test_gather_points.py

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

from mmcv.ops import gather_points


@pytest.mark.skipif(
    not torch.cuda.is_available(), reason='requires CUDA support')
def test_gather_points():
    features = torch.tensor([[[
        -1.6095, -0.1029, -0.8876, -1.2447, -2.4031, 0.3708, -1.1586, -1.4967,
        -0.4800, 0.2252
    ],
                              [
                                  1.9138, 3.4979, 1.6854, 1.5631, 3.6776,
                                  3.1154, 2.1705, 2.5221, 2.0411, 3.1446
                              ],
                              [
                                  -1.4173, 0.3073, -1.4339, -1.4340, -1.2770,
                                  -0.2867, -1.4162, -1.4044, -1.4245, -1.4074
                              ]],
                             [[
                                 0.2160, 0.0842, 0.3661, -0.2749, -0.4909,
                                 -0.6066, -0.8773, -0.0745, -0.9496, 0.1434
                             ],
                              [
                                  1.3644, 1.8087, 1.6855, 1.9563, 1.2746,
                                  1.9662, 0.9566, 1.8778, 1.1437, 1.3639
                              ],
                              [
                                  -0.7172, 0.1692, 0.2241, 0.0721, -0.7540,
                                  0.0462, -0.6227, 0.3223, -0.6944, -0.5294
                              ]]]).cuda()

    idx = torch.tensor([[0, 1, 4, 0, 0, 0], [0, 5, 6, 0, 0, 0]]).int().cuda()

    output = gather_points(features, idx)
    expected_output = torch.tensor(
        [[[-1.6095, -0.1029, -2.4031, -1.6095, -1.6095, -1.6095],
          [1.9138, 3.4979, 3.6776, 1.9138, 1.9138, 1.9138],
          [-1.4173, 0.3073, -1.2770, -1.4173, -1.4173, -1.4173]],
         [[0.2160, -0.6066, -0.8773, 0.2160, 0.2160, 0.2160],
          [1.3644, 1.9662, 0.9566, 1.3644, 1.3644, 1.3644],
          [-0.7172, 0.0462, -0.6227, -0.7172, -0.7172, -0.7172]]]).cuda()

    assert torch.allclose(output, expected_output)

    # test fp16
    output_half = gather_points(features.half(), idx)
    assert torch.allclose(output_half, expected_output.half())