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import torch
import torch.nn as nn
import antialiased_cnns
class DownLayer(nn.Module):
def __init__(self, in_channels, out_channels):
super(DownLayer, self).__init__()
self.layer = nn.Sequential(
nn.MaxPool2d(kernel_size=2, stride=1),
antialiased_cnns.BlurPool(in_channels, stride=2),
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.LeakyReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
nn.LeakyReLU(inplace=True)
)
def forward(self, x):
return self.layer(x)
class UpLayer(nn.Module):
def __init__(self, in_channels, out_channels):
super(UpLayer, self).__init__()
# Conv transpose upsampling
self.blur_upsample = nn.Sequential(
nn.ConvTranspose2d(in_channels, out_channels, kernel_size=2, stride=2, padding=0),
antialiased_cnns.BlurPool(out_channels, stride=1)
)
self.layer = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.LeakyReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
nn.LeakyReLU(inplace=True)
)
def forward(self, x, skip):
x = self.blur_upsample(x)
x = torch.cat([x, skip], dim=1) # Concatenate with skip connection
return self.layer(x)
class UNet(nn.Module):
def __init__(self):
super(UNet, self).__init__()
self.init_conv = nn.Sequential(
nn.Conv2d(5, 64, kernel_size=3, padding=1), # output: 512 x 512 x 64
nn.LeakyReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, padding=1), # output: 512 x 512 x 64
nn.LeakyReLU(inplace=True)
)
self.down1 = DownLayer(64, 128) # output: 256 x 256 x 128
self.down2 = DownLayer(128, 256) # output: 128 x 128 x 256
self.down3 = DownLayer(256, 512) # output: 64 x 64 x 512
self.down4 = DownLayer(512, 1024) # output: 32 x 32 x 1024
self.up1 = UpLayer(1024, 512) # output: 64 x 64 x 512
self.up2 = UpLayer(512, 256) # output: 128 x 128 x 256
self.up3 = UpLayer(256, 128) # output: 256 x 256 x 128
self.up4 = UpLayer(128, 64) # output: 512 x 512 x 64
self.final_conv = nn.Conv2d(64, 3, kernel_size=1) # output: 512 x 512 x 3
def forward(self, x):
x0 = self.init_conv(x)
x1 = self.down1(x0)
x2 = self.down2(x1)
x3 = self.down3(x2)
x4 = self.down4(x3)
x = self.up1(x4, x3)
x = self.up2(x, x2)
x = self.up3(x, x1)
x = self.up4(x, x0)
x = self.final_conv(x)
return x
class PatchGANDiscriminator(nn.Module):
def __init__(self, input_channels=3):
super(PatchGANDiscriminator, self).__init__()
self.model = nn.Sequential(
nn.Conv2d(input_channels, 64, kernel_size=4, stride=2, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(256, 1, kernel_size=4, stride=1, padding=1)
# Output layer with 1 channel for binary classification
)
def forward(self, x):
return self.model(x)
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