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fix(t4metric_v2): filter GT by annotation num_lidar_pts #224

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fix(t4metric_v2): filter GT by annotation num_lidar_pts #224

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16 changes: 7 additions & 9 deletions autoware_ml/detection3d/evaluation/t4metric/t4metric_v2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,6 @@
import torch
from mmdet3d.registry import METRICS
from mmdet3d.structures import LiDARInstance3DBoxes
from mmdet3d.structures.ops import box_np_ops
from mmengine.dist import get_world_size
from mmengine.evaluator import BaseMetric
from mmengine.logging import MessageHub, MMLogger
Expand Down Expand Up @@ -438,11 +437,10 @@ def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None:
# Skip processing if result pickle already exists
return

batch_points = data_batch["inputs"]["points"]
for data_sample, points in zip(data_samples, batch_points):
for data_sample in data_samples:
current_time = data_sample["timestamp"]
scene_id = self._parse_scene_id(data_sample["lidar_path"])
frame_ground_truth = self._parse_ground_truth_from_sample(current_time, data_sample, points)
frame_ground_truth = self._parse_ground_truth_from_sample(current_time, data_sample)
perception_frame = self._parse_predictions_from_sample(current_time, data_sample, frame_ground_truth)
self._save_perception_frame(scene_id, data_sample["sample_idx"], perception_frame)

Expand Down Expand Up @@ -1384,7 +1382,7 @@ def _parse_scene_id(self, lidar_path: str) -> str:
except ValueError:
return _UNKNOWN

def _parse_ground_truth_from_sample(self, time: float, data_sample: Dict[str, Any], points) -> FrameGroundTruth:
def _parse_ground_truth_from_sample(self, time: float, data_sample: Dict[str, Any]) -> FrameGroundTruth:
"""Parses ground truth objects from the given data sample.

Args:
Expand Down Expand Up @@ -1415,10 +1413,10 @@ def _parse_ground_truth_from_sample(self, time: float, data_sample: Dict[str, An
num_lidar_pts: np.ndarray = eval_info.get("num_lidar_pts", np.array([]))

if self.min_num_points > 0 and len(bboxes):
points_cpu = points.cpu().numpy()
indices = box_np_ops.points_in_rbbox(points_cpu[:, :3], bboxes[:, :7])
num_points_in_gt = indices.sum(0)
bboxes_mask = num_points_in_gt >= self.min_num_points
# Filter by the annotation's keyframe point count (nuScenes/Waymo convention)
# so the evaluation GT set stays independent of the model input pipeline
# (sweep count, remove_close, range filter).
bboxes_mask = num_lidar_pts >= self.min_num_points
bboxes = bboxes[bboxes_mask]
gt_labels_3d = gt_labels_3d[bboxes_mask]
num_lidar_pts = num_lidar_pts[bboxes_mask]
Expand Down