metrics

Bases: Metric

Packed 3D-detection mean average precision as a torchmetrics metric.

A stateful wrapper of mean_average_precision3d: each update appends one batch's packed predictions and ground truth, and compute returns {"mAP@t": ...} (averaged over the classes present in the targets) for each IoU threshold. An ignore_mask passed to update is stored as the predictions' ignore_mask entry, excluding the flagged predictions from scoring entirely (the KITTI min-height rule). The state is a per-process list of batches (not gathered across processes), so run detection validation on a single device.

Parameters:

• iou_thresholds (Sequence[float], default: (0.25, 0.5) ) –

  IoU thresholds at which mAP@t is reported.

• interpolation (Interpolation, default: 'all' ) –

  AP interpolation: "all" integrates the full precision-recall curve; "r11" / "r40" sample the KITTI 11- / 40-point recall grids.

• kwargs (Any, default: {} ) –

  Forwarded to torchmetrics.Metric.

Methods:

• update –

  Append one batch's packed predictions and ground truth.

• compute –

  Score the accumulated batches and return one mAP@t entry per IoU threshold.

update(
    preds: Detection3D,
    target: Boxes3D,
    ignore_mask: OptTensor = None,
) -> None

compute() -> Dict[str, float]

Bases: Metric

Packed 3D-detection per-class average precision as a torchmetrics metric.

A stateful wrapper of average_precision3d: each update appends one batch's packed predictions and ground truth, and compute returns one AP/<class> entry per class plus their mean as mAP, each class matched at its own IoU threshold (the KITTI / nuScenes convention, e.g. Car@0.7 and Pedestrian/Cyclist@0.5). Targets may carry an ignore_mask so predictions overlapping an ignore region are not counted as false positives. An ignore_mask passed to update is stored as the predictions' ignore_mask entry, excluding the flagged predictions from scoring entirely (the KITTI min-height rule). The state is a per-process list of batches (not gathered across processes), so run detection validation on a single device.

Parameters:

• iou_per_class (Mapping[int, float]) –

  Mapping of class index to the IoU threshold used to match its boxes. Keys are coerced to int (YAML / OmegaConf mappings may arrive with string keys).

• class_names (Optional[Sequence[str]], default: None ) –

  Optional class names used in the returned keys (defaults to the class index).

• interpolation (Interpolation, default: 'all' ) –

  AP interpolation: "all" integrates the full precision-recall curve; "r11" / "r40" sample the KITTI 11- / 40-point recall grids.

• kwargs (Any, default: {} ) –

  Forwarded to torchmetrics.Metric.

Methods:

• update –

  Append one batch's packed predictions and ground truth.

• compute –

  Score the accumulated batches and return one AP/<class> entry per class plus their mAP.

update(
    preds: Detection3D,
    target: Boxes3D,
    ignore_mask: OptTensor = None,
) -> None

compute() -> Dict[str, float]

Bases: Metric

The official nuScenes detection metrics as a torchmetrics metric.

A stateful wrapper of nuscenes_detection_metrics: each update appends one batch's packed predictions and ground truth together with the optional velocity, attribute and point-count extras, and compute returns the functional's flat dict (AP/<class>, mAP, the five TP errors and the NDS). The predictions' velocity entry and the velocity argument are appended to the boxes as \((v_x, v_y)\) columns, the \((M, 9)\) layout the functional scores; prediction attributes are derived from the accumulated prediction velocities in compute with the standard speed heuristic (nuscenes_velocity_attributes). Sample indices are offset per update by the number of samples seen so far (read off the batch tensors), so scenes of different updates never collide. The state is a per-process list of batches (not gathered across processes), so run detection validation on a single device.

Parameters:

• class_names (Sequence[str]) –

  Class name per label index; barrier and traffic_cone get their official special handling by name.

• class_ranges (Optional[Mapping[str, float]], default: None ) –

  Maximum BEV evaluation range per class name; defaults to the official ranges.

• dist_thresholds (Sequence[float], default: (0.5, 1.0, 2.0, 4.0) ) –

  Matching thresholds in meters the AP is averaged over.

• tp_threshold (float, default: 2.0 ) –

  Matching threshold in meters of the TP-error metrics.

• max_boxes_per_sample (int, default: 500 ) –

  Per-sample cap on scored predictions (highest scores kept).

• min_recall (float, default: 0.1 ) –

  Recall up to which the AP and TP-error curves are clipped.

• min_precision (float, default: 0.1 ) –

  Precision subtracted before the AP mean.

• kwargs (Any, default: {} ) –

  Forwarded to torchmetrics.Metric.

Methods:

• update –

  Append one batch's packed predictions and ground truth with the optional nuScenes extras.

• compute –

  Score the accumulated samples and return the official nuScenes metrics, NDS included.

update(
    preds: Detection3D,
    target: Boxes3D,
    *,
    velocity: OptTensor = None,
    num_points: OptTensor = None,
    attribute: OptTensor = None,
) -> None

compute() -> Dict[str, float]

Bases: Metric

Point-mask instance-segmentation AP as a torchmetrics metric.

A stateful wrapper of instance_average_precision: each update appends one scene's instance_matches record (the compact per-scene reduction of predicted masks against ground-truth instances), and compute returns {"AP/<class>": ..., "mAP": ..., "mAP@0.5": ..., "mAP@0.25": ...} following the standard indoor instance-segmentation protocol. The state is a per-process list of records (not gathered across processes), so run instance validation on a single device.

Parameters:

• num_classes (int) –

  Number of instance classes.

• class_names (Optional[Sequence[str]], default: None ) –

  Optional names for the AP/<class> keys; falls back to the class index.

• min_points (int, default: 100 ) –

  Minimum point count for a prediction or ground-truth instance to be scored; smaller ground-truth instances count as ignore regions.

• kwargs (Any, default: {} ) –

  Forwarded to torchmetrics.Metric.

Methods:

• update –

  Append one scene's instance_matches record.

• compute –

  Score the accumulated scene records and return the per-class average precisions and their mean.

update(match: Mapping[str, Tensor]) -> None

compute() -> Dict[str, float]

Bases: Metric

ShapeNetPart instance / class mean IoU as a torchmetrics metric.

A stateful wrapper of part_iou: each shape is scored only over the part labels its category owns (a part absent from both the prediction and the target counts as IoU \(1\)), per-category IoU sums and shape counts accumulate across update calls (summed across processes), and compute returns the protocol's two numbers: ins_mIoU (mean over shapes) and cls_mIoU (mean per category, then over the categories seen). The category of each shape is read off its target labels, since every category owns a disjoint part range.

Parameters:

• part_ids (Optional[Sequence[Sequence[int]]], default: None ) –

  Part labels owned by each category; defaults to the 16-category / 50-part ShapeNetPart table (ShapeNetPart.seg_ids).

• restrict_to_category (bool, default: False ) –

  If True, the argmax of 2-D preds is taken over the shape's own category parts only (logits of the other parts are masked out), the protocol of PointNet, Point-MAE and Point-M2AE. The default is the global argmax over all parts (DGCNN, PointNeXt).

• kwargs (Any, default: {} ) –

  Forwarded to torchmetrics.Metric.

Methods:

• update –

  Score one packed batch of shapes.

• compute –

  Reduce the accumulated per-category IoU sums into ins_mIoU and cls_mIoU.

update(
    preds: Tensor, target: Tensor, batch: Tensor
) -> None

compute() -> Dict[str, Tensor]