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729628a 6342806 729628a 6342806 729628a 6342806 729628a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 | """Quality gate benchmarking against vendor ground truth."""
from __future__ import annotations
import json
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
from sensorflow.metrics.perception_3d import (
bev_iou,
compute_map_mar_by_frame,
mean_orientation_error,
mean_position_error,
)
from sensorflow.metrics.resource_profile import compute_resource_profile
from sensorflow.metrics.temporal_mot import (
compute_id_swap_rate,
compute_track_fragmentation_rate,
)
from sensorflow.schemas.unified_frame import UnifiedSequence
DEFAULT_THRESHOLDS = {
"map_3d": 0.65,
"orientation_error_deg": 5.0,
"id_swap_rate": 0.02,
"track_fragmentation_rate": 0.05,
"position_error_m": 2.0,
}
class QualityGate:
"""Benchmark automated tracks against vendor ground truth."""
def __init__(self, thresholds: Optional[Dict[str, float]] = None):
self.thresholds = thresholds or DEFAULT_THRESHOLDS.copy()
def evaluate(
self,
sequence: UnifiedSequence,
pred_tracks: List[Dict],
stages_run: Optional[List[str]] = None,
) -> Dict[str, Any]:
# Matching is scoped per frame: a prediction may only match ground
# truth from the same timestamp. Pooling boxes across all frames
# (the previous behavior) allowed physically impossible cross-frame
# matches, inflating mAP/mAR and corrupting error statistics.
preds_by_frame, gts_by_frame = self._collect_boxes_by_frame(sequence, pred_tracks)
all_preds = [b for boxes in preds_by_frame.values() for b in boxes]
all_gts = [b for boxes in gts_by_frame.values() for b in boxes]
map_metrics = compute_map_mar_by_frame(preds_by_frame, gts_by_frame)
matched_pairs = []
for fid in sorted(set(preds_by_frame) | set(gts_by_frame)):
matched_pairs.extend(self._match_for_errors(
preds_by_frame.get(fid, []), gts_by_frame.get(fid, [])))
orient_err = mean_orientation_error(matched_pairs)
pos_err = mean_position_error(matched_pairs)
gt_tracks = self._build_gt_tracks(sequence)
id_swap = compute_id_swap_rate(pred_tracks, gt_tracks)
fragmentation = compute_track_fragmentation_rate(pred_tracks, gt_tracks)
resource = compute_resource_profile(sequence.frames, stages_run)
metric_card = {
"map_3d": map_metrics["map_3d"],
"mar_3d": map_metrics["mar_3d"],
"mean_iou_3d": map_metrics["mean_iou_3d"],
"orientation_error_deg": orient_err,
"position_error_m": pos_err,
"id_swap_rate": id_swap,
"track_fragmentation_rate": fragmentation,
"process_units": resource["process_units"],
"compute_cycles": resource["compute_cycles"],
}
failures = self._check_thresholds(metric_card)
passed = len(failures) == 0
quality_report = {
"total_predictions": len(all_preds),
"total_ground_truth": len(all_gts),
"quality_score": self._quality_score(metric_card),
"metric_card": metric_card,
"passed": passed,
"failures": failures,
"issue_summary": {
"high_orientation_error": 1 if orient_err > self.thresholds["orientation_error_deg"] else 0,
"high_position_error": 1 if pos_err > self.thresholds["position_error_m"] else 0,
"id_swaps": 1 if id_swap > self.thresholds["id_swap_rate"] else 0,
"fragmentation": 1 if fragmentation > self.thresholds["track_fragmentation_rate"] else 0,
},
"recommendations": self._recommendations(metric_card, failures),
}
return {"metric_card": metric_card, "quality_report": quality_report, "passed": passed}
def _collect_boxes(
self,
sequence: UnifiedSequence,
pred_tracks: List[Dict],
) -> Tuple[List[List[float]], List[List[float]]]:
all_preds, all_gts = [], []
for track in pred_tracks:
for fr in track.get("frames", []):
all_preds.append(fr["bbox_3d"])
for frame in sequence.frames:
for gt in frame.ground_truth:
all_gts.append(gt.bbox_3d)
return all_preds, all_gts
def _collect_boxes_by_frame(
self,
sequence: UnifiedSequence,
