dacapo
Subpackages
dacapo.blockwisedacapo.compute_contextdacapo.experimentsdacapo.experiments.architecturesdacapo.experiments.architectures.architecturedacapo.experiments.architectures.architecture_configdacapo.experiments.architectures.cnnectome_unetdacapo.experiments.architectures.cnnectome_unet_configdacapo.experiments.architectures.dummy_architecturedacapo.experiments.architectures.dummy_architecture_config
dacapo.experiments.arraytypesdacapo.experiments.arraytypes.annotationsdacapo.experiments.arraytypes.arraytypedacapo.experiments.arraytypes.binarydacapo.experiments.arraytypes.distancesdacapo.experiments.arraytypes.embeddingdacapo.experiments.arraytypes.intensitiesdacapo.experiments.arraytypes.maskdacapo.experiments.arraytypes.probabilities
dacapo.experiments.datasplitsdacapo.experiments.datasplits.datasetsdacapo.experiments.datasplits.keysdacapo.experiments.datasplits.datasplitdacapo.experiments.datasplits.datasplit_configdacapo.experiments.datasplits.datasplit_generatordacapo.experiments.datasplits.dummy_datasplitdacapo.experiments.datasplits.dummy_datasplit_configdacapo.experiments.datasplits.train_validate_datasplitdacapo.experiments.datasplits.train_validate_datasplit_config
dacapo.experiments.startsdacapo.experiments.tasksdacapo.experiments.tasks.evaluatorsdacapo.experiments.tasks.lossesdacapo.experiments.tasks.post_processorsdacapo.experiments.tasks.predictorsdacapo.experiments.tasks.affinities_taskdacapo.experiments.tasks.affinities_task_configdacapo.experiments.tasks.distance_taskdacapo.experiments.tasks.distance_task_configdacapo.experiments.tasks.dummy_taskdacapo.experiments.tasks.dummy_task_configdacapo.experiments.tasks.hot_distance_taskdacapo.experiments.tasks.hot_distance_task_configdacapo.experiments.tasks.inner_distance_taskdacapo.experiments.tasks.inner_distance_task_configdacapo.experiments.tasks.one_hot_taskdacapo.experiments.tasks.one_hot_task_configdacapo.experiments.tasks.pretrained_taskdacapo.experiments.tasks.pretrained_task_configdacapo.experiments.tasks.taskdacapo.experiments.tasks.task_config
dacapo.experiments.trainersdacapo.experiments.trainers.gp_augmentsdacapo.experiments.trainers.optimizersdacapo.experiments.trainers.dummy_trainerdacapo.experiments.trainers.dummy_trainer_configdacapo.experiments.trainers.gunpowder_trainerdacapo.experiments.trainers.gunpowder_trainer_configdacapo.experiments.trainers.trainerdacapo.experiments.trainers.trainer_config
dacapo.experiments.modeldacapo.experiments.rundacapo.experiments.run_configdacapo.experiments.training_iteration_statsdacapo.experiments.training_statsdacapo.experiments.validation_iteration_scoresdacapo.experiments.validation_scores
dacapo.extdacapo.gpdacapo.storedacapo.store.array_storedacapo.store.config_storedacapo.store.conversion_hooksdacapo.store.converterdacapo.store.create_storedacapo.store.file_config_storedacapo.store.file_stats_storedacapo.store.local_array_storedacapo.store.local_weights_storedacapo.store.mongo_config_storedacapo.store.mongo_stats_storedacapo.store.stats_storedacapo.store.weights_store
dacapo.utils
Submodules
Package Contents
Classes
Functions
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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. |
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Validate a run at a given iteration. Loads the weights from a previously |
- class dacapo.Options
- classmethod instance(**kwargs) DaCapoConfig
- dacapo.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.train(run_name: str)
Train a run
- dacapo.validate(run_name: str, iteration: int, num_workers: int = 1, output_dtype: str = 'uint8', overwrite: bool = True)
Validate a run at a given iteration. Loads the weights from a previously stored checkpoint. Returns the best parameters and scores for this iteration.