mirror of
https://github.com/open-mmlab/mmcv.git
synced 2025-06-03 21:54:52 +08:00
c0f5492ee9
* add ext ops, support parrots * fix lint * fix lint * update op from mmdetection * support non-pytorch env * fix import bug * test not import mmcv.op * rename mmcv.op to mmcv.ops * fix compile warning * 1. fix syncbn warning in pytorch 1.5 2. support only cpu compile 3. add point_sample from mmdet * fix text bug * update docstrings * fix line endings * minor updates * remove non_local from ops * bug fix for nonlocal2d * rename ops_ext to _ext and _ext to _flow_warp_ext * update the doc * try clang-format github action * fix github action * add ops to api.rst * fix cpp format * fix clang format issues * remove .clang-format Co-authored-by: Kai Chen <chenkaidev@gmail.com>
42 lines
1.8 KiB
Python
42 lines
1.8 KiB
Python
import numpy as np
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import torch
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class TestBBox(object):
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def _test_bbox_overlaps(self, dtype=torch.float):
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if not torch.cuda.is_available():
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return
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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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