ab_upt_config

Classes

AnchorBranchedUPTConfig

!!! abstract "Usage Documentation"

Module Contents

class ab_upt_config.AnchorBranchedUPTConfig(/, **data)

Bases: pydantic.BaseModel

!!! abstract “Usage Documentation”

[Models](../concepts/models.md)

A base class for creating Pydantic models.

Parameters:

data (Any)

__class_vars__

The names of the class variables defined on the model.

__private_attributes__

Metadata about the private attributes of the model.

__signature__

The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__

Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__

The core schema of the model.

__pydantic_custom_init__

Whether the model has a custom __init__ function.

__pydantic_decorators__

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__

Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__

The name of the post-init method for the model, if defined.

__pydantic_root_model__

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__

The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_fields__

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects.

__pydantic_computed_fields__

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_extra__

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_fields_set__

The names of fields explicitly set during instantiation.

__pydantic_private__

Values of private attributes set on the model instance.

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.

supernode_pooling_config: emmi.schemas.modules.encoder.SupernodePoolingConfig
transformer_block_config: emmi.schemas.modules.blocks.transformer_block_config.TransformerBlockConfig
geometry_depth: int = None

Number of transformer blocks in the geometry encoder.

hidden_dim: int = None

Hidden dimension of the model.

physics_blocks: list[Literal['shared', 'cross', 'joint', 'perceiver']]

Types of physics blocks to use in the model. Options are “shared”, “cross”, “joint”, and “perceiver”. Shared: Self-attention within a branch (surface or volume). Attention blocks share weights between surface and volume. Cross: Cross-attention between surface and volume branches. Weights are shared between surface and volume. Joint: Joint attention over surface and volume points. I.e. full self-attention over both surface and volume points. Perceiver: Perceiver-style attention blocks.

num_surface_blocks: int = None

Number of transformer blocks in the surface decoder. Weights are not shared with the volume decoder.

num_volume_blocks: int = None

Number of transformer blocks in the volume decoder. Weights are not shared with the surface decoder.

init_weights: emmi.types.InitWeightsMode = None

Weight initialization of linear layers. Defaults to “truncnormal002”.

drop_path_rate: float = None

Drop path rate for stochastic depth. Defaults to 0.0 (no drop path).