def METHOD_NAME(ex_rois, gt_rois):
    """
    compute bounding box regression targets from ex_rois to gt_rois
    :param ex_rois: [N, 4]
    :param gt_rois: [N, 4]
    :return: [N, 4]
    """
    assert ex_rois.shape[0] == gt_rois.shape[0], 'inconsistent rois number'
    ex_widths = ex_rois[:, 2] - ex_rois[:, 0] + 1.0
    ex_heights = ex_rois[:, 3] - ex_rois[:, 1] + 1.0
    ex_ctr_x = ex_rois[:, 0] + 0.5 * (ex_widths - 1.0)
    ex_ctr_y = ex_rois[:, 1] + 0.5 * (ex_heights - 1.0)
    gt_widths = gt_rois[:, 2] - gt_rois[:, 0] + 1.0
    gt_heights = gt_rois[:, 3] - gt_rois[:, 1] + 1.0
    gt_ctr_x = gt_rois[:, 0] + 0.5 * (gt_widths - 1.0)
    gt_ctr_y = gt_rois[:, 1] + 0.5 * (gt_heights - 1.0)
    targets_dx = (gt_ctr_x - ex_ctr_x) / (ex_widths + 1e-14)
    targets_dy = (gt_ctr_y - ex_ctr_y) / (ex_heights + 1e-14)
    targets_dw = np.log(gt_widths / ex_widths)
    targets_dh = np.log(gt_heights / ex_heights)
    if gt_rois.shape[1] <= 4:
        targets = np.vstack(
            (targets_dx, targets_dy, targets_dw, targets_dh)).transpose()
        return targets
    else:
        targets = [targets_dx, targets_dy, targets_dw, targets_dh]
        # if config.USE_BLUR:
        #  for i in range(4, gt_rois.shape[1]):
        #    t = gt_rois[:,i]
        #    targets.append(t)
        targets = np.vstack(targets).transpose()
        return targets