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Commit b8166f31 authored by rusty1s's avatar rusty1s
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linting and interface changes

parent cd7dbf25
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Showing with 517 additions and 555 deletions
csrc/cpu/utils/cloud.h 100755 → 100644
View file @ b8166f31
// Author: Peiyuan Liao (alexander_liao@outlook.com) #pragma once
//
# pragma once
#include <ATen/ATen.h> #include <ATen/ATen.h>
#include <algorithm>
#include <cmath> #include <cmath>
#include <vector> #include <iomanip>
#include <unordered_map> #include <iostream>
#include <map> #include <map>
#include <algorithm>
#include <numeric> #include <numeric>
#include <iostream> #include <unordered_map>
#include <iomanip> #include <vector>
#include <time.h> #include <time.h>
template <typename scalar_t> struct PointCloud {
template<typename scalar_t> std::vector<std::vector<scalar_t> *> pts;
struct PointCloud
{ void set(std::vector<scalar_t> new_pts, int dim) {
std::vector<std::vector<scalar_t>*> pts;
std::vector<std::vector<scalar_t> *> temp(new_pts.size() / dim);
void set(std::vector<scalar_t> new_pts, int dim){ for (size_t i = 0; i < new_pts.size(); i++) {
if (i % dim == 0) {
std::vector<std::vector<scalar_t>*> temp(new_pts.size()/dim); std::vector<scalar_t> *point = new std::vector<scalar_t>(dim);
for(size_t i=0; i < new_pts.size(); i++){
if(i%dim == 0){ for (size_t j = 0; j < (size_t)dim; j++) {
std::vector<scalar_t>* point = new std::vector<scalar_t>(dim); (*point)[j] = new_pts[i + j];
}
for (size_t j = 0; j < (size_t)dim; j++) { temp[i / dim] = point;
(*point)[j]=new_pts[i+j]; }
} }
temp[i/dim] = point;
} pts = temp;
} }
void set_batch(std::vector<scalar_t> new_pts, size_t begin, long size,
pts = temp; int dim) {
} std::vector<std::vector<scalar_t> *> temp(size);
void set_batch(std::vector<scalar_t> new_pts, size_t begin, long size, int dim){ for (size_t i = 0; i < (size_t)size; i++) {
std::vector<std::vector<scalar_t>*> temp(size); std::vector<scalar_t> *point = new std::vector<scalar_t>(dim);
for(size_t i=0; i < (size_t)size; i++){ for (size_t j = 0; j < (size_t)dim; j++) {
std::vector<scalar_t>* point = new std::vector<scalar_t>(dim); (*point)[j] = new_pts[dim * (begin + i) + j];
for (size_t j = 0; j < (size_t)dim; j++) { }
(*point)[j] = new_pts[dim*(begin+i)+j];
} temp[i] = point;
}
temp[i] = point; pts = temp;
}
}
pts = temp; // Must return the number of data points.
} inline size_t kdtree_get_point_count() const { return pts.size(); }
// Must return the number of data points // Returns the dim'th component of the idx'th point in the class:
inline size_t kdtree_get_point_count() const { return pts.size(); } inline scalar_t kdtree_get_pt(const size_t idx, const size_t dim) const {
return (*pts[idx])[dim];
// Returns the dim'th component of the idx'th point in the class: }
inline scalar_t kdtree_get_pt(const size_t idx, const size_t dim) const
{ // Optional bounding-box computation: return false to default to a standard
return (*pts[idx])[dim]; // bbox computation loop.
} // Return true if the BBOX was already computed by the class and returned in
// "bb" so it can be avoided to redo it again. Look at bb.size() to find out
// Optional bounding-box computation: return false to default to a standard bbox computation loop. // the expected dimensionality (e.g. 2 or 3 for point clouds)
// Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again. template <class BBOX> bool kdtree_get_bbox(BBOX & /* bb */) const {
// Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds) return false;
template <class BBOX> }
bool kdtree_get_bbox(BBOX& /* bb */) const { return false; }
}; };
csrc/cpu/utils/neighbors.cpp 100755 → 100644
View file @ b8166f31
#include "cloud.h" #include "cloud.h"
#include "nanoflann.hpp" #include "nanoflann.hpp"
#include <set>
#include <cstdint> #include <cstdint>
#include <thread>
#include <iostream> #include <iostream>
#include <set>
#include <thread>
typedef struct thread_struct { typedef struct thread_struct {
void* kd_tree; void *kd_tree;
void* matches; void *matches;
void* queries; void *queries;
size_t* max_count; size_t *max_count;
std::mutex* ct_m; std::mutex *ct_m;
std::mutex* tree_m; std::mutex *tree_m;
size_t start; size_t start;
size_t end; size_t end;
double search_radius; double search_radius;
bool small; bool small;
bool option; bool option;
size_t k; size_t k;
} thread_args; } thread_args;
template<typename scalar_t> template <typename scalar_t> void thread_routine(thread_args *targs) {
void thread_routine(thread_args* targs) { typedef nanoflann::KDTreeSingleIndexAdaptor<
