add v0.19.1 release

docs/Makefile

@@ -24,7 +24,7 @@ docset: html
 	convert $(SPHINXPROJ).docset/icon@2x.png -resize 16x16 $(SPHINXPROJ).docset/icon.png
 
 html-noplot:  # Avoids running the gallery examples, which may take time
-	$(SPHINXBUILD) -D plot_gallery=0 -b html $(ASPHINXOPTS) "${SOURCEDIR}" "$(BUILDDIR)"/html
+	$(SPHINXBUILD) -D plot_gallery=0 -b html "${SOURCEDIR}" "$(BUILDDIR)"/html
 	@echo
 	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
 
@@ -32,6 +32,8 @@ clean:
 	rm -rf $(BUILDDIR)/*
 	rm -rf $(SOURCEDIR)/auto_examples/  # sphinx-gallery
 	rm -rf $(SOURCEDIR)/gen_modules/  # sphinx-gallery
+	rm -rf $(SOURCEDIR)/generated/  # autosummary
+	rm -rf $(SOURCEDIR)/models/generated  # autosummary
 
 .PHONY: help Makefile docset
 

docs/requirements.txt

-sphinx==2.4.4
-sphinx-gallery>=0.9.0
-sphinx-copybutton>=0.3.1
 matplotlib
 numpy
--e git+git://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
+sphinx-copybutton>=0.3.1
+sphinx-gallery>=0.11.1
+sphinx==5.0.0
+tabulate
+-e git+https://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
+pycocotools

docs/source/_static/css/custom_torchvision.css

-/* This rule (and possibly this entire file) should be removed once
+/* This rule should be removed once
 https://github.com/pytorch/pytorch_sphinx_theme/issues/125 is fixed.
 
 We override the rule so that the links to the notebooks aren't hidden in the
@@ -10,3 +10,26 @@ article.pytorch-article .sphx-glr-download-link-note.admonition.note,
 article.pytorch-article .reference.download.internal, article.pytorch-article .sphx-glr-signature {
   display: block;
 }
+
+/* These 2 rules below are for the weight tables (generated in conf.py) to look
+ * better. In particular we make their row height shorter */
+.table-weights td, .table-weights th {
+  margin-bottom: 0.2rem;
+  padding: 0 !important;
+  line-height: 1 !important;
+}
+.table-weights p {
+    margin-bottom: 0.2rem !important;
+}
+
+/* Fix for Sphinx gallery 0.11
+See https://github.com/sphinx-gallery/sphinx-gallery/issues/990
+*/
+article.pytorch-article .sphx-glr-thumbnails .sphx-glr-thumbcontainer {
+    width: unset;
+    margin-right: 0;
+    margin-left: 0;
+}
+article.pytorch-article div.section div.wy-table-responsive tbody td {
+    width: 50%;
+}

docs/source/_templates/class.rst

+.. role:: hidden
+    :class: hidden-section
+.. currentmodule:: {{ module }}
+
+
+{{ name | underline}}
+
+.. autoclass:: {{ name }}
+    :members:

docs/source/_templates/class_dataset.rst

+.. role:: hidden
+    :class: hidden-section
+.. currentmodule:: {{ module }}
+
+
+{{ name | underline}}
+
+.. autoclass:: {{ name }}
+    :members:
+        __getitem__,
+        {% if "category_name" in methods %} category_name {% endif %}
+    :special-members:

docs/source/_templates/function.rst

+.. role:: hidden
+    :class: hidden-section
+.. currentmodule:: {{ module }}
+
+
+{{ name | underline}}
+
+.. autofunction:: {{ name }}

docs/source/beta_status.py

+from docutils import nodes
+from docutils.parsers.rst import Directive
+
+
+class BetaStatus(Directive):
+    has_content = True
+    text = "The {api_name} is in Beta stage, and backward compatibility is not guaranteed."
+    node = nodes.warning
+
+    def run(self):
+        text = self.text.format(api_name=" ".join(self.content))
+        return [self.node("", nodes.paragraph("", "", nodes.Text(text)))]
+
+
+def setup(app):
+    app.add_directive("betastatus", BetaStatus)
+    return {
+        "version": "0.1",
+        "parallel_read_safe": True,
+        "parallel_write_safe": True,
+    }

docs/source/datasets.rst

-torchvision.datasets
-====================
+.. _datasets:
+
+Datasets
+========
+
+Torchvision provides many built-in datasets in the ``torchvision.datasets``
+module, as well as utility classes for building your own datasets.
+
+Built-in datasets
+-----------------
 
