DynamicViT

This repository contains PyTorch implementation for DynamicViT.

Created by Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, Cho-Jui Hsieh

intro

Model Zoo

We provide our DynamicViT models pretrained on ImageNet:

name arch rho [email protected] [email protected] FLOPs url
DynamicViT-256/0.7 deit_256 0.7 76.532 93.118 1.3G Google Drive / Tsinghua Cloud
DynamicViT-384/0.7 deit_small 0.7 79.316 94.676 2.9G Google Drive / Tsinghua Cloud
DynamicViT-LV-S/0.5 lvvit_s 0.5 81.970 95.756 3.7G Google Drive / Tsinghua Cloud
DynamicViT-LV-S/0.7 lvvit_s 0.7 83.076 96.252 4.6G Google Drive / Tsinghua Cloud
DynamicViT-LV-M/0.7 lvvit_m 0.7 83.816 96.584 8.5G Google Drive / Tsinghua Cloud

Usage

Requirements

  • torch>=1.7.0
  • torchvision>=0.8.1
  • timm==0.4.5

Data preparation: download and extract ImageNet images from http://image-net.org/. The directory structure should be

│ILSVRC2012/
├──train/
│  ├── n01440764
│  │   ├── n01440764_10026.JPEG
│  │   ├── n01440764_10027.JPEG
│  │   ├── ......
│  ├── ......
├──val/
│  ├── n01440764
│  │   ├── ILSVRC2012_val_00000293.JPEG
│  │   ├── ILSVRC2012_val_00002138.JPEG
│  │   ├── ......
│  ├── ......

Model preparation: download pre-trained DeiT and LV-ViT models for training DynamicViT:

sh download_pretrain.sh

Demo

We provide a Jupyter notebook where you can run the visualization of DynamicViT.

To run the demo, you need to install matplotlib.

demo

Evaluation

To evaluate a pre-trained DynamicViT model on ImageNet val with a single GPU, run:

python infer.py --data-path /path/to/ILSVRC2012/ --arch arch_name --model-path /path/to/model --base_rate 0.7 

Training

To train DynamicViT models on ImageNet, run:

DeiT-small

python -m torch.distributed.launch --nproc_per_node=8 --use_env main_dynamic_vit.py  --output_dir logs/dynamic-vit_deit-small --arch deit_small --input-size 224 --batch-size 96 --data-path /path/to/ILSVRC2012/ --epochs 30 --dist-eval --distill --base_rate 0.7

LV-ViT-S

python -m torch.distributed.launch --nproc_per_node=8 --use_env main_dynamic_vit.py  --output_dir logs/dynamic-vit_lvvit-s --arch lvvit_s --input-size 224 --batch-size 64 --data-path /path/to/ILSVRC2012/ --epochs 30 --dist-eval --distill --base_rate 0.7

LV-ViT-M

python -m torch.distributed.launch --nproc_per_node=8 --use_env main_dynamic_vit.py  --output_dir logs/dynamic-vit_lvvit-m --arch lvvit_m --input-size 224 --batch-size 48 --data-path /path/to/ILSVRC2012/ --epochs 30 --dist-eval --distill --base_rate 0.7

You can train models with different keeping ratio by adjusting base_rate. DynamicViT can also achieve comparable performance with only 15 epochs training (around 0.1% lower accuracy).

GitHub

https://github.com/raoyongming/DynamicViT