt_bboxes.new_zeros(num_points, + num_gts), inside_gt_bbox_mask + center_prior_list = [] + for slvl_points, stride in zip(anchor_points_list, self.strides): + # slvl_points: points from single level in FPN, has shape (h*w, 2) + # single_level_points has shape (h*w, num_gt, 2) + single_level_points = slvl_points[:, None, :].expand( + (slvl_points.size(0), len(gt_bboxes), 2)) + gt_center_x = ((gt_bboxes[:, 0] + gt_bboxes[:, 2]) / 2) + gt_center_y = ((gt_bboxes[:, 1] + gt_bboxes[:, 3]) / 2) + gt_center = torch.stack((gt_center_x, gt_center_y), dim=1) + gt_center = gt_center[None] + # instance_center has shape (1, num_gt, 2) + instance_center = self.mean[labels][None] + # instance_sigma has shape (1, num_gt, 2) + instance_sigma = self.sigma[labels][None] + # distance has shape (num_points, num_gt, 2) + distance = (((single_level_points - gt_center) / float(stride) - + instance_center)**2) + center_prior = torch.exp(-distance / + (2 * instance_sigma**2)).prod(dim=-1) + center_prior_list.append(center_prior) + center_prior_weights = torch.cat(center_prior_list, dim=0) + + if self.force_topk: + gt_inds_no_points_inside = torch.nonzero( + inside_gt_bbox_mask.sum(0) == 0).reshape(-1) + if gt_inds_no_points_inside.numel(): + topk_center_index = \ + center_prior_weights[:, gt_inds_no_points_inside].topk( + self.topk, + dim=0)[1] + temp_mask = inside_gt_bbox_mask[:, gt_inds_no_points_inside] + inside_gt_bbox_mask[:, gt_inds_no_points_inside] = \ + torch.scatter(temp_mask, + dim=0, + index=topk_center_index, + src=torch.ones_like( + topk_center_index, + dtype=torch.bool)) + + center_prior_weights[~inside_gt_bbox_mask] = 0 + return center_prior_weights, inside_gt_bbox_mask + + +@HEADS.register_module() +class AutoAssignHead(FCOSHead): + """AutoAssignHead head used in `AutoAssign. + + `_. + + Args: + force_topk (bool): Used in center prior initialization to + handle extremely small gt. Default is False. + topk (int): The number of points used to calculate the + center prior when no point falls in gt_bbox. Only work when + force_topk if True. Defaults to 9. + pos_loss_weight (float): The loss weight of positive loss + and with default value 0.25. + neg_loss_weight (float): The loss weight of negative loss + and with default value 0.75. + center_loss_weight (float): The loss weight of center prior + loss and with default value 0.75. + """ + + def __init__(self, + *args, + force_topk=False, + topk=9, + pos_loss_weight=0.25, + neg_loss_weight=0.75, + center_loss_weight=0.75, + **kwargs): + super().__init__(*args, conv_bias=True, **kwargs) + self.center_prior = CenterPrior( + force_topk=force_topk, + topk=topk, + num_classes=self.num_classes, + strides=self.strides) + self.pos_loss_weight = pos_loss_weight + self.neg_loss_weight = neg_loss_weight + self.center_loss_weight = center_loss_weight + + def init_weights(self): + """Initialize weights of the head. + + In particular, we have special initialization for classified conv's and + regression conv's bias + """ + + super(AutoAssignHead, self).init_weights() + bias_cls = bias_init_with_prob(0.02) + normal_init(self.conv_cls, std=0.01, bias=bias_cls) + normal_init(self.conv_reg, std=0.01, bias=4.0) + + def _get_points_single(self, + featmap_size, + stride, + dtype, + device, + flatten=False): + """Almost the same as the implementation in fcos, we remove half stride + offset to align with the original implementation.""" + + y, x = super(FCOSHead, + self)._get_points_single(featmap_size, stride, dtype, + device) + points = torch.stack((x.reshape(-1) * stride, y.reshape(-1) * stride), + dim=-1) + return points + + def forward_single(self, x, scale, stride): + """Forward features of a single scale level. + + Args: + x (Tensor): FPN feature maps of the specified stride. + scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize + the bbox prediction. + stride (int): The corresponding stride for feature maps, only + used to normalize the bbox prediction when self.norm_on_bbox + is True. + + Returns: + tuple: scores for each class, bbox predictions and centerness \ + predictions of input feature maps. + """ + cls_score, bbox_pred, cls_feat, reg_feat = super( + FCOSHead, self).forward_single(x) + centerness = self.conv_centerness(reg_feat) + # scale the bbox_pred of different level + # float to avoid overflow when enabling FP16 + bbox_pred = scale(bbox_pred).float() + bbox_pred = F.relu(bbox_pred) + bbox_pred *= stride + return cls_score, bbox_pred, centerness + + def get_pos_loss_single(self, cls_score, objectness, reg_loss, gt_labels, + center_prior_weights): + """Calculate the positive loss of all points in gt_bboxes. + + Args: + cls_score (Tensor): All category scores for each point on + the feature map. The shape is (num_points, num_class). + objectness (Tensor): Foreground probability of all points, + has shape (num_points, 1). + reg_loss (Tensor): The regression loss of each gt_bbox and each + prediction box, has shape of (num_points, num_gt). + gt_labels (Tensor): The zeros based gt_labels of all gt + with shape of (num_gt,). + center_prior_weights (Tensor): Float tensor with shape + of (num_points, num_gt). Each value represents + the center weighting coefficient. + + Returns: + tuple[Tensor]: + + - pos_loss (Tensor): The positive loss of all points + in the gt_bboxes. + """ + # p_loc: localization confidence + p_loc = torch.exp(-reg_loss) + # p_cls: classification confidence + p_cls = (cls_score * objectness)[:, gt_labels] + # p_pos: joint confidence indicator + p_pos = p_cls * p_loc + + # 3 is a hyper-parameter to control the contributions of high and + # low confidence locations towards positive losses. + confidence_weight = torch.exp(p_pos * 3) + p_pos_weight = (confidence_weight * center_prior_weights) / ( + (confidence_weight * center_prior_weights).sum( + 0, keepdim=True)).clamp(min=EPS) + reweighted_p_pos = (p_pos * p_pos_weight).sum(0) + pos_loss = F.binary_cross_entropy( + reweighted_p_pos, + torch.ones_like(reweighted_p_pos), + reduction='none') + pos_loss = pos_loss.sum() * self.pos_loss_weight + return pos_loss, + + def get_neg_loss_single(self, cls_score, objectness, gt_labels, ious, + inside_gt_bbox_mask): + """Calculate the negative loss of all points in feature map. + + Args: + cls_score (Tensor): All category scores for each point on + the feature map. The shape is (num_points, num_class). + objectness (Tensor): Foreground probability of all points + and is shape of (num_points, 1). + gt_labels (Tensor): The zeros based label of all gt with shape of + (num_gt). + ious (Tensor): Float tensor with shape of (num_points, num_gt). + Each value represent the iou of pred_bbox and gt_bboxes. + inside_gt_bbox_mask (Tensor): Tensor of bool type, + with shape of (num_points, num_gt), each + value is used to mark whether this point falls + within a certain gt. + + Returns: + tuple[Tensor]: + + - neg_loss (Tensor): The negative loss of all points + in the feature map. + """ + num_gts = len(gt_labels) + joint_conf = (cls_score * objectness) + p_neg_weight = torch.ones_like(joint_conf) + if num_gts > 0: + # the order of dinmension would affect the value of + # p_neg_weight, we strictly follow the original + # implementation. + inside_gt_bbox_mask = inside_gt_bbox_mask.permute(1, 0) + ious = ious.permute(1, 0) + + foreground_idxs = torch.nonzero(inside_gt_bbox_mask, as_tuple=True) + temp_weight = (1 / (1 - ious[foreground_idxs]).clamp_(EPS)) + + def normalize(x): + return (x - x.min() + EPS) / (x.max() - x.min() + EPS) + + for instance_idx in range(num_gts): + idxs = foreground_idxs[0] == instance_idx + if idxs.any(): + temp_weight[idxs] = normalize(temp_weight[idxs]) + + p_neg_weight[foreground_idxs[1], + gt_labels[foreground_idxs[0]]] = 1 - temp_weight + + logits = (joint_conf * p_neg_weight) + neg_loss = ( + logits**2 * F.binary_cross_entropy( + logits, torch.zeros_like(logits), reduction='none')) + neg_loss = neg_loss.sum() * self.neg_loss_weight + return neg_loss, + + @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'objectnesses')) + def loss(self, + cls_scores, + bbox_preds, + objectnesses, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute loss of the head. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level, + each is a 4D-tensor, the channel number is + num_points * num_classes. + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level, each is a 4D-tensor, the channel number is + num_points * 4. + objectnesses (list[Tensor]): objectness for each scale level, each + is a 4D-tensor, the channel number is num_points * 1. + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + + assert len(cls_scores) == len(bbox_preds) == len(objectnesses) + all_num_gt = sum([len(item) for item in gt_bboxes]) + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + all_level_points = self.get_points(featmap_sizes, bbox_preds[0].dtype, + bbox_preds[0].device) + inside_gt_bbox_mask_list, bbox_targets_list = self.get_targets( + all_level_points, gt_bboxes) + + center_prior_weight_list = [] + temp_inside_gt_bbox_mask_list = [] + for gt_bboxe, gt_label, inside_gt_bbox_mask in zip( + gt_bboxes, gt_labels, inside_gt_bbox_mask_list): + center_prior_weight, inside_gt_bbox_mask = \ + self.center_prior(all_level_points, gt_bboxe, gt_label, + inside_gt_bbox_mask) + center_prior_weight_list.append(center_prior_weight) + temp_inside_gt_bbox_mask_list.append(inside_gt_bbox_mask) + inside_gt_bbox_mask_list = temp_inside_gt_bbox_mask_list + + mlvl_points = torch.cat(all_level_points, dim=0) + bbox_preds = levels_to_images(bbox_preds) + cls_scores = levels_to_images(cls_scores) + objectnesses = levels_to_images(objectnesses) + + reg_loss_list = [] + ious_list = [] + num_points = len(mlvl_points) + + for bbox_pred, gt_bboxe, inside_gt_bbox_mask in zip( + bbox_preds, bbox_targets_list, inside_gt_bbox_mask_list): + temp_num_gt = gt_bboxe.size(1) + expand_mlvl_points = mlvl_points[:, None, :].expand( + num_points, temp_num_gt, 2).reshape(-1, 2) + gt_bboxe = gt_bboxe.reshape(-1, 4) + expand_bbox_pred = bbox_pred[:, None, :].expand( + num_points, temp_num_gt, 4).reshape(-1, 4) + decoded_bbox_preds = distance2bbox(expand_mlvl_points, + expand_bbox_pred) + decoded_target_preds = distance2bbox(expand_mlvl_points, gt_bboxe) + with torch.no_grad(): + ious = bbox_overlaps( + decoded_bbox_preds, decoded_target_preds, is_aligned=True) + ious = ious.reshape(num_points, temp_num_gt) + if temp_num_gt: + ious = ious.max( + dim=-1, keepdim=True).values.repeat(1, temp_num_gt) + else: + ious = ious.new_zeros(num_points, temp_num_gt) + ious[~inside_gt_bbox_mask] = 0 + ious_list.append(ious) + loss_bbox = self.loss_bbox( + decoded_bbox_preds, + decoded_target_preds, + weight=None, + reduction_override='none') + reg_loss_list.append(loss_bbox.reshape(num_points, temp_num_gt)) + + cls_scores = [item.sigmoid() for item in cls_scores] + objectnesses = [item.sigmoid() for item in objectnesses] + pos_loss_list, = multi_apply(self.get_pos_loss_single, cls_scores, + objectnesses, reg_loss_list, gt_labels, + center_prior_weight_list) + pos_avg_factor = reduce_mean( + bbox_pred.new_tensor(all_num_gt)).clamp_(min=1) + pos_loss = sum(pos_loss_list) / pos_avg_factor + + neg_loss_list, = multi_apply(self.get_neg_loss_single, cls_scores, + objectnesses, gt_labels, ious_list, + inside_gt_bbox_mask_list) + neg_avg_factor = sum(item.data.sum() + for item in center_prior_weight_list) + neg_avg_factor = reduce_mean(neg_avg_factor).clamp_(min=1) + neg_loss = sum(neg_loss_list) / neg_avg_factor + + center_loss = [] + for i in range(len(img_metas)): + + if inside_gt_bbox_mask_list[i].any(): + center_loss.append( + len(gt_bboxes[i]) / + center_prior_weight_list[i].sum().clamp_(min=EPS)) + # when width or height of gt_bbox is smaller than stride of p3 + else: + center_loss.append(center_prior_weight_list[i].sum() * 0) + + center_loss = torch.stack(center_loss).mean() * self.center_loss_weight + + # avoid dead lock in DDP + if all_num_gt == 0: + pos_loss = bbox_preds[0].sum() * 0 + dummy_center_prior_loss = self.center_prior.mean.sum( + ) * 0 + self.center_prior.sigma.sum() * 0 + center_loss = objectnesses[0].sum() * 0 + dummy_center_prior_loss + + loss = dict( + loss_pos=pos_loss, loss_neg=neg_loss, loss_center=center_loss) + + return loss + + def get_targets(self, points, gt_bboxes_list): + """Compute regression targets and each point inside or outside gt_bbox + in multiple images. + + Args: + points (list[Tensor]): Points of all fpn level, each has shape + (num_points, 2). + gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image, + each has shape (num_gt, 4). + + Returns: + tuple(list[Tensor]): + + - inside_gt_bbox_mask_list (list[Tensor]): Each + Tensor is with bool type and shape of + (num_points, num_gt), each value + is used to mark whether this point falls + within a certain gt. + - concat_lvl_bbox_targets (list[Tensor]): BBox + targets of each level. Each tensor has shape + (num_points, num_gt, 4). + """ + + concat_points = torch.cat(points, dim=0) + # the number of points per img, per lvl + num_points = [center.size(0) for center in points] + inside_gt_bbox_mask_list, bbox_targets_list = multi_apply( + self._get_target_single, gt_bboxes_list, points=concat_points) + bbox_targets_list = [ + list(bbox_targets.split(num_points, 0)) + for bbox_targets in bbox_targets_list + ] + concat_lvl_bbox_targets = [ + torch.cat(item, dim=0) for item in bbox_targets_list + ] + return inside_gt_bbox_mask_list, concat_lvl_bbox_targets + + def _get_target_single(self, gt_bboxes, points): + """Compute regression targets and each point inside or outside gt_bbox + for a single image. + + Args: + gt_bboxes (Tensor): gt_bbox of single image, has shape + (num_gt,) + points (Tensor): Points of all fpn level, has shape + (num_points, 2). + + Returns: + tuple[Tensor]: Containing the following Tensors: + + - inside_gt_bbox_mask (Tensor): Bool tensor with shape + (num_points, num_gt), each value is used to mark + whether this point falls within a certain gt. + - bbox_targets (Tensor): BBox targets of each points with + each gt_bboxes, has shape (num_points, num_gt, 4). + """ + num_points = points.size(0) + num_gts = gt_bboxes.size(0) + gt_bboxes = gt_bboxes[None].expand(num_points, num_gts, 4) + xs, ys = points[:, 0], points[:, 1] + xs = xs[:, None] + ys = ys[:, None] + left = xs - gt_bboxes[..., 0] + right = gt_bboxes[..., 2] - xs + top = ys - gt_bboxes[..., 1] + bottom = gt_bboxes[..., 3] - ys + bbox_targets = torch.stack((left, top, right, bottom), -1) + if num_gts: + inside_gt_bbox_mask = bbox_targets.min(-1)[0] > 0 + else: + inside_gt_bbox_mask = bbox_targets.new_zeros((num_points, num_gts), + dtype=torch.bool) + + return inside_gt_bbox_mask, bbox_targets diff --git a/mmdet/models/dense_heads/base_dense_head.py b/mmdet/models/dense_heads/base_dense_head.py new file mode 100644 index 0000000..108eeb2 --- /dev/null +++ b/mmdet/models/dense_heads/base_dense_head.py @@ -0,0 +1,59 @@ +from abc import ABCMeta, abstractmethod + +from mmcv.runner import BaseModule + + +class BaseDenseHead(BaseModule, metaclass=ABCMeta): + """Base class for DenseHeads.""" + + def __init__(self, init_cfg=None): + super(BaseDenseHead, self).__init__(init_cfg) + + @abstractmethod + def loss(self, **kwargs): + """Compute losses of the head.""" + pass + + @abstractmethod + def get_bboxes(self, **kwargs): + """Transform network output for a batch into bbox predictions.""" + pass + + def forward_train(self, + x, + img_metas, + gt_bboxes, + gt_labels=None, + gt_bboxes_ignore=None, + proposal_cfg=None, + **kwargs): + """ + Args: + x (list[Tensor]): Features from FPN. + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes (Tensor): Ground truth bboxes of the image, + shape (num_gts, 4). + gt_labels (Tensor): Ground truth labels of each box, + shape (num_gts,). + gt_bboxes_ignore (Tensor): Ground truth bboxes to be + ignored, shape (num_ignored_gts, 4). + proposal_cfg (mmcv.Config): Test / postprocessing configuration, + if None, test_cfg would be used + + Returns: + tuple: + losses: (dict[str, Tensor]): A dictionary of loss components. + proposal_list (list[Tensor]): Proposals of each image. + """ + outs = self(x) + if gt_labels is None: + loss_inputs = outs + (gt_bboxes, img_metas) + else: + loss_inputs = outs + (gt_bboxes, gt_labels, img_metas) + losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore) + if proposal_cfg is None: + return losses + else: + proposal_list = self.get_bboxes(*outs, img_metas, cfg=proposal_cfg) + return losses, proposal_list diff --git a/mmdet/models/dense_heads/cascade_rpn_head.py b/mmdet/models/dense_heads/cascade_rpn_head.py new file mode 100644 index 0000000..def5bc4 --- /dev/null +++ b/mmdet/models/dense_heads/cascade_rpn_head.py @@ -0,0 +1,785 @@ +from __future__ import division +import copy +import warnings + +import torch +import torch.nn as nn +from mmcv import ConfigDict +from mmcv.ops import DeformConv2d, batched_nms +from mmcv.runner import BaseModule, ModuleList + +from mmdet.core import (RegionAssigner, build_assigner, build_sampler, + images_to_levels, multi_apply) +from ..builder import HEADS, build_head +from .base_dense_head import BaseDenseHead +from .rpn_head import RPNHead + + +class AdaptiveConv(BaseModule): + """AdaptiveConv used to adapt the sampling location with the anchors. + + Args: + in_channels (int): Number of channels in the input image + out_channels (int): Number of channels produced by the convolution + kernel_size (int or tuple): Size of the conv kernel. Default: 3 + stride (int or tuple, optional): Stride of the convolution. Default: 1 + padding (int or tuple, optional): Zero-padding added to both sides of + the input. Default: 1 + dilation (int or tuple, optional): Spacing between kernel elements. + Default: 3 + groups (int, optional): Number of blocked connections from input + channels to output channels. Default: 1 + bias (bool, optional): If set True, adds a learnable bias to the + output. Default: False. + type (str, optional): Type of adaptive conv, can be either 'offset' + (arbitrary anchors) or 'dilation' (uniform anchor). + Default: 'dilation'. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + dilation=3, + groups=1, + bias=False, + type='dilation', + init_cfg=dict( + type='Normal', std=0.01, override=dict(name='conv'))): + super(AdaptiveConv, self).__init__(init_cfg) + assert type in ['offset', 'dilation'] + self.adapt_type = type + + assert kernel_size == 3, 'Adaptive conv only supports kernels 3' + if self.adapt_type == 'offset': + assert stride == 1 and padding == 1 and groups == 1, \ + 'Adaptive conv offset mode only supports padding: {1}, ' \ + f'stride: {1}, groups: {1}' + self.conv = DeformConv2d( + in_channels, + out_channels, + kernel_size, + padding=padding, + stride=stride, + groups=groups, + bias=bias) + else: + self.conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size, + padding=dilation, + dilation=dilation) + + def forward(self, x, offset): + """Forward function.""" + if self.adapt_type == 'offset': + N, _, H, W = x.shape + assert offset is not None + assert H * W == offset.shape[1] + # reshape [N, NA, 18] to (N, 18, H, W) + offset = offset.permute(0, 2, 1).reshape(N, -1, H, W) + offset = offset.contiguous() + x = self.conv(x, offset) + else: + assert offset is None + x = self.conv(x) + return x + + +@HEADS.register_module() +class StageCascadeRPNHead(RPNHead): + """Stage of CascadeRPNHead. + + Args: + in_channels (int): Number of channels in the input feature map. + anchor_generator (dict): anchor generator config. + adapt_cfg (dict): adaptation config. + bridged_feature (bool, optional): whether update rpn feature. + Default: False. + with_cls (bool, optional): wheather use classification branch. + Default: True. + sampling (bool, optional): wheather use sampling. Default: True. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + in_channels, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[1.0], + strides=[4, 8, 16, 32, 64]), + adapt_cfg=dict(type='dilation', dilation=3), + bridged_feature=False, + with_cls=True, + sampling=True, + init_cfg=None, + **kwargs): + self.with_cls = with_cls + self.anchor_strides = anchor_generator['strides'] + self.anchor_scales = anchor_generator['scales'] + self.bridged_feature = bridged_feature + self.adapt_cfg = adapt_cfg + super(StageCascadeRPNHead, self).__init__( + in_channels, + anchor_generator=anchor_generator, + init_cfg=init_cfg, + **kwargs) + + # override sampling and sampler + self.sampling = sampling + if self.train_cfg: + self.assigner = build_assigner(self.train_cfg.assigner) + # use PseudoSampler when sampling is False + if self.sampling and hasattr(self.train_cfg, 'sampler'): + sampler_cfg = self.train_cfg.sampler + else: + sampler_cfg = dict(type='PseudoSampler') + self.sampler = build_sampler(sampler_cfg, context=self) + + if init_cfg is None: + self.init_cfg = dict( + type='Normal', std=0.01, override=[dict(name='rpn_reg')]) + if self.with_cls: + self.init_cfg['override'].append(dict(name='rpn_cls')) + + def _init_layers(self): + """Init layers of a CascadeRPN stage.""" + self.rpn_conv = AdaptiveConv(self.in_channels, self.feat_channels, + **self.adapt_cfg) + if self.with_cls: + self.rpn_cls = nn.Conv2d(self.feat_channels, + self.num_anchors * self.cls_out_channels, + 1) + self.rpn_reg = nn.Conv2d(self.feat_channels, self.num_anchors * 4, 1) + self.relu = nn.ReLU(inplace=True) + + def forward_single(self, x, offset): + """Forward function of single scale.""" + bridged_x = x + x = self.relu(self.rpn_conv(x, offset)) + if self.bridged_feature: + bridged_x = x # update feature + cls_score = self.rpn_cls(x) if self.with_cls else None + bbox_pred = self.rpn_reg(x) + return bridged_x, cls_score, bbox_pred + + def forward(self, feats, offset_list=None): + """Forward function.""" + if offset_list is None: + offset_list = [None for _ in range(len(feats))] + return multi_apply(self.forward_single, feats, offset_list) + + def _region_targets_single(self, + anchors, + valid_flags, + gt_bboxes, + gt_bboxes_ignore, + gt_labels, + img_meta, + featmap_sizes, + label_channels=1): + """Get anchor targets based on region for single level.""" + assign_result = self.assigner.assign( + anchors, + valid_flags, + gt_bboxes, + img_meta, + featmap_sizes, + self.anchor_scales[0], + self.anchor_strides, + gt_bboxes_ignore=gt_bboxes_ignore, + gt_labels=None, + allowed_border=self.train_cfg.allowed_border) + flat_anchors = torch.cat(anchors) + sampling_result = self.sampler.sample(assign_result, flat_anchors, + gt_bboxes) + + num_anchors = flat_anchors.shape[0] + bbox_targets = torch.zeros_like(flat_anchors) + bbox_weights = torch.zeros_like(flat_anchors) + labels = flat_anchors.new_zeros(num_anchors, dtype=torch.long) + label_weights = flat_anchors.new_zeros(num_anchors, dtype=torch.float) + + pos_inds = sampling_result.pos_inds + neg_inds = sampling_result.neg_inds + if len(pos_inds) > 0: + if not self.reg_decoded_bbox: + pos_bbox_targets = self.bbox_coder.encode( + sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes) + else: + pos_bbox_targets = sampling_result.pos_gt_bboxes + bbox_targets[pos_inds, :] = pos_bbox_targets + bbox_weights[pos_inds, :] = 1.0 + if gt_labels is None: + labels[pos_inds] = 1 + else: + labels[pos_inds] = gt_labels[ + sampling_result.pos_assigned_gt_inds] + if self.train_cfg.pos_weight <= 0: + label_weights[pos_inds] = 1.0 + else: + label_weights[pos_inds] = self.train_cfg.pos_weight + if len(neg_inds) > 0: + label_weights[neg_inds] = 1.0 + + return (labels, label_weights, bbox_targets, bbox_weights, pos_inds, + neg_inds) + + def region_targets(self, + anchor_list, + valid_flag_list, + gt_bboxes_list, + img_metas, + featmap_sizes, + gt_bboxes_ignore_list=None, + gt_labels_list=None, + label_channels=1, + unmap_outputs=True): + """See :func:`StageCascadeRPNHead.get_targets`.""" + num_imgs = len(img_metas) + assert len(anchor_list) == len(valid_flag_list) == num_imgs + + # anchor number of multi levels + num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] + + # compute targets for each image + if gt_bboxes_ignore_list is None: + gt_bboxes_ignore_list = [None for _ in range(num_imgs)] + if gt_labels_list is None: + gt_labels_list = [None for _ in range(num_imgs)] + (all_labels, all_label_weights, all_bbox_targets, all_bbox_weights, + pos_inds_list, neg_inds_list) = multi_apply( + self._region_targets_single, + anchor_list, + valid_flag_list, + gt_bboxes_list, + gt_bboxes_ignore_list, + gt_labels_list, + img_metas, + featmap_sizes=featmap_sizes, + label_channels=label_channels) + # no valid anchors + if any([labels is None for labels in all_labels]): + return None + # sampled anchors of all images + num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list]) + num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list]) + # split targets to a list w.r.t. multiple levels + labels_list = images_to_levels(all_labels, num_level_anchors) + label_weights_list = images_to_levels(all_label_weights, + num_level_anchors) + bbox_targets_list = images_to_levels(all_bbox_targets, + num_level_anchors) + bbox_weights_list = images_to_levels(all_bbox_weights, + num_level_anchors) + return (labels_list, label_weights_list, bbox_targets_list, + bbox_weights_list, num_total_pos, num_total_neg) + + def get_targets(self, + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + featmap_sizes, + gt_bboxes_ignore=None, + label_channels=1): + """Compute regression and classification targets for anchors. + + Args: + anchor_list (list[list]): Multi level anchors of each image. + valid_flag_list (list[list]): Multi level valid flags of each + image. + gt_bboxes (list[Tensor]): Ground truth bboxes of each image. + img_metas (list[dict]): Meta info of each image. + featmap_sizes (list[Tensor]): Feature mapsize each level + gt_bboxes_ignore (list[Tensor]): Ignore bboxes of each images + label_channels (int): Channel of label. + + Returns: + cls_reg_targets (tuple) + """ + if isinstance(self.assigner, RegionAssigner): + cls_reg_targets = self.region_targets( + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + featmap_sizes, + gt_bboxes_ignore_list=gt_bboxes_ignore, + label_channels=label_channels) + else: + cls_reg_targets = super(StageCascadeRPNHead, self).get_targets( + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + label_channels=label_channels) + return cls_reg_targets + + def anchor_offset(self, anchor_list, anchor_strides, featmap_sizes): + """ Get offest for deformable conv based on anchor shape + NOTE: currently support deformable kernel_size=3 and dilation=1 + + Args: + anchor_list (list[list[tensor])): [NI, NLVL, NA, 4] list of + multi-level anchors + anchor_strides (list[int]): anchor stride of each level + + Returns: + offset_list (list[tensor]): [NLVL, NA, 2, 18]: offset of DeformConv + kernel. + """ + + def _shape_offset(anchors, stride, ks=3, dilation=1): + # currently support kernel_size=3 and dilation=1 + assert ks == 3 and dilation == 1 + pad = (ks - 1) // 2 + idx = torch.arange(-pad, pad + 1, dtype=dtype, device=device) + yy, xx = torch.meshgrid(idx, idx) # return order matters + xx = xx.reshape(-1) + yy = yy.reshape(-1) + w = (anchors[:, 2] - anchors[:, 0]) / stride + h = (anchors[:, 3] - anchors[:, 1]) / stride + w = w / (ks - 1) - dilation + h = h / (ks - 1) - dilation + offset_x = w[:, None] * xx # (NA, ks**2) + offset_y = h[:, None] * yy # (NA, ks**2) + return offset_x, offset_y + + def _ctr_offset(anchors, stride, featmap_size): + feat_h, feat_w = featmap_size + assert len(anchors) == feat_h * feat_w + + x = (anchors[:, 0] + anchors[:, 2]) * 0.5 + y = (anchors[:, 1] + anchors[:, 3]) * 0.5 + # compute centers on feature map + x = x / stride + y = y / stride + # compute predefine centers + xx = torch.arange(0, feat_w, device=anchors.device) + yy = torch.arange(0, feat_h, device=anchors.device) + yy, xx = torch.meshgrid(yy, xx) + xx = xx.reshape(-1).type_as(x) + yy = yy.reshape(-1).type_as(y) + + offset_x = x - xx # (NA, ) + offset_y = y - yy # (NA, ) + return offset_x, offset_y + + num_imgs = len(anchor_list) + num_lvls = len(anchor_list[0]) + dtype = anchor_list[0][0].dtype + device = anchor_list[0][0].device + num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] + + offset_list = [] + for i in range(num_imgs): + mlvl_offset = [] + for lvl in range(num_lvls): + c_offset_x, c_offset_y = _ctr_offset(anchor_list[i][lvl], + anchor_strides[lvl], + featmap_sizes[lvl]) + s_offset_x, s_offset_y = _shape_offset(anchor_list[i][lvl], + anchor_strides[lvl]) + + # offset = ctr_offset + shape_offset + offset_x = s_offset_x + c_offset_x[:, None] + offset_y = s_offset_y + c_offset_y[:, None] + + # offset order (y0, x0, y1, x2, .., y8, x8, y9, x9) + offset = torch.stack([offset_y, offset_x], dim=-1) + offset = offset.reshape(offset.size(0), -1) # [NA, 2*ks**2] + mlvl_offset.append(offset) + offset_list.append(torch.cat(mlvl_offset)) # [totalNA, 2*ks**2] + offset_list = images_to_levels(offset_list, num_level_anchors) + return offset_list + + def loss_single(self, cls_score, bbox_pred, anchors, labels, label_weights, + bbox_targets, bbox_weights, num_total_samples): + """Loss function on single scale.""" + # classification loss + if self.with_cls: + labels = labels.reshape(-1) + label_weights = label_weights.reshape(-1) + cls_score = cls_score.permute(0, 2, 3, + 1).reshape(-1, self.cls_out_channels) + loss_cls = self.loss_cls( + cls_score, labels, label_weights, avg_factor=num_total_samples) + # regression loss + bbox_targets = bbox_targets.reshape(-1, 4) + bbox_weights = bbox_weights.reshape(-1, 4) + bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4) + if self.reg_decoded_bbox: + # When the regression loss (e.g. `IouLoss`, `GIouLoss`) + # is applied directly on the decoded bounding boxes, it + # decodes the already encoded coordinates to absolute format. + anchors = anchors.reshape(-1, 4) + bbox_pred = self.bbox_coder.decode(anchors, bbox_pred) + loss_reg = self.loss_bbox( + bbox_pred, + bbox_targets, + bbox_weights, + avg_factor=num_total_samples) + if self.with_cls: + return loss_cls, loss_reg + return None, loss_reg + + def loss(self, + anchor_list, + valid_flag_list, + cls_scores, + bbox_preds, + gt_bboxes, + img_metas, + gt_bboxes_ignore=None): + """Compute losses of the head. + + Args: + anchor_list (list[list]): Multi level anchors of each image. + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (N, num_anchors * num_classes, H, W) + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_anchors * 4, H, W) + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. Default: None + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + featmap_sizes = [featmap.size()[-2:] for featmap in bbox_preds] + label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 + cls_reg_targets = self.get_targets( + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + featmap_sizes, + gt_bboxes_ignore=gt_bboxes_ignore, + label_channels=label_channels) + if cls_reg_targets is None: + return None + (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, + num_total_pos, num_total_neg) = cls_reg_targets + if self.sampling: + num_total_samples = num_total_pos + num_total_neg + else: + # 200 is hard-coded average factor, + # which follows guided anchoring. + num_total_samples = sum([label.numel() + for label in labels_list]) / 200.0 + + # change per image, per level anchor_list to per_level, per_image + mlvl_anchor_list = list(zip(*anchor_list)) + # concat mlvl_anchor_list + mlvl_anchor_list = [ + torch.cat(anchors, dim=0) for anchors in mlvl_anchor_list + ] + + losses = multi_apply( + self.loss_single, + cls_scores, + bbox_preds, + mlvl_anchor_list, + labels_list, + label_weights_list, + bbox_targets_list, + bbox_weights_list, + num_total_samples=num_total_samples) + if self.with_cls: + return dict(loss_rpn_cls=losses[0], loss_rpn_reg=losses[1]) + return dict(loss_rpn_reg=losses[1]) + + def get_bboxes(self, + anchor_list, + cls_scores, + bbox_preds, + img_metas, + cfg, + rescale=False): + """Get proposal predict.""" + assert len(cls_scores) == len(bbox_preds) + num_levels = len(cls_scores) + + result_list = [] + for img_id in range(len(img_metas)): + cls_score_list = [ + cls_scores[i][img_id].detach() for i in range(num_levels) + ] + bbox_pred_list = [ + bbox_preds[i][img_id].detach() for i in range(num_levels) + ] + img_shape = img_metas[img_id]['img_shape'] + scale_factor = img_metas[img_id]['scale_factor'] + proposals = self._get_bboxes_single(cls_score_list, bbox_pred_list, + anchor_list[img_id], img_shape, + scale_factor, cfg, rescale) + result_list.append(proposals) + return result_list + + def refine_bboxes(self, anchor_list, bbox_preds, img_metas): + """Refine bboxes through stages.""" + num_levels = len(bbox_preds) + new_anchor_list = [] + for img_id in range(len(img_metas)): + mlvl_anchors = [] + for i in range(num_levels): + bbox_pred = bbox_preds[i][img_id].detach() + bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4) + img_shape = img_metas[img_id]['img_shape'] + bboxes = self.bbox_coder.decode(anchor_list[img_id][i], + bbox_pred, img_shape) + mlvl_anchors.append(bboxes) + new_anchor_list.append(mlvl_anchors) + return new_anchor_list + + # TODO: temporary plan + def _get_bboxes_single(self, + cls_scores, + bbox_preds, + mlvl_anchors, + img_shape, + scale_factor, + cfg, + rescale=False): + """Transform outputs for a single batch item into bbox predictions. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (num_anchors * num_classes, H, W). + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (num_anchors * 4, H, W). + mlvl_anchors (list[Tensor]): Box reference for each scale level + with shape (num_total_anchors, 4). + img_shape (tuple[int]): Shape of the input image, + (height, width, 3). + scale_factor (ndarray): Scale factor of the image arange as + (w_scale, h_scale, w_scale, h_scale). + cfg (mmcv.Config): Test / postprocessing configuration, + if None, test_cfg would be used. + rescale (bool): If True, return boxes in original image space. + + Returns: + Tensor: Labeled boxes have the shape of (n,5), where the + first 4 columns are bounding box positions + (tl_x, tl_y, br_x, br_y) and the 5-th column is a score + between 0 and 1. + """ + cfg = self.test_cfg if cfg is None else cfg + cfg = copy.deepcopy(cfg) + # bboxes from different level should be independent during NMS, + # level_ids are used as labels for batched NMS to separate them + level_ids = [] + mlvl_scores = [] + mlvl_bbox_preds = [] + mlvl_valid_anchors = [] + for idx in range(len(cls_scores)): + rpn_cls_score = cls_scores[idx] + rpn_bbox_pred = bbox_preds[idx] + assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:] + rpn_cls_score = rpn_cls_score.permute(1, 2, 0) + if self.use_sigmoid_cls: + rpn_cls_score = rpn_cls_score.reshape(-1) + scores = rpn_cls_score.sigmoid() + else: + rpn_cls_score = rpn_cls_score.reshape(-1, 2) + # We set FG labels to [0, num_class-1] and BG label to + # num_class in RPN head since mmdet v2.5, which is unified to + # be consistent with other head since mmdet v2.0. In mmdet v2.0 + # to v2.4 we keep BG label as 0 and FG label as 1 in rpn head. + scores = rpn_cls_score.softmax(dim=1)[:, 0] + rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1, 4) + anchors = mlvl_anchors[idx] + if cfg.nms_pre > 0 and scores.shape[0] > cfg.nms_pre: + # sort is faster than topk + # _, topk_inds = scores.topk(cfg.nms_pre) + if torch.onnx.is_in_onnx_export(): + # sort op will be converted to TopK in onnx + # and k<=3480 in TensorRT + _, topk_inds = scores.topk(cfg.nms_pre) + scores = scores[topk_inds] + else: + ranked_scores, rank_inds = scores.sort(descending=True) + topk_inds = rank_inds[:cfg.nms_pre] + scores = ranked_scores[:cfg.nms_pre] + rpn_bbox_pred = rpn_bbox_pred[topk_inds, :] + anchors = anchors[topk_inds, :] + mlvl_scores.append(scores) + mlvl_bbox_preds.append(rpn_bbox_pred) + mlvl_valid_anchors.append(anchors) + level_ids.append( + scores.new_full((scores.size(0), ), idx, dtype=torch.long)) + + scores = torch.cat(mlvl_scores) + anchors = torch.cat(mlvl_valid_anchors) + rpn_bbox_pred = torch.cat(mlvl_bbox_preds) + proposals = self.bbox_coder.decode( + anchors, rpn_bbox_pred, max_shape=img_shape) + ids = torch.cat(level_ids) + + # Skip nonzero op while exporting to ONNX + if cfg.min_bbox_size > 0 and (not torch.onnx.is_in_onnx_export()): + w = proposals[:, 2] - proposals[:, 0] + h = proposals[:, 3] - proposals[:, 1] + valid_inds = torch.nonzero( + (w >= cfg.min_bbox_size) + & (h >= cfg.min_bbox_size), + as_tuple=False).squeeze() + if valid_inds.sum().item() != len(proposals): + proposals = proposals[valid_inds, :] + scores = scores[valid_inds] + ids = ids[valid_inds] + + # deprecate arguments warning + if 'nms' not in cfg or 'max_num' in cfg or 'nms_thr' in cfg: + warnings.warn( + 'In rpn_proposal or test_cfg, ' + 'nms_thr has been moved to a dict named nms as ' + 'iou_threshold, max_num has been renamed as max_per_img, ' + 'name of original arguments and the way to specify ' + 'iou_threshold of NMS will be deprecated.') + if 'nms' not in cfg: + cfg.nms = ConfigDict(dict(type='nms', iou_threshold=cfg.nms_thr)) + if 'max_num' in cfg: + if 'max_per_img' in cfg: + assert cfg.max_num == cfg.max_per_img, f'You ' \ + f'set max_num and ' \ + f'max_per_img at the same time, but get {cfg.max_num} ' \ + f'and {cfg.max_per_img} respectively' \ + 'Please delete max_num which will be deprecated.' + else: + cfg.max_per_img = cfg.max_num + if 'nms_thr' in cfg: + assert cfg.nms.iou_threshold == cfg.nms_thr, f'You set' \ + f' iou_threshold in nms and ' \ + f'nms_thr at the same time, but get' \ + f' {cfg.nms.iou_threshold} and {cfg.nms_thr}' \ + f' respectively. Please delete the nms_thr ' \ + f'which will be deprecated.' + + dets, keep = batched_nms(proposals, scores, ids, cfg.nms) + return dets[:cfg.max_per_img] + + +@HEADS.register_module() +class CascadeRPNHead(BaseDenseHead): + """The CascadeRPNHead will predict more accurate region proposals, which is + required for two-stage detectors (such as Fast/Faster R-CNN). CascadeRPN + consists of a sequence of RPNStage to progressively improve the accuracy of + the detected proposals. + + More details can be found in ``https://arxiv.org/abs/1909.06720``. + + Args: + num_stages (int): number of CascadeRPN stages. + stages (list[dict]): list of configs to build the stages. + train_cfg (list[dict]): list of configs at training time each stage. + test_cfg (dict): config at testing time. + """ + + def __init__(self, num_stages, stages, train_cfg, test_cfg, init_cfg=None): + super(CascadeRPNHead, self).__init__(init_cfg) + assert num_stages == len(stages) + self.num_stages = num_stages + # Be careful! Pretrained weights cannot be loaded when use + # nn.ModuleList + self.stages = ModuleList() + for i in range(len(stages)): + train_cfg_i = train_cfg[i] if train_cfg is not None else None + stages[i].update(train_cfg=train_cfg_i) + stages[i].update(test_cfg=test_cfg) + self.stages.append(build_head(stages[i])) + self.train_cfg = train_cfg + self.test_cfg = test_cfg + + def loss(self): + """loss() is implemented in StageCascadeRPNHead.""" + pass + + def get_bboxes(self): + """get_bboxes() is implemented in StageCascadeRPNHead.""" + pass + + def forward_train(self, + x, + img_metas, + gt_bboxes, + gt_labels=None, + gt_bboxes_ignore=None, + proposal_cfg=None): + """Forward train function.""" + assert gt_labels is None, 'RPN does not require gt_labels' + + featmap_sizes = [featmap.size()[-2:] for featmap in x] + device = x[0].device + anchor_list, valid_flag_list = self.stages[0].get_anchors( + featmap_sizes, img_metas, device=device) + + losses = dict() + + for i in range(self.num_stages): + stage = self.stages[i] + + if stage.adapt_cfg['type'] == 'offset': + offset_list = stage.anchor_offset(anchor_list, + stage.anchor_strides, + featmap_sizes) + else: + offset_list = None + x, cls_score, bbox_pred = stage(x, offset_list) + rpn_loss_inputs = (anchor_list, valid_flag_list, cls_score, + bbox_pred, gt_bboxes, img_metas) + stage_loss = stage.loss(*rpn_loss_inputs) + for name, value in stage_loss.items(): + losses['s{}.{}'.format(i, name)] = value + + # refine boxes + if i < self.num_stages - 1: + anchor_list = stage.refine_bboxes(anchor_list, bbox_pred, + img_metas) + if proposal_cfg is None: + return losses + else: + proposal_list = self.stages[-1].get_bboxes(anchor_list, cls_score, + bbox_pred, img_metas, + self.test_cfg) + return losses, proposal_list + + def simple_test_rpn(self, x, img_metas): + """Simple forward test function.""" + featmap_sizes = [featmap.size()[-2:] for featmap in x] + device = x[0].device + anchor_list, _ = self.stages[0].get_anchors( + featmap_sizes, img_metas, device=device) + + for i in range(self.num_stages): + stage = self.stages[i] + if stage.adapt_cfg['type'] == 'offset': + offset_list = stage.anchor_offset(anchor_list, + stage.anchor_strides, + featmap_sizes) + else: + offset_list = None + x, cls_score, bbox_pred = stage(x, offset_list) + if i < self.num_stages - 1: + anchor_list = stage.refine_bboxes(anchor_list, bbox_pred, + img_metas) + + proposal_list = self.stages[-1].get_bboxes(anchor_list, cls_score, + bbox_pred, img_metas, + self.test_cfg) + return proposal_list + + def aug_test_rpn(self, x, img_metas): + """Augmented forward test function.""" + raise NotImplementedError diff --git a/mmdet/models/dense_heads/centripetal_head.py b/mmdet/models/dense_heads/centripetal_head.py new file mode 100644 index 0000000..a9d3ddf --- /dev/null +++ b/mmdet/models/dense_heads/centripetal_head.py @@ -0,0 +1,426 @@ +import torch.nn as nn +from mmcv.cnn import ConvModule, normal_init +from mmcv.ops import DeformConv2d + +from mmdet.core import multi_apply +from ..builder import HEADS, build_loss +from .corner_head import CornerHead + + +@HEADS.register_module() +class CentripetalHead(CornerHead): + """Head of CentripetalNet: Pursuing High-quality Keypoint Pairs for Object + Detection. + + CentripetalHead inherits from :class:`CornerHead`. It removes the + embedding branch and adds guiding shift and centripetal shift branches. + More details can be found in the `paper + `_ . + + Args: + num_classes (int): Number of categories excluding the background + category. + in_channels (int): Number of channels in the input feature map. + num_feat_levels (int): Levels of feature from the previous module. 2 + for HourglassNet-104 and 1 for HourglassNet-52. HourglassNet-104 + outputs the final feature and intermediate supervision feature and + HourglassNet-52 only outputs the final feature. Default: 2. + corner_emb_channels (int): Channel of embedding vector. Default: 1. + train_cfg (dict | None): Training config. Useless in CornerHead, + but we keep this variable for SingleStageDetector. Default: None. + test_cfg (dict | None): Testing config of CornerHead. Default: None. + loss_heatmap (dict | None): Config of corner heatmap loss. Default: + GaussianFocalLoss. + loss_embedding (dict | None): Config of corner embedding loss. Default: + AssociativeEmbeddingLoss. + loss_offset (dict | None): Config of corner offset loss. Default: + SmoothL1Loss. + loss_guiding_shift (dict): Config of guiding shift loss. Default: + SmoothL1Loss. + loss_centripetal_shift (dict): Config of centripetal shift loss. + Default: SmoothL1Loss. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + *args, + centripetal_shift_channels=2, + guiding_shift_channels=2, + feat_adaption_conv_kernel=3, + loss_guiding_shift=dict( + type='SmoothL1Loss', beta=1.0, loss_weight=0.05), + loss_centripetal_shift=dict( + type='SmoothL1Loss', beta=1.0, loss_weight=1), + init_cfg=None, + **kwargs): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + assert centripetal_shift_channels == 2, ( + 'CentripetalHead only support centripetal_shift_channels == 2') + self.centripetal_shift_channels = centripetal_shift_channels + assert guiding_shift_channels == 2, ( + 'CentripetalHead only support guiding_shift_channels == 2') + self.guiding_shift_channels = guiding_shift_channels + self.feat_adaption_conv_kernel = feat_adaption_conv_kernel + super(CentripetalHead, self).__init__( + *args, init_cfg=init_cfg, **kwargs) + self.loss_guiding_shift = build_loss(loss_guiding_shift) + self.loss_centripetal_shift = build_loss(loss_centripetal_shift) + + def _init_centripetal_layers(self): + """Initialize centripetal layers. + + Including feature adaption deform convs (feat_adaption), deform offset + prediction convs (dcn_off), guiding shift (guiding_shift) and + centripetal shift ( centripetal_shift). Each branch has two parts: + prefix `tl_` for top-left and `br_` for bottom-right. + """ + self.tl_feat_adaption = nn.ModuleList() + self.br_feat_adaption = nn.ModuleList() + self.tl_dcn_offset = nn.ModuleList() + self.br_dcn_offset = nn.ModuleList() + self.tl_guiding_shift = nn.ModuleList() + self.br_guiding_shift = nn.ModuleList() + self.tl_centripetal_shift = nn.ModuleList() + self.br_centripetal_shift = nn.ModuleList() + + for _ in range(self.num_feat_levels): + self.tl_feat_adaption.append( + DeformConv2d(self.in_channels, self.in_channels, + self.feat_adaption_conv_kernel, 1, 1)) + self.br_feat_adaption.append( + DeformConv2d(self.in_channels, self.in_channels, + self.feat_adaption_conv_kernel, 1, 1)) + + self.tl_guiding_shift.append( + self._make_layers( + out_channels=self.guiding_shift_channels, + in_channels=self.in_channels)) + self.br_guiding_shift.append( + self._make_layers( + out_channels=self.guiding_shift_channels, + in_channels=self.in_channels)) + + self.tl_dcn_offset.append( + ConvModule( + self.guiding_shift_channels, + self.feat_adaption_conv_kernel**2 * + self.guiding_shift_channels, + 1, + bias=False, + act_cfg=None)) + self.br_dcn_offset.append( + ConvModule( + self.guiding_shift_channels, + self.feat_adaption_conv_kernel**2 * + self.guiding_shift_channels, + 1, + bias=False, + act_cfg=None)) + + self.tl_centripetal_shift.append( + self._make_layers( + out_channels=self.centripetal_shift_channels, + in_channels=self.in_channels)) + self.br_centripetal_shift.append( + self._make_layers( + out_channels=self.centripetal_shift_channels, + in_channels=self.in_channels)) + + def _init_layers(self): + """Initialize layers for CentripetalHead. + + Including two parts: CornerHead layers and CentripetalHead layers + """ + super()._init_layers() # using _init_layers in CornerHead + self._init_centripetal_layers() + + def init_weights(self): + super(CentripetalHead, self).init_weights() + for i in range(self.num_feat_levels): + normal_init(self.tl_feat_adaption[i], std=0.01) + normal_init(self.br_feat_adaption[i], std=0.01) + normal_init(self.tl_dcn_offset[i].conv, std=0.1) + normal_init(self.br_dcn_offset[i].conv, std=0.1) + _ = [x.conv.reset_parameters() for x in self.tl_guiding_shift[i]] + _ = [x.conv.reset_parameters() for x in self.br_guiding_shift[i]] + _ = [ + x.conv.reset_parameters() for x in self.tl_centripetal_shift[i] + ] + _ = [ + x.conv.reset_parameters() for x in self.br_centripetal_shift[i] + ] + + def forward_single(self, x, lvl_ind): + """Forward feature of a single level. + + Args: + x (Tensor): Feature of a single level. + lvl_ind (int): Level index of current feature. + + Returns: + tuple[Tensor]: A tuple of CentripetalHead's output for current + feature level. Containing the following Tensors: + + - tl_heat (Tensor): Predicted top-left corner heatmap. + - br_heat (Tensor): Predicted bottom-right corner heatmap. + - tl_off (Tensor): Predicted top-left offset heatmap. + - br_off (Tensor): Predicted bottom-right offset heatmap. + - tl_guiding_shift (Tensor): Predicted top-left guiding shift + heatmap. + - br_guiding_shift (Tensor): Predicted bottom-right guiding + shift heatmap. + - tl_centripetal_shift (Tensor): Predicted top-left centripetal + shift heatmap. + - br_centripetal_shift (Tensor): Predicted bottom-right + centripetal shift heatmap. + """ + tl_heat, br_heat, _, _, tl_off, br_off, tl_pool, br_pool = super( + ).forward_single( + x, lvl_ind, return_pool=True) + + tl_guiding_shift = self.tl_guiding_shift[lvl_ind](tl_pool) + br_guiding_shift = self.br_guiding_shift[lvl_ind](br_pool) + + tl_dcn_offset = self.tl_dcn_offset[lvl_ind](tl_guiding_shift.detach()) + br_dcn_offset = self.br_dcn_offset[lvl_ind](br_guiding_shift.detach()) + + tl_feat_adaption = self.tl_feat_adaption[lvl_ind](tl_pool, + tl_dcn_offset) + br_feat_adaption = self.br_feat_adaption[lvl_ind](br_pool, + br_dcn_offset) + + tl_centripetal_shift = self.tl_centripetal_shift[lvl_ind]( + tl_feat_adaption) + br_centripetal_shift = self.br_centripetal_shift[lvl_ind]( + br_feat_adaption) + + result_list = [ + tl_heat, br_heat, tl_off, br_off, tl_guiding_shift, + br_guiding_shift, tl_centripetal_shift, br_centripetal_shift + ] + return result_list + + def loss(self, + tl_heats, + br_heats, + tl_offs, + br_offs, + tl_guiding_shifts, + br_guiding_shifts, + tl_centripetal_shifts, + br_centripetal_shifts, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute losses of the head. + + Args: + tl_heats (list[Tensor]): Top-left corner heatmaps for each level + with shape (N, num_classes, H, W). + br_heats (list[Tensor]): Bottom-right corner heatmaps for each + level with shape (N, num_classes, H, W). + tl_offs (list[Tensor]): Top-left corner offsets for each level + with shape (N, corner_offset_channels, H, W). + br_offs (list[Tensor]): Bottom-right corner offsets for each level + with shape (N, corner_offset_channels, H, W). + tl_guiding_shifts (list[Tensor]): Top-left guiding shifts for each + level with shape (N, guiding_shift_channels, H, W). + br_guiding_shifts (list[Tensor]): Bottom-right guiding shifts for + each level with shape (N, guiding_shift_channels, H, W). + tl_centripetal_shifts (list[Tensor]): Top-left centripetal shifts + for each level with shape (N, centripetal_shift_channels, H, + W). + br_centripetal_shifts (list[Tensor]): Bottom-right centripetal + shifts for each level with shape (N, + centripetal_shift_channels, H, W). + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [left, top, right, bottom] format. + gt_labels (list[Tensor]): Class indices corresponding to each box. + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (list[Tensor] | None): Specify which bounding + boxes can be ignored when computing the loss. + + Returns: + dict[str, Tensor]: A dictionary of loss components. Containing the + following losses: + + - det_loss (list[Tensor]): Corner keypoint losses of all + feature levels. + - off_loss (list[Tensor]): Corner offset losses of all feature + levels. + - guiding_loss (list[Tensor]): Guiding shift losses of all + feature levels. + - centripetal_loss (list[Tensor]): Centripetal shift losses of + all feature levels. + """ + targets = self.get_targets( + gt_bboxes, + gt_labels, + tl_heats[-1].shape, + img_metas[0]['pad_shape'], + with_corner_emb=self.with_corner_emb, + with_guiding_shift=True, + with_centripetal_shift=True) + mlvl_targets = [targets for _ in range(self.num_feat_levels)] + [det_losses, off_losses, guiding_losses, centripetal_losses + ] = multi_apply(self.loss_single, tl_heats, br_heats, tl_offs, + br_offs, tl_guiding_shifts, br_guiding_shifts, + tl_centripetal_shifts, br_centripetal_shifts, + mlvl_targets) + loss_dict = dict( + det_loss=det_losses, + off_loss=off_losses, + guiding_loss=guiding_losses, + centripetal_loss=centripetal_losses) + return loss_dict + + def loss_single(self, tl_hmp, br_hmp, tl_off, br_off, tl_guiding_shift, + br_guiding_shift, tl_centripetal_shift, + br_centripetal_shift, targets): + """Compute losses for single level. + + Args: + tl_hmp (Tensor): Top-left corner heatmap for current level with + shape (N, num_classes, H, W). + br_hmp (Tensor): Bottom-right corner heatmap for current level with + shape (N, num_classes, H, W). + tl_off (Tensor): Top-left corner offset for current level with + shape (N, corner_offset_channels, H, W). + br_off (Tensor): Bottom-right corner offset for current level with + shape (N, corner_offset_channels, H, W). + tl_guiding_shift (Tensor): Top-left guiding shift for current level + with shape (N, guiding_shift_channels, H, W). + br_guiding_shift (Tensor): Bottom-right guiding shift for current + level with shape (N, guiding_shift_channels, H, W). + tl_centripetal_shift (Tensor): Top-left centripetal shift for + current level with shape (N, centripetal_shift_channels, H, W). + br_centripetal_shift (Tensor): Bottom-right centripetal shift for + current level with shape (N, centripetal_shift_channels, H, W). + targets (dict): Corner target generated by `get_targets`. + + Returns: + tuple[torch.Tensor]: Losses of the head's differnet branches + containing the following losses: + + - det_loss (Tensor): Corner keypoint loss. + - off_loss (Tensor): Corner offset loss. + - guiding_loss (Tensor): Guiding shift loss. + - centripetal_loss (Tensor): Centripetal shift loss. + """ + targets['corner_embedding'] = None + + det_loss, _, _, off_loss = super().loss_single(tl_hmp, br_hmp, None, + None, tl_off, br_off, + targets) + + gt_tl_guiding_shift = targets['topleft_guiding_shift'] + gt_br_guiding_shift = targets['bottomright_guiding_shift'] + gt_tl_centripetal_shift = targets['topleft_centripetal_shift'] + gt_br_centripetal_shift = targets['bottomright_centripetal_shift'] + + gt_tl_heatmap = targets['topleft_heatmap'] + gt_br_heatmap = targets['bottomright_heatmap'] + # We only compute the offset loss at the real corner position. + # The value of real corner would be 1 in heatmap ground truth. + # The mask is computed in class agnostic mode and its shape is + # batch * 1 * width * height. + tl_mask = gt_tl_heatmap.eq(1).sum(1).gt(0).unsqueeze(1).type_as( + gt_tl_heatmap) + br_mask = gt_br_heatmap.eq(1).sum(1).gt(0).unsqueeze(1).type_as( + gt_br_heatmap) + + # Guiding shift loss + tl_guiding_loss = self.loss_guiding_shift( + tl_guiding_shift, + gt_tl_guiding_shift, + tl_mask, + avg_factor=tl_mask.sum()) + br_guiding_loss = self.loss_guiding_shift( + br_guiding_shift, + gt_br_guiding_shift, + br_mask, + avg_factor=br_mask.sum()) + guiding_loss = (tl_guiding_loss + br_guiding_loss) / 2.0 + # Centripetal shift loss + tl_centripetal_loss = self.loss_centripetal_shift( + tl_centripetal_shift, + gt_tl_centripetal_shift, + tl_mask, + avg_factor=tl_mask.sum()) + br_centripetal_loss = self.loss_centripetal_shift( + br_centripetal_shift, + gt_br_centripetal_shift, + br_mask, + avg_factor=br_mask.sum()) + centripetal_loss = (tl_centripetal_loss + br_centripetal_loss) / 2.0 + + return det_loss, off_loss, guiding_loss, centripetal_loss + + def get_bboxes(self, + tl_heats, + br_heats, + tl_offs, + br_offs, + tl_guiding_shifts, + br_guiding_shifts, + tl_centripetal_shifts, + br_centripetal_shifts, + img_metas, + rescale=False, + with_nms=True): + """Transform network output for a batch into bbox predictions. + + Args: + tl_heats (list[Tensor]): Top-left corner heatmaps for each level + with shape (N, num_classes, H, W). + br_heats (list[Tensor]): Bottom-right corner heatmaps for each + level with shape (N, num_classes, H, W). + tl_offs (list[Tensor]): Top-left corner offsets for each level + with shape (N, corner_offset_channels, H, W). + br_offs (list[Tensor]): Bottom-right corner offsets for each level + with shape (N, corner_offset_channels, H, W). + tl_guiding_shifts (list[Tensor]): Top-left guiding shifts for each + level with shape (N, guiding_shift_channels, H, W). Useless in + this function, we keep this arg because it's the raw output + from CentripetalHead. + br_guiding_shifts (list[Tensor]): Bottom-right guiding shifts for + each level with shape (N, guiding_shift_channels, H, W). + Useless in this function, we keep this arg because it's the + raw output from CentripetalHead. + tl_centripetal_shifts (list[Tensor]): Top-left centripetal shifts + for each level with shape (N, centripetal_shift_channels, H, + W). + br_centripetal_shifts (list[Tensor]): Bottom-right centripetal + shifts for each level with shape (N, + centripetal_shift_channels, H, W). + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + rescale (bool): If True, return boxes in original image space. + Default: False. + with_nms (bool): If True, do nms before return boxes. + Default: True. + """ + assert tl_heats[-1].shape[0] == br_heats[-1].shape[0] == len(img_metas) + result_list = [] + for img_id in range(len(img_metas)): + result_list.append( + self._get_bboxes_single( + tl_heats[-1][img_id:img_id + 1, :], + br_heats[-1][img_id:img_id + 1, :], + tl_offs[-1][img_id:img_id + 1, :], + br_offs[-1][img_id:img_id + 1, :], + img_metas[img_id], + tl_emb=None, + br_emb=None, + tl_centripetal_shift=tl_centripetal_shifts[-1][ + img_id:img_id + 1, :], + br_centripetal_shift=br_centripetal_shifts[-1][ + img_id:img_id + 1, :], + rescale=rescale, + with_nms=with_nms)) + + return result_list diff --git a/mmdet/models/dense_heads/corner_head.py b/mmdet/models/dense_heads/corner_head.py new file mode 100644 index 0000000..957ed55 --- /dev/null +++ b/mmdet/models/dense_heads/corner_head.py @@ -0,0 +1,1084 @@ +from logging import warning +from math import ceil, log + +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule, bias_init_with_prob +from mmcv.ops import CornerPool, batched_nms +from mmcv.runner import BaseModule + +from mmdet.core import multi_apply +from ..builder import HEADS, build_loss +from ..utils import gaussian_radius, gen_gaussian_target +from .base_dense_head import BaseDenseHead + + +class BiCornerPool(BaseModule): + """Bidirectional Corner Pooling Module (TopLeft, BottomRight, etc.) + + Args: + in_channels (int): Input channels of module. + out_channels (int): Output channels of module. + feat_channels (int): Feature channels of module. + directions (list[str]): Directions of two CornerPools. + norm_cfg (dict): Dictionary to construct and config norm layer. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + in_channels, + directions, + feat_channels=128, + out_channels=128, + norm_cfg=dict(type='BN', requires_grad=True), + init_cfg=None): + super(BiCornerPool, self).__init__(init_cfg) + self.direction1_conv = ConvModule( + in_channels, feat_channels, 3, padding=1, norm_cfg=norm_cfg) + self.direction2_conv = ConvModule( + in_channels, feat_channels, 3, padding=1, norm_cfg=norm_cfg) + + self.aftpool_conv = ConvModule( + feat_channels, + out_channels, + 3, + padding=1, + norm_cfg=norm_cfg, + act_cfg=None) + + self.conv1 = ConvModule( + in_channels, out_channels, 1, norm_cfg=norm_cfg, act_cfg=None) + self.conv2 = ConvModule( + in_channels, out_channels, 3, padding=1, norm_cfg=norm_cfg) + + self.direction1_pool = CornerPool(directions[0]) + self.direction2_pool = CornerPool(directions[1]) + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + """Forward features from the upstream network. + + Args: + x (tensor): Input feature of BiCornerPool. + + Returns: + conv2 (tensor): Output feature of BiCornerPool. + """ + direction1_conv = self.direction1_conv(x) + direction2_conv = self.direction2_conv(x) + direction1_feat = self.direction1_pool(direction1_conv) + direction2_feat = self.direction2_pool(direction2_conv) + aftpool_conv = self.aftpool_conv(direction1_feat + direction2_feat) + conv1 = self.conv1(x) + relu = self.relu(aftpool_conv + conv1) + conv2 = self.conv2(relu) + return conv2 + + +@HEADS.register_module() +class CornerHead(BaseDenseHead): + """Head of CornerNet: Detecting Objects as Paired Keypoints. + + Code is modified from the `official github repo + `_ . + + More details can be found in the `paper + `_ . + + Args: + num_classes (int): Number of categories excluding the background + category. + in_channels (int): Number of channels in the input feature map. + num_feat_levels (int): Levels of feature from the previous module. 2 + for HourglassNet-104 and 1 for HourglassNet-52. Because + HourglassNet-104 outputs the final feature and intermediate + supervision feature and HourglassNet-52 only outputs the final + feature. Default: 2. + corner_emb_channels (int): Channel of embedding vector. Default: 1. + train_cfg (dict | None): Training config. Useless in CornerHead, + but we keep this variable for SingleStageDetector. Default: None. + test_cfg (dict | None): Testing config of CornerHead. Default: None. + loss_heatmap (dict | None): Config of corner heatmap loss. Default: + GaussianFocalLoss. + loss_embedding (dict | None): Config of corner embedding loss. Default: + AssociativeEmbeddingLoss. + loss_offset (dict | None): Config of corner offset loss. Default: + SmoothL1Loss. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + num_classes, + in_channels, + num_feat_levels=2, + corner_emb_channels=1, + train_cfg=None, + test_cfg=None, + loss_heatmap=dict( + type='GaussianFocalLoss', + alpha=2.0, + gamma=4.0, + loss_weight=1), + loss_embedding=dict( + type='AssociativeEmbeddingLoss', + pull_weight=0.25, + push_weight=0.25), + loss_offset=dict( + type='SmoothL1Loss', beta=1.0, loss_weight=1), + init_cfg=None): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + super(CornerHead, self).__init__(init_cfg) + self.num_classes = num_classes + self.in_channels = in_channels + self.corner_emb_channels = corner_emb_channels + self.with_corner_emb = self.corner_emb_channels > 0 + self.corner_offset_channels = 2 + self.num_feat_levels = num_feat_levels + self.loss_heatmap = build_loss( + loss_heatmap) if loss_heatmap is not None else None + self.loss_embedding = build_loss( + loss_embedding) if loss_embedding is not None else None + self.loss_offset = build_loss( + loss_offset) if loss_offset is not None else None + self.train_cfg = train_cfg + self.test_cfg = test_cfg + + self._init_layers() + + def _make_layers(self, out_channels, in_channels=256, feat_channels=256): + """Initialize conv sequential for CornerHead.""" + return nn.Sequential( + ConvModule(in_channels, feat_channels, 3, padding=1), + ConvModule( + feat_channels, out_channels, 1, norm_cfg=None, act_cfg=None)) + + def _init_corner_kpt_layers(self): + """Initialize corner keypoint layers. + + Including corner heatmap branch and corner offset branch. Each branch + has two parts: prefix `tl_` for top-left and `br_` for bottom-right. + """ + self.tl_pool, self.br_pool = nn.ModuleList(), nn.ModuleList() + self.tl_heat, self.br_heat = nn.ModuleList(), nn.ModuleList() + self.tl_off, self.br_off = nn.ModuleList(), nn.ModuleList() + + for _ in range(self.num_feat_levels): + self.tl_pool.append( + BiCornerPool( + self.in_channels, ['top', 'left'], + out_channels=self.in_channels)) + self.br_pool.append( + BiCornerPool( + self.in_channels, ['bottom', 'right'], + out_channels=self.in_channels)) + + self.tl_heat.append( + self._make_layers( + out_channels=self.num_classes, + in_channels=self.in_channels)) + self.br_heat.append( + self._make_layers( + out_channels=self.num_classes, + in_channels=self.in_channels)) + + self.tl_off.append( + self._make_layers( + out_channels=self.corner_offset_channels, + in_channels=self.in_channels)) + self.br_off.append( + self._make_layers( + out_channels=self.corner_offset_channels, + in_channels=self.in_channels)) + + def _init_corner_emb_layers(self): + """Initialize corner embedding layers. + + Only include corner embedding branch with two parts: prefix `tl_` for + top-left and `br_` for bottom-right. + """ + self.tl_emb, self.br_emb = nn.ModuleList(), nn.ModuleList() + + for _ in range(self.num_feat_levels): + self.tl_emb.append( + self._make_layers( + out_channels=self.corner_emb_channels, + in_channels=self.in_channels)) + self.br_emb.append( + self._make_layers( + out_channels=self.corner_emb_channels, + in_channels=self.in_channels)) + + def _init_layers(self): + """Initialize layers for CornerHead. + + Including two parts: corner keypoint layers and corner embedding layers + """ + self._init_corner_kpt_layers() + if self.with_corner_emb: + self._init_corner_emb_layers() + + def init_weights(self): + super(CornerHead, self).init_weights() + bias_init = bias_init_with_prob(0.1) + for i in range(self.num_feat_levels): + # The initialization of parameters are different between + # nn.Conv2d and ConvModule. Our experiments show that + # using the original initialization of nn.Conv2d increases + # the final mAP by about 0.2% + self.tl_heat[i][-1].conv.reset_parameters() + self.tl_heat[i][-1].conv.bias.data.fill_(bias_init) + self.br_heat[i][-1].conv.reset_parameters() + self.br_heat[i][-1].conv.bias.data.fill_(bias_init) + self.tl_off[i][-1].conv.reset_parameters() + self.br_off[i][-1].conv.reset_parameters() + if self.with_corner_emb: + self.tl_emb[i][-1].conv.reset_parameters() + self.br_emb[i][-1].conv.reset_parameters() + + def forward(self, feats): + """Forward features from the upstream network. + + Args: + feats (tuple[Tensor]): Features from the upstream network, each is + a 4D-tensor. + + Returns: + tuple: Usually a tuple of corner heatmaps, offset heatmaps and + embedding heatmaps. + - tl_heats (list[Tensor]): Top-left corner heatmaps for all + levels, each is a 4D-tensor, the channels number is + num_classes. + - br_heats (list[Tensor]): Bottom-right corner heatmaps for all + levels, each is a 4D-tensor, the channels number is + num_classes. + - tl_embs (list[Tensor] | list[None]): Top-left embedding + heatmaps for all levels, each is a 4D-tensor or None. + If not None, the channels number is corner_emb_channels. + - br_embs (list[Tensor] | list[None]): Bottom-right embedding + heatmaps for all levels, each is a 4D-tensor or None. + If not None, the channels number is corner_emb_channels. + - tl_offs (list[Tensor]): Top-left offset heatmaps for all + levels, each is a 4D-tensor. The channels number is + corner_offset_channels. + - br_offs (list[Tensor]): Bottom-right offset heatmaps for all + levels, each is a 4D-tensor. The channels number is + corner_offset_channels. + """ + lvl_ind = list(range(self.num_feat_levels)) + return multi_apply(self.forward_single, feats, lvl_ind) + + def forward_single(self, x, lvl_ind, return_pool=False): + """Forward feature of a single level. + + Args: + x (Tensor): Feature of a single level. + lvl_ind (int): Level index of current feature. + return_pool (bool): Return corner pool feature or not. + + Returns: + tuple[Tensor]: A tuple of CornerHead's output for current feature + level. Containing the following Tensors: + + - tl_heat (Tensor): Predicted top-left corner heatmap. + - br_heat (Tensor): Predicted bottom-right corner heatmap. + - tl_emb (Tensor | None): Predicted top-left embedding heatmap. + None for `self.with_corner_emb == False`. + - br_emb (Tensor | None): Predicted bottom-right embedding + heatmap. None for `self.with_corner_emb == False`. + - tl_off (Tensor): Predicted top-left offset heatmap. + - br_off (Tensor): Predicted bottom-right offset heatmap. + - tl_pool (Tensor): Top-left corner pool feature. Not must + have. + - br_pool (Tensor): Bottom-right corner pool feature. Not must + have. + """ + tl_pool = self.tl_pool[lvl_ind](x) + tl_heat = self.tl_heat[lvl_ind](tl_pool) + br_pool = self.br_pool[lvl_ind](x) + br_heat = self.br_heat[lvl_ind](br_pool) + + tl_emb, br_emb = None, None + if self.with_corner_emb: + tl_emb = self.tl_emb[lvl_ind](tl_pool) + br_emb = self.br_emb[lvl_ind](br_pool) + + tl_off = self.tl_off[lvl_ind](tl_pool) + br_off = self.br_off[lvl_ind](br_pool) + + result_list = [tl_heat, br_heat, tl_emb, br_emb, tl_off, br_off] + if return_pool: + result_list.append(tl_pool) + result_list.append(br_pool) + + return result_list + + def get_targets(self, + gt_bboxes, + gt_labels, + feat_shape, + img_shape, + with_corner_emb=False, + with_guiding_shift=False, + with_centripetal_shift=False): + """Generate corner targets. + + Including corner heatmap, corner offset. + + Optional: corner embedding, corner guiding shift, centripetal shift. + + For CornerNet, we generate corner heatmap, corner offset and corner + embedding from this function. + + For CentripetalNet, we generate corner heatmap, corner offset, guiding + shift and centripetal shift from this function. + + Args: + gt_bboxes (list[Tensor]): Ground truth bboxes of each image, each + has shape (num_gt, 4). + gt_labels (list[Tensor]): Ground truth labels of each box, each has + shape (num_gt,). + feat_shape (list[int]): Shape of output feature, + [batch, channel, height, width]. + img_shape (list[int]): Shape of input image, + [height, width, channel]. + with_corner_emb (bool): Generate corner embedding target or not. + Default: False. + with_guiding_shift (bool): Generate guiding shift target or not. + Default: False. + with_centripetal_shift (bool): Generate centripetal shift target or + not. Default: False. + + Returns: + dict: Ground truth of corner heatmap, corner offset, corner + embedding, guiding shift and centripetal shift. Containing the + following keys: + + - topleft_heatmap (Tensor): Ground truth top-left corner + heatmap. + - bottomright_heatmap (Tensor): Ground truth bottom-right + corner heatmap. + - topleft_offset (Tensor): Ground truth top-left corner offset. + - bottomright_offset (Tensor): Ground truth bottom-right corner + offset. + - corner_embedding (list[list[list[int]]]): Ground truth corner + embedding. Not must have. + - topleft_guiding_shift (Tensor): Ground truth top-left corner + guiding shift. Not must have. + - bottomright_guiding_shift (Tensor): Ground truth bottom-right + corner guiding shift. Not must have. + - topleft_centripetal_shift (Tensor): Ground truth top-left + corner centripetal shift. Not must have. + - bottomright_centripetal_shift (Tensor): Ground truth + bottom-right corner centripetal shift. Not must have. + """ + batch_size, _, height, width = feat_shape + img_h, img_w = img_shape[:2] + + width_ratio = float(width / img_w) + height_ratio = float(height / img_h) + + gt_tl_heatmap = gt_bboxes[-1].new_zeros( + [batch_size, self.num_classes, height, width]) + gt_br_heatmap = gt_bboxes[-1].new_zeros( + [batch_size, self.num_classes, height, width]) + gt_tl_offset = gt_bboxes[-1].new_zeros([batch_size, 2, height, width]) + gt_br_offset = gt_bboxes[-1].new_zeros([batch_size, 2, height, width]) + + if with_corner_emb: + match = [] + + # Guiding shift is a kind of offset, from center to corner + if with_guiding_shift: + gt_tl_guiding_shift = gt_bboxes[-1].new_zeros( + [batch_size, 2, height, width]) + gt_br_guiding_shift = gt_bboxes[-1].new_zeros( + [batch_size, 2, height, width]) + # Centripetal shift is also a kind of offset, from center to corner + # and normalized by log. + if with_centripetal_shift: + gt_tl_centripetal_shift = gt_bboxes[-1].new_zeros( + [batch_size, 2, height, width]) + gt_br_centripetal_shift = gt_bboxes[-1].new_zeros( + [batch_size, 2, height, width]) + + for batch_id in range(batch_size): + # Ground truth of corner embedding per image is a list of coord set + corner_match = [] + for box_id in range(len(gt_labels[batch_id])): + left, top, right, bottom = gt_bboxes[batch_id][box_id] + center_x = (left + right) / 2.0 + center_y = (top + bottom) / 2.0 + label = gt_labels[batch_id][box_id] + + # Use coords in the feature level to generate ground truth + scale_left = left * width_ratio + scale_right = right * width_ratio + scale_top = top * height_ratio + scale_bottom = bottom * height_ratio + scale_center_x = center_x * width_ratio + scale_center_y = center_y * height_ratio + + # Int coords on feature map/ground truth tensor + left_idx = int(min(scale_left, width - 1)) + right_idx = int(min(scale_right, width - 1)) + top_idx = int(min(scale_top, height - 1)) + bottom_idx = int(min(scale_bottom, height - 1)) + + # Generate gaussian heatmap + scale_box_width = ceil(scale_right - scale_left) + scale_box_height = ceil(scale_bottom - scale_top) + radius = gaussian_radius((scale_box_height, scale_box_width), + min_overlap=0.3) + radius = max(0, int(radius)) + gt_tl_heatmap[batch_id, label] = gen_gaussian_target( + gt_tl_heatmap[batch_id, label], [left_idx, top_idx], + radius) + gt_br_heatmap[batch_id, label] = gen_gaussian_target( + gt_br_heatmap[batch_id, label], [right_idx, bottom_idx], + radius) + + # Generate corner offset + left_offset = scale_left - left_idx + top_offset = scale_top - top_idx + right_offset = scale_right - right_idx + bottom_offset = scale_bottom - bottom_idx + gt_tl_offset[batch_id, 0, top_idx, left_idx] = left_offset + gt_tl_offset[batch_id, 1, top_idx, left_idx] = top_offset + gt_br_offset[batch_id, 0, bottom_idx, right_idx] = right_offset + gt_br_offset[batch_id, 1, bottom_idx, + right_idx] = bottom_offset + + # Generate corner embedding + if with_corner_emb: + corner_match.append([[top_idx, left_idx], + [bottom_idx, right_idx]]) + # Generate guiding shift + if with_guiding_shift: + gt_tl_guiding_shift[batch_id, 0, top_idx, + left_idx] = scale_center_x - left_idx + gt_tl_guiding_shift[batch_id, 1, top_idx, + left_idx] = scale_center_y - top_idx + gt_br_guiding_shift[batch_id, 0, bottom_idx, + right_idx] = right_idx - scale_center_x + gt_br_guiding_shift[ + batch_id, 1, bottom_idx, + right_idx] = bottom_idx - scale_center_y + # Generate centripetal shift + if with_centripetal_shift: + gt_tl_centripetal_shift[batch_id, 0, top_idx, + left_idx] = log(scale_center_x - + scale_left) + gt_tl_centripetal_shift[batch_id, 1, top_idx, + left_idx] = log(scale_center_y - + scale_top) + gt_br_centripetal_shift[batch_id, 0, bottom_idx, + right_idx] = log(scale_right - + scale_center_x) + gt_br_centripetal_shift[batch_id, 1, bottom_idx, + right_idx] = log(scale_bottom - + scale_center_y) + + if with_corner_emb: + match.append(corner_match) + + target_result = dict( + topleft_heatmap=gt_tl_heatmap, + topleft_offset=gt_tl_offset, + bottomright_heatmap=gt_br_heatmap, + bottomright_offset=gt_br_offset) + + if with_corner_emb: + target_result.update(corner_embedding=match) + if with_guiding_shift: + target_result.update( + topleft_guiding_shift=gt_tl_guiding_shift, + bottomright_guiding_shift=gt_br_guiding_shift) + if with_centripetal_shift: + target_result.update( + topleft_centripetal_shift=gt_tl_centripetal_shift, + bottomright_centripetal_shift=gt_br_centripetal_shift) + + return target_result + + def loss(self, + tl_heats, + br_heats, + tl_embs, + br_embs, + tl_offs, + br_offs, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute losses of the head. + + Args: + tl_heats (list[Tensor]): Top-left corner heatmaps for each level + with shape (N, num_classes, H, W). + br_heats (list[Tensor]): Bottom-right corner heatmaps for each + level with shape (N, num_classes, H, W). + tl_embs (list[Tensor]): Top-left corner embeddings for each level + with shape (N, corner_emb_channels, H, W). + br_embs (list[Tensor]): Bottom-right corner embeddings for each + level with shape (N, corner_emb_channels, H, W). + tl_offs (list[Tensor]): Top-left corner offsets for each level + with shape (N, corner_offset_channels, H, W). + br_offs (list[Tensor]): Bottom-right corner offsets for each level + with shape (N, corner_offset_channels, H, W). + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [left, top, right, bottom] format. + gt_labels (list[Tensor]): Class indices corresponding to each box. + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (list[Tensor] | None): Specify which bounding + boxes can be ignored when computing the loss. + + Returns: + dict[str, Tensor]: A dictionary of loss components. Containing the + following losses: + + - det_loss (list[Tensor]): Corner keypoint losses of all + feature levels. + - pull_loss (list[Tensor]): Part one of AssociativeEmbedding + losses of all feature levels. + - push_loss (list[Tensor]): Part two of AssociativeEmbedding + losses of all feature levels. + - off_loss (list[Tensor]): Corner offset losses of all feature + levels. + """ + targets = self.get_targets( + gt_bboxes, + gt_labels, + tl_heats[-1].shape, + img_metas[0]['pad_shape'], + with_corner_emb=self.with_corner_emb) + mlvl_targets = [targets for _ in range(self.num_feat_levels)] + det_losses, pull_losses, push_losses, off_losses = multi_apply( + self.loss_single, tl_heats, br_heats, tl_embs, br_embs, tl_offs, + br_offs, mlvl_targets) + loss_dict = dict(det_loss=det_losses, off_loss=off_losses) + if self.with_corner_emb: + loss_dict.update(pull_loss=pull_losses, push_loss=push_losses) + return loss_dict + + def loss_single(self, tl_hmp, br_hmp, tl_emb, br_emb, tl_off, br_off, + targets): + """Compute losses for single level. + + Args: + tl_hmp (Tensor): Top-left corner heatmap for current level with + shape (N, num_classes, H, W). + br_hmp (Tensor): Bottom-right corner heatmap for current level with + shape (N, num_classes, H, W). + tl_emb (Tensor): Top-left corner embedding for current level with + shape (N, corner_emb_channels, H, W). + br_emb (Tensor): Bottom-right corner embedding for current level + with shape (N, corner_emb_channels, H, W). + tl_off (Tensor): Top-left corner offset for current level with + shape (N, corner_offset_channels, H, W). + br_off (Tensor): Bottom-right corner offset for current level with + shape (N, corner_offset_channels, H, W). + targets (dict): Corner target generated by `get_targets`. + + Returns: + tuple[torch.Tensor]: Losses of the head's differnet branches + containing the following losses: + + - det_loss (Tensor): Corner keypoint loss. + - pull_loss (Tensor): Part one of AssociativeEmbedding loss. + - push_loss (Tensor): Part two of AssociativeEmbedding loss. + - off_loss (Tensor): Corner offset loss. + """ + gt_tl_hmp = targets['topleft_heatmap'] + gt_br_hmp = targets['bottomright_heatmap'] + gt_tl_off = targets['topleft_offset'] + gt_br_off = targets['bottomright_offset'] + gt_embedding = targets['corner_embedding'] + + # Detection loss + tl_det_loss = self.loss_heatmap( + tl_hmp.sigmoid(), + gt_tl_hmp, + avg_factor=max(1, + gt_tl_hmp.eq(1).sum())) + br_det_loss = self.loss_heatmap( + br_hmp.sigmoid(), + gt_br_hmp, + avg_factor=max(1, + gt_br_hmp.eq(1).sum())) + det_loss = (tl_det_loss + br_det_loss) / 2.0 + + # AssociativeEmbedding loss + if self.with_corner_emb and self.loss_embedding is not None: + pull_loss, push_loss = self.loss_embedding(tl_emb, br_emb, + gt_embedding) + else: + pull_loss, push_loss = None, None + + # Offset loss + # We only compute the offset loss at the real corner position. + # The value of real corner would be 1 in heatmap ground truth. + # The mask is computed in class agnostic mode and its shape is + # batch * 1 * width * height. + tl_off_mask = gt_tl_hmp.eq(1).sum(1).gt(0).unsqueeze(1).type_as( + gt_tl_hmp) + br_off_mask = gt_br_hmp.eq(1).sum(1).gt(0).unsqueeze(1).type_as( + gt_br_hmp) + tl_off_loss = self.loss_offset( + tl_off, + gt_tl_off, + tl_off_mask, + avg_factor=max(1, tl_off_mask.sum())) + br_off_loss = self.loss_offset( + br_off, + gt_br_off, + br_off_mask, + avg_factor=max(1, br_off_mask.sum())) + + off_loss = (tl_off_loss + br_off_loss) / 2.0 + + return det_loss, pull_loss, push_loss, off_loss + + def get_bboxes(self, + tl_heats, + br_heats, + tl_embs, + br_embs, + tl_offs, + br_offs, + img_metas, + rescale=False, + with_nms=True): + """Transform network output for a batch into bbox predictions. + + Args: + tl_heats (list[Tensor]): Top-left corner heatmaps for each level + with shape (N, num_classes, H, W). + br_heats (list[Tensor]): Bottom-right corner heatmaps for each + level with shape (N, num_classes, H, W). + tl_embs (list[Tensor]): Top-left corner embeddings for each level + with shape (N, corner_emb_channels, H, W). + br_embs (list[Tensor]): Bottom-right corner embeddings for each + level with shape (N, corner_emb_channels, H, W). + tl_offs (list[Tensor]): Top-left corner offsets for each level + with shape (N, corner_offset_channels, H, W). + br_offs (list[Tensor]): Bottom-right corner offsets for each level + with shape (N, corner_offset_channels, H, W). + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + rescale (bool): If True, return boxes in original image space. + Default: False. + with_nms (bool): If True, do nms before return boxes. + Default: True. + """ + assert tl_heats[-1].shape[0] == br_heats[-1].shape[0] == len(img_metas) + result_list = [] + for img_id in range(len(img_metas)): + result_list.append( + self._get_bboxes_single( + tl_heats[-1][img_id:img_id + 1, :], + br_heats[-1][img_id:img_id + 1, :], + tl_offs[-1][img_id:img_id + 1, :], + br_offs[-1][img_id:img_id + 1, :], + img_metas[img_id], + tl_emb=tl_embs[-1][img_id:img_id + 1, :], + br_emb=br_embs[-1][img_id:img_id + 1, :], + rescale=rescale, + with_nms=with_nms)) + + return result_list + + def _get_bboxes_single(self, + tl_heat, + br_heat, + tl_off, + br_off, + img_meta, + tl_emb=None, + br_emb=None, + tl_centripetal_shift=None, + br_centripetal_shift=None, + rescale=False, + with_nms=True): + """Transform outputs for a single batch item into bbox predictions. + + Args: + tl_heat (Tensor): Top-left corner heatmap for current level with + shape (N, num_classes, H, W). + br_heat (Tensor): Bottom-right corner heatmap for current level + with shape (N, num_classes, H, W). + tl_off (Tensor): Top-left corner offset for current level with + shape (N, corner_offset_channels, H, W). + br_off (Tensor): Bottom-right corner offset for current level with + shape (N, corner_offset_channels, H, W). + img_meta (dict): Meta information of current image, e.g., + image size, scaling factor, etc. + tl_emb (Tensor): Top-left corner embedding for current level with + shape (N, corner_emb_channels, H, W). + br_emb (Tensor): Bottom-right corner embedding for current level + with shape (N, corner_emb_channels, H, W). + tl_centripetal_shift: Top-left corner's centripetal shift for + current level with shape (N, 2, H, W). + br_centripetal_shift: Bottom-right corner's centripetal shift for + current level with shape (N, 2, H, W). + rescale (bool): If True, return boxes in original image space. + Default: False. + with_nms (bool): If True, do nms before return boxes. + Default: True. + """ + if isinstance(img_meta, (list, tuple)): + img_meta = img_meta[0] + + batch_bboxes, batch_scores, batch_clses = self.decode_heatmap( + tl_heat=tl_heat.sigmoid(), + br_heat=br_heat.sigmoid(), + tl_off=tl_off, + br_off=br_off, + tl_emb=tl_emb, + br_emb=br_emb, + tl_centripetal_shift=tl_centripetal_shift, + br_centripetal_shift=br_centripetal_shift, + img_meta=img_meta, + k=self.test_cfg.corner_topk, + kernel=self.test_cfg.local_maximum_kernel, + distance_threshold=self.test_cfg.distance_threshold) + + if rescale: + batch_bboxes /= batch_bboxes.new_tensor(img_meta['scale_factor']) + + bboxes = batch_bboxes.view([-1, 4]) + scores = batch_scores.view([-1, 1]) + clses = batch_clses.view([-1, 1]) + + idx = scores.argsort(dim=0, descending=True) + bboxes = bboxes[idx].view([-1, 4]) + scores = scores[idx].view(-1) + clses = clses[idx].view(-1) + + detections = torch.cat([bboxes, scores.unsqueeze(-1)], -1) + keepinds = (detections[:, -1] > -0.1) + detections = detections[keepinds] + labels = clses[keepinds] + + if with_nms: + detections, labels = self._bboxes_nms(detections, labels, + self.test_cfg) + + return detections, labels + + def _bboxes_nms(self, bboxes, labels, cfg): + if labels.numel() == 0: + return bboxes, labels + + if 'nms_cfg' in cfg: + warning.warn('nms_cfg in test_cfg will be deprecated. ' + 'Please rename it as nms') + if 'nms' not in cfg: + cfg.nms = cfg.nms_cfg + + out_bboxes, keep = batched_nms(bboxes[:, :4], bboxes[:, -1], labels, + cfg.nms) + out_labels = labels[keep] + + if len(out_bboxes) > 0: + idx = torch.argsort(out_bboxes[:, -1], descending=True) + idx = idx[:cfg.max_per_img] + out_bboxes = out_bboxes[idx] + out_labels = out_labels[idx] + + return out_bboxes, out_labels + + def _gather_feat(self, feat, ind, mask=None): + """Gather feature according to index. + + Args: + feat (Tensor): Target feature map. + ind (Tensor): Target coord index. + mask (Tensor | None): Mask of featuremap. Default: None. + + Returns: + feat (Tensor): Gathered feature. + """ + dim = feat.size(2) + ind = ind.unsqueeze(2).repeat(1, 1, dim) + feat = feat.gather(1, ind) + if mask is not None: + mask = mask.unsqueeze(2).expand_as(feat) + feat = feat[mask] + feat = feat.view(-1, dim) + return feat + + def _local_maximum(self, heat, kernel=3): + """Extract local maximum pixel with given kernel. + + Args: + heat (Tensor): Target heatmap. + kernel (int): Kernel size of max pooling. Default: 3. + + Returns: + heat (Tensor): A heatmap where local maximum pixels maintain its + own value and other positions are 0. + """ + pad = (kernel - 1) // 2 + hmax = F.max_pool2d(heat, kernel, stride=1, padding=pad) + keep = (hmax == heat).float() + return heat * keep + + def _transpose_and_gather_feat(self, feat, ind): + """Transpose and gather feature according to index. + + Args: + feat (Tensor): Target feature map. + ind (Tensor): Target coord index. + + Returns: + feat (Tensor): Transposed and gathered feature. + """ + feat = feat.permute(0, 2, 3, 1).contiguous() + feat = feat.view(feat.size(0), -1, feat.size(3)) + feat = self._gather_feat(feat, ind) + return feat + + def _topk(self, scores, k=20): + """Get top k positions from heatmap. + + Args: + scores (Tensor): Target heatmap with shape + [batch, num_classes, height, width]. + k (int): Target number. Default: 20. + + Returns: + tuple[torch.Tensor]: Scores, indexes, categories and coords of + topk keypoint. Containing following Tensors: + + - topk_scores (Tensor): Max scores of each topk keypoint. + - topk_inds (Tensor): Indexes of each topk keypoint. + - topk_clses (Tensor): Categories of each topk keypoint. + - topk_ys (Tensor): Y-coord of each topk keypoint. + - topk_xs (Tensor): X-coord of each topk keypoint. + """ + batch, _, height, width = scores.size() + topk_scores, topk_inds = torch.topk(scores.view(batch, -1), k) + topk_clses = topk_inds // (height * width) + topk_inds = topk_inds % (height * width) + topk_ys = topk_inds // width + topk_xs = (topk_inds % width).int().float() + return topk_scores, topk_inds, topk_clses, topk_ys, topk_xs + + def decode_heatmap(self, + tl_heat, + br_heat, + tl_off, + br_off, + tl_emb=None, + br_emb=None, + tl_centripetal_shift=None, + br_centripetal_shift=None, + img_meta=None, + k=100, + kernel=3, + distance_threshold=0.5, + num_dets=1000): + """Transform outputs for a single batch item into raw bbox predictions. + + Args: + tl_heat (Tensor): Top-left corner heatmap for current level with + shape (N, num_classes, H, W). + br_heat (Tensor): Bottom-right corner heatmap for current level + with shape (N, num_classes, H, W). + tl_off (Tensor): Top-left corner offset for current level with + shape (N, corner_offset_channels, H, W). + br_off (Tensor): Bottom-right corner offset for current level with + shape (N, corner_offset_channels, H, W). + tl_emb (Tensor | None): Top-left corner embedding for current + level with shape (N, corner_emb_channels, H, W). + br_emb (Tensor | None): Bottom-right corner embedding for current + level with shape (N, corner_emb_channels, H, W). + tl_centripetal_shift (Tensor | None): Top-left centripetal shift + for current level with shape (N, 2, H, W). + br_centripetal_shift (Tensor | None): Bottom-right centripetal + shift for current level with shape (N, 2, H, W). + img_meta (dict): Meta information of current image, e.g., + image size, scaling factor, etc. + k (int): Get top k corner keypoints from heatmap. + kernel (int): Max pooling kernel for extract local maximum pixels. + distance_threshold (float): Distance threshold. Top-left and + bottom-right corner keypoints with feature distance less than + the threshold will be regarded as keypoints from same object. + num_dets (int): Num of raw boxes before doing nms. + + Returns: + tuple[torch.Tensor]: Decoded output of CornerHead, containing the + following Tensors: + + - bboxes (Tensor): Coords of each box. + - scores (Tensor): Scores of each box. + - clses (Tensor): Categories of each box. + """ + with_embedding = tl_emb is not None and br_emb is not None + with_centripetal_shift = ( + tl_centripetal_shift is not None + and br_centripetal_shift is not None) + assert with_embedding + with_centripetal_shift == 1 + batch, _, height, width = tl_heat.size() + inp_h, inp_w, _ = img_meta['pad_shape'] + + # perform nms on heatmaps + tl_heat = self._local_maximum(tl_heat, kernel=kernel) + br_heat = self._local_maximum(br_heat, kernel=kernel) + + tl_scores, tl_inds, tl_clses, tl_ys, tl_xs = self._topk(tl_heat, k=k) + br_scores, br_inds, br_clses, br_ys, br_xs = self._topk(br_heat, k=k) + + # We use repeat instead of expand here because expand is a + # shallow-copy function. Thus it could cause unexpected testing result + # sometimes. Using expand will decrease about 10% mAP during testing + # compared to repeat. + tl_ys = tl_ys.view(batch, k, 1).repeat(1, 1, k) + tl_xs = tl_xs.view(batch, k, 1).repeat(1, 1, k) + br_ys = br_ys.view(batch, 1, k).repeat(1, k, 1) + br_xs = br_xs.view(batch, 1, k).repeat(1, k, 1) + + tl_off = self._transpose_and_gather_feat(tl_off, tl_inds) + tl_off = tl_off.view(batch, k, 1, 2) + br_off = self._transpose_and_gather_feat(br_off, br_inds) + br_off = br_off.view(batch, 1, k, 2) + + tl_xs = tl_xs + tl_off[..., 0] + tl_ys = tl_ys + tl_off[..., 1] + br_xs = br_xs + br_off[..., 0] + br_ys = br_ys + br_off[..., 1] + + if with_centripetal_shift: + tl_centripetal_shift = self._transpose_and_gather_feat( + tl_centripetal_shift, tl_inds).view(batch, k, 1, 2).exp() + br_centripetal_shift = self._transpose_and_gather_feat( + br_centripetal_shift, br_inds).view(batch, 1, k, 2).exp() + + tl_ctxs = tl_xs + tl_centripetal_shift[..., 0] + tl_ctys = tl_ys + tl_centripetal_shift[..., 1] + br_ctxs = br_xs - br_centripetal_shift[..., 0] + br_ctys = br_ys - br_centripetal_shift[..., 1] + + # all possible boxes based on top k corners (ignoring class) + tl_xs *= (inp_w / width) + tl_ys *= (inp_h / height) + br_xs *= (inp_w / width) + br_ys *= (inp_h / height) + + if with_centripetal_shift: + tl_ctxs *= (inp_w / width) + tl_ctys *= (inp_h / height) + br_ctxs *= (inp_w / width) + br_ctys *= (inp_h / height) + + x_off = img_meta['border'][2] + y_off = img_meta['border'][0] + + tl_xs -= x_off + tl_ys -= y_off + br_xs -= x_off + br_ys -= y_off + + tl_xs *= tl_xs.gt(0.0).type_as(tl_xs) + tl_ys *= tl_ys.gt(0.0).type_as(tl_ys) + br_xs *= br_xs.gt(0.0).type_as(br_xs) + br_ys *= br_ys.gt(0.0).type_as(br_ys) + + bboxes = torch.stack((tl_xs, tl_ys, br_xs, br_ys), dim=3) + area_bboxes = ((br_xs - tl_xs) * (br_ys - tl_ys)).abs() + + if with_centripetal_shift: + tl_ctxs -= x_off + tl_ctys -= y_off + br_ctxs -= x_off + br_ctys -= y_off + + tl_ctxs *= tl_ctxs.gt(0.0).type_as(tl_ctxs) + tl_ctys *= tl_ctys.gt(0.0).type_as(tl_ctys) + br_ctxs *= br_ctxs.gt(0.0).type_as(br_ctxs) + br_ctys *= br_ctys.gt(0.0).type_as(br_ctys) + + ct_bboxes = torch.stack((tl_ctxs, tl_ctys, br_ctxs, br_ctys), + dim=3) + area_ct_bboxes = ((br_ctxs - tl_ctxs) * (br_ctys - tl_ctys)).abs() + + rcentral = torch.zeros_like(ct_bboxes) + # magic nums from paper section 4.1 + mu = torch.ones_like(area_bboxes) / 2.4 + mu[area_bboxes > 3500] = 1 / 2.1 # large bbox have smaller mu + + bboxes_center_x = (bboxes[..., 0] + bboxes[..., 2]) / 2 + bboxes_center_y = (bboxes[..., 1] + bboxes[..., 3]) / 2 + rcentral[..., 0] = bboxes_center_x - mu * (bboxes[..., 2] - + bboxes[..., 0]) / 2 + rcentral[..., 1] = bboxes_center_y - mu * (bboxes[..., 3] - + bboxes[..., 1]) / 2 + rcentral[..., 2] = bboxes_center_x + mu * (bboxes[..., 2] - + bboxes[..., 0]) / 2 + rcentral[..., 3] = bboxes_center_y + mu * (bboxes[..., 3] - + bboxes[..., 1]) / 2 + area_rcentral = ((rcentral[..., 2] - rcentral[..., 0]) * + (rcentral[..., 3] - rcentral[..., 1])).abs() + dists = area_ct_bboxes / area_rcentral + + tl_ctx_inds = (ct_bboxes[..., 0] <= rcentral[..., 0]) | ( + ct_bboxes[..., 0] >= rcentral[..., 2]) + tl_cty_inds = (ct_bboxes[..., 1] <= rcentral[..., 1]) | ( + ct_bboxes[..., 1] >= rcentral[..., 3]) + br_ctx_inds = (ct_bboxes[..., 2] <= rcentral[..., 0]) | ( + ct_bboxes[..., 2] >= rcentral[..., 2]) + br_cty_inds = (ct_bboxes[..., 3] <= rcentral[..., 1]) | ( + ct_bboxes[..., 3] >= rcentral[..., 3]) + + if with_embedding: + tl_emb = self._transpose_and_gather_feat(tl_emb, tl_inds) + tl_emb = tl_emb.view(batch, k, 1) + br_emb = self._transpose_and_gather_feat(br_emb, br_inds) + br_emb = br_emb.view(batch, 1, k) + dists = torch.abs(tl_emb - br_emb) + + tl_scores = tl_scores.view(batch, k, 1).repeat(1, 1, k) + br_scores = br_scores.view(batch, 1, k).repeat(1, k, 1) + + scores = (tl_scores + br_scores) / 2 # scores for all possible boxes + + # tl and br should have same class + tl_clses = tl_clses.view(batch, k, 1).repeat(1, 1, k) + br_clses = br_clses.view(batch, 1, k).repeat(1, k, 1) + cls_inds = (tl_clses != br_clses) + + # reject boxes based on distances + dist_inds = dists > distance_threshold + + # reject boxes based on widths and heights + width_inds = (br_xs <= tl_xs) + height_inds = (br_ys <= tl_ys) + + scores[cls_inds] = -1 + scores[width_inds] = -1 + scores[height_inds] = -1 + scores[dist_inds] = -1 + if with_centripetal_shift: + scores[tl_ctx_inds] = -1 + scores[tl_cty_inds] = -1 + scores[br_ctx_inds] = -1 + scores[br_cty_inds] = -1 + + scores = scores.view(batch, -1) + scores, inds = torch.topk(scores, num_dets) + scores = scores.unsqueeze(2) + + bboxes = bboxes.view(batch, -1, 4) + bboxes = self._gather_feat(bboxes, inds) + + clses = tl_clses.contiguous().view(batch, -1, 1) + clses = self._gather_feat(clses, inds).float() + + return bboxes, scores, clses diff --git a/mmdet/models/dense_heads/deformable_detr_head.py b/mmdet/models/dense_heads/deformable_detr_head.py new file mode 100644 index 0000000..92c980a --- /dev/null +++ b/mmdet/models/dense_heads/deformable_detr_head.py @@ -0,0 +1,317 @@ +import copy + +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import Linear, bias_init_with_prob, constant_init +from mmcv.runner import force_fp32 + +from mmdet.core import multi_apply +from mmdet.models.utils.transformer import inverse_sigmoid +from ..builder import HEADS +from .detr_head import DETRHead + + +@HEADS.register_module() +class DeformableDETRHead(DETRHead): + """Head of DeformDETR: Deformable DETR: Deformable Transformers for End-to- + End Object Detection. + + Code is modified from the `official github repo + `_. + + More details can be found in the `paper + `_ . + + Args: + with_box_refine (bool): Whether to refine the reference points + in the decoder. Defaults to False. + as_two_stage (bool) : Whether to generate the proposal from + the outputs of encoder. + transformer (obj:`ConfigDict`): ConfigDict is used for building + the Encoder and Decoder. + """ + + def __init__(self, + *args, + with_box_refine=False, + as_two_stage=False, + transformer=None, + **kwargs): + self.with_box_refine = with_box_refine + self.as_two_stage = as_two_stage + if self.as_two_stage: + transformer['as_two_stage'] = self.as_two_stage + + super(DeformableDETRHead, self).__init__( + *args, transformer=transformer, **kwargs) + + def _init_layers(self): + """Initialize classification branch and regression branch of head.""" + + fc_cls = Linear(self.embed_dims, self.cls_out_channels) + reg_branch = [] + for _ in range(self.num_reg_fcs): + reg_branch.append(Linear(self.embed_dims, self.embed_dims)) + reg_branch.append(nn.ReLU()) + reg_branch.append(Linear(self.embed_dims, 4)) + reg_branch = nn.Sequential(*reg_branch) + + def _get_clones(module, N): + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + + # last reg_branch is used to generate proposal from + # encode feature map when as_two_stage is True. + num_pred = (self.transformer.decoder.num_layers + 1) if \ + self.as_two_stage else self.transformer.decoder.num_layers + + if self.with_box_refine: + self.cls_branches = _get_clones(fc_cls, num_pred) + self.reg_branches = _get_clones(reg_branch, num_pred) + else: + + self.cls_branches = nn.ModuleList( + [fc_cls for _ in range(num_pred)]) + self.reg_branches = nn.ModuleList( + [reg_branch for _ in range(num_pred)]) + + if not self.as_two_stage: + self.query_embedding = nn.Embedding(self.num_query, + self.embed_dims * 2) + + def init_weights(self): + """Initialize weights of the DeformDETR head.""" + self.transformer.init_weights() + if self.loss_cls.use_sigmoid: + bias_init = bias_init_with_prob(0.01) + for m in self.cls_branches: + nn.init.constant_(m.bias, bias_init) + for m in self.reg_branches: + constant_init(m[-1], 0, bias=0) + nn.init.constant_(self.reg_branches[0][-1].bias.data[2:], -2.0) + if self.as_two_stage: + for m in self.reg_branches: + nn.init.constant_(m[-1].bias.data[2:], 0.0) + + def forward(self, mlvl_feats, img_metas): + """Forward function. + + Args: + mlvl_feats (tuple[Tensor]): Features from the upstream + network, each is a 4D-tensor with shape + (N, C, H, W). + img_metas (list[dict]): List of image information. + + Returns: + all_cls_scores (Tensor): Outputs from the classification head, \ + shape [nb_dec, bs, num_query, cls_out_channels]. Note \ + cls_out_channels should includes background. + all_bbox_preds (Tensor): Sigmoid outputs from the regression \ + head with normalized coordinate format (cx, cy, w, h). \ + Shape [nb_dec, bs, num_query, 4]. + enc_outputs_class (Tensor): The score of each point on encode \ + feature map, has shape (N, h*w, num_class). Only when \ + as_two_stage is Ture it would be returned, otherwise \ + `None` would be returned. + enc_outputs_coord (Tensor): The proposal generate from the \ + encode feature map, has shape (N, h*w, 4). Only when \ + as_two_stage is Ture it would be returned, otherwise \ + `None` would be returned. + """ + + batch_size = mlvl_feats[0].size(0) + input_img_h, input_img_w = img_metas[0]['batch_input_shape'] + img_masks = mlvl_feats[0].new_ones( + (batch_size, input_img_h, input_img_w)) + for img_id in range(batch_size): + img_h, img_w, _ = img_metas[img_id]['img_shape'] + img_masks[img_id, :img_h, :img_w] = 0 + + mlvl_masks = [] + mlvl_positional_encodings = [] + for feat in mlvl_feats: + mlvl_masks.append( + F.interpolate(img_masks[None], + size=feat.shape[-2:]).to(torch.bool).squeeze(0)) + mlvl_positional_encodings.append( + self.positional_encoding(mlvl_masks[-1])) + + query_embeds = None + if not self.as_two_stage: + query_embeds = self.query_embedding.weight + hs, init_reference, inter_references, \ + enc_outputs_class, enc_outputs_coord = self.transformer( + mlvl_feats, + mlvl_masks, + query_embeds, + mlvl_positional_encodings, + reg_branches=self.reg_branches if self.with_box_refine else None, # noqa:E501 + cls_branches=self.cls_branches if self.as_two_stage else None # noqa:E501 + ) + hs = hs.permute(0, 2, 1, 3) + outputs_classes = [] + outputs_coords = [] + + for lvl in range(hs.shape[0]): + if lvl == 0: + reference = init_reference + else: + reference = inter_references[lvl - 1] + reference = inverse_sigmoid(reference) + outputs_class = self.cls_branches[lvl](hs[lvl]) + tmp = self.reg_branches[lvl](hs[lvl]) + if reference.shape[-1] == 4: + tmp += reference + else: + assert reference.shape[-1] == 2 + tmp[..., :2] += reference + outputs_coord = tmp.sigmoid() + outputs_classes.append(outputs_class) + outputs_coords.append(outputs_coord) + + outputs_classes = torch.stack(outputs_classes) + outputs_coords = torch.stack(outputs_coords) + if self.as_two_stage: + return outputs_classes, outputs_coords, \ + enc_outputs_class, \ + enc_outputs_coord.sigmoid() + else: + return outputs_classes, outputs_coords, \ + None, None + + @force_fp32(apply_to=('all_cls_scores_list', 'all_bbox_preds_list')) + def loss(self, + all_cls_scores, + all_bbox_preds, + enc_cls_scores, + enc_bbox_preds, + gt_bboxes_list, + gt_labels_list, + img_metas, + gt_bboxes_ignore=None): + """"Loss function. + + Args: + all_cls_scores (Tensor): Classification score of all + decoder layers, has shape + [nb_dec, bs, num_query, cls_out_channels]. + all_bbox_preds (Tensor): Sigmoid regression + outputs of all decode layers. Each is a 4D-tensor with + normalized coordinate format (cx, cy, w, h) and shape + [nb_dec, bs, num_query, 4]. + enc_cls_scores (Tensor): Classification scores of + points on encode feature map , has shape + (N, h*w, num_classes). Only be passed when as_two_stage is + True, otherwise is None. + enc_bbox_preds (Tensor): Regression results of each points + on the encode feature map, has shape (N, h*w, 4). Only be + passed when as_two_stage is True, otherwise is None. + gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image + with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels_list (list[Tensor]): Ground truth class indices for each + image with shape (num_gts, ). + img_metas (list[dict]): List of image meta information. + gt_bboxes_ignore (list[Tensor], optional): Bounding boxes + which can be ignored for each image. Default None. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + assert gt_bboxes_ignore is None, \ + f'{self.__class__.__name__} only supports ' \ + f'for gt_bboxes_ignore setting to None.' + + num_dec_layers = len(all_cls_scores) + all_gt_bboxes_list = [gt_bboxes_list for _ in range(num_dec_layers)] + all_gt_labels_list = [gt_labels_list for _ in range(num_dec_layers)] + all_gt_bboxes_ignore_list = [ + gt_bboxes_ignore for _ in range(num_dec_layers) + ] + img_metas_list = [img_metas for _ in range(num_dec_layers)] + + losses_cls, losses_bbox, losses_iou = multi_apply( + self.loss_single, all_cls_scores, all_bbox_preds, + all_gt_bboxes_list, all_gt_labels_list, img_metas_list, + all_gt_bboxes_ignore_list) + + loss_dict = dict() + # loss of proposal generated from encode feature map. + if enc_cls_scores is not None: + binary_labels_list = [ + torch.zeros_like(gt_labels_list[i]) + for i in range(len(img_metas)) + ] + enc_loss_cls, enc_losses_bbox, enc_losses_iou = \ + self.loss_single(enc_cls_scores, enc_bbox_preds, + gt_bboxes_list, binary_labels_list, + img_metas, gt_bboxes_ignore) + loss_dict['enc_loss_cls'] = enc_loss_cls + loss_dict['enc_loss_bbox'] = enc_losses_bbox + loss_dict['enc_loss_iou'] = enc_losses_iou + + # loss from the last decoder layer + loss_dict['loss_cls'] = losses_cls[-1] + loss_dict['loss_bbox'] = losses_bbox[-1] + loss_dict['loss_iou'] = losses_iou[-1] + # loss from other decoder layers + num_dec_layer = 0 + for loss_cls_i, loss_bbox_i, loss_iou_i in zip(losses_cls[:-1], + losses_bbox[:-1], + losses_iou[:-1]): + loss_dict[f'd{num_dec_layer}.loss_cls'] = loss_cls_i + loss_dict[f'd{num_dec_layer}.loss_bbox'] = loss_bbox_i + loss_dict[f'd{num_dec_layer}.loss_iou'] = loss_iou_i + num_dec_layer += 1 + return loss_dict + + @force_fp32(apply_to=('all_cls_scores_list', 'all_bbox_preds_list')) + def get_bboxes(self, + all_cls_scores, + all_bbox_preds, + enc_cls_scores, + enc_bbox_preds, + img_metas, + rescale=False): + """Transform network outputs for a batch into bbox predictions. + + Args: + all_cls_scores (Tensor): Classification score of all + decoder layers, has shape + [nb_dec, bs, num_query, cls_out_channels]. + all_bbox_preds (Tensor): Sigmoid regression + outputs of all decode layers. Each is a 4D-tensor with + normalized coordinate format (cx, cy, w, h) and shape + [nb_dec, bs, num_query, 4]. + enc_cls_scores (Tensor): Classification scores of + points on encode feature map , has shape + (N, h*w, num_classes). Only be passed when as_two_stage is + True, otherwise is None. + enc_bbox_preds (Tensor): Regression results of each points + on the encode feature map, has shape (N, h*w, 4). Only be + passed when as_two_stage is True, otherwise is None. + img_metas (list[dict]): Meta information of each image. + rescale (bool, optional): If True, return boxes in original + image space. Defalut False. + + Returns: + list[list[Tensor, Tensor]]: Each item in result_list is 2-tuple. \ + The first item is an (n, 5) tensor, where the first 4 columns \ + are bounding box positions (tl_x, tl_y, br_x, br_y) and the \ + 5-th column is a score between 0 and 1. The second item is a \ + (n,) tensor where each item is the predicted class label of \ + the corresponding box. + """ + cls_scores = all_cls_scores[-1] + bbox_preds = all_bbox_preds[-1] + + result_list = [] + for img_id in range(len(img_metas)): + cls_score = cls_scores[img_id] + bbox_pred = bbox_preds[img_id] + img_shape = img_metas[img_id]['img_shape'] + scale_factor = img_metas[img_id]['scale_factor'] + proposals = self._get_bboxes_single(cls_score, bbox_pred, + img_shape, scale_factor, + rescale) + result_list.append(proposals) + return result_list diff --git a/mmdet/models/dense_heads/dense_test_mixins.py b/mmdet/models/dense_heads/dense_test_mixins.py new file mode 100644 index 0000000..dd81364 --- /dev/null +++ b/mmdet/models/dense_heads/dense_test_mixins.py @@ -0,0 +1,100 @@ +from inspect import signature + +import torch + +from mmdet.core import bbox2result, bbox_mapping_back, multiclass_nms + + +class BBoxTestMixin(object): + """Mixin class for test time augmentation of bboxes.""" + + def merge_aug_bboxes(self, aug_bboxes, aug_scores, img_metas): + """Merge augmented detection bboxes and scores. + + Args: + aug_bboxes (list[Tensor]): shape (n, 4*#class) + aug_scores (list[Tensor] or None): shape (n, #class) + img_shapes (list[Tensor]): shape (3, ). + + Returns: + tuple: (bboxes, scores) + """ + recovered_bboxes = [] + for bboxes, img_info in zip(aug_bboxes, img_metas): + img_shape = img_info[0]['img_shape'] + scale_factor = img_info[0]['scale_factor'] + flip = img_info[0]['flip'] + flip_direction = img_info[0]['flip_direction'] + bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip, + flip_direction) + recovered_bboxes.append(bboxes) + bboxes = torch.cat(recovered_bboxes, dim=0) + if aug_scores is None: + return bboxes + else: + scores = torch.cat(aug_scores, dim=0) + return bboxes, scores + + def aug_test_bboxes(self, feats, img_metas, rescale=False): + """Test det bboxes with test time augmentation. + + Args: + feats (list[Tensor]): the outer list indicates test-time + augmentations and inner Tensor should have a shape NxCxHxW, + which contains features for all images in the batch. + img_metas (list[list[dict]]): the outer list indicates test-time + augs (multiscale, flip, etc.) and the inner list indicates + images in a batch. each dict has image information. + rescale (bool, optional): Whether to rescale the results. + Defaults to False. + + Returns: + list[ndarray]: bbox results of each class + """ + # check with_nms argument + gb_sig = signature(self.get_bboxes) + gb_args = [p.name for p in gb_sig.parameters.values()] + if hasattr(self, '_get_bboxes'): + gbs_sig = signature(self._get_bboxes) + else: + gbs_sig = signature(self._get_bboxes_single) + gbs_args = [p.name for p in gbs_sig.parameters.values()] + assert ('with_nms' in gb_args) and ('with_nms' in gbs_args), \ + f'{self.__class__.__name__}' \ + ' does not support test-time augmentation' + + aug_bboxes = [] + aug_scores = [] + aug_factors = [] # score_factors for NMS + for x, img_meta in zip(feats, img_metas): + # only one image in the batch + outs = self.forward(x) + bbox_inputs = outs + (img_meta, self.test_cfg, False, False) + bbox_outputs = self.get_bboxes(*bbox_inputs)[0] + aug_bboxes.append(bbox_outputs[0]) + aug_scores.append(bbox_outputs[1]) + # bbox_outputs of some detectors (e.g., ATSS, FCOS, YOLOv3) + # contains additional element to adjust scores before NMS + if len(bbox_outputs) >= 3: + aug_factors.append(bbox_outputs[2]) + + # after merging, bboxes will be rescaled to the original image size + merged_bboxes, merged_scores = self.merge_aug_bboxes( + aug_bboxes, aug_scores, img_metas) + merged_factors = torch.cat(aug_factors, dim=0) if aug_factors else None + det_bboxes, det_labels = multiclass_nms( + merged_bboxes, + merged_scores, + self.test_cfg.score_thr, + self.test_cfg.nms, + self.test_cfg.max_per_img, + score_factors=merged_factors) + + if rescale: + _det_bboxes = det_bboxes + else: + _det_bboxes = det_bboxes.clone() + _det_bboxes[:, :4] *= det_bboxes.new_tensor( + img_metas[0][0]['scale_factor']) + bbox_results = bbox2result(_det_bboxes, det_labels, self.num_classes) + return bbox_results diff --git a/mmdet/models/dense_heads/detr_head.py b/mmdet/models/dense_heads/detr_head.py new file mode 100644 index 0000000..6a2363a --- /dev/null +++ b/mmdet/models/dense_heads/detr_head.py @@ -0,0 +1,688 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import Conv2d, Linear, build_activation_layer +from mmcv.cnn.bricks.transformer import FFN, build_positional_encoding +from mmcv.runner import force_fp32 + +from mmdet.core import (bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh, + build_assigner, build_sampler, multi_apply, + reduce_mean) +from mmdet.models.utils import build_transformer +from ..builder import HEADS, build_loss +from .anchor_free_head import AnchorFreeHead +from cccu import counter + +@HEADS.register_module() +class DETRHead(AnchorFreeHead): + """Implements the DETR transformer head. + + See `paper: End-to-End Object Detection with Transformers + `_ for details. + + Args: + num_classes (int): Number of categories excluding the background. + in_channels (int): Number of channels in the input feature map. + num_query (int): Number of query in Transformer. + num_reg_fcs (int, optional): Number of fully-connected layers used in + `FFN`, which is then used for the regression head. Default 2. + transformer (obj:`mmcv.ConfigDict`|dict): Config for transformer. + Default: None. + sync_cls_avg_factor (bool): Whether to sync the avg_factor of + all ranks. Default to False. + positional_encoding (obj:`mmcv.ConfigDict`|dict): + Config for position encoding. + loss_cls (obj:`mmcv.ConfigDict`|dict): Config of the + classification loss. Default `CrossEntropyLoss`. + loss_bbox (obj:`mmcv.ConfigDict`|dict): Config of the + regression loss. Default `L1Loss`. + loss_iou (obj:`mmcv.ConfigDict`|dict): Config of the + regression iou loss. Default `GIoULoss`. + tran_cfg (obj:`mmcv.ConfigDict`|dict): Training config of + transformer head. + test_cfg (obj:`mmcv.ConfigDict`|dict): Testing config of + transformer head. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + _version = 2 + + def __init__(self, + num_classes, + in_channels, + num_query=100, + num_reg_fcs=2, + transformer=None, + sync_cls_avg_factor=False, + positional_encoding=dict( + type='SinePositionalEncoding', + num_feats=128, + normalize=True), + loss_cls=dict( + type='CrossEntropyLoss', + bg_cls_weight=0.1, + use_sigmoid=False, + loss_weight=1.0, + class_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + train_cfg=dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='ClassificationCost', weight=1.), + reg_cost=dict(type='BBoxL1Cost', weight=5.0), + iou_cost=dict( + type='IoUCost', iou_mode='giou', weight=2.0))), + test_cfg=dict(max_per_img=100), + init_cfg=None, + **kwargs): + # NOTE here use `AnchorFreeHead` instead of `TransformerHead`, + # since it brings inconvenience when the initialization of + # `AnchorFreeHead` is called. + super(AnchorFreeHead, self).__init__(init_cfg) + self.bg_cls_weight = 0 + self.sync_cls_avg_factor = sync_cls_avg_factor + class_weight = loss_cls.get('class_weight', None) + if class_weight is not None and (self.__class__ is DETRHead): + assert isinstance(class_weight, float), 'Expected ' \ + 'class_weight to have type float. Found ' \ + f'{type(class_weight)}.' + # NOTE following the official DETR rep0, bg_cls_weight means + # relative classification weight of the no-object class. + bg_cls_weight = loss_cls.get('bg_cls_weight', class_weight) + assert isinstance(bg_cls_weight, float), 'Expected ' \ + 'bg_cls_weight to have type float. Found ' \ + f'{type(bg_cls_weight)}.' + class_weight = torch.ones(num_classes + 1) * class_weight + # set background class as the last indice + class_weight[num_classes] = bg_cls_weight + loss_cls.update({'class_weight': class_weight}) + if 'bg_cls_weight' in loss_cls: + loss_cls.pop('bg_cls_weight') + self.bg_cls_weight = bg_cls_weight + + if train_cfg: + assert 'assigner' in train_cfg, 'assigner should be provided '\ + 'when train_cfg is set.' + assigner = train_cfg['assigner'] + assert loss_cls['loss_weight'] == assigner['cls_cost']['weight'], \ + 'The classification weight for loss and matcher should be' \ + 'exactly the same.' + assert loss_bbox['loss_weight'] == assigner['reg_cost'][ + 'weight'], 'The regression L1 weight for loss and matcher ' \ + 'should be exactly the same.' + assert loss_iou['loss_weight'] == assigner['iou_cost']['weight'], \ + 'The regression iou weight for loss and matcher should be' \ + 'exactly the same.' + self.assigner = build_assigner(assigner) + # DETR sampling=False, so use PseudoSampler + sampler_cfg = dict(type='PseudoSampler') + self.sampler = build_sampler(sampler_cfg, context=self) + self.num_query = num_query + self.num_classes = num_classes + self.in_channels = in_channels + self.num_reg_fcs = num_reg_fcs + self.train_cfg = train_cfg + self.test_cfg = test_cfg + self.fp16_enabled = False + self.loss_cls = build_loss(loss_cls) + self.loss_bbox = build_loss(loss_bbox) + self.loss_iou = build_loss(loss_iou) + + if self.loss_cls.use_sigmoid: + self.cls_out_channels = num_classes + else: + self.cls_out_channels = num_classes + 1 + self.act_cfg = transformer.get('act_cfg', + dict(type='ReLU', inplace=True)) + self.activate = build_activation_layer(self.act_cfg) + self.positional_encoding = build_positional_encoding( + positional_encoding) + self.transformer = build_transformer(transformer) + self.embed_dims = self.transformer.embed_dims + assert 'num_feats' in positional_encoding + num_feats = positional_encoding['num_feats'] + assert num_feats * 2 == self.embed_dims, 'embed_dims should' \ + f' be exactly 2 times of num_feats. Found {self.embed_dims}' \ + f' and {num_feats}.' + self._init_layers() + + def _init_layers(self): + """Initialize layers of the transformer head.""" + self.input_proj = Conv2d( + self.in_channels, self.embed_dims, kernel_size=1) + self.fc_cls = Linear(self.embed_dims, self.cls_out_channels) + self.reg_ffn = FFN( + self.embed_dims, + self.embed_dims, + self.num_reg_fcs, + self.act_cfg, + dropout=0.0, + add_residual=False) + self.fc_reg = Linear(self.embed_dims, 4) + self.query_embedding = nn.Embedding(self.num_query, self.embed_dims) + + def init_weights(self): + """Initialize weights of the transformer head.""" + # The initialization for transformer is important + self.transformer.init_weights() + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs): + """load checkpoints.""" + # NOTE here use `AnchorFreeHead` instead of `TransformerHead`, + # since `AnchorFreeHead._load_from_state_dict` should not be + # called here. Invoking the default `Module._load_from_state_dict` + # is enough. + + # Names of some parameters in has been changed. + version = local_metadata.get('version', None) + if (version is None or version < 2) and self.__class__ is DETRHead: + convert_dict = { + '.self_attn.': '.attentions.0.', + '.ffn.': '.ffns.0.', + '.multihead_attn.': '.attentions.1.', + '.decoder.norm.': '.decoder.post_norm.' + } + for k in state_dict.keys(): + for ori_key, convert_key in convert_dict.items(): + if ori_key in k: + convert_key = k.replace(ori_key, convert_key) + state_dict[convert_key] = state_dict[k] + del state_dict[k] + + super(AnchorFreeHead, + self)._load_from_state_dict(state_dict, prefix, local_metadata, + strict, missing_keys, + unexpected_keys, error_msgs) + + def forward(self, feats, img_metas): + """Forward function. + + Args: + feats (tuple[Tensor]): Features from the upstream network, each is + a 4D-tensor. + img_metas (list[dict]): List of image information. + + Returns: + tuple[list[Tensor], list[Tensor]]: Outputs for all scale levels. + + - all_cls_scores_list (list[Tensor]): Classification scores \ + for each scale level. Each is a 4D-tensor with shape \ + [nb_dec, bs, num_query, cls_out_channels]. Note \ + `cls_out_channels` should includes background. + - all_bbox_preds_list (list[Tensor]): Sigmoid regression \ + outputs for each scale level. Each is a 4D-tensor with \ + normalized coordinate format (cx, cy, w, h) and shape \ + [nb_dec, bs, num_query, 4]. + """ + num_levels = len(feats) + img_metas_list = [img_metas for _ in range(num_levels)] + return multi_apply(self.forward_single, feats, img_metas_list) + + def forward_single(self, x, img_metas): + """"Forward function for a single feature level. + + Args: + x (Tensor): Input feature from backbone's single stage, shape + [bs, c, h, w]. + img_metas (list[dict]): List of image information. + + Returns: + all_cls_scores (Tensor): Outputs from the classification head, + shape [nb_dec, bs, num_query, cls_out_channels]. Note + cls_out_channels should includes background. + all_bbox_preds (Tensor): Sigmoid outputs from the regression + head with normalized coordinate format (cx, cy, w, h). + Shape [nb_dec, bs, num_query, 4]. + """ + # construct binary masks which used for the transformer. + # NOTE following the official DETR repo, non-zero values representing + # ignored positions, while zero values means valid positions. + batch_size = x.size(0) + input_img_h, input_img_w = img_metas[0]['batch_input_shape'] + masks = x.new_ones((batch_size, input_img_h, input_img_w)) + for img_id in range(batch_size): + img_h, img_w, _ = img_metas[img_id]['img_shape'] + masks[img_id, :img_h, :img_w] = 0 + + x = self.input_proj(x) + # interpolate masks to have the same spatial shape with x + masks = F.interpolate( + masks.unsqueeze(1), size=x.shape[-2:]).to(torch.bool).squeeze(1) + # position encoding + pos_embed = self.positional_encoding(masks) # [bs, embed_dim, h, w] + # outs_dec: [nb_dec, bs, num_query, embed_dim] + outs_dec, _ = self.transformer(x, masks, self.query_embedding.weight, + pos_embed) + + all_cls_scores = self.fc_cls(outs_dec) + all_bbox_preds = self.fc_reg(self.activate( + self.reg_ffn(outs_dec))).sigmoid() + return all_cls_scores, all_bbox_preds + + @force_fp32(apply_to=('all_cls_scores_list', 'all_bbox_preds_list')) + def loss(self, + all_cls_scores_list, + all_bbox_preds_list, + gt_bboxes_list, + gt_labels_list, + img_metas, + gt_bboxes_ignore=None): + """"Loss function. + + Only outputs from the last feature level are used for computing + losses by default. + + Args: + all_cls_scores_list (list[Tensor]): Classification outputs + for each feature level. Each is a 4D-tensor with shape + [nb_dec, bs, num_query, cls_out_channels]. + all_bbox_preds_list (list[Tensor]): Sigmoid regression + outputs for each feature level. Each is a 4D-tensor with + normalized coordinate format (cx, cy, w, h) and shape + [nb_dec, bs, num_query, 4]. + gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image + with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels_list (list[Tensor]): Ground truth class indices for each + image with shape (num_gts, ). + img_metas (list[dict]): List of image meta information. + gt_bboxes_ignore (list[Tensor], optional): Bounding boxes + which can be ignored for each image. Default None. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + # NOTE defaultly only the outputs from the last feature scale is used. + all_cls_scores = all_cls_scores_list[-1] + all_bbox_preds = all_bbox_preds_list[-1] + assert gt_bboxes_ignore is None, \ + 'Only supports for gt_bboxes_ignore setting to None.' + + num_dec_layers = len(all_cls_scores) + all_gt_bboxes_list = [gt_bboxes_list for _ in range(num_dec_layers)] + all_gt_labels_list = [gt_labels_list for _ in range(num_dec_layers)] + all_gt_bboxes_ignore_list = [ + gt_bboxes_ignore for _ in range(num_dec_layers) + ] + img_metas_list = [img_metas for _ in range(num_dec_layers)] + + losses_cls, losses_bbox, losses_iou = multi_apply( + self.loss_single, all_cls_scores, all_bbox_preds, + all_gt_bboxes_list, all_gt_labels_list, img_metas_list, + all_gt_bboxes_ignore_list) + + loss_dict = dict() + # loss from the last decoder layer + loss_dict['loss_cls'] = losses_cls[-1] + loss_dict['loss_bbox'] = losses_bbox[-1] + loss_dict['loss_iou'] = losses_iou[-1] + # loss from other decoder layers + num_dec_layer = 0 + for loss_cls_i, loss_bbox_i, loss_iou_i in zip(losses_cls[:-1], + losses_bbox[:-1], + losses_iou[:-1]): + loss_dict[f'd{num_dec_layer}.loss_cls'] = loss_cls_i + loss_dict[f'd{num_dec_layer}.loss_bbox'] = loss_bbox_i + loss_dict[f'd{num_dec_layer}.loss_iou'] = loss_iou_i + num_dec_layer += 1 + return loss_dict + + def loss_single(self, + cls_scores, + bbox_preds, + gt_bboxes_list, + gt_labels_list, + img_metas, + gt_bboxes_ignore_list=None): + """"Loss function for outputs from a single decoder layer of a single + feature level. + + Args: + cls_scores (Tensor): Box score logits from a single decoder layer + for all images. Shape [bs, num_query, cls_out_channels]. + bbox_preds (Tensor): Sigmoid outputs from a single decoder layer + for all images, with normalized coordinate (cx, cy, w, h) and + shape [bs, num_query, 4]. + gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image + with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels_list (list[Tensor]): Ground truth class indices for each + image with shape (num_gts, ). + img_metas (list[dict]): List of image meta information. + gt_bboxes_ignore_list (list[Tensor], optional): Bounding + boxes which can be ignored for each image. Default None. + + Returns: + dict[str, Tensor]: A dictionary of loss components for outputs from + a single decoder layer. + """ + num_imgs = cls_scores.size(0) + cls_scores_list = [cls_scores[i] for i in range(num_imgs)] + bbox_preds_list = [bbox_preds[i] for i in range(num_imgs)] + cls_reg_targets = self.get_targets(cls_scores_list, bbox_preds_list, + gt_bboxes_list, gt_labels_list, + img_metas, gt_bboxes_ignore_list) + (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, + num_total_pos, num_total_neg) = cls_reg_targets + labels = torch.cat(labels_list, 0) + label_weights = torch.cat(label_weights_list, 0) + bbox_targets = torch.cat(bbox_targets_list, 0) + bbox_weights = torch.cat(bbox_weights_list, 0) + + # classification loss + cls_scores = cls_scores.reshape(-1, self.cls_out_channels) + # construct weighted avg_factor to match with the official DETR repo + cls_avg_factor = num_total_pos * 1.0 + \ + num_total_neg * self.bg_cls_weight + if self.sync_cls_avg_factor: + cls_avg_factor = reduce_mean( + cls_scores.new_tensor([cls_avg_factor])) + cls_avg_factor = max(cls_avg_factor, 1) + + cls_avg_factor = max(cls_avg_factor, 1) + loss_cls = self.loss_cls( + cls_scores, labels, label_weights, avg_factor=cls_avg_factor) + + # Compute the average number of gt boxes accross all gpus, for + # normalization purposes + num_total_pos = loss_cls.new_tensor([num_total_pos]) + num_total_pos = torch.clamp(reduce_mean(num_total_pos), min=1).item() + + # construct factors used for rescale bboxes + factors = [] + for img_meta, bbox_pred in zip(img_metas, bbox_preds): + img_h, img_w, _ = img_meta['img_shape'] + factor = bbox_pred.new_tensor([img_w, img_h, img_w, + img_h]).unsqueeze(0).repeat( + bbox_pred.size(0), 1) + factors.append(factor) + factors = torch.cat(factors, 0) + + # DETR regress the relative position of boxes (cxcywh) in the image, + # thus the learning target is normalized by the image size. So here + # we need to re-scale them for calculating IoU loss + bbox_preds = bbox_preds.reshape(-1, 4) + bboxes = bbox_cxcywh_to_xyxy(bbox_preds) * factors + bboxes_gt = bbox_cxcywh_to_xyxy(bbox_targets) * factors + + # regression IoU loss, defaultly GIoU loss + loss_iou = self.loss_iou( + bboxes, bboxes_gt, bbox_weights, avg_factor=num_total_pos) + + # regression L1 loss + loss_bbox = self.loss_bbox( + bbox_preds, bbox_targets, bbox_weights, avg_factor=num_total_pos) + return loss_cls, loss_bbox, loss_iou + + def get_targets(self, + cls_scores_list, + bbox_preds_list, + gt_bboxes_list, + gt_labels_list, + img_metas, + gt_bboxes_ignore_list=None): + """"Compute regression and classification targets for a batch image. + + Outputs from a single decoder layer of a single feature level are used. + + Args: + cls_scores_list (list[Tensor]): Box score logits from a single + decoder layer for each image with shape [num_query, + cls_out_channels]. + bbox_preds_list (list[Tensor]): Sigmoid outputs from a single + decoder layer for each image, with normalized coordinate + (cx, cy, w, h) and shape [num_query, 4]. + gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image + with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels_list (list[Tensor]): Ground truth class indices for each + image with shape (num_gts, ). + img_metas (list[dict]): List of image meta information. + gt_bboxes_ignore_list (list[Tensor], optional): Bounding + boxes which can be ignored for each image. Default None. + + Returns: + tuple: a tuple containing the following targets. + + - labels_list (list[Tensor]): Labels for all images. + - label_weights_list (list[Tensor]): Label weights for all \ + images. + - bbox_targets_list (list[Tensor]): BBox targets for all \ + images. + - bbox_weights_list (list[Tensor]): BBox weights for all \ + images. + - num_total_pos (int): Number of positive samples in all \ + images. + - num_total_neg (int): Number of negative samples in all \ + images. + """ + assert gt_bboxes_ignore_list is None, \ + 'Only supports for gt_bboxes_ignore setting to None.' + num_imgs = len(cls_scores_list) + gt_bboxes_ignore_list = [ + gt_bboxes_ignore_list for _ in range(num_imgs) + ] + + (labels_list, label_weights_list, bbox_targets_list, + bbox_weights_list, pos_inds_list, neg_inds_list) = multi_apply( + self._get_target_single, cls_scores_list, bbox_preds_list, + gt_bboxes_list, gt_labels_list, img_metas, gt_bboxes_ignore_list) + num_total_pos = sum((inds.numel() for inds in pos_inds_list)) + num_total_neg = sum((inds.numel() for inds in neg_inds_list)) + return (labels_list, label_weights_list, bbox_targets_list, + bbox_weights_list, num_total_pos, num_total_neg) + + def _get_target_single(self, + cls_score, + bbox_pred, + gt_bboxes, + gt_labels, + img_meta, + gt_bboxes_ignore=None): + """"Compute regression and classification targets for one image. + + Outputs from a single decoder layer of a single feature level are used. + + Args: + cls_score (Tensor): Box score logits from a single decoder layer + for one image. Shape [num_query, cls_out_channels]. + bbox_pred (Tensor): Sigmoid outputs from a single decoder layer + for one image, with normalized coordinate (cx, cy, w, h) and + shape [num_query, 4]. + gt_bboxes (Tensor): Ground truth bboxes for one image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (Tensor): Ground truth class indices for one image + with shape (num_gts, ). + img_meta (dict): Meta information for one image. + gt_bboxes_ignore (Tensor, optional): Bounding boxes + which can be ignored. Default None. + + Returns: + tuple[Tensor]: a tuple containing the following for one image. + + - labels (Tensor): Labels of each image. + - label_weights (Tensor]): Label weights of each image. + - bbox_targets (Tensor): BBox targets of each image. + - bbox_weights (Tensor): BBox weights of each image. + - pos_inds (Tensor): Sampled positive indices for each image. + - neg_inds (Tensor): Sampled negative indices for each image. + """ + + num_bboxes = bbox_pred.size(0) + # assigner and sampler + assign_result = self.assigner.assign(bbox_pred, cls_score, gt_bboxes, + gt_labels, img_meta, + gt_bboxes_ignore) + sampling_result = self.sampler.sample(assign_result, bbox_pred, + gt_bboxes) + pos_inds = sampling_result.pos_inds + neg_inds = sampling_result.neg_inds + + # label targets + labels = gt_bboxes.new_full((num_bboxes, ), + self.num_classes, + dtype=torch.long) + labels[pos_inds] = gt_labels[sampling_result.pos_assigned_gt_inds] + label_weights = gt_bboxes.new_ones(num_bboxes) + + # bbox targets + bbox_targets = torch.zeros_like(bbox_pred) + bbox_weights = torch.zeros_like(bbox_pred) + bbox_weights[pos_inds] = 1.0 + img_h, img_w, _ = img_meta['img_shape'] + + # DETR regress the relative position of boxes (cxcywh) in the image. + # Thus the learning target should be normalized by the image size, also + # the box format should be converted from defaultly x1y1x2y2 to cxcywh. + factor = bbox_pred.new_tensor([img_w, img_h, img_w, + img_h]).unsqueeze(0) + pos_gt_bboxes_normalized = sampling_result.pos_gt_bboxes / factor + pos_gt_bboxes_targets = bbox_xyxy_to_cxcywh(pos_gt_bboxes_normalized) + bbox_targets[pos_inds] = pos_gt_bboxes_targets + return (labels, label_weights, bbox_targets, bbox_weights, pos_inds, + neg_inds) + + # over-write because img_metas are needed as inputs for bbox_head. + def forward_train(self, + x, + img_metas, + gt_bboxes, + gt_labels=None, + gt_bboxes_ignore=None, + proposal_cfg=None, + **kwargs): + """Forward function for training mode. + + Args: + x (list[Tensor]): Features from backbone. + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes (Tensor): Ground truth bboxes of the image, + shape (num_gts, 4). + gt_labels (Tensor): Ground truth labels of each box, + shape (num_gts,). + gt_bboxes_ignore (Tensor): Ground truth bboxes to be + ignored, shape (num_ignored_gts, 4). + proposal_cfg (mmcv.Config): Test / postprocessing configuration, + if None, test_cfg would be used. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + assert proposal_cfg is None, '"proposal_cfg" must be None' + outs = self(x, img_metas) + if gt_labels is None: + loss_inputs = outs + (gt_bboxes, img_metas) + else: + loss_inputs = outs + (gt_bboxes, gt_labels, img_metas) + losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore) + return losses + + @force_fp32(apply_to=('all_cls_scores_list', 'all_bbox_preds_list')) + def get_bboxes(self, + all_cls_scores_list, + all_bbox_preds_list, + img_metas, + rescale=False): + """Transform network outputs for a batch into bbox predictions. + + Args: + all_cls_scores_list (list[Tensor]): Classification outputs + for each feature level. Each is a 4D-tensor with shape + [nb_dec, bs, num_query, cls_out_channels]. + all_bbox_preds_list (list[Tensor]): Sigmoid regression + outputs for each feature level. Each is a 4D-tensor with + normalized coordinate format (cx, cy, w, h) and shape + [nb_dec, bs, num_query, 4]. + img_metas (list[dict]): Meta information of each image. + rescale (bool, optional): If True, return boxes in original + image space. Default False. + + Returns: + list[list[Tensor, Tensor]]: Each item in result_list is 2-tuple. \ + The first item is an (n, 5) tensor, where the first 4 columns \ + are bounding box positions (tl_x, tl_y, br_x, br_y) and the \ + 5-th column is a score between 0 and 1. The second item is a \ + (n,) tensor where each item is the predicted class label of \ + the corresponding box. + """ + # NOTE defaultly only using outputs from the last feature level, + # and only the outputs from the last decoder layer is used. + cls_scores = all_cls_scores_list[-1][-1] + bbox_preds = all_bbox_preds_list[-1][-1] + + result_list = [] + for img_id in range(len(img_metas)): + cls_score = cls_scores[img_id] + bbox_pred = bbox_preds[img_id] + img_shape = img_metas[img_id]['img_shape'] + scale_factor = img_metas[img_id]['scale_factor'] + proposals = self._get_bboxes_single(cls_score, bbox_pred, + img_shape, scale_factor, + rescale) + result_list.append(proposals) + + return result_list + + def _get_bboxes_single(self, + cls_score, + bbox_pred, + img_shape, + scale_factor, + rescale=False): + """Transform outputs from the last decoder layer into bbox predictions + for each image. + + Args: + cls_score (Tensor): Box score logits from the last decoder layer + for each image. Shape [num_query, cls_out_channels]. + bbox_pred (Tensor): Sigmoid outputs from the last decoder layer + for each image, with coordinate format (cx, cy, w, h) and + shape [num_query, 4]. + img_shape (tuple[int]): Shape of input image, (height, width, 3). + scale_factor (ndarray, optional): Scale factor of the image arange + as (w_scale, h_scale, w_scale, h_scale). + rescale (bool, optional): If True, return boxes in original image + space. Default False. + + Returns: + tuple[Tensor]: Results of detected bboxes and labels. + + - det_bboxes: Predicted bboxes with shape [num_query, 5], \ + where the first 4 columns are bounding box positions \ + (tl_x, tl_y, br_x, br_y) and the 5-th column are scores \ + between 0 and 1. + - det_labels: Predicted labels of the corresponding box with \ + shape [num_query]. + """ + assert len(cls_score) == len(bbox_pred) + max_per_img = self.test_cfg.get('max_per_img', self.num_query) + # exclude background + if self.loss_cls.use_sigmoid: + cls_score = cls_score.sigmoid() + scores, indexs = cls_score.view(-1).topk(max_per_img) + det_labels = indexs % self.num_classes + bbox_index = indexs // self.num_classes + bbox_pred = bbox_pred[bbox_index] + else: + scores, det_labels = F.softmax(cls_score, dim=-1)[..., :-1].max(-1) + + ct = counter(log_name='detr_query_predcls_temp.txt', matrixshape=(100, 81)) + for i, det_label in enumerate(det_labels): + ct.m[i, det_label] += 1 + ct.record() + + scores, bbox_index = scores.topk(max_per_img) + bbox_pred = bbox_pred[bbox_index] + det_labels = det_labels[bbox_index] + + det_bboxes = bbox_cxcywh_to_xyxy(bbox_pred) + det_bboxes[:, 0::2] = det_bboxes[:, 0::2] * img_shape[1] + det_bboxes[:, 1::2] = det_bboxes[:, 1::2] * img_shape[0] + det_bboxes[:, 0::2].clamp_(min=0, max=img_shape[1]) + det_bboxes[:, 1::2].clamp_(min=0, max=img_shape[0]) + if rescale: + det_bboxes /= det_bboxes.new_tensor(scale_factor) + det_bboxes = torch.cat((det_bboxes, scores.unsqueeze(1)), -1) + + return det_bboxes, det_labels diff --git a/mmdet/models/dense_heads/embedding_rpn_head.py b/mmdet/models/dense_heads/embedding_rpn_head.py new file mode 100644 index 0000000..a96532a --- /dev/null +++ b/mmdet/models/dense_heads/embedding_rpn_head.py @@ -0,0 +1,108 @@ +import torch +import torch.nn as nn +from mmcv.runner import BaseModule + +from mmdet.models.builder import HEADS +from ...core import bbox_cxcywh_to_xyxy + + +@HEADS.register_module() +class EmbeddingRPNHead(BaseModule): + """RPNHead in the `Sparse R-CNN `_ . + + Unlike traditional RPNHead, this module does not need FPN input, but just + decode `init_proposal_bboxes` and expand the first dimension of + `init_proposal_bboxes` and `init_proposal_features` to the batch_size. + + Args: + num_proposals (int): Number of init_proposals. Default 100. + proposal_feature_channel (int): Channel number of + init_proposal_feature. Defaults to 256. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + num_proposals=100, + proposal_feature_channel=256, + init_cfg=None, + **kwargs): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + super(EmbeddingRPNHead, self).__init__(init_cfg) + self.num_proposals = num_proposals + self.proposal_feature_channel = proposal_feature_channel + self._init_layers() + + def _init_layers(self): + """Initialize a sparse set of proposal boxes and proposal features.""" + self.init_proposal_bboxes = nn.Embedding(self.num_proposals, 4) + self.init_proposal_features = nn.Embedding( + self.num_proposals, self.proposal_feature_channel) + + def init_weights(self): + """Initialize the init_proposal_bboxes as normalized. + + [c_x, c_y, w, h], and we initialize it to the size of the entire + image. + """ + super(EmbeddingRPNHead, self).init_weights() + nn.init.constant_(self.init_proposal_bboxes.weight[:, :2], 0.5) + nn.init.constant_(self.init_proposal_bboxes.weight[:, 2:], 1) + + def _decode_init_proposals(self, imgs, img_metas): + """Decode init_proposal_bboxes according to the size of images and + expand dimension of init_proposal_features to batch_size. + + Args: + imgs (list[Tensor]): List of FPN features. + img_metas (list[dict]): List of meta-information of + images. Need the img_shape to decode the init_proposals. + + Returns: + Tuple(Tensor): + + - proposals (Tensor): Decoded proposal bboxes, + has shape (batch_size, num_proposals, 4). + - init_proposal_features (Tensor): Expanded proposal + features, has shape + (batch_size, num_proposals, proposal_feature_channel). + - imgs_whwh (Tensor): Tensor with shape + (batch_size, 4), the dimension means + [img_width, img_height, img_width, img_height]. + """ + proposals = self.init_proposal_bboxes.weight.clone() + proposals = bbox_cxcywh_to_xyxy(proposals) + num_imgs = len(imgs[0]) + imgs_whwh = [] + for meta in img_metas: + h, w, _ = meta['img_shape'] + imgs_whwh.append(imgs[0].new_tensor([[w, h, w, h]])) + imgs_whwh = torch.cat(imgs_whwh, dim=0) + imgs_whwh = imgs_whwh[:, None, :] + + # imgs_whwh has shape (batch_size, 1, 4) + # The shape of proposals change from (num_proposals, 4) + # to (batch_size ,num_proposals, 4) + proposals = proposals * imgs_whwh + + init_proposal_features = self.init_proposal_features.weight.clone() + init_proposal_features = init_proposal_features[None].expand( + num_imgs, *init_proposal_features.size()) + + return proposals.detach(), init_proposal_features, imgs_whwh + + def forward_dummy(self, img, img_metas): + """Dummy forward function. + + Used in flops calculation. + """ + return self._decode_init_proposals(img, img_metas) + + def forward_train(self, img, img_metas): + """Forward function in training stage.""" + return self._decode_init_proposals(img, img_metas) + + def simple_test_rpn(self, img, img_metas): + """Forward function in testing stage.""" + return self._decode_init_proposals(img, img_metas) diff --git a/mmdet/models/dense_heads/fcos_head.py b/mmdet/models/dense_heads/fcos_head.py new file mode 100644 index 0000000..39fe575 --- /dev/null +++ b/mmdet/models/dense_heads/fcos_head.py @@ -0,0 +1,650 @@ +import os +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import Scale +from mmcv.runner import force_fp32 + +from mmdet.core import distance2bbox, multi_apply, multiclass_nms, reduce_mean +from ..builder import HEADS, build_loss +from .anchor_free_head import AnchorFreeHead + +INF = 1e8 + + +@HEADS.register_module() +class FCOSHead(AnchorFreeHead): + """Anchor-free head used in `FCOS `_. + + The FCOS head does not use anchor boxes. Instead bounding boxes are + predicted at each pixel and a centerness measure is used to suppress + low-quality predictions. + Here norm_on_bbox, centerness_on_reg, dcn_on_last_conv are training + tricks used in official repo, which will bring remarkable mAP gains + of up to 4.9. Please see https://github.com/tianzhi0549/FCOS for + more detail. + + Args: + num_classes (int): Number of categories excluding the background + category. + in_channels (int): Number of channels in the input feature map. + strides (list[int] | list[tuple[int, int]]): Strides of points + in multiple feature levels. Default: (4, 8, 16, 32, 64). + regress_ranges (tuple[tuple[int, int]]): Regress range of multiple + level points. + center_sampling (bool): If true, use center sampling. Default: False. + center_sample_radius (float): Radius of center sampling. Default: 1.5. + norm_on_bbox (bool): If true, normalize the regression targets + with FPN strides. Default: False. + centerness_on_reg (bool): If true, position centerness on the + regress branch. Please refer to https://github.com/tianzhi0549/FCOS/issues/89#issuecomment-516877042. + Default: False. + conv_bias (bool | str): If specified as `auto`, it will be decided by the + norm_cfg. Bias of conv will be set as True if `norm_cfg` is None, otherwise + False. Default: "auto". + loss_cls (dict): Config of classification loss. + loss_bbox (dict): Config of localization loss. + loss_centerness (dict): Config of centerness loss. + norm_cfg (dict): dictionary to construct and config norm layer. + Default: norm_cfg=dict(type='GN', num_groups=32, requires_grad=True). + init_cfg (dict or list[dict], optional): Initialization config dict. + + Example: + >>> self = FCOSHead(11, 7) + >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]] + >>> cls_score, bbox_pred, centerness = self.forward(feats) + >>> assert len(cls_score) == len(self.scales) + """ # noqa: E501 + + def __init__(self, + num_classes, + in_channels, + regress_ranges=((-1, 64), (64, 128), (128, 256), (256, 512), + (512, INF)), + center_sampling=False, + center_sample_radius=1.5, + norm_on_bbox=False, + centerness_on_reg=False, + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='IoULoss', loss_weight=1.0), + loss_centerness=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0), + norm_cfg=dict(type='GN', num_groups=32, requires_grad=True), + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=dict( + type='Normal', + name='conv_cls', + std=0.01, + bias_prob=0.01)), + **kwargs): + self.regress_ranges = regress_ranges + self.center_sampling = center_sampling + self.center_sample_radius = center_sample_radius + self.norm_on_bbox = norm_on_bbox + self.centerness_on_reg = centerness_on_reg + super().__init__( + num_classes, + in_channels, + loss_cls=loss_cls, + loss_bbox=loss_bbox, + norm_cfg=norm_cfg, + init_cfg=init_cfg, + **kwargs) + self.loss_centerness = build_loss(loss_centerness) + + def _init_layers(self): + """Initialize layers of the head.""" + super()._init_layers() + self.conv_centerness = nn.Conv2d( + self.feat_channels, int(os.environ['A']), 3, padding=1) + self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides]) + + def forward(self, feats): + """Forward features from the upstream network. + + Args: + feats (tuple[Tensor]): Features from the upstream network, each is + a 4D-tensor. + + Returns: + tuple: + cls_scores (list[Tensor]): Box scores for each scale level, \ + each is a 4D-tensor, the channel number is \ + num_points * num_classes. + bbox_preds (list[Tensor]): Box energies / deltas for each \ + scale level, each is a 4D-tensor, the channel number is \ + num_points * 4. + centernesses (list[Tensor]): centerness for each scale level, \ + each is a 4D-tensor, the channel number is num_points * 1. + """ + return multi_apply(self.forward_single, feats, self.scales, + self.strides) + + def forward_single(self, x, scale, stride): + """Forward features of a single scale level. + + Args: + x (Tensor): FPN feature maps of the specified stride. + scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize + the bbox prediction. + stride (int): The corresponding stride for feature maps, only + used to normalize the bbox prediction when self.norm_on_bbox + is True. + + Returns: + tuple: scores for each class, bbox predictions and centerness \ + predictions of input feature maps. + """ + cls_score, bbox_pred, cls_feat, reg_feat = super().forward_single(x) + if self.centerness_on_reg: + centerness = self.conv_centerness(reg_feat)[:, 0:1, :, :] + else: + centerness = self.conv_centerness(cls_feat)[:, 0:1, :, :] + # scale the bbox_pred of different level + # float to avoid overflow when enabling FP16 + bbox_pred = scale(bbox_pred).float() + if self.norm_on_bbox: + bbox_pred = F.relu(bbox_pred) + if not self.training: + bbox_pred *= stride + else: + bbox_pred = bbox_pred.exp() + return cls_score, bbox_pred, centerness + + @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'centernesses')) + def loss(self, + cls_scores, + bbox_preds, + centernesses, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute loss of the head. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level, + each is a 4D-tensor, the channel number is + num_points * num_classes. + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level, each is a 4D-tensor, the channel number is + num_points * 4. + centernesses (list[Tensor]): centerness for each scale level, each + is a 4D-tensor, the channel number is num_points * 1. + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + assert len(cls_scores) == len(bbox_preds) == len(centernesses) + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + all_level_points = self.get_points(featmap_sizes, bbox_preds[0].dtype, + bbox_preds[0].device) + labels, bbox_targets = self.get_targets(all_level_points, gt_bboxes, + gt_labels) + + num_imgs = cls_scores[0].size(0) + # flatten cls_scores, bbox_preds and centerness + flatten_cls_scores = [ + cls_score.permute(0, 2, 3, 1).reshape(-1, self.cls_out_channels) + for cls_score in cls_scores + ] + flatten_bbox_preds = [ + bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4) + for bbox_pred in bbox_preds + ] + flatten_centerness = [ + centerness.permute(0, 2, 3, 1).reshape(-1) + for centerness in centernesses + ] + flatten_cls_scores = torch.cat(flatten_cls_scores) + flatten_bbox_preds = torch.cat(flatten_bbox_preds) + flatten_centerness = torch.cat(flatten_centerness) + flatten_labels = torch.cat(labels) + flatten_bbox_targets = torch.cat(bbox_targets) + # repeat points to align with bbox_preds + flatten_points = torch.cat( + [points.repeat(num_imgs, 1) for points in all_level_points]) + + # FG cat_id: [0, num_classes -1], BG cat_id: num_classes + bg_class_ind = self.num_classes + pos_inds = ((flatten_labels >= 0) + & (flatten_labels < bg_class_ind)).nonzero().reshape(-1) + num_pos = torch.tensor( + len(pos_inds), dtype=torch.float, device=bbox_preds[0].device) + num_pos = max(reduce_mean(num_pos), 1.0) + loss_cls = self.loss_cls( + flatten_cls_scores, flatten_labels, avg_factor=num_pos) + + pos_bbox_preds = flatten_bbox_preds[pos_inds] + pos_centerness = flatten_centerness[pos_inds] + pos_bbox_targets = flatten_bbox_targets[pos_inds] + pos_centerness_targets = self.centerness_target(pos_bbox_targets) + # centerness weighted iou loss + centerness_denorm = max( + reduce_mean(pos_centerness_targets.sum().detach()), 1e-6) + + if len(pos_inds) > 0: + pos_points = flatten_points[pos_inds] + pos_decoded_bbox_preds = distance2bbox(pos_points, pos_bbox_preds) + pos_decoded_target_preds = distance2bbox(pos_points, + pos_bbox_targets) + loss_bbox = self.loss_bbox( + pos_decoded_bbox_preds, + pos_decoded_target_preds, + weight=pos_centerness_targets, + avg_factor=centerness_denorm) + loss_centerness = self.loss_centerness( + pos_centerness, pos_centerness_targets, avg_factor=num_pos) + else: + loss_bbox = pos_bbox_preds.sum() + loss_centerness = pos_centerness.sum() + + return dict( + loss_cls=loss_cls, + loss_bbox=loss_bbox, + loss_centerness=loss_centerness) + + @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'centernesses')) + def get_bboxes(self, + cls_scores, + bbox_preds, + centernesses, + img_metas, + cfg=None, + rescale=False, + with_nms=True): + """Transform network output for a batch into bbox predictions. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + with shape (N, num_points * num_classes, H, W). + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_points * 4, H, W). + centernesses (list[Tensor]): Centerness for each scale level with + shape (N, num_points * 1, H, W). + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + cfg (mmcv.Config | None): Test / postprocessing configuration, + if None, test_cfg would be used. Default: None. + rescale (bool): If True, return boxes in original image space. + Default: False. + with_nms (bool): If True, do nms before return boxes. + Default: True. + + Returns: + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is an (n, 5) tensor, where 5 represent + (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. + The shape of the second tensor in the tuple is (n,), and + each element represents the class label of the corresponding + box. + """ + assert len(cls_scores) == len(bbox_preds) + num_levels = len(cls_scores) + + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + mlvl_points = self.get_points(featmap_sizes, bbox_preds[0].dtype, + bbox_preds[0].device) + + cls_score_list = [cls_scores[i].detach() for i in range(num_levels)] + bbox_pred_list = [bbox_preds[i].detach() for i in range(num_levels)] + centerness_pred_list = [ + centernesses[i].detach() for i in range(num_levels) + ] + if torch.onnx.is_in_onnx_export(): + assert len( + img_metas + ) == 1, 'Only support one input image while in exporting to ONNX' + img_shapes = img_metas[0]['img_shape_for_onnx'] + else: + img_shapes = [ + img_metas[i]['img_shape'] + for i in range(cls_scores[0].shape[0]) + ] + scale_factors = [ + img_metas[i]['scale_factor'] for i in range(cls_scores[0].shape[0]) + ] + result_list = self._get_bboxes(cls_score_list, bbox_pred_list, + centerness_pred_list, mlvl_points, + img_shapes, scale_factors, cfg, rescale, + with_nms) + return result_list + + def _get_bboxes(self, + cls_scores, + bbox_preds, + centernesses, + mlvl_points, + img_shapes, + scale_factors, + cfg, + rescale=False, + with_nms=True): + """Transform outputs for a single batch item into bbox predictions. + + Args: + cls_scores (list[Tensor]): Box scores for a single scale level + with shape (N, num_points * num_classes, H, W). + bbox_preds (list[Tensor]): Box energies / deltas for a single scale + level with shape (N, num_points * 4, H, W). + centernesses (list[Tensor]): Centerness for a single scale level + with shape (N, num_points, H, W). + mlvl_points (list[Tensor]): Box reference for a single scale level + with shape (num_total_points, 4). + img_shapes (list[tuple[int]]): Shape of the input image, + list[(height, width, 3)]. + scale_factors (list[ndarray]): Scale factor of the image arrange as + (w_scale, h_scale, w_scale, h_scale). + cfg (mmcv.Config | None): Test / postprocessing configuration, + if None, test_cfg would be used. + rescale (bool): If True, return boxes in original image space. + Default: False. + with_nms (bool): If True, do nms before return boxes. + Default: True. + + Returns: + tuple(Tensor): + det_bboxes (Tensor): BBox predictions in shape (n, 5), where + the first 4 columns are bounding box positions + (tl_x, tl_y, br_x, br_y) and the 5-th column is a score + between 0 and 1. + det_labels (Tensor): A (n,) tensor where each item is the + predicted class label of the corresponding box. + """ + cfg = self.test_cfg if cfg is None else cfg + assert len(cls_scores) == len(bbox_preds) == len(mlvl_points) + device = cls_scores[0].device + batch_size = cls_scores[0].shape[0] + # convert to tensor to keep tracing + nms_pre_tensor = torch.tensor( + cfg.get('nms_pre', -1), device=device, dtype=torch.long) + mlvl_bboxes = [] + mlvl_scores = [] + mlvl_centerness = [] + for cls_score, bbox_pred, centerness, points in zip( + cls_scores, bbox_preds, centernesses, mlvl_points): + assert cls_score.size()[-2:] == bbox_pred.size()[-2:] + scores = cls_score.permute(0, 2, 3, 1).reshape( + batch_size, -1, self.cls_out_channels).sigmoid() + centerness = centerness.permute(0, 2, 3, + 1).reshape(batch_size, + -1).sigmoid() + + bbox_pred = bbox_pred.permute(0, 2, 3, + 1).reshape(batch_size, -1, 4) + points = points.expand(batch_size, -1, 2) + # Get top-k prediction + from mmdet.core.export import get_k_for_topk + nms_pre = get_k_for_topk(nms_pre_tensor, bbox_pred.shape[1]) + if nms_pre > 0: + max_scores, _ = (scores * centerness[..., None]).max(-1) + _, topk_inds = max_scores.topk(nms_pre) + batch_inds = torch.arange(batch_size).view( + -1, 1).expand_as(topk_inds).long() + # Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501 + if torch.onnx.is_in_onnx_export(): + transformed_inds = bbox_pred.shape[ + 1] * batch_inds + topk_inds + points = points.reshape(-1, + 2)[transformed_inds, :].reshape( + batch_size, -1, 2) + bbox_pred = bbox_pred.reshape( + -1, 4)[transformed_inds, :].reshape(batch_size, -1, 4) + scores = scores.reshape( + -1, self.num_classes)[transformed_inds, :].reshape( + batch_size, -1, self.num_classes) + centerness = centerness.reshape( + -1, 1)[transformed_inds].reshape(batch_size, -1) + else: + points = points[batch_inds, topk_inds, :] + bbox_pred = bbox_pred[batch_inds, topk_inds, :] + scores = scores[batch_inds, topk_inds, :] + centerness = centerness[batch_inds, topk_inds] + + bboxes = distance2bbox(points, bbox_pred, max_shape=img_shapes) + mlvl_bboxes.append(bboxes) + mlvl_scores.append(scores) + mlvl_centerness.append(centerness) + + batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1) + if rescale: + batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor( + scale_factors).unsqueeze(1) + batch_mlvl_scores = torch.cat(mlvl_scores, dim=1) + batch_mlvl_centerness = torch.cat(mlvl_centerness, dim=1) + + # Replace multiclass_nms with ONNX::NonMaxSuppression in deployment + if torch.onnx.is_in_onnx_export() and with_nms: + from mmdet.core.export import add_dummy_nms_for_onnx + batch_mlvl_scores = batch_mlvl_scores * ( + batch_mlvl_centerness.unsqueeze(2)) + max_output_boxes_per_class = cfg.nms.get( + 'max_output_boxes_per_class', 200) + iou_threshold = cfg.nms.get('iou_threshold', 0.5) + score_threshold = cfg.score_thr + nms_pre = cfg.get('deploy_nms_pre', -1) + return add_dummy_nms_for_onnx(batch_mlvl_bboxes, batch_mlvl_scores, + max_output_boxes_per_class, + iou_threshold, score_threshold, + nms_pre, cfg.max_per_img) + # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 + # BG cat_id: num_class + padding = batch_mlvl_scores.new_zeros(batch_size, + batch_mlvl_scores.shape[1], 1) + batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1) + + if with_nms: + det_results = [] + for (mlvl_bboxes, mlvl_scores, + mlvl_centerness) in zip(batch_mlvl_bboxes, batch_mlvl_scores, + batch_mlvl_centerness): + det_bbox, det_label = multiclass_nms( + mlvl_bboxes, + mlvl_scores, + cfg.score_thr, + cfg.nms, + cfg.max_per_img, + score_factors=mlvl_centerness) + det_results.append(tuple([det_bbox, det_label])) + else: + det_results = [ + tuple(mlvl_bs) + for mlvl_bs in zip(batch_mlvl_bboxes, batch_mlvl_scores, + batch_mlvl_centerness) + ] + return det_results + + def _get_points_single(self, + featmap_size, + stride, + dtype, + device, + flatten=False): + """Get points according to feature map sizes.""" + y, x = super()._get_points_single(featmap_size, stride, dtype, device) + points = torch.stack((x.reshape(-1) * stride, y.reshape(-1) * stride), + dim=-1) + stride // 2 + return points + + def get_targets(self, points, gt_bboxes_list, gt_labels_list): + """Compute regression, classification and centerness targets for points + in multiple images. + + Args: + points (list[Tensor]): Points of each fpn level, each has shape + (num_points, 2). + gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image, + each has shape (num_gt, 4). + gt_labels_list (list[Tensor]): Ground truth labels of each box, + each has shape (num_gt,). + + Returns: + tuple: + concat_lvl_labels (list[Tensor]): Labels of each level. \ + concat_lvl_bbox_targets (list[Tensor]): BBox targets of each \ + level. + """ + assert len(points) == len(self.regress_ranges) + num_levels = len(points) + # expand regress ranges to align with points + expanded_regress_ranges = [ + points[i].new_tensor(self.regress_ranges[i])[None].expand_as( + points[i]) for i in range(num_levels) + ] + # concat all levels points and regress ranges + concat_regress_ranges = torch.cat(expanded_regress_ranges, dim=0) + concat_points = torch.cat(points, dim=0) + + # the number of points per img, per lvl + num_points = [center.size(0) for center in points] + + # get labels and bbox_targets of each image + labels_list, bbox_targets_list = multi_apply( + self._get_target_single, + gt_bboxes_list, + gt_labels_list, + points=concat_points, + regress_ranges=concat_regress_ranges, + num_points_per_lvl=num_points) + + # split to per img, per level + labels_list = [labels.split(num_points, 0) for labels in labels_list] + bbox_targets_list = [ + bbox_targets.split(num_points, 0) + for bbox_targets in bbox_targets_list + ] + + # concat per level image + concat_lvl_labels = [] + concat_lvl_bbox_targets = [] + for i in range(num_levels): + concat_lvl_labels.append( + torch.cat([labels[i] for labels in labels_list])) + bbox_targets = torch.cat( + [bbox_targets[i] for bbox_targets in bbox_targets_list]) + if self.norm_on_bbox: + bbox_targets = bbox_targets / self.strides[i] + concat_lvl_bbox_targets.append(bbox_targets) + return concat_lvl_labels, concat_lvl_bbox_targets + + def _get_target_single(self, gt_bboxes, gt_labels, points, regress_ranges, + num_points_per_lvl): + """Compute regression and classification targets for a single image.""" + num_points = points.size(0) + num_gts = gt_labels.size(0) + if num_gts == 0: + return gt_labels.new_full((num_points,), self.num_classes), \ + gt_bboxes.new_zeros((num_points, 4)) + + areas = (gt_bboxes[:, 2] - gt_bboxes[:, 0]) * ( + gt_bboxes[:, 3] - gt_bboxes[:, 1]) + # TODO: figure out why these two are different + # areas = areas[None].expand(num_points, num_gts) + areas = areas[None].repeat(num_points, 1) + regress_ranges = regress_ranges[:, None, :].expand( + num_points, num_gts, 2) + gt_bboxes = gt_bboxes[None].expand(num_points, num_gts, 4) + xs, ys = points[:, 0], points[:, 1] + xs = xs[:, None].expand(num_points, num_gts) + ys = ys[:, None].expand(num_points, num_gts) + + left = xs - gt_bboxes[..., 0] + right = gt_bboxes[..., 2] - xs + top = ys - gt_bboxes[..., 1] + bottom = gt_bboxes[..., 3] - ys + bbox_targets = torch.stack((left, top, right, bottom), -1) + + if self.center_sampling: + # condition1: inside a `center bbox` + radius = self.center_sample_radius + center_xs = (gt_bboxes[..., 0] + gt_bboxes[..., 2]) / 2 + center_ys = (gt_bboxes[..., 1] + gt_bboxes[..., 3]) / 2 + center_gts = torch.zeros_like(gt_bboxes) + stride = center_xs.new_zeros(center_xs.shape) + + # project the points on current lvl back to the `original` sizes + lvl_begin = 0 + for lvl_idx, num_points_lvl in enumerate(num_points_per_lvl): + lvl_end = lvl_begin + num_points_lvl + stride[lvl_begin:lvl_end] = self.strides[lvl_idx] * radius + lvl_begin = lvl_end + + x_mins = center_xs - stride + y_mins = center_ys - stride + x_maxs = center_xs + stride + y_maxs = center_ys + stride + center_gts[..., 0] = torch.where(x_mins > gt_bboxes[..., 0], + x_mins, gt_bboxes[..., 0]) + center_gts[..., 1] = torch.where(y_mins > gt_bboxes[..., 1], + y_mins, gt_bboxes[..., 1]) + center_gts[..., 2] = torch.where(x_maxs > gt_bboxes[..., 2], + gt_bboxes[..., 2], x_maxs) + center_gts[..., 3] = torch.where(y_maxs > gt_bboxes[..., 3], + gt_bboxes[..., 3], y_maxs) + + cb_dist_left = xs - center_gts[..., 0] + cb_dist_right = center_gts[..., 2] - xs + cb_dist_top = ys - center_gts[..., 1] + cb_dist_bottom = center_gts[..., 3] - ys + center_bbox = torch.stack( + (cb_dist_left, cb_dist_top, cb_dist_right, cb_dist_bottom), -1) + inside_gt_bbox_mask = center_bbox.min(-1)[0] > 0 + else: + # condition1: inside a gt bbox + inside_gt_bbox_mask = bbox_targets.min(-1)[0] > 0 + + # condition2: limit the regression range for each location + max_regress_distance = bbox_targets.max(-1)[0] + inside_regress_range = ( + (max_regress_distance >= regress_ranges[..., 0]) + & (max_regress_distance <= regress_ranges[..., 1])) + + # if there are still more than one objects for a location, + # we choose the one with minimal area + areas[inside_gt_bbox_mask == 0] = INF + areas[inside_regress_range == 0] = INF + min_area, min_area_inds = areas.min(dim=1) + + labels = gt_labels[min_area_inds] + labels[min_area == INF] = self.num_classes # set as BG + bbox_targets = bbox_targets[range(num_points), min_area_inds] + + return labels, bbox_targets + + def centerness_target(self, pos_bbox_targets): + """Compute centerness targets. + + Args: + pos_bbox_targets (Tensor): BBox targets of positive bboxes in shape + (num_pos, 4) + + Returns: + Tensor: Centerness target. + """ + # only calculate pos centerness targets, otherwise there may be nan + left_right = pos_bbox_targets[:, [0, 2]] + top_bottom = pos_bbox_targets[:, [1, 3]] + if len(left_right) == 0: + centerness_targets = left_right[..., 0] + else: + centerness_targets = ( + left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) * ( + top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0]) + return torch.sqrt(centerness_targets) diff --git a/mmdet/models/dense_heads/fovea_head.py b/mmdet/models/dense_heads/fovea_head.py new file mode 100644 index 0000000..657a879 --- /dev/null +++ b/mmdet/models/dense_heads/fovea_head.py @@ -0,0 +1,348 @@ +import torch +import torch.nn as nn +from mmcv.cnn import ConvModule +from mmcv.ops import DeformConv2d +from mmcv.runner import BaseModule + +from mmdet.core import multi_apply, multiclass_nms +from ..builder import HEADS +from .anchor_free_head import AnchorFreeHead + +INF = 1e8 + + +class FeatureAlign(BaseModule): + + def __init__(self, + in_channels, + out_channels, + kernel_size=3, + deform_groups=4, + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.1, + override=dict( + type='Normal', name='conv_adaption', std=0.01))): + super(FeatureAlign, self).__init__(init_cfg) + offset_channels = kernel_size * kernel_size * 2 + self.conv_offset = nn.Conv2d( + 4, deform_groups * offset_channels, 1, bias=False) + self.conv_adaption = DeformConv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + padding=(kernel_size - 1) // 2, + deform_groups=deform_groups) + self.relu = nn.ReLU(inplace=True) + + def forward(self, x, shape): + offset = self.conv_offset(shape) + x = self.relu(self.conv_adaption(x, offset)) + return x + + +@HEADS.register_module() +class FoveaHead(AnchorFreeHead): + """FoveaBox: Beyond Anchor-based Object Detector + https://arxiv.org/abs/1904.03797 + """ + + def __init__(self, + num_classes, + in_channels, + base_edge_list=(16, 32, 64, 128, 256), + scale_ranges=((8, 32), (16, 64), (32, 128), (64, 256), (128, + 512)), + sigma=0.4, + with_deform=False, + deform_groups=4, + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=dict( + type='Normal', + name='conv_cls', + std=0.01, + bias_prob=0.01)), + **kwargs): + self.base_edge_list = base_edge_list + self.scale_ranges = scale_ranges + self.sigma = sigma + self.with_deform = with_deform + self.deform_groups = deform_groups + super().__init__(num_classes, in_channels, init_cfg=init_cfg, **kwargs) + + def _init_layers(self): + # box branch + super()._init_reg_convs() + self.conv_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1) + + # cls branch + if not self.with_deform: + super()._init_cls_convs() + self.conv_cls = nn.Conv2d( + self.feat_channels, self.cls_out_channels, 3, padding=1) + else: + self.cls_convs = nn.ModuleList() + self.cls_convs.append( + ConvModule( + self.feat_channels, (self.feat_channels * 4), + 3, + stride=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + bias=self.norm_cfg is None)) + self.cls_convs.append( + ConvModule((self.feat_channels * 4), (self.feat_channels * 4), + 1, + stride=1, + padding=0, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + bias=self.norm_cfg is None)) + self.feature_adaption = FeatureAlign( + self.feat_channels, + self.feat_channels, + kernel_size=3, + deform_groups=self.deform_groups) + self.conv_cls = nn.Conv2d( + int(self.feat_channels * 4), + self.cls_out_channels, + 3, + padding=1) + + def forward_single(self, x): + cls_feat = x + reg_feat = x + for reg_layer in self.reg_convs: + reg_feat = reg_layer(reg_feat) + bbox_pred = self.conv_reg(reg_feat) + if self.with_deform: + cls_feat = self.feature_adaption(cls_feat, bbox_pred.exp()) + for cls_layer in self.cls_convs: + cls_feat = cls_layer(cls_feat) + cls_score = self.conv_cls(cls_feat) + return cls_score, bbox_pred + + def _get_points_single(self, *args, **kwargs): + y, x = super()._get_points_single(*args, **kwargs) + return y + 0.5, x + 0.5 + + def loss(self, + cls_scores, + bbox_preds, + gt_bbox_list, + gt_label_list, + img_metas, + gt_bboxes_ignore=None): + assert len(cls_scores) == len(bbox_preds) + + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + points = self.get_points(featmap_sizes, bbox_preds[0].dtype, + bbox_preds[0].device) + num_imgs = cls_scores[0].size(0) + flatten_cls_scores = [ + cls_score.permute(0, 2, 3, 1).reshape(-1, self.cls_out_channels) + for cls_score in cls_scores + ] + flatten_bbox_preds = [ + bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4) + for bbox_pred in bbox_preds + ] + flatten_cls_scores = torch.cat(flatten_cls_scores) + flatten_bbox_preds = torch.cat(flatten_bbox_preds) + flatten_labels, flatten_bbox_targets = self.get_targets( + gt_bbox_list, gt_label_list, featmap_sizes, points) + + # FG cat_id: [0, num_classes -1], BG cat_id: num_classes + pos_inds = ((flatten_labels >= 0) + & (flatten_labels < self.num_classes)).nonzero().view(-1) + num_pos = len(pos_inds) + + loss_cls = self.loss_cls( + flatten_cls_scores, flatten_labels, avg_factor=num_pos + num_imgs) + if num_pos > 0: + pos_bbox_preds = flatten_bbox_preds[pos_inds] + pos_bbox_targets = flatten_bbox_targets[pos_inds] + pos_weights = pos_bbox_targets.new_zeros( + pos_bbox_targets.size()) + 1.0 + loss_bbox = self.loss_bbox( + pos_bbox_preds, + pos_bbox_targets, + pos_weights, + avg_factor=num_pos) + else: + loss_bbox = torch.tensor( + 0, + dtype=flatten_bbox_preds.dtype, + device=flatten_bbox_preds.device) + return dict(loss_cls=loss_cls, loss_bbox=loss_bbox) + + def get_targets(self, gt_bbox_list, gt_label_list, featmap_sizes, points): + label_list, bbox_target_list = multi_apply( + self._get_target_single, + gt_bbox_list, + gt_label_list, + featmap_size_list=featmap_sizes, + point_list=points) + flatten_labels = [ + torch.cat([ + labels_level_img.flatten() for labels_level_img in labels_level + ]) for labels_level in zip(*label_list) + ] + flatten_bbox_targets = [ + torch.cat([ + bbox_targets_level_img.reshape(-1, 4) + for bbox_targets_level_img in bbox_targets_level + ]) for bbox_targets_level in zip(*bbox_target_list) + ] + flatten_labels = torch.cat(flatten_labels) + flatten_bbox_targets = torch.cat(flatten_bbox_targets) + return flatten_labels, flatten_bbox_targets + + def _get_target_single(self, + gt_bboxes_raw, + gt_labels_raw, + featmap_size_list=None, + point_list=None): + + gt_areas = torch.sqrt((gt_bboxes_raw[:, 2] - gt_bboxes_raw[:, 0]) * + (gt_bboxes_raw[:, 3] - gt_bboxes_raw[:, 1])) + label_list = [] + bbox_target_list = [] + # for each pyramid, find the cls and box target + for base_len, (lower_bound, upper_bound), stride, featmap_size, \ + (y, x) in zip(self.base_edge_list, self.scale_ranges, + self.strides, featmap_size_list, point_list): + # FG cat_id: [0, num_classes -1], BG cat_id: num_classes + labels = gt_labels_raw.new_zeros(featmap_size) + self.num_classes + bbox_targets = gt_bboxes_raw.new(featmap_size[0], featmap_size[1], + 4) + 1 + # scale assignment + hit_indices = ((gt_areas >= lower_bound) & + (gt_areas <= upper_bound)).nonzero().flatten() + if len(hit_indices) == 0: + label_list.append(labels) + bbox_target_list.append(torch.log(bbox_targets)) + continue + _, hit_index_order = torch.sort(-gt_areas[hit_indices]) + hit_indices = hit_indices[hit_index_order] + gt_bboxes = gt_bboxes_raw[hit_indices, :] / stride + gt_labels = gt_labels_raw[hit_indices] + half_w = 0.5 * (gt_bboxes[:, 2] - gt_bboxes[:, 0]) + half_h = 0.5 * (gt_bboxes[:, 3] - gt_bboxes[:, 1]) + # valid fovea area: left, right, top, down + pos_left = torch.ceil( + gt_bboxes[:, 0] + (1 - self.sigma) * half_w - 0.5).long(). \ + clamp(0, featmap_size[1] - 1) + pos_right = torch.floor( + gt_bboxes[:, 0] + (1 + self.sigma) * half_w - 0.5).long(). \ + clamp(0, featmap_size[1] - 1) + pos_top = torch.ceil( + gt_bboxes[:, 1] + (1 - self.sigma) * half_h - 0.5).long(). \ + clamp(0, featmap_size[0] - 1) + pos_down = torch.floor( + gt_bboxes[:, 1] + (1 + self.sigma) * half_h - 0.5).long(). \ + clamp(0, featmap_size[0] - 1) + for px1, py1, px2, py2, label, (gt_x1, gt_y1, gt_x2, gt_y2) in \ + zip(pos_left, pos_top, pos_right, pos_down, gt_labels, + gt_bboxes_raw[hit_indices, :]): + labels[py1:py2 + 1, px1:px2 + 1] = label + bbox_targets[py1:py2 + 1, px1:px2 + 1, 0] = \ + (stride * x[py1:py2 + 1, px1:px2 + 1] - gt_x1) / base_len + bbox_targets[py1:py2 + 1, px1:px2 + 1, 1] = \ + (stride * y[py1:py2 + 1, px1:px2 + 1] - gt_y1) / base_len + bbox_targets[py1:py2 + 1, px1:px2 + 1, 2] = \ + (gt_x2 - stride * x[py1:py2 + 1, px1:px2 + 1]) / base_len + bbox_targets[py1:py2 + 1, px1:px2 + 1, 3] = \ + (gt_y2 - stride * y[py1:py2 + 1, px1:px2 + 1]) / base_len + bbox_targets = bbox_targets.clamp(min=1. / 16, max=16.) + label_list.append(labels) + bbox_target_list.append(torch.log(bbox_targets)) + return label_list, bbox_target_list + + def get_bboxes(self, + cls_scores, + bbox_preds, + img_metas, + cfg=None, + rescale=None): + assert len(cls_scores) == len(bbox_preds) + num_levels = len(cls_scores) + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + points = self.get_points( + featmap_sizes, + bbox_preds[0].dtype, + bbox_preds[0].device, + flatten=True) + result_list = [] + for img_id in range(len(img_metas)): + cls_score_list = [ + cls_scores[i][img_id].detach() for i in range(num_levels) + ] + bbox_pred_list = [ + bbox_preds[i][img_id].detach() for i in range(num_levels) + ] + img_shape = img_metas[img_id]['img_shape'] + scale_factor = img_metas[img_id]['scale_factor'] + det_bboxes = self._get_bboxes_single(cls_score_list, + bbox_pred_list, featmap_sizes, + points, img_shape, + scale_factor, cfg, rescale) + result_list.append(det_bboxes) + return result_list + + def _get_bboxes_single(self, + cls_scores, + bbox_preds, + featmap_sizes, + point_list, + img_shape, + scale_factor, + cfg, + rescale=False): + cfg = self.test_cfg if cfg is None else cfg + assert len(cls_scores) == len(bbox_preds) == len(point_list) + det_bboxes = [] + det_scores = [] + for cls_score, bbox_pred, featmap_size, stride, base_len, (y, x) \ + in zip(cls_scores, bbox_preds, featmap_sizes, self.strides, + self.base_edge_list, point_list): + assert cls_score.size()[-2:] == bbox_pred.size()[-2:] + scores = cls_score.permute(1, 2, 0).reshape( + -1, self.cls_out_channels).sigmoid() + bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4).exp() + nms_pre = cfg.get('nms_pre', -1) + if (nms_pre > 0) and (scores.shape[0] > nms_pre): + max_scores, _ = scores.max(dim=1) + _, topk_inds = max_scores.topk(nms_pre) + bbox_pred = bbox_pred[topk_inds, :] + scores = scores[topk_inds, :] + y = y[topk_inds] + x = x[topk_inds] + x1 = (stride * x - base_len * bbox_pred[:, 0]). \ + clamp(min=0, max=img_shape[1] - 1) + y1 = (stride * y - base_len * bbox_pred[:, 1]). \ + clamp(min=0, max=img_shape[0] - 1) + x2 = (stride * x + base_len * bbox_pred[:, 2]). \ + clamp(min=0, max=img_shape[1] - 1) + y2 = (stride * y + base_len * bbox_pred[:, 3]). \ + clamp(min=0, max=img_shape[0] - 1) + bboxes = torch.stack([x1, y1, x2, y2], -1) + det_bboxes.append(bboxes) + det_scores.append(scores) + det_bboxes = torch.cat(det_bboxes) + if rescale: + det_bboxes /= det_bboxes.new_tensor(scale_factor) + det_scores = torch.cat(det_scores) + padding = det_scores.new_zeros(det_scores.shape[0], 1) + # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 + # BG cat_id: num_class + det_scores = torch.cat([det_scores, padding], dim=1) + det_bboxes, det_labels = multiclass_nms(det_bboxes, det_scores, + cfg.score_thr, cfg.nms, + cfg.max_per_img) + return det_bboxes, det_labels diff --git a/mmdet/models/dense_heads/free_anchor_retina_head.py b/mmdet/models/dense_heads/free_anchor_retina_head.py new file mode 100644 index 0000000..79879fd --- /dev/null +++ b/mmdet/models/dense_heads/free_anchor_retina_head.py @@ -0,0 +1,270 @@ +import torch +import torch.nn.functional as F + +from mmdet.core import bbox_overlaps +from ..builder import HEADS +from .retina_head import RetinaHead + +EPS = 1e-12 + + +@HEADS.register_module() +class FreeAnchorRetinaHead(RetinaHead): + """FreeAnchor RetinaHead used in https://arxiv.org/abs/1909.02466. + + Args: + num_classes (int): Number of categories excluding the background + category. + in_channels (int): Number of channels in the input feature map. + stacked_convs (int): Number of conv layers in cls and reg tower. + Default: 4. + conv_cfg (dict): dictionary to construct and config conv layer. + Default: None. + norm_cfg (dict): dictionary to construct and config norm layer. + Default: norm_cfg=dict(type='GN', num_groups=32, + requires_grad=True). + pre_anchor_topk (int): Number of boxes that be token in each bag. + bbox_thr (float): The threshold of the saturated linear function. It is + usually the same with the IoU threshold used in NMS. + gamma (float): Gamma parameter in focal loss. + alpha (float): Alpha parameter in focal loss. + """ # noqa: W605 + + def __init__(self, + num_classes, + in_channels, + stacked_convs=4, + conv_cfg=None, + norm_cfg=None, + pre_anchor_topk=50, + bbox_thr=0.6, + gamma=2.0, + alpha=0.5, + **kwargs): + super(FreeAnchorRetinaHead, + self).__init__(num_classes, in_channels, stacked_convs, conv_cfg, + norm_cfg, **kwargs) + + self.pre_anchor_topk = pre_anchor_topk + self.bbox_thr = bbox_thr + self.gamma = gamma + self.alpha = alpha + + def loss(self, + cls_scores, + bbox_preds, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute losses of the head. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (N, num_anchors * num_classes, H, W) + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_anchors * 4, H, W) + gt_bboxes (list[Tensor]): each item are the truth boxes for each + image in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + assert len(featmap_sizes) == len(self.anchor_generator.base_anchors) + + anchor_list, _ = self.get_anchors(featmap_sizes, img_metas) + anchors = [torch.cat(anchor) for anchor in anchor_list] + + # concatenate each level + cls_scores = [ + cls.permute(0, 2, 3, + 1).reshape(cls.size(0), -1, self.cls_out_channels) + for cls in cls_scores + ] + bbox_preds = [ + bbox_pred.permute(0, 2, 3, 1).reshape(bbox_pred.size(0), -1, 4) + for bbox_pred in bbox_preds + ] + cls_scores = torch.cat(cls_scores, dim=1) + bbox_preds = torch.cat(bbox_preds, dim=1) + + cls_prob = torch.sigmoid(cls_scores) + box_prob = [] + num_pos = 0 + positive_losses = [] + for _, (anchors_, gt_labels_, gt_bboxes_, cls_prob_, + bbox_preds_) in enumerate( + zip(anchors, gt_labels, gt_bboxes, cls_prob, bbox_preds)): + + with torch.no_grad(): + if len(gt_bboxes_) == 0: + image_box_prob = torch.zeros( + anchors_.size(0), + self.cls_out_channels).type_as(bbox_preds_) + else: + # box_localization: a_{j}^{loc}, shape: [j, 4] + pred_boxes = self.bbox_coder.decode(anchors_, bbox_preds_) + + # object_box_iou: IoU_{ij}^{loc}, shape: [i, j] + object_box_iou = bbox_overlaps(gt_bboxes_, pred_boxes) + + # object_box_prob: P{a_{j} -> b_{i}}, shape: [i, j] + t1 = self.bbox_thr + t2 = object_box_iou.max( + dim=1, keepdim=True).values.clamp(min=t1 + 1e-12) + object_box_prob = ((object_box_iou - t1) / + (t2 - t1)).clamp( + min=0, max=1) + + # object_cls_box_prob: P{a_{j} -> b_{i}}, shape: [i, c, j] + num_obj = gt_labels_.size(0) + indices = torch.stack([ + torch.arange(num_obj).type_as(gt_labels_), gt_labels_ + ], + dim=0) + object_cls_box_prob = torch.sparse_coo_tensor( + indices, object_box_prob) + + # image_box_iou: P{a_{j} \in A_{+}}, shape: [c, j] + """ + from "start" to "end" implement: + image_box_iou = torch.sparse.max(object_cls_box_prob, + dim=0).t() + + """ + # start + box_cls_prob = torch.sparse.sum( + object_cls_box_prob, dim=0).to_dense() + + indices = torch.nonzero(box_cls_prob, as_tuple=False).t_() + if indices.numel() == 0: + image_box_prob = torch.zeros( + anchors_.size(0), + self.cls_out_channels).type_as(object_box_prob) + else: + nonzero_box_prob = torch.where( + (gt_labels_.unsqueeze(dim=-1) == indices[0]), + object_box_prob[:, indices[1]], + torch.tensor([ + 0 + ]).type_as(object_box_prob)).max(dim=0).values + + # upmap to shape [j, c] + image_box_prob = torch.sparse_coo_tensor( + indices.flip([0]), + nonzero_box_prob, + size=(anchors_.size(0), + self.cls_out_channels)).to_dense() + # end + + box_prob.append(image_box_prob) + + # construct bags for objects + match_quality_matrix = bbox_overlaps(gt_bboxes_, anchors_) + _, matched = torch.topk( + match_quality_matrix, + self.pre_anchor_topk, + dim=1, + sorted=False) + del match_quality_matrix + + # matched_cls_prob: P_{ij}^{cls} + matched_cls_prob = torch.gather( + cls_prob_[matched], 2, + gt_labels_.view(-1, 1, 1).repeat(1, self.pre_anchor_topk, + 1)).squeeze(2) + + # matched_box_prob: P_{ij}^{loc} + matched_anchors = anchors_[matched] + matched_object_targets = self.bbox_coder.encode( + matched_anchors, + gt_bboxes_.unsqueeze(dim=1).expand_as(matched_anchors)) + loss_bbox = self.loss_bbox( + bbox_preds_[matched], + matched_object_targets, + reduction_override='none').sum(-1) + matched_box_prob = torch.exp(-loss_bbox) + + # positive_losses: {-log( Mean-max(P_{ij}^{cls} * P_{ij}^{loc}) )} + num_pos += len(gt_bboxes_) + positive_losses.append( + self.positive_bag_loss(matched_cls_prob, matched_box_prob)) + positive_loss = torch.cat(positive_losses).sum() / max(1, num_pos) + + # box_prob: P{a_{j} \in A_{+}} + box_prob = torch.stack(box_prob, dim=0) + + # negative_loss: + # \sum_{j}{ FL((1 - P{a_{j} \in A_{+}}) * (1 - P_{j}^{bg})) } / n||B|| + negative_loss = self.negative_bag_loss(cls_prob, box_prob).sum() / max( + 1, num_pos * self.pre_anchor_topk) + + # avoid the absence of gradients in regression subnet + # when no ground-truth in a batch + if num_pos == 0: + positive_loss = bbox_preds.sum() * 0 + + losses = { + 'positive_bag_loss': positive_loss, + 'negative_bag_loss': negative_loss + } + return losses + + def positive_bag_loss(self, matched_cls_prob, matched_box_prob): + """Compute positive bag loss. + + :math:`-log( Mean-max(P_{ij}^{cls} * P_{ij}^{loc}) )`. + + :math:`P_{ij}^{cls}`: matched_cls_prob, classification probability of matched samples. + + :math:`P_{ij}^{loc}`: matched_box_prob, box probability of matched samples. + + Args: + matched_cls_prob (Tensor): Classification probabilty of matched + samples in shape (num_gt, pre_anchor_topk). + matched_box_prob (Tensor): BBox probability of matched samples, + in shape (num_gt, pre_anchor_topk). + + Returns: + Tensor: Positive bag loss in shape (num_gt,). + """ # noqa: E501, W605 + # bag_prob = Mean-max(matched_prob) + matched_prob = matched_cls_prob * matched_box_prob + weight = 1 / torch.clamp(1 - matched_prob, 1e-12, None) + weight /= weight.sum(dim=1).unsqueeze(dim=-1) + bag_prob = (weight * matched_prob).sum(dim=1) + # positive_bag_loss = -self.alpha * log(bag_prob) + return self.alpha * F.binary_cross_entropy( + bag_prob, torch.ones_like(bag_prob), reduction='none') + + def negative_bag_loss(self, cls_prob, box_prob): + """Compute negative bag loss. + + :math:`FL((1 - P_{a_{j} \in A_{+}}) * (1 - P_{j}^{bg}))`. + + :math:`P_{a_{j} \in A_{+}}`: Box_probability of matched samples. + + :math:`P_{j}^{bg}`: Classification probability of negative samples. + + Args: + cls_prob (Tensor): Classification probability, in shape + (num_img, num_anchors, num_classes). + box_prob (Tensor): Box probability, in shape + (num_img, num_anchors, num_classes). + + Returns: + Tensor: Negative bag loss in shape (num_img, num_anchors, num_classes). + """ # noqa: E501, W605 + prob = cls_prob * (1 - box_prob) + # There are some cases when neg_prob = 0. + # This will cause the neg_prob.log() to be inf without clamp. + prob = prob.clamp(min=EPS, max=1 - EPS) + negative_bag_loss = prob**self.gamma * F.binary_cross_entropy( + prob, torch.zeros_like(prob), reduction='none') + return (1 - self.alpha) * negative_bag_loss diff --git a/mmdet/models/dense_heads/fsaf_head.py b/mmdet/models/dense_heads/fsaf_head.py new file mode 100644 index 0000000..6aa442d --- /dev/null +++ b/mmdet/models/dense_heads/fsaf_head.py @@ -0,0 +1,432 @@ +import numpy as np +import torch +from mmcv.runner import force_fp32 + +from mmdet.core import (anchor_inside_flags, images_to_levels, multi_apply, + unmap) +from ..builder import HEADS +from ..losses.accuracy import accuracy +from ..losses.utils import weight_reduce_loss +from .retina_head import RetinaHead + + +@HEADS.register_module() +class FSAFHead(RetinaHead): + """Anchor-free head used in `FSAF `_. + + The head contains two subnetworks. The first classifies anchor boxes and + the second regresses deltas for the anchors (num_anchors is 1 for anchor- + free methods) + + Args: + *args: Same as its base class in :class:`RetinaHead` + score_threshold (float, optional): The score_threshold to calculate + positive recall. If given, prediction scores lower than this value + is counted as incorrect prediction. Default to None. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + **kwargs: Same as its base class in :class:`RetinaHead` + + Example: + >>> import torch + >>> self = FSAFHead(11, 7) + >>> x = torch.rand(1, 7, 32, 32) + >>> cls_score, bbox_pred = self.forward_single(x) + >>> # Each anchor predicts a score for each class except background + >>> cls_per_anchor = cls_score.shape[1] / self.num_anchors + >>> box_per_anchor = bbox_pred.shape[1] / self.num_anchors + >>> assert cls_per_anchor == self.num_classes + >>> assert box_per_anchor == 4 + """ + + def __init__(self, *args, score_threshold=None, init_cfg=None, **kwargs): + # The positive bias in self.retina_reg conv is to prevent predicted \ + # bbox with 0 area + if init_cfg is None: + init_cfg = dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=[ + dict( + type='Normal', + name='retina_cls', + std=0.01, + bias_prob=0.01), + dict( + type='Normal', name='retina_reg', std=0.01, bias=0.25) + ]) + super().__init__(*args, init_cfg=init_cfg, **kwargs) + self.score_threshold = score_threshold + + def forward_single(self, x): + """Forward feature map of a single scale level. + + Args: + x (Tensor): Feature map of a single scale level. + + Returns: + tuple (Tensor): + cls_score (Tensor): Box scores for each scale level + Has shape (N, num_points * num_classes, H, W). + bbox_pred (Tensor): Box energies / deltas for each scale + level with shape (N, num_points * 4, H, W). + """ + cls_score, bbox_pred = super().forward_single(x) + # relu: TBLR encoder only accepts positive bbox_pred + return cls_score, self.relu(bbox_pred) + + def _get_targets_single(self, + flat_anchors, + valid_flags, + gt_bboxes, + gt_bboxes_ignore, + gt_labels, + img_meta, + label_channels=1, + unmap_outputs=True): + """Compute regression and classification targets for anchors in a + single image. + + Most of the codes are the same with the base class + :obj: `AnchorHead`, except that it also collects and returns + the matched gt index in the image (from 0 to num_gt-1). If the + anchor bbox is not matched to any gt, the corresponding value in + pos_gt_inds is -1. + """ + inside_flags = anchor_inside_flags(flat_anchors, valid_flags, + img_meta['img_shape'][:2], + self.train_cfg.allowed_border) + if not inside_flags.any(): + return (None, ) * 7 + # Assign gt and sample anchors + anchors = flat_anchors[inside_flags.type(torch.bool), :] + assign_result = self.assigner.assign( + anchors, gt_bboxes, gt_bboxes_ignore, + None if self.sampling else gt_labels) + + sampling_result = self.sampler.sample(assign_result, anchors, + gt_bboxes) + + num_valid_anchors = anchors.shape[0] + bbox_targets = torch.zeros_like(anchors) + bbox_weights = torch.zeros_like(anchors) + labels = anchors.new_full((num_valid_anchors, ), + self.num_classes, + dtype=torch.long) + label_weights = anchors.new_zeros((num_valid_anchors, label_channels), + dtype=torch.float) + pos_gt_inds = anchors.new_full((num_valid_anchors, ), + -1, + dtype=torch.long) + + pos_inds = sampling_result.pos_inds + neg_inds = sampling_result.neg_inds + + if len(pos_inds) > 0: + if not self.reg_decoded_bbox: + pos_bbox_targets = self.bbox_coder.encode( + sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes) + else: + # When the regression loss (e.g. `IouLoss`, `GIouLoss`) + # is applied directly on the decoded bounding boxes, both + # the predicted boxes and regression targets should be with + # absolute coordinate format. + pos_bbox_targets = sampling_result.pos_gt_bboxes + bbox_targets[pos_inds, :] = pos_bbox_targets + bbox_weights[pos_inds, :] = 1.0 + # The assigned gt_index for each anchor. (0-based) + pos_gt_inds[pos_inds] = sampling_result.pos_assigned_gt_inds + if gt_labels is None: + # Only rpn gives gt_labels as None + # Foreground is the first class + labels[pos_inds] = 0 + else: + labels[pos_inds] = gt_labels[ + sampling_result.pos_assigned_gt_inds] + if self.train_cfg.pos_weight <= 0: + label_weights[pos_inds] = 1.0 + else: + label_weights[pos_inds] = self.train_cfg.pos_weight + + if len(neg_inds) > 0: + label_weights[neg_inds] = 1.0 + + # shadowed_labels is a tensor composed of tuples + # (anchor_inds, class_label) that indicate those anchors lying in the + # outer region of a gt or overlapped by another gt with a smaller + # area. + # + # Therefore, only the shadowed labels are ignored for loss calculation. + # the key `shadowed_labels` is defined in :obj:`CenterRegionAssigner` + shadowed_labels = assign_result.get_extra_property('shadowed_labels') + if shadowed_labels is not None and shadowed_labels.numel(): + if len(shadowed_labels.shape) == 2: + idx_, label_ = shadowed_labels[:, 0], shadowed_labels[:, 1] + assert (labels[idx_] != label_).all(), \ + 'One label cannot be both positive and ignored' + label_weights[idx_, label_] = 0 + else: + label_weights[shadowed_labels] = 0 + + # map up to original set of anchors + if unmap_outputs: + num_total_anchors = flat_anchors.size(0) + labels = unmap(labels, num_total_anchors, inside_flags) + label_weights = unmap(label_weights, num_total_anchors, + inside_flags) + bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags) + bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags) + pos_gt_inds = unmap( + pos_gt_inds, num_total_anchors, inside_flags, fill=-1) + + return (labels, label_weights, bbox_targets, bbox_weights, pos_inds, + neg_inds, sampling_result, pos_gt_inds) + + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) + def loss(self, + cls_scores, + bbox_preds, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute loss of the head. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (N, num_points * num_classes, H, W). + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_points * 4, H, W). + gt_bboxes (list[Tensor]): each item are the truth boxes for each + image in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + for i in range(len(bbox_preds)): # loop over fpn level + # avoid 0 area of the predicted bbox + bbox_preds[i] = bbox_preds[i].clamp(min=1e-4) + # TODO: It may directly use the base-class loss function. + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + assert len(featmap_sizes) == self.anchor_generator.num_levels + batch_size = len(gt_bboxes) + device = cls_scores[0].device + anchor_list, valid_flag_list = self.get_anchors( + featmap_sizes, img_metas, device=device) + label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 + cls_reg_targets = self.get_targets( + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + gt_labels_list=gt_labels, + label_channels=label_channels) + if cls_reg_targets is None: + return None + (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, + num_total_pos, num_total_neg, + pos_assigned_gt_inds_list) = cls_reg_targets + + num_gts = np.array(list(map(len, gt_labels))) + num_total_samples = ( + num_total_pos + num_total_neg if self.sampling else num_total_pos) + # anchor number of multi levels + num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] + # concat all level anchors and flags to a single tensor + concat_anchor_list = [] + for i in range(len(anchor_list)): + concat_anchor_list.append(torch.cat(anchor_list[i])) + all_anchor_list = images_to_levels(concat_anchor_list, + num_level_anchors) + losses_cls, losses_bbox = multi_apply( + self.loss_single, + cls_scores, + bbox_preds, + all_anchor_list, + labels_list, + label_weights_list, + bbox_targets_list, + bbox_weights_list, + num_total_samples=num_total_samples) + + # `pos_assigned_gt_inds_list` (length: fpn_levels) stores the assigned + # gt index of each anchor bbox in each fpn level. + cum_num_gts = list(np.cumsum(num_gts)) # length of batch_size + for i, assign in enumerate(pos_assigned_gt_inds_list): + # loop over fpn levels + for j in range(1, batch_size): + # loop over batch size + # Convert gt indices in each img to those in the batch + assign[j][assign[j] >= 0] += int(cum_num_gts[j - 1]) + pos_assigned_gt_inds_list[i] = assign.flatten() + labels_list[i] = labels_list[i].flatten() + num_gts = sum(map(len, gt_labels)) # total number of gt in the batch + # The unique label index of each gt in the batch + label_sequence = torch.arange(num_gts, device=device) + # Collect the average loss of each gt in each level + with torch.no_grad(): + loss_levels, = multi_apply( + self.collect_loss_level_single, + losses_cls, + losses_bbox, + pos_assigned_gt_inds_list, + labels_seq=label_sequence) + # Shape: (fpn_levels, num_gts). Loss of each gt at each fpn level + loss_levels = torch.stack(loss_levels, dim=0) + # Locate the best fpn level for loss back-propagation + if loss_levels.numel() == 0: # zero gt + argmin = loss_levels.new_empty((num_gts, ), dtype=torch.long) + else: + _, argmin = loss_levels.min(dim=0) + + # Reweight the loss of each (anchor, label) pair, so that only those + # at the best gt level are back-propagated. + losses_cls, losses_bbox, pos_inds = multi_apply( + self.reweight_loss_single, + losses_cls, + losses_bbox, + pos_assigned_gt_inds_list, + labels_list, + list(range(len(losses_cls))), + min_levels=argmin) + num_pos = torch.cat(pos_inds, 0).sum().float() + pos_recall = self.calculate_pos_recall(cls_scores, labels_list, + pos_inds) + + if num_pos == 0: # No gt + avg_factor = num_pos + float(num_total_neg) + else: + avg_factor = num_pos + for i in range(len(losses_cls)): + losses_cls[i] /= avg_factor + losses_bbox[i] /= avg_factor + return dict( + loss_cls=losses_cls, + loss_bbox=losses_bbox, + num_pos=num_pos / batch_size, + pos_recall=pos_recall) + + def calculate_pos_recall(self, cls_scores, labels_list, pos_inds): + """Calculate positive recall with score threshold. + + Args: + cls_scores (list[Tensor]): Classification scores at all fpn levels. + Each tensor is in shape (N, num_classes * num_anchors, H, W) + labels_list (list[Tensor]): The label that each anchor is assigned + to. Shape (N * H * W * num_anchors, ) + pos_inds (list[Tensor]): List of bool tensors indicating whether + the anchor is assigned to a positive label. + Shape (N * H * W * num_anchors, ) + + Returns: + Tensor: A single float number indicating the positive recall. + """ + with torch.no_grad(): + num_class = self.num_classes + scores = [ + cls.permute(0, 2, 3, 1).reshape(-1, num_class)[pos] + for cls, pos in zip(cls_scores, pos_inds) + ] + labels = [ + label.reshape(-1)[pos] + for label, pos in zip(labels_list, pos_inds) + ] + scores = torch.cat(scores, dim=0) + labels = torch.cat(labels, dim=0) + if self.use_sigmoid_cls: + scores = scores.sigmoid() + else: + scores = scores.softmax(dim=1) + + return accuracy(scores, labels, thresh=self.score_threshold) + + def collect_loss_level_single(self, cls_loss, reg_loss, assigned_gt_inds, + labels_seq): + """Get the average loss in each FPN level w.r.t. each gt label. + + Args: + cls_loss (Tensor): Classification loss of each feature map pixel, + shape (num_anchor, num_class) + reg_loss (Tensor): Regression loss of each feature map pixel, + shape (num_anchor, 4) + assigned_gt_inds (Tensor): It indicates which gt the prior is + assigned to (0-based, -1: no assignment). shape (num_anchor), + labels_seq: The rank of labels. shape (num_gt) + + Returns: + shape: (num_gt), average loss of each gt in this level + """ + if len(reg_loss.shape) == 2: # iou loss has shape (num_prior, 4) + reg_loss = reg_loss.sum(dim=-1) # sum loss in tblr dims + if len(cls_loss.shape) == 2: + cls_loss = cls_loss.sum(dim=-1) # sum loss in class dims + loss = cls_loss + reg_loss + assert loss.size(0) == assigned_gt_inds.size(0) + # Default loss value is 1e6 for a layer where no anchor is positive + # to ensure it will not be chosen to back-propagate gradient + losses_ = loss.new_full(labels_seq.shape, 1e6) + for i, l in enumerate(labels_seq): + match = assigned_gt_inds == l + if match.any(): + losses_[i] = loss[match].mean() + return losses_, + + def reweight_loss_single(self, cls_loss, reg_loss, assigned_gt_inds, + labels, level, min_levels): + """Reweight loss values at each level. + + Reassign loss values at each level by masking those where the + pre-calculated loss is too large. Then return the reduced losses. + + Args: + cls_loss (Tensor): Element-wise classification loss. + Shape: (num_anchors, num_classes) + reg_loss (Tensor): Element-wise regression loss. + Shape: (num_anchors, 4) + assigned_gt_inds (Tensor): The gt indices that each anchor bbox + is assigned to. -1 denotes a negative anchor, otherwise it is the + gt index (0-based). Shape: (num_anchors, ), + labels (Tensor): Label assigned to anchors. Shape: (num_anchors, ). + level (int): The current level index in the pyramid + (0-4 for RetinaNet) + min_levels (Tensor): The best-matching level for each gt. + Shape: (num_gts, ), + + Returns: + tuple: + - cls_loss: Reduced corrected classification loss. Scalar. + - reg_loss: Reduced corrected regression loss. Scalar. + - pos_flags (Tensor): Corrected bool tensor indicating the + final positive anchors. Shape: (num_anchors, ). + """ + loc_weight = torch.ones_like(reg_loss) + cls_weight = torch.ones_like(cls_loss) + pos_flags = assigned_gt_inds >= 0 # positive pixel flag + pos_indices = torch.nonzero(pos_flags, as_tuple=False).flatten() + + if pos_flags.any(): # pos pixels exist + pos_assigned_gt_inds = assigned_gt_inds[pos_flags] + zeroing_indices = (min_levels[pos_assigned_gt_inds] != level) + neg_indices = pos_indices[zeroing_indices] + + if neg_indices.numel(): + pos_flags[neg_indices] = 0 + loc_weight[neg_indices] = 0 + # Only the weight corresponding to the label is + # zeroed out if not selected + zeroing_labels = labels[neg_indices] + assert (zeroing_labels >= 0).all() + cls_weight[neg_indices, zeroing_labels] = 0 + + # Weighted loss for both cls and reg loss + cls_loss = weight_reduce_loss(cls_loss, cls_weight, reduction='sum') + reg_loss = weight_reduce_loss(reg_loss, loc_weight, reduction='sum') + + return cls_loss, reg_loss, pos_flags diff --git a/mmdet/models/dense_heads/ga_retina_head.py b/mmdet/models/dense_heads/ga_retina_head.py new file mode 100644 index 0000000..cc83bd5 --- /dev/null +++ b/mmdet/models/dense_heads/ga_retina_head.py @@ -0,0 +1,112 @@ +import torch.nn as nn +from mmcv.cnn import ConvModule +from mmcv.ops import MaskedConv2d + +from ..builder import HEADS +from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead + + +@HEADS.register_module() +class GARetinaHead(GuidedAnchorHead): + """Guided-Anchor-based RetinaNet head.""" + + def __init__(self, + num_classes, + in_channels, + stacked_convs=4, + conv_cfg=None, + norm_cfg=None, + init_cfg=None, + **kwargs): + if init_cfg is None: + init_cfg = dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=[ + dict( + type='Normal', + name='conv_loc', + std=0.01, + bias_prob=0.01), + dict( + type='Normal', + name='retina_cls', + std=0.01, + bias_prob=0.01) + ]) + self.stacked_convs = stacked_convs + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + super(GARetinaHead, self).__init__( + num_classes, in_channels, init_cfg=init_cfg, **kwargs) + + def _init_layers(self): + """Initialize layers of the head.""" + self.relu = nn.ReLU(inplace=True) + self.cls_convs = nn.ModuleList() + self.reg_convs = nn.ModuleList() + for i in range(self.stacked_convs): + chn = self.in_channels if i == 0 else self.feat_channels + self.cls_convs.append( + ConvModule( + chn, + self.feat_channels, + 3, + stride=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + self.reg_convs.append( + ConvModule( + chn, + self.feat_channels, + 3, + stride=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + + self.conv_loc = nn.Conv2d(self.feat_channels, 1, 1) + self.conv_shape = nn.Conv2d(self.feat_channels, self.num_anchors * 2, + 1) + self.feature_adaption_cls = FeatureAdaption( + self.feat_channels, + self.feat_channels, + kernel_size=3, + deform_groups=self.deform_groups) + self.feature_adaption_reg = FeatureAdaption( + self.feat_channels, + self.feat_channels, + kernel_size=3, + deform_groups=self.deform_groups) + self.retina_cls = MaskedConv2d( + self.feat_channels, + self.num_anchors * self.cls_out_channels, + 3, + padding=1) + self.retina_reg = MaskedConv2d( + self.feat_channels, self.num_anchors * 4, 3, padding=1) + + def forward_single(self, x): + """Forward feature map of a single scale level.""" + cls_feat = x + reg_feat = x + for cls_conv in self.cls_convs: + cls_feat = cls_conv(cls_feat) + for reg_conv in self.reg_convs: + reg_feat = reg_conv(reg_feat) + + loc_pred = self.conv_loc(cls_feat) + shape_pred = self.conv_shape(reg_feat) + + cls_feat = self.feature_adaption_cls(cls_feat, shape_pred) + reg_feat = self.feature_adaption_reg(reg_feat, shape_pred) + + if not self.training: + mask = loc_pred.sigmoid()[0] >= self.loc_filter_thr + else: + mask = None + cls_score = self.retina_cls(cls_feat, mask) + bbox_pred = self.retina_reg(reg_feat, mask) + return cls_score, bbox_pred, shape_pred, loc_pred diff --git a/mmdet/models/dense_heads/ga_rpn_head.py b/mmdet/models/dense_heads/ga_rpn_head.py new file mode 100644 index 0000000..4fc6d30 --- /dev/null +++ b/mmdet/models/dense_heads/ga_rpn_head.py @@ -0,0 +1,177 @@ +import copy +import warnings + +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv import ConfigDict +from mmcv.ops import nms + +from ..builder import HEADS +from .guided_anchor_head import GuidedAnchorHead +from .rpn_test_mixin import RPNTestMixin + + +@HEADS.register_module() +class GARPNHead(RPNTestMixin, GuidedAnchorHead): + """Guided-Anchor-based RPN head.""" + + def __init__(self, + in_channels, + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=dict( + type='Normal', + name='conv_loc', + std=0.01, + bias_prob=0.01)), + **kwargs): + super(GARPNHead, self).__init__( + 1, in_channels, init_cfg=init_cfg, **kwargs) + + def _init_layers(self): + """Initialize layers of the head.""" + self.rpn_conv = nn.Conv2d( + self.in_channels, self.feat_channels, 3, padding=1) + super(GARPNHead, self)._init_layers() + + def forward_single(self, x): + """Forward feature of a single scale level.""" + + x = self.rpn_conv(x) + x = F.relu(x, inplace=True) + (cls_score, bbox_pred, shape_pred, + loc_pred) = super(GARPNHead, self).forward_single(x) + return cls_score, bbox_pred, shape_pred, loc_pred + + def loss(self, + cls_scores, + bbox_preds, + shape_preds, + loc_preds, + gt_bboxes, + img_metas, + gt_bboxes_ignore=None): + losses = super(GARPNHead, self).loss( + cls_scores, + bbox_preds, + shape_preds, + loc_preds, + gt_bboxes, + None, + img_metas, + gt_bboxes_ignore=gt_bboxes_ignore) + return dict( + loss_rpn_cls=losses['loss_cls'], + loss_rpn_bbox=losses['loss_bbox'], + loss_anchor_shape=losses['loss_shape'], + loss_anchor_loc=losses['loss_loc']) + + def _get_bboxes_single(self, + cls_scores, + bbox_preds, + mlvl_anchors, + mlvl_masks, + img_shape, + scale_factor, + cfg, + rescale=False): + cfg = self.test_cfg if cfg is None else cfg + + cfg = copy.deepcopy(cfg) + + # deprecate arguments warning + if 'nms' not in cfg or 'max_num' in cfg or 'nms_thr' in cfg: + warnings.warn( + 'In rpn_proposal or test_cfg, ' + 'nms_thr has been moved to a dict named nms as ' + 'iou_threshold, max_num has been renamed as max_per_img, ' + 'name of original arguments and the way to specify ' + 'iou_threshold of NMS will be deprecated.') + if 'nms' not in cfg: + cfg.nms = ConfigDict(dict(type='nms', iou_threshold=cfg.nms_thr)) + if 'max_num' in cfg: + if 'max_per_img' in cfg: + assert cfg.max_num == cfg.max_per_img, f'You ' \ + f'set max_num and max_per_img at the same time, ' \ + f'but get {cfg.max_num} ' \ + f'and {cfg.max_per_img} respectively' \ + 'Please delete max_num which will be deprecated.' + else: + cfg.max_per_img = cfg.max_num + if 'nms_thr' in cfg: + assert cfg.nms.iou_threshold == cfg.nms_thr, f'You set ' \ + f'iou_threshold in nms and ' \ + f'nms_thr at the same time, but get ' \ + f'{cfg.nms.iou_threshold} and {cfg.nms_thr}' \ + f' respectively. Please delete the ' \ + f'nms_thr which will be deprecated.' + + assert cfg.nms.get('type', 'nms') == 'nms', 'GARPNHead only support ' \ + 'naive nms.' + + mlvl_proposals = [] + for idx in range(len(cls_scores)): + rpn_cls_score = cls_scores[idx] + rpn_bbox_pred = bbox_preds[idx] + anchors = mlvl_anchors[idx] + mask = mlvl_masks[idx] + assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:] + # if no location is kept, end. + if mask.sum() == 0: + continue + rpn_cls_score = rpn_cls_score.permute(1, 2, 0) + if self.use_sigmoid_cls: + rpn_cls_score = rpn_cls_score.reshape(-1) + scores = rpn_cls_score.sigmoid() + else: + rpn_cls_score = rpn_cls_score.reshape(-1, 2) + # remind that we set FG labels to [0, num_class-1] + # since mmdet v2.0 + # BG cat_id: num_class + scores = rpn_cls_score.softmax(dim=1)[:, :-1] + # filter scores, bbox_pred w.r.t. mask. + # anchors are filtered in get_anchors() beforehand. + scores = scores[mask] + rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1, + 4)[mask, :] + if scores.dim() == 0: + rpn_bbox_pred = rpn_bbox_pred.unsqueeze(0) + anchors = anchors.unsqueeze(0) + scores = scores.unsqueeze(0) + # filter anchors, bbox_pred, scores w.r.t. scores + if cfg.nms_pre > 0 and scores.shape[0] > cfg.nms_pre: + _, topk_inds = scores.topk(cfg.nms_pre) + rpn_bbox_pred = rpn_bbox_pred[topk_inds, :] + anchors = anchors[topk_inds, :] + scores = scores[topk_inds] + # get proposals w.r.t. anchors and rpn_bbox_pred + proposals = self.bbox_coder.decode( + anchors, rpn_bbox_pred, max_shape=img_shape) + # filter out too small bboxes + if cfg.min_bbox_size > 0: + w = proposals[:, 2] - proposals[:, 0] + h = proposals[:, 3] - proposals[:, 1] + valid_inds = torch.nonzero( + (w >= cfg.min_bbox_size) & (h >= cfg.min_bbox_size), + as_tuple=False).squeeze() + proposals = proposals[valid_inds, :] + scores = scores[valid_inds] + # NMS in current level + proposals, _ = nms(proposals, scores, cfg.nms.iou_threshold) + proposals = proposals[:cfg.nms_post, :] + mlvl_proposals.append(proposals) + proposals = torch.cat(mlvl_proposals, 0) + if cfg.get('nms_across_levels', False): + # NMS across multi levels + proposals, _ = nms(proposals[:, :4], proposals[:, -1], + cfg.nms.iou_threshold) + proposals = proposals[:cfg.max_per_img, :] + else: + scores = proposals[:, 4] + num = min(cfg.max_per_img, proposals.shape[0]) + _, topk_inds = scores.topk(num) + proposals = proposals[topk_inds, :] + return proposals diff --git a/mmdet/models/dense_heads/gfl_head.py b/mmdet/models/dense_heads/gfl_head.py new file mode 100644 index 0000000..a62cf7a --- /dev/null +++ b/mmdet/models/dense_heads/gfl_head.py @@ -0,0 +1,648 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule, Scale +from mmcv.runner import force_fp32 + +from mmdet.core import (anchor_inside_flags, bbox2distance, bbox_overlaps, + build_assigner, build_sampler, distance2bbox, + images_to_levels, multi_apply, multiclass_nms, + reduce_mean, unmap) +from ..builder import HEADS, build_loss +from .anchor_head import AnchorHead + + +class Integral(nn.Module): + """A fixed layer for calculating integral result from distribution. + + This layer calculates the target location by :math: `sum{P(y_i) * y_i}`, + P(y_i) denotes the softmax vector that represents the discrete distribution + y_i denotes the discrete set, usually {0, 1, 2, ..., reg_max} + + Args: + reg_max (int): The maximal value of the discrete set. Default: 16. You + may want to reset it according to your new dataset or related + settings. + """ + + def __init__(self, reg_max=16): + super(Integral, self).