File size: 5,201 Bytes
2c924d3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch import nn
from torch.autograd import Function

from ..utils import ext_loader

ext_module = ext_loader.load_ext(
    '_ext',
    ['dynamic_point_to_voxel_forward', 'dynamic_point_to_voxel_backward'])


class _DynamicScatter(Function):

    @staticmethod
    def forward(ctx, feats, coors, reduce_type='max'):
        """convert kitti points(N, >=3) to voxels.

        Args:
            feats (torch.Tensor): [N, C]. Points features to be reduced
                into voxels.
            coors (torch.Tensor): [N, ndim]. Corresponding voxel coordinates
                (specifically multi-dim voxel index) of each points.
            reduce_type (str, optional): Reduce op. support 'max', 'sum' and
                'mean'. Default: 'max'.

        Returns:
            voxel_feats (torch.Tensor): [M, C]. Reduced features, input
                features that shares the same voxel coordinates are reduced to
                one row.
            voxel_coors (torch.Tensor): [M, ndim]. Voxel coordinates.
        """
        results = ext_module.dynamic_point_to_voxel_forward(
            feats, coors, reduce_type)
        (voxel_feats, voxel_coors, point2voxel_map,
         voxel_points_count) = results
        ctx.reduce_type = reduce_type
        ctx.save_for_backward(feats, voxel_feats, point2voxel_map,
                              voxel_points_count)
        ctx.mark_non_differentiable(voxel_coors)
        return voxel_feats, voxel_coors

    @staticmethod
    def backward(ctx, grad_voxel_feats, grad_voxel_coors=None):
        (feats, voxel_feats, point2voxel_map,
         voxel_points_count) = ctx.saved_tensors
        grad_feats = torch.zeros_like(feats)
        # TODO: whether to use index put or use cuda_backward
        # To use index put, need point to voxel index
        ext_module.dynamic_point_to_voxel_backward(
            grad_feats, grad_voxel_feats.contiguous(), feats, voxel_feats,
            point2voxel_map, voxel_points_count, ctx.reduce_type)
        return grad_feats, None, None


dynamic_scatter = _DynamicScatter.apply


class DynamicScatter(nn.Module):
    """Scatters points into voxels, used in the voxel encoder with dynamic
    voxelization.

    Note:
        The CPU and GPU implementation get the same output, but have numerical
        difference after summation and division (e.g., 5e-7).

    Args:
        voxel_size (list): list [x, y, z] size of three dimension.
        point_cloud_range (list): The coordinate range of points, [x_min,
            y_min, z_min, x_max, y_max, z_max].
        average_points (bool): whether to use avg pooling to scatter points
            into voxel.
    """

    def __init__(self, voxel_size, point_cloud_range, average_points: bool):
        super().__init__()

        self.voxel_size = voxel_size
        self.point_cloud_range = point_cloud_range
        self.average_points = average_points

    def forward_single(self, points, coors):
        """Scatters points into voxels.

        Args:
            points (torch.Tensor): Points to be reduced into voxels.
            coors (torch.Tensor): Corresponding voxel coordinates (specifically
                multi-dim voxel index) of each points.

        Returns:
            voxel_feats (torch.Tensor): Reduced features, input features that
                shares the same voxel coordinates are reduced to one row.
            voxel_coors (torch.Tensor): Voxel coordinates.
        """
        reduce = 'mean' if self.average_points else 'max'
        return dynamic_scatter(points.contiguous(), coors.contiguous(), reduce)

    def forward(self, points, coors):
        """Scatters points/features into voxels.

        Args:
            points (torch.Tensor): Points to be reduced into voxels.
            coors (torch.Tensor): Corresponding voxel coordinates (specifically
                multi-dim voxel index) of each points.

        Returns:
            voxel_feats (torch.Tensor): Reduced features, input features that
                shares the same voxel coordinates are reduced to one row.
            voxel_coors (torch.Tensor): Voxel coordinates.
        """
        if coors.size(-1) == 3:
            return self.forward_single(points, coors)
        else:
            batch_size = coors[-1, 0] + 1
            voxels, voxel_coors = [], []
            for i in range(batch_size):
                inds = torch.where(coors[:, 0] == i)
                voxel, voxel_coor = self.forward_single(
                    points[inds], coors[inds][:, 1:])
                coor_pad = nn.functional.pad(
                    voxel_coor, (1, 0), mode='constant', value=i)
                voxel_coors.append(coor_pad)
                voxels.append(voxel)
            features = torch.cat(voxels, dim=0)
            feature_coors = torch.cat(voxel_coors, dim=0)

            return features, feature_coors

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