def boxes_iou_bev_gpu(boxes_a,
boxes_b,
box_mode='wlh',
metric='rotate_iou',
rect=False):
"""
This rotate boxes_iou_bev_gpu is about 9.25x faster than box_np_ops.riou_cc.
The box_np_ops.iou_jit is about 4.1x faster than the nearest boxes_iou_bev_gpu.
:param boxes_a: (M, 7), [x, y, z, h, w, l, ry], torch tensor with type float32
:param boxes_b: (N, 7), [x, y, z, h, w, l, ry], torch tensor with type float32
:return:
ans_iou: (M, N)
"""
if metric == 'rotate_iou':
boxes_a_bev = utils.boxes3d_to_bev_torch(boxes_a, box_mode, rect)
boxes_b_bev = utils.boxes3d_to_bev_torch(boxes_b, box_mode, rect)
elif metric == 'nearest_iou':
boxes_a_bev = utils.rbbox2d_to_near_bbox_torch(boxes_a, box_mode,
rect).cuda()
boxes_b_bev = utils.rbbox2d_to_near_bbox_torch(boxes_b, box_mode,
rect).cuda()
else:
raise NotImplementedError
ans_iou = torch.cuda.FloatTensor(
torch.Size((boxes_a.shape[0], boxes_b.shape[0]))).zero_()
iou3d_cuda.boxes_iou_bev_gpu(boxes_a_bev.contiguous(),
boxes_b_bev.contiguous(), ans_iou)
return ans_iou
def boxes_iou_bev(self,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 boxes_iou_bev(boxes_a, boxes_b):
"""
:param boxes_a: (M, 5)
:param boxes_b: (N, 5)
:return:
ans_iou: (M, N)
"""
if boxes_a.numel() == 0 or boxes_b.numel() == 0:
return torch.empty((1, 1))
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