Refactor the get_weights API

references/classification/train.py

@@ -158,8 +158,7 @@ def load_data(traindir, valdir, args):
                 crop_size=val_crop_size, resize_size=val_resize_size, interpolation=interpolation
             )
         else:
-            fn = PM.quantization.__dict__[args.model] if hasattr(args, "backend") else PM.__dict__[args.model]
-            weights = PM._api.get_weight(fn, args.weights)
+            weights = PM.get_weight(args.weights)
             preprocessing = weights.transforms()
 
         dataset_test = torchvision.datasets.ImageFolder(

references/detection/train.py

@@ -53,8 +53,7 @@ def get_transform(train, args):
     elif not args.weights:
         return presets.DetectionPresetEval()
     else:
-        fn = PM.detection.__dict__[args.model]
-        weights = PM._api.get_weight(fn, args.weights)
+        weights = PM.get_weight(args.weights)
         return weights.transforms()
 
 

references/segmentation/train.py

@@ -38,8 +38,7 @@ def get_transform(train, args):
     elif not args.weights:
         return presets.SegmentationPresetEval(base_size=520)
     else:
-        fn = PM.segmentation.__dict__[args.model]
-        weights = PM._api.get_weight(fn, args.weights)
+        weights = PM.get_weight(args.weights)
         return weights.transforms()
 
 

references/video_classification/train.py

@@ -160,8 +160,7 @@ def main(args):
     if not args.weights:
         transform_test = presets.VideoClassificationPresetEval((128, 171), (112, 112))
     else:
-        fn = PM.video.__dict__[args.model]
-        weights = PM._api.get_weight(fn, args.weights)
+        weights = PM.get_weight(args.weights)
         transform_test = weights.transforms()
 
     if args.cache_dataset and os.path.exists(cache_path):

test/test_models.py

@@ -22,7 +22,11 @@
 
 def get_models_from_module(module):
     # TODO add a registration mechanism to torchvision.models
-    return [v for k, v in module.__dict__.items() if callable(v) and k[0].lower() == k[0] and k[0] != "_"]
+    return [
+        v
+        for k, v in module.__dict__.items()
+        if callable(v) and k[0].lower() == k[0] and k[0] != "_" and k != "get_weight"
+    ]
 
 
 @pytest.fixture

test/test_prototype_models.py

@@ -24,6 +24,19 @@ def _get_parent_module(model_fn):
     return module
 
 
+def _get_model_weights(model_fn):
+    module = _get_parent_module(model_fn)
+    weights_name = "_QuantizedWeights" if module.__name__.split(".")[-1] == "quantization" else "_Weights"
+    try:
+        return next(
+            v
+            for k, v in module.__dict__.items()
+            if k.endswith(weights_name) and k.replace(weights_name, "").lower() == model_fn.__name__
+        )
+    except StopIteration:
+        return None
+
+
 def _build_model(fn, **kwargs):
     try:
         model = fn(**kwargs)
@@ -36,24 +49,22 @@ def _build_model(fn, **kwargs):
 
 
 @pytest.mark.parametrize(
-    "model_fn, name, weight",
+    "name, weight",
     [
-        (models.resnet50, "ImageNet1K_V1", models.ResNet50_Weights.ImageNet1K_V1),
-        (models.resnet50, "default", models.ResNet50_Weights.ImageNet1K_V2),
+        ("ResNet50_Weights.ImageNet1K_V1", models.ResNet50_Weights.ImageNet1K_V1),
+        ("ResNet50_Weights.default", models.ResNet50_Weights.ImageNet1K_V2),
         (
-            models.quantization.resnet50,
-            "default",
+            "ResNet50_QuantizedWeights.default",
             models.quantization.ResNet50_QuantizedWeights.ImageNet1K_FBGEMM_V2,
         ),
         (
-            models.quantization.resnet50,
-            "ImageNet1K_FBGEMM_V1",
+            "ResNet50_QuantizedWeights.ImageNet1K_FBGEMM_V1",
             models.quantization.ResNet50_QuantizedWeights.ImageNet1K_FBGEMM_V1,
         ),
     ],
 )
-def test_get_weight(model_fn, name, weight):
-    assert models._api.get_weight(model_fn, name) == weight
+def test_get_weight(name, weight):
+    assert models.get_weight(name) == weight
 
 
 @pytest.mark.parametrize(
@@ -65,10 +76,9 @@ def test_get_weight(model_fn, name, weight):
     + TM.get_models_from_module(models.video),
 )
 def test_naming_conventions(model_fn):
-    model_name = model_fn.__name__
-    module = _get_parent_module(model_fn)
-    weights_name = "_QuantizedWeights" if module.__name__.split(".")[-1] == "quantization" else "_Weights"
-    assert model_name in set(x.replace(weights_name, "").lower() for x in module.__dict__ if x.endswith(weights_name))
+    weights_enum = _get_model_weights(model_fn)
+    assert weights_enum is not None
+    assert len(weights_enum) == 0 or hasattr(weights_enum, "default")
 