typedef nanoflann::KDTreeSingleIndexAdaptor< nanoflann::L2_Adaptor<scalar_t, PointCloud<scalar_t> > , PointCloud<scalar_t>> my_kd_tree_t; nanoflann::L2_Adaptor<scalar_t, PointCloud<scalar_t>>,
typedef std::vector< std::vector<std::pair<size_t, scalar_t> > > kd_pair; PointCloud<scalar_t>>
my_kd_tree_t* index = (my_kd_tree_t*) targs->kd_tree; my_kd_tree_t;
kd_pair* matches = (kd_pair*)targs->matches; typedef std::vector<std::vector<std::pair<size_t, scalar_t>>> kd_pair;
PointCloud<scalar_t>* pcd_query = (PointCloud<scalar_t>*)targs->queries; my_kd_tree_t *index = (my_kd_tree_t *)targs->kd_tree;
size_t* max_count = targs->max_count; kd_pair *matches = (kd_pair *)targs->matches;
std::mutex* ct_m = targs->ct_m; PointCloud<scalar_t> *pcd_query = (PointCloud<scalar_t> *)targs->queries;
std::mutex* tree_m = targs->tree_m; size_t *max_count = targs->max_count;
double eps; std::mutex *ct_m = targs->ct_m;
if (targs->small) { std::mutex *tree_m = targs->tree_m;
eps = 0.000001; double eps;
} if (targs->small) {
else { eps = 0.000001;
eps = 0; } else {
} eps = 0;
double search_radius = (double) targs->search_radius; }
size_t start = targs->start; double search_radius = (double)targs->search_radius;
size_t end = targs->end; size_t start = targs->start;
auto k = targs->k; size_t end = targs->end;
for (size_t i = start; i < end; i++) { auto k = targs->k;
for (size_t i = start; i < end; i++) {
std::vector<scalar_t> p0 = *(((*pcd_query).pts)[i]);
std::vector<scalar_t> p0 = *(((*pcd_query).pts)[i]);
scalar_t* query_pt = new scalar_t[p0.size()];
std::copy(p0.begin(), p0.end(), query_pt); scalar_t *query_pt = new scalar_t[p0.size()];
(*matches)[i].reserve(*max_count); std::copy(p0.begin(), p0.end(), query_pt);
std::vector<std::pair<size_t, scalar_t> > ret_matches; (*matches)[i].reserve(*max_count);
std::vector<size_t>* knn_ret_matches = new std::vector<size_t>(k); std::vector<std::pair<size_t, scalar_t>> ret_matches;
std::vector<scalar_t>* knn_dist_matches = new std::vector<scalar_t>(k); std::vector<size_t> *knn_ret_matches = new std::vector<size_t>(k);
std::vector<scalar_t> *knn_dist_matches = new std::vector<scalar_t>(k);
tree_m->lock();
tree_m->lock();
size_t nMatches;
if (targs->option){ size_t nMatches;
nMatches = index->radiusSearch(query_pt, (scalar_t)(search_radius+eps), ret_matches, nanoflann::SearchParams()); if (targs->option) {
} nMatches = index->radiusSearch(query_pt, (scalar_t)(search_radius + eps),
else { ret_matches, nanoflann::SearchParams());
nMatches = index->knnSearch(query_pt, k, &(*knn_ret_matches)[0],&(* knn_dist_matches)[0]); } else {
auto temp = new std::vector<std::pair<size_t, scalar_t> >((*knn_dist_matches).size()); nMatches = index->knnSearch(query_pt, k, &(*knn_ret_matches)[0],
for (size_t j = 0; j < (*knn_ret_matches).size(); j++){ &(*knn_dist_matches)[0]);
(*temp)[j] = std::make_pair( (*knn_ret_matches)[j],(*knn_dist_matches)[j] ); auto temp = new std::vector<std::pair<size_t, scalar_t>>(
} (*knn_dist_matches).size());
ret_matches = *temp; for (size_t j = 0; j < (*knn_ret_matches).size(); j++) {
} (*temp)[j] =
tree_m->unlock(); std::make_pair((*knn_ret_matches)[j], (*knn_dist_matches)[j]);
}
(*matches)[i] = ret_matches; ret_matches = *temp;
}
ct_m->lock(); tree_m->unlock();
if(*max_count < nMatches) {
*max_count = nMatches; (*matches)[i] = ret_matches;
}
ct_m->unlock(); ct_m->lock();
if (*max_count < nMatches) {
} *max_count = nMatches;
}
ct_m->unlock();
}
} }
template<typename scalar_t> template <typename scalar_t>
size_t nanoflann_neighbors(std::vector<scalar_t>& queries, std::vector<scalar_t>& supports, size_t nanoflann_neighbors(std::vector<scalar_t> &queries,
std::vector<size_t>*& neighbors_indices, double radius, int dim, std::vector<scalar_t> &supports,
int64_t max_num, int64_t n_threads, int64_t k, int option){ std::vector<size_t> *&neighbors_indices,
double radius, int dim, int64_t max_num,
const scalar_t search_radius = static_cast<scalar_t>(radius*radius); int64_t n_threads, int64_t k, int option) {
// Counting vector const scalar_t search_radius = static_cast<scalar_t>(radius * radius);
size_t* max_count = new size_t();
*max_count = 1; // Counting vector
size_t *max_count = new size_t();
size_t ssize = supports.size(); *max_count = 1;
// CLoud variable
PointCloud<scalar_t> pcd; size_t ssize = supports.size();
pcd.set(supports, dim); // CLoud variable
// Cloud query PointCloud<scalar_t> pcd;
PointCloud<scalar_t>* pcd_query = new PointCloud<scalar_t>(); pcd.set(supports, dim);
(*pcd_query).set(queries, dim); // Cloud query
PointCloud<scalar_t> *pcd_query = new PointCloud<scalar_t>();
// Tree parameters (*pcd_query).set(queries, dim);
nanoflann::KDTreeSingleIndexAdaptorParams tree_params(15 /* max leaf */);
// Tree parameters
