Rename prototype weight names to comply with PEP8

test/test_prototype_models.py

@@ -54,15 +54,15 @@ def _build_model(fn, **kwargs):
 @pytest.mark.parametrize(
     "name, weight",
     [
-        ("ResNet50_Weights.ImageNet1K_V1", models.ResNet50_Weights.ImageNet1K_V1),
-        ("ResNet50_Weights.default", models.ResNet50_Weights.ImageNet1K_V2),
+        ("ResNet50_Weights.IMAGENET1K_V1", models.ResNet50_Weights.IMAGENET1K_V1),
+        ("ResNet50_Weights.DEFAULT", models.ResNet50_Weights.IMAGENET1K_V2),
         (
-            "ResNet50_QuantizedWeights.default",
-            models.quantization.ResNet50_QuantizedWeights.ImageNet1K_FBGEMM_V2,
+            "ResNet50_QuantizedWeights.DEFAULT",
+            models.quantization.ResNet50_QuantizedWeights.IMAGENET1K_FBGEMM_V2,
         ),
         (
-            "ResNet50_QuantizedWeights.ImageNet1K_FBGEMM_V1",
-            models.quantization.ResNet50_QuantizedWeights.ImageNet1K_FBGEMM_V1,
+            "ResNet50_QuantizedWeights.IMAGENET1K_FBGEMM_V1",
+            models.quantization.ResNet50_QuantizedWeights.IMAGENET1K_FBGEMM_V1,
         ),
     ],
 )
@@ -83,7 +83,7 @@ def test_naming_conventions(model_fn):
     weights_enum = _get_model_weights(model_fn)
     print(weights_enum)
     assert weights_enum is not None
-    assert len(weights_enum) == 0 or hasattr(weights_enum, "default")
+    assert len(weights_enum) == 0 or hasattr(weights_enum, "DEFAULT")
 
 
 @pytest.mark.parametrize(
@@ -117,25 +117,29 @@ def test_schema_meta_validation(model_fn):
 
     problematic_weights = {}
     incorrect_params = []
+    bad_names = []
     for w in weights_enum:
         missing_fields = fields - set(w.meta.keys())
         if missing_fields:
             problematic_weights[w] = missing_fields
-        if w == weights_enum.default:
+        if w == weights_enum.DEFAULT:
             if module_name == "quantization":
-                # parametes() cound doesn't work well with quantization, so we check against the non-quantized
+                # parameters() count doesn't work well with quantization, so we check against the non-quantized
                 unquantized_w = w.meta.get("unquantized")
                 if unquantized_w is not None and w.meta.get("num_params") != unquantized_w.meta.get("num_params"):
                     incorrect_params.append(w)
             else:
                 if w.meta.get("num_params") != sum(p.numel() for p in model_fn(weights=w).parameters()):
                     incorrect_params.append(w)
         else:
-            if w.meta.get("num_params") != weights_enum.default.meta.get("num_params"):
+            if w.meta.get("num_params") != weights_enum.DEFAULT.meta.get("num_params"):
                 incorrect_params.append(w)
+        if not w.name.isupper():
+            bad_names.append(w)
 
     assert not problematic_weights
     assert not incorrect_params
+    assert not bad_names
 
 
 @pytest.mark.parametrize("model_fn", TM.get_models_from_module(models))

torchvision/prototype/models/_api.py

@@ -80,7 +80,7 @@ def __getattr__(self, name):
 
 def get_weight(name: str) -> WeightsEnum:
     """
-    Gets the weight enum value by its full name. Example: "ResNet50_Weights.ImageNet1K_V1"
+    Gets the weight enum value by its full name. Example: "ResNet50_Weights.IMAGENET1K_V1"
 
     Args:
         name (str): The name of the weight enum entry.

