Reinstate and deprecate `model_urls` and `quant_model_urls` (#5992)

* Reinstate and deprecate `model_urls` and `quant_model_urls`

* Apply suggestions from code review
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

* Move todo location

* Add alexnet

* Add densenet

* Add efficientnet

* Add googlenet.

* Add inception.

* Add mobilenetv3

* Add regnet

* Add resnet

* Add shufflenetv2

* Fix linter

* Add squeezenet

* Add vgg

* Add vit

* Add quantized googlenet

* Add quantized inceptionv3

* Add quantized mobilenet_v3

* Add quantized resnet

* Add quantized shufflenetv2

* Fix incorrect imports

* Add faster_rcnn

* Add fcos

* Add keypoint rcnn

* Add mask rcnn

* Add retinanet

* Add ssd

* Add ssdlite

* Add deeplabv3

* Add fcn

* Add lraspp.

* Add video resnet

* Removing weights for shufflenetv2_x1.5 and shufflenetv2_x2.0

* Update the comments
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

torchvision/models/_utils.py

@@ -134,7 +134,8 @@ def kwonly_to_pos_or_kw(fn: Callable[..., D]) -> Callable[..., D]:
             keyword_only_kwargs = dict(zip(keyword_only_params, keyword_only_args))
             warnings.warn(
                 f"Using {sequence_to_str(tuple(keyword_only_kwargs.keys()), separate_last='and ')} as positional "
-                f"parameter(s) is deprecated. Please use keyword parameter(s) instead."
+                f"parameter(s) is deprecated since 0.13 and will be removed in 0.15. Please use keyword parameter(s) "
+                f"instead."
             )
             kwargs.update(keyword_only_kwargs)
 
@@ -205,11 +206,13 @@ def handle_legacy_interface(**weights: Tuple[str, Union[Optional[W], Callable[[D
 
                 if not pretrained_positional:
                     warnings.warn(
-                        f"The parameter '{pretrained_param}' is deprecated, please use '{weights_param}' instead."
+                        f"The parameter '{pretrained_param}' is deprecated since 0.13 and will be removed in 0.15, "
+                        f"please use '{weights_param}' instead."
                     )
 
                 msg = (
-                    f"Arguments other than a weight enum or `None` for '{weights_param}' are deprecated. "
+                    f"Arguments other than a weight enum or `None` for '{weights_param}' are deprecated since 0.13 and "
+                    f"will be removed in 0.15. "
                     f"The current behavior is equivalent to passing `{weights_param}={default_weights_arg}`."
                 )
                 if pretrained_arg:
@@ -242,3 +245,12 @@ def _ovewrite_value_param(param: Optional[V], new_value: V) -> V:
         if param != new_value:
             raise ValueError(f"The parameter '{param}' expected value {new_value} but got {param} instead.")
     return new_value
+
+
+class _ModelURLs(dict):
+    def __getitem__(self, item):
+        warnings.warn(
+            "Accessing the model URLs via the internal dictionary of the module is deprecated since 0.13 and will "
+            "be removed in 0.15. Please access them via the appropriate Weights Enum instead."
+        )
+        return super().__getitem__(item)

torchvision/models/alexnet.py

@@ -111,3 +111,14 @@ def alexnet(*, weights: Optional[AlexNet_Weights] = None, progress: bool = True,
         model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model
+
+
+# The dictionary below is internal implementation detail and will be removed in v0.15
+from ._utils import _ModelURLs
+
+
+model_urls = _ModelURLs(
+    {
+        "alexnet": AlexNet_Weights.IMAGENET1K_V1.url,
+    }
+)

torchvision/models/densenet.py

@@ -430,3 +430,17 @@ def densenet201(*, weights: Optional[DenseNet201_Weights] = None, progress: bool
     weights = DenseNet201_Weights.verify(weights)
 
