Cleanup Models prototype implementation (#4940)

* Disable WeightEntry to pass-through `Weights.verify()`

* Rename `Weights.state_dict()` to `Weights.get_state_dict()`

* Add TODO for missing doc.

* Moving warning messages for googlenet.

* Upper-case global `_COMMON_META` var

* Replace argument with parameter in all warnings.

torchvision/prototype/models/_api.py

@@ -50,7 +50,7 @@ class Weights(Enum):
         if obj is not None:
             if type(obj) is str:
                 obj = cls.from_str(obj)
-            elif not isinstance(obj, cls) and not isinstance(obj, WeightEntry):
+            elif not isinstance(obj, cls):
                 raise TypeError(
                     f"Invalid Weight class provided; expected {cls.__name__} but received {obj.__class__.__name__}."
                 )
@@ -63,7 +63,7 @@ class Weights(Enum):
                 return v
         raise ValueError(f"Invalid value {value} for enum {cls.__name__}.")
 
-    def state_dict(self, progress: bool) -> OrderedDict:
+    def get_state_dict(self, progress: bool) -> OrderedDict:
         return load_state_dict_from_url(self.url, progress=progress)
 
     def __repr__(self):
@@ -90,7 +90,7 @@ def get_weight(fn: Callable, weight_name: str) -> Weights:
     """
     sig = signature(fn)
     if "weights" not in sig.parameters:
-        raise ValueError("The method is missing the 'weights' argument.")
+        raise ValueError("The method is missing the 'weights' parameter.")
 
     ann = signature(fn).parameters["weights"].annotation
     weights_class = None

torchvision/prototype/models/alexnet.py

@@ -30,7 +30,7 @@ class AlexNetWeights(Weights):
 
 def alexnet(weights: Optional[AlexNetWeights] = None, progress: bool = True, **kwargs: Any) -> AlexNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = AlexNetWeights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
     weights = AlexNetWeights.verify(weights)
     if weights is not None:
@@ -39,6 +39,6 @@ def alexnet(weights: Optional[AlexNetWeights] = None, progress: bool = True, **k
     model = AlexNet(**kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model

torchvision/prototype/models/densenet.py

@@ -34,7 +34,7 @@ def _load_state_dict(model: nn.Module, weights: Weights, progress: bool) -> None
         r"^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$"
     )
 
-    state_dict = weights.state_dict(progress=progress)
+    state_dict = weights.get_state_dict(progress=progress)
     for key in list(state_dict.keys()):
         res = pattern.match(key)
         if res:
@@ -63,11 +63,11 @@ def _densenet(
     return model
 
 
-_common_meta = {
+_COMMON_META = {
     "size": (224, 224),
     "categories": _IMAGENET_CATEGORIES,
     "interpolation": InterpolationMode.BILINEAR,
-    "recipe": None,  # weights ported from LuaTorch
+    "recipe": None,  # TODO: add here a URL to documentation stating that the weights were ported from LuaTorch
 }
 
 
@@ -76,7 +76,7 @@ class DenseNet121Weights(Weights):
         url="https://download.pytorch.org/models/densenet121-a639ec97.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "acc@1": 74.434,
             "acc@5": 91.972,
         },
@@ -88,7 +88,7 @@ class DenseNet161Weights(Weights):
         url="https://download.pytorch.org/models/densenet161-8d451a50.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "acc@1": 77.138,
             "acc@5": 93.560,
         },
@@ -100,7 +100,7 @@ class DenseNet169Weights(Weights):
         url="https://download.pytorch.org/models/densenet169-b2777c0a.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "acc@1": 75.600,
             "acc@5": 92.806,
         },
@@ -112,7 +112,7 @@ class DenseNet201Weights(Weights):
         url="https://download.pytorch.org/models/densenet201-c1103571.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "acc@1": 76.896,
             "acc@5": 93.370,
         },
@@ -121,7 +121,7 @@ class DenseNet201Weights(Weights):
 
 def densenet121(weights: Optional[DenseNet121Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = DenseNet121Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
     weights = DenseNet121Weights.verify(weights)
 
