mirror of https://github.com/open-mmlab/mmcv.git
43 lines
1.8 KiB
Python
43 lines
1.8 KiB
Python
import numpy as np
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import pytest
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import torch
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@pytest.mark.skipif(
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not torch.cuda.is_available(), reason='requires CUDA support')
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class TestBBox(object):
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def _test_bbox_overlaps(self, dtype=torch.float):
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from mmcv.ops import bbox_overlaps
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b1 = torch.tensor([[1.0, 1.0, 3.0, 4.0], [2.0, 2.0, 3.0, 4.0],
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[7.0, 7.0, 8.0, 8.0]]).cuda().type(dtype)
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b2 = torch.tensor([[0.0, 2.0, 2.0, 5.0], [2.0, 1.0, 3.0,
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3.0]]).cuda().type(dtype)
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should_output = np.array([[0.33333334, 0.5], [0.2, 0.5], [0.0, 0.0]])
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out = bbox_overlaps(b1, b2, offset=1)
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assert np.allclose(out.cpu().numpy(), should_output, 1e-2)
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b1 = torch.tensor([[1.0, 1.0, 3.0, 4.0], [2.0, 2.0, 3.0,
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4.0]]).cuda().type(dtype)
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b2 = torch.tensor([[0.0, 2.0, 2.0, 5.0], [2.0, 1.0, 3.0,
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3.0]]).cuda().type(dtype)
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should_output = np.array([0.33333334, 0.5])
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out = bbox_overlaps(b1, b2, aligned=True, offset=1)
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assert np.allclose(out.cpu().numpy(), should_output, 1e-2)
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b1 = torch.tensor([[0.0, 0.0, 3.0, 3.0]]).cuda().type(dtype)
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b1 = torch.tensor([[0.0, 0.0, 3.0, 3.0]]).cuda().type(dtype)
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b2 = torch.tensor([[4.0, 0.0, 5.0, 3.0], [3.0, 0.0, 4.0, 3.0],
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[2.0, 0.0, 3.0, 3.0], [1.0, 0.0, 2.0,
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3.0]]).cuda().type(dtype)
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should_output = np.array([0, 0.2, 0.5, 0.5])
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out = bbox_overlaps(b1, b2, offset=1)
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assert np.allclose(out.cpu().numpy(), should_output, 1e-2)
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def test_bbox_overlaps_float(self):
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self._test_bbox_overlaps(torch.float)
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def test_bbox_overlaps_half(self):
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self._test_bbox_overlaps(torch.half)
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