// KDTree type definition nanoflann::KDTreeSingleIndexAdaptorParams tree_params(15 /* max leaf */);
typedef nanoflann::KDTreeSingleIndexAdaptor< nanoflann::L2_Adaptor<scalar_t, PointCloud<scalar_t> > , PointCloud<scalar_t>> my_kd_tree_t;
typedef std::vector< std::vector<std::pair<size_t, scalar_t> > > kd_pair; // KDTree type definition
typedef nanoflann::KDTreeSingleIndexAdaptor<
// Pointer to trees nanoflann::L2_Adaptor<scalar_t, PointCloud<scalar_t>>,
my_kd_tree_t* index; PointCloud<scalar_t>>
index = new my_kd_tree_t(dim, pcd, tree_params); my_kd_tree_t;
index->buildIndex(); typedef std::vector<std::vector<std::pair<size_t, scalar_t>>> kd_pair;
// Search neigbors indices
// Pointer to trees
// Search params my_kd_tree_t *index;
nanoflann::SearchParams search_params; index = new my_kd_tree_t(dim, pcd, tree_params);
// search_params.sorted = true; index->buildIndex();
kd_pair* list_matches = new kd_pair((*pcd_query).pts.size()); // Search neigbors indices
// single threaded routine // Search params
if (n_threads == 1){ nanoflann::SearchParams search_params;
size_t i0 = 0; // search_params.sorted = true;
double eps; kd_pair *list_matches = new kd_pair((*pcd_query).pts.size());
if (ssize < 10) {
eps = 0.000001; // single threaded routine
} if (n_threads == 1) {
else { size_t i0 = 0;
eps = 0; double eps;
} if (ssize < 10) {
eps = 0.000001;
for (auto& p : (*pcd_query).pts){ } else {
auto p0 = *p; eps = 0;
// Find neighbors }
scalar_t* query_pt = new scalar_t[dim];
std::copy(p0.begin(), p0.end(), query_pt); for (auto &p : (*pcd_query).pts) {
auto p0 = *p;
(*list_matches)[i0].reserve(*max_count); // Find neighbors
std::vector<std::pair<size_t, scalar_t> > ret_matches; scalar_t *query_pt = new scalar_t[dim];
std::vector<size_t>* knn_ret_matches = new std::vector<size_t>(k); std::copy(p0.begin(), p0.end(), query_pt);
std::vector<scalar_t>* knn_dist_matches = new std::vector<scalar_t>(k);
(*list_matches)[i0].reserve(*max_count);
size_t nMatches; std::vector<std::pair<size_t, scalar_t>> ret_matches;
if (!!(option)){ std::vector<size_t> *knn_ret_matches = new std::vector<size_t>(k);
nMatches = index->radiusSearch(query_pt, (scalar_t)(search_radius+eps), ret_matches, search_params); std::vector<scalar_t> *knn_dist_matches = new std::vector<scalar_t>(k);
}
else { size_t nMatches;
nMatches = index->knnSearch(query_pt, (size_t)k, &(*knn_ret_matches)[0],&(* knn_dist_matches)[0]); if (!!(option)) {
auto temp = new std::vector<std::pair<size_t, scalar_t> >((*knn_dist_matches).size()); nMatches =
for (size_t j = 0; j < (*knn_ret_matches).size(); j++){ index->radiusSearch(query_pt, (scalar_t)(search_radius + eps),
(*temp)[j] = std::make_pair( (*knn_ret_matches)[j],(*knn_dist_matches)[j] ); ret_matches, search_params);
} } else {
ret_matches = *temp; nMatches = index->knnSearch(query_pt, (size_t)k, &(*knn_ret_matches)[0],
} &(*knn_dist_matches)[0]);
(*list_matches)[i0] = ret_matches; auto temp = new std::vector<std::pair<size_t, scalar_t>>(
if(*max_count < nMatches) *max_count = nMatches; (*knn_dist_matches).size());
i0++; for (size_t j = 0; j < (*knn_ret_matches).size(); j++) {
(*temp)[j] =
} std::make_pair((*knn_ret_matches)[j], (*knn_dist_matches)[j]);
} }
else {// Multi-threaded routine ret_matches = *temp;
std::mutex* mtx = new std::mutex(); }
std::mutex* mtx_tree = new std::mutex(); (*list_matches)[i0] = ret_matches;
if (*max_count < nMatches)
size_t n_queries = (*pcd_query).pts.size(); *max_count = nMatches;
size_t actual_threads = std::min((long long)n_threads, (long long)n_queries); i0++;
}
std::vector<std::thread*> tid(actual_threads); } else { // Multi-threaded routine
std::mutex *mtx = new std::mutex();
size_t start, end; std::mutex *mtx_tree = new std::mutex();
size_t length;
if (n_queries) { size_t n_queries = (*pcd_query).pts.size();
length = 1; size_t actual_threads =
} std::min((long long)n_threads, (long long)n_queries);
else {
auto res = std::lldiv((long long)n_queries, (long long)n_threads); std::vector<std::thread *> tid(actual_threads);
length = (size_t)res.quot;
} size_t start, end;
for (size_t t = 0; t < actual_threads; t++) { size_t length;
start = t*length; if (n_queries) {
if (t == actual_threads-1) { length = 1;
end = n_queries; } else {
} auto res = std::lldiv((long long)n_queries, (long long)n_threads);
else { length = (size_t)res.quot;
end = (t+1)*length; }
} for (size_t t = 0; t < actual_threads; t++) {
thread_args* targs = new thread_args(); start = t * length;
targs->kd_tree = index; if (t == actual_threads - 1) {
targs->matches = list_matches; end = n_queries;
targs->max_count = max_count; } else {
targs->ct_m = mtx; end = (t + 1) * length;
targs->tree_m = mtx_tree; }
targs->search_radius = search_radius; thread_args *targs = new thread_args();
