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CTAI_flask/core/net/unet.py

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+import torch.nn as nn
+import torch
+from torch import autograd
+
+class DoubleConv(nn.Module):
+    def __init__(self, in_ch, out_ch):
+        super(DoubleConv, self).__init__()
+        self.conv = nn.Sequential(
+            nn.Conv2d(in_ch, out_ch, 3, padding=1),
+            nn.BatchNorm2d(out_ch),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(out_ch, out_ch, 3, padding=1),
+            nn.BatchNorm2d(out_ch),
+            nn.ReLU(inplace=True)
+        )
+
+    def forward(self, input):
+        return self.conv(input)
+
+
+class Unet(nn.Module):
+    def __init__(self,in_ch,out_ch):
+        super(Unet, self).__init__()
+
+        self.conv1 = DoubleConv(in_ch, 64)
+        self.pool1 = nn.MaxPool2d(2)
+        self.conv2 = DoubleConv(64, 128)
+        self.pool2 = nn.MaxPool2d(2)
+        self.conv3 = DoubleConv(128, 256)
+        self.pool3 = nn.MaxPool2d(2)
+        self.conv4 = DoubleConv(256, 512)
+        self.pool4 = nn.MaxPool2d(2)
+        self.conv5 = DoubleConv(512, 1024)
+        self.up6 = nn.ConvTranspose2d(1024, 512, 2, stride=2)
+        self.conv6 = DoubleConv(1024, 512)
+        self.up7 = nn.ConvTranspose2d(512, 256, 2, stride=2)
+        self.conv7 = DoubleConv(512, 256)
+        self.up8 = nn.ConvTranspose2d(256, 128, 2, stride=2)
+        self.conv8 = DoubleConv(256, 128)
+        self.up9 = nn.ConvTranspose2d(128, 64, 2, stride=2)
+        self.conv9 = DoubleConv(128, 64)
+        self.conv10 = nn.Conv2d(64,out_ch, 1)
+
+    def forward(self,x):
+        c1=self.conv1(x)
+        p1=self.pool1(c1)
+        c2=self.conv2(p1)
+        p2=self.pool2(c2)
+        c3=self.conv3(p2)
+        p3=self.pool3(c3)
+        c4=self.conv4(p3)
+        p4=self.pool4(c4)
+        c5=self.conv5(p4)
+        up_6= self.up6(c5)
+        merge6 = torch.cat([up_6, c4], dim=1)
+        c6=self.conv6(merge6)
+        up_7=self.up7(c6)
+        merge7 = torch.cat([up_7, c3], dim=1)
+        c7=self.conv7(merge7)
+        up_8=self.up8(c7)
+        merge8 = torch.cat([up_8, c2], dim=1)
+        c8=self.conv8(merge8)
+        up_9=self.up9(c8)
+        merge9=torch.cat([up_9,c1],dim=1)
+        c9=self.conv9(merge9)
+        c10=self.conv10(c9)
+        out = nn.Sigmoid()(c10)
+        return out