Magnety / Dynamic-Vision-Transformer

Accelerating T2t-ViT by 1.6-3.6x.

Dynamic-Vision-Transformer (Pytorch)

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

• Not All Images are Worth 16x16 Words: Dynamic Vision Transformers with Adaptive Sequence Length

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:

@article{wang2021not,
        title = {Not All Images are Worth 16x16 Words: Dynamic Vision Transformers with Adaptive Sequence Length},
       author = {Wang, Yulin and Huang, Rui and Song, Shiji and Huang, Zeyi and Huang, Gao},
      journal = {arXiv preprint arXiv:2105.15075},
         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

Backbone	# of Exits	# of Tokens	Links
T2T-ViT-12	3	7x7-10x10-14x14	Tsinghua Cloud / Google Drive

• 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 3.7.7
• pytorch 1.3.1
• torchvision 0.4.2

Evaluate Pre-trained Models

Read the evaluation results saved in pre-trained models

CUDA_VISIBLE_DEVICES=0 python inference.py --batch_size 128 --model DVT_T2t_vit_12 --checkpoint_path PATH_TO_CHECKPOINTS  --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 128 --model DVT_T2t_vit_12 --checkpoint_path PATH_TO_CHECKPOINTS  --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 128 --model DVT_T2t_vit_12 --checkpoint_path PATH_TO_CHECKPOINTS  --eval_mode 2

The dataset is expected to be prepared as follows:

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

Contact

If you have any question, please feel free to contact the authors. Yulin Wang: [e-mail].

Acknowledgment

Our code of T2T-ViT from here.

To Do

• Update the code for training.