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mmcv/tests/test_ops/test_group_points.py

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Add copyright pre-commit-hook (#1742) * first commit * Add copyright pre-commit-hook
2022-02-24 09:24:25 +08:00
# Copyright (c) OpenMMLab. All rights reserved.
[Feature] Add group points ops from mmdet3d (#1415) * add op (group points) and its related ops (ball query and knn) in mmdet3d * refactor code * fix typo * refactor code * fix typo * refactor code * make input contiguous Co-authored-by: zhouzaida <zhouzaida@163.com>
2021-10-23 14:01:31 +08:00
import pytest
import torch
from mmcv.ops import grouping_operation
@pytest.mark.skipif(
not torch.cuda.is_available(), reason='requires CUDA support')
def test_grouping_points():
idx = torch.tensor([[[0, 0, 0], [3, 3, 3], [8, 8, 8], [0, 0, 0], [0, 0, 0],
[0, 0, 0]],
[[0, 0, 0], [6, 6, 6], [9, 9, 9], [0, 0, 0], [0, 0, 0],
[0, 0, 0]]]).int().cuda()
[Features] Add stack ball query and stack group points ops (#2292) * add stack sa model ops * fix lint * fix lint * fix comments * fix bug * fix lint * fix comments * fix lint * fix lint * fix
2022-10-28 00:42:10 +08:00
features = torch.tensor([[[
[Feature] Add group points ops from mmdet3d (#1415) * add op (group points) and its related ops (ball query and knn) in mmdet3d * refactor code * fix typo * refactor code * fix typo * refactor code * make input contiguous Co-authored-by: zhouzaida <zhouzaida@163.com>
2021-10-23 14:01:31 +08:00
0.5798, -0.7981, -0.9280, -1.3311, 1.3687, 0.9277, -0.4164, -1.8274,
0.9268, 0.8414
],
[
5.4247, 1.5113, 2.3944, 1.4740, 5.0300,
5.1030, 1.9360, 2.1939, 2.1581, 3.4666
],
[
-1.6266, -1.0281, -1.0393, -1.6931, -1.3982,
-0.5732, -1.0830, -1.7561, -1.6786, -1.6967
]],
[[
-0.0380, -0.1880, -1.5724, 0.6905, -0.3190,
0.7798, -0.3693, -0.9457, -0.2942, -1.8527
],
[
1.1773, 1.5009, 2.6399, 5.9242, 1.0962,
2.7346, 6.0865, 1.5555, 4.3303, 2.8229
],
[
-0.6646, -0.6870, -0.1125, -0.2224, -0.3445,
-1.4049, 0.4990, -0.7037, -0.9924, 0.0386
]]]).cuda()
[Features] Add stack ball query and stack group points ops (#2292) * add stack sa model ops * fix lint * fix lint * fix comments * fix bug * fix lint * fix comments * fix lint * fix lint * fix
2022-10-28 00:42:10 +08:00
output = grouping_operation(features, idx)
[Feature] Add group points ops from mmdet3d (#1415) * add op (group points) and its related ops (ball query and knn) in mmdet3d * refactor code * fix typo * refactor code * fix typo * refactor code * make input contiguous Co-authored-by: zhouzaida <zhouzaida@163.com>
2021-10-23 14:01:31 +08:00
expected_output = torch.tensor([[[[0.5798, 0.5798, 0.5798],
[-1.3311, -1.3311, -1.3311],
[0.9268, 0.9268, 0.9268],
[0.5798, 0.5798, 0.5798],
[0.5798, 0.5798, 0.5798],
[0.5798, 0.5798, 0.5798]],
[[5.4247, 5.4247, 5.4247],
[1.4740, 1.4740, 1.4740],
[2.1581, 2.1581, 2.1581],
[5.4247, 5.4247, 5.4247],
[5.4247, 5.4247, 5.4247],
[5.4247, 5.4247, 5.4247]],
[[-1.6266, -1.6266, -1.6266],
[-1.6931, -1.6931, -1.6931],
[-1.6786, -1.6786, -1.6786],
[-1.6266, -1.6266, -1.6266],
[-1.6266, -1.6266, -1.6266],
[-1.6266, -1.6266, -1.6266]]],
[[[-0.0380, -0.0380, -0.0380],
[-0.3693, -0.3693, -0.3693],
[-1.8527, -1.8527, -1.8527],
[-0.0380, -0.0380, -0.0380],
[-0.0380, -0.0380, -0.0380],
[-0.0380, -0.0380, -0.0380]],
[[1.1773, 1.1773, 1.1773],
[6.0865, 6.0865, 6.0865],
[2.8229, 2.8229, 2.8229],
[1.1773, 1.1773, 1.1773],
[1.1773, 1.1773, 1.1773],
[1.1773, 1.1773, 1.1773]],
[[-0.6646, -0.6646, -0.6646],
[0.4990, 0.4990, 0.4990],
[0.0386, 0.0386, 0.0386],
[-0.6646, -0.6646, -0.6646],
[-0.6646, -0.6646, -0.6646],
[-0.6646, -0.6646, -0.6646]]]]).cuda()
assert torch.allclose(output, expected_output)
