生成器网络为Unet
判别器为patchGAN
代码示例：
class DoubleConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(DoubleConv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 3, 1, 1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, 3, 1, 1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
)
def forward(self, x):
return self.conv(x)
class GeneratorNet(nn.Module):
def __init__(
self, in_channels=3, out_channels=1, features=[64, 128, 256, 512],
):
super(GeneratorNet, self).__init__()
self.ups = nn.ModuleList()
self.downs = nn.ModuleList()
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
# Down part of UNET : Refer to pdf file in architecture of generator to understand more about U-net
for feature in features:
self.downs.append(DoubleConv(in_channels, feature))
in_channels = feature
# Up part of UNET: Refer to pdf file in architecture of generator to understand more about U-net
for feature in reversed(features):
self.ups.append(
nn.ConvTranspose2d(
feature*2, feature, kernel_size=2, stride=2,
)
)
self.ups.append(DoubleConv(feature*2, feature))
self.bottleneck = DoubleConv(features[-1], features[-1]*2)
self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1)
def forward(self, x):
skip_connections = []
for down in self.downs:
x = down(x)
skip_connections.append(x)
x = self.pool(x)
x = self.bottleneck(x)
skip_connections = skip_connections[::-1]
for idx in range(0, len(self.ups), 2):
x = self.ups[idx](x)
skip_connection = skip_connections[idx//2]
if x.shape != skip_connection.shape:
x = TF.resize(x, size=skip_connection.shape[2:])
concat_skip = torch.cat((skip_connection, x), dim=1)
x = self.ups[idx+1](concat_skip)
return self.final_conv(x)
class DiscriminatorNet(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.disc = nn.Sequential(
nn.Conv2d(in_channels, 64, 3, 1, 1), # layer 1 64x256x256
nn.LeakyReLU(0.01, inplace=True),
nn.MaxPool2d(2), # layer 2 64x128x128
nn.Conv2d(64, 128, 3, 1, 1), # layer 3 128x128x128
nn.LeakyReLU(0.01, inplace=True),
nn.MaxPool2d(2), # layer 4 128x64x64
nn.Conv2d(128, 256, 3, 1, 1), # layer 5 256x64x64
nn.LeakyReLU(0.01, inplace=True), # batch norm next
nn.BatchNorm2d(256),
nn.MaxPool2d(2), # layer 6 256x32x32
nn.Conv2d(256, 256, 3, 1, 1), # layer 7 256x32x32
nn.LeakyReLU(0.01, inplace=True), # batch norm next
nn.BatchNorm2d(256),
nn.MaxPool2d(2), # layer 8 256x16x16
nn.Conv2d(256, 256, 3, 1, 1), # layer 9 256x16x16
nn.LeakyReLU(0.01, inplace=True), # batch norm next
nn.BatchNorm2d(256),
nn.Conv2d(256, 1, 3, 1, 1), # layer 10 1x16x16
)
def forward(self, x,y):
input = torch.cat([x,y], axis=1)
return self.disc(input)
