Rename prototype weight names to comply with PEP8 (#5257)

* renamed ImageNet weights

* renamed COCO weights

* renamed COCO with VOC labels weights

* renamed Kinetics 400 weights

* rename default with DEFAULT

* update test

* fix typos

* update test

* update test

* update test

* indent as w was weight_enum

* revert

* Adding back the capitalization test
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

torchvision/prototype/models/quantization/googlenet.py

@@ -24,7 +24,7 @@ __all__ = [
 
 
 class GoogLeNet_QuantizedWeights(WeightsEnum):
-    ImageNet1K_FBGEMM_V1 = Weights(
+    IMAGENET1K_FBGEMM_V1 = Weights(
         url="https://download.pytorch.org/models/quantized/googlenet_fbgemm-c00238cf.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -39,20 +39,20 @@ class GoogLeNet_QuantizedWeights(WeightsEnum):
             "backend": "fbgemm",
             "quantization": "ptq",
             "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-models",
-            "unquantized": GoogLeNet_Weights.ImageNet1K_V1,
+            "unquantized": GoogLeNet_Weights.IMAGENET1K_V1,
             "acc@1": 69.826,
             "acc@5": 89.404,
         },
     )
-    default = ImageNet1K_FBGEMM_V1
+    DEFAULT = IMAGENET1K_FBGEMM_V1
 
 
 @handle_legacy_interface(
     weights=(
         "pretrained",
-        lambda kwargs: GoogLeNet_QuantizedWeights.ImageNet1K_FBGEMM_V1
+        lambda kwargs: GoogLeNet_QuantizedWeights.IMAGENET1K_FBGEMM_V1
         if kwargs.get("quantize", False)
-        else GoogLeNet_Weights.ImageNet1K_V1,
+        else GoogLeNet_Weights.IMAGENET1K_V1,
     )
 )
 def googlenet(

torchvision/prototype/models/quantization/inception.py

@@ -23,7 +23,7 @@ __all__ = [
 
 
 class Inception_V3_QuantizedWeights(WeightsEnum):
-    ImageNet1K_FBGEMM_V1 = Weights(
+    IMAGENET1K_FBGEMM_V1 = Weights(
         url="https://download.pytorch.org/models/quantized/inception_v3_google_fbgemm-71447a44.pth",
         transforms=partial(ImageNetEval, crop_size=299, resize_size=342),
         meta={
@@ -38,20 +38,20 @@ class Inception_V3_QuantizedWeights(WeightsEnum):
             "backend": "fbgemm",
             "quantization": "ptq",
             "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-models",
-            "unquantized": Inception_V3_Weights.ImageNet1K_V1,
+            "unquantized": Inception_V3_Weights.IMAGENET1K_V1,
             "acc@1": 77.176,
             "acc@5": 93.354,
         },
     )
-    default = ImageNet1K_FBGEMM_V1
+    DEFAULT = IMAGENET1K_FBGEMM_V1
 
 
 @handle_legacy_interface(
     weights=(
         "pretrained",
-        lambda kwargs: Inception_V3_QuantizedWeights.ImageNet1K_FBGEMM_V1
+        lambda kwargs: Inception_V3_QuantizedWeights.IMAGENET1K_FBGEMM_V1
         if kwargs.get("quantize", False)
-        else Inception_V3_Weights.ImageNet1K_V1,
+        else Inception_V3_Weights.IMAGENET1K_V1,
     )
 )
 def inception_v3(

torchvision/prototype/models/quantization/mobilenetv2.py

@@ -24,7 +24,7 @@ __all__ = [
 
 
 class MobileNet_V2_QuantizedWeights(WeightsEnum):
-    ImageNet1K_QNNPACK_V1 = Weights(
+    IMAGENET1K_QNNPACK_V1 = Weights(
         url="https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -39,20 +39,20 @@ class MobileNet_V2_QuantizedWeights(WeightsEnum):
             "backend": "qnnpack",
             "quantization": "qat",
             "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#qat-mobilenetv2",
-            "unquantized": MobileNet_V2_Weights.ImageNet1K_V1,
+            "unquantized": MobileNet_V2_Weights.IMAGENET1K_V1,
             "acc@1": 71.658,
             "acc@5": 90.150,
         },
     )
-    default = ImageNet1K_QNNPACK_V1
+    DEFAULT = IMAGENET1K_QNNPACK_V1
 
 
 @handle_legacy_interface(
     weights=(
         "pretrained",
-        lambda kwargs: MobileNet_V2_QuantizedWeights.ImageNet1K_QNNPACK_V1
+        lambda kwargs: MobileNet_V2_QuantizedWeights.IMAGENET1K_QNNPACK_V1
         if kwargs.get("quantize", False)
-        else MobileNet_V2_Weights.ImageNet1K_V1,
+        else MobileNet_V2_Weights.IMAGENET1K_V1,
     )
 )
 def mobilenet_v2(

torchvision/prototype/models/quantization/mobilenetv3.py

@@ -57,7 +57,7 @@ def _mobilenet_v3_model(
 
 
 class MobileNet_V3_Large_QuantizedWeights(WeightsEnum):
-    ImageNet1K_QNNPACK_V1 = Weights(
+    IMAGENET1K_QNNPACK_V1 = Weights(
         url="https://download.pytorch.org/models/quantized/mobilenet_v3_large_qnnpack-5bcacf28.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -72,20 +72,20 @@ class MobileNet_V3_Large_QuantizedWeights(WeightsEnum):
             "backend": "qnnpack",
             "quantization": "qat",
             "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#qat-mobilenetv3",
-            "unquantized": MobileNet_V3_Large_Weights.ImageNet1K_V1,
+            "unquantized": MobileNet_V3_Large_Weights.IMAGENET1K_V1,
             "acc@1": 73.004,
             "acc@5": 90.858,
         },
     )
-    default = ImageNet1K_QNNPACK_V1
+    DEFAULT = IMAGENET1K_QNNPACK_V1
 