targs->queries = pcd_query; targs->kd_tree = index;
targs->start = start; targs->matches = list_matches;
targs->end = end; targs->max_count = max_count;
if (ssize < 10) { targs->ct_m = mtx;
targs->small = true; targs->tree_m = mtx_tree;
} targs->search_radius = search_radius;
else { targs->queries = pcd_query;
targs->small = false; targs->start = start;
} targs->end = end;
targs->option = !!(option); if (ssize < 10) {
targs->k = k; targs->small = true;
std::thread* temp = new std::thread(thread_routine<scalar_t>, targs); } else {
tid[t] = temp; targs->small = false;
} }
targs->option = !!(option);
for (size_t t = 0; t < actual_threads; t++){ targs->k = k;
tid[t]->join(); std::thread *temp = new std::thread(thread_routine<scalar_t>, targs);
} tid[t] = temp;
} }
// Reserve the memory for (size_t t = 0; t < actual_threads; t++) {
if(max_num > 0) { tid[t]->join();
*max_count = max_num; }
} }
size_t size = 0; // total number of edges // Reserve the memory
for (auto& inds : *list_matches){ if (max_num > 0) {
if(inds.size() <= *max_count) *max_count = max_num;
size += inds.size(); }
else
size += *max_count; size_t size = 0; // total number of edges
} for (auto &inds : *list_matches) {
if (inds.size() <= *max_count)
neighbors_indices->resize(size*2); size += inds.size();
size_t i1 = 0; // index of the query points else
size_t u = 0; // curent index of the neighbors_indices size += *max_count;
for (auto& inds : *list_matches){ }
for (size_t j = 0; j < *max_count; j++){
if(j < inds.size()){ neighbors_indices->resize(size * 2);
(*neighbors_indices)[u] = inds[j].first; size_t i1 = 0; // index of the query points
(*neighbors_indices)[u + 1] = i1; size_t u = 0; // curent index of the neighbors_indices
u += 2; for (auto &inds : *list_matches) {
} for (size_t j = 0; j < *max_count; j++) {
} if (j < inds.size()) {
i1++; (*neighbors_indices)[u] = inds[j].first;
} (*neighbors_indices)[u + 1] = i1;
u += 2;
return *max_count; }
}
i1++;
}
return *max_count;
} }
template<typename scalar_t> template <typename scalar_t>
size_t batch_nanoflann_neighbors (std::vector<scalar_t>& queries, size_t batch_nanoflann_neighbors(std::vector<scalar_t> &queries,
std::vector<scalar_t>& supports, std::vector<scalar_t> &supports,
std::vector<long>& q_batches, std::vector<long> &q_batches,
std::vector<long>& s_batches, std::vector<long> &s_batches,
std::vector<size_t>*& neighbors_indices, std::vector<size_t> *&neighbors_indices,
double radius, int dim, int64_t max_num, int64_t k, int option){ double radius, int dim, int64_t max_num,
int64_t k, int option) {
// indices // Indices.
size_t i0 = 0; size_t i0 = 0;
// Square radius // Square radius.
const scalar_t r2 = static_cast<scalar_t>(radius*radius); const scalar_t r2 = static_cast<scalar_t>(radius * radius);
// Counting vector // Counting vector.
size_t max_count = 0; size_t max_count = 0;
// batch index // Batch index.
size_t b = 0; size_t b = 0;
size_t sum_qb = 0; size_t sum_qb = 0;
size_t sum_sb = 0; size_t sum_sb = 0;
double eps; double eps;
if (supports.size() < 10){ if (supports.size() < 10) {
eps = 0.000001; eps = 0.000001;
} } else {
else { eps = 0;
eps = 0; }
} // Nanoflann related variables.
// Nanoflann related variables
// Cloud variable.
// CLoud variable PointCloud<scalar_t> current_cloud;
PointCloud<scalar_t> current_cloud; PointCloud<scalar_t> query_pcd;
PointCloud<scalar_t> query_pcd; query_pcd.set(queries, dim);
query_pcd.set(queries, dim); std::vector<std::vector<std::pair<size_t, scalar_t>>> all_inds_dists(
std::vector<std::vector<std::pair<size_t, scalar_t> > > all_inds_dists(query_pcd.pts.size()); query_pcd.pts.size());
// Tree parameters // Tree parameters.
nanoflann::KDTreeSingleIndexAdaptorParams tree_params(10 /* max leaf */); nanoflann::KDTreeSingleIndexAdaptorParams tree_params(10 /* max leaf */);
// KDTree type definition // KDTree type definition.
typedef nanoflann::KDTreeSingleIndexAdaptor< nanoflann::L2_Adaptor<scalar_t, PointCloud<scalar_t> > , PointCloud<scalar_t>> my_kd_tree_t; typedef nanoflann::KDTreeSingleIndexAdaptor<
nanoflann::L2_Adaptor<scalar_t, PointCloud<scalar_t>>,
// Pointer to trees PointCloud<scalar_t>>
my_kd_tree_t* index; my_kd_tree_t;
// Build KDTree for the first batch element
current_cloud.set_batch(supports, sum_sb, s_batches[b], dim); // Pointer to trees.
index = new my_kd_tree_t(dim, current_cloud, tree_params); my_kd_tree_t *index;
index->buildIndex(); // Build KDTree for the first batch element.
// Search neigbors indices current_cloud.set_batch(supports, sum_sb, s_batches[b], dim);
// Search params index = new my_kd_tree_t(dim, current_cloud, tree_params);
nanoflann::SearchParams search_params; index->buildIndex();
search_params.sorted = true; // Search neigbors indices.
