Provide helper methods for parameter checks to reduce duplicate code.

torchvision/prototype/models/_utils.py

@@ -0,0 +1,20 @@
+import warnings
+from typing import Any, Dict, Optional, TypeVar
+
+from ._api import Weights
+
+
+W = TypeVar("W", bound=Weights)
+
+
+def _deprecated_param(
+    deprecated_param: str, new_param: str, default_value: Optional[W], kwargs: Dict[str, Any]
+) -> Optional[W]:
+    warnings.warn(f"The parameter '{deprecated_param}' is deprecated, please use {new_param} instead.")
+    if kwargs.pop(deprecated_param):
+        if default_value is not None:
+            return default_value
+        else:
+            raise ValueError("No checkpoint is available for model.")
+    else:
+        return None

torchvision/prototype/models/alexnet.py

@@ -1,4 +1,3 @@
-import warnings
 from functools import partial
 from typing import Any, Optional
 
@@ -8,6 +7,7 @@
 from ...models.alexnet import AlexNet
 from ._api import Weights, WeightEntry
 from ._meta import _IMAGENET_CATEGORIES
+from ._utils import _deprecated_param
 
 
 __all__ = ["AlexNet", "AlexNetWeights", "alexnet"]
@@ -30,9 +30,9 @@ class AlexNetWeights(Weights):
 
 def alexnet(weights: Optional[AlexNetWeights] = None, progress: bool = True, **kwargs: Any) -> AlexNet:
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = AlexNetWeights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param("pretrained", "weights", AlexNetWeights.ImageNet1K_RefV1, kwargs)
     weights = AlexNetWeights.verify(weights)
+
     if weights is not None:
         kwargs["num_classes"] = len(weights.meta["categories"])
 

torchvision/prototype/models/densenet.py

@@ -1,5 +1,4 @@
 import re
-import warnings
 from functools import partial
 from typing import Any, Optional, Tuple
 
@@ -10,6 +9,7 @@
 from ...models.densenet import DenseNet
 from ._api import Weights, WeightEntry
 from ._meta import _IMAGENET_CATEGORIES
+from ._utils import _deprecated_param
 
 
 __all__ = [
@@ -121,35 +121,31 @@ class DenseNet201Weights(Weights):
 
 def densenet121(weights: Optional[DenseNet121Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = DenseNet121Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
+        weights = _deprecated_param("pretrained", "weights", DenseNet121Weights.ImageNet1K_Community, kwargs)
     weights = DenseNet121Weights.verify(weights)
 
     return _densenet(32, (6, 12, 24, 16), 64, weights, progress, **kwargs)
 
 
 def densenet161(weights: Optional[DenseNet161Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = DenseNet161Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
+        weights = _deprecated_param("pretrained", "weights", DenseNet161Weights.ImageNet1K_Community, kwargs)
     weights = DenseNet161Weights.verify(weights)
 
     return _densenet(48, (6, 12, 36, 24), 96, weights, progress, **kwargs)
 
 
 def densenet169(weights: Optional[DenseNet169Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = DenseNet169Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
+        weights = _deprecated_param("pretrained", "weights", DenseNet169Weights.ImageNet1K_Community, kwargs)
     weights = DenseNet169Weights.verify(weights)
 
     return _densenet(32, (6, 12, 32, 32), 64, weights, progress, **kwargs)
 
 
 def densenet201(weights: Optional[DenseNet201Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = DenseNet201Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
+        weights = _deprecated_param("pretrained", "weights", DenseNet201Weights.ImageNet1K_Community, kwargs)
     weights = DenseNet201Weights.verify(weights)
 
     return _densenet(32, (6, 12, 48, 32), 64, weights, progress, **kwargs)

torchvision/prototype/models/detection/faster_rcnn.py

@@ -1,4 +1,3 @@
-import warnings
 from typing import Any, Optional, Union
 
 from torchvision.prototype.transforms import CocoEval
@@ -15,6 +14,7 @@
 )
 from .._api import Weights, WeightEntry
 from .._meta import _COCO_CATEGORIES
+from .._utils import _deprecated_param
 from ..mobilenetv3 import MobileNetV3LargeWeights, mobilenet_v3_large
 from ..resnet import ResNet50Weights, resnet50
 