__init__() + self.reg_max = reg_max + self.register_buffer('project', + torch.linspace(0, self.reg_max, self.reg_max + 1)) + + def forward(self, x): + """Forward feature from the regression head to get integral result of + bounding box location. + + Args: + x (Tensor): Features of the regression head, shape (N, 4*(n+1)), + n is self.reg_max. + + Returns: + x (Tensor): Integral result of box locations, i.e., distance + offsets from the box center in four directions, shape (N, 4). + """ + x = F.softmax(x.reshape(-1, self.reg_max + 1), dim=1) + x = F.linear(x, self.project.type_as(x)).reshape(-1, 4) + return x + + +@HEADS.register_module() +class GFLHead(AnchorHead): + """Generalized Focal Loss: Learning Qualified and Distributed Bounding + Boxes for Dense Object Detection. + + GFL head structure is similar with ATSS, however GFL uses + 1) joint representation for classification and localization quality, and + 2) flexible General distribution for bounding box locations, + which are supervised by + Quality Focal Loss (QFL) and Distribution Focal Loss (DFL), respectively + + https://arxiv.org/abs/2006.04388 + + Args: + num_classes (int): Number of categories excluding the background + category. + in_channels (int): Number of channels in the input feature map. + stacked_convs (int): Number of conv layers in cls and reg tower. + Default: 4. + conv_cfg (dict): dictionary to construct and config conv layer. + Default: None. + norm_cfg (dict): dictionary to construct and config norm layer. + Default: dict(type='GN', num_groups=32, requires_grad=True). + loss_qfl (dict): Config of Quality Focal Loss (QFL). + reg_max (int): Max value of integral set :math: `{0, ..., reg_max}` + in QFL setting. Default: 16. + init_cfg (dict or list[dict], optional): Initialization config dict. + Example: + >>> self = GFLHead(11, 7) + >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]] + >>> cls_quality_score, bbox_pred = self.forward(feats) + >>> assert len(cls_quality_score) == len(self.scales) + """ + + def __init__(self, + num_classes, + in_channels, + stacked_convs=4, + conv_cfg=None, + norm_cfg=dict(type='GN', num_groups=32, requires_grad=True), + loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25), + reg_max=16, + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=dict( + type='Normal', + name='gfl_cls', + std=0.01, + bias_prob=0.01)), + **kwargs): + self.stacked_convs = stacked_convs + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.reg_max = reg_max + super(GFLHead, self).__init__( + num_classes, in_channels, init_cfg=init_cfg, **kwargs) + + self.sampling = False + if self.train_cfg: + self.assigner = build_assigner(self.train_cfg.assigner) + # SSD sampling=False so use PseudoSampler + sampler_cfg = dict(type='PseudoSampler') + self.sampler = build_sampler(sampler_cfg, context=self) + + self.integral = Integral(self.reg_max) + self.loss_dfl = build_loss(loss_dfl) + + def _init_layers(self): + """Initialize layers of the head.""" + self.relu = nn.ReLU(inplace=True) + self.cls_convs = nn.ModuleList() + self.reg_convs = nn.ModuleList() + for i in range(self.stacked_convs): + chn = self.in_channels if i == 0 else self.feat_channels + self.cls_convs.append( + ConvModule( + chn, + self.feat_channels, + 3, + stride=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + self.reg_convs.append( + ConvModule( + chn, + self.feat_channels, + 3, + stride=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + assert self.num_anchors == 1, 'anchor free version' + self.gfl_cls = nn.Conv2d( + self.feat_channels, self.cls_out_channels, 3, padding=1) + self.gfl_reg = nn.Conv2d( + self.feat_channels, 4 * (self.reg_max + 1), 3, padding=1) + self.scales = nn.ModuleList( + [Scale(1.0) for _ in self.anchor_generator.strides]) + + def forward(self, feats): + """Forward features from the upstream network. + + Args: + feats (tuple[Tensor]): Features from the upstream network, each is + a 4D-tensor. + + Returns: + tuple: Usually a tuple of classification scores and bbox prediction + cls_scores (list[Tensor]): Classification and quality (IoU) + joint scores for all scale levels, each is a 4D-tensor, + the channel number is num_classes. + bbox_preds (list[Tensor]): Box distribution logits for all + scale levels, each is a 4D-tensor, the channel number is + 4*(n+1), n is max value of integral set. + """ + return multi_apply(self.forward_single, feats, self.scales) + + def forward_single(self, x, scale): + """Forward feature of a single scale level. + + Args: + x (Tensor): Features of a single scale level. + scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize + the bbox prediction. + + Returns: + tuple: + cls_score (Tensor): Cls and quality joint scores for a single + scale level the channel number is num_classes. + bbox_pred (Tensor): Box distribution logits for a single scale + level, the channel number is 4*(n+1), n is max value of + integral set. + """ + cls_feat = x + reg_feat = x + for cls_conv in self.cls_convs: + cls_feat = cls_conv(cls_feat) + for reg_conv in self.reg_convs: + reg_feat = reg_conv(reg_feat) + cls_score = self.gfl_cls(cls_feat) + bbox_pred = scale(self.gfl_reg(reg_feat)).float() + return cls_score, bbox_pred + + def anchor_center(self, anchors): + """Get anchor centers from anchors. + + Args: + anchors (Tensor): Anchor list with shape (N, 4), "xyxy" format. + + Returns: + Tensor: Anchor centers with shape (N, 2), "xy" format. + """ + anchors_cx = (anchors[..., 2] + anchors[..., 0]) / 2 + anchors_cy = (anchors[..., 3] + anchors[..., 1]) / 2 + return torch.stack([anchors_cx, anchors_cy], dim=-1) + + def loss_single(self, anchors, cls_score, bbox_pred, labels, label_weights, + bbox_targets, stride, num_total_samples): + """Compute loss of a single scale level. + + Args: + anchors (Tensor): Box reference for each scale level with shape + (N, num_total_anchors, 4). + cls_score (Tensor): Cls and quality joint scores for each scale + level has shape (N, num_classes, H, W). + bbox_pred (Tensor): Box distribution logits for each scale + level with shape (N, 4*(n+1), H, W), n is max value of integral + set. + labels (Tensor): Labels of each anchors with shape + (N, num_total_anchors). + label_weights (Tensor): Label weights of each anchor with shape + (N, num_total_anchors) + bbox_targets (Tensor): BBox regression targets of each anchor wight + shape (N, num_total_anchors, 4). + stride (tuple): Stride in this scale level. + num_total_samples (int): Number of positive samples that is + reduced over all GPUs. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + assert stride[0] == stride[1], 'h stride is not equal to w stride!' + anchors = anchors.reshape(-1, 4) + cls_score = cls_score.permute(0, 2, 3, + 1).reshape(-1, self.cls_out_channels) + bbox_pred = bbox_pred.permute(0, 2, 3, + 1).reshape(-1, 4 * (self.reg_max + 1)) + bbox_targets = bbox_targets.reshape(-1, 4) + labels = labels.reshape(-1) + label_weights = label_weights.reshape(-1) + + # FG cat_id: [0, num_classes -1], BG cat_id: num_classes + bg_class_ind = self.num_classes + pos_inds = ((labels >= 0) + & (labels < bg_class_ind)).nonzero().squeeze(1) + score = label_weights.new_zeros(labels.shape) + + if len(pos_inds) > 0: + pos_bbox_targets = bbox_targets[pos_inds] + pos_bbox_pred = bbox_pred[pos_inds] + pos_anchors = anchors[pos_inds] + pos_anchor_centers = self.anchor_center(pos_anchors) / stride[0] + + weight_targets = cls_score.detach().sigmoid() + weight_targets = weight_targets.max(dim=1)[0][pos_inds] + pos_bbox_pred_corners = self.integral(pos_bbox_pred) + pos_decode_bbox_pred = distance2bbox(pos_anchor_centers, + pos_bbox_pred_corners) + pos_decode_bbox_targets = pos_bbox_targets / stride[0] + score[pos_inds] = bbox_overlaps( + pos_decode_bbox_pred.detach(), + pos_decode_bbox_targets, + is_aligned=True) + pred_corners = pos_bbox_pred.reshape(-1, self.reg_max + 1) + target_corners = bbox2distance(pos_anchor_centers, + pos_decode_bbox_targets, + self.reg_max).reshape(-1) + + # regression loss + loss_bbox = self.loss_bbox( + pos_decode_bbox_pred, + pos_decode_bbox_targets, + weight=weight_targets, + avg_factor=1.0) + + # dfl loss + loss_dfl = self.loss_dfl( + pred_corners, + target_corners, + weight=weight_targets[:, None].expand(-1, 4).reshape(-1), + avg_factor=4.0) + else: + loss_bbox = bbox_pred.sum() * 0 + loss_dfl = bbox_pred.sum() * 0 + weight_targets = bbox_pred.new_tensor(0) + + # cls (qfl) loss + loss_cls = self.loss_cls( + cls_score, (labels, score), + weight=label_weights, + avg_factor=num_total_samples) + + return loss_cls, loss_bbox, loss_dfl, weight_targets.sum() + + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) + def loss(self, + cls_scores, + bbox_preds, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute losses of the head. + + Args: + cls_scores (list[Tensor]): Cls and quality scores for each scale + level has shape (N, num_classes, H, W). + bbox_preds (list[Tensor]): Box distribution logits for each scale + level with shape (N, 4*(n+1), H, W), n is max value of integral + set. + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (list[Tensor] | None): specify which bounding + boxes can be ignored when computing the loss. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + assert len(featmap_sizes) == self.anchor_generator.num_levels + + device = cls_scores[0].device + anchor_list, valid_flag_list = self.get_anchors( + featmap_sizes, img_metas, device=device) + label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 + + cls_reg_targets = self.get_targets( + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + gt_labels_list=gt_labels, + label_channels=label_channels) + if cls_reg_targets is None: + return None + + (anchor_list, labels_list, label_weights_list, bbox_targets_list, + bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets + + num_total_samples = reduce_mean( + torch.tensor(num_total_pos, dtype=torch.float, + device=device)).item() + num_total_samples = max(num_total_samples, 1.0) + + losses_cls, losses_bbox, losses_dfl,\ + avg_factor = multi_apply( + self.loss_single, + anchor_list, + cls_scores, + bbox_preds, + labels_list, + label_weights_list, + bbox_targets_list, + self.anchor_generator.strides, + num_total_samples=num_total_samples) + + avg_factor = sum(avg_factor) + avg_factor = reduce_mean(avg_factor).clamp_(min=1).item() + losses_bbox = list(map(lambda x: x / avg_factor, losses_bbox)) + losses_dfl = list(map(lambda x: x / avg_factor, losses_dfl)) + return dict( + loss_cls=losses_cls, loss_bbox=losses_bbox, loss_dfl=losses_dfl) + + def _get_bboxes(self, + cls_scores, + bbox_preds, + mlvl_anchors, + img_shapes, + scale_factors, + cfg, + rescale=False, + with_nms=True): + """Transform outputs for a single batch item into labeled boxes. + + Args: + cls_scores (list[Tensor]): Box scores for a single scale level + has shape (N, num_classes, H, W). + bbox_preds (list[Tensor]): Box distribution logits for a single + scale level with shape (N, 4*(n+1), H, W), n is max value of + integral set. + mlvl_anchors (list[Tensor]): Box reference for a single scale level + with shape (num_total_anchors, 4). + img_shapes (list[tuple[int]]): Shape of the input image, + list[(height, width, 3)]. + scale_factors (list[ndarray]): Scale factor of the image arange as + (w_scale, h_scale, w_scale, h_scale). + cfg (mmcv.Config | None): Test / postprocessing configuration, + if None, test_cfg would be used. + rescale (bool): If True, return boxes in original image space. + Default: False. + with_nms (bool): If True, do nms before return boxes. + Default: True. + + Returns: + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is an (n, 5) tensor, where 5 represent + (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. + The shape of the second tensor in the tuple is (n,), and + each element represents the class label of the corresponding + box. + """ + cfg = self.test_cfg if cfg is None else cfg + assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors) + batch_size = cls_scores[0].shape[0] + + mlvl_bboxes = [] + mlvl_scores = [] + for cls_score, bbox_pred, stride, anchors in zip( + cls_scores, bbox_preds, self.anchor_generator.strides, + mlvl_anchors): + assert cls_score.size()[-2:] == bbox_pred.size()[-2:] + assert stride[0] == stride[1] + scores = cls_score.permute(0, 2, 3, 1).reshape( + batch_size, -1, self.cls_out_channels).sigmoid() + bbox_pred = bbox_pred.permute(0, 2, 3, 1) + + bbox_pred = self.integral(bbox_pred) * stride[0] + bbox_pred = bbox_pred.reshape(batch_size, -1, 4) + + nms_pre = cfg.get('nms_pre', -1) + if nms_pre > 0 and scores.shape[1] > nms_pre: + max_scores, _ = scores.max(-1) + _, topk_inds = max_scores.topk(nms_pre) + batch_inds = torch.arange(batch_size).view( + -1, 1).expand_as(topk_inds).long() + anchors = anchors[topk_inds, :] + bbox_pred = bbox_pred[batch_inds, topk_inds, :] + scores = scores[batch_inds, topk_inds, :] + else: + anchors = anchors.expand_as(bbox_pred) + + bboxes = distance2bbox( + self.anchor_center(anchors), bbox_pred, max_shape=img_shapes) + mlvl_bboxes.append(bboxes) + mlvl_scores.append(scores) + + batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1) + if rescale: + batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor( + scale_factors).unsqueeze(1) + + batch_mlvl_scores = torch.cat(mlvl_scores, dim=1) + # Add a dummy background class to the backend when using sigmoid + # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 + # BG cat_id: num_class + padding = batch_mlvl_scores.new_zeros(batch_size, + batch_mlvl_scores.shape[1], 1) + batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1) + + if with_nms: + det_results = [] + for (mlvl_bboxes, mlvl_scores) in zip(batch_mlvl_bboxes, + batch_mlvl_scores): + det_bbox, det_label = multiclass_nms(mlvl_bboxes, mlvl_scores, + cfg.score_thr, cfg.nms, + cfg.max_per_img) + det_results.append(tuple([det_bbox, det_label])) + else: + det_results = [ + tuple(mlvl_bs) + for mlvl_bs in zip(batch_mlvl_bboxes, batch_mlvl_scores) + ] + return det_results + + def get_targets(self, + anchor_list, + valid_flag_list, + gt_bboxes_list, + img_metas, + gt_bboxes_ignore_list=None, + gt_labels_list=None, + label_channels=1, + unmap_outputs=True): + """Get targets for GFL head. + + This method is almost the same as `AnchorHead.get_targets()`. Besides + returning the targets as the parent method does, it also returns the + anchors as the first element of the returned tuple. + """ + num_imgs = len(img_metas) + assert len(anchor_list) == len(valid_flag_list) == num_imgs + + # anchor number of multi levels + num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] + num_level_anchors_list = [num_level_anchors] * num_imgs + + # concat all level anchors and flags to a single tensor + for i in range(num_imgs): + assert len(anchor_list[i]) == len(valid_flag_list[i]) + anchor_list[i] = torch.cat(anchor_list[i]) + valid_flag_list[i] = torch.cat(valid_flag_list[i]) + + # compute targets for each image + if gt_bboxes_ignore_list is None: + gt_bboxes_ignore_list = [None for _ in range(num_imgs)] + if gt_labels_list is None: + gt_labels_list = [None for _ in range(num_imgs)] + (all_anchors, all_labels, all_label_weights, all_bbox_targets, + all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply( + self._get_target_single, + anchor_list, + valid_flag_list, + num_level_anchors_list, + gt_bboxes_list, + gt_bboxes_ignore_list, + gt_labels_list, + img_metas, + label_channels=label_channels, + unmap_outputs=unmap_outputs) + # no valid anchors + if any([labels is None for labels in all_labels]): + return None + # sampled anchors of all images + num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list]) + num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list]) + # split targets to a list w.r.t. multiple levels + anchors_list = images_to_levels(all_anchors, num_level_anchors) + labels_list = images_to_levels(all_labels, num_level_anchors) + label_weights_list = images_to_levels(all_label_weights, + num_level_anchors) + bbox_targets_list = images_to_levels(all_bbox_targets, + num_level_anchors) + bbox_weights_list = images_to_levels(all_bbox_weights, + num_level_anchors) + return (anchors_list, labels_list, label_weights_list, + bbox_targets_list, bbox_weights_list, num_total_pos, + num_total_neg) + + def _get_target_single(self, + flat_anchors, + valid_flags, + num_level_anchors, + gt_bboxes, + gt_bboxes_ignore, + gt_labels, + img_meta, + label_channels=1, + unmap_outputs=True): + """Compute regression, classification targets for anchors in a single + image. + + Args: + flat_anchors (Tensor): Multi-level anchors of the image, which are + concatenated into a single tensor of shape (num_anchors, 4) + valid_flags (Tensor): Multi level valid flags of the image, + which are concatenated into a single tensor of + shape (num_anchors,). + num_level_anchors Tensor): Number of anchors of each scale level. + gt_bboxes (Tensor): Ground truth bboxes of the image, + shape (num_gts, 4). + gt_bboxes_ignore (Tensor): Ground truth bboxes to be + ignored, shape (num_ignored_gts, 4). + gt_labels (Tensor): Ground truth labels of each box, + shape (num_gts,). + img_meta (dict): Meta info of the image. + label_channels (int): Channel of label. + unmap_outputs (bool): Whether to map outputs back to the original + set of anchors. + + Returns: + tuple: N is the number of total anchors in the image. + anchors (Tensor): All anchors in the image with shape (N, 4). + labels (Tensor): Labels of all anchors in the image with shape + (N,). + label_weights (Tensor): Label weights of all anchor in the + image with shape (N,). + bbox_targets (Tensor): BBox targets of all anchors in the + image with shape (N, 4). + bbox_weights (Tensor): BBox weights of all anchors in the + image with shape (N, 4). + pos_inds (Tensor): Indices of positive anchor with shape + (num_pos,). + neg_inds (Tensor): Indices of negative anchor with shape + (num_neg,). + """ + inside_flags = anchor_inside_flags(flat_anchors, valid_flags, + img_meta['img_shape'][:2], + self.train_cfg.allowed_border) + if not inside_flags.any(): + return (None, ) * 7 + # assign gt and sample anchors + anchors = flat_anchors[inside_flags, :] + + num_level_anchors_inside = self.get_num_level_anchors_inside( + num_level_anchors, inside_flags) + assign_result = self.assigner.assign(anchors, num_level_anchors_inside, + gt_bboxes, gt_bboxes_ignore, + gt_labels) + + sampling_result = self.sampler.sample(assign_result, anchors, + gt_bboxes) + + num_valid_anchors = anchors.shape[0] + bbox_targets = torch.zeros_like(anchors) + bbox_weights = torch.zeros_like(anchors) + labels = anchors.new_full((num_valid_anchors, ), + self.num_classes, + dtype=torch.long) + label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float) + + pos_inds = sampling_result.pos_inds + neg_inds = sampling_result.neg_inds + if len(pos_inds) > 0: + pos_bbox_targets = sampling_result.pos_gt_bboxes + bbox_targets[pos_inds, :] = pos_bbox_targets + bbox_weights[pos_inds, :] = 1.0 + if gt_labels is None: + # Only rpn gives gt_labels as None + # Foreground is the first class + labels[pos_inds] = 0 + else: + labels[pos_inds] = gt_labels[ + sampling_result.pos_assigned_gt_inds] + if self.train_cfg.pos_weight <= 0: + label_weights[pos_inds] = 1.0 + else: + label_weights[pos_inds] = self.train_cfg.pos_weight + if len(neg_inds) > 0: + label_weights[neg_inds] = 1.0 + + # map up to original set of anchors + if unmap_outputs: + num_total_anchors = flat_anchors.size(0) + anchors = unmap(anchors, num_total_anchors, inside_flags) + labels = unmap( + labels, num_total_anchors, inside_flags, fill=self.num_classes) + label_weights = unmap(label_weights, num_total_anchors, + inside_flags) + bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags) + bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags) + + return (anchors, labels, label_weights, bbox_targets, bbox_weights, + pos_inds, neg_inds) + + def get_num_level_anchors_inside(self, num_level_anchors, inside_flags): + split_inside_flags = torch.split(inside_flags, num_level_anchors) + num_level_anchors_inside = [ + int(flags.sum()) for flags in split_inside_flags + ] + return num_level_anchors_inside diff --git a/mmdet/models/dense_heads/guided_anchor_head.py b/mmdet/models/dense_heads/guided_anchor_head.py new file mode 100644 index 0000000..7c0166b --- /dev/null +++ b/mmdet/models/dense_heads/guided_anchor_head.py @@ -0,0 +1,858 @@ +import torch +import torch.nn as nn +from mmcv.ops import DeformConv2d, MaskedConv2d +from mmcv.runner import BaseModule, force_fp32 + +from mmdet.core import (anchor_inside_flags, build_anchor_generator, + build_assigner, build_bbox_coder, build_sampler, + calc_region, images_to_levels, multi_apply, + multiclass_nms, unmap) +from ..builder import HEADS, build_loss +from .anchor_head import AnchorHead + + +class FeatureAdaption(BaseModule): + """Feature Adaption Module. + + Feature Adaption Module is implemented based on DCN v1. + It uses anchor shape prediction rather than feature map to + predict offsets of deform conv layer. + + Args: + in_channels (int): Number of channels in the input feature map. + out_channels (int): Number of channels in the output feature map. + kernel_size (int): Deformable conv kernel size. + deform_groups (int): Deformable conv group size. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size=3, + deform_groups=4, + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.1, + override=dict( + type='Normal', name='conv_adaption', std=0.01))): + super(FeatureAdaption, self).__init__(init_cfg) + offset_channels = kernel_size * kernel_size * 2 + self.conv_offset = nn.Conv2d( + 2, deform_groups * offset_channels, 1, bias=False) + self.conv_adaption = DeformConv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + padding=(kernel_size - 1) // 2, + deform_groups=deform_groups) + self.relu = nn.ReLU(inplace=True) + + def forward(self, x, shape): + offset = self.conv_offset(shape.detach()) + x = self.relu(self.conv_adaption(x, offset)) + return x + + +@HEADS.register_module() +class GuidedAnchorHead(AnchorHead): + """Guided-Anchor-based head (GA-RPN, GA-RetinaNet, etc.). + + This GuidedAnchorHead will predict high-quality feature guided + anchors and locations where anchors will be kept in inference. + There are mainly 3 categories of bounding-boxes. + + - Sampled 9 pairs for target assignment. (approxes) + - The square boxes where the predicted anchors are based on. (squares) + - Guided anchors. + + Please refer to https://arxiv.org/abs/1901.03278 for more details. + + Args: + num_classes (int): Number of classes. + in_channels (int): Number of channels in the input feature map. + feat_channels (int): Number of hidden channels. + approx_anchor_generator (dict): Config dict for approx generator + square_anchor_generator (dict): Config dict for square generator + anchor_coder (dict): Config dict for anchor coder + bbox_coder (dict): Config dict for bbox coder + reg_decoded_bbox (bool): If true, the regression loss would be + applied directly on decoded bounding boxes, converting both + the predicted boxes and regression targets to absolute + coordinates format. Default False. It should be `True` when + using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head. + deform_groups: (int): Group number of DCN in + FeatureAdaption module. + loc_filter_thr (float): Threshold to filter out unconcerned regions. + loss_loc (dict): Config of location loss. + loss_shape (dict): Config of anchor shape loss. + loss_cls (dict): Config of classification loss. + loss_bbox (dict): Config of bbox regression loss. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__( + self, + num_classes, + in_channels, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=8, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[8], + strides=[4, 8, 16, 32, 64]), + anchor_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0] + ), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0] + ), + reg_decoded_bbox=False, + deform_groups=4, + loc_filter_thr=0.01, + train_cfg=None, + test_cfg=None, + loss_loc=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0), + init_cfg=dict(type='Normal', layer='Conv2d', std=0.01, + override=dict(type='Normal', + name='conv_loc', + std=0.01, + bias_prob=0.01))): # yapf: disable + super(AnchorHead, self).__init__(init_cfg) + self.in_channels = in_channels + self.num_classes = num_classes + self.feat_channels = feat_channels + self.deform_groups = deform_groups + self.loc_filter_thr = loc_filter_thr + + # build approx_anchor_generator and square_anchor_generator + assert (approx_anchor_generator['octave_base_scale'] == + square_anchor_generator['scales'][0]) + assert (approx_anchor_generator['strides'] == + square_anchor_generator['strides']) + self.approx_anchor_generator = build_anchor_generator( + approx_anchor_generator) + self.square_anchor_generator = build_anchor_generator( + square_anchor_generator) + self.approxs_per_octave = self.approx_anchor_generator \ + .num_base_anchors[0] + + self.reg_decoded_bbox = reg_decoded_bbox + + # one anchor per location + self.num_anchors = 1 + self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False) + self.loc_focal_loss = loss_loc['type'] in ['FocalLoss'] + self.sampling = loss_cls['type'] not in ['FocalLoss'] + self.ga_sampling = train_cfg is not None and hasattr( + train_cfg, 'ga_sampler') + if self.use_sigmoid_cls: + self.cls_out_channels = self.num_classes + else: + self.cls_out_channels = self.num_classes + 1 + + # build bbox_coder + self.anchor_coder = build_bbox_coder(anchor_coder) + self.bbox_coder = build_bbox_coder(bbox_coder) + + # build losses + self.loss_loc = build_loss(loss_loc) + self.loss_shape = build_loss(loss_shape) + self.loss_cls = build_loss(loss_cls) + self.loss_bbox = build_loss(loss_bbox) + + self.train_cfg = train_cfg + self.test_cfg = test_cfg + + if self.train_cfg: + self.assigner = build_assigner(self.train_cfg.assigner) + # use PseudoSampler when sampling is False + if self.sampling and hasattr(self.train_cfg, 'sampler'): + sampler_cfg = self.train_cfg.sampler + else: + sampler_cfg = dict(type='PseudoSampler') + self.sampler = build_sampler(sampler_cfg, context=self) + + self.ga_assigner = build_assigner(self.train_cfg.ga_assigner) + if self.ga_sampling: + ga_sampler_cfg = self.train_cfg.ga_sampler + else: + ga_sampler_cfg = dict(type='PseudoSampler') + self.ga_sampler = build_sampler(ga_sampler_cfg, context=self) + + self.fp16_enabled = False + + self._init_layers() + + def _init_layers(self): + self.relu = nn.ReLU(inplace=True) + self.conv_loc = nn.Conv2d(self.in_channels, 1, 1) + self.conv_shape = nn.Conv2d(self.in_channels, self.num_anchors * 2, 1) + self.feature_adaption = FeatureAdaption( + self.in_channels, + self.feat_channels, + kernel_size=3, + deform_groups=self.deform_groups) + self.conv_cls = MaskedConv2d(self.feat_channels, + self.num_anchors * self.cls_out_channels, + 1) + self.conv_reg = MaskedConv2d(self.feat_channels, self.num_anchors * 4, + 1) + + def forward_single(self, x): + loc_pred = self.conv_loc(x) + shape_pred = self.conv_shape(x) + x = self.feature_adaption(x, shape_pred) + # masked conv is only used during inference for speed-up + if not self.training: + mask = loc_pred.sigmoid()[0] >= self.loc_filter_thr + else: + mask = None + cls_score = self.conv_cls(x, mask) + bbox_pred = self.conv_reg(x, mask) + return cls_score, bbox_pred, shape_pred, loc_pred + + def forward(self, feats): + return multi_apply(self.forward_single, feats) + + def get_sampled_approxs(self, featmap_sizes, img_metas, device='cuda'): + """Get sampled approxs and inside flags according to feature map sizes. + + Args: + featmap_sizes (list[tuple]): Multi-level feature map sizes. + img_metas (list[dict]): Image meta info. + device (torch.device | str): device for returned tensors + + Returns: + tuple: approxes of each image, inside flags of each image + """ + num_imgs = len(img_metas) + + # since feature map sizes of all images are the same, we only compute + # approxes for one time + multi_level_approxs = self.approx_anchor_generator.grid_anchors( + featmap_sizes, device=device) + approxs_list = [multi_level_approxs for _ in range(num_imgs)] + + # for each image, we compute inside flags of multi level approxes + inside_flag_list = [] + for img_id, img_meta in enumerate(img_metas): + multi_level_flags = [] + multi_level_approxs = approxs_list[img_id] + + # obtain valid flags for each approx first + multi_level_approx_flags = self.approx_anchor_generator \ + .valid_flags(featmap_sizes, + img_meta['pad_shape'], + device=device) + + for i, flags in enumerate(multi_level_approx_flags): + approxs = multi_level_approxs[i] + inside_flags_list = [] + for i in range(self.approxs_per_octave): + split_valid_flags = flags[i::self.approxs_per_octave] + split_approxs = approxs[i::self.approxs_per_octave, :] + inside_flags = anchor_inside_flags( + split_approxs, split_valid_flags, + img_meta['img_shape'][:2], + self.train_cfg.allowed_border) + inside_flags_list.append(inside_flags) + # inside_flag for a position is true if any anchor in this + # position is true + inside_flags = ( + torch.stack(inside_flags_list, 0).sum(dim=0) > 0) + multi_level_flags.append(inside_flags) + inside_flag_list.append(multi_level_flags) + return approxs_list, inside_flag_list + + def get_anchors(self, + featmap_sizes, + shape_preds, + loc_preds, + img_metas, + use_loc_filter=False, + device='cuda'): + """Get squares according to feature map sizes and guided anchors. + + Args: + featmap_sizes (list[tuple]): Multi-level feature map sizes. + shape_preds (list[tensor]): Multi-level shape predictions. + loc_preds (list[tensor]): Multi-level location predictions. + img_metas (list[dict]): Image meta info. + use_loc_filter (bool): Use loc filter or not. + device (torch.device | str): device for returned tensors + + Returns: + tuple: square approxs of each image, guided anchors of each image, + loc masks of each image + """ + num_imgs = len(img_metas) + num_levels = len(featmap_sizes) + + # since feature map sizes of all images are the same, we only compute + # squares for one time + multi_level_squares = self.square_anchor_generator.grid_anchors( + featmap_sizes, device=device) + squares_list = [multi_level_squares for _ in range(num_imgs)] + + # for each image, we compute multi level guided anchors + guided_anchors_list = [] + loc_mask_list = [] + for img_id, img_meta in enumerate(img_metas): + multi_level_guided_anchors = [] + multi_level_loc_mask = [] + for i in range(num_levels): + squares = squares_list[img_id][i] + shape_pred = shape_preds[i][img_id] + loc_pred = loc_preds[i][img_id] + guided_anchors, loc_mask = self._get_guided_anchors_single( + squares, + shape_pred, + loc_pred, + use_loc_filter=use_loc_filter) + multi_level_guided_anchors.append(guided_anchors) + multi_level_loc_mask.append(loc_mask) + guided_anchors_list.append(multi_level_guided_anchors) + loc_mask_list.append(multi_level_loc_mask) + return squares_list, guided_anchors_list, loc_mask_list + + def _get_guided_anchors_single(self, + squares, + shape_pred, + loc_pred, + use_loc_filter=False): + """Get guided anchors and loc masks for a single level. + + Args: + square (tensor): Squares of a single level. + shape_pred (tensor): Shape predections of a single level. + loc_pred (tensor): Loc predections of a single level. + use_loc_filter (list[tensor]): Use loc filter or not. + + Returns: + tuple: guided anchors, location masks + """ + # calculate location filtering mask + loc_pred = loc_pred.sigmoid().detach() + if use_loc_filter: + loc_mask = loc_pred >= self.loc_filter_thr + else: + loc_mask = loc_pred >= 0.0 + mask = loc_mask.permute(1, 2, 0).expand(-1, -1, self.num_anchors) + mask = mask.contiguous().view(-1) + # calculate guided anchors + squares = squares[mask] + anchor_deltas = shape_pred.permute(1, 2, 0).contiguous().view( + -1, 2).detach()[mask] + bbox_deltas = anchor_deltas.new_full(squares.size(), 0) + bbox_deltas[:, 2:] = anchor_deltas + guided_anchors = self.anchor_coder.decode( + squares, bbox_deltas, wh_ratio_clip=1e-6) + return guided_anchors, mask + + def ga_loc_targets(self, gt_bboxes_list, featmap_sizes): + """Compute location targets for guided anchoring. + + Each feature map is divided into positive, negative and ignore regions. + - positive regions: target 1, weight 1 + - ignore regions: target 0, weight 0 + - negative regions: target 0, weight 0.1 + + Args: + gt_bboxes_list (list[Tensor]): Gt bboxes of each image. + featmap_sizes (list[tuple]): Multi level sizes of each feature + maps. + + Returns: + tuple + """ + anchor_scale = self.approx_anchor_generator.octave_base_scale + anchor_strides = self.approx_anchor_generator.strides + # Currently only supports same stride in x and y direction. + for stride in anchor_strides: + assert (stride[0] == stride[1]) + anchor_strides = [stride[0] for stride in anchor_strides] + + center_ratio = self.train_cfg.center_ratio + ignore_ratio = self.train_cfg.ignore_ratio + img_per_gpu = len(gt_bboxes_list) + num_lvls = len(featmap_sizes) + r1 = (1 - center_ratio) / 2 + r2 = (1 - ignore_ratio) / 2 + all_loc_targets = [] + all_loc_weights = [] + all_ignore_map = [] + for lvl_id in range(num_lvls): + h, w = featmap_sizes[lvl_id] + loc_targets = torch.zeros( + img_per_gpu, + 1, + h, + w, + device=gt_bboxes_list[0].device, + dtype=torch.float32) + loc_weights = torch.full_like(loc_targets, -1) + ignore_map = torch.zeros_like(loc_targets) + all_loc_targets.append(loc_targets) + all_loc_weights.append(loc_weights) + all_ignore_map.append(ignore_map) + for img_id in range(img_per_gpu): + gt_bboxes = gt_bboxes_list[img_id] + scale = torch.sqrt((gt_bboxes[:, 2] - gt_bboxes[:, 0]) * + (gt_bboxes[:, 3] - gt_bboxes[:, 1])) + min_anchor_size = scale.new_full( + (1, ), float(anchor_scale * anchor_strides[0])) + # assign gt bboxes to different feature levels w.r.t. their scales + target_lvls = torch.floor( + torch.log2(scale) - torch.log2(min_anchor_size) + 0.5) + target_lvls = target_lvls.clamp(min=0, max=num_lvls - 1).long() + for gt_id in range(gt_bboxes.size(0)): + lvl = target_lvls[gt_id].item() + # rescaled to corresponding feature map + gt_ = gt_bboxes[gt_id, :4] / anchor_strides[lvl] + # calculate ignore regions + ignore_x1, ignore_y1, ignore_x2, ignore_y2 = calc_region( + gt_, r2, featmap_sizes[lvl]) + # calculate positive (center) regions + ctr_x1, ctr_y1, ctr_x2, ctr_y2 = calc_region( + gt_, r1, featmap_sizes[lvl]) + all_loc_targets[lvl][img_id, 0, ctr_y1:ctr_y2 + 1, + ctr_x1:ctr_x2 + 1] = 1 + all_loc_weights[lvl][img_id, 0, ignore_y1:ignore_y2 + 1, + ignore_x1:ignore_x2 + 1] = 0 + all_loc_weights[lvl][img_id, 0, ctr_y1:ctr_y2 + 1, + ctr_x1:ctr_x2 + 1] = 1 + # calculate ignore map on nearby low level feature + if lvl > 0: + d_lvl = lvl - 1 + # rescaled to corresponding feature map + gt_ = gt_bboxes[gt_id, :4] / anchor_strides[d_lvl] + ignore_x1, ignore_y1, ignore_x2, ignore_y2 = calc_region( + gt_, r2, featmap_sizes[d_lvl]) + all_ignore_map[d_lvl][img_id, 0, ignore_y1:ignore_y2 + 1, + ignore_x1:ignore_x2 + 1] = 1 + # calculate ignore map on nearby high level feature + if lvl < num_lvls - 1: + u_lvl = lvl + 1 + # rescaled to corresponding feature map + gt_ = gt_bboxes[gt_id, :4] / anchor_strides[u_lvl] + ignore_x1, ignore_y1, ignore_x2, ignore_y2 = calc_region( + gt_, r2, featmap_sizes[u_lvl]) + all_ignore_map[u_lvl][img_id, 0, ignore_y1:ignore_y2 + 1, + ignore_x1:ignore_x2 + 1] = 1 + for lvl_id in range(num_lvls): + # ignore negative regions w.r.t. ignore map + all_loc_weights[lvl_id][(all_loc_weights[lvl_id] < 0) + & (all_ignore_map[lvl_id] > 0)] = 0 + # set negative regions with weight 0.1 + all_loc_weights[lvl_id][all_loc_weights[lvl_id] < 0] = 0.1 + # loc average factor to balance loss + loc_avg_factor = sum( + [t.size(0) * t.size(-1) * t.size(-2) + for t in all_loc_targets]) / 200 + return all_loc_targets, all_loc_weights, loc_avg_factor + + def _ga_shape_target_single(self, + flat_approxs, + inside_flags, + flat_squares, + gt_bboxes, + gt_bboxes_ignore, + img_meta, + unmap_outputs=True): + """Compute guided anchoring targets. + + This function returns sampled anchors and gt bboxes directly + rather than calculates regression targets. + + Args: + flat_approxs (Tensor): flat approxs of a single image, + shape (n, 4) + inside_flags (Tensor): inside flags of a single image, + shape (n, ). + flat_squares (Tensor): flat squares of a single image, + shape (approxs_per_octave * n, 4) + gt_bboxes (Tensor): Ground truth bboxes of a single image. + img_meta (dict): Meta info of a single image. + approxs_per_octave (int): number of approxs per octave + cfg (dict): RPN train configs. + unmap_outputs (bool): unmap outputs or not. + + Returns: + tuple + """ + if not inside_flags.any(): + return (None, ) * 5 + # assign gt and sample anchors + expand_inside_flags = inside_flags[:, None].expand( + -1, self.approxs_per_octave).reshape(-1) + approxs = flat_approxs[expand_inside_flags, :] + squares = flat_squares[inside_flags, :] + + assign_result = self.ga_assigner.assign(approxs, squares, + self.approxs_per_octave, + gt_bboxes, gt_bboxes_ignore) + sampling_result = self.ga_sampler.sample(assign_result, squares, + gt_bboxes) + + bbox_anchors = torch.zeros_like(squares) + bbox_gts = torch.zeros_like(squares) + bbox_weights = torch.zeros_like(squares) + + pos_inds = sampling_result.pos_inds + neg_inds = sampling_result.neg_inds + if len(pos_inds) > 0: + bbox_anchors[pos_inds, :] = sampling_result.pos_bboxes + bbox_gts[pos_inds, :] = sampling_result.pos_gt_bboxes + bbox_weights[pos_inds, :] = 1.0 + + # map up to original set of anchors + if unmap_outputs: + num_total_anchors = flat_squares.size(0) + bbox_anchors = unmap(bbox_anchors, num_total_anchors, inside_flags) + bbox_gts = unmap(bbox_gts, num_total_anchors, inside_flags) + bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags) + + return (bbox_anchors, bbox_gts, bbox_weights, pos_inds, neg_inds) + + def ga_shape_targets(self, + approx_list, + inside_flag_list, + square_list, + gt_bboxes_list, + img_metas, + gt_bboxes_ignore_list=None, + unmap_outputs=True): + """Compute guided anchoring targets. + + Args: + approx_list (list[list]): Multi level approxs of each image. + inside_flag_list (list[list]): Multi level inside flags of each + image. + square_list (list[list]): Multi level squares of each image. + gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image. + img_metas (list[dict]): Meta info of each image. + gt_bboxes_ignore_list (list[Tensor]): ignore list of gt bboxes. + unmap_outputs (bool): unmap outputs or not. + + Returns: + tuple + """ + num_imgs = len(img_metas) + assert len(approx_list) == len(inside_flag_list) == len( + square_list) == num_imgs + # anchor number of multi levels + num_level_squares = [squares.size(0) for squares in square_list[0]] + # concat all level anchors and flags to a single tensor + inside_flag_flat_list = [] + approx_flat_list = [] + square_flat_list = [] + for i in range(num_imgs): + assert len(square_list[i]) == len(inside_flag_list[i]) + inside_flag_flat_list.append(torch.cat(inside_flag_list[i])) + approx_flat_list.append(torch.cat(approx_list[i])) + square_flat_list.append(torch.cat(square_list[i])) + + # compute targets for each image + if gt_bboxes_ignore_list is None: + gt_bboxes_ignore_list = [None for _ in range(num_imgs)] + (all_bbox_anchors, all_bbox_gts, all_bbox_weights, pos_inds_list, + neg_inds_list) = multi_apply( + self._ga_shape_target_single, + approx_flat_list, + inside_flag_flat_list, + square_flat_list, + gt_bboxes_list, + gt_bboxes_ignore_list, + img_metas, + unmap_outputs=unmap_outputs) + # no valid anchors + if any([bbox_anchors is None for bbox_anchors in all_bbox_anchors]): + return None + # sampled anchors of all images + num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list]) + num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list]) + # split targets to a list w.r.t. multiple levels + bbox_anchors_list = images_to_levels(all_bbox_anchors, + num_level_squares) + bbox_gts_list = images_to_levels(all_bbox_gts, num_level_squares) + bbox_weights_list = images_to_levels(all_bbox_weights, + num_level_squares) + return (bbox_anchors_list, bbox_gts_list, bbox_weights_list, + num_total_pos, num_total_neg) + + def loss_shape_single(self, shape_pred, bbox_anchors, bbox_gts, + anchor_weights, anchor_total_num): + shape_pred = shape_pred.permute(0, 2, 3, 1).contiguous().view(-1, 2) + bbox_anchors = bbox_anchors.contiguous().view(-1, 4) + bbox_gts = bbox_gts.contiguous().view(-1, 4) + anchor_weights = anchor_weights.contiguous().view(-1, 4) + bbox_deltas = bbox_anchors.new_full(bbox_anchors.size(), 0) + bbox_deltas[:, 2:] += shape_pred + # filter out negative samples to speed-up weighted_bounded_iou_loss + inds = torch.nonzero( + anchor_weights[:, 0] > 0, as_tuple=False).squeeze(1) + bbox_deltas_ = bbox_deltas[inds] + bbox_anchors_ = bbox_anchors[inds] + bbox_gts_ = bbox_gts[inds] + anchor_weights_ = anchor_weights[inds] + pred_anchors_ = self.anchor_coder.decode( + bbox_anchors_, bbox_deltas_, wh_ratio_clip=1e-6) + loss_shape = self.loss_shape( + pred_anchors_, + bbox_gts_, + anchor_weights_, + avg_factor=anchor_total_num) + return loss_shape + + def loss_loc_single(self, loc_pred, loc_target, loc_weight, + loc_avg_factor): + loss_loc = self.loss_loc( + loc_pred.reshape(-1, 1), + loc_target.reshape(-1).long(), + loc_weight.reshape(-1), + avg_factor=loc_avg_factor) + return loss_loc + + @force_fp32( + apply_to=('cls_scores', 'bbox_preds', 'shape_preds', 'loc_preds')) + def loss(self, + cls_scores, + bbox_preds, + shape_preds, + loc_preds, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + assert len(featmap_sizes) == self.approx_anchor_generator.num_levels + + device = cls_scores[0].device + + # get loc targets + loc_targets, loc_weights, loc_avg_factor = self.ga_loc_targets( + gt_bboxes, featmap_sizes) + + # get sampled approxes + approxs_list, inside_flag_list = self.get_sampled_approxs( + featmap_sizes, img_metas, device=device) + # get squares and guided anchors + squares_list, guided_anchors_list, _ = self.get_anchors( + featmap_sizes, shape_preds, loc_preds, img_metas, device=device) + + # get shape targets + shape_targets = self.ga_shape_targets(approxs_list, inside_flag_list, + squares_list, gt_bboxes, + img_metas) + if shape_targets is None: + return None + (bbox_anchors_list, bbox_gts_list, anchor_weights_list, anchor_fg_num, + anchor_bg_num) = shape_targets + anchor_total_num = ( + anchor_fg_num if not self.ga_sampling else anchor_fg_num + + anchor_bg_num) + + # get anchor targets + label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 + cls_reg_targets = self.get_targets( + guided_anchors_list, + inside_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + gt_labels_list=gt_labels, + label_channels=label_channels) + if cls_reg_targets is None: + return None + (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, + num_total_pos, num_total_neg) = cls_reg_targets + num_total_samples = ( + num_total_pos + num_total_neg if self.sampling else num_total_pos) + + # anchor number of multi levels + num_level_anchors = [ + anchors.size(0) for anchors in guided_anchors_list[0] + ] + # concat all level anchors to a single tensor + concat_anchor_list = [] + for i in range(len(guided_anchors_list)): + concat_anchor_list.append(torch.cat(guided_anchors_list[i])) + all_anchor_list = images_to_levels(concat_anchor_list, + num_level_anchors) + + # get classification and bbox regression losses + losses_cls, losses_bbox = multi_apply( + self.loss_single, + cls_scores, + bbox_preds, + all_anchor_list, + labels_list, + label_weights_list, + bbox_targets_list, + bbox_weights_list, + num_total_samples=num_total_samples) + + # get anchor location loss + losses_loc = [] + for i in range(len(loc_preds)): + loss_loc = self.loss_loc_single( + loc_preds[i], + loc_targets[i], + loc_weights[i], + loc_avg_factor=loc_avg_factor) + losses_loc.append(loss_loc) + + # get anchor shape loss + losses_shape = [] + for i in range(len(shape_preds)): + loss_shape = self.loss_shape_single( + shape_preds[i], + bbox_anchors_list[i], + bbox_gts_list[i], + anchor_weights_list[i], + anchor_total_num=anchor_total_num) + losses_shape.append(loss_shape) + + return dict( + loss_cls=losses_cls, + loss_bbox=losses_bbox, + loss_shape=losses_shape, + loss_loc=losses_loc) + + @force_fp32( + apply_to=('cls_scores', 'bbox_preds', 'shape_preds', 'loc_preds')) + def get_bboxes(self, + cls_scores, + bbox_preds, + shape_preds, + loc_preds, + img_metas, + cfg=None, + rescale=False): + assert len(cls_scores) == len(bbox_preds) == len(shape_preds) == len( + loc_preds) + num_levels = len(cls_scores) + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + device = cls_scores[0].device + # get guided anchors + _, guided_anchors, loc_masks = self.get_anchors( + featmap_sizes, + shape_preds, + loc_preds, + img_metas, + use_loc_filter=not self.training, + device=device) + result_list = [] + for img_id in range(len(img_metas)): + cls_score_list = [ + cls_scores[i][img_id].detach() for i in range(num_levels) + ] + bbox_pred_list = [ + bbox_preds[i][img_id].detach() for i in range(num_levels) + ] + guided_anchor_list = [ + guided_anchors[img_id][i].detach() for i in range(num_levels) + ] + loc_mask_list = [ + loc_masks[img_id][i].detach() for i in range(num_levels) + ] + img_shape = img_metas[img_id]['img_shape'] + scale_factor = img_metas[img_id]['scale_factor'] + proposals = self._get_bboxes_single(cls_score_list, bbox_pred_list, + guided_anchor_list, + loc_mask_list, img_shape, + scale_factor, cfg, rescale) + result_list.append(proposals) + return result_list + + def _get_bboxes_single(self, + cls_scores, + bbox_preds, + mlvl_anchors, + mlvl_masks, + img_shape, + scale_factor, + cfg, + rescale=False): + cfg = self.test_cfg if cfg is None else cfg + assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors) + mlvl_bboxes = [] + mlvl_scores = [] + for cls_score, bbox_pred, anchors, mask in zip(cls_scores, bbox_preds, + mlvl_anchors, + mlvl_masks): + assert cls_score.size()[-2:] == bbox_pred.size()[-2:] + # if no location is kept, end. + if mask.sum() == 0: + continue + # reshape scores and bbox_pred + cls_score = cls_score.permute(1, 2, + 0).reshape(-1, self.cls_out_channels) + if self.use_sigmoid_cls: + scores = cls_score.sigmoid() + else: + scores = cls_score.softmax(-1) + bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4) + # filter scores, bbox_pred w.r.t. mask. + # anchors are filtered in get_anchors() beforehand. + scores = scores[mask, :] + bbox_pred = bbox_pred[mask, :] + if scores.dim() == 0: + anchors = anchors.unsqueeze(0) + scores = scores.unsqueeze(0) + bbox_pred = bbox_pred.unsqueeze(0) + # filter anchors, bbox_pred, scores w.r.t. scores + nms_pre = cfg.get('nms_pre', -1) + if nms_pre > 0 and scores.shape[0] > nms_pre: + if self.use_sigmoid_cls: + max_scores, _ = scores.max(dim=1) + else: + # remind that we set FG labels to [0, num_class-1] + # since mmdet v2.0 + # BG cat_id: num_class + max_scores, _ = scores[:, :-1].max(dim=1) + _, topk_inds = max_scores.topk(nms_pre) + anchors = anchors[topk_inds, :] + bbox_pred = bbox_pred[topk_inds, :] + scores = scores[topk_inds, :] + bboxes = self.bbox_coder.decode( + anchors, bbox_pred, max_shape=img_shape) + mlvl_bboxes.append(bboxes) + mlvl_scores.append(scores) + mlvl_bboxes = torch.cat(mlvl_bboxes) + if rescale: + mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor) + mlvl_scores = torch.cat(mlvl_scores) + if self.use_sigmoid_cls: + # Add a dummy background class to the backend when using sigmoid + # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 + # BG cat_id: num_class + padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1) + mlvl_scores = torch.cat([mlvl_scores, padding], dim=1) + # multi class NMS + det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores, + cfg.score_thr, cfg.nms, + cfg.max_per_img) + return det_bboxes, det_labels diff --git a/mmdet/models/dense_heads/ld_head.py b/mmdet/models/dense_heads/ld_head.py new file mode 100644 index 0000000..501e1f7 --- /dev/null +++ b/mmdet/models/dense_heads/ld_head.py @@ -0,0 +1,261 @@ +import torch +from mmcv.runner import force_fp32 + +from mmdet.core import (bbox2distance, bbox_overlaps, distance2bbox, + multi_apply, reduce_mean) +from ..builder import HEADS, build_loss +from .gfl_head import GFLHead + + +@HEADS.register_module() +class LDHead(GFLHead): + """Localization distillation Head. (Short description) + + It utilizes the learned bbox distributions to transfer the localization + dark knowledge from teacher to student. Original paper: `Localization + Distillation for Object Detection. `_ + + Args: + num_classes (int): Number of categories excluding the background + category. + in_channels (int): Number of channels in the input feature map. + loss_ld (dict): Config of Localization Distillation Loss (LD), + T is the temperature for distillation. + """ + + def __init__(self, + num_classes, + in_channels, + loss_ld=dict( + type='LocalizationDistillationLoss', + loss_weight=0.25, + T=10), + **kwargs): + + super(LDHead, self).__init__(num_classes, in_channels, **kwargs) + self.loss_ld = build_loss(loss_ld) + + def loss_single(self, anchors, cls_score, bbox_pred, labels, label_weights, + bbox_targets, stride, soft_targets, num_total_samples): + """Compute loss of a single scale level. + + Args: + anchors (Tensor): Box reference for each scale level with shape + (N, num_total_anchors, 4). + cls_score (Tensor): Cls and quality joint scores for each scale + level has shape (N, num_classes, H, W). + bbox_pred (Tensor): Box distribution logits for each scale + level with shape (N, 4*(n+1), H, W), n is max value of integral + set. + labels (Tensor): Labels of each anchors with shape + (N, num_total_anchors). + label_weights (Tensor): Label weights of each anchor with shape + (N, num_total_anchors) + bbox_targets (Tensor): BBox regression targets of each anchor wight + shape (N, num_total_anchors, 4). + stride (tuple): Stride in this scale level. + num_total_samples (int): Number of positive samples that is + reduced over all GPUs. + + Returns: + dict[tuple, Tensor]: Loss components and weight targets. + """ + assert stride[0] == stride[1], 'h stride is not equal to w stride!' + anchors = anchors.reshape(-1, 4) + cls_score = cls_score.permute(0, 2, 3, + 1).reshape(-1, self.cls_out_channels) + bbox_pred = bbox_pred.permute(0, 2, 3, + 1).reshape(-1, 4 * (self.reg_max + 1)) + soft_targets = soft_targets.permute(0, 2, 3, + 1).reshape(-1, + 4 * (self.reg_max + 1)) + + bbox_targets = bbox_targets.reshape(-1, 4) + labels = labels.reshape(-1) + label_weights = label_weights.reshape(-1) + + # FG cat_id: [0, num_classes -1], BG cat_id: num_classes + bg_class_ind = self.num_classes + pos_inds = ((labels >= 0) + & (labels < bg_class_ind)).nonzero().squeeze(1) + score = label_weights.new_zeros(labels.shape) + + if len(pos_inds) > 0: + pos_bbox_targets = bbox_targets[pos_inds] + pos_bbox_pred = bbox_pred[pos_inds] + pos_anchors = anchors[pos_inds] + pos_anchor_centers = self.anchor_center(pos_anchors) / stride[0] + + weight_targets = cls_score.detach().sigmoid() + weight_targets = weight_targets.max(dim=1)[0][pos_inds] + pos_bbox_pred_corners = self.integral(pos_bbox_pred) + pos_decode_bbox_pred = distance2bbox(pos_anchor_centers, + pos_bbox_pred_corners) + pos_decode_bbox_targets = pos_bbox_targets / stride[0] + score[pos_inds] = bbox_overlaps( + pos_decode_bbox_pred.detach(), + pos_decode_bbox_targets, + is_aligned=True) + pred_corners = pos_bbox_pred.reshape(-1, self.reg_max + 1) + pos_soft_targets = soft_targets[pos_inds] + soft_corners = pos_soft_targets.reshape(-1, self.reg_max + 1) + + target_corners = bbox2distance(pos_anchor_centers, + pos_decode_bbox_targets, + self.reg_max).reshape(-1) + + # regression loss + loss_bbox = self.loss_bbox( + pos_decode_bbox_pred, + pos_decode_bbox_targets, + weight=weight_targets, + avg_factor=1.0) + + # dfl loss + loss_dfl = self.loss_dfl( + pred_corners, + target_corners, + weight=weight_targets[:, None].expand(-1, 4).reshape(-1), + avg_factor=4.0) + + # ld loss + loss_ld = self.loss_ld( + pred_corners, + soft_corners, + weight=weight_targets[:, None].expand(-1, 4).reshape(-1), + avg_factor=4.0) + + else: + loss_ld = bbox_pred.sum() * 0 + loss_bbox = bbox_pred.sum() * 0 + loss_dfl = bbox_pred.sum() * 0 + weight_targets = bbox_pred.new_tensor(0) + + # cls (qfl) loss + loss_cls = self.loss_cls( + cls_score, (labels, score), + weight=label_weights, + avg_factor=num_total_samples) + + return loss_cls, loss_bbox, loss_dfl, loss_ld, weight_targets.sum() + + def forward_train(self, + x, + out_teacher, + img_metas, + gt_bboxes, + gt_labels=None, + gt_bboxes_ignore=None, + proposal_cfg=None, + **kwargs): + """ + Args: + x (list[Tensor]): Features from FPN. + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes (Tensor): Ground truth bboxes of the image, + shape (num_gts, 4). + gt_labels (Tensor): Ground truth labels of each box, + shape (num_gts,). + gt_bboxes_ignore (Tensor): Ground truth bboxes to be + ignored, shape (num_ignored_gts, 4). + proposal_cfg (mmcv.Config): Test / postprocessing configuration, + if None, test_cfg would be used + + Returns: + tuple[dict, list]: The loss components and proposals of each image. + + - losses (dict[str, Tensor]): A dictionary of loss components. + - proposal_list (list[Tensor]): Proposals of each image. + """ + outs = self(x) + soft_target = out_teacher[1] + if gt_labels is None: + loss_inputs = outs + (gt_bboxes, soft_target, img_metas) + else: + loss_inputs = outs + (gt_bboxes, gt_labels, soft_target, img_metas) + losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore) + if proposal_cfg is None: + return losses + else: + proposal_list = self.get_bboxes(*outs, img_metas, cfg=proposal_cfg) + return losses, proposal_list + + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) + def loss(self, + cls_scores, + bbox_preds, + gt_bboxes, + gt_labels, + soft_target, + img_metas, + gt_bboxes_ignore=None): + """Compute losses of the head. + + Args: + cls_scores (list[Tensor]): Cls and quality scores for each scale + level has shape (N, num_classes, H, W). + bbox_preds (list[Tensor]): Box distribution logits for each scale + level with shape (N, 4*(n+1), H, W), n is max value of integral + set. + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (list[Tensor] | None): specify which bounding + boxes can be ignored when computing the loss. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + assert len(featmap_sizes) == self.anchor_generator.num_levels + + device = cls_scores[0].device + anchor_list, valid_flag_list = self.get_anchors( + featmap_sizes, img_metas, device=device) + label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 + + cls_reg_targets = self.get_targets( + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + gt_labels_list=gt_labels, + label_channels=label_channels) + if cls_reg_targets is None: + return None + + (anchor_list, labels_list, label_weights_list, bbox_targets_list, + bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets + + num_total_samples = reduce_mean( + torch.tensor(num_total_pos, dtype=torch.float, + device=device)).item() + num_total_samples = max(num_total_samples, 1.0) + + losses_cls, losses_bbox, losses_dfl, losses_ld, \ + avg_factor = multi_apply( + self.loss_single, + anchor_list, + cls_scores, + bbox_preds, + labels_list, + label_weights_list, + bbox_targets_list, + self.anchor_generator.strides, + soft_target, + num_total_samples=num_total_samples) + + avg_factor = sum(avg_factor) + 1e-6 + avg_factor = reduce_mean(avg_factor).item() + losses_bbox = [x / avg_factor for x in losses_bbox] + losses_dfl = [x / avg_factor for x in losses_dfl] + return dict( + loss_cls=losses_cls, + loss_bbox=losses_bbox, + loss_dfl=losses_dfl, + loss_ld=losses_ld) diff --git a/mmdet/models/dense_heads/nasfcos_head.py b/mmdet/models/dense_heads/nasfcos_head.py new file mode 100644 index 0000000..086ebf8 --- /dev/null +++ b/mmdet/models/dense_heads/nasfcos_head.py @@ -0,0 +1,79 @@ +import copy + +import torch.nn as nn +from mmcv.cnn import ConvModule, Scale + +from mmdet.models.dense_heads.fcos_head import FCOSHead +from ..builder import HEADS + + +@HEADS.register_module() +class NASFCOSHead(FCOSHead): + """Anchor-free head used in `NASFCOS `_. + + It is quite similar with FCOS head, except for the searched structure of + classification branch and bbox regression branch, where a structure of + "dconv3x3, conv3x3, dconv3x3, conv1x1" is utilized instead. + """ + + def __init__(self, *args, init_cfg=None, **kwargs): + if init_cfg is None: + init_cfg = [ + dict(type='Caffe2Xavier', layer=['ConvModule', 'Conv2d']), + dict( + type='Normal', + std=0.01, + override=[ + dict(name='conv_reg'), + dict(name='conv_centerness'), + dict( + name='conv_cls', + type='Normal', + std=0.01, + bias_prob=0.01) + ]), + ] + super(NASFCOSHead, self).__init__(*args, init_cfg=init_cfg, **kwargs) + + def _init_layers(self): + """Initialize layers of the head.""" + dconv3x3_config = dict( + type='DCNv2', + kernel_size=3, + use_bias=True, + deform_groups=2, + padding=1) + conv3x3_config = dict(type='Conv', kernel_size=3, padding=1) + conv1x1_config = dict(type='Conv', kernel_size=1) + + self.arch_config = [ + dconv3x3_config, conv3x3_config, dconv3x3_config, conv1x1_config + ] + self.cls_convs = nn.ModuleList() + self.reg_convs = nn.ModuleList() + for i, op_ in enumerate(self.arch_config): + op = copy.deepcopy(op_) + chn = self.in_channels if i == 0 else self.feat_channels + assert isinstance(op, dict) + use_bias = op.pop('use_bias', False) + padding = op.pop('padding', 0) + kernel_size = op.pop('kernel_size') + module = ConvModule( + chn, + self.feat_channels, + kernel_size, + stride=1, + padding=padding, + norm_cfg=self.norm_cfg, + bias=use_bias, + conv_cfg=op) + + self.cls_convs.append(copy.deepcopy(module)) + self.reg_convs.append(copy.deepcopy(module)) + + self.conv_cls = nn.Conv2d( + self.feat_channels, self.cls_out_channels, 3, padding=1) + self.conv_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1) + self.conv_centerness = nn.Conv2d(self.feat_channels, 1, 3, padding=1) + + self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides]) diff --git a/mmdet/models/dense_heads/paa_head.py b/mmdet/models/dense_heads/paa_head.py new file mode 100644 index 0000000..e067b01 --- /dev/null +++ b/mmdet/models/dense_heads/paa_head.py @@ -0,0 +1,671 @@ +import numpy as np +import torch +from mmcv.runner import force_fp32 + +from mmdet.core import multi_apply, multiclass_nms +from mmdet.core.bbox.iou_calculators import bbox_overlaps +from mmdet.models import HEADS +from mmdet.models.dense_heads import ATSSHead + +EPS = 1e-12 +try: + import sklearn.mixture as skm +except ImportError: + skm = None + + +def levels_to_images(mlvl_tensor): + """Concat multi-level feature maps by image. + + [feature_level0, feature_level1...] -> [feature_image0, feature_image1...] + Convert the shape of each element in mlvl_tensor from (N, C, H, W) to + (N, H*W , C), then split the element to N elements with shape (H*W, C), and + concat elements in same image of all level along first dimension. + + Args: + mlvl_tensor (list[torch.Tensor]): list of Tensor which collect from + corresponding level. Each element is of shape (N, C, H, W) + + Returns: + list[torch.Tensor]: A list that contains N tensors and each tensor is + of shape (num_elements, C) + """ + batch_size = mlvl_tensor[0].size(0) + batch_list = [[] for _ in range(batch_size)] + channels = mlvl_tensor[0].size(1) + for t in mlvl_tensor: + t = t.permute(0, 2, 3, 1) + t = t.view(batch_size, -1, channels).contiguous() + for img in range(batch_size): + batch_list[img].append(t[img]) + return [torch.cat(item, 0) for item in batch_list] + + +@HEADS.register_module() +class PAAHead(ATSSHead): + """Head of PAAAssignment: Probabilistic Anchor Assignment with IoU + Prediction for Object Detection. + + Code is modified from the `official github repo + `_. + + More details can be found in the `paper + `_ . + + Args: + topk (int): Select topk samples with smallest loss in + each level. + score_voting (bool): Whether to use score voting in post-process. + covariance_type : String describing the type of covariance parameters + to be used in :class:`sklearn.mixture.GaussianMixture`. + It must be one of: + + - 'full': each component has its own general covariance matrix + - 'tied': all components share the same general covariance matrix + - 'diag': each component has its own diagonal covariance matrix + - 'spherical': each component has its own single variance + Default: 'diag'. From 'full' to 'spherical', the gmm fitting + process is faster yet the performance could be influenced. For most + cases, 'diag' should be a good choice. + """ + + def __init__(self, + *args, + topk=9, + score_voting=True, + covariance_type='diag', + **kwargs): + # topk used in paa reassign process + self.topk = topk + self.with_score_voting = score_voting + self.covariance_type = covariance_type + super(PAAHead, self).__init__(*args, **kwargs) + + @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'iou_preds')) + def loss(self, + cls_scores, + bbox_preds, + iou_preds, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute losses of the head. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (N, num_anchors * num_classes, H, W) + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_anchors * 4, H, W) + iou_preds (list[Tensor]): iou_preds for each scale + level with shape (N, num_anchors * 1, H, W) + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (list[Tensor] | None): Specify which bounding + boxes can be ignored when are computing the loss. + + Returns: + dict[str, Tensor]: A dictionary of loss gmm_assignment. + """ + + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + assert len(featmap_sizes) == self.anchor_generator.num_levels + + device = cls_scores[0].device + anchor_list, valid_flag_list = self.get_anchors( + featmap_sizes, img_metas, device=device) + label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 + cls_reg_targets = self.get_targets( + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + gt_labels_list=gt_labels, + label_channels=label_channels, + ) + (labels, labels_weight, bboxes_target, bboxes_weight, pos_inds, + pos_gt_index) = cls_reg_targets + cls_scores = levels_to_images(cls_scores) + cls_scores = [ + item.reshape(-1, self.cls_out_channels) for item in cls_scores + ] + bbox_preds = levels_to_images(bbox_preds) + bbox_preds = [item.reshape(-1, 4) for item in bbox_preds] + iou_preds = levels_to_images(iou_preds) + iou_preds = [item.reshape(-1, 1) for item in iou_preds] + pos_losses_list, = multi_apply(self.get_pos_loss, anchor_list, + cls_scores, bbox_preds, labels, + labels_weight, bboxes_target, + bboxes_weight, pos_inds) + + with torch.no_grad(): + reassign_labels, reassign_label_weight, \ + reassign_bbox_weights, num_pos = multi_apply( + self.paa_reassign, + pos_losses_list, + labels, + labels_weight, + bboxes_weight, + pos_inds, + pos_gt_index, + anchor_list) + num_pos = sum(num_pos) + # convert all tensor list to a flatten tensor + cls_scores = torch.cat(cls_scores, 0).view(-1, cls_scores[0].size(-1)) + bbox_preds = torch.cat(bbox_preds, 0).view(-1, bbox_preds[0].size(-1)) + iou_preds = torch.cat(iou_preds, 0).view(-1, iou_preds[0].size(-1)) + labels = torch.cat(reassign_labels, 0).view(-1) + flatten_anchors = torch.cat( + [torch.cat(item, 0) for item in anchor_list]) + labels_weight = torch.cat(reassign_label_weight, 0).view(-1) + bboxes_target = torch.cat(bboxes_target, + 0).view(-1, bboxes_target[0].size(-1)) + + pos_inds_flatten = ((labels >= 0) + & + (labels < self.num_classes)).nonzero().reshape(-1) + + losses_cls = self.loss_cls( + cls_scores, + labels, + labels_weight, + avg_factor=max(num_pos, len(img_metas))) # avoid num_pos=0 + if num_pos: + pos_bbox_pred = self.bbox_coder.decode( + flatten_anchors[pos_inds_flatten], + bbox_preds[pos_inds_flatten]) + pos_bbox_target = bboxes_target[pos_inds_flatten] + iou_target = bbox_overlaps( + pos_bbox_pred.detach(), pos_bbox_target, is_aligned=True) + losses_iou = self.loss_centerness( + iou_preds[pos_inds_flatten], + iou_target.unsqueeze(-1), + avg_factor=num_pos) + losses_bbox = self.loss_bbox( + pos_bbox_pred, + pos_bbox_target, + iou_target.clamp(min=EPS), + avg_factor=iou_target.sum()) + else: + losses_iou = iou_preds.sum() * 0 + losses_bbox = bbox_preds.sum() * 0 + + return dict( + loss_cls=losses_cls, loss_bbox=losses_bbox, loss_iou=losses_iou) + + def get_pos_loss(self, anchors, cls_score, bbox_pred, label, label_weight, + bbox_target, bbox_weight, pos_inds): + """Calculate loss of all potential positive samples obtained from first + match process. + + Args: + anchors (list[Tensor]): Anchors of each scale. + cls_score (Tensor): Box scores of single image with shape + (num_anchors, num_classes) + bbox_pred (Tensor): Box energies / deltas of single image + with shape (num_anchors, 4) + label (Tensor): classification target of each anchor with + shape (num_anchors,) + label_weight (Tensor): Classification loss weight of each + anchor with shape (num_anchors). + bbox_target (dict): Regression target of each anchor with + shape (num_anchors, 4). + bbox_weight (Tensor): Bbox weight of each anchor with shape + (num_anchors, 4). + pos_inds (Tensor): Index of all positive samples got from + first assign process. + + Returns: + Tensor: Losses of all positive samples in single image. + """ + if not len(pos_inds): + return cls_score.new([]), + anchors_all_level = torch.cat(anchors, 0) + pos_scores = cls_score[pos_inds] + pos_bbox_pred = bbox_pred[pos_inds] + pos_label = label[pos_inds] + pos_label_weight = label_weight[pos_inds] + pos_bbox_target = bbox_target[pos_inds] + pos_bbox_weight = bbox_weight[pos_inds] + pos_anchors = anchors_all_level[pos_inds] + pos_bbox_pred = self.bbox_coder.decode(pos_anchors, pos_bbox_pred) + + # to keep loss dimension + loss_cls = self.loss_cls( + pos_scores, + pos_label, + pos_label_weight, + avg_factor=self.loss_cls.loss_weight, + reduction_override='none') + + loss_bbox = self.loss_bbox( + pos_bbox_pred, + pos_bbox_target, + pos_bbox_weight, + avg_factor=self.loss_cls.loss_weight, + reduction_override='none') + + loss_cls = loss_cls.sum(-1) + pos_loss = loss_bbox + loss_cls + return pos_loss, + + def paa_reassign(self, pos_losses, label, label_weight, bbox_weight, + pos_inds, pos_gt_inds, anchors): + """Fit loss to GMM distribution and separate positive, ignore, negative + samples again with GMM model. + + Args: + pos_losses (Tensor): Losses of all positive samples in + single image. + label (Tensor): classification target of each anchor with + shape (num_anchors,) + label_weight (Tensor): Classification loss weight of each + anchor with shape (num_anchors). + bbox_weight (Tensor): Bbox weight of each anchor with shape + (num_anchors, 4). + pos_inds (Tensor): Index of all positive samples got from + first assign process. + pos_gt_inds (Tensor): Gt_index of all positive samples got + from first assign process. + anchors (list[Tensor]): Anchors of each scale. + + Returns: + tuple: Usually returns a tuple containing learning targets. + + - label (Tensor): classification target of each anchor after + paa assign, with shape (num_anchors,) + - label_weight (Tensor): Classification loss weight of each + anchor after paa assign, with shape (num_anchors). + - bbox_weight (Tensor): Bbox weight of each anchor with shape + (num_anchors, 4). + - num_pos (int): The number of positive samples after paa + assign. + """ + if not len(pos_inds): + return label, label_weight, bbox_weight, 0 + label = label.clone() + label_weight = label_weight.clone() + bbox_weight = bbox_weight.clone() + num_gt = pos_gt_inds.max() + 1 + num_level = len(anchors) + num_anchors_each_level = [item.size(0) for item in anchors] + num_anchors_each_level.insert(0, 0) + inds_level_interval = np.cumsum(num_anchors_each_level) + pos_level_mask = [] + for i in range(num_level): + mask = (pos_inds >= inds_level_interval[i]) & ( + pos_inds < inds_level_interval[i + 1]) + pos_level_mask.append(mask) + pos_inds_after_paa = [label.new_tensor([])] + ignore_inds_after_paa = [label.new_tensor([])] + for gt_ind in range(num_gt): + pos_inds_gmm = [] + pos_loss_gmm = [] + gt_mask = pos_gt_inds == gt_ind + for level in range(num_level): + level_mask = pos_level_mask[level] + level_gt_mask = level_mask & gt_mask + value, topk_inds = pos_losses[level_gt_mask].topk( + min(level_gt_mask.sum(), self.topk), largest=False) + pos_inds_gmm.append(pos_inds[level_gt_mask][topk_inds]) + pos_loss_gmm.append(value) + pos_inds_gmm = torch.cat(pos_inds_gmm) + pos_loss_gmm = torch.cat(pos_loss_gmm) + # fix gmm need at least two sample + if len(pos_inds_gmm) < 2: + continue + device = pos_inds_gmm.device + pos_loss_gmm, sort_inds = pos_loss_gmm.sort() + pos_inds_gmm = pos_inds_gmm[sort_inds] + pos_loss_gmm = pos_loss_gmm.view(-1, 1).cpu().numpy() + min_loss, max_loss = pos_loss_gmm.min(), pos_loss_gmm.max() + means_init = np.array([min_loss, max_loss]).reshape(2, 1) + weights_init = np.array([0.5, 0.5]) + precisions_init = np.array([1.0, 1.0]).reshape(2, 1, 1) # full + if self.covariance_type == 'spherical': + precisions_init = precisions_init.reshape(2) + elif self.covariance_type == 'diag': + precisions_init = precisions_init.reshape(2, 1) + elif self.covariance_type == 'tied': + precisions_init = np.array([[1.0]]) + if skm is None: + raise ImportError('Please run "pip install sklearn" ' + 'to install sklearn first.') + gmm = skm.GaussianMixture( + 2, + weights_init=weights_init, + means_init=means_init, + precisions_init=precisions_init, + covariance_type=self.covariance_type) + gmm.fit(pos_loss_gmm) + gmm_assignment = gmm.predict(pos_loss_gmm) + scores = gmm.score_samples(pos_loss_gmm) + gmm_assignment = torch.from_numpy(gmm_assignment).to(device) + scores = torch.from_numpy(scores).to(device) + + pos_inds_temp, ignore_inds_temp = self.gmm_separation_scheme( + gmm_assignment, scores, pos_inds_gmm) + pos_inds_after_paa.append(pos_inds_temp) + ignore_inds_after_paa.append(ignore_inds_temp) + + pos_inds_after_paa = torch.cat(pos_inds_after_paa) + ignore_inds_after_paa = torch.cat(ignore_inds_after_paa) + reassign_mask = (pos_inds.unsqueeze(1) != pos_inds_after_paa).all(1) + reassign_ids = pos_inds[reassign_mask] + label[reassign_ids] = self.num_classes + label_weight[ignore_inds_after_paa] = 0 + bbox_weight[reassign_ids] = 0 + num_pos = len(pos_inds_after_paa) + return label, label_weight, bbox_weight, num_pos + + def gmm_separation_scheme(self, gmm_assignment, scores, pos_inds_gmm): + """A general separation scheme for gmm model. + + It separates a GMM distribution of candidate samples into three + parts, 0 1 and uncertain areas, and you can implement other + separation schemes by rewriting this function. + + Args: + gmm_assignment (Tensor): The prediction of GMM which is of shape + (num_samples,). The 0/1 value indicates the distribution + that each sample comes from. + scores (Tensor): The probability of sample coming from the + fit GMM distribution. The tensor is of shape (num_samples,). + pos_inds_gmm (Tensor): All the indexes of samples which are used + to fit GMM model. The tensor is of shape (num_samples,) + + Returns: + tuple[Tensor]: The indices of positive and ignored samples. + + - pos_inds_temp (Tensor): Indices of positive samples. + - ignore_inds_temp (Tensor): Indices of ignore samples. + """ + # The implementation is (c) in Fig.3 in origin paper instead of (b). + # You can refer to issues such as + # https://github.com/kkhoot/PAA/issues/8 and + # https://github.com/kkhoot/PAA/issues/9. + fgs = gmm_assignment == 0 + pos_inds_temp = fgs.new_tensor([], dtype=torch.long) + ignore_inds_temp = fgs.new_tensor([], dtype=torch.long) + if fgs.nonzero().numel(): + _, pos_thr_ind = scores[fgs].topk(1) + pos_inds_temp = pos_inds_gmm[fgs][:pos_thr_ind + 1] + ignore_inds_temp = pos_inds_gmm.new_tensor([]) + return pos_inds_temp, ignore_inds_temp + + def get_targets( + self, + anchor_list, + valid_flag_list, + gt_bboxes_list, + img_metas, + gt_bboxes_ignore_list=None, + gt_labels_list=None, + label_channels=1, + unmap_outputs=True, + ): + """Get targets for PAA head. + + This method is almost the same as `AnchorHead.get_targets()`. We direct + return the results from _get_targets_single instead map it to levels + by images_to_levels function. + + Args: + anchor_list (list[list[Tensor]]): Multi level anchors of each + image. The outer list indicates images, and the inner list + corresponds to feature levels of the image. Each element of + the inner list is a tensor of shape (num_anchors, 4). + valid_flag_list (list[list[Tensor]]): Multi level valid flags of + each image. The outer list indicates images, and the inner list + corresponds to feature levels of the image. Each element of + the inner list is a tensor of shape (num_anchors, ) + gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image. + img_metas (list[dict]): Meta info of each image. + gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be + ignored. + gt_labels_list (list[Tensor]): Ground truth labels of each box. + label_channels (int): Channel of label. + unmap_outputs (bool): Whether to map outputs back to the original + set of anchors. + + Returns: + tuple: Usually returns a tuple containing learning targets. + + - labels (list[Tensor]): Labels of all anchors, each with + shape (num_anchors,). + - label_weights (list[Tensor]): Label weights of all anchor. + each with shape (num_anchors,). + - bbox_targets (list[Tensor]): BBox targets of all anchors. + each with shape (num_anchors, 4). + - bbox_weights (list[Tensor]): BBox weights of all anchors. + each with shape (num_anchors, 4). + - pos_inds (list[Tensor]): Contains all index of positive + sample in all anchor. + - gt_inds (list[Tensor]): Contains all gt_index of positive + sample in all anchor. + """ + + num_imgs = len(img_metas) + assert len(anchor_list) == len(valid_flag_list) == num_imgs + concat_anchor_list = [] + concat_valid_flag_list = [] + for i in range(num_imgs): + assert len(anchor_list[i]) == len(valid_flag_list[i]) + concat_anchor_list.append(torch.cat(anchor_list[i])) + concat_valid_flag_list.append(torch.cat(valid_flag_list[i])) + + # compute targets for each image + if gt_bboxes_ignore_list is None: + gt_bboxes_ignore_list = [None for _ in range(num_imgs)] + if gt_labels_list is None: + gt_labels_list = [None for _ in range(num_imgs)] + results = multi_apply( + self._get_targets_single, + concat_anchor_list, + concat_valid_flag_list, + gt_bboxes_list, + gt_bboxes_ignore_list, + gt_labels_list, + img_metas, + label_channels=label_channels, + unmap_outputs=unmap_outputs) + + (labels, label_weights, bbox_targets, bbox_weights, valid_pos_inds, + valid_neg_inds, sampling_result) = results + + # Due to valid flag of anchors, we have to calculate the real pos_inds + # in origin anchor set. + pos_inds = [] + for i, single_labels in enumerate(labels): + pos_mask = (0 <= single_labels) & ( + single_labels < self.num_classes) + pos_inds.append(pos_mask.nonzero().view(-1)) + + gt_inds = [item.pos_assigned_gt_inds for item in sampling_result] + return (labels, label_weights, bbox_targets, bbox_weights, pos_inds, + gt_inds) + + def _get_targets_single(self, + flat_anchors, + valid_flags, + gt_bboxes, + gt_bboxes_ignore, + gt_labels, + img_meta, + label_channels=1, + unmap_outputs=True): + """Compute regression and classification targets for anchors in a + single image. + + This method is same as `AnchorHead._get_targets_single()`. + """ + assert unmap_outputs, 'We must map outputs back to the original' \ + 'set of anchors in PAAhead' + return super(ATSSHead, self)._get_targets_single( + flat_anchors, + valid_flags, + gt_bboxes, + gt_bboxes_ignore, + gt_labels, + img_meta, + label_channels=1, + unmap_outputs=True) + + def _get_bboxes(self, + cls_scores, + bbox_preds, + iou_preds, + mlvl_anchors, + img_shapes, + scale_factors, + cfg, + rescale=False, + with_nms=True): + """Transform outputs for a single batch item into labeled boxes. + + This method is almost same as `ATSSHead._get_bboxes()`. + We use sqrt(iou_preds * cls_scores) in NMS process instead of just + cls_scores. Besides, score voting is used when `` score_voting`` + is set to True. + """ + assert with_nms, 'PAA only supports "with_nms=True" now' + assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors) + batch_size = cls_scores[0].shape[0] + + mlvl_bboxes = [] + mlvl_scores = [] + mlvl_iou_preds = [] + for cls_score, bbox_pred, iou_preds, anchors in zip( + cls_scores, bbox_preds, iou_preds, mlvl_anchors): + assert cls_score.size()[-2:] == bbox_pred.size()[-2:] + + scores = cls_score.permute(0, 2, 3, 1).reshape( + batch_size, -1, self.cls_out_channels).sigmoid() + bbox_pred = bbox_pred.permute(0, 2, 3, + 1).reshape(batch_size, -1, 4) + iou_preds = iou_preds.permute(0, 2, 3, 1).reshape(batch_size, + -1).sigmoid() + + nms_pre = cfg.get('nms_pre', -1) + if nms_pre > 0 and scores.shape[1] > nms_pre: + max_scores, _ = (scores * iou_preds[..., None]).sqrt().max(-1) + _, topk_inds = max_scores.topk(nms_pre) + batch_inds = torch.arange(batch_size).view( + -1, 1).expand_as(topk_inds).long() + anchors = anchors[topk_inds, :] + bbox_pred = bbox_pred[batch_inds, topk_inds, :] + scores = scores[batch_inds, topk_inds, :] + iou_preds = iou_preds[batch_inds, topk_inds] + else: + anchors = anchors.expand_as(bbox_pred) + + bboxes = self.bbox_coder.decode( + anchors, bbox_pred, max_shape=img_shapes) + mlvl_bboxes.append(bboxes) + mlvl_scores.append(scores) + mlvl_iou_preds.append(iou_preds) + + batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1) + if rescale: + batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor( + scale_factors).unsqueeze(1) + batch_mlvl_scores = torch.cat(mlvl_scores, dim=1) + # Add a dummy background class to the backend when using sigmoid + # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 + # BG cat_id: num_class + padding = batch_mlvl_scores.new_zeros(batch_size, + batch_mlvl_scores.shape[1], 1) + batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1) + batch_mlvl_iou_preds = torch.cat(mlvl_iou_preds, dim=1) + batch_mlvl_nms_scores = (batch_mlvl_scores * + batch_mlvl_iou_preds[..., None]).sqrt() + + det_results = [] + for (mlvl_bboxes, mlvl_scores) in zip(batch_mlvl_bboxes, + batch_mlvl_nms_scores): + det_bbox, det_label = multiclass_nms( + mlvl_bboxes, + mlvl_scores, + cfg.score_thr, + cfg.nms, + cfg.max_per_img, + score_factors=None) + if self.with_score_voting and len(det_bbox) > 0: + det_bbox, det_label = self.score_voting( + det_bbox, det_label, mlvl_bboxes, mlvl_scores, + cfg.score_thr) + det_results.append(tuple([det_bbox, det_label])) + + return det_results + + def score_voting(self, det_bboxes, det_labels, mlvl_bboxes, + mlvl_nms_scores, score_thr): + """Implementation of score voting method works on each remaining boxes + after NMS procedure. + + Args: + det_bboxes (Tensor): Remaining boxes after NMS procedure, + with shape (k, 5), each dimension means + (x1, y1, x2, y2, score). + det_labels (Tensor): The label of remaining boxes, with shape + (k, 1),Labels are 0-based. + mlvl_bboxes (Tensor): All boxes before the NMS procedure, + with shape (num_anchors,4). + mlvl_nms_scores (Tensor): The scores of all boxes which is used + in the NMS procedure, with shape (num_anchors, num_class) + mlvl_iou_preds (Tensor): The predictions of IOU of all boxes + before the NMS procedure, with shape (num_anchors, 1) + score_thr (float): The score threshold of bboxes. + + Returns: + tuple: Usually returns a tuple containing voting results. + + - det_bboxes_voted (Tensor): Remaining boxes after + score voting procedure, with shape (k, 5), each + dimension means (x1, y1, x2, y2, score). + - det_labels_voted (Tensor): Label of remaining bboxes + after voting, with shape (num_anchors,). + """ + candidate_mask = mlvl_nms_scores > score_thr + candidate_mask_nonzeros = candidate_mask.nonzero() + candidate_inds = candidate_mask_nonzeros[:, 0] + candidate_labels = candidate_mask_nonzeros[:, 1] + candidate_bboxes = mlvl_bboxes[candidate_inds] + candidate_scores = mlvl_nms_scores[candidate_mask] + det_bboxes_voted = [] + det_labels_voted = [] + for cls in range(self.cls_out_channels): + candidate_cls_mask = candidate_labels == cls + if not candidate_cls_mask.any(): + continue + candidate_cls_scores = candidate_scores[candidate_cls_mask] + candidate_cls_bboxes = candidate_bboxes[candidate_cls_mask] + det_cls_mask = det_labels == cls + det_cls_bboxes = det_bboxes[det_cls_mask].view( + -1, det_bboxes.size(-1)) + det_candidate_ious = bbox_overlaps(det_cls_bboxes[:, :4], + candidate_cls_bboxes) + for det_ind in range(len(det_cls_bboxes)): + single_det_ious = det_candidate_ious[det_ind] + pos_ious_mask = single_det_ious > 0.01 + pos_ious = single_det_ious[pos_ious_mask] + pos_bboxes = candidate_cls_bboxes[pos_ious_mask] + pos_scores = candidate_cls_scores[pos_ious_mask] + pis = (torch.exp(-(1 - pos_ious)**2 / 0.025) * + pos_scores)[:, None] + voted_box = torch.sum( + pis * pos_bboxes, dim=0) / torch.sum( + pis, dim=0) + voted_score = det_cls_bboxes[det_ind][-1:][None, :] + det_bboxes_voted.append( + torch.cat((voted_box[None, :], voted_score), dim=1)) + det_labels_voted.append(cls) + + det_bboxes_voted = torch.cat(det_bboxes_voted, dim=0) + det_labels_voted = det_labels.new_tensor(det_labels_voted) + return det_bboxes_voted, det_labels_voted diff --git a/mmdet/models/dense_heads/pisa_retinanet_head.py b/mmdet/models/dense_heads/pisa_retinanet_head.py new file mode 100644 index 0000000..bd87b9a --- /dev/null +++ b/mmdet/models/dense_heads/pisa_retinanet_head.py @@ -0,0 +1,154 @@ +import torch +from mmcv.runner import force_fp32 + +from mmdet.core import images_to_levels +from ..builder import HEADS +from ..losses import carl_loss, isr_p +from .retina_head import RetinaHead + + +@HEADS.register_module() +class PISARetinaHead(RetinaHead): + """PISA Retinanet Head. + + The head owns the same structure with Retinanet Head, but differs in two + aspects: + 1. Importance-based Sample Reweighting Positive (ISR-P) is applied to + change the positive loss weights. + 2. Classification-aware regression loss is adopted as a third loss. + """ + + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) + def loss(self, + cls_scores, + bbox_preds, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute losses of the head. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (N, num_anchors * num_classes, H, W) + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_anchors * 4, H, W) + gt_bboxes (list[Tensor]): Ground truth bboxes of each image + with shape (num_obj, 4). + gt_labels (list[Tensor]): Ground truth labels of each image + with shape (num_obj, 4). + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (list[Tensor]): Ignored gt bboxes of each image. + Default: None. + + Returns: + dict: Loss dict, comprise classification loss, regression loss and + carl loss. + """ + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + assert len(featmap_sizes) == self.anchor_generator.num_levels + + device = cls_scores[0].device + + anchor_list, valid_flag_list = self.get_anchors( + featmap_sizes, img_metas, device=device) + label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 + cls_reg_targets = self.get_targets( + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + gt_labels_list=gt_labels, + label_channels=label_channels, + return_sampling_results=True) + if cls_reg_targets is None: + return None + (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, + num_total_pos, num_total_neg, sampling_results_list) = cls_reg_targets + num_total_samples = ( + num_total_pos + num_total_neg if self.sampling else num_total_pos) + + # anchor number of multi levels + num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] + # concat all level anchors and flags to a single tensor + concat_anchor_list = [] + for i in range(len(anchor_list)): + concat_anchor_list.append(torch.cat(anchor_list[i])) + all_anchor_list = images_to_levels(concat_anchor_list, + num_level_anchors) + + num_imgs = len(img_metas) + flatten_cls_scores = [ + cls_score.permute(0, 2, 3, 1).reshape(num_imgs, -1, label_channels) + for cls_score in cls_scores + ] + flatten_cls_scores = torch.cat( + flatten_cls_scores, dim=1).reshape(-1, + flatten_cls_scores[0].size(-1)) + flatten_bbox_preds = [ + bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1, 4) + for bbox_pred in bbox_preds + ] + flatten_bbox_preds = torch.cat( + flatten_bbox_preds, dim=1).view(-1, flatten_bbox_preds[0].size(-1)) + flatten_labels = torch.cat(labels_list, dim=1).reshape(-1) + flatten_label_weights = torch.cat( + label_weights_list, dim=1).reshape(-1) + flatten_anchors = torch.cat(all_anchor_list, dim=1).reshape(-1, 4) + flatten_bbox_targets = torch.cat( + bbox_targets_list, dim=1).reshape(-1, 4) + flatten_bbox_weights = torch.cat( + bbox_weights_list, dim=1).reshape(-1, 4) + + # Apply ISR-P + isr_cfg = self.train_cfg.get('isr', None) + if isr_cfg is not None: + all_targets = (flatten_labels, flatten_label_weights, + flatten_bbox_targets, flatten_bbox_weights) + with torch.no_grad(): + all_targets = isr_p( + flatten_cls_scores, + flatten_bbox_preds, + all_targets, + flatten_anchors, + sampling_results_list, + bbox_coder=self.bbox_coder, + loss_cls=self.loss_cls, + num_class=self.num_classes, + **self.train_cfg.isr) + (flatten_labels, flatten_label_weights, flatten_bbox_targets, + flatten_bbox_weights) = all_targets + + # For convenience we compute loss once instead separating by fpn level, + # so that we don't need to separate the weights by level again. + # The result should be the same + losses_cls = self.loss_cls( + flatten_cls_scores, + flatten_labels, + flatten_label_weights, + avg_factor=num_total_samples) + losses_bbox = self.loss_bbox( + flatten_bbox_preds, + flatten_bbox_targets, + flatten_bbox_weights, + avg_factor=num_total_samples) + loss_dict = dict(loss_cls=losses_cls, loss_bbox=losses_bbox) + + # CARL Loss + carl_cfg = self.train_cfg.get('carl', None) + if carl_cfg is not None: + loss_carl = carl_loss( + flatten_cls_scores, + flatten_labels, + flatten_bbox_preds, + flatten_bbox_targets, + self.loss_bbox, + **self.train_cfg.carl, + avg_factor=num_total_pos, + sigmoid=True, + num_class=self.num_classes) + loss_dict.update(loss_carl) + + return loss_dict diff --git a/mmdet/models/dense_heads/pisa_ssd_head.py b/mmdet/models/dense_heads/pisa_ssd_head.py new file mode 100644 index 0000000..90ef3c8 --- /dev/null +++ b/mmdet/models/dense_heads/pisa_ssd_head.py @@ -0,0 +1,139 @@ +import torch + +from mmdet.core import multi_apply +from ..builder import HEADS +from ..losses import CrossEntropyLoss, SmoothL1Loss, carl_loss, isr_p +from .ssd_head import SSDHead + + +# TODO: add loss evaluator for SSD +@HEADS.register_module() +class PISASSDHead(SSDHead): + + def loss(self, + cls_scores, + bbox_preds, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute losses of the head. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (N, num_anchors * num_classes, H, W) + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_anchors * 4, H, W) + gt_bboxes (list[Tensor]): Ground truth bboxes of each image + with shape (num_obj, 4). + gt_labels (list[Tensor]): Ground truth labels of each image + with shape (num_obj, 4). + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (list[Tensor]): Ignored gt bboxes of each image. + Default: None. + + Returns: + dict: Loss dict, comprise classification loss regression loss and + carl loss. + """ + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + assert len(featmap_sizes) == self.anchor_generator.num_levels + + device = cls_scores[0].device + + anchor_list, valid_flag_list = self.get_anchors( + featmap_sizes, img_metas, device=device) + cls_reg_targets = self.get_targets( + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + gt_labels_list=gt_labels, + label_channels=1, + unmap_outputs=False, + return_sampling_results=True) + if cls_reg_targets is None: + return None + (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, + num_total_pos, num_total_neg, sampling_results_list) = cls_reg_targets + + num_images = len(img_metas) + all_cls_scores = torch.cat([ + s.permute(0, 2, 3, 1).reshape( + num_images, -1, self.cls_out_channels) for s in cls_scores + ], 1) + all_labels = torch.cat(labels_list, -1).view(num_images, -1) + all_label_weights = torch.cat(label_weights_list, + -1).view(num_images, -1) + all_bbox_preds = torch.cat([ + b.permute(0, 2, 3, 1).reshape(num_images, -1, 4) + for b in bbox_preds + ], -2) + all_bbox_targets = torch.cat(bbox_targets_list, + -2).view(num_images, -1, 4) + all_bbox_weights = torch.cat(bbox_weights_list, + -2).view(num_images, -1, 4) + + # concat all level anchors to a single tensor + all_anchors = [] + for i in range(num_images): + all_anchors.append(torch.cat(anchor_list[i])) + + isr_cfg = self.train_cfg.get('isr', None) + all_targets = (all_labels.view(-1), all_label_weights.view(-1), + all_bbox_targets.view(-1, + 4), all_bbox_weights.view(-1, 4)) + # apply ISR-P + if isr_cfg is not None: + all_targets = isr_p( + all_cls_scores.view(-1, all_cls_scores.size(-1)), + all_bbox_preds.view(-1, 4), + all_targets, + torch.cat(all_anchors), + sampling_results_list, + loss_cls=CrossEntropyLoss(), + bbox_coder=self.bbox_coder, + **self.train_cfg.isr, + num_class=self.num_classes) + (new_labels, new_label_weights, new_bbox_targets, + new_bbox_weights) = all_targets + all_labels = new_labels.view(all_labels.shape) + all_label_weights = new_label_weights.view(all_label_weights.shape) + all_bbox_targets = new_bbox_targets.view(all_bbox_targets.shape) + all_bbox_weights = new_bbox_weights.view(all_bbox_weights.shape) + + # add CARL loss + carl_loss_cfg = self.train_cfg.get('carl', None) + if carl_loss_cfg is not None: + loss_carl = carl_loss( + all_cls_scores.view(-1, all_cls_scores.size(-1)), + all_targets[0], + all_bbox_preds.view(-1, 4), + all_targets[2], + SmoothL1Loss(beta=1.), + **self.train_cfg.carl, + avg_factor=num_total_pos, + num_class=self.num_classes) + + # check NaN and Inf + assert torch.isfinite(all_cls_scores).all().item(), \ + 'classification scores become infinite or NaN!' + assert torch.isfinite(all_bbox_preds).all().item(), \ + 'bbox predications become infinite or NaN!' + + losses_cls, losses_bbox = multi_apply( + self.loss_single, + all_cls_scores, + all_bbox_preds, + all_anchors, + all_labels, + all_label_weights, + all_bbox_targets, + all_bbox_weights, + num_total_samples=num_total_pos) + loss_dict = dict(loss_cls=losses_cls, loss_bbox=losses_bbox) + if carl_loss_cfg is not None: + loss_dict.update(loss_carl) + return loss_dict diff --git a/mmdet/models/dense_heads/query_generator.py b/mmdet/models/dense_heads/query_generator.py new file mode 100644 index 0000000..a33d237 --- /dev/null +++ b/mmdet/models/dense_heads/query_generator.py @@ -0,0 +1,85 @@ +import torch +import torch.nn as nn +from mmcv.runner import BaseModule + +from mmdet.models.builder import HEADS +from ...core import bbox_cxcywh_to_xyxy + + +@HEADS.register_module() +class InitialQueryGenerator(BaseModule): + """ + This module produces initial content vector $\mathbf{q}$ and positional vector $(x, y, z, r)$. + Note that the initial positional vector is **not** learnable. + """ + + def __init__(self, + num_query=100, + content_dim=256, + init_cfg=None, + **kwargs): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + super(InitialQueryGenerator, self).__init__(init_cfg) + self.num_query = num_query + self.content_dim = content_dim + self._init_layers() + + def _init_layers(self): + self.init_proposal_bboxes = nn.Embedding(self.num_query, 4) + self.init_content_features = nn.Embedding( + self.num_query, self.content_dim) + + def init_weights(self): + super(InitialQueryGenerator, self).init_weights() + nn.init.constant_(self.init_proposal_bboxes.weight[:, :2], 0.5) + nn.init.constant_(self.init_proposal_bboxes.weight[:, 2:], 1) + + def _decode_init_proposals(self, imgs, img_metas): + """ + Hacks based on 'sparse_roi_head.py'. + For the positional vector, we first compute (x, y, z, r) that fully covers an image. + """ + proposals = self.init_proposal_bboxes.weight.clone() + proposals = bbox_cxcywh_to_xyxy(proposals) + num_imgs = len(imgs[0]) + imgs_whwh = [] + for meta in img_metas: + h, w, _ = meta['img_shape'] + imgs_whwh.append(imgs[0].new_tensor([[w, h, w, h]])) + imgs_whwh = torch.cat(imgs_whwh, dim=0) + imgs_whwh = imgs_whwh[:, None, :] + + proposals = proposals * imgs_whwh + + xy = 0.5 * (proposals[..., 0:2] + proposals[..., 2:4]) + wh = proposals[..., 2:4] - proposals[..., 0:2] + z = (wh).prod(-1, keepdim=True).sqrt().log2() + r = (wh[..., 1:2]/wh[..., 0:1]).log2() + + # NOTE: xyzr **not** learnable + xyzr = torch.cat([xy, z, r], dim=-1).detach() + + init_content_features = self.init_content_features.weight.clone() + init_content_features = init_content_features[None].expand( + num_imgs, *init_content_features.size()) + + init_content_features = torch.layer_norm( + init_content_features, normalized_shape=[init_content_features.size(-1)]) + + return xyzr, init_content_features, imgs_whwh + + def forward_dummy(self, img, img_metas): + """Dummy forward function. + + Used in flops calculation. + """ + return self._decode_init_proposals(img, img_metas) + + def forward_train(self, img, img_metas): + """Forward function in training stage.""" + return self._decode_init_proposals(img, img_metas) + + def simple_test_rpn(self, img, img_metas): + """Forward function in testing stage.""" + return self._decode_init_proposals(img, img_metas) diff --git a/mmdet/models/dense_heads/reppoints_head.py b/mmdet/models/dense_heads/reppoints_head.py new file mode 100644 index 0000000..c7dcb02 --- /dev/null +++ b/mmdet/models/dense_heads/reppoints_head.py @@ -0,0 +1,764 @@ +import numpy as np +import torch +import torch.nn as nn +from mmcv.cnn import ConvModule +from mmcv.ops import DeformConv2d + +from mmdet.core import (PointGenerator, build_assigner, build_sampler, + images_to_levels, multi_apply, multiclass_nms, unmap) +from ..builder import HEADS, build_loss +from .anchor_free_head import AnchorFreeHead + + +@HEADS.register_module() +class RepPointsHead(AnchorFreeHead): + """RepPoint head. + + Args: + point_feat_channels (int): Number of channels of points features. + gradient_mul (float): The multiplier to gradients from + points refinement and recognition. + point_strides (Iterable): points strides. + point_base_scale (int): bbox scale for assigning labels. + loss_cls (dict): Config of classification loss. + loss_bbox_init (dict): Config of initial points loss. + loss_bbox_refine (dict): Config of points loss in refinement. + use_grid_points (bool): If we use bounding box representation, the + reppoints is represented as grid points on the bounding box. + center_init (bool): Whether to use center point assignment. + transform_method (str): The methods to transform RepPoints to bbox. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ # noqa: W605 + + def __init__(self, + num_classes, + in_channels, + point_feat_channels=256, + num_points=9, + gradient_mul=0.1, + point_strides=[8, 16, 32, 64, 128], + point_base_scale=4, + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox_init=dict( + type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=0.5), + loss_bbox_refine=dict( + type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0), + use_grid_points=False, + center_init=True, + transform_method='moment', + moment_mul=0.01, + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=dict( + type='Normal', + name='reppoints_cls_out', + std=0.01, + bias_prob=0.01)), + **kwargs): + self.num_points = num_points + self.point_feat_channels = point_feat_channels + self.use_grid_points = use_grid_points + self.center_init = center_init + + # we use deform conv to extract points features + self.dcn_kernel = int(np.sqrt(num_points)) + self.dcn_pad = int((self.dcn_kernel - 1) / 2) + assert self.dcn_kernel * self.dcn_kernel == num_points, \ + 'The points number should be a square number.' + assert self.dcn_kernel % 2 == 1, \ + 'The points number should be an odd square number.' + dcn_base = np.arange(-self.dcn_pad, + self.dcn_pad + 1).astype(np.float64) + dcn_base_y = np.repeat(dcn_base, self.dcn_kernel) + dcn_base_x = np.tile(dcn_base, self.dcn_kernel) + dcn_base_offset = np.stack([dcn_base_y, dcn_base_x], axis=1).reshape( + (-1)) + self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1) + + super().__init__( + num_classes, + in_channels, + loss_cls=loss_cls, + init_cfg=init_cfg, + **kwargs) + + self.gradient_mul = gradient_mul + self.point_base_scale = point_base_scale + self.point_strides = point_strides + self.point_generators = [PointGenerator() for _ in self.point_strides] + + self.sampling = loss_cls['type'] not in ['FocalLoss'] + if self.train_cfg: + self.init_assigner = build_assigner(self.train_cfg.init.assigner) + self.refine_assigner = build_assigner( + self.train_cfg.refine.assigner) + # use PseudoSampler when sampling is False + if self.sampling and hasattr(self.train_cfg, 'sampler'): + sampler_cfg = self.train_cfg.sampler + else: + sampler_cfg = dict(type='PseudoSampler') + self.sampler = build_sampler(sampler_cfg, context=self) + self.transform_method = transform_method + if self.transform_method == 'moment': + self.moment_transfer = nn.Parameter( + data=torch.zeros(2), requires_grad=True) + self.moment_mul = moment_mul + + self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False) + if self.use_sigmoid_cls: + self.cls_out_channels = self.num_classes + else: + self.cls_out_channels = self.num_classes + 1 + self.loss_bbox_init = build_loss(loss_bbox_init) + self.loss_bbox_refine = build_loss(loss_bbox_refine) + + def _init_layers(self): + """Initialize layers of the head.""" + self.relu = nn.ReLU(inplace=True) + self.cls_convs = nn.ModuleList() + self.reg_convs = nn.ModuleList() + for i in range(self.stacked_convs): + chn = self.in_channels if i == 0 else self.feat_channels + self.cls_convs.append( + ConvModule( + chn, + self.feat_channels, + 3, + stride=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + self.reg_convs.append( + ConvModule( + chn, + self.feat_channels, + 3, + stride=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + pts_out_dim = 4 if self.use_grid_points else 2 * self.num_points + self.reppoints_cls_conv = DeformConv2d(self.feat_channels, + self.point_feat_channels, + self.dcn_kernel, 1, + self.dcn_pad) + self.reppoints_cls_out = nn.Conv2d(self.point_feat_channels, + self.cls_out_channels, 1, 1, 0) + self.reppoints_pts_init_conv = nn.Conv2d(self.feat_channels, + self.point_feat_channels, 3, + 1, 1) + self.reppoints_pts_init_out = nn.Conv2d(self.point_feat_channels, + pts_out_dim, 1, 1, 0) + self.reppoints_pts_refine_conv = DeformConv2d(self.feat_channels, + self.point_feat_channels, + self.dcn_kernel, 1, + self.dcn_pad) + self.reppoints_pts_refine_out = nn.Conv2d(self.point_feat_channels, + pts_out_dim, 1, 1, 0) + + def points2bbox(self, pts, y_first=True): + """Converting the points set into bounding box. + + :param pts: the input points sets (fields), each points + set (fields) is represented as 2n scalar. + :param y_first: if y_first=True, the point set is represented as + [y1, x1, y2, x2 ... yn, xn], otherwise the point set is + represented as [x1, y1, x2, y2 ... xn, yn]. + :return: each points set is converting to a bbox [x1, y1, x2, y2]. + """ + pts_reshape = pts.view(pts.shape[0], -1, 2, *pts.shape[2:]) + pts_y = pts_reshape[:, :, 0, ...] if y_first else pts_reshape[:, :, 1, + ...] + pts_x = pts_reshape[:, :, 1, ...] if y_first else pts_reshape[:, :, 0, + ...] + if self.transform_method == 'minmax': + bbox_left = pts_x.min(dim=1, keepdim=True)[0] + bbox_right = pts_x.max(dim=1, keepdim=True)[0] + bbox_up = pts_y.min(dim=1, keepdim=True)[0] + bbox_bottom = pts_y.max(dim=1, keepdim=True)[0] + bbox = torch.cat([bbox_left, bbox_up, bbox_right, bbox_bottom], + dim=1) + elif self.transform_method == 'partial_minmax': + pts_y = pts_y[:, :4, ...] + pts_x = pts_x[:, :4, ...] + bbox_left = pts_x.min(dim=1, keepdim=True)[0] + bbox_right = pts_x.max(dim=1, keepdim=True)[0] + bbox_up = pts_y.min(dim=1, keepdim=True)[0] + bbox_bottom = pts_y.max(dim=1, keepdim=True)[0] + bbox = torch.cat([bbox_left, bbox_up, bbox_right, bbox_bottom], + dim=1) + elif self.transform_method == 'moment': + pts_y_mean = pts_y.mean(dim=1, keepdim=True) + pts_x_mean = pts_x.mean(dim=1, keepdim=True) + pts_y_std = torch.std(pts_y - pts_y_mean, dim=1, keepdim=True) + pts_x_std = torch.std(pts_x - pts_x_mean, dim=1, keepdim=True) + moment_transfer = (self.moment_transfer * self.moment_mul) + ( + self.moment_transfer.detach() * (1 - self.moment_mul)) + moment_width_transfer = moment_transfer[0] + moment_height_transfer = moment_transfer[1] + half_width = pts_x_std * torch.exp(moment_width_transfer) + half_height = pts_y_std * torch.exp(moment_height_transfer) + bbox = torch.cat([ + pts_x_mean - half_width, pts_y_mean - half_height, + pts_x_mean + half_width, pts_y_mean + half_height + ], + dim=1) + else: + raise NotImplementedError + return bbox + + def gen_grid_from_reg(self, reg, previous_boxes): + """Base on the previous bboxes and regression values, we compute the + regressed bboxes and generate the grids on the bboxes. + + :param reg: the regression value to previous bboxes. + :param previous_boxes: previous bboxes. + :return: generate grids on the regressed bboxes. + """ + b, _, h, w = reg.shape + bxy = (previous_boxes[:, :2, ...] + previous_boxes[:, 2:, ...]) / 2. + bwh = (previous_boxes[:, 2:, ...] - + previous_boxes[:, :2, ...]).clamp(min=1e-6) + grid_topleft = bxy + bwh * reg[:, :2, ...] - 0.5 * bwh * torch.exp( + reg[:, 2:, ...]) + grid_wh = bwh * torch.exp(reg[:, 2:, ...]) + grid_left = grid_topleft[:, [0], ...] + grid_top = grid_topleft[:, [1], ...] + grid_width = grid_wh[:, [0], ...] + grid_height = grid_wh[:, [1], ...] + intervel = torch.linspace(0., 1., self.dcn_kernel).view( + 1, self.dcn_kernel, 1, 1).type_as(reg) + grid_x = grid_left + grid_width * intervel + grid_x = grid_x.unsqueeze(1).repeat(1, self.dcn_kernel, 1, 1, 1) + grid_x = grid_x.view(b, -1, h, w) + grid_y = grid_top + grid_height * intervel + grid_y = grid_y.unsqueeze(2).repeat(1, 1, self.dcn_kernel, 1, 1) + grid_y = grid_y.view(b, -1, h, w) + grid_yx = torch.stack([grid_y, grid_x], dim=2) + grid_yx = grid_yx.view(b, -1, h, w) + regressed_bbox = torch.cat([ + grid_left, grid_top, grid_left + grid_width, grid_top + grid_height + ], 1) + return grid_yx, regressed_bbox + + def forward(self, feats): + return multi_apply(self.forward_single, feats) + + def forward_single(self, x): + """Forward feature map of a single FPN level.""" + dcn_base_offset = self.dcn_base_offset.type_as(x) + # If we use center_init, the initial reppoints is from center points. + # If we use bounding bbox representation, the initial reppoints is + # from regular grid placed on a pre-defined bbox. + if self.use_grid_points or not self.center_init: + scale = self.point_base_scale / 2 + points_init = dcn_base_offset / dcn_base_offset.max() * scale + bbox_init = x.new_tensor([-scale, -scale, scale, + scale]).view(1, 4, 1, 1) + else: + points_init = 0 + cls_feat = x + pts_feat = x + for cls_conv in self.cls_convs: + cls_feat = cls_conv(cls_feat) + for reg_conv in self.reg_convs: + pts_feat = reg_conv(pts_feat) + # initialize reppoints + pts_out_init = self.reppoints_pts_init_out( + self.relu(self.reppoints_pts_init_conv(pts_feat))) + if self.use_grid_points: + pts_out_init, bbox_out_init = self.gen_grid_from_reg( + pts_out_init, bbox_init.detach()) + else: + pts_out_init = pts_out_init + points_init + # refine and classify reppoints + pts_out_init_grad_mul = (1 - self.gradient_mul) * pts_out_init.detach( + ) + self.gradient_mul * pts_out_init + dcn_offset = pts_out_init_grad_mul - dcn_base_offset + cls_out = self.reppoints_cls_out( + self.relu(self.reppoints_cls_conv(cls_feat, dcn_offset))) + pts_out_refine = self.reppoints_pts_refine_out( + self.relu(self.reppoints_pts_refine_conv(pts_feat, dcn_offset))) + if self.use_grid_points: + pts_out_refine, bbox_out_refine = self.gen_grid_from_reg( + pts_out_refine, bbox_out_init.detach()) + else: + pts_out_refine = pts_out_refine + pts_out_init.detach() + return cls_out, pts_out_init, pts_out_refine + + def get_points(self, featmap_sizes, img_metas, device): + """Get points according to feature map sizes. + + Args: + featmap_sizes (list[tuple]): Multi-level feature map sizes. + img_metas (list[dict]): Image meta info. + + Returns: + tuple: points of each image, valid flags of each image + """ + num_imgs = len(img_metas) + num_levels = len(featmap_sizes) + + # since feature map sizes of all images are the same, we only compute + # points center for one time + multi_level_points = [] + for i in range(num_levels): + points = self.point_generators[i].grid_points( + featmap_sizes[i], self.point_strides[i], device) + multi_level_points.append(points) + points_list = [[point.clone() for point in multi_level_points] + for _ in range(num_imgs)] + + # for each image, we compute valid flags of multi level grids + valid_flag_list = [] + for img_id, img_meta in enumerate(img_metas): + multi_level_flags = [] + for i in range(num_levels): + point_stride = self.point_strides[i] + feat_h, feat_w = featmap_sizes[i] + h, w = img_meta['pad_shape'][:2] + valid_feat_h = min(int(np.ceil(h / point_stride)), feat_h) + valid_feat_w = min(int(np.ceil(w / point_stride)), feat_w) + flags = self.point_generators[i].valid_flags( + (feat_h, feat_w), (valid_feat_h, valid_feat_w), device) + multi_level_flags.append(flags) + valid_flag_list.append(multi_level_flags) + + return points_list, valid_flag_list + + def centers_to_bboxes(self, point_list): + """Get bboxes according to center points. + + Only used in :class:`MaxIoUAssigner`. + """ + bbox_list = [] + for i_img, point in enumerate(point_list): + bbox = [] + for i_lvl in range(len(self.point_strides)): + scale = self.point_base_scale * self.point_strides[i_lvl] * 0.5 + bbox_shift = torch.Tensor([-scale, -scale, scale, + scale]).view(1, 4).type_as(point[0]) + bbox_center = torch.cat( + [point[i_lvl][:, :2], point[i_lvl][:, :2]], dim=1) + bbox.append(bbox_center + bbox_shift) + bbox_list.append(bbox) + return bbox_list + + def offset_to_pts(self, center_list, pred_list): + """Change from point offset to point coordinate.""" + pts_list = [] + for i_lvl in range(len(self.point_strides)): + pts_lvl = [] + for i_img in range(len(center_list)): + pts_center = center_list[i_img][i_lvl][:, :2].repeat( + 1, self.num_points) + pts_shift = pred_list[i_lvl][i_img] + yx_pts_shift = pts_shift.permute(1, 2, 0).view( + -1, 2 * self.num_points) + y_pts_shift = yx_pts_shift[..., 0::2] + x_pts_shift = yx_pts_shift[..., 1::2] + xy_pts_shift = torch.stack([x_pts_shift, y_pts_shift], -1) + xy_pts_shift = xy_pts_shift.view(*yx_pts_shift.shape[:-1], -1) + pts = xy_pts_shift * self.point_strides[i_lvl] + pts_center + pts_lvl.append(pts) + pts_lvl = torch.stack(pts_lvl, 0) + pts_list.append(pts_lvl) + return pts_list + + def _point_target_single(self, + flat_proposals, + valid_flags, + gt_bboxes, + gt_bboxes_ignore, + gt_labels, + label_channels=1, + stage='init', + unmap_outputs=True): + inside_flags = valid_flags + if not inside_flags.any(): + return (None, ) * 7 + # assign gt and sample proposals + proposals = flat_proposals[inside_flags, :] + + if stage == 'init': + assigner = self.init_assigner + pos_weight = self.train_cfg.init.pos_weight + else: + assigner = self.refine_assigner + pos_weight = self.train_cfg.refine.pos_weight + assign_result = assigner.assign(proposals, gt_bboxes, gt_bboxes_ignore, + None if self.sampling else gt_labels) + sampling_result = self.sampler.sample(assign_result, proposals, + gt_bboxes) + + num_valid_proposals = proposals.shape[0] + bbox_gt = proposals.new_zeros([num_valid_proposals, 4]) + pos_proposals = torch.zeros_like(proposals) + proposals_weights = proposals.new_zeros([num_valid_proposals, 4]) + labels = proposals.new_full((num_valid_proposals, ), + self.num_classes, + dtype=torch.long) + label_weights = proposals.new_zeros( + num_valid_proposals, dtype=torch.float) + + pos_inds = sampling_result.pos_inds + neg_inds = sampling_result.neg_inds + if len(pos_inds) > 0: + pos_gt_bboxes = sampling_result.pos_gt_bboxes + bbox_gt[pos_inds, :] = pos_gt_bboxes + pos_proposals[pos_inds, :] = proposals[pos_inds, :] + proposals_weights[pos_inds, :] = 1.0 + if gt_labels is None: + # Only rpn gives gt_labels as None + # Foreground is the first class + labels[pos_inds] = 0 + else: + labels[pos_inds] = gt_labels[ + sampling_result.pos_assigned_gt_inds] + if pos_weight <= 0: + label_weights[pos_inds] = 1.0 + else: + label_weights[pos_inds] = pos_weight + if len(neg_inds) > 0: + label_weights[neg_inds] = 1.0 + + # map up to original set of proposals + if unmap_outputs: + num_total_proposals = flat_proposals.size(0) + labels = unmap(labels, num_total_proposals, inside_flags) + label_weights = unmap(label_weights, num_total_proposals, + inside_flags) + bbox_gt = unmap(bbox_gt, num_total_proposals, inside_flags) + pos_proposals = unmap(pos_proposals, num_total_proposals, + inside_flags) + proposals_weights = unmap(proposals_weights, num_total_proposals, + inside_flags) + + return (labels, label_weights, bbox_gt, pos_proposals, + proposals_weights, pos_inds, neg_inds) + + def get_targets(self, + proposals_list, + valid_flag_list, + gt_bboxes_list, + img_metas, + gt_bboxes_ignore_list=None, + gt_labels_list=None, + stage='init', + label_channels=1, + unmap_outputs=True): + """Compute corresponding GT box and classification targets for + proposals. + + Args: + proposals_list (list[list]): Multi level points/bboxes of each + image. + valid_flag_list (list[list]): Multi level valid flags of each + image. + gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image. + img_metas (list[dict]): Meta info of each image. + gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be + ignored. + gt_bboxes_list (list[Tensor]): Ground truth labels of each box. + stage (str): `init` or `refine`. Generate target for init stage or + refine stage + label_channels (int): Channel of label. + unmap_outputs (bool): Whether to map outputs back to the original + set of anchors. + + Returns: + tuple: + - labels_list (list[Tensor]): Labels of each level. + - label_weights_list (list[Tensor]): Label weights of each level. # noqa: E501 + - bbox_gt_list (list[Tensor]): Ground truth bbox of each level. + - proposal_list (list[Tensor]): Proposals(points/bboxes) of each level. # noqa: E501 + - proposal_weights_list (list[Tensor]): Proposal weights of each level. # noqa: E501 + - num_total_pos (int): Number of positive samples in all images. # noqa: E501 + - num_total_neg (int): Number of negative samples in all images. # noqa: E501 + """ + assert stage in ['init', 'refine'] + num_imgs = len(img_metas) + assert len(proposals_list) == len(valid_flag_list) == num_imgs + + # points number of multi levels + num_level_proposals = [points.size(0) for points in proposals_list[0]] + + # concat all level points and flags to a single tensor + for i in range(num_imgs): + assert len(proposals_list[i]) == len(valid_flag_list[i]) + proposals_list[i] = torch.cat(proposals_list[i]) + valid_flag_list[i] = torch.cat(valid_flag_list[i]) + + # compute targets for each image + if gt_bboxes_ignore_list is None: + gt_bboxes_ignore_list = [None for _ in range(num_imgs)] + if gt_labels_list is None: + gt_labels_list = [None for _ in range(num_imgs)] + (all_labels, all_label_weights, all_bbox_gt, all_proposals, + all_proposal_weights, pos_inds_list, neg_inds_list) = multi_apply( + self._point_target_single, + proposals_list, + valid_flag_list, + gt_bboxes_list, + gt_bboxes_ignore_list, + gt_labels_list, + stage=stage, + label_channels=label_channels, + unmap_outputs=unmap_outputs) + # no valid points + if any([labels is None for labels in all_labels]): + return None + # sampled points of all images + num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list]) + num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list]) + labels_list = images_to_levels(all_labels, num_level_proposals) + label_weights_list = images_to_levels(all_label_weights, + num_level_proposals) + bbox_gt_list = images_to_levels(all_bbox_gt, num_level_proposals) + proposals_list = images_to_levels(all_proposals, num_level_proposals) + proposal_weights_list = images_to_levels(all_proposal_weights, + num_level_proposals) + return (labels_list, label_weights_list, bbox_gt_list, proposals_list, + proposal_weights_list, num_total_pos, num_total_neg) + + def loss_single(self, cls_score, pts_pred_init, pts_pred_refine, labels, + label_weights, bbox_gt_init, bbox_weights_init, + bbox_gt_refine, bbox_weights_refine, stride, + num_total_samples_init, num_total_samples_refine): + # classification loss + labels = labels.reshape(-1) + label_weights = label_weights.reshape(-1) + cls_score = cls_score.permute(0, 2, 3, + 1).reshape(-1, self.cls_out_channels) + cls_score = cls_score.contiguous() + loss_cls = self.loss_cls( + cls_score, + labels, + label_weights, + avg_factor=num_total_samples_refine) + + # points loss + bbox_gt_init = bbox_gt_init.reshape(-1, 4) + bbox_weights_init = bbox_weights_init.reshape(-1, 4) + bbox_pred_init = self.points2bbox( + pts_pred_init.reshape(-1, 2 * self.num_points), y_first=False) + bbox_gt_refine = bbox_gt_refine.reshape(-1, 4) + bbox_weights_refine = bbox_weights_refine.reshape(-1, 4) + bbox_pred_refine = self.points2bbox( + pts_pred_refine.reshape(-1, 2 * self.num_points), y_first=False) + normalize_term = self.point_base_scale * stride + loss_pts_init = self.loss_bbox_init( + bbox_pred_init / normalize_term, + bbox_gt_init / normalize_term, + bbox_weights_init, + avg_factor=num_total_samples_init) + loss_pts_refine = self.loss_bbox_refine( + bbox_pred_refine / normalize_term, + bbox_gt_refine / normalize_term, + bbox_weights_refine, + avg_factor=num_total_samples_refine) + return loss_cls, loss_pts_init, loss_pts_refine + + def loss(self, + cls_scores, + pts_preds_init, + pts_preds_refine, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + assert len(featmap_sizes) == len(self.point_generators) + device = cls_scores[0].device + label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 + + # target for initial stage + center_list, valid_flag_list = self.get_points(featmap_sizes, + img_metas, device) + pts_coordinate_preds_init = self.offset_to_pts(center_list, + pts_preds_init) + if self.train_cfg.init.assigner['type'] == 'PointAssigner': + # Assign target for center list + candidate_list = center_list + else: + # transform center list to bbox list and + # assign target for bbox list + bbox_list = self.centers_to_bboxes(center_list) + candidate_list = bbox_list + cls_reg_targets_init = self.get_targets( + candidate_list, + valid_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + gt_labels_list=gt_labels, + stage='init', + label_channels=label_channels) + (*_, bbox_gt_list_init, candidate_list_init, bbox_weights_list_init, + num_total_pos_init, num_total_neg_init) = cls_reg_targets_init + num_total_samples_init = ( + num_total_pos_init + + num_total_neg_init if self.sampling else num_total_pos_init) + + # target for refinement stage + center_list, valid_flag_list = self.get_points(featmap_sizes, + img_metas, device) + pts_coordinate_preds_refine = self.offset_to_pts( + center_list, pts_preds_refine) + bbox_list = [] + for i_img, center in enumerate(center_list): + bbox = [] + for i_lvl in range(len(pts_preds_refine)): + bbox_preds_init = self.points2bbox( + pts_preds_init[i_lvl].detach()) + bbox_shift = bbox_preds_init * self.point_strides[i_lvl] + bbox_center = torch.cat( + [center[i_lvl][:, :2], center[i_lvl][:, :2]], dim=1) + bbox.append(bbox_center + + bbox_shift[i_img].permute(1, 2, 0).reshape(-1, 4)) + bbox_list.append(bbox) + cls_reg_targets_refine = self.get_targets( + bbox_list, + valid_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + gt_labels_list=gt_labels, + stage='refine', + label_channels=label_channels) + (labels_list, label_weights_list, bbox_gt_list_refine, + candidate_list_refine, bbox_weights_list_refine, num_total_pos_refine, + num_total_neg_refine) = cls_reg_targets_refine + num_total_samples_refine = ( + num_total_pos_refine + + num_total_neg_refine if self.sampling else num_total_pos_refine) + + # compute loss + losses_cls, losses_pts_init, losses_pts_refine = multi_apply( + self.loss_single, + cls_scores, + pts_coordinate_preds_init, + pts_coordinate_preds_refine, + labels_list, + label_weights_list, + bbox_gt_list_init, + bbox_weights_list_init, + bbox_gt_list_refine, + bbox_weights_list_refine, + self.point_strides, + num_total_samples_init=num_total_samples_init, + num_total_samples_refine=num_total_samples_refine) + loss_dict_all = { + 'loss_cls': losses_cls, + 'loss_pts_init': losses_pts_init, + 'loss_pts_refine': losses_pts_refine + } + return loss_dict_all + + def get_bboxes(self, + cls_scores, + pts_preds_init, + pts_preds_refine, + img_metas, + cfg=None, + rescale=False, + with_nms=True): + assert len(cls_scores) == len(pts_preds_refine) + device = cls_scores[0].device + bbox_preds_refine = [ + self.points2bbox(pts_pred_refine) + for pts_pred_refine in pts_preds_refine + ] + num_levels = len(cls_scores) + mlvl_points = [ + self.point_generators[i].grid_points(cls_scores[i].size()[-2:], + self.point_strides[i], device) + for i in range(num_levels) + ] + result_list = [] + for img_id in range(len(img_metas)): + cls_score_list = [ + cls_scores[i][img_id].detach() for i in range(num_levels) + ] + bbox_pred_list = [ + bbox_preds_refine[i][img_id].detach() + for i in range(num_levels) + ] + img_shape = img_metas[img_id]['img_shape'] + scale_factor = img_metas[img_id]['scale_factor'] + proposals = self._get_bboxes_single(cls_score_list, bbox_pred_list, + mlvl_points, img_shape, + scale_factor, cfg, rescale, + with_nms) + result_list.append(proposals) + return result_list + + def _get_bboxes_single(self, + cls_scores, + bbox_preds, + mlvl_points, + img_shape, + scale_factor, + cfg, + rescale=False, + with_nms=True): + cfg = self.test_cfg if cfg is None else cfg + assert len(cls_scores) == len(bbox_preds) == len(mlvl_points) + mlvl_bboxes = [] + mlvl_scores = [] + for i_lvl, (cls_score, bbox_pred, points) in enumerate( + zip(cls_scores, bbox_preds, mlvl_points)): + assert cls_score.size()[-2:] == bbox_pred.size()[-2:] + cls_score = cls_score.permute(1, 2, + 0).reshape(-1, self.cls_out_channels) + if self.use_sigmoid_cls: + scores = cls_score.sigmoid() + else: + scores = cls_score.softmax(-1) + bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4) + nms_pre = cfg.get('nms_pre', -1) + if nms_pre > 0 and scores.shape[0] > nms_pre: + if self.use_sigmoid_cls: + max_scores, _ = scores.max(dim=1) + else: + # remind that we set FG labels to [0, num_class-1] + # since mmdet v2.0 + # BG cat_id: num_class + max_scores, _ = scores[:, :-1].max(dim=1) + _, topk_inds = max_scores.topk(nms_pre) + points = points[topk_inds, :] + bbox_pred = bbox_pred[topk_inds, :] + scores = scores[topk_inds, :] + bbox_pos_center = torch.cat([points[:, :2], points[:, :2]], dim=1) + bboxes = bbox_pred * self.point_strides[i_lvl] + bbox_pos_center + x1 = bboxes[:, 0].clamp(min=0, max=img_shape[1]) + y1 = bboxes[:, 1].clamp(min=0, max=img_shape[0]) + x2 = bboxes[:, 2].clamp(min=0, max=img_shape[1]) + y2 = bboxes[:, 3].clamp(min=0, max=img_shape[0]) + bboxes = torch.stack([x1, y1, x2, y2], dim=-1) + mlvl_bboxes.append(bboxes) + mlvl_scores.append(scores) + mlvl_bboxes = torch.cat(mlvl_bboxes) + if rescale: + mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor) + mlvl_scores = torch.cat(mlvl_scores) + if self.use_sigmoid_cls: + # Add a dummy background class to the backend when using sigmoid + # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 + # BG cat_id: num_class + padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1) + mlvl_scores = torch.cat([mlvl_scores, padding], dim=1) + if with_nms: + det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores, + cfg.score_thr, cfg.nms, + cfg.max_per_img) + return det_bboxes, det_labels + else: + return mlvl_bboxes, mlvl_scores diff --git a/mmdet/models/dense_heads/retina_head.py b/mmdet/models/dense_heads/retina_head.py new file mode 100644 index 0000000..698aec5 --- /dev/null +++ b/mmdet/models/dense_heads/retina_head.py @@ -0,0 +1,114 @@ +import torch.nn as nn +from mmcv.cnn import ConvModule + +from ..builder import HEADS +from .anchor_head import AnchorHead + + +@HEADS.register_module() +class RetinaHead(AnchorHead): + r"""An anchor-based head used in `RetinaNet + `_. + + The head contains two subnetworks. The first classifies anchor boxes and + the second regresses deltas for the anchors. + + Example: + >>> import torch + >>> self = RetinaHead(11, 7) + >>> x = torch.rand(1, 7, 32, 32) + >>> cls_score, bbox_pred = self.forward_single(x) + >>> # Each anchor predicts a score for each class except background + >>> cls_per_anchor = cls_score.shape[1] / self.num_anchors + >>> box_per_anchor = bbox_pred.shape[1] / self.num_anchors + >>> assert cls_per_anchor == (self.num_classes) + >>> assert box_per_anchor == 4 + """ + + def __init__(self, + num_classes, + in_channels, + stacked_convs=4, + conv_cfg=None, + norm_cfg=None, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=dict( + type='Normal', + name='retina_cls', + std=0.01, + bias_prob=0.01)), + **kwargs): + self.stacked_convs = stacked_convs + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + super(RetinaHead, self).__init__( + num_classes, + in_channels, + anchor_generator=anchor_generator, + init_cfg=init_cfg, + **kwargs) + + def _init_layers(self): + """Initialize layers of the head.""" + self.relu = nn.ReLU(inplace=True) + self.cls_convs = nn.ModuleList() + self.reg_convs = nn.ModuleList() + for i in range(self.stacked_convs): + chn = self.in_channels if i == 0 else self.feat_channels + self.cls_convs.append( + ConvModule( + chn, + self.feat_channels, + 3, + stride=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + self.reg_convs.append( + ConvModule( + chn, + self.feat_channels, + 3, + stride=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + self.retina_cls = nn.Conv2d( + self.feat_channels, + self.num_anchors * self.cls_out_channels, + 3, + padding=1) + self.retina_reg = nn.Conv2d( + self.feat_channels, self.num_anchors * 4, 3, padding=1) + + def forward_single(self, x): + """Forward feature of a single scale level. + + Args: + x (Tensor): Features of a single scale level. + + Returns: + tuple: + cls_score (Tensor): Cls scores for a single scale level + the channels number is num_anchors * num_classes. + bbox_pred (Tensor): Box energies / deltas for a single scale + level, the channels number is num_anchors * 4. + """ + cls_feat = x + reg_feat = x + for cls_conv in self.cls_convs: + cls_feat = cls_conv(cls_feat) + for reg_conv in self.reg_convs: + reg_feat = reg_conv(reg_feat) + cls_score = self.retina_cls(cls_feat) + bbox_pred = self.retina_reg(reg_feat) + return cls_score, bbox_pred diff --git a/mmdet/models/dense_heads/retina_sepbn_head.py b/mmdet/models/dense_heads/retina_sepbn_head.py new file mode 100644 index 0000000..6fda7fc --- /dev/null +++ b/mmdet/models/dense_heads/retina_sepbn_head.py @@ -0,0 +1,117 @@ +import torch.nn as nn +from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init + +from ..builder import HEADS +from .anchor_head import AnchorHead + + +@HEADS.register_module() +class RetinaSepBNHead(AnchorHead): + """"RetinaHead with separate BN. + + In RetinaHead, conv/norm layers are shared across different FPN levels, + while in RetinaSepBNHead, conv layers are shared across different FPN + levels, but BN layers are separated. + """ + + def __init__(self, + num_classes, + num_ins, + in_channels, + stacked_convs=4, + conv_cfg=None, + norm_cfg=None, + init_cfg=None, + **kwargs): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + self.stacked_convs = stacked_convs + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.num_ins = num_ins + super(RetinaSepBNHead, self).__init__( + num_classes, in_channels, init_cfg=init_cfg, **kwargs) + + def _init_layers(self): + """Initialize layers of the head.""" + self.relu = nn.ReLU(inplace=True) + self.cls_convs = nn.ModuleList() + self.reg_convs = nn.ModuleList() + for i in range(self.num_ins): + cls_convs = nn.ModuleList() + reg_convs = nn.ModuleList() + for i in range(self.stacked_convs): + chn = self.in_channels if i == 0 else self.feat_channels + cls_convs.append( + ConvModule( + chn, + self.feat_channels, + 3, + stride=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + reg_convs.append( + ConvModule( + chn, + self.feat_channels, + 3, + stride=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + self.cls_convs.append(cls_convs) + self.reg_convs.append(reg_convs) + for i in range(self.stacked_convs): + for j in range(1, self.num_ins): + self.cls_convs[j][i].conv = self.cls_convs[0][i].conv + self.reg_convs[j][i].conv = self.reg_convs[0][i].conv + self.retina_cls = nn.Conv2d( + self.feat_channels, + self.num_anchors * self.cls_out_channels, + 3, + padding=1) + self.retina_reg = nn.Conv2d( + self.feat_channels, self.num_anchors * 4, 3, padding=1) + + def init_weights(self): + """Initialize weights of the head.""" + super(RetinaSepBNHead, self).init_weights() + for m in self.cls_convs[0]: + normal_init(m.conv, std=0.01) + for m in self.reg_convs[0]: + normal_init(m.conv, std=0.01) + bias_cls = bias_init_with_prob(0.01) + normal_init(self.retina_cls, std=0.01, bias=bias_cls) + normal_init(self.retina_reg, std=0.01) + + def forward(self, feats): + """Forward features from the upstream network. + + Args: + feats (tuple[Tensor]): Features from the upstream network, each is + a 4D-tensor. + + Returns: + tuple: Usually a tuple of classification scores and bbox prediction + cls_scores (list[Tensor]): Classification scores for all scale + levels, each is a 4D-tensor, the channels number is + num_anchors * num_classes. + bbox_preds (list[Tensor]): Box energies / deltas for all scale + levels, each is a 4D-tensor, the channels number is + num_anchors * 4. + """ + cls_scores = [] + bbox_preds = [] + for i, x in enumerate(feats): + cls_feat = feats[i] + reg_feat = feats[i] + for cls_conv in self.cls_convs[i]: + cls_feat = cls_conv(cls_feat) + for reg_conv in self.reg_convs[i]: + reg_feat = reg_conv(reg_feat) + cls_score = self.retina_cls(cls_feat) + bbox_pred = self.retina_reg(reg_feat) + cls_scores.append(cls_score) + bbox_preds.append(bbox_pred) + return cls_scores, bbox_preds diff --git a/mmdet/models/dense_heads/rpn_head.py b/mmdet/models/dense_heads/rpn_head.py new file mode 100644 index 0000000..3e6ae64 --- /dev/null +++ b/mmdet/models/dense_heads/rpn_head.py @@ -0,0 +1,250 @@ +import copy +import warnings + +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv import ConfigDict +from mmcv.ops import batched_nms + +from ..builder import HEADS +from .anchor_head import AnchorHead +from .rpn_test_mixin import RPNTestMixin + + +@HEADS.register_module() +class RPNHead(RPNTestMixin, AnchorHead): + """RPN head. + + Args: + in_channels (int): Number of channels in the input feature map. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ # noqa: W605 + + def __init__(self, + in_channels, + init_cfg=dict(type='Normal', layer='Conv2d', std=0.01), + **kwargs): + super(RPNHead, self).__init__( + 1, in_channels, init_cfg=init_cfg, **kwargs) + + def _init_layers(self): + """Initialize layers of the head.""" + self.rpn_conv = nn.Conv2d( + self.in_channels, self.feat_channels, 3, padding=1) + self.rpn_cls = nn.Conv2d(self.feat_channels, + self.num_anchors * self.cls_out_channels, 1) + self.rpn_reg = nn.Conv2d(self.feat_channels, self.num_anchors * 4, 1) + + def forward_single(self, x): + """Forward feature map of a single scale level.""" + x = self.rpn_conv(x) + x = F.relu(x, inplace=True) + rpn_cls_score = self.rpn_cls(x) + rpn_bbox_pred = self.rpn_reg(x) + return rpn_cls_score, rpn_bbox_pred + + def loss(self, + cls_scores, + bbox_preds, + gt_bboxes, + img_metas, + gt_bboxes_ignore=None): + """Compute losses of the head. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (N, num_anchors * num_classes, H, W) + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_anchors * 4, H, W) + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + losses = super(RPNHead, self).loss( + cls_scores, + bbox_preds, + gt_bboxes, + None, + img_metas, + gt_bboxes_ignore=gt_bboxes_ignore) + return dict( + loss_rpn_cls=losses['loss_cls'], loss_rpn_bbox=losses['loss_bbox']) + + def _get_bboxes(self, + cls_scores, + bbox_preds, + mlvl_anchors, + img_shapes, + scale_factors, + cfg, + rescale=False): + """Transform outputs for a single batch item into bbox predictions. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (N, num_anchors * num_classes, H, W). + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_anchors * 4, H, W). + mlvl_anchors (list[Tensor]): Box reference for each scale level + with shape (num_total_anchors, 4). + img_shapes (list[tuple[int]]): Shape of the input image, + (height, width, 3). + scale_factors (list[ndarray]): Scale factor of the image arange as + (w_scale, h_scale, w_scale, h_scale). + cfg (mmcv.Config): Test / postprocessing configuration, + if None, test_cfg would be used. + rescale (bool): If True, return boxes in original image space. + + Returns: + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is an (n, 5) tensor, where the first 4 columns + are bounding box positions (tl_x, tl_y, br_x, br_y) and the + 5-th column is a score between 0 and 1. The second item is a + (n,) tensor where each item is the predicted class labelof the + corresponding box. + """ + cfg = self.test_cfg if cfg is None else cfg + cfg = copy.deepcopy(cfg) + # bboxes from different level should be independent during NMS, + # level_ids are used as labels for batched NMS to separate them + level_ids = [] + mlvl_scores = [] + mlvl_bbox_preds = [] + mlvl_valid_anchors = [] + batch_size = cls_scores[0].shape[0] + nms_pre_tensor = torch.tensor( + cfg.nms_pre, device=cls_scores[0].device, dtype=torch.long) + for idx in range(len(cls_scores)): + rpn_cls_score = cls_scores[idx] + rpn_bbox_pred = bbox_preds[idx] + assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:] + rpn_cls_score = rpn_cls_score.permute(0, 2, 3, 1) + if self.use_sigmoid_cls: + rpn_cls_score = rpn_cls_score.reshape(batch_size, -1) + scores = rpn_cls_score.sigmoid() + else: + rpn_cls_score = rpn_cls_score.reshape(batch_size, -1, 2) + # We set FG labels to [0, num_class-1] and BG label to + # num_class in RPN head since mmdet v2.5, which is unified to + # be consistent with other head since mmdet v2.0. In mmdet v2.0 + # to v2.4 we keep BG label as 0 and FG label as 1 in rpn head. + scores = rpn_cls_score.softmax(-1)[..., 0] + rpn_bbox_pred = rpn_bbox_pred.permute(0, 2, 3, 1).reshape( + batch_size, -1, 4) + anchors = mlvl_anchors[idx] + anchors = anchors.expand_as(rpn_bbox_pred) + # Get top-k prediction + from mmdet.core.export import get_k_for_topk + nms_pre = get_k_for_topk(nms_pre_tensor, rpn_bbox_pred.shape[1]) + if nms_pre > 0: + _, topk_inds = scores.topk(nms_pre) + batch_inds = torch.arange(batch_size).view( + -1, 1).expand_as(topk_inds) + # Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501 + if torch.onnx.is_in_onnx_export(): + # Mind k<=3480 in TensorRT for TopK + transformed_inds = scores.shape[1] * batch_inds + topk_inds + scores = scores.reshape(-1, 1)[transformed_inds].reshape( + batch_size, -1) + rpn_bbox_pred = rpn_bbox_pred.reshape( + -1, 4)[transformed_inds, :].reshape(batch_size, -1, 4) + anchors = anchors.reshape(-1, + 4)[transformed_inds, :].reshape( + batch_size, -1, 4) + else: + # sort is faster than topk + ranked_scores, rank_inds = scores.sort(descending=True) + topk_inds = rank_inds[:, :cfg.nms_pre] + scores = ranked_scores[:, :cfg.nms_pre] + batch_inds = torch.arange(batch_size).view( + -1, 1).expand_as(topk_inds) + rpn_bbox_pred = rpn_bbox_pred[batch_inds, topk_inds, :] + anchors = anchors[batch_inds, topk_inds, :] + + mlvl_scores.append(scores) + mlvl_bbox_preds.append(rpn_bbox_pred) + mlvl_valid_anchors.append(anchors) + level_ids.append( + scores.new_full(( + batch_size, + scores.size(1), + ), + idx, + dtype=torch.long)) + + batch_mlvl_scores = torch.cat(mlvl_scores, dim=1) + batch_mlvl_anchors = torch.cat(mlvl_valid_anchors, dim=1) + batch_mlvl_rpn_bbox_pred = torch.cat(mlvl_bbox_preds, dim=1) + batch_mlvl_proposals = self.bbox_coder.decode( + batch_mlvl_anchors, batch_mlvl_rpn_bbox_pred, max_shape=img_shapes) + batch_mlvl_ids = torch.cat(level_ids, dim=1) + + # deprecate arguments warning + if 'nms' not in cfg or 'max_num' in cfg or 'nms_thr' in cfg: + warnings.warn( + 'In rpn_proposal or test_cfg, ' + 'nms_thr has been moved to a dict named nms as ' + 'iou_threshold, max_num has been renamed as max_per_img, ' + 'name of original arguments and the way to specify ' + 'iou_threshold of NMS will be deprecated.') + if 'nms' not in cfg: + cfg.nms = ConfigDict(dict(type='nms', iou_threshold=cfg.nms_thr)) + if 'max_num' in cfg: + if 'max_per_img' in cfg: + assert cfg.max_num == cfg.max_per_img, f'You ' \ + f'set max_num and ' \ + f'max_per_img at the same time, but get {cfg.max_num} ' \ + f'and {cfg.max_per_img} respectively' \ + 'Please delete max_num which will be deprecated.' + else: + cfg.max_per_img = cfg.max_num + if 'nms_thr' in cfg: + assert cfg.nms.iou_threshold == cfg.nms_thr, f'You set' \ + f' iou_threshold in nms and ' \ + f'nms_thr at the same time, but get' \ + f' {cfg.nms.iou_threshold} and {cfg.nms_thr}' \ + f' respectively. Please delete the nms_thr ' \ + f'which will be deprecated.' + + # Replace batched_nms with ONNX::NonMaxSuppression in deployment + if torch.onnx.is_in_onnx_export(): + from mmdet.core.export import add_dummy_nms_for_onnx + batch_mlvl_scores = batch_mlvl_scores.unsqueeze(2) + score_threshold = cfg.nms.get('score_thr', 0.0) + nms_pre = cfg.get('deploy_nms_pre', cfg.max_per_img) + dets, _ = add_dummy_nms_for_onnx(batch_mlvl_proposals, + batch_mlvl_scores, + cfg.max_per_img, + cfg.nms.iou_threshold, + score_threshold, nms_pre, + cfg.max_per_img) + return dets + + result_list = [] + for (mlvl_proposals, mlvl_scores, + mlvl_ids) in zip(batch_mlvl_proposals, batch_mlvl_scores, + batch_mlvl_ids): + # Skip nonzero op while exporting to ONNX + if cfg.min_bbox_size > 0 and (not torch.onnx.is_in_onnx_export()): + w = mlvl_proposals[:, 2] - mlvl_proposals[:, 0] + h = mlvl_proposals[:, 3] - mlvl_proposals[:, 1] + valid_ind = torch.nonzero( + (w >= cfg.min_bbox_size) + & (h >= cfg.min_bbox_size), + as_tuple=False).squeeze() + if valid_ind.sum().item() != len(mlvl_proposals): + mlvl_proposals = mlvl_proposals[valid_ind, :] + mlvl_scores = mlvl_scores[valid_ind] + mlvl_ids = mlvl_ids[valid_ind] + + dets, keep = batched_nms(mlvl_proposals, mlvl_scores, mlvl_ids, + cfg.nms) + result_list.append(dets[:cfg.max_per_img]) + return result_list diff --git a/mmdet/models/dense_heads/rpn_test_mixin.py b/mmdet/models/dense_heads/rpn_test_mixin.py new file mode 100644 index 0000000..4ce5c66 --- /dev/null +++ b/mmdet/models/dense_heads/rpn_test_mixin.py @@ -0,0 +1,59 @@ +import sys + +from mmdet.core import merge_aug_proposals + +if sys.version_info >= (3, 7): + from mmdet.utils.contextmanagers import completed + + +class RPNTestMixin(object): + """Test methods of RPN.""" + + if sys.version_info >= (3, 7): + + async def async_simple_test_rpn(self, x, img_metas): + sleep_interval = self.test_cfg.pop('async_sleep_interval', 0.025) + async with completed( + __name__, 'rpn_head_forward', + sleep_interval=sleep_interval): + rpn_outs = self(x) + + proposal_list = self.get_bboxes(*rpn_outs, img_metas) + return proposal_list + + def simple_test_rpn(self, x, img_metas): + """Test without augmentation. + + Args: + x (tuple[Tensor]): Features from the upstream network, each is + a 4D-tensor. + img_metas (list[dict]): Meta info of each image. + + Returns: + list[Tensor]: Proposals of each image. + """ + rpn_outs = self(x) + proposal_list = self.get_bboxes(*rpn_outs, img_metas) + return proposal_list + + def aug_test_rpn(self, feats, img_metas): + samples_per_gpu = len(img_metas[0]) + aug_proposals = [[] for _ in range(samples_per_gpu)] + for x, img_meta in zip(feats, img_metas): + proposal_list = self.simple_test_rpn(x, img_meta) + for i, proposals in enumerate(proposal_list): + aug_proposals[i].append(proposals) + # reorganize the order of 'img_metas' to match the dimensions + # of 'aug_proposals' + aug_img_metas = [] + for i in range(samples_per_gpu): + aug_img_meta = [] + for j in range(len(img_metas)): + aug_img_meta.append(img_metas[j][i]) + aug_img_metas.append(aug_img_meta) + # after merging, proposals will be rescaled to the original image size + merged_proposals = [ + merge_aug_proposals(proposals, aug_img_meta, self.test_cfg) + for proposals, aug_img_meta in zip(aug_proposals, aug_img_metas) + ] + return merged_proposals diff --git a/mmdet/models/dense_heads/sabl_retina_head.py b/mmdet/models/dense_heads/sabl_retina_head.py new file mode 100644 index 0000000..8c9a439 --- /dev/null +++ b/mmdet/models/dense_heads/sabl_retina_head.py @@ -0,0 +1,621 @@ +import numpy as np +import torch +import torch.nn as nn +from mmcv.cnn import ConvModule +from mmcv.runner import force_fp32 + +from mmdet.core import (build_anchor_generator, build_assigner, + build_bbox_coder, build_sampler, images_to_levels, + multi_apply, multiclass_nms, unmap) +from ..builder import HEADS, build_loss +from .base_dense_head import BaseDenseHead +from .guided_anchor_head import GuidedAnchorHead + + +@HEADS.register_module() +class SABLRetinaHead(BaseDenseHead): + """Side-Aware Boundary Localization (SABL) for RetinaNet. + + The anchor generation, assigning and sampling in SABLRetinaHead + are the same as GuidedAnchorHead for guided anchoring. + + Please refer to https://arxiv.org/abs/1912.04260 for more details. + + Args: + num_classes (int): Number of classes. + in_channels (int): Number of channels in the input feature map. + stacked_convs (int): Number of Convs for classification \ + and regression branches. Defaults to 4. + feat_channels (int): Number of hidden channels. \ + Defaults to 256. + approx_anchor_generator (dict): Config dict for approx generator. + square_anchor_generator (dict): Config dict for square generator. + conv_cfg (dict): Config dict for ConvModule. Defaults to None. + norm_cfg (dict): Config dict for Norm Layer. Defaults to None. + bbox_coder (dict): Config dict for bbox coder. + reg_decoded_bbox (bool): If true, the regression loss would be + applied directly on decoded bounding boxes, converting both + the predicted boxes and regression targets to absolute + coordinates format. Default False. It should be `True` when + using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head. + train_cfg (dict): Training config of SABLRetinaHead. + test_cfg (dict): Testing config of SABLRetinaHead. + loss_cls (dict): Config of classification loss. + loss_bbox_cls (dict): Config of classification loss for bbox branch. + loss_bbox_reg (dict): Config of regression loss for bbox branch. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + num_classes, + in_channels, + stacked_convs=4, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128]), + conv_cfg=None, + norm_cfg=None, + bbox_coder=dict( + type='BucketingBBoxCoder', + num_buckets=14, + scale_factor=3.0), + reg_decoded_bbox=False, + train_cfg=None, + test_cfg=None, + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.5), + loss_bbox_reg=dict( + type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5), + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=dict( + type='Normal', + name='retina_cls', + std=0.01, + bias_prob=0.01))): + super(SABLRetinaHead, self).__init__(init_cfg) + self.in_channels = in_channels + self.num_classes = num_classes + self.feat_channels = feat_channels + self.num_buckets = bbox_coder['num_buckets'] + self.side_num = int(np.ceil(self.num_buckets / 2)) + + assert (approx_anchor_generator['octave_base_scale'] == + square_anchor_generator['scales'][0]) + assert (approx_anchor_generator['strides'] == + square_anchor_generator['strides']) + + self.approx_anchor_generator = build_anchor_generator( + approx_anchor_generator) + self.square_anchor_generator = build_anchor_generator( + square_anchor_generator) + self.approxs_per_octave = ( + self.approx_anchor_generator.num_base_anchors[0]) + + # one anchor per location + self.num_anchors = 1 + self.stacked_convs = stacked_convs + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + + self.reg_decoded_bbox = reg_decoded_bbox + + self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False) + self.sampling = loss_cls['type'] not in [ + 'FocalLoss', 'GHMC', 'QualityFocalLoss' + ] + if self.use_sigmoid_cls: + self.cls_out_channels = num_classes + else: + self.cls_out_channels = num_classes + 1 + + self.bbox_coder = build_bbox_coder(bbox_coder) + self.loss_cls = build_loss(loss_cls) + self.loss_bbox_cls = build_loss(loss_bbox_cls) + self.loss_bbox_reg = build_loss(loss_bbox_reg) + + self.train_cfg = train_cfg + self.test_cfg = test_cfg + + if self.train_cfg: + self.assigner = build_assigner(self.train_cfg.assigner) + # use PseudoSampler when sampling is False + if self.sampling and hasattr(self.train_cfg, 'sampler'): + sampler_cfg = self.train_cfg.sampler + else: + sampler_cfg = dict(type='PseudoSampler') + self.sampler = build_sampler(sampler_cfg, context=self) + + self.fp16_enabled = False + self._init_layers() + + def _init_layers(self): + self.relu = nn.ReLU(inplace=True) + self.cls_convs = nn.ModuleList() + self.reg_convs = nn.ModuleList() + for i in range(self.stacked_convs): + chn = self.in_channels if i == 0 else self.feat_channels + self.cls_convs.append( + ConvModule( + chn, + self.feat_channels, + 3, + stride=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + self.reg_convs.append( + ConvModule( + chn, + self.feat_channels, + 3, + stride=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + self.retina_cls = nn.Conv2d( + self.feat_channels, self.cls_out_channels, 3, padding=1) + self.retina_bbox_reg = nn.Conv2d( + self.feat_channels, self.side_num * 4, 3, padding=1) + self.retina_bbox_cls = nn.Conv2d( + self.feat_channels, self.side_num * 4, 3, padding=1) + + def forward_single(self, x): + cls_feat = x + reg_feat = x + for cls_conv in self.cls_convs: + cls_feat = cls_conv(cls_feat) + for reg_conv in self.reg_convs: + reg_feat = reg_conv(reg_feat) + cls_score = self.retina_cls(cls_feat) + bbox_cls_pred = self.retina_bbox_cls(reg_feat) + bbox_reg_pred = self.retina_bbox_reg(reg_feat) + bbox_pred = (bbox_cls_pred, bbox_reg_pred) + return cls_score, bbox_pred + + def forward(self, feats): + return multi_apply(self.forward_single, feats) + + def get_anchors(self, featmap_sizes, img_metas, device='cuda'): + """Get squares according to feature map sizes and guided anchors. + + Args: + featmap_sizes (list[tuple]): Multi-level feature map sizes. + img_metas (list[dict]): Image meta info. + device (torch.device | str): device for returned tensors + + Returns: + tuple: square approxs of each image + """ + num_imgs = len(img_metas) + + # since feature map sizes of all images are the same, we only compute + # squares for one time + multi_level_squares = self.square_anchor_generator.grid_anchors( + featmap_sizes, device=device) + squares_list = [multi_level_squares for _ in range(num_imgs)] + + return squares_list + + def get_target(self, + approx_list, + inside_flag_list, + square_list, + gt_bboxes_list, + img_metas, + gt_bboxes_ignore_list=None, + gt_labels_list=None, + label_channels=None, + sampling=True, + unmap_outputs=True): + """Compute bucketing targets. + Args: + approx_list (list[list]): Multi level approxs of each image. + inside_flag_list (list[list]): Multi level inside flags of each + image. + square_list (list[list]): Multi level squares of each image. + gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image. + img_metas (list[dict]): Meta info of each image. + gt_bboxes_ignore_list (list[Tensor]): ignore list of gt bboxes. + gt_bboxes_list (list[Tensor]): Gt bboxes of each image. + label_channels (int): Channel of label. + sampling (bool): Sample Anchors or not. + unmap_outputs (bool): unmap outputs or not. + + Returns: + tuple: Returns a tuple containing learning targets. + + - labels_list (list[Tensor]): Labels of each level. + - label_weights_list (list[Tensor]): Label weights of each \ + level. + - bbox_cls_targets_list (list[Tensor]): BBox cls targets of \ + each level. + - bbox_cls_weights_list (list[Tensor]): BBox cls weights of \ + each level. + - bbox_reg_targets_list (list[Tensor]): BBox reg targets of \ + each level. + - bbox_reg_weights_list (list[Tensor]): BBox reg weights of \ + each level. + - num_total_pos (int): Number of positive samples in all \ + images. + - num_total_neg (int): Number of negative samples in all \ + images. + """ + num_imgs = len(img_metas) + assert len(approx_list) == len(inside_flag_list) == len( + square_list) == num_imgs + # anchor number of multi levels + num_level_squares = [squares.size(0) for squares in square_list[0]] + # concat all level anchors and flags to a single tensor + inside_flag_flat_list = [] + approx_flat_list = [] + square_flat_list = [] + for i in range(num_imgs): + assert len(square_list[i]) == len(inside_flag_list[i]) + inside_flag_flat_list.append(torch.cat(inside_flag_list[i])) + approx_flat_list.append(torch.cat(approx_list[i])) + square_flat_list.append(torch.cat(square_list[i])) + + # compute targets for each image + if gt_bboxes_ignore_list is None: + gt_bboxes_ignore_list = [None for _ in range(num_imgs)] + if gt_labels_list is None: + gt_labels_list = [None for _ in range(num_imgs)] + (all_labels, all_label_weights, all_bbox_cls_targets, + all_bbox_cls_weights, all_bbox_reg_targets, all_bbox_reg_weights, + pos_inds_list, neg_inds_list) = multi_apply( + self._get_target_single, + approx_flat_list, + inside_flag_flat_list, + square_flat_list, + gt_bboxes_list, + gt_bboxes_ignore_list, + gt_labels_list, + img_metas, + label_channels=label_channels, + sampling=sampling, + unmap_outputs=unmap_outputs) + # no valid anchors + if any([labels is None for labels in all_labels]): + return None + # sampled anchors of all images + num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list]) + num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list]) + # split targets to a list w.r.t. multiple levels + labels_list = images_to_levels(all_labels, num_level_squares) + label_weights_list = images_to_levels(all_label_weights, + num_level_squares) + bbox_cls_targets_list = images_to_levels(all_bbox_cls_targets, + num_level_squares) + bbox_cls_weights_list = images_to_levels(all_bbox_cls_weights, + num_level_squares) + bbox_reg_targets_list = images_to_levels(all_bbox_reg_targets, + num_level_squares) + bbox_reg_weights_list = images_to_levels(all_bbox_reg_weights, + num_level_squares) + return (labels_list, label_weights_list, bbox_cls_targets_list, + bbox_cls_weights_list, bbox_reg_targets_list, + bbox_reg_weights_list, num_total_pos, num_total_neg) + + def _get_target_single(self, + flat_approxs, + inside_flags, + flat_squares, + gt_bboxes, + gt_bboxes_ignore, + gt_labels, + img_meta, + label_channels=None, + sampling=True, + unmap_outputs=True): + """Compute regression and classification targets for anchors in a + single image. + + Args: + flat_approxs (Tensor): flat approxs of a single image, + shape (n, 4) + inside_flags (Tensor): inside flags of a single image, + shape (n, ). + flat_squares (Tensor): flat squares of a single image, + shape (approxs_per_octave * n, 4) + gt_bboxes (Tensor): Ground truth bboxes of a single image, \ + shape (num_gts, 4). + gt_bboxes_ignore (Tensor): Ground truth bboxes to be + ignored, shape (num_ignored_gts, 4). + gt_labels (Tensor): Ground truth labels of each box, + shape (num_gts,). + img_meta (dict): Meta info of the image. + label_channels (int): Channel of label. + sampling (bool): Sample Anchors or not. + unmap_outputs (bool): unmap outputs or not. + + Returns: + tuple: + + - labels_list (Tensor): Labels in a single image + - label_weights (Tensor): Label weights in a single image + - bbox_cls_targets (Tensor): BBox cls targets in a single image + - bbox_cls_weights (Tensor): BBox cls weights in a single image + - bbox_reg_targets (Tensor): BBox reg targets in a single image + - bbox_reg_weights (Tensor): BBox reg weights in a single image + - num_total_pos (int): Number of positive samples \ + in a single image + - num_total_neg (int): Number of negative samples \ + in a single image + """ + if not inside_flags.any(): + return (None, ) * 8 + # assign gt and sample anchors + expand_inside_flags = inside_flags[:, None].expand( + -1, self.approxs_per_octave).reshape(-1) + approxs = flat_approxs[expand_inside_flags, :] + squares = flat_squares[inside_flags, :] + + assign_result = self.assigner.assign(approxs, squares, + self.approxs_per_octave, + gt_bboxes, gt_bboxes_ignore) + sampling_result = self.sampler.sample(assign_result, squares, + gt_bboxes) + + num_valid_squares = squares.shape[0] + bbox_cls_targets = squares.new_zeros( + (num_valid_squares, self.side_num * 4)) + bbox_cls_weights = squares.new_zeros( + (num_valid_squares, self.side_num * 4)) + bbox_reg_targets = squares.new_zeros( + (num_valid_squares, self.side_num * 4)) + bbox_reg_weights = squares.new_zeros( + (num_valid_squares, self.side_num * 4)) + labels = squares.new_full((num_valid_squares, ), + self.num_classes, + dtype=torch.long) + label_weights = squares.new_zeros(num_valid_squares, dtype=torch.float) + + pos_inds = sampling_result.pos_inds + neg_inds = sampling_result.neg_inds + if len(pos_inds) > 0: + (pos_bbox_reg_targets, pos_bbox_reg_weights, pos_bbox_cls_targets, + pos_bbox_cls_weights) = self.bbox_coder.encode( + sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes) + + bbox_cls_targets[pos_inds, :] = pos_bbox_cls_targets + bbox_reg_targets[pos_inds, :] = pos_bbox_reg_targets + bbox_cls_weights[pos_inds, :] = pos_bbox_cls_weights + bbox_reg_weights[pos_inds, :] = pos_bbox_reg_weights + if gt_labels is None: + # Only rpn gives gt_labels as None + # Foreground is the first class + labels[pos_inds] = 0 + else: + labels[pos_inds] = gt_labels[ + sampling_result.pos_assigned_gt_inds] + if self.train_cfg.pos_weight <= 0: + label_weights[pos_inds] = 1.0 + else: + label_weights[pos_inds] = self.train_cfg.pos_weight + if len(neg_inds) > 0: + label_weights[neg_inds] = 1.0 + + # map up to original set of anchors + if unmap_outputs: + num_total_anchors = flat_squares.size(0) + labels = unmap( + labels, num_total_anchors, inside_flags, fill=self.num_classes) + label_weights = unmap(label_weights, num_total_anchors, + inside_flags) + bbox_cls_targets = unmap(bbox_cls_targets, num_total_anchors, + inside_flags) + bbox_cls_weights = unmap(bbox_cls_weights, num_total_anchors, + inside_flags) + bbox_reg_targets = unmap(bbox_reg_targets, num_total_anchors, + inside_flags) + bbox_reg_weights = unmap(bbox_reg_weights, num_total_anchors, + inside_flags) + return (labels, label_weights, bbox_cls_targets, bbox_cls_weights, + bbox_reg_targets, bbox_reg_weights, pos_inds, neg_inds) + + def loss_single(self, cls_score, bbox_pred, labels, label_weights, + bbox_cls_targets, bbox_cls_weights, bbox_reg_targets, + bbox_reg_weights, num_total_samples): + # classification loss + labels = labels.reshape(-1) + label_weights = label_weights.reshape(-1) + cls_score = cls_score.permute(0, 2, 3, + 1).reshape(-1, self.cls_out_channels) + loss_cls = self.loss_cls( + cls_score, labels, label_weights, avg_factor=num_total_samples) + # regression loss + bbox_cls_targets = bbox_cls_targets.reshape(-1, self.side_num * 4) + bbox_cls_weights = bbox_cls_weights.reshape(-1, self.side_num * 4) + bbox_reg_targets = bbox_reg_targets.reshape(-1, self.side_num * 4) + bbox_reg_weights = bbox_reg_weights.reshape(-1, self.side_num * 4) + (bbox_cls_pred, bbox_reg_pred) = bbox_pred + bbox_cls_pred = bbox_cls_pred.permute(0, 2, 3, 1).reshape( + -1, self.side_num * 4) + bbox_reg_pred = bbox_reg_pred.permute(0, 2, 3, 1).reshape( + -1, self.side_num * 4) + loss_bbox_cls = self.loss_bbox_cls( + bbox_cls_pred, + bbox_cls_targets.long(), + bbox_cls_weights, + avg_factor=num_total_samples * 4 * self.side_num) + loss_bbox_reg = self.loss_bbox_reg( + bbox_reg_pred, + bbox_reg_targets, + bbox_reg_weights, + avg_factor=num_total_samples * 4 * self.bbox_coder.offset_topk) + return loss_cls, loss_bbox_cls, loss_bbox_reg + + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) + def loss(self, + cls_scores, + bbox_preds, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + assert len(featmap_sizes) == self.approx_anchor_generator.num_levels + + device = cls_scores[0].device + + # get sampled approxes + approxs_list, inside_flag_list = GuidedAnchorHead.get_sampled_approxs( + self, featmap_sizes, img_metas, device=device) + + square_list = self.get_anchors(featmap_sizes, img_metas, device=device) + + label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 + + cls_reg_targets = self.get_target( + approxs_list, + inside_flag_list, + square_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + gt_labels_list=gt_labels, + label_channels=label_channels, + sampling=self.sampling) + if cls_reg_targets is None: + return None + (labels_list, label_weights_list, bbox_cls_targets_list, + bbox_cls_weights_list, bbox_reg_targets_list, bbox_reg_weights_list, + num_total_pos, num_total_neg) = cls_reg_targets + num_total_samples = ( + num_total_pos + num_total_neg if self.sampling else num_total_pos) + losses_cls, losses_bbox_cls, losses_bbox_reg = multi_apply( + self.loss_single, + cls_scores, + bbox_preds, + labels_list, + label_weights_list, + bbox_cls_targets_list, + bbox_cls_weights_list, + bbox_reg_targets_list, + bbox_reg_weights_list, + num_total_samples=num_total_samples) + return dict( + loss_cls=losses_cls, + loss_bbox_cls=losses_bbox_cls, + loss_bbox_reg=losses_bbox_reg) + + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) + def get_bboxes(self, + cls_scores, + bbox_preds, + img_metas, + cfg=None, + rescale=False): + assert len(cls_scores) == len(bbox_preds) + num_levels = len(cls_scores) + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + + device = cls_scores[0].device + mlvl_anchors = self.get_anchors( + featmap_sizes, img_metas, device=device) + result_list = [] + for img_id in range(len(img_metas)): + cls_score_list = [ + cls_scores[i][img_id].detach() for i in range(num_levels) + ] + bbox_cls_pred_list = [ + bbox_preds[i][0][img_id].detach() for i in range(num_levels) + ] + bbox_reg_pred_list = [ + bbox_preds[i][1][img_id].detach() for i in range(num_levels) + ] + img_shape = img_metas[img_id]['img_shape'] + scale_factor = img_metas[img_id]['scale_factor'] + proposals = self.get_bboxes_single(cls_score_list, + bbox_cls_pred_list, + bbox_reg_pred_list, + mlvl_anchors[img_id], img_shape, + scale_factor, cfg, rescale) + result_list.append(proposals) + return result_list + + def get_bboxes_single(self, + cls_scores, + bbox_cls_preds, + bbox_reg_preds, + mlvl_anchors, + img_shape, + scale_factor, + cfg, + rescale=False): + cfg = self.test_cfg if cfg is None else cfg + mlvl_bboxes = [] + mlvl_scores = [] + mlvl_confids = [] + assert len(cls_scores) == len(bbox_cls_preds) == len( + bbox_reg_preds) == len(mlvl_anchors) + for cls_score, bbox_cls_pred, bbox_reg_pred, anchors in zip( + cls_scores, bbox_cls_preds, bbox_reg_preds, mlvl_anchors): + assert cls_score.size()[-2:] == bbox_cls_pred.size( + )[-2:] == bbox_reg_pred.size()[-2::] + cls_score = cls_score.permute(1, 2, + 0).reshape(-1, self.cls_out_channels) + if self.use_sigmoid_cls: + scores = cls_score.sigmoid() + else: + scores = cls_score.softmax(-1) + bbox_cls_pred = bbox_cls_pred.permute(1, 2, 0).reshape( + -1, self.side_num * 4) + bbox_reg_pred = bbox_reg_pred.permute(1, 2, 0).reshape( + -1, self.side_num * 4) + nms_pre = cfg.get('nms_pre', -1) + if nms_pre > 0 and scores.shape[0] > nms_pre: + if self.use_sigmoid_cls: + max_scores, _ = scores.max(dim=1) + else: + max_scores, _ = scores[:, :-1].max(dim=1) + _, topk_inds = max_scores.topk(nms_pre) + anchors = anchors[topk_inds, :] + bbox_cls_pred = bbox_cls_pred[topk_inds, :] + bbox_reg_pred = bbox_reg_pred[topk_inds, :] + scores = scores[topk_inds, :] + bbox_preds = [ + bbox_cls_pred.contiguous(), + bbox_reg_pred.contiguous() + ] + bboxes, confids = self.bbox_coder.decode( + anchors.contiguous(), bbox_preds, max_shape=img_shape) + mlvl_bboxes.append(bboxes) + mlvl_scores.append(scores) + mlvl_confids.append(confids) + mlvl_bboxes = torch.cat(mlvl_bboxes) + if rescale: + mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor) + mlvl_scores = torch.cat(mlvl_scores) + mlvl_confids = torch.cat(mlvl_confids) + if self.use_sigmoid_cls: + padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1) + mlvl_scores = torch.cat([mlvl_scores, padding], dim=1) + det_bboxes, det_labels = multiclass_nms( + mlvl_bboxes, + mlvl_scores, + cfg.score_thr, + cfg.nms, + cfg.max_per_img, + score_factors=mlvl_confids) + return det_bboxes, det_labels diff --git a/mmdet/models/dense_heads/ssd_head.py b/mmdet/models/dense_heads/ssd_head.py new file mode 100644 index 0000000..1e2151a --- /dev/null +++ b/mmdet/models/dense_heads/ssd_head.py @@ -0,0 +1,264 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.runner import ModuleList, force_fp32 + +from mmdet.core import (build_anchor_generator, build_assigner, + build_bbox_coder, build_sampler, multi_apply) +from ..builder import HEADS +from ..losses import smooth_l1_loss +from .anchor_head import AnchorHead + + +# TODO: add loss evaluator for SSD +@HEADS.register_module() +class SSDHead(AnchorHead): + """SSD head used in https://arxiv.org/abs/1512.02325. + + Args: + num_classes (int): Number of categories excluding the background + category. + in_channels (int): Number of channels in the input feature map. + anchor_generator (dict): Config dict for anchor generator + bbox_coder (dict): Config of bounding box coder. + reg_decoded_bbox (bool): If true, the regression loss would be + applied directly on decoded bounding boxes, converting both + the predicted boxes and regression targets to absolute + coordinates format. Default False. It should be `True` when + using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head. + train_cfg (dict): Training config of anchor head. + test_cfg (dict): Testing config of anchor head. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ # noqa: W605 + + def __init__(self, + num_classes=80, + in_channels=(512, 1024, 512, 256, 256, 256), + anchor_generator=dict( + type='SSDAnchorGenerator', + scale_major=False, + input_size=300, + strides=[8, 16, 32, 64, 100, 300], + ratios=([2], [2, 3], [2, 3], [2, 3], [2], [2]), + basesize_ratio_range=(0.1, 0.9)), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=True, + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0], + ), + reg_decoded_bbox=False, + train_cfg=None, + test_cfg=None, + init_cfg=dict( + type='Xavier', + layer='Conv2d', + distribution='uniform', + bias=0)): + super(AnchorHead, self).__init__(init_cfg) + self.num_classes = num_classes + self.in_channels = in_channels + self.cls_out_channels = num_classes + 1 # add background class + self.anchor_generator = build_anchor_generator(anchor_generator) + num_anchors = self.anchor_generator.num_base_anchors + + reg_convs = [] + cls_convs = [] + for i in range(len(in_channels)): + reg_convs.append( + nn.Conv2d( + in_channels[i], + num_anchors[i] * 4, + kernel_size=3, + padding=1)) + cls_convs.append( + nn.Conv2d( + in_channels[i], + num_anchors[i] * (num_classes + 1), + kernel_size=3, + padding=1)) + self.reg_convs = ModuleList(reg_convs) + self.cls_convs = ModuleList(cls_convs) + + self.bbox_coder = build_bbox_coder(bbox_coder) + self.reg_decoded_bbox = reg_decoded_bbox + self.use_sigmoid_cls = False + self.cls_focal_loss = False + self.train_cfg = train_cfg + self.test_cfg = test_cfg + # set sampling=False for archor_target + self.sampling = False + if self.train_cfg: + self.assigner = build_assigner(self.train_cfg.assigner) + # SSD sampling=False so use PseudoSampler + sampler_cfg = dict(type='PseudoSampler') + self.sampler = build_sampler(sampler_cfg, context=self) + self.fp16_enabled = False + + def forward(self, feats): + """Forward features from the upstream network. + + Args: + feats (tuple[Tensor]): Features from the upstream network, each is + a 4D-tensor. + + Returns: + tuple: + cls_scores (list[Tensor]): Classification scores for all scale + levels, each is a 4D-tensor, the channels number is + num_anchors * num_classes. + bbox_preds (list[Tensor]): Box energies / deltas for all scale + levels, each is a 4D-tensor, the channels number is + num_anchors * 4. + """ + cls_scores = [] + bbox_preds = [] + for feat, reg_conv, cls_conv in zip(feats, self.reg_convs, + self.cls_convs): + cls_scores.append(cls_conv(feat)) + bbox_preds.append(reg_conv(feat)) + return cls_scores, bbox_preds + + def loss_single(self, cls_score, bbox_pred, anchor, labels, label_weights, + bbox_targets, bbox_weights, num_total_samples): + """Compute loss of a single image. + + Args: + cls_score (Tensor): Box scores for eachimage + Has shape (num_total_anchors, num_classes). + bbox_pred (Tensor): Box energies / deltas for each image + level with shape (num_total_anchors, 4). + anchors (Tensor): Box reference for each scale level with shape + (num_total_anchors, 4). + labels (Tensor): Labels of each anchors with shape + (num_total_anchors,). + label_weights (Tensor): Label weights of each anchor with shape + (num_total_anchors,) + bbox_targets (Tensor): BBox regression targets of each anchor wight + shape (num_total_anchors, 4). + bbox_weights (Tensor): BBox regression loss weights of each anchor + with shape (num_total_anchors, 4). + num_total_samples (int): If sampling, num total samples equal to + the number of total anchors; Otherwise, it is the number of + positive anchors. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + + loss_cls_all = F.cross_entropy( + cls_score, labels, reduction='none') * label_weights + # FG cat_id: [0, num_classes -1], BG cat_id: num_classes + pos_inds = ((labels >= 0) & + (labels < self.num_classes)).nonzero().reshape(-1) + neg_inds = (labels == self.num_classes).nonzero().view(-1) + + num_pos_samples = pos_inds.size(0) + num_neg_samples = self.train_cfg.neg_pos_ratio * num_pos_samples + if num_neg_samples > neg_inds.size(0): + num_neg_samples = neg_inds.size(0) + topk_loss_cls_neg, _ = loss_cls_all[neg_inds].topk(num_neg_samples) + loss_cls_pos = loss_cls_all[pos_inds].sum() + loss_cls_neg = topk_loss_cls_neg.sum() + loss_cls = (loss_cls_pos + loss_cls_neg) / num_total_samples + + if self.reg_decoded_bbox: + # When the regression loss (e.g. `IouLoss`, `GIouLoss`) + # is applied directly on the decoded bounding boxes, it + # decodes the already encoded coordinates to absolute format. + bbox_pred = self.bbox_coder.decode(anchor, bbox_pred) + + loss_bbox = smooth_l1_loss( + bbox_pred, + bbox_targets, + bbox_weights, + beta=self.train_cfg.smoothl1_beta, + avg_factor=num_total_samples) + return loss_cls[None], loss_bbox + + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) + def loss(self, + cls_scores, + bbox_preds, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute losses of the head. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (N, num_anchors * num_classes, H, W) + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_anchors * 4, H, W) + gt_bboxes (list[Tensor]): each item are the truth boxes for each + image in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + assert len(featmap_sizes) == self.anchor_generator.num_levels + + device = cls_scores[0].device + + anchor_list, valid_flag_list = self.get_anchors( + featmap_sizes, img_metas, device=device) + cls_reg_targets = self.get_targets( + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + gt_labels_list=gt_labels, + label_channels=1, + unmap_outputs=False) + if cls_reg_targets is None: + return None + (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, + num_total_pos, num_total_neg) = cls_reg_targets + + num_images = len(img_metas) + all_cls_scores = torch.cat([ + s.permute(0, 2, 3, 1).reshape( + num_images, -1, self.cls_out_channels) for s in cls_scores + ], 1) + all_labels = torch.cat(labels_list, -1).view(num_images, -1) + all_label_weights = torch.cat(label_weights_list, + -1).view(num_images, -1) + all_bbox_preds = torch.cat([ + b.permute(0, 2, 3, 1).reshape(num_images, -1, 4) + for b in bbox_preds + ], -2) + all_bbox_targets = torch.cat(bbox_targets_list, + -2).view(num_images, -1, 4) + all_bbox_weights = torch.cat(bbox_weights_list, + -2).view(num_images, -1, 4) + + # concat all level anchors to a single tensor + all_anchors = [] + for i in range(num_images): + all_anchors.append(torch.cat(anchor_list[i])) + + # check NaN and Inf + assert torch.isfinite(all_cls_scores).all().item(), \ + 'classification scores become infinite or NaN!' + assert torch.isfinite(all_bbox_preds).all().item(), \ + 'bbox predications become infinite or NaN!' + + losses_cls, losses_bbox = multi_apply( + self.loss_single, + all_cls_scores, + all_bbox_preds, + all_anchors, + all_labels, + all_label_weights, + all_bbox_targets, + all_bbox_weights, + num_total_samples=num_total_pos) + return dict(loss_cls=losses_cls, loss_bbox=losses_bbox) diff --git a/mmdet/models/dense_heads/vfnet_head.py b/mmdet/models/dense_heads/vfnet_head.py new file mode 100644 index 0000000..6d887d5 --- /dev/null +++ b/mmdet/models/dense_heads/vfnet_head.py @@ -0,0 +1,791 @@ +import numpy as np +import torch +import torch.nn as nn +from mmcv.cnn import ConvModule, Scale +from mmcv.ops import DeformConv2d +from mmcv.runner import force_fp32 + +from mmdet.core import (bbox2distance, bbox_overlaps, build_anchor_generator, + build_assigner, build_sampler, distance2bbox, + multi_apply, multiclass_nms, reduce_mean) +from ..builder import HEADS, build_loss +from .atss_head import ATSSHead +from .fcos_head import FCOSHead + +INF = 1e8 + + +@HEADS.register_module() +class VFNetHead(ATSSHead, FCOSHead): + """Head of `VarifocalNet (VFNet): An IoU-aware Dense Object + Detector.`_. + + The VFNet predicts IoU-aware classification scores which mix the + object presence confidence and object localization accuracy as the + detection score. It is built on the FCOS architecture and uses ATSS + for defining positive/negative training examples. The VFNet is trained + with Varifocal Loss and empolys star-shaped deformable convolution to + extract features for a bbox. + + Args: + num_classes (int): Number of categories excluding the background + category. + in_channels (int): Number of channels in the input feature map. + regress_ranges (tuple[tuple[int, int]]): Regress range of multiple + level points. + center_sampling (bool): If true, use center sampling. Default: False. + center_sample_radius (float): Radius of center sampling. Default: 1.5. + sync_num_pos (bool): If true, synchronize the number of positive + examples across GPUs. Default: True + gradient_mul (float): The multiplier to gradients from bbox refinement + and recognition. Default: 0.1. + bbox_norm_type (str): The bbox normalization type, 'reg_denom' or + 'stride'. Default: reg_denom + loss_cls_fl (dict): Config of focal loss. + use_vfl (bool): If true, use varifocal loss for training. + Default: True. + loss_cls (dict): Config of varifocal loss. + loss_bbox (dict): Config of localization loss, GIoU Loss. + loss_bbox (dict): Config of localization refinement loss, GIoU Loss. + norm_cfg (dict): dictionary to construct and config norm layer. + Default: norm_cfg=dict(type='GN', num_groups=32, + requires_grad=True). + use_atss (bool): If true, use ATSS to define positive/negative + examples. Default: True. + anchor_generator (dict): Config of anchor generator for ATSS. + init_cfg (dict or list[dict], optional): Initialization config dict. + + Example: + >>> self = VFNetHead(11, 7) + >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]] + >>> cls_score, bbox_pred, bbox_pred_refine= self.forward(feats) + >>> assert len(cls_score) == len(self.scales) + """ # noqa: E501 + + def __init__(self, + num_classes, + in_channels, + regress_ranges=((-1, 64), (64, 128), (128, 256), (256, 512), + (512, INF)), + center_sampling=False, + center_sample_radius=1.5, + sync_num_pos=True, + gradient_mul=0.1, + bbox_norm_type='reg_denom', + loss_cls_fl=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + use_vfl=True, + loss_cls=dict( + type='VarifocalLoss', + use_sigmoid=True, + alpha=0.75, + gamma=2.0, + iou_weighted=True, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=1.5), + loss_bbox_refine=dict(type='GIoULoss', loss_weight=2.0), + norm_cfg=dict(type='GN', num_groups=32, requires_grad=True), + use_atss=True, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + center_offset=0.0, + strides=[8, 16, 32, 64, 128]), + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=dict( + type='Normal', + name='vfnet_cls', + std=0.01, + bias_prob=0.01)), + **kwargs): + # dcn base offsets, adapted from reppoints_head.py + self.num_dconv_points = 9 + self.dcn_kernel = int(np.sqrt(self.num_dconv_points)) + self.dcn_pad = int((self.dcn_kernel - 1) / 2) + dcn_base = np.arange(-self.dcn_pad, + self.dcn_pad + 1).astype(np.float64) + dcn_base_y = np.repeat(dcn_base, self.dcn_kernel) + dcn_base_x = np.tile(dcn_base, self.dcn_kernel) + dcn_base_offset = np.stack([dcn_base_y, dcn_base_x], axis=1).reshape( + (-1)) + self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1) + + super(FCOSHead, self).__init__( + num_classes, + in_channels, + norm_cfg=norm_cfg, + init_cfg=init_cfg, + **kwargs) + self.regress_ranges = regress_ranges + self.reg_denoms = [ + regress_range[-1] for regress_range in regress_ranges + ] + self.reg_denoms[-1] = self.reg_denoms[-2] * 2 + self.center_sampling = center_sampling + self.center_sample_radius = center_sample_radius + self.sync_num_pos = sync_num_pos + self.bbox_norm_type = bbox_norm_type + self.gradient_mul = gradient_mul + self.use_vfl = use_vfl + if self.use_vfl: + self.loss_cls = build_loss(loss_cls) + else: + self.loss_cls = build_loss(loss_cls_fl) + self.loss_bbox = build_loss(loss_bbox) + self.loss_bbox_refine = build_loss(loss_bbox_refine) + + # for getting ATSS targets + self.use_atss = use_atss + self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False) + self.anchor_generator = build_anchor_generator(anchor_generator) + self.anchor_center_offset = anchor_generator['center_offset'] + self.num_anchors = self.anchor_generator.num_base_anchors[0] + self.sampling = False + if self.train_cfg: + self.assigner = build_assigner(self.train_cfg.assigner) + sampler_cfg = dict(type='PseudoSampler') + self.sampler = build_sampler(sampler_cfg, context=self) + + def _init_layers(self): + """Initialize layers of the head.""" + super(FCOSHead, self)._init_cls_convs() + super(FCOSHead, self)._init_reg_convs() + self.relu = nn.ReLU(inplace=True) + self.vfnet_reg_conv = ConvModule( + self.feat_channels, + self.feat_channels, + 3, + stride=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + bias=self.conv_bias) + self.vfnet_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1) + self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides]) + + self.vfnet_reg_refine_dconv = DeformConv2d( + self.feat_channels, + self.feat_channels, + self.dcn_kernel, + 1, + padding=self.dcn_pad) + self.vfnet_reg_refine = nn.Conv2d(self.feat_channels, 4, 3, padding=1) + self.scales_refine = nn.ModuleList([Scale(1.0) for _ in self.strides]) + + self.vfnet_cls_dconv = DeformConv2d( + self.feat_channels, + self.feat_channels, + self.dcn_kernel, + 1, + padding=self.dcn_pad) + self.vfnet_cls = nn.Conv2d( + self.feat_channels, self.cls_out_channels, 3, padding=1) + + def forward(self, feats): + """Forward features from the upstream network. + + Args: + feats (tuple[Tensor]): Features from the upstream network, each is + a 4D-tensor. + + Returns: + tuple: + cls_scores (list[Tensor]): Box iou-aware scores for each scale + level, each is a 4D-tensor, the channel number is + num_points * num_classes. + bbox_preds (list[Tensor]): Box offsets for each + scale level, each is a 4D-tensor, the channel number is + num_points * 4. + bbox_preds_refine (list[Tensor]): Refined Box offsets for + each scale level, each is a 4D-tensor, the channel + number is num_points * 4. + """ + return multi_apply(self.forward_single, feats, self.scales, + self.scales_refine, self.strides, self.reg_denoms) + + def forward_single(self, x, scale, scale_refine, stride, reg_denom): + """Forward features of a single scale level. + + Args: + x (Tensor): FPN feature maps of the specified stride. + scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize + the bbox prediction. + scale_refine (:obj: `mmcv.cnn.Scale`): Learnable scale module to + resize the refined bbox prediction. + stride (int): The corresponding stride for feature maps, + used to normalize the bbox prediction when + bbox_norm_type = 'stride'. + reg_denom (int): The corresponding regression range for feature + maps, only used to normalize the bbox prediction when + bbox_norm_type = 'reg_denom'. + + Returns: + tuple: iou-aware cls scores for each box, bbox predictions and + refined bbox predictions of input feature maps. + """ + cls_feat = x + reg_feat = x + + for cls_layer in self.cls_convs: + cls_feat = cls_layer(cls_feat) + + for reg_layer in self.reg_convs: + reg_feat = reg_layer(reg_feat) + + # predict the bbox_pred of different level + reg_feat_init = self.vfnet_reg_conv(reg_feat) + if self.bbox_norm_type == 'reg_denom': + bbox_pred = scale( + self.vfnet_reg(reg_feat_init)).float().exp() * reg_denom + elif self.bbox_norm_type == 'stride': + bbox_pred = scale( + self.vfnet_reg(reg_feat_init)).float().exp() * stride + else: + raise NotImplementedError + + # compute star deformable convolution offsets + # converting dcn_offset to reg_feat.dtype thus VFNet can be + # trained with FP16 + dcn_offset = self.star_dcn_offset(bbox_pred, self.gradient_mul, + stride).to(reg_feat.dtype) + + # refine the bbox_pred + reg_feat = self.relu(self.vfnet_reg_refine_dconv(reg_feat, dcn_offset)) + bbox_pred_refine = scale_refine( + self.vfnet_reg_refine(reg_feat)).float().exp() + bbox_pred_refine = bbox_pred_refine * bbox_pred.detach() + + # predict the iou-aware cls score + cls_feat = self.relu(self.vfnet_cls_dconv(cls_feat, dcn_offset)) + cls_score = self.vfnet_cls(cls_feat) + + return cls_score, bbox_pred, bbox_pred_refine + + def star_dcn_offset(self, bbox_pred, gradient_mul, stride): + """Compute the star deformable conv offsets. + + Args: + bbox_pred (Tensor): Predicted bbox distance offsets (l, r, t, b). + gradient_mul (float): Gradient multiplier. + stride (int): The corresponding stride for feature maps, + used to project the bbox onto the feature map. + + Returns: + dcn_offsets (Tensor): The offsets for deformable convolution. + """ + dcn_base_offset = self.dcn_base_offset.type_as(bbox_pred) + bbox_pred_grad_mul = (1 - gradient_mul) * bbox_pred.detach() + \ + gradient_mul * bbox_pred + # map to the feature map scale + bbox_pred_grad_mul = bbox_pred_grad_mul / stride + N, C, H, W = bbox_pred.size() + + x1 = bbox_pred_grad_mul[:, 0, :, :] + y1 = bbox_pred_grad_mul[:, 1, :, :] + x2 = bbox_pred_grad_mul[:, 2, :, :] + y2 = bbox_pred_grad_mul[:, 3, :, :] + bbox_pred_grad_mul_offset = bbox_pred.new_zeros( + N, 2 * self.num_dconv_points, H, W) + bbox_pred_grad_mul_offset[:, 0, :, :] = -1.0 * y1 # -y1 + bbox_pred_grad_mul_offset[:, 1, :, :] = -1.0 * x1 # -x1 + bbox_pred_grad_mul_offset[:, 2, :, :] = -1.0 * y1 # -y1 + bbox_pred_grad_mul_offset[:, 4, :, :] = -1.0 * y1 # -y1 + bbox_pred_grad_mul_offset[:, 5, :, :] = x2 # x2 + bbox_pred_grad_mul_offset[:, 7, :, :] = -1.0 * x1 # -x1 + bbox_pred_grad_mul_offset[:, 11, :, :] = x2 # x2 + bbox_pred_grad_mul_offset[:, 12, :, :] = y2 # y2 + bbox_pred_grad_mul_offset[:, 13, :, :] = -1.0 * x1 # -x1 + bbox_pred_grad_mul_offset[:, 14, :, :] = y2 # y2 + bbox_pred_grad_mul_offset[:, 16, :, :] = y2 # y2 + bbox_pred_grad_mul_offset[:, 17, :, :] = x2 # x2 + dcn_offset = bbox_pred_grad_mul_offset - dcn_base_offset + + return dcn_offset + + @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'bbox_preds_refine')) + def loss(self, + cls_scores, + bbox_preds, + bbox_preds_refine, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute loss of the head. + + Args: + cls_scores (list[Tensor]): Box iou-aware scores for each scale + level, each is a 4D-tensor, the channel number is + num_points * num_classes. + bbox_preds (list[Tensor]): Box offsets for each + scale level, each is a 4D-tensor, the channel number is + num_points * 4. + bbox_preds_refine (list[Tensor]): Refined Box offsets for + each scale level, each is a 4D-tensor, the channel + number is num_points * 4. + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + Default: None. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + assert len(cls_scores) == len(bbox_preds) == len(bbox_preds_refine) + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + all_level_points = self.get_points(featmap_sizes, bbox_preds[0].dtype, + bbox_preds[0].device) + labels, label_weights, bbox_targets, bbox_weights = self.get_targets( + cls_scores, all_level_points, gt_bboxes, gt_labels, img_metas, + gt_bboxes_ignore) + + num_imgs = cls_scores[0].size(0) + # flatten cls_scores, bbox_preds and bbox_preds_refine + flatten_cls_scores = [ + cls_score.permute(0, 2, 3, + 1).reshape(-1, + self.cls_out_channels).contiguous() + for cls_score in cls_scores + ] + flatten_bbox_preds = [ + bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4).contiguous() + for bbox_pred in bbox_preds + ] + flatten_bbox_preds_refine = [ + bbox_pred_refine.permute(0, 2, 3, 1).reshape(-1, 4).contiguous() + for bbox_pred_refine in bbox_preds_refine + ] + flatten_cls_scores = torch.cat(flatten_cls_scores) + flatten_bbox_preds = torch.cat(flatten_bbox_preds) + flatten_bbox_preds_refine = torch.cat(flatten_bbox_preds_refine) + flatten_labels = torch.cat(labels) + flatten_bbox_targets = torch.cat(bbox_targets) + # repeat points to align with bbox_preds + flatten_points = torch.cat( + [points.repeat(num_imgs, 1) for points in all_level_points]) + + # FG cat_id: [0, num_classes - 1], BG cat_id: num_classes + bg_class_ind = self.num_classes + pos_inds = torch.where( + ((flatten_labels >= 0) & (flatten_labels < bg_class_ind)) > 0)[0] + num_pos = len(pos_inds) + + pos_bbox_preds = flatten_bbox_preds[pos_inds] + pos_bbox_preds_refine = flatten_bbox_preds_refine[pos_inds] + pos_labels = flatten_labels[pos_inds] + + # sync num_pos across all gpus + if self.sync_num_pos: + num_pos_avg_per_gpu = reduce_mean( + pos_inds.new_tensor(num_pos).float()).item() + num_pos_avg_per_gpu = max(num_pos_avg_per_gpu, 1.0) + else: + num_pos_avg_per_gpu = num_pos + + pos_bbox_targets = flatten_bbox_targets[pos_inds] + pos_points = flatten_points[pos_inds] + + pos_decoded_bbox_preds = distance2bbox(pos_points, pos_bbox_preds) + pos_decoded_target_preds = distance2bbox(pos_points, pos_bbox_targets) + iou_targets_ini = bbox_overlaps( + pos_decoded_bbox_preds, + pos_decoded_target_preds.detach(), + is_aligned=True).clamp(min=1e-6) + bbox_weights_ini = iou_targets_ini.clone().detach() + bbox_avg_factor_ini = reduce_mean( + bbox_weights_ini.sum()).clamp_(min=1).item() + + pos_decoded_bbox_preds_refine = \ + distance2bbox(pos_points, pos_bbox_preds_refine) + iou_targets_rf = bbox_overlaps( + pos_decoded_bbox_preds_refine, + pos_decoded_target_preds.detach(), + is_aligned=True).clamp(min=1e-6) + bbox_weights_rf = iou_targets_rf.clone().detach() + bbox_avg_factor_rf = reduce_mean( + bbox_weights_rf.sum()).clamp_(min=1).item() + + if num_pos > 0: + loss_bbox = self.loss_bbox( + pos_decoded_bbox_preds, + pos_decoded_target_preds.detach(), + weight=bbox_weights_ini, + avg_factor=bbox_avg_factor_ini) + + loss_bbox_refine = self.loss_bbox_refine( + pos_decoded_bbox_preds_refine, + pos_decoded_target_preds.detach(), + weight=bbox_weights_rf, + avg_factor=bbox_avg_factor_rf) + + # build IoU-aware cls_score targets + if self.use_vfl: + pos_ious = iou_targets_rf.clone().detach() + cls_iou_targets = torch.zeros_like(flatten_cls_scores) + cls_iou_targets[pos_inds, pos_labels] = pos_ious + else: + loss_bbox = pos_bbox_preds.sum() * 0 + loss_bbox_refine = pos_bbox_preds_refine.sum() * 0 + if self.use_vfl: + cls_iou_targets = torch.zeros_like(flatten_cls_scores) + + if self.use_vfl: + loss_cls = self.loss_cls( + flatten_cls_scores, + cls_iou_targets, + avg_factor=num_pos_avg_per_gpu) + else: + loss_cls = self.loss_cls( + flatten_cls_scores, + flatten_labels, + weight=label_weights, + avg_factor=num_pos_avg_per_gpu) + + return dict( + loss_cls=loss_cls, + loss_bbox=loss_bbox, + loss_bbox_rf=loss_bbox_refine) + + @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'bbox_preds_refine')) + def get_bboxes(self, + cls_scores, + bbox_preds, + bbox_preds_refine, + img_metas, + cfg=None, + rescale=None, + with_nms=True): + """Transform network outputs for a batch into bbox predictions. + + Args: + cls_scores (list[Tensor]): Box iou-aware scores for each scale + level with shape (N, num_points * num_classes, H, W). + bbox_preds (list[Tensor]): Box offsets for each scale + level with shape (N, num_points * 4, H, W). + bbox_preds_refine (list[Tensor]): Refined Box offsets for + each scale level with shape (N, num_points * 4, H, W). + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + cfg (mmcv.Config): Test / postprocessing configuration, + if None, test_cfg would be used. Default: None. + rescale (bool): If True, return boxes in original image space. + Default: False. + with_nms (bool): If True, do nms before returning boxes. + Default: True. + + Returns: + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is an (n, 5) tensor, where the first 4 columns + are bounding box positions (tl_x, tl_y, br_x, br_y) and the + 5-th column is a score between 0 and 1. The second item is a + (n,) tensor where each item is the predicted class label of + the corresponding box. + """ + assert len(cls_scores) == len(bbox_preds) == len(bbox_preds_refine) + num_levels = len(cls_scores) + + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + mlvl_points = self.get_points(featmap_sizes, bbox_preds[0].dtype, + bbox_preds[0].device) + result_list = [] + for img_id in range(len(img_metas)): + cls_score_list = [ + cls_scores[i][img_id].detach() for i in range(num_levels) + ] + bbox_pred_list = [ + bbox_preds_refine[i][img_id].detach() + for i in range(num_levels) + ] + img_shape = img_metas[img_id]['img_shape'] + scale_factor = img_metas[img_id]['scale_factor'] + det_bboxes = self._get_bboxes_single(cls_score_list, + bbox_pred_list, mlvl_points, + img_shape, scale_factor, cfg, + rescale, with_nms) + result_list.append(det_bboxes) + return result_list + + def _get_bboxes_single(self, + cls_scores, + bbox_preds, + mlvl_points, + img_shape, + scale_factor, + cfg, + rescale=False, + with_nms=True): + """Transform outputs for a single batch item into bbox predictions. + + Args: + cls_scores (list[Tensor]): Box iou-aware scores for a single scale + level with shape (num_points * num_classes, H, W). + bbox_preds (list[Tensor]): Box offsets for a single scale + level with shape (num_points * 4, H, W). + mlvl_points (list[Tensor]): Box reference for a single scale level + with shape (num_total_points, 4). + img_shape (tuple[int]): Shape of the input image, + (height, width, 3). + scale_factor (ndarray): Scale factor of the image arrange as + (w_scale, h_scale, w_scale, h_scale). + cfg (mmcv.Config | None): Test / postprocessing configuration, + if None, test_cfg would be used. + rescale (bool): If True, return boxes in original image space. + Default: False. + with_nms (bool): If True, do nms before returning boxes. + Default: True. + + Returns: + tuple(Tensor): + det_bboxes (Tensor): BBox predictions in shape (n, 5), where + the first 4 columns are bounding box positions + (tl_x, tl_y, br_x, br_y) and the 5-th column is a score + between 0 and 1. + det_labels (Tensor): A (n,) tensor where each item is the + predicted class label of the corresponding box. + """ + cfg = self.test_cfg if cfg is None else cfg + assert len(cls_scores) == len(bbox_preds) == len(mlvl_points) + mlvl_bboxes = [] + mlvl_scores = [] + for cls_score, bbox_pred, points in zip(cls_scores, bbox_preds, + mlvl_points): + assert cls_score.size()[-2:] == bbox_pred.size()[-2:] + scores = cls_score.permute(1, 2, 0).reshape( + -1, self.cls_out_channels).contiguous().sigmoid() + bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4).contiguous() + + nms_pre = cfg.get('nms_pre', -1) + if 0 < nms_pre < scores.shape[0]: + max_scores, _ = scores.max(dim=1) + _, topk_inds = max_scores.topk(nms_pre) + points = points[topk_inds, :] + bbox_pred = bbox_pred[topk_inds, :] + scores = scores[topk_inds, :] + bboxes = distance2bbox(points, bbox_pred, max_shape=img_shape) + mlvl_bboxes.append(bboxes) + mlvl_scores.append(scores) + mlvl_bboxes = torch.cat(mlvl_bboxes) + if rescale: + mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor) + mlvl_scores = torch.cat(mlvl_scores) + padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1) + # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 + # BG cat_id: num_class + mlvl_scores = torch.cat([mlvl_scores, padding], dim=1) + if with_nms: + det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores, + cfg.score_thr, cfg.nms, + cfg.max_per_img) + return det_bboxes, det_labels + else: + return mlvl_bboxes, mlvl_scores + + def _get_points_single(self, + featmap_size, + stride, + dtype, + device, + flatten=False): + """Get points according to feature map sizes.""" + h, w = featmap_size + x_range = torch.arange( + 0, w * stride, stride, dtype=dtype, device=device) + y_range = torch.arange( + 0, h * stride, stride, dtype=dtype, device=device) + y, x = torch.meshgrid(y_range, x_range) + # to be compatible with anchor points in ATSS + if self.use_atss: + points = torch.stack( + (x.reshape(-1), y.reshape(-1)), dim=-1) + \ + stride * self.anchor_center_offset + else: + points = torch.stack( + (x.reshape(-1), y.reshape(-1)), dim=-1) + stride // 2 + return points + + def get_targets(self, cls_scores, mlvl_points, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore): + """A wrapper for computing ATSS and FCOS targets for points in multiple + images. + + Args: + cls_scores (list[Tensor]): Box iou-aware scores for each scale + level with shape (N, num_points * num_classes, H, W). + mlvl_points (list[Tensor]): Points of each fpn level, each has + shape (num_points, 2). + gt_bboxes (list[Tensor]): Ground truth bboxes of each image, + each has shape (num_gt, 4). + gt_labels (list[Tensor]): Ground truth labels of each box, + each has shape (num_gt,). + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | Tensor): Ground truth bboxes to be + ignored, shape (num_ignored_gts, 4). + + Returns: + tuple: + labels_list (list[Tensor]): Labels of each level. + label_weights (Tensor/None): Label weights of all levels. + bbox_targets_list (list[Tensor]): Regression targets of each + level, (l, t, r, b). + bbox_weights (Tensor/None): Bbox weights of all levels. + """ + if self.use_atss: + return self.get_atss_targets(cls_scores, mlvl_points, gt_bboxes, + gt_labels, img_metas, + gt_bboxes_ignore) + else: + self.norm_on_bbox = False + return self.get_fcos_targets(mlvl_points, gt_bboxes, gt_labels) + + def _get_target_single(self, *args, **kwargs): + """Avoid ambiguity in multiple inheritance.""" + if self.use_atss: + return ATSSHead._get_target_single(self, *args, **kwargs) + else: + return FCOSHead._get_target_single(self, *args, **kwargs) + + def get_fcos_targets(self, points, gt_bboxes_list, gt_labels_list): + """Compute FCOS regression and classification targets for points in + multiple images. + + Args: + points (list[Tensor]): Points of each fpn level, each has shape + (num_points, 2). + gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image, + each has shape (num_gt, 4). + gt_labels_list (list[Tensor]): Ground truth labels of each box, + each has shape (num_gt,). + + Returns: + tuple: + labels (list[Tensor]): Labels of each level. + label_weights: None, to be compatible with ATSS targets. + bbox_targets (list[Tensor]): BBox targets of each level. + bbox_weights: None, to be compatible with ATSS targets. + """ + labels, bbox_targets = FCOSHead.get_targets(self, points, + gt_bboxes_list, + gt_labels_list) + label_weights = None + bbox_weights = None + return labels, label_weights, bbox_targets, bbox_weights + + def get_atss_targets(self, + cls_scores, + mlvl_points, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """A wrapper for computing ATSS targets for points in multiple images. + + Args: + cls_scores (list[Tensor]): Box iou-aware scores for each scale + level with shape (N, num_points * num_classes, H, W). + mlvl_points (list[Tensor]): Points of each fpn level, each has + shape (num_points, 2). + gt_bboxes (list[Tensor]): Ground truth bboxes of each image, + each has shape (num_gt, 4). + gt_labels (list[Tensor]): Ground truth labels of each box, + each has shape (num_gt,). + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | Tensor): Ground truth bboxes to be + ignored, shape (num_ignored_gts, 4). Default: None. + + Returns: + tuple: + labels_list (list[Tensor]): Labels of each level. + label_weights (Tensor): Label weights of all levels. + bbox_targets_list (list[Tensor]): Regression targets of each + level, (l, t, r, b). + bbox_weights (Tensor): Bbox weights of all levels. + """ + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + assert len(featmap_sizes) == self.anchor_generator.num_levels + + device = cls_scores[0].device + anchor_list, valid_flag_list = self.get_anchors( + featmap_sizes, img_metas, device=device) + label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 + + cls_reg_targets = ATSSHead.get_targets( + self, + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + gt_labels_list=gt_labels, + label_channels=label_channels, + unmap_outputs=True) + if cls_reg_targets is None: + return None + + (anchor_list, labels_list, label_weights_list, bbox_targets_list, + bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets + + bbox_targets_list = [ + bbox_targets.reshape(-1, 4) for bbox_targets in bbox_targets_list + ] + + num_imgs = len(img_metas) + # transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format + bbox_targets_list = self.transform_bbox_targets( + bbox_targets_list, mlvl_points, num_imgs) + + labels_list = [labels.reshape(-1) for labels in labels_list] + label_weights_list = [ + label_weights.reshape(-1) for label_weights in label_weights_list + ] + bbox_weights_list = [ + bbox_weights.reshape(-1) for bbox_weights in bbox_weights_list + ] + label_weights = torch.cat(label_weights_list) + bbox_weights = torch.cat(bbox_weights_list) + return labels_list, label_weights, bbox_targets_list, bbox_weights + + def transform_bbox_targets(self, decoded_bboxes, mlvl_points, num_imgs): + """Transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format. + + Args: + decoded_bboxes (list[Tensor]): Regression targets of each level, + in the form of (x1, y1, x2, y2). + mlvl_points (list[Tensor]): Points of each fpn level, each has + shape (num_points, 2). + num_imgs (int): the number of images in a batch. + + Returns: + bbox_targets (list[Tensor]): Regression targets of each level in + the form of (l, t, r, b). + """ + # TODO: Re-implemented in Class PointCoder + assert len(decoded_bboxes) == len(mlvl_points) + num_levels = len(decoded_bboxes) + mlvl_points = [points.repeat(num_imgs, 1) for points in mlvl_points] + bbox_targets = [] + for i in range(num_levels): + bbox_target = bbox2distance(mlvl_points[i], decoded_bboxes[i]) + bbox_targets.append(bbox_target) + + return bbox_targets + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs): + """Override the method in the parent class to avoid changing para's + name.""" + pass diff --git a/mmdet/models/dense_heads/yolact_head.py b/mmdet/models/dense_heads/yolact_head.py new file mode 100644 index 0000000..a666151 --- /dev/null +++ b/mmdet/models/dense_heads/yolact_head.py @@ -0,0 +1,942 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule, ModuleList, force_fp32 + +from mmdet.core import build_sampler, fast_nms, images_to_levels, multi_apply +from ..builder import HEADS, build_loss +from .anchor_head import AnchorHead + + +@HEADS.register_module() +class YOLACTHead(AnchorHead): + """YOLACT box head used in https://arxiv.org/abs/1904.02689. + + Note that YOLACT head is a light version of RetinaNet head. + Four differences are described as follows: + + 1. YOLACT box head has three-times fewer anchors. + 2. YOLACT box head shares the convs for box and cls branches. + 3. YOLACT box head uses OHEM instead of Focal loss. + 4. YOLACT box head predicts a set of mask coefficients for each box. + + Args: + num_classes (int): Number of categories excluding the background + category. + in_channels (int): Number of channels in the input feature map. + anchor_generator (dict): Config dict for anchor generator + loss_cls (dict): Config of classification loss. + loss_bbox (dict): Config of localization loss. + num_head_convs (int): Number of the conv layers shared by + box and cls branches. + num_protos (int): Number of the mask coefficients. + use_ohem (bool): If true, ``loss_single_OHEM`` will be used for + cls loss calculation. If false, ``loss_single`` will be used. + conv_cfg (dict): Dictionary to construct and config conv layer. + norm_cfg (dict): Dictionary to construct and config norm layer. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + num_classes, + in_channels, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=3, + scales_per_octave=1, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + reduction='none', + loss_weight=1.0), + loss_bbox=dict( + type='SmoothL1Loss', beta=1.0, loss_weight=1.5), + num_head_convs=1, + num_protos=32, + use_ohem=True, + conv_cfg=None, + norm_cfg=None, + init_cfg=dict( + type='Xavier', + distribution='uniform', + bias=0, + layer='Conv2d'), + **kwargs): + self.num_head_convs = num_head_convs + self.num_protos = num_protos + self.use_ohem = use_ohem + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + super(YOLACTHead, self).__init__( + num_classes, + in_channels, + loss_cls=loss_cls, + loss_bbox=loss_bbox, + anchor_generator=anchor_generator, + init_cfg=init_cfg, + **kwargs) + if self.use_ohem: + sampler_cfg = dict(type='PseudoSampler') + self.sampler = build_sampler(sampler_cfg, context=self) + self.sampling = False + + def _init_layers(self): + """Initialize layers of the head.""" + self.relu = nn.ReLU(inplace=True) + self.head_convs = ModuleList() + for i in range(self.num_head_convs): + chn = self.in_channels if i == 0 else self.feat_channels + self.head_convs.append( + ConvModule( + chn, + self.feat_channels, + 3, + stride=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + self.conv_cls = nn.Conv2d( + self.feat_channels, + self.num_anchors * self.cls_out_channels, + 3, + padding=1) + self.conv_reg = nn.Conv2d( + self.feat_channels, self.num_anchors * 4, 3, padding=1) + self.conv_coeff = nn.Conv2d( + self.feat_channels, + self.num_anchors * self.num_protos, + 3, + padding=1) + + def forward_single(self, x): + """Forward feature of a single scale level. + + Args: + x (Tensor): Features of a single scale level. + + Returns: + tuple: + cls_score (Tensor): Cls scores for a single scale level \ + the channels number is num_anchors * num_classes. + bbox_pred (Tensor): Box energies / deltas for a single scale \ + level, the channels number is num_anchors * 4. + coeff_pred (Tensor): Mask coefficients for a single scale \ + level, the channels number is num_anchors * num_protos. + """ + for head_conv in self.head_convs: + x = head_conv(x) + cls_score = self.conv_cls(x) + bbox_pred = self.conv_reg(x) + coeff_pred = self.conv_coeff(x).tanh() + return cls_score, bbox_pred, coeff_pred + + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) + def loss(self, + cls_scores, + bbox_preds, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """A combination of the func:``AnchorHead.loss`` and + func:``SSDHead.loss``. + + When ``self.use_ohem == True``, it functions like ``SSDHead.loss``, + otherwise, it follows ``AnchorHead.loss``. Besides, it additionally + returns ``sampling_results``. