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Architectures

Source

Deep-learning model configurations.

ArchitectureConfig

Bases: BaseModel

Base Pydantic model for all model architectures.

The model_dump method allows removing the architecture key from the model.

architecture instance-attribute

Name of the architecture.

model_dump(**kwargs)

Dump the model as a dictionary, ignoring the architecture keyword.

Parameters:

  • **kwargs (Any, default: {} ) –

    Additional keyword arguments from Pydantic BaseModel model_dump method.

Returns:

  • {str: Any} –

    Model as a dictionary.

LVAEConfig

Bases: ArchitectureConfig

LVAE model.

decoder_conv_strides = Field(default=[2, 2], validate_default=True) class-attribute instance-attribute

Dimensions (2D or 3D) of the convolutional layers.

input_shape = Field(default=(64, 64), validate_default=True) class-attribute instance-attribute

Shape of the input patch (Z, Y, X) or (Y, X) if the data is 2D.

is_3D()

Return whether the model is 3D or not.

Returns:

  • bool –

    Whether the model is 3D or not.

model_dump(**kwargs)

Dump the model as a dictionary, ignoring the architecture keyword.

Parameters:

  • **kwargs (Any, default: {} ) –

    Additional keyword arguments from Pydantic BaseModel model_dump method.

Returns:

  • {str: Any} –

    Model as a dictionary.

set_3D(is_3D)

Set 3D model by setting the conv_dims parameters.

Parameters:

  • is_3D (bool) –

    Whether the algorithm is 3D or not.

validate_conv_strides()

Validate the convolutional strides.

Returns:

  • list –

    Validated strides.

Raises:

  • ValueError –

    If the number of strides is not 2.

validate_decoder_even(decoder_n_filters) classmethod

Validate that num_channels_init is even.

Parameters:

  • decoder_n_filters (int) –

    Number of channels.

Returns:

  • int –

    Validated number of channels.

Raises:

  • ValueError –

    If the number of channels is odd.

validate_encoder_even(encoder_n_filters) classmethod

Validate that num_channels_init is even.

Parameters:

  • encoder_n_filters (int) –

    Number of channels.

Returns:

  • int –

    Validated number of channels.

Raises:

  • ValueError –

    If the number of channels is odd.

validate_input_shape(input_shape) classmethod

Validate the input shape.

Parameters:

  • input_shape (list) –

    Shape of the input patch.

Returns:

  • list –

    Validated input shape.

Raises:

  • ValueError –

    If the number of dimensions is not 3 or 4.

validate_multiscale_count()

Validate the multiscale count.

Returns:

  • Self –

    The validated model.

validate_z_dims(z_dims)

Validate the z_dims.

Parameters:

  • z_dims (tuple) –

    Tuple of z dimensions.

Returns:

  • tuple –

    Validated z dimensions.

Raises:

  • ValueError –

    If the number of z dimensions is not 4.

UNetConfig

Bases: ArchitectureConfig

Pydantic model for a N2V(2)-compatible UNet.

Attributes:

  • depth (int) –

    Depth of the model, between 1 and 10 (default 2).

  • num_channels_init (int) –

    Number of filters of the first level of the network, should be even and minimum 8 (default 96).

architecture instance-attribute

Name of the architecture.

conv_dims = Field(default=2, validate_default=True) class-attribute instance-attribute

Dimensions (2D or 3D) of the convolutional layers.

depth = Field(default=2, ge=1, le=10, validate_default=True) class-attribute instance-attribute

Number of levels in the UNet.

final_activation = Field(default='None', validate_default=True) class-attribute instance-attribute

Final activation function.

in_channels = Field(default=1, ge=1, validate_default=True) class-attribute instance-attribute

Number of channels in the input to the model.

independent_channels = Field(default=True, validate_default=True) class-attribute instance-attribute

Whether information is processed independently in each channel, used to train channels independently.

n2v2 = Field(default=False, validate_default=True) class-attribute instance-attribute

Whether to use N2V2 architecture modifications, with blur pool layers and fewer skip connections.

num_channels_init = Field(default=32, ge=8, le=1024, validate_default=True) class-attribute instance-attribute

Number of convolutional filters in the first layer of the UNet.

num_classes = Field(default=1, ge=1, validate_default=True) class-attribute instance-attribute

Number of classes or channels in the model output.

residual = Field(default=False, validate_default=True) class-attribute instance-attribute

Whether to add a residual connection from the input to the output.

use_batch_norm = Field(default=True, validate_default=True) class-attribute instance-attribute

Whether to use batch normalization in the model.

get_num_input_channels()

Get the number of input channels.

Returns:

  • int –

    Number of input channels.

get_num_output_channels()

Get the number of output channels.

Returns:

  • int –

    Number of output channels.

is_3D()

Return whether the model is 3D or not.

This method is used in the NG configuration validation to check that the model dimensions match the data dimensions.

Returns:

  • bool –

    Whether the model is 3D or not.

model_dump(**kwargs)

Dump the model as a dictionary, ignoring the architecture keyword.

Parameters:

  • **kwargs (Any, default: {} ) –

    Additional keyword arguments from Pydantic BaseModel model_dump method.

Returns:

  • {str: Any} –

    Model as a dictionary.

set_3D(is_3D)

Set 3D model by setting the conv_dims parameters.

Parameters:

  • is_3D (bool) –

    Whether the algorithm is 3D or not.

uses_batch_norm()

Return whether the model uses batch normalization.

Returns:

  • bool –

    Whether the model uses batch normalization.

validate_num_channels_init(num_channels_init) classmethod

Validate that num_channels_init is even.

Parameters:

  • num_channels_init (int) –

    Number of channels.

Returns:

  • int –

    Validated number of channels.

Raises:

  • ValueError –

    If the number of channels is odd.