helios.data.transforms
======================

.. py:module:: helios.data.transforms


Attributes
----------

.. autoapisummary::

   helios.data.transforms.TRANSFORM_REGISTRY


Classes
-------

.. autoapisummary::

   helios.data.transforms.ToImageTensor


Functions
---------

.. autoapisummary::

   helios.data.transforms.create_transform


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

.. py:data:: TRANSFORM_REGISTRY

   Global instance of the registry for transforms.

   .. rubric:: Example

   .. code-block:: python

       import helios.data.transforms as hldt

       # This automatically registers your dataset.
       @hldt.TRANSFORM_REGISTRY.register()
       class MyTransform:
           ...

       # Alternatively you can manually register a dataset like this:
       hldt.TRANSFORM_REGISTRY.register(MyTransform)

.. py:function:: create_transform(type_name: str, *args: Any, **kwargs: Any) -> torch.nn.Module

   Create a transform of the given type.

   This uses TRANSFORM_REGISTRY to look-up transform types, so ensure your transforms
   have been registered before using this function.

   :param type_name: the type of the transform to create.
   :param args: positional arguments to pass into the transform.
   :param kwargs: keyword arguments to pass into the transform.

   :returns: The constructed transform.


.. py:class:: ToImageTensor(dtype: torch.dtype = torch.float32, scale: bool = True)

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


   Convert an image (or list of images) to tensor(s).

   An image is meant to be a tensor, ndarray, or PIL image. The shape expected to be
   either [H, W, C] or [C, H, W].

   :param dtype: the output type of the tensors.
   :param scale: if true, scale the values to the valid range. Defaults to true.


   .. py:method:: forward(img: numpy.typing.NDArray | list[numpy.typing.NDArray] | tuple[numpy.typing.NDArray, Ellipsis]) -> torch.Tensor | list[torch.Tensor] | tuple[torch.Tensor, Ellipsis]

      Convert the input image(s) into tensor(s).

      The return type will match the type of the input. So, if the input is a single
      image, then the output will be a single tensor. If the input is a list or a tuple
      of images, the output will be a list or tuple of tensors.

      :param img: image(s) to convert.

      :returns: The converted images.