// Search params.
for (auto& p : query_pcd.pts){ nanoflann::SearchParams search_params;
auto p0 = *p; search_params.sorted = true;
// Check if we changed batch
for (auto &p : query_pcd.pts) {
scalar_t* query_pt = new scalar_t[dim]; auto p0 = *p;
std::copy(p0.begin(), p0.end(), query_pt); // Check if we changed batch.
if (i0 == sum_qb + q_batches[b]){ scalar_t *query_pt = new scalar_t[dim];
sum_qb += q_batches[b]; std::copy(p0.begin(), p0.end(), query_pt);
sum_sb += s_batches[b];
b++; if (i0 == sum_qb + q_batches[b]) {
sum_qb += q_batches[b];
// Change the points sum_sb += s_batches[b];
current_cloud.pts.clear(); b++;
current_cloud.set_batch(supports, sum_sb, s_batches[b], dim);
// Build KDTree of the current element of the batch // Change the points.
delete index; current_cloud.pts.clear();
index = new my_kd_tree_t(dim, current_cloud, tree_params); current_cloud.set_batch(supports, sum_sb, s_batches[b], dim);
index->buildIndex(); // Build KDTree of the current element of the batch.
} delete index;
// Initial guess of neighbors size index = new my_kd_tree_t(dim, current_cloud, tree_params);
all_inds_dists[i0].reserve(max_count); index->buildIndex();
// Find neighbors }
// Initial guess of neighbors size.
size_t nMatches; all_inds_dists[i0].reserve(max_count);
if (!!option) {
nMatches = index->radiusSearch(query_pt, r2+eps, all_inds_dists[i0], search_params); // Find neighbors.
// Update max count size_t nMatches;
} if (!!option) {
else { nMatches = index->radiusSearch(query_pt, r2 + eps, all_inds_dists[i0],
std::vector<size_t>* knn_ret_matches = new std::vector<size_t>(k); search_params);
std::vector<scalar_t>* knn_dist_matches = new std::vector<scalar_t>(k); // Update max count.
nMatches = index->knnSearch(query_pt, (size_t)k, &(*knn_ret_matches)[0],&(*knn_dist_matches)[0]); } else {
auto temp = new std::vector<std::pair<size_t, scalar_t> >((*knn_dist_matches).size()); std::vector<size_t> *knn_ret_matches = new std::vector<size_t>(k);
for (size_t j = 0; j < (*knn_ret_matches).size(); j++){ std::vector<scalar_t> *knn_dist_matches = new std::vector<scalar_t>(k);
(*temp)[j] = std::make_pair( (*knn_ret_matches)[j],(*knn_dist_matches)[j] ); nMatches = index->knnSearch(query_pt, (size_t)k, &(*knn_ret_matches)[0],
} &(*knn_dist_matches)[0]);
all_inds_dists[i0] = *temp; auto temp = new std::vector<std::pair<size_t, scalar_t>>(
} (*knn_dist_matches).size());
if (nMatches > max_count) for (size_t j = 0; j < (*knn_ret_matches).size(); j++) {
max_count = nMatches; (*temp)[j] =
// Increment query idx std::make_pair((*knn_ret_matches)[j], (*knn_dist_matches)[j]);
i0++; }
} all_inds_dists[i0] = *temp;
}
if (nMatches > max_count)
max_count = nMatches;
// how many neighbors do we keep i0++;
if(max_num > 0) { }
max_count = max_num;
} // How many neighbors do we keep.
// Reserve the memory if (max_num > 0) {
max_count = max_num;
size_t size = 0; // total number of edges }
for (auto& inds_dists : all_inds_dists){
if(inds_dists.size() <= max_count) size_t size = 0; // Total number of edges.
size += inds_dists.size(); for (auto &inds_dists : all_inds_dists) {
else if (inds_dists.size() <= max_count)
size += max_count; size += inds_dists.size();
} else
size += max_count;
neighbors_indices->resize(size * 2); }
i0 = 0;
sum_sb = 0; neighbors_indices->resize(size * 2);
sum_qb = 0; i0 = 0;
b = 0; sum_sb = 0;
size_t u = 0; sum_qb = 0;
for (auto& inds_dists : all_inds_dists){ b = 0;
if (i0 == sum_qb + q_batches[b]){ size_t u = 0;
sum_qb += q_batches[b]; for (auto &inds_dists : all_inds_dists) {
sum_sb += s_batches[b]; if (i0 == sum_qb + q_batches[b]) {
b++; sum_qb += q_batches[b];
} sum_sb += s_batches[b];
for (size_t j = 0; j < max_count; j++){ b++;
if (j < inds_dists.size()){ }
(*neighbors_indices)[u] = inds_dists[j].first + sum_sb; for (size_t j = 0; j < max_count; j++) {
(*neighbors_indices)[u + 1] = i0; if (j < inds_dists.size()) {
u += 2; (*neighbors_indices)[u] = inds_dists[j].first + sum_sb;
} (*neighbors_indices)[u + 1] = i0;
} u += 2;
i0++; }
} }
i0++;
return max_count; }
return max_count;
} }
torch_cluster/knn.py
View file @ b8166f31
from typing import Optional from typing import Optional
import torch import torch
import numpy as np
@torch.jit.script
def knn(x: torch.Tensor, y: torch.Tensor, k: int, def knn(x: torch.Tensor, y: torch.Tensor, k: int,
batch_x: Optional[torch.Tensor] = None, batch_x: Optional[torch.Tensor] = None,
batch_y: Optional[torch.Tensor] = None, batch_y: Optional[torch.Tensor] = None, cosine: bool = False,
cosine: bool = False, n_threads: int = 1) -> torch.Tensor: num_workers: int = 1) -> torch.Tensor:
r"""Finds for each element in :obj:`y` the :obj:`k` nearest points in r"""Finds for each element in :obj:`y` the :obj:`k` nearest points in
:obj:`x`. :obj:`x`.
