simplify model builders (#5001)

* simplify model builders

* cleanup

* refactor kwonly to pos or kw handling

* put weight verification back

* revert num categories checks

* fix default weights

* cleanup

* remove manual parameter map

* refactor decorator interface

* address review comments

* cleanup

* refactor callable default

* fix type annotation

* process ungrouped models

* cleanup

* mroe cleanup

* use decorator for detection models

* add decorator for quantization models

* add decorator for segmentation  models

* add decorator for video  models

* remove old helpers

* fix resnet50

* Adding verification back on InceptionV3

* Add kwargs in DeeplabeV3

* Add kwargs on FCN

* Fix typing on Deeplab

* Fix typing on FCN
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

test/test_prototype_models.py

@@ -5,6 +5,8 @@ import test_models as TM
 import torch
 from common_utils import cpu_and_gpu, run_on_env_var
 from torchvision.prototype import models
+from torchvision.prototype.models._api import WeightsEnum, Weights
+from torchvision.prototype.models._utils import handle_legacy_interface
 
 run_if_test_with_prototype = run_on_env_var(
     "PYTORCH_TEST_WITH_PROTOTYPE",
@@ -164,3 +166,87 @@ def test_old_vs_new_factory(model_fn, dev):
 
 def test_smoke():
     import torchvision.prototype.models  # noqa: F401
+
+
+# With this filter, every unexpected warning will be turned into an error
+@pytest.mark.filterwarnings("error")
+class TestHandleLegacyInterface:
+    class TestWeights(WeightsEnum):
+        Sentinel = Weights(url="https://pytorch.org", transforms=lambda x: x, meta=dict())
+
+    @pytest.mark.parametrize(
+        "kwargs",
+        [
+            pytest.param(dict(), id="empty"),
+            pytest.param(dict(weights=None), id="None"),
+            pytest.param(dict(weights=TestWeights.Sentinel), id="Weights"),
+        ],
+    )
+    def test_no_warn(self, kwargs):
+        @handle_legacy_interface(weights=("pretrained", self.TestWeights.Sentinel))
+        def builder(*, weights=None):
+            pass
+
+        builder(**kwargs)
+
+    @pytest.mark.parametrize("pretrained", (True, False))
+    def test_pretrained_pos(self, pretrained):
+        @handle_legacy_interface(weights=("pretrained", self.TestWeights.Sentinel))
+        def builder(*, weights=None):
+            pass
+
+        with pytest.warns(UserWarning, match="positional"):
+            builder(pretrained)
+
+    @pytest.mark.parametrize("pretrained", (True, False))
+    def test_pretrained_kw(self, pretrained):
+        @handle_legacy_interface(weights=("pretrained", self.TestWeights.Sentinel))
+        def builder(*, weights=None):
+            pass
+
+        with pytest.warns(UserWarning, match="deprecated"):
+            builder(pretrained)
+
+    @pytest.mark.parametrize("pretrained", (True, False))
+    @pytest.mark.parametrize("positional", (True, False))
+    def test_equivalent_behavior_weights(self, pretrained, positional):
+        @handle_legacy_interface(weights=("pretrained", self.TestWeights.Sentinel))
+        def builder(*, weights=None):
+            pass
+
+        args, kwargs = ((pretrained,), dict()) if positional else ((), dict(pretrained=pretrained))
+        with pytest.warns(UserWarning, match=f"weights={self.TestWeights.Sentinel if pretrained else None}"):
+            builder(*args, **kwargs)
+
+    def test_multi_params(self):
+        weights_params = ("weights", "weights_other")
+        pretrained_params = [param.replace("weights", "pretrained") for param in weights_params]
+
+        @handle_legacy_interface(
+            **{
+                weights_param: (pretrained_param, self.TestWeights.Sentinel)
+                for weights_param, pretrained_param in zip(weights_params, pretrained_params)
+            }
+        )
+        def builder(*, weights=None, weights_other=None):
+            pass
+
+        for pretrained_param in pretrained_params:
+            with pytest.warns(UserWarning, match="deprecated"):
+                builder(**{pretrained_param: True})
+
+    def test_default_callable(self):
+        @handle_legacy_interface(
+            weights=(
+                "pretrained",
+                lambda kwargs: self.TestWeights.Sentinel if kwargs["flag"] else None,
+            )
+        )
+        def builder(*, weights=None, flag):
+            pass
+
+        with pytest.warns(UserWarning, match="deprecated"):
+            builder(pretrained=True, flag=True)
+
+        with pytest.raises(ValueError, match="weights"):
+            builder(pretrained=True, flag=False)

torchvision/prototype/models/_utils.py

+import functools
 import warnings
-from typing import Any, Dict, Optional, TypeVar
+from typing import Any, Dict, Optional, TypeVar, Callable, Tuple, Union
 
-from ._api import WeightsEnum
+from torch import nn
+from torchvision.prototype.utils._internal import kwonly_to_pos_or_kw
 
