text: # Copyright 2022 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
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  """Tensorflow implementation of non max suppression."""
  
  # Import libraries
  import tensorflow as tf
  
  from official.vision.ops import box_ops
  
  
  NMS_TILE_SIZE = 512
  
  
  def _self_suppression(iou, _, iou_sum):
    batch_size = tf.shape(iou)[0]
    can_suppress_others = tf.cast(
        tf.reshape(tf.reduce_max(iou, 1) <= 0.5, [batch_size, -1, 1]), iou.dtype)
    iou_suppressed = tf.reshape(
        tf.cast(tf.reduce_max(can_suppress_others * iou, 1) <= 0.5, iou.dtype),
        [batch_size, -1, 1]) * iou
    iou_sum_new = tf.reduce_sum(iou_suppressed, [1, 2])
    return [
        iou_suppressed,
        tf.reduce_any(iou_sum - iou_sum_new > 0.5), iou_sum_new
    ]
  
  
  def _cross_suppression(boxes, box_slice, iou_threshold, inner_idx):
    batch_size = tf.shape(boxes)[0]
    new_slice = tf.slice(boxes, [0, inner_idx * NMS_TILE_SIZE, 0],
                         [batch_size, NMS_TILE_SIZE, 4])
    iou = box_ops.bbox_overlap(new_slice, box_slice)
    ret_slice = tf.expand_dims(
        tf.cast(tf.reduce_all(iou < iou_threshold, [1]), box_slice.dtype),
        2) * box_slice
    return boxes, ret_slice, iou_threshold, inner_idx + 1
  
  
  def _suppression_loop_body(boxes, iou_threshold, output_size, idx):
    """Process boxes in the range [idx*NMS_TILE_SIZE, (idx+1)*NMS_TILE_SIZE).
  
    Args:
      boxes: a tensor with a shape of [batch_size, anchors, 4].
      iou_threshold: a float representing the threshold for deciding whether boxes
        overlap too much with respect to IOU.
      output_size: an int32 tensor of size [batch_size]. Representing the number
        of selected boxes for each batch.
      idx: an integer scalar representing induction variable.
  
    Returns:
      boxes: updated boxes.
      iou_threshold: pass down iou_threshold to the next iteration.
      output_size: the updated output_size.
      idx: the updated induction variable.
    """
    num_tiles = tf.shape(boxes)[1] // NMS_TILE_SIZE
    batch_size = tf.shape(boxes)[0]
  
    # Iterates over tiles that can possibly suppress the current tile.
    box_slice = tf.slice(boxes, [0, idx * NMS_TILE_SIZE, 0],
                         [batch_size, NMS_TILE_SIZE, 4])
    _, box_slice, _, _ = tf.while_loop(
        lambda _boxes, _box_slice, _threshold, inner_idx: inner_idx < idx,
        _cross_suppression, [boxes, box_slice, iou_threshold,
                             tf.constant(0)])
  
    # Iterates over the current tile to compute self-suppression.
    iou = box_ops.bbox_overlap(box_slice, box_slice)
    mask = tf.expand_dims(
        tf.reshape(tf.range(NMS_TILE_SIZE), [1, -1]) > tf.reshape(
            tf.range(NMS_TILE_SIZE), [-1, 1]), 0)
    iou *= tf.cast(tf.logical_and(mask, iou >= iou_threshold), iou.dtype)
    suppressed_iou, _, _ = tf.while_loop(
        lambda _iou, loop_condition, _iou_sum: loop_condition, _self_suppression,
        [iou, tf.constant(True),
         tf.reduce_sum(iou, [1, 2])])
    suppressed_box = tf.reduce_sum(suppressed_iou, 1) > 0
    box_slice *= tf.expand_dims(1.0 - tf.cast(suppressed_box, box_slice.dtype), 2)
  
    # Uses box_slice to update the input boxes.
    mask = tf.reshape(
        tf.cast(tf.equal(tf.range(num_tiles), idx), boxes.dtype), [1, -1, 1, 1])
    boxes = tf.tile(tf.expand_dims(
        box_slice, [1]), [1, num_tiles, 1, 1]) * mask + tf.reshape(
            boxes, [batch_size, num_tiles, NMS_TILE_SIZE, 4]) * (1 - mask)
    boxes = tf.reshape(boxes, [batch_size, -1, 4])
  
    # Updates output_size.
    output_size += tf.reduce_sum(
        tf.cast(tf.reduce_any(box_slice > 0, [2]), tf.int32), [1])
    return boxes, iou_threshold, output_size, idx + 1
  
  
  def sorted_non_max_suppression_padded(scores,
                                        boxes,
                                        max_output_size,
                                        iou_threshold):
    """A wrapper that handles non-maximum suppression.
  
