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

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

.. autoapi-nested-parse::

   wandb logger with add image interface.



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

Classes
~~~~~~~

.. autoapisummary::

   anomalib.utils.loggers.wandb.AnomalibWandbLogger




.. 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.