+from ._api import WeightsEnum
 
 W = TypeVar("W", bound=WeightsEnum)
+M = TypeVar("M", bound=nn.Module)
 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.")
+def handle_legacy_interface(**weights: Tuple[str, Union[Optional[W], Callable[[Dict[str, Any]], Optional[W]]]]):
+    """Decorates a model builder with the new interface to make it compatible with the old.
+
+    In particular this handles two things:
+
+    1. Allows positional parameters again, but emits a deprecation warning in case they are used. See
+        :func:`torchvision.prototype.utils._internal.kwonly_to_pos_or_kw` for details.
+    2. Handles the default value change from ``pretrained=False`` to ``weights=None`` and ``pretrained=True`` to
+        ``weights=Weights`` and emits a deprecation warning with instructions for the new interface.
+
+    Args:
+        **weights (Tuple[str, Union[Optional[W], Callable[[Dict[str, Any]], Optional[W]]]]): Deprecated parameter
+            name and default value for the legacy ``pretrained=True``. The default value can be a callable in which
+            case it will be called with a dictionary of the keyword arguments. The only key that is guaranteed to be in
+            the dictionary is the deprecated parameter name passed as first element in the tuple. All other parameters
+            should be accessed with :meth:`~dict.get`.
+    """
+
+    def outer_wrapper(builder: Callable[..., M]) -> Callable[..., M]:
+        @kwonly_to_pos_or_kw
+        @functools.wraps(builder)
+        def inner_wrapper(*args: Any, **kwargs: Any) -> M:
+            for weights_param, (pretrained_param, default) in weights.items():  # type: ignore[union-attr]
+                # If neither the weights nor the pretrained parameter as passed, or the weights argument already use
+                # the new style arguments, there is nothing to do. Note that we cannot use `None` as sentinel for the
+                # weight argument, since it is a valid value.
+                sentinel = object()
+                weights_arg = kwargs.get(weights_param, sentinel)
+                if (
+                    (weights_param not in kwargs and pretrained_param not in kwargs)
+                    or isinstance(weights_arg, WeightsEnum)
+                    or weights_arg is None
+                ):
+                    continue
+
+                # If the pretrained parameter was passed as positional argument, it is now mapped to
+                # `kwargs[weights_param]`. This happens because the @kwonly_to_pos_or_kw decorator uses the current
+                # signature to infer the names of positionally passed arguments and thus has no knowledge that there
+                # used to be a pretrained parameter.
+                pretrained_positional = weights_arg is not sentinel
+                if pretrained_positional:
+                    # We put the pretrained argument under its legacy name in the keyword argument dictionary to have a
+                    # unified access to the value if the default value is a callable.
+                    kwargs[pretrained_param] = pretrained_arg = kwargs.pop(weights_param)
                 else:
-        return None
+                    pretrained_arg = kwargs[pretrained_param]
 
+                if pretrained_arg:
+                    default_weights_arg = default(kwargs) if callable(default) else default
+                    if not isinstance(default_weights_arg, WeightsEnum):
+                        raise ValueError(f"No weights available for model {builder.__name__}")
+                else:
+                    default_weights_arg = None
 
-def _deprecated_positional(kwargs: Dict[str, Any], deprecated_param: str, new_param: str, default_value: V) -> None:
+                if not pretrained_positional:
                     warnings.warn(
-        f"The positional parameter '{deprecated_param}' is deprecated, please use keyword parameter '{new_param}'"
-        + " instead."
+                        f"The parameter '{pretrained_param}' is deprecated, please use '{weights_param}' instead."
+                    )
+
+                msg = (
+                    f"Arguments other than a weight enum or `None` for '{weights_param}' are deprecated. "
+                    f"The current behavior is equivalent to passing `{weights_param}={default_weights_arg}`."
+                )
+                if pretrained_arg:
+                    msg = (
+                        f"{msg} You can also use `{weights_param}={type(default_weights_arg).__name__}.default` "
+                        f"to get the most up-to-date weights."
                     )
-    kwargs[deprecated_param] = default_value
+                warnings.warn(msg)
+
+                del kwargs[pretrained_param]
+                kwargs[weights_param] = default_weights_arg
+
+            return builder(*args, **kwargs)
+
+        return inner_wrapper
+
+    return outer_wrapper
 
 
 def _ovewrite_named_param(kwargs: Dict[str, Any], param: str, new_value: V) -> None:

torchvision/prototype/models/alexnet.py

@@ -7,7 +7,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ...models.alexnet import AlexNet
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
-from ._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
+from ._utils import handle_legacy_interface, _ovewrite_named_param
 
 
 __all__ = ["AlexNet", "AlexNet_Weights", "alexnet"]
@@ -29,11 +29,8 @@ class AlexNet_Weights(WeightsEnum):
     default = ImageNet1K_V1
 
 
-def alexnet(weights: Optional[AlexNet_Weights] = None, progress: bool = True, **kwargs: Any) -> AlexNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", AlexNet_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", AlexNet_Weights.ImageNet1K_V1))
+def alexnet(*, weights: Optional[AlexNet_Weights] = None, progress: bool = True, **kwargs: Any) -> AlexNet:
     weights = AlexNet_Weights.verify(weights)
 
     if weights is not None:

torchvision/prototype/models/densenet.py

@@ -9,7 +9,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ...models.densenet import DenseNet
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
-from ._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
+from ._utils import handle_legacy_interface, _ovewrite_named_param
 
 
 __all__ = [
@@ -123,41 +123,29 @@ class DenseNet201_Weights(WeightsEnum):
     default = ImageNet1K_V1
 
 
-def densenet121(weights: Optional[DenseNet121_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", DenseNet121_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", DenseNet121_Weights.ImageNet1K_V1))
+def densenet121(*, weights: Optional[DenseNet121_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     weights = DenseNet121_Weights.verify(weights)
 
     return _densenet(32, (6, 12, 24, 16), 64, weights, progress, **kwargs)
 
 
-def densenet161(weights: Optional[DenseNet161_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", DenseNet161_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", DenseNet161_Weights.ImageNet1K_V1))
+def densenet161(*, weights: Optional[DenseNet161_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     weights = DenseNet161_Weights.verify(weights)
 
     return _densenet(48, (6, 12, 36, 24), 96, weights, progress, **kwargs)
 
 
-def densenet169(weights: Optional[DenseNet169_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", DenseNet169_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", DenseNet169_Weights.ImageNet1K_V1))
+def densenet169(*, weights: Optional[DenseNet169_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     weights = DenseNet169_Weights.verify(weights)
 
     return _densenet(32, (6, 12, 32, 32), 64, weights, progress, **kwargs)
 
