Update training references from legacy models (#4830)

* Update training references from legacy models.

* Refactoring to share common parts.

references/classification/README.md

@@ -42,20 +42,20 @@ torchrun --nproc_per_node=8 train.py --model inception_v3\
       --val-resize-size 342 --val-crop-size 299 --train-crop-size 299 --test-only --pretrained
 ```
 
-### ResNext-50 32x4d
+### ResNet
 ```
-torchrun --nproc_per_node=8 train.py\
-    --model resnext50_32x4d --epochs 100
+torchrun --nproc_per_node=8 train.py --model $MODEL
 ```
 
+Here `$MODEL` is one of `resnet18`, `resnet34`, `resnet50`, `resnet101` or `resnet152`.
 
-### ResNext-101 32x8d
-
+### ResNext
 ```
 torchrun --nproc_per_node=8 train.py\
-    --model resnext101_32x8d --epochs 100
+    --model $MODEL --epochs 100
 ```
 
+Here `$MODEL` is one of `resnext50_32x4d` or `resnext101_32x8d`.
 Note that the above command corresponds to a single node with 8 GPUs. If you use
 a different number of GPUs and/or a different batch size, then the learning rate
 should be scaled accordingly. For example, the pretrained model provided by

torchvision/prototype/models/alexnet.py

@@ -13,15 +13,14 @@ from ._meta import _IMAGENET_CATEGORIES
 __all__ = ["AlexNet", "AlexNetWeights", "alexnet"]
 
 
-_common_meta = {"size": (224, 224), "categories": _IMAGENET_CATEGORIES, "interpolation": InterpolationMode.BILINEAR}
-
-
 class AlexNetWeights(Weights):
     ImageNet1K_RefV1 = WeightEntry(
         url="https://download.pytorch.org/models/alexnet-owt-7be5be79.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
-            **_common_meta,
+            "size": (224, 224),
+            "categories": _IMAGENET_CATEGORIES,
+            "interpolation": InterpolationMode.BILINEAR,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg",
             "acc@1": 56.522,
             "acc@5": 79.066,

torchvision/prototype/models/densenet.py

@@ -63,16 +63,20 @@ def _densenet(
     return model
 
 
-_common_meta = {"size": (224, 224), "categories": _IMAGENET_CATEGORIES, "interpolation": InterpolationMode.BILINEAR}
+_common_meta = {
+    "size": (224, 224),
+    "categories": _IMAGENET_CATEGORIES,
+    "interpolation": InterpolationMode.BILINEAR,
+    "recipe": None,  # weights ported from LuaTorch
+}
 
 
 class DenseNet121Weights(Weights):
-    ImageNet1K_RefV1 = WeightEntry(
+    ImageNet1K_Community = WeightEntry(
         url="https://download.pytorch.org/models/densenet121-a639ec97.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "",
             "acc@1": 74.434,
             "acc@5": 91.972,
         },
@@ -80,12 +84,11 @@ class DenseNet121Weights(Weights):
 
 
 class DenseNet161Weights(Weights):
-    ImageNet1K_RefV1 = WeightEntry(
+    ImageNet1K_Community = WeightEntry(
         url="https://download.pytorch.org/models/densenet161-8d451a50.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "",
             "acc@1": 77.138,
             "acc@5": 93.560,
         },
@@ -93,12 +96,11 @@ class DenseNet161Weights(Weights):
 
 
 class DenseNet169Weights(Weights):
-    ImageNet1K_RefV1 = WeightEntry(
+    ImageNet1K_Community = WeightEntry(
         url="https://download.pytorch.org/models/densenet169-b2777c0a.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "",
             "acc@1": 75.600,
             "acc@5": 92.806,
         },
@@ -106,12 +108,11 @@ class DenseNet169Weights(Weights):
 
 
 class DenseNet201Weights(Weights):
-    ImageNet1K_RefV1 = WeightEntry(
+    ImageNet1K_Community = WeightEntry(
         url="https://download.pytorch.org/models/densenet201-c1103571.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "",
             "acc@1": 76.896,
             "acc@5": 93.370,
         },
@@ -121,7 +122,7 @@ 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.")
-        weights = DenseNet121Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+        weights = DenseNet121Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
     weights = DenseNet121Weights.verify(weights)
 
     return _densenet(32, (6, 12, 24, 16), 64, weights, progress, **kwargs)
@@ -130,7 +131,7 @@ 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.")
