Activating More Pixels in Image Super-Resolution Transformer

Related tags

Deep Learning HAT
Overview

HAT [Paper Link]

Activating More Pixels in Image Super-Resolution Transformer

Xiangyu Chen, Xintao Wang, Jiantao Zhou and Chao Dong

BibTeX

@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}
}

Environment

Installation

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

How To Test

  • 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.

Results

The inference results on benchmark datasets are available at Google Drive or Baidu Netdisk (access code: 63p5).

This repo is still being updated. The training codes will be released soon.

Issues
  • About position encoding and attention mask

    About position encoding and attention mask

    Hello,

    Thanks for your great work!

    What is the difference in implementing position encoding and attention mask in overlapped cross-window attention? I mean that overlapped cross-window attention is different from the vanilla one, since the window size of Q and K are different, and I think using original RPE and attention mask does not make sense.

    Could you please give me some hints? Thanks in advance.

    opened by mrluin 1
Owner
XyChen
PhD. Student,Computer Vision
XyChen
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