torchvision/prototype/models/_utils.py

@@ -78,7 +78,7 @@ def inner_wrapper(*args: Any, **kwargs: Any) -> M:
                 )
                 if pretrained_arg:
                     msg = (
-                        f"{msg} You can also use `{weights_param}={type(default_weights_arg).__name__}.default` "
+                        f"{msg} You can also use `{weights_param}={type(default_weights_arg).__name__}.DEFAULT` "
                         f"to get the most up-to-date weights."
                     )
                 warnings.warn(msg)

torchvision/prototype/models/alexnet.py

@@ -14,7 +14,7 @@
 
 
 class AlexNet_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/alexnet-owt-7be5be79.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -31,10 +31,10 @@ class AlexNet_Weights(WeightsEnum):
             "acc@5": 79.066,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
-@handle_legacy_interface(weights=("pretrained", AlexNet_Weights.ImageNet1K_V1))
+@handle_legacy_interface(weights=("pretrained", AlexNet_Weights.IMAGENET1K_V1))
 def alexnet(*, weights: Optional[AlexNet_Weights] = None, progress: bool = True, **kwargs: Any) -> AlexNet:
     weights = AlexNet_Weights.verify(weights)
 

torchvision/prototype/models/convnext.py

@@ -178,7 +178,7 @@ def forward(self, x: Tensor) -> Tensor:
 
 
 class ConvNeXt_Tiny_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/convnext_tiny-47b116bd.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=236),
         meta={
@@ -195,10 +195,10 @@ class ConvNeXt_Tiny_Weights(WeightsEnum):
             "acc@5": 96.146,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
-@handle_legacy_interface(weights=("pretrained", ConvNeXt_Tiny_Weights.ImageNet1K_V1))
+@handle_legacy_interface(weights=("pretrained", ConvNeXt_Tiny_Weights.IMAGENET1K_V1))
 def convnext_tiny(*, weights: Optional[ConvNeXt_Tiny_Weights] = None, progress: bool = True, **kwargs: Any) -> ConvNeXt:
     r"""ConvNeXt model architecture from the
     `"A ConvNet for the 2020s" <https://arxiv.org/abs/2201.03545>`_ paper.

torchvision/prototype/models/densenet.py

@@ -76,7 +76,7 @@ def _densenet(
 
 
 class DenseNet121_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/densenet121-a639ec97.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -86,11 +86,11 @@ class DenseNet121_Weights(WeightsEnum):
             "acc@5": 91.972,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
 class DenseNet161_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/densenet161-8d451a50.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -100,11 +100,11 @@ class DenseNet161_Weights(WeightsEnum):
             "acc@5": 93.560,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
 class DenseNet169_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/densenet169-b2777c0a.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -114,11 +114,11 @@ class DenseNet169_Weights(WeightsEnum):
             "acc@5": 92.806,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
 class DenseNet201_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/densenet201-c1103571.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -128,31 +128,31 @@ class DenseNet201_Weights(WeightsEnum):
             "acc@5": 93.370,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
-@handle_legacy_interface(weights=("pretrained", DenseNet121_Weights.ImageNet1K_V1))
+@handle_legacy_interface(weights=("pretrained", DenseNet121_Weights.IMAGENET1K_V1))
 def densenet121(*, weights: Optional[DenseNet121_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     weights = DenseNet121_Weights.verify(weights)
 
     return _densenet(32, (6, 12, 24, 16), 64, weights, progress, **kwargs)
 
 
-@handle_legacy_interface(weights=("pretrained", DenseNet161_Weights.ImageNet1K_V1))
+@handle_legacy_interface(weights=("pretrained", DenseNet161_Weights.IMAGENET1K_V1))
 def densenet161(*, weights: Optional[DenseNet161_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     weights = DenseNet161_Weights.verify(weights)
 
     return _densenet(48, (6, 12, 36, 24), 96, weights, progress, **kwargs)
 
 
-@handle_legacy_interface(weights=("pretrained", DenseNet169_Weights.ImageNet1K_V1))
+@handle_legacy_interface(weights=("pretrained", DenseNet169_Weights.IMAGENET1K_V1))
 def densenet169(*, weights: Optional[DenseNet169_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     weights = DenseNet169_Weights.verify(weights)
 
     return _densenet(32, (6, 12, 32, 32), 64, weights, progress, **kwargs)
 
