HAT [Paper Link] 
Xiangyu Chen, Xintao Wang, Jiantao Zhou and Chao Dong
@article{chen2022activating,
title={Activating More Pixels in Image Super-Resolution Transformer},
author={Chen, Xiangyu and Wang, Xintao and Zhou, Jiantao and Dong, Chao},
journal={arXiv preprint arXiv:2205.04437},
year={2022}
}**Google Scholar has unknown bugs for indexing this paper recently, while it can still be cited by the above BibTeX.**
- ✅ 2022-05-09: Release the first version of the paper at Arxiv.
- ✅ 2022-05-20: Release the codes, models and results of HAT.
- ✅ 2022-08-29: Add a Replicate demo for SRx4.
- ✅ 2022-09-25: Add the tile mode for inference with limited GPU memory.
- ✅ 2022-11-24: Upload a GAN-based HAT model for Real-World SR (Real_HAT_GAN_SRx4.pth).
- ✅ 2023-03-19: Update paper to CVPR version. Small HAT models are added.
- ✅ 2023-04-05: Upload the HAT-S codes, models and results.
- (To do) Add the tile mode for Replicate demo.
- (To do) Update the Replicate demo for Real-World SR.
- (To do) Upload the training configs for the Real-World GAN-based HAT model.
- (To do) Add HAT models for Multiple Image Restoration tasks.
Benchmark results on SRx4 without x2 pretraining. Mulit-Adds are calculated for a 64x64 input.
| Model | Params(M) | Multi-Adds(G) | Set5 | Set14 | BSD100 | Urban100 | Manga109 |
|---|---|---|---|---|---|---|---|
| SwinIR | 11.9 | 53.6 | 32.92 | 29.09 | 27.92 | 27.45 | 32.03 |
| HAT-S | 9.6 | 54.9 | 32.92 | 29.15 | 27.97 | 27.87 | 32.35 |
| HAT | 20.8 | 102.4 | 33.04 | 29.23 | 28.00 | 27.97 | 32.48 |
- PyTorch >= 1.7 (Recommend NOT using torch 1.8!!! It would cause abnormal performance.)
- BasicSR == 1.3.4.9
pip install -r requirements.txt
python setup.py develop
Without implementing the codes, chaiNNer is a nice tool to run our models.
Otherwise,
- Refer to
./options/testfor the configuration file of the model to be tested, and prepare the testing data and pretrained model. - The pretrained models are available at Google Drive or Baidu Netdisk (access code: qyrl).
- Then run the follwing codes (taking
HAT_SRx4_ImageNet-pretrain.pthas an example):
python hat/test.py -opt options/test/HAT_SRx4_ImageNet-pretrain.yml
The testing results will be saved in the ./results folder.
- Refer to
./options/test/HAT_SRx4_ImageNet-LR.ymlfor inference without the ground truth image.
Note that the tile mode is also provided for limited GPU memory when testing. You can modify the specific settings of the tile mode in your custom testing option by referring to ./options/test/HAT_tile_example.yml.
- Refer to
./options/trainfor the configuration file of the model to train. - Preparation of training data can refer to this page. ImageNet dataset can be downloaded at the official website.
- The training command is like
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 hat/train.py -opt options/train/train_HAT_SRx2_from_scratch.yml --launcher pytorch
- Note that the default batch size per gpu is 4, which will cost about 20G memory for each GPU.
The training logs and weights will be saved in the ./experiments folder.
The inference results on benchmark datasets are available at Google Drive or Baidu Netdisk (access code: 63p5).
If you have any question, please email [email protected] or join in the Wechat group of BasicSR to discuss with the authors.
创建LFHAT的python虚拟环境
conda create --name LFHAT python=3.8
安装1.9.1版本的pytorch
pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html
// 如果报错则适当替换相近版本进行安装
在阿里源下安装requirements
pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
执行安装脚本
python setup.py develop