 All datasets are subclasses of :class:`torch.utils.data.Dataset`
 i.e, they have ``__getitem__`` and ``__len__`` methods implemented.
@@ -19,242 +27,157 @@ All the datasets have almost similar API. They all have two common arguments:
 ``transform`` and  ``target_transform`` to transform the input and target respectively.
 You can also create your own datasets using the provided :ref:`base classes <base_classes_datasets>`.
 
-Caltech
-~~~~~~~
-
-.. autoclass:: Caltech101
-  :members: __getitem__
-  :special-members:
-
-.. autoclass:: Caltech256
-  :members: __getitem__
-  :special-members:
-
-CelebA
-~~~~~~
-
-.. autoclass:: CelebA
-  :members: __getitem__
-  :special-members:
-
-CIFAR
-~~~~~
-
-.. autoclass:: CIFAR10
-  :members: __getitem__
-  :special-members:
-
-.. autoclass:: CIFAR100
-
-Cityscapes
-~~~~~~~~~~
-
-.. note ::
-    Requires Cityscape to be downloaded.
-
-.. autoclass:: Cityscapes
-  :members: __getitem__
-  :special-members:
-
-COCO
-~~~~
-
-.. note ::
-    These require the `COCO API to be installed`_
-
-.. _COCO API to be installed: https://github.com/pdollar/coco/tree/master/PythonAPI
-
-
-Captions
-^^^^^^^^
-
-.. autoclass:: CocoCaptions
-  :members: __getitem__
-  :special-members:
-
-
-Detection
-^^^^^^^^^
-
-.. autoclass:: CocoDetection
-  :members: __getitem__
-  :special-members:
-
-
-EMNIST
-~~~~~~
-
-.. autoclass:: EMNIST
-
-FakeData
-~~~~~~~~
-
-.. autoclass:: FakeData
-
-Fashion-MNIST
-~~~~~~~~~~~~~
-
-.. autoclass:: FashionMNIST
-
-Flickr
-~~~~~~
-
-.. autoclass:: Flickr8k
-  :members: __getitem__
-  :special-members:
-
-.. autoclass:: Flickr30k
-  :members: __getitem__
-  :special-members:
-
-HMDB51
-~~~~~~~
-
-.. autoclass:: HMDB51
-  :members: __getitem__
-  :special-members:
-
-ImageNet
-~~~~~~~~~~~
-
-.. autoclass:: ImageNet
-
-.. note ::
-    This requires `scipy` to be installed
-
-Kinetics-400
+Image classification
+~~~~~~~~~~~~~~~~~~~~
+
+.. autosummary::
+    :toctree: generated/
+    :template: class_dataset.rst
+
+    Caltech101
+    Caltech256
+    CelebA
+    CIFAR10
+    CIFAR100
+    Country211
+    DTD
+    EMNIST
+    EuroSAT
+    FakeData
+    FashionMNIST
+    FER2013
+    FGVCAircraft
+    Flickr8k
+    Flickr30k
+    Flowers102
+    Food101
+    GTSRB
+    INaturalist
+    ImageNet
+    Imagenette
+    KMNIST
+    LFWPeople
+    LSUN
+    MNIST
+    Omniglot
+    OxfordIIITPet
+    Places365
+    PCAM
+    QMNIST
+    RenderedSST2
+    SEMEION
+    SBU
+    StanfordCars
+    STL10
+    SUN397
+    SVHN
+    USPS
+
+Image detection or segmentation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autosummary::
+    :toctree: generated/
+    :template: class_dataset.rst
+
+    CocoDetection
+    CelebA
+    Cityscapes
+    Kitti
+    OxfordIIITPet
+    SBDataset
+    VOCSegmentation
+    VOCDetection
+    WIDERFace
+
+Optical Flow
 ~~~~~~~~~~~~
 
-.. autoclass:: Kinetics400
-  :members: __getitem__
-  :special-members:
-
-KITTI
-~~~~~~~~~
-
-.. autoclass:: Kitti
-  :members: __getitem__
-  :special-members:
-
-KMNIST
-~~~~~~~~~~~~~
-
-.. autoclass:: KMNIST
-
-LSUN
-~~~~
-
-.. autoclass:: LSUN
-  :members: __getitem__
-  :special-members:
-
-MNIST
-~~~~~
-
-.. autoclass:: MNIST
-
-Omniglot
-~~~~~~~~
-
-.. autoclass:: Omniglot
-
-PhotoTour
-~~~~~~~~~
-
-.. autoclass:: PhotoTour
-  :members: __getitem__
-  :special-members:
-
-Places365
-~~~~~~~~~
-
-.. autoclass:: Places365
-  :members: __getitem__
-  :special-members:
-
-QMNIST
-~~~~~~
-
-.. autoclass:: QMNIST
-
-SBD
-~~~~~~
-
-.. autoclass:: SBDataset
-  :members: __getitem__
-  :special-members:
-
-SBU
-~~~
-
-.. autoclass:: SBU
-  :members: __getitem__
-  :special-members:
-
-SEMEION
-~~~~~~~
-
-.. autoclass:: SEMEION
-  :members: __getitem__
-  :special-members:
-
-STL10
-~~~~~
-
-.. autoclass:: STL10
-  :members: __getitem__
-  :special-members:
-
-SVHN
-~~~~~
+.. autosummary::
+    :toctree: generated/
+    :template: class_dataset.rst
+
+    FlyingChairs
+    FlyingThings3D
+    HD1K
+    KittiFlow
+    Sintel
+
+Stereo Matching
+~~~~~~~~~~~~~~~
+
+.. autosummary::
+    :toctree: generated/
+    :template: class_dataset.rst
+
+    CarlaStereo
+    Kitti2012Stereo
+    Kitti2015Stereo
+    CREStereo
+    FallingThingsStereo
+    SceneFlowStereo
+    SintelStereo
+    InStereo2k
+    ETH3DStereo
+    Middlebury2014Stereo
+
+Image pairs
+~~~~~~~~~~~
 
-.. autoclass:: SVHN
-  :members: __getitem__
-  :special-members:
+.. autosummary::
+    :toctree: generated/
+    :template: class_dataset.rst
 
-UCF101
-~~~~~~~
+    LFWPairs
+    PhotoTour
 
-.. autoclass:: UCF101
-  :members: __getitem__
-  :special-members:
+Image captioning
+~~~~~~~~~~~~~~~~
 
-USPS
-~~~~~
+.. autosummary::
+    :toctree: generated/
+    :template: class_dataset.rst
 
-.. autoclass:: USPS
-  :members: __getitem__
-  :special-members:
+    CocoCaptions
 
-VOC
-~~~~~~
+Video classification
+~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: VOCSegmentation
-  :members: __getitem__
-  :special-members:
+.. autosummary::
+    :toctree: generated/
+    :template: class_dataset.rst
 
-.. autoclass:: VOCDetection
-  :members: __getitem__
-  :special-members:
+    HMDB51
+    Kinetics
+    UCF101
 
-WIDERFace
-~~~~~~~~~
+Video prediction
+~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: WIDERFace
-  :members: __getitem__
-  :special-members:
+.. autosummary::
+    :toctree: generated/
+    :template: class_dataset.rst
 
+    MovingMNIST
 
 .. _base_classes_datasets:
 
 Base classes for custom datasets
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+--------------------------------
+
+.. autosummary::
+    :toctree: generated/
+    :template: class.rst
+
+    DatasetFolder
+    ImageFolder
+    VisionDataset
 
-.. autoclass:: DatasetFolder
-  :members: __getitem__, find_classes, make_dataset
-  :special-members:
+Transforms v2
+-------------
 
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
 
-.. autoclass:: ImageFolder
-  :members: __getitem__
-  :special-members:
+    wrap_dataset_for_transforms_v2

docs/source/docutils.conf

+# Necessary for the table generated by autosummary to look decent
+[html writers]
+table_style: colwidths-auto

docs/source/feature_extraction.rst

+Feature extraction for model inspection
+=======================================
+
+.. currentmodule:: torchvision.models.feature_extraction
+
+The ``torchvision.models.feature_extraction`` package contains
+feature extraction utilities that let us tap into our models to access intermediate
+transformations of our inputs. This could be useful for a variety of
+applications in computer vision. Just a few examples are:
+
+- Visualizing feature maps.
+- Extracting features to compute image descriptors for tasks like facial
+  recognition, copy-detection, or image retrieval.
+- Passing selected features to downstream sub-networks for end-to-end training
+  with a specific task in mind. For example, passing a hierarchy of features
+  to a Feature Pyramid Network with object detection heads.
+
+Torchvision provides :func:`create_feature_extractor` for this purpose.
+It works by following roughly these steps:
+
+1. Symbolically tracing the model to get a graphical representation of
+   how it transforms the input, step by step.
+2. Setting the user-selected graph nodes as outputs.
+3. Removing all redundant nodes (anything downstream of the output nodes).
+4. Generating python code from the resulting graph and bundling that into a
+   PyTorch module together with the graph itself.
+
+|
+
+The `torch.fx documentation <https://pytorch.org/docs/stable/fx.html>`_
+provides a more general and detailed explanation of the above procedure and
+the inner workings of the symbolic tracing.
+
+.. _about-node-names:
+
+**About Node Names**
+
+In order to specify which nodes should be output nodes for extracted
+features, one should be familiar with the node naming convention used here