-        weights = DenseNet161Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+        weights = DenseNet161Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
     weights = DenseNet161Weights.verify(weights)
 
     return _densenet(48, (6, 12, 36, 24), 96, weights, progress, **kwargs)
@@ -139,7 +140,7 @@ 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.")
-        weights = DenseNet169Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+        weights = DenseNet169Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
     weights = DenseNet169Weights.verify(weights)
 
     return _densenet(32, (6, 12, 32, 32), 64, weights, progress, **kwargs)
@@ -148,7 +149,7 @@ 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.")
-        weights = DenseNet201Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+        weights = DenseNet201Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
     weights = DenseNet201Weights.verify(weights)
 
     return _densenet(32, (6, 12, 48, 32), 64, weights, progress, **kwargs)

torchvision/prototype/models/efficientnet.py

@@ -62,7 +62,11 @@ def _efficientnet(
     return model
 
 
-_common_meta = {"categories": _IMAGENET_CATEGORIES, "interpolation": InterpolationMode.BICUBIC}
+_common_meta = {
+    "categories": _IMAGENET_CATEGORIES,
+    "interpolation": InterpolationMode.BICUBIC,
+    "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#efficientnet",
+}
 
 
 class EfficientNetB0Weights(Weights):
@@ -72,7 +76,6 @@ class EfficientNetB0Weights(Weights):
         meta={
             **_common_meta,
             "size": (224, 224),
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#efficientnet",
             "acc@1": 77.692,
             "acc@5": 93.532,
         },
@@ -86,7 +89,6 @@ class EfficientNetB1Weights(Weights):
         meta={
             **_common_meta,
             "size": (240, 240),
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#efficientnet",
             "acc@1": 78.642,
             "acc@5": 94.186,
         },
@@ -100,7 +102,6 @@ class EfficientNetB2Weights(Weights):
         meta={
             **_common_meta,
             "size": (288, 288),
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#efficientnet",
             "acc@1": 80.608,
             "acc@5": 95.310,
         },
@@ -114,7 +115,6 @@ class EfficientNetB3Weights(Weights):
         meta={
             **_common_meta,
             "size": (300, 300),
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#efficientnet",
             "acc@1": 82.008,
             "acc@5": 96.054,
         },
@@ -128,7 +128,6 @@ class EfficientNetB4Weights(Weights):
         meta={
             **_common_meta,
             "size": (380, 380),
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#efficientnet",
             "acc@1": 83.384,
             "acc@5": 96.594,
         },
@@ -142,7 +141,6 @@ class EfficientNetB5Weights(Weights):
         meta={
             **_common_meta,
             "size": (456, 456),
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#efficientnet",
             "acc@1": 83.444,
             "acc@5": 96.628,
         },
@@ -156,7 +154,6 @@ class EfficientNetB6Weights(Weights):
         meta={
             **_common_meta,
             "size": (528, 528),
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#efficientnet",
             "acc@1": 84.008,
             "acc@5": 96.916,
         },
@@ -170,7 +167,6 @@ class EfficientNetB7Weights(Weights):
         meta={
             **_common_meta,
             "size": (600, 600),
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#efficientnet",
             "acc@1": 84.122,
             "acc@5": 96.908,
         },

torchvision/prototype/models/googlenet.py

@@ -13,15 +13,14 @@ from ._meta import _IMAGENET_CATEGORIES
 __all__ = ["GoogLeNet", "GoogLeNetOutputs", "_GoogLeNetOutputs", "GoogLeNetWeights", "googlenet"]
 
 
-_common_meta = {"size": (224, 224), "categories": _IMAGENET_CATEGORIES, "interpolation": InterpolationMode.BILINEAR}
-
-
 class GoogLeNetWeights(Weights):
     ImageNet1K_Community = WeightEntry(
