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

@@ -242,7 +242,7 @@ 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,
@@ -316,6 +316,9 @@ 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/inception.py

@@ -128,6 +128,7 @@ def __init__(
         num_classes: int = 1000,
         aux_logits: bool = True,
         transform_input: bool = False,
+        init_weights: Optional[bool] = None,
     ) -> None:
         super().__init__(
             num_classes=num_classes,
@@ -142,6 +143,7 @@ def __init__(
                 QuantizableInceptionE,
                 QuantizableInceptionAux,
             ],
+            init_weights=init_weights,
         )
         self.quant = torch.ao.quantization.QuantStub()
         self.dequant = torch.ao.quantization.DeQuantStub()