test_roipoint_pool3d.py

# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch

from mmcv.ops import RoIPointPool3d
from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE


@pytest.mark.parametrize('device', [
    pytest.param(
        'cuda',
        marks=pytest.mark.skipif(
            not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
    pytest.param(
        'mlu',
        marks=pytest.mark.skipif(
            not IS_MLU_AVAILABLE, reason='requires MLU support'))
])
@pytest.mark.parametrize('dtype', [
    torch.float, torch.half,
    pytest.param(
        torch.double,
        marks=pytest.mark.skipif(
            IS_MLU_AVAILABLE, reason='MLU does not support for double'))
])
def test_roipoint(device, dtype):
    points = torch.tensor(
        [[1, 2, 3.3], [1.2, 2.5, 3.0], [0.8, 2.1, 3.5], [1.6, 2.6, 3.6],
         [0.8, 1.2, 3.9], [-9.2, 21.0, 18.2], [3.8, 7.9, 6.3],
         [4.7, 3.5, -12.2], [3.8, 7.6, -2], [-10.6, -12.9, -20], [-16, -18, 9],
         [-21.3, -52, -5], [0, 0, 0], [6, 7, 8], [-2, -3, -4]],
        dtype=dtype).unsqueeze(0).to(device)
    feats = points.clone()
    rois = torch.tensor([[[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 0.3],
                          [-10.0, 23.0, 16.0, 10, 20, 20, 0.5]]],
                        dtype=dtype).to(device)

    roipoint_pool3d = RoIPointPool3d(num_sampled_points=4)
    roi_feat, empty_flag = roipoint_pool3d(points, feats, rois)
    expected_roi_feat = torch.tensor(
        [[[[1, 2, 3.3, 1, 2, 3.3], [1.2, 2.5, 3, 1.2, 2.5, 3],
           [0.8, 2.1, 3.5, 0.8, 2.1, 3.5], [1.6, 2.6, 3.6, 1.6, 2.6, 3.6]],
          [[-9.2, 21, 18.2, -9.2, 21, 18.2], [-9.2, 21, 18.2, -9.2, 21, 18.2],
           [-9.2, 21, 18.2, -9.2, 21, 18.2], [-9.2, 21, 18.2, -9.2, 21, 18.2]]]
         ],
        dtype=dtype).to(device)
    expected_empty_flag = torch.tensor([[0, 0]]).int().to(device)

    assert torch.allclose(roi_feat, expected_roi_feat)
    assert torch.allclose(empty_flag, expected_empty_flag)