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Reproduce the Stereo SR Results

1. Data Preparation

Follow previous works, our models are trained with Flickr1024 and Middlebury datasets, which is exactly the same as iPASSR. Please visit their homepage and follow their instructions to download and prepare the datasets.

Download and prepare the train set and place it in ./datasets/StereoSR

Download and prepare the evaluation data and place it in ./datasets/StereoSR/test

The structure of datasets directory should be like

    datasets
    ├── StereoSR
    │   ├── patches_x2
    │   │   ├── 000001
    │   │   ├── 000002
    │   │   ├── ...
    │   │   ├── 298142
    │   │   └── 298143
    │   ├── patches_x4
    │   │   ├── 000001
    │   │   ├── 000002
    │   │   ├── ...
    │   │   ├── 049019
    │   │   └── 049020
    |   ├── test
    │   |   ├── Flickr1024
    │   │   │   ├── hr
    │   │   │   ├── lr_x2
    │   │   │   └── lr_x4
    │   |   ├── KITTI2012
    │   │   │   ├── hr
    │   │   │   ├── lr_x2
    │   │   │   └── lr_x4
    │   |   ├── KITTI2015
    │   │   │   ├── hr
    │   │   │   ├── lr_x2
    │   │   │   └── lr_x4
    │   │   └── Middlebury
    │   │       ├── hr
    │   │       ├── lr_x2
    │   │       └── lr_x4

2. Evaluation

Download the pretrain model in ./experiments/pretrained_models/

name scale #Params PSNR SSIM pretrained models configs
NAFSSR-T x4 0.46M 23.69 0.7384 gdrive | baidu train | test
NAFSSR-S x4 1.56M 23.88 0.7468 gdrive | baidu train | test
NAFSSR-B x4 6.80M 24.07 0.7551 gdrive | baidu train | test
NAFSSR-L x4 23.83M 24.17 0.7589 gdrive | baidu train | test
NAFSSR-T x2 0.46M 28.94 0.9128 gdrive | baidu train | test
NAFSSR-S x2 1.56M 29.19 0.9160 gdrive | baidu train | test
NAFSSR-B x2 6.80M 29.54 0.7551 gdrive | baidu train | test
NAFSSR-L x2 23.79M 29.68 0.9221 gdrive | baidu train | test

PSNR/SSIM are evaluate on Flickr1024 test set.

Testing on KITTI2012, KITTI2015, Middlebury, Flickr1024 datasets

  • NAFSSR-T for 4x SR:
python -m torch.distributed.launch --nproc_per_node=1 --master_port=4321 basicsr/test.py -opt ./options/test/NAFSSR/NAFSSR-T_x4.yml --launcher pytorch
  • NAFSSR-S for 4x SR:
python -m torch.distributed.launch --nproc_per_node=1 --master_port=4321 basicsr/test.py -opt ./options/test/NAFSSR/NAFSSR-S_x4.yml --launcher pytorch
  • NAFSSR-B for 4x SR:
python -m torch.distributed.launch --nproc_per_node=1 --master_port=4321 basicsr/test.py -opt ./options/test/NAFSSR/NAFSSR-B_x4.yml --launcher pytorch
  • NAFSSR-L for 4x SR:
python -m torch.distributed.launch --nproc_per_node=1 --master_port=4321 basicsr/test.py -opt ./options/test/NAFSSR/NAFSSR-L_x4.yml --launcher pytorch
  • NAFSSR-L for 2x SR:
python -m torch.distributed.launch --nproc_per_node=1 --master_port=4321 basicsr/test.py -opt ./options/test/NAFSSR/NAFSSR-L_x2.yml --launcher pytorch
  • Test by a single gpu by default. Set --nproc_per_node to # of gpus for distributed validation.

3. Training

  • NAFNet-B for 4x SR:

    python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt options/train/NAFSSR/NAFSSR-B_x4.yml --launcher pytorch

  • NAFNet-S for 4x SR:

    python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt options/train/NAFSSR/NAFSSR-S_x4.yml --launcher pytorch

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