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>

torchvision/prototype/models/quantization/resnet.py

@@ -13,7 +13,7 @@ from ....models.quantization.resnet 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 ..resnet import ResNet18_Weights, ResNet50_Weights, ResNeXt101_32X8D_Weights
 
 
@@ -125,63 +125,62 @@ class ResNeXt101_32X8D_QuantizedWeights(WeightsEnum):
     default = ImageNet1K_FBGEMM_V2
 
 
+@handle_legacy_interface(
+    weights=(
+        "pretrained",
+        lambda kwargs: ResNet18_QuantizedWeights.ImageNet1K_FBGEMM_V1
+        if kwargs.get("quantize", False)
+        else ResNet18_Weights.ImageNet1K_V1,
+    )
+)
 def resnet18(
+    *,
     weights: Optional[Union[ResNet18_QuantizedWeights, ResNet18_Weights]] = None,
     progress: bool = True,
     quantize: bool = False,
     **kwargs: Any,
 ) -> QuantizableResNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        default_value = ResNet18_QuantizedWeights.ImageNet1K_FBGEMM_V1 if quantize else ResNet18_Weights.ImageNet1K_V1
-        weights = _deprecated_param(kwargs, "pretrained", "weights", default_value)  # type: ignore[assignment]
-    if quantize:
-        weights = ResNet18_QuantizedWeights.verify(weights)
-    else:
-        weights = ResNet18_Weights.verify(weights)
+    weights = (ResNet18_QuantizedWeights if quantize else ResNet18_Weights).verify(weights)
 
     return _resnet(QuantizableBasicBlock, [2, 2, 2, 2], weights, progress, quantize, **kwargs)
 
 
+@handle_legacy_interface(
+    weights=(
+        "pretrained",
+        lambda kwargs: ResNet50_QuantizedWeights.ImageNet1K_FBGEMM_V1
+        if kwargs.get("quantize", False)
+        else ResNet50_Weights.ImageNet1K_V1,
+    )
+)
 def resnet50(
+    *,
     weights: Optional[Union[ResNet50_QuantizedWeights, ResNet50_Weights]] = None,
     progress: bool = True,
     quantize: bool = False,
     **kwargs: Any,
 ) -> QuantizableResNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        default_value = ResNet50_QuantizedWeights.ImageNet1K_FBGEMM_V1 if quantize else ResNet50_Weights.ImageNet1K_V1
-        weights = _deprecated_param(kwargs, "pretrained", "weights", default_value)  # type: ignore[assignment]
-    if quantize:
-        weights = ResNet50_QuantizedWeights.verify(weights)
-    else:
-        weights = ResNet50_Weights.verify(weights)
+    weights = (ResNet50_QuantizedWeights if quantize else ResNet50_Weights).verify(weights)
 
     return _resnet(QuantizableBottleneck, [3, 4, 6, 3], weights, progress, quantize, **kwargs)
 
 
+@handle_legacy_interface(
+    weights=(
+        "pretrained",
+        lambda kwargs: ResNeXt101_32X8D_QuantizedWeights.ImageNet1K_FBGEMM_V1
+        if kwargs.get("quantize", False)
+        else ResNeXt101_32X8D_Weights.ImageNet1K_V1,
+    )
+)
 def resnext101_32x8d(
+    *,
     weights: Optional[Union[ResNeXt101_32X8D_QuantizedWeights, ResNeXt101_32X8D_Weights]] = None,
     progress: bool = True,
     quantize: bool = False,
     **kwargs: Any,
 ) -> QuantizableResNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        default_value = (
-            ResNeXt101_32X8D_QuantizedWeights.ImageNet1K_FBGEMM_V1
-            if quantize
-            else ResNeXt101_32X8D_Weights.ImageNet1K_V1
-        )
-        weights = _deprecated_param(kwargs, "pretrained", "weights", default_value)  # type: ignore[assignment]
-    if quantize:
-        weights = ResNeXt101_32X8D_QuantizedWeights.verify(weights)
-    else:
-        weights = ResNeXt101_32X8D_Weights.verify(weights)
+    weights = (ResNeXt101_32X8D_QuantizedWeights if quantize else ResNeXt101_32X8D_Weights).verify(weights)
 
     _ovewrite_named_param(kwargs, "groups", 32)
     _ovewrite_named_param(kwargs, "width_per_group", 8)

torchvision/prototype/models/quantization/shufflenetv2.py

@@ -11,7 +11,7 @@ from ....models.quantization.shufflenetv2 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 ..shufflenetv2 import ShuffleNet_V2_X0_5_Weights, ShuffleNet_V2_X1_0_Weights
 
 
@@ -27,6 +27,7 @@ __all__ = [
 def _shufflenetv2(
     stages_repeats: List[int],
     stages_out_channels: List[int],
+    *,
     weights: Optional[WeightsEnum],
     progress: bool,
     quantize: bool,
@@ -87,47 +88,43 @@ class ShuffleNet_V2_X1_0_QuantizedWeights(WeightsEnum):
     default = ImageNet1K_FBGEMM_V1
 
