Loggers#

Load PyTorch Lightning Loggers.

class anomalib.loggers.AnomalibCometLogger(api_key=None, save_dir=None, project_name=None, rest_api_key=None, experiment_name=None, experiment_key=None, offline=False, prefix='', **kwargs)#

Bases: ImageLoggerBase, CometLogger

Logger for comet.

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

Note

Same as the CometLogger provided by PyTorch Lightning and the doc string is reproduced below.

Track your parameters, metrics, source code and more using Comet.

Install it with pip:

pip install comet-ml

Comet requires either an API Key (online mode) or a local directory path (offline mode).

Parameters:

api_key (str | None) – Required in online mode. API key, found on Comet.ml. If not given, this will be loaded from the environment variable COMET_API_KEY or ~/.comet.config if either exists. Defaults to None.
save_dir (str | None) – Required in offline mode. The path for the directory to save local comet logs. If given, this also sets the directory for saving checkpoints. Defaults to None.
project_name (str | None) – Optional. Send your experiment to a specific project. Otherwise will be sent to Uncategorized Experiments. If the project name does not already exist, Comet.ml will create a new project. Defaults to None.
rest_api_key (str | None) – Optional. Rest API key found in Comet.ml settings. This is used to determine version number Defaults to None.
experiment_name (str | None) – Optional. String representing the name for this particular experiment on Comet.ml. Defaults to None.
experiment_key (str | None) – Optional. If set, restores from existing experiment. Defaults to None.
offline (bool) – If api_key and save_dir are both given, this determines whether the experiment will be in online or offline mode. This is useful if you use save_dir to control the checkpoints directory and have a ~/.comet.config file but still want to run offline experiments. Defaults to None.
prefix (str) – A string to put at the beginning of metric keys. Defaults to "".
kwargs – Additional arguments like workspace, log_code, etc. used by CometExperiment can be passed as keyword arguments in this logger.

Raises:

ModuleNotFoundError – If required Comet package is not installed on the device.
MisconfigurationException – If neither api_key nor save_dir are passed as arguments.

Example

>>> from anomalib.loggers import AnomalibCometLogger
>>> from anomalib.engine import Engine
...
>>> comet_logger = AnomalibCometLogger()
>>> engine =  Engine(logger=comet_logger)

See also

Comet Documentation

add_image(image, name=None, **kwargs)#

Interface to add image to comet logger.

Parameters:

image (np.ndarray | Figure) – Image to log.
name (str | None) – The tag of the image Defaults to None.
kwargs – Accepts only global_step (int). The step at which to log the image.

Return type:

None

class anomalib.loggers.AnomalibMLFlowLogger(experiment_name='anomalib_logs', run_name=None, tracking_uri=None, save_dir='./mlruns', log_model=False, prefix='', **kwargs)#

Bases: ImageLoggerBase, MLFlowLogger

Logger for MLFlow.

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

Note

Same as the MLFlowLogger provided by PyTorch Lightning and the doc string is reproduced below.

Track your parameters, metrics, source code and more using MLFlow.

Install it with pip:

pip install mlflow

Parameters:

experiment_name (str | None) – The name of the experiment.
run_name (str | None) – Name of the new run. The run_name is internally stored as a mlflow.runName tag. If the mlflow.runName tag has already been set in tags, the value is overridden by the run_name.
tracking_uri (str | None) – Address of local or remote tracking server. If not provided, defaults to MLFLOW_TRACKING_URI environment variable if set, otherwise it falls back to file:<save_dir>.
save_dir (str | None) – A path to a local directory where the MLflow runs get saved. Defaults to ./mlruns if tracking_uri is not provided. Has no effect if tracking_uri is provided.
log_model (Optional[Literal[True, False, 'all']]) –
Log checkpoints created by ModelCheckpoint as MLFlow artifacts.
- if log_model == 'all', checkpoints are logged during training.
- if log_model == True, checkpoints are logged at the end of training, except when save_top_k == -1 which also logs every checkpoint during training.
- if log_model == False (default), no checkpoint is logged.
prefix (str | None) – A string to put at the beginning of metric keys. Defaults to ''.
kwargs – Additional arguments like tags, artifact_location etc. used by MLFlowExperiment can be passed as keyword arguments in this logger.

