Datapoint -> TVTensor; datapoint[s] -> tv_tensor[s]

docs/source/conf.py

@@ -88,8 +88,8 @@ def __init__(self, src_dir):
         "plot_transforms_e2e.py",
         "plot_cutmix_mixup.py",
         "plot_custom_transforms.py",
-        "plot_datapoints.py",
-        "plot_custom_datapoints.py",
+        "plot_tv_tensors.py",
+        "plot_custom_tv_tensors.py",
     ]
 
     def __call__(self, filename):

docs/source/index.rst

@@ -32,7 +32,7 @@ architectures, and common image transformations for computer vision.
    :caption: Package Reference
 
    transforms
-   datapoints
+   tv_tensors
    models
    datasets
    utils

docs/source/transforms.rst

@@ -30,12 +30,12 @@ tasks (image classification, detection, segmentation, video classification).
 .. code:: python
 
     # Detection (re-using imports and transforms from above)
-    from torchvision import datapoints
+    from torchvision import tv_tensors
 
     img = torch.randint(0, 256, size=(3, H, W), dtype=torch.uint8)
     bboxes = torch.randint(0, H // 2, size=(3, 4))
     bboxes[:, 2:] += bboxes[:, :2]
-    bboxes = datapoints.BoundingBoxes(bboxes, format="XYXY", canvas_size=(H, W))
+    bboxes = tv_tensors.BoundingBoxes(bboxes, format="XYXY", canvas_size=(H, W))
 
     # The same transforms can be used!
     img, bboxes = transforms(img, bboxes)
@@ -183,8 +183,8 @@ Transforms are available as classes like
 This is very much like the :mod:`torch.nn` package which defines both classes
 and functional equivalents in :mod:`torch.nn.functional`.
 
-The functionals support PIL images, pure tensors, or :ref:`datapoints
-<datapoints>`, e.g. both ``resize(image_tensor)`` and ``resize(bboxes)`` are
+The functionals support PIL images, pure tensors, or :ref:`tv_tensors
+<tv_tensors>`, e.g. both ``resize(image_tensor)`` and ``resize(bboxes)`` are
 valid.
 
 .. note::

docs/source/datapoints.rst → docs/source/tv_tensors.rst

@@ -1,13 +1,13 @@
-.. _datapoints:
+.. _tv_tensors:
 
-Datapoints
+TVTensors
 ==========
 
-.. currentmodule:: torchvision.datapoints
+.. currentmodule:: torchvision.tv_tensors
 
-Datapoints are tensor subclasses which the :mod:`~torchvision.transforms.v2` v2 transforms use under the hood to
+TVTensors are tensor subclasses which the :mod:`~torchvision.transforms.v2` v2 transforms use under the hood to
 dispatch their inputs to the appropriate lower-level kernels. Most users do not
-need to manipulate datapoints directly and can simply rely on dataset wrapping -
+need to manipulate tv_tensors directly and can simply rely on dataset wrapping -
 see e.g. :ref:`sphx_glr_auto_examples_transforms_plot_transforms_e2e.py`.
 
 .. autosummary::
@@ -19,6 +19,6 @@ see e.g. :ref:`sphx_glr_auto_examples_transforms_plot_transforms_e2e.py`.
     BoundingBoxFormat
     BoundingBoxes
     Mask
-    Datapoint
+    TVTensor
     set_return_type
     wrap

gallery/others/plot_video_api.py

@@ -86,7 +86,7 @@
 print("PTS for first five frames ", ptss[:5])
 print("Total number of frames: ", len(frames))
 approx_nf = metadata['audio']['duration'][0] * metadata['audio']['framerate'][0]
-print("Approx total number of datapoints we can expect: ", approx_nf)
+print("Approx total number of tv_tensors we can expect: ", approx_nf)
 print("Read data size: ", frames[0].size(0) * len(frames))
 
 # %%
@@ -170,7 +170,7 @@ def example_read_video(video_object, start=0, end=None, read_video=True, read_au
     return video_frames, audio_frames, (video_pts, audio_pts), video_object.get_metadata()
 
 
-# Total number of frames should be 327 for video and 523264 datapoints for audio
+# Total number of frames should be 327 for video and 523264 tv_tensors for audio
 vf, af, info, meta = example_read_video(video)
 print(vf.size(), af.size())
 

gallery/transforms/helpers.py

@@ -1,7 +1,7 @@
 import matplotlib.pyplot as plt
 import torch
 from torchvision.utils import draw_bounding_boxes, draw_segmentation_masks
-from torchvision import datapoints
+from torchvision import tv_tensors
 from torchvision.transforms.v2 import functional as F
 
 
@@ -22,7 +22,7 @@ def plot(imgs, row_title=None, **imshow_kwargs):
                 if isinstance(target, dict):
                     boxes = target.get("boxes")
                     masks = target.get("masks")
-                elif isinstance(target, datapoints.BoundingBoxes):
+                elif isinstance(target, tv_tensors.BoundingBoxes):
                     boxes = target
                 else:
                     raise ValueError(f"Unexpected target type: {type(target)}")

gallery/transforms/plot_custom_transforms.py

@@ -13,7 +13,7 @@
 
 # %%
 import torch
-from torchvision import datapoints
+from torchvision import tv_tensors
 from torchvision.transforms import v2
 
 
@@ -62,7 +62,7 @@ def forward(self, img, bboxes, label):  # we assume inputs are always structured
 
 H, W = 256, 256
 img = torch.rand(3, H, W)
-bboxes = datapoints.BoundingBoxes(
+bboxes = tv_tensors.BoundingBoxes(
     torch.tensor([[0, 10, 10, 20], [50, 50, 70, 70]]),
     format="XYXY",
     canvas_size=(H, W)
@@ -74,9 +74,9 @@ def forward(self, img, bboxes, label):  # we assume inputs are always structured
 print(f"Output image shape: {out_img.shape}\nout_bboxes = {out_bboxes}\n{out_label = }")
 # %%
 # .. note::
-#     While working with datapoint classes in your code, make sure to
+#     While working with tv_tensor classes in your code, make sure to
 #     familiarize yourself with this section:
-#     :ref:`datapoint_unwrapping_behaviour`
+#     :ref:`tv_tensor_unwrapping_behaviour`
 #
 # Supporting arbitrary input structures
 # =====================================
@@ -111,7 +111,7 @@ def forward(self, img, bboxes, label):  # we assume inputs are always structured
 # In brief, the core logic is to unpack the input into a flat list using `pytree
 # <https://github.com/pytorch/pytorch/blob/main/torch/utils/_pytree.py>`_, and
 # then transform only the entries that can be transformed (the decision is made
-# based on the **class** of the entries, as all datapoints are
+# based on the **class** of the entries, as all tv_tensors are
 # tensor-subclasses) plus some custom logic that is out of score here - check the
 # code for details. The (potentially transformed) entries are then repacked and
 # returned, in the same structure as the input.

gallery/transforms/plot_custom_datapoints.py → gallery/transforms/plot_custom_tv_tensors.py

@@ -1,62 +1,62 @@
 """
 =====================================
-How to write your own Datapoint class
+How to write your own TVTensor class
 =====================================
 
 .. note::
-    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_custom_datapoints.ipynb>`_
-    or :ref:`go to the end <sphx_glr_download_auto_examples_transforms_plot_custom_datapoints.py>` to download the full example code.
+    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_custom_tv_tensors.ipynb>`_
+    or :ref:`go to the end <sphx_glr_download_auto_examples_transforms_plot_custom_tv_tensors.py>` to download the full example code.
 
 This guide is intended for advanced users and downstream library maintainers. We explain how to
-write your own datapoint class, and how to make it compatible with the built-in
+write your own tv_tensor class, and how to make it compatible with the built-in
 Torchvision v2 transforms. Before continuing, make sure you have read
-:ref:`sphx_glr_auto_examples_transforms_plot_datapoints.py`.
+:ref:`sphx_glr_auto_examples_transforms_plot_tv_tensors.py`.
 """
 
 # %%
 import torch
-from torchvision import datapoints
+from torchvision import tv_tensors
 from torchvision.transforms import v2
 
 # %%
 # We will create a very simple class that just inherits from the base
-# :class:`~torchvision.datapoints.Datapoint` class. It will be enough to cover
+# :class:`~torchvision.tv_tensors.TVTensor` class. It will be enough to cover
 # what you need to know to implement your more elaborate uses-cases. If you need
 # to create a class that carries meta-data, take a look at how the
-# :class:`~torchvision.datapoints.BoundingBoxes` class is `implemented
-# <https://github.com/pytorch/vision/blob/main/torchvision/datapoints/_bounding_box.py>`_.
+# :class:`~torchvision.tv_tensors.BoundingBoxes` class is `implemented
+# <https://github.com/pytorch/vision/blob/main/torchvision/tv_tensors/_bounding_box.py>`_.
 