 
+@handle_legacy_interface(
+    weights=(
+        "pretrained",
+        lambda kwargs: ShuffleNet_V2_X0_5_QuantizedWeights.ImageNet1K_FBGEMM_V1
+        if kwargs.get("quantize", False)
+        else ShuffleNet_V2_X0_5_Weights.ImageNet1K_V1,
+    )
+)
 def shufflenet_v2_x0_5(
+    *,
     weights: Optional[Union[ShuffleNet_V2_X0_5_QuantizedWeights, ShuffleNet_V2_X0_5_Weights]] = None,
     progress: bool = True,
     quantize: bool = False,
     **kwargs: Any,
 ) -> QuantizableShuffleNetV2:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        default_value = (
-            ShuffleNet_V2_X0_5_QuantizedWeights.ImageNet1K_FBGEMM_V1
-            if quantize
-            else ShuffleNet_V2_X0_5_Weights.ImageNet1K_V1
+    weights = (ShuffleNet_V2_X0_5_QuantizedWeights if quantize else ShuffleNet_V2_X0_5_Weights).verify(weights)
+    return _shufflenetv2(
+        [4, 8, 4], [24, 48, 96, 192, 1024], weights=weights, progress=progress, quantize=quantize, **kwargs
     )
-        weights = _deprecated_param(kwargs, "pretrained", "weights", default_value)  # type: ignore[assignment]
-    if quantize:
-        weights = ShuffleNet_V2_X0_5_QuantizedWeights.verify(weights)
-    else:
-        weights = ShuffleNet_V2_X0_5_Weights.verify(weights)
-
-    return _shufflenetv2([4, 8, 4], [24, 48, 96, 192, 1024], weights, progress, quantize, **kwargs)
 
 
+@handle_legacy_interface(
+    weights=(
+        "pretrained",
+        lambda kwargs: ShuffleNet_V2_X1_0_QuantizedWeights.ImageNet1K_FBGEMM_V1
+        if kwargs.get("quantize", False)
+        else ShuffleNet_V2_X1_0_Weights.ImageNet1K_V1,
+    )
+)
 def shufflenet_v2_x1_0(
+    *,
     weights: Optional[Union[ShuffleNet_V2_X1_0_QuantizedWeights, ShuffleNet_V2_X1_0_Weights]] = None,
     progress: bool = True,
     quantize: bool = False,
     **kwargs: Any,
 ) -> QuantizableShuffleNetV2:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        default_value = (
-            ShuffleNet_V2_X1_0_QuantizedWeights.ImageNet1K_FBGEMM_V1
-            if quantize
-            else ShuffleNet_V2_X1_0_Weights.ImageNet1K_V1
+    weights = (ShuffleNet_V2_X1_0_QuantizedWeights if quantize else ShuffleNet_V2_X1_0_Weights).verify(weights)
+    return _shufflenetv2(
+        [4, 8, 4], [24, 116, 232, 464, 1024], weights=weights, progress=progress, quantize=quantize, **kwargs
     )
-        weights = _deprecated_param(kwargs, "pretrained", "weights", default_value)  # type: ignore[assignment]
-    if quantize:
-        weights = ShuffleNet_V2_X1_0_QuantizedWeights.verify(weights)
-    else:
-        weights = ShuffleNet_V2_X1_0_Weights.verify(weights)
-
-    return _shufflenetv2([4, 8, 4], [24, 116, 232, 464, 1024], weights, progress, quantize, **kwargs)

torchvision/prototype/models/regnet.py

@@ -8,7 +8,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ...models.regnet import RegNet, BlockParams
 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__ = [
@@ -260,33 +260,24 @@ class RegNet_X_32GF_Weights(WeightsEnum):
     default = ImageNet1K_V1
 
 
-def regnet_y_400mf(weights: Optional[RegNet_Y_400MF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", RegNet_Y_400MF_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", RegNet_Y_400MF_Weights.ImageNet1K_V1))
+def regnet_y_400mf(*, weights: Optional[RegNet_Y_400MF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
     weights = RegNet_Y_400MF_Weights.verify(weights)
 
     params = BlockParams.from_init_params(depth=16, w_0=48, w_a=27.89, w_m=2.09, group_width=8, se_ratio=0.25, **kwargs)
     return _regnet(params, weights, progress, **kwargs)
 
 
-def regnet_y_800mf(weights: Optional[RegNet_Y_800MF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", RegNet_Y_800MF_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", RegNet_Y_800MF_Weights.ImageNet1K_V1))
+def regnet_y_800mf(*, weights: Optional[RegNet_Y_800MF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
     weights = RegNet_Y_800MF_Weights.verify(weights)
 
     params = BlockParams.from_init_params(depth=14, w_0=56, w_a=38.84, w_m=2.4, group_width=16, se_ratio=0.25, **kwargs)
     return _regnet(params, weights, progress, **kwargs)
 
 
-def regnet_y_1_6gf(weights: Optional[RegNet_Y_1_6GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", RegNet_Y_1_6GF_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", RegNet_Y_1_6GF_Weights.ImageNet1K_V1))
+def regnet_y_1_6gf(*, weights: Optional[RegNet_Y_1_6GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
     weights = RegNet_Y_1_6GF_Weights.verify(weights)
 
     params = BlockParams.from_init_params(
@@ -295,11 +286,8 @@ def regnet_y_1_6gf(weights: Optional[RegNet_Y_1_6GF_Weights] = None, progress: b
     return _regnet(params, weights, progress, **kwargs)
 
 
-def regnet_y_3_2gf(weights: Optional[RegNet_Y_3_2GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", RegNet_Y_3_2GF_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", RegNet_Y_3_2GF_Weights.ImageNet1K_V1))
+def regnet_y_3_2gf(*, weights: Optional[RegNet_Y_3_2GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
     weights = RegNet_Y_3_2GF_Weights.verify(weights)
 
     params = BlockParams.from_init_params(
@@ -308,11 +296,8 @@ def regnet_y_3_2gf(weights: Optional[RegNet_Y_3_2GF_Weights] = None, progress: b
     return _regnet(params, weights, progress, **kwargs)
 
