Use is to compare type of objects

torchvision/models/quantization/googlenet.py

@@ -167,5 +167,5 @@ def fuse_model(self) -> None:
         """
 
         for m in self.modules():
-            if type(m) == QuantizableBasicConv2d:
+            if type(m) is QuantizableBasicConv2d:
                 m.fuse_model()

torchvision/models/quantization/inception.py

@@ -247,5 +247,5 @@ def fuse_model(self) -> None:
         """
 
         for m in self.modules():
-            if type(m) == QuantizableBasicConv2d:
+            if type(m) is QuantizableBasicConv2d:
                 m.fuse_model()

torchvision/models/quantization/mobilenetv2.py

@@ -30,7 +30,7 @@ def forward(self, x: Tensor) -> Tensor:
 
     def fuse_model(self) -> None:
         for idx in range(len(self.conv)):
-            if type(self.conv[idx]) == nn.Conv2d:
+            if type(self.conv[idx]) is nn.Conv2d:
                 fuse_modules(self.conv, [str(idx), str(idx + 1)], inplace=True)
 
 
@@ -54,9 +54,9 @@ def forward(self, x: Tensor) -> Tensor:
 
     def fuse_model(self) -> None:
         for m in self.modules():
-            if type(m) == ConvNormActivation:
+            if type(m) is ConvNormActivation:
                 fuse_modules(m, ["0", "1", "2"], inplace=True)
-            if type(m) == QuantizableInvertedResidual:
+            if type(m) is QuantizableInvertedResidual:
                 m.fuse_model()
 
 

torchvision/models/quantization/mobilenetv3.py

@@ -99,12 +99,12 @@ def forward(self, x: Tensor) -> Tensor:
 
     def fuse_model(self) -> None:
         for m in self.modules():
-            if type(m) == ConvNormActivation:
+            if type(m) is ConvNormActivation:
                 modules_to_fuse = ["0", "1"]
-                if len(m) == 3 and type(m[2]) == nn.ReLU:
+                if len(m) == 3 and type(m[2]) is nn.ReLU:
                     modules_to_fuse.append("2")
                 fuse_modules(m, modules_to_fuse, inplace=True)
-            elif type(m) == QuantizableSqueezeExcitation:
+            elif type(m) is QuantizableSqueezeExcitation:
                 m.fuse_model()
 
 

torchvision/models/quantization/resnet.py

@@ -104,7 +104,7 @@ def fuse_model(self) -> None:
 
         fuse_modules(self, ["conv1", "bn1", "relu"], inplace=True)
         for m in self.modules():
-            if type(m) == QuantizableBottleneck or type(m) == QuantizableBasicBlock:
+            if type(m) is QuantizableBottleneck or type(m) is QuantizableBasicBlock:
                 m.fuse_model()
 
 

torchvision/models/quantization/shufflenetv2.py

@@ -68,7 +68,7 @@ def fuse_model(self) -> None:
             if name in ["conv1", "conv5"]:
                 torch.quantization.fuse_modules(m, [["0", "1", "2"]], inplace=True)
         for m in self.modules():
-            if type(m) == QuantizableInvertedResidual:
+            if type(m) is QuantizableInvertedResidual:
                 if len(m.branch1._modules.items()) > 0:
                     torch.quantization.fuse_modules(m.branch1, [["0", "1"], ["2", "3", "4"]], inplace=True)
                 torch.quantization.fuse_modules(

torchvision/models/quantization/utils.py

@@ -9,7 +9,7 @@ def _replace_relu(module: nn.Module) -> None:
         # Checking for explicit type instead of instance
         # as we only want to replace modules of the exact type
         # not inherited classes
-        if type(mod) == nn.ReLU or type(mod) == nn.ReLU6:
+        if type(mod) is nn.ReLU or type(mod) is nn.ReLU6:
             reassign[name] = nn.ReLU(inplace=False)
 
     for key, value in reassign.items():