1 vin.modules.volume_encoder

vin.modules.volume_encoder

Multiscale 3D encoding for deterministic volumetric evidence.

1.1 Classes

Name	Description
VolumeEncoder	Encode fixed evidence and support without changing the grid geometry.
VolumeEncoderConfig	Configure a native-Torch dense evidence-volume encoder.

1.1.1 VolumeEncoder

vin.modules.volume_encoder.VolumeEncoder(config)

Encode fixed evidence and support without changing the grid geometry.

The input projection receives the deterministic evidence channels together with a separate binary support channel. The full encoder refines the fine grid, constructs a 2C half-resolution carrier, and fuses its upsampled context back into the original grid. Learned features never redefine support: callers retain and sample the supplied mask independently.

1.1.1.1 Attributes

Name	Description
output_channels	Return the encoded feature width.

1.1.1.2 Methods

Name	Description
forward	Encode one or more evidence grids while preserving their resolution.

1.1.1.2.1 forward

vin.modules.volume_encoder.VolumeEncoder.forward(evidence, availability)

Encode one or more evidence grids while preserving their resolution.

1.1.1.2.1.1 Parameters

Name	Type	Description	Default
evidence Tensor["B C D H W", float32]		Deterministic volume channels. C must equal VolumeEncoderConfig.input_channels.	required
availability Tensor["B 1 D H W", bool]		Explicit measured support. It is an input feature and remains owned by the caller.	required

1.1.1.2.1.2 Returns

Name	Type	Description
	Tensor	Tensor["B C_e D H W", float] learned features on the input
	Tensor	device. Gradients propagate to floating evidence and encoder weights.

1.1.1.2.1.3 Raises

Name	Type	Description
	ValueError	If ranks, shapes, dtype, device, or finite-value contracts are violated, or a full encoder receives a grid too small for its coarse scale.

1.1.2 VolumeEncoderConfig

vin.modules.volume_encoder.VolumeEncoderConfig()

Configure a native-Torch dense evidence-volume encoder.

1.1.2.1 Attributes

Name	Description
kind	Closed serialization identity for the multiscale encoder.
input_channels	Deterministic evidence channels, excluding the explicit support mask.
width	Fine-scale learned feature width; the coarse scale uses twice this width.
max_group_norm_groups	Largest permitted GroupNorm group count, reduced to a channel divisor.
projection_only	Use only the input projection for the matched spatial-capacity ablation.
target_type	Return the configured dense encoder type.