 
-def regnet_y_8gf(weights: Optional[RegNet_Y_8GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", RegNet_Y_8GF_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", RegNet_Y_8GF_Weights.ImageNet1K_V1))
+def regnet_y_8gf(*, weights: Optional[RegNet_Y_8GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
     weights = RegNet_Y_8GF_Weights.verify(weights)
 
     params = BlockParams.from_init_params(
@@ -321,11 +306,8 @@ def regnet_y_8gf(weights: Optional[RegNet_Y_8GF_Weights] = None, progress: bool
     return _regnet(params, weights, progress, **kwargs)
 
 
-def regnet_y_16gf(weights: Optional[RegNet_Y_16GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", RegNet_Y_16GF_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", RegNet_Y_16GF_Weights.ImageNet1K_V1))
+def regnet_y_16gf(*, weights: Optional[RegNet_Y_16GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
     weights = RegNet_Y_16GF_Weights.verify(weights)
 
     params = BlockParams.from_init_params(
@@ -334,11 +316,8 @@ def regnet_y_16gf(weights: Optional[RegNet_Y_16GF_Weights] = None, progress: boo
     return _regnet(params, weights, progress, **kwargs)
 
 
-def regnet_y_32gf(weights: Optional[RegNet_Y_32GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", RegNet_Y_32GF_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", RegNet_Y_32GF_Weights.ImageNet1K_V1))
+def regnet_y_32gf(*, weights: Optional[RegNet_Y_32GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
     weights = RegNet_Y_32GF_Weights.verify(weights)
 
     params = BlockParams.from_init_params(
@@ -347,77 +326,56 @@ def regnet_y_32gf(weights: Optional[RegNet_Y_32GF_Weights] = None, progress: boo
     return _regnet(params, weights, progress, **kwargs)
 
 
-def regnet_x_400mf(weights: Optional[RegNet_X_400MF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", RegNet_X_400MF_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", RegNet_X_400MF_Weights.ImageNet1K_V1))
+def regnet_x_400mf(*, weights: Optional[RegNet_X_400MF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
     weights = RegNet_X_400MF_Weights.verify(weights)
 
     params = BlockParams.from_init_params(depth=22, w_0=24, w_a=24.48, w_m=2.54, group_width=16, **kwargs)
     return _regnet(params, weights, progress, **kwargs)
 
 
-def regnet_x_800mf(weights: Optional[RegNet_X_800MF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", RegNet_X_800MF_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", RegNet_X_800MF_Weights.ImageNet1K_V1))
+def regnet_x_800mf(*, weights: Optional[RegNet_X_800MF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
     weights = RegNet_X_800MF_Weights.verify(weights)
 
     params = BlockParams.from_init_params(depth=16, w_0=56, w_a=35.73, w_m=2.28, group_width=16, **kwargs)
     return _regnet(params, weights, progress, **kwargs)
 
 
-def regnet_x_1_6gf(weights: Optional[RegNet_X_1_6GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", RegNet_X_1_6GF_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", RegNet_X_1_6GF_Weights.ImageNet1K_V1))
+def regnet_x_1_6gf(*, weights: Optional[RegNet_X_1_6GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
     weights = RegNet_X_1_6GF_Weights.verify(weights)
 
     params = BlockParams.from_init_params(depth=18, w_0=80, w_a=34.01, w_m=2.25, group_width=24, **kwargs)
     return _regnet(params, weights, progress, **kwargs)
 
 
-def regnet_x_3_2gf(weights: Optional[RegNet_X_3_2GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", RegNet_X_3_2GF_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", RegNet_X_3_2GF_Weights.ImageNet1K_V1))
+def regnet_x_3_2gf(*, weights: Optional[RegNet_X_3_2GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
     weights = RegNet_X_3_2GF_Weights.verify(weights)
 
     params = BlockParams.from_init_params(depth=25, w_0=88, w_a=26.31, w_m=2.25, group_width=48, **kwargs)
     return _regnet(params, weights, progress, **kwargs)
 
 
-def regnet_x_8gf(weights: Optional[RegNet_X_8GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", RegNet_X_8GF_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", RegNet_X_8GF_Weights.ImageNet1K_V1))
+def regnet_x_8gf(*, weights: Optional[RegNet_X_8GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
     weights = RegNet_X_8GF_Weights.verify(weights)
 
     params = BlockParams.from_init_params(depth=23, w_0=80, w_a=49.56, w_m=2.88, group_width=120, **kwargs)
     return _regnet(params, weights, progress, **kwargs)
 
 
-def regnet_x_16gf(weights: Optional[RegNet_X_16GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", RegNet_X_16GF_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", RegNet_X_16GF_Weights.ImageNet1K_V1))
+def regnet_x_16gf(*, weights: Optional[RegNet_X_16GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
     weights = RegNet_X_16GF_Weights.verify(weights)
 
     params = BlockParams.from_init_params(depth=22, w_0=216, w_a=55.59, w_m=2.1, group_width=128, **kwargs)
     return _regnet(params, weights, progress, **kwargs)
 
 
-def regnet_x_32gf(weights: Optional[RegNet_X_32GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", RegNet_X_32GF_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", RegNet_X_32GF_Weights.ImageNet1K_V1))
+def regnet_x_32gf(*, weights: Optional[RegNet_X_32GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
     weights = RegNet_X_32GF_Weights.verify(weights)
 
     params = BlockParams.from_init_params(depth=23, w_0=320, w_a=69.86, w_m=2.0, group_width=168, **kwargs)

torchvision/prototype/models/resnet.py

@@ -7,7 +7,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ...models.resnet import BasicBlock, Bottleneck, ResNet
 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__ = [
@@ -250,61 +250,45 @@ class Wide_ResNet101_2_Weights(WeightsEnum):
     default = ImageNet1K_V2
 
