PyTorch code for our paper "Attention in Attention Network for Image Super-Resolution"

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Deep Learning A2N
Overview

Under construction...

Attention in Attention Network for Image Super-Resolution (A2N)

This repository is an PyTorch implementation of the paper

"Attention in Attention Network for Image Super-Resolution" [arXiv]

Visual results in the paper are availble at Google Drive or Baidu Netdisk (password: 7t74).

Unofficial TensorFlow implementation: https://github.com/Anuj040/superres

Test

Dependecies: torch==0.4.1

You can download the test sets from Google Drive. Put the test data in ../Data/benchmark/.

python main.py  --scale 4 --data_test Set5 --pre_train ./experiment/model/aan_x4.pt --chop --test_only

If you use CPU, please add "--cpu".

Train

Training data preparation

  1. Download DIV2K training data from DIV2K dataset or SNU_CVLab.
  2. Specify '--dir_data' in option.py based on the data path.

For more informaiton, please refer to EDSR(PyTorch).

Training

python main.py --scale 2 --patch_size 128 --reset --chop

python main.py --scale 3 --patch_size 192 --reset --chop

python main.py --scale 4 --patch_size 256 --reset --chop

Citation

If you have any question or suggestion, welcome to email me at here.

If you find our work helpful in your resarch or work, please cite the following papers.

@misc{chen2021attention,
      title={Attention in Attention Network for Image Super-Resolution}, 
      author={Haoyu Chen and Jinjin Gu and Zhi Zhang},
      year={2021},
      eprint={2104.09497},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Acknowledgements

This code is built on EDSR (PyTorch) and PAN. We thank the authors for sharing their codes.

Comments
  • About pre-trained model

    About pre-trained model

    Thank you for your amazing work! I would like to know if the pre-trained model is provided? I can't find it , sry. And I notice that the methods of reconstruction module and Upsample module are nearest-neighbor interpolation and bilinear interpolation, respectively.Have you tried using sub-pixel related methods? And how is the result? Thanks again.

    opened by jjlinghu 2
  • Mismatch in the model architecture implementation and the one given in the paper

    Mismatch in the model architecture implementation and the one given in the paper

    Thanks a lot for an interesting work on super-resolution and an easy to understand repository. I am working to implement this repo in tensorflow (link). I noticed that the following skip connection is not shown in the Figure 3. of https://arxiv.org/abs/2104.09497. Just wanted to confirm that. https://github.com/haoyuc/A2N/blob/a04e78a3f3735553c4514521dd5b6937eee40aae/code/model/aan.py#L152

    Thanks a lot for your time. PS: If you feel my implementation worthy of reference, please consider that. Would be immensely grateful.

    opened by Anuj040 2
  • Suggestion

    Suggestion

    Hi, @haoyuc Thanks for your work For the convenience of comparison, can you share the pre-trained model or benchmark results. Looking for you work and good luck!

    opened by Senwang98 2
  • 关于注意力分支的问题

    关于注意力分支的问题

    您好,在看完您的文章后,我有一些不清楚的地方想向您请教一下,Attention Branch中使用的是通道-空间注意嘛?为什么我只能看到的是空间注意力机制。还有就是我不清楚,注意力机制的最后一步,为什么又加了一个卷积,K4的作用是什么呢?我的问题可能比较基础,希望您有空的话,可以解决一下我的疑惑。谢谢您的指教。 image image

    opened by zyaqin 2
  • 对于注意力分支的选择

    对于注意力分支的选择

    对您的工作很感兴趣,请问一下看您论文里写的 非注意和通道-空间注意的组合具有最好的表现,您代码中用的是通道-空间注意吗?还有就是请问一下 单纯采用通道注意力和非注意力的组合,加上您的dropout模块,效果会变得不好吗?dropout模块只能在非注意和通道-空间注意或空间注意的组合才有正面效果吗? 谢谢您的指教

    opened by Lewis0427 1
  • Problem with loading data

    Problem with loading data

    Thank you for sharing codes to us. While reproducing the code, I came across some minor issues that I would like to ask you for advice on, as follows: KE0_L@XDKXGJW@(6P4X1AP1 I carefully checked the code and found that the output of the data loader had only three parts, namely hr, lr and filename, and I was puzzled as to why there were four parts (lr,hr,_,idx_scale) of output in the code. Looking forward to your reply, thanks!

    opened by zyaqin 4
Owner
Haoyu Chen
Haoyu Chen
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