Spaces:
Running
on
L40S
Running
on
L40S
File size: 6,576 Bytes
d7e58f0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 |
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from mmcv.ops import (RoIAwarePool3d, points_in_boxes_all, points_in_boxes_cpu,
points_in_boxes_part)
@pytest.mark.skipif(
not torch.cuda.is_available(), reason='requires CUDA support')
def test_RoIAwarePool3d():
roiaware_pool3d_max = RoIAwarePool3d(
out_size=4, max_pts_per_voxel=128, mode='max')
roiaware_pool3d_avg = RoIAwarePool3d(
out_size=4, max_pts_per_voxel=128, mode='avg')
rois = torch.tensor(
[[1.0, 2.0, 3.0, 5.0, 4.0, 6.0, -0.3 - np.pi / 2],
[-10.0, 23.0, 16.0, 20.0, 10.0, 20.0, -0.5 - np.pi / 2]],
dtype=torch.float32).cuda(
) # boxes (m, 7) with bottom center in lidar coordinate
pts = 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).cuda() # points (n, 3) in lidar coordinate
pts_feature = pts.clone()
pooled_features_max = roiaware_pool3d_max(
rois=rois, pts=pts, pts_feature=pts_feature)
assert pooled_features_max.shape == torch.Size([2, 4, 4, 4, 3])
assert torch.allclose(pooled_features_max.sum(),
torch.tensor(51.100).cuda(), 1e-3)
pooled_features_avg = roiaware_pool3d_avg(
rois=rois, pts=pts, pts_feature=pts_feature)
assert pooled_features_avg.shape == torch.Size([2, 4, 4, 4, 3])
assert torch.allclose(pooled_features_avg.sum(),
torch.tensor(49.750).cuda(), 1e-3)
@pytest.mark.skipif(
not torch.cuda.is_available(), reason='requires CUDA support')
def test_points_in_boxes_part():
boxes = 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(
) # boxes (b, t, 7) with bottom center in lidar coordinate
pts = 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], [6, 4, 9]]],
dtype=torch.float32).cuda() # points (b, m, 3) in lidar coordinate
point_indices = points_in_boxes_part(points=pts, boxes=boxes)
expected_point_indices = torch.tensor(
[[0, 0, 0, 0, 0, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1, -1]],
dtype=torch.int32).cuda()
assert point_indices.shape == torch.Size([2, 8])
assert (point_indices == expected_point_indices).all()
boxes = torch.tensor([[[0.0, 0.0, 0.0, 1.0, 20.0, 1.0, 0.523598]]],
dtype=torch.float32).cuda() # 30 degrees
pts = torch.tensor(
[[[4, 6.928, 0], [6.928, 4, 0], [4, -6.928, 0], [6.928, -4, 0],
[-4, 6.928, 0], [-6.928, 4, 0], [-4, -6.928, 0], [-6.928, -4, 0]]],
dtype=torch.float32).cuda()
point_indices = points_in_boxes_part(points=pts, boxes=boxes)
expected_point_indices = torch.tensor([[-1, -1, 0, -1, 0, -1, -1, -1]],
dtype=torch.int32).cuda()
assert (point_indices == expected_point_indices).all()
def test_points_in_boxes_cpu():
boxes = 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
) # boxes (m, 7) with bottom center in lidar coordinate
pts = 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) # points (n, 3) in lidar coordinate
point_indices = points_in_boxes_cpu(points=pts, boxes=boxes)
expected_point_indices = torch.tensor(
[[[1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [0, 1], [0, 0], [0, 0],
[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]],
dtype=torch.int32)
assert point_indices.shape == torch.Size([1, 15, 2])
assert (point_indices == expected_point_indices).all()
boxes = torch.tensor([[[0.0, 0.0, 0.0, 1.0, 20.0, 1.0, 0.523598]]],
dtype=torch.float32) # 30 degrees
pts = torch.tensor(
[[[4, 6.928, 0], [6.928, 4, 0], [4, -6.928, 0], [6.928, -4, 0],
[-4, 6.928, 0], [-6.928, 4, 0], [-4, -6.928, 0], [-6.928, -4, 0]]],
dtype=torch.float32)
point_indices = points_in_boxes_cpu(points=pts, boxes=boxes)
expected_point_indices = torch.tensor(
[[[0], [0], [1], [0], [1], [0], [0], [0]]], dtype=torch.int32)
assert (point_indices == expected_point_indices).all()
@pytest.mark.skipif(
not torch.cuda.is_available(), reason='requires CUDA support')
def test_points_in_boxes_all():
boxes = 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(
) # boxes (m, 7) with bottom center in lidar coordinate
pts = 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).cuda() # points (n, 3) in lidar coordinate
point_indices = points_in_boxes_all(points=pts, boxes=boxes)
expected_point_indices = torch.tensor(
[[[1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [0, 1], [0, 0], [0, 0],
[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]],
dtype=torch.int32).cuda()
assert point_indices.shape == torch.Size([1, 15, 2])
assert (point_indices == expected_point_indices).all()
if torch.cuda.device_count() > 1:
pts = pts.to('cuda:1')
boxes = boxes.to('cuda:1')
expected_point_indices = expected_point_indices.to('cuda:1')
point_indices = points_in_boxes_all(points=pts, boxes=boxes)
assert point_indices.shape == torch.Size([1, 15, 2])
assert (point_indices == expected_point_indices).all()
|