 
-def resnet18(weights: Optional[ResNet18_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", ResNet18_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", ResNet18_Weights.ImageNet1K_V1))
+def resnet18(*, weights: Optional[ResNet18_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet:
     weights = ResNet18_Weights.verify(weights)
 
     return _resnet(BasicBlock, [2, 2, 2, 2], weights, progress, **kwargs)
 
 
-def resnet34(weights: Optional[ResNet34_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", ResNet34_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", ResNet34_Weights.ImageNet1K_V1))
+def resnet34(*, weights: Optional[ResNet34_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet:
     weights = ResNet34_Weights.verify(weights)
 
     return _resnet(BasicBlock, [3, 4, 6, 3], weights, progress, **kwargs)
 
 
-def resnet50(weights: Optional[ResNet50_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", ResNet50_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", ResNet50_Weights.ImageNet1K_V1))
+def resnet50(*, weights: Optional[ResNet50_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet:
     weights = ResNet50_Weights.verify(weights)
 
     return _resnet(Bottleneck, [3, 4, 6, 3], weights, progress, **kwargs)
 
 
-def resnet101(weights: Optional[ResNet101_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", ResNet101_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", ResNet101_Weights.ImageNet1K_V1))
+def resnet101(*, weights: Optional[ResNet101_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet:
     weights = ResNet101_Weights.verify(weights)
 
     return _resnet(Bottleneck, [3, 4, 23, 3], weights, progress, **kwargs)
 
 
-def resnet152(weights: Optional[ResNet152_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", ResNet152_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", ResNet152_Weights.ImageNet1K_V1))
+def resnet152(*, weights: Optional[ResNet152_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet:
     weights = ResNet152_Weights.verify(weights)
 
     return _resnet(Bottleneck, [3, 8, 36, 3], weights, progress, **kwargs)
 
 
-def resnext50_32x4d(weights: Optional[ResNeXt50_32X4D_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", ResNeXt50_32X4D_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", ResNeXt50_32X4D_Weights.ImageNet1K_V1))
+def resnext50_32x4d(
+    *, weights: Optional[ResNeXt50_32X4D_Weights] = None, progress: bool = True, **kwargs: Any
+) -> ResNet:
     weights = ResNeXt50_32X4D_Weights.verify(weights)
 
     _ovewrite_named_param(kwargs, "groups", 32)
@@ -312,13 +296,10 @@ def resnext50_32x4d(weights: Optional[ResNeXt50_32X4D_Weights] = None, progress:
     return _resnet(Bottleneck, [3, 4, 6, 3], weights, progress, **kwargs)
 
 
+@handle_legacy_interface(weights=("pretrained", ResNeXt101_32X8D_Weights.ImageNet1K_V1))
 def resnext101_32x8d(
-    weights: Optional[ResNeXt101_32X8D_Weights] = None, progress: bool = True, **kwargs: Any
+    *, weights: Optional[ResNeXt101_32X8D_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> ResNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", ResNeXt101_32X8D_Weights.ImageNet1K_V1)
     weights = ResNeXt101_32X8D_Weights.verify(weights)
 
     _ovewrite_named_param(kwargs, "groups", 32)
@@ -326,24 +307,20 @@ def resnext101_32x8d(
     return _resnet(Bottleneck, [3, 4, 23, 3], weights, progress, **kwargs)
 
 
-def wide_resnet50_2(weights: Optional[Wide_ResNet50_2_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", Wide_ResNet50_2_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", Wide_ResNet50_2_Weights.ImageNet1K_V1))
+def wide_resnet50_2(
+    *, weights: Optional[Wide_ResNet50_2_Weights] = None, progress: bool = True, **kwargs: Any
+) -> ResNet:
     weights = Wide_ResNet50_2_Weights.verify(weights)
 
     _ovewrite_named_param(kwargs, "width_per_group", 64 * 2)
     return _resnet(Bottleneck, [3, 4, 6, 3], weights, progress, **kwargs)
 
 
+@handle_legacy_interface(weights=("pretrained", Wide_ResNet101_2_Weights.ImageNet1K_V1))
 def wide_resnet101_2(
-    weights: Optional[Wide_ResNet101_2_Weights] = None, progress: bool = True, **kwargs: Any
+    *, weights: Optional[Wide_ResNet101_2_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> ResNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", Wide_ResNet101_2_Weights.ImageNet1K_V1)
     weights = Wide_ResNet101_2_Weights.verify(weights)
 
     _ovewrite_named_param(kwargs, "width_per_group", 64 * 2)

torchvision/prototype/models/segmentation/deeplabv3.py

@@ -7,7 +7,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ....models.segmentation.deeplabv3 import DeepLabV3, _deeplabv3_mobilenetv3, _deeplabv3_resnet
 from .._api import WeightsEnum, Weights
 from .._meta import _VOC_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, resnet101
 from ..resnet import ResNet50_Weights, ResNet101_Weights
@@ -72,7 +72,12 @@ class DeepLabV3_MobileNet_V3_Large_Weights(WeightsEnum):
     default = CocoWithVocLabels_V1
 
