mirror of https://github.com/open-mmlab/mmcv.git
68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import pytest
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import torch
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from mmcv.ops import gather_points
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from mmcv.utils import IS_CUDA_AVAILABLE, IS_NPU_AVAILABLE
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class TestGatherPoints:
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@pytest.mark.parametrize('device', [
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pytest.param(
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'cuda',
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marks=pytest.mark.skipif(
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not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
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pytest.param(
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'npu',
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marks=pytest.mark.skipif(
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not IS_NPU_AVAILABLE, reason='requires NPU support'))
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])
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def test_gather_points_all_close(self, device):
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features = torch.tensor(
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[[[
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-1.6095, -0.1029, -0.8876, -1.2447, -2.4031, 0.3708, -1.1586,
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-1.4967, -0.4800, 0.2252
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],
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[
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1.9138, 3.4979, 1.6854, 1.5631, 3.6776, 3.1154, 2.1705,
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2.5221, 2.0411, 3.1446
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],
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[
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-1.4173, 0.3073, -1.4339, -1.4340, -1.2770, -0.2867, -1.4162,
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-1.4044, -1.4245, -1.4074
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]],
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[[
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0.2160, 0.0842, 0.3661, -0.2749, -0.4909, -0.6066, -0.8773,
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-0.0745, -0.9496, 0.1434
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],
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[
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1.3644, 1.8087, 1.6855, 1.9563, 1.2746, 1.9662, 0.9566,
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1.8778, 1.1437, 1.3639
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],
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[
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-0.7172, 0.1692, 0.2241, 0.0721, -0.7540, 0.0462, -0.6227,
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0.3223, -0.6944, -0.5294
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]]],
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dtype=torch.float,
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device=device)
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idx = torch.tensor([[0, 1, 4, 0, 0, 0], [0, 5, 6, 0, 0, 0]],
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dtype=torch.int32,
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device=device)
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output = gather_points(features, idx)
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expected_output = torch.tensor(
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[[[-1.6095, -0.1029, -2.4031, -1.6095, -1.6095, -1.6095],
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[1.9138, 3.4979, 3.6776, 1.9138, 1.9138, 1.9138],
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[-1.4173, 0.3073, -1.2770, -1.4173, -1.4173, -1.4173]],
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[[0.2160, -0.6066, -0.8773, 0.2160, 0.2160, 0.2160],
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[1.3644, 1.9662, 0.9566, 1.3644, 1.3644, 1.3644],
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[-0.7172, 0.0462, -0.6227, -0.7172, -0.7172, -0.7172]]],
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dtype=torch.float,
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device=device)
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assert torch.allclose(output, expected_output)
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# test fp16
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output_half = gather_points(features.half(), idx)
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assert torch.allclose(output_half, expected_output.half())
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