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https://github.com/microsoft/esvit
EsViT: Efficient self-supervised Vision Transformers
https://github.com/microsoft/esvit
self-supervised-learning vision-transformers
Last synced: 6 days ago
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EsViT: Efficient self-supervised Vision Transformers
- Host: GitHub
- URL: https://github.com/microsoft/esvit
- Owner: microsoft
- License: mit
- Created: 2021-07-01T19:19:39.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-08-28T01:38:18.000Z (about 1 year ago)
- Last Synced: 2024-08-06T20:08:01.264Z (3 months ago)
- Topics: self-supervised-learning, vision-transformers
- Language: Python
- Homepage:
- Size: 1.88 MB
- Stars: 402
- Watchers: 12
- Forks: 42
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
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README
# Efficient Self-Supervised Vision Transformers (EsViT)
[](https://paperswithcode.com/sota/self-supervised-image-classification-on?p=efficient-self-supervised-vision-transformers)
[[Paper]](https://arxiv.org/abs/2106.09785) [[Slides]](http://chunyuan.li/assets/pdf/esvit_talk_chunyl.pdf)
PyTorch implementation for [EsViT](https://arxiv.org/abs/2106.09785) (accepted in ICLR, 2022), built with two techniques:
- A multi-stage Transformer architecture. Three multi-stage Transformer variants are implemented under the folder [`models`](./models).
- A non-contrastive region-level matching pre-train task. The region-level matching task is implemented in function `DDINOLoss(nn.Module)` (Line 648) in [`main_esvit.py`](./main_esvit.py). Please use `--use_dense_prediction True`, otherwise only the view-level task is used.
Figure: Efficiency vs accuracy comparison under the linear classification protocol on ImageNet. Left: Throughput of all SoTA SSL vision systems, circle sizes indicates model parameter counts; Right: performance over varied parameter counts for models with moderate (throughout/#parameters) ratio. Please refer Section 4.1 for details.
## Updates
* [08/19/2022] Organizing ECCV Workshop [*Computer Vision in the Wild (CVinW)*](https://computer-vision-in-the-wild.github.io/eccv-2022/), where two challenges are hosted to evaluate the zero-shot, few-shot and full-shot performance of pre-trained vision models in downstream tasks:
- [``*Image Classification in the Wild (ICinW)*''](https://eval.ai/web/challenges/challenge-page/1832/overview) Challenge evaluates on 20 image classification tasks.
- [``*Object Detection in the Wild (ODinW)*''](https://eval.ai/web/challenges/challenge-page/1839/overview) Challenge evaluates on 35 object detection tasks.
$\qquad$ [ 
[Workshop]](https://computer-vision-in-the-wild.github.io/eccv-2022/) $\qquad$ [
[IC Challenge] ](https://eval.ai/web/challenges/challenge-page/1832/overview)
$\qquad$ [
[OD Challenge] ](https://eval.ai/web/challenges/challenge-page/1839/overview)
* [06/19/2022] Released the evaluation benchmark used in EsVIT. It contains 20 downstream image classification tasks. [[ELEVATER Benchmark]](https://computer-vision-in-the-wild.github.io/ELEVATER/) [[Toolkit]](https://github.com/Computer-Vision-in-the-Wild/Elevater_Toolkit_IC) [[Paper]](https://arxiv.org/abs/2204.08790)
## Pretrained models
You can download the full checkpoint (trained with both view-level and region-level tasks, batch size=512 and ImageNet-1K.), which contains backbone and projection head weights for both student and teacher networks.
Note: The data is on Azure Storage Blob, a SAS with Read permission is provided. Please append the following SAS at the end of each link to download:
```bash
?sp=r&st=2023-08-28T01:36:35Z&se=3023-08-28T09:36:35Z&sv=2022-11-02&sr=c&sig=coos9vSl4Xk6S6KvqZffkVCUb7Ug%2FFR9cfyc3xacMJI%3D
```
- EsViT (Swin) with network configurations of increased model capacities, **pre-trained with both view-level and region-level tasks**. ResNet-50 trained with both tasks is shown as a reference.
