add example for v2 wrapping for custom datasets

gallery/plot_datapoints.py

@@ -20,6 +20,7 @@
 torchvision.disable_beta_transforms_warning()
 
 from torchvision import datapoints
+from torchvision.transforms.v2 import functional as F
 
 
 ########################################################################################################################
@@ -93,6 +94,68 @@
 # built-in datasets. Meaning, if your custom dataset subclasses from a built-in one and the output type is the same, you
 # also don't have to wrap manually.
 #
+# If you have a custom dataset, for example the ``PennFudanDataset`` from
+# `this tutorial <https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html>`_, you have two options:
+#
+# 1. Perform the wrapping inside ``__getitem__``:
+
+class PennFudanDataset(torch.utils.data.Dataset):
+    ...
+
+    def __getitem__(self, item):
+        ...
+
+        target["boxes"] = datapoints.BoundingBox(
+            boxes,
+            format=datapoints.BoundingBoxFormat.XYXY,
+            spatial_size=F.get_spatial_size(img),
+        )
+        target["labels"] = labels
+        target["masks"] = datapoints.Mask(masks)
+
+        ...
+
+        if self.transforms is not None:
+            img, target = self.transforms(img, target)
+
+        ...
+
+########################################################################################################################
+# 2. Perform the wrapping inside a custom transformation at the beginning of your pipeline:
+
+
+class WrapPennFudanDataset:
+    def __call__(self, img, target):
+        target["boxes"] = datapoints.BoundingBox(
+            target["boxes"],
+            format=datapoints.BoundingBoxFormat.XYXY,
+            spatial_size=F.get_spatial_size(img),
+        )
+        target["masks"] = datapoints.Mask(target["masks"])
+        return img, target
+
+
+...
+
+
+def get_transform(train):
+    transforms = []
+    transforms.append(WrapPennFudanDataset())
+    transforms.append(T.PILToTensor())
+    ...
+
+########################################################################################################################
+# .. note::
+#
+#    If both :class:`~torchvision.datapoints.BoundingBox`'es and :class:`~torchvision.datapoints.Mask`'s are included in
+#    the sample, ``torchvision.transforms.v2`` will transform them both. Meaning, if you don't need both, dropping or
+#    at least not wrapping the obsolete parts, can lead to a significant performance boost.
+#
+#    For example, if you are using the ``PennFudanDataset`` for object detection, not wrapping the masks avoids
+#    transforming them over and over again in the pipeline just to ultimately ignoring them. In general, it would be
+#    even better to not load the masks at all, but this is not possible in this example, since the bounding boxes are
+#    generated from the masks.
+#
 # How do the datapoints behave inside a computation?
 # --------------------------------------------------
 #
@@ -101,6 +164,7 @@
 # Since for most operations involving datapoints, it cannot be safely inferred whether the result should retain the
 # datapoint type, we choose to return a plain tensor instead of a datapoint (this might change, see note below):
 
+
 assert isinstance(image, datapoints.Image)
 
 new_image = image + 0

torchvision/datapoints/_dataset_wrapper.py

@@ -124,7 +124,9 @@ def __init__(self, dataset, target_keys):
         if not isinstance(dataset, datasets.VisionDataset):
             raise TypeError(
                 f"This wrapper is meant for subclasses of `torchvision.datasets.VisionDataset`, "
-                f"but got a '{dataset_cls.__name__}' instead."
+                f"but got a '{dataset_cls.__name__}' instead.\n"
+                f"For an example of how to perform the wrapping for custom datasets, see\n\n"
+                "https://pytorch.org/vision/main/auto_examples/plot_datapoints.html#do-i-have-to-wrap-the-output-of-the-datasets-myself"
             )
 
         for cls in dataset_cls.mro():