@@ -130,7 +130,7 @@ def densenet121(weights: Optional[DenseNet121Weights] = None, progress: bool = T
 
 def densenet161(weights: Optional[DenseNet161Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = DenseNet161Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
     weights = DenseNet161Weights.verify(weights)
 
@@ -139,7 +139,7 @@ def densenet161(weights: Optional[DenseNet161Weights] = None, progress: bool = T
 
 def densenet169(weights: Optional[DenseNet169Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = DenseNet169Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
     weights = DenseNet169Weights.verify(weights)
 
@@ -148,7 +148,7 @@ def densenet169(weights: Optional[DenseNet169Weights] = None, progress: bool = T
 
 def densenet201(weights: Optional[DenseNet201Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = DenseNet201Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
     weights = DenseNet201Weights.verify(weights)
 

torchvision/prototype/models/detection/faster_rcnn.py

@@ -30,7 +30,7 @@ __all__ = [
 ]
 
 
-_common_meta = {
+_COMMON_META = {
     "categories": _COCO_CATEGORIES,
     "interpolation": InterpolationMode.BILINEAR,
 }
@@ -41,7 +41,7 @@ class FasterRCNNResNet50FPNWeights(Weights):
         url="https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth",
         transforms=CocoEval,
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-resnet-50-fpn",
             "map": 37.0,
         },
@@ -53,7 +53,7 @@ class FasterRCNNMobileNetV3LargeFPNWeights(Weights):
         url="https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_fpn-fb6a3cc7.pth",
         transforms=CocoEval,
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-mobilenetv3-large-fpn",
             "map": 32.8,
         },
@@ -65,7 +65,7 @@ class FasterRCNNMobileNetV3Large320FPNWeights(Weights):
         url="https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_320_fpn-907ea3f9.pth",
         transforms=CocoEval,
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-mobilenetv3-large-320-fpn",
             "map": 22.8,
         },
@@ -81,11 +81,11 @@ def fasterrcnn_resnet50_fpn(
     **kwargs: Any,
 ) -> FasterRCNN:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = FasterRCNNResNet50FPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
     weights = FasterRCNNResNet50FPNWeights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The argument pretrained_backbone is deprecated, please use weights_backbone instead.")
+        warnings.warn("The parameter pretrained_backbone is deprecated, please use weights_backbone instead.")
         weights_backbone = ResNet50Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained_backbone") else None
     weights_backbone = ResNet50Weights.verify(weights_backbone)
 
@@ -102,7 +102,7 @@ def fasterrcnn_resnet50_fpn(
     model = FasterRCNN(backbone, num_classes=num_classes, **kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
         if weights == FasterRCNNResNet50FPNWeights.Coco_RefV1:
             overwrite_eps(model, 0.0)
 
@@ -142,7 +142,7 @@ def _fasterrcnn_mobilenet_v3_large_fpn(
     )
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model
 
@@ -156,11 +156,11 @@ def fasterrcnn_mobilenet_v3_large_fpn(
     **kwargs: Any,
 ) -> FasterRCNN:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = FasterRCNNMobileNetV3LargeFPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
     weights = FasterRCNNMobileNetV3LargeFPNWeights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The argument pretrained_backbone is deprecated, please use weights_backbone instead.")
+        warnings.warn("The parameter pretrained_backbone is deprecated, please use weights_backbone instead.")
         weights_backbone = MobileNetV3LargeWeights.ImageNet1K_RefV1 if kwargs.pop("pretrained_backbone") else None
     weights_backbone = MobileNetV3LargeWeights.verify(weights_backbone)
 
@@ -188,11 +188,11 @@ def fasterrcnn_mobilenet_v3_large_320_fpn(
     **kwargs: Any,
 ) -> FasterRCNN:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = FasterRCNNMobileNetV3Large320FPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
     weights = FasterRCNNMobileNetV3Large320FPNWeights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The argument pretrained_backbone is deprecated, please use weights_backbone instead.")
+        warnings.warn("The parameter pretrained_backbone is deprecated, please use weights_backbone instead.")
         weights_backbone = MobileNetV3LargeWeights.ImageNet1K_RefV1 if kwargs.pop("pretrained_backbone") else None
     weights_backbone = MobileNetV3LargeWeights.verify(weights_backbone)
 

torchvision/prototype/models/detection/keypoint_rcnn.py

@@ -22,7 +22,7 @@ __all__ = [
 ]
 
