A wrapper that handles non-maximum suppression.

• 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[itile_size : (i+1)tile_size] for j in range(i - 1): suppressing_tile = boxes[jtile_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

`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.

`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.

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