 
-def densenet201(weights: Optional[DenseNet201_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", DenseNet201_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", DenseNet201_Weights.ImageNet1K_V1))
+def densenet201(*, weights: Optional[DenseNet201_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     weights = DenseNet201_Weights.verify(weights)
 
     return _densenet(32, (6, 12, 48, 32), 64, weights, progress, **kwargs)

torchvision/prototype/models/detection/faster_rcnn.py

@@ -14,7 +14,7 @@ from ....models.detection.faster_rcnn import (
 )
 from .._api import WeightsEnum, Weights
 from .._meta import _COCO_CATEGORIES
-from .._utils import _deprecated_param, _deprecated_positional, _ovewrite_value_param
+from .._utils import handle_legacy_interface, _ovewrite_value_param
 from ..mobilenetv3 import MobileNet_V3_Large_Weights, mobilenet_v3_large
 from ..resnet import ResNet50_Weights, resnet50
 
@@ -75,7 +75,12 @@ class FasterRCNN_MobileNet_V3_Large_320_FPN_Weights(WeightsEnum):
     default = Coco_V1
 
 
+@handle_legacy_interface(
+    weights=("pretrained", FasterRCNN_ResNet50_FPN_Weights.Coco_V1),
+    weights_backbone=("pretrained_backbone", ResNet50_Weights.ImageNet1K_V1),
+)
 def fasterrcnn_resnet50_fpn(
+    *,
     weights: Optional[FasterRCNN_ResNet50_FPN_Weights] = None,
     progress: bool = True,
     num_classes: Optional[int] = None,
@@ -83,17 +88,7 @@ def fasterrcnn_resnet50_fpn(
     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:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", FasterRCNN_ResNet50_FPN_Weights.Coco_V1)
     weights = FasterRCNN_ResNet50_FPN_Weights.verify(weights)
-    if type(weights_backbone) == bool and weights_backbone:
-        _deprecated_positional(kwargs, "pretrained_backbone", "weights_backbone", True)
-    if "pretrained_backbone" in kwargs:
-        weights_backbone = _deprecated_param(
-            kwargs, "pretrained_backbone", "weights_backbone", ResNet50_Weights.ImageNet1K_V1
-        )
     weights_backbone = ResNet50_Weights.verify(weights_backbone)
 
     if weights is not None:
@@ -119,6 +114,7 @@ def fasterrcnn_resnet50_fpn(
 
 
 def _fasterrcnn_mobilenet_v3_large_fpn(
+    *,
     weights: Optional[Union[FasterRCNN_MobileNet_V3_Large_FPN_Weights, FasterRCNN_MobileNet_V3_Large_320_FPN_Weights]],
     progress: bool,
     num_classes: Optional[int],
@@ -158,7 +154,12 @@ def _fasterrcnn_mobilenet_v3_large_fpn(
     return model
 
 
+@handle_legacy_interface(
+    weights=("pretrained", FasterRCNN_MobileNet_V3_Large_FPN_Weights.Coco_V1),
+    weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.ImageNet1K_V1),
+)
 def fasterrcnn_mobilenet_v3_large_fpn(
+    *,
     weights: Optional[FasterRCNN_MobileNet_V3_Large_FPN_Weights] = None,
     progress: bool = True,
     num_classes: Optional[int] = None,
@@ -166,17 +167,7 @@ def fasterrcnn_mobilenet_v3_large_fpn(
     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:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", FasterRCNN_MobileNet_V3_Large_FPN_Weights.Coco_V1)
     weights = FasterRCNN_MobileNet_V3_Large_FPN_Weights.verify(weights)
-    if type(weights_backbone) == bool and weights_backbone:
-        _deprecated_positional(kwargs, "pretrained_backbone", "weights_backbone", True)
-    if "pretrained_backbone" in kwargs:
-        weights_backbone = _deprecated_param(
-            kwargs, "pretrained_backbone", "weights_backbone", MobileNet_V3_Large_Weights.ImageNet1K_V1
-        )
     weights_backbone = MobileNet_V3_Large_Weights.verify(weights_backbone)
 
     defaults = {
@@ -185,16 +176,21 @@ def fasterrcnn_mobilenet_v3_large_fpn(
 
     kwargs = {**defaults, **kwargs}
     return _fasterrcnn_mobilenet_v3_large_fpn(
-        weights,
-        progress,
-        num_classes,
-        weights_backbone,
-        trainable_backbone_layers,
+        weights=weights,
+        progress=progress,
+        num_classes=num_classes,
+        weights_backbone=weights_backbone,
+        trainable_backbone_layers=trainable_backbone_layers,
         **kwargs,
     )
 
 
+@handle_legacy_interface(
+    weights=("pretrained", FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.Coco_V1),
+    weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.ImageNet1K_V1),
+)
 def fasterrcnn_mobilenet_v3_large_320_fpn(
+    *,
     weights: Optional[FasterRCNN_MobileNet_V3_Large_320_FPN_Weights] = None,
     progress: bool = True,
     num_classes: Optional[int] = None,
@@ -202,19 +198,8 @@ def fasterrcnn_mobilenet_v3_large_320_fpn(
     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:
-        weights = _deprecated_param(
-            kwargs, "pretrained", "weights", FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.Coco_V1
-        )
+
     weights = FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.verify(weights)
-    if type(weights_backbone) == bool and weights_backbone:
-        _deprecated_positional(kwargs, "pretrained_backbone", "weights_backbone", True)
-    if "pretrained_backbone" in kwargs:
-        weights_backbone = _deprecated_param(
-            kwargs, "pretrained_backbone", "weights_backbone", MobileNet_V3_Large_Weights.ImageNet1K_V1
-        )
     weights_backbone = MobileNet_V3_Large_Weights.verify(weights_backbone)
 
