| | |
| | 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) |
| |
|
