PyTorch implementation for EsViT (accepted in ICLR, 2022), built with two techniques:
- A multi-stage Transformer architecture. Three multi-stage Transformer variants are implemented under the folder
models
. - A non-contrastive region-level matching pre-train task. The region-level matching task is implemented in function
DDINOLoss(nn.Module)
(Line 648) inmain_esvit.py
. Please use--use_dense_prediction True
, otherwise only the view-level task is used.
- [08/19/2022] Organizing ECCV Workshop Computer Vision in the Wild (CVinW), 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)'' Challenge evaluates on 20 image classification tasks.
- ``Object Detection in the Wild (ODinW)'' Challenge evaluates on 35 object detection tasks.
- [06/19/2022] Released the evaluation benchmark used in EsVIT. It contains 20 downstream image classification tasks. [ELEVATER Benchmark] [Toolkit] [Paper]
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:
?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 View-level and Region-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 |
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
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 theYAML
file underexperiments/imagenet/*/*.yaml
. The default window size=7; To consider a multi-stage architecture with window size=14, please choose yaml files withwindow14
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 $OUT_PATH --batch_size_per_gpu 32 --epochs 300 --teacher_temp 0.07 --warmup_epochs 10 --warmup_teacher_temp_epochs 30 --norm_last_layer false --use_dense_prediction True --aug-opt dino_aug --cfg experiments/imagenet/vil/vil_small/base.yaml MODEL.SPEC.MSVIT.ARCH 'l1,h3,d96,n2,s1,g1,p4,f7,a0_l2,h6,d192,n2,s1,g1,p2,f7,a0_l3,h12,d384,n6,s0,g1,p2,f7,a0_l4,h24,d768,n2,s0,g0,p2,f7,a0' MODEL.SPEC.MSVIT.MODE 1 MODEL.SPEC.MSVIT.VIL_MODE_SWITCH 0.75
To train on 2 nodes with 16 GPUs each (total 32 GPUs) for Swin-Small model size:
OUT_PATH=$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 $OUT_PATH/checkpoint0200.pth
To train a supervised linear classifier on frozen weights on a single node with 4 gpus, run eval_linear.py
. To train a k-NN classifier on frozen weights on a single node with 4 gpus, run eval_knn.py
. Please specify --arch
, --cfg
and --pretrained_weights
to choose a pre-trained checkpoint. If you want to evaluate the last checkpoint of EsViT with Swin-T, you can run for example:
PROJ_PATH=your_esvit_project_path
DATA_PATH=$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
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
.
If you find this repository useful, please consider giving a star ⭐ and citation 🍺:
@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}
}
[Swin Transformers] [Vision Longformer] [Convolutional vision Transformers (CvT)] [Focal Transformers]
Our implementation is built partly upon packages: [Dino] [Timm]
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