[Feature] Migrate occluded metric to 3.x.

docs/en/user_guides/useful_tools.md

@@ -509,3 +509,78 @@ python tools/analysis_tools/confusion_matrix.py ${CONFIG}  ${DETECTION_RESULTS}
 And you will get a confusion matrix like this:
 
 ![confusion_matrix_example](https://user-images.githubusercontent.com/12907710/140513068-994cdbf4-3a4a-48f0-8fd8-2830d93fd963.png)
+
+## COCO Separated & Occluded Mask Metric
+
+Detecting occluded objects still remains a challenge for state-of-the-art object detectors.
+We implemented the metric presented in paper [A Tri-Layer Plugin to Improve Occluded Detection](https://arxiv.org/abs/2210.10046) to calculate the recall of separated and occluded masks.
+
+There are two ways to use this metric:
+
+### Offline evaluation
+
+We provide a script to calculate the metric with a dumped prediction file.
+
+First, use the `tools/test.py` script to dump the detection results:
+
+```shell
+python tools/test.py ${CONFIG} ${MODEL_PATH} --out results.pkl
+```
+
+Then, run the `tools/analysis_tools/coco_occluded_separated_recall.py` script to get the recall of separated and occluded masks:
+
+```shell
+python tools/analysis_tools/coco_occluded_separated_recall.py results.pkl --out occluded_separated_recall.json
+```
+
+The output should be like this:
+
+```
+loading annotations into memory...
+Done (t=0.51s)
+creating index...
+index created!
+processing detection results...
+[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 5000/5000, 109.3 task/s, elapsed: 46s, ETA:     0s
+computing occluded mask recall...
+[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 5550/5550, 780.5 task/s, elapsed: 7s, ETA:     0s
+COCO occluded mask recall: 58.79%
+COCO occluded mask success num: 3263
+computing separated mask recall...
+[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 3522/3522, 778.3 task/s, elapsed: 5s, ETA:     0s
+COCO separated mask recall: 31.94%
+COCO separated mask success num: 1125
+
++-----------+--------+-------------+
+| mask type | recall | num correct |
++-----------+--------+-------------+
+| occluded  | 58.79% | 3263        |
+| separated | 31.94% | 1125        |
++-----------+--------+-------------+
+Evaluation results have been saved to occluded_separated_recall.json.
+```
+
+### Online evaluation
+
+We implement `CocoOccludedSeparatedMetric` which inherits from the `CocoMetic`.
+To evaluate the recall of separated and occluded masks during training, just replace the evaluator metric type with `'CocoOccludedSeparatedMetric'` in your config:
+
+```python
+val_evaluator = dict(
+    type='CocoOccludedSeparatedMetric',  # modify this
+    ann_file=data_root + 'annotations/instances_val2017.json',
+    metric=['bbox', 'segm'],
+    format_only=False)
+test_evaluator = val_evaluator
+```
+
+Please cite the paper if you use this metric:
+
+```latex
+@article{zhan2022triocc,
+    title={A Tri-Layer Plugin to Improve Occluded Detection},
+    author={Zhan, Guanqi and Xie, Weidi and Zisserman, Andrew},
+    journal={British Machine Vision Conference},
+    year={2022}
+}
+```

docs/zh_cn/user_guides/useful_tools.md

@@ -485,3 +485,78 @@ python tools/analysis_tools/confusion_matrix.py ${CONFIG}  ${DETECTION_RESULTS}
 最后你可以得到如图的混淆矩阵：
 