...@@ -19,13 +19,18 @@ def knn(x: torch.Tensor, y: torch.Tensor, k: int, ...@@ -19,13 +19,18 @@ def knn(x: torch.Tensor, y: torch.Tensor, k: int,
k (int): The number of neighbors. k (int): The number of neighbors.
batch_x (LongTensor, optional): Batch vector batch_x (LongTensor, optional): Batch vector
:math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each
node to a specific example. (default: :obj:`None`) node to a specific example. :obj:`batch_x` needs to be sorted.
(default: :obj:`None`)
batch_y (LongTensor, optional): Batch vector batch_y (LongTensor, optional): Batch vector
:math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^M`, which assigns each :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^M`, which assigns each
node to a specific example. (default: :obj:`None`) node to a specific example. :obj:`batch_y` needs to be sorted.
cosine (boolean, optional): If :obj:`True`, will use the cosine (default: :obj:`None`)
distance instead of euclidean distance to find nearest neighbors. cosine (boolean, optional): If :obj:`True`, will use the Cosine
(default: :obj:`False`) distance instead of the Euclidean distance to find nearest
neighbors. (default: :obj:`False`)
num_workers (int): Number of workers to use for computation. Has no
effect in case :obj:`batch_x` or :obj:`batch_y` is not
:obj:`None`, or the input lies on the GPU. (default: :obj:`1`)
:rtype: :class:`LongTensor` :rtype: :class:`LongTensor`
...@@ -44,62 +49,36 @@ def knn(x: torch.Tensor, y: torch.Tensor, k: int, ...@@ -44,62 +49,36 @@ def knn(x: torch.Tensor, y: torch.Tensor, k: int,
x = x.view(-1, 1) if x.dim() == 1 else x x = x.view(-1, 1) if x.dim() == 1 else x
y = y.view(-1, 1) if y.dim() == 1 else y y = y.view(-1, 1) if y.dim() == 1 else y
def is_sorted(x): if batch_x is not None:
return (np.diff(x.detach().cpu()) >= 0).all() assert x.size(0) == batch_x.numel()
batch_size = int(batch_x.max()) + 1
if x.is_cuda:
if batch_x is not None:
assert x.size(0) == batch_x.numel()
assert is_sorted(batch_x)
batch_size = int(batch_x.max()) + 1
deg = x.new_zeros(batch_size, dtype=torch.long) deg = x.new_zeros(batch_size, dtype=torch.long)
deg.scatter_add_(0, batch_x, torch.ones_like(batch_x)) deg.scatter_add_(0, batch_x, torch.ones_like(batch_x))
ptr_x = deg.new_zeros(batch_size + 1) ptr_x = deg.new_zeros(batch_size + 1)
torch.cumsum(deg, 0, out=ptr_x[1:]) torch.cumsum(deg, 0, out=ptr_x[1:])
else:
ptr_x = torch.tensor([0, x.size(0)], device=x.device)
if batch_y is not None: if batch_y is not None:
assert y.size(0) == batch_y.numel() assert y.size(0) == batch_y.numel()
assert is_sorted(batch_y) batch_size = int(batch_y.max()) + 1
batch_size = int(batch_y.max()) + 1
deg = y.new_zeros(batch_size, dtype=torch.long) deg = y.new_zeros(batch_size, dtype=torch.long)
deg.scatter_add_(0, batch_y, torch.ones_like(batch_y)) deg.scatter_add_(0, batch_y, torch.ones_like(batch_y))
ptr_y = deg.new_zeros(batch_size + 1) ptr_y = deg.new_zeros(batch_size + 1)
torch.cumsum(deg, 0, out=ptr_y[1:]) torch.cumsum(deg, 0, out=ptr_y[1:])
else:
ptr_y = torch.tensor([0, y.size(0)], device=y.device)
return torch.ops.torch_cluster.knn(x, y, ptr_x,
ptr_y, k, cosine, n_threads)
else: else:
assert x.dim() == 2 ptr_y = torch.tensor([0, y.size(0)], device=y.device)
if batch_x is not None:
assert batch_x.dim() == 1
assert is_sorted(batch_x)
assert x.size(0) == batch_x.size(0)
assert y.dim() == 2
if batch_y is not None:
assert batch_y.dim() == 1
assert is_sorted(batch_y)
assert y.size(0) == batch_y.size(0)
assert x.size(1) == y.size(1)
if cosine:
raise NotImplementedError('`cosine` argument not supported on CPU')
return torch.ops.torch_cluster.knn(x, y, batch_x, batch_y, return torch.ops.torch_cluster.knn(x, y, ptr_x, ptr_y, k, cosine,
k, cosine, n_threads) num_workers)
@torch.jit.script
def knn_graph(x: torch.Tensor, k: int, batch: Optional[torch.Tensor] = None, def knn_graph(x: torch.Tensor, k: int, batch: Optional[torch.Tensor] = None,
loop: bool = False, flow: str = 'source_to_target', loop: bool = False, flow: str = 'source_to_target',
cosine: bool = False, n_threads: int = 1) -> torch.Tensor: cosine: bool = False, num_workers: int = 1) -> torch.Tensor:
r"""Computes graph edges to the nearest :obj:`k` points. r"""Computes graph edges to the nearest :obj:`k` points.
