# reproduce the SIDD dataset results 



### 1. Data Preparation

##### Download the train set and place it in ```./datasets/SIDD/Data```:

* google drive [link](https://drive.google.com/file/d/1UHjWZzLPGweA9ZczmV8lFSRcIxqiOVJw/view?usp=sharing) or 百度网盘 [link](https://pan.baidu.com/s/1EnBVjrfFBiXIRPBgjFrifg), (提取码: sl6h)
* ```python scripts/data_preparation/sidd.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/SIDD/val/```:

  * google drive [link](https://drive.google.com/file/d/1gZx_K2vmiHalRNOb1aj93KuUQ2guOlLp/view?usp=sharing) or 百度网盘 [link](https://pan.baidu.com/s/1I9N5fDa4SNP0nuHEy6k-rw), (提取码: 59d7)
  * it should be like ```./datasets/SIDD/val/input_crops.lmdb``` and ```./datasets/SIDD/val/gt_crops.lmdb```



### 2. Training

* NAFNet-SIDD-width32:

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

* NAFNet-SIDD-width64:

  ```
  python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt options/train/SIDD/NAFNet-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-SIDD-width32**: google drive [link](https://drive.google.com/file/d/1lsByk21Xw-6aW7epCwOQxvm6HYCQZPHZ/view?usp=sharing) or 百度网盘 [link](https://pan.baidu.com/s/1Xses38SWl-7wuyuhaGNhaw) (提取码: um97)

  * **NAFNet-SIDD-width64**: google drive [link](https://drive.google.com/file/d/14Fht1QQJ2gMlk4N1ERCRuElg8JfjrWWR/view?usp=sharing) or 百度网盘 [link](https://pan.baidu.com/s/198kYyVSrY_xZF0jGv9U0sQ) (提取码: dton)

    

##### Testing on SIDD dataset	

  * NAFNet-SIDD-width32:

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

  * NAFNet-SIDD-width64:

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

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