2 · 训练分配和评价匹配解决两个问题 · 实现代码

def assign_training_anchors(anchors, gt_boxes, positive_iou=0.50):
    """D2L-style target assignment: threshold positives + forced GT coverage.

    One anchor has at most one GT, but several anchors may target the same GT.
    Returns GT index per anchor (-1 means background in this simplified lab).
    Scores and predicted classes are intentionally absent from the inputs.
    """
    if not 0 <= positive_iou <= 1:
        raise ValueError("positive_iou must be in [0,1]")
    ious = box_iou(anchors, gt_boxes)
    n_anchor, n_gt = ious.shape
    assigned = np.full(n_anchor, -1, dtype=int)
    forced = np.zeros(n_anchor, dtype=bool)
    if n_gt == 0:
        return assigned, forced
    if n_anchor < n_gt:
        raise ValueError("Forced GT coverage requires anchors >= GTs")
    best_gt = ious.argmax(axis=1)
    best_iou = ious[np.arange(n_anchor), best_gt]
    positive = best_iou >= positive_iou
    assigned[positive] = best_gt[positive]
    remaining = ious.copy()
    for _ in range(n_gt):
        anchor_i, gt_i = np.unravel_index(np.argmax(remaining), remaining.shape)
        assigned[anchor_i] = gt_i
        forced[anchor_i] = True
        remaining[anchor_i, :] = -1.0
        remaining[:, gt_i] = -1.0
    return assigned, forced

print("0-based GT targets (-1=background):", assign_training_anchors(PRED, GT)[0])