 
-_common_meta = {"categories": _COCO_PERSON_CATEGORIES, "keypoint_names": _COCO_PERSON_KEYPOINT_NAMES}
+_COMMON_META = {"categories": _COCO_PERSON_CATEGORIES, "keypoint_names": _COCO_PERSON_KEYPOINT_NAMES}
 
 
 class KeypointRCNNResNet50FPNWeights(Weights):
@@ -30,7 +30,7 @@ class KeypointRCNNResNet50FPNWeights(Weights):
         url="https://download.pytorch.org/models/keypointrcnn_resnet50_fpn_coco-9f466800.pth",
         transforms=CocoEval,
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "recipe": "https://github.com/pytorch/vision/issues/1606",
             "box_map": 50.6,
             "kp_map": 61.1,
@@ -40,7 +40,7 @@ class KeypointRCNNResNet50FPNWeights(Weights):
         url="https://download.pytorch.org/models/keypointrcnn_resnet50_fpn_coco-fc266e95.pth",
         transforms=CocoEval,
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#keypoint-r-cnn",
             "box_map": 54.6,
             "kp_map": 65.0,
@@ -58,7 +58,7 @@ def keypointrcnn_resnet50_fpn(
     **kwargs: Any,
 ) -> KeypointRCNN:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         pretrained = kwargs.pop("pretrained")
         if type(pretrained) == str and pretrained == "legacy":
             weights = KeypointRCNNResNet50FPNWeights.Coco_RefV1_Legacy
@@ -68,7 +68,7 @@ def keypointrcnn_resnet50_fpn(
             weights = None
     weights = KeypointRCNNResNet50FPNWeights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The argument pretrained_backbone is deprecated, please use weights_backbone instead.")
+        warnings.warn("The parameter pretrained_backbone is deprecated, please use weights_backbone instead.")
         weights_backbone = ResNet50Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained_backbone") else None
     weights_backbone = ResNet50Weights.verify(weights_backbone)
 
@@ -86,7 +86,7 @@ def keypointrcnn_resnet50_fpn(
     model = KeypointRCNN(backbone, num_classes, num_keypoints=num_keypoints, **kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
         if weights == KeypointRCNNResNet50FPNWeights.Coco_RefV1:
             overwrite_eps(model, 0.0)
 

torchvision/prototype/models/detection/mask_rcnn.py

@@ -46,11 +46,11 @@ def maskrcnn_resnet50_fpn(
     **kwargs: Any,
 ) -> MaskRCNN:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = MaskRCNNResNet50FPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
     weights = MaskRCNNResNet50FPNWeights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The argument pretrained_backbone is deprecated, please use weights_backbone instead.")
+        warnings.warn("The parameter pretrained_backbone is deprecated, please use weights_backbone instead.")
         weights_backbone = ResNet50Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained_backbone") else None
     weights_backbone = ResNet50Weights.verify(weights_backbone)
 
@@ -67,7 +67,7 @@ def maskrcnn_resnet50_fpn(
     model = MaskRCNN(backbone, num_classes=num_classes, **kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
         if weights == MaskRCNNResNet50FPNWeights.Coco_RefV1:
             overwrite_eps(model, 0.0)
 

torchvision/prototype/models/detection/retinanet.py

@@ -46,11 +46,11 @@ def retinanet_resnet50_fpn(
     **kwargs: Any,
 ) -> RetinaNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = RetinaNetResNet50FPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
     weights = RetinaNetResNet50FPNWeights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The argument pretrained_backbone is deprecated, please use weights_backbone instead.")
+        warnings.warn("The parameter pretrained_backbone is deprecated, please use weights_backbone instead.")
         weights_backbone = ResNet50Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained_backbone") else None
     weights_backbone = ResNet50Weights.verify(weights_backbone)
 