     return _densenet(32, (6, 12, 48, 32), 64, weights, progress, **kwargs)
+
+
+# The dictionary below is internal implementation detail and will be removed in v0.15
+from ._utils import _ModelURLs
+
+
+model_urls = _ModelURLs(
+    {
+        "densenet121": DenseNet121_Weights.IMAGENET1K_V1.url,
+        "densenet169": DenseNet169_Weights.IMAGENET1K_V1.url,
+        "densenet201": DenseNet201_Weights.IMAGENET1K_V1.url,
+        "densenet161": DenseNet161_Weights.IMAGENET1K_V1.url,
+    }
+)

torchvision/models/detection/faster_rcnn.py

@@ -804,3 +804,16 @@ def fasterrcnn_mobilenet_v3_large_fpn(
         trainable_backbone_layers=trainable_backbone_layers,
         **kwargs,
     )
+
+
+# The dictionary below is internal implementation detail and will be removed in v0.15
+from .._utils import _ModelURLs
+
+
+model_urls = _ModelURLs(
+    {
+        "fasterrcnn_resnet50_fpn_coco": FasterRCNN_ResNet50_FPN_Weights.COCO_V1.url,
+        "fasterrcnn_mobilenet_v3_large_320_fpn_coco": FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.COCO_V1.url,
+        "fasterrcnn_mobilenet_v3_large_fpn_coco": FasterRCNN_MobileNet_V3_Large_FPN_Weights.COCO_V1.url,
+    }
+)

torchvision/models/detection/fcos.py

@@ -758,3 +758,14 @@ def fcos_resnet50_fpn(
         model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model
+
+
+# The dictionary below is internal implementation detail and will be removed in v0.15
+from .._utils import _ModelURLs
+
+
+model_urls = _ModelURLs(
+    {
+        "fcos_resnet50_fpn_coco": FCOS_ResNet50_FPN_Weights.COCO_V1.url,
+    }
+)

torchvision/models/detection/keypoint_rcnn.py

@@ -454,3 +454,16 @@ def keypointrcnn_resnet50_fpn(
             overwrite_eps(model, 0.0)
 
     return model
+
+
+# The dictionary below is internal implementation detail and will be removed in v0.15
+from .._utils import _ModelURLs
+
+
+model_urls = _ModelURLs(
+    {
+        # legacy model for BC reasons, see https://github.com/pytorch/vision/issues/1606
+        "keypointrcnn_resnet50_fpn_coco_legacy": KeypointRCNN_ResNet50_FPN_Weights.COCO_LEGACY.url,
+        "keypointrcnn_resnet50_fpn_coco": KeypointRCNN_ResNet50_FPN_Weights.COCO_V1.url,
+    }
+)

torchvision/models/detection/mask_rcnn.py

@@ -565,3 +565,14 @@ def maskrcnn_resnet50_fpn_v2(
         model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model
+
+
+# The dictionary below is internal implementation detail and will be removed in v0.15
+from .._utils import _ModelURLs
+
+
+model_urls = _ModelURLs(
+    {
+        "maskrcnn_resnet50_fpn_coco": MaskRCNN_ResNet50_FPN_Weights.COCO_V1.url,
+    }
+)

torchvision/models/detection/retinanet.py

@@ -879,3 +879,14 @@ def retinanet_resnet50_fpn_v2(
         model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model
+
+
+# The dictionary below is internal implementation detail and will be removed in v0.15
+from .._utils import _ModelURLs
+
+
+model_urls = _ModelURLs(
+    {
+        "retinanet_resnet50_fpn_coco": RetinaNet_ResNet50_FPN_Weights.COCO_V1.url,
+    }
+)

torchvision/models/detection/ssd.py

@@ -672,3 +672,25 @@ def ssd300_vgg16(
         model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model
+
+
+# The dictionary below is internal implementation detail and will be removed in v0.15
+from .._utils import _ModelURLs
+
+
+model_urls = _ModelURLs(
+    {
+        "ssd300_vgg16_coco": SSD300_VGG16_Weights.COCO_V1.url,
+    }
+)
+
+
+backbone_urls = _ModelURLs(
+    {
+        # 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.
+        # Ref: https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
+        # Only the `features` weights have proper values, those on the `classifier` module are filled with nans.
+        "vgg16_features": VGG16_Weights.IMAGENET1K_FEATURES.url,
+    }
+)

