:py:mod:`mipcandy.data.transform`
=================================

.. py:module:: mipcandy.data.transform

.. autodoc2-docstring:: mipcandy.data.transform
   :allowtitles:

Module Contents
---------------

Classes
~~~~~~~

.. list-table::
   :class: autosummary longtable
   :align: left

   * - :py:obj:`JointTransform <mipcandy.data.transform.JointTransform>`
     -
   * - :py:obj:`MONAITransform <mipcandy.data.transform.MONAITransform>`
     -

API
~~~

.. py:class:: JointTransform(*, transform: mipcandy.types.Transform | None = None, image_only: mipcandy.types.Transform | None = None, label_only: mipcandy.types.Transform | None = None, keys: tuple[str, str] = ('image', 'label'), order: tuple[mipcandy.data.transform._Order, mipcandy.data.transform._Order, mipcandy.data.transform._Order] = ('transform', 'image_only', 'label_only'))
   :canonical: mipcandy.data.transform.JointTransform

   Bases: :py:obj:`torch.nn.Module`

   .. py:method:: forward(image: torch.Tensor, label: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]
      :canonical: mipcandy.data.transform.JointTransform.forward

      .. autodoc2-docstring:: mipcandy.data.transform.JointTransform.forward

.. py:class:: MONAITransform(transform: mipcandy.types.Transform, *, keys: tuple[str, str] = ('image', 'label'))
   :canonical: mipcandy.data.transform.MONAITransform

   Bases: :py:obj:`torch.nn.Module`

   .. py:method:: forward(data: torch.Tensor | dict[str, torch.Tensor]) -> torch.Tensor | dict[str, torch.Tensor]
      :canonical: mipcandy.data.transform.MONAITransform.forward

      .. autodoc2-docstring:: mipcandy.data.transform.MONAITransform.forward