File size: 10,875 Bytes
b4f7b8c |
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 |
import torch
import torch.nn as nn
import torch.nn.functional as F
class CNR2d(nn.Module):
def __init__(self, nch_in, nch_out, kernel_size=4, stride=1, padding=1, norm='bnorm', relu=0.0, drop=[], bias=[]):
super().__init__()
if bias == []:
if norm == 'bnorm':
bias = False
else:
bias = True
layers = []
layers += [Conv2d(nch_in, nch_out, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias)]
if norm != []:
layers += [Norm2d(nch_out, norm)]
if relu != []:
layers += [ReLU(relu)]
if drop != []:
layers += [nn.Dropout2d(drop)]
self.cbr = nn.Sequential(*layers)
def forward(self, x):
return self.cbr(x)
class DECNR2d(nn.Module):
def __init__(self, nch_in, nch_out, kernel_size=4, stride=1, padding=1, output_padding=0, norm='bnorm', relu=0.0, drop=[], bias=[]):
super().__init__()
if bias == []:
if norm == 'bnorm':
bias = False
else:
bias = True
layers = []
layers += [Deconv2d(nch_in, nch_out, kernel_size=kernel_size, stride=stride, padding=padding, output_padding=output_padding, bias=bias)]
if norm != []:
layers += [Norm2d(nch_out, norm)]
if relu != []:
layers += [ReLU(relu)]
if drop != []:
layers += [nn.Dropout2d(drop)]
self.decbr = nn.Sequential(*layers)
def forward(self, x):
return self.decbr(x)
class ResBlock(nn.Module):
def __init__(self, nch_in, nch_out, kernel_size=3, stride=1, padding=1, padding_mode='reflection', norm='inorm', relu=0.0, drop=[], bias=[]):
super().__init__()
if bias == []:
if norm == 'bnorm':
bias = False
else:
bias = True
layers = []
# 1st conv
layers += [Padding(padding, padding_mode=padding_mode)]
layers += [CNR2d(nch_in, nch_out, kernel_size=kernel_size, stride=stride, padding=0, norm=norm, relu=relu)]
if drop != []:
layers += [nn.Dropout2d(drop)]
# 2nd conv
layers += [Padding(padding, padding_mode=padding_mode)]
layers += [CNR2d(nch_in, nch_out, kernel_size=kernel_size, stride=stride, padding=0, norm=norm, relu=[])]
self.resblk = nn.Sequential(*layers)
def forward(self, x):
return x + self.resblk(x)
class ResBlock_cat(nn.Module):
def __init__(self, nch_in, nch_out, kernel_size=3, stride=1, padding=1, padding_mode='reflection', norm='inorm', relu=0.0, drop=[], bias=[]):
super().__init__()
if bias == []:
if norm == 'bnorm':
bias = False
else:
bias = True
layers = []
# 1st conv
layers += [Padding(padding, padding_mode=padding_mode)]
layers += [CNR2d(nch_in*2, nch_out, kernel_size=kernel_size, stride=stride, padding=0, norm=norm, relu=relu)]
if drop != []:
layers += [nn.Dropout2d(drop)]
# 2nd conv
layers += [Padding(padding, padding_mode=padding_mode)]
layers += [CNR2d(nch_in, nch_out, kernel_size=kernel_size, stride=stride, padding=0, norm=norm, relu=[])]
self.resblk = nn.Sequential(*layers)
def forward(self,x,y):
output = x + self.resblk(torch.cat([x,y],dim=1))
return output
class LinearBlock(nn.Module):
def __init__(self, input_dim, output_dim, norm='none', activation='relu'):
super(LinearBlock, self).__init__()
use_bias = True
# initialize fully connected layer
if norm == 'sn':
self.fc = SpectralNorm(nn.Linear(input_dim, output_dim, bias=use_bias))
else:
self.fc = nn.Linear(input_dim, output_dim, bias=use_bias)
# initialize normalization
norm_dim = output_dim
if norm == 'bn':
self.norm = nn.BatchNorm1d(norm_dim)
elif norm == 'in':
self.norm = nn.InstanceNorm1d(norm_dim)
elif norm == 'ln':
self.norm = LayerNorm(norm_dim)
elif norm == 'none' or norm == 'sn':
self.norm = None
else:
assert 0, "Unsupported normalization: {}".format(norm)
# initialize activation
if activation == 'relu':
self.activation = nn.ReLU(inplace=True)
elif activation == 'lrelu':
self.activation = nn.LeakyReLU(0.2, inplace=True)
elif activation == 'prelu':
self.activation = nn.PReLU()
elif activation == 'selu':
self.activation = nn.SELU(inplace=True)
elif activation == 'tanh':
self.activation = nn.Tanh()
elif activation == 'none':
self.activation = None
else:
assert 0, "Unsupported activation: {}".format(activation)
def forward(self, x):
out = self.fc(x)
if self.norm:
out = self.norm(out)
if self.activation:
out = self.activation(out)
return out
class MLP(nn.Module):
def __init__(self, input_dim, output_dim, dim, n_blk, norm='none', activ='relu'):
super(MLP, self).__init__()
self.model = []
self.model += [LinearBlock(input_dim, dim, norm=norm, activation=activ)]
for i in range(n_blk - 2):
self.model += [LinearBlock(dim, dim, norm=norm, activation=activ)]
