omniloader.data.npy
===================

.. py:module:: omniloader.data.npy

.. autoapi-nested-parse::

   A dataset over a per-sample ``.npy`` folder layout.

   :class:`NpyFolderDataset` reads the common feature-extraction layout where each
   sample is a directory of ``.npy`` files, one per named value::

       root/
         <sample_key>/
           video.npy
           audio.npy
           valence.npy
           ...

   Arrays are memory-mapped (``mmap_mode="r"``) so large features are not copied into
   RAM until sliced. Every sample directory is expected to contain the same set of
   files; the first sample defines the key set unless ``keys`` is given.



Classes
-------

.. autoapisummary::

   omniloader.data.npy.NpyFolderDataset


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

.. py:class:: NpyFolderDataset(root: str | pathlib.Path, keys: collections.abc.Sequence[str] | None = None, mmap: bool = True)

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


   Read samples from a directory of per-sample ``.npy`` sub-folders.

   :param root: Directory whose immediate sub-directories are samples.
   :param keys: Optional value names (``.npy`` stems) to load. When ``None``, the
                files present in the first sample directory define the key set.
   :param mmap: Memory-map the arrays instead of reading them fully. Defaults to
                ``True``.

   :raises FileNotFoundError: If ``root`` has no sample sub-directories.


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

      Number of sample directories.



   .. py:method:: __getitem__(index: int) -> dict[str, Any]

      Load the sample at ``index`` as a dict of tensors plus its key.

      :param index: Sample position.

      :returns: A dict mapping each key to a tensor, plus a ``key`` metadata string
                (the sample directory name).