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YangLiu9208 · Jun 7, 2023 · 33c95d9 · 33c95d9
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diff --git a/model.rar b/model.rar
diff --git a/model/__init__.py b/model/__init__.py
diff --git a/model/__pycache__/__init__.cpython-37.pyc b/model/__pycache__/__init__.cpython-37.pyc
diff --git a/model/__pycache__/embedding.cpython-37.pyc b/model/__pycache__/embedding.cpython-37.pyc
diff --git a/model/backbone/TRN.py b/model/backbone/TRN.py
diff --git a/model/backbone/TRN_resnet18.py b/model/backbone/TRN_resnet18.py
diff --git a/model/backbone/TRN_resnet50.py b/model/backbone/TRN_resnet50.py
diff --git a/model/backbone/TSN.py b/model/backbone/TSN.py
diff --git a/model/backbone/__init__.py b/model/backbone/__init__.py
@@ -0,0 +1,15 @@
+from .inception.google import GoogleNet
+from .inception.v1bn import InceptionV1BN
+from .resnet import ResNet50, ResNet18, Semantic_ResNet18, Semantic_ResNet50
+from .vggnet import VggNet16
+from .vggnet import SeVggNet16
+from .vggnet import SeFusionVGG16
+from .vggnet import SeFusionVGG16_MMAct
+from .vggnet import SeFusionVGG16_Berkeley
+from .vggnet import SemanticFusionVGG16
+from .vggnet import SemanticFusionVGG16_MMAct
+from .vggnet import SemanticFusionVGG16_Berkeley
+from .TSN import TSN
+from .TRN import TRN
+from .TRN_resnet18 import TRN_resnet18
+from .TRN_resnet50 import TRN_resnet50
diff --git a/model/backbone/__pycache__/TRN.cpython-37.pyc b/model/backbone/__pycache__/TRN.cpython-37.pyc
diff --git a/model/backbone/__pycache__/TRN_resnet18.cpython-37.pyc b/model/backbone/__pycache__/TRN_resnet18.cpython-37.pyc
diff --git a/model/backbone/__pycache__/TRN_resnet50.cpython-37.pyc b/model/backbone/__pycache__/TRN_resnet50.cpython-37.pyc
diff --git a/model/backbone/__pycache__/TRNmodule.cpython-37.pyc b/model/backbone/__pycache__/TRNmodule.cpython-37.pyc
diff --git a/model/backbone/__pycache__/TSM.cpython-37.pyc b/model/backbone/__pycache__/TSM.cpython-37.pyc
diff --git a/model/backbone/__pycache__/TSN.cpython-37.pyc b/model/backbone/__pycache__/TSN.cpython-37.pyc
diff --git a/model/backbone/__pycache__/__init__.cpython-37.pyc b/model/backbone/__pycache__/__init__.cpython-37.pyc
diff --git a/model/backbone/__pycache__/basic_ops.cpython-37.pyc b/model/backbone/__pycache__/basic_ops.cpython-37.pyc
diff --git a/model/backbone/__pycache__/resnet.cpython-37.pyc b/model/backbone/__pycache__/resnet.cpython-37.pyc
diff --git a/model/backbone/__pycache__/transforms.cpython-37.pyc b/model/backbone/__pycache__/transforms.cpython-37.pyc
diff --git a/model/backbone/__pycache__/vggnet.cpython-37.pyc b/model/backbone/__pycache__/vggnet.cpython-37.pyc
diff --git a/model/backbone/inception/__init__.py b/model/backbone/inception/__init__.py
diff --git a/model/backbone/inception/__pycache__/__init__.cpython-37.pyc b/model/backbone/inception/__pycache__/__init__.cpython-37.pyc
diff --git a/model/backbone/inception/__pycache__/google.cpython-37.pyc b/model/backbone/inception/__pycache__/google.cpython-37.pyc
diff --git a/model/backbone/inception/__pycache__/v1bn.cpython-37.pyc b/model/backbone/inception/__pycache__/v1bn.cpython-37.pyc
diff --git a/model/backbone/resnet.py b/model/backbone/resnet.py
@@ -0,0 +1,106 @@
+import torchvision
+import torch.nn as nn
+
+__all__ = ['ResNet18', 'ResNet50','Semantic_ResNet18','Semantic_ResNet50']
+
+
+class ResNet18(nn.Module):
+    #output_size = 512
+
+    def __init__(self,n_classes):
+        super(ResNet18, self).__init__()
