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Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer, arxiv

PaddlePaddle training/validation code and pretrained models for Shuffle Transformer.

The official pytorch implementation is here.

This implementation is developed by PaddleViT.

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Shuffle Transformer Model Overview

Update

  • Update (2022-03-24): Code is refactored and bugs are fixed.
  • Update (2021-08-11): Model FLOPs and # params are uploaded.
  • Update (2021-08-11): Code is released and ported weights are uploaded.

Models Zoo

Model Acc@1 Acc@5 #Params FLOPs Image Size Crop_pct Interpolation Link
shuffle_vit_tiny 82.39 96.05 28.5M 4.6G 224 0.875 bicubic google/baidu
shuffle_vit_small 83.53 96.57 50.1M 8.8G 224 0.875 bicubic google/baidu
shuffle_vit_base 83.95 96.91 88.4M 15.5G 224 0.875 bicubic google/baidu

*The results are evaluated on ImageNet2012 validation set.

Data Preparation

ImageNet2012 dataset is used in the following file structure:

│imagenet/
├──train_list.txt
├──val_list.txt
├──train/
│  ├── n01440764
│  │   ├── n01440764_10026.JPEG
│  │   ├── n01440764_10027.JPEG
│  │   ├── ......
│  ├── ......
├──val/
│  ├── n01440764
│  │   ├── ILSVRC2012_val_00000293.JPEG
│  │   ├── ILSVRC2012_val_00002138.JPEG
│  │   ├── ......
│  ├── ......
  • train_list.txt: list of relative paths and labels of training images. You can download it from: google/baidu
  • val_list.txt: list of relative paths and labels of validation images. You can download it from: google/baidu

Usage

To use the model with pretrained weights, download the .pdparam weight file and change related file paths in the following python scripts. The model config files are located in ./configs/.

For example, assume weight file is downloaded in ./shuffle_vit_tiny_patch4_window7_224.pdparams, to use the shuffle_vit_tiny_patch4_window7_224 model in python:

from config import get_config
from shuffle_transformer import build_shuffle_transformer as build_model
# config files in ./configs/
config = get_config('./configs/shuffle_vit_tiny_patch4_window7_224.yaml')
# build model
model = build_model(config)
# load pretrained weights
model_state_dict = paddle.load('./shuffle_vit_tiny_patch4_window7_224.pdparams')
model.set_state_dict(model_state_dict)

Evaluation

To evaluate model performance on ImageNet2012, run the following script using command line:

sh run_eval_multi.sh

or

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python main_multi_gpu.py \
-cfg='./configs/shuffle_vit_tiny_patch4_window7_224.yaml' \
-dataset='imagenet2012' \
-batch_size=256 \
-data_path='/dataset/imagenet' \
-eval \
-pretrained='./shuffle_vit_tiny_patch4_window7_224.pdparams' \
-amp

Note: if you have only 1 GPU, change device number to CUDA_VISIBLE_DEVICES=0 would run the evaluation on single GPU.

Training

To train the model on ImageNet2012, run the following script using command line:

sh run_train_multi.sh

or

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python main_multi_gpu.py \
-cfg='./configs/shuffle_vit_tiny_patch4_window7_224.yaml' \
-dataset='imagenet2012' \
-batch_size=256 \
-data_path='/dataset/imagenet' \
-amp

Note: it is highly recommanded to run the training using multiple GPUs / multi-node GPUs.

Reference

@article{huang2021shuffle,
  title={Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer},
  author={Huang, Zilong and Ben, Youcheng and Luo, Guozhong and Cheng, Pei and Yu, Gang and Fu, Bin},
  journal={arXiv preprint arXiv:2106.03650},
  year={2021}
}