ncalab.data.growingncadataset
=============================

.. py:module:: ncalab.data.growingncadataset


Classes
-------

.. autoapisummary::

   ncalab.data.growingncadataset.GrowingNCADataset


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

.. py:class:: GrowingNCADataset(image: numpy.ndarray, num_channels: int, batch_size: int = 8)

   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.

   Dedicated dataset for "growing" tasks, like growing emoji.

   The idea is to train a model solely for the purpose to generate ("grow")
   a fixed image. Hence, this Dataset class only stores multiple copies of the
   same image.

   :param image [np.ndarray]: Input image.
   :param num_channels [int]: Total number of image channels (including hidden)
   :param batch_size [int]: Output batch size. Defaults to 8.


   .. py:attribute:: batch_size
      :value: 8



   .. py:attribute:: image


   .. py:attribute:: seed


   .. py:method:: __len__()


   .. py:method:: __getitem__(idx)