'Function to perform non-maximal supression.
Arguments:
detections (Tensor): detection boxes (box_count, 5), each row has
(x1, y1, x2, y2, score). Assume the boxes have already
been sorted based on score in descending order
output_mask (Tensor): pre-allocated buffer for mask output from the kernel
threshold (float): box overlap threshold, boxes with smaller overlaps will be kept
normalized (bool): whether box coordinates are normalized to image dimensions
Outputs:
keep_ind (list): list of indices'

def nms(self, detections, threshold, normalized=False):

    if (normalized is True):
        offset = 0
    else:
        offset = 1
    dets = detections.get()
    keep = np.where((dets[:, 4] != 0))[0]
    x1 = dets[(keep, 0)]
    y1 = dets[(keep, 1)]
    x2 = dets[(keep, 2)]
    y2 = dets[(keep, 3)]
    scores = dets[(keep, 4)]
    areas = (((x2 - x1) + offset) * ((y2 - y1) + offset))
    order = scores.argsort()[::(-1)]
    keep = []
    while (order.size > 0):
        i = order[0]
        keep.append(i)
        xx1 = np.maximum(x1[i], x1[order[1:]])
        yy1 = np.maximum(y1[i], y1[order[1:]])
        xx2 = np.minimum(x2[i], x2[order[1:]])
        yy2 = np.minimum(y2[i], y2[order[1:]])
        w = np.maximum(0.0, ((xx2 - xx1) + offset))
        h = np.maximum(0.0, ((yy2 - yy1) + offset))
        inter = (w * h)
        ovr = (inter / ((areas[i] + areas[order[1:]]) - inter))
        inds = np.where((ovr <= threshold))[0]
        order = order[(inds + 1)]
    return keep
