Is calculating AP50 in this manner correct?

[FindOne1]

Comparing the AP calculation method in YOLOv5, it seems that in YOLOX, when computing map50, only the average precision is taken into account. However, doesn't AP represent the area under the precision-recall curve?
Therefore, the AP50 values printed using these two methods are different.
Here are two code snippets for comparison：
yolox:

def per_class_AP_table(coco_eval, class_names=COCO_CLASSES, headers=["class", "AP"], colums=6):
    per_class_AP = {}
    precisions = coco_eval.eval["precision"]
    # dimension of precisions: [TxRxKxAxM]
    # precision has dims (iou, recall, cls, area range, max dets)
    assert len(class_names) == precisions.shape[2]

    for idx, name in enumerate(class_names):
        # area range index 0: all area ranges
        # max dets index -1: typically 100 per image
        precision = precisions[:, :, idx, 0, -1]
        precision = precision[precision > -1]
        ap = np.mean(precision) if precision.size else float("nan")
        per_class_AP[name] = float(ap * 100)

    num_cols = min(colums, len(per_class_AP) * len(headers))
    result_pair = [x for pair in per_class_AP.items() for x in pair]
    row_pair = itertools.zip_longest(*[result_pair[i::num_cols] for i in range(num_cols)])
    table_headers = headers * (num_cols // len(headers))
    table = tabulate(
        row_pair, tablefmt="pipe", floatfmt=".3f", headers=table_headers, numalign="left",
    )
    return table

yolov5:

def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16,sample_rate=1000):
    """ Compute the average precision, given the recall and precision curves.
    Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
    # Arguments
        tp:  True positives (nparray, nx1 or nx10).
        conf:  Objectness value from 0-1 (nparray).
        pred_cls:  Predicted object classes (nparray).
        target_cls:  True object classes (nparray).
        plot:  Plot precision-recall curve at mAP@0.5
        save_dir:  Plot save directory
    # Returns
        The average precision as computed in py-faster-rcnn.
    """

    # Sort by objectness
    i = np.argsort(-conf)
    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]

    # Find unique classes
    unique_classes, nt = np.unique(target_cls, return_counts=True)
    nc = unique_classes.shape[0]  # number of classes, number of detections

    # Create Precision-Recall curve and compute AP for each class
    px, py = np.linspace(0, 1, sample_rate), []  # for plotting
    ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, sample_rate)), np.zeros((nc, sample_rate))
    for ci, c in enumerate(unique_classes):
        i = pred_cls == c
        n_l = nt[ci]  # number of labels
        n_p = i.sum()  # number of predictions
        if n_p == 0 or n_l == 0:
            continue

        # Accumulate FPs and TPs
        fpc = (1 - tp[i]).cumsum(0)
        tpc = tp[i].cumsum(0)

        # Recall
        recall = tpc / (n_l + eps)  # recall curve
        r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0)  # negative x, xp because xp decreases

        # Precision
        precision = tpc / (tpc + fpc)  # precision curve
        p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1)  # p at pr_score

        # AP from recall-precision curve
        for j in range(tp.shape[1]):
            ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
            if plot and j == 0:
                py.append(np.interp(px, mrec, mpre))  # precision at mAP@0.5

    # Compute F1 (harmonic mean of precision and recall)
    f1 = 2 * p * r / (p + r + eps)
    # print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!',f1.shape)
    names = [v for k, v in names.items() if k in unique_classes]  # list: only classes that have data
    names = dict(enumerate(names))  # to dict
    if plot:
        plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names)
        plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1')
        plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision')
        plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall')

    i = smooth(f1.mean(0), 0.1).argmax()  # max F1 index
    p, r, f1 = p[:, i], r[:, i], f1[:, i]
    tp = (r * nt).round()  # true positives
    fp = (tp / (p + eps) - tp).round()  # false positives
    return tp, fp, p, r, f1, ap, unique_classes.astype(int)