:py:mod:`anomalib.data`
=======================

.. py:module:: anomalib.data

.. autoapi-nested-parse::

   Anomalib Datasets.



Subpackages
-----------
.. toctree::
   :titlesonly:
   :maxdepth: 3

   utils/index.rst


Submodules
----------
.. toctree::
   :titlesonly:
   :maxdepth: 1

   btech/index.rst
   folder/index.rst
   inference/index.rst
   mvtec/index.rst


Package Contents
----------------

Classes
~~~~~~~

.. autoapisummary::

   anomalib.data.BTech
   anomalib.data.Folder
   anomalib.data.InferenceDataset
   anomalib.data.MVTec



Functions
~~~~~~~~~

.. autoapisummary::

   anomalib.data.get_datamodule



.. py:class:: BTech(root: str, category: str, image_size: Optional[Union[int, Tuple[int, int]]] = None, train_batch_size: int = 32, test_batch_size: int = 32, num_workers: int = 8, task: str = 'segmentation', transform_config_train: Optional[Union[str, albumentations.Compose]] = None, transform_config_val: Optional[Union[str, albumentations.Compose]] = None, seed: Optional[int] = None, create_validation_set: bool = False)

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

   BTechDataModule Lightning Data Module.

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

      Download the dataset if not available.


   .. py:method:: setup(stage: Optional[str] = None) -> None

      Setup train, validation and test data.

      BTech dataset uses BTech dataset structure, which is the reason for
      using `anomalib.data.btech.BTech` class to get the dataset items.

      :param stage: Optional[str]:  Train/Val/Test stages. (Default value = None)


   .. py:method:: train_dataloader() -> pytorch_lightning.utilities.types.TRAIN_DATALOADERS

      Get train dataloader.


   .. py:method:: val_dataloader() -> pytorch_lightning.utilities.types.EVAL_DATALOADERS

      Get validation dataloader.


   .. py:method:: test_dataloader() -> pytorch_lightning.utilities.types.EVAL_DATALOADERS

      Get test dataloader.


   .. py:method:: predict_dataloader() -> pytorch_lightning.utilities.types.EVAL_DATALOADERS

      Get predict dataloader.



.. py:class:: Folder(root: Union[str, pathlib.Path], normal_dir: str = 'normal', abnormal_dir: str = 'abnormal', task: str = 'classification', normal_test_dir: Optional[Union[pathlib.Path, str]] = None, mask_dir: Optional[Union[pathlib.Path, str]] = None, extensions: Optional[Tuple[str, Ellipsis]] = None, split_ratio: float = 0.2, seed: Optional[int] = None, image_size: Optional[Union[int, Tuple[int, int]]] = None, train_batch_size: int = 32, test_batch_size: int = 32, num_workers: int = 8, transform_config_train: Optional[Union[str, albumentations.Compose]] = None, transform_config_val: Optional[Union[str, albumentations.Compose]] = None, create_validation_set: bool = False)

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

   Folder Lightning Data Module.

   .. py:method:: setup(stage: Optional[str] = None) -> None

      Setup train, validation and test data.

      :param stage: Optional[str]:  Train/Val/Test stages. (Default value = None)


   .. py:method:: train_dataloader() -> pytorch_lightning.utilities.types.TRAIN_DATALOADERS

      Get train dataloader.


   .. py:method:: val_dataloader() -> pytorch_lightning.utilities.types.EVAL_DATALOADERS

      Get validation dataloader.


   .. py:method:: test_dataloader() -> pytorch_lightning.utilities.types.EVAL_DATALOADERS

      Get test dataloader.


   .. py:method:: predict_dataloader() -> pytorch_lightning.utilities.types.EVAL_DATALOADERS

      Get predict dataloader.



.. py:class:: InferenceDataset(path: Union[str, pathlib.Path], pre_process: Optional[anomalib.pre_processing.PreProcessor] = None, image_size: Optional[Union[int, Tuple[int, int]]] = None, transform_config: Optional[Union[str, albumentations.Compose]] = None)

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

   Inference Dataset to perform prediction.

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

      Get the number of images in the given path.


   .. py:method:: __getitem__(index: int) -> Any

      Get the image based on the `index`.



.. py:class:: MVTec(root: str, category: str, image_size: Optional[Union[int, Tuple[int, int]]] = None, train_batch_size: int = 32, test_batch_size: int = 32, num_workers: int = 8, task: str = 'segmentation', transform_config_train: Optional[Union[str, albumentations.Compose]] = None, transform_config_val: Optional[Union[str, albumentations.Compose]] = None, seed: Optional[int] = None, create_validation_set: bool = False)

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

   MVTec AD Lightning Data Module.

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

      Download the dataset if not available.


   .. py:method:: setup(stage: Optional[str] = None) -> None

      Setup train, validation and test data.

      :param stage: Optional[str]:  Train/Val/Test stages. (Default value = None)


   .. py:method:: train_dataloader() -> pytorch_lightning.utilities.types.TRAIN_DATALOADERS

      Get train dataloader.


   .. py:method:: val_dataloader() -> pytorch_lightning.utilities.types.EVAL_DATALOADERS

      Get validation dataloader.


   .. py:method:: test_dataloader() -> pytorch_lightning.utilities.types.EVAL_DATALOADERS

      Get test dataloader.


   .. py:method:: predict_dataloader() -> pytorch_lightning.utilities.types.EVAL_DATALOADERS

      Get predict dataloader.



.. py:function:: get_datamodule(config: Union[omegaconf.DictConfig, omegaconf.ListConfig]) -> pytorch_lightning.LightningDataModule

   Get Anomaly Datamodule.

   :param config: Configuration of the anomaly model.
   :type config: Union[DictConfig, ListConfig]

   :returns: PyTorch Lightning DataModule