         url="https://download.pytorch.org/models/googlenet-1378be20.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
-            **_common_meta,
+            "size": (224, 224),
+            "categories": _IMAGENET_CATEGORIES,
+            "interpolation": InterpolationMode.BILINEAR,
             "recipe": "https://github.com/TheCodez/examples/blob/inception/imagenet/README.md#googlenet",
             "acc@1": 69.778,
             "acc@5": 89.530,

torchvision/prototype/models/inception.py

@@ -13,15 +13,14 @@ from ._meta import _IMAGENET_CATEGORIES
 __all__ = ["Inception3", "InceptionOutputs", "_InceptionOutputs", "Inception3Weights", "inception_v3"]
 
 
-_common_meta = {"size": (299, 299), "categories": _IMAGENET_CATEGORIES, "interpolation": InterpolationMode.BILINEAR}
-
-
 class Inception3Weights(Weights):
     ImageNet1K_TFV1 = WeightEntry(
         url="https://download.pytorch.org/models/inception_v3_google-0cc3c7bd.pth",
         transforms=partial(ImageNetEval, crop_size=299, resize_size=342),
         meta={
-            **_common_meta,
+            "size": (299, 299),
+            "categories": _IMAGENET_CATEGORIES,
+            "interpolation": InterpolationMode.BILINEAR,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#inception-v3",
             "acc@1": 77.294,
             "acc@5": 93.450,

torchvision/prototype/models/mnasnet.py

@@ -23,7 +23,12 @@ __all__ = [
 ]
 
 
-_common_meta = {"size": (224, 224), "categories": _IMAGENET_CATEGORIES, "interpolation": InterpolationMode.BILINEAR}
+_common_meta = {
+    "size": (224, 224),
+    "categories": _IMAGENET_CATEGORIES,
+    "interpolation": InterpolationMode.BILINEAR,
+    "recipe": "https://github.com/1e100/mnasnet_trainer",
+}
 
 
 class MNASNet0_5Weights(Weights):
@@ -32,7 +37,6 @@ class MNASNet0_5Weights(Weights):
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "https://github.com/1e100/mnasnet_trainer",
             "acc@1": 67.734,
             "acc@5": 87.490,
         },
@@ -50,7 +54,6 @@ class MNASNet1_0Weights(Weights):
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "https://github.com/1e100/mnasnet_trainer",
             "acc@1": 73.456,
             "acc@5": 91.510,
         },

torchvision/prototype/models/mobilenetv2.py

@@ -13,15 +13,14 @@ from ._meta import _IMAGENET_CATEGORIES
 __all__ = ["MobileNetV2", "MobileNetV2Weights", "mobilenet_v2"]
 
 
-_common_meta = {"size": (224, 224), "categories": _IMAGENET_CATEGORIES, "interpolation": InterpolationMode.BILINEAR}
-
-
 class MobileNetV2Weights(Weights):
     ImageNet1K_RefV1 = WeightEntry(
         url="https://download.pytorch.org/models/mobilenet_v2-b0353104.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
-            **_common_meta,
+            "size": (224, 224),
+            "categories": _IMAGENET_CATEGORIES,
+            "interpolation": InterpolationMode.BILINEAR,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv2",
             "acc@1": 71.878,
             "acc@5": 90.286,

torchvision/prototype/models/mobilenetv3.py

@@ -37,7 +37,12 @@ def _mobilenet_v3(
     return model
 
 
-_common_meta = {"size": (224, 224), "categories": _IMAGENET_CATEGORIES, "interpolation": InterpolationMode.BILINEAR}
+_common_meta = {
+    "size": (224, 224),
+    "categories": _IMAGENET_CATEGORIES,
+    "interpolation": InterpolationMode.BILINEAR,
+    "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv3-large--small",
+}
 
 
 class MobileNetV3LargeWeights(Weights):
@@ -46,7 +51,6 @@ class MobileNetV3LargeWeights(Weights):
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv3-large--small",
             "acc@1": 74.042,
             "acc@5": 91.340,
         },
@@ -59,7 +63,6 @@ class MobileNetV3SmallWeights(Weights):
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv3-large--small",
             "acc@1": 67.668,
             "acc@5": 87.402,
         },

torchvision/prototype/models/resnet.py

@@ -60,7 +60,7 @@ class ResNet18Weights(Weights):
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "",
