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

Model:

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

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

3. Evaluation

Save your pretrain model weight in `./experiments/pretrained_models/`

Testing on REDS dataset

Model:

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

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