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unet.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:265778b4608a9caa5bc5e534811950602334f146a745111722c4ced36f8c53ac
+size 31107237

unet.py

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+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+
+
+class UNet(nn.Module):
+
+    def __init__(self, in_channels=3, out_channels=1, init_features=32):
+        super(UNet, self).__init__()
+
+        features = init_features
+        self.encoder1 = UNet._block(in_channels, features, name="enc1")
+        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.encoder2 = UNet._block(features, features * 2, name="enc2")
+        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.encoder3 = UNet._block(features * 2, features * 4, name="enc3")
+        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.encoder4 = UNet._block(features * 4, features * 8, name="enc4")
+        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        self.bottleneck = UNet._block(features * 8, features * 16, name="bottleneck")
+
+        self.upconv4 = nn.ConvTranspose2d(
+            features * 16, features * 8, kernel_size=2, stride=2
+        )
+        self.decoder4 = UNet._block((features * 8) * 2, features * 8, name="dec4")
+        self.upconv3 = nn.ConvTranspose2d(
+            features * 8, features * 4, kernel_size=2, stride=2
+        )
+        self.decoder3 = UNet._block((features * 4) * 2, features * 4, name="dec3")
+        self.upconv2 = nn.ConvTranspose2d(
+            features * 4, features * 2, kernel_size=2, stride=2
+        )
+        self.decoder2 = UNet._block((features * 2) * 2, features * 2, name="dec2")
+        self.upconv1 = nn.ConvTranspose2d(
+            features * 2, features, kernel_size=2, stride=2
+        )
+        self.decoder1 = UNet._block(features * 2, features, name="dec1")
+
+        self.conv = nn.Conv2d(
+            in_channels=features, out_channels=out_channels, kernel_size=1
+        )
+
+    def forward(self, x):
+        enc1 = self.encoder1(x)
+        enc2 = self.encoder2(self.pool1(enc1))
+        enc3 = self.encoder3(self.pool2(enc2))
+        enc4 = self.encoder4(self.pool3(enc3))
+
+        bottleneck = self.bottleneck(self.pool4(enc4))
+
+        dec4 = self.upconv4(bottleneck)
+        dec4 = torch.cat((dec4, enc4), dim=1)
+        dec4 = self.decoder4(dec4)
+        dec3 = self.upconv3(dec4)
+        dec3 = torch.cat((dec3, enc3), dim=1)
+        dec3 = self.decoder3(dec3)
+        dec2 = self.upconv2(dec3)
+        dec2 = torch.cat((dec2, enc2), dim=1)
+        dec2 = self.decoder2(dec2)
+        dec1 = self.upconv1(dec2)
+        dec1 = torch.cat((dec1, enc1), dim=1)
+        dec1 = self.decoder1(dec1)
+        return torch.sigmoid(self.conv(dec1))
+
+    @staticmethod
+    def _block(in_channels, features, name):
+        return nn.Sequential(
+            OrderedDict(
+                [
+                    (
+                        name + "conv1",
+                        nn.Conv2d(
+                            in_channels=in_channels,
+                            out_channels=features,
+                            kernel_size=3,
+                            padding=1,
+                            bias=False,
+                        ),
+                    ),
+                    (name + "norm1", nn.BatchNorm2d(num_features=features)),
+                    (name + "relu1", nn.ReLU(inplace=True)),
+                    (
+                        name + "conv2",
+                        nn.Conv2d(
+                            in_channels=features,
+                            out_channels=features,
+                            kernel_size=3,
+                            padding=1,
+                            bias=False,
+                        ),
+                    ),
+                    (name + "norm2", nn.BatchNorm2d(num_features=features)),
+                    (name + "relu2", nn.ReLU(inplace=True)),
+                ]
+            )
+        )