     defaults = {
@@ -227,10 +212,10 @@ def fasterrcnn_mobilenet_v3_large_320_fpn(
 
     kwargs = {**defaults, **kwargs}
     return _fasterrcnn_mobilenet_v3_large_fpn(
-        weights,
-        progress,
-        num_classes,
-        weights_backbone,
-        trainable_backbone_layers,
+        weights=weights,
+        progress=progress,
+        num_classes=num_classes,
+        weights_backbone=weights_backbone,
+        trainable_backbone_layers=trainable_backbone_layers,
         **kwargs,
     )

torchvision/prototype/models/detection/keypoint_rcnn.py

@@ -11,7 +11,7 @@ from ....models.detection.keypoint_rcnn import (
 )
 from .._api import WeightsEnum, Weights
 from .._meta import _COCO_PERSON_CATEGORIES, _COCO_PERSON_KEYPOINT_NAMES
-from .._utils import _deprecated_param, _deprecated_positional, _ovewrite_value_param
+from .._utils import handle_legacy_interface, _ovewrite_value_param
 from ..resnet import ResNet50_Weights, resnet50
 
 
@@ -49,7 +49,17 @@ class KeypointRCNN_ResNet50_FPN_Weights(WeightsEnum):
     default = Coco_V1
 
 
+@handle_legacy_interface(
+    weights=(
+        "pretrained",
+        lambda kwargs: KeypointRCNN_ResNet50_FPN_Weights.Coco_Legacy
+        if kwargs["pretrained"] == "legacy"
+        else KeypointRCNN_ResNet50_FPN_Weights.Coco_V1,
+    ),
+    weights_backbone=("pretrained_backbone", ResNet50_Weights.ImageNet1K_V1),
+)
 def keypointrcnn_resnet50_fpn(
+    *,
     weights: Optional[KeypointRCNN_ResNet50_FPN_Weights] = None,
     progress: bool = True,
     num_classes: Optional[int] = None,
@@ -58,21 +68,7 @@ def keypointrcnn_resnet50_fpn(
     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:
-        default_value = KeypointRCNN_ResNet50_FPN_Weights.Coco_V1
-        if kwargs["pretrained"] == "legacy":
-            default_value = KeypointRCNN_ResNet50_FPN_Weights.Coco_Legacy
-            kwargs["pretrained"] = True
-        weights = _deprecated_param(kwargs, "pretrained", "weights", default_value)
     weights = KeypointRCNN_ResNet50_FPN_Weights.verify(weights)
-    if type(weights_backbone) == bool and weights_backbone:
-        _deprecated_positional(kwargs, "pretrained_backbone", "weights_backbone", True)
-    if "pretrained_backbone" in kwargs:
-        weights_backbone = _deprecated_param(
-            kwargs, "pretrained_backbone", "weights_backbone", ResNet50_Weights.ImageNet1K_V1
-        )
     weights_backbone = ResNet50_Weights.verify(weights_backbone)
 
     if weights is not None:

torchvision/prototype/models/detection/mask_rcnn.py

@@ -12,7 +12,7 @@ from ....models.detection.mask_rcnn import (
 )
 from .._api import WeightsEnum, Weights
 from .._meta import _COCO_CATEGORIES
-from .._utils import _deprecated_param, _deprecated_positional, _ovewrite_value_param
+from .._utils import handle_legacy_interface, _ovewrite_value_param
 from ..resnet import ResNet50_Weights, resnet50
 
 
@@ -38,7 +38,12 @@ class MaskRCNN_ResNet50_FPN_Weights(WeightsEnum):
     default = Coco_V1
 
 
+@handle_legacy_interface(
+    weights=("pretrained", MaskRCNN_ResNet50_FPN_Weights.Coco_V1),
+    weights_backbone=("pretrained_backbone", ResNet50_Weights.ImageNet1K_V1),
+)
 def maskrcnn_resnet50_fpn(
+    *,
     weights: Optional[MaskRCNN_ResNet50_FPN_Weights] = None,
     progress: bool = True,
     num_classes: Optional[int] = None,
@@ -46,17 +51,7 @@ def maskrcnn_resnet50_fpn(
     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:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", MaskRCNN_ResNet50_FPN_Weights.Coco_V1)
     weights = MaskRCNN_ResNet50_FPN_Weights.verify(weights)
-    if type(weights_backbone) == bool and weights_backbone:
-        _deprecated_positional(kwargs, "pretrained_backbone", "weights_backbone", True)
-    if "pretrained_backbone" in kwargs:
-        weights_backbone = _deprecated_param(
-            kwargs, "pretrained_backbone", "weights_backbone", ResNet50_Weights.ImageNet1K_V1
-        )
     weights_backbone = ResNet50_Weights.verify(weights_backbone)
 
     if weights is not None:

torchvision/prototype/models/detection/retinanet.py

@@ -13,7 +13,7 @@ from ....models.detection.retinanet import (
 )
 from .._api import WeightsEnum, Weights
 from .._meta import _COCO_CATEGORIES
-from .._utils import _deprecated_param, _deprecated_positional, _ovewrite_value_param
+from .._utils import handle_legacy_interface, _ovewrite_value_param
 from ..resnet import ResNet50_Weights, resnet50
 
 
@@ -38,7 +38,12 @@ class RetinaNet_ResNet50_FPN_Weights(WeightsEnum):
     default = Coco_V1
 
