kyusuncho / Uformer-NIA

Modified Uformer for NIA Dataset

Uformer: A General U-Shaped Transformer for Image Restoration (CVPR 2022): Modified for NIA Dataset

Paper: https://arxiv.org/abs/2106.03106

Modifications of the Original Code

We made a new generate_patches python file that splits the NIA deblurring data into training and test datasets.

Package dependencies

The project is built with PyTorch 1.7.1, Python3.7, CUDA10.1. For package dependencies, you can install them by:

pip install -r requirements.txt

Pretrained model

• Uformer32_denoising_sidd.pth [Google Drive]: PSNR 39.77 dB.
• Uformer16_denoising_sidd.pth [Google Drive]: PSNR 39.65 dB.

Results from the pretrained model

• Uformer_B: SIDD | DND | GoPro | HIDE | RealBlur-J | RealBlur-R | DPDD
• Uformer32: SIDD | DND

Training

Denoising

To train Uformer32(embed_dim=32) on SIDD, we use 2 V100 GPUs and run for 250 epochs:

python3 ./train.py --arch Uformer --batch_size 32 --gpu '0,1' \
    --train_ps 128 --train_dir ../datasets/denoising/sidd/train --env 32_0705_1 \
    --val_dir ../datasets/denoising/sidd/val --embed_dim 32 --warmup

More configuration can be founded in train.sh.

Evaluation

Denoising

To evaluate Uformer32 on SIDD, you can run:

python3 ./test.py --arch Uformer --batch_size 1 --gpu '0' \
    --input_dir ../datasets/denoising/sidd/val --result_dir YOUR_RESULT_DIR \
    --weights YOUR_PRETRAINED_MODEL_PATH --embed_dim 32 

Computational Cost

We provide a simple script to calculate the flops by ourselves, a simple script has been added in model.py. You can change the configuration and run it via:

python3 model.py

The manual calculation of GMacs in this repo differs slightly from the main paper, but they do not influence the conclusion. We will correct the paper later.

Citation

If you find this project useful in your research, please consider citing:

@inproceedings{Wang2022Uformer,
	title={Uformer: A General U-Shaped Transformer for Image Restoration},
	author={Wang, Zhendong and Cun, Xiaodong and Bao, Jianmin and Zhou, Wengang and Liu, Jianzhuang and Li, Houqiang},
	booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year={2022}
}

Acknowledgement

This code borrows heavily from MIRNet and SwinTransformer.