 ![confusion_matrix_example](https://user-images.githubusercontent.com/12907710/140513068-994cdbf4-3a4a-48f0-8fd8-2830d93fd963.png)
+
+## COCO 分离和遮挡实例分割性能评估
+
+对于最先进的目标检测器来说，检测被遮挡的物体仍然是一个挑战。
+我们实现了论文 [A Tri-Layer Plugin to Improve Occluded Detection](https://arxiv.org/abs/2210.10046) 中提出的指标来计算分离和遮挡目标的召回率。
+
+使用此评价指标有两种方法：
+
+### 离线评测
+
+我们提供了一个脚本对存储后的检测结果文件计算指标。
+
+首先，使用 `tools/test.py` 脚本存储检测结果：
+
+```shell
+python tools/test.py ${CONFIG} ${MODEL_PATH} --out results.pkl
+```
+
+然后，运行 `tools/analysis_tools/coco_occluded_separated_recall.py` 脚本来计算分离和遮挡目标的掩码的召回率:
+
+```shell
+python tools/analysis_tools/coco_occluded_separated_recall.py results.pkl --out occluded_separated_recall.json
+```
+
+输出如下：
+
+```
+loading annotations into memory...
+Done (t=0.51s)
+creating index...
+index created!
+processing detection results...
+[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 5000/5000, 109.3 task/s, elapsed: 46s, ETA:     0s
+computing occluded mask recall...
+[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 5550/5550, 780.5 task/s, elapsed: 7s, ETA:     0s
+COCO occluded mask recall: 58.79%
+COCO occluded mask success num: 3263
+computing separated mask recall...
+[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 3522/3522, 778.3 task/s, elapsed: 5s, ETA:     0s
+COCO separated mask recall: 31.94%
+COCO separated mask success num: 1125
+
++-----------+--------+-------------+
+| mask type | recall | num correct |
++-----------+--------+-------------+
+| occluded  | 58.79% | 3263        |
+| separated | 31.94% | 1125        |
++-----------+--------+-------------+
+Evaluation results have been saved to occluded_separated_recall.json.
+```
+
+### 在线评测
+
+我们实现继承自 `CocoMetic` 的 `CocoOccludedSeparatedMetric`。
+要在训练期间评估分离和遮挡掩码的召回率，只需在配置中将 evaluator 类型替换为 `CocoOccludedSeparatedMetric`：
+
+```python
+val_evaluator = dict(
+    type='CocoOccludedSeparatedMetric',  # 修改此处
+    ann_file=data_root + 'annotations/instances_val2017.json',
+    metric=['bbox', 'segm'],
+    format_only=False)
+test_evaluator = val_evaluator
+```
+
+如果您使用了此指标，请引用论文：
+
+```latex
+@article{zhan2022triocc,
+    title={A Tri-Layer Plugin to Improve Occluded Detection},
+    author={Zhan, Guanqi and Xie, Weidi and Zisserman, Andrew},
+    journal={British Machine Vision Conference},
+    year={2022}
+}
+```

mmdet/evaluation/metrics/__init__.py

@@ -1,14 +1,17 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .cityscapes_metric import CityScapesMetric
 from .coco_metric import CocoMetric
+from .coco_occluded_metric import CocoOccludedSeparatedMetric
 from .coco_panoptic_metric import CocoPanopticMetric
 from .crowdhuman_metric import CrowdHumanMetric
+from .dump_det_results import DumpDetResults
 from .dump_proposals_metric import DumpProposals
 from .lvis_metric import LVISMetric
 from .openimages_metric import OpenImagesMetric
 from .voc_metric import VOCMetric
 
 __all__ = [
     'CityScapesMetric', 'CocoMetric', 'CocoPanopticMetric', 'OpenImagesMetric',
-    'VOCMetric', 'LVISMetric', 'CrowdHumanMetric', 'DumpProposals'
+    'VOCMetric', 'LVISMetric', 'CrowdHumanMetric', 'DumpProposals',
+    'CocoOccludedSeparatedMetric', 'DumpDetResults'
 ]

mmdet/evaluation/metrics/coco_metric.py

@@ -7,6 +7,7 @@
 from typing import Dict, List, Optional, Sequence, Union
 
 import numpy as np
+import torch
 from mmengine.evaluator import BaseMetric
 from mmengine.fileio import FileClient, dump, load
 from mmengine.logging import MMLogger
@@ -350,7 +351,8 @@ def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None:
             # encode mask to RLE
             if 'masks' in pred:
                 result['masks'] = encode_mask_results(
-                    pred['masks'].detach().cpu().numpy())
+                    pred['masks'].detach().cpu().numpy()) if isinstance(
+                        pred['masks'], torch.Tensor) else pred['masks']
             # some detectors use different scores for bbox and mask
             if 'mask_scores' in pred:
                 result['mask_scores'] = pred['mask_scores'].cpu().numpy()