@@ -70,7 +70,7 @@ def retinanet_resnet50_fpn(
     model = RetinaNet(backbone, num_classes, **kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
         if weights == RetinaNetResNet50FPNWeights.Coco_RefV1:
             overwrite_eps(model, 0.0)
 

torchvision/prototype/models/detection/ssd.py

@@ -44,16 +44,16 @@ def ssd300_vgg16(
     **kwargs: Any,
 ) -> SSD:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = SSD300VGG16Weights.Coco_RefV1 if kwargs.pop("pretrained") else None
     weights = SSD300VGG16Weights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The argument pretrained_backbone is deprecated, please use weights_backbone instead.")
+        warnings.warn("The parameter pretrained_backbone is deprecated, please use weights_backbone instead.")
         weights_backbone = VGG16Weights.ImageNet1K_Features if kwargs.pop("pretrained_backbone") else None
     weights_backbone = VGG16Weights.verify(weights_backbone)
 
     if "size" in kwargs:
-        warnings.warn("The size of the model is already fixed; ignoring the argument.")
+        warnings.warn("The size of the model is already fixed; ignoring the parameter.")
 
     if weights is not None:
         weights_backbone = None
@@ -81,6 +81,6 @@ def ssd300_vgg16(
     model = SSD(backbone, anchor_generator, (300, 300), num_classes, **kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model

torchvision/prototype/models/detection/ssdlite.py

@@ -50,16 +50,16 @@ def ssdlite320_mobilenet_v3_large(
     **kwargs: Any,
 ) -> SSD:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = SSDlite320MobileNetV3LargeFPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
     weights = SSDlite320MobileNetV3LargeFPNWeights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The argument pretrained_backbone is deprecated, please use weights_backbone instead.")
+        warnings.warn("The parameter pretrained_backbone is deprecated, please use weights_backbone instead.")
         weights_backbone = MobileNetV3LargeWeights.ImageNet1K_RefV1 if kwargs.pop("pretrained_backbone") else None
     weights_backbone = MobileNetV3LargeWeights.verify(weights_backbone)
 
     if "size" in kwargs:
-        warnings.warn("The size of the model is already fixed; ignoring the argument.")
+        warnings.warn("The size of the model is already fixed; ignoring the parameter.")
 
     if weights is not None:
         weights_backbone = None
@@ -114,6 +114,6 @@ def ssdlite320_mobilenet_v3_large(
     )
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model

torchvision/prototype/models/efficientnet.py

@@ -57,12 +57,12 @@ def _efficientnet(
     model = EfficientNet(inverted_residual_setting, dropout, **kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model
 
