.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/transforms/plot_custom_tv_tensors.py" .. LINE NUMBERS ARE GIVEN BELOW. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_transforms_plot_custom_tv_tensors.py: ==================================== How to write your own TVTensor class ==================================== .. note:: Try on `collab `_ or :ref:`go to the end ` to download the full example code. This guide is intended for advanced users and downstream library maintainers. We explain how to 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`. .. GENERATED FROM PYTHON SOURCE LINES 17-21 .. code-block:: Python import torch from torchvision import tv_tensors from torchvision.transforms import v2 .. GENERATED FROM PYTHON SOURCE LINES 22-28 We will create a very simple class that just inherits from the base :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.tv_tensors.BoundingBoxes` class is `implemented `_. .. GENERATED FROM PYTHON SOURCE LINES 28-37 .. code-block:: Python class MyTVTensor(tv_tensors.TVTensor): pass my_dp = MyTVTensor([1, 2, 3]) my_dp .. rst-class:: sphx-glr-script-out .. code-block:: none MyTVTensor([1., 2., 3.]) .. GENERATED FROM PYTHON SOURCE LINES 38-46 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 MyTVTensor class, and register it to the functional API. .. GENERATED FROM PYTHON SOURCE LINES 46-57 .. code-block:: Python from torchvision.transforms.v2 import functional as F @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 tv_tensors.wrap(out, like=my_dp) .. GENERATED FROM PYTHON SOURCE LINES 58-71 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:: In our call to ``register_kernel`` above we used a string ``functional="hflip"`` to refer to the functional we want to hook into. We could also have used the functional *itself*, i.e. ``@register_kernel(functional=F.hflip, ...)``. Now that we have registered our kernel, we can call the functional API on a ``MyTVTensor`` instance: .. GENERATED FROM PYTHON SOURCE LINES 71-75 .. code-block:: Python my_dp = MyTVTensor(torch.rand(3, 256, 256)) _ = F.hflip(my_dp) .. rst-class:: sphx-glr-script-out .. code-block:: none Flipping! .. GENERATED FROM PYTHON SOURCE LINES 76-78 And we can also use the :class:`~torchvision.transforms.v2.RandomHorizontalFlip` transform, since it relies on :func:`~torchvision.transforms.v2.functional.hflip` internally: .. GENERATED FROM PYTHON SOURCE LINES 78-81 .. code-block:: Python t = v2.RandomHorizontalFlip(p=1) _ = t(my_dp) .. rst-class:: sphx-glr-script-out .. code-block:: none Flipping! .. GENERATED FROM PYTHON SOURCE LINES 82-104 .. note:: We cannot register a kernel for a transform class, we can only register a kernel for a **functional**. The reason we can't register a transform class is because one transform may internally rely on more than one functional, so in general we can't register a single kernel for a given class. .. _param_forwarding: Parameter forwarding, and ensuring future compatibility of your kernels ----------------------------------------------------------------------- The functional API that you're hooking into is public and therefore **backward** compatible: we guarantee that the parameters of these functionals won't be removed or renamed without a proper deprecation cycle. However, we don't guarantee **forward** compatibility, and we may add new parameters in the future. Imagine that in a future version, Torchvision adds a new ``inplace`` parameter to its :func:`~torchvision.transforms.v2.functional.hflip` functional. If you already defined and registered your own kernel as .. GENERATED FROM PYTHON SOURCE LINES 104-111 .. code-block:: Python def hflip_my_tv_tensor(my_dp): # noqa print("Flipping!") out = my_dp.flip(-1) return tv_tensors.wrap(out, like=my_dp) .. GENERATED FROM PYTHON SOURCE LINES 112-120 then calling ``F.hflip(my_dp)`` will **fail**, because ``hflip`` will try to pass the new ``inplace`` parameter to your kernel, but your kernel doesn't accept it. For this reason, we recommend to always define your kernels with ``*args, **kwargs`` in their signature, as done above. This way, your kernel will be able to accept any new parameter that we may add in the future. (Technically, adding `**kwargs` only should be enough). .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.004 seconds) .. _sphx_glr_download_auto_examples_transforms_plot_custom_tv_tensors.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_custom_tv_tensors.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_custom_tv_tensors.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_