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.. "auto_examples/plot_datapoints.py"
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.. only:: html
.. note::
:class: sphx-glr-download-link-note
:ref:`Go to the end <sphx_glr_download_auto_examples_plot_datapoints.py>`
to download the full example code
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_plot_datapoints.py:
==============
Datapoints FAQ
==============
The :mod:`torchvision.datapoints` namespace was introduced together with ``torchvision.transforms.v2``. This example
showcases what these datapoints are and how they behave. 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 datapoints 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 datapoints.
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.. code-block:: default
import PIL.Image
import torch
import torchvision
# We are using BETA APIs, so we deactivate the associated warning, thereby acknowledging that
# some APIs may slightly change in the future
torchvision.disable_beta_transforms_warning()
from torchvision import datapoints
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What are datapoints?
--------------------
Datapoints are zero-copy tensor subclasses:
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.. code-block:: default
tensor = torch.rand(3, 256, 256)
image = datapoints.Image(tensor)
assert isinstance(image, torch.Tensor)
assert image.data_ptr() == tensor.data_ptr()
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Under the hood, they are needed in :mod:`torchvision.transforms.v2` to correctly dispatch to the appropriate function
for the input data.
What datapoints are supported?
------------------------------
So far :mod:`torchvision.datapoints` supports four types of datapoints:
* :class:`~torchvision.datapoints.Image`
* :class:`~torchvision.datapoints.Video`
* :class:`~torchvision.datapoints.BoundingBox`
* :class:`~torchvision.datapoints.Mask`
How do I construct a datapoint?
-------------------------------
Each datapoint class takes any tensor-like data that can be turned into a :class:`~torch.Tensor`
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.. code-block:: default
image = datapoints.Image([[[[0, 1], [1, 0]]]])
print(image)
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.. code-block:: none
Image([[[[0, 1],
[1, 0]]]], )
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Similar to other PyTorch creations ops, the constructor also takes the ``dtype``, ``device``, and ``requires_grad``
parameters.
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.. code-block:: default
float_image = datapoints.Image([[[0, 1], [1, 0]]], dtype=torch.float32, requires_grad=True)
print(float_image)
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.. code-block:: none
Image([[[0., 1.],
[1., 0.]]], grad_fn=<AliasBackward0>, )
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In addition, :class:`~torchvision.datapoints.Image` and :class:`~torchvision.datapoints.Mask` also take a
:class:`PIL.Image.Image` directly:
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.. code-block:: default
image = datapoints.Image(PIL.Image.open("assets/astronaut.jpg"))
print(image.shape, image.dtype)
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.. code-block:: none
torch.Size([3, 512, 512]) torch.uint8
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In general, the datapoints can also store additional metadata that complements the underlying tensor. For example,
:class:`~torchvision.datapoints.BoundingBox` stores the coordinate format as well as the spatial size of the
corresponding image alongside the actual values:
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.. code-block:: default
bounding_box = datapoints.BoundingBox(
[17, 16, 344, 495], format=datapoints.BoundingBoxFormat.XYXY, spatial_size=image.shape[-2:]
)
print(bounding_box)
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.. code-block:: none
BoundingBox([ 17, 16, 344, 495], format=BoundingBoxFormat.XYXY, spatial_size=torch.Size([512, 512]))
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Do I have to wrap the output of the datasets myself?
----------------------------------------------------
Only if you are using custom datasets. For the built-in ones, you can use
:func:`torchvision.datasets.wrap_dataset_for_transforms_v2`. Note that the function also supports subclasses of the
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.
How do the datapoints behave inside a computation?
--------------------------------------------------
Datapoints look and feel just like regular tensors. Everything that is supported on a plain :class:`torch.Tensor`
also works on datapoints.
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):
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.. code-block:: default
assert isinstance(image, datapoints.Image)
new_image = image + 0
assert isinstance(new_image, torch.Tensor) and not isinstance(new_image, datapoints.Image)
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.. note::
This "unwrapping" behaviour is something we're actively seeking feedback on. If you find this surprising or if you
have any suggestions on how to better support your use-cases, please reach out to us via this issue:
https://github.com/pytorch/vision/issues/7319
There are two exceptions to this rule:
1. The operations :meth:`~torch.Tensor.clone`, :meth:`~torch.Tensor.to`, and :meth:`~torch.Tensor.requires_grad_`
retain the datapoint type.
2. Inplace operations on datapoints cannot change the type of the datapoint they are called on. However, if you use
the flow style, the returned value will be unwrapped:
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.. code-block:: default
image = datapoints.Image([[[0, 1], [1, 0]]])
new_image = image.add_(1).mul_(2)
assert isinstance(image, torch.Tensor)
print(image)
assert isinstance(new_image, torch.Tensor) and not isinstance(new_image, datapoints.Image)
assert (new_image == image).all()
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.. code-block:: none
Image([[[2, 4],
[4, 2]]], )
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**Total running time of the script:** ( 0 minutes 0.059 seconds)
.. _sphx_glr_download_auto_examples_plot_datapoints.py:
.. only:: html
.. container:: sphx-glr-footer sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_datapoints.py <plot_datapoints.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_datapoints.ipynb <plot_datapoints.ipynb>`
.. only:: html
.. rst-class:: sphx-glr-signature
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