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (N, num_anchors * num_classes, H, W) + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_anchors * 4, H, W) + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): Class indices corresponding to each box + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | list[Tensor]): Specify which bounding + boxes can be ignored when computing the loss. Default: None + + Returns: + tuple: + dict[str, Tensor]: A dictionary of loss components. + List[:obj:``SamplingResult``]: Sampler results for each image. + """ + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + assert len(featmap_sizes) == self.anchor_generator.num_levels + + device = cls_scores[0].device + + anchor_list, valid_flag_list = self.get_anchors( + featmap_sizes, img_metas, device=device) + label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 + cls_reg_targets = self.get_targets( + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + gt_labels_list=gt_labels, + label_channels=label_channels, + unmap_outputs=not self.use_ohem, + return_sampling_results=True) + if cls_reg_targets is None: + return None + (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, + num_total_pos, num_total_neg, sampling_results) = cls_reg_targets + + if self.use_ohem: + num_images = len(img_metas) + all_cls_scores = torch.cat([ + s.permute(0, 2, 3, 1).reshape( + num_images, -1, self.cls_out_channels) for s in cls_scores + ], 1) + all_labels = torch.cat(labels_list, -1).view(num_images, -1) + all_label_weights = torch.cat(label_weights_list, + -1).view(num_images, -1) + all_bbox_preds = torch.cat([ + b.permute(0, 2, 3, 1).reshape(num_images, -1, 4) + for b in bbox_preds + ], -2) + all_bbox_targets = torch.cat(bbox_targets_list, + -2).view(num_images, -1, 4) + all_bbox_weights = torch.cat(bbox_weights_list, + -2).view(num_images, -1, 4) + + # concat all level anchors to a single tensor + all_anchors = [] + for i in range(num_images): + all_anchors.append(torch.cat(anchor_list[i])) + + # check NaN and Inf + assert torch.isfinite(all_cls_scores).all().item(), \ + 'classification scores become infinite or NaN!' + assert torch.isfinite(all_bbox_preds).all().item(), \ + 'bbox predications become infinite or NaN!' + + losses_cls, losses_bbox = multi_apply( + self.loss_single_OHEM, + all_cls_scores, + all_bbox_preds, + all_anchors, + all_labels, + all_label_weights, + all_bbox_targets, + all_bbox_weights, + num_total_samples=num_total_pos) + else: + num_total_samples = ( + num_total_pos + + num_total_neg if self.sampling else num_total_pos) + + # anchor number of multi levels + num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] + # concat all level anchors and flags to a single tensor + concat_anchor_list = [] + for i in range(len(anchor_list)): + concat_anchor_list.append(torch.cat(anchor_list[i])) + all_anchor_list = images_to_levels(concat_anchor_list, + num_level_anchors) + losses_cls, losses_bbox = multi_apply( + self.loss_single, + cls_scores, + bbox_preds, + all_anchor_list, + labels_list, + label_weights_list, + bbox_targets_list, + bbox_weights_list, + num_total_samples=num_total_samples) + + return dict( + loss_cls=losses_cls, loss_bbox=losses_bbox), sampling_results + + def loss_single_OHEM(self, cls_score, bbox_pred, anchors, labels, + label_weights, bbox_targets, bbox_weights, + num_total_samples): + """"See func:``SSDHead.loss``.""" + loss_cls_all = self.loss_cls(cls_score, labels, label_weights) + + # FG cat_id: [0, num_classes -1], BG cat_id: num_classes + pos_inds = ((labels >= 0) & (labels < self.num_classes)).nonzero( + as_tuple=False).reshape(-1) + neg_inds = (labels == self.num_classes).nonzero( + as_tuple=False).view(-1) + + num_pos_samples = pos_inds.size(0) + if num_pos_samples == 0: + num_neg_samples = neg_inds.size(0) + else: + num_neg_samples = self.train_cfg.neg_pos_ratio * num_pos_samples + if num_neg_samples > neg_inds.size(0): + num_neg_samples = neg_inds.size(0) + topk_loss_cls_neg, _ = loss_cls_all[neg_inds].topk(num_neg_samples) + loss_cls_pos = loss_cls_all[pos_inds].sum() + loss_cls_neg = topk_loss_cls_neg.sum() + loss_cls = (loss_cls_pos + loss_cls_neg) / num_total_samples + if self.reg_decoded_bbox: + # When the regression loss (e.g. `IouLoss`, `GIouLoss`) + # is applied directly on the decoded bounding boxes, it + # decodes the already encoded coordinates to absolute format. + bbox_pred = self.bbox_coder.decode(anchors, bbox_pred) + loss_bbox = self.loss_bbox( + bbox_pred, + bbox_targets, + bbox_weights, + avg_factor=num_total_samples) + return loss_cls[None], loss_bbox + + @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'coeff_preds')) + def get_bboxes(self, + cls_scores, + bbox_preds, + coeff_preds, + img_metas, + cfg=None, + rescale=False): + """"Similiar to func:``AnchorHead.get_bboxes``, but additionally + processes coeff_preds. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + with shape (N, num_anchors * num_classes, H, W) + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_anchors * 4, H, W) + coeff_preds (list[Tensor]): Mask coefficients for each scale + level with shape (N, num_anchors * num_protos, H, W) + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + cfg (mmcv.Config | None): Test / postprocessing configuration, + if None, test_cfg would be used + rescale (bool): If True, return boxes in original image space. + Default: False. + + Returns: + list[tuple[Tensor, Tensor, Tensor]]: Each item in result_list is + a 3-tuple. The first item is an (n, 5) tensor, where the + first 4 columns are bounding box positions + (tl_x, tl_y, br_x, br_y) and the 5-th column is a score + between 0 and 1. The second item is an (n,) tensor where each + item is the predicted class label of the corresponding box. + The third item is an (n, num_protos) tensor where each item + is the predicted mask coefficients of instance inside the + corresponding box. + """ + assert len(cls_scores) == len(bbox_preds) + num_levels = len(cls_scores) + + device = cls_scores[0].device + featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)] + mlvl_anchors = self.anchor_generator.grid_anchors( + featmap_sizes, device=device) + + det_bboxes = [] + det_labels = [] + det_coeffs = [] + for img_id in range(len(img_metas)): + cls_score_list = [ + cls_scores[i][img_id].detach() for i in range(num_levels) + ] + bbox_pred_list = [ + bbox_preds[i][img_id].detach() for i in range(num_levels) + ] + coeff_pred_list = [ + coeff_preds[i][img_id].detach() for i in range(num_levels) + ] + img_shape = img_metas[img_id]['img_shape'] + scale_factor = img_metas[img_id]['scale_factor'] + bbox_res = self._get_bboxes_single(cls_score_list, bbox_pred_list, + coeff_pred_list, mlvl_anchors, + img_shape, scale_factor, cfg, + rescale) + det_bboxes.append(bbox_res[0]) + det_labels.append(bbox_res[1]) + det_coeffs.append(bbox_res[2]) + return det_bboxes, det_labels, det_coeffs + + def _get_bboxes_single(self, + cls_score_list, + bbox_pred_list, + coeff_preds_list, + mlvl_anchors, + img_shape, + scale_factor, + cfg, + rescale=False): + """"Similiar to func:``AnchorHead._get_bboxes_single``, but + additionally processes coeff_preds_list and uses fast NMS instead of + traditional NMS. + + Args: + cls_score_list (list[Tensor]): Box scores for a single scale level + Has shape (num_anchors * num_classes, H, W). + bbox_pred_list (list[Tensor]): Box energies / deltas for a single + scale level with shape (num_anchors * 4, H, W). + coeff_preds_list (list[Tensor]): Mask coefficients for a single + scale level with shape (num_anchors * num_protos, H, W). + mlvl_anchors (list[Tensor]): Box reference for a single scale level + with shape (num_total_anchors, 4). + img_shape (tuple[int]): Shape of the input image, + (height, width, 3). + scale_factor (ndarray): Scale factor of the image arange as + (w_scale, h_scale, w_scale, h_scale). + cfg (mmcv.Config): Test / postprocessing configuration, + if None, test_cfg would be used. + rescale (bool): If True, return boxes in original image space. + + Returns: + tuple[Tensor, Tensor, Tensor]: The first item is an (n, 5) tensor, + where the first 4 columns are bounding box positions + (tl_x, tl_y, br_x, br_y) and the 5-th column is a score between + 0 and 1. The second item is an (n,) tensor where each item is + the predicted class label of the corresponding box. The third + item is an (n, num_protos) tensor where each item is the + predicted mask coefficients of instance inside the + corresponding box. + """ + cfg = self.test_cfg if cfg is None else cfg + assert len(cls_score_list) == len(bbox_pred_list) == len(mlvl_anchors) + mlvl_bboxes = [] + mlvl_scores = [] + mlvl_coeffs = [] + for cls_score, bbox_pred, coeff_pred, anchors in \ + zip(cls_score_list, bbox_pred_list, + coeff_preds_list, mlvl_anchors): + assert cls_score.size()[-2:] == bbox_pred.size()[-2:] + cls_score = cls_score.permute(1, 2, + 0).reshape(-1, self.cls_out_channels) + if self.use_sigmoid_cls: + scores = cls_score.sigmoid() + else: + scores = cls_score.softmax(-1) + bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4) + coeff_pred = coeff_pred.permute(1, 2, + 0).reshape(-1, self.num_protos) + nms_pre = cfg.get('nms_pre', -1) + if nms_pre > 0 and scores.shape[0] > nms_pre: + # Get maximum scores for foreground classes. + if self.use_sigmoid_cls: + max_scores, _ = scores.max(dim=1) + else: + # remind that we set FG labels to [0, num_class-1] + # since mmdet v2.0 + # BG cat_id: num_class + max_scores, _ = scores[:, :-1].max(dim=1) + _, topk_inds = max_scores.topk(nms_pre) + anchors = anchors[topk_inds, :] + bbox_pred = bbox_pred[topk_inds, :] + scores = scores[topk_inds, :] + coeff_pred = coeff_pred[topk_inds, :] + bboxes = self.bbox_coder.decode( + anchors, bbox_pred, max_shape=img_shape) + mlvl_bboxes.append(bboxes) + mlvl_scores.append(scores) + mlvl_coeffs.append(coeff_pred) + mlvl_bboxes = torch.cat(mlvl_bboxes) + if rescale: + mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor) + mlvl_scores = torch.cat(mlvl_scores) + mlvl_coeffs = torch.cat(mlvl_coeffs) + if self.use_sigmoid_cls: + # Add a dummy background class to the backend when using sigmoid + # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 + # BG cat_id: num_class + padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1) + mlvl_scores = torch.cat([mlvl_scores, padding], dim=1) + det_bboxes, det_labels, det_coeffs = fast_nms(mlvl_bboxes, mlvl_scores, + mlvl_coeffs, + cfg.score_thr, + cfg.iou_thr, cfg.top_k, + cfg.max_per_img) + return det_bboxes, det_labels, det_coeffs + + +@HEADS.register_module() +class YOLACTSegmHead(BaseModule): + """YOLACT segmentation head used in https://arxiv.org/abs/1904.02689. + + Apply a semantic segmentation loss on feature space using layers that are + only evaluated during training to increase performance with no speed + penalty. + + Args: + in_channels (int): Number of channels in the input feature map. + num_classes (int): Number of categories excluding the background + category. + loss_segm (dict): Config of semantic segmentation loss. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + num_classes, + in_channels=256, + loss_segm=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0), + init_cfg=dict( + type='Xavier', + distribution='uniform', + override=dict(name='segm_conv'))): + super(YOLACTSegmHead, self).__init__(init_cfg) + self.in_channels = in_channels + self.num_classes = num_classes + self.loss_segm = build_loss(loss_segm) + self._init_layers() + self.fp16_enabled = False + + def _init_layers(self): + """Initialize layers of the head.""" + self.segm_conv = nn.Conv2d( + self.in_channels, self.num_classes, kernel_size=1) + + def forward(self, x): + """Forward feature from the upstream network. + + Args: + x (Tensor): Feature from the upstream network, which is + a 4D-tensor. + + Returns: + Tensor: Predicted semantic segmentation map with shape + (N, num_classes, H, W). + """ + return self.segm_conv(x) + + @force_fp32(apply_to=('segm_pred', )) + def loss(self, segm_pred, gt_masks, gt_labels): + """Compute loss of the head. + + Args: + segm_pred (list[Tensor]): Predicted semantic segmentation map + with shape (N, num_classes, H, W). + gt_masks (list[Tensor]): Ground truth masks for each image with + the same shape of the input image. + gt_labels (list[Tensor]): Class indices corresponding to each box. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + loss_segm = [] + num_imgs, num_classes, mask_h, mask_w = segm_pred.size() + for idx in range(num_imgs): + cur_segm_pred = segm_pred[idx] + cur_gt_masks = gt_masks[idx].float() + cur_gt_labels = gt_labels[idx] + segm_targets = self.get_targets(cur_segm_pred, cur_gt_masks, + cur_gt_labels) + if segm_targets is None: + loss = self.loss_segm(cur_segm_pred, + torch.zeros_like(cur_segm_pred), + torch.zeros_like(cur_segm_pred)) + else: + loss = self.loss_segm( + cur_segm_pred, + segm_targets, + avg_factor=num_imgs * mask_h * mask_w) + loss_segm.append(loss) + return dict(loss_segm=loss_segm) + + def get_targets(self, segm_pred, gt_masks, gt_labels): + """Compute semantic segmentation targets for each image. + + Args: + segm_pred (Tensor): Predicted semantic segmentation map + with shape (num_classes, H, W). + gt_masks (Tensor): Ground truth masks for each image with + the same shape of the input image. + gt_labels (Tensor): Class indices corresponding to each box. + + Returns: + Tensor: Semantic segmentation targets with shape + (num_classes, H, W). + """ + if gt_masks.size(0) == 0: + return None + num_classes, mask_h, mask_w = segm_pred.size() + with torch.no_grad(): + downsampled_masks = F.interpolate( + gt_masks.unsqueeze(0), (mask_h, mask_w), + mode='bilinear', + align_corners=False).squeeze(0) + downsampled_masks = downsampled_masks.gt(0.5).float() + segm_targets = torch.zeros_like(segm_pred, requires_grad=False) + for obj_idx in range(downsampled_masks.size(0)): + segm_targets[gt_labels[obj_idx] - 1] = torch.max( + segm_targets[gt_labels[obj_idx] - 1], + downsampled_masks[obj_idx]) + return segm_targets + + +@HEADS.register_module() +class YOLACTProtonet(BaseModule): + """YOLACT mask head used in https://arxiv.org/abs/1904.02689. + + This head outputs the mask prototypes for YOLACT. + + Args: + in_channels (int): Number of channels in the input feature map. + proto_channels (tuple[int]): Output channels of protonet convs. + proto_kernel_sizes (tuple[int]): Kernel sizes of protonet convs. + include_last_relu (Bool): If keep the last relu of protonet. + num_protos (int): Number of prototypes. + num_classes (int): Number of categories excluding the background + category. + loss_mask_weight (float): Reweight the mask loss by this factor. + max_masks_to_train (int): Maximum number of masks to train for + each image. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + num_classes, + in_channels=256, + proto_channels=(256, 256, 256, None, 256, 32), + proto_kernel_sizes=(3, 3, 3, -2, 3, 1), + include_last_relu=True, + num_protos=32, + loss_mask_weight=1.0, + max_masks_to_train=100, + init_cfg=dict( + type='Xavier', + distribution='uniform', + override=dict(name='protonet'))): + super(YOLACTProtonet, self).__init__(init_cfg) + self.in_channels = in_channels + self.proto_channels = proto_channels + self.proto_kernel_sizes = proto_kernel_sizes + self.include_last_relu = include_last_relu + self.protonet = self._init_layers() + + self.loss_mask_weight = loss_mask_weight + self.num_protos = num_protos + self.num_classes = num_classes + self.max_masks_to_train = max_masks_to_train + self.fp16_enabled = False + + def _init_layers(self): + """A helper function to take a config setting and turn it into a + network.""" + # Possible patterns: + # ( 256, 3) -> conv + # ( 256,-2) -> deconv + # (None,-2) -> bilinear interpolate + in_channels = self.in_channels + protonets = ModuleList() + for num_channels, kernel_size in zip(self.proto_channels, + self.proto_kernel_sizes): + if kernel_size > 0: + layer = nn.Conv2d( + in_channels, + num_channels, + kernel_size, + padding=kernel_size // 2) + else: + if num_channels is None: + layer = InterpolateModule( + scale_factor=-kernel_size, + mode='bilinear', + align_corners=False) + else: + layer = nn.ConvTranspose2d( + in_channels, + num_channels, + -kernel_size, + padding=kernel_size // 2) + protonets.append(layer) + protonets.append(nn.ReLU(inplace=True)) + in_channels = num_channels if num_channels is not None \ + else in_channels + if not self.include_last_relu: + protonets = protonets[:-1] + return nn.Sequential(*protonets) + + def forward(self, x, coeff_pred, bboxes, img_meta, sampling_results=None): + """Forward feature from the upstream network to get prototypes and + linearly combine the prototypes, using masks coefficients, into + instance masks. Finally, crop the instance masks with given bboxes. + + Args: + x (Tensor): Feature from the upstream network, which is + a 4D-tensor. + coeff_pred (list[Tensor]): Mask coefficients for each scale + level with shape (N, num_anchors * num_protos, H, W). + bboxes (list[Tensor]): Box used for cropping with shape + (N, num_anchors * 4, H, W). During training, they are + ground truth boxes. During testing, they are predicted + boxes. + img_meta (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + sampling_results (List[:obj:``SamplingResult``]): Sampler results + for each image. + + Returns: + list[Tensor]: Predicted instance segmentation masks. + """ + prototypes = self.protonet(x) + prototypes = prototypes.permute(0, 2, 3, 1).contiguous() + + num_imgs = x.size(0) + # Training state + if self.training: + coeff_pred_list = [] + for coeff_pred_per_level in coeff_pred: + coeff_pred_per_level = \ + coeff_pred_per_level.permute( + 0, 2, 3, 1).reshape(num_imgs, -1, self.num_protos) + coeff_pred_list.append(coeff_pred_per_level) + coeff_pred = torch.cat(coeff_pred_list, dim=1) + + mask_pred_list = [] + for idx in range(num_imgs): + cur_prototypes = prototypes[idx] + cur_coeff_pred = coeff_pred[idx] + cur_bboxes = bboxes[idx] + cur_img_meta = img_meta[idx] + + # Testing state + if not self.training: + bboxes_for_cropping = cur_bboxes + else: + cur_sampling_results = sampling_results[idx] + pos_assigned_gt_inds = \ + cur_sampling_results.pos_assigned_gt_inds + bboxes_for_cropping = cur_bboxes[pos_assigned_gt_inds].clone() + pos_inds = cur_sampling_results.pos_inds + cur_coeff_pred = cur_coeff_pred[pos_inds] + + # Linearly combine the prototypes with the mask coefficients + mask_pred = cur_prototypes @ cur_coeff_pred.t() + mask_pred = torch.sigmoid(mask_pred) + + h, w = cur_img_meta['img_shape'][:2] + bboxes_for_cropping[:, 0] /= w + bboxes_for_cropping[:, 1] /= h + bboxes_for_cropping[:, 2] /= w + bboxes_for_cropping[:, 3] /= h + + mask_pred = self.crop(mask_pred, bboxes_for_cropping) + mask_pred = mask_pred.permute(2, 0, 1).contiguous() + mask_pred_list.append(mask_pred) + return mask_pred_list + + @force_fp32(apply_to=('mask_pred', )) + def loss(self, mask_pred, gt_masks, gt_bboxes, img_meta, sampling_results): + """Compute loss of the head. + + Args: + mask_pred (list[Tensor]): Predicted prototypes with shape + (num_classes, H, W). + gt_masks (list[Tensor]): Ground truth masks for each image with + the same shape of the input image. + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + img_meta (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + sampling_results (List[:obj:``SamplingResult``]): Sampler results + for each image. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + loss_mask = [] + num_imgs = len(mask_pred) + total_pos = 0 + for idx in range(num_imgs): + cur_mask_pred = mask_pred[idx] + cur_gt_masks = gt_masks[idx].float() + cur_gt_bboxes = gt_bboxes[idx] + cur_img_meta = img_meta[idx] + cur_sampling_results = sampling_results[idx] + + pos_assigned_gt_inds = cur_sampling_results.pos_assigned_gt_inds + num_pos = pos_assigned_gt_inds.size(0) + # Since we're producing (near) full image masks, + # it'd take too much vram to backprop on every single mask. + # Thus we select only a subset. + if num_pos > self.max_masks_to_train: + perm = torch.randperm(num_pos) + select = perm[:self.max_masks_to_train] + cur_mask_pred = cur_mask_pred[select] + pos_assigned_gt_inds = pos_assigned_gt_inds[select] + num_pos = self.max_masks_to_train + total_pos += num_pos + + gt_bboxes_for_reweight = cur_gt_bboxes[pos_assigned_gt_inds] + + mask_targets = self.get_targets(cur_mask_pred, cur_gt_masks, + pos_assigned_gt_inds) + if num_pos == 0: + loss = cur_mask_pred.sum() * 0. + elif mask_targets is None: + loss = F.binary_cross_entropy(cur_mask_pred, + torch.zeros_like(cur_mask_pred), + torch.zeros_like(cur_mask_pred)) + else: + cur_mask_pred = torch.clamp(cur_mask_pred, 0, 1) + loss = F.binary_cross_entropy( + cur_mask_pred, mask_targets, + reduction='none') * self.loss_mask_weight + + h, w = cur_img_meta['img_shape'][:2] + gt_bboxes_width = (gt_bboxes_for_reweight[:, 2] - + gt_bboxes_for_reweight[:, 0]) / w + gt_bboxes_height = (gt_bboxes_for_reweight[:, 3] - + gt_bboxes_for_reweight[:, 1]) / h + loss = loss.mean(dim=(1, + 2)) / gt_bboxes_width / gt_bboxes_height + loss = torch.sum(loss) + loss_mask.append(loss) + + if total_pos == 0: + total_pos += 1 # avoid nan + loss_mask = [x / total_pos for x in loss_mask] + + return dict(loss_mask=loss_mask) + + def get_targets(self, mask_pred, gt_masks, pos_assigned_gt_inds): + """Compute instance segmentation targets for each image. + + Args: + mask_pred (Tensor): Predicted prototypes with shape + (num_classes, H, W). + gt_masks (Tensor): Ground truth masks for each image with + the same shape of the input image. + pos_assigned_gt_inds (Tensor): GT indices of the corresponding + positive samples. + Returns: + Tensor: Instance segmentation targets with shape + (num_instances, H, W). + """ + if gt_masks.size(0) == 0: + return None + mask_h, mask_w = mask_pred.shape[-2:] + gt_masks = F.interpolate( + gt_masks.unsqueeze(0), (mask_h, mask_w), + mode='bilinear', + align_corners=False).squeeze(0) + gt_masks = gt_masks.gt(0.5).float() + mask_targets = gt_masks[pos_assigned_gt_inds] + return mask_targets + + def get_seg_masks(self, mask_pred, label_pred, img_meta, rescale): + """Resize, binarize, and format the instance mask predictions. + + Args: + mask_pred (Tensor): shape (N, H, W). + label_pred (Tensor): shape (N, ). + img_meta (dict): Meta information of each image, e.g., + image size, scaling factor, etc. + rescale (bool): If rescale is False, then returned masks will + fit the scale of imgs[0]. + Returns: + list[ndarray]: Mask predictions grouped by their predicted classes. + """ + ori_shape = img_meta['ori_shape'] + scale_factor = img_meta['scale_factor'] + if rescale: + img_h, img_w = ori_shape[:2] + else: + img_h = np.round(ori_shape[0] * scale_factor[1]).astype(np.int32) + img_w = np.round(ori_shape[1] * scale_factor[0]).astype(np.int32) + + cls_segms = [[] for _ in range(self.num_classes)] + if mask_pred.size(0) == 0: + return cls_segms + + mask_pred = F.interpolate( + mask_pred.unsqueeze(0), (img_h, img_w), + mode='bilinear', + align_corners=False).squeeze(0) > 0.5 + mask_pred = mask_pred.cpu().numpy().astype(np.uint8) + + for m, l in zip(mask_pred, label_pred): + cls_segms[l].append(m) + return cls_segms + + def crop(self, masks, boxes, padding=1): + """Crop predicted masks by zeroing out everything not in the predicted + bbox. + + Args: + masks (Tensor): shape [H, W, N]. + boxes (Tensor): bbox coords in relative point form with + shape [N, 4]. + + Return: + Tensor: The cropped masks. + """ + h, w, n = masks.size() + x1, x2 = self.sanitize_coordinates( + boxes[:, 0], boxes[:, 2], w, padding, cast=False) + y1, y2 = self.sanitize_coordinates( + boxes[:, 1], boxes[:, 3], h, padding, cast=False) + + rows = torch.arange( + w, device=masks.device, dtype=x1.dtype).view(1, -1, + 1).expand(h, w, n) + cols = torch.arange( + h, device=masks.device, dtype=x1.dtype).view(-1, 1, + 1).expand(h, w, n) + + masks_left = rows >= x1.view(1, 1, -1) + masks_right = rows < x2.view(1, 1, -1) + masks_up = cols >= y1.view(1, 1, -1) + masks_down = cols < y2.view(1, 1, -1) + + crop_mask = masks_left * masks_right * masks_up * masks_down + + return masks * crop_mask.float() + + def sanitize_coordinates(self, x1, x2, img_size, padding=0, cast=True): + """Sanitizes the input coordinates so that x1 < x2, x1 != x2, x1 >= 0, + and x2 <= image_size. Also converts from relative to absolute + coordinates and casts the results to long tensors. + + Warning: this does things in-place behind the scenes so + copy if necessary. + + Args: + _x1 (Tensor): shape (N, ). + _x2 (Tensor): shape (N, ). + img_size (int): Size of the input image. + padding (int): x1 >= padding, x2 <= image_size-padding. + cast (bool): If cast is false, the result won't be cast to longs. + + Returns: + tuple: + x1 (Tensor): Sanitized _x1. + x2 (Tensor): Sanitized _x2. + """ + x1 = x1 * img_size + x2 = x2 * img_size + if cast: + x1 = x1.long() + x2 = x2.long() + x1 = torch.min(x1, x2) + x2 = torch.max(x1, x2) + x1 = torch.clamp(x1 - padding, min=0) + x2 = torch.clamp(x2 + padding, max=img_size) + return x1, x2 + + +class InterpolateModule(BaseModule): + """This is a module version of F.interpolate. + + Any arguments you give it just get passed along for the ride. + """ + + def __init__(self, *args, init_cfg=None, **kwargs): + super().__init__(init_cfg) + + self.args = args + self.kwargs = kwargs + + def forward(self, x): + """Forward features from the upstream network.""" + return F.interpolate(x, *self.args, **self.kwargs) diff --git a/mmdet/models/dense_heads/yolo_head.py b/mmdet/models/dense_heads/yolo_head.py new file mode 100644 index 0000000..6939127 --- /dev/null +++ b/mmdet/models/dense_heads/yolo_head.py @@ -0,0 +1,604 @@ +# Copyright (c) 2019 Western Digital Corporation or its affiliates. + +import warnings + +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule +from mmcv.runner import force_fp32 + +from mmdet.core import (build_anchor_generator, build_assigner, + build_bbox_coder, build_sampler, images_to_levels, + multi_apply, multiclass_nms) +from ..builder import HEADS, build_loss +from .base_dense_head import BaseDenseHead +from .dense_test_mixins import BBoxTestMixin + + +@HEADS.register_module() +class YOLOV3Head(BaseDenseHead, BBoxTestMixin): + """YOLOV3Head Paper link: https://arxiv.org/abs/1804.02767. + + Args: + num_classes (int): The number of object classes (w/o background) + in_channels (List[int]): Number of input channels per scale. + out_channels (List[int]): The number of output channels per scale + before the final 1x1 layer. Default: (1024, 512, 256). + anchor_generator (dict): Config dict for anchor generator + bbox_coder (dict): Config of bounding box coder. + featmap_strides (List[int]): The stride of each scale. + Should be in descending order. Default: (32, 16, 8). + one_hot_smoother (float): Set a non-zero value to enable label-smooth + Default: 0. + conv_cfg (dict): Config dict for convolution layer. Default: None. + norm_cfg (dict): Dictionary to construct and config norm layer. + Default: dict(type='BN', requires_grad=True) + act_cfg (dict): Config dict for activation layer. + Default: dict(type='LeakyReLU', negative_slope=0.1). + loss_cls (dict): Config of classification loss. + loss_conf (dict): Config of confidence loss. + loss_xy (dict): Config of xy coordinate loss. + loss_wh (dict): Config of wh coordinate loss. + train_cfg (dict): Training config of YOLOV3 head. Default: None. + test_cfg (dict): Testing config of YOLOV3 head. Default: None. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + num_classes, + in_channels, + out_channels=(1024, 512, 256), + anchor_generator=dict( + type='YOLOAnchorGenerator', + base_sizes=[[(116, 90), (156, 198), (373, 326)], + [(30, 61), (62, 45), (59, 119)], + [(10, 13), (16, 30), (33, 23)]], + strides=[32, 16, 8]), + bbox_coder=dict(type='YOLOBBoxCoder'), + featmap_strides=[32, 16, 8], + one_hot_smoother=0., + conv_cfg=None, + norm_cfg=dict(type='BN', requires_grad=True), + act_cfg=dict(type='LeakyReLU', negative_slope=0.1), + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0), + loss_conf=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0), + loss_xy=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0), + loss_wh=dict(type='MSELoss', loss_weight=1.0), + train_cfg=None, + test_cfg=None, + init_cfg=dict( + type='Normal', std=0.01, + override=dict(name='convs_pred'))): + super(YOLOV3Head, self).__init__(init_cfg) + # Check params + assert (len(in_channels) == len(out_channels) == len(featmap_strides)) + + self.num_classes = num_classes + self.in_channels = in_channels + self.out_channels = out_channels + self.featmap_strides = featmap_strides + self.train_cfg = train_cfg + self.test_cfg = test_cfg + if self.train_cfg: + self.assigner = build_assigner(self.train_cfg.assigner) + if hasattr(self.train_cfg, 'sampler'): + sampler_cfg = self.train_cfg.sampler + else: + sampler_cfg = dict(type='PseudoSampler') + self.sampler = build_sampler(sampler_cfg, context=self) + + self.one_hot_smoother = one_hot_smoother + + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + + self.bbox_coder = build_bbox_coder(bbox_coder) + self.anchor_generator = build_anchor_generator(anchor_generator) + + self.loss_cls = build_loss(loss_cls) + self.loss_conf = build_loss(loss_conf) + self.loss_xy = build_loss(loss_xy) + self.loss_wh = build_loss(loss_wh) + # usually the numbers of anchors for each level are the same + # except SSD detectors + self.num_anchors = self.anchor_generator.num_base_anchors[0] + assert len( + self.anchor_generator.num_base_anchors) == len(featmap_strides) + self._init_layers() + + @property + def num_levels(self): + return len(self.featmap_strides) + + @property + def num_attrib(self): + """int: number of attributes in pred_map, bboxes (4) + + objectness (1) + num_classes""" + + return 5 + self.num_classes + + def _init_layers(self): + self.convs_bridge = nn.ModuleList() + self.convs_pred = nn.ModuleList() + for i in range(self.num_levels): + conv_bridge = ConvModule( + self.in_channels[i], + self.out_channels[i], + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + conv_pred = nn.Conv2d(self.out_channels[i], + self.num_anchors * self.num_attrib, 1) + + self.convs_bridge.append(conv_bridge) + self.convs_pred.append(conv_pred) + + def forward(self, feats): + """Forward features from the upstream network. + + Args: + feats (tuple[Tensor]): Features from the upstream network, each is + a 4D-tensor. + + Returns: + tuple[Tensor]: A tuple of multi-level predication map, each is a + 4D-tensor of shape (batch_size, 5+num_classes, height, width). + """ + + assert len(feats) == self.num_levels + pred_maps = [] + for i in range(self.num_levels): + x = feats[i] + x = self.convs_bridge[i](x) + pred_map = self.convs_pred[i](x) + pred_maps.append(pred_map) + + return tuple(pred_maps), + + @force_fp32(apply_to=('pred_maps', )) + def get_bboxes(self, + pred_maps, + img_metas, + cfg=None, + rescale=False, + with_nms=True): + """Transform network output for a batch into bbox predictions. + + Args: + pred_maps (list[Tensor]): Raw predictions for a batch of images. + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + cfg (mmcv.Config | None): Test / postprocessing configuration, + if None, test_cfg would be used. Default: None. + rescale (bool): If True, return boxes in original image space. + Default: False. + with_nms (bool): If True, do nms before return boxes. + Default: True. + + Returns: + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is an (n, 5) tensor, where 5 represent + (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. + The shape of the second tensor in the tuple is (n,), and + each element represents the class label of the corresponding + box. + """ + num_levels = len(pred_maps) + pred_maps_list = [pred_maps[i].detach() for i in range(num_levels)] + scale_factors = [ + img_metas[i]['scale_factor'] + for i in range(pred_maps_list[0].shape[0]) + ] + result_list = self._get_bboxes(pred_maps_list, scale_factors, cfg, + rescale, with_nms) + return result_list + + def _get_bboxes(self, + pred_maps_list, + scale_factors, + cfg, + rescale=False, + with_nms=True): + """Transform outputs for a single batch item into bbox predictions. + + Args: + pred_maps_list (list[Tensor]): Prediction maps for different scales + of each single image in the batch. + scale_factors (list(ndarray)): Scale factor of the image arrange as + (w_scale, h_scale, w_scale, h_scale). + cfg (mmcv.Config | None): Test / postprocessing configuration, + if None, test_cfg would be used. + rescale (bool): If True, return boxes in original image space. + Default: False. + with_nms (bool): If True, do nms before return boxes. + Default: True. + + Returns: + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is an (n, 5) tensor, where 5 represent + (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. + The shape of the second tensor in the tuple is (n,), and + each element represents the class label of the corresponding + box. + """ + cfg = self.test_cfg if cfg is None else cfg + assert len(pred_maps_list) == self.num_levels + + device = pred_maps_list[0].device + batch_size = pred_maps_list[0].shape[0] + + featmap_sizes = [ + pred_maps_list[i].shape[-2:] for i in range(self.num_levels) + ] + multi_lvl_anchors = self.anchor_generator.grid_anchors( + featmap_sizes, device) + # convert to tensor to keep tracing + nms_pre_tensor = torch.tensor( + cfg.get('nms_pre', -1), device=device, dtype=torch.long) + + multi_lvl_bboxes = [] + multi_lvl_cls_scores = [] + multi_lvl_conf_scores = [] + for i in range(self.num_levels): + # get some key info for current scale + pred_map = pred_maps_list[i] + stride = self.featmap_strides[i] + # (b,h, w, num_anchors*num_attrib) -> + # (b,h*w*num_anchors, num_attrib) + pred_map = pred_map.permute(0, 2, 3, + 1).reshape(batch_size, -1, + self.num_attrib) + # Inplace operation like + # ```pred_map[..., :2] = \torch.sigmoid(pred_map[..., :2])``` + # would create constant tensor when exporting to onnx + pred_map_conf = torch.sigmoid(pred_map[..., :2]) + pred_map_rest = pred_map[..., 2:] + pred_map = torch.cat([pred_map_conf, pred_map_rest], dim=-1) + pred_map_boxes = pred_map[..., :4] + multi_lvl_anchor = multi_lvl_anchors[i] + multi_lvl_anchor = multi_lvl_anchor.expand_as(pred_map_boxes) + bbox_pred = self.bbox_coder.decode(multi_lvl_anchor, + pred_map_boxes, stride) + # conf and cls + conf_pred = torch.sigmoid(pred_map[..., 4]) + cls_pred = torch.sigmoid(pred_map[..., 5:]).view( + batch_size, -1, self.num_classes) # Cls pred one-hot. + + # Get top-k prediction + from mmdet.core.export import get_k_for_topk + nms_pre = get_k_for_topk(nms_pre_tensor, bbox_pred.shape[1]) + if nms_pre > 0: + _, topk_inds = conf_pred.topk(nms_pre) + batch_inds = torch.arange(batch_size).view( + -1, 1).expand_as(topk_inds).long() + # Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501 + if torch.onnx.is_in_onnx_export(): + transformed_inds = ( + bbox_pred.shape[1] * batch_inds + topk_inds) + bbox_pred = bbox_pred.reshape( + -1, 4)[transformed_inds, :].reshape(batch_size, -1, 4) + cls_pred = cls_pred.reshape( + -1, self.num_classes)[transformed_inds, :].reshape( + batch_size, -1, self.num_classes) + conf_pred = conf_pred.reshape(-1, + 1)[transformed_inds].reshape( + batch_size, -1) + else: + bbox_pred = bbox_pred[batch_inds, topk_inds, :] + cls_pred = cls_pred[batch_inds, topk_inds, :] + conf_pred = conf_pred[batch_inds, topk_inds] + # Save the result of current scale + multi_lvl_bboxes.append(bbox_pred) + multi_lvl_cls_scores.append(cls_pred) + multi_lvl_conf_scores.append(conf_pred) + + # Merge the results of different scales together + batch_mlvl_bboxes = torch.cat(multi_lvl_bboxes, dim=1) + batch_mlvl_scores = torch.cat(multi_lvl_cls_scores, dim=1) + batch_mlvl_conf_scores = torch.cat(multi_lvl_conf_scores, dim=1) + + # Replace multiclass_nms with ONNX::NonMaxSuppression in deployment + if torch.onnx.is_in_onnx_export() and with_nms: + from mmdet.core.export import add_dummy_nms_for_onnx + conf_thr = cfg.get('conf_thr', -1) + score_thr = cfg.get('score_thr', -1) + # follow original pipeline of YOLOv3 + if conf_thr > 0: + mask = (batch_mlvl_conf_scores >= conf_thr).float() + batch_mlvl_conf_scores *= mask + if score_thr > 0: + mask = (batch_mlvl_scores > score_thr).float() + batch_mlvl_scores *= mask + batch_mlvl_conf_scores = batch_mlvl_conf_scores.unsqueeze( + 2).expand_as(batch_mlvl_scores) + batch_mlvl_scores = batch_mlvl_scores * batch_mlvl_conf_scores + max_output_boxes_per_class = cfg.nms.get( + 'max_output_boxes_per_class', 200) + iou_threshold = cfg.nms.get('iou_threshold', 0.5) + # keep aligned with original pipeline, improve + # mAP by 1% for YOLOv3 in ONNX + score_threshold = 0 + nms_pre = cfg.get('deploy_nms_pre', -1) + return add_dummy_nms_for_onnx( + batch_mlvl_bboxes, + batch_mlvl_scores, + max_output_boxes_per_class, + iou_threshold, + score_threshold, + nms_pre, + cfg.max_per_img, + ) + + if with_nms and (batch_mlvl_conf_scores.size(0) == 0): + return torch.zeros((0, 5)), torch.zeros((0, )) + + if rescale: + batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor( + scale_factors).unsqueeze(1) + + # In mmdet 2.x, the class_id for background is num_classes. + # i.e., the last column. + padding = batch_mlvl_scores.new_zeros(batch_size, + batch_mlvl_scores.shape[1], 1) + batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1) + + # Support exporting to onnx without nms + if with_nms and cfg.get('nms', None) is not None: + det_results = [] + for (mlvl_bboxes, mlvl_scores, + mlvl_conf_scores) in zip(batch_mlvl_bboxes, batch_mlvl_scores, + batch_mlvl_conf_scores): + # Filtering out all predictions with conf < conf_thr + conf_thr = cfg.get('conf_thr', -1) + if conf_thr > 0 and (not torch.onnx.is_in_onnx_export()): + # TensorRT not support NonZero + # add as_tuple=False for compatibility in Pytorch 1.6 + # flatten would create a Reshape op with constant values, + # and raise RuntimeError when doing inference in ONNX + # Runtime with a different input image (#4221). + conf_inds = mlvl_conf_scores.ge(conf_thr).nonzero( + as_tuple=False).squeeze(1) + mlvl_bboxes = mlvl_bboxes[conf_inds, :] + mlvl_scores = mlvl_scores[conf_inds, :] + mlvl_conf_scores = mlvl_conf_scores[conf_inds] + + det_bboxes, det_labels = multiclass_nms( + mlvl_bboxes, + mlvl_scores, + cfg.score_thr, + cfg.nms, + cfg.max_per_img, + score_factors=mlvl_conf_scores) + det_results.append(tuple([det_bboxes, det_labels])) + + else: + det_results = [ + tuple(mlvl_bs) + for mlvl_bs in zip(batch_mlvl_bboxes, batch_mlvl_scores, + batch_mlvl_conf_scores) + ] + return det_results + + @force_fp32(apply_to=('pred_maps', )) + def loss(self, + pred_maps, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute loss of the head. + + Args: + pred_maps (list[Tensor]): Prediction map for each scale level, + shape (N, num_anchors * num_attrib, H, W) + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + num_imgs = len(img_metas) + device = pred_maps[0][0].device + + featmap_sizes = [ + pred_maps[i].shape[-2:] for i in range(self.num_levels) + ] + multi_level_anchors = self.anchor_generator.grid_anchors( + featmap_sizes, device) + anchor_list = [multi_level_anchors for _ in range(num_imgs)] + + responsible_flag_list = [] + for img_id in range(len(img_metas)): + responsible_flag_list.append( + self.anchor_generator.responsible_flags( + featmap_sizes, gt_bboxes[img_id], device)) + + target_maps_list, neg_maps_list = self.get_targets( + anchor_list, responsible_flag_list, gt_bboxes, gt_labels) + + losses_cls, losses_conf, losses_xy, losses_wh = multi_apply( + self.loss_single, pred_maps, target_maps_list, neg_maps_list) + + return dict( + loss_cls=losses_cls, + loss_conf=losses_conf, + loss_xy=losses_xy, + loss_wh=losses_wh) + + def loss_single(self, pred_map, target_map, neg_map): + """Compute loss of a single image from a batch. + + Args: + pred_map (Tensor): Raw predictions for a single level. + target_map (Tensor): The Ground-Truth target for a single level. + neg_map (Tensor): The negative masks for a single level. + + Returns: + tuple: + loss_cls (Tensor): Classification loss. + loss_conf (Tensor): Confidence loss. + loss_xy (Tensor): Regression loss of x, y coordinate. + loss_wh (Tensor): Regression loss of w, h coordinate. + """ + + num_imgs = len(pred_map) + pred_map = pred_map.permute(0, 2, 3, + 1).reshape(num_imgs, -1, self.num_attrib) + neg_mask = neg_map.float() + pos_mask = target_map[..., 4] + pos_and_neg_mask = neg_mask + pos_mask + pos_mask = pos_mask.unsqueeze(dim=-1) + if torch.max(pos_and_neg_mask) > 1.: + warnings.warn('There is overlap between pos and neg sample.') + pos_and_neg_mask = pos_and_neg_mask.clamp(min=0., max=1.) + + pred_xy = pred_map[..., :2] + pred_wh = pred_map[..., 2:4] + pred_conf = pred_map[..., 4] + pred_label = pred_map[..., 5:] + + target_xy = target_map[..., :2] + target_wh = target_map[..., 2:4] + target_conf = target_map[..., 4] + target_label = target_map[..., 5:] + + loss_cls = self.loss_cls(pred_label, target_label, weight=pos_mask) + loss_conf = self.loss_conf( + pred_conf, target_conf, weight=pos_and_neg_mask) + loss_xy = self.loss_xy(pred_xy, target_xy, weight=pos_mask) + loss_wh = self.loss_wh(pred_wh, target_wh, weight=pos_mask) + + return loss_cls, loss_conf, loss_xy, loss_wh + + def get_targets(self, anchor_list, responsible_flag_list, gt_bboxes_list, + gt_labels_list): + """Compute target maps for anchors in multiple images. + + Args: + anchor_list (list[list[Tensor]]): Multi level anchors of each + image. The outer list indicates images, and the inner list + corresponds to feature levels of the image. Each element of + the inner list is a tensor of shape (num_total_anchors, 4). + responsible_flag_list (list[list[Tensor]]): Multi level responsible + flags of each image. Each element is a tensor of shape + (num_total_anchors, ) + gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image. + gt_labels_list (list[Tensor]): Ground truth labels of each box. + + Returns: + tuple: Usually returns a tuple containing learning targets. + - target_map_list (list[Tensor]): Target map of each level. + - neg_map_list (list[Tensor]): Negative map of each level. + """ + num_imgs = len(anchor_list) + + # anchor number of multi levels + num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] + + results = multi_apply(self._get_targets_single, anchor_list, + responsible_flag_list, gt_bboxes_list, + gt_labels_list) + + all_target_maps, all_neg_maps = results + assert num_imgs == len(all_target_maps) == len(all_neg_maps) + target_maps_list = images_to_levels(all_target_maps, num_level_anchors) + neg_maps_list = images_to_levels(all_neg_maps, num_level_anchors) + + return target_maps_list, neg_maps_list + + def _get_targets_single(self, anchors, responsible_flags, gt_bboxes, + gt_labels): + """Generate matching bounding box prior and converted GT. + + Args: + anchors (list[Tensor]): Multi-level anchors of the image. + responsible_flags (list[Tensor]): Multi-level responsible flags of + anchors + gt_bboxes (Tensor): Ground truth bboxes of single image. + gt_labels (Tensor): Ground truth labels of single image. + + Returns: + tuple: + target_map (Tensor): Predication target map of each + scale level, shape (num_total_anchors, + 5+num_classes) + neg_map (Tensor): Negative map of each scale level, + shape (num_total_anchors,) + """ + + anchor_strides = [] + for i in range(len(anchors)): + anchor_strides.append( + torch.tensor(self.featmap_strides[i], + device=gt_bboxes.device).repeat(len(anchors[i]))) + concat_anchors = torch.cat(anchors) + concat_responsible_flags = torch.cat(responsible_flags) + + anchor_strides = torch.cat(anchor_strides) + assert len(anchor_strides) == len(concat_anchors) == \ + len(concat_responsible_flags) + assign_result = self.assigner.assign(concat_anchors, + concat_responsible_flags, + gt_bboxes) + sampling_result = self.sampler.sample(assign_result, concat_anchors, + gt_bboxes) + + target_map = concat_anchors.new_zeros( + concat_anchors.size(0), self.num_attrib) + + target_map[sampling_result.pos_inds, :4] = self.bbox_coder.encode( + sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes, + anchor_strides[sampling_result.pos_inds]) + + target_map[sampling_result.pos_inds, 4] = 1 + + gt_labels_one_hot = F.one_hot( + gt_labels, num_classes=self.num_classes).float() + if self.one_hot_smoother != 0: # label smooth + gt_labels_one_hot = gt_labels_one_hot * ( + 1 - self.one_hot_smoother + ) + self.one_hot_smoother / self.num_classes + target_map[sampling_result.pos_inds, 5:] = gt_labels_one_hot[ + sampling_result.pos_assigned_gt_inds] + + neg_map = concat_anchors.new_zeros( + concat_anchors.size(0), dtype=torch.uint8) + neg_map[sampling_result.neg_inds] = 1 + + return target_map, neg_map + + def aug_test(self, feats, img_metas, rescale=False): + """Test function with test time augmentation. + + Args: + feats (list[Tensor]): the outer list indicates test-time + augmentations and inner Tensor should have a shape NxCxHxW, + which contains features for all images in the batch. + img_metas (list[list[dict]]): the outer list indicates test-time + augs (multiscale, flip, etc.) and the inner list indicates + images in a batch. each dict has image information. + rescale (bool, optional): Whether to rescale the results. + Defaults to False. + + Returns: + list[ndarray]: bbox results of each class + """ + return self.aug_test_bboxes(feats, img_metas, rescale=rescale) diff --git a/mmdet/models/dense_heads/yolof_head.py b/mmdet/models/dense_heads/yolof_head.py new file mode 100644 index 0000000..e15d4d4 --- /dev/null +++ b/mmdet/models/dense_heads/yolof_head.py @@ -0,0 +1,415 @@ +import torch +import torch.nn as nn +from mmcv.cnn import (ConvModule, bias_init_with_prob, constant_init, is_norm, + normal_init) +from mmcv.runner import force_fp32 + +from mmdet.core import anchor_inside_flags, multi_apply, reduce_mean, unmap +from ..builder import HEADS +from .anchor_head import AnchorHead + +INF = 1e8 + + +def levels_to_images(mlvl_tensor): + """Concat multi-level feature maps by image. + + [feature_level0, feature_level1...] -> [feature_image0, feature_image1...] + Convert the shape of each element in mlvl_tensor from (N, C, H, W) to + (N, H*W , C), then split the element to N elements with shape (H*W, C), and + concat elements in same image of all level along first dimension. + + Args: + mlvl_tensor (list[torch.Tensor]): list of Tensor which collect from + corresponding level. Each element is of shape (N, C, H, W) + + Returns: + list[torch.Tensor]: A list that contains N tensors and each tensor is + of shape (num_elements, C) + """ + batch_size = mlvl_tensor[0].size(0) + batch_list = [[] for _ in range(batch_size)] + channels = mlvl_tensor[0].size(1) + for t in mlvl_tensor: + t = t.permute(0, 2, 3, 1) + t = t.view(batch_size, -1, channels).contiguous() + for img in range(batch_size): + batch_list[img].append(t[img]) + return [torch.cat(item, 0) for item in batch_list] + + +@HEADS.register_module() +class YOLOFHead(AnchorHead): + """YOLOFHead Paper link: https://arxiv.org/abs/2103.09460. + + Args: + num_classes (int): The number of object classes (w/o background) + in_channels (List[int]): The number of input channels per scale. + cls_num_convs (int): The number of convolutions of cls branch. + Default 2. + reg_num_convs (int): The number of convolutions of reg branch. + Default 4. + norm_cfg (dict): Dictionary to construct and config norm layer. + """ + + def __init__(self, + num_classes, + in_channels, + num_cls_convs=2, + num_reg_convs=4, + norm_cfg=dict(type='BN', requires_grad=True), + **kwargs): + self.num_cls_convs = num_cls_convs + self.num_reg_convs = num_reg_convs + self.norm_cfg = norm_cfg + super(YOLOFHead, self).__init__(num_classes, in_channels, **kwargs) + + def _init_layers(self): + cls_subnet = [] + bbox_subnet = [] + for i in range(self.num_cls_convs): + cls_subnet.append( + ConvModule( + self.in_channels, + self.in_channels, + kernel_size=3, + padding=1, + norm_cfg=self.norm_cfg)) + for i in range(self.num_reg_convs): + bbox_subnet.append( + ConvModule( + self.in_channels, + self.in_channels, + kernel_size=3, + padding=1, + norm_cfg=self.norm_cfg)) + self.cls_subnet = nn.Sequential(*cls_subnet) + self.bbox_subnet = nn.Sequential(*bbox_subnet) + self.cls_score = nn.Conv2d( + self.in_channels, + self.num_anchors * self.num_classes, + kernel_size=3, + stride=1, + padding=1) + self.bbox_pred = nn.Conv2d( + self.in_channels, + self.num_anchors * 4, + kernel_size=3, + stride=1, + padding=1) + self.object_pred = nn.Conv2d( + self.in_channels, + self.num_anchors, + kernel_size=3, + stride=1, + padding=1) + + def init_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + normal_init(m, mean=0, std=0.01) + if is_norm(m): + constant_init(m, 1) + + # Use prior in model initialization to improve stability + bias_cls = bias_init_with_prob(0.01) + torch.nn.init.constant_(self.cls_score.bias, bias_cls) + + def forward_single(self, feature): + cls_score = self.cls_score(self.cls_subnet(feature)) + N, _, H, W = cls_score.shape + cls_score = cls_score.view(N, -1, self.num_classes, H, W) + + reg_feat = self.bbox_subnet(feature) + bbox_reg = self.bbox_pred(reg_feat) + objectness = self.object_pred(reg_feat) + + # implicit objectness + objectness = objectness.view(N, -1, 1, H, W) + normalized_cls_score = cls_score + objectness - torch.log( + 1. + torch.clamp(cls_score.exp(), max=INF) + + torch.clamp(objectness.exp(), max=INF)) + normalized_cls_score = normalized_cls_score.view(N, -1, H, W) + return normalized_cls_score, bbox_reg + + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) + def loss(self, + cls_scores, + bbox_preds, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute losses of the head. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (batch, num_anchors * num_classes, h, w) + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (batch, num_anchors * 4, h, w) + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. Default: None + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + assert len(cls_scores) == 1 + assert self.anchor_generator.num_levels == 1 + + device = cls_scores[0].device + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + anchor_list, valid_flag_list = self.get_anchors( + featmap_sizes, img_metas, device=device) + + # The output level is always 1 + anchor_list = [anchors[0] for anchors in anchor_list] + valid_flag_list = [valid_flags[0] for valid_flags in valid_flag_list] + + cls_scores_list = levels_to_images(cls_scores) + bbox_preds_list = levels_to_images(bbox_preds) + + label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 + cls_reg_targets = self.get_targets( + cls_scores_list, + bbox_preds_list, + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + gt_labels_list=gt_labels, + label_channels=label_channels) + if cls_reg_targets is None: + return None + (batch_labels, batch_label_weights, num_total_pos, num_total_neg, + batch_bbox_weights, batch_pos_predicted_boxes, + batch_target_boxes) = cls_reg_targets + + flatten_labels = batch_labels.reshape(-1) + batch_label_weights = batch_label_weights.reshape(-1) + cls_score = cls_scores[0].permute(0, 2, 3, + 1).reshape(-1, self.cls_out_channels) + + num_total_samples = (num_total_pos + + num_total_neg) if self.sampling else num_total_pos + num_total_samples = reduce_mean( + cls_score.new_tensor(num_total_samples)).clamp_(1.0).item() + + # classification loss + loss_cls = self.loss_cls( + cls_score, + flatten_labels, + batch_label_weights, + avg_factor=num_total_samples) + + # regression loss + if batch_pos_predicted_boxes.shape[0] == 0: + # no pos sample + loss_bbox = batch_pos_predicted_boxes.sum() * 0 + else: + loss_bbox = self.loss_bbox( + batch_pos_predicted_boxes, + batch_target_boxes, + batch_bbox_weights.float(), + avg_factor=num_total_samples) + + return dict(loss_cls=loss_cls, loss_bbox=loss_bbox) + + def get_targets(self, + cls_scores_list, + bbox_preds_list, + anchor_list, + valid_flag_list, + gt_bboxes_list, + img_metas, + gt_bboxes_ignore_list=None, + gt_labels_list=None, + label_channels=1, + unmap_outputs=True): + """Compute regression and classification targets for anchors in + multiple images. + + Args: + cls_scores_list (list[Tensor])： Classification scores of + each image. each is a 4D-tensor, the shape is + (h * w, num_anchors * num_classes). + bbox_preds_list (list[Tensor])： Bbox preds of each image. + each is a 4D-tensor, the shape is (h * w, num_anchors * 4). + anchor_list (list[Tensor]): Anchors of each image. Each element of + is a tensor of shape (h * w * num_anchors, 4). + valid_flag_list (list[Tensor]): Valid flags of each image. Each + element of is a tensor of shape (h * w * num_anchors, ) + gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image. + img_metas (list[dict]): Meta info of each image. + gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be + ignored. + gt_labels_list (list[Tensor]): Ground truth labels of each box. + label_channels (int): Channel of label. + unmap_outputs (bool): Whether to map outputs back to the original + set of anchors. + + Returns: + tuple: Usually returns a tuple containing learning targets. + + - batch_labels (Tensor): Label of all images. Each element \ + of is a tensor of shape (batch, h * w * num_anchors) + - batch_label_weights (Tensor): Label weights of all images \ + of is a tensor of shape (batch, h * w * num_anchors) + - num_total_pos (int): Number of positive samples in all \ + images. + - num_total_neg (int): Number of negative samples in all \ + images. + additional_returns: This function enables user-defined returns from + `self._get_targets_single`. These returns are currently refined + to properties at each feature map (i.e. having HxW dimension). + The results will be concatenated after the end + """ + num_imgs = len(img_metas) + assert len(anchor_list) == len(valid_flag_list) == num_imgs + + # compute targets for each image + if gt_bboxes_ignore_list is None: + gt_bboxes_ignore_list = [None for _ in range(num_imgs)] + if gt_labels_list is None: + gt_labels_list = [None for _ in range(num_imgs)] + results = multi_apply( + self._get_targets_single, + bbox_preds_list, + anchor_list, + valid_flag_list, + gt_bboxes_list, + gt_bboxes_ignore_list, + gt_labels_list, + img_metas, + label_channels=label_channels, + unmap_outputs=unmap_outputs) + (all_labels, all_label_weights, pos_inds_list, neg_inds_list, + sampling_results_list) = results[:5] + rest_results = list(results[5:]) # user-added return values + # no valid anchors + if any([labels is None for labels in all_labels]): + return None + # sampled anchors of all images + num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list]) + num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list]) + + batch_labels = torch.stack(all_labels, 0) + batch_label_weights = torch.stack(all_label_weights, 0) + + res = (batch_labels, batch_label_weights, num_total_pos, num_total_neg) + for i, rests in enumerate(rest_results): # user-added return values + rest_results[i] = torch.cat(rests, 0) + + return res + tuple(rest_results) + + def _get_targets_single(self, + bbox_preds, + flat_anchors, + valid_flags, + gt_bboxes, + gt_bboxes_ignore, + gt_labels, + img_meta, + label_channels=1, + unmap_outputs=True): + """Compute regression and classification targets for anchors in a + single image. + + Args: + bbox_preds (Tensor): Bbox prediction of the image, which + shape is (h * w ,4) + flat_anchors (Tensor): Anchors of the image, which shape is + (h * w * num_anchors ,4) + valid_flags (Tensor): Valid flags of the image, which shape is + (h * w * num_anchors,). + gt_bboxes (Tensor): Ground truth bboxes of the image, + shape (num_gts, 4). + gt_bboxes_ignore (Tensor): Ground truth bboxes to be + ignored, shape (num_ignored_gts, 4). + img_meta (dict): Meta info of the image. + gt_labels (Tensor): Ground truth labels of each box, + shape (num_gts,). + label_channels (int): Channel of label. + unmap_outputs (bool): Whether to map outputs back to the original + set of anchors. + + Returns: + tuple: + labels (Tensor): Labels of image, which shape is + (h * w * num_anchors, ). + label_weights (Tensor): Label weights of image, which shape is + (h * w * num_anchors, ). + pos_inds (Tensor): Pos index of image. + neg_inds (Tensor): Neg index of image. + sampling_result (obj:`SamplingResult`): Sampling result. + pos_bbox_weights (Tensor): The Weight of using to calculate + the bbox branch loss, which shape is (num, ). + pos_predicted_boxes (Tensor): boxes predicted value of + using to calculate the bbox branch loss, which shape is + (num, 4). + pos_target_boxes (Tensor): boxes target value of + using to calculate the bbox branch loss, which shape is + (num, 4). + """ + inside_flags = anchor_inside_flags(flat_anchors, valid_flags, + img_meta['img_shape'][:2], + self.train_cfg.allowed_border) + if not inside_flags.any(): + return (None, ) * 8 + # assign gt and sample anchors + anchors = flat_anchors[inside_flags, :] + bbox_preds = bbox_preds.reshape(-1, 4) + bbox_preds = bbox_preds[inside_flags, :] + + # decoded bbox + decoder_bbox_preds = self.bbox_coder.decode(anchors, bbox_preds) + assign_result = self.assigner.assign( + decoder_bbox_preds, anchors, gt_bboxes, gt_bboxes_ignore, + None if self.sampling else gt_labels) + + pos_bbox_weights = assign_result.get_extra_property('pos_idx') + pos_predicted_boxes = assign_result.get_extra_property( + 'pos_predicted_boxes') + pos_target_boxes = assign_result.get_extra_property('target_boxes') + + sampling_result = self.sampler.sample(assign_result, anchors, + gt_bboxes) + num_valid_anchors = anchors.shape[0] + labels = anchors.new_full((num_valid_anchors, ), + self.num_classes, + dtype=torch.long) + label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float) + + pos_inds = sampling_result.pos_inds + neg_inds = sampling_result.neg_inds + if len(pos_inds) > 0: + if gt_labels is None: + # Only rpn gives gt_labels as None + # Foreground is the first class since v2.5.0 + labels[pos_inds] = 0 + else: + labels[pos_inds] = gt_labels[ + sampling_result.pos_assigned_gt_inds] + if self.train_cfg.pos_weight <= 0: + label_weights[pos_inds] = 1.0 + else: + label_weights[pos_inds] = self.train_cfg.pos_weight + if len(neg_inds) > 0: + label_weights[neg_inds] = 1.0 + + # map up to original set of anchors + if unmap_outputs: + num_total_anchors = flat_anchors.size(0) + labels = unmap( + labels, num_total_anchors, inside_flags, + fill=self.num_classes) # fill bg label + label_weights = unmap(label_weights, num_total_anchors, + inside_flags) + + return (labels, label_weights, pos_inds, neg_inds, sampling_result, + pos_bbox_weights, pos_predicted_boxes, pos_target_boxes) diff --git a/mmdet/models/detectors/__init__.py b/mmdet/models/detectors/__init__.py new file mode 100644 index 0000000..4d1a2f6 --- /dev/null +++ b/mmdet/models/detectors/__init__.py @@ -0,0 +1,44 @@ +from .atss import ATSS +from .autoassign import AutoAssign +from .base import BaseDetector +from .cascade_rcnn import CascadeRCNN +from .cornernet import CornerNet +from .deformable_detr import DeformableDETR +from .detr import DETR +from .fast_rcnn import FastRCNN +from .faster_rcnn import FasterRCNN +from .fcos import FCOS +from .fovea import FOVEA +from .fsaf import FSAF +from .gfl import GFL +from .grid_rcnn import GridRCNN +from .htc import HybridTaskCascade +from .kd_one_stage import KnowledgeDistillationSingleStageDetector +from .mask_rcnn import MaskRCNN +from .mask_scoring_rcnn import MaskScoringRCNN +from .nasfcos import NASFCOS +from .paa import PAA +from .point_rend import PointRend +from .reppoints_detector import RepPointsDetector +from .retinanet import RetinaNet +from .rpn import RPN +from .scnet import SCNet +from .single_stage import SingleStageDetector +from .sparse_rcnn import SparseRCNN +from .trident_faster_rcnn import TridentFasterRCNN +from .two_stage import TwoStageDetector +from .vfnet import VFNet +from .yolact import YOLACT +from .yolo import YOLOV3 +from .yolof import YOLOF +from .query_based import QueryBased + +__all__ = [ + 'ATSS', 'BaseDetector', 'SingleStageDetector', 'TwoStageDetector', 'RPN', + 'KnowledgeDistillationSingleStageDetector', 'FastRCNN', 'FasterRCNN', + 'MaskRCNN', 'CascadeRCNN', 'HybridTaskCascade', 'RetinaNet', 'FCOS', + 'GridRCNN', 'MaskScoringRCNN', 'RepPointsDetector', 'FOVEA', 'FSAF', + 'NASFCOS', 'PointRend', 'GFL', 'CornerNet', 'PAA', 'YOLOV3', 'YOLACT', + 'VFNet', 'DETR', 'TridentFasterRCNN', 'SparseRCNN', 'SCNet', + 'DeformableDETR', 'AutoAssign', 'YOLOF', 'QueryBased' +] diff --git 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+@DETECTORS.register_module() +class ATSS(SingleStageDetector): + """Implementation of `ATSS `_.""" + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(ATSS, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/detectors/autoassign.py b/mmdet/models/detectors/autoassign.py new file mode 100644 index 0000000..1bc0309 --- /dev/null +++ b/mmdet/models/detectors/autoassign.py @@ -0,0 +1,18 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class AutoAssign(SingleStageDetector): + """Implementation of `AutoAssign: Differentiable Label Assignment for Dense + Object Detection `_.""" + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None): + super(AutoAssign, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained) diff --git a/mmdet/models/detectors/base.py b/mmdet/models/detectors/base.py new file mode 100644 index 0000000..6ee7c90 --- /dev/null +++ b/mmdet/models/detectors/base.py @@ -0,0 +1,341 @@ +from abc import ABCMeta, abstractmethod +from collections import OrderedDict + +import mmcv +import numpy as np +import torch +import torch.distributed as dist +from mmcv.runner import BaseModule, auto_fp16 + +from mmdet.core.visualization import imshow_det_bboxes + + +class BaseDetector(BaseModule, metaclass=ABCMeta): + """Base class for detectors.""" + + def __init__(self, init_cfg=None): + super(BaseDetector, self).__init__(init_cfg) + self.fp16_enabled = False + + @property + def with_neck(self): + """bool: whether the detector has a neck""" + return hasattr(self, 'neck') and self.neck is not None + + # TODO: these properties need to be carefully handled + # for both single stage & two stage detectors + @property + def with_shared_head(self): + """bool: whether the detector has a shared head in the RoI Head""" + return hasattr(self, 'roi_head') and self.roi_head.with_shared_head + + @property + def with_bbox(self): + """bool: whether the detector has a bbox head""" + return ((hasattr(self, 'roi_head') and self.roi_head.with_bbox) + or (hasattr(self, 'bbox_head') and self.bbox_head is not None)) + + @property + def with_mask(self): + """bool: whether the detector has a mask head""" + return ((hasattr(self, 'roi_head') and self.roi_head.with_mask) + or (hasattr(self, 'mask_head') and self.mask_head is not None)) + + @abstractmethod + def extract_feat(self, imgs): + """Extract features from images.""" + pass + + def extract_feats(self, imgs): + """Extract features from multiple images. + + Args: + imgs (list[torch.Tensor]): A list of images. The images are + augmented from the same image but in different ways. + + Returns: + list[torch.Tensor]: Features of different images + """ + assert isinstance(imgs, list) + return [self.extract_feat(img) for img in imgs] + + def forward_train(self, imgs, img_metas, **kwargs): + """ + Args: + img (list[Tensor]): List of tensors of shape (1, C, H, W). + Typically these should be mean centered and std scaled. + img_metas (list[dict]): List of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys, see + :class:`mmdet.datasets.pipelines.Collect`. + kwargs (keyword arguments): Specific to concrete implementation. + """ + # NOTE the batched image size information may be useful, e.g. + # in DETR, this is needed for the construction of masks, which is + # then used for the transformer_head. + batch_input_shape = tuple(imgs[0].size()[-2:]) + for img_meta in img_metas: + img_meta['batch_input_shape'] = batch_input_shape + + async def async_simple_test(self, img, img_metas, **kwargs): + raise NotImplementedError + + @abstractmethod + def simple_test(self, img, img_metas, **kwargs): + pass + + @abstractmethod + def aug_test(self, imgs, img_metas, **kwargs): + """Test function with test time augmentation.""" + pass + + async def aforward_test(self, *, img, img_metas, **kwargs): + for var, name in [(img, 'img'), (img_metas, 'img_metas')]: + if not isinstance(var, list): + raise TypeError(f'{name} must be a list, but got {type(var)}') + + num_augs = len(img) + if num_augs != len(img_metas): + raise ValueError(f'num of augmentations ({len(img)}) ' + f'!= num of image metas ({len(img_metas)})') + # TODO: remove the restriction of samples_per_gpu == 1 when prepared + samples_per_gpu = img[0].size(0) + assert samples_per_gpu == 1 + + if num_augs == 1: + return await self.async_simple_test(img[0], img_metas[0], **kwargs) + else: + raise NotImplementedError + + def forward_test(self, imgs, img_metas, **kwargs): + """ + Args: + imgs (List[Tensor]): the outer list indicates test-time + augmentations and inner Tensor should have a shape NxCxHxW, + which contains all images in the batch. + img_metas (List[List[dict]]): the outer list indicates test-time + augs (multiscale, flip, etc.) and the inner list indicates + images in a batch. + """ + for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]: + if not isinstance(var, list): + raise TypeError(f'{name} must be a list, but got {type(var)}') + + num_augs = len(imgs) + if num_augs != len(img_metas): + raise ValueError(f'num of augmentations ({len(imgs)}) ' + f'!= num of image meta ({len(img_metas)})') + + # NOTE the batched image size information may be useful, e.g. + # in DETR, this is needed for the construction of masks, which is + # then used for the transformer_head. + for img, img_meta in zip(imgs, img_metas): + batch_size = len(img_meta) + for img_id in range(batch_size): + img_meta[img_id]['batch_input_shape'] = tuple(img.size()[-2:]) + + if num_augs == 1: + # proposals (List[List[Tensor]]): the outer list indicates + # test-time augs (multiscale, flip, etc.) and the inner list + # indicates images in a batch. + # The Tensor should have a shape Px4, where P is the number of + # proposals. + if 'proposals' in kwargs: + kwargs['proposals'] = kwargs['proposals'][0] + return self.simple_test(imgs[0], img_metas[0], **kwargs) + else: + assert imgs[0].size(0) == 1, 'aug test does not support ' \ + 'inference with batch size ' \ + f'{imgs[0].size(0)}' + # TODO: support test augmentation for predefined proposals + assert 'proposals' not in kwargs + return self.aug_test(imgs, img_metas, **kwargs) + + @auto_fp16(apply_to=('img', )) + def forward(self, img, img_metas, return_loss=True, **kwargs): + """Calls either :func:`forward_train` or :func:`forward_test` depending + on whether ``return_loss`` is ``True``. + + Note this setting will change the expected inputs. When + ``return_loss=True``, img and img_meta are single-nested (i.e. Tensor + and List[dict]), and when ``resturn_loss=False``, img and img_meta + should be double nested (i.e. List[Tensor], List[List[dict]]), with + the outer list indicating test time augmentations. + """ + if return_loss: + return self.forward_train(img, img_metas, **kwargs) + else: + return self.forward_test(img, img_metas, **kwargs) + + def _parse_losses(self, losses): + """Parse the raw outputs (losses) of the network. + + Args: + losses (dict): Raw output of the network, which usually contain + losses and other necessary infomation. + + Returns: + tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor \ + which may be a weighted sum of all losses, log_vars contains \ + all the variables to be sent to the logger. + """ + log_vars = OrderedDict() + for loss_name, loss_value in losses.items(): + if isinstance(loss_value, torch.Tensor): + log_vars[loss_name] = loss_value.mean() + elif isinstance(loss_value, list): + log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value) + else: + raise TypeError( + f'{loss_name} is not a tensor or list of tensors') + + loss = sum(_value for _key, _value in log_vars.items() + if 'loss' in _key) + + log_vars['loss'] = loss + for loss_name, loss_value in log_vars.items(): + # reduce loss when distributed training + if dist.is_available() and dist.is_initialized(): + loss_value = loss_value.data.clone() + dist.all_reduce(loss_value.div_(dist.get_world_size())) + log_vars[loss_name] = loss_value.item() + + return loss, log_vars + + def train_step(self, data, optimizer): + """The iteration step during training. + + This method defines an iteration step during training, except for the + back propagation and optimizer updating, which are done in an optimizer + hook. Note that in some complicated cases or models, the whole process + including back propagation and optimizer updating is also defined in + this method, such as GAN. + + Args: + data (dict): The output of dataloader. + optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of + runner is passed to ``train_step()``. This argument is unused + and reserved. + + Returns: + dict: It should contain at least 3 keys: ``loss``, ``log_vars``, \ + ``num_samples``. + + - ``loss`` is a tensor for back propagation, which can be a \ + weighted sum of multiple losses. + - ``log_vars`` contains all the variables to be sent to the + logger. + - ``num_samples`` indicates the batch size (when the model is \ + DDP, it means the batch size on each GPU), which is used for \ + averaging the logs. + """ + losses = self(**data) + loss, log_vars = self._parse_losses(losses) + + outputs = dict( + loss=loss, log_vars=log_vars, num_samples=len(data['img_metas'])) + + return outputs + + def val_step(self, data, optimizer): + """The iteration step during validation. + + This method shares the same signature as :func:`train_step`, but used + during val epochs. Note that the evaluation after training epochs is + not implemented with this method, but an evaluation hook. + """ + losses = self(**data) + loss, log_vars = self._parse_losses(losses) + + outputs = dict( + loss=loss, log_vars=log_vars, num_samples=len(data['img_metas'])) + + return outputs + + def show_result(self, + img, + result, + score_thr=0.3, + bbox_color=(72, 101, 241), + text_color=(72, 101, 241), + mask_color=None, + thickness=2, + font_size=13, + win_name='', + show=False, + wait_time=0, + out_file=None): + """Draw `result` over `img`. + + Args: + img (str or Tensor): The image to be displayed. + result (Tensor or tuple): The results to draw over `img` + bbox_result or (bbox_result, segm_result). + score_thr (float, optional): Minimum score of bboxes to be shown. + Default: 0.3. + bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines. + The tuple of color should be in BGR order. Default: 'green' + text_color (str or tuple(int) or :obj:`Color`):Color of texts. + The tuple of color should be in BGR order. Default: 'green' + mask_color (None or str or tuple(int) or :obj:`Color`): + Color of masks. The tuple of color should be in BGR order. + Default: None + thickness (int): Thickness of lines. Default: 2 + font_size (int): Font size of texts. Default: 13 + win_name (str): The window name. Default: '' + wait_time (float): Value of waitKey param. + Default: 0. + show (bool): Whether to show the image. + Default: False. + out_file (str or None): The filename to write the image. + Default: None. + + Returns: + img (Tensor): Only if not `show` or `out_file` + """ + img = mmcv.imread(img) + img = img.copy() + if isinstance(result, tuple): + bbox_result, segm_result = result + if isinstance(segm_result, tuple): + segm_result = segm_result[0] # ms rcnn + else: + bbox_result, segm_result = result, None + bboxes = np.vstack(bbox_result) + labels = [ + np.full(bbox.shape[0], i, dtype=np.int32) + for i, bbox in enumerate(bbox_result) + ] + labels = np.concatenate(labels) + # draw segmentation masks + segms = None + if segm_result is not None and len(labels) > 0: # non empty + segms = mmcv.concat_list(segm_result) + if isinstance(segms[0], torch.Tensor): + segms = torch.stack(segms, dim=0).detach().cpu().numpy() + else: + segms = np.stack(segms, axis=0) + # if out_file specified, do not show image in window + if out_file is not None: + show = False + # draw bounding boxes + img = imshow_det_bboxes( + img, + bboxes, + labels, + segms, + class_names=self.CLASSES, + score_thr=score_thr, + bbox_color=bbox_color, + text_color=text_color, + mask_color=mask_color, + thickness=thickness, + font_size=font_size, + win_name=win_name, + show=show, + wait_time=wait_time, + out_file=out_file) + + if not (show or out_file): + return img diff --git a/mmdet/models/detectors/cascade_rcnn.py b/mmdet/models/detectors/cascade_rcnn.py new file mode 100644 index 0000000..8a41789 --- /dev/null +++ b/mmdet/models/detectors/cascade_rcnn.py @@ -0,0 +1,48 @@ +from ..builder import DETECTORS +from .two_stage import TwoStageDetector + + +@DETECTORS.register_module() +class CascadeRCNN(TwoStageDetector): + r"""Implementation of `Cascade R-CNN: Delving into High Quality Object + Detection `_""" + + def __init__(self, + backbone, + neck=None, + rpn_head=None, + roi_head=None, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(CascadeRCNN, self).__init__( + backbone=backbone, + neck=neck, + rpn_head=rpn_head, + roi_head=roi_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) + + def show_result(self, data, result, **kwargs): + """Show prediction results of the detector. + + Args: + data (str or np.ndarray): Image filename or loaded image. + result (Tensor or tuple): The results to draw over `img` + bbox_result or (bbox_result, segm_result). + + Returns: + np.ndarray: The image with bboxes drawn on it. + """ + if self.with_mask: + ms_bbox_result, ms_segm_result = result + if isinstance(ms_bbox_result, dict): + result = (ms_bbox_result['ensemble'], + ms_segm_result['ensemble']) + else: + if isinstance(result, dict): + result = result['ensemble'] + return super(CascadeRCNN, self).show_result(data, result, **kwargs) diff --git a/mmdet/models/detectors/cornernet.py b/mmdet/models/detectors/cornernet.py new file mode 100644 index 0000000..b6dc603 --- /dev/null +++ b/mmdet/models/detectors/cornernet.py @@ -0,0 +1,96 @@ +import torch + +from mmdet.core import bbox2result, bbox_mapping_back +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class CornerNet(SingleStageDetector): + """CornerNet. + + This detector is the implementation of the paper `CornerNet: Detecting + Objects as Paired Keypoints `_ . + """ + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(CornerNet, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) + + def merge_aug_results(self, aug_results, img_metas): + """Merge augmented detection bboxes and score. + + Args: + aug_results (list[list[Tensor]]): Det_bboxes and det_labels of each + image. + img_metas (list[list[dict]]): Meta information of each image, e.g., + image size, scaling factor, etc. + + Returns: + tuple: (bboxes, labels) + """ + recovered_bboxes, aug_labels = [], [] + for bboxes_labels, img_info in zip(aug_results, img_metas): + img_shape = img_info[0]['img_shape'] # using shape before padding + scale_factor = img_info[0]['scale_factor'] + flip = img_info[0]['flip'] + bboxes, labels = bboxes_labels + bboxes, scores = bboxes[:, :4], bboxes[:, -1:] + bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip) + recovered_bboxes.append(torch.cat([bboxes, scores], dim=-1)) + aug_labels.append(labels) + + bboxes = torch.cat(recovered_bboxes, dim=0) + labels = torch.cat(aug_labels) + + if bboxes.shape[0] > 0: + out_bboxes, out_labels = self.bbox_head._bboxes_nms( + bboxes, labels, self.bbox_head.test_cfg) + else: + out_bboxes, out_labels = bboxes, labels + + return out_bboxes, out_labels + + def aug_test(self, imgs, img_metas, rescale=False): + """Augment testing of CornerNet. + + Args: + imgs (list[Tensor]): Augmented images. + img_metas (list[list[dict]]): Meta information of each image, e.g., + image size, scaling factor, etc. + rescale (bool): If True, return boxes in original image space. + Default: False. + + Note: + ``imgs`` must including flipped image pairs. + + Returns: + list[list[np.ndarray]]: BBox results of each image and classes. + The outer list corresponds to each image. The inner list + corresponds to each class. + """ + img_inds = list(range(len(imgs))) + + assert img_metas[0][0]['flip'] + img_metas[1][0]['flip'], ( + 'aug test must have flipped image pair') + aug_results = [] + for ind, flip_ind in zip(img_inds[0::2], img_inds[1::2]): + img_pair = torch.cat([imgs[ind], imgs[flip_ind]]) + x = self.extract_feat(img_pair) + outs = self.bbox_head(x) + bbox_list = self.bbox_head.get_bboxes( + *outs, [img_metas[ind], img_metas[flip_ind]], False, False) + aug_results.append(bbox_list[0]) + aug_results.append(bbox_list[1]) + + bboxes, labels = self.merge_aug_results(aug_results, img_metas) + bbox_results = bbox2result(bboxes, labels, self.bbox_head.num_classes) + + return [bbox_results] diff --git a/mmdet/models/detectors/deformable_detr.py b/mmdet/models/detectors/deformable_detr.py new file mode 100644 index 0000000..947550f --- /dev/null +++ b/mmdet/models/detectors/deformable_detr.py @@ -0,0 +1,9 @@ +from ..builder import DETECTORS +from .detr import DETR + + +@DETECTORS.register_module() +class DeformableDETR(DETR): + + def __init__(self, *args, **kwargs): + super(DETR, self).__init__(*args, **kwargs) diff --git a/mmdet/models/detectors/detr.py b/mmdet/models/detectors/detr.py new file mode 100644 index 0000000..2ba2136 --- /dev/null +++ b/mmdet/models/detectors/detr.py @@ -0,0 +1,47 @@ +from mmdet.core import bbox2result +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class DETR(SingleStageDetector): + r"""Implementation of `DETR: End-to-End Object Detection with + Transformers `_""" + + def __init__(self, + backbone, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(DETR, self).__init__(backbone, None, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) + + def simple_test(self, img, img_metas, rescale=False): + """Test function without test time augmentation. + + Args: + imgs (list[torch.Tensor]): List of multiple images + img_metas (list[dict]): List of image information. + rescale (bool, optional): Whether to rescale the results. + Defaults to False. + + Returns: + list[list[np.ndarray]]: BBox results of each image and classes. + The outer list corresponds to each image. The inner list + corresponds to each class. + """ + batch_size = len(img_metas) + assert batch_size == 1, 'Currently only batch_size 1 for inference ' \ + f'mode is supported. Found batch_size {batch_size}.' + x = self.extract_feat(img) + outs = self.bbox_head(x, img_metas) + bbox_list = self.bbox_head.get_bboxes( + *outs, img_metas, rescale=rescale) + + bbox_results = [ + bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes) + for det_bboxes, det_labels in bbox_list + ] + return bbox_results diff --git a/mmdet/models/detectors/fast_rcnn.py b/mmdet/models/detectors/fast_rcnn.py new file mode 100644 index 0000000..4dd5619 --- /dev/null +++ b/mmdet/models/detectors/fast_rcnn.py @@ -0,0 +1,54 @@ +from ..builder import DETECTORS +from .two_stage import TwoStageDetector + + +@DETECTORS.register_module() +class FastRCNN(TwoStageDetector): + """Implementation of `Fast R-CNN `_""" + + def __init__(self, + backbone, + roi_head, + train_cfg, + test_cfg, + neck=None, + pretrained=None, + init_cfg=None): + super(FastRCNN, self).__init__( + backbone=backbone, + neck=neck, + roi_head=roi_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) + + def forward_test(self, imgs, img_metas, proposals, **kwargs): + """ + Args: + imgs (List[Tensor]): the outer list indicates test-time + augmentations and inner Tensor should have a shape NxCxHxW, + which contains all images in the batch. + img_metas (List[List[dict]]): the outer list indicates test-time + augs (multiscale, flip, etc.) and the inner list indicates + images in a batch. + proposals (List[List[Tensor]]): the outer list indicates test-time + augs (multiscale, flip, etc.) and the inner list indicates + images in a batch. The Tensor should have a shape Px4, where + P is the number of proposals. + """ + for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]: + if not isinstance(var, list): + raise TypeError(f'{name} must be a list, but got {type(var)}') + + num_augs = len(imgs) + if num_augs != len(img_metas): + raise ValueError(f'num of augmentations ({len(imgs)}) ' + f'!= num of image meta ({len(img_metas)})') + + if num_augs == 1: + return self.simple_test(imgs[0], img_metas[0], proposals[0], + **kwargs) + else: + # TODO: support test-time augmentation + assert NotImplementedError diff --git a/mmdet/models/detectors/faster_rcnn.py b/mmdet/models/detectors/faster_rcnn.py new file mode 100644 index 0000000..f6a7244 --- /dev/null +++ b/mmdet/models/detectors/faster_rcnn.py @@ -0,0 +1,26 @@ +from ..builder import DETECTORS +from .two_stage import TwoStageDetector + + +@DETECTORS.register_module() +class FasterRCNN(TwoStageDetector): + """Implementation of `Faster R-CNN `_""" + + def __init__(self, + backbone, + rpn_head, + roi_head, + train_cfg, + test_cfg, + neck=None, + pretrained=None, + init_cfg=None): + super(FasterRCNN, self).__init__( + backbone=backbone, + neck=neck, + rpn_head=rpn_head, + roi_head=roi_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) diff --git a/mmdet/models/detectors/fcos.py b/mmdet/models/detectors/fcos.py new file mode 100644 index 0000000..df1d0bc --- /dev/null +++ b/mmdet/models/detectors/fcos.py @@ -0,0 +1,18 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class FCOS(SingleStageDetector): + """Implementation of `FCOS `_""" + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(FCOS, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/detectors/fovea.py b/mmdet/models/detectors/fovea.py new file mode 100644 index 0000000..f7c7562 --- /dev/null +++ b/mmdet/models/detectors/fovea.py @@ -0,0 +1,18 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class FOVEA(SingleStageDetector): + """Implementation of `FoveaBox `_""" + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(FOVEA, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/detectors/fsaf.py b/mmdet/models/detectors/fsaf.py new file mode 100644 index 0000000..b859c72 --- /dev/null +++ b/mmdet/models/detectors/fsaf.py @@ -0,0 +1,18 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class FSAF(SingleStageDetector): + """Implementation of `FSAF `_""" + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(FSAF, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/detectors/gfl.py b/mmdet/models/detectors/gfl.py new file mode 100644 index 0000000..29bdb6b --- /dev/null +++ b/mmdet/models/detectors/gfl.py @@ -0,0 +1,17 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class GFL(SingleStageDetector): + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(GFL, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/detectors/grid_rcnn.py b/mmdet/models/detectors/grid_rcnn.py new file mode 100644 index 0000000..1bd3594 --- /dev/null +++ b/mmdet/models/detectors/grid_rcnn.py @@ -0,0 +1,31 @@ +from ..builder import DETECTORS +from .two_stage import TwoStageDetector + + +@DETECTORS.register_module() +class GridRCNN(TwoStageDetector): + """Grid R-CNN. + + This detector is the implementation of: + - Grid R-CNN (https://arxiv.org/abs/1811.12030) + - Grid R-CNN Plus: Faster and Better (https://arxiv.org/abs/1906.05688) + """ + + def __init__(self, + backbone, + rpn_head, + roi_head, + train_cfg, + test_cfg, + neck=None, + pretrained=None, + init_cfg=None): + super(GridRCNN, self).__init__( + backbone=backbone, + neck=neck, + rpn_head=rpn_head, + roi_head=roi_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) diff --git a/mmdet/models/detectors/htc.py b/mmdet/models/detectors/htc.py new file mode 100644 index 0000000..d9efdf4 --- /dev/null +++ b/mmdet/models/detectors/htc.py @@ -0,0 +1,15 @@ +from ..builder import DETECTORS +from .cascade_rcnn import CascadeRCNN + + +@DETECTORS.register_module() +class HybridTaskCascade(CascadeRCNN): + """Implementation of `HTC `_""" + + def __init__(self, **kwargs): + super(HybridTaskCascade, self).__init__(**kwargs) + + @property + def with_semantic(self): + """bool: whether the detector has a semantic head""" + return self.roi_head.with_semantic diff --git a/mmdet/models/detectors/kd_one_stage.py b/mmdet/models/detectors/kd_one_stage.py new file mode 100644 index 0000000..671ec19 --- /dev/null +++ b/mmdet/models/detectors/kd_one_stage.py @@ -0,0 +1,100 @@ +import mmcv +import torch +from mmcv.runner import load_checkpoint + +from .. import build_detector +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class KnowledgeDistillationSingleStageDetector(SingleStageDetector): + r"""Implementation of `Distilling the Knowledge in a Neural Network. + `_. + + Args: + teacher_config (str | dict): Config file path + or the config object of teacher model. + teacher_ckpt (str, optional): Checkpoint path of teacher model. + If left as None, the model will not load any weights. + """ + + def __init__(self, + backbone, + neck, + bbox_head, + teacher_config, + teacher_ckpt=None, + eval_teacher=True, + train_cfg=None, + test_cfg=None, + pretrained=None): + super().__init__(backbone, neck, bbox_head, train_cfg, test_cfg, + pretrained) + self.eval_teacher = eval_teacher + # Build teacher model + if isinstance(teacher_config, str): + teacher_config = mmcv.Config.fromfile(teacher_config) + self.teacher_model = build_detector(teacher_config['model']) + if teacher_ckpt is not None: + load_checkpoint( + self.teacher_model, teacher_ckpt, map_location='cpu') + + def forward_train(self, + img, + img_metas, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None): + """ + Args: + img (Tensor): Input images of shape (N, C, H, W). + Typically these should be mean centered and std scaled. + img_metas (list[dict]): A List of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + :class:`mmdet.datasets.pipelines.Collect`. + gt_bboxes (list[Tensor]): Each item are the truth boxes for each + image in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): Class indices corresponding to each box + gt_bboxes_ignore (None | list[Tensor]): Specify which bounding + boxes can be ignored when computing the loss. + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + x = self.extract_feat(img) + with torch.no_grad(): + teacher_x = self.teacher_model.extract_feat(img) + out_teacher = self.teacher_model.bbox_head(teacher_x) + losses = self.bbox_head.forward_train(x, out_teacher, img_metas, + gt_bboxes, gt_labels, + gt_bboxes_ignore) + return losses + + def cuda(self, device=None): + """Since teacher_model is registered as a plain object, it is necessary + to put the teacher model to cuda when calling cuda function.""" + self.teacher_model.cuda(device=device) + return super().cuda(device=device) + + def train(self, mode=True): + """Set the same train mode for teacher and student model.""" + if self.eval_teacher: + self.teacher_model.train(False) + else: + self.teacher_model.train(mode) + super().train(mode) + + def __setattr__(self, name, value): + """Set attribute, i.e. self.name = value + + This reloading prevent the teacher model from being registered as a + nn.Module. The teacher module is registered as a plain object, so that + the teacher parameters will not show up when calling + ``self.parameters``, ``self.modules``, ``self.children`` methods. + """ + if name == 'teacher_model': + object.__setattr__(self, name, value) + else: + super().__setattr__(name, value) diff --git a/mmdet/models/detectors/mask_rcnn.py b/mmdet/models/detectors/mask_rcnn.py new file mode 100644 index 0000000..29ea62d --- /dev/null +++ b/mmdet/models/detectors/mask_rcnn.py @@ -0,0 +1,26 @@ +from ..builder import DETECTORS +from .two_stage import TwoStageDetector + + +@DETECTORS.register_module() +class MaskRCNN(TwoStageDetector): + """Implementation of `Mask R-CNN `_""" + + def __init__(self, + backbone, + rpn_head, + roi_head, + train_cfg, + test_cfg, + neck=None, + pretrained=None, + init_cfg=None): + super(MaskRCNN, self).__init__( + backbone=backbone, + neck=neck, + rpn_head=rpn_head, + roi_head=roi_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) diff --git a/mmdet/models/detectors/mask_scoring_rcnn.py b/mmdet/models/detectors/mask_scoring_rcnn.py new file mode 100644 index 0000000..86c6053 --- /dev/null +++ b/mmdet/models/detectors/mask_scoring_rcnn.py @@ -0,0 +1,29 @@ +from ..builder import DETECTORS +from .two_stage import TwoStageDetector + + +@DETECTORS.register_module() +class MaskScoringRCNN(TwoStageDetector): + """Mask Scoring RCNN. + + https://arxiv.org/abs/1903.00241 + """ + + def __init__(self, + backbone, + rpn_head, + roi_head, + train_cfg, + test_cfg, + neck=None, + pretrained=None, + init_cfg=None): + super(MaskScoringRCNN, self).__init__( + backbone=backbone, + neck=neck, + rpn_head=rpn_head, + roi_head=roi_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) diff --git a/mmdet/models/detectors/nasfcos.py b/mmdet/models/detectors/nasfcos.py new file mode 100644 index 0000000..6f3446f --- /dev/null +++ b/mmdet/models/detectors/nasfcos.py @@ -0,0 +1,21 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class NASFCOS(SingleStageDetector): + """NAS-FCOS: Fast Neural Architecture Search for Object Detection. + + https://arxiv.org/abs/1906.0442 + """ + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(NASFCOS, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/detectors/paa.py b/mmdet/models/detectors/paa.py new file mode 100644 index 0000000..afc8059 --- /dev/null +++ b/mmdet/models/detectors/paa.py @@ -0,0 +1,18 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class PAA(SingleStageDetector): + """Implementation of `PAA `_.""" + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(PAA, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/detectors/point_rend.py b/mmdet/models/detectors/point_rend.py new file mode 100644 index 0000000..72c4bac --- /dev/null +++ b/mmdet/models/detectors/point_rend.py @@ -0,0 +1,31 @@ +from ..builder import DETECTORS +from .two_stage import TwoStageDetector + + +@DETECTORS.register_module() +class PointRend(TwoStageDetector): + """PointRend: Image Segmentation as Rendering + + This detector is the implementation of + `PointRend `_. + + """ + + def __init__(self, + backbone, + rpn_head, + roi_head, + train_cfg, + test_cfg, + neck=None, + pretrained=None, + init_cfg=None): + super(PointRend, self).__init__( + backbone=backbone, + neck=neck, + rpn_head=rpn_head, + roi_head=roi_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) diff --git a/mmdet/models/detectors/query_based.py b/mmdet/models/detectors/query_based.py new file mode 100644 index 0000000..51fc20c --- /dev/null +++ b/mmdet/models/detectors/query_based.py @@ -0,0 +1,11 @@ +from ..builder import DETECTORS +from .two_stage import TwoStageDetector +from .sparse_rcnn import SparseRCNN + + +@DETECTORS.register_module() +class QueryBased(SparseRCNN): + ''' + We hack and build our model into Sparse RCNN framework implementation + in mmdetection. + ''' diff --git a/mmdet/models/detectors/reppoints_detector.py b/mmdet/models/detectors/reppoints_detector.py new file mode 100644 index 0000000..3636a60 --- /dev/null +++ b/mmdet/models/detectors/reppoints_detector.py @@ -0,0 +1,23 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class RepPointsDetector(SingleStageDetector): + """RepPoints: Point Set Representation for Object Detection. + + This detector is the implementation of: + - RepPoints detector (https://arxiv.org/pdf/1904.11490) + """ + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(RepPointsDetector, + self).__init__(backbone, neck, bbox_head, train_cfg, test_cfg, + pretrained, init_cfg) diff --git a/mmdet/models/detectors/retinanet.py b/mmdet/models/detectors/retinanet.py new file mode 100644 index 0000000..6aa29f2 --- /dev/null +++ b/mmdet/models/detectors/retinanet.py @@ -0,0 +1,18 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class RetinaNet(SingleStageDetector): + """Implementation of `RetinaNet `_""" + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(RetinaNet, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/detectors/rpn.py b/mmdet/models/detectors/rpn.py new file mode 100644 index 0000000..bddb38b --- /dev/null +++ b/mmdet/models/detectors/rpn.py @@ -0,0 +1,149 @@ +import mmcv +import torch +from mmcv.image import tensor2imgs + +from mmdet.core import bbox_mapping +from ..builder import DETECTORS, build_backbone, build_head, build_neck +from .base import BaseDetector + + +@DETECTORS.register_module() +class RPN(BaseDetector): + """Implementation of Region Proposal Network.""" + + def __init__(self, + backbone, + neck, + rpn_head, + train_cfg, + test_cfg, + pretrained=None, + init_cfg=None): + super(RPN, self).__init__(init_cfg) + backbone.pretrained = pretrained + self.backbone = build_backbone(backbone) + self.neck = build_neck(neck) if neck is not None else None + rpn_train_cfg = train_cfg.rpn if train_cfg is not None else None + rpn_head.update(train_cfg=rpn_train_cfg) + rpn_head.update(test_cfg=test_cfg.rpn) + self.rpn_head = build_head(rpn_head) + self.train_cfg = train_cfg + self.test_cfg = test_cfg + + def extract_feat(self, img): + """Extract features. + + Args: + img (torch.Tensor): Image tensor with shape (n, c, h ,w). + + Returns: + list[torch.Tensor]: Multi-level features that may have + different resolutions. + """ + x = self.backbone(img) + if self.with_neck: + x = self.neck(x) + return x + + def forward_dummy(self, img): + """Dummy forward function.""" + x = self.extract_feat(img) + rpn_outs = self.rpn_head(x) + return rpn_outs + + def forward_train(self, + img, + img_metas, + gt_bboxes=None, + gt_bboxes_ignore=None): + """ + Args: + img (Tensor): Input images of shape (N, C, H, W). + Typically these should be mean centered and std scaled. + img_metas (list[dict]): A List of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + :class:`mmdet.datasets.pipelines.Collect`. + gt_bboxes (list[Tensor]): Each item are the truth boxes for each + image in [tl_x, tl_y, br_x, br_y] format. + gt_bboxes_ignore (None | list[Tensor]): Specify which bounding + boxes can be ignored when computing the loss. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + if (isinstance(self.train_cfg.rpn, dict) + and self.train_cfg.rpn.get('debug', False)): + self.rpn_head.debug_imgs = tensor2imgs(img) + + x = self.extract_feat(img) + losses = self.rpn_head.forward_train(x, img_metas, gt_bboxes, None, + gt_bboxes_ignore) + return losses + + def simple_test(self, img, img_metas, rescale=False): + """Test function without test time augmentation. + + Args: + imgs (list[torch.Tensor]): List of multiple images + img_metas (list[dict]): List of image information. + rescale (bool, optional): Whether to rescale the results. + Defaults to False. + + Returns: + list[np.ndarray]: proposals + """ + x = self.extract_feat(img) + # get origin input shape to onnx dynamic input shape + if torch.onnx.is_in_onnx_export(): + img_shape = torch._shape_as_tensor(img)[2:] + img_metas[0]['img_shape_for_onnx'] = img_shape + proposal_list = self.rpn_head.simple_test_rpn(x, img_metas) + if rescale: + for proposals, meta in zip(proposal_list, img_metas): + proposals[:, :4] /= proposals.new_tensor(meta['scale_factor']) + if torch.onnx.is_in_onnx_export(): + return proposal_list + + return [proposal.cpu().numpy() for proposal in proposal_list] + + def aug_test(self, imgs, img_metas, rescale=False): + """Test function with test time augmentation. + + Args: + imgs (list[torch.Tensor]): List of multiple images + img_metas (list[dict]): List of image information. + rescale (bool, optional): Whether to rescale the results. + Defaults to False. + + Returns: + list[np.ndarray]: proposals + """ + proposal_list = self.rpn_head.aug_test_rpn( + self.extract_feats(imgs), img_metas) + if not rescale: + for proposals, img_meta in zip(proposal_list, img_metas[0]): + img_shape = img_meta['img_shape'] + scale_factor = img_meta['scale_factor'] + flip = img_meta['flip'] + flip_direction = img_meta['flip_direction'] + proposals[:, :4] = bbox_mapping(proposals[:, :4], img_shape, + scale_factor, flip, + flip_direction) + return [proposal.cpu().numpy() for proposal in proposal_list] + + def show_result(self, data, result, top_k=20, **kwargs): + """Show RPN proposals on the image. + + Args: + data (str or np.ndarray): Image filename or loaded image. + result (Tensor or tuple): The results to draw over `img` + bbox_result or (bbox_result, segm_result). + top_k (int): Plot the first k bboxes only + if set positive. Default: 20 + + Returns: + np.ndarray: The image with bboxes drawn on it. + """ + mmcv.imshow_bboxes(data, result, top_k=top_k) diff --git a/mmdet/models/detectors/scnet.py b/mmdet/models/detectors/scnet.py new file mode 100644 index 0000000..04a2347 --- /dev/null +++ b/mmdet/models/detectors/scnet.py @@ -0,0 +1,10 @@ +from ..builder import DETECTORS +from .cascade_rcnn import CascadeRCNN + + +@DETECTORS.register_module() +class SCNet(CascadeRCNN): + """Implementation of `SCNet `_""" + + def __init__(self, **kwargs): + super(SCNet, self).__init__(**kwargs) diff --git a/mmdet/models/detectors/single_stage.py b/mmdet/models/detectors/single_stage.py new file mode 100644 index 0000000..d01ebf3 --- /dev/null +++ b/mmdet/models/detectors/single_stage.py @@ -0,0 +1,137 @@ +import torch + +from mmdet.core import bbox2result +from ..builder import DETECTORS, build_backbone, build_head, build_neck +from .base import BaseDetector + + +@DETECTORS.register_module() +class SingleStageDetector(BaseDetector): + """Base class for single-stage detectors. + + Single-stage detectors directly and densely predict bounding boxes on the + output features of the backbone+neck. + """ + + def __init__(self, + backbone, + neck=None, + bbox_head=None, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(SingleStageDetector, self).__init__(init_cfg) + backbone.pretrained = pretrained + self.backbone = build_backbone(backbone) + if neck is not None: + self.neck = build_neck(neck) + bbox_head.update(train_cfg=train_cfg) + bbox_head.update(test_cfg=test_cfg) + self.bbox_head = build_head(bbox_head) + self.train_cfg = train_cfg + self.test_cfg = test_cfg + + def extract_feat(self, img): + """Directly extract features from the backbone+neck.""" + x = self.backbone(img) + if self.with_neck: + x = self.neck(x) + return x + + def forward_dummy(self, img): + """Used for computing network flops. + + See `mmdetection/tools/analysis_tools/get_flops.py` + """ + x = self.extract_feat(img) + outs = self.bbox_head(x) + return outs + + def forward_train(self, + img, + img_metas, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None): + """ + Args: + img (Tensor): Input images of shape (N, C, H, W). + Typically these should be mean centered and std scaled. + img_metas (list[dict]): A List of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + :class:`mmdet.datasets.pipelines.Collect`. + gt_bboxes (list[Tensor]): Each item are the truth boxes for each + image in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): Class indices corresponding to each box + gt_bboxes_ignore (None | list[Tensor]): Specify which bounding + boxes can be ignored when computing the loss. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + super(SingleStageDetector, self).forward_train(img, img_metas) + x = self.extract_feat(img) + losses = self.bbox_head.forward_train(x, img_metas, gt_bboxes, + gt_labels, gt_bboxes_ignore) + return losses + + def simple_test(self, img, img_metas, rescale=False): + """Test function without test time augmentation. + + Args: + imgs (list[torch.Tensor]): List of multiple images + img_metas (list[dict]): List of image information. + rescale (bool, optional): Whether to rescale the results. + Defaults to False. + + Returns: + list[list[np.ndarray]]: BBox results of each image and classes. + The outer list corresponds to each image. The inner list + corresponds to each class. + """ + x = self.extract_feat(img) + outs = self.bbox_head(x) + # get origin input shape to support onnx dynamic shape + if torch.onnx.is_in_onnx_export(): + # get shape as tensor + img_shape = torch._shape_as_tensor(img)[2:] + img_metas[0]['img_shape_for_onnx'] = img_shape + bbox_list = self.bbox_head.get_bboxes( + *outs, img_metas, rescale=rescale) + # skip post-processing when exporting to ONNX + if torch.onnx.is_in_onnx_export(): + return bbox_list + + bbox_results = [ + bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes) + for det_bboxes, det_labels in bbox_list + ] + return bbox_results + + def aug_test(self, imgs, img_metas, rescale=False): + """Test function with test time augmentation. + + Args: + imgs (list[Tensor]): the outer list indicates test-time + augmentations and inner Tensor should have a shape NxCxHxW, + which contains all images in the batch. + img_metas (list[list[dict]]): the outer list indicates test-time + augs (multiscale, flip, etc.) and the inner list indicates + images in a batch. each dict has image information. + rescale (bool, optional): Whether to rescale the results. + Defaults to False. + + Returns: + list[list[np.ndarray]]: BBox results of each image and classes. + The outer list corresponds to each image. The inner list + corresponds to each class. + """ + assert hasattr(self.bbox_head, 'aug_test'), \ + f'{self.bbox_head.__class__.__name__}' \ + ' does not support test-time augmentation' + + feats = self.extract_feats(imgs) + return [self.bbox_head.aug_test(feats, img_metas, rescale=rescale)] diff --git a/mmdet/models/detectors/sparse_rcnn.py b/mmdet/models/detectors/sparse_rcnn.py new file mode 100644 index 0000000..0f21c99 --- /dev/null +++ b/mmdet/models/detectors/sparse_rcnn.py @@ -0,0 +1,117 @@ +from ..builder import DETECTORS +from .two_stage import TwoStageDetector +import cccu + +@DETECTORS.register_module() +class SparseRCNN(TwoStageDetector): + r"""Implementation of `Sparse R-CNN: End-to-End Object Detection with + Learnable Proposals `_""" + + def __init__(self, *args, **kwargs): + super(SparseRCNN, self).__init__(*args, **kwargs) + assert self.with_rpn, 'Sparse R-CNN do not support external proposals' + + def forward_train(self, + img, + img_metas, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + gt_masks=None, + proposals=None, + **kwargs): + """Forward function of SparseR-CNN in train stage. + + Args: + img (Tensor): of shape (N, C, H, W) encoding input images. + Typically these should be mean centered and std scaled. + img_metas (list[dict]): list of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + :class:`mmdet.datasets.pipelines.Collect`. + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + gt_bboxes_ignore (None | list[Tensor): specify which bounding + boxes can be ignored when computing the loss. + gt_masks (List[Tensor], optional) : Segmentation masks for + each box. But we don't support it in this architecture. + proposals (List[Tensor], optional): override rpn proposals with + custom proposals. Use when `with_rpn` is False. + + Returns: + dict[str, Tensor]: a dictionary of loss components + """ + + assert proposals is None, 'Sparse R-CNN does not support' \ + ' external proposals' + assert gt_masks is None, 'Sparse R-CNN does not instance segmentation' + ### Fangyi: painter start + ''' + painter = cccu.painter(canvas_name='adamixer-sample-p.jpg') + painter.loadimg(cccu.rescale(cccu.MetaReader(img_metas[0]['filename'], print_detail=False), dim=(1333, 800))) + ''' + ### Fangyi: painter end + x = self.extract_feat(img) + proposal_boxes, proposal_features, imgs_whwh = \ + self.rpn_head.forward_train(x, img_metas) + roi_losses = self.roi_head.forward_train( + x, + proposal_boxes, + proposal_features, + img_metas, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=gt_bboxes_ignore, + gt_masks=gt_masks, + imgs_whwh=imgs_whwh) + return roi_losses + + def simple_test(self, img, img_metas, rescale=False): + """Test function without test time augmentation. + + Args: + imgs (list[torch.Tensor]): List of multiple images + img_metas (list[dict]): List of image information. + rescale (bool): Whether to rescale the results. + Defaults to False. + + Returns: + list[list[np.ndarray]]: BBox results of each image and classes. + The outer list corresponds to each image. The inner list + corresponds to each class. + """ + painter = cccu.painter(canvas_name='adamixer-sample-p.jpg') + painter.loadimg(cccu.rescale(cccu.MetaReader(img_metas[0]['filename'], print_detail=False), dim=(1333, 800))) + x = self.extract_feat(img) + proposal_boxes, proposal_features, imgs_whwh = \ + self.rpn_head.simple_test_rpn(x, img_metas) + bbox_results = self.roi_head.simple_test( + x, + proposal_boxes, + proposal_features, + img_metas, + imgs_whwh=imgs_whwh, + rescale=rescale) + return bbox_results + + def forward_dummy(self, img): + """Used for computing network flops. + + See `mmdetection/tools/analysis_tools/get_flops.py` + """ + # backbone + x = self.extract_feat(img) + # rpn + num_imgs = len(img) + dummy_img_metas = [ + dict(img_shape=(800, 1333, 3)) for _ in range(num_imgs) + ] + proposal_boxes, proposal_features, imgs_whwh = \ + self.rpn_head.simple_test_rpn(x, dummy_img_metas) + # roi_head + roi_outs = self.roi_head.forward_dummy(x, proposal_boxes, + proposal_features, + dummy_img_metas) + return roi_outs diff --git a/mmdet/models/detectors/trident_faster_rcnn.py b/mmdet/models/detectors/trident_faster_rcnn.py new file mode 100644 index 0000000..c72065e --- /dev/null +++ b/mmdet/models/detectors/trident_faster_rcnn.py @@ -0,0 +1,68 @@ +from ..builder import DETECTORS +from .faster_rcnn import FasterRCNN + + +@DETECTORS.register_module() +class TridentFasterRCNN(FasterRCNN): + """Implementation of `TridentNet `_""" + + def __init__(self, + backbone, + rpn_head, + roi_head, + train_cfg, + test_cfg, + neck=None, + pretrained=None, + init_cfg=None): + + super(TridentFasterRCNN, self).__init__( + backbone=backbone, + neck=neck, + rpn_head=rpn_head, + roi_head=roi_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) + assert self.backbone.num_branch == self.roi_head.num_branch + assert self.backbone.test_branch_idx == self.roi_head.test_branch_idx + self.num_branch = self.backbone.num_branch + self.test_branch_idx = self.backbone.test_branch_idx + + def simple_test(self, img, img_metas, proposals=None, rescale=False): + """Test without augmentation.""" + assert self.with_bbox, 'Bbox head must be implemented.' + x = self.extract_feat(img) + if proposals is None: + num_branch = (self.num_branch if self.test_branch_idx == -1 else 1) + trident_img_metas = img_metas * num_branch + proposal_list = self.rpn_head.simple_test_rpn(x, trident_img_metas) + else: + proposal_list = proposals + + return self.roi_head.simple_test( + x, proposal_list, trident_img_metas, rescale=rescale) + + def aug_test(self, imgs, img_metas, rescale=False): + """Test with augmentations. + + If rescale is False, then returned bboxes and masks will fit the scale + of imgs[0]. + """ + x = self.extract_feats(imgs) + num_branch = (self.num_branch if self.test_branch_idx == -1 else 1) + trident_img_metas = [img_metas * num_branch for img_metas in img_metas] + proposal_list = self.rpn_head.aug_test_rpn(x, trident_img_metas) + return self.roi_head.aug_test( + x, proposal_list, img_metas, rescale=rescale) + + def forward_train(self, img, img_metas, gt_bboxes, gt_labels, **kwargs): + """make copies of img and gts to fit multi-branch.""" + trident_gt_bboxes = tuple(gt_bboxes * self.num_branch) + trident_gt_labels = tuple(gt_labels * self.num_branch) + trident_img_metas = tuple(img_metas * self.num_branch) + + return super(TridentFasterRCNN, + self).forward_train(img, trident_img_metas, + trident_gt_bboxes, trident_gt_labels) diff --git a/mmdet/models/detectors/two_stage.py b/mmdet/models/detectors/two_stage.py new file mode 100644 index 0000000..1cee2a8 --- /dev/null +++ b/mmdet/models/detectors/two_stage.py @@ -0,0 +1,194 @@ +import torch + +from ..builder import DETECTORS, build_backbone, build_head, build_neck +from .base import BaseDetector + + +@DETECTORS.register_module() +class TwoStageDetector(BaseDetector): + """Base class for two-stage detectors. + + Two-stage detectors typically consisting of a region proposal network and a + task-specific regression head. + """ + + def __init__(self, + backbone, + neck=None, + rpn_head=None, + roi_head=None, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(TwoStageDetector, self).__init__(init_cfg) + backbone.pretrained = pretrained + self.backbone = build_backbone(backbone) + + if neck is not None: + self.neck = build_neck(neck) + + if rpn_head is not None: + rpn_train_cfg = train_cfg.rpn if train_cfg is not None else None + rpn_head_ = rpn_head.copy() + rpn_head_.update(train_cfg=rpn_train_cfg, test_cfg=test_cfg.rpn) + self.rpn_head = build_head(rpn_head_) + + if roi_head is not None: + # update train and test cfg here for now + # TODO: refactor assigner & sampler + rcnn_train_cfg = train_cfg.rcnn if train_cfg is not None else None + roi_head.update(train_cfg=rcnn_train_cfg) + roi_head.update(test_cfg=test_cfg.rcnn) + roi_head.pretrained = pretrained + self.roi_head = build_head(roi_head) + + self.train_cfg = train_cfg + self.test_cfg = test_cfg + + @property + def with_rpn(self): + """bool: whether the detector has RPN""" + return hasattr(self, 'rpn_head') and self.rpn_head is not None + + @property + def with_roi_head(self): + """bool: whether the detector has a RoI head""" + return hasattr(self, 'roi_head') and self.roi_head is not None + + def extract_feat(self, img): + """Directly extract features from the backbone+neck.""" + x = self.backbone(img) + if self.with_neck: + x = self.neck(x) + return x + + def forward_dummy(self, img): + """Used for computing network flops. + + See `mmdetection/tools/analysis_tools/get_flops.py` + """ + outs = () + # backbone + x = self.extract_feat(img) + # rpn + if self.with_rpn: + rpn_outs = self.rpn_head(x) + outs = outs + (rpn_outs, ) + proposals = torch.randn(1000, 4).to(img.device) + # roi_head + roi_outs = self.roi_head.forward_dummy(x, proposals) + outs = outs + (roi_outs, ) + return outs + + def forward_train(self, + img, + img_metas, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + gt_masks=None, + proposals=None, + **kwargs): + """ + Args: + img (Tensor): of shape (N, C, H, W) encoding input images. + Typically these should be mean centered and std scaled. + + img_metas (list[dict]): list of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmdet/datasets/pipelines/formatting.py:Collect`. + + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + + gt_labels (list[Tensor]): class indices corresponding to each box + + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + + gt_masks (None | Tensor) : true segmentation masks for each box + used if the architecture supports a segmentation task. + + proposals : override rpn proposals with custom proposals. Use when + `with_rpn` is False. + + Returns: + dict[str, Tensor]: a dictionary of loss components + """ + x = self.extract_feat(img) + + losses = dict() + + # RPN forward and loss + if self.with_rpn: + proposal_cfg = self.train_cfg.get('rpn_proposal', + self.test_cfg.rpn) + rpn_losses, proposal_list = self.rpn_head.forward_train( + x, + img_metas, + gt_bboxes, + gt_labels=None, + gt_bboxes_ignore=gt_bboxes_ignore, + proposal_cfg=proposal_cfg) + losses.update(rpn_losses) + else: + proposal_list = proposals + + roi_losses = self.roi_head.forward_train(x, img_metas, proposal_list, + gt_bboxes, gt_labels, + gt_bboxes_ignore, gt_masks, + **kwargs) + losses.update(roi_losses) + + return losses + + async def async_simple_test(self, + img, + img_meta, + proposals=None, + rescale=False): + """Async test without augmentation.""" + assert self.with_bbox, 'Bbox head must be implemented.' + x = self.extract_feat(img) + + if proposals is None: + proposal_list = await self.rpn_head.async_simple_test_rpn( + x, img_meta) + else: + proposal_list = proposals + + return await self.roi_head.async_simple_test( + x, proposal_list, img_meta, rescale=rescale) + + def simple_test(self, img, img_metas, proposals=None, rescale=False): + """Test without augmentation.""" + assert self.with_bbox, 'Bbox head must be implemented.' + + x = self.extract_feat(img) + + # get origin input shape to onnx dynamic input shape + if torch.onnx.is_in_onnx_export(): + img_shape = torch._shape_as_tensor(img)[2:] + img_metas[0]['img_shape_for_onnx'] = img_shape + + if proposals is None: + proposal_list = self.rpn_head.simple_test_rpn(x, img_metas) + else: + proposal_list = proposals + + return self.roi_head.simple_test( + x, proposal_list, img_metas, rescale=rescale) + + def aug_test(self, imgs, img_metas, rescale=False): + """Test with augmentations. + + If rescale is False, then returned bboxes and masks will fit the scale + of imgs[0]. + """ + x = self.extract_feats(imgs) + proposal_list = self.rpn_head.aug_test_rpn(x, img_metas) + return self.roi_head.aug_test( + x, proposal_list, img_metas, rescale=rescale) diff --git a/mmdet/models/detectors/vfnet.py b/mmdet/models/detectors/vfnet.py new file mode 100644 index 0000000..cd34d71 --- /dev/null +++ b/mmdet/models/detectors/vfnet.py @@ -0,0 +1,19 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class VFNet(SingleStageDetector): + """Implementation of `VarifocalNet + (VFNet).`_""" + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(VFNet, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/detectors/yolact.py b/mmdet/models/detectors/yolact.py new file mode 100644 index 0000000..d40a091 --- /dev/null +++ b/mmdet/models/detectors/yolact.py @@ -0,0 +1,141 @@ +import torch + +from mmdet.core import bbox2result +from ..builder import DETECTORS, build_head +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class YOLACT(SingleStageDetector): + """Implementation of `YOLACT `_""" + + def __init__(self, + backbone, + neck, + bbox_head, + segm_head, + mask_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(YOLACT, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) + self.segm_head = build_head(segm_head) + self.mask_head = build_head(mask_head) + + def forward_dummy(self, img): + """Used for computing network flops. + + See `mmdetection/tools/analysis_tools/get_flops.py` + """ + raise NotImplementedError + + def forward_train(self, + img, + img_metas, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + gt_masks=None): + """ + Args: + img (Tensor): of shape (N, C, H, W) encoding input images. + Typically these should be mean centered and std scaled. + img_metas (list[dict]): list of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmdet/datasets/pipelines/formatting.py:Collect`. + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + gt_masks (None | Tensor) : true segmentation masks for each box + used if the architecture supports a segmentation task. + + Returns: + dict[str, Tensor]: a dictionary of loss components + """ + # convert Bitmap mask or Polygon Mask to Tensor here + gt_masks = [ + gt_mask.to_tensor(dtype=torch.uint8, device=img.device) + for gt_mask in gt_masks + ] + + x = self.extract_feat(img) + + cls_score, bbox_pred, coeff_pred = self.bbox_head(x) + bbox_head_loss_inputs = (cls_score, bbox_pred) + (gt_bboxes, gt_labels, + img_metas) + losses, sampling_results = self.bbox_head.loss( + *bbox_head_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore) + + segm_head_outs = self.segm_head(x[0]) + loss_segm = self.segm_head.loss(segm_head_outs, gt_masks, gt_labels) + losses.update(loss_segm) + + mask_pred = self.mask_head(x[0], coeff_pred, gt_bboxes, img_metas, + sampling_results) + loss_mask = self.mask_head.loss(mask_pred, gt_masks, gt_bboxes, + img_metas, sampling_results) + losses.update(loss_mask) + + # check NaN and Inf + for loss_name in losses.keys(): + assert torch.isfinite(torch.stack(losses[loss_name]))\ + .all().item(), '{} becomes infinite or NaN!'\ + .format(loss_name) + + return losses + + def simple_test(self, img, img_metas, rescale=False): + """Test function without test time augmentation.""" + x = self.extract_feat(img) + + cls_score, bbox_pred, coeff_pred = self.bbox_head(x) + + bbox_inputs = (cls_score, bbox_pred, + coeff_pred) + (img_metas, self.test_cfg, rescale) + det_bboxes, det_labels, det_coeffs = self.bbox_head.get_bboxes( + *bbox_inputs) + bbox_results = [ + bbox2result(det_bbox, det_label, self.bbox_head.num_classes) + for det_bbox, det_label in zip(det_bboxes, det_labels) + ] + + num_imgs = len(img_metas) + scale_factors = tuple(meta['scale_factor'] for meta in img_metas) + if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes): + segm_results = [[[] for _ in range(self.mask_head.num_classes)] + for _ in range(num_imgs)] + else: + # if det_bboxes is rescaled to the original image size, we need to + # rescale it back to the testing scale to obtain RoIs. + if rescale and not isinstance(scale_factors[0], float): + scale_factors = [ + torch.from_numpy(scale_factor).to(det_bboxes[0].device) + for scale_factor in scale_factors + ] + _bboxes = [ + det_bboxes[i][:, :4] * + scale_factors[i] if rescale else det_bboxes[i][:, :4] + for i in range(len(det_bboxes)) + ] + mask_preds = self.mask_head(x[0], det_coeffs, _bboxes, img_metas) + # apply mask post-processing to each image individually + segm_results = [] + for i in range(num_imgs): + if det_bboxes[i].shape[0] == 0: + segm_results.append( + [[] for _ in range(self.mask_head.num_classes)]) + else: + segm_result = self.mask_head.get_seg_masks( + mask_preds[i], det_labels[i], img_metas[i], rescale) + segm_results.append(segm_result) + return list(zip(bbox_results, segm_results)) + + def aug_test(self, imgs, img_metas, rescale=False): + """Test with augmentations.""" + raise NotImplementedError diff --git a/mmdet/models/detectors/yolo.py b/mmdet/models/detectors/yolo.py new file mode 100644 index 0000000..bd1f89e --- /dev/null +++ b/mmdet/models/detectors/yolo.py @@ -0,0 +1,19 @@ +# Copyright (c) 2019 Western Digital Corporation or its affiliates. + +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class YOLOV3(SingleStageDetector): + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(YOLOV3, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/detectors/yolof.py b/mmdet/models/detectors/yolof.py new file mode 100644 index 0000000..dc7b3ad --- /dev/null +++ b/mmdet/models/detectors/yolof.py @@ -0,0 +1,18 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class YOLOF(SingleStageDetector): + r"""Implementation of `You Only Look One-level Feature + `_""" + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None): + super(YOLOF, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained) diff --git a/mmdet/models/losses/__init__.py b/mmdet/models/losses/__init__.py new file mode 100644 index 0000000..297aa22 --- /dev/null +++ b/mmdet/models/losses/__init__.py @@ -0,0 +1,29 @@ +from .accuracy import Accuracy, accuracy +from .ae_loss import AssociativeEmbeddingLoss +from .balanced_l1_loss import BalancedL1Loss, balanced_l1_loss +from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy, + cross_entropy, mask_cross_entropy) +from .focal_loss import FocalLoss, sigmoid_focal_loss +from .gaussian_focal_loss import GaussianFocalLoss +from .gfocal_loss import DistributionFocalLoss, QualityFocalLoss +from .ghm_loss import GHMC, GHMR +from .iou_loss import (BoundedIoULoss, CIoULoss, DIoULoss, GIoULoss, IoULoss, + bounded_iou_loss, iou_loss) +from .kd_loss import KnowledgeDistillationKLDivLoss +from .mse_loss import MSELoss, mse_loss +from .pisa_loss import carl_loss, isr_p +from .smooth_l1_loss import L1Loss, SmoothL1Loss, l1_loss, smooth_l1_loss +from .utils import reduce_loss, weight_reduce_loss, weighted_loss +from .varifocal_loss import VarifocalLoss + +__all__ = [ + 'accuracy', 'Accuracy', 'cross_entropy', 'binary_cross_entropy', + 'mask_cross_entropy', 'CrossEntropyLoss', 'sigmoid_focal_loss', + 'FocalLoss', 'smooth_l1_loss', 'SmoothL1Loss', 'balanced_l1_loss', + 'BalancedL1Loss', 'mse_loss', 'MSELoss', 'iou_loss', 'bounded_iou_loss', + 'IoULoss', 'BoundedIoULoss', 'GIoULoss', 'DIoULoss', 'CIoULoss', 'GHMC', + 'GHMR', 'reduce_loss', 'weight_reduce_loss', 'weighted_loss', 'L1Loss', + 'l1_loss', 'isr_p', 'carl_loss', 'AssociativeEmbeddingLoss', + 'GaussianFocalLoss', 'QualityFocalLoss', 'DistributionFocalLoss', + 'VarifocalLoss', 'KnowledgeDistillationKLDivLoss' +] diff --git a/mmdet/models/losses/__pycache__/__init__.cpython-37.pyc b/mmdet/models/losses/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..6999a0d Binary files /dev/null and b/mmdet/models/losses/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/models/losses/__pycache__/accuracy.cpython-37.pyc b/mmdet/models/losses/__pycache__/accuracy.cpython-37.pyc new file mode 100644 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Defaults to 1. + thresh (float, optional): If not None, predictions with scores under + this threshold are considered incorrect. Default to None. + + Returns: + float | tuple[float]: If the input ``topk`` is a single integer, + the function will return a single float as accuracy. If + ``topk`` is a tuple containing multiple integers, the + function will return a tuple containing accuracies of + each ``topk`` number. + """ + assert isinstance(topk, (int, tuple)) + if isinstance(topk, int): + topk = (topk, ) + return_single = True + else: + return_single = False + + maxk = max(topk) + if pred.size(0) == 0: + accu = [pred.new_tensor(0.) for i in range(len(topk))] + return accu[0] if return_single else accu + assert pred.ndim == 2 and target.ndim == 1 + assert pred.size(0) == target.size(0) + assert maxk <= pred.size(1), \ + f'maxk {maxk} exceeds pred dimension {pred.size(1)}' + pred_value, pred_label = pred.topk(maxk, dim=1) + pred_label = pred_label.t() # transpose to shape (maxk, N) + correct = pred_label.eq(target.view(1, -1).expand_as(pred_label)) + if thresh is not None: + # Only prediction values larger than thresh are counted as correct + correct = correct & (pred_value > thresh).t() + res = [] + for k in topk: + correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True) + res.append(correct_k.mul_(100.0 / pred.size(0))) + return res[0] if return_single else res + + +class Accuracy(nn.Module): + + def __init__(self, topk=(1, ), thresh=None): + """Module to calculate the accuracy. + + Args: + topk (tuple, optional): The criterion used to calculate the + accuracy. Defaults to (1,). + thresh (float, optional): If not None, predictions with scores + under this threshold are considered incorrect. Default to None. + """ + super().__init__() + self.topk = topk + self.thresh = thresh + + def forward(self, pred, target): + """Forward function to calculate accuracy. + + Args: + pred (torch.Tensor): Prediction of models. + target (torch.Tensor): Target for each prediction. + + Returns: + tuple[float]: The accuracies under different topk criterions. + """ + return accuracy(pred, target, self.topk, self.thresh) diff --git a/mmdet/models/losses/ae_loss.py b/mmdet/models/losses/ae_loss.py new file mode 100644 index 0000000..cff472a --- /dev/null +++ b/mmdet/models/losses/ae_loss.py @@ -0,0 +1,102 @@ +import mmcv +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ..builder import LOSSES + + +@mmcv.jit(derivate=True, coderize=True) +def ae_loss_per_image(tl_preds, br_preds, match): + """Associative Embedding Loss in one image. + + Associative Embedding Loss including two parts: pull loss and push loss. + Pull loss makes embedding vectors from same object closer to each other. + Push loss distinguish embedding vector from different objects, and makes + the gap between them is large enough. + + During computing, usually there are 3 cases: + - no object in image: both pull loss and push loss will be 0. + - one object in image: push loss will be 0 and pull loss is computed + by the two corner of the only object. + - more than one objects in image: pull loss is computed by corner pairs + from each object, push loss is computed by each object with all + other objects. We use confusion matrix with 0 in diagonal to + compute the push loss. + + Args: + tl_preds (tensor): Embedding feature map of left-top corner. + br_preds (tensor): Embedding feature map of bottim-right corner. + match (list): Downsampled coordinates pair of each ground truth box. + """ + + tl_list, br_list, me_list = [], [], [] + if len(match) == 0: # no object in image + pull_loss = tl_preds.sum() * 0. + push_loss = tl_preds.sum() * 0. + else: + for m in match: + [tl_y, tl_x], [br_y, br_x] = m + tl_e = tl_preds[:, tl_y, tl_x].view(-1, 1) + br_e = br_preds[:, br_y, br_x].view(-1, 1) + tl_list.append(tl_e) + br_list.append(br_e) + me_list.append((tl_e + br_e) / 2.0) + + tl_list = torch.cat(tl_list) + br_list = torch.cat(br_list) + me_list = torch.cat(me_list) + + assert tl_list.size() == br_list.size() + + # N is object number in image, M is dimension of embedding vector + N, M = tl_list.size() + + pull_loss = (tl_list - me_list).pow(2) + (br_list - me_list).pow(2) + pull_loss = pull_loss.sum() / N + + margin = 1 # exp setting of CornerNet, details in section 3.3 of paper + + # confusion matrix of push loss + conf_mat = me_list.expand((N, N, M)).permute(1, 0, 2) - me_list + conf_weight = 1 - torch.eye(N).type_as(me_list) + conf_mat = conf_weight * (margin - conf_mat.sum(-1).abs()) + + if N > 1: # more than one object in current image + push_loss = F.relu(conf_mat).sum() / (N * (N - 1)) + else: + push_loss = tl_preds.sum() * 0. + + return pull_loss, push_loss + + +@LOSSES.register_module() +class AssociativeEmbeddingLoss(nn.Module): + """Associative Embedding Loss. + + More details can be found in + `Associative Embedding `_ and + `CornerNet `_ . + Code is modified from `kp_utils.py `_ # noqa: E501 + + Args: + pull_weight (float): Loss weight for corners from same object. + push_weight (float): Loss weight for corners from different object. + """ + + def __init__(self, pull_weight=0.25, push_weight=0.25): + super(AssociativeEmbeddingLoss, self).__init__() + self.pull_weight = pull_weight + self.push_weight = push_weight + + def forward(self, pred, target, match): + """Forward function.""" + batch = pred.size(0) + pull_all, push_all = 0.0, 0.0 + for i in range(batch): + pull, push = ae_loss_per_image(pred[i], target[i], match[i]) + + pull_all += self.pull_weight * pull + push_all += self.push_weight * push + + return pull_all, push_all diff --git a/mmdet/models/losses/balanced_l1_loss.py b/mmdet/models/losses/balanced_l1_loss.py new file mode 100644 index 0000000..7bcd13f --- /dev/null +++ b/mmdet/models/losses/balanced_l1_loss.py @@ -0,0 +1,120 @@ +import mmcv +import numpy as np +import torch +import torch.nn as nn + +from ..builder import LOSSES +from .utils import weighted_loss + + +@mmcv.jit(derivate=True, coderize=True) +@weighted_loss +def balanced_l1_loss(pred, + target, + beta=1.0, + alpha=0.5, + gamma=1.5, + reduction='mean'): + """Calculate balanced L1 loss. + + Please see the `Libra R-CNN `_ + + Args: + pred (torch.Tensor): The prediction with shape (N, 4). + target (torch.Tensor): The learning target of the prediction with + shape (N, 4). + beta (float): The loss is a piecewise function of prediction and target + and ``beta`` serves as a threshold for the difference between the + prediction and target. Defaults to 1.0. + alpha (float): The denominator ``alpha`` in the balanced L1 loss. + Defaults to 0.5. + gamma (float): The ``gamma`` in the balanced L1 loss. + Defaults to 1.5. + reduction (str, optional): The method that reduces the loss to a + scalar. Options are "none", "mean" and "sum". + + Returns: + torch.Tensor: The calculated loss + """ + assert beta > 0 + assert pred.size() == target.size() and target.numel() > 0 + + diff = torch.abs(pred - target) + b = np.e**(gamma / alpha) - 1 + loss = torch.where( + diff < beta, alpha / b * + (b * diff + 1) * torch.log(b * diff / beta + 1) - alpha * diff, + gamma * diff + gamma / b - alpha * beta) + + return loss + + +@LOSSES.register_module() +class BalancedL1Loss(nn.Module): + """Balanced L1 Loss. + + arXiv: https://arxiv.org/pdf/1904.02701.pdf (CVPR 2019) + + Args: + alpha (float): The denominator ``alpha`` in the balanced L1 loss. + Defaults to 0.5. + gamma (float): The ``gamma`` in the balanced L1 loss. Defaults to 1.5. + beta (float, optional): The loss is a piecewise function of prediction + and target. ``beta`` serves as a threshold for the difference + between the prediction and target. Defaults to 1.0. + reduction (str, optional): The method that reduces the loss to a + scalar. Options are "none", "mean" and "sum". + loss_weight (float, optional): The weight of the loss. Defaults to 1.0 + """ + + def __init__(self, + alpha=0.5, + gamma=1.5, + beta=1.0, + reduction='mean', + loss_weight=1.0): + super(BalancedL1Loss, self).__init__() + self.alpha = alpha + self.gamma = gamma + self.beta = beta + self.reduction = reduction + self.loss_weight = loss_weight + + def forward(self, + pred, + target, + weight=None, + avg_factor=None, + reduction_override=None, + **kwargs): + """Forward function of loss. + + Args: + pred (torch.Tensor): The prediction with shape (N, 4). + target (torch.Tensor): The learning target of the prediction with + shape (N, 4). + weight (torch.Tensor, optional): Sample-wise loss weight with + shape (N, ). + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + reduction_override (str, optional): The reduction method used to + override the original reduction method of the loss. + Options are "none", "mean" and "sum". + + Returns: + torch.Tensor: The calculated loss + """ + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + loss_bbox = self.loss_weight * balanced_l1_loss( + pred, + target, + weight, + alpha=self.alpha, + gamma=self.gamma, + beta=self.beta, + reduction=reduction, + avg_factor=avg_factor, + **kwargs) + return loss_bbox diff --git a/mmdet/models/losses/cross_entropy_loss.py b/mmdet/models/losses/cross_entropy_loss.py new file mode 100644 index 0000000..5799415 --- /dev/null +++ b/mmdet/models/losses/cross_entropy_loss.py @@ -0,0 +1,214 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ..builder import LOSSES +from .utils import weight_reduce_loss + + +def cross_entropy(pred, + label, + weight=None, + reduction='mean', + avg_factor=None, + class_weight=None): + """Calculate the CrossEntropy loss. + + Args: + pred (torch.Tensor): The prediction with shape (N, C), C is the number + of classes. + label (torch.Tensor): The learning label of the prediction. + weight (torch.Tensor, optional): Sample-wise loss weight. + reduction (str, optional): The method used to reduce the loss. + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + class_weight (list[float], optional): The weight for each class. + + Returns: + torch.Tensor: The calculated loss + """ + # element-wise losses + loss = F.cross_entropy(pred, label, weight=class_weight, reduction='none') + + # apply weights and do the reduction + if weight is not None: + weight = weight.float() + loss = weight_reduce_loss( + loss, weight=weight, reduction=reduction, avg_factor=avg_factor) + + return loss + + +def _expand_onehot_labels(labels, label_weights, label_channels): + bin_labels = labels.new_full((labels.size(0), label_channels), 0) + inds = torch.nonzero( + (labels >= 0) & (labels < label_channels), as_tuple=False).squeeze() + if inds.numel() > 0: + bin_labels[inds, labels[inds]] = 1 + + if label_weights is None: + bin_label_weights = None + else: + bin_label_weights = label_weights.view(-1, 1).expand( + label_weights.size(0), label_channels) + + return bin_labels, bin_label_weights + + +def binary_cross_entropy(pred, + label, + weight=None, + reduction='mean', + avg_factor=None, + class_weight=None): + """Calculate the binary CrossEntropy loss. + + Args: + pred (torch.Tensor): The prediction with shape (N, 1). + label (torch.Tensor): The learning label of the prediction. + weight (torch.Tensor, optional): Sample-wise loss weight. + reduction (str, optional): The method used to reduce the loss. + Options are "none", "mean" and "sum". + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + class_weight (list[float], optional): The weight for each class. + + Returns: + torch.Tensor: The calculated loss + """ + if pred.dim() != label.dim(): + label, weight = _expand_onehot_labels(label, weight, pred.size(-1)) + + # weighted element-wise losses + if weight is not None: + weight = weight.float() + loss = F.binary_cross_entropy_with_logits( + pred, label.float(), pos_weight=class_weight, reduction='none') + # do the reduction for the weighted loss + loss = weight_reduce_loss( + loss, weight, reduction=reduction, avg_factor=avg_factor) + + return loss + + +def mask_cross_entropy(pred, + target, + label, + reduction='mean', + avg_factor=None, + class_weight=None): + """Calculate the CrossEntropy loss for masks. + + Args: + pred (torch.Tensor): The prediction with shape (N, C, *), C is the + number of classes. The trailing * indicates arbitrary shape. + target (torch.Tensor): The learning label of the prediction. + label (torch.Tensor): ``label`` indicates the class label of the mask + corresponding object. This will be used to select the mask in the + of the class which the object belongs to when the mask prediction + if not class-agnostic. + reduction (str, optional): The method used to reduce the loss. + Options are "none", "mean" and "sum". + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + class_weight (list[float], optional): The weight for each class. + + Returns: + torch.Tensor: The calculated loss + + Example: + >>> N, C = 3, 11 + >>> H, W = 2, 2 + >>> pred = torch.randn(N, C, H, W) * 1000 + >>> target = torch.rand(N, H, W) + >>> label = torch.randint(0, C, size=(N,)) + >>> reduction = 'mean' + >>> avg_factor = None + >>> class_weights = None + >>> loss = mask_cross_entropy(pred, target, label, reduction, + >>> avg_factor, class_weights) + >>> assert loss.shape == (1,) + """ + # TODO: handle these two reserved arguments + assert reduction == 'mean' and avg_factor is None + num_rois = pred.size()[0] + inds = torch.arange(0, num_rois, dtype=torch.long, device=pred.device) + pred_slice = pred[inds, label].squeeze(1) + return F.binary_cross_entropy_with_logits( + pred_slice, target, weight=class_weight, reduction='mean')[None] + + +@LOSSES.register_module() +class CrossEntropyLoss(nn.Module): + + def __init__(self, + use_sigmoid=False, + use_mask=False, + reduction='mean', + class_weight=None, + loss_weight=1.0): + """CrossEntropyLoss. + + Args: + use_sigmoid (bool, optional): Whether the prediction uses sigmoid + of softmax. Defaults to False. + use_mask (bool, optional): Whether to use mask cross entropy loss. + Defaults to False. + reduction (str, optional): . Defaults to 'mean'. + Options are "none", "mean" and "sum". + class_weight (list[float], optional): Weight of each class. + Defaults to None. + loss_weight (float, optional): Weight of the loss. Defaults to 1.0. + """ + super(CrossEntropyLoss, self).__init__() + assert (use_sigmoid is False) or (use_mask is False) + self.use_sigmoid = use_sigmoid + self.use_mask = use_mask + self.reduction = reduction + self.loss_weight = loss_weight + self.class_weight = class_weight + + if self.use_sigmoid: + self.cls_criterion = binary_cross_entropy + elif self.use_mask: + self.cls_criterion = mask_cross_entropy + else: + self.cls_criterion = cross_entropy + + def forward(self, + cls_score, + label, + weight=None, + avg_factor=None, + reduction_override=None, + **kwargs): + """Forward function. + + Args: + cls_score (torch.Tensor): The prediction. + label (torch.Tensor): The learning label of the prediction. + weight (torch.Tensor, optional): Sample-wise loss weight. + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + reduction (str, optional): The method used to reduce the loss. + Options are "none", "mean" and "sum". + Returns: + torch.Tensor: The calculated loss + """ + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + if self.class_weight is not None: + class_weight = cls_score.new_tensor( + self.class_weight, device=cls_score.device) + else: + class_weight = None + loss_cls = self.loss_weight * self.cls_criterion( + cls_score, + label, + weight, + class_weight=class_weight, + reduction=reduction, + avg_factor=avg_factor, + **kwargs) + return loss_cls diff --git a/mmdet/models/losses/focal_loss.py b/mmdet/models/losses/focal_loss.py new file mode 100644 index 0000000..493907c --- /dev/null +++ b/mmdet/models/losses/focal_loss.py @@ -0,0 +1,181 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.ops import sigmoid_focal_loss as _sigmoid_focal_loss + +from ..builder import LOSSES +from .utils import weight_reduce_loss + + +# This method is only for debugging +def py_sigmoid_focal_loss(pred, + target, + weight=None, + gamma=2.0, + alpha=0.25, + reduction='mean', + avg_factor=None): + """PyTorch version of `Focal Loss `_. + + Args: + pred (torch.Tensor): The prediction with shape (N, C), C is the + number of classes + target (torch.Tensor): The learning label of the prediction. + weight (torch.Tensor, optional): Sample-wise loss weight. + gamma (float, optional): The gamma for calculating the modulating + factor. Defaults to 2.0. + alpha (float, optional): A balanced form for Focal Loss. + Defaults to 0.25. + reduction (str, optional): The method used to reduce the loss into + a scalar. Defaults to 'mean'. + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + """ + pred_sigmoid = pred.sigmoid() + target = target.type_as(pred) + pt = (1 - pred_sigmoid) * target + pred_sigmoid * (1 - target) + focal_weight = (alpha * target + (1 - alpha) * + (1 - target)) * pt.pow(gamma) + loss = F.binary_cross_entropy_with_logits( + pred, target, reduction='none') * focal_weight + if weight is not None: + if weight.shape != loss.shape: + if weight.size(0) == loss.size(0): + # For most cases, weight is of shape (num_priors, ), + # which means it does not have the second axis num_class + weight = weight.view(-1, 1) + else: + # Sometimes, weight per anchor per class is also needed. e.g. + # in FSAF. But it may be flattened of shape + # (num_priors x num_class, ), while loss is still of shape + # (num_priors, num_class). + assert weight.numel() == loss.numel() + weight = weight.view(loss.size(0), -1) + assert weight.ndim == loss.ndim + loss = weight_reduce_loss(loss, weight, reduction, avg_factor) + return loss + + +def sigmoid_focal_loss(pred, + target, + weight=None, + gamma=2.0, + alpha=0.25, + reduction='mean', + avg_factor=None): + r"""A warpper of cuda version `Focal Loss + `_. + + Args: + pred (torch.Tensor): The prediction with shape (N, C), C is the number + of classes. + target (torch.Tensor): The learning label of the prediction. + weight (torch.Tensor, optional): Sample-wise loss weight. + gamma (float, optional): The gamma for calculating the modulating + factor. Defaults to 2.0. + alpha (float, optional): A balanced form for Focal Loss. + Defaults to 0.25. + reduction (str, optional): The method used to reduce the loss into + a scalar. Defaults to 'mean'. Options are "none", "mean" and "sum". + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + """ + # Function.apply does not accept keyword arguments, so the decorator + # "weighted_loss" is not applicable + loss = _sigmoid_focal_loss(pred.contiguous(), target, gamma, alpha, None, + 'none') + if weight is not None: + if weight.shape != loss.shape: + if weight.size(0) == loss.size(0): + # For most cases, weight is of shape (num_priors, ), + # which means it does not have the second axis num_class + weight = weight.view(-1, 1) + else: + # Sometimes, weight per anchor per class is also needed. e.g. + # in FSAF. But it may be flattened of shape + # (num_priors x num_class, ), while loss is still of shape + # (num_priors, num_class). + assert weight.numel() == loss.numel() + weight = weight.view(loss.size(0), -1) + assert weight.ndim == loss.ndim + loss = weight_reduce_loss(loss, weight, reduction, avg_factor) + return loss + + +@LOSSES.register_module() +class FocalLoss(nn.Module): + + def __init__(self, + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + reduction='mean', + loss_weight=1.0): + """`Focal Loss `_ + + Args: + use_sigmoid (bool, optional): Whether to the prediction is + used for sigmoid or softmax. Defaults to True. + gamma (float, optional): The gamma for calculating the modulating + factor. Defaults to 2.0. + alpha (float, optional): A balanced form for Focal Loss. + Defaults to 0.25. + reduction (str, optional): The method used to reduce the loss into + a scalar. Defaults to 'mean'. Options are "none", "mean" and + "sum". + loss_weight (float, optional): Weight of loss. Defaults to 1.0. + """ + super(FocalLoss, self).__init__() + assert use_sigmoid is True, 'Only sigmoid focal loss supported now.' + self.use_sigmoid = use_sigmoid + self.gamma = gamma + self.alpha = alpha + self.reduction = reduction + self.loss_weight = loss_weight + + def forward(self, + pred, + target, + weight=None, + avg_factor=None, + reduction_override=None): + """Forward function. + + Args: + pred (torch.Tensor): The prediction. + target (torch.Tensor): The learning label of the prediction. + weight (torch.Tensor, optional): The weight of loss for each + prediction. Defaults to None. + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + reduction_override (str, optional): The reduction method used to + override the original reduction method of the loss. + Options are "none", "mean" and "sum". + + Returns: + torch.Tensor: The calculated loss + """ + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + if self.use_sigmoid: + if torch.cuda.is_available() and pred.is_cuda: + calculate_loss_func = sigmoid_focal_loss + else: + num_classes = pred.size(1) + target = F.one_hot(target, num_classes=num_classes + 1) + target = target[:, :num_classes] + calculate_loss_func = py_sigmoid_focal_loss + + loss_cls = self.loss_weight * calculate_loss_func( + pred, + target, + weight, + gamma=self.gamma, + alpha=self.alpha, + reduction=reduction, + avg_factor=avg_factor) + + else: + raise NotImplementedError + return loss_cls diff --git a/mmdet/models/losses/gaussian_focal_loss.py b/mmdet/models/losses/gaussian_focal_loss.py new file mode 100644 index 0000000..e45506a --- /dev/null +++ b/mmdet/models/losses/gaussian_focal_loss.py @@ -0,0 +1,91 @@ +import mmcv +import torch.nn as nn + +from ..builder import LOSSES +from .utils import weighted_loss + + +@mmcv.jit(derivate=True, coderize=True) +@weighted_loss +def gaussian_focal_loss(pred, gaussian_target, alpha=2.0, gamma=4.0): + """`Focal Loss `_ for targets in gaussian + distribution. + + Args: + pred (torch.Tensor): The prediction. + gaussian_target (torch.Tensor): The learning target of the prediction + in gaussian distribution. + alpha (float, optional): A balanced form for Focal Loss. + Defaults to 2.0. + gamma (float, optional): The gamma for calculating the modulating + factor. Defaults to 4.0. + """ + eps = 1e-12 + pos_weights = gaussian_target.eq(1) + neg_weights = (1 - gaussian_target).pow(gamma) + pos_loss = -(pred + eps).log() * (1 - pred).pow(alpha) * pos_weights + neg_loss = -(1 - pred + eps).log() * pred.pow(alpha) * neg_weights + return pos_loss + neg_loss + + +@LOSSES.register_module() +class GaussianFocalLoss(nn.Module): + """GaussianFocalLoss is a variant of focal loss. + + More details can be found in the `paper + `_ + Code is modified from `kp_utils.py + `_ # noqa: E501 + Please notice that the target in GaussianFocalLoss is a gaussian heatmap, + not 0/1 binary target. + + Args: + alpha (float): Power of prediction. + gamma (float): Power of target for negative samples. + reduction (str): Options are "none", "mean" and "sum". + loss_weight (float): Loss weight of current loss. + """ + + def __init__(self, + alpha=2.0, + gamma=4.0, + reduction='mean', + loss_weight=1.0): + super(GaussianFocalLoss, self).__init__() + self.alpha = alpha + self.gamma = gamma + self.reduction = reduction + self.loss_weight = loss_weight + + def forward(self, + pred, + target, + weight=None, + avg_factor=None, + reduction_override=None): + """Forward function. + + Args: + pred (torch.Tensor): The prediction. + target (torch.Tensor): The learning target of the prediction + in gaussian distribution. + weight (torch.Tensor, optional): The weight of loss for each + prediction. Defaults to None. + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + reduction_override (str, optional): The reduction method used to + override the original reduction method of the loss. + Defaults to None. + """ + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + loss_reg = self.loss_weight * gaussian_focal_loss( + pred, + target, + weight, + alpha=self.alpha, + gamma=self.gamma, + reduction=reduction, + avg_factor=avg_factor) + return loss_reg diff --git a/mmdet/models/losses/gfocal_loss.py b/mmdet/models/losses/gfocal_loss.py new file mode 100644 index 0000000..9d3b883 --- /dev/null +++ b/mmdet/models/losses/gfocal_loss.py @@ -0,0 +1,188 @@ +import mmcv +import torch.nn as nn +import torch.nn.functional as F + +from ..builder import LOSSES +from .utils import weighted_loss + + +@mmcv.jit(derivate=True, coderize=True) +@weighted_loss +def quality_focal_loss(pred, target, beta=2.0): + r"""Quality Focal Loss (QFL) is from `Generalized Focal Loss: Learning + Qualified and Distributed Bounding Boxes for Dense Object Detection + `_. + + Args: + pred (torch.Tensor): Predicted joint representation of classification + and quality (IoU) estimation with shape (N, C), C is the number of + classes. + target (tuple([torch.Tensor])): Target category label with shape (N,) + and target quality label with shape (N,). + beta (float): The beta parameter for calculating the modulating factor. + Defaults to 2.0. + + Returns: + torch.Tensor: Loss tensor with shape (N,). + """ + assert len(target) == 2, """target for QFL must be a tuple of two elements, + including category label and quality label, respectively""" + # label denotes the category id, score denotes the quality score + label, score = target + + # negatives are supervised by 0 quality score + pred_sigmoid = pred.sigmoid() + scale_factor = pred_sigmoid + zerolabel = scale_factor.new_zeros(pred.shape) + loss = F.binary_cross_entropy_with_logits( + pred, zerolabel, reduction='none') * scale_factor.pow(beta) + + # FG cat_id: [0, num_classes -1], BG cat_id: num_classes + bg_class_ind = pred.size(1) + pos = ((label >= 0) & (label < bg_class_ind)).nonzero().squeeze(1) + pos_label = label[pos].long() + # positives are supervised by bbox quality (IoU) score + scale_factor = score[pos] - pred_sigmoid[pos, pos_label] + loss[pos, pos_label] = F.binary_cross_entropy_with_logits( + pred[pos, pos_label], score[pos], + reduction='none') * scale_factor.abs().pow(beta) + + loss = loss.sum(dim=1, keepdim=False) + return loss + + +@mmcv.jit(derivate=True, coderize=True) +@weighted_loss +def distribution_focal_loss(pred, label): + r"""Distribution Focal Loss (DFL) is from `Generalized Focal Loss: Learning + Qualified and Distributed Bounding Boxes for Dense Object Detection + `_. + + Args: + pred (torch.Tensor): Predicted general distribution of bounding boxes + (before softmax) with shape (N, n+1), n is the max value of the + integral set `{0, ..., n}` in paper. + label (torch.Tensor): Target distance label for bounding boxes with + shape (N,). + + Returns: + torch.Tensor: Loss tensor with shape (N,). + """ + dis_left = label.long() + dis_right = dis_left + 1 + weight_left = dis_right.float() - label + weight_right = label - dis_left.float() + loss = F.cross_entropy(pred, dis_left, reduction='none') * weight_left \ + + F.cross_entropy(pred, dis_right, reduction='none') * weight_right + return loss + + +@LOSSES.register_module() +class QualityFocalLoss(nn.Module): + r"""Quality Focal Loss (QFL) is a variant of `Generalized Focal Loss: + Learning Qualified and Distributed Bounding Boxes for Dense Object + Detection `_. + + Args: + use_sigmoid (bool): Whether sigmoid operation is conducted in QFL. + Defaults to True. + beta (float): The beta parameter for calculating the modulating factor. + Defaults to 2.0. + reduction (str): Options are "none", "mean" and "sum". + loss_weight (float): Loss weight of current loss. + """ + + def __init__(self, + use_sigmoid=True, + beta=2.0, + reduction='mean', + loss_weight=1.0): + super(QualityFocalLoss, self).__init__() + assert use_sigmoid is True, 'Only sigmoid in QFL supported now.' + self.use_sigmoid = use_sigmoid + self.beta = beta + self.reduction = reduction + self.loss_weight = loss_weight + + def forward(self, + pred, + target, + weight=None, + avg_factor=None, + reduction_override=None): + """Forward function. + + Args: + pred (torch.Tensor): Predicted joint representation of + classification and quality (IoU) estimation with shape (N, C), + C is the number of classes. + target (tuple([torch.Tensor])): Target category label with shape + (N,) and target quality label with shape (N,). + weight (torch.Tensor, optional): The weight of loss for each + prediction. Defaults to None. + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + reduction_override (str, optional): The reduction method used to + override the original reduction method of the loss. + Defaults to None. + """ + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + if self.use_sigmoid: + loss_cls = self.loss_weight * quality_focal_loss( + pred, + target, + weight, + beta=self.beta, + reduction=reduction, + avg_factor=avg_factor) + else: + raise NotImplementedError + return loss_cls + + +@LOSSES.register_module() +class DistributionFocalLoss(nn.Module): + r"""Distribution Focal Loss (DFL) is a variant of `Generalized Focal Loss: + Learning Qualified and Distributed Bounding Boxes for Dense Object + Detection `_. + + Args: + reduction (str): Options are `'none'`, `'mean'` and `'sum'`. + loss_weight (float): Loss weight of current loss. + """ + + def __init__(self, reduction='mean', loss_weight=1.0): + super(DistributionFocalLoss, self).__init__() + self.reduction = reduction + self.loss_weight = loss_weight + + def forward(self, + pred, + target, + weight=None, + avg_factor=None, + reduction_override=None): + """Forward function. + + Args: + pred (torch.Tensor): Predicted general distribution of bounding + boxes (before softmax) with shape (N, n+1), n is the max value + of the integral set `{0, ..., n}` in paper. + target (torch.Tensor): Target distance label for bounding boxes + with shape (N,). + weight (torch.Tensor, optional): The weight of loss for each + prediction. Defaults to None. + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + reduction_override (str, optional): The reduction method used to + override the original reduction method of the loss. + Defaults to None. + """ + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + loss_cls = self.loss_weight * distribution_focal_loss( + pred, target, weight, reduction=reduction, avg_factor=avg_factor) + return loss_cls diff --git a/mmdet/models/losses/ghm_loss.py b/mmdet/models/losses/ghm_loss.py new file mode 100644 index 0000000..8969a23 --- /dev/null +++ b/mmdet/models/losses/ghm_loss.py @@ -0,0 +1,172 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ..builder import LOSSES + + +def _expand_onehot_labels(labels, label_weights, label_channels): + bin_labels = labels.new_full((labels.size(0), label_channels), 0) + inds = torch.nonzero( + (labels >= 0) & (labels < label_channels), as_tuple=False).squeeze() + if inds.numel() > 0: + bin_labels[inds, labels[inds]] = 1 + bin_label_weights = label_weights.view(-1, 1).expand( + label_weights.size(0), label_channels) + return bin_labels, bin_label_weights + + +# TODO: code refactoring to make it consistent with other losses +@LOSSES.register_module() +class GHMC(nn.Module): + """GHM Classification Loss. + + Details of the theorem can be viewed in the paper + `Gradient Harmonized Single-stage Detector + `_. + + Args: + bins (int): Number of the unit regions for distribution calculation. + momentum (float): The parameter for moving average. + use_sigmoid (bool): Can only be true for BCE based loss now. + loss_weight (float): The weight of the total GHM-C loss. + """ + + def __init__(self, bins=10, momentum=0, use_sigmoid=True, loss_weight=1.0): + super(GHMC, self).__init__() + self.bins = bins + self.momentum = momentum + edges = torch.arange(bins + 1).float() / bins + self.register_buffer('edges', edges) + self.edges[-1] += 1e-6 + if momentum > 0: + acc_sum = torch.zeros(bins) + self.register_buffer('acc_sum', acc_sum) + self.use_sigmoid = use_sigmoid + if not self.use_sigmoid: + raise NotImplementedError + self.loss_weight = loss_weight + + def forward(self, pred, target, label_weight, *args, **kwargs): + """Calculate the GHM-C loss. + + Args: + pred (float tensor of size [batch_num, class_num]): + The direct prediction of classification fc layer. + target (float tensor of size [batch_num, class_num]): + Binary class target for each sample. + label_weight (float tensor of size [batch_num, class_num]): + the value is 1 if the sample is valid and 0 if ignored. + Returns: + The gradient harmonized loss. + """ + # the target should be binary class label + if pred.dim() != target.dim(): + target, label_weight = _expand_onehot_labels( + target, label_weight, pred.size(-1)) + target, label_weight = target.float(), label_weight.float() + edges = self.edges + mmt = self.momentum + weights = torch.zeros_like(pred) + + # gradient length + g = torch.abs(pred.sigmoid().detach() - target) + + valid = label_weight > 0 + tot = max(valid.float().sum().item(), 1.0) + n = 0 # n valid bins + for i in range(self.bins): + inds = (g >= edges[i]) & (g < edges[i + 1]) & valid + num_in_bin = inds.sum().item() + if num_in_bin > 0: + if mmt > 0: + self.acc_sum[i] = mmt * self.acc_sum[i] \ + + (1 - mmt) * num_in_bin + weights[inds] = tot / self.acc_sum[i] + else: + weights[inds] = tot / num_in_bin + n += 1 + if n > 0: + weights = weights / n + + loss = F.binary_cross_entropy_with_logits( + pred, target, weights, reduction='sum') / tot + return loss * self.loss_weight + + +# TODO: code refactoring to make it consistent with other losses +@LOSSES.register_module() +class GHMR(nn.Module): + """GHM Regression Loss. + + Details of the theorem can be viewed in the paper + `Gradient Harmonized Single-stage Detector + `_. + + Args: + mu (float): The parameter for the Authentic Smooth L1 loss. + bins (int): Number of the unit regions for distribution calculation. + momentum (float): The parameter for moving average. + loss_weight (float): The weight of the total GHM-R loss. + """ + + def __init__(self, mu=0.02, bins=10, momentum=0, loss_weight=1.0): + super(GHMR, self).__init__() + self.mu = mu + self.bins = bins + edges = torch.arange(bins + 1).float() / bins + self.register_buffer('edges', edges) + self.edges[-1] = 1e3 + self.momentum = momentum + if momentum > 0: + acc_sum = torch.zeros(bins) + self.register_buffer('acc_sum', acc_sum) + self.loss_weight = loss_weight + + # TODO: support reduction parameter + def forward(self, pred, target, label_weight, avg_factor=None): + """Calculate the GHM-R loss. + + Args: + pred (float tensor of size [batch_num, 4 (* class_num)]): + The prediction of box regression layer. Channel number can be 4 + or 4 * class_num depending on whether it is class-agnostic. + target (float tensor of size [batch_num, 4 (* class_num)]): + The target regression values with the same size of pred. + label_weight (float tensor of size [batch_num, 4 (* class_num)]): + The weight of each sample, 0 if ignored. + Returns: + The gradient harmonized loss. + """ + mu = self.mu + edges = self.edges + mmt = self.momentum + + # ASL1 loss + diff = pred - target + loss = torch.sqrt(diff * diff + mu * mu) - mu + + # gradient length + g = torch.abs(diff / torch.sqrt(mu * mu + diff * diff)).detach() + weights = torch.zeros_like(g) + + valid = label_weight > 0 + tot = max(label_weight.float().sum().item(), 1.0) + n = 0 # n: valid bins + for i in range(self.bins): + inds = (g >= edges[i]) & (g < edges[i + 1]) & valid + num_in_bin = inds.sum().item() + if num_in_bin > 0: + n += 1 + if mmt > 0: + self.acc_sum[i] = mmt * self.acc_sum[i] \ + + (1 - mmt) * num_in_bin + weights[inds] = tot / self.acc_sum[i] + else: + weights[inds] = tot / num_in_bin + if n > 0: + weights /= n + + loss = loss * weights + loss = loss.sum() / tot + return loss * self.loss_weight diff --git a/mmdet/models/losses/iou_loss.py b/mmdet/models/losses/iou_loss.py new file mode 100644 index 0000000..4c11a9a --- /dev/null +++ b/mmdet/models/losses/iou_loss.py @@ -0,0 +1,446 @@ +import math + +import mmcv +import torch +import torch.nn as nn + +from mmdet.core import bbox_overlaps +from ..builder import LOSSES +from .utils import weighted_loss + + +@mmcv.jit(derivate=True, coderize=True) +@weighted_loss +def iou_loss(pred, target, linear=False, eps=1e-6): + """IoU loss. + + Computing the IoU loss between a set of predicted bboxes and target bboxes. + The loss is calculated as negative log of IoU. + + Args: + pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2), + shape (n, 4). + target (torch.Tensor): Corresponding gt bboxes, shape (n, 4). + linear (bool, optional): If True, use linear scale of loss instead of + log scale. Default: False. + eps (float): Eps to avoid log(0). + + Return: + torch.Tensor: Loss tensor. + """ + ious = bbox_overlaps(pred, target, is_aligned=True).clamp(min=eps) + if linear: + loss = 1 - ious + else: + loss = -ious.log() + return loss + + +@mmcv.jit(derivate=True, coderize=True) +@weighted_loss +def bounded_iou_loss(pred, target, beta=0.2, eps=1e-3): + """BIoULoss. + + This is an implementation of paper + `Improving Object Localization with Fitness NMS and Bounded IoU Loss. + `_. + + Args: + pred (torch.Tensor): Predicted bboxes. + target (torch.Tensor): Target bboxes. + beta (float): beta parameter in smoothl1. + eps (float): eps to avoid NaN. + """ + pred_ctrx = (pred[:, 0] + pred[:, 2]) * 0.5 + pred_ctry = (pred[:, 1] + pred[:, 3]) * 0.5 + pred_w = pred[:, 2] - pred[:, 0] + pred_h = pred[:, 3] - pred[:, 1] + with torch.no_grad(): + target_ctrx = (target[:, 0] + target[:, 2]) * 0.5 + target_ctry = (target[:, 1] + target[:, 3]) * 0.5 + target_w = target[:, 2] - target[:, 0] + target_h = target[:, 3] - target[:, 1] + + dx = target_ctrx - pred_ctrx + dy = target_ctry - pred_ctry + + loss_dx = 1 - torch.max( + (target_w - 2 * dx.abs()) / + (target_w + 2 * dx.abs() + eps), torch.zeros_like(dx)) + loss_dy = 1 - torch.max( + (target_h - 2 * dy.abs()) / + (target_h + 2 * dy.abs() + eps), torch.zeros_like(dy)) + loss_dw = 1 - torch.min(target_w / (pred_w + eps), pred_w / + (target_w + eps)) + loss_dh = 1 - torch.min(target_h / (pred_h + eps), pred_h / + (target_h + eps)) + loss_comb = torch.stack([loss_dx, loss_dy, loss_dw, loss_dh], + dim=-1).view(loss_dx.size(0), -1) + + loss = torch.where(loss_comb < beta, 0.5 * loss_comb * loss_comb / beta, + loss_comb - 0.5 * beta) + return loss + + +@mmcv.jit(derivate=True, coderize=True) +@weighted_loss +def giou_loss(pred, target, eps=1e-7): + r"""`Generalized Intersection over Union: A Metric and A Loss for Bounding + Box Regression `_. + + Args: + pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2), + shape (n, 4). + target (torch.Tensor): Corresponding gt bboxes, shape (n, 4). + eps (float): Eps to avoid log(0). + + Return: + Tensor: Loss tensor. + """ + gious = bbox_overlaps(pred, target, mode='giou', is_aligned=True, eps=eps) + loss = 1 - gious + return loss + + +@mmcv.jit(derivate=True, coderize=True) +@weighted_loss +def diou_loss(pred, target, eps=1e-7): + r"""`Implementation of Distance-IoU Loss: Faster and Better + Learning for Bounding Box Regression, https://arxiv.org/abs/1911.08287`_. + + Code is modified from https://github.com/Zzh-tju/DIoU. + + Args: + pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2), + shape (n, 4). + target (Tensor): Corresponding gt bboxes, shape (n, 4). + eps (float): Eps to avoid log(0). + Return: + Tensor: Loss tensor. + """ + # overlap + lt = torch.max(pred[:, :2], target[:, :2]) + rb = torch.min(pred[:, 2:], target[:, 2:]) + wh = (rb - lt).clamp(min=0) + overlap = wh[:, 0] * wh[:, 1] + + # union + ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1]) + ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1]) + union = ap + ag - overlap + eps + + # IoU + ious = overlap / union + + # enclose area + enclose_x1y1 = torch.min(pred[:, :2], target[:, :2]) + enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:]) + enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0) + + cw = enclose_wh[:, 0] + ch = enclose_wh[:, 1] + + c2 = cw**2 + ch**2 + eps + + b1_x1, b1_y1 = pred[:, 0], pred[:, 1] + b1_x2, b1_y2 = pred[:, 2], pred[:, 3] + b2_x1, b2_y1 = target[:, 0], target[:, 1] + b2_x2, b2_y2 = target[:, 2], target[:, 3] + + left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4 + right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4 + rho2 = left + right + + # DIoU + dious = ious - rho2 / c2 + loss = 1 - dious + return loss + + +@mmcv.jit(derivate=True, coderize=True) +@weighted_loss +def ciou_loss(pred, target, eps=1e-7): + r"""`Implementation of paper `Enhancing Geometric Factors into + Model Learning and Inference for Object Detection and Instance + Segmentation `_. + + Code is modified from https://github.com/Zzh-tju/CIoU. + + Args: + pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2), + shape (n, 4). + target (Tensor): Corresponding gt bboxes, shape (n, 4). + eps (float): Eps to avoid log(0). + Return: + Tensor: Loss tensor. + """ + # overlap + lt = torch.max(pred[:, :2], target[:, :2]) + rb = torch.min(pred[:, 2:], target[:, 2:]) + wh = (rb - lt).clamp(min=0) + overlap = wh[:, 0] * wh[:, 1] + + # union + ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1]) + ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1]) + union = ap + ag - overlap + eps + + # IoU + ious = overlap / union + + # enclose area + enclose_x1y1 = torch.min(pred[:, :2], target[:, :2]) + enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:]) + enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0) + + cw = enclose_wh[:, 0] + ch = enclose_wh[:, 1] + + c2 = cw**2 + ch**2 + eps + + b1_x1, b1_y1 = pred[:, 0], pred[:, 1] + b1_x2, b1_y2 = pred[:, 2], pred[:, 3] + b2_x1, b2_y1 = target[:, 0], target[:, 1] + b2_x2, b2_y2 = target[:, 2], target[:, 3] + + w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps + w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps + + left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4 + right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4 + rho2 = left + right + + factor = 4 / math.pi**2 + v = factor * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2) + + # CIoU + cious = ious - (rho2 / c2 + v**2 / (1 - ious + v)) + loss = 1 - cious + return loss + + +@LOSSES.register_module() +class IoULoss(nn.Module): + """IoULoss. + + Computing the IoU loss between a set of predicted bboxes and target bboxes. + + Args: + linear (bool): If True, use linear scale of loss instead of log scale. + Default: False. + eps (float): Eps to avoid log(0). + reduction (str): Options are "none", "mean" and "sum". + loss_weight (float): Weight of loss. + """ + + def __init__(self, + linear=False, + eps=1e-6, + reduction='mean', + loss_weight=1.0): + super(IoULoss, self).__init__() + self.linear = linear + self.eps = eps + self.reduction = reduction + self.loss_weight = loss_weight + + def forward(self, + pred, + target, + weight=None, + avg_factor=None, + reduction_override=None, + **kwargs): + """Forward function. + + Args: + pred (torch.Tensor): The prediction. + target (torch.Tensor): The learning target of the prediction. + weight (torch.Tensor, optional): The weight of loss for each + prediction. Defaults to None. + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + reduction_override (str, optional): The reduction method used to + override the original reduction method of the loss. + Defaults to None. Options are "none", "mean" and "sum". + """ + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + if (weight is not None) and (not torch.any(weight > 0)) and ( + reduction != 'none'): + if pred.dim() == weight.dim() + 1: + weight = weight.unsqueeze(1) + return (pred * weight).sum() # 0 + if weight is not None and weight.dim() > 1: + # TODO: remove this in the future + # reduce the weight of shape (n, 4) to (n,) to match the + # iou_loss of shape (n,) + assert weight.shape == pred.shape + weight = weight.mean(-1) + loss = self.loss_weight * iou_loss( + pred, + target, + weight, + linear=self.linear, + eps=self.eps, + reduction=reduction, + avg_factor=avg_factor, + **kwargs) + return loss + + +@LOSSES.register_module() +class BoundedIoULoss(nn.Module): + + def __init__(self, beta=0.2, eps=1e-3, reduction='mean', loss_weight=1.0): + super(BoundedIoULoss, self).__init__() + self.beta = beta + self.eps = eps + self.reduction = reduction + self.loss_weight = loss_weight + + def forward(self, + pred, + target, + weight=None, + avg_factor=None, + reduction_override=None, + **kwargs): + if weight is not None and not torch.any(weight > 0): + if pred.dim() == weight.dim() + 1: + weight = weight.unsqueeze(1) + return (pred * weight).sum() # 0 + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + loss = self.loss_weight * bounded_iou_loss( + pred, + target, + weight, + beta=self.beta, + eps=self.eps, + reduction=reduction, + avg_factor=avg_factor, + **kwargs) + return loss + + +@LOSSES.register_module() +class GIoULoss(nn.Module): + + def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0): + super(GIoULoss, self).__init__() + self.eps = eps + self.reduction = reduction + self.loss_weight = loss_weight + + def forward(self, + pred, + target, + weight=None, + avg_factor=None, + reduction_override=None, + **kwargs): + if weight is not None and not torch.any(weight > 0): + if pred.dim() == weight.dim() + 1: + weight = weight.unsqueeze(1) + return (pred * weight).sum() # 0 + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + if weight is not None and weight.dim() > 1: + # TODO: remove this in the future + # reduce the weight of shape (n, 4) to (n,) to match the + # giou_loss of shape (n,) + assert weight.shape == pred.shape + weight = weight.mean(-1) + loss = self.loss_weight * giou_loss( + pred, + target, + weight, + eps=self.eps, + reduction=reduction, + avg_factor=avg_factor, + **kwargs) + return loss + + +@LOSSES.register_module() +class DIoULoss(nn.Module): + + def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0): + super(DIoULoss, self).__init__() + self.eps = eps + self.reduction = reduction + self.loss_weight = loss_weight + + def forward(self, + pred, + target, + weight=None, + avg_factor=None, + reduction_override=None, + **kwargs): + if weight is not None and not torch.any(weight > 0): + if pred.dim() == weight.dim() + 1: + weight = weight.unsqueeze(1) + return (pred * weight).sum() # 0 + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + if weight is not None and weight.dim() > 1: + # TODO: remove this in the future + # reduce the weight of shape (n, 4) to (n,) to match the + # giou_loss of shape (n,) + assert weight.shape == pred.shape + weight = weight.mean(-1) + loss = self.loss_weight * diou_loss( + pred, + target, + weight, + eps=self.eps, + reduction=reduction, + avg_factor=avg_factor, + **kwargs) + return loss + + +@LOSSES.register_module() +class CIoULoss(nn.Module): + + def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0): + super(CIoULoss, self).__init__() + self.eps = eps + self.reduction = reduction + self.loss_weight = loss_weight + + def forward(self, + pred, + target, + weight=None, + avg_factor=None, + reduction_override=None, + **kwargs): + if weight is not None and not torch.any(weight > 0): + if pred.dim() == weight.dim() + 1: + weight = weight.unsqueeze(1) + return (pred * weight).sum() # 0 + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + if weight is not None and weight.dim() > 1: + # TODO: remove this in the future + # reduce the weight of shape (n, 4) to (n,) to match the + # giou_loss of shape (n,) + assert weight.shape == pred.shape + weight = weight.mean(-1) + loss = self.loss_weight * ciou_loss( + pred, + target, + weight, + eps=self.eps, + reduction=reduction, + avg_factor=avg_factor, + **kwargs) + return loss diff --git a/mmdet/models/losses/kd_loss.py b/mmdet/models/losses/kd_loss.py new file mode 100644 index 0000000..f3abb68 --- /dev/null +++ b/mmdet/models/losses/kd_loss.py @@ -0,0 +1,87 @@ +import mmcv +import torch.nn as nn +import torch.nn.functional as F + +from ..builder import LOSSES +from .utils import weighted_loss + + +@mmcv.jit(derivate=True, coderize=True) +@weighted_loss +def knowledge_distillation_kl_div_loss(pred, + soft_label, + T, + detach_target=True): + r"""Loss function for knowledge distilling using KL divergence. + + Args: + pred (Tensor): Predicted logits with shape (N, n + 1). + soft_label (Tensor): Target logits with shape (N, N + 1). + T (int): Temperature for distillation. + detach_target (bool): Remove soft_label from automatic differentiation + + Returns: + torch.Tensor: Loss tensor with shape (N,). + """ + assert pred.size() == soft_label.size() + target = F.softmax(soft_label / T, dim=1) + if detach_target: + target = target.detach() + + kd_loss = F.kl_div( + F.log_softmax(pred / T, dim=1), target, reduction='none').mean(1) * ( + T * T) + + return kd_loss + + +@LOSSES.register_module() +class KnowledgeDistillationKLDivLoss(nn.Module): + """Loss function for knowledge distilling using KL divergence. + + Args: + reduction (str): Options are `'none'`, `'mean'` and `'sum'`. + loss_weight (float): Loss weight of current loss. + T (int): Temperature for distillation. + """ + + def __init__(self, reduction='mean', loss_weight=1.0, T=10): + super(KnowledgeDistillationKLDivLoss, self).__init__() + assert T >= 1 + self.reduction = reduction + self.loss_weight = loss_weight + self.T = T + + def forward(self, + pred, + soft_label, + weight=None, + avg_factor=None, + reduction_override=None): + """Forward function. + + Args: + pred (Tensor): Predicted logits with shape (N, n + 1). + soft_label (Tensor): Target logits with shape (N, N + 1). + weight (torch.Tensor, optional): The weight of loss for each + prediction. Defaults to None. + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + reduction_override (str, optional): The reduction method used to + override the original reduction method of the loss. + Defaults to None. + """ + assert reduction_override in (None, 'none', 'mean', 'sum') + + reduction = ( + reduction_override if reduction_override else self.reduction) + + loss_kd = self.loss_weight * knowledge_distillation_kl_div_loss( + pred, + soft_label, + weight, + reduction=reduction, + avg_factor=avg_factor, + T=self.T) + + return loss_kd diff --git a/mmdet/models/losses/mse_loss.py b/mmdet/models/losses/mse_loss.py new file mode 100644 index 0000000..68d0575 --- /dev/null +++ b/mmdet/models/losses/mse_loss.py @@ -0,0 +1,49 @@ +import torch.nn as nn +import torch.nn.functional as F + +from ..builder import LOSSES +from .utils import weighted_loss + + +@weighted_loss +def mse_loss(pred, target): + """Warpper of mse loss.""" + return F.mse_loss(pred, target, reduction='none') + + +@LOSSES.register_module() +class MSELoss(nn.Module): + """MSELoss. + + Args: + reduction (str, optional): The method that reduces the loss to a + scalar. Options are "none", "mean" and "sum". + loss_weight (float, optional): The weight of the loss. Defaults to 1.0 + """ + + def __init__(self, reduction='mean', loss_weight=1.0): + super().__init__() + self.reduction = reduction + self.loss_weight = loss_weight + + def forward(self, pred, target, weight=None, avg_factor=None): + """Forward function of loss. + + Args: + pred (torch.Tensor): The prediction. + target (torch.Tensor): The learning target of the prediction. + weight (torch.Tensor, optional): Weight of the loss for each + prediction. Defaults to None. + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + + Returns: + torch.Tensor: The calculated loss + """ + loss = self.loss_weight * mse_loss( + pred, + target, + weight, + reduction=self.reduction, + avg_factor=avg_factor) + return loss diff --git a/mmdet/models/losses/pisa_loss.py b/mmdet/models/losses/pisa_loss.py new file mode 100644 index 0000000..4a48adf --- /dev/null +++ b/mmdet/models/losses/pisa_loss.py @@ -0,0 +1,183 @@ +import mmcv +import torch + +from mmdet.core import bbox_overlaps + + +@mmcv.jit(derivate=True, coderize=True) +def isr_p(cls_score, + bbox_pred, + bbox_targets, + rois, + sampling_results, + loss_cls, + bbox_coder, + k=2, + bias=0, + num_class=80): + """Importance-based Sample Reweighting (ISR_P), positive part. + + Args: + cls_score (Tensor): Predicted classification scores. + bbox_pred (Tensor): Predicted bbox deltas. + bbox_targets (tuple[Tensor]): A tuple of bbox targets, the are + labels, label_weights, bbox_targets, bbox_weights, respectively. + rois (Tensor): Anchors (single_stage) in shape (n, 4) or RoIs + (two_stage) in shape (n, 5). + sampling_results (obj): Sampling results. + loss_cls (func): Classification loss func of the head. + bbox_coder (obj): BBox coder of the head. + k (float): Power of the non-linear mapping. + bias (float): Shift of the non-linear mapping. + num_class (int): Number of classes, default: 80. + + Return: + tuple([Tensor]): labels, imp_based_label_weights, bbox_targets, + bbox_target_weights + """ + + labels, label_weights, bbox_targets, bbox_weights = bbox_targets + pos_label_inds = ((labels >= 0) & + (labels < num_class)).nonzero().reshape(-1) + pos_labels = labels[pos_label_inds] + + # if no positive samples, return the original targets + num_pos = float(pos_label_inds.size(0)) + if num_pos == 0: + return labels, label_weights, bbox_targets, bbox_weights + + # merge pos_assigned_gt_inds of per image to a single tensor + gts = list() + last_max_gt = 0 + for i in range(len(sampling_results)): + gt_i = sampling_results[i].pos_assigned_gt_inds + gts.append(gt_i + last_max_gt) + if len(gt_i) != 0: + last_max_gt = gt_i.max() + 1 + gts = torch.cat(gts) + assert len(gts) == num_pos + + cls_score = cls_score.detach() + bbox_pred = bbox_pred.detach() + + # For single stage detectors, rois here indicate anchors, in shape (N, 4) + # For two stage detectors, rois are in shape (N, 5) + if rois.size(-1) == 5: + pos_rois = rois[pos_label_inds][:, 1:] + else: + pos_rois = rois[pos_label_inds] + + if bbox_pred.size(-1) > 4: + bbox_pred = bbox_pred.view(bbox_pred.size(0), -1, 4) + pos_delta_pred = bbox_pred[pos_label_inds, pos_labels].view(-1, 4) + else: + pos_delta_pred = bbox_pred[pos_label_inds].view(-1, 4) + + # compute iou of the predicted bbox and the corresponding GT + pos_delta_target = bbox_targets[pos_label_inds].view(-1, 4) + pos_bbox_pred = bbox_coder.decode(pos_rois, pos_delta_pred) + target_bbox_pred = bbox_coder.decode(pos_rois, pos_delta_target) + ious = bbox_overlaps(pos_bbox_pred, target_bbox_pred, is_aligned=True) + + pos_imp_weights = label_weights[pos_label_inds] + # Two steps to compute IoU-HLR. Samples are first sorted by IoU locally, + # then sorted again within the same-rank group + max_l_num = pos_labels.bincount().max() + for label in pos_labels.unique(): + l_inds = (pos_labels == label).nonzero().view(-1) + l_gts = gts[l_inds] + for t in l_gts.unique(): + t_inds = l_inds[l_gts == t] + t_ious = ious[t_inds] + _, t_iou_rank_idx = t_ious.sort(descending=True) + _, t_iou_rank = t_iou_rank_idx.sort() + ious[t_inds] += max_l_num - t_iou_rank.float() + l_ious = ious[l_inds] + _, l_iou_rank_idx = l_ious.sort(descending=True) + _, l_iou_rank = l_iou_rank_idx.sort() # IoU-HLR + # linearly map HLR to label weights + pos_imp_weights[l_inds] *= (max_l_num - l_iou_rank.float()) / max_l_num + + pos_imp_weights = (bias + pos_imp_weights * (1 - bias)).pow(k) + + # normalize to make the new weighted loss value equal to the original loss + pos_loss_cls = loss_cls( + cls_score[pos_label_inds], pos_labels, reduction_override='none') + if pos_loss_cls.dim() > 1: + ori_pos_loss_cls = pos_loss_cls * label_weights[pos_label_inds][:, + None] + new_pos_loss_cls = pos_loss_cls * pos_imp_weights[:, None] + else: + ori_pos_loss_cls = pos_loss_cls * label_weights[pos_label_inds] + new_pos_loss_cls = pos_loss_cls * pos_imp_weights + pos_loss_cls_ratio = ori_pos_loss_cls.sum() / new_pos_loss_cls.sum() + pos_imp_weights = pos_imp_weights * pos_loss_cls_ratio + label_weights[pos_label_inds] = pos_imp_weights + + bbox_targets = labels, label_weights, bbox_targets, bbox_weights + return bbox_targets + + +@mmcv.jit(derivate=True, coderize=True) +def carl_loss(cls_score, + labels, + bbox_pred, + bbox_targets, + loss_bbox, + k=1, + bias=0.2, + avg_factor=None, + sigmoid=False, + num_class=80): + """Classification-Aware Regression Loss (CARL). + + Args: + cls_score (Tensor): Predicted classification scores. + labels (Tensor): Targets of classification. + bbox_pred (Tensor): Predicted bbox deltas. + bbox_targets (Tensor): Target of bbox regression. + loss_bbox (func): Regression loss func of the head. + bbox_coder (obj): BBox coder of the head. + k (float): Power of the non-linear mapping. + bias (float): Shift of the non-linear mapping. + avg_factor (int): Average factor used in regression loss. + sigmoid (bool): Activation of the classification score. + num_class (int): Number of classes, default: 80. + + Return: + dict: CARL loss dict. + """ + pos_label_inds = ((labels >= 0) & + (labels < num_class)).nonzero().reshape(-1) + if pos_label_inds.numel() == 0: + return dict(loss_carl=cls_score.sum()[None] * 0.) + pos_labels = labels[pos_label_inds] + + # multiply pos_cls_score with the corresponding bbox weight + # and remain gradient + if sigmoid: + pos_cls_score = cls_score.sigmoid()[pos_label_inds, pos_labels] + else: + pos_cls_score = cls_score.softmax(-1)[pos_label_inds, pos_labels] + carl_loss_weights = (bias + (1 - bias) * pos_cls_score).pow(k) + + # normalize carl_loss_weight to make its sum equal to num positive + num_pos = float(pos_cls_score.size(0)) + weight_ratio = num_pos / carl_loss_weights.sum() + carl_loss_weights *= weight_ratio + + if avg_factor is None: + avg_factor = bbox_targets.size(0) + # if is class agnostic, bbox pred is in shape (N, 4) + # otherwise, bbox pred is in shape (N, #classes, 4) + if bbox_pred.size(-1) > 4: + bbox_pred = bbox_pred.view(bbox_pred.size(0), -1, 4) + pos_bbox_preds = bbox_pred[pos_label_inds, pos_labels] + else: + pos_bbox_preds = bbox_pred[pos_label_inds] + ori_loss_reg = loss_bbox( + pos_bbox_preds, + bbox_targets[pos_label_inds], + reduction_override='none') / avg_factor + loss_carl = (ori_loss_reg * carl_loss_weights[:, None]).sum() + return dict(loss_carl=loss_carl[None]) diff --git a/mmdet/models/losses/smooth_l1_loss.py b/mmdet/models/losses/smooth_l1_loss.py new file mode 100644 index 0000000..ec9c98a --- /dev/null +++ b/mmdet/models/losses/smooth_l1_loss.py @@ -0,0 +1,139 @@ +import mmcv +import torch +import torch.nn as nn + +from ..builder import LOSSES +from .utils import weighted_loss + + +@mmcv.jit(derivate=True, coderize=True) +@weighted_loss +def smooth_l1_loss(pred, target, beta=1.0): + """Smooth L1 loss. + + Args: + pred (torch.Tensor): The prediction. + target (torch.Tensor): The learning target of the prediction. + beta (float, optional): The threshold in the piecewise function. + Defaults to 1.0. + + Returns: + torch.Tensor: Calculated loss + """ + assert beta > 0 + assert pred.size() == target.size() and target.numel() > 0 + diff = torch.abs(pred - target) + loss = torch.where(diff < beta, 0.5 * diff * diff / beta, + diff - 0.5 * beta) + return loss + + +@mmcv.jit(derivate=True, coderize=True) +@weighted_loss +def l1_loss(pred, target): + """L1 loss. + + Args: + pred (torch.Tensor): The prediction. + target (torch.Tensor): The learning target of the prediction. + + Returns: + torch.Tensor: Calculated loss + """ + assert pred.size() == target.size() and target.numel() > 0 + loss = torch.abs(pred - target) + return loss + + +@LOSSES.register_module() +class SmoothL1Loss(nn.Module): + """Smooth L1 loss. + + Args: + beta (float, optional): The threshold in the piecewise function. + Defaults to 1.0. + reduction (str, optional): The method to reduce the loss. + Options are "none", "mean" and "sum". Defaults to "mean". + loss_weight (float, optional): The weight of loss. + """ + + def __init__(self, beta=1.0, reduction='mean', loss_weight=1.0): + super(SmoothL1Loss, self).__init__() + self.beta = beta + self.reduction = reduction + self.loss_weight = loss_weight + + def forward(self, + pred, + target, + weight=None, + avg_factor=None, + reduction_override=None, + **kwargs): + """Forward function. + + Args: + pred (torch.Tensor): The prediction. + target (torch.Tensor): The learning target of the prediction. + weight (torch.Tensor, optional): The weight of loss for each + prediction. Defaults to None. + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + reduction_override (str, optional): The reduction method used to + override the original reduction method of the loss. + Defaults to None. + """ + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + loss_bbox = self.loss_weight * smooth_l1_loss( + pred, + target, + weight, + beta=self.beta, + reduction=reduction, + avg_factor=avg_factor, + **kwargs) + return loss_bbox + + +@LOSSES.register_module() +class L1Loss(nn.Module): + """L1 loss. + + Args: + reduction (str, optional): The method to reduce the loss. + Options are "none", "mean" and "sum". + loss_weight (float, optional): The weight of loss. + """ + + def __init__(self, reduction='mean', loss_weight=1.0): + super(L1Loss, self).__init__() + self.reduction = reduction + self.loss_weight = loss_weight + + def forward(self, + pred, + target, + weight=None, + avg_factor=None, + reduction_override=None): + """Forward function. + + Args: + pred (torch.Tensor): The prediction. + target (torch.Tensor): The learning target of the prediction. + weight (torch.Tensor, optional): The weight of loss for each + prediction. Defaults to None. + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + reduction_override (str, optional): The reduction method used to + override the original reduction method of the loss. + Defaults to None. + """ + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + loss_bbox = self.loss_weight * l1_loss( + pred, target, weight, reduction=reduction, avg_factor=avg_factor) + return loss_bbox diff --git a/mmdet/models/losses/utils.py b/mmdet/models/losses/utils.py new file mode 100644 index 0000000..4756d7f --- /dev/null +++ b/mmdet/models/losses/utils.py @@ -0,0 +1,100 @@ +import functools + +import mmcv +import torch.nn.functional as F + + +def reduce_loss(loss, reduction): + """Reduce loss as specified. + + Args: + loss (Tensor): Elementwise loss tensor. + reduction (str): Options are "none", "mean" and "sum". + + Return: + Tensor: Reduced loss tensor. + """ + reduction_enum = F._Reduction.get_enum(reduction) + # none: 0, elementwise_mean:1, sum: 2 + if reduction_enum == 0: + return loss + elif reduction_enum == 1: + return loss.mean() + elif reduction_enum == 2: + return loss.sum() + + +@mmcv.jit(derivate=True, coderize=True) +def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None): + """Apply element-wise weight and reduce loss. + + Args: + loss (Tensor): Element-wise loss. + weight (Tensor): Element-wise weights. + reduction (str): Same as built-in losses of PyTorch. + avg_factor (float): Avarage factor when computing the mean of losses. + + Returns: + Tensor: Processed loss values. + """ + # if weight is specified, apply element-wise weight + if weight is not None: + loss = loss * weight + + # if avg_factor is not specified, just reduce the loss + if avg_factor is None: + loss = reduce_loss(loss, reduction) + else: + # if reduction is mean, then average the loss by avg_factor + if reduction == 'mean': + loss = loss.sum() / avg_factor + # if reduction is 'none', then do nothing, otherwise raise an error + elif reduction != 'none': + raise ValueError('avg_factor can not be used with reduction="sum"') + return loss + + +def weighted_loss(loss_func): + """Create a weighted version of a given loss function. + + To use this decorator, the loss function must have the signature like + `loss_func(pred, target, **kwargs)`. The function only needs to compute + element-wise loss without any reduction. This decorator will add weight + and reduction arguments to the function. The decorated function will have + the signature like `loss_func(pred, target, weight=None, reduction='mean', + avg_factor=None, **kwargs)`. + + :Example: + + >>> import torch + >>> @weighted_loss + >>> def l1_loss(pred, target): + >>> return (pred - target).abs() + + >>> pred = torch.Tensor([0, 2, 3]) + >>> target = torch.Tensor([1, 1, 1]) + >>> weight = torch.Tensor([1, 0, 1]) + + >>> l1_loss(pred, target) + tensor(1.3333) + >>> l1_loss(pred, target, weight) + tensor(1.) + >>> l1_loss(pred, target, reduction='none') + tensor([1., 1., 2.]) + >>> l1_loss(pred, target, weight, avg_factor=2) + tensor(1.5000) + """ + + @functools.wraps(loss_func) + def wrapper(pred, + target, + weight=None, + reduction='mean', + avg_factor=None, + **kwargs): + # get element-wise loss + loss = loss_func(pred, target, **kwargs) + loss = weight_reduce_loss(loss, weight, reduction, avg_factor) + return loss + + return wrapper diff --git a/mmdet/models/losses/varifocal_loss.py b/mmdet/models/losses/varifocal_loss.py new file mode 100644 index 0000000..7f00bd6 --- /dev/null +++ b/mmdet/models/losses/varifocal_loss.py @@ -0,0 +1,133 @@ +import mmcv +import torch.nn as nn +import torch.nn.functional as F + +from ..builder import LOSSES +from .utils import weight_reduce_loss + + +@mmcv.jit(derivate=True, coderize=True) +def varifocal_loss(pred, + target, + weight=None, + alpha=0.75, + gamma=2.0, + iou_weighted=True, + reduction='mean', + avg_factor=None): + """`Varifocal Loss `_ + + Args: + pred (torch.Tensor): The prediction with shape (N, C), C is the + number of classes + target (torch.Tensor): The learning target of the iou-aware + classification score with shape (N, C), C is the number of classes. + weight (torch.Tensor, optional): The weight of loss for each + prediction. Defaults to None. + alpha (float, optional): A balance factor for the negative part of + Varifocal Loss, which is different from the alpha of Focal Loss. + Defaults to 0.75. + gamma (float, optional): The gamma for calculating the modulating + factor. Defaults to 2.0. + iou_weighted (bool, optional): Whether to weight the loss of the + positive example with the iou target. Defaults to True. + reduction (str, optional): The method used to reduce the loss into + a scalar. Defaults to 'mean'. Options are "none", "mean" and + "sum". + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + """ + # pred and target should be of the same size + assert pred.size() == target.size() + pred_sigmoid = pred.sigmoid() + target = target.type_as(pred) + if iou_weighted: + focal_weight = target * (target > 0.0).float() + \ + alpha * (pred_sigmoid - target).abs().pow(gamma) * \ + (target <= 0.0).float() + else: + focal_weight = (target > 0.0).float() + \ + alpha * (pred_sigmoid - target).abs().pow(gamma) * \ + (target <= 0.0).float() + loss = F.binary_cross_entropy_with_logits( + pred, target, reduction='none') * focal_weight + loss = weight_reduce_loss(loss, weight, reduction, avg_factor) + return loss + + +@LOSSES.register_module() +class VarifocalLoss(nn.Module): + + def __init__(self, + use_sigmoid=True, + alpha=0.75, + gamma=2.0, + iou_weighted=True, + reduction='mean', + loss_weight=1.0): + """`Varifocal Loss `_ + + Args: + use_sigmoid (bool, optional): Whether the prediction is + used for sigmoid or softmax. Defaults to True. + alpha (float, optional): A balance factor for the negative part of + Varifocal Loss, which is different from the alpha of Focal + Loss. Defaults to 0.75. + gamma (float, optional): The gamma for calculating the modulating + factor. Defaults to 2.0. + iou_weighted (bool, optional): Whether to weight the loss of the + positive examples with the iou target. Defaults to True. + reduction (str, optional): The method used to reduce the loss into + a scalar. Defaults to 'mean'. Options are "none", "mean" and + "sum". + loss_weight (float, optional): Weight of loss. Defaults to 1.0. + """ + super(VarifocalLoss, self).__init__() + assert use_sigmoid is True, \ + 'Only sigmoid varifocal loss supported now.' + assert alpha >= 0.0 + self.use_sigmoid = use_sigmoid + self.alpha = alpha + self.gamma = gamma + self.iou_weighted = iou_weighted + self.reduction = reduction + self.loss_weight = loss_weight + + def forward(self, + pred, + target, + weight=None, + avg_factor=None, + reduction_override=None): + """Forward function. + + Args: + pred (torch.Tensor): The prediction. + target (torch.Tensor): The learning target of the prediction. + weight (torch.Tensor, optional): The weight of loss for each + prediction. Defaults to None. + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + reduction_override (str, optional): The reduction method used to + override the original reduction method of the loss. + Options are "none", "mean" and "sum". + + Returns: + torch.Tensor: The calculated loss + """ + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + if self.use_sigmoid: + loss_cls = self.loss_weight * varifocal_loss( + pred, + target, + weight, + alpha=self.alpha, + gamma=self.gamma, + iou_weighted=self.iou_weighted, + reduction=reduction, + avg_factor=avg_factor) + else: + raise NotImplementedError + return loss_cls diff --git a/mmdet/models/necks/__init__.py b/mmdet/models/necks/__init__.py new file mode 100644 index 0000000..31c2bf5 --- /dev/null +++ b/mmdet/models/necks/__init__.py @@ -0,0 +1,18 @@ +from .bfp import BFP +from .channel_mapper import ChannelMapper +from .dilated_encoder import DilatedEncoder +from .fpg import FPG +from .fpn import FPN +from .identity_fpn import ChannelMapping +from .fpn_carafe import FPN_CARAFE +from .hrfpn import HRFPN +from .nas_fpn import NASFPN +from .nasfcos_fpn import NASFCOS_FPN +from .pafpn import PAFPN +from .rfp import RFP +from .yolo_neck import YOLOV3Neck + +__all__ = [ + 'FPN', 'BFP', 'ChannelMapper', 'HRFPN', 'NASFPN', 'FPN_CARAFE', 'PAFPN', + 'NASFCOS_FPN', 'RFP', 'YOLOV3Neck', 'FPG', 'DilatedEncoder', 'ChannelMapping' +] diff --git a/mmdet/models/necks/__pycache__/__init__.cpython-37.pyc b/mmdet/models/necks/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..cff817d Binary files /dev/null and b/mmdet/models/necks/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/models/necks/__pycache__/bfp.cpython-37.pyc b/mmdet/models/necks/__pycache__/bfp.cpython-37.pyc new file mode 100644 index 0000000..59c099f Binary files /dev/null and b/mmdet/models/necks/__pycache__/bfp.cpython-37.pyc differ diff --git a/mmdet/models/necks/__pycache__/channel_mapper.cpython-37.pyc 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This module is used in Libra R-CNN (CVPR 2019), see + the paper `Libra R-CNN: Towards Balanced Learning for Object Detection + `_ for details. + + Args: + in_channels (int): Number of input channels (feature maps of all levels + should have the same channels). + num_levels (int): Number of input feature levels. + conv_cfg (dict): The config dict for convolution layers. + norm_cfg (dict): The config dict for normalization layers. + refine_level (int): Index of integration and refine level of BSF in + multi-level features from bottom to top. + refine_type (str): Type of the refine op, currently support + [None, 'conv', 'non_local']. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + in_channels, + num_levels, + refine_level=2, + refine_type=None, + conv_cfg=None, + norm_cfg=None, + init_cfg=dict( + type='Xavier', layer='Conv2d', distribution='uniform')): + super(BFP, self).__init__(init_cfg) + assert refine_type in [None, 'conv', 'non_local'] + + self.in_channels = in_channels + self.num_levels = num_levels + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + + self.refine_level = refine_level + self.refine_type = refine_type + assert 0 <= self.refine_level < self.num_levels + + if self.refine_type == 'conv': + self.refine = ConvModule( + self.in_channels, + self.in_channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg) + elif self.refine_type == 'non_local': + self.refine = NonLocal2d( + self.in_channels, + reduction=1, + use_scale=False, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg) + + def forward(self, inputs): + """Forward function.""" + assert len(inputs) == self.num_levels + + # step 1: gather multi-level features by resize and average + feats = [] + gather_size = inputs[self.refine_level].size()[2:] + for i in range(self.num_levels): + if i < self.refine_level: + gathered = F.adaptive_max_pool2d( + inputs[i], output_size=gather_size) + else: + gathered = F.interpolate( + inputs[i], size=gather_size, mode='nearest') + feats.append(gathered) + + bsf = sum(feats) / len(feats) + + # step 2: refine gathered features + if self.refine_type is not None: + bsf = self.refine(bsf) + + # step 3: scatter refined features to multi-levels by a residual path + outs = [] + for i in range(self.num_levels): + out_size = inputs[i].size()[2:] + if i < self.refine_level: + residual = F.interpolate(bsf, size=out_size, mode='nearest') + else: + residual = F.adaptive_max_pool2d(bsf, output_size=out_size) + outs.append(residual + inputs[i]) + + return tuple(outs) diff --git a/mmdet/models/necks/channel_mapper.py b/mmdet/models/necks/channel_mapper.py new file mode 100644 index 0000000..673bb16 --- /dev/null +++ b/mmdet/models/necks/channel_mapper.py @@ -0,0 +1,99 @@ +import torch.nn as nn +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule + +from ..builder import NECKS + + +@NECKS.register_module() +class ChannelMapper(BaseModule): + r"""Channel Mapper to reduce/increase channels of backbone features. + + This is used to reduce/increase channels of backbone features. + + Args: + in_channels (List[int]): Number of input channels per scale. + out_channels (int): Number of output channels (used at each scale). + kernel_size (int, optional): kernel_size for reducing channels (used + at each scale). Default: 3. + conv_cfg (dict, optional): Config dict for convolution layer. + Default: None. + norm_cfg (dict, optional): Config dict for normalization layer. + Default: None. + act_cfg (dict, optional): Config dict for activation layer in + ConvModule. Default: dict(type='ReLU'). + num_outs (int, optional): Number of output feature maps. There + would be extra_convs when num_outs larger than the lenghth + of in_channels. + init_cfg (dict or list[dict], optional): Initialization config dict. + Example: + >>> import torch + >>> in_channels = [2, 3, 5, 7] + >>> scales = [340, 170, 84, 43] + >>> inputs = [torch.rand(1, c, s, s) + ... for c, s in zip(in_channels, scales)] + >>> self = ChannelMapper(in_channels, 11, 3).eval() + >>> outputs = self.forward(inputs) + >>> for i in range(len(outputs)): + ... print(f'outputs[{i}].shape = {outputs[i].shape}') + outputs[0].shape = torch.Size([1, 11, 340, 340]) + outputs[1].shape = torch.Size([1, 11, 170, 170]) + outputs[2].shape = torch.Size([1, 11, 84, 84]) + outputs[3].shape = torch.Size([1, 11, 43, 43]) + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size=3, + conv_cfg=None, + norm_cfg=None, + act_cfg=dict(type='ReLU'), + num_outs=None, + init_cfg=dict( + type='Xavier', layer='Conv2d', distribution='uniform')): + super(ChannelMapper, self).__init__(init_cfg) + assert isinstance(in_channels, list) + self.extra_convs = None + if num_outs is None: + num_outs = len(in_channels) + self.convs = nn.ModuleList() + for in_channel in in_channels: + self.convs.append( + ConvModule( + in_channel, + out_channels, + kernel_size, + padding=(kernel_size - 1) // 2, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg)) + if num_outs > len(in_channels): + self.extra_convs = nn.ModuleList() + for i in range(len(in_channels), num_outs): + if i == len(in_channels): + in_channel = in_channels[-1] + else: + in_channel = out_channels + self.extra_convs.append( + ConvModule( + in_channel, + out_channels, + 3, + stride=2, + padding=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg)) + + def forward(self, inputs): + """Forward function.""" + assert len(inputs) == len(self.convs) + outs = [self.convs[i](inputs[i]) for i in range(len(inputs))] + if self.extra_convs: + for i in range(len(self.extra_convs)): + if i == 0: + outs.append(self.extra_convs[0](inputs[-1])) + else: + outs.append(self.extra_convs[i](outs[-1])) + return tuple(outs) diff --git a/mmdet/models/necks/dilated_encoder.py b/mmdet/models/necks/dilated_encoder.py new file mode 100644 index 0000000..e97d5cc --- /dev/null +++ b/mmdet/models/necks/dilated_encoder.py @@ -0,0 +1,107 @@ +import torch.nn as nn +from mmcv.cnn import (ConvModule, caffe2_xavier_init, constant_init, is_norm, + normal_init) +from torch.nn import BatchNorm2d + +from ..builder import NECKS + + +class Bottleneck(nn.Module): + """Bottleneck block for DilatedEncoder used in `YOLOF. + + `. + + The Bottleneck contains three ConvLayers and one residual connection. + + Args: + in_channels (int): The number of input channels. + mid_channels (int): The number of middle output channels. + dilation (int): Dilation rate. + norm_cfg (dict): Dictionary to construct and config norm layer. + """ + + def __init__(self, + in_channels, + mid_channels, + dilation, + norm_cfg=dict(type='BN', requires_grad=True)): + super(Bottleneck, self).__init__() + self.conv1 = ConvModule( + in_channels, mid_channels, 1, norm_cfg=norm_cfg) + self.conv2 = ConvModule( + mid_channels, + mid_channels, + 3, + padding=dilation, + dilation=dilation, + norm_cfg=norm_cfg) + self.conv3 = ConvModule( + mid_channels, in_channels, 1, norm_cfg=norm_cfg) + + def forward(self, x): + identity = x + out = self.conv1(x) + out = self.conv2(out) + out = self.conv3(out) + out = out + identity + return out + + +@NECKS.register_module() +class DilatedEncoder(nn.Module): + """Dilated Encoder for YOLOF `. + + This module contains two types of components: + - the original FPN lateral convolution layer and fpn convolution layer, + which are 1x1 conv + 3x3 conv + - the dilated residual block + + Args: + in_channels (int): The number of input channels. + out_channels (int): The number of output channels. + block_mid_channels (int): The number of middle block output channels + num_residual_blocks (int): The number of residual blocks. + """ + + def __init__(self, in_channels, out_channels, block_mid_channels, + num_residual_blocks): + super(DilatedEncoder, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.block_mid_channels = block_mid_channels + self.num_residual_blocks = num_residual_blocks + self.block_dilations = [2, 4, 6, 8] + self._init_layers() + + def _init_layers(self): + self.lateral_conv = nn.Conv2d( + self.in_channels, self.out_channels, kernel_size=1) + self.lateral_norm = BatchNorm2d(self.out_channels) + self.fpn_conv = nn.Conv2d( + self.out_channels, self.out_channels, kernel_size=3, padding=1) + self.fpn_norm = BatchNorm2d(self.out_channels) + encoder_blocks = [] + for i in range(self.num_residual_blocks): + dilation = self.block_dilations[i] + encoder_blocks.append( + Bottleneck( + self.out_channels, + self.block_mid_channels, + dilation=dilation)) + self.dilated_encoder_blocks = nn.Sequential(*encoder_blocks) + + def init_weights(self): + caffe2_xavier_init(self.lateral_conv) + caffe2_xavier_init(self.fpn_conv) + for m in [self.lateral_norm, self.fpn_norm]: + constant_init(m, 1) + for m in self.dilated_encoder_blocks.modules(): + if isinstance(m, nn.Conv2d): + normal_init(m, mean=0, std=0.01) + if is_norm(m): + constant_init(m, 1) + + def forward(self, feature): + out = self.lateral_norm(self.lateral_conv(feature[-1])) + out = self.fpn_norm(self.fpn_conv(out)) + return self.dilated_encoder_blocks(out), diff --git a/mmdet/models/necks/fpg.py b/mmdet/models/necks/fpg.py new file mode 100644 index 0000000..2b65dba --- /dev/null +++ b/mmdet/models/necks/fpg.py @@ -0,0 +1,405 @@ +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule + +from ..builder import NECKS + + +class Transition(BaseModule): + """Base class for transition. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + """ + + def __init__(self, in_channels, out_channels, init_cfg=None): + super().__init__(init_cfg) + self.in_channels = in_channels + self.out_channels = out_channels + + def forward(x): + pass + + +class UpInterpolationConv(Transition): + """A transition used for up-sampling. + + Up-sample the input by interpolation then refines the feature by + a convolution layer. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + scale_factor (int): Up-sampling factor. Default: 2. + mode (int): Interpolation mode. Default: nearest. + align_corners (bool): Whether align corners when interpolation. + Default: None. + kernel_size (int): Kernel size for the conv. Default: 3. + """ + + def __init__(self, + in_channels, + out_channels, + scale_factor=2, + mode='nearest', + align_corners=None, + kernel_size=3, + init_cfg=None, + **kwargs): + super().__init__(in_channels, out_channels, init_cfg) + self.mode = mode + self.scale_factor = scale_factor + self.align_corners = align_corners + self.conv = ConvModule( + in_channels, + out_channels, + kernel_size, + padding=(kernel_size - 1) // 2, + **kwargs) + + def forward(self, x): + x = F.interpolate( + x, + scale_factor=self.scale_factor, + mode=self.mode, + align_corners=self.align_corners) + x = self.conv(x) + return x + + +class LastConv(Transition): + """A transition used for refining the output of the last stage. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + num_inputs (int): Number of inputs of the FPN features. + kernel_size (int): Kernel size for the conv. Default: 3. + """ + + def __init__(self, + in_channels, + out_channels, + num_inputs, + kernel_size=3, + init_cfg=None, + **kwargs): + super().__init__(in_channels, out_channels, init_cfg) + self.num_inputs = num_inputs + self.conv_out = ConvModule( + in_channels, + out_channels, + kernel_size, + padding=(kernel_size - 1) // 2, + **kwargs) + + def forward(self, inputs): + assert len(inputs) == self.num_inputs + return self.conv_out(inputs[-1]) + + +@NECKS.register_module() +class FPG(BaseModule): + """FPG. + + Implementation of `Feature Pyramid Grids (FPG) + `_. + This implementation only gives the basic structure stated in the paper. + But users can implement different type of transitions to fully explore the + the potential power of the structure of FPG. + + Args: + in_channels (int): Number of input channels (feature maps of all levels + should have the same channels). + out_channels (int): Number of output channels (used at each scale) + num_outs (int): Number of output scales. + stack_times (int): The number of times the pyramid architecture will + be stacked. + paths (list[str]): Specify the path order of each stack level. + Each element in the list should be either 'bu' (bottom-up) or + 'td' (top-down). + inter_channels (int): Number of inter channels. + same_up_trans (dict): Transition that goes down at the same stage. + same_down_trans (dict): Transition that goes up at the same stage. + across_lateral_trans (dict): Across-pathway same-stage + across_down_trans (dict): Across-pathway bottom-up connection. + across_up_trans (dict): Across-pathway top-down connection. + across_skip_trans (dict): Across-pathway skip connection. + output_trans (dict): Transition that trans the output of the + last stage. + start_level (int): Index of the start input backbone level used to + build the feature pyramid. Default: 0. + end_level (int): Index of the end input backbone level (exclusive) to + build the feature pyramid. Default: -1, which means the last level. + add_extra_convs (bool): It decides whether to add conv + layers on top of the original feature maps. Default to False. + If True, its actual mode is specified by `extra_convs_on_inputs`. + norm_cfg (dict): Config dict for normalization layer. Default: None. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + transition_types = { + 'conv': ConvModule, + 'interpolation_conv': UpInterpolationConv, + 'last_conv': LastConv, + } + + def __init__(self, + in_channels, + out_channels, + num_outs, + stack_times, + paths, + inter_channels=None, + same_down_trans=None, + same_up_trans=dict( + type='conv', kernel_size=3, stride=2, padding=1), + across_lateral_trans=dict(type='conv', kernel_size=1), + across_down_trans=dict(type='conv', kernel_size=3), + across_up_trans=None, + across_skip_trans=dict(type='identity'), + output_trans=dict(type='last_conv', kernel_size=3), + start_level=0, + end_level=-1, + add_extra_convs=False, + norm_cfg=None, + skip_inds=None, + init_cfg=[ + dict(type='Caffe2Xavier', layer='Conv2d'), + dict( + type='Constant', + layer=[ + '_BatchNorm', '_InstanceNorm', 'GroupNorm', + 'LayerNorm' + ], + val=1.0) + ]): + super(FPG, self).__init__(init_cfg) + assert isinstance(in_channels, list) + self.in_channels = in_channels + self.out_channels = out_channels + self.num_ins = len(in_channels) + self.num_outs = num_outs + if inter_channels is None: + self.inter_channels = [out_channels for _ in range(num_outs)] + elif isinstance(inter_channels, int): + self.inter_channels = [inter_channels for _ in range(num_outs)] + else: + assert isinstance(inter_channels, list) + assert len(inter_channels) == num_outs + self.inter_channels = inter_channels + self.stack_times = stack_times + self.paths = paths + assert isinstance(paths, list) and len(paths) == stack_times + for d in paths: + assert d in ('bu', 'td') + + self.same_down_trans = same_down_trans + self.same_up_trans = same_up_trans + self.across_lateral_trans = across_lateral_trans + self.across_down_trans = across_down_trans + self.across_up_trans = across_up_trans + self.output_trans = output_trans + self.across_skip_trans = across_skip_trans + + self.with_bias = norm_cfg is None + # skip inds must be specified if across skip trans is not None + if self.across_skip_trans is not None: + skip_inds is not None + self.skip_inds = skip_inds + assert len(self.skip_inds[0]) <= self.stack_times + + if end_level == -1: + self.backbone_end_level = self.num_ins + assert num_outs >= self.num_ins - start_level + else: + # if end_level < inputs, no extra level is allowed + self.backbone_end_level = end_level + assert end_level <= len(in_channels) + assert num_outs == end_level - start_level + self.start_level = start_level + self.end_level = end_level + self.add_extra_convs = add_extra_convs + + # build lateral 1x1 convs to reduce channels + self.lateral_convs = nn.ModuleList() + for i in range(self.start_level, self.backbone_end_level): + l_conv = nn.Conv2d(self.in_channels[i], + self.inter_channels[i - self.start_level], 1) + self.lateral_convs.append(l_conv) + + extra_levels = num_outs - self.backbone_end_level + self.start_level + self.extra_downsamples = nn.ModuleList() + for i in range(extra_levels): + if self.add_extra_convs: + fpn_idx = self.backbone_end_level - self.start_level + i + extra_conv = nn.Conv2d( + self.inter_channels[fpn_idx - 1], + self.inter_channels[fpn_idx], + 3, + stride=2, + padding=1) + self.extra_downsamples.append(extra_conv) + else: + self.extra_downsamples.append(nn.MaxPool2d(1, stride=2)) + + self.fpn_transitions = nn.ModuleList() # stack times + for s in range(self.stack_times): + stage_trans = nn.ModuleList() # num of feature levels + for i in range(self.num_outs): + # same, across_lateral, across_down, across_up + trans = nn.ModuleDict() + if s in self.skip_inds[i]: + stage_trans.append(trans) + continue + # build same-stage down trans (used in bottom-up paths) + if i == 0 or self.same_up_trans is None: + same_up_trans = None + else: + same_up_trans = self.build_trans( + self.same_up_trans, self.inter_channels[i - 1], + self.inter_channels[i]) + trans['same_up'] = same_up_trans + # build same-stage up trans (used in top-down paths) + if i == self.num_outs - 1 or self.same_down_trans is None: + same_down_trans = None + else: + same_down_trans = self.build_trans( + self.same_down_trans, self.inter_channels[i + 1], + self.inter_channels[i]) + trans['same_down'] = same_down_trans + # build across lateral trans + across_lateral_trans = self.build_trans( + self.across_lateral_trans, self.inter_channels[i], + self.inter_channels[i]) + trans['across_lateral'] = across_lateral_trans + # build across down trans + if i == self.num_outs - 1 or self.across_down_trans is None: + across_down_trans = None + else: + across_down_trans = self.build_trans( + self.across_down_trans, self.inter_channels[i + 1], + self.inter_channels[i]) + trans['across_down'] = across_down_trans + # build across up trans + if i == 0 or self.across_up_trans is None: + across_up_trans = None + else: + across_up_trans = self.build_trans( + self.across_up_trans, self.inter_channels[i - 1], + self.inter_channels[i]) + trans['across_up'] = across_up_trans + if self.across_skip_trans is None: + across_skip_trans = None + else: + across_skip_trans = self.build_trans( + self.across_skip_trans, self.inter_channels[i - 1], + self.inter_channels[i]) + trans['across_skip'] = across_skip_trans + # build across_skip trans + stage_trans.append(trans) + self.fpn_transitions.append(stage_trans) + + self.output_transition = nn.ModuleList() # output levels + for i in range(self.num_outs): + trans = self.build_trans( + self.output_trans, + self.inter_channels[i], + self.out_channels, + num_inputs=self.stack_times + 1) + self.output_transition.append(trans) + + self.relu = nn.ReLU(inplace=True) + + def build_trans(self, cfg, in_channels, out_channels, **extra_args): + cfg_ = cfg.copy() + trans_type = cfg_.pop('type') + trans_cls = self.transition_types[trans_type] + return trans_cls(in_channels, out_channels, **cfg_, **extra_args) + + def fuse(self, fuse_dict): + out = None + for item in fuse_dict.values(): + if item is not None: + if out is None: + out = item + else: + out = out + item + return out + + def forward(self, inputs): + assert len(inputs) == len(self.in_channels) + + # build all levels from original feature maps + feats = [ + lateral_conv(inputs[i + self.start_level]) + for i, lateral_conv in enumerate(self.lateral_convs) + ] + for downsample in self.extra_downsamples: + feats.append(downsample(feats[-1])) + + outs = [feats] + + for i in range(self.stack_times): + current_outs = outs[-1] + next_outs = [] + direction = self.paths[i] + for j in range(self.num_outs): + if i in self.skip_inds[j]: + next_outs.append(outs[-1][j]) + continue + # feature level + if direction == 'td': + lvl = self.num_outs - j - 1 + else: + lvl = j + # get transitions + if direction == 'td': + same_trans = self.fpn_transitions[i][lvl]['same_down'] + else: + same_trans = self.fpn_transitions[i][lvl]['same_up'] + across_lateral_trans = self.fpn_transitions[i][lvl][ + 'across_lateral'] + across_down_trans = self.fpn_transitions[i][lvl]['across_down'] + across_up_trans = self.fpn_transitions[i][lvl]['across_up'] + across_skip_trans = self.fpn_transitions[i][lvl]['across_skip'] + # init output + to_fuse = dict( + same=None, lateral=None, across_up=None, across_down=None) + # same downsample/upsample + if same_trans is not None: + to_fuse['same'] = same_trans(next_outs[-1]) + # across lateral + if across_lateral_trans is not None: + to_fuse['lateral'] = across_lateral_trans( + current_outs[lvl]) + # across downsample + if lvl > 0 and across_up_trans is not None: + to_fuse['across_up'] = across_up_trans(current_outs[lvl - + 1]) + # across upsample + if (lvl < self.num_outs - 1 and across_down_trans is not None): + to_fuse['across_down'] = across_down_trans( + current_outs[lvl + 1]) + if across_skip_trans is not None: + to_fuse['across_skip'] = across_skip_trans(outs[0][lvl]) + x = self.fuse(to_fuse) + next_outs.append(x) + + if direction == 'td': + outs.append(next_outs[::-1]) + else: + outs.append(next_outs) + + # output trans + final_outs = [] + for i in range(self.num_outs): + lvl_out_list = [] + for s in range(len(outs)): + lvl_out_list.append(outs[s][i]) + lvl_out = self.output_transition[i](lvl_out_list) + final_outs.append(lvl_out) + + return final_outs diff --git a/mmdet/models/necks/fpn.py b/mmdet/models/necks/fpn.py new file mode 100644 index 0000000..ad637fd --- /dev/null +++ b/mmdet/models/necks/fpn.py @@ -0,0 +1,217 @@ +import warnings + +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule, auto_fp16 + +from ..builder import NECKS + + +@NECKS.register_module() +class FPN(BaseModule): + r"""Feature Pyramid Network. + + This is an implementation of paper `Feature Pyramid Networks for Object + Detection `_. + + Args: + in_channels (List[int]): Number of input channels per scale. + out_channels (int): Number of output channels (used at each scale) + num_outs (int): Number of output scales. + start_level (int): Index of the start input backbone level used to + build the feature pyramid. Default: 0. + end_level (int): Index of the end input backbone level (exclusive) to + build the feature pyramid. Default: -1, which means the last level. + add_extra_convs (bool | str): If bool, it decides whether to add conv + layers on top of the original feature maps. Default to False. + If True, its actual mode is specified by `extra_convs_on_inputs`. + If str, it specifies the source feature map of the extra convs. + Only the following options are allowed + + - 'on_input': Last feat map of neck inputs (i.e. backbone feature). + - 'on_lateral': Last feature map after lateral convs. + - 'on_output': The last output feature map after fpn convs. + extra_convs_on_inputs (bool, deprecated): Whether to apply extra convs + on the original feature from the backbone. If True, + it is equivalent to `add_extra_convs='on_input'`. If False, it is + equivalent to set `add_extra_convs='on_output'`. Default to True. + relu_before_extra_convs (bool): Whether to apply relu before the extra + conv. Default: False. + no_norm_on_lateral (bool): Whether to apply norm on lateral. + Default: False. + conv_cfg (dict): Config dict for convolution layer. Default: None. + norm_cfg (dict): Config dict for normalization layer. Default: None. + act_cfg (str): Config dict for activation layer in ConvModule. + Default: None. + upsample_cfg (dict): Config dict for interpolate layer. + Default: `dict(mode='nearest')` + init_cfg (dict or list[dict], optional): Initialization config dict. + + Example: + >>> import torch + >>> in_channels = [2, 3, 5, 7] + >>> scales = [340, 170, 84, 43] + >>> inputs = [torch.rand(1, c, s, s) + ... for c, s in zip(in_channels, scales)] + >>> self = FPN(in_channels, 11, len(in_channels)).eval() + >>> outputs = self.forward(inputs) + >>> for i in range(len(outputs)): + ... print(f'outputs[{i}].shape = {outputs[i].shape}') + outputs[0].shape = torch.Size([1, 11, 340, 340]) + outputs[1].shape = torch.Size([1, 11, 170, 170]) + outputs[2].shape = torch.Size([1, 11, 84, 84]) + outputs[3].shape = torch.Size([1, 11, 43, 43]) + """ + + def __init__(self, + in_channels, + out_channels, + num_outs, + start_level=0, + end_level=-1, + add_extra_convs=False, + extra_convs_on_inputs=True, + relu_before_extra_convs=False, + no_norm_on_lateral=False, + conv_cfg=None, + norm_cfg=None, + act_cfg=None, + upsample_cfg=dict(mode='nearest'), + init_cfg=dict( + type='Xavier', layer='Conv2d', distribution='uniform')): + super(FPN, self).__init__(init_cfg) + assert isinstance(in_channels, list) + self.in_channels = in_channels + self.out_channels = out_channels + self.num_ins = len(in_channels) + self.num_outs = num_outs + self.relu_before_extra_convs = relu_before_extra_convs + self.no_norm_on_lateral = no_norm_on_lateral + self.fp16_enabled = False + self.upsample_cfg = upsample_cfg.copy() + + if end_level == -1: + self.backbone_end_level = self.num_ins + assert num_outs >= self.num_ins - start_level + else: + # if end_level < inputs, no extra level is allowed + self.backbone_end_level = end_level + assert end_level <= len(in_channels) + assert num_outs == end_level - start_level + self.start_level = start_level + self.end_level = end_level + self.add_extra_convs = add_extra_convs + assert isinstance(add_extra_convs, (str, bool)) + if isinstance(add_extra_convs, str): + # Extra_convs_source choices: 'on_input', 'on_lateral', 'on_output' + assert add_extra_convs in ('on_input', 'on_lateral', 'on_output') + elif add_extra_convs: # True + if extra_convs_on_inputs: + # TODO: deprecate `extra_convs_on_inputs` + warnings.simplefilter('once') + warnings.warn( + '"extra_convs_on_inputs" will be deprecated in v2.9.0,' + 'Please use "add_extra_convs"', DeprecationWarning) + self.add_extra_convs = 'on_input' + else: + self.add_extra_convs = 'on_output' + + self.lateral_convs = nn.ModuleList() + self.fpn_convs = nn.ModuleList() + + for i in range(self.start_level, self.backbone_end_level): + l_conv = ConvModule( + in_channels[i], + out_channels, + 1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg if not self.no_norm_on_lateral else None, + act_cfg=act_cfg, + inplace=False) + fpn_conv = ConvModule( + out_channels, + out_channels, + 3, + padding=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg, + inplace=False) + + self.lateral_convs.append(l_conv) + self.fpn_convs.append(fpn_conv) + + # add extra conv layers (e.g., RetinaNet) + extra_levels = num_outs - self.backbone_end_level + self.start_level + if self.add_extra_convs and extra_levels >= 1: + for i in range(extra_levels): + if i == 0 and self.add_extra_convs == 'on_input': + in_channels = self.in_channels[self.backbone_end_level - 1] + else: + in_channels = out_channels + extra_fpn_conv = ConvModule( + in_channels, + out_channels, + 3, + stride=2, + padding=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg, + inplace=False) + self.fpn_convs.append(extra_fpn_conv) + + @auto_fp16() + def forward(self, inputs): + """Forward function.""" + assert len(inputs) == len(self.in_channels) + + # build laterals + laterals = [ + lateral_conv(inputs[i + self.start_level]) + for i, lateral_conv in enumerate(self.lateral_convs) + ] + + # build top-down path + used_backbone_levels = len(laterals) + for i in range(used_backbone_levels - 1, 0, -1): + # In some cases, fixing `scale factor` (e.g. 2) is preferred, but + # it cannot co-exist with `size` in `F.interpolate`. + if 'scale_factor' in self.upsample_cfg: + laterals[i - 1] += F.interpolate(laterals[i], + **self.upsample_cfg) + else: + prev_shape = laterals[i - 1].shape[2:] + laterals[i - 1] += F.interpolate( + laterals[i], size=prev_shape, **self.upsample_cfg) + + # build outputs + # part 1: from original levels + outs = [ + self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels) + ] + # part 2: add extra levels + if self.num_outs > len(outs): + # use max pool to get more levels on top of outputs + # (e.g., Faster R-CNN, Mask R-CNN) + if not self.add_extra_convs: + for i in range(self.num_outs - used_backbone_levels): + outs.append(F.max_pool2d(outs[-1], 1, stride=2)) + # add conv layers on top of original feature maps (RetinaNet) + else: + if self.add_extra_convs == 'on_input': + extra_source = inputs[self.backbone_end_level - 1] + elif self.add_extra_convs == 'on_lateral': + extra_source = laterals[-1] + elif self.add_extra_convs == 'on_output': + extra_source = outs[-1] + else: + raise NotImplementedError + outs.append(self.fpn_convs[used_backbone_levels](extra_source)) + for i in range(used_backbone_levels + 1, self.num_outs): + if self.relu_before_extra_convs: + outs.append(self.fpn_convs[i](F.relu(outs[-1]))) + else: + outs.append(self.fpn_convs[i](outs[-1])) + return tuple(outs) diff --git a/mmdet/models/necks/fpn_carafe.py b/mmdet/models/necks/fpn_carafe.py new file mode 100644 index 0000000..ccc78ec --- /dev/null +++ b/mmdet/models/necks/fpn_carafe.py @@ -0,0 +1,274 @@ +import torch.nn as nn +from mmcv.cnn import ConvModule, build_upsample_layer, xavier_init +from mmcv.ops.carafe import CARAFEPack +from mmcv.runner import BaseModule, ModuleList + +from ..builder import NECKS + + +@NECKS.register_module() +class FPN_CARAFE(BaseModule): + """FPN_CARAFE is a more flexible implementation of FPN. It allows more + choice for upsample methods during the top-down pathway. + + It can reproduce the performance of ICCV 2019 paper + CARAFE: Content-Aware ReAssembly of FEatures + Please refer to https://arxiv.org/abs/1905.02188 for more details. + + Args: + in_channels (list[int]): Number of channels for each input feature map. + out_channels (int): Output channels of feature pyramids. + num_outs (int): Number of output stages. + start_level (int): Start level of feature pyramids. + (Default: 0) + end_level (int): End level of feature pyramids. + (Default: -1 indicates the last level). + norm_cfg (dict): Dictionary to construct and config norm layer. + activate (str): Type of activation function in ConvModule + (Default: None indicates w/o activation). + order (dict): Order of components in ConvModule. + upsample (str): Type of upsample layer. + upsample_cfg (dict): Dictionary to construct and config upsample layer. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + in_channels, + out_channels, + num_outs, + start_level=0, + end_level=-1, + norm_cfg=None, + act_cfg=None, + order=('conv', 'norm', 'act'), + upsample_cfg=dict( + type='carafe', + up_kernel=5, + up_group=1, + encoder_kernel=3, + encoder_dilation=1), + init_cfg=None): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + super(FPN_CARAFE, self).__init__(init_cfg) + assert isinstance(in_channels, list) + self.in_channels = in_channels + self.out_channels = out_channels + self.num_ins = len(in_channels) + self.num_outs = num_outs + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + self.with_bias = norm_cfg is None + self.upsample_cfg = upsample_cfg.copy() + self.upsample = self.upsample_cfg.get('type') + self.relu = nn.ReLU(inplace=False) + + self.order = order + assert order in [('conv', 'norm', 'act'), ('act', 'conv', 'norm')] + + assert self.upsample in [ + 'nearest', 'bilinear', 'deconv', 'pixel_shuffle', 'carafe', None + ] + if self.upsample in ['deconv', 'pixel_shuffle']: + assert hasattr( + self.upsample_cfg, + 'upsample_kernel') and self.upsample_cfg.upsample_kernel > 0 + self.upsample_kernel = self.upsample_cfg.pop('upsample_kernel') + + if end_level == -1: + self.backbone_end_level = self.num_ins + assert num_outs >= self.num_ins - start_level + else: + # if end_level < inputs, no extra level is allowed + self.backbone_end_level = end_level + assert end_level <= len(in_channels) + assert num_outs == end_level - start_level + self.start_level = start_level + self.end_level = end_level + + self.lateral_convs = ModuleList() + self.fpn_convs = ModuleList() + self.upsample_modules = ModuleList() + + for i in range(self.start_level, self.backbone_end_level): + l_conv = ConvModule( + in_channels[i], + out_channels, + 1, + norm_cfg=norm_cfg, + bias=self.with_bias, + act_cfg=act_cfg, + inplace=False, + order=self.order) + fpn_conv = ConvModule( + out_channels, + out_channels, + 3, + padding=1, + norm_cfg=self.norm_cfg, + bias=self.with_bias, + act_cfg=act_cfg, + inplace=False, + order=self.order) + if i != self.backbone_end_level - 1: + upsample_cfg_ = self.upsample_cfg.copy() + if self.upsample == 'deconv': + upsample_cfg_.update( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=self.upsample_kernel, + stride=2, + padding=(self.upsample_kernel - 1) // 2, + output_padding=(self.upsample_kernel - 1) // 2) + elif self.upsample == 'pixel_shuffle': + upsample_cfg_.update( + in_channels=out_channels, + out_channels=out_channels, + scale_factor=2, + upsample_kernel=self.upsample_kernel) + elif self.upsample == 'carafe': + upsample_cfg_.update(channels=out_channels, scale_factor=2) + else: + # suppress warnings + align_corners = (None + if self.upsample == 'nearest' else False) + upsample_cfg_.update( + scale_factor=2, + mode=self.upsample, + align_corners=align_corners) + upsample_module = build_upsample_layer(upsample_cfg_) + self.upsample_modules.append(upsample_module) + self.lateral_convs.append(l_conv) + self.fpn_convs.append(fpn_conv) + + # add extra conv layers (e.g., RetinaNet) + extra_out_levels = ( + num_outs - self.backbone_end_level + self.start_level) + if extra_out_levels >= 1: + for i in range(extra_out_levels): + in_channels = ( + self.in_channels[self.backbone_end_level - + 1] if i == 0 else out_channels) + extra_l_conv = ConvModule( + in_channels, + out_channels, + 3, + stride=2, + padding=1, + norm_cfg=norm_cfg, + bias=self.with_bias, + act_cfg=act_cfg, + inplace=False, + order=self.order) + if self.upsample == 'deconv': + upsampler_cfg_ = dict( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=self.upsample_kernel, + stride=2, + padding=(self.upsample_kernel - 1) // 2, + output_padding=(self.upsample_kernel - 1) // 2) + elif self.upsample == 'pixel_shuffle': + upsampler_cfg_ = dict( + in_channels=out_channels, + out_channels=out_channels, + scale_factor=2, + upsample_kernel=self.upsample_kernel) + elif self.upsample == 'carafe': + upsampler_cfg_ = dict( + channels=out_channels, + scale_factor=2, + **self.upsample_cfg) + else: + # suppress warnings + align_corners = (None + if self.upsample == 'nearest' else False) + upsampler_cfg_ = dict( + scale_factor=2, + mode=self.upsample, + align_corners=align_corners) + upsampler_cfg_['type'] = self.upsample + upsample_module = build_upsample_layer(upsampler_cfg_) + extra_fpn_conv = ConvModule( + out_channels, + out_channels, + 3, + padding=1, + norm_cfg=self.norm_cfg, + bias=self.with_bias, + act_cfg=act_cfg, + inplace=False, + order=self.order) + self.upsample_modules.append(upsample_module) + self.fpn_convs.append(extra_fpn_conv) + self.lateral_convs.append(extra_l_conv) + + # default init_weights for conv(msra) and norm in ConvModule + def init_weights(self): + """Initialize the weights of module.""" + super(FPN_CARAFE, self).init_weights() + for m in self.modules(): + if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)): + xavier_init(m, distribution='uniform') + for m in self.modules(): + if isinstance(m, CARAFEPack): + m.init_weights() + + def slice_as(self, src, dst): + """Slice ``src`` as ``dst`` + + Note: + ``src`` should have the same or larger size than ``dst``. + + Args: + src (torch.Tensor): Tensors to be sliced. + dst (torch.Tensor): ``src`` will be sliced to have the same + size as ``dst``. + + Returns: + torch.Tensor: Sliced tensor. + """ + assert (src.size(2) >= dst.size(2)) and (src.size(3) >= dst.size(3)) + if src.size(2) == dst.size(2) and src.size(3) == dst.size(3): + return src + else: + return src[:, :, :dst.size(2), :dst.size(3)] + + def tensor_add(self, a, b): + """Add tensors ``a`` and ``b`` that might have different sizes.""" + if a.size() == b.size(): + c = a + b + else: + c = a + self.slice_as(b, a) + return c + + def forward(self, inputs): + """Forward function.""" + assert len(inputs) == len(self.in_channels) + + # build laterals + laterals = [] + for i, lateral_conv in enumerate(self.lateral_convs): + if i <= self.backbone_end_level - self.start_level: + input = inputs[min(i + self.start_level, len(inputs) - 1)] + else: + input = laterals[-1] + lateral = lateral_conv(input) + laterals.append(lateral) + + # build top-down path + for i in range(len(laterals) - 1, 0, -1): + if self.upsample is not None: + upsample_feat = self.upsample_modules[i - 1](laterals[i]) + else: + upsample_feat = laterals[i] + laterals[i - 1] = self.tensor_add(laterals[i - 1], upsample_feat) + + # build outputs + num_conv_outs = len(self.fpn_convs) + outs = [] + for i in range(num_conv_outs): + out = self.fpn_convs[i](laterals[i]) + outs.append(out) + return tuple(outs) diff --git a/mmdet/models/necks/hrfpn.py b/mmdet/models/necks/hrfpn.py new file mode 100644 index 0000000..75e6c95 --- /dev/null +++ b/mmdet/models/necks/hrfpn.py @@ -0,0 +1,99 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule +from torch.utils.checkpoint import checkpoint + +from ..builder import NECKS + + +@NECKS.register_module() +class HRFPN(BaseModule): + """HRFPN (High Resolution Feature Pyrmamids) + + paper: `High-Resolution Representations for Labeling Pixels and Regions + `_. + + Args: + in_channels (list): number of channels for each branch. + out_channels (int): output channels of feature pyramids. + num_outs (int): number of output stages. + pooling_type (str): pooling for generating feature pyramids + from {MAX, AVG}. + conv_cfg (dict): dictionary to construct and config conv layer. + norm_cfg (dict): dictionary to construct and config norm layer. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. + stride (int): stride of 3x3 convolutional layers + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + in_channels, + out_channels, + num_outs=5, + pooling_type='AVG', + conv_cfg=None, + norm_cfg=None, + with_cp=False, + stride=1, + init_cfg=dict(type='Caffe2Xavier', layer='Conv2d')): + super(HRFPN, self).__init__(init_cfg) + assert isinstance(in_channels, list) + self.in_channels = in_channels + self.out_channels = out_channels + self.num_ins = len(in_channels) + self.num_outs = num_outs + self.with_cp = with_cp + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + + self.reduction_conv = ConvModule( + sum(in_channels), + out_channels, + kernel_size=1, + conv_cfg=self.conv_cfg, + act_cfg=None) + + self.fpn_convs = nn.ModuleList() + for i in range(self.num_outs): + self.fpn_convs.append( + ConvModule( + out_channels, + out_channels, + kernel_size=3, + padding=1, + stride=stride, + conv_cfg=self.conv_cfg, + act_cfg=None)) + + if pooling_type == 'MAX': + self.pooling = F.max_pool2d + else: + self.pooling = F.avg_pool2d + + def forward(self, inputs): + """Forward function.""" + assert len(inputs) == self.num_ins + outs = [inputs[0]] + for i in range(1, self.num_ins): + outs.append( + F.interpolate(inputs[i], scale_factor=2**i, mode='bilinear')) + out = torch.cat(outs, dim=1) + if out.requires_grad and self.with_cp: + out = checkpoint(self.reduction_conv, out) + else: + out = self.reduction_conv(out) + outs = [out] + for i in range(1, self.num_outs): + outs.append(self.pooling(out, kernel_size=2**i, stride=2**i)) + outputs = [] + + for i in range(self.num_outs): + if outs[i].requires_grad and self.with_cp: + tmp_out = checkpoint(self.fpn_convs[i], outs[i]) + else: + tmp_out = self.fpn_convs[i](outs[i]) + outputs.append(tmp_out) + return tuple(outputs) diff --git a/mmdet/models/necks/identity_fpn.py b/mmdet/models/necks/identity_fpn.py new file mode 100644 index 0000000..8269a72 --- /dev/null +++ b/mmdet/models/necks/identity_fpn.py @@ -0,0 +1,146 @@ +import warnings + +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule, auto_fp16 + +from ..builder import NECKS + + +@NECKS.register_module() +class ChannelMapping(BaseModule): + r""" + Simply transform multi-scale features to the same channel by a linear layer + """ + + def __init__(self, + in_channels, + out_channels, + num_outs, + start_level=0, + end_level=-1, + add_extra_convs=False, + extra_convs_on_inputs=True, + relu_before_extra_convs=False, + no_norm_on_lateral=False, + conv_cfg=None, + norm_cfg=None, + act_cfg=None, + upsample_cfg=dict(mode='nearest'), + init_cfg=dict( + type='Xavier', layer='Conv2d', distribution='uniform')): + super(ChannelMapping, self).__init__(init_cfg) + assert isinstance(in_channels, list) + self.in_channels = in_channels + self.out_channels = out_channels + self.num_ins = len(in_channels) + self.num_outs = num_outs + self.relu_before_extra_convs = relu_before_extra_convs + self.no_norm_on_lateral = no_norm_on_lateral + self.fp16_enabled = False + self.upsample_cfg = upsample_cfg.copy() + + if end_level == -1: + self.backbone_end_level = self.num_ins + assert num_outs >= self.num_ins - start_level + else: + # if end_level < inputs, no extra level is allowed + self.backbone_end_level = end_level + assert end_level <= len(in_channels) + assert num_outs == end_level - start_level + self.start_level = start_level + self.end_level = end_level + self.add_extra_convs = add_extra_convs + assert isinstance(add_extra_convs, (str, bool)) + if isinstance(add_extra_convs, str): + # Extra_convs_source choices: 'on_input', 'on_lateral', 'on_output' + assert add_extra_convs in ('on_input', 'on_lateral', 'on_output') + elif add_extra_convs: # True + if extra_convs_on_inputs: + # TODO: deprecate `extra_convs_on_inputs` + warnings.simplefilter('once') + warnings.warn( + '"extra_convs_on_inputs" will be deprecated in v2.9.0,' + 'Please use "add_extra_convs"', DeprecationWarning) + self.add_extra_convs = 'on_input' + else: + self.add_extra_convs = 'on_output' + + self.lateral_convs = nn.ModuleList() + self.fpn_convs = nn.ModuleList() + + for i in range(self.start_level, self.backbone_end_level): + l_conv = ConvModule( + in_channels[i], + out_channels, + 1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg if not self.no_norm_on_lateral else None, + act_cfg=act_cfg, + inplace=False) + + self.lateral_convs.append(l_conv) + self.fpn_convs.append(nn.Identity()) + + # add extra conv layers (e.g., RetinaNet) + extra_levels = num_outs - self.backbone_end_level + self.start_level + if self.add_extra_convs and extra_levels >= 1: + for i in range(extra_levels): + if i == 0 and self.add_extra_convs == 'on_input': + in_channels = self.in_channels[self.backbone_end_level - 1] + else: + in_channels = out_channels + extra_fpn_conv = nn.AvgPool2d(3, 2, 1) + self.fpn_convs.append(extra_fpn_conv) + + self.gn_out = nn.GroupNorm(32, out_channels) + + @auto_fp16() + def forward(self, inputs): + """Forward function.""" + assert len(inputs) == len(self.in_channels) + + # build laterals + laterals = [ + lateral_conv(inputs[i + self.start_level]) + for i, lateral_conv in enumerate(self.lateral_convs) + ] + + # build top-down path + used_backbone_levels = len(laterals) + + # NOTE: self.fpn_convs are all the identity + outs = [ + self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels) + ] + + if self.num_outs > len(outs): + # use max pool to get more levels on top of outputs + # (e.g., Faster R-CNN, Mask R-CNN) + if not self.add_extra_convs: + for i in range(self.num_outs - used_backbone_levels): + outs.append(F.max_pool2d(outs[-1], 1, stride=2)) + # add conv layers on top of original feature maps (RetinaNet) + else: + if self.add_extra_convs == 'on_input': + extra_source = inputs[self.backbone_end_level - 1] + elif self.add_extra_convs == 'on_lateral': + extra_source = laterals[-1] + elif self.add_extra_convs == 'on_output': + extra_source = outs[-1] + else: + raise NotImplementedError + outs.append(self.fpn_convs[used_backbone_levels](extra_source)) + for i in range(used_backbone_levels + 1, self.num_outs): + if self.relu_before_extra_convs: + outs.append(self.fpn_convs[i](F.relu(outs[-1]))) + else: + outs.append(self.fpn_convs[i](outs[-1])) + + outs = [ + self.gn_out(out) for i, out in enumerate(outs) + ] + + return tuple(outs) diff --git a/mmdet/models/necks/nas_fpn.py b/mmdet/models/necks/nas_fpn.py new file mode 100644 index 0000000..fca3496 --- /dev/null +++ b/mmdet/models/necks/nas_fpn.py @@ -0,0 +1,157 @@ +import torch.nn as nn +from mmcv.cnn import ConvModule +from mmcv.ops.merge_cells import GlobalPoolingCell, SumCell +from mmcv.runner import BaseModule, ModuleList + +from ..builder import NECKS + + +@NECKS.register_module() +class NASFPN(BaseModule): + """NAS-FPN. + + Implementation of `NAS-FPN: Learning Scalable Feature Pyramid Architecture + for Object Detection `_ + + Args: + in_channels (List[int]): Number of input channels per scale. + out_channels (int): Number of output channels (used at each scale) + num_outs (int): Number of output scales. + stack_times (int): The number of times the pyramid architecture will + be stacked. + start_level (int): Index of the start input backbone level used to + build the feature pyramid. Default: 0. + end_level (int): Index of the end input backbone level (exclusive) to + build the feature pyramid. Default: -1, which means the last level. + add_extra_convs (bool): It decides whether to add conv + layers on top of the original feature maps. Default to False. + If True, its actual mode is specified by `extra_convs_on_inputs`. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + in_channels, + out_channels, + num_outs, + stack_times, + start_level=0, + end_level=-1, + add_extra_convs=False, + norm_cfg=None, + init_cfg=dict(type='Caffe2Xavier', layer='Conv2d')): + super(NASFPN, self).__init__(init_cfg) + assert isinstance(in_channels, list) + self.in_channels = in_channels + self.out_channels = out_channels + self.num_ins = len(in_channels) # num of input feature levels + self.num_outs = num_outs # num of output feature levels + self.stack_times = stack_times + self.norm_cfg = norm_cfg + + if end_level == -1: + self.backbone_end_level = self.num_ins + assert num_outs >= self.num_ins - start_level + else: + # if end_level < inputs, no extra level is allowed + self.backbone_end_level = end_level + assert end_level <= len(in_channels) + assert num_outs == end_level - start_level + self.start_level = start_level + self.end_level = end_level + self.add_extra_convs = add_extra_convs + + # add lateral connections + self.lateral_convs = nn.ModuleList() + for i in range(self.start_level, self.backbone_end_level): + l_conv = ConvModule( + in_channels[i], + out_channels, + 1, + norm_cfg=norm_cfg, + act_cfg=None) + self.lateral_convs.append(l_conv) + + # add extra downsample layers (stride-2 pooling or conv) + extra_levels = num_outs - self.backbone_end_level + self.start_level + self.extra_downsamples = nn.ModuleList() + for i in range(extra_levels): + extra_conv = ConvModule( + out_channels, out_channels, 1, norm_cfg=norm_cfg, act_cfg=None) + self.extra_downsamples.append( + nn.Sequential(extra_conv, nn.MaxPool2d(2, 2))) + + # add NAS FPN connections + self.fpn_stages = ModuleList() + for _ in range(self.stack_times): + stage = nn.ModuleDict() + # gp(p6, p4) -> p4_1 + stage['gp_64_4'] = GlobalPoolingCell( + in_channels=out_channels, + out_channels=out_channels, + out_norm_cfg=norm_cfg) + # sum(p4_1, p4) -> p4_2 + stage['sum_44_4'] = SumCell( + in_channels=out_channels, + out_channels=out_channels, + out_norm_cfg=norm_cfg) + # sum(p4_2, p3) -> p3_out + stage['sum_43_3'] = SumCell( + in_channels=out_channels, + out_channels=out_channels, + out_norm_cfg=norm_cfg) + # sum(p3_out, p4_2) -> p4_out + stage['sum_34_4'] = SumCell( + in_channels=out_channels, + out_channels=out_channels, + out_norm_cfg=norm_cfg) + # sum(p5, gp(p4_out, p3_out)) -> p5_out + stage['gp_43_5'] = GlobalPoolingCell(with_out_conv=False) + stage['sum_55_5'] = SumCell( + in_channels=out_channels, + out_channels=out_channels, + out_norm_cfg=norm_cfg) + # sum(p7, gp(p5_out, p4_2)) -> p7_out + stage['gp_54_7'] = GlobalPoolingCell(with_out_conv=False) + stage['sum_77_7'] = SumCell( + in_channels=out_channels, + out_channels=out_channels, + out_norm_cfg=norm_cfg) + # gp(p7_out, p5_out) -> p6_out + stage['gp_75_6'] = GlobalPoolingCell( + in_channels=out_channels, + out_channels=out_channels, + out_norm_cfg=norm_cfg) + self.fpn_stages.append(stage) + + def forward(self, inputs): + """Forward function.""" + # build P3-P5 + feats = [ + lateral_conv(inputs[i + self.start_level]) + for i, lateral_conv in enumerate(self.lateral_convs) + ] + # build P6-P7 on top of P5 + for downsample in self.extra_downsamples: + feats.append(downsample(feats[-1])) + + p3, p4, p5, p6, p7 = feats + + for stage in self.fpn_stages: + # gp(p6, p4) -> p4_1 + p4_1 = stage['gp_64_4'](p6, p4, out_size=p4.shape[-2:]) + # sum(p4_1, p4) -> p4_2 + p4_2 = stage['sum_44_4'](p4_1, p4, out_size=p4.shape[-2:]) + # sum(p4_2, p3) -> p3_out + p3 = stage['sum_43_3'](p4_2, p3, out_size=p3.shape[-2:]) + # sum(p3_out, p4_2) -> p4_out + p4 = stage['sum_34_4'](p3, p4_2, out_size=p4.shape[-2:]) + # sum(p5, gp(p4_out, p3_out)) -> p5_out + p5_tmp = stage['gp_43_5'](p4, p3, out_size=p5.shape[-2:]) + p5 = stage['sum_55_5'](p5, p5_tmp, out_size=p5.shape[-2:]) + # sum(p7, gp(p5_out, p4_2)) -> p7_out + p7_tmp = stage['gp_54_7'](p5, p4_2, out_size=p7.shape[-2:]) + p7 = stage['sum_77_7'](p7, p7_tmp, out_size=p7.shape[-2:]) + # gp(p7_out, p5_out) -> p6_out + p6 = stage['gp_75_6'](p7, p5, out_size=p6.shape[-2:]) + + return p3, p4, p5, p6, p7 diff --git a/mmdet/models/necks/nasfcos_fpn.py b/mmdet/models/necks/nasfcos_fpn.py new file mode 100644 index 0000000..77a3ffd --- /dev/null +++ b/mmdet/models/necks/nasfcos_fpn.py @@ -0,0 +1,168 @@ +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule, caffe2_xavier_init +from mmcv.ops.merge_cells import ConcatCell +from mmcv.runner import BaseModule + +from ..builder import NECKS + + +@NECKS.register_module() +class NASFCOS_FPN(BaseModule): + """FPN structure in NASFPN. + + Implementation of paper `NAS-FCOS: Fast Neural Architecture Search for + Object Detection `_ + + Args: + in_channels (List[int]): Number of input channels per scale. + out_channels (int): Number of output channels (used at each scale) + num_outs (int): Number of output scales. + start_level (int): Index of the start input backbone level used to + build the feature pyramid. Default: 0. + end_level (int): Index of the end input backbone level (exclusive) to + build the feature pyramid. Default: -1, which means the last level. + add_extra_convs (bool): It decides whether to add conv + layers on top of the original feature maps. Default to False. + If True, its actual mode is specified by `extra_convs_on_inputs`. + conv_cfg (dict): dictionary to construct and config conv layer. + norm_cfg (dict): dictionary to construct and config norm layer. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + in_channels, + out_channels, + num_outs, + start_level=1, + end_level=-1, + add_extra_convs=False, + conv_cfg=None, + norm_cfg=None, + init_cfg=None): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + super(NASFCOS_FPN, self).__init__(init_cfg) + assert isinstance(in_channels, list) + self.in_channels = in_channels + self.out_channels = out_channels + self.num_ins = len(in_channels) + self.num_outs = num_outs + self.norm_cfg = norm_cfg + self.conv_cfg = conv_cfg + + if end_level == -1: + self.backbone_end_level = self.num_ins + assert num_outs >= self.num_ins - start_level + else: + self.backbone_end_level = end_level + assert end_level <= len(in_channels) + assert num_outs == end_level - start_level + self.start_level = start_level + self.end_level = end_level + self.add_extra_convs = add_extra_convs + + self.adapt_convs = nn.ModuleList() + for i in range(self.start_level, self.backbone_end_level): + adapt_conv = ConvModule( + in_channels[i], + out_channels, + 1, + stride=1, + padding=0, + bias=False, + norm_cfg=dict(type='BN'), + act_cfg=dict(type='ReLU', inplace=False)) + self.adapt_convs.append(adapt_conv) + + # C2 is omitted according to the paper + extra_levels = num_outs - self.backbone_end_level + self.start_level + + def build_concat_cell(with_input1_conv, with_input2_conv): + cell_conv_cfg = dict( + kernel_size=1, padding=0, bias=False, groups=out_channels) + return ConcatCell( + in_channels=out_channels, + out_channels=out_channels, + with_out_conv=True, + out_conv_cfg=cell_conv_cfg, + out_norm_cfg=dict(type='BN'), + out_conv_order=('norm', 'act', 'conv'), + with_input1_conv=with_input1_conv, + with_input2_conv=with_input2_conv, + input_conv_cfg=conv_cfg, + input_norm_cfg=norm_cfg, + upsample_mode='nearest') + + # Denote c3=f0, c4=f1, c5=f2 for convince + self.fpn = nn.ModuleDict() + self.fpn['c22_1'] = build_concat_cell(True, True) + self.fpn['c22_2'] = build_concat_cell(True, True) + self.fpn['c32'] = build_concat_cell(True, False) + self.fpn['c02'] = build_concat_cell(True, False) + self.fpn['c42'] = build_concat_cell(True, True) + self.fpn['c36'] = build_concat_cell(True, True) + self.fpn['c61'] = build_concat_cell(True, True) # f9 + self.extra_downsamples = nn.ModuleList() + for i in range(extra_levels): + extra_act_cfg = None if i == 0 \ + else dict(type='ReLU', inplace=False) + self.extra_downsamples.append( + ConvModule( + out_channels, + out_channels, + 3, + stride=2, + padding=1, + act_cfg=extra_act_cfg, + order=('act', 'norm', 'conv'))) + + def forward(self, inputs): + """Forward function.""" + feats = [ + adapt_conv(inputs[i + self.start_level]) + for i, adapt_conv in enumerate(self.adapt_convs) + ] + + for (i, module_name) in enumerate(self.fpn): + idx_1, idx_2 = int(module_name[1]), int(module_name[2]) + res = self.fpn[module_name](feats[idx_1], feats[idx_2]) + feats.append(res) + + ret = [] + for (idx, input_idx) in zip([9, 8, 7], [1, 2, 3]): # add P3, P4, P5 + feats1, feats2 = feats[idx], feats[5] + feats2_resize = F.interpolate( + feats2, + size=feats1.size()[2:], + mode='bilinear', + align_corners=False) + + feats_sum = feats1 + feats2_resize + ret.append( + F.interpolate( + feats_sum, + size=inputs[input_idx].size()[2:], + mode='bilinear', + align_corners=False)) + + for submodule in self.extra_downsamples: + ret.append(submodule(ret[-1])) + + return tuple(ret) + + def init_weights(self): + """Initialize the weights of module.""" + super(NASFCOS_FPN, self).init_weights() + for module in self.fpn.values(): + if hasattr(module, 'conv_out'): + caffe2_xavier_init(module.out_conv.conv) + + for modules in [ + self.adapt_convs.modules(), + self.extra_downsamples.modules() + ]: + for module in modules: + if isinstance(module, nn.Conv2d): + caffe2_xavier_init(module) diff --git a/mmdet/models/necks/pafpn.py b/mmdet/models/necks/pafpn.py new file mode 100644 index 0000000..1ae40c6 --- /dev/null +++ b/mmdet/models/necks/pafpn.py @@ -0,0 +1,154 @@ +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule +from mmcv.runner import auto_fp16 + +from ..builder import NECKS +from .fpn import FPN + + +@NECKS.register_module() +class PAFPN(FPN): + """Path Aggregation Network for Instance Segmentation. + + This is an implementation of the `PAFPN in Path Aggregation Network + `_. + + Args: + in_channels (List[int]): Number of input channels per scale. + out_channels (int): Number of output channels (used at each scale) + num_outs (int): Number of output scales. + start_level (int): Index of the start input backbone level used to + build the feature pyramid. Default: 0. + end_level (int): Index of the end input backbone level (exclusive) to + build the feature pyramid. Default: -1, which means the last level. + add_extra_convs (bool): Whether to add conv layers on top of the + original feature maps. Default: False. + extra_convs_on_inputs (bool): Whether to apply extra conv on + the original feature from the backbone. Default: False. + relu_before_extra_convs (bool): Whether to apply relu before the extra + conv. Default: False. + no_norm_on_lateral (bool): Whether to apply norm on lateral. + Default: False. + conv_cfg (dict): Config dict for convolution layer. Default: None. + norm_cfg (dict): Config dict for normalization layer. Default: None. + act_cfg (str): Config dict for activation layer in ConvModule. + Default: None. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + in_channels, + out_channels, + num_outs, + start_level=0, + end_level=-1, + add_extra_convs=False, + extra_convs_on_inputs=True, + relu_before_extra_convs=False, + no_norm_on_lateral=False, + conv_cfg=None, + norm_cfg=None, + act_cfg=None, + init_cfg=dict( + type='Xavier', layer='Conv2d', distribution='uniform')): + super(PAFPN, self).__init__( + in_channels, + out_channels, + num_outs, + start_level, + end_level, + add_extra_convs, + extra_convs_on_inputs, + relu_before_extra_convs, + no_norm_on_lateral, + conv_cfg, + norm_cfg, + act_cfg, + init_cfg=init_cfg) + # add extra bottom up pathway + self.downsample_convs = nn.ModuleList() + self.pafpn_convs = nn.ModuleList() + for i in range(self.start_level + 1, self.backbone_end_level): + d_conv = ConvModule( + out_channels, + out_channels, + 3, + stride=2, + padding=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg, + inplace=False) + pafpn_conv = ConvModule( + out_channels, + out_channels, + 3, + padding=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg, + inplace=False) + self.downsample_convs.append(d_conv) + self.pafpn_convs.append(pafpn_conv) + + @auto_fp16() + def forward(self, inputs): + """Forward function.""" + assert len(inputs) == len(self.in_channels) + + # build laterals + laterals = [ + lateral_conv(inputs[i + self.start_level]) + for i, lateral_conv in enumerate(self.lateral_convs) + ] + + # build top-down path + used_backbone_levels = len(laterals) + for i in range(used_backbone_levels - 1, 0, -1): + prev_shape = laterals[i - 1].shape[2:] + laterals[i - 1] += F.interpolate( + laterals[i], size=prev_shape, mode='nearest') + + # build outputs + # part 1: from original levels + inter_outs = [ + self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels) + ] + + # part 2: add bottom-up path + for i in range(0, used_backbone_levels - 1): + inter_outs[i + 1] += self.downsample_convs[i](inter_outs[i]) + + outs = [] + outs.append(inter_outs[0]) + outs.extend([ + self.pafpn_convs[i - 1](inter_outs[i]) + for i in range(1, used_backbone_levels) + ]) + + # part 3: add extra levels + if self.num_outs > len(outs): + # use max pool to get more levels on top of outputs + # (e.g., Faster R-CNN, Mask R-CNN) + if not self.add_extra_convs: + for i in range(self.num_outs - used_backbone_levels): + outs.append(F.max_pool2d(outs[-1], 1, stride=2)) + # add conv layers on top of original feature maps (RetinaNet) + else: + if self.add_extra_convs == 'on_input': + orig = inputs[self.backbone_end_level - 1] + outs.append(self.fpn_convs[used_backbone_levels](orig)) + elif self.add_extra_convs == 'on_lateral': + outs.append(self.fpn_convs[used_backbone_levels]( + laterals[-1])) + elif self.add_extra_convs == 'on_output': + outs.append(self.fpn_convs[used_backbone_levels](outs[-1])) + else: + raise NotImplementedError + for i in range(used_backbone_levels + 1, self.num_outs): + if self.relu_before_extra_convs: + outs.append(self.fpn_convs[i](F.relu(outs[-1]))) + else: + outs.append(self.fpn_convs[i](outs[-1])) + return tuple(outs) diff --git a/mmdet/models/necks/rfp.py b/mmdet/models/necks/rfp.py new file mode 100644 index 0000000..200e243 --- /dev/null +++ b/mmdet/models/necks/rfp.py @@ -0,0 +1,134 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import constant_init, xavier_init +from mmcv.runner import BaseModule, ModuleList + +from ..builder import NECKS, build_backbone +from .fpn import FPN + + +class ASPP(BaseModule): + """ASPP (Atrous Spatial Pyramid Pooling) + + This is an implementation of the ASPP module used in DetectoRS + (https://arxiv.org/pdf/2006.02334.pdf) + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of channels produced by this module + dilations (tuple[int]): Dilations of the four branches. + Default: (1, 3, 6, 1) + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + in_channels, + out_channels, + dilations=(1, 3, 6, 1), + init_cfg=dict(type='Kaiming', layer='Conv2d')): + super().__init__(init_cfg) + assert dilations[-1] == 1 + self.aspp = nn.ModuleList() + for dilation in dilations: + kernel_size = 3 if dilation > 1 else 1 + padding = dilation if dilation > 1 else 0 + conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=1, + dilation=dilation, + padding=padding, + bias=True) + self.aspp.append(conv) + self.gap = nn.AdaptiveAvgPool2d(1) + + def forward(self, x): + avg_x = self.gap(x) + out = [] + for aspp_idx in range(len(self.aspp)): + inp = avg_x if (aspp_idx == len(self.aspp) - 1) else x + out.append(F.relu_(self.aspp[aspp_idx](inp))) + out[-1] = out[-1].expand_as(out[-2]) + out = torch.cat(out, dim=1) + return out + + +@NECKS.register_module() +class RFP(FPN): + """RFP (Recursive Feature Pyramid) + + This is an implementation of RFP in `DetectoRS + `_. Different from standard FPN, the + input of RFP should be multi level features along with origin input image + of backbone. + + Args: + rfp_steps (int): Number of unrolled steps of RFP. + rfp_backbone (dict): Configuration of the backbone for RFP. + aspp_out_channels (int): Number of output channels of ASPP module. + aspp_dilations (tuple[int]): Dilation rates of four branches. + Default: (1, 3, 6, 1) + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + rfp_steps, + rfp_backbone, + aspp_out_channels, + aspp_dilations=(1, 3, 6, 1), + init_cfg=None, + **kwargs): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + super().__init__(init_cfg=init_cfg, **kwargs) + self.rfp_steps = rfp_steps + # Be careful! Pretrained weights cannot be loaded when use + # nn.ModuleList + self.rfp_modules = ModuleList() + for rfp_idx in range(1, rfp_steps): + rfp_module = build_backbone(rfp_backbone) + self.rfp_modules.append(rfp_module) + self.rfp_aspp = ASPP(self.out_channels, aspp_out_channels, + aspp_dilations) + self.rfp_weight = nn.Conv2d( + self.out_channels, + 1, + kernel_size=1, + stride=1, + padding=0, + bias=True) + + def init_weights(self): + # Avoid using super().init_weights(), which may alter the default + # initialization of the modules in self.rfp_modules that have missing + # keys in the pretrained checkpoint. + for convs in [self.lateral_convs, self.fpn_convs]: + for m in convs.modules(): + if isinstance(m, nn.Conv2d): + xavier_init(m, distribution='uniform') + for rfp_idx in range(self.rfp_steps - 1): + self.rfp_modules[rfp_idx].init_weights() + constant_init(self.rfp_weight, 0) + + def forward(self, inputs): + inputs = list(inputs) + assert len(inputs) == len(self.in_channels) + 1 # +1 for input image + img = inputs.pop(0) + # FPN forward + x = super().forward(tuple(inputs)) + for rfp_idx in range(self.rfp_steps - 1): + rfp_feats = [x[0]] + list( + self.rfp_aspp(x[i]) for i in range(1, len(x))) + x_idx = self.rfp_modules[rfp_idx].rfp_forward(img, rfp_feats) + # FPN forward + x_idx = super().forward(x_idx) + x_new = [] + for ft_idx in range(len(x_idx)): + add_weight = torch.sigmoid(self.rfp_weight(x_idx[ft_idx])) + x_new.append(add_weight * x_idx[ft_idx] + + (1 - add_weight) * x[ft_idx]) + x = x_new + return x diff --git a/mmdet/models/necks/yolo_neck.py b/mmdet/models/necks/yolo_neck.py new file mode 100644 index 0000000..8f6aac7 --- /dev/null +++ b/mmdet/models/necks/yolo_neck.py @@ -0,0 +1,137 @@ +# Copyright (c) 2019 Western Digital Corporation or its affiliates. + +import torch +import torch.nn.functional as F +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule + +from ..builder import NECKS + + +class DetectionBlock(BaseModule): + """Detection block in YOLO neck. + + Let out_channels = n, the DetectionBlock contains: + Six ConvLayers, 1 Conv2D Layer and 1 YoloLayer. + The first 6 ConvLayers are formed the following way: + 1x1xn, 3x3x2n, 1x1xn, 3x3x2n, 1x1xn, 3x3x2n. + The Conv2D layer is 1x1x255. + Some block will have branch after the fifth ConvLayer. + The input channel is arbitrary (in_channels) + + Args: + in_channels (int): The number of input channels. + out_channels (int): The number of output channels. + conv_cfg (dict): Config dict for convolution layer. Default: None. + norm_cfg (dict): Dictionary to construct and config norm layer. + Default: dict(type='BN', requires_grad=True) + act_cfg (dict): Config dict for activation layer. + Default: dict(type='LeakyReLU', negative_slope=0.1). + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + in_channels, + out_channels, + conv_cfg=None, + norm_cfg=dict(type='BN', requires_grad=True), + act_cfg=dict(type='LeakyReLU', negative_slope=0.1), + init_cfg=None): + super(DetectionBlock, self).__init__(init_cfg) + double_out_channels = out_channels * 2 + + # shortcut + cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg) + self.conv1 = ConvModule(in_channels, out_channels, 1, **cfg) + self.conv2 = ConvModule( + out_channels, double_out_channels, 3, padding=1, **cfg) + self.conv3 = ConvModule(double_out_channels, out_channels, 1, **cfg) + self.conv4 = ConvModule( + out_channels, double_out_channels, 3, padding=1, **cfg) + self.conv5 = ConvModule(double_out_channels, out_channels, 1, **cfg) + + def forward(self, x): + tmp = self.conv1(x) + tmp = self.conv2(tmp) + tmp = self.conv3(tmp) + tmp = self.conv4(tmp) + out = self.conv5(tmp) + return out + + +@NECKS.register_module() +class YOLOV3Neck(BaseModule): + """The neck of YOLOV3. + + It can be treated as a simplified version of FPN. It + will take the result from Darknet backbone and do some upsampling and + concatenation. It will finally output the detection result. + + Note: + The input feats should be from top to bottom. + i.e., from high-lvl to low-lvl + But YOLOV3Neck will process them in reversed order. + i.e., from bottom (high-lvl) to top (low-lvl) + + Args: + num_scales (int): The number of scales / stages. + in_channels (int): The number of input channels. + out_channels (int): The number of output channels. + conv_cfg (dict): Config dict for convolution layer. Default: None. + norm_cfg (dict): Dictionary to construct and config norm layer. + Default: dict(type='BN', requires_grad=True) + act_cfg (dict): Config dict for activation layer. + Default: dict(type='LeakyReLU', negative_slope=0.1). + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + num_scales, + in_channels, + out_channels, + conv_cfg=None, + norm_cfg=dict(type='BN', requires_grad=True), + act_cfg=dict(type='LeakyReLU', negative_slope=0.1), + init_cfg=None): + super(YOLOV3Neck, self).__init__(init_cfg) + assert (num_scales == len(in_channels) == len(out_channels)) + self.num_scales = num_scales + self.in_channels = in_channels + self.out_channels = out_channels + + # shortcut + cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg) + + # To support arbitrary scales, the code looks awful, but it works. + # Better solution is welcomed. + self.detect1 = DetectionBlock(in_channels[0], out_channels[0], **cfg) + for i in range(1, self.num_scales): + in_c, out_c = self.in_channels[i], self.out_channels[i] + self.add_module(f'conv{i}', ConvModule(in_c, out_c, 1, **cfg)) + # in_c + out_c : High-lvl feats will be cat with low-lvl feats + self.add_module(f'detect{i+1}', + DetectionBlock(in_c + out_c, out_c, **cfg)) + + def forward(self, feats): + assert len(feats) == self.num_scales + + # processed from bottom (high-lvl) to top (low-lvl) + outs = [] + out = self.detect1(feats[-1]) + outs.append(out) + + for i, x in enumerate(reversed(feats[:-1])): + conv = getattr(self, f'conv{i+1}') + tmp = conv(out) + + # Cat with low-lvl feats + tmp = F.interpolate(tmp, scale_factor=2) + tmp = torch.cat((tmp, x), 1) + + detect = getattr(self, f'detect{i+2}') + out = detect(tmp) + outs.append(out) + + return tuple(outs) diff --git a/mmdet/models/roi_heads/__init__.py b/mmdet/models/roi_heads/__init__.py new file mode 100644 index 0000000..2cee5b8 --- /dev/null +++ b/mmdet/models/roi_heads/__init__.py @@ -0,0 +1,43 @@ +from .base_roi_head import BaseRoIHead +from .bbox_heads import (BBoxHead, ConvFCBBoxHead, DIIHead, + DoubleConvFCBBoxHead, SABLHead, SCNetBBoxHead, + Shared2FCBBoxHead, Shared4Conv1FCBBoxHead) +from .cascade_roi_head import CascadeRoIHead +from .double_roi_head import DoubleHeadRoIHead +from .dynamic_roi_head import DynamicRoIHead +from .grid_roi_head import GridRoIHead +from .htc_roi_head import HybridTaskCascadeRoIHead +from .mask_heads import (CoarseMaskHead, FCNMaskHead, FeatureRelayHead, + FusedSemanticHead, GlobalContextHead, GridHead, + HTCMaskHead, MaskIoUHead, MaskPointHead, + SCNetMaskHead, SCNetSemanticHead) +from .mask_scoring_roi_head import MaskScoringRoIHead +from .pisa_roi_head import PISARoIHead +from .point_rend_roi_head import PointRendRoIHead +from .roi_extractors import (BaseRoIExtractor, GenericRoIExtractor, + SingleRoIExtractor) +from .scnet_roi_head import SCNetRoIHead +from .shared_heads import ResLayer +from .sparse_roi_head import SparseRoIHead +from .standard_roi_head import StandardRoIHead +from .trident_roi_head import TridentRoIHead +from .adamixer_decoder import AdaMixerDecoder +from .adamixer_decoder_aq import AdaMixerDecoder_aq +from .adamixer_decoder_caq import AdaMixerDecoder_caq +from .adamixer_decoder_aql import AdaMixerDecoder_aql +from .adamixer_decoder_Qrecycle import AdaMixerDecoder_Qrecycle +from .adamixer_decoder_Qrecycle_optimize import AdaMixerDecoder_Qrecycle_optimize +from .adamixer_decoder_stodepth import AdaMixerDecoder_stodepth +__all__ = [ + 'BaseRoIHead', 'CascadeRoIHead', 'DoubleHeadRoIHead', 'MaskScoringRoIHead', + 'HybridTaskCascadeRoIHead', 'GridRoIHead', 'ResLayer', 'BBoxHead', + 'ConvFCBBoxHead', 'DIIHead', 'SABLHead', 'Shared2FCBBoxHead', + 'StandardRoIHead', 'Shared4Conv1FCBBoxHead', 'DoubleConvFCBBoxHead', + 'FCNMaskHead', 'HTCMaskHead', 'FusedSemanticHead', 'GridHead', + 'MaskIoUHead', 'BaseRoIExtractor', 'GenericRoIExtractor', + 'SingleRoIExtractor', 'PISARoIHead', 'PointRendRoIHead', 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/dev/null and b/mmdet/models/roi_heads/__pycache__/trident_roi_head.cpython-37.pyc differ diff --git a/mmdet/models/roi_heads/adamixer_decoder.py b/mmdet/models/roi_heads/adamixer_decoder.py new file mode 100644 index 0000000..9600816 --- /dev/null +++ b/mmdet/models/roi_heads/adamixer_decoder.py @@ -0,0 +1,252 @@ +import torch + +from mmdet.core import bbox2result, bbox2roi, bbox_xyxy_to_cxcywh +from mmdet.core.bbox.samplers import PseudoSampler +from ..builder import HEADS +from .cascade_roi_head import CascadeRoIHead +import cccu +import os +DEBUG = 'DEBUG' in os.environ + + +@HEADS.register_module() +class AdaMixerDecoder(CascadeRoIHead): + _DEBUG = -1 + + def __init__(self, + num_stages=6, + stage_loss_weights=(1, 1, 1, 1, 1, 1), + content_dim=256, + featmap_strides=[4, 8, 16, 32], + bbox_head=None, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + assert bbox_head is not None + assert len(stage_loss_weights) == num_stages + self.featmap_strides = featmap_strides + self.num_stages = num_stages + self.stage_loss_weights = stage_loss_weights + self.content_dim = content_dim + super(AdaMixerDecoder, self).__init__( + num_stages, + stage_loss_weights, + bbox_roi_extractor=dict( + # This does not mean that our method need RoIAlign. We put this + # as a placeholder to satisfy the argument for the parent class + # 'CascadeRoIHead'. + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=bbox_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) + # train_cfg would be None when run the test.py + if train_cfg is not None: + for stage in range(num_stages): + assert isinstance(self.bbox_sampler[stage], PseudoSampler) + # self.writer = cccu.TextWriter('./out/gt_assigned_2_querys.txt') + self.recoder = [] + + def _bbox_forward(self, stage, img_feat, query_xyzr, query_content, img_metas): + num_imgs = len(img_metas) + bbox_head = self.bbox_head[stage] + + cls_score, delta_xyzr, query_content = bbox_head(img_feat, query_xyzr, + query_content, + featmap_strides=self.featmap_strides) + + query_xyzr, decoded_bboxes = self.bbox_head[stage].refine_xyzr( + query_xyzr, + delta_xyzr) + + bboxes_list = [bboxes for bboxes in decoded_bboxes] + + bbox_results = dict( + cls_score=cls_score, + query_xyzr=query_xyzr, + decode_bbox_pred=decoded_bboxes, + query_content=query_content, + detach_cls_score_list=[ + cls_score[i].detach() for i in range(num_imgs) + ], + detach_bboxes_list=[item.detach() for item in bboxes_list], + bboxes_list=bboxes_list, + ) + if DEBUG: + with torch.no_grad(): + torch.save( + bbox_results, 'demo/bbox_results_{}.pth'.format(AdaMixerDecoder._DEBUG)) + AdaMixerDecoder._DEBUG += 1 + return bbox_results + + def forward_train(self, + x, + query_xyzr, + query_content, + img_metas, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + imgs_whwh=None, + gt_masks=None): + + num_imgs = len(img_metas) + num_queries = query_xyzr.size(1) + imgs_whwh = imgs_whwh.repeat(1, num_queries, 1) + all_stage_bbox_results = [] + all_stage_loss = {} + + for stage in range(self.num_stages): + bbox_results = self._bbox_forward(stage, x, query_xyzr, query_content, + img_metas) + all_stage_bbox_results.append(bbox_results) + if gt_bboxes_ignore is None: + # TODO support ignore + gt_bboxes_ignore = [None for _ in range(num_imgs)] + sampling_results = [] + cls_pred_list = bbox_results['detach_cls_score_list'] + bboxes_list = bbox_results['detach_bboxes_list'] + + query_xyzr = bbox_results['query_xyzr'].detach() + query_content = bbox_results['query_content'] + + if self.stage_loss_weights[stage] <= 0: + continue + + for i in range(num_imgs): + normalize_bbox_ccwh = bbox_xyxy_to_cxcywh(bboxes_list[i] / + imgs_whwh[i]) + assign_result = self.bbox_assigner[stage].assign( + normalize_bbox_ccwh, cls_pred_list[i], gt_bboxes[i], + gt_labels[i], img_metas[i]) + saving = assign_result.gt_inds.tolist() + # self.writer.record([img_metas[i]['ori_filename'], stage, saving]) + sampling_result = self.bbox_sampler[stage].sample( + assign_result, bboxes_list[i], gt_bboxes[i]) + sampling_results.append(sampling_result) + bbox_targets = self.bbox_head[stage].get_targets( + sampling_results, gt_bboxes, gt_labels, self.train_cfg[stage], + True) + + cls_score = bbox_results['cls_score'] + decode_bbox_pred = bbox_results['decode_bbox_pred'] + + single_stage_loss = self.bbox_head[stage].loss( + cls_score.view(-1, cls_score.size(-1)), + decode_bbox_pred.view(-1, 4), + *bbox_targets, + imgs_whwh=imgs_whwh) + for key, value in single_stage_loss.items(): + all_stage_loss[f'stage{stage}_{key}'] = value * \ + self.stage_loss_weights[stage] + # self.writer.save() + # self.writer.auto_split(interval=10000) + return all_stage_loss + + def simple_test(self, + x, + query_xyzr, + query_content, + img_metas, + imgs_whwh, + rescale=False): + assert self.with_bbox, 'Bbox head must be implemented.' + if DEBUG: + torch.save(img_metas, 'demo/img_metas.pth') + + num_imgs = len(img_metas) + record = {i:[] for i in range(self.num_stages)} + for stage in range(self.num_stages): + bbox_results = self._bbox_forward( + stage, x, query_xyzr, query_content, img_metas) + query_content = bbox_results['query_content'] + cls_score = bbox_results['cls_score'] + bboxes_list = bbox_results['detach_bboxes_list'] + query_xyzr = bbox_results['query_xyzr'] + # record[stage].append(cls_score[0].cpu().tolist()) + # record[stage].append(bboxes_list[0].cpu().tolist()) + + num_classes = self.bbox_head[-1].num_classes + det_bboxes = [] + det_labels = [] + + if self.bbox_head[-1].loss_cls.use_sigmoid: + cls_score = cls_score.sigmoid() + else: + cls_score = cls_score.softmax(-1)[..., :-1] + + for img_id in range(num_imgs): + + cls_score_per_img = cls_score[img_id] + scores_per_img, topk_indices = cls_score_per_img.flatten( + 0, 1).topk( + self.test_cfg.max_per_img, sorted=False) + labels_per_img = topk_indices % num_classes + # a = topk_indices // num_classes + bbox_pred_per_img = bboxes_list[img_id][topk_indices // + num_classes] + + ''' # fangyi modify start + # The following is a my implementation for testing where each query only produce one result + cls_score_per_img = cls_score[img_id] + scores_per_img, labels_per_img = torch.max(cls_score_per_img, dim=1) + bbox_pred_per_img = bboxes_list[img_id] + # record query bias on class + ct = cccu.counter(log_name='adamixer_query_predcls_temp.txt', matrixshape=(300, 80)) + for i, label_per_img in enumerate(labels_per_img): + ct.m[i, label_per_img] += 1 + ct.record() + # filter out low confident # proved not useful at all + # index = (scores_per_img > 0.02).nonzero().squeeze() + # if len(index.shape) != 0: + # scores_per_img = scores_per_img[index] + # labels_per_img = labels_per_img[index] + # bbox_pred_per_img = bbox_pred_per_img[index] + ''' # fangyi modify end + + if rescale: + scale_factor = img_metas[img_id]['scale_factor'] + record['scale_factor'] = scale_factor.tolist() + bbox_pred_per_img /= bbox_pred_per_img.new_tensor(scale_factor) + det_bboxes.append( + torch.cat([bbox_pred_per_img, scores_per_img[:, None]], dim=1)) + det_labels.append(labels_per_img) + + bbox_results = [ + bbox2result(det_bboxes[i], det_labels[i], num_classes) + for i in range(num_imgs) + ] + + #self.recoder.append(record) + if len(self.recoder) == 5000: + cccu.MetaSaver(self.recoder, 'metaquery.pkl') + + return bbox_results + + def aug_test(self, x, bboxes_list, img_metas, rescale=False): + raise NotImplementedError() + + def forward_dummy(self, x, + query_xyzr, + query_content, + img_metas): + """Dummy forward function when do the flops computing.""" + all_stage_bbox_results = [] + + num_imgs = len(img_metas) + if self.with_bbox: + for stage in range(self.num_stages): + bbox_results = self._bbox_forward(stage, x, + query_xyzr, + query_content, + img_metas) + all_stage_bbox_results.append(bbox_results) + query_content = bbox_results['query_content'] + query_xyzr = bbox_results['query_xyzr'] + return all_stage_bbox_results diff --git a/mmdet/models/roi_heads/adamixer_decoder_Qrecycle.py b/mmdet/models/roi_heads/adamixer_decoder_Qrecycle.py new file mode 100644 index 0000000..d3f3f25 --- /dev/null +++ b/mmdet/models/roi_heads/adamixer_decoder_Qrecycle.py @@ -0,0 +1,274 @@ +import torch + +from mmdet.core import bbox2result, bbox2roi, bbox_xyxy_to_cxcywh +from mmdet.core.bbox.samplers import PseudoSampler +from ..builder import HEADS +from .cascade_roi_head import CascadeRoIHead +import cccu +import os +DEBUG = 'DEBUG' in os.environ + + +@HEADS.register_module() +class AdaMixerDecoder_Qrecycle(CascadeRoIHead): # Fangyi: accumulated query with loss + _DEBUG = -1 + + def __init__(self, + num_stages=6, + stage_loss_weights=(1, 1, 1, 1, 1, 1), + content_dim=256, + featmap_strides=[4, 8, 16, 32], + bbox_head=None, + train_cfg=None, + test_cfg=None, + pretrained=None, + recycle=[], + start_q=None, + end_q =None, + init_cfg=None): + assert bbox_head is not None + assert len(stage_loss_weights) == num_stages + self.featmap_strides = featmap_strides + self.num_stages = num_stages + self.stage_loss_weights = stage_loss_weights + self.content_dim = content_dim + self.recycle = recycle + self.start_q = start_q + self.end_q = end_q + super(AdaMixerDecoder_Qrecycle, self).__init__( + num_stages, + stage_loss_weights, + bbox_roi_extractor=dict( + # This does not mean that our method need RoIAlign. We put this + # as a placeholder to satisfy the argument for the parent class + # 'CascadeRoIHead'. + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=bbox_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) + # train_cfg would be None when run the test.py + if train_cfg is not None: + for stage in range(num_stages): + assert isinstance(self.bbox_sampler[stage], PseudoSampler) + + def _bbox_forward(self, stage, img_feat, query_xyzr, query_content, img_metas): + num_imgs = len(img_metas) + bbox_head = self.bbox_head[stage] + + cls_score, delta_xyzr, query_content = bbox_head(img_feat, query_xyzr, + query_content, + featmap_strides=self.featmap_strides) + + query_xyzr, decoded_bboxes = self.bbox_head[stage].refine_xyzr( + query_xyzr, + delta_xyzr) + + bboxes_list = [bboxes for bboxes in decoded_bboxes] + + bbox_results = dict( + cls_score=cls_score, + query_xyzr=query_xyzr, + decode_bbox_pred=decoded_bboxes, + query_content=query_content, + detach_cls_score_list=[ + cls_score[i].detach() for i in range(num_imgs) + ], + detach_bboxes_list=[item.detach() for item in bboxes_list], + bboxes_list=bboxes_list, + ) + if DEBUG: + with torch.no_grad(): + torch.save( + bbox_results, 'demo/bbox_results_{}.pth'.format(AdaMixerDecoder_Qrecycle._DEBUG)) + AdaMixerDecoder_Qrecycle._DEBUG += 1 + return bbox_results + + def forward_train(self, + x, + query_xyzr, + query_content, + img_metas, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + imgs_whwh=None, + gt_masks=None): + + num_imgs = len(img_metas) + num_queries = query_xyzr.size(1) + imgs_whwh = imgs_whwh.repeat(1, num_queries, 1) + all_stage_bbox_results = [] + all_stage_loss = {} + + query_xyzr_list_reserve = [query_xyzr] + query_content_list_reserve = [query_content] + + for stage in range(self.num_stages): + single_stage_group_loss = [] + start_q = self.start_q[stage] + end_q = self.end_q[stage] + query_xyzr_list = query_xyzr_list_reserve.copy()[start_q:end_q] + query_content_list = query_content_list_reserve.copy()[start_q:end_q] + for groupid, (query_xyzr, query_content) in enumerate(zip(query_xyzr_list, query_content_list)): + bbox_results = self._bbox_forward(stage, x, query_xyzr, query_content, + img_metas) + all_stage_bbox_results.append(bbox_results) + if gt_bboxes_ignore is None: + # TODO support ignore + gt_bboxes_ignore = [None for _ in range(num_imgs)] + sampling_results = [] + cls_pred_list = bbox_results['detach_cls_score_list'] + bboxes_list = bbox_results['detach_bboxes_list'] + + query_xyzr_new = bbox_results['query_xyzr'].detach() + query_content_new = bbox_results['query_content'] + # TODO: detach query content for noisy querys because not going to use them anyway? + # TODO: only append important query groups, e.x. from the last layer + query_xyzr_list_reserve.append(query_xyzr_new) + query_content_list_reserve.append(query_content_new) + + for i in range(num_imgs): + normalize_bbox_ccwh = bbox_xyxy_to_cxcywh(bboxes_list[i] / + imgs_whwh[i]) + assign_result = self.bbox_assigner[stage].assign( + normalize_bbox_ccwh, cls_pred_list[i], gt_bboxes[i], + gt_labels[i], img_metas[i]) + sampling_result = self.bbox_sampler[stage].sample( + assign_result, bboxes_list[i], gt_bboxes[i]) + sampling_results.append(sampling_result) + bbox_targets = self.bbox_head[stage].get_targets( + sampling_results, gt_bboxes, gt_labels, self.train_cfg[stage], + True) + + cls_score = bbox_results['cls_score'] + decode_bbox_pred = bbox_results['decode_bbox_pred'] + + single_stage_group_loss.append(self.bbox_head[stage].loss( + cls_score.view(-1, cls_score.size(-1)), + decode_bbox_pred.view(-1, 4), + *bbox_targets, + imgs_whwh=imgs_whwh) + ) + + # TODO: weight group loss: for the most important group weight it the highest + for groupid, single_stage_single_group_loss in enumerate(single_stage_group_loss): + if groupid == 0: + for key, value in single_stage_single_group_loss.items(): + all_stage_loss[f'stage{stage}_{key}'] = value * \ + self.stage_loss_weights[stage] + else: + for key, value in single_stage_single_group_loss.items(): + all_stage_loss[f'stage{stage}_{key}'] += value * \ + self.stage_loss_weights[stage] + + return all_stage_loss + + + def simple_test(self, + x, + query_xyzr, + query_content, + img_metas, + imgs_whwh, + rescale=False): + assert self.with_bbox, 'Bbox head must be implemented.' + if DEBUG: + torch.save(img_metas, 'demo/img_metas.pth') + + num_imgs = len(img_metas) + + for stage in range(self.num_stages): + s = stage + + #query_xyzr_keep = query_xyzr + #query_content_keep = query_content + + bbox_results = self._bbox_forward( + s, x, query_xyzr, query_content, img_metas) + query_content = bbox_results['query_content'] + cls_score = bbox_results['cls_score'] + bboxes_list = bbox_results['detach_bboxes_list'] + query_xyzr = bbox_results['query_xyzr'] + + #query_xyzr = (query_xyzr * alpha + query_xyzr_keep) / (1+alpha) + #query_content = (query_content * alpha + query_content_keep) / (1+alpha) + + num_classes = self.bbox_head[-1].num_classes + det_bboxes = [] + det_labels = [] + + if self.bbox_head[-1].loss_cls.use_sigmoid: + cls_score = cls_score.sigmoid() + else: + cls_score = cls_score.softmax(-1)[..., :-1] + + for img_id in range(num_imgs): + + cls_score_per_img = cls_score[img_id] + scores_per_img, topk_indices = cls_score_per_img.flatten( + 0, 1).topk( + self.test_cfg.max_per_img, sorted=False) + labels_per_img = topk_indices % num_classes + # a = topk_indices // num_classes + bbox_pred_per_img = bboxes_list[img_id][topk_indices // + num_classes] + + ''' # fangyi modify start + # The following is a my implementation for testing where each query only produce one result + cls_score_per_img = cls_score[img_id] + scores_per_img, labels_per_img = torch.max(cls_score_per_img, dim=1) + bbox_pred_per_img = bboxes_list[img_id] + # record query bias on class + ct = cccu.counter(log_name='adamixer_query_predcls_temp.txt', matrixshape=(300, 80)) + for i, label_per_img in enumerate(labels_per_img): + ct.m[i, label_per_img] += 1 + ct.record() + # filter out low confident # proved not useful at all + # index = (scores_per_img > 0.02).nonzero().squeeze() + # if len(index.shape) != 0: + # scores_per_img = scores_per_img[index] + # labels_per_img = labels_per_img[index] + # bbox_pred_per_img = bbox_pred_per_img[index] + ''' # fangyi modify end + + if rescale: + scale_factor = img_metas[img_id]['scale_factor'] + bbox_pred_per_img /= bbox_pred_per_img.new_tensor(scale_factor) + det_bboxes.append( + torch.cat([bbox_pred_per_img, scores_per_img[:, None]], dim=1)) + det_labels.append(labels_per_img) + + bbox_results = [ + bbox2result(det_bboxes[i], det_labels[i], num_classes) + for i in range(num_imgs) + ] + + return bbox_results + + def aug_test(self, x, bboxes_list, img_metas, rescale=False): + raise NotImplementedError() + + def forward_dummy(self, x, + query_xyzr, + query_content, + img_metas): + """Dummy forward function when do the flops computing.""" + all_stage_bbox_results = [] + + num_imgs = len(img_metas) + if self.with_bbox: + for stage in range(self.num_stages): + bbox_results = self._bbox_forward(stage, x, + query_xyzr, + query_content, + img_metas) + all_stage_bbox_results.append(bbox_results) + query_content = bbox_results['query_content'] + query_xyzr = bbox_results['query_xyzr'] + return all_stage_bbox_results diff --git a/mmdet/models/roi_heads/adamixer_decoder_Qrecycle_optimize.py b/mmdet/models/roi_heads/adamixer_decoder_Qrecycle_optimize.py new file mode 100644 index 0000000..cc44e37 --- /dev/null +++ b/mmdet/models/roi_heads/adamixer_decoder_Qrecycle_optimize.py @@ -0,0 +1,294 @@ +import torch + +from mmdet.core import bbox2result, bbox2roi, bbox_xyxy_to_cxcywh +from mmdet.core.bbox.samplers import PseudoSampler +from ..builder import HEADS +from .cascade_roi_head import CascadeRoIHead +import cccu +import os +DEBUG = 'DEBUG' in os.environ + + +@HEADS.register_module() +class AdaMixerDecoder_Qrecycle_optimize(CascadeRoIHead): # Fangyi: accumulated query with loss + _DEBUG = -1 + + def __init__(self, + num_stages=6, + stage_loss_weights=(1, 1, 1, 1, 1, 1), + content_dim=256, + featmap_strides=[4, 8, 16, 32], + bbox_head=None, + train_cfg=None, + test_cfg=None, + pretrained=None, + recycle=[], + start_q=None, + end_q =None, + init_cfg=None): + assert bbox_head is not None + assert len(stage_loss_weights) == num_stages + self.featmap_strides = featmap_strides + self.num_stages = num_stages + self.stage_loss_weights = stage_loss_weights + self.content_dim = content_dim + self.recycle = recycle + self.start_q = start_q + self.end_q = end_q + super(AdaMixerDecoder_Qrecycle_optimize, self).__init__( + num_stages, + stage_loss_weights, + bbox_roi_extractor=dict( + # This does not mean that our method need RoIAlign. We put this + # as a placeholder to satisfy the argument for the parent class + # 'CascadeRoIHead'. + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=bbox_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) + # train_cfg would be None when run the test.py + if train_cfg is not None: + for stage in range(num_stages): + assert isinstance(self.bbox_sampler[stage], PseudoSampler) + + def _bbox_forward(self, stage, img_feat, query_xyzr, query_content, img_metas): + num_imgs = len(img_metas) + bbox_head = self.bbox_head[stage] + + cls_score, delta_xyzr, query_content = bbox_head(img_feat, query_xyzr, + query_content, + featmap_strides=self.featmap_strides) + + query_xyzr, decoded_bboxes = self.bbox_head[stage].refine_xyzr( + query_xyzr, + delta_xyzr) + + bboxes_list = [bboxes for bboxes in decoded_bboxes] + + bbox_results = dict( + cls_score=cls_score, + query_xyzr=query_xyzr, + decode_bbox_pred=decoded_bboxes, + query_content=query_content, + detach_cls_score_list=[ + cls_score[i].detach() for i in range(num_imgs) + ], + detach_bboxes_list=[item.detach() for item in bboxes_list], + bboxes_list=bboxes_list, + ) + if DEBUG: + with torch.no_grad(): + torch.save( + bbox_results, 'demo/bbox_results_{}.pth'.format(AdaMixerDecoder_Qrecycle_optimize._DEBUG)) + AdaMixerDecoder_Qrecycle_optimize._DEBUG += 1 + return bbox_results + + def forward_train(self, + x, + query_xyzr, + query_content, + img_metas, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + imgs_whwh=None, + gt_masks=None): + + num_imgs = len(img_metas) + num_queries = query_xyzr.size(1) + imgs_whwh_keep = imgs_whwh.repeat(1, num_queries, 1) + imgs_whwh = imgs_whwh.repeat(1, num_queries, 1) + + batchsize = len(img_metas) + x_keep = [_ for _ in x] + img_metas_keep = img_metas.copy() + gt_bboxes_keep = gt_bboxes.copy() + gt_labels_keep = gt_labels.copy() + + + all_stage_bbox_results = [] + all_stage_loss = {} + query_xyzr_list_reserve = [query_xyzr] + query_content_list_reserve = [query_content] + + for stage in range(self.num_stages): + + single_stage_group_loss_new = [] + start_q = self.start_q[stage] + end_q = self.end_q[stage] + query_xyzr_list = query_xyzr_list_reserve.copy()[start_q:end_q] + query_content_list = query_content_list_reserve.copy()[start_q:end_q] + + query_xyzr = torch.cat(query_xyzr_list, dim=0) + query_content = torch.cat(query_content_list, dim=0) + + fakesetsize = int(len(query_xyzr) / batchsize) + x_new = [x_.repeat(fakesetsize, 1, 1, 1) for x_ in x_keep] + img_metas_ = img_metas_keep * fakesetsize + gt_bboxes_ = gt_bboxes_keep * fakesetsize + gt_labels_ = gt_labels_keep * fakesetsize + imgs_whwh_ = imgs_whwh_keep.repeat(fakesetsize, 1, 1) + + bbox_results = self._bbox_forward(stage, x_new, query_xyzr, query_content, + img_metas_) + all_stage_bbox_results.append(bbox_results) + if gt_bboxes_ignore is None: + # TODO support ignore + gt_bboxes_ignore = [None for _ in range(num_imgs)] + sampling_results = [] + cls_pred_list = bbox_results['detach_cls_score_list'] + bboxes_list = bbox_results['detach_bboxes_list'] + + query_xyzr_new = bbox_results['query_xyzr'].detach() + query_content_new = bbox_results['query_content'] + # TODO: detach query content for noisy querys because not going to use them anyway? + # TODO: only append important query groups, e.x. from the last layer + + query_xyzr_list_reserve.extend([query_xyzr_new[i*2:i*2+batchsize] for i in range(fakesetsize)]) + query_content_list_reserve.extend([query_content_new[i*2:i*2+batchsize] for i in range(fakesetsize)]) + + for i in range(num_imgs*fakesetsize): + normalize_bbox_ccwh = bbox_xyxy_to_cxcywh(bboxes_list[i] / + imgs_whwh_[i]) + assign_result = self.bbox_assigner[stage].assign( + normalize_bbox_ccwh, cls_pred_list[i], gt_bboxes_[i], + gt_labels_[i], img_metas_[i]) + sampling_result = self.bbox_sampler[stage].sample( + assign_result, bboxes_list[i], gt_bboxes_[i]) + sampling_results.append(sampling_result) + bbox_targets = self.bbox_head[stage].get_targets( + sampling_results, gt_bboxes_, gt_labels_, self.train_cfg[stage], + True) + + cls_score = bbox_results['cls_score'] + decode_bbox_pred = bbox_results['decode_bbox_pred'] + + single_stage_group_loss_new.append(self.bbox_head[stage].loss( + cls_score.view(-1, cls_score.size(-1)), + decode_bbox_pred.view(-1, 4), + *bbox_targets, + imgs_whwh=imgs_whwh_) + ) + + # TODO: weight group loss: for the most important group weight it the highest + for groupid, single_stage_single_group_loss in enumerate(single_stage_group_loss_new): + if groupid == 0: + for key, value in single_stage_single_group_loss.items(): + all_stage_loss[f'stage{stage}_{key}'] = value * \ + self.stage_loss_weights[stage] + else: + for key, value in single_stage_single_group_loss.items(): + all_stage_loss[f'stage{stage}_{key}'] += value * \ + self.stage_loss_weights[stage] + + return all_stage_loss + + + def simple_test(self, + x, + query_xyzr, + query_content, + img_metas, + imgs_whwh, + rescale=False): + assert self.with_bbox, 'Bbox head must be implemented.' + if DEBUG: + torch.save(img_metas, 'demo/img_metas.pth') + + num_imgs = len(img_metas) + + for stage in range(self.num_stages): + s = stage + + #query_xyzr_keep = query_xyzr + #query_content_keep = query_content + + bbox_results = self._bbox_forward( + s, x, query_xyzr, query_content, img_metas) + query_content = bbox_results['query_content'] + cls_score = bbox_results['cls_score'] + bboxes_list = bbox_results['detach_bboxes_list'] + query_xyzr = bbox_results['query_xyzr'] + + #query_xyzr = (query_xyzr * alpha + query_xyzr_keep) / (1+alpha) + #query_content = (query_content * alpha + query_content_keep) / (1+alpha) + + num_classes = self.bbox_head[-1].num_classes + det_bboxes = [] + det_labels = [] + + if self.bbox_head[-1].loss_cls.use_sigmoid: + cls_score = cls_score.sigmoid() + else: + cls_score = cls_score.softmax(-1)[..., :-1] + + for img_id in range(num_imgs): + + cls_score_per_img = cls_score[img_id] + scores_per_img, topk_indices = cls_score_per_img.flatten( + 0, 1).topk( + self.test_cfg.max_per_img, sorted=False) + labels_per_img = topk_indices % num_classes + # a = topk_indices // num_classes + bbox_pred_per_img = bboxes_list[img_id][topk_indices // + num_classes] + + ''' # fangyi modify start + # The following is a my implementation for testing where each query only produce one result + cls_score_per_img = cls_score[img_id] + scores_per_img, labels_per_img = torch.max(cls_score_per_img, dim=1) + bbox_pred_per_img = bboxes_list[img_id] + # record query bias on class + ct = cccu.counter(log_name='adamixer_query_predcls_temp.txt', matrixshape=(300, 80)) + for i, label_per_img in enumerate(labels_per_img): + ct.m[i, label_per_img] += 1 + ct.record() + # filter out low confident # proved not useful at all + # index = (scores_per_img > 0.02).nonzero().squeeze() + # if len(index.shape) != 0: + # scores_per_img = scores_per_img[index] + # labels_per_img = labels_per_img[index] + # bbox_pred_per_img = bbox_pred_per_img[index] + ''' # fangyi modify end + + if rescale: + scale_factor = img_metas[img_id]['scale_factor'] + bbox_pred_per_img /= bbox_pred_per_img.new_tensor(scale_factor) + det_bboxes.append( + torch.cat([bbox_pred_per_img, scores_per_img[:, None]], dim=1)) + det_labels.append(labels_per_img) + + bbox_results = [ + bbox2result(det_bboxes[i], det_labels[i], num_classes) + for i in range(num_imgs) + ] + + return bbox_results + + def aug_test(self, x, bboxes_list, img_metas, rescale=False): + raise NotImplementedError() + + def forward_dummy(self, x, + query_xyzr, + query_content, + img_metas): + """Dummy forward function when do the flops computing.""" + all_stage_bbox_results = [] + + num_imgs = len(img_metas) + if self.with_bbox: + for stage in range(self.num_stages): + bbox_results = self._bbox_forward(stage, x, + query_xyzr, + query_content, + img_metas) + all_stage_bbox_results.append(bbox_results) + query_content = bbox_results['query_content'] + query_xyzr = bbox_results['query_xyzr'] + return all_stage_bbox_results diff --git a/mmdet/models/roi_heads/adamixer_decoder_aq.py b/mmdet/models/roi_heads/adamixer_decoder_aq.py new file mode 100644 index 0000000..f3df0cf --- /dev/null +++ b/mmdet/models/roi_heads/adamixer_decoder_aq.py @@ -0,0 +1,260 @@ +import torch + +from mmdet.core import bbox2result, bbox2roi, bbox_xyxy_to_cxcywh +from mmdet.core.bbox.samplers import PseudoSampler +from ..builder import HEADS +from .cascade_roi_head import CascadeRoIHead +import cccu +import os +DEBUG = 'DEBUG' in os.environ + + +@HEADS.register_module() +class AdaMixerDecoder_aq(CascadeRoIHead): + _DEBUG = -1 + + def __init__(self, + num_stages=6, + stage_loss_weights=(1, 1, 1, 1, 1, 1), + content_dim=256, + featmap_strides=[4, 8, 16, 32], + bbox_head=None, + train_cfg=None, + test_cfg=None, + pretrained=None, + recycle=[], + init_cfg=None): + assert bbox_head is not None + assert len(stage_loss_weights) == num_stages + self.featmap_strides = featmap_strides + self.num_stages = num_stages + self.stage_loss_weights = stage_loss_weights + self.content_dim = content_dim + self.recycle = recycle + super(AdaMixerDecoder_aq, self).__init__( + num_stages, + stage_loss_weights, + bbox_roi_extractor=dict( + # This does not mean that our method need RoIAlign. We put this + # as a placeholder to satisfy the argument for the parent class + # 'CascadeRoIHead'. + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=bbox_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) + # train_cfg would be None when run the test.py + if train_cfg is not None: + for stage in range(num_stages): + assert isinstance(self.bbox_sampler[stage], PseudoSampler) + + def _bbox_forward(self, stage, img_feat, query_xyzr, query_content, img_metas): + num_imgs = len(img_metas) + bbox_head = self.bbox_head[stage] + + cls_score, delta_xyzr, query_content = bbox_head(img_feat, query_xyzr, + query_content, + featmap_strides=self.featmap_strides) + + query_xyzr, decoded_bboxes = self.bbox_head[stage].refine_xyzr( + query_xyzr, + delta_xyzr) + + bboxes_list = [bboxes for bboxes in decoded_bboxes] + + bbox_results = dict( + cls_score=cls_score, + query_xyzr=query_xyzr, + decode_bbox_pred=decoded_bboxes, + query_content=query_content, + detach_cls_score_list=[ + cls_score[i].detach() for i in range(num_imgs) + ], + detach_bboxes_list=[item.detach() for item in bboxes_list], + bboxes_list=bboxes_list, + ) + if DEBUG: + with torch.no_grad(): + torch.save( + bbox_results, 'demo/bbox_results_{}.pth'.format(AdaMixerDecoder._DEBUG)) + AdaMixerDecoder_aq._DEBUG += 1 + return bbox_results + + def forward_train(self, + x, + query_xyzr, + query_content, + img_metas, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + imgs_whwh=None, + gt_masks=None): + + num_imgs = len(img_metas) + imgs_whwh_keep = imgs_whwh + all_stage_bbox_results = [] + all_stage_loss = {} + + for stage in range(self.num_stages): + bbox_results = self._bbox_forward(stage, x, query_xyzr, query_content, + img_metas) + all_stage_bbox_results.append(bbox_results) + if gt_bboxes_ignore is None: + # TODO support ignore + gt_bboxes_ignore = [None for _ in range(num_imgs)] + sampling_results = [] + cls_pred_list = bbox_results['detach_cls_score_list'] + bboxes_list = bbox_results['detach_bboxes_list'] + + query_xyzr_new = bbox_results['query_xyzr'].detach() + query_content_new = bbox_results['query_content'] + + num_queries = query_xyzr_new.size(1) + imgs_whwh = imgs_whwh_keep.repeat(1, num_queries, 1) + if (stage >= self.recycle[0]) and (stage < self.recycle[1]): + query_xyzr = torch.cat((query_xyzr, query_xyzr_new), dim=1) + query_content = torch.cat((query_content, query_content_new), dim=1) + else: + query_xyzr = query_xyzr_new + query_content = query_content_new + + + if self.stage_loss_weights[stage] <= 0: + continue + + for i in range(num_imgs): + normalize_bbox_ccwh = bbox_xyxy_to_cxcywh(bboxes_list[i] / + imgs_whwh[i]) + assign_result = self.bbox_assigner[stage].assign( + normalize_bbox_ccwh, cls_pred_list[i], gt_bboxes[i], + gt_labels[i], img_metas[i]) + sampling_result = self.bbox_sampler[stage].sample( + assign_result, bboxes_list[i], gt_bboxes[i]) + sampling_results.append(sampling_result) + bbox_targets = self.bbox_head[stage].get_targets( + sampling_results, gt_bboxes, gt_labels, self.train_cfg[stage], + True) + + cls_score = bbox_results['cls_score'] + decode_bbox_pred = bbox_results['decode_bbox_pred'] + + single_stage_loss = self.bbox_head[stage].loss( + cls_score.view(-1, cls_score.size(-1)), + decode_bbox_pred.view(-1, 4), + *bbox_targets, + imgs_whwh=imgs_whwh) + for key, value in single_stage_loss.items(): + all_stage_loss[f'stage{stage}_{key}'] = value * \ + self.stage_loss_weights[stage] + + return all_stage_loss + + def simple_test(self, + x, + query_xyzr, + query_content, + img_metas, + imgs_whwh, + rescale=False): + assert self.with_bbox, 'Bbox head must be implemented.' + if DEBUG: + torch.save(img_metas, 'demo/img_metas.pth') + + num_imgs = len(img_metas) + + for stage in range(self.num_stages): + bbox_results = self._bbox_forward( + stage, x, query_xyzr, query_content, img_metas) + query_content_new = bbox_results['query_content'] + cls_score = bbox_results['cls_score'] + bboxes_list = bbox_results['detach_bboxes_list'] + query_xyzr_new = bbox_results['query_xyzr'] + + if (stage >= self.recycle[0]) and (stage < self.recycle[1]): + query_xyzr = torch.cat((query_xyzr, query_xyzr_new), dim=1) + query_content = torch.cat((query_content, query_content_new), dim=1) + else: + query_xyzr = query_xyzr_new + query_content = query_content_new + + + num_classes = self.bbox_head[-1].num_classes + det_bboxes = [] + det_labels = [] + + if self.bbox_head[-1].loss_cls.use_sigmoid: + cls_score = cls_score.sigmoid() + else: + cls_score = cls_score.softmax(-1)[..., :-1] + + for img_id in range(num_imgs): + + cls_score_per_img = cls_score[img_id] + scores_per_img, topk_indices = cls_score_per_img.flatten( + 0, 1).topk( + self.test_cfg.max_per_img, sorted=False) + labels_per_img = topk_indices % num_classes + a = topk_indices // num_classes + bbox_pred_per_img = bboxes_list[img_id][topk_indices // + num_classes] + + ''' # fangyi modify start + # The following is a my implementation for testing where each query only produce one result + cls_score_per_img = cls_score[img_id] + scores_per_img, labels_per_img = torch.max(cls_score_per_img, dim=1) + bbox_pred_per_img = bboxes_list[img_id] + + # record query bias on class + ct = cccu.counter(log_name='adamixer_query_predcls_temp.txt', matrixshape=(300, 80)) + for i, label_per_img in enumerate(labels_per_img): + ct.m[i, label_per_img] += 1 + ct.record() + # filter out low confident # proved not useful at all + # index = (scores_per_img > 0.02).nonzero().squeeze() + # if len(index.shape) != 0: + # scores_per_img = scores_per_img[index] + # labels_per_img = labels_per_img[index] + # bbox_pred_per_img = bbox_pred_per_img[index] + ''' # fangyi modify end + + if rescale: + scale_factor = img_metas[img_id]['scale_factor'] + bbox_pred_per_img /= bbox_pred_per_img.new_tensor(scale_factor) + det_bboxes.append( + torch.cat([bbox_pred_per_img, scores_per_img[:, None]], dim=1)) + det_labels.append(labels_per_img) + + bbox_results = [ + bbox2result(det_bboxes[i], det_labels[i], num_classes) + for i in range(num_imgs) + ] + + return bbox_results + + def aug_test(self, x, bboxes_list, img_metas, rescale=False): + raise NotImplementedError() + + def forward_dummy(self, x, + query_xyzr, + query_content, + img_metas): + """Dummy forward function when do the flops computing.""" + all_stage_bbox_results = [] + + num_imgs = len(img_metas) + if self.with_bbox: + for stage in range(self.num_stages): + bbox_results = self._bbox_forward(stage, x, + query_xyzr, + query_content, + img_metas) + all_stage_bbox_results.append(bbox_results) + query_content = bbox_results['query_content'] + query_xyzr = bbox_results['query_xyzr'] + return all_stage_bbox_results diff --git a/mmdet/models/roi_heads/adamixer_decoder_aql.py b/mmdet/models/roi_heads/adamixer_decoder_aql.py new file mode 100644 index 0000000..5403089 --- /dev/null +++ b/mmdet/models/roi_heads/adamixer_decoder_aql.py @@ -0,0 +1,469 @@ +import torch + +from mmdet.core import bbox2result, bbox2roi, bbox_xyxy_to_cxcywh +from mmdet.core.bbox.samplers import PseudoSampler +from ..builder import HEADS +from .cascade_roi_head import CascadeRoIHead +import cccu +import os +DEBUG = 'DEBUG' in os.environ + + +@HEADS.register_module() +class AdaMixerDecoder_aql(CascadeRoIHead): # Fangyi: accumulated query with loss + _DEBUG = -1 + + def __init__(self, + num_stages=6, + stage_loss_weights=(1, 1, 1, 1, 1, 1), + content_dim=256, + featmap_strides=[4, 8, 16, 32], + bbox_head=None, + train_cfg=None, + test_cfg=None, + pretrained=None, + recycle=[], + init_cfg=None): + assert bbox_head is not None + assert len(stage_loss_weights) == num_stages + self.featmap_strides = featmap_strides + self.num_stages = num_stages + self.stage_loss_weights = stage_loss_weights + self.content_dim = content_dim + self.recycle = recycle + super(AdaMixerDecoder_aql, self).__init__( + num_stages, + stage_loss_weights, + bbox_roi_extractor=dict( + # This does not mean that our method need RoIAlign. We put this + # as a placeholder to satisfy the argument for the parent class + # 'CascadeRoIHead'. + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=bbox_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) + # train_cfg would be None when run the test.py + if train_cfg is not None: + for stage in range(num_stages): + assert isinstance(self.bbox_sampler[stage], PseudoSampler) + + def _bbox_forward(self, stage, img_feat, query_xyzr, query_content, img_metas): + num_imgs = len(img_metas) + bbox_head = self.bbox_head[stage] + + cls_score, delta_xyzr, query_content = bbox_head(img_feat, query_xyzr, + query_content, + featmap_strides=self.featmap_strides) + + query_xyzr, decoded_bboxes = self.bbox_head[stage].refine_xyzr( + query_xyzr, + delta_xyzr) + + bboxes_list = [bboxes for bboxes in decoded_bboxes] + + bbox_results = dict( + cls_score=cls_score, + query_xyzr=query_xyzr, + decode_bbox_pred=decoded_bboxes, + query_content=query_content, + detach_cls_score_list=[ + cls_score[i].detach() for i in range(num_imgs) + ], + detach_bboxes_list=[item.detach() for item in bboxes_list], + bboxes_list=bboxes_list, + ) + if DEBUG: + with torch.no_grad(): + torch.save( + bbox_results, 'demo/bbox_results_{}.pth'.format(AdaMixerDecoder_aql._DEBUG)) + AdaMixerDecoder_aql._DEBUG += 1 + return bbox_results + + def forward_train(self, + x, + query_xyzr, + query_content, + img_metas, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + imgs_whwh=None, + gt_masks=None): + + num_imgs = len(img_metas) + num_queries = query_xyzr.size(1) + imgs_whwh_keep = imgs_whwh.repeat(1, num_queries, 1) + all_stage_bbox_results = [] + all_stage_loss = {} + + query_xyzr_list_reserve = [query_xyzr] + query_content_list_reserve = [query_content] + query_xyzr_list_reserve_last = [] + query_content_list_reserve_last = [] + + batchsize = len(img_metas) + fakesetsize = 2 # 8 will reduce 16 hours; 2 will reduce 9 hours; 4 will reduce 15 hours + x_keep = [_ for _ in x] + img_metas_keep = img_metas.copy() + gt_bboxes_keep = gt_bboxes.copy() + gt_labels_keep = gt_labels.copy() + for stage in range(self.num_stages+1): + + if stage == self.num_stages: # at the latest stage + query_xyzr = torch.cat(query_xyzr_list_reserve_last, dim=0) + query_content = torch.cat(query_content_list_reserve_last, dim=0) + setsize = int(len(query_content) / batchsize) + if setsize > fakesetsize: + single_stage_group_loss = [] + num_group = int(setsize / fakesetsize) + + # x = [x_.repeat(fakesetsize, 1, 1, 1) for x_ in x_keep] + # img_metas = img_metas_keep * fakesetsize + # gt_bboxes = gt_bboxes_keep * fakesetsize + # gt_labels = gt_labels_keep * fakesetsize + # imgs_whwh = imgs_whwh_keep.repeat(fakesetsize, 1, 1) + + for groupid in range(num_group): + query_xyzr_this_group = query_xyzr[fakesetsize * batchsize * groupid:fakesetsize * batchsize * ( + groupid + 1)] + query_content_this_group = query_content[ + fakesetsize * batchsize * groupid:fakesetsize * batchsize * ( + groupid + 1)] + bbox_results = self._bbox_forward(stage-1, x, query_xyzr_this_group, query_content_this_group, + img_metas) + # all_stage_bbox_results.append(bbox_results) + if gt_bboxes_ignore is None: + # TODO support ignore + gt_bboxes_ignore = [None for _ in range(num_imgs)] + sampling_results = [] + cls_pred_list = bbox_results['detach_cls_score_list'] + bboxes_list = bbox_results['detach_bboxes_list'] + + # query_xyzr_new = bbox_results['query_xyzr'].detach() + # query_content_new = bbox_results['query_content'] + # TODO: detach query content for noisy querys because not going to use them anyway? + # TODO: only append important query groups, e.x. from the last layer + # query_xyzr_list_reserve.append(query_xyzr_new) + # query_content_list_reserve.append(query_content_new) + + for i in range(num_imgs * fakesetsize): + normalize_bbox_ccwh = bbox_xyxy_to_cxcywh(bboxes_list[i] / + imgs_whwh[i]) + assign_result = self.bbox_assigner[stage-1].assign( + normalize_bbox_ccwh, cls_pred_list[i], gt_bboxes[i], + gt_labels[i], img_metas[i]) + sampling_result = self.bbox_sampler[stage-1].sample( + assign_result, bboxes_list[i], gt_bboxes[i]) + sampling_results.append(sampling_result) + bbox_targets = self.bbox_head[stage-1].get_targets( + sampling_results, gt_bboxes, gt_labels, self.train_cfg[stage-1], + True) + + cls_score = bbox_results['cls_score'] + decode_bbox_pred = bbox_results['decode_bbox_pred'] + + single_stage_group_loss.append(self.bbox_head[stage-1].loss( + cls_score.view(-1, cls_score.size(-1)), + decode_bbox_pred.view(-1, 4), + *bbox_targets, + imgs_whwh=imgs_whwh) + ) + + # TODO: weight group loss: for the most important group weight it the highest + # TODO: multiply fakesetsize for each loss or not multiply? Do not forget to modify the setsize below + for groupid, single_stage_single_group_loss in enumerate(single_stage_group_loss): + if groupid == 0: + for key, value in single_stage_single_group_loss.items(): + all_stage_loss[f'stage{stage}_{key}'] = value * \ + self.stage_loss_weights[stage-1] * fakesetsize + else: + for key, value in single_stage_single_group_loss.items(): + all_stage_loss[f'stage{stage}_{key}'] += value * \ + self.stage_loss_weights[stage-1] * fakesetsize + else: + # x = [x_.repeat(setsize, 1, 1, 1) for x_ in x_keep] + # img_metas = img_metas_keep * setsize + # gt_bboxes = gt_bboxes_keep * setsize + # gt_labels = gt_labels_keep * setsize + # imgs_whwh = imgs_whwh_keep.repeat(setsize, 1, 1) + + bbox_results = self._bbox_forward(stage-1, x, query_xyzr, query_content, + img_metas) + all_stage_bbox_results.append(bbox_results) + if gt_bboxes_ignore is None: + # TODO support ignore + gt_bboxes_ignore = [None for _ in range(num_imgs)] + sampling_results = [] + cls_pred_list = bbox_results['detach_cls_score_list'] + bboxes_list = bbox_results['detach_bboxes_list'] + + # query_xyzr_new = bbox_results['query_xyzr'].detach() + # query_content_new = bbox_results['query_content'] + # TODO: detach query content for noisy querys because not going to use them anyway? + # TODO: only append important query groups, e.x. from the last layer + # query_xyzr_list_reserve.append(query_xyzr_new) + # query_content_list_reserve.append(query_content_new) + + for i in range(num_imgs * setsize): + normalize_bbox_ccwh = bbox_xyxy_to_cxcywh(bboxes_list[i] / + imgs_whwh[i]) + assign_result = self.bbox_assigner[stage-1].assign( + normalize_bbox_ccwh, cls_pred_list[i], gt_bboxes[i], + gt_labels[i], img_metas[i]) + sampling_result = self.bbox_sampler[stage-1].sample( + assign_result, bboxes_list[i], gt_bboxes[i]) + sampling_results.append(sampling_result) + bbox_targets = self.bbox_head[stage-1].get_targets( + sampling_results, gt_bboxes, gt_labels, self.train_cfg[stage-1], + True) + + cls_score = bbox_results['cls_score'] + decode_bbox_pred = bbox_results['decode_bbox_pred'] + + single_stage_group_loss = self.bbox_head[stage-1].loss( + cls_score.view(-1, cls_score.size(-1)), + decode_bbox_pred.view(-1, 4), + *bbox_targets, + imgs_whwh=imgs_whwh) + + # TODO: multiply setsize for each loss or not multiply? Do not forget to modify the fakesetsize above + for key, value in single_stage_group_loss.items(): + all_stage_loss[f'stage{stage}_{key}'] = value * \ + self.stage_loss_weights[stage-1] * setsize + + return all_stage_loss + + query_xyzr = torch.cat(query_xyzr_list_reserve, dim=0) + query_content = torch.cat(query_content_list_reserve, dim=0) + setsize = int(len(query_content) / batchsize) + + if setsize > fakesetsize: + single_stage_group_loss = [] + num_group = int(setsize / fakesetsize) + + x = [x_.repeat(fakesetsize, 1, 1, 1) for x_ in x_keep] + img_metas = img_metas_keep * fakesetsize + gt_bboxes = gt_bboxes_keep * fakesetsize + gt_labels = gt_labels_keep * fakesetsize + imgs_whwh = imgs_whwh_keep.repeat(fakesetsize, 1, 1) + + for groupid in range(num_group): + query_xyzr_this_group = query_xyzr[fakesetsize*batchsize*groupid:fakesetsize*batchsize*(groupid+1)] + query_content_this_group = query_content[fakesetsize*batchsize*groupid:fakesetsize*batchsize*(groupid+1)] + bbox_results = self._bbox_forward(stage, x, query_xyzr_this_group, query_content_this_group, + img_metas) + # all_stage_bbox_results.append(bbox_results) + if gt_bboxes_ignore is None: + # TODO support ignore + gt_bboxes_ignore = [None for _ in range(num_imgs)] + sampling_results = [] + cls_pred_list = bbox_results['detach_cls_score_list'] + bboxes_list = bbox_results['detach_bboxes_list'] + + query_xyzr_new = bbox_results['query_xyzr'].detach() + query_content_new = bbox_results['query_content'] + # TODO: detach query content for noisy querys because not going to use them anyway? + # TODO: only append important query groups, e.x. from the last layer + if stage == self.num_stages - 1: + query_xyzr_list_reserve_last.append(query_xyzr_new) + query_content_list_reserve_last.append(query_content_new) + else: + query_xyzr_list_reserve.append(query_xyzr_new) + query_content_list_reserve.append(query_content_new) + + for i in range(num_imgs * fakesetsize): + normalize_bbox_ccwh = bbox_xyxy_to_cxcywh(bboxes_list[i] / + imgs_whwh[i]) + assign_result = self.bbox_assigner[stage].assign( + normalize_bbox_ccwh, cls_pred_list[i], gt_bboxes[i], + gt_labels[i], img_metas[i]) + sampling_result = self.bbox_sampler[stage].sample( + assign_result, bboxes_list[i], gt_bboxes[i]) + sampling_results.append(sampling_result) + bbox_targets = self.bbox_head[stage].get_targets( + sampling_results, gt_bboxes, gt_labels, self.train_cfg[stage], + True) + + cls_score = bbox_results['cls_score'] + decode_bbox_pred = bbox_results['decode_bbox_pred'] + + single_stage_group_loss.append(self.bbox_head[stage].loss( + cls_score.view(-1, cls_score.size(-1)), + decode_bbox_pred.view(-1, 4), + *bbox_targets, + imgs_whwh=imgs_whwh) + ) + + # TODO: weight group loss: for the most important group weight it the highest + # TODO: multiply fakesetsize for each loss or not multiply? Do not forget to modify the setsize below + for groupid, single_stage_single_group_loss in enumerate(single_stage_group_loss): + if groupid == 0: + for key, value in single_stage_single_group_loss.items(): + all_stage_loss[f'stage{stage}_{key}'] = value * \ + self.stage_loss_weights[stage] * fakesetsize + else: + for key, value in single_stage_single_group_loss.items(): + all_stage_loss[f'stage{stage}_{key}'] += value * \ + self.stage_loss_weights[stage] * fakesetsize + else: + x = [x_.repeat(setsize, 1, 1, 1) for x_ in x_keep] + img_metas = img_metas_keep * setsize + gt_bboxes = gt_bboxes_keep * setsize + gt_labels = gt_labels_keep * setsize + imgs_whwh = imgs_whwh_keep.repeat(setsize, 1, 1) + + bbox_results = self._bbox_forward(stage, x, query_xyzr, query_content, + img_metas) + all_stage_bbox_results.append(bbox_results) + if gt_bboxes_ignore is None: + # TODO support ignore + gt_bboxes_ignore = [None for _ in range(num_imgs)] + sampling_results = [] + cls_pred_list = bbox_results['detach_cls_score_list'] + bboxes_list = bbox_results['detach_bboxes_list'] + + query_xyzr_new = bbox_results['query_xyzr'].detach() + query_content_new = bbox_results['query_content'] + # TODO: detach query content for noisy querys because not going to use them anyway? + # TODO: only append important query groups, e.x. from the last layer + if stage == self.num_stages - 1: + query_xyzr_list_reserve_last.append(query_xyzr_new) + query_content_list_reserve_last.append(query_content_new) + else: + query_xyzr_list_reserve.append(query_xyzr_new) + query_content_list_reserve.append(query_content_new) + + for i in range(num_imgs * setsize): + normalize_bbox_ccwh = bbox_xyxy_to_cxcywh(bboxes_list[i] / + imgs_whwh[i]) + assign_result = self.bbox_assigner[stage].assign( + normalize_bbox_ccwh, cls_pred_list[i], gt_bboxes[i], + gt_labels[i], img_metas[i]) + sampling_result = self.bbox_sampler[stage].sample( + assign_result, bboxes_list[i], gt_bboxes[i]) + sampling_results.append(sampling_result) + bbox_targets = self.bbox_head[stage].get_targets( + sampling_results, gt_bboxes, gt_labels, self.train_cfg[stage], + True) + + cls_score = bbox_results['cls_score'] + decode_bbox_pred = bbox_results['decode_bbox_pred'] + + single_stage_group_loss = self.bbox_head[stage].loss( + cls_score.view(-1, cls_score.size(-1)), + decode_bbox_pred.view(-1, 4), + *bbox_targets, + imgs_whwh=imgs_whwh) + + # TODO: multiply setsize for each loss or not multiply? Do not forget to modify the fakesetsize above + for key, value in single_stage_group_loss.items(): + all_stage_loss[f'stage{stage}_{key}'] = value * \ + self.stage_loss_weights[stage] * setsize + + #print(all_stage_loss) + #print(all_stage_lossa) + + def simple_test(self, + x, + query_xyzr, + query_content, + img_metas, + imgs_whwh, + rescale=False): + assert self.with_bbox, 'Bbox head must be implemented.' + if DEBUG: + torch.save(img_metas, 'demo/img_metas.pth') + + num_imgs = len(img_metas) + + for stage in range(2): + if stage >= 1: + s = 5 + else: + s = stage + bbox_results = self._bbox_forward( + s, x, query_xyzr, query_content, img_metas) + query_content = bbox_results['query_content'] + cls_score = bbox_results['cls_score'] + bboxes_list = bbox_results['detach_bboxes_list'] + query_xyzr = bbox_results['query_xyzr'] + + num_classes = self.bbox_head[-1].num_classes + det_bboxes = [] + det_labels = [] + + if self.bbox_head[-1].loss_cls.use_sigmoid: + cls_score = cls_score.sigmoid() + else: + cls_score = cls_score.softmax(-1)[..., :-1] + + for img_id in range(num_imgs): + + cls_score_per_img = cls_score[img_id] + scores_per_img, topk_indices = cls_score_per_img.flatten( + 0, 1).topk( + self.test_cfg.max_per_img, sorted=False) + labels_per_img = topk_indices % num_classes + # a = topk_indices // num_classes + bbox_pred_per_img = bboxes_list[img_id][topk_indices // + num_classes] + + ''' # fangyi modify start + # The following is a my implementation for testing where each query only produce one result + cls_score_per_img = cls_score[img_id] + scores_per_img, labels_per_img = torch.max(cls_score_per_img, dim=1) + bbox_pred_per_img = bboxes_list[img_id] + # record query bias on class + ct = cccu.counter(log_name='adamixer_query_predcls_temp.txt', matrixshape=(300, 80)) + for i, label_per_img in enumerate(labels_per_img): + ct.m[i, label_per_img] += 1 + ct.record() + # filter out low confident # proved not useful at all + # index = (scores_per_img > 0.02).nonzero().squeeze() + # if len(index.shape) != 0: + # scores_per_img = scores_per_img[index] + # labels_per_img = labels_per_img[index] + # bbox_pred_per_img = bbox_pred_per_img[index] + ''' # fangyi modify end + + if rescale: + scale_factor = img_metas[img_id]['scale_factor'] + bbox_pred_per_img /= bbox_pred_per_img.new_tensor(scale_factor) + det_bboxes.append( + torch.cat([bbox_pred_per_img, scores_per_img[:, None]], dim=1)) + det_labels.append(labels_per_img) + + bbox_results = [ + bbox2result(det_bboxes[i], det_labels[i], num_classes) + for i in range(num_imgs) + ] + + return bbox_results + + def aug_test(self, x, bboxes_list, img_metas, rescale=False): + raise NotImplementedError() + + def forward_dummy(self, x, + query_xyzr, + query_content, + img_metas): + """Dummy forward function when do the flops computing.""" + all_stage_bbox_results = [] + + num_imgs = len(img_metas) + if self.with_bbox: + for stage in range(self.num_stages): + bbox_results = self._bbox_forward(stage, x, + query_xyzr, + query_content, + img_metas) + all_stage_bbox_results.append(bbox_results) + query_content = bbox_results['query_content'] + query_xyzr = bbox_results['query_xyzr'] + return all_stage_bbox_results diff --git a/mmdet/models/roi_heads/adamixer_decoder_aql2.py b/mmdet/models/roi_heads/adamixer_decoder_aql2.py new file mode 100644 index 0000000..7d3f06a --- /dev/null +++ b/mmdet/models/roi_heads/adamixer_decoder_aql2.py @@ -0,0 +1,260 @@ +import torch + +from mmdet.core import bbox2result, bbox2roi, bbox_xyxy_to_cxcywh +from mmdet.core.bbox.samplers import PseudoSampler +from ..builder import HEADS +from .cascade_roi_head import CascadeRoIHead +import cccu +import os +DEBUG = 'DEBUG' in os.environ + + +@HEADS.register_module() +class AdaMixerDecoder_aql(CascadeRoIHead): # Fangyi: accumulated query with loss + _DEBUG = -1 + + def __init__(self, + num_stages=6, + stage_loss_weights=(1, 1, 1, 1, 1, 1), + content_dim=256, + featmap_strides=[4, 8, 16, 32], + bbox_head=None, + train_cfg=None, + test_cfg=None, + pretrained=None, + recycle=[], + init_cfg=None): + assert bbox_head is not None + assert len(stage_loss_weights) == num_stages + self.featmap_strides = featmap_strides + self.num_stages = num_stages + self.stage_loss_weights = stage_loss_weights + self.content_dim = content_dim + self.recycle = recycle + super(AdaMixerDecoder_aql, self).__init__( + num_stages, + stage_loss_weights, + bbox_roi_extractor=dict( + # This does not mean that our method need RoIAlign. We put this + # as a placeholder to satisfy the argument for the parent class + # 'CascadeRoIHead'. + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=bbox_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) + # train_cfg would be None when run the test.py + if train_cfg is not None: + for stage in range(num_stages): + assert isinstance(self.bbox_sampler[stage], PseudoSampler) + + def _bbox_forward(self, stage, img_feat, query_xyzr, query_content, img_metas): + num_imgs = len(img_metas) + bbox_head = self.bbox_head[stage] + + cls_score, delta_xyzr, query_content = bbox_head(img_feat, query_xyzr, + query_content, + featmap_strides=self.featmap_strides) + + query_xyzr, decoded_bboxes = self.bbox_head[stage].refine_xyzr( + query_xyzr, + delta_xyzr) + + bboxes_list = [bboxes for bboxes in decoded_bboxes] + + bbox_results = dict( + cls_score=cls_score, + query_xyzr=query_xyzr, + decode_bbox_pred=decoded_bboxes, + query_content=query_content, + detach_cls_score_list=[ + cls_score[i].detach() for i in range(num_imgs) + ], + detach_bboxes_list=[item.detach() for item in bboxes_list], + bboxes_list=bboxes_list, + ) + if DEBUG: + with torch.no_grad(): + torch.save( + bbox_results, 'demo/bbox_results_{}.pth'.format(AdaMixerDecoder_aql._DEBUG)) + AdaMixerDecoder_aql._DEBUG += 1 + return bbox_results + + def forward_train(self, + x, + query_xyzr, + query_content, + img_metas, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + imgs_whwh=None, + gt_masks=None): + + num_imgs = len(img_metas) + num_queries = query_xyzr.size(1) + imgs_whwh = imgs_whwh.repeat(1, num_queries, 1) + all_stage_bbox_results = [] + all_stage_loss = {} + + query_xyzr_list_reserve = [query_xyzr] + query_content_list_reserve = [query_content] + + for stage in range(self.num_stages): + single_stage_group_loss = [] + query_xyzr_list = query_xyzr_list_reserve.copy() + query_content_list = query_content_list_reserve.copy() + for groupid, (query_xyzr, query_content) in enumerate(zip(query_xyzr_list, query_content_list)): + bbox_results = self._bbox_forward(stage, x, query_xyzr, query_content, + img_metas) + all_stage_bbox_results.append(bbox_results) + if gt_bboxes_ignore is None: + # TODO support ignore + gt_bboxes_ignore = [None for _ in range(num_imgs)] + sampling_results = [] + cls_pred_list = bbox_results['detach_cls_score_list'] + bboxes_list = bbox_results['detach_bboxes_list'] + + query_xyzr_new = bbox_results['query_xyzr'].detach() + query_content_new = bbox_results['query_content'] + # TODO: detach query content for noisy querys because not going to use them anyway? + # TODO: only append important query groups, e.x. from the last layer + query_xyzr_list_reserve.append(query_xyzr_new) + query_content_list_reserve.append(query_content_new) + + for i in range(num_imgs): + normalize_bbox_ccwh = bbox_xyxy_to_cxcywh(bboxes_list[i] / + imgs_whwh[i]) + assign_result = self.bbox_assigner[stage].assign( + normalize_bbox_ccwh, cls_pred_list[i], gt_bboxes[i], + gt_labels[i], img_metas[i]) + sampling_result = self.bbox_sampler[stage].sample( + assign_result, bboxes_list[i], gt_bboxes[i]) + sampling_results.append(sampling_result) + bbox_targets = self.bbox_head[stage].get_targets( + sampling_results, gt_bboxes, gt_labels, self.train_cfg[stage], + True) + + cls_score = bbox_results['cls_score'] + decode_bbox_pred = bbox_results['decode_bbox_pred'] + + single_stage_group_loss.append(self.bbox_head[stage].loss( + cls_score.view(-1, cls_score.size(-1)), + decode_bbox_pred.view(-1, 4), + *bbox_targets, + imgs_whwh=imgs_whwh) + ) + + # TODO: weight group loss: for the most important group weight it the highest + for groupid, single_stage_single_group_loss in enumerate(single_stage_group_loss): + if groupid == 0: + for key, value in single_stage_single_group_loss.items(): + all_stage_loss[f'stage{stage}_{key}'] = value * \ + self.stage_loss_weights[stage] + else: + for key, value in single_stage_single_group_loss.items(): + all_stage_loss[f'stage{stage}_{key}'] += value * \ + self.stage_loss_weights[stage] + + return all_stage_loss + + + def simple_test(self, + x, + query_xyzr, + query_content, + img_metas, + imgs_whwh, + rescale=False): + assert self.with_bbox, 'Bbox head must be implemented.' + if DEBUG: + torch.save(img_metas, 'demo/img_metas.pth') + + num_imgs = len(img_metas) + + for stage in range(self.num_stages): + bbox_results = self._bbox_forward( + stage, x, query_xyzr, query_content, img_metas) + query_content = bbox_results['query_content'] + cls_score = bbox_results['cls_score'] + bboxes_list = bbox_results['detach_bboxes_list'] + query_xyzr = bbox_results['query_xyzr'] + + num_classes = self.bbox_head[-1].num_classes + det_bboxes = [] + det_labels = [] + + if self.bbox_head[-1].loss_cls.use_sigmoid: + cls_score = cls_score.sigmoid() + else: + cls_score = cls_score.softmax(-1)[..., :-1] + + for img_id in range(num_imgs): + + cls_score_per_img = cls_score[img_id] + scores_per_img, topk_indices = cls_score_per_img.flatten( + 0, 1).topk( + self.test_cfg.max_per_img, sorted=False) + labels_per_img = topk_indices % num_classes + # a = topk_indices // num_classes + bbox_pred_per_img = bboxes_list[img_id][topk_indices // + num_classes] + + ''' # fangyi modify start + # The following is a my implementation for testing where each query only produce one result + cls_score_per_img = cls_score[img_id] + scores_per_img, labels_per_img = torch.max(cls_score_per_img, dim=1) + bbox_pred_per_img = bboxes_list[img_id] + # record query bias on class + ct = cccu.counter(log_name='adamixer_query_predcls_temp.txt', matrixshape=(300, 80)) + for i, label_per_img in enumerate(labels_per_img): + ct.m[i, label_per_img] += 1 + ct.record() + # filter out low confident # proved not useful at all + # index = (scores_per_img > 0.02).nonzero().squeeze() + # if len(index.shape) != 0: + # scores_per_img = scores_per_img[index] + # labels_per_img = labels_per_img[index] + # bbox_pred_per_img = bbox_pred_per_img[index] + ''' # fangyi modify end + + if rescale: + scale_factor = img_metas[img_id]['scale_factor'] + bbox_pred_per_img /= bbox_pred_per_img.new_tensor(scale_factor) + det_bboxes.append( + torch.cat([bbox_pred_per_img, scores_per_img[:, None]], dim=1)) + det_labels.append(labels_per_img) + + bbox_results = [ + bbox2result(det_bboxes[i], det_labels[i], num_classes) + for i in range(num_imgs) + ] + + return bbox_results + + def aug_test(self, x, bboxes_list, img_metas, rescale=False): + raise NotImplementedError() + + def forward_dummy(self, x, + query_xyzr, + query_content, + img_metas): + """Dummy forward function when do the flops computing.""" + all_stage_bbox_results = [] + + num_imgs = len(img_metas) + if self.with_bbox: + for stage in range(self.num_stages): + bbox_results = self._bbox_forward(stage, x, + query_xyzr, + query_content, + img_metas) + all_stage_bbox_results.append(bbox_results) + query_content = bbox_results['query_content'] + query_xyzr = bbox_results['query_xyzr'] + return all_stage_bbox_results diff --git a/mmdet/models/roi_heads/adamixer_decoder_caq.py b/mmdet/models/roi_heads/adamixer_decoder_caq.py new file mode 100644 index 0000000..990f19a --- /dev/null +++ b/mmdet/models/roi_heads/adamixer_decoder_caq.py @@ -0,0 +1,268 @@ +import torch + +from mmdet.core import bbox2result, bbox2roi, bbox_xyxy_to_cxcywh +from mmdet.core.bbox.samplers import PseudoSampler +from ..builder import HEADS +from .cascade_roi_head import CascadeRoIHead +import cccu +import os +DEBUG = 'DEBUG' in os.environ + + +@HEADS.register_module() +class AdaMixerDecoder_caq(CascadeRoIHead): + _DEBUG = -1 + + def __init__(self, + num_stages=6, + stage_loss_weights=(1, 1, 1, 1, 1, 1), + content_dim=256, + featmap_strides=[4, 8, 16, 32], + bbox_head=None, + train_cfg=None, + test_cfg=None, + pretrained=None, + recycle=[], + init_cfg=None): + assert bbox_head is not None + assert len(stage_loss_weights) == num_stages + self.featmap_strides = featmap_strides + self.num_stages = num_stages + self.stage_loss_weights = stage_loss_weights + self.content_dim = content_dim + self.recycle = recycle + super(AdaMixerDecoder_caq, self).__init__( + num_stages, + stage_loss_weights, + bbox_roi_extractor=dict( + # This does not mean that our method need RoIAlign. We put this + # as a placeholder to satisfy the argument for the parent class + # 'CascadeRoIHead'. + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=bbox_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) + # train_cfg would be None when run the test.py + if train_cfg is not None: + for stage in range(num_stages): + assert isinstance(self.bbox_sampler[stage], PseudoSampler) + + def _bbox_forward(self, stage, img_feat, query_xyzr, query_content, img_metas): + num_imgs = len(img_metas) + bbox_head = self.bbox_head[stage] + + cls_score, delta_xyzr, query_content = bbox_head(img_feat, query_xyzr, + query_content, + featmap_strides=self.featmap_strides) + + query_xyzr, decoded_bboxes = self.bbox_head[stage].refine_xyzr( + query_xyzr, + delta_xyzr) + + bboxes_list = [bboxes for bboxes in decoded_bboxes] + + bbox_results = dict( + cls_score=cls_score, + query_xyzr=query_xyzr, + decode_bbox_pred=decoded_bboxes, + query_content=query_content, + detach_cls_score_list=[ + cls_score[i].detach() for i in range(num_imgs) + ], + detach_bboxes_list=[item.detach() for item in bboxes_list], + bboxes_list=bboxes_list, + ) + if DEBUG: + with torch.no_grad(): + torch.save( + bbox_results, 'demo/bbox_results_{}.pth'.format(AdaMixerDecoder._DEBUG)) + AdaMixerDecoder_caq._DEBUG += 1 + return bbox_results + + def forward_train(self, + x, + query_xyzr, + query_content, + img_metas, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + imgs_whwh=None, + gt_masks=None): + + num_imgs = len(img_metas) + imgs_whwh_keep = imgs_whwh + all_stage_bbox_results = [] + all_stage_loss = {} + + + for stage in range(self.num_stages): + bbox_results = self._bbox_forward(stage, x, query_xyzr, query_content, + img_metas) + all_stage_bbox_results.append(bbox_results) + if gt_bboxes_ignore is None: + # TODO support ignore + gt_bboxes_ignore = [None for _ in range(num_imgs)] + sampling_results = [] + cls_pred_list = bbox_results['detach_cls_score_list'] + bboxes_list = bbox_results['detach_bboxes_list'] + + query_xyzr = bbox_results['query_xyzr'].detach() + query_content = bbox_results['query_content'] + + num_queries = query_xyzr.size(1) + imgs_whwh = imgs_whwh_keep.repeat(1, num_queries, 1) + + if stage == self.recycle[0]: # collection starts from this stage default 0 + query_xyzr_new = query_xyzr + query_content_new = query_content + elif self.recycle[0] < stage <= self.recycle[-1]: + query_xyzr_new = torch.cat((query_xyzr_new, query_xyzr), dim=1) + query_content_new = torch.cat((query_content_new, query_content), dim=1) + + if stage == self.recycle[-1]: # collection starts from this stage default 4 + query_xyzr = query_xyzr_new # get ready to feed to stage [-1] + query_content = query_content_new + + if self.stage_loss_weights[stage] <= 0: + continue + + for i in range(num_imgs): + normalize_bbox_ccwh = bbox_xyxy_to_cxcywh(bboxes_list[i] / + imgs_whwh[i]) + assign_result = self.bbox_assigner[stage].assign( + normalize_bbox_ccwh, cls_pred_list[i], gt_bboxes[i], + gt_labels[i], img_metas[i]) + sampling_result = self.bbox_sampler[stage].sample( + assign_result, bboxes_list[i], gt_bboxes[i]) + sampling_results.append(sampling_result) + bbox_targets = self.bbox_head[stage].get_targets( + sampling_results, gt_bboxes, gt_labels, self.train_cfg[stage], + True) + + cls_score = bbox_results['cls_score'] + decode_bbox_pred = bbox_results['decode_bbox_pred'] + + single_stage_loss = self.bbox_head[stage].loss( + cls_score.view(-1, cls_score.size(-1)), + decode_bbox_pred.view(-1, 4), + *bbox_targets, + imgs_whwh=imgs_whwh) + for key, value in single_stage_loss.items(): + all_stage_loss[f'stage{stage}_{key}'] = value * \ + self.stage_loss_weights[stage] + + return all_stage_loss + + def simple_test(self, + x, + query_xyzr, + query_content, + img_metas, + imgs_whwh, + rescale=False): + assert self.with_bbox, 'Bbox head must be implemented.' + if DEBUG: + torch.save(img_metas, 'demo/img_metas.pth') + + num_imgs = len(img_metas) + + for stage in range(self.num_stages): + bbox_results = self._bbox_forward( + stage, x, query_xyzr, query_content, img_metas) + query_content = bbox_results['query_content'] + cls_score = bbox_results['cls_score'] + bboxes_list = bbox_results['detach_bboxes_list'] + query_xyzr = bbox_results['query_xyzr'] + + if stage == self.recycle[0]: # collection starts from this stage default 0 + query_xyzr_new = query_xyzr + query_content_new = query_content + elif self.recycle[0] < stage <= self.recycle[-1]: + query_xyzr_new = torch.cat((query_xyzr_new, query_xyzr), dim=1) + query_content_new = torch.cat((query_content_new, query_content), dim=1) + + if stage == self.recycle[-1]: # collection starts from this stage default 4 + query_xyzr = query_xyzr_new # get ready to feed to stage [-1] + query_content = query_content_new + + + num_classes = self.bbox_head[-1].num_classes + det_bboxes = [] + det_labels = [] + + if self.bbox_head[-1].loss_cls.use_sigmoid: + cls_score = cls_score.sigmoid() + else: + cls_score = cls_score.softmax(-1)[..., :-1] + + for img_id in range(num_imgs): + + cls_score_per_img = cls_score[img_id] + scores_per_img, topk_indices = cls_score_per_img.flatten( + 0, 1).topk( + self.test_cfg.max_per_img, sorted=False) + labels_per_img = topk_indices % num_classes + a = topk_indices // num_classes + bbox_pred_per_img = bboxes_list[img_id][topk_indices // + num_classes] + + ''' # fangyi modify start + # The following is a my implementation for testing where each query only produce one result + cls_score_per_img = cls_score[img_id] + scores_per_img, labels_per_img = torch.max(cls_score_per_img, dim=1) + bbox_pred_per_img = bboxes_list[img_id] + # record query bias on class + ct = cccu.counter(log_name='adamixer_query_predcls_temp.txt', matrixshape=(300, 80)) + for i, label_per_img in enumerate(labels_per_img): + ct.m[i, label_per_img] += 1 + ct.record() + # filter out low confident # proved not useful at all + # index = (scores_per_img > 0.02).nonzero().squeeze() + # if len(index.shape) != 0: + # scores_per_img = scores_per_img[index] + # labels_per_img = labels_per_img[index] + # bbox_pred_per_img = bbox_pred_per_img[index] + ''' # fangyi modify end + + if rescale: + scale_factor = img_metas[img_id]['scale_factor'] + bbox_pred_per_img /= bbox_pred_per_img.new_tensor(scale_factor) + det_bboxes.append( + torch.cat([bbox_pred_per_img, scores_per_img[:, None]], dim=1)) + det_labels.append(labels_per_img) + + bbox_results = [ + bbox2result(det_bboxes[i], det_labels[i], num_classes) + for i in range(num_imgs) + ] + + return bbox_results + + def aug_test(self, x, bboxes_list, img_metas, rescale=False): + raise NotImplementedError() + + def forward_dummy(self, x, + query_xyzr, + query_content, + img_metas): + """Dummy forward function when do the flops computing.""" + all_stage_bbox_results = [] + + num_imgs = len(img_metas) + if self.with_bbox: + for stage in range(self.num_stages): + bbox_results = self._bbox_forward(stage, x, + query_xyzr, + query_content, + img_metas) + all_stage_bbox_results.append(bbox_results) + query_content = bbox_results['query_content'] + query_xyzr = bbox_results['query_xyzr'] + return all_stage_bbox_results diff --git a/mmdet/models/roi_heads/adamixer_decoder_stodepth.py b/mmdet/models/roi_heads/adamixer_decoder_stodepth.py new file mode 100644 index 0000000..84b94cd --- /dev/null +++ b/mmdet/models/roi_heads/adamixer_decoder_stodepth.py @@ -0,0 +1,261 @@ +import torch + +from mmdet.core import bbox2result, bbox2roi, bbox_xyxy_to_cxcywh +from mmdet.core.bbox.samplers import PseudoSampler +from ..builder import HEADS +from .cascade_roi_head import CascadeRoIHead +import cccu +import os +import numpy as np +DEBUG = 'DEBUG' in os.environ +import torch.distributed as dist + +@HEADS.register_module() +class AdaMixerDecoder_stodepth(CascadeRoIHead): + _DEBUG = -1 + + def __init__(self, + num_stages=6, + stage_loss_weights=(1, 1, 1, 1, 1, 1), + content_dim=256, + featmap_strides=[4, 8, 16, 32], + bbox_head=None, + train_cfg=None, + test_cfg=None, + pretrained=None, + Pl=None, + init_cfg=None): + assert bbox_head is not None + assert len(stage_loss_weights) == num_stages + self.featmap_strides = featmap_strides + self.num_stages = num_stages + self.stage_loss_weights = stage_loss_weights + self.content_dim = content_dim + self.Pl = Pl + super(AdaMixerDecoder_stodepth, self).__init__( + num_stages, + stage_loss_weights, + bbox_roi_extractor=dict( + # This does not mean that our method need RoIAlign. We put this + # as a placeholder to satisfy the argument for the parent class + # 'CascadeRoIHead'. + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=bbox_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) + # train_cfg would be None when run the test.py + if train_cfg is not None: + for stage in range(num_stages): + assert isinstance(self.bbox_sampler[stage], PseudoSampler) + + def _bbox_forward(self, stage, img_feat, query_xyzr, query_content, img_metas): + num_imgs = len(img_metas) + bbox_head = self.bbox_head[stage] + + cls_score, delta_xyzr, query_content = bbox_head(img_feat, query_xyzr, + query_content, + featmap_strides=self.featmap_strides) + + query_xyzr, decoded_bboxes = self.bbox_head[stage].refine_xyzr( + query_xyzr, + delta_xyzr) + + bboxes_list = [bboxes for bboxes in decoded_bboxes] + + bbox_results = dict( + cls_score=cls_score, + query_xyzr=query_xyzr, + decode_bbox_pred=decoded_bboxes, + query_content=query_content, + detach_cls_score_list=[ + cls_score[i].detach() for i in range(num_imgs) + ], + detach_bboxes_list=[item.detach() for item in bboxes_list], + bboxes_list=bboxes_list, + ) + if DEBUG: + with torch.no_grad(): + torch.save( + bbox_results, 'demo/bbox_results_{}.pth'.format(AdaMixerDecoder_stodepth._DEBUG)) + AdaMixerDecoder_stodepth._DEBUG += 1 + return bbox_results + + def forward_train(self, + x, + query_xyzr, + query_content, + img_metas, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + imgs_whwh=None, + gt_masks=None): + + num_imgs = len(img_metas) + num_queries = query_xyzr.size(1) + imgs_whwh = imgs_whwh.repeat(1, num_queries, 1) + all_stage_bbox_results = [] + all_stage_loss = {} + device = x[0].get_device() + skipper = torch.Tensor([-1, -1, -1, -1, -1, -1]).to(device) + for stage in range(self.num_stages): + if dist.get_rank() == 0: + Pl = self.Pl[stage] + if not np.random.binomial(1, Pl): + skipper[stage]= True + else: + skipper[stage] = False + else: + skipper[stage] = -1 + # print('before', stage, device, skipper) + dist.broadcast(skipper, src=0) + if skipper[stage] is -1: + raise TypeError + # print('after', stage, device, skipper) + if skipper[stage]: + # print(f'skipping stage {stage} at device {device}') + continue + + bbox_results = self._bbox_forward(stage, x, query_xyzr, query_content, + img_metas) + all_stage_bbox_results.append(bbox_results) + if gt_bboxes_ignore is None: + # TODO support ignore + gt_bboxes_ignore = [None for _ in range(num_imgs)] + sampling_results = [] + cls_pred_list = bbox_results['detach_cls_score_list'] + bboxes_list = bbox_results['detach_bboxes_list'] + + query_xyzr = bbox_results['query_xyzr'].detach() + query_content = bbox_results['query_content'] + + if self.stage_loss_weights[stage] <= 0: + continue + + for i in range(num_imgs): + normalize_bbox_ccwh = bbox_xyxy_to_cxcywh(bboxes_list[i] / + imgs_whwh[i]) + assign_result = self.bbox_assigner[stage].assign( + normalize_bbox_ccwh, cls_pred_list[i], gt_bboxes[i], + gt_labels[i], img_metas[i]) + sampling_result = self.bbox_sampler[stage].sample( + assign_result, bboxes_list[i], gt_bboxes[i]) + sampling_results.append(sampling_result) + bbox_targets = self.bbox_head[stage].get_targets( + sampling_results, gt_bboxes, gt_labels, self.train_cfg[stage], + True) + + cls_score = bbox_results['cls_score'] + decode_bbox_pred = bbox_results['decode_bbox_pred'] + + single_stage_loss = self.bbox_head[stage].loss( + cls_score.view(-1, cls_score.size(-1)), + decode_bbox_pred.view(-1, 4), + *bbox_targets, + imgs_whwh=imgs_whwh) + for key, value in single_stage_loss.items(): + all_stage_loss[f'stage{stage}_{key}'] = value * \ + self.stage_loss_weights[stage] + + return all_stage_loss + + def simple_test(self, + x, + query_xyzr, + query_content, + img_metas, + imgs_whwh, + rescale=False): + assert self.with_bbox, 'Bbox head must be implemented.' + if DEBUG: + torch.save(img_metas, 'demo/img_metas.pth') + + num_imgs = len(img_metas) + + for stage in range(self.num_stages): + bbox_results = self._bbox_forward( + stage, x, query_xyzr, query_content, img_metas) + query_content = bbox_results['query_content'] + cls_score = bbox_results['cls_score'] + bboxes_list = bbox_results['detach_bboxes_list'] + query_xyzr = bbox_results['query_xyzr'] + + num_classes = self.bbox_head[-1].num_classes + det_bboxes = [] + det_labels = [] + + if self.bbox_head[-1].loss_cls.use_sigmoid: + cls_score = cls_score.sigmoid() + else: + cls_score = cls_score.softmax(-1)[..., :-1] + + for img_id in range(num_imgs): + + cls_score_per_img = cls_score[img_id] + scores_per_img, topk_indices = cls_score_per_img.flatten( + 0, 1).topk( + self.test_cfg.max_per_img, sorted=False) + labels_per_img = topk_indices % num_classes + # a = topk_indices // num_classes + bbox_pred_per_img = bboxes_list[img_id][topk_indices // + num_classes] + + ''' # fangyi modify start + # The following is a my implementation for testing where each query only produce one result + cls_score_per_img = cls_score[img_id] + scores_per_img, labels_per_img = torch.max(cls_score_per_img, dim=1) + bbox_pred_per_img = bboxes_list[img_id] + # record query bias on class + ct = cccu.counter(log_name='adamixer_query_predcls_temp.txt', matrixshape=(300, 80)) + for i, label_per_img in enumerate(labels_per_img): + ct.m[i, label_per_img] += 1 + ct.record() + # filter out low confident # proved not useful at all + # index = (scores_per_img > 0.02).nonzero().squeeze() + # if len(index.shape) != 0: + # scores_per_img = scores_per_img[index] + # labels_per_img = labels_per_img[index] + # bbox_pred_per_img = bbox_pred_per_img[index] + ''' # fangyi modify end + + if rescale: + scale_factor = img_metas[img_id]['scale_factor'] + bbox_pred_per_img /= bbox_pred_per_img.new_tensor(scale_factor) + det_bboxes.append( + torch.cat([bbox_pred_per_img, scores_per_img[:, None]], dim=1)) + det_labels.append(labels_per_img) + + bbox_results = [ + bbox2result(det_bboxes[i], det_labels[i], num_classes) + for i in range(num_imgs) + ] + + return bbox_results + + def aug_test(self, x, bboxes_list, img_metas, rescale=False): + raise NotImplementedError() + + def forward_dummy(self, x, + query_xyzr, + query_content, + img_metas): + """Dummy forward function when do the flops computing.""" + all_stage_bbox_results = [] + + num_imgs = len(img_metas) + if self.with_bbox: + for stage in range(self.num_stages): + bbox_results = self._bbox_forward(stage, x, + query_xyzr, + query_content, + img_metas) + all_stage_bbox_results.append(bbox_results) + query_content = bbox_results['query_content'] + query_xyzr = bbox_results['query_xyzr'] + return all_stage_bbox_results diff --git a/mmdet/models/roi_heads/base_roi_head.py b/mmdet/models/roi_heads/base_roi_head.py new file mode 100644 index 0000000..423af25 --- /dev/null +++ b/mmdet/models/roi_heads/base_roi_head.py @@ -0,0 +1,102 @@ +from abc import ABCMeta, abstractmethod + +from mmcv.runner import BaseModule + +from ..builder import build_shared_head + + +class BaseRoIHead(BaseModule, metaclass=ABCMeta): + """Base class for RoIHeads.""" + + def __init__(self, + bbox_roi_extractor=None, + bbox_head=None, + mask_roi_extractor=None, + mask_head=None, + shared_head=None, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(BaseRoIHead, self).__init__(init_cfg) + self.train_cfg = train_cfg + self.test_cfg = test_cfg + if shared_head is not None: + shared_head.pretrained = pretrained + self.shared_head = build_shared_head(shared_head) + + if bbox_head is not None: + self.init_bbox_head(bbox_roi_extractor, bbox_head) + + if mask_head is not None: + self.init_mask_head(mask_roi_extractor, mask_head) + + self.init_assigner_sampler() + + @property + def with_bbox(self): + """bool: whether the RoI head contains a `bbox_head`""" + return hasattr(self, 'bbox_head') and self.bbox_head is not None + + @property + def with_mask(self): + """bool: whether the RoI head contains a `mask_head`""" + return hasattr(self, 'mask_head') and self.mask_head is not None + + @property + def with_shared_head(self): + """bool: whether the RoI head contains a `shared_head`""" + return hasattr(self, 'shared_head') and self.shared_head is not None + + @abstractmethod + def init_bbox_head(self): + """Initialize ``bbox_head``""" + pass + + @abstractmethod + def init_mask_head(self): + """Initialize ``mask_head``""" + pass + + @abstractmethod + def init_assigner_sampler(self): + """Initialize assigner and sampler.""" + pass + + @abstractmethod + def forward_train(self, + x, + img_meta, + proposal_list, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + gt_masks=None, + **kwargs): + """Forward function during training.""" + + async def async_simple_test(self, + x, + proposal_list, + img_metas, + proposals=None, + rescale=False, + **kwargs): + """Asynchronized test function.""" + raise NotImplementedError + + def simple_test(self, + x, + proposal_list, + img_meta, + proposals=None, + rescale=False, + **kwargs): + """Test without augmentation.""" + + def aug_test(self, x, proposal_list, img_metas, rescale=False, **kwargs): + """Test with augmentations. + + If rescale is False, then returned bboxes and masks will fit the scale + of imgs[0]. + """ diff --git a/mmdet/models/roi_heads/bbox_heads/__init__.py b/mmdet/models/roi_heads/bbox_heads/__init__.py new file mode 100644 index 0000000..12ff503 --- /dev/null +++ b/mmdet/models/roi_heads/bbox_heads/__init__.py @@ -0,0 +1,14 @@ +from .bbox_head import BBoxHead +from .convfc_bbox_head import (ConvFCBBoxHead, Shared2FCBBoxHead, + Shared4Conv1FCBBoxHead) +from .dii_head import DIIHead +from .double_bbox_head import DoubleConvFCBBoxHead +from .sabl_head import SABLHead +from .scnet_bbox_head import SCNetBBoxHead +from .adamixer_decoder_stage import AdaMixerDecoderStage + +__all__ = [ + 'BBoxHead', 'ConvFCBBoxHead', 'Shared2FCBBoxHead', + 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multi_apply +from mmdet.models.builder import HEADS, build_loss +from mmdet.models.dense_heads.atss_head import reduce_mean +from mmdet.models.losses import accuracy +from mmdet.models.utils import build_transformer +from .bbox_head import BBoxHead + +from .sampling_3d_operator import sampling_3d +from .adaptive_mixing_operator import AdaptiveMixing + +from mmdet.core import bbox_overlaps + +import os +import cccu + +DEBUG = 'DEBUG' in os.environ + + +def dprint(*args, **kwargs): + import os + if 'DEBUG' in os.environ: + print(*args, **kwargs) + + +def decode_box(xyzr): + scale = 2.00 ** xyzr[..., 2:3] + ratio = 2.00 ** torch.cat([xyzr[..., 3:4] * -0.5, + xyzr[..., 3:4] * 0.5], dim=-1) + wh = scale * ratio + xy = xyzr[..., 0:2] + roi = torch.cat([xy - wh * 0.5, xy + wh * 0.5], dim=-1) + return roi + + +def make_sample_points(offset, num_group, xyzr): + ''' + offset_yx: [B, L, num_group*3], normalized by stride + + return: [B, H, W, num_group, 3] + ''' + B, L, _ = offset.shape + + offset = offset.view(B, L, 1, num_group, 3) + + roi_cc = xyzr[..., :2] + scale = 2.00 ** xyzr[..., 2:3] + ratio = 2.00 ** torch.cat([xyzr[..., 3:4] * -0.5, + xyzr[..., 3:4] * 0.5], dim=-1) + roi_wh = scale * ratio + + roi_lvl = xyzr[..., 2:3].view(B, L, 1, 1, 1) + + offset_yx = offset[..., :2] * roi_wh.view(B, L, 1, 1, 2) + sample_yx = roi_cc.contiguous().view(B, L, 1, 1, 2) \ + + offset_yx + + sample_lvl = roi_lvl + offset[..., 2:3] + + #### Fangyi: painter starts from here + ''' + painter = cccu.painter('adamixer-sample-p.jpg', palette_length=400) + # painter.show() + for j, (sample, roi) in enumerate(zip(sample_yx[0], roi_cc[0])): + painter = cccu.painter('adamixer-sample-p.jpg', palette_length=400) + painter.draw_apoint((int(roi.detach().cpu().numpy()[0]), int(roi.detach().cpu().numpy()[1])), + color=j, thickness=4, replace_canvas=0) + for i, off in enumerate(sample[0]): + painter.draw_apoint((int(off.detach().cpu().numpy()[0]), int(off.detach().cpu().numpy()[1])), + color=j, thickness=1, replace_canvas=0) + #IWantToSeeTheJthQuery = 1 + #if j > IWantToSeeTheJthQuery: + # break + #if j == IWantToSeeTheJthQuery: + # painter.show() + painter.show() + ''' + #### Fangyi: painter ends at here + return torch.cat([sample_yx, sample_lvl], dim=-1) + + +class AdaptiveSamplingMixing(nn.Module): + _DEBUG = 0 + + def __init__(self, + in_points=32, + out_points=128, + n_groups=4, + content_dim=256, + feat_channels=None + ): + super(AdaptiveSamplingMixing, self).__init__() + self.in_points = in_points + self.out_points = out_points + self.n_groups = n_groups + self.content_dim = content_dim + self.feat_channels = feat_channels if feat_channels is not None else self.content_dim + + self.sampling_offset_generator = nn.Sequential( + nn.Linear(content_dim, in_points * n_groups * 3) + ) + + self.norm = nn.LayerNorm(content_dim) + + self.adaptive_mixing = AdaptiveMixing( + self.feat_channels, + query_dim=self.content_dim, + in_points=self.in_points, + out_points=self.out_points, + n_groups=self.n_groups, + ) + + self.init_weights() + + @torch.no_grad() + def init_weights(self): + nn.init.zeros_(self.sampling_offset_generator[-1].weight) + nn.init.zeros_(self.sampling_offset_generator[-1].bias) + + bias = self.sampling_offset_generator[-1].bias.data.view( + self.n_groups, self.in_points, 3) + + # if in_points are squared number, then initialize + # to sampling on grids regularly, not used in most + # of our experiments. + if int(self.in_points ** 0.5) ** 2 == self.in_points: + h = int(self.in_points ** 0.5) + y = torch.linspace(-0.5, 0.5, h + 1) + 0.5 / h + yp = y[:-1] + y = yp.view(-1, 1).repeat(1, h) + x = yp.view(1, -1).repeat(h, 1) + y = y.flatten(0, 1)[None, :, None] + x = x.flatten(0, 1)[None, :, None] + bias[:, :, 0:2] = torch.cat([y, x], dim=-1) + else: + bandwidth = 0.5 * 1.0 + nn.init.uniform_(bias, -bandwidth, bandwidth) + + # initialize sampling delta z + nn.init.constant_(bias[:, :, 2:3], -1.0) + + self.adaptive_mixing.init_weights() + + def forward(self, x, query_feat, query_xyzr, featmap_strides): + offset = self.sampling_offset_generator(query_feat) + + sample_points_xyz = make_sample_points( + offset, self.n_groups * self.in_points, + query_xyzr, + ) + + if DEBUG: + torch.save( + sample_points_xyz, 'demo/sample_xy_{}.pth'.format(AdaptiveSamplingMixing._DEBUG)) + + sampled_feature, _ = sampling_3d(sample_points_xyz, x, + featmap_strides=featmap_strides, + n_points=self.in_points, + ) + + if DEBUG: + torch.save( + sampled_feature, 'demo/sample_feature_{}.pth'.format(AdaptiveSamplingMixing._DEBUG)) + AdaptiveSamplingMixing._DEBUG += 1 + + query_feat = self.adaptive_mixing(sampled_feature, query_feat) + query_feat = self.norm(query_feat) + + return query_feat + + +def position_embedding(token_xyzr, num_feats, temperature=10000): + assert token_xyzr.size(-1) == 4 + term = token_xyzr.new_tensor([1000, 1000, 1, 1]).view(1, 1, -1) + token_xyzr = token_xyzr / term + dim_t = torch.arange( + num_feats, dtype=torch.float32, device=token_xyzr.device) + dim_t = (temperature ** (2 * (dim_t // 2) / num_feats)).view(1, 1, 1, -1) + pos_x = token_xyzr[..., None] / dim_t + pos_x = torch.stack( + (pos_x[..., 0::2].sin(), pos_x[..., 1::2].cos()), + dim=4).flatten(2) + return pos_x + + +@HEADS.register_module() +class AdaMixerDecoderStage(BBoxHead): + _DEBUG = -1 + + def __init__(self, + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=1, + feedforward_channels=2048, + content_dim=256, + feat_channels=256, + dropout=0.0, + ffn_act_cfg=dict(type='ReLU', inplace=True), + in_points=32, + out_points=128, + n_groups=4, + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + init_cfg=None, + **kwargs): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + super(AdaMixerDecoderStage, self).__init__( + num_classes=num_classes, + reg_decoded_bbox=True, + reg_class_agnostic=True, + init_cfg=init_cfg, + **kwargs) + self.loss_iou = build_loss(loss_iou) + self.content_dim = content_dim + self.fp16_enabled = False + self.attention = MultiheadAttention(content_dim, num_heads, dropout) + self.attention_norm = build_norm_layer(dict(type='LN'), content_dim)[1] + + self.ffn = FFN( + content_dim, + feedforward_channels, + num_ffn_fcs, + act_cfg=ffn_act_cfg, + dropout=dropout) + self.ffn_norm = build_norm_layer(dict(type='LN'), content_dim)[1] + + self.cls_fcs = nn.ModuleList() + for _ in range(num_cls_fcs): + self.cls_fcs.append( + nn.Linear(content_dim, content_dim, bias=True)) + self.cls_fcs.append( + build_norm_layer(dict(type='LN'), content_dim)[1]) + self.cls_fcs.append( + build_activation_layer(dict(type='ReLU', inplace=True))) + + # over load the self.fc_cls in BBoxHead + if self.loss_cls.use_sigmoid: + self.fc_cls = nn.Linear(content_dim, self.num_classes) + else: + self.fc_cls = nn.Linear(content_dim, self.num_classes + 1) + + self.reg_fcs = nn.ModuleList() + for _ in range(num_reg_fcs): + self.reg_fcs.append( + nn.Linear(content_dim, content_dim, bias=True)) + self.reg_fcs.append( + build_norm_layer(dict(type='LN'), content_dim)[1]) + self.reg_fcs.append( + build_activation_layer(dict(type='ReLU', inplace=True))) + # over load the self.fc_cls in BBoxHead + self.fc_reg = nn.Linear(content_dim, 4) + + self.in_points = in_points + self.n_groups = n_groups + self.out_points = out_points + + self.sampling_n_mixing = AdaptiveSamplingMixing( + content_dim=content_dim, # query dim + feat_channels=feat_channels, + in_points=self.in_points, + out_points=self.out_points, + n_groups=self.n_groups + ) + + self.iof_tau = nn.Parameter(torch.ones(self.attention.num_heads, )) + + @torch.no_grad() + def init_weights(self): + super(AdaMixerDecoderStage, self).init_weights() + for n, m in self.named_modules(): + if isinstance(m, nn.Linear): + m.reset_parameters() + nn.init.xavier_uniform_(m.weight) + + if self.loss_cls.use_sigmoid: + bias_init = bias_init_with_prob(0.01) + nn.init.constant_(self.fc_cls.bias, bias_init) + + nn.init.zeros_(self.fc_reg.weight) + nn.init.zeros_(self.fc_reg.bias) + + nn.init.uniform_(self.iof_tau, 0.0, 4.0) + + self.sampling_n_mixing.init_weights() + + @auto_fp16() + def forward(self, + x, + query_xyzr, + query_content, + featmap_strides): + N, n_query = query_content.shape[:2] + + AdaMixerDecoderStage._DEBUG += 1 + + with torch.no_grad(): + rois = decode_box(query_xyzr) + roi_box_batched = rois.view(N, n_query, 4) + iof = bbox_overlaps(roi_box_batched, roi_box_batched, mode='iof')[ + :, None, :, :] + iof = (iof + 1e-7).log() + pe = position_embedding(query_xyzr, query_content.size(-1) // 4) + + '''IoF''' + attn_bias = (iof * self.iof_tau.view(1, -1, 1, 1)).flatten(0, 1) + + query_content = query_content.permute(1, 0, 2) + pe = pe.permute(1, 0, 2) + '''sinusoidal positional embedding''' + query_content_attn = query_content + pe + query_content = self.attention( + query_content_attn, + attn_mask=attn_bias, + ) + query_content = self.attention_norm(query_content) + query_content = query_content.permute(1, 0, 2) + + ''' adaptive 3D sampling and mixing ''' + query_content = self.sampling_n_mixing( + x, query_content, query_xyzr, featmap_strides) + + # FFN + query_content = self.ffn_norm(self.ffn(query_content)) + + cls_feat = query_content + reg_feat = query_content + + for cls_layer in self.cls_fcs: + cls_feat = cls_layer(cls_feat) + for reg_layer in self.reg_fcs: + reg_feat = reg_layer(reg_feat) + + cls_score = self.fc_cls(cls_feat).view(N, n_query, -1) + xyzr_delta = self.fc_reg(reg_feat).view(N, n_query, -1) + + return cls_score, xyzr_delta, query_content.view(N, n_query, -1) + + def refine_xyzr(self, xyzr, xyzr_delta, return_bbox=True): + z = xyzr[..., 2:3] + new_xy = xyzr[..., 0:2] + xyzr_delta[..., 0:2] * (2 ** z) + new_zr = xyzr[..., 2:4] + xyzr_delta[..., 2:4] + xyzr = torch.cat([new_xy, new_zr], dim=-1) + if return_bbox: + return xyzr, decode_box(xyzr) + else: + return xyzr + + @force_fp32(apply_to=('cls_score', 'bbox_pred')) + def loss(self, + cls_score, + bbox_pred, + labels, + label_weights, + bbox_targets, + bbox_weights, + imgs_whwh=None, + reduction_override=None, + **kwargs): + losses = dict() + bg_class_ind = self.num_classes + + pos_inds = (labels >= 0) & (labels < bg_class_ind) + num_pos = pos_inds.sum().float() + avg_factor = reduce_mean(num_pos) + if cls_score is not None: + if cls_score.numel() > 0: + losses['loss_cls'] = self.loss_cls( + cls_score, + labels, + label_weights, + avg_factor=avg_factor, + reduction_override=reduction_override) + losses['pos_acc'] = accuracy(cls_score[pos_inds], + labels[pos_inds]) + if bbox_pred is not None: + # 0~self.num_classes-1 are FG, self.num_classes is BG + # do not perform bounding box regression for BG anymore. + if pos_inds.any(): + pos_bbox_pred = bbox_pred.reshape(bbox_pred.size(0), + 4)[pos_inds.type(torch.bool)] + imgs_whwh = imgs_whwh.reshape(bbox_pred.size(0), + 4)[pos_inds.type(torch.bool)] + losses['loss_bbox'] = self.loss_bbox( + pos_bbox_pred / imgs_whwh, + bbox_targets[pos_inds.type(torch.bool)] / imgs_whwh, + bbox_weights[pos_inds.type(torch.bool)], + avg_factor=avg_factor) + losses['loss_iou'] = self.loss_iou( + pos_bbox_pred, + bbox_targets[pos_inds.type(torch.bool)], + bbox_weights[pos_inds.type(torch.bool)], + avg_factor=avg_factor) + else: + losses['loss_bbox'] = bbox_pred.sum() * 0 + losses['loss_iou'] = bbox_pred.sum() * 0 + return losses + + def _get_target_single(self, pos_inds, neg_inds, pos_bboxes, neg_bboxes, + pos_gt_bboxes, pos_gt_labels, cfg): + num_pos = pos_bboxes.size(0) + num_neg = neg_bboxes.size(0) + num_samples = num_pos + num_neg + + # original implementation uses new_zeros since BG are set to be 0 + # now use empty & fill because BG cat_id = num_classes, + # FG cat_id = [0, num_classes-1] + labels = pos_bboxes.new_full((num_samples,), + self.num_classes, + dtype=torch.long) + label_weights = pos_bboxes.new_zeros(num_samples) + bbox_targets = pos_bboxes.new_zeros(num_samples, 4) + bbox_weights = pos_bboxes.new_zeros(num_samples, 4) + if num_pos > 0: + labels[pos_inds] = pos_gt_labels + pos_weight = 1.0 if cfg.pos_weight <= 0 else cfg.pos_weight + label_weights[pos_inds] = pos_weight + if not self.reg_decoded_bbox: + pos_bbox_targets = self.bbox_coder.encode( + pos_bboxes, pos_gt_bboxes) + else: + pos_bbox_targets = pos_gt_bboxes + bbox_targets[pos_inds, :] = pos_bbox_targets + bbox_weights[pos_inds, :] = 1 + if num_neg > 0: + label_weights[neg_inds] = 1.0 + + return labels, label_weights, bbox_targets, bbox_weights + + def get_targets(self, + sampling_results, + gt_bboxes, + gt_labels, + rcnn_train_cfg, + concat=True): + pos_inds_list = [res.pos_inds for res in sampling_results] + neg_inds_list = [res.neg_inds for res in sampling_results] + pos_bboxes_list = [res.pos_bboxes for res in sampling_results] + neg_bboxes_list = [res.neg_bboxes for res in sampling_results] + pos_gt_bboxes_list = [res.pos_gt_bboxes for res in sampling_results] + pos_gt_labels_list = [res.pos_gt_labels for res in sampling_results] + labels, label_weights, bbox_targets, bbox_weights = multi_apply( + self._get_target_single, + pos_inds_list, + neg_inds_list, + pos_bboxes_list, + neg_bboxes_list, + pos_gt_bboxes_list, + pos_gt_labels_list, + cfg=rcnn_train_cfg) + if concat: + labels = torch.cat(labels, 0) + label_weights = torch.cat(label_weights, 0) + bbox_targets = torch.cat(bbox_targets, 0) + bbox_weights = torch.cat(bbox_weights, 0) + return labels, label_weights, bbox_targets, bbox_weights diff --git a/mmdet/models/roi_heads/bbox_heads/adaptive_mixing_operator.py b/mmdet/models/roi_heads/bbox_heads/adaptive_mixing_operator.py new file mode 100644 index 0000000..96d23d4 --- /dev/null +++ b/mmdet/models/roi_heads/bbox_heads/adaptive_mixing_operator.py @@ -0,0 +1,162 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def dprint(*args, **kwargs): + import os + if 'DEBUG' in os.environ: + print(*args, **kwargs) + + +_dump_i = 0 + + +# this requires a customized MMCV to include +# the flops updated in `__user_flops_handle__` +class SRShadowForFlops(nn.Module): + def __init__(self, in_dim, in_points, n_groups, query_dim=None, + out_dim=None, out_points=None, **kwargs): + super(SRShadowForFlops, self).__init__() + out_dim = out_dim if out_dim is not None else in_dim + out_points = out_points if out_points is not None else in_points + query_dim = query_dim if query_dim is not None else in_dim + + self.query_dim = query_dim + self.in_dim = in_dim + self.in_points = in_points + self.n_groups = n_groups + self.out_dim = out_dim + self.out_points = out_points + + def forward(self, x, query): + pass + + @staticmethod + def __user_flops_handle__(module, input, output): + B, num_query, num_group, num_point, num_channel = input[0].shape + + eff_in_dim = module.in_dim//num_group + eff_out_dim = module.out_dim//num_group + in_points = module.in_points + out_points = module.out_points + + step1 = B*num_query*num_group*in_points*eff_in_dim*eff_out_dim + step2 = B*num_query*num_group*eff_out_dim*in_points*out_points + module.__flops__ += int(step1+step2) + pass + + +class AdaptiveMixing(nn.Module): + def __init__(self, in_dim, in_points, n_groups, query_dim=None, + out_dim=None, out_points=None, sampling_rate=None): + super(AdaptiveMixing, self).__init__() + out_dim = out_dim if out_dim is not None else in_dim + out_points = out_points if out_points is not None else in_points + query_dim = query_dim if query_dim is not None else in_dim + sampling_rate = sampling_rate if sampling_rate is not None else 1 + + self.query_dim = query_dim + self.in_dim = in_dim + self.in_points = in_points//sampling_rate + self.n_groups = n_groups + self.out_dim = out_dim + self.out_points = out_points + + self.eff_in_dim = in_dim//n_groups + self.eff_out_dim = out_dim//n_groups + + self.m_parameters = self.eff_in_dim * self.eff_out_dim + self.s_parameters = self.in_points * self.out_points + + self.total_parameters = self.m_parameters + self.s_parameters + + self.parameter_generator = nn.Sequential( + nn.Linear(self.query_dim, self.n_groups*self.total_parameters), + ) + + self.out_proj = nn.Linear( + self.eff_out_dim*self.out_points*self.n_groups, self.query_dim, bias=True + ) + + self.act = nn.ReLU(inplace=True) + + # virtual modules for FLOPs calculation + local_dict = locals() + local_dict.pop('self') + self.shadow = SRShadowForFlops(**local_dict) + + self.init_weights() + + @torch.no_grad() + def init_weights(self): + nn.init.zeros_(self.parameter_generator[-1].weight) + + def forward(self, x, query): + + # Calculate FLOPs + self.shadow(x, query) + B, N, g, P, C = x.size() + # batch, num_query, group, point, channel + G = self.n_groups + assert g == G + # assert C*g == self.in_dim + + # query: B, N, C + # x: B, N, G, Px, Cx + + global _dump_i + + '''generate mixing parameters''' + params = self.parameter_generator(query) + params = params.reshape(B*N, G, -1) + + out = x.reshape(B*N, G, P, C) + + M, S = params.split( + [self.m_parameters, self.s_parameters], 2) + + '''you can choose one implementation below''' + if False: + out = out.reshape( + B*N*G, P, C + ) + + M = M.reshape( + B*N*G, self.eff_in_dim, self.eff_in_dim) + S = S.reshape( + B*N*G, self.out_points, self.in_points) + + '''adaptive channel mixing''' + out = torch.bmm(out, M) + out = F.layer_norm(out, [out.size(-2), out.size(-1)]) + out = self.act(out) + + '''adaptive spatial mixing''' + out = torch.bmm(S, out) # implicitly transpose and matmul + out = F.layer_norm(out, [out.size(-2), out.size(-1)]) + out = self.act(out) + else: + M = M.reshape( + B*N, G, self.eff_in_dim, self.eff_in_dim) + S = S.reshape( + B*N, G, self.out_points, self.in_points) + + '''adaptive channel mixing''' + + out = torch.matmul(out, M) + out = F.layer_norm(out, [out.size(-2), out.size(-1)]) + out = self.act(out) + + '''adaptive spatial mixing''' + out = torch.matmul(S, out) # implicitly transpose and matmul + out = F.layer_norm(out, [out.size(-2), out.size(-1)]) + out = self.act(out) + + '''linear transfomation to query dim''' + out = out.reshape(B, N, -1) + out = self.out_proj(out) + + out = query + out + + return out diff --git a/mmdet/models/roi_heads/bbox_heads/bbox_head.py b/mmdet/models/roi_heads/bbox_heads/bbox_head.py new file mode 100644 index 0000000..d5aef47 --- /dev/null +++ b/mmdet/models/roi_heads/bbox_heads/bbox_head.py @@ -0,0 +1,527 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.runner import BaseModule, auto_fp16, force_fp32 +from torch.nn.modules.utils import _pair + +from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms +from mmdet.models.builder import HEADS, build_loss +from mmdet.models.losses import accuracy + + +@HEADS.register_module() +class BBoxHead(BaseModule): + """Simplest RoI head, with only two fc layers for classification and + regression respectively.""" + + def __init__(self, + with_avg_pool=False, + with_cls=True, + with_reg=True, + roi_feat_size=7, + in_channels=256, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=True, + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + reg_decoded_bbox=False, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict( + type='SmoothL1Loss', beta=1.0, loss_weight=1.0), + init_cfg=None): + super(BBoxHead, self).__init__(init_cfg) + assert with_cls or with_reg + self.with_avg_pool = with_avg_pool + self.with_cls = with_cls + self.with_reg = with_reg + self.roi_feat_size = _pair(roi_feat_size) + self.roi_feat_area = self.roi_feat_size[0] * self.roi_feat_size[1] + self.in_channels = in_channels + self.num_classes = num_classes + self.reg_class_agnostic = reg_class_agnostic + self.reg_decoded_bbox = reg_decoded_bbox + self.fp16_enabled = False + + self.bbox_coder = build_bbox_coder(bbox_coder) + self.loss_cls = build_loss(loss_cls) + self.loss_bbox = build_loss(loss_bbox) + + in_channels = self.in_channels + if self.with_avg_pool: + self.avg_pool = nn.AvgPool2d(self.roi_feat_size) + else: + in_channels *= self.roi_feat_area + if self.with_cls: + # need to add background class + self.fc_cls = nn.Linear(in_channels, num_classes + 1) + if self.with_reg: + out_dim_reg = 4 if reg_class_agnostic else 4 * num_classes + self.fc_reg = nn.Linear(in_channels, out_dim_reg) + self.debug_imgs = None + if init_cfg is None: + self.init_cfg = [] + if self.with_cls: + self.init_cfg += [ + dict( + type='Normal', std=0.01, override=dict(name='fc_cls')) + ] + if self.with_reg: + self.init_cfg += [ + dict( + type='Normal', std=0.001, override=dict(name='fc_reg')) + ] + + @auto_fp16() + def forward(self, x): + if self.with_avg_pool: + x = self.avg_pool(x) + x = x.view(x.size(0), -1) + cls_score = self.fc_cls(x) if self.with_cls else None + bbox_pred = self.fc_reg(x) if self.with_reg else None + return cls_score, bbox_pred + + def _get_target_single(self, pos_bboxes, neg_bboxes, pos_gt_bboxes, + pos_gt_labels, cfg): + """Calculate the ground truth for proposals in the single image + according to the sampling results. + + Args: + pos_bboxes (Tensor): Contains all the positive boxes, + has shape (num_pos, 4), the last dimension 4 + represents [tl_x, tl_y, br_x, br_y]. + neg_bboxes (Tensor): Contains all the negative boxes, + has shape (num_neg, 4), the last dimension 4 + represents [tl_x, tl_y, br_x, br_y]. + pos_gt_bboxes (Tensor): Contains all the gt_boxes, + has shape (num_gt, 4), the last dimension 4 + represents [tl_x, tl_y, br_x, br_y]. + pos_gt_labels (Tensor): Contains all the gt_labels, + has shape (num_gt). + cfg (obj:`ConfigDict`): `train_cfg` of R-CNN. + + Returns: + Tuple[Tensor]: Ground truth for proposals + in a single image. Containing the following Tensors: + + - labels(Tensor): Gt_labels for all proposals, has + shape (num_proposals,). + - label_weights(Tensor): Labels_weights for all + proposals, has shape (num_proposals,). + - bbox_targets(Tensor):Regression target for all + proposals, has shape (num_proposals, 4), the + last dimension 4 represents [tl_x, tl_y, br_x, br_y]. + - bbox_weights(Tensor):Regression weights for all + proposals, has shape (num_proposals, 4). + """ + num_pos = pos_bboxes.size(0) + num_neg = neg_bboxes.size(0) + num_samples = num_pos + num_neg + + # original implementation uses new_zeros since BG are set to be 0 + # now use empty & fill because BG cat_id = num_classes, + # FG cat_id = [0, num_classes-1] + labels = pos_bboxes.new_full((num_samples, ), + self.num_classes, + dtype=torch.long) + label_weights = pos_bboxes.new_zeros(num_samples) + bbox_targets = pos_bboxes.new_zeros(num_samples, 4) + bbox_weights = pos_bboxes.new_zeros(num_samples, 4) + if num_pos > 0: + labels[:num_pos] = pos_gt_labels + pos_weight = 1.0 if cfg.pos_weight <= 0 else cfg.pos_weight + label_weights[:num_pos] = pos_weight + if not self.reg_decoded_bbox: + pos_bbox_targets = self.bbox_coder.encode( + pos_bboxes, pos_gt_bboxes) + else: + # When the regression loss (e.g. `IouLoss`, `GIouLoss`) + # is applied directly on the decoded bounding boxes, both + # the predicted boxes and regression targets should be with + # absolute coordinate format. + pos_bbox_targets = pos_gt_bboxes + bbox_targets[:num_pos, :] = pos_bbox_targets + bbox_weights[:num_pos, :] = 1 + if num_neg > 0: + label_weights[-num_neg:] = 1.0 + + return labels, label_weights, bbox_targets, bbox_weights + + def get_targets(self, + sampling_results, + gt_bboxes, + gt_labels, + rcnn_train_cfg, + concat=True): + """Calculate the ground truth for all samples in a batch according to + the sampling_results. + + Almost the same as the implementation in bbox_head, we passed + additional parameters pos_inds_list and neg_inds_list to + `_get_target_single` function. + + Args: + sampling_results (List[obj:SamplingResults]): Assign results of + all images in a batch after sampling. + gt_bboxes (list[Tensor]): Gt_bboxes of all images in a batch, + each tensor has shape (num_gt, 4), the last dimension 4 + represents [tl_x, tl_y, br_x, br_y]. + gt_labels (list[Tensor]): Gt_labels of all images in a batch, + each tensor has shape (num_gt,). + rcnn_train_cfg (obj:ConfigDict): `train_cfg` of RCNN. + concat (bool): Whether to concatenate the results of all + the images in a single batch. + + Returns: + Tuple[Tensor]: Ground truth for proposals in a single image. + Containing the following list of Tensors: + + - labels (list[Tensor],Tensor): Gt_labels for all + proposals in a batch, each tensor in list has + shape (num_proposals,) when `concat=False`, otherwise + just a single tensor has shape (num_all_proposals,). + - label_weights (list[Tensor]): Labels_weights for + all proposals in a batch, each tensor in list has + shape (num_proposals,) when `concat=False`, otherwise + just a single tensor has shape (num_all_proposals,). + - bbox_targets (list[Tensor],Tensor): Regression target + for all proposals in a batch, each tensor in list + has shape (num_proposals, 4) when `concat=False`, + otherwise just a single tensor has shape + (num_all_proposals, 4), the last dimension 4 represents + [tl_x, tl_y, br_x, br_y]. + - bbox_weights (list[tensor],Tensor): Regression weights for + all proposals in a batch, each tensor in list has shape + (num_proposals, 4) when `concat=False`, otherwise just a + single tensor has shape (num_all_proposals, 4). + """ + pos_bboxes_list = [res.pos_bboxes for res in sampling_results] + neg_bboxes_list = [res.neg_bboxes for res in sampling_results] + pos_gt_bboxes_list = [res.pos_gt_bboxes for res in sampling_results] + pos_gt_labels_list = [res.pos_gt_labels for res in sampling_results] + labels, label_weights, bbox_targets, bbox_weights = multi_apply( + self._get_target_single, + pos_bboxes_list, + neg_bboxes_list, + pos_gt_bboxes_list, + pos_gt_labels_list, + cfg=rcnn_train_cfg) + + if concat: + labels = torch.cat(labels, 0) + label_weights = torch.cat(label_weights, 0) + bbox_targets = torch.cat(bbox_targets, 0) + bbox_weights = torch.cat(bbox_weights, 0) + return labels, label_weights, bbox_targets, bbox_weights + + @force_fp32(apply_to=('cls_score', 'bbox_pred')) + def loss(self, + cls_score, + bbox_pred, + rois, + labels, + label_weights, + bbox_targets, + bbox_weights, + reduction_override=None): + losses = dict() + if cls_score is not None: + avg_factor = max(torch.sum(label_weights > 0).float().item(), 1.) + if cls_score.numel() > 0: + losses['loss_cls'] = self.loss_cls( + cls_score, + labels, + label_weights, + avg_factor=avg_factor, + reduction_override=reduction_override) + losses['acc'] = accuracy(cls_score, labels) + if bbox_pred is not None: + bg_class_ind = self.num_classes + # 0~self.num_classes-1 are FG, self.num_classes is BG + pos_inds = (labels >= 0) & (labels < bg_class_ind) + # do not perform bounding box regression for BG anymore. + if pos_inds.any(): + if self.reg_decoded_bbox: + # When the regression loss (e.g. `IouLoss`, + # `GIouLoss`, `DIouLoss`) is applied directly on + # the decoded bounding boxes, it decodes the + # already encoded coordinates to absolute format. + bbox_pred = self.bbox_coder.decode(rois[:, 1:], bbox_pred) + if self.reg_class_agnostic: + pos_bbox_pred = bbox_pred.view( + bbox_pred.size(0), 4)[pos_inds.type(torch.bool)] + else: + pos_bbox_pred = bbox_pred.view( + bbox_pred.size(0), -1, + 4)[pos_inds.type(torch.bool), + labels[pos_inds.type(torch.bool)]] + losses['loss_bbox'] = self.loss_bbox( + pos_bbox_pred, + bbox_targets[pos_inds.type(torch.bool)], + bbox_weights[pos_inds.type(torch.bool)], + avg_factor=bbox_targets.size(0), + reduction_override=reduction_override) + else: + losses['loss_bbox'] = bbox_pred[pos_inds].sum() + return losses + + @force_fp32(apply_to=('cls_score', 'bbox_pred')) + def get_bboxes(self, + rois, + cls_score, + bbox_pred, + img_shape, + scale_factor, + rescale=False, + cfg=None): + """Transform network output for a batch into bbox predictions. + + If the input rois has batch dimension, the function would be in + `batch_mode` and return is a tuple[list[Tensor], list[Tensor]], + otherwise, the return is a tuple[Tensor, Tensor]. + + Args: + rois (Tensor): Boxes to be transformed. Has shape (num_boxes, 5) + or (B, num_boxes, 5) + cls_score (list[Tensor] or Tensor): Box scores for + each scale level, each is a 4D-tensor, the channel number is + num_points * num_classes. + bbox_pred (Tensor, optional): Box energies / deltas for each scale + level, each is a 4D-tensor, the channel number is + num_classes * 4. + img_shape (Sequence[int] or torch.Tensor or Sequence[ + Sequence[int]], optional): Maximum bounds for boxes, specifies + (H, W, C) or (H, W). If rois shape is (B, num_boxes, 4), then + the max_shape should be a Sequence[Sequence[int]] + and the length of max_shape should also be B. + scale_factor (tuple[ndarray] or ndarray): Scale factor of the + image arange as (w_scale, h_scale, w_scale, h_scale). In + `batch_mode`, the scale_factor shape is tuple[ndarray]. + rescale (bool): If True, return boxes in original image space. + Default: False. + cfg (obj:`ConfigDict`): `test_cfg` of Bbox Head. Default: None + + Returns: + tuple[list[Tensor], list[Tensor]] or tuple[Tensor, Tensor]: + If the input has a batch dimension, the return value is + a tuple of the list. The first list contains the boxes of + the corresponding image in a batch, each tensor has the + shape (num_boxes, 5) and last dimension 5 represent + (tl_x, tl_y, br_x, br_y, score). Each Tensor in the second + list is the labels with shape (num_boxes, ). The length of + both lists should be equal to batch_size. Otherwise return + value is a tuple of two tensors, the first tensor is the + boxes with scores, the second tensor is the labels, both + have the same shape as the first case. + """ + if isinstance(cls_score, list): + cls_score = sum(cls_score) / float(len(cls_score)) + + scores = F.softmax( + cls_score, dim=-1) if cls_score is not None else None + + batch_mode = True + if rois.ndim == 2: + # e.g. AugTest, Cascade R-CNN, HTC, SCNet... + batch_mode = False + + # add batch dimension + if scores is not None: + scores = scores.unsqueeze(0) + if bbox_pred is not None: + bbox_pred = bbox_pred.unsqueeze(0) + rois = rois.unsqueeze(0) + + if bbox_pred is not None: + bboxes = self.bbox_coder.decode( + rois[..., 1:], bbox_pred, max_shape=img_shape) + else: + bboxes = rois[..., 1:].clone() + if img_shape is not None: + max_shape = bboxes.new_tensor(img_shape)[..., :2] + min_xy = bboxes.new_tensor(0) + max_xy = torch.cat( + [max_shape] * 2, dim=-1).flip(-1).unsqueeze(-2) + bboxes = torch.where(bboxes < min_xy, min_xy, bboxes) + bboxes = torch.where(bboxes > max_xy, max_xy, bboxes) + + if rescale and bboxes.size(-2) > 0: + if not isinstance(scale_factor, tuple): + scale_factor = tuple([scale_factor]) + # B, 1, bboxes.size(-1) + scale_factor = bboxes.new_tensor(scale_factor).unsqueeze(1).repeat( + 1, 1, + bboxes.size(-1) // 4) + bboxes /= scale_factor + + # Replace multiclass_nms with ONNX::NonMaxSuppression in deployment + if torch.onnx.is_in_onnx_export(): + from mmdet.core.export import add_dummy_nms_for_onnx + batch_size = scores.shape[0] + # ignore background class + scores = scores[..., :self.num_classes] + labels = torch.arange( + self.num_classes, dtype=torch.long).to(scores.device) + labels = labels.view(1, 1, -1).expand_as(scores) + labels = labels.reshape(batch_size, -1) + scores = scores.reshape(batch_size, -1) + bboxes = bboxes.reshape(batch_size, -1, 4) + + max_size = torch.max(img_shape) + # Offset bboxes of each class so that bboxes of different labels + # do not overlap. + offsets = (labels * max_size + 1).unsqueeze(2) + bboxes_for_nms = bboxes + offsets + max_output_boxes_per_class = cfg.nms.get( + 'max_output_boxes_per_class', cfg.max_per_img) + iou_threshold = cfg.nms.get('iou_threshold', 0.5) + score_threshold = cfg.score_thr + nms_pre = cfg.get('deploy_nms_pre', -1) + batch_dets, labels = add_dummy_nms_for_onnx( + bboxes_for_nms, + scores.unsqueeze(2), + max_output_boxes_per_class, + iou_threshold, + score_threshold, + pre_top_k=nms_pre, + after_top_k=cfg.max_per_img, + labels=labels) + # Offset the bboxes back after dummy nms. + offsets = (labels * max_size + 1).unsqueeze(2) + # Indexing + inplace operation fails with dynamic shape in ONNX + # original style: batch_dets[..., :4] -= offsets + bboxes, scores = batch_dets[..., 0:4], batch_dets[..., 4:5] + bboxes -= offsets + batch_dets = torch.cat([bboxes, scores], dim=2) + return batch_dets, labels + det_bboxes = [] + det_labels = [] + for (bbox, score) in zip(bboxes, scores): + if cfg is not None: + det_bbox, det_label = multiclass_nms(bbox, score, + cfg.score_thr, cfg.nms, + cfg.max_per_img) + else: + det_bbox, det_label = bbox, score + det_bboxes.append(det_bbox) + det_labels.append(det_label) + + if not batch_mode: + det_bboxes = det_bboxes[0] + det_labels = det_labels[0] + return det_bboxes, det_labels + + @force_fp32(apply_to=('bbox_preds', )) + def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas): + """Refine bboxes during training. + + Args: + rois (Tensor): Shape (n*bs, 5), where n is image number per GPU, + and bs is the sampled RoIs per image. The first column is + the image id and the next 4 columns are x1, y1, x2, y2. + labels (Tensor): Shape (n*bs, ). + bbox_preds (Tensor): Shape (n*bs, 4) or (n*bs, 4*#class). + pos_is_gts (list[Tensor]): Flags indicating if each positive bbox + is a gt bbox. + img_metas (list[dict]): Meta info of each image. + + Returns: + list[Tensor]: Refined bboxes of each image in a mini-batch. + + Example: + >>> # xdoctest: +REQUIRES(module:kwarray) + >>> import kwarray + >>> import numpy as np + >>> from mmdet.core.bbox.demodata import random_boxes + >>> self = BBoxHead(reg_class_agnostic=True) + >>> n_roi = 2 + >>> n_img = 4 + >>> scale = 512 + >>> rng = np.random.RandomState(0) + >>> img_metas = [{'img_shape': (scale, scale)} + ... for _ in range(n_img)] + >>> # Create rois in the expected format + >>> roi_boxes = random_boxes(n_roi, scale=scale, rng=rng) + >>> img_ids = torch.randint(0, n_img, (n_roi,)) + >>> img_ids = img_ids.float() + >>> rois = torch.cat([img_ids[:, None], roi_boxes], dim=1) + >>> # Create other args + >>> labels = torch.randint(0, 2, (n_roi,)).long() + >>> bbox_preds = random_boxes(n_roi, scale=scale, rng=rng) + >>> # For each image, pretend random positive boxes are gts + >>> is_label_pos = (labels.numpy() > 0).astype(np.int) + >>> lbl_per_img = kwarray.group_items(is_label_pos, + ... img_ids.numpy()) + >>> pos_per_img = [sum(lbl_per_img.get(gid, [])) + ... for gid in range(n_img)] + >>> pos_is_gts = [ + >>> torch.randint(0, 2, (npos,)).byte().sort( + >>> descending=True)[0] + >>> for npos in pos_per_img + >>> ] + >>> bboxes_list = self.refine_bboxes(rois, labels, bbox_preds, + >>> pos_is_gts, img_metas) + >>> print(bboxes_list) + """ + img_ids = rois[:, 0].long().unique(sorted=True) + assert img_ids.numel() <= len(img_metas) + + bboxes_list = [] + for i in range(len(img_metas)): + inds = torch.nonzero( + rois[:, 0] == i, as_tuple=False).squeeze(dim=1) + num_rois = inds.numel() + + bboxes_ = rois[inds, 1:] + label_ = labels[inds] + bbox_pred_ = bbox_preds[inds] + img_meta_ = img_metas[i] + pos_is_gts_ = pos_is_gts[i] + + bboxes = self.regress_by_class(bboxes_, label_, bbox_pred_, + img_meta_) + + # filter gt bboxes + pos_keep = 1 - pos_is_gts_ + keep_inds = pos_is_gts_.new_ones(num_rois) + keep_inds[:len(pos_is_gts_)] = pos_keep + + bboxes_list.append(bboxes[keep_inds.type(torch.bool)]) + + return bboxes_list + + @force_fp32(apply_to=('bbox_pred', )) + def regress_by_class(self, rois, label, bbox_pred, img_meta): + """Regress the bbox for the predicted class. Used in Cascade R-CNN. + + Args: + rois (Tensor): shape (n, 4) or (n, 5) + label (Tensor): shape (n, ) + bbox_pred (Tensor): shape (n, 4*(#class)) or (n, 4) + img_meta (dict): Image meta info. + + Returns: + Tensor: Regressed bboxes, the same shape as input rois. + """ + assert rois.size(1) == 4 or rois.size(1) == 5, repr(rois.shape) + + if not self.reg_class_agnostic: + label = label * 4 + inds = torch.stack((label, label + 1, label + 2, label + 3), 1) + bbox_pred = torch.gather(bbox_pred, 1, inds) + assert bbox_pred.size(1) == 4 + + if rois.size(1) == 4: + new_rois = self.bbox_coder.decode( + rois, bbox_pred, max_shape=img_meta['img_shape']) + else: + bboxes = self.bbox_coder.decode( + rois[:, 1:], bbox_pred, max_shape=img_meta['img_shape']) + new_rois = torch.cat((rois[:, [0]], bboxes), dim=1) + + return new_rois diff --git a/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py b/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py new file mode 100644 index 0000000..f047505 --- /dev/null +++ b/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py @@ -0,0 +1,210 @@ +import torch.nn as nn +from mmcv.cnn import ConvModule + +from mmdet.models.builder import HEADS +from .bbox_head import BBoxHead + + +@HEADS.register_module() +class ConvFCBBoxHead(BBoxHead): + r"""More general bbox head, with shared conv and fc layers and two optional + separated branches. + + .. code-block:: none + + /-> cls convs -> cls fcs -> cls + shared convs -> shared fcs + \-> reg convs -> reg fcs -> reg + """ # noqa: W605 + + def __init__(self, + num_shared_convs=0, + num_shared_fcs=0, + num_cls_convs=0, + num_cls_fcs=0, + num_reg_convs=0, + num_reg_fcs=0, + conv_out_channels=256, + fc_out_channels=1024, + conv_cfg=None, + norm_cfg=None, + init_cfg=None, + *args, + **kwargs): + super(ConvFCBBoxHead, self).__init__( + *args, init_cfg=init_cfg, **kwargs) + assert (num_shared_convs + num_shared_fcs + num_cls_convs + + num_cls_fcs + num_reg_convs + num_reg_fcs > 0) + if num_cls_convs > 0 or num_reg_convs > 0: + assert num_shared_fcs == 0 + if not self.with_cls: + assert num_cls_convs == 0 and num_cls_fcs == 0 + if not self.with_reg: + assert num_reg_convs == 0 and num_reg_fcs == 0 + self.num_shared_convs = num_shared_convs + self.num_shared_fcs = num_shared_fcs + self.num_cls_convs = num_cls_convs + self.num_cls_fcs = num_cls_fcs + self.num_reg_convs = num_reg_convs + self.num_reg_fcs = num_reg_fcs + self.conv_out_channels = conv_out_channels + self.fc_out_channels = fc_out_channels + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + + # add shared convs and fcs + self.shared_convs, self.shared_fcs, last_layer_dim = \ + self._add_conv_fc_branch( + self.num_shared_convs, self.num_shared_fcs, self.in_channels, + True) + self.shared_out_channels = last_layer_dim + + # add cls specific branch + self.cls_convs, self.cls_fcs, self.cls_last_dim = \ + self._add_conv_fc_branch( + self.num_cls_convs, self.num_cls_fcs, self.shared_out_channels) + + # add reg specific branch + self.reg_convs, self.reg_fcs, self.reg_last_dim = \ + self._add_conv_fc_branch( + self.num_reg_convs, self.num_reg_fcs, self.shared_out_channels) + + if self.num_shared_fcs == 0 and not self.with_avg_pool: + if self.num_cls_fcs == 0: + self.cls_last_dim *= self.roi_feat_area + if self.num_reg_fcs == 0: + self.reg_last_dim *= self.roi_feat_area + + self.relu = nn.ReLU(inplace=True) + # reconstruct fc_cls and fc_reg since input channels are changed + if self.with_cls: + self.fc_cls = nn.Linear(self.cls_last_dim, self.num_classes + 1) + if self.with_reg: + out_dim_reg = (4 if self.reg_class_agnostic else 4 * + self.num_classes) + self.fc_reg = nn.Linear(self.reg_last_dim, out_dim_reg) + + if init_cfg is None: + self.init_cfg += [ + dict( + type='Xavier', + layer='Linear', + override=[ + dict(name='shared_fcs'), + dict(name='cls_fcs'), + dict(name='reg_fcs') + ]) + ] + + def _add_conv_fc_branch(self, + num_branch_convs, + num_branch_fcs, + in_channels, + is_shared=False): + """Add shared or separable branch. + + convs -> avg pool (optional) -> fcs + """ + last_layer_dim = in_channels + # add branch specific conv layers + branch_convs = nn.ModuleList() + if num_branch_convs > 0: + for i in range(num_branch_convs): + conv_in_channels = ( + last_layer_dim if i == 0 else self.conv_out_channels) + branch_convs.append( + ConvModule( + conv_in_channels, + self.conv_out_channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + last_layer_dim = self.conv_out_channels + # add branch specific fc layers + branch_fcs = nn.ModuleList() + if num_branch_fcs > 0: + # for shared branch, only consider self.with_avg_pool + # for separated branches, also consider self.num_shared_fcs + if (is_shared + or self.num_shared_fcs == 0) and not self.with_avg_pool: + last_layer_dim *= self.roi_feat_area + for i in range(num_branch_fcs): + fc_in_channels = ( + last_layer_dim if i == 0 else self.fc_out_channels) + branch_fcs.append( + nn.Linear(fc_in_channels, self.fc_out_channels)) + last_layer_dim = self.fc_out_channels + return branch_convs, branch_fcs, last_layer_dim + + def forward(self, x): + # shared part + if self.num_shared_convs > 0: + for conv in self.shared_convs: + x = conv(x) + + if self.num_shared_fcs > 0: + if self.with_avg_pool: + x = self.avg_pool(x) + + x = x.flatten(1) + + for fc in self.shared_fcs: + x = self.relu(fc(x)) + # separate branches + x_cls = x + x_reg = x + + for conv in self.cls_convs: + x_cls = conv(x_cls) + if x_cls.dim() > 2: + if self.with_avg_pool: + x_cls = self.avg_pool(x_cls) + x_cls = x_cls.flatten(1) + for fc in self.cls_fcs: + x_cls = self.relu(fc(x_cls)) + + for conv in self.reg_convs: + x_reg = conv(x_reg) + if x_reg.dim() > 2: + if self.with_avg_pool: + x_reg = self.avg_pool(x_reg) + x_reg = x_reg.flatten(1) + for fc in self.reg_fcs: + x_reg = self.relu(fc(x_reg)) + + cls_score = self.fc_cls(x_cls) if self.with_cls else None + bbox_pred = self.fc_reg(x_reg) if self.with_reg else None + return cls_score, bbox_pred + + +@HEADS.register_module() +class Shared2FCBBoxHead(ConvFCBBoxHead): + + def __init__(self, fc_out_channels=1024, *args, **kwargs): + super(Shared2FCBBoxHead, self).__init__( + num_shared_convs=0, + num_shared_fcs=2, + num_cls_convs=0, + num_cls_fcs=0, + num_reg_convs=0, + num_reg_fcs=0, + fc_out_channels=fc_out_channels, + *args, + **kwargs) + + +@HEADS.register_module() +class Shared4Conv1FCBBoxHead(ConvFCBBoxHead): + + def __init__(self, fc_out_channels=1024, *args, **kwargs): + super(Shared4Conv1FCBBoxHead, self).__init__( + num_shared_convs=4, + num_shared_fcs=1, + num_cls_convs=0, + num_cls_fcs=0, + num_reg_convs=0, + num_reg_fcs=0, + fc_out_channels=fc_out_channels, + *args, + **kwargs) diff --git a/mmdet/models/roi_heads/bbox_heads/dii_head.py b/mmdet/models/roi_heads/bbox_heads/dii_head.py new file mode 100644 index 0000000..cf708eb --- /dev/null +++ b/mmdet/models/roi_heads/bbox_heads/dii_head.py @@ -0,0 +1,421 @@ +import torch +import torch.nn as nn +from mmcv.cnn import (bias_init_with_prob, build_activation_layer, + build_norm_layer) +from mmcv.cnn.bricks.transformer import FFN, MultiheadAttention +from mmcv.runner import auto_fp16, force_fp32 + +from mmdet.core import multi_apply +from mmdet.models.builder import HEADS, build_loss +from mmdet.models.dense_heads.atss_head import reduce_mean +from mmdet.models.losses import accuracy +from mmdet.models.utils import build_transformer +from .bbox_head import BBoxHead + + +@HEADS.register_module() +class DIIHead(BBoxHead): + r"""Dynamic Instance Interactive Head for `Sparse R-CNN: End-to-End Object + Detection with Learnable Proposals `_ + + Args: + num_classes (int): Number of class in dataset. + Defaults to 80. + num_ffn_fcs (int): The number of fully-connected + layers in FFNs. Defaults to 2. + num_heads (int): The hidden dimension of FFNs. + Defaults to 8. + num_cls_fcs (int): The number of fully-connected + layers in classification subnet. Defaults to 1. + num_reg_fcs (int): The number of fully-connected + layers in regression subnet. Defaults to 3. + feedforward_channels (int): The hidden dimension + of FFNs. Defaults to 2048 + in_channels (int): Hidden_channels of MultiheadAttention. + Defaults to 256. + dropout (float): Probability of drop the channel. + Defaults to 0.0 + ffn_act_cfg (dict): The activation config for FFNs. + dynamic_conv_cfg (dict): The convolution config + for DynamicConv. + loss_iou (dict): The config for iou or giou loss. + + """ + + def __init__(self, + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=3, + feedforward_channels=2048, + in_channels=256, + dropout=0.0, + ffn_act_cfg=dict(type='ReLU', inplace=True), + dynamic_conv_cfg=dict( + type='DynamicConv', + in_channels=256, + feat_channels=64, + out_channels=256, + input_feat_shape=7, + act_cfg=dict(type='ReLU', inplace=True), + norm_cfg=dict(type='LN')), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + init_cfg=None, + **kwargs): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + super(DIIHead, self).__init__( + num_classes=num_classes, + reg_decoded_bbox=True, + reg_class_agnostic=True, + init_cfg=init_cfg, + **kwargs) + self.loss_iou = build_loss(loss_iou) + self.in_channels = in_channels + self.fp16_enabled = False + self.attention = MultiheadAttention(in_channels, num_heads, dropout) + self.attention_norm = build_norm_layer(dict(type='LN'), in_channels)[1] + + self.instance_interactive_conv = build_transformer(dynamic_conv_cfg) + self.instance_interactive_conv_dropout = nn.Dropout(dropout) + self.instance_interactive_conv_norm = build_norm_layer( + dict(type='LN'), in_channels)[1] + + self.ffn = FFN( + in_channels, + feedforward_channels, + num_ffn_fcs, + act_cfg=ffn_act_cfg, + dropout=dropout) + self.ffn_norm = build_norm_layer(dict(type='LN'), in_channels)[1] + + self.cls_fcs = nn.ModuleList() + for _ in range(num_cls_fcs): + self.cls_fcs.append( + nn.Linear(in_channels, in_channels, bias=False)) + self.cls_fcs.append( + build_norm_layer(dict(type='LN'), in_channels)[1]) + self.cls_fcs.append( + build_activation_layer(dict(type='ReLU', inplace=True))) + + # over load the self.fc_cls in BBoxHead + if self.loss_cls.use_sigmoid: + self.fc_cls = nn.Linear(in_channels, self.num_classes) + else: + self.fc_cls = nn.Linear(in_channels, self.num_classes + 1) + + self.reg_fcs = nn.ModuleList() + for _ in range(num_reg_fcs): + self.reg_fcs.append( + nn.Linear(in_channels, in_channels, bias=False)) + self.reg_fcs.append( + build_norm_layer(dict(type='LN'), in_channels)[1]) + self.reg_fcs.append( + build_activation_layer(dict(type='ReLU', inplace=True))) + # over load the self.fc_cls in BBoxHead + self.fc_reg = nn.Linear(in_channels, 4) + + assert self.reg_class_agnostic, 'DIIHead only ' \ + 'suppport `reg_class_agnostic=True` ' + assert self.reg_decoded_bbox, 'DIIHead only ' \ + 'suppport `reg_decoded_bbox=True`' + + def init_weights(self): + """Use xavier initialization for all weight parameter and set + classification head bias as a specific value when use focal loss.""" + super(DIIHead, self).init_weights() + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + else: + # adopt the default initialization for + # the weight and bias of the layer norm + pass + if self.loss_cls.use_sigmoid: + bias_init = bias_init_with_prob(0.01) + nn.init.constant_(self.fc_cls.bias, bias_init) + + @auto_fp16() + def forward(self, roi_feat, proposal_feat): + """Forward function of Dynamic Instance Interactive Head. + + Args: + roi_feat (Tensor): Roi-pooling features with shape + (batch_size*num_proposals, feature_dimensions, + pooling_h , pooling_w). + proposal_feat (Tensor): Intermediate feature get from + diihead in last stage, has shape + (batch_size, num_proposals, feature_dimensions) + + Returns: + tuple[Tensor]: Usually a tuple of classification scores + and bbox prediction and a intermediate feature. + + - cls_scores (Tensor): Classification scores for + all proposals, has shape + (batch_size, num_proposals, num_classes). + - bbox_preds (Tensor): Box energies / deltas for + all proposals, has shape + (batch_size, num_proposals, 4). + - obj_feat (Tensor): Object feature before classification + and regression subnet, has shape + (batch_size, num_proposal, feature_dimensions). + """ + N, num_proposals = proposal_feat.shape[:2] + + # Self attention + proposal_feat = proposal_feat.permute(1, 0, 2) + proposal_feat = self.attention_norm(self.attention(proposal_feat)) + + # instance interactive + proposal_feat = proposal_feat.permute(1, 0, + 2).reshape(-1, self.in_channels) + proposal_feat_iic = self.instance_interactive_conv( + proposal_feat, roi_feat) + proposal_feat = proposal_feat + self.instance_interactive_conv_dropout( + proposal_feat_iic) + obj_feat = self.instance_interactive_conv_norm(proposal_feat) + + # FFN + obj_feat = self.ffn_norm(self.ffn(obj_feat)) + + cls_feat = obj_feat + reg_feat = obj_feat + + for cls_layer in self.cls_fcs: + cls_feat = cls_layer(cls_feat) + for reg_layer in self.reg_fcs: + reg_feat = reg_layer(reg_feat) + + cls_score = self.fc_cls(cls_feat).view(N, num_proposals, -1) + bbox_delta = self.fc_reg(reg_feat).view(N, num_proposals, -1) + + return cls_score, bbox_delta, obj_feat.view(N, num_proposals, -1) + + @force_fp32(apply_to=('cls_score', 'bbox_pred')) + def loss(self, + cls_score, + bbox_pred, + labels, + label_weights, + bbox_targets, + bbox_weights, + imgs_whwh=None, + reduction_override=None, + **kwargs): + """"Loss function of DIIHead, get loss of all images. + + Args: + cls_score (Tensor): Classification prediction + results of all class, has shape + (batch_size * num_proposals_single_image, num_classes) + bbox_pred (Tensor): Regression prediction results, + has shape + (batch_size * num_proposals_single_image, 4), the last + dimension 4 represents [tl_x, tl_y, br_x, br_y]. + labels (Tensor): Label of each proposals, has shape + (batch_size * num_proposals_single_image + label_weights (Tensor): Classification loss + weight of each proposals, has shape + (batch_size * num_proposals_single_image + bbox_targets (Tensor): Regression targets of each + proposals, has shape + (batch_size * num_proposals_single_image, 4), + the last dimension 4 represents + [tl_x, tl_y, br_x, br_y]. + bbox_weights (Tensor): Regression loss weight of each + proposals's coordinate, has shape + (batch_size * num_proposals_single_image, 4), + imgs_whwh (Tensor): imgs_whwh (Tensor): Tensor with\ + shape (batch_size, num_proposals, 4), the last + dimension means + [img_width,img_height, img_width, img_height]. + reduction_override (str, optional): The reduction + method used to override the original reduction + method of the loss. Options are "none", + "mean" and "sum". Defaults to None, + + Returns: + dict[str, Tensor]: Dictionary of loss components + """ + losses = dict() + bg_class_ind = self.num_classes + # note in spare rcnn num_gt == num_pos + pos_inds = (labels >= 0) & (labels < bg_class_ind) + num_pos = pos_inds.sum().float() + avg_factor = reduce_mean(num_pos) + if cls_score is not None: + if cls_score.numel() > 0: + losses['loss_cls'] = self.loss_cls( + cls_score, + labels, + label_weights, + avg_factor=avg_factor, + reduction_override=reduction_override) + losses['pos_acc'] = accuracy(cls_score[pos_inds], + labels[pos_inds]) + if bbox_pred is not None: + # 0~self.num_classes-1 are FG, self.num_classes is BG + # do not perform bounding box regression for BG anymore. + if pos_inds.any(): + pos_bbox_pred = bbox_pred.reshape(bbox_pred.size(0), + 4)[pos_inds.type(torch.bool)] + imgs_whwh = imgs_whwh.reshape(bbox_pred.size(0), + 4)[pos_inds.type(torch.bool)] + losses['loss_bbox'] = self.loss_bbox( + pos_bbox_pred / imgs_whwh, + bbox_targets[pos_inds.type(torch.bool)] / imgs_whwh, + bbox_weights[pos_inds.type(torch.bool)], + avg_factor=avg_factor) + losses['loss_iou'] = self.loss_iou( + pos_bbox_pred, + bbox_targets[pos_inds.type(torch.bool)], + bbox_weights[pos_inds.type(torch.bool)], + avg_factor=avg_factor) + else: + losses['loss_bbox'] = bbox_pred.sum() * 0 + losses['loss_iou'] = bbox_pred.sum() * 0 + return losses + + def _get_target_single(self, pos_inds, neg_inds, pos_bboxes, neg_bboxes, + pos_gt_bboxes, pos_gt_labels, cfg): + """Calculate the ground truth for proposals in the single image + according to the sampling results. + + Almost the same as the implementation in `bbox_head`, + we add pos_inds and neg_inds to select positive and + negative samples instead of selecting the first num_pos + as positive samples. + + Args: + pos_inds (Tensor): The length is equal to the + positive sample numbers contain all index + of the positive sample in the origin proposal set. + neg_inds (Tensor): The length is equal to the + negative sample numbers contain all index + of the negative sample in the origin proposal set. + pos_bboxes (Tensor): Contains all the positive boxes, + has shape (num_pos, 4), the last dimension 4 + represents [tl_x, tl_y, br_x, br_y]. + neg_bboxes (Tensor): Contains all the negative boxes, + has shape (num_neg, 4), the last dimension 4 + represents [tl_x, tl_y, br_x, br_y]. + pos_gt_bboxes (Tensor): Contains all the gt_boxes, + has shape (num_gt, 4), the last dimension 4 + represents [tl_x, tl_y, br_x, br_y]. + pos_gt_labels (Tensor): Contains all the gt_labels, + has shape (num_gt). + cfg (obj:`ConfigDict`): `train_cfg` of R-CNN. + + Returns: + Tuple[Tensor]: Ground truth for proposals in a single image. + Containing the following Tensors: + + - labels(Tensor): Gt_labels for all proposals, has + shape (num_proposals,). + - label_weights(Tensor): Labels_weights for all proposals, has + shape (num_proposals,). + - bbox_targets(Tensor):Regression target for all proposals, has + shape (num_proposals, 4), the last dimension 4 + represents [tl_x, tl_y, br_x, br_y]. + - bbox_weights(Tensor):Regression weights for all proposals, + has shape (num_proposals, 4). + """ + num_pos = pos_bboxes.size(0) + num_neg = neg_bboxes.size(0) + num_samples = num_pos + num_neg + + # original implementation uses new_zeros since BG are set to be 0 + # now use empty & fill because BG cat_id = num_classes, + # FG cat_id = [0, num_classes-1] + labels = pos_bboxes.new_full((num_samples, ), + self.num_classes, + dtype=torch.long) + label_weights = pos_bboxes.new_zeros(num_samples) + bbox_targets = pos_bboxes.new_zeros(num_samples, 4) + bbox_weights = pos_bboxes.new_zeros(num_samples, 4) + if num_pos > 0: + labels[pos_inds] = pos_gt_labels + pos_weight = 1.0 if cfg.pos_weight <= 0 else cfg.pos_weight + label_weights[pos_inds] = pos_weight + if not self.reg_decoded_bbox: + pos_bbox_targets = self.bbox_coder.encode( + pos_bboxes, pos_gt_bboxes) + else: + pos_bbox_targets = pos_gt_bboxes + bbox_targets[pos_inds, :] = pos_bbox_targets + bbox_weights[pos_inds, :] = 1 + if num_neg > 0: + label_weights[neg_inds] = 1.0 + + return labels, label_weights, bbox_targets, bbox_weights + + def get_targets(self, + sampling_results, + gt_bboxes, + gt_labels, + rcnn_train_cfg, + concat=True): + """Calculate the ground truth for all samples in a batch according to + the sampling_results. + + Almost the same as the implementation in bbox_head, we passed + additional parameters pos_inds_list and neg_inds_list to + `_get_target_single` function. + + Args: + sampling_results (List[obj:SamplingResults]): Assign results of + all images in a batch after sampling. + gt_bboxes (list[Tensor]): Gt_bboxes of all images in a batch, + each tensor has shape (num_gt, 4), the last dimension 4 + represents [tl_x, tl_y, br_x, br_y]. + gt_labels (list[Tensor]): Gt_labels of all images in a batch, + each tensor has shape (num_gt,). + rcnn_train_cfg (obj:`ConfigDict`): `train_cfg` of RCNN. + concat (bool): Whether to concatenate the results of all + the images in a single batch. + + Returns: + Tuple[Tensor]: Ground truth for proposals in a single image. + Containing the following list of Tensors: + + - labels (list[Tensor],Tensor): Gt_labels for all + proposals in a batch, each tensor in list has + shape (num_proposals,) when `concat=False`, otherwise just + a single tensor has shape (num_all_proposals,). + - label_weights (list[Tensor]): Labels_weights for + all proposals in a batch, each tensor in list has shape + (num_proposals,) when `concat=False`, otherwise just a + single tensor has shape (num_all_proposals,). + - bbox_targets (list[Tensor],Tensor): Regression target + for all proposals in a batch, each tensor in list has + shape (num_proposals, 4) when `concat=False`, otherwise + just a single tensor has shape (num_all_proposals, 4), + the last dimension 4 represents [tl_x, tl_y, br_x, br_y]. + - bbox_weights (list[tensor],Tensor): Regression weights for + all proposals in a batch, each tensor in list has shape + (num_proposals, 4) when `concat=False`, otherwise just a + single tensor has shape (num_all_proposals, 4). + """ + pos_inds_list = [res.pos_inds for res in sampling_results] + neg_inds_list = [res.neg_inds for res in sampling_results] + pos_bboxes_list = [res.pos_bboxes for res in sampling_results] + neg_bboxes_list = [res.neg_bboxes for res in sampling_results] + pos_gt_bboxes_list = [res.pos_gt_bboxes for res in sampling_results] + pos_gt_labels_list = [res.pos_gt_labels for res in sampling_results] + labels, label_weights, bbox_targets, bbox_weights = multi_apply( + self._get_target_single, + pos_inds_list, + neg_inds_list, + pos_bboxes_list, + neg_bboxes_list, + pos_gt_bboxes_list, + pos_gt_labels_list, + cfg=rcnn_train_cfg) + if concat: + labels = torch.cat(labels, 0) + label_weights = torch.cat(label_weights, 0) + bbox_targets = torch.cat(bbox_targets, 0) + bbox_weights = torch.cat(bbox_weights, 0) + return labels, label_weights, bbox_targets, bbox_weights diff --git a/mmdet/models/roi_heads/bbox_heads/double_bbox_head.py b/mmdet/models/roi_heads/bbox_heads/double_bbox_head.py new file mode 100644 index 0000000..26687e0 --- /dev/null +++ b/mmdet/models/roi_heads/bbox_heads/double_bbox_head.py @@ -0,0 +1,177 @@ +import torch.nn as nn +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule, ModuleList + +from mmdet.models.backbones.resnet import Bottleneck +from mmdet.models.builder import HEADS +from .bbox_head import BBoxHead + + +class BasicResBlock(BaseModule): + """Basic residual block. + + This block is a little different from the block in the ResNet backbone. + The kernel size of conv1 is 1 in this block while 3 in ResNet BasicBlock. + + Args: + in_channels (int): Channels of the input feature map. + out_channels (int): Channels of the output feature map. + conv_cfg (dict): The config dict for convolution layers. + norm_cfg (dict): The config dict for normalization layers. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + in_channels, + out_channels, + conv_cfg=None, + norm_cfg=dict(type='BN'), + init_cfg=None): + super(BasicResBlock, self).__init__(init_cfg) + + # main path + self.conv1 = ConvModule( + in_channels, + in_channels, + kernel_size=3, + padding=1, + bias=False, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg) + self.conv2 = ConvModule( + in_channels, + out_channels, + kernel_size=1, + bias=False, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=None) + + # identity path + self.conv_identity = ConvModule( + in_channels, + out_channels, + kernel_size=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=None) + + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + identity = x + + x = self.conv1(x) + x = self.conv2(x) + + identity = self.conv_identity(identity) + out = x + identity + + out = self.relu(out) + return out + + +@HEADS.register_module() +class DoubleConvFCBBoxHead(BBoxHead): + r"""Bbox head used in Double-Head R-CNN + + .. code-block:: none + + /-> cls + /-> shared convs -> + \-> reg + roi features + /-> cls + \-> shared fc -> + \-> reg + """ # noqa: W605 + + def __init__(self, + num_convs=0, + num_fcs=0, + conv_out_channels=1024, + fc_out_channels=1024, + conv_cfg=None, + norm_cfg=dict(type='BN'), + init_cfg=dict( + type='Normal', + override=[ + dict(type='Normal', name='fc_cls', std=0.01), + dict(type='Normal', name='fc_reg', std=0.001), + dict( + type='Xavier', + name='fc_branch', + distribution='uniform') + ]), + **kwargs): + kwargs.setdefault('with_avg_pool', True) + super(DoubleConvFCBBoxHead, self).__init__(init_cfg=init_cfg, **kwargs) + assert self.with_avg_pool + assert num_convs > 0 + assert num_fcs > 0 + self.num_convs = num_convs + self.num_fcs = num_fcs + self.conv_out_channels = conv_out_channels + self.fc_out_channels = fc_out_channels + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + + # increase the channel of input features + self.res_block = BasicResBlock(self.in_channels, + self.conv_out_channels) + + # add conv heads + self.conv_branch = self._add_conv_branch() + # add fc heads + self.fc_branch = self._add_fc_branch() + + out_dim_reg = 4 if self.reg_class_agnostic else 4 * self.num_classes + self.fc_reg = nn.Linear(self.conv_out_channels, out_dim_reg) + + self.fc_cls = nn.Linear(self.fc_out_channels, self.num_classes + 1) + self.relu = nn.ReLU(inplace=True) + + def _add_conv_branch(self): + """Add the fc branch which consists of a sequential of conv layers.""" + branch_convs = ModuleList() + for i in range(self.num_convs): + branch_convs.append( + Bottleneck( + inplanes=self.conv_out_channels, + planes=self.conv_out_channels // 4, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + return branch_convs + + def _add_fc_branch(self): + """Add the fc branch which consists of a sequential of fc layers.""" + branch_fcs = ModuleList() + for i in range(self.num_fcs): + fc_in_channels = ( + self.in_channels * + self.roi_feat_area if i == 0 else self.fc_out_channels) + branch_fcs.append(nn.Linear(fc_in_channels, self.fc_out_channels)) + return branch_fcs + + def forward(self, x_cls, x_reg): + # conv head + x_conv = self.res_block(x_reg) + + for conv in self.conv_branch: + x_conv = conv(x_conv) + + if self.with_avg_pool: + x_conv = self.avg_pool(x_conv) + + x_conv = x_conv.view(x_conv.size(0), -1) + bbox_pred = self.fc_reg(x_conv) + + # fc head + x_fc = x_cls.view(x_cls.size(0), -1) + for fc in self.fc_branch: + x_fc = self.relu(fc(x_fc)) + + cls_score = self.fc_cls(x_fc) + + return cls_score, bbox_pred diff --git a/mmdet/models/roi_heads/bbox_heads/sabl_head.py b/mmdet/models/roi_heads/bbox_heads/sabl_head.py new file mode 100644 index 0000000..07c542e --- /dev/null +++ b/mmdet/models/roi_heads/bbox_heads/sabl_head.py @@ -0,0 +1,583 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule, force_fp32 + +from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms +from mmdet.models.builder import HEADS, build_loss +from mmdet.models.losses import accuracy + + +@HEADS.register_module() +class SABLHead(BaseModule): + """Side-Aware Boundary Localization (SABL) for RoI-Head. + + Side-Aware features are extracted by conv layers + with an attention mechanism. + Boundary Localization with Bucketing and Bucketing Guided Rescoring + are implemented in BucketingBBoxCoder. + + Please refer to https://arxiv.org/abs/1912.04260 for more details. + + Args: + cls_in_channels (int): Input channels of cls RoI feature. \ + Defaults to 256. + reg_in_channels (int): Input channels of reg RoI feature. \ + Defaults to 256. + roi_feat_size (int): Size of RoI features. Defaults to 7. + reg_feat_up_ratio (int): Upsample ratio of reg features. \ + Defaults to 2. + reg_pre_kernel (int): Kernel of 2D conv layers before \ + attention pooling. Defaults to 3. + reg_post_kernel (int): Kernel of 1D conv layers after \ + attention pooling. Defaults to 3. + reg_pre_num (int): Number of pre convs. Defaults to 2. + reg_post_num (int): Number of post convs. Defaults to 1. + num_classes (int): Number of classes in dataset. Defaults to 80. + cls_out_channels (int): Hidden channels in cls fcs. Defaults to 1024. + reg_offset_out_channels (int): Hidden and output channel \ + of reg offset branch. Defaults to 256. + reg_cls_out_channels (int): Hidden and output channel \ + of reg cls branch. Defaults to 256. + num_cls_fcs (int): Number of fcs for cls branch. Defaults to 1. + num_reg_fcs (int): Number of fcs for reg branch.. Defaults to 0. + reg_class_agnostic (bool): Class agnostic regresion or not. \ + Defaults to True. + norm_cfg (dict): Config of norm layers. Defaults to None. + bbox_coder (dict): Config of bbox coder. Defaults 'BucketingBBoxCoder'. + loss_cls (dict): Config of classification loss. + loss_bbox_cls (dict): Config of classification loss for bbox branch. + loss_bbox_reg (dict): Config of regression loss for bbox branch. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + num_classes, + cls_in_channels=256, + reg_in_channels=256, + roi_feat_size=7, + reg_feat_up_ratio=2, + reg_pre_kernel=3, + reg_post_kernel=3, + reg_pre_num=2, + reg_post_num=1, + cls_out_channels=1024, + reg_offset_out_channels=256, + reg_cls_out_channels=256, + num_cls_fcs=1, + num_reg_fcs=0, + reg_class_agnostic=True, + norm_cfg=None, + bbox_coder=dict( + type='BucketingBBoxCoder', + num_buckets=14, + scale_factor=1.7), + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0), + loss_bbox_reg=dict( + type='SmoothL1Loss', beta=0.1, loss_weight=1.0), + init_cfg=None): + super(SABLHead, self).