 
+@handle_legacy_interface(
+    weights=("pretrained", DeepLabV3_ResNet50_Weights.CocoWithVocLabels_V1),
+    weights_backbone=("pretrained_backbone", ResNet50_Weights.ImageNet1K_V1),
+)
 def deeplabv3_resnet50(
+    *,
     weights: Optional[DeepLabV3_ResNet50_Weights] = None,
     progress: bool = True,
     num_classes: Optional[int] = None,
@@ -80,17 +85,7 @@ def deeplabv3_resnet50(
     weights_backbone: Optional[ResNet50_Weights] = None,
     **kwargs: Any,
 ) -> DeepLabV3:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", DeepLabV3_ResNet50_Weights.CocoWithVocLabels_V1)
     weights = DeepLabV3_ResNet50_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:
@@ -109,7 +104,12 @@ def deeplabv3_resnet50(
     return model
 
 
+@handle_legacy_interface(
+    weights=("pretrained", DeepLabV3_ResNet101_Weights.CocoWithVocLabels_V1),
+    weights_backbone=("pretrained_backbone", ResNet101_Weights.ImageNet1K_V1),
+)
 def deeplabv3_resnet101(
+    *,
     weights: Optional[DeepLabV3_ResNet101_Weights] = None,
     progress: bool = True,
     num_classes: Optional[int] = None,
@@ -117,17 +117,7 @@ def deeplabv3_resnet101(
     weights_backbone: Optional[ResNet101_Weights] = None,
     **kwargs: Any,
 ) -> DeepLabV3:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", DeepLabV3_ResNet101_Weights.CocoWithVocLabels_V1)
     weights = DeepLabV3_ResNet101_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", ResNet101_Weights.ImageNet1K_V1
-        )
     weights_backbone = ResNet101_Weights.verify(weights_backbone)
 
     if weights is not None:
@@ -146,7 +136,12 @@ def deeplabv3_resnet101(
     return model
 
 
+@handle_legacy_interface(
+    weights=("pretrained", DeepLabV3_MobileNet_V3_Large_Weights.CocoWithVocLabels_V1),
+    weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.ImageNet1K_V1),
+)
 def deeplabv3_mobilenet_v3_large(
+    *,
     weights: Optional[DeepLabV3_MobileNet_V3_Large_Weights] = None,
     progress: bool = True,
     num_classes: Optional[int] = None,
@@ -154,19 +149,7 @@ def deeplabv3_mobilenet_v3_large(
     weights_backbone: Optional[MobileNet_V3_Large_Weights] = None,
     **kwargs: Any,
 ) -> DeepLabV3:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(
-            kwargs, "pretrained", "weights", DeepLabV3_MobileNet_V3_Large_Weights.CocoWithVocLabels_V1
-        )
     weights = DeepLabV3_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 weights is not None:

torchvision/prototype/models/segmentation/fcn.py

@@ -7,7 +7,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ....models.segmentation.fcn import FCN, _fcn_resnet
 from .._api import WeightsEnum, Weights
 from .._meta import _VOC_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, ResNet101_Weights, resnet50, resnet101
 
 
@@ -48,7 +48,12 @@ class FCN_ResNet101_Weights(WeightsEnum):
     default = CocoWithVocLabels_V1
 
 
+@handle_legacy_interface(
+    weights=("pretrained", FCN_ResNet50_Weights.CocoWithVocLabels_V1),
+    weights_backbone=("pretrained_backbone", ResNet50_Weights.ImageNet1K_V1),
+)
 def fcn_resnet50(
+    *,
     weights: Optional[FCN_ResNet50_Weights] = None,
     progress: bool = True,
     num_classes: Optional[int] = None,
@@ -56,17 +61,7 @@ def fcn_resnet50(
     weights_backbone: Optional[ResNet50_Weights] = None,
     **kwargs: Any,
 ) -> FCN:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", FCN_ResNet50_Weights.CocoWithVocLabels_V1)
     weights = FCN_ResNet50_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:
@@ -85,7 +80,12 @@ def fcn_resnet50(
     return model
 
 
+@handle_legacy_interface(
+    weights=("pretrained", FCN_ResNet101_Weights.CocoWithVocLabels_V1),
+    weights_backbone=("pretrained_backbone", ResNet101_Weights.ImageNet1K_V1),
+)
 def fcn_resnet101(
+    *,
     weights: Optional[FCN_ResNet101_Weights] = None,
     progress: bool = True,
     num_classes: Optional[int] = None,
@@ -93,17 +93,7 @@ def fcn_resnet101(
     weights_backbone: Optional[ResNet101_Weights] = None,
     **kwargs: Any,
 ) -> FCN:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", FCN_ResNet101_Weights.CocoWithVocLabels_V1)
     weights = FCN_ResNet101_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", ResNet101_Weights.ImageNet1K_V1
-        )
     weights_backbone = ResNet101_Weights.verify(weights_backbone)
 
     if weights is not None:

torchvision/prototype/models/segmentation/lraspp.py

@@ -7,7 +7,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ....models.segmentation.lraspp import LRASPP, _lraspp_mobilenetv3
 from .._api import WeightsEnum, Weights
 from .._meta import _VOC_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
 
 
@@ -29,7 +29,12 @@ class LRASPP_MobileNet_V3_Large_Weights(WeightsEnum):
     default = CocoWithVocLabels_V1
 
 
+@handle_legacy_interface(
+    weights=("pretrained", LRASPP_MobileNet_V3_Large_Weights.CocoWithVocLabels_V1),
+    weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.ImageNet1K_V1),
+)
 def lraspp_mobilenet_v3_large(
+    *,
     weights: Optional[LRASPP_MobileNet_V3_Large_Weights] = None,
     progress: bool = True,
     num_classes: Optional[int] = None,
@@ -39,19 +44,7 @@ def lraspp_mobilenet_v3_large(
     if kwargs.pop("aux_loss", False):
         raise NotImplementedError("This model does not use auxiliary loss")
 
