Remove warnings pytest models

test/test_datasets.py

@@ -617,7 +617,6 @@ class VOCSegmentationTestCase(datasets_utils.ImageDatasetTestCase):
             year=[f"20{year:02d}" for year in range(7, 13)], image_set=("train", "val", "trainval")
         ),
         dict(year="2007", image_set="test"),
-        dict(year="2007-test", image_set="test"),
     )
 
     def inject_fake_data(self, tmpdir, config):

test/test_models.py

@@ -244,7 +244,7 @@ def _check_input_backprop(model, inputs):
 # The following contains configuration parameters for all models which are used by
 # the _test_*_model methods.
 _model_params = {
-    "inception_v3": {"input_shape": (1, 3, 299, 299)},
+    "inception_v3": {"input_shape": (1, 3, 299, 299), "init_weights": True},
     "retinanet_resnet50_fpn": {
         "num_classes": 20,
         "score_thresh": 0.01,
@@ -318,6 +318,7 @@ def _check_input_backprop(model, inputs):
     "s3d": {
         "input_shape": (1, 3, 16, 224, 224),
     },
+    "googlenet": {"init_weights": True},
 }
 # speeding up slow models:
 slow_models = [

torchvision/models/quantization/googlenet.py

@@ -39,7 +39,7 @@ def fuse_model(self, is_qat: Optional[bool] = None) -> None:
 
 class QuantizableInception(Inception):
     def __init__(self, *args: Any, **kwargs: Any) -> None:
-        super().__init__(conv_block=QuantizableBasicConv2d, *args, **kwargs)  # type: ignore[misc]
+        super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs)  # type: ignore[misc]
         self.cat = nn.quantized.FloatFunctional()
 
     def forward(self, x: Tensor) -> Tensor:
@@ -50,7 +50,7 @@ def forward(self, x: Tensor) -> Tensor:
 class QuantizableInceptionAux(InceptionAux):
     # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
     def __init__(self, *args: Any, **kwargs: Any) -> None:
-        super().__init__(conv_block=QuantizableBasicConv2d, *args, **kwargs)  # type: ignore[misc]
+        super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs)  # type: ignore[misc]
         self.relu = nn.ReLU()
 
     def forward(self, x: Tensor) -> Tensor:
@@ -75,7 +75,7 @@ class QuantizableGoogLeNet(GoogLeNet):
     # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
     def __init__(self, *args: Any, **kwargs: Any) -> None:
         super().__init__(  # type: ignore[misc]
-            blocks=[QuantizableBasicConv2d, QuantizableInception, QuantizableInceptionAux], *args, **kwargs
+            *args, blocks=[QuantizableBasicConv2d, QuantizableInception, QuantizableInceptionAux], **kwargs
         )
         self.quant = torch.ao.quantization.QuantStub()
         self.dequant = torch.ao.quantization.DeQuantStub()

torchvision/models/quantization/inception.py

@@ -41,7 +41,7 @@ def fuse_model(self, is_qat: Optional[bool] = None) -> None:
 class QuantizableInceptionA(inception_module.InceptionA):
     # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
     def __init__(self, *args: Any, **kwargs: Any) -> None:
-        super().__init__(conv_block=QuantizableBasicConv2d, *args, **kwargs)  # type: ignore[misc]
+        super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs)  # type: ignore[misc]
         self.myop = nn.quantized.FloatFunctional()
 
     def forward(self, x: Tensor) -> Tensor:
@@ -52,7 +52,7 @@ def forward(self, x: Tensor) -> Tensor:
 class QuantizableInceptionB(inception_module.InceptionB):
     # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
     def __init__(self, *args: Any, **kwargs: Any) -> None:
-        super().__init__(conv_block=QuantizableBasicConv2d, *args, **kwargs)  # type: ignore[misc]
+        super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs)  # type: ignore[misc]
         self.myop = nn.quantized.FloatFunctional()
 
     def forward(self, x: Tensor) -> Tensor:
@@ -63,7 +63,7 @@ def forward(self, x: Tensor) -> Tensor:
 class QuantizableInceptionC(inception_module.InceptionC):
     # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
     def __init__(self, *args: Any, **kwargs: Any) -> None:
-        super().__init__(conv_block=QuantizableBasicConv2d, *args, **kwargs)  # type: ignore[misc]
+        super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs)  # type: ignore[misc]
         self.myop = nn.quantized.FloatFunctional()
 
     def forward(self, x: Tensor) -> Tensor:
@@ -74,7 +74,7 @@ def forward(self, x: Tensor) -> Tensor:
 class QuantizableInceptionD(inception_module.InceptionD):
     # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
     def __init__(self, *args: Any, **kwargs: Any) -> None:
-        super().__init__(conv_block=QuantizableBasicConv2d, *args, **kwargs)  # type: ignore[misc]
+        super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs)  # type: ignore[misc]
         self.myop = nn.quantized.FloatFunctional()
 
     def forward(self, x: Tensor) -> Tensor:
@@ -85,7 +85,7 @@ def forward(self, x: Tensor) -> Tensor:
 class QuantizableInceptionE(inception_module.InceptionE):
     # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
     def __init__(self, *args: Any, **kwargs: Any) -> None:
-        super().__init__(conv_block=QuantizableBasicConv2d, *args, **kwargs)  # type: ignore[misc]
+        super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs)  # type: ignore[misc]
         self.myop1 = nn.quantized.FloatFunctional()
         self.myop2 = nn.quantized.FloatFunctional()
         self.myop3 = nn.quantized.FloatFunctional()
@@ -119,20 +119,13 @@ def forward(self, x: Tensor) -> Tensor:
 class QuantizableInceptionAux(inception_module.InceptionAux):
     # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
     def __init__(self, *args: Any, **kwargs: Any) -> None:
-        super().__init__(conv_block=QuantizableBasicConv2d, *args, **kwargs)  # type: ignore[misc]
+        super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs)  # type: ignore[misc]
 
 
 class QuantizableInception3(inception_module.Inception3):
-    def __init__(
-        self,
-        num_classes: int = 1000,
-        aux_logits: bool = True,
-        transform_input: bool = False,
-    ) -> None:
-        super().__init__(
-            num_classes=num_classes,
-            aux_logits=aux_logits,
-            transform_input=transform_input,
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(  # type: ignore[misc]
+            *args,
             inception_blocks=[
                 QuantizableBasicConv2d,
                 QuantizableInceptionA,
@@ -142,6 +135,7 @@ def __init__(
                 QuantizableInceptionE,
                 QuantizableInceptionAux,
             ],
+            **kwargs,
         )
         self.quant = torch.ao.quantization.QuantStub()
         self.dequant = torch.ao.quantization.DeQuantStub()

torchvision/models/quantization/mobilenetv3.py

@@ -83,7 +83,7 @@ def _load_from_state_dict(
 class QuantizableInvertedResidual(InvertedResidual):
     # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
     def __init__(self, *args: Any, **kwargs: Any) -> None:
-        super().__init__(se_layer=QuantizableSqueezeExcitation, *args, **kwargs)  # type: ignore[misc]
+        super().__init__(*args, se_layer=QuantizableSqueezeExcitation, **kwargs)  # type: ignore[misc]
         self.skip_add = nn.quantized.FloatFunctional()
 
     def forward(self, x: Tensor) -> Tensor: