More Multiweight support cleanups (#4948)

* Updated the link for densenet recipe.

* Set default value of `num_classes` and `num_keypoints` to `None`

* Provide helper methods for parameter checks to reduce duplicate code.

* Throw errors on silent config overwrites from weight meta-data and legacy builders.

* Changing order of arguments + fixing mypy.

* Make the builders fully BC.

* Add "default" weights support that returns always the best weights.

test/test_prototype_models.py

@@ -31,14 +31,19 @@ def get_models_with_module_names(module):
 
 
 @pytest.mark.parametrize(
-    "model_fn, weight",
+    "model_fn, name, weight",
     [
-        (models.resnet50, models.ResNet50Weights.ImageNet1K_RefV2),
-        (models.quantization.resnet50, models.quantization.QuantizedResNet50Weights.ImageNet1K_FBGEMM_RefV1),
+        (models.resnet50, "ImageNet1K_RefV1", models.ResNet50Weights.ImageNet1K_RefV1),
+        (models.resnet50, "default", models.ResNet50Weights.ImageNet1K_RefV2),
+        (
+            models.quantization.resnet50,
+            "ImageNet1K_FBGEMM_RefV1",
+            models.quantization.QuantizedResNet50Weights.ImageNet1K_FBGEMM_RefV1,
+        ),
     ],
 )
-def test_get_weight(model_fn, weight):
-    assert models._api.get_weight(model_fn, weight.name) == weight
+def test_get_weight(model_fn, name, weight):
+    assert models._api.get_weight(model_fn, name) == weight
 
 
 @pytest.mark.parametrize("model_fn", TM.get_models_from_module(models))

torchvision/prototype/models/_api.py

@@ -30,6 +30,7 @@ class WeightEntry:
     url: str
     transforms: Callable
     meta: Dict[str, Any]
+    default: bool
 
 
 class Weights(Enum):
@@ -59,7 +60,7 @@ class Weights(Enum):
     @classmethod
     def from_str(cls, value: str) -> "Weights":
         for v in cls:
-            if v._name_ == value:
+            if v._name_ == value or (value == "default" and v.default):
                 return v
         raise ValueError(f"Invalid value {value} for enum {cls.__name__}.")
 

torchvision/prototype/models/_utils.py

+import warnings
+from typing import Any, Dict, Optional, TypeVar
+
+from ._api import Weights
+
+
+W = TypeVar("W", bound=Weights)
+V = TypeVar("V")
+
+
+def _deprecated_param(
+    kwargs: Dict[str, Any], deprecated_param: str, new_param: str, default_value: Optional[W]
+) -> Optional[W]:
+    warnings.warn(f"The parameter '{deprecated_param}' is deprecated, please use '{new_param}' instead.")
+    if kwargs.pop(deprecated_param):
+        if default_value is not None:
+            return default_value
+        else:
+            raise ValueError("No checkpoint is available for model.")
+    else:
+        return None
+
+
+def _deprecated_positional(kwargs: Dict[str, Any], deprecated_param: str, new_param: str, default_value: V) -> None:
+    warnings.warn(
+        f"The positional parameter '{deprecated_param}' is deprecated, please use keyword parameter '{new_param}'"
+        + " instead."
+    )
+    kwargs[deprecated_param] = default_value
+
+
+def _ovewrite_named_param(kwargs: Dict[str, Any], param: str, new_value: V) -> None:
+    if param in kwargs:
+        if kwargs[param] != new_value:
+            raise ValueError(f"The parameter '{param}' expected value {new_value} but got {kwargs[param]} instead.")
+    else:
+        kwargs[param] = new_value
+
+
+def _ovewrite_value_param(param: Optional[V], new_value: V) -> V:
+    if param is not None:
+        if param != new_value:
+            raise ValueError(f"The parameter '{param}' expected value {new_value} but got {param} instead.")
+    return new_value

torchvision/prototype/models/alexnet.py

-import warnings
 from functools import partial
 from typing import Any, Optional
 
@@ -8,6 +7,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ...models.alexnet import AlexNet
 from ._api import Weights, WeightEntry
 from ._meta import _IMAGENET_CATEGORIES
+from ._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
 
 
 __all__ = ["AlexNet", "AlexNetWeights", "alexnet"]
@@ -25,16 +25,19 @@ class AlexNetWeights(Weights):
             "acc@1": 56.522,
             "acc@5": 79.066,
         },
+        default=True,
     )
 
 
 def alexnet(weights: Optional[AlexNetWeights] = None, progress: bool = True, **kwargs: Any) -> AlexNet:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = AlexNetWeights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", AlexNetWeights.ImageNet1K_RefV1)
     weights = AlexNetWeights.verify(weights)
+
     if weights is not None:
-        kwargs["num_classes"] = len(weights.meta["categories"])
+        _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
 
     model = AlexNet(**kwargs)
 

torchvision/prototype/models/densenet.py

 import re
-import warnings
 from functools import partial
 from typing import Any, Optional, Tuple
 
@@ -10,6 +9,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ...models.densenet import DenseNet
 from ._api import Weights, WeightEntry
 from ._meta import _IMAGENET_CATEGORIES
+from ._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
 
 
 __all__ = [
@@ -53,7 +53,7 @@ def _densenet(
     **kwargs: Any,
 ) -> DenseNet:
     if weights is not None:
-        kwargs["num_classes"] = len(weights.meta["categories"])
+        _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
 
     model = DenseNet(growth_rate, block_config, num_init_features, **kwargs)
 
@@ -67,7 +67,7 @@ _COMMON_META = {
     "size": (224, 224),
     "categories": _IMAGENET_CATEGORIES,
     "interpolation": InterpolationMode.BILINEAR,
-    "recipe": None,  # TODO: add here a URL to documentation stating that the weights were ported from LuaTorch
+    "recipe": "https://github.com/pytorch/vision/pull/116",
 }
 
 
@@ -80,6 +80,7 @@ class DenseNet121Weights(Weights):
             "acc@1": 74.434,
             "acc@5": 91.972,
         },
+        default=True,
     )
 
 
@@ -92,6 +93,7 @@ class DenseNet161Weights(Weights):
             "acc@1": 77.138,
             "acc@5": 93.560,
         },
+        default=True,
     )
 
 
@@ -104,6 +106,7 @@ class DenseNet169Weights(Weights):
             "acc@1": 75.600,
             "acc@5": 92.806,
         },
+        default=True,
     )
 
 
@@ -116,40 +119,45 @@ class DenseNet201Weights(Weights):
             "acc@1": 76.896,
             "acc@5": 93.370,
         },
+        default=True,
     )
 
 
 def densenet121(weights: Optional[DenseNet121Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = DenseNet121Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", DenseNet121Weights.ImageNet1K_Community)
     weights = DenseNet121Weights.verify(weights)
 
     return _densenet(32, (6, 12, 24, 16), 64, weights, progress, **kwargs)
 
 
 def densenet161(weights: Optional[DenseNet161Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = DenseNet161Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", DenseNet161Weights.ImageNet1K_Community)
     weights = DenseNet161Weights.verify(weights)
 
     return _densenet(48, (6, 12, 36, 24), 96, weights, progress, **kwargs)
 
 
 def densenet169(weights: Optional[DenseNet169Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = DenseNet169Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", DenseNet169Weights.ImageNet1K_Community)
     weights = DenseNet169Weights.verify(weights)
 
     return _densenet(32, (6, 12, 32, 32), 64, weights, progress, **kwargs)
 
 
 def densenet201(weights: Optional[DenseNet201Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = DenseNet201Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", DenseNet201Weights.ImageNet1K_Community)
     weights = DenseNet201Weights.verify(weights)
 
     return _densenet(32, (6, 12, 48, 32), 64, weights, progress, **kwargs)

torchvision/prototype/models/detection/faster_rcnn.py

-import warnings
 from typing import Any, Optional, Union
 
 from torchvision.prototype.transforms import CocoEval
@@ -15,6 +14,7 @@ from ....models.detection.faster_rcnn import (
 )
 from .._api import Weights, WeightEntry
 from .._meta import _COCO_CATEGORIES
+from .._utils import _deprecated_param, _deprecated_positional, _ovewrite_value_param
 from ..mobilenetv3 import MobileNetV3LargeWeights, mobilenet_v3_large
 from ..resnet import ResNet50Weights, resnet50
 
@@ -45,6 +45,7 @@ class FasterRCNNResNet50FPNWeights(Weights):
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-resnet-50-fpn",
             "map": 37.0,
         },
+        default=True,
     )
 
 
@@ -57,6 +58,7 @@ class FasterRCNNMobileNetV3LargeFPNWeights(Weights):
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-mobilenetv3-large-fpn",
             "map": 32.8,
         },
+        default=True,
     )
 
 
@@ -69,29 +71,36 @@ class FasterRCNNMobileNetV3Large320FPNWeights(Weights):
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-mobilenetv3-large-320-fpn",
             "map": 22.8,
         },
+        default=True,
     )
 
 
 def fasterrcnn_resnet50_fpn(
     weights: Optional[FasterRCNNResNet50FPNWeights] = None,
-    weights_backbone: Optional[ResNet50Weights] = None,
     progress: bool = True,
-    num_classes: int = 91,
+    num_classes: Optional[int] = None,
+    weights_backbone: Optional[ResNet50Weights] = None,
     trainable_backbone_layers: Optional[int] = None,
     **kwargs: Any,
 ) -> FasterRCNN:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = FasterRCNNResNet50FPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", FasterRCNNResNet50FPNWeights.Coco_RefV1)
     weights = FasterRCNNResNet50FPNWeights.verify(weights)
+    if type(weights_backbone) == bool and weights_backbone:
+        _deprecated_positional(kwargs, "pretrained_backbone", "weights_backbone", True)
     if "pretrained_backbone" in kwargs:
-        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 = _deprecated_param(
+            kwargs, "pretrained_backbone", "weights_backbone", ResNet50Weights.ImageNet1K_RefV1
+        )
     weights_backbone = ResNet50Weights.verify(weights_backbone)
 
     if weights is not None:
         weights_backbone = None
-        num_classes = len(weights.meta["categories"])
+        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
+    elif num_classes is None:
+        num_classes = 91
 
     trainable_backbone_layers = _validate_trainable_layers(
         weights is not None or weights_backbone is not None, trainable_backbone_layers, 5, 3
@@ -110,16 +119,18 @@ def fasterrcnn_resnet50_fpn(
 
 
 def _fasterrcnn_mobilenet_v3_large_fpn(
-    weights: Optional[Union[FasterRCNNMobileNetV3LargeFPNWeights, FasterRCNNMobileNetV3Large320FPNWeights]] = None,
-    weights_backbone: Optional[MobileNetV3LargeWeights] = None,
-    progress: bool = True,
-    num_classes: int = 91,
-    trainable_backbone_layers: Optional[int] = None,
+    weights: Optional[Union[FasterRCNNMobileNetV3LargeFPNWeights, FasterRCNNMobileNetV3Large320FPNWeights]],
+    progress: bool,
+    num_classes: Optional[int],
+    weights_backbone: Optional[MobileNetV3LargeWeights],
+    trainable_backbone_layers: Optional[int],
     **kwargs: Any,
 ) -> FasterRCNN:
     if weights is not None:
         weights_backbone = None
-        num_classes = len(weights.meta["categories"])
+        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
+    elif num_classes is None:
+        num_classes = 91
 
     trainable_backbone_layers = _validate_trainable_layers(
         weights is not None or weights_backbone is not None, trainable_backbone_layers, 6, 3
@@ -149,19 +160,23 @@ def _fasterrcnn_mobilenet_v3_large_fpn(
 
 def fasterrcnn_mobilenet_v3_large_fpn(
     weights: Optional[FasterRCNNMobileNetV3LargeFPNWeights] = None,
-    weights_backbone: Optional[MobileNetV3LargeWeights] = None,
     progress: bool = True,
-    num_classes: int = 91,
+    num_classes: Optional[int] = None,
+    weights_backbone: Optional[MobileNetV3LargeWeights] = None,
     trainable_backbone_layers: Optional[int] = None,
     **kwargs: Any,
 ) -> FasterRCNN:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = FasterRCNNMobileNetV3LargeFPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", FasterRCNNMobileNetV3LargeFPNWeights.Coco_RefV1)
     weights = FasterRCNNMobileNetV3LargeFPNWeights.verify(weights)
+    if type(weights_backbone) == bool and weights_backbone:
+        _deprecated_positional(kwargs, "pretrained_backbone", "weights_backbone", True)
     if "pretrained_backbone" in kwargs:
-        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 = _deprecated_param(
+            kwargs, "pretrained_backbone", "weights_backbone", MobileNetV3LargeWeights.ImageNet1K_RefV1
+        )
     weights_backbone = MobileNetV3LargeWeights.verify(weights_backbone)
 
     defaults = {
@@ -171,9 +186,9 @@ def fasterrcnn_mobilenet_v3_large_fpn(
     kwargs = {**defaults, **kwargs}
     return _fasterrcnn_mobilenet_v3_large_fpn(
         weights,
-        weights_backbone,
         progress,
         num_classes,
+        weights_backbone,
         trainable_backbone_layers,
         **kwargs,
     )
@@ -181,19 +196,23 @@ def fasterrcnn_mobilenet_v3_large_fpn(
 
 def fasterrcnn_mobilenet_v3_large_320_fpn(
     weights: Optional[FasterRCNNMobileNetV3Large320FPNWeights] = None,
-    weights_backbone: Optional[MobileNetV3LargeWeights] = None,
     progress: bool = True,
-    num_classes: int = 91,
+    num_classes: Optional[int] = None,
+    weights_backbone: Optional[MobileNetV3LargeWeights] = None,
     trainable_backbone_layers: Optional[int] = None,
     **kwargs: Any,
 ) -> FasterRCNN:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = FasterRCNNMobileNetV3Large320FPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", FasterRCNNMobileNetV3Large320FPNWeights.Coco_RefV1)
     weights = FasterRCNNMobileNetV3Large320FPNWeights.verify(weights)
+    if type(weights_backbone) == bool and weights_backbone:
+        _deprecated_positional(kwargs, "pretrained_backbone", "weights_backbone", True)
     if "pretrained_backbone" in kwargs:
-        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 = _deprecated_param(
+            kwargs, "pretrained_backbone", "weights_backbone", MobileNetV3LargeWeights.ImageNet1K_RefV1
+        )
     weights_backbone = MobileNetV3LargeWeights.verify(weights_backbone)
 
     defaults = {
@@ -207,9 +226,9 @@ def fasterrcnn_mobilenet_v3_large_320_fpn(
     kwargs = {**defaults, **kwargs}
     return _fasterrcnn_mobilenet_v3_large_fpn(
         weights,
-        weights_backbone,
         progress,
         num_classes,
+        weights_backbone,
         trainable_backbone_layers,
         **kwargs,
     )

torchvision/prototype/models/detection/keypoint_rcnn.py

-import warnings
 from typing import Any, Optional
 
 from torchvision.prototype.transforms import CocoEval
@@ -12,6 +11,7 @@ from ....models.detection.keypoint_rcnn import (
 )
 from .._api import Weights, WeightEntry
 from .._meta import _COCO_PERSON_CATEGORIES, _COCO_PERSON_KEYPOINT_NAMES
+from .._utils import _deprecated_param, _deprecated_positional, _ovewrite_value_param
 from ..resnet import ResNet50Weights, resnet50
 
 
@@ -35,6 +35,7 @@ class KeypointRCNNResNet50FPNWeights(Weights):
             "box_map": 50.6,
             "kp_map": 61.1,
         },
+        default=False,
     )
     Coco_RefV1 = WeightEntry(
         url="https://download.pytorch.org/models/keypointrcnn_resnet50_fpn_coco-fc266e95.pth",
@@ -45,37 +46,45 @@ class KeypointRCNNResNet50FPNWeights(Weights):
             "box_map": 54.6,
             "kp_map": 65.0,
         },
+        default=True,
     )
 
 
 def keypointrcnn_resnet50_fpn(
     weights: Optional[KeypointRCNNResNet50FPNWeights] = None,
-    weights_backbone: Optional[ResNet50Weights] = None,
     progress: bool = True,
-    num_classes: int = 2,
-    num_keypoints: int = 17,
+    num_classes: Optional[int] = None,
+    num_keypoints: Optional[int] = None,
+    weights_backbone: Optional[ResNet50Weights] = None,
     trainable_backbone_layers: Optional[int] = None,
     **kwargs: Any,
 ) -> KeypointRCNN:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        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
-        elif type(pretrained) == bool and pretrained:
-            weights = KeypointRCNNResNet50FPNWeights.Coco_RefV1
-        else:
-            weights = None
+        default_value = KeypointRCNNResNet50FPNWeights.Coco_RefV1
+        if kwargs["pretrained"] == "legacy":
+            default_value = KeypointRCNNResNet50FPNWeights.Coco_RefV1_Legacy
+            kwargs["pretrained"] = True
+        weights = _deprecated_param(kwargs, "pretrained", "weights", default_value)
     weights = KeypointRCNNResNet50FPNWeights.verify(weights)
+    if type(weights_backbone) == bool and weights_backbone:
+        _deprecated_positional(kwargs, "pretrained_backbone", "weights_backbone", True)
     if "pretrained_backbone" in kwargs:
-        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 = _deprecated_param(
+            kwargs, "pretrained_backbone", "weights_backbone", ResNet50Weights.ImageNet1K_RefV1
+        )
     weights_backbone = ResNet50Weights.verify(weights_backbone)
 
     if weights is not None:
         weights_backbone = None
-        num_classes = len(weights.meta["categories"])
-        num_keypoints = len(weights.meta["keypoint_names"])
+        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
+        num_keypoints = _ovewrite_value_param(num_keypoints, len(weights.meta["keypoint_names"]))
+    else:
+        if num_classes is None:
+            num_classes = 2
+        if num_keypoints is None:
+            num_keypoints = 17
 
     trainable_backbone_layers = _validate_trainable_layers(
         weights is not None or weights_backbone is not None, trainable_backbone_layers, 5, 3

torchvision/prototype/models/detection/mask_rcnn.py

-import warnings
 from typing import Any, Optional
 
 from torchvision.prototype.transforms import CocoEval
@@ -13,6 +12,7 @@ from ....models.detection.mask_rcnn import (
 )
 from .._api import Weights, WeightEntry
 from .._meta import _COCO_CATEGORIES
+from .._utils import _deprecated_param, _deprecated_positional, _ovewrite_value_param
 from ..resnet import ResNet50Weights, resnet50
 
 
@@ -34,29 +34,36 @@ class MaskRCNNResNet50FPNWeights(Weights):
             "box_map": 37.9,
             "mask_map": 34.6,
         },
+        default=True,
     )
 
 
 def maskrcnn_resnet50_fpn(
     weights: Optional[MaskRCNNResNet50FPNWeights] = None,
-    weights_backbone: Optional[ResNet50Weights] = None,
     progress: bool = True,
-    num_classes: int = 91,
+    num_classes: Optional[int] = None,
+    weights_backbone: Optional[ResNet50Weights] = None,
     trainable_backbone_layers: Optional[int] = None,
     **kwargs: Any,
 ) -> MaskRCNN:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = MaskRCNNResNet50FPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", MaskRCNNResNet50FPNWeights.Coco_RefV1)
     weights = MaskRCNNResNet50FPNWeights.verify(weights)
+    if type(weights_backbone) == bool and weights_backbone:
+        _deprecated_positional(kwargs, "pretrained_backbone", "weights_backbone", True)
     if "pretrained_backbone" in kwargs:
-        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 = _deprecated_param(
+            kwargs, "pretrained_backbone", "weights_backbone", ResNet50Weights.ImageNet1K_RefV1
+        )
     weights_backbone = ResNet50Weights.verify(weights_backbone)
 
     if weights is not None:
         weights_backbone = None
-        num_classes = len(weights.meta["categories"])
+        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
+    elif num_classes is None:
+        num_classes = 91
 
     trainable_backbone_layers = _validate_trainable_layers(
         weights is not None or weights_backbone is not None, trainable_backbone_layers, 5, 3

torchvision/prototype/models/detection/retinanet.py

-import warnings
 from typing import Any, Optional
 
 from torchvision.prototype.transforms import CocoEval
@@ -14,6 +13,7 @@ from ....models.detection.retinanet import (
 )
 from .._api import Weights, WeightEntry
 from .._meta import _COCO_CATEGORIES
+from .._utils import _deprecated_param, _deprecated_positional, _ovewrite_value_param
 from ..resnet import ResNet50Weights, resnet50
 
 
@@ -34,29 +34,36 @@ class RetinaNetResNet50FPNWeights(Weights):
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#retinanet",
             "map": 36.4,
         },
+        default=True,
     )
 
 
 def retinanet_resnet50_fpn(
     weights: Optional[RetinaNetResNet50FPNWeights] = None,
-    weights_backbone: Optional[ResNet50Weights] = None,
     progress: bool = True,
-    num_classes: int = 91,
+    num_classes: Optional[int] = None,
+    weights_backbone: Optional[ResNet50Weights] = None,
     trainable_backbone_layers: Optional[int] = None,
     **kwargs: Any,
 ) -> RetinaNet:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = RetinaNetResNet50FPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", RetinaNetResNet50FPNWeights.Coco_RefV1)
     weights = RetinaNetResNet50FPNWeights.verify(weights)
+    if type(weights_backbone) == bool and weights_backbone:
+        _deprecated_positional(kwargs, "pretrained_backbone", "weights_backbone", True)
     if "pretrained_backbone" in kwargs:
-        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 = _deprecated_param(
+            kwargs, "pretrained_backbone", "weights_backbone", ResNet50Weights.ImageNet1K_RefV1
+        )
     weights_backbone = ResNet50Weights.verify(weights_backbone)
 
     if weights is not None:
         weights_backbone = None
-        num_classes = len(weights.meta["categories"])
+        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
+    elif num_classes is None:
+        num_classes = 91
 
     trainable_backbone_layers = _validate_trainable_layers(
         weights is not None or weights_backbone is not None, trainable_backbone_layers, 5, 3

torchvision/prototype/models/detection/ssd.py

@@ -12,6 +12,7 @@ from ....models.detection.ssd import (
 )
 from .._api import Weights, WeightEntry
 from .._meta import _COCO_CATEGORIES
+from .._utils import _deprecated_param, _deprecated_positional, _ovewrite_value_param
 from ..vgg import VGG16Weights, vgg16
 
 
@@ -32,24 +33,29 @@ class SSD300VGG16Weights(Weights):
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#ssd300-vgg16",
             "map": 25.1,
         },
+        default=True,
     )
 
 
 def ssd300_vgg16(
     weights: Optional[SSD300VGG16Weights] = None,
-    weights_backbone: Optional[VGG16Weights] = None,
     progress: bool = True,
-    num_classes: int = 91,
+    num_classes: Optional[int] = None,
+    weights_backbone: Optional[VGG16Weights] = None,
     trainable_backbone_layers: Optional[int] = None,
     **kwargs: Any,
 ) -> SSD:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = SSD300VGG16Weights.Coco_RefV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", SSD300VGG16Weights.Coco_RefV1)
     weights = SSD300VGG16Weights.verify(weights)
+    if type(weights_backbone) == bool and weights_backbone:
+        _deprecated_positional(kwargs, "pretrained_backbone", "weights_backbone", True)
     if "pretrained_backbone" in kwargs:
-        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 = _deprecated_param(
+            kwargs, "pretrained_backbone", "weights_backbone", VGG16Weights.ImageNet1K_Features
+        )
     weights_backbone = VGG16Weights.verify(weights_backbone)
 
     if "size" in kwargs:
@@ -57,7 +63,9 @@ def ssd300_vgg16(
 
     if weights is not None:
         weights_backbone = None
-        num_classes = len(weights.meta["categories"])
+        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
+    elif num_classes is None:
+        num_classes = 91
 
     trainable_backbone_layers = _validate_trainable_layers(
         weights is not None or weights_backbone is not None, trainable_backbone_layers, 5, 4

torchvision/prototype/models/detection/ssdlite.py

@@ -17,6 +17,7 @@ from ....models.detection.ssdlite import (
 )
 from .._api import Weights, WeightEntry
 from .._meta import _COCO_CATEGORIES
+from .._utils import _deprecated_param, _deprecated_positional, _ovewrite_value_param
 from ..mobilenetv3 import MobileNetV3LargeWeights, mobilenet_v3_large
 
 
@@ -37,25 +38,30 @@ class SSDlite320MobileNetV3LargeFPNWeights(Weights):
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#ssdlite320-mobilenetv3-large",
             "map": 21.3,
         },
+        default=True,
     )
 
 
 def ssdlite320_mobilenet_v3_large(
     weights: Optional[SSDlite320MobileNetV3LargeFPNWeights] = None,
-    weights_backbone: Optional[MobileNetV3LargeWeights] = None,
     progress: bool = True,
-    num_classes: int = 91,
+    num_classes: Optional[int] = None,
+    weights_backbone: Optional[MobileNetV3LargeWeights] = None,
     trainable_backbone_layers: Optional[int] = None,
     norm_layer: Optional[Callable[..., nn.Module]] = None,
     **kwargs: Any,
 ) -> SSD:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = SSDlite320MobileNetV3LargeFPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", SSDlite320MobileNetV3LargeFPNWeights.Coco_RefV1)
     weights = SSDlite320MobileNetV3LargeFPNWeights.verify(weights)
+    if type(weights_backbone) == bool and weights_backbone:
+        _deprecated_positional(kwargs, "pretrained_backbone", "weights_backbone", True)
     if "pretrained_backbone" in kwargs:
-        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 = _deprecated_param(
+            kwargs, "pretrained_backbone", "weights_backbone", MobileNetV3LargeWeights.ImageNet1K_RefV1
+        )
     weights_backbone = MobileNetV3LargeWeights.verify(weights_backbone)
 
     if "size" in kwargs:
@@ -63,7 +69,9 @@ def ssdlite320_mobilenet_v3_large(
 
     if weights is not None:
         weights_backbone = None
-        num_classes = len(weights.meta["categories"])
+        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
+    elif num_classes is None:
+        num_classes = 91
 
     trainable_backbone_layers = _validate_trainable_layers(
         weights is not None or weights_backbone is not None, trainable_backbone_layers, 6, 6

torchvision/prototype/models/efficientnet.py

-import warnings
 from functools import partial
 from typing import Any, Optional
 
@@ -9,6 +8,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ...models.efficientnet import EfficientNet, MBConvConfig
 from ._api import Weights, WeightEntry
 from ._meta import _IMAGENET_CATEGORIES
+from ._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
 
 
 __all__ = [
@@ -41,7 +41,7 @@ def _efficientnet(
     **kwargs: Any,
 ) -> EfficientNet:
     if weights is not None:
-        kwargs["num_classes"] = len(weights.meta["categories"])
+        _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
 
     bneck_conf = partial(MBConvConfig, width_mult=width_mult, depth_mult=depth_mult)
     inverted_residual_setting = [
@@ -79,6 +79,7 @@ class EfficientNetB0Weights(Weights):
             "acc@1": 77.692,
             "acc@5": 93.532,
         },
+        default=True,
     )
 
 
@@ -92,6 +93,7 @@ class EfficientNetB1Weights(Weights):
             "acc@1": 78.642,
             "acc@5": 94.186,
         },
+        default=True,
     )
 
 
@@ -105,6 +107,7 @@ class EfficientNetB2Weights(Weights):
             "acc@1": 80.608,
             "acc@5": 95.310,
         },
+        default=True,
     )
 
 
@@ -118,6 +121,7 @@ class EfficientNetB3Weights(Weights):
             "acc@1": 82.008,
             "acc@5": 96.054,
         },
+        default=True,
     )
 
 
@@ -131,6 +135,7 @@ class EfficientNetB4Weights(Weights):
             "acc@1": 83.384,
             "acc@5": 96.594,
         },
+        default=True,
     )
 
 
@@ -144,6 +149,7 @@ class EfficientNetB5Weights(Weights):
             "acc@1": 83.444,
             "acc@5": 96.628,
         },
+        default=True,
     )
 
 
@@ -157,6 +163,7 @@ class EfficientNetB6Weights(Weights):
             "acc@1": 84.008,
             "acc@5": 96.916,
         },
+        default=True,
     )
 
