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#include <ATen/TensorUtils.h>
#include <torch/extension.h>
// #include "voxelization.h"
namespace {
template <typename T, typename T_int>
void dynamic_voxelize_kernel(const torch::TensorAccessor<T, 2> points,
torch::TensorAccessor<T_int, 2> coors,
const std::vector<float> voxel_size,
const std::vector<float> coors_range,
const std::vector<int> grid_size,
const int num_points, const int num_features,
const int NDim) {
const int ndim_minus_1 = NDim - 1;
bool failed = false;
// int coor[NDim];
int* coor = new int[NDim]();
int c;
for (int i = 0; i < num_points; ++i) {
failed = false;
for (int j = 0; j < NDim; ++j) {
c = floor((points[i][j] - coors_range[j]) / voxel_size[j]);
// necessary to rm points out of range
if ((c < 0 || c >= grid_size[j])) {
failed = true;
break;
}
coor[j] = c;
}
for (int k = 0; k < NDim; ++k) {
if (failed)
coors[i][k] = -1;
else
coors[i][k] = coor[k];
}
}
delete[] coor;
return;
}
template <typename T, typename T_int>
void hard_voxelize_kernel(const torch::TensorAccessor<T, 2> points,
torch::TensorAccessor<T, 3> voxels,
torch::TensorAccessor<T_int, 2> coors,
torch::TensorAccessor<T_int, 1> num_points_per_voxel,
torch::TensorAccessor<T_int, 3> coor_to_voxelidx,
int& voxel_num, const std::vector<float> voxel_size,
const std::vector<float> coors_range,
const std::vector<int> grid_size,
const int max_points, const int max_voxels,
const int num_points, const int num_features,
const int NDim) {
// declare a temp coors
at::Tensor temp_coors = at::zeros(
{num_points, NDim}, at::TensorOptions().dtype(at::kInt).device(at::kCPU));
// First use dynamic voxelization to get coors,
// then check max points/voxels constraints
dynamic_voxelize_kernel<T, int>(points, temp_coors.accessor<int, 2>(),
voxel_size, coors_range, grid_size,
num_points, num_features, NDim);
int voxelidx, num;
auto coor = temp_coors.accessor<int, 2>();
for (int i = 0; i < num_points; ++i) {
// T_int* coor = temp_coors.data_ptr<int>() + i * NDim;
if (coor[i][0] == -1) continue;
voxelidx = coor_to_voxelidx[coor[i][0]][coor[i][1]][coor[i][2]];
// record voxel
if (voxelidx == -1) {
voxelidx = voxel_num;
if (max_voxels != -1 && voxel_num >= max_voxels) continue;
voxel_num += 1;
coor_to_voxelidx[coor[i][0]][coor[i][1]][coor[i][2]] = voxelidx;
for (int k = 0; k < NDim; ++k) {
coors[voxelidx][k] = coor[i][k];
}
}
// put points into voxel
num = num_points_per_voxel[voxelidx];
if (max_points == -1 || num < max_points) {
for (int k = 0; k < num_features; ++k) {
voxels[voxelidx][num][k] = points[i][k];
}
num_points_per_voxel[voxelidx] += 1;
}
}
return;
}
} // namespace
namespace voxelization {
int hard_voxelize_cpu(const at::Tensor& points, at::Tensor& voxels,
at::Tensor& coors, at::Tensor& num_points_per_voxel,
const std::vector<float> voxel_size,
const std::vector<float> coors_range,
const int max_points, const int max_voxels,
const int NDim = 3) {
// current version tooks about 0.02s_0.03s for one frame on cpu
// check device
AT_ASSERTM(points.device().is_cpu(), "points must be a CPU tensor");
std::vector<int> grid_size(NDim);
const int num_points = points.size(0);
const int num_features = points.size(1);
for (int i = 0; i < NDim; ++i) {
grid_size[i] =
round((coors_range[NDim + i] - coors_range[i]) / voxel_size[i]);
}
// coors, num_points_per_voxel, coor_to_voxelidx are int Tensor
// printf("cpu coor_to_voxelidx size: [%d, %d, %d]\n", grid_size[2],
// grid_size[1], grid_size[0]);
at::Tensor coor_to_voxelidx =
-at::ones({grid_size[2], grid_size[1], grid_size[0]}, coors.options());
int voxel_num = 0;
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
points.scalar_type(), "hard_voxelize_forward", [&] {
hard_voxelize_kernel<scalar_t, int>(
points.accessor<scalar_t, 2>(), voxels.accessor<scalar_t, 3>(),
coors.accessor<int, 2>(), num_points_per_voxel.accessor<int, 1>(),
coor_to_voxelidx.accessor<int, 3>(), voxel_num, voxel_size,
coors_range, grid_size, max_points, max_voxels, num_points,
num_features, NDim);
});
return voxel_num;
}
void dynamic_voxelize_cpu(const at::Tensor& points, at::Tensor& coors,
const std::vector<float> voxel_size,
const std::vector<float> coors_range,
const int NDim = 3) {
// check device
AT_ASSERTM(points.device().is_cpu(), "points must be a CPU tensor");
std::vector<int> grid_size(NDim);
const int num_points = points.size(0);
const int num_features = points.size(1);
for (int i = 0; i < NDim; ++i) {
grid_size[i] =
round((coors_range[NDim + i] - coors_range[i]) / voxel_size[i]);
}
// coors, num_points_per_voxel, coor_to_voxelidx are int Tensor
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
points.scalar_type(), "hard_voxelize_forward", [&] {
dynamic_voxelize_kernel<scalar_t, int>(
points.accessor<scalar_t, 2>(), coors.accessor<int, 2>(),
voxel_size, coors_range, grid_size, num_points, num_features, NDim);
});
return;
}
} // namespace voxelization
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