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}
}

• PyTorch >= 1.7
• BasicSR == 1.3.4.9

pip install -r requirements.txt
python setup.py develop

• Refer to ./options/test for 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.pth as 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/train for 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.