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43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
""" Full assembly of the parts to form the complete network """
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import torch.nn.functional as F
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from .unet_parts import *
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class UNet(nn.Module):
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def __init__(self, n_channels, n_classes, bilinear=True):
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super(UNet, self).__init__()
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self.n_channels = n_channels
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self.n_classes = n_classes
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self.bilinear = bilinear
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self.inc = DoubleConv(n_channels, 64)
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self.down1 = Down(64, 128)
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self.down2 = Down(128, 256)
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self.down3 = Down(256, 512)
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self.down4 = Down(512, 512)
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self.up1 = Up(1024, 256, bilinear)
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self.up2 = Up(512, 128, bilinear)
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self.up3 = Up(256, 64, bilinear)
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self.up4 = Up(128, 64, bilinear)
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self.outc = OutConv(64, n_classes)
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def forward(self, x):
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x1 = self.inc(x)
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x2 = self.down1(x1)
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x3 = self.down2(x2)
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x4 = self.down3(x3)
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x5 = self.down4(x4)
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x = self.up1(x5, x4)
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x = self.up2(x, x3)
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x = self.up3(x, x2)
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x = self.up4(x, x1)
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logits = self.outc(x)
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return logits
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if self.n_classes > 1:
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return F.softmax(x, dim=1)
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else:
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return torch.sigmoid(x)
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