 
-_common_meta = {
+_COMMON_META = {
     "categories": _IMAGENET_CATEGORIES,
     "interpolation": InterpolationMode.BICUBIC,
     "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#efficientnet",
@@ -74,7 +74,7 @@ class EfficientNetB0Weights(Weights):
         url="https://download.pytorch.org/models/efficientnet_b0_rwightman-3dd342df.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=256, interpolation=InterpolationMode.BICUBIC),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "size": (224, 224),
             "acc@1": 77.692,
             "acc@5": 93.532,
@@ -87,7 +87,7 @@ class EfficientNetB1Weights(Weights):
         url="https://download.pytorch.org/models/efficientnet_b1_rwightman-533bc792.pth",
         transforms=partial(ImageNetEval, crop_size=240, resize_size=256, interpolation=InterpolationMode.BICUBIC),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "size": (240, 240),
             "acc@1": 78.642,
             "acc@5": 94.186,
@@ -100,7 +100,7 @@ class EfficientNetB2Weights(Weights):
         url="https://download.pytorch.org/models/efficientnet_b2_rwightman-bcdf34b7.pth",
         transforms=partial(ImageNetEval, crop_size=288, resize_size=288, interpolation=InterpolationMode.BICUBIC),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "size": (288, 288),
             "acc@1": 80.608,
             "acc@5": 95.310,
@@ -113,7 +113,7 @@ class EfficientNetB3Weights(Weights):
         url="https://download.pytorch.org/models/efficientnet_b3_rwightman-cf984f9c.pth",
         transforms=partial(ImageNetEval, crop_size=300, resize_size=320, interpolation=InterpolationMode.BICUBIC),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "size": (300, 300),
             "acc@1": 82.008,
             "acc@5": 96.054,
@@ -126,7 +126,7 @@ class EfficientNetB4Weights(Weights):
         url="https://download.pytorch.org/models/efficientnet_b4_rwightman-7eb33cd5.pth",
         transforms=partial(ImageNetEval, crop_size=380, resize_size=384, interpolation=InterpolationMode.BICUBIC),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "size": (380, 380),
             "acc@1": 83.384,
             "acc@5": 96.594,
@@ -139,7 +139,7 @@ class EfficientNetB5Weights(Weights):
         url="https://download.pytorch.org/models/efficientnet_b5_lukemelas-b6417697.pth",
         transforms=partial(ImageNetEval, crop_size=456, resize_size=456, interpolation=InterpolationMode.BICUBIC),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "size": (456, 456),
             "acc@1": 83.444,
             "acc@5": 96.628,
@@ -152,7 +152,7 @@ class EfficientNetB6Weights(Weights):
         url="https://download.pytorch.org/models/efficientnet_b6_lukemelas-c76e70fd.pth",
         transforms=partial(ImageNetEval, crop_size=528, resize_size=528, interpolation=InterpolationMode.BICUBIC),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "size": (528, 528),
             "acc@1": 84.008,
             "acc@5": 96.916,
@@ -165,7 +165,7 @@ class EfficientNetB7Weights(Weights):
         url="https://download.pytorch.org/models/efficientnet_b7_lukemelas-dcc49843.pth",
         transforms=partial(ImageNetEval, crop_size=600, resize_size=600, interpolation=InterpolationMode.BICUBIC),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "size": (600, 600),
             "acc@1": 84.122,
             "acc@5": 96.908,
@@ -177,7 +177,7 @@ def efficientnet_b0(
     weights: Optional[EfficientNetB0Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = EfficientNetB0Weights.ImageNet1K_TimmV1 if kwargs.pop("pretrained") else None
     weights = EfficientNetB0Weights.verify(weights)
     return _efficientnet(width_mult=1.0, depth_mult=1.0, dropout=0.2, weights=weights, progress=progress, **kwargs)
@@ -187,7 +187,7 @@ def efficientnet_b1(
     weights: Optional[EfficientNetB1Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = EfficientNetB1Weights.ImageNet1K_TimmV1 if kwargs.pop("pretrained") else None
     weights = EfficientNetB1Weights.verify(weights)
     return _efficientnet(width_mult=1.0, depth_mult=1.1, dropout=0.2, weights=weights, progress=progress, **kwargs)
@@ -197,7 +197,7 @@ def efficientnet_b2(
     weights: Optional[EfficientNetB2Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = EfficientNetB2Weights.ImageNet1K_TimmV1 if kwargs.pop("pretrained") else None
     weights = EfficientNetB2Weights.verify(weights)
     return _efficientnet(width_mult=1.1, depth_mult=1.2, dropout=0.3, weights=weights, progress=progress, **kwargs)
@@ -207,7 +207,7 @@ def efficientnet_b3(
     weights: Optional[EfficientNetB3Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = EfficientNetB3Weights.ImageNet1K_TimmV1 if kwargs.pop("pretrained") else None
     weights = EfficientNetB3Weights.verify(weights)
     return _efficientnet(width_mult=1.2, depth_mult=1.4, dropout=0.3, weights=weights, progress=progress, **kwargs)
@@ -217,7 +217,7 @@ def efficientnet_b4(
     weights: Optional[EfficientNetB4Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = EfficientNetB4Weights.ImageNet1K_TimmV1 if kwargs.pop("pretrained") else None
     weights = EfficientNetB4Weights.verify(weights)
     return _efficientnet(width_mult=1.4, depth_mult=1.8, dropout=0.4, weights=weights, progress=progress, **kwargs)
@@ -227,7 +227,7 @@ def efficientnet_b5(
     weights: Optional[EfficientNetB5Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = EfficientNetB5Weights.ImageNet1K_TFV1 if kwargs.pop("pretrained") else None
     weights = EfficientNetB5Weights.verify(weights)
     return _efficientnet(
@@ -245,7 +245,7 @@ def efficientnet_b6(
     weights: Optional[EfficientNetB6Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = EfficientNetB6Weights.ImageNet1K_TFV1 if kwargs.pop("pretrained") else None
     weights = EfficientNetB6Weights.verify(weights)
     return _efficientnet(
@@ -263,7 +263,7 @@ def efficientnet_b7(
     weights: Optional[EfficientNetB7Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = EfficientNetB7Weights.ImageNet1K_TFV1 if kwargs.pop("pretrained") else None
     weights = EfficientNetB7Weights.verify(weights)
     return _efficientnet(