__init__(init_cfg) + self.cls_in_channels = cls_in_channels + self.reg_in_channels = reg_in_channels + self.roi_feat_size = roi_feat_size + self.reg_feat_up_ratio = int(reg_feat_up_ratio) + self.num_buckets = bbox_coder['num_buckets'] + assert self.reg_feat_up_ratio // 2 >= 1 + self.up_reg_feat_size = roi_feat_size * self.reg_feat_up_ratio + assert self.up_reg_feat_size == bbox_coder['num_buckets'] + self.reg_pre_kernel = reg_pre_kernel + self.reg_post_kernel = reg_post_kernel + self.reg_pre_num = reg_pre_num + self.reg_post_num = reg_post_num + self.num_classes = num_classes + self.cls_out_channels = cls_out_channels + self.reg_offset_out_channels = reg_offset_out_channels + self.reg_cls_out_channels = reg_cls_out_channels + self.num_cls_fcs = num_cls_fcs + self.num_reg_fcs = num_reg_fcs + self.reg_class_agnostic = reg_class_agnostic + assert self.reg_class_agnostic + self.norm_cfg = norm_cfg + + self.bbox_coder = build_bbox_coder(bbox_coder) + self.loss_cls = build_loss(loss_cls) + self.loss_bbox_cls = build_loss(loss_bbox_cls) + self.loss_bbox_reg = build_loss(loss_bbox_reg) + + self.cls_fcs = self._add_fc_branch(self.num_cls_fcs, + self.cls_in_channels, + self.roi_feat_size, + self.cls_out_channels) + + self.side_num = int(np.ceil(self.num_buckets / 2)) + + if self.reg_feat_up_ratio > 1: + self.upsample_x = nn.ConvTranspose1d( + reg_in_channels, + reg_in_channels, + self.reg_feat_up_ratio, + stride=self.reg_feat_up_ratio) + self.upsample_y = nn.ConvTranspose1d( + reg_in_channels, + reg_in_channels, + self.reg_feat_up_ratio, + stride=self.reg_feat_up_ratio) + + self.reg_pre_convs = nn.ModuleList() + for i in range(self.reg_pre_num): + reg_pre_conv = ConvModule( + reg_in_channels, + reg_in_channels, + kernel_size=reg_pre_kernel, + padding=reg_pre_kernel // 2, + norm_cfg=norm_cfg, + act_cfg=dict(type='ReLU')) + self.reg_pre_convs.append(reg_pre_conv) + + self.reg_post_conv_xs = nn.ModuleList() + for i in range(self.reg_post_num): + reg_post_conv_x = ConvModule( + reg_in_channels, + reg_in_channels, + kernel_size=(1, reg_post_kernel), + padding=(0, reg_post_kernel // 2), + norm_cfg=norm_cfg, + act_cfg=dict(type='ReLU')) + self.reg_post_conv_xs.append(reg_post_conv_x) + self.reg_post_conv_ys = nn.ModuleList() + for i in range(self.reg_post_num): + reg_post_conv_y = ConvModule( + reg_in_channels, + reg_in_channels, + kernel_size=(reg_post_kernel, 1), + padding=(reg_post_kernel // 2, 0), + norm_cfg=norm_cfg, + act_cfg=dict(type='ReLU')) + self.reg_post_conv_ys.append(reg_post_conv_y) + + self.reg_conv_att_x = nn.Conv2d(reg_in_channels, 1, 1) + self.reg_conv_att_y = nn.Conv2d(reg_in_channels, 1, 1) + + self.fc_cls = nn.Linear(self.cls_out_channels, self.num_classes + 1) + self.relu = nn.ReLU(inplace=True) + + self.reg_cls_fcs = self._add_fc_branch(self.num_reg_fcs, + self.reg_in_channels, 1, + self.reg_cls_out_channels) + self.reg_offset_fcs = self._add_fc_branch(self.num_reg_fcs, + self.reg_in_channels, 1, + self.reg_offset_out_channels) + self.fc_reg_cls = nn.Linear(self.reg_cls_out_channels, 1) + self.fc_reg_offset = nn.Linear(self.reg_offset_out_channels, 1) + + if init_cfg is None: + self.init_cfg = [ + dict( + type='Xavier', + layer='Linear', + distribution='uniform', + override=[ + dict(type='Normal', name='reg_conv_att_x', std=0.01), + dict(type='Normal', name='reg_conv_att_y', std=0.01), + dict(type='Normal', name='fc_reg_cls', std=0.01), + dict(type='Normal', name='fc_cls', std=0.01), + dict(type='Normal', name='fc_reg_offset', std=0.001) + ]) + ] + if self.reg_feat_up_ratio > 1: + self.init_cfg += [ + dict( + type='Kaiming', + distribution='normal', + override=[ + dict(name='upsample_x'), + dict(name='upsample_y') + ]) + ] + + def _add_fc_branch(self, num_branch_fcs, in_channels, roi_feat_size, + fc_out_channels): + in_channels = in_channels * roi_feat_size * roi_feat_size + branch_fcs = nn.ModuleList() + for i in range(num_branch_fcs): + fc_in_channels = (in_channels if i == 0 else fc_out_channels) + branch_fcs.append(nn.Linear(fc_in_channels, fc_out_channels)) + return branch_fcs + + def cls_forward(self, cls_x): + cls_x = cls_x.view(cls_x.size(0), -1) + for fc in self.cls_fcs: + cls_x = self.relu(fc(cls_x)) + cls_score = self.fc_cls(cls_x) + return cls_score + + def attention_pool(self, reg_x): + """Extract direction-specific features fx and fy with attention + methanism.""" + reg_fx = reg_x + reg_fy = reg_x + reg_fx_att = self.reg_conv_att_x(reg_fx).sigmoid() + reg_fy_att = self.reg_conv_att_y(reg_fy).sigmoid() + reg_fx_att = reg_fx_att / reg_fx_att.sum(dim=2).unsqueeze(2) + reg_fy_att = reg_fy_att / reg_fy_att.sum(dim=3).unsqueeze(3) + reg_fx = (reg_fx * reg_fx_att).sum(dim=2) + reg_fy = (reg_fy * reg_fy_att).sum(dim=3) + return reg_fx, reg_fy + + def side_aware_feature_extractor(self, reg_x): + """Refine and extract side-aware features without split them.""" + for reg_pre_conv in self.reg_pre_convs: + reg_x = reg_pre_conv(reg_x) + reg_fx, reg_fy = self.attention_pool(reg_x) + + if self.reg_post_num > 0: + reg_fx = reg_fx.unsqueeze(2) + reg_fy = reg_fy.unsqueeze(3) + for i in range(self.reg_post_num): + reg_fx = self.reg_post_conv_xs[i](reg_fx) + reg_fy = self.reg_post_conv_ys[i](reg_fy) + reg_fx = reg_fx.squeeze(2) + reg_fy = reg_fy.squeeze(3) + if self.reg_feat_up_ratio > 1: + reg_fx = self.relu(self.upsample_x(reg_fx)) + reg_fy = self.relu(self.upsample_y(reg_fy)) + reg_fx = torch.transpose(reg_fx, 1, 2) + reg_fy = torch.transpose(reg_fy, 1, 2) + return reg_fx.contiguous(), reg_fy.contiguous() + + def reg_pred(self, x, offset_fcs, cls_fcs): + """Predict bucketing estimation (cls_pred) and fine regression (offset + pred) with side-aware features.""" + x_offset = x.view(-1, self.reg_in_channels) + x_cls = x.view(-1, self.reg_in_channels) + + for fc in offset_fcs: + x_offset = self.relu(fc(x_offset)) + for fc in cls_fcs: + x_cls = self.relu(fc(x_cls)) + offset_pred = self.fc_reg_offset(x_offset) + cls_pred = self.fc_reg_cls(x_cls) + + offset_pred = offset_pred.view(x.size(0), -1) + cls_pred = cls_pred.view(x.size(0), -1) + + return offset_pred, cls_pred + + def side_aware_split(self, feat): + """Split side-aware features aligned with orders of bucketing + targets.""" + l_end = int(np.ceil(self.up_reg_feat_size / 2)) + r_start = int(np.floor(self.up_reg_feat_size / 2)) + feat_fl = feat[:, :l_end] + feat_fr = feat[:, r_start:].flip(dims=(1, )) + feat_fl = feat_fl.contiguous() + feat_fr = feat_fr.contiguous() + feat = torch.cat([feat_fl, feat_fr], dim=-1) + return feat + + def bbox_pred_split(self, bbox_pred, num_proposals_per_img): + """Split batch bbox prediction back to each image.""" + bucket_cls_preds, bucket_offset_preds = bbox_pred + bucket_cls_preds = bucket_cls_preds.split(num_proposals_per_img, 0) + bucket_offset_preds = bucket_offset_preds.split( + num_proposals_per_img, 0) + bbox_pred = tuple(zip(bucket_cls_preds, bucket_offset_preds)) + return bbox_pred + + def reg_forward(self, reg_x): + outs = self.side_aware_feature_extractor(reg_x) + edge_offset_preds = [] + edge_cls_preds = [] + reg_fx = outs[0] + reg_fy = outs[1] + offset_pred_x, cls_pred_x = self.reg_pred(reg_fx, self.reg_offset_fcs, + self.reg_cls_fcs) + offset_pred_y, cls_pred_y = self.reg_pred(reg_fy, self.reg_offset_fcs, + self.reg_cls_fcs) + offset_pred_x = self.side_aware_split(offset_pred_x) + offset_pred_y = self.side_aware_split(offset_pred_y) + cls_pred_x = self.side_aware_split(cls_pred_x) + cls_pred_y = self.side_aware_split(cls_pred_y) + edge_offset_preds = torch.cat([offset_pred_x, offset_pred_y], dim=-1) + edge_cls_preds = torch.cat([cls_pred_x, cls_pred_y], dim=-1) + + return (edge_cls_preds, edge_offset_preds) + + def forward(self, x): + + bbox_pred = self.reg_forward(x) + cls_score = self.cls_forward(x) + + return cls_score, bbox_pred + + def get_targets(self, sampling_results, gt_bboxes, gt_labels, + rcnn_train_cfg): + pos_proposals = [res.pos_bboxes for res in sampling_results] + neg_proposals = [res.neg_bboxes for res in sampling_results] + pos_gt_bboxes = [res.pos_gt_bboxes for res in sampling_results] + pos_gt_labels = [res.pos_gt_labels for res in sampling_results] + cls_reg_targets = self.bucket_target(pos_proposals, neg_proposals, + pos_gt_bboxes, pos_gt_labels, + rcnn_train_cfg) + (labels, label_weights, bucket_cls_targets, bucket_cls_weights, + bucket_offset_targets, bucket_offset_weights) = cls_reg_targets + return (labels, label_weights, (bucket_cls_targets, + bucket_offset_targets), + (bucket_cls_weights, bucket_offset_weights)) + + def bucket_target(self, + pos_proposals_list, + neg_proposals_list, + pos_gt_bboxes_list, + pos_gt_labels_list, + rcnn_train_cfg, + concat=True): + (labels, label_weights, bucket_cls_targets, bucket_cls_weights, + bucket_offset_targets, bucket_offset_weights) = multi_apply( + self._bucket_target_single, + pos_proposals_list, + neg_proposals_list, + pos_gt_bboxes_list, + pos_gt_labels_list, + cfg=rcnn_train_cfg) + + if concat: + labels = torch.cat(labels, 0) + label_weights = torch.cat(label_weights, 0) + bucket_cls_targets = torch.cat(bucket_cls_targets, 0) + bucket_cls_weights = torch.cat(bucket_cls_weights, 0) + bucket_offset_targets = torch.cat(bucket_offset_targets, 0) + bucket_offset_weights = torch.cat(bucket_offset_weights, 0) + return (labels, label_weights, bucket_cls_targets, bucket_cls_weights, + bucket_offset_targets, bucket_offset_weights) + + def _bucket_target_single(self, pos_proposals, neg_proposals, + pos_gt_bboxes, pos_gt_labels, cfg): + """Compute bucketing estimation targets and fine regression targets for + a single image. + + Args: + pos_proposals (Tensor): positive proposals of a single image, + Shape (n_pos, 4) + neg_proposals (Tensor): negative proposals of a single image, + Shape (n_neg, 4). + pos_gt_bboxes (Tensor): gt bboxes assigned to positive proposals + of a single image, Shape (n_pos, 4). + pos_gt_labels (Tensor): gt labels assigned to positive proposals + of a single image, Shape (n_pos, ). + cfg (dict): Config of calculating targets + + Returns: + tuple: + + - labels (Tensor): Labels in a single image. \ + Shape (n,). + - label_weights (Tensor): Label weights in a single image.\ + Shape (n,) + - bucket_cls_targets (Tensor): Bucket cls targets in \ + a single image. Shape (n, num_buckets*2). + - bucket_cls_weights (Tensor): Bucket cls weights in \ + a single image. Shape (n, num_buckets*2). + - bucket_offset_targets (Tensor): Bucket offset targets \ + in a single image. Shape (n, num_buckets*2). + - bucket_offset_targets (Tensor): Bucket offset weights \ + in a single image. Shape (n, num_buckets*2). + """ + num_pos = pos_proposals.size(0) + num_neg = neg_proposals.size(0) + num_samples = num_pos + num_neg + labels = pos_gt_bboxes.new_full((num_samples, ), + self.num_classes, + dtype=torch.long) + label_weights = pos_proposals.new_zeros(num_samples) + bucket_cls_targets = pos_proposals.new_zeros(num_samples, + 4 * self.side_num) + bucket_cls_weights = pos_proposals.new_zeros(num_samples, + 4 * self.side_num) + bucket_offset_targets = pos_proposals.new_zeros( + num_samples, 4 * self.side_num) + bucket_offset_weights = pos_proposals.new_zeros( + num_samples, 4 * self.side_num) + if num_pos > 0: + labels[:num_pos] = pos_gt_labels + label_weights[:num_pos] = 1.0 + (pos_bucket_offset_targets, pos_bucket_offset_weights, + pos_bucket_cls_targets, + pos_bucket_cls_weights) = self.bbox_coder.encode( + pos_proposals, pos_gt_bboxes) + bucket_cls_targets[:num_pos, :] = pos_bucket_cls_targets + bucket_cls_weights[:num_pos, :] = pos_bucket_cls_weights + bucket_offset_targets[:num_pos, :] = pos_bucket_offset_targets + bucket_offset_weights[:num_pos, :] = pos_bucket_offset_weights + if num_neg > 0: + label_weights[-num_neg:] = 1.0 + return (labels, label_weights, bucket_cls_targets, bucket_cls_weights, + bucket_offset_targets, bucket_offset_weights) + + def loss(self, + cls_score, + bbox_pred, + rois, + labels, + label_weights, + bbox_targets, + bbox_weights, + reduction_override=None): + losses = dict() + if cls_score is not None: + avg_factor = max(torch.sum(label_weights > 0).float().item(), 1.) + losses['loss_cls'] = self.loss_cls( + cls_score, + labels, + label_weights, + avg_factor=avg_factor, + reduction_override=reduction_override) + losses['acc'] = accuracy(cls_score, labels) + + if bbox_pred is not None: + bucket_cls_preds, bucket_offset_preds = bbox_pred + bucket_cls_targets, bucket_offset_targets = bbox_targets + bucket_cls_weights, bucket_offset_weights = bbox_weights + # edge cls + bucket_cls_preds = bucket_cls_preds.view(-1, self.side_num) + bucket_cls_targets = bucket_cls_targets.view(-1, self.side_num) + bucket_cls_weights = bucket_cls_weights.view(-1, self.side_num) + losses['loss_bbox_cls'] = self.loss_bbox_cls( + bucket_cls_preds, + bucket_cls_targets, + bucket_cls_weights, + avg_factor=bucket_cls_targets.size(0), + reduction_override=reduction_override) + + losses['loss_bbox_reg'] = self.loss_bbox_reg( + bucket_offset_preds, + bucket_offset_targets, + bucket_offset_weights, + avg_factor=bucket_offset_targets.size(0), + reduction_override=reduction_override) + + return losses + + @force_fp32(apply_to=('cls_score', 'bbox_pred')) + def get_bboxes(self, + rois, + cls_score, + bbox_pred, + img_shape, + scale_factor, + rescale=False, + cfg=None): + if isinstance(cls_score, list): + cls_score = sum(cls_score) / float(len(cls_score)) + scores = F.softmax(cls_score, dim=1) if cls_score is not None else None + + if bbox_pred is not None: + bboxes, confids = self.bbox_coder.decode(rois[:, 1:], bbox_pred, + img_shape) + else: + bboxes = rois[:, 1:].clone() + confids = None + if img_shape is not None: + bboxes[:, [0, 2]].clamp_(min=0, max=img_shape[1] - 1) + bboxes[:, [1, 3]].clamp_(min=0, max=img_shape[0] - 1) + + if rescale and bboxes.size(0) > 0: + if isinstance(scale_factor, float): + bboxes /= scale_factor + else: + bboxes /= torch.from_numpy(scale_factor).to(bboxes.device) + + if cfg is None: + return bboxes, scores + else: + det_bboxes, det_labels = multiclass_nms( + bboxes, + scores, + cfg.score_thr, + cfg.nms, + cfg.max_per_img, + score_factors=confids) + + return det_bboxes, det_labels + + @force_fp32(apply_to=('bbox_preds', )) + def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas): + """Refine bboxes during training. + + Args: + rois (Tensor): Shape (n*bs, 5), where n is image number per GPU, + and bs is the sampled RoIs per image. + labels (Tensor): Shape (n*bs, ). + bbox_preds (list[Tensor]): Shape [(n*bs, num_buckets*2), \ + (n*bs, num_buckets*2)]. + pos_is_gts (list[Tensor]): Flags indicating if each positive bbox + is a gt bbox. + img_metas (list[dict]): Meta info of each image. + + Returns: + list[Tensor]: Refined bboxes of each image in a mini-batch. + """ + img_ids = rois[:, 0].long().unique(sorted=True) + assert img_ids.numel() == len(img_metas) + + bboxes_list = [] + for i in range(len(img_metas)): + inds = torch.nonzero( + rois[:, 0] == i, as_tuple=False).squeeze(dim=1) + num_rois = inds.numel() + + bboxes_ = rois[inds, 1:] + label_ = labels[inds] + edge_cls_preds, edge_offset_preds = bbox_preds + edge_cls_preds_ = edge_cls_preds[inds] + edge_offset_preds_ = edge_offset_preds[inds] + bbox_pred_ = [edge_cls_preds_, edge_offset_preds_] + img_meta_ = img_metas[i] + pos_is_gts_ = pos_is_gts[i] + + bboxes = self.regress_by_class(bboxes_, label_, bbox_pred_, + img_meta_) + # filter gt bboxes + pos_keep = 1 - pos_is_gts_ + keep_inds = pos_is_gts_.new_ones(num_rois) + keep_inds[:len(pos_is_gts_)] = pos_keep + + bboxes_list.append(bboxes[keep_inds.type(torch.bool)]) + + return bboxes_list + + @force_fp32(apply_to=('bbox_pred', )) + def regress_by_class(self, rois, label, bbox_pred, img_meta): + """Regress the bbox for the predicted class. Used in Cascade R-CNN. + + Args: + rois (Tensor): shape (n, 4) or (n, 5) + label (Tensor): shape (n, ) + bbox_pred (list[Tensor]): shape [(n, num_buckets *2), \ + (n, num_buckets *2)] + img_meta (dict): Image meta info. + + Returns: + Tensor: Regressed bboxes, the same shape as input rois. + """ + assert rois.size(1) == 4 or rois.size(1) == 5 + + if rois.size(1) == 4: + new_rois, _ = self.bbox_coder.decode(rois, bbox_pred, + img_meta['img_shape']) + else: + bboxes, _ = self.bbox_coder.decode(rois[:, 1:], bbox_pred, + img_meta['img_shape']) + new_rois = torch.cat((rois[:, [0]], bboxes), dim=1) + + return new_rois diff --git a/mmdet/models/roi_heads/bbox_heads/sampling_3d_operator.py b/mmdet/models/roi_heads/bbox_heads/sampling_3d_operator.py new file mode 100644 index 0000000..c025950 --- /dev/null +++ b/mmdet/models/roi_heads/bbox_heads/sampling_3d_operator.py @@ -0,0 +1,108 @@ +import torch +from torch._C import dtype +import torch.nn as nn +import torch.nn.functional as F + + +def sampling_each_level(sample_points: torch.Tensor, + value: torch.Tensor, + weight=None, + n_points=1): + B1, n_queries, _t, n_groups_points, _ = sample_points.shape + assert _t == 1 + B2, C_feat, H_feat, W_feat = value.shape + assert B1 == B2 + B = B1 + + n_groups = n_groups_points//n_points + n_channels = C_feat//n_groups + + sample_points = sample_points \ + .view(B, n_queries, n_groups, n_points, 2) \ + .permute(0, 2, 1, 3, 4).flatten(0, 1) + sample_points = sample_points*2.0-1.0 + + # `sampling_points` now has the shape [B*n_groups, n_queries, n_points, 2] + + value = value.view(B*n_groups, n_channels, H_feat, W_feat) + out = F.grid_sample( + value, sample_points, + mode='bilinear', padding_mode='zeros', align_corners=False, + ) + + # `out`` now has the shape [B*n_groups, C, n_queries, n_points] + + if weight is not None: + weight = weight.view(B, n_queries, n_groups, n_points) \ + .permute(0, 2, 1, 3).flatten(0, 1).unsqueeze(1) + # `weight`` has the shape [B*n_groups, 1, n_queries, n_points] + out *= weight + + return out \ + .view(B, n_groups, n_channels, n_queries, n_points) \ + .permute(0, 3, 1, 4, 2) + + # `out`` has shape [B, n_queries, n_groups, n_points, n_channels] + + +def translate_to_linear_weight(ref: torch.Tensor, num_total, + tau=2.0, featmap_strides=None): + if featmap_strides is None: + grid = torch.arange(num_total, device=ref.device, dtype=ref.dtype).view( + *[len(ref.shape)*[1, ]+[-1, ]]) + else: + grid = torch.as_tensor( + featmap_strides, device=ref.device, dtype=ref.dtype) + grid = grid.log2().view(*[len(ref.shape)*[1, ]+[-1, ]]) + + ref = ref.unsqueeze(-1).clone() + l2 = (ref-grid).pow(2.0).div(tau).abs().neg() + weight = torch.softmax(l2, dim=-1) + + return weight + + +def sampling_3d( + sample_points: torch.Tensor, + multi_lvl_values, + featmap_strides, + n_points: int = 1, + num_levels: int = None, + tau=2.0, +): + B, n_queries, _t, n_groups_points, _ = sample_points.shape + assert _t == 1 + B, C_feat, _, _ = multi_lvl_values[0].shape + + n_groups = n_groups_points//n_points + n_channels = C_feat//n_groups + + if num_levels is None: + num_levels = len(featmap_strides) + + sample_points_xy = sample_points[..., 0:2] + + sample_points_z = sample_points[..., 2].clone() + sample_points_lvl_weight = translate_to_linear_weight( + sample_points_z, num_levels, + tau=tau, featmap_strides=featmap_strides) + + sample_points_lvl_weight_list = sample_points_lvl_weight.unbind(-1) + + out = sample_points.new_zeros( + B, n_queries, n_groups, n_points, n_channels) + + for i in range(num_levels): + value = multi_lvl_values[i] + lvl_weights = sample_points_lvl_weight_list[i] + + stride = featmap_strides[i] + + mapping_size = value.new_tensor( + [value.size(3), value.size(2)]).view(1, 1, 1, 1, -1) * stride + normalized_xy = sample_points_xy/mapping_size + + out += sampling_each_level(normalized_xy, value, + weight=lvl_weights, n_points=n_points) + + return out, None diff --git a/mmdet/models/roi_heads/bbox_heads/scnet_bbox_head.py b/mmdet/models/roi_heads/bbox_heads/scnet_bbox_head.py new file mode 100644 index 0000000..35758f4 --- /dev/null +++ b/mmdet/models/roi_heads/bbox_heads/scnet_bbox_head.py @@ -0,0 +1,76 @@ +from mmdet.models.builder import HEADS +from .convfc_bbox_head import ConvFCBBoxHead + + +@HEADS.register_module() +class SCNetBBoxHead(ConvFCBBoxHead): + """BBox head for `SCNet `_. + + This inherits ``ConvFCBBoxHead`` with modified forward() function, allow us + to get intermediate shared feature. + """ + + def _forward_shared(self, x): + """Forward function for shared part.""" + if self.num_shared_convs > 0: + for conv in self.shared_convs: + x = conv(x) + + if self.num_shared_fcs > 0: + if self.with_avg_pool: + x = self.avg_pool(x) + + x = x.flatten(1) + + for fc in self.shared_fcs: + x = self.relu(fc(x)) + + return x + + def _forward_cls_reg(self, x): + """Forward function for classification and regression parts.""" + x_cls = x + x_reg = x + + for conv in self.cls_convs: + x_cls = conv(x_cls) + if x_cls.dim() > 2: + if self.with_avg_pool: + x_cls = self.avg_pool(x_cls) + x_cls = x_cls.flatten(1) + for fc in self.cls_fcs: + x_cls = self.relu(fc(x_cls)) + + for conv in self.reg_convs: + x_reg = conv(x_reg) + if x_reg.dim() > 2: + if self.with_avg_pool: + x_reg = self.avg_pool(x_reg) + x_reg = x_reg.flatten(1) + for fc in self.reg_fcs: + x_reg = self.relu(fc(x_reg)) + + cls_score = self.fc_cls(x_cls) if self.with_cls else None + bbox_pred = self.fc_reg(x_reg) if self.with_reg else None + + return cls_score, bbox_pred + + def forward(self, x, return_shared_feat=False): + """Forward function. + + Args: + x (Tensor): input features + return_shared_feat (bool): If True, return cls-reg-shared feature. + + Return: + out (tuple[Tensor]): contain ``cls_score`` and ``bbox_pred``, + if ``return_shared_feat`` is True, append ``x_shared`` to the + returned tuple. + """ + x_shared = self._forward_shared(x) + out = self._forward_cls_reg(x_shared) + + if return_shared_feat: + out += (x_shared, ) + + return out diff --git a/mmdet/models/roi_heads/cascade_roi_head.py b/mmdet/models/roi_heads/cascade_roi_head.py new file mode 100644 index 0000000..f58c451 --- /dev/null +++ b/mmdet/models/roi_heads/cascade_roi_head.py @@ -0,0 +1,495 @@ +import torch +import torch.nn as nn +from mmcv.runner import ModuleList + +from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, build_assigner, + build_sampler, merge_aug_bboxes, merge_aug_masks, + multiclass_nms) +from ..builder import HEADS, build_head, build_roi_extractor +from .base_roi_head import BaseRoIHead +from .test_mixins import BBoxTestMixin, MaskTestMixin + + +@HEADS.register_module() +class CascadeRoIHead(BaseRoIHead, BBoxTestMixin, MaskTestMixin): + """Cascade roi head including one bbox head and one mask head. + + https://arxiv.org/abs/1712.00726 + """ + + def __init__(self, + num_stages, + stage_loss_weights, + bbox_roi_extractor=None, + bbox_head=None, + mask_roi_extractor=None, + mask_head=None, + shared_head=None, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + assert bbox_roi_extractor is not None + assert bbox_head is not None + assert shared_head is None, \ + 'Shared head is not supported in Cascade RCNN anymore' + + self.num_stages = num_stages + self.stage_loss_weights = stage_loss_weights + super(CascadeRoIHead, self).__init__( + bbox_roi_extractor=bbox_roi_extractor, + bbox_head=bbox_head, + mask_roi_extractor=mask_roi_extractor, + mask_head=mask_head, + shared_head=shared_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) + + def init_bbox_head(self, bbox_roi_extractor, bbox_head): + """Initialize box head and box roi extractor. + + Args: + bbox_roi_extractor (dict): Config of box roi extractor. + bbox_head (dict): Config of box in box head. + """ + self.bbox_roi_extractor = ModuleList() + self.bbox_head = ModuleList() + if not isinstance(bbox_roi_extractor, list): + bbox_roi_extractor = [ + bbox_roi_extractor for _ in range(self.num_stages) + ] + if not isinstance(bbox_head, list): + bbox_head = [bbox_head for _ in range(self.num_stages)] + assert len(bbox_roi_extractor) == len(bbox_head) == self.num_stages + for roi_extractor, head in zip(bbox_roi_extractor, bbox_head): + self.bbox_roi_extractor.append(build_roi_extractor(roi_extractor)) + self.bbox_head.append(build_head(head)) + + def init_mask_head(self, mask_roi_extractor, mask_head): + """Initialize mask head and mask roi extractor. + + Args: + mask_roi_extractor (dict): Config of mask roi extractor. + mask_head (dict): Config of mask in mask head. + """ + self.mask_head = nn.ModuleList() + if not isinstance(mask_head, list): + mask_head = [mask_head for _ in range(self.num_stages)] + assert len(mask_head) == self.num_stages + for head in mask_head: + self.mask_head.append(build_head(head)) + if mask_roi_extractor is not None: + self.share_roi_extractor = False + self.mask_roi_extractor = ModuleList() + if not isinstance(mask_roi_extractor, list): + mask_roi_extractor = [ + mask_roi_extractor for _ in range(self.num_stages) + ] + assert len(mask_roi_extractor) == self.num_stages + for roi_extractor in mask_roi_extractor: + self.mask_roi_extractor.append( + build_roi_extractor(roi_extractor)) + else: + self.share_roi_extractor = True + self.mask_roi_extractor = self.bbox_roi_extractor + + def init_assigner_sampler(self): + """Initialize assigner and sampler for each stage.""" + self.bbox_assigner = [] + self.bbox_sampler = [] + if self.train_cfg is not None: + for idx, rcnn_train_cfg in enumerate(self.train_cfg): + self.bbox_assigner.append( + build_assigner(rcnn_train_cfg.assigner)) + self.current_stage = idx + self.bbox_sampler.append( + build_sampler(rcnn_train_cfg.sampler, context=self)) + + def forward_dummy(self, x, proposals): + """Dummy forward function.""" + # bbox head + outs = () + rois = bbox2roi([proposals]) + if self.with_bbox: + for i in range(self.num_stages): + bbox_results = self._bbox_forward(i, x, rois) + outs = outs + (bbox_results['cls_score'], + bbox_results['bbox_pred']) + # mask heads + if self.with_mask: + mask_rois = rois[:100] + for i in range(self.num_stages): + mask_results = self._mask_forward(i, x, mask_rois) + outs = outs + (mask_results['mask_pred'], ) + return outs + + def _bbox_forward(self, stage, x, rois): + """Box head forward function used in both training and testing.""" + bbox_roi_extractor = self.bbox_roi_extractor[stage] + bbox_head = self.bbox_head[stage] + bbox_feats = bbox_roi_extractor(x[:bbox_roi_extractor.num_inputs], + rois) + # do not support caffe_c4 model anymore + cls_score, bbox_pred = bbox_head(bbox_feats) + + bbox_results = dict( + cls_score=cls_score, bbox_pred=bbox_pred, bbox_feats=bbox_feats) + return bbox_results + + def _bbox_forward_train(self, stage, x, sampling_results, gt_bboxes, + gt_labels, rcnn_train_cfg): + """Run forward function and calculate loss for box head in training.""" + rois = bbox2roi([res.bboxes for res in sampling_results]) + bbox_results = self._bbox_forward(stage, x, rois) + bbox_targets = self.bbox_head[stage].get_targets( + sampling_results, gt_bboxes, gt_labels, rcnn_train_cfg) + loss_bbox = self.bbox_head[stage].loss(bbox_results['cls_score'], + bbox_results['bbox_pred'], rois, + *bbox_targets) + + bbox_results.update( + loss_bbox=loss_bbox, rois=rois, bbox_targets=bbox_targets) + return bbox_results + + def _mask_forward(self, stage, x, rois): + """Mask head forward function used in both training and testing.""" + mask_roi_extractor = self.mask_roi_extractor[stage] + mask_head = self.mask_head[stage] + mask_feats = mask_roi_extractor(x[:mask_roi_extractor.num_inputs], + rois) + # do not support caffe_c4 model anymore + mask_pred = mask_head(mask_feats) + + mask_results = dict(mask_pred=mask_pred) + return mask_results + + def _mask_forward_train(self, + stage, + x, + sampling_results, + gt_masks, + rcnn_train_cfg, + bbox_feats=None): + """Run forward function and calculate loss for mask head in + training.""" + pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results]) + mask_results = self._mask_forward(stage, x, pos_rois) + + mask_targets = self.mask_head[stage].get_targets( + sampling_results, gt_masks, rcnn_train_cfg) + pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results]) + loss_mask = self.mask_head[stage].loss(mask_results['mask_pred'], + mask_targets, pos_labels) + + mask_results.update(loss_mask=loss_mask) + return mask_results + + def forward_train(self, + x, + img_metas, + proposal_list, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + gt_masks=None): + """ + Args: + x (list[Tensor]): list of multi-level img features. + img_metas (list[dict]): list of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmdet/datasets/pipelines/formatting.py:Collect`. + proposals (list[Tensors]): list of region proposals. + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + gt_masks (None | Tensor) : true segmentation masks for each box + used if the architecture supports a segmentation task. + + Returns: + dict[str, Tensor]: a dictionary of loss components + """ + losses = dict() + for i in range(self.num_stages): + self.current_stage = i + rcnn_train_cfg = self.train_cfg[i] + lw = self.stage_loss_weights[i] + + # assign gts and sample proposals + sampling_results = [] + if self.with_bbox or self.with_mask: + bbox_assigner = self.bbox_assigner[i] + bbox_sampler = self.bbox_sampler[i] + num_imgs = len(img_metas) + if gt_bboxes_ignore is None: + gt_bboxes_ignore = [None for _ in range(num_imgs)] + + for j in range(num_imgs): + assign_result = bbox_assigner.assign( + proposal_list[j], gt_bboxes[j], gt_bboxes_ignore[j], + gt_labels[j]) + sampling_result = bbox_sampler.sample( + assign_result, + proposal_list[j], + gt_bboxes[j], + gt_labels[j], + feats=[lvl_feat[j][None] for lvl_feat in x]) + sampling_results.append(sampling_result) + + # bbox head forward and loss + bbox_results = self._bbox_forward_train(i, x, sampling_results, + gt_bboxes, gt_labels, + rcnn_train_cfg) + + for name, value in bbox_results['loss_bbox'].items(): + losses[f's{i}.{name}'] = ( + value * lw if 'loss' in name else value) + + # mask head forward and loss + if self.with_mask: + mask_results = self._mask_forward_train( + i, x, sampling_results, gt_masks, rcnn_train_cfg, + bbox_results['bbox_feats']) + for name, value in mask_results['loss_mask'].items(): + losses[f's{i}.{name}'] = ( + value * lw if 'loss' in name else value) + + # refine bboxes + if i < self.num_stages - 1: + pos_is_gts = [res.pos_is_gt for res in sampling_results] + # bbox_targets is a tuple + roi_labels = bbox_results['bbox_targets'][0] + with torch.no_grad(): + roi_labels = torch.where( + roi_labels == self.bbox_head[i].num_classes, + bbox_results['cls_score'][:, :-1].argmax(1), + roi_labels) + proposal_list = self.bbox_head[i].refine_bboxes( + bbox_results['rois'], roi_labels, + bbox_results['bbox_pred'], pos_is_gts, img_metas) + + return losses + + def simple_test(self, x, proposal_list, img_metas, rescale=False): + """Test without augmentation.""" + assert self.with_bbox, 'Bbox head must be implemented.' + num_imgs = len(proposal_list) + img_shapes = tuple(meta['img_shape'] for meta in img_metas) + ori_shapes = tuple(meta['ori_shape'] for meta in img_metas) + scale_factors = tuple(meta['scale_factor'] for meta in img_metas) + + # "ms" in variable names means multi-stage + ms_bbox_result = {} + ms_segm_result = {} + ms_scores = [] + rcnn_test_cfg = self.test_cfg + + rois = bbox2roi(proposal_list) + for i in range(self.num_stages): + bbox_results = self._bbox_forward(i, x, rois) + + # split batch bbox prediction back to each image + cls_score = bbox_results['cls_score'] + bbox_pred = bbox_results['bbox_pred'] + num_proposals_per_img = tuple( + len(proposals) for proposals in proposal_list) + rois = rois.split(num_proposals_per_img, 0) + cls_score = cls_score.split(num_proposals_per_img, 0) + if isinstance(bbox_pred, torch.Tensor): + bbox_pred = bbox_pred.split(num_proposals_per_img, 0) + else: + bbox_pred = self.bbox_head[i].bbox_pred_split( + bbox_pred, num_proposals_per_img) + ms_scores.append(cls_score) + + if i < self.num_stages - 1: + bbox_label = [s[:, :-1].argmax(dim=1) for s in cls_score] + rois = torch.cat([ + self.bbox_head[i].regress_by_class(rois[j], bbox_label[j], + bbox_pred[j], + img_metas[j]) + for j in range(num_imgs) + ]) + + # average scores of each image by stages + cls_score = [ + sum([score[i] for score in ms_scores]) / float(len(ms_scores)) + for i in range(num_imgs) + ] + + # apply bbox post-processing to each image individually + det_bboxes = [] + det_labels = [] + for i in range(num_imgs): + det_bbox, det_label = self.bbox_head[-1].get_bboxes( + rois[i], + cls_score[i], + bbox_pred[i], + img_shapes[i], + scale_factors[i], + rescale=rescale, + cfg=rcnn_test_cfg) + det_bboxes.append(det_bbox) + det_labels.append(det_label) + + if torch.onnx.is_in_onnx_export(): + return det_bboxes, det_labels + bbox_results = [ + bbox2result(det_bboxes[i], det_labels[i], + self.bbox_head[-1].num_classes) + for i in range(num_imgs) + ] + ms_bbox_result['ensemble'] = bbox_results + + if self.with_mask: + if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes): + mask_classes = self.mask_head[-1].num_classes + segm_results = [[[] for _ in range(mask_classes)] + for _ in range(num_imgs)] + else: + if rescale and not isinstance(scale_factors[0], float): + scale_factors = [ + torch.from_numpy(scale_factor).to(det_bboxes[0].device) + for scale_factor in scale_factors + ] + _bboxes = [ + det_bboxes[i][:, :4] * + scale_factors[i] if rescale else det_bboxes[i][:, :4] + for i in range(len(det_bboxes)) + ] + mask_rois = bbox2roi(_bboxes) + num_mask_rois_per_img = tuple( + _bbox.size(0) for _bbox in _bboxes) + aug_masks = [] + for i in range(self.num_stages): + mask_results = self._mask_forward(i, x, mask_rois) + mask_pred = mask_results['mask_pred'] + # split batch mask prediction back to each image + mask_pred = mask_pred.split(num_mask_rois_per_img, 0) + aug_masks.append( + [m.sigmoid().cpu().numpy() for m in mask_pred]) + + # apply mask post-processing to each image individually + segm_results = [] + for i in range(num_imgs): + if det_bboxes[i].shape[0] == 0: + segm_results.append( + [[] + for _ in range(self.mask_head[-1].num_classes)]) + else: + aug_mask = [mask[i] for mask in aug_masks] + merged_masks = merge_aug_masks( + aug_mask, [[img_metas[i]]] * self.num_stages, + rcnn_test_cfg) + segm_result = self.mask_head[-1].get_seg_masks( + merged_masks, _bboxes[i], det_labels[i], + rcnn_test_cfg, ori_shapes[i], scale_factors[i], + rescale) + segm_results.append(segm_result) + ms_segm_result['ensemble'] = segm_results + + if self.with_mask: + results = list( + zip(ms_bbox_result['ensemble'], ms_segm_result['ensemble'])) + else: + results = ms_bbox_result['ensemble'] + + return results + + def aug_test(self, features, proposal_list, img_metas, rescale=False): + """Test with augmentations. + + If rescale is False, then returned bboxes and masks will fit the scale + of imgs[0]. + """ + rcnn_test_cfg = self.test_cfg + aug_bboxes = [] + aug_scores = [] + for x, img_meta in zip(features, img_metas): + # only one image in the batch + img_shape = img_meta[0]['img_shape'] + scale_factor = img_meta[0]['scale_factor'] + flip = img_meta[0]['flip'] + flip_direction = img_meta[0]['flip_direction'] + + proposals = bbox_mapping(proposal_list[0][:, :4], img_shape, + scale_factor, flip, flip_direction) + # "ms" in variable names means multi-stage + ms_scores = [] + + rois = bbox2roi([proposals]) + for i in range(self.num_stages): + bbox_results = self._bbox_forward(i, x, rois) + ms_scores.append(bbox_results['cls_score']) + + if i < self.num_stages - 1: + bbox_label = bbox_results['cls_score'][:, :-1].argmax( + dim=1) + rois = self.bbox_head[i].regress_by_class( + rois, bbox_label, bbox_results['bbox_pred'], + img_meta[0]) + + cls_score = sum(ms_scores) / float(len(ms_scores)) + bboxes, scores = self.bbox_head[-1].get_bboxes( + rois, + cls_score, + bbox_results['bbox_pred'], + img_shape, + scale_factor, + rescale=False, + cfg=None) + aug_bboxes.append(bboxes) + aug_scores.append(scores) + + # after merging, bboxes will be rescaled to the original image size + merged_bboxes, merged_scores = merge_aug_bboxes( + aug_bboxes, aug_scores, img_metas, rcnn_test_cfg) + det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores, + rcnn_test_cfg.score_thr, + rcnn_test_cfg.nms, + rcnn_test_cfg.max_per_img) + + bbox_result = bbox2result(det_bboxes, det_labels, + self.bbox_head[-1].num_classes) + + if self.with_mask: + if det_bboxes.shape[0] == 0: + segm_result = [[[] + for _ in range(self.mask_head[-1].num_classes)] + ] + else: + aug_masks = [] + aug_img_metas = [] + for x, img_meta in zip(features, img_metas): + img_shape = img_meta[0]['img_shape'] + scale_factor = img_meta[0]['scale_factor'] + flip = img_meta[0]['flip'] + flip_direction = img_meta[0]['flip_direction'] + _bboxes = bbox_mapping(det_bboxes[:, :4], img_shape, + scale_factor, flip, flip_direction) + mask_rois = bbox2roi([_bboxes]) + for i in range(self.num_stages): + mask_results = self._mask_forward(i, x, mask_rois) + aug_masks.append( + mask_results['mask_pred'].sigmoid().cpu().numpy()) + aug_img_metas.append(img_meta) + merged_masks = merge_aug_masks(aug_masks, aug_img_metas, + self.test_cfg) + + ori_shape = img_metas[0][0]['ori_shape'] + segm_result = self.mask_head[-1].get_seg_masks( + merged_masks, + det_bboxes, + det_labels, + rcnn_test_cfg, + ori_shape, + scale_factor=1.0, + rescale=False) + return [(bbox_result, segm_result)] + else: + return [bbox_result] diff --git a/mmdet/models/roi_heads/double_roi_head.py b/mmdet/models/roi_heads/double_roi_head.py new file mode 100644 index 0000000..a1aa6c8 --- /dev/null +++ b/mmdet/models/roi_heads/double_roi_head.py @@ -0,0 +1,33 @@ +from ..builder import HEADS +from .standard_roi_head import StandardRoIHead + + +@HEADS.register_module() +class DoubleHeadRoIHead(StandardRoIHead): + """RoI head for Double Head RCNN. + + https://arxiv.org/abs/1904.06493 + """ + + def __init__(self, reg_roi_scale_factor, **kwargs): + super(DoubleHeadRoIHead, self).__init__(**kwargs) + self.reg_roi_scale_factor = reg_roi_scale_factor + + def _bbox_forward(self, x, rois): + """Box head forward function used in both training and testing time.""" + bbox_cls_feats = self.bbox_roi_extractor( + x[:self.bbox_roi_extractor.num_inputs], rois) + bbox_reg_feats = self.bbox_roi_extractor( + x[:self.bbox_roi_extractor.num_inputs], + rois, + roi_scale_factor=self.reg_roi_scale_factor) + if self.with_shared_head: + bbox_cls_feats = self.shared_head(bbox_cls_feats) + bbox_reg_feats = self.shared_head(bbox_reg_feats) + cls_score, bbox_pred = self.bbox_head(bbox_cls_feats, bbox_reg_feats) + + bbox_results = dict( + cls_score=cls_score, + bbox_pred=bbox_pred, + bbox_feats=bbox_cls_feats) + return bbox_results diff --git a/mmdet/models/roi_heads/dynamic_roi_head.py b/mmdet/models/roi_heads/dynamic_roi_head.py new file mode 100644 index 0000000..89427a9 --- /dev/null +++ b/mmdet/models/roi_heads/dynamic_roi_head.py @@ -0,0 +1,154 @@ +import numpy as np +import torch + +from mmdet.core import bbox2roi +from mmdet.models.losses import SmoothL1Loss +from ..builder import HEADS +from .standard_roi_head import StandardRoIHead + +EPS = 1e-15 + + +@HEADS.register_module() +class DynamicRoIHead(StandardRoIHead): + """RoI head for `Dynamic R-CNN `_.""" + + def __init__(self, **kwargs): + super(DynamicRoIHead, self).__init__(**kwargs) + assert isinstance(self.bbox_head.loss_bbox, SmoothL1Loss) + # the IoU history of the past `update_iter_interval` iterations + self.iou_history = [] + # the beta history of the past `update_iter_interval` iterations + self.beta_history = [] + + def forward_train(self, + x, + img_metas, + proposal_list, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + gt_masks=None): + """Forward function for training. + + Args: + x (list[Tensor]): list of multi-level img features. + + img_metas (list[dict]): list of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmdet/datasets/pipelines/formatting.py:Collect`. + + proposals (list[Tensors]): list of region proposals. + + gt_bboxes (list[Tensor]): each item are the truth boxes for each + image in [tl_x, tl_y, br_x, br_y] format. + + gt_labels (list[Tensor]): class indices corresponding to each box + + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + + gt_masks (None | Tensor) : true segmentation masks for each box + used if the architecture supports a segmentation task. + + Returns: + dict[str, Tensor]: a dictionary of loss components + """ + # assign gts and sample proposals + if self.with_bbox or self.with_mask: + num_imgs = len(img_metas) + if gt_bboxes_ignore is None: + gt_bboxes_ignore = [None for _ in range(num_imgs)] + sampling_results = [] + cur_iou = [] + for i in range(num_imgs): + assign_result = self.bbox_assigner.assign( + proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i], + gt_labels[i]) + sampling_result = self.bbox_sampler.sample( + assign_result, + proposal_list[i], + gt_bboxes[i], + gt_labels[i], + feats=[lvl_feat[i][None] for lvl_feat in x]) + # record the `iou_topk`-th largest IoU in an image + iou_topk = min(self.train_cfg.dynamic_rcnn.iou_topk, + len(assign_result.max_overlaps)) + ious, _ = torch.topk(assign_result.max_overlaps, iou_topk) + cur_iou.append(ious[-1].item()) + sampling_results.append(sampling_result) + # average the current IoUs over images + cur_iou = np.mean(cur_iou) + self.iou_history.append(cur_iou) + + losses = dict() + # bbox head forward and loss + if self.with_bbox: + bbox_results = self._bbox_forward_train(x, sampling_results, + gt_bboxes, gt_labels, + img_metas) + losses.update(bbox_results['loss_bbox']) + + # mask head forward and loss + if self.with_mask: + mask_results = self._mask_forward_train(x, sampling_results, + bbox_results['bbox_feats'], + gt_masks, img_metas) + losses.update(mask_results['loss_mask']) + + # update IoU threshold and SmoothL1 beta + update_iter_interval = self.train_cfg.dynamic_rcnn.update_iter_interval + if len(self.iou_history) % update_iter_interval == 0: + new_iou_thr, new_beta = self.update_hyperparameters() + + return losses + + def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels, + img_metas): + num_imgs = len(img_metas) + rois = bbox2roi([res.bboxes for res in sampling_results]) + bbox_results = self._bbox_forward(x, rois) + + bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes, + gt_labels, self.train_cfg) + # record the `beta_topk`-th smallest target + # `bbox_targets[2]` and `bbox_targets[3]` stand for bbox_targets + # and bbox_weights, respectively + pos_inds = bbox_targets[3][:, 0].nonzero().squeeze(1) + num_pos = len(pos_inds) + cur_target = bbox_targets[2][pos_inds, :2].abs().mean(dim=1) + beta_topk = min(self.train_cfg.dynamic_rcnn.beta_topk * num_imgs, + num_pos) + cur_target = torch.kthvalue(cur_target, beta_topk)[0].item() + self.beta_history.append(cur_target) + loss_bbox = self.bbox_head.loss(bbox_results['cls_score'], + bbox_results['bbox_pred'], rois, + *bbox_targets) + + bbox_results.update(loss_bbox=loss_bbox) + return bbox_results + + def update_hyperparameters(self): + """Update hyperparameters like IoU thresholds for assigner and beta for + SmoothL1 loss based on the training statistics. + + Returns: + tuple[float]: the updated ``iou_thr`` and ``beta``. + """ + new_iou_thr = max(self.train_cfg.dynamic_rcnn.initial_iou, + np.mean(self.iou_history)) + self.iou_history = [] + self.bbox_assigner.pos_iou_thr = new_iou_thr + self.bbox_assigner.neg_iou_thr = new_iou_thr + self.bbox_assigner.min_pos_iou = new_iou_thr + if (np.median(self.beta_history) < EPS): + # avoid 0 or too small value for new_beta + new_beta = self.bbox_head.loss_bbox.beta + else: + new_beta = min(self.train_cfg.dynamic_rcnn.initial_beta, + np.median(self.beta_history)) + self.beta_history = [] + self.bbox_head.loss_bbox.beta = new_beta + return new_iou_thr, new_beta diff --git a/mmdet/models/roi_heads/grid_roi_head.py b/mmdet/models/roi_heads/grid_roi_head.py new file mode 100644 index 0000000..c2b08f4 --- /dev/null +++ b/mmdet/models/roi_heads/grid_roi_head.py @@ -0,0 +1,164 @@ +import torch + +from mmdet.core import bbox2result, bbox2roi +from ..builder import HEADS, build_head, build_roi_extractor +from .standard_roi_head import StandardRoIHead + + +@HEADS.register_module() +class GridRoIHead(StandardRoIHead): + """Grid roi head for Grid R-CNN. + + https://arxiv.org/abs/1811.12030 + """ + + def __init__(self, grid_roi_extractor, grid_head, **kwargs): + assert grid_head is not None + super(GridRoIHead, self).__init__(**kwargs) + if grid_roi_extractor is not None: + self.grid_roi_extractor = build_roi_extractor(grid_roi_extractor) + self.share_roi_extractor = False + else: + self.share_roi_extractor = True + self.grid_roi_extractor = self.bbox_roi_extractor + self.grid_head = build_head(grid_head) + + def _random_jitter(self, sampling_results, img_metas, amplitude=0.15): + """Ramdom jitter positive proposals for training.""" + for sampling_result, img_meta in zip(sampling_results, img_metas): + bboxes = sampling_result.pos_bboxes + random_offsets = bboxes.new_empty(bboxes.shape[0], 4).uniform_( + -amplitude, amplitude) + # before jittering + cxcy = (bboxes[:, 2:4] + bboxes[:, :2]) / 2 + wh = (bboxes[:, 2:4] - bboxes[:, :2]).abs() + # after jittering + new_cxcy = cxcy + wh * random_offsets[:, :2] + new_wh = wh * (1 + random_offsets[:, 2:]) + # xywh to xyxy + new_x1y1 = (new_cxcy - new_wh / 2) + new_x2y2 = (new_cxcy + new_wh / 2) + new_bboxes = torch.cat([new_x1y1, new_x2y2], dim=1) + # clip bboxes + max_shape = img_meta['img_shape'] + if max_shape is not None: + new_bboxes[:, 0::2].clamp_(min=0, max=max_shape[1] - 1) + new_bboxes[:, 1::2].clamp_(min=0, max=max_shape[0] - 1) + + sampling_result.pos_bboxes = new_bboxes + return sampling_results + + def forward_dummy(self, x, proposals): + """Dummy forward function.""" + # bbox head + outs = () + rois = bbox2roi([proposals]) + if self.with_bbox: + bbox_results = self._bbox_forward(x, rois) + outs = outs + (bbox_results['cls_score'], + bbox_results['bbox_pred']) + + # grid head + grid_rois = rois[:100] + grid_feats = self.grid_roi_extractor( + x[:self.grid_roi_extractor.num_inputs], grid_rois) + if self.with_shared_head: + grid_feats = self.shared_head(grid_feats) + grid_pred = self.grid_head(grid_feats) + outs = outs + (grid_pred, ) + + # mask head + if self.with_mask: + mask_rois = rois[:100] + mask_results = self._mask_forward(x, mask_rois) + outs = outs + (mask_results['mask_pred'], ) + return outs + + def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels, + img_metas): + """Run forward function and calculate loss for box head in training.""" + bbox_results = super(GridRoIHead, + self)._bbox_forward_train(x, sampling_results, + gt_bboxes, gt_labels, + img_metas) + + # Grid head forward and loss + sampling_results = self._random_jitter(sampling_results, img_metas) + pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results]) + + # GN in head does not support zero shape input + if pos_rois.shape[0] == 0: + return bbox_results + + grid_feats = self.grid_roi_extractor( + x[:self.grid_roi_extractor.num_inputs], pos_rois) + if self.with_shared_head: + grid_feats = self.shared_head(grid_feats) + # Accelerate training + max_sample_num_grid = self.train_cfg.get('max_num_grid', 192) + sample_idx = torch.randperm( + grid_feats.shape[0])[:min(grid_feats.shape[0], max_sample_num_grid + )] + grid_feats = grid_feats[sample_idx] + + grid_pred = self.grid_head(grid_feats) + + grid_targets = self.grid_head.get_targets(sampling_results, + self.train_cfg) + grid_targets = grid_targets[sample_idx] + + loss_grid = self.grid_head.loss(grid_pred, grid_targets) + + bbox_results['loss_bbox'].update(loss_grid) + return bbox_results + + def simple_test(self, + x, + proposal_list, + img_metas, + proposals=None, + rescale=False): + """Test without augmentation.""" + assert self.with_bbox, 'Bbox head must be implemented.' + + det_bboxes, det_labels = self.simple_test_bboxes( + x, img_metas, proposal_list, self.test_cfg, rescale=False) + # pack rois into bboxes + grid_rois = bbox2roi([det_bbox[:, :4] for det_bbox in det_bboxes]) + if grid_rois.shape[0] != 0: + grid_feats = self.grid_roi_extractor( + x[:len(self.grid_roi_extractor.featmap_strides)], grid_rois) + self.grid_head.test_mode = True + grid_pred = self.grid_head(grid_feats) + # split batch grid head prediction back to each image + num_roi_per_img = tuple(len(det_bbox) for det_bbox in det_bboxes) + grid_pred = { + k: v.split(num_roi_per_img, 0) + for k, v in grid_pred.items() + } + + # apply bbox post-processing to each image individually + bbox_results = [] + num_imgs = len(det_bboxes) + for i in range(num_imgs): + if det_bboxes[i].shape[0] == 0: + bbox_results.append(grid_rois.new_tensor([])) + else: + det_bbox = self.grid_head.get_bboxes( + det_bboxes[i], grid_pred['fused'][i], [img_metas[i]]) + if rescale: + det_bbox[:, :4] /= img_metas[i]['scale_factor'] + bbox_results.append( + bbox2result(det_bbox, det_labels[i], + self.bbox_head.num_classes)) + else: + bbox_results = [ + grid_rois.new_tensor([]) for _ in range(len(det_bboxes)) + ] + + if not self.with_mask: + return bbox_results + else: + segm_results = self.simple_test_mask( + x, img_metas, det_bboxes, det_labels, rescale=rescale) + return list(zip(bbox_results, segm_results)) diff --git a/mmdet/models/roi_heads/htc_roi_head.py b/mmdet/models/roi_heads/htc_roi_head.py new file mode 100644 index 0000000..4903ecc --- /dev/null +++ b/mmdet/models/roi_heads/htc_roi_head.py @@ -0,0 +1,578 @@ +import torch +import torch.nn.functional as F + +from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, merge_aug_bboxes, + merge_aug_masks, multiclass_nms) +from ..builder import HEADS, build_head, build_roi_extractor +from .cascade_roi_head import CascadeRoIHead + + +@HEADS.register_module() +class HybridTaskCascadeRoIHead(CascadeRoIHead): + """Hybrid task cascade roi head including one bbox head and one mask head. + + https://arxiv.org/abs/1901.07518 + """ + + def __init__(self, + num_stages, + stage_loss_weights, + semantic_roi_extractor=None, + semantic_head=None, + semantic_fusion=('bbox', 'mask'), + interleaved=True, + mask_info_flow=True, + **kwargs): + super(HybridTaskCascadeRoIHead, + self).__init__(num_stages, stage_loss_weights, **kwargs) + assert self.with_bbox and self.with_mask + assert not self.with_shared_head # shared head is not supported + + if semantic_head is not None: + self.semantic_roi_extractor = build_roi_extractor( + semantic_roi_extractor) + self.semantic_head = build_head(semantic_head) + + self.semantic_fusion = semantic_fusion + self.interleaved = interleaved + self.mask_info_flow = mask_info_flow + + @property + def with_semantic(self): + """bool: whether the head has semantic head""" + if hasattr(self, 'semantic_head') and self.semantic_head is not None: + return True + else: + return False + + def forward_dummy(self, x, proposals): + """Dummy forward function.""" + outs = () + # semantic head + if self.with_semantic: + _, semantic_feat = self.semantic_head(x) + else: + semantic_feat = None + # bbox heads + rois = bbox2roi([proposals]) + for i in range(self.num_stages): + bbox_results = self._bbox_forward( + i, x, rois, semantic_feat=semantic_feat) + outs = outs + (bbox_results['cls_score'], + bbox_results['bbox_pred']) + # mask heads + if self.with_mask: + mask_rois = rois[:100] + mask_roi_extractor = self.mask_roi_extractor[-1] + mask_feats = mask_roi_extractor( + x[:len(mask_roi_extractor.featmap_strides)], mask_rois) + if self.with_semantic and 'mask' in self.semantic_fusion: + mask_semantic_feat = self.semantic_roi_extractor( + [semantic_feat], mask_rois) + mask_feats += mask_semantic_feat + last_feat = None + for i in range(self.num_stages): + mask_head = self.mask_head[i] + if self.mask_info_flow: + mask_pred, last_feat = mask_head(mask_feats, last_feat) + else: + mask_pred = mask_head(mask_feats) + outs = outs + (mask_pred, ) + return outs + + def _bbox_forward_train(self, + stage, + x, + sampling_results, + gt_bboxes, + gt_labels, + rcnn_train_cfg, + semantic_feat=None): + """Run forward function and calculate loss for box head in training.""" + bbox_head = self.bbox_head[stage] + rois = bbox2roi([res.bboxes for res in sampling_results]) + bbox_results = self._bbox_forward( + stage, x, rois, semantic_feat=semantic_feat) + + bbox_targets = bbox_head.get_targets(sampling_results, gt_bboxes, + gt_labels, rcnn_train_cfg) + loss_bbox = bbox_head.loss(bbox_results['cls_score'], + bbox_results['bbox_pred'], rois, + *bbox_targets) + + bbox_results.update( + loss_bbox=loss_bbox, + rois=rois, + bbox_targets=bbox_targets, + ) + return bbox_results + + def _mask_forward_train(self, + stage, + x, + sampling_results, + gt_masks, + rcnn_train_cfg, + semantic_feat=None): + """Run forward function and calculate loss for mask head in + training.""" + mask_roi_extractor = self.mask_roi_extractor[stage] + mask_head = self.mask_head[stage] + pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results]) + mask_feats = mask_roi_extractor(x[:mask_roi_extractor.num_inputs], + pos_rois) + + # semantic feature fusion + # element-wise sum for original features and pooled semantic features + if self.with_semantic and 'mask' in self.semantic_fusion: + mask_semantic_feat = self.semantic_roi_extractor([semantic_feat], + pos_rois) + if mask_semantic_feat.shape[-2:] != mask_feats.shape[-2:]: + mask_semantic_feat = F.adaptive_avg_pool2d( + mask_semantic_feat, mask_feats.shape[-2:]) + mask_feats += mask_semantic_feat + + # mask information flow + # forward all previous mask heads to obtain last_feat, and fuse it + # with the normal mask feature + if self.mask_info_flow: + last_feat = None + for i in range(stage): + last_feat = self.mask_head[i]( + mask_feats, last_feat, return_logits=False) + mask_pred = mask_head(mask_feats, last_feat, return_feat=False) + else: + mask_pred = mask_head(mask_feats, return_feat=False) + + mask_targets = mask_head.get_targets(sampling_results, gt_masks, + rcnn_train_cfg) + pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results]) + loss_mask = mask_head.loss(mask_pred, mask_targets, pos_labels) + + mask_results = dict(loss_mask=loss_mask) + return mask_results + + def _bbox_forward(self, stage, x, rois, semantic_feat=None): + """Box head forward function used in both training and testing.""" + bbox_roi_extractor = self.bbox_roi_extractor[stage] + bbox_head = self.bbox_head[stage] + bbox_feats = bbox_roi_extractor( + x[:len(bbox_roi_extractor.featmap_strides)], rois) + if self.with_semantic and 'bbox' in self.semantic_fusion: + bbox_semantic_feat = self.semantic_roi_extractor([semantic_feat], + rois) + if bbox_semantic_feat.shape[-2:] != bbox_feats.shape[-2:]: + bbox_semantic_feat = F.adaptive_avg_pool2d( + bbox_semantic_feat, bbox_feats.shape[-2:]) + bbox_feats += bbox_semantic_feat + cls_score, bbox_pred = bbox_head(bbox_feats) + + bbox_results = dict(cls_score=cls_score, bbox_pred=bbox_pred) + return bbox_results + + def _mask_forward_test(self, stage, x, bboxes, semantic_feat=None): + """Mask head forward function for testing.""" + mask_roi_extractor = self.mask_roi_extractor[stage] + mask_head = self.mask_head[stage] + mask_rois = bbox2roi([bboxes]) + mask_feats = mask_roi_extractor( + x[:len(mask_roi_extractor.featmap_strides)], mask_rois) + if self.with_semantic and 'mask' in self.semantic_fusion: + mask_semantic_feat = self.semantic_roi_extractor([semantic_feat], + mask_rois) + if mask_semantic_feat.shape[-2:] != mask_feats.shape[-2:]: + mask_semantic_feat = F.adaptive_avg_pool2d( + mask_semantic_feat, mask_feats.shape[-2:]) + mask_feats += mask_semantic_feat + if self.mask_info_flow: + last_feat = None + last_pred = None + for i in range(stage): + mask_pred, last_feat = self.mask_head[i](mask_feats, last_feat) + if last_pred is not None: + mask_pred = mask_pred + last_pred + last_pred = mask_pred + mask_pred = mask_head(mask_feats, last_feat, return_feat=False) + if last_pred is not None: + mask_pred = mask_pred + last_pred + else: + mask_pred = mask_head(mask_feats) + return mask_pred + + def forward_train(self, + x, + img_metas, + proposal_list, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + gt_masks=None, + gt_semantic_seg=None): + """ + Args: + x (list[Tensor]): list of multi-level img features. + + img_metas (list[dict]): list of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmdet/datasets/pipelines/formatting.py:Collect`. + + proposal_list (list[Tensors]): list of region proposals. + + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + + gt_labels (list[Tensor]): class indices corresponding to each box + + gt_bboxes_ignore (None, list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + + gt_masks (None, Tensor) : true segmentation masks for each box + used if the architecture supports a segmentation task. + + gt_semantic_seg (None, list[Tensor]): semantic segmentation masks + used if the architecture supports semantic segmentation task. + + Returns: + dict[str, Tensor]: a dictionary of loss components + """ + # semantic segmentation part + # 2 outputs: segmentation prediction and embedded features + losses = dict() + if self.with_semantic: + semantic_pred, semantic_feat = self.semantic_head(x) + loss_seg = self.semantic_head.loss(semantic_pred, gt_semantic_seg) + losses['loss_semantic_seg'] = loss_seg + else: + semantic_feat = None + + for i in range(self.num_stages): + self.current_stage = i + rcnn_train_cfg = self.train_cfg[i] + lw = self.stage_loss_weights[i] + + # assign gts and sample proposals + sampling_results = [] + bbox_assigner = self.bbox_assigner[i] + bbox_sampler = self.bbox_sampler[i] + num_imgs = len(img_metas) + if gt_bboxes_ignore is None: + gt_bboxes_ignore = [None for _ in range(num_imgs)] + + for j in range(num_imgs): + assign_result = bbox_assigner.assign(proposal_list[j], + gt_bboxes[j], + gt_bboxes_ignore[j], + gt_labels[j]) + sampling_result = bbox_sampler.sample( + assign_result, + proposal_list[j], + gt_bboxes[j], + gt_labels[j], + feats=[lvl_feat[j][None] for lvl_feat in x]) + sampling_results.append(sampling_result) + + # bbox head forward and loss + bbox_results = \ + self._bbox_forward_train( + i, x, sampling_results, gt_bboxes, gt_labels, + rcnn_train_cfg, semantic_feat) + roi_labels = bbox_results['bbox_targets'][0] + + for name, value in bbox_results['loss_bbox'].items(): + losses[f's{i}.{name}'] = ( + value * lw if 'loss' in name else value) + + # mask head forward and loss + if self.with_mask: + # interleaved execution: use regressed bboxes by the box branch + # to train the mask branch + if self.interleaved: + pos_is_gts = [res.pos_is_gt for res in sampling_results] + with torch.no_grad(): + proposal_list = self.bbox_head[i].refine_bboxes( + bbox_results['rois'], roi_labels, + bbox_results['bbox_pred'], pos_is_gts, img_metas) + # re-assign and sample 512 RoIs from 512 RoIs + sampling_results = [] + for j in range(num_imgs): + assign_result = bbox_assigner.assign( + proposal_list[j], gt_bboxes[j], + gt_bboxes_ignore[j], gt_labels[j]) + sampling_result = bbox_sampler.sample( + assign_result, + proposal_list[j], + gt_bboxes[j], + gt_labels[j], + feats=[lvl_feat[j][None] for lvl_feat in x]) + sampling_results.append(sampling_result) + mask_results = self._mask_forward_train( + i, x, sampling_results, gt_masks, rcnn_train_cfg, + semantic_feat) + for name, value in mask_results['loss_mask'].items(): + losses[f's{i}.{name}'] = ( + value * lw if 'loss' in name else value) + + # refine bboxes (same as Cascade R-CNN) + if i < self.num_stages - 1 and not self.interleaved: + pos_is_gts = [res.pos_is_gt for res in sampling_results] + with torch.no_grad(): + proposal_list = self.bbox_head[i].refine_bboxes( + bbox_results['rois'], roi_labels, + bbox_results['bbox_pred'], pos_is_gts, img_metas) + + return losses + + def simple_test(self, x, proposal_list, img_metas, rescale=False): + """Test without augmentation.""" + if self.with_semantic: + _, semantic_feat = self.semantic_head(x) + else: + semantic_feat = None + + num_imgs = len(proposal_list) + img_shapes = tuple(meta['img_shape'] for meta in img_metas) + ori_shapes = tuple(meta['ori_shape'] for meta in img_metas) + scale_factors = tuple(meta['scale_factor'] for meta in img_metas) + + # "ms" in variable names means multi-stage + ms_bbox_result = {} + ms_segm_result = {} + ms_scores = [] + rcnn_test_cfg = self.test_cfg + + rois = bbox2roi(proposal_list) + for i in range(self.num_stages): + bbox_head = self.bbox_head[i] + bbox_results = self._bbox_forward( + i, x, rois, semantic_feat=semantic_feat) + # split batch bbox prediction back to each image + cls_score = bbox_results['cls_score'] + bbox_pred = bbox_results['bbox_pred'] + num_proposals_per_img = tuple(len(p) for p in proposal_list) + rois = rois.split(num_proposals_per_img, 0) + cls_score = cls_score.split(num_proposals_per_img, 0) + bbox_pred = bbox_pred.split(num_proposals_per_img, 0) + ms_scores.append(cls_score) + + if i < self.num_stages - 1: + bbox_label = [s[:, :-1].argmax(dim=1) for s in cls_score] + rois = torch.cat([ + bbox_head.regress_by_class(rois[i], bbox_label[i], + bbox_pred[i], img_metas[i]) + for i in range(num_imgs) + ]) + + # average scores of each image by stages + cls_score = [ + sum([score[i] for score in ms_scores]) / float(len(ms_scores)) + for i in range(num_imgs) + ] + + # apply bbox post-processing to each image individually + det_bboxes = [] + det_labels = [] + for i in range(num_imgs): + det_bbox, det_label = self.bbox_head[-1].get_bboxes( + rois[i], + cls_score[i], + bbox_pred[i], + img_shapes[i], + scale_factors[i], + rescale=rescale, + cfg=rcnn_test_cfg) + det_bboxes.append(det_bbox) + det_labels.append(det_label) + bbox_result = [ + bbox2result(det_bboxes[i], det_labels[i], + self.bbox_head[-1].num_classes) + for i in range(num_imgs) + ] + ms_bbox_result['ensemble'] = bbox_result + + if self.with_mask: + if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes): + mask_classes = self.mask_head[-1].num_classes + segm_results = [[[] for _ in range(mask_classes)] + for _ in range(num_imgs)] + else: + if rescale and not isinstance(scale_factors[0], float): + scale_factors = [ + torch.from_numpy(scale_factor).to(det_bboxes[0].device) + for scale_factor in scale_factors + ] + _bboxes = [ + det_bboxes[i][:, :4] * + scale_factors[i] if rescale else det_bboxes[i] + for i in range(num_imgs) + ] + mask_rois = bbox2roi(_bboxes) + aug_masks = [] + mask_roi_extractor = self.mask_roi_extractor[-1] + mask_feats = mask_roi_extractor( + x[:len(mask_roi_extractor.featmap_strides)], mask_rois) + if self.with_semantic and 'mask' in self.semantic_fusion: + mask_semantic_feat = self.semantic_roi_extractor( + [semantic_feat], mask_rois) + mask_feats += mask_semantic_feat + last_feat = None + + num_bbox_per_img = tuple(len(_bbox) for _bbox in _bboxes) + for i in range(self.num_stages): + mask_head = self.mask_head[i] + if self.mask_info_flow: + mask_pred, last_feat = mask_head(mask_feats, last_feat) + else: + mask_pred = mask_head(mask_feats) + + # split batch mask prediction back to each image + mask_pred = mask_pred.split(num_bbox_per_img, 0) + aug_masks.append( + [mask.sigmoid().cpu().numpy() for mask in mask_pred]) + + # apply mask post-processing to each image individually + segm_results = [] + for i in range(num_imgs): + if det_bboxes[i].shape[0] == 0: + segm_results.append( + [[] + for _ in range(self.mask_head[-1].num_classes)]) + else: + aug_mask = [mask[i] for mask in aug_masks] + merged_mask = merge_aug_masks( + aug_mask, [[img_metas[i]]] * self.num_stages, + rcnn_test_cfg) + segm_result = self.mask_head[-1].get_seg_masks( + merged_mask, _bboxes[i], det_labels[i], + rcnn_test_cfg, ori_shapes[i], scale_factors[i], + rescale) + segm_results.append(segm_result) + ms_segm_result['ensemble'] = segm_results + + if self.with_mask: + results = list( + zip(ms_bbox_result['ensemble'], ms_segm_result['ensemble'])) + else: + results = ms_bbox_result['ensemble'] + + return results + + def aug_test(self, img_feats, proposal_list, img_metas, rescale=False): + """Test with augmentations. + + If rescale is False, then returned bboxes and masks will fit the scale + of imgs[0]. + """ + if self.with_semantic: + semantic_feats = [ + self.semantic_head(feat)[1] for feat in img_feats + ] + else: + semantic_feats = [None] * len(img_metas) + + rcnn_test_cfg = self.test_cfg + aug_bboxes = [] + aug_scores = [] + for x, img_meta, semantic in zip(img_feats, img_metas, semantic_feats): + # only one image in the batch + img_shape = img_meta[0]['img_shape'] + scale_factor = img_meta[0]['scale_factor'] + flip = img_meta[0]['flip'] + flip_direction = img_meta[0]['flip_direction'] + + proposals = bbox_mapping(proposal_list[0][:, :4], img_shape, + scale_factor, flip, flip_direction) + # "ms" in variable names means multi-stage + ms_scores = [] + + rois = bbox2roi([proposals]) + for i in range(self.num_stages): + bbox_head = self.bbox_head[i] + bbox_results = self._bbox_forward( + i, x, rois, semantic_feat=semantic) + ms_scores.append(bbox_results['cls_score']) + + if i < self.num_stages - 1: + bbox_label = bbox_results['cls_score'].argmax(dim=1) + rois = bbox_head.regress_by_class( + rois, bbox_label, bbox_results['bbox_pred'], + img_meta[0]) + + cls_score = sum(ms_scores) / float(len(ms_scores)) + bboxes, scores = self.bbox_head[-1].get_bboxes( + rois, + cls_score, + bbox_results['bbox_pred'], + img_shape, + scale_factor, + rescale=False, + cfg=None) + aug_bboxes.append(bboxes) + aug_scores.append(scores) + + # after merging, bboxes will be rescaled to the original image size + merged_bboxes, merged_scores = merge_aug_bboxes( + aug_bboxes, aug_scores, img_metas, rcnn_test_cfg) + det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores, + rcnn_test_cfg.score_thr, + rcnn_test_cfg.nms, + rcnn_test_cfg.max_per_img) + + bbox_result = bbox2result(det_bboxes, det_labels, + self.bbox_head[-1].num_classes) + + if self.with_mask: + if det_bboxes.shape[0] == 0: + segm_result = [[[] + for _ in range(self.mask_head[-1].num_classes)] + ] + else: + aug_masks = [] + aug_img_metas = [] + for x, img_meta, semantic in zip(img_feats, img_metas, + semantic_feats): + img_shape = img_meta[0]['img_shape'] + scale_factor = img_meta[0]['scale_factor'] + flip = img_meta[0]['flip'] + flip_direction = img_meta[0]['flip_direction'] + _bboxes = bbox_mapping(det_bboxes[:, :4], img_shape, + scale_factor, flip, flip_direction) + mask_rois = bbox2roi([_bboxes]) + mask_feats = self.mask_roi_extractor[-1]( + x[:len(self.mask_roi_extractor[-1].featmap_strides)], + mask_rois) + if self.with_semantic: + semantic_feat = semantic + mask_semantic_feat = self.semantic_roi_extractor( + [semantic_feat], mask_rois) + if mask_semantic_feat.shape[-2:] != mask_feats.shape[ + -2:]: + mask_semantic_feat = F.adaptive_avg_pool2d( + mask_semantic_feat, mask_feats.shape[-2:]) + mask_feats += mask_semantic_feat + last_feat = None + for i in range(self.num_stages): + mask_head = self.mask_head[i] + if self.mask_info_flow: + mask_pred, last_feat = mask_head( + mask_feats, last_feat) + else: + mask_pred = mask_head(mask_feats) + aug_masks.append(mask_pred.sigmoid().cpu().numpy()) + aug_img_metas.append(img_meta) + merged_masks = merge_aug_masks(aug_masks, aug_img_metas, + self.test_cfg) + + ori_shape = img_metas[0][0]['ori_shape'] + segm_result = self.mask_head[-1].get_seg_masks( + merged_masks, + det_bboxes, + det_labels, + rcnn_test_cfg, + ori_shape, + scale_factor=1.0, + rescale=False) + return [(bbox_result, segm_result)] + else: + return [bbox_result] diff --git a/mmdet/models/roi_heads/mask_heads/__init__.py b/mmdet/models/roi_heads/mask_heads/__init__.py new file mode 100644 index 0000000..abfbe26 --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/__init__.py @@ -0,0 +1,17 @@ +from .coarse_mask_head import CoarseMaskHead +from .fcn_mask_head import FCNMaskHead +from .feature_relay_head import FeatureRelayHead +from .fused_semantic_head import FusedSemanticHead +from .global_context_head import GlobalContextHead +from .grid_head import GridHead +from .htc_mask_head import HTCMaskHead +from .mask_point_head import MaskPointHead +from .maskiou_head import MaskIoUHead +from .scnet_mask_head import SCNetMaskHead +from .scnet_semantic_head import SCNetSemanticHead + +__all__ = [ + 'FCNMaskHead', 'HTCMaskHead', 'FusedSemanticHead', 'GridHead', + 'MaskIoUHead', 'CoarseMaskHead', 'MaskPointHead', 'SCNetMaskHead', + 'SCNetSemanticHead', 'GlobalContextHead', 'FeatureRelayHead' +] diff --git 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a/mmdet/models/roi_heads/mask_heads/coarse_mask_head.py b/mmdet/models/roi_heads/mask_heads/coarse_mask_head.py new file mode 100644 index 0000000..e58cae1 --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/coarse_mask_head.py @@ -0,0 +1,99 @@ +from mmcv.cnn import ConvModule, Linear +from mmcv.runner import ModuleList, auto_fp16 + +from mmdet.models.builder import HEADS +from .fcn_mask_head import FCNMaskHead + + +@HEADS.register_module() +class CoarseMaskHead(FCNMaskHead): + """Coarse mask head used in PointRend. + + Compared with standard ``FCNMaskHead``, ``CoarseMaskHead`` will downsample + the input feature map instead of upsample it. + + Args: + num_convs (int): Number of conv layers in the head. Default: 0. + num_fcs (int): Number of fc layers in the head. Default: 2. + fc_out_channels (int): Number of output channels of fc layer. + Default: 1024. + downsample_factor (int): The factor that feature map is downsampled by. + Default: 2. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + num_convs=0, + num_fcs=2, + fc_out_channels=1024, + downsample_factor=2, + init_cfg=dict( + type='Xavier', + override=[ + dict(name='fcs'), + dict(type='Constant', val=0.001, name='fc_logits') + ]), + *arg, + **kwarg): + super(CoarseMaskHead, self).__init__( + *arg, + num_convs=num_convs, + upsample_cfg=dict(type=None), + init_cfg=None, + **kwarg) + self.init_cfg = init_cfg + self.num_fcs = num_fcs + assert self.num_fcs > 0 + self.fc_out_channels = fc_out_channels + self.downsample_factor = downsample_factor + assert self.downsample_factor >= 1 + # remove conv_logit + delattr(self, 'conv_logits') + + if downsample_factor > 1: + downsample_in_channels = ( + self.conv_out_channels + if self.num_convs > 0 else self.in_channels) + self.downsample_conv = ConvModule( + downsample_in_channels, + self.conv_out_channels, + kernel_size=downsample_factor, + stride=downsample_factor, + padding=0, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg) + else: + self.downsample_conv = None + + self.output_size = (self.roi_feat_size[0] // downsample_factor, + self.roi_feat_size[1] // downsample_factor) + self.output_area = self.output_size[0] * self.output_size[1] + + last_layer_dim = self.conv_out_channels * self.output_area + + self.fcs = ModuleList() + for i in range(num_fcs): + fc_in_channels = ( + last_layer_dim if i == 0 else self.fc_out_channels) + self.fcs.append(Linear(fc_in_channels, self.fc_out_channels)) + last_layer_dim = self.fc_out_channels + output_channels = self.num_classes * self.output_area + self.fc_logits = Linear(last_layer_dim, output_channels) + + def init_weights(self): + super(FCNMaskHead, self).init_weights() + + @auto_fp16() + def forward(self, x): + for conv in self.convs: + x = conv(x) + + if self.downsample_conv is not None: + x = self.downsample_conv(x) + + x = x.flatten(1) + for fc in self.fcs: + x = self.relu(fc(x)) + mask_pred = self.fc_logits(x).view( + x.size(0), self.num_classes, *self.output_size) + return mask_pred diff --git a/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py b/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py new file mode 100644 index 0000000..4204b68 --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py @@ -0,0 +1,393 @@ +import os + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import Conv2d, ConvModule, build_upsample_layer +from mmcv.ops.carafe import CARAFEPack +from mmcv.runner import BaseModule, ModuleList, auto_fp16, force_fp32 +from torch.nn.modules.utils import _pair + +from mmdet.core import mask_target +from mmdet.models.builder import HEADS, build_loss + +BYTES_PER_FLOAT = 4 +# TODO: This memory limit may be too much or too little. It would be better to +# determine it based on available resources. +GPU_MEM_LIMIT = 1024**3 # 1 GB memory limit + + +@HEADS.register_module() +class FCNMaskHead(BaseModule): + + def __init__(self, + num_convs=4, + roi_feat_size=14, + in_channels=256, + conv_kernel_size=3, + conv_out_channels=256, + num_classes=80, + class_agnostic=False, + upsample_cfg=dict(type='deconv', scale_factor=2), + conv_cfg=None, + norm_cfg=None, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0), + init_cfg=None): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + super(FCNMaskHead, self).__init__(init_cfg) + self.upsample_cfg = upsample_cfg.copy() + if self.upsample_cfg['type'] not in [ + None, 'deconv', 'nearest', 'bilinear', 'carafe' + ]: + raise ValueError( + f'Invalid upsample method {self.upsample_cfg["type"]}, ' + 'accepted methods are "deconv", "nearest", "bilinear", ' + '"carafe"') + self.num_convs = num_convs + # WARN: roi_feat_size is reserved and not used + self.roi_feat_size = _pair(roi_feat_size) + self.in_channels = in_channels + self.conv_kernel_size = conv_kernel_size + self.conv_out_channels = conv_out_channels + self.upsample_method = self.upsample_cfg.get('type') + self.scale_factor = self.upsample_cfg.pop('scale_factor', None) + self.num_classes = num_classes + self.class_agnostic = class_agnostic + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.fp16_enabled = False + self.loss_mask = build_loss(loss_mask) + + self.convs = ModuleList() + for i in range(self.num_convs): + in_channels = ( + self.in_channels if i == 0 else self.conv_out_channels) + padding = (self.conv_kernel_size - 1) // 2 + self.convs.append( + ConvModule( + in_channels, + self.conv_out_channels, + self.conv_kernel_size, + padding=padding, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg)) + upsample_in_channels = ( + self.conv_out_channels if self.num_convs > 0 else in_channels) + upsample_cfg_ = self.upsample_cfg.copy() + if self.upsample_method is None: + self.upsample = None + elif self.upsample_method == 'deconv': + upsample_cfg_.update( + in_channels=upsample_in_channels, + out_channels=self.conv_out_channels, + kernel_size=self.scale_factor, + stride=self.scale_factor) + self.upsample = build_upsample_layer(upsample_cfg_) + elif self.upsample_method == 'carafe': + upsample_cfg_.update( + channels=upsample_in_channels, scale_factor=self.scale_factor) + self.upsample = build_upsample_layer(upsample_cfg_) + else: + # suppress warnings + align_corners = (None + if self.upsample_method == 'nearest' else False) + upsample_cfg_.update( + scale_factor=self.scale_factor, + mode=self.upsample_method, + align_corners=align_corners) + self.upsample = build_upsample_layer(upsample_cfg_) + + out_channels = 1 if self.class_agnostic else self.num_classes + logits_in_channel = ( + self.conv_out_channels + if self.upsample_method == 'deconv' else upsample_in_channels) + self.conv_logits = Conv2d(logits_in_channel, out_channels, 1) + self.relu = nn.ReLU(inplace=True) + self.debug_imgs = None + + def init_weights(self): + super(FCNMaskHead, self).init_weights() + for m in [self.upsample, self.conv_logits]: + if m is None: + continue + elif isinstance(m, CARAFEPack): + m.init_weights() + else: + nn.init.kaiming_normal_( + m.weight, mode='fan_out', nonlinearity='relu') + nn.init.constant_(m.bias, 0) + + @auto_fp16() + def forward(self, x): + for conv in self.convs: + x = conv(x) + if self.upsample is not None: + x = self.upsample(x) + if self.upsample_method == 'deconv': + x = self.relu(x) + mask_pred = self.conv_logits(x) + return mask_pred + + def get_targets(self, sampling_results, gt_masks, rcnn_train_cfg): + pos_proposals = [res.pos_bboxes for res in sampling_results] + pos_assigned_gt_inds = [ + res.pos_assigned_gt_inds for res in sampling_results + ] + mask_targets = mask_target(pos_proposals, pos_assigned_gt_inds, + gt_masks, rcnn_train_cfg) + return mask_targets + + @force_fp32(apply_to=('mask_pred', )) + def loss(self, mask_pred, mask_targets, labels): + """ + Example: + >>> from mmdet.models.roi_heads.mask_heads.fcn_mask_head import * # NOQA + >>> N = 7 # N = number of extracted ROIs + >>> C, H, W = 11, 32, 32 + >>> # Create example instance of FCN Mask Head. + >>> # There are lots of variations depending on the configuration + >>> self = FCNMaskHead(num_classes=C, num_convs=1) + >>> inputs = torch.rand(N, self.in_channels, H, W) + >>> mask_pred = self.forward(inputs) + >>> sf = self.scale_factor + >>> labels = torch.randint(0, C, size=(N,)) + >>> # With the default properties the mask targets should indicate + >>> # a (potentially soft) single-class label + >>> mask_targets = torch.rand(N, H * sf, W * sf) + >>> loss = self.loss(mask_pred, mask_targets, labels) + >>> print('loss = {!r}'.format(loss)) + """ + loss = dict() + if mask_pred.size(0) == 0: + loss_mask = mask_pred.sum() + else: + if self.class_agnostic: + loss_mask = self.loss_mask(mask_pred, mask_targets, + torch.zeros_like(labels)) + else: + loss_mask = self.loss_mask(mask_pred, mask_targets, labels) + loss['loss_mask'] = loss_mask + return loss + + def get_seg_masks(self, mask_pred, det_bboxes, det_labels, rcnn_test_cfg, + ori_shape, scale_factor, rescale): + """Get segmentation masks from mask_pred and bboxes. + + Args: + mask_pred (Tensor or ndarray): shape (n, #class, h, w). + For single-scale testing, mask_pred is the direct output of + model, whose type is Tensor, while for multi-scale testing, + it will be converted to numpy array outside of this method. + det_bboxes (Tensor): shape (n, 4/5) + det_labels (Tensor): shape (n, ) + rcnn_test_cfg (dict): rcnn testing config + ori_shape (Tuple): original image height and width, shape (2,) + scale_factor(float | Tensor): If ``rescale is True``, box + coordinates are divided by this scale factor to fit + ``ori_shape``. + rescale (bool): If True, the resulting masks will be rescaled to + ``ori_shape``. + + Returns: + list[list]: encoded masks. The c-th item in the outer list + corresponds to the c-th class. Given the c-th outer list, the + i-th item in that inner list is the mask for the i-th box with + class label c. + + Example: + >>> import mmcv + >>> from mmdet.models.roi_heads.mask_heads.fcn_mask_head import * # NOQA + >>> N = 7 # N = number of extracted ROIs + >>> C, H, W = 11, 32, 32 + >>> # Create example instance of FCN Mask Head. + >>> self = FCNMaskHead(num_classes=C, num_convs=0) + >>> inputs = torch.rand(N, self.in_channels, H, W) + >>> mask_pred = self.forward(inputs) + >>> # Each input is associated with some bounding box + >>> det_bboxes = torch.Tensor([[1, 1, 42, 42 ]] * N) + >>> det_labels = torch.randint(0, C, size=(N,)) + >>> rcnn_test_cfg = mmcv.Config({'mask_thr_binary': 0, }) + >>> ori_shape = (H * 4, W * 4) + >>> scale_factor = torch.FloatTensor((1, 1)) + >>> rescale = False + >>> # Encoded masks are a list for each category. + >>> encoded_masks = self.get_seg_masks( + >>> mask_pred, det_bboxes, det_labels, rcnn_test_cfg, ori_shape, + >>> scale_factor, rescale + >>> ) + >>> assert len(encoded_masks) == C + >>> assert sum(list(map(len, encoded_masks))) == N + """ + if isinstance(mask_pred, torch.Tensor): + mask_pred = mask_pred.sigmoid() + else: + mask_pred = det_bboxes.new_tensor(mask_pred) + + device = mask_pred.device + cls_segms = [[] for _ in range(self.num_classes) + ] # BG is not included in num_classes + bboxes = det_bboxes[:, :4] + labels = det_labels + # No need to consider rescale and scale_factor while exporting to ONNX + if torch.onnx.is_in_onnx_export(): + img_h, img_w = ori_shape[:2] + else: + if rescale: + img_h, img_w = ori_shape[:2] + else: + if isinstance(scale_factor, float): + img_h = np.round(ori_shape[0] * scale_factor).astype( + np.int32) + img_w = np.round(ori_shape[1] * scale_factor).astype( + np.int32) + else: + w_scale, h_scale = scale_factor[0], scale_factor[1] + img_h = np.round(ori_shape[0] * h_scale.item()).astype( + np.int32) + img_w = np.round(ori_shape[1] * w_scale.item()).astype( + np.int32) + scale_factor = 1.0 + + if not isinstance(scale_factor, (float, torch.Tensor)): + scale_factor = bboxes.new_tensor(scale_factor) + bboxes = bboxes / scale_factor + + # support exporting to ONNX + if torch.onnx.is_in_onnx_export(): + threshold = rcnn_test_cfg.mask_thr_binary + if not self.class_agnostic: + box_inds = torch.arange(mask_pred.shape[0]) + mask_pred = mask_pred[box_inds, labels][:, None] + masks, _ = _do_paste_mask( + mask_pred, bboxes, img_h, img_w, skip_empty=False) + if threshold >= 0: + masks = (masks >= threshold).to(dtype=torch.bool) + else: + # TensorRT backend does not have data type of uint8 + is_trt_backend = os.environ.get( + 'ONNX_BACKEND') == 'MMCVTensorRT' + target_dtype = torch.int32 if is_trt_backend else torch.uint8 + masks = (masks * 255).to(dtype=target_dtype) + return masks + + N = len(mask_pred) + # The actual implementation split the input into chunks, + # and paste them chunk by chunk. + if device.type == 'cpu': + # CPU is most efficient when they are pasted one by one with + # skip_empty=True, so that it performs minimal number of + # operations. + num_chunks = N + else: + # GPU benefits from parallelism for larger chunks, + # but may have memory issue + num_chunks = int( + np.ceil(N * img_h * img_w * BYTES_PER_FLOAT / GPU_MEM_LIMIT)) + assert (num_chunks <= + N), 'Default GPU_MEM_LIMIT is too small; try increasing it' + chunks = torch.chunk(torch.arange(N, device=device), num_chunks) + + threshold = rcnn_test_cfg.mask_thr_binary + im_mask = torch.zeros( + N, + img_h, + img_w, + device=device, + dtype=torch.bool if threshold >= 0 else torch.uint8) + + if not self.class_agnostic: + mask_pred = mask_pred[range(N), labels][:, None] + + for inds in chunks: + masks_chunk, spatial_inds = _do_paste_mask( + mask_pred[inds], + bboxes[inds], + img_h, + img_w, + skip_empty=device.type == 'cpu') + + if threshold >= 0: + masks_chunk = (masks_chunk >= threshold).to(dtype=torch.bool) + else: + # for visualization and debugging + masks_chunk = (masks_chunk * 255).to(dtype=torch.uint8) + + im_mask[(inds, ) + spatial_inds] = masks_chunk + + for i in range(N): + cls_segms[labels[i]].append(im_mask[i].detach().cpu().numpy()) + return cls_segms + + +def _do_paste_mask(masks, boxes, img_h, img_w, skip_empty=True): + """Paste instance masks according to boxes. + + This implementation is modified from + https://github.com/facebookresearch/detectron2/ + + Args: + masks (Tensor): N, 1, H, W + boxes (Tensor): N, 4 + img_h (int): Height of the image to be pasted. + img_w (int): Width of the image to be pasted. + skip_empty (bool): Only paste masks within the region that + tightly bound all boxes, and returns the results this region only. + An important optimization for CPU. + + Returns: + tuple: (Tensor, tuple). The first item is mask tensor, the second one + is the slice object. + If skip_empty == False, the whole image will be pasted. It will + return a mask of shape (N, img_h, img_w) and an empty tuple. + If skip_empty == True, only area around the mask will be pasted. + A mask of shape (N, h', w') and its start and end coordinates + in the original image will be returned. + """ + # On GPU, paste all masks together (up to chunk size) + # by using the entire image to sample the masks + # Compared to pasting them one by one, + # this has more operations but is faster on COCO-scale dataset. + device = masks.device + if skip_empty: + x0_int, y0_int = torch.clamp( + boxes.min(dim=0).values.floor()[:2] - 1, + min=0).to(dtype=torch.int32) + x1_int = torch.clamp( + boxes[:, 2].max().ceil() + 1, max=img_w).to(dtype=torch.int32) + y1_int = torch.clamp( + boxes[:, 3].max().ceil() + 1, max=img_h).to(dtype=torch.int32) + else: + x0_int, y0_int = 0, 0 + x1_int, y1_int = img_w, img_h + x0, y0, x1, y1 = torch.split(boxes, 1, dim=1) # each is Nx1 + + N = masks.shape[0] + + img_y = torch.arange(y0_int, y1_int, device=device).to(torch.float32) + 0.5 + img_x = torch.arange(x0_int, x1_int, device=device).to(torch.float32) + 0.5 + img_y = (img_y - y0) / (y1 - y0) * 2 - 1 + img_x = (img_x - x0) / (x1 - x0) * 2 - 1 + # img_x, img_y have shapes (N, w), (N, h) + # IsInf op is not supported with ONNX<=1.7.0 + if not torch.onnx.is_in_onnx_export(): + if torch.isinf(img_x).any(): + inds = torch.where(torch.isinf(img_x)) + img_x[inds] = 0 + if torch.isinf(img_y).any(): + inds = torch.where(torch.isinf(img_y)) + img_y[inds] = 0 + + gx = img_x[:, None, :].expand(N, img_y.size(1), img_x.size(1)) + gy = img_y[:, :, None].expand(N, img_y.size(1), img_x.size(1)) + grid = torch.stack([gx, gy], dim=3) + + img_masks = F.grid_sample( + masks.to(dtype=torch.float32), grid, align_corners=False) + + if skip_empty: + return img_masks[:, 0], (slice(y0_int, y1_int), slice(x0_int, x1_int)) + else: + return img_masks[:, 0], () diff --git a/mmdet/models/roi_heads/mask_heads/feature_relay_head.py b/mmdet/models/roi_heads/mask_heads/feature_relay_head.py new file mode 100644 index 0000000..b4cd382 --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/feature_relay_head.py @@ -0,0 +1,52 @@ +import torch.nn as nn +from mmcv.runner import BaseModule, auto_fp16 + +from mmdet.models.builder import HEADS + + +@HEADS.register_module() +class FeatureRelayHead(BaseModule): + """Feature Relay Head used in `SCNet `_. + + Args: + in_channels (int, optional): number of input channels. Default: 256. + conv_out_channels (int, optional): number of output channels before + classification layer. Default: 256. + roi_feat_size (int, optional): roi feat size at box head. Default: 7. + scale_factor (int, optional): scale factor to match roi feat size + at mask head. Default: 2. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + in_channels=1024, + out_conv_channels=256, + roi_feat_size=7, + scale_factor=2, + init_cfg=dict(type='Kaiming', layer='Linear')): + super(FeatureRelayHead, self).__init__(init_cfg) + assert isinstance(roi_feat_size, int) + + self.in_channels = in_channels + self.out_conv_channels = out_conv_channels + self.roi_feat_size = roi_feat_size + self.out_channels = (roi_feat_size**2) * out_conv_channels + self.scale_factor = scale_factor + self.fp16_enabled = False + + self.fc = nn.Linear(self.in_channels, self.out_channels) + self.upsample = nn.Upsample( + scale_factor=scale_factor, mode='bilinear', align_corners=True) + + @auto_fp16() + def forward(self, x): + """Forward function.""" + N, in_C = x.shape + if N > 0: + out_C = self.out_conv_channels + out_HW = self.roi_feat_size + x = self.fc(x) + x = x.reshape(N, out_C, out_HW, out_HW) + x = self.upsample(x) + return x + return None diff --git a/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py b/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py new file mode 100644 index 0000000..85a64fb --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py @@ -0,0 +1,106 @@ +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule, auto_fp16, force_fp32 + +from mmdet.models.builder import HEADS + + +@HEADS.register_module() +class FusedSemanticHead(BaseModule): + r"""Multi-level fused semantic segmentation head. + + .. code-block:: none + + in_1 -> 1x1 conv --- + | + in_2 -> 1x1 conv -- | + || + in_3 -> 1x1 conv - || + ||| /-> 1x1 conv (mask prediction) + in_4 -> 1x1 conv -----> 3x3 convs (*4) + | \-> 1x1 conv (feature) + in_5 -> 1x1 conv --- + """ # noqa: W605 + + def __init__(self, + num_ins, + fusion_level, + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=183, + ignore_label=255, + loss_weight=0.2, + conv_cfg=None, + norm_cfg=None, + init_cfg=dict( + type='Kaiming', override=dict(name='conv_logits'))): + super(FusedSemanticHead, self).__init__(init_cfg) + self.num_ins = num_ins + self.fusion_level = fusion_level + self.num_convs = num_convs + self.in_channels = in_channels + self.conv_out_channels = conv_out_channels + self.num_classes = num_classes + self.ignore_label = ignore_label + self.loss_weight = loss_weight + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.fp16_enabled = False + + self.lateral_convs = nn.ModuleList() + for i in range(self.num_ins): + self.lateral_convs.append( + ConvModule( + self.in_channels, + self.in_channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + inplace=False)) + + self.convs = nn.ModuleList() + for i in range(self.num_convs): + in_channels = self.in_channels if i == 0 else conv_out_channels + self.convs.append( + ConvModule( + in_channels, + conv_out_channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + self.conv_embedding = ConvModule( + conv_out_channels, + conv_out_channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg) + self.conv_logits = nn.Conv2d(conv_out_channels, self.num_classes, 1) + + self.criterion = nn.CrossEntropyLoss(ignore_index=ignore_label) + + @auto_fp16() + def forward(self, feats): + x = self.lateral_convs[self.fusion_level](feats[self.fusion_level]) + fused_size = tuple(x.shape[-2:]) + for i, feat in enumerate(feats): + if i != self.fusion_level: + feat = F.interpolate( + feat, size=fused_size, mode='bilinear', align_corners=True) + x += self.lateral_convs[i](feat) + + for i in range(self.num_convs): + x = self.convs[i](x) + + mask_pred = self.conv_logits(x) + x = self.conv_embedding(x) + return mask_pred, x + + @force_fp32(apply_to=('mask_pred', )) + def loss(self, mask_pred, labels): + labels = labels.squeeze(1).long() + loss_semantic_seg = self.criterion(mask_pred, labels) + loss_semantic_seg *= self.loss_weight + return loss_semantic_seg diff --git a/mmdet/models/roi_heads/mask_heads/global_context_head.py b/mmdet/models/roi_heads/mask_heads/global_context_head.py new file mode 100644 index 0000000..4f61994 --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/global_context_head.py @@ -0,0 +1,100 @@ +import torch.nn as nn +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule, auto_fp16, force_fp32 + +from mmdet.models.builder import HEADS +from mmdet.models.utils import ResLayer, SimplifiedBasicBlock + + +@HEADS.register_module() +class GlobalContextHead(BaseModule): + """Global context head used in `SCNet `_. + + Args: + num_convs (int, optional): number of convolutional layer in GlbCtxHead. + Default: 4. + in_channels (int, optional): number of input channels. Default: 256. + conv_out_channels (int, optional): number of output channels before + classification layer. Default: 256. + num_classes (int, optional): number of classes. Default: 80. + loss_weight (float, optional): global context loss weight. Default: 1. + conv_cfg (dict, optional): config to init conv layer. Default: None. + norm_cfg (dict, optional): config to init norm layer. Default: None. + conv_to_res (bool, optional): if True, 2 convs will be grouped into + 1 `SimplifiedBasicBlock` using a skip connection. Default: False. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_weight=1.0, + conv_cfg=None, + norm_cfg=None, + conv_to_res=False, + init_cfg=dict( + type='Normal', std=0.01, override=dict(name='fc'))): + super(GlobalContextHead, self).__init__(init_cfg) + self.num_convs = num_convs + self.in_channels = in_channels + self.conv_out_channels = conv_out_channels + self.num_classes = num_classes + self.loss_weight = loss_weight + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.conv_to_res = conv_to_res + self.fp16_enabled = False + + if self.conv_to_res: + num_res_blocks = num_convs // 2 + self.convs = ResLayer( + SimplifiedBasicBlock, + in_channels, + self.conv_out_channels, + num_res_blocks, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg) + self.num_convs = num_res_blocks + else: + self.convs = nn.ModuleList() + for i in range(self.num_convs): + in_channels = self.in_channels if i == 0 else conv_out_channels + self.convs.append( + ConvModule( + in_channels, + conv_out_channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + + self.pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Linear(conv_out_channels, num_classes) + + self.criterion = nn.BCEWithLogitsLoss() + + @auto_fp16() + def forward(self, feats): + """Forward function.""" + x = feats[-1] + for i in range(self.num_convs): + x = self.convs[i](x) + x = self.pool(x) + + # multi-class prediction + mc_pred = x.reshape(x.size(0), -1) + mc_pred = self.fc(mc_pred) + + return mc_pred, x + + @force_fp32(apply_to=('pred', )) + def loss(self, pred, labels): + """Loss function.""" + labels = [lbl.unique() for lbl in labels] + targets = pred.new_zeros(pred.size()) + for i, label in enumerate(labels): + targets[i, label] = 1.0 + loss = self.loss_weight * self.criterion(pred, targets) + return loss diff --git a/mmdet/models/roi_heads/mask_heads/grid_head.py b/mmdet/models/roi_heads/mask_heads/grid_head.py new file mode 100644 index 0000000..2d6ef67 --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/grid_head.py @@ -0,0 +1,362 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule + +from mmdet.models.builder import HEADS, build_loss + + +@HEADS.register_module() +class GridHead(BaseModule): + + def __init__(self, + grid_points=9, + num_convs=8, + roi_feat_size=14, + in_channels=256, + conv_kernel_size=3, + point_feat_channels=64, + deconv_kernel_size=4, + class_agnostic=False, + loss_grid=dict( + type='CrossEntropyLoss', use_sigmoid=True, + loss_weight=15), + conv_cfg=None, + norm_cfg=dict(type='GN', num_groups=36), + init_cfg=[ + dict(type='Kaiming', layer=['Conv2d', 'Linear']), + dict( + type='Normal', + layer='ConvTranspose2d', + std=0.001, + override=dict( + type='Normal', + name='deconv2', + std=0.001, + bias=-np.log(0.99 / 0.01))) + ]): + super(GridHead, self).__init__(init_cfg) + self.grid_points = grid_points + self.num_convs = num_convs + self.roi_feat_size = roi_feat_size + self.in_channels = in_channels + self.conv_kernel_size = conv_kernel_size + self.point_feat_channels = point_feat_channels + self.conv_out_channels = self.point_feat_channels * self.grid_points + self.class_agnostic = class_agnostic + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + if isinstance(norm_cfg, dict) and norm_cfg['type'] == 'GN': + assert self.conv_out_channels % norm_cfg['num_groups'] == 0 + + assert self.grid_points >= 4 + self.grid_size = int(np.sqrt(self.grid_points)) + if self.grid_size * self.grid_size != self.grid_points: + raise ValueError('grid_points must be a square number') + + # the predicted heatmap is half of whole_map_size + if not isinstance(self.roi_feat_size, int): + raise ValueError('Only square RoIs are supporeted in Grid R-CNN') + self.whole_map_size = self.roi_feat_size * 4 + + # compute point-wise sub-regions + self.sub_regions = self.calc_sub_regions() + + self.convs = [] + for i in range(self.num_convs): + in_channels = ( + self.in_channels if i == 0 else self.conv_out_channels) + stride = 2 if i == 0 else 1 + padding = (self.conv_kernel_size - 1) // 2 + self.convs.append( + ConvModule( + in_channels, + self.conv_out_channels, + self.conv_kernel_size, + stride=stride, + padding=padding, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + bias=True)) + self.convs = nn.Sequential(*self.convs) + + self.deconv1 = nn.ConvTranspose2d( + self.conv_out_channels, + self.conv_out_channels, + kernel_size=deconv_kernel_size, + stride=2, + padding=(deconv_kernel_size - 2) // 2, + groups=grid_points) + self.norm1 = nn.GroupNorm(grid_points, self.conv_out_channels) + self.deconv2 = nn.ConvTranspose2d( + self.conv_out_channels, + grid_points, + kernel_size=deconv_kernel_size, + stride=2, + padding=(deconv_kernel_size - 2) // 2, + groups=grid_points) + + # find the 4-neighbor of each grid point + self.neighbor_points = [] + grid_size = self.grid_size + for i in range(grid_size): # i-th column + for j in range(grid_size): # j-th row + neighbors = [] + if i > 0: # left: (i - 1, j) + neighbors.append((i - 1) * grid_size + j) + if j > 0: # up: (i, j - 1) + neighbors.append(i * grid_size + j - 1) + if j < grid_size - 1: # down: (i, j + 1) + neighbors.append(i * grid_size + j + 1) + if i < grid_size - 1: # right: (i + 1, j) + neighbors.append((i + 1) * grid_size + j) + self.neighbor_points.append(tuple(neighbors)) + # total edges in the grid + self.num_edges = sum([len(p) for p in self.neighbor_points]) + + self.forder_trans = nn.ModuleList() # first-order feature transition + self.sorder_trans = nn.ModuleList() # second-order feature transition + for neighbors in self.neighbor_points: + fo_trans = nn.ModuleList() + so_trans = nn.ModuleList() + for _ in range(len(neighbors)): + # each transition module consists of a 5x5 depth-wise conv and + # 1x1 conv. + fo_trans.append( + nn.Sequential( + nn.Conv2d( + self.point_feat_channels, + self.point_feat_channels, + 5, + stride=1, + padding=2, + groups=self.point_feat_channels), + nn.Conv2d(self.point_feat_channels, + self.point_feat_channels, 1))) + so_trans.append( + nn.Sequential( + nn.Conv2d( + self.point_feat_channels, + self.point_feat_channels, + 5, + 1, + 2, + groups=self.point_feat_channels), + nn.Conv2d(self.point_feat_channels, + self.point_feat_channels, 1))) + self.forder_trans.append(fo_trans) + self.sorder_trans.append(so_trans) + + self.loss_grid = build_loss(loss_grid) + + def forward(self, x): + assert x.shape[-1] == x.shape[-2] == self.roi_feat_size + # RoI feature transformation, downsample 2x + x = self.convs(x) + + c = self.point_feat_channels + # first-order fusion + x_fo = [None for _ in range(self.grid_points)] + for i, points in enumerate(self.neighbor_points): + x_fo[i] = x[:, i * c:(i + 1) * c] + for j, point_idx in enumerate(points): + x_fo[i] = x_fo[i] + self.forder_trans[i][j]( + x[:, point_idx * c:(point_idx + 1) * c]) + + # second-order fusion + x_so = [None for _ in range(self.grid_points)] + for i, points in enumerate(self.neighbor_points): + x_so[i] = x[:, i * c:(i + 1) * c] + for j, point_idx in enumerate(points): + x_so[i] = x_so[i] + self.sorder_trans[i][j](x_fo[point_idx]) + + # predicted heatmap with fused features + x2 = torch.cat(x_so, dim=1) + x2 = self.deconv1(x2) + x2 = F.relu(self.norm1(x2), inplace=True) + heatmap = self.deconv2(x2) + + # predicted heatmap with original features (applicable during training) + if self.training: + x1 = x + x1 = self.deconv1(x1) + x1 = F.relu(self.norm1(x1), inplace=True) + heatmap_unfused = self.deconv2(x1) + else: + heatmap_unfused = heatmap + + return dict(fused=heatmap, unfused=heatmap_unfused) + + def calc_sub_regions(self): + """Compute point specific representation regions. + + See Grid R-CNN Plus (https://arxiv.org/abs/1906.05688) for details. + """ + # to make it consistent with the original implementation, half_size + # is computed as 2 * quarter_size, which is smaller + half_size = self.whole_map_size // 4 * 2 + sub_regions = [] + for i in range(self.grid_points): + x_idx = i // self.grid_size + y_idx = i % self.grid_size + if x_idx == 0: + sub_x1 = 0 + elif x_idx == self.grid_size - 1: + sub_x1 = half_size + else: + ratio = x_idx / (self.grid_size - 1) - 0.25 + sub_x1 = max(int(ratio * self.whole_map_size), 0) + + if y_idx == 0: + sub_y1 = 0 + elif y_idx == self.grid_size - 1: + sub_y1 = half_size + else: + ratio = y_idx / (self.grid_size - 1) - 0.25 + sub_y1 = max(int(ratio * self.whole_map_size), 0) + sub_regions.append( + (sub_x1, sub_y1, sub_x1 + half_size, sub_y1 + half_size)) + return sub_regions + + def get_targets(self, sampling_results, rcnn_train_cfg): + # mix all samples (across images) together. + pos_bboxes = torch.cat([res.pos_bboxes for res in sampling_results], + dim=0).cpu() + pos_gt_bboxes = torch.cat( + [res.pos_gt_bboxes for res in sampling_results], dim=0).cpu() + assert pos_bboxes.shape == pos_gt_bboxes.shape + + # expand pos_bboxes to 2x of original size + x1 = pos_bboxes[:, 0] - (pos_bboxes[:, 2] - pos_bboxes[:, 0]) / 2 + y1 = pos_bboxes[:, 1] - (pos_bboxes[:, 3] - pos_bboxes[:, 1]) / 2 + x2 = pos_bboxes[:, 2] + (pos_bboxes[:, 2] - pos_bboxes[:, 0]) / 2 + y2 = pos_bboxes[:, 3] + (pos_bboxes[:, 3] - pos_bboxes[:, 1]) / 2 + pos_bboxes = torch.stack([x1, y1, x2, y2], dim=-1) + pos_bbox_ws = (pos_bboxes[:, 2] - pos_bboxes[:, 0]).unsqueeze(-1) + pos_bbox_hs = (pos_bboxes[:, 3] - pos_bboxes[:, 1]).unsqueeze(-1) + + num_rois = pos_bboxes.shape[0] + map_size = self.whole_map_size + # this is not the final target shape + targets = torch.zeros((num_rois, self.grid_points, map_size, map_size), + dtype=torch.float) + + # pre-compute interpolation factors for all grid points. + # the first item is the factor of x-dim, and the second is y-dim. + # for a 9-point grid, factors are like (1, 0), (0.5, 0.5), (0, 1) + factors = [] + for j in range(self.grid_points): + x_idx = j // self.grid_size + y_idx = j % self.grid_size + factors.append((1 - x_idx / (self.grid_size - 1), + 1 - y_idx / (self.grid_size - 1))) + + radius = rcnn_train_cfg.pos_radius + radius2 = radius**2 + for i in range(num_rois): + # ignore small bboxes + if (pos_bbox_ws[i] <= self.grid_size + or pos_bbox_hs[i] <= self.grid_size): + continue + # for each grid point, mark a small circle as positive + for j in range(self.grid_points): + factor_x, factor_y = factors[j] + gridpoint_x = factor_x * pos_gt_bboxes[i, 0] + ( + 1 - factor_x) * pos_gt_bboxes[i, 2] + gridpoint_y = factor_y * pos_gt_bboxes[i, 1] + ( + 1 - factor_y) * pos_gt_bboxes[i, 3] + + cx = int((gridpoint_x - pos_bboxes[i, 0]) / pos_bbox_ws[i] * + map_size) + cy = int((gridpoint_y - pos_bboxes[i, 1]) / pos_bbox_hs[i] * + map_size) + + for x in range(cx - radius, cx + radius + 1): + for y in range(cy - radius, cy + radius + 1): + if x >= 0 and x < map_size and y >= 0 and y < map_size: + if (x - cx)**2 + (y - cy)**2 <= radius2: + targets[i, j, y, x] = 1 + # reduce the target heatmap size by a half + # proposed in Grid R-CNN Plus (https://arxiv.org/abs/1906.05688). + sub_targets = [] + for i in range(self.grid_points): + sub_x1, sub_y1, sub_x2, sub_y2 = self.sub_regions[i] + sub_targets.append(targets[:, [i], sub_y1:sub_y2, sub_x1:sub_x2]) + sub_targets = torch.cat(sub_targets, dim=1) + sub_targets = sub_targets.to(sampling_results[0].pos_bboxes.device) + return sub_targets + + def loss(self, grid_pred, grid_targets): + loss_fused = self.loss_grid(grid_pred['fused'], grid_targets) + loss_unfused = self.loss_grid(grid_pred['unfused'], grid_targets) + loss_grid = loss_fused + loss_unfused + return dict(loss_grid=loss_grid) + + def get_bboxes(self, det_bboxes, grid_pred, img_metas): + # TODO: refactoring + assert det_bboxes.shape[0] == grid_pred.shape[0] + det_bboxes = det_bboxes.cpu() + cls_scores = det_bboxes[:, [4]] + det_bboxes = det_bboxes[:, :4] + grid_pred = grid_pred.sigmoid().cpu() + + R, c, h, w = grid_pred.shape + half_size = self.whole_map_size // 4 * 2 + assert h == w == half_size + assert c == self.grid_points + + # find the point with max scores in the half-sized heatmap + grid_pred = grid_pred.view(R * c, h * w) + pred_scores, pred_position = grid_pred.max(dim=1) + xs = pred_position % w + ys = pred_position // w + + # get the position in the whole heatmap instead of half-sized heatmap + for i in range(self.grid_points): + xs[i::self.grid_points] += self.sub_regions[i][0] + ys[i::self.grid_points] += self.sub_regions[i][1] + + # reshape to (num_rois, grid_points) + pred_scores, xs, ys = tuple( + map(lambda x: x.view(R, c), [pred_scores, xs, ys])) + + # get expanded pos_bboxes + widths = (det_bboxes[:, 2] - det_bboxes[:, 0]).unsqueeze(-1) + heights = (det_bboxes[:, 3] - det_bboxes[:, 1]).unsqueeze(-1) + x1 = (det_bboxes[:, 0, None] - widths / 2) + y1 = (det_bboxes[:, 1, None] - heights / 2) + # map the grid point to the absolute coordinates + abs_xs = (xs.float() + 0.5) / w * widths + x1 + abs_ys = (ys.float() + 0.5) / h * heights + y1 + + # get the grid points indices that fall on the bbox boundaries + x1_inds = [i for i in range(self.grid_size)] + y1_inds = [i * self.grid_size for i in range(self.grid_size)] + x2_inds = [ + self.grid_points - self.grid_size + i + for i in range(self.grid_size) + ] + y2_inds = [(i + 1) * self.grid_size - 1 for i in range(self.grid_size)] + + # voting of all grid points on some boundary + bboxes_x1 = (abs_xs[:, x1_inds] * pred_scores[:, x1_inds]).sum( + dim=1, keepdim=True) / ( + pred_scores[:, x1_inds].sum(dim=1, keepdim=True)) + bboxes_y1 = (abs_ys[:, y1_inds] * pred_scores[:, y1_inds]).sum( + dim=1, keepdim=True) / ( + pred_scores[:, y1_inds].sum(dim=1, keepdim=True)) + bboxes_x2 = (abs_xs[:, x2_inds] * pred_scores[:, x2_inds]).sum( + dim=1, keepdim=True) / ( + pred_scores[:, x2_inds].sum(dim=1, keepdim=True)) + bboxes_y2 = (abs_ys[:, y2_inds] * pred_scores[:, y2_inds]).sum( + dim=1, keepdim=True) / ( + pred_scores[:, y2_inds].sum(dim=1, keepdim=True)) + + bbox_res = torch.cat( + [bboxes_x1, bboxes_y1, bboxes_x2, bboxes_y2, cls_scores], dim=1) + bbox_res[:, [0, 2]].clamp_(min=0, max=img_metas[0]['img_shape'][1]) + bbox_res[:, [1, 3]].clamp_(min=0, max=img_metas[0]['img_shape'][0]) + + return bbox_res diff --git a/mmdet/models/roi_heads/mask_heads/htc_mask_head.py b/mmdet/models/roi_heads/mask_heads/htc_mask_head.py new file mode 100644 index 0000000..0f435ec --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/htc_mask_head.py @@ -0,0 +1,38 @@ +from mmcv.cnn import ConvModule + +from mmdet.models.builder import HEADS +from .fcn_mask_head import FCNMaskHead + + +@HEADS.register_module() +class HTCMaskHead(FCNMaskHead): + + def __init__(self, with_conv_res=True, *args, **kwargs): + super(HTCMaskHead, self).__init__(*args, **kwargs) + self.with_conv_res = with_conv_res + if self.with_conv_res: + self.conv_res = ConvModule( + self.conv_out_channels, + self.conv_out_channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg) + + def forward(self, x, res_feat=None, return_logits=True, return_feat=True): + if res_feat is not None: + assert self.with_conv_res + res_feat = self.conv_res(res_feat) + x = x + res_feat + for conv in self.convs: + x = conv(x) + res_feat = x + outs = [] + if return_logits: + x = self.upsample(x) + if self.upsample_method == 'deconv': + x = self.relu(x) + mask_pred = self.conv_logits(x) + outs.append(mask_pred) + if return_feat: + outs.append(res_feat) + return outs if len(outs) > 1 else outs[0] diff --git a/mmdet/models/roi_heads/mask_heads/mask_point_head.py b/mmdet/models/roi_heads/mask_heads/mask_point_head.py new file mode 100644 index 0000000..030ae37 --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/mask_point_head.py @@ -0,0 +1,300 @@ +# Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend/point_head/point_head.py # noqa + +import torch +import torch.nn as nn +from mmcv.cnn import ConvModule +from mmcv.ops import point_sample, rel_roi_point_to_rel_img_point +from mmcv.runner import BaseModule + +from mmdet.models.builder import HEADS, build_loss + + +@HEADS.register_module() +class MaskPointHead(BaseModule): + """A mask point head use in PointRend. + + ``MaskPointHead`` use shared multi-layer perceptron (equivalent to + nn.Conv1d) to predict the logit of input points. The fine-grained feature + and coarse feature will be concatenate together for predication. + + Args: + num_fcs (int): Number of fc layers in the head. Default: 3. + in_channels (int): Number of input channels. Default: 256. + fc_channels (int): Number of fc channels. Default: 256. + num_classes (int): Number of classes for logits. Default: 80. + class_agnostic (bool): Whether use class agnostic classification. + If so, the output channels of logits will be 1. Default: False. + coarse_pred_each_layer (bool): Whether concatenate coarse feature with + the output of each fc layer. Default: True. + conv_cfg (dict | None): Dictionary to construct and config conv layer. + Default: dict(type='Conv1d')) + norm_cfg (dict | None): Dictionary to construct and config norm layer. + Default: None. + loss_point (dict): Dictionary to construct and config loss layer of + point head. Default: dict(type='CrossEntropyLoss', use_mask=True, + loss_weight=1.0). + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + num_classes, + num_fcs=3, + in_channels=256, + fc_channels=256, + class_agnostic=False, + coarse_pred_each_layer=True, + conv_cfg=dict(type='Conv1d'), + norm_cfg=None, + act_cfg=dict(type='ReLU'), + loss_point=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0), + init_cfg=dict( + type='Normal', std=0.001, + override=dict(name='fc_logits'))): + super().__init__(init_cfg) + self.num_fcs = num_fcs + self.in_channels = in_channels + self.fc_channels = fc_channels + self.num_classes = num_classes + self.class_agnostic = class_agnostic + self.coarse_pred_each_layer = coarse_pred_each_layer + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.loss_point = build_loss(loss_point) + + fc_in_channels = in_channels + num_classes + self.fcs = nn.ModuleList() + for _ in range(num_fcs): + fc = ConvModule( + fc_in_channels, + fc_channels, + kernel_size=1, + stride=1, + padding=0, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + self.fcs.append(fc) + fc_in_channels = fc_channels + fc_in_channels += num_classes if self.coarse_pred_each_layer else 0 + + out_channels = 1 if self.class_agnostic else self.num_classes + self.fc_logits = nn.Conv1d( + fc_in_channels, out_channels, kernel_size=1, stride=1, padding=0) + + def forward(self, fine_grained_feats, coarse_feats): + """Classify each point base on fine grained and coarse feats. + + Args: + fine_grained_feats (Tensor): Fine grained feature sampled from FPN, + shape (num_rois, in_channels, num_points). + coarse_feats (Tensor): Coarse feature sampled from CoarseMaskHead, + shape (num_rois, num_classes, num_points). + + Returns: + Tensor: Point classification results, + shape (num_rois, num_class, num_points). + """ + + x = torch.cat([fine_grained_feats, coarse_feats], dim=1) + for fc in self.fcs: + x = fc(x) + if self.coarse_pred_each_layer: + x = torch.cat((x, coarse_feats), dim=1) + return self.fc_logits(x) + + def get_targets(self, rois, rel_roi_points, sampling_results, gt_masks, + cfg): + """Get training targets of MaskPointHead for all images. + + Args: + rois (Tensor): Region of Interest, shape (num_rois, 5). + rel_roi_points: Points coordinates relative to RoI, shape + (num_rois, num_points, 2). + sampling_results (:obj:`SamplingResult`): Sampling result after + sampling and assignment. + gt_masks (Tensor) : Ground truth segmentation masks of + corresponding boxes, shape (num_rois, height, width). + cfg (dict): Training cfg. + + Returns: + Tensor: Point target, shape (num_rois, num_points). + """ + + num_imgs = len(sampling_results) + rois_list = [] + rel_roi_points_list = [] + for batch_ind in range(num_imgs): + inds = (rois[:, 0] == batch_ind) + rois_list.append(rois[inds]) + rel_roi_points_list.append(rel_roi_points[inds]) + pos_assigned_gt_inds_list = [ + res.pos_assigned_gt_inds for res in sampling_results + ] + cfg_list = [cfg for _ in range(num_imgs)] + + point_targets = map(self._get_target_single, rois_list, + rel_roi_points_list, pos_assigned_gt_inds_list, + gt_masks, cfg_list) + point_targets = list(point_targets) + + if len(point_targets) > 0: + point_targets = torch.cat(point_targets) + + return point_targets + + def _get_target_single(self, rois, rel_roi_points, pos_assigned_gt_inds, + gt_masks, cfg): + """Get training target of MaskPointHead for each image.""" + num_pos = rois.size(0) + num_points = cfg.num_points + if num_pos > 0: + gt_masks_th = ( + gt_masks.to_tensor(rois.dtype, rois.device).index_select( + 0, pos_assigned_gt_inds)) + gt_masks_th = gt_masks_th.unsqueeze(1) + rel_img_points = rel_roi_point_to_rel_img_point( + rois, rel_roi_points, gt_masks_th.shape[2:]) + point_targets = point_sample(gt_masks_th, + rel_img_points).squeeze(1) + else: + point_targets = rois.new_zeros((0, num_points)) + return point_targets + + def loss(self, point_pred, point_targets, labels): + """Calculate loss for MaskPointHead. + + Args: + point_pred (Tensor): Point predication result, shape + (num_rois, num_classes, num_points). + point_targets (Tensor): Point targets, shape (num_roi, num_points). + labels (Tensor): Class label of corresponding boxes, + shape (num_rois, ) + + Returns: + dict[str, Tensor]: a dictionary of point loss components + """ + + loss = dict() + if self.class_agnostic: + loss_point = self.loss_point(point_pred, point_targets, + torch.zeros_like(labels)) + else: + loss_point = self.loss_point(point_pred, point_targets, labels) + loss['loss_point'] = loss_point + return loss + + def _get_uncertainty(self, mask_pred, labels): + """Estimate uncertainty based on pred logits. + + We estimate uncertainty as L1 distance between 0.0 and the logits + prediction in 'mask_pred' for the foreground class in `classes`. + + Args: + mask_pred (Tensor): mask predication logits, shape (num_rois, + num_classes, mask_height, mask_width). + + labels (list[Tensor]): Either predicted or ground truth label for + each predicted mask, of length num_rois. + + Returns: + scores (Tensor): Uncertainty scores with the most uncertain + locations having the highest uncertainty score, + shape (num_rois, 1, mask_height, mask_width) + """ + if mask_pred.shape[1] == 1: + gt_class_logits = mask_pred.clone() + else: + inds = torch.arange(mask_pred.shape[0], device=mask_pred.device) + gt_class_logits = mask_pred[inds, labels].unsqueeze(1) + return -torch.abs(gt_class_logits) + + def get_roi_rel_points_train(self, mask_pred, labels, cfg): + """Get ``num_points`` most uncertain points with random points during + train. + + Sample points in [0, 1] x [0, 1] coordinate space based on their + uncertainty. The uncertainties are calculated for each point using + '_get_uncertainty()' function that takes point's logit prediction as + input. + + Args: + mask_pred (Tensor): A tensor of shape (num_rois, num_classes, + mask_height, mask_width) for class-specific or class-agnostic + prediction. + labels (list): The ground truth class for each instance. + cfg (dict): Training config of point head. + + Returns: + point_coords (Tensor): A tensor of shape (num_rois, num_points, 2) + that contains the coordinates sampled points. + """ + num_points = cfg.num_points + oversample_ratio = cfg.oversample_ratio + importance_sample_ratio = cfg.importance_sample_ratio + assert oversample_ratio >= 1 + assert 0 <= importance_sample_ratio <= 1 + batch_size = mask_pred.shape[0] + num_sampled = int(num_points * oversample_ratio) + point_coords = torch.rand( + batch_size, num_sampled, 2, device=mask_pred.device) + point_logits = point_sample(mask_pred, point_coords) + # It is crucial to calculate uncertainty based on the sampled + # prediction value for the points. Calculating uncertainties of the + # coarse predictions first and sampling them for points leads to + # incorrect results. To illustrate this: assume uncertainty func( + # logits)=-abs(logits), a sampled point between two coarse + # predictions with -1 and 1 logits has 0 logits, and therefore 0 + # uncertainty value. However, if we calculate uncertainties for the + # coarse predictions first, both will have -1 uncertainty, + # and sampled point will get -1 uncertainty. + point_uncertainties = self._get_uncertainty(point_logits, labels) + num_uncertain_points = int(importance_sample_ratio * num_points) + num_random_points = num_points - num_uncertain_points + idx = torch.topk( + point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1] + shift = num_sampled * torch.arange( + batch_size, dtype=torch.long, device=mask_pred.device) + idx += shift[:, None] + point_coords = point_coords.view(-1, 2)[idx.view(-1), :].view( + batch_size, num_uncertain_points, 2) + if num_random_points > 0: + rand_roi_coords = torch.rand( + batch_size, num_random_points, 2, device=mask_pred.device) + point_coords = torch.cat((point_coords, rand_roi_coords), dim=1) + return point_coords + + def get_roi_rel_points_test(self, mask_pred, pred_label, cfg): + """Get ``num_points`` most uncertain points during test. + + Args: + mask_pred (Tensor): A tensor of shape (num_rois, num_classes, + mask_height, mask_width) for class-specific or class-agnostic + prediction. + pred_label (list): The predication class for each instance. + cfg (dict): Testing config of point head. + + Returns: + point_indices (Tensor): A tensor of shape (num_rois, num_points) + that contains indices from [0, mask_height x mask_width) of the + most uncertain points. + point_coords (Tensor): A tensor of shape (num_rois, num_points, 2) + that contains [0, 1] x [0, 1] normalized coordinates of the + most uncertain points from the [mask_height, mask_width] grid . + """ + num_points = cfg.subdivision_num_points + uncertainty_map = self._get_uncertainty(mask_pred, pred_label) + num_rois, _, mask_height, mask_width = uncertainty_map.shape + h_step = 1.0 / mask_height + w_step = 1.0 / mask_width + + uncertainty_map = uncertainty_map.view(num_rois, + mask_height * mask_width) + num_points = min(mask_height * mask_width, num_points) + point_indices = uncertainty_map.topk(num_points, dim=1)[1] + point_coords = uncertainty_map.new_zeros(num_rois, num_points, 2) + point_coords[:, :, 0] = w_step / 2.0 + (point_indices % + mask_width).float() * w_step + point_coords[:, :, 1] = h_step / 2.0 + (point_indices // + mask_width).float() * h_step + return point_indices, point_coords diff --git a/mmdet/models/roi_heads/mask_heads/maskiou_head.py b/mmdet/models/roi_heads/mask_heads/maskiou_head.py new file mode 100644 index 0000000..fc117ff --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/maskiou_head.py @@ -0,0 +1,182 @@ +import numpy as np +import torch +import torch.nn as nn +from mmcv.cnn import Conv2d, Linear, MaxPool2d +from mmcv.runner import BaseModule, force_fp32 +from torch.nn.modules.utils import _pair + +from mmdet.models.builder import HEADS, build_loss + + +@HEADS.register_module() +class MaskIoUHead(BaseModule): + """Mask IoU Head. + + This head predicts the IoU of predicted masks and corresponding gt masks. + """ + + def __init__(self, + num_convs=4, + num_fcs=2, + roi_feat_size=14, + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + num_classes=80, + loss_iou=dict(type='MSELoss', loss_weight=0.5), + init_cfg=[ + dict(type='Kaiming', override=dict(name='convs')), + dict(type='Caffe2Xavier', override=dict(name='fcs')), + dict( + type='Normal', + std=0.01, + override=dict(name='fc_mask_iou')) + ]): + super(MaskIoUHead, self).__init__(init_cfg) + self.in_channels = in_channels + self.conv_out_channels = conv_out_channels + self.fc_out_channels = fc_out_channels + self.num_classes = num_classes + self.fp16_enabled = False + + self.convs = nn.ModuleList() + for i in range(num_convs): + if i == 0: + # concatenation of mask feature and mask prediction + in_channels = self.in_channels + 1 + else: + in_channels = self.conv_out_channels + stride = 2 if i == num_convs - 1 else 1 + self.convs.append( + Conv2d( + in_channels, + self.conv_out_channels, + 3, + stride=stride, + padding=1)) + + roi_feat_size = _pair(roi_feat_size) + pooled_area = (roi_feat_size[0] // 2) * (roi_feat_size[1] // 2) + self.fcs = nn.ModuleList() + for i in range(num_fcs): + in_channels = ( + self.conv_out_channels * + pooled_area if i == 0 else self.fc_out_channels) + self.fcs.append(Linear(in_channels, self.fc_out_channels)) + + self.fc_mask_iou = Linear(self.fc_out_channels, self.num_classes) + self.relu = nn.ReLU() + self.max_pool = MaxPool2d(2, 2) + self.loss_iou = build_loss(loss_iou) + + def forward(self, mask_feat, mask_pred): + mask_pred = mask_pred.sigmoid() + mask_pred_pooled = self.max_pool(mask_pred.unsqueeze(1)) + + x = torch.cat((mask_feat, mask_pred_pooled), 1) + + for conv in self.convs: + x = self.relu(conv(x)) + x = x.flatten(1) + for fc in self.fcs: + x = self.relu(fc(x)) + mask_iou = self.fc_mask_iou(x) + return mask_iou + + @force_fp32(apply_to=('mask_iou_pred', )) + def loss(self, mask_iou_pred, mask_iou_targets): + pos_inds = mask_iou_targets > 0 + if pos_inds.sum() > 0: + loss_mask_iou = self.loss_iou(mask_iou_pred[pos_inds], + mask_iou_targets[pos_inds]) + else: + loss_mask_iou = mask_iou_pred.sum() * 0 + return dict(loss_mask_iou=loss_mask_iou) + + @force_fp32(apply_to=('mask_pred', )) + def get_targets(self, sampling_results, gt_masks, mask_pred, mask_targets, + rcnn_train_cfg): + """Compute target of mask IoU. + + Mask IoU target is the IoU of the predicted mask (inside a bbox) and + the gt mask of corresponding gt mask (the whole instance). + The intersection area is computed inside the bbox, and the gt mask area + is computed with two steps, firstly we compute the gt area inside the + bbox, then divide it by the area ratio of gt area inside the bbox and + the gt area of the whole instance. + + Args: + sampling_results (list[:obj:`SamplingResult`]): sampling results. + gt_masks (BitmapMask | PolygonMask): Gt masks (the whole instance) + of each image, with the same shape of the input image. + mask_pred (Tensor): Predicted masks of each positive proposal, + shape (num_pos, h, w). + mask_targets (Tensor): Gt mask of each positive proposal, + binary map of the shape (num_pos, h, w). + rcnn_train_cfg (dict): Training config for R-CNN part. + + Returns: + Tensor: mask iou target (length == num positive). + """ + pos_proposals = [res.pos_bboxes for res in sampling_results] + pos_assigned_gt_inds = [ + res.pos_assigned_gt_inds for res in sampling_results + ] + + # compute the area ratio of gt areas inside the proposals and + # the whole instance + area_ratios = map(self._get_area_ratio, pos_proposals, + pos_assigned_gt_inds, gt_masks) + area_ratios = torch.cat(list(area_ratios)) + assert mask_targets.size(0) == area_ratios.size(0) + + mask_pred = (mask_pred > rcnn_train_cfg.mask_thr_binary).float() + mask_pred_areas = mask_pred.sum((-1, -2)) + + # mask_pred and mask_targets are binary maps + overlap_areas = (mask_pred * mask_targets).sum((-1, -2)) + + # compute the mask area of the whole instance + gt_full_areas = mask_targets.sum((-1, -2)) / (area_ratios + 1e-7) + + mask_iou_targets = overlap_areas / ( + mask_pred_areas + gt_full_areas - overlap_areas) + return mask_iou_targets + + def _get_area_ratio(self, pos_proposals, pos_assigned_gt_inds, gt_masks): + """Compute area ratio of the gt mask inside the proposal and the gt + mask of the corresponding instance.""" + num_pos = pos_proposals.size(0) + if num_pos > 0: + area_ratios = [] + proposals_np = pos_proposals.cpu().numpy() + pos_assigned_gt_inds = pos_assigned_gt_inds.cpu().numpy() + # compute mask areas of gt instances (batch processing for speedup) + gt_instance_mask_area = gt_masks.areas + for i in range(num_pos): + gt_mask = gt_masks[pos_assigned_gt_inds[i]] + + # crop the gt mask inside the proposal + bbox = proposals_np[i, :].astype(np.int32) + gt_mask_in_proposal = gt_mask.crop(bbox) + + ratio = gt_mask_in_proposal.areas[0] / ( + gt_instance_mask_area[pos_assigned_gt_inds[i]] + 1e-7) + area_ratios.append(ratio) + area_ratios = torch.from_numpy(np.stack(area_ratios)).float().to( + pos_proposals.device) + else: + area_ratios = pos_proposals.new_zeros((0, )) + return area_ratios + + @force_fp32(apply_to=('mask_iou_pred', )) + def get_mask_scores(self, mask_iou_pred, det_bboxes, det_labels): + """Get the mask scores. + + mask_score = bbox_score * mask_iou + """ + inds = range(det_labels.size(0)) + mask_scores = mask_iou_pred[inds, det_labels] * det_bboxes[inds, -1] + mask_scores = mask_scores.cpu().numpy() + det_labels = det_labels.cpu().numpy() + return [mask_scores[det_labels == i] for i in range(self.num_classes)] diff --git a/mmdet/models/roi_heads/mask_heads/scnet_mask_head.py b/mmdet/models/roi_heads/mask_heads/scnet_mask_head.py new file mode 100644 index 0000000..983a2d9 --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/scnet_mask_head.py @@ -0,0 +1,27 @@ +from mmdet.models.builder import HEADS +from mmdet.models.utils import ResLayer, SimplifiedBasicBlock +from .fcn_mask_head import FCNMaskHead + + +@HEADS.register_module() +class SCNetMaskHead(FCNMaskHead): + """Mask head for `SCNet `_. + + Args: + conv_to_res (bool, optional): if True, change the conv layers to + ``SimplifiedBasicBlock``. + """ + + def __init__(self, conv_to_res=True, **kwargs): + super(SCNetMaskHead, self).__init__(**kwargs) + self.conv_to_res = conv_to_res + if conv_to_res: + assert self.conv_kernel_size == 3 + self.num_res_blocks = self.num_convs // 2 + self.convs = ResLayer( + SimplifiedBasicBlock, + self.in_channels, + self.conv_out_channels, + self.num_res_blocks, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg) diff --git a/mmdet/models/roi_heads/mask_heads/scnet_semantic_head.py b/mmdet/models/roi_heads/mask_heads/scnet_semantic_head.py new file mode 100644 index 0000000..df85a01 --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/scnet_semantic_head.py @@ -0,0 +1,27 @@ +from mmdet.models.builder import HEADS +from mmdet.models.utils import ResLayer, SimplifiedBasicBlock +from .fused_semantic_head import FusedSemanticHead + + +@HEADS.register_module() +class SCNetSemanticHead(FusedSemanticHead): + """Mask head for `SCNet `_. + + Args: + conv_to_res (bool, optional): if True, change the conv layers to + ``SimplifiedBasicBlock``. + """ + + def __init__(self, conv_to_res=True, **kwargs): + super(SCNetSemanticHead, self).__init__(**kwargs) + self.conv_to_res = conv_to_res + if self.conv_to_res: + num_res_blocks = self.num_convs // 2 + self.convs = ResLayer( + SimplifiedBasicBlock, + self.in_channels, + self.conv_out_channels, + num_res_blocks, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg) + self.num_convs = num_res_blocks diff --git a/mmdet/models/roi_heads/mask_scoring_roi_head.py b/mmdet/models/roi_heads/mask_scoring_roi_head.py new file mode 100644 index 0000000..e12700c --- /dev/null +++ b/mmdet/models/roi_heads/mask_scoring_roi_head.py @@ -0,0 +1,112 @@ +import torch + +from mmdet.core import bbox2roi +from ..builder import HEADS, build_head +from .standard_roi_head import StandardRoIHead + + +@HEADS.register_module() +class MaskScoringRoIHead(StandardRoIHead): + """Mask Scoring RoIHead for Mask Scoring RCNN. + + https://arxiv.org/abs/1903.00241 + """ + + def __init__(self, mask_iou_head, **kwargs): + assert mask_iou_head is not None + super(MaskScoringRoIHead, self).__init__(**kwargs) + self.mask_iou_head = build_head(mask_iou_head) + + def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks, + img_metas): + """Run forward function and calculate loss for Mask head in + training.""" + pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results]) + mask_results = super(MaskScoringRoIHead, + self)._mask_forward_train(x, sampling_results, + bbox_feats, gt_masks, + img_metas) + if mask_results['loss_mask'] is None: + return mask_results + + # mask iou head forward and loss + pos_mask_pred = mask_results['mask_pred'][ + range(mask_results['mask_pred'].size(0)), pos_labels] + mask_iou_pred = self.mask_iou_head(mask_results['mask_feats'], + pos_mask_pred) + pos_mask_iou_pred = mask_iou_pred[range(mask_iou_pred.size(0)), + pos_labels] + + mask_iou_targets = self.mask_iou_head.get_targets( + sampling_results, gt_masks, pos_mask_pred, + mask_results['mask_targets'], self.train_cfg) + loss_mask_iou = self.mask_iou_head.loss(pos_mask_iou_pred, + mask_iou_targets) + mask_results['loss_mask'].update(loss_mask_iou) + return mask_results + + def simple_test_mask(self, + x, + img_metas, + det_bboxes, + det_labels, + rescale=False): + """Obtain mask prediction without augmentation.""" + # image shapes of images in the batch + ori_shapes = tuple(meta['ori_shape'] for meta in img_metas) + scale_factors = tuple(meta['scale_factor'] for meta in img_metas) + + num_imgs = len(det_bboxes) + if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes): + num_classes = self.mask_head.num_classes + segm_results = [[[] for _ in range(num_classes)] + for _ in range(num_imgs)] + mask_scores = [[[] for _ in range(num_classes)] + for _ in range(num_imgs)] + else: + # if det_bboxes is rescaled to the original image size, we need to + # rescale it back to the testing scale to obtain RoIs. + if rescale and not isinstance(scale_factors[0], float): + scale_factors = [ + torch.from_numpy(scale_factor).to(det_bboxes[0].device) + for scale_factor in scale_factors + ] + _bboxes = [ + det_bboxes[i][:, :4] * + scale_factors[i] if rescale else det_bboxes[i] + for i in range(num_imgs) + ] + mask_rois = bbox2roi(_bboxes) + mask_results = self._mask_forward(x, mask_rois) + concat_det_labels = torch.cat(det_labels) + # get mask scores with mask iou head + mask_feats = mask_results['mask_feats'] + mask_pred = mask_results['mask_pred'] + mask_iou_pred = self.mask_iou_head( + mask_feats, mask_pred[range(concat_det_labels.size(0)), + concat_det_labels]) + # split batch mask prediction back to each image + num_bboxes_per_img = tuple(len(_bbox) for _bbox in _bboxes) + mask_preds = mask_pred.split(num_bboxes_per_img, 0) + mask_iou_preds = mask_iou_pred.split(num_bboxes_per_img, 0) + + # apply mask post-processing to each image individually + segm_results = [] + mask_scores = [] + for i in range(num_imgs): + if det_bboxes[i].shape[0] == 0: + segm_results.append( + [[] for _ in range(self.mask_head.num_classes)]) + mask_scores.append( + [[] for _ in range(self.mask_head.num_classes)]) + else: + segm_result = self.mask_head.get_seg_masks( + mask_preds[i], _bboxes[i], det_labels[i], + self.test_cfg, ori_shapes[i], scale_factors[i], + rescale) + # get mask scores with mask iou head + mask_score = self.mask_iou_head.get_mask_scores( + mask_iou_preds[i], det_bboxes[i], det_labels[i]) + segm_results.append(segm_result) + mask_scores.append(mask_score) + return list(zip(segm_results, mask_scores)) diff --git a/mmdet/models/roi_heads/pisa_roi_head.py b/mmdet/models/roi_heads/pisa_roi_head.py new file mode 100644 index 0000000..e011136 --- /dev/null +++ b/mmdet/models/roi_heads/pisa_roi_head.py @@ -0,0 +1,159 @@ +from mmdet.core import bbox2roi +from ..builder import HEADS +from ..losses.pisa_loss import carl_loss, isr_p +from .standard_roi_head import StandardRoIHead + + +@HEADS.register_module() +class PISARoIHead(StandardRoIHead): + r"""The RoI head for `Prime Sample Attention in Object Detection + `_.""" + + def forward_train(self, + x, + img_metas, + proposal_list, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + gt_masks=None): + """Forward function for training. + + Args: + x (list[Tensor]): List of multi-level img features. + img_metas (list[dict]): List of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmdet/datasets/pipelines/formatting.py:Collect`. + proposals (list[Tensors]): List of region proposals. + gt_bboxes (list[Tensor]): Each item are the truth boxes for each + image in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): Class indices corresponding to each box + gt_bboxes_ignore (list[Tensor], optional): Specify which bounding + boxes can be ignored when computing the loss. + gt_masks (None | Tensor) : True segmentation masks for each box + used if the architecture supports a segmentation task. + + Returns: + dict[str, Tensor]: a dictionary of loss components + """ + # assign gts and sample proposals + if self.with_bbox or self.with_mask: + num_imgs = len(img_metas) + if gt_bboxes_ignore is None: + gt_bboxes_ignore = [None for _ in range(num_imgs)] + sampling_results = [] + neg_label_weights = [] + for i in range(num_imgs): + assign_result = self.bbox_assigner.assign( + proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i], + gt_labels[i]) + sampling_result = self.bbox_sampler.sample( + assign_result, + proposal_list[i], + gt_bboxes[i], + gt_labels[i], + feats=[lvl_feat[i][None] for lvl_feat in x]) + # neg label weight is obtained by sampling when using ISR-N + neg_label_weight = None + if isinstance(sampling_result, tuple): + sampling_result, neg_label_weight = sampling_result + sampling_results.append(sampling_result) + neg_label_weights.append(neg_label_weight) + + losses = dict() + # bbox head forward and loss + if self.with_bbox: + bbox_results = self._bbox_forward_train( + x, + sampling_results, + gt_bboxes, + gt_labels, + img_metas, + neg_label_weights=neg_label_weights) + losses.update(bbox_results['loss_bbox']) + + # mask head forward and loss + if self.with_mask: + mask_results = self._mask_forward_train(x, sampling_results, + bbox_results['bbox_feats'], + gt_masks, img_metas) + losses.update(mask_results['loss_mask']) + + return losses + + def _bbox_forward(self, x, rois): + """Box forward function used in both training and testing.""" + # TODO: a more flexible way to decide which feature maps to use + bbox_feats = self.bbox_roi_extractor( + x[:self.bbox_roi_extractor.num_inputs], rois) + if self.with_shared_head: + bbox_feats = self.shared_head(bbox_feats) + cls_score, bbox_pred = self.bbox_head(bbox_feats) + + bbox_results = dict( + cls_score=cls_score, bbox_pred=bbox_pred, bbox_feats=bbox_feats) + return bbox_results + + def _bbox_forward_train(self, + x, + sampling_results, + gt_bboxes, + gt_labels, + img_metas, + neg_label_weights=None): + """Run forward function and calculate loss for box head in training.""" + rois = bbox2roi([res.bboxes for res in sampling_results]) + + bbox_results = self._bbox_forward(x, rois) + + bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes, + gt_labels, self.train_cfg) + + # neg_label_weights obtained by sampler is image-wise, mapping back to + # the corresponding location in label weights + if neg_label_weights[0] is not None: + label_weights = bbox_targets[1] + cur_num_rois = 0 + for i in range(len(sampling_results)): + num_pos = sampling_results[i].pos_inds.size(0) + num_neg = sampling_results[i].neg_inds.size(0) + label_weights[cur_num_rois + num_pos:cur_num_rois + num_pos + + num_neg] = neg_label_weights[i] + cur_num_rois += num_pos + num_neg + + cls_score = bbox_results['cls_score'] + bbox_pred = bbox_results['bbox_pred'] + + # Apply ISR-P + isr_cfg = self.train_cfg.get('isr', None) + if isr_cfg is not None: + bbox_targets = isr_p( + cls_score, + bbox_pred, + bbox_targets, + rois, + sampling_results, + self.bbox_head.loss_cls, + self.bbox_head.bbox_coder, + **isr_cfg, + num_class=self.bbox_head.num_classes) + loss_bbox = self.bbox_head.loss(cls_score, bbox_pred, rois, + *bbox_targets) + + # Add CARL Loss + carl_cfg = self.train_cfg.get('carl', None) + if carl_cfg is not None: + loss_carl = carl_loss( + cls_score, + bbox_targets[0], + bbox_pred, + bbox_targets[2], + self.bbox_head.loss_bbox, + **carl_cfg, + num_class=self.bbox_head.num_classes) + loss_bbox.update(loss_carl) + + bbox_results.update(loss_bbox=loss_bbox) + return bbox_results diff --git a/mmdet/models/roi_heads/point_rend_roi_head.py b/mmdet/models/roi_heads/point_rend_roi_head.py new file mode 100644 index 0000000..af43a21 --- /dev/null +++ b/mmdet/models/roi_heads/point_rend_roi_head.py @@ -0,0 +1,209 @@ +# Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend # noqa + +import torch +import torch.nn.functional as F +from mmcv.ops import point_sample, rel_roi_point_to_rel_img_point + +from mmdet.core import bbox2roi, bbox_mapping, merge_aug_masks +from .. import builder +from ..builder import HEADS +from .standard_roi_head import StandardRoIHead + + +@HEADS.register_module() +class PointRendRoIHead(StandardRoIHead): + """`PointRend `_.""" + + def __init__(self, point_head, *args, **kwargs): + super().__init__(*args, **kwargs) + assert self.with_bbox and self.with_mask + self.init_point_head(point_head) + + def init_point_head(self, point_head): + """Initialize ``point_head``""" + self.point_head = builder.build_head(point_head) + + def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks, + img_metas): + """Run forward function and calculate loss for mask head and point head + in training.""" + mask_results = super()._mask_forward_train(x, sampling_results, + bbox_feats, gt_masks, + img_metas) + if mask_results['loss_mask'] is not None: + loss_point = self._mask_point_forward_train( + x, sampling_results, mask_results['mask_pred'], gt_masks, + img_metas) + mask_results['loss_mask'].update(loss_point) + + return mask_results + + def _mask_point_forward_train(self, x, sampling_results, mask_pred, + gt_masks, img_metas): + """Run forward function and calculate loss for point head in + training.""" + pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results]) + rel_roi_points = self.point_head.get_roi_rel_points_train( + mask_pred, pos_labels, cfg=self.train_cfg) + rois = bbox2roi([res.pos_bboxes for res in sampling_results]) + + fine_grained_point_feats = self._get_fine_grained_point_feats( + x, rois, rel_roi_points, img_metas) + coarse_point_feats = point_sample(mask_pred, rel_roi_points) + mask_point_pred = self.point_head(fine_grained_point_feats, + coarse_point_feats) + mask_point_target = self.point_head.get_targets( + rois, rel_roi_points, sampling_results, gt_masks, self.train_cfg) + loss_mask_point = self.point_head.loss(mask_point_pred, + mask_point_target, pos_labels) + + return loss_mask_point + + def _get_fine_grained_point_feats(self, x, rois, rel_roi_points, + img_metas): + """Sample fine grained feats from each level feature map and + concatenate them together.""" + num_imgs = len(img_metas) + fine_grained_feats = [] + for idx in range(self.mask_roi_extractor.num_inputs): + feats = x[idx] + spatial_scale = 1. / float( + self.mask_roi_extractor.featmap_strides[idx]) + point_feats = [] + for batch_ind in range(num_imgs): + # unravel batch dim + feat = feats[batch_ind].unsqueeze(0) + inds = (rois[:, 0].long() == batch_ind) + if inds.any(): + rel_img_points = rel_roi_point_to_rel_img_point( + rois[inds], rel_roi_points[inds], feat.shape[2:], + spatial_scale).unsqueeze(0) + point_feat = point_sample(feat, rel_img_points) + point_feat = point_feat.squeeze(0).transpose(0, 1) + point_feats.append(point_feat) + fine_grained_feats.append(torch.cat(point_feats, dim=0)) + return torch.cat(fine_grained_feats, dim=1) + + def _mask_point_forward_test(self, x, rois, label_pred, mask_pred, + img_metas): + """Mask refining process with point head in testing.""" + refined_mask_pred = mask_pred.clone() + for subdivision_step in range(self.test_cfg.subdivision_steps): + refined_mask_pred = F.interpolate( + refined_mask_pred, + scale_factor=self.test_cfg.scale_factor, + mode='bilinear', + align_corners=False) + # If `subdivision_num_points` is larger or equal to the + # resolution of the next step, then we can skip this step + num_rois, channels, mask_height, mask_width = \ + refined_mask_pred.shape + if (self.test_cfg.subdivision_num_points >= + self.test_cfg.scale_factor**2 * mask_height * mask_width + and + subdivision_step < self.test_cfg.subdivision_steps - 1): + continue + point_indices, rel_roi_points = \ + self.point_head.get_roi_rel_points_test( + refined_mask_pred, label_pred, cfg=self.test_cfg) + fine_grained_point_feats = self._get_fine_grained_point_feats( + x, rois, rel_roi_points, img_metas) + coarse_point_feats = point_sample(mask_pred, rel_roi_points) + mask_point_pred = self.point_head(fine_grained_point_feats, + coarse_point_feats) + + point_indices = point_indices.unsqueeze(1).expand(-1, channels, -1) + refined_mask_pred = refined_mask_pred.reshape( + num_rois, channels, mask_height * mask_width) + refined_mask_pred = refined_mask_pred.scatter_( + 2, point_indices, mask_point_pred) + refined_mask_pred = refined_mask_pred.view(num_rois, channels, + mask_height, mask_width) + + return refined_mask_pred + + def simple_test_mask(self, + x, + img_metas, + det_bboxes, + det_labels, + rescale=False): + """Obtain mask prediction without augmentation.""" + ori_shapes = tuple(meta['ori_shape'] for meta in img_metas) + scale_factors = tuple(meta['scale_factor'] for meta in img_metas) + num_imgs = len(det_bboxes) + if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes): + segm_results = [[[] for _ in range(self.mask_head.num_classes)] + for _ in range(num_imgs)] + else: + # if det_bboxes is rescaled to the original image size, we need to + # rescale it back to the testing scale to obtain RoIs. + if rescale and not isinstance(scale_factors[0], float): + scale_factors = [ + torch.from_numpy(scale_factor).to(det_bboxes[0].device) + for scale_factor in scale_factors + ] + _bboxes = [ + det_bboxes[i][:, :4] * + scale_factors[i] if rescale else det_bboxes[i][:, :4] + for i in range(len(det_bboxes)) + ] + mask_rois = bbox2roi(_bboxes) + mask_results = self._mask_forward(x, mask_rois) + # split batch mask prediction back to each image + mask_pred = mask_results['mask_pred'] + num_mask_roi_per_img = [len(det_bbox) for det_bbox in det_bboxes] + mask_preds = mask_pred.split(num_mask_roi_per_img, 0) + mask_rois = mask_rois.split(num_mask_roi_per_img, 0) + + # apply mask post-processing to each image individually + segm_results = [] + for i in range(num_imgs): + if det_bboxes[i].shape[0] == 0: + segm_results.append( + [[] for _ in range(self.mask_head.num_classes)]) + else: + x_i = [xx[[i]] for xx in x] + mask_rois_i = mask_rois[i] + mask_rois_i[:, 0] = 0 # TODO: remove this hack + mask_pred_i = self._mask_point_forward_test( + x_i, mask_rois_i, det_labels[i], mask_preds[i], + [img_metas]) + segm_result = self.mask_head.get_seg_masks( + mask_pred_i, _bboxes[i], det_labels[i], self.test_cfg, + ori_shapes[i], scale_factors[i], rescale) + segm_results.append(segm_result) + return segm_results + + def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels): + """Test for mask head with test time augmentation.""" + if det_bboxes.shape[0] == 0: + segm_result = [[] for _ in range(self.mask_head.num_classes)] + else: + aug_masks = [] + for x, img_meta in zip(feats, img_metas): + img_shape = img_meta[0]['img_shape'] + scale_factor = img_meta[0]['scale_factor'] + flip = img_meta[0]['flip'] + _bboxes = bbox_mapping(det_bboxes[:, :4], img_shape, + scale_factor, flip) + mask_rois = bbox2roi([_bboxes]) + mask_results = self._mask_forward(x, mask_rois) + mask_results['mask_pred'] = self._mask_point_forward_test( + x, mask_rois, det_labels, mask_results['mask_pred'], + img_metas) + # convert to numpy array to save memory + aug_masks.append( + mask_results['mask_pred'].sigmoid().cpu().numpy()) + merged_masks = merge_aug_masks(aug_masks, img_metas, self.test_cfg) + + ori_shape = img_metas[0][0]['ori_shape'] + segm_result = self.mask_head.get_seg_masks( + merged_masks, + det_bboxes, + det_labels, + self.test_cfg, + ori_shape, + scale_factor=1.0, + rescale=False) + return segm_result diff --git a/mmdet/models/roi_heads/roi_extractors/__init__.py b/mmdet/models/roi_heads/roi_extractors/__init__.py new file mode 100644 index 0000000..59e2d6d --- /dev/null +++ b/mmdet/models/roi_heads/roi_extractors/__init__.py @@ -0,0 +1,5 @@ +from .base_roi_extractor import BaseRoIExtractor +from .generic_roi_extractor import GenericRoIExtractor +from .single_level_roi_extractor import SingleRoIExtractor + +__all__ = ['BaseRoIExtractor', 'SingleRoIExtractor', 'GenericRoIExtractor'] diff --git a/mmdet/models/roi_heads/roi_extractors/__pycache__/__init__.cpython-37.pyc b/mmdet/models/roi_heads/roi_extractors/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..5ce3ef4 Binary files /dev/null and b/mmdet/models/roi_heads/roi_extractors/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/models/roi_heads/roi_extractors/__pycache__/base_roi_extractor.cpython-37.pyc b/mmdet/models/roi_heads/roi_extractors/__pycache__/base_roi_extractor.cpython-37.pyc new file mode 100644 index 0000000..e21cb99 Binary files /dev/null and b/mmdet/models/roi_heads/roi_extractors/__pycache__/base_roi_extractor.cpython-37.pyc differ diff --git a/mmdet/models/roi_heads/roi_extractors/__pycache__/generic_roi_extractor.cpython-37.pyc b/mmdet/models/roi_heads/roi_extractors/__pycache__/generic_roi_extractor.cpython-37.pyc new file mode 100644 index 0000000..12ca47b Binary files /dev/null and b/mmdet/models/roi_heads/roi_extractors/__pycache__/generic_roi_extractor.cpython-37.pyc differ diff --git a/mmdet/models/roi_heads/roi_extractors/__pycache__/single_level_roi_extractor.cpython-37.pyc b/mmdet/models/roi_heads/roi_extractors/__pycache__/single_level_roi_extractor.cpython-37.pyc new file mode 100644 index 0000000..6983b9b Binary files /dev/null and b/mmdet/models/roi_heads/roi_extractors/__pycache__/single_level_roi_extractor.cpython-37.pyc differ diff --git a/mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py b/mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py new file mode 100644 index 0000000..704ccf2 --- /dev/null +++ b/mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py @@ -0,0 +1,87 @@ +from abc import ABCMeta, abstractmethod + +import torch +import torch.nn as nn +from mmcv import ops +from mmcv.runner import BaseModule + + +class BaseRoIExtractor(BaseModule, metaclass=ABCMeta): + """Base class for RoI extractor. + + Args: + roi_layer (dict): Specify RoI layer type and arguments. + out_channels (int): Output channels of RoI layers. + featmap_strides (int): Strides of input feature maps. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + roi_layer, + out_channels, + featmap_strides, + init_cfg=None): + super(BaseRoIExtractor, self).__init__(init_cfg) + self.roi_layers = self.build_roi_layers(roi_layer, featmap_strides) + self.out_channels = out_channels + self.featmap_strides = featmap_strides + self.fp16_enabled = False + + @property + def num_inputs(self): + """int: Number of input feature maps.""" + return len(self.featmap_strides) + + def build_roi_layers(self, layer_cfg, featmap_strides): + """Build RoI operator to extract feature from each level feature map. + + Args: + layer_cfg (dict): Dictionary to construct and config RoI layer + operation. Options are modules under ``mmcv/ops`` such as + ``RoIAlign``. + featmap_strides (List[int]): The stride of input feature map w.r.t + to the original image size, which would be used to scale RoI + coordinate (original image coordinate system) to feature + coordinate system. + + Returns: + nn.ModuleList: The RoI extractor modules for each level feature + map. + """ + + cfg = layer_cfg.copy() + layer_type = cfg.pop('type') + assert hasattr(ops, layer_type) + layer_cls = getattr(ops, layer_type) + roi_layers = nn.ModuleList( + [layer_cls(spatial_scale=1 / s, **cfg) for s in featmap_strides]) + return roi_layers + + def roi_rescale(self, rois, scale_factor): + """Scale RoI coordinates by scale factor. + + Args: + rois (torch.Tensor): RoI (Region of Interest), shape (n, 5) + scale_factor (float): Scale factor that RoI will be multiplied by. + + Returns: + torch.Tensor: Scaled RoI. + """ + + cx = (rois[:, 1] + rois[:, 3]) * 0.5 + cy = (rois[:, 2] + rois[:, 4]) * 0.5 + w = rois[:, 3] - rois[:, 1] + h = rois[:, 4] - rois[:, 2] + new_w = w * scale_factor + new_h = h * scale_factor + x1 = cx - new_w * 0.5 + x2 = cx + new_w * 0.5 + y1 = cy - new_h * 0.5 + y2 = cy + new_h * 0.5 + new_rois = torch.stack((rois[:, 0], x1, y1, x2, y2), dim=-1) + return new_rois + + @abstractmethod + def forward(self, feats, rois, roi_scale_factor=None): + pass diff --git a/mmdet/models/roi_heads/roi_extractors/generic_roi_extractor.py b/mmdet/models/roi_heads/roi_extractors/generic_roi_extractor.py new file mode 100644 index 0000000..80c25bb --- /dev/null +++ b/mmdet/models/roi_heads/roi_extractors/generic_roi_extractor.py @@ -0,0 +1,83 @@ +from mmcv.cnn.bricks import build_plugin_layer +from mmcv.runner import force_fp32 + +from mmdet.models.builder import ROI_EXTRACTORS +from .base_roi_extractor import BaseRoIExtractor + + +@ROI_EXTRACTORS.register_module() +class GenericRoIExtractor(BaseRoIExtractor): + """Extract RoI features from all level feature maps levels. + + This is the implementation of `A novel Region of Interest Extraction Layer + for Instance Segmentation `_. + + Args: + aggregation (str): The method to aggregate multiple feature maps. + Options are 'sum', 'concat'. Default: 'sum'. + pre_cfg (dict | None): Specify pre-processing modules. Default: None. + post_cfg (dict | None): Specify post-processing modules. Default: None. + kwargs (keyword arguments): Arguments that are the same + as :class:`BaseRoIExtractor`. + """ + + def __init__(self, + aggregation='sum', + pre_cfg=None, + post_cfg=None, + **kwargs): + super(GenericRoIExtractor, self).__init__(**kwargs) + + assert aggregation in ['sum', 'concat'] + + self.aggregation = aggregation + self.with_post = post_cfg is not None + self.with_pre = pre_cfg is not None + # build pre/post processing modules + if self.with_post: + self.post_module = build_plugin_layer(post_cfg, '_post_module')[1] + if self.with_pre: + self.pre_module = build_plugin_layer(pre_cfg, '_pre_module')[1] + + @force_fp32(apply_to=('feats', ), out_fp16=True) + def forward(self, feats, rois, roi_scale_factor=None): + """Forward function.""" + if len(feats) == 1: + return self.roi_layers[0](feats[0], rois) + + out_size = self.roi_layers[0].output_size + num_levels = len(feats) + roi_feats = feats[0].new_zeros( + rois.size(0), self.out_channels, *out_size) + + # some times rois is an empty tensor + if roi_feats.shape[0] == 0: + return roi_feats + + if roi_scale_factor is not None: + rois = self.roi_rescale(rois, roi_scale_factor) + + # mark the starting channels for concat mode + start_channels = 0 + for i in range(num_levels): + roi_feats_t = self.roi_layers[i](feats[i], rois) + end_channels = start_channels + roi_feats_t.size(1) + if self.with_pre: + # apply pre-processing to a RoI extracted from each layer + roi_feats_t = self.pre_module(roi_feats_t) + if self.aggregation == 'sum': + # and sum them all + roi_feats += roi_feats_t + else: + # and concat them along channel dimension + roi_feats[:, start_channels:end_channels] = roi_feats_t + # update channels starting position + start_channels = end_channels + # check if concat channels match at the end + if self.aggregation == 'concat': + assert start_channels == self.out_channels + + if self.with_post: + # apply post-processing before return the result + roi_feats = self.post_module(roi_feats) + return roi_feats diff --git a/mmdet/models/roi_heads/roi_extractors/single_level_roi_extractor.py b/mmdet/models/roi_heads/roi_extractors/single_level_roi_extractor.py new file mode 100644 index 0000000..856c234 --- /dev/null +++ b/mmdet/models/roi_heads/roi_extractors/single_level_roi_extractor.py @@ -0,0 +1,111 @@ +import torch +from mmcv.runner import force_fp32 + +from mmdet.models.builder import ROI_EXTRACTORS +from .base_roi_extractor import BaseRoIExtractor + + +@ROI_EXTRACTORS.register_module() +class SingleRoIExtractor(BaseRoIExtractor): + """Extract RoI features from a single level feature map. + + If there are multiple input feature levels, each RoI is mapped to a level + according to its scale. The mapping rule is proposed in + `FPN `_. + + Args: + roi_layer (dict): Specify RoI layer type and arguments. + out_channels (int): Output channels of RoI layers. + featmap_strides (List[int]): Strides of input feature maps. + finest_scale (int): Scale threshold of mapping to level 0. Default: 56. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + roi_layer, + out_channels, + featmap_strides, + finest_scale=56, + init_cfg=None): + super(SingleRoIExtractor, self).__init__(roi_layer, out_channels, + featmap_strides, init_cfg) + self.finest_scale = finest_scale + + def map_roi_levels(self, rois, num_levels): + """Map rois to corresponding feature levels by scales. + + - scale < finest_scale * 2: level 0 + - finest_scale * 2 <= scale < finest_scale * 4: level 1 + - finest_scale * 4 <= scale < finest_scale * 8: level 2 + - scale >= finest_scale * 8: level 3 + + Args: + rois (Tensor): Input RoIs, shape (k, 5). + num_levels (int): Total level number. + + Returns: + Tensor: Level index (0-based) of each RoI, shape (k, ) + """ + scale = torch.sqrt( + (rois[:, 3] - rois[:, 1]) * (rois[:, 4] - rois[:, 2])) + target_lvls = torch.floor(torch.log2(scale / self.finest_scale + 1e-6)) + target_lvls = target_lvls.clamp(min=0, max=num_levels - 1).long() + return target_lvls + + @force_fp32(apply_to=('feats', ), out_fp16=True) + def forward(self, feats, rois, roi_scale_factor=None): + """Forward function.""" + out_size = self.roi_layers[0].output_size + num_levels = len(feats) + expand_dims = (-1, self.out_channels * out_size[0] * out_size[1]) + if torch.onnx.is_in_onnx_export(): + # Work around to export mask-rcnn to onnx + roi_feats = rois[:, :1].clone().detach() + roi_feats = roi_feats.expand(*expand_dims) + roi_feats = roi_feats.reshape(-1, self.out_channels, *out_size) + roi_feats = roi_feats * 0 + else: + roi_feats = feats[0].new_zeros( + rois.size(0), self.out_channels, *out_size) + # TODO: remove this when parrots supports + if torch.__version__ == 'parrots': + roi_feats.requires_grad = True + + if num_levels == 1: + if len(rois) == 0: + return roi_feats + return self.roi_layers[0](feats[0], rois) + + target_lvls = self.map_roi_levels(rois, num_levels) + + if roi_scale_factor is not None: + rois = self.roi_rescale(rois, roi_scale_factor) + + for i in range(num_levels): + mask = target_lvls == i + if torch.onnx.is_in_onnx_export(): + # To keep all roi_align nodes exported to onnx + # and skip nonzero op + mask = mask.float().unsqueeze(-1).expand(*expand_dims).reshape( + roi_feats.shape) + roi_feats_t = self.roi_layers[i](feats[i], rois) + roi_feats_t *= mask + roi_feats += roi_feats_t + continue + inds = mask.nonzero(as_tuple=False).squeeze(1) + if inds.numel() > 0: + rois_ = rois[inds] + roi_feats_t = self.roi_layers[i](feats[i], rois_) + roi_feats[inds] = roi_feats_t + else: + # Sometimes some pyramid levels will not be used for RoI + # feature extraction and this will cause an incomplete + # computation graph in one GPU, which is different from those + # in other GPUs and will cause a hanging error. + # Therefore, we add it to ensure each feature pyramid is + # included in the computation graph to avoid runtime bugs. + roi_feats += sum( + x.view(-1)[0] + for x in self.parameters()) * 0. + feats[i].sum() * 0. + return roi_feats diff --git a/mmdet/models/roi_heads/scnet_roi_head.py b/mmdet/models/roi_heads/scnet_roi_head.py new file mode 100644 index 0000000..84789fb --- /dev/null +++ b/mmdet/models/roi_heads/scnet_roi_head.py @@ -0,0 +1,561 @@ +import torch +import torch.nn.functional as F + +from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, merge_aug_bboxes, + merge_aug_masks, multiclass_nms) +from ..builder import HEADS, build_head, build_roi_extractor +from .cascade_roi_head import CascadeRoIHead + + +@HEADS.register_module() +class SCNetRoIHead(CascadeRoIHead): + """RoIHead for `SCNet `_. + + Args: + num_stages (int): number of cascade stages. + stage_loss_weights (list): loss weight of cascade stages. + semantic_roi_extractor (dict): config to init semantic roi extractor. + semantic_head (dict): config to init semantic head. + feat_relay_head (dict): config to init feature_relay_head. + glbctx_head (dict): config to init global context head. + """ + + def __init__(self, + num_stages, + stage_loss_weights, + semantic_roi_extractor=None, + semantic_head=None, + feat_relay_head=None, + glbctx_head=None, + **kwargs): + super(SCNetRoIHead, self).__init__(num_stages, stage_loss_weights, + **kwargs) + assert self.with_bbox and self.with_mask + assert not self.with_shared_head # shared head is not supported + + if semantic_head is not None: + self.semantic_roi_extractor = build_roi_extractor( + semantic_roi_extractor) + self.semantic_head = build_head(semantic_head) + + if feat_relay_head is not None: + self.feat_relay_head = build_head(feat_relay_head) + + if glbctx_head is not None: + self.glbctx_head = build_head(glbctx_head) + + def init_mask_head(self, mask_roi_extractor, mask_head): + """Initialize ``mask_head``""" + if mask_roi_extractor is not None: + self.mask_roi_extractor = build_roi_extractor(mask_roi_extractor) + self.mask_head = build_head(mask_head) + + @property + def with_semantic(self): + """bool: whether the head has semantic head""" + return hasattr(self, + 'semantic_head') and self.semantic_head is not None + + @property + def with_feat_relay(self): + """bool: whether the head has feature relay head""" + return (hasattr(self, 'feat_relay_head') + and self.feat_relay_head is not None) + + @property + def with_glbctx(self): + """bool: whether the head has global context head""" + return hasattr(self, 'glbctx_head') and self.glbctx_head is not None + + def _fuse_glbctx(self, roi_feats, glbctx_feat, rois): + """Fuse global context feats with roi feats.""" + assert roi_feats.size(0) == rois.size(0) + img_inds = torch.unique(rois[:, 0].cpu(), sorted=True).long() + fused_feats = torch.zeros_like(roi_feats) + for img_id in img_inds: + inds = (rois[:, 0] == img_id.item()) + fused_feats[inds] = roi_feats[inds] + glbctx_feat[img_id] + return fused_feats + + def _slice_pos_feats(self, feats, sampling_results): + """Get features from pos rois.""" + num_rois = [res.bboxes.size(0) for res in sampling_results] + num_pos_rois = [res.pos_bboxes.size(0) for res in sampling_results] + inds = torch.zeros(sum(num_rois), dtype=torch.bool) + start = 0 + for i in range(len(num_rois)): + start = 0 if i == 0 else start + num_rois[i - 1] + stop = start + num_pos_rois[i] + inds[start:stop] = 1 + sliced_feats = feats[inds] + return sliced_feats + + def _bbox_forward(self, + stage, + x, + rois, + semantic_feat=None, + glbctx_feat=None): + """Box head forward function used in both training and testing.""" + bbox_roi_extractor = self.bbox_roi_extractor[stage] + bbox_head = self.bbox_head[stage] + bbox_feats = bbox_roi_extractor( + x[:len(bbox_roi_extractor.featmap_strides)], rois) + if self.with_semantic and semantic_feat is not None: + bbox_semantic_feat = self.semantic_roi_extractor([semantic_feat], + rois) + if bbox_semantic_feat.shape[-2:] != bbox_feats.shape[-2:]: + bbox_semantic_feat = F.adaptive_avg_pool2d( + bbox_semantic_feat, bbox_feats.shape[-2:]) + bbox_feats += bbox_semantic_feat + if self.with_glbctx and glbctx_feat is not None: + bbox_feats = self._fuse_glbctx(bbox_feats, glbctx_feat, rois) + cls_score, bbox_pred, relayed_feat = bbox_head( + bbox_feats, return_shared_feat=True) + + bbox_results = dict( + cls_score=cls_score, + bbox_pred=bbox_pred, + relayed_feat=relayed_feat) + return bbox_results + + def _mask_forward(self, + x, + rois, + semantic_feat=None, + glbctx_feat=None, + relayed_feat=None): + """Mask head forward function used in both training and testing.""" + mask_feats = self.mask_roi_extractor( + x[:self.mask_roi_extractor.num_inputs], rois) + if self.with_semantic and semantic_feat is not None: + mask_semantic_feat = self.semantic_roi_extractor([semantic_feat], + rois) + if mask_semantic_feat.shape[-2:] != mask_feats.shape[-2:]: + mask_semantic_feat = F.adaptive_avg_pool2d( + mask_semantic_feat, mask_feats.shape[-2:]) + mask_feats += mask_semantic_feat + if self.with_glbctx and glbctx_feat is not None: + mask_feats = self._fuse_glbctx(mask_feats, glbctx_feat, rois) + if self.with_feat_relay and relayed_feat is not None: + mask_feats = mask_feats + relayed_feat + mask_pred = self.mask_head(mask_feats) + mask_results = dict(mask_pred=mask_pred) + + return mask_results + + def _bbox_forward_train(self, + stage, + x, + sampling_results, + gt_bboxes, + gt_labels, + rcnn_train_cfg, + semantic_feat=None, + glbctx_feat=None): + """Run forward function and calculate loss for box head in training.""" + bbox_head = self.bbox_head[stage] + rois = bbox2roi([res.bboxes for res in sampling_results]) + bbox_results = self._bbox_forward( + stage, + x, + rois, + semantic_feat=semantic_feat, + glbctx_feat=glbctx_feat) + + bbox_targets = bbox_head.get_targets(sampling_results, gt_bboxes, + gt_labels, rcnn_train_cfg) + loss_bbox = bbox_head.loss(bbox_results['cls_score'], + bbox_results['bbox_pred'], rois, + *bbox_targets) + + bbox_results.update( + loss_bbox=loss_bbox, rois=rois, bbox_targets=bbox_targets) + return bbox_results + + def _mask_forward_train(self, + x, + sampling_results, + gt_masks, + rcnn_train_cfg, + semantic_feat=None, + glbctx_feat=None, + relayed_feat=None): + """Run forward function and calculate loss for mask head in + training.""" + pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results]) + mask_results = self._mask_forward( + x, + pos_rois, + semantic_feat=semantic_feat, + glbctx_feat=glbctx_feat, + relayed_feat=relayed_feat) + + mask_targets = self.mask_head.get_targets(sampling_results, gt_masks, + rcnn_train_cfg) + pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results]) + loss_mask = self.mask_head.loss(mask_results['mask_pred'], + mask_targets, pos_labels) + + mask_results = loss_mask + return mask_results + + def forward_train(self, + x, + img_metas, + proposal_list, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + gt_masks=None, + gt_semantic_seg=None): + """ + Args: + x (list[Tensor]): list of multi-level img features. + + img_metas (list[dict]): list of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmdet/datasets/pipelines/formatting.py:Collect`. + + proposal_list (list[Tensors]): list of region proposals. + + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + + gt_labels (list[Tensor]): class indices corresponding to each box + + gt_bboxes_ignore (None, list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + + gt_masks (None, Tensor) : true segmentation masks for each box + used if the architecture supports a segmentation task. + + gt_semantic_seg (None, list[Tensor]): semantic segmentation masks + used if the architecture supports semantic segmentation task. + + Returns: + dict[str, Tensor]: a dictionary of loss components + """ + losses = dict() + + # semantic segmentation branch + if self.with_semantic: + semantic_pred, semantic_feat = self.semantic_head(x) + loss_seg = self.semantic_head.loss(semantic_pred, gt_semantic_seg) + losses['loss_semantic_seg'] = loss_seg + else: + semantic_feat = None + + # global context branch + if self.with_glbctx: + mc_pred, glbctx_feat = self.glbctx_head(x) + loss_glbctx = self.glbctx_head.loss(mc_pred, gt_labels) + losses['loss_glbctx'] = loss_glbctx + else: + glbctx_feat = None + + for i in range(self.num_stages): + self.current_stage = i + rcnn_train_cfg = self.train_cfg[i] + lw = self.stage_loss_weights[i] + + # assign gts and sample proposals + sampling_results = [] + bbox_assigner = self.bbox_assigner[i] + bbox_sampler = self.bbox_sampler[i] + num_imgs = len(img_metas) + if gt_bboxes_ignore is None: + gt_bboxes_ignore = [None for _ in range(num_imgs)] + + for j in range(num_imgs): + assign_result = bbox_assigner.assign(proposal_list[j], + gt_bboxes[j], + gt_bboxes_ignore[j], + gt_labels[j]) + sampling_result = bbox_sampler.sample( + assign_result, + proposal_list[j], + gt_bboxes[j], + gt_labels[j], + feats=[lvl_feat[j][None] for lvl_feat in x]) + sampling_results.append(sampling_result) + + bbox_results = \ + self._bbox_forward_train( + i, x, sampling_results, gt_bboxes, gt_labels, + rcnn_train_cfg, semantic_feat, glbctx_feat) + roi_labels = bbox_results['bbox_targets'][0] + + for name, value in bbox_results['loss_bbox'].items(): + losses[f's{i}.{name}'] = ( + value * lw if 'loss' in name else value) + + # refine boxes + if i < self.num_stages - 1: + pos_is_gts = [res.pos_is_gt for res in sampling_results] + with torch.no_grad(): + proposal_list = self.bbox_head[i].refine_bboxes( + bbox_results['rois'], roi_labels, + bbox_results['bbox_pred'], pos_is_gts, img_metas) + + if self.with_feat_relay: + relayed_feat = self._slice_pos_feats(bbox_results['relayed_feat'], + sampling_results) + relayed_feat = self.feat_relay_head(relayed_feat) + else: + relayed_feat = None + + mask_results = self._mask_forward_train(x, sampling_results, gt_masks, + rcnn_train_cfg, semantic_feat, + glbctx_feat, relayed_feat) + mask_lw = sum(self.stage_loss_weights) + losses['loss_mask'] = mask_lw * mask_results['loss_mask'] + + return losses + + def simple_test(self, x, proposal_list, img_metas, rescale=False): + """Test without augmentation.""" + if self.with_semantic: + _, semantic_feat = self.semantic_head(x) + else: + semantic_feat = None + + if self.with_glbctx: + mc_pred, glbctx_feat = self.glbctx_head(x) + else: + glbctx_feat = None + + num_imgs = len(proposal_list) + img_shapes = tuple(meta['img_shape'] for meta in img_metas) + ori_shapes = tuple(meta['ori_shape'] for meta in img_metas) + scale_factors = tuple(meta['scale_factor'] for meta in img_metas) + + # "ms" in variable names means multi-stage + ms_scores = [] + rcnn_test_cfg = self.test_cfg + + rois = bbox2roi(proposal_list) + for i in range(self.num_stages): + bbox_head = self.bbox_head[i] + bbox_results = self._bbox_forward( + i, + x, + rois, + semantic_feat=semantic_feat, + glbctx_feat=glbctx_feat) + # split batch bbox prediction back to each image + cls_score = bbox_results['cls_score'] + bbox_pred = bbox_results['bbox_pred'] + num_proposals_per_img = tuple(len(p) for p in proposal_list) + rois = rois.split(num_proposals_per_img, 0) + cls_score = cls_score.split(num_proposals_per_img, 0) + bbox_pred = bbox_pred.split(num_proposals_per_img, 0) + ms_scores.append(cls_score) + + if i < self.num_stages - 1: + bbox_label = [s[:, :-1].argmax(dim=1) for s in cls_score] + rois = torch.cat([ + bbox_head.regress_by_class(rois[i], bbox_label[i], + bbox_pred[i], img_metas[i]) + for i in range(num_imgs) + ]) + + # average scores of each image by stages + cls_score = [ + sum([score[i] for score in ms_scores]) / float(len(ms_scores)) + for i in range(num_imgs) + ] + + # apply bbox post-processing to each image individually + det_bboxes = [] + det_labels = [] + for i in range(num_imgs): + det_bbox, det_label = self.bbox_head[-1].get_bboxes( + rois[i], + cls_score[i], + bbox_pred[i], + img_shapes[i], + scale_factors[i], + rescale=rescale, + cfg=rcnn_test_cfg) + det_bboxes.append(det_bbox) + det_labels.append(det_label) + det_bbox_results = [ + bbox2result(det_bboxes[i], det_labels[i], + self.bbox_head[-1].num_classes) + for i in range(num_imgs) + ] + + if self.with_mask: + if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes): + mask_classes = self.mask_head.num_classes + det_segm_results = [[[] for _ in range(mask_classes)] + for _ in range(num_imgs)] + else: + if rescale and not isinstance(scale_factors[0], float): + scale_factors = [ + torch.from_numpy(scale_factor).to(det_bboxes[0].device) + for scale_factor in scale_factors + ] + _bboxes = [ + det_bboxes[i][:, :4] * + scale_factors[i] if rescale else det_bboxes[i] + for i in range(num_imgs) + ] + mask_rois = bbox2roi(_bboxes) + + # get relay feature on mask_rois + bbox_results = self._bbox_forward( + -1, + x, + mask_rois, + semantic_feat=semantic_feat, + glbctx_feat=glbctx_feat) + relayed_feat = bbox_results['relayed_feat'] + relayed_feat = self.feat_relay_head(relayed_feat) + + mask_results = self._mask_forward( + x, + mask_rois, + semantic_feat=semantic_feat, + glbctx_feat=glbctx_feat, + relayed_feat=relayed_feat) + mask_pred = mask_results['mask_pred'] + + # split batch mask prediction back to each image + num_bbox_per_img = tuple(len(_bbox) for _bbox in _bboxes) + mask_preds = mask_pred.split(num_bbox_per_img, 0) + + # apply mask post-processing to each image individually + det_segm_results = [] + for i in range(num_imgs): + if det_bboxes[i].shape[0] == 0: + det_segm_results.append( + [[] for _ in range(self.mask_head.num_classes)]) + else: + segm_result = self.mask_head.get_seg_masks( + mask_preds[i], _bboxes[i], det_labels[i], + self.test_cfg, ori_shapes[i], scale_factors[i], + rescale) + det_segm_results.append(segm_result) + + # return results + if self.with_mask: + return list(zip(det_bbox_results, det_segm_results)) + else: + return det_bbox_results + + def aug_test(self, img_feats, proposal_list, img_metas, rescale=False): + if self.with_semantic: + semantic_feats = [ + self.semantic_head(feat)[1] for feat in img_feats + ] + else: + semantic_feats = [None] * len(img_metas) + + if self.with_glbctx: + glbctx_feats = [self.glbctx_head(feat)[1] for feat in img_feats] + else: + glbctx_feats = [None] * len(img_metas) + + rcnn_test_cfg = self.test_cfg + aug_bboxes = [] + aug_scores = [] + for x, img_meta, semantic_feat, glbctx_feat in zip( + img_feats, img_metas, semantic_feats, glbctx_feats): + # only one image in the batch + img_shape = img_meta[0]['img_shape'] + scale_factor = img_meta[0]['scale_factor'] + flip = img_meta[0]['flip'] + + proposals = bbox_mapping(proposal_list[0][:, :4], img_shape, + scale_factor, flip) + # "ms" in variable names means multi-stage + ms_scores = [] + + rois = bbox2roi([proposals]) + for i in range(self.num_stages): + bbox_head = self.bbox_head[i] + bbox_results = self._bbox_forward( + i, + x, + rois, + semantic_feat=semantic_feat, + glbctx_feat=glbctx_feat) + ms_scores.append(bbox_results['cls_score']) + if i < self.num_stages - 1: + bbox_label = bbox_results['cls_score'].argmax(dim=1) + rois = bbox_head.regress_by_class( + rois, bbox_label, bbox_results['bbox_pred'], + img_meta[0]) + + cls_score = sum(ms_scores) / float(len(ms_scores)) + bboxes, scores = self.bbox_head[-1].get_bboxes( + rois, + cls_score, + bbox_results['bbox_pred'], + img_shape, + scale_factor, + rescale=False, + cfg=None) + aug_bboxes.append(bboxes) + aug_scores.append(scores) + + # after merging, bboxes will be rescaled to the original image size + merged_bboxes, merged_scores = merge_aug_bboxes( + aug_bboxes, aug_scores, img_metas, rcnn_test_cfg) + det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores, + rcnn_test_cfg.score_thr, + rcnn_test_cfg.nms, + rcnn_test_cfg.max_per_img) + + det_bbox_results = bbox2result(det_bboxes, det_labels, + self.bbox_head[-1].num_classes) + + if self.with_mask: + if det_bboxes.shape[0] == 0: + det_segm_results = [[] + for _ in range(self.mask_head.num_classes)] + else: + aug_masks = [] + for x, img_meta, semantic_feat, glbctx_feat in zip( + img_feats, img_metas, semantic_feats, glbctx_feats): + img_shape = img_meta[0]['img_shape'] + scale_factor = img_meta[0]['scale_factor'] + flip = img_meta[0]['flip'] + _bboxes = bbox_mapping(det_bboxes[:, :4], img_shape, + scale_factor, flip) + mask_rois = bbox2roi([_bboxes]) + # get relay feature on mask_rois + bbox_results = self._bbox_forward( + -1, + x, + mask_rois, + semantic_feat=semantic_feat, + glbctx_feat=glbctx_feat) + relayed_feat = bbox_results['relayed_feat'] + relayed_feat = self.feat_relay_head(relayed_feat) + mask_results = self._mask_forward( + x, + mask_rois, + semantic_feat=semantic_feat, + glbctx_feat=glbctx_feat, + relayed_feat=relayed_feat) + mask_pred = mask_results['mask_pred'] + aug_masks.append(mask_pred.sigmoid().cpu().numpy()) + merged_masks = merge_aug_masks(aug_masks, img_metas, + self.test_cfg) + ori_shape = img_metas[0][0]['ori_shape'] + det_segm_results = self.mask_head.get_seg_masks( + merged_masks, + det_bboxes, + det_labels, + rcnn_test_cfg, + ori_shape, + scale_factor=1.0, + rescale=False) + return [(det_bbox_results, det_segm_results)] + else: + return [det_bbox_results] diff --git a/mmdet/models/roi_heads/shared_heads/__init__.py b/mmdet/models/roi_heads/shared_heads/__init__.py new file mode 100644 index 0000000..bbe7014 --- /dev/null +++ b/mmdet/models/roi_heads/shared_heads/__init__.py @@ -0,0 +1,3 @@ +from .res_layer import ResLayer + +__all__ = ['ResLayer'] diff --git a/mmdet/models/roi_heads/shared_heads/__pycache__/__init__.cpython-37.pyc b/mmdet/models/roi_heads/shared_heads/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..73b9949 Binary files /dev/null and b/mmdet/models/roi_heads/shared_heads/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/models/roi_heads/shared_heads/__pycache__/res_layer.cpython-37.pyc b/mmdet/models/roi_heads/shared_heads/__pycache__/res_layer.cpython-37.pyc new file mode 100644 index 0000000..aaa7d28 Binary files /dev/null and b/mmdet/models/roi_heads/shared_heads/__pycache__/res_layer.cpython-37.pyc differ diff --git a/mmdet/models/roi_heads/shared_heads/res_layer.py b/mmdet/models/roi_heads/shared_heads/res_layer.py new file mode 100644 index 0000000..01d6cb7 --- /dev/null +++ b/mmdet/models/roi_heads/shared_heads/res_layer.py @@ -0,0 +1,79 @@ +import warnings + +import torch.nn as nn +from mmcv.runner import BaseModule, auto_fp16 + +from mmdet.models.backbones import ResNet +from mmdet.models.builder import SHARED_HEADS +from mmdet.models.utils import ResLayer as _ResLayer + + +@SHARED_HEADS.register_module() +class ResLayer(BaseModule): + + def __init__(self, + depth, + stage=3, + stride=2, + dilation=1, + style='pytorch', + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + with_cp=False, + dcn=None, + pretrained=None, + init_cfg=None): + super(ResLayer, self).__init__(init_cfg) + + self.norm_eval = norm_eval + self.norm_cfg = norm_cfg + self.stage = stage + self.fp16_enabled = False + block, stage_blocks = ResNet.arch_settings[depth] + stage_block = stage_blocks[stage] + planes = 64 * 2**stage + inplanes = 64 * 2**(stage - 1) * block.expansion + + res_layer = _ResLayer( + block, + inplanes, + planes, + stage_block, + stride=stride, + dilation=dilation, + style=style, + with_cp=with_cp, + norm_cfg=self.norm_cfg, + dcn=dcn) + self.add_module(f'layer{stage + 1}', res_layer) + + assert not (init_cfg and pretrained), \ + 'init_cfg and pretrained cannot be setting at the same time' + if isinstance(pretrained, str): + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' + 'please use "init_cfg" instead') + self.init_cfg = dict(type='Pretrained', checkpoint=pretrained) + elif pretrained is None: + if init_cfg is None: + self.init_cfg = [ + dict(type='Kaiming', layer='Conv2d'), + dict( + type='Constant', + val=1, + layer=['_BatchNorm', 'GroupNorm']) + ] + else: + raise TypeError('pretrained must be a str or None') + + @auto_fp16() + def forward(self, x): + res_layer = getattr(self, f'layer{self.stage + 1}') + out = res_layer(x) + return out + + def train(self, mode=True): + super(ResLayer, self).train(mode) + if self.norm_eval: + for m in self.modules(): + if isinstance(m, nn.BatchNorm2d): + m.eval() diff --git a/mmdet/models/roi_heads/sparse_roi_head.py b/mmdet/models/roi_heads/sparse_roi_head.py new file mode 100644 index 0000000..c249613 --- /dev/null +++ b/mmdet/models/roi_heads/sparse_roi_head.py @@ -0,0 +1,318 @@ +import torch + +from mmdet.core import bbox2result, bbox2roi, bbox_xyxy_to_cxcywh +from mmdet.core.bbox.samplers import PseudoSampler +from ..builder import HEADS +from .cascade_roi_head import CascadeRoIHead + + +@HEADS.register_module() +class SparseRoIHead(CascadeRoIHead): + r"""The RoIHead for `Sparse R-CNN: End-to-End Object Detection with + Learnable Proposals `_ + + Args: + num_stages (int): Number of stage whole iterative process. + Defaults to 6. + stage_loss_weights (Tuple[float]): The loss + weight of each stage. By default all stages have + the same weight 1. + bbox_roi_extractor (dict): Config of box roi extractor. + bbox_head (dict): Config of box head. + train_cfg (dict, optional): Configuration information in train stage. + Defaults to None. + test_cfg (dict, optional): Configuration information in test stage. + Defaults to None. + pretrained (str, optional): model pretrained path. Default: None + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + + """ + + def __init__(self, + num_stages=6, + stage_loss_weights=(1, 1, 1, 1, 1, 1), + proposal_feature_channel=256, + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='DIIHead', + num_classes=80, + num_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=3, + feedforward_channels=2048, + hidden_channels=256, + dropout=0.0, + roi_feat_size=7, + ffn_act_cfg=dict(type='ReLU', inplace=True)), + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + assert bbox_roi_extractor is not None + assert bbox_head is not None + assert len(stage_loss_weights) == num_stages + self.num_stages = num_stages + self.stage_loss_weights = stage_loss_weights + self.proposal_feature_channel = proposal_feature_channel + super(SparseRoIHead, self).__init__( + num_stages, + stage_loss_weights, + bbox_roi_extractor=bbox_roi_extractor, + bbox_head=bbox_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) + # train_cfg would be None when run the test.py + if train_cfg is not None: + for stage in range(num_stages): + assert isinstance(self.bbox_sampler[stage], PseudoSampler), \ + 'Sparse R-CNN only support `PseudoSampler`' + + def _bbox_forward(self, stage, x, rois, object_feats, img_metas): + """Box head forward function used in both training and testing. Returns + all regression, classification results and a intermediate feature. + + Args: + stage (int): The index of current stage in + iterative process. + x (List[Tensor]): List of FPN features + rois (Tensor): Rois in total batch. With shape (num_proposal, 5). + the last dimension 5 represents (img_index, x1, y1, x2, y2). + object_feats (Tensor): The object feature extracted from + the previous stage. + img_metas (dict): meta information of images. + + Returns: + dict[str, Tensor]: a dictionary of bbox head outputs, + Containing the following results: + + - cls_score (Tensor): The score of each class, has + shape (batch_size, num_proposals, num_classes) + when use focal loss or + (batch_size, num_proposals, num_classes+1) + otherwise. + - decode_bbox_pred (Tensor): The regression results + with shape (batch_size, num_proposal, 4). + The last dimension 4 represents + [tl_x, tl_y, br_x, br_y]. + - object_feats (Tensor): The object feature extracted + from current stage + - detach_cls_score_list (list[Tensor]): The detached + classification results, length is batch_size, and + each tensor has shape (num_proposal, num_classes). + - detach_proposal_list (list[tensor]): The detached + regression results, length is batch_size, and each + tensor has shape (num_proposal, 4). The last + dimension 4 represents [tl_x, tl_y, br_x, br_y]. + """ + num_imgs = len(img_metas) + bbox_roi_extractor = self.bbox_roi_extractor[stage] + bbox_head = self.bbox_head[stage] + bbox_feats = bbox_roi_extractor(x[:bbox_roi_extractor.num_inputs], + rois) + cls_score, bbox_pred, object_feats = bbox_head(bbox_feats, + object_feats) + proposal_list = self.bbox_head[stage].refine_bboxes( + rois, + rois.new_zeros(len(rois)), # dummy arg + bbox_pred.view(-1, bbox_pred.size(-1)), + [rois.new_zeros(object_feats.size(1)) for _ in range(num_imgs)], + img_metas) + bbox_results = dict( + cls_score=cls_score, + decode_bbox_pred=torch.cat(proposal_list), + object_feats=object_feats, + # detach then use it in label assign + detach_cls_score_list=[ + cls_score[i].detach() for i in range(num_imgs) + ], + detach_proposal_list=[item.detach() for item in proposal_list]) + + return bbox_results + + def forward_train(self, + x, + proposal_boxes, + proposal_features, + img_metas, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + imgs_whwh=None, + gt_masks=None): + """Forward function in training stage. + + Args: + x (list[Tensor]): list of multi-level img features. + proposals (Tensor): Decoded proposal bboxes, has shape + (batch_size, num_proposals, 4) + proposal_features (Tensor): Expanded proposal + features, has shape + (batch_size, num_proposals, proposal_feature_channel) + img_metas (list[dict]): list of image info dict where + each dict has: 'img_shape', 'scale_factor', 'flip', + and may also contain 'filename', 'ori_shape', + 'pad_shape', and 'img_norm_cfg'. For details on the + values of these keys see + `mmdet/datasets/pipelines/formatting.py:Collect`. + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + imgs_whwh (Tensor): Tensor with shape (batch_size, 4), + the dimension means + [img_width,img_height, img_width, img_height]. + gt_masks (None | Tensor) : true segmentation masks for each box + used if the architecture supports a segmentation task. + + Returns: + dict[str, Tensor]: a dictionary of loss components of all stage. + """ + + num_imgs = len(img_metas) + num_proposals = proposal_boxes.size(1) + imgs_whwh = imgs_whwh.repeat(1, num_proposals, 1) + all_stage_bbox_results = [] + proposal_list = [proposal_boxes[i] for i in range(len(proposal_boxes))] + object_feats = proposal_features + all_stage_loss = {} + for stage in range(self.num_stages): + rois = bbox2roi(proposal_list) + bbox_results = self._bbox_forward(stage, x, rois, object_feats, + img_metas) + all_stage_bbox_results.append(bbox_results) + if gt_bboxes_ignore is None: + # TODO support ignore + gt_bboxes_ignore = [None for _ in range(num_imgs)] + sampling_results = [] + cls_pred_list = bbox_results['detach_cls_score_list'] + proposal_list = bbox_results['detach_proposal_list'] + for i in range(num_imgs): + normalize_bbox_ccwh = bbox_xyxy_to_cxcywh(proposal_list[i] / + imgs_whwh[i]) + assign_result = self.bbox_assigner[stage].assign( + normalize_bbox_ccwh, cls_pred_list[i], gt_bboxes[i], + gt_labels[i], img_metas[i]) + sampling_result = self.bbox_sampler[stage].sample( + assign_result, proposal_list[i], gt_bboxes[i]) + sampling_results.append(sampling_result) + bbox_targets = self.bbox_head[stage].get_targets( + sampling_results, gt_bboxes, gt_labels, self.train_cfg[stage], + True) + cls_score = bbox_results['cls_score'] + decode_bbox_pred = bbox_results['decode_bbox_pred'] + + single_stage_loss = self.bbox_head[stage].loss( + cls_score.view(-1, cls_score.size(-1)), + decode_bbox_pred.view(-1, 4), + *bbox_targets, + imgs_whwh=imgs_whwh) + for key, value in single_stage_loss.items(): + all_stage_loss[f'stage{stage}_{key}'] = value * \ + self.stage_loss_weights[stage] + object_feats = bbox_results['object_feats'] + + return all_stage_loss + + def simple_test(self, + x, + proposal_boxes, + proposal_features, + img_metas, + imgs_whwh, + rescale=False): + """Test without augmentation. + + Args: + x (list[Tensor]): list of multi-level img features. + proposal_boxes (Tensor): Decoded proposal bboxes, has shape + (batch_size, num_proposals, 4) + proposal_features (Tensor): Expanded proposal + features, has shape + (batch_size, num_proposals, proposal_feature_channel) + img_metas (dict): meta information of images. + imgs_whwh (Tensor): Tensor with shape (batch_size, 4), + the dimension means + [img_width,img_height, img_width, img_height]. + rescale (bool): If True, return boxes in original image + space. Defaults to False. + + Returns: + bbox_results (list[tuple[np.ndarray]]): \ + [[cls1_det, cls2_det, ...], ...]. \ + The outer list indicates images, and the inner \ + list indicates per-class detected bboxes. The \ + np.ndarray has shape (num_det, 5) and the last \ + dimension 5 represents (x1, y1, x2, y2, score). + """ + assert self.with_bbox, 'Bbox head must be implemented.' + # Decode initial proposals + num_imgs = len(img_metas) + proposal_list = [proposal_boxes[i] for i in range(num_imgs)] + object_feats = proposal_features + for stage in range(self.num_stages): + rois = bbox2roi(proposal_list) + bbox_results = self._bbox_forward(stage, x, rois, object_feats, + img_metas) + object_feats = bbox_results['object_feats'] + cls_score = bbox_results['cls_score'] + proposal_list = bbox_results['detach_proposal_list'] + + num_classes = self.bbox_head[-1].num_classes + det_bboxes = [] + det_labels = [] + + if self.bbox_head[-1].loss_cls.use_sigmoid: + cls_score = cls_score.sigmoid() + else: + cls_score = cls_score.softmax(-1)[..., :-1] + + for img_id in range(num_imgs): + cls_score_per_img = cls_score[img_id] + scores_per_img, topk_indices = cls_score_per_img.flatten( + 0, 1).topk( + self.test_cfg.max_per_img, sorted=False) + labels_per_img = topk_indices % num_classes + bbox_pred_per_img = proposal_list[img_id][topk_indices // + num_classes] + if rescale: + scale_factor = img_metas[img_id]['scale_factor'] + bbox_pred_per_img /= bbox_pred_per_img.new_tensor(scale_factor) + det_bboxes.append( + torch.cat([bbox_pred_per_img, scores_per_img[:, None]], dim=1)) + det_labels.append(labels_per_img) + + bbox_results = [ + bbox2result(det_bboxes[i], det_labels[i], num_classes) + for i in range(num_imgs) + ] + + return bbox_results + + def aug_test(self, features, proposal_list, img_metas, rescale=False): + raise NotImplementedError('Sparse R-CNN does not support `aug_test`') + + def forward_dummy(self, x, proposal_boxes, proposal_features, img_metas): + """Dummy forward function when do the flops computing.""" + all_stage_bbox_results = [] + proposal_list = [proposal_boxes[i] for i in range(len(proposal_boxes))] + object_feats = proposal_features + if self.with_bbox: + for stage in range(self.num_stages): + rois = bbox2roi(proposal_list) + bbox_results = self._bbox_forward(stage, x, rois, object_feats, + img_metas) + + all_stage_bbox_results.append(bbox_results) + proposal_list = bbox_results['detach_proposal_list'] + object_feats = bbox_results['object_feats'] + return all_stage_bbox_results diff --git a/mmdet/models/roi_heads/standard_roi_head.py b/mmdet/models/roi_heads/standard_roi_head.py new file mode 100644 index 0000000..6ebdba8 --- /dev/null +++ b/mmdet/models/roi_heads/standard_roi_head.py @@ -0,0 +1,277 @@ +import torch + +from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler +from ..builder import HEADS, build_head, build_roi_extractor +from .base_roi_head import BaseRoIHead +from .test_mixins import BBoxTestMixin, MaskTestMixin + + +@HEADS.register_module() +class StandardRoIHead(BaseRoIHead, BBoxTestMixin, MaskTestMixin): + """Simplest base roi head including one bbox head and one mask head.""" + + def init_assigner_sampler(self): + """Initialize assigner and sampler.""" + self.bbox_assigner = None + self.bbox_sampler = None + if self.train_cfg: + self.bbox_assigner = build_assigner(self.train_cfg.assigner) + self.bbox_sampler = build_sampler( + self.train_cfg.sampler, context=self) + + def init_bbox_head(self, bbox_roi_extractor, bbox_head): + """Initialize ``bbox_head``""" + self.bbox_roi_extractor = build_roi_extractor(bbox_roi_extractor) + self.bbox_head = build_head(bbox_head) + + def init_mask_head(self, mask_roi_extractor, mask_head): + """Initialize ``mask_head``""" + if mask_roi_extractor is not None: + self.mask_roi_extractor = build_roi_extractor(mask_roi_extractor) + self.share_roi_extractor = False + else: + self.share_roi_extractor = True + self.mask_roi_extractor = self.bbox_roi_extractor + self.mask_head = build_head(mask_head) + + def forward_dummy(self, x, proposals): + """Dummy forward function.""" + # bbox head + outs = () + rois = bbox2roi([proposals]) + if self.with_bbox: + bbox_results = self._bbox_forward(x, rois) + outs = outs + (bbox_results['cls_score'], + bbox_results['bbox_pred']) + # mask head + if self.with_mask: + mask_rois = rois[:100] + mask_results = self._mask_forward(x, mask_rois) + outs = outs + (mask_results['mask_pred'], ) + return outs + + def forward_train(self, + x, + img_metas, + proposal_list, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + gt_masks=None): + """ + Args: + x (list[Tensor]): list of multi-level img features. + img_metas (list[dict]): list of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmdet/datasets/pipelines/formatting.py:Collect`. + proposals (list[Tensors]): list of region proposals. + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + gt_masks (None | Tensor) : true segmentation masks for each box + used if the architecture supports a segmentation task. + + Returns: + dict[str, Tensor]: a dictionary of loss components + """ + # assign gts and sample proposals + if self.with_bbox or self.with_mask: + num_imgs = len(img_metas) + if gt_bboxes_ignore is None: + gt_bboxes_ignore = [None for _ in range(num_imgs)] + sampling_results = [] + for i in range(num_imgs): + assign_result = self.bbox_assigner.assign( + proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i], + gt_labels[i]) + sampling_result = self.bbox_sampler.sample( + assign_result, + proposal_list[i], + gt_bboxes[i], + gt_labels[i], + feats=[lvl_feat[i][None] for lvl_feat in x]) + sampling_results.append(sampling_result) + + losses = dict() + # bbox head forward and loss + if self.with_bbox: + bbox_results = self._bbox_forward_train(x, sampling_results, + gt_bboxes, gt_labels, + img_metas) + losses.update(bbox_results['loss_bbox']) + + # mask head forward and loss + if self.with_mask: + mask_results = self._mask_forward_train(x, sampling_results, + bbox_results['bbox_feats'], + gt_masks, img_metas) + losses.update(mask_results['loss_mask']) + + return losses + + def _bbox_forward(self, x, rois): + """Box head forward function used in both training and testing.""" + # TODO: a more flexible way to decide which feature maps to use + bbox_feats = self.bbox_roi_extractor( + x[:self.bbox_roi_extractor.num_inputs], rois) + if self.with_shared_head: + bbox_feats = self.shared_head(bbox_feats) + cls_score, bbox_pred = self.bbox_head(bbox_feats) + + bbox_results = dict( + cls_score=cls_score, bbox_pred=bbox_pred, bbox_feats=bbox_feats) + return bbox_results + + def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels, + img_metas): + """Run forward function and calculate loss for box head in training.""" + rois = bbox2roi([res.bboxes for res in sampling_results]) + bbox_results = self._bbox_forward(x, rois) + + bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes, + gt_labels, self.train_cfg) + loss_bbox = self.bbox_head.loss(bbox_results['cls_score'], + bbox_results['bbox_pred'], rois, + *bbox_targets) + + bbox_results.update(loss_bbox=loss_bbox) + return bbox_results + + def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks, + img_metas): + """Run forward function and calculate loss for mask head in + training.""" + if not self.share_roi_extractor: + pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results]) + mask_results = self._mask_forward(x, pos_rois) + else: + pos_inds = [] + device = bbox_feats.device + for res in sampling_results: + pos_inds.append( + torch.ones( + res.pos_bboxes.shape[0], + device=device, + dtype=torch.uint8)) + pos_inds.append( + torch.zeros( + res.neg_bboxes.shape[0], + device=device, + dtype=torch.uint8)) + pos_inds = torch.cat(pos_inds) + + mask_results = self._mask_forward( + x, pos_inds=pos_inds, bbox_feats=bbox_feats) + + mask_targets = self.mask_head.get_targets(sampling_results, gt_masks, + self.train_cfg) + pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results]) + loss_mask = self.mask_head.loss(mask_results['mask_pred'], + mask_targets, pos_labels) + + mask_results.update(loss_mask=loss_mask, mask_targets=mask_targets) + return mask_results + + def _mask_forward(self, x, rois=None, pos_inds=None, bbox_feats=None): + """Mask head forward function used in both training and testing.""" + assert ((rois is not None) ^ + (pos_inds is not None and bbox_feats is not None)) + if rois is not None: + mask_feats = self.mask_roi_extractor( + x[:self.mask_roi_extractor.num_inputs], rois) + if self.with_shared_head: + mask_feats = self.shared_head(mask_feats) + else: + assert bbox_feats is not None + mask_feats = bbox_feats[pos_inds] + + mask_pred = self.mask_head(mask_feats) + mask_results = dict(mask_pred=mask_pred, mask_feats=mask_feats) + return mask_results + + async def async_simple_test(self, + x, + proposal_list, + img_metas, + proposals=None, + rescale=False): + """Async test without augmentation.""" + assert self.with_bbox, 'Bbox head must be implemented.' + + det_bboxes, det_labels = await self.async_test_bboxes( + x, img_metas, proposal_list, self.test_cfg, rescale=rescale) + bbox_results = bbox2result(det_bboxes, det_labels, + self.bbox_head.num_classes) + if not self.with_mask: + return bbox_results + else: + segm_results = await self.async_test_mask( + x, + img_metas, + det_bboxes, + det_labels, + rescale=rescale, + mask_test_cfg=self.test_cfg.get('mask')) + return bbox_results, segm_results + + def simple_test(self, + x, + proposal_list, + img_metas, + proposals=None, + rescale=False): + """Test without augmentation.""" + assert self.with_bbox, 'Bbox head must be implemented.' + + det_bboxes, det_labels = self.simple_test_bboxes( + x, img_metas, proposal_list, self.test_cfg, rescale=rescale) + if torch.onnx.is_in_onnx_export(): + if self.with_mask: + segm_results = self.simple_test_mask( + x, img_metas, det_bboxes, det_labels, rescale=rescale) + return det_bboxes, det_labels, segm_results + return det_bboxes, det_labels + + bbox_results = [ + bbox2result(det_bboxes[i], det_labels[i], + self.bbox_head.num_classes) + for i in range(len(det_bboxes)) + ] + + if not self.with_mask: + return bbox_results + else: + segm_results = self.simple_test_mask( + x, img_metas, det_bboxes, det_labels, rescale=rescale) + return list(zip(bbox_results, segm_results)) + + def aug_test(self, x, proposal_list, img_metas, rescale=False): + """Test with augmentations. + + If rescale is False, then returned bboxes and masks will fit the scale + of imgs[0]. + """ + det_bboxes, det_labels = self.aug_test_bboxes(x, img_metas, + proposal_list, + self.test_cfg) + + if rescale: + _det_bboxes = det_bboxes + else: + _det_bboxes = det_bboxes.clone() + _det_bboxes[:, :4] *= det_bboxes.new_tensor( + img_metas[0][0]['scale_factor']) + bbox_results = bbox2result(_det_bboxes, det_labels, + self.bbox_head.num_classes) + + # det_bboxes always keep the original scale + if self.with_mask: + segm_results = self.aug_test_mask(x, img_metas, det_bboxes, + det_labels) + return [(bbox_results, segm_results)] + else: + return [bbox_results] diff --git a/mmdet/models/roi_heads/test_mixins.py b/mmdet/models/roi_heads/test_mixins.py new file mode 100644 index 0000000..930d737 --- /dev/null +++ b/mmdet/models/roi_heads/test_mixins.py @@ -0,0 +1,370 @@ +import logging +import sys + +import torch + +from mmdet.core import (bbox2roi, bbox_mapping, merge_aug_bboxes, + merge_aug_masks, multiclass_nms) + +logger = logging.getLogger(__name__) + +if sys.version_info >= (3, 7): + from mmdet.utils.contextmanagers import completed + + +class BBoxTestMixin(object): + + if sys.version_info >= (3, 7): + + async def async_test_bboxes(self, + x, + img_metas, + proposals, + rcnn_test_cfg, + rescale=False, + bbox_semaphore=None, + global_lock=None): + """Asynchronized test for box head without augmentation.""" + rois = bbox2roi(proposals) + roi_feats = self.bbox_roi_extractor( + x[:len(self.bbox_roi_extractor.featmap_strides)], rois) + if self.with_shared_head: + roi_feats = self.shared_head(roi_feats) + sleep_interval = rcnn_test_cfg.get('async_sleep_interval', 0.017) + + async with completed( + __name__, 'bbox_head_forward', + sleep_interval=sleep_interval): + cls_score, bbox_pred = self.bbox_head(roi_feats) + + img_shape = img_metas[0]['img_shape'] + scale_factor = img_metas[0]['scale_factor'] + det_bboxes, det_labels = self.bbox_head.get_bboxes( + rois, + cls_score, + bbox_pred, + img_shape, + scale_factor, + rescale=rescale, + cfg=rcnn_test_cfg) + return det_bboxes, det_labels + + def simple_test_bboxes(self, + x, + img_metas, + proposals, + rcnn_test_cfg, + rescale=False): + """Test only det bboxes without augmentation. + + Args: + x (tuple[Tensor]): Feature maps of all scale level. + img_metas (list[dict]): Image meta info. + proposals (Tensor or List[Tensor]): Region proposals. + rcnn_test_cfg (obj:`ConfigDict`): `test_cfg` of R-CNN. + rescale (bool): If True, return boxes in original image space. + Default: False. + + Returns: + tuple[list[Tensor], list[Tensor]]: The first list contains + the boxes of the corresponding image in a batch, each + tensor has the shape (num_boxes, 5) and last dimension + 5 represent (tl_x, tl_y, br_x, br_y, score). Each Tensor + in the second list is the labels with shape (num_boxes, ). + The length of both lists should be equal to batch_size. + """ + # get origin input shape to support onnx dynamic input shape + if torch.onnx.is_in_onnx_export(): + assert len( + img_metas + ) == 1, 'Only support one input image while in exporting to ONNX' + img_shapes = img_metas[0]['img_shape_for_onnx'] + else: + img_shapes = tuple(meta['img_shape'] for meta in img_metas) + scale_factors = tuple(meta['scale_factor'] for meta in img_metas) + + # The length of proposals of different batches may be different. + # In order to form a batch, a padding operation is required. + if isinstance(proposals, list): + # padding to form a batch + max_size = max([proposal.size(0) for proposal in proposals]) + for i, proposal in enumerate(proposals): + supplement = proposal.new_full( + (max_size - proposal.size(0), proposal.size(1)), 0) + proposals[i] = torch.cat((supplement, proposal), dim=0) + rois = torch.stack(proposals, dim=0) + else: + rois = proposals + + batch_index = torch.arange( + rois.size(0), device=rois.device).float().view(-1, 1, 1).expand( + rois.size(0), rois.size(1), 1) + rois = torch.cat([batch_index, rois[..., :4]], dim=-1) + batch_size = rois.shape[0] + num_proposals_per_img = rois.shape[1] + + # Eliminate the batch dimension + rois = rois.view(-1, 5) + bbox_results = self._bbox_forward(x, rois) + cls_score = bbox_results['cls_score'] + bbox_pred = bbox_results['bbox_pred'] + + # Recover the batch dimension + rois = rois.reshape(batch_size, num_proposals_per_img, rois.size(-1)) + cls_score = cls_score.reshape(batch_size, num_proposals_per_img, + cls_score.size(-1)) + + if not torch.onnx.is_in_onnx_export(): + # remove padding, ignore batch_index when calculating mask + supplement_mask = rois.abs()[..., 1:].sum(dim=-1) == 0 + cls_score[supplement_mask, :] = 0 + + # bbox_pred would be None in some detector when with_reg is False, + # e.g. Grid R-CNN. + if bbox_pred is not None: + # the bbox prediction of some detectors like SABL is not Tensor + if isinstance(bbox_pred, torch.Tensor): + bbox_pred = bbox_pred.reshape(batch_size, + num_proposals_per_img, + bbox_pred.size(-1)) + if not torch.onnx.is_in_onnx_export(): + bbox_pred[supplement_mask, :] = 0 + else: + # TODO: Looking forward to a better way + # For SABL + bbox_preds = self.bbox_head.bbox_pred_split( + bbox_pred, num_proposals_per_img) + # apply bbox post-processing to each image individually + det_bboxes = [] + det_labels = [] + for i in range(len(proposals)): + # remove padding + supplement_mask = proposals[i].abs().sum(dim=-1) == 0 + for bbox in bbox_preds[i]: + bbox[supplement_mask] = 0 + det_bbox, det_label = self.bbox_head.get_bboxes( + rois[i], + cls_score[i], + bbox_preds[i], + img_shapes[i], + scale_factors[i], + rescale=rescale, + cfg=rcnn_test_cfg) + det_bboxes.append(det_bbox) + det_labels.append(det_label) + return det_bboxes, det_labels + else: + bbox_pred = None + + return self.bbox_head.get_bboxes( + rois, + cls_score, + bbox_pred, + img_shapes, + scale_factors, + rescale=rescale, + cfg=rcnn_test_cfg) + + def aug_test_bboxes(self, feats, img_metas, proposal_list, rcnn_test_cfg): + """Test det bboxes with test time augmentation.""" + aug_bboxes = [] + aug_scores = [] + for x, img_meta in zip(feats, img_metas): + # only one image in the batch + img_shape = img_meta[0]['img_shape'] + scale_factor = img_meta[0]['scale_factor'] + flip = img_meta[0]['flip'] + flip_direction = img_meta[0]['flip_direction'] + # TODO more flexible + proposals = bbox_mapping(proposal_list[0][:, :4], img_shape, + scale_factor, flip, flip_direction) + rois = bbox2roi([proposals]) + bbox_results = self._bbox_forward(x, rois) + bboxes, scores = self.bbox_head.get_bboxes( + rois, + bbox_results['cls_score'], + bbox_results['bbox_pred'], + img_shape, + scale_factor, + rescale=False, + cfg=None) + aug_bboxes.append(bboxes) + aug_scores.append(scores) + # after merging, bboxes will be rescaled to the original image size + merged_bboxes, merged_scores = merge_aug_bboxes( + aug_bboxes, aug_scores, img_metas, rcnn_test_cfg) + det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores, + rcnn_test_cfg.score_thr, + rcnn_test_cfg.nms, + rcnn_test_cfg.max_per_img) + return det_bboxes, det_labels + + +class MaskTestMixin(object): + + if sys.version_info >= (3, 7): + + async def async_test_mask(self, + x, + img_metas, + det_bboxes, + det_labels, + rescale=False, + mask_test_cfg=None): + """Asynchronized test for mask head without augmentation.""" + # image shape of the first image in the batch (only one) + ori_shape = img_metas[0]['ori_shape'] + scale_factor = img_metas[0]['scale_factor'] + if det_bboxes.shape[0] == 0: + segm_result = [[] for _ in range(self.mask_head.num_classes)] + else: + if rescale and not isinstance(scale_factor, + (float, torch.Tensor)): + scale_factor = det_bboxes.new_tensor(scale_factor) + _bboxes = ( + det_bboxes[:, :4] * + scale_factor if rescale else det_bboxes) + mask_rois = bbox2roi([_bboxes]) + mask_feats = self.mask_roi_extractor( + x[:len(self.mask_roi_extractor.featmap_strides)], + mask_rois) + + if self.with_shared_head: + mask_feats = self.shared_head(mask_feats) + if mask_test_cfg and mask_test_cfg.get('async_sleep_interval'): + sleep_interval = mask_test_cfg['async_sleep_interval'] + else: + sleep_interval = 0.035 + async with completed( + __name__, + 'mask_head_forward', + sleep_interval=sleep_interval): + mask_pred = self.mask_head(mask_feats) + segm_result = self.mask_head.get_seg_masks( + mask_pred, _bboxes, det_labels, self.test_cfg, ori_shape, + scale_factor, rescale) + return segm_result + + def simple_test_mask(self, + x, + img_metas, + det_bboxes, + det_labels, + rescale=False): + """Simple test for mask head without augmentation.""" + # image shapes of images in the batch + ori_shapes = tuple(meta['ori_shape'] for meta in img_metas) + scale_factors = tuple(meta['scale_factor'] for meta in img_metas) + + if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes): + if torch.onnx.is_in_onnx_export(): + raise RuntimeError('[ONNX Error] Can not record MaskHead ' + 'as it has not been executed this time') + segm_results = [[[] for _ in range(self.mask_head.num_classes)] + for _ in range(len(det_bboxes))] + return segm_results + + # The length of proposals of different batches may be different. + # In order to form a batch, a padding operation is required. + if isinstance(det_bboxes, list): + # padding to form a batch + max_size = max([bboxes.size(0) for bboxes in det_bboxes]) + for i, (bbox, label) in enumerate(zip(det_bboxes, det_labels)): + supplement_bbox = bbox.new_full( + (max_size - bbox.size(0), bbox.size(1)), 0) + supplement_label = label.new_full((max_size - label.size(0), ), + 0) + det_bboxes[i] = torch.cat((supplement_bbox, bbox), dim=0) + det_labels[i] = torch.cat((supplement_label, label), dim=0) + det_bboxes = torch.stack(det_bboxes, dim=0) + det_labels = torch.stack(det_labels, dim=0) + + batch_size = det_bboxes.size(0) + num_proposals_per_img = det_bboxes.shape[1] + + # if det_bboxes is rescaled to the original image size, we need to + # rescale it back to the testing scale to obtain RoIs. + det_bboxes = det_bboxes[..., :4] + if rescale: + if not isinstance(scale_factors[0], float): + scale_factors = det_bboxes.new_tensor(scale_factors) + det_bboxes = det_bboxes * scale_factors.unsqueeze(1) + + batch_index = torch.arange( + det_bboxes.size(0), device=det_bboxes.device).float().view( + -1, 1, 1).expand(det_bboxes.size(0), det_bboxes.size(1), 1) + mask_rois = torch.cat([batch_index, det_bboxes], dim=-1) + mask_rois = mask_rois.view(-1, 5) + mask_results = self._mask_forward(x, mask_rois) + mask_pred = mask_results['mask_pred'] + + # Support get_seg_masks exporting to ONNX + if torch.onnx.is_in_onnx_export(): + max_shape = img_metas[0]['img_shape_for_onnx'] + num_det = det_bboxes.shape[1] + det_bboxes = det_bboxes.reshape(-1, 4) + det_labels = det_labels.reshape(-1) + segm_results = self.mask_head.get_seg_masks( + mask_pred, det_bboxes, det_labels, self.test_cfg, max_shape, + scale_factors[0], rescale) + segm_results = segm_results.reshape(batch_size, num_det, + max_shape[0], max_shape[1]) + return segm_results + # Recover the batch dimension + mask_preds = mask_pred.reshape(batch_size, num_proposals_per_img, + *mask_pred.shape[1:]) + + # apply mask post-processing to each image individually + segm_results = [] + for i in range(batch_size): + mask_pred = mask_preds[i] + det_bbox = det_bboxes[i] + det_label = det_labels[i] + + # remove padding + supplement_mask = det_bbox.abs().sum(dim=-1) != 0 + mask_pred = mask_pred[supplement_mask] + det_bbox = det_bbox[supplement_mask] + det_label = det_label[supplement_mask] + + if det_label.shape[0] == 0: + segm_results.append([[] + for _ in range(self.mask_head.num_classes) + ]) + else: + segm_result = self.mask_head.get_seg_masks( + mask_pred, det_bbox, det_label, self.test_cfg, + ori_shapes[i], scale_factors[i], rescale) + segm_results.append(segm_result) + return segm_results + + def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels): + """Test for mask head with test time augmentation.""" + if det_bboxes.shape[0] == 0: + segm_result = [[] for _ in range(self.mask_head.num_classes)] + else: + aug_masks = [] + for x, img_meta in zip(feats, img_metas): + img_shape = img_meta[0]['img_shape'] + scale_factor = img_meta[0]['scale_factor'] + flip = img_meta[0]['flip'] + flip_direction = img_meta[0]['flip_direction'] + _bboxes = bbox_mapping(det_bboxes[:, :4], img_shape, + scale_factor, flip, flip_direction) + mask_rois = bbox2roi([_bboxes]) + mask_results = self._mask_forward(x, mask_rois) + # convert to numpy array to save memory + aug_masks.append( + mask_results['mask_pred'].sigmoid().cpu().numpy()) + merged_masks = merge_aug_masks(aug_masks, img_metas, self.test_cfg) + + ori_shape = img_metas[0][0]['ori_shape'] + segm_result = self.mask_head.get_seg_masks( + merged_masks, + det_bboxes, + det_labels, + self.test_cfg, + ori_shape, + scale_factor=1.0, + rescale=False) + return segm_result diff --git a/mmdet/models/roi_heads/trident_roi_head.py b/mmdet/models/roi_heads/trident_roi_head.py new file mode 100644 index 0000000..245569e --- /dev/null +++ b/mmdet/models/roi_heads/trident_roi_head.py @@ -0,0 +1,119 @@ +import torch +from mmcv.ops import batched_nms + +from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, merge_aug_bboxes, + multiclass_nms) +from mmdet.models.roi_heads.standard_roi_head import StandardRoIHead +from ..builder import HEADS + + +@HEADS.register_module() +class TridentRoIHead(StandardRoIHead): + """Trident roi head. + + Args: + num_branch (int): Number of branches in TridentNet. + test_branch_idx (int): In inference, all 3 branches will be used + if `test_branch_idx==-1`, otherwise only branch with index + `test_branch_idx` will be used. + """ + + def __init__(self, num_branch, test_branch_idx, **kwargs): + self.num_branch = num_branch + self.test_branch_idx = test_branch_idx + super(TridentRoIHead, self).