 
+@handle_legacy_interface(
+    weights=("pretrained", RetinaNet_ResNet50_FPN_Weights.Coco_V1),
+    weights_backbone=("pretrained_backbone", ResNet50_Weights.ImageNet1K_V1),
+)
 def retinanet_resnet50_fpn(
+    *,
     weights: Optional[RetinaNet_ResNet50_FPN_Weights] = None,
     progress: bool = True,
     num_classes: Optional[int] = None,
@@ -46,17 +51,7 @@ def retinanet_resnet50_fpn(
     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:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", RetinaNet_ResNet50_FPN_Weights.Coco_V1)
     weights = RetinaNet_ResNet50_FPN_Weights.verify(weights)
-    if type(weights_backbone) == bool and weights_backbone:
-        _deprecated_positional(kwargs, "pretrained_backbone", "weights_backbone", True)
-    if "pretrained_backbone" in kwargs:
-        weights_backbone = _deprecated_param(
-            kwargs, "pretrained_backbone", "weights_backbone", ResNet50_Weights.ImageNet1K_V1
-        )
     weights_backbone = ResNet50_Weights.verify(weights_backbone)
 
     if weights is not None:

torchvision/prototype/models/detection/ssd.py

@@ -12,7 +12,7 @@ from ....models.detection.ssd import (
 )
 from .._api import WeightsEnum, Weights
 from .._meta import _COCO_CATEGORIES
-from .._utils import _deprecated_param, _deprecated_positional, _ovewrite_value_param
+from .._utils import handle_legacy_interface, _ovewrite_value_param
 from ..vgg import VGG16_Weights, vgg16
 
 
@@ -37,7 +37,12 @@ class SSD300_VGG16_Weights(WeightsEnum):
     default = Coco_V1
 
 
+@handle_legacy_interface(
+    weights=("pretrained", SSD300_VGG16_Weights.Coco_V1),
+    weights_backbone=("pretrained_backbone", VGG16_Weights.ImageNet1K_Features),
+)
 def ssd300_vgg16(
+    *,
     weights: Optional[SSD300_VGG16_Weights] = None,
     progress: bool = True,
     num_classes: Optional[int] = None,
@@ -45,17 +50,7 @@ def ssd300_vgg16(
     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:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", SSD300_VGG16_Weights.Coco_V1)
     weights = SSD300_VGG16_Weights.verify(weights)
-    if type(weights_backbone) == bool and weights_backbone:
-        _deprecated_positional(kwargs, "pretrained_backbone", "weights_backbone", True)
-    if "pretrained_backbone" in kwargs:
-        weights_backbone = _deprecated_param(
-            kwargs, "pretrained_backbone", "weights_backbone", VGG16_Weights.ImageNet1K_Features
-        )
     weights_backbone = VGG16_Weights.verify(weights_backbone)
 
     if "size" in kwargs:

torchvision/prototype/models/detection/ssdlite.py

@@ -17,7 +17,7 @@ from ....models.detection.ssdlite import (
 )
 from .._api import WeightsEnum, Weights
 from .._meta import _COCO_CATEGORIES
-from .._utils import _deprecated_param, _deprecated_positional, _ovewrite_value_param
+from .._utils import handle_legacy_interface, _ovewrite_value_param
 from ..mobilenetv3 import MobileNet_V3_Large_Weights, mobilenet_v3_large
 
 
@@ -42,7 +42,12 @@ class SSDLite320_MobileNet_V3_Large_Weights(WeightsEnum):
     default = Coco_V1
 
 
+@handle_legacy_interface(
+    weights=("pretrained", SSDLite320_MobileNet_V3_Large_Weights.Coco_V1),
+    weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.ImageNet1K_V1),
+)
 def ssdlite320_mobilenet_v3_large(
+    *,
     weights: Optional[SSDLite320_MobileNet_V3_Large_Weights] = None,
     progress: bool = True,
     num_classes: Optional[int] = None,
@@ -51,17 +56,7 @@ def ssdlite320_mobilenet_v3_large(
     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:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", SSDLite320_MobileNet_V3_Large_Weights.Coco_V1)
     weights = SSDLite320_MobileNet_V3_Large_Weights.verify(weights)
-    if type(weights_backbone) == bool and weights_backbone:
-        _deprecated_positional(kwargs, "pretrained_backbone", "weights_backbone", True)
-    if "pretrained_backbone" in kwargs:
-        weights_backbone = _deprecated_param(
-            kwargs, "pretrained_backbone", "weights_backbone", MobileNet_V3_Large_Weights.ImageNet1K_V1
-        )
     weights_backbone = MobileNet_V3_Large_Weights.verify(weights_backbone)
 
     if "size" in kwargs:

torchvision/prototype/models/efficientnet.py

@@ -8,7 +8,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ...models.efficientnet import EfficientNet, MBConvConfig
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
-from ._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
+from ._utils import handle_legacy_interface, _ovewrite_named_param
 
 
 __all__ = [
@@ -181,73 +181,55 @@ class EfficientNet_B7_Weights(WeightsEnum):
     default = ImageNet1K_V1
 
 
+@handle_legacy_interface(weights=("pretrained", EfficientNet_B0_Weights.ImageNet1K_V1))
 def efficientnet_b0(
-    weights: Optional[EfficientNet_B0_Weights] = None, progress: bool = True, **kwargs: Any
+    *, weights: Optional[EfficientNet_B0_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", EfficientNet_B0_Weights.ImageNet1K_V1)
     weights = EfficientNet_B0_Weights.verify(weights)
 
     return _efficientnet(width_mult=1.0, depth_mult=1.0, dropout=0.2, weights=weights, progress=progress, **kwargs)
 
 
+@handle_legacy_interface(weights=("pretrained", EfficientNet_B1_Weights.ImageNet1K_V1))
 def efficientnet_b1(
-    weights: Optional[EfficientNet_B1_Weights] = None, progress: bool = True, **kwargs: Any
+    *, weights: Optional[EfficientNet_B1_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", EfficientNet_B1_Weights.ImageNet1K_V1)
     weights = EfficientNet_B1_Weights.verify(weights)
 
     return _efficientnet(width_mult=1.0, depth_mult=1.1, dropout=0.2, weights=weights, progress=progress, **kwargs)
 
