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| import torch | |
| import torch.nn as nn | |
| class UNetBlock(nn.Module): | |
| def __init__(self, in_channels, out_channels, down=True, use_dropout=False): | |
| super().__init__() | |
| if down: | |
| self.model = nn.Sequential( | |
| nn.Conv2d(in_channels, out_channels, 4, 2, 1, bias=False), | |
| nn.BatchNorm2d(out_channels), | |
| nn.LeakyReLU(0.2, inplace=True) | |
| ) | |
| else: | |
| layers = [ | |
| nn.ConvTranspose2d(in_channels, out_channels, 4, 2, 1, bias=False), | |
| nn.BatchNorm2d(out_channels), | |
| nn.ReLU(inplace=True) | |
| ] | |
| if use_dropout: | |
| layers.append(nn.Dropout(0.5)) | |
| self.model = nn.Sequential(*layers) | |
| def forward(self, x): | |
| return self.model(x) | |
| class GeneratorUNet(nn.Module): | |
| def __init__(self, in_channels=3, out_channels=3): | |
| super().__init__() | |
| self.down1 = UNetBlock(in_channels, 64, down=True) | |
| self.down2 = UNetBlock(64, 128, down=True) | |
| self.down3 = UNetBlock(128, 256, down=True) | |
| self.down4 = UNetBlock(256, 512, down=True) | |
| self.down5 = UNetBlock(512, 512, down=True) | |
| self.down6 = UNetBlock(512, 512, down=True) | |
| self.down7 = UNetBlock(512, 512, down=True) | |
| self.down8 = UNetBlock(512, 512, down=True) | |
| self.up1 = UNetBlock(512, 512, down=False, use_dropout=True) | |
| self.up2 = UNetBlock(1024, 512, down=False, use_dropout=True) | |
| self.up3 = UNetBlock(1024, 512, down=False, use_dropout=True) | |
| self.up4 = UNetBlock(1024, 512, down=False) | |
| self.up5 = UNetBlock(1024, 256, down=False) | |
| self.up6 = UNetBlock(512, 128, down=False) | |
| self.up7 = UNetBlock(256, 64, down=False) | |
| self.final = nn.Sequential( | |
| nn.ConvTranspose2d(128, out_channels, 4, 2, 1), | |
| nn.Tanh() | |
| ) | |
| def forward(self, x): | |
| d1 = self.down1(x) | |
| d2 = self.down2(d1) | |
| d3 = self.down3(d2) | |
| d4 = self.down4(d3) | |
| d5 = self.down5(d4) | |
| d6 = self.down6(d5) | |
| d7 = self.down7(d6) | |
| d8 = self.down8(d7) | |
| u1 = self.up1(d8) | |
| u2 = self.up2(torch.cat([u1, d7], 1)) | |
| u3 = self.up3(torch.cat([u2, d6], 1)) | |
| u4 = self.up4(torch.cat([u3, d5], 1)) | |
| u5 = self.up5(torch.cat([u4, d4], 1)) | |
| u6 = self.up6(torch.cat([u5, d3], 1)) | |
| u7 = self.up7(torch.cat([u6, d2], 1)) | |
| return self.final(torch.cat([u7, d1], 1)) |