GoPro.md

reproduce the GoPro dataset results

1. Data Preparation

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

• google drive or 百度网盘
• it should be like ./datasets/GoPro/train/input and ./datasets/GoPro/train/target
• python scripts/data_preparation/gopro.py to crop the train image pairs to 512x512 patches and make the data into lmdb format.

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

• google drive or 百度网盘
• it should be like ./datasets/GoPro/test/input.lmdb and ./datasets/GoPro/test/target.lmdb

2. Training

• NAFNet-GoPro-width32:

  python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt options/train/GoPro/NAFNet-width32.yml --launcher pytorch
  

• NAFNet-GoPro-width64:

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

• Baseline-GoPro-width32:

  python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt options/train/GoPro/Baseline-width32.yml --launcher pytorch
  

• Baseline-GoPro-width64:

  python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt options/train/GoPro/Baseline-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-GoPro-width32: google drive or 百度网盘
• NAFNet-GoPro-width64: google drive or 百度网盘
• Baseline-GoPro-width32: google drive or 百度网盘
• Baseline-GoPro-width64: google drive or 百度网盘

Testing on GoPro dataset

• NAFNet-GoPro-width32:

python -m torch.distributed.launch --nproc_per_node=1 --master_port=4321 basicsr/test.py -opt ./options/test/GoPro/NAFNet-width32.yml --launcher pytorch

• NAFNet-GoPro-width64:

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

• Baseline-GoPro-width32:

python -m torch.distributed.launch --nproc_per_node=1 --master_port=4321 basicsr/test.py -opt ./options/test/GoPro/Baseline-width32.yml --launcher pytorch

• Baseline-GoPro-width64:

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

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