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Algorithms

Source

Algorithm configurations.

CAREAlgorithm

Bases: UNetBasedAlgorithm

CARE algorithm configuration.

Attributes:

  • algorithm (care) –

    CARE Algorithm name.

  • loss ({mae, mse}) –

    CARE-compatible loss function.

algorithm = 'care' class-attribute instance-attribute

CARE Algorithm name.

loss = 'mae' class-attribute instance-attribute

CARE-compatible loss function.

lr_scheduler = LrSchedulerConfig() class-attribute instance-attribute

Learning rate scheduler to use, defined in SupportedLrScheduler.

model instance-attribute

UNet without a final activation function, without the n2v2 modifications, and without independent channels for mismatching input/output channel numbers.

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()

Get the algorithm description.

Returns:

  • str –

    Algorithm description.

get_algorithm_friendly_name()

Get the algorithm friendly name.

Returns:

  • str –

    Friendly name of the algorithm.

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_supervised() classmethod

Return whether the algorithm is supervised.

Returns:

  • bool –

    Whether the algorithm is supervised.

uses_batch_norm()

Return whether the model uses batch normalization.

Returns:

  • bool –

    Whether the model uses batch normalization.

HDNAlgorithm

Bases: VAEBasedAlgorithm

HDN algorithm configuration.

optimizer = OptimizerConfig() class-attribute instance-attribute

Optimizer to use, defined in SupportedOptimizer.

__str__()

Pretty string representing the configuration.

Returns:

  • str –

    Pretty string.

algorithm_cross_validation()

Validate the algorithm model based on algorithm.

Returns:

  • Self –

    The validated model.

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()

Get the algorithm description.

Returns:

  • str –

    Algorithm description.

get_algorithm_friendly_name()

Get the algorithm friendly name.

Returns:

  • str –

    Friendly name of the algorithm.

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_compatible_algorithms() classmethod

Get the list of compatible algorithms.

Returns:

  • list of str –

    List of compatible algorithms.

output_channels_validation()

Validate the consistency between number of out channels and noise models.

Returns:

  • Self –

    The validated model.

predict_logvar_validation()

Validate the consistency of predict_logvar throughout the model.

Returns:

  • Self –

    The validated model.

MicroSplitAlgorithm

Bases: VAEBasedAlgorithm

MicroSplit algorithm configuration.

optimizer = OptimizerConfig() class-attribute instance-attribute

Optimizer to use, defined in SupportedOptimizer.

__str__()

Pretty string representing the configuration.

Returns:

  • str –

    Pretty string.

algorithm_cross_validation()

Validate the algorithm model based on algorithm.

Returns:

  • Self –

    The validated model.

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()

Get the algorithm description.

Returns:

  • str –

    Algorithm description.

get_algorithm_friendly_name()

Get the algorithm friendly name.

Returns:

  • str –

    Friendly name of the algorithm.

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_compatible_algorithms() classmethod

Get the list of compatible algorithms.

Returns:

  • list of str –

    List of compatible algorithms.

output_channels_validation()

Validate the consistency between number of out channels and noise models.

Returns:

  • Self –

    The validated model.

predict_logvar_validation()

Validate the consistency of predict_logvar throughout the model.

Returns:

  • Self –

    The validated model.

N2NAlgorithm

Bases: UNetBasedAlgorithm

Noise2Noise Algorithm configuration.

algorithm = 'n2n' class-attribute instance-attribute

N2N Algorithm name.

loss = 'mae' class-attribute instance-attribute

N2N-compatible loss function.

lr_scheduler = LrSchedulerConfig() class-attribute instance-attribute

Learning rate scheduler to use, defined in SupportedLrScheduler.

model instance-attribute

UNet without a final activation function, without the n2v2 modifications, and without independent channels for mismatching input/output channel numbers.

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()

Get the algorithm description.

Returns:

  • str –

    Algorithm description.

get_algorithm_friendly_name()

Get the algorithm friendly name.

Returns:

  • str –

    Friendly name of the algorithm.

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_supervised() classmethod

Return whether the algorithm is supervised.

Returns:

  • bool –

    Whether the algorithm is supervised.

uses_batch_norm()

Return whether the model uses batch normalization.

Returns:

  • bool –

    Whether the model uses batch normalization.

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.

PN2VAlgorithm

Bases: UNetBasedAlgorithm

PN2V Algorithm configuration.

algorithm = 'pn2v' class-attribute instance-attribute

PN2V Algorithm name.

loss = 'pn2v' class-attribute instance-attribute

PN2V loss function (uses N2V loss with noise model).

lr_scheduler = LrSchedulerConfig() class-attribute instance-attribute

Learning rate scheduler to use, defined in SupportedLrScheduler.

noise_model instance-attribute

Noise model configuration for probabilistic denoising.

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 structPN2V.

Returns:

  • bool –

    Whether the configuration is using structPN2V.

set_n2v2(use_n2v2)

Set the configuration to use PN2V2 or the vanilla Probabilistic Noise2Void.

This method ensures that PN2V2 is set correctly and remain coherent, as opposed to setting the different parameters individually.

Parameters:

  • use_n2v2 (bool) –

    Whether to use PN2V2.

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 validated configuration.

Raises:

  • ValueError –

    If N2V2 is used with the wrong pixel manipulation strategy.

UNetBasedAlgorithm

Bases: BaseModel

General UNet-based algorithm configuration.

This Pydantic model validates the parameters governing the components of the training algorithm: which algorithm, loss function, model architecture, optimizer, and learning rate scheduler to use.

Currently, we only support N2V, CARE, N2N, and PN2V algorithms. In order to train these algorithms, use the corresponding configuration child classes (e.g. N2VAlgorithm) to ensure coherent parameters (e.g. specific losses).

Attributes:

Raises:

  • ValueError –

    Algorithm parameter type validation errors.

  • ValueError –

    If the algorithm, loss and model are not compatible.

algorithm instance-attribute

Algorithm name, as defined in SupportedAlgorithm.

loss instance-attribute

Loss function to use, as defined in SupportedLoss.

lr_scheduler = LrSchedulerConfig() class-attribute instance-attribute

Learning rate scheduler to use, defined in SupportedLrScheduler.

model instance-attribute

UNet model configuration.

optimizer = OptimizerConfig() class-attribute instance-attribute

Optimizer to use, defined in SupportedOptimizer.

__str__()

Pretty string representing the configuration.

Returns:

  • str –

    Pretty string.

get_num_input_channels()

Get the number of input channels.

Returns:

  • int –

    Number of input channels.

uses_batch_norm()

Return whether the model uses batch normalization.

Returns:

  • bool –

    Whether the model uses batch normalization.

VAEBasedAlgorithm

Bases: BaseModel

VAE-based algorithm configuration.

optimizer = OptimizerConfig() class-attribute instance-attribute

Optimizer to use, defined in SupportedOptimizer.

__str__()

Pretty string representing the configuration.

Returns:

  • str –

    Pretty string.

algorithm_cross_validation()

Validate the algorithm model based on algorithm.

Returns:

  • Self –

    The validated model.

get_compatible_algorithms() classmethod

Get the list of compatible algorithms.

Returns:

  • list of str –

    List of compatible algorithms.

output_channels_validation()

Validate the consistency between number of out channels and noise models.

Returns:

  • Self –

    The validated model.

predict_logvar_validation()

Validate the consistency of predict_logvar throughout the model.

Returns:

  • Self –

    The validated model.