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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import pytest | |
| import torch | |
| from mmcv.ops import ball_query | |
| def test_ball_query(): | |
| new_xyz = torch.tensor([[[-0.0740, 1.3147, -1.3625], | |
| [-2.2769, 2.7817, -0.2334], | |
| [-0.4003, 2.4666, -0.5116], | |
| [-0.0740, 1.3147, -1.3625], | |
| [-0.0740, 1.3147, -1.3625]], | |
| [[-2.0289, 2.4952, -0.1708], | |
| [-2.0668, 6.0278, -0.4875], | |
| [0.4066, 1.4211, -0.2947], | |
| [-2.0289, 2.4952, -0.1708], | |
| [-2.0289, 2.4952, -0.1708]]]).cuda() | |
| xyz = torch.tensor([[[-0.0740, 1.3147, -1.3625], [0.5555, 1.0399, -1.3634], | |
| [-0.4003, 2.4666, | |
| -0.5116], [-0.5251, 2.4379, -0.8466], | |
| [-0.9691, 1.1418, | |
| -1.3733], [-0.2232, 0.9561, -1.3626], | |
| [-2.2769, 2.7817, -0.2334], | |
| [-0.2822, 1.3192, -1.3645], [0.1533, 1.5024, -1.0432], | |
| [0.4917, 1.1529, -1.3496]], | |
| [[-2.0289, 2.4952, | |
| -0.1708], [-0.7188, 0.9956, -0.5096], | |
| [-2.0668, 6.0278, -0.4875], [-1.9304, 3.3092, 0.6610], | |
| [0.0949, 1.4332, 0.3140], [-1.2879, 2.0008, -0.7791], | |
| [-0.7252, 0.9611, -0.6371], [0.4066, 1.4211, -0.2947], | |
| [0.3220, 1.4447, 0.3548], [-0.9744, 2.3856, | |
| -1.2000]]]).cuda() | |
| idx = ball_query(0, 0.2, 5, xyz, new_xyz) | |
| expected_idx = torch.tensor([[[0, 0, 0, 0, 0], [6, 6, 6, 6, 6], | |
| [2, 2, 2, 2, 2], [0, 0, 0, 0, 0], | |
| [0, 0, 0, 0, 0]], | |
| [[0, 0, 0, 0, 0], [2, 2, 2, 2, 2], | |
| [7, 7, 7, 7, 7], [0, 0, 0, 0, 0], | |
| [0, 0, 0, 0, 0]]]).cuda() | |
| assert torch.all(idx == expected_idx) | |
| # test dilated ball query | |
| idx = ball_query(0.2, 0.4, 5, xyz, new_xyz) | |
| expected_idx = torch.tensor([[[0, 5, 7, 0, 0], [6, 6, 6, 6, 6], | |
| [2, 3, 2, 2, 2], [0, 5, 7, 0, 0], | |
| [0, 5, 7, 0, 0]], | |
| [[0, 0, 0, 0, 0], [2, 2, 2, 2, 2], | |
| [7, 7, 7, 7, 7], [0, 0, 0, 0, 0], | |
| [0, 0, 0, 0, 0]]]).cuda() | |
| assert torch.all(idx == expected_idx) | |