+            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet",
             "acc@1": 69.758,
             "acc@5": 89.078,
         },
@@ -73,7 +73,7 @@ class ResNet34Weights(Weights):
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "",
+            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet",
             "acc@1": 73.314,
             "acc@5": 91.420,
         },
@@ -86,7 +86,7 @@ class ResNet50Weights(Weights):
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification",
+            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet",
             "acc@1": 76.130,
             "acc@5": 92.862,
         },
@@ -109,7 +109,7 @@ class ResNet101Weights(Weights):
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "",
+            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet",
             "acc@1": 77.374,
             "acc@5": 93.546,
         },
@@ -132,7 +132,7 @@ class ResNet152Weights(Weights):
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "",
+            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet",
             "acc@1": 78.312,
             "acc@5": 94.046,
         },
@@ -155,7 +155,7 @@ class ResNeXt50_32x4dWeights(Weights):
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "",
+            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnext",
             "acc@1": 77.618,
             "acc@5": 93.698,
         },
@@ -168,7 +168,7 @@ class ResNeXt101_32x8dWeights(Weights):
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "",
+            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnext",
             "acc@1": 79.312,
             "acc@5": 94.526,
         },
@@ -176,12 +176,12 @@ class ResNeXt101_32x8dWeights(Weights):
 
 
 class WideResNet50_2Weights(Weights):
-    ImageNet1K_RefV1 = WeightEntry(
+    ImageNet1K_Community = WeightEntry(
         url="https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "",
+            "recipe": "https://github.com/pytorch/vision/pull/912#issue-445437439",
             "acc@1": 78.468,
             "acc@5": 94.086,
         },
@@ -189,12 +189,12 @@ class WideResNet50_2Weights(Weights):
 
 
 class WideResNet101_2Weights(Weights):
-    ImageNet1K_RefV1 = WeightEntry(
+    ImageNet1K_Community = WeightEntry(
         url="https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "",
+            "recipe": "https://github.com/pytorch/vision/pull/912#issue-445437439",
             "acc@1": 78.848,
             "acc@5": 94.284,
         },
@@ -275,7 +275,7 @@ def resnext101_32x8d(weights: Optional[ResNeXt101_32x8dWeights] = None, progress
 def wide_resnet50_2(weights: Optional[WideResNet50_2Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet:
     if "pretrained" in kwargs:
         warnings.warn("The argument pretrained is deprecated, please use weights instead.")
-        weights = WideResNet50_2Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+        weights = WideResNet50_2Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
 
     weights = WideResNet50_2Weights.verify(weights)
     kwargs["width_per_group"] = 64 * 2
@@ -285,7 +285,7 @@ def wide_resnet50_2(weights: Optional[WideResNet50_2Weights] = None, progress: b
 def wide_resnet101_2(weights: Optional[WideResNet101_2Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet:
     if "pretrained" in kwargs:
         warnings.warn("The argument pretrained is deprecated, please use weights instead.")
-        weights = WideResNet101_2Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+        weights = WideResNet101_2Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
 
     weights = WideResNet101_2Weights.verify(weights)
     kwargs["width_per_group"] = 64 * 2

torchvision/prototype/models/segmentation/deeplabv3.py

@@ -22,12 +22,15 @@ __all__ = [
 ]
 
 
+_common_meta = {"categories": _VOC_CATEGORIES}
+
+
 class DeepLabV3ResNet50Weights(Weights):
     CocoWithVocLabels_RefV1 = WeightEntry(
         url="https://download.pytorch.org/models/deeplabv3_resnet50_coco-cd0a2569.pth",
         transforms=partial(VocEval, resize_size=520),