__init__(**kwargs) + + def merge_trident_bboxes(self, trident_det_bboxes, trident_det_labels): + """Merge bbox predictions of each branch.""" + if trident_det_bboxes.numel() == 0: + det_bboxes = trident_det_bboxes.new_zeros((0, 5)) + det_labels = trident_det_bboxes.new_zeros((0, ), dtype=torch.long) + else: + nms_bboxes = trident_det_bboxes[:, :4] + nms_scores = trident_det_bboxes[:, 4].contiguous() + nms_inds = trident_det_labels + nms_cfg = self.test_cfg['nms'] + det_bboxes, keep = batched_nms(nms_bboxes, nms_scores, nms_inds, + nms_cfg) + det_labels = trident_det_labels[keep] + if self.test_cfg['max_per_img'] > 0: + det_labels = det_labels[:self.test_cfg['max_per_img']] + det_bboxes = det_bboxes[:self.test_cfg['max_per_img']] + + return det_bboxes, det_labels + + def simple_test(self, + x, + proposal_list, + img_metas, + proposals=None, + rescale=False): + """Test without augmentation as follows: + + 1. Compute prediction bbox and label per branch. + 2. Merge predictions of each branch according to scores of + bboxes, i.e., bboxes with higher score are kept to give + top-k prediction. + """ + assert self.with_bbox, 'Bbox head must be implemented.' + det_bboxes_list, det_labels_list = self.simple_test_bboxes( + x, img_metas, proposal_list, self.test_cfg, rescale=rescale) + num_branch = self.num_branch if self.test_branch_idx == -1 else 1 + for _ in range(len(det_bboxes_list)): + if det_bboxes_list[_].shape[0] == 0: + det_bboxes_list[_] = det_bboxes_list[_].new_empty((0, 5)) + det_bboxes, det_labels = [], [] + for i in range(len(img_metas) // num_branch): + det_result = self.merge_trident_bboxes( + torch.cat(det_bboxes_list[i * num_branch:(i + 1) * + num_branch]), + torch.cat(det_labels_list[i * num_branch:(i + 1) * + num_branch])) + det_bboxes.append(det_result[0]) + det_labels.append(det_result[1]) + + bbox_results = [ + bbox2result(det_bboxes[i], det_labels[i], + self.bbox_head.num_classes) + for i in range(len(det_bboxes)) + ] + return bbox_results + + def aug_test_bboxes(self, feats, img_metas, proposal_list, rcnn_test_cfg): + """Test det bboxes with test time augmentation.""" + aug_bboxes = [] + aug_scores = [] + for x, img_meta in zip(feats, img_metas): + # only one image in the batch + img_shape = img_meta[0]['img_shape'] + scale_factor = img_meta[0]['scale_factor'] + flip = img_meta[0]['flip'] + flip_direction = img_meta[0]['flip_direction'] + + trident_bboxes, trident_scores = [], [] + for branch_idx in range(len(proposal_list)): + proposals = bbox_mapping(proposal_list[0][:, :4], img_shape, + scale_factor, flip, flip_direction) + rois = bbox2roi([proposals]) + bbox_results = self._bbox_forward(x, rois) + bboxes, scores = self.bbox_head.get_bboxes( + rois, + bbox_results['cls_score'], + bbox_results['bbox_pred'], + img_shape, + scale_factor, + rescale=False, + cfg=None) + trident_bboxes.append(bboxes) + trident_scores.append(scores) + + aug_bboxes.append(torch.cat(trident_bboxes, 0)) + aug_scores.append(torch.cat(trident_scores, 0)) + # after merging, bboxes will be rescaled to the original image size + merged_bboxes, merged_scores = merge_aug_bboxes( + aug_bboxes, aug_scores, img_metas, rcnn_test_cfg) + det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores, + rcnn_test_cfg.score_thr, + rcnn_test_cfg.nms, + rcnn_test_cfg.max_per_img) + return det_bboxes, det_labels diff --git a/mmdet/models/utils/__init__.py b/mmdet/models/utils/__init__.py new file mode 100644 index 0000000..32dd7e2 --- /dev/null +++ b/mmdet/models/utils/__init__.py @@ -0,0 +1,14 @@ +from .builder import build_transformer +from .gaussian_target import gaussian_radius, gen_gaussian_target +from .positional_encoding import (LearnedPositionalEncoding, + SinePositionalEncoding) +from .res_layer import ResLayer, SimplifiedBasicBlock +from .transformer import (DetrTransformerDecoder, DetrTransformerDecoderLayer, + DynamicConv, Transformer) + +__all__ = [ + 'ResLayer', 'gaussian_radius', 'gen_gaussian_target', + 'DetrTransformerDecoderLayer', 'DetrTransformerDecoder', 'Transformer', + 'build_transformer', 'SinePositionalEncoding', 'LearnedPositionalEncoding', + 'DynamicConv', 'SimplifiedBasicBlock' +] diff --git a/mmdet/models/utils/__pycache__/__init__.cpython-37.pyc b/mmdet/models/utils/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..af5559e Binary files /dev/null and b/mmdet/models/utils/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/models/utils/__pycache__/builder.cpython-37.pyc b/mmdet/models/utils/__pycache__/builder.cpython-37.pyc new file mode 100644 index 0000000..0a107ca Binary files /dev/null and b/mmdet/models/utils/__pycache__/builder.cpython-37.pyc differ diff --git a/mmdet/models/utils/__pycache__/gaussian_target.cpython-37.pyc b/mmdet/models/utils/__pycache__/gaussian_target.cpython-37.pyc new file mode 100644 index 0000000..8a6921f Binary files /dev/null and b/mmdet/models/utils/__pycache__/gaussian_target.cpython-37.pyc differ diff --git a/mmdet/models/utils/__pycache__/positional_encoding.cpython-37.pyc b/mmdet/models/utils/__pycache__/positional_encoding.cpython-37.pyc new file mode 100644 index 0000000..f76a724 Binary files /dev/null and b/mmdet/models/utils/__pycache__/positional_encoding.cpython-37.pyc differ diff --git a/mmdet/models/utils/__pycache__/res_layer.cpython-37.pyc b/mmdet/models/utils/__pycache__/res_layer.cpython-37.pyc new file mode 100644 index 0000000..41b547d Binary files /dev/null and b/mmdet/models/utils/__pycache__/res_layer.cpython-37.pyc differ diff --git a/mmdet/models/utils/__pycache__/transformer.cpython-37.pyc b/mmdet/models/utils/__pycache__/transformer.cpython-37.pyc new file mode 100644 index 0000000..05282c3 Binary files /dev/null and b/mmdet/models/utils/__pycache__/transformer.cpython-37.pyc differ diff --git a/mmdet/models/utils/builder.py b/mmdet/models/utils/builder.py new file mode 100644 index 0000000..5686850 --- /dev/null +++ b/mmdet/models/utils/builder.py @@ -0,0 +1,8 @@ +from mmcv.utils import Registry, build_from_cfg + +TRANSFORMER = Registry('Transformer') + + +def build_transformer(cfg, default_args=None): + """Builder for Transformer.""" + return build_from_cfg(cfg, TRANSFORMER, default_args) diff --git a/mmdet/models/utils/gaussian_target.py b/mmdet/models/utils/gaussian_target.py new file mode 100644 index 0000000..7bb7160 --- /dev/null +++ b/mmdet/models/utils/gaussian_target.py @@ -0,0 +1,185 @@ +from math import sqrt + +import torch + + +def gaussian2D(radius, sigma=1, dtype=torch.float32, device='cpu'): + """Generate 2D gaussian kernel. + + Args: + radius (int): Radius of gaussian kernel. + sigma (int): Sigma of gaussian function. Default: 1. + dtype (torch.dtype): Dtype of gaussian tensor. Default: torch.float32. + device (str): Device of gaussian tensor. Default: 'cpu'. + + Returns: + h (Tensor): Gaussian kernel with a + ``(2 * radius + 1) * (2 * radius + 1)`` shape. + """ + x = torch.arange( + -radius, radius + 1, dtype=dtype, device=device).view(1, -1) + y = torch.arange( + -radius, radius + 1, dtype=dtype, device=device).view(-1, 1) + + h = (-(x * x + y * y) / (2 * sigma * sigma)).exp() + + h[h < torch.finfo(h.dtype).eps * h.max()] = 0 + return h + + +def gen_gaussian_target(heatmap, center, radius, k=1): + """Generate 2D gaussian heatmap. + + Args: + heatmap (Tensor): Input heatmap, the gaussian kernel will cover on + it and maintain the max value. + center (list[int]): Coord of gaussian kernel's center. + radius (int): Radius of gaussian kernel. + k (int): Coefficient of gaussian kernel. Default: 1. + + Returns: + out_heatmap (Tensor): Updated heatmap covered by gaussian kernel. + """ + diameter = 2 * radius + 1 + gaussian_kernel = gaussian2D( + radius, sigma=diameter / 6, dtype=heatmap.dtype, device=heatmap.device) + + x, y = center + + height, width = heatmap.shape[:2] + + left, right = min(x, radius), min(width - x, radius + 1) + top, bottom = min(y, radius), min(height - y, radius + 1) + + masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right] + masked_gaussian = gaussian_kernel[radius - top:radius + bottom, + radius - left:radius + right] + out_heatmap = heatmap + torch.max( + masked_heatmap, + masked_gaussian * k, + out=out_heatmap[y - top:y + bottom, x - left:x + right]) + + return out_heatmap + + +def gaussian_radius(det_size, min_overlap): + r"""Generate 2D gaussian radius. + + This function is modified from the `official github repo + `_. + + Given ``min_overlap``, radius could computed by a quadratic equation + according to Vieta's formulas. + + There are 3 cases for computing gaussian radius, details are following: + + - Explanation of figure: ``lt`` and ``br`` indicates the left-top and + bottom-right corner of ground truth box. ``x`` indicates the + generated corner at the limited position when ``radius=r``. + + - Case1: one corner is inside the gt box and the other is outside. + + .. code:: text + + |< width >| + + lt-+----------+ - + | | | ^ + +--x----------+--+ + | | | | + | | | | height + | | overlap | | + | | | | + | | | | v + +--+---------br--+ - + | | | + +----------+--x + + To ensure IoU of generated box and gt box is larger than ``min_overlap``: + + .. math:: + \cfrac{(w-r)*(h-r)}{w*h+(w+h)r-r^2} \ge {iou} \quad\Rightarrow\quad + {r^2-(w+h)r+\cfrac{1-iou}{1+iou}*w*h} \ge 0 \\ + {a} = 1,\quad{b} = {-(w+h)},\quad{c} = {\cfrac{1-iou}{1+iou}*w*h} + {r} \le \cfrac{-b-\sqrt{b^2-4*a*c}}{2*a} + + - Case2: both two corners are inside the gt box. + + .. code:: text + + |< width >| + + lt-+----------+ - + | | | ^ + +--x-------+ | + | | | | + | |overlap| | height + | | | | + | +-------x--+ + | | | v + +----------+-br - + + To ensure IoU of generated box and gt box is larger than ``min_overlap``: + + .. math:: + \cfrac{(w-2*r)*(h-2*r)}{w*h} \ge {iou} \quad\Rightarrow\quad + {4r^2-2(w+h)r+(1-iou)*w*h} \ge 0 \\ + {a} = 4,\quad {b} = {-2(w+h)},\quad {c} = {(1-iou)*w*h} + {r} \le \cfrac{-b-\sqrt{b^2-4*a*c}}{2*a} + + - Case3: both two corners are outside the gt box. + + .. code:: text + + |< width >| + + x--+----------------+ + | | | + +-lt-------------+ | - + | | | | ^ + | | | | + | | overlap | | height + | | | | + | | | | v + | +------------br--+ - + | | | + +----------------+--x + + To ensure IoU of generated box and gt box is larger than ``min_overlap``: + + .. math:: + \cfrac{w*h}{(w+2*r)*(h+2*r)} \ge {iou} \quad\Rightarrow\quad + {4*iou*r^2+2*iou*(w+h)r+(iou-1)*w*h} \le 0 \\ + {a} = {4*iou},\quad {b} = {2*iou*(w+h)},\quad {c} = {(iou-1)*w*h} \\ + {r} \le \cfrac{-b+\sqrt{b^2-4*a*c}}{2*a} + + Args: + det_size (list[int]): Shape of object. + min_overlap (float): Min IoU with ground truth for boxes generated by + keypoints inside the gaussian kernel. + + Returns: + radius (int): Radius of gaussian kernel. + """ + height, width = det_size + + a1 = 1 + b1 = (height + width) + c1 = width * height * (1 - min_overlap) / (1 + min_overlap) + sq1 = sqrt(b1**2 - 4 * a1 * c1) + r1 = (b1 - sq1) / (2 * a1) + + a2 = 4 + b2 = 2 * (height + width) + c2 = (1 - min_overlap) * width * height + sq2 = sqrt(b2**2 - 4 * a2 * c2) + r2 = (b2 - sq2) / (2 * a2) + + a3 = 4 * min_overlap + b3 = -2 * min_overlap * (height + width) + c3 = (min_overlap - 1) * width * height + sq3 = sqrt(b3**2 - 4 * a3 * c3) + r3 = (b3 + sq3) / (2 * a3) + return min(r1, r2, r3) diff --git a/mmdet/models/utils/positional_encoding.py b/mmdet/models/utils/positional_encoding.py new file mode 100644 index 0000000..18a3f68 --- /dev/null +++ b/mmdet/models/utils/positional_encoding.py @@ -0,0 +1,157 @@ +import math + +import torch +import torch.nn as nn +from mmcv.cnn.bricks.transformer import POSITIONAL_ENCODING +from mmcv.runner import BaseModule + + +@POSITIONAL_ENCODING.register_module() +class SinePositionalEncoding(BaseModule): + """Position encoding with sine and cosine functions. + + See `End-to-End Object Detection with Transformers + `_ for details. + + Args: + num_feats (int): The feature dimension for each position + along x-axis or y-axis. Note the final returned dimension + for each position is 2 times of this value. + temperature (int, optional): The temperature used for scaling + the position embedding. Defaults to 10000. + normalize (bool, optional): Whether to normalize the position + embedding. Defaults to False. + scale (float, optional): A scale factor that scales the position + embedding. The scale will be used only when `normalize` is True. + Defaults to 2*pi. + eps (float, optional): A value added to the denominator for + numerical stability. Defaults to 1e-6. + offset (float): offset add to embed when do the normalization. + Defaults to 0. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + num_feats, + temperature=10000, + normalize=False, + scale=2 * math.pi, + eps=1e-6, + offset=0., + init_cfg=None): + super(SinePositionalEncoding, self).__init__(init_cfg) + if normalize: + assert isinstance(scale, (float, int)), 'when normalize is set,' \ + 'scale should be provided and in float or int type, ' \ + f'found {type(scale)}' + self.num_feats = num_feats + self.temperature = temperature + self.normalize = normalize + self.scale = scale + self.eps = eps + self.offset = offset + + def forward(self, mask): + """Forward function for `SinePositionalEncoding`. + + Args: + mask (Tensor): ByteTensor mask. Non-zero values representing + ignored positions, while zero values means valid positions + for this image. Shape [bs, h, w]. + + Returns: + pos (Tensor): Returned position embedding with shape + [bs, num_feats*2, h, w]. + """ + not_mask = ~mask + y_embed = not_mask.cumsum(1, dtype=torch.float32) + x_embed = not_mask.cumsum(2, dtype=torch.float32) + if self.normalize: + y_embed = (y_embed + self.offset) / \ + (y_embed[:, -1:, :] + self.eps) * self.scale + x_embed = (x_embed + self.offset) / \ + (x_embed[:, :, -1:] + self.eps) * self.scale + dim_t = torch.arange( + self.num_feats, dtype=torch.float32, device=mask.device) + dim_t = self.temperature**(2 * (dim_t // 2) / self.num_feats) + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack( + (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), + dim=4).flatten(3) + pos_y = torch.stack( + (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), + dim=4).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + def __repr__(self): + """str: a string that describes the module""" + repr_str = self.__class__.__name__ + repr_str += f'(num_feats={self.num_feats}, ' + repr_str += f'temperature={self.temperature}, ' + repr_str += f'normalize={self.normalize}, ' + repr_str += f'scale={self.scale}, ' + repr_str += f'eps={self.eps})' + return repr_str + + +@POSITIONAL_ENCODING.register_module() +class LearnedPositionalEncoding(BaseModule): + """Position embedding with learnable embedding weights. + + Args: + num_feats (int): The feature dimension for each position + along x-axis or y-axis. The final returned dimension for + each position is 2 times of this value. + row_num_embed (int, optional): The dictionary size of row embeddings. + Default 50. + col_num_embed (int, optional): The dictionary size of col embeddings. + Default 50. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + num_feats, + row_num_embed=50, + col_num_embed=50, + init_cfg=dict(type='Uniform', layer='Embedding')): + super(LearnedPositionalEncoding, self).__init__(init_cfg) + self.row_embed = nn.Embedding(row_num_embed, num_feats) + self.col_embed = nn.Embedding(col_num_embed, num_feats) + self.num_feats = num_feats + self.row_num_embed = row_num_embed + self.col_num_embed = col_num_embed + + def forward(self, mask): + """Forward function for `LearnedPositionalEncoding`. + + Args: + mask (Tensor): ByteTensor mask. Non-zero values representing + ignored positions, while zero values means valid positions + for this image. Shape [bs, h, w]. + + Returns: + pos (Tensor): Returned position embedding with shape + [bs, num_feats*2, h, w]. + """ + h, w = mask.shape[-2:] + x = torch.arange(w, device=mask.device) + y = torch.arange(h, device=mask.device) + x_embed = self.col_embed(x) + y_embed = self.row_embed(y) + pos = torch.cat( + (x_embed.unsqueeze(0).repeat(h, 1, 1), y_embed.unsqueeze(1).repeat( + 1, w, 1)), + dim=-1).permute(2, 0, + 1).unsqueeze(0).repeat(mask.shape[0], 1, 1, 1) + return pos + + def __repr__(self): + """str: a string that describes the module""" + repr_str = self.__class__.__name__ + repr_str += f'(num_feats={self.num_feats}, ' + repr_str += f'row_num_embed={self.row_num_embed}, ' + repr_str += f'col_num_embed={self.col_num_embed})' + return repr_str diff --git a/mmdet/models/utils/res_layer.py b/mmdet/models/utils/res_layer.py new file mode 100644 index 0000000..825880d --- /dev/null +++ b/mmdet/models/utils/res_layer.py @@ -0,0 +1,189 @@ +from mmcv.cnn import build_conv_layer, build_norm_layer +from mmcv.runner import BaseModule, Sequential +from torch import nn as nn + + +class ResLayer(Sequential): + """ResLayer to build ResNet style backbone. + + Args: + block (nn.Module): block used to build ResLayer. + inplanes (int): inplanes of block. + planes (int): planes of block. + num_blocks (int): number of blocks. + stride (int): stride of the first block. Default: 1 + avg_down (bool): Use AvgPool instead of stride conv when + downsampling in the bottleneck. Default: False + conv_cfg (dict): dictionary to construct and config conv layer. + Default: None + norm_cfg (dict): dictionary to construct and config norm layer. + Default: dict(type='BN') + downsample_first (bool): Downsample at the first block or last block. + False for Hourglass, True for ResNet. Default: True + """ + + def __init__(self, + block, + inplanes, + planes, + num_blocks, + stride=1, + avg_down=False, + conv_cfg=None, + norm_cfg=dict(type='BN'), + downsample_first=True, + **kwargs): + self.block = block + + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + downsample = [] + conv_stride = stride + if avg_down: + conv_stride = 1 + downsample.append( + nn.AvgPool2d( + kernel_size=stride, + stride=stride, + ceil_mode=True, + count_include_pad=False)) + downsample.extend([ + build_conv_layer( + conv_cfg, + inplanes, + planes * block.expansion, + kernel_size=1, + stride=conv_stride, + bias=False), + build_norm_layer(norm_cfg, planes * block.expansion)[1] + ]) + downsample = nn.Sequential(*downsample) + + layers = [] + if downsample_first: + layers.append( + block( + inplanes=inplanes, + planes=planes, + stride=stride, + downsample=downsample, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + **kwargs)) + inplanes = planes * block.expansion + for _ in range(1, num_blocks): + layers.append( + block( + inplanes=inplanes, + planes=planes, + stride=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + **kwargs)) + + else: # downsample_first=False is for HourglassModule + for _ in range(num_blocks - 1): + layers.append( + block( + inplanes=inplanes, + planes=inplanes, + stride=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + **kwargs)) + layers.append( + block( + inplanes=inplanes, + planes=planes, + stride=stride, + downsample=downsample, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + **kwargs)) + super(ResLayer, self).__init__(*layers) + + +class SimplifiedBasicBlock(BaseModule): + """Simplified version of original basic residual block. This is used in + `SCNet `_. + + - Norm layer is now optional + - Last ReLU in forward function is removed + """ + expansion = 1 + + def __init__(self, + inplanes, + planes, + stride=1, + dilation=1, + downsample=None, + style='pytorch', + with_cp=False, + conv_cfg=None, + norm_cfg=dict(type='BN'), + dcn=None, + plugins=None, + init_fg=None): + super(SimplifiedBasicBlock, self).__init__(init_fg) + assert dcn is None, 'Not implemented yet.' + assert plugins is None, 'Not implemented yet.' + assert not with_cp, 'Not implemented yet.' + self.with_norm = norm_cfg is not None + with_bias = True if norm_cfg is None else False + self.conv1 = build_conv_layer( + conv_cfg, + inplanes, + planes, + 3, + stride=stride, + padding=dilation, + dilation=dilation, + bias=with_bias) + if self.with_norm: + self.norm1_name, norm1 = build_norm_layer( + norm_cfg, planes, postfix=1) + self.add_module(self.norm1_name, norm1) + self.conv2 = build_conv_layer( + conv_cfg, planes, planes, 3, padding=1, bias=with_bias) + if self.with_norm: + self.norm2_name, norm2 = build_norm_layer( + norm_cfg, planes, postfix=2) + self.add_module(self.norm2_name, norm2) + + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + self.dilation = dilation + self.with_cp = with_cp + + @property + def norm1(self): + """nn.Module: normalization layer after the first convolution layer""" + return getattr(self, self.norm1_name) if self.with_norm else None + + @property + def norm2(self): + """nn.Module: normalization layer after the second convolution layer""" + return getattr(self, self.norm2_name) if self.with_norm else None + + def forward(self, x): + """Forward function.""" + + identity = x + + out = self.conv1(x) + if self.with_norm: + out = self.norm1(out) + out = self.relu(out) + + out = self.conv2(out) + if self.with_norm: + out = self.norm2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + + return out diff --git a/mmdet/models/utils/transformer.py b/mmdet/models/utils/transformer.py new file mode 100644 index 0000000..360d0a5 --- /dev/null +++ b/mmdet/models/utils/transformer.py @@ -0,0 +1,1253 @@ +import torch.nn.functional as F +import math + +import torch +import torch.nn as nn +from typing import Sequence +from itertools import repeat +import collections.abc + +from mmcv.cnn import build_activation_layer, build_norm_layer, xavier_init, build_conv_layer +from mmcv.cnn.bricks.registry import (TRANSFORMER_LAYER, + TRANSFORMER_LAYER_SEQUENCE) +from mmcv.cnn.bricks.transformer import (BaseTransformerLayer, + MultiScaleDeformableAttention, + TransformerLayerSequence, + build_transformer_layer_sequence) +from mmcv.runner.base_module import BaseModule +from torch.nn.init import normal_ + +from mmdet.models.utils.builder import TRANSFORMER + + +def _ntuple(n): + + def parse(x): + if isinstance(x, collections.abc.Iterable): + return x + return tuple(repeat(x, n)) + + return parse + + +to_1tuple = _ntuple(1) +to_2tuple = _ntuple(2) +to_3tuple = _ntuple(3) +to_4tuple = _ntuple(4) +to_ntuple = _ntuple + + +def nlc_to_nchw(x, hw_shape): + """Convert [N, L, C] shape tensor to [N, C, H, W] shape tensor. + Args: + x (Tensor): The input tensor of shape [N, L, C] before conversion. + hw_shape (Sequence[int]): The height and width of output feature map. + Returns: + Tensor: The output tensor of shape [N, C, H, W] after conversion. + """ + H, W = hw_shape + assert len(x.shape) == 3 + B, L, C = x.shape + assert L == H * W, 'The seq_len does not match H, W' + return x.transpose(1, 2).reshape(B, C, H, W).contiguous() + + +def nchw_to_nlc(x): + """Flatten [N, C, H, W] shape tensor to [N, L, C] shape tensor. + Args: + x (Tensor): The input tensor of shape [N, C, H, W] before conversion. + Returns: + Tensor: The output tensor of shape [N, L, C] after conversion. + """ + assert len(x.shape) == 4 + return x.flatten(2).transpose(1, 2).contiguous() + + +class AdaptivePadding(nn.Module): + """Applies padding to input (if needed) so that input can get fully covered + by filter you specified. It support two modes "same" and "corner". The + "same" mode is same with "SAME" padding mode in TensorFlow, pad zero around + input. The "corner" mode would pad zero to bottom right. + Args: + kernel_size (int | tuple): Size of the kernel: + stride (int | tuple): Stride of the filter. Default: 1: + dilation (int | tuple): Spacing between kernel elements. + Default: 1 + padding (str): Support "same" and "corner", "corner" mode + would pad zero to bottom right, and "same" mode would + pad zero around input. Default: "corner". + Example: + >>> kernel_size = 16 + >>> stride = 16 + >>> dilation = 1 + >>> input = torch.rand(1, 1, 15, 17) + >>> adap_pad = AdaptivePadding( + >>> kernel_size=kernel_size, + >>> stride=stride, + >>> dilation=dilation, + >>> padding="corner") + >>> out = adap_pad(input) + >>> assert (out.shape[2], out.shape[3]) == (16, 32) + >>> input = torch.rand(1, 1, 16, 17) + >>> out = adap_pad(input) + >>> assert (out.shape[2], out.shape[3]) == (16, 32) + """ + + def __init__(self, kernel_size=1, stride=1, dilation=1, padding='corner'): + + super(AdaptivePadding, self).__init__() + + assert padding in ('same', 'corner') + + kernel_size = to_2tuple(kernel_size) + stride = to_2tuple(stride) + padding = to_2tuple(padding) + dilation = to_2tuple(dilation) + + self.padding = padding + self.kernel_size = kernel_size + self.stride = stride + self.dilation = dilation + + def get_pad_shape(self, input_shape): + input_h, input_w = input_shape + kernel_h, kernel_w = self.kernel_size + stride_h, stride_w = self.stride + output_h = math.ceil(input_h / stride_h) + output_w = math.ceil(input_w / stride_w) + pad_h = max((output_h - 1) * stride_h + + (kernel_h - 1) * self.dilation[0] + 1 - input_h, 0) + pad_w = max((output_w - 1) * stride_w + + (kernel_w - 1) * self.dilation[1] + 1 - input_w, 0) + return pad_h, pad_w + + def forward(self, x): + pad_h, pad_w = self.get_pad_shape(x.size()[-2:]) + if pad_h > 0 or pad_w > 0: + if self.padding == 'corner': + x = F.pad(x, [0, pad_w, 0, pad_h]) + elif self.padding == 'same': + x = F.pad(x, [ + pad_w // 2, pad_w - pad_w // 2, pad_h // 2, + pad_h - pad_h // 2 + ]) + return x + + +class PatchEmbed(BaseModule): + """Image to Patch Embedding. + We use a conv layer to implement PatchEmbed. + Args: + in_channels (int): The num of input channels. Default: 3 + embed_dims (int): The dimensions of embedding. Default: 768 + conv_type (str): The config dict for embedding + conv layer type selection. Default: "Conv2d. + kernel_size (int): The kernel_size of embedding conv. Default: 16. + stride (int): The slide stride of embedding conv. + Default: None (Would be set as `kernel_size`). + padding (int | tuple | string ): The padding length of + embedding conv. When it is a string, it means the mode + of adaptive padding, support "same" and "corner" now. + Default: "corner". + dilation (int): The dilation rate of embedding conv. Default: 1. + bias (bool): Bias of embed conv. Default: True. + norm_cfg (dict, optional): Config dict for normalization layer. + Default: None. + input_size (int | tuple | None): The size of input, which will be + used to calculate the out size. Only work when `dynamic_size` + is False. Default: None. + init_cfg (`mmcv.ConfigDict`, optional): The Config for initialization. + Default: None. + """ + + def __init__( + self, + in_channels=3, + embed_dims=768, + conv_type='Conv2d', + kernel_size=16, + stride=16, + padding='corner', + dilation=1, + bias=True, + norm_cfg=None, + input_size=None, + init_cfg=None, + ): + super(PatchEmbed, self).__init__(init_cfg=init_cfg) + + self.embed_dims = embed_dims + if stride is None: + stride = kernel_size + + kernel_size = to_2tuple(kernel_size) + stride = to_2tuple(stride) + dilation = to_2tuple(dilation) + + if isinstance(padding, str): + self.adap_padding = AdaptivePadding( + kernel_size=kernel_size, + stride=stride, + dilation=dilation, + padding=padding) + # disable the padding of conv + padding = 0 + else: + self.adap_padding = None + padding = to_2tuple(padding) + + self.projection = build_conv_layer( + dict(type=conv_type), + in_channels=in_channels, + out_channels=embed_dims, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + bias=bias) + + if norm_cfg is not None: + self.norm = build_norm_layer(norm_cfg, embed_dims)[1] + else: + self.norm = None + + if input_size: + input_size = to_2tuple(input_size) + # `init_out_size` would be used outside to + # calculate the num_patches + # when `use_abs_pos_embed` outside + self.init_input_size = input_size + if self.adap_padding: + pad_h, pad_w = self.adap_padding.get_pad_shape(input_size) + input_h, input_w = input_size + input_h = input_h + pad_h + input_w = input_w + pad_w + input_size = (input_h, input_w) + + # https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html + h_out = (input_size[0] + 2 * padding[0] - dilation[0] * + (kernel_size[0] - 1) - 1) // stride[0] + 1 + w_out = (input_size[1] + 2 * padding[1] - dilation[1] * + (kernel_size[1] - 1) - 1) // stride[1] + 1 + self.init_out_size = (h_out, w_out) + else: + self.init_input_size = None + self.init_out_size = None + + def forward(self, x): + """ + Args: + x (Tensor): Has shape (B, C, H, W). In most case, C is 3. + Returns: + tuple: Contains merged results and its spatial shape. + - x (Tensor): Has shape (B, out_h * out_w, embed_dims) + - out_size (tuple[int]): Spatial shape of x, arrange as + (out_h, out_w). + """ + + if self.adap_padding: + x = self.adap_padding(x) + + x = self.projection(x) + out_size = (x.shape[2], x.shape[3]) + x = x.flatten(2).transpose(1, 2) + if self.norm is not None: + x = self.norm(x) + return x, out_size + + +class PatchMerging(BaseModule): + """Merge patch feature map. + This layer groups feature map by kernel_size, and applies norm and linear + layers to the grouped feature map. Our implementation uses `nn.Unfold` to + merge patch, which is about 25% faster than original implementation. + Instead, we need to modify pretrained models for compatibility. + Args: + in_channels (int): The num of input channels. + to gets fully covered by filter and stride you specified.. + Default: True. + out_channels (int): The num of output channels. + kernel_size (int | tuple, optional): the kernel size in the unfold + layer. Defaults to 2. + stride (int | tuple, optional): the stride of the sliding blocks in the + unfold layer. Default: None. (Would be set as `kernel_size`) + padding (int | tuple | string ): The padding length of + embedding conv. When it is a string, it means the mode + of adaptive padding, support "same" and "corner" now. + Default: "corner". + dilation (int | tuple, optional): dilation parameter in the unfold + layer. Default: 1. + bias (bool, optional): Whether to add bias in linear layer or not. + Defaults: False. + norm_cfg (dict, optional): Config dict for normalization layer. + Default: dict(type='LN'). + init_cfg (dict, optional): The extra config for initialization. + Default: None. + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size=2, + stride=None, + padding='corner', + dilation=1, + bias=False, + norm_cfg=dict(type='LN'), + init_cfg=None): + super().__init__(init_cfg=init_cfg) + self.in_channels = in_channels + self.out_channels = out_channels + if stride: + stride = stride + else: + stride = kernel_size + + kernel_size = to_2tuple(kernel_size) + stride = to_2tuple(stride) + dilation = to_2tuple(dilation) + + if isinstance(padding, str): + self.adap_padding = AdaptivePadding( + kernel_size=kernel_size, + stride=stride, + dilation=dilation, + padding=padding) + # disable the padding of unfold + padding = 0 + else: + self.adap_padding = None + + padding = to_2tuple(padding) + self.sampler = nn.Unfold( + kernel_size=kernel_size, + dilation=dilation, + padding=padding, + stride=stride) + + sample_dim = kernel_size[0] * kernel_size[1] * in_channels + + if norm_cfg is not None: + self.norm = build_norm_layer(norm_cfg, sample_dim)[1] + else: + self.norm = None + + self.reduction = nn.Linear(sample_dim, out_channels, bias=bias) + + def forward(self, x, input_size): + """ + Args: + x (Tensor): Has shape (B, H*W, C_in). + input_size (tuple[int]): The spatial shape of x, arrange as (H, W). + Default: None. + Returns: + tuple: Contains merged results and its spatial shape. + - x (Tensor): Has shape (B, Merged_H * Merged_W, C_out) + - out_size (tuple[int]): Spatial shape of x, arrange as + (Merged_H, Merged_W). + """ + B, L, C = x.shape + assert isinstance(input_size, Sequence), f'Expect ' \ + f'input_size is ' \ + f'`Sequence` ' \ + f'but get {input_size}' + + H, W = input_size + assert L == H * W, 'input feature has wrong size' + + x = x.view(B, H, W, C).permute([0, 3, 1, 2]) # B, C, H, W + # Use nn.Unfold to merge patch. About 25% faster than original method, + # but need to modify pretrained model for compatibility + + if self.adap_padding: + x = self.adap_padding(x) + H, W = x.shape[-2:] + + x = self.sampler(x) + # if kernel_size=2 and stride=2, x should has shape (B, 4*C, H/2*W/2) + + out_h = (H + 2 * self.sampler.padding[0] - self.sampler.dilation[0] * + (self.sampler.kernel_size[0] - 1) - + 1) // self.sampler.stride[0] + 1 + out_w = (W + 2 * self.sampler.padding[1] - self.sampler.dilation[1] * + (self.sampler.kernel_size[1] - 1) - + 1) // self.sampler.stride[1] + 1 + + output_size = (out_h, out_w) + x = x.transpose(1, 2) # B, H/2*W/2, 4*C + x = self.norm(x) if self.norm else x + x = self.reduction(x) + return x, output_size + + +def inverse_sigmoid(x, eps=1e-5): + """Inverse function of sigmoid. + + Args: + x (Tensor): The tensor to do the + inverse. + eps (float): EPS avoid numerical + overflow. Defaults 1e-5. + Returns: + Tensor: The x has passed the inverse + function of sigmoid, has same + shape with input. + """ + x = x.clamp(min=0, max=1) + x1 = x.clamp(min=eps) + x2 = (1 - x).clamp(min=eps) + return torch.log(x1 / x2) + + +@TRANSFORMER_LAYER.register_module() +class DetrTransformerDecoderLayer(BaseTransformerLayer): + """Implements decoder layer in DETR transformer. + + Args: + attn_cfgs (list[`mmcv.ConfigDict`] | list[dict] | dict )): + Configs for self_attention or cross_attention, the order + should be consistent with it in `operation_order`. If it is + a dict, it would be expand to the number of attention in + `operation_order`. + feedforward_channels (int): The hidden dimension for FFNs. + ffn_dropout (float): Probability of an element to be zeroed + in ffn. Default 0.0. + operation_order (tuple[str]): The execution order of operation + in transformer. Such as ('self_attn', 'norm', 'ffn', 'norm'). + Default：None + act_cfg (dict): The activation config for FFNs. Default: `LN` + norm_cfg (dict): Config dict for normalization layer. + Default: `LN`. + ffn_num_fcs (int): The number of fully-connected layers in FFNs. + Default：2. + """ + + def __init__(self, + attn_cfgs, + feedforward_channels, + ffn_dropout=0.0, + operation_order=None, + act_cfg=dict(type='ReLU', inplace=True), + norm_cfg=dict(type='LN'), + ffn_num_fcs=2, + **kwargs): + super(DetrTransformerDecoderLayer, self).__init__( + attn_cfgs=attn_cfgs, + feedforward_channels=feedforward_channels, + ffn_dropout=ffn_dropout, + operation_order=operation_order, + act_cfg=act_cfg, + norm_cfg=norm_cfg, + ffn_num_fcs=ffn_num_fcs, + **kwargs) + assert len(operation_order) == 6 + assert set(operation_order) == set( + ['self_attn', 'norm', 'cross_attn', 'ffn']) + + +@TRANSFORMER_LAYER_SEQUENCE.register_module() +class DetrTransformerEncoder(TransformerLayerSequence): + """TransformerEncoder of DETR. + + Args: + post_norm_cfg (dict): Config of last normalization layer. Default： + `LN`. Only used when `self.pre_norm` is `True` + """ + + def __init__(self, *args, post_norm_cfg=dict(type='LN'), **kwargs): + super(DetrTransformerEncoder, self).__init__(*args, **kwargs) + if post_norm_cfg is not None: + self.post_norm = build_norm_layer( + post_norm_cfg, self.embed_dims)[1] if self.pre_norm else None + else: + assert not self.pre_norm, f'Use prenorm in ' \ + f'{self.__class__.__name__},' \ + f'Please specify post_norm_cfg' + self.post_norm = None + + def forward(self, *args, **kwargs): + """Forward function for `TransformerCoder`. + + Returns: + Tensor: forwarded results with shape [num_query, bs, embed_dims]. + """ + x = super(DetrTransformerEncoder, self).forward(*args, **kwargs) + if self.post_norm is not None: + x = self.post_norm(x) + return x + + +@TRANSFORMER_LAYER_SEQUENCE.register_module() +class DetrTransformerDecoder(TransformerLayerSequence): + """Implements the decoder in DETR transformer. + + Args: + return_intermediate (bool): Whether to return intermediate outputs. + post_norm_cfg (dict): Config of last normalization layer. Default： + `LN`. + """ + + def __init__(self, + *args, + post_norm_cfg=dict(type='LN'), + return_intermediate=False, + **kwargs): + + super(DetrTransformerDecoder, self).__init__(*args, **kwargs) + self.return_intermediate = return_intermediate + if post_norm_cfg is not None: + self.post_norm = build_norm_layer(post_norm_cfg, + self.embed_dims)[1] + else: + self.post_norm = None + + def forward(self, query, *args, **kwargs): + """Forward function for `TransformerDecoder`. + + Args: + query (Tensor): Input query with shape + `(num_query, bs, embed_dims)`. + + Returns: + Tensor: Results with shape [1, num_query, bs, embed_dims] when + return_intermediate is `False`, otherwise it has shape + [num_layers, num_query, bs, embed_dims]. + """ + if not self.return_intermediate: + x = super().forward(query, *args, **kwargs) + if self.post_norm: + x = self.post_norm(x)[None] + return x + + intermediate = [] + for layer in self.layers: + query = layer(query, *args, **kwargs) + if self.return_intermediate: + if self.post_norm is not None: + intermediate.append(self.post_norm(query)) + else: + intermediate.append(query) + return torch.stack(intermediate) + + +@TRANSFORMER.register_module() +class Transformer(BaseModule): + """Implements the DETR transformer. + + Following the official DETR implementation, this module copy-paste + from torch.nn.Transformer with modifications: + + * positional encodings are passed in MultiheadAttention + * extra LN at the end of encoder is removed + * decoder returns a stack of activations from all decoding layers + + See `paper: End-to-End Object Detection with Transformers + `_ for details. + + Args: + encoder (`mmcv.ConfigDict` | Dict): Config of + TransformerEncoder. Defaults to None. + decoder ((`mmcv.ConfigDict` | Dict)): Config of + TransformerDecoder. Defaults to None + init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. + Defaults to None. + """ + + def __init__(self, encoder=None, decoder=None, init_cfg=None): + super(Transformer, self).__init__(init_cfg=init_cfg) + self.encoder = build_transformer_layer_sequence(encoder) + self.decoder = build_transformer_layer_sequence(decoder) + self.embed_dims = self.encoder.embed_dims + + def init_weights(self): + # follow the official DETR to init parameters + for m in self.modules(): + if hasattr(m, 'weight') and m.weight.dim() > 1: + xavier_init(m, distribution='uniform') + self._is_init = True + + def forward(self, x, mask, query_embed, pos_embed): + """Forward function for `Transformer`. + + Args: + x (Tensor): Input query with shape [bs, c, h, w] where + c = embed_dims. + mask (Tensor): The key_padding_mask used for encoder and decoder, + with shape [bs, h, w]. + query_embed (Tensor): The query embedding for decoder, with shape + [num_query, c]. + pos_embed (Tensor): The positional encoding for encoder and + decoder, with the same shape as `x`. + + Returns: + tuple[Tensor]: results of decoder containing the following tensor. + + - out_dec: Output from decoder. If return_intermediate_dec \ + is True output has shape [num_dec_layers, bs, + num_query, embed_dims], else has shape [1, bs, \ + num_query, embed_dims]. + - memory: Output results from encoder, with shape \ + [bs, embed_dims, h, w]. + """ + bs, c, h, w = x.shape + x = x.flatten(2).permute(2, 0, 1) # [bs, c, h, w] -> [h*w, bs, c] + pos_embed = pos_embed.flatten(2).permute(2, 0, 1) + query_embed = query_embed.unsqueeze(1).repeat( + 1, bs, 1) # [num_query, dim] -> [num_query, bs, dim] + mask = mask.flatten(1) # [bs, h, w] -> [bs, h*w] + memory = self.encoder( + query=x, + key=None, + value=None, + query_pos=pos_embed, + query_key_padding_mask=mask) + target = torch.zeros_like(query_embed) + # out_dec: [num_layers, num_query, bs, dim] + out_dec = self.decoder( + query=target, + key=memory, + value=memory, + key_pos=pos_embed, + query_pos=query_embed, + key_padding_mask=mask) + out_dec = out_dec.transpose(1, 2) + memory = memory.permute(1, 2, 0).reshape(bs, c, h, w) + return out_dec, memory + + +@TRANSFORMER_LAYER_SEQUENCE.register_module() +class DeformableDetrTransformerDecoder(TransformerLayerSequence): + """Implements the decoder in DETR transformer. + + Args: + return_intermediate (bool): Whether to return intermediate outputs. + coder_norm_cfg (dict): Config of last normalization layer. Default： + `LN`. + """ + + def __init__(self, *args, return_intermediate=False, **kwargs): + + super(DeformableDetrTransformerDecoder, self).__init__(*args, **kwargs) + self.return_intermediate = return_intermediate + + def forward(self, + query, + *args, + reference_points=None, + valid_ratios=None, + reg_branches=None, + **kwargs): + """Forward function for `TransformerDecoder`. + + Args: + query (Tensor): Input query with shape + `(num_query, bs, embed_dims)`. + reference_points (Tensor): The reference + points of offset. has shape + (bs, num_query, 4) when as_two_stage, + otherwise has shape ((bs, num_query, 2). + valid_ratios (Tensor): The radios of valid + points on the feature map, has shape + (bs, num_levels, 2) + reg_branch: (obj:`nn.ModuleList`): Used for + refining the regression results. Only would + be passed when with_box_refine is True, + otherwise would be passed a `None`. + + Returns: + Tensor: Results with shape [1, num_query, bs, embed_dims] when + return_intermediate is `False`, otherwise it has shape + [num_layers, num_query, bs, embed_dims]. + """ + output = query + intermediate = [] + intermediate_reference_points = [] + for lid, layer in enumerate(self.layers): + if reference_points.shape[-1] == 4: + reference_points_input = reference_points[:, :, None] * \ + torch.cat([valid_ratios, valid_ratios], -1)[:, None] + else: + assert reference_points.shape[-1] == 2 + reference_points_input = reference_points[:, :, None] * \ + valid_ratios[:, None] + output = layer( + output, + *args, + reference_points=reference_points_input, + **kwargs) + output = output.permute(1, 0, 2) + + if reg_branches is not None: + tmp = reg_branches[lid](output) + if reference_points.shape[-1] == 4: + new_reference_points = tmp + inverse_sigmoid( + reference_points) + new_reference_points = new_reference_points.sigmoid() + else: + assert reference_points.shape[-1] == 2 + new_reference_points = tmp + new_reference_points[..., :2] = tmp[ + ..., :2] + inverse_sigmoid(reference_points) + new_reference_points = new_reference_points.sigmoid() + reference_points = new_reference_points.detach() + + output = output.permute(1, 0, 2) + if self.return_intermediate: + intermediate.append(output) + intermediate_reference_points.append(reference_points) + + if self.return_intermediate: + return torch.stack(intermediate), torch.stack( + intermediate_reference_points) + + return output, reference_points + + +@TRANSFORMER.register_module() +class DeformableDetrTransformer(Transformer): + """Implements the DeformableDETR transformer. + + Args: + as_two_stage (bool): Generate query from encoder features. + Default: False. + num_feature_levels (int): Number of feature maps from FPN: + Default: 4. + two_stage_num_proposals (int): Number of proposals when set + `as_two_stage` as True. Default: 300. + """ + + def __init__(self, + as_two_stage=False, + num_feature_levels=4, + two_stage_num_proposals=300, + **kwargs): + super(DeformableDetrTransformer, self).__init__(**kwargs) + self.as_two_stage = as_two_stage + self.num_feature_levels = num_feature_levels + self.two_stage_num_proposals = two_stage_num_proposals + self.embed_dims = self.encoder.embed_dims + self.init_layers() + + def init_layers(self): + """Initialize layers of the DeformableDetrTransformer.""" + self.level_embeds = nn.Parameter( + torch.Tensor(self.num_feature_levels, self.embed_dims)) + + if self.as_two_stage: + self.enc_output = nn.Linear(self.embed_dims, self.embed_dims) + self.enc_output_norm = nn.LayerNorm(self.embed_dims) + self.pos_trans = nn.Linear(self.embed_dims * 2, + self.embed_dims * 2) + self.pos_trans_norm = nn.LayerNorm(self.embed_dims * 2) + else: + self.reference_points = nn.Linear(self.embed_dims, 2) + + def init_weights(self): + """Initialize the transformer weights.""" + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + for m in self.modules(): + if isinstance(m, MultiScaleDeformableAttention): + m.init_weight() + if not self.as_two_stage: + xavier_init(self.reference_points, distribution='uniform', bias=0.) + normal_(self.level_embeds) + + def gen_encoder_output_proposals(self, memory, memory_padding_mask, + spatial_shapes): + """Generate proposals from encoded memory. + + Args: + memory (Tensor) : The output of encoder, + has shape (bs, num_key, embed_dim). num_key is + equal the number of points on feature map from + all level. + memory_padding_mask (Tensor): Padding mask for memory. + has shape (bs, num_key). + spatial_shapes (Tensor): The shape of all feature maps. + has shape (num_level, 2). + + Returns: + tuple: A tuple of feature map and bbox prediction. + + - output_memory (Tensor): The input of decoder, \ + has shape (bs, num_key, embed_dim). num_key is \ + equal the number of points on feature map from \ + all levels. + - output_proposals (Tensor): The normalized proposal \ + after a inverse sigmoid, has shape \ + (bs, num_keys, 4). + """ + + N, S, C = memory.shape + proposals = [] + _cur = 0 + for lvl, (H, W) in enumerate(spatial_shapes): + mask_flatten_ = memory_padding_mask[:, _cur:(_cur + H * W)].view( + N, H, W, 1) + valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1) + valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1) + + grid_y, grid_x = torch.meshgrid( + torch.linspace( + 0, H - 1, H, dtype=torch.float32, device=memory.device), + torch.linspace( + 0, W - 1, W, dtype=torch.float32, device=memory.device)) + grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) + + scale = torch.cat([valid_W.unsqueeze(-1), + valid_H.unsqueeze(-1)], 1).view(N, 1, 1, 2) + grid = (grid.unsqueeze(0).expand(N, -1, -1, -1) + 0.5) / scale + wh = torch.ones_like(grid) * 0.05 * (2.0**lvl) + proposal = torch.cat((grid, wh), -1).view(N, -1, 4) + proposals.append(proposal) + _cur += (H * W) + output_proposals = torch.cat(proposals, 1) + output_proposals_valid = ((output_proposals > 0.01) & + (output_proposals < 0.99)).all( + -1, keepdim=True) + output_proposals = torch.log(output_proposals / (1 - output_proposals)) + output_proposals = output_proposals.masked_fill( + memory_padding_mask.unsqueeze(-1), float('inf')) + output_proposals = output_proposals.masked_fill( + ~output_proposals_valid, float('inf')) + + output_memory = memory + output_memory = output_memory.masked_fill( + memory_padding_mask.unsqueeze(-1), float(0)) + output_memory = output_memory.masked_fill(~output_proposals_valid, + float(0)) + output_memory = self.enc_output_norm(self.enc_output(output_memory)) + return output_memory, output_proposals + + @staticmethod + def get_reference_points(spatial_shapes, valid_ratios, device): + """Get the reference points used in decoder. + + Args: + spatial_shapes (Tensor): The shape of all + feature maps, has shape (num_level, 2). + valid_ratios (Tensor): The radios of valid + points on the feature map, has shape + (bs, num_levels, 2) + device (obj:`device`): The device where + reference_points should be. + + Returns: + Tensor: reference points used in decoder, has \ + shape (bs, num_keys, num_levels, 2). + """ + reference_points_list = [] + for lvl, (H, W) in enumerate(spatial_shapes): + # TODO check this 0.5 + ref_y, ref_x = torch.meshgrid( + torch.linspace( + 0.5, H - 0.5, H, dtype=torch.float32, device=device), + torch.linspace( + 0.5, W - 0.5, W, dtype=torch.float32, device=device)) + ref_y = ref_y.reshape(-1)[None] / ( + valid_ratios[:, None, lvl, 1] * H) + ref_x = ref_x.reshape(-1)[None] / ( + valid_ratios[:, None, lvl, 0] * W) + ref = torch.stack((ref_x, ref_y), -1) + reference_points_list.append(ref) + reference_points = torch.cat(reference_points_list, 1) + reference_points = reference_points[:, :, None] * valid_ratios[:, None] + return reference_points + + def get_valid_ratio(self, mask): + """Get the valid radios of feature maps of all level.""" + _, H, W = mask.shape + valid_H = torch.sum(~mask[:, :, 0], 1) + valid_W = torch.sum(~mask[:, 0, :], 1) + valid_ratio_h = valid_H.float() / H + valid_ratio_w = valid_W.float() / W + valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1) + return valid_ratio + + def get_proposal_pos_embed(self, + proposals, + num_pos_feats=128, + temperature=10000): + """Get the position embedding of proposal.""" + scale = 2 * math.pi + dim_t = torch.arange( + num_pos_feats, dtype=torch.float32, device=proposals.device) + dim_t = temperature**(2 * (dim_t // 2) / num_pos_feats) + # N, L, 4 + proposals = proposals.sigmoid() * scale + # N, L, 4, 128 + pos = proposals[:, :, :, None] / dim_t + # N, L, 4, 64, 2 + pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), + dim=4).flatten(2) + return pos + + def forward(self, + mlvl_feats, + mlvl_masks, + query_embed, + mlvl_pos_embeds, + reg_branches=None, + cls_branches=None, + **kwargs): + """Forward function for `Transformer`. + + Args: + mlvl_feats (list(Tensor)): Input queries from + different level. Each element has shape + [bs, embed_dims, h, w]. + mlvl_masks (list(Tensor)): The key_padding_mask from + different level used for encoder and decoder, + each element has shape [bs, h, w]. + query_embed (Tensor): The query embedding for decoder, + with shape [num_query, c]. + mlvl_pos_embeds (list(Tensor)): The positional encoding + of feats from different level, has the shape + [bs, embed_dims, h, w]. + reg_branches (obj:`nn.ModuleList`): Regression heads for + feature maps from each decoder layer. Only would + be passed when + `with_box_refine` is Ture. Default to None. + cls_branches (obj:`nn.ModuleList`): Classification heads + for feature maps from each decoder layer. Only would + be passed when `as_two_stage` + is Ture. Default to None. + + + Returns: + tuple[Tensor]: results of decoder containing the following tensor. + + - inter_states: Outputs from decoder. If + return_intermediate_dec is True output has shape \ + (num_dec_layers, bs, num_query, embed_dims), else has \ + shape (1, bs, num_query, embed_dims). + - init_reference_out: The initial value of reference \ + points, has shape (bs, num_queries, 4). + - inter_references_out: The internal value of reference \ + points in decoder, has shape \ + (num_dec_layers, bs,num_query, embed_dims) + - enc_outputs_class: The classification score of \ + proposals generated from \ + encoder's feature maps, has shape \ + (batch, h*w, num_classes). \ + Only would be returned when `as_two_stage` is True, \ + otherwise None. + - enc_outputs_coord_unact: The regression results \ + generated from encoder's feature maps., has shape \ + (batch, h*w, 4). Only would \ + be returned when `as_two_stage` is True, \ + otherwise None. + """ + assert self.as_two_stage or query_embed is not None + + feat_flatten = [] + mask_flatten = [] + lvl_pos_embed_flatten = [] + spatial_shapes = [] + for lvl, (feat, mask, pos_embed) in enumerate( + zip(mlvl_feats, mlvl_masks, mlvl_pos_embeds)): + bs, c, h, w = feat.shape + spatial_shape = (h, w) + spatial_shapes.append(spatial_shape) + feat = feat.flatten(2).transpose(1, 2) + mask = mask.flatten(1) + pos_embed = pos_embed.flatten(2).transpose(1, 2) + lvl_pos_embed = pos_embed + self.level_embeds[lvl].view(1, 1, -1) + lvl_pos_embed_flatten.append(lvl_pos_embed) + feat_flatten.append(feat) + mask_flatten.append(mask) + feat_flatten = torch.cat(feat_flatten, 1) + mask_flatten = torch.cat(mask_flatten, 1) + lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) + spatial_shapes = torch.as_tensor( + spatial_shapes, dtype=torch.long, device=feat_flatten.device) + level_start_index = torch.cat((spatial_shapes.new_zeros( + (1, )), spatial_shapes.prod(1).cumsum(0)[:-1])) + valid_ratios = torch.stack( + [self.get_valid_ratio(m) for m in mlvl_masks], 1) + + reference_points = \ + self.get_reference_points(spatial_shapes, + valid_ratios, + device=feat.device) + + feat_flatten = feat_flatten.permute(1, 0, 2) # (H*W, bs, embed_dims) + lvl_pos_embed_flatten = lvl_pos_embed_flatten.permute( + 1, 0, 2) # (H*W, bs, embed_dims) + memory = self.encoder( + query=feat_flatten, + key=None, + value=None, + query_pos=lvl_pos_embed_flatten, + query_key_padding_mask=mask_flatten, + spatial_shapes=spatial_shapes, + reference_points=reference_points, + level_start_index=level_start_index, + valid_ratios=valid_ratios, + **kwargs) + + memory = memory.permute(1, 0, 2) + bs, _, c = memory.shape + if self.as_two_stage: + output_memory, output_proposals = \ + self.gen_encoder_output_proposals( + memory, mask_flatten, spatial_shapes) + enc_outputs_class = cls_branches[self.decoder.num_layers]( + output_memory) + enc_outputs_coord_unact = \ + reg_branches[ + self.decoder.num_layers](output_memory) + output_proposals + + topk = self.two_stage_num_proposals + topk_proposals = torch.topk( + enc_outputs_class[..., 0], topk, dim=1)[1] + topk_coords_unact = torch.gather( + enc_outputs_coord_unact, 1, + topk_proposals.unsqueeze(-1).repeat(1, 1, 4)) + topk_coords_unact = topk_coords_unact.detach() + reference_points = topk_coords_unact.sigmoid() + init_reference_out = reference_points + pos_trans_out = self.pos_trans_norm( + self.pos_trans(self.get_proposal_pos_embed(topk_coords_unact))) + query_pos, query = torch.split(pos_trans_out, c, dim=2) + else: + query_pos, query = torch.split(query_embed, c, dim=1) + query_pos = query_pos.unsqueeze(0).expand(bs, -1, -1) + query = query.unsqueeze(0).expand(bs, -1, -1) + reference_points = self.reference_points(query_pos).sigmoid() + init_reference_out = reference_points + + # decoder + query = query.permute(1, 0, 2) + memory = memory.permute(1, 0, 2) + query_pos = query_pos.permute(1, 0, 2) + inter_states, inter_references = self.decoder( + query=query, + key=None, + value=memory, + query_pos=query_pos, + key_padding_mask=mask_flatten, + reference_points=reference_points, + spatial_shapes=spatial_shapes, + level_start_index=level_start_index, + valid_ratios=valid_ratios, + reg_branches=reg_branches, + **kwargs) + + inter_references_out = inter_references + if self.as_two_stage: + return inter_states, init_reference_out,\ + inter_references_out, enc_outputs_class,\ + enc_outputs_coord_unact + return inter_states, init_reference_out, \ + inter_references_out, None, None + + +@TRANSFORMER.register_module() +class DynamicConv(BaseModule): + """Implements Dynamic Convolution. + + This module generate parameters for each sample and + use bmm to implement 1*1 convolution. Code is modified + from the `official github repo `_ . + + Args: + in_channels (int): The input feature channel. + Defaults to 256. + feat_channels (int): The inner feature channel. + Defaults to 64. + out_channels (int, optional): The output feature channel. + When not specified, it will be set to `in_channels` + by default + input_feat_shape (int): The shape of input feature. + Defaults to 7. + act_cfg (dict): The activation config for DynamicConv. + norm_cfg (dict): Config dict for normalization layer. Default + layer normalization. + init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. + Default: None. + """ + + def __init__(self, + in_channels=256, + feat_channels=64, + out_channels=None, + input_feat_shape=7, + act_cfg=dict(type='ReLU', inplace=True), + norm_cfg=dict(type='LN'), + init_cfg=None): + super(DynamicConv, self).__init__(init_cfg) + self.in_channels = in_channels + self.feat_channels = feat_channels + self.out_channels_raw = out_channels + self.input_feat_shape = input_feat_shape + self.act_cfg = act_cfg + self.norm_cfg = norm_cfg + self.out_channels = out_channels if out_channels else in_channels + + self.num_params_in = self.in_channels * self.feat_channels + self.num_params_out = self.out_channels * self.feat_channels + self.dynamic_layer = nn.Linear( + self.in_channels, self.num_params_in + self.num_params_out) + + self.norm_in = build_norm_layer(norm_cfg, self.feat_channels)[1] + self.norm_out = build_norm_layer(norm_cfg, self.out_channels)[1] + + self.activation = build_activation_layer(act_cfg) + + num_output = self.out_channels * input_feat_shape**2 + self.fc_layer = nn.Linear(num_output, self.out_channels) + self.fc_norm = build_norm_layer(norm_cfg, self.out_channels)[1] + + def forward(self, param_feature, input_feature): + """Forward function for `DynamicConv`. + + Args: + param_feature (Tensor): The feature can be used + to generate the parameter, has shape + (num_all_proposals, in_channels). + input_feature (Tensor): Feature that + interact with parameters, has shape + (num_all_proposals, in_channels, H, W). + + Returns: + Tensor: The output feature has shape + (num_all_proposals, out_channels). + """ + num_proposals = param_feature.size(0) + input_feature = input_feature.view(num_proposals, self.in_channels, + -1).permute(2, 0, 1) + + input_feature = input_feature.permute(1, 0, 2) + parameters = self.dynamic_layer(param_feature) + + param_in = parameters[:, :self.num_params_in].view( + -1, self.in_channels, self.feat_channels) + param_out = parameters[:, -self.num_params_out:].view( + -1, self.feat_channels, self.out_channels) + + # input_feature has shape (num_all_proposals, H*W, in_channels) + # param_in has shape (num_all_proposals, in_channels, feat_channels) + # feature has shape (num_all_proposals, H*W, feat_channels) + features = torch.bmm(input_feature, param_in) + features = self.norm_in(features) + features = self.activation(features) + + # param_out has shape (batch_size, feat_channels, out_channels) + features = torch.bmm(features, param_out) + features = self.norm_out(features) + features = self.activation(features) + + features = features.flatten(1) + features = self.fc_layer(features) + features = self.fc_norm(features) + features = self.activation(features) + + return features + + +@TRANSFORMER.register_module() +class DynamicConvV2(BaseModule): + """Implements Dynamic Convolution. + + This module generate parameters for each sample and + use bmm to implement 1*1 convolution. Code is modified + from the `official github repo `_ . + + Args: + in_channels (int): The input feature channel. + Defaults to 256. + feat_channels (int): The inner feature channel. + Defaults to 64. + out_channels (int, optional): The output feature channel. + When not specified, it will be set to `in_channels` + by default + input_feat_shape (int): The shape of input feature. + Defaults to 7. + act_cfg (dict): The activation config for DynamicConv. + norm_cfg (dict): Config dict for normalization layer. Default + layer normalization. + init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. + Default: None. + """ + + def __init__(self, + in_channels=256, + feat_channels=64, + out_channels=None, + input_feat_shape=7, + act_cfg=dict(type='ReLU', inplace=True), + norm_cfg=dict(type='LN'), + init_cfg=None): + super(DynamicConvV2, self).__init__(init_cfg) + self.in_channels = in_channels + self.feat_channels = feat_channels + self.out_channels_raw = out_channels + self.input_feat_shape = input_feat_shape + self.act_cfg = act_cfg + self.norm_cfg = norm_cfg + self.out_channels = out_channels if out_channels else in_channels + + self.num_params_in = self.in_channels * self.feat_channels + self.num_params_out = self.out_channels * self.feat_channels + self.dynamic_layer = nn.Linear( + self.in_channels, self.num_params_in + self.num_params_out) + + self.norm_in = build_norm_layer(norm_cfg, self.feat_channels)[1] + self.norm_out = build_norm_layer(norm_cfg, self.out_channels)[1] + + self.activation = build_activation_layer(act_cfg) + + num_output = self.out_channels * input_feat_shape**2 + self.fc_layer = nn.Linear(num_output, self.out_channels) + self.fc_norm = build_norm_layer(norm_cfg, self.out_channels)[1] + + def forward(self, param_feature, input_feature): + """Forward function for `DynamicConv`. + + Args: + param_feature (Tensor): The feature can be used + to generate the parameter, has shape + (num_all_proposals, in_channels). + input_feature (Tensor): Feature that + interact with parameters, has shape + (num_all_proposals, in_channels, H, W). + + Returns: + Tensor: The output feature has shape + (num_all_proposals, out_channels). + """ + num_proposals = param_feature.size(0) + input_feature = input_feature.view(num_proposals, self.in_channels, + -1).permute(2, 0, 1) + + input_feature = input_feature.permute(1, 0, 2) + parameters = self.dynamic_layer(param_feature) + + param_in = parameters[:, :self.num_params_in].view( + -1, self.in_channels, self.feat_channels) + param_out = parameters[:, -self.num_params_out:].view( + -1, self.feat_channels, self.out_channels) + + # input_feature has shape (num_all_proposals, H*W, in_channels) + # param_in has shape (num_all_proposals, in_channels, feat_channels) + # feature has shape (num_all_proposals, H*W, feat_channels) + features = torch.bmm(input_feature, param_in) + features = self.norm_in(features) + features = self.activation(features) + + # param_out has shape (batch_size, feat_channels, out_channels) + features = torch.bmm(features, param_out) + features = self.norm_out(features) + features = self.activation(features) + + features = features.flatten(1) + features = self.fc_layer(features) + features = self.fc_norm(features) + features = self.activation(features) + + return features diff --git a/mmdet/temp.txt b/mmdet/temp.txt new file mode 100644 index 0000000..903cdce --- /dev/null +++ b/mmdet/temp.txt @@ -0,0 +1 @@ +2021-09-05 08:52:14,820 - mmdet - INFO - Epoch [2][6200/7330] lr: 2.500e-05, eta: 8:16:53, time: 0.393, data_time: 0.011, memory: 5936, stage0_loss_cls: 0.6445, stage0_pos_acc: 76.4297, stage0_loss_bbox: 0.5958, stage0_loss_iou: 1.2016, stage1_loss_cls: 0.5746, stage1_pos_acc: 82.5276, stage1_loss_bbox: 0.3948, stage1_loss_iou: 0.8817, stage2_loss_cls: 0.5487, stage2_pos_acc: 83.2970, stage2_loss_bbox: 0.3510, stage2_loss_iou: 0.7903, stage3_loss_cls: 0.5386, stage3_pos_acc: 83.6222, stage3_loss_bbox: 0.3360, stage3_loss_iou: 0.7589, stage4_loss_cls: 0.5294, stage4_pos_acc: 84.0369, stage4_loss_bbox: 0.3378, stage4_loss_iou: 0.7592, stage5_loss_cls: 0.5311, stage5_pos_acc: 84.3230, stage5_loss_bbox: 0.3365, stage5_loss_iou: 0.7567, loss: 10.8672, grad_norm: 37.3838 \ No newline at end of file diff --git a/mmdet/utils/__init__.py b/mmdet/utils/__init__.py new file mode 100644 index 0000000..ac489e2 --- /dev/null +++ b/mmdet/utils/__init__.py @@ -0,0 +1,4 @@ +from .collect_env import collect_env +from .logger import get_root_logger + +__all__ = ['get_root_logger', 'collect_env'] diff --git a/mmdet/utils/__pycache__/__init__.cpython-37.pyc b/mmdet/utils/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..d9d0152 Binary files /dev/null and b/mmdet/utils/__pycache__/__init__.cpython-37.pyc differ diff --git a/mmdet/utils/__pycache__/collect_env.cpython-37.pyc b/mmdet/utils/__pycache__/collect_env.cpython-37.pyc new file mode 100644 index 0000000..bdc2169 Binary files /dev/null and b/mmdet/utils/__pycache__/collect_env.cpython-37.pyc differ diff --git a/mmdet/utils/__pycache__/contextmanagers.cpython-37.pyc b/mmdet/utils/__pycache__/contextmanagers.cpython-37.pyc new file mode 100644 index 0000000..f732802 Binary files /dev/null and b/mmdet/utils/__pycache__/contextmanagers.cpython-37.pyc differ diff --git a/mmdet/utils/__pycache__/logger.cpython-37.pyc b/mmdet/utils/__pycache__/logger.cpython-37.pyc new file mode 100644 index 0000000..4c1b724 Binary files /dev/null and b/mmdet/utils/__pycache__/logger.cpython-37.pyc differ diff --git a/mmdet/utils/__pycache__/util_mixins.cpython-37.pyc b/mmdet/utils/__pycache__/util_mixins.cpython-37.pyc new file mode 100644 index 0000000..f7986cb Binary files /dev/null and b/mmdet/utils/__pycache__/util_mixins.cpython-37.pyc differ diff --git a/mmdet/utils/collect_env.py b/mmdet/utils/collect_env.py new file mode 100644 index 0000000..89c064a --- /dev/null +++ b/mmdet/utils/collect_env.py @@ -0,0 +1,16 @@ +from mmcv.utils import collect_env as collect_base_env +from mmcv.utils import get_git_hash + +import mmdet + + +def collect_env(): + """Collect the information of the running environments.""" + env_info = collect_base_env() + env_info['MMDetection'] = mmdet.__version__ + '+' + get_git_hash()[:7] + return env_info + + +if __name__ == '__main__': + for name, val in collect_env().items(): + print(f'{name}: {val}') diff --git a/mmdet/utils/contextmanagers.py b/mmdet/utils/contextmanagers.py new file mode 100644 index 0000000..38a6392 --- /dev/null +++ b/mmdet/utils/contextmanagers.py @@ -0,0 +1,121 @@ +import asyncio +import contextlib +import logging +import os +import time +from typing import List + +import torch + +logger = logging.getLogger(__name__) + +DEBUG_COMPLETED_TIME = bool(os.environ.get('DEBUG_COMPLETED_TIME', False)) + + +@contextlib.asynccontextmanager +async def completed(trace_name='', + name='', + sleep_interval=0.05, + streams: List[torch.cuda.Stream] = None): + """Async context manager that waits for work to complete on given CUDA + streams.""" + if not torch.cuda.is_available(): + yield + return + + stream_before_context_switch = torch.cuda.current_stream() + if not streams: + streams = [stream_before_context_switch] + else: + streams = [s if s else stream_before_context_switch for s in streams] + + end_events = [ + torch.cuda.Event(enable_timing=DEBUG_COMPLETED_TIME) for _ in streams + ] + + if DEBUG_COMPLETED_TIME: + start = torch.cuda.Event(enable_timing=True) + stream_before_context_switch.record_event(start) + + cpu_start = time.monotonic() + logger.debug('%s %s starting, streams: %s', trace_name, name, streams) + grad_enabled_before = torch.is_grad_enabled() + try: + yield + finally: + current_stream = torch.cuda.current_stream() + assert current_stream == stream_before_context_switch + + if DEBUG_COMPLETED_TIME: + cpu_end = time.monotonic() + for i, stream in enumerate(streams): + event = end_events[i] + stream.record_event(event) + + grad_enabled_after = torch.is_grad_enabled() + + # observed change of torch.is_grad_enabled() during concurrent run of + # async_test_bboxes code + assert (grad_enabled_before == grad_enabled_after + ), 'Unexpected is_grad_enabled() value change' + + are_done = [e.query() for e in end_events] + logger.debug('%s %s completed: %s streams: %s', trace_name, name, + are_done, streams) + with torch.cuda.stream(stream_before_context_switch): + while not all(are_done): + await asyncio.sleep(sleep_interval) + are_done = [e.query() for e in end_events] + logger.debug( + '%s %s completed: %s streams: %s', + trace_name, + name, + are_done, + streams, + ) + + current_stream = torch.cuda.current_stream() + assert current_stream == stream_before_context_switch + + if DEBUG_COMPLETED_TIME: + cpu_time = (cpu_end - cpu_start) * 1000 + stream_times_ms = '' + for i, stream in enumerate(streams): + elapsed_time = start.elapsed_time(end_events[i]) + stream_times_ms += f' {stream} {elapsed_time:.2f} ms' + logger.info('%s %s %.2f ms %s', trace_name, name, cpu_time, + stream_times_ms) + + +@contextlib.asynccontextmanager +async def concurrent(streamqueue: asyncio.Queue, + trace_name='concurrent', + name='stream'): + """Run code concurrently in different streams. + + :param streamqueue: asyncio.Queue instance. + + Queue tasks define the pool of streams used for concurrent execution. + """ + if not torch.cuda.is_available(): + yield + return + + initial_stream = torch.cuda.current_stream() + + with torch.cuda.stream(initial_stream): + stream = await streamqueue.get() + assert isinstance(stream, torch.cuda.Stream) + + try: + with torch.cuda.stream(stream): + logger.debug('%s %s is starting, stream: %s', trace_name, name, + stream) + yield + current = torch.cuda.current_stream() + assert current == stream + logger.debug('%s %s has finished, stream: %s', trace_name, + name, stream) + finally: + streamqueue.task_done() + streamqueue.put_nowait(stream) diff --git a/mmdet/utils/logger.py b/mmdet/utils/logger.py new file mode 100644 index 0000000..6fc6e6b --- /dev/null +++ b/mmdet/utils/logger.py @@ -0,0 +1,19 @@ +import logging + +from mmcv.utils import get_logger + + +def get_root_logger(log_file=None, log_level=logging.INFO): + """Get root logger. + + Args: + log_file (str, optional): File path of log. Defaults to None. + log_level (int, optional): The level of logger. + Defaults to logging.INFO. + + Returns: + :obj:`logging.Logger`: The obtained logger + """ + logger = get_logger(name='mmdet', log_file=log_file, log_level=log_level) + + return logger diff --git a/mmdet/utils/profiling.py b/mmdet/utils/profiling.py new file mode 100644 index 0000000..4be9222 --- /dev/null +++ b/mmdet/utils/profiling.py @@ -0,0 +1,39 @@ +import contextlib +import sys +import time + +import torch + +if sys.version_info >= (3, 7): + + @contextlib.contextmanager + def profile_time(trace_name, + name, + enabled=True, + stream=None, + end_stream=None): + """Print time spent by CPU and GPU. + + Useful as a temporary context manager to find sweet spots of code + suitable for async implementation. + """ + if (not enabled) or not torch.cuda.is_available(): + yield + return + stream = stream if stream else torch.cuda.current_stream() + end_stream = end_stream if end_stream else stream + start = torch.cuda.Event(enable_timing=True) + end = torch.cuda.Event(enable_timing=True) + stream.record_event(start) + try: + cpu_start = time.monotonic() + yield + finally: + cpu_end = time.monotonic() + end_stream.record_event(end) + end.synchronize() + cpu_time = (cpu_end - cpu_start) * 1000 + gpu_time = start.elapsed_time(end) + msg = f'{trace_name} {name} cpu_time {cpu_time:.2f} ms ' + msg += f'gpu_time {gpu_time:.2f} ms stream {stream}' + print(msg, end_stream) diff --git a/mmdet/utils/util_mixins.py b/mmdet/utils/util_mixins.py new file mode 100644 index 0000000..69669a3 --- /dev/null +++ b/mmdet/utils/util_mixins.py @@ -0,0 +1,104 @@ +"""This module defines the :class:`NiceRepr` mixin class, which defines a +``__repr__`` and ``__str__`` method that only depend on a custom ``__nice__`` +method, which you must define. This means you only have to overload one +function instead of two. Furthermore, if the object defines a ``__len__`` +method, then the ``__nice__`` method defaults to something sensible, otherwise +it is treated as abstract and raises ``NotImplementedError``. + +To use simply have your object inherit from :class:`NiceRepr` +(multi-inheritance should be ok). + +This code was copied from the ubelt library: https://github.com/Erotemic/ubelt + +Example: + >>> # Objects that define __nice__ have a default __str__ and __repr__ + >>> class Student(NiceRepr): + ... def __init__(self, name): + ... self.name = name + ... def __nice__(self): + ... return self.name + >>> s1 = Student('Alice') + >>> s2 = Student('Bob') + >>> print(f's1 = {s1}') + >>> print(f's2 = {s2}') + s1 = + s2 = + +Example: + >>> # Objects that define __len__ have a default __nice__ + >>> class Group(NiceRepr): + ... def __init__(self, data): + ... self.data = data + ... def __len__(self): + ... return len(self.data) + >>> g = Group([1, 2, 3]) + >>> print(f'g = {g}') + g = +""" +import warnings + + +class NiceRepr(object): + """Inherit from this class and define ``__nice__`` to "nicely" print your + objects. + + Defines ``__str__`` and ``__repr__`` in terms of ``__nice__`` function + Classes that inherit from :class:`NiceRepr` should redefine ``__nice__``. + If the inheriting class has a ``__len__``, method then the default + ``__nice__`` method will return its length. + + Example: + >>> class Foo(NiceRepr): + ... def __nice__(self): + ... return 'info' + >>> foo = Foo() + >>> assert str(foo) == '' + >>> assert repr(foo).startswith('>> class Bar(NiceRepr): + ... pass + >>> bar = Bar() + >>> import pytest + >>> with pytest.warns(None) as record: + >>> assert 'object at' in str(bar) + >>> assert 'object at' in repr(bar) + + Example: + >>> class Baz(NiceRepr): + ... def __len__(self): + ... return 5 + >>> baz = Baz() + >>> assert str(baz) == '' + """ + + def __nice__(self): + """str: a "nice" summary string describing this module""" + if hasattr(self, '__len__'): + # It is a common pattern for objects to use __len__ in __nice__ + # As a convenience we define a default __nice__ for these objects + return str(len(self)) + else: + # In all other cases force the subclass to overload __nice__ + raise NotImplementedError( + f'Define the __nice__ method for {self.__class__!r}') + + def __repr__(self): + """str: the string of the module""" + try: + nice = self.__nice__() + classname = self.__class__.__name__ + return f'<{classname}({nice}) at {hex(id(self))}>' + except NotImplementedError as ex: + warnings.warn(str(ex), category=RuntimeWarning) + return object.__repr__(self) + + def __str__(self): + """str: the string of the module""" + try: + classname = self.__class__.__name__ + nice = self.__nice__() + return f'<{classname}({nice})>' + except NotImplementedError as ex: + warnings.warn(str(ex), category=RuntimeWarning) + return object.__repr__(self) diff --git a/mmdet/utils/util_random.py b/mmdet/utils/util_random.py new file mode 100644 index 0000000..e313e99 --- /dev/null +++ b/mmdet/utils/util_random.py @@ -0,0 +1,33 @@ +"""Helpers for random number generators.""" +import numpy as np + + +def ensure_rng(rng=None): + """Coerces input into a random number generator. + + If the input is None, then a global random state is returned. + + If the input is a numeric value, then that is used as a seed to construct a + random state. Otherwise the input is returned as-is. + + Adapted from [1]_. + + Args: + rng (int | numpy.random.RandomState | None): + if None, then defaults to the global rng. Otherwise this can be an + integer or a RandomState class + Returns: + (numpy.random.RandomState) : rng - + a numpy random number generator + + References: + .. [1] https://gitlab.kitware.com/computer-vision/kwarray/blob/master/kwarray/util_random.py#L270 # noqa: E501 + """ + + if rng is None: + rng = np.random.mtrand._rand + elif isinstance(rng, int): + rng = np.random.RandomState(rng) + else: + rng = rng + return rng diff --git a/mmdet/version.py b/mmdet/version.py new file mode 100644 index 0000000..a96372a --- /dev/null +++ b/mmdet/version.py @@ -0,0 +1,19 @@ +# Copyright (c) Open-MMLab. All rights reserved. + +__version__ = '2.12.0' +short_version = __version__ + + +def parse_version_info(version_str): + version_info = [] + for x in version_str.split('.'): + if x.isdigit(): + version_info.append(int(x)) + elif x.find('rc') != -1: + patch_version = x.split('rc') + version_info.append(int(patch_version[0])) + version_info.append(f'rc{patch_version[1]}') + return tuple(version_info) + + +version_info = parse_version_info(__version__) diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 0000000..9796e87 --- /dev/null +++ b/pytest.ini @@ -0,0 +1,7 @@ +[pytest] +addopts = --xdoctest --xdoctest-style=auto +norecursedirs = .git ignore build __pycache__ data docker docs .eggs + +filterwarnings= default + ignore:.*No cfgstr given in Cacher constructor or call.*:Warning + ignore:.*Define the __nice__ method for.*:Warning diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..6981bd7 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,4 @@ +-r requirements/build.txt +-r requirements/optional.txt +-r requirements/runtime.txt +-r requirements/tests.txt diff --git a/requirements/build.txt b/requirements/build.txt new file mode 100644 index 0000000..8155829 --- /dev/null +++ b/requirements/build.txt @@ -0,0 +1,3 @@ +# These must be installed before building mmdetection +cython +numpy diff --git a/requirements/docs.txt b/requirements/docs.txt new file mode 100644 index 0000000..89fbf86 --- /dev/null +++ b/requirements/docs.txt @@ -0,0 +1,4 @@ +recommonmark +sphinx +sphinx_markdown_tables +sphinx_rtd_theme diff --git a/requirements/optional.txt b/requirements/optional.txt new file mode 100644 index 0000000..ac9688b --- /dev/null +++ b/requirements/optional.txt @@ -0,0 +1,5 @@ +albumentations>=0.3.2 +cityscapesscripts +imagecorruptions +scipy +sklearn diff --git a/requirements/readthedocs.txt b/requirements/readthedocs.txt new file mode 100644 index 0000000..0542bfc --- /dev/null +++ b/requirements/readthedocs.txt @@ -0,0 +1,3 @@ +mmcv +torch +torchvision diff --git a/requirements/runtime.txt b/requirements/runtime.txt new file mode 100644 index 0000000..17efe68 --- /dev/null +++ b/requirements/runtime.txt @@ -0,0 +1,6 @@ +matplotlib +numpy +pycocotools; platform_system == "Linux" +pycocotools-windows; platform_system == "Windows" +six +terminaltables diff --git a/requirements/tests.txt b/requirements/tests.txt new file mode 100644 index 0000000..5f3de01 --- /dev/null +++ b/requirements/tests.txt @@ -0,0 +1,13 @@ +asynctest +codecov +flake8 +interrogate +isort==4.3.21 +# Note: used for kwarray.group_items, this may be ported to mmcv in the future. +kwarray +onnx==1.7.0 +onnxruntime==1.5.1 +pytest +ubelt +xdoctest>=0.10.0 +yapf diff --git a/resources/coco_test_12510.jpg b/resources/coco_test_12510.jpg new file mode 100644 index 0000000..1271ae1 Binary files /dev/null and b/resources/coco_test_12510.jpg differ diff --git a/resources/corruptions_sev_3.png b/resources/corruptions_sev_3.png new file mode 100644 index 0000000..bbbd19a Binary files /dev/null and b/resources/corruptions_sev_3.png differ diff --git a/resources/data_pipeline.png b/resources/data_pipeline.png new file mode 100644 index 0000000..6ac3fee Binary files /dev/null and b/resources/data_pipeline.png differ diff --git a/resources/loss_curve.png b/resources/loss_curve.png new file mode 100644 index 0000000..0242555 Binary files /dev/null and b/resources/loss_curve.png differ diff --git a/resources/mmdet-logo.png b/resources/mmdet-logo.png new file mode 100644 index 0000000..a0b6fbd Binary files /dev/null and b/resources/mmdet-logo.png differ diff --git a/resources/qq_group_qrcode.jpg b/resources/qq_group_qrcode.jpg new file mode 100644 index 0000000..4173474 Binary files /dev/null and b/resources/qq_group_qrcode.jpg differ diff --git a/resources/zhihu_qrcode.jpg b/resources/zhihu_qrcode.jpg new file mode 100644 index 0000000..c745fb0 Binary files /dev/null and b/resources/zhihu_qrcode.jpg differ diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 0000000..78eb65e --- /dev/null +++ b/setup.cfg @@ -0,0 +1,13 @@ +[isort] +line_length = 79 +multi_line_output = 0 +known_standard_library = setuptools +known_first_party = mmdet +known_third_party = PIL,asynctest,cityscapesscripts,cv2,gather_models,matplotlib,mmcv,numpy,onnx,onnxruntime,pycocotools,pytest,seaborn,six,terminaltables,torch,ts +no_lines_before = STDLIB,LOCALFOLDER +default_section = THIRDPARTY + +[yapf] +BASED_ON_STYLE = pep8 +BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true +SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true diff --git a/setup.py b/setup.py new file mode 100755 index 0000000..55eea6b --- /dev/null +++ b/setup.py @@ -0,0 +1,161 @@ +#!/usr/bin/env python +import os +from setuptools import find_packages, setup + +import torch +from torch.utils.cpp_extension import (BuildExtension, CppExtension, + CUDAExtension) + + +def readme(): + with open('README.md', encoding='utf-8') as f: + content = f.read() + return content + + +version_file = 'mmdet/version.py' + + +def get_version(): + with open(version_file, 'r') as f: + exec(compile(f.read(), version_file, 'exec')) + return locals()['__version__'] + + +def make_cuda_ext(name, module, sources, sources_cuda=[]): + + define_macros = [] + extra_compile_args = {'cxx': []} + + if torch.cuda.is_available() or os.getenv('FORCE_CUDA', '0') == '1': + define_macros += [('WITH_CUDA', None)] + extension = CUDAExtension + extra_compile_args['nvcc'] = [ + '-D__CUDA_NO_HALF_OPERATORS__', + '-D__CUDA_NO_HALF_CONVERSIONS__', + '-D__CUDA_NO_HALF2_OPERATORS__', + ] + sources += sources_cuda + else: + print(f'Compiling {name} without CUDA') + extension = CppExtension + + return extension( + name=f'{module}.{name}', + sources=[os.path.join(*module.split('.'), p) for p in sources], + define_macros=define_macros, + extra_compile_args=extra_compile_args) + + +def parse_requirements(fname='requirements.txt', with_version=True): + """Parse the package dependencies listed in a requirements file but strips + specific versioning information. + + Args: + fname (str): path to requirements file + with_version (bool, default=False): if True include version specs + + Returns: + List[str]: list of requirements items + + CommandLine: + python -c "import setup; print(setup.parse_requirements())" + """ + import sys + from os.path import exists + import re + require_fpath = fname + + def parse_line(line): + """Parse information from a line in a requirements text file.""" + if line.startswith('-r '): + # Allow specifying requirements in other files + target = line.split(' ')[1] + for info in parse_require_file(target): + yield info + else: + info = {'line': line} + if line.startswith('-e '): + info['package'] = line.split('#egg=')[1] + elif '@git+' in line: + info['package'] = line + else: + # Remove versioning from the package + pat = '(' + '|'.join(['>=', '==', '>']) + ')' + parts = re.split(pat, line, maxsplit=1) + parts = [p.strip() for p in parts] + + info['package'] = parts[0] + if len(parts) > 1: + op, rest = parts[1:] + if ';' in rest: + # Handle platform specific dependencies + # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies + version, platform_deps = map(str.strip, + rest.split(';')) + info['platform_deps'] = platform_deps + else: + version = rest # NOQA + info['version'] = (op, version) + yield info + + def parse_require_file(fpath): + with open(fpath, 'r') as f: + for line in f.readlines(): + line = line.strip() + if line and not line.startswith('#'): + for info in parse_line(line): + yield info + + def gen_packages_items(): + if exists(require_fpath): + for info in parse_require_file(require_fpath): + parts = [info['package']] + if with_version and 'version' in info: + parts.extend(info['version']) + if not sys.version.startswith('3.4'): + # apparently package_deps are broken in 3.4 + platform_deps = info.get('platform_deps') + if platform_deps is not None: + parts.append(';' + platform_deps) + item = ''.join(parts) + yield item + + packages = list(gen_packages_items()) + return packages + + +if __name__ == '__main__': + setup( + name='mmdet', + version=get_version(), + description='OpenMMLab Detection Toolbox and Benchmark', + long_description=readme(), + long_description_content_type='text/markdown', + author='OpenMMLab', + author_email='openmmlab@gmail.com', + keywords='computer vision, object detection', + url='https://github.com/open-mmlab/mmdetection', + packages=find_packages(exclude=('configs', 'tools', 'demo')), + classifiers=[ + 'Development Status :: 5 - Production/Stable', + 'License :: OSI Approved :: Apache Software License', + 'Operating System :: OS Independent', + 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3.6', + 'Programming Language :: Python :: 3.7', + 'Programming Language :: Python :: 3.8', + ], + license='Apache License 2.0', + setup_requires=parse_requirements('requirements/build.txt'), + tests_require=parse_requirements('requirements/tests.txt'), + install_requires=parse_requirements('requirements/runtime.txt'), + extras_require={ + 'all': parse_requirements('requirements.txt'), + 'tests': parse_requirements('requirements/tests.txt'), + 'build': parse_requirements('requirements/build.txt'), + 'optional': parse_requirements('requirements/optional.txt'), + }, + ext_modules=[], + cmdclass={'build_ext': BuildExtension}, + zip_safe=False) diff --git a/test_module.py b/test_module.py new file mode 100644 index 0000000..091fb10 --- /dev/null +++ b/test_module.py @@ -0,0 +1,96 @@ +import torch +from mmdet.models.dense_heads import query_generator + +from mmdet.models.roi_heads.bbox_heads.adaptive_mixing_operator import AdaptiveMixing +from mmdet.models.dense_heads.query_generator import InitialQueryGenerator +from mmdet.models.detectors import QueryBased + +num_stages = 6 +num_proposals = 100 +QUERY_DIM = 256 +FEAT_DIM = 256 +FF_DIM = 2048 + +# P_in for spatial mixing in the paper. +in_points_list = [32, ] * num_stages + +# P_out for spatial mixing in the paper. Also named as `out_points` in this codebase. +out_patterns_list = [128, ] * num_stages + +# G for the mixer grouping in the paper. Also named as n_head in this codebase. +n_group_list = [4, ] * num_stages + +detector = QueryBased( + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='ChannelMapping', + in_channels=[256, 512, 1024, 2048], + out_channels=FEAT_DIM, + start_level=0, + add_extra_convs='on_output', + num_outs=4), + rpn_head=dict( + type='InitialQueryGenerator', + num_query=num_proposals, + content_dim=QUERY_DIM), + roi_head=dict( + type='AdaMixerDecoder', + num_stages=num_stages, + stage_loss_weights=[1] * num_stages, + content_dim=QUERY_DIM, + bbox_head=[ + dict( + type='AdaMixerDecoderStage', + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=1, + feedforward_channels=FF_DIM, + content_dim=QUERY_DIM, + feat_channels=FEAT_DIM, + dropout=0.0, + in_points=in_points_list[stage_idx], + out_points=out_patterns_list[stage_idx], + n_groups=n_group_list[stage_idx], + ffn_act_cfg=dict(type='ReLU', inplace=True), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + # NOTE: The following argument is a placeholder to hack the code. No real effects for decoding or updating bounding boxes. + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=False, + target_means=[0., 0., 0., 0.], + target_stds=[0.5, 0.5, 1., 1.])) for stage_idx in range(num_stages) + ]), + # training and testing settings + train_cfg=dict( + rpn=None, + rcnn=[ + dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0), + iou_cost=dict(type='IoUCost', iou_mode='giou', + weight=2.0)), + sampler=dict(type='PseudoSampler'), + pos_weight=1) for _ in range(num_stages) + ]), + test_cfg=dict(rpn=None, rcnn=dict(max_per_img=num_proposals)) +) diff --git a/tests/test_data/test_datasets/test_coco_dataset.py b/tests/test_data/test_datasets/test_coco_dataset.py new file mode 100644 index 0000000..13b6c7f --- /dev/null +++ b/tests/test_data/test_datasets/test_coco_dataset.py @@ -0,0 +1,57 @@ +import os.path as osp +import tempfile + +import mmcv +import pytest + +from mmdet.datasets import CocoDataset + + +def _create_ids_error_coco_json(json_name): + image = { + 'id': 0, + 'width': 640, + 'height': 640, + 'file_name': 'fake_name.jpg', + } + + annotation_1 = { + 'id': 1, + 'image_id': 0, + 'category_id': 0, + 'area': 400, + 'bbox': [50, 60, 20, 20], + 'iscrowd': 0, + } + + annotation_2 = { + 'id': 1, + 'image_id': 0, + 'category_id': 0, + 'area': 900, + 'bbox': [100, 120, 30, 30], + 'iscrowd': 0, + } + + categories = [{ + 'id': 0, + 'name': 'car', + 'supercategory': 'car', + }] + + fake_json = { + 'images': [image], + 'annotations': [annotation_1, annotation_2], + 'categories': categories + } + mmcv.dump(fake_json, json_name) + + +def test_coco_annotation_ids_unique(): + tmp_dir = tempfile.TemporaryDirectory() + fake_json_file = osp.join(tmp_dir.name, 'fake_data.json') + _create_ids_error_coco_json(fake_json_file) + + # test annotation ids not unique error + with pytest.raises(AssertionError): + CocoDataset(ann_file=fake_json_file, classes=('car', ), pipeline=[]) diff --git a/tests/test_data/test_datasets/test_common.py b/tests/test_data/test_datasets/test_common.py new file mode 100644 index 0000000..6d9e3fd --- /dev/null +++ b/tests/test_data/test_datasets/test_common.py @@ -0,0 +1,357 @@ +import copy +import logging +import os +import os.path as osp +import tempfile +from unittest.mock import MagicMock, patch + +import mmcv +import numpy as np +import pytest +import torch +import torch.nn as nn +from mmcv.runner import EpochBasedRunner +from torch.utils.data import DataLoader + +from mmdet.core.evaluation import DistEvalHook, EvalHook +from mmdet.datasets import DATASETS, CocoDataset, CustomDataset, build_dataset + + +def _create_dummy_coco_json(json_name): + image = { + 'id': 0, + 'width': 640, + 'height': 640, + 'file_name': 'fake_name.jpg', + } + + annotation_1 = { + 'id': 1, + 'image_id': 0, + 'category_id': 0, + 'area': 400, + 'bbox': [50, 60, 20, 20], + 'iscrowd': 0, + } + + annotation_2 = { + 'id': 2, + 'image_id': 0, + 'category_id': 0, + 'area': 900, + 'bbox': [100, 120, 30, 30], + 'iscrowd': 0, + } + + annotation_3 = { + 'id': 3, + 'image_id': 0, + 'category_id': 0, + 'area': 1600, + 'bbox': [150, 160, 40, 40], + 'iscrowd': 0, + } + + annotation_4 = { + 'id': 4, + 'image_id': 0, + 'category_id': 0, + 'area': 10000, + 'bbox': [250, 260, 100, 100], + 'iscrowd': 0, + } + + categories = [{ + 'id': 0, + 'name': 'car', + 'supercategory': 'car', + }] + + fake_json = { + 'images': [image], + 'annotations': + [annotation_1, annotation_2, annotation_3, annotation_4], + 'categories': categories + } + + mmcv.dump(fake_json, json_name) + + +def _create_dummy_custom_pkl(pkl_name): + fake_pkl = [{ + 'filename': 'fake_name.jpg', + 'width': 640, + 'height': 640, + 'ann': { + 'bboxes': + np.array([[50, 60, 70, 80], [100, 120, 130, 150], + [150, 160, 190, 200], [250, 260, 350, 360]]), + 'labels': + np.array([0, 0, 0, 0]) + } + }] + mmcv.dump(fake_pkl, pkl_name) + + +def _create_dummy_results(): + boxes = [ + np.array([[50, 60, 70, 80, 1.0], [100, 120, 130, 150, 0.98], + [150, 160, 190, 200, 0.96], [250, 260, 350, 360, 0.95]]) + ] + return [boxes] + + +@pytest.mark.parametrize('config_path', + ['./configs/_base_/datasets/voc0712.py']) +def test_dataset_init(config_path): + if not os.path.exists('./data'): + os.symlink('./tests/data', './data') + data_config = mmcv.Config.fromfile(config_path) + if 'data' not in data_config: + return + stage_names = ['train', 'val', 'test'] + for stage_name in stage_names: + dataset_config = copy.deepcopy(data_config.data.get(stage_name)) + dataset = build_dataset(dataset_config) + dataset[0] + os.unlink('./data') + + +def test_dataset_evaluation(): + tmp_dir = tempfile.TemporaryDirectory() + # create dummy data + fake_json_file = osp.join(tmp_dir.name, 'fake_data.json') + _create_dummy_coco_json(fake_json_file) + + # test single coco dataset evaluation + coco_dataset = CocoDataset( + ann_file=fake_json_file, classes=('car', ), pipeline=[]) + fake_results = _create_dummy_results() + eval_results = coco_dataset.evaluate(fake_results, classwise=True) + assert eval_results['bbox_mAP'] == 1 + assert eval_results['bbox_mAP_50'] == 1 + assert eval_results['bbox_mAP_75'] == 1 + + # test concat dataset evaluation + fake_concat_results = _create_dummy_results() + _create_dummy_results() + + # build concat dataset through two config dict + coco_cfg = dict( + type='CocoDataset', + ann_file=fake_json_file, + classes=('car', ), + pipeline=[]) + concat_cfgs = [coco_cfg, coco_cfg] + concat_dataset = build_dataset(concat_cfgs) + eval_results = concat_dataset.evaluate(fake_concat_results) + assert eval_results['0_bbox_mAP'] == 1 + assert eval_results['0_bbox_mAP_50'] == 1 + assert eval_results['0_bbox_mAP_75'] == 1 + assert eval_results['1_bbox_mAP'] == 1 + assert eval_results['1_bbox_mAP_50'] == 1 + assert eval_results['1_bbox_mAP_75'] == 1 + + # build concat dataset through concatenated ann_file + coco_cfg = dict( + type='CocoDataset', + ann_file=[fake_json_file, fake_json_file], + classes=('car', ), + pipeline=[]) + concat_dataset = build_dataset(coco_cfg) + eval_results = concat_dataset.evaluate(fake_concat_results) + assert eval_results['0_bbox_mAP'] == 1 + assert eval_results['0_bbox_mAP_50'] == 1 + assert eval_results['0_bbox_mAP_75'] == 1 + assert eval_results['1_bbox_mAP'] == 1 + assert eval_results['1_bbox_mAP_50'] == 1 + assert eval_results['1_bbox_mAP_75'] == 1 + + # create dummy data + fake_pkl_file = osp.join(tmp_dir.name, 'fake_data.pkl') + _create_dummy_custom_pkl(fake_pkl_file) + + # test single custom dataset evaluation + custom_dataset = CustomDataset( + ann_file=fake_pkl_file, classes=('car', ), pipeline=[]) + fake_results = _create_dummy_results() + eval_results = custom_dataset.evaluate(fake_results) + assert eval_results['mAP'] == 1 + + # test concat dataset evaluation + fake_concat_results = _create_dummy_results() + _create_dummy_results() + + # build concat dataset through two config dict + custom_cfg = dict( + type='CustomDataset', + ann_file=fake_pkl_file, + classes=('car', ), + pipeline=[]) + concat_cfgs = [custom_cfg, custom_cfg] + concat_dataset = build_dataset(concat_cfgs) + eval_results = concat_dataset.evaluate(fake_concat_results) + assert eval_results['0_mAP'] == 1 + assert eval_results['1_mAP'] == 1 + + # build concat dataset through concatenated ann_file + concat_cfg = dict( + type='CustomDataset', + ann_file=[fake_pkl_file, fake_pkl_file], + classes=('car', ), + pipeline=[]) + concat_dataset = build_dataset(concat_cfg) + eval_results = concat_dataset.evaluate(fake_concat_results) + assert eval_results['0_mAP'] == 1 + assert eval_results['1_mAP'] == 1 + + # build concat dataset through explict type + concat_cfg = dict( + type='ConcatDataset', + datasets=[custom_cfg, custom_cfg], + separate_eval=False) + concat_dataset = build_dataset(concat_cfg) + eval_results = concat_dataset.evaluate(fake_concat_results, metric='mAP') + assert eval_results['mAP'] == 1 + assert len(concat_dataset.datasets[0].data_infos) == \ + len(concat_dataset.datasets[1].data_infos) + assert len(concat_dataset.datasets[0].data_infos) == 1 + tmp_dir.cleanup() + + +@patch('mmdet.apis.single_gpu_test', MagicMock) +@patch('mmdet.apis.multi_gpu_test', MagicMock) +@pytest.mark.parametrize('EvalHookParam', (EvalHook, DistEvalHook)) +def test_evaluation_hook(EvalHookParam): + # create dummy data + dataloader = DataLoader(torch.ones((5, 2))) + + # 0.1. dataloader is not a DataLoader object + with pytest.raises(TypeError): + EvalHookParam(dataloader=MagicMock(), interval=-1) + + # 0.2. negative interval + with pytest.raises(ValueError): + EvalHookParam(dataloader, interval=-1) + + # 1. start=None, interval=1: perform evaluation after each epoch. + runner = _build_demo_runner() + evalhook = EvalHookParam(dataloader, interval=1) + evalhook.evaluate = MagicMock() + runner.register_hook(evalhook) + runner.run([dataloader], [('train', 1)], 2) + assert evalhook.evaluate.call_count == 2 # after epoch 1 & 2 + + # 2. start=1, interval=1: perform evaluation after each epoch. + runner = _build_demo_runner() + + evalhook = EvalHookParam(dataloader, start=1, interval=1) + evalhook.evaluate = MagicMock() + runner.register_hook(evalhook) + runner.run([dataloader], [('train', 1)], 2) + assert evalhook.evaluate.call_count == 2 # after epoch 1 & 2 + + # 3. start=None, interval=2: perform evaluation after epoch 2, 4, 6, etc + runner = _build_demo_runner() + evalhook = EvalHookParam(dataloader, interval=2) + evalhook.evaluate = MagicMock() + runner.register_hook(evalhook) + runner.run([dataloader], [('train', 1)], 2) + assert evalhook.evaluate.call_count == 1 # after epoch 2 + + # 4. start=1, interval=2: perform evaluation after epoch 1, 3, 5, etc + runner = _build_demo_runner() + evalhook = EvalHookParam(dataloader, start=1, interval=2) + evalhook.evaluate = MagicMock() + runner.register_hook(evalhook) + runner.run([dataloader], [('train', 1)], 3) + assert evalhook.evaluate.call_count == 2 # after epoch 1 & 3 + + # 5. start=0/negative, interval=1: perform evaluation after each epoch and + # before epoch 1. + runner = _build_demo_runner() + evalhook = EvalHookParam(dataloader, start=0) + evalhook.evaluate = MagicMock() + runner.register_hook(evalhook) + runner.run([dataloader], [('train', 1)], 2) + assert evalhook.evaluate.call_count == 3 # before epoch1 and after e1 & e2 + + runner = _build_demo_runner() + with pytest.warns(UserWarning): + evalhook = EvalHookParam(dataloader, start=-2) + evalhook.evaluate = MagicMock() + runner.register_hook(evalhook) + runner.run([dataloader], [('train', 1)], 2) + assert evalhook.evaluate.call_count == 3 # before epoch1 and after e1 & e2 + + # 6. resuming from epoch i, start = x (x<=i), interval =1: perform + # evaluation after each epoch and before the first epoch. + runner = _build_demo_runner() + evalhook = EvalHookParam(dataloader, start=1) + evalhook.evaluate = MagicMock() + runner.register_hook(evalhook) + runner._epoch = 2 + runner.run([dataloader], [('train', 1)], 3) + assert evalhook.evaluate.call_count == 2 # before & after epoch 3 + + # 7. resuming from epoch i, start = i+1/None, interval =1: perform + # evaluation after each epoch. + runner = _build_demo_runner() + evalhook = EvalHookParam(dataloader, start=2) + evalhook.evaluate = MagicMock() + runner.register_hook(evalhook) + runner._epoch = 1 + runner.run([dataloader], [('train', 1)], 3) + assert evalhook.evaluate.call_count == 2 # after epoch 2 & 3 + + +def _build_demo_runner(): + + class Model(nn.Module): + + def __init__(self): + super().__init__() + self.linear = nn.Linear(2, 1) + + def forward(self, x): + return self.linear(x) + + def train_step(self, x, optimizer, **kwargs): + return dict(loss=self(x)) + + def val_step(self, x, optimizer, **kwargs): + return dict(loss=self(x)) + + model = Model() + tmp_dir = tempfile.mkdtemp() + + runner = EpochBasedRunner( + model=model, work_dir=tmp_dir, logger=logging.getLogger()) + return runner + + +@pytest.mark.parametrize('classes, expected_length', [(['bus'], 2), + (['car'], 1), + (['bus', 'car'], 2)]) +def test_allow_empty_images(classes, expected_length): + dataset_class = DATASETS.get('CocoDataset') + # Filter empty images + filtered_dataset = dataset_class( + ann_file='tests/data/coco_sample.json', + img_prefix='tests/data', + pipeline=[], + classes=classes, + filter_empty_gt=True) + + # Get all + full_dataset = dataset_class( + ann_file='tests/data/coco_sample.json', + img_prefix='tests/data', + pipeline=[], + classes=classes, + filter_empty_gt=False) + + assert len(filtered_dataset) == expected_length + assert len(filtered_dataset.img_ids) == expected_length + assert len(full_dataset) == 3 + assert len(full_dataset.img_ids) == 3 + assert filtered_dataset.CLASSES == classes + assert full_dataset.CLASSES == classes diff --git a/tests/test_data/test_datasets/test_custom_dataset.py b/tests/test_data/test_datasets/test_custom_dataset.py new file mode 100644 index 0000000..bda4499 --- /dev/null +++ b/tests/test_data/test_datasets/test_custom_dataset.py @@ -0,0 +1,88 @@ +from unittest.mock import MagicMock, patch + +import pytest + +from mmdet.datasets import DATASETS + + +@patch('mmdet.datasets.CocoDataset.load_annotations', MagicMock()) +@patch('mmdet.datasets.CustomDataset.load_annotations', MagicMock()) +@patch('mmdet.datasets.XMLDataset.load_annotations', MagicMock()) +@patch('mmdet.datasets.CityscapesDataset.load_annotations', MagicMock()) +@patch('mmdet.datasets.CocoDataset._filter_imgs', MagicMock) +@patch('mmdet.datasets.CustomDataset._filter_imgs', MagicMock) +@patch('mmdet.datasets.XMLDataset._filter_imgs', MagicMock) +@patch('mmdet.datasets.CityscapesDataset._filter_imgs', MagicMock) +@pytest.mark.parametrize('dataset', + ['CocoDataset', 'VOCDataset', 'CityscapesDataset']) +def test_custom_classes_override_default(dataset): + dataset_class = DATASETS.get(dataset) + if dataset in ['CocoDataset', 'CityscapesDataset']: + dataset_class.coco = MagicMock() + dataset_class.cat_ids = MagicMock() + + original_classes = dataset_class.CLASSES + + # Test setting classes as a tuple + custom_dataset = dataset_class( + ann_file=MagicMock(), + pipeline=[], + classes=('bus', 'car'), + test_mode=True, + img_prefix='VOC2007' if dataset == 'VOCDataset' else '') + + assert custom_dataset.CLASSES != original_classes + assert custom_dataset.CLASSES == ('bus', 'car') + print(custom_dataset) + + # Test setting classes as a list + custom_dataset = dataset_class( + ann_file=MagicMock(), + pipeline=[], + classes=['bus', 'car'], + test_mode=True, + img_prefix='VOC2007' if dataset == 'VOCDataset' else '') + + assert custom_dataset.CLASSES != original_classes + assert custom_dataset.CLASSES == ['bus', 'car'] + print(custom_dataset) + + # Test overriding not a subset + custom_dataset = dataset_class( + ann_file=MagicMock(), + pipeline=[], + classes=['foo'], + test_mode=True, + img_prefix='VOC2007' if dataset == 'VOCDataset' else '') + + assert custom_dataset.CLASSES != original_classes + assert custom_dataset.CLASSES == ['foo'] + print(custom_dataset) + + # Test default behavior + custom_dataset = dataset_class( + ann_file=MagicMock(), + pipeline=[], + classes=None, + test_mode=True, + img_prefix='VOC2007' if dataset == 'VOCDataset' else '') + + assert custom_dataset.CLASSES == original_classes + print(custom_dataset) + + # Test sending file path + import tempfile + tmp_file = tempfile.NamedTemporaryFile() + with open(tmp_file.name, 'w') as f: + f.write('bus\ncar\n') + custom_dataset = dataset_class( + ann_file=MagicMock(), + pipeline=[], + classes=tmp_file.name, + test_mode=True, + img_prefix='VOC2007' if dataset == 'VOCDataset' else '') + tmp_file.close() + + assert custom_dataset.CLASSES != original_classes + assert custom_dataset.CLASSES == ['bus', 'car'] + print(custom_dataset) diff --git a/tests/test_data/test_datasets/test_dataset_wrapper.py b/tests/test_data/test_datasets/test_dataset_wrapper.py new file mode 100644 index 0000000..c08c990 --- /dev/null +++ b/tests/test_data/test_datasets/test_dataset_wrapper.py @@ -0,0 +1,80 @@ +import bisect +import math +from collections import defaultdict +from unittest.mock import MagicMock + +import numpy as np + +from mmdet.datasets import (ClassBalancedDataset, ConcatDataset, CustomDataset, + RepeatDataset) + + +def test_dataset_wrapper(): + CustomDataset.load_annotations = MagicMock() + CustomDataset.__getitem__ = MagicMock(side_effect=lambda idx: idx) + dataset_a = CustomDataset( + ann_file=MagicMock(), pipeline=[], test_mode=True, img_prefix='') + len_a = 10 + cat_ids_list_a = [ + np.random.randint(0, 80, num).tolist() + for num in np.random.randint(1, 20, len_a) + ] + dataset_a.data_infos = MagicMock() + dataset_a.data_infos.__len__.return_value = len_a + dataset_a.get_cat_ids = MagicMock( + side_effect=lambda idx: cat_ids_list_a[idx]) + dataset_b = CustomDataset( + ann_file=MagicMock(), pipeline=[], test_mode=True, img_prefix='') + len_b = 20 + cat_ids_list_b = [ + np.random.randint(0, 80, num).tolist() + for num in np.random.randint(1, 20, len_b) + ] + dataset_b.data_infos = MagicMock() + dataset_b.data_infos.__len__.return_value = len_b + dataset_b.get_cat_ids = MagicMock( + side_effect=lambda idx: cat_ids_list_b[idx]) + + concat_dataset = ConcatDataset([dataset_a, dataset_b]) + assert concat_dataset[5] == 5 + assert concat_dataset[25] == 15 + assert concat_dataset.get_cat_ids(5) == cat_ids_list_a[5] + assert concat_dataset.get_cat_ids(25) == cat_ids_list_b[15] + assert len(concat_dataset) == len(dataset_a) + len(dataset_b) + + repeat_dataset = RepeatDataset(dataset_a, 10) + assert repeat_dataset[5] == 5 + assert repeat_dataset[15] == 5 + assert repeat_dataset[27] == 7 + assert repeat_dataset.get_cat_ids(5) == cat_ids_list_a[5] + assert repeat_dataset.get_cat_ids(15) == cat_ids_list_a[5] + assert repeat_dataset.get_cat_ids(27) == cat_ids_list_a[7] + assert len(repeat_dataset) == 10 * len(dataset_a) + + category_freq = defaultdict(int) + for cat_ids in cat_ids_list_a: + cat_ids = set(cat_ids) + for cat_id in cat_ids: + category_freq[cat_id] += 1 + for k, v in category_freq.items(): + category_freq[k] = v / len(cat_ids_list_a) + + mean_freq = np.mean(list(category_freq.values())) + repeat_thr = mean_freq + + category_repeat = { + cat_id: max(1.0, math.sqrt(repeat_thr / cat_freq)) + for cat_id, cat_freq in category_freq.items() + } + + repeat_factors = [] + for cat_ids in cat_ids_list_a: + cat_ids = set(cat_ids) + repeat_factor = max({category_repeat[cat_id] for cat_id in cat_ids}) + repeat_factors.append(math.ceil(repeat_factor)) + repeat_factors_cumsum = np.cumsum(repeat_factors) + repeat_factor_dataset = ClassBalancedDataset(dataset_a, repeat_thr) + assert len(repeat_factor_dataset) == repeat_factors_cumsum[-1] + for idx in np.random.randint(0, len(repeat_factor_dataset), 3): + assert repeat_factor_dataset[idx] == bisect.bisect_right( + repeat_factors_cumsum, idx) diff --git a/tests/test_data/test_datasets/test_xml_dataset.py b/tests/test_data/test_datasets/test_xml_dataset.py new file mode 100644 index 0000000..ebdd9e6 --- /dev/null +++ b/tests/test_data/test_datasets/test_xml_dataset.py @@ -0,0 +1,22 @@ +import pytest + +from mmdet.datasets import DATASETS + + +def test_xml_dataset(): + dataconfig = { + 'ann_file': 'data/VOCdevkit/VOC2007/ImageSets/Main/test.txt', + 'img_prefix': 'data/VOCdevkit/VOC2007/', + 'pipeline': [{ + 'type': 'LoadImageFromFile' + }] + } + XMLDataset = DATASETS.get('XMLDataset') + + class XMLDatasetSubClass(XMLDataset): + CLASSES = None + + # get_ann_info and _filter_imgs of XMLDataset + # would use self.CLASSES, we added CLASSES not NONE + with pytest.raises(AssertionError): + XMLDatasetSubClass(**dataconfig) diff --git a/tests/test_data/test_pipelines/test_formatting.py b/tests/test_data/test_pipelines/test_formatting.py new file mode 100644 index 0000000..8a2a375 --- /dev/null +++ b/tests/test_data/test_pipelines/test_formatting.py @@ -0,0 +1,23 @@ +import os.path as osp + +from mmcv.utils import build_from_cfg + +from mmdet.datasets.builder import PIPELINES + + +def test_default_format_bundle(): + results = dict( + img_prefix=osp.join(osp.dirname(__file__), '../../data'), + img_info=dict(filename='color.jpg')) + load = dict(type='LoadImageFromFile') + load = build_from_cfg(load, PIPELINES) + bundle = dict(type='DefaultFormatBundle') + bundle = build_from_cfg(bundle, PIPELINES) + results = load(results) + assert 'pad_shape' not in results + assert 'scale_factor' not in results + assert 'img_norm_cfg' not in results + results = bundle(results) + assert 'pad_shape' in results + assert 'scale_factor' in results + assert 'img_norm_cfg' in results diff --git a/tests/test_data/test_pipelines/test_loading.py b/tests/test_data/test_pipelines/test_loading.py new file mode 100644 index 0000000..b7ecd7f --- /dev/null +++ b/tests/test_data/test_pipelines/test_loading.py @@ -0,0 +1,90 @@ +import copy +import os.path as osp + +import mmcv +import numpy as np + +from mmdet.datasets.pipelines import (LoadImageFromFile, LoadImageFromWebcam, + LoadMultiChannelImageFromFiles) + + +class TestLoading(object): + + @classmethod + def setup_class(cls): + cls.data_prefix = osp.join(osp.dirname(__file__), '../../data') + + def test_load_img(self): + results = dict( + img_prefix=self.data_prefix, img_info=dict(filename='color.jpg')) + transform = LoadImageFromFile() + results = transform(copy.deepcopy(results)) + assert results['filename'] == osp.join(self.data_prefix, 'color.jpg') + assert results['ori_filename'] == 'color.jpg' + assert results['img'].shape == (288, 512, 3) + assert results['img'].dtype == np.uint8 + assert results['img_shape'] == (288, 512, 3) + assert results['ori_shape'] == (288, 512, 3) + assert repr(transform) == transform.__class__.__name__ + \ + "(to_float32=False, color_type='color', " + \ + "file_client_args={'backend': 'disk'})" + + # no img_prefix + results = dict( + img_prefix=None, img_info=dict(filename='tests/data/color.jpg')) + transform = LoadImageFromFile() + results = transform(copy.deepcopy(results)) + assert results['filename'] == 'tests/data/color.jpg' + assert results['ori_filename'] == 'tests/data/color.jpg' + assert results['img'].shape == (288, 512, 3) + + # to_float32 + transform = LoadImageFromFile(to_float32=True) + results = transform(copy.deepcopy(results)) + assert results['img'].dtype == np.float32 + + # gray image + results = dict( + img_prefix=self.data_prefix, img_info=dict(filename='gray.jpg')) + transform = LoadImageFromFile() + results = transform(copy.deepcopy(results)) + assert results['img'].shape == (288, 512, 3) + assert results['img'].dtype == np.uint8 + + transform = LoadImageFromFile(color_type='unchanged') + results = transform(copy.deepcopy(results)) + assert results['img'].shape == (288, 512) + assert results['img'].dtype == np.uint8 + + def test_load_multi_channel_img(self): + results = dict( + img_prefix=self.data_prefix, + img_info=dict(filename=['color.jpg', 'color.jpg'])) + transform = LoadMultiChannelImageFromFiles() + results = transform(copy.deepcopy(results)) + assert results['filename'] == [ + osp.join(self.data_prefix, 'color.jpg'), + osp.join(self.data_prefix, 'color.jpg') + ] + assert results['ori_filename'] == ['color.jpg', 'color.jpg'] + assert results['img'].shape == (288, 512, 3, 2) + assert results['img'].dtype == np.uint8 + assert results['img_shape'] == (288, 512, 3, 2) + assert results['ori_shape'] == (288, 512, 3, 2) + assert results['pad_shape'] == (288, 512, 3, 2) + assert results['scale_factor'] == 1.0 + assert repr(transform) == transform.__class__.__name__ + \ + "(to_float32=False, color_type='unchanged', " + \ + "file_client_args={'backend': 'disk'})" + + def test_load_webcam_img(self): + img = mmcv.imread(osp.join(self.data_prefix, 'color.jpg')) + results = dict(img=img) + transform = LoadImageFromWebcam() + results = transform(copy.deepcopy(results)) + assert results['filename'] is None + assert results['ori_filename'] is None + assert results['img'].shape == (288, 512, 3) + assert results['img'].dtype == np.uint8 + assert results['img_shape'] == (288, 512, 3) + assert results['ori_shape'] == (288, 512, 3) diff --git a/tests/test_data/test_pipelines/test_sampler.py b/tests/test_data/test_pipelines/test_sampler.py new file mode 100644 index 0000000..1ba5c56 --- /dev/null +++ b/tests/test_data/test_pipelines/test_sampler.py @@ -0,0 +1,328 @@ +import torch + +from mmdet.core.bbox.assigners import MaxIoUAssigner +from mmdet.core.bbox.samplers import (OHEMSampler, RandomSampler, + ScoreHLRSampler) + + +def test_random_sampler(): + assigner = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ignore_iof_thr=0.5, + ignore_wrt_candidates=False, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + gt_labels = torch.LongTensor([1, 2]) + gt_bboxes_ignore = torch.Tensor([ + [30, 30, 40, 40], + ]) + assign_result = assigner.assign( + bboxes, + gt_bboxes, + gt_bboxes_ignore=gt_bboxes_ignore, + gt_labels=gt_labels) + + sampler = RandomSampler( + num=10, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=True) + + sample_result = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels) + + assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) + assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds) + + +def test_random_sampler_empty_gt(): + assigner = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ignore_iof_thr=0.5, + ignore_wrt_candidates=False, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.empty(0, 4) + gt_labels = torch.empty(0, ).long() + assign_result = assigner.assign(bboxes, gt_bboxes, gt_labels=gt_labels) + + sampler = RandomSampler( + num=10, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=True) + + sample_result = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels) + + assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) + assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds) + + +def test_random_sampler_empty_pred(): + assigner = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ignore_iof_thr=0.5, + ignore_wrt_candidates=False, + ) + bboxes = torch.empty(0, 4) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + gt_labels = torch.LongTensor([1, 2]) + assign_result = assigner.assign(bboxes, gt_bboxes, gt_labels=gt_labels) + + sampler = RandomSampler( + num=10, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=True) + + sample_result = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels) + + assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) + assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds) + + +def _context_for_ohem(): + import sys + from os.path import dirname + sys.path.insert(0, dirname(dirname(dirname(__file__)))) + from test_forward import _get_detector_cfg + + model = _get_detector_cfg( + 'faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py') + model['pretrained'] = None + + from mmdet.models import build_detector + context = build_detector(model).roi_head + return context + + +def test_ohem_sampler(): + + assigner = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ignore_iof_thr=0.5, + ignore_wrt_candidates=False, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + gt_labels = torch.LongTensor([1, 2]) + gt_bboxes_ignore = torch.Tensor([ + [30, 30, 40, 40], + ]) + assign_result = assigner.assign( + bboxes, + gt_bboxes, + gt_bboxes_ignore=gt_bboxes_ignore, + gt_labels=gt_labels) + + context = _context_for_ohem() + + sampler = OHEMSampler( + num=10, + pos_fraction=0.5, + context=context, + neg_pos_ub=-1, + add_gt_as_proposals=True) + + feats = [torch.rand(1, 256, int(2**i), int(2**i)) for i in [6, 5, 4, 3, 2]] + sample_result = sampler.sample( + assign_result, bboxes, gt_bboxes, gt_labels, feats=feats) + + assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) + assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds) + + +def test_ohem_sampler_empty_gt(): + + assigner = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ignore_iof_thr=0.5, + ignore_wrt_candidates=False, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.empty(0, 4) + gt_labels = torch.LongTensor([]) + gt_bboxes_ignore = torch.Tensor([]) + assign_result = assigner.assign( + bboxes, + gt_bboxes, + gt_bboxes_ignore=gt_bboxes_ignore, + gt_labels=gt_labels) + + context = _context_for_ohem() + + sampler = OHEMSampler( + num=10, + pos_fraction=0.5, + context=context, + neg_pos_ub=-1, + add_gt_as_proposals=True) + + feats = [torch.rand(1, 256, int(2**i), int(2**i)) for i in [6, 5, 4, 3, 2]] + + sample_result = sampler.sample( + assign_result, bboxes, gt_bboxes, gt_labels, feats=feats) + + assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) + assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds) + + +def test_ohem_sampler_empty_pred(): + assigner = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ignore_iof_thr=0.5, + ignore_wrt_candidates=False, + ) + bboxes = torch.empty(0, 4) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_labels = torch.LongTensor([1, 2, 2, 3]) + gt_bboxes_ignore = torch.Tensor([]) + assign_result = assigner.assign( + bboxes, + gt_bboxes, + gt_bboxes_ignore=gt_bboxes_ignore, + gt_labels=gt_labels) + + context = _context_for_ohem() + + sampler = OHEMSampler( + num=10, + pos_fraction=0.5, + context=context, + neg_pos_ub=-1, + add_gt_as_proposals=True) + + feats = [torch.rand(1, 256, int(2**i), int(2**i)) for i in [6, 5, 4, 3, 2]] + + sample_result = sampler.sample( + assign_result, bboxes, gt_bboxes, gt_labels, feats=feats) + + assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) + assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds) + + +def test_random_sample_result(): + from mmdet.core.bbox.samplers.sampling_result import SamplingResult + SamplingResult.random(num_gts=0, num_preds=0) + SamplingResult.random(num_gts=0, num_preds=3) + SamplingResult.random(num_gts=3, num_preds=3) + SamplingResult.random(num_gts=0, num_preds=3) + SamplingResult.random(num_gts=7, num_preds=7) + SamplingResult.random(num_gts=7, num_preds=64) + SamplingResult.random(num_gts=24, num_preds=3) + + for i in range(3): + SamplingResult.random(rng=i) + + +def test_score_hlr_sampler_empty_pred(): + assigner = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ignore_iof_thr=0.5, + ignore_wrt_candidates=False, + ) + context = _context_for_ohem() + sampler = ScoreHLRSampler( + num=10, + pos_fraction=0.5, + context=context, + neg_pos_ub=-1, + add_gt_as_proposals=True) + gt_bboxes_ignore = torch.Tensor([]) + feats = [torch.rand(1, 256, int(2**i), int(2**i)) for i in [6, 5, 4, 3, 2]] + + # empty bbox + bboxes = torch.empty(0, 4) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_labels = torch.LongTensor([1, 2, 2, 3]) + assign_result = assigner.assign( + bboxes, + gt_bboxes, + gt_bboxes_ignore=gt_bboxes_ignore, + gt_labels=gt_labels) + sample_result, _ = sampler.sample( + assign_result, bboxes, gt_bboxes, gt_labels, feats=feats) + assert len(sample_result.neg_inds) == 0 + assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) + assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds) + + # empty gt + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.empty(0, 4) + gt_labels = torch.LongTensor([]) + assign_result = assigner.assign( + bboxes, + gt_bboxes, + gt_bboxes_ignore=gt_bboxes_ignore, + gt_labels=gt_labels) + sample_result, _ = sampler.sample( + assign_result, bboxes, gt_bboxes, gt_labels, feats=feats) + assert len(sample_result.pos_inds) == 0 + assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) + assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds) + + # non-empty input + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_labels = torch.LongTensor([1, 2, 2, 3]) + assign_result = assigner.assign( + bboxes, + gt_bboxes, + gt_bboxes_ignore=gt_bboxes_ignore, + gt_labels=gt_labels) + sample_result, _ = sampler.sample( + assign_result, bboxes, gt_bboxes, gt_labels, feats=feats) + assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) + assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds) diff --git a/tests/test_data/test_pipelines/test_transform/test_img_augment.py b/tests/test_data/test_pipelines/test_transform/test_img_augment.py new file mode 100644 index 0000000..8f7dd9e --- /dev/null +++ b/tests/test_data/test_pipelines/test_transform/test_img_augment.py @@ -0,0 +1,203 @@ +import copy + +import mmcv +import numpy as np +from mmcv.utils import build_from_cfg +from numpy.testing import assert_array_equal + +from mmdet.core.mask import BitmapMasks, PolygonMasks +from mmdet.datasets.builder import PIPELINES + + +def construct_toy_data(poly2mask=True): + img = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.uint8) + img = np.stack([img, img, img], axis=-1) + results = dict() + # image + results['img'] = img + results['img_shape'] = img.shape + results['img_fields'] = ['img'] + # bboxes + results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore'] + results['gt_bboxes'] = np.array([[0., 0., 2., 1.]], dtype=np.float32) + results['gt_bboxes_ignore'] = np.array([[2., 0., 3., 1.]], + dtype=np.float32) + # labels + results['gt_labels'] = np.array([1], dtype=np.int64) + # masks + results['mask_fields'] = ['gt_masks'] + if poly2mask: + gt_masks = np.array([[0, 1, 1, 0], [0, 1, 0, 0]], + dtype=np.uint8)[None, :, :] + results['gt_masks'] = BitmapMasks(gt_masks, 2, 4) + else: + raw_masks = [[np.array([1, 0, 2, 0, 2, 1, 1, 1], dtype=np.float)]] + results['gt_masks'] = PolygonMasks(raw_masks, 2, 4) + # segmentations + results['seg_fields'] = ['gt_semantic_seg'] + results['gt_semantic_seg'] = img[..., 0] + return results + + +def test_adjust_color(): + results = construct_toy_data() + # test wighout aug + transform = dict(type='ColorTransform', prob=0, level=10) + transform_module = build_from_cfg(transform, PIPELINES) + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], results['img']) + + # test with factor 1 + img = results['img'] + transform = dict(type='ColorTransform', prob=1, level=10) + transform_module = build_from_cfg(transform, PIPELINES) + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], img) + + # test with factor 0 + transform_module.factor = 0 + img_gray = mmcv.bgr2gray(img.copy()) + img_r = np.stack([img_gray, img_gray, img_gray], axis=-1) + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], img_r) + + # test with factor 0.5 + transform_module.factor = 0.5 + results_transformed = transform_module(copy.deepcopy(results)) + img = results['img'] + assert_array_equal( + results_transformed['img'], + np.round(np.clip((img * 0.5 + img_r * 0.5), 0, 255)).astype(img.dtype)) + + +def test_imequalize(nb_rand_test=100): + + def _imequalize(img): + # equalize the image using PIL.ImageOps.equalize + from PIL import ImageOps, Image + img = Image.fromarray(img) + equalized_img = np.asarray(ImageOps.equalize(img)) + return equalized_img + + results = construct_toy_data() + # test wighout aug + transform = dict(type='EqualizeTransform', prob=0) + transform_module = build_from_cfg(transform, PIPELINES) + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], results['img']) + + # test equalize with case step=0 + transform = dict(type='EqualizeTransform', prob=1.) + transform_module = build_from_cfg(transform, PIPELINES) + img = np.array([[0, 0, 0], [120, 120, 120], [255, 255, 255]], + dtype=np.uint8) + img = np.stack([img, img, img], axis=-1) + results['img'] = img + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], img) + + # test equalize with randomly sampled image. + for _ in range(nb_rand_test): + img = np.clip(np.random.uniform(0, 1, (1000, 1200, 3)) * 260, 0, + 255).astype(np.uint8) + results['img'] = img + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], _imequalize(img)) + + +def test_adjust_brightness(nb_rand_test=100): + + def _adjust_brightness(img, factor): + # adjust the brightness of image using + # PIL.ImageEnhance.Brightness + from PIL.ImageEnhance import Brightness + from PIL import Image + img = Image.fromarray(img) + brightened_img = Brightness(img).enhance(factor) + return np.asarray(brightened_img) + + results = construct_toy_data() + # test wighout aug + transform = dict(type='BrightnessTransform', level=10, prob=0) + transform_module = build_from_cfg(transform, PIPELINES) + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], results['img']) + + # test case with factor 1.0 + transform = dict(type='BrightnessTransform', level=10, prob=1.) + transform_module = build_from_cfg(transform, PIPELINES) + transform_module.factor = 1.0 + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], results['img']) + + # test case with factor 0.0 + transform_module.factor = 0.0 + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], + np.zeros_like(results['img'])) + + # test with randomly sampled images and factors. + for _ in range(nb_rand_test): + img = np.clip(np.random.uniform(0, 1, (1000, 1200, 3)) * 260, 0, + 255).astype(np.uint8) + factor = np.random.uniform() + transform_module.factor = factor + results['img'] = img + np.testing.assert_allclose( + transform_module(copy.deepcopy(results))['img'].astype(np.int32), + _adjust_brightness(img, factor).astype(np.int32), + rtol=0, + atol=1) + + +def test_adjust_contrast(nb_rand_test=100): + + def _adjust_contrast(img, factor): + from PIL.ImageEnhance import Contrast + from PIL import Image + # Image.fromarray defaultly supports RGB, not BGR. + # convert from BGR to RGB + img = Image.fromarray(img[..., ::-1], mode='RGB') + contrasted_img = Contrast(img).enhance(factor) + # convert from RGB to BGR + return np.asarray(contrasted_img)[..., ::-1] + + results = construct_toy_data() + # test wighout aug + transform = dict(type='ContrastTransform', level=10, prob=0) + transform_module = build_from_cfg(transform, PIPELINES) + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], results['img']) + + # test case with factor 1.0 + transform = dict(type='ContrastTransform', level=10, prob=1.) + transform_module = build_from_cfg(transform, PIPELINES) + transform_module.factor = 1.0 + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], results['img']) + + # test case with factor 0.0 + transform_module.factor = 0.0 + results_transformed = transform_module(copy.deepcopy(results)) + np.testing.assert_allclose( + results_transformed['img'], + _adjust_contrast(results['img'], 0.), + rtol=0, + atol=1) + + # test adjust_contrast with randomly sampled images and factors. + for _ in range(nb_rand_test): + img = np.clip(np.random.uniform(0, 1, (1200, 1000, 3)) * 260, 0, + 255).astype(np.uint8) + factor = np.random.uniform() + transform_module.factor = factor + results['img'] = img + results_transformed = transform_module(copy.deepcopy(results)) + # Note the gap (less_equal 1) between PIL.ImageEnhance.Contrast + # and mmcv.adjust_contrast comes from the gap that converts from + # a color image to gray image using mmcv or PIL. + np.testing.assert_allclose( + transform_module(copy.deepcopy(results))['img'].astype(np.int32), + _adjust_contrast(results['img'], factor).astype(np.int32), + rtol=0, + atol=1) diff --git a/tests/test_data/test_pipelines/test_transform/test_models_aug_test.py b/tests/test_data/test_pipelines/test_transform/test_models_aug_test.py new file mode 100644 index 0000000..fc12355 --- /dev/null +++ b/tests/test_data/test_pipelines/test_transform/test_models_aug_test.py @@ -0,0 +1,128 @@ +import os.path as osp + +import mmcv +import torch +from mmcv.parallel import collate +from mmcv.utils import build_from_cfg + +from mmdet.datasets.builder import PIPELINES +from mmdet.models import build_detector + + +def model_aug_test_template(cfg_file): + # get config + cfg = mmcv.Config.fromfile(cfg_file) + # init model + cfg.model.pretrained = None + cfg.model.train_cfg = None + model = build_detector(cfg.model) + + # init test pipeline and set aug test + load_cfg, multi_scale_cfg = cfg.test_pipeline + multi_scale_cfg['flip'] = True + multi_scale_cfg['img_scale'] = [(1333, 800), (800, 600), (640, 480)] + + load = build_from_cfg(load_cfg, PIPELINES) + transform = build_from_cfg(multi_scale_cfg, PIPELINES) + + results = dict( + img_prefix=osp.join(osp.dirname(__file__), '../../../data'), + img_info=dict(filename='color.jpg')) + results = transform(load(results)) + assert len(results['img']) == 6 + assert len(results['img_metas']) == 6 + + results['img'] = [collate([x]) for x in results['img']] + results['img_metas'] = [collate([x]).data[0] for x in results['img_metas']] + # aug test the model + model.eval() + with torch.no_grad(): + aug_result = model(return_loss=False, rescale=True, **results) + return aug_result + + +def test_aug_test_size(): + results = dict( + img_prefix=osp.join(osp.dirname(__file__), '../../../data'), + img_info=dict(filename='color.jpg')) + + # Define simple pipeline + load = dict(type='LoadImageFromFile') + load = build_from_cfg(load, PIPELINES) + + # get config + transform = dict( + type='MultiScaleFlipAug', + transforms=[], + img_scale=[(1333, 800), (800, 600), (640, 480)], + flip=True, + flip_direction=['horizontal', 'vertical']) + multi_aug_test_module = build_from_cfg(transform, PIPELINES) + + results = load(results) + results = multi_aug_test_module(load(results)) + # len(["original", "horizontal", "vertical"]) * + # len([(1333, 800), (800, 600), (640, 480)]) + assert len(results['img']) == 9 + + +def test_cascade_rcnn_aug_test(): + aug_result = model_aug_test_template( + 'configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py') + assert len(aug_result[0]) == 80 + + +def test_mask_rcnn_aug_test(): + aug_result = model_aug_test_template( + 'configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py') + assert len(aug_result[0]) == 2 + assert len(aug_result[0][0]) == 80 + assert len(aug_result[0][1]) == 80 + + +def test_htc_aug_test(): + aug_result = model_aug_test_template('configs/htc/htc_r50_fpn_1x_coco.py') + assert len(aug_result[0]) == 2 + assert len(aug_result[0][0]) == 80 + assert len(aug_result[0][1]) == 80 + + +def test_scnet_aug_test(): + aug_result = model_aug_test_template( + 'configs/scnet/scnet_r50_fpn_1x_coco.py') + assert len(aug_result[0]) == 2 + assert len(aug_result[0][0]) == 80 + assert len(aug_result[0][1]) == 80 + + +def test_cornernet_aug_test(): + # get config + cfg = mmcv.Config.fromfile( + 'configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py') + # init model + cfg.model.pretrained = None + cfg.model.train_cfg = None + model = build_detector(cfg.model) + + # init test pipeline and set aug test + load_cfg, multi_scale_cfg = cfg.test_pipeline + multi_scale_cfg['flip'] = True + multi_scale_cfg['scale_factor'] = [0.5, 1.0, 2.0] + + load = build_from_cfg(load_cfg, PIPELINES) + transform = build_from_cfg(multi_scale_cfg, PIPELINES) + + results = dict( + img_prefix=osp.join(osp.dirname(__file__), '../../../data'), + img_info=dict(filename='color.jpg')) + results = transform(load(results)) + assert len(results['img']) == 6 + assert len(results['img_metas']) == 6 + + results['img'] = [collate([x]) for x in results['img']] + results['img_metas'] = [collate([x]).data[0] for x in results['img_metas']] + # aug test the model + model.eval() + with torch.no_grad(): + aug_result = model(return_loss=False, rescale=True, **results) + assert len(aug_result[0]) == 80 diff --git a/tests/test_data/test_pipelines/test_transform/test_rotate.py b/tests/test_data/test_pipelines/test_transform/test_rotate.py new file mode 100644 index 0000000..c440451 --- /dev/null +++ b/tests/test_data/test_pipelines/test_transform/test_rotate.py @@ -0,0 +1,224 @@ +import copy + +import numpy as np +import pytest +from mmcv.utils import build_from_cfg + +from mmdet.core.mask import BitmapMasks, PolygonMasks +from mmdet.datasets.builder import PIPELINES + + +def construct_toy_data(poly2mask=True): + img = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.uint8) + img = np.stack([img, img, img], axis=-1) + results = dict() + # image + results['img'] = img + results['img_shape'] = img.shape + results['img_fields'] = ['img'] + # bboxes + results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore'] + results['gt_bboxes'] = np.array([[0., 0., 2., 1.]], dtype=np.float32) + results['gt_bboxes_ignore'] = np.array([[2., 0., 3., 1.]], + dtype=np.float32) + # labels + results['gt_labels'] = np.array([1], dtype=np.int64) + # masks + results['mask_fields'] = ['gt_masks'] + if poly2mask: + gt_masks = np.array([[0, 1, 1, 0], [0, 1, 0, 0]], + dtype=np.uint8)[None, :, :] + results['gt_masks'] = BitmapMasks(gt_masks, 2, 4) + else: + raw_masks = [[np.array([0, 0, 2, 0, 2, 1, 0, 1], dtype=np.float)]] + results['gt_masks'] = PolygonMasks(raw_masks, 2, 4) + # segmentations + results['seg_fields'] = ['gt_semantic_seg'] + results['gt_semantic_seg'] = img[..., 0] + return results + + +def _check_fields(results, results_rotated, keys): + for key in keys: + if isinstance(results[key], (BitmapMasks, PolygonMasks)): + assert np.equal(results[key].to_ndarray(), + results_rotated[key].to_ndarray()).all() + else: + assert np.equal(results[key], results_rotated[key]).all() + + +def check_rotate(results, results_rotated): + # check image + _check_fields(results, results_rotated, results.get('img_fields', ['img'])) + # check bboxes + _check_fields(results, results_rotated, results.get('bbox_fields', [])) + # check masks + _check_fields(results, results_rotated, results.get('mask_fields', [])) + # check segmentations + _check_fields(results, results_rotated, results.get('seg_fields', [])) + # _check gt_labels + if 'gt_labels' in results: + assert np.equal(results['gt_labels'], + results_rotated['gt_labels']).all() + + +def test_rotate(): + # test assertion for invalid type of max_rotate_angle + with pytest.raises(AssertionError): + transform = dict(type='Rotate', level=1, max_rotate_angle=(30, )) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid type of scale + with pytest.raises(AssertionError): + transform = dict(type='Rotate', level=2, scale=(1.2, )) + build_from_cfg(transform, PIPELINES) + + # test ValueError for invalid type of img_fill_val + with pytest.raises(ValueError): + transform = dict( + type='Rotate', level=2, img_fill_val=[ + 128, + ]) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid number of elements in center + with pytest.raises(AssertionError): + transform = dict(type='Rotate', level=2, center=(0.5, )) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid type of center + with pytest.raises(AssertionError): + transform = dict(type='Rotate', level=2, center=[0, 0]) + build_from_cfg(transform, PIPELINES) + + # test case when no rotate aug (level=0) + results = construct_toy_data() + img_fill_val = (104, 116, 124) + seg_ignore_label = 255 + transform = dict( + type='Rotate', + level=0, + prob=1., + img_fill_val=img_fill_val, + seg_ignore_label=seg_ignore_label, + ) + rotate_module = build_from_cfg(transform, PIPELINES) + results_wo_rotate = rotate_module(copy.deepcopy(results)) + check_rotate(results, results_wo_rotate) + + # test case when no rotate aug (prob<=0) + transform = dict( + type='Rotate', level=10, prob=0., img_fill_val=img_fill_val, scale=0.6) + rotate_module = build_from_cfg(transform, PIPELINES) + results_wo_rotate = rotate_module(copy.deepcopy(results)) + check_rotate(results, results_wo_rotate) + + # test clockwise rotation with angle 90 + results = construct_toy_data() + img_fill_val = 128 + transform = dict( + type='Rotate', + level=10, + max_rotate_angle=90, + img_fill_val=img_fill_val, + # set random_negative_prob to 0 for clockwise rotation + random_negative_prob=0., + prob=1.) + rotate_module = build_from_cfg(transform, PIPELINES) + results_rotated = rotate_module(copy.deepcopy(results)) + img_r = np.array([[img_fill_val, 6, 2, img_fill_val], + [img_fill_val, 7, 3, img_fill_val]]).astype(np.uint8) + img_r = np.stack([img_r, img_r, img_r], axis=-1) + results_gt = copy.deepcopy(results) + results_gt['img'] = img_r + results_gt['gt_bboxes'] = np.array([[1., 0., 2., 1.]], dtype=np.float32) + results_gt['gt_bboxes_ignore'] = np.empty((0, 4), dtype=np.float32) + gt_masks = np.array([[0, 1, 1, 0], [0, 0, 1, 0]], + dtype=np.uint8)[None, :, :] + results_gt['gt_masks'] = BitmapMasks(gt_masks, 2, 4) + results_gt['gt_semantic_seg'] = np.array( + [[255, 6, 2, 255], [255, 7, 3, + 255]]).astype(results['gt_semantic_seg'].dtype) + check_rotate(results_gt, results_rotated) + + # test clockwise rotation with angle 90, PolygonMasks + results = construct_toy_data(poly2mask=False) + results_rotated = rotate_module(copy.deepcopy(results)) + gt_masks = [[np.array([2, 0, 2, 1, 1, 1, 1, 0], dtype=np.float)]] + results_gt['gt_masks'] = PolygonMasks(gt_masks, 2, 4) + check_rotate(results_gt, results_rotated) + + # test counter-clockwise roatation with angle 90, + # and specify the ratation center + img_fill_val = (104, 116, 124) + transform = dict( + type='Rotate', + level=10, + max_rotate_angle=90, + center=(0, 0), + img_fill_val=img_fill_val, + # set random_negative_prob to 1 for counter-clockwise rotation + random_negative_prob=1., + prob=1.) + results = construct_toy_data() + rotate_module = build_from_cfg(transform, PIPELINES) + results_rotated = rotate_module(copy.deepcopy(results)) + results_gt = copy.deepcopy(results) + h, w = results['img'].shape[:2] + img_r = np.stack([ + np.ones((h, w)) * img_fill_val[0], + np.ones((h, w)) * img_fill_val[1], + np.ones((h, w)) * img_fill_val[2] + ], + axis=-1).astype(np.uint8) + img_r[0, 0, :] = 1 + img_r[0, 1, :] = 5 + results_gt['img'] = img_r + results_gt['gt_bboxes'] = np.empty((0, 4), dtype=np.float32) + results_gt['gt_bboxes_ignore'] = np.empty((0, 4), dtype=np.float32) + results_gt['gt_labels'] = np.empty((0, ), dtype=np.int64) + gt_masks = np.empty((0, h, w), dtype=np.uint8) + results_gt['gt_masks'] = BitmapMasks(gt_masks, h, w) + gt_seg = (np.ones((h, w)) * 255).astype(results['gt_semantic_seg'].dtype) + gt_seg[0, 0], gt_seg[0, 1] = 1, 5 + results_gt['gt_semantic_seg'] = gt_seg + check_rotate(results_gt, results_rotated) + + transform = dict( + type='Rotate', + level=10, + max_rotate_angle=90, + center=(0), + img_fill_val=img_fill_val, + random_negative_prob=1., + prob=1.) + rotate_module = build_from_cfg(transform, PIPELINES) + results_rotated = rotate_module(copy.deepcopy(results)) + check_rotate(results_gt, results_rotated) + + # test counter-clockwise roatation with angle 90, + # and specify the ratation center, PolygonMasks + results = construct_toy_data(poly2mask=False) + results_rotated = rotate_module(copy.deepcopy(results)) + gt_masks = [[np.array([0, 0, 0, 0, 1, 0, 1, 0], dtype=np.float)]] + results_gt['gt_masks'] = PolygonMasks(gt_masks, 2, 4) + check_rotate(results_gt, results_rotated) + + # test AutoAugment equipped with Rotate + policies = [[dict(type='Rotate', level=10, prob=1.)]] + autoaug = dict(type='AutoAugment', policies=policies) + autoaug_module = build_from_cfg(autoaug, PIPELINES) + autoaug_module(copy.deepcopy(results)) + + policies = [[ + dict(type='Rotate', level=10, prob=1.), + dict( + type='Rotate', + level=8, + max_rotate_angle=90, + center=(0), + img_fill_val=img_fill_val) + ]] + autoaug = dict(type='AutoAugment', policies=policies) + autoaug_module = build_from_cfg(autoaug, PIPELINES) + autoaug_module(copy.deepcopy(results)) diff --git a/tests/test_data/test_pipelines/test_transform/test_shear.py b/tests/test_data/test_pipelines/test_transform/test_shear.py new file mode 100644 index 0000000..3d63812 --- /dev/null +++ b/tests/test_data/test_pipelines/test_transform/test_shear.py @@ -0,0 +1,217 @@ +import copy + +import numpy as np +import pytest +from mmcv.utils import build_from_cfg + +from mmdet.core.mask import BitmapMasks, PolygonMasks +from mmdet.datasets.builder import PIPELINES + + +def construct_toy_data(poly2mask=True): + img = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.uint8) + img = np.stack([img, img, img], axis=-1) + results = dict() + # image + results['img'] = img + results['img_shape'] = img.shape + results['img_fields'] = ['img'] + # bboxes + results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore'] + results['gt_bboxes'] = np.array([[0., 0., 2., 1.]], dtype=np.float32) + results['gt_bboxes_ignore'] = np.array([[2., 0., 3., 1.]], + dtype=np.float32) + # labels + results['gt_labels'] = np.array([1], dtype=np.int64) + # masks + results['mask_fields'] = ['gt_masks'] + if poly2mask: + gt_masks = np.array([[0, 1, 1, 0], [0, 1, 0, 0]], + dtype=np.uint8)[None, :, :] + results['gt_masks'] = BitmapMasks(gt_masks, 2, 4) + else: + raw_masks = [[np.array([1, 0, 2, 0, 2, 1, 1, 1], dtype=np.float)]] + results['gt_masks'] = PolygonMasks(raw_masks, 2, 4) + + # segmentations + results['seg_fields'] = ['gt_semantic_seg'] + results['gt_semantic_seg'] = img[..., 0] + return results + + +def _check_fields(results, results_sheared, keys): + for key in keys: + if isinstance(results[key], (BitmapMasks, PolygonMasks)): + assert np.equal(results[key].to_ndarray(), + results_sheared[key].to_ndarray()).all() + else: + assert np.equal(results[key], results_sheared[key]).all() + + +def check_shear(results, results_sheared): + # _check_keys(results, results_sheared) + # check image + _check_fields(results, results_sheared, results.get('img_fields', ['img'])) + # check bboxes + _check_fields(results, results_sheared, results.get('bbox_fields', [])) + # check masks + _check_fields(results, results_sheared, results.get('mask_fields', [])) + # check segmentations + _check_fields(results, results_sheared, results.get('seg_fields', [])) + # check gt_labels + if 'gt_labels' in results: + assert np.equal(results['gt_labels'], + results_sheared['gt_labels']).all() + + +def test_shear(): + # test assertion for invalid type of max_shear_magnitude + with pytest.raises(AssertionError): + transform = dict(type='Shear', level=1, max_shear_magnitude=(0.5, )) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid value of max_shear_magnitude + with pytest.raises(AssertionError): + transform = dict(type='Shear', level=2, max_shear_magnitude=1.2) + build_from_cfg(transform, PIPELINES) + + # test ValueError for invalid type of img_fill_val + with pytest.raises(ValueError): + transform = dict(type='Shear', level=2, img_fill_val=[128]) + build_from_cfg(transform, PIPELINES) + + results = construct_toy_data() + # test case when no shear aug (level=0, direction='horizontal') + img_fill_val = (104, 116, 124) + seg_ignore_label = 255 + transform = dict( + type='Shear', + level=0, + prob=1., + img_fill_val=img_fill_val, + seg_ignore_label=seg_ignore_label, + direction='horizontal') + shear_module = build_from_cfg(transform, PIPELINES) + results_wo_shear = shear_module(copy.deepcopy(results)) + check_shear(results, results_wo_shear) + + # test case when no shear aug (level=0, direction='vertical') + transform = dict( + type='Shear', + level=0, + prob=1., + img_fill_val=img_fill_val, + seg_ignore_label=seg_ignore_label, + direction='vertical') + shear_module = build_from_cfg(transform, PIPELINES) + results_wo_shear = shear_module(copy.deepcopy(results)) + check_shear(results, results_wo_shear) + + # test case when no shear aug (prob<=0) + transform = dict( + type='Shear', + level=10, + prob=0., + img_fill_val=img_fill_val, + direction='vertical') + shear_module = build_from_cfg(transform, PIPELINES) + results_wo_shear = shear_module(copy.deepcopy(results)) + check_shear(results, results_wo_shear) + + # test shear horizontally, magnitude=1 + transform = dict( + type='Shear', + level=10, + prob=1., + img_fill_val=img_fill_val, + direction='horizontal', + max_shear_magnitude=1., + random_negative_prob=0.) + shear_module = build_from_cfg(transform, PIPELINES) + results_sheared = shear_module(copy.deepcopy(results)) + results_gt = copy.deepcopy(results) + img_s = np.array([[1, 2, 3, 4], [0, 5, 6, 7]], dtype=np.uint8) + img_s = np.stack([img_s, img_s, img_s], axis=-1) + img_s[1, 0, :] = np.array(img_fill_val) + results_gt['img'] = img_s + results_gt['gt_bboxes'] = np.array([[0., 0., 3., 1.]], dtype=np.float32) + results_gt['gt_bboxes_ignore'] = np.array([[2., 0., 4., 1.]], + dtype=np.float32) + gt_masks = np.array([[0, 1, 1, 0], [0, 0, 1, 0]], + dtype=np.uint8)[None, :, :] + results_gt['gt_masks'] = BitmapMasks(gt_masks, 2, 4) + results_gt['gt_semantic_seg'] = np.array( + [[1, 2, 3, 4], [255, 5, 6, 7]], dtype=results['gt_semantic_seg'].dtype) + check_shear(results_gt, results_sheared) + + # test PolygonMasks with shear horizontally, magnitude=1 + results = construct_toy_data(poly2mask=False) + results_sheared = shear_module(copy.deepcopy(results)) + gt_masks = [[np.array([1, 0, 2, 0, 3, 1, 2, 1], dtype=np.float)]] + results_gt['gt_masks'] = PolygonMasks(gt_masks, 2, 4) + check_shear(results_gt, results_sheared) + + # test shear vertically, magnitude=-1 + img_fill_val = 128 + results = construct_toy_data() + transform = dict( + type='Shear', + level=10, + prob=1., + img_fill_val=img_fill_val, + direction='vertical', + max_shear_magnitude=1., + random_negative_prob=1.) + shear_module = build_from_cfg(transform, PIPELINES) + results_sheared = shear_module(copy.deepcopy(results)) + results_gt = copy.deepcopy(results) + img_s = np.array([[1, 6, img_fill_val, img_fill_val], + [5, img_fill_val, img_fill_val, img_fill_val]], + dtype=np.uint8) + img_s = np.stack([img_s, img_s, img_s], axis=-1) + results_gt['img'] = img_s + results_gt['gt_bboxes'] = np.empty((0, 4), dtype=np.float32) + results_gt['gt_labels'] = np.empty((0, ), dtype=np.int64) + results_gt['gt_bboxes_ignore'] = np.empty((0, 4), dtype=np.float32) + gt_masks = np.array([[0, 1, 0, 0], [0, 0, 0, 0]], + dtype=np.uint8)[None, :, :] + results_gt['gt_masks'] = BitmapMasks(gt_masks, 2, 4) + results_gt['gt_semantic_seg'] = np.array( + [[1, 6, 255, 255], [5, 255, 255, 255]], + dtype=results['gt_semantic_seg'].dtype) + check_shear(results_gt, results_sheared) + + # test PolygonMasks with shear vertically, magnitude=-1 + results = construct_toy_data(poly2mask=False) + results_sheared = shear_module(copy.deepcopy(results)) + gt_masks = [[np.array([1, 0, 2, 0, 2, 0, 1, 0], dtype=np.float)]] + results_gt['gt_masks'] = PolygonMasks(gt_masks, 2, 4) + check_shear(results_gt, results_sheared) + + results = construct_toy_data() + # same mask for BitmapMasks and PolygonMasks + results['gt_masks'] = BitmapMasks( + np.array([[0, 1, 1, 0], [0, 1, 1, 0]], dtype=np.uint8)[None, :, :], 2, + 4) + results['gt_bboxes'] = np.array([[1., 0., 2., 1.]], dtype=np.float32) + results_sheared_bitmap = shear_module(copy.deepcopy(results)) + check_shear(results_sheared_bitmap, results_sheared) + + # test AutoAugment equipped with Shear + policies = [[dict(type='Shear', level=10, prob=1.)]] + autoaug = dict(type='AutoAugment', policies=policies) + autoaug_module = build_from_cfg(autoaug, PIPELINES) + autoaug_module(copy.deepcopy(results)) + + policies = [[ + dict(type='Shear', level=10, prob=1.), + dict( + type='Shear', + level=8, + img_fill_val=img_fill_val, + direction='vertical', + max_shear_magnitude=1.) + ]] + autoaug = dict(type='AutoAugment', policies=policies) + autoaug_module = build_from_cfg(autoaug, PIPELINES) + autoaug_module(copy.deepcopy(results)) diff --git a/tests/test_data/test_pipelines/test_transform/test_transform.py b/tests/test_data/test_pipelines/test_transform/test_transform.py new file mode 100644 index 0000000..b69d5ef --- /dev/null +++ b/tests/test_data/test_pipelines/test_transform/test_transform.py @@ -0,0 +1,792 @@ +import copy +import os.path as osp + +import mmcv +import numpy as np +import pytest +import torch +from mmcv.utils import build_from_cfg + +from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps +from mmdet.datasets.builder import PIPELINES + + +def test_resize(): + # test assertion if img_scale is a list + with pytest.raises(AssertionError): + transform = dict(type='Resize', img_scale=[1333, 800], keep_ratio=True) + build_from_cfg(transform, PIPELINES) + + # test assertion if len(img_scale) while ratio_range is not None + with pytest.raises(AssertionError): + transform = dict( + type='Resize', + img_scale=[(1333, 800), (1333, 600)], + ratio_range=(0.9, 1.1), + keep_ratio=True) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid multiscale_mode + with pytest.raises(AssertionError): + transform = dict( + type='Resize', + img_scale=[(1333, 800), (1333, 600)], + keep_ratio=True, + multiscale_mode='2333') + build_from_cfg(transform, PIPELINES) + + # test assertion if both scale and scale_factor are setted + with pytest.raises(AssertionError): + results = dict( + img_prefix=osp.join(osp.dirname(__file__), '../../../data'), + img_info=dict(filename='color.jpg')) + load = dict(type='LoadImageFromFile') + load = build_from_cfg(load, PIPELINES) + transform = dict(type='Resize', img_scale=(1333, 800), keep_ratio=True) + transform = build_from_cfg(transform, PIPELINES) + results = load(results) + results['scale'] = (1333, 800) + results['scale_factor'] = 1.0 + results = transform(results) + + transform = dict(type='Resize', img_scale=(1333, 800), keep_ratio=True) + resize_module = build_from_cfg(transform, PIPELINES) + + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + results['img'] = img + results['img2'] = copy.deepcopy(img) + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + # Set initial values for default meta_keys + results['pad_shape'] = img.shape + results['img_fields'] = ['img', 'img2'] + + results = resize_module(results) + assert np.equal(results['img'], results['img2']).all() + + results.pop('scale') + results.pop('scale_factor') + transform = dict( + type='Resize', + img_scale=(1280, 800), + multiscale_mode='value', + keep_ratio=False) + resize_module = build_from_cfg(transform, PIPELINES) + results = resize_module(results) + assert np.equal(results['img'], results['img2']).all() + assert results['img_shape'] == (800, 1280, 3) + + +def test_flip(): + # test assertion for invalid flip_ratio + with pytest.raises(AssertionError): + transform = dict(type='RandomFlip', flip_ratio=1.5) + build_from_cfg(transform, PIPELINES) + # test assertion for 0 <= sum(flip_ratio) <= 1 + with pytest.raises(AssertionError): + transform = dict( + type='RandomFlip', + flip_ratio=[0.7, 0.8], + direction=['horizontal', 'vertical']) + build_from_cfg(transform, PIPELINES) + + # test assertion for mismatch between number of flip_ratio and direction + with pytest.raises(AssertionError): + transform = dict(type='RandomFlip', flip_ratio=[0.4, 0.5]) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid direction + with pytest.raises(AssertionError): + transform = dict( + type='RandomFlip', flip_ratio=1., direction='horizonta') + build_from_cfg(transform, PIPELINES) + + transform = dict(type='RandomFlip', flip_ratio=1.) + flip_module = build_from_cfg(transform, PIPELINES) + + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + original_img = copy.deepcopy(img) + results['img'] = img + results['img2'] = copy.deepcopy(img) + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + # Set initial values for default meta_keys + results['pad_shape'] = img.shape + results['scale_factor'] = 1.0 + results['img_fields'] = ['img', 'img2'] + + results = flip_module(results) + assert np.equal(results['img'], results['img2']).all() + + flip_module = build_from_cfg(transform, PIPELINES) + results = flip_module(results) + assert np.equal(results['img'], results['img2']).all() + assert np.equal(original_img, results['img']).all() + + # test flip_ratio is float, direction is list + transform = dict( + type='RandomFlip', + flip_ratio=0.9, + direction=['horizontal', 'vertical', 'diagonal']) + flip_module = build_from_cfg(transform, PIPELINES) + + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + original_img = copy.deepcopy(img) + results['img'] = img + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + # Set initial values for default meta_keys + results['pad_shape'] = img.shape + results['scale_factor'] = 1.0 + results['img_fields'] = ['img'] + results = flip_module(results) + if results['flip']: + assert np.array_equal( + mmcv.imflip(original_img, results['flip_direction']), + results['img']) + else: + assert np.array_equal(original_img, results['img']) + + # test flip_ratio is list, direction is list + transform = dict( + type='RandomFlip', + flip_ratio=[0.3, 0.3, 0.2], + direction=['horizontal', 'vertical', 'diagonal']) + flip_module = build_from_cfg(transform, PIPELINES) + + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + original_img = copy.deepcopy(img) + results['img'] = img + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + # Set initial values for default meta_keys + results['pad_shape'] = img.shape + results['scale_factor'] = 1.0 + results['img_fields'] = ['img'] + results = flip_module(results) + if results['flip']: + assert np.array_equal( + mmcv.imflip(original_img, results['flip_direction']), + results['img']) + else: + assert np.array_equal(original_img, results['img']) + + +def test_random_crop(): + # test assertion for invalid random crop + with pytest.raises(AssertionError): + transform = dict(type='RandomCrop', crop_size=(-1, 0)) + build_from_cfg(transform, PIPELINES) + + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + results['img'] = img + + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + # TODO: add img_fields test + results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore'] + # Set initial values for default meta_keys + results['pad_shape'] = img.shape + results['scale_factor'] = 1.0 + + def create_random_bboxes(num_bboxes, img_w, img_h): + bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2)) + bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2)) + bboxes = np.concatenate((bboxes_left_top, bboxes_right_bottom), 1) + bboxes = (bboxes * np.array([img_w, img_h, img_w, img_h])).astype( + np.int) + return bboxes + + h, w, _ = img.shape + gt_bboxes = create_random_bboxes(8, w, h) + gt_bboxes_ignore = create_random_bboxes(2, w, h) + results['gt_bboxes'] = gt_bboxes + results['gt_bboxes_ignore'] = gt_bboxes_ignore + transform = dict(type='RandomCrop', crop_size=(h - 20, w - 20)) + crop_module = build_from_cfg(transform, PIPELINES) + results = crop_module(results) + assert results['img'].shape[:2] == (h - 20, w - 20) + # All bboxes should be reserved after crop + assert results['img_shape'][:2] == (h - 20, w - 20) + assert results['gt_bboxes'].shape[0] == 8 + assert results['gt_bboxes_ignore'].shape[0] == 2 + + def area(bboxes): + return np.prod(bboxes[:, 2:4] - bboxes[:, 0:2], axis=1) + + assert (area(results['gt_bboxes']) <= area(gt_bboxes)).all() + assert (area(results['gt_bboxes_ignore']) <= area(gt_bboxes_ignore)).all() + + # test assertion for invalid crop_type + with pytest.raises(ValueError): + transform = dict( + type='RandomCrop', crop_size=(1, 1), crop_type='unknown') + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid crop_size + with pytest.raises(AssertionError): + transform = dict( + type='RandomCrop', crop_type='relative', crop_size=(0, 0)) + build_from_cfg(transform, PIPELINES) + + def _construct_toy_data(): + img = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.uint8) + img = np.stack([img, img, img], axis=-1) + results = dict() + # image + results['img'] = img + results['img_shape'] = img.shape + results['img_fields'] = ['img'] + # bboxes + results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore'] + results['gt_bboxes'] = np.array([[0., 0., 2., 1.]], dtype=np.float32) + results['gt_bboxes_ignore'] = np.array([[2., 0., 3., 1.]], + dtype=np.float32) + # labels + results['gt_labels'] = np.array([1], dtype=np.int64) + return results + + # test crop_type "relative_range" + results = _construct_toy_data() + transform = dict( + type='RandomCrop', + crop_type='relative_range', + crop_size=(0.3, 0.7), + allow_negative_crop=True) + transform_module = build_from_cfg(transform, PIPELINES) + results_transformed = transform_module(copy.deepcopy(results)) + h, w = results_transformed['img_shape'][:2] + assert int(2 * 0.3 + 0.5) <= h <= int(2 * 1 + 0.5) + assert int(4 * 0.7 + 0.5) <= w <= int(4 * 1 + 0.5) + + # test crop_type "relative" + transform = dict( + type='RandomCrop', + crop_type='relative', + crop_size=(0.3, 0.7), + allow_negative_crop=True) + transform_module = build_from_cfg(transform, PIPELINES) + results_transformed = transform_module(copy.deepcopy(results)) + h, w = results_transformed['img_shape'][:2] + assert h == int(2 * 0.3 + 0.5) and w == int(4 * 0.7 + 0.5) + + # test crop_type "absolute" + transform = dict( + type='RandomCrop', + crop_type='absolute', + crop_size=(1, 2), + allow_negative_crop=True) + transform_module = build_from_cfg(transform, PIPELINES) + results_transformed = transform_module(copy.deepcopy(results)) + h, w = results_transformed['img_shape'][:2] + assert h == 1 and w == 2 + + # test crop_type "absolute_range" + transform = dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(1, 20), + allow_negative_crop=True) + transform_module = build_from_cfg(transform, PIPELINES) + results_transformed = transform_module(copy.deepcopy(results)) + h, w = results_transformed['img_shape'][:2] + assert 1 <= h <= 2 and 1 <= w <= 4 + + +def test_min_iou_random_crop(): + + def create_random_bboxes(num_bboxes, img_w, img_h): + bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2)) + bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2)) + bboxes = np.concatenate((bboxes_left_top, bboxes_right_bottom), 1) + bboxes = (bboxes * np.array([img_w, img_h, img_w, img_h])).astype( + np.int) + return bboxes + + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + results['img'] = img + + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore'] + # Set initial values for default meta_keys + results['pad_shape'] = img.shape + results['scale_factor'] = 1.0 + h, w, _ = img.shape + gt_bboxes = create_random_bboxes(1, w, h) + gt_bboxes_ignore = create_random_bboxes(1, w, h) + results['gt_bboxes'] = gt_bboxes + results['gt_bboxes_ignore'] = gt_bboxes_ignore + transform = dict(type='MinIoURandomCrop') + crop_module = build_from_cfg(transform, PIPELINES) + + # Test for img_fields + results_test = copy.deepcopy(results) + results_test['img1'] = results_test['img'] + results_test['img_fields'] = ['img', 'img1'] + with pytest.raises(AssertionError): + crop_module(results_test) + results = crop_module(results) + patch = np.array([0, 0, results['img_shape'][1], results['img_shape'][0]]) + ious = bbox_overlaps(patch.reshape(-1, 4), + results['gt_bboxes']).reshape(-1) + ious_ignore = bbox_overlaps( + patch.reshape(-1, 4), results['gt_bboxes_ignore']).reshape(-1) + mode = crop_module.mode + if mode == 1: + assert np.equal(results['gt_bboxes'], gt_bboxes).all() + assert np.equal(results['gt_bboxes_ignore'], gt_bboxes_ignore).all() + else: + assert (ious >= mode).all() + assert (ious_ignore >= mode).all() + + +def test_pad(): + # test assertion if both size_divisor and size is None + with pytest.raises(AssertionError): + transform = dict(type='Pad') + build_from_cfg(transform, PIPELINES) + + transform = dict(type='Pad', size_divisor=32) + transform = build_from_cfg(transform, PIPELINES) + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + original_img = copy.deepcopy(img) + results['img'] = img + results['img2'] = copy.deepcopy(img) + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + # Set initial values for default meta_keys + results['pad_shape'] = img.shape + results['scale_factor'] = 1.0 + results['img_fields'] = ['img', 'img2'] + + results = transform(results) + assert np.equal(results['img'], results['img2']).all() + # original img already divisible by 32 + assert np.equal(results['img'], original_img).all() + img_shape = results['img'].shape + assert img_shape[0] % 32 == 0 + assert img_shape[1] % 32 == 0 + + resize_transform = dict( + type='Resize', img_scale=(1333, 800), keep_ratio=True) + resize_module = build_from_cfg(resize_transform, PIPELINES) + results = resize_module(results) + results = transform(results) + img_shape = results['img'].shape + assert np.equal(results['img'], results['img2']).all() + assert img_shape[0] % 32 == 0 + assert img_shape[1] % 32 == 0 + + +def test_normalize(): + img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True) + transform = dict(type='Normalize', **img_norm_cfg) + transform = build_from_cfg(transform, PIPELINES) + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + original_img = copy.deepcopy(img) + results['img'] = img + results['img2'] = copy.deepcopy(img) + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + # Set initial values for default meta_keys + results['pad_shape'] = img.shape + results['scale_factor'] = 1.0 + results['img_fields'] = ['img', 'img2'] + + results = transform(results) + assert np.equal(results['img'], results['img2']).all() + + mean = np.array(img_norm_cfg['mean']) + std = np.array(img_norm_cfg['std']) + converted_img = (original_img[..., ::-1] - mean) / std + assert np.allclose(results['img'], converted_img) + + +def test_albu_transform(): + results = dict( + img_prefix=osp.join(osp.dirname(__file__), '../../../data'), + img_info=dict(filename='color.jpg')) + + # Define simple pipeline + load = dict(type='LoadImageFromFile') + load = build_from_cfg(load, PIPELINES) + + albu_transform = dict( + type='Albu', transforms=[dict(type='ChannelShuffle', p=1)]) + albu_transform = build_from_cfg(albu_transform, PIPELINES) + + normalize = dict(type='Normalize', mean=[0] * 3, std=[0] * 3, to_rgb=True) + normalize = build_from_cfg(normalize, PIPELINES) + + # Execute transforms + results = load(results) + results = albu_transform(results) + results = normalize(results) + + assert results['img'].dtype == np.float32 + + +def test_random_center_crop_pad(): + # test assertion for invalid crop_size while test_mode=False + with pytest.raises(AssertionError): + transform = dict( + type='RandomCenterCropPad', + crop_size=(-1, 0), + test_mode=False, + test_pad_mode=None) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid ratios while test_mode=False + with pytest.raises(AssertionError): + transform = dict( + type='RandomCenterCropPad', + crop_size=(511, 511), + ratios=(1.0), + test_mode=False, + test_pad_mode=None) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid mean, std and to_rgb + with pytest.raises(AssertionError): + transform = dict( + type='RandomCenterCropPad', + crop_size=(511, 511), + mean=None, + std=None, + to_rgb=None, + test_mode=False, + test_pad_mode=None) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid crop_size while test_mode=True + with pytest.raises(AssertionError): + transform = dict( + type='RandomCenterCropPad', + crop_size=(511, 511), + ratios=None, + border=None, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True, + test_mode=True, + test_pad_mode=('logical_or', 127)) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid ratios while test_mode=True + with pytest.raises(AssertionError): + transform = dict( + type='RandomCenterCropPad', + crop_size=None, + ratios=(0.9, 1.0, 1.1), + border=None, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True, + test_mode=True, + test_pad_mode=('logical_or', 127)) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid border while test_mode=True + with pytest.raises(AssertionError): + transform = dict( + type='RandomCenterCropPad', + crop_size=None, + ratios=None, + border=128, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True, + test_mode=True, + test_pad_mode=('logical_or', 127)) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid test_pad_mode while test_mode=True + with pytest.raises(AssertionError): + transform = dict( + type='RandomCenterCropPad', + crop_size=None, + ratios=None, + border=None, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True, + test_mode=True, + test_pad_mode=('do_nothing', 100)) + build_from_cfg(transform, PIPELINES) + + results = dict( + img_prefix=osp.join(osp.dirname(__file__), '../../../data'), + img_info=dict(filename='color.jpg')) + + load = dict(type='LoadImageFromFile', to_float32=True) + load = build_from_cfg(load, PIPELINES) + results = load(results) + test_results = copy.deepcopy(results) + + def create_random_bboxes(num_bboxes, img_w, img_h): + bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2)) + bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2)) + bboxes = np.concatenate((bboxes_left_top, bboxes_right_bottom), 1) + bboxes = (bboxes * np.array([img_w, img_h, img_w, img_h])).astype( + np.int) + return bboxes + + h, w, _ = results['img_shape'] + gt_bboxes = create_random_bboxes(8, w, h) + gt_bboxes_ignore = create_random_bboxes(2, w, h) + results['gt_bboxes'] = gt_bboxes + results['gt_bboxes_ignore'] = gt_bboxes_ignore + train_transform = dict( + type='RandomCenterCropPad', + crop_size=(h - 20, w - 20), + ratios=(1.0, ), + border=128, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True, + test_mode=False, + test_pad_mode=None) + crop_module = build_from_cfg(train_transform, PIPELINES) + train_results = crop_module(results) + assert train_results['img'].shape[:2] == (h - 20, w - 20) + # All bboxes should be reserved after crop + assert train_results['pad_shape'][:2] == (h - 20, w - 20) + assert train_results['gt_bboxes'].shape[0] == 8 + assert train_results['gt_bboxes_ignore'].shape[0] == 2 + + test_transform = dict( + type='RandomCenterCropPad', + crop_size=None, + ratios=None, + border=None, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True, + test_mode=True, + test_pad_mode=('logical_or', 127)) + crop_module = build_from_cfg(test_transform, PIPELINES) + + test_results = crop_module(test_results) + assert test_results['img'].shape[:2] == (h | 127, w | 127) + assert test_results['pad_shape'][:2] == (h | 127, w | 127) + assert 'border' in test_results + + +def test_multi_scale_flip_aug(): + # test assertion if give both scale_factor and img_scale + with pytest.raises(AssertionError): + transform = dict( + type='MultiScaleFlipAug', + scale_factor=1.0, + img_scale=[(1333, 800)], + transforms=[dict(type='Resize')]) + build_from_cfg(transform, PIPELINES) + + # test assertion if both scale_factor and img_scale are None + with pytest.raises(AssertionError): + transform = dict( + type='MultiScaleFlipAug', + scale_factor=None, + img_scale=None, + transforms=[dict(type='Resize')]) + build_from_cfg(transform, PIPELINES) + + # test assertion if img_scale is not tuple or list of tuple + with pytest.raises(AssertionError): + transform = dict( + type='MultiScaleFlipAug', + img_scale=[1333, 800], + transforms=[dict(type='Resize')]) + build_from_cfg(transform, PIPELINES) + + # test assertion if flip_direction is not str or list of str + with pytest.raises(AssertionError): + transform = dict( + type='MultiScaleFlipAug', + img_scale=[(1333, 800)], + flip_direction=1, + transforms=[dict(type='Resize')]) + build_from_cfg(transform, PIPELINES) + + scale_transform = dict( + type='MultiScaleFlipAug', + img_scale=[(1333, 800), (1333, 640)], + transforms=[dict(type='Resize', keep_ratio=True)]) + transform = build_from_cfg(scale_transform, PIPELINES) + + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + results['img'] = img + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + # Set initial values for default meta_keys + results['pad_shape'] = img.shape + results['img_fields'] = ['img'] + + scale_results = transform(copy.deepcopy(results)) + assert len(scale_results['img']) == 2 + assert scale_results['img'][0].shape == (750, 1333, 3) + assert scale_results['img_shape'][0] == (750, 1333, 3) + assert scale_results['img'][1].shape == (640, 1138, 3) + assert scale_results['img_shape'][1] == (640, 1138, 3) + + scale_factor_transform = dict( + type='MultiScaleFlipAug', + scale_factor=[0.8, 1.0, 1.2], + transforms=[dict(type='Resize', keep_ratio=False)]) + transform = build_from_cfg(scale_factor_transform, PIPELINES) + scale_factor_results = transform(copy.deepcopy(results)) + assert len(scale_factor_results['img']) == 3 + assert scale_factor_results['img'][0].shape == (230, 409, 3) + assert scale_factor_results['img_shape'][0] == (230, 409, 3) + assert scale_factor_results['img'][1].shape == (288, 512, 3) + assert scale_factor_results['img_shape'][1] == (288, 512, 3) + assert scale_factor_results['img'][2].shape == (345, 614, 3) + assert scale_factor_results['img_shape'][2] == (345, 614, 3) + + # test pipeline of coco_detection + results = dict( + img_prefix=osp.join(osp.dirname(__file__), '../../../data'), + img_info=dict(filename='color.jpg')) + load_cfg, multi_scale_cfg = mmcv.Config.fromfile( + 'configs/_base_/datasets/coco_detection.py').test_pipeline + load = build_from_cfg(load_cfg, PIPELINES) + transform = build_from_cfg(multi_scale_cfg, PIPELINES) + results = transform(load(results)) + assert len(results['img']) == 1 + assert len(results['img_metas']) == 1 + assert isinstance(results['img'][0], torch.Tensor) + assert isinstance(results['img_metas'][0], mmcv.parallel.DataContainer) + assert results['img_metas'][0].data['ori_shape'] == (288, 512, 3) + assert results['img_metas'][0].data['img_shape'] == (750, 1333, 3) + assert results['img_metas'][0].data['pad_shape'] == (768, 1344, 3) + assert results['img_metas'][0].data['scale_factor'].tolist() == [ + 2.603515625, 2.6041667461395264, 2.603515625, 2.6041667461395264 + ] + + +def test_cutout(): + # test n_holes + with pytest.raises(AssertionError): + transform = dict(type='CutOut', n_holes=(5, 3), cutout_shape=(8, 8)) + build_from_cfg(transform, PIPELINES) + with pytest.raises(AssertionError): + transform = dict(type='CutOut', n_holes=(3, 4, 5), cutout_shape=(8, 8)) + build_from_cfg(transform, PIPELINES) + # test cutout_shape and cutout_ratio + with pytest.raises(AssertionError): + transform = dict(type='CutOut', n_holes=1, cutout_shape=8) + build_from_cfg(transform, PIPELINES) + with pytest.raises(AssertionError): + transform = dict(type='CutOut', n_holes=1, cutout_ratio=0.2) + build_from_cfg(transform, PIPELINES) + # either of cutout_shape and cutout_ratio should be given + with pytest.raises(AssertionError): + transform = dict(type='CutOut', n_holes=1) + build_from_cfg(transform, PIPELINES) + with pytest.raises(AssertionError): + transform = dict( + type='CutOut', + n_holes=1, + cutout_shape=(2, 2), + cutout_ratio=(0.4, 0.4)) + build_from_cfg(transform, PIPELINES) + + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + + results['img'] = img + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + results['pad_shape'] = img.shape + results['img_fields'] = ['img'] + + transform = dict(type='CutOut', n_holes=1, cutout_shape=(10, 10)) + cutout_module = build_from_cfg(transform, PIPELINES) + cutout_result = cutout_module(copy.deepcopy(results)) + assert cutout_result['img'].sum() < img.sum() + + transform = dict(type='CutOut', n_holes=1, cutout_ratio=(0.8, 0.8)) + cutout_module = build_from_cfg(transform, PIPELINES) + cutout_result = cutout_module(copy.deepcopy(results)) + assert cutout_result['img'].sum() < img.sum() + + transform = dict( + type='CutOut', + n_holes=(2, 4), + cutout_shape=[(10, 10), (15, 15)], + fill_in=(255, 255, 255)) + cutout_module = build_from_cfg(transform, PIPELINES) + cutout_result = cutout_module(copy.deepcopy(results)) + assert cutout_result['img'].sum() > img.sum() + + transform = dict( + type='CutOut', + n_holes=1, + cutout_ratio=(0.8, 0.8), + fill_in=(255, 255, 255)) + cutout_module = build_from_cfg(transform, PIPELINES) + cutout_result = cutout_module(copy.deepcopy(results)) + assert cutout_result['img'].sum() > img.sum() + + +def test_random_shift(): + # test assertion for invalid shift_ratio + with pytest.raises(AssertionError): + transform = dict(type='RandomShift', shift_ratio=1.5) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid max_shift_px + with pytest.raises(AssertionError): + transform = dict(type='RandomShift', max_shift_px=-1) + build_from_cfg(transform, PIPELINES) + + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + results['img'] = img + # TODO: add img_fields test + results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore'] + + def create_random_bboxes(num_bboxes, img_w, img_h): + bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2)) + bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2)) + bboxes = np.concatenate((bboxes_left_top, bboxes_right_bottom), 1) + bboxes = (bboxes * np.array([img_w, img_h, img_w, img_h])).astype( + np.int) + return bboxes + + h, w, _ = img.shape + gt_bboxes = create_random_bboxes(8, w, h) + gt_bboxes_ignore = create_random_bboxes(2, w, h) + results['gt_labels'] = torch.ones(gt_bboxes.shape[0]) + results['gt_bboxes'] = gt_bboxes + results['gt_bboxes_ignore'] = gt_bboxes_ignore + transform = dict(type='RandomShift', shift_ratio=1.0) + random_shift_module = build_from_cfg(transform, PIPELINES) + results = random_shift_module(results) + + assert results['img'].shape[:2] == (h, w) + assert results['gt_labels'].shape[0] == results['gt_bboxes'].shape[0] diff --git a/tests/test_data/test_pipelines/test_transform/test_translate.py b/tests/test_data/test_pipelines/test_transform/test_translate.py new file mode 100644 index 0000000..87f37d0 --- /dev/null +++ b/tests/test_data/test_pipelines/test_transform/test_translate.py @@ -0,0 +1,515 @@ +import copy + +import numpy as np +import pycocotools.mask as maskUtils +import pytest +from mmcv.utils import build_from_cfg + +from mmdet.core.mask import BitmapMasks, PolygonMasks +from mmdet.datasets.builder import PIPELINES + + +def _check_keys(results, results_translated): + assert len(set(results.keys()).difference(set( + results_translated.keys()))) == 0 + assert len(set(results_translated.keys()).difference(set( + results.keys()))) == 0 + + +def _pad(h, w, c, pad_val, axis=-1, dtype=np.float32): + assert isinstance(pad_val, (int, float, tuple)) + if isinstance(pad_val, (int, float)): + pad_val = tuple([pad_val] * c) + assert len(pad_val) == c + pad_data = np.stack([np.ones((h, w)) * pad_val[i] for i in range(c)], + axis=axis).astype(dtype) + return pad_data + + +def _construct_img(results): + h, w = results['img_info']['height'], results['img_info']['width'] + img = np.random.uniform(0, 1, (h, w, 3)) * 255 + img = img.astype(np.uint8) + results['img'] = img + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + results['img_fields'] = ['img'] + + +def _construct_ann_info(h=427, w=640, c=3): + bboxes = np.array( + [[222.62, 217.82, 241.81, 238.93], [50.5, 329.7, 130.23, 384.96], + [175.47, 331.97, 254.8, 389.26]], + dtype=np.float32) + labels = np.array([9, 2, 2], dtype=np.int64) + bboxes_ignore = np.array([[59., 253., 311., 337.]], dtype=np.float32) + masks = [ + [[222.62, 217.82, 222.62, 238.93, 241.81, 238.93, 240.85, 218.78]], + [[ + 69.19, 332.17, 82.39, 330.25, 97.24, 329.7, 114.01, 331.35, 116.76, + 337.39, 119.78, 343.17, 128.03, 344.54, 128.86, 347.84, 124.18, + 350.59, 129.96, 358.01, 130.23, 366.54, 129.13, 377.81, 125.28, + 382.48, 119.78, 381.93, 117.31, 377.54, 116.21, 379.46, 114.83, + 382.21, 107.14, 383.31, 105.49, 378.36, 77.99, 377.54, 75.79, + 381.11, 69.74, 381.93, 66.72, 378.91, 65.07, 377.81, 63.15, 379.19, + 62.32, 383.31, 52.7, 384.96, 50.5, 379.46, 51.32, 375.61, 51.6, + 370.11, 51.6, 364.06, 53.52, 354.99, 56.27, 344.54, 59.57, 336.29, + 66.45, 332.72 + ]], + [[ + 175.47, 386.86, 175.87, 376.44, 177.08, 351.2, 189.1, 332.77, + 194.31, 331.97, 236.37, 332.77, 244.79, 342.39, 246.79, 346.79, + 248.39, 345.99, 251.6, 345.59, 254.8, 348.0, 254.8, 351.6, 250.0, + 352.0, 250.0, 354.81, 251.6, 358.41, 251.6, 364.42, 251.6, 370.03, + 252.8, 378.04, 252.8, 384.05, 250.8, 387.26, 246.39, 387.66, + 245.19, 386.46, 242.38, 388.86, 233.97, 389.26, 232.77, 388.06, + 232.77, 383.65, 195.91, 381.25, 195.91, 384.86, 191.1, 384.86, + 187.49, 385.26, 186.69, 382.85, 184.29, 382.45, 183.09, 387.26, + 178.68, 388.46, 176.28, 387.66 + ]] + ] + return dict( + bboxes=bboxes, labels=labels, bboxes_ignore=bboxes_ignore, masks=masks) + + +def _load_bboxes(results): + ann_info = results['ann_info'] + results['gt_bboxes'] = ann_info['bboxes'].copy() + results['bbox_fields'] = ['gt_bboxes'] + gt_bboxes_ignore = ann_info.get('bboxes_ignore', None) + if gt_bboxes_ignore is not None: + results['gt_bboxes_ignore'] = gt_bboxes_ignore.copy() + results['bbox_fields'].append('gt_bboxes_ignore') + + +def _load_labels(results): + results['gt_labels'] = results['ann_info']['labels'].copy() + + +def _poly2mask(mask_ann, img_h, img_w): + if isinstance(mask_ann, list): + # polygon -- a single object might consist of multiple parts + # we merge all parts into one mask rle code + rles = maskUtils.frPyObjects(mask_ann, img_h, img_w) + rle = maskUtils.merge(rles) + elif isinstance(mask_ann['counts'], list): + # uncompressed RLE + rle = maskUtils.frPyObjects(mask_ann, img_h, img_w) + else: + # rle + rle = mask_ann + mask = maskUtils.decode(rle) + return mask + + +def _process_polygons(polygons): + polygons = [np.array(p) for p in polygons] + valid_polygons = [] + for polygon in polygons: + if len(polygon) % 2 == 0 and len(polygon) >= 6: + valid_polygons.append(polygon) + return valid_polygons + + +def _load_masks(results, poly2mask=True): + h, w = results['img_info']['height'], results['img_info']['width'] + gt_masks = results['ann_info']['masks'] + if poly2mask: + gt_masks = BitmapMasks([_poly2mask(mask, h, w) for mask in gt_masks], + h, w) + else: + gt_masks = PolygonMasks( + [_process_polygons(polygons) for polygons in gt_masks], h, w) + results['gt_masks'] = gt_masks + results['mask_fields'] = ['gt_masks'] + + +def _construct_semantic_seg(results): + h, w = results['img_info']['height'], results['img_info']['width'] + seg_toy = (np.random.uniform(0, 1, (h, w)) * 255).astype(np.uint8) + results['gt_semantic_seg'] = seg_toy + results['seg_fields'] = ['gt_semantic_seg'] + + +def construct_toy_data(poly2mask=True): + img_info = dict(height=427, width=640) + ann_info = _construct_ann_info(h=img_info['height'], w=img_info['width']) + results = dict(img_info=img_info, ann_info=ann_info) + # construct image, similar to 'LoadImageFromFile' + _construct_img(results) + # 'LoadAnnotations' (bboxes, labels, masks, semantic_seg) + _load_bboxes(results) + _load_labels(results) + _load_masks(results, poly2mask) + _construct_semantic_seg(results) + return results + + +def test_translate(): + # test assertion for invalid value of level + with pytest.raises(AssertionError): + transform = dict(type='Translate', level=-1) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid type of level + with pytest.raises(AssertionError): + transform = dict(type='Translate', level=[1]) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid prob + with pytest.raises(AssertionError): + transform = dict(type='Translate', level=1, prob=-0.5) + build_from_cfg(transform, PIPELINES) + + # test assertion for the num of elements in tuple img_fill_val + with pytest.raises(AssertionError): + transform = dict( + type='Translate', level=1, img_fill_val=(128, 128, 128, 128)) + build_from_cfg(transform, PIPELINES) + + # test ValueError for invalid type of img_fill_val + with pytest.raises(ValueError): + transform = dict( + type='Translate', level=1, img_fill_val=[128, 128, 128]) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid value of img_fill_val + with pytest.raises(AssertionError): + transform = dict( + type='Translate', level=1, img_fill_val=(128, -1, 256)) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid value of direction + with pytest.raises(AssertionError): + transform = dict( + type='Translate', level=1, img_fill_val=128, direction='diagonal') + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid type of max_translate_offset + with pytest.raises(AssertionError): + transform = dict( + type='Translate', + level=1, + img_fill_val=128, + max_translate_offset=(250., )) + build_from_cfg(transform, PIPELINES) + + # construct toy data example for unit test + results = construct_toy_data() + + def _check_bbox_mask(results, + results_translated, + offset, + direction, + min_size=0.): + # The key correspondence from bboxes to labels and masks. + bbox2label = { + 'gt_bboxes': 'gt_labels', + 'gt_bboxes_ignore': 'gt_labels_ignore' + } + bbox2mask = { + 'gt_bboxes': 'gt_masks', + 'gt_bboxes_ignore': 'gt_masks_ignore' + } + + def _translate_bbox(bboxes, offset, direction, max_h, max_w): + if direction == 'horizontal': + bboxes[:, 0::2] = bboxes[:, 0::2] + offset + elif direction == 'vertical': + bboxes[:, 1::2] = bboxes[:, 1::2] + offset + else: + raise ValueError + bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, max_w) + bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, max_h) + return bboxes + + h, w, c = results_translated['img'].shape + for key in results_translated.get('bbox_fields', []): + label_key, mask_key = bbox2label[key], bbox2mask[key] + # check length of key + if label_key in results: + assert len(results_translated[key]) == len( + results_translated[label_key]) + if mask_key in results: + assert len(results_translated[key]) == len( + results_translated[mask_key]) + # construct gt_bboxes + gt_bboxes = _translate_bbox( + copy.deepcopy(results[key]), offset, direction, h, w) + valid_inds = (gt_bboxes[:, 2] - gt_bboxes[:, 0] > min_size) & ( + gt_bboxes[:, 3] - gt_bboxes[:, 1] > min_size) + gt_bboxes = gt_bboxes[valid_inds] + # check bbox + assert np.equal(gt_bboxes, results_translated[key]).all() + + # construct gt_masks + if mask_key not in results: + # e.g. 