An official implementation about our paper: Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning(ACCV2020)
If you use Anaconda, you can run these commands to build a env that can run our code correctly:
#First step: build a env and enter it
conda create -n AAF python=3.6
conda activate AAF
#Second step: install pytorch and torchvision via conda
conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=9.0 -c pytorch
#Final step: install other dependecies via pip
pip install -r requirements.txt
Now, you have build a conda env for running code successfully!
Firstly, please refer to DIV2K to get DIV2K which is a widely-used dataset for super-resolution. Put it in any folder, and modify the parameter dataset.dir_root
in yaml(e.g. yaml/train_sd_x4.yaml
) as this folder. Our method will scan files according to dataset.dir_root/dataset.dir_lr
.
Perform below script to train the model from scratch.
python train.py --config AAF_SD_x4 #AAF-SD for x4 scale
Here, AAF_SD_x4 can be change as:
AAF_S_x4 #AAF-S for x4 scale
AAF_SD_x4 #AAF-SD for x4 scale
AAF_M_x4 #AAF-M for x4 scale
AAF_L_x4 #AAF-L for x4 scale
AAF_S_x3 #AAF-S for x3 scale
AAF_SD_x3 #AAF-SD for x3 scale
AAF_M_x3 #AAF-M for x3 scale
AAF_L_x3 #AAF-L for x3 scale
AAF_S_x2* #AAF-S for x2 scale
AAF_SD_x2 #AAF-SD for x2 scale
AAF_M_x2* #AAF-M for x2 scale
AAF_L_x2* #AAF-L for x2 scale
Please put the testing data (such like DIV2K_test_LR_bicubic/X4
) that you want to check results in ./test_img
, now you will have:
|- ./test_img/
|--- DIV2K_test_LR_bicubic/
|------ X4/
|--------- 0901x4.png
|--------- 0902x4.png
|--------- ...
We provide the pretrained model of AAF in ./checkpoint
, so you can test images directly. Run this commands to obatain the results. They can be seen in ./results/AAF_SD_x4
:
python test.py --config AAF_SD_x4
./yaml/*.yaml
.If you think the paper is helpful for your research, please cite:
@inproceedings{wang2020Lightweight,
author = {Xuehui Wang, Qing Wang, Yuzhi Zhao, Junchi Yan, Lei Fan, and Long Chen.},
title = {Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning},
journal = {ACCV},
year = {2020}
}
If you have any question, please contact wangxh228@mail2.sysu.edu.cn