Cleanup Models prototype implementation

torchvision/prototype/models/_api.py

@@ -50,7 +50,7 @@ def verify(cls, obj: Any) -> Any:
         if obj is not None:
             if type(obj) is str:
                 obj = cls.from_str(obj)
-            elif not isinstance(obj, cls) and not isinstance(obj, WeightEntry):
+            elif not isinstance(obj, cls):
                 raise TypeError(
                     f"Invalid Weight class provided; expected {cls.__name__} but received {obj.__class__.__name__}."
                 )
@@ -63,7 +63,7 @@ def from_str(cls, value: str) -> "Weights":
                 return v
         raise ValueError(f"Invalid value {value} for enum {cls.__name__}.")
 
-    def state_dict(self, progress: bool) -> OrderedDict:
+    def get_state_dict(self, progress: bool) -> OrderedDict:
         return load_state_dict_from_url(self.url, progress=progress)
 
     def __repr__(self):
@@ -90,7 +90,7 @@ def get_weight(fn: Callable, weight_name: str) -> Weights:
     """
     sig = signature(fn)
     if "weights" not in sig.parameters:
-        raise ValueError("The method is missing the 'weights' argument.")
+        raise ValueError("The method is missing the 'weights' parameter.")
 
     ann = signature(fn).parameters["weights"].annotation
     weights_class = None

torchvision/prototype/models/alexnet.py

@@ -30,7 +30,7 @@ class AlexNetWeights(Weights):
 
 def alexnet(weights: Optional[AlexNetWeights] = None, progress: bool = True, **kwargs: Any) -> AlexNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = AlexNetWeights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
     weights = AlexNetWeights.verify(weights)
     if weights is not None:
@@ -39,6 +39,6 @@ def alexnet(weights: Optional[AlexNetWeights] = None, progress: bool = True, **k
     model = AlexNet(**kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model

torchvision/prototype/models/densenet.py

@@ -34,7 +34,7 @@ def _load_state_dict(model: nn.Module, weights: Weights, progress: bool) -> None
         r"^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$"
     )
 
-    state_dict = weights.state_dict(progress=progress)
+    state_dict = weights.get_state_dict(progress=progress)
     for key in list(state_dict.keys()):
         res = pattern.match(key)
         if res:
@@ -63,11 +63,11 @@ def _densenet(
     return model
 
 
-_common_meta = {
+_COMMON_META = {
     "size": (224, 224),
     "categories": _IMAGENET_CATEGORIES,
     "interpolation": InterpolationMode.BILINEAR,
-    "recipe": None,  # weights ported from LuaTorch
+    "recipe": None,  # TODO: add here a URL to documentation stating that the weights were ported from LuaTorch
 }
 
 
@@ -76,7 +76,7 @@ class DenseNet121Weights(Weights):
         url="https://download.pytorch.org/models/densenet121-a639ec97.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "acc@1": 74.434,
             "acc@5": 91.972,
         },
@@ -88,7 +88,7 @@ class DenseNet161Weights(Weights):
         url="https://download.pytorch.org/models/densenet161-8d451a50.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "acc@1": 77.138,
             "acc@5": 93.560,
         },
@@ -100,7 +100,7 @@ class DenseNet169Weights(Weights):
         url="https://download.pytorch.org/models/densenet169-b2777c0a.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "acc@1": 75.600,
             "acc@5": 92.806,
         },
@@ -112,7 +112,7 @@ class DenseNet201Weights(Weights):
         url="https://download.pytorch.org/models/densenet201-c1103571.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "acc@1": 76.896,
             "acc@5": 93.370,
         },
@@ -121,7 +121,7 @@ class DenseNet201Weights(Weights):
 
 def densenet121(weights: Optional[DenseNet121Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = DenseNet121Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
     weights = DenseNet121Weights.verify(weights)
 
@@ -130,7 +130,7 @@ def densenet121(weights: Optional[DenseNet121Weights] = None, progress: bool = T
 
 def densenet161(weights: Optional[DenseNet161Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = DenseNet161Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
     weights = DenseNet161Weights.verify(weights)
 
@@ -139,7 +139,7 @@ def densenet161(weights: Optional[DenseNet161Weights] = None, progress: bool = T
 
 def densenet169(weights: Optional[DenseNet169Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = DenseNet169Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
     weights = DenseNet169Weights.verify(weights)
 