+        pretrained = torchvision.models.resnet18(pretrained=True)
+
+        for module_name in ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3', 'layer4', 'avgpool']:
+            self.add_module(module_name, getattr(pretrained, module_name))
+        self.fc = nn.Linear(512, n_classes)
+    def forward(self, x, get_ha=False):
+        x = self.maxpool(self.relu(self.bn1(self.conv1(x))))
+        b1 = self.layer1(x)
+        b2 = self.layer2(b1)
+        b3 = self.layer3(b2)
+        b4 = self.layer4(b3)
+        pool = self.avgpool(b4)
+        pool = pool.view(pool.size(0), -1)
+        out  = self.fc(pool)
+        if get_ha:
+            return b1, b2, b3, b4, pool, out
+
+        return out
+
+
+class ResNet50(nn.Module):
+    #output_size = 2048
+
+    def __init__(self, n_classes):
+        super(ResNet50, self).__init__()
+        pretrained = torchvision.models.resnet50(pretrained=True)
+
+        for module_name in ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3', 'layer4', 'avgpool']:
+            self.add_module(module_name, getattr(pretrained, module_name))
+        self.fc = nn.Linear(2048, n_classes)
+    def forward(self, x, get_ha=False):
+        x = self.maxpool(self.relu(self.bn1(self.conv1(x))))
+        b1 = self.layer1(x)
+        b2 = self.layer2(b1)
+        b3 = self.layer3(b2)
+        b4 = self.layer4(b3)
+        pool = self.avgpool(b4)
+        pool = pool.view(pool.size(0), -1)
+        out  = self.fc(pool)
+        if get_ha:
+            return b1, b2, b3, b4, pool, out
+
+        return out
+
+class Semantic_ResNet18(nn.Module):
+    #output_size = 512
+
+    def __init__(self,n_classes):
+        super(Semantic_ResNet18, self).__init__()
+        pretrained = torchvision.models.resnet18(pretrained=True)
+
+        for module_name in ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3', 'layer4', 'avgpool']:
+            self.add_module(module_name, getattr(pretrained, module_name))
+        self.fc1 = nn.Linear(512, 300)
+        self.fc2 = nn.Linear(300, n_classes)
+    def forward(self, x, get_ha=False):
+        x = self.maxpool(self.relu(self.bn1(self.conv1(x))))
+        b1 = self.layer1(x)
+        b2 = self.layer2(b1)
+        b3 = self.layer3(b2)
+        b4 = self.layer4(b3)
+        pool = self.avgpool(b4)
+        pool = pool.view(pool.size(0), -1)
+        semantic  = self.fc1(pool)
+        out  = self.fc2(semantic)
+        if get_ha:
+            return b1, b2, b3, b4, semantic, out
+
+        return out
+
+class Semantic_ResNet50(nn.Module):
+    #output_size = 2048
+
+    def __init__(self,n_classes):
+        super(Semantic_ResNet50, self).__init__()
+        pretrained = torchvision.models.resnet50(pretrained=True)
+
+        for module_name in ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3', 'layer4', 'avgpool']:
+            self.add_module(module_name, getattr(pretrained, module_name))
+        self.fc1 = nn.Linear(2048, 300)
+        self.fc2 = nn.Linear(300, n_classes)
+    def forward(self, x, get_ha=False):
+        x = self.maxpool(self.relu(self.bn1(self.conv1(x))))
+        b1 = self.layer1(x)
+        b2 = self.layer2(b1)
+        b3 = self.layer3(b2)
+        b4 = self.layer4(b3)
+        pool = self.avgpool(b4)
+        pool = pool.view(pool.size(0), -1)
+        semantic  = self.fc1(pool)
+        out  = self.fc2(semantic)
+        if get_ha:
+            return b1, b2, b3, b4, semantic, out
+
+        return out