 
+@handle_legacy_interface(weights=("pretrained", EfficientNet_B2_Weights.ImageNet1K_V1))
 def efficientnet_b2(
-    weights: Optional[EfficientNet_B2_Weights] = None, progress: bool = True, **kwargs: Any
+    *, weights: Optional[EfficientNet_B2_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", EfficientNet_B2_Weights.ImageNet1K_V1)
     weights = EfficientNet_B2_Weights.verify(weights)
 
     return _efficientnet(width_mult=1.1, depth_mult=1.2, dropout=0.3, weights=weights, progress=progress, **kwargs)
 
 
+@handle_legacy_interface(weights=("pretrained", EfficientNet_B3_Weights.ImageNet1K_V1))
 def efficientnet_b3(
-    weights: Optional[EfficientNet_B3_Weights] = None, progress: bool = True, **kwargs: Any
+    *, weights: Optional[EfficientNet_B3_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", EfficientNet_B3_Weights.ImageNet1K_V1)
     weights = EfficientNet_B3_Weights.verify(weights)
 
     return _efficientnet(width_mult=1.2, depth_mult=1.4, dropout=0.3, weights=weights, progress=progress, **kwargs)
 
 
+@handle_legacy_interface(weights=("pretrained", EfficientNet_B4_Weights.ImageNet1K_V1))
 def efficientnet_b4(
-    weights: Optional[EfficientNet_B4_Weights] = None, progress: bool = True, **kwargs: Any
+    *, weights: Optional[EfficientNet_B4_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", EfficientNet_B4_Weights.ImageNet1K_V1)
     weights = EfficientNet_B4_Weights.verify(weights)
 
     return _efficientnet(width_mult=1.4, depth_mult=1.8, dropout=0.4, weights=weights, progress=progress, **kwargs)
 
 
+@handle_legacy_interface(weights=("pretrained", EfficientNet_B5_Weights.ImageNet1K_V1))
 def efficientnet_b5(
-    weights: Optional[EfficientNet_B5_Weights] = None, progress: bool = True, **kwargs: Any
+    *, weights: Optional[EfficientNet_B5_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", EfficientNet_B5_Weights.ImageNet1K_V1)
     weights = EfficientNet_B5_Weights.verify(weights)
 
     return _efficientnet(
@@ -261,13 +243,10 @@ def efficientnet_b5(
     )
 
 
+@handle_legacy_interface(weights=("pretrained", EfficientNet_B6_Weights.ImageNet1K_V1))
 def efficientnet_b6(
-    weights: Optional[EfficientNet_B6_Weights] = None, progress: bool = True, **kwargs: Any
+    *, weights: Optional[EfficientNet_B6_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", EfficientNet_B6_Weights.ImageNet1K_V1)
     weights = EfficientNet_B6_Weights.verify(weights)
 
     return _efficientnet(
@@ -281,13 +260,10 @@ def efficientnet_b6(
     )
 
 
+@handle_legacy_interface(weights=("pretrained", EfficientNet_B7_Weights.ImageNet1K_V1))
 def efficientnet_b7(
-    weights: Optional[EfficientNet_B7_Weights] = None, progress: bool = True, **kwargs: Any
+    *, weights: Optional[EfficientNet_B7_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", EfficientNet_B7_Weights.ImageNet1K_V1)
     weights = EfficientNet_B7_Weights.verify(weights)
 
     return _efficientnet(

torchvision/prototype/models/googlenet.py

@@ -8,7 +8,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ...models.googlenet import GoogLeNet, GoogLeNetOutputs, _GoogLeNetOutputs
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
-from ._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
+from ._utils import handle_legacy_interface, _ovewrite_named_param
 
 
 __all__ = ["GoogLeNet", "GoogLeNetOutputs", "_GoogLeNetOutputs", "GoogLeNet_Weights", "googlenet"]
@@ -30,11 +30,8 @@ class GoogLeNet_Weights(WeightsEnum):
     default = ImageNet1K_V1
 
 
-def googlenet(weights: Optional[GoogLeNet_Weights] = None, progress: bool = True, **kwargs: Any) -> GoogLeNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", GoogLeNet_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", GoogLeNet_Weights.ImageNet1K_V1))
+def googlenet(*, weights: Optional[GoogLeNet_Weights] = None, progress: bool = True, **kwargs: Any) -> GoogLeNet:
     weights = GoogLeNet_Weights.verify(weights)
 
     original_aux_logits = kwargs.get("aux_logits", False)

torchvision/prototype/models/inception.py

@@ -7,7 +7,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ...models.inception import Inception3, InceptionOutputs, _InceptionOutputs
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
-from ._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
+from ._utils import handle_legacy_interface, _ovewrite_named_param
 
 
 __all__ = ["Inception3", "InceptionOutputs", "_InceptionOutputs", "Inception_V3_Weights", "inception_v3"]
@@ -29,11 +29,8 @@ class Inception_V3_Weights(WeightsEnum):
     default = ImageNet1K_V1
 
 
-def inception_v3(weights: Optional[Inception_V3_Weights] = None, progress: bool = True, **kwargs: Any) -> Inception3:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", Inception_V3_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", Inception_V3_Weights.ImageNet1K_V1))
+def inception_v3(*, weights: Optional[Inception_V3_Weights] = None, progress: bool = True, **kwargs: Any) -> Inception3:
     weights = Inception_V3_Weights.verify(weights)
 
     original_aux_logits = kwargs.get("aux_logits", True)

torchvision/prototype/models/mnasnet.py

@@ -7,7 +7,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ...models.mnasnet import MNASNet
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
-from ._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
+from ._utils import handle_legacy_interface, _ovewrite_named_param
 
 
 __all__ = [
@@ -79,41 +79,29 @@ def _mnasnet(alpha: float, weights: Optional[WeightsEnum], progress: bool, **kwa
     return model
 
 
-def mnasnet0_5(weights: Optional[MNASNet0_5_Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", MNASNet0_5_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", MNASNet0_5_Weights.ImageNet1K_V1))
+def mnasnet0_5(*, weights: Optional[MNASNet0_5_Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
     weights = MNASNet0_5_Weights.verify(weights)
 
     return _mnasnet(0.5, weights, progress, **kwargs)
 