 
 @handle_legacy_interface(
     weights=(
         "pretrained",
-        lambda kwargs: MobileNet_V3_Large_QuantizedWeights.ImageNet1K_QNNPACK_V1
+        lambda kwargs: MobileNet_V3_Large_QuantizedWeights.IMAGENET1K_QNNPACK_V1
         if kwargs.get("quantize", False)
-        else MobileNet_V3_Large_Weights.ImageNet1K_V1,
+        else MobileNet_V3_Large_Weights.IMAGENET1K_V1,
     )
 )
 def mobilenet_v3_large(

torchvision/prototype/models/quantization/resnet.py

@@ -66,7 +66,7 @@ _COMMON_META = {
 
 
 class ResNet18_QuantizedWeights(WeightsEnum):
-    ImageNet1K_FBGEMM_V1 = Weights(
+    IMAGENET1K_FBGEMM_V1 = Weights(
         url="https://download.pytorch.org/models/quantized/resnet18_fbgemm_16fa66dd.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -74,16 +74,16 @@ class ResNet18_QuantizedWeights(WeightsEnum):
             "architecture": "ResNet",
             "publication_year": 2015,
             "num_params": 11689512,
-            "unquantized": ResNet18_Weights.ImageNet1K_V1,
+            "unquantized": ResNet18_Weights.IMAGENET1K_V1,
             "acc@1": 69.494,
             "acc@5": 88.882,
         },
     )
-    default = ImageNet1K_FBGEMM_V1
+    DEFAULT = IMAGENET1K_FBGEMM_V1
 
 
 class ResNet50_QuantizedWeights(WeightsEnum):
-    ImageNet1K_FBGEMM_V1 = Weights(
+    IMAGENET1K_FBGEMM_V1 = Weights(
         url="https://download.pytorch.org/models/quantized/resnet50_fbgemm_bf931d71.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -91,12 +91,12 @@ class ResNet50_QuantizedWeights(WeightsEnum):
             "architecture": "ResNet",
             "publication_year": 2015,
             "num_params": 25557032,
-            "unquantized": ResNet50_Weights.ImageNet1K_V1,
+            "unquantized": ResNet50_Weights.IMAGENET1K_V1,
             "acc@1": 75.920,
             "acc@5": 92.814,
         },
     )
-    ImageNet1K_FBGEMM_V2 = Weights(
+    IMAGENET1K_FBGEMM_V2 = Weights(
         url="https://download.pytorch.org/models/quantized/resnet50_fbgemm-23753f79.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -104,16 +104,16 @@ class ResNet50_QuantizedWeights(WeightsEnum):
             "architecture": "ResNet",
             "publication_year": 2015,
             "num_params": 25557032,
-            "unquantized": ResNet50_Weights.ImageNet1K_V2,
+            "unquantized": ResNet50_Weights.IMAGENET1K_V2,
             "acc@1": 80.282,
             "acc@5": 94.976,
         },
     )
-    default = ImageNet1K_FBGEMM_V2
+    DEFAULT = IMAGENET1K_FBGEMM_V2
 
 
 class ResNeXt101_32X8D_QuantizedWeights(WeightsEnum):
-    ImageNet1K_FBGEMM_V1 = Weights(
+    IMAGENET1K_FBGEMM_V1 = Weights(
         url="https://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm_09835ccf.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -121,12 +121,12 @@ class ResNeXt101_32X8D_QuantizedWeights(WeightsEnum):
             "architecture": "ResNeXt",
             "publication_year": 2016,
             "num_params": 88791336,
-            "unquantized": ResNeXt101_32X8D_Weights.ImageNet1K_V1,
+            "unquantized": ResNeXt101_32X8D_Weights.IMAGENET1K_V1,
             "acc@1": 78.986,
             "acc@5": 94.480,
         },
     )
-    ImageNet1K_FBGEMM_V2 = Weights(
+    IMAGENET1K_FBGEMM_V2 = Weights(
         url="https://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm-ee16d00c.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -134,20 +134,20 @@ class ResNeXt101_32X8D_QuantizedWeights(WeightsEnum):
             "architecture": "ResNeXt",
             "publication_year": 2016,
             "num_params": 88791336,
-            "unquantized": ResNeXt101_32X8D_Weights.ImageNet1K_V2,
+            "unquantized": ResNeXt101_32X8D_Weights.IMAGENET1K_V2,
             "acc@1": 82.574,
             "acc@5": 96.132,
         },
     )
-    default = ImageNet1K_FBGEMM_V2
+    DEFAULT = IMAGENET1K_FBGEMM_V2
 
 
 @handle_legacy_interface(
     weights=(
         "pretrained",
-        lambda kwargs: ResNet18_QuantizedWeights.ImageNet1K_FBGEMM_V1
+        lambda kwargs: ResNet18_QuantizedWeights.IMAGENET1K_FBGEMM_V1
         if kwargs.get("quantize", False)
-        else ResNet18_Weights.ImageNet1K_V1,
+        else ResNet18_Weights.IMAGENET1K_V1,
     )
 )
 def resnet18(
@@ -165,9 +165,9 @@ def resnet18(
 @handle_legacy_interface(
     weights=(
         "pretrained",
-        lambda kwargs: ResNet50_QuantizedWeights.ImageNet1K_FBGEMM_V1
+        lambda kwargs: ResNet50_QuantizedWeights.IMAGENET1K_FBGEMM_V1
         if kwargs.get("quantize", False)
-        else ResNet50_Weights.ImageNet1K_V1,
+        else ResNet50_Weights.IMAGENET1K_V1,
     )
 )
 def resnet50(
@@ -185,9 +185,9 @@ def resnet50(
 @handle_legacy_interface(
     weights=(
         "pretrained",
-        lambda kwargs: ResNeXt101_32X8D_QuantizedWeights.ImageNet1K_FBGEMM_V1
+        lambda kwargs: ResNeXt101_32X8D_QuantizedWeights.IMAGENET1K_FBGEMM_V1
         if kwargs.get("quantize", False)
-        else ResNeXt101_32X8D_Weights.ImageNet1K_V1,
+        else ResNeXt101_32X8D_Weights.IMAGENET1K_V1,
     )
 )
 def resnext101_32x8d(

