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# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcv.ops import RoIPointPool3d
@pytest.mark.skipif(
not torch.cuda.is_available(), reason='requires CUDA support')
def test_roipoint():
feats = 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=torch.float32).unsqueeze(0).cuda()
points = feats.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=torch.float32).cuda()
roipoint_pool3d = RoIPointPool3d(num_sampled_points=4)
roi_feat, empty_flag = roipoint_pool3d(feats, points, 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]]]]).cuda()
expected_empty_flag = torch.tensor([[0, 0]]).int().cuda()
assert torch.allclose(roi_feat, expected_roi_feat)
assert torch.allclose(empty_flag, expected_empty_flag)
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