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N2V Algorithm Config

Source

N2V Algorithm configuration.

N2VAlgorithm

Bases: UNetBasedAlgorithm

N2V Algorithm configuration.

algorithm = 'n2v' class-attribute instance-attribute

N2V Algorithm name.

loss = 'n2v' class-attribute instance-attribute

N2V loss function.

lr_scheduler = LrSchedulerConfig() class-attribute instance-attribute

Learning rate scheduler to use, defined in SupportedLrScheduler.

model instance-attribute

Model parameters.

monitor_metric = 'val_loss' class-attribute instance-attribute

Metric to monitor for the learning rate scheduler. Used in the returned dict of PyTorch Lightning configure_optimizers method.

n2v_config = N2VManipulateConfig() class-attribute instance-attribute

Noise2Void pixel manipulation configuration.

optimizer = OptimizerConfig() class-attribute instance-attribute

Optimizer to use, defined in SupportedOptimizer.

__str__()

Pretty string representing the configuration.

Returns:

  • str –

    Pretty string.

get_algorithm_citations()

Return a list of citation entries of the current algorithm.

This is used to generate the model description for the BioImage Model Zoo.

Returns:

  • List[CiteEntry] –

    List of citation entries.

get_algorithm_description()

Return a description of the algorithm.

This method is used to generate the README of the BioImage Model Zoo export.

Returns:

  • str –

    Description of the algorithm.

get_algorithm_friendly_name()

Get the friendly name of the algorithm.

Returns:

  • str –

    Friendly name.

get_algorithm_keywords()

Get algorithm keywords.

Returns:

get_algorithm_references()

Get the algorithm references.

This is used to generate the README of the BioImage Model Zoo export.

Returns:

  • str –

    Algorithm references.

get_num_input_channels()

Get the number of input channels.

Returns:

  • int –

    Number of input channels.

is_struct_n2v()

Check if the configuration is using structN2V.

Returns:

  • bool –

    Whether the configuration is using structN2V.

is_supervised() classmethod

Return whether the algorithm is supervised.

Returns:

  • bool –

    Whether the algorithm is supervised.

set_n2v2(use_n2v2)

Set the configuration to use N2V2 or the vanilla Noise2Void.

This method ensures that N2V2 is set correctly and remain coherent, as opposed to setting the different parameters individually.

Parameters:

  • use_n2v2 (bool) –

    Whether to use N2V2.

uses_batch_norm()

Return whether the model uses batch normalization.

Returns:

  • bool –

    Whether the model uses batch normalization.

validate_n2v2()

Validate that the N2V2 strategy and models are set correctly.

Returns:

  • Self –

    The validateed configuration.

Raises:

  • ValueError –

    If N2V2 is used with the wrong pixel manipulation strategy.