Efficient Self-Supervised Vision Transformers (EsViT)

[Paper] [Slides]

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) in 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), 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.

$\qquad$ [Workshop] $\qquad$ [IC Challenge] $\qquad$ [OD Challenge]

• [06/19/2022] Released the evaluation benchmark used in EsVIT. It contains 20 downstream image classification tasks. [ELEVATER Benchmark] [Toolkit] [Paper]

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:

?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

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 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 ⭐ 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}
}

Related Projects/Codebase

[Swin Transformers] [Vision Longformer] [Convolutional vision Transformers (CvT)] [Focal Transformers]

Acknowledgement

Our implementation is built partly upon packages: [Dino] [Timm]

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