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(
-            kwargs, "pretrained", "weights", LRASPP_MobileNet_V3_Large_Weights.CocoWithVocLabels_V1
-        )
     weights = LRASPP_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 weights is not None:

torchvision/prototype/models/shufflenetv2.py

@@ -7,7 +7,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ...models.shufflenetv2 import ShuffleNetV2
 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__ = [
@@ -82,49 +82,37 @@ class ShuffleNet_V2_X2_0_Weights(WeightsEnum):
     pass
 
 
+@handle_legacy_interface(weights=("pretrained", ShuffleNet_V2_X0_5_Weights.ImageNet1K_V1))
 def shufflenet_v2_x0_5(
-    weights: Optional[ShuffleNet_V2_X0_5_Weights] = None, progress: bool = True, **kwargs: Any
+    *, weights: Optional[ShuffleNet_V2_X0_5_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> ShuffleNetV2:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", ShuffleNet_V2_X0_5_Weights.ImageNet1K_V1)
     weights = ShuffleNet_V2_X0_5_Weights.verify(weights)
 
     return _shufflenetv2(weights, progress, [4, 8, 4], [24, 48, 96, 192, 1024], **kwargs)
 
 
+@handle_legacy_interface(weights=("pretrained", ShuffleNet_V2_X1_0_Weights.ImageNet1K_V1))
 def shufflenet_v2_x1_0(
-    weights: Optional[ShuffleNet_V2_X1_0_Weights] = None, progress: bool = True, **kwargs: Any
+    *, weights: Optional[ShuffleNet_V2_X1_0_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> ShuffleNetV2:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", ShuffleNet_V2_X1_0_Weights.ImageNet1K_V1)
     weights = ShuffleNet_V2_X1_0_Weights.verify(weights)
 
     return _shufflenetv2(weights, progress, [4, 8, 4], [24, 116, 232, 464, 1024], **kwargs)
 
 
+@handle_legacy_interface(weights=("pretrained", None))
 def shufflenet_v2_x1_5(
-    weights: Optional[ShuffleNet_V2_X1_5_Weights] = None, progress: bool = True, **kwargs: Any
+    *, weights: Optional[ShuffleNet_V2_X1_5_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> ShuffleNetV2:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", None)
     weights = ShuffleNet_V2_X1_5_Weights.verify(weights)
 
     return _shufflenetv2(weights, progress, [4, 8, 4], [24, 176, 352, 704, 1024], **kwargs)
 
 
+@handle_legacy_interface(weights=("pretrained", None))
 def shufflenet_v2_x2_0(
-    weights: Optional[ShuffleNet_V2_X2_0_Weights] = None, progress: bool = True, **kwargs: Any
+    *, weights: Optional[ShuffleNet_V2_X2_0_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> ShuffleNetV2:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", None)
     weights = ShuffleNet_V2_X2_0_Weights.verify(weights)
 
     return _shufflenetv2(weights, progress, [4, 8, 4], [24, 244, 488, 976, 2048], **kwargs)

torchvision/prototype/models/squeezenet.py

@@ -7,7 +7,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ...models.squeezenet import SqueezeNet
 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__ = ["SqueezeNet", "SqueezeNet1_0_Weights", "SqueezeNet1_1_Weights", "squeezenet1_0", "squeezenet1_1"]
@@ -47,11 +47,10 @@ class SqueezeNet1_1_Weights(WeightsEnum):
     default = ImageNet1K_V1
 
 
-def squeezenet1_0(weights: Optional[SqueezeNet1_0_Weights] = None, progress: bool = True, **kwargs: Any) -> SqueezeNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", SqueezeNet1_0_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", SqueezeNet1_0_Weights.ImageNet1K_V1))
+def squeezenet1_0(
+    *, weights: Optional[SqueezeNet1_0_Weights] = None, progress: bool = True, **kwargs: Any
+) -> SqueezeNet:
     weights = SqueezeNet1_0_Weights.verify(weights)
 
     if weights is not None:
@@ -65,11 +64,10 @@ def squeezenet1_0(weights: Optional[SqueezeNet1_0_Weights] = None, progress: boo
     return model
 
 
-def squeezenet1_1(weights: Optional[SqueezeNet1_1_Weights] = None, progress: bool = True, **kwargs: Any) -> SqueezeNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", SqueezeNet1_1_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", SqueezeNet1_1_Weights.ImageNet1K_V1))
+def squeezenet1_1(
+    *, weights: Optional[SqueezeNet1_1_Weights] = None, progress: bool = True, **kwargs: Any
+) -> SqueezeNet:
     weights = SqueezeNet1_1_Weights.verify(weights)
 
     if weights is not None:

torchvision/prototype/models/vgg.py

@@ -7,7 +7,7 @@ from torchvision.transforms.functional import InterpolationMode
 from ...models.vgg import VGG, make_layers, cfgs
 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__ = [
@@ -169,81 +169,57 @@ class VGG19_BN_Weights(WeightsEnum):
     default = ImageNet1K_V1
 
 
-def vgg11(weights: Optional[VGG11_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", VGG11_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", VGG11_Weights.ImageNet1K_V1))
+def vgg11(*, weights: Optional[VGG11_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
     weights = VGG11_Weights.verify(weights)
 
     return _vgg("A", False, weights, progress, **kwargs)
 
 
-def vgg11_bn(weights: Optional[VGG11_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", VGG11_BN_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", VGG11_BN_Weights.ImageNet1K_V1))
+def vgg11_bn(*, weights: Optional[VGG11_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
     weights = VGG11_BN_Weights.verify(weights)
 
     return _vgg("A", True, weights, progress, **kwargs)
 
