dacapo.experiments.tasks.post_processors.post_processor

Classes

PostProcessor

Base class of all post-processors.

Module Contents

class dacapo.experiments.tasks.post_processors.post_processor.PostProcessor

Base class of all post-processors.

A post-processor takes a model’s prediction and converts it into the final output (e.g., per-voxel class probabilities into a semantic segmentation). A post-processor can have multiple parameters, which can be enumerated using the enumerate_parameters method. The process method takes a set of parameters and applies the post-processing to the prediction.

prediction_array_identifier: The identifier of the array containing the model’s prediction.

enumerate_parameters(): Enumerate all possible parameters of this post-processor.

set_prediction(): Set the prediction array identifier.

process(): Convert predictions into the final output.

Note

This class is abstract. Subclasses must implement the abstract methods. Once created, the values of its attributes cannot be changed.

abstract enumerate_parameters() → Iterable[dacapo.experiments.tasks.post_processors.post_processor_parameters.PostProcessorParameters]

Enumerate all possible parameters of this post-processor.

Returns:: An iterable of PostProcessorParameters instances.
Raises:: NotImplementedError – If the method is not implemented in the subclass.

Examples

>>> post_processor = MyPostProcessor()
>>> for parameters in post_processor.enumerate_parameters():
...     print(parameters)
MyPostProcessorParameters(param1=0.0, param2=0.0)
MyPostProcessorParameters(param1=0.0, param2=1.0)
MyPostProcessorParameters(param1=1.0, param2=0.0)
MyPostProcessorParameters(param1=1.0, param2=1.0)

Note

This method must be implemented in the subclass. It should return an iterable of PostProcessorParameters instances.

abstract set_prediction(prediction_array_identifier: dacapo.store.local_array_store.LocalArrayIdentifier) → None

Set the prediction array identifier.

Parameters:: prediction_array_identifier – The identifier of the array containing the model’s prediction.
Raises:: NotImplementedError – If the method is not implemented in the subclass.

Examples

>>> post_processor = MyPostProcessor()
>>> post_processor.set_prediction("prediction")

Note

This method must be implemented in the subclass. It should set the prediction_array_identifier attribute.

abstract process(parameters: dacapo.experiments.tasks.post_processors.post_processor_parameters.PostProcessorParameters, output_array_identifier: dacapo.store.local_array_store.LocalArrayIdentifier, num_workers: int = 16, chunk_size: funlib.geometry.Coordinate = Coordinate((64, 64, 64))) → dacapo.experiments.datasplits.datasets.arrays.Array

Convert predictions into the final output.

Parameters:

parameters – The parameters of the post-processor.
output_array_identifier – The identifier of the array to store the output.
num_workers – The number of workers to use.
chunk_size – The size of the chunks to process.

Returns:

The output array.

Raises:

NotImplementedError – If the method is not implemented in the subclass.

Examples

>>> post_processor = MyPostProcessor()
>>> post_processor.set_prediction("prediction")
>>> parameters = MyPostProcessorParameters(param1=0.0, param2=0.0)
>>> output = post_processor.process(parameters, "output")

Note

This method must be implemented in the subclass. It should convert the model’s prediction into the final output.