Create ResNet_for_CC.py

ResNet_for_CC.py

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+import torch
+import torch.nn as nn
+import torchvision.models as models
+
+
+class ResClassifier(nn.Module):
+    def __init__(self, class_num=14):
+        super(ResClassifier, self).__init__()
+        self.fc1 = nn.Sequential(
+            nn.Linear(128, 64),
+            nn.BatchNorm1d(64, affine=True),
+            nn.ReLU(inplace=True),
+            nn.Dropout()
+        )
+        self.fc2 = nn.Sequential(
+            nn.Linear(64, 64),
+            nn.BatchNorm1d(64, affine=True),
+            nn.ReLU(inplace=True),
+            nn.Dropout()
+        )
+        self.fc3 = nn.Linear(64, class_num)
+
+    def forward(self, x):
+        fc1_emb = self.fc1(x)
+        fc2_emb = self.fc2(fc1_emb)
+        logit = self.fc3(fc2_emb)
+        return logit
+
+class CC_model(nn.Module):
+    def __init__(self, num_classes1=14, num_classes2=None):
+
+        if num_classes2 is None:
+            num_classes2 = num_classes1
+
+        super(CC_model, self).__init__()
+        assert num_classes1 == num_classes2
+        self.num_classes = num_classes1
+        self.model_resnet = models.resnet50(weights='ResNet50_Weights.DEFAULT')
+        num_ftrs = self.model_resnet.fc.in_features
+        self.model_resnet.fc = nn.Identity()
+        self.classification_fc = nn.Linear(num_ftrs, num_classes1)
+        self.dr = nn.Linear(num_ftrs, 128)
+        self.fc1 = ResClassifier(num_classes1)
+        self.fc2 = ResClassifier(num_classes1)
+
+    def forward(self, x, detach_feature=False):
+        feature = self.model_resnet(x)
+        res_out = self.classification_fc(feature)
+        if detach_feature:
+            feature = feature.detach()
+        dr_feature = self.dr(feature)
+        out1 = self.fc1(dr_feature)
+        out2 = self.fc2(dr_feature)
+        output_mean = (out1 + out2)
+        return output_mean