Training and testing codes for USRNet, DnCNN, FFDNet, SRMD, DPSR, MSRResNet, ESRGAN, BSRGAN, SwinIR

Kai Zhang

Computer Vision Lab, ETH Zurich, Switzerland

---

• News (2021-12-23): Our techniques are adopted in https://www.amemori.ai/.

• News (2021-12-23): Real image denoising.

• 

• News (2021-09-09): Add main_download_pretrained_models.py to download pre-trained models.

• News (2021-09-08): Add matlab code to zoom local part of an image for the purpose of comparison between different results.

• News (2021-09-07): We upload the training code of SwinIR and provide an interactive online Colob demo for real-world image SR. Try to super-resolve your own images on Colab!

Real-World Image (x4)	BSRGAN, ICCV2021	Real-ESRGAN	SwinIR (ours)

• News (2021-08-31): We upload the training code of BSRGAN.

• News (2021-08-24): We upload the BSRGAN degradation model.

• News (2021-08-22): Support multi-feature-layer VGG perceptual loss and UNet discriminator.

• News (2021-08-18): We upload the extended BSRGAN degradation model. It is slightly different from our published version.

• News (2021-06-03): Add testing codes of GPEN (CVPR21) for face image enhancement: main_test_face_enhancement.py

• News (2021-05-13): Add PatchGAN discriminator.

• News (2021-05-12): Support distributed training, see also https://github.com/xinntao/BasicSR/blob/master/docs/TrainTest.md.

• News (2021-01): BSRGAN for blind real image super-resolution will be added.

• Pull requests are welcome!

• Correction (2020-10): If you use multiple GPUs for GAN training, remove or comment Line 105 to enable DataParallel for fast training

• News (2020-10): Add utils_receptivefield.py to calculate receptive field.

• News (2020-8): A deep plug-and-play image restoration toolbox is released at cszn/DPIR.

• Tips (2020-8): Use this to avoid out of memory issue.

• News (2020-7): Add main_challenge_sr.py to get FLOPs, #Params, Runtime, #Activations, #Conv, and Max Memory Allocated.

from utils.utils_modelsummary import get_model_activation, get_model_flops
input_dim = (3, 256, 256)  # set the input dimension
activations, num_conv2d = get_model_activation(model, input_dim)
logger.info('{:>16s} : {:<.4f} [M]'.format('#Activations', activations/10**6))
logger.info('{:>16s} : {:<d}'.format('#Conv2d', num_conv2d))
flops = get_model_flops(model, input_dim, False)
logger.info('{:>16s} : {:<.4f} [G]'.format('FLOPs', flops/10**9))
num_parameters = sum(map(lambda x: x.numel(), model.parameters()))
logger.info('{:>16s} : {:<.4f} [M]'.format('#Params', num_parameters/10**6))

• News (2020-6): Add USRNet (CVPR 2020) for training and testing.
  • Network Architecture
  • Dataset

Clone repo

git clone https://github.com/cszn/KAIR.git

pip install -r requirement.txt

Training

You should modify the json file from options first, for example, setting "gpu_ids": [0,1,2,3] if 4 GPUs are used, setting "dataroot_H": "trainsets/trainH" if path of the high quality dataset is trainsets/trainH.

• Training with DataParallel - PSNR

python main_train_psnr.py --opt options/train_msrresnet_psnr.json

• Training with DataParallel - GAN

python main_train_gan.py --opt options/train_msrresnet_gan.json

• Training with DistributedDataParallel - PSNR - 4 GPUs

python -m torch.distributed.launch --nproc_per_node=4 --master_port=1234 main_train_psnr.py --opt options/train_msrresnet_psnr.json  --dist True

• Training with DistributedDataParallel - PSNR - 8 GPUs

python -m torch.distributed.launch --nproc_per_node=8 --master_port=1234 main_train_psnr.py --opt options/train_msrresnet_psnr.json  --dist True

• Training with DistributedDataParallel - GAN - 4 GPUs

python -m torch.distributed.launch --nproc_per_node=4 --master_port=1234 main_train_gan.py --opt options/train_msrresnet_gan.json  --dist True

• Training with DistributedDataParallel - GAN - 8 GPUs

python -m torch.distributed.launch --nproc_per_node=8 --master_port=1234 main_train_gan.py --opt options/train_msrresnet_gan.json  --dist True

• Kill distributed training processes of main_train_gan.py

kill $(ps aux | grep main_train_gan.py | grep -v grep | awk '{print $2}')

---

Method	Original Link
DnCNN	https://github.com/cszn/DnCNN
FDnCNN	https://github.com/cszn/DnCNN
FFDNet	https://github.com/cszn/FFDNet
SRMD	https://github.com/cszn/SRMD
DPSR-SRResNet	https://github.com/cszn/DPSR
SRResNet	https://github.com/xinntao/BasicSR
ESRGAN	https://github.com/xinntao/ESRGAN
RRDB	https://github.com/xinntao/ESRGAN
IMDB	https://github.com/Zheng222/IMDN
USRNet	https://github.com/cszn/USRNet
DRUNet	https://github.com/cszn/DPIR
DPIR	https://github.com/cszn/DPIR
BSRGAN	https://github.com/cszn/BSRGAN
SwinIR	https://github.com/JingyunLiang/SwinIR

Network architectures

• USRNet

  

• DnCNN

  

• IRCNN denoiser

• FFDNet

  

• SRMD

  

• SRResNet, SRGAN, RRDB, ESRGAN

  