[Features] Add stack ball query and stack group points ops (#2292) * add stack sa model ops * fix lint * fix lint * fix comments * fix bug * fix lint * fix comments * fix lint * fix lint * fix
2022-10-28 00:42:10 +08:00
@pytest.mark.skipif(
not torch.cuda.is_available(), reason='requires CUDA support')
def test_stack_grouping_points():
idx = torch.tensor([[0, 0, 0], [3, 3, 3], [8, 8, 8], [1, 1, 1], [0, 0, 0],
[2, 2, 2], [0, 0, 0], [6, 6, 6], [9, 9, 9], [0, 0, 0],
[1, 1, 1], [0, 0, 0]]).int().cuda()
features = torch.tensor([[
0.5798, -0.7981, -0.9280, -1.3311, 1.3687, 0.9277, -0.4164, -1.8274,
0.9268, 0.8414
],
[
5.4247, 1.5113, 2.3944, 1.4740, 5.0300,
5.1030, 1.9360, 2.1939, 2.1581, 3.4666
],
[
-1.6266, -1.0281, -1.0393, -1.6931, -1.3982,
-0.5732, -1.0830, -1.7561, -1.6786, -1.6967
],
[
-0.0380, -0.1880, -1.5724, 0.6905, -0.3190,
0.7798, -0.3693, -0.9457, -0.2942, -1.8527
],
[
1.1773, 1.5009, 2.6399, 5.9242, 1.0962,
2.7346, 6.0865, 1.5555, 4.3303, 2.8229
],
[
-0.6646, -0.6870, -0.1125, -0.2224, -0.3445,
-1.4049, 0.4990, -0.7037, -0.9924, 0.0386
]]).float().cuda()
features_batch_cnt = torch.tensor([3, 3]).int().cuda()
indices_batch_cnt = torch.tensor([6, 6]).int().cuda()
output = grouping_operation(features, idx, features_batch_cnt,
indices_batch_cnt)
expected_output = torch.Tensor([[[0.5798, 0.5798, 0.5798],
[-0.7981, -0.7981, -0.7981],
[-0.9280, -0.9280, -0.9280],
[-1.3311, -1.3311, -1.3311],
[1.3687, 1.3687, 1.3687],
[0.9277, 0.9277, 0.9277],
[-0.4164, -0.4164, -0.4164],
[-1.8274, -1.8274, -1.8274],
[0.9268, 0.9268, 0.9268],
[0.8414, 0.8414, 0.8414]],
[[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000]],
[[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000]],
[[5.4247, 5.4247, 5.4247],
[1.5113, 1.5113, 1.5113],
[2.3944, 2.3944, 2.3944],
[1.4740, 1.4740, 1.4740],
[5.0300, 5.0300, 5.0300],
[5.1030, 5.1030, 5.1030],
[1.9360, 1.9360, 1.9360],
[2.1939, 2.1939, 2.1939],
[2.1581, 2.1581, 2.1581],
[3.4666, 3.4666, 3.4666]],
[[0.5798, 0.5798, 0.5798],
[-0.7981, -0.7981, -0.7981],
[-0.9280, -0.9280, -0.9280],
[-1.3311, -1.3311, -1.3311],
[1.3687, 1.3687, 1.3687],
[0.9277, 0.9277, 0.9277],
[-0.4164, -0.4164, -0.4164],
[-1.8274, -1.8274, -1.8274],
[0.9268, 0.9268, 0.9268],
[0.8414, 0.8414, 0.8414]],
[[-1.6266, -1.6266, -1.6266],
[-1.0281, -1.0281, -1.0281],
[-1.0393, -1.0393, -1.0393],
[-1.6931, -1.6931, -1.6931],
[-1.3982, -1.3982, -1.3982],
[-0.5732, -0.5732, -0.5732],
[-1.0830, -1.0830, -1.0830],
[-1.7561, -1.7561, -1.7561],
[-1.6786, -1.6786, -1.6786],
[-1.6967, -1.6967, -1.6967]],
[[-0.0380, -0.0380, -0.0380],
[-0.1880, -0.1880, -0.1880],
[-1.5724, -1.5724, -1.5724],
[0.6905, 0.6905, 0.6905],
[-0.3190, -0.3190, -0.3190],
[0.7798, 0.7798, 0.7798],
[-0.3693, -0.3693, -0.3693],
[-0.9457, -0.9457, -0.9457],
[-0.2942, -0.2942, -0.2942],
[-1.8527, -1.8527, -1.8527]],
[[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000]],
[[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000]],
[[-0.0380, -0.0380, -0.0380],
[-0.1880, -0.1880, -0.1880],
[-1.5724, -1.5724, -1.5724],
[0.6905, 0.6905, 0.6905],
[-0.3190, -0.3190, -0.3190],
[0.7798, 0.7798, 0.7798],
[-0.3693, -0.3693, -0.3693],
[-0.9457, -0.9457, -0.9457],
[-0.2942, -0.2942, -0.2942],
[-1.8527, -1.8527, -1.8527]],
[[1.1773, 1.1773, 1.1773],
[1.5009, 1.5009, 1.5009],
[2.6399, 2.6399, 2.6399],
[5.9242, 5.9242, 5.9242],
[1.0962, 1.0962, 1.0962],
[2.7346, 2.7346, 2.7346],
[6.0865, 6.0865, 6.0865],
[1.5555, 1.5555, 1.5555],
[4.3303, 4.3303, 4.3303],
[2.8229, 2.8229, 2.8229]],
[[-0.0380, -0.0380, -0.0380],
[-0.1880, -0.1880, -0.1880],
[-1.5724, -1.5724, -1.5724],
[0.6905, 0.6905, 0.6905],
[-0.3190, -0.3190, -0.3190],
[0.7798, 0.7798, 0.7798],
[-0.3693, -0.3693, -0.3693],
[-0.9457, -0.9457, -0.9457],
[-0.2942, -0.2942, -0.2942],
[-1.8527, -1.8527,
-1.8527]]]).cuda().float()
assert torch.allclose(output, expected_output)