Args:
        anchor_labels: fHxfWxNA
        anchor_boxes: fHxfWxNAx4, encoded
        label_logits:  fHxfWxNA
        box_logits: fHxfWxNAx4

    Returns:
        label_loss, box_loss

def rpn_losses(anchor_labels, anchor_boxes, label_logits, box_logits):
    """
    Args:
        anchor_labels: fHxfWxNA
        anchor_boxes: fHxfWxNAx4, encoded
        label_logits:  fHxfWxNA
        box_logits: fHxfWxNAx4

    Returns:
        label_loss, box_loss
    """
    with tf.device('/cpu:0'):
        valid_mask = tf.stop_gradient(tf.not_equal(anchor_labels, -1))
        pos_mask = tf.stop_gradient(tf.equal(anchor_labels, 1))
        nr_valid = tf.stop_gradient(tf.count_nonzero(valid_mask, dtype=tf.int32), name='num_valid_anchor')
        nr_pos = tf.identity(tf.count_nonzero(pos_mask, dtype=tf.int32), name='num_pos_anchor')
        # nr_pos is guaranteed >0 in C4. But in FPN. even nr_valid could be 0.

        valid_anchor_labels = tf.boolean_mask(anchor_labels, valid_mask)
    valid_label_logits = tf.boolean_mask(label_logits, valid_mask)

    with tf.name_scope('label_metrics'):
        valid_label_prob = tf.nn.sigmoid(valid_label_logits)
        summaries = []
        with tf.device('/cpu:0'):
            for th in [0.5, 0.2, 0.1]:
                valid_prediction = tf.cast(valid_label_prob > th, tf.int32)
                nr_pos_prediction = tf.reduce_sum(valid_prediction, name='num_pos_prediction')
                pos_prediction_corr = tf.count_nonzero(
                    tf.logical_and(
                        valid_label_prob > th,
                        tf.equal(valid_prediction, valid_anchor_labels)),
                    dtype=tf.int32)
                placeholder = 0.5   # A small value will make summaries appear lower.
                recall = tf.cast(tf.truediv(pos_prediction_corr, nr_pos), tf.float32)
                recall = tf.where(tf.equal(nr_pos, 0), placeholder, recall, name='recall_th{}'.format(th))
                precision = tf.cast(tf.truediv(pos_prediction_corr, nr_pos_prediction), tf.float32)
                precision = tf.where(tf.equal(nr_pos_prediction, 0),
                                     placeholder, precision, name='precision_th{}'.format(th))
                summaries.extend([precision, recall])
        add_moving_summary(*summaries)

    # Per-level loss summaries in FPN may appear lower due to the use of a small placeholder.
    # But the total RPN loss will be fine.  TODO make the summary op smarter
    placeholder = 0.
    label_loss = tf.nn.sigmoid_cross_entropy_with_logits(
        labels=tf.cast(valid_anchor_labels, tf.float32), logits=valid_label_logits)
    label_loss = tf.reduce_sum(label_loss) * (1. / cfg.RPN.BATCH_PER_IM)
    label_loss = tf.where(tf.equal(nr_valid, 0), placeholder, label_loss, name='label_loss')

    pos_anchor_boxes = tf.boolean_mask(anchor_boxes, pos_mask)
    pos_box_logits = tf.boolean_mask(box_logits, pos_mask)
    delta = 1.0 / 9
    box_loss = tf.losses.huber_loss(
        pos_anchor_boxes, pos_box_logits, delta=delta,
        reduction=tf.losses.Reduction.SUM) / delta
    box_loss = box_loss * (1. / cfg.RPN.BATCH_PER_IM)
    box_loss = tf.where(tf.equal(nr_pos, 0), placeholder, box_loss, name='box_loss')

    add_moving_summary(label_loss, box_loss, nr_valid, nr_pos)
    return [label_loss, box_loss]