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[Feature] Add interpolate ops from mmdet3d (#1355)  
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import pytest
import torch

from mmcv.ops import three_nn


@pytest.mark.skipif(
    not torch.cuda.is_available(), reason='requires CUDA support')
def test_three_nn():
    known = torch.tensor([[[-1.8373, 3.5605,
                            -0.7867], [0.7615, 2.9420, 0.2314],
                           [-0.6503, 3.6637, -1.0622],
                           [-1.8373, 3.5605, -0.7867],
                           [-1.8373, 3.5605, -0.7867]],
                          [[-1.3399, 1.9991, -0.3698],
                           [-0.0799, 0.9698,
                            -0.8457], [0.0858, 2.4721, -0.1928],
                           [-1.3399, 1.9991, -0.3698],
                           [-1.3399, 1.9991, -0.3698]]]).cuda()

    unknown = torch.tensor([[[-1.8373, 3.5605, -0.7867],
                             [0.7615, 2.9420, 0.2314],
                             [-0.6503, 3.6637, -1.0622],
                             [-1.5237, 2.3976, -0.8097],
                             [-0.0722, 3.4017, -0.2880],
                             [0.5198, 3.0661, -0.4605],
                             [-2.0185, 3.5019, -0.3236],
                             [0.5098, 3.1020, 0.5799],
                             [-1.6137, 3.8443, -0.5269],
                             [0.7341, 2.9626, -0.3189]],
                            [[-1.3399, 1.9991, -0.3698],
                             [-0.0799, 0.9698, -0.8457],
                             [0.0858, 2.4721, -0.1928],
                             [-0.9022, 1.6560, -1.3090],
                             [0.1156, 1.6901, -0.4366],
                             [-0.6477, 2.3576, -0.1563],
                             [-0.8482, 1.1466, -1.2704],
                             [-0.8753, 2.0845, -0.3460],
                             [-0.5621, 1.4233, -1.2858],
                             [-0.5883, 1.3114, -1.2899]]]).cuda()

    dist, idx = three_nn(unknown, known)
    expected_dist = torch.tensor([[[0.0000, 0.0000, 0.0000],
                                   [0.0000, 2.0463, 2.8588],
                                   [0.0000, 1.2229, 1.2229],
                                   [1.2047, 1.2047, 1.2047],
                                   [1.0011, 1.0845, 1.8411],
                                   [0.7433, 1.4451, 2.4304],
                                   [0.5007, 0.5007, 0.5007],
                                   [0.4587, 2.0875, 2.7544],
                                   [0.4450, 0.4450, 0.4450],
                                   [0.5514, 1.7206, 2.6811]],
                                  [[0.0000, 0.0000, 0.0000],
                                   [0.0000, 1.6464, 1.6952],
                                   [0.0000, 1.5125, 1.5125],
                                   [1.0915, 1.0915, 1.0915],
                                   [0.8197, 0.8511, 1.4894],
                                   [0.7433, 0.8082, 0.8082],
                                   [0.8955, 1.3340, 1.3340],
                                   [0.4730, 0.4730, 0.4730],
                                   [0.7949, 1.3325, 1.3325],
                                   [0.7566, 1.3727, 1.3727]]]).cuda()
    expected_idx = torch.tensor([[[0, 3, 4], [1, 2, 0], [2, 0, 3], [0, 3, 4],
                                  [2, 1, 0], [1, 2, 0], [0, 3, 4], [1, 2, 0],
                                  [0, 3, 4], [1, 2, 0]],
                                 [[0, 3, 4], [1, 2, 0], [2, 0, 3], [0, 3, 4],
                                  [2, 1, 0], [2, 0, 3], [1, 0, 3], [0, 3, 4],
                                  [1, 0, 3], [1, 0, 3]]]).cuda()

    assert torch.allclose(dist, expected_dist, 1e-4)
    assert torch.all(idx == expected_idx)