Sample some boxes from all proposals for training.
    #fg is guaranteed to be > 0, because ground truth boxes will be added as proposals.

    Args:
        boxes: nx4 region proposals, floatbox
        gt_boxes: mx4, floatbox
        gt_labels: m, int32

    Returns:
        A BoxProposals instance.
        sampled_boxes: tx4 floatbox, the rois
        sampled_labels: t int64 labels, in [0, #class). Positive means foreground.
        fg_inds_wrt_gt: #fg indices, each in range [0, m-1].
            It contains the matching GT of each foreground roi.

def sample_fast_rcnn_targets(boxes, gt_boxes, gt_labels):
    """
    Sample some boxes from all proposals for training.
    #fg is guaranteed to be > 0, because ground truth boxes will be added as proposals.

    Args:
        boxes: nx4 region proposals, floatbox
        gt_boxes: mx4, floatbox
        gt_labels: m, int32

    Returns:
        A BoxProposals instance.
        sampled_boxes: tx4 floatbox, the rois
        sampled_labels: t int64 labels, in [0, #class). Positive means foreground.
        fg_inds_wrt_gt: #fg indices, each in range [0, m-1].
            It contains the matching GT of each foreground roi.
    """
    iou = pairwise_iou(boxes, gt_boxes)     # nxm
    proposal_metrics(iou)

    # add ground truth as proposals as well
    boxes = tf.concat([boxes, gt_boxes], axis=0)    # (n+m) x 4
    iou = tf.concat([iou, tf.eye(tf.shape(gt_boxes)[0])], axis=0)   # (n+m) x m
    # #proposal=n+m from now on

    def sample_fg_bg(iou):
        fg_mask = tf.reduce_max(iou, axis=1) >= cfg.FRCNN.FG_THRESH

        fg_inds = tf.reshape(tf.where(fg_mask), [-1])
        num_fg = tf.minimum(int(
            cfg.FRCNN.BATCH_PER_IM * cfg.FRCNN.FG_RATIO),
            tf.size(fg_inds), name='num_fg')
        fg_inds = tf.random_shuffle(fg_inds)[:num_fg]

        bg_inds = tf.reshape(tf.where(tf.logical_not(fg_mask)), [-1])
        num_bg = tf.minimum(
            cfg.FRCNN.BATCH_PER_IM - num_fg,
            tf.size(bg_inds), name='num_bg')
        bg_inds = tf.random_shuffle(bg_inds)[:num_bg]

        add_moving_summary(num_fg, num_bg)
        return fg_inds, bg_inds

    fg_inds, bg_inds = sample_fg_bg(iou)
    # fg,bg indices w.r.t proposals

    best_iou_ind = tf.argmax(iou, axis=1)   # #proposal, each in 0~m-1
    fg_inds_wrt_gt = tf.gather(best_iou_ind, fg_inds)   # num_fg

    all_indices = tf.concat([fg_inds, bg_inds], axis=0)   # indices w.r.t all n+m proposal boxes
    ret_boxes = tf.gather(boxes, all_indices)

    ret_labels = tf.concat(
        [tf.gather(gt_labels, fg_inds_wrt_gt),
         tf.zeros_like(bg_inds, dtype=tf.int64)], axis=0)
    # stop the gradient -- they are meant to be training targets
    return BoxProposals(
        tf.stop_gradient(ret_boxes, name='sampled_proposal_boxes'),
        tf.stop_gradient(ret_labels, name='sampled_labels'),
        tf.stop_gradient(fg_inds_wrt_gt))