 
-def vgg13(weights: Optional[VGG13_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", VGG13_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", VGG13_Weights.ImageNet1K_V1))
+def vgg13(*, weights: Optional[VGG13_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
     weights = VGG13_Weights.verify(weights)
 
     return _vgg("B", False, weights, progress, **kwargs)
 
 
-def vgg13_bn(weights: Optional[VGG13_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", VGG13_BN_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", VGG13_BN_Weights.ImageNet1K_V1))
+def vgg13_bn(*, weights: Optional[VGG13_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
     weights = VGG13_BN_Weights.verify(weights)
 
     return _vgg("B", True, weights, progress, **kwargs)
 
 
-def vgg16(weights: Optional[VGG16_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", VGG16_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", VGG16_Weights.ImageNet1K_V1))
+def vgg16(*, weights: Optional[VGG16_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
     weights = VGG16_Weights.verify(weights)
 
     return _vgg("D", False, weights, progress, **kwargs)
 
 
-def vgg16_bn(weights: Optional[VGG16_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", VGG16_BN_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", VGG16_BN_Weights.ImageNet1K_V1))
+def vgg16_bn(*, weights: Optional[VGG16_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
     weights = VGG16_BN_Weights.verify(weights)
 
     return _vgg("D", True, weights, progress, **kwargs)
 
 
-def vgg19(weights: Optional[VGG19_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", VGG19_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", VGG19_Weights.ImageNet1K_V1))
+def vgg19(*, weights: Optional[VGG19_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
     weights = VGG19_Weights.verify(weights)
 
     return _vgg("E", False, weights, progress, **kwargs)
 
 
-def vgg19_bn(weights: Optional[VGG19_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", VGG19_BN_Weights.ImageNet1K_V1)
+@handle_legacy_interface(weights=("pretrained", VGG19_BN_Weights.ImageNet1K_V1))
+def vgg19_bn(*, weights: Optional[VGG19_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
     weights = VGG19_BN_Weights.verify(weights)
 
     return _vgg("E", True, weights, progress, **kwargs)

torchvision/prototype/models/video/resnet.py

@@ -17,7 +17,7 @@ from ....models.video.resnet import (
 )
 from .._api import WeightsEnum, Weights
 from .._meta import _KINETICS400_CATEGORIES
-from .._utils import _deprecated_param, _deprecated_positional, _ovewrite_named_param
+from .._utils import handle_legacy_interface, _ovewrite_named_param
 
 
 __all__ = [
@@ -98,11 +98,8 @@ class R2Plus1D_18_Weights(WeightsEnum):
     default = Kinetics400_V1
 
 
-def r3d_18(weights: Optional[R3D_18_Weights] = None, progress: bool = True, **kwargs: Any) -> VideoResNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", R3D_18_Weights.Kinetics400_V1)
+@handle_legacy_interface(weights=("pretrained", R3D_18_Weights.Kinetics400_V1))
+def r3d_18(*, weights: Optional[R3D_18_Weights] = None, progress: bool = True, **kwargs: Any) -> VideoResNet:
     weights = R3D_18_Weights.verify(weights)
 
     return _video_resnet(
@@ -116,11 +113,8 @@ def r3d_18(weights: Optional[R3D_18_Weights] = None, progress: bool = True, **kw
     )
 
 
-def mc3_18(weights: Optional[MC3_18_Weights] = None, progress: bool = True, **kwargs: Any) -> VideoResNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", MC3_18_Weights.Kinetics400_V1)
+@handle_legacy_interface(weights=("pretrained", MC3_18_Weights.Kinetics400_V1))
+def mc3_18(*, weights: Optional[MC3_18_Weights] = None, progress: bool = True, **kwargs: Any) -> VideoResNet:
     weights = MC3_18_Weights.verify(weights)
 
     return _video_resnet(
@@ -134,11 +128,8 @@ def mc3_18(weights: Optional[MC3_18_Weights] = None, progress: bool = True, **kw
     )
 
 
-def r2plus1d_18(weights: Optional[R2Plus1D_18_Weights] = None, progress: bool = True, **kwargs: Any) -> VideoResNet:
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", R2Plus1D_18_Weights.Kinetics400_V1)
+@handle_legacy_interface(weights=("pretrained", R2Plus1D_18_Weights.Kinetics400_V1))
+def r2plus1d_18(*, weights: Optional[R2Plus1D_18_Weights] = None, progress: bool = True, **kwargs: Any) -> VideoResNet:
     weights = R2Plus1D_18_Weights.verify(weights)
 
     return _video_resnet(

torchvision/prototype/models/vision_transformer.py

@@ -12,7 +12,7 @@ import torch.nn as nn
 from torch import Tensor
 
 from ._api import WeightsEnum
-from ._utils import _deprecated_param, _deprecated_positional
+from ._utils import handle_legacy_interface
 
 
 __all__ = [
@@ -279,7 +279,8 @@ def _vision_transformer(
     return model
 
 
-def vit_b_16(weights: Optional[ViT_B_16_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer:
+@handle_legacy_interface(weights=("pretrained", None))
+def vit_b_16(*, weights: Optional[ViT_B_16_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer:
     """
     Constructs a vit_b_16 architecture from
     `"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" <https://arxiv.org/abs/2010.11929>`_.
@@ -289,10 +290,6 @@ def vit_b_16(weights: Optional[ViT_B_16_Weights] = None, progress: bool = True,
             Default: None.
         progress (bool, optional): If True, displays a progress bar of the download to stderr. Default: True.
     """
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", None)
     weights = ViT_B_16_Weights.verify(weights)
 
     return _vision_transformer(
@@ -307,7 +304,8 @@ def vit_b_16(weights: Optional[ViT_B_16_Weights] = None, progress: bool = True,
     )
 
