neuro_morpho.data.data_loader
=============================

.. py:module:: neuro_morpho.data.data_loader

.. autoapi-nested-parse::

   Data Loader for the NeuroMorpho dataset.



Classes
-------

.. autoapisummary::

   neuro_morpho.data.data_loader.NeuroMorphoDataset


Functions
---------

.. autoapisummary::

   neuro_morpho.data.data_loader.build_dataloader


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

.. py:class:: NeuroMorphoDataset(x_dir: str | pathlib.Path, y_dir: str | pathlib.Path, aug_transform: torchvision.transforms.v2.Transform = None, pre_aug_x_transform: torchvision.transforms.v2.Transform = None, pre_aug_y_transform: torchvision.transforms.v2.Transform = None, post_aug_x_transform: torchvision.transforms.v2.Transform = None, post_aug_y_transform: torchvision.transforms.v2.Transform = None)

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


   NeuroMorpho Dataset.

   This dataset is used to load images and their corresponding labels for
   training and testing.


   .. py:attribute:: img_files


   .. py:attribute:: lbl_files


   .. py:attribute:: aug_transform
      :value: None



   .. py:attribute:: pre_aug_x_transform
      :value: None



   .. py:attribute:: pre_aug_y_transform
      :value: None



   .. py:attribute:: post_aug_x_transform
      :value: None



   .. py:attribute:: post_aug_y_transform
      :value: None



   .. py:method:: __getitem__(index: int) -> tuple[torch.Tensor, torch.Tensor | tuple[torch.Tensor, Ellipsis]]

      Get an item from the dataset.

      :param index: Index of the item.
      :type index: int

      :returns: Tuple containing the image and label.
      :rtype: tuple



   .. py:method:: __len__() -> int

      Get the length of the dataset.

      :returns: Length of the dataset.
      :rtype: int



.. py:function:: build_dataloader(x_dir: str | pathlib.Path, y_dir: str | pathlib.Path, batch_size: int = 1, shuffle: bool = True, num_workers: int = 0, aug_transform: torchvision.transforms.v2.Transform = None, pre_aug_x_transform: torchvision.transforms.v2.Transform = None, post_aug_x_transform: torchvision.transforms.v2.Transform = None, pre_aug_y_transform: torchvision.transforms.v2.Transform = None, post_aug_y_transform: torchvision.transforms.v2.Transform = None) -> torch.utils.data.DataLoader

   Build a DataLoader for the dataset.

   :param x_dir: Directory containing the input images.
   :type x_dir: str|Path
   :param y_dir: Directory containing the label images.
   :type y_dir: str|Path
   :param batch_size: Batch size. Defaults to 1.
   :type batch_size: int, optional
   :param shuffle: Whether to shuffle the data. Defaults to True.
   :type shuffle: bool, optional
   :param num_workers: Number of workers. Defaults to 0.
   :type num_workers: int, optional
   :param aug_transform: Transform to be applied to the
                         data for augmentation. Defaults to None.
   :type aug_transform: v2.Transform, optional
   :param pre_aug_x_transform: Transform to be applied to the input
                               images for normalization. Defaults to None.
   :type pre_aug_x_transform: v2.Transform, optional
   :param post_aug_x_transform: Transform to be applied to the input
                                images after augmentation. Defaults to None.
   :type post_aug_x_transform: v2.Transform, optional
   :param pre_aug_y_transform: Transform to be applied to the label
                               images for normalization. Defaults to None.
   :type pre_aug_y_transform: v2.Transform, optional
   :param post_aug_y_transform: Transform to be applied to the label
                                images after augmentation. Defaults to None.
   :type post_aug_y_transform: v2.Transform, optional

   :returns: DataLoader for the dataset.
   :rtype: td.DataLoader