pred_tracks: List[Dict],
) -> Tuple[Dict[str, List[List[float]]], Dict[str, List[List[float]]]]:
preds_by_frame: Dict[str, List[List[float]]] = {}
gts_by_frame: Dict[str, List[List[float]]] = {}
for track in pred_tracks:
for fr in track.get("frames", []):
preds_by_frame.setdefault(fr["frame_id"], []).append(fr["bbox_3d"])
for frame in sequence.frames:
for gt in frame.ground_truth:
gts_by_frame.setdefault(frame.frame_id, []).append(gt.bbox_3d)
return preds_by_frame, gts_by_frame
def _match_for_errors(
self,
preds: List[List[float]],
gts: List[List[float]],
) -> List[Tuple[List[float], List[float]]]:
pairs = []
used_gt = set()
for pred in preds:
best_iou, best_gi = 0, -1
for gi, gt in enumerate(gts):
if gi in used_gt:
continue
iou = bev_iou(pred, gt)
if iou > best_iou:
best_iou = iou
best_gi = gi
if best_gi >= 0 and best_iou > 0.3:
pairs.append((pred, gts[best_gi]))
used_gt.add(best_gi)
return pairs
def _build_gt_tracks(self, sequence: UnifiedSequence) -> List[Dict]:
by_instance: Dict[str, List[Dict]] = {}
for frame in sequence.frames:
for gt in frame.ground_truth:
by_instance.setdefault(gt.instance_id, []).append({
"frame_id": frame.frame_id,
"bbox_3d": gt.bbox_3d,
})
return [
{"instance_id": iid, "frames": frames}
for iid, frames in by_instance.items()
]
def _check_thresholds(self, metrics: Dict[str, float]) -> List[Dict]:
failures = []
checks = [
("map_3d", metrics["map_3d"], self.thresholds["map_3d"], ">="),
("orientation_error_deg", metrics["orientation_error_deg"], self.thresholds["orientation_error_deg"], "<="),
("id_swap_rate", metrics["id_swap_rate"], self.thresholds["id_swap_rate"], "<="),
("track_fragmentation_rate", metrics["track_fragmentation_rate"], self.thresholds["track_fragmentation_rate"], "<="),
("position_error_m", metrics["position_error_m"], self.thresholds["position_error_m"], "<="),
]
for name, value, threshold, op in checks:
failed = (value < threshold) if op == ">=" else (value > threshold)
if failed:
failures.append({
"metric": name,
"value": value,
"threshold": threshold,
"operator": op,
})
return failures
def _quality_score(self, metrics: Dict[str, float]) -> float:
def safe(v, default=0.0):
import math
return default if v is None or (isinstance(v, float) and math.isnan(v)) else v
score = (
safe(metrics.get("map_3d", 0)) * 40 +
(1 - min(safe(metrics.get("orientation_error_deg", 0)) / 45, 1)) * 20 +
(1 - min(safe(metrics.get("id_swap_rate", 0)) / 0.1, 1)) * 20 +
(1 - min(safe(metrics.get("track_fragmentation_rate", 0)) / 0.2, 1)) * 20
)
return round(min(100, max(0, score)), 1)
def _recommendations(self, metrics: Dict, failures: List[Dict]) -> List[str]:
recs = []
if metrics["orientation_error_deg"] > self.thresholds["orientation_error_deg"]:
recs.append("Improve 3D orientation estimation via tighter SAM mask-to-LiDAR alignment.")
if metrics["id_swap_rate"] > self.thresholds["id_swap_rate"]:
recs.append("Increase tracker velocity penalty or reduce association gate during occlusions.")
if metrics["track_fragmentation_rate"] > self.thresholds["track_fragmentation_rate"]:
recs.append("Extend max_age for ghost tracks and review occlusion handling.")
if metrics["map_3d"] < self.thresholds["map_3d"]:
recs.append("Retrain or tune perception model; verify LiDAR-camera calibration.")
if not recs:
recs.append("All quality gate thresholds met. Proceed to launch gate.")
return recs
def save_results(self, sequence_id: str, results: Dict[str, Any]) -> Path:
out_dir = Path("runs/pipeline") / sequence_id / "benchmark"
out_dir.mkdir(parents=True, exist_ok=True)
with open(out_dir / "metric_card.json", "w") as f:
json.dump(results["metric_card"], f, indent=2)
with open(out_dir / "quality_report.json", "w") as f:
json.dump(results["quality_report"], f, indent=2)
return out_dir
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