@@ -148,7 +148,7 @@ def densenet169(weights: Optional[DenseNet169Weights] = None, progress: bool = T
 
 def densenet201(weights: Optional[DenseNet201Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = DenseNet201Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
     weights = DenseNet201Weights.verify(weights)
 

torchvision/prototype/models/detection/faster_rcnn.py

@@ -30,7 +30,7 @@
 ]
 
 
-_common_meta = {
+_COMMON_META = {
     "categories": _COCO_CATEGORIES,
     "interpolation": InterpolationMode.BILINEAR,
 }
@@ -41,7 +41,7 @@ class FasterRCNNResNet50FPNWeights(Weights):
         url="https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth",
         transforms=CocoEval,
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-resnet-50-fpn",
             "map": 37.0,
         },
@@ -53,7 +53,7 @@ class FasterRCNNMobileNetV3LargeFPNWeights(Weights):
         url="https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_fpn-fb6a3cc7.pth",
         transforms=CocoEval,
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-mobilenetv3-large-fpn",
             "map": 32.8,
         },
@@ -65,7 +65,7 @@ class FasterRCNNMobileNetV3Large320FPNWeights(Weights):
         url="https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_320_fpn-907ea3f9.pth",
         transforms=CocoEval,
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-mobilenetv3-large-320-fpn",
             "map": 22.8,
         },
@@ -81,11 +81,11 @@ def fasterrcnn_resnet50_fpn(
     **kwargs: Any,
 ) -> FasterRCNN:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = FasterRCNNResNet50FPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
     weights = FasterRCNNResNet50FPNWeights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The argument pretrained_backbone is deprecated, please use weights_backbone instead.")
+        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 = ResNet50Weights.verify(weights_backbone)
 
@@ -102,7 +102,7 @@ def fasterrcnn_resnet50_fpn(
     model = FasterRCNN(backbone, num_classes=num_classes, **kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
         if weights == FasterRCNNResNet50FPNWeights.Coco_RefV1:
             overwrite_eps(model, 0.0)
 
@@ -142,7 +142,7 @@ def _fasterrcnn_mobilenet_v3_large_fpn(
     )
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model
 
@@ -156,11 +156,11 @@ def fasterrcnn_mobilenet_v3_large_fpn(
     **kwargs: Any,
 ) -> FasterRCNN:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = FasterRCNNMobileNetV3LargeFPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
     weights = FasterRCNNMobileNetV3LargeFPNWeights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The argument pretrained_backbone is deprecated, please use weights_backbone instead.")
+        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 = MobileNetV3LargeWeights.verify(weights_backbone)
 
@@ -188,11 +188,11 @@ def fasterrcnn_mobilenet_v3_large_320_fpn(
     **kwargs: Any,
 ) -> FasterRCNN:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = FasterRCNNMobileNetV3Large320FPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
     weights = FasterRCNNMobileNetV3Large320FPNWeights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The argument pretrained_backbone is deprecated, please use weights_backbone instead.")
+        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 = MobileNetV3LargeWeights.verify(weights_backbone)
 

torchvision/prototype/models/detection/keypoint_rcnn.py

@@ -22,15 +22,15 @@
 ]
 
 
-_common_meta = {"categories": _COCO_PERSON_CATEGORIES, "keypoint_names": _COCO_PERSON_KEYPOINT_NAMES}
+_COMMON_META = {"categories": _COCO_PERSON_CATEGORIES, "keypoint_names": _COCO_PERSON_KEYPOINT_NAMES}
 
 
 class KeypointRCNNResNet50FPNWeights(Weights):
     Coco_RefV1_Legacy = WeightEntry(
         url="https://download.pytorch.org/models/keypointrcnn_resnet50_fpn_coco-9f466800.pth",
         transforms=CocoEval,
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "recipe": "https://github.com/pytorch/vision/issues/1606",
             "box_map": 50.6,
             "kp_map": 61.1,
@@ -40,7 +40,7 @@ class KeypointRCNNResNet50FPNWeights(Weights):
         url="https://download.pytorch.org/models/keypointrcnn_resnet50_fpn_coco-fc266e95.pth",
         transforms=CocoEval,
         meta={
-            **_common_meta,
+            **_COMMON_META,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#keypoint-r-cnn",
             "box_map": 54.6,
             "kp_map": 65.0,
@@ -58,7 +58,7 @@ def keypointrcnn_resnet50_fpn(
     **kwargs: Any,
 ) -> KeypointRCNN:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        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
@@ -68,7 +68,7 @@ def keypointrcnn_resnet50_fpn(
             weights = None
     weights = KeypointRCNNResNet50FPNWeights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The argument pretrained_backbone is deprecated, please use weights_backbone instead.")
+        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 = ResNet50Weights.verify(weights_backbone)
 
