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#include "radius_cpu.h"
#include "utils.h"
#include "utils/KDTreeVectorOfVectorsAdaptor.h"
#include "utils/nanoflann.hpp"
torch::Tensor radius_cpu(torch::Tensor x, torch::Tensor y,
torch::optional<torch::Tensor> ptr_x,
torch::optional<torch::Tensor> ptr_y, double r,
int64_t max_num_neighbors, int64_t num_workers) {
CHECK_CPU(x);
CHECK_INPUT(x.dim() == 2);
CHECK_CPU(y);
CHECK_INPUT(y.dim() == 2);
if (ptr_x.has_value()) {
CHECK_CPU(ptr_x.value());
CHECK_INPUT(ptr_x.value().dim() == 1);
}
if (ptr_y.has_value()) {
CHECK_CPU(ptr_y.value());
CHECK_INPUT(ptr_y.value().dim() == 1);
}
std::vector<size_t> out_vec = std::vector<size_t>();
AT_DISPATCH_ALL_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, x.scalar_type(), "radius_cpu", [&] {
// See: nanoflann/examples/vector_of_vectors_example.cpp
auto x_data = x.data_ptr<scalar_t>();
auto y_data = y.data_ptr<scalar_t>();
typedef std::vector<std::vector<scalar_t>> vec_t;
nanoflann::SearchParams params;
params.sorted = false;
if (!ptr_x.has_value()) { // Single example.
vec_t pts(x.size(0));
for (int64_t i = 0; i < x.size(0); i++) {
pts[i].resize(x.size(1));
for (int64_t j = 0; j < x.size(1); j++) {
pts[i][j] = x_data[i * x.size(1) + j];
}
}
typedef KDTreeVectorOfVectorsAdaptor<vec_t, scalar_t> my_kd_tree_t;
my_kd_tree_t mat_index(x.size(1), pts, 10);
mat_index.index->buildIndex();
for (int64_t i = 0; i < y.size(0); i++) {
std::vector<std::pair<size_t, scalar_t>> ret_matches;
size_t num_matches = mat_index.index->radiusSearch(
y_data + i * y.size(1), r * r, ret_matches, params);
for (size_t j = 0; j < std::min(num_matches, (size_t)max_num_neighbors);
j++) {
out_vec.push_back(ret_matches[j].first);
out_vec.push_back(i);
}
}
} else { // Batch-wise.
auto ptr_x_data = ptr_x.value().data_ptr<int64_t>();
auto ptr_y_data = ptr_y.value().data_ptr<int64_t>();
for (int64_t b = 0; b < ptr_x.value().size(0) - 1; b++) {
auto x_start = ptr_x_data[b], x_end = ptr_x_data[b + 1];
auto y_start = ptr_y_data[b], y_end = ptr_y_data[b + 1];
if (x_start == x_end || y_start == y_end)
continue;
vec_t pts(x_end - x_start);
for (int64_t i = 0; i < x_end - x_start; i++) {
pts[i].resize(x.size(1));
for (int64_t j = 0; j < x.size(1); j++) {
pts[i][j] = x_data[(i + x_start) * x.size(1) + j];
}
}
typedef KDTreeVectorOfVectorsAdaptor<vec_t, scalar_t> my_kd_tree_t;
my_kd_tree_t mat_index(x.size(1), pts, 10);
mat_index.index->buildIndex();
for (int64_t i = y_start; i < y_end; i++) {
std::vector<std::pair<size_t, scalar_t>> ret_matches;
size_t num_matches = mat_index.index->radiusSearch(
y_data + i * y.size(1), r * r, ret_matches, params);
for (size_t j = 0;
j < std::min(num_matches, (size_t)max_num_neighbors); j++) {
out_vec.push_back(x_start + ret_matches[j].first);
out_vec.push_back(i);
}
}
}
}
});
const int64_t size = out_vec.size() / 2;
auto out = torch::from_blob(out_vec.data(), {size, 2},
x.options().dtype(torch::kLong));
return out.t().index_select(0, torch::tensor({1, 0}));
}
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