noether.modeling.modules.layers.vectors_conditioner¶

Classes¶

`VectorsConditionerConfig`	Configuration for `VectorsConditioner`.
`VectorsConditioner`	Embeds a set of named vectors into a single conditioning vector.

Module Contents¶

class noether.modeling.modules.layers.vectors_conditioner.VectorsConditionerConfig(/, **data)¶

Bases: pydantic.BaseModel

Configuration for VectorsConditioner.

All conditioning inputs are expected to be normalized to [-1, 1]; the underlying sine-cosine embedding runs in NeRF mode.

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.

Parameters:: data (Any)

hidden_dim: int = None¶: Dimension of the per-vector embedding and per-vector MLP.

conditioning_spec: noether.data.schemas.FieldDimSpec¶: Mapping from input vector name to its feature dimension, e.g. {"angle": 1, "shape_params": 3}.

condition_dim: int | None = None¶: Dimension of the final conditioning vector. Defaults to hidden_dim if None.

max_frequency: float = None¶: Highest frequency band, in units of π, for the NeRF-mode sine-cosine embedding. Pick based on the smallest spatial scale you need to resolve in normalized coordinates (rough heuristic: 1 / typical_input_spacing).

init_weights: noether.core.types.InitWeightsMode = 'truncnormal002'¶: Weight initialization for MLPs.

class noether.modeling.modules.layers.vectors_conditioner.VectorsConditioner(config)¶

Bases: torch.nn.Module

Embeds a set of named vectors into a single conditioning vector.

Each input vector named in config.conditioning_spec is encoded with a NeRF-mode ContinuousSincosEmbed followed by a per-vector MLP. The resulting per-vector embeddings are concatenated and projected to condition_dim by a shared MLP.

Note

All input vectors must be normalized to [-1, 1]. The underlying sine-cosine embedding uses NeRF-style frequencies tuned for that range; values outside it will alias and produce uninformative embeddings.

Parameters:: config (VectorsConditionerConfig) – configuration for the VectorsConditioner. See VectorsConditionerConfig for available options.

hidden_dim¶

condition_dim¶

conditioning_spec¶

embedder¶

shared_mlp¶

forward(**conditioning_inputs)¶

Embed a set of named vectors into a single conditioning vector.

All vectors declared in config.conditioning_spec must be supplied as keyword arguments matching the spec names. Inputs must be normalized to [-1, 1].

Parameters:: **conditioning_inputs (torch.Tensor) – Vectors with shape (batch_size, num_features), keyed by the names declared in config.conditioning_spec. The num_features of each vector must match the dimension declared in the spec. All inputs must share the same batch_size.
Returns:: Conditioning vector with shape (batch_size, condition_dim).
Raises:: ValueError – If the supplied inputs don’t match the spec (wrong number of vectors, missing key, wrong rank, or wrong feature dimension).
Return type:: torch.Tensor

Example

conditioner = VectorsConditioner(
    VectorsConditionerConfig(
        hidden_dim=64,
        conditioning_spec={"angle": 1, "shape_params": 3},
        condition_dim=128,
        max_frequency=1024,
    )
)
# Inputs normalized to [-1, 1].
angle = torch.tensor([[0.5], [-0.2]])  # shape (batch_size, 1)
shape_params = torch.tensor([[0.1, -0.3, 0.7], [-0.5, 0.2, -0.8]])  # shape (batch_size, 3)
condition = conditioner(angle=angle, shape_params=shape_params)
# condition.shape == (2, 128)