`dacapo.apply`

Module Contents

`apply`(run_name, input_container, input_dataset, ...[, ...])	Load weights and apply a model to a dataset. If iteration is None, the best iteration based on the criterion is used. If roi is None, the whole input dataset is used.
`apply_run`(run, iteration, parameters, ...[, roi, ...])	Apply the model to a dataset. If roi is None, the whole input dataset is used. Assumes model is already loaded.

dacapo.apply.apply(run_name: str, input_container: pathlib.Path | str, input_dataset: str, output_path: pathlib.Path | str, validation_dataset: dacapo.experiments.datasplits.datasets.dataset.Dataset | str | None = None, criterion: str = 'voi', iteration: int | None = None, parameters: dacapo.experiments.tasks.post_processors.post_processor_parameters.PostProcessorParameters | str | None = None, roi: funlib.geometry.Roi | str | None = None, num_workers: int = 12, output_dtype: numpy.dtype | str = np.uint8, overwrite: bool = True, file_format: str = 'zarr'): Load weights and apply a model to a dataset. If iteration is None, the best iteration based on the criterion is used. If roi is None, the whole input dataset is used.

dacapo.apply.apply_run(run: dacapo.experiments.run.Run, iteration: int, parameters: dacapo.experiments.tasks.post_processors.post_processor_parameters.PostProcessorParameters, input_array_identifier: dacapo.store.array_store.LocalArrayIdentifier, prediction_array_identifier: dacapo.store.array_store.LocalArrayIdentifier, output_array_identifier: dacapo.store.array_store.LocalArrayIdentifier, roi: funlib.geometry.Roi | None = None, num_workers: int = 12, output_dtype: numpy.dtype | str = np.uint8, overwrite: bool = True): Apply the model to a dataset. If roi is None, the whole input dataset is used. Assumes model is already loaded.