mapping_networks.config.LossConfig

class mapping_networks.config.LossConfig(*, task: TaskLossConfig = <factory>, lambda_stability: float = 0.1, lambda_smoothness: float = 0.01, lambda_alignment: float = 0.01, stability_epsilon: float = 0.01, stability_num_samples: int = 1, smoothness_method: str = 'stochastic', smoothness_projections: int = 4, trainable_coefficients: bool = False, enable_stability: bool = False, enable_smoothness: bool = False, enable_alignment: bool = False)

Configuration for the composite mapping loss.

Controls which auxiliary losses are enabled and their weighting coefficients.

Example:

config = LossConfig(
    enable_stability=True,
    lambda_stability=0.1,
    smoothness_method="stochastic",
)

__init__(**data: Any) → None

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Methods

__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
construct([_fields_set])
copy(*[, include, exclude, update, deep])	Returns a copy of the model.
dict(*[, include, exclude, by_alias, ...])
from_orm(obj)
json(*[, include, exclude, by_alias, ...])
model_construct([_fields_set])	Creates a new instance of the Model class with validated data.
model_copy(*[, update, deep])	!!! abstract "Usage Documentation"
model_dump(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
model_dump_json(*[, indent, ensure_ascii, ...])	!!! abstract "Usage Documentation"
model_json_schema(by_alias, ref_template, ...)	Generates a JSON schema for a model class.
model_parametrized_name(params)	Compute the class name for parametrizations of generic classes.
model_post_init(context, /)	Override this method to perform additional initialization after __init__ and model_construct.
model_rebuild(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, extra, ...])	Validate a pydantic model instance.
model_validate_json(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
model_validate_strings(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
parse_file(path, *[, content_type, ...])
parse_obj(obj)
parse_raw(b, *[, content_type, encoding, ...])
schema([by_alias, ref_template])
schema_json(*[, by_alias, ref_template])
update_forward_refs(**localns)
validate(value)

Attributes

model_computed_fields
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extra	Get extra fields set during validation.
model_fields
model_fields_set	Returns the set of fields that have been explicitly set on this model instance.
task
lambda_stability
lambda_smoothness
lambda_alignment
stability_epsilon
stability_num_samples
smoothness_method
smoothness_projections
trainable_coefficients
enable_stability
enable_smoothness
enable_alignment