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STGAN/encoder1_1.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+class encoder1(nn.Module):
+    def __init__(self, num_layers=10, num_features=64, out_num=2):
+        super(encoder1, self).__init__()
+
+        layers = [nn.Sequential(nn.Conv2d(3, num_features, kernel_size=3, stride=1, padding=1),
+                                nn.ReLU(inplace=True))]
+        for i in range(num_layers - 2):
+            layers.append(nn.Sequential(nn.Conv2d(num_features, num_features, kernel_size=3, padding=1),
+                                        nn.BatchNorm2d(num_features),
+                                        nn.ReLU(inplace=True)))
+        layers.append(nn.Sequential(nn.Conv2d(num_features, 1, kernel_size=3, padding=1),
+                                        nn.BatchNorm2d(1),
+                                        nn.ReLU(inplace=True)))
+        self.layers = nn.Sequential(*layers)
+        self.layers2 = nn.Sequential(
+            nn.Conv2d(1, 32, kernel_size=5, stride=1, padding=2),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2))
+        self.layers3 = nn.Sequential(
+            nn.Conv2d(32, 64, kernel_size=5, stride=1, padding=2),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2))
+        self.layers4=nn.Sequential(nn.Linear(65536, 512), nn.ReLU(), nn.Linear(512, 256), 
+                              nn.ReLU(), nn.Linear(256, out_num), nn.Sigmoid())
+
+
+
+    def forward(self, inputs):
+        output1 = self.layers(inputs)
+        output1 = self.layers2(output1)
+        output1 = self.layers3(output1)
+        output2 = output1.reshape(output1.size(0), -1)
+        output2 = self.layers4(output2)
+
+        return output2

STGAN/encoder2_1.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+class encoder2(nn.Module):
+    def __init__(self, num_layers=10, num_features=64, out_num=1):
+        super(encoder2, self).__init__()
+
+        layers = [nn.Sequential(nn.Conv2d(3, num_features, kernel_size=3, stride=1, padding=1),
+                                nn.ReLU(inplace=True))]
+        for i in range(num_layers - 2):
+            layers.append(nn.Sequential(nn.Conv2d(num_features, num_features, kernel_size=3, padding=1),
+                                        nn.BatchNorm2d(num_features),
+                                        nn.ReLU(inplace=True)))
+        self.layers = nn.Sequential(*layers)
+        self.layer_2=nn.Sequential(nn.Conv2d(num_features, out_num, kernel_size=3, padding=1),
+                                        nn.BatchNorm2d(out_num),
+                                        nn.ReLU(inplace=True))
+
+
+
+    def forward(self, inputs):
+        output = self.layers(inputs)
+        output = self.layer_2(output)
+
+        return output