iou3d_utils.py

import torch

from . import iou3d_cuda


def boxes_iou_bev(boxes_a, boxes_b):
    """
    :param boxes_a: (M, 5)
    :param boxes_b: (N, 5)
    :return:
        ans_iou: (M, N)
    """

    ans_iou = torch.cuda.FloatTensor(
        torch.Size((boxes_a.shape[0], boxes_b.shape[0]))).zero_()

    iou3d_cuda.boxes_iou_bev_gpu(boxes_a.contiguous(), boxes_b.contiguous(),
                                 ans_iou)

    return ans_iou


def nms_gpu(boxes, scores, thresh):
    """
    :param boxes: (N, 5) [x1, y1, x2, y2, ry]
    :param scores: (N)
    :param thresh:
    :return:
    """
    # areas = (x2 - x1) * (y2 - y1)
    order = scores.sort(0, descending=True)[1]

    boxes = boxes[order].contiguous()

    keep = torch.LongTensor(boxes.size(0))
    num_out = iou3d_cuda.nms_gpu(boxes, keep, thresh)
    return order[keep[:num_out].cuda()].contiguous()


def nms_normal_gpu(boxes, scores, thresh):
    """
    :param boxes: (N, 5) [x1, y1, x2, y2, ry]
    :param scores: (N)
    :param thresh:
    :return:
    """
    # areas = (x2 - x1) * (y2 - y1)
    order = scores.sort(0, descending=True)[1]

    boxes = boxes[order].contiguous()

    keep = torch.LongTensor(boxes.size(0))
    num_out = iou3d_cuda.nms_normal_gpu(boxes, keep, thresh)
    return order[keep[:num_out].cuda()].contiguous()