torchvision/prototype/models/googlenet.py

@@ -30,7 +30,7 @@ class GoogLeNetWeights(Weights):
 
 def googlenet(weights: Optional[GoogLeNetWeights] = None, progress: bool = True, **kwargs: Any) -> GoogLeNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = GoogLeNetWeights.ImageNet1K_TFV1 if kwargs.pop("pretrained") else None
     weights = GoogLeNetWeights.verify(weights)
 
@@ -38,10 +38,6 @@ def googlenet(weights: Optional[GoogLeNetWeights] = None, progress: bool = True,
     if weights is not None:
         if "transform_input" not in kwargs:
             kwargs["transform_input"] = True
-        if original_aux_logits:
-            warnings.warn(
-                "auxiliary heads in the pretrained googlenet model are NOT pretrained, so make sure to train them"
-            )
         kwargs["aux_logits"] = True
         kwargs["init_weights"] = False
         kwargs["num_classes"] = len(weights.meta["categories"])
@@ -49,10 +45,14 @@ def googlenet(weights: Optional[GoogLeNetWeights] = None, progress: bool = True,
     model = GoogLeNet(**kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
         if not original_aux_logits:
             model.aux_logits = False
             model.aux1 = None  # type: ignore[assignment]
             model.aux2 = None  # type: ignore[assignment]
+        else:
+            warnings.warn(
+                "auxiliary heads in the pretrained googlenet model are NOT pretrained, so make sure to train them"
+            )
 
     return model

torchvision/prototype/models/inception.py

@@ -30,7 +30,7 @@ class InceptionV3Weights(Weights):
 
 def inception_v3(weights: Optional[InceptionV3Weights] = None, progress: bool = True, **kwargs: Any) -> Inception3:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = InceptionV3Weights.ImageNet1K_TFV1 if kwargs.pop("pretrained") else None
     weights = InceptionV3Weights.verify(weights)
 
@@ -45,7 +45,7 @@ def inception_v3(weights: Optional[InceptionV3Weights] = None, progress: bool =
     model = Inception3(**kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
         if not original_aux_logits:
             model.aux_logits = False
             model.AuxLogits = None

torchvision/prototype/models/mnasnet.py

@@ -23,7 +23,7 @@ __all__ = [
 ]
 
 
-_common_meta = {
+_COMMON_META = {
     "size": (224, 224),
     "categories": _IMAGENET_CATEGORIES,
     "interpolation": InterpolationMode.BILINEAR,
@@ -36,7 +36,7 @@ class MNASNet0_5Weights(Weights):
         url="https://download.pytorch.org/models/mnasnet0.5_top1_67.823-3ffadce67e.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "acc@1": 67.734,
             "acc@5": 87.490,
         },
@@ -53,7 +53,7 @@ class MNASNet1_0Weights(Weights):
         url="https://download.pytorch.org/models/mnasnet1.0_top1_73.512-f206786ef8.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "acc@1": 73.456,
             "acc@5": 91.510,
         },
@@ -72,14 +72,14 @@ def _mnasnet(alpha: float, weights: Optional[Weights], progress: bool, **kwargs:
     model = MNASNet(alpha, **kwargs)
 
     if weights:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model
 
 
 def mnasnet0_5(weights: Optional[MNASNet0_5Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = MNASNet0_5Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
 
     weights = MNASNet0_5Weights.verify(weights)
@@ -89,7 +89,7 @@ def mnasnet0_5(weights: Optional[MNASNet0_5Weights] = None, progress: bool = Tru
 
 def mnasnet0_75(weights: Optional[MNASNet0_75Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         if kwargs.pop("pretrained"):
             raise ValueError("No checkpoint is available for model type mnasnet0_75")
 