Args: Args:
...@@ -108,7 +87,8 @@ def knn_graph(x: torch.Tensor, k: int, batch: Optional[torch.Tensor] = None, ...@@ -108,7 +87,8 @@ def knn_graph(x: torch.Tensor, k: int, batch: Optional[torch.Tensor] = None,
k (int): The number of neighbors. k (int): The number of neighbors.
batch (LongTensor, optional): Batch vector batch (LongTensor, optional): Batch vector
:math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each
node to a specific example. (default: :obj:`None`) node to a specific example. :obj:`batch` needs to be sorted.
(default: :obj:`None`)
loop (bool, optional): If :obj:`True`, the graph will contain loop (bool, optional): If :obj:`True`, the graph will contain
self-loops. (default: :obj:`False`) self-loops. (default: :obj:`False`)
flow (string, optional): The flow direction when using in combination flow (string, optional): The flow direction when using in combination
...@@ -117,6 +97,9 @@ def knn_graph(x: torch.Tensor, k: int, batch: Optional[torch.Tensor] = None, ...@@ -117,6 +97,9 @@ def knn_graph(x: torch.Tensor, k: int, batch: Optional[torch.Tensor] = None,
cosine (boolean, optional): If :obj:`True`, will use the cosine cosine (boolean, optional): If :obj:`True`, will use the cosine
distance instead of euclidean distance to find nearest neighbors. distance instead of euclidean distance to find nearest neighbors.
(default: :obj:`False`) (default: :obj:`False`)
num_workers (int): Number of workers to use for computation. Has no
effect in case :obj:`batch` is not :obj:`None`, or the input lies
on the GPU. (default: :obj:`1`)
:rtype: :class:`LongTensor` :rtype: :class:`LongTensor`
...@@ -131,8 +114,8 @@ def knn_graph(x: torch.Tensor, k: int, batch: Optional[torch.Tensor] = None, ...@@ -131,8 +114,8 @@ def knn_graph(x: torch.Tensor, k: int, batch: Optional[torch.Tensor] = None,
""" """
assert flow in ['source_to_target', 'target_to_source'] assert flow in ['source_to_target', 'target_to_source']
row, col = knn(x, x, k if loop else k + 1, batch, batch, row, col = knn(x, x, k if loop else k + 1, batch, batch, cosine,
cosine=cosine, n_threads=n_threads) num_workers)
row, col = (col, row) if flow == 'source_to_target' else (row, col) row, col = (col, row) if flow == 'source_to_target' else (row, col)
if not loop: if not loop:
mask = row != col mask = row != col
......
torch_cluster/radius.py
View file @ b8166f31
from typing import Optional from typing import Optional
import torch import torch
import numpy as np
@torch.jit.script
def radius(x: torch.Tensor, y: torch.Tensor, r: float, def radius(x: torch.Tensor, y: torch.Tensor, r: float,
batch_x: Optional[torch.Tensor] = None, batch_x: Optional[torch.Tensor] = None,
batch_y: Optional[torch.Tensor] = None, batch_y: Optional[torch.Tensor] = None, max_num_neighbors: int = 32,
max_num_neighbors: int = 32, n_threads: int = 1) -> torch.Tensor: num_workers: int = 1) -> torch.Tensor:
r"""Finds for each element in :obj:`y` all points in :obj:`x` within r"""Finds for each element in :obj:`y` all points in :obj:`x` within
distance :obj:`r`. distance :obj:`r`.
...@@ -16,17 +17,19 @@ def radius(x: torch.Tensor, y: torch.Tensor, r: float, ...@@ -16,17 +17,19 @@ def radius(x: torch.Tensor, y: torch.Tensor, r: float,
y (Tensor): Node feature matrix y (Tensor): Node feature matrix
:math:`\mathbf{Y} \in \mathbb{R}^{M \times F}`. :math:`\mathbf{Y} \in \mathbb{R}^{M \times F}`.
r (float): The radius. r (float): The radius.
batch_x (LongTensor, optional): Batch vector (must be sorted) batch_x (LongTensor, optional): Batch vector
:math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each
node to a specific example. (default: :obj:`None`) node to a specific example. :obj:`batch_x` needs to be sorted.
batch_y (LongTensor, optional): Batch vector (must be sorted) (default: :obj:`None`)
batch_y (LongTensor, optional): Batch vector
:math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^M`, which assigns each :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^M`, which assigns each
node to a specific example. (default: :obj:`None`) node to a specific example. :obj:`batch_y` needs to be sorted.