+(which differs slightly from that used in ``torch.fx``). A node name is
+specified as a ``.`` separated path walking the module hierarchy from top level
+module down to leaf operation or leaf module. For instance ``"layer4.2.relu"``
+in ResNet-50 represents the output of the ReLU of the 2nd block of the 4th
+layer of the ``ResNet`` module. Here are some finer points to keep in mind:
+
+- When specifying node names for :func:`create_feature_extractor`, you may
+  provide a truncated version of a node name as a shortcut. To see how this
+  works, try creating a ResNet-50 model and printing the node names with
+  ``train_nodes, _ = get_graph_node_names(model) print(train_nodes)`` and
+  observe that the last node pertaining to ``layer4`` is
+  ``"layer4.2.relu_2"``. One may specify ``"layer4.2.relu_2"`` as the return
+  node, or just ``"layer4"`` as this, by convention, refers to the last node
+  (in order of execution) of ``layer4``.
+- If a certain module or operation is repeated more than once, node names get
+  an additional ``_{int}`` postfix to disambiguate. For instance, maybe the
+  addition (``+``) operation is used three times in the same ``forward``
+  method. Then there would be ``"path.to.module.add"``,
+  ``"path.to.module.add_1"``, ``"path.to.module.add_2"``. The counter is
+  maintained within the scope of the direct parent. So in ResNet-50 there is
+  a ``"layer4.1.add"`` and a ``"layer4.2.add"``. Because the addition
+  operations reside in different blocks, there is no need for a postfix to
+  disambiguate.
+
+
+**An Example**
+
+Here is an example of how we might extract features for MaskRCNN:
+
+.. code-block:: python
+
+  import torch
+  from torchvision.models import resnet50
+  from torchvision.models.feature_extraction import get_graph_node_names
+  from torchvision.models.feature_extraction import create_feature_extractor
+  from torchvision.models.detection.mask_rcnn import MaskRCNN
+  from torchvision.models.detection.backbone_utils import LastLevelMaxPool
+  from torchvision.ops.feature_pyramid_network import FeaturePyramidNetwork
+
+
+  # To assist you in designing the feature extractor you may want to print out
+  # the available nodes for resnet50.
+  m = resnet50()
+  train_nodes, eval_nodes = get_graph_node_names(resnet50())
+
+  # The lists returned, are the names of all the graph nodes (in order of
+  # execution) for the input model traced in train mode and in eval mode
+  # respectively. You'll find that `train_nodes` and `eval_nodes` are the same
+  # for this example. But if the model contains control flow that's dependent
+  # on the training mode, they may be different.
+
+  # To specify the nodes you want to extract, you could select the final node
+  # that appears in each of the main layers:
+  return_nodes = {
+      # node_name: user-specified key for output dict
+      'layer1.2.relu_2': 'layer1',
+      'layer2.3.relu_2': 'layer2',
+      'layer3.5.relu_2': 'layer3',
+      'layer4.2.relu_2': 'layer4',
+  }
+
+  # But `create_feature_extractor` can also accept truncated node specifications
+  # like "layer1", as it will just pick the last node that's a descendent of
+  # of the specification. (Tip: be careful with this, especially when a layer
+  # has multiple outputs. It's not always guaranteed that the last operation
+  # performed is the one that corresponds to the output you desire. You should
+  # consult the source code for the input model to confirm.)
+  return_nodes = {
+      'layer1': 'layer1',
+      'layer2': 'layer2',
+      'layer3': 'layer3',
+      'layer4': 'layer4',
+  }
+
+  # Now you can build the feature extractor. This returns a module whose forward
+  # method returns a dictionary like:
+  # {
+  #     'layer1': output of layer 1,
+  #     'layer2': output of layer 2,
+  #     'layer3': output of layer 3,
+  #     'layer4': output of layer 4,
+  # }
+  create_feature_extractor(m, return_nodes=return_nodes)
