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Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer

This repository contains the PyTorch code for Evo-ViT (accepted by AAAI-22).

This work proposes a slow-fast token evolution approach to accelerate vanilla vision transformers of both flat and deep-narrow structures without additional pre-training and fine-tuning procedures. For details please see Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer by Yifan Xu*, Zhijie Zhang*, Mengdan Zhang, Kekai Sheng, Ke Li, Weiming Dong, Liqing Zhang, Changsheng Xu, and Xing Sun.

Our code is based on pytorch-image-models, DeiT, and LeViT.

Preparation

Download and extract ImageNet train and val images from http://image-net.org/. The directory structure is the standard layout for the torchvision datasets.ImageFolder, and the training and validation data is expected to be in the train/ folder and val folder respectively.

/path/to/imagenet/
  train/
    class1/
      img1.jpeg
    class2/
      img2.jpeg
  val/
    class1/
      img3.jpeg
    class/2
      img4.jpeg

All distillation settings are conducted with a teacher model RegNetY-160, which is available at teacher checkpoint.

Install the requirements by running:

pip3 install -r requirements.txt

NOTE that all experiments in the paper are conducted under cuda11.0. If necessary, please install the following packages under the environment with CUDA version 11.0: torch1.7.0-cu110, torchvision-0.8.1-cu110.

Model Zoo

We provide our Evo-ViT models pretrained on ImageNet:

The input image resolution is 224 × 224 unless specified. * denotes the input image resolution is 384 × 384.

Usage

Evaluation

To evaluate a pre-trained model, run:

python3 main_deit.py --model evo_deit_small_patch16_224 --eval --resume /path/to/checkpoint.pth --batch-size 256 --data-path /path/to/imagenet

Training with input resolution of 224

To train Evo-ViT on ImageNet on a single node with 8 gpus for 300 epochs, run:

Evo-ViT-T

python3 -m torch.distributed.launch --nproc_per_node=8 --use_env main_deit.py --model evo_deit_tiny_patch16_224 --drop-path 0 --batch-size 256 --data-path /path/to/imagenet --output_dir /path/to/save

Evo-ViT-S

python3 -m torch.distributed.launch --nproc_per_node=8 --use_env main_deit.py --model evo_deit_small_patch16_224 --batch-size 128 --data-path /path/to/imagenet --output_dir /path/to/save

Sometimes loss Nan happens in the early training epochs of DeiT-B, which is described in this issue. Our solution is to reduce the batch size to 128, load a warmup checkpoint trained for 9 epochs, and train Evo-ViT for the remaining 291 epochs. To train Evo-ViT-B on ImageNet on a single node with 8 gpus for 300 epochs, run:

python3 -m torch.distributed.launch --nproc_per_node=8 --use_env main_deit.py --model evo_deit_base_patch16_224 --batch-size 128 --data-path /path/to/imagenet --output_dir /path/to/save --resume /path/to/warmup_checkpoint.pth

To train Evo-LeViT-128 on ImageNet on a single node with 8 gpus for 300 epochs, run:

python3 -m torch.distributed.launch --nproc_per_node=8 --use_env main_levit.py --model EvoLeViT_128 --batch-size 256 --data-path /path/to/imagenet --output_dir /path/to/save

The other models of Evo-LeViT are trained with the same command as mentioned above.

Training with input resolution of 384

To train Evo-ViT-B* on ImageNet on 2 nodes with 8 gpus each for 300 epochs, run:

python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=$NODE_SIZE  --node_rank=$NODE_RANK --master_port=$MASTER_PORT --master_addr=$MASTER_ADDR main_deit.py --model evo_deit_base_patch16_384 --input-size 384 --batch-size 64 --data-path /path/to/imagenet --output_dir /path/to/save

To train Evo-ViT-S* on ImageNet on a single node with 8 gpus for 300 epochs, run:

python3 -m torch.distributed.launch --nproc_per_node=8 --use_env main_deit.py --model evo_deit_small_patch16_384 --batch-size 128 --input-size 384 --data-path /path/to/imagenet --output_dir /path/to/save"

To train Evo-LeViT-384* on ImageNet on a single node with 8 gpus for 300 epochs, run:

python3 -m torch.distributed.launch --nproc_per_node=8 --use_env main_levit.py --model EvoLeViT_384_384 --input-size 384 --batch-size 128 --data-path /path/to/imagenet --output_dir /path/to/save

The other models of Evo-LeViT* are trained with the same command of Evo-LeViT-384*.

Testing inference throughput

To test inference throughput, first modify the model name in line 153 of benchmark.py. Then, run:

python3 benchmark.py

The defauld input resolution is 224. To test inference throughput with input resolution of 384, please add the parameter "--img_size 384"

Visualization of token selection

The visualization code is modified from DynamicViT.

To visualize a batch of ImageNet val images, run:

python3 visualize.py --model evo_deit_small_vis_patch16_224 --resume /path/to/checkpoint.pth --output_dir /path/to/save --data-path /path/to/imagenet --batch-size 64 

To visualize a single image, run:

python3 visualize.py --model evo_deit_small_vis_patch16_224 --resume /path/to/checkpoint.pth --output_dir /path/to/save --img-path ./imgs/a.jpg --save-name evo_test

Add parameter '--layer-wise-prune' if the visualized model is not trained with layer-to-stage training strategy.

The visualization results of Evo-ViT-S are as follows:

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{evo-vit,
  title={Evo-vit: Slow-fast token evolution for dynamic vision transformer},
  author={Xu, Yifan and Zhang, Zhijie and Zhang, Mengdan and Sheng, Kekai and Li, Ke and Dong, Weiming and Zhang, Liqing and Xu, Changsheng and Sun, Xing},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={36},
  number={3},
  pages={2964--2972},
  year={2022}
}