PAN [:zap: 272K parameters]

Lowest parameters in AIM2020 Efficient Super Resolution.

Paper | Video

Efficient Image Super-Resolution Using Pixel Attention

Authors: Hengyuan Zhao, Xiangtao Kong, Jingwen He, Yu Qiao, Chao Dong

Dependencies

• Python >= 3.6 (Recommend to use Anaconda)
• PyTorch >= 1.5.0
• NVIDIA GPU + CUDA
• Python packages: pip install numpy opencv-python lmdb
• [option] Python packages: pip install tensorboardX, for visualizing curves.

Codes

• Our codes version based on mmsr.
• This codes provide the testing and training code.

How to Test

1. Clone this github repo.

git clone https://github.com/zhaohengyuan1/PAN.git
cd PAN

2. Download the five test datasets (Set5, Set14, B100, Urban100, Manga109) from Google Drive

3. Pretrained models have be placed in ./experiments/pretrained_models/ folder. More models can be download from Google Drive.

4. Run test. We provide x2,x3,x4 pretrained models.

cd codes
python test.py -opt option/test/test_PANx4.yml

More testing commond can be found in ./codes/run_scripts.sh file. 5. The output results will be sorted in ./results. (We have been put our testing log file in ./results) We also provide our testing results on five benchmark datasets on Google Drive

How to Train

1. Download DIV2K and Flickr2K from Google Drive or Baidu Drive

2. Generate Training patches. Modified the path of your training datasets in ./codes/data_scripts/extract_subimages.py file.

3. Run Training.

python train.py -opt options/train/train_PANx4.yml

4. More training commond can be found in ./codes/run_scripts.sh file.

Testing the Parameters, Mult-Adds and Running Time

1. Testing the parameters and Mult-Adds.

python test_summary.py

2. Testing the Running Time.

python test_running_time.py

Related Work on AIM2020

Enhanced Quadratic Video Interpolation (winning solution of AIM2020 VTSR Challenge) paper | code

Mics

Contact hy.zhao1 at siat.ac.cn for any questions or comments.