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/inputand./datasets/GoPro/train/target python scripts/data_preparation/gopro.pyto 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.lmdband./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 pytorchNAFNet-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 pytorchBaseline-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 pytorchBaseline-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 pytorch8 gpus by default. Set
--nproc_per_nodeto # 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_nodeto # of gpus for distributed validation.