Spaces:
Build error
Build error
File size: 5,286 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 |
# 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
|