CLI

CLI#

Anomalib CLI.

class anomalib.cli.AnomalibCLI(save_config_callback=<class 'lightning.pytorch.cli.SaveConfigCallback'>, save_config_kwargs=None, trainer_class=<class 'lightning.pytorch.trainer.trainer.Trainer'>, trainer_defaults=None, seed_everything_default=True, parser_kwargs=None, args=None, run=True, auto_configure_optimizers=True)#

Bases: LightningCLI

Implementation of a fully configurable CLI tool for anomalib.

The advantage of this tool is its flexibility to configure the pipeline from both the CLI and a configuration file (.yaml or .json). It is even possible to use both the CLI and a configuration file simultaneously. For more details, the reader could refer to PyTorch Lightning CLI documentation.

save_config_kwargs is set to overwrite=True so that the SaveConfigCallback overwrites the config if it already exists.

add_arguments_to_parser(parser)#: Extend trainer’s arguments to add engine arguments. :rtype: None

Note

Since Engine parameters are manually added, any change to the Engine class should be reflected manually.

add_benchmark_arguments(parser)#

Add benchmark arguments to the parser.

Return type:: None

Example

$ anomalib benchmark –benchmark_config tools/benchmarking/benchmark_params.yaml

add_export_arguments(parser)#

Add export arguments to the parser.

Return type:: None

add_hpo_arguments(parser)#

Add hyperparameter optimization arguments.

Return type:: None

add_predict_arguments(parser)#

Add predict arguments to the parser.

Return type:: None

add_train_arguments(parser)#

Add train arguments to the parser.

Return type:: None

static anomalib_subcommands()#

Return a dictionary of subcommands and their description.

Return type:: dict[str, dict[str, str]]

before_instantiate_classes()#

Modify the configuration to properly instantiate classes and sets up tiler.

Return type:: None

benchmark()#

Run benchmark subcommand.

Return type:: None

property export: Callable[[...], Path | None]#: Export the model using engine’s export method.

property fit: Callable[[...], None]#: Fit the model using engine’s fit method.

hpo()#

Run hpo subcommand.

Return type:: None

init_parser(**kwargs)#

Method that instantiates the argument parser.

Return type:: LightningArgumentParser

instantiate_classes()#

Instantiate classes depending on the subcommand.

For trainer related commands it instantiates all the model, datamodule and trainer classes. But for subcommands we do not want to instantiate any trainer specific classes such as datamodule, model, etc This is because the subcommand is responsible for instantiating and executing code based on the passed config

Return type:: None

instantiate_engine()#: Instantiate the engine. :rtype: Engine

Note

Most of the code in this method is taken from LightningCLI’s instantiate_trainer method. Refer to that method for more details.

property predict: Callable[[...], List[Any] | List[List[Any]] | None]#: Predict using engine’s predict method.

static subcommands()#

Skip predict subcommand as it is added later.

Return type:: dict[str, set[str]]

property test: Callable[[...], List[Mapping[str, float]]]#: Test the model using engine’s test method.

property train: Callable[[...], List[Mapping[str, float]]]#: Train the model using engine’s train method.

property validate: Callable[[...], List[Mapping[str, float]] | None]#: Validate the model using engine’s validate method.

CLI

Contents

CLI#