def eval_class(gt_annos,
dt_annos,
current_classes,
difficultys,
metric,
min_overlaps,
compute_aos=False,
num_parts=50):
"""Kitti eval. support 2d/bev/3d/aos eval. support 0.5:0.05:0.95 coco AP.
Args:
gt_annos: dict, must from get_label_annos() in kitti_common.py
dt_annos: dict, must from get_label_annos() in kitti_common.py
current_classes: list of int, 0: car, 1: pedestrian, 2: cyclist
difficultys: list of int. eval difficulty, 0: easy, 1: normal, 2: hard
metric: eval type. 0: bbox, 1: bev, 2: 3d
min_overlaps: float, min overlap. format: [num_overlap, metric, class].
num_parts: int. a parameter for fast calculate algorithm
Returns:
dict of recall, precision and aos
"""
assert len(gt_annos) == len(dt_annos)
num_examples = len(gt_annos)
split_parts = get_split_parts(num_examples, num_parts)
def eval_class(gt_annos, dt_annos, current_classes, difficultys, metric, min_overlaps, compute_aos=False, num_parts=50): """Kitti eval. support 2d/bev/3d/aos eval. support 0.5:0.05:0.95 coco AP. Args: gt_annos: dict, must from get_label_annos() in kitti_common.py dt_annos: dict, must from get_label_annos() in kitti_common.py current_classes: list of int, 0: car, 1: pedestrian, 2: cyclist difficultys: list of int. eval difficulty, 0: easy, 1: normal, 2: hard metric: eval type. 0: bbox, 1: bev, 2: 3d min_overlaps: float, min overlap. format: [num_overlap, metric, class]. num_parts: int. a parameter for fast calculate algorithm Returns: dict of recall, precision and aos """ assert len(gt_annos) == len(dt_annos) num_examples = len(gt_annos) split_parts = get_split_parts(num_examples, num_parts)
The question is what does the param N_SAMPLE_PTS mean here? at the last line