File size: 11,971 Bytes
82ea528 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def sub_mean(x):
mean = x.mean(2, keepdim=True).mean(3, keepdim=True)
x -= mean
return x, mean
def InOutPaddings(x):
w, h = x.size(3), x.size(2)
padding_width, padding_height = 0, 0
if w != ((w >> 7) << 7):
padding_width = (((w >> 7) + 1) << 7) - w
if h != ((h >> 7) << 7):
padding_height = (((h >> 7) + 1) << 7) - h
paddingInput = nn.ReflectionPad2d(padding=[padding_width // 2, padding_width - padding_width // 2,
padding_height // 2, padding_height - padding_height // 2])
paddingOutput = nn.ReflectionPad2d(padding=[0 - padding_width // 2, padding_width // 2 - padding_width,
0 - padding_height // 2, padding_height // 2 - padding_height])
return paddingInput, paddingOutput
class ConvNorm(nn.Module):
def __init__(self, in_feat, out_feat, kernel_size, stride=1, norm=False):
super(ConvNorm, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = nn.ReflectionPad2d(reflection_padding)
self.conv = nn.Conv2d(in_feat, out_feat, stride=stride, kernel_size=kernel_size, bias=True)
self.norm = norm
if norm == 'IN':
self.norm = nn.InstanceNorm2d(out_feat, track_running_stats=True)
elif norm == 'BN':
self.norm = nn.BatchNorm2d(out_feat)
def forward(self, x):
out = self.reflection_pad(x)
out = self.conv(out)
if self.norm:
out = self.norm(out)
return out
class UpConvNorm(nn.Module):
def __init__(self, in_channels, out_channels, mode='transpose', norm=False):
super(UpConvNorm, self).__init__()
if mode == 'transpose':
self.upconv = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1)
elif mode == 'shuffle':
self.upconv = nn.Sequential(
ConvNorm(in_channels, 4*out_channels, kernel_size=3, stride=1, norm=norm),
PixelShuffle(2))
else:
# out_channels is always going to be the same as in_channels
self.upconv = nn.Sequential(
nn.Upsample(mode='bilinear', scale_factor=2, align_corners=False),
ConvNorm(in_channels, out_channels, kernel_size=1, stride=1, norm=norm))
def forward(self, x):
out = self.upconv(x)
return out
class meanShift(nn.Module):
def __init__(self, rgbRange, rgbMean, sign, nChannel=3):
super(meanShift, self).__init__()
if nChannel == 1:
l = rgbMean[0] * rgbRange * float(sign)
self.shifter = nn.Conv2d(1, 1, kernel_size=1, stride=1, padding=0)
self.shifter.weight.data = torch.eye(1).view(1, 1, 1, 1)
self.shifter.bias.data = torch.Tensor([l])
elif nChannel == 3:
r = rgbMean[0] * rgbRange * float(sign)
g = rgbMean[1] * rgbRange * float(sign)
b = rgbMean[2] * rgbRange * float(sign)
self.shifter = nn.Conv2d(3, 3, kernel_size=1, stride=1, padding=0)
self.shifter.weight.data = torch.eye(3).view(3, 3, 1, 1)
self.shifter.bias.data = torch.Tensor([r, g, b])
else:
r = rgbMean[0] * rgbRange * float(sign)
g = rgbMean[1] * rgbRange * float(sign)
b = rgbMean[2] * rgbRange * float(sign)
self.shifter = nn.Conv2d(6, 6, kernel_size=1, stride=1, padding=0)
self.shifter.weight.data = torch.eye(6).view(6, 6, 1, 1)
self.shifter.bias.data = torch.Tensor([r, g, b, r, g, b])
# Freeze the meanShift layer
for params in self.shifter.parameters():
params.requires_grad = False
def forward(self, x):
x = self.shifter(x)
return x
""" CONV - (BN) - RELU - CONV - (BN) """
class ResBlock(nn.Module):
def __init__(self, in_feat, out_feat, kernel_size=3, reduction=False, bias=True, # 'reduction' is just for placeholder
norm=False, act=nn.ReLU(True), downscale=False):
super(ResBlock, self).__init__()
self.body = nn.Sequential(
ConvNorm(in_feat, out_feat, kernel_size=kernel_size, stride=2 if downscale else 1),
act,
ConvNorm(out_feat, out_feat, kernel_size=kernel_size, stride=1)
)
self.downscale = None
if downscale:
self.downscale = nn.Conv2d(in_feat, out_feat, kernel_size=1, stride=2)
def forward(self, x):
res = x
out = self.body(x)
if self.downscale is not None:
res = self.downscale(res)
out += res
return out
## Channel Attention (CA) Layer
class CALayer(nn.Module):
def __init__(self, channel, reduction=16):
super(CALayer, self).__init__()
# global average pooling: feature --> point
self.avg_pool = nn.AdaptiveAvgPool2d(1)
# feature channel downscale and upscale --> channel weight
self.conv_du = nn.Sequential(
nn.Conv2d(channel, channel // reduction, 1, padding=0, bias=True),
nn.ReLU(inplace=True),
nn.Conv2d(channel // reduction, channel, 1, padding=0, bias=True),
nn.Sigmoid()
)
def forward(self, x):
y = self.avg_pool(x)
y = self.conv_du(y)
return x * y, y
## Residual Channel Attention Block (RCAB)
class RCAB(nn.Module):
def __init__(self, in_feat, out_feat, kernel_size, reduction, bias=True,
norm=False, act=nn.ReLU(True), downscale=False, return_ca=False):
super(RCAB, self).__init__()
self.body = nn.Sequential(
ConvNorm(in_feat, out_feat, kernel_size, stride=2 if downscale else 1, norm=norm),
act,