arch
params
tasks
linear
k-nn
download
logs
ResNet-50
23M
V+R
75.7%
71.3%
full ckpt
train
linear
knn
EsViT (Swin-T, W=7)
28M
V+R
78.0%
75.7%
full ckpt
train
linear
knn
EsViT (Swin-S, W=7)
49M
V+R
79.5%
77.7%
full ckpt
train
linear
knn
EsViT (Swin-B, W=7)
87M
V+R
80.4%
78.9%
full ckpt
train
linear
knn
EsViT (Swin-T, W=14)
28M
V+R
78.7%
77.0%
full ckpt
train
linear
knn
EsViT (Swin-S, W=14)
49M
V+R
80.8%
79.1%
full ckpt
train
linear
knn
EsViT (Swin-B, W=14)
87M
V+R
81.3%
79.3%
full ckpt
train
linear
knn
- EsViT with view-level task only
arch
params
tasks
linear
k-nn
download
logs
ResNet-50
23M
V
75.0%
69.1%
full ckpt
train
linear
knn
EsViT (Swin-T, W=7)
28M
V
77.0%
74.2%
full ckpt
train
linear
knn
EsViT (Swin-S, W=7)
49M
V
79.2%
76.9%
full ckpt
train
linear
knn
EsViT (Swin-B, W=7)
87M
V
79.6%
77.7%
full ckpt
train
linear
knn
- EsViT (Swin-T, W=7) with different pre-train datasets (view-level task only)
arch
params
batch size
pre-train dataset
linear
k-nn
download
logs
EsViT
28M
1024
ImageNet-1K
77.1%
73.7%
full ckpt
train
linear
knn
EsViT
28M
1024
WebVision-v1
75.4%
69.4%
full ckpt
train
linear
knn
EsViT
28M
1024
OpenImages-v4
69.6%
60.3%
full ckpt
train
linear
knn
EsViT
28M
1024
ImageNet-22K
73.5%
66.1%
full ckpt
train
linear
knn
- EsViT with more multi-stage vision Transformer architectures, pre-trained with **V**iew-level and **R**egion-level tasks.
arch
params
pre-train task
linear
k-nn
download
logs
EsViT (ViL, W=7)
28M
V
77.3%
73.9%
full ckpt
train
linear
knn
EsViT (ViL, W=7)
28M
V+R
77.5%
74.5%
full ckpt
train
linear
knn
EsViT (CvT, W=7)
29M
V
77.6%
74.8%
full ckpt
train
linear
knn
EsViT (CvT, W=7)
29M
V+R
78.5%
76.7%
full ckpt
train
linear
knn
## Pre-training
### One-node training
To train on 1 node with 16 GPUs for Swin-T model size:
```
PROJ_PATH=your_esvit_project_path
DATA_PATH=$PROJ_PATH/project/data/imagenet
OUT_PATH=$PROJ_PATH/output/esvit_exp/ssl/swin_tiny_imagenet/
python -m torch.distributed.launch --nproc_per_node=16 main_esvit.py --arch swin_tiny --data_path $DATA_PATH/train --output_dir $OUT_PATH --batch_size_per_gpu 32 --epochs 300 --teacher_temp 0.07 --warmup_epochs 10 --warmup_teacher_temp_epochs 30 --norm_last_layer false --use_dense_prediction True --cfg experiments/imagenet/swin/swin_tiny_patch4_window7_224.yaml
```
The main training script is [`main_esvit.py`](./main_esvit.py) and conducts the training loop, taking the following options (among others) as arguments:
- `--use_dense_prediction`: whether or not to use the region matching task in pre-training
- `--arch`: switch between different sparse self-attention in the multi-stage Transformer architecture. Example architecture choices for EsViT training include [`swin_tiny`, `swin_small`, `swin_base`, `swin_large`,`cvt_tiny`, `vil_2262`]. The configuration files should be adjusted accrodingly, we provide example below. One may specify the network configuration by editing the `YAML` file under `experiments/imagenet/*/*.yaml`. The default window size=7; To consider a multi-stage architecture with window size=14, please choose yaml files with `window14` in filenames.
To train on 1 node with 16 GPUs for Convolutional vision Transformer (CvT) models:
```
python -m torch.distributed.launch --nproc_per_node=16 main_evsit.py --arch cvt_tiny --data_path $DATA_PATH/train --output_dir $OUT_PATH --batch_size_per_gpu 32 --epochs 300 --teacher_temp 0.07 --warmup_epochs 10 --warmup_teacher_temp_epochs 30 --norm_last_layer false --use_dense_prediction True --aug-opt dino_aug --cfg experiments/imagenet/cvt_v4/s1.yaml
```
To train on 1 node with 16 GPUs for Vision Longformer (ViL) models:
```
python -m torch.distributed.launch --nproc_per_node=16 main_evsit.py --arch vil_2262 --data_path $DATA_PATH/train --output_dir $OUT_PATH --batch_size_per_gpu 32 --epochs 300 --teacher_temp 0.07 --warmup_epochs 10 --warmup_teacher_temp_epochs 30 --norm_last_layer false --use_dense_prediction True --aug-opt dino_aug --cfg experiments/imagenet/vil/vil_small/base.yaml MODEL.SPEC.MSVIT.ARCH 'l1,h3,d96,n2,s1,g1,p4,f7,a0_l2,h6,d192,n2,s1,g1,p2,f7,a0_l3,h12,d384,n6,s0,g1,p2,f7,a0_l4,h24,d768,n2,s0,g0,p2,f7,a0' MODEL.SPEC.MSVIT.MODE 1 MODEL.SPEC.MSVIT.VIL_MODE_SWITCH 0.75
```
### Multi-node training
To train on 2 nodes with 16 GPUs each (total 32 GPUs) for Swin-Small model size:
```
