:py:mod:`anomalib.utils.loggers`
================================

.. py:module:: anomalib.utils.loggers

.. autoapi-nested-parse::

   Load PyTorch Lightning Loggers.



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

   base/index.rst
   tensorboard/index.rst
   wandb/index.rst


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

Classes
~~~~~~~

.. autoapisummary::

   anomalib.utils.loggers.AnomalibTensorBoardLogger
   anomalib.utils.loggers.AnomalibWandbLogger



Functions
~~~~~~~~~

.. autoapisummary::

   anomalib.utils.loggers.configure_logger
   anomalib.utils.loggers.get_experiment_logger



.. py:class:: AnomalibTensorBoardLogger(save_dir: str, name: Optional[str] = 'default', version: Optional[Union[int, str]] = None, log_graph: bool = False, default_hp_metric: bool = True, prefix: str = '', **kwargs)

   Bases: :py:obj:`anomalib.utils.loggers.base.ImageLoggerBase`, :py:obj:`pytorch_lightning.loggers.tensorboard.TensorBoardLogger`

   Logger for tensorboard.

   Adds interface for `add_image` in the logger rather than calling the experiment object.

   .. note:: Same as the Tensorboard Logger provided by PyTorch Lightning and the doc string is reproduced below.

   Logs are saved to
   ``os.path.join(save_dir, name, version)``. This is the default logger in Lightning, it comes
   preinstalled.

   .. rubric:: Example

   >>> from pytorch_lightning import Trainer
   >>> from anomalib.utils.loggers import AnomalibTensorBoardLogger
   >>> logger = AnomalibTensorBoardLogger("tb_logs", name="my_model")
   >>> trainer = Trainer(logger=logger)

   :param save_dir: Save directory
   :type save_dir: str
   :param name: Experiment name. Defaults to ``'default'``. If it is the empty string then no
                per-experiment subdirectory is used.
   :type name: Optional, str
   :param version: Experiment version. If version is not specified the logger inspects the save
                   directory for existing versions, then automatically assigns the next available version.
                   If it is a string then it is used as the run-specific subdirectory name,
                   otherwise ``'version_${version}'`` is used.
   :type version: Optional, int, str
   :param log_graph: Adds the computational graph to tensorboard. This requires that
                     the user has defined the `self.example_input_array` attribute in their
                     model.
   :type log_graph: bool
   :param default_hp_metric: Enables a placeholder metric with key `hp_metric` when `log_hyperparams` is
                             called without a metric (otherwise calls to log_hyperparams without a metric are ignored).
   :type default_hp_metric: bool
   :param prefix: A string to put at the beginning of metric keys.
   :type prefix: str
   :param \*\*kwargs: Additional arguments like `comment`, `filename_suffix`, etc. used by
                      :class:`SummaryWriter` can be passed as keyword arguments in this logger.

   .. py:method:: add_image(self, image: Union[numpy.ndarray, matplotlib.figure.Figure], name: Optional[str] = None, **kwargs: Any)

      Interface to add image to tensorboard logger.

      :param image: Image to log
      :type image: Union[np.ndarray, Figure]
      :param name: The tag of the image
      :type name: Optional[str]
      :param kwargs: Accepts only `global_step` (int). The step at which to log the image.



.. py:class:: AnomalibWandbLogger(name: Optional[str] = None, save_dir: Optional[str] = None, offline: Optional[bool] = False, id: Optional[str] = None, anonymous: Optional[bool] = None, version: Optional[str] = None, project: Optional[str] = None, log_model: Union[str, bool] = False, experiment=None, prefix: Optional[str] = '', **kwargs)

   Bases: :py:obj:`anomalib.utils.loggers.base.ImageLoggerBase`, :py:obj:`pytorch_lightning.loggers.wandb.WandbLogger`

   Logger for wandb.

   Adds interface for `add_image` in the logger rather than calling the experiment object.

   .. note:: Same as the wandb Logger provided by PyTorch Lightning and the doc string is reproduced below.

   Log using `Weights and Biases <https://www.wandb.com/>`_.

   Install it with pip:

   .. code-block:: bash

       $ pip install wandb

   :param name: Display name for the run.
   :param save_dir: Path where data is saved (wandb dir by default).
   :param offline: Run offline (data can be streamed later to wandb servers).
   :param id: Sets the version, mainly used to resume a previous run.
   :param version: Same as id.
   :param anonymous: Enables or explicitly disables anonymous logging.
   :param project: The name of the project to which this run will belong.
   :param log_model: Save checkpoints in wandb dir to upload on W&B servers.
   :param prefix: A string to put at the beginning of metric keys.
   :param experiment: WandB experiment object. Automatically set when creating a run.
   :param \*\*kwargs: Arguments passed to :func:`wandb.init` like `entity`, `group`, `tags`, etc.

   :raises ImportError: If required WandB package is not installed on the device.
   :raises MisconfigurationException: If both ``log_model`` and ``offline``is set to ``True``.

   .. rubric:: Example

   >>> from anomalib.utils.loggers import AnomalibWandbLogger
   >>> from pytorch_lightning import Trainer
   >>> wandb_logger = AnomalibWandbLogger()
   >>> trainer = Trainer(logger=wandb_logger)

   Note: When logging manually through `wandb.log` or `trainer.logger.experiment.log`,
   make sure to use `commit=False` so the logging step does not increase.

   .. seealso::

      - `Tutorial <https://colab.research.google.com/drive/16d1uctGaw2y9KhGBlINNTsWpmlXdJwRW?usp=sharing>`__
        on how to use W&B with PyTorch Lightning
      - `W&B Documentation <https://docs.wandb.ai/integrations/lightning>`__

   .. py:method:: add_image(self, image: Union[numpy.ndarray, matplotlib.figure.Figure], name: Optional[str] = None, **kwargs: Any)

      Interface to add image to wandb logger.

      :param image: Image to log
      :type image: Union[np.ndarray, Figure]
      :param name: The tag of the image
      :type name: Optional[str]


   .. py:method:: save(self) -> None

      Upload images to wandb server.

      .. note:: There is a limit on the number of images that can be logged together to the `wandb` server.



.. py:function:: configure_logger(level: Union[int, str] = logging.INFO)

   Get console logger by name.

   :param level: Logger Level. Defaults to logging.INFO.
   :type level: Union[int, str], optional

   :returns: The expected logger.
   :rtype: Logger


.. py:function:: get_experiment_logger(config: Union[omegaconf.dictconfig.DictConfig, omegaconf.listconfig.ListConfig]) -> Union[pytorch_lightning.loggers.LightningLoggerBase, Iterable[pytorch_lightning.loggers.LightningLoggerBase], bool]

   Return a logger based on the choice of logger in the config file.

   :param config: config.yaml file for the corresponding anomalib model.
   :type config: DictConfig

   :raises ValueError: for any logger types apart from false and tensorboard

   :returns: Logger
   :rtype: Union[LightningLoggerBase, Iterable[LightningLoggerBase], bool]