         meta={
-            "categories": _VOC_CATEGORIES,
+            **_common_meta,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/segmentation#deeplabv3_resnet50",
             "mIoU": 66.4,
             "acc": 92.4,
@@ -40,7 +43,7 @@ class DeepLabV3ResNet101Weights(Weights):
         url="https://download.pytorch.org/models/deeplabv3_resnet101_coco-586e9e4e.pth",
         transforms=partial(VocEval, resize_size=520),
         meta={
-            "categories": _VOC_CATEGORIES,
+            **_common_meta,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/segmentation#fcn_resnet101",
             "mIoU": 67.4,
             "acc": 92.4,
@@ -53,7 +56,7 @@ class DeepLabV3MobileNetV3LargeWeights(Weights):
         url="https://download.pytorch.org/models/deeplabv3_mobilenet_v3_large-fc3c493d.pth",
         transforms=partial(VocEval, resize_size=520),
         meta={
-            "categories": _VOC_CATEGORIES,
+            **_common_meta,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/segmentation#deeplabv3_mobilenet_v3_large",
             "mIoU": 60.3,
             "acc": 91.2,

torchvision/prototype/models/segmentation/fcn.py

@@ -12,12 +12,15 @@ from ..resnet import ResNet50Weights, ResNet101Weights, resnet50, resnet101
 __all__ = ["FCN", "FCNResNet50Weights", "FCNResNet101Weights", "fcn_resnet50", "fcn_resnet101"]
 
 
+_common_meta = {"categories": _VOC_CATEGORIES}
+
+
 class FCNResNet50Weights(Weights):
     CocoWithVocLabels_RefV1 = WeightEntry(
         url="https://download.pytorch.org/models/fcn_resnet50_coco-1167a1af.pth",
         transforms=partial(VocEval, resize_size=520),
         meta={
-            "categories": _VOC_CATEGORIES,
+            **_common_meta,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/segmentation#fcn_resnet50",
             "mIoU": 60.5,
             "acc": 91.4,
@@ -30,7 +33,7 @@ class FCNResNet101Weights(Weights):
         url="https://download.pytorch.org/models/fcn_resnet101_coco-7ecb50ca.pth",
         transforms=partial(VocEval, resize_size=520),
         meta={
-            "categories": _VOC_CATEGORIES,
+            **_common_meta,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/segmentation#deeplabv3_resnet101",
             "mIoU": 63.7,
             "acc": 91.9,

torchvision/prototype/models/shufflenetv2.py

@@ -40,16 +40,20 @@ def _shufflenetv2(
     return model
 
 
-_common_meta = {"size": (224, 224), "categories": _IMAGENET_CATEGORIES, "interpolation": InterpolationMode.BILINEAR}
+_common_meta = {
+    "size": (224, 224),
+    "categories": _IMAGENET_CATEGORIES,
+    "interpolation": InterpolationMode.BILINEAR,
+    "recipe": "https://github.com/barrh/Shufflenet-v2-Pytorch/tree/v0.1.0",
+}
 
 
 class ShuffleNetV2_x0_5Weights(Weights):
-    ImageNet1K_RefV1 = WeightEntry(
+    ImageNet1K_Community = WeightEntry(
         url="https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "",
             "acc@1": 69.362,
             "acc@5": 88.316,
         },
@@ -57,12 +61,11 @@ class ShuffleNetV2_x0_5Weights(Weights):
 
 
 class ShuffleNetV2_x1_0Weights(Weights):
-    ImageNet1K_RefV1 = WeightEntry(
+    ImageNet1K_Community = WeightEntry(
         url="https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth",
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "",
             "acc@1": 60.552,
             "acc@5": 81.746,
         },
@@ -82,7 +85,7 @@ def shufflenet_v2_x0_5(
 ) -> ShuffleNetV2:
     if "pretrained" in kwargs:
         warnings.warn("The argument pretrained is deprecated, please use weights instead.")
-        weights = ShuffleNetV2_x0_5Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+        weights = ShuffleNetV2_x0_5Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
     weights = ShuffleNetV2_x0_5Weights.verify(weights)
 
     return _shufflenetv2(weights, progress, [4, 8, 4], [24, 48, 96, 192, 1024], **kwargs)
@@ -93,7 +96,7 @@ def shufflenet_v2_x1_0(
 ) -> ShuffleNetV2:
     if "pretrained" in kwargs:
         warnings.warn("The argument pretrained is deprecated, please use weights instead.")
