[Add] ResNet50

backbone.py

@@ -389,6 +389,38 @@ def _forward_impl(self, x: Tensor) -> Tensor:
 
         return x
 
+class ResNet50(torchvision.models.resnet.ResNet):
+    def __init__(self, track_bn=True):
+        def norm_layer(*args, **kwargs):
+            return nn.BatchNorm2d(*args, **kwargs, track_running_stats=track_bn)
+        super().__init__(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], norm_layer=norm_layer)
+        del self.fc
+        self.final_feat_dim = 2048
+
+    def load_imagenet_weights(self, progress=True):
+        state_dict = load_state_dict_from_url(torchvision.models.resnet.model_urls['resnet50'],
+                                              progress=progress)
+        missing, unexpected = self.load_state_dict(state_dict, strict=False)
+        if len(missing) > 0:
+            raise AssertionError('Model code may be incorrect')
+
+    def _forward_impl(self, x: Tensor) -> Tensor:
+        # See note [TorchScript super()]
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = torch.flatten(x, 1)
+        # x = self.fc(x)
+
+        return x
 
 
 ##########################################################################################################
@@ -626,6 +658,7 @@ def forward(self, x, is_feat=False):
 _backbone_class_map = {
     'resnet10': ResNet10,
     'resnet18': ResNet18,
+    'resnet50': ResNet50,
 }
 
 

paths.py

@@ -24,6 +24,7 @@
 BACKBONE_KEYS = {
     'resnet10': 'resnet10',
     'resnet18': 'resnet18',
+    'resnet50': 'resnet50',
 }
 
 MODEL_KEYS = {