 
-class MyDatapoint(datapoints.Datapoint):
+class MyTVTensor(tv_tensors.TVTensor):
     pass
 
 
-my_dp = MyDatapoint([1, 2, 3])
+my_dp = MyTVTensor([1, 2, 3])
 my_dp
 
 # %%
-# Now that we have defined our custom Datapoint class, we want it to be
+# Now that we have defined our custom TVTensor class, we want it to be
 # compatible with the built-in torchvision transforms, and the functional API.
 # For that, we need to implement a kernel which performs the core of the
 # transformation, and then "hook" it to the functional that we want to support
 # via :func:`~torchvision.transforms.v2.functional.register_kernel`.
 #
 # We illustrate this process below: we create a kernel for the "horizontal flip"
-# operation of our MyDatapoint class, and register it to the functional API.
+# operation of our MyTVTensor class, and register it to the functional API.
 
 from torchvision.transforms.v2 import functional as F
 
 
-@F.register_kernel(functional="hflip", datapoint_cls=MyDatapoint)
-def hflip_my_datapoint(my_dp, *args, **kwargs):
+@F.register_kernel(functional="hflip", tv_tensor_cls=MyTVTensor)
+def hflip_my_tv_tensor(my_dp, *args, **kwargs):
     print("Flipping!")
     out = my_dp.flip(-1)
-    return datapoints.wrap(out, like=my_dp)
+    return tv_tensors.wrap(out, like=my_dp)
 
 
 # %%
-# To understand why :func:`~torchvision.datapoints.wrap` is used, see
-# :ref:`datapoint_unwrapping_behaviour`. Ignore the ``*args, **kwargs`` for now,
+# To understand why :func:`~torchvision.tv_tensors.wrap` is used, see
+# :ref:`tv_tensor_unwrapping_behaviour`. Ignore the ``*args, **kwargs`` for now,
 # we will explain it below in :ref:`param_forwarding`.
 #
 # .. note::
@@ -67,9 +67,9 @@ def hflip_my_datapoint(my_dp, *args, **kwargs):
 #     ``@register_kernel(functional=F.hflip, ...)``.
 #
 # Now that we have registered our kernel, we can call the functional API on a
-# ``MyDatapoint`` instance:
+# ``MyTVTensor`` instance:
 
-my_dp = MyDatapoint(torch.rand(3, 256, 256))
+my_dp = MyTVTensor(torch.rand(3, 256, 256))
 _ = F.hflip(my_dp)
 
 # %%
@@ -102,10 +102,10 @@ def hflip_my_datapoint(my_dp, *args, **kwargs):
 # to its :func:`~torchvision.transforms.v2.functional.hflip` functional. If you
 # already defined and registered your own kernel as
 
-def hflip_my_datapoint(my_dp):  # noqa
+def hflip_my_tv_tensor(my_dp):  # noqa
     print("Flipping!")
     out = my_dp.flip(-1)
-    return datapoints.wrap(out, like=my_dp)
+    return tv_tensors.wrap(out, like=my_dp)
 
 
 # %%

gallery/transforms/plot_transforms_e2e.py

@@ -23,7 +23,7 @@
 import torch
 import torch.utils.data
 
-from torchvision import models, datasets, datapoints
+from torchvision import models, datasets, tv_tensors
 from torchvision.transforms import v2
 
 torch.manual_seed(0)
@@ -72,7 +72,7 @@
 # %%
 # We used the ``target_keys`` parameter to specify the kind of output we're
 # interested in. Our dataset now returns a target which is dict where the values
-# are :ref:`Datapoints <what_are_datapoints>` (all are :class:`torch.Tensor`
+# are :ref:`TVTensors <what_are_tv_tensors>` (all are :class:`torch.Tensor`
 # subclasses). We're dropped all unncessary keys from the previous output, but
 # if you need any of the original keys e.g. "image_id", you can still ask for
 # it.
@@ -103,7 +103,7 @@
     [
         v2.ToImage(),
         v2.RandomPhotometricDistort(p=1),
-        v2.RandomZoomOut(fill={datapoints.Image: (123, 117, 104), "others": 0}),
+        v2.RandomZoomOut(fill={tv_tensors.Image: (123, 117, 104), "others": 0}),
         v2.RandomIoUCrop(),
         v2.RandomHorizontalFlip(p=1),
         v2.SanitizeBoundingBoxes(),