Dynamic-Vision-Transformer (NeurIPS 2021)

This repo contains the official PyTorch code and pre-trained models for the Dynamic Vision Transformer (DVT).

• Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition

We also provide an implementation under the MindSpore framework and train DVT on a cluster of Ascend AI processors. Code and pre-trained models will be available at here.

Update on 2021/10/02: Release the Training Code.

Update on 2021/06/01: Release Pre-trained Models and the Inference Code on ImageNet.

Introduction

We develop a Dynamic Vision Transformer (DVT) to automatically configure a proper number of tokens for each individual image, leading to a significant improvement in computational efficiency, both theoretically and empirically.

Citation

If you find this work valuable or use our code in your own research, please consider citing us with the following bibtex:

@inproceedings{wang2021not,
        title = {Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition},
       author = {Wang, Yulin and Huang, Rui and Song, Shiji and Huang, Zeyi and Huang, Gao},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
         year = {2021}
}

Results

• Top-1 accuracy on ImageNet v.s. GFLOPs

• Top-1 accuracy on CIFAR v.s. GFLOPs

• Top-1 accuracy on ImageNet v.s. Throughput

• Visualization

Pre-trained Models

• What are contained in the checkpoints:

**.pth.tar
├── model_state_dict: state dictionaries of the model
├── flops: a list containing the GFLOPs corresponding to exiting at each exit
├── anytime_classification: Top-1 accuracy of each exit
├── dynamic_threshold: the confidence thresholds used in budgeted batch classification
├── budgeted_batch_classification: results of budgeted batch classification (a two-item list, [0] and [1] correspond to the two coordinates of a curve)

Requirements

• python 3.7.10
• pytorch 1.8.1
• torchvision 0.9.1
• apex
• pyyaml

Data Preparation

• The ImageNet dataset should be prepared as follows:

ImageNet
├── train
│   ├── folder 1 (class 1)
│   ├── folder 2 (class 1)
│   ├── ...
├── val
│   ├── folder 1 (class 1)
│   ├── folder 2 (class 1)
│   ├── ...

Evaluate Pre-trained Models

• Read the evaluation results saved in pre-trained models

CUDA_VISIBLE_DEVICES=0 python inference.py --model {DVT_T2t_vit_12, DVT_T2t_vit_14, DVT_Deit_small} --checkpoint_path PATH_TO_CHECKPOINT  --eval_mode 0

• Read the confidence thresholds saved in pre-trained models and infer the model on the validation set

CUDA_VISIBLE_DEVICES=0 python inference.py --data_url PATH_TO_DATASET --batch_size 64 --model {DVT_T2t_vit_12, DVT_T2t_vit_14, DVT_Deit_small} --checkpoint_path PATH_TO_CHECKPOINT  --eval_mode 1

• Determine confidence thresholds on the training set and infer the model on the validation set

CUDA_VISIBLE_DEVICES=0 python inference.py --data_url PATH_TO_DATASET --batch_size 64 --model {DVT_T2t_vit_12, DVT_T2t_vit_14, DVT_Deit_small} --checkpoint_path PATH_TO_CHECKPOINT  --eval_mode 2

Train

• Train DVT_T2t_vit_12/14 on ImageNet

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 main.py PATH_TO_DATASET --model DVT_T2t_vit_12 --b 128 --lr 2e-3 --weight-decay .03 --amp --img-size 224

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 main.py PATH_TO_DATASET --model DVT_T2t_vit_14 --b 64 --lr 5e-4 --weight-decay .05 --amp --img-size 224

• We use NVIDIA APEX AMP for efficient training. It would be ok to turn it off (by removing '--amp'). But the GPU memory cost will significantly increase.

Transfer DVT to CIFAR-10/100

We finetune our pretrained DVT_T2t_vit_12/14 to CIFAR-10/100 in the same way as T2T-ViT.

Contact

This is a re-implementation version. If you have any question, please feel free to contact the authors. Yulin Wang: [email protected].

Acknowledgment

Our code of T2T-ViT is from here. Our code of DeiT is from here.