Dynamic-Vision-Transformer (NeurIPS 2021)

This repo contains the official MindSpore code for the Dynamic Vision Transformer (DVT).

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

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.

Training

You have to execute script from "src" directory. It will create directory "../results/{DATETIME}__{EXPERIMENT_NAME}" and place results there.

bash scripts/train_ascend.sh {0-7} EXPERIMENT_NAME --config=CONFIG_PATH --device {Ascend (default)|GPU} [TRAIN.PY_ARGUMENTS]

# training for feature reuse and releation reuse
bash scripts/train_ascend.sh 0-7 deit_dvt_12_49_196_w_f_w_r_adamw_originhead_dataaug_mixup --config=configs/local/vit_dvt/deit_dvt_12_49_196_w_f_w_r_adamw_originhead_dataaug_mixup.yml.j2

# training for feature reuse and w/o releation reuse
bash scripts/train_ascend.sh 0-7 deit_dvt_12_49_196_w_f_n_r_adamw_originhead_dataaug_mixup --config=configs/local/vit_dvt/deit_dvt_12_49_196_w_f_n_r_adamw_originhead_dataaug_mixup.yml.j2

# training for w/o feature reuse and releation reuse
bash scripts/train_ascend.sh 0-7 deit_dvt_12_49_196_n_f_w_r_adamw_originhead_dataaug_mixup --config=configs/local/vit_dvt/deit_dvt_12_49_196_n_f_w_r_adamw_originhead_dataaug_mixup.yml.j2

# training for w/o feature reuse and w/o releation reuse
bash scripts/train_ascend.sh 0-7 deit_dvt_12_49_196_n_f_n_r_adamw_originhead_dataaug_mixup --config=configs/local/vit_dvt/deit_dvt_12_49_196_n_f_n_r_adamw_originhead_dataaug_mixup.yml.j2

# inference for feature reuse and releation reuse
bash scripts/inference_ascend.sh 0 deit_dvt_12_49_196_w_f_w_r_adamw_originhead_dataaug_mixup_inference --config=configs/local/vit_dvt/deit_dvt_12_49_196_w_f_w_r_adamw_originhead_dataaug_mixup_inference.yml.j2

Results

• Models Overview

model	flops	acc
deit-s/16	4.608	78.67
deit-s/32	1.145	72.116
vit-b/16	17.58	79.1
vit-b/32	4.41	73.972

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

• Visualization

Requirements

• Mindspore 1.5 (https://www.mindspore.cn/install/en)
• jinja2 (https://anaconda.org/anaconda/jinja2)
• tqdm (for GPU only)
• mpi4py (for GPU only)

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}
}

Contact

This is a MindSpore implementation version. If you have any question, please feel free to contact Yulin Wang: [email protected] and Guanfu Chen: [email protected].