torchvision/models/detection/ssdlite.py

@@ -321,3 +321,14 @@ def ssdlite320_mobilenet_v3_large(
         model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model
+
+
+# The dictionary below is internal implementation detail and will be removed in v0.15
+from .._utils import _ModelURLs
+
+
+model_urls = _ModelURLs(
+    {
+        "ssdlite320_mobilenet_v3_large_coco": SSDLite320_MobileNet_V3_Large_Weights.COCO_V1.url,
+    }
+)

torchvision/models/efficientnet.py

@@ -1028,3 +1028,23 @@ def efficientnet_v2_l(
         norm_layer=partial(nn.BatchNorm2d, eps=1e-03),
         **kwargs,
     )
+
+
+# The dictionary below is internal implementation detail and will be removed in v0.15
+from ._utils import _ModelURLs
+
+
+model_urls = _ModelURLs(
+    {
+        # Weights ported from https://github.com/rwightman/pytorch-image-models/
+        "efficientnet_b0": EfficientNet_B0_Weights.IMAGENET1K_V1.url,
+        "efficientnet_b1": EfficientNet_B1_Weights.IMAGENET1K_V1.url,
+        "efficientnet_b2": EfficientNet_B2_Weights.IMAGENET1K_V1.url,
+        "efficientnet_b3": EfficientNet_B3_Weights.IMAGENET1K_V1.url,
+        "efficientnet_b4": EfficientNet_B4_Weights.IMAGENET1K_V1.url,
+        # Weights ported from https://github.com/lukemelas/EfficientNet-PyTorch/
+        "efficientnet_b5": EfficientNet_B5_Weights.IMAGENET1K_V1.url,
+        "efficientnet_b6": EfficientNet_B6_Weights.IMAGENET1K_V1.url,
+        "efficientnet_b7": EfficientNet_B7_Weights.IMAGENET1K_V1.url,
+    }
+)

torchvision/models/googlenet.py

@@ -339,3 +339,15 @@ def googlenet(*, weights: Optional[GoogLeNet_Weights] = None, progress: bool = T
             )
 
     return model
+
+
+# The dictionary below is internal implementation detail and will be removed in v0.15
+from ._utils import _ModelURLs
+
+
+model_urls = _ModelURLs(
+    {
+        # GoogLeNet ported from TensorFlow
+        "googlenet": GoogLeNet_Weights.IMAGENET1K_V1.url,
+    }
+)

torchvision/models/inception.py

@@ -471,3 +471,15 @@ def inception_v3(*, weights: Optional[Inception_V3_Weights] = None, progress: bo
             model.AuxLogits = None
 
     return model
+
+
+# The dictionary below is internal implementation detail and will be removed in v0.15
+from ._utils import _ModelURLs
+
+
+model_urls = _ModelURLs(
+    {
+        # Inception v3 ported from TensorFlow
+        "inception_v3_google": Inception_V3_Weights.IMAGENET1K_V1.url,
+    }
+)

torchvision/models/mobilenetv2.py

@@ -263,3 +263,14 @@ def mobilenet_v2(
         model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model
+
+
+# The dictionary below is internal implementation detail and will be removed in v0.15
+from ._utils import _ModelURLs
+
+
+model_urls = _ModelURLs(
+    {
+        "mobilenet_v2": MobileNet_V2_Weights.IMAGENET1K_V1.url,
+    }
+)