torchvision/prototype/models/quantization/shufflenetv2.py

@@ -65,41 +65,41 @@ _COMMON_META = {
 
 
 class ShuffleNet_V2_X0_5_QuantizedWeights(WeightsEnum):
-    ImageNet1K_FBGEMM_V1 = Weights(
+    IMAGENET1K_FBGEMM_V1 = Weights(
         url="https://download.pytorch.org/models/quantized/shufflenetv2_x0.5_fbgemm-00845098.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 1366792,
-            "unquantized": ShuffleNet_V2_X0_5_Weights.ImageNet1K_V1,
+            "unquantized": ShuffleNet_V2_X0_5_Weights.IMAGENET1K_V1,
             "acc@1": 57.972,
             "acc@5": 79.780,
         },
     )
-    default = ImageNet1K_FBGEMM_V1
+    DEFAULT = IMAGENET1K_FBGEMM_V1
 
 
 class ShuffleNet_V2_X1_0_QuantizedWeights(WeightsEnum):
-    ImageNet1K_FBGEMM_V1 = Weights(
+    IMAGENET1K_FBGEMM_V1 = Weights(
         url="https://download.pytorch.org/models/quantized/shufflenetv2_x1_fbgemm-db332c57.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 2278604,
-            "unquantized": ShuffleNet_V2_X1_0_Weights.ImageNet1K_V1,
+            "unquantized": ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1,
             "acc@1": 68.360,
             "acc@5": 87.582,
         },
     )
-    default = ImageNet1K_FBGEMM_V1
+    DEFAULT = IMAGENET1K_FBGEMM_V1
 
 
 @handle_legacy_interface(
     weights=(
         "pretrained",
-        lambda kwargs: ShuffleNet_V2_X0_5_QuantizedWeights.ImageNet1K_FBGEMM_V1
+        lambda kwargs: ShuffleNet_V2_X0_5_QuantizedWeights.IMAGENET1K_FBGEMM_V1
         if kwargs.get("quantize", False)
-        else ShuffleNet_V2_X0_5_Weights.ImageNet1K_V1,
+        else ShuffleNet_V2_X0_5_Weights.IMAGENET1K_V1,
     )
 )
 def shufflenet_v2_x0_5(
@@ -118,9 +118,9 @@ def shufflenet_v2_x0_5(
 @handle_legacy_interface(
     weights=(
         "pretrained",
-        lambda kwargs: ShuffleNet_V2_X1_0_QuantizedWeights.ImageNet1K_FBGEMM_V1
+        lambda kwargs: ShuffleNet_V2_X1_0_QuantizedWeights.IMAGENET1K_FBGEMM_V1
         if kwargs.get("quantize", False)
-        else ShuffleNet_V2_X1_0_Weights.ImageNet1K_V1,
+        else ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1,
     )
 )
 def shufflenet_v2_x1_0(

torchvision/prototype/models/regnet.py

@@ -75,7 +75,7 @@ def _regnet(
 
 
 class RegNet_Y_400MF_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_y_400mf-c65dace8.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -86,7 +86,7 @@ class RegNet_Y_400MF_Weights(WeightsEnum):
             "acc@5": 91.716,
         },
     )
-    ImageNet1K_V2 = Weights(
+    IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_y_400mf-e6988f5f.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -97,11 +97,11 @@ class RegNet_Y_400MF_Weights(WeightsEnum):
             "acc@5": 92.742,
         },
     )
-    default = ImageNet1K_V2
+    DEFAULT = IMAGENET1K_V2
 
 
 class RegNet_Y_800MF_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_y_800mf-1b27b58c.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -112,7 +112,7 @@ class RegNet_Y_800MF_Weights(WeightsEnum):
             "acc@5": 93.136,
         },
     )
-    ImageNet1K_V2 = Weights(
+    IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_y_800mf-58fc7688.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -123,11 +123,11 @@ class RegNet_Y_800MF_Weights(WeightsEnum):
             "acc@5": 94.502,
         },
     )
-    default = ImageNet1K_V2
+    DEFAULT = IMAGENET1K_V2
 
 
 class RegNet_Y_1_6GF_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_y_1_6gf-b11a554e.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -138,7 +138,7 @@ class RegNet_Y_1_6GF_Weights(WeightsEnum):
             "acc@5": 93.966,
         },
     )
-    ImageNet1K_V2 = Weights(
+    IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_y_1_6gf-0d7bc02a.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -149,11 +149,11 @@ class RegNet_Y_1_6GF_Weights(WeightsEnum):
             "acc@5": 95.444,
         },
     )
-    default = ImageNet1K_V2
+    DEFAULT = IMAGENET1K_V2
 
 
 class RegNet_Y_3_2GF_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_y_3_2gf-b5a9779c.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -164,7 +164,7 @@ class RegNet_Y_3_2GF_Weights(WeightsEnum):
             "acc@5": 94.576,
         },
     )
-    ImageNet1K_V2 = Weights(
+    IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_y_3_2gf-9180c971.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -175,11 +175,11 @@ class RegNet_Y_3_2GF_Weights(WeightsEnum):
             "acc@5": 95.972,
         },
     )
-    default = ImageNet1K_V2
+    DEFAULT = IMAGENET1K_V2
 
 
 class RegNet_Y_8GF_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_y_8gf-d0d0e4a8.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -190,7 +190,7 @@ class RegNet_Y_8GF_Weights(WeightsEnum):
             "acc@5": 95.048,
         },
     )
-    ImageNet1K_V2 = Weights(
+    IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_y_8gf-dc2b1b54.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -201,11 +201,11 @@ class RegNet_Y_8GF_Weights(WeightsEnum):
             "acc@5": 96.330,
         },
     )
-    default = ImageNet1K_V2
+    DEFAULT = IMAGENET1K_V2
 