'gt_masks_ignore' + continue + masks, masks_translated = results[mask_key].to_ndarray( + ), results_translated[mask_key].to_ndarray() + assert masks.dtype == masks_translated.dtype + if direction == 'horizontal': + masks_pad = _pad( + h, + abs(offset), + masks.shape[0], + 0, + axis=0, + dtype=masks.dtype) + if offset <= 0: + # left shift + gt_masks = np.concatenate( + (masks[:, :, -offset:], masks_pad), axis=-1) + else: + # right shift + gt_masks = np.concatenate( + (masks_pad, masks[:, :, :-offset]), axis=-1) + else: + masks_pad = _pad( + abs(offset), + w, + masks.shape[0], + 0, + axis=0, + dtype=masks.dtype) + if offset <= 0: + # top shift + gt_masks = np.concatenate( + (masks[:, -offset:, :], masks_pad), axis=1) + else: + # bottom shift + gt_masks = np.concatenate( + (masks_pad, masks[:, :-offset, :]), axis=1) + gt_masks = gt_masks[valid_inds] + # check masks + assert np.equal(gt_masks, masks_translated).all() + + def _check_img_seg(results, results_translated, keys, offset, fill_val, + direction): + for key in keys: + assert isinstance(results_translated[key], type(results[key])) + # assert type(results[key]) == type(results_translated[key]) + data, data_translated = results[key], results_translated[key] + if 'mask' in key: + data, data_translated = data.to_ndarray( + ), data_translated.to_ndarray() + assert data.dtype == data_translated.dtype + if 'img' in key: + data, data_translated = data.transpose( + (2, 0, 1)), data_translated.transpose((2, 0, 1)) + elif 'seg' in key: + data, data_translated = data[None, :, :], data_translated[ + None, :, :] + c, h, w = data.shape + if direction == 'horizontal': + data_pad = _pad( + h, abs(offset), c, fill_val, axis=0, dtype=data.dtype) + if offset <= 0: + # left shift + data_gt = np.concatenate((data[:, :, -offset:], data_pad), + axis=-1) + else: + # right shift + data_gt = np.concatenate((data_pad, data[:, :, :-offset]), + axis=-1) + else: + data_pad = _pad( + abs(offset), w, c, fill_val, axis=0, dtype=data.dtype) + if offset <= 0: + # top shift + data_gt = np.concatenate((data[:, -offset:, :], data_pad), + axis=1) + else: + # bottom shift + data_gt = np.concatenate((data_pad, data[:, :-offset, :]), + axis=1) + if 'mask' in key: + # TODO assertion here. ``data_translated`` must be a subset + # (or equal) of ``data_gt`` + pass + else: + assert np.equal(data_gt, data_translated).all() + + def check_translate(results, + results_translated, + offset, + img_fill_val, + seg_ignore_label, + direction, + min_size=0): + # check keys + _check_keys(results, results_translated) + # check image + _check_img_seg(results, results_translated, + results.get('img_fields', ['img']), offset, + img_fill_val, direction) + # check segmentation map + _check_img_seg(results, results_translated, + results.get('seg_fields', []), offset, seg_ignore_label, + direction) + # check masks and bboxes + _check_bbox_mask(results, results_translated, offset, direction, + min_size) + + # test case when level=0 (without translate aug) + img_fill_val = (104, 116, 124) + seg_ignore_label = 255 + transform = dict( + type='Translate', + level=0, + prob=1.0, + img_fill_val=img_fill_val, + seg_ignore_label=seg_ignore_label) + translate_module = build_from_cfg(transform, PIPELINES) + results_wo_translate = translate_module(copy.deepcopy(results)) + check_translate( + copy.deepcopy(results), + results_wo_translate, + 0, + img_fill_val, + seg_ignore_label, + 'horizontal', + ) + + # test case when level>0 and translate horizontally (left shift). + transform = dict( + type='Translate', + level=8, + prob=1.0, + img_fill_val=img_fill_val, + random_negative_prob=1.0, + seg_ignore_label=seg_ignore_label) + translate_module = build_from_cfg(transform, PIPELINES) + offset = translate_module.offset + results_translated = translate_module(copy.deepcopy(results)) + check_translate( + copy.deepcopy(results), + results_translated, + -offset, + img_fill_val, + seg_ignore_label, + 'horizontal', + ) + + # test case when level>0 and translate horizontally (right shift). + translate_module.random_negative_prob = 0.0 + results_translated = translate_module(copy.deepcopy(results)) + check_translate( + copy.deepcopy(results), + results_translated, + offset, + img_fill_val, + seg_ignore_label, + 'horizontal', + ) + + # test case when level>0 and translate vertically (top shift). + transform = dict( + type='Translate', + level=10, + prob=1.0, + img_fill_val=img_fill_val, + seg_ignore_label=seg_ignore_label, + random_negative_prob=1.0, + direction='vertical') + translate_module = build_from_cfg(transform, PIPELINES) + offset = translate_module.offset + results_translated = translate_module(copy.deepcopy(results)) + check_translate( + copy.deepcopy(results), results_translated, -offset, img_fill_val, + seg_ignore_label, 'vertical') + + # test case when level>0 and translate vertically (bottom shift). + translate_module.random_negative_prob = 0.0 + results_translated = translate_module(copy.deepcopy(results)) + check_translate( + copy.deepcopy(results), results_translated, offset, img_fill_val, + seg_ignore_label, 'vertical') + + # test case when no translation is called (prob<=0) + transform = dict( + type='Translate', + level=8, + prob=0.0, + img_fill_val=img_fill_val, + random_negative_prob=0.0, + seg_ignore_label=seg_ignore_label) + translate_module = build_from_cfg(transform, PIPELINES) + results_translated = translate_module(copy.deepcopy(results)) + + # test translate vertically with PolygonMasks (top shift) + results = construct_toy_data(False) + transform = dict( + type='Translate', + level=10, + prob=1.0, + img_fill_val=img_fill_val, + seg_ignore_label=seg_ignore_label, + direction='vertical') + translate_module = build_from_cfg(transform, PIPELINES) + offset = translate_module.offset + translate_module.random_negative_prob = 1.0 + results_translated = translate_module(copy.deepcopy(results)) + + def _translated_gt(masks, direction, offset, out_shape): + translated_masks = [] + for poly_per_obj in masks: + translated_poly_per_obj = [] + for p in poly_per_obj: + p = p.copy() + if direction == 'horizontal': + p[0::2] = np.clip(p[0::2] + offset, 0, out_shape[1]) + elif direction == 'vertical': + p[1::2] = np.clip(p[1::2] + offset, 0, out_shape[0]) + if PolygonMasks([[p]], *out_shape).areas[0] > 0: + # filter invalid (area=0) + translated_poly_per_obj.append(p) + if len(translated_poly_per_obj): + translated_masks.append(translated_poly_per_obj) + translated_masks = PolygonMasks(translated_masks, *out_shape) + return translated_masks + + h, w = results['img_shape'][:2] + for key in results.get('mask_fields', []): + masks = results[key] + translated_gt = _translated_gt(masks, 'vertical', -offset, (h, w)) + assert np.equal(results_translated[key].to_ndarray(), + translated_gt.to_ndarray()).all() + + # test translate horizontally with PolygonMasks (right shift) + results = construct_toy_data(False) + transform = dict( + type='Translate', + level=8, + prob=1.0, + img_fill_val=img_fill_val, + random_negative_prob=0.0, + seg_ignore_label=seg_ignore_label) + translate_module = build_from_cfg(transform, PIPELINES) + offset = translate_module.offset + results_translated = translate_module(copy.deepcopy(results)) + h, w = results['img_shape'][:2] + for key in results.get('mask_fields', []): + masks = results[key] + translated_gt = _translated_gt(masks, 'horizontal', offset, (h, w)) + assert np.equal(results_translated[key].to_ndarray(), + translated_gt.to_ndarray()).all() + + # test AutoAugment equipped with Translate + policies = [[dict(type='Translate', level=10, prob=1.)]] + autoaug = dict(type='AutoAugment', policies=policies) + autoaug_module = build_from_cfg(autoaug, PIPELINES) + autoaug_module(copy.deepcopy(results)) + + policies = [[ + dict(type='Translate', level=10, prob=1.), + dict( + type='Translate', + level=8, + img_fill_val=img_fill_val, + direction='vertical') + ]] + autoaug = dict(type='AutoAugment', policies=policies) + autoaug_module = build_from_cfg(autoaug, PIPELINES) + autoaug_module(copy.deepcopy(results)) diff --git a/tests/test_data/test_utils.py b/tests/test_data/test_utils.py new file mode 100644 index 0000000..cd612f2 --- /dev/null +++ b/tests/test_data/test_utils.py @@ -0,0 +1,79 @@ +import pytest + +from mmdet.datasets import get_loading_pipeline, replace_ImageToTensor + + +def test_replace_ImageToTensor(): + # with MultiScaleFlipAug + pipelines = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize'), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) + ] + expected_pipelines = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize'), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img']), + ]) + ] + with pytest.warns(UserWarning): + assert expected_pipelines == replace_ImageToTensor(pipelines) + + # without MultiScaleFlipAug + pipelines = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize'), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ] + expected_pipelines = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize'), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img']), + ] + with pytest.warns(UserWarning): + assert expected_pipelines == replace_ImageToTensor(pipelines) + + +def test_get_loading_pipeline(): + pipelines = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) + ] + expected_pipelines = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True) + ] + assert expected_pipelines == \ + get_loading_pipeline(pipelines) diff --git a/tests/test_metrics/test_box_overlap.py b/tests/test_metrics/test_box_overlap.py new file mode 100644 index 0000000..94c6400 --- /dev/null +++ b/tests/test_metrics/test_box_overlap.py @@ -0,0 +1,105 @@ +import numpy as np +import pytest +import torch + +from mmdet.core import BboxOverlaps2D, bbox_overlaps + + +def test_bbox_overlaps_2d(eps=1e-7): + + def _construct_bbox(num_bbox=None): + img_h = int(np.random.randint(3, 1000)) + img_w = int(np.random.randint(3, 1000)) + if num_bbox is None: + num_bbox = np.random.randint(1, 10) + x1y1 = torch.rand((num_bbox, 2)) + x2y2 = torch.max(torch.rand((num_bbox, 2)), x1y1) + bboxes = torch.cat((x1y1, x2y2), -1) + bboxes[:, 0::2] *= img_w + bboxes[:, 1::2] *= img_h + return bboxes, num_bbox + + # is_aligned is True, bboxes.size(-1) == 5 (include score) + self = BboxOverlaps2D() + bboxes1, num_bbox = _construct_bbox() + bboxes2, _ = _construct_bbox(num_bbox) + bboxes1 = torch.cat((bboxes1, torch.rand((num_bbox, 1))), 1) + bboxes2 = torch.cat((bboxes2, torch.rand((num_bbox, 1))), 1) + gious = self(bboxes1, bboxes2, 'giou', True) + assert gious.size() == (num_bbox, ), gious.size() + assert torch.all(gious >= -1) and torch.all(gious <= 1) + + # is_aligned is True, bboxes1.size(-2) == 0 + bboxes1 = torch.empty((0, 4)) + bboxes2 = torch.empty((0, 4)) + gious = self(bboxes1, bboxes2, 'giou', True) + assert gious.size() == (0, ), gious.size() + assert torch.all(gious == torch.empty((0, ))) + assert torch.all(gious >= -1) and torch.all(gious <= 1) + + # is_aligned is True, and bboxes.ndims > 2 + bboxes1, num_bbox = _construct_bbox() + bboxes2, _ = _construct_bbox(num_bbox) + bboxes1 = bboxes1.unsqueeze(0).repeat(2, 1, 1) + # test assertion when batch dim is not the same + with pytest.raises(AssertionError): + self(bboxes1, bboxes2.unsqueeze(0).repeat(3, 1, 1), 'giou', True) + bboxes2 = bboxes2.unsqueeze(0).repeat(2, 1, 1) + gious = self(bboxes1, bboxes2, 'giou', True) + assert torch.all(gious >= -1) and torch.all(gious <= 1) + assert gious.size() == (2, num_bbox) + bboxes1 = bboxes1.unsqueeze(0).repeat(2, 1, 1, 1) + bboxes2 = bboxes2.unsqueeze(0).repeat(2, 1, 1, 1) + gious = self(bboxes1, bboxes2, 'giou', True) + assert torch.all(gious >= -1) and torch.all(gious <= 1) + assert gious.size() == (2, 2, num_bbox) + + # is_aligned is False + bboxes1, num_bbox1 = _construct_bbox() + bboxes2, num_bbox2 = _construct_bbox() + gious = self(bboxes1, bboxes2, 'giou') + assert torch.all(gious >= -1) and torch.all(gious <= 1) + assert gious.size() == (num_bbox1, num_bbox2) + + # is_aligned is False, and bboxes.ndims > 2 + bboxes1 = bboxes1.unsqueeze(0).repeat(2, 1, 1) + bboxes2 = bboxes2.unsqueeze(0).repeat(2, 1, 1) + gious = self(bboxes1, bboxes2, 'giou') + assert torch.all(gious >= -1) and torch.all(gious <= 1) + assert gious.size() == (2, num_bbox1, num_bbox2) + bboxes1 = bboxes1.unsqueeze(0) + bboxes2 = bboxes2.unsqueeze(0) + gious = self(bboxes1, bboxes2, 'giou') + assert torch.all(gious >= -1) and torch.all(gious <= 1) + assert gious.size() == (1, 2, num_bbox1, num_bbox2) + + # is_aligned is False, bboxes1.size(-2) == 0 + gious = self(torch.empty(1, 2, 0, 4), bboxes2, 'giou') + assert torch.all(gious == torch.empty(1, 2, 0, bboxes2.size(-2))) + assert torch.all(gious >= -1) and torch.all(gious <= 1) + + # test allclose between bbox_overlaps and the original official + # implementation. + bboxes1 = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [32, 32, 38, 42], + ]) + bboxes2 = torch.FloatTensor([ + [0, 0, 10, 20], + [0, 10, 10, 19], + [10, 10, 20, 20], + ]) + gious = bbox_overlaps(bboxes1, bboxes2, 'giou', is_aligned=True, eps=eps) + gious = gious.numpy().round(4) + # the gt is got with four decimal precision. + expected_gious = np.array([0.5000, -0.0500, -0.8214]) + assert np.allclose(gious, expected_gious, rtol=0, atol=eps) + + # test mode 'iof' + ious = bbox_overlaps(bboxes1, bboxes2, 'iof', is_aligned=True, eps=eps) + assert torch.all(ious >= -1) and torch.all(ious <= 1) + assert ious.size() == (bboxes1.size(0), ) + ious = bbox_overlaps(bboxes1, bboxes2, 'iof', eps=eps) + assert torch.all(ious >= -1) and torch.all(ious <= 1) + assert ious.size() == (bboxes1.size(0), bboxes2.size(0)) diff --git a/tests/test_metrics/test_losses.py b/tests/test_metrics/test_losses.py new file mode 100644 index 0000000..5370f0e --- /dev/null +++ b/tests/test_metrics/test_losses.py @@ -0,0 +1,167 @@ +import pytest +import torch + +from mmdet.models import Accuracy, build_loss + + +def test_ce_loss(): + # use_mask and use_sigmoid cannot be true at the same time + with pytest.raises(AssertionError): + loss_cfg = dict( + type='CrossEntropyLoss', + use_mask=True, + use_sigmoid=True, + loss_weight=1.0) + build_loss(loss_cfg) + + # test loss with class weights + loss_cls_cfg = dict( + type='CrossEntropyLoss', + use_sigmoid=False, + class_weight=[0.8, 0.2], + loss_weight=1.0) + loss_cls = build_loss(loss_cls_cfg) + fake_pred = torch.Tensor([[100, -100]]) + fake_label = torch.Tensor([1]).long() + assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(40.)) + + loss_cls_cfg = dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0) + loss_cls = build_loss(loss_cls_cfg) + assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(200.)) + + +def test_varifocal_loss(): + # only sigmoid version of VarifocalLoss is implemented + with pytest.raises(AssertionError): + loss_cfg = dict( + type='VarifocalLoss', use_sigmoid=False, loss_weight=1.0) + build_loss(loss_cfg) + + # test that alpha should be greater than 0 + with pytest.raises(AssertionError): + loss_cfg = dict( + type='VarifocalLoss', + alpha=-0.75, + gamma=2.0, + use_sigmoid=True, + loss_weight=1.0) + build_loss(loss_cfg) + + # test that pred and target should be of the same size + loss_cls_cfg = dict( + type='VarifocalLoss', + use_sigmoid=True, + alpha=0.75, + gamma=2.0, + iou_weighted=True, + reduction='mean', + loss_weight=1.0) + loss_cls = build_loss(loss_cls_cfg) + with pytest.raises(AssertionError): + fake_pred = torch.Tensor([[100.0, -100.0]]) + fake_target = torch.Tensor([[1.0]]) + loss_cls(fake_pred, fake_target) + + # test the calculation + loss_cls = build_loss(loss_cls_cfg) + fake_pred = torch.Tensor([[100.0, -100.0]]) + fake_target = torch.Tensor([[1.0, 0.0]]) + assert torch.allclose(loss_cls(fake_pred, fake_target), torch.tensor(0.0)) + + # test the loss with weights + loss_cls = build_loss(loss_cls_cfg) + fake_pred = torch.Tensor([[0.0, 100.0]]) + fake_target = torch.Tensor([[1.0, 1.0]]) + fake_weight = torch.Tensor([0.0, 1.0]) + assert torch.allclose( + loss_cls(fake_pred, fake_target, fake_weight), torch.tensor(0.0)) + + +def test_kd_loss(): + # test that temeprature should be greater than 1 + with pytest.raises(AssertionError): + loss_cfg = dict( + type='KnowledgeDistillationKLDivLoss', loss_weight=1.0, T=0.5) + build_loss(loss_cfg) + + # test that pred and target should be of the same size + loss_cls_cfg = dict( + type='KnowledgeDistillationKLDivLoss', loss_weight=1.0, T=1) + loss_cls = build_loss(loss_cls_cfg) + with pytest.raises(AssertionError): + fake_pred = torch.Tensor([[100, -100]]) + fake_label = torch.Tensor([1]).long() + loss_cls(fake_pred, fake_label) + + # test the calculation + loss_cls = build_loss(loss_cls_cfg) + fake_pred = torch.Tensor([[100.0, 100.0]]) + fake_target = torch.Tensor([[1.0, 1.0]]) + assert torch.allclose(loss_cls(fake_pred, fake_target), torch.tensor(0.0)) + + # test the loss with weights + loss_cls = build_loss(loss_cls_cfg) + fake_pred = torch.Tensor([[100.0, -100.0], [100.0, 100.0]]) + fake_target = torch.Tensor([[1.0, 0.0], [1.0, 1.0]]) + fake_weight = torch.Tensor([0.0, 1.0]) + assert torch.allclose( + loss_cls(fake_pred, fake_target, fake_weight), torch.tensor(0.0)) + + +def test_accuracy(): + # test for empty pred + pred = torch.empty(0, 4) + label = torch.empty(0) + accuracy = Accuracy(topk=1) + acc = accuracy(pred, label) + assert acc.item() == 0 + + pred = torch.Tensor([[0.2, 0.3, 0.6, 0.5], [0.1, 0.1, 0.2, 0.6], + [0.9, 0.0, 0.0, 0.1], [0.4, 0.7, 0.1, 0.1], + [0.0, 0.0, 0.99, 0]]) + # test for top1 + true_label = torch.Tensor([2, 3, 0, 1, 2]).long() + accuracy = Accuracy(topk=1) + acc = accuracy(pred, true_label) + assert acc.item() == 100 + + # test for top1 with score thresh=0.8 + true_label = torch.Tensor([2, 3, 0, 1, 2]).long() + accuracy = Accuracy(topk=1, thresh=0.8) + acc = accuracy(pred, true_label) + assert acc.item() == 40 + + # test for top2 + accuracy = Accuracy(topk=2) + label = torch.Tensor([3, 2, 0, 0, 2]).long() + acc = accuracy(pred, label) + assert acc.item() == 100 + + # test for both top1 and top2 + accuracy = Accuracy(topk=(1, 2)) + true_label = torch.Tensor([2, 3, 0, 1, 2]).long() + acc = accuracy(pred, true_label) + for a in acc: + assert a.item() == 100 + + # topk is larger than pred class number + with pytest.raises(AssertionError): + accuracy = Accuracy(topk=5) + accuracy(pred, true_label) + + # wrong topk type + with pytest.raises(AssertionError): + accuracy = Accuracy(topk='wrong type') + accuracy(pred, true_label) + + # label size is larger than required + with pytest.raises(AssertionError): + label = torch.Tensor([2, 3, 0, 1, 2, 0]).long() # size mismatch + accuracy = Accuracy() + accuracy(pred, label) + + # wrong pred dimension + with pytest.raises(AssertionError): + accuracy = Accuracy() + accuracy(pred[:, :, None], true_label) diff --git a/tests/test_models/test_backbones/__init__.py b/tests/test_models/test_backbones/__init__.py new file mode 100644 index 0000000..ce4596a --- /dev/null +++ b/tests/test_models/test_backbones/__init__.py @@ -0,0 +1,3 @@ +from .utils import check_norm_state, is_block, is_norm + +__all__ = ['is_block', 'is_norm', 'check_norm_state'] diff --git a/tests/test_models/test_backbones/test_hourglass.py b/tests/test_models/test_backbones/test_hourglass.py new file mode 100644 index 0000000..363c94d --- /dev/null +++ b/tests/test_models/test_backbones/test_hourglass.py @@ -0,0 +1,44 @@ +import pytest +import torch + +from mmdet.models.backbones.hourglass import HourglassNet + + +def test_hourglass_backbone(): + with pytest.raises(AssertionError): + # HourglassNet's num_stacks should larger than 0 + HourglassNet(num_stacks=0) + + with pytest.raises(AssertionError): + # len(stage_channels) should equal len(stage_blocks) + HourglassNet( + stage_channels=[256, 256, 384, 384, 384], + stage_blocks=[2, 2, 2, 2, 2, 4]) + + with pytest.raises(AssertionError): + # len(stage_channels) should lagrer than downsample_times + HourglassNet( + downsample_times=5, + stage_channels=[256, 256, 384, 384, 384], + stage_blocks=[2, 2, 2, 2, 2]) + + # Test HourglassNet-52 + model = HourglassNet(num_stacks=1) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 256, 256) + feat = model(imgs) + assert len(feat) == 1 + assert feat[0].shape == torch.Size([1, 256, 64, 64]) + + # Test HourglassNet-104 + model = HourglassNet(num_stacks=2) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 256, 256) + feat = model(imgs) + assert len(feat) == 2 + assert feat[0].shape == torch.Size([1, 256, 64, 64]) + assert feat[1].shape == torch.Size([1, 256, 64, 64]) diff --git a/tests/test_models/test_backbones/test_regnet.py b/tests/test_models/test_backbones/test_regnet.py new file mode 100644 index 0000000..81d4abc --- /dev/null +++ b/tests/test_models/test_backbones/test_regnet.py @@ -0,0 +1,58 @@ +import pytest +import torch + +from mmdet.models.backbones import RegNet + +regnet_test_data = [ + ('regnetx_400mf', + dict(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22, + bot_mul=1.0), [32, 64, 160, 384]), + ('regnetx_800mf', + dict(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16, + bot_mul=1.0), [64, 128, 288, 672]), + ('regnetx_1.6gf', + dict(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18, + bot_mul=1.0), [72, 168, 408, 912]), + ('regnetx_3.2gf', + dict(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25, + bot_mul=1.0), [96, 192, 432, 1008]), + ('regnetx_4.0gf', + dict(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23, + bot_mul=1.0), [80, 240, 560, 1360]), + ('regnetx_6.4gf', + dict(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17, + bot_mul=1.0), [168, 392, 784, 1624]), + ('regnetx_8.0gf', + dict(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23, + bot_mul=1.0), [80, 240, 720, 1920]), + ('regnetx_12gf', + dict(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, + bot_mul=1.0), [224, 448, 896, 2240]), +] + + +@pytest.mark.parametrize('arch_name,arch,out_channels', regnet_test_data) +def test_regnet_backbone(arch_name, arch, out_channels): + with pytest.raises(AssertionError): + # ResNeXt depth should be in [50, 101, 152] + RegNet(arch_name + '233') + + # Test RegNet with arch_name + model = RegNet(arch_name) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, out_channels[0], 56, 56]) + assert feat[1].shape == torch.Size([1, out_channels[1], 28, 28]) + assert feat[2].shape == torch.Size([1, out_channels[2], 14, 14]) + assert feat[3].shape == torch.Size([1, out_channels[3], 7, 7]) + + # Test RegNet with arch + model = RegNet(arch) + assert feat[0].shape == torch.Size([1, out_channels[0], 56, 56]) + assert feat[1].shape == torch.Size([1, out_channels[1], 28, 28]) + assert feat[2].shape == torch.Size([1, out_channels[2], 14, 14]) + assert feat[3].shape == torch.Size([1, out_channels[3], 7, 7]) diff --git a/tests/test_models/test_backbones/test_renext.py b/tests/test_models/test_backbones/test_renext.py new file mode 100644 index 0000000..d01443e --- /dev/null +++ b/tests/test_models/test_backbones/test_renext.py @@ -0,0 +1,105 @@ +import pytest +import torch + +from mmdet.models.backbones import ResNeXt +from mmdet.models.backbones.resnext import Bottleneck as BottleneckX +from .utils import is_block + + +def test_renext_bottleneck(): + with pytest.raises(AssertionError): + # Style must be in ['pytorch', 'caffe'] + BottleneckX(64, 64, groups=32, base_width=4, style='tensorflow') + + # Test ResNeXt Bottleneck structure + block = BottleneckX( + 64, 64, groups=32, base_width=4, stride=2, style='pytorch') + assert block.conv2.stride == (2, 2) + assert block.conv2.groups == 32 + assert block.conv2.out_channels == 128 + + # Test ResNeXt Bottleneck with DCN + dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False) + with pytest.raises(AssertionError): + # conv_cfg must be None if dcn is not None + BottleneckX( + 64, + 64, + groups=32, + base_width=4, + dcn=dcn, + conv_cfg=dict(type='Conv')) + BottleneckX(64, 64, dcn=dcn) + + # Test ResNeXt Bottleneck forward + block = BottleneckX(64, 16, groups=32, base_width=4) + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + # Test ResNeXt Bottleneck forward with plugins + plugins = [ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + stages=(False, False, True, True), + position='after_conv2') + ] + block = BottleneckX(64, 16, groups=32, base_width=4, plugins=plugins) + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + +def test_resnext_backbone(): + with pytest.raises(KeyError): + # ResNeXt depth should be in [50, 101, 152] + ResNeXt(depth=18) + + # Test ResNeXt with group 32, base_width 4 + model = ResNeXt(depth=50, groups=32, base_width=4) + for m in model.modules(): + if is_block(m): + assert m.conv2.groups == 32 + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) + + +regnet_test_data = [ + ('regnetx_400mf', + dict(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22, + bot_mul=1.0), [32, 64, 160, 384]), + ('regnetx_800mf', + dict(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16, + bot_mul=1.0), [64, 128, 288, 672]), + ('regnetx_1.6gf', + dict(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18, + bot_mul=1.0), [72, 168, 408, 912]), + ('regnetx_3.2gf', + dict(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25, + bot_mul=1.0), [96, 192, 432, 1008]), + ('regnetx_4.0gf', + dict(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23, + bot_mul=1.0), [80, 240, 560, 1360]), + ('regnetx_6.4gf', + dict(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17, + bot_mul=1.0), [168, 392, 784, 1624]), + ('regnetx_8.0gf', + dict(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23, + bot_mul=1.0), [80, 240, 720, 1920]), + ('regnetx_12gf', + dict(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, + bot_mul=1.0), [224, 448, 896, 2240]), +] diff --git a/tests/test_models/test_backbones/test_res2net.py b/tests/test_models/test_backbones/test_res2net.py new file mode 100644 index 0000000..95d0118 --- /dev/null +++ b/tests/test_models/test_backbones/test_res2net.py @@ -0,0 +1,62 @@ +import pytest +import torch + +from mmdet.models.backbones import Res2Net +from mmdet.models.backbones.res2net import Bottle2neck +from .utils import is_block + + +def test_res2net_bottle2neck(): + with pytest.raises(AssertionError): + # Style must be in ['pytorch', 'caffe'] + Bottle2neck(64, 64, base_width=26, scales=4, style='tensorflow') + + with pytest.raises(AssertionError): + # Scale must be larger than 1 + Bottle2neck(64, 64, base_width=26, scales=1, style='pytorch') + + # Test Res2Net Bottle2neck structure + block = Bottle2neck( + 64, 64, base_width=26, stride=2, scales=4, style='pytorch') + assert block.scales == 4 + + # Test Res2Net Bottle2neck with DCN + dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False) + with pytest.raises(AssertionError): + # conv_cfg must be None if dcn is not None + Bottle2neck( + 64, + 64, + base_width=26, + scales=4, + dcn=dcn, + conv_cfg=dict(type='Conv')) + Bottle2neck(64, 64, dcn=dcn) + + # Test Res2Net Bottle2neck forward + block = Bottle2neck(64, 16, base_width=26, scales=4) + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + +def test_res2net_backbone(): + with pytest.raises(KeyError): + # Res2Net depth should be in [50, 101, 152] + Res2Net(depth=18) + + # Test Res2Net with scales 4, base_width 26 + model = Res2Net(depth=50, scales=4, base_width=26) + for m in model.modules(): + if is_block(m): + assert m.scales == 4 + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) diff --git a/tests/test_models/test_backbones/test_resnest.py b/tests/test_models/test_backbones/test_resnest.py new file mode 100644 index 0000000..2243591 --- /dev/null +++ b/tests/test_models/test_backbones/test_resnest.py @@ -0,0 +1,43 @@ +import pytest +import torch + +from mmdet.models.backbones import ResNeSt +from mmdet.models.backbones.resnest import Bottleneck as BottleneckS + + +def test_resnest_bottleneck(): + with pytest.raises(AssertionError): + # Style must be in ['pytorch', 'caffe'] + BottleneckS(64, 64, radix=2, reduction_factor=4, style='tensorflow') + + # Test ResNeSt Bottleneck structure + block = BottleneckS( + 64, 256, radix=2, reduction_factor=4, stride=2, style='pytorch') + assert block.avd_layer.stride == 2 + assert block.conv2.channels == 256 + + # Test ResNeSt Bottleneck forward + block = BottleneckS(64, 16, radix=2, reduction_factor=4) + x = torch.randn(2, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([2, 64, 56, 56]) + + +def test_resnest_backbone(): + with pytest.raises(KeyError): + # ResNeSt depth should be in [50, 101, 152, 200] + ResNeSt(depth=18) + + # Test ResNeSt with radix 2, reduction_factor 4 + model = ResNeSt( + depth=50, radix=2, reduction_factor=4, out_indices=(0, 1, 2, 3)) + model.init_weights() + model.train() + + imgs = torch.randn(2, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([2, 256, 56, 56]) + assert feat[1].shape == torch.Size([2, 512, 28, 28]) + assert feat[2].shape == torch.Size([2, 1024, 14, 14]) + assert feat[3].shape == torch.Size([2, 2048, 7, 7]) diff --git a/tests/test_models/test_backbones/test_resnet.py b/tests/test_models/test_backbones/test_resnet.py new file mode 100644 index 0000000..afbdf1c --- /dev/null +++ b/tests/test_models/test_backbones/test_resnet.py @@ -0,0 +1,666 @@ +import pytest +import torch +from mmcv import assert_params_all_zeros +from mmcv.ops import DeformConv2dPack +from torch.nn.modules import AvgPool2d, GroupNorm +from torch.nn.modules.batchnorm import _BatchNorm + +from mmdet.models.backbones import ResNet, ResNetV1d +from mmdet.models.backbones.resnet import BasicBlock, Bottleneck +from mmdet.models.utils import ResLayer, SimplifiedBasicBlock +from .utils import check_norm_state, is_block, is_norm + + +def test_resnet_basic_block(): + with pytest.raises(AssertionError): + # Not implemented yet. + dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False) + BasicBlock(64, 64, dcn=dcn) + + with pytest.raises(AssertionError): + # Not implemented yet. + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv3') + ] + BasicBlock(64, 64, plugins=plugins) + + with pytest.raises(AssertionError): + # Not implemented yet + plugins = [ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + position='after_conv2') + ] + BasicBlock(64, 64, plugins=plugins) + + # test BasicBlock structure and forward + block = BasicBlock(64, 64) + assert block.conv1.in_channels == 64 + assert block.conv1.out_channels == 64 + assert block.conv1.kernel_size == (3, 3) + assert block.conv2.in_channels == 64 + assert block.conv2.out_channels == 64 + assert block.conv2.kernel_size == (3, 3) + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + # Test BasicBlock with checkpoint forward + block = BasicBlock(64, 64, with_cp=True) + assert block.with_cp + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + +def test_resnet_bottleneck(): + with pytest.raises(AssertionError): + # Style must be in ['pytorch', 'caffe'] + Bottleneck(64, 64, style='tensorflow') + + with pytest.raises(AssertionError): + # Allowed positions are 'after_conv1', 'after_conv2', 'after_conv3' + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv4') + ] + Bottleneck(64, 16, plugins=plugins) + + with pytest.raises(AssertionError): + # Need to specify different postfix to avoid duplicate plugin name + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv3'), + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv3') + ] + Bottleneck(64, 16, plugins=plugins) + + with pytest.raises(KeyError): + # Plugin type is not supported + plugins = [dict(cfg=dict(type='WrongPlugin'), position='after_conv3')] + Bottleneck(64, 16, plugins=plugins) + + # Test Bottleneck with checkpoint forward + block = Bottleneck(64, 16, with_cp=True) + assert block.with_cp + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + # Test Bottleneck style + block = Bottleneck(64, 64, stride=2, style='pytorch') + assert block.conv1.stride == (1, 1) + assert block.conv2.stride == (2, 2) + block = Bottleneck(64, 64, stride=2, style='caffe') + assert block.conv1.stride == (2, 2) + assert block.conv2.stride == (1, 1) + + # Test Bottleneck DCN + dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False) + with pytest.raises(AssertionError): + Bottleneck(64, 64, dcn=dcn, conv_cfg=dict(type='Conv')) + block = Bottleneck(64, 64, dcn=dcn) + assert isinstance(block.conv2, DeformConv2dPack) + + # Test Bottleneck forward + block = Bottleneck(64, 16) + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + # Test Bottleneck with 1 ContextBlock after conv3 + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv3') + ] + block = Bottleneck(64, 16, plugins=plugins) + assert block.context_block.in_channels == 64 + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + # Test Bottleneck with 1 GeneralizedAttention after conv2 + plugins = [ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + position='after_conv2') + ] + block = Bottleneck(64, 16, plugins=plugins) + assert block.gen_attention_block.in_channels == 16 + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + # Test Bottleneck with 1 GeneralizedAttention after conv2, 1 NonLocal2D + # after conv2, 1 ContextBlock after conv3 + plugins = [ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + position='after_conv2'), + dict(cfg=dict(type='NonLocal2d'), position='after_conv2'), + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv3') + ] + block = Bottleneck(64, 16, plugins=plugins) + assert block.gen_attention_block.in_channels == 16 + assert block.nonlocal_block.in_channels == 16 + assert block.context_block.in_channels == 64 + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + # Test Bottleneck with 1 ContextBlock after conv2, 2 ContextBlock after + # conv3 + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=1), + position='after_conv2'), + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=2), + position='after_conv3'), + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=3), + position='after_conv3') + ] + block = Bottleneck(64, 16, plugins=plugins) + assert block.context_block1.in_channels == 16 + assert block.context_block2.in_channels == 64 + assert block.context_block3.in_channels == 64 + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + +def test_simplied_basic_block(): + with pytest.raises(AssertionError): + # Not implemented yet. + dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False) + SimplifiedBasicBlock(64, 64, dcn=dcn) + + with pytest.raises(AssertionError): + # Not implemented yet. + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv3') + ] + SimplifiedBasicBlock(64, 64, plugins=plugins) + + with pytest.raises(AssertionError): + # Not implemented yet + plugins = [ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + position='after_conv2') + ] + SimplifiedBasicBlock(64, 64, plugins=plugins) + + with pytest.raises(AssertionError): + # Not implemented yet + SimplifiedBasicBlock(64, 64, with_cp=True) + + # test SimplifiedBasicBlock structure and forward + block = SimplifiedBasicBlock(64, 64) + assert block.conv1.in_channels == 64 + assert block.conv1.out_channels == 64 + assert block.conv1.kernel_size == (3, 3) + assert block.conv2.in_channels == 64 + assert block.conv2.out_channels == 64 + assert block.conv2.kernel_size == (3, 3) + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + # test SimplifiedBasicBlock without norm + block = SimplifiedBasicBlock(64, 64, norm_cfg=None) + assert block.norm1 is None + assert block.norm2 is None + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + +def test_resnet_res_layer(): + # Test ResLayer of 3 Bottleneck w\o downsample + layer = ResLayer(Bottleneck, 64, 16, 3) + assert len(layer) == 3 + assert layer[0].conv1.in_channels == 64 + assert layer[0].conv1.out_channels == 16 + for i in range(1, len(layer)): + assert layer[i].conv1.in_channels == 64 + assert layer[i].conv1.out_channels == 16 + for i in range(len(layer)): + assert layer[i].downsample is None + x = torch.randn(1, 64, 56, 56) + x_out = layer(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + # Test ResLayer of 3 Bottleneck with downsample + layer = ResLayer(Bottleneck, 64, 64, 3) + assert layer[0].downsample[0].out_channels == 256 + for i in range(1, len(layer)): + assert layer[i].downsample is None + x = torch.randn(1, 64, 56, 56) + x_out = layer(x) + assert x_out.shape == torch.Size([1, 256, 56, 56]) + + # Test ResLayer of 3 Bottleneck with stride=2 + layer = ResLayer(Bottleneck, 64, 64, 3, stride=2) + assert layer[0].downsample[0].out_channels == 256 + assert layer[0].downsample[0].stride == (2, 2) + for i in range(1, len(layer)): + assert layer[i].downsample is None + x = torch.randn(1, 64, 56, 56) + x_out = layer(x) + assert x_out.shape == torch.Size([1, 256, 28, 28]) + + # Test ResLayer of 3 Bottleneck with stride=2 and average downsample + layer = ResLayer(Bottleneck, 64, 64, 3, stride=2, avg_down=True) + assert isinstance(layer[0].downsample[0], AvgPool2d) + assert layer[0].downsample[1].out_channels == 256 + assert layer[0].downsample[1].stride == (1, 1) + for i in range(1, len(layer)): + assert layer[i].downsample is None + x = torch.randn(1, 64, 56, 56) + x_out = layer(x) + assert x_out.shape == torch.Size([1, 256, 28, 28]) + + # Test ResLayer of 3 BasicBlock with stride=2 and downsample_first=False + layer = ResLayer(BasicBlock, 64, 64, 3, stride=2, downsample_first=False) + assert layer[2].downsample[0].out_channels == 64 + assert layer[2].downsample[0].stride == (2, 2) + for i in range(len(layer) - 1): + assert layer[i].downsample is None + x = torch.randn(1, 64, 56, 56) + x_out = layer(x) + assert x_out.shape == torch.Size([1, 64, 28, 28]) + + +def test_resnest_stem(): + # Test default stem_channels + model = ResNet(50) + assert model.stem_channels == 64 + assert model.conv1.out_channels == 64 + assert model.norm1.num_features == 64 + + # Test default stem_channels, with base_channels=32 + model = ResNet(50, base_channels=32) + assert model.stem_channels == 32 + assert model.conv1.out_channels == 32 + assert model.norm1.num_features == 32 + assert model.layer1[0].conv1.in_channels == 32 + + # Test stem_channels=64 + model = ResNet(50, stem_channels=64) + assert model.stem_channels == 64 + assert model.conv1.out_channels == 64 + assert model.norm1.num_features == 64 + assert model.layer1[0].conv1.in_channels == 64 + + # Test stem_channels=64, with base_channels=32 + model = ResNet(50, stem_channels=64, base_channels=32) + assert model.stem_channels == 64 + assert model.conv1.out_channels == 64 + assert model.norm1.num_features == 64 + assert model.layer1[0].conv1.in_channels == 64 + + # Test stem_channels=128 + model = ResNet(depth=50, stem_channels=128) + model.init_weights() + model.train() + assert model.conv1.out_channels == 128 + assert model.layer1[0].conv1.in_channels == 128 + + # Test V1d stem_channels + model = ResNetV1d(depth=50, stem_channels=128) + model.init_weights() + model.train() + assert model.stem[0].out_channels == 64 + assert model.stem[1].num_features == 64 + assert model.stem[3].out_channels == 64 + assert model.stem[4].num_features == 64 + assert model.stem[6].out_channels == 128 + assert model.stem[7].num_features == 128 + assert model.layer1[0].conv1.in_channels == 128 + + +def test_resnet_backbone(): + """Test resnet backbone.""" + with pytest.raises(KeyError): + # ResNet depth should be in [18, 34, 50, 101, 152] + ResNet(20) + + with pytest.raises(AssertionError): + # In ResNet: 1 <= num_stages <= 4 + ResNet(50, num_stages=0) + + with pytest.raises(AssertionError): + # len(stage_with_dcn) == num_stages + dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False) + ResNet(50, dcn=dcn, stage_with_dcn=(True, )) + + with pytest.raises(AssertionError): + # len(stage_with_plugin) == num_stages + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True), + position='after_conv3') + ] + ResNet(50, plugins=plugins) + + with pytest.raises(AssertionError): + # In ResNet: 1 <= num_stages <= 4 + ResNet(50, num_stages=5) + + with pytest.raises(AssertionError): + # len(strides) == len(dilations) == num_stages + ResNet(50, strides=(1, ), dilations=(1, 1), num_stages=3) + + with pytest.raises(TypeError): + # pretrained must be a string path + model = ResNet(50, pretrained=0) + model.init_weights() + + with pytest.raises(AssertionError): + # Style must be in ['pytorch', 'caffe'] + ResNet(50, style='tensorflow') + + # Test ResNet50 norm_eval=True + model = ResNet(50, norm_eval=True) + model.init_weights() + model.train() + assert check_norm_state(model.modules(), False) + + # Test ResNet50 with torchvision pretrained weight + model = ResNet( + depth=50, norm_eval=True, pretrained='torchvision://resnet50') + model.init_weights() + model.train() + assert check_norm_state(model.modules(), False) + + # Test ResNet50 with first stage frozen + frozen_stages = 1 + model = ResNet(50, frozen_stages=frozen_stages) + model.init_weights() + model.train() + assert model.norm1.training is False + for layer in [model.conv1, model.norm1]: + for param in layer.parameters(): + assert param.requires_grad is False + for i in range(1, frozen_stages + 1): + layer = getattr(model, f'layer{i}') + for mod in layer.modules(): + if isinstance(mod, _BatchNorm): + assert mod.training is False + for param in layer.parameters(): + assert param.requires_grad is False + + # Test ResNet50V1d with first stage frozen + model = ResNetV1d(depth=50, frozen_stages=frozen_stages) + assert len(model.stem) == 9 + model.init_weights() + model.train() + assert check_norm_state(model.stem, False) + for param in model.stem.parameters(): + assert param.requires_grad is False + for i in range(1, frozen_stages + 1): + layer = getattr(model, f'layer{i}') + for mod in layer.modules(): + if isinstance(mod, _BatchNorm): + assert mod.training is False + for param in layer.parameters(): + assert param.requires_grad is False + + # Test ResNet18 forward + model = ResNet(18) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 64, 56, 56]) + assert feat[1].shape == torch.Size([1, 128, 28, 28]) + assert feat[2].shape == torch.Size([1, 256, 14, 14]) + assert feat[3].shape == torch.Size([1, 512, 7, 7]) + + # Test ResNet18 with checkpoint forward + model = ResNet(18, with_cp=True) + for m in model.modules(): + if is_block(m): + assert m.with_cp + + # Test ResNet50 with BatchNorm forward + model = ResNet(50) + for m in model.modules(): + if is_norm(m): + assert isinstance(m, _BatchNorm) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) + + # Test ResNet50 with layers 1, 2, 3 out forward + model = ResNet(50, out_indices=(0, 1, 2)) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 3 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + + # Test ResNet50 with checkpoint forward + model = ResNet(50, with_cp=True) + for m in model.modules(): + if is_block(m): + assert m.with_cp + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) + + # Test ResNet50 with GroupNorm forward + model = ResNet( + 50, norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)) + for m in model.modules(): + if is_norm(m): + assert isinstance(m, GroupNorm) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) + + # Test ResNet50 with 1 GeneralizedAttention after conv2, 1 NonLocal2D + # after conv2, 1 ContextBlock after conv3 in layers 2, 3, 4 + plugins = [ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + stages=(False, True, True, True), + position='after_conv2'), + dict(cfg=dict(type='NonLocal2d'), position='after_conv2'), + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, False), + position='after_conv3') + ] + model = ResNet(50, plugins=plugins) + for m in model.layer1.modules(): + if is_block(m): + assert not hasattr(m, 'context_block') + assert not hasattr(m, 'gen_attention_block') + assert m.nonlocal_block.in_channels == 64 + for m in model.layer2.modules(): + if is_block(m): + assert m.nonlocal_block.in_channels == 128 + assert m.gen_attention_block.in_channels == 128 + assert m.context_block.in_channels == 512 + + for m in model.layer3.modules(): + if is_block(m): + assert m.nonlocal_block.in_channels == 256 + assert m.gen_attention_block.in_channels == 256 + assert m.context_block.in_channels == 1024 + + for m in model.layer4.modules(): + if is_block(m): + assert m.nonlocal_block.in_channels == 512 + assert m.gen_attention_block.in_channels == 512 + assert not hasattr(m, 'context_block') + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) + + # Test ResNet50 with 1 ContextBlock after conv2, 1 ContextBlock after + # conv3 in layers 2, 3, 4 + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=1), + stages=(False, True, True, False), + position='after_conv3'), + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=2), + stages=(False, True, True, False), + position='after_conv3') + ] + + model = ResNet(50, plugins=plugins) + for m in model.layer1.modules(): + if is_block(m): + assert not hasattr(m, 'context_block') + assert not hasattr(m, 'context_block1') + assert not hasattr(m, 'context_block2') + for m in model.layer2.modules(): + if is_block(m): + assert not hasattr(m, 'context_block') + assert m.context_block1.in_channels == 512 + assert m.context_block2.in_channels == 512 + + for m in model.layer3.modules(): + if is_block(m): + assert not hasattr(m, 'context_block') + assert m.context_block1.in_channels == 1024 + assert m.context_block2.in_channels == 1024 + + for m in model.layer4.modules(): + if is_block(m): + assert not hasattr(m, 'context_block') + assert not hasattr(m, 'context_block1') + assert not hasattr(m, 'context_block2') + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) + + # Test ResNet50 zero initialization of residual + model = ResNet(50, zero_init_residual=True) + model.init_weights() + for m in model.modules(): + if isinstance(m, Bottleneck): + assert assert_params_all_zeros(m.norm3) + elif isinstance(m, BasicBlock): + assert assert_params_all_zeros(m.norm2) + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) + + # Test ResNetV1d forward + model = ResNetV1d(depth=50) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) diff --git a/tests/test_models/test_backbones/test_trident_resnet.py b/tests/test_models/test_backbones/test_trident_resnet.py new file mode 100644 index 0000000..ebb4415 --- /dev/null +++ b/tests/test_models/test_backbones/test_trident_resnet.py @@ -0,0 +1,180 @@ +import pytest +import torch + +from mmdet.models.backbones import TridentResNet +from mmdet.models.backbones.trident_resnet import TridentBottleneck + + +def test_trident_resnet_bottleneck(): + trident_dilations = (1, 2, 3) + test_branch_idx = 1 + concat_output = True + trident_build_config = (trident_dilations, test_branch_idx, concat_output) + + with pytest.raises(AssertionError): + # Style must be in ['pytorch', 'caffe'] + TridentBottleneck( + *trident_build_config, inplanes=64, planes=64, style='tensorflow') + + with pytest.raises(AssertionError): + # Allowed positions are 'after_conv1', 'after_conv2', 'after_conv3' + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv4') + ] + TridentBottleneck( + *trident_build_config, inplanes=64, planes=16, plugins=plugins) + + with pytest.raises(AssertionError): + # Need to specify different postfix to avoid duplicate plugin name + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv3'), + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv3') + ] + TridentBottleneck( + *trident_build_config, inplanes=64, planes=16, plugins=plugins) + + with pytest.raises(KeyError): + # Plugin type is not supported + plugins = [dict(cfg=dict(type='WrongPlugin'), position='after_conv3')] + TridentBottleneck( + *trident_build_config, inplanes=64, planes=16, plugins=plugins) + + # Test Bottleneck with checkpoint forward + block = TridentBottleneck( + *trident_build_config, inplanes=64, planes=16, with_cp=True) + assert block.with_cp + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56]) + + # Test Bottleneck style + block = TridentBottleneck( + *trident_build_config, + inplanes=64, + planes=64, + stride=2, + style='pytorch') + assert block.conv1.stride == (1, 1) + assert block.conv2.stride == (2, 2) + block = TridentBottleneck( + *trident_build_config, inplanes=64, planes=64, stride=2, style='caffe') + assert block.conv1.stride == (2, 2) + assert block.conv2.stride == (1, 1) + + # Test Bottleneck forward + block = TridentBottleneck(*trident_build_config, inplanes=64, planes=16) + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56]) + + # Test Bottleneck with 1 ContextBlock after conv3 + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv3') + ] + block = TridentBottleneck( + *trident_build_config, inplanes=64, planes=16, plugins=plugins) + assert block.context_block.in_channels == 64 + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56]) + + # Test Bottleneck with 1 GeneralizedAttention after conv2 + plugins = [ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + position='after_conv2') + ] + block = TridentBottleneck( + *trident_build_config, inplanes=64, planes=16, plugins=plugins) + assert block.gen_attention_block.in_channels == 16 + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56]) + + # Test Bottleneck with 1 GeneralizedAttention after conv2, 1 NonLocal2D + # after conv2, 1 ContextBlock after conv3 + plugins = [ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + position='after_conv2'), + dict(cfg=dict(type='NonLocal2d'), position='after_conv2'), + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv3') + ] + block = TridentBottleneck( + *trident_build_config, inplanes=64, planes=16, plugins=plugins) + assert block.gen_attention_block.in_channels == 16 + assert block.nonlocal_block.in_channels == 16 + assert block.context_block.in_channels == 64 + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56]) + + # Test Bottleneck with 1 ContextBlock after conv2, 2 ContextBlock after + # conv3 + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=1), + position='after_conv2'), + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=2), + position='after_conv3'), + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=3), + position='after_conv3') + ] + block = TridentBottleneck( + *trident_build_config, inplanes=64, planes=16, plugins=plugins) + assert block.context_block1.in_channels == 16 + assert block.context_block2.in_channels == 64 + assert block.context_block3.in_channels == 64 + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56]) + + +def test_trident_resnet_backbone(): + tridentresnet_config = dict( + num_branch=3, + test_branch_idx=1, + strides=(1, 2, 2), + dilations=(1, 1, 1), + trident_dilations=(1, 2, 3), + out_indices=(2, ), + ) + """Test tridentresnet backbone.""" + with pytest.raises(AssertionError): + # TridentResNet depth should be in [50, 101, 152] + TridentResNet(18, **tridentresnet_config) + + with pytest.raises(AssertionError): + # In TridentResNet: num_stages == 3 + TridentResNet(50, num_stages=4, **tridentresnet_config) + + model = TridentResNet(50, num_stages=3, **tridentresnet_config) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 1 + assert feat[0].shape == torch.Size([3, 1024, 14, 14]) diff --git a/tests/test_models/test_backbones/utils.py b/tests/test_models/test_backbones/utils.py new file mode 100644 index 0000000..5314e4d --- /dev/null +++ b/tests/test_models/test_backbones/utils.py @@ -0,0 +1,31 @@ +from torch.nn.modules import GroupNorm +from torch.nn.modules.batchnorm import _BatchNorm + +from mmdet.models.backbones.res2net import Bottle2neck +from mmdet.models.backbones.resnet import BasicBlock, Bottleneck +from mmdet.models.backbones.resnext import Bottleneck as BottleneckX +from mmdet.models.utils import SimplifiedBasicBlock + + +def is_block(modules): + """Check if is ResNet building block.""" + if isinstance(modules, (BasicBlock, Bottleneck, BottleneckX, Bottle2neck, + SimplifiedBasicBlock)): + return True + return False + + +def is_norm(modules): + """Check if is one of the norms.""" + if isinstance(modules, (GroupNorm, _BatchNorm)): + return True + return False + + +def check_norm_state(modules, train_state): + """Check if norm layer is in correct train state.""" + for mod in modules: + if isinstance(mod, _BatchNorm): + if mod.training != train_state: + return False + return True diff --git a/tests/test_models/test_dense_heads/test_anchor_head.py b/tests/test_models/test_dense_heads/test_anchor_head.py new file mode 100644 index 0000000..23cb364 --- /dev/null +++ b/tests/test_models/test_dense_heads/test_anchor_head.py @@ -0,0 +1,69 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import AnchorHead + + +def test_anchor_head_loss(): + """Tests anchor head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + + cfg = mmcv.Config( + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False)) + self = AnchorHead(num_classes=4, in_channels=1, train_cfg=cfg) + + # Anchor head expects a multiple levels of features per image + feat = [ + torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2))) + for i in range(len(self.anchor_generator.strides)) + ] + cls_scores, bbox_preds = self.forward(feat) + + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_cls_loss = sum(empty_gt_losses['loss_cls']) + empty_box_loss = sum(empty_gt_losses['loss_bbox']) + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + onegt_cls_loss = sum(one_gt_losses['loss_cls']) + onegt_box_loss = sum(one_gt_losses['loss_bbox']) + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' diff --git a/tests/test_models/test_dense_heads/test_atss_head.py b/tests/test_models/test_dense_heads/test_atss_head.py new file mode 100644 index 0000000..3757a34 --- /dev/null +++ b/tests/test_models/test_dense_heads/test_atss_head.py @@ -0,0 +1,76 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import ATSSHead + + +def test_atss_head_loss(): + """Tests atss head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + train_cfg = mmcv.Config( + dict( + assigner=dict(type='ATSSAssigner', topk=9), + allowed_border=-1, + pos_weight=-1, + debug=False)) + self = ATSSHead( + num_classes=4, + in_channels=1, + train_cfg=train_cfg, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=2.0)) + feat = [ + torch.rand(1, 1, s // feat_size, s // feat_size) + for feat_size in [4, 8, 16, 32, 64] + ] + cls_scores, bbox_preds, centernesses = self.forward(feat) + + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, centernesses, + gt_bboxes, gt_labels, img_metas, + gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_cls_loss = sum(empty_gt_losses['loss_cls']) + empty_box_loss = sum(empty_gt_losses['loss_bbox']) + empty_centerness_loss = sum(empty_gt_losses['loss_centerness']) + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + assert empty_centerness_loss.item() == 0, ( + 'there should be no centerness loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, centernesses, gt_bboxes, + gt_labels, img_metas, gt_bboxes_ignore) + onegt_cls_loss = sum(one_gt_losses['loss_cls']) + onegt_box_loss = sum(one_gt_losses['loss_bbox']) + onegt_centerness_loss = sum(one_gt_losses['loss_centerness']) + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' + assert onegt_centerness_loss.item() > 0, ( + 'centerness loss should be non-zero') diff --git a/tests/test_models/test_dense_heads/test_autoassign_head.py b/tests/test_models/test_dense_heads/test_autoassign_head.py new file mode 100644 index 0000000..ebcf6fe --- /dev/null +++ b/tests/test_models/test_dense_heads/test_autoassign_head.py @@ -0,0 +1,91 @@ +import mmcv +import torch + +from mmdet.models.dense_heads.autoassign_head import AutoAssignHead +from mmdet.models.dense_heads.paa_head import levels_to_images + + +def test_autoassign_head_loss(): + """Tests autoassign head loss when truth is empty and non-empty.""" + + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + train_cfg = mmcv.Config( + dict(assigner=None, allowed_border=-1, pos_weight=-1, debug=False)) + self = AutoAssignHead( + num_classes=4, + in_channels=1, + train_cfg=train_cfg, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=1.3)) + feat = [ + torch.rand(1, 1, s // feat_size, s // feat_size) + for feat_size in [4, 8, 16, 32, 64] + ] + self.init_weights() + cls_scores, bbox_preds, objectnesses = self(feat) + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, objectnesses, + gt_bboxes, gt_labels, img_metas, + gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_pos_loss = empty_gt_losses['loss_pos'] + empty_neg_loss = empty_gt_losses['loss_neg'] + empty_center_loss = empty_gt_losses['loss_center'] + assert empty_neg_loss.item() > 0, 'cls loss should be non-zero' + assert empty_pos_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + assert empty_center_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, objectnesses, gt_bboxes, + gt_labels, img_metas, gt_bboxes_ignore) + onegt_pos_loss = one_gt_losses['loss_pos'] + onegt_neg_loss = one_gt_losses['loss_neg'] + onegt_center_loss = one_gt_losses['loss_center'] + assert onegt_pos_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_neg_loss.item() > 0, 'box loss should be non-zero' + assert onegt_center_loss.item() > 0, 'box loss should be non-zero' + n, c, h, w = 10, 4, 20, 20 + mlvl_tensor = [torch.ones(n, c, h, w) for i in range(5)] + results = levels_to_images(mlvl_tensor) + assert len(results) == n + assert results[0].size() == (h * w * 5, c) + cls_scores = [torch.ones(2, 4, 5, 5)] + bbox_preds = [torch.ones(2, 4, 5, 5)] + iou_preds = [torch.ones(2, 1, 5, 5)] + mlvl_anchors = [torch.ones(5 * 5, 2)] + img_shape = None + scale_factor = [0.5, 0.5] + cfg = mmcv.Config( + dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) + rescale = False + self._get_bboxes( + cls_scores, + bbox_preds, + iou_preds, + mlvl_anchors, + img_shape, + scale_factor, + cfg, + rescale=rescale) diff --git a/tests/test_models/test_dense_heads/test_corner_head.py b/tests/test_models/test_dense_heads/test_corner_head.py new file mode 100644 index 0000000..91d1c21 --- /dev/null +++ b/tests/test_models/test_dense_heads/test_corner_head.py @@ -0,0 +1,166 @@ +import torch + +from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps +from mmdet.models.dense_heads import CornerHead + + +def test_corner_head_loss(): + """Tests corner head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + + self = CornerHead(num_classes=4, in_channels=1) + + # Corner head expects a multiple levels of features per image + feat = [ + torch.rand(1, 1, s // 4, s // 4) for _ in range(self.num_feat_levels) + ] + tl_heats, br_heats, tl_embs, br_embs, tl_offs, br_offs = self.forward(feat) + + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + + gt_bboxes_ignore = None + empty_gt_losses = self.loss(tl_heats, br_heats, tl_embs, br_embs, tl_offs, + br_offs, gt_bboxes, gt_labels, img_metas, + gt_bboxes_ignore) + empty_det_loss = sum(empty_gt_losses['det_loss']) + empty_push_loss = sum(empty_gt_losses['push_loss']) + empty_pull_loss = sum(empty_gt_losses['pull_loss']) + empty_off_loss = sum(empty_gt_losses['off_loss']) + assert empty_det_loss.item() > 0, 'det loss should be non-zero' + assert empty_push_loss.item() == 0, ( + 'there should be no push loss when there are no true boxes') + assert empty_pull_loss.item() == 0, ( + 'there should be no pull loss when there are no true boxes') + assert empty_off_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(tl_heats, br_heats, tl_embs, br_embs, tl_offs, + br_offs, gt_bboxes, gt_labels, img_metas, + gt_bboxes_ignore) + onegt_det_loss = sum(one_gt_losses['det_loss']) + onegt_push_loss = sum(one_gt_losses['push_loss']) + onegt_pull_loss = sum(one_gt_losses['pull_loss']) + onegt_off_loss = sum(one_gt_losses['off_loss']) + assert onegt_det_loss.item() > 0, 'det loss should be non-zero' + assert onegt_push_loss.item() == 0, ( + 'there should be no push loss when there are only one true box') + assert onegt_pull_loss.item() > 0, 'pull loss should be non-zero' + assert onegt_off_loss.item() > 0, 'off loss should be non-zero' + + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874], + [123.6667, 123.8757, 138.6326, 251.8874]]), + ] + gt_labels = [torch.LongTensor([2, 3])] + + # equalize the corners' embedding value of different objects to make the + # push_loss larger than 0 + gt_bboxes_ind = (gt_bboxes[0] // 4).int().tolist() + for tl_emb_feat, br_emb_feat in zip(tl_embs, br_embs): + tl_emb_feat[:, :, gt_bboxes_ind[0][1], + gt_bboxes_ind[0][0]] = tl_emb_feat[:, :, + gt_bboxes_ind[1][1], + gt_bboxes_ind[1][0]] + br_emb_feat[:, :, gt_bboxes_ind[0][3], + gt_bboxes_ind[0][2]] = br_emb_feat[:, :, + gt_bboxes_ind[1][3], + gt_bboxes_ind[1][2]] + + two_gt_losses = self.loss(tl_heats, br_heats, tl_embs, br_embs, tl_offs, + br_offs, gt_bboxes, gt_labels, img_metas, + gt_bboxes_ignore) + twogt_det_loss = sum(two_gt_losses['det_loss']) + twogt_push_loss = sum(two_gt_losses['push_loss']) + twogt_pull_loss = sum(two_gt_losses['pull_loss']) + twogt_off_loss = sum(two_gt_losses['off_loss']) + assert twogt_det_loss.item() > 0, 'det loss should be non-zero' + assert twogt_push_loss.item() > 0, 'push loss should be non-zero' + assert twogt_pull_loss.item() > 0, 'pull loss should be non-zero' + assert twogt_off_loss.item() > 0, 'off loss should be non-zero' + + +def test_corner_head_encode_and_decode_heatmap(): + """Tests corner head generating and decoding the heatmap.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3), + 'border': (0, 0, 0, 0) + }] + + gt_bboxes = [ + torch.Tensor([[10, 20, 200, 240], [40, 50, 100, 200], + [10, 20, 200, 240]]) + ] + gt_labels = [torch.LongTensor([1, 1, 2])] + + self = CornerHead(num_classes=4, in_channels=1, corner_emb_channels=1) + + feat = [ + torch.rand(1, 1, s // 4, s // 4) for _ in range(self.num_feat_levels) + ] + + targets = self.get_targets( + gt_bboxes, + gt_labels, + feat[0].shape, + img_metas[0]['pad_shape'], + with_corner_emb=self.with_corner_emb) + + gt_tl_heatmap = targets['topleft_heatmap'] + gt_br_heatmap = targets['bottomright_heatmap'] + gt_tl_offset = targets['topleft_offset'] + gt_br_offset = targets['bottomright_offset'] + embedding = targets['corner_embedding'] + [top, left], [bottom, right] = embedding[0][0] + gt_tl_embedding_heatmap = torch.zeros([1, 1, s // 4, s // 4]) + gt_br_embedding_heatmap = torch.zeros([1, 1, s // 4, s // 4]) + gt_tl_embedding_heatmap[0, 0, top, left] = 1 + gt_br_embedding_heatmap[0, 0, bottom, right] = 1 + + batch_bboxes, batch_scores, batch_clses = self.decode_heatmap( + tl_heat=gt_tl_heatmap, + br_heat=gt_br_heatmap, + tl_off=gt_tl_offset, + br_off=gt_br_offset, + tl_emb=gt_tl_embedding_heatmap, + br_emb=gt_br_embedding_heatmap, + img_meta=img_metas[0], + k=100, + kernel=3, + distance_threshold=0.5) + + bboxes = batch_bboxes.view(-1, 4) + scores = batch_scores.view(-1, 1) + clses = batch_clses.view(-1, 1) + + idx = scores.argsort(dim=0, descending=True) + bboxes = bboxes[idx].view(-1, 4) + scores = scores[idx].view(-1) + clses = clses[idx].view(-1) + + valid_bboxes = bboxes[torch.where(scores > 0.05)] + valid_labels = clses[torch.where(scores > 0.05)] + max_coordinate = valid_bboxes.max() + offsets = valid_labels.to(valid_bboxes) * (max_coordinate + 1) + gt_offsets = gt_labels[0].to(gt_bboxes[0]) * (max_coordinate + 1) + + offset_bboxes = valid_bboxes + offsets[:, None] + offset_gtbboxes = gt_bboxes[0] + gt_offsets[:, None] + + iou_matrix = bbox_overlaps(offset_bboxes.numpy(), offset_gtbboxes.numpy()) + assert (iou_matrix == 1).sum() == 3 diff --git a/tests/test_models/test_dense_heads/test_detr_head.py b/tests/test_models/test_dense_heads/test_detr_head.py new file mode 100644 index 0000000..51f97d4 --- /dev/null +++ b/tests/test_models/test_dense_heads/test_detr_head.py @@ -0,0 +1,103 @@ +import torch +from mmcv import ConfigDict + +from mmdet.models.dense_heads import DETRHead + + +def test_detr_head_loss(): + """Tests transformer head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3), + 'batch_input_shape': (s, s) + }] + config = ConfigDict( + dict( + type='DETRHead', + num_classes=80, + in_channels=200, + transformer=dict( + type='Transformer', + encoder=dict( + type='DetrTransformerEncoder', + num_layers=6, + transformerlayers=dict( + type='BaseTransformerLayer', + attn_cfgs=[ + dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1) + ], + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'ffn', 'norm'))), + decoder=dict( + type='DetrTransformerDecoder', + return_intermediate=True, + num_layers=6, + transformerlayers=dict( + type='DetrTransformerDecoderLayer', + attn_cfgs=dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1), + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'cross_attn', + 'norm', 'ffn', 'norm')), + )), + positional_encoding=dict( + type='SinePositionalEncoding', num_feats=128, normalize=True), + loss_cls=dict( + type='CrossEntropyLoss', + bg_cls_weight=0.1, + use_sigmoid=False, + loss_weight=1.0, + class_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0))) + + self = DETRHead(**config) + self.init_weights() + feat = [torch.rand(1, 200, 10, 10)] + cls_scores, bbox_preds = self.forward(feat, img_metas) + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + for key, loss in empty_gt_losses.items(): + if 'cls' in key: + assert loss.item() > 0, 'cls loss should be non-zero' + elif 'bbox' in key: + assert loss.item( + ) == 0, 'there should be no box loss when there are no true boxes' + elif 'iou' in key: + assert loss.item( + ) == 0, 'there should be no iou loss when there are no true boxes' + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + for loss in one_gt_losses.values(): + assert loss.item( + ) > 0, 'cls loss, or box loss, or iou loss should be non-zero' + + # test forward_train + self.forward_train(feat, img_metas, gt_bboxes, gt_labels) + + # test inference mode + self.get_bboxes(cls_scores, bbox_preds, img_metas, rescale=True) diff --git a/tests/test_models/test_dense_heads/test_fcos_head.py b/tests/test_models/test_dense_heads/test_fcos_head.py new file mode 100644 index 0000000..663e815 --- /dev/null +++ b/tests/test_models/test_dense_heads/test_fcos_head.py @@ -0,0 +1,63 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import FCOSHead + + +def test_fcos_head_loss(): + """Tests fcos head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + train_cfg = mmcv.Config( + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False)) + # since Focal Loss is not supported on CPU + self = FCOSHead( + num_classes=4, + in_channels=1, + train_cfg=train_cfg, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)) + feat = [ + torch.rand(1, 1, s // feat_size, s // feat_size) + for feat_size in [4, 8, 16, 32, 64] + ] + cls_scores, bbox_preds, centerness = self.forward(feat) + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, centerness, gt_bboxes, + gt_labels, img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_cls_loss = empty_gt_losses['loss_cls'] + empty_box_loss = empty_gt_losses['loss_bbox'] + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, centerness, gt_bboxes, + gt_labels, img_metas, gt_bboxes_ignore) + onegt_cls_loss = one_gt_losses['loss_cls'] + onegt_box_loss = one_gt_losses['loss_bbox'] + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' diff --git a/tests/test_models/test_dense_heads/test_fsaf_head.py b/tests/test_models/test_dense_heads/test_fsaf_head.py new file mode 100644 index 0000000..1d85937 --- /dev/null +++ b/tests/test_models/test_dense_heads/test_fsaf_head.py @@ -0,0 +1,81 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import FSAFHead + + +def test_fsaf_head_loss(): + """Tests anchor head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + + cfg = dict( + reg_decoded_bbox=True, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=1, + scales_per_octave=1, + ratios=[1.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict(type='TBLRBBoxCoder', normalizer=4.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0, + reduction='none'), + loss_bbox=dict( + type='IoULoss', eps=1e-6, loss_weight=1.0, reduction='none')) + + train_cfg = mmcv.Config( + dict( + assigner=dict( + type='CenterRegionAssigner', + pos_scale=0.2, + neg_scale=0.2, + min_pos_iof=0.01), + allowed_border=-1, + pos_weight=-1, + debug=False)) + head = FSAFHead(num_classes=4, in_channels=1, train_cfg=train_cfg, **cfg) + if torch.cuda.is_available(): + head.cuda() + # FSAF head expects a multiple levels of features per image + feat = [ + torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2))).cuda() + for i in range(len(head.anchor_generator.strides)) + ] + cls_scores, bbox_preds = head.forward(feat) + gt_bboxes_ignore = None + + # When truth is non-empty then both cls and box loss should be nonzero + # for random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]).cuda(), + ] + gt_labels = [torch.LongTensor([2]).cuda()] + one_gt_losses = head.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + onegt_cls_loss = sum(one_gt_losses['loss_cls']) + onegt_box_loss = sum(one_gt_losses['loss_bbox']) + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' + + # Test that empty ground truth encourages the network to predict bkg + gt_bboxes = [torch.empty((0, 4)).cuda()] + gt_labels = [torch.LongTensor([]).cuda()] + + empty_gt_losses = head.loss(cls_scores, bbox_preds, gt_bboxes, + gt_labels, img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there + # should be no box loss. + empty_cls_loss = sum(empty_gt_losses['loss_cls']) + empty_box_loss = sum(empty_gt_losses['loss_bbox']) + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') diff --git a/tests/test_models/test_dense_heads/test_ga_anchor_head.py b/tests/test_models/test_dense_heads/test_ga_anchor_head.py new file mode 100644 index 0000000..4da346d --- /dev/null +++ b/tests/test_models/test_dense_heads/test_ga_anchor_head.py @@ -0,0 +1,90 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import GuidedAnchorHead + + +def test_ga_anchor_head_loss(): + """Tests anchor head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + + cfg = mmcv.Config( + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + center_ratio=0.2, + ignore_ratio=0.5, + pos_weight=-1, + debug=False)) + head = GuidedAnchorHead(num_classes=4, in_channels=4, train_cfg=cfg) + + # Anchor head expects a multiple levels of features per image + if torch.cuda.is_available(): + head.cuda() + feat = [ + torch.rand(1, 4, s // (2**(i + 2)), s // (2**(i + 2))).cuda() + for i in range(len(head.approx_anchor_generator.base_anchors)) + ] + cls_scores, bbox_preds, shape_preds, loc_preds = head.forward(feat) + + # Test that empty ground truth encourages the network to predict + # background + gt_bboxes = [torch.empty((0, 4)).cuda()] + gt_labels = [torch.LongTensor([]).cuda()] + + gt_bboxes_ignore = None + + empty_gt_losses = head.loss(cls_scores, bbox_preds, shape_preds, + loc_preds, gt_bboxes, gt_labels, img_metas, + gt_bboxes_ignore) + + # When there is no truth, the cls loss should be nonzero but there + # should be no box loss. + empty_cls_loss = sum(empty_gt_losses['loss_cls']) + empty_box_loss = sum(empty_gt_losses['loss_bbox']) + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero + # for random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]).cuda(), + ] + gt_labels = [torch.LongTensor([2]).cuda()] + one_gt_losses = head.loss(cls_scores, bbox_preds, shape_preds, + loc_preds, gt_bboxes, gt_labels, img_metas, + gt_bboxes_ignore) + onegt_cls_loss = sum(one_gt_losses['loss_cls']) + onegt_box_loss = sum(one_gt_losses['loss_bbox']) + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' diff --git a/tests/test_models/test_dense_heads/test_gfl_head.py b/tests/test_models/test_dense_heads/test_gfl_head.py new file mode 100644 index 0000000..b035a57 --- /dev/null +++ b/tests/test_models/test_dense_heads/test_gfl_head.py @@ -0,0 +1,73 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import GFLHead + + +def test_gfl_head_loss(): + """Tests gfl head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + train_cfg = mmcv.Config( + dict( + assigner=dict(type='ATSSAssigner', topk=9), + allowed_border=-1, + pos_weight=-1, + debug=False)) + self = GFLHead( + num_classes=4, + in_channels=1, + train_cfg=train_cfg, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128]), + loss_cls=dict( + type='QualityFocalLoss', + use_sigmoid=True, + beta=2.0, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=2.0)) + feat = [ + torch.rand(1, 1, s // feat_size, s // feat_size) + for feat_size in [4, 8, 16, 32, 64] + ] + cls_scores, bbox_preds = self.forward(feat) + + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_cls_loss = sum(empty_gt_losses['loss_cls']) + empty_box_loss = sum(empty_gt_losses['loss_bbox']) + empty_dfl_loss = sum(empty_gt_losses['loss_dfl']) + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + assert empty_dfl_loss.item() == 0, ( + 'there should be no dfl loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + onegt_cls_loss = sum(one_gt_losses['loss_cls']) + onegt_box_loss = sum(one_gt_losses['loss_bbox']) + onegt_dfl_loss = sum(one_gt_losses['loss_dfl']) + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' + assert onegt_dfl_loss.item() > 0, 'dfl loss should be non-zero' diff --git a/tests/test_models/test_dense_heads/test_ld_head.py b/tests/test_models/test_dense_heads/test_ld_head.py new file mode 100644 index 0000000..6a7541a --- /dev/null +++ b/tests/test_models/test_dense_heads/test_ld_head.py @@ -0,0 +1,120 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import GFLHead, LDHead + + +def test_ld_head_loss(): + """Tests vfnet head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + train_cfg = mmcv.Config( + dict( + assigner=dict(type='ATSSAssigner', topk=9, ignore_iof_thr=0.1), + allowed_border=-1, + pos_weight=-1, + debug=False)) + + self = LDHead( + num_classes=4, + in_channels=1, + train_cfg=train_cfg, + loss_ld=dict(type='KnowledgeDistillationKLDivLoss', loss_weight=1.0), + loss_cls=dict( + type='QualityFocalLoss', + use_sigmoid=True, + beta=2.0, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=2.0), + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128])) + + teacher_model = GFLHead( + num_classes=4, + in_channels=1, + train_cfg=train_cfg, + loss_cls=dict( + type='QualityFocalLoss', + use_sigmoid=True, + beta=2.0, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=2.0), + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128])) + + feat = [ + torch.rand(1, 1, s // feat_size, s // feat_size) + for feat_size in [4, 8, 16, 32, 64] + ] + cls_scores, bbox_preds = self.forward(feat) + rand_soft_target = teacher_model.forward(feat)[1] + + # Test that empty ground truth encourages the network to predict + # background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + gt_bboxes_ignore = None + + empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + rand_soft_target, img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero, ld loss should + # be non-negative but there should be no box loss. + empty_cls_loss = sum(empty_gt_losses['loss_cls']) + empty_box_loss = sum(empty_gt_losses['loss_bbox']) + empty_ld_loss = sum(empty_gt_losses['loss_ld']) + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + assert empty_ld_loss.item() >= 0, 'ld loss should be non-negative' + + # When truth is non-empty then both cls and box loss should be nonzero + # for random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + rand_soft_target, img_metas, gt_bboxes_ignore) + onegt_cls_loss = sum(one_gt_losses['loss_cls']) + onegt_box_loss = sum(one_gt_losses['loss_bbox']) + + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' + + gt_bboxes_ignore = gt_bboxes + + # When truth is non-empty but ignored then the cls loss should be nonzero, + # but there should be no box loss. + ignore_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + rand_soft_target, img_metas, gt_bboxes_ignore) + ignore_cls_loss = sum(ignore_gt_losses['loss_cls']) + ignore_box_loss = sum(ignore_gt_losses['loss_bbox']) + + assert ignore_cls_loss.item() > 0, 'cls loss should be non-zero' + assert ignore_box_loss.item() == 0, 'gt bbox ignored loss should be zero' + + # When truth is non-empty and not ignored then both cls and box loss should + # be nonzero for random inputs + gt_bboxes_ignore = [torch.randn(1, 4)] + + not_ignore_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, + gt_labels, rand_soft_target, img_metas, + gt_bboxes_ignore) + not_ignore_cls_loss = sum(not_ignore_gt_losses['loss_cls']) + not_ignore_box_loss = sum(not_ignore_gt_losses['loss_bbox']) + + assert not_ignore_cls_loss.item() > 0, 'cls loss should be non-zero' + assert not_ignore_box_loss.item( + ) > 0, 'gt bbox not ignored loss should be non-zero' diff --git a/tests/test_models/test_dense_heads/test_paa_head.py b/tests/test_models/test_dense_heads/test_paa_head.py new file mode 100644 index 0000000..262e89d --- /dev/null +++ b/tests/test_models/test_dense_heads/test_paa_head.py @@ -0,0 +1,122 @@ +import mmcv +import numpy as np +import torch + +from mmdet.models.dense_heads import PAAHead, paa_head +from mmdet.models.dense_heads.paa_head import levels_to_images + + +def test_paa_head_loss(): + """Tests paa head loss when truth is empty and non-empty.""" + + class mock_skm(object): + + def GaussianMixture(self, *args, **kwargs): + return self + + def fit(self, loss): + pass + + def predict(self, loss): + components = np.zeros_like(loss, dtype=np.long) + return components.reshape(-1) + + def score_samples(self, loss): + scores = np.random.random(len(loss)) + return scores + + paa_head.skm = mock_skm() + + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + train_cfg = mmcv.Config( + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.1, + neg_iou_thr=0.1, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False)) + # since Focal Loss is not supported on CPU + self = PAAHead( + num_classes=4, + in_channels=1, + train_cfg=train_cfg, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=1.3), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)) + feat = [ + torch.rand(1, 1, s // feat_size, s // feat_size) + for feat_size in [4, 8, 16, 32, 64] + ] + self.init_weights() + cls_scores, bbox_preds, iou_preds = self(feat) + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, iou_preds, gt_bboxes, + gt_labels, img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_cls_loss = empty_gt_losses['loss_cls'] + empty_box_loss = empty_gt_losses['loss_bbox'] + empty_iou_loss = empty_gt_losses['loss_iou'] + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + assert empty_iou_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, iou_preds, gt_bboxes, + gt_labels, img_metas, gt_bboxes_ignore) + onegt_cls_loss = one_gt_losses['loss_cls'] + onegt_box_loss = one_gt_losses['loss_bbox'] + onegt_iou_loss = one_gt_losses['loss_iou'] + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' + assert onegt_iou_loss.item() > 0, 'box loss should be non-zero' + n, c, h, w = 10, 4, 20, 20 + mlvl_tensor = [torch.ones(n, c, h, w) for i in range(5)] + results = levels_to_images(mlvl_tensor) + assert len(results) == n + assert results[0].size() == (h * w * 5, c) + assert self.with_score_voting + cls_scores = [torch.ones(2, 4, 5, 5)] + bbox_preds = [torch.ones(2, 4, 5, 5)] + iou_preds = [torch.ones(2, 1, 5, 5)] + mlvl_anchors = [torch.ones(2, 5 * 5, 4)] + img_shape = None + scale_factor = [0.5, 0.5] + cfg = mmcv.Config( + dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) + rescale = False + self._get_bboxes( + cls_scores, + bbox_preds, + iou_preds, + mlvl_anchors, + img_shape, + scale_factor, + cfg, + rescale=rescale) diff --git a/tests/test_models/test_dense_heads/test_pisa_head.py b/tests/test_models/test_dense_heads/test_pisa_head.py new file mode 100644 index 0000000..6b1d42d --- /dev/null +++ b/tests/test_models/test_dense_heads/test_pisa_head.py @@ -0,0 +1,244 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import PISARetinaHead, PISASSDHead +from mmdet.models.roi_heads import PISARoIHead + + +def test_pisa_retinanet_head_loss(): + """Tests pisa retinanet head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + + cfg = mmcv.Config( + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + isr=dict(k=2., bias=0.), + carl=dict(k=1., bias=0.2), + allowed_border=0, + pos_weight=-1, + debug=False)) + self = PISARetinaHead(num_classes=4, in_channels=1, train_cfg=cfg) + + # Anchor head expects a multiple levels of features per image + feat = [ + torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2))) + for i in range(len(self.anchor_generator.strides)) + ] + cls_scores, bbox_preds = self.forward(feat) + + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_cls_loss = empty_gt_losses['loss_cls'].sum() + empty_box_loss = empty_gt_losses['loss_bbox'].sum() + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + onegt_cls_loss = one_gt_losses['loss_cls'].sum() + onegt_box_loss = one_gt_losses['loss_bbox'].sum() + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' + + +def test_pisa_ssd_head_loss(): + """Tests pisa ssd head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + + cfg = mmcv.Config( + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0., + ignore_iof_thr=-1, + gt_max_assign_all=False), + isr=dict(k=2., bias=0.), + carl=dict(k=1., bias=0.2), + smoothl1_beta=1., + allowed_border=-1, + pos_weight=-1, + neg_pos_ratio=3, + debug=False)) + ssd_anchor_generator = dict( + type='SSDAnchorGenerator', + scale_major=False, + input_size=300, + strides=[1], + ratios=([2], ), + basesize_ratio_range=(0.15, 0.9)) + self = PISASSDHead( + num_classes=4, + in_channels=(1, ), + train_cfg=cfg, + anchor_generator=ssd_anchor_generator) + + # Anchor head expects a multiple levels of features per image + feat = [ + torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2))) + for i in range(len(self.anchor_generator.strides)) + ] + cls_scores, bbox_preds = self.forward(feat) + + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_cls_loss = sum(empty_gt_losses['loss_cls']) + empty_box_loss = sum(empty_gt_losses['loss_bbox']) + # SSD is special, #pos:#neg = 1: 3, so empth gt will also lead loss cls = 0 + assert empty_cls_loss.item() == 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + onegt_cls_loss = sum(one_gt_losses['loss_cls']) + onegt_box_loss = sum(one_gt_losses['loss_bbox']) + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' + + +def test_pisa_roi_head_loss(): + """Tests pisa roi head loss when truth is empty and non-empty.""" + train_cfg = mmcv.Config( + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='ScoreHLRSampler', + num=4, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True, + k=0.5, + bias=0.), + isr=dict(k=2., bias=0.), + carl=dict(k=1., bias=0.2), + allowed_border=0, + pos_weight=-1, + debug=False)) + + bbox_roi_extractor = dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=1, + featmap_strides=[1]) + + bbox_head = dict( + type='Shared2FCBBoxHead', + in_channels=1, + fc_out_channels=2, + roi_feat_size=7, + num_classes=4, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)) + + self = PISARoIHead(bbox_roi_extractor, bbox_head, train_cfg=train_cfg) + + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + + # Anchor head expects a multiple levels of features per image + feat = [ + torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2))) + for i in range(1) + ] + + proposal_list = [ + torch.Tensor([[22.6667, 22.8757, 238.6326, 151.8874], [0, 3, 5, 7]]) + ] + + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + gt_bboxes_ignore = None + + empty_gt_losses = self.forward_train(feat, img_metas, proposal_list, + gt_bboxes, gt_labels, + gt_bboxes_ignore) + + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_cls_loss = empty_gt_losses['loss_cls'].sum() + empty_box_loss = empty_gt_losses['loss_bbox'].sum() + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + + one_gt_losses = self.forward_train(feat, img_metas, proposal_list, + gt_bboxes, gt_labels, gt_bboxes_ignore) + onegt_cls_loss = one_gt_losses['loss_cls'].sum() + onegt_box_loss = one_gt_losses['loss_bbox'].sum() + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' diff --git a/tests/test_models/test_dense_heads/test_sabl_retina_head.py b/tests/test_models/test_dense_heads/test_sabl_retina_head.py new file mode 100644 index 0000000..c958be6 --- /dev/null +++ b/tests/test_models/test_dense_heads/test_sabl_retina_head.py @@ -0,0 +1,75 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import SABLRetinaHead + + +def test_sabl_retina_head_loss(): + """Tests anchor head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + + cfg = mmcv.Config( + dict( + assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False)) + head = SABLRetinaHead( + num_classes=4, + in_channels=3, + feat_channels=10, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + train_cfg=cfg) + if torch.cuda.is_available(): + head.cuda() + # Anchor head expects a multiple levels of features per image + feat = [ + torch.rand(1, 3, s // (2**(i + 2)), s // (2**(i + 2))).cuda() + for i in range(len(head.approx_anchor_generator.base_anchors)) + ] + cls_scores, bbox_preds = head.forward(feat) + + # Test that empty ground truth encourages the network + # to predict background + gt_bboxes = [torch.empty((0, 4)).cuda()] + gt_labels = [torch.LongTensor([]).cuda()] + + gt_bboxes_ignore = None + empty_gt_losses = head.loss(cls_scores, bbox_preds, gt_bboxes, + gt_labels, img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there + # should be no box loss. + empty_cls_loss = sum(empty_gt_losses['loss_cls']) + empty_box_cls_loss = sum(empty_gt_losses['loss_bbox_cls']) + empty_box_reg_loss = sum(empty_gt_losses['loss_bbox_reg']) + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_cls_loss.item() == 0, ( + 'there should be no box cls loss when there are no true boxes') + assert empty_box_reg_loss.item() == 0, ( + 'there should be no box reg loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should + # be nonzero for random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]).cuda(), + ] + gt_labels = [torch.LongTensor([2]).cuda()] + one_gt_losses = head.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + onegt_cls_loss = sum(one_gt_losses['loss_cls']) + onegt_box_cls_loss = sum(one_gt_losses['loss_bbox_cls']) + onegt_box_reg_loss = sum(one_gt_losses['loss_bbox_reg']) + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_cls_loss.item() > 0, 'box loss cls should be non-zero' + assert onegt_box_reg_loss.item() > 0, 'box loss reg should be non-zero' diff --git a/tests/test_models/test_dense_heads/test_vfnet_head.py b/tests/test_models/test_dense_heads/test_vfnet_head.py new file mode 100644 index 0000000..4fd43dd --- /dev/null +++ b/tests/test_models/test_dense_heads/test_vfnet_head.py @@ -0,0 +1,62 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import VFNetHead + + +def test_vfnet_head_loss(): + """Tests vfnet head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + train_cfg = mmcv.Config( + dict( + assigner=dict(type='ATSSAssigner', topk=9), + allowed_border=-1, + pos_weight=-1, + debug=False)) + # since Focal Loss is not supported on CPU + self = VFNetHead( + num_classes=4, + in_channels=1, + train_cfg=train_cfg, + loss_cls=dict(type='VarifocalLoss', use_sigmoid=True, loss_weight=1.0)) + if torch.cuda.is_available(): + self.cuda() + feat = [ + torch.rand(1, 1, s // feat_size, s // feat_size).cuda() + for feat_size in [4, 8, 16, 32, 64] + ] + cls_scores, bbox_preds, bbox_preds_refine = self.forward(feat) + # Test that empty ground truth encourages the network to predict + # background + gt_bboxes = [torch.empty((0, 4)).cuda()] + gt_labels = [torch.LongTensor([]).cuda()] + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, bbox_preds_refine, + gt_bboxes, gt_labels, img_metas, + gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there + # should be no box loss. + empty_cls_loss = empty_gt_losses['loss_cls'] + empty_box_loss = empty_gt_losses['loss_bbox'] + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero + # for random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]).cuda(), + ] + gt_labels = [torch.LongTensor([2]).cuda()] + one_gt_losses = self.loss(cls_scores, bbox_preds, bbox_preds_refine, + gt_bboxes, gt_labels, img_metas, + gt_bboxes_ignore) + onegt_cls_loss = one_gt_losses['loss_cls'] + onegt_box_loss = one_gt_losses['loss_bbox'] + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' diff --git a/tests/test_models/test_dense_heads/test_yolact_head.py b/tests/test_models/test_dense_heads/test_yolact_head.py new file mode 100644 index 0000000..aff57c4 --- /dev/null +++ b/tests/test_models/test_dense_heads/test_yolact_head.py @@ -0,0 +1,136 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import YOLACTHead, YOLACTProtonet, YOLACTSegmHead + + +def test_yolact_head_loss(): + """Tests yolact head losses when truth is empty and non-empty.""" + s = 550 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + train_cfg = mmcv.Config( + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0., + ignore_iof_thr=-1, + gt_max_assign_all=False), + smoothl1_beta=1., + allowed_border=-1, + pos_weight=-1, + neg_pos_ratio=3, + debug=False, + min_gt_box_wh=[4.0, 4.0])) + bbox_head = YOLACTHead( + num_classes=80, + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=3, + scales_per_octave=1, + base_sizes=[8, 16, 32, 64, 128], + ratios=[0.5, 1.0, 2.0], + strides=[550.0 / x for x in [69, 35, 18, 9, 5]], + centers=[(550 * 0.5 / x, 550 * 0.5 / x) + for x in [69, 35, 18, 9, 5]]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + reduction='none', + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.5), + num_head_convs=1, + num_protos=32, + use_ohem=True, + train_cfg=train_cfg) + segm_head = YOLACTSegmHead( + in_channels=256, + num_classes=80, + loss_segm=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)) + mask_head = YOLACTProtonet( + num_classes=80, + in_channels=256, + num_protos=32, + max_masks_to_train=100, + loss_mask_weight=6.125) + feat = [ + torch.rand(1, 256, feat_size, feat_size) + for feat_size in [69, 35, 18, 9, 5] + ] + cls_score, bbox_pred, coeff_pred = bbox_head.forward(feat) + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + gt_masks = [torch.empty((0, 550, 550))] + gt_bboxes_ignore = None + empty_gt_losses, sampling_results = bbox_head.loss( + cls_score, + bbox_pred, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_cls_loss = sum(empty_gt_losses['loss_cls']) + empty_box_loss = sum(empty_gt_losses['loss_bbox']) + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # Test segm head and mask head + segm_head_outs = segm_head(feat[0]) + empty_segm_loss = segm_head.loss(segm_head_outs, gt_masks, gt_labels) + mask_pred = mask_head(feat[0], coeff_pred, gt_bboxes, img_metas, + sampling_results) + empty_mask_loss = mask_head.loss(mask_pred, gt_masks, gt_bboxes, img_metas, + sampling_results) + # When there is no truth, the segm and mask loss should be zero. + empty_segm_loss = sum(empty_segm_loss['loss_segm']) + empty_mask_loss = sum(empty_mask_loss['loss_mask']) + assert empty_segm_loss.item() == 0, ( + 'there should be no segm loss when there are no true boxes') + assert empty_mask_loss == 0, ( + 'there should be no mask loss when there are no true boxes') + + # When truth is non-empty then cls, box, mask, segm loss should be + # nonzero for random inputs. + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + gt_masks = [(torch.rand((1, 550, 550)) > 0.5).float()] + + one_gt_losses, sampling_results = bbox_head.loss( + cls_score, + bbox_pred, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=gt_bboxes_ignore) + one_gt_cls_loss = sum(one_gt_losses['loss_cls']) + one_gt_box_loss = sum(one_gt_losses['loss_bbox']) + assert one_gt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert one_gt_box_loss.item() > 0, 'box loss should be non-zero' + + one_gt_segm_loss = segm_head.loss(segm_head_outs, gt_masks, gt_labels) + mask_pred = mask_head(feat[0], coeff_pred, gt_bboxes, img_metas, + sampling_results) + one_gt_mask_loss = mask_head.loss(mask_pred, gt_masks, gt_bboxes, + img_metas, sampling_results) + one_gt_segm_loss = sum(one_gt_segm_loss['loss_segm']) + one_gt_mask_loss = sum(one_gt_mask_loss['loss_mask']) + assert one_gt_segm_loss.item() > 0, 'segm loss should be non-zero' + assert one_gt_mask_loss.item() > 0, 'mask loss should be non-zero' diff --git a/tests/test_models/test_dense_heads/test_yolof_head.py b/tests/test_models/test_dense_heads/test_yolof_head.py new file mode 100644 index 0000000..ef21b66 --- /dev/null +++ b/tests/test_models/test_dense_heads/test_yolof_head.py @@ -0,0 +1,75 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import YOLOFHead + + +def test_yolof_head_loss(): + """Tests yolof head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + train_cfg = mmcv.Config( + dict( + assigner=dict( + type='UniformAssigner', + pos_ignore_thr=0.15, + neg_ignore_thr=0.7), + allowed_border=-1, + pos_weight=-1, + debug=False)) + self = YOLOFHead( + num_classes=4, + in_channels=1, + reg_decoded_bbox=True, + train_cfg=train_cfg, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[1, 2, 4, 8, 16], + strides=[32]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1., 1., 1., 1.], + add_ctr_clamp=True, + ctr_clamp=32), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=1.0)) + feat = [torch.rand(1, 1, s // 32, s // 32)] + cls_scores, bbox_preds = self.forward(feat) + + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_cls_loss = empty_gt_losses['loss_cls'] + empty_box_loss = empty_gt_losses['loss_bbox'] + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + onegt_cls_loss = one_gt_losses['loss_cls'] + onegt_box_loss = one_gt_losses['loss_bbox'] + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' diff --git a/tests/test_models/test_forward.py b/tests/test_models/test_forward.py new file mode 100644 index 0000000..01955b2 --- /dev/null +++ b/tests/test_models/test_forward.py @@ -0,0 +1,609 @@ +"""pytest tests/test_forward.py.""" +import copy +from os.path import dirname, exists, join + +import numpy as np +import pytest +import torch + + +def _get_config_directory(): + """Find the predefined detector config directory.""" + try: + # Assume we are running in the source mmdetection repo + repo_dpath = dirname(dirname(dirname(__file__))) + except NameError: + # For IPython development when this __file__ is not defined + import mmdet + repo_dpath = dirname(dirname(mmdet.__file__)) + config_dpath = join(repo_dpath, 'configs') + if not exists(config_dpath): + raise Exception('Cannot find config path') + return config_dpath + + +def _get_config_module(fname): + """Load a configuration as a python module.""" + from mmcv import Config + config_dpath = _get_config_directory() + config_fpath = join(config_dpath, fname) + config_mod = Config.fromfile(config_fpath) + return config_mod + + +def _get_detector_cfg(fname): + """Grab configs necessary to create a detector. + + These are deep copied to allow for safe modification of parameters without + influencing other tests. + """ + config = _get_config_module(fname) + model = copy.deepcopy(config.model) + return model + + +def test_sparse_rcnn_forward(): + config_path = 'sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py' + model = _get_detector_cfg(config_path) + model['pretrained'] = None + from mmdet.models import build_detector + detector = build_detector(model) + detector.init_weights() + input_shape = (1, 3, 550, 550) + mm_inputs = _demo_mm_inputs(input_shape, num_items=[5]) + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + # Test forward train with non-empty truth batch + detector = detector + imgs = imgs + detector.train() + gt_bboxes = mm_inputs['gt_bboxes'] + gt_bboxes = [item for item in gt_bboxes] + gt_labels = mm_inputs['gt_labels'] + gt_labels = [item for item in gt_labels] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + return_loss=True) + assert isinstance(losses, dict) + loss, _ = detector._parse_losses(losses) + assert float(loss.item()) > 0 + detector.forward_dummy(imgs) + + # Test forward train with an empty truth batch + mm_inputs = _demo_mm_inputs(input_shape, num_items=[0]) + imgs = mm_inputs.pop('imgs') + imgs = imgs + img_metas = mm_inputs.pop('img_metas') + gt_bboxes = mm_inputs['gt_bboxes'] + gt_bboxes = [item for item in gt_bboxes] + gt_labels = mm_inputs['gt_labels'] + gt_labels = [item for item in gt_labels] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + return_loss=True) + assert isinstance(losses, dict) + loss, _ = detector._parse_losses(losses) + assert float(loss.item()) > 0 + + # Test forward test + detector.eval() + with torch.no_grad(): + img_list = [g[None, :] for g in imgs] + batch_results = [] + for one_img, one_meta in zip(img_list, img_metas): + result = detector.forward([one_img], [[one_meta]], + rescale=True, + return_loss=False) + batch_results.append(result) + + +def test_rpn_forward(): + model = _get_detector_cfg('rpn/rpn_r50_fpn_1x_coco.py') + model['pretrained'] = None + + from mmdet.models import build_detector + detector = build_detector(model) + + input_shape = (1, 3, 224, 224) + mm_inputs = _demo_mm_inputs(input_shape) + + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + + # Test forward train + gt_bboxes = mm_inputs['gt_bboxes'] + losses = detector.forward( + imgs, img_metas, gt_bboxes=gt_bboxes, return_loss=True) + assert isinstance(losses, dict) + + # Test forward test + with torch.no_grad(): + img_list = [g[None, :] for g in imgs] + batch_results = [] + for one_img, one_meta in zip(img_list, img_metas): + result = detector.forward([one_img], [[one_meta]], + return_loss=False) + batch_results.append(result) + + +@pytest.mark.parametrize( + 'cfg_file', + [ + 'retinanet/retinanet_r50_fpn_1x_coco.py', + 'guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py', + 'ghm/retinanet_ghm_r50_fpn_1x_coco.py', + 'fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py', + 'foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py', + # 'free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py', + # 'atss/atss_r50_fpn_1x_coco.py', # not ready for topk + 'reppoints/reppoints_moment_r50_fpn_1x_coco.py', + 'yolo/yolov3_d53_mstrain-608_273e_coco.py' + ]) +def test_single_stage_forward_gpu(cfg_file): + if not torch.cuda.is_available(): + import pytest + pytest.skip('test requires GPU and torch+cuda') + + model = _get_detector_cfg(cfg_file) + model['pretrained'] = None + + from mmdet.models import build_detector + detector = build_detector(model) + + input_shape = (2, 3, 224, 224) + mm_inputs = _demo_mm_inputs(input_shape) + + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + + detector = detector.cuda() + imgs = imgs.cuda() + # Test forward train + gt_bboxes = [b.cuda() for b in mm_inputs['gt_bboxes']] + gt_labels = [g.cuda() for g in mm_inputs['gt_labels']] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + return_loss=True) + assert isinstance(losses, dict) + + # Test forward test + with torch.no_grad(): + img_list = [g[None, :] for g in imgs] + batch_results = [] + for one_img, one_meta in zip(img_list, img_metas): + result = detector.forward([one_img], [[one_meta]], + return_loss=False) + batch_results.append(result) + + +def test_faster_rcnn_ohem_forward(): + model = _get_detector_cfg( + 'faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py') + model['pretrained'] = None + + from mmdet.models import build_detector + detector = build_detector(model) + + input_shape = (1, 3, 256, 256) + + # Test forward train with a non-empty truth batch + mm_inputs = _demo_mm_inputs(input_shape, num_items=[10]) + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + gt_bboxes = mm_inputs['gt_bboxes'] + gt_labels = mm_inputs['gt_labels'] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + return_loss=True) + assert isinstance(losses, dict) + loss, _ = detector._parse_losses(losses) + assert float(loss.item()) > 0 + + # Test forward train with an empty truth batch + mm_inputs = _demo_mm_inputs(input_shape, num_items=[0]) + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + gt_bboxes = mm_inputs['gt_bboxes'] + gt_labels = mm_inputs['gt_labels'] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + return_loss=True) + assert isinstance(losses, dict) + loss, _ = detector._parse_losses(losses) + assert float(loss.item()) > 0 + + +@pytest.mark.parametrize('cfg_file', [ + 'cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py', + 'mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py', + 'grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py', + 'ms_rcnn/ms_rcnn_r50_fpn_1x_coco.py', + 'htc/htc_r50_fpn_1x_coco.py', + 'scnet/scnet_r50_fpn_20e_coco.py', +]) +def test_two_stage_forward(cfg_file): + models_with_semantic = [ + 'htc/htc_r50_fpn_1x_coco.py', + 'scnet/scnet_r50_fpn_20e_coco.py', + ] + if cfg_file in models_with_semantic: + with_semantic = True + else: + with_semantic = False + + model = _get_detector_cfg(cfg_file) + model['pretrained'] = None + + from mmdet.models import build_detector + detector = build_detector(model) + + input_shape = (1, 3, 256, 256) + + # Test forward train with a non-empty truth batch + mm_inputs = _demo_mm_inputs( + input_shape, num_items=[10], with_semantic=with_semantic) + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + losses = detector.forward(imgs, img_metas, return_loss=True, **mm_inputs) + assert isinstance(losses, dict) + loss, _ = detector._parse_losses(losses) + loss.requires_grad_(True) + assert float(loss.item()) > 0 + loss.backward() + + # Test forward train with an empty truth batch + mm_inputs = _demo_mm_inputs( + input_shape, num_items=[0], with_semantic=with_semantic) + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + losses = detector.forward(imgs, img_metas, return_loss=True, **mm_inputs) + assert isinstance(losses, dict) + loss, _ = detector._parse_losses(losses) + loss.requires_grad_(True) + assert float(loss.item()) > 0 + loss.backward() + + # Test forward test + with torch.no_grad(): + img_list = [g[None, :] for g in imgs] + batch_results = [] + for one_img, one_meta in zip(img_list, img_metas): + result = detector.forward([one_img], [[one_meta]], + return_loss=False) + batch_results.append(result) + + +@pytest.mark.parametrize( + 'cfg_file', ['ghm/retinanet_ghm_r50_fpn_1x_coco.py', 'ssd/ssd300_coco.py']) +def test_single_stage_forward_cpu(cfg_file): + model = _get_detector_cfg(cfg_file) + model['pretrained'] = None + + from mmdet.models import build_detector + detector = build_detector(model) + + input_shape = (1, 3, 300, 300) + mm_inputs = _demo_mm_inputs(input_shape) + + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + + # Test forward train + gt_bboxes = mm_inputs['gt_bboxes'] + gt_labels = mm_inputs['gt_labels'] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + return_loss=True) + assert isinstance(losses, dict) + + # Test forward test + with torch.no_grad(): + img_list = [g[None, :] for g in imgs] + batch_results = [] + for one_img, one_meta in zip(img_list, img_metas): + result = detector.forward([one_img], [[one_meta]], + return_loss=False) + batch_results.append(result) + + +def _demo_mm_inputs(input_shape=(1, 3, 300, 300), + num_items=None, num_classes=10, + with_semantic=False): # yapf: disable + """Create a superset of inputs needed to run test or train batches. + + Args: + input_shape (tuple): + input batch dimensions + + num_items (None | List[int]): + specifies the number of boxes in each batch item + + num_classes (int): + number of different labels a box might have + """ + from mmdet.core import BitmapMasks + + (N, C, H, W) = input_shape + + rng = np.random.RandomState(0) + + imgs = rng.rand(*input_shape) + + img_metas = [{ + 'img_shape': (H, W, C), + 'ori_shape': (H, W, C), + 'pad_shape': (H, W, C), + 'filename': '.png', + 'scale_factor': 1.0, + 'flip': False, + 'flip_direction': None, + } for _ in range(N)] + + gt_bboxes = [] + gt_labels = [] + gt_masks = [] + + for batch_idx in range(N): + if num_items is None: + num_boxes = rng.randint(1, 10) + else: + num_boxes = num_items[batch_idx] + + cx, cy, bw, bh = rng.rand(num_boxes, 4).T + + tl_x = ((cx * W) - (W * bw / 2)).clip(0, W) + tl_y = ((cy * H) - (H * bh / 2)).clip(0, H) + br_x = ((cx * W) + (W * bw / 2)).clip(0, W) + br_y = ((cy * H) + (H * bh / 2)).clip(0, H) + + boxes = np.vstack([tl_x, tl_y, br_x, br_y]).T + class_idxs = rng.randint(1, num_classes, size=num_boxes) + + gt_bboxes.append(torch.FloatTensor(boxes)) + gt_labels.append(torch.LongTensor(class_idxs)) + + mask = np.random.randint(0, 2, (len(boxes), H, W), dtype=np.uint8) + gt_masks.append(BitmapMasks(mask, H, W)) + + mm_inputs = { + 'imgs': torch.FloatTensor(imgs).requires_grad_(True), + 'img_metas': img_metas, + 'gt_bboxes': gt_bboxes, + 'gt_labels': gt_labels, + 'gt_bboxes_ignore': None, + 'gt_masks': gt_masks, + } + + if with_semantic: + # assume gt_semantic_seg using scale 1/8 of the img + gt_semantic_seg = np.random.randint( + 0, num_classes, (1, 1, H // 8, W // 8), dtype=np.uint8) + mm_inputs.update( + {'gt_semantic_seg': torch.ByteTensor(gt_semantic_seg)}) + + return mm_inputs + + +def test_yolact_forward(): + model = _get_detector_cfg('yolact/yolact_r50_1x8_coco.py') + model['pretrained'] = None + + from mmdet.models import build_detector + detector = build_detector(model) + + input_shape = (1, 3, 100, 100) + mm_inputs = _demo_mm_inputs(input_shape) + + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + + # Test forward train + detector.train() + gt_bboxes = mm_inputs['gt_bboxes'] + gt_labels = mm_inputs['gt_labels'] + gt_masks = mm_inputs['gt_masks'] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + gt_masks=gt_masks, + return_loss=True) + assert isinstance(losses, dict) + + # Test forward test + detector.eval() + with torch.no_grad(): + img_list = [g[None, :] for g in imgs] + batch_results = [] + for one_img, one_meta in zip(img_list, img_metas): + result = detector.forward([one_img], [[one_meta]], + rescale=True, + return_loss=False) + batch_results.append(result) + + +def test_detr_forward(): + model = _get_detector_cfg('detr/detr_r50_8x2_150e_coco.py') + model['pretrained'] = None + + from mmdet.models import build_detector + detector = build_detector(model) + + input_shape = (1, 3, 100, 100) + mm_inputs = _demo_mm_inputs(input_shape) + + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + + # Test forward train with non-empty truth batch + detector.train() + gt_bboxes = mm_inputs['gt_bboxes'] + gt_labels = mm_inputs['gt_labels'] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + return_loss=True) + assert isinstance(losses, dict) + loss, _ = detector._parse_losses(losses) + assert float(loss.item()) > 0 + + # Test forward train with an empty truth batch + mm_inputs = _demo_mm_inputs(input_shape, num_items=[0]) + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + gt_bboxes = mm_inputs['gt_bboxes'] + gt_labels = mm_inputs['gt_labels'] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + return_loss=True) + assert isinstance(losses, dict) + loss, _ = detector._parse_losses(losses) + assert float(loss.item()) > 0 + + # Test forward test + detector.eval() + with torch.no_grad(): + img_list = [g[None, :] for g in imgs] + batch_results = [] + for one_img, one_meta in zip(img_list, img_metas): + result = detector.forward([one_img], [[one_meta]], + rescale=True, + return_loss=False) + batch_results.append(result) + + +def test_kd_single_stage_forward(): + model = _get_detector_cfg('ld/ld_r18_gflv1_r101_fpn_coco_1x.py') + model['pretrained'] = None + + from mmdet.models import build_detector + detector = build_detector(model) + + input_shape = (1, 3, 100, 100) + mm_inputs = _demo_mm_inputs(input_shape) + + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + + # Test forward train with non-empty truth batch + detector.train() + gt_bboxes = mm_inputs['gt_bboxes'] + gt_labels = mm_inputs['gt_labels'] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + return_loss=True) + assert isinstance(losses, dict) + loss, _ = detector._parse_losses(losses) + assert float(loss.item()) > 0 + + # Test forward train with an empty truth batch + mm_inputs = _demo_mm_inputs(input_shape, num_items=[0]) + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + gt_bboxes = mm_inputs['gt_bboxes'] + gt_labels = mm_inputs['gt_labels'] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + return_loss=True) + assert isinstance(losses, dict) + loss, _ = detector._parse_losses(losses) + assert float(loss.item()) > 0 + + # Test forward test + detector.eval() + with torch.no_grad(): + img_list = [g[None, :] for g in imgs] + batch_results = [] + for one_img, one_meta in zip(img_list, img_metas): + result = detector.forward([one_img], [[one_meta]], + rescale=True, + return_loss=False) + batch_results.append(result) + + +def test_inference_detector(): + from mmdet.apis import inference_detector + from mmdet.models import build_detector + from mmcv import ConfigDict + + # small RetinaNet + num_class = 3 + model_dict = dict( + type='RetinaNet', + pretrained=None, + backbone=dict( + type='ResNet', + depth=18, + num_stages=4, + out_indices=(3, ), + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='pytorch'), + neck=None, + bbox_head=dict( + type='RetinaHead', + num_classes=num_class, + in_channels=512, + stacked_convs=1, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5], + strides=[32]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + ), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) + + rng = np.random.RandomState(0) + img1 = rng.rand(100, 100, 3) + img2 = rng.rand(100, 100, 3) + + model = build_detector(ConfigDict(model_dict)) + config = _get_config_module('retinanet/retinanet_r50_fpn_1x_coco.py') + model.cfg = config + # test single image + result = inference_detector(model, img1) + assert len(result) == num_class + # test multiple image + result = inference_detector(model, [img1, img2]) + assert len(result) == 2 and len(result[0]) == num_class diff --git a/tests/test_models/test_loss.py b/tests/test_models/test_loss.py new file mode 100644 index 0000000..cd5f0a0 --- /dev/null +++ b/tests/test_models/test_loss.py @@ -0,0 +1,16 @@ +import pytest +import torch + +from mmdet.models.losses import (BoundedIoULoss, CIoULoss, DIoULoss, GIoULoss, + IoULoss) + + +@pytest.mark.parametrize( + 'loss_class', [IoULoss, BoundedIoULoss, GIoULoss, DIoULoss, CIoULoss]) +def test_iou_type_loss_zeros_weight(loss_class): + pred = torch.rand((10, 4)) + target = torch.rand((10, 4)) + weight = torch.zeros(10) + + loss = loss_class()(pred, target, weight) + assert loss == 0. diff --git a/tests/test_models/test_necks.py b/tests/test_models/test_necks.py new file mode 100644 index 0000000..312c591 --- /dev/null +++ b/tests/test_models/test_necks.py @@ -0,0 +1,248 @@ +import pytest +import torch +from torch.nn.modules.batchnorm import _BatchNorm + +from mmdet.models.necks import FPN, ChannelMapper, DilatedEncoder + + +def test_fpn(): + """Tests fpn.""" + s = 64 + in_channels = [8, 16, 32, 64] + feat_sizes = [s // 2**i for i in range(4)] # [64, 32, 16, 8] + out_channels = 8 + # `num_outs` is not equal to len(in_channels) - start_level + with pytest.raises(AssertionError): + FPN(in_channels=in_channels, + out_channels=out_channels, + start_level=1, + num_outs=2) + + # `end_level` is larger than len(in_channels) - 1 + with pytest.raises(AssertionError): + FPN(in_channels=in_channels, + out_channels=out_channels, + start_level=1, + end_level=4, + num_outs=2) + + # `num_outs` is not equal to end_level - start_level + with pytest.raises(AssertionError): + FPN(in_channels=in_channels, + out_channels=out_channels, + start_level=1, + end_level=3, + num_outs=1) + + # Invalid `add_extra_convs` option + with pytest.raises(AssertionError): + FPN(in_channels=in_channels, + out_channels=out_channels, + start_level=1, + add_extra_convs='on_xxx', + num_outs=5) + + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + start_level=1, + add_extra_convs=True, + num_outs=5) + + # FPN expects a multiple levels of features per image + feats = [ + torch.rand(1, in_channels[i], feat_sizes[i], feat_sizes[i]) + for i in range(len(in_channels)) + ] + outs = fpn_model(feats) + assert fpn_model.add_extra_convs == 'on_input' + assert len(outs) == fpn_model.num_outs + for i in range(fpn_model.num_outs): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + + # Tests for fpn with no extra convs (pooling is used instead) + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + start_level=1, + add_extra_convs=False, + num_outs=5) + outs = fpn_model(feats) + assert len(outs) == fpn_model.num_outs + assert not fpn_model.add_extra_convs + for i in range(fpn_model.num_outs): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + + # Tests for fpn with lateral bns + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + start_level=1, + add_extra_convs=True, + no_norm_on_lateral=False, + norm_cfg=dict(type='BN', requires_grad=True), + num_outs=5) + outs = fpn_model(feats) + assert len(outs) == fpn_model.num_outs + assert fpn_model.add_extra_convs == 'on_input' + for i in range(fpn_model.num_outs): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + bn_exist = False + for m in fpn_model.modules(): + if isinstance(m, _BatchNorm): + bn_exist = True + assert bn_exist + + # Bilinear upsample + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + start_level=1, + add_extra_convs=True, + upsample_cfg=dict(mode='bilinear', align_corners=True), + num_outs=5) + fpn_model(feats) + outs = fpn_model(feats) + assert len(outs) == fpn_model.num_outs + assert fpn_model.add_extra_convs == 'on_input' + for i in range(fpn_model.num_outs): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + + # Scale factor instead of fixed upsample size upsample + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + start_level=1, + add_extra_convs=True, + upsample_cfg=dict(scale_factor=2), + num_outs=5) + outs = fpn_model(feats) + assert len(outs) == fpn_model.num_outs + for i in range(fpn_model.num_outs): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + + # Extra convs source is 'inputs' + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs='on_input', + start_level=1, + num_outs=5) + assert fpn_model.add_extra_convs == 'on_input' + outs = fpn_model(feats) + assert len(outs) == fpn_model.num_outs + for i in range(fpn_model.num_outs): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + + # Extra convs source is 'laterals' + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs='on_lateral', + start_level=1, + num_outs=5) + assert fpn_model.add_extra_convs == 'on_lateral' + outs = fpn_model(feats) + assert len(outs) == fpn_model.num_outs + for i in range(fpn_model.num_outs): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + + # Extra convs source is 'outputs' + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs='on_output', + start_level=1, + num_outs=5) + assert fpn_model.add_extra_convs == 'on_output' + outs = fpn_model(feats) + assert len(outs) == fpn_model.num_outs + for i in range(fpn_model.num_outs): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + + # extra_convs_on_inputs=False is equal to extra convs source is 'on_output' + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs=True, + extra_convs_on_inputs=False, + start_level=1, + num_outs=5, + ) + assert fpn_model.add_extra_convs == 'on_output' + outs = fpn_model(feats) + assert len(outs) == fpn_model.num_outs + for i in range(fpn_model.num_outs): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + + # extra_convs_on_inputs=True is equal to extra convs source is 'on_input' + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs=True, + extra_convs_on_inputs=True, + start_level=1, + num_outs=5, + ) + assert fpn_model.add_extra_convs == 'on_input' + outs = fpn_model(feats) + assert len(outs) == fpn_model.num_outs + for i in range(fpn_model.num_outs): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + + +def test_channel_mapper(): + """Tests ChannelMapper.""" + s = 64 + in_channels = [8, 16, 32, 64] + feat_sizes = [s // 2**i for i in range(4)] # [64, 32, 16, 8] + out_channels = 8 + kernel_size = 3 + feats = [ + torch.rand(1, in_channels[i], feat_sizes[i], feat_sizes[i]) + for i in range(len(in_channels)) + ] + + # in_channels must be a list + with pytest.raises(AssertionError): + channel_mapper = ChannelMapper( + in_channels=10, out_channels=out_channels, kernel_size=kernel_size) + # the length of channel_mapper's inputs must be equal to the length of + # in_channels + with pytest.raises(AssertionError): + channel_mapper = ChannelMapper( + in_channels=in_channels[:-1], + out_channels=out_channels, + kernel_size=kernel_size) + channel_mapper(feats) + + channel_mapper = ChannelMapper( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size) + + outs = channel_mapper(feats) + assert len(outs) == len(feats) + for i in range(len(feats)): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + + +def test_dilated_encoder(): + in_channels = 16 + out_channels = 32 + out_shape = 34 + dilated_encoder = DilatedEncoder(in_channels, out_channels, 16, 2) + feat = [torch.rand(1, in_channels, 34, 34)] + out_feat = dilated_encoder(feat)[0] + assert out_feat.shape == (1, out_channels, out_shape, out_shape) diff --git a/tests/test_models/test_roi_heads/__init__.py b/tests/test_models/test_roi_heads/__init__.py new file mode 100644 index 0000000..9bb6402 --- /dev/null +++ b/tests/test_models/test_roi_heads/__init__.py @@ -0,0 +1,3 @@ +from .utils import _dummy_bbox_sampling + +__all__ = ['_dummy_bbox_sampling'] diff --git a/tests/test_models/test_roi_heads/test_bbox_head.py b/tests/test_models/test_roi_heads/test_bbox_head.py new file mode 100644 index 0000000..a7506b9 --- /dev/null +++ b/tests/test_models/test_roi_heads/test_bbox_head.py @@ -0,0 +1,255 @@ +import mmcv +import pytest +import torch + +from mmdet.core import bbox2roi +from mmdet.models.roi_heads.bbox_heads import BBoxHead +from .utils import _dummy_bbox_sampling + + +def test_bbox_head_loss(): + """Tests bbox head loss when truth is empty and non-empty.""" + self = BBoxHead(in_channels=8, roi_feat_size=3) + + # Dummy proposals + proposal_list = [ + torch.Tensor([[23.6667, 23.8757, 228.6326, 153.8874]]), + ] + + target_cfg = mmcv.Config(dict(pos_weight=1)) + + # Test bbox loss when truth is empty + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + + sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes, + gt_labels) + + bbox_targets = self.get_targets(sampling_results, gt_bboxes, gt_labels, + target_cfg) + labels, label_weights, bbox_targets, bbox_weights = bbox_targets + + # Create dummy features "extracted" for each sampled bbox + num_sampled = sum(len(res.bboxes) for res in sampling_results) + rois = bbox2roi([res.bboxes for res in sampling_results]) + dummy_feats = torch.rand(num_sampled, 8 * 3 * 3) + cls_scores, bbox_preds = self.forward(dummy_feats) + + losses = self.loss(cls_scores, bbox_preds, rois, labels, label_weights, + bbox_targets, bbox_weights) + assert losses.get('loss_cls', 0) > 0, 'cls-loss should be non-zero' + assert losses.get('loss_bbox', 0) == 0, 'empty gt loss should be zero' + + # Test bbox loss when truth is non-empty + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + + sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes, + gt_labels) + rois = bbox2roi([res.bboxes for res in sampling_results]) + + bbox_targets = self.get_targets(sampling_results, gt_bboxes, gt_labels, + target_cfg) + labels, label_weights, bbox_targets, bbox_weights = bbox_targets + + # Create dummy features "extracted" for each sampled bbox + num_sampled = sum(len(res.bboxes) for res in sampling_results) + dummy_feats = torch.rand(num_sampled, 8 * 3 * 3) + cls_scores, bbox_preds = self.forward(dummy_feats) + + losses = self.loss(cls_scores, bbox_preds, rois, labels, label_weights, + bbox_targets, bbox_weights) + assert losses.get('loss_cls', 0) > 0, 'cls-loss should be non-zero' + assert losses.get('loss_bbox', 0) > 0, 'box-loss should be non-zero' + + +@pytest.mark.parametrize(['num_sample', 'num_batch'], [[2, 2], [0, 2], [0, 0]]) +def test_bbox_head_get_bboxes(num_sample, num_batch): + self = BBoxHead(reg_class_agnostic=True) + + num_class = 6 + rois = torch.rand((num_sample, 5)) + cls_score = torch.rand((num_sample, num_class)) + bbox_pred = torch.rand((num_sample, 4)) + scale_factor = 2.0 + det_bboxes, det_labels = self.get_bboxes( + rois, cls_score, bbox_pred, None, scale_factor, rescale=True) + if num_sample == 0: + assert len(det_bboxes) == 0 and len(det_labels) == 0 + else: + assert det_bboxes.shape == bbox_pred.shape + assert det_labels.shape == cls_score.shape + + rois = torch.rand((num_batch, num_sample, 5)) + cls_score = torch.rand((num_batch, num_sample, num_class)) + bbox_pred = torch.rand((num_batch, num_sample, 4)) + det_bboxes, det_labels = self.get_bboxes( + rois, cls_score, bbox_pred, None, scale_factor, rescale=True) + assert len(det_bboxes) == num_batch and len(det_labels) == num_batch + + +def test_refine_boxes(): + """Mirrors the doctest in + ``mmdet.models.bbox_heads.bbox_head.BBoxHead.refine_boxes`` but checks for + multiple values of n_roi / n_img.""" + self = BBoxHead(reg_class_agnostic=True) + + test_settings = [ + + # Corner case: less rois than images + { + 'n_roi': 2, + 'n_img': 4, + 'rng': 34285940 + }, + + # Corner case: no images + { + 'n_roi': 0, + 'n_img': 0, + 'rng': 52925222 + }, + + # Corner cases: few images / rois + { + 'n_roi': 1, + 'n_img': 1, + 'rng': 1200281 + }, + { + 'n_roi': 2, + 'n_img': 1, + 'rng': 1200282 + }, + { + 'n_roi': 2, + 'n_img': 2, + 'rng': 1200283 + }, + { + 'n_roi': 1, + 'n_img': 2, + 'rng': 1200284 + }, + + # Corner case: no rois few images + { + 'n_roi': 0, + 'n_img': 1, + 'rng': 23955860 + }, + { + 'n_roi': 0, + 'n_img': 2, + 'rng': 25830516 + }, + + # Corner case: no rois many images + { + 'n_roi': 0, + 'n_img': 10, + 'rng': 671346 + }, + { + 'n_roi': 0, + 'n_img': 20, + 'rng': 699807 + }, + + # Corner case: cal_similarity num rois and images + { + 'n_roi': 20, + 'n_img': 20, + 'rng': 1200238 + }, + { + 'n_roi': 10, + 'n_img': 20, + 'rng': 1200238 + }, + { + 'n_roi': 5, + 'n_img': 5, + 'rng': 1200238 + }, + + # ---------------------------------- + # Common case: more rois than images + { + 'n_roi': 100, + 'n_img': 1, + 'rng': 337156 + }, + { + 'n_roi': 150, + 'n_img': 2, + 'rng': 275898 + }, + { + 'n_roi': 500, + 'n_img': 5, + 'rng': 4903221 + }, + ] + + for demokw in test_settings: + try: + n_roi = demokw['n_roi'] + n_img = demokw['n_img'] + rng = demokw['rng'] + + print(f'Test refine_boxes case: {demokw!r}') + tup = _demodata_refine_boxes(n_roi, n_img, rng=rng) + rois, labels, bbox_preds, pos_is_gts, img_metas = tup + bboxes_list = self.refine_bboxes(rois, labels, bbox_preds, + pos_is_gts, img_metas) + assert len(bboxes_list) == n_img + assert sum(map(len, bboxes_list)) <= n_roi + assert all(b.shape[1] == 4 for b in bboxes_list) + except Exception: + print(f'Test failed with demokw={demokw!r}') + raise + + +def _demodata_refine_boxes(n_roi, n_img, rng=0): + """Create random test data for the + ``mmdet.models.bbox_heads.bbox_head.BBoxHead.refine_boxes`` method.""" + import numpy as np + from mmdet.core.bbox.demodata import random_boxes + from mmdet.core.bbox.demodata import ensure_rng + try: + import kwarray + except ImportError: + import pytest + pytest.skip('kwarray is required for this test') + scale = 512 + rng = ensure_rng(rng) + img_metas = [{'img_shape': (scale, scale)} for _ in range(n_img)] + # Create rois in the expected format + roi_boxes = random_boxes(n_roi, scale=scale, rng=rng) + if n_img == 0: + assert n_roi == 0, 'cannot have any rois if there are no images' + img_ids = torch.empty((0, ), dtype=torch.long) + roi_boxes = torch.empty((0, 4), dtype=torch.float32) + else: + img_ids = rng.randint(0, n_img, (n_roi, )) + img_ids = torch.from_numpy(img_ids) + rois = torch.cat([img_ids[:, None].float(), roi_boxes], dim=1) + # Create other args + labels = rng.randint(0, 2, (n_roi, )) + labels = torch.from_numpy(labels).long() + bbox_preds = random_boxes(n_roi, scale=scale, rng=rng) + # For each image, pretend random positive boxes are gts + is_label_pos = (labels.numpy() > 0).astype(np.int) + lbl_per_img = kwarray.group_items(is_label_pos, img_ids.numpy()) + pos_per_img = [sum(lbl_per_img.get(gid, [])) for gid in range(n_img)] + # randomly generate with numpy then sort with torch + _pos_is_gts = [ + rng.randint(0, 2, (npos, )).astype(np.uint8) for npos in pos_per_img + ] + pos_is_gts = [ + torch.from_numpy(p).sort(descending=True)[0] for p in _pos_is_gts + ] + return rois, labels, bbox_preds, pos_is_gts, img_metas diff --git a/tests/test_models/test_roi_heads/test_mask_head.py b/tests/test_models/test_roi_heads/test_mask_head.py new file mode 100644 index 0000000..31826cd --- /dev/null +++ b/tests/test_models/test_roi_heads/test_mask_head.py @@ -0,0 +1,69 @@ +import mmcv +import torch + +from mmdet.models.roi_heads.mask_heads import FCNMaskHead, MaskIoUHead +from .utils import _dummy_bbox_sampling + + +def test_mask_head_loss(): + """Test mask head loss when mask target is empty.""" + self = FCNMaskHead( + num_convs=1, + roi_feat_size=6, + in_channels=8, + conv_out_channels=8, + num_classes=8) + + # Dummy proposals + proposal_list = [ + torch.Tensor([[23.6667, 23.8757, 228.6326, 153.8874]]), + ] + + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes, + gt_labels) + + # create dummy mask + import numpy as np + from mmdet.core import BitmapMasks + dummy_mask = np.random.randint(0, 2, (1, 160, 240), dtype=np.uint8) + gt_masks = [BitmapMasks(dummy_mask, 160, 240)] + + # create dummy train_cfg + train_cfg = mmcv.Config(dict(mask_size=12, mask_thr_binary=0.5)) + + # Create dummy features "extracted" for each sampled bbox + num_sampled = sum(len(res.bboxes) for res in sampling_results) + dummy_feats = torch.rand(num_sampled, 8, 6, 6) + + mask_pred = self.forward(dummy_feats) + mask_targets = self.get_targets(sampling_results, gt_masks, train_cfg) + pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results]) + loss_mask = self.loss(mask_pred, mask_targets, pos_labels) + + onegt_mask_loss = sum(loss_mask['loss_mask']) + assert onegt_mask_loss.item() > 0, 'mask loss should be non-zero' + + # test mask_iou_head + mask_iou_head = MaskIoUHead( + num_convs=1, + num_fcs=1, + roi_feat_size=6, + in_channels=8, + conv_out_channels=8, + fc_out_channels=8, + num_classes=8) + + pos_mask_pred = mask_pred[range(mask_pred.size(0)), pos_labels] + mask_iou_pred = mask_iou_head(dummy_feats, pos_mask_pred) + pos_mask_iou_pred = mask_iou_pred[range(mask_iou_pred.size(0)), pos_labels] + + mask_iou_targets = mask_iou_head.get_targets(sampling_results, gt_masks, + pos_mask_pred, mask_targets, + train_cfg) + loss_mask_iou = mask_iou_head.loss(pos_mask_iou_pred, mask_iou_targets) + onegt_mask_iou_loss = loss_mask_iou['loss_mask_iou'].sum() + assert onegt_mask_iou_loss.item() >= 0 diff --git a/tests/test_models/test_roi_heads/test_roi_extractor.py b/tests/test_models/test_roi_heads/test_roi_extractor.py new file mode 100644 index 0000000..22743f2 --- /dev/null +++ b/tests/test_models/test_roi_heads/test_roi_extractor.py @@ -0,0 +1,113 @@ +import pytest +import torch + +from mmdet.models.roi_heads.roi_extractors import GenericRoIExtractor + + +def test_groie(): + # test with pre/post + cfg = dict( + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False)) + + groie = GenericRoIExtractor(**cfg) + + feats = ( + torch.rand((1, 256, 200, 336)), + torch.rand((1, 256, 100, 168)), + torch.rand((1, 256, 50, 84)), + torch.rand((1, 256, 25, 42)), + ) + + rois = torch.tensor([[0.0000, 587.8285, 52.1405, 886.2484, 341.5644]]) + + res = groie(feats, rois) + assert res.shape == torch.Size([1, 256, 7, 7]) + + # test w.o. pre/post + cfg = dict( + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]) + + groie = GenericRoIExtractor(**cfg) + + feats = ( + torch.rand((1, 256, 200, 336)), + torch.rand((1, 256, 100, 168)), + torch.rand((1, 256, 50, 84)), + torch.rand((1, 256, 25, 42)), + ) + + rois = torch.tensor([[0.0000, 587.8285, 52.1405, 886.2484, 341.5644]]) + + res = groie(feats, rois) + assert res.shape == torch.Size([1, 256, 7, 7]) + + # test w.o. pre/post concat + cfg = dict( + aggregation='concat', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256 * 4, + featmap_strides=[4, 8, 16, 32]) + + groie = GenericRoIExtractor(**cfg) + + feats = ( + torch.rand((1, 256, 200, 336)), + torch.rand((1, 256, 100, 168)), + torch.rand((1, 256, 50, 84)), + torch.rand((1, 256, 25, 42)), + ) + + rois = torch.tensor([[0.0000, 587.8285, 52.1405, 886.2484, 341.5644]]) + + res = groie(feats, rois) + assert res.shape == torch.Size([1, 1024, 7, 7]) + + # test not supported aggregate method + with pytest.raises(AssertionError): + cfg = dict( + aggregation='not support', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=1024, + featmap_strides=[4, 8, 16, 32]) + _ = GenericRoIExtractor(**cfg) + + # test concat channels number + cfg = dict( + aggregation='concat', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256 * 5, # 256*5 != 256*4 + featmap_strides=[4, 8, 16, 32]) + + groie = GenericRoIExtractor(**cfg) + + feats = ( + torch.rand((1, 256, 200, 336)), + torch.rand((1, 256, 100, 168)), + torch.rand((1, 256, 50, 84)), + torch.rand((1, 256, 25, 42)), + ) + + rois = torch.tensor([[0.0000, 587.8285, 52.1405, 886.2484, 341.5644]]) + + # out_channels does not sum of feat channels + with pytest.raises(AssertionError): + _ = groie(feats, rois) diff --git a/tests/test_models/test_roi_heads/test_sabl_bbox_head.py b/tests/test_models/test_roi_heads/test_sabl_bbox_head.py new file mode 100644 index 0000000..0517808 --- /dev/null +++ b/tests/test_models/test_roi_heads/test_sabl_bbox_head.py @@ -0,0 +1,76 @@ +import mmcv +import torch + +from mmdet.core import bbox2roi +from mmdet.models.roi_heads.bbox_heads import SABLHead +from .utils import _dummy_bbox_sampling + + +def test_sabl_bbox_head_loss(): + """Tests bbox head loss when truth is empty and non-empty.""" + self = SABLHead( + num_classes=4, + cls_in_channels=3, + reg_in_channels=3, + cls_out_channels=3, + reg_offset_out_channels=3, + reg_cls_out_channels=3, + roi_feat_size=7) + + # Dummy proposals + proposal_list = [ + torch.Tensor([[23.6667, 23.8757, 228.6326, 153.8874]]), + ] + + target_cfg = mmcv.Config(dict(pos_weight=1)) + + # Test bbox loss when truth is empty + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + + sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes, + gt_labels) + + bbox_targets = self.get_targets(sampling_results, gt_bboxes, gt_labels, + target_cfg) + labels, label_weights, bbox_targets, bbox_weights = bbox_targets + + # Create dummy features "extracted" for each sampled bbox + num_sampled = sum(len(res.bboxes) for res in sampling_results) + rois = bbox2roi([res.bboxes for res in sampling_results]) + dummy_feats = torch.rand(num_sampled, 3, 7, 7) + cls_scores, bbox_preds = self.forward(dummy_feats) + + losses = self.loss(cls_scores, bbox_preds, rois, labels, label_weights, + bbox_targets, bbox_weights) + assert losses.get('loss_cls', 0) > 0, 'cls-loss should be non-zero' + assert losses.get('loss_bbox_cls', + 0) == 0, 'empty gt bbox-cls-loss should be zero' + assert losses.get('loss_bbox_reg', + 0) == 0, 'empty gt bbox-reg-loss should be zero' + + # Test bbox loss when truth is non-empty + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + + sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes, + gt_labels) + rois = bbox2roi([res.bboxes for res in sampling_results]) + + bbox_targets = self.get_targets(sampling_results, gt_bboxes, gt_labels, + target_cfg) + labels, label_weights, bbox_targets, bbox_weights = bbox_targets + + # Create dummy features "extracted" for each sampled bbox + num_sampled = sum(len(res.bboxes) for res in sampling_results) + dummy_feats = torch.rand(num_sampled, 3, 7, 7) + cls_scores, bbox_preds = self.forward(dummy_feats) + + losses = self.loss(cls_scores, bbox_preds, rois, labels, label_weights, + bbox_targets, bbox_weights) + assert losses.get('loss_bbox_cls', + 0) > 0, 'empty gt bbox-cls-loss should be zero' + assert losses.get('loss_bbox_reg', + 0) > 0, 'empty gt bbox-reg-loss should be zero' diff --git a/tests/test_models/test_roi_heads/utils.py b/tests/test_models/test_roi_heads/utils.py new file mode 100644 index 0000000..e5c6d58 --- /dev/null +++ b/tests/test_models/test_roi_heads/utils.py @@ -0,0 +1,37 @@ +import torch + +from mmdet.core import build_assigner, build_sampler + + +def _dummy_bbox_sampling(proposal_list, gt_bboxes, gt_labels): + """Create sample results that can be passed to BBoxHead.get_targets.""" + num_imgs = 1 + feat = torch.rand(1, 1, 3, 3) + assign_config = dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + ignore_iof_thr=-1) + sampler_config = dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True) + bbox_assigner = build_assigner(assign_config) + bbox_sampler = build_sampler(sampler_config) + gt_bboxes_ignore = [None for _ in range(num_imgs)] + sampling_results = [] + for i in range(num_imgs): + assign_result = bbox_assigner.assign(proposal_list[i], gt_bboxes[i], + gt_bboxes_ignore[i], gt_labels[i]) + sampling_result = bbox_sampler.sample( + assign_result, + proposal_list[i], + gt_bboxes[i], + gt_labels[i], + feats=feat) + sampling_results.append(sampling_result) + + return sampling_results diff --git a/tests/test_models/test_utils/test_position_encoding.py b/tests/test_models/test_utils/test_position_encoding.py new file mode 100644 index 0000000..94fdd47 --- /dev/null +++ b/tests/test_models/test_utils/test_position_encoding.py @@ -0,0 +1,38 @@ +import pytest +import torch + +from mmdet.models.utils import (LearnedPositionalEncoding, + SinePositionalEncoding) + + +def test_sine_positional_encoding(num_feats=16, batch_size=2): + # test invalid type of scale + with pytest.raises(AssertionError): + module = SinePositionalEncoding( + num_feats, scale=(3., ), normalize=True) + + module = SinePositionalEncoding(num_feats) + h, w = 10, 6 + mask = torch.rand(batch_size, h, w) > 0.5 + assert not module.normalize + out = module(mask) + assert out.shape == (batch_size, num_feats * 2, h, w) + + # set normalize + module = SinePositionalEncoding(num_feats, normalize=True) + assert module.normalize + out = module(mask) + assert out.shape == (batch_size, num_feats * 2, h, w) + + +def test_learned_positional_encoding(num_feats=16, + row_num_embed=10, + col_num_embed=10, + batch_size=2): + module = LearnedPositionalEncoding(num_feats, row_num_embed, col_num_embed) + assert module.row_embed.weight.shape == (row_num_embed, num_feats) + assert module.col_embed.weight.shape == (col_num_embed, num_feats) + h, w = 10, 6 + mask = torch.rand(batch_size, h, w) > 0.5 + out = module(mask) + assert out.shape == (batch_size, num_feats * 2, h, w) diff --git a/tests/test_models/test_utils/test_transformer.py b/tests/test_models/test_utils/test_transformer.py new file mode 100644 index 0000000..6058b2a --- /dev/null +++ b/tests/test_models/test_utils/test_transformer.py @@ -0,0 +1,110 @@ +import pytest +from mmcv.utils import ConfigDict + +from mmdet.models.utils.transformer import (DetrTransformerDecoder, + DetrTransformerEncoder, + Transformer) + + +def test_detr_transformer_dencoder_encoder_layer(): + config = ConfigDict( + dict( + return_intermediate=True, + num_layers=6, + transformerlayers=dict( + type='DetrTransformerDecoderLayer', + attn_cfgs=dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1), + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=( + 'norm', + 'self_attn', + 'norm', + 'cross_attn', + 'norm', + 'ffn', + )))) + assert DetrTransformerDecoder(**config).layers[0].pre_norm + assert len(DetrTransformerDecoder(**config).layers) == 6 + + DetrTransformerDecoder(**config) + with pytest.raises(AssertionError): + config = ConfigDict( + dict( + return_intermediate=True, + num_layers=6, + transformerlayers=[ + dict( + type='DetrTransformerDecoderLayer', + attn_cfgs=dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1), + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'cross_attn', + 'norm', 'ffn', 'norm')) + ] * 5)) + DetrTransformerDecoder(**config) + + config = ConfigDict( + dict( + num_layers=6, + transformerlayers=dict( + type='DetrTransformerDecoderLayer', + attn_cfgs=dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1), + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=('norm', 'self_attn', 'norm', 'cross_attn', + 'norm', 'ffn', 'norm')))) + + with pytest.raises(AssertionError): + # len(operation_order) == 6 + DetrTransformerEncoder(**config) + + +def test_transformer(): + config = ConfigDict( + dict( + encoder=dict( + type='DetrTransformerEncoder', + num_layers=6, + transformerlayers=dict( + type='BaseTransformerLayer', + attn_cfgs=[ + dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1) + ], + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'ffn', 'norm'))), + decoder=dict( + type='DetrTransformerDecoder', + return_intermediate=True, + num_layers=6, + transformerlayers=dict( + type='DetrTransformerDecoderLayer', + attn_cfgs=dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1), + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'cross_attn', 'norm', + 'ffn', 'norm')), + ))) + transformer = Transformer(**config) + transformer.init_weights() diff --git a/tests/test_onnx/__init__.py b/tests/test_onnx/__init__.py new file mode 100644 index 0000000..320516c --- /dev/null +++ b/tests/test_onnx/__init__.py @@ -0,0 +1,3 @@ +from .utils import ort_validate + +__all__ = ['ort_validate'] diff --git a/tests/test_onnx/test_head.py b/tests/test_onnx/test_head.py new file mode 100644 index 0000000..e79ee2c --- /dev/null +++ b/tests/test_onnx/test_head.py @@ -0,0 +1,380 @@ +import os.path as osp +from functools import partial + +import mmcv +import pytest +import torch +from mmcv.cnn import Scale + +from mmdet import digit_version +from mmdet.models.dense_heads import (FCOSHead, FSAFHead, RetinaHead, SSDHead, + YOLOV3Head) +from .utils import ort_validate + +data_path = osp.join(osp.dirname(__file__), 'data') + +if digit_version(torch.__version__) <= digit_version('1.5.0'): + pytest.skip( + 'ort backend does not support version below 1.5.0', + allow_module_level=True) + + +def retinanet_config(): + """RetinanNet Head Config.""" + head_cfg = dict( + stacked_convs=6, + feat_channels=2, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0])) + + test_cfg = mmcv.Config( + dict( + deploy_nms_pre=0, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) + + model = RetinaHead( + num_classes=4, in_channels=1, test_cfg=test_cfg, **head_cfg) + model.requires_grad_(False) + + return model + + +def test_retina_head_forward_single(): + """Test RetinaNet Head single forward in torch and onnxruntime env.""" + retina_model = retinanet_config() + + feat = torch.rand(1, retina_model.in_channels, 32, 32) + # validate the result between the torch and ort + ort_validate(retina_model.forward_single, feat) + + +def test_retina_head_forward(): + """Test RetinaNet Head forward in torch and onnxruntime env.""" + retina_model = retinanet_config() + s = 128 + # RetinaNet head expects a multiple levels of features per image + feats = [ + torch.rand(1, retina_model.in_channels, s // (2**(i + 2)), + s // (2**(i + 2))) # [32, 16, 8, 4, 2] + for i in range(len(retina_model.anchor_generator.strides)) + ] + ort_validate(retina_model.forward, feats) + + +def test_retinanet_head_get_bboxes(): + """Test RetinaNet Head _get_bboxes() in torch and onnxruntime env.""" + retina_model = retinanet_config() + s = 128 + img_metas = [{ + 'img_shape_for_onnx': torch.Tensor([s, s]), + 'scale_factor': 1, + 'pad_shape': (s, s, 3), + 'img_shape': (s, s, 2) + }] + + # The data of retina_head_get_bboxes.pkl contains two parts: + # cls_score(list(Tensor)) and bboxes(list(Tensor)), + # where each torch.Tensor is generated by torch.rand(). + # the cls_score's size: (1, 36, 32, 32), (1, 36, 16, 16), + # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2). + # the bboxes's size: (1, 36, 32, 32), (1, 36, 16, 16), + # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2) + retina_head_data = 'retina_head_get_bboxes.pkl' + feats = mmcv.load(osp.join(data_path, retina_head_data)) + cls_score = feats[:5] + bboxes = feats[5:] + + retina_model.get_bboxes = partial( + retina_model.get_bboxes, img_metas=img_metas, with_nms=False) + ort_validate(retina_model.get_bboxes, (cls_score, bboxes)) + + +def yolo_config(): + """YoloV3 Head Config.""" + head_cfg = dict( + anchor_generator=dict( + type='YOLOAnchorGenerator', + base_sizes=[[(116, 90), (156, 198), (373, 326)], + [(30, 61), (62, 45), (59, 119)], + [(10, 13), (16, 30), (33, 23)]], + strides=[32, 16, 8]), + bbox_coder=dict(type='YOLOBBoxCoder')) + + test_cfg = mmcv.Config( + dict( + deploy_nms_pre=0, + min_bbox_size=0, + score_thr=0.05, + conf_thr=0.005, + nms=dict(type='nms', iou_threshold=0.45), + max_per_img=100)) + + model = YOLOV3Head( + num_classes=4, + in_channels=[1, 1, 1], + out_channels=[16, 8, 4], + test_cfg=test_cfg, + **head_cfg) + model.requires_grad_(False) + # yolov3 need eval() + model.cpu().eval() + return model + + +def test_yolov3_head_forward(): + """Test Yolov3 head forward() in torch and ort env.""" + yolo_model = yolo_config() + + # Yolov3 head expects a multiple levels of features per image + feats = [ + torch.rand(1, 1, 64 // (2**(i + 2)), 64 // (2**(i + 2))) + for i in range(len(yolo_model.in_channels)) + ] + ort_validate(yolo_model.forward, feats) + + +def test_yolov3_head_get_bboxes(): + """Test yolov3 head get_bboxes() in torch and ort env.""" + yolo_model = yolo_config() + s = 128 + img_metas = [{ + 'img_shape_for_onnx': torch.Tensor([s, s]), + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + + # The data of yolov3_head_get_bboxes.pkl contains + # a list of torch.Tensor, where each torch.Tensor + # is generated by torch.rand and each tensor size is: + # (1, 27, 32, 32), (1, 27, 16, 16), (1, 27, 8, 8). + yolo_head_data = 'yolov3_head_get_bboxes.pkl' + pred_maps = mmcv.load(osp.join(data_path, yolo_head_data)) + + yolo_model.get_bboxes = partial( + yolo_model.get_bboxes, img_metas=img_metas, with_nms=False) + ort_validate(yolo_model.get_bboxes, pred_maps) + + +def fcos_config(): + """FCOS Head Config.""" + test_cfg = mmcv.Config( + dict( + deploy_nms_pre=0, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) + + model = FCOSHead(num_classes=4, in_channels=1, test_cfg=test_cfg) + + model.requires_grad_(False) + return model + + +def test_fcos_head_forward_single(): + """Test fcos forward single in torch and ort env.""" + fcos_model = fcos_config() + + feat = torch.rand(1, fcos_model.in_channels, 32, 32) + fcos_model.forward_single = partial( + fcos_model.forward_single, + scale=Scale(1.0).requires_grad_(False), + stride=(4, )) + ort_validate(fcos_model.forward_single, feat) + + +def test_fcos_head_forward(): + """Test fcos forward in mutil-level feature map.""" + fcos_model = fcos_config() + s = 128 + feats = [ + torch.rand(1, 1, s // feat_size, s // feat_size) + for feat_size in [4, 8, 16, 32, 64] + ] + ort_validate(fcos_model.forward, feats) + + +def test_fcos_head_get_bboxes(): + """Test fcos head get_bboxes() in ort.""" + fcos_model = fcos_config() + s = 128 + img_metas = [{ + 'img_shape_for_onnx': torch.Tensor([s, s]), + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + + cls_scores = [ + torch.rand(1, fcos_model.num_classes, s // feat_size, s // feat_size) + for feat_size in [4, 8, 16, 32, 64] + ] + bboxes = [ + torch.rand(1, 4, s // feat_size, s // feat_size) + for feat_size in [4, 8, 16, 32, 64] + ] + centerness = [ + torch.rand(1, 1, s // feat_size, s // feat_size) + for feat_size in [4, 8, 16, 32, 64] + ] + + fcos_model.get_bboxes = partial( + fcos_model.get_bboxes, img_metas=img_metas, with_nms=False) + ort_validate(fcos_model.get_bboxes, (cls_scores, bboxes, centerness)) + + +def fsaf_config(): + """FSAF Head Config.""" + cfg = dict( + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=1, + scales_per_octave=1, + ratios=[1.0], + strides=[8, 16, 32, 64, 128])) + + test_cfg = mmcv.Config( + dict( + deploy_nms_pre=0, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) + + model = FSAFHead(num_classes=4, in_channels=1, test_cfg=test_cfg, **cfg) + model.requires_grad_(False) + return model + + +def test_fsaf_head_forward_single(): + """Test RetinaNet Head forward_single() in torch and onnxruntime env.""" + fsaf_model = fsaf_config() + + feat = torch.rand(1, fsaf_model.in_channels, 32, 32) + ort_validate(fsaf_model.forward_single, feat) + + +def test_fsaf_head_forward(): + """Test RetinaNet Head forward in torch and onnxruntime env.""" + fsaf_model = fsaf_config() + s = 128 + feats = [ + torch.rand(1, fsaf_model.in_channels, s // (2**(i + 2)), + s // (2**(i + 2))) + for i in range(len(fsaf_model.anchor_generator.strides)) + ] + ort_validate(fsaf_model.forward, feats) + + +def test_fsaf_head_get_bboxes(): + """Test RetinaNet Head get_bboxes in torch and onnxruntime env.""" + fsaf_model = fsaf_config() + s = 256 + img_metas = [{ + 'img_shape_for_onnx': torch.Tensor([s, s]), + 'scale_factor': 1, + 'pad_shape': (s, s, 3), + 'img_shape': (s, s, 2) + }] + + # The data of fsaf_head_get_bboxes.pkl contains two parts: + # cls_score(list(Tensor)) and bboxes(list(Tensor)), + # where each torch.Tensor is generated by torch.rand(). + # the cls_score's size: (1, 4, 64, 64), (1, 4, 32, 32), + # (1, 4, 16, 16), (1, 4, 8, 8), (1, 4, 4, 4). + # the bboxes's size: (1, 4, 64, 64), (1, 4, 32, 32), + # (1, 4, 16, 16), (1, 4, 8, 8), (1, 4, 4, 4). + fsaf_head_data = 'fsaf_head_get_bboxes.pkl' + feats = mmcv.load(osp.join(data_path, fsaf_head_data)) + cls_score = feats[:5] + bboxes = feats[5:] + + fsaf_model.get_bboxes = partial( + fsaf_model.get_bboxes, img_metas=img_metas, with_nms=False) + ort_validate(fsaf_model.get_bboxes, (cls_score, bboxes)) + + +def ssd_config(): + """SSD Head Config.""" + cfg = dict( + anchor_generator=dict( + type='SSDAnchorGenerator', + scale_major=False, + input_size=300, + basesize_ratio_range=(0.15, 0.9), + strides=[8, 16, 32, 64, 100, 300], + ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2])) + + test_cfg = mmcv.Config( + dict( + deploy_nms_pre=0, + nms=dict(type='nms', iou_threshold=0.45), + min_bbox_size=0, + score_thr=0.02, + max_per_img=200)) + + model = SSDHead( + num_classes=4, + in_channels=(4, 8, 4, 2, 2, 2), + test_cfg=test_cfg, + **cfg) + + model.requires_grad_(False) + return model + + +def test_ssd_head_forward(): + """Test SSD Head forward in torch and onnxruntime env.""" + ssd_model = ssd_config() + + featmap_size = [38, 19, 10, 6, 5, 3, 1] + + feats = [ + torch.rand(1, ssd_model.in_channels[i], featmap_size[i], + featmap_size[i]) for i in range(len(ssd_model.in_channels)) + ] + ort_validate(ssd_model.forward, feats) + + +def test_ssd_head_get_bboxes(): + """Test SSD Head get_bboxes in torch and onnxruntime env.""" + ssd_model = ssd_config() + s = 300 + img_metas = [{ + 'img_shape_for_onnx': torch.Tensor([s, s]), + 'scale_factor': 1, + 'pad_shape': (s, s, 3), + 'img_shape': (s, s, 2) + }] + + # The data of ssd_head_get_bboxes.pkl contains two parts: + # cls_score(list(Tensor)) and bboxes(list(Tensor)), + # where each torch.Tensor is generated by torch.rand(). + # the cls_score's size: (1, 20, 38, 38), (1, 30, 19, 19), + # (1, 30, 10, 10), (1, 30, 5, 5), (1, 20, 3, 3), (1, 20, 1, 1). + # the bboxes's size: (1, 16, 38, 38), (1, 24, 19, 19), + # (1, 24, 10, 10), (1, 24, 5, 5), (1, 16, 3, 3), (1, 16, 1, 1). + ssd_head_data = 'ssd_head_get_bboxes.pkl' + feats = mmcv.load(osp.join(data_path, ssd_head_data)) + cls_score = feats[:6] + bboxes = feats[6:] + + ssd_model.get_bboxes = partial( + ssd_model.get_bboxes, img_metas=img_metas, with_nms=False) + ort_validate(ssd_model.get_bboxes, (cls_score, bboxes)) diff --git a/tests/test_onnx/test_neck.py b/tests/test_onnx/test_neck.py new file mode 100644 index 0000000..f16a3dc --- /dev/null +++ b/tests/test_onnx/test_neck.py @@ -0,0 +1,162 @@ +import os.path as osp + +import mmcv +import pytest +import torch + +from mmdet import digit_version +from mmdet.models.necks import FPN, YOLOV3Neck +from .utils import ort_validate + +if digit_version(torch.__version__) <= digit_version('1.5.0'): + pytest.skip( + 'ort backend does not support version below 1.5.0', + allow_module_level=True) + +# Control the returned model of fpn_neck_config() +fpn_test_step_names = { + 'fpn_normal': 0, + 'fpn_wo_extra_convs': 1, + 'fpn_lateral_bns': 2, + 'fpn_bilinear_upsample': 3, + 'fpn_scale_factor': 4, + 'fpn_extra_convs_inputs': 5, + 'fpn_extra_convs_laterals': 6, + 'fpn_extra_convs_outputs': 7, +} + +# Control the returned model of yolo_neck_config() +yolo_test_step_names = {'yolo_normal': 0} + +data_path = osp.join(osp.dirname(__file__), 'data') + + +def fpn_neck_config(test_step_name): + """Return the class containing the corresponding attributes according to + the fpn_test_step_names.""" + s = 64 + in_channels = [8, 16, 32, 64] + feat_sizes = [s // 2**i for i in range(4)] # [64, 32, 16, 8] + out_channels = 8 + + feats = [ + torch.rand(1, in_channels[i], feat_sizes[i], feat_sizes[i]) + for i in range(len(in_channels)) + ] + + if (fpn_test_step_names[test_step_name] == 0): + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs=True, + num_outs=5) + elif (fpn_test_step_names[test_step_name] == 1): + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs=False, + num_outs=5) + elif (fpn_test_step_names[test_step_name] == 2): + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs=True, + no_norm_on_lateral=False, + norm_cfg=dict(type='BN', requires_grad=True), + num_outs=5) + elif (fpn_test_step_names[test_step_name] == 3): + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs=True, + upsample_cfg=dict(mode='bilinear', align_corners=True), + num_outs=5) + elif (fpn_test_step_names[test_step_name] == 4): + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs=True, + upsample_cfg=dict(scale_factor=2), + num_outs=5) + elif (fpn_test_step_names[test_step_name] == 5): + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs='on_input', + num_outs=5) + elif (fpn_test_step_names[test_step_name] == 6): + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs='on_lateral', + num_outs=5) + elif (fpn_test_step_names[test_step_name] == 7): + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs='on_output', + num_outs=5) + return fpn_model, feats + + +def yolo_neck_config(test_step_name): + """Config yolov3 Neck.""" + in_channels = [16, 8, 4] + out_channels = [8, 4, 2] + + # The data of yolov3_neck.pkl contains a list of + # torch.Tensor, where each torch.Tensor is generated by + # torch.rand and each tensor size is: + # (1, 4, 64, 64), (1, 8, 32, 32), (1, 16, 16, 16). + yolov3_neck_data = 'yolov3_neck.pkl' + feats = mmcv.load(osp.join(data_path, yolov3_neck_data)) + + if (yolo_test_step_names[test_step_name] == 0): + yolo_model = YOLOV3Neck( + in_channels=in_channels, out_channels=out_channels, num_scales=3) + return yolo_model, feats + + +def test_fpn_normal(): + outs = fpn_neck_config('fpn_normal') + ort_validate(*outs) + + +def test_fpn_wo_extra_convs(): + outs = fpn_neck_config('fpn_wo_extra_convs') + ort_validate(*outs) + + +def test_fpn_lateral_bns(): + outs = fpn_neck_config('fpn_lateral_bns') + ort_validate(*outs) + + +def test_fpn_bilinear_upsample(): + outs = fpn_neck_config('fpn_bilinear_upsample') + ort_validate(*outs) + + +def test_fpn_scale_factor(): + outs = fpn_neck_config('fpn_scale_factor') + ort_validate(*outs) + + +def test_fpn_extra_convs_inputs(): + outs = fpn_neck_config('fpn_extra_convs_inputs') + ort_validate(*outs) + + +def test_fpn_extra_convs_laterals(): + outs = fpn_neck_config('fpn_extra_convs_laterals') + ort_validate(*outs) + + +def test_fpn_extra_convs_outputs(): + outs = fpn_neck_config('fpn_extra_convs_outputs') + ort_validate(*outs) + + +def test_yolo_normal(): + outs = yolo_neck_config('yolo_normal') + ort_validate(*outs) diff --git a/tests/test_onnx/utils.py b/tests/test_onnx/utils.py new file mode 100644 index 0000000..89b9c13 --- /dev/null +++ b/tests/test_onnx/utils.py @@ -0,0 +1,136 @@ +import os +import os.path as osp +import warnings + +import numpy as np +import onnx +import onnxruntime as ort +import torch +import torch.nn as nn + +ort_custom_op_path = '' +try: + from mmcv.ops import get_onnxruntime_op_path + ort_custom_op_path = get_onnxruntime_op_path() +except (ImportError, ModuleNotFoundError): + warnings.warn('If input model has custom op from mmcv, \ + you may have to build mmcv with ONNXRuntime from source.') + + +class WrapFunction(nn.Module): + """Wrap the function to be tested for torch.onnx.export tracking.""" + + def __init__(self, wrapped_function): + super(WrapFunction, self).__init__() + self.wrapped_function = wrapped_function + + def forward(self, *args, **kwargs): + return self.wrapped_function(*args, **kwargs) + + +def ort_validate(model, feats, onnx_io='tmp.onnx'): + """Validate the output of the onnxruntime backend is the same as the output + generated by torch. + + Args: + model (nn.Module | function): the function of model or model + to be verified. + feats (tuple(list(torch.Tensor)) | list(torch.Tensor) | torch.Tensor): + the input of model. + onnx_io (str): the name of onnx output file. + """ + # if model is not an instance of nn.Module, then it is a normal + # function and it should be wrapped. + if isinstance(model, nn.Module): + wrap_model = model + else: + wrap_model = WrapFunction(model) + wrap_model.cpu().eval() + with torch.no_grad(): + torch.onnx.export( + wrap_model, + feats, + onnx_io, + export_params=True, + keep_initializers_as_inputs=True, + do_constant_folding=True, + verbose=False, + opset_version=11) + + if isinstance(feats, tuple): + ort_feats = [] + for feat in feats: + ort_feats += feat + else: + ort_feats = feats + # default model name: tmp.onnx + onnx_outputs = get_ort_model_output(ort_feats) + + # remove temp file + if osp.exists(onnx_io): + os.remove(onnx_io) + + if isinstance(feats, tuple): + torch_outputs = convert_result_list(wrap_model.forward(*feats)) + else: + torch_outputs = convert_result_list(wrap_model.forward(feats)) + torch_outputs = [ + torch_output.detach().numpy() for torch_output in torch_outputs + ] + + # match torch_outputs and onnx_outputs + for i in range(len(onnx_outputs)): + np.testing.assert_allclose( + torch_outputs[i], onnx_outputs[i], rtol=1e-03, atol=1e-05) + + +def get_ort_model_output(feat, onnx_io='tmp.onnx'): + """Run the model in onnxruntime env. + + Args: + feat (list[Tensor]): A list of tensors from torch.rand, + each is a 4D-tensor. + + Returns: + list[np.array]: onnxruntime infer result, each is a np.array + """ + + onnx_model = onnx.load(onnx_io) + onnx.checker.check_model(onnx_model) + + session_options = ort.SessionOptions() + # register custom op for onnxruntime + if osp.exists(ort_custom_op_path): + session_options.register_custom_ops_library(ort_custom_op_path) + sess = ort.InferenceSession(onnx_io, session_options) + if isinstance(feat, torch.Tensor): + onnx_outputs = sess.run(None, + {sess.get_inputs()[0].name: feat.numpy()}) + else: + onnx_outputs = sess.run(None, { + sess.get_inputs()[i].name: feat[i].numpy() + for i in range(len(feat)) + }) + return onnx_outputs + + +def convert_result_list(outputs): + """Convert the torch forward outputs containing tuple or list to a list + only containing torch.Tensor. + + Args: + output (list(Tensor) | tuple(list(Tensor) | ...): the outputs + in torch env, maybe containing nested structures such as list + or tuple. + + Returns: + list(Tensor): a list only containing torch.Tensor + """ + # recursive end condition + if isinstance(outputs, torch.Tensor): + return [outputs] + + ret = [] + for sub in outputs: + ret += convert_result_list(sub) + return ret diff --git a/tests/test_runtime/async_benchmark.py b/tests/test_runtime/async_benchmark.py new file mode 100644 index 0000000..9116752 --- /dev/null +++ b/tests/test_runtime/async_benchmark.py @@ -0,0 +1,101 @@ +import asyncio +import os +import shutil +import urllib + +import mmcv +import torch + +from mmdet.apis import (async_inference_detector, inference_detector, + init_detector) +from mmdet.utils.contextmanagers import concurrent +from mmdet.utils.profiling import profile_time + + +async def main(): + """Benchmark between async and synchronous inference interfaces. + + Sample runs for 20 demo images on K80 GPU, model - mask_rcnn_r50_fpn_1x: + + async sync + + 7981.79 ms 9660.82 ms + 8074.52 ms 9660.94 ms + 7976.44 ms 9406.83 ms + + Async variant takes about 0.83-0.85 of the time of the synchronous + interface. + """ + project_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__))) + project_dir = os.path.join(project_dir, '..') + + config_file = os.path.join( + project_dir, 'configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py') + checkpoint_file = os.path.join( + project_dir, + 'checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth') + + if not os.path.exists(checkpoint_file): + url = ('http://download.openmmlab.com/mmdetection/v2.0' + '/mask_rcnn/mask_rcnn_r50_fpn_1x_coco' + '/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth') + print(f'Downloading {url} ...') + local_filename, _ = urllib.request.urlretrieve(url) + os.makedirs(os.path.dirname(checkpoint_file), exist_ok=True) + shutil.move(local_filename, checkpoint_file) + print(f'Saved as {checkpoint_file}') + else: + print(f'Using existing checkpoint {checkpoint_file}') + + device = 'cuda:0' + model = init_detector( + config_file, checkpoint=checkpoint_file, device=device) + + # queue is used for concurrent inference of multiple images + streamqueue = asyncio.Queue() + # queue size defines concurrency level + streamqueue_size = 4 + + for _ in range(streamqueue_size): + streamqueue.put_nowait(torch.cuda.Stream(device=device)) + + # test a single image and show the results + img = mmcv.imread(os.path.join(project_dir, 'demo/demo.jpg')) + + # warmup + await async_inference_detector(model, img) + + async def detect(img): + async with concurrent(streamqueue): + return await async_inference_detector(model, img) + + num_of_images = 20 + with profile_time('benchmark', 'async'): + tasks = [ + asyncio.create_task(detect(img)) for _ in range(num_of_images) + ] + async_results = await asyncio.gather(*tasks) + + with torch.cuda.stream(torch.cuda.default_stream()): + with profile_time('benchmark', 'sync'): + sync_results = [ + inference_detector(model, img) for _ in range(num_of_images) + ] + + result_dir = os.path.join(project_dir, 'demo') + model.show_result( + img, + async_results[0], + score_thr=0.5, + show=False, + out_file=os.path.join(result_dir, 'result_async.jpg')) + model.show_result( + img, + sync_results[0], + score_thr=0.5, + show=False, + out_file=os.path.join(result_dir, 'result_sync.jpg')) + + +if __name__ == '__main__': + asyncio.run(main()) diff --git a/tests/test_runtime/test_async.py b/tests/test_runtime/test_async.py new file mode 100644 index 0000000..e9733f6 --- /dev/null +++ b/tests/test_runtime/test_async.py @@ -0,0 +1,82 @@ +"""Tests for async interface.""" + +import asyncio +import os +import sys + +import asynctest +import mmcv +import torch + +from mmdet.apis import async_inference_detector, init_detector + +if sys.version_info >= (3, 7): + from mmdet.utils.contextmanagers import concurrent + + +class AsyncTestCase(asynctest.TestCase): + use_default_loop = False + forbid_get_event_loop = True + + TEST_TIMEOUT = int(os.getenv('ASYNCIO_TEST_TIMEOUT', '30')) + + def _run_test_method(self, method): + result = method() + if asyncio.iscoroutine(result): + self.loop.run_until_complete( + asyncio.wait_for(result, timeout=self.TEST_TIMEOUT)) + + +class MaskRCNNDetector: + + def __init__(self, + model_config, + checkpoint=None, + streamqueue_size=3, + device='cuda:0'): + + self.streamqueue_size = streamqueue_size + self.device = device + # build the model and load checkpoint + self.model = init_detector( + model_config, checkpoint=None, device=self.device) + self.streamqueue = None + + async def init(self): + self.streamqueue = asyncio.Queue() + for _ in range(self.streamqueue_size): + stream = torch.cuda.Stream(device=self.device) + self.streamqueue.put_nowait(stream) + + if sys.version_info >= (3, 7): + + async def apredict(self, img): + if isinstance(img, str): + img = mmcv.imread(img) + async with concurrent(self.streamqueue): + result = await async_inference_detector(self.model, img) + return result + + +class AsyncInferenceTestCase(AsyncTestCase): + + if sys.version_info >= (3, 7): + + async def test_simple_inference(self): + if not torch.cuda.is_available(): + import pytest + + pytest.skip('test requires GPU and torch+cuda') + + ori_grad_enabled = torch.is_grad_enabled() + root_dir = os.path.dirname(os.path.dirname(__name__)) + model_config = os.path.join( + root_dir, 'configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py') + detector = MaskRCNNDetector(model_config) + await detector.init() + img_path = os.path.join(root_dir, 'demo/demo.jpg') + bboxes, _ = await detector.apredict(img_path) + self.assertTrue(bboxes) + # asy inference detector will hack grad_enabled, + # so restore here to avoid it to influence other tests + torch.set_grad_enabled(ori_grad_enabled) diff --git a/tests/test_runtime/test_config.py b/tests/test_runtime/test_config.py new file mode 100644 index 0000000..83fb64e --- /dev/null +++ b/tests/test_runtime/test_config.py @@ -0,0 +1,418 @@ +from os.path import dirname, exists, join, relpath +from unittest.mock import Mock + +import pytest +import torch +from mmcv.runner import build_optimizer + +from mmdet.core import BitmapMasks, PolygonMasks +from mmdet.datasets.builder import DATASETS +from mmdet.datasets.utils import NumClassCheckHook + + +def _get_config_directory(): + """Find the predefined detector config directory.""" + try: + # Assume we are running in the source mmdetection repo + repo_dpath = dirname(dirname(__file__)) + repo_dpath = join(repo_dpath, '..') + except NameError: + # For IPython development when this __file__ is not defined + import mmdet + repo_dpath = dirname(dirname(mmdet.__file__)) + config_dpath = join(repo_dpath, 'configs') + if not exists(config_dpath): + raise Exception('Cannot find config path') + return config_dpath + + +def _check_numclasscheckhook(detector, config_mod): + dummy_runner = Mock() + dummy_runner.model = detector + + def get_dataset_name_classes(dataset): + # deal with `RepeatDataset`,`ConcatDataset`,`ClassBalancedDataset`.. + if isinstance(dataset, (list, tuple)): + dataset = dataset[0] + while ('dataset' in dataset): + dataset = dataset['dataset'] + # ConcatDataset + if isinstance(dataset, (list, tuple)): + dataset = dataset[0] + return dataset['type'], dataset.get('classes', None) + + compatible_check = NumClassCheckHook() + dataset_name, CLASSES = get_dataset_name_classes( + config_mod['data']['train']) + if CLASSES is None: + CLASSES = DATASETS.get(dataset_name).CLASSES + dummy_runner.data_loader.dataset.CLASSES = CLASSES + compatible_check.before_train_epoch(dummy_runner) + + dummy_runner.data_loader.dataset.CLASSES = None + compatible_check.before_train_epoch(dummy_runner) + + dataset_name, CLASSES = get_dataset_name_classes(config_mod['data']['val']) + if CLASSES is None: + CLASSES = DATASETS.get(dataset_name).CLASSES + dummy_runner.data_loader.dataset.CLASSES = CLASSES + compatible_check.before_val_epoch(dummy_runner) + dummy_runner.data_loader.dataset.CLASSES = None + compatible_check.before_val_epoch(dummy_runner) + + +def test_config_build_detector(): + """Test that all detection models defined in the configs can be + initialized.""" + from mmcv import Config + from mmdet.models import build_detector + + config_dpath = _get_config_directory() + print(f'Found config_dpath = {config_dpath}') + + import glob + config_fpaths = list(glob.glob(join(config_dpath, '**', '*.py'))) + config_fpaths = [p for p in config_fpaths if p.find('_base_') == -1] + config_names = [relpath(p, config_dpath) for p in config_fpaths] + + print(f'Using {len(config_names)} config files') + + for config_fname in config_names: + config_fpath = join(config_dpath, config_fname) + config_mod = Config.fromfile(config_fpath) + config_mod.model + print(f'Building detector, config_fpath = {config_fpath}') + + # Remove pretrained keys to allow for testing in an offline environment + if 'pretrained' in config_mod.model: + config_mod.model['pretrained'] = None + + detector = build_detector(config_mod.model) + assert detector is not None + + _check_numclasscheckhook(detector, config_mod) + + optimizer = build_optimizer(detector, config_mod.optimizer) + assert isinstance(optimizer, torch.optim.Optimizer) + + if 'roi_head' in config_mod.model.keys(): + # for two stage detector + # detectors must have bbox head + assert detector.roi_head.with_bbox and detector.with_bbox + assert detector.roi_head.with_mask == detector.with_mask + + head_config = config_mod.model['roi_head'] + _check_roi_head(head_config, detector.roi_head) + + # else: + # # for single stage detector + # # detectors must have bbox head + # # assert detector.with_bbox + # head_config = config_mod.model['bbox_head'] + # _check_bbox_head(head_config, detector.bbox_head) + + +def _check_roi_head(config, head): + # check consistency between head_config and roi_head + assert config['type'] == head.__class__.__name__ + + # check roi_align + bbox_roi_cfg = config.bbox_roi_extractor + bbox_roi_extractor = head.bbox_roi_extractor + _check_roi_extractor(bbox_roi_cfg, bbox_roi_extractor) + + # check bbox head infos + bbox_cfg = config.bbox_head + bbox_head = head.bbox_head + _check_bbox_head(bbox_cfg, bbox_head) + + if head.with_mask: + # check roi_align + if config.mask_roi_extractor: + mask_roi_cfg = config.mask_roi_extractor + mask_roi_extractor = head.mask_roi_extractor + _check_roi_extractor(mask_roi_cfg, mask_roi_extractor, + bbox_roi_extractor) + + # check mask head infos + mask_head = head.mask_head + mask_cfg = config.mask_head + _check_mask_head(mask_cfg, mask_head) + + # check arch specific settings, e.g., cascade/htc + if config['type'] in ['CascadeRoIHead', 'HybridTaskCascadeRoIHead']: + assert config.num_stages == len(head.bbox_head) + assert config.num_stages == len(head.bbox_roi_extractor) + + if head.with_mask: + assert config.num_stages == len(head.mask_head) + assert config.num_stages == len(head.mask_roi_extractor) + + elif config['type'] in ['MaskScoringRoIHead']: + assert (hasattr(head, 'mask_iou_head') + and head.mask_iou_head is not None) + mask_iou_cfg = config.mask_iou_head + mask_iou_head = head.mask_iou_head + assert (mask_iou_cfg.fc_out_channels == + mask_iou_head.fc_mask_iou.in_features) + + elif config['type'] in ['GridRoIHead']: + grid_roi_cfg = config.grid_roi_extractor + grid_roi_extractor = head.grid_roi_extractor + _check_roi_extractor(grid_roi_cfg, grid_roi_extractor, + bbox_roi_extractor) + + config.grid_head.grid_points = head.grid_head.grid_points + + +def _check_roi_extractor(config, roi_extractor, prev_roi_extractor=None): + import torch.nn as nn + # Separate roi_extractor and prev_roi_extractor checks for flexibility + if isinstance(roi_extractor, nn.ModuleList): + roi_extractor = roi_extractor[0] + if prev_roi_extractor and isinstance(prev_roi_extractor, nn.ModuleList): + prev_roi_extractor = prev_roi_extractor[0] + + assert (len(config.featmap_strides) == len(roi_extractor.roi_layers)) + assert (config.out_channels == roi_extractor.out_channels) + from torch.nn.modules.utils import _pair + assert (_pair(config.roi_layer.output_size) == + roi_extractor.roi_layers[0].output_size) + + if 'use_torchvision' in config.roi_layer: + assert (config.roi_layer.use_torchvision == + roi_extractor.roi_layers[0].use_torchvision) + elif 'aligned' in config.roi_layer: + assert ( + config.roi_layer.aligned == roi_extractor.roi_layers[0].aligned) + + if prev_roi_extractor: + assert (roi_extractor.roi_layers[0].aligned == + prev_roi_extractor.roi_layers[0].aligned) + assert (roi_extractor.roi_layers[0].use_torchvision == + prev_roi_extractor.roi_layers[0].use_torchvision) + + +def _check_mask_head(mask_cfg, mask_head): + import torch.nn as nn + if isinstance(mask_cfg, list): + for single_mask_cfg, single_mask_head in zip(mask_cfg, mask_head): + _check_mask_head(single_mask_cfg, single_mask_head) + elif isinstance(mask_head, nn.ModuleList): + for single_mask_head in mask_head: + _check_mask_head(mask_cfg, single_mask_head) + else: + assert mask_cfg['type'] == mask_head.__class__.__name__ + assert mask_cfg.in_channels == mask_head.in_channels + class_agnostic = mask_cfg.get('class_agnostic', False) + out_dim = (1 if class_agnostic else mask_cfg.num_classes) + if hasattr(mask_head, 'conv_logits'): + assert (mask_cfg.conv_out_channels == + mask_head.conv_logits.in_channels) + assert mask_head.conv_logits.out_channels == out_dim + else: + assert mask_cfg.fc_out_channels == mask_head.fc_logits.in_features + assert (mask_head.fc_logits.out_features == out_dim * + mask_head.output_area) + + +def _check_bbox_head(bbox_cfg, bbox_head): + import torch.nn as nn + if isinstance(bbox_cfg, list): + for single_bbox_cfg, single_bbox_head in zip(bbox_cfg, bbox_head): + _check_bbox_head(single_bbox_cfg, single_bbox_head) + elif isinstance(bbox_head, nn.ModuleList): + for single_bbox_head in bbox_head: + _check_bbox_head(bbox_cfg, single_bbox_head) + else: + assert bbox_cfg['type'] == bbox_head.__class__.__name__ + if bbox_cfg['type'] == 'SABLHead': + assert bbox_cfg.cls_in_channels == bbox_head.cls_in_channels + assert bbox_cfg.reg_in_channels == bbox_head.reg_in_channels + + cls_out_channels = bbox_cfg.get('cls_out_channels', 1024) + assert (cls_out_channels == bbox_head.fc_cls.in_features) + assert (bbox_cfg.num_classes + 1 == bbox_head.fc_cls.out_features) + + elif bbox_cfg['type'] == 'DIIHead': + assert bbox_cfg['num_ffn_fcs'] == bbox_head.ffn.num_fcs + # 3 means FC and LN and Relu + assert bbox_cfg['num_cls_fcs'] == len(bbox_head.cls_fcs) // 3 + assert bbox_cfg['num_reg_fcs'] == len(bbox_head.reg_fcs) // 3 + assert bbox_cfg['in_channels'] == bbox_head.in_channels + assert bbox_cfg['in_channels'] == bbox_head.fc_cls.in_features + assert bbox_cfg['in_channels'] == bbox_head.fc_reg.in_features + assert bbox_cfg['in_channels'] == bbox_head.attention.embed_dims + assert bbox_cfg[ + 'feedforward_channels'] == bbox_head.ffn.feedforward_channels + + else: + assert bbox_cfg.in_channels == bbox_head.in_channels + with_cls = bbox_cfg.get('with_cls', True) + + if with_cls: + fc_out_channels = bbox_cfg.get('fc_out_channels', 2048) + assert (fc_out_channels == bbox_head.fc_cls.in_features) + assert (bbox_cfg.num_classes + + 1 == bbox_head.fc_cls.out_features) + with_reg = bbox_cfg.get('with_reg', True) + if with_reg: + out_dim = (4 if bbox_cfg.reg_class_agnostic else 4 * + bbox_cfg.num_classes) + assert bbox_head.fc_reg.out_features == out_dim + + +def _check_anchorhead(config, head): + # check consistency between head_config and roi_head + assert config['type'] == head.__class__.__name__ + assert config.in_channels == head.in_channels + + num_classes = ( + config.num_classes - + 1 if config.loss_cls.get('use_sigmoid', False) else config.num_classes) + if config['type'] == 'ATSSHead': + assert (config.feat_channels == head.atss_cls.in_channels) + assert (config.feat_channels == head.atss_reg.in_channels) + assert (config.feat_channels == head.atss_centerness.in_channels) + elif config['type'] == 'SABLRetinaHead': + assert (config.feat_channels == head.retina_cls.in_channels) + assert (config.feat_channels == head.retina_bbox_reg.in_channels) + assert (config.feat_channels == head.retina_bbox_cls.in_channels) + else: + assert (config.in_channels == head.conv_cls.in_channels) + assert (config.in_channels == head.conv_reg.in_channels) + assert (head.conv_cls.out_channels == num_classes * head.num_anchors) + assert head.fc_reg.out_channels == 4 * head.num_anchors + + +# Only tests a representative subset of configurations +# TODO: test pipelines using Albu, current Albu throw None given empty GT +@pytest.mark.parametrize( + 'config_rpath', + [ + 'wider_face/ssd300_wider_face.py', + 'pascal_voc/ssd300_voc0712.py', + 'pascal_voc/ssd512_voc0712.py', + # 'albu_example/mask_rcnn_r50_fpn_1x.py', + 'foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py', + 'mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py', + 'mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py', + 'fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py' + ]) +def test_config_data_pipeline(config_rpath): + """Test whether the data pipeline is valid and can process corner cases. + + CommandLine: + xdoctest -m tests/test_runtime/ + test_config.py test_config_build_data_pipeline + """ + from mmcv import Config + from mmdet.datasets.pipelines import Compose + import numpy as np + + config_dpath = _get_config_directory() + print(f'Found config_dpath = {config_dpath}') + + def dummy_masks(h, w, num_obj=3, mode='bitmap'): + assert mode in ('polygon', 'bitmap') + if mode == 'bitmap': + masks = np.random.randint(0, 2, (num_obj, h, w), dtype=np.uint8) + masks = BitmapMasks(masks, h, w) + else: + masks = [] + for i in range(num_obj): + masks.append([]) + masks[-1].append( + np.random.uniform(0, min(h - 1, w - 1), (8 + 4 * i, ))) + masks[-1].append( + np.random.uniform(0, min(h - 1, w - 1), (10 + 4 * i, ))) + masks = PolygonMasks(masks, h, w) + return masks + + config_fpath = join(config_dpath, config_rpath) + cfg = Config.fromfile(config_fpath) + + # remove loading pipeline + loading_pipeline = cfg.train_pipeline.pop(0) + loading_ann_pipeline = cfg.train_pipeline.pop(0) + cfg.test_pipeline.pop(0) + + train_pipeline = Compose(cfg.train_pipeline) + test_pipeline = Compose(cfg.test_pipeline) + + print(f'Building data pipeline, config_fpath = {config_fpath}') + + print(f'Test training data pipeline: \n{train_pipeline!r}') + img = np.random.randint(0, 255, size=(888, 666, 3), dtype=np.uint8) + if loading_pipeline.get('to_float32', False): + img = img.astype(np.float32) + mode = 'bitmap' if loading_ann_pipeline.get('poly2mask', + True) else 'polygon' + results = dict( + filename='test_img.png', + ori_filename='test_img.png', + img=img, + img_shape=img.shape, + ori_shape=img.shape, + gt_bboxes=np.array([[35.2, 11.7, 39.7, 15.7]], dtype=np.float32), + gt_labels=np.array([1], dtype=np.int64), + gt_masks=dummy_masks(img.shape[0], img.shape[1], mode=mode), + ) + results['img_fields'] = ['img'] + results['bbox_fields'] = ['gt_bboxes'] + results['mask_fields'] = ['gt_masks'] + output_results = train_pipeline(results) + assert output_results is not None + + print(f'Test testing data pipeline: \n{test_pipeline!r}') + results = dict( + filename='test_img.png', + ori_filename='test_img.png', + img=img, + img_shape=img.shape, + ori_shape=img.shape, + gt_bboxes=np.array([[35.2, 11.7, 39.7, 15.7]], dtype=np.float32), + gt_labels=np.array([1], dtype=np.int64), + gt_masks=dummy_masks(img.shape[0], img.shape[1], mode=mode), + ) + results['img_fields'] = ['img'] + results['bbox_fields'] = ['gt_bboxes'] + results['mask_fields'] = ['gt_masks'] + output_results = test_pipeline(results) + assert output_results is not None + + # test empty GT + print('Test empty GT with training data pipeline: ' + f'\n{train_pipeline!r}') + results = dict( + filename='test_img.png', + ori_filename='test_img.png', + img=img, + img_shape=img.shape, + ori_shape=img.shape, + gt_bboxes=np.zeros((0, 4), dtype=np.float32), + gt_labels=np.array([], dtype=np.int64), + gt_masks=dummy_masks(img.shape[0], img.shape[1], num_obj=0, mode=mode), + ) + results['img_fields'] = ['img'] + results['bbox_fields'] = ['gt_bboxes'] + results['mask_fields'] = ['gt_masks'] + output_results = train_pipeline(results) + assert output_results is not None + + print(f'Test empty GT with testing data pipeline: \n{test_pipeline!r}') + results = dict( + filename='test_img.png', + ori_filename='test_img.png', + img=img, + img_shape=img.shape, + ori_shape=img.shape, + gt_bboxes=np.zeros((0, 4), dtype=np.float32), + gt_labels=np.array([], dtype=np.int64), + gt_masks=dummy_masks(img.shape[0], img.shape[1], num_obj=0, mode=mode), + ) + results['img_fields'] = ['img'] + results['bbox_fields'] = ['gt_bboxes'] + results['mask_fields'] = ['gt_masks'] + output_results = test_pipeline(results) + assert output_results is not None diff --git a/tests/test_runtime/test_eval_hook.py b/tests/test_runtime/test_eval_hook.py new file mode 100644 index 0000000..2231aa7 --- /dev/null +++ b/tests/test_runtime/test_eval_hook.py @@ -0,0 +1,263 @@ +import os.path as osp +import tempfile +import unittest.mock as mock +from collections import OrderedDict +from unittest.mock import MagicMock, patch + +import pytest +import torch +import torch.nn as nn +from mmcv.runner import EpochBasedRunner, build_optimizer +from mmcv.utils import get_logger +from torch.utils.data import DataLoader, Dataset + +from mmdet.core import DistEvalHook, EvalHook + + +class ExampleDataset(Dataset): + + def __init__(self): + self.index = 0 + self.eval_result = [0.1, 0.4, 0.3, 0.7, 0.2, 0.05, 0.4, 0.6] + + def __getitem__(self, idx): + results = dict(imgs=torch.tensor([1])) + return results + + def __len__(self): + return 1 + + @mock.create_autospec + def evaluate(self, results, logger=None): + pass + + +class EvalDataset(ExampleDataset): + + def evaluate(self, results, logger=None): + mean_ap = self.eval_result[self.index] + output = OrderedDict(mAP=mean_ap, index=self.index, score=mean_ap) + self.index += 1 + return output + + +class ExampleModel(nn.Module): + + def __init__(self): + super().__init__() + self.conv = nn.Linear(1, 1) + self.test_cfg = None + + def forward(self, imgs, rescale=False, return_loss=False): + return imgs + + def train_step(self, data_batch, optimizer, **kwargs): + outputs = { + 'loss': 0.5, + 'log_vars': { + 'accuracy': 0.98 + }, + 'num_samples': 1 + } + return outputs + + +@pytest.mark.skipif( + not torch.cuda.is_available(), reason='requires CUDA support') +@patch('mmdet.apis.single_gpu_test', MagicMock) +@patch('mmdet.apis.multi_gpu_test', MagicMock) +@pytest.mark.parametrize('EvalHookCls', (EvalHook, DistEvalHook)) +def test_eval_hook(EvalHookCls): + with pytest.raises(TypeError): + # dataloader must be a pytorch DataLoader + test_dataset = ExampleDataset() + data_loader = [ + DataLoader( + test_dataset, + batch_size=1, + sampler=None, + num_worker=0, + shuffle=False) + ] + EvalHookCls(data_loader) + + with pytest.raises(KeyError): + # rule must be in keys of rule_map + test_dataset = ExampleDataset() + data_loader = DataLoader( + test_dataset, + batch_size=1, + sampler=None, + num_workers=0, + shuffle=False) + EvalHookCls(data_loader, save_best='auto', rule='unsupport') + + with pytest.raises(ValueError): + # key_indicator must be valid when rule_map is None + test_dataset = ExampleDataset() + data_loader = DataLoader( + test_dataset, + batch_size=1, + sampler=None, + num_workers=0, + shuffle=False) + EvalHookCls(data_loader, save_best='unsupport') + + optimizer_cfg = dict( + type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) + + test_dataset = ExampleDataset() + loader = DataLoader(test_dataset, batch_size=1) + model = ExampleModel() + optimizer = build_optimizer(model, optimizer_cfg) + + data_loader = DataLoader(test_dataset, batch_size=1) + eval_hook = EvalHookCls(data_loader, save_best=None) + with tempfile.TemporaryDirectory() as tmpdir: + logger = get_logger('test_eval') + runner = EpochBasedRunner( + model=model, + batch_processor=None, + optimizer=optimizer, + work_dir=tmpdir, + logger=logger) + runner.register_hook(eval_hook) + runner.run([loader], [('train', 1)], 1) + assert runner.meta is None or 'best_score' not in runner.meta[ + 'hook_msgs'] + assert runner.meta is None or 'best_ckpt' not in runner.meta[ + 'hook_msgs'] + + # when `save_best` is set to 'auto', first metric will be used. + loader = DataLoader(EvalDataset(), batch_size=1) + model = ExampleModel() + data_loader = DataLoader(EvalDataset(), batch_size=1) + eval_hook = EvalHookCls(data_loader, interval=1, save_best='auto') + + with tempfile.TemporaryDirectory() as tmpdir: + logger = get_logger('test_eval') + runner = EpochBasedRunner( + model=model, + batch_processor=None, + optimizer=optimizer, + work_dir=tmpdir, + logger=logger) + runner.register_checkpoint_hook(dict(interval=1)) + runner.register_hook(eval_hook) + runner.run([loader], [('train', 1)], 8) + + real_path = osp.join(tmpdir, 'epoch_4.pth') + link_path = osp.join(tmpdir, 'best_mAP.pth') + + assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path) + assert osp.exists(link_path) + assert runner.meta['hook_msgs']['best_score'] == 0.7 + + loader = DataLoader(EvalDataset(), batch_size=1) + model = ExampleModel() + data_loader = DataLoader(EvalDataset(), batch_size=1) + eval_hook = EvalHookCls(data_loader, interval=1, save_best='mAP') + + with tempfile.TemporaryDirectory() as tmpdir: + logger = get_logger('test_eval') + runner = EpochBasedRunner( + model=model, + batch_processor=None, + optimizer=optimizer, + work_dir=tmpdir, + logger=logger) + runner.register_checkpoint_hook(dict(interval=1)) + runner.register_hook(eval_hook) + runner.run([loader], [('train', 1)], 8) + + real_path = osp.join(tmpdir, 'epoch_4.pth') + link_path = osp.join(tmpdir, 'best_mAP.pth') + + assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path) + assert osp.exists(link_path) + assert runner.meta['hook_msgs']['best_score'] == 0.7 + + data_loader = DataLoader(EvalDataset(), batch_size=1) + eval_hook = EvalHookCls( + data_loader, interval=1, save_best='score', rule='greater') + with tempfile.TemporaryDirectory() as tmpdir: + logger = get_logger('test_eval') + runner = EpochBasedRunner( + model=model, + batch_processor=None, + optimizer=optimizer, + work_dir=tmpdir, + logger=logger) + runner.register_checkpoint_hook(dict(interval=1)) + runner.register_hook(eval_hook) + runner.run([loader], [('train', 1)], 8) + + real_path = osp.join(tmpdir, 'epoch_4.pth') + link_path = osp.join(tmpdir, 'best_score.pth') + + assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path) + assert osp.exists(link_path) + assert runner.meta['hook_msgs']['best_score'] == 0.7 + + data_loader = DataLoader(EvalDataset(), batch_size=1) + eval_hook = EvalHookCls(data_loader, save_best='mAP', rule='less') + with tempfile.TemporaryDirectory() as tmpdir: + logger = get_logger('test_eval') + runner = EpochBasedRunner( + model=model, + batch_processor=None, + optimizer=optimizer, + work_dir=tmpdir, + logger=logger) + runner.register_checkpoint_hook(dict(interval=1)) + runner.register_hook(eval_hook) + runner.run([loader], [('train', 1)], 8) + + real_path = osp.join(tmpdir, 'epoch_6.pth') + link_path = osp.join(tmpdir, 'best_mAP.pth') + + assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path) + assert osp.exists(link_path) + assert runner.meta['hook_msgs']['best_score'] == 0.05 + + data_loader = DataLoader(EvalDataset(), batch_size=1) + eval_hook = EvalHookCls(data_loader, save_best='mAP') + with tempfile.TemporaryDirectory() as tmpdir: + logger = get_logger('test_eval') + runner = EpochBasedRunner( + model=model, + batch_processor=None, + optimizer=optimizer, + work_dir=tmpdir, + logger=logger) + runner.register_checkpoint_hook(dict(interval=1)) + runner.register_hook(eval_hook) + runner.run([loader], [('train', 1)], 2) + + real_path = osp.join(tmpdir, 'epoch_2.pth') + link_path = osp.join(tmpdir, 'best_mAP.pth') + + assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path) + assert osp.exists(link_path) + assert runner.meta['hook_msgs']['best_score'] == 0.4 + + resume_from = osp.join(tmpdir, 'latest.pth') + loader = DataLoader(ExampleDataset(), batch_size=1) + eval_hook = EvalHookCls(data_loader, save_best='mAP') + runner = EpochBasedRunner( + model=model, + batch_processor=None, + optimizer=optimizer, + work_dir=tmpdir, + logger=logger) + runner.register_checkpoint_hook(dict(interval=1)) + runner.register_hook(eval_hook) + runner.resume(resume_from) + runner.run([loader], [('train', 1)], 8) + + real_path = osp.join(tmpdir, 'epoch_4.pth') + link_path = osp.join(tmpdir, 'best_mAP.pth') + + assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path) + assert osp.exists(link_path) + assert runner.meta['hook_msgs']['best_score'] == 0.7 diff --git a/tests/test_runtime/test_fp16.py b/tests/test_runtime/test_fp16.py new file mode 100644 index 0000000..afcfe26 --- /dev/null +++ b/tests/test_runtime/test_fp16.py @@ -0,0 +1,300 @@ +import numpy as np +import pytest +import torch +import torch.nn as nn +from mmcv.runner import auto_fp16, force_fp32 +from mmcv.runner.fp16_utils import cast_tensor_type + + +def test_cast_tensor_type(): + inputs = torch.FloatTensor([5.]) + src_type = torch.float32 + dst_type = torch.int32 + outputs = cast_tensor_type(inputs, src_type, dst_type) + assert isinstance(outputs, torch.Tensor) + assert outputs.dtype == dst_type + + inputs = 'tensor' + src_type = str + dst_type = str + outputs = cast_tensor_type(inputs, src_type, dst_type) + assert isinstance(outputs, str) + + inputs = np.array([5.]) + src_type = np.ndarray + dst_type = np.ndarray + outputs = cast_tensor_type(inputs, src_type, dst_type) + assert isinstance(outputs, np.ndarray) + + inputs = dict( + tensor_a=torch.FloatTensor([1.]), tensor_b=torch.FloatTensor([2.])) + src_type = torch.float32 + dst_type = torch.int32 + outputs = cast_tensor_type(inputs, src_type, dst_type) + assert isinstance(outputs, dict) + assert outputs['tensor_a'].dtype == dst_type + assert outputs['tensor_b'].dtype == dst_type + + inputs = [torch.FloatTensor([1.]), torch.FloatTensor([2.])] + src_type = torch.float32 + dst_type = torch.int32 + outputs = cast_tensor_type(inputs, src_type, dst_type) + assert isinstance(outputs, list) + assert outputs[0].dtype == dst_type + assert outputs[1].dtype == dst_type + + inputs = 5 + outputs = cast_tensor_type(inputs, None, None) + assert isinstance(outputs, int) + + +def test_auto_fp16(): + + with pytest.raises(TypeError): + # ExampleObject is not a subclass of nn.Module + + class ExampleObject(object): + + @auto_fp16() + def __call__(self, x): + return x + + model = ExampleObject() + input_x = torch.ones(1, dtype=torch.float32) + model(input_x) + + # apply to all input args + class ExampleModule(nn.Module): + + @auto_fp16() + def forward(self, x, y): + return x, y + + model = ExampleModule() + input_x = torch.ones(1, dtype=torch.float32) + input_y = torch.ones(1, dtype=torch.float32) + output_x, output_y = model(input_x, input_y) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.float32 + + model.fp16_enabled = True + output_x, output_y = model(input_x, input_y) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.half + + if torch.cuda.is_available(): + model.cuda() + output_x, output_y = model(input_x.cuda(), input_y.cuda()) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.half + + # apply to specified input args + class ExampleModule(nn.Module): + + @auto_fp16(apply_to=('x', )) + def forward(self, x, y): + return x, y + + model = ExampleModule() + input_x = torch.ones(1, dtype=torch.float32) + input_y = torch.ones(1, dtype=torch.float32) + output_x, output_y = model(input_x, input_y) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.float32 + + model.fp16_enabled = True + output_x, output_y = model(input_x, input_y) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.float32 + + if torch.cuda.is_available(): + model.cuda() + output_x, output_y = model(input_x.cuda(), input_y.cuda()) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.float32 + + # apply to optional input args + class ExampleModule(nn.Module): + + @auto_fp16(apply_to=('x', 'y')) + def forward(self, x, y=None, z=None): + return x, y, z + + model = ExampleModule() + input_x = torch.ones(1, dtype=torch.float32) + input_y = torch.ones(1, dtype=torch.float32) + input_z = torch.ones(1, dtype=torch.float32) + output_x, output_y, output_z = model(input_x, y=input_y, z=input_z) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.float32 + assert output_z.dtype == torch.float32 + + model.fp16_enabled = True + output_x, output_y, output_z = model(input_x, y=input_y, z=input_z) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.half + assert output_z.dtype == torch.float32 + + if torch.cuda.is_available(): + model.cuda() + output_x, output_y, output_z = model( + input_x.cuda(), y=input_y.cuda(), z=input_z.cuda()) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.half + assert output_z.dtype == torch.float32 + + # out_fp32=True + class ExampleModule(nn.Module): + + @auto_fp16(apply_to=('x', 'y'), out_fp32=True) + def forward(self, x, y=None, z=None): + return x, y, z + + model = ExampleModule() + input_x = torch.ones(1, dtype=torch.half) + input_y = torch.ones(1, dtype=torch.float32) + input_z = torch.ones(1, dtype=torch.float32) + output_x, output_y, output_z = model(input_x, y=input_y, z=input_z) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.float32 + assert output_z.dtype == torch.float32 + + model.fp16_enabled = True + output_x, output_y, output_z = model(input_x, y=input_y, z=input_z) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.float32 + assert output_z.dtype == torch.float32 + + if torch.cuda.is_available(): + model.cuda() + output_x, output_y, output_z = model( + input_x.cuda(), y=input_y.cuda(), z=input_z.cuda()) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.float32 + assert output_z.dtype == torch.float32 + + +def test_force_fp32(): + + with pytest.raises(TypeError): + # ExampleObject is not a subclass of nn.Module + + class ExampleObject(object): + + @force_fp32() + def __call__(self, x): + return x + + model = ExampleObject() + input_x = torch.ones(1, dtype=torch.float32) + model(input_x) + + # apply to all input args + class ExampleModule(nn.Module): + + @force_fp32() + def forward(self, x, y): + return x, y + + model = ExampleModule() + input_x = torch.ones(1, dtype=torch.half) + input_y = torch.ones(1, dtype=torch.half) + output_x, output_y = model(input_x, input_y) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.half + + model.fp16_enabled = True + output_x, output_y = model(input_x, input_y) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.float32 + + if torch.cuda.is_available(): + model.cuda() + output_x, output_y = model(input_x.cuda(), input_y.cuda()) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.float32 + + # apply to specified input args + class ExampleModule(nn.Module): + + @force_fp32(apply_to=('x', )) + def forward(self, x, y): + return x, y + + model = ExampleModule() + input_x = torch.ones(1, dtype=torch.half) + input_y = torch.ones(1, dtype=torch.half) + output_x, output_y = model(input_x, input_y) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.half + + model.fp16_enabled = True + output_x, output_y = model(input_x, input_y) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.half + + if torch.cuda.is_available(): + model.cuda() + output_x, output_y = model(input_x.cuda(), input_y.cuda()) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.half + + # apply to optional input args + class ExampleModule(nn.Module): + + @force_fp32(apply_to=('x', 'y')) + def forward(self, x, y=None, z=None): + return x, y, z + + model = ExampleModule() + input_x = torch.ones(1, dtype=torch.half) + input_y = torch.ones(1, dtype=torch.half) + input_z = torch.ones(1, dtype=torch.half) + output_x, output_y, output_z = model(input_x, y=input_y, z=input_z) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.half + assert output_z.dtype == torch.half + + model.fp16_enabled = True + output_x, output_y, output_z = model(input_x, y=input_y, z=input_z) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.float32 + assert output_z.dtype == torch.half + + if torch.cuda.is_available(): + model.cuda() + output_x, output_y, output_z = model( + input_x.cuda(), y=input_y.cuda(), z=input_z.cuda()) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.float32 + assert output_z.dtype == torch.half + + # out_fp16=True + class ExampleModule(nn.Module): + + @force_fp32(apply_to=('x', 'y'), out_fp16=True) + def forward(self, x, y=None, z=None): + return x, y, z + + model = ExampleModule() + input_x = torch.ones(1, dtype=torch.float32) + input_y = torch.ones(1, dtype=torch.half) + input_z = torch.ones(1, dtype=torch.half) + output_x, output_y, output_z = model(input_x, y=input_y, z=input_z) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.half + assert output_z.dtype == torch.half + + model.fp16_enabled = True + output_x, output_y, output_z = model(input_x, y=input_y, z=input_z) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.half + assert output_z.dtype == torch.half + + if torch.cuda.is_available(): + model.cuda() + output_x, output_y, output_z = model( + input_x.cuda(), y=input_y.cuda(), z=input_z.cuda()) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.half + assert output_z.dtype == torch.half diff --git a/tests/test_utils/test_anchor.py b/tests/test_utils/test_anchor.py new file mode 100644 index 0000000..4af29cc --- /dev/null +++ b/tests/test_utils/test_anchor.py @@ -0,0 +1,410 @@ +""" +CommandLine: + pytest tests/test_utils/test_anchor.py + xdoctest tests/test_utils/test_anchor.py zero + +""" +import torch + + +def test_standard_anchor_generator(): + from mmdet.core.anchor import build_anchor_generator + anchor_generator_cfg = dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8]) + + anchor_generator = build_anchor_generator(anchor_generator_cfg) + assert anchor_generator is not None + + +def test_strides(): + from mmdet.core import AnchorGenerator + # Square strides + self = AnchorGenerator([10], [1.], [1.], [10]) + anchors = self.grid_anchors([(2, 2)], device='cpu') + + expected_anchors = torch.tensor([[-5., -5., 5., 5.], [5., -5., 15., 5.], + [-5., 5., 5., 15.], [5., 5., 15., 15.]]) + + assert torch.equal(anchors[0], expected_anchors) + + # Different strides in x and y direction + self = AnchorGenerator([(10, 20)], [1.], [1.], [10]) + anchors = self.grid_anchors([(2, 2)], device='cpu') + + expected_anchors = torch.tensor([[-5., -5., 5., 5.], [5., -5., 15., 5.], + [-5., 15., 5., 25.], [5., 15., 15., 25.]]) + + assert torch.equal(anchors[0], expected_anchors) + + +def test_ssd_anchor_generator(): + from mmdet.core.anchor import build_anchor_generator + if torch.cuda.is_available(): + device = 'cuda' + else: + device = 'cpu' + + anchor_generator_cfg = dict( + type='SSDAnchorGenerator', + scale_major=False, + input_size=300, + basesize_ratio_range=(0.15, 0.9), + strides=[8, 16, 32, 64, 100, 300], + ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]) + + featmap_sizes = [(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)] + anchor_generator = build_anchor_generator(anchor_generator_cfg) + + # check base anchors + expected_base_anchors = [ + torch.Tensor([[-6.5000, -6.5000, 14.5000, 14.5000], + [-11.3704, -11.3704, 19.3704, 19.3704], + [-10.8492, -3.4246, 18.8492, 11.4246], + [-3.4246, -10.8492, 11.4246, 18.8492]]), + torch.Tensor([[-14.5000, -14.5000, 30.5000, 30.5000], + [-25.3729, -25.3729, 41.3729, 41.3729], + [-23.8198, -7.9099, 39.8198, 23.9099], + [-7.9099, -23.8198, 23.9099, 39.8198], + [-30.9711, -4.9904, 46.9711, 20.9904], + [-4.9904, -30.9711, 20.9904, 46.9711]]), + torch.Tensor([[-33.5000, -33.5000, 65.5000, 65.5000], + [-45.5366, -45.5366, 77.5366, 77.5366], + [-54.0036, -19.0018, 86.0036, 51.0018], + [-19.0018, -54.0036, 51.0018, 86.0036], + [-69.7365, -12.5788, 101.7365, 44.5788], + [-12.5788, -69.7365, 44.5788, 101.7365]]), + torch.Tensor([[-44.5000, -44.5000, 108.5000, 108.5000], + [-56.9817, -56.9817, 120.9817, 120.9817], + [-76.1873, -22.0937, 140.1873, 86.0937], + [-22.0937, -76.1873, 86.0937, 140.1873], + [-100.5019, -12.1673, 164.5019, 76.1673], + [-12.1673, -100.5019, 76.1673, 164.5019]]), + torch.Tensor([[-53.5000, -53.5000, 153.5000, 153.5000], + [-66.2185, -66.2185, 166.2185, 166.2185], + [-96.3711, -23.1855, 196.3711, 123.1855], + [-23.1855, -96.3711, 123.1855, 196.3711]]), + torch.Tensor([[19.5000, 19.5000, 280.5000, 280.5000], + [6.6342, 6.6342, 293.3658, 293.3658], + [-34.5549, 57.7226, 334.5549, 242.2774], + [57.7226, -34.5549, 242.2774, 334.5549]]), + ] + base_anchors = anchor_generator.base_anchors + for i, base_anchor in enumerate(base_anchors): + assert base_anchor.allclose(expected_base_anchors[i]) + + # check valid flags + expected_valid_pixels = [5776, 2166, 600, 150, 36, 4] + multi_level_valid_flags = anchor_generator.valid_flags( + featmap_sizes, (300, 300), device) + for i, single_level_valid_flag in enumerate(multi_level_valid_flags): + assert single_level_valid_flag.sum() == expected_valid_pixels[i] + + # check number of base anchors for each level + assert anchor_generator.num_base_anchors == [4, 6, 6, 6, 4, 4] + + # check anchor generation + anchors = anchor_generator.grid_anchors(featmap_sizes, device) + assert len(anchors) == 6 + + +def test_anchor_generator_with_tuples(): + from mmdet.core.anchor import build_anchor_generator + if torch.cuda.is_available(): + device = 'cuda' + else: + device = 'cpu' + + anchor_generator_cfg = dict( + type='SSDAnchorGenerator', + scale_major=False, + input_size=300, + basesize_ratio_range=(0.15, 0.9), + strides=[8, 16, 32, 64, 100, 300], + ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]) + + featmap_sizes = [(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)] + anchor_generator = build_anchor_generator(anchor_generator_cfg) + anchors = anchor_generator.grid_anchors(featmap_sizes, device) + + anchor_generator_cfg_tuples = dict( + type='SSDAnchorGenerator', + scale_major=False, + input_size=300, + basesize_ratio_range=(0.15, 0.9), + strides=[(8, 8), (16, 16), (32, 32), (64, 64), (100, 100), (300, 300)], + ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]) + + anchor_generator_tuples = build_anchor_generator( + anchor_generator_cfg_tuples) + anchors_tuples = anchor_generator_tuples.grid_anchors( + featmap_sizes, device) + for anchor, anchor_tuples in zip(anchors, anchors_tuples): + assert torch.equal(anchor, anchor_tuples) + + +def test_yolo_anchor_generator(): + from mmdet.core.anchor import build_anchor_generator + if torch.cuda.is_available(): + device = 'cuda' + else: + device = 'cpu' + + anchor_generator_cfg = dict( + type='YOLOAnchorGenerator', + strides=[32, 16, 8], + base_sizes=[ + [(116, 90), (156, 198), (373, 326)], + [(30, 61), (62, 45), (59, 119)], + [(10, 13), (16, 30), (33, 23)], + ]) + + featmap_sizes = [(14, 18), (28, 36), (56, 72)] + anchor_generator = build_anchor_generator(anchor_generator_cfg) + + # check base anchors + expected_base_anchors = [ + torch.Tensor([[-42.0000, -29.0000, 74.0000, 61.0000], + [-62.0000, -83.0000, 94.0000, 115.0000], + [-170.5000, -147.0000, 202.5000, 179.0000]]), + torch.Tensor([[-7.0000, -22.5000, 23.0000, 38.5000], + [-23.0000, -14.5000, 39.0000, 30.5000], + [-21.5000, -51.5000, 37.5000, 67.5000]]), + torch.Tensor([[-1.0000, -2.5000, 9.0000, 10.5000], + [-4.0000, -11.0000, 12.0000, 19.0000], + [-12.5000, -7.5000, 20.5000, 15.5000]]) + ] + base_anchors = anchor_generator.base_anchors + for i, base_anchor in enumerate(base_anchors): + assert base_anchor.allclose(expected_base_anchors[i]) + + # check number of base anchors for each level + assert anchor_generator.num_base_anchors == [3, 3, 3] + + # check anchor generation + anchors = anchor_generator.grid_anchors(featmap_sizes, device) + assert len(anchors) == 3 + + +def test_retina_anchor(): + from mmdet.models import build_head + if torch.cuda.is_available(): + device = 'cuda' + else: + device = 'cpu' + + # head configs modified from + # configs/nas_fpn/retinanet_r50_fpn_crop640_50e.py + bbox_head = dict( + type='RetinaSepBNHead', + num_classes=4, + num_ins=5, + in_channels=4, + stacked_convs=1, + feat_channels=4, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0])) + + retina_head = build_head(bbox_head) + assert retina_head.anchor_generator is not None + + # use the featmap sizes in NASFPN setting to test retina head + featmap_sizes = [(80, 80), (40, 40), (20, 20), (10, 10), (5, 5)] + # check base anchors + expected_base_anchors = [ + torch.Tensor([[-22.6274, -11.3137, 22.6274, 11.3137], + [-28.5088, -14.2544, 28.5088, 14.2544], + [-35.9188, -17.9594, 35.9188, 17.9594], + [-16.0000, -16.0000, 16.0000, 16.0000], + [-20.1587, -20.1587, 20.1587, 20.1587], + [-25.3984, -25.3984, 25.3984, 25.3984], + [-11.3137, -22.6274, 11.3137, 22.6274], + [-14.2544, -28.5088, 14.2544, 28.5088], + [-17.9594, -35.9188, 17.9594, 35.9188]]), + torch.Tensor([[-45.2548, -22.6274, 45.2548, 22.6274], + [-57.0175, -28.5088, 57.0175, 28.5088], + [-71.8376, -35.9188, 71.8376, 35.9188], + [-32.0000, -32.0000, 32.0000, 32.0000], + [-40.3175, -40.3175, 40.3175, 40.3175], + [-50.7968, -50.7968, 50.7968, 50.7968], + [-22.6274, -45.2548, 22.6274, 45.2548], + [-28.5088, -57.0175, 28.5088, 57.0175], + [-35.9188, -71.8376, 35.9188, 71.8376]]), + torch.Tensor([[-90.5097, -45.2548, 90.5097, 45.2548], + [-114.0350, -57.0175, 114.0350, 57.0175], + [-143.6751, -71.8376, 143.6751, 71.8376], + [-64.0000, -64.0000, 64.0000, 64.0000], + [-80.6349, -80.6349, 80.6349, 80.6349], + [-101.5937, -101.5937, 101.5937, 101.5937], + [-45.2548, -90.5097, 45.2548, 90.5097], + [-57.0175, -114.0350, 57.0175, 114.0350], + [-71.8376, -143.6751, 71.8376, 143.6751]]), + torch.Tensor([[-181.0193, -90.5097, 181.0193, 90.5097], + [-228.0701, -114.0350, 228.0701, 114.0350], + [-287.3503, -143.6751, 287.3503, 143.6751], + [-128.0000, -128.0000, 128.0000, 128.0000], + [-161.2699, -161.2699, 161.2699, 161.2699], + [-203.1873, -203.1873, 203.1873, 203.1873], + [-90.5097, -181.0193, 90.5097, 181.0193], + [-114.0350, -228.0701, 114.0350, 228.0701], + [-143.6751, -287.3503, 143.6751, 287.3503]]), + torch.Tensor([[-362.0387, -181.0193, 362.0387, 181.0193], + [-456.1401, -228.0701, 456.1401, 228.0701], + [-574.7006, -287.3503, 574.7006, 287.3503], + [-256.0000, -256.0000, 256.0000, 256.0000], + [-322.5398, -322.5398, 322.5398, 322.5398], + [-406.3747, -406.3747, 406.3747, 406.3747], + [-181.0193, -362.0387, 181.0193, 362.0387], + [-228.0701, -456.1401, 228.0701, 456.1401], + [-287.3503, -574.7006, 287.3503, 574.7006]]) + ] + base_anchors = retina_head.anchor_generator.base_anchors + for i, base_anchor in enumerate(base_anchors): + assert base_anchor.allclose(expected_base_anchors[i]) + + # check valid flags + expected_valid_pixels = [57600, 14400, 3600, 900, 225] + multi_level_valid_flags = retina_head.anchor_generator.valid_flags( + featmap_sizes, (640, 640), device) + for i, single_level_valid_flag in enumerate(multi_level_valid_flags): + assert single_level_valid_flag.sum() == expected_valid_pixels[i] + + # check number of base anchors for each level + assert retina_head.anchor_generator.num_base_anchors == [9, 9, 9, 9, 9] + + # check anchor generation + anchors = retina_head.anchor_generator.grid_anchors(featmap_sizes, device) + assert len(anchors) == 5 + + +def test_guided_anchor(): + from mmdet.models import build_head + if torch.cuda.is_available(): + device = 'cuda' + else: + device = 'cpu' + # head configs modified from + # configs/guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py + bbox_head = dict( + type='GARetinaHead', + num_classes=8, + in_channels=4, + stacked_convs=1, + feat_channels=4, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128])) + + ga_retina_head = build_head(bbox_head) + assert ga_retina_head.approx_anchor_generator is not None + + # use the featmap sizes in NASFPN setting to test ga_retina_head + featmap_sizes = [(100, 152), (50, 76), (25, 38), (13, 19), (7, 10)] + # check base anchors + expected_approxs = [ + torch.Tensor([[-22.6274, -11.3137, 22.6274, 11.3137], + [-28.5088, -14.2544, 28.5088, 14.2544], + [-35.9188, -17.9594, 35.9188, 17.9594], + [-16.0000, -16.0000, 16.0000, 16.0000], + [-20.1587, -20.1587, 20.1587, 20.1587], + [-25.3984, -25.3984, 25.3984, 25.3984], + [-11.3137, -22.6274, 11.3137, 22.6274], + [-14.2544, -28.5088, 14.2544, 28.5088], + [-17.9594, -35.9188, 17.9594, 35.9188]]), + torch.Tensor([[-45.2548, -22.6274, 45.2548, 22.6274], + [-57.0175, -28.5088, 57.0175, 28.5088], + [-71.8376, -35.9188, 71.8376, 35.9188], + [-32.0000, -32.0000, 32.0000, 32.0000], + [-40.3175, -40.3175, 40.3175, 40.3175], + [-50.7968, -50.7968, 50.7968, 50.7968], + [-22.6274, -45.2548, 22.6274, 45.2548], + [-28.5088, -57.0175, 28.5088, 57.0175], + [-35.9188, -71.8376, 35.9188, 71.8376]]), + torch.Tensor([[-90.5097, -45.2548, 90.5097, 45.2548], + [-114.0350, -57.0175, 114.0350, 57.0175], + [-143.6751, -71.8376, 143.6751, 71.8376], + [-64.0000, -64.0000, 64.0000, 64.0000], + [-80.6349, -80.6349, 80.6349, 80.6349], + [-101.5937, -101.5937, 101.5937, 101.5937], + [-45.2548, -90.5097, 45.2548, 90.5097], + [-57.0175, -114.0350, 57.0175, 114.0350], + [-71.8376, -143.6751, 71.8376, 143.6751]]), + torch.Tensor([[-181.0193, -90.5097, 181.0193, 90.5097], + [-228.0701, -114.0350, 228.0701, 114.0350], + [-287.3503, -143.6751, 287.3503, 143.6751], + [-128.0000, -128.0000, 128.0000, 128.0000], + [-161.2699, -161.2699, 161.2699, 161.2699], + [-203.1873, -203.1873, 203.1873, 203.1873], + [-90.5097, -181.0193, 90.5097, 181.0193], + [-114.0350, -228.0701, 114.0350, 228.0701], + [-143.6751, -287.3503, 143.6751, 287.3503]]), + torch.Tensor([[-362.0387, -181.0193, 362.0387, 181.0193], + [-456.1401, -228.0701, 456.1401, 228.0701], + [-574.7006, -287.3503, 574.7006, 287.3503], + [-256.0000, -256.0000, 256.0000, 256.0000], + [-322.5398, -322.5398, 322.5398, 322.5398], + [-406.3747, -406.3747, 406.3747, 406.3747], + [-181.0193, -362.0387, 181.0193, 362.0387], + [-228.0701, -456.1401, 228.0701, 456.1401], + [-287.3503, -574.7006, 287.3503, 574.7006]]) + ] + approxs = ga_retina_head.approx_anchor_generator.base_anchors + for i, base_anchor in enumerate(approxs): + assert base_anchor.allclose(expected_approxs[i]) + + # check valid flags + expected_valid_pixels = [136800, 34200, 8550, 2223, 630] + multi_level_valid_flags = ga_retina_head.approx_anchor_generator \ + .valid_flags(featmap_sizes, (800, 1216), device) + for i, single_level_valid_flag in enumerate(multi_level_valid_flags): + assert single_level_valid_flag.sum() == expected_valid_pixels[i] + + # check number of base anchors for each level + assert ga_retina_head.approx_anchor_generator.num_base_anchors == [ + 9, 9, 9, 9, 9 + ] + + # check approx generation + squares = ga_retina_head.square_anchor_generator.grid_anchors( + featmap_sizes, device) + assert len(squares) == 5 + + expected_squares = [ + torch.Tensor([[-16., -16., 16., 16.]]), + torch.Tensor([[-32., -32., 32., 32]]), + torch.Tensor([[-64., -64., 64., 64.]]), + torch.Tensor([[-128., -128., 128., 128.]]), + torch.Tensor([[-256., -256., 256., 256.]]) + ] + squares = ga_retina_head.square_anchor_generator.base_anchors + for i, base_anchor in enumerate(squares): + assert base_anchor.allclose(expected_squares[i]) + + # square_anchor_generator does not check valid flags + # check number of base anchors for each level + assert (ga_retina_head.square_anchor_generator.num_base_anchors == [ + 1, 1, 1, 1, 1 + ]) + + # check square generation + anchors = ga_retina_head.square_anchor_generator.grid_anchors( + featmap_sizes, device) + assert len(anchors) == 5 diff --git a/tests/test_utils/test_assigner.py b/tests/test_utils/test_assigner.py new file mode 100644 index 0000000..949234b --- /dev/null +++ b/tests/test_utils/test_assigner.py @@ -0,0 +1,497 @@ +"""Tests the Assigner objects. + +CommandLine: + pytest tests/test_utils/test_assigner.py + xdoctest tests/test_utils/test_assigner.py zero +""" +import torch + +from mmdet.core.bbox.assigners import (ApproxMaxIoUAssigner, + CenterRegionAssigner, HungarianAssigner, + MaxIoUAssigner, PointAssigner, + UniformAssigner) + + +def test_max_iou_assigner(): + self = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + gt_labels = torch.LongTensor([2, 3]) + assign_result = self.assign(bboxes, gt_bboxes, gt_labels=gt_labels) + assert len(assign_result.gt_inds) == 4 + assert len(assign_result.labels) == 4 + + expected_gt_inds = torch.LongTensor([1, 0, 2, 0]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_max_iou_assigner_with_ignore(): + self = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ignore_iof_thr=0.5, + ignore_wrt_candidates=False, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [30, 32, 40, 42], + ]) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + gt_bboxes_ignore = torch.Tensor([ + [30, 30, 40, 40], + ]) + assign_result = self.assign( + bboxes, gt_bboxes, gt_bboxes_ignore=gt_bboxes_ignore) + + expected_gt_inds = torch.LongTensor([1, 0, 2, -1]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_max_iou_assigner_with_empty_gt(): + """Test corner case where an image might have no true detections.""" + self = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.empty(0, 4) + assign_result = self.assign(bboxes, gt_bboxes) + + expected_gt_inds = torch.LongTensor([0, 0, 0, 0]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_max_iou_assigner_with_empty_boxes(): + """Test corner case where a network might predict no boxes.""" + self = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ) + bboxes = torch.empty((0, 4)) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + gt_labels = torch.LongTensor([2, 3]) + + # Test with gt_labels + assign_result = self.assign(bboxes, gt_bboxes, gt_labels=gt_labels) + assert len(assign_result.gt_inds) == 0 + assert tuple(assign_result.labels.shape) == (0, ) + + # Test without gt_labels + assign_result = self.assign(bboxes, gt_bboxes, gt_labels=None) + assert len(assign_result.gt_inds) == 0 + assert assign_result.labels is None + + +def test_max_iou_assigner_with_empty_boxes_and_ignore(): + """Test corner case where a network might predict no boxes and + ignore_iof_thr is on.""" + self = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ignore_iof_thr=0.5, + ) + bboxes = torch.empty((0, 4)) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + gt_bboxes_ignore = torch.Tensor([ + [30, 30, 40, 40], + ]) + gt_labels = torch.LongTensor([2, 3]) + + # Test with gt_labels + assign_result = self.assign( + bboxes, + gt_bboxes, + gt_labels=gt_labels, + gt_bboxes_ignore=gt_bboxes_ignore) + assert len(assign_result.gt_inds) == 0 + assert tuple(assign_result.labels.shape) == (0, ) + + # Test without gt_labels + assign_result = self.assign( + bboxes, gt_bboxes, gt_labels=None, gt_bboxes_ignore=gt_bboxes_ignore) + assert len(assign_result.gt_inds) == 0 + assert assign_result.labels is None + + +def test_max_iou_assigner_with_empty_boxes_and_gt(): + """Test corner case where a network might predict no boxes and no gt.""" + self = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ) + bboxes = torch.empty((0, 4)) + gt_bboxes = torch.empty((0, 4)) + assign_result = self.assign(bboxes, gt_bboxes) + assert len(assign_result.gt_inds) == 0 + + +def test_point_assigner(): + self = PointAssigner() + points = torch.FloatTensor([ # [x, y, stride] + [0, 0, 1], + [10, 10, 1], + [5, 5, 1], + [32, 32, 1], + ]) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + assign_result = self.assign(points, gt_bboxes) + expected_gt_inds = torch.LongTensor([1, 2, 1, 0]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_point_assigner_with_empty_gt(): + """Test corner case where an image might have no true detections.""" + self = PointAssigner() + points = torch.FloatTensor([ # [x, y, stride] + [0, 0, 1], + [10, 10, 1], + [5, 5, 1], + [32, 32, 1], + ]) + gt_bboxes = torch.FloatTensor([]) + assign_result = self.assign(points, gt_bboxes) + + expected_gt_inds = torch.LongTensor([0, 0, 0, 0]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_point_assigner_with_empty_boxes_and_gt(): + """Test corner case where an image might predict no points and no gt.""" + self = PointAssigner() + points = torch.FloatTensor([]) + gt_bboxes = torch.FloatTensor([]) + assign_result = self.assign(points, gt_bboxes) + assert len(assign_result.gt_inds) == 0 + + +def test_approx_iou_assigner(): + self = ApproxMaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + approxs_per_octave = 1 + approxs = bboxes + squares = bboxes + assign_result = self.assign(approxs, squares, approxs_per_octave, + gt_bboxes) + + expected_gt_inds = torch.LongTensor([1, 0, 2, 0]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_approx_iou_assigner_with_empty_gt(): + """Test corner case where an image might have no true detections.""" + self = ApproxMaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.FloatTensor([]) + approxs_per_octave = 1 + approxs = bboxes + squares = bboxes + assign_result = self.assign(approxs, squares, approxs_per_octave, + gt_bboxes) + + expected_gt_inds = torch.LongTensor([0, 0, 0, 0]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_approx_iou_assigner_with_empty_boxes(): + """Test corner case where an network might predict no boxes.""" + self = ApproxMaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ) + bboxes = torch.empty((0, 4)) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + approxs_per_octave = 1 + approxs = bboxes + squares = bboxes + assign_result = self.assign(approxs, squares, approxs_per_octave, + gt_bboxes) + assert len(assign_result.gt_inds) == 0 + + +def test_approx_iou_assigner_with_empty_boxes_and_gt(): + """Test corner case where an network might predict no boxes and no gt.""" + self = ApproxMaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ) + bboxes = torch.empty((0, 4)) + gt_bboxes = torch.empty((0, 4)) + approxs_per_octave = 1 + approxs = bboxes + squares = bboxes + assign_result = self.assign(approxs, squares, approxs_per_octave, + gt_bboxes) + assert len(assign_result.gt_inds) == 0 + + +def test_random_assign_result(): + """Test random instantiation of assign result to catch corner cases.""" + from mmdet.core.bbox.assigners.assign_result import AssignResult + AssignResult.random() + + AssignResult.random(num_gts=0, num_preds=0) + AssignResult.random(num_gts=0, num_preds=3) + AssignResult.random(num_gts=3, num_preds=3) + AssignResult.random(num_gts=0, num_preds=3) + AssignResult.random(num_gts=7, num_preds=7) + AssignResult.random(num_gts=7, num_preds=64) + AssignResult.random(num_gts=24, num_preds=3) + + +def test_center_region_assigner(): + self = CenterRegionAssigner(pos_scale=0.3, neg_scale=1) + bboxes = torch.FloatTensor([[0, 0, 10, 10], [10, 10, 20, 20], [8, 8, 9, + 9]]) + gt_bboxes = torch.FloatTensor([ + [0, 0, 11, 11], # match bboxes[0] + [10, 10, 20, 20], # match bboxes[1] + [4.5, 4.5, 5.5, 5.5], # match bboxes[0] but area is too small + [0, 0, 10, 10], # match bboxes[1] and has a smaller area than gt[0] + ]) + gt_labels = torch.LongTensor([2, 3, 4, 5]) + assign_result = self.assign(bboxes, gt_bboxes, gt_labels=gt_labels) + assert len(assign_result.gt_inds) == 3 + assert len(assign_result.labels) == 3 + expected_gt_inds = torch.LongTensor([4, 2, 0]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + shadowed_labels = assign_result.get_extra_property('shadowed_labels') + # [8, 8, 9, 9] in the shadowed region of [0, 0, 11, 11] (label: 2) + assert torch.any(shadowed_labels == torch.LongTensor([[2, 2]])) + # [8, 8, 9, 9] in the shadowed region of [0, 0, 10, 10] (label: 5) + assert torch.any(shadowed_labels == torch.LongTensor([[2, 5]])) + # [0, 0, 10, 10] is already assigned to [4.5, 4.5, 5.5, 5.5]. + # Therefore, [0, 0, 11, 11] (label: 2) is shadowed + assert torch.any(shadowed_labels == torch.LongTensor([[0, 2]])) + + +def test_center_region_assigner_with_ignore(): + self = CenterRegionAssigner( + pos_scale=0.5, + neg_scale=1, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + ]) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 10], # match bboxes[0] + [10, 10, 20, 20], # match bboxes[1] + ]) + gt_bboxes_ignore = torch.FloatTensor([ + [0, 0, 10, 10], # match bboxes[0] + ]) + gt_labels = torch.LongTensor([1, 2]) + assign_result = self.assign( + bboxes, + gt_bboxes, + gt_bboxes_ignore=gt_bboxes_ignore, + gt_labels=gt_labels) + assert len(assign_result.gt_inds) == 2 + assert len(assign_result.labels) == 2 + + expected_gt_inds = torch.LongTensor([-1, 2]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_center_region_assigner_with_empty_bboxes(): + self = CenterRegionAssigner( + pos_scale=0.5, + neg_scale=1, + ) + bboxes = torch.empty((0, 4)).float() + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 10], # match bboxes[0] + [10, 10, 20, 20], # match bboxes[1] + ]) + gt_labels = torch.LongTensor([1, 2]) + assign_result = self.assign(bboxes, gt_bboxes, gt_labels=gt_labels) + assert assign_result.gt_inds is None or assign_result.gt_inds.numel() == 0 + assert assign_result.labels is None or assign_result.labels.numel() == 0 + + +def test_center_region_assigner_with_empty_gts(): + self = CenterRegionAssigner( + pos_scale=0.5, + neg_scale=1, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + ]) + gt_bboxes = torch.empty((0, 4)).float() + gt_labels = torch.empty((0, )).long() + assign_result = self.assign(bboxes, gt_bboxes, gt_labels=gt_labels) + assert len(assign_result.gt_inds) == 2 + expected_gt_inds = torch.LongTensor([0, 0]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_hungarian_match_assigner(): + self = HungarianAssigner() + assert self.iou_cost.iou_mode == 'giou' + + # test no gt bboxes + bbox_pred = torch.rand((10, 4)) + cls_pred = torch.rand((10, 81)) + gt_bboxes = torch.empty((0, 4)).float() + gt_labels = torch.empty((0, )).long() + img_meta = dict(img_shape=(10, 8, 3)) + assign_result = self.assign(bbox_pred, cls_pred, gt_bboxes, gt_labels, + img_meta) + assert torch.all(assign_result.gt_inds == 0) + assert torch.all(assign_result.labels == -1) + + # test with gt bboxes + gt_bboxes = torch.FloatTensor([[0, 0, 5, 7], [3, 5, 7, 8]]) + gt_labels = torch.LongTensor([1, 20]) + assign_result = self.assign(bbox_pred, cls_pred, gt_bboxes, gt_labels, + img_meta) + assert torch.all(assign_result.gt_inds > -1) + assert (assign_result.gt_inds > 0).sum() == gt_bboxes.size(0) + assert (assign_result.labels > -1).sum() == gt_bboxes.size(0) + + # test iou mode + self = HungarianAssigner( + iou_cost=dict(type='IoUCost', iou_mode='iou', weight=1.0)) + assert self.iou_cost.iou_mode == 'iou' + assign_result = self.assign(bbox_pred, cls_pred, gt_bboxes, gt_labels, + img_meta) + assert torch.all(assign_result.gt_inds > -1) + assert (assign_result.gt_inds > 0).sum() == gt_bboxes.size(0) + assert (assign_result.labels > -1).sum() == gt_bboxes.size(0) + + # test focal loss mode + self = HungarianAssigner( + iou_cost=dict(type='IoUCost', iou_mode='giou', weight=1.0), + cls_cost=dict(type='FocalLossCost', weight=1.)) + assert self.iou_cost.iou_mode == 'giou' + assign_result = self.assign(bbox_pred, cls_pred, gt_bboxes, gt_labels, + img_meta) + assert torch.all(assign_result.gt_inds > -1) + assert (assign_result.gt_inds > 0).sum() == gt_bboxes.size(0) + assert (assign_result.labels > -1).sum() == gt_bboxes.size(0) + + +def test_uniform_assigner(): + self = UniformAssigner(0.15, 0.7, 1) + pred_bbox = torch.FloatTensor([ + [1, 1, 12, 8], + [4, 4, 20, 20], + [1, 5, 15, 15], + [30, 5, 32, 42], + ]) + anchor = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + gt_labels = torch.LongTensor([2, 3]) + assign_result = self.assign( + pred_bbox, anchor, gt_bboxes, gt_labels=gt_labels) + assert len(assign_result.gt_inds) == 4 + assert len(assign_result.labels) == 4 + + expected_gt_inds = torch.LongTensor([-1, 0, 2, 0]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_uniform_assigner_with_empty_gt(): + """Test corner case where an image might have no true detections.""" + self = UniformAssigner(0.15, 0.7, 1) + pred_bbox = torch.FloatTensor([ + [1, 1, 12, 8], + [4, 4, 20, 20], + [1, 5, 15, 15], + [30, 5, 32, 42], + ]) + anchor = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.empty(0, 4) + assign_result = self.assign(pred_bbox, anchor, gt_bboxes) + + expected_gt_inds = torch.LongTensor([0, 0, 0, 0]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_uniform_assigner_with_empty_boxes(): + """Test corner case where a network might predict no boxes.""" + self = UniformAssigner(0.15, 0.7, 1) + pred_bbox = torch.empty((0, 4)) + anchor = torch.empty((0, 4)) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + gt_labels = torch.LongTensor([2, 3]) + + # Test with gt_labels + assign_result = self.assign( + pred_bbox, anchor, gt_bboxes, gt_labels=gt_labels) + assert len(assign_result.gt_inds) == 0 + assert tuple(assign_result.labels.shape) == (0, ) + + # Test without gt_labels + assign_result = self.assign(pred_bbox, anchor, gt_bboxes, gt_labels=None) + assert len(assign_result.gt_inds) == 0 diff --git a/tests/test_utils/test_coder.py b/tests/test_utils/test_coder.py new file mode 100644 index 0000000..2dca413 --- /dev/null +++ b/tests/test_utils/test_coder.py @@ -0,0 +1,109 @@ +import pytest +import torch + +from mmdet.core.bbox.coder import (DeltaXYWHBBoxCoder, TBLRBBoxCoder, + YOLOBBoxCoder) + + +def test_yolo_bbox_coder(): + coder = YOLOBBoxCoder() + bboxes = torch.Tensor([[-42., -29., 74., 61.], [-10., -29., 106., 61.], + [22., -29., 138., 61.], [54., -29., 170., 61.]]) + pred_bboxes = torch.Tensor([[0.4709, 0.6152, 0.1690, -0.4056], + [0.5399, 0.6653, 0.1162, -0.4162], + [0.4654, 0.6618, 0.1548, -0.4301], + [0.4786, 0.6197, 0.1896, -0.4479]]) + grid_size = 32 + expected_decode_bboxes = torch.Tensor( + [[-53.6102, -10.3096, 83.7478, 49.6824], + [-15.8700, -8.3901, 114.4236, 50.9693], + [11.1822, -8.0924, 146.6034, 50.4476], + [41.2068, -8.9232, 181.4236, 48.5840]]) + assert expected_decode_bboxes.allclose( + coder.decode(bboxes, pred_bboxes, grid_size)) + + +def test_delta_bbox_coder(): + coder = DeltaXYWHBBoxCoder() + + rois = torch.Tensor([[0., 0., 1., 1.], [0., 0., 1., 1.], [0., 0., 1., 1.], + [5., 5., 5., 5.]]) + deltas = torch.Tensor([[0., 0., 0., 0.], [1., 1., 1., 1.], + [0., 0., 2., -1.], [0.7, -1.9, -0.5, 0.3]]) + expected_decode_bboxes = torch.Tensor([[0.0000, 0.0000, 1.0000, 1.0000], + [0.1409, 0.1409, 2.8591, 2.8591], + [0.0000, 0.3161, 4.1945, 0.6839], + [5.0000, 5.0000, 5.0000, 5.0000]]) + + out = coder.decode(rois, deltas, max_shape=(32, 32)) + assert expected_decode_bboxes.allclose(out, atol=1e-04) + out = coder.decode(rois, deltas, max_shape=torch.Tensor((32, 32))) + assert expected_decode_bboxes.allclose(out, atol=1e-04) + + batch_rois = rois.unsqueeze(0).repeat(2, 1, 1) + batch_deltas = deltas.unsqueeze(0).repeat(2, 1, 1) + batch_out = coder.decode(batch_rois, batch_deltas, max_shape=(32, 32))[0] + assert out.allclose(batch_out) + batch_out = coder.decode( + batch_rois, batch_deltas, max_shape=[(32, 32), (32, 32)])[0] + assert out.allclose(batch_out) + + # test max_shape is not equal to batch + with pytest.raises(AssertionError): + coder.decode( + batch_rois, batch_deltas, max_shape=[(32, 32), (32, 32), (32, 32)]) + + rois = torch.zeros((0, 4)) + deltas = torch.zeros((0, 4)) + out = coder.decode(rois, deltas, max_shape=(32, 32)) + assert rois.shape == out.shape + + # test add_ctr_clamp + coder = DeltaXYWHBBoxCoder(add_ctr_clamp=True, ctr_clamp=2) + + rois = torch.Tensor([[0., 0., 6., 6.], [0., 0., 1., 1.], [0., 0., 1., 1.], + [5., 5., 5., 5.]]) + deltas = torch.Tensor([[1., 1., 2., 2.], [1., 1., 1., 1.], + [0., 0., 2., -1.], [0.7, -1.9, -0.5, 0.3]]) + expected_decode_bboxes = torch.Tensor([[0.0000, 0.0000, 27.1672, 27.1672], + [0.1409, 0.1409, 2.8591, 2.8591], + [0.0000, 0.3161, 4.1945, 0.6839], + [5.0000, 5.0000, 5.0000, 5.0000]]) + + out = coder.decode(rois, deltas, max_shape=(32, 32)) + assert expected_decode_bboxes.allclose(out, atol=1e-04) + + +def test_tblr_bbox_coder(): + coder = TBLRBBoxCoder(normalizer=15.) + + rois = torch.Tensor([[0., 0., 1., 1.], [0., 0., 1., 1.], [0., 0., 1., 1.], + [5., 5., 5., 5.]]) + deltas = torch.Tensor([[0., 0., 0., 0.], [1., 1., 1., 1.], + [0., 0., 2., -1.], [0.7, -1.9, -0.5, 0.3]]) + expected_decode_bboxes = torch.Tensor([[0.5000, 0.5000, 0.5000, 0.5000], + [0.0000, 0.0000, 12.0000, 13.0000], + [0.0000, 0.5000, 0.0000, 0.5000], + [5.0000, 5.0000, 5.0000, 5.0000]]) + + out = coder.decode(rois, deltas, max_shape=(13, 12)) + assert expected_decode_bboxes.allclose(out) + out = coder.decode(rois, deltas, max_shape=torch.Tensor((13, 12))) + assert expected_decode_bboxes.allclose(out) + + batch_rois = rois.unsqueeze(0).repeat(2, 1, 1) + batch_deltas = deltas.unsqueeze(0).repeat(2, 1, 1) + batch_out = coder.decode(batch_rois, batch_deltas, max_shape=(13, 12))[0] + assert out.allclose(batch_out) + batch_out = coder.decode( + batch_rois, batch_deltas, max_shape=[(13, 12), (13, 12)])[0] + assert out.allclose(batch_out) + + # test max_shape is not equal to batch + with pytest.raises(AssertionError): + coder.decode(batch_rois, batch_deltas, max_shape=[(13, 12)]) + + rois = torch.zeros((0, 4)) + deltas = torch.zeros((0, 4)) + out = coder.decode(rois, deltas, max_shape=(32, 32)) + assert rois.shape == out.shape diff --git a/tests/test_utils/test_masks.py b/tests/test_utils/test_masks.py new file mode 100644 index 0000000..808cf08 --- /dev/null +++ b/tests/test_utils/test_masks.py @@ -0,0 +1,655 @@ +import numpy as np +import pytest +import torch + +from mmdet.core import BitmapMasks, PolygonMasks + + +def dummy_raw_bitmap_masks(size): + """ + Args: + size (tuple): expected shape of dummy masks, (H, W) or (N, H, W) + + Return: + ndarray: dummy mask + """ + return np.random.randint(0, 2, size, dtype=np.uint8) + + +def dummy_raw_polygon_masks(size): + """ + Args: + size (tuple): expected shape of dummy masks, (N, H, W) + + Return: + list[list[ndarray]]: dummy mask + """ + num_obj, heigt, width = size + polygons = [] + for _ in range(num_obj): + num_points = np.random.randint(5) * 2 + 6 + polygons.append([np.random.uniform(0, min(heigt, width), num_points)]) + return polygons + + +def dummy_bboxes(num, max_height, max_width): + x1y1 = np.random.randint(0, min(max_height // 2, max_width // 2), (num, 2)) + wh = np.random.randint(0, min(max_height // 2, max_width // 2), (num, 2)) + x2y2 = x1y1 + wh + return np.concatenate([x1y1, x2y2], axis=1).squeeze().astype(np.float32) + + +def test_bitmap_mask_init(): + # init with empty ndarray masks + raw_masks = np.empty((0, 28, 28), dtype=np.uint8) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + assert len(bitmap_masks) == 0 + assert bitmap_masks.height == 28 + assert bitmap_masks.width == 28 + + # init with empty list masks + raw_masks = [] + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + assert len(bitmap_masks) == 0 + assert bitmap_masks.height == 28 + assert bitmap_masks.width == 28 + + # init with ndarray masks contain 3 instances + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + assert len(bitmap_masks) == 3 + assert bitmap_masks.height == 28 + assert bitmap_masks.width == 28 + + # init with list masks contain 3 instances + raw_masks = [dummy_raw_bitmap_masks((28, 28)) for _ in range(3)] + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + assert len(bitmap_masks) == 3 + assert bitmap_masks.height == 28 + assert bitmap_masks.width == 28 + + # init with raw masks of unsupported type + with pytest.raises(AssertionError): + raw_masks = [[dummy_raw_bitmap_masks((28, 28))]] + BitmapMasks(raw_masks, 28, 28) + + +def test_bitmap_mask_rescale(): + # rescale with empty bitmap masks + raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + rescaled_masks = bitmap_masks.rescale((56, 72)) + assert len(rescaled_masks) == 0 + assert rescaled_masks.height == 56 + assert rescaled_masks.width == 56 + + # rescale with bitmap masks contain 1 instances + raw_masks = np.array([[[1, 0, 0, 0], [0, 1, 0, 1]]]) + bitmap_masks = BitmapMasks(raw_masks, 2, 4) + rescaled_masks = bitmap_masks.rescale((8, 8)) + assert len(rescaled_masks) == 1 + assert rescaled_masks.height == 4 + assert rescaled_masks.width == 8 + truth = np.array([[[1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0], + [0, 0, 1, 1, 0, 0, 1, 1], [0, 0, 1, 1, 0, 0, 1, 1]]]) + assert (rescaled_masks.masks == truth).all() + + +def test_bitmap_mask_resize(): + # resize with empty bitmap masks + raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + resized_masks = bitmap_masks.resize((56, 72)) + assert len(resized_masks) == 0 + assert resized_masks.height == 56 + assert resized_masks.width == 72 + + # resize with bitmap masks contain 1 instances + raw_masks = np.diag(np.ones(4, dtype=np.uint8))[np.newaxis, ...] + bitmap_masks = BitmapMasks(raw_masks, 4, 4) + resized_masks = bitmap_masks.resize((8, 8)) + assert len(resized_masks) == 1 + assert resized_masks.height == 8 + assert resized_masks.width == 8 + truth = np.array([[[1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0], + [0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 0], + [0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0], + [0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 1, 1]]]) + assert (resized_masks.masks == truth).all() + + # resize to non-square + raw_masks = np.diag(np.ones(4, dtype=np.uint8))[np.newaxis, ...] + bitmap_masks = BitmapMasks(raw_masks, 4, 4) + resized_masks = bitmap_masks.resize((4, 8)) + assert len(resized_masks) == 1 + assert resized_masks.height == 4 + assert resized_masks.width == 8 + truth = np.array([[[1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 0], + [0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1]]]) + assert (resized_masks.masks == truth).all() + + +def test_bitmap_mask_flip(): + # flip with empty bitmap masks + raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + flipped_masks = bitmap_masks.flip(flip_direction='horizontal') + assert len(flipped_masks) == 0 + assert flipped_masks.height == 28 + assert flipped_masks.width == 28 + + # horizontally flip with bitmap masks contain 3 instances + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + flipped_masks = bitmap_masks.flip(flip_direction='horizontal') + flipped_flipped_masks = flipped_masks.flip(flip_direction='horizontal') + assert flipped_masks.masks.shape == (3, 28, 28) + assert (bitmap_masks.masks == flipped_flipped_masks.masks).all() + assert (flipped_masks.masks == raw_masks[:, :, ::-1]).all() + + # vertically flip with bitmap masks contain 3 instances + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + flipped_masks = bitmap_masks.flip(flip_direction='vertical') + flipped_flipped_masks = flipped_masks.flip(flip_direction='vertical') + assert len(flipped_masks) == 3 + assert flipped_masks.height == 28 + assert flipped_masks.width == 28 + assert (bitmap_masks.masks == flipped_flipped_masks.masks).all() + assert (flipped_masks.masks == raw_masks[:, ::-1, :]).all() + + # diagonal flip with bitmap masks contain 3 instances + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + flipped_masks = bitmap_masks.flip(flip_direction='diagonal') + flipped_flipped_masks = flipped_masks.flip(flip_direction='diagonal') + assert len(flipped_masks) == 3 + assert flipped_masks.height == 28 + assert flipped_masks.width == 28 + assert (bitmap_masks.masks == flipped_flipped_masks.masks).all() + assert (flipped_masks.masks == raw_masks[:, ::-1, ::-1]).all() + + +def test_bitmap_mask_pad(): + # pad with empty bitmap masks + raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + padded_masks = bitmap_masks.pad((56, 56)) + assert len(padded_masks) == 0 + assert padded_masks.height == 56 + assert padded_masks.width == 56 + + # pad with bitmap masks contain 3 instances + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + padded_masks = bitmap_masks.pad((56, 56)) + assert len(padded_masks) == 3 + assert padded_masks.height == 56 + assert padded_masks.width == 56 + assert (padded_masks.masks[:, 28:, 28:] == 0).all() + + +def test_bitmap_mask_crop(): + # crop with empty bitmap masks + dummy_bbox = np.array([0, 10, 10, 27], dtype=np.int) + raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + cropped_masks = bitmap_masks.crop(dummy_bbox) + assert len(cropped_masks) == 0 + assert cropped_masks.height == 17 + assert cropped_masks.width == 10 + + # crop with bitmap masks contain 3 instances + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + cropped_masks = bitmap_masks.crop(dummy_bbox) + assert len(cropped_masks) == 3 + assert cropped_masks.height == 17 + assert cropped_masks.width == 10 + x1, y1, x2, y2 = dummy_bbox + assert (cropped_masks.masks == raw_masks[:, y1:y2, x1:x2]).all() + + # crop with invalid bbox + with pytest.raises(AssertionError): + dummy_bbox = dummy_bboxes(2, 28, 28) + bitmap_masks.crop(dummy_bbox) + + +def test_bitmap_mask_crop_and_resize(): + dummy_bbox = dummy_bboxes(5, 28, 28) + inds = np.random.randint(0, 3, (5, )) + + # crop and resize with empty bitmap masks + raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + cropped_resized_masks = bitmap_masks.crop_and_resize( + dummy_bbox, (56, 56), inds) + assert len(cropped_resized_masks) == 0 + assert cropped_resized_masks.height == 56 + assert cropped_resized_masks.width == 56 + + # crop and resize with bitmap masks contain 3 instances + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + cropped_resized_masks = bitmap_masks.crop_and_resize( + dummy_bbox, (56, 56), inds) + assert len(cropped_resized_masks) == 5 + assert cropped_resized_masks.height == 56 + assert cropped_resized_masks.width == 56 + + +def test_bitmap_mask_expand(): + # expand with empty bitmap masks + raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + expanded_masks = bitmap_masks.expand(56, 56, 12, 14) + assert len(expanded_masks) == 0 + assert expanded_masks.height == 56 + assert expanded_masks.width == 56 + + # expand with bitmap masks contain 3 instances + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + expanded_masks = bitmap_masks.expand(56, 56, 12, 14) + assert len(expanded_masks) == 3 + assert expanded_masks.height == 56 + assert expanded_masks.width == 56 + assert (expanded_masks.masks[:, :12, :14] == 0).all() + assert (expanded_masks.masks[:, 12 + 28:, 14 + 28:] == 0).all() + + +def test_bitmap_mask_area(): + # area of empty bitmap mask + raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + assert bitmap_masks.areas.sum() == 0 + + # area of bitmap masks contain 3 instances + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + areas = bitmap_masks.areas + assert len(areas) == 3 + assert (areas == raw_masks.sum((1, 2))).all() + + +def test_bitmap_mask_to_ndarray(): + # empty bitmap masks to ndarray + raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + ndarray_masks = bitmap_masks.to_ndarray() + assert isinstance(ndarray_masks, np.ndarray) + assert ndarray_masks.shape == (0, 28, 28) + + # bitmap masks contain 3 instances to ndarray + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + ndarray_masks = bitmap_masks.to_ndarray() + assert isinstance(ndarray_masks, np.ndarray) + assert ndarray_masks.shape == (3, 28, 28) + assert (ndarray_masks == raw_masks).all() + + +def test_bitmap_mask_to_tensor(): + # empty bitmap masks to tensor + raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + tensor_masks = bitmap_masks.to_tensor(dtype=torch.uint8, device='cpu') + assert isinstance(tensor_masks, torch.Tensor) + assert tensor_masks.shape == (0, 28, 28) + + # bitmap masks contain 3 instances to tensor + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + tensor_masks = bitmap_masks.to_tensor(dtype=torch.uint8, device='cpu') + assert isinstance(tensor_masks, torch.Tensor) + assert tensor_masks.shape == (3, 28, 28) + assert (tensor_masks.numpy() == raw_masks).all() + + +def test_bitmap_mask_index(): + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + assert (bitmap_masks[0].masks == raw_masks[0]).all() + assert (bitmap_masks[range(2)].masks == raw_masks[range(2)]).all() + + +def test_bitmap_mask_iter(): + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + for i, bitmap_mask in enumerate(bitmap_masks): + assert bitmap_mask.shape == (28, 28) + assert (bitmap_mask == raw_masks[i]).all() + + +def test_polygon_mask_init(): + # init with empty masks + raw_masks = [] + polygon_masks = BitmapMasks(raw_masks, 28, 28) + assert len(polygon_masks) == 0 + assert polygon_masks.height == 28 + assert polygon_masks.width == 28 + + # init with masks contain 3 instances + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + assert isinstance(polygon_masks.masks, list) + assert isinstance(polygon_masks.masks[0], list) + assert isinstance(polygon_masks.masks[0][0], np.ndarray) + assert len(polygon_masks) == 3 + assert polygon_masks.height == 28 + assert polygon_masks.width == 28 + assert polygon_masks.to_ndarray().shape == (3, 28, 28) + + # init with raw masks of unsupported type + with pytest.raises(AssertionError): + raw_masks = [[[]]] + PolygonMasks(raw_masks, 28, 28) + + raw_masks = [dummy_raw_polygon_masks((3, 28, 28))] + PolygonMasks(raw_masks, 28, 28) + + +def test_polygon_mask_rescale(): + # rescale with empty polygon masks + raw_masks = dummy_raw_polygon_masks((0, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + rescaled_masks = polygon_masks.rescale((56, 72)) + assert len(rescaled_masks) == 0 + assert rescaled_masks.height == 56 + assert rescaled_masks.width == 56 + assert rescaled_masks.to_ndarray().shape == (0, 56, 56) + + # rescale with polygon masks contain 3 instances + raw_masks = [[np.array([1, 1, 3, 1, 4, 3, 2, 4, 1, 3], dtype=np.float)]] + polygon_masks = PolygonMasks(raw_masks, 5, 5) + rescaled_masks = polygon_masks.rescale((12, 10)) + assert len(rescaled_masks) == 1 + assert rescaled_masks.height == 10 + assert rescaled_masks.width == 10 + assert rescaled_masks.to_ndarray().shape == (1, 10, 10) + truth = np.array( + [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [0, 0, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], + [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [0, 0, 0, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], + np.uint8) + assert (rescaled_masks.to_ndarray() == truth).all() + + +def test_polygon_mask_resize(): + # resize with empty polygon masks + raw_masks = dummy_raw_polygon_masks((0, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + resized_masks = polygon_masks.resize((56, 72)) + assert len(resized_masks) == 0 + assert resized_masks.height == 56 + assert resized_masks.width == 72 + assert resized_masks.to_ndarray().shape == (0, 56, 72) + + # resize with polygon masks contain 1 instance 1 part + raw_masks1 = [[np.array([1, 1, 3, 1, 4, 3, 2, 4, 1, 3], dtype=np.float)]] + polygon_masks1 = PolygonMasks(raw_masks1, 5, 5) + resized_masks1 = polygon_masks1.resize((10, 10)) + assert len(resized_masks1) == 1 + assert resized_masks1.height == 10 + assert resized_masks1.width == 10 + assert resized_masks1.to_ndarray().shape == (1, 10, 10) + truth1 = np.array( + [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [0, 0, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], + [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [0, 0, 0, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], + np.uint8) + assert (resized_masks1.to_ndarray() == truth1).all() + + # resize with polygon masks contain 1 instance 2 part + raw_masks2 = [[ + np.array([0., 0., 1., 0., 1., 1.]), + np.array([1., 1., 2., 1., 2., 2., 1., 2.]) + ]] + polygon_masks2 = PolygonMasks(raw_masks2, 3, 3) + resized_masks2 = polygon_masks2.resize((6, 6)) + assert len(resized_masks2) == 1 + assert resized_masks2.height == 6 + assert resized_masks2.width == 6 + assert resized_masks2.to_ndarray().shape == (1, 6, 6) + truth2 = np.array( + [[0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0], + [0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]], np.uint8) + assert (resized_masks2.to_ndarray() == truth2).all() + + # resize with polygon masks contain 2 instances + raw_masks3 = [raw_masks1[0], raw_masks2[0]] + polygon_masks3 = PolygonMasks(raw_masks3, 5, 5) + resized_masks3 = polygon_masks3.resize((10, 10)) + assert len(resized_masks3) == 2 + assert resized_masks3.height == 10 + assert resized_masks3.width == 10 + assert resized_masks3.to_ndarray().shape == (2, 10, 10) + truth3 = np.stack([truth1, np.pad(truth2, ((0, 4), (0, 4)), 'constant')]) + assert (resized_masks3.to_ndarray() == truth3).all() + + # resize to non-square + raw_masks4 = [[np.array([1, 1, 3, 1, 4, 3, 2, 4, 1, 3], dtype=np.float)]] + polygon_masks4 = PolygonMasks(raw_masks4, 5, 5) + resized_masks4 = polygon_masks4.resize((5, 10)) + assert len(resized_masks4) == 1 + assert resized_masks4.height == 5 + assert resized_masks4.width == 10 + assert resized_masks4.to_ndarray().shape == (1, 5, 10) + truth4 = np.array( + [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], + [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], np.uint8) + assert (resized_masks4.to_ndarray() == truth4).all() + + +def test_polygon_mask_flip(): + # flip with empty polygon masks + raw_masks = dummy_raw_polygon_masks((0, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + flipped_masks = polygon_masks.flip(flip_direction='horizontal') + assert len(flipped_masks) == 0 + assert flipped_masks.height == 28 + assert flipped_masks.width == 28 + assert flipped_masks.to_ndarray().shape == (0, 28, 28) + + # TODO: fixed flip correctness checking after v2.0_coord is merged + # horizontally flip with polygon masks contain 3 instances + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + flipped_masks = polygon_masks.flip(flip_direction='horizontal') + flipped_flipped_masks = flipped_masks.flip(flip_direction='horizontal') + assert len(flipped_masks) == 3 + assert flipped_masks.height == 28 + assert flipped_masks.width == 28 + assert flipped_masks.to_ndarray().shape == (3, 28, 28) + assert (polygon_masks.to_ndarray() == flipped_flipped_masks.to_ndarray() + ).all() + + # vertically flip with polygon masks contain 3 instances + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + flipped_masks = polygon_masks.flip(flip_direction='vertical') + flipped_flipped_masks = flipped_masks.flip(flip_direction='vertical') + assert len(flipped_masks) == 3 + assert flipped_masks.height == 28 + assert flipped_masks.width == 28 + assert flipped_masks.to_ndarray().shape == (3, 28, 28) + assert (polygon_masks.to_ndarray() == flipped_flipped_masks.to_ndarray() + ).all() + + # diagonal flip with polygon masks contain 3 instances + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + flipped_masks = polygon_masks.flip(flip_direction='diagonal') + flipped_flipped_masks = flipped_masks.flip(flip_direction='diagonal') + assert len(flipped_masks) == 3 + assert flipped_masks.height == 28 + assert flipped_masks.width == 28 + assert flipped_masks.to_ndarray().shape == (3, 28, 28) + assert (polygon_masks.to_ndarray() == flipped_flipped_masks.to_ndarray() + ).all() + + +def test_polygon_mask_crop(): + dummy_bbox = np.array([0, 10, 10, 27], dtype=np.int) + # crop with empty polygon masks + raw_masks = dummy_raw_polygon_masks((0, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + cropped_masks = polygon_masks.crop(dummy_bbox) + assert len(cropped_masks) == 0 + assert cropped_masks.height == 17 + assert cropped_masks.width == 10 + assert cropped_masks.to_ndarray().shape == (0, 17, 10) + + # crop with polygon masks contain 1 instances + raw_masks = [[np.array([1., 3., 5., 1., 5., 6., 1, 6])]] + polygon_masks = PolygonMasks(raw_masks, 7, 7) + bbox = np.array([0, 0, 3, 4]) + cropped_masks = polygon_masks.crop(bbox) + assert len(cropped_masks) == 1 + assert cropped_masks.height == 4 + assert cropped_masks.width == 3 + assert cropped_masks.to_ndarray().shape == (1, 4, 3) + truth = np.array([[0, 0, 0], [0, 0, 0], [0, 0, 1], [0, 1, 1]]) + assert (cropped_masks.to_ndarray() == truth).all() + + # crop with invalid bbox + with pytest.raises(AssertionError): + dummy_bbox = dummy_bboxes(2, 28, 28) + polygon_masks.crop(dummy_bbox) + + +def test_polygon_mask_pad(): + # pad with empty polygon masks + raw_masks = dummy_raw_polygon_masks((0, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + padded_masks = polygon_masks.pad((56, 56)) + assert len(padded_masks) == 0 + assert padded_masks.height == 56 + assert padded_masks.width == 56 + assert padded_masks.to_ndarray().shape == (0, 56, 56) + + # pad with polygon masks contain 3 instances + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + padded_masks = polygon_masks.pad((56, 56)) + assert len(padded_masks) == 3 + assert padded_masks.height == 56 + assert padded_masks.width == 56 + assert padded_masks.to_ndarray().shape == (3, 56, 56) + assert (padded_masks.to_ndarray()[:, 28:, 28:] == 0).all() + + +def test_polygon_mask_expand(): + with pytest.raises(NotImplementedError): + raw_masks = dummy_raw_polygon_masks((0, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + polygon_masks.expand(56, 56, 10, 17) + + +def test_polygon_mask_crop_and_resize(): + dummy_bbox = dummy_bboxes(5, 28, 28) + inds = np.random.randint(0, 3, (5, )) + + # crop and resize with empty polygon masks + raw_masks = dummy_raw_polygon_masks((0, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + cropped_resized_masks = polygon_masks.crop_and_resize( + dummy_bbox, (56, 56), inds) + assert len(cropped_resized_masks) == 0 + assert cropped_resized_masks.height == 56 + assert cropped_resized_masks.width == 56 + assert cropped_resized_masks.to_ndarray().shape == (0, 56, 56) + + # crop and resize with polygon masks contain 3 instances + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + cropped_resized_masks = polygon_masks.crop_and_resize( + dummy_bbox, (56, 56), inds) + assert len(cropped_resized_masks) == 5 + assert cropped_resized_masks.height == 56 + assert cropped_resized_masks.width == 56 + assert cropped_resized_masks.to_ndarray().shape == (5, 56, 56) + + +def test_polygon_mask_area(): + # area of empty polygon masks + raw_masks = dummy_raw_polygon_masks((0, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + assert polygon_masks.areas.sum() == 0 + + # area of polygon masks contain 1 instance + # here we hack a case that the gap between the area of bitmap and polygon + # is minor + raw_masks = [[np.array([1, 1, 5, 1, 3, 4])]] + polygon_masks = PolygonMasks(raw_masks, 6, 6) + polygon_area = polygon_masks.areas + bitmap_area = polygon_masks.to_bitmap().areas + assert len(polygon_area) == 1 + assert np.isclose(polygon_area, bitmap_area).all() + + +def test_polygon_mask_to_bitmap(): + # polygon masks contain 3 instances to bitmap + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + bitmap_masks = polygon_masks.to_bitmap() + assert (polygon_masks.to_ndarray() == bitmap_masks.to_ndarray()).all() + + +def test_polygon_mask_to_ndarray(): + # empty polygon masks to ndarray + raw_masks = dummy_raw_polygon_masks((0, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + ndarray_masks = polygon_masks.to_ndarray() + assert isinstance(ndarray_masks, np.ndarray) + assert ndarray_masks.shape == (0, 28, 28) + + # polygon masks contain 3 instances to ndarray + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + ndarray_masks = polygon_masks.to_ndarray() + assert isinstance(ndarray_masks, np.ndarray) + assert ndarray_masks.shape == (3, 28, 28) + + +def test_polygon_to_tensor(): + # empty polygon masks to tensor + raw_masks = dummy_raw_polygon_masks((0, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + tensor_masks = polygon_masks.to_tensor(dtype=torch.uint8, device='cpu') + assert isinstance(tensor_masks, torch.Tensor) + assert tensor_masks.shape == (0, 28, 28) + + # polygon masks contain 3 instances to tensor + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + tensor_masks = polygon_masks.to_tensor(dtype=torch.uint8, device='cpu') + assert isinstance(tensor_masks, torch.Tensor) + assert tensor_masks.shape == (3, 28, 28) + assert (tensor_masks.numpy() == polygon_masks.to_ndarray()).all() + + +def test_polygon_mask_index(): + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + # index by integer + polygon_masks[0] + # index by list + polygon_masks[[0, 1]] + # index by ndarray + polygon_masks[np.asarray([0, 1])] + with pytest.raises(ValueError): + # invalid index + polygon_masks[torch.Tensor([1, 2])] + + +def test_polygon_mask_iter(): + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + for i, polygon_mask in enumerate(polygon_masks): + assert np.equal(polygon_mask, raw_masks[i]).all() diff --git a/tests/test_utils/test_misc.py b/tests/test_utils/test_misc.py new file mode 100644 index 0000000..16be906 --- /dev/null +++ b/tests/test_utils/test_misc.py @@ -0,0 +1,92 @@ +import numpy as np +import pytest +import torch + +from mmdet.core.bbox import distance2bbox +from mmdet.core.mask.structures import BitmapMasks, PolygonMasks +from mmdet.core.utils import mask2ndarray + + +def dummy_raw_polygon_masks(size): + """ + Args: + size (tuple): expected shape of dummy masks, (N, H, W) + + Return: + list[list[ndarray]]: dummy mask + """ + num_obj, heigt, width = size + polygons = [] + for _ in range(num_obj): + num_points = np.random.randint(5) * 2 + 6 + polygons.append([np.random.uniform(0, min(heigt, width), num_points)]) + return polygons + + +def test_mask2ndarray(): + raw_masks = np.ones((3, 28, 28)) + bitmap_mask = BitmapMasks(raw_masks, 28, 28) + output_mask = mask2ndarray(bitmap_mask) + assert np.allclose(raw_masks, output_mask) + + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + output_mask = mask2ndarray(polygon_masks) + assert output_mask.shape == (3, 28, 28) + + raw_masks = np.ones((3, 28, 28)) + output_mask = mask2ndarray(raw_masks) + assert np.allclose(raw_masks, output_mask) + + raw_masks = torch.ones((3, 28, 28)) + output_mask = mask2ndarray(raw_masks) + assert np.allclose(raw_masks, output_mask) + + # test unsupported type + raw_masks = [] + with pytest.raises(TypeError): + output_mask = mask2ndarray(raw_masks) + + +def test_distance2bbox(): + point = torch.Tensor([[74., 61.], [-29., 106.], [138., 61.], [29., 170.]]) + + distance = torch.Tensor([[0., 0, 1., 1.], [1., 2., 10., 6.], + [22., -29., 138., 61.], [54., -29., 170., 61.]]) + expected_decode_bboxes = torch.Tensor([[74., 61., 75., 62.], + [0., 104., 0., 112.], + [100., 90., 100., 120.], + [0., 120., 100., 120.]]) + out_bbox = distance2bbox(point, distance, max_shape=(120, 100)) + assert expected_decode_bboxes.allclose(out_bbox) + out = distance2bbox(point, distance, max_shape=torch.Tensor((120, 100))) + assert expected_decode_bboxes.allclose(out) + + batch_point = point.unsqueeze(0).repeat(2, 1, 1) + batch_distance = distance.unsqueeze(0).repeat(2, 1, 1) + batch_out = distance2bbox( + batch_point, batch_distance, max_shape=(120, 100))[0] + assert out.allclose(batch_out) + batch_out = distance2bbox( + batch_point, batch_distance, max_shape=[(120, 100), (120, 100)])[0] + assert out.allclose(batch_out) + + batch_out = distance2bbox(point, batch_distance, max_shape=(120, 100))[0] + assert out.allclose(batch_out) + + # test max_shape is not equal to batch + with pytest.raises(AssertionError): + distance2bbox( + batch_point, + batch_distance, + max_shape=[(120, 100), (120, 100), (32, 32)]) + + rois = torch.zeros((0, 4)) + deltas = torch.zeros((0, 4)) + out = distance2bbox(rois, deltas, max_shape=(120, 100)) + assert rois.shape == out.shape + + rois = torch.zeros((2, 0, 4)) + deltas = torch.zeros((2, 0, 4)) + out = distance2bbox(rois, deltas, max_shape=(120, 100)) + assert rois.shape == out.shape diff --git a/tests/test_utils/test_version.py b/tests/test_utils/test_version.py new file mode 100644 index 0000000..6ddf45c --- /dev/null +++ b/tests/test_utils/test_version.py @@ -0,0 +1,15 @@ +from mmdet import digit_version + + +def test_version_check(): + assert digit_version('1.0.5') > digit_version('1.0.5rc0') + assert digit_version('1.0.5') > digit_version('1.0.4rc0') + assert digit_version('1.0.5') > digit_version('1.0rc0') + assert digit_version('1.0.0') > digit_version('0.6.2') + assert digit_version('1.0.0') > digit_version('0.2.16') + assert digit_version('1.0.5rc0') > digit_version('1.0.0rc0') + assert digit_version('1.0.0rc1') > digit_version('1.0.0rc0') + assert digit_version('1.0.0rc2') > digit_version('1.0.0rc0') + assert digit_version('1.0.0rc2') > digit_version('1.0.0rc1') + assert digit_version('1.0.1rc1') > digit_version('1.0.0rc1') + assert digit_version('1.0.0') > digit_version('1.0.0rc1') diff --git a/tests/test_utils/test_visualization.py b/tests/test_utils/test_visualization.py new file mode 100644 index 0000000..9c7969b --- /dev/null +++ b/tests/test_utils/test_visualization.py @@ -0,0 +1,127 @@ +# Copyright (c) Open-MMLab. All rights reserved. +import os +import os.path as osp +import tempfile + +import mmcv +import numpy as np +import pytest +import torch + +from mmdet.core import visualization as vis + + +def test_color(): + assert vis.color_val_matplotlib(mmcv.Color.blue) == (0., 0., 1.) + assert vis.color_val_matplotlib('green') == (0., 1., 0.) + assert vis.color_val_matplotlib((1, 2, 3)) == (3 / 255, 2 / 255, 1 / 255) + assert vis.color_val_matplotlib(100) == (100 / 255, 100 / 255, 100 / 255) + assert vis.color_val_matplotlib(np.zeros(3, dtype=np.int)) == (0., 0., 0.) + # forbid white color + with pytest.raises(TypeError): + vis.color_val_matplotlib([255, 255, 255]) + # forbid float + with pytest.raises(TypeError): + vis.color_val_matplotlib(1.0) + # overflowed + with pytest.raises(AssertionError): + vis.color_val_matplotlib((0, 0, 500)) + + +def test_imshow_det_bboxes(): + tmp_filename = osp.join(tempfile.gettempdir(), 'det_bboxes_image', + 'image.jpg') + image = np.ones((10, 10, 3), np.uint8) + bbox = np.array([[2, 1, 3, 3], [3, 4, 6, 6]]) + label = np.array([0, 1]) + out_image = vis.imshow_det_bboxes( + image, bbox, label, out_file=tmp_filename, show=False) + assert osp.isfile(tmp_filename) + assert image.shape == out_image.shape + assert not np.allclose(image, out_image) + os.remove(tmp_filename) + + # test grayscale images + image = np.ones((10, 10), np.uint8) + bbox = np.array([[2, 1, 3, 3], [3, 4, 6, 6]]) + label = np.array([0, 1]) + out_image = vis.imshow_det_bboxes( + image, bbox, label, out_file=tmp_filename, show=False) + assert osp.isfile(tmp_filename) + assert image.shape == out_image.shape[:2] + os.remove(tmp_filename) + + # test shaped (0,) + image = np.ones((10, 10, 3), np.uint8) + bbox = np.ones((0, 4)) + label = np.ones((0, )) + vis.imshow_det_bboxes( + image, bbox, label, out_file=tmp_filename, show=False) + assert osp.isfile(tmp_filename) + os.remove(tmp_filename) + + # test mask + image = np.ones((10, 10, 3), np.uint8) + bbox = np.array([[2, 1, 3, 3], [3, 4, 6, 6]]) + label = np.array([0, 1]) + segms = np.random.random((2, 10, 10)) > 0.5 + segms = np.array(segms, np.int32) + vis.imshow_det_bboxes( + image, bbox, label, segms, out_file=tmp_filename, show=False) + assert osp.isfile(tmp_filename) + os.remove(tmp_filename) + + # test tensor mask type error + with pytest.raises(AttributeError): + segms = torch.tensor(segms) + vis.imshow_det_bboxes(image, bbox, label, segms, show=False) + + +def test_imshow_gt_det_bboxes(): + tmp_filename = osp.join(tempfile.gettempdir(), 'det_bboxes_image', + 'image.jpg') + image = np.ones((10, 10, 3), np.uint8) + bbox = np.array([[2, 1, 3, 3], [3, 4, 6, 6]]) + label = np.array([0, 1]) + annotation = dict(gt_bboxes=bbox, gt_labels=label) + det_result = np.array([[2, 1, 3, 3, 0], [3, 4, 6, 6, 1]]) + result = [det_result] + out_image = vis.imshow_gt_det_bboxes( + image, annotation, result, out_file=tmp_filename, show=False) + assert osp.isfile(tmp_filename) + assert image.shape == out_image.shape + assert not np.allclose(image, out_image) + os.remove(tmp_filename) + + # test grayscale images + image = np.ones((10, 10), np.uint8) + bbox = np.array([[2, 1, 3, 3], [3, 4, 6, 6]]) + label = np.array([0, 1]) + annotation = dict(gt_bboxes=bbox, gt_labels=label) + det_result = np.array([[2, 1, 3, 3, 0], [3, 4, 6, 6, 1]]) + result = [det_result] + vis.imshow_gt_det_bboxes( + image, annotation, result, out_file=tmp_filename, show=False) + assert osp.isfile(tmp_filename) + os.remove(tmp_filename) + + # test numpy mask + gt_mask = np.ones((2, 10, 10)) + annotation['gt_masks'] = gt_mask + vis.imshow_gt_det_bboxes( + image, annotation, result, out_file=tmp_filename, show=False) + assert osp.isfile(tmp_filename) + os.remove(tmp_filename) + + # test tensor mask + gt_mask = torch.ones((2, 10, 10)) + annotation['gt_masks'] = gt_mask + vis.imshow_gt_det_bboxes( + image, annotation, result, out_file=tmp_filename, show=False) + assert osp.isfile(tmp_filename) + os.remove(tmp_filename) + + # test unsupported type + annotation['gt_masks'] = [] + with pytest.raises(TypeError): + vis.imshow_gt_det_bboxes(image, annotation, result, show=False) diff --git a/tools/analysis_tools/analyze_logs.py b/tools/analysis_tools/analyze_logs.py new file mode 100644 index 0000000..83464f7 --- /dev/null +++ b/tools/analysis_tools/analyze_logs.py @@ -0,0 +1,179 @@ +import argparse +import json +from collections import defaultdict + +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns + + +def cal_train_time(log_dicts, args): + for i, log_dict in enumerate(log_dicts): + print(f'{"-" * 5}Analyze train time of {args.json_logs[i]}{"-" * 5}') + all_times = [] + for epoch in log_dict.keys(): + if args.include_outliers: + all_times.append(log_dict[epoch]['time']) + else: + all_times.append(log_dict[epoch]['time'][1:]) + all_times = np.array(all_times) + epoch_ave_time = all_times.mean(-1) + slowest_epoch = epoch_ave_time.argmax() + fastest_epoch = epoch_ave_time.argmin() + std_over_epoch = epoch_ave_time.std() + print(f'slowest epoch {slowest_epoch + 1}, ' + f'average time is {epoch_ave_time[slowest_epoch]:.4f}') + print(f'fastest epoch {fastest_epoch + 1}, ' + f'average time is {epoch_ave_time[fastest_epoch]:.4f}') + print(f'time std over epochs is {std_over_epoch:.4f}') + print(f'average iter time: {np.mean(all_times):.4f} s/iter') + print() + + +def plot_curve(log_dicts, args): + if args.backend is not None: + plt.switch_backend(args.backend) + sns.set_style(args.style) + # if legend is None, use {filename}_{key} as legend + legend = args.legend + if legend is None: + legend = [] + for json_log in args.json_logs: + for metric in args.keys: + legend.append(f'{json_log}_{metric}') + assert len(legend) == (len(args.json_logs) * len(args.keys)) + metrics = args.keys + + num_metrics = len(metrics) + for i, log_dict in enumerate(log_dicts): + epochs = list(log_dict.keys()) + for j, metric in enumerate(metrics): + print(f'plot curve of {args.json_logs[i]}, metric is {metric}') + if metric not in log_dict[epochs[0]]: + raise KeyError( + f'{args.json_logs[i]} does not contain metric {metric}') + + if 'mAP' in metric: + xs = np.arange(1, max(epochs) + 1) + ys = [] + for epoch in epochs: + ys += log_dict[epoch][metric] + ax = plt.gca() + ax.set_xticks(xs) + plt.xlabel('epoch') + plt.plot(xs, ys, label=legend[i * num_metrics + j], marker='o') + else: + xs = [] + ys = [] + num_iters_per_epoch = log_dict[epochs[0]]['iter'][-1] + for epoch in epochs: + iters = log_dict[epoch]['iter'] + if log_dict[epoch]['mode'][-1] == 'val': + iters = iters[:-1] + xs.append( + np.array(iters) + (epoch - 1) * num_iters_per_epoch) + ys.append(np.array(log_dict[epoch][metric][:len(iters)])) + xs = np.concatenate(xs) + ys = np.concatenate(ys) + plt.xlabel('iter') + plt.plot( + xs, ys, label=legend[i * num_metrics + j], linewidth=0.5) + plt.legend() + if args.title is not None: + plt.title(args.title) + if args.out is None: + plt.show() + else: + print(f'save curve to: {args.out}') + plt.savefig(args.out) + plt.cla() + + +def add_plot_parser(subparsers): + parser_plt = subparsers.add_parser( + 'plot_curve', help='parser for plotting curves') + parser_plt.add_argument( + 'json_logs', + type=str, + nargs='+', + help='path of train log in json format') + parser_plt.add_argument( + '--keys', + type=str, + nargs='+', + default=['bbox_mAP'], + help='the metric that you want to plot') + parser_plt.add_argument('--title', type=str, help='title of figure') + parser_plt.add_argument( + '--legend', + type=str, + nargs='+', + default=None, + help='legend of each plot') + parser_plt.add_argument( + '--backend', type=str, default=None, help='backend of plt') + parser_plt.add_argument( + '--style', type=str, default='dark', help='style of plt') + parser_plt.add_argument('--out', type=str, default=None) + + +def add_time_parser(subparsers): + parser_time = subparsers.add_parser( + 'cal_train_time', + help='parser for computing the average time per training iteration') + parser_time.add_argument( + 'json_logs', + type=str, + nargs='+', + help='path of train log in json format') + parser_time.add_argument( + '--include-outliers', + action='store_true', + help='include the first value of every epoch when computing ' + 'the average time') + + +def parse_args(): + parser = argparse.ArgumentParser(description='Analyze Json Log') + # currently only support plot curve and calculate average train time + subparsers = parser.add_subparsers(dest='task', help='task parser') + add_plot_parser(subparsers) + add_time_parser(subparsers) + args = parser.parse_args() + return args + + +def load_json_logs(json_logs): + # load and convert json_logs to log_dict, key is epoch, value is a sub dict + # keys of sub dict is different metrics, e.g. memory, bbox_mAP + # value of sub dict is a list of corresponding values of all iterations + log_dicts = [dict() for _ in json_logs] + for json_log, log_dict in zip(json_logs, log_dicts): + with open(json_log, 'r') as log_file: + for line in log_file: + log = json.loads(line.strip()) + # skip lines without `epoch` field + if 'epoch' not in log: + continue + epoch = log.pop('epoch') + if epoch not in log_dict: + log_dict[epoch] = defaultdict(list) + for k, v in log.items(): + log_dict[epoch][k].append(v) + return log_dicts + + +def main(): + args = parse_args() + + json_logs = args.json_logs + for json_log in json_logs: + assert json_log.endswith('.json') + + log_dicts = load_json_logs(json_logs) + + eval(args.task)(log_dicts, args) + + +if __name__ == '__main__': + main() diff --git a/tools/analysis_tools/analyze_results.py b/tools/analysis_tools/analyze_results.py new file mode 100644 index 0000000..fc6b4d9 --- /dev/null +++ b/tools/analysis_tools/analyze_results.py @@ -0,0 +1,202 @@ +import argparse +import os.path as osp + +import mmcv +import numpy as np +from mmcv import Config, DictAction + +from mmdet.core.evaluation import eval_map +from mmdet.core.visualization import imshow_gt_det_bboxes +from mmdet.datasets import build_dataset, get_loading_pipeline + + +def bbox_map_eval(det_result, annotation): + """Evaluate mAP of single image det result. + + Args: + det_result (list[list]): [[cls1_det, cls2_det, ...], ...]. + The outer list indicates images, and the inner list indicates + per-class detected bboxes. + annotation (dict): Ground truth annotations where keys of + annotations are: + + - bboxes: numpy array of shape (n, 4) + - labels: numpy array of shape (n, ) + - bboxes_ignore (optional): numpy array of shape (k, 4) + - labels_ignore (optional): numpy array of shape (k, ) + + Returns: + float: mAP + """ + + # use only bbox det result + if isinstance(det_result, tuple): + bbox_det_result = [det_result[0]] + else: + bbox_det_result = [det_result] + # mAP + iou_thrs = np.linspace( + .5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True) + mean_aps = [] + for thr in iou_thrs: + mean_ap, _ = eval_map( + bbox_det_result, [annotation], iou_thr=thr, logger='silent') + mean_aps.append(mean_ap) + return sum(mean_aps) / len(mean_aps) + + +class ResultVisualizer(object): + """Display and save evaluation results. + + Args: + show (bool): Whether to show the image. Default: True + wait_time (float): Value of waitKey param. Default: 0. + score_thr (float): Minimum score of bboxes to be shown. + Default: 0 + """ + + def __init__(self, show=False, wait_time=0, score_thr=0): + self.show = show + self.wait_time = wait_time + self.score_thr = score_thr + + def _save_image_gts_results(self, dataset, results, mAPs, out_dir=None): + mmcv.mkdir_or_exist(out_dir) + + for mAP_info in mAPs: + index, mAP = mAP_info + data_info = dataset.prepare_train_img(index) + + # calc save file path + filename = data_info['filename'] + if data_info['img_prefix'] is not None: + filename = osp.join(data_info['img_prefix'], filename) + else: + filename = data_info['filename'] + fname, name = osp.splitext(osp.basename(filename)) + save_filename = fname + '_' + str(round(mAP, 3)) + name + out_file = osp.join(out_dir, save_filename) + imshow_gt_det_bboxes( + data_info['img'], + data_info, + results[index], + dataset.CLASSES, + show=self.show, + score_thr=self.score_thr, + wait_time=self.wait_time, + out_file=out_file) + + def evaluate_and_show(self, + dataset, + results, + topk=20, + show_dir='work_dir', + eval_fn=None): + """Evaluate and show results. + + Args: + dataset (Dataset): A PyTorch dataset. + results (list): Det results from test results pkl file + topk (int): Number of the highest topk and + lowest topk after evaluation index sorting. Default: 20 + show_dir (str, optional): The filename to write the image. + Default: 'work_dir' + eval_fn (callable, optional): Eval function, Default: None + """ + + assert topk > 0 + if (topk * 2) > len(dataset): + topk = len(dataset) // 2 + + if eval_fn is None: + eval_fn = bbox_map_eval + else: + assert callable(eval_fn) + + prog_bar = mmcv.ProgressBar(len(results)) + _mAPs = {} + for i, (result, ) in enumerate(zip(results)): + # self.dataset[i] should not call directly + # because there is a risk of mismatch + data_info = dataset.prepare_train_img(i) + mAP = eval_fn(result, data_info['ann_info']) + _mAPs[i] = mAP + prog_bar.update() + + # descending select topk image + _mAPs = list(sorted(_mAPs.items(), key=lambda kv: kv[1])) + good_mAPs = _mAPs[-topk:] + bad_mAPs = _mAPs[:topk] + + good_dir = osp.abspath(osp.join(show_dir, 'good')) + bad_dir = osp.abspath(osp.join(show_dir, 'bad')) + self._save_image_gts_results(dataset, results, good_mAPs, good_dir) + self._save_image_gts_results(dataset, results, bad_mAPs, bad_dir) + + +def parse_args(): + parser = argparse.ArgumentParser( + description='MMDet eval image prediction result for each') + parser.add_argument('config', help='test config file path') + parser.add_argument( + 'prediction_path', help='prediction path where test pkl result') + parser.add_argument( + 'show_dir', help='directory where painted images will be saved') + parser.add_argument('--show', action='store_true', help='show results') + parser.add_argument( + '--wait-time', + type=float, + default=0, + help='the interval of show (s), 0 is block') + parser.add_argument( + '--topk', + default=20, + type=int, + help='saved Number of the highest topk ' + 'and lowest topk after index sorting') + parser.add_argument( + '--show-score-thr', + type=float, + default=0, + help='score threshold (default: 0.)') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + + mmcv.check_file_exist(args.prediction_path) + + cfg = Config.fromfile(args.config) + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + cfg.data.test.test_mode = True + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) + + cfg.data.test.pop('samples_per_gpu', 0) + cfg.data.test.pipeline = get_loading_pipeline(cfg.data.train.pipeline) + dataset = build_dataset(cfg.data.test) + outputs = mmcv.load(args.prediction_path) + + result_visualizer = ResultVisualizer(args.show, args.wait_time, + args.show_score_thr) + result_visualizer.evaluate_and_show( + dataset, outputs, topk=args.topk, show_dir=args.show_dir) + + +if __name__ == '__main__': + main() diff --git a/tools/analysis_tools/benchmark.py b/tools/analysis_tools/benchmark.py new file mode 100644 index 0000000..76ecc3a --- /dev/null +++ b/tools/analysis_tools/benchmark.py @@ -0,0 +1,113 @@ +import argparse +import time + +import torch +from mmcv import Config, DictAction +from mmcv.cnn import fuse_conv_bn +from mmcv.parallel import MMDataParallel +from mmcv.runner import load_checkpoint, wrap_fp16_model + +from mmdet.datasets import (build_dataloader, build_dataset, + replace_ImageToTensor) +from mmdet.models import build_detector + + +def parse_args(): + parser = argparse.ArgumentParser(description='MMDet benchmark a model') + parser.add_argument('config', help='test config file path') + parser.add_argument('checkpoint', help='checkpoint file') + parser.add_argument( + '--log-interval', default=50, help='interval of logging') + parser.add_argument( + '--fuse-conv-bn', + action='store_true', + help='Whether to fuse conv and bn, this will slightly increase' + 'the inference speed') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + + cfg = Config.fromfile(args.config) + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) + # set cudnn_benchmark + if cfg.get('cudnn_benchmark', False): + torch.backends.cudnn.benchmark = True + cfg.model.pretrained = None + cfg.data.test.test_mode = True + + # build the dataloader + samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1) + if samples_per_gpu > 1: + # Replace 'ImageToTensor' to 'DefaultFormatBundle' + cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline) + dataset = build_dataset(cfg.data.test) + data_loader = build_dataloader( + dataset, + samples_per_gpu=1, + workers_per_gpu=cfg.data.workers_per_gpu, + dist=False, + shuffle=False) + + # build the model and load checkpoint + cfg.model.train_cfg = None + model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg')) + fp16_cfg = cfg.get('fp16', None) + if fp16_cfg is not None: + wrap_fp16_model(model) + load_checkpoint(model, args.checkpoint, map_location='cpu') + if args.fuse_conv_bn: + model = fuse_conv_bn(model) + + model = MMDataParallel(model, device_ids=[0]) + + model.eval() + + # the first several iterations may be very slow so skip them + num_warmup = 5 + pure_inf_time = 0 + + # benchmark with 2000 image and take the average + for i, data in enumerate(data_loader): + + torch.cuda.synchronize() + start_time = time.perf_counter() + + with torch.no_grad(): + model(return_loss=False, rescale=True, **data) + + torch.cuda.synchronize() + elapsed = time.perf_counter() - start_time + + if i >= num_warmup: + pure_inf_time += elapsed + if (i + 1) % args.log_interval == 0: + fps = (i + 1 - num_warmup) / pure_inf_time + print(f'Done image [{i + 1:<3}/ 2000], fps: {fps:.1f} img / s') + + if (i + 1) == 2000: + pure_inf_time += elapsed + fps = (i + 1 - num_warmup) / pure_inf_time + print(f'Overall fps: {fps:.1f} img / s') + break + + +if __name__ == '__main__': + main() diff --git a/tools/analysis_tools/coco_error_analysis.py b/tools/analysis_tools/coco_error_analysis.py new file mode 100644 index 0000000..722efe6 --- /dev/null +++ b/tools/analysis_tools/coco_error_analysis.py @@ -0,0 +1,338 @@ +import copy +import os +from argparse import ArgumentParser +from multiprocessing import Pool + +import matplotlib.pyplot as plt +import numpy as np +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval + + +def makeplot(rs, ps, outDir, class_name, iou_type): + cs = np.vstack([ + np.ones((2, 3)), + np.array([0.31, 0.51, 0.74]), + np.array([0.75, 0.31, 0.30]), + np.array([0.36, 0.90, 0.38]), + np.array([0.50, 0.39, 0.64]), + np.array([1, 0.6, 0]), + ]) + areaNames = ['allarea', 'small', 'medium', 'large'] + types = ['C75', 'C50', 'Loc', 'Sim', 'Oth', 'BG', 'FN'] + for i in range(len(areaNames)): + area_ps = ps[..., i, 0] + figure_title = iou_type + '-' + class_name + '-' + areaNames[i] + aps = [ps_.mean() for ps_ in area_ps] + ps_curve = [ + ps_.mean(axis=1) if ps_.ndim > 1 else ps_ for ps_ in area_ps + ] + ps_curve.insert(0, np.zeros(ps_curve[0].shape)) + fig = plt.figure() + ax = plt.subplot(111) + for k in range(len(types)): + ax.plot(rs, ps_curve[k + 1], color=[0, 0, 0], linewidth=0.5) + ax.fill_between( + rs, + ps_curve[k], + ps_curve[k + 1], + color=cs[k], + label=str(f'[{aps[k]:.3f}]' + types[k]), + ) + plt.xlabel('recall') + plt.ylabel('precision') + plt.xlim(0, 1.0) + plt.ylim(0, 1.0) + plt.title(figure_title) + plt.legend() + # plt.show() + fig.savefig(outDir + f'/{figure_title}.png') + plt.close(fig) + + +def autolabel(ax, rects): + """Attach a text label above each bar in *rects*, displaying its height.""" + for rect in rects: + height = rect.get_height() + if height > 0 and height <= 1: # for percent values + text_label = '{:2.0f}'.format(height * 100) + else: + text_label = '{:2.0f}'.format(height) + ax.annotate( + text_label, + xy=(rect.get_x() + rect.get_width() / 2, height), + xytext=(0, 3), # 3 points vertical offset + textcoords='offset points', + ha='center', + va='bottom', + fontsize='x-small', + ) + + +def makebarplot(rs, ps, outDir, class_name, iou_type): + areaNames = ['allarea', 'small', 'medium', 'large'] + types = ['C75', 'C50', 'Loc', 'Sim', 'Oth', 'BG', 'FN'] + fig, ax = plt.subplots() + x = np.arange(len(areaNames)) # the areaNames locations + width = 0.60 # the width of the bars + rects_list = [] + figure_title = iou_type + '-' + class_name + '-' + 'ap bar plot' + for i in range(len(types) - 1): + type_ps = ps[i, ..., 0] + aps = [ps_.mean() for ps_ in type_ps.T] + rects_list.append( + ax.bar( + x - width / 2 + (i + 1) * width / len(types), + aps, + width / len(types), + label=types[i], + )) + + # Add some text for labels, title and custom x-axis tick labels, etc. + ax.set_ylabel('Mean Average Precision (mAP)') + ax.set_title(figure_title) + ax.set_xticks(x) + ax.set_xticklabels(areaNames) + ax.legend() + + # Add score texts over bars + for rects in rects_list: + autolabel(ax, rects) + + # Save plot + fig.savefig(outDir + f'/{figure_title}.png') + plt.close(fig) + + +def get_gt_area_group_numbers(cocoEval): + areaRng = cocoEval.params.areaRng + areaRngStr = [str(aRng) for aRng in areaRng] + areaRngLbl = cocoEval.params.areaRngLbl + areaRngStr2areaRngLbl = dict(zip(areaRngStr, areaRngLbl)) + areaRngLbl2Number = dict.fromkeys(areaRngLbl, 0) + for evalImg in cocoEval.evalImgs: + if evalImg: + for gtIgnore in evalImg['gtIgnore']: + if not gtIgnore: + aRngLbl = areaRngStr2areaRngLbl[str(evalImg['aRng'])] + areaRngLbl2Number[aRngLbl] += 1 + return areaRngLbl2Number + + +def make_gt_area_group_numbers_plot(cocoEval, outDir, verbose=True): + areaRngLbl2Number = get_gt_area_group_numbers(cocoEval) + areaRngLbl = areaRngLbl2Number.keys() + if verbose: + print('number of annotations per area group:', areaRngLbl2Number) + + # Init figure + fig, ax = plt.subplots() + x = np.arange(len(areaRngLbl)) # the areaNames locations + width = 0.60 # the width of the bars + figure_title = 'number of annotations per area group' + + rects = ax.bar(x, areaRngLbl2Number.values(), width) + + # Add some text for labels, title and custom x-axis tick labels, etc. + ax.set_ylabel('Number of annotations') + ax.set_title(figure_title) + ax.set_xticks(x) + ax.set_xticklabels(areaRngLbl) + + # Add score texts over bars + autolabel(ax, rects) + + # Save plot + fig.tight_layout() + fig.savefig(outDir + f'/{figure_title}.png') + plt.close(fig) + + +def make_gt_area_histogram_plot(cocoEval, outDir): + n_bins = 100 + areas = [ann['area'] for ann in cocoEval.cocoGt.anns.values()] + + # init figure + figure_title = 'gt annotation areas histogram plot' + fig, ax = plt.subplots() + + # Set the number of bins + ax.hist(np.sqrt(areas), bins=n_bins) + + # Add some text for labels, title and custom x-axis tick labels, etc. + ax.set_xlabel('Squareroot Area') + ax.set_ylabel('Number of annotations') + ax.set_title(figure_title) + + # Save plot + fig.tight_layout() + fig.savefig(outDir + f'/{figure_title}.png') + plt.close(fig) + + +def analyze_individual_category(k, + cocoDt, + cocoGt, + catId, + iou_type, + areas=None): + nm = cocoGt.loadCats(catId)[0] + print(f'--------------analyzing {k + 1}-{nm["name"]}---------------') + ps_ = {} + dt = copy.deepcopy(cocoDt) + nm = cocoGt.loadCats(catId)[0] + imgIds = cocoGt.getImgIds() + dt_anns = dt.dataset['annotations'] + select_dt_anns = [] + for ann in dt_anns: + if ann['category_id'] == catId: + select_dt_anns.append(ann) + dt.dataset['annotations'] = select_dt_anns + dt.createIndex() + # compute precision but ignore superclass confusion + gt = copy.deepcopy(cocoGt) + child_catIds = gt.getCatIds(supNms=[nm['supercategory']]) + for idx, ann in enumerate(gt.dataset['annotations']): + if ann['category_id'] in child_catIds and ann['category_id'] != catId: + gt.dataset['annotations'][idx]['ignore'] = 1 + gt.dataset['annotations'][idx]['iscrowd'] = 1 + gt.dataset['annotations'][idx]['category_id'] = catId + cocoEval = COCOeval(gt, copy.deepcopy(dt), iou_type) + cocoEval.params.imgIds = imgIds + cocoEval.params.maxDets = [100] + cocoEval.params.iouThrs = [0.1] + cocoEval.params.useCats = 1 + if areas: + cocoEval.params.areaRng = [[0**2, areas[2]], [0**2, areas[0]], + [areas[0], areas[1]], [areas[1], areas[2]]] + cocoEval.evaluate() + cocoEval.accumulate() + ps_supercategory = cocoEval.eval['precision'][0, :, k, :, :] + ps_['ps_supercategory'] = ps_supercategory + # compute precision but ignore any class confusion + gt = copy.deepcopy(cocoGt) + for idx, ann in enumerate(gt.dataset['annotations']): + if ann['category_id'] != catId: + gt.dataset['annotations'][idx]['ignore'] = 1 + gt.dataset['annotations'][idx]['iscrowd'] = 1 + gt.dataset['annotations'][idx]['category_id'] = catId + cocoEval = COCOeval(gt, copy.deepcopy(dt), iou_type) + cocoEval.params.imgIds = imgIds + cocoEval.params.maxDets = [100] + cocoEval.params.iouThrs = [0.1] + cocoEval.params.useCats = 1 + if areas: + cocoEval.params.areaRng = [[0**2, areas[2]], [0**2, areas[0]], + [areas[0], areas[1]], [areas[1], areas[2]]] + cocoEval.evaluate() + cocoEval.accumulate() + ps_allcategory = cocoEval.eval['precision'][0, :, k, :, :] + ps_['ps_allcategory'] = ps_allcategory + return k, ps_ + + +def analyze_results(res_file, + ann_file, + res_types, + out_dir, + extraplots=None, + areas=None): + for res_type in res_types: + assert res_type in ['bbox', 'segm'] + if areas: + assert len(areas) == 3, '3 integers should be specified as areas, \ + representing 3 area regions' + + directory = os.path.dirname(out_dir + '/') + if not os.path.exists(directory): + print(f'-------------create {out_dir}-----------------') + os.makedirs(directory) + + cocoGt = COCO(ann_file) + cocoDt = cocoGt.loadRes(res_file) + imgIds = cocoGt.getImgIds() + for res_type in res_types: + res_out_dir = out_dir + '/' + res_type + '/' + res_directory = os.path.dirname(res_out_dir) + if not os.path.exists(res_directory): + print(f'-------------create {res_out_dir}-----------------') + os.makedirs(res_directory) + iou_type = res_type + cocoEval = COCOeval( + copy.deepcopy(cocoGt), copy.deepcopy(cocoDt), iou_type) + cocoEval.params.imgIds = imgIds + cocoEval.params.iouThrs = [0.75, 0.5, 0.1] + cocoEval.params.maxDets = [100] + if areas: + cocoEval.params.areaRng = [[0**2, areas[2]], [0**2, areas[0]], + [areas[0], areas[1]], + [areas[1], areas[2]]] + cocoEval.evaluate() + cocoEval.accumulate() + ps = cocoEval.eval['precision'] + ps = np.vstack([ps, np.zeros((4, *ps.shape[1:]))]) + catIds = cocoGt.getCatIds() + recThrs = cocoEval.params.recThrs + with Pool(processes=48) as pool: + args = [(k, cocoDt, cocoGt, catId, iou_type, areas) + for k, catId in enumerate(catIds)] + analyze_results = pool.starmap(analyze_individual_category, args) + for k, catId in enumerate(catIds): + nm = cocoGt.loadCats(catId)[0] + print(f'--------------saving {k + 1}-{nm["name"]}---------------') + analyze_result = analyze_results[k] + assert k == analyze_result[0] + ps_supercategory = analyze_result[1]['ps_supercategory'] + ps_allcategory = analyze_result[1]['ps_allcategory'] + # compute precision but ignore superclass confusion + ps[3, :, k, :, :] = ps_supercategory + # compute precision but ignore any class confusion + ps[4, :, k, :, :] = ps_allcategory + # fill in background and false negative errors and plot + ps[ps == -1] = 0 + ps[5, :, k, :, :] = ps[4, :, k, :, :] > 0 + ps[6, :, k, :, :] = 1.0 + makeplot(recThrs, ps[:, :, k], res_out_dir, nm['name'], iou_type) + if extraplots: + makebarplot(recThrs, ps[:, :, k], res_out_dir, nm['name'], + iou_type) + makeplot(recThrs, ps, res_out_dir, 'allclass', iou_type) + if extraplots: + makebarplot(recThrs, ps, res_out_dir, 'allclass', iou_type) + make_gt_area_group_numbers_plot( + cocoEval=cocoEval, outDir=res_out_dir, verbose=True) + make_gt_area_histogram_plot(cocoEval=cocoEval, outDir=res_out_dir) + + +def main(): + parser = ArgumentParser(description='COCO Error Analysis Tool') + parser.add_argument('result', help='result file (json format) path') + parser.add_argument('out_dir', help='dir to save analyze result images') + parser.add_argument( + '--ann', + default='data/coco/annotations/instances_val2017.json', + help='annotation file path') + parser.add_argument( + '--types', type=str, nargs='+', default=['bbox'], help='result types') + parser.add_argument( + '--extraplots', + action='store_true', + help='export extra bar/stat plots') + parser.add_argument( + '--areas', + type=int, + nargs='+', + default=[1024, 9216, 10000000000], + help='area regions') + args = parser.parse_args() + analyze_results( + args.result, + args.ann, + args.types, + out_dir=args.out_dir, + extraplots=args.extraplots, + areas=args.areas) + + +if __name__ == '__main__': + main() diff --git a/tools/analysis_tools/ensemble.py b/tools/analysis_tools/ensemble.py new file mode 100644 index 0000000..883b746 --- /dev/null +++ b/tools/analysis_tools/ensemble.py @@ -0,0 +1,125 @@ +import argparse + +import mmcv +from mmcv import Config, DictAction + +from mmdet.datasets import build_dataset +import numpy as np +from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, build_assigner, + build_sampler, merge_aug_bboxes, merge_aug_masks, + multiclass_nms) +from mmcv.ops.nms import batched_nms +import torch, torchvision + + +def parse_args(): + parser = argparse.ArgumentParser(description='Evaluate metric of the ' + 'results saved in pkl format') + parser.add_argument('config', help='Config of the model') + parser.add_argument('--pkls', nargs="+", default=["a", "b"], help='Results in pickle format') + parser.add_argument( + '--format-only', + action='store_true', + help='Format the output results without perform evaluation. It is' + 'useful when you want to format the result to a specific format and ' + 'submit it to the test server') + parser.add_argument( + '--eval', + type=str, + nargs='+', + help='Evaluation metrics, which depends on the dataset, e.g., "bbox",' + ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + parser.add_argument( + '--eval-options', + nargs='+', + action=DictAction, + help='custom options for evaluation, the key-value pair in xxx=yyy ' + 'format will be kwargs for dataset.evaluate() function') + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + + cfg = Config.fromfile(args.config) + assert args.eval or args.format_only, ( + 'Please specify at least one operation (eval/format the results) with ' + 'the argument "--eval", "--format-only"') + if args.eval and args.format_only: + raise ValueError('--eval and --format_only cannot be both specified') + + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) + cfg.data.test.test_mode = True + + dataset = build_dataset(cfg.data.test) + + pkls = args.pkls + outputs_list = [] + for pkl in pkls: + outputs_list.append(mmcv.load(pkl)) + + kwargs = {} if args.eval_options is None else args.eval_options + if args.format_only: + raise NotImplementedError + dataset.format_results(outputs, **kwargs) + if args.eval: + eval_kwargs = cfg.get('evaluation', {}).copy() + # hard-code way to remove EvalHook args + for key in [ + 'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best', + 'rule' + ]: + eval_kwargs.pop(key, None) + eval_kwargs.update(dict(metric=args.eval, **kwargs)) + + outputs = ensemble(outputs_list) + + print(dataset.evaluate(outputs, **eval_kwargs)) + + +def ensemble(outs): + img_num = len(outs[0]) + out_num = len(outs) + final = [] + base = outs[0] + other = outs[1:] + # collect all to base + for out in other: + for imgid, img in enumerate(out): + for clsid, cls in enumerate(img): + base[imgid][clsid] = np.concatenate((base[imgid][clsid], cls), axis=0) + + nms_cfg = {'type': 'nms', 'iou_threshold': 0.7} + + for imgid, img in enumerate(base): + img_new = [] + for cls in img: + boxes = torch.Tensor(cls[:, 0:4]).contiguous() + scores = torch.Tensor(cls[:, 4]).contiguous() + idxs = torch.Tensor([1]*len(scores)).contiguous() + dets, keep = batched_nms(boxes, scores, idxs, nms_cfg, class_agnostic=True) + + img_new.append(dets.numpy()) + final.append(img_new) + + + return final + +if __name__ == '__main__': + main() diff --git a/tools/analysis_tools/eval_metric.py b/tools/analysis_tools/eval_metric.py new file mode 100644 index 0000000..5732719 --- /dev/null +++ b/tools/analysis_tools/eval_metric.py @@ -0,0 +1,83 @@ +import argparse + +import mmcv +from mmcv import Config, DictAction + +from mmdet.datasets import build_dataset + + +def parse_args(): + parser = argparse.ArgumentParser(description='Evaluate metric of the ' + 'results saved in pkl format') + parser.add_argument('config', help='Config of the model') + parser.add_argument('pkl_results', help='Results in pickle format') + parser.add_argument( + '--format-only', + action='store_true', + help='Format the output results without perform evaluation. It is' + 'useful when you want to format the result to a specific format and ' + 'submit it to the test server') + parser.add_argument( + '--eval', + type=str, + nargs='+', + help='Evaluation metrics, which depends on the dataset, e.g., "bbox",' + ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + parser.add_argument( + '--eval-options', + nargs='+', + action=DictAction, + help='custom options for evaluation, the key-value pair in xxx=yyy ' + 'format will be kwargs for dataset.evaluate() function') + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + + cfg = Config.fromfile(args.config) + assert args.eval or args.format_only, ( + 'Please specify at least one operation (eval/format the results) with ' + 'the argument "--eval", "--format-only"') + if args.eval and args.format_only: + raise ValueError('--eval and --format_only cannot be both specified') + + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) + cfg.data.test.test_mode = True + + dataset = build_dataset(cfg.data.test) + outputs = mmcv.load(args.pkl_results) + + kwargs = {} if args.eval_options is None else args.eval_options + if args.format_only: + dataset.format_results(outputs, **kwargs) + if args.eval: + eval_kwargs = cfg.get('evaluation', {}).copy() + # hard-code way to remove EvalHook args + for key in [ + 'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best', + 'rule' + ]: + eval_kwargs.pop(key, None) + eval_kwargs.update(dict(metric=args.eval, **kwargs)) + print(dataset.evaluate(outputs, **eval_kwargs)) + + +if __name__ == '__main__': + main() diff --git a/tools/analysis_tools/get_flops.py b/tools/analysis_tools/get_flops.py new file mode 100644 index 0000000..0bf682e --- /dev/null +++ b/tools/analysis_tools/get_flops.py @@ -0,0 +1,88 @@ +import argparse + +import torch +from mmcv import Config, DictAction + +from mmdet.models import build_detector + +try: + from mmcv.cnn import get_model_complexity_info +except ImportError: + raise ImportError('Please upgrade mmcv to >0.6.2') + + +def parse_args(): + parser = argparse.ArgumentParser(description='Train a detector') + parser.add_argument('config', help='train config file path') + parser.add_argument( + '--shape', + type=int, + nargs='+', + default=[1280, 800], + help='input image size') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + args = parser.parse_args() + return args + + +def main(): + + args = parse_args() + + if len(args.shape) == 1: + input_shape = (3, args.shape[0], args.shape[0]) + elif len(args.shape) == 2: + input_shape = (3, ) + tuple(args.shape) + else: + raise ValueError('invalid input shape') + + cfg = Config.fromfile(args.config) + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) + + model = build_detector( + cfg.model, + train_cfg=cfg.get('train_cfg'), + test_cfg=cfg.get('test_cfg')) + if torch.cuda.is_available(): + model.cuda() + model.eval() + torch.save(model.state_dict(), 'demo/last_model.pth') + + if hasattr(model, 'forward_dummy'): + model.forward = model.forward_dummy + else: + raise NotImplementedError( + 'FLOPs counter is currently not currently supported with {}'. + format(model.__class__.__name__)) + with open('flops.txt', 'w') as f: + import io + stream_str = io.StringIO() + flops, params = get_model_complexity_info( + model, input_shape, ost=stream_str) + text = stream_str.getvalue() + f.write(text) + + split_line = '=' * 30 + print(f'{split_line}\nInput shape: {input_shape}\n' + f'Flops: {flops}\nParams: {params}\n{split_line}') + print('!!!Please be cautious if you use the results in papers. ' + 'You may need to check if all ops are supported and verify that the ' + 'flops computation is correct.') + + +if __name__ == '__main__': + main() diff --git a/tools/analysis_tools/map_factory.py b/tools/analysis_tools/map_factory.py new file mode 100644 index 0000000..e31964a --- /dev/null +++ b/tools/analysis_tools/map_factory.py @@ -0,0 +1,710 @@ +import argparse + +import mmcv, copy +from mmcv import Config, DictAction + +from mmdet.datasets import build_dataset +import numpy as np +from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, build_assigner, + build_sampler, merge_aug_bboxes, merge_aug_masks, + multiclass_nms) +from mmcv.ops.nms import batched_nms +import torch, torchvision +from mmdet.datasets.api_wrappers import COCOeval +import pickle5 + +def parse_args(): + parser = argparse.ArgumentParser(description='Evaluate metric of the ' + 'results saved in pkl format') + parser.add_argument('config', help='Config of the model') + parser.add_argument('--pkls', nargs="+", default=["a", "b"], help='Results in pickle format') + parser.add_argument( + '--format-only', + action='store_true', + help='Format the output results without perform evaluation. It is' + 'useful when you want to format the result to a specific format and ' + 'submit it to the test server') + parser.add_argument( + '--eval', + type=str, + nargs='+', + help='Evaluation metrics, which depends on the dataset, e.g., "bbox",' + ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + parser.add_argument( + '--eval-options', + nargs='+', + action=DictAction, + help='custom options for evaluation, the key-value pair in xxx=yyy ' + 'format will be kwargs for dataset.evaluate() function') + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + + cfg = Config.fromfile(args.config) + assert args.eval or args.format_only, ( + 'Please specify at least one operation (eval/format the results) with ' + 'the argument "--eval", "--format-only"') + if args.eval and args.format_only: + raise ValueError('--eval and --format_only cannot be both specified') + + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) + cfg.data.test.test_mode = True + + dataset = build_dataset(cfg.data.test) + + #pkl = mmcv.load(args.pkls[0]) + with open(args.pkls[0], "rb") as f: + pkl = pickle5.load(f) + + outs5 = postproc_deform(pkl, 5, 1) + outs4 = postproc_deform(pkl, 4, 1) + outs3 = postproc_deform(pkl, 3, 1) + outs2 = postproc_deform(pkl, 2, 1) + outs1 = postproc_deform(pkl, 1, 1) + outs0 = postproc_deform(pkl, 0, 1) + + # outs = organize(pkl) + #outs5 = postproc(pkl, 5, 1) + #outs4 = postproc(pkl, 4, 1) + #outs3 = postproc(pkl, 3,1) + #outs2 = postproc(pkl, 2,1) + #outs1 = postproc(pkl, 1,1) + #outs0 = postproc(pkl, 0,1) + + import numpy as np + + for iou_thrs in list(np.arange(0.5, 1, 0.05)): + try: + cocoEval5 = preeval(base=outs5, dataset=dataset, iou_thrs=iou_thrs) + cocoEval4 = preeval(base=outs4, dataset=dataset, iou_thrs=iou_thrs) + cocoEval3 = preeval(base=outs3, dataset=dataset, iou_thrs=iou_thrs) + cocoEval2 = preeval(base=outs2, dataset=dataset, iou_thrs=iou_thrs) + cocoEval1 = preeval(base=outs1, dataset=dataset, iou_thrs=iou_thrs) + cocoEval0 = preeval(base=outs0, dataset=dataset, iou_thrs=iou_thrs) + + #cocoEval5.evaluate() + #inter_r2 = cocoEval5.evalImgs + #cocoEval5.accumulate() + #cocoEval5.summarize() + + cocoEval5.evalImgs = fangyi(base=cocoEval5, supts=[cocoEval4, + cocoEval3, + cocoEval2, + cocoEval1, + cocoEval0, + ]) + + cocoEval5._paramsEval = copy.deepcopy(cocoEval5.params) + #cocoEval5.eval = motivation_verification(evalImgs5, + # [evalImgs5], + # cocoEval5.params) + cocoEval5.accumulate() + cocoEval5.summarize() + except: + 1 + + +def fangyi(base, supts): + # FP, FN, TP, TN = 0, grief1, grief2 = 0, sorrow = 0 + # for an image i, + # for a query q, stage6 prediction is q6 + # if q6 is FP: FP += 1 + # check if q1-5 exists TP or FP with lower score, if true, grief1+=1, + # if q6 is TP: TP += 1 + # check if q1-5 exists same TP but with higher score, if true, sorrow+=1 + # for a gt + # if gt is FN: FN += 1 + # check if q1-5 exists TP, if true, grief2+=1, + # + # sorrow rate = sorrow / TP: among all TP, how many of them could have a higher confident score + # the higher, the worse + # grief rate = grief / (FP + FN): among all mistakes that q6 predicts, so many of them could have a correct detection + # the higher, the worse + # if q6 is a TN: + # check if q1-5 exists TN and score < q6, if true, sorrow+=1 + + p = base.params + p.imgIds = list(np.unique(p.imgIds)) + if p.useCats: + p.catIds = list(np.unique(p.catIds)) + p.maxDets = sorted(p.maxDets) + + base.params = p + base._prepare() + # loop through images, area range, max detection number + base.ious = {(imgId, catId): base.computeIoU(imgId, catId) \ + for imgId in p.imgIds + for catId in p.catIds} + for supt in supts: + supt.params = p + supt._prepare() + supt.ious = {(imgId, catId): supt.computeIoU(imgId, catId) \ + for imgId in p.imgIds + for catId in p.catIds} + + maxDet = p.maxDets[-1] + aRng = p.areaRng[0] + + res = [] + FP, FN, TP, TN, grief, sorrow, all_pred, all_gt, grief1, grief2 = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 + FP2, FN2, TP2, TN2, = 0, 0, 0, 0 + + for catId in p.catIds: + for imgId in p.imgIds: + gt = base._gts[imgId, catId] + dt = base._dts[imgId, catId] + if len(gt) == 0 and len(dt) == 0: + res.append(None) + continue + + for g in gt: + if g['ignore'] or (g['area'] < aRng[0] or g['area'] > aRng[1]): + g['_ignore'] = 1 + else: + g['_ignore'] = 0 + + # sort dt highest score first, sort gt ignore last + gtind = np.argsort([g['_ignore'] for g in gt], kind='mergesort') + gt = [gt[i] for i in gtind] + dtind = np.argsort([-d['score'] for d in dt], kind='mergesort') + dt = [dt[i] for i in dtind[0:maxDet]] + iscrowd = [int(o['iscrowd']) for o in gt] + # load computed ious + ious = base.ious[imgId, catId][:, gtind] if len(base.ious[imgId, catId]) > 0 else base.ious[ + imgId, catId] + + T = len(p.iouThrs) + G = len(gt) + D = len(dt) + + gtm = np.zeros((T, G)) # gtm positive number == TP number; gtm 0 number == FN number + dtm = np.zeros((T, D)) # dtm positive number == TP number; dtm 0 number == FP number + gtIg = np.array([g['_ignore'] for g in gt]) + dtIg = np.zeros((T, D)) + if not len(ious) == 0: + for tind, t in enumerate(p.iouThrs): + for dind, d in enumerate(dt): + # information about best match so far (m=-1 -> unmatched) + iou = min([t, 1 - 1e-10]) + m = -1 + for gind, g in enumerate(gt): + # if this gt already matched, and not a crowd, continue + if gtm[tind, gind] > 0 and not iscrowd[gind]: + continue + # if dt matched to reg gt, and on ignore gt, stop + if m > -1 and gtIg[m] == 0 and gtIg[gind] == 1: + break + # continue to next gt unless better match made + if ious[dind, gind] < iou: + continue + # if match successful and best so far, store appropriately + iou = ious[dind, gind] + m = gind + g_reserve = g + iou_reserve = iou + # if match made store id of match for both dt and gt + if m == -1: + FP2 += 1 + ckfp1, ckfp2 = check_supts_FP(imgId, catId, d, supts, t) + if ckfp1: + grief1 += 1 + grief += 1 + dt[dind]['score'] = ckfp2 + continue + dtIg[tind, dind] = gtIg[m] + if gtIg[m] == 0 and not (d['area'] < aRng[0] or d['area'] > aRng[1]): + TP2 += 1 + cktp1, cktp2, cktp3, cktp4 = check_supts_TP(imgId, catId, g_reserve, d, supts, t) + if cktp1: + sorrow += 1 + dt[dind]['bbox'] = cktp4['bbox'] + dt[dind]['area'] = cktp4['area'] + dt[dind]['score'] = cktp4['score'] + dt[dind]['segmentation'] = cktp4['segmentation'] + + dtm[tind, dind] = gt[m]['id'] + gtm[tind, m] = d['id'] + else: + for dind, d in enumerate(dt): + FP2 += 1 + ckfp1, ckfp2 = check_supts_FP(imgId, catId, d, supts, t) + if ckfp1: + grief1 += 1 + grief += 1 + dt[dind]['score'] = ckfp2 + + #if not len(ious) == 0: + for gind, g in enumerate(gt): + if gtm[tind, gind] == 0 and gtIg[gind] == 0: + FN2 += 1 + if check_supts_FN(imgId, catId, g, supts, t): + grief2 += 1 + grief += 1 + + # set unmatched detections outside of area range to ignore + a = np.array([d['area'] < aRng[0] or d['area'] > aRng[1] for d in dt]).reshape((1, len(dt))) + dtIg = np.logical_or(dtIg, np.logical_and(dtm == 0, np.repeat(a, T, 0))) + + res.append({ + 'image_id': imgId, + 'category_id': catId, + 'aRng': aRng, + 'maxDet': maxDet, + 'dtIds': [d['id'] for d in dt], + 'gtIds': [g['id'] for g in gt], + 'dtMatches': dtm, + 'gtMatches': gtm, + 'dtScores': [d['score'] for d in dt], + 'gtIgnore': gtIg, + 'dtIgnore': dtIg,}) + + tps = np.logical_and(dtm, np.logical_not(dtIg)) + fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg)) + npig = np.count_nonzero(gtIg == 0) # number of gt + fns = npig - tps.sum() + fns2 = np.logical_and(np.logical_not(gtm), np.logical_not(gtIg)) + assert fns == fns2.sum() + tps2 = np.logical_and(gtm, np.logical_not(gtIg)) + assert tps.sum() == tps2.sum() + TP += tps.sum() + FP += fps.sum() + all_gt += npig + all_pred = TP + FP + FN += fns + + print('TP, FP, all_gt, FN', TP, FP, all_gt, FN) + print('TP2:{}, FP2:{}, FN2:{}, sorrow:{}, sorrow rate:{}, grief1:{}, grief2:{}, grief:{}, grief1 rate{}, grief2 rate{} , grief rate{}'.format( + TP2, FP2, FN2, sorrow, sorrow/TP2, grief1, grief2, grief, grief1/FP2, grief2/FN2, grief/(FP2 + FN2))) + return res + + +def check_supts_FN(imgId, catId, g, supts, t): + gt_realid = g['id'] + p = supts[0].params + maxDet = p.maxDets[-1] + aRng = p.areaRng[0] + for supt in supts: + gt = supt._gts[imgId, catId] + dt = supt._dts[imgId, catId] + if len(gt) == 0 and len(dt) == 0: + continue + + for g in gt: + if g['ignore'] or (g['area'] < aRng[0] or g['area'] > aRng[1]): + g['_ignore'] = 1 + else: + g['_ignore'] = 0 + + # sort dt highest score first, sort gt ignore last + gtind = np.argsort([g['_ignore'] for g in gt], kind='mergesort') + gt = [gt[i] for i in gtind] + dtind = np.argsort([-d['score'] for d in dt], kind='mergesort') + dt = [dt[i] for i in dtind[0:maxDet]] + iscrowd = [int(o['iscrowd']) for o in gt] + # load computed ious + ious = supt.ious[imgId, catId][:, gtind] if len(supt.ious[imgId, catId]) > 0 else supt.ious[ + imgId, catId] + + T = len(p.iouThrs) + G = len(gt) + D = len(dt) + + gtm = np.zeros((T, G)) # gtm positive number == TP number; gtm 0 number == FN number + dtm = np.zeros((T, D)) # dtm positive number == TP number; dtm 0 number == FP number + gtIg = np.array([g['_ignore'] for g in gt]) + dtIg = np.zeros((T, D)) + if not len(ious) == 0: + for tind, t in enumerate(p.iouThrs): + for dind, d in enumerate(dt): + # information about best match so far (m=-1 -> unmatched) + iou = min([t, 1 - 1e-10]) + m = -1 + for gind, g in enumerate(gt): + # if this gt already matched, and not a crowd, continue + if gtm[tind, gind] > 0 and not iscrowd[gind]: + continue + # if dt matched to reg gt, and on ignore gt, stop + if m > -1 and gtIg[m] == 0 and gtIg[gind] == 1: + break + # continue to next gt unless better match made + if ious[dind, gind] < iou: + continue + # if match successful and best so far, store appropriately + iou = ious[dind, gind] + m = gind + g_reserve = g + # if match made store id of match for both dt and gt + if m == -1: + continue + dtIg[tind, dind] = gtIg[m] + if gtIg[m] == 0 and not (d['area'] < aRng[0] or d['area'] > aRng[1]): + # TP + if g_reserve['id'] == gt_realid: + if d['score'] > 0.1: + return True + dtm[tind, dind] = gt[m]['id'] + gtm[tind, m] = d['id'] + + return False + + +def check_supts_FP(imgId, catId, d, supts, t): + qry_id = d['fangyi_query'] + base_score = d['score'] + p = supts[0].params + maxDet = p.maxDets[-1] + aRng = p.areaRng[0] + FP_success = 0 + for supt in supts: + gt = supt._gts[imgId, catId] + dt = supt._dts[imgId, catId] + if len(gt) == 0 and len(dt) == 0: + continue + + for g in gt: + if g['ignore'] or (g['area'] < aRng[0] or g['area'] > aRng[1]): + g['_ignore'] = 1 + else: + g['_ignore'] = 0 + + # sort dt highest score first, sort gt ignore last + gtind = np.argsort([g['_ignore'] for g in gt], kind='mergesort') + gt = [gt[i] for i in gtind] + dtind = np.argsort([-d['score'] for d in dt], kind='mergesort') + dt = [dt[i] for i in dtind[0:maxDet]] + iscrowd = [int(o['iscrowd']) for o in gt] + # load computed ious + ious = supt.ious[imgId, catId][:, gtind] if len(supt.ious[imgId, catId]) > 0 else supt.ious[ + imgId, catId] + + T = len(p.iouThrs) + G = len(gt) + D = len(dt) + + gtm = np.zeros((T, G)) # gtm positive number == TP number; gtm 0 number == FN number + dtm = np.zeros((T, D)) # dtm positive number == TP number; dtm 0 number == FP number + gtIg = np.array([g['_ignore'] for g in gt]) + dtIg = np.zeros((T, D)) + if not len(ious) == 0: + for tind, t in enumerate(p.iouThrs): + for dind, d in enumerate(dt): + # information about best match so far (m=-1 -> unmatched) + iou = min([t, 1 - 1e-10]) + m = -1 + for gind, g in enumerate(gt): + # if this gt already matched, and not a crowd, continue + if gtm[tind, gind] > 0 and not iscrowd[gind]: + continue + # if dt matched to reg gt, and on ignore gt, stop + if m > -1 and gtIg[m] == 0 and gtIg[gind] == 1: + break + # continue to next gt unless better match made + if ious[dind, gind] < iou: + continue + # if match successful and best so far, store appropriately + iou = ious[dind, gind] + m = gind + g_reserve = g + # if match made store id of match for both dt and gt + if m == -1: + # FP + if qry_id == d['fangyi_query']: + # print(base_score, d['score']) + if base_score > d['score']: + FP_success = 1 + base_score = d['score'] + continue + dtIg[tind, dind] = gtIg[m] + if gtIg[m] == 0 and not (d['area'] < aRng[0] or d['area'] > aRng[1]): + 1 + # if d['fangyi_query'] == qry_id: + # return 2 + dtm[tind, dind] = gt[m]['id'] + gtm[tind, m] = d['id'] + else: + for dind, d in enumerate(dt): + # FP + if qry_id == d['fangyi_query']: + if base_score > d['score']: + FP_success = 1 + base_score = d['score'] + + return FP_success, base_score + + +def check_supts_TP(imgId, catId, g, d, supts, t): + gt_realid = g['id'] + qry_id = d['fangyi_query'] + base_score = d['score'] + p = supts[0].params + maxDet = p.maxDets[-1] + aRng = p.areaRng[0] + success = False + reserve = None + for supt in supts: + gt = supt._gts[imgId, catId] + dt = supt._dts[imgId, catId] + if len(gt) == 0 or len(dt) == 0: + continue + + for g in gt: + if g['ignore'] or (g['area'] < aRng[0] or g['area'] > aRng[1]): + g['_ignore'] = 1 + else: + g['_ignore'] = 0 + + # sort dt highest score first, sort gt ignore last + gtind = np.argsort([g['_ignore'] for g in gt], kind='mergesort') + gt = [gt[i] for i in gtind] + dtind = np.argsort([-d['score'] for d in dt], kind='mergesort') + dt = [dt[i] for i in dtind[0:maxDet]] + iscrowd = [int(o['iscrowd']) for o in gt] + # load computed ious + ious = supt.ious[imgId, catId][:, gtind] if len(supt.ious[imgId, catId]) > 0 else supt.ious[imgId, catId] + + gtIg = np.array([g['_ignore'] for g in gt]) + + if not len(ious) == 0: + for dind, d in enumerate(dt): + if not d['fangyi_query'] == qry_id: + continue + for gind, g in enumerate(gt): + if not g['id'] == gt_realid: + continue + iou = ious[dind, gind] + if iou >= t: + if d['score'] > base_score: + success = True + #t = iou + base_score = d['score'] + reserve = d + + + return success, t, base_score, reserve + + +def preeval(base, dataset, iou_thrs): + cocoGt = dataset.coco + preds = dataset._det2json(base) + cocoDt = cocoGt.loadRes(preds) + + cocoEval = COCOeval(cocoGt, cocoDt, 'bbox') # initialize a class here + cocoEval.params.catIds = dataset.cat_ids + cocoEval.params.imgIds = dataset.img_ids + cocoEval.params.maxDets = [100] + cocoEval.params.iouThrs = [iou_thrs] + cocoEval.params.areaRng = [[0, 10000000000.0]] + cocoEval.params.areaRngLbl = ['all'] + + return cocoEval + + +def motivation_verification(base, others, p): + k_list = list(range(80)) + i_list = list(range(5000)) + T, A, M = 1, 1, 1 + R = 101 + K = 80 + precision = -np.ones((T, R, K, A, M)) # -1 for the precision of absent categories + recall = -np.ones((T, K, A, M)) + scores = -np.ones((T, R, K, A, M)) + for k in k_list: + Nk = k * 5000 + E = [base[k + i*80] for i in i_list] + E = [e for e in E if not e is None] + dtScores = np.concatenate([e['dtScores'] for e in E]) + + inds = np.argsort(-dtScores, kind='mergesort') + dtScoresSorted = dtScores[inds] + + dtm = np.concatenate([e['dtMatches'] for e in E], axis=1)[:, inds] + dtIg = np.concatenate([e['dtIgnore'] for e in E], axis=1)[:, inds] + gtIg = np.concatenate([e['gtIgnore'] for e in E]) + npig = np.count_nonzero(gtIg == 0) + + if npig == 0: + continue + tps = np.logical_and( dtm, np.logical_not(dtIg) ) + fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg) ) + + tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float) + fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float) + for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)): + tp = np.array(tp) + fp = np.array(fp) + nd = len(tp) + rc = tp / npig + pr = tp / (fp+tp+np.spacing(1)) + q = np.zeros((R,)) + ss = np.zeros((R,)) + + if nd: + recall[t,k,0,0] = rc[-1] + else: + recall[t,k,0,0] = 0 + + pr = pr.tolist(); q = q.tolist() + + for i in range(nd-1, 0, -1): + if pr[i] > pr[i-1]: + pr[i-1] = pr[i] + + inds = np.searchsorted(rc, p.recThrs, side='left') + try: + for ri, pi in enumerate(inds): + q[ri] = pr[pi] + ss[ri] = dtScoresSorted[pi] + except: + pass + precision[t,:,k,0,0] = np.array(q) + scores[t,:,k,0,0] = np.array(ss) + + eval = { + 'counts': [T, R, K, A, M], + 'precision': precision, + 'recall': recall, + 'scores': scores, + } + return eval + +def organize(outs): + # FP, FN, TP, TN = 0, grief = 0, sorrow = 0 + # for an image i, + # for a query q, stage6 prediction is q6 + # if q6 is FP: FP += 1 + # check if q1-5 exists TP or FP with lower score, if true, grief+=1, + # if q6 is TP: TP += 1 + # check if q1-5 exists same TP but with higher score, if true, sorrow+=1 + # for a gt + # if gt is FN: FN += 1 + # check if q1-5 exists TP, if true, grief+=1, + # + # sorrow rate = sorrow / TP: among all TP, how many of them could have a higher confident score + # the higher, the worse + # grief rate = grief / (FP + FN): among all mistakes that q6 predicts, so many of them could have a correct detection + # the higher, the worse + # if q6 is a TN: + # check if q1-5 exists TN and score < q6, if true, sorrow+=1 + + allres = [] + for img in outs: + scale_factor = img['scale_factor'] + l_det_bboxes, l_det_labels = [], [] + for stage in range(6): + cls_score, bboxes_list = img[stage] + cls_score_per_img = torch.sigmoid(torch.Tensor(cls_score)) + scores_per_img, labels_per_img = torch.max(cls_score_per_img, dim=1) + bbox_pred_per_img = torch.Tensor(bboxes_list) + bbox_pred_per_img /= bbox_pred_per_img.new_tensor(scale_factor) + + queryid = torch.Tensor(list(range(len(scores_per_img)))).float() + det_bboxes = torch.cat([bbox_pred_per_img, scores_per_img[:, None], labels_per_img[:, None].float(), queryid[:, None]], dim=1) # （100， 5） + det_labels = labels_per_img # （100，） + l_det_bboxes.append(det_bboxes[:, :, None]) + l_det_labels.append(det_labels[:, None]) + + bboxes = torch.cat(l_det_bboxes, dim=2).permute(0, 2, 1).contiguous().numpy() + labels = torch.cat(l_det_labels, dim=1).numpy() + + res = [bboxes[labels[:, -1] == i, :] for i in range(80)] + allres.append(res) + + return allres + +def postproc(outs, stageid=5, mmdet_implementation=False): + res = [] + for img in outs: + cls_score, bboxes_list = img[stageid] + cls_score_per_img = torch.sigmoid(torch.Tensor(cls_score)) + queryid = torch.Tensor(list(range(len(cls_score_per_img)))).float() + if mmdet_implementation: + scores_per_img, topk_indices = cls_score_per_img.flatten(0, 1).topk(100, sorted=False) + labels_per_img = topk_indices % 80 + bbox_pred_per_img = torch.Tensor(bboxes_list)[topk_indices // 80] + queryid = queryid[topk_indices // 80] + else: + scores_per_img, labels_per_img = torch.max(cls_score_per_img, dim=1) + bbox_pred_per_img = torch.Tensor(bboxes_list) + + scale_factor = img['scale_factor'] + bbox_pred_per_img /= bbox_pred_per_img.new_tensor(scale_factor) + + det_bboxes = [torch.cat([bbox_pred_per_img, scores_per_img[:, None], labels_per_img[:, None].float(), queryid[:, None]], dim=1)] + det_labels = [labels_per_img] + res.extend([bbox2result(det_bboxes[i], det_labels[i], 80) for i in range(1)]) + + return res + +def postproc_deform(outs, stageid=5, mmdet_implementation=False): + res = [] + for img in outs: + cls_score, bboxes_list = img[stageid] + cls_score = torch.sigmoid(torch.Tensor(cls_score[0])) + bbox_pred = torch.Tensor(bboxes_list[0]) + img_shape = img['img_shape'] + scale_factor = img['scale_factor'] + queryid = torch.Tensor(list(range(len(cls_score)))).float() + + if mmdet_implementation: + scores, topk_indices = cls_score.flatten(0, 1).topk(100, sorted=False) + det_labels = topk_indices % 80 + bbox_pred = torch.Tensor(bbox_pred)[topk_indices // 80] + queryid = queryid[topk_indices // 80] + else: + scores, det_labels = torch.max(cls_score, dim=1) + bbox_pred = torch.Tensor(bbox_pred) + + det_bboxes = bbox_cxcywh_to_xyxy(bbox_pred) + det_bboxes[:, 0::2] = det_bboxes[:, 0::2] * img_shape[1] + det_bboxes[:, 1::2] = det_bboxes[:, 1::2] * img_shape[0] + det_bboxes[:, 0::2].clamp_(min=0, max=img_shape[1]) + det_bboxes[:, 1::2].clamp_(min=0, max=img_shape[0]) + + det_bboxes /= det_bboxes.new_tensor(scale_factor) + # det_bboxes = torch.cat((det_bboxes, scores.unsqueeze(1)), -1) + + scores_per_img = scores + labels_per_img = det_labels + bbox_pred_per_img = det_bboxes + + det_bboxes = [torch.cat([bbox_pred_per_img, scores_per_img[:, None], labels_per_img[:, None].float(), queryid[:, None]], dim=1)] + det_labels = [labels_per_img] + res.extend([bbox2result(det_bboxes[i], det_labels[i], 80) for i in range(1)]) + + return res + + +def bbox_cxcywh_to_xyxy(bbox): + """Convert bbox coordinates from (cx, cy, w, h) to (x1, y1, x2, y2). + + Args: + bbox (Tensor): Shape (n, 4) for bboxes. + + Returns: + Tensor: Converted bboxes. + """ + cx, cy, w, h = bbox.split((1, 1, 1, 1), dim=-1) + bbox_new = [(cx - 0.5 * w), (cy - 0.5 * h), (cx + 0.5 * w), (cy + 0.5 * h)] + return torch.cat(bbox_new, dim=-1) + + +if __name__ == '__main__': + main() diff --git a/tools/analysis_tools/robustness_eval.py b/tools/analysis_tools/robustness_eval.py new file mode 100644 index 0000000..cc2e27b --- /dev/null +++ b/tools/analysis_tools/robustness_eval.py @@ -0,0 +1,250 @@ +import os.path as osp +from argparse import ArgumentParser + +import mmcv +import numpy as np + + +def print_coco_results(results): + + def _print(result, ap=1, iouThr=None, areaRng='all', maxDets=100): + titleStr = 'Average Precision' if ap == 1 else 'Average Recall' + typeStr = '(AP)' if ap == 1 else '(AR)' + iouStr = '0.50:0.95' \ + if iouThr is None else f'{iouThr:0.2f}' + iStr = f' {titleStr:<18} {typeStr} @[ IoU={iouStr:<9} | ' + iStr += f'area={areaRng:>6s} | maxDets={maxDets:>3d} ] = {result:0.3f}' + print(iStr) + + stats = np.zeros((12, )) + stats[0] = _print(results[0], 1) + stats[1] = _print(results[1], 1, iouThr=.5) + stats[2] = _print(results[2], 1, iouThr=.75) + stats[3] = _print(results[3], 1, areaRng='small') + stats[4] = _print(results[4], 1, areaRng='medium') + stats[5] = _print(results[5], 1, areaRng='large') + stats[6] = _print(results[6], 0, maxDets=1) + stats[7] = _print(results[7], 0, maxDets=10) + stats[8] = _print(results[8], 0) + stats[9] = _print(results[9], 0, areaRng='small') + stats[10] = _print(results[10], 0, areaRng='medium') + stats[11] = _print(results[11], 0, areaRng='large') + + +def get_coco_style_results(filename, + task='bbox', + metric=None, + prints='mPC', + aggregate='benchmark'): + + assert aggregate in ['benchmark', 'all'] + + if prints == 'all': + prints = ['P', 'mPC', 'rPC'] + elif isinstance(prints, str): + prints = [prints] + for p in prints: + assert p in ['P', 'mPC', 'rPC'] + + if metric is None: + metrics = [ + 'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'AR1', 'AR10', 'AR100', + 'ARs', 'ARm', 'ARl' + ] + elif isinstance(metric, list): + metrics = metric + else: + metrics = [metric] + + for metric_name in metrics: + assert metric_name in [ + 'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'AR1', 'AR10', 'AR100', + 'ARs', 'ARm', 'ARl' + ] + + eval_output = mmcv.load(filename) + + num_distortions = len(list(eval_output.keys())) + results = np.zeros((num_distortions, 6, len(metrics)), dtype='float32') + + for corr_i, distortion in enumerate(eval_output): + for severity in eval_output[distortion]: + for metric_j, metric_name in enumerate(metrics): + mAP = eval_output[distortion][severity][task][metric_name] + results[corr_i, severity, metric_j] = mAP + + P = results[0, 0, :] + if aggregate == 'benchmark': + mPC = np.mean(results[:15, 1:, :], axis=(0, 1)) + else: + mPC = np.mean(results[:, 1:, :], axis=(0, 1)) + rPC = mPC / P + + print(f'\nmodel: {osp.basename(filename)}') + if metric is None: + if 'P' in prints: + print(f'Performance on Clean Data [P] ({task})') + print_coco_results(P) + if 'mPC' in prints: + print(f'Mean Performance under Corruption [mPC] ({task})') + print_coco_results(mPC) + if 'rPC' in prints: + print(f'Relative Performance under Corruption [rPC] ({task})') + print_coco_results(rPC) + else: + if 'P' in prints: + print(f'Performance on Clean Data [P] ({task})') + for metric_i, metric_name in enumerate(metrics): + print(f'{metric_name:5} = {P[metric_i]:0.3f}') + if 'mPC' in prints: + print(f'Mean Performance under Corruption [mPC] ({task})') + for metric_i, metric_name in enumerate(metrics): + print(f'{metric_name:5} = {mPC[metric_i]:0.3f}') + if 'rPC' in prints: + print(f'Relative Performance under Corruption [rPC] ({task})') + for metric_i, metric_name in enumerate(metrics): + print(f'{metric_name:5} => {rPC[metric_i] * 100:0.1f} %') + + return results + + +def get_voc_style_results(filename, prints='mPC', aggregate='benchmark'): + + assert aggregate in ['benchmark', 'all'] + + if prints == 'all': + prints = ['P', 'mPC', 'rPC'] + elif isinstance(prints, str): + prints = [prints] + for p in prints: + assert p in ['P', 'mPC', 'rPC'] + + eval_output = mmcv.load(filename) + + num_distortions = len(list(eval_output.keys())) + results = np.zeros((num_distortions, 6, 20), dtype='float32') + + for i, distortion in enumerate(eval_output): + for severity in eval_output[distortion]: + mAP = [ + eval_output[distortion][severity][j]['ap'] + for j in range(len(eval_output[distortion][severity])) + ] + results[i, severity, :] = mAP + + P = results[0, 0, :] + if aggregate == 'benchmark': + mPC = np.mean(results[:15, 1:, :], axis=(0, 1)) + else: + mPC = np.mean(results[:, 1:, :], axis=(0, 1)) + rPC = mPC / P + + print(f'\nmodel: {osp.basename(filename)}') + if 'P' in prints: + print(f'Performance on Clean Data [P] in AP50 = {np.mean(P):0.3f}') + if 'mPC' in prints: + print('Mean Performance under Corruption [mPC] in AP50 = ' + f'{np.mean(mPC):0.3f}') + if 'rPC' in prints: + print('Relative Performance under Corruption [rPC] in % = ' + f'{np.mean(rPC) * 100:0.1f}') + + return np.mean(results, axis=2, keepdims=True) + + +def get_results(filename, + dataset='coco', + task='bbox', + metric=None, + prints='mPC', + aggregate='benchmark'): + assert dataset in ['coco', 'voc', 'cityscapes'] + + if dataset in ['coco', 'cityscapes']: + results = get_coco_style_results( + filename, + task=task, + metric=metric, + prints=prints, + aggregate=aggregate) + elif dataset == 'voc': + if task != 'bbox': + print('Only bbox analysis is supported for Pascal VOC') + print('Will report bbox results\n') + if metric not in [None, ['AP'], ['AP50']]: + print('Only the AP50 metric is supported for Pascal VOC') + print('Will report AP50 metric\n') + results = get_voc_style_results( + filename, prints=prints, aggregate=aggregate) + + return results + + +def get_distortions_from_file(filename): + + eval_output = mmcv.load(filename) + + return get_distortions_from_results(eval_output) + + +def get_distortions_from_results(eval_output): + distortions = [] + for i, distortion in enumerate(eval_output): + distortions.append(distortion.replace('_', ' ')) + return distortions + + +def main(): + parser = ArgumentParser(description='Corruption Result Analysis') + parser.add_argument('filename', help='result file path') + parser.add_argument( + '--dataset', + type=str, + choices=['coco', 'voc', 'cityscapes'], + default='coco', + help='dataset type') + parser.add_argument( + '--task', + type=str, + nargs='+', + choices=['bbox', 'segm'], + default=['bbox'], + help='task to report') + parser.add_argument( + '--metric', + nargs='+', + choices=[ + None, 'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'AR1', 'AR10', + 'AR100', 'ARs', 'ARm', 'ARl' + ], + default=None, + help='metric to report') + parser.add_argument( + '--prints', + type=str, + nargs='+', + choices=['P', 'mPC', 'rPC'], + default='mPC', + help='corruption benchmark metric to print') + parser.add_argument( + '--aggregate', + type=str, + choices=['all', 'benchmark'], + default='benchmark', + help='aggregate all results or only those \ + for benchmark corruptions') + + args = parser.parse_args() + + for task in args.task: + get_results( + args.filename, + dataset=args.dataset, + task=task, + metric=args.metric, + prints=args.prints, + aggregate=args.aggregate) + + +if __name__ == '__main__': + main() diff --git a/tools/analysis_tools/stage_wise_error_analysis.py b/tools/analysis_tools/stage_wise_error_analysis.py new file mode 100644 index 0000000..4c8645c --- /dev/null +++ b/tools/analysis_tools/stage_wise_error_analysis.py @@ -0,0 +1,673 @@ +import argparse + +import mmcv +from mmcv import Config, DictAction + +from mmdet.datasets import build_dataset +import numpy as np +from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, build_assigner, + build_sampler, merge_aug_bboxes, merge_aug_masks, + multiclass_nms) +from mmcv.ops.nms import batched_nms +import torch, torchvision +from mmdet.datasets.api_wrappers import COCOeval +import pickle5 + +def parse_args(): + parser = argparse.ArgumentParser(description='Evaluate metric of the ' + 'results saved in pkl format') + parser.add_argument('config', help='Config of the model') + parser.add_argument('--pkls', nargs="+", default=["a", "b"], help='Results in pickle format') + parser.add_argument( + '--format-only', + action='store_true', + help='Format the output results without perform evaluation. It is' + 'useful when you want to format the result to a specific format and ' + 'submit it to the test server') + parser.add_argument( + '--eval', + type=str, + nargs='+', + help='Evaluation metrics, which depends on the dataset, e.g., "bbox",' + ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + parser.add_argument( + '--eval-options', + nargs='+', + action=DictAction, + help='custom options for evaluation, the key-value pair in xxx=yyy ' + 'format will be kwargs for dataset.evaluate() function') + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + + cfg = Config.fromfile(args.config) + assert args.eval or args.format_only, ( + 'Please specify at least one operation (eval/format the results) with ' + 'the argument "--eval", "--format-only"') + if args.eval and args.format_only: + raise ValueError('--eval and --format_only cannot be both specified') + + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) + cfg.data.test.test_mode = True + + dataset = build_dataset(cfg.data.test) + + #pkl = mmcv.load(args.pkls[0]) + with open(args.pkls[0], "rb") as f: + pkl = pickle5.load(f) + + #outs5 = postproc_deform(pkl, 5) + #outs4 = postproc_deform(pkl, 4) + #outs3 = postproc_deform(pkl, 3) + #outs2 = postproc_deform(pkl, 2) + #outs1 = postproc_deform(pkl, 1) + #outs0 = postproc_deform(pkl, 0) + + # outs = organize(pkl) + outs5 = postproc(pkl, 5) + outs4 = postproc(pkl, 4) + outs3 = postproc(pkl, 3) + outs2 = postproc(pkl, 2) + outs1 = postproc(pkl, 1) + outs0 = postproc(pkl, 0) + + iou_thrs = 0.75 + + cocoEval5 = preeval(base=outs5, dataset=dataset, iou_thrs=iou_thrs) + cocoEval4 = preeval(base=outs4, dataset=dataset, iou_thrs=iou_thrs) + cocoEval3 = preeval(base=outs3, dataset=dataset, iou_thrs=iou_thrs) + cocoEval2 = preeval(base=outs2, dataset=dataset, iou_thrs=iou_thrs) + cocoEval1 = preeval(base=outs1, dataset=dataset, iou_thrs=iou_thrs) + cocoEval0 = preeval(base=outs0, dataset=dataset, iou_thrs=iou_thrs) + + evalImgs5 = fangyi(base=cocoEval5, supts=[cocoEval4, + cocoEval3, + cocoEval2, + cocoEval1, + cocoEval0, + ]) + + #cocoEval5.eval = motivation_verification(evalImgs5, + # [evalImgs5], + # cocoEval5.params) + #cocoEval5.summarize() + + +def fangyi(base, supts): + # FP, FN, TP, TN = 0, grief1, grief2 = 0, sorrow = 0 + # for an image i, + # for a query q, stage6 prediction is q6 + # if q6 is FP: FP += 1 + # check if q1-5 exists TP or FP with lower score, if true, grief1+=1, + # if q6 is TP: TP += 1 + # check if q1-5 exists same TP but with higher score, if true, sorrow+=1 + # for a gt + # if gt is FN: FN += 1 + # check if q1-5 exists TP, if true, grief2+=1, + # + # sorrow rate = sorrow / TP: among all TP, how many of them could have a higher confident score + # the higher, the worse + # grief rate = grief / (FP + FN): among all mistakes that q6 predicts, so many of them could have a correct detection + # the higher, the worse + # if q6 is a TN: + # check if q1-5 exists TN and score < q6, if true, sorrow+=1 + + p = base.params + p.imgIds = list(np.unique(p.imgIds)) + if p.useCats: + p.catIds = list(np.unique(p.catIds)) + p.maxDets = sorted(p.maxDets) + + base.params = p + base._prepare() + # loop through images, area range, max detection number + base.ious = {(imgId, catId): base.computeIoU(imgId, catId) \ + for imgId in p.imgIds + for catId in p.catIds} + for supt in supts: + supt.params = p + supt._prepare() + supt.ious = {(imgId, catId): supt.computeIoU(imgId, catId) \ + for imgId in p.imgIds + for catId in p.catIds} + + maxDet = p.maxDets[-1] + aRng = p.areaRng[0] + + res = [] + FP, FN, TP, TN, grief, sorrow, all_pred, all_gt, grief1, grief2 = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 + FP2, FN2, TP2, TN2, = 0, 0, 0, 0 + + for imgId in p.imgIds: + total_q_num = 0 + for catId in p.catIds: + gt = base._gts[imgId, catId] + dt = base._dts[imgId, catId] + if len(gt) == 0 and len(dt) == 0: + res.append(None) + continue + + for g in gt: + if g['ignore'] or (g['area'] < aRng[0] or g['area'] > aRng[1]): + g['_ignore'] = 1 + else: + g['_ignore'] = 0 + + # sort dt highest score first, sort gt ignore last + gtind = np.argsort([g['_ignore'] for g in gt], kind='mergesort') + gt = [gt[i] for i in gtind] + dtind = np.argsort([-d['score'] for d in dt], kind='mergesort') + dt = [dt[i] for i in dtind[0:maxDet]] + iscrowd = [int(o['iscrowd']) for o in gt] + # load computed ious + ious = base.ious[imgId, catId][:, gtind] if len(base.ious[imgId, catId]) > 0 else base.ious[ + imgId, catId] + + T = len(p.iouThrs) + G = len(gt) + D = len(dt) + + gtm = np.zeros((T, G)) # gtm positive number == TP number; gtm 0 number == FN number + dtm = np.zeros((T, D)) # dtm positive number == TP number; dtm 0 number == FP number + gtIg = np.array([g['_ignore'] for g in gt]) + dtIg = np.zeros((T, D)) + if not len(ious) == 0: + for tind, t in enumerate(p.iouThrs): + for dind, d in enumerate(dt): + # information about best match so far (m=-1 -> unmatched) + iou = min([t, 1 - 1e-10]) + m = -1 + for gind, g in enumerate(gt): + # if this gt already matched, and not a crowd, continue + if gtm[tind, gind] > 0 and not iscrowd[gind]: + continue + # if dt matched to reg gt, and on ignore gt, stop + if m > -1 and gtIg[m] == 0 and gtIg[gind] == 1: + break + # continue to next gt unless better match made + if ious[dind, gind] < iou: + continue + # if match successful and best so far, store appropriately + iou = ious[dind, gind] + m = gind + g_reserve = g + iou_reserve = iou + # if match made store id of match for both dt and gt + if m == -1: + FP2 += 1 + if check_supts_FP(imgId, catId, d, supts, t): + grief1 += 1 + grief += 1 + continue + dtIg[tind, dind] = gtIg[m] + if gtIg[m] == 0 and not (d['area'] < aRng[0] or d['area'] > aRng[1]): + TP2 += 1 + if check_supts_TP(imgId, catId, g_reserve, d, supts, iou_reserve): + sorrow += 1 + dtm[tind, dind] = gt[m]['id'] + gtm[tind, m] = d['id'] + else: + for dind, d in enumerate(dt): + FP2 += 1 + if check_supts_FP(imgId, catId, d, supts, t): + grief1 += 1 + grief += 1 + + #if not len(ious) == 0: + for gind, g in enumerate(gt): + if gtm[tind, gind] == 0 and gtIg[gind] == 0: + FN2 += 1 + if check_supts_FN(imgId, catId, g, supts, t): + grief2 += 1 + grief += 1 + + # set unmatched detections outside of area range to ignore + a = np.array([d['area'] < aRng[0] or d['area'] > aRng[1] for d in dt]).reshape((1, len(dt))) + dtIg = np.logical_or(dtIg, np.logical_and(dtm == 0, np.repeat(a, T, 0))) + + res.append({ + 'image_id': imgId, + 'category_id': catId, + 'aRng': aRng, + 'maxDet': maxDet, + 'dtIds': [d['id'] for d in dt], + 'gtIds': [g['id'] for g in gt], + 'dtMatches': dtm, + 'gtMatches': gtm, + 'dtScores': [d['score'] for d in dt], + 'gtIgnore': gtIg, + 'dtIgnore': dtIg,}) + + tps = np.logical_and(dtm, np.logical_not(dtIg)) + fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg)) + npig = np.count_nonzero(gtIg == 0) # number of gt + fns = npig - tps.sum() + fns2 = np.logical_and(np.logical_not(gtm), np.logical_not(gtIg)) + assert fns == fns2.sum() + tps2 = np.logical_and(gtm, np.logical_not(gtIg)) + assert tps.sum() == tps2.sum() + TP += tps.sum() + FP += fps.sum() + all_gt += npig + all_pred = TP + FP + FN += fns + + print('TP, FP, all_gt, FN', TP, FP, all_gt, FN) + print('TP2:{}, FP2:{}, FN2:{}, sorrow:{}, sorrow rate:{}, grief1:{}, grief2:{}, grief:{}, grief1 rate{}, grief2 rate{} , grief rate{}'.format( + TP2, FP2, FN2, sorrow, sorrow/TP2, grief1, grief2, grief, grief1/FP2, grief2/FN2, grief/(FP2 + FN2))) + return res + + +def check_supts_FN(imgId, catId, g, supts, t): + gt_realid = g['id'] + p = supts[0].params + maxDet = p.maxDets[-1] + aRng = p.areaRng[0] + for supt in supts: + gt = supt._gts[imgId, catId] + dt = supt._dts[imgId, catId] + if len(gt) == 0 and len(dt) == 0: + continue + + for g in gt: + if g['ignore'] or (g['area'] < aRng[0] or g['area'] > aRng[1]): + g['_ignore'] = 1 + else: + g['_ignore'] = 0 + + # sort dt highest score first, sort gt ignore last + gtind = np.argsort([g['_ignore'] for g in gt], kind='mergesort') + gt = [gt[i] for i in gtind] + dtind = np.argsort([-d['score'] for d in dt], kind='mergesort') + dt = [dt[i] for i in dtind[0:maxDet]] + iscrowd = [int(o['iscrowd']) for o in gt] + # load computed ious + ious = supt.ious[imgId, catId][:, gtind] if len(supt.ious[imgId, catId]) > 0 else supt.ious[ + imgId, catId] + + T = len(p.iouThrs) + G = len(gt) + D = len(dt) + + gtm = np.zeros((T, G)) # gtm positive number == TP number; gtm 0 number == FN number + dtm = np.zeros((T, D)) # dtm positive number == TP number; dtm 0 number == FP number + gtIg = np.array([g['_ignore'] for g in gt]) + dtIg = np.zeros((T, D)) + if not len(ious) == 0: + for tind, t in enumerate(p.iouThrs): + for dind, d in enumerate(dt): + # information about best match so far (m=-1 -> unmatched) + iou = min([t, 1 - 1e-10]) + m = -1 + for gind, g in enumerate(gt): + # if this gt already matched, and not a crowd, continue + if gtm[tind, gind] > 0 and not iscrowd[gind]: + continue + # if dt matched to reg gt, and on ignore gt, stop + if m > -1 and gtIg[m] == 0 and gtIg[gind] == 1: + break + # continue to next gt unless better match made + if ious[dind, gind] < iou: + continue + # if match successful and best so far, store appropriately + iou = ious[dind, gind] + m = gind + g_reserve = g + # if match made store id of match for both dt and gt + if m == -1: + continue + dtIg[tind, dind] = gtIg[m] + if gtIg[m] == 0 and not (d['area'] < aRng[0] or d['area'] > aRng[1]): + # TP + if g_reserve['id'] == gt_realid: + if d['score'] > 0.1: + return True + dtm[tind, dind] = gt[m]['id'] + gtm[tind, m] = d['id'] + + return False + + +def check_supts_FP(imgId, catId, d, supts, t): + qry_id = d['fangyi_query'] + base_score = d['score'] + p = supts[0].params + maxDet = p.maxDets[-1] + aRng = p.areaRng[0] + + for supt in supts: + gt = supt._gts[imgId, catId] + dt = supt._dts[imgId, catId] + if len(gt) == 0 and len(dt) == 0: + continue + + for g in gt: + if g['ignore'] or (g['area'] < aRng[0] or g['area'] > aRng[1]): + g['_ignore'] = 1 + else: + g['_ignore'] = 0 + + # sort dt highest score first, sort gt ignore last + gtind = np.argsort([g['_ignore'] for g in gt], kind='mergesort') + gt = [gt[i] for i in gtind] + dtind = np.argsort([-d['score'] for d in dt], kind='mergesort') + dt = [dt[i] for i in dtind[0:maxDet]] + iscrowd = [int(o['iscrowd']) for o in gt] + # load computed ious + ious = supt.ious[imgId, catId][:, gtind] if len(supt.ious[imgId, catId]) > 0 else supt.ious[ + imgId, catId] + + T = len(p.iouThrs) + G = len(gt) + D = len(dt) + + gtm = np.zeros((T, G)) # gtm positive number == TP number; gtm 0 number == FN number + dtm = np.zeros((T, D)) # dtm positive number == TP number; dtm 0 number == FP number + gtIg = np.array([g['_ignore'] for g in gt]) + dtIg = np.zeros((T, D)) + if not len(ious) == 0: + for tind, t in enumerate(p.iouThrs): + for dind, d in enumerate(dt): + # information about best match so far (m=-1 -> unmatched) + iou = min([t, 1 - 1e-10]) + m = -1 + for gind, g in enumerate(gt): + # if this gt already matched, and not a crowd, continue + if gtm[tind, gind] > 0 and not iscrowd[gind]: + continue + # if dt matched to reg gt, and on ignore gt, stop + if m > -1 and gtIg[m] == 0 and gtIg[gind] == 1: + break + # continue to next gt unless better match made + if ious[dind, gind] < iou: + continue + # if match successful and best so far, store appropriately + iou = ious[dind, gind] + m = gind + g_reserve = g + # if match made store id of match for both dt and gt + if m == -1: + # FP + if qry_id == d['fangyi_query']: + if base_score > d['score']: + return True + continue + dtIg[tind, dind] = gtIg[m] + if gtIg[m] == 0 and not (d['area'] < aRng[0] or d['area'] > aRng[1]): + if d['fangyi_query'] == qry_id: + return True + dtm[tind, dind] = gt[m]['id'] + gtm[tind, m] = d['id'] + else: + for dind, d in enumerate(dt): + # FP + if qry_id == d['fangyi_query']: + if base_score > d['score']: + return True + + return False + + +def check_supts_TP(imgId, catId, g, d, supts, t): + gt_realid = g['id'] + qry_id = d['fangyi_query'] + base_score = d['score'] + p = supts[0].params + maxDet = p.maxDets[-1] + aRng = p.areaRng[0] + + for supt in supts: + gt = supt._gts[imgId, catId] + dt = supt._dts[imgId, catId] + if len(gt) == 0 or len(dt) == 0: + continue + + for g in gt: + if g['ignore'] or (g['area'] < aRng[0] or g['area'] > aRng[1]): + g['_ignore'] = 1 + else: + g['_ignore'] = 0 + + # sort dt highest score first, sort gt ignore last + gtind = np.argsort([g['_ignore'] for g in gt], kind='mergesort') + gt = [gt[i] for i in gtind] + dtind = np.argsort([-d['score'] for d in dt], kind='mergesort') + dt = [dt[i] for i in dtind[0:maxDet]] + iscrowd = [int(o['iscrowd']) for o in gt] + # load computed ious + ious = supt.ious[imgId, catId][:, gtind] if len(supt.ious[imgId, catId]) > 0 else supt.ious[imgId, catId] + + gtIg = np.array([g['_ignore'] for g in gt]) + if not len(ious) == 0: + for dind, d in enumerate(dt): + if not d['fangyi_query'] == qry_id: + continue + for gind, g in enumerate(gt): + if not g['id'] == gt_realid: + continue + iou = ious[dind, gind] + if iou >= t: + if d['score'] > base_score: + return True + + return False + + +def preeval(base, dataset, iou_thrs): + cocoGt = dataset.coco + preds = dataset._det2json(base) + cocoDt = cocoGt.loadRes(preds) + + cocoEval = COCOeval(cocoGt, cocoDt, 'bbox') # initialize a class here + cocoEval.params.catIds = dataset.cat_ids + cocoEval.params.imgIds = dataset.img_ids + cocoEval.params.maxDets = [300] + cocoEval.params.iouThrs = [iou_thrs] + cocoEval.params.areaRng = [[0, 10000000000.0]] + cocoEval.params.areaRngLbl = ['all'] + + return cocoEval + + +def motivation_verification(base, others, p): + k_list = list(range(80)) + i_list = list(range(5000)) + T, A, M = 1, 1, 1 + R = 101 + K = 80 + precision = -np.ones((T, R, K, A, M)) # -1 for the precision of absent categories + recall = -np.ones((T, K, A, M)) + scores = -np.ones((T, R, K, A, M)) + for k in k_list: + Nk = k * 5000 + E = [base[k + i*80] for i in i_list] + E = [e for e in E if not e is None] + dtScores = np.concatenate([e['dtScores'] for e in E]) + + inds = np.argsort(-dtScores, kind='mergesort') + dtScoresSorted = dtScores[inds] + + dtm = np.concatenate([e['dtMatches'] for e in E], axis=1)[:, inds] + dtIg = np.concatenate([e['dtIgnore'] for e in E], axis=1)[:, inds] + gtIg = np.concatenate([e['gtIgnore'] for e in E]) + npig = np.count_nonzero(gtIg == 0) + + if npig == 0: + continue + tps = np.logical_and( dtm, np.logical_not(dtIg) ) + fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg) ) + + tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float) + fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float) + for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)): + tp = np.array(tp) + fp = np.array(fp) + nd = len(tp) + rc = tp / npig + pr = tp / (fp+tp+np.spacing(1)) + q = np.zeros((R,)) + ss = np.zeros((R,)) + + if nd: + recall[t,k,0,0] = rc[-1] + else: + recall[t,k,0,0] = 0 + + pr = pr.tolist(); q = q.tolist() + + for i in range(nd-1, 0, -1): + if pr[i] > pr[i-1]: + pr[i-1] = pr[i] + + inds = np.searchsorted(rc, p.recThrs, side='left') + try: + for ri, pi in enumerate(inds): + q[ri] = pr[pi] + ss[ri] = dtScoresSorted[pi] + except: + pass + precision[t,:,k,0,0] = np.array(q) + scores[t,:,k,0,0] = np.array(ss) + + eval = { + 'counts': [T, R, K, A, M], + 'precision': precision, + 'recall': recall, + 'scores': scores, + } + return eval + +def organize(outs): + # FP, FN, TP, TN = 0, grief = 0, sorrow = 0 + # for an image i, + # for a query q, stage6 prediction is q6 + # if q6 is FP: FP += 1 + # check if q1-5 exists TP or FP with lower score, if true, grief+=1, + # if q6 is TP: TP += 1 + # check if q1-5 exists same TP but with higher score, if true, sorrow+=1 + # for a gt + # if gt is FN: FN += 1 + # check if q1-5 exists TP, if true, grief+=1, + # + # sorrow rate = sorrow / TP: among all TP, how many of them could have a higher confident score + # the higher, the worse + # grief rate = grief / (FP + FN): among all mistakes that q6 predicts, so many of them could have a correct detection + # the higher, the worse + # if q6 is a TN: + # check if q1-5 exists TN and score < q6, if true, sorrow+=1 + + allres = [] + for img in outs: + scale_factor = img['scale_factor'] + l_det_bboxes, l_det_labels = [], [] + for stage in range(6): + cls_score, bboxes_list = img[stage] + cls_score_per_img = torch.sigmoid(torch.Tensor(cls_score)) + scores_per_img, labels_per_img = torch.max(cls_score_per_img, dim=1) + bbox_pred_per_img = torch.Tensor(bboxes_list) + bbox_pred_per_img /= bbox_pred_per_img.new_tensor(scale_factor) + + queryid = torch.Tensor(list(range(len(scores_per_img)))).float() + det_bboxes = torch.cat([bbox_pred_per_img, scores_per_img[:, None], labels_per_img[:, None].float(), queryid[:, None]], dim=1) # （100， 5） + det_labels = labels_per_img # （100，） + l_det_bboxes.append(det_bboxes[:, :, None]) + l_det_labels.append(det_labels[:, None]) + + bboxes = torch.cat(l_det_bboxes, dim=2).permute(0, 2, 1).contiguous().numpy() + labels = torch.cat(l_det_labels, dim=1).numpy() + + res = [bboxes[labels[:, -1] == i, :] for i in range(80)] + allres.append(res) + + return allres + +def postproc(outs, stageid=5, mmdet_implementation=False): + res = [] + for img in outs: + cls_score, bboxes_list = img[stageid] + cls_score_per_img = torch.sigmoid(torch.Tensor(cls_score)) + + if mmdet_implementation: + scores_per_img, topk_indices = cls_score_per_img.flatten(0, 1).topk(100, sorted=False) + labels_per_img = topk_indices % 80 + bbox_pred_per_img = torch.Tensor(bboxes_list)[topk_indices // 80] + else: + scores_per_img, labels_per_img = torch.max(cls_score_per_img, dim=1) + bbox_pred_per_img = torch.Tensor(bboxes_list) + + scale_factor = img['scale_factor'] + bbox_pred_per_img /= bbox_pred_per_img.new_tensor(scale_factor) + queryid = torch.Tensor(list(range(len(scores_per_img)))).float() + det_bboxes = [torch.cat([bbox_pred_per_img, scores_per_img[:, None], labels_per_img[:, None].float(), queryid[:, None]], dim=1)] + det_labels = [labels_per_img] + res.extend([bbox2result(det_bboxes[i], det_labels[i], 80) for i in range(1)]) + + return res + +def postproc_deform(outs, stageid=5, mmdet_implementation=False): + res = [] + for img in outs: + cls_score, bboxes_list = img[stageid] + cls_score = torch.sigmoid(torch.Tensor(cls_score[0])) + bbox_pred = torch.Tensor(bboxes_list[0]) + img_shape = img['img_shape'] + scale_factor = img['scale_factor'] + + scores, det_labels = torch.max(cls_score, dim=1) + bbox_pred = torch.Tensor(bbox_pred) + + det_bboxes = bbox_cxcywh_to_xyxy(bbox_pred) + det_bboxes[:, 0::2] = det_bboxes[:, 0::2] * img_shape[1] + det_bboxes[:, 1::2] = det_bboxes[:, 1::2] * img_shape[0] + det_bboxes[:, 0::2].clamp_(min=0, max=img_shape[1]) + det_bboxes[:, 1::2].clamp_(min=0, max=img_shape[0]) + + det_bboxes /= det_bboxes.new_tensor(scale_factor) + # det_bboxes = torch.cat((det_bboxes, scores.unsqueeze(1)), -1) + + scores_per_img = scores + labels_per_img = det_labels + bbox_pred_per_img = det_bboxes + + queryid = torch.Tensor(list(range(len(scores_per_img)))).float() + det_bboxes = [torch.cat([bbox_pred_per_img, scores_per_img[:, None], labels_per_img[:, None].float(), queryid[:, None]], dim=1)] + det_labels = [labels_per_img] + res.extend([bbox2result(det_bboxes[i], det_labels[i], 80) for i in range(1)]) + + return res + + +def bbox_cxcywh_to_xyxy(bbox): + """Convert bbox coordinates from (cx, cy, w, h) to (x1, y1, x2, y2). + + Args: + bbox (Tensor): Shape (n, 4) for bboxes. + + Returns: + Tensor: Converted bboxes. + """ + cx, cy, w, h = bbox.split((1, 1, 1, 1), dim=-1) + bbox_new = [(cx - 0.5 * w), (cy - 0.5 * h), (cx + 0.5 * w), (cy + 0.5 * h)] + return torch.cat(bbox_new, dim=-1) + + +if __name__ == '__main__': + main() diff --git a/tools/analysis_tools/test_robustness.py b/tools/analysis_tools/test_robustness.py new file mode 100644 index 0000000..ae30c01 --- /dev/null +++ b/tools/analysis_tools/test_robustness.py @@ -0,0 +1,390 @@ +import argparse +import copy +import os +import os.path as osp + +import mmcv +import torch +from mmcv import DictAction +from mmcv.parallel import MMDataParallel, MMDistributedDataParallel +from mmcv.runner import (get_dist_info, init_dist, load_checkpoint, + wrap_fp16_model) +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval +from tools.analysis_tools.robustness_eval import get_results + +from mmdet import datasets +from mmdet.apis import multi_gpu_test, set_random_seed, single_gpu_test +from mmdet.core import eval_map +from mmdet.datasets import build_dataloader, build_dataset +from mmdet.models import build_detector + + +def coco_eval_with_return(result_files, + result_types, + coco, + max_dets=(100, 300, 1000)): + for res_type in result_types: + assert res_type in ['proposal', 'bbox', 'segm', 'keypoints'] + + if mmcv.is_str(coco): + coco = COCO(coco) + assert isinstance(coco, COCO) + + eval_results = {} + for res_type in result_types: + result_file = result_files[res_type] + assert result_file.endswith('.json') + + coco_dets = coco.loadRes(result_file) + img_ids = coco.getImgIds() + iou_type = 'bbox' if res_type == 'proposal' else res_type + cocoEval = COCOeval(coco, coco_dets, iou_type) + cocoEval.params.imgIds = img_ids + if res_type == 'proposal': + cocoEval.params.useCats = 0 + cocoEval.params.maxDets = list(max_dets) + cocoEval.evaluate() + cocoEval.accumulate() + cocoEval.summarize() + if res_type == 'segm' or res_type == 'bbox': + metric_names = [ + 'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'AR1', 'AR10', + 'AR100', 'ARs', 'ARm', 'ARl' + ] + eval_results[res_type] = { + metric_names[i]: cocoEval.stats[i] + for i in range(len(metric_names)) + } + else: + eval_results[res_type] = cocoEval.stats + + return eval_results + + +def voc_eval_with_return(result_file, + dataset, + iou_thr=0.5, + logger='print', + only_ap=True): + det_results = mmcv.load(result_file) + annotations = [dataset.get_ann_info(i) for i in range(len(dataset))] + if hasattr(dataset, 'year') and dataset.year == 2007: + dataset_name = 'voc07' + else: + dataset_name = dataset.CLASSES + mean_ap, eval_results = eval_map( + det_results, + annotations, + scale_ranges=None, + iou_thr=iou_thr, + dataset=dataset_name, + logger=logger) + + if only_ap: + eval_results = [{ + 'ap': eval_results[i]['ap'] + } for i in range(len(eval_results))] + + return mean_ap, eval_results + + +def parse_args(): + parser = argparse.ArgumentParser(description='MMDet test detector') + parser.add_argument('config', help='test config file path') + parser.add_argument('checkpoint', help='checkpoint file') + parser.add_argument('--out', help='output result file') + parser.add_argument( + '--corruptions', + type=str, + nargs='+', + default='benchmark', + choices=[ + 'all', 'benchmark', 'noise', 'blur', 'weather', 'digital', + 'holdout', 'None', 'gaussian_noise', 'shot_noise', 'impulse_noise', + 'defocus_blur', 'glass_blur', 'motion_blur', 'zoom_blur', 'snow', + 'frost', 'fog', 'brightness', 'contrast', 'elastic_transform', + 'pixelate', 'jpeg_compression', 'speckle_noise', 'gaussian_blur', + 'spatter', 'saturate' + ], + help='corruptions') + parser.add_argument( + '--severities', + type=int, + nargs='+', + default=[0, 1, 2, 3, 4, 5], + help='corruption severity levels') + parser.add_argument( + '--eval', + type=str, + nargs='+', + choices=['proposal', 'proposal_fast', 'bbox', 'segm', 'keypoints'], + help='eval types') + parser.add_argument( + '--iou-thr', + type=float, + default=0.5, + help='IoU threshold for pascal voc evaluation') + parser.add_argument( + '--summaries', + type=bool, + default=False, + help='Print summaries for every corruption and severity') + parser.add_argument( + '--workers', type=int, default=32, help='workers per gpu') + parser.add_argument('--show', action='store_true', help='show results') + parser.add_argument( + '--show-dir', help='directory where painted images will be saved') + parser.add_argument( + '--show-score-thr', + type=float, + default=0.3, + help='score threshold (default: 0.3)') + parser.add_argument('--tmpdir', help='tmp dir for writing some results') + parser.add_argument('--seed', type=int, default=None, help='random seed') + parser.add_argument( + '--launcher', + choices=['none', 'pytorch', 'slurm', 'mpi'], + default='none', + help='job launcher') + parser.add_argument('--local_rank', type=int, default=0) + parser.add_argument( + '--final-prints', + type=str, + nargs='+', + choices=['P', 'mPC', 'rPC'], + default='mPC', + help='corruption benchmark metric to print at the end') + parser.add_argument( + '--final-prints-aggregate', + type=str, + choices=['all', 'benchmark'], + default='benchmark', + help='aggregate all results or only those for benchmark corruptions') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + args = parser.parse_args() + if 'LOCAL_RANK' not in os.environ: + os.environ['LOCAL_RANK'] = str(args.local_rank) + return args + + +def main(): + args = parse_args() + + assert args.out or args.show or args.show_dir, \ + ('Please specify at least one operation (save or show the results) ' + 'with the argument "--out", "--show" or "show-dir"') + + if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): + raise ValueError('The output file must be a pkl file.') + + cfg = mmcv.Config.fromfile(args.config) + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) + # set cudnn_benchmark + if cfg.get('cudnn_benchmark', False): + torch.backends.cudnn.benchmark = True + cfg.model.pretrained = None + cfg.data.test.test_mode = True + if args.workers == 0: + args.workers = cfg.data.workers_per_gpu + + # init distributed env first, since logger depends on the dist info. + if args.launcher == 'none': + distributed = False + else: + distributed = True + init_dist(args.launcher, **cfg.dist_params) + + # set random seeds + if args.seed is not None: + set_random_seed(args.seed) + + if 'all' in args.corruptions: + corruptions = [ + 'gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur', + 'glass_blur', 'motion_blur', 'zoom_blur', 'snow', 'frost', 'fog', + 'brightness', 'contrast', 'elastic_transform', 'pixelate', + 'jpeg_compression', 'speckle_noise', 'gaussian_blur', 'spatter', + 'saturate' + ] + elif 'benchmark' in args.corruptions: + corruptions = [ + 'gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur', + 'glass_blur', 'motion_blur', 'zoom_blur', 'snow', 'frost', 'fog', + 'brightness', 'contrast', 'elastic_transform', 'pixelate', + 'jpeg_compression' + ] + elif 'noise' in args.corruptions: + corruptions = ['gaussian_noise', 'shot_noise', 'impulse_noise'] + elif 'blur' in args.corruptions: + corruptions = [ + 'defocus_blur', 'glass_blur', 'motion_blur', 'zoom_blur' + ] + elif 'weather' in args.corruptions: + corruptions = ['snow', 'frost', 'fog', 'brightness'] + elif 'digital' in args.corruptions: + corruptions = [ + 'contrast', 'elastic_transform', 'pixelate', 'jpeg_compression' + ] + elif 'holdout' in args.corruptions: + corruptions = ['speckle_noise', 'gaussian_blur', 'spatter', 'saturate'] + elif 'None' in args.corruptions: + corruptions = ['None'] + args.severities = [0] + else: + corruptions = args.corruptions + + rank, _ = get_dist_info() + aggregated_results = {} + for corr_i, corruption in enumerate(corruptions): + aggregated_results[corruption] = {} + for sev_i, corruption_severity in enumerate(args.severities): + # evaluate severity 0 (= no corruption) only once + if corr_i > 0 and corruption_severity == 0: + aggregated_results[corruption][0] = \ + aggregated_results[corruptions[0]][0] + continue + + test_data_cfg = copy.deepcopy(cfg.data.test) + # assign corruption and severity + if corruption_severity > 0: + corruption_trans = dict( + type='Corrupt', + corruption=corruption, + severity=corruption_severity) + # TODO: hard coded "1", we assume that the first step is + # loading images, which needs to be fixed in the future + test_data_cfg['pipeline'].insert(1, corruption_trans) + + # print info + print(f'\nTesting {corruption} at severity {corruption_severity}') + + # build the dataloader + # TODO: support multiple images per gpu + # (only minor changes are needed) + dataset = build_dataset(test_data_cfg) + data_loader = build_dataloader( + dataset, + samples_per_gpu=1, + workers_per_gpu=args.workers, + dist=distributed, + shuffle=False) + + # build the model and load checkpoint + cfg.model.train_cfg = None + model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg')) + fp16_cfg = cfg.get('fp16', None) + if fp16_cfg is not None: + wrap_fp16_model(model) + checkpoint = load_checkpoint( + model, args.checkpoint, map_location='cpu') + # old versions did not save class info in checkpoints, + # this walkaround is for backward compatibility + if 'CLASSES' in checkpoint.get('meta', {}): + model.CLASSES = checkpoint['meta']['CLASSES'] + else: + model.CLASSES = dataset.CLASSES + + if not distributed: + model = MMDataParallel(model, device_ids=[0]) + show_dir = args.show_dir + if show_dir is not None: + show_dir = osp.join(show_dir, corruption) + show_dir = osp.join(show_dir, str(corruption_severity)) + if not osp.exists(show_dir): + osp.makedirs(show_dir) + outputs = single_gpu_test(model, data_loader, args.show, + show_dir, args.show_score_thr) + else: + model = MMDistributedDataParallel( + model.cuda(), + device_ids=[torch.cuda.current_device()], + broadcast_buffers=False) + outputs = multi_gpu_test(model, data_loader, args.tmpdir) + + if args.out and rank == 0: + eval_results_filename = ( + osp.splitext(args.out)[0] + '_results' + + osp.splitext(args.out)[1]) + mmcv.dump(outputs, args.out) + eval_types = args.eval + if cfg.dataset_type == 'VOCDataset': + if eval_types: + for eval_type in eval_types: + if eval_type == 'bbox': + test_dataset = mmcv.runner.obj_from_dict( + cfg.data.test, datasets) + logger = 'print' if args.summaries else None + mean_ap, eval_results = \ + voc_eval_with_return( + args.out, test_dataset, + args.iou_thr, logger) + aggregated_results[corruption][ + corruption_severity] = eval_results + else: + print('\nOnly "bbox" evaluation \ + is supported for pascal voc') + else: + if eval_types: + print(f'Starting evaluate {" and ".join(eval_types)}') + if eval_types == ['proposal_fast']: + result_file = args.out + else: + if not isinstance(outputs[0], dict): + result_files = dataset.results2json( + outputs, args.out) + else: + for name in outputs[0]: + print(f'\nEvaluating {name}') + outputs_ = [out[name] for out in outputs] + result_file = args.out + + f'.{name}' + result_files = dataset.results2json( + outputs_, result_file) + eval_results = coco_eval_with_return( + result_files, eval_types, dataset.coco) + aggregated_results[corruption][ + corruption_severity] = eval_results + else: + print('\nNo task was selected for evaluation;' + '\nUse --eval to select a task') + + # save results after each evaluation + mmcv.dump(aggregated_results, eval_results_filename) + + if rank == 0: + # print final results + print('\nAggregated results:') + prints = args.final_prints + aggregate = args.final_prints_aggregate + + if cfg.dataset_type == 'VOCDataset': + get_results( + eval_results_filename, + dataset='voc', + prints=prints, + aggregate=aggregate) + else: + get_results( + eval_results_filename, + dataset='coco', + prints=prints, + aggregate=aggregate) + + +if __name__ == '__main__': + main() diff --git a/tools/dataset_converters/cityscapes.py b/tools/dataset_converters/cityscapes.py new file mode 100644 index 0000000..bde3dac --- /dev/null +++ b/tools/dataset_converters/cityscapes.py @@ -0,0 +1,151 @@ +import argparse +import glob +import os.path as osp + +import cityscapesscripts.helpers.labels as CSLabels +import mmcv +import numpy as np +import pycocotools.mask as maskUtils + + +def collect_files(img_dir, gt_dir): + suffix = 'leftImg8bit.png' + files = [] + for img_file in glob.glob(osp.join(img_dir, '**/*.png')): + assert img_file.endswith(suffix), img_file + inst_file = gt_dir + img_file[ + len(img_dir):-len(suffix)] + 'gtFine_instanceIds.png' + # Note that labelIds are not converted to trainId for seg map + segm_file = gt_dir + img_file[ + len(img_dir):-len(suffix)] + 'gtFine_labelIds.png' + files.append((img_file, inst_file, segm_file)) + assert len(files), f'No images found in {img_dir}' + print(f'Loaded {len(files)} images from {img_dir}') + + return files + + +def collect_annotations(files, nproc=1): + print('Loading annotation images') + if nproc > 1: + images = mmcv.track_parallel_progress( + load_img_info, files, nproc=nproc) + else: + images = mmcv.track_progress(load_img_info, files) + + return images + + +def load_img_info(files): + img_file, inst_file, segm_file = files + inst_img = mmcv.imread(inst_file, 'unchanged') + # ids < 24 are stuff labels (filtering them first is about 5% faster) + unique_inst_ids = np.unique(inst_img[inst_img >= 24]) + anno_info = [] + for inst_id in unique_inst_ids: + # For non-crowd annotations, inst_id // 1000 is the label_id + # Crowd annotations have <1000 instance ids + label_id = inst_id // 1000 if inst_id >= 1000 else inst_id + label = CSLabels.id2label[label_id] + if not label.hasInstances or label.ignoreInEval: + continue + + category_id = label.id + iscrowd = int(inst_id < 1000) + mask = np.asarray(inst_img == inst_id, dtype=np.uint8, order='F') + mask_rle = maskUtils.encode(mask[:, :, None])[0] + + area = maskUtils.area(mask_rle) + # convert to COCO style XYWH format + bbox = maskUtils.toBbox(mask_rle) + + # for json encoding + mask_rle['counts'] = mask_rle['counts'].decode() + + anno = dict( + iscrowd=iscrowd, + category_id=category_id, + bbox=bbox.tolist(), + area=area.tolist(), + segmentation=mask_rle) + anno_info.append(anno) + video_name = osp.basename(osp.dirname(img_file)) + img_info = dict( + # remove img_prefix for filename + file_name=osp.join(video_name, osp.basename(img_file)), + height=inst_img.shape[0], + width=inst_img.shape[1], + anno_info=anno_info, + segm_file=osp.join(video_name, osp.basename(segm_file))) + + return img_info + + +def cvt_annotations(image_infos, out_json_name): + out_json = dict() + img_id = 0 + ann_id = 0 + out_json['images'] = [] + out_json['categories'] = [] + out_json['annotations'] = [] + for image_info in image_infos: + image_info['id'] = img_id + anno_infos = image_info.pop('anno_info') + out_json['images'].append(image_info) + for anno_info in anno_infos: + anno_info['image_id'] = img_id + anno_info['id'] = ann_id + out_json['annotations'].append(anno_info) + ann_id += 1 + img_id += 1 + for label in CSLabels.labels: + if label.hasInstances and not label.ignoreInEval: + cat = dict(id=label.id, name=label.name) + out_json['categories'].append(cat) + + if len(out_json['annotations']) == 0: + out_json.pop('annotations') + + mmcv.dump(out_json, out_json_name) + return out_json + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Convert Cityscapes annotations to COCO format') + parser.add_argument('cityscapes_path', help='cityscapes data path') + parser.add_argument('--img-dir', default='leftImg8bit', type=str) + parser.add_argument('--gt-dir', default='gtFine', type=str) + parser.add_argument('-o', '--out-dir', help='output path') + parser.add_argument( + '--nproc', default=1, type=int, help='number of process') + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + cityscapes_path = args.cityscapes_path + out_dir = args.out_dir if args.out_dir else cityscapes_path + mmcv.mkdir_or_exist(out_dir) + + img_dir = osp.join(cityscapes_path, args.img_dir) + gt_dir = osp.join(cityscapes_path, args.gt_dir) + + set_name = dict( + train='instancesonly_filtered_gtFine_train.json', + val='instancesonly_filtered_gtFine_val.json', + test='instancesonly_filtered_gtFine_test.json') + + for split, json_name in set_name.items(): + print(f'Converting {split} into {json_name}') + with mmcv.Timer( + print_tmpl='It took {}s to convert Cityscapes annotation'): + files = collect_files( + osp.join(img_dir, split), osp.join(gt_dir, split)) + image_infos = collect_annotations(files, nproc=args.nproc) + cvt_annotations(image_infos, osp.join(out_dir, json_name)) + + +if __name__ == '__main__': + main() diff --git a/tools/dataset_converters/pascal_voc.py b/tools/dataset_converters/pascal_voc.py new file mode 100644 index 0000000..f109307 --- /dev/null +++ b/tools/dataset_converters/pascal_voc.py @@ -0,0 +1,236 @@ +import argparse +import os.path as osp +import xml.etree.ElementTree as ET + +import mmcv +import numpy as np + +from mmdet.core import voc_classes + +label_ids = {name: i for i, name in enumerate(voc_classes())} + + +def parse_xml(args): + xml_path, img_path = args + tree = ET.parse(xml_path) + root = tree.getroot() + size = root.find('size') + w = int(size.find('width').text) + h = int(size.find('height').text) + bboxes = [] + labels = [] + bboxes_ignore = [] + labels_ignore = [] + for obj in root.findall('object'): + name = obj.find('name').text + label = label_ids[name] + difficult = int(obj.find('difficult').text) + bnd_box = obj.find('bndbox') + bbox = [ + int(bnd_box.find('xmin').text), + int(bnd_box.find('ymin').text), + int(bnd_box.find('xmax').text), + int(bnd_box.find('ymax').text) + ] + if difficult: + bboxes_ignore.append(bbox) + labels_ignore.append(label) + else: + bboxes.append(bbox) + labels.append(label) + if not bboxes: + bboxes = np.zeros((0, 4)) + labels = np.zeros((0, )) + else: + bboxes = np.array(bboxes, ndmin=2) - 1 + labels = np.array(labels) + if not bboxes_ignore: + bboxes_ignore = np.zeros((0, 4)) + labels_ignore = np.zeros((0, )) + else: + bboxes_ignore = np.array(bboxes_ignore, ndmin=2) - 1 + labels_ignore = np.array(labels_ignore) + annotation = { + 'filename': img_path, + 'width': w, + 'height': h, + 'ann': { + 'bboxes': bboxes.astype(np.float32), + 'labels': labels.astype(np.int64), + 'bboxes_ignore': bboxes_ignore.astype(np.float32), + 'labels_ignore': labels_ignore.astype(np.int64) + } + } + return annotation + + +def cvt_annotations(devkit_path, years, split, out_file): + if not isinstance(years, list): + years = [years] + annotations = [] + for year in years: + filelist = osp.join(devkit_path, + f'VOC{year}/ImageSets/Main/{split}.txt') + if not osp.isfile(filelist): + print(f'filelist does not exist: {filelist}, ' + f'skip voc{year} {split}') + return + img_names = mmcv.list_from_file(filelist) + xml_paths = [ + osp.join(devkit_path, f'VOC{year}/Annotations/{img_name}.xml') + for img_name in img_names + ] + img_paths = [ + f'VOC{year}/JPEGImages/{img_name}.jpg' for img_name in img_names + ] + part_annotations = mmcv.track_progress(parse_xml, + list(zip(xml_paths, img_paths))) + annotations.extend(part_annotations) + if out_file.endswith('json'): + annotations = cvt_to_coco_json(annotations) + mmcv.dump(annotations, out_file) + return annotations + + +def cvt_to_coco_json(annotations): + image_id = 0 + annotation_id = 0 + coco = dict() + coco['images'] = [] + coco['type'] = 'instance' + coco['categories'] = [] + coco['annotations'] = [] + image_set = set() + + def addAnnItem(annotation_id, image_id, category_id, bbox, difficult_flag): + annotation_item = dict() + annotation_item['segmentation'] = [] + + seg = [] + # bbox[] is x1,y1,x2,y2 + # left_top + seg.append(int(bbox[0])) + seg.append(int(bbox[1])) + # left_bottom + seg.append(int(bbox[0])) + seg.append(int(bbox[3])) + # right_bottom + seg.append(int(bbox[2])) + seg.append(int(bbox[3])) + # right_top + seg.append(int(bbox[2])) + seg.append(int(bbox[1])) + + annotation_item['segmentation'].append(seg) + + xywh = np.array( + [bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]]) + annotation_item['area'] = int(xywh[2] * xywh[3]) + if difficult_flag == 1: + annotation_item['ignore'] = 0 + annotation_item['iscrowd'] = 1 + else: + annotation_item['ignore'] = 0 + annotation_item['iscrowd'] = 0 + annotation_item['image_id'] = int(image_id) + annotation_item['bbox'] = xywh.astype(int).tolist() + annotation_item['category_id'] = int(category_id) + annotation_item['id'] = int(annotation_id) + coco['annotations'].append(annotation_item) + return annotation_id + 1 + + for category_id, name in enumerate(voc_classes()): + category_item = dict() + category_item['supercategory'] = str('none') + category_item['id'] = int(category_id) + category_item['name'] = str(name) + coco['categories'].append(category_item) + + for ann_dict in annotations: + file_name = ann_dict['filename'] + ann = ann_dict['ann'] + assert file_name not in image_set + image_item = dict() + image_item['id'] = int(image_id) + image_item['file_name'] = str(file_name) + image_item['height'] = int(ann_dict['height']) + image_item['width'] = int(ann_dict['width']) + coco['images'].append(image_item) + image_set.add(file_name) + + bboxes = ann['bboxes'][:, :4] + labels = ann['labels'] + for bbox_id in range(len(bboxes)): + bbox = bboxes[bbox_id] + label = labels[bbox_id] + annotation_id = addAnnItem( + annotation_id, image_id, label, bbox, difficult_flag=0) + + bboxes_ignore = ann['bboxes_ignore'][:, :4] + labels_ignore = ann['labels_ignore'] + for bbox_id in range(len(bboxes_ignore)): + bbox = bboxes_ignore[bbox_id] + label = labels_ignore[bbox_id] + annotation_id = addAnnItem( + annotation_id, image_id, label, bbox, difficult_flag=1) + + image_id += 1 + + return coco + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Convert PASCAL VOC annotations to mmdetection format') + parser.add_argument('devkit_path', help='pascal voc devkit path') + parser.add_argument('-o', '--out-dir', help='output path') + parser.add_argument( + '--out-format', + default='pkl', + choices=('pkl', 'coco'), + help='output format, "coco" indicates coco annotation format') + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + devkit_path = args.devkit_path + out_dir = args.out_dir if args.out_dir else devkit_path + mmcv.mkdir_or_exist(out_dir) + + years = [] + if osp.isdir(osp.join(devkit_path, 'VOC2007')): + years.append('2007') + if osp.isdir(osp.join(devkit_path, 'VOC2012')): + years.append('2012') + if '2007' in years and '2012' in years: + years.append(['2007', '2012']) + if not years: + raise IOError(f'The devkit path {devkit_path} contains neither ' + '"VOC2007" nor "VOC2012" subfolder') + out_fmt = f'.{args.out_format}' + if args.out_format == 'coco': + out_fmt = '.json' + for year in years: + if year == '2007': + prefix = 'voc07' + elif year == '2012': + prefix = 'voc12' + elif year == ['2007', '2012']: + prefix = 'voc0712' + for split in ['train', 'val', 'trainval']: + dataset_name = prefix + '_' + split + print(f'processing {dataset_name} ...') + cvt_annotations(devkit_path, year, split, + osp.join(out_dir, dataset_name + out_fmt)) + if not isinstance(year, list): + dataset_name = prefix + '_test' + print(f'processing {dataset_name} ...') + cvt_annotations(devkit_path, year, 'test', + osp.join(out_dir, dataset_name + out_fmt)) + print('Done!') + + +if __name__ == '__main__': + main() diff --git a/tools/deployment/mmdet2torchserve.py b/tools/deployment/mmdet2torchserve.py new file mode 100644 index 0000000..d1d8501 --- /dev/null +++ b/tools/deployment/mmdet2torchserve.py @@ -0,0 +1,109 @@ +from argparse import ArgumentParser, Namespace +from pathlib import Path +from tempfile import TemporaryDirectory + +import mmcv + +try: + from model_archiver.model_packaging import package_model + from model_archiver.model_packaging_utils import ModelExportUtils +except ImportError: + package_model = None + + +def mmdet2torchserve( + config_file: str, + checkpoint_file: str, + output_folder: str, + model_name: str, + model_version: str = '1.0', + force: bool = False, +): + """Converts MMDetection model (config + checkpoint) to TorchServe `.mar`. + + Args: + config_file: + In MMDetection config format. + The contents vary for each task repository. + checkpoint_file: + In MMDetection checkpoint format. + The contents vary for each task repository. + output_folder: + Folder where `{model_name}.mar` will be created. + The file created will be in TorchServe archive format. + model_name: + If not None, used for naming the `{model_name}.mar` file + that will be created under `output_folder`. + If None, `{Path(checkpoint_file).stem}` will be used. + model_version: + Model's version. + force: + If True, if there is an existing `{model_name}.mar` + file under `output_folder` it will be overwritten. + """ + mmcv.mkdir_or_exist(output_folder) + + config = mmcv.Config.fromfile(config_file) + + with TemporaryDirectory() as tmpdir: + config.dump(f'{tmpdir}/config.py') + + args = Namespace( + **{ + 'model_file': f'{tmpdir}/config.py', + 'serialized_file': checkpoint_file, + 'handler': f'{Path(__file__).parent}/mmdet_handler.py', + 'model_name': model_name or Path(checkpoint_file).stem, + 'version': model_version, + 'export_path': output_folder, + 'force': force, + 'requirements_file': None, + 'extra_files': None, + 'runtime': 'python', + 'archive_format': 'default' + }) + manifest = ModelExportUtils.generate_manifest_json(args) + package_model(args, manifest) + + +def parse_args(): + parser = ArgumentParser( + description='Convert MMDetection models to TorchServe `.mar` format.') + parser.add_argument('config', type=str, help='config file path') + parser.add_argument('checkpoint', type=str, help='checkpoint file path') + parser.add_argument( + '--output-folder', + type=str, + required=True, + help='Folder where `{model_name}.mar` will be created.') + parser.add_argument( + '--model-name', + type=str, + default=None, + help='If not None, used for naming the `{model_name}.mar`' + 'file that will be created under `output_folder`.' + 'If None, `{Path(checkpoint_file).stem}` will be used.') + parser.add_argument( + '--model-version', + type=str, + default='1.0', + help='Number used for versioning.') + parser.add_argument( + '-f', + '--force', + action='store_true', + help='overwrite the existing `{model_name}.mar`') + args = parser.parse_args() + + return args + + +if __name__ == '__main__': + args = parse_args() + + if package_model is None: + raise ImportError('`torch-model-archiver` is required.' + 'Try: pip install torch-model-archiver') + + mmdet2torchserve(args.config, args.checkpoint, args.output_folder, + args.model_name, args.model_version, args.force) diff --git a/tools/deployment/mmdet_handler.py b/tools/deployment/mmdet_handler.py new file mode 100644 index 0000000..568fcd2 --- /dev/null +++ b/tools/deployment/mmdet_handler.py @@ -0,0 +1,69 @@ +import base64 +import os + +import mmcv +import torch +from ts.torch_handler.base_handler import BaseHandler + +from mmdet.apis import inference_detector, init_detector + + +class MMdetHandler(BaseHandler): + threshold = 0.5 + + def initialize(self, context): + properties = context.system_properties + self.map_location = 'cuda' if torch.cuda.is_available() else 'cpu' + self.device = torch.device(self.map_location + ':' + + str(properties.get('gpu_id')) if torch.cuda. + is_available() else self.map_location) + self.manifest = context.manifest + + model_dir = properties.get('model_dir') + serialized_file = self.manifest['model']['serializedFile'] + checkpoint = os.path.join(model_dir, serialized_file) + self.config_file = os.path.join(model_dir, 'config.py') + + self.model = init_detector(self.config_file, checkpoint, self.device) + self.initialized = True + + def preprocess(self, data): + images = [] + + for row in data: + image = row.get('data') or row.get('body') + if isinstance(image, str): + image = base64.b64decode(image) + image = mmcv.imfrombytes(image) + images.append(image) + + return images + + def inference(self, data, *args, **kwargs): + results = inference_detector(self.model, data) + return results + + def postprocess(self, data): + # Format output following the example ObjectDetectionHandler format + output = [] + for image_index, image_result in enumerate(data): + output.append([]) + if isinstance(image_result, tuple): + bbox_result, segm_result = image_result + if isinstance(segm_result, tuple): + segm_result = segm_result[0] # ms rcnn + else: + bbox_result, segm_result = image_result, None + + for class_index, class_result in enumerate(bbox_result): + class_name = self.model.CLASSES[class_index] + for bbox in class_result: + bbox_coords = bbox[:-1].tolist() + score = float(bbox[-1]) + if score >= self.threshold: + output[image_index].append({ + class_name: bbox_coords, + 'score': score + }) + + return output diff --git a/tools/deployment/onnx2tensorrt.py b/tools/deployment/onnx2tensorrt.py new file mode 100644 index 0000000..e2ecbda --- /dev/null +++ b/tools/deployment/onnx2tensorrt.py @@ -0,0 +1,206 @@ +import argparse +import os +import os.path as osp + +import numpy as np +import onnx +import onnxruntime as ort +import torch +from mmcv.ops import get_onnxruntime_op_path +from mmcv.tensorrt import (TRTWraper, is_tensorrt_plugin_loaded, onnx2trt, + save_trt_engine) + +from mmdet.core import get_classes +from mmdet.core.export import preprocess_example_input +from mmdet.core.visualization.image import imshow_det_bboxes + + +def get_GiB(x: int): + """return x GiB.""" + return x * (1 << 30) + + +def onnx2tensorrt(onnx_file, + trt_file, + input_config, + verify=False, + show=False, + dataset='coco', + workspace_size=1, + verbose=False): + import tensorrt as trt + onnx_model = onnx.load(onnx_file) + input_shape = input_config['input_shape'] + # create trt engine and wraper + opt_shape_dict = {'input': [input_shape, input_shape, input_shape]} + max_workspace_size = get_GiB(workspace_size) + trt_engine = onnx2trt( + onnx_model, + opt_shape_dict, + log_level=trt.Logger.VERBOSE if verbose else trt.Logger.ERROR, + fp16_mode=False, + max_workspace_size=max_workspace_size) + save_dir, _ = osp.split(trt_file) + if save_dir: + os.makedirs(save_dir, exist_ok=True) + save_trt_engine(trt_engine, trt_file) + print(f'Successfully created TensorRT engine: {trt_file}') + + if verify: + one_img, one_meta = preprocess_example_input(input_config) + input_img_cpu = one_img.detach().cpu().numpy() + input_img_cuda = one_img.cuda() + img = one_meta['show_img'] + + # Get results from ONNXRuntime + ort_custom_op_path = get_onnxruntime_op_path() + session_options = ort.SessionOptions() + if osp.exists(ort_custom_op_path): + session_options.register_custom_ops_library(ort_custom_op_path) + sess = ort.InferenceSession(onnx_file, session_options) + output_names = [_.name for _ in sess.get_outputs()] + ort_outputs = sess.run(None, { + 'input': input_img_cpu, + }) + with_mask = len(output_names) == 3 + ort_outputs = [_.squeeze(0) for _ in ort_outputs] + ort_dets, ort_labels = ort_outputs[:2] + ort_masks = ort_outputs[2] if with_mask else None + ort_shapes = [_.shape for _ in ort_outputs] + print(f'ONNX Runtime output names: {output_names}, \ + output shapes: {ort_shapes}') + + # Get results from TensorRT + trt_model = TRTWraper(trt_file, ['input'], output_names) + with torch.no_grad(): + trt_outputs = trt_model({'input': input_img_cuda}) + trt_outputs = [ + trt_outputs[_].detach().cpu().numpy().squeeze(0) + for _ in output_names + ] + trt_dets, trt_labels = trt_outputs[:2] + trt_shapes = [_.shape for _ in trt_outputs] + print(f'TensorRT output names: {output_names}, \ + output shapes: {trt_shapes}') + trt_masks = trt_outputs[2] if with_mask else None + + # Show detection outputs + if show: + CLASSES = get_classes(dataset) + score_thr = 0.35 + imshow_det_bboxes( + img.copy(), + trt_dets, + trt_labels, + segms=trt_masks, + class_names=CLASSES, + score_thr=score_thr, + win_name='TensorRT') + imshow_det_bboxes( + img.copy(), + ort_dets, + ort_labels, + segms=ort_masks, + class_names=CLASSES, + score_thr=score_thr, + win_name='ONNXRuntime') + # Compare results + np.testing.assert_allclose(ort_dets, trt_dets, rtol=1e-03, atol=1e-05) + np.testing.assert_allclose(ort_labels, trt_labels) + if with_mask: + np.testing.assert_allclose( + ort_masks, trt_masks, rtol=1e-03, atol=1e-05) + print('The numerical values are the same ' + + 'between ONNXRuntime and TensorRT') + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Convert MMDetection models from ONNX to TensorRT') + parser.add_argument('model', help='Filename of input ONNX model') + parser.add_argument( + '--trt-file', + type=str, + default='tmp.trt', + help='Filename of output TensorRT engine') + parser.add_argument( + '--input-img', type=str, default='', help='Image for test') + parser.add_argument( + '--show', action='store_true', help='Whether to show output results') + parser.add_argument( + '--dataset', type=str, default='coco', help='Dataset name') + parser.add_argument( + '--verify', + action='store_true', + help='Verify the outputs of ONNXRuntime and TensorRT') + parser.add_argument( + '--verbose', + action='store_true', + help='Whether to verbose logging messages while creating \ + TensorRT engine. Defaults to False.') + parser.add_argument( + '--to-rgb', + action='store_false', + help='Feed model with RGB or BGR image. Default is RGB.') + parser.add_argument( + '--shape', + type=int, + nargs='+', + default=[400, 600], + help='Input size of the model') + parser.add_argument( + '--mean', + type=float, + nargs='+', + default=[123.675, 116.28, 103.53], + help='Mean value used for preprocess input data') + parser.add_argument( + '--std', + type=float, + nargs='+', + default=[58.395, 57.12, 57.375], + help='Variance value used for preprocess input data') + parser.add_argument( + '--workspace-size', + type=int, + default=1, + help='Max workspace size in GiB') + args = parser.parse_args() + return args + + +if __name__ == '__main__': + + assert is_tensorrt_plugin_loaded(), 'TensorRT plugin should be compiled.' + args = parse_args() + + if not args.input_img: + args.input_img = osp.join(osp.dirname(__file__), '../demo/demo.jpg') + + if len(args.shape) == 1: + input_shape = (1, 3, args.shape[0], args.shape[0]) + elif len(args.shape) == 2: + input_shape = (1, 3) + tuple(args.shape) + else: + raise ValueError('invalid input shape') + + assert len(args.mean) == 3 + assert len(args.std) == 3 + + normalize_cfg = {'mean': args.mean, 'std': args.std, 'to_rgb': args.to_rgb} + input_config = { + 'input_shape': input_shape, + 'input_path': args.input_img, + 'normalize_cfg': normalize_cfg + } + + # Create TensorRT engine + onnx2tensorrt( + args.model, + args.trt_file, + input_config, + verify=args.verify, + show=args.show, + dataset=args.dataset, + workspace_size=args.workspace_size, + verbose=args.verbose) diff --git a/tools/deployment/pytorch2onnx.py b/tools/deployment/pytorch2onnx.py new file mode 100644 index 0000000..343402b --- /dev/null +++ b/tools/deployment/pytorch2onnx.py @@ -0,0 +1,275 @@ +import argparse +import os.path as osp +import warnings + +import numpy as np +import onnx +import onnxruntime as rt +import torch +from mmcv import DictAction + +from mmdet.core.export import (build_model_from_cfg, + generate_inputs_and_wrap_model, + preprocess_example_input) + + +def pytorch2onnx(config_path, + checkpoint_path, + input_img, + input_shape, + opset_version=11, + show=False, + output_file='tmp.onnx', + verify=False, + normalize_cfg=None, + dataset='coco', + test_img=None, + do_simplify=False, + cfg_options=None, + dynamic_export=None): + + input_config = { + 'input_shape': input_shape, + 'input_path': input_img, + 'normalize_cfg': normalize_cfg + } + + # prepare original model and meta for verifying the onnx model + orig_model = build_model_from_cfg( + config_path, checkpoint_path, cfg_options=cfg_options) + one_img, one_meta = preprocess_example_input(input_config) + model, tensor_data = generate_inputs_and_wrap_model( + config_path, checkpoint_path, input_config, cfg_options=cfg_options) + output_names = ['dets', 'labels'] + if model.with_mask: + output_names.append('masks') + dynamic_axes = None + if dynamic_export: + dynamic_axes = { + 'input': { + 0: 'batch', + 2: 'width', + 3: 'height' + }, + 'dets': { + 0: 'batch', + 1: 'num_dets', + }, + 'labels': { + 0: 'batch', + 1: 'num_dets', + }, + } + if model.with_mask: + dynamic_axes['masks'] = {0: 'batch', 1: 'num_dets'} + + torch.onnx.export( + model, + tensor_data, + output_file, + input_names=['input'], + output_names=output_names, + export_params=True, + keep_initializers_as_inputs=True, + do_constant_folding=True, + verbose=show, + opset_version=opset_version, + dynamic_axes=dynamic_axes) + + model.forward = orig_model.forward + + # get the custom op path + ort_custom_op_path = '' + try: + from mmcv.ops import get_onnxruntime_op_path + ort_custom_op_path = get_onnxruntime_op_path() + except (ImportError, ModuleNotFoundError): + warnings.warn('If input model has custom op from mmcv, \ + you may have to build mmcv with ONNXRuntime from source.') + + if do_simplify: + from mmdet import digit_version + import onnxsim + + min_required_version = '0.3.0' + assert digit_version(onnxsim.__version__) >= digit_version( + min_required_version + ), f'Requires to install onnx-simplify>={min_required_version}' + + input_dic = {'input': one_img.detach().cpu().numpy()} + onnxsim.simplify( + output_file, input_data=input_dic, custom_lib=ort_custom_op_path) + print(f'Successfully exported ONNX model: {output_file}') + + if verify: + from mmdet.core import get_classes, bbox2result + from mmdet.apis import show_result_pyplot + + model.CLASSES = get_classes(dataset) + num_classes = len(model.CLASSES) + # check by onnx + onnx_model = onnx.load(output_file) + onnx.checker.check_model(onnx_model) + if dynamic_export: + # scale up to test dynamic shape + h, w = [int((_ * 1.5) // 32 * 32) for _ in input_shape[2:]] + input_config['input_shape'] = (1, 3, h, w) + if test_img is not None: + input_config['input_path'] = test_img + one_img, one_meta = preprocess_example_input(input_config) + tensor_data = [one_img] + + # get pytorch output + pytorch_results = model(tensor_data, [[one_meta]], return_loss=False) + pytorch_results = pytorch_results[0] + # get onnx output + input_all = [node.name for node in onnx_model.graph.input] + input_initializer = [ + node.name for node in onnx_model.graph.initializer + ] + net_feed_input = list(set(input_all) - set(input_initializer)) + assert (len(net_feed_input) == 1) + session_options = rt.SessionOptions() + # register custom op for ONNX Runtime + if osp.exists(ort_custom_op_path): + session_options.register_custom_ops_library(ort_custom_op_path) + feed_input_img = one_img.detach().numpy() + if dynamic_export: + # test batch with two input images + feed_input_img = np.vstack([feed_input_img, feed_input_img]) + sess = rt.InferenceSession(output_file, session_options) + onnx_outputs = sess.run(None, {net_feed_input[0]: feed_input_img}) + output_names = [_.name for _ in sess.get_outputs()] + output_shapes = [_.shape for _ in onnx_outputs] + print(f'ONNX Runtime output names: {output_names}, \ + output shapes: {output_shapes}') + # get last image's outputs + onnx_outputs = [_[-1] for _ in onnx_outputs] + ort_dets, ort_labels = onnx_outputs[:2] + onnx_results = bbox2result(ort_dets, ort_labels, num_classes) + if model.with_mask: + segm_results = onnx_outputs[2] + cls_segms = [[] for _ in range(num_classes)] + for i in range(ort_dets.shape[0]): + cls_segms[ort_labels[i]].append(segm_results[i]) + onnx_results = (onnx_results, cls_segms) + # visualize predictions + if show: + show_result_pyplot( + model, one_meta['show_img'], pytorch_results, title='Pytorch') + show_result_pyplot( + model, one_meta['show_img'], onnx_results, title='ONNXRuntime') + + # compare a part of result + if model.with_mask: + compare_pairs = list(zip(onnx_results, pytorch_results)) + else: + compare_pairs = [(onnx_results, pytorch_results)] + err_msg = 'The numerical values are different between Pytorch' + \ + ' and ONNX, but it does not necessarily mean the' + \ + ' exported ONNX model is problematic.' + # check the numerical value + for onnx_res, pytorch_res in compare_pairs: + for o_res, p_res in zip(onnx_res, pytorch_res): + np.testing.assert_allclose( + o_res, p_res, rtol=1e-03, atol=1e-05, err_msg=err_msg) + print('The numerical values are the same between Pytorch and ONNX') + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Convert MMDetection models to ONNX') + parser.add_argument('config', help='test config file path') + parser.add_argument('checkpoint', help='checkpoint file') + parser.add_argument('--input-img', type=str, help='Images for input') + parser.add_argument( + '--show', + action='store_true', + help='Show onnx graph and detection outputs') + parser.add_argument('--output-file', type=str, default='tmp.onnx') + parser.add_argument('--opset-version', type=int, default=11) + parser.add_argument( + '--test-img', type=str, default=None, help='Images for test') + parser.add_argument( + '--dataset', type=str, default='coco', help='Dataset name') + parser.add_argument( + '--verify', + action='store_true', + help='verify the onnx model output against pytorch output') + parser.add_argument( + '--simplify', + action='store_true', + help='Whether to simplify onnx model.') + parser.add_argument( + '--shape', + type=int, + nargs='+', + default=[800, 1216], + help='input image size') + parser.add_argument( + '--mean', + type=float, + nargs='+', + default=[123.675, 116.28, 103.53], + help='mean value used for preprocess input data') + parser.add_argument( + '--std', + type=float, + nargs='+', + default=[58.395, 57.12, 57.375], + help='variance value used for preprocess input data') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='Override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + parser.add_argument( + '--dynamic-export', + action='store_true', + help='Whether to export onnx with dynamic axis.') + args = parser.parse_args() + return args + + +if __name__ == '__main__': + args = parse_args() + + assert args.opset_version == 11, 'MMDet only support opset 11 now' + + if not args.input_img: + args.input_img = osp.join( + osp.dirname(__file__), '../../tests/data/color.jpg') + + if len(args.shape) == 1: + input_shape = (1, 3, args.shape[0], args.shape[0]) + elif len(args.shape) == 2: + input_shape = (1, 3) + tuple(args.shape) + else: + raise ValueError('invalid input shape') + + assert len(args.mean) == 3 + assert len(args.std) == 3 + + normalize_cfg = {'mean': args.mean, 'std': args.std} + + # convert model to onnx file + pytorch2onnx( + args.config, + args.checkpoint, + args.input_img, + input_shape, + opset_version=args.opset_version, + show=args.show, + output_file=args.output_file, + verify=args.verify, + normalize_cfg=normalize_cfg, + dataset=args.dataset, + test_img=args.test_img, + do_simplify=args.simplify, + cfg_options=args.cfg_options, + dynamic_export=args.dynamic_export) diff --git a/tools/deployment/test.py b/tools/deployment/test.py new file mode 100644 index 0000000..770589d --- /dev/null +++ b/tools/deployment/test.py @@ -0,0 +1,132 @@ +import argparse + +import mmcv +from mmcv import Config, DictAction +from mmcv.parallel import MMDataParallel + +from mmdet.apis import single_gpu_test +from mmdet.core.export.model_wrappers import ONNXRuntimeDetector +from mmdet.datasets import (build_dataloader, build_dataset, + replace_ImageToTensor) + + +def parse_args(): + parser = argparse.ArgumentParser( + description='MMDet test (and eval) an ONNX model using ONNXRuntime') + parser.add_argument('config', help='test config file path') + parser.add_argument('model', help='Input model file') + parser.add_argument('--out', help='output result file in pickle format') + parser.add_argument( + '--format-only', + action='store_true', + help='Format the output results without perform evaluation. It is' + 'useful when you want to format the result to a specific format and ' + 'submit it to the test server') + parser.add_argument( + '--eval', + type=str, + nargs='+', + help='evaluation metrics, which depends on the dataset, e.g., "bbox",' + ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC') + parser.add_argument('--show', action='store_true', help='show results') + parser.add_argument( + '--show-dir', help='directory where painted images will be saved') + parser.add_argument( + '--show-score-thr', + type=float, + default=0.3, + help='score threshold (default: 0.3)') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + parser.add_argument( + '--eval-options', + nargs='+', + action=DictAction, + help='custom options for evaluation, the key-value pair in xxx=yyy ' + 'format will be kwargs for dataset.evaluate() function') + + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + + assert args.out or args.eval or args.format_only or args.show \ + or args.show_dir, \ + ('Please specify at least one operation (save/eval/format/show the ' + 'results / save the results) with the argument "--out", "--eval"' + ', "--format-only", "--show" or "--show-dir"') + + if args.eval and args.format_only: + raise ValueError('--eval and --format_only cannot be both specified') + + if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): + raise ValueError('The output file must be a pkl file.') + + cfg = Config.fromfile(args.config) + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + + # in case the test dataset is concatenated + samples_per_gpu = 1 + if isinstance(cfg.data.test, dict): + cfg.data.test.test_mode = True + samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1) + if samples_per_gpu > 1: + # Replace 'ImageToTensor' to 'DefaultFormatBundle' + cfg.data.test.pipeline = replace_ImageToTensor( + cfg.data.test.pipeline) + elif isinstance(cfg.data.test, list): + for ds_cfg in cfg.data.test: + ds_cfg.test_mode = True + samples_per_gpu = max( + [ds_cfg.pop('samples_per_gpu', 1) for ds_cfg in cfg.data.test]) + if samples_per_gpu > 1: + for ds_cfg in cfg.data.test: + ds_cfg.pipeline = replace_ImageToTensor(ds_cfg.pipeline) + + # build the dataloader + dataset = build_dataset(cfg.data.test) + data_loader = build_dataloader( + dataset, + samples_per_gpu=samples_per_gpu, + workers_per_gpu=cfg.data.workers_per_gpu, + dist=False, + shuffle=False) + + model = ONNXRuntimeDetector( + args.model, class_names=dataset.CLASSES, device_id=0) + + model = MMDataParallel(model, device_ids=[0]) + outputs = single_gpu_test(model, data_loader, args.show, args.show_dir, + args.show_score_thr) + + if args.out: + print(f'\nwriting results to {args.out}') + mmcv.dump(outputs, args.out) + kwargs = {} if args.eval_options is None else args.eval_options + if args.format_only: + dataset.format_results(outputs, **kwargs) + if args.eval: + eval_kwargs = cfg.get('evaluation', {}).copy() + # hard-code way to remove EvalHook args + for key in [ + 'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best', + 'rule' + ]: + eval_kwargs.pop(key, None) + eval_kwargs.update(dict(metric=args.eval, **kwargs)) + print(dataset.evaluate(outputs, **eval_kwargs)) + + +if __name__ == '__main__': + main() diff --git a/tools/dist_test.sh b/tools/dist_test.sh new file mode 100755 index 0000000..3c74ec6 --- /dev/null +++ b/tools/dist_test.sh @@ -0,0 +1,10 @@ +#!/usr/bin/env bash + +CONFIG=$1 +CHECKPOINT=$2 +GPUS=$3 +PORT=${PORT:-29500} + +PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ +python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \ + $(dirname "$0")/test.py $CONFIG $CHECKPOINT --launcher pytorch ${@:4} diff --git a/tools/dist_train.sh b/tools/dist_train.sh new file mode 100755 index 0000000..5b43fff --- /dev/null +++ b/tools/dist_train.sh @@ -0,0 +1,9 @@ +#!/usr/bin/env bash + +CONFIG=$1 +GPUS=$2 +PORT=${PORT:-29500} + +PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ +python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \ + $(dirname "$0")/train.py $CONFIG --launcher pytorch ${@:3} diff --git a/tools/misc/browse_dataset.py b/tools/misc/browse_dataset.py new file mode 100644 index 0000000..0c9385f --- /dev/null +++ b/tools/misc/browse_dataset.py @@ -0,0 +1,96 @@ +import argparse +import os +from pathlib import Path + +import mmcv +from mmcv import Config, DictAction + +from mmdet.core.utils import mask2ndarray +from mmdet.core.visualization import imshow_det_bboxes +from mmdet.datasets.builder import build_dataset + + +def parse_args(): + parser = argparse.ArgumentParser(description='Browse a dataset') + parser.add_argument('config', help='train config file path') + parser.add_argument( + '--skip-type', + type=str, + nargs='+', + default=['DefaultFormatBundle', 'Normalize', 'Collect'], + help='skip some useless pipeline') + parser.add_argument( + '--output-dir', + default=None, + type=str, + help='If there is no display interface, you can save it') + parser.add_argument('--not-show', default=False, action='store_true') + parser.add_argument( + '--show-interval', + type=float, + default=2, + help='the interval of show (s)') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + args = parser.parse_args() + return args + + +def retrieve_data_cfg(config_path, skip_type, cfg_options): + cfg = Config.fromfile(config_path) + if cfg_options is not None: + cfg.merge_from_dict(cfg_options) + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) + train_data_cfg = cfg.data.train + train_data_cfg['pipeline'] = [ + x for x in train_data_cfg.pipeline if x['type'] not in skip_type + ] + + return cfg + + +def main(): + args = parse_args() + cfg = retrieve_data_cfg(args.config, args.skip_type, args.cfg_options) + + dataset = build_dataset(cfg.data.train) + + progress_bar = mmcv.ProgressBar(len(dataset)) + + for item in dataset: + filename = os.path.join(args.output_dir, + Path(item['filename']).name + ) if args.output_dir is not None else None + + gt_masks = item.get('gt_masks', None) + if gt_masks is not None: + gt_masks = mask2ndarray(gt_masks) + + imshow_det_bboxes( + item['img'], + item['gt_bboxes'], + item['gt_labels'], + gt_masks, + class_names=dataset.CLASSES, + show=not args.not_show, + wait_time=args.show_interval, + out_file=filename, + bbox_color=(255, 102, 61), + text_color=(255, 102, 61)) + + progress_bar.update() + + +if __name__ == '__main__': + main() diff --git a/tools/misc/print_config.py b/tools/misc/print_config.py new file mode 100644 index 0000000..3627f81 --- /dev/null +++ b/tools/misc/print_config.py @@ -0,0 +1,54 @@ +import argparse +import warnings + +from mmcv import Config, DictAction + + +def parse_args(): + parser = argparse.ArgumentParser(description='Print the whole config') + parser.add_argument('config', help='config file path') + parser.add_argument( + '--options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file (deprecate), ' + 'change to --cfg-options instead.') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + args = parser.parse_args() + + if args.options and args.cfg_options: + raise ValueError( + '--options and --cfg-options cannot be both ' + 'specified, --options is deprecated in favor of --cfg-options') + if args.options: + warnings.warn('--options is deprecated in favor of --cfg-options') + args.cfg_options = args.options + + return args + + +def main(): + args = parse_args() + + cfg = Config.fromfile(args.config) + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) + print(f'Config:\n{cfg.pretty_text}') + + +if __name__ == '__main__': + main() diff --git a/tools/model_converters/detectron2pytorch.py b/tools/model_converters/detectron2pytorch.py new file mode 100644 index 0000000..961e6f5 --- /dev/null +++ b/tools/model_converters/detectron2pytorch.py @@ -0,0 +1,82 @@ +import argparse +from collections import OrderedDict + +import mmcv +import torch + +arch_settings = {50: (3, 4, 6, 3), 101: (3, 4, 23, 3)} + + +def convert_bn(blobs, state_dict, caffe_name, torch_name, converted_names): + # detectron replace bn with affine channel layer + state_dict[torch_name + '.bias'] = torch.from_numpy(blobs[caffe_name + + '_b']) + state_dict[torch_name + '.weight'] = torch.from_numpy(blobs[caffe_name + + '_s']) + bn_size = state_dict[torch_name + '.weight'].size() + state_dict[torch_name + '.running_mean'] = torch.zeros(bn_size) + state_dict[torch_name + '.running_var'] = torch.ones(bn_size) + converted_names.add(caffe_name + '_b') + converted_names.add(caffe_name + '_s') + + +def convert_conv_fc(blobs, state_dict, caffe_name, torch_name, + converted_names): + state_dict[torch_name + '.weight'] = torch.from_numpy(blobs[caffe_name + + '_w']) + converted_names.add(caffe_name + '_w') + if caffe_name + '_b' in blobs: + state_dict[torch_name + '.bias'] = torch.from_numpy(blobs[caffe_name + + '_b']) + converted_names.add(caffe_name + '_b') + + +def convert(src, dst, depth): + """Convert keys in detectron pretrained ResNet models to pytorch style.""" + # load arch_settings + if depth not in arch_settings: + raise ValueError('Only support ResNet-50 and ResNet-101 currently') + block_nums = arch_settings[depth] + # load caffe model + caffe_model = mmcv.load(src, encoding='latin1') + blobs = caffe_model['blobs'] if 'blobs' in caffe_model else caffe_model + # convert to pytorch style + state_dict = OrderedDict() + converted_names = set() + convert_conv_fc(blobs, state_dict, 'conv1', 'conv1', converted_names) + convert_bn(blobs, state_dict, 'res_conv1_bn', 'bn1', converted_names) + for i in range(1, len(block_nums) + 1): + for j in range(block_nums[i - 1]): + if j == 0: + convert_conv_fc(blobs, state_dict, f'res{i + 1}_{j}_branch1', + f'layer{i}.{j}.downsample.0', converted_names) + convert_bn(blobs, state_dict, f'res{i + 1}_{j}_branch1_bn', + f'layer{i}.{j}.downsample.1', converted_names) + for k, letter in enumerate(['a', 'b', 'c']): + convert_conv_fc(blobs, state_dict, + f'res{i + 1}_{j}_branch2{letter}', + f'layer{i}.{j}.conv{k+1}', converted_names) + convert_bn(blobs, state_dict, + f'res{i + 1}_{j}_branch2{letter}_bn', + f'layer{i}.{j}.bn{k + 1}', converted_names) + # check if all layers are converted + for key in blobs: + if key not in converted_names: + print(f'Not Convert: {key}') + # save checkpoint + checkpoint = dict() + checkpoint['state_dict'] = state_dict + torch.save(checkpoint, dst) + + +def main(): + parser = argparse.ArgumentParser(description='Convert model keys') + parser.add_argument('src', help='src detectron model path') + parser.add_argument('dst', help='save path') + parser.add_argument('depth', type=int, help='ResNet model depth') + args = parser.parse_args() + convert(args.src, args.dst, args.depth) + + +if __name__ == '__main__': + main() diff --git a/tools/model_converters/publish_model.py b/tools/model_converters/publish_model.py new file mode 100644 index 0000000..c20e7e3 --- /dev/null +++ b/tools/model_converters/publish_model.py @@ -0,0 +1,39 @@ +import argparse +import subprocess + +import torch + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Process a checkpoint to be published') + parser.add_argument('in_file', help='input checkpoint filename') + parser.add_argument('out_file', help='output checkpoint filename') + args = parser.parse_args() + return args + + +def process_checkpoint(in_file, out_file): + checkpoint = torch.load(in_file, map_location='cpu') + # remove optimizer for smaller file size + if 'optimizer' in checkpoint: + del checkpoint['optimizer'] + # if it is necessary to remove some sensitive data in checkpoint['meta'], + # add the code here. + torch.save(checkpoint, out_file) + sha = subprocess.check_output(['sha256sum', out_file]).decode() + if out_file.endswith('.pth'): + out_file_name = out_file[:-4] + else: + out_file_name = out_file + final_file = out_file_name + f'-{sha[:8]}.pth' + subprocess.Popen(['mv', out_file, final_file]) + + +def main(): + args = parse_args() + process_checkpoint(args.in_file, args.out_file) + + +if __name__ == '__main__': + main() diff --git a/tools/model_converters/regnet2mmdet.py b/tools/model_converters/regnet2mmdet.py new file mode 100644 index 0000000..9f4e316 --- /dev/null +++ b/tools/model_converters/regnet2mmdet.py @@ -0,0 +1,89 @@ +import argparse +from collections import OrderedDict + +import torch + + +def convert_stem(model_key, model_weight, state_dict, converted_names): + new_key = model_key.replace('stem.conv', 'conv1') + new_key = new_key.replace('stem.bn', 'bn1') + state_dict[new_key] = model_weight + converted_names.add(model_key) + print(f'Convert {model_key} to {new_key}') + + +def convert_head(model_key, model_weight, state_dict, converted_names): + new_key = model_key.replace('head.fc', 'fc') + state_dict[new_key] = model_weight + converted_names.add(model_key) + print(f'Convert {model_key} to {new_key}') + + +def convert_reslayer(model_key, model_weight, state_dict, converted_names): + split_keys = model_key.split('.') + layer, block, module = split_keys[:3] + block_id = int(block[1:]) + layer_name = f'layer{int(layer[1:])}' + block_name = f'{block_id - 1}' + + if block_id == 1 and module == 'bn': + new_key = f'{layer_name}.{block_name}.downsample.1.{split_keys[-1]}' + elif block_id == 1 and module == 'proj': + new_key = f'{layer_name}.{block_name}.downsample.0.{split_keys[-1]}' + elif module == 'f': + if split_keys[3] == 'a_bn': + module_name = 'bn1' + elif split_keys[3] == 'b_bn': + module_name = 'bn2' + elif split_keys[3] == 'c_bn': + module_name = 'bn3' + elif split_keys[3] == 'a': + module_name = 'conv1' + elif split_keys[3] == 'b': + module_name = 'conv2' + elif split_keys[3] == 'c': + module_name = 'conv3' + new_key = f'{layer_name}.{block_name}.{module_name}.{split_keys[-1]}' + else: + raise ValueError(f'Unsupported conversion of key {model_key}') + print(f'Convert {model_key} to {new_key}') + state_dict[new_key] = model_weight + converted_names.add(model_key) + + +def convert(src, dst): + """Convert keys in pycls pretrained RegNet models to mmdet style.""" + # load caffe model + regnet_model = torch.load(src) + blobs = regnet_model['model_state'] + # convert to pytorch style + state_dict = OrderedDict() + converted_names = set() + for key, weight in blobs.items(): + if 'stem' in key: + convert_stem(key, weight, state_dict, converted_names) + elif 'head' in key: + convert_head(key, weight, state_dict, converted_names) + elif key.startswith('s'): + convert_reslayer(key, weight, state_dict, converted_names) + + # check if all layers are converted + for key in blobs: + if key not in converted_names: + print(f'not converted: {key}') + # save checkpoint + checkpoint = dict() + checkpoint['state_dict'] = state_dict + torch.save(checkpoint, dst) + + +def main(): + parser = argparse.ArgumentParser(description='Convert model keys') + parser.add_argument('src', help='src detectron model path') + parser.add_argument('dst', help='save path') + args = parser.parse_args() + convert(args.src, args.dst) + + +if __name__ == '__main__': + main() diff --git a/tools/model_converters/upgrade_model_version.py b/tools/model_converters/upgrade_model_version.py new file mode 100644 index 0000000..232c8bc --- /dev/null +++ b/tools/model_converters/upgrade_model_version.py @@ -0,0 +1,209 @@ +import argparse +import re +import tempfile +from collections import OrderedDict + +import torch +from mmcv import Config + + +def is_head(key): + valid_head_list = [ + 'bbox_head', 'mask_head', 'semantic_head', 'grid_head', 'mask_iou_head' + ] + + return any(key.startswith(h) for h in valid_head_list) + + +def parse_config(config_strings): + temp_file = tempfile.NamedTemporaryFile() + config_path = f'{temp_file.name}.py' + with open(config_path, 'w') as f: + f.write(config_strings) + + config = Config.fromfile(config_path) + is_two_stage = True + is_ssd = False + is_retina = False + reg_cls_agnostic = False + if 'rpn_head' not in config.model: + is_two_stage = False + # check whether it is SSD + if config.model.bbox_head.type == 'SSDHead': + is_ssd = True + elif config.model.bbox_head.type == 'RetinaHead': + is_retina = True + elif isinstance(config.model['bbox_head'], list): + reg_cls_agnostic = True + elif 'reg_class_agnostic' in config.model.bbox_head: + reg_cls_agnostic = config.model.bbox_head \ + .reg_class_agnostic + temp_file.close() + return is_two_stage, is_ssd, is_retina, reg_cls_agnostic + + +def reorder_cls_channel(val, num_classes=81): + # bias + if val.dim() == 1: + new_val = torch.cat((val[1:], val[:1]), dim=0) + # weight + else: + out_channels, in_channels = val.shape[:2] + # conv_cls for softmax output + if out_channels != num_classes and out_channels % num_classes == 0: + new_val = val.reshape(-1, num_classes, in_channels, *val.shape[2:]) + new_val = torch.cat((new_val[:, 1:], new_val[:, :1]), dim=1) + new_val = new_val.reshape(val.size()) + # fc_cls + elif out_channels == num_classes: + new_val = torch.cat((val[1:], val[:1]), dim=0) + # agnostic | retina_cls | rpn_cls + else: + new_val = val + + return new_val + + +def truncate_cls_channel(val, num_classes=81): + + # bias + if val.dim() == 1: + if val.size(0) % num_classes == 0: + new_val = val[:num_classes - 1] + else: + new_val = val + # weight + else: + out_channels, in_channels = val.shape[:2] + # conv_logits + if out_channels % num_classes == 0: + new_val = val.reshape(num_classes, in_channels, *val.shape[2:])[1:] + new_val = new_val.reshape(-1, *val.shape[1:]) + # agnostic + else: + new_val = val + + return new_val + + +def truncate_reg_channel(val, num_classes=81): + # bias + if val.dim() == 1: + # fc_reg | rpn_reg + if val.size(0) % num_classes == 0: + new_val = val.reshape(num_classes, -1)[:num_classes - 1] + new_val = new_val.reshape(-1) + # agnostic + else: + new_val = val + # weight + else: + out_channels, in_channels = val.shape[:2] + # fc_reg | rpn_reg + if out_channels % num_classes == 0: + new_val = val.reshape(num_classes, -1, in_channels, + *val.shape[2:])[1:] + new_val = new_val.reshape(-1, *val.shape[1:]) + # agnostic + else: + new_val = val + + return new_val + + +def convert(in_file, out_file, num_classes): + """Convert keys in checkpoints. + + There can be some breaking changes during the development of mmdetection, + and this tool is used for upgrading checkpoints trained with old versions + to the latest one. + """ + checkpoint = torch.load(in_file) + in_state_dict = checkpoint.pop('state_dict') + out_state_dict = OrderedDict() + meta_info = checkpoint['meta'] + is_two_stage, is_ssd, is_retina, reg_cls_agnostic = parse_config( + '#' + meta_info['config']) + if meta_info['mmdet_version'] <= '0.5.3' and is_retina: + upgrade_retina = True + else: + upgrade_retina = False + + # MMDetection v2.5.0 unifies the class order in RPN + # if the model is trained in version=2.5.0 + if meta_info['mmdet_version'] < '2.5.0': + upgrade_rpn = True + else: + upgrade_rpn = False + + for key, val in in_state_dict.items(): + new_key = key + new_val = val + if is_two_stage and is_head(key): + new_key = 'roi_head.{}'.format(key) + + # classification + if upgrade_rpn: + m = re.search( + r'(conv_cls|retina_cls|rpn_cls|fc_cls|fcos_cls|' + r'fovea_cls).(weight|bias)', new_key) + else: + m = re.search( + r'(conv_cls|retina_cls|fc_cls|fcos_cls|' + r'fovea_cls).(weight|bias)', new_key) + if m is not None: + print(f'reorder cls channels of {new_key}') + new_val = reorder_cls_channel(val, num_classes) + + # regression + if upgrade_rpn: + m = re.search(r'(fc_reg).(weight|bias)', new_key) + else: + m = re.search(r'(fc_reg|rpn_reg).(weight|bias)', new_key) + if m is not None and not reg_cls_agnostic: + print(f'truncate regression channels of {new_key}') + new_val = truncate_reg_channel(val, num_classes) + + # mask head + m = re.search(r'(conv_logits).(weight|bias)', new_key) + if m is not None: + print(f'truncate mask prediction channels of {new_key}') + new_val = truncate_cls_channel(val, num_classes) + + m = re.search(r'(cls_convs|reg_convs).\d.(weight|bias)', key) + # Legacy issues in RetinaNet since V1.x + # Use ConvModule instead of nn.Conv2d in RetinaNet + # cls_convs.0.weight -> cls_convs.0.conv.weight + if m is not None and upgrade_retina: + param = m.groups()[1] + new_key = key.replace(param, f'conv.{param}') + out_state_dict[new_key] = val + print(f'rename the name of {key} to {new_key}') + continue + + m = re.search(r'(cls_convs).\d.(weight|bias)', key) + if m is not None and is_ssd: + print(f'reorder cls channels of {new_key}') + new_val = reorder_cls_channel(val, num_classes) + + out_state_dict[new_key] = new_val + checkpoint['state_dict'] = out_state_dict + torch.save(checkpoint, out_file) + + +def main(): + parser = argparse.ArgumentParser(description='Upgrade model version') + parser.add_argument('in_file', help='input checkpoint file') + parser.add_argument('out_file', help='output checkpoint file') + parser.add_argument( + '--num-classes', + type=int, + default=81, + help='number of classes of the original model') + args = parser.parse_args() + convert(args.in_file, args.out_file, args.num_classes) + + +if __name__ == '__main__': + main() diff --git a/tools/slurm_test.sh b/tools/slurm_test.sh new file mode 100755 index 0000000..6dd67e5 --- /dev/null +++ b/tools/slurm_test.sh @@ -0,0 +1,24 @@ +#!/usr/bin/env bash + +set -x + +PARTITION=$1 +JOB_NAME=$2 +CONFIG=$3 +CHECKPOINT=$4 +GPUS=${GPUS:-8} +GPUS_PER_NODE=${GPUS_PER_NODE:-8} +CPUS_PER_TASK=${CPUS_PER_TASK:-5} +PY_ARGS=${@:5} +SRUN_ARGS=${SRUN_ARGS:-""} + +PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ +srun -p ${PARTITION} \ + --job-name=${JOB_NAME} \ + --gres=gpu:${GPUS_PER_NODE} \ + --ntasks=${GPUS} \ + --ntasks-per-node=${GPUS_PER_NODE} \ + --cpus-per-task=${CPUS_PER_TASK} \ + --kill-on-bad-exit=1 \ + ${SRUN_ARGS} \ + python -u tools/test.py ${CONFIG} ${CHECKPOINT} --launcher="slurm" ${PY_ARGS} diff --git a/tools/slurm_train.sh b/tools/slurm_train.sh new file mode 100755 index 0000000..b3feb3d --- /dev/null +++ b/tools/slurm_train.sh @@ -0,0 +1,24 @@ +#!/usr/bin/env bash + +set -x + +PARTITION=$1 +JOB_NAME=$2 +CONFIG=$3 +WORK_DIR=$4 +GPUS=${GPUS:-8} +GPUS_PER_NODE=${GPUS_PER_NODE:-8} +CPUS_PER_TASK=${CPUS_PER_TASK:-5} +SRUN_ARGS=${SRUN_ARGS:-""} +PY_ARGS=${@:5} + +PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ +srun -p ${PARTITION} \ + --job-name=${JOB_NAME} \ + --gres=gpu:${GPUS_PER_NODE} \ + --ntasks=${GPUS} \ + --ntasks-per-node=${GPUS_PER_NODE} \ + --cpus-per-task=${CPUS_PER_TASK} \ + --kill-on-bad-exit=1 \ + ${SRUN_ARGS} \ + python -u tools/train.py ${CONFIG} --work-dir=${WORK_DIR} --launcher="slurm" ${PY_ARGS} diff --git a/tools/test.py b/tools/test.py new file mode 100644 index 0000000..01e15fc --- /dev/null +++ b/tools/test.py @@ -0,0 +1,221 @@ +import argparse +import os +import warnings + +import mmcv +import torch +from mmcv import Config, DictAction +from mmcv.cnn import fuse_conv_bn +from mmcv.parallel import MMDataParallel, MMDistributedDataParallel +from mmcv.runner import (get_dist_info, init_dist, load_checkpoint, + wrap_fp16_model) + +from mmdet.apis import multi_gpu_test, single_gpu_test +from mmdet.datasets import (build_dataloader, build_dataset, + replace_ImageToTensor) +from mmdet.models import build_detector + + +def parse_args(): + parser = argparse.ArgumentParser( + description='MMDet test (and eval) a model') + parser.add_argument('config', help='test config file path') + parser.add_argument('checkpoint', help='checkpoint file') + parser.add_argument('--out', help='output result file in pickle format') + parser.add_argument( + '--fuse-conv-bn', + action='store_true', + help='Whether to fuse conv and bn, this will slightly increase' + 'the inference speed') + parser.add_argument( + '--format-only', + action='store_true', + help='Format the output results without perform evaluation. It is' + 'useful when you want to format the result to a specific format and ' + 'submit it to the test server') + parser.add_argument( + '--eval', + type=str, + nargs='+', + help='evaluation metrics, which depends on the dataset, e.g., "bbox",' + ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC') + parser.add_argument('--show', action='store_true', help='show results') + parser.add_argument( + '--show-dir', help='directory where painted images will be saved') + parser.add_argument( + '--show-score-thr', + type=float, + default=0.3, + help='score threshold (default: 0.3)') + parser.add_argument( + '--gpu-collect', + action='store_true', + help='whether to use gpu to collect results.') + parser.add_argument( + '--tmpdir', + help='tmp directory used for collecting results from multiple ' + 'workers, available when gpu-collect is not specified') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + parser.add_argument( + '--options', + nargs='+', + action=DictAction, + help='custom options for evaluation, the key-value pair in xxx=yyy ' + 'format will be kwargs for dataset.evaluate() function (deprecate), ' + 'change to --eval-options instead.') + parser.add_argument( + '--eval-options', + nargs='+', + action=DictAction, + help='custom options for evaluation, the key-value pair in xxx=yyy ' + 'format will be kwargs for dataset.evaluate() function') + parser.add_argument( + '--launcher', + choices=['none', 'pytorch', 'slurm', 'mpi'], + default='none', + help='job launcher') + parser.add_argument('--local_rank', type=int, default=0) + args = parser.parse_args() + if 'LOCAL_RANK' not in os.environ: + os.environ['LOCAL_RANK'] = str(args.local_rank) + + if args.options and args.eval_options: + raise ValueError( + '--options and --eval-options cannot be both ' + 'specified, --options is deprecated in favor of --eval-options') + if args.options: + warnings.warn('--options is deprecated in favor of --eval-options') + args.eval_options = args.options + return args + + +def main(): + args = parse_args() + + assert args.out or args.eval or args.format_only or args.show \ + or args.show_dir, \ + ('Please specify at least one operation (save/eval/format/show the ' + 'results / save the results) with the argument "--out", "--eval"' + ', "--format-only", "--show" or "--show-dir"') + + if args.eval and args.format_only: + raise ValueError('--eval and --format_only cannot be both specified') + + if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): + raise ValueError('The output file must be a pkl file.') + + cfg = Config.fromfile(args.config) + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) + # set cudnn_benchmark + if cfg.get('cudnn_benchmark', False): + torch.backends.cudnn.benchmark = True + cfg.model.pretrained = None + if cfg.model.get('neck'): + if isinstance(cfg.model.neck, list): + for neck_cfg in cfg.model.neck: + if neck_cfg.get('rfp_backbone'): + if neck_cfg.rfp_backbone.get('pretrained'): + neck_cfg.rfp_backbone.pretrained = None + elif cfg.model.neck.get('rfp_backbone'): + if cfg.model.neck.rfp_backbone.get('pretrained'): + cfg.model.neck.rfp_backbone.pretrained = None + + # in case the test dataset is concatenated + samples_per_gpu = 1 + if isinstance(cfg.data.test, dict): + cfg.data.test.test_mode = True + samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1) + if samples_per_gpu > 1: + # Replace 'ImageToTensor' to 'DefaultFormatBundle' + cfg.data.test.pipeline = replace_ImageToTensor( + cfg.data.test.pipeline) + elif isinstance(cfg.data.test, list): + for ds_cfg in cfg.data.test: + ds_cfg.test_mode = True + samples_per_gpu = max( + [ds_cfg.pop('samples_per_gpu', 1) for ds_cfg in cfg.data.test]) + if samples_per_gpu > 1: + for ds_cfg in cfg.data.test: + ds_cfg.pipeline = replace_ImageToTensor(ds_cfg.pipeline) + + # init distributed env first, since logger depends on the dist info. + if args.launcher == 'none': + distributed = False + else: + distributed = True + init_dist(args.launcher, **cfg.dist_params) + + # build the dataloader + dataset = build_dataset(cfg.data.test) + data_loader = build_dataloader( + dataset, + samples_per_gpu=samples_per_gpu, + workers_per_gpu=cfg.data.workers_per_gpu, + dist=distributed, + shuffle=False) + + # build the model and load checkpoint + cfg.model.train_cfg = None + model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg')) + fp16_cfg = cfg.get('fp16', None) + if fp16_cfg is not None: + wrap_fp16_model(model) + if args.checkpoint != 'none': + checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') + if args.fuse_conv_bn: + model = fuse_conv_bn(model) + # old versions did not save class info in checkpoints, this walkaround is + # for backward compatibility + if args.checkpoint != 'none' and 'CLASSES' in checkpoint.get('meta', {}): + model.CLASSES = checkpoint['meta']['CLASSES'] + else: + model.CLASSES = dataset.CLASSES + + if not distributed: + model = MMDataParallel(model, device_ids=[0]) + outputs = single_gpu_test(model, data_loader, args.show, args.show_dir, + args.show_score_thr) + else: + model = MMDistributedDataParallel( + model.cuda(), + device_ids=[torch.cuda.current_device()], + broadcast_buffers=False) + outputs = multi_gpu_test(model, data_loader, args.tmpdir, + args.gpu_collect) + + rank, _ = get_dist_info() + if rank == 0: + if args.out: + print(f'\nwriting results to {args.out}') + mmcv.dump(outputs, args.out) + kwargs = {} if args.eval_options is None else args.eval_options + if args.format_only: + dataset.format_results(outputs, **kwargs) + if args.eval: + eval_kwargs = cfg.get('evaluation', {}).copy() + # hard-code way to remove EvalHook args + for key in [ + 'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best', + 'rule' + ]: + eval_kwargs.pop(key, None) + eval_kwargs.update(dict(metric=args.eval, **kwargs)) + print(dataset.evaluate(outputs, **eval_kwargs)) + + +if __name__ == '__main__': + main() diff --git a/tools/train.py b/tools/train.py new file mode 100644 index 0000000..718c8f9 --- /dev/null +++ b/tools/train.py @@ -0,0 +1,203 @@ +import argparse +import copy +import os +import os.path as osp +import time +import warnings + +import mmcv +import torch +from mmcv import Config, DictAction +from mmcv.runner import get_dist_info, init_dist +from mmcv.utils import get_git_hash + +from mmdet import __version__ +from mmdet.apis import set_random_seed, train_detector +from mmdet.datasets import build_dataset +from mmdet.models import build_detector +from mmdet.utils import collect_env, get_root_logger + + +def parse_args(): + parser = argparse.ArgumentParser(description='Train a detector') + parser.add_argument('config', help='train config file path') + parser.add_argument('--work-dir', help='the dir to save logs and models') + parser.add_argument( + '--resume-from', help='the checkpoint file to resume from') + parser.add_argument( + '--no-validate', + action='store_true', + help='whether not to evaluate the checkpoint during training') + group_gpus = parser.add_mutually_exclusive_group() + group_gpus.add_argument( + '--gpus', + type=int, + help='number of gpus to use ' + '(only applicable to non-distributed training)') + group_gpus.add_argument( + '--gpu-ids', + type=int, + nargs='+', + help='ids of gpus to use ' + '(only applicable to non-distributed training)') + parser.add_argument('--seed', type=int, default=None, help='random seed') + parser.add_argument( + '--deterministic', + action='store_true', + help='whether to set deterministic options for CUDNN backend.') + parser.add_argument( + '--options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file (deprecate), ' + 'change to --cfg-options instead.') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + parser.add_argument( + '--launcher', + choices=['none', 'pytorch', 'slurm', 'mpi'], + default='none', + help='job launcher') + parser.add_argument('--local_rank', type=int, default=0) + args = parser.parse_args() + if 'LOCAL_RANK' not in os.environ: + os.environ['LOCAL_RANK'] = str(args.local_rank) + + if args.options and args.cfg_options: + raise ValueError( + '--options and --cfg-options cannot be both ' + 'specified, --options is deprecated in favor of --cfg-options') + if args.options: + warnings.warn('--options is deprecated in favor of --cfg-options') + args.cfg_options = args.options + + return args + + +def main(): + args = parse_args() + + cfg = Config.fromfile(args.config) + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) + # set cudnn_benchmark + if cfg.get('cudnn_benchmark', False): + torch.backends.cudnn.benchmark = True + + # work_dir is determined in this priority: CLI > segment in file > filename + if args.work_dir is not None: + # update configs according to CLI args if args.work_dir is not None + cfg.work_dir = args.work_dir + elif cfg.get('work_dir', None) is None: + # use config filename as default work_dir if cfg.work_dir is None + # cfg.work_dir = osp.join('./work_dirs', + # osp.splitext(osp.basename(args.config))[0]) + cfg.work_dir = osp.join(cfg.get('work_dir_prefix', './work_dirs'), + osp.splitext(osp.basename(args.config))[0]) + cfg.work_dir = cfg.work_dir + cfg.get('postfix', '') + if args.resume_from is not None: + cfg.resume_from = args.resume_from + if args.gpu_ids is not None: + cfg.gpu_ids = args.gpu_ids + else: + cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus) + + # init distributed env first, since logger depends on the dist info. + if args.launcher == 'none': + distributed = False + else: + distributed = True + init_dist(args.launcher, **cfg.dist_params) + # re-set gpu_ids with distributed training mode + _, world_size = get_dist_info() + cfg.gpu_ids = range(world_size) + + # create work_dir + mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir)) + # dump config + cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config))) + # init the logger before other steps + timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) + log_file = osp.join(cfg.work_dir, f'{timestamp}.log') + logger = get_root_logger(log_file=log_file, log_level=cfg.log_level) + + # init the meta dict to record some important information such as + # environment info and seed, which will be logged + meta = dict() + # log env info + env_info_dict = collect_env() + env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()]) + dash_line = '-' * 60 + '\n' + logger.info('Environment info:\n' + dash_line + env_info + '\n' + + dash_line) + meta['env_info'] = env_info + meta['config'] = cfg.pretty_text + # log some basic info + logger.info(f'Distributed training: {distributed}') + logger.info(f'Config:\n{cfg.pretty_text}') + + if args.local_rank == 0: + import shutil + import os + if os.path.exists(cfg.work_dir + '/mmdet'): + shutil.rmtree(cfg.work_dir + '/mmdet') + + if os.path.exists(cfg.work_dir + '/config'): + shutil.rmtree(cfg.work_dir + '/config') + + shutil.copytree('./mmdet', cfg.work_dir + '/mmdet') + shutil.copytree('./configs', cfg.work_dir + '/config') + + # set random seeds + if args.seed is not None: + logger.info(f'Set random seed to {args.seed}, ' + f'deterministic: {args.deterministic}') + set_random_seed(args.seed, deterministic=args.deterministic) + cfg.seed = args.seed + meta['seed'] = args.seed + meta['exp_name'] = osp.basename(args.config) + + model = build_detector( + cfg.model, + train_cfg=cfg.get('train_cfg'), + test_cfg=cfg.get('test_cfg')) + model.init_weights() + + datasets = [build_dataset(cfg.data.train)] + if len(cfg.workflow) == 2: + val_dataset = copy.deepcopy(cfg.data.val) + val_dataset.pipeline = cfg.data.train.pipeline + datasets.append(build_dataset(val_dataset)) + if cfg.checkpoint_config is not None: + # save mmdet version, config file content and class names in + # checkpoints as meta data + cfg.checkpoint_config.meta = dict( + mmdet_version=__version__ + get_git_hash()[:7], + CLASSES=datasets[0].CLASSES) + # add an attribute for visualization convenience + model.CLASSES = datasets[0].CLASSES + train_detector( + model, + datasets, + cfg, + distributed=distributed, + validate=(not args.no_validate), + timestamp=timestamp, + meta=meta) + + +if __name__ == '__main__': + main() diff --git a/visualize_sampling_points.ipynb b/visualize_sampling_points.ipynb new file mode 100644 index 0000000..a786209 --- /dev/null +++ b/visualize_sampling_points.ipynb @@ -0,0 +1,239 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Use load_from_local loader\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/gaoziteng/local_workspace/adamixer-github/AdaMixer/mmdet/datasets/utils.py:68: UserWarning: \"ImageToTensor\" pipeline is replaced by \"DefaultFormatBundle\" for batch inference. It is recommended to manually replace it in the test data pipeline in your config file.\n", + " 'data pipeline in your config file.', UserWarning)\n" + ] + } + ], + "source": [ + "import os\n", + "os.environ['DEBUG'] = '1'\n", + "\n", + "%rm demo/*.pth\n", + "\n", + "import torch\n", + "\n", + "\n", + "from PIL import Image\n", + "from mmdet.apis import init_detector, inference_detector, show_result_pyplot\n", + "import sys\n", + "import os.path\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "config_file = './configs/adamixer/adamixer_r50_1x_coco.py'\n", + "# path to checkpoint or simply `None` for not loading checkpoint\n", + "checkpoint_file = None\n", + "\n", + "model = init_detector(config_file, checkpoint_file, device='cuda:0')\n", + "\n", + "for m in model.modules():\n", + " if hasattr(m, '_DEBUG'):\n", + " setattr(type(m), '_DEBUG', 0)\n", + "\n", + "IMG = 'data/coco/val2017/000000057597.jpg'\n", + "IMG_IND = IMG[-16:-4]\n", + "\n", + "Image.open(IMG).save('demo/testin.jpg')\n", + "result = inference_detector(model, IMG)\n", + "show_result_pyplot(model, IMG, result, score_thr=0.3,\n", + " out_file='demo/result.jpg')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image.open('demo/result.jpg')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.return_types.topk(\n", + "values=tensor([0.8366, 0.8110, 0.7830, 0.5122, 0.4761]),\n", + "indices=tensor([80, 6, 99, 90, 15]))" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scores = torch.load('demo/bbox_results_5.pth', map_location='cpu')['cls_score']\n", + "category = 0\n", + "scores[0, :, category].sigmoid().topk(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for STAGE in range(6):\n", + " P = torch.load('demo/sample_xy_{}.pth'.format(STAGE), map_location='cpu').detach()\n", + " metas = torch.load('demo/img_metas.pth', map_location='cpu')\n", + "\n", + " sf = metas[0]['scale_factor']\n", + "\n", + " xy = P[0, 99, 0] \n", + "\n", + " plt.figure(figsize=(16, 16))\n", + " plt.imshow(Image.open('demo/testin.jpg'))\n", + " xy = xy.view(4, -1, 3)\n", + " for i in range(4):\n", + " sub_xy = xy[i]\n", + " plt.scatter(sub_xy[:, 0]/ sf[0], sub_xy[:, 1]/ sf[0], s=4.0 ** (sub_xy[:, 2]-4), alpha=0.7)\n", + " plt.axis('off')\n", + " plt.savefig('sampling-points_image-{}_stage-{}.jpg'.format(IMG_IND, STAGE), bbox_inches='tight', pad_inches=0)\n", + " plt.show()\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "adamixer", + "language": "python", + "name": "adamixer" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}