dacapo.experiments.tasks.post_processors.dummy_post_processor

Classes

DummyPostProcessor

Dummy post-processor that stores some dummy data. The dummy data is a 10x10x10

Module Contents

class dacapo.experiments.tasks.post_processors.dummy_post_processor.DummyPostProcessor(detection_threshold: float)

Dummy post-processor that stores some dummy data. The dummy data is a 10x10x10 array filled with the value of the min_size parameter. The min_size parameter is specified in the parameters of the post-processor. The post-processor has a detection threshold that is used to determine if an object is detected.

detection_threshold: The detection threshold.

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.

detection_threshold

enumerate_parameters() → Iterable[dacapo.experiments.tasks.post_processors.dummy_post_processor_parameters.DummyPostProcessorParameters]

Enumerate all possible parameters of this post-processor. Should return instances of PostProcessorParameters.

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

Examples

>>> post_processor = DummyPostProcessor()
>>> for parameters in post_processor.enumerate_parameters():
...     print(parameters)
DummyPostProcessorParameters(id=0, min_size=1)
DummyPostProcessorParameters(id=1, min_size=2)
DummyPostProcessorParameters(id=2, min_size=3)
DummyPostProcessorParameters(id=3, min_size=4)
DummyPostProcessorParameters(id=4, min_size=5)
DummyPostProcessorParameters(id=5, min_size=6)
DummyPostProcessorParameters(id=6, min_size=7)
DummyPostProcessorParameters(id=7, min_size=8)
DummyPostProcessorParameters(id=8, min_size=9)
DummyPostProcessorParameters(id=9, min_size=10)

Note

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

set_prediction(prediction_array_identifier)

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

Note

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

process(parameters, output_array_identifier, *args, **kwargs)

Convert predictions into the final output.

Parameters:

parameters – The parameters of the post-processor.
output_array_identifier – The identifier of the output array.
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 = DummyPostProcessor()
>>> post_processor.process(parameters, "output")

Note

This method must be implemented in the subclass. It should process the predictions and store the output in the output array.