Greedily selects a subset of bounding boxes in descending order of score.

This operation performs non_max_suppression on the inputs per batch, across all classes. Prunes away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes are supplied as [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm is agnostic to where the origin is in the coordinate system. Also note that this algorithm is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The output of this operation is the final boxes, scores and classes tensor returned after performing non_max_suppression.

boxes A 4-D float Tensor of shape [batch_size, num_boxes, q, 4]. If q is 1 then same boxes are used for all classes otherwise, if q is equal to number of classes, class-specific boxes are used.
scores A 3-D float Tensor of shape [batch_size, num_boxes, num_classes] representing a single score corresponding to each box (each row of boxes).
max_output_size_per_class A scalar integer Tensor representing the maximum number of boxes to be selected by non-max suppression per class
max_total_size A int32 scalar representing maximum number of boxes retained over all classes. Note that setting this value to a large number may result in OOM error depending on the system workload.
iou_threshold A float representing the threshold for deciding whether boxes overlap too much with respect to IOU.
score_threshold A float representing the threshold for deciding when to remove boxes based on score.
pad_per_class If false, the output nmsed boxes, scores and classes are padded/clipped to max_total_size. If true, the output nmsed boxes, scores