OUT_PATH=$PROJ_PATH/exp_output/esvit_exp/swin/swin_small/bl_lr0.0005_gpu16_bs16_multicrop_epoch300_dino_aug_window14
python main_evsit_mnodes.py --num_nodes 2 --num_gpus_per_node 16 --data_path $DATA_PATH/train --output_dir $OUT_PATH/continued_from0200_dense --batch_size_per_gpu 16 --arch swin_small --zip_mode True --epochs 300 --teacher_temp 0.07 --warmup_epochs 10 --warmup_teacher_temp_epochs 30 --norm_last_layer false --cfg experiments/imagenet/swin/swin_small_patch4_window14_224.yaml --use_dense_prediction True --pretrained_weights_ckpt $OUT_PATH/checkpoint0200.pth
```
## Evaluation:
### k-NN and Linear classification on ImageNet
To train a supervised linear classifier on frozen weights on a single node with 4 gpus, run `eval_linear.py`. To train a k-NN classifier on frozen weights on a single node with 4 gpus, run `eval_knn.py`. Please specify `--arch`, `--cfg` and `--pretrained_weights` to choose a pre-trained checkpoint. If you want to evaluate the last checkpoint of EsViT with Swin-T, you can run for example:
```
PROJ_PATH=your_esvit_project_path
DATA_PATH=$PROJ_PATH/project/data/imagenet
OUT_PATH=$PROJ_PATH/exp_output/esvit_exp/swin/swin_tiny/bl_lr0.0005_gpu16_bs32_dense_multicrop_epoch300
CKPT_PATH=$PROJ_PATH/exp_output/esvit_exp/swin/swin_tiny/bl_lr0.0005_gpu16_bs32_dense_multicrop_epoch300/checkpoint.pth
python -m torch.distributed.launch --nproc_per_node=4 eval_linear.py --data_path $DATA_PATH --output_dir $OUT_PATH/lincls/epoch0300 --pretrained_weights $CKPT_PATH --checkpoint_key teacher --batch_size_per_gpu 256 --arch swin_tiny --cfg experiments/imagenet/swin/swin_tiny_patch4_window7_224.yaml --n_last_blocks 4 --num_labels 1000 MODEL.NUM_CLASSES 0
python -m torch.distributed.launch --nproc_per_node=4 eval_knn.py --data_path $DATA_PATH --dump_features $OUT_PATH/features/epoch0300 --pretrained_weights $CKPT_PATH --checkpoint_key teacher --batch_size_per_gpu 256 --arch swin_tiny --cfg experiments/imagenet/swin/swin_tiny_patch4_window7_224.yaml MODEL.NUM_CLASSES 0
```
## Analysis/Visualization of correspondence and attention maps
You can analyze the learned models by running `python run_analysis.py`. One example to analyze EsViT (Swin-T) is shown.
For an invidiual image (with path `--image_path $IMG_PATH`), we visualize the attention maps and correspondence of the last layer:
```
python run_analysis.py --arch swin_tiny --image_path $IMG_PATH --output_dir $OUT_PATH --pretrained_weights $CKPT_PATH --learning ssl --seed $SEED --cfg experiments/imagenet/swin/swin_tiny_patch4_window7_224.yaml --vis_attention True --vis_correspondence True MODEL.NUM_CLASSES 0
```
For an image dataset (with path `--data_path $DATA_PATH`), we quantatively measure the correspondence:
```
python run_analysis.py --arch swin_tiny --data_path $DATA_PATH --output_dir $OUT_PATH --pretrained_weights $CKPT_PATH --learning ssl --seed $SEED --cfg experiments/imagenet/swin/swin_tiny_patch4_window7_224.yaml --measure_correspondence True MODEL.NUM_CLASSES 0
```
For more examples, please see `scripts/scripts_local/run_analysis.sh`.
## Citation
If you find this repository useful, please consider giving a star :star: and citation :beer::
```
@article{li2021esvit,
title={Efficient Self-supervised Vision Transformers for Representation Learning},
author={Li, Chunyuan and Yang, Jianwei and Zhang, Pengchuan and Gao, Mei and Xiao, Bin and Dai, Xiyang and Yuan, Lu and Gao, Jianfeng},
journal={International Conference on Learning Representations (ICLR)},
year={2022}
}
```
#### Related Projects/Codebase
[[Swin Transformers](https://github.com/microsoft/Swin-Transformer)] [[Vision Longformer](https://github.com/microsoft/vision-longformer)] [[Convolutional vision Transformers (CvT)](https://github.com/microsoft/CvT)] [[Focal Transformers](https://github.com/microsoft/Focal-Transformer)]
#### Acknowledgement
Our implementation is built partly upon packages: [[Dino](https://github.com/facebookresearch/dino)] [[Timm](https://github.com/rwightman/pytorch-image-models)]
## Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [[email protected]](mailto:[email protected]) with any additional questions or comments.
## Trademarks
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trademarks or logos is subject to and must follow
[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
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