lingvo.tasks.car.kitti_ap_metric module

Average Precision metric class for KITTI.

class lingvo.tasks.car.kitti_ap_metric.KITTIAPMetrics(params)[source]

Bases: lingvo.tasks.car.ap_metric.APMetrics

The KITTI implementation of AP metric.

_GetData(classid, difficulty=None, distance=None, num_points=None, rotation=None)[source]

Returns groundtruth and prediction for the classid in a NestedMap.

Parameters

  • classid – int32 specifying the class

  • difficulty – String in [easy, moderate, hard]. If None specified, all difficulty levels are permitted.

  • distance – int32 specifying a binned Euclidean distance of the ground truth bounding box. If None is specified, all distances are selected.

  • num_points – int32 specifying a binned number of laser points within the ground truth bounding box. If None is specified, all boxes are selected.

  • rotation – int32 specifying a binned rotation within the ground truth bounding box. If None is specified, all boxes are selected.

Returns

NestedMap containing iou_threshold, groundtruth and predictions for specified, classid, difficulty level and binned distance. If no bboxes are found with these parameters, returns None.

_BuildMetric(feed_data, classid)[source]

Construct tensors and the feed_dict for KITTI metric op.

Parameters

  • feed_data – a NestedMap returned by _GetData()

  • classid – integer. Unused in this implementation.

Returns

  • scalar_metrics: a dict mapping all the metric names to fetch tensors.

  • curves: a dict mapping all the curve names to fetch tensors.

  • feed_dict: a dict mapping the tensors in feed_tensors to feed values.

Return type

A tuple of 3 dicts

_ComputeFinalMetrics(classids=None, difficulty=None, distance=None, num_points=None, rotation=None)[source]

Compute precision-recall curves as well as average precision.

Parameters

  • classids – A list of N int32.

  • difficulty – String in [easy, moderate, hard]. If None specified, all difficulty levels are permitted.

  • distance – int32 specifying a binned Euclidean distance of the ground truth bounding box. If None is specified, all distances are selected.

  • num_points – int32 specifying a binned number of laser points within the ground truth bounding box. If None is specified, all boxes are selected.

  • rotation – int32 specifying a binned rotation within the ground truth bounding box. If None is specified, all boxes are selected.

Returns

dict. Each entry in the dict is a list of C (number of classes) dicts containing mapping from metric names to individual results. Individual entries may be the following items. - scalars: A list of C (number of classes) dicts mapping metric names to scalar values. - curves: A list of C dicts mapping metrics names to np.float32 arrays of shape [NumberOfPrecisionRecallPoints()+1, 2]. In the last dimension, 0 indexes precision and 1 indexes recall. - calibrations: A list of C dicts mapping metrics names to np.float32 arrays of shape [number of predictions, 2]. The first column is the predicted probabilty and the second column is 0 or 1 indicating that the prediction matched a ground truth item.