 
-def mnasnet0_75(weights: Optional[MNASNet0_75_Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", None)
+@handle_legacy_interface(weights=("pretrained", None))
+def mnasnet0_75(*, weights: Optional[MNASNet0_75_Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
     weights = MNASNet0_75_Weights.verify(weights)
 
     return _mnasnet(0.75, weights, progress, **kwargs)
 
 
-def mnasnet1_0(weights: Optional[MNASNet1_0_Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", MNASNet1_0_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", MNASNet1_0_Weights.ImageNet1K_V1))
+def mnasnet1_0(*, weights: Optional[MNASNet1_0_Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
     weights = MNASNet1_0_Weights.verify(weights)
 
     return _mnasnet(1.0, weights, progress, **kwargs)
 
 
-def mnasnet1_3(weights: Optional[MNASNet1_3_Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", None)
+@handle_legacy_interface(weights=("pretrained", None))
+def mnasnet1_3(*, weights: Optional[MNASNet1_3_Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
     weights = MNASNet1_3_Weights.verify(weights)
 
     return _mnasnet(1.3, weights, progress, **kwargs)

torchvision/prototype/models/mobilenetv2.py

@@ -7,7 +7,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ...models.mobilenetv2 import MobileNetV2
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
-from ._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
+from ._utils import handle_legacy_interface, _ovewrite_named_param
 
 
 __all__ = ["MobileNetV2", "MobileNet_V2_Weights", "mobilenet_v2"]
@@ -29,11 +29,10 @@ class MobileNet_V2_Weights(WeightsEnum):
     default = ImageNet1K_V1
 
 
-def mobilenet_v2(weights: Optional[MobileNet_V2_Weights] = None, progress: bool = True, **kwargs: Any) -> MobileNetV2:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", MobileNet_V2_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", MobileNet_V2_Weights.ImageNet1K_V1))
+def mobilenet_v2(
+    *, weights: Optional[MobileNet_V2_Weights] = None, progress: bool = True, **kwargs: Any
+) -> MobileNetV2:
     weights = MobileNet_V2_Weights.verify(weights)
 
     if weights is not None:

torchvision/prototype/models/mobilenetv3.py

@@ -7,7 +7,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ...models.mobilenetv3 import MobileNetV3, _mobilenet_v3_conf, InvertedResidualConfig
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
-from ._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
+from ._utils import handle_legacy_interface, _ovewrite_named_param
 
 
 __all__ = [
@@ -82,26 +82,20 @@ class MobileNet_V3_Small_Weights(WeightsEnum):
     default = ImageNet1K_V1
 
 
+@handle_legacy_interface(weights=("pretrained", MobileNet_V3_Large_Weights.ImageNet1K_V1))
 def mobilenet_v3_large(
-    weights: Optional[MobileNet_V3_Large_Weights] = None, progress: bool = True, **kwargs: Any
+    *, weights: Optional[MobileNet_V3_Large_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> MobileNetV3:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", MobileNet_V3_Large_Weights.ImageNet1K_V1)
     weights = MobileNet_V3_Large_Weights.verify(weights)
 
     inverted_residual_setting, last_channel = _mobilenet_v3_conf("mobilenet_v3_large", **kwargs)
     return _mobilenet_v3(inverted_residual_setting, last_channel, weights, progress, **kwargs)
 
 
+@handle_legacy_interface(weights=("pretrained", MobileNet_V3_Small_Weights.ImageNet1K_V1))
 def mobilenet_v3_small(
-    weights: Optional[MobileNet_V3_Small_Weights] = None, progress: bool = True, **kwargs: Any
+    *, weights: Optional[MobileNet_V3_Small_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> MobileNetV3:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", MobileNet_V3_Small_Weights.ImageNet1K_V1)
     weights = MobileNet_V3_Small_Weights.verify(weights)
 
     inverted_residual_setting, last_channel = _mobilenet_v3_conf("mobilenet_v3_small", **kwargs)

torchvision/prototype/models/quantization/googlenet.py

@@ -12,7 +12,7 @@ from ....models.quantization.googlenet import (
 )
 from .._api import WeightsEnum, Weights
 from .._meta import _IMAGENET_CATEGORIES
-from .._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
+from .._utils import handle_legacy_interface, _ovewrite_named_param
 from ..googlenet import GoogLeNet_Weights
 
 
@@ -42,21 +42,22 @@ class GoogLeNet_QuantizedWeights(WeightsEnum):
     default = ImageNet1K_FBGEMM_V1
 
 
+@handle_legacy_interface(
+    weights=(
+        "pretrained",
+        lambda kwargs: GoogLeNet_QuantizedWeights.ImageNet1K_FBGEMM_V1
+        if kwargs.get("quantize", False)
+        else GoogLeNet_Weights.ImageNet1K_V1,
+    )
+)
 def googlenet(
+    *,
     weights: Optional[Union[GoogLeNet_QuantizedWeights, GoogLeNet_Weights]] = None,
     progress: bool = True,
     quantize: bool = False,
     **kwargs: Any,
 ) -> QuantizableGoogLeNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        default_value = GoogLeNet_QuantizedWeights.ImageNet1K_FBGEMM_V1 if quantize else GoogLeNet_Weights.ImageNet1K_V1
-        weights = _deprecated_param(kwargs, "pretrained", "weights", default_value)  # type: ignore[assignment]
-    if quantize:
-        weights = GoogLeNet_QuantizedWeights.verify(weights)
-    else:
-        weights = GoogLeNet_Weights.verify(weights)
+    weights = (GoogLeNet_QuantizedWeights if quantize else GoogLeNet_Weights).verify(weights)
 
     original_aux_logits = kwargs.get("aux_logits", False)
     if weights is not None:

torchvision/prototype/models/quantization/inception.py

@@ -11,7 +11,7 @@ from ....models.quantization.inception import (
 )
 from .._api import WeightsEnum, Weights
 from .._meta import _IMAGENET_CATEGORIES
-from .._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
+from .._utils import handle_legacy_interface, _ovewrite_named_param
 from ..inception import Inception_V3_Weights
 