 
-def vit_b_32(weights: Optional[ViT_B_32_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer:
+@handle_legacy_interface(weights=("pretrained", None))
+def vit_b_32(*, weights: Optional[ViT_B_32_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer:
     """
     Constructs a vit_b_32 architecture from
     `"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" <https://arxiv.org/abs/2010.11929>`_.
@@ -317,10 +315,6 @@ def vit_b_32(weights: Optional[ViT_B_32_Weights] = None, progress: bool = True,
             Default: None.
         progress (bool, optional): If True, displays a progress bar of the download to stderr. Default: True.
     """
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", None)
     weights = ViT_B_32_Weights.verify(weights)
 
     return _vision_transformer(
@@ -335,7 +329,8 @@ def vit_b_32(weights: Optional[ViT_B_32_Weights] = None, progress: bool = True,
     )
 
 
-def vit_l_16(weights: Optional[ViT_L_16_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer:
+@handle_legacy_interface(weights=("pretrained", None))
+def vit_l_16(*, weights: Optional[ViT_L_16_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer:
     """
     Constructs a vit_l_16 architecture from
     `"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" <https://arxiv.org/abs/2010.11929>`_.
@@ -345,10 +340,6 @@ def vit_l_16(weights: Optional[ViT_L_16_Weights] = None, progress: bool = True,
             Default: None.
         progress (bool, optional): If True, displays a progress bar of the download to stderr. Default: True.
     """
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", None)
     weights = ViT_L_16_Weights.verify(weights)
 
     return _vision_transformer(
@@ -363,7 +354,8 @@ def vit_l_16(weights: Optional[ViT_L_16_Weights] = None, progress: bool = True,
     )
 
 
-def vit_l_32(weights: Optional[ViT_L_32_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer:
+@handle_legacy_interface(weights=("pretrained", None))
+def vit_l_32(*, weights: Optional[ViT_L_32_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer:
     """
     Constructs a vit_l_32 architecture from
     `"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" <https://arxiv.org/abs/2010.11929>`_.
@@ -373,10 +365,6 @@ def vit_l_32(weights: Optional[ViT_L_32_Weights] = None, progress: bool = True,
             Default: None.
         progress (bool, optional): If True, displays a progress bar of the download to stderr. Default: True.
     """
-    if type(weights) == bool and weights:
-        _deprecated_positional(kwargs, "pretrained", "weights", True)
-    if "pretrained" in kwargs:
-        weights = _deprecated_param(kwargs, "pretrained", "weights", None)
     weights = ViT_L_32_Weights.verify(weights)
 
     return _vision_transformer(

torchvision/prototype/utils/_internal.py

 import collections.abc
 import difflib
 import enum
+import functools
+import inspect
 import os
 import os.path
 import textwrap
+import warnings
 from typing import Collection, Sequence, Callable, Any, Iterator, NoReturn, Mapping, TypeVar, Iterable, Tuple, cast
 
 __all__ = [
@@ -13,6 +16,7 @@ __all__ = [
     "FrozenMapping",
     "make_repr",
     "FrozenBunch",
+    "kwonly_to_pos_or_kw",
 ]
 
 
@@ -126,3 +130,54 @@ class FrozenBunch(FrozenMapping):
 
     def __repr__(self) -> str:
         return make_repr(type(self).__name__, self.items())
+
+
+def kwonly_to_pos_or_kw(fn: Callable[..., D]) -> Callable[..., D]:
+    """Decorates a function that uses keyword only parameters to also allow them being passed as positionals.
+
+    For example, consider the use case of changing the signature of ``old_fn`` into the one from ``new_fn``:
+
+    .. code::
+
+        def old_fn(foo, bar, baz=None):
+            ...
+
+        def new_fn(foo, *, bar, baz=None):
+            ...
+
+    Calling ``old_fn("foo", "bar, "baz")`` was valid, but the same call is no longer valid with ``new_fn``. To keep BC
+    and at the same time warn the user of the deprecation, this decorator can be used:
+
+    .. code::
+
+        @kwonly_to_pos_or_kw
+        def new_fn(foo, *, bar, baz=None):
+            ...
+
+        new_fn("foo", "bar, "baz")
+    """
+    params = inspect.signature(fn).parameters
+
+    try:
+        keyword_only_start_idx = next(
+            idx for idx, param in enumerate(params.values()) if param.kind == param.KEYWORD_ONLY
+        )
+    except StopIteration:
+        raise TypeError(f"Found no keyword-only parameter on function '{fn.__name__}'") from None
+
+    keyword_only_params = tuple(inspect.signature(fn).parameters)[keyword_only_start_idx:]
+
+    @functools.wraps(fn)
+    def wrapper(*args: Any, **kwargs: Any) -> D:
+        args, keyword_only_args = args[:keyword_only_start_idx], args[keyword_only_start_idx:]
+        if keyword_only_args:
+            keyword_only_kwargs = dict(zip(keyword_only_params, keyword_only_args))
+            warnings.warn(
+                f"Using {sequence_to_str(tuple(keyword_only_kwargs.keys()), separate_last='and ')} as positional "
+                f"parameter(s) is deprecated. Please use keyword parameter(s) instead."
+            )
+            kwargs.update(keyword_only_kwargs)
+
+        return fn(*args, **kwargs)
+
+    return wrapper