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Optimize Metropolis2D IoU evaluation #313

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Optimize Metropolis2D IoU evaluation #313

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26 changes: 21 additions & 5 deletions evaluation/cosmos3/reasoner/vlmevalkit/vlmeval/dataset/metropolis2d/detection_2d_dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -292,22 +292,38 @@ def evaluate(self, eval_file, **judge_kwargs):
total_pred += len(pred_boxes_car)

# Sort predictions by confidence (descending)
pred_boxes_car = sorted(pred_boxes_car, key=lambda x: x.get('score', 1.0), reverse=True)
pred_boxes_car = sorted(
pred_boxes_car,
key=lambda x: x.get('score', 1.0),
reverse=True
)

# Precompute IoU matrix once for all prediction/ground-truth pairs.
num_preds = len(pred_boxes_car)
num_gts = len(gt_boxes_car)

iou_matrix = np.zeros((num_preds, num_gts), dtype=np.float32)

for pred_idx, pred in enumerate(pred_boxes_car):
pred_bbox = pred['bbox']

for gt_idx, gt in enumerate(gt_boxes_car):
iou_matrix[pred_idx, gt_idx] = compute_2d_iou(
pred_bbox, gt['bbox']
)
# Match predictions to ground truth at each IoU threshold
for iou_thresh in iou_thresholds:
gt_matched = [False] * len(gt_boxes_car)

for pred in pred_boxes_car:
pred_bbox = pred['bbox']
for pred_idx, pred in enumerate(pred_boxes_car):
confidence = pred.get('score', 1.0)
best_iou = 0.0
best_gt_idx = -1

for gt_idx, gt in enumerate(gt_boxes_car):
for gt_idx in range(num_gts):
if gt_matched[gt_idx]:
continue
iou = compute_2d_iou(pred_bbox, gt['bbox'])
iou = iou_matrix[pred_idx, gt_idx]
if iou > best_iou:
best_iou = iou
best_gt_idx = gt_idx
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