 
-@handle_legacy_interface(weights=("pretrained", DenseNet201_Weights.ImageNet1K_V1))
+@handle_legacy_interface(weights=("pretrained", DenseNet201_Weights.IMAGENET1K_V1))
 def densenet201(*, weights: Optional[DenseNet201_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     weights = DenseNet201_Weights.verify(weights)
 

torchvision/prototype/models/detection/faster_rcnn.py

@@ -40,7 +40,7 @@
 
 
 class FasterRCNN_ResNet50_FPN_Weights(WeightsEnum):
-    Coco_V1 = Weights(
+    COCO_V1 = Weights(
         url="https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth",
         transforms=CocoEval,
         meta={
@@ -50,11 +50,11 @@ class FasterRCNN_ResNet50_FPN_Weights(WeightsEnum):
             "map": 37.0,
         },
     )
-    default = Coco_V1
+    DEFAULT = COCO_V1
 
 
 class FasterRCNN_MobileNet_V3_Large_FPN_Weights(WeightsEnum):
-    Coco_V1 = Weights(
+    COCO_V1 = Weights(
         url="https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_fpn-fb6a3cc7.pth",
         transforms=CocoEval,
         meta={
@@ -64,11 +64,11 @@ class FasterRCNN_MobileNet_V3_Large_FPN_Weights(WeightsEnum):
             "map": 32.8,
         },
     )
-    default = Coco_V1
+    DEFAULT = COCO_V1
 
 
 class FasterRCNN_MobileNet_V3_Large_320_FPN_Weights(WeightsEnum):
-    Coco_V1 = Weights(
+    COCO_V1 = Weights(
         url="https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_320_fpn-907ea3f9.pth",
         transforms=CocoEval,
         meta={
@@ -78,12 +78,12 @@ class FasterRCNN_MobileNet_V3_Large_320_FPN_Weights(WeightsEnum):
             "map": 22.8,
         },
     )
-    default = Coco_V1
+    DEFAULT = COCO_V1
 
 
 @handle_legacy_interface(
-    weights=("pretrained", FasterRCNN_ResNet50_FPN_Weights.Coco_V1),
-    weights_backbone=("pretrained_backbone", ResNet50_Weights.ImageNet1K_V1),
+    weights=("pretrained", FasterRCNN_ResNet50_FPN_Weights.COCO_V1),
+    weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1),
 )
 def fasterrcnn_resnet50_fpn(
     *,
@@ -113,7 +113,7 @@ def fasterrcnn_resnet50_fpn(
 
     if weights is not None:
         model.load_state_dict(weights.get_state_dict(progress=progress))
-        if weights == FasterRCNN_ResNet50_FPN_Weights.Coco_V1:
+        if weights == FasterRCNN_ResNet50_FPN_Weights.COCO_V1:
             overwrite_eps(model, 0.0)
 
     return model
@@ -161,8 +161,8 @@ def _fasterrcnn_mobilenet_v3_large_fpn(
 
 
 @handle_legacy_interface(
-    weights=("pretrained", FasterRCNN_MobileNet_V3_Large_FPN_Weights.Coco_V1),
-    weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.ImageNet1K_V1),
+    weights=("pretrained", FasterRCNN_MobileNet_V3_Large_FPN_Weights.COCO_V1),
+    weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.IMAGENET1K_V1),
 )
 def fasterrcnn_mobilenet_v3_large_fpn(
     *,
@@ -192,8 +192,8 @@ def fasterrcnn_mobilenet_v3_large_fpn(
 
 
 @handle_legacy_interface(
-    weights=("pretrained", FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.Coco_V1),
-    weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.ImageNet1K_V1),
+    weights=("pretrained", FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.COCO_V1),
+    weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.IMAGENET1K_V1),
 )
 def fasterrcnn_mobilenet_v3_large_320_fpn(
     *,

torchvision/prototype/models/detection/fcos.py

@@ -38,12 +38,12 @@ class FCOS_ResNet50_FPN_Weights(WeightsEnum):
             "map": 39.2,
         },
     )
-    default = COCO_V1
+    DEFAULT = COCO_V1
 
 
 @handle_legacy_interface(
     weights=("pretrained", FCOS_ResNet50_FPN_Weights.COCO_V1),
-    weights_backbone=("pretrained_backbone", ResNet50_Weights.ImageNet1K_V1),
+    weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1),
 )
 def fcos_resnet50_fpn(
     *,

torchvision/prototype/models/detection/keypoint_rcnn.py

@@ -34,7 +34,7 @@
 
 
 class KeypointRCNN_ResNet50_FPN_Weights(WeightsEnum):
-    Coco_Legacy = Weights(
+    COCO_LEGACY = Weights(
         url="https://download.pytorch.org/models/keypointrcnn_resnet50_fpn_coco-9f466800.pth",
         transforms=CocoEval,
         meta={
@@ -45,7 +45,7 @@ class KeypointRCNN_ResNet50_FPN_Weights(WeightsEnum):
             "map_kp": 61.1,
         },
     )
-    Coco_V1 = Weights(
+    COCO_V1 = Weights(
         url="https://download.pytorch.org/models/keypointrcnn_resnet50_fpn_coco-fc266e95.pth",
         transforms=CocoEval,
         meta={
@@ -56,17 +56,17 @@ class KeypointRCNN_ResNet50_FPN_Weights(WeightsEnum):
             "map_kp": 65.0,
         },
     )
-    default = Coco_V1
+    DEFAULT = COCO_V1
 
 
 @handle_legacy_interface(
     weights=(
         "pretrained",
-        lambda kwargs: KeypointRCNN_ResNet50_FPN_Weights.Coco_Legacy
+        lambda kwargs: KeypointRCNN_ResNet50_FPN_Weights.COCO_LEGACY
         if kwargs["pretrained"] == "legacy"
-        else KeypointRCNN_ResNet50_FPN_Weights.Coco_V1,
+        else KeypointRCNN_ResNet50_FPN_Weights.COCO_V1,
     ),
-    weights_backbone=("pretrained_backbone", ResNet50_Weights.ImageNet1K_V1),
+    weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1),
 )
 def keypointrcnn_resnet50_fpn(
     *,
@@ -101,7 +101,7 @@ def keypointrcnn_resnet50_fpn(
 
     if weights is not None:
         model.load_state_dict(weights.get_state_dict(progress=progress))
-        if weights == KeypointRCNN_ResNet50_FPN_Weights.Coco_V1:
+        if weights == KeypointRCNN_ResNet50_FPN_Weights.COCO_V1:
             overwrite_eps(model, 0.0)
 
     return model

torchvision/prototype/models/detection/mask_rcnn.py

@@ -24,7 +24,7 @@
 
 
 class MaskRCNN_ResNet50_FPN_Weights(WeightsEnum):
-    Coco_V1 = Weights(
+    COCO_V1 = Weights(
         url="https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth",
         transforms=CocoEval,
         meta={
@@ -39,12 +39,12 @@ class MaskRCNN_ResNet50_FPN_Weights(WeightsEnum):
             "map_mask": 34.6,
         },
     )
-    default = Coco_V1
+    DEFAULT = COCO_V1
 
 
 @handle_legacy_interface(
-    weights=("pretrained", MaskRCNN_ResNet50_FPN_Weights.Coco_V1),
-    weights_backbone=("pretrained_backbone", ResNet50_Weights.ImageNet1K_V1),
+    weights=("pretrained", MaskRCNN_ResNet50_FPN_Weights.COCO_V1),
+    weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1),
 )
 def maskrcnn_resnet50_fpn(
     *,
@@ -74,7 +74,7 @@ def maskrcnn_resnet50_fpn(
 
     if weights is not None:
         model.load_state_dict(weights.get_state_dict(progress=progress))
-        if weights == MaskRCNN_ResNet50_FPN_Weights.Coco_V1:
+        if weights == MaskRCNN_ResNet50_FPN_Weights.COCO_V1:
             overwrite_eps(model, 0.0)
 
     return model