partially enable type checking for .models (#2668)

* partially enable mypy for .models

* fix existing errors

* ignore error instead of using Union

mypy.ini

@@ -4,15 +4,23 @@ files = torchvision
 show_error_codes = True
 pretty = True
 
-;[mypy-torchvision.datasets.*]
+[mypy-torchvision.io._video_opt.*]
 
-;ignore_errors = True
+ignore_errors = True
 
-[mypy-torchvision.io._video_opt.*]
+[mypy-torchvision.io.*]
+
+ignore_errors = True
+
+[mypy-torchvision.models.detection.*]
+
+ignore_errors = True
+
+[mypy-torchvision.models.densenet.*]
 
 ignore_errors = True
 
-[mypy-torchvision.models.*]
+[mypy-torchvision.models.quantization.*]
 
 ignore_errors = True
 

torchvision/models/googlenet.py

@@ -193,12 +193,11 @@ class GoogLeNet(nn.Module):
         return x, aux2, aux1
 
     @torch.jit.unused
-    def eager_outputs(self, x, aux2, aux1):
-        # type: (Tensor, Optional[Tensor], Optional[Tensor]) -> GoogLeNetOutputs
+    def eager_outputs(self, x: Tensor, aux2: Tensor, aux1: Optional[Tensor]) -> GoogLeNetOutputs:
         if self.training and self.aux_logits:
             return _GoogLeNetOutputs(x, aux2, aux1)
         else:
-            return x
+            return x   # type: ignore[return-value]
 
     def forward(self, x):
         # type: (Tensor) -> GoogLeNetOutputs

torchvision/models/inception.py

@@ -188,12 +188,11 @@ class Inception3(nn.Module):
         return x, aux
 
     @torch.jit.unused
-    def eager_outputs(self, x, aux):
-        # type: (Tensor, Optional[Tensor]) -> InceptionOutputs
+    def eager_outputs(self, x: torch.Tensor, aux: Optional[Tensor]) -> InceptionOutputs:
         if self.training and self.aux_logits:
             return InceptionOutputs(x, aux)
         else:
-            return x
+            return x  # type: ignore[return-value]
 
     def forward(self, x):
         x = self._transform_input(x)