Changes on top of the original work for faster SwinIR training

In order to train large swinIR model efficiently for enhance/restoration (x1) and super-resolution (x2, x3, x4...) scenarios, following changes were made:

1. Fp16 training support
2. Tensorboard support
3. Added bash scripts for distributed training on single and multiple nodes
4. Requires absolute paths in config file as opposed to relative in the original codebase

• For SwinIR training instructions specific to my changes, please check README_SwinIR
  
  

---

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

Kai Zhang

Computer Vision Lab, ETH Zurich, Switzerland

---

• 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}
}