# Copyright (c) OpenMMLab. All rights reserved. import pytest import torch from mmcv.ops import ball_query @pytest.mark.skipif( not torch.cuda.is_available(), reason='requires CUDA support') 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)