    Assumption:
      * The boxes are sorted by scores unless the box is a dot (all coordinates
        are zero).
      * Boxes with higher scores can be used to suppress boxes with lower scores.
  
    The overal design of the algorithm is to handle boxes tile-by-tile:
  
    boxes = boxes.pad_to_multiply_of(tile_size)
    num_tiles = len(boxes) // tile_size
    output_boxes = []
    for i in range(num_tiles):
      box_tile = boxes[i*tile_size : (i+1)*tile_size]
      for j in range(i - 1):
        suppressing_tile = boxes[j*tile_size : (j+1)*tile_size]
        iou = bbox_overlap(box_tile, suppressing_tile)
        # if the box is suppressed in iou, clear it to a dot
        box_tile *= _update_boxes(iou)
      # Iteratively handle the diagnal tile.
      iou = _box_overlap(box_tile, box_tile)
      iou_changed = True
      while iou_changed:
        # boxes that are not suppressed by anything else
        suppressing_boxes = _get_suppressing_boxes(iou)
        # boxes that are suppressed by suppressing_boxes
        suppressed_boxes = _get_suppressed_boxes(iou, suppressing_boxes)
        # clear iou to 0 for boxes that are suppressed, as they cannot be used
        # to suppress other boxes any more
        new_iou = _clear_iou(iou, suppressed_boxes)
        iou_changed = (new_iou != iou)
        iou = new_iou
      # remaining boxes that can still suppress others, are selected boxes.
      output_boxes.append(_get_suppressing_boxes(iou))
      if len(output_boxes) >= max_output_size:
        break
  
    Args:
      scores: a tensor with a shape of [batch_size, anchors].
      boxes: a tensor with a shape of [batch_size, anchors, 4].
      max_output_size: a scalar integer `Tensor` representing the maximum number
        of boxes to be selected by non max suppression.
      iou_threshold: a float representing the threshold for deciding whether boxes
        overlap too much with respect to IOU.
  
    Returns:
      nms_scores: a tensor with a shape of [batch_size, anchors]. It has same
        dtype as input scores.
      nms_proposals: a tensor with a shape of [batch_size, anchors, 4]. It has
        same dtype as input boxes.
    """
    batch_size = tf.shape(boxes)[0]
    num_boxes = tf.shape(boxes)[1]
    pad = tf.cast(
        tf.math.ceil(tf.cast(num_boxes, tf.float32) / NMS_TILE_SIZE),
        tf.int32) * NMS_TILE_SIZE - num_boxes
    boxes = tf.pad(tf.cast(boxes, tf.float32), [[0, 0], [0, pad], [0, 0]])
    scores = tf.pad(
        tf.cast(scores, tf.float32), [[0, 0], [0, pad]], constant_values=-1)
    num_boxes += pad
  
    def _loop_cond(unused_boxes, unused_threshold, output_size, idx):
      return tf.logical_and(
          tf.reduce_min(output_size) < max_output_size,
          idx < num_boxes // NMS_TILE_SIZE)
  
    selected_boxes, _, output_size, _ = tf.while_loop(
        _loop_cond, _suppression_loop_body, [
            boxes, iou_threshold,
            tf.zeros([batch_size], tf.int32),
            tf.constant(0)
        ])
    idx = num_boxes - tf.cast(
        tf.nn.top_k(
            tf.cast(tf.reduce_any(selected_boxes > 0, [2]), tf.int32) *
            tf.expand_dims(tf.range(num_boxes, 0, -1), 0), max_output_size)[0],
        tf.int32)
    idx = tf.minimum(idx, num_boxes - 1)
    idx = tf.reshape(
        idx + tf.reshape(tf.range(batch_size) * num_boxes, [-1, 1]), [-1])
    boxes = tf.reshape(
        tf.gather(tf.reshape(boxes, [-1, 4]), idx),
        [batch_size, max_output_size, 4])
    boxes = boxes * tf.cast(
        tf.reshape(tf.range(max_output_size), [1, -1, 1]) < tf.reshape(
            output_size, [-1, 1, 1]), boxes.dtype)
    scores = tf.reshape(
        tf.gather(tf.reshape(scores, [-1, 1]), idx),
        [batch_size, max_output_size])
    scores = scores * tf.cast(
        tf.reshape(tf.range(max_output_size), [1, -1]) < tf.reshape(
            output_size, [-1, 1]), scores.dtype)
    return scores, boxes
  