self.model += [LinearBlock(dim, output_dim, norm='none', activation='none')] # no output activations
self.model = nn.Sequential(*self.model)
def forward(self, x):
return self.model(x.view(x.size(0), -1))
class CNR1d(nn.Module):
def __init__(self, nch_in, nch_out, norm='bnorm', relu=0.0, drop=[]):
super().__init__()
if norm == 'bnorm':
bias = False
else:
bias = True
layers = []
layers += [nn.Linear(nch_in, nch_out, bias=bias)]
if norm != []:
layers += [Norm2d(nch_out, norm)]
if relu != []:
layers += [ReLU(relu)]
if drop != []:
layers += [nn.Dropout2d(drop)]
self.cbr = nn.Sequential(*layers)
def forward(self, x):
return self.cbr(x)
class Conv2d(nn.Module):
def __init__(self, nch_in, nch_out, kernel_size=4, stride=1, padding=1, bias=True):
super(Conv2d, self).__init__()
self.conv = nn.Conv2d(nch_in, nch_out, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias)
def forward(self, x):
return self.conv(x)
class Deconv2d(nn.Module):
def __init__(self, nch_in, nch_out, kernel_size=4, stride=1, padding=1, output_padding=0, bias=True):
super(Deconv2d, self).__init__()
self.deconv = nn.ConvTranspose2d(nch_in, nch_out, kernel_size=kernel_size, stride=stride, padding=padding, output_padding=output_padding, bias=bias)
# layers = [nn.Upsample(scale_factor=2, mode='bilinear'),
# nn.ReflectionPad2d(1),
# nn.Conv2d(nch_in , nch_out, kernel_size=3, stride=1, padding=0)]
#
# self.deconv = nn.Sequential(*layers)
def forward(self, x):
return self.deconv(x)
class Linear(nn.Module):
def __init__(self, nch_in, nch_out):
super(Linear, self).__init__()
self.linear = nn.Linear(nch_in, nch_out)
def forward(self, x):
return self.linear(x)
class Norm2d(nn.Module):
def __init__(self, nch, norm_mode):
super(Norm2d, self).__init__()
if norm_mode == 'bnorm':
self.norm = nn.BatchNorm2d(nch)
elif norm_mode == 'inorm':
self.norm = nn.InstanceNorm2d(nch)
def forward(self, x):
return self.norm(x)
class ReLU(nn.Module):
def __init__(self, relu):
super(ReLU, self).__init__()
if relu > 0:
self.relu = nn.LeakyReLU(relu, True)
elif relu == 0:
self.relu = nn.ReLU(True)
def forward(self, x):
return self.relu(x)
class Padding(nn.Module):
def __init__(self, padding, padding_mode='zeros', value=0):
super(Padding, self).__init__()
if padding_mode == 'reflection':
self. padding = nn.ReflectionPad2d(padding)
elif padding_mode == 'replication':
self.padding = nn.ReplicationPad2d(padding)
elif padding_mode == 'constant':
self.padding = nn.ConstantPad2d(padding, value)
elif padding_mode == 'zeros':
self.padding = nn.ZeroPad2d(padding)
def forward(self, x):
return self.padding(x)
class Pooling2d(nn.Module):
def __init__(self, nch=[], pool=2, type='avg'):
super().__init__()
if type == 'avg':
self.pooling = nn.AvgPool2d(pool)
elif type == 'max':
self.pooling = nn.MaxPool2d(pool)
elif type == 'conv':
self.pooling = nn.Conv2d(nch, nch, kernel_size=pool, stride=pool)
def forward(self, x):
return self.pooling(x)
class UnPooling2d(nn.Module):
def __init__(self, nch=[], pool=2, type='nearest'):
super().__init__()
if type == 'nearest':
self.unpooling = nn.Upsample(scale_factor=pool, mode='nearest', align_corners=True)
elif type == 'bilinear':
self.unpooling = nn.Upsample(scale_factor=pool, mode='bilinear', align_corners=True)
elif type == 'conv':
self.unpooling = nn.ConvTranspose2d(nch, nch, kernel_size=pool, stride=pool)
def forward(self, x):
return self.unpooling(x)
class Concat(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x1, x2):
diffy = x2.size()[2] - x1.size()[2]
diffx = x2.size()[3] - x1.size()[3]
x1 = F.pad(x1, [diffx // 2, diffx - diffx // 2,
diffy // 2, diffy - diffy // 2])
return torch.cat([x2, x1], dim=1)
class TV1dLoss(nn.Module):
def __init__(self):
super(TV1dLoss, self).__init__()
def forward(self, input):
# loss = torch.mean(torch.abs(input[:, :, :, :-1] - input[:, :, :, 1:])) + \
# torch.mean(torch.abs(input[:, :, :-1, :] - input[:, :, 1:, :]))
loss = torch.mean(torch.abs(input[:, :-1] - input[:, 1:]))
return loss
class TV2dLoss(nn.Module):
def __init__(self):
super(TV2dLoss, self).__init__()
def forward(self, input):
loss = torch.mean(torch.abs(input[:, :, :, :-1] - input[:, :, :, 1:])) + \
torch.mean(torch.abs(input[:, :, :-1, :] - input[:, :, 1:, :]))
return loss
class SSIM2dLoss(nn.Module):
def __init__(self):
super(SSIM2dLoss, self).__init__()
def forward(self, input, targer):
loss = 0
return loss
|