def accumulate(
dt_root_fpath: Path, gt_fpath: Path, cfg: DetectionCfg
) -> Tuple[DefaultDict[str, np.ndarray], DefaultDict[str, int]]:
"""Accumulate the true/false positives (boolean flags) and true positive errors for each class.
Args:
dt_root_fpath: Detections root folder file path.
gt_fpath: Ground truth file path.
cfg: Detection configuration.
Returns:
cls_to_accum: Class to accumulated statistics dictionary of shape |C| -> (N, K + S) where C
is the number of detection classes, K is the number of true positive thresholds used for
AP computation, and S is the number of true positive errors.
cls_to_ninst: Mapping of shape |C| -> (1,) the class names to the number of instances in the ground
truth dataset.
"""
log_id = gt_fpath.parents[1].stem
logger.info(f"log_id = {log_id}")
ts = gt_fpath.stem.split("_")[-1]
dt_fpath = dt_root_fpath / f"{log_id}/per_sweep_annotations_amodal/" f"tracked_object_labels_{ts}.json"
dts = np.array(read_label(str(dt_fpath)))
gts = np.array(read_label(str(gt_fpath)))
cls_to_accum = defaultdict(list)
cls_to_ninst = defaultdict(int)
for class_name in cfg.dt_classes:
dt_filtered = filter_instances(
dts,
class_name,
filter_metric=cfg.dt_metric,
max_detection_range=cfg.max_dt_range,
)
gt_filtered = filter_instances(
gts,
class_name,
filter_metric=cfg.dt_metric,
max_detection_range=cfg.max_dt_range,
)
gt_filtered = remove_duplicate_instances(gt_filtered, cfg)
logger.info(f"{dt_filtered.shape[0]} detections")
logger.info(f"{gt_filtered.shape[0]} ground truth")
if dt_filtered.shape[0] > 0:
ranked_detections, scores = rank(dt_filtered)
metrics = assign(ranked_detections, gt_filtered, cfg)
cls_to_accum[class_name] = np.hstack((metrics, scores))
cls_to_ninst[class_name] = gt_filtered.shape[0]
return cls_to_accum, cls_to_ninst
def __str__(self) -> str:
frame_lidar = self.num_lidar_frame
frame_image_ring = self.num_ring_camera_frame
frame_image_stereo = self.num_stereo_camera_frame
num_images = [len(self.image_list[cam]) for cam in CAMERA_LIST]
num_annotations = [len(object_label.read_label(label)) for label in self.label_list]
start_time = self.lidar_timestamp_list[0]
end_time = self.lidar_timestamp_list[-1]
time_in_sec = (end_time - start_time) * 10 ** (-9)
return f"""
def get_label_object(self, idx: int, log_id: Optional[str] = None) -> List[ObjectLabelRecord]:
"""Get label corresponding to frame index idx (in lidar frame).
Args:
idx: Lidar frame index
log_id: ID of log to search (default: current log)
Returns:
List of ObjectLabelRecord info for a particular index
"""
assert self.lidar_timestamp_list is not None
assert self._lidar_timestamp_list is not None
assert self.label_list is not None
assert self._label_list is not None
if log_id is None:
log_id = self.current_log
assert idx < len(self._lidar_timestamp_list[log_id])
return object_label.read_label(self._label_list[log_id][idx])
def accumulate(
dt_root_fpath: Path, gt_fpath: Path, cfg: DetectionCfg,
avm: Optional[ArgoverseMap]
) -> Tuple[DefaultDict[str, np.ndarray], DefaultDict[str, int]]:
"""Accumulate the true/false positives (boolean flags) and true positive errors for each class.
Args:
dt_root_fpath: Detections root folder file path.
gt_fpath: Ground truth file path.
cfg: Detection configuration.
Returns:
cls_to_accum: Class to accumulated statistics dictionary of shape |C| -> (N, K + S) where C
is the number of detection classes, K is the number of true positive thresholds used for
AP computation, and S is the number of true positive errors.
cls_to_ninst: Mapping of shape |C| -> (1,) the class names to the number of instances in the ground
truth dataset.
"""
log_id = gt_fpath.parents[1].stem
logger.info(f"log_id = {log_id}")
ts = int(gt_fpath.stem.split("_")[-1])
dt_fpath = dt_root_fpath / f"{log_id}/per_sweep_annotations_amodal/" f"tracked_object_labels_{ts}.json"
dts = np.array(read_label(str(dt_fpath)))
gts = np.array(read_label(str(gt_fpath)))
if cfg.eval_only_roi_instances and avm is not None:
# go up 3 levels, because hierarchy is as follows:
# {gt_root_fpath}/{log_id}/per_sweep_annotations_amodal/{gt_root_fname}
gt_root_fpath = Path(gt_fpath).parents[2]
city_SE3_egovehicle = get_city_SE3_egovehicle_at_sensor_t(
ts, str(gt_root_fpath), log_id)
if city_SE3_egovehicle is not None:
log_city_name = read_city_name(
os.path.join(gt_root_fpath, log_id, "city_info.json"))
dts = filter_objs_to_roi(dts, avm, city_SE3_egovehicle,
log_city_name)
gts = filter_objs_to_roi(gts, avm, city_SE3_egovehicle,
log_city_name)
cls_to_accum = defaultdict(list)
cls_to_ninst = defaultdict(int)
for class_name in cfg.dt_classes:
dt_filtered = filter_instances(
dts,
class_name,
filter_metric=cfg.dt_metric,
max_detection_range=cfg.max_dt_range,
)
gt_filtered = filter_instances(
gts,
class_name,
filter_metric=cfg.dt_metric,
max_detection_range=cfg.max_dt_range,
)
gt_filtered = remove_duplicate_instances(gt_filtered, cfg)
logger.info(f"{dt_filtered.shape[0]} detections")
logger.info(f"{gt_filtered.shape[0]} ground truth")
if dt_filtered.shape[0] > 0:
ranked_detections, scores = rank(dt_filtered)
metrics = assign(ranked_detections, gt_filtered, cfg)
cls_to_accum[class_name] = np.hstack((metrics, scores))
cls_to_ninst[class_name] = gt_filtered.shape[0]
return cls_to_accum, cls_to_ninst