(default: :obj:`None`)
max_num_neighbors (int, optional): The maximum number of neighbors to max_num_neighbors (int, optional): The maximum number of neighbors to
return for each element in :obj:`y`. (default: :obj:`32`) return for each element in :obj:`y`. (default: :obj:`32`)
n_threads (int): number of threads when the input is on CPU. Note num_workers (int): Number of workers to use for computation. Has no
that this has no effect when batch_x or batch_y is not None, or effect in case :obj:`batch_x` or :obj:`batch_y` is not
x is on GPU. (default: :obj:`1`) :obj:`None`, or the input lies on the GPU. (default: :obj:`1`)
.. code-block:: python .. code-block:: python
...@@ -43,71 +46,49 @@ def radius(x: torch.Tensor, y: torch.Tensor, r: float, ...@@ -43,71 +46,49 @@ def radius(x: torch.Tensor, y: torch.Tensor, r: float,
x = x.view(-1, 1) if x.dim() == 1 else x x = x.view(-1, 1) if x.dim() == 1 else x
y = y.view(-1, 1) if y.dim() == 1 else y y = y.view(-1, 1) if y.dim() == 1 else y
def is_sorted(x): if batch_x is not None:
return (np.diff(x.detach().cpu()) >= 0).all() assert x.size(0) == batch_x.numel()
batch_size = int(batch_x.max()) + 1
if x.is_cuda:
if batch_x is not None: deg = x.new_zeros(batch_size, dtype=torch.long)
assert x.size(0) == batch_x.numel() deg.scatter_add_(0, batch_x, torch.ones_like(batch_x))
assert is_sorted(batch_x)
batch_size = int(batch_x.max()) + 1 ptr_x = deg.new_zeros(batch_size + 1)
torch.cumsum(deg, 0, out=ptr_x[1:])
deg = x.new_zeros(batch_size, dtype=torch.long)
deg.scatter_add_(0, batch_x, torch.ones_like(batch_x))
ptr_x = deg.new_zeros(batch_size + 1)
torch.cumsum(deg, 0, out=ptr_x[1:])
else:
ptr_x = None
if batch_y is not None:
assert y.size(0) == batch_y.numel()
assert is_sorted(batch_y)
batch_size = int(batch_y.max()) + 1
deg = y.new_zeros(batch_size, dtype=torch.long)
deg.scatter_add_(0, batch_y, torch.ones_like(batch_y))
ptr_y = deg.new_zeros(batch_size + 1)
torch.cumsum(deg, 0, out=ptr_y[1:])
else:
ptr_y = None
result = torch.ops.torch_cluster.radius(x, y, ptr_x, ptr_y, r,
max_num_neighbors, n_threads)
else: else:
assert x.dim() == 2 ptr_x = None
if batch_x is not None:
assert batch_x.dim() == 1
assert is_sorted(batch_x)
assert x.size(0) == batch_x.size(0)
assert y.dim() == 2 if batch_y is not None:
if batch_y is not None: assert y.size(0) == batch_y.numel()
assert batch_y.dim() == 1 batch_size = int(batch_y.max()) + 1
assert is_sorted(batch_y)
assert y.size(0) == batch_y.size(0)
assert x.size(1) == y.size(1)
result = torch.ops.torch_cluster.radius(x, y, batch_x, batch_y, r, deg = y.new_zeros(batch_size, dtype=torch.long)
max_num_neighbors, n_threads) deg.scatter_add_(0, batch_y, torch.ones_like(batch_y))
ptr_y = deg.new_zeros(batch_size + 1)
torch.cumsum(deg, 0, out=ptr_y[1:])
else:
ptr_y = None
return result return torch.ops.torch_cluster.radius(x, y, ptr_x, ptr_y, r,
max_num_neighbors, num_workers)
@torch.jit.script
def radius_graph(x: torch.Tensor, r: float, def radius_graph(x: torch.Tensor, r: float,
batch: Optional[torch.Tensor] = None, loop: bool = False, batch: Optional[torch.Tensor] = None, loop: bool = False,
max_num_neighbors: int = 32, max_num_neighbors: int = 32, flow: str = 'source_to_target',
flow: str = 'source_to_target', num_workers: int = 1) -> torch.Tensor:
n_threads: int = 1) -> torch.Tensor:
r"""Computes graph edges to all points within a given distance. r"""Computes graph edges to all points within a given distance.
Args: Args:
x (Tensor): Node feature matrix x (Tensor): Node feature matrix
:math:`\mathbf{X} \in \mathbb{R}^{N \times F}`. :math:`\mathbf{X} \in \mathbb{R}^{N \times F}`.
r (float): The radius. r (float): The radius.
batch (LongTensor, optional): Batch vector (must be sorted) batch (LongTensor, optional): Batch vector
:math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each
node to a specific example. (default: :obj:`None`) node to a specific example. :obj:`batch` needs to be sorted.
(default: :obj:`None`)
loop (bool, optional): If :obj:`True`, the graph will contain loop (bool, optional): If :obj:`True`, the graph will contain
self-loops. (default: :obj:`False`) self-loops. (default: :obj:`False`)
max_num_neighbors (int, optional): The maximum number of neighbors to max_num_neighbors (int, optional): The maximum number of neighbors to
...@@ -115,9 +96,9 @@ def radius_graph(x: torch.Tensor, r: float, ...@@ -115,9 +96,9 @@ def radius_graph(x: torch.Tensor, r: float,
flow (string, optional): The flow direction when using in combination flow (string, optional): The flow direction when using in combination
with message passing (:obj:`"source_to_target"` or with message passing (:obj:`"source_to_target"` or
:obj:`"target_to_source"`). (default: :obj:`"source_to_target"`) :obj:`"target_to_source"`). (default: :obj:`"source_to_target"`)
n_threads (int): number of threads when the input is on CPU. Note num_workers (int): Number of workers to use for computation. Has no
that this has no effect when batch_x or batch_y is not None, or effect in case :obj:`batch` is not :obj:`None`, or the input lies
x is on GPU. (default: :obj:`1`) on the GPU. (default: :obj:`1`)
:rtype: :class:`LongTensor` :rtype: :class:`LongTensor`
...@@ -134,7 +115,7 @@ def radius_graph(x: torch.Tensor, r: float, ...@@ -134,7 +115,7 @@ def radius_graph(x: torch.Tensor, r: float,
assert flow in ['source_to_target', 'target_to_source'] assert flow in ['source_to_target', 'target_to_source']
row, col = radius(x, x, r, batch, batch, row, col = radius(x, x, r, batch, batch,
max_num_neighbors if loop else max_num_neighbors + 1, max_num_neighbors if loop else max_num_neighbors + 1,
n_threads) num_workers)
row, col = (col, row) if flow == 'source_to_target' else (row, col) row, col = (col, row) if flow == 'source_to_target' else (row, col)
if not loop: if not loop:
mask = row != col mask = row != col
......
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