 
@@ -170,66 +177,79 @@ class EfficientNetB7Weights(Weights):
             "acc@1": 84.122,
             "acc@5": 96.908,
         },
+        default=True,
     )
 
 
 def efficientnet_b0(
     weights: Optional[EfficientNetB0Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = EfficientNetB0Weights.ImageNet1K_TimmV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", EfficientNetB0Weights.ImageNet1K_TimmV1)
     weights = EfficientNetB0Weights.verify(weights)
+
     return _efficientnet(width_mult=1.0, depth_mult=1.0, dropout=0.2, weights=weights, progress=progress, **kwargs)
 
 
 def efficientnet_b1(
     weights: Optional[EfficientNetB1Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = EfficientNetB1Weights.ImageNet1K_TimmV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", EfficientNetB1Weights.ImageNet1K_TimmV1)
     weights = EfficientNetB1Weights.verify(weights)
+
     return _efficientnet(width_mult=1.0, depth_mult=1.1, dropout=0.2, weights=weights, progress=progress, **kwargs)
 
 
 def efficientnet_b2(
     weights: Optional[EfficientNetB2Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = EfficientNetB2Weights.ImageNet1K_TimmV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", EfficientNetB2Weights.ImageNet1K_TimmV1)
     weights = EfficientNetB2Weights.verify(weights)
+
     return _efficientnet(width_mult=1.1, depth_mult=1.2, dropout=0.3, weights=weights, progress=progress, **kwargs)
 
 
 def efficientnet_b3(
     weights: Optional[EfficientNetB3Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = EfficientNetB3Weights.ImageNet1K_TimmV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", EfficientNetB3Weights.ImageNet1K_TimmV1)
     weights = EfficientNetB3Weights.verify(weights)
+
     return _efficientnet(width_mult=1.2, depth_mult=1.4, dropout=0.3, weights=weights, progress=progress, **kwargs)
 
 
 def efficientnet_b4(
     weights: Optional[EfficientNetB4Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = EfficientNetB4Weights.ImageNet1K_TimmV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", EfficientNetB4Weights.ImageNet1K_TimmV1)
     weights = EfficientNetB4Weights.verify(weights)
+
     return _efficientnet(width_mult=1.4, depth_mult=1.8, dropout=0.4, weights=weights, progress=progress, **kwargs)
 
 
 def efficientnet_b5(
     weights: Optional[EfficientNetB5Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = EfficientNetB5Weights.ImageNet1K_TFV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", EfficientNetB5Weights.ImageNet1K_TFV1)
     weights = EfficientNetB5Weights.verify(weights)
+
     return _efficientnet(
         width_mult=1.6,
         depth_mult=2.2,
@@ -244,10 +264,12 @@ def efficientnet_b5(
 def efficientnet_b6(
     weights: Optional[EfficientNetB6Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = EfficientNetB6Weights.ImageNet1K_TFV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", EfficientNetB6Weights.ImageNet1K_TFV1)
     weights = EfficientNetB6Weights.verify(weights)
+
     return _efficientnet(
         width_mult=1.8,
         depth_mult=2.6,
@@ -262,10 +284,12 @@ def efficientnet_b6(
 def efficientnet_b7(
     weights: Optional[EfficientNetB7Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = EfficientNetB7Weights.ImageNet1K_TFV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", EfficientNetB7Weights.ImageNet1K_TFV1)
     weights = EfficientNetB7Weights.verify(weights)
+
     return _efficientnet(
         width_mult=2.0,
         depth_mult=3.1,

torchvision/prototype/models/googlenet.py

@@ -8,6 +8,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ...models.googlenet import GoogLeNet, GoogLeNetOutputs, _GoogLeNetOutputs
 from ._api import Weights, WeightEntry
 from ._meta import _IMAGENET_CATEGORIES
+from ._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
 
 
 __all__ = ["GoogLeNet", "GoogLeNetOutputs", "_GoogLeNetOutputs", "GoogLeNetWeights", "googlenet"]
@@ -25,22 +26,24 @@ class GoogLeNetWeights(Weights):
             "acc@1": 69.778,
             "acc@5": 89.530,
         },
+        default=True,
     )
 
 
 def googlenet(weights: Optional[GoogLeNetWeights] = None, progress: bool = True, **kwargs: Any) -> GoogLeNet:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = GoogLeNetWeights.ImageNet1K_TFV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", GoogLeNetWeights.ImageNet1K_TFV1)
     weights = GoogLeNetWeights.verify(weights)
 
     original_aux_logits = kwargs.get("aux_logits", False)
     if weights is not None:
         if "transform_input" not in kwargs:
-            kwargs["transform_input"] = True
-        kwargs["aux_logits"] = True
-        kwargs["init_weights"] = False
-        kwargs["num_classes"] = len(weights.meta["categories"])
+            _ovewrite_named_param(kwargs, "transform_input", True)
+        _ovewrite_named_param(kwargs, "aux_logits", True)
+        _ovewrite_named_param(kwargs, "init_weights", False)
+        _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
 
     model = GoogLeNet(**kwargs)
 

torchvision/prototype/models/inception.py

-import warnings
 from functools import partial
 from typing import Any, Optional
 
@@ -8,6 +7,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ...models.inception import Inception3, InceptionOutputs, _InceptionOutputs
 from ._api import Weights, WeightEntry
 from ._meta import _IMAGENET_CATEGORIES
+from ._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
 
 
 __all__ = ["Inception3", "InceptionOutputs", "_InceptionOutputs", "InceptionV3Weights", "inception_v3"]
@@ -25,22 +25,24 @@ class InceptionV3Weights(Weights):
             "acc@1": 77.294,
             "acc@5": 93.450,
         },
+        default=True,
     )
 
 
 def inception_v3(weights: Optional[InceptionV3Weights] = None, progress: bool = True, **kwargs: Any) -> Inception3:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = InceptionV3Weights.ImageNet1K_TFV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", InceptionV3Weights.ImageNet1K_TFV1)
     weights = InceptionV3Weights.verify(weights)
 
     original_aux_logits = kwargs.get("aux_logits", True)
     if weights is not None:
         if "transform_input" not in kwargs:
-            kwargs["transform_input"] = True
-        kwargs["aux_logits"] = True
-        kwargs["init_weights"] = False
-        kwargs["num_classes"] = len(weights.meta["categories"])
+            _ovewrite_named_param(kwargs, "transform_input", True)
+        _ovewrite_named_param(kwargs, "aux_logits", True)
+        _ovewrite_named_param(kwargs, "init_weights", False)
+        _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
 
     model = Inception3(**kwargs)
 

torchvision/prototype/models/mnasnet.py

-import warnings
 from functools import partial
 from typing import Any, Optional
 
