WDSR

This implements training of WDSR on the DIV2K_x2 dataset.

• Reference impkementation:

url=https://github.com/ychfan/wdsr
branch=master
commit_id=b78256293c435ef34e8eab3098484777c0ca0e10

WDSR Detail

For Details, see src/models/wdsr.py

Requirements

• Install PyTorch (pytorch.org)
• pip install -r requirements.txt Note: pillow recommends installing a newer version. If the corresponding torchvision version cannot be installed directly, you can use the source code to install the corresponding version. The source code reference link: Suggestion the pillow is 9.1.0 and the torchvision is 0.6.0
• The DIV2k Dataset can be downloaded from Reference impkementation (readme), find DIV2K dataset: DIVerse 2K resolution high quality images as used for the NTIRE challenge on super-resolution @ CVPR 2017 link , download Train Data (HR images), Validation Data (HR images), Train Data Track 1 bicubic downscaling x2 (LR images), Validation Data Track 1 bicubic downscaling x2 (LR images).Move the datasets to directory ./data/DIV2K/

Training

To train a model, run trainer.py with the desired model architecture and the path to the DIV2K dataset:

# 1p training full
# 备注： 目标精度34.75；验收精度35.3625
bash test/train_full_1p.sh 

# 1p train perf
bash test/train_performance_1p.sh 

# 1p testing
bash test/eval_1p.sh 

# 8p training full
# 备注： 目标精度34.75；验收精度33.7371
bash test/train_full_8p.sh 

# 8p train perf
bash test/train_performance_8p.sh 

# 8p testing
bash test/train_eval_8p.sh --pre_train_model=real_pre_train_model

# demo
# 请将要测试图片路径作为lr_image的参数传入，输出会在output_sr 文件夹
python3 demo.py --pre_train_model real_pre_train_model --lr_image 0801x2.png

wdsr training result

PSNR (dB)	Npu_nums	Epochs	AMP_Type
35.3625	1	30	O2
34.3368	8	30	O2