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import torch
import torch.nn as nn
class UNet(nn.Module):
def __init__(self):
super(UNet, self).__init__()
def conv_block(in_channels, out_channels):
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
)
# Encoder
self.enc1 = conv_block(3, 64)
self.enc2 = conv_block(64, 128)
self.enc3 = conv_block(128, 256)
self.enc4 = conv_block(256, 512)
self.pool = nn.MaxPool2d(2)
# Bottleneck
self.bottleneck = conv_block(512, 1024)
# Decoder
self.upconv4 = nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2)
self.dec4 = conv_block(1024, 512)
self.upconv3 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
self.dec3 = conv_block(512, 256)
self.upconv2 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
self.dec2 = conv_block(256, 128)
self.upconv1 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
self.dec1 = conv_block(128, 64)
self.conv_last = nn.Conv2d(64, 1, kernel_size=1)
def forward(self, x):
c1 = self.enc1(x)
p1 = self.pool(c1)
c2 = self.enc2(p1)
p2 = self.pool(c2)
c3 = self.enc3(p2)
p3 = self.pool(c3)
c4 = self.enc4(p3)
p4 = self.pool(c4)
bottleneck = self.bottleneck(p4)
u4 = self.upconv4(bottleneck)
u4 = torch.cat([u4, c4], dim=1)
d4 = self.dec4(u4)
u3 = self.upconv3(d4)
u3 = torch.cat([u3, c3], dim=1)
d3 = self.dec3(u3)
u2 = self.upconv2(d3)
u2 = torch.cat([u2, c2], dim=1)
d2 = self.dec2(u2)
u1 = self.upconv1(d2)
u1 = torch.cat([u1, c1], dim=1)
d1 = self.dec1(u1)
return torch.sigmoid(self.conv_last(d1)) # sigmoid kept (matches BCELoss training) |