| """ Full assembly of the parts to form the complete network """ |
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|
| from .vanilla_unet_parts import * |
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|
| class UNet(nn.Module): |
| def __init__(self, n_channels, n_classes, bilinear=False): |
| super(UNet, self).__init__() |
| self.n_channels = n_channels |
| self.n_classes = n_classes |
| self.bilinear = bilinear |
|
|
| self.inc = (DoubleConv(n_channels, 64)) |
| self.down1 = (Down(64, 128)) |
| self.down2 = (Down(128, 256)) |
| self.down3 = (Down(256, 512)) |
| factor = 2 if bilinear else 1 |
| self.down4 = (Down(512, 1024 // factor)) |
| self.up1 = (Up(1024, 512 // factor, bilinear)) |
| self.up2 = (Up(512, 256 // factor, bilinear)) |
| self.up3 = (Up(256, 128 // factor, bilinear)) |
| self.up4 = (Up(128, 64, bilinear)) |
| self.outc = (OutConv(64, n_classes)) |
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|
| def forward(self, x): |
| x1 = self.inc(x) |
| x2 = self.down1(x1) |
| x3 = self.down2(x2) |
| x4 = self.down3(x3) |
| x5 = self.down4(x4) |
| x = self.up1(x5, x4) |
| x = self.up2(x, x3) |
| x = self.up3(x, x2) |
| x = self.up4(x, x1) |
| logits = self.outc(x) |
| return logits |
|
|
| def use_checkpointing(self): |
| self.inc = torch.utils.checkpoint(self.inc) |
| self.down1 = torch.utils.checkpoint(self.down1) |
| self.down2 = torch.utils.checkpoint(self.down2) |
| self.down3 = torch.utils.checkpoint(self.down3) |
| self.down4 = torch.utils.checkpoint(self.down4) |
| self.up1 = torch.utils.checkpoint(self.up1) |
| self.up2 = torch.utils.checkpoint(self.up2) |
| self.up3 = torch.utils.checkpoint(self.up3) |
| self.up4 = torch.utils.checkpoint(self.up4) |
| self.outc = torch.utils.checkpoint(self.outc) |
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|
| class UNetForFID(UNet): |
| def __init__(self, n_channels, n_classes, bilinear=False): |
| super(UNetForFID, self).__init__(n_channels, n_classes, bilinear=bilinear) |
|
|
| def forward(self, x): |
| x1 = self.inc(x) |
| x2 = self.down1(x1) |
| x3 = self.down2(x2) |
| x4 = self.down3(x3) |
| x5 = self.down4(x4) |
| x = self.up1(x5, x4) |
| x = self.up2(x, x3) |
| x = self.up3(x, x2) |
| x = self.up4(x, x1) |
| x = (self.outc(x)).sigmoid() |
| x = F.adaptive_avg_pool2d(x, (32, 32)) |
| |
| return x.view(x.shape[0], -1, 1, 1), None |
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|