torchvision/models/mobilenetv3.py

@@ -414,3 +414,15 @@ def mobilenet_v3_small(
 
     inverted_residual_setting, last_channel = _mobilenet_v3_conf("mobilenet_v3_small", **kwargs)
     return _mobilenet_v3(inverted_residual_setting, last_channel, weights, progress, **kwargs)
+
+
+# The dictionary below is internal implementation detail and will be removed in v0.15
+from ._utils import _ModelURLs
+
+
+model_urls = _ModelURLs(
+    {
+        "mobilenet_v3_large": MobileNet_V3_Large_Weights.IMAGENET1K_V1.url,
+        "mobilenet_v3_small": MobileNet_V3_Small_Weights.IMAGENET1K_V1.url,
+    }
+)

torchvision/models/quantization/googlenet.py

@@ -200,3 +200,16 @@ def googlenet(
             )
 
     return model
+
+
+# The dictionary below is internal implementation detail and will be removed in v0.15
+from .._utils import _ModelURLs
+from ..googlenet import model_urls  # noqa: F401
+
+
+quant_model_urls = _ModelURLs(
+    {
+        # fp32 GoogLeNet ported from TensorFlow, with weights quantized in PyTorch
+        "googlenet_fbgemm": GoogLeNet_QuantizedWeights.IMAGENET1K_FBGEMM_V1.url,
+    }
+)

torchvision/models/quantization/inception.py

@@ -251,3 +251,16 @@ def inception_v3(
             model.AuxLogits = None
 
     return model
+
+
+# The dictionary below is internal implementation detail and will be removed in v0.15
+from .._utils import _ModelURLs
+from ..inception import model_urls  # noqa: F401
+
+
+quant_model_urls = _ModelURLs(
+    {
+        # fp32 weights ported from TensorFlow, quantized in PyTorch
+        "inception_v3_google_fbgemm": Inception_V3_QuantizedWeights.IMAGENET1K_FBGEMM_V1.url,
+    }
+)

torchvision/models/quantization/mobilenetv2.py

@@ -131,3 +131,15 @@ def mobilenet_v2(
         model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model
+
+
+# The dictionary below is internal implementation detail and will be removed in v0.15
+from .._utils import _ModelURLs
+from ..mobilenetv2 import model_urls  # noqa: F401
+
+
+quant_model_urls = _ModelURLs(
+    {
+        "mobilenet_v2_qnnpack": MobileNet_V2_QuantizedWeights.IMAGENET1K_QNNPACK_V1.url,
+    }
+)

torchvision/models/quantization/mobilenetv3.py

@@ -211,3 +211,15 @@ def mobilenet_v3_large(
 
     inverted_residual_setting, last_channel = _mobilenet_v3_conf("mobilenet_v3_large", **kwargs)
     return _mobilenet_v3_model(inverted_residual_setting, last_channel, weights, progress, quantize, **kwargs)
+
+
+# The dictionary below is internal implementation detail and will be removed in v0.15
+from .._utils import _ModelURLs
+from ..mobilenetv3 import model_urls  # noqa: F401
+
+
+quant_model_urls = _ModelURLs(
+    {
+        "mobilenet_v3_large_qnnpack": MobileNet_V3_Large_QuantizedWeights.IMAGENET1K_QNNPACK_V1.url,
+    }
+)

torchvision/models/quantization/resnet.py

@@ -362,3 +362,17 @@ def resnext101_64x4d(
     _ovewrite_named_param(kwargs, "groups", 64)
     _ovewrite_named_param(kwargs, "width_per_group", 4)
     return _resnet(QuantizableBottleneck, [3, 4, 23, 3], weights, progress, quantize, **kwargs)
+
+
+# The dictionary below is internal implementation detail and will be removed in v0.15
+from .._utils import _ModelURLs
+from ..resnet import model_urls  # noqa: F401
+
+
+quant_model_urls = _ModelURLs(
+    {
+        "resnet18_fbgemm": ResNet18_QuantizedWeights.IMAGENET1K_FBGEMM_V1.url,
+        "resnet50_fbgemm": ResNet50_QuantizedWeights.IMAGENET1K_FBGEMM_V1.url,
+        "resnext101_32x8d_fbgemm": ResNeXt101_32X8D_QuantizedWeights.IMAGENET1K_FBGEMM_V1.url,
+    }
+)