code: # Copyright (c) OpenMMLab. All rights reserved.
  import numpy as np
  import pytest
  import torch
  
  from mmcv.device.mlu import IS_MLU_AVAILABLE
  from mmcv.utils import IS_CUDA_AVAILABLE
  
  
  class TestBBox(object):
  
      def _test_bbox_overlaps(self, device, dtype=torch.float):
          from mmcv.ops import bbox_overlaps
          b1 = torch.tensor([[1.0, 1.0, 3.0, 4.0], [2.0, 2.0, 3.0, 4.0],
                             [7.0, 7.0, 8.0, 8.0]]).to(device).type(dtype)
          b2 = torch.tensor([[0.0, 2.0, 2.0, 5.0], [2.0, 1.0, 3.0,
                                                    3.0]]).to(device).type(dtype)
          should_output = np.array([[0.33333334, 0.5], [0.2, 0.5], [0.0, 0.0]])
          out = bbox_overlaps(b1, b2, offset=1)
          assert np.allclose(out.cpu().numpy(), should_output, 1e-2)
  
          b1 = torch.tensor([[1.0, 1.0, 3.0, 4.0], [2.0, 2.0, 3.0,
                                                    4.0]]).to(device).type(dtype)
          b2 = torch.tensor([[0.0, 2.0, 2.0, 5.0], [2.0, 1.0, 3.0,
                                                    3.0]]).to(device).type(dtype)
          should_output = np.array([0.33333334, 0.5])
          out = bbox_overlaps(b1, b2, aligned=True, offset=1)
          assert np.allclose(out.cpu().numpy(), should_output, 1e-2)
  
          b1 = torch.tensor([[0.0, 0.0, 3.0, 3.0]]).to(device).type(dtype)
          b2 = torch.tensor([[4.0, 0.0, 5.0, 3.0], [3.0, 0.0, 4.0, 3.0],
                             [2.0, 0.0, 3.0, 3.0], [1.0, 0.0, 2.0,
                                                    3.0]]).to(device).type(dtype)
          should_output = np.array([0, 0.2, 0.5, 0.5])
          out = bbox_overlaps(b1, b2, offset=1)
          assert np.allclose(out.cpu().numpy(), should_output, 1e-2)
  
      @pytest.mark.parametrize('device', [
          pytest.param(
              'cuda',
              marks=pytest.mark.skipif(
                  not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
          pytest.param(
              'mlu',
              marks=pytest.mark.skipif(
                  not IS_MLU_AVAILABLE, reason='requires MLU support'))
      ])
      def test_bbox_overlaps_float(self, device):
          self._test_bbox_overlaps(device, dtype=torch.float)
  
      @pytest.mark.parametrize('device', [
          pytest.param(
              'cuda',
              marks=pytest.mark.skipif(
                  not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
          pytest.param(
              'mlu',
              marks=pytest.mark.skipif(
                  not IS_MLU_AVAILABLE, reason='requires MLU support'))
      ])
      def test_bbox_overlaps_half(self, device):
          self._test_bbox_overlaps(device, dtype=torch.half)
  
apis: numpy.array torch.tensor