NAFNet/docs/REDS.md

reproduce the REDS dataset results

1. Data Preparation

Download the train set and place it in ./datasets/REDS/train:

• google drive (link and link) or SNU CVLab Server (link and link)
• it should be like ./datasets/REDS/train/train_blur_jpeg and ./datasets/REDS/train/train_sharp
• python scripts/data_preparation/reds.py to make the data into lmdb format.

Download the evaluation data (in lmdb format) and place it in ./datasets/REDS/val/:

• google drive or 百度网盘,
• it should be like ./datasets/REDS/val/blur_300.lmdb and ./datasets/REDS/val/sharp_300.lmdb

2. Training

• NAFNet-REDS-width64:

  python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt options/train/REDS/NAFNet-width64.yml --launcher pytorch
  

• 8 gpus by default. Set --nproc_per_node to # of gpus for distributed validation.

3. Evaluation

Download the pretrain model in ./experiments/pretrained_models/

• NAFNet-REDS-width64: google drive or 百度网盘

Testing on REDS dataset

• NAFNet-REDS-width64:

python -m torch.distributed.launch --nproc_per_node=1 --master_port=4321 basicsr/test.py -opt ./options/test/REDS/NAFNet-width64.yml --launcher pytorch

• Test by a single gpu by default. Set --nproc_per_node to # of gpus for distributed validation.