 
 @pytest.mark.parametrize("model_fn", TM.get_models_from_module(models))

torchvision/prototype/models/__init__.py

@@ -15,3 +15,4 @@
 from . import quantization
 from . import segmentation
 from . import video
+from ._api import get_weight

torchvision/prototype/models/_api.py

@@ -1,7 +1,9 @@
+import importlib
+import inspect
+import sys
 from collections import OrderedDict
 from dataclasses import dataclass, fields
 from enum import Enum
-from inspect import signature
 from typing import Any, Callable, Dict
 
 from ..._internally_replaced_utils import load_state_dict_from_url
@@ -30,7 +32,6 @@ class Weights:
     url: str
     transforms: Callable
     meta: Dict[str, Any]
-    default: bool
 
 
 class WeightsEnum(Enum):
@@ -50,7 +51,7 @@ def __init__(self, value: Weights):
     def verify(cls, obj: Any) -> Any:
         if obj is not None:
             if type(obj) is str:
-                obj = cls.from_str(obj)
+                obj = cls.from_str(obj.replace(cls.__name__ + ".", ""))
             elif not isinstance(obj, cls):
                 raise TypeError(
                     f"Invalid Weight class provided; expected {cls.__name__} but received {obj.__class__.__name__}."
@@ -59,9 +60,14 @@ def verify(cls, obj: Any) -> Any:
 
     @classmethod
     def from_str(cls, value: str) -> "WeightsEnum":
-        for v in cls:
-            if v._name_ == value or (value == "default" and v.default):
-                return v
+        if value == "default":
+            default_value = getattr(cls, "default", None)
+            if default_value is not None:
+                return default_value
+        else:
+            for v in cls:
+                if v._name_ == value:
+                    return v
         raise ValueError(f"Invalid value {value} for enum {cls.__name__}.")
 
     def get_state_dict(self, progress: bool) -> OrderedDict:
@@ -78,41 +84,37 @@ def __getattr__(self, name):
         return super().__getattr__(name)
 
 
-def get_weight(fn: Callable, weight_name: str) -> WeightsEnum:
+def get_weight(name: str) -> WeightsEnum:
     """
-    Gets the weight enum of a specific model builder method and weight name combination.
+    Gets the weight enum value by its full name. Example: ``ResNet50_Weights.ImageNet1K_V1``
 
     Args:
-        fn (Callable): The builder method used to create the model.
-        weight_name (str): The name of the weight enum entry of the specific model.
+        name (str): The name of the weight enum entry.
 
     Returns:
         WeightsEnum: The requested weight enum.
     """
-    sig = signature(fn)
-    if "weights" not in sig.parameters:
-        raise ValueError("The method is missing the 'weights' parameter.")
+    try:
+        enum_name, value_name = name.split(".")
+    except ValueError:
+        raise ValueError(f"Invalid weight name provided: '{name}'.")
+
+    base_module_name = ".".join(sys.modules[__name__].__name__.split(".")[:-1])
+    base_module = importlib.import_module(base_module_name)
+    model_modules = [base_module] + [
+        x[1] for x in inspect.getmembers(base_module, inspect.ismodule) if x[1].__file__.endswith("__init__.py")
+    ]
 
-    ann = signature(fn).parameters["weights"].annotation
     weights_enum = None
-    if isinstance(ann, type) and issubclass(ann, WeightsEnum):
-        weights_enum = ann
-    else:
-        # handle cases like Union[Optional, T]
-        # TODO: Replace ann.__args__ with typing.get_args(ann) after python >= 3.8
-        for t in ann.__args__:  # type: ignore[union-attr]
-            if isinstance(t, type) and issubclass(t, WeightsEnum):
-                # ensure the name exists. handles builders with multiple types of weights like in quantization
-                try:
-                    t.from_str(weight_name)
-                except ValueError:
-                    continue
-                weights_enum = t
-                break
+    for m in model_modules:
+        potential_class = m.__dict__.get(enum_name, None)
+        if potential_class is not None and issubclass(potential_class, WeightsEnum):
+            weights_enum = potential_class
+            break
 
     if weights_enum is None:
         raise ValueError(
             "The weight class for the specific method couldn't be retrieved. Make sure the typing info is correct."
         )
 