+
+  # Let's put all that together to wrap resnet50 with MaskRCNN
+
+  # MaskRCNN requires a backbone with an attached FPN
+  class Resnet50WithFPN(torch.nn.Module):
+      def __init__(self):
+          super(Resnet50WithFPN, self).__init__()
+          # Get a resnet50 backbone
+          m = resnet50()
+          # Extract 4 main layers (note: MaskRCNN needs this particular name
+          # mapping for return nodes)
+          self.body = create_feature_extractor(
+              m, return_nodes={f'layer{k}': str(v)
+                               for v, k in enumerate([1, 2, 3, 4])})
+          # Dry run to get number of channels for FPN
+          inp = torch.randn(2, 3, 224, 224)
+          with torch.no_grad():
+              out = self.body(inp)
+          in_channels_list = [o.shape[1] for o in out.values()]
+          # Build FPN
+          self.out_channels = 256
+          self.fpn = FeaturePyramidNetwork(
+              in_channels_list, out_channels=self.out_channels,
+              extra_blocks=LastLevelMaxPool())
+
+      def forward(self, x):
+          x = self.body(x)
+          x = self.fpn(x)
+          return x
+
+
+  # Now we can build our model!
+  model = MaskRCNN(Resnet50WithFPN(), num_classes=91).eval()
+
+
+API Reference
+-------------
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    create_feature_extractor
+    get_graph_node_names

docs/source/index.rst

@@ -31,18 +31,21 @@ architectures, and common image transformations for computer vision.
    :maxdepth: 2
    :caption: Package Reference
 
-   datasets
-   io
-   models
-   ops
    transforms
+   tv_tensors
+   models
+   datasets
    utils
+   ops
+   io
+   feature_extraction
 
 .. toctree::
    :maxdepth: 1
-   :caption: Examples
+   :caption: Examples and training references
 
    auto_examples/index
+   training_references
 
 .. automodule:: torchvision
    :members:
@@ -58,3 +61,9 @@ architectures, and common image transformations for computer vision.
    TorchElastic <https://pytorch.org/elastic/>
    TorchServe <https://pytorch.org/serve>
    PyTorch on XLA Devices <http://pytorch.org/xla/>
+
+
+Indices
+-------
+
+* :ref:`genindex`

docs/source/io.rst

-torchvision.io
-==============
+Decoding / Encoding images and videos
+=====================================
 
 .. currentmodule:: torchvision.io
 
 The :mod:`torchvision.io` package provides functions for performing IO
-operations. They are currently specific to reading and writing video and
-images.
+operations. They are currently specific to reading and writing images and
+videos.
+
+Images
+------
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    read_image
+    decode_image
+    encode_jpeg
+    decode_jpeg
+    write_jpeg
+    decode_gif
+    encode_png
+    decode_png
+    write_png
+    read_file
+    write_file
+
+.. autosummary::
+    :toctree: generated/
+    :template: class.rst
+
+    ImageReadMode
+
+
 
 Video
 -----
 
-.. autofunction:: read_video
-
-.. autofunction:: read_video_timestamps
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
 
-.. autofunction:: write_video
+    read_video
+    read_video_timestamps
+    write_video
 
 
 Fine-grained video API
-----------------------
+^^^^^^^^^^^^^^^^^^^^^^
 
 In addition to the :mod:`read_video` function, we provide a high-performance 
 lower-level API for more fine-grained control compared to the :mod:`read_video` function.
 It does all this whilst fully supporting torchscript.
 
-.. autoclass:: VideoReader
-    :members: __next__, get_metadata, set_current_stream, seek
+.. betastatus:: fine-grained video API
+
+.. autosummary::
+    :toctree: generated/
+    :template: class.rst
+
+    VideoReader
 
 
 Example of inspecting a video:
@@ -54,29 +88,3 @@ Example of inspecting a video:
     # the constructor we select a default video stream, but
     # in practice, we can set whichever stream we would like 
     video.set_current_stream("video:0")
