zhwzhong / dan Goto Github PK

This is an official implementation of Unfolding the Alternating Optimization for Blind Super Resolution

If this repo works for you, please cite our paper:

@article{luo2020unfolding,
  title={Unfolding the Alternating Optimization for Blind Super Resolution},
  author={Luo, Zhengxiong and Huang, Yan and Li, Shang and Wang, Liang and Tan, Tieniu},
  journal={Advances in Neural Information Processing Systems (NeurIPS)},
  volume={33},
  year={2020}
}

This repo is build on the basis of [MMSR] and [IKC]

• Add more pretrained weights and update the results of DANv1 !

• Add pretrained weights and update the results of about [IKC]!

• Add DANv2

• python3
• pytorch >= 1.5
• NVIDIA GPU + CUDA
• Python packages: pip3 install numpy opencv-python lmdb pyyaml

Pretrained weights of DANv1 and IKC are available at BaiduYun(Password: cbjv) or GoogleDrive. Download the weights to checkpoints

.
|-- checkpoints
`-- |-- DANv1
    |   |-- ...
    `--IKC
        |-- ... 

We use DIV2K and Flickr2K as our training datasets.

For evaluation of Setting 1, we use five datasets, i.e., Set5, Set14, Urban100, BSD100 and Manga109.

We use DIV2KRK for evaluation of Setting 2.

To train a model on the full dataset(DIV2K+Flickr2K, totally 3450 images), download datasets from official websites. After download, run codes/scripts/generate_mod_blur_LR_bic.py to generate LRblur/LR/HR/Bicubic datasets paths. (You need to modify the file paths by yourself.)

python3 codes/scripts/generate_mod_blur_LR_bic.py

For efficient IO, run codes/scripts/create_lmdb.py to transform datasets to binary files. (You need to modify the file paths by yourself.)

python3 codes/scripts/create_lmdb.py

For single GPU:

cd codes/config/DANv1
python3 train.py -opt=options/setting1/train_setting1_x4.yml

For distributed training

cd codes/config/DANv1
python3 -m torch.distributed.launch --nproc_per_node=8 --master_poer=4321 train.py -opt=options/setting1/train_setting1_x4.yml --launcher pytorch

cd codes/config/DANv1
python3 test.py -opt=options/setting1/test_setting1_x4.yml

cd codes/config/DANv1
python3 inference.py -input_dir=/path/to/real/images/ -output_dir=/path/to/save/sr/results/