@@ -81,12 +81,12 @@ def fasterrcnn_resnet50_fpn(
     **kwargs: Any,
 ) -> FasterRCNN:
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = FasterRCNNResNet50FPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param("pretrained", "weights", FasterRCNNResNet50FPNWeights.Coco_RefV1, kwargs)
     weights = FasterRCNNResNet50FPNWeights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The parameter pretrained_backbone is deprecated, please use weights_backbone instead.")
-        weights_backbone = ResNet50Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained_backbone") else None
+        weights_backbone = _deprecated_param(
+            "pretrained_backbone", "weights_backbone", ResNet50Weights.ImageNet1K_RefV1, kwargs
+        )
     weights_backbone = ResNet50Weights.verify(weights_backbone)
 
     if weights is not None:
@@ -160,12 +160,12 @@ def fasterrcnn_mobilenet_v3_large_fpn(
     **kwargs: Any,
 ) -> FasterRCNN:
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = FasterRCNNMobileNetV3LargeFPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param("pretrained", "weights", FasterRCNNMobileNetV3LargeFPNWeights.Coco_RefV1, kwargs)
     weights = FasterRCNNMobileNetV3LargeFPNWeights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The parameter pretrained_backbone is deprecated, please use weights_backbone instead.")
-        weights_backbone = MobileNetV3LargeWeights.ImageNet1K_RefV1 if kwargs.pop("pretrained_backbone") else None
+        weights_backbone = _deprecated_param(
+            "pretrained_backbone", "weights_backbone", MobileNetV3LargeWeights.ImageNet1K_RefV1, kwargs
+        )
     weights_backbone = MobileNetV3LargeWeights.verify(weights_backbone)
 
     defaults = {
@@ -192,12 +192,12 @@ def fasterrcnn_mobilenet_v3_large_320_fpn(
     **kwargs: Any,
 ) -> FasterRCNN:
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = FasterRCNNMobileNetV3Large320FPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param("pretrained", "weights", FasterRCNNMobileNetV3Large320FPNWeights.Coco_RefV1, kwargs)
     weights = FasterRCNNMobileNetV3Large320FPNWeights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The parameter pretrained_backbone is deprecated, please use weights_backbone instead.")
-        weights_backbone = MobileNetV3LargeWeights.ImageNet1K_RefV1 if kwargs.pop("pretrained_backbone") else None
+        weights_backbone = _deprecated_param(
+            "pretrained_backbone", "weights_backbone", MobileNetV3LargeWeights.ImageNet1K_RefV1, kwargs
+        )
     weights_backbone = MobileNetV3LargeWeights.verify(weights_backbone)
 
     defaults = {

torchvision/prototype/models/detection/keypoint_rcnn.py

@@ -1,4 +1,3 @@
-import warnings
 from typing import Any, Optional
 
 from torchvision.prototype.transforms import CocoEval
@@ -12,6 +11,7 @@
 )
 from .._api import Weights, WeightEntry
 from .._meta import _COCO_PERSON_CATEGORIES, _COCO_PERSON_KEYPOINT_NAMES
+from .._utils import _deprecated_param
 from ..resnet import ResNet50Weights, resnet50
 
 
@@ -58,18 +58,16 @@ def keypointrcnn_resnet50_fpn(
     **kwargs: Any,
 ) -> KeypointRCNN:
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        pretrained = kwargs.pop("pretrained")
-        if type(pretrained) == str and pretrained == "legacy":
-            weights = KeypointRCNNResNet50FPNWeights.Coco_RefV1_Legacy
-        elif type(pretrained) == bool and pretrained:
-            weights = KeypointRCNNResNet50FPNWeights.Coco_RefV1
-        else:
-            weights = None
+        default_value = KeypointRCNNResNet50FPNWeights.Coco_RefV1
+        if kwargs["pretrained"] == "legacy":
+            default_value = KeypointRCNNResNet50FPNWeights.Coco_RefV1_Legacy
+            kwargs["pretrained"] = True
+        weights = _deprecated_param("pretrained", "weights", default_value, kwargs)
     weights = KeypointRCNNResNet50FPNWeights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The parameter pretrained_backbone is deprecated, please use weights_backbone instead.")
-        weights_backbone = ResNet50Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained_backbone") else None
+        weights_backbone = _deprecated_param(
+            "pretrained_backbone", "weights_backbone", ResNet50Weights.ImageNet1K_RefV1, kwargs
+        )
     weights_backbone = ResNet50Weights.verify(weights_backbone)
 
     if weights is not None:

torchvision/prototype/models/detection/mask_rcnn.py

@@ -1,4 +1,3 @@
-import warnings
 from typing import Any, Optional
 
 from torchvision.prototype.transforms import CocoEval
@@ -13,6 +12,7 @@
 )
 from .._api import Weights, WeightEntry
 from .._meta import _COCO_CATEGORIES
+from .._utils import _deprecated_param
 from ..resnet import ResNet50Weights, resnet50
 
