ops_nms {torchvisionlib}

Performs non-maximum suppression (NMS) on the boxes

Description

Performs non-maximum suppression (NMS) on the boxes according to their intersection-over-union (IoU).

Usage

ops_nms(boxes, scores, iou_threshold)

Arguments

boxes

Tensor[N,4] boxes to perform NMS on. They are expected to be in ⁠(x1, y1, x2, y2)⁠ format with ⁠0 <= x1 < x2⁠ and ⁠0 <= y1 < y2⁠.

scores

Tensor[N] scores for each one of the boxes.

iou_threshold

float discards all overlapping boxes with IoU > iou_threshold.

Details

NMS iteratively removes lower scoring boxes which have an IoU greater than iou_threshold with another (higher scoring) box.

If multiple boxes have the exact same score and satisfy the IoU criterion with respect to a reference box, the selected box is not guaranteed to be the same between CPU and GPU. This is similar to the behavior of argsort in PyTorch when repeated values are present.

Value

int64 tensor with the indices of the elements that have been kept by NMS, sorted in decreasing order of scores

Examples

if (torchvisionlib_is_installed()) {
  ops_nms(torch::torch_rand(3, 4), torch::torch_rand(3), 0.5)
}