@@ -8,6 +7,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ...models.mnasnet import MNASNet
 from ._api import Weights, WeightEntry
 from ._meta import _IMAGENET_CATEGORIES
+from ._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
 
 
 __all__ = [
@@ -40,6 +40,7 @@ class MNASNet0_5Weights(Weights):
             "acc@1": 67.734,
             "acc@5": 87.490,
         },
+        default=True,
     )
 
 
@@ -57,6 +58,7 @@ class MNASNet1_0Weights(Weights):
             "acc@1": 73.456,
             "acc@5": 91.510,
         },
+        default=True,
     )
 
 
@@ -67,7 +69,7 @@ class MNASNet1_3Weights(Weights):
 
 def _mnasnet(alpha: float, weights: Optional[Weights], progress: bool, **kwargs: Any) -> MNASNet:
     if weights is not None:
-        kwargs["num_classes"] = len(weights.meta["categories"])
+        _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
 
     model = MNASNet(alpha, **kwargs)
 
@@ -78,41 +80,40 @@ def _mnasnet(alpha: float, weights: Optional[Weights], progress: bool, **kwargs:
 
 
 def mnasnet0_5(weights: Optional[MNASNet0_5Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = MNASNet0_5Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
-
+        weights = _deprecated_param(kwargs, "pretrained", "weights", MNASNet0_5Weights.ImageNet1K_Community)
     weights = MNASNet0_5Weights.verify(weights)
 
     return _mnasnet(0.5, weights, progress, **kwargs)
 
 
 def mnasnet0_75(weights: Optional[MNASNet0_75Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        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")
-
+        weights = _deprecated_param(kwargs, "pretrained", "weights", None)
     weights = MNASNet0_75Weights.verify(weights)
 
     return _mnasnet(0.75, weights, progress, **kwargs)
 
 
 def mnasnet1_0(weights: Optional[MNASNet1_0Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = MNASNet1_0Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", MNASNet1_0Weights.ImageNet1K_Community)
     weights = MNASNet1_0Weights.verify(weights)
 
     return _mnasnet(1.0, weights, progress, **kwargs)
 
 
 def mnasnet1_3(weights: Optional[MNASNet1_3Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        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")
-
+        weights = _deprecated_param(kwargs, "pretrained", "weights", None)
     weights = MNASNet1_3Weights.verify(weights)
 
     return _mnasnet(1.3, weights, progress, **kwargs)

torchvision/prototype/models/mobilenetv2.py

-import warnings
 from functools import partial
 from typing import Any, Optional
 
@@ -8,6 +7,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ...models.mobilenetv2 import MobileNetV2
 from ._api import Weights, WeightEntry
 from ._meta import _IMAGENET_CATEGORIES
+from ._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
 
 
 __all__ = ["MobileNetV2", "MobileNetV2Weights", "mobilenet_v2"]
@@ -25,17 +25,19 @@ class MobileNetV2Weights(Weights):
             "acc@1": 71.878,
             "acc@5": 90.286,
         },
+        default=True,
     )
 
 
 def mobilenet_v2(weights: Optional[MobileNetV2Weights] = None, progress: bool = True, **kwargs: Any) -> MobileNetV2:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = MobileNetV2Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", MobileNetV2Weights.ImageNet1K_RefV1)
     weights = MobileNetV2Weights.verify(weights)
 
     if weights is not None:
-        kwargs["num_classes"] = len(weights.meta["categories"])
+        _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
 
     model = MobileNetV2(**kwargs)
 

torchvision/prototype/models/mobilenetv3.py

-import warnings
 from functools import partial
 from typing import Any, Optional, List
 
@@ -8,6 +7,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ...models.mobilenetv3 import MobileNetV3, _mobilenet_v3_conf, InvertedResidualConfig
 from ._api import Weights, WeightEntry
 from ._meta import _IMAGENET_CATEGORIES
+from ._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
 
 
 __all__ = [
@@ -27,7 +27,7 @@ def _mobilenet_v3(
     **kwargs: Any,
 ) -> MobileNetV3:
     if weights is not None:
-        kwargs["num_classes"] = len(weights.meta["categories"])
+        _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
 
     model = MobileNetV3(inverted_residual_setting, last_channel, **kwargs)
 
@@ -54,6 +54,7 @@ class MobileNetV3LargeWeights(Weights):
             "acc@1": 74.042,
             "acc@5": 91.340,
         },
+        default=False,
     )
     ImageNet1K_RefV2 = WeightEntry(
         url="https://download.pytorch.org/models/mobilenet_v3_large-5c1a4163.pth",
@@ -64,6 +65,7 @@ class MobileNetV3LargeWeights(Weights):
             "acc@1": 75.274,
             "acc@5": 92.566,
         },
+        default=True,
     )
 
 
@@ -77,15 +79,17 @@ class MobileNetV3SmallWeights(Weights):
             "acc@1": 67.668,
             "acc@5": 87.402,
         },
+        default=True,
     )
 
 
 def mobilenet_v3_large(
     weights: Optional[MobileNetV3LargeWeights] = None, progress: bool = True, **kwargs: Any
 ) -> MobileNetV3:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = MobileNetV3LargeWeights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", MobileNetV3LargeWeights.ImageNet1K_RefV1)
     weights = MobileNetV3LargeWeights.verify(weights)
 
     inverted_residual_setting, last_channel = _mobilenet_v3_conf("mobilenet_v3_large", **kwargs)
@@ -95,9 +99,10 @@ def mobilenet_v3_large(
 def mobilenet_v3_small(
     weights: Optional[MobileNetV3SmallWeights] = None, progress: bool = True, **kwargs: Any
 ) -> MobileNetV3:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = MobileNetV3SmallWeights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param(kwargs, "pretrained", "weights", MobileNetV3SmallWeights.ImageNet1K_RefV1)
     weights = MobileNetV3SmallWeights.verify(weights)
 
     inverted_residual_setting, last_channel = _mobilenet_v3_conf("mobilenet_v3_small", **kwargs)

torchvision/prototype/models/quantization/googlenet.py

@@ -12,6 +12,7 @@ from ....models.quantization.googlenet import (
 )
 from .._api import Weights, WeightEntry
 from .._meta import _IMAGENET_CATEGORIES
+from .._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
 from ..googlenet import GoogLeNetWeights
 