 
@@ -46,12 +46,12 @@ def maskrcnn_resnet50_fpn(
     **kwargs: Any,
 ) -> MaskRCNN:
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = MaskRCNNResNet50FPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param("pretrained", "weights", MaskRCNNResNet50FPNWeights.Coco_RefV1, kwargs)
     weights = MaskRCNNResNet50FPNWeights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The parameter pretrained_backbone is deprecated, please use weights_backbone instead.")
-        weights_backbone = ResNet50Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained_backbone") else None
+        weights_backbone = _deprecated_param(
+            "pretrained_backbone", "weights_backbone", ResNet50Weights.ImageNet1K_RefV1, kwargs
+        )
     weights_backbone = ResNet50Weights.verify(weights_backbone)
 
     if weights is not None:

torchvision/prototype/models/detection/retinanet.py

@@ -1,4 +1,3 @@
-import warnings
 from typing import Any, Optional
 
 from torchvision.prototype.transforms import CocoEval
@@ -14,6 +13,7 @@
 )
 from .._api import Weights, WeightEntry
 from .._meta import _COCO_CATEGORIES
+from .._utils import _deprecated_param
 from ..resnet import ResNet50Weights, resnet50
 
 
@@ -46,12 +46,12 @@ def retinanet_resnet50_fpn(
     **kwargs: Any,
 ) -> RetinaNet:
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = RetinaNetResNet50FPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param("pretrained", "weights", RetinaNetResNet50FPNWeights.Coco_RefV1, kwargs)
     weights = RetinaNetResNet50FPNWeights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The parameter pretrained_backbone is deprecated, please use weights_backbone instead.")
-        weights_backbone = ResNet50Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained_backbone") else None
+        weights_backbone = _deprecated_param(
+            "pretrained_backbone", "weights_backbone", ResNet50Weights.ImageNet1K_RefV1, kwargs
+        )
     weights_backbone = ResNet50Weights.verify(weights_backbone)
 
     if weights is not None:

torchvision/prototype/models/detection/ssd.py

@@ -12,6 +12,7 @@
 )
 from .._api import Weights, WeightEntry
 from .._meta import _COCO_CATEGORIES
+from .._utils import _deprecated_param
 from ..vgg import VGG16Weights, vgg16
 
 
@@ -44,12 +45,12 @@ def ssd300_vgg16(
     **kwargs: Any,
 ) -> SSD:
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = SSD300VGG16Weights.Coco_RefV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param("pretrained", "weights", SSD300VGG16Weights.Coco_RefV1, kwargs)
     weights = SSD300VGG16Weights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The parameter pretrained_backbone is deprecated, please use weights_backbone instead.")
-        weights_backbone = VGG16Weights.ImageNet1K_Features if kwargs.pop("pretrained_backbone") else None
+        weights_backbone = _deprecated_param(
+            "pretrained_backbone", "weights_backbone", VGG16Weights.ImageNet1K_Features, kwargs
+        )
     weights_backbone = VGG16Weights.verify(weights_backbone)
 
     if "size" in kwargs:

torchvision/prototype/models/detection/ssdlite.py

@@ -17,6 +17,7 @@
 )
 from .._api import Weights, WeightEntry
 from .._meta import _COCO_CATEGORIES
+from .._utils import _deprecated_param
 from ..mobilenetv3 import MobileNetV3LargeWeights, mobilenet_v3_large
 
 
@@ -50,12 +51,12 @@ def ssdlite320_mobilenet_v3_large(
     **kwargs: Any,
 ) -> SSD:
     if "pretrained" in kwargs:
-        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
-        weights = SSDlite320MobileNetV3LargeFPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
+        weights = _deprecated_param("pretrained", "weights", SSDlite320MobileNetV3LargeFPNWeights.Coco_RefV1, kwargs)
     weights = SSDlite320MobileNetV3LargeFPNWeights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The parameter pretrained_backbone is deprecated, please use weights_backbone instead.")
-        weights_backbone = MobileNetV3LargeWeights.ImageNet1K_RefV1 if kwargs.pop("pretrained_backbone") else None
+        weights_backbone = _deprecated_param(
+            "pretrained_backbone", "weights_backbone", MobileNetV3LargeWeights.ImageNet1K_RefV1, kwargs
+        )
     weights_backbone = MobileNetV3LargeWeights.verify(weights_backbone)
 
     if "size" in kwargs: