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Normalization Config

Source

Pydantic models for normalization strategies.

MeanStdConfig

Bases: BaseModel

Mean and standard deviation normalization configuration.

Holds mean and standard deviation statistics for input and target, used to normalize data. Each statistic can be a single float (applied globally to all channels) or a list of floats (one per channel). If not provided, statistics can be computed automatically.

Attributes:

  • name (Literal['mean_std']) –

    Identifier for the mean-std normalization scheme.

  • input_means (float | list[float] | None) –

    Means for input normalization. None for automatic computation.

  • input_stds (float | list[float] | None) –

    Standard deviations for input normalization. None for automatic computation.

  • target_means (float | list[float] | None) –

    Means for target normalization. None for automatic computation.

  • target_stds (float | list[float] | None) –

    Standard deviations for target normalization. None for automatic computation.

  • per_channel (bool) –

    When True (default), statistics are computed independently for each channel. When False, a single statistic is computed across all channels.

needs_computation()

Check if statistics need to be computed.

Returns:

  • bool –

    True if input statistics are missing, False otherwise.

set_input_stats(means, stds)

Set input means and stds together to avoid validation errors.

Parameters:

  • means (list[float]) –

    Mean values per channel.

  • stds (list[float]) –

    Standard deviation values per channel.

set_target_stats(means, stds)

Set target means and stds together to avoid validation errors.

Parameters:

  • means (list[float]) –

    Mean values per channel.

  • stds (list[float]) –

    Standard deviation values per channel.

validate_global_stats_single_element(v, info) classmethod

Validate stats length against the per_channel parameter.

Parameters:

  • v (OptionalFloatStats) –

    Value to validate.

  • info (ValidationInfo) –

    Validated values.

Returns:

  • OptionalFloatStats –

    Validate value.

validate_means_stds()

Validate that means and stds are provided in pairs or set to None.

Returns:

  • Self –

    The validated model instance.

Raises:

  • ValueError –

    If only one of means or stds is provided for input or target, or if paired lists have mismatched lengths.

validate_size(n_input_channels, n_output_channels)

Validate that statistics sizes match the number of channels.

Parameters:

  • n_input_channels (int) –

    The number of input channels to validate against.

  • n_output_channels (int) –

    The number of output channels to validate against.

Raises:

  • ValueError –

    If any provided statistics list does not match the expected size.

MinMaxConfig

Bases: BaseModel

Min-max normalization configuration.

Stores minimum and maximum statistics for scaling data into a desired range. Each statistic can be a single float (applied globally to all channels) or a list of floats (one per channel). If not provided, statistics can be computed automatically.

Attributes:

  • name (Literal['min_max']) –

    Identifier for min-max normalization.

  • input_mins (float | list[float] | None) –

    Minimum values for input normalization. None for automatic computation.

  • input_maxes (float | list[float] | None) –

    Maximum values for input normalization. None for automatic computation.

  • target_mins (float | list[float] | None) –

    Minimum values for target normalization. None for automatic computation.

  • target_maxes (float | list[float] | None) –

    Maximum values for target normalization. None for automatic computation.

  • per_channel (bool) –

    When True (default), statistics are computed independently for each channel. When False, a single statistic is computed across all channels.

needs_computation()

Check if min/max values need to be computed.

Returns:

  • bool –

    True if input statistics are missing, False otherwise.

set_input_range(mins, maxes)

Set input mins and maxes together to avoid validation errors.

Parameters:

  • mins (list[float]) –

    Minimum values per channel.

  • maxes (list[float]) –

    Maximum values per channel.

set_target_range(mins, maxes)

Set target mins and maxes together to avoid validation errors.

Parameters:

  • mins (list[float]) –

    Minimum values per channel.

  • maxes (list[float]) –

    Maximum values per channel.

validate_global_stats_single_element(v, info) classmethod

Validate stats length against the per_channel parameter.

Parameters:

  • v (OptionalFloatStats) –

    Value to validate.

  • info (ValidationInfo) –

    Validated values.

Returns:

  • OptionalFloatStats –

    Validate value.

validate_mins_maxes()

Validate that mins and maxes are provided in pairs or both None.

Returns:

  • Self –

    The validated model instance.

Raises:

  • ValueError –

    If only one of mins or maxes is provided for input or target, or if paired lists have mismatched lengths.

validate_size(n_input_channels, n_output_channels)

Validate that statistics sizes match the number of channels.

Parameters:

  • n_input_channels (int) –

    The number of input channels to validate against.

  • n_output_channels (int) –

    The number of output channels to validate against.

Raises:

  • ValueError –

    If any provided statistics list does not match the expected size.

NoNormConfig

Bases: BaseModel

No normalization configuration.

Indicates that no normalization should be applied.

Attributes:

  • name (Literal['none']) –

    Identifier for no normalization scheme.

needs_computation()

Check if statistics need to be computed.

Returns:

  • bool –

    Always False, as no statistics are required.

validate_size(*args, **kwargs)

No validation needed.

Parameters:

  • *args (Any, default: () ) –

    Parameters will be ignored.

  • **kwargs (Any, default: {} ) –

    Parameters will be ignored.

QuantileConfig

Bases: BaseModel

Quantile normalization configuration.

Normalizes data using quantile-based range scaling. Quantile levels can be specified as a single value (applied to all channels) or a list (one per channel). If not provided, quantile values can be computed automatically.

Attributes:

  • name (Literal['quantile']) –

    Identifier for quantile normalization.

  • lower_quantiles (float | list[float]) –

    Lower quantile level(s). Values must be in [0, 1).

  • upper_quantiles (float | list[float]) –

    Upper quantile level(s). Values must be in (0, 1].

  • input_lower_quantile_values (float | list[float] | None) –

    Computed lower quantile values for input.

  • input_upper_quantile_values (float | list[float] | None) –

    Computed upper quantile values for input.

  • target_lower_quantile_values (float | list[float] | None) –

    Computed lower quantile values for target.

  • target_upper_quantile_values (float | list[float] | None) –

    Computed upper quantile values for target.

  • per_channel (bool) –

    When True (default), quantile values are computed independently for each channel. When False, a single quantile is computed across all channels.

needs_computation()

Check if quantile values need to be computed.

Returns:

  • bool –

    True if quantile values need to be computed.

set_input_quantile_values(lower, upper)

Set input quantile values together to avoid validation errors.

Parameters:

  • lower (list[float]) –

    Lower quantile values per channel.

  • upper (list[float]) –

    Upper quantile values per channel.

set_target_quantile_values(lower, upper)

Set target quantile values together to avoid validation errors.

Parameters:

  • lower (list[float]) –

    Lower quantile values per channel.

  • upper (list[float]) –

    Upper quantile values per channel.

validate_global_stats_single_element(v, info) classmethod

Validate stats length against the per_channel parameter.

Parameters:

  • v (OptionalFloatStats) –

    Value to validate.

  • info (ValidationInfo) –

    Validated values.

Returns:

  • OptionalFloatStats –

    Validate value.

validate_quantile_levels()

Validate quantile levels are in valid range and properly ordered.

Returns:

  • Self –

    The validated model instance.

validate_quantile_values()

Validate that computed quantile value lists are provided in pairs.

Returns:

  • Self –

    The validated model instance.

validate_size(n_input_channels, n_output_channels)

Validate that statistics sizes match the number of channels.

Parameters:

  • n_input_channels (int) –

    The number of input channels to validate against.

  • n_output_channels (int) –

    The number of output channels to validate against.

Raises:

  • ValueError –

    If any provided statistics list does not match the expected size.