 
@@ -37,6 +38,7 @@ class QuantizedGoogLeNetWeights(Weights):
             "acc@1": 69.826,
             "acc@5": 89.404,
         },
+        default=True,
     )
 
 
@@ -46,13 +48,13 @@ def googlenet(
     quantize: bool = False,
     **kwargs: Any,
 ) -> QuantizableGoogLeNet:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        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:
-            weights = None
-
+        default_value = (
+            QuantizedGoogLeNetWeights.ImageNet1K_FBGEMM_TFV1 if quantize else GoogLeNetWeights.ImageNet1K_TFV1
+        )
+        weights = _deprecated_param(kwargs, "pretrained", "weights", default_value)  # type: ignore[assignment]
     if quantize:
         weights = QuantizedGoogLeNetWeights.verify(weights)
     else:
@@ -61,12 +63,12 @@ def googlenet(
     original_aux_logits = kwargs.get("aux_logits", False)
     if weights is not None:
         if "transform_input" not in kwargs:
-            kwargs["transform_input"] = True
-        kwargs["aux_logits"] = True
-        kwargs["init_weights"] = False
-        kwargs["num_classes"] = len(weights.meta["categories"])
+            _ovewrite_named_param(kwargs, "transform_input", True)
+        _ovewrite_named_param(kwargs, "aux_logits", True)
+        _ovewrite_named_param(kwargs, "init_weights", False)
+        _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
         if "backend" in weights.meta:
-            kwargs["backend"] = weights.meta["backend"]
+            _ovewrite_named_param(kwargs, "backend", weights.meta["backend"])
     backend = kwargs.pop("backend", "fbgemm")
 
     model = QuantizableGoogLeNet(**kwargs)

torchvision/prototype/models/quantization/inception.py

-import warnings
 from functools import partial
 from typing import Any, Optional, Union
 
@@ -12,6 +11,7 @@ from ....models.quantization.inception import (
 )
 from .._api import Weights, WeightEntry
 from .._meta import _IMAGENET_CATEGORIES
+from .._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
 from ..inception import InceptionV3Weights
 
 
@@ -37,6 +37,7 @@ class QuantizedInceptionV3Weights(Weights):
             "acc@1": 77.176,
             "acc@5": 93.354,
         },
+        default=True,
     )
 
 
@@ -46,15 +47,13 @@ def inception_v3(
     quantize: bool = False,
     **kwargs: Any,
 ) -> QuantizableInception3:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        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
-            )
-        else:
-            weights = None
-
+        default_value = (
+            QuantizedInceptionV3Weights.ImageNet1K_FBGEMM_TFV1 if quantize else InceptionV3Weights.ImageNet1K_TFV1
+        )
+        weights = _deprecated_param(kwargs, "pretrained", "weights", default_value)  # type: ignore[assignment]
     if quantize:
         weights = QuantizedInceptionV3Weights.verify(weights)
     else:
@@ -63,11 +62,11 @@ def inception_v3(
     original_aux_logits = kwargs.get("aux_logits", False)
     if weights is not None:
         if "transform_input" not in kwargs:
-            kwargs["transform_input"] = True
-        kwargs["aux_logits"] = True
-        kwargs["num_classes"] = len(weights.meta["categories"])
+            _ovewrite_named_param(kwargs, "transform_input", True)
+        _ovewrite_named_param(kwargs, "aux_logits", True)
+        _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
         if "backend" in weights.meta:
-            kwargs["backend"] = weights.meta["backend"]
+            _ovewrite_named_param(kwargs, "backend", weights.meta["backend"])
     backend = kwargs.pop("backend", "fbgemm")
 
     model = QuantizableInception3(**kwargs)

torchvision/prototype/models/quantization/mobilenetv2.py

-import warnings
 from functools import partial
 from typing import Any, Optional, Union
 
@@ -13,6 +12,7 @@ from ....models.quantization.mobilenetv2 import (
 )
 from .._api import Weights, WeightEntry
 from .._meta import _IMAGENET_CATEGORIES
+from .._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
 from ..mobilenetv2 import MobileNetV2Weights
 
 
@@ -38,6 +38,7 @@ class QuantizedMobileNetV2Weights(Weights):
             "acc@1": 71.658,
             "acc@5": 90.150,
         },
+        default=True,
     )
 
 
@@ -47,26 +48,22 @@ def mobilenet_v2(
     quantize: bool = False,
     **kwargs: Any,
 ) -> QuantizableMobileNetV2:
+    if type(weights) == bool and weights:
+        _deprecated_positional(kwargs, "pretrained", "weights", True)
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        if kwargs.pop("pretrained"):
-            weights = (
-                QuantizedMobileNetV2Weights.ImageNet1K_QNNPACK_RefV1
-                if quantize
-                else MobileNetV2Weights.ImageNet1K_RefV1
-            )
-        else:
-            weights = None
-
+        default_value = (
+            QuantizedMobileNetV2Weights.ImageNet1K_QNNPACK_RefV1 if quantize else MobileNetV2Weights.ImageNet1K_RefV1
+        )
+        weights = _deprecated_param(kwargs, "pretrained", "weights", default_value)  # type: ignore[assignment]
     if quantize:
         weights = QuantizedMobileNetV2Weights.verify(weights)
     else:
         weights = MobileNetV2Weights.verify(weights)
 
     if weights is not None:
-        kwargs["num_classes"] = len(weights.meta["categories"])
+        _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
         if "backend" in weights.meta:
-            kwargs["backend"] = weights.meta["backend"]
+            _ovewrite_named_param(kwargs, "backend", weights.meta["backend"])
     backend = kwargs.pop("backend", "qnnpack")
 
     model = QuantizableMobileNetV2(block=QuantizableInvertedResidual, **kwargs)