| |
| import numpy as np |
| import pytest |
| import torch |
| from mmengine.utils import digit_version |
|
|
| from mmcv.utils import (IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE, IS_MPS_AVAILABLE, |
| IS_NPU_AVAILABLE) |
|
|
|
|
| class TestBBox: |
|
|
| def _test_bbox_overlaps(self, device='cpu', 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) |
|
|
| b1 = torch.tensor([[10.0 + i, 10.0 + i, 30.0 + i, 30.0 + i] |
| for i in range(1000)]).to(device).type(dtype) |
| b2 = torch.tensor([[20.0 + i, 20.0 + i, 40.0 + i, 40.0 + i] |
| for i in range(1000)]).to(device).type(dtype) |
| should_output = np.array([1 / 7] * 1000) |
| out = bbox_overlaps(b1, b2, aligned=True) |
| assert np.allclose(out.cpu().numpy(), should_output, 1e-2) |
|
|
| @pytest.mark.parametrize('device', [ |
| 'cpu', |
| 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')), |
| pytest.param( |
| 'mps', |
| marks=pytest.mark.skipif( |
| not IS_MPS_AVAILABLE |
| or digit_version(torch.__version__) >= digit_version('2.1.0'), |
| reason='requires MPS support')), |
| pytest.param( |
| 'npu', |
| marks=pytest.mark.skipif( |
| not IS_NPU_AVAILABLE, reason='requires NPU 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')), |
| pytest.param( |
| 'npu', |
| marks=pytest.mark.skipif( |
| not IS_NPU_AVAILABLE, reason='requires NPU support')) |
| ]) |
| def test_bbox_overlaps_half(self, device): |
| self._test_bbox_overlaps(device, dtype=torch.half) |
|
|