Example

>>> from anomalib.loggers import AnomalibMLFlowLogger
>>> from anomalib.engine import Engine
...
>>> mlflow_logger = AnomalibMLFlowLogger()
>>> engine = Engine(logger=mlflow_logger)

See also

MLFlow Documentation.

add_image(image, name=None, **kwargs)#

Interface to log images in the mlflow loggers.

Parameters:

image (np.ndarray | Figure) – Image to log.
name (str | None) – The tag of the image defaults to None.
kwargs – Additional keyword arguments that are only used if image is of type Figure. These arguments are passed directly to the method that saves the figure. If image is a NumPy array, kwargs has no effect.

Return type:

None

class anomalib.loggers.AnomalibTensorBoardLogger(save_dir, name='default', version=None, log_graph=False, default_hp_metric=True, prefix='', **kwargs)#

Bases: ImageLoggerBase, 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.

Example

>>> from anomalib.engine import Engine
>>> from anomalib.loggers import AnomalibTensorBoardLogger
...
>>> logger = AnomalibTensorBoardLogger("tb_logs", name="my_model")
>>> engine =  Engine(logger=logger)

Parameters:

save_dir (str) – Save directory
name (str | None) – Experiment name. Defaults to 'default'. If it is the empty string then no per-experiment subdirectory is used. Default: 'default'.
version (int | str | None) – 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. Defaults to None
log_graph (bool) – Adds the computational graph to tensorboard. This requires that the user has defined the self.example_input_array attribute in their model. Defaults to False.
default_hp_metric (bool) – 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). Defaults to True.
prefix (str) – A string to put at the beginning of metric keys. Defaults to ''.
**kwargs – Additional arguments like comment, filename_suffix, etc. used by SummaryWriter can be passed as keyword arguments in this logger.

add_image(image, name=None, **kwargs)#

Interface to add image to tensorboard logger.

Parameters:

image (np.ndarray | Figure) – Image to log
name (str | None) – The tag of the image Defaults to None.
kwargs – Accepts only global_step (int). The step at which to log the image.

Return type:

None

class anomalib.loggers.AnomalibWandbLogger(name=None, save_dir='.', version=None, offline=False, dir=None, id=None, anonymous=None, project=None, log_model=False, experiment=None, prefix='', checkpoint_name=None, **kwargs)#

Bases: ImageLoggerBase, 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.

Install it with pip:

$ pip install wandb

Parameters:

name (str | None) – Display name for the run. Defaults to None.
save_dir (Union[str, Path]) – Path where data is saved (wandb dir by default). Defaults to None.
version (str | None) – Sets the version, mainly used to resume a previous run.
offline (bool) – Run offline (data can be streamed later to wandb servers). Defaults to False.
dir (Union[str, Path, None]) – Alias for save_dir.
id (str | None) – Sets the version, mainly used to resume a previous run. Defaults to None.
anonymous (bool | None) – Enables or explicitly disables anonymous logging. Defaults to None.
version – Same as id. Defaults to None.
project (str | None) – The name of the project to which this run will belong. Defaults to None.
log_model (Union[Literal['all'], bool]) – Save checkpoints in wandb dir to upload on W&B servers. Defaults to False.
experiment (Run | RunDisabled | None) – WandB experiment object. Automatically set when creating a run. Defaults to None.
prefix (str) – A string to put at the beginning of metric keys. Defaults to ''.
**kwargs – Arguments passed to wandb.init() like entity, group, tags, etc.

Raises:

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

Example

>>> from anomalib.loggers import AnomalibWandbLogger
>>> from anomalib.engine import Engine
...
>>> wandb_logger = AnomalibWandbLogger()
>>> engine =  Engine(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.

Loggers

Contents

Loggers#