Codes for "Efficient Long-Range Attention Network for Image Super-resolution"

Related tags

Audio ELAN
Overview

ELAN

Codes for "Efficient Long-Range Attention Network for Image Super-resolution", arxiv link.

Dependencies & Installation

Please refer to the following simple steps for installation. Datas can be download from Baidu cloud disk [url] (pwd: al4m)

git clone https://github.com/xindongzhang/ELAN.git
cd ELAN
conda env create -f environment.yml
conda activate elan

Training

cd ELAN
python train.py --config ./configs/elan_light_x4.yml

Citation

If SimpleIR helps your research or work, please consider citing the following works:


@article{zhang2022efficient,
  title={Efficient Long-Range Attention Network for Image Super-resolution},
  author={Zhang, Xindong and Zeng, Hui and Guo, Shi and Zhang, Lei},
  journal={arXiv preprint arXiv:2203.06697},
  year={2022}
}
Issues
  • split_with_sizes expects split_sizes to sum exactly to 128 (input tensor's size at dimension 1), but got split_sizes=[42, 42, 42]

    split_with_sizes expects split_sizes to sum exactly to 128 (input tensor's size at dimension 1), but got split_sizes=[42, 42, 42]

    输入 input = torch.randn(1, 3, 320, 180) 报错如题 。 请问怎么修改

    网络配置:

    scale: 4

    rgb_range: 255

    colors: 3

    m_elan: 24

    c_elan: 60

    n_share: 1

    r_expand: 2

    act_type: 'relu'

    window_sizes: [4, 8, 16]

    opened by yumulinfeng1 0
  • Tensor Shape size issue

    Tensor Shape size issue

    Hello, I am running the code and in second epoch, it says the in and out tensor size issue. The in is 3,256,256 and out is 3,248,258. I tried to use resize function. I have used the following configuration: batch_size: 8 data_repeat: 80 data_augment: 1 epochs: 1000 lr: 0.00025

    Please guide!

    opened by mshaheryar91 9
  • Positional Encoding

    Positional Encoding

    Hello,

    Thanks for your great work, an efficient and neat transformer framework is essential for low-level vision I think.

    According to your work, I tried discard the attention mask and positional encoding in SwinIR, the training and inference speed is largely improved, and the attention mask has slight effect on performance. However, the performance severely droped after removing RPE in original SwinIR.

    Could you please give me some hints about how can we discard RPE (and attention mask) correctly? directly removing codes related to positional encoding or removing positional encoding should incorporate someother necessary elements?

    Looking forward to your reply, thanks~

    opened by mrluin 1
  • Shifted Window and Shared Attention Patterns in Consecutive GMSAs

    Shifted Window and Shared Attention Patterns in Consecutive GMSAs

    Hello,

    The paper is very interesting to me, since SwinIR suffers from high memory consumption and slow convergence. I recently have two questions about the proposed framework.

    Firstly, two consecutive GMSAs can share the attention maps, while the shifted window is applied to partition neighboring pixels together, which should derive different attention patterns. How is it addressed or is interleaved sharing mechanism adopted?

    Secondly, the results in Table 3 show the reduction of FLOPs and Latency by using the shifted mechanism. How could this method reduce the computational footprint? Is it solely due to the removal of the masking and relative positional encoding used in SwinIR?

    Finally, could you present the convergence of ELAN, compared with SwinIR and other CNN-based models? It can provide a more comprehensive comparison and better show the advantages of ELAN.

    Thanks a lot.

    BTW, the neat model architecture is definitely appealing.

    opened by CVPR-Anonymous 0
Owner
xindong zhang
xindong zhang
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