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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 |