add docstring for dataset wrapper (#7333)

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

docs/source/datasets.rst

@@ -169,3 +169,12 @@ Base classes for custom datasets
     DatasetFolder
     ImageFolder
     VisionDataset
+
+Transforms v2
+-------------
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    wrap_dataset_for_transforms_v2

torchvision/datapoints/_dataset_wrapper.py

@@ -14,8 +14,71 @@ from torchvision.transforms.v2 import functional as F
 __all__ = ["wrap_dataset_for_transforms_v2"]
 
 
-# TODO: naming!
 def wrap_dataset_for_transforms_v2(dataset):
+    """[BETA] Wrap a ``torchvision.dataset`` for usage with :mod:`torchvision.transforms.v2`.
+
+    .. v2betastatus:: wrap_dataset_for_transforms_v2 function
+
+    Example:
+        >>> dataset = torchvision.datasets.CocoDetection(...)
+        >>> dataset = wrap_dataset_for_transforms_v2(dataset)
+
+    .. note::
+
+       For now, only the most popular datasets are supported. Furthermore, the wrapper only supports dataset
+       configurations that are fully supported by ``torchvision.transforms.v2``. If you encounter an error prompting you
+       to raise an issue to ``torchvision`` for a dataset or configuration that you need, please do so.
+
+    The dataset samples are wrapped according to the description below.
+
+    Special cases:
+
+        * :class:`~torchvision.datasets.CocoDetection`: Instead of returning the target as list of dicts, the wrapper
+          returns a dict of lists. In addition, the key-value-pairs ``"boxes"`` (in ``XYXY`` coordinate format),
+          ``"masks"`` and ``"labels"`` are added and wrap the data in the corresponding ``torchvision.datapoints``.
+          The original keys are preserved.
+        * :class:`~torchvision.datasets.VOCDetection`: The key-value-pairs ``"boxes"`` and ``"labels"`` are added to
+          the target and wrap the data in the corresponding ``torchvision.datapoints``. The original keys are
+          preserved.
+        * :class:`~torchvision.datasets.CelebA`: The target for ``target_type="bbox"`` is converted to the ``XYXY``
+          coordinate format and wrapped into a :class:`~torchvision.datapoints.BoundingBox` datapoint.
+        * :class:`~torchvision.datasets.Kitti`: Instead returning the target as list of dictsthe wrapper returns a dict
+          of lists. In addition, the key-value-pairs ``"boxes"`` and ``"labels"`` are added and wrap the data
+          in the corresponding ``torchvision.datapoints``. The original keys are preserved.
+        * :class:`~torchvision.datasets.OxfordIIITPet`: The target for ``target_type="segmentation"`` is wrapped into a
+          :class:`~torchvision.datapoints.Mask` datapoint.
+        * :class:`~torchvision.datasets.Cityscapes`: The target for ``target_type="semantic"`` is wrapped into a
+          :class:`~torchvision.datapoints.Mask` datapoint. The target for ``target_type="instance"`` is *replaced* by
+          a dictionary with the key-value-pairs ``"masks"`` (as :class:`~torchvision.datapoints.Mask` datapoint) and
+          ``"labels"``.
+        * :class:`~torchvision.datasets.WIDERFace`: The value for key ``"bbox"`` in the target is converted to ``XYXY``
+          coordinate format and wrapped into a :class:`~torchvision.datapoints.BoundingBox` datapoint.
+
+    Image classification datasets
+
+        This wrapper is a no-op for image classification datasets, since they were already fully supported by
+        :mod:`torchvision.transforms` and thus no change is needed for :mod:`torchvision.transforms.v2`.
+
+    Segmentation datasets
+
+        Segmentation datasets, e.g. :class:`~torchvision.datasets.VOCSegmentation` return a two-tuple of
+        :class:`PIL.Image.Image`'s. This wrapper leaves the image as is (first item), while wrapping the
+        segmentation mask into a :class:`~torchvision.datapoints.Mask` (second item).
+
+    Video classification datasets
+
+        Video classification datasets, e.g. :class:`~torchvision.datasets.Kinetics` return a three-tuple containing a
+        :class:`torch.Tensor` for the video and audio and a :class:`int` as label. This wrapper wraps the video into a
+        :class:`~torchvision.datapoints.Video` while leaving the other items as is.
+
+        .. note::
+
+            Only datasets constructed with ``output_format="TCHW"`` are supported, since the alternative
+            ``output_format="THWC"`` is not supported by :mod:`torchvision.transforms.v2`.
+
+    Args:
+        dataset: the dataset instance to wrap for compatibility with transforms v2.
+    """
     return VisionDatasetDatapointWrapper(dataset)