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-## 环境配置:
-
-conda create -n DynamicVit python=3.6
-
-conda activate DynamicVit
-
-conda deactivate
-
-conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 -c pytorch
-
-pip3 install timm==0.4.5
+# SPViT: Enabling Faster Vision Transformers via Latency-aware Soft Token Pruning
 
+This repository contains PyTorch training code for the ECCV 2022 paper.
 
-## 下载prertained model
-见 download_pretrain.sh
+[arXiv](https://arxiv.org/abs/2211.10801) |  [ECCV 2022](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136710618.pdf)
 
+<p align="center">
+  <img src="./plot_time_acc_table.png" width=60%> <br>
+  Comparison of different models with various accuracy-training time trade-off..
+</p>
 
+## Usage
 
-## 命令
+### Environment Settings
 
-举例：跑deit-small, 用3keep+senet的代码.
-
-python3 -u -m torch.distributed.launch --nproc_per_node=8 --use_env main_l2_vit_3keep_senet.py --output_dir logs/3keep_senet --arch deit_small --input-size 224 --batch-size 256 --data-path /data/ImageNet_new/ --epochs 30 --dist-eval --distill --base_rate 0.7 2>&1 | tee -i 3keep_senet.log
+```
+conda create -n DynamicVit python=3.6
 
-跑deit-base
+conda activate DynamicVit
 
-python3 -u -m torch.distributed.launch --nproc_per_node=8 --use_env main_l2_vit_3keep_senet.py --output_dir logs/deit_base_3keep_senet_256_60_5e-4 --arch deit_base --input-size 224 --batch-size 256 --data-path /data/ImageNet_new/ --epochs 60 --dist-eval --distill --base_rate 0.7 2>&1 | tee -i deit_base_3keep_senet_256_60_5e-4.log
+conda deactivate
 
+conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 -c pytorch
 
-## 调参 link
+pip3 install timm==0.4.5
+```
+
+### Data preparation 
+download and extract ImageNet images from http://image-net.org/. The directory structure should be
+
+```
+│ILSVRC2012/
+├──train/
+│  ├── n01440764
+│  │   ├── n01440764_10026.JPEG
+│  │   ├── n01440764_10027.JPEG
+│  │   ├── ......
+│  ├── ......
+├──val/
+│  ├── n01440764
+│  │   ├── ILSVRC2012_val_00000293.JPEG
+│  │   ├── ILSVRC2012_val_00002138.JPEG
+│  │   ├── ......
+│  ├── ......
+```
+
+### Download prertained model for training 
+
+```
+sh download_pretrain.sh
+```
+
+### Training
+
+
+**DeiT-S**
+
+```
+CUDA_VISIBLE_DEVICES="0,1,2,3"  python3 -u -m torch.distributed.launch --nproc_per_node=4 --use_env main_l2_vit_3keep_senet.py 
+                                --output_dir logs/3keep_senet 
+                                --arch deit_small 
+                                --input-size 224 
+                                --batch-size 256 
+                                --data-path /data/ImageNet_new/ 
+                                --epochs 30 
+                                --dist-eval 
+                                --distill 
+                                --base_rate 0.7 
+```
+**DeiT-B**
+
+```
+CUDA_VISIBLE_DEVICES="0,1,2,3"  python3 -u -m torch.distributed.launch --nproc_per_node=8 --use_env main_l2_vit_3keep_senet.py 
+                                --output_dir logs/deit_base_3keep_senet_256_60_5e-4 
+                                --arch deit_base 
+                                --input-size 224 
+                                --batch-size 256 
+                                --data-path /data/ImageNet_new/ 
+                                --epochs 60 
+                                --dist-eval 
+                                --distill 
+                                --base_rate 0.7 
+
+```
+
+
+### Some hyperparameter tunning results 
 https://docs.google.com/spreadsheets/d/1k25sS_-mmQyIvpIrn32GUw3eRuYcCy0cN0OSOq0QGFI/edit?usp=sharing
 
-## score 数据记录
+### score 
 https://drive.google.com/drive/folders/1diICKopeYL7H84Wsr0Xxh30e9xh6RX2d?usp=sharing
 
-## 可选择是否使用全精度训练。关闭 amp 功能。在 engine_l2.py 的 train_one_epoch() 和 evaluate()
-![](fig/1.png)
+### Full precision training 
+To turn off the amp function, go to ```engine_l2.py```  ```train_one_epoch()``` and ```evaluate()```
 
-## vit.py 文件改动，生成 vit_l2.py [对于 one keep token]
+<p align="center">
+  <img src="fig/1.png" width=60%> <br>
+</p>
 
-### 生成 multihead-predictor 类
-### VisionTransformerDiffPruning 的 forward()
 
-## vit.py 文件改动，生成 vit_l2_3keep.py [对于 three keep tokens]
+## Acknowledgements
 
+Our code is based on [pytorch-image-models](https://github.com/rwightman/pytorch-image-models), [DeiT](https://github.com/facebookresearch/deit), [DynamicViT](https://github.com/raoyongming/DynamicViT).
 
-### 生成 multihead-predictor 类
-### VisionTransformerDiffPruning 的 forward()
+## Citation
+If you find our work useful in your research, please consider citing:
+```
+@inproceedings{kong2022spvit,
+  title={SPViT: Enabling Faster Vision Transformers via Latency-Aware Soft Token Pruning},
+  author={Kong, Zhenglun and Dong, Peiyan and Ma, Xiaolong and Meng, Xin and Niu, Wei and Sun, Mengshu and Shen, Xuan and Yuan, Geng and Ren, Bin and Tang, Hao and others},
+  booktitle={Computer Vision--ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part XI},
+  pages={620--640},
+  year={2022},
+  organization={Springer}
+}
+```
 
-###环境：因为加入了torch.nan_to_num(x, nan=4.0)，需要用torch 1.8.0