 
 class RegNet_Y_16GF_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_y_16gf-9e6ed7dd.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -216,7 +216,7 @@ class RegNet_Y_16GF_Weights(WeightsEnum):
             "acc@5": 95.240,
         },
     )
-    ImageNet1K_V2 = Weights(
+    IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_y_16gf-3e4a00f9.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -227,11 +227,11 @@ class RegNet_Y_16GF_Weights(WeightsEnum):
             "acc@5": 96.328,
         },
     )
-    default = ImageNet1K_V2
+    DEFAULT = IMAGENET1K_V2
 
 
 class RegNet_Y_32GF_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_y_32gf-4dee3f7a.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -242,7 +242,7 @@ class RegNet_Y_32GF_Weights(WeightsEnum):
             "acc@5": 95.340,
         },
     )
-    ImageNet1K_V2 = Weights(
+    IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_y_32gf-8db6d4b5.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -253,7 +253,7 @@ class RegNet_Y_32GF_Weights(WeightsEnum):
             "acc@5": 96.498,
         },
     )
-    default = ImageNet1K_V2
+    DEFAULT = IMAGENET1K_V2
 
 
 class RegNet_Y_128GF_Weights(WeightsEnum):
@@ -262,7 +262,7 @@ class RegNet_Y_128GF_Weights(WeightsEnum):
 
 
 class RegNet_X_400MF_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_x_400mf-adf1edd5.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -273,7 +273,7 @@ class RegNet_X_400MF_Weights(WeightsEnum):
             "acc@5": 90.950,
         },
     )
-    ImageNet1K_V2 = Weights(
+    IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_x_400mf-62229a5f.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -284,11 +284,11 @@ class RegNet_X_400MF_Weights(WeightsEnum):
             "acc@5": 92.322,
         },
     )
-    default = ImageNet1K_V2
+    DEFAULT = IMAGENET1K_V2
 
 
 class RegNet_X_800MF_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_x_800mf-ad17e45c.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -299,7 +299,7 @@ class RegNet_X_800MF_Weights(WeightsEnum):
             "acc@5": 92.348,
         },
     )
-    ImageNet1K_V2 = Weights(
+    IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_x_800mf-94a99ebd.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -310,11 +310,11 @@ class RegNet_X_800MF_Weights(WeightsEnum):
             "acc@5": 93.826,
         },
     )
-    default = ImageNet1K_V2
+    DEFAULT = IMAGENET1K_V2
 
 
 class RegNet_X_1_6GF_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_x_1_6gf-e3633e7f.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -325,7 +325,7 @@ class RegNet_X_1_6GF_Weights(WeightsEnum):
             "acc@5": 93.440,
         },
     )
-    ImageNet1K_V2 = Weights(
+    IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_x_1_6gf-a12f2b72.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -336,11 +336,11 @@ class RegNet_X_1_6GF_Weights(WeightsEnum):
             "acc@5": 94.922,
         },
     )
-    default = ImageNet1K_V2
+    DEFAULT = IMAGENET1K_V2
 
 
 class RegNet_X_3_2GF_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_x_3_2gf-f342aeae.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -351,7 +351,7 @@ class RegNet_X_3_2GF_Weights(WeightsEnum):
             "acc@5": 93.992,
         },
     )
-    ImageNet1K_V2 = Weights(
+    IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_x_3_2gf-7071aa85.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -362,11 +362,11 @@ class RegNet_X_3_2GF_Weights(WeightsEnum):
             "acc@5": 95.430,
         },
     )
-    default = ImageNet1K_V2
+    DEFAULT = IMAGENET1K_V2
 
 
 class RegNet_X_8GF_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_x_8gf-03ceed89.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -377,7 +377,7 @@ class RegNet_X_8GF_Weights(WeightsEnum):
             "acc@5": 94.686,
         },
     )
-    ImageNet1K_V2 = Weights(
+    IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_x_8gf-2b70d774.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -388,11 +388,11 @@ class RegNet_X_8GF_Weights(WeightsEnum):
             "acc@5": 95.678,
         },
     )
-    default = ImageNet1K_V2
+    DEFAULT = IMAGENET1K_V2
 
 
 class RegNet_X_16GF_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_x_16gf-2007eb11.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -403,7 +403,7 @@ class RegNet_X_16GF_Weights(WeightsEnum):
             "acc@5": 94.944,
         },
     )
-    ImageNet1K_V2 = Weights(
+    IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_x_16gf-ba3796d7.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -414,11 +414,11 @@ class RegNet_X_16GF_Weights(WeightsEnum):
             "acc@5": 96.196,
         },
     )
-    default = ImageNet1K_V2
+    DEFAULT = IMAGENET1K_V2
 
 
 class RegNet_X_32GF_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_x_32gf-9d47f8d0.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -429,7 +429,7 @@ class RegNet_X_32GF_Weights(WeightsEnum):
             "acc@5": 95.248,
         },
     )
-    ImageNet1K_V2 = Weights(
+    IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_x_32gf-6eb8fdc6.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -440,10 +440,10 @@ class RegNet_X_32GF_Weights(WeightsEnum):
             "acc@5": 96.288,
         },
     )
-    default = ImageNet1K_V2
+    DEFAULT = IMAGENET1K_V2
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
@@ -451,7 +451,7 @@ def regnet_y_400mf(*, weights: Optional[RegNet_Y_400MF_Weights] = None, progress
     return _regnet(params, weights, progress, **kwargs)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
@@ -459,7 +459,7 @@ def regnet_y_800mf(*, weights: Optional[RegNet_Y_800MF_Weights] = None, progress
     return _regnet(params, weights, progress, **kwargs)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
@@ -469,7 +469,7 @@ def regnet_y_1_6gf(*, weights: Optional[RegNet_Y_1_6GF_Weights] = None, progress
     return _regnet(params, weights, progress, **kwargs)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
@@ -479,7 +479,7 @@ def regnet_y_3_2gf(*, weights: Optional[RegNet_Y_3_2GF_Weights] = None, progress
     return _regnet(params, weights, progress, **kwargs)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
@@ -489,7 +489,7 @@ def regnet_y_8gf(*, weights: Optional[RegNet_Y_8GF_Weights] = None, progress: bo
     return _regnet(params, weights, progress, **kwargs)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
@@ -499,7 +499,7 @@ def regnet_y_16gf(*, weights: Optional[RegNet_Y_16GF_Weights] = None, progress:
     return _regnet(params, weights, progress, **kwargs)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
@@ -519,7 +519,7 @@ def regnet_y_128gf(*, weights: Optional[RegNet_Y_128GF_Weights] = None, progress
     return _regnet(params, weights, progress, **kwargs)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
@@ -527,7 +527,7 @@ def regnet_x_400mf(*, weights: Optional[RegNet_X_400MF_Weights] = None, progress
     return _regnet(params, weights, progress, **kwargs)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
@@ -535,7 +535,7 @@ def regnet_x_800mf(*, weights: Optional[RegNet_X_800MF_Weights] = None, progress
     return _regnet(params, weights, progress, **kwargs)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
@@ -543,7 +543,7 @@ def regnet_x_1_6gf(*, weights: Optional[RegNet_X_1_6GF_Weights] = None, progress
     return _regnet(params, weights, progress, **kwargs)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
@@ -551,7 +551,7 @@ def regnet_x_3_2gf(*, weights: Optional[RegNet_X_3_2GF_Weights] = None, progress
     return _regnet(params, weights, progress, **kwargs)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
@@ -559,7 +559,7 @@ def regnet_x_8gf(*, weights: Optional[RegNet_X_8GF_Weights] = None, progress: bo
     return _regnet(params, weights, progress, **kwargs)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
@@ -567,7 +567,7 @@ def regnet_x_16gf(*, weights: Optional[RegNet_X_16GF_Weights] = None, progress:
     return _regnet(params, weights, progress, **kwargs)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 

torchvision/prototype/models/resnet.py

@@ -61,7 +61,7 @@ _COMMON_META = {
 
 
 class ResNet18_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/resnet18-f37072fd.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -74,11 +74,11 @@ class ResNet18_Weights(WeightsEnum):
             "acc@5": 89.078,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
 class ResNet34_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/resnet34-b627a593.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -91,11 +91,11 @@ class ResNet34_Weights(WeightsEnum):
             "acc@5": 91.420,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
 class ResNet50_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/resnet50-0676ba61.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -108,7 +108,7 @@ class ResNet50_Weights(WeightsEnum):
             "acc@5": 92.862,
         },
     )
-    ImageNet1K_V2 = Weights(
+    IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/resnet50-11ad3fa6.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -121,11 +121,11 @@ class ResNet50_Weights(WeightsEnum):
             "acc@5": 95.434,
         },
     )
-    default = ImageNet1K_V2
+    DEFAULT = IMAGENET1K_V2
 
 
 class ResNet101_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/resnet101-63fe2227.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -138,7 +138,7 @@ class ResNet101_Weights(WeightsEnum):
             "acc@5": 93.546,
         },
     )
-    ImageNet1K_V2 = Weights(
+    IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/resnet101-cd907fc2.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -151,11 +151,11 @@ class ResNet101_Weights(WeightsEnum):
             "acc@5": 95.780,
         },
     )
-    default = ImageNet1K_V2
+    DEFAULT = IMAGENET1K_V2
 
 
 class ResNet152_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/resnet152-394f9c45.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -168,7 +168,7 @@ class ResNet152_Weights(WeightsEnum):
             "acc@5": 94.046,
         },
     )
-    ImageNet1K_V2 = Weights(
+    IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/resnet152-f82ba261.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -181,11 +181,11 @@ class ResNet152_Weights(WeightsEnum):
             "acc@5": 96.002,
         },
     )
-    default = ImageNet1K_V2
+    DEFAULT = IMAGENET1K_V2
 
 
 class ResNeXt50_32X4D_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -198,7 +198,7 @@ class ResNeXt50_32X4D_Weights(WeightsEnum):
             "acc@5": 93.698,
         },
     )
-    ImageNet1K_V2 = Weights(
+    IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/resnext50_32x4d-1a0047aa.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -211,11 +211,11 @@ class ResNeXt50_32X4D_Weights(WeightsEnum):
             "acc@5": 95.340,
         },
     )
-    default = ImageNet1K_V2
+    DEFAULT = IMAGENET1K_V2
 
 
 class ResNeXt101_32X8D_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -228,7 +228,7 @@ class ResNeXt101_32X8D_Weights(WeightsEnum):
             "acc@5": 94.526,
         },
     )
-    ImageNet1K_V2 = Weights(
+    IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/resnext101_32x8d-110c445d.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -241,11 +241,11 @@ class ResNeXt101_32X8D_Weights(WeightsEnum):
             "acc@5": 96.228,
         },
     )
-    default = ImageNet1K_V2
+    DEFAULT = IMAGENET1K_V2
 
 
 class Wide_ResNet50_2_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -258,7 +258,7 @@ class Wide_ResNet50_2_Weights(WeightsEnum):
             "acc@5": 94.086,
         },
     )
-    ImageNet1K_V2 = Weights(
+    IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/wide_resnet50_2-9ba9bcbe.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -271,11 +271,11 @@ class Wide_ResNet50_2_Weights(WeightsEnum):
             "acc@5": 95.758,
         },
     )
-    default = ImageNet1K_V2
+    DEFAULT = IMAGENET1K_V2
 
 
 class Wide_ResNet101_2_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -288,7 +288,7 @@ class Wide_ResNet101_2_Weights(WeightsEnum):
             "acc@5": 94.284,
         },
     )
-    ImageNet1K_V2 = Weights(
+    IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/wide_resnet101_2-d733dc28.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
         meta={
@@ -301,45 +301,45 @@ class Wide_ResNet101_2_Weights(WeightsEnum):
             "acc@5": 96.020,
         },
     )
-    default = ImageNet1K_V2
+    DEFAULT = IMAGENET1K_V2
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
 
-@handle_legacy_interface(weights=("pretrained", 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:
@@ -350,7 +350,7 @@ def resnext50_32x4d(
     return _resnet(Bottleneck, [3, 4, 6, 3], weights, progress, **kwargs)
 
 
-@handle_legacy_interface(weights=("pretrained", ResNeXt101_32X8D_Weights.ImageNet1K_V1))
+@handle_legacy_interface(weights=("pretrained", ResNeXt101_32X8D_Weights.IMAGENET1K_V1))
 def resnext101_32x8d(
     *, weights: Optional[ResNeXt101_32X8D_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> ResNet:
@@ -361,7 +361,7 @@ def resnext101_32x8d(
     return _resnet(Bottleneck, [3, 4, 23, 3], weights, progress, **kwargs)
 
 
-@handle_legacy_interface(weights=("pretrained", 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:
@@ -371,7 +371,7 @@ def wide_resnet50_2(
     return _resnet(Bottleneck, [3, 4, 6, 3], weights, progress, **kwargs)
 
 
-@handle_legacy_interface(weights=("pretrained", Wide_ResNet101_2_Weights.ImageNet1K_V1))
+@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
 ) -> ResNet:

torchvision/prototype/models/segmentation/deeplabv3.py

@@ -34,7 +34,7 @@ _COMMON_META = {
 
 
 class DeepLabV3_ResNet50_Weights(WeightsEnum):
-    CocoWithVocLabels_V1 = Weights(
+    COCO_WITH_VOC_LABELS_V1 = Weights(
         url="https://download.pytorch.org/models/deeplabv3_resnet50_coco-cd0a2569.pth",
         transforms=partial(VocEval, resize_size=520),
         meta={
@@ -45,11 +45,11 @@ class DeepLabV3_ResNet50_Weights(WeightsEnum):
             "acc": 92.4,
         },
     )
-    default = CocoWithVocLabels_V1
+    DEFAULT = COCO_WITH_VOC_LABELS_V1
 
 
 class DeepLabV3_ResNet101_Weights(WeightsEnum):
-    CocoWithVocLabels_V1 = Weights(
+    COCO_WITH_VOC_LABELS_V1 = Weights(
         url="https://download.pytorch.org/models/deeplabv3_resnet101_coco-586e9e4e.pth",
         transforms=partial(VocEval, resize_size=520),
         meta={
@@ -60,11 +60,11 @@ class DeepLabV3_ResNet101_Weights(WeightsEnum):
             "acc": 92.4,
         },
     )
-    default = CocoWithVocLabels_V1
+    DEFAULT = COCO_WITH_VOC_LABELS_V1
 
 
 class DeepLabV3_MobileNet_V3_Large_Weights(WeightsEnum):
-    CocoWithVocLabels_V1 = Weights(
+    COCO_WITH_VOC_LABELS_V1 = Weights(
         url="https://download.pytorch.org/models/deeplabv3_mobilenet_v3_large-fc3c493d.pth",
         transforms=partial(VocEval, resize_size=520),
         meta={
@@ -75,12 +75,12 @@ class DeepLabV3_MobileNet_V3_Large_Weights(WeightsEnum):
             "acc": 91.2,
         },
     )
-    default = CocoWithVocLabels_V1
+    DEFAULT = COCO_WITH_VOC_LABELS_V1
 
 
 @handle_legacy_interface(
-    weights=("pretrained", DeepLabV3_ResNet50_Weights.CocoWithVocLabels_V1),
-    weights_backbone=("pretrained_backbone", ResNet50_Weights.ImageNet1K_V1),
+    weights=("pretrained", DeepLabV3_ResNet50_Weights.COCO_WITH_VOC_LABELS_V1),
+    weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1),
 )
 def deeplabv3_resnet50(
     *,
@@ -111,8 +111,8 @@ def deeplabv3_resnet50(
 
 
 @handle_legacy_interface(
-    weights=("pretrained", DeepLabV3_ResNet101_Weights.CocoWithVocLabels_V1),
-    weights_backbone=("pretrained_backbone", ResNet101_Weights.ImageNet1K_V1),
+    weights=("pretrained", DeepLabV3_ResNet101_Weights.COCO_WITH_VOC_LABELS_V1),
+    weights_backbone=("pretrained_backbone", ResNet101_Weights.IMAGENET1K_V1),
 )
 def deeplabv3_resnet101(
     *,
@@ -143,8 +143,8 @@ def deeplabv3_resnet101(
 
 
 @handle_legacy_interface(
-    weights=("pretrained", DeepLabV3_MobileNet_V3_Large_Weights.CocoWithVocLabels_V1),
-    weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.ImageNet1K_V1),
+    weights=("pretrained", DeepLabV3_MobileNet_V3_Large_Weights.COCO_WITH_VOC_LABELS_V1),
+    weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.IMAGENET1K_V1),
 )
 def deeplabv3_mobilenet_v3_large(
     *,

torchvision/prototype/models/segmentation/fcn.py

@@ -24,7 +24,7 @@ _COMMON_META = {
 
 
 class FCN_ResNet50_Weights(WeightsEnum):
-    CocoWithVocLabels_V1 = Weights(
+    COCO_WITH_VOC_LABELS_V1 = Weights(
         url="https://download.pytorch.org/models/fcn_resnet50_coco-1167a1af.pth",
         transforms=partial(VocEval, resize_size=520),
         meta={
@@ -35,11 +35,11 @@ class FCN_ResNet50_Weights(WeightsEnum):
             "acc": 91.4,
         },
     )
-    default = CocoWithVocLabels_V1
+    DEFAULT = COCO_WITH_VOC_LABELS_V1
 
 
 class FCN_ResNet101_Weights(WeightsEnum):
-    CocoWithVocLabels_V1 = Weights(
+    COCO_WITH_VOC_LABELS_V1 = Weights(
         url="https://download.pytorch.org/models/fcn_resnet101_coco-7ecb50ca.pth",
         transforms=partial(VocEval, resize_size=520),
         meta={
@@ -50,12 +50,12 @@ class FCN_ResNet101_Weights(WeightsEnum):
             "acc": 91.9,
         },
     )
-    default = CocoWithVocLabels_V1
+    DEFAULT = COCO_WITH_VOC_LABELS_V1
 
 
 @handle_legacy_interface(
-    weights=("pretrained", FCN_ResNet50_Weights.CocoWithVocLabels_V1),
-    weights_backbone=("pretrained_backbone", ResNet50_Weights.ImageNet1K_V1),
+    weights=("pretrained", FCN_ResNet50_Weights.COCO_WITH_VOC_LABELS_V1),
+    weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1),
 )
 def fcn_resnet50(
     *,
@@ -86,8 +86,8 @@ def fcn_resnet50(
 
 
 @handle_legacy_interface(
-    weights=("pretrained", FCN_ResNet101_Weights.CocoWithVocLabels_V1),
-    weights_backbone=("pretrained_backbone", ResNet101_Weights.ImageNet1K_V1),
+    weights=("pretrained", FCN_ResNet101_Weights.COCO_WITH_VOC_LABELS_V1),
+    weights_backbone=("pretrained_backbone", ResNet101_Weights.IMAGENET1K_V1),
 )
 def fcn_resnet101(
     *,

torchvision/prototype/models/segmentation/lraspp.py

@@ -15,7 +15,7 @@ __all__ = ["LRASPP", "LRASPP_MobileNet_V3_Large_Weights", "lraspp_mobilenet_v3_l
 
 
 class LRASPP_MobileNet_V3_Large_Weights(WeightsEnum):
-    CocoWithVocLabels_V1 = Weights(
+    COCO_WITH_VOC_LABELS_V1 = Weights(
         url="https://download.pytorch.org/models/lraspp_mobilenet_v3_large-d234d4ea.pth",
         transforms=partial(VocEval, resize_size=520),
         meta={
@@ -30,12 +30,12 @@ class LRASPP_MobileNet_V3_Large_Weights(WeightsEnum):
             "acc": 91.2,
         },
     )
-    default = CocoWithVocLabels_V1
+    DEFAULT = COCO_WITH_VOC_LABELS_V1
 
 
 @handle_legacy_interface(
-    weights=("pretrained", LRASPP_MobileNet_V3_Large_Weights.CocoWithVocLabels_V1),
-    weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.ImageNet1K_V1),
+    weights=("pretrained", LRASPP_MobileNet_V3_Large_Weights.COCO_WITH_VOC_LABELS_V1),
+    weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.IMAGENET1K_V1),
 )
 def lraspp_mobilenet_v3_large(
     *,

torchvision/prototype/models/shufflenetv2.py

@@ -53,7 +53,7 @@ _COMMON_META = {
 
 
 class ShuffleNet_V2_X0_5_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -63,11 +63,11 @@ class ShuffleNet_V2_X0_5_Weights(WeightsEnum):
             "acc@5": 88.316,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
 class ShuffleNet_V2_X1_0_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -77,7 +77,7 @@ class ShuffleNet_V2_X1_0_Weights(WeightsEnum):
             "acc@5": 81.746,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
 class ShuffleNet_V2_X1_5_Weights(WeightsEnum):
@@ -88,7 +88,7 @@ class ShuffleNet_V2_X2_0_Weights(WeightsEnum):
     pass
 
 
-@handle_legacy_interface(weights=("pretrained", ShuffleNet_V2_X0_5_Weights.ImageNet1K_V1))
+@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
 ) -> ShuffleNetV2:
@@ -97,7 +97,7 @@ def shufflenet_v2_x0_5(
     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))
+@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
 ) -> ShuffleNetV2:

torchvision/prototype/models/squeezenet.py

@@ -25,7 +25,7 @@ _COMMON_META = {
 
 
 class SqueezeNet1_0_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/squeezenet1_0-b66bff10.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -36,11 +36,11 @@ class SqueezeNet1_0_Weights(WeightsEnum):
             "acc@5": 80.420,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
 class SqueezeNet1_1_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/squeezenet1_1-b8a52dc0.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -51,10 +51,10 @@ class SqueezeNet1_1_Weights(WeightsEnum):
             "acc@5": 80.624,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
-@handle_legacy_interface(weights=("pretrained", 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:
@@ -71,7 +71,7 @@ def squeezenet1_0(
     return model
 
 
-@handle_legacy_interface(weights=("pretrained", 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:

torchvision/prototype/models/vgg.py

@@ -53,7 +53,7 @@ _COMMON_META = {
 
 
 class VGG11_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vgg11-8a719046.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -63,11 +63,11 @@ class VGG11_Weights(WeightsEnum):
             "acc@5": 88.628,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
 class VGG11_BN_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vgg11_bn-6002323d.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -77,11 +77,11 @@ class VGG11_BN_Weights(WeightsEnum):
             "acc@5": 89.810,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
 class VGG13_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vgg13-19584684.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -91,11 +91,11 @@ class VGG13_Weights(WeightsEnum):
             "acc@5": 89.246,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
 class VGG13_BN_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vgg13_bn-abd245e5.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -105,11 +105,11 @@ class VGG13_BN_Weights(WeightsEnum):
             "acc@5": 90.374,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
 class VGG16_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vgg16-397923af.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -122,7 +122,7 @@ class VGG16_Weights(WeightsEnum):
     # We port the features of a VGG16 backbone trained by amdegroot because unlike the one on TorchVision, it uses the
     # same input standardization method as the paper. Only the `features` weights have proper values, those on the
     # `classifier` module are filled with nans.
-    ImageNet1K_Features = Weights(
+    IMAGENET1K_FEATURES = Weights(
         url="https://download.pytorch.org/models/vgg16_features-amdegroot-88682ab5.pth",
         transforms=partial(
             ImageNetEval, crop_size=224, mean=(0.48235, 0.45882, 0.40784), std=(1.0 / 255.0, 1.0 / 255.0, 1.0 / 255.0)
@@ -136,11 +136,11 @@ class VGG16_Weights(WeightsEnum):
             "acc@5": float("nan"),
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
 class VGG16_BN_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vgg16_bn-6c64b313.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -150,11 +150,11 @@ class VGG16_BN_Weights(WeightsEnum):
             "acc@5": 91.516,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
 class VGG19_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vgg19-dcbb9e9d.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -164,11 +164,11 @@ class VGG19_Weights(WeightsEnum):
             "acc@5": 90.876,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
 class VGG19_BN_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vgg19_bn-c79401a0.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -178,59 +178,59 @@ class VGG19_BN_Weights(WeightsEnum):
             "acc@5": 91.842,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 

torchvision/prototype/models/video/resnet.py

@@ -63,7 +63,7 @@ _COMMON_META = {
 
 
 class R3D_18_Weights(WeightsEnum):
-    Kinetics400_V1 = Weights(
+    KINETICS400_V1 = Weights(
         url="https://download.pytorch.org/models/r3d_18-b3b3357e.pth",
         transforms=partial(Kinect400Eval, crop_size=(112, 112), resize_size=(128, 171)),
         meta={
@@ -74,11 +74,11 @@ class R3D_18_Weights(WeightsEnum):
             "acc@5": 75.45,
         },
     )
-    default = Kinetics400_V1
+    DEFAULT = KINETICS400_V1
 
 
 class MC3_18_Weights(WeightsEnum):
-    Kinetics400_V1 = Weights(
+    KINETICS400_V1 = Weights(
         url="https://download.pytorch.org/models/mc3_18-a90a0ba3.pth",
         transforms=partial(Kinect400Eval, crop_size=(112, 112), resize_size=(128, 171)),
         meta={
@@ -89,11 +89,11 @@ class MC3_18_Weights(WeightsEnum):
             "acc@5": 76.29,
         },
     )
-    default = Kinetics400_V1
+    DEFAULT = KINETICS400_V1
 
 
 class R2Plus1D_18_Weights(WeightsEnum):
-    Kinetics400_V1 = Weights(
+    KINETICS400_V1 = Weights(
         url="https://download.pytorch.org/models/r2plus1d_18-91a641e6.pth",
         transforms=partial(Kinect400Eval, crop_size=(112, 112), resize_size=(128, 171)),
         meta={
@@ -104,10 +104,10 @@ class R2Plus1D_18_Weights(WeightsEnum):
             "acc@5": 78.81,
         },
     )
-    default = Kinetics400_V1
+    DEFAULT = KINETICS400_V1
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
@@ -122,7 +122,7 @@ def r3d_18(*, weights: Optional[R3D_18_Weights] = None, progress: bool = True, *
     )
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 
@@ -137,7 +137,7 @@ def mc3_18(*, weights: Optional[MC3_18_Weights] = None, progress: bool = True, *
     )
 
 
-@handle_legacy_interface(weights=("pretrained", 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)
 

torchvision/prototype/models/vision_transformer.py

@@ -36,7 +36,7 @@ _COMMON_META = {
 
 
 class ViT_B_16_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vit_b_16-c867db91.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -49,11 +49,11 @@ class ViT_B_16_Weights(WeightsEnum):
             "acc@5": 95.318,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
 class ViT_B_32_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vit_b_32-d86f8d99.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -66,11 +66,11 @@ class ViT_B_32_Weights(WeightsEnum):
             "acc@5": 92.466,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
 class ViT_L_16_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vit_l_16-852ce7e3.pth",
         transforms=partial(ImageNetEval, crop_size=224, resize_size=242),
         meta={
@@ -83,11 +83,11 @@ class ViT_L_16_Weights(WeightsEnum):
             "acc@5": 94.638,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
 class ViT_L_32_Weights(WeightsEnum):
-    ImageNet1K_V1 = Weights(
+    IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vit_l_32-c7638314.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
@@ -100,7 +100,7 @@ class ViT_L_32_Weights(WeightsEnum):
             "acc@5": 93.07,
         },
     )
-    default = ImageNet1K_V1
+    DEFAULT = IMAGENET1K_V1
 
 
 def _vision_transformer(
@@ -134,7 +134,7 @@ def _vision_transformer(
     return model
 
 
-@handle_legacy_interface(weights=("pretrained", ViT_B_16_Weights.ImageNet1K_V1))
+@handle_legacy_interface(weights=("pretrained", ViT_B_16_Weights.IMAGENET1K_V1))
 def vit_b_16(*, weights: Optional[ViT_B_16_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer:
     weights = ViT_B_16_Weights.verify(weights)
 
@@ -150,7 +150,7 @@ def vit_b_16(*, weights: Optional[ViT_B_16_Weights] = None, progress: bool = Tru
     )
 
 
-@handle_legacy_interface(weights=("pretrained", ViT_B_32_Weights.ImageNet1K_V1))
+@handle_legacy_interface(weights=("pretrained", ViT_B_32_Weights.IMAGENET1K_V1))
 def vit_b_32(*, weights: Optional[ViT_B_32_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer:
     weights = ViT_B_32_Weights.verify(weights)
 
@@ -166,7 +166,7 @@ def vit_b_32(*, weights: Optional[ViT_B_32_Weights] = None, progress: bool = Tru
     )
 
 
-@handle_legacy_interface(weights=("pretrained", ViT_L_16_Weights.ImageNet1K_V1))
+@handle_legacy_interface(weights=("pretrained", ViT_L_16_Weights.IMAGENET1K_V1))
 def vit_l_16(*, weights: Optional[ViT_L_16_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer:
     weights = ViT_L_16_Weights.verify(weights)
 
@@ -182,7 +182,7 @@ def vit_l_16(*, weights: Optional[ViT_L_16_Weights] = None, progress: bool = Tru
     )
 
 
-@handle_legacy_interface(weights=("pretrained", ViT_L_32_Weights.ImageNet1K_V1))
+@handle_legacy_interface(weights=("pretrained", ViT_L_32_Weights.IMAGENET1K_V1))
 def vit_l_32(*, weights: Optional[ViT_L_32_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer:
     weights = ViT_L_32_Weights.verify(weights)