Added revamped docs for DenseNet (#5878)

* added dense net docs

* fix models_new.rst

Author: lezwon

docs/source/models/densenet.rst

@@ -0,0 +1,27 @@
+DenseNet
+========
+
+.. currentmodule:: torchvision.models
+
+The DenseNet model is based on the `Densely Connected Convolutional Networks
+<https://arxiv.org/abs/1608.06993>`_ paper.
+
+
+Model builders
+--------------
+
+The following model builders can be used to instantiate a DenseNet model, with or
+without pre-trained weights. All the model builders internally rely on the
+``torchvision.models.densenet.DenseNet`` base class. Please refer to the `source
+code
+<https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_ for
+more details about this class.
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    densenet121
+    densenet161
+    densenet169
+    densenet201

docs/source/models_new.rst

@@ -37,6 +37,7 @@ weights:
    :maxdepth: 1
 
    models/convnext
+   models/densenet
    models/efficientnet
    models/efficientnetv2
    models/regnet

torchvision/models/densenet.py

@@ -339,14 +339,23 @@ class DenseNet201_Weights(WeightsEnum):
 @handle_legacy_interface(weights=("pretrained", DenseNet121_Weights.IMAGENET1K_V1))
 def densenet121(*, weights: Optional[DenseNet121_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     r"""Densenet-121 model from
-    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
+    `Densely Connected Convolutional Networks <https://arxiv.org/abs/1608.06993>`_.
     The required minimum input size of the model is 29x29.
 
     Args:
-        weights (DenseNet121_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
-        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
-          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
+        weights (:class:`~torchvision.models.DenseNet121_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.DenseNet121_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.densenet.DenseNet``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.DenseNet121_Weights
+        :members:
     """
     weights = DenseNet121_Weights.verify(weights)
 
@@ -356,14 +365,23 @@ def densenet121(*, weights: Optional[DenseNet121_Weights] = None, progress: bool
 @handle_legacy_interface(weights=("pretrained", DenseNet161_Weights.IMAGENET1K_V1))
 def densenet161(*, weights: Optional[DenseNet161_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     r"""Densenet-161 model from
-    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
+    `Densely Connected Convolutional Networks <https://arxiv.org/abs/1608.06993>`_.
     The required minimum input size of the model is 29x29.
 
     Args:
-        weights (DenseNet161_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
-        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
-          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
+        weights (:class:`~torchvision.models.DenseNet161_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.DenseNet161_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.densenet.DenseNet``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.DenseNet161_Weights
+        :members:
     """
     weights = DenseNet161_Weights.verify(weights)
 
@@ -373,14 +391,23 @@ def densenet161(*, weights: Optional[DenseNet161_Weights] = None, progress: bool
 @handle_legacy_interface(weights=("pretrained", DenseNet169_Weights.IMAGENET1K_V1))
 def densenet169(*, weights: Optional[DenseNet169_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     r"""Densenet-169 model from
-    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
+    `Densely Connected Convolutional Networks <https://arxiv.org/abs/1608.06993>`_.
     The required minimum input size of the model is 29x29.
 
     Args:
-        weights (DenseNet169_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
-        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
-          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
+        weights (:class:`~torchvision.models.DenseNet169_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.DenseNet169_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.densenet.DenseNet``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.DenseNet169_Weights
+        :members:
     """
     weights = DenseNet169_Weights.verify(weights)
 
@@ -390,14 +417,23 @@ def densenet169(*, weights: Optional[DenseNet169_Weights] = None, progress: bool
 @handle_legacy_interface(weights=("pretrained", DenseNet201_Weights.IMAGENET1K_V1))
 def densenet201(*, weights: Optional[DenseNet201_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
     r"""Densenet-201 model from
-    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
+    `Densely Connected Convolutional Networks <https://arxiv.org/abs/1608.06993>`_.
     The required minimum input size of the model is 29x29.
 
     Args:
-        weights (DenseNet201_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
-        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
-          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
+        weights (:class:`~torchvision.models.DenseNet201_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.DenseNet201_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.densenet.DenseNet``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.DenseNet201_Weights
+        :members:
     """
     weights = DenseNet201_Weights.verify(weights)