sc2_datasets.lightning.datamodules.sc2_datamodule
=================================================

.. py:module:: sc2_datasets.lightning.datamodules.sc2_datamodule


Classes
-------

.. autoapisummary::

   sc2_datasets.lightning.datamodules.sc2_datamodule.SC2DataModuleSingleJSON
   sc2_datasets.lightning.datamodules.sc2_datamodule.SC2DataModule


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

.. py:class:: SC2DataModuleSingleJSON(dataset_name: str, unpack_dir: pathlib.Path, download: bool = True, download_dir: pathlib.Path | str | None = None, dataset_url: str = '', transform: Callable | None = None, validator: Callable | None = None)

   Bases: :py:obj:`pytorch_lightning.LightningDataModule`


   .. py:attribute:: dataset_name


   .. py:attribute:: unpack_dir


   .. py:attribute:: download
      :value: True



   .. py:attribute:: download_dir
      :value: None



   .. py:attribute:: dataset_url
      :value: ''



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



   .. py:attribute:: validator
      :value: None



   .. py:method:: prepare_data() -> None


   .. py:method:: setup(stage: str | None = None) -> None


   .. py:method:: train_dataloader() -> torch.utils.data.dataloader.DataLoader


   .. py:method:: val_dataloader() -> torch.utils.data.dataloader.DataLoader


   .. py:method:: test_dataloader() -> torch.utils.data.dataloader.DataLoader


   .. py:method:: teardown(stage)


.. py:class:: SC2DataModule(replaypacks: list[sc2_datasets.available_replaypacks.DatasetProperties], download_dir: pathlib.Path | str = Path('./data/download').resolve(), unpack_dir: pathlib.Path | str = Path('./data/unpack').resolve(), download: bool = True, transform: Callable = None, batch_size: int = 256, num_workers: int = 0, unpack_n_workers: int = 16, validator: Callable | None = None)

   Bases: :py:obj:`pytorch_lightning.LightningDataModule`


   Defines a LightningDataModule abstraction for some StarCraft II DataModule.

   :param replaypacks: Specifies a list of properties of replaypacks that will be used for downloading.
   :type replaypacks: list[DatasetProperties]
   :param download_dir: Specifies the path where the dataset will be downloaded,        by default "./data/download"
   :type download_dir: Path | str, optional
   :param unpack_dir: Specifies the path where the dataset will be unpacked        into a custom directory structure, by default "./data/unpack"
   :type unpack_dir: Path | str, optional
   :param download: If the underlying dataset should be downloaded, by default True
   :type download: bool, optional
   :param transform: Specifies the PyTorch transforms to be used        on the replaypack (dataset),
                     Deprecated since version v1.5: Will be removed in v1.7.0, by default None
   :type transform: Callable, optional
   :param batch_size: The size of collating individual        fetched data samples, by default 256
   :type batch_size: int, optional
   :param num_workers: How many sub-processes        to use for data loading, by default 0
   :type num_workers: int, optional
   :param unpack_n_workers: The number of workers        that will be used for unpacking the archive, by default 16
   :type unpack_n_workers: int, optional
   :param validator: Specifies the validation option for fetched data,        this can also act as a filtering function that will be        applied for the entirety of the dataset, by default None
   :type validator: Callable | None, optional


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



   .. py:attribute:: batch_size
      :value: 256



   .. py:attribute:: num_workers
      :value: 0



   .. py:attribute:: download_dir


   .. py:attribute:: unpack_dir


   .. py:attribute:: download
      :value: True



   .. py:attribute:: unpack_n_workers
      :value: 16



   .. py:attribute:: validator
      :value: None



   .. py:attribute:: replaypacks


   .. py:method:: prepare_data() -> None


   .. py:method:: setup(stage: str | None = None) -> None


   .. py:method:: train_dataloader() -> torch.utils.data.dataloader.DataLoader


   .. py:method:: val_dataloader() -> torch.utils.data.dataloader.DataLoader


   .. py:method:: test_dataloader() -> torch.utils.data.dataloader.DataLoader


   .. py:method:: teardown(stage: str | None = None) -> None