File size: 5,286 Bytes
251e479
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch import nn
from torch.autograd import Function
from torch.nn.modules.utils import _pair

from ..utils import ext_loader

ext_module = ext_loader.load_ext(
    '_ext', ['dynamic_voxelize_forward', 'hard_voxelize_forward'])


class _Voxelization(Function):

    @staticmethod
    def forward(ctx,
                points,
                voxel_size,
                coors_range,
                max_points=35,
                max_voxels=20000):
        """Convert kitti points(N, >=3) to voxels.

        Args:
            points (torch.Tensor): [N, ndim]. Points[:, :3] contain xyz points
                and points[:, 3:] contain other information like reflectivity.
            voxel_size (tuple or float): The size of voxel with the shape of
                [3].
            coors_range (tuple or float): The coordinate range of voxel with
                the shape of [6].
            max_points (int, optional): maximum points contained in a voxel. if
                max_points=-1, it means using dynamic_voxelize. Default: 35.
            max_voxels (int, optional): maximum voxels this function create.
                for second, 20000 is a good choice. Users should shuffle points
                before call this function because max_voxels may drop points.
                Default: 20000.

        Returns:
            voxels_out (torch.Tensor): Output voxels with the shape of [M,
                max_points, ndim]. Only contain points and returned when
                max_points != -1.
            coors_out (torch.Tensor): Output coordinates with the shape of
                [M, 3].
            num_points_per_voxel_out (torch.Tensor): Num points per voxel with
                the shape of [M]. Only returned when max_points != -1.
        """
        if max_points == -1 or max_voxels == -1:
            coors = points.new_zeros(size=(points.size(0), 3), dtype=torch.int)
            ext_module.dynamic_voxelize_forward(points, coors, voxel_size,
                                                coors_range, 3)
            return coors
        else:
            voxels = points.new_zeros(
                size=(max_voxels, max_points, points.size(1)))
            coors = points.new_zeros(size=(max_voxels, 3), dtype=torch.int)
            num_points_per_voxel = points.new_zeros(
                size=(max_voxels, ), dtype=torch.int)
            voxel_num = ext_module.hard_voxelize_forward(
                points, voxels, coors, num_points_per_voxel, voxel_size,
                coors_range, max_points, max_voxels, 3)
            # select the valid voxels
            voxels_out = voxels[:voxel_num]
            coors_out = coors[:voxel_num]
            num_points_per_voxel_out = num_points_per_voxel[:voxel_num]
            return voxels_out, coors_out, num_points_per_voxel_out


voxelization = _Voxelization.apply


class Voxelization(nn.Module):
    """Convert kitti points(N, >=3) to voxels.

    Please refer to `PVCNN <https://arxiv.org/abs/1907.03739>`_ for more
    details.

    Args:
        voxel_size (tuple or float): The size of voxel with the shape of [3].
        point_cloud_range (tuple or float): The coordinate range of voxel with
            the shape of [6].
        max_num_points (int): maximum points contained in a voxel. if
            max_points=-1, it means using dynamic_voxelize.
        max_voxels (int, optional): maximum voxels this function create.
            for second, 20000 is a good choice. Users should shuffle points
            before call this function because max_voxels may drop points.
            Default: 20000.
    """

    def __init__(self,
                 voxel_size,
                 point_cloud_range,
                 max_num_points,
                 max_voxels=20000):
        super().__init__()

        self.voxel_size = voxel_size
        self.point_cloud_range = point_cloud_range
        self.max_num_points = max_num_points
        if isinstance(max_voxels, tuple):
            self.max_voxels = max_voxels
        else:
            self.max_voxels = _pair(max_voxels)

        point_cloud_range = torch.tensor(
            point_cloud_range, dtype=torch.float32)
        voxel_size = torch.tensor(voxel_size, dtype=torch.float32)
        grid_size = (point_cloud_range[3:] -
                     point_cloud_range[:3]) / voxel_size
        grid_size = torch.round(grid_size).long()
        input_feat_shape = grid_size[:2]
        self.grid_size = grid_size
        # the origin shape is as [x-len, y-len, z-len]
        # [w, h, d] -> [d, h, w]
        self.pcd_shape = [*input_feat_shape, 1][::-1]

    def forward(self, input):
        if self.training:
            max_voxels = self.max_voxels[0]
        else:
            max_voxels = self.max_voxels[1]

        return voxelization(input, self.voxel_size, self.point_cloud_range,
                            self.max_num_points, max_voxels)

    def __repr__(self):
        s = self.__class__.__name__ + '('
        s += 'voxel_size=' + str(self.voxel_size)
        s += ', point_cloud_range=' + str(self.point_cloud_range)
        s += ', max_num_points=' + str(self.max_num_points)
        s += ', max_voxels=' + str(self.max_voxels)
        s += ')'
        return s