@@ -86,7 +86,7 @@ def keypointrcnn_resnet50_fpn(
     model = KeypointRCNN(backbone, num_classes, num_keypoints=num_keypoints, **kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
         if weights == KeypointRCNNResNet50FPNWeights.Coco_RefV1:
             overwrite_eps(model, 0.0)
 

torchvision/prototype/models/detection/mask_rcnn.py

@@ -46,11 +46,11 @@ def maskrcnn_resnet50_fpn(
     **kwargs: Any,
 ) -> MaskRCNN:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = MaskRCNNResNet50FPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
     weights = MaskRCNNResNet50FPNWeights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The argument pretrained_backbone is deprecated, please use weights_backbone instead.")
+        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 = ResNet50Weights.verify(weights_backbone)
 
@@ -67,7 +67,7 @@ def maskrcnn_resnet50_fpn(
     model = MaskRCNN(backbone, num_classes=num_classes, **kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
         if weights == MaskRCNNResNet50FPNWeights.Coco_RefV1:
             overwrite_eps(model, 0.0)
 

torchvision/prototype/models/detection/retinanet.py

@@ -46,11 +46,11 @@ def retinanet_resnet50_fpn(
     **kwargs: Any,
 ) -> RetinaNet:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = RetinaNetResNet50FPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
     weights = RetinaNetResNet50FPNWeights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The argument pretrained_backbone is deprecated, please use weights_backbone instead.")
+        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 = ResNet50Weights.verify(weights_backbone)
 
@@ -70,7 +70,7 @@ def retinanet_resnet50_fpn(
     model = RetinaNet(backbone, num_classes, **kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
         if weights == RetinaNetResNet50FPNWeights.Coco_RefV1:
             overwrite_eps(model, 0.0)
 

torchvision/prototype/models/detection/ssd.py

@@ -44,16 +44,16 @@ def ssd300_vgg16(
     **kwargs: Any,
 ) -> SSD:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = SSD300VGG16Weights.Coco_RefV1 if kwargs.pop("pretrained") else None
     weights = SSD300VGG16Weights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The argument pretrained_backbone is deprecated, please use weights_backbone instead.")
+        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 = VGG16Weights.verify(weights_backbone)
 
     if "size" in kwargs:
-        warnings.warn("The size of the model is already fixed; ignoring the argument.")
+        warnings.warn("The size of the model is already fixed; ignoring the parameter.")
 
     if weights is not None:
         weights_backbone = None
@@ -81,6 +81,6 @@ def ssd300_vgg16(
     model = SSD(backbone, anchor_generator, (300, 300), num_classes, **kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model

torchvision/prototype/models/detection/ssdlite.py

@@ -50,16 +50,16 @@ def ssdlite320_mobilenet_v3_large(
     **kwargs: Any,
 ) -> SSD:
     if "pretrained" in kwargs:
-        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        warnings.warn("The parameter pretrained is deprecated, please use weights instead.")
         weights = SSDlite320MobileNetV3LargeFPNWeights.Coco_RefV1 if kwargs.pop("pretrained") else None
     weights = SSDlite320MobileNetV3LargeFPNWeights.verify(weights)
     if "pretrained_backbone" in kwargs:
-        warnings.warn("The argument pretrained_backbone is deprecated, please use weights_backbone instead.")
+        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 = MobileNetV3LargeWeights.verify(weights_backbone)
 
     if "size" in kwargs:
-        warnings.warn("The size of the model is already fixed; ignoring the argument.")
+        warnings.warn("The size of the model is already fixed; ignoring the parameter.")
 
     if weights is not None:
         weights_backbone = None
@@ -114,6 +114,6 @@ def ssdlite320_mobilenet_v3_large(
     )
 
     if weights is not None:
-        model.load_state_dict(weights.state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model