-        weights = ShuffleNetV2_x1_0Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+        weights = ShuffleNetV2_x1_0Weights.ImageNet1K_Community if kwargs.pop("pretrained") else None
     weights = ShuffleNetV2_x1_0Weights.verify(weights)
 
     return _shufflenetv2(weights, progress, [4, 8, 4], [24, 116, 232, 464, 1024], **kwargs)

torchvision/prototype/models/squeezenet.py

@@ -13,7 +13,12 @@ from ._meta import _IMAGENET_CATEGORIES
 __all__ = ["SqueezeNet", "SqueezeNet1_0Weights", "SqueezeNet1_1Weights", "squeezenet1_0", "squeezenet1_1"]
 
 
-_common_meta = {"size": (224, 224), "categories": _IMAGENET_CATEGORIES, "interpolation": InterpolationMode.BILINEAR}
+_common_meta = {
+    "size": (224, 224),
+    "categories": _IMAGENET_CATEGORIES,
+    "interpolation": InterpolationMode.BILINEAR,
+    "recipe": "https://github.com/pytorch/vision/pull/49#issuecomment-277560717",
+}
 
 
 class SqueezeNet1_0Weights(Weights):
@@ -22,7 +27,6 @@ class SqueezeNet1_0Weights(Weights):
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "https://github.com/pytorch/vision/pull/49#issuecomment-277560717",
             "acc@1": 58.092,
             "acc@5": 80.420,
         },
@@ -35,7 +39,6 @@ class SqueezeNet1_1Weights(Weights):
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "https://github.com/pytorch/vision/pull/49#issuecomment-277560717",
             "acc@1": 58.178,
             "acc@5": 80.624,
         },

torchvision/prototype/models/vgg.py

@@ -40,7 +40,12 @@ def _vgg(arch: str, cfg: str, batch_norm: bool, weights: Optional[Weights], prog
     return model
 
 
-_common_meta = {"size": (224, 224), "categories": _IMAGENET_CATEGORIES, "interpolation": InterpolationMode.BILINEAR}
+_common_meta = {
+    "size": (224, 224),
+    "categories": _IMAGENET_CATEGORIES,
+    "interpolation": InterpolationMode.BILINEAR,
+    "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg",
+}
 
 
 class VGG11Weights(Weights):
@@ -49,7 +54,6 @@ class VGG11Weights(Weights):
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg",
             "acc@1": 69.020,
             "acc@5": 88.628,
         },
@@ -62,7 +66,6 @@ class VGG11BNWeights(Weights):
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg",
             "acc@1": 70.370,
             "acc@5": 89.810,
         },
@@ -75,7 +78,6 @@ class VGG13Weights(Weights):
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg",
             "acc@1": 69.928,
             "acc@5": 89.246,
         },
@@ -88,7 +90,6 @@ class VGG13BNWeights(Weights):
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg",
             "acc@1": 71.586,
             "acc@5": 90.374,
         },
@@ -101,7 +102,6 @@ class VGG16Weights(Weights):
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg",
             "acc@1": 71.592,
             "acc@5": 90.382,
         },
@@ -114,7 +114,6 @@ class VGG16BNWeights(Weights):
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg",
             "acc@1": 73.360,
             "acc@5": 91.516,
         },
@@ -127,7 +126,6 @@ class VGG19Weights(Weights):
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg",
             "acc@1": 72.376,
             "acc@5": 90.876,
         },
@@ -140,7 +138,6 @@ class VGG19BNWeights(Weights):
         transforms=partial(ImageNetEval, crop_size=224),
         meta={
             **_common_meta,
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg",
             "acc@1": 74.218,
             "acc@5": 91.842,
         },

torchvision/prototype/models/video/resnet.py

@@ -51,7 +51,12 @@ def _video_resnet(
     return model
 
 
-_common_meta = {"size": (112, 112), "categories": _KINETICS400_CATEGORIES, "interpolation": InterpolationMode.BILINEAR}
+_common_meta = {
+    "size": (112, 112),
+    "categories": _KINETICS400_CATEGORIES,
+    "interpolation": InterpolationMode.BILINEAR,
+    "recipe": "https://github.com/pytorch/vision/tree/main/references/video_classification",
+}
 
 
 class R3D_18Weights(Weights):
@@ -60,7 +65,6 @@ class R3D_18Weights(Weights):
         transforms=partial(Kinect400Eval, resize_size=(128, 171), crop_size=(112, 112)),
         meta={
             **_common_meta,
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/video_classification",
             "acc@1": 52.75,
             "acc@5": 75.45,
         },
@@ -73,7 +77,6 @@ class MC3_18Weights(Weights):
         transforms=partial(Kinect400Eval, resize_size=(128, 171), crop_size=(112, 112)),
         meta={
             **_common_meta,
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/video_classification",
             "acc@1": 53.90,
             "acc@5": 76.29,
         },
@@ -86,7 +89,6 @@ class R2Plus1D_18Weights(Weights):
         transforms=partial(Kinect400Eval, resize_size=(128, 171), crop_size=(112, 112)),
         meta={
             **_common_meta,
-            "recipe": "https://github.com/pytorch/vision/tree/main/references/video_classification",
             "acc@1": 57.50,
             "acc@5": 78.81,
         },