Authors: Hengyuan Zhao, Xiangtao Kong, Jingwen He, Yu Qiao, Chao Dong
- 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.
- Our codes version based on mmsr.
- This codes provide the testing and training code.
- Clone this github repo.
git clone https://github.com/zhaohengyuan1/PAN.git
cd PAN
-
Download the five test datasets (Set5, Set14, B100, Urban100, Manga109) from Google Drive
-
Pretrained models have be placed in
./experiments/pretrained_models/folder. More models can be download from Google Drive. -
Run test. We provide
x2,x3,x4pretrained 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
-
Download DIV2K and Flickr2K from Google Drive or Baidu Drive
-
Generate Training patches. Modified the path of your training datasets in
./codes/data_scripts/extract_subimages.pyfile. -
Run Training.
python train.py -opt options/train/train_PANx4.yml
- More training commond can be found in
./codes/run_scripts.shfile.
- Testing the parameters and Mult-Adds.
python test_summary.py
- Testing the Running Time.
python test_running_time.py
Enhanced Quadratic Video Interpolation (winning solution of AIM2020 VTSR Challenge) paper | code
Contact hy.zhao1 at siat.ac.cn for any questions or comments.