| import torch | |
| import torch.nn as nn | |
| class ConvBlock(nn.Module): | |
| def __init__(self, in_channels, out_channels): | |
| super(ConvBlock, self).__init__() | |
| self.conv = 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) | |
| ) | |
| def forward(self, x): | |
| return self.conv(x) | |
| class UpConv(nn.Module): | |
| def __init__(self, in_channels, out_channels): | |
| super(UpConv, self).__init__() | |
| self.up = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=2, stride=2) | |
| def forward(self, x): | |
| return self.up(x) | |
| class UNet(nn.Module): | |
| def __init__(self, in_channels=3, out_channels=1): | |
| super(UNet, self).__init__() | |
| self.encoder1 = ConvBlock(in_channels, 64) | |
| self.encoder2 = ConvBlock(64, 128) | |
| self.encoder3 = ConvBlock(128, 256) | |
| self.encoder4 = ConvBlock(256, 512) | |
| self.pool = nn.MaxPool2d(kernel_size=2, stride=2) | |
| self.bottleneck = ConvBlock(512, 1024) | |
| self.upconv4 = UpConv(1024, 512) | |
| self.decoder4 = ConvBlock(1024, 512) | |
| self.upconv3 = UpConv(512, 256) | |
| self.decoder3 = ConvBlock(512, 256) | |
| self.upconv2 = UpConv(256, 128) | |
| self.decoder2 = ConvBlock(256, 128) | |
| self.upconv1 = UpConv(128, 64) | |
| self.decoder1 = ConvBlock(128, 64) | |
| self.final_conv = nn.Conv2d(64, out_channels, kernel_size=1) | |
| def forward(self, x): | |
| enc1 = self.encoder1(x) | |
| enc2 = self.encoder2(self.pool(enc1)) | |
| enc3 = self.encoder3(self.pool(enc2)) | |
| enc4 = self.encoder4(self.pool(enc3)) | |
| bottleneck = self.bottleneck(self.pool(enc4)) | |
| dec4 = self.upconv4(bottleneck) | |
| dec4 = torch.cat((enc4, dec4), dim=1) | |
| dec4 = self.decoder4(dec4) | |
| dec3 = self.upconv3(dec4) | |
| dec3 = torch.cat((enc3, dec3), dim=1) | |
| dec3 = self.decoder3(dec3) | |
| dec2 = self.upconv2(dec3) | |
| dec2 = torch.cat((enc2, dec2), dim=1) | |
| dec2 = self.decoder2(dec2) | |
| dec1 = self.upconv1(dec2) | |
| dec1 = torch.cat((enc1, dec1), dim=1) | |
| dec1 = self.decoder1(dec1) | |
| return self.final_conv(dec1) |