-    return weights_enum.from_str(weight_name)
+    return weights_enum.from_str(value_name)

torchvision/prototype/models/alexnet.py

@@ -25,8 +25,8 @@ class AlexNet_Weights(WeightsEnum):
             "acc@1": 56.522,
             "acc@5": 79.066,
         },
-        default=True,
     )
+    default = ImageNet1K_V1
 
 
 def alexnet(weights: Optional[AlexNet_Weights] = None, progress: bool = True, **kwargs: Any) -> AlexNet:

torchvision/prototype/models/densenet.py

@@ -80,8 +80,8 @@ class DenseNet121_Weights(WeightsEnum):
             "acc@1": 74.434,
             "acc@5": 91.972,
         },
-        default=True,
     )
+    default = ImageNet1K_V1
 
 
 class DenseNet161_Weights(WeightsEnum):
@@ -93,8 +93,8 @@ class DenseNet161_Weights(WeightsEnum):
             "acc@1": 77.138,
             "acc@5": 93.560,
         },
-        default=True,
     )
+    default = ImageNet1K_V1
 
 
 class DenseNet169_Weights(WeightsEnum):
@@ -106,8 +106,8 @@ class DenseNet169_Weights(WeightsEnum):
             "acc@1": 75.600,
             "acc@5": 92.806,
         },
-        default=True,
     )
+    default = ImageNet1K_V1
 
 
 class DenseNet201_Weights(WeightsEnum):
@@ -119,8 +119,8 @@ class DenseNet201_Weights(WeightsEnum):
             "acc@1": 76.896,
             "acc@5": 93.370,
         },
-        default=True,
     )
+    default = ImageNet1K_V1
 
 
 def densenet121(weights: Optional[DenseNet121_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:

torchvision/prototype/models/detection/faster_rcnn.py

@@ -45,8 +45,8 @@ class FasterRCNN_ResNet50_FPN_Weights(WeightsEnum):
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-resnet-50-fpn",
             "map": 37.0,
         },
-        default=True,
     )
+    default = Coco_V1
 
 
 class FasterRCNN_MobileNet_V3_Large_FPN_Weights(WeightsEnum):
@@ -58,8 +58,8 @@ class FasterRCNN_MobileNet_V3_Large_FPN_Weights(WeightsEnum):
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-mobilenetv3-large-fpn",
             "map": 32.8,
         },
-        default=True,
     )
+    default = Coco_V1
 
 
 class FasterRCNN_MobileNet_V3_Large_320_FPN_Weights(WeightsEnum):
@@ -71,8 +71,8 @@ class FasterRCNN_MobileNet_V3_Large_320_FPN_Weights(WeightsEnum):
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-mobilenetv3-large-320-fpn",
             "map": 22.8,
         },
-        default=True,
     )
+    default = Coco_V1
 
 
 def fasterrcnn_resnet50_fpn(

torchvision/prototype/models/detection/keypoint_rcnn.py

@@ -35,7 +35,6 @@ class KeypointRCNN_ResNet50_FPN_Weights(WeightsEnum):
             "box_map": 50.6,
             "kp_map": 61.1,
         },
-        default=False,
     )
     Coco_V1 = Weights(
         url="https://download.pytorch.org/models/keypointrcnn_resnet50_fpn_coco-fc266e95.pth",
@@ -46,8 +45,8 @@ class KeypointRCNN_ResNet50_FPN_Weights(WeightsEnum):
             "box_map": 54.6,
             "kp_map": 65.0,
         },
-        default=True,
     )
+    default = Coco_V1
 
 
 def keypointrcnn_resnet50_fpn(

torchvision/prototype/models/detection/mask_rcnn.py

@@ -34,8 +34,8 @@ class MaskRCNN_ResNet50_FPN_Weights(WeightsEnum):
             "box_map": 37.9,
             "mask_map": 34.6,
         },
-        default=True,
     )
+    default = Coco_V1
 
 
 def maskrcnn_resnet50_fpn(

torchvision/prototype/models/detection/retinanet.py

@@ -34,8 +34,8 @@ class RetinaNet_ResNet50_FPN_Weights(WeightsEnum):
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#retinanet",
             "map": 36.4,
         },
-        default=True,
     )
+    default = Coco_V1
 
 
 def retinanet_resnet50_fpn(

torchvision/prototype/models/detection/ssd.py

@@ -33,8 +33,8 @@ class SSD300_VGG16_Weights(WeightsEnum):
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#ssd300-vgg16",
             "map": 25.1,
         },
-        default=True,
     )
+    default = Coco_V1
 
 
 def ssd300_vgg16(

torchvision/prototype/models/detection/ssdlite.py

@@ -38,8 +38,8 @@ class SSDLite320_MobileNet_V3_Large_Weights(WeightsEnum):
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#ssdlite320-mobilenetv3-large",
             "map": 21.3,
         },
-        default=True,
     )
+    default = Coco_V1
 
 
 def ssdlite320_mobilenet_v3_large(