def fpn_map_rois_to_levels(boxes):
    """
    Assign boxes to level 2~5.

    Args:
        boxes (nx4):

    Returns:
        [tf.Tensor]: 4 tensors for level 2-5. Each tensor is a vector of indices of boxes in its level.
        [tf.Tensor]: 4 tensors, the gathered boxes in each level.

    Be careful that the returned tensor could be empty.
    """
    sqrtarea = tf.sqrt(tf_area(boxes))
    level = tf.cast(tf.floor(
        4 + tf.math.log(sqrtarea * (1. / 224) + 1e-6) * (1.0 / np.log(2))), tf.int32)

    # RoI levels range from 2~5 (not 6)
    level_ids = [
        tf.where(level <= 2),
        tf.where(tf.equal(level, 3)),   # == is not supported
        tf.where(tf.equal(level, 4)),
        tf.where(level >= 5)]
    level_ids = [tf.reshape(x, [-1], name='roi_level{}_id'.format(i + 2))
                 for i, x in enumerate(level_ids)]
    num_in_levels = [tf.size(x, name='num_roi_level{}'.format(i + 2))
                     for i, x in enumerate(level_ids)]
    add_moving_summary(*num_in_levels)

    level_boxes = [tf.gather(boxes, ids) for ids in level_ids]
    return level_ids, level_boxes

    Assign boxes to level 2~5.

    Args:
        boxes (nx4):

    Returns:
        [tf.Tensor]: 4 tensors for level 2-5. Each tensor is a vector of indices of boxes in its level.
        [tf.Tensor]: 4 tensors, the gathered boxes in each level.

    Be careful that the returned tensor could be empty.
    
examples/FasterRCNN/modeling/model_fpn.py