1 vin.types.backbone

vin.types.backbone

EVL backbone exchange containers for VIN scorer inputs.

This module owns the typed payloads shared by EVL adapters, offline stores, VIN models, and target-selection code. The aggregate aria_nbv.vin.types package re-exports these names for compatibility, but this file is the canonical definition owner.

1.1 Classes

Name Description
EvlBackboneOutput Actor-visible EVL evidence exchanged with VIN scorers and caches.

1.1.1 EvlBackboneOutput

vin.types.backbone.EvlBackboneOutput(
    t_world_voxel,
    voxel_extent,
    voxel_feat=None,
    occ_feat=None,
    obb_feat=None,
    occ_pr=None,
    occ_input=None,
    free_input=None,
    free_input_provenance=None,
    counts=None,
    counts_m=None,
    voxel_select_t=None,
    cent_pr=None,
    bbox_pr=None,
    clas_pr=None,
    cent_pr_nms=None,
    obbs_pr_nms=None,
    obb_pred=None,
    obb_pred_viz=None,
    obb_pred_sem_id_to_name=None,
    obb_pred_probs_full=None,
    obb_pred_probs_full_viz=None,
    pts_world=None,
    feat2d_upsampled=dict(),
    token2d=dict(),
)

Actor-visible EVL evidence exchanged with VIN scorers and caches.

The tensors are local to the EVL model configuration and checkpoint that produced them; they are learned evidence, not ground truth. This container does not embed source, config, or checkpoint hashes. Any durable offline payload must therefore pair it with provenance metadata that identifies the EVL source, resolved configuration, and checkpoint content.

1.1.1.1 Attributes

Name Type Description
t_world_voxel PoseTW PoseTW["B 12"] world←voxel pose for the voxel grid.
voxel_extent Tensor Tensor["6", float32] voxel grid extent in voxel frame [x_min,x_max,y_min,y_max,z_min,z_max] (meters).
voxel_feat Tensor | None Optional Tensor["B F D H W", float32] raw voxel features from the 3D backbone.
occ_feat Tensor | None Optional Tensor["B C D H W", float32] neck features for occupancy.
obb_feat Tensor | None Optional Tensor["B C D H W", float32] neck features for OBB detection.
occ_pr Tensor | None Optional Tensor["B 1 D H W", float32] EVL occupancy probability.
occ_input Tensor | None Optional Tensor["B 1 D H W", float32] voxelized occupied evidence from input points.
free_input Tensor | None Optional Tensor["B 1 D H W", float32] voxelized free-space evidence.
free_input_provenance FreeInputProvenance | None Native EVL output or the explicit derived construction.
counts Tensor | None Optional Tensor["B D H W", int64] per-voxel observation counts.
counts_m Tensor | None Optional Tensor["B D H W", int64] masked/debug variant of counts.
voxel_select_t Tensor | None Optional Tensor["B 1", int64] frame index anchoring the voxel grid.
cent_pr Tensor | None Optional Tensor["B 1 D H W", float32] centerness probabilities.
bbox_pr Tensor | None Optional Tensor["B 7 D H W", float32] bounding box regressions.
clas_pr Tensor | None Optional Tensor["B K D H W", float32] class probabilities.
cent_pr_nms Tensor | None Optional Tensor["B 1 D H W", float32] centerness after NMS.
obbs_pr_nms ObbTW | None Optional ObbTW["B M 34"] OBB predictions after NMS (voxel frame).
obb_pred ObbTW | None Optional ObbTW["B M 34"] OBB predictions in snippet coordinates.
obb_pred_viz ObbTW | None Optional ObbTW["B M 34"] visualization OBB predictions in snippet coordinates.
obb_pred_sem_id_to_name dict[int, str] | None Optional sparse semantic id to class-name map aligned with EVL taxonomy.
obb_pred_probs_full list[Tensor] | None Optional list of per-OBB class probability tensors.
obb_pred_probs_full_viz list[Tensor] | None Optional list of per-OBB class probability tensors for visualization.
pts_world Tensor | None Optional Tensor["B (D·H·W) 3", float32] world-space voxel centers.
feat2d_upsampled dict[str, Tensor] Per-stream 2D feature maps keyed by stream name.
token2d dict[str, Tensor] Per-stream 2D tokens keyed by stream name.

1.1.1.2 Methods

Name Description
to_serializable Serialize this backbone output into a cache-friendly CPU payload.
from_serializable Reconstruct one backbone output from a serialized payload.
to Move all tensors to the specified device.
1.1.1.2.1 to_serializable
vin.types.backbone.EvlBackboneOutput.to_serializable(include_fields=None)

Serialize this backbone output into a cache-friendly CPU payload.

1.1.1.2.1.1 Parameters
Name Type Description Default
include_fields set[str] | None Optional dataclass field names to include. When omitted, all fields are serialized. None
1.1.1.2.1.2 Returns
Name Type Description
dict[str, Any] CPU-only payload suitable for immutable offline-store records.
1.1.1.2.2 from_serializable
vin.types.backbone.EvlBackboneOutput.from_serializable(
    payload,
    *,
    device,
    include_fields=None,
)

Reconstruct one backbone output from a serialized payload.

1.1.1.2.2.1 Parameters
Name Type Description Default
payload dict[str, Any] Serialized payload produced by to_serializable. required
device torch.device Destination device for tensors and wrappers. required
include_fields set[str] | None Optional subset of fields to decode. None
1.1.1.2.2.2 Returns
Name Type Description
'EvlBackboneOutput' Reconstructed backbone output.
1.1.1.2.3 to
vin.types.backbone.EvlBackboneOutput.to(device)

Move all tensors to the specified device.

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