Update badnet_m.py

badnet_m.py

@@ -1,36 +1,51 @@
 from torch import nn
+import torchvision
 
 class BadNet(nn.Module):
+    # def __init__(self, input_channel, output_label) -> None:
+    # 目前只假设cifar10
+    def __init__(self, output_label) -> None:
+        super(BadNet, self).__init__()
+        self.model = torchvision.models.resnet18(pretrained=True)
+        num_features = self.model.fc.out_features
+        self.fc = nn.Linear(in_features=num_features, out_features=output_label)
+        
+    
+    def forward(self, xs):
+        out = self.model(xs)
+        return self.fc(out)
 
-    def __init__(self, input_channels, output_num):
-        super().__init__()
-        self.conv1 = nn.Sequential(
-            nn.Conv2d(in_channels=input_channels, out_channels=16, kernel_size=5, stride=1),
-            nn.ReLU(),
-            nn.AvgPool2d(kernel_size=2, stride=2)
-        )
+# class BadNet(nn.Module):
 
-        self.conv2 = nn.Sequential(
-            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1),
-            nn.ReLU(),
-            nn.AvgPool2d(kernel_size=2, stride=2)
-        )
-        fc1_input_features = 800 if input_channels == 3 else 512
-        self.fc1 = nn.Sequential(
-            nn.Linear(in_features=fc1_input_features, out_features=512),
-            nn.ReLU()
-        )
-        self.fc2 = nn.Sequential(
-            nn.Linear(in_features=512, out_features=output_num),
-            nn.Softmax(dim=-1)
-        )
-        self.dropout = nn.Dropout(p=.5)
+#     def __init__(self, input_channels, output_num):
+#         super().__init__()
+#         self.conv1 = nn.Sequential(
+#             nn.Conv2d(in_channels=input_channels, out_channels=16, kernel_size=5, stride=1),
+#             nn.ReLU(),
+#             nn.AvgPool2d(kernel_size=2, stride=2)
+#         )
 
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.conv2(x)
-        print(x.shape)
-        x = x.view(x.size(0), -1)
-        x = self.fc1(x)
-        x = self.fc2(x)
-        return x
+#         self.conv2 = nn.Sequential(
+#             nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1),
+#             nn.ReLU(),
+#             nn.AvgPool2d(kernel_size=2, stride=2)
+#         )
+#         fc1_input_features = 800 if input_channels == 3 else 512
+#         self.fc1 = nn.Sequential(
+#             nn.Linear(in_features=fc1_input_features, out_features=512),
+#             nn.ReLU()
+#         )
+#         self.fc2 = nn.Sequential(
+#             nn.Linear(in_features=512, out_features=output_num),
+#             nn.Softmax(dim=-1)
+#         )
+#         self.dropout = nn.Dropout(p=.5)
+
+#     def forward(self, x):
+#         x = self.conv1(x)
+#         x = self.conv2(x)
+#         print(x.shape)
+#         x = x.view(x.size(0), -1)
+#         x = self.fc1(x)
+#         x = self.fc2(x)
+#         return x