@@ -100,7 +100,7 @@ def mnasnet0_75(weights: Optional[MNASNet0_75Weights] = None, progress: bool = T
 
 def mnasnet1_0(weights: Optional[MNASNet1_0Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = MNASNet1_0Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
     weights = MNASNet1_0Weights.verify(weights)
 
@@ -109,7 +109,7 @@ def mnasnet1_0(weights: Optional[MNASNet1_0Weights] = None, progress: bool = Tru
 
 def mnasnet1_3(weights: Optional[MNASNet1_3Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         if kwargs.pop("pretrained"):
             raise ValueError("No checkpoint is available for model type mnasnet1_3")
 

torchvision/prototype/models/mobilenetv2.py

@@ -30,7 +30,7 @@ class MobileNetV2Weights(Weights):
 
 def mobilenet_v2(weights: Optional[MobileNetV2Weights] = None, progress: bool = True, **kwargs: Any) -> MobileNetV2:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = MobileNetV2Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
     weights = MobileNetV2Weights.verify(weights)
 
@@ -40,6 +40,6 @@ def mobilenet_v2(weights: Optional[MobileNetV2Weights] = None, progress: bool =
     model = MobileNetV2(**kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model

torchvision/prototype/models/mobilenetv3.py

@@ -32,12 +32,12 @@ def _mobilenet_v3(
     model = MobileNetV3(inverted_residual_setting, last_channel, **kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model
 
 
-_common_meta = {
+_COMMON_META = {
     "size": (224, 224),
     "categories": _IMAGENET_CATEGORIES,
     "interpolation": InterpolationMode.BILINEAR,
@@ -50,7 +50,7 @@ class MobileNetV3LargeWeights(Weights):
         url="https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "acc@1": 74.042,
             "acc@5": 91.340,
         },
@@ -62,7 +62,7 @@ class MobileNetV3SmallWeights(Weights):
         url="https://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "acc@1": 67.668,
             "acc@5": 87.402,
         },
@@ -73,7 +73,7 @@ def mobilenet_v3_large(
     weights: Optional[MobileNetV3LargeWeights] = None, progress: bool = True, **kwargs: Any
 ) -> MobileNetV3:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = MobileNetV3LargeWeights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
     weights = MobileNetV3LargeWeights.verify(weights)
 
@@ -85,7 +85,7 @@ def mobilenet_v3_small(
     weights: Optional[MobileNetV3SmallWeights] = None, progress: bool = True, **kwargs: Any
 ) -> MobileNetV3:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = MobileNetV3SmallWeights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
     weights = MobileNetV3SmallWeights.verify(weights)
 

torchvision/prototype/models/quantization/googlenet.py

@@ -47,7 +47,7 @@ def googlenet(
     **kwargs: Any,
 ) -> QuantizableGoogLeNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         if kwargs.pop("pretrained"):
             weights = QuantizedGoogLeNetWeights.ImageNet1K_FBGEMM_TFV1 if quantize else GoogLeNetWeights.ImageNet1K_TFV1
         else:
@@ -62,10 +62,6 @@ def googlenet(
     if weights is not None:
         if "transform_input" not in kwargs:
             kwargs["transform_input"] = True
-        if original_aux_logits:
-            warnings.warn(
-                "auxiliary heads in the pretrained googlenet model are NOT pretrained, so make sure to train them"
-            )
         kwargs["aux_logits"] = True
         kwargs["init_weights"] = False
         kwargs["num_classes"] = len(weights.meta["categories"])
@@ -79,10 +75,14 @@ def googlenet(
         quantize_model(model, backend)
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
         if not original_aux_logits:
             model.aux_logits = False
             model.aux1 = None  # type: ignore[assignment]
             model.aux2 = None  # type: ignore[assignment]
+        else:
+            warnings.warn(
+                "auxiliary heads in the pretrained googlenet model are NOT pretrained, so make sure to train them"
+            )
 
