Add revamped docs for `ShuffleNet V2` (#5921)

* Update ShuffleNetV2 docstring

* Add entry for ShuffleNet V2 in new doc

* Minor fixes
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

docs/source/models/shufflenetv2.rst

+ShuffleNet V2
+=============
+
+.. currentmodule:: torchvision.models
+
+The ShuffleNet V2 model is based on the `ShuffleNet V2: Practical Guidelines for Efficient
+CNN Architecture Design <https://arxiv.org/abs/1807.11164>`__ paper.
+
+
+Model builders
+--------------
+
+The following model builders can be used to instantiate a ShuffleNetV2 model, with or
+without pre-trained weights. All the model builders internally rely on the
+``torchvision.models.shufflenetv2.ShuffleNetV2`` base class. Please refer to the `source
+code
+<https://github.com/pytorch/vision/blob/main/torchvision/models/shufflenetv2.py>`_ for
+more details about this class.
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    shufflenet_v2_x0_5
+    shufflenet_v2_x1_0
+    shufflenet_v2_x1_5
+    shufflenet_v2_x2_0

docs/source/models_new.rst

@@ -48,6 +48,7 @@ weights:
    models/regnet
    models/resnet
    models/resnext
+   models/shufflenetv2
    models/squeezenet
    models/swin_transformer
    models/vgg

torchvision/models/shufflenetv2.py

@@ -261,13 +261,25 @@ def shufflenet_v2_x0_5(
     *, weights: Optional[ShuffleNet_V2_X0_5_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> ShuffleNetV2:
     """
-    Constructs a ShuffleNetV2 with 0.5x output channels, as described in
-    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
-    <https://arxiv.org/abs/1807.11164>`_.
+    Constructs a ShuffleNetV2 architecture with 0.5x output channels, as described in
+    `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
+    <https://arxiv.org/abs/1807.11164>`__.
 
     Args:
-        weights (ShuffleNet_V2_X0_5_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
+        weights (:class:`~torchvision.models.ShuffleNet_V2_X0_5_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.ShuffleNet_V2_X0_5_Weights` below for
+            more details, and possible values. By default, no pre-trained
+            weights are used.
+        progress (bool, optional): If True, displays a progress bar of the
+            download to stderr. Default is True.
+        **kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/shufflenetv2.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.ShuffleNet_V2_X0_5_Weights
+        :members:
     """
     weights = ShuffleNet_V2_X0_5_Weights.verify(weights)
 
@@ -279,13 +291,25 @@ def shufflenet_v2_x1_0(
     *, weights: Optional[ShuffleNet_V2_X1_0_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> ShuffleNetV2:
     """
-    Constructs a ShuffleNetV2 with 1.0x output channels, as described in
-    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
-    <https://arxiv.org/abs/1807.11164>`_.
+    Constructs a ShuffleNetV2 architecture with 1.0x output channels, as described in
+    `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
+    <https://arxiv.org/abs/1807.11164>`__.
 
     Args:
-        weights (ShuffleNet_V2_X1_0_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
+        weights (:class:`~torchvision.models.ShuffleNet_V2_X1_0_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.ShuffleNet_V2_X1_0_Weights` below for
+            more details, and possible values. By default, no pre-trained
+            weights are used.
+        progress (bool, optional): If True, displays a progress bar of the
+            download to stderr. Default is True.
+        **kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/shufflenetv2.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.ShuffleNet_V2_X1_0_Weights
+        :members:
     """
     weights = ShuffleNet_V2_X1_0_Weights.verify(weights)
 
@@ -297,13 +321,25 @@ def shufflenet_v2_x1_5(
     *, weights: Optional[ShuffleNet_V2_X1_5_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> ShuffleNetV2:
     """
-    Constructs a ShuffleNetV2 with 1.5x output channels, as described in
-    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
-    <https://arxiv.org/abs/1807.11164>`_.
+    Constructs a ShuffleNetV2 architecture with 1.5x output channels, as described in
+    `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
+    <https://arxiv.org/abs/1807.11164>`__.
 
     Args:
-        weights (ShuffleNet_V2_X1_5_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
+        weights (:class:`~torchvision.models.ShuffleNet_V2_X1_5_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.ShuffleNet_V2_X1_5_Weights` below for
+            more details, and possible values. By default, no pre-trained
+            weights are used.
+        progress (bool, optional): If True, displays a progress bar of the
+            download to stderr. Default is True.
+        **kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/shufflenetv2.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.ShuffleNet_V2_X1_5_Weights
+        :members:
     """
     weights = ShuffleNet_V2_X1_5_Weights.verify(weights)
 
@@ -315,13 +351,25 @@ def shufflenet_v2_x2_0(
     *, weights: Optional[ShuffleNet_V2_X2_0_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> ShuffleNetV2:
     """
-    Constructs a ShuffleNetV2 with 2.0x output channels, as described in
-    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
-    <https://arxiv.org/abs/1807.11164>`_.
+    Constructs a ShuffleNetV2 architecture with 2.0x output channels, as described in
+    `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
+    <https://arxiv.org/abs/1807.11164>`__.
 
     Args:
-        weights (ShuffleNet_V2_X2_0_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
+        weights (:class:`~torchvision.models.ShuffleNet_V2_X2_0_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.ShuffleNet_V2_X2_0_Weights` below for
+            more details, and possible values. By default, no pre-trained
+            weights are used.
+        progress (bool, optional): If True, displays a progress bar of the
+            download to stderr. Default is True.
+        **kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/shufflenetv2.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.ShuffleNet_V2_X2_0_Weights
+        :members:
     """
     weights = ShuffleNet_V2_X2_0_Weights.verify(weights)