tv_tensor -> TVTensor where it matters (pytorch#7904)

Co-authored-by: Philip Meier <[email protected]>

docs/source/transforms.rst

@@ -183,7 +183,7 @@ 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:`tv_tensors
+The functionals support PIL images, pure tensors, or :ref:`TVTensors
 <tv_tensors>`, e.g. both ``resize(image_tensor)`` and ``resize(bboxes)`` are
 valid.
 

docs/source/tv_tensors.rst

@@ -5,10 +5,11 @@ TVTensors
 
 .. currentmodule:: torchvision.tv_tensors
 
-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 tv_tensors directly and can simply rely on dataset wrapping -
-see e.g. :ref:`sphx_glr_auto_examples_transforms_plot_transforms_e2e.py`.
+TVTensors are :class:`torch.Tensor` subclasses which the v2 :ref:`transforms
+<transforms>` use under the hood to dispatch their inputs to the appropriate
+lower-level kernels. Most users do not need to manipulate TVTensors directly and
+can simply rely on dataset wrapping - see e.g.
+:ref:`sphx_glr_auto_examples_transforms_plot_transforms_e2e.py`.
 
 .. autosummary::
     :toctree: generated/

gallery/transforms/plot_custom_transforms.py

@@ -74,7 +74,7 @@ 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 tv_tensor classes in your code, make sure to
+#     While working with TVTensor classes in your code, make sure to
 #     familiarize yourself with this section:
 #     :ref:`tv_tensor_unwrapping_behaviour`
 #
@@ -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 tv_tensors are
+# based on the **class** of the entries, as all TVTensors 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_tv_tensors.py

@@ -1,14 +1,14 @@
 """
-=====================================
+====================================
 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_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 tv_tensor class, and how to make it compatible with the built-in
+write your own TVTensor 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_tv_tensors.py`.
 """

gallery/transforms/plot_transforms_getting_started.py

@@ -115,7 +115,7 @@
 # segmentation, or videos (:class:`torchvision.tv_tensors.Video`), we could have
 # passed them to the transforms in exactly the same way.
 #
-# By now you likely have a few questions: what are these tv_tensors, how do we
+# By now you likely have a few questions: what are these TVTensors, how do we
 # use them, and what is the expected input/output of those transforms? We'll
 # answer these in the next sections.
 
@@ -126,15 +126,15 @@
 # What are TVTensors?
 # --------------------
 #
-# TVTensors are :class:`torch.Tensor` subclasses. The available tv_tensors are
+# TVTensors are :class:`torch.Tensor` subclasses. The available TVTensors are
 # :class:`~torchvision.tv_tensors.Image`,
 # :class:`~torchvision.tv_tensors.BoundingBoxes`,
 # :class:`~torchvision.tv_tensors.Mask`, and
 # :class:`~torchvision.tv_tensors.Video`.
 #
 # TVTensors look and feel just like regular tensors - they **are** tensors.
 # Everything that is supported on a plain :class:`torch.Tensor` like ``.sum()``
-# or any ``torch.*`` operator will also work on a tv_tensor:
+# or any ``torch.*`` operator will also work on a TVTensor:
 
 img_dp = tv_tensors.Image(torch.randint(0, 256, (3, 256, 256), dtype=torch.uint8))
 
@@ -146,7 +146,7 @@
 # transform a given input, the transforms first look at the **class** of the
 # object, and dispatch to the appropriate implementation accordingly.
 #
-# You don't need to know much more about tv_tensors at this point, but advanced
+# You don't need to know much more about TVTensors at this point, but advanced
 # users who want to learn more can refer to
 # :ref:`sphx_glr_auto_examples_transforms_plot_tv_tensors.py`.
 #
@@ -234,9 +234,9 @@
 # Torchvision also supports datasets for object detection or segmentation like
 # :class:`torchvision.datasets.CocoDetection`. Those datasets predate
 # the existence of the :mod:`torchvision.transforms.v2` module and of the
-# tv_tensors, so they don't return tv_tensors out of the box.
+# TVTensors, so they don't return TVTensors out of the box.
 #
-# An easy way to force those datasets to return tv_tensors and to make them
+# An easy way to force those datasets to return TVTensors and to make them
 # compatible with v2 transforms is to use the
 # :func:`torchvision.datasets.wrap_dataset_for_transforms_v2` function:
 #
@@ -246,7 +246,7 @@
 #
 #    dataset = CocoDetection(..., transforms=my_transforms)
 #    dataset = wrap_dataset_for_transforms_v2(dataset)
-#    # Now the dataset returns tv_tensors!
+#    # Now the dataset returns TVTensors!
 #
 # Using your own datasets
 # ^^^^^^^^^^^^^^^^^^^^^^^

gallery/transforms/plot_tv_tensors.py

@@ -9,18 +9,18 @@
 
 
 TVTensors are Tensor subclasses introduced together with
-``torchvision.transforms.v2``. This example showcases what these tv_tensors are
+``torchvision.transforms.v2``. This example showcases what these TVTensors are
 and how they behave.
 
 .. warning::
 
-    **Intended Audience** Unless you're writing your own transforms or your own tv_tensors, you
+    **Intended Audience** Unless you're writing your own transforms or your own TVTensors, you
     probably do not need to read this guide. This is a fairly low-level topic
     that most users will not need to worry about: you do not need to understand
-    the internals of tv_tensors to efficiently rely on
+    the internals of TVTensors to efficiently rely on
     ``torchvision.transforms.v2``. It may however be useful for advanced users
     trying to implement their own datasets, transforms, or work directly with
-    the tv_tensors.
+    the TVTensors.
 """
 
 # %%
@@ -31,8 +31,8 @@
 
 
 # %%
-# What are tv_tensors?
-# --------------------
+# What are TVTensors?
+# -------------------
 #
 # TVTensors are zero-copy tensor subclasses:
 
@@ -46,31 +46,31 @@
 # Under the hood, they are needed in :mod:`torchvision.transforms.v2` to correctly dispatch to the appropriate function
 # for the input data.
 #
-# :mod:`torchvision.tv_tensors` supports four types of tv_tensors:
+# :mod:`torchvision.tv_tensors` supports four types of TVTensors:
 #
 # * :class:`~torchvision.tv_tensors.Image`
 # * :class:`~torchvision.tv_tensors.Video`
 # * :class:`~torchvision.tv_tensors.BoundingBoxes`
 # * :class:`~torchvision.tv_tensors.Mask`
 #
-# What can I do with a tv_tensor?
-# -------------------------------
+# What can I do with a TVTensor?
+# ------------------------------
 #
 # TVTensors look and feel just like regular tensors - they **are** tensors.
 # Everything that is supported on a plain :class:`torch.Tensor` like ``.sum()`` or
-# any ``torch.*`` operator will also work on tv_tensors. See
+# any ``torch.*`` operator will also work on TVTensors. See
 # :ref:`tv_tensor_unwrapping_behaviour` for a few gotchas.
 
 # %%
 # .. _tv_tensor_creation:
 #
-# How do I construct a tv_tensor?
-# -------------------------------
+# How do I construct a TVTensor?
+# ------------------------------
 #
 # Using the constructor
 # ^^^^^^^^^^^^^^^^^^^^^
 #
-# Each tv_tensor class takes any tensor-like data that can be turned into a :class:`~torch.Tensor`
+# Each TVTensor class takes any tensor-like data that can be turned into a :class:`~torch.Tensor`
 
 image = tv_tensors.Image([[[[0, 1], [1, 0]]]])
 print(image)
@@ -92,7 +92,7 @@
 print(image.shape, image.dtype)
 
 # %%
-# Some tv_tensors require additional metadata to be passed in ordered to be constructed. For example,
+# Some TVTensors require additional metadata to be passed in ordered to be constructed. For example,
 # :class:`~torchvision.tv_tensors.BoundingBoxes` requires the coordinate format as well as the size of the
 # corresponding image (``canvas_size``) alongside the actual values. These
 # metadata are required to properly transform the bounding boxes.
@@ -109,7 +109,7 @@
 # ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 #
 # You can also use the :func:`~torchvision.tv_tensors.wrap` function to wrap a tensor object
-# into a tv_tensor. This is useful when you already have an object of the
+# into a TVTensor. This is useful when you already have an object of the
 # desired type, which typically happens when writing transforms: you just want
 # to wrap the output like the input.
 
@@ -125,7 +125,7 @@
 # .. _tv_tensor_unwrapping_behaviour:
 #
 # I had a TVTensor but now I have a Tensor. Help!
-# ------------------------------------------------
+# -----------------------------------------------
 #
 # By default, operations on :class:`~torchvision.tv_tensors.TVTensor` objects
 # will return a pure Tensor:
@@ -151,7 +151,7 @@
 # But I want a TVTensor back!
 # ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 #
-# You can re-wrap a pure tensor into a tv_tensor by just calling the tv_tensor
+# You can re-wrap a pure tensor into a TVTensor by just calling the TVTensor
 # constructor, or by using the :func:`~torchvision.tv_tensors.wrap` function
 # (see more details above in :ref:`tv_tensor_creation`):
 
@@ -164,7 +164,7 @@
 # as a global config setting for the whole program, or as a context manager
 # (read its docs to learn more about caveats):
 
-with tv_tensors.set_return_type("tv_tensor"):
+with tv_tensors.set_return_type("TVTensor"):
     new_bboxes = bboxes + 3
 assert isinstance(new_bboxes, tv_tensors.BoundingBoxes)
 
@@ -203,17 +203,17 @@
 # There are a few exceptions to this "unwrapping" rule:
 # :meth:`~torch.Tensor.clone`, :meth:`~torch.Tensor.to`,
 # :meth:`torch.Tensor.detach`, and :meth:`~torch.Tensor.requires_grad_` retain
-# the tv_tensor type.
+# the TVTensor type.
 #
-# Inplace operations on tv_tensors like ``obj.add_()`` will preserve the type of
+# Inplace operations on TVTensors like ``obj.add_()`` will preserve the type of
 # ``obj``. However, the **returned** value of inplace operations will be a pure
 # tensor:
 
 image = tv_tensors.Image([[[0, 1], [1, 0]]])
 
 new_image = image.add_(1).mul_(2)
 
-# image got transformed in-place and is still an Image tv_tensor, but new_image
+# image got transformed in-place and is still a TVTensor Image, but new_image
 # is a Tensor. They share the same underlying data and they're equal, just
 # different classes.
 assert isinstance(image, tv_tensors.Image)