• IMDN

  -----

Testing

Method	model_zoo
main_test_dncnn.py	dncnn_15.pth, dncnn_25.pth, dncnn_50.pth, dncnn_gray_blind.pth, dncnn_color_blind.pth, dncnn3.pth
main_test_ircnn_denoiser.py	ircnn_gray.pth, ircnn_color.pth
main_test_fdncnn.py	fdncnn_gray.pth, fdncnn_color.pth, fdncnn_gray_clip.pth, fdncnn_color_clip.pth
main_test_ffdnet.py	ffdnet_gray.pth, ffdnet_color.pth, ffdnet_gray_clip.pth, ffdnet_color_clip.pth
main_test_srmd.py	srmdnf_x2.pth, srmdnf_x3.pth, srmdnf_x4.pth, srmd_x2.pth, srmd_x3.pth, srmd_x4.pth
	The above models are converted from MatConvNet.
main_test_dpsr.py	dpsr_x2.pth, dpsr_x3.pth, dpsr_x4.pth, dpsr_x4_gan.pth
main_test_msrresnet.py	msrresnet_x4_psnr.pth, msrresnet_x4_gan.pth
main_test_rrdb.py	rrdb_x4_psnr.pth, rrdb_x4_esrgan.pth
main_test_imdn.py	imdn_x4.pth

model_zoo

• download link https://drive.google.com/drive/folders/13kfr3qny7S2xwG9h7v95F5mkWs0OmU0D

trainsets

• https://github.com/xinntao/BasicSR/blob/master/docs/DatasetPreparation.md
• train400
• DIV2K
• Flickr2K
• optional: use split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=512, p_overlap=96, p_max=800) to get trainsets/trainH with small images for fast data loading

testsets

• https://github.com/xinntao/BasicSR/blob/master/docs/DatasetPreparation.md
• set12
• bsd68
• cbsd68
• kodak24
• srbsd68
• set5
• set14
• cbsd100
• urban100
• manga109

References

@inproceedings{liang2021swinir,
title={SwinIR: Image Restoration Using Swin Transformer},
author={Liang, Jingyun and Cao, Jiezhang and Sun, Guolei and Zhang, Kai and Van Gool, Luc and Timofte, Radu},
booktitle={IEEE International Conference on Computer Vision Workshops},
year={2021}
}
@inproceedings{zhang2021designing,
title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution},
author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu},
booktitle={IEEE International Conference on Computer Vision},
year={2021}
}
@article{zhang2021plug, % DPIR & DRUNet & IRCNN
  title={Plug-and-Play Image Restoration with Deep Denoiser Prior},
  author={Zhang, Kai and Li, Yawei and Zuo, Wangmeng and Zhang, Lei and Van Gool, Luc and Timofte, Radu},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2021}
}
@inproceedings{zhang2020aim, % efficientSR_challenge
  title={AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results},
  author={Kai Zhang and Martin Danelljan and Yawei Li and Radu Timofte and others},
  booktitle={European Conference on Computer Vision Workshops},
  year={2020}
}
@inproceedings{zhang2020deep, % USRNet
  title={Deep unfolding network for image super-resolution},
  author={Zhang, Kai and Van Gool, Luc and Timofte, Radu},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
  pages={3217--3226},
  year={2020}
}
@article{zhang2017beyond, % DnCNN
  title={Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising},
  author={Zhang, Kai and Zuo, Wangmeng and Chen, Yunjin and Meng, Deyu and Zhang, Lei},
  journal={IEEE Transactions on Image Processing},
  volume={26},
  number={7},
  pages={3142--3155},
  year={2017}
}
@inproceedings{zhang2017learning, % IRCNN
title={Learning deep CNN denoiser prior for image restoration},
author={Zhang, Kai and Zuo, Wangmeng and Gu, Shuhang and Zhang, Lei},
booktitle={IEEE conference on computer vision and pattern recognition},
pages={3929--3938},
year={2017}
}
@article{zhang2018ffdnet, % FFDNet, FDnCNN
  title={FFDNet: Toward a fast and flexible solution for CNN-based image denoising},
  author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
  journal={IEEE Transactions on Image Processing},
  volume={27},
  number={9},
  pages={4608--4622},
  year={2018}
}
@inproceedings{zhang2018learning, % SRMD
  title={Learning a single convolutional super-resolution network for multiple degradations},
  author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
  pages={3262--3271},
  year={2018}
}
@inproceedings{zhang2019deep, % DPSR
  title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
  author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
  pages={1671--1681},
  year={2019}
}
@InProceedings{wang2018esrgan, % ESRGAN, MSRResNet
    author = {Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Loy, Chen Change},
    title = {ESRGAN: Enhanced super-resolution generative adversarial networks},
    booktitle = {The European Conference on Computer Vision Workshops (ECCVW)},
    month = {September},
    year = {2018}
}
@inproceedings{hui2019lightweight, % IMDN
  title={Lightweight Image Super-Resolution with Information Multi-distillation Network},
  author={Hui, Zheng and Gao, Xinbo and Yang, Yunchu and Wang, Xiumei},
  booktitle={Proceedings of the 27th ACM International Conference on Multimedia (ACM MM)},
  pages={2024--2032},
  year={2019}
}
@inproceedings{zhang2019aim, % IMDN
  title={AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results},
  author={Kai Zhang and Shuhang Gu and Radu Timofte and others},
  booktitle={IEEE International Conference on Computer Vision Workshops},
  year={2019}
}
@inproceedings{yang2021gan,
    title={GAN Prior Embedded Network for Blind Face Restoration in the Wild},
    author={Tao Yang, Peiran Ren, Xuansong Xie, and Lei Zhang},
    booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
    year={2021}
}