update

2022-01-10 17:04:25 +08:00
parent 3783ef0e75
commit 591c650dd9
5 changed files with 314 additions and 174 deletions
@@ -0,0 +1,67 @@
+import torch
+import torch.nn as nn
+
+class Discriminator(nn.Module):
+    def __init__(self, input_nc, norm_layer=nn.BatchNorm2d, use_sigmoid=False):
+        super(Discriminator, self).__init__()
+
+        kw = 4
+        padw = 1
+        self.down1 = nn.Sequential(
+            nn.Conv2d(input_nc, 64, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(64),
+            nn.LeakyReLU(0.2, True)
+        )
+        self.down2 = nn.Sequential(
+            nn.Conv2d(64, 128, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(128),
+            nn.LeakyReLU(0.2, True)
+        )
+        self.down3 = nn.Sequential(
+            nn.Conv2d(128, 256, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(256),
+            nn.LeakyReLU(0.2, True)
+        )
+        self.down4 = nn.Sequential(
+            nn.Conv2d(256, 512, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(512),
+            nn.LeakyReLU(0.2, True)
+        )
+        self.down5 = nn.Sequential(
+            nn.Conv2d(512, 512, kernel_size=kw, stride=2, padding=padw),
+            norm_layer(512),
+            nn.LeakyReLU(0.2, True)
+        )
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(512, 512, kernel_size=kw, stride=1, padding=padw),
+            norm_layer(512),
+            nn.LeakyReLU(0.2, True)
+        )
+
+        if use_sigmoid:
+            self.conv2 = nn.Sequential(
+                nn.Conv2d(512, 1, kernel_size=kw, stride=1, padding=padw),
+                nn.Sigmoid()
+            )
+        else:
+            self.conv2 = nn.Sequential(
+                nn.Conv2d(512, 1, kernel_size=kw, stride=1, padding=padw)
+            )
+
+    def forward(self, input):
+        out = []
+        x = self.down1(input)
+        #out.append(x)
+        x = self.down2(x)
+        #out.append(x)
+        x = self.down3(x)
+        #out.append(x)
+        x = self.down4(x)
+        x = self.down5(x)
+        out.append(x)
+        x = self.conv1(x)
+        out.append(x)
+        x = self.conv2(x)
+        out.append(x)
+        
+        return out