minerva.data.datasets.context_dataset
=====================================

.. py:module:: minerva.data.datasets.context_dataset


Classes
-------

.. autoapisummary::

   minerva.data.datasets.context_dataset.ContextDataset


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

.. py:class:: ContextDataset(readers, transform = None)

   Bases: :py:obj:`torch.utils.data.Dataset`


   An abstract class representing a :class:`Dataset`.

   All datasets that represent a map from keys to data samples should subclass
   it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a
   data sample for a given key. Subclasses could also optionally overwrite
   :meth:`__len__`, which is expected to return the size of the dataset by many
   :class:`~torch.utils.data.Sampler` implementations and the default options
   of :class:`~torch.utils.data.DataLoader`. Subclasses could also
   optionally implement :meth:`__getitems__`, for speedup batched samples
   loading. This method accepts list of indices of samples of batch and returns
   list of samples.

   .. note::
     :class:`~torch.utils.data.DataLoader` by default constructs an index
     sampler that yields integral indices.  To make it work with a map-style
     dataset with non-integral indices/keys, a custom sampler must be provided.

   A PyTorch Dataset class for handling paired image and mask data with optional context transformations.

   Parameters
   ----------
   readers : Tuple[_Reader, _Reader]
       A tuple containing two reader objects. The first reader should provide images,
       and the second reader should provide corresponding masks. Both readers must
       support indexing and have the same length.
   transform : Optional[_Transform], default=None
       An optional transformation function or callable that takes a tuple of (image, mask)
       and returns a transformed tuple of (image, mask). If None, no transformations
       are applied.


   .. py:method:: __getitem__(idx)


   .. py:method:: __len__()


   .. py:attribute:: readers


   .. py:attribute:: transform
      :value: None