CSWin-Transformer

This repo is the official implementation of "CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows".

Introduction

CSWin Transformer (the name CSWin stands for Cross-Shaped Window) is introduced in arxiv, which is a new general-purpose backbone for computer vision. It is a hierarchical Transformer and replaces the traditional full attention with our newly proposed cross-shaped window self-attention. The cross-shaped window self-attention mechanism computes self-attention in the horizontal and vertical stripes in parallel that from a cross-shaped window, with each stripe obtained by splitting the input feature into stripes of equal width. With CSWin, we could realize global attention with a limited computation cost.

CSWin Transformer achieves strong performance on ImageNet classification (87.5 on val with only 97G flops) and ADE20K semantic segmentation (55.7 mIoU on val), surpassing previous models by a large margin.

Main Results on ImageNet

model	pretrain	resolution	acc@1	#params	FLOPs	22K model	1K model
CSWin-T	ImageNet-1K	224x224	82.8	23M	4.3G	-	model
CSWin-S	ImageNet-1k	224x224	83.6	35M	6.9G	-	model
CSWin-B	ImageNet-1k	224x224	84.2	78M	15.0G	-	model
CSWin-B	ImageNet-1k	384x384	85.5	78M	47.0G	-	model
CSWin-L	ImageNet-22k	224x224	86.5	173M	31.5G	model	model
CSWin-L	ImageNet-22k	384x384	87.5	173M	96.8G	-	model

Main Results on Downstream Tasks

COCO Object Detection

backbone	Method	pretrain	lr Schd	box mAP	mask mAP	#params	FLOPS
CSwin-T	Mask R-CNN	ImageNet-1K	3x	49.0	43.6	42M	279G
CSwin-S	Mask R-CNN	ImageNet-1K	3x	50.0	44.5	54M	342G
CSwin-B	Mask R-CNN	ImageNet-1K	3x	50.8	44.9	97M	526G
CSwin-T	Cascade Mask R-CNN	ImageNet-1K	3x	52.5	45.3	80M	757G
CSwin-S	Cascade Mask R-CNN	ImageNet-1K	3x	53.7	46.4	92M	820G
CSwin-B	Cascade Mask R-CNN	ImageNet-1K	3x	53.9	46.4	135M	1004G

ADE20K Semantic Segmentation (val)

Backbone	Method	pretrain	Crop Size	Lr Schd	mIoU	mIoU (ms+flip)	#params	FLOPs
CSwin-T	Semantic FPN	ImageNet-1K	512x512	80K	48.2	-	26M	202G
CSwin-S	Semantic FPN	ImageNet-1K	512x512	80K	49.2	-	39M	271G
CSwin-B	Semantic FPN	ImageNet-1K	512x512	80K	49.9	-	81M	464G
CSwin-T	UPerNet	ImageNet-1K	512x512	160K	49.3	50.7	60M	959G
CSwin-S	UperNet	ImageNet-1K	512x512	160K	50.4	51.5	65M	1027G
CSwin-B	UperNet	ImageNet-1K	512x512	160K	51.1	52.2	109M	1222G
CSwin-B	UPerNet	ImageNet-22K	640x640	160K	51.8	52.6	109M	1941G
CSwin-L	UperNet	ImageNet-22K	640x640	160K	53.4	55.7	208M	2745G

pretrained models and code could be found at segmentation

Requirements

timm==0.3.4, pytorch>=1.4, opencv, ... , run:

bash install_req.sh

Apex for mixed precision training is used for finetuning. To install apex, run:

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

Data prepare: ImageNet with the following folder structure, you can extract imagenet by this script.

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

Train

Train the three lite variants: CSWin-Tiny, CSWin-Small and CSWin-Base:

bash train.sh 8 --data <data path> --model CSWin_64_12211_tiny_224 -b 256 --lr 2e-3 --weight-decay .05 --amp --img-size 224 --warmup-epochs 20 --model-ema-decay 0.99984 --drop-path 0.2

bash train.sh 8 --data <data path> --model CSWin_64_24322_small_224 -b 256 --lr 2e-3 --weight-decay .05 --amp --img-size 224 --warmup-epochs 20 --model-ema-decay 0.99984 --drop-path 0.4

bash train.sh 8 --data <data path> --model CSWin_96_24322_base_224 -b 128 --lr 1e-3 --weight-decay .1 --amp --img-size 224 --warmup-epochs 20 --model-ema-decay 0.99992 --drop-path 0.5

If you want to train our CSWin on images with 384x384 resolution, please use '--img-size 384'.

If the GPU memory is not enough, please use '-b 128 --lr 1e-3 --model-ema-decay 0.99992' or use checkpoint '--use-chk'.

Finetune

Finetune CSWin-Base with 384x384 resolution:

bash finetune.sh 8 --data <data path> --model CSWin_96_24322_base_384 -b 32 --lr 5e-6 --min-lr 5e-7 --weight-decay 1e-8 --amp --img-size 384 --warmup-epochs 0 --model-ema-decay 0.9998 --finetune <pretrained 224 model> --epochs 20 --mixup 0.1 --cooldown-epochs 10 --drop-path 0.7 --ema-finetune --lr-scale 1 --cutmix 0.1

Finetune ImageNet-22K pretrained CSWin-Large with 224x224 resolution:

bash finetune.sh 8 --data <data path> --model CSWin_144_24322_large_224 -b 64 --lr 2.5e-4 --min-lr 5e-7 --weight-decay 1e-8 --amp --img-size 224 --warmup-epochs 0 --model-ema-decay 0.9996 --finetune <22k-pretrained model> --epochs 30 --mixup 0.01 --cooldown-epochs 10 --interpolation bicubic  --lr-scale 0.05 --drop-path 0.2 --cutmix 0.3 --use-chk --fine-22k --ema-finetune

If the GPU memory is not enough, please use checkpoint '--use-chk'.

Cite CSWin Transformer

@misc{dong2021cswin,
      title={CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows}, 
        author={Xiaoyi Dong and Jianmin Bao and Dongdong Chen and Weiming Zhang and Nenghai Yu and Lu Yuan and Dong Chen and Baining Guo},
        year={2021},
        eprint={2107.00652},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
}

Acknowledgement

This repository is built using the timm library and the DeiT repository.

License

This project is licensed under the license found in the LICENSE file in the root directory of this source tree.

Microsoft Open Source Code of Conduct

Contact Information

For help or issues using CSWin Transformer, please submit a GitHub issue.

For other communications related to CSWin Transformer, please contact Jianmin Bao (jianbao@microsoft.com), Dong Chen (doch@microsoft.com).