Spaces:
Running
Running
File size: 6,718 Bytes
2514fb4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 |
import math
import torch.nn as nn
import models.basicblock as B
import functools
import torch.nn.functional as F
import torch.nn.init as init
"""
# --------------------------------------------
# modified SRResNet
# -- MSRResNet0 (v0.0)
# -- MSRResNet1 (v0.1)
# --------------------------------------------
References:
@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 Concerence 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 concerence on computer vision and pattern recognition},
pages={4681--4690},
year={2017}
}
# --------------------------------------------
"""
# --------------------------------------------
# modified SRResNet v0.0
# https://github.com/xinntao/ESRGAN
# --------------------------------------------
class MSRResNet0(nn.Module):
def __init__(self, in_nc=3, out_nc=3, nc=64, nb=16, upscale=4, act_mode='R', upsample_mode='upconv'):
"""
in_nc: channel number of input
out_nc: channel number of output
nc: channel number
nb: number of residual blocks
upscale: up-scale factor
act_mode: activation function
upsample_mode: 'upconv' | 'pixelshuffle' | 'convtranspose'
"""
super(MSRResNet0, self).__init__()
assert 'R' in act_mode or 'L' in act_mode, 'Examples of activation function: R, L, BR, BL, IR, IL'
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
# --------------------------------------------
# modified SRResNet v0.1
# https://github.com/xinntao/ESRGAN
# --------------------------------------------
class MSRResNet1(nn.Module):
def __init__(self, in_nc=3, out_nc=3, nc=64, nb=16, upscale=4, act_mode='R', upsample_mode='upconv'):
super(MSRResNet1, self).__init__()
self.upscale = upscale
self.conv_first = nn.Conv2d(in_nc, nc, 3, 1, 1, bias=True)
basic_block = functools.partial(ResidualBlock_noBN, nc=nc)
self.recon_trunk = make_layer(basic_block, nb)
# upsampling
if self.upscale == 2:
self.upconv1 = nn.Conv2d(nc, nc * 4, 3, 1, 1, bias=True)
self.pixel_shuffle = nn.PixelShuffle(2)
elif self.upscale == 3:
self.upconv1 = nn.Conv2d(nc, nc * 9, 3, 1, 1, bias=True)
self.pixel_shuffle = nn.PixelShuffle(3)
elif self.upscale == 4:
self.upconv1 = nn.Conv2d(nc, nc * 4, 3, 1, 1, bias=True)
self.upconv2 = nn.Conv2d(nc, nc * 4, 3, 1, 1, bias=True)
self.pixel_shuffle = nn.PixelShuffle(2)
self.HRconv = nn.Conv2d(nc, nc, 3, 1, 1, bias=True)
self.conv_last = nn.Conv2d(nc, out_nc, 3, 1, 1, bias=True)
# activation function
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
# initialization
initialize_weights([self.conv_first, self.upconv1, self.HRconv, self.conv_last], 0.1)
if self.upscale == 4:
initialize_weights(self.upconv2, 0.1)
def forward(self, x):
fea = self.lrelu(self.conv_first(x))
out = self.recon_trunk(fea)
if self.upscale == 4:
out = self.lrelu(self.pixel_shuffle(self.upconv1(out)))
out = self.lrelu(self.pixel_shuffle(self.upconv2(out)))
elif self.upscale == 3 or self.upscale == 2:
out = self.lrelu(self.pixel_shuffle(self.upconv1(out)))
out = self.conv_last(self.lrelu(self.HRconv(out)))
base = F.interpolate(x, scale_factor=self.upscale, mode='bilinear', align_corners=False)
out += base
return out
def initialize_weights(net_l, scale=1):
if not isinstance(net_l, list):
net_l = [net_l]
for net in net_l:
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, a=0, mode='fan_in')
m.weight.data *= scale # for residual block
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
init.kaiming_normal_(m.weight, a=0, mode='fan_in')
m.weight.data *= scale
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
init.constant_(m.weight, 1)
init.constant_(m.bias.data, 0.0)
def make_layer(block, n_layers):
layers = []
for _ in range(n_layers):
layers.append(block())
return nn.Sequential(*layers)
class ResidualBlock_noBN(nn.Module):
'''Residual block w/o BN
---Conv-ReLU-Conv-+-
|________________|
'''
def __init__(self, nc=64):
super(ResidualBlock_noBN, self).__init__()
self.conv1 = nn.Conv2d(nc, nc, 3, 1, 1, bias=True)
self.conv2 = nn.Conv2d(nc, nc, 3, 1, 1, bias=True)
# initialization
initialize_weights([self.conv1, self.conv2], 0.1)
def forward(self, x):
identity = x
out = F.relu(self.conv1(x), inplace=True)
out = self.conv2(out)
return identity + out
|