-
-
-Image
------
-
-.. autoclass:: ImageReadMode
-
-.. autofunction:: read_image
-
-.. autofunction:: decode_image
-
-.. autofunction:: encode_jpeg
-
-.. autofunction:: decode_jpeg
-
-.. autofunction:: write_jpeg
-
-.. autofunction:: encode_png
-
-.. autofunction:: decode_png
-
-.. autofunction:: write_png
-
-.. autofunction:: read_file
-
-.. autofunction:: write_file

docs/source/models/alexnet.rst

+AlexNet
+=======
+
+.. currentmodule:: torchvision.models
+
+The AlexNet model was originally introduced in the
+`ImageNet Classification with Deep Convolutional Neural Networks
+<https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html>`__
+paper. The implemented architecture is slightly different from the original one,
+and is based on `One weird trick for parallelizing convolutional neural networks
+<https://arxiv.org/abs/1404.5997>`__.
+
+
+Model builders
+--------------
+
+The following model builders can be used to instantiate an AlexNet model, with or
+without pre-trained weights. All the model builders internally rely on the
+``torchvision.models.alexnet.AlexNet`` base class. Please refer to the `source
+code
+<https://github.com/pytorch/vision/blob/main/torchvision/models/alexnet.py>`_ for
+more details about this class.
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    alexnet

docs/source/models/convnext.rst

+ConvNeXt
+========
+
+.. currentmodule:: torchvision.models
+
+The ConvNeXt model is based on the `A ConvNet for the 2020s
+<https://arxiv.org/abs/2201.03545>`_ paper.
+
+
+Model builders
+--------------
+
+The following model builders can be used to instantiate a ConvNeXt model, with or
+without pre-trained weights. All the model builders internally rely on the
+``torchvision.models.convnext.ConvNeXt`` base class. Please refer to the `source code
+<https://github.com/pytorch/vision/blob/main/torchvision/models/convnext.py>`_ for
+more details about this class.
+
+.. autosummary::
+   :toctree: generated/
+   :template: function.rst
+
+   convnext_tiny
+   convnext_small
+   convnext_base
+   convnext_large

docs/source/models/deeplabv3.rst

+DeepLabV3
+=========
+
+.. currentmodule:: torchvision.models.segmentation
+
+The DeepLabV3 model is based on the `Rethinking Atrous Convolution for Semantic
+Image Segmentation <https://arxiv.org/abs/1706.05587>`__ paper.
+
+.. betastatus:: segmentation module
+
+
+Model builders
+--------------
+
+The following model builders can be used to instantiate a DeepLabV3 model with
+different backbones, with or without pre-trained weights. All the model builders
+internally rely on the ``torchvision.models.segmentation.deeplabv3.DeepLabV3`` base class. Please
+refer to the `source code
+<https://github.com/pytorch/vision/blob/main/torchvision/models/segmentation/deeplabv3.py>`_
+for more details about this class.
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    deeplabv3_mobilenet_v3_large
+    deeplabv3_resnet50
+    deeplabv3_resnet101

docs/source/models/densenet.rst

+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/efficientnet.rst

+EfficientNet
+============
+
+.. currentmodule:: torchvision.models
+
+The EfficientNet model is based on the `EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks <https://arxiv.org/abs/1905.11946>`__
+paper.
+
+
+Model builders
+--------------
+
+The following model builders can be used to instantiate an EfficientNet model, with or
+without pre-trained weights. All the model builders internally rely on the
+``torchvision.models.efficientnet.EfficientNet`` base class. Please refer to the `source
+code
+<https://github.com/pytorch/vision/blob/main/torchvision/models/efficientnet.py>`_ for
+more details about this class.
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    efficientnet_b0
+    efficientnet_b1
+    efficientnet_b2
+    efficientnet_b3
+    efficientnet_b4
+    efficientnet_b5
+    efficientnet_b6
+    efficientnet_b7