mnist_dataset
=============

.. py:module:: mnist_dataset

.. autoapi-nested-parse::

   HDF5-backed MNIST Dataset for PyTorch.

   This implements a lightweight dataset that lazily opens an HDF5 file and
   returns (image_tensor, label) pairs. Images are returned as uint8 tensors
   scaled to [0,1] if a transform isn't provided.



Classes
-------

.. autoapisummary::

   mnist_dataset.MnistH5Dataset


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

.. py:class:: MnistH5Dataset(h5_path: str | pathlib.Path, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None)

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


   .. py:attribute:: h5_path


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



   .. py:attribute:: target_transform
      :value: None



   .. py:attribute:: _h5
      :value: None



   .. py:method:: _ensure_open()


   .. py:method:: __len__()

      Return the number of samples in the dataset.

      This reads the ``labels`` dataset's first dimension to determine
      the length and therefore triggers lazy opening of the HDF5 file.



   .. py:method:: __getitem__(idx)

      Return the (image, label) pair for the given index.

      Parameters
      ----------
      idx : int
          Index of the requested sample.

      Returns
      -------
      (torch.Tensor, int)
          A tuple of (image_tensor, label). The image tensor is a float
          tensor scaled to [0,1] with shape (C, H, W). Label is an int and
          may have been transformed by ``target_transform`` if provided.

      Raises
      ------
      IndexError
          If the index is out of bounds for the dataset.



   .. py:method:: close()

      Close the underlying HDF5 file handle if open.

      This is safe to call multiple times. It is also invoked from
      ``__del__`` to ensure resources are released when the dataset is
      garbage-collected.



   .. py:method:: __del__()