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| import math | |
| import torch.nn as nn | |
| import models.basicblock as B | |
| """ | |
| # -------------------------------------------- | |
| # modified SRResNet | |
| # -- MSRResNet_prior (for DPSR) | |
| # -------------------------------------------- | |
| References: | |
| @inproceedings{zhang2019deep, | |
| 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, | |
| title={Esrgan: Enhanced super-resolution generative adversarial networks}, | |
| author={Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Change Loy, Chen}, | |
| booktitle={European Conference on Computer Vision (ECCV)}, | |
| pages={0--0}, | |
| year={2018} | |
| } | |
| @inproceedings{ledig2017photo, | |
| title={Photo-realistic single image super-resolution using a generative adversarial network}, | |
| author={Ledig, Christian and Theis, Lucas and Husz{\'a}r, Ferenc and Caballero, Jose and Cunningham, Andrew and Acosta, Alejandro and Aitken, Andrew and Tejani, Alykhan and Totz, Johannes and Wang, Zehan and others}, | |
| booktitle={IEEE conference on computer vision and pattern recognition}, | |
| pages={4681--4690}, | |
| year={2017} | |
| } | |
| # -------------------------------------------- | |
| """ | |
| # -------------------------------------------- | |
| # MSRResNet super-resolver prior for DPSR | |
| # https://github.com/cszn/DPSR | |
| # https://github.com/cszn/DPSR/blob/master/models/network_srresnet.py | |
| # -------------------------------------------- | |
| class MSRResNet_prior(nn.Module): | |
| def __init__(self, in_nc=4, out_nc=3, nc=96, nb=16, upscale=4, act_mode='R', upsample_mode='upconv'): | |
| super(MSRResNet_prior, self).__init__() | |
| n_upscale = int(math.log(upscale, 2)) | |
| if upscale == 3: | |
| n_upscale = 1 | |
| m_head = B.conv(in_nc, nc, mode='C') | |
| m_body = [B.ResBlock(nc, nc, mode='C'+act_mode+'C') for _ in range(nb)] | |
| m_body.append(B.conv(nc, nc, mode='C')) | |
| if upsample_mode == 'upconv': | |
| upsample_block = B.upsample_upconv | |
| elif upsample_mode == 'pixelshuffle': | |
| upsample_block = B.upsample_pixelshuffle | |
| elif upsample_mode == 'convtranspose': | |
| upsample_block = B.upsample_convtranspose | |
| else: | |
| raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode)) | |
| if upscale == 3: | |
| m_uper = upsample_block(nc, nc, mode='3'+act_mode) | |
| else: | |
| m_uper = [upsample_block(nc, nc, mode='2'+act_mode) for _ in range(n_upscale)] | |
| H_conv0 = B.conv(nc, nc, mode='C'+act_mode) | |
| H_conv1 = B.conv(nc, out_nc, bias=False, mode='C') | |
| m_tail = B.sequential(H_conv0, H_conv1) | |
| self.model = B.sequential(m_head, B.ShortcutBlock(B.sequential(*m_body)), *m_uper, m_tail) | |
| def forward(self, x): | |
| x = self.model(x) | |
| return x | |
| class SRResNet(nn.Module): | |
| def __init__(self, in_nc=3, out_nc=3, nc=64, nb=16, upscale=4, act_mode='R', upsample_mode='upconv'): | |
| super(SRResNet, self).__init__() | |
| n_upscale = int(math.log(upscale, 2)) | |
| if upscale == 3: | |
| n_upscale = 1 | |
| m_head = B.conv(in_nc, nc, mode='C') | |
| m_body = [B.ResBlock(nc, nc, mode='C'+act_mode+'C') for _ in range(nb)] | |
| m_body.append(B.conv(nc, nc, mode='C')) | |
| if upsample_mode == 'upconv': | |
| upsample_block = B.upsample_upconv | |
| elif upsample_mode == 'pixelshuffle': | |
| upsample_block = B.upsample_pixelshuffle | |
| elif upsample_mode == 'convtranspose': | |
| upsample_block = B.upsample_convtranspose | |
| else: | |
| raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode)) | |
| if upscale == 3: | |
| m_uper = upsample_block(nc, nc, mode='3'+act_mode) | |
| else: | |
| m_uper = [upsample_block(nc, nc, mode='2'+act_mode) for _ in range(n_upscale)] | |
| H_conv0 = B.conv(nc, nc, mode='C'+act_mode) | |
| H_conv1 = B.conv(nc, out_nc, bias=False, mode='C') | |
| m_tail = B.sequential(H_conv0, H_conv1) | |
| self.model = B.sequential(m_head, B.ShortcutBlock(B.sequential(*m_body)), *m_uper, m_tail) | |
| def forward(self, x): | |
| x = self.model(x) | |
| return x |