ConvNorm(out_feat, out_feat, kernel_size, stride=1, norm=norm),
CALayer(out_feat, reduction)
)
self.downscale = downscale
if downscale:
self.downConv = nn.Conv2d(in_feat, out_feat, kernel_size=3, stride=2, padding=1)
self.return_ca = return_ca
def forward(self, x):
res = x
out, ca = self.body(x)
if self.downscale:
res = self.downConv(res)
out += res
if self.return_ca:
return out, ca
else:
return out
## Residual Group (RG)
class ResidualGroup(nn.Module):
def __init__(self, Block, n_resblocks, n_feat, kernel_size, reduction, act, norm=False):
super(ResidualGroup, self).__init__()
modules_body = [Block(n_feat, n_feat, kernel_size, reduction, bias=True, norm=norm, act=act)
for _ in range(n_resblocks)]
modules_body.append(ConvNorm(n_feat, n_feat, kernel_size, stride=1, norm=norm))
self.body = nn.Sequential(*modules_body)
def forward(self, x):
res = self.body(x)
res += x
return res
def pixel_shuffle(input, scale_factor):
batch_size, channels, in_height, in_width = input.size()
out_channels = int(int(channels / scale_factor) / scale_factor)
out_height = int(in_height * scale_factor)
out_width = int(in_width * scale_factor)
if scale_factor >= 1:
input_view = input.contiguous().view(batch_size, out_channels, scale_factor, scale_factor, in_height, in_width)
shuffle_out = input_view.permute(0, 1, 4, 2, 5, 3).contiguous()
else:
block_size = int(1 / scale_factor)
input_view = input.contiguous().view(batch_size, channels, out_height, block_size, out_width, block_size)
shuffle_out = input_view.permute(0, 1, 3, 5, 2, 4).contiguous()
return shuffle_out.view(batch_size, out_channels, out_height, out_width)
class PixelShuffle(nn.Module):
def __init__(self, scale_factor):
super(PixelShuffle, self).__init__()
self.scale_factor = scale_factor
def forward(self, x):
return pixel_shuffle(x, self.scale_factor)
def extra_repr(self):
return 'scale_factor={}'.format(self.scale_factor)
def conv(in_channels, out_channels, kernel_size,
stride=1, bias=True, groups=1):
return nn.Conv2d(
in_channels,
out_channels,
kernel_size=kernel_size,
padding=kernel_size//2,
stride=1,
bias=bias,
groups=groups)
def conv1x1(in_channels, out_channels, stride=1, bias=True, groups=1):
return nn.Conv2d(
in_channels,
out_channels,
kernel_size=1,
stride=stride,
bias=bias,
groups=groups)
def conv3x3(in_channels, out_channels, stride=1,
padding=1, bias=True, groups=1):
return nn.Conv2d(
in_channels,
out_channels,
kernel_size=3,
stride=stride,
padding=padding,
bias=bias,
groups=groups)
def conv5x5(in_channels, out_channels, stride=1,
padding=2, bias=True, groups=1):
return nn.Conv2d(
in_channels,
out_channels,
kernel_size=5,
stride=stride,
padding=padding,
bias=bias,
groups=groups)
def conv7x7(in_channels, out_channels, stride=1,
padding=3, bias=True, groups=1):
return nn.Conv2d(
in_channels,
out_channels,
kernel_size=7,
stride=stride,
padding=padding,
bias=bias,
groups=groups)
def upconv2x2(in_channels, out_channels, mode='shuffle'):
if mode == 'transpose':
return nn.ConvTranspose2d(
in_channels,
out_channels,
kernel_size=4,
stride=2,
padding=1)
elif mode == 'shuffle':
return nn.Sequential(
conv3x3(in_channels, 4*out_channels),
PixelShuffle(2))
else:
# out_channels is always going to be the same as in_channels
return nn.Sequential(
nn.Upsample(mode='bilinear', scale_factor=2, align_corners=False),
conv1x1(in_channels, out_channels))
class Interpolation(nn.Module):
def __init__(self, n_resgroups, n_resblocks, n_feats,
reduction=16, act=nn.LeakyReLU(0.2, True), norm=False):
super(Interpolation, self).__init__()
# define modules: head, body, tail
self.headConv = conv3x3(n_feats * 2, n_feats)
modules_body = [
ResidualGroup(
RCAB,
n_resblocks=n_resblocks,
n_feat=n_feats,
kernel_size=3,
reduction=reduction,
act=act,
norm=norm)
for _ in range(n_resgroups)]
self.body = nn.Sequential(*modules_body)
self.tailConv = conv3x3(n_feats, n_feats)
def forward(self, x0, x1):
# Build input tensor
x = torch.cat([x0, x1], dim=1)
x = self.headConv(x)
res = self.body(x)
res += x
out = self.tailConv(res)
return out
class Interpolation_res(nn.Module):
def __init__(self, n_resgroups, n_resblocks, n_feats,
act=nn.LeakyReLU(0.2, True), norm=False):
super(Interpolation_res, self).__init__()
# define modules: head, body, tail (reduces concatenated inputs to n_feat)
self.headConv = conv3x3(n_feats * 2, n_feats)
modules_body = [ResidualGroup(ResBlock, n_resblocks=n_resblocks, n_feat=n_feats, kernel_size=3,
reduction=0, act=act, norm=norm)
for _ in range(n_resgroups)]
self.body = nn.Sequential(*modules_body)
self.tailConv = conv3x3(n_feats, n_feats)
def forward(self, x0, x1):
# Build input tensor
x = torch.cat([x0, x1], dim=1)
x = self.headConv(x)
res = x
for m in self.body:
res = m(res)
res += x
x = self.tailConv(res)
return x |