     return model

torchvision/prototype/models/quantization/inception.py

@@ -47,7 +47,7 @@ def inception_v3(
     **kwargs: Any,
 ) -> QuantizableInception3:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         if kwargs.pop("pretrained"):
             weights = (
                 QuantizedInceptionV3Weights.ImageNet1K_FBGEMM_TFV1 if quantize else InceptionV3Weights.ImageNet1K_TFV1
@@ -79,7 +79,7 @@ def inception_v3(
         if quantize and not original_aux_logits:
             model.aux_logits = False
             model.AuxLogits = None
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
         if not quantize and not original_aux_logits:
             model.aux_logits = False
             model.AuxLogits = None

torchvision/prototype/models/quantization/mobilenetv2.py

@@ -48,7 +48,7 @@ def mobilenet_v2(
     **kwargs: Any,
 ) -> QuantizableMobileNetV2:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         if kwargs.pop("pretrained"):
             weights = (
                 QuantizedMobileNetV2Weights.ImageNet1K_QNNPACK_RefV1
@@ -75,6 +75,6 @@ def mobilenet_v2(
         quantize_model(model, backend)
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model

torchvision/prototype/models/quantization/mobilenetv3.py

@@ -47,7 +47,7 @@ def _mobilenet_v3_model(
         torch.quantization.prepare_qat(model, inplace=True)
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
 
     if quantize:
         torch.quantization.convert(model, inplace=True)
@@ -81,7 +81,7 @@ def mobilenet_v3_large(
     **kwargs: Any,
 ) -> QuantizableMobileNetV3:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         if kwargs.pop("pretrained"):
             weights = (
                 QuantizedMobileNetV3LargeWeights.ImageNet1K_QNNPACK_RefV1

torchvision/prototype/models/quantization/resnet.py

@@ -48,12 +48,12 @@ def _resnet(
         quantize_model(model, backend)
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model
 
 
-_common_meta = {
+_COMMON_META = {
     "size": (224, 224),
     "categories": _IMAGENET_CATEGORIES,
     "interpolation": InterpolationMode.BILINEAR,
@@ -68,7 +68,7 @@ class QuantizedResNet18Weights(Weights):
         url="https://download.pytorch.org/models/quantized/resnet18_fbgemm_16fa66dd.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "unquantized": ResNet18Weights.ImageNet1K_RefV1,
             "acc@1": 69.494,
             "acc@5": 88.882,
@@ -81,7 +81,7 @@ class QuantizedResNet50Weights(Weights):
         url="https://download.pytorch.org/models/quantized/resnet50_fbgemm_bf931d71.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "unquantized": ResNet50Weights.ImageNet1K_RefV1,
             "acc@1": 75.920,
             "acc@5": 92.814,
@@ -94,7 +94,7 @@ class QuantizedResNeXt101_32x8dWeights(Weights):
         url="https://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm_09835ccf.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "unquantized": ResNeXt101_32x8dWeights.ImageNet1K_RefV1,
             "acc@1": 78.986,
             "acc@5": 94.480,
@@ -109,7 +109,7 @@ def resnet18(
     **kwargs: Any,
 ) -> QuantizableResNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         if kwargs.pop("pretrained"):
             weights = QuantizedResNet18Weights.ImageNet1K_FBGEMM_RefV1 if quantize else ResNet18Weights.ImageNet1K_RefV1
         else:
@@ -130,7 +130,7 @@ def resnet50(
     **kwargs: Any,
 ) -> QuantizableResNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         if kwargs.pop("pretrained"):
             weights = QuantizedResNet50Weights.ImageNet1K_FBGEMM_RefV1 if quantize else ResNet50Weights.ImageNet1K_RefV1
         else:
@@ -151,7 +151,7 @@ def resnext101_32x8d(
     **kwargs: Any,
 ) -> QuantizableResNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         if kwargs.pop("pretrained"):
             weights = (
                 QuantizedResNeXt101_32x8dWeights.ImageNet1K_FBGEMM_RefV1