 
@@ -41,23 +41,22 @@ class Inception_V3_QuantizedWeights(WeightsEnum):
     default = ImageNet1K_FBGEMM_V1
 
 
+@handle_legacy_interface(
+    weights=(
+        "pretrained",
+        lambda kwargs: Inception_V3_QuantizedWeights.ImageNet1K_FBGEMM_V1
+        if kwargs.get("quantize", False)
+        else Inception_V3_Weights.ImageNet1K_V1,
+    )
+)
 def inception_v3(
+    *,
     weights: Optional[Union[Inception_V3_QuantizedWeights, Inception_V3_Weights]] = None,
     progress: bool = True,
     quantize: bool = False,
     **kwargs: Any,
 ) -> QuantizableInception3:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        default_value = (
-            Inception_V3_QuantizedWeights.ImageNet1K_FBGEMM_V1 if quantize else Inception_V3_Weights.ImageNet1K_V1
-        )
-        weights = _deprecated_param(kwargs, "pretrained", "weights", default_value)  # type: ignore[assignment]
-    if quantize:
-        weights = Inception_V3_QuantizedWeights.verify(weights)
-    else:
-        weights = Inception_V3_Weights.verify(weights)
+    weights = (Inception_V3_QuantizedWeights if quantize else Inception_V3_Weights).verify(weights)
 
     original_aux_logits = kwargs.get("aux_logits", False)
     if weights is not None:

torchvision/prototype/models/quantization/mobilenetv2.py

@@ -12,7 +12,7 @@ from ....models.quantization.mobilenetv2 import (
 )
 from .._api import WeightsEnum, Weights
 from .._meta import _IMAGENET_CATEGORIES
-from .._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
+from .._utils import handle_legacy_interface, _ovewrite_named_param
 from ..mobilenetv2 import MobileNet_V2_Weights
 
 
@@ -42,23 +42,22 @@ class MobileNet_V2_QuantizedWeights(WeightsEnum):
     default = ImageNet1K_QNNPACK_V1
 
 
+@handle_legacy_interface(
+    weights=(
+        "pretrained",
+        lambda kwargs: MobileNet_V2_QuantizedWeights.ImageNet1K_QNNPACK_V1
+        if kwargs.get("quantize", False)
+        else MobileNet_V2_Weights.ImageNet1K_V1,
+    )
+)
 def mobilenet_v2(
+    *,
     weights: Optional[Union[MobileNet_V2_QuantizedWeights, MobileNet_V2_Weights]] = None,
     progress: bool = True,
     quantize: bool = False,
     **kwargs: Any,
 ) -> QuantizableMobileNetV2:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        default_value = (
-            MobileNet_V2_QuantizedWeights.ImageNet1K_QNNPACK_V1 if quantize else MobileNet_V2_Weights.ImageNet1K_V1
-        )
-        weights = _deprecated_param(kwargs, "pretrained", "weights", default_value)  # type: ignore[assignment]
-    if quantize:
-        weights = MobileNet_V2_QuantizedWeights.verify(weights)
-    else:
-        weights = MobileNet_V2_Weights.verify(weights)
+    weights = (MobileNet_V2_QuantizedWeights if quantize else MobileNet_V2_Weights).verify(weights)
 
     if weights is not None:
         _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))

torchvision/prototype/models/quantization/mobilenetv3.py

@@ -13,7 +13,7 @@ from ....models.quantization.mobilenetv3 import (
 )
 from .._api import WeightsEnum, Weights
 from .._meta import _IMAGENET_CATEGORIES
-from .._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
+from .._utils import handle_legacy_interface, _ovewrite_named_param
 from ..mobilenetv3 import MobileNet_V3_Large_Weights, _mobilenet_v3_conf
 
 
@@ -75,25 +75,22 @@ class MobileNet_V3_Large_QuantizedWeights(WeightsEnum):
     default = ImageNet1K_QNNPACK_V1
 
 
+@handle_legacy_interface(
+    weights=(
+        "pretrained",
+        lambda kwargs: MobileNet_V3_Large_QuantizedWeights.ImageNet1K_QNNPACK_V1
+        if kwargs.get("quantize", False)
+        else MobileNet_V3_Large_Weights.ImageNet1K_V1,
+    )
+)
 def mobilenet_v3_large(
+    *,
     weights: Optional[Union[MobileNet_V3_Large_QuantizedWeights, MobileNet_V3_Large_Weights]] = None,
     progress: bool = True,
     quantize: bool = False,
     **kwargs: Any,
 ) -> QuantizableMobileNetV3:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        default_value = (
-            MobileNet_V3_Large_QuantizedWeights.ImageNet1K_QNNPACK_V1
-            if quantize
-            else MobileNet_V3_Large_Weights.ImageNet1K_V1
-        )
-        weights = _deprecated_param(kwargs, "pretrained", "weights", default_value)  # type: ignore[assignment]
-    if quantize:
-        weights = MobileNet_V3_Large_QuantizedWeights.verify(weights)
-    else:
-        weights = MobileNet_V3_Large_Weights.verify(weights)
+    weights = (MobileNet_V3_Large_QuantizedWeights if quantize else MobileNet_V3_Large_Weights).verify(weights)
 
     inverted_residual_setting, last_channel = _mobilenet_v3_conf("mobilenet_v3_large", **kwargs)
     return _mobilenet_v3_model(inverted_residual_setting, last_channel, weights, progress, quantize, **kwargs)