add nvcc flags [ci skip]

.travis.yml

@@ -59,7 +59,8 @@ install:
   - conda install pytorch=${TORCH_VERSION} ${TOOLKIT} -c pytorch --yes
   - source script/torch.sh
   - pip install flake8 codecov
-  - python setup.py install
+  - pip install scipy==1.4.1
+  - pip install .[test]
 script:
   - flake8 .
   - python setup.py test

CMakeLists.txt

 cmake_minimum_required(VERSION 3.0)
 project(torchcluster)
 set(CMAKE_CXX_STANDARD 14)
-set(TORCHCLUSTER_VERSION 1.5.4)
+set(TORCHCLUSTER_VERSION 1.5.5)
 
 option(WITH_CUDA "Enable CUDA support" OFF)
 
@@ -74,3 +74,8 @@ if(WITH_CUDA)
     csrc/cuda/rw_cuda.h
     DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/${PROJECT_NAME}/cuda)
 endif()
+
+if(WITH_CUDA)
+  set_property(TARGET torch_cuda PROPERTY INTERFACE_COMPILE_OPTIONS "")
+  set_property(TARGET torch_cpu PROPERTY INTERFACE_COMPILE_OPTIONS "")
+endif()

README.md

@@ -107,7 +107,7 @@ from torch_cluster import graclus_cluster
 
 row = torch.tensor([0, 1, 1, 2])
 col = torch.tensor([1, 0, 2, 1])
-weight = torch.Tensor([1, 1, 1, 1])  # Optional edge weights.
+weight = torch.tensor([1., 1., 1., 1.])  # Optional edge weights.
 
 cluster = graclus_cluster(row, col, weight)
 ```
@@ -125,7 +125,7 @@ A clustering algorithm, which overlays a regular grid of user-defined size over
 import torch
 from torch_cluster import grid_cluster
 
-pos = torch.Tensor([[0, 0], [11, 9], [2, 8], [2, 2], [8, 3]])
+pos = torch.tensor([[0., 0.], [11., 9.], [2., 8.], [2., 2.], [8., 3.]])
 size = torch.Tensor([5, 5])
 
 cluster = grid_cluster(pos, size)
@@ -144,7 +144,7 @@ A sampling algorithm, which iteratively samples the most distant point with rega
 import torch
 from torch_cluster import fps
 
-x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]])
+x = torch.tensor([[-1., -1.], [-1., 1.], [1., -1.], [1., 1.]])
 batch = torch.tensor([0, 0, 0, 0])
 index = fps(x, batch, ratio=0.5, random_start=False)
 ```
@@ -158,11 +158,21 @@ tensor([0, 3])
 
 Computes graph edges to the nearest *k* points.
 
+**Args:**
+
+* **x** *(Tensor)*: Node feature matrix of shape `[N, F]`.
+* **r** *(float)*: The radius.
+* **batch** *(LongTensor, optional)*: Batch vector of shape `[N]`, which assigns each node to a specific example. `batch` needs to be sorted. (default: `None`)
+* **loop** *(bool, optional)*: If `True`, the graph will contain self-loops. (default: `False`)
+* **flow** *(string, optional)*: The flow direction when using in combination with message passing (`"source_to_target"` or `"target_to_source"`). (default: `"source_to_target"`)
+* **cosine** *(boolean, optional)*: If `True`, will use the Cosine distance instead of Euclidean distance to find nearest neighbors. (default: `False`)
+* **num_workers** *(int)*: Number of workers to use for computation. Has no effect in case `batch` is not `None`, or the input lies on the GPU. (default: `1`)
+
 ```python
 import torch
 from torch_cluster import knn_graph
 
-x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]])
+x = torch.tensor([[-1., -1.], [-1., 1.], [1., -1.], [1., 1.]])
 batch = torch.tensor([0, 0, 0, 0])
 edge_index = knn_graph(x, k=2, batch=batch, loop=False)
 ```
@@ -177,11 +187,21 @@ tensor([[1, 2, 0, 3, 0, 3, 1, 2],
 
 Computes graph edges to all points within a given distance.
 
+**Args:**
+
+* **x** *(Tensor)*: Node feature matrix of shape `[N, F]`.
+* **r** *(float)*: The radius.
+* **batch** *(LongTensor, optional)*: Batch vector of shape `[N]`, which assigns each node to a specific example. `batch` needs to be sorted. (default: `None`)
+* **loop** *(bool, optional)*: If `True`, the graph will contain self-loops. (default: `False`)
+* **max_num_neighbors** *(int, optional)*: The maximum number of neighbors to return for each element. (default: `32`)
+* **flow** *(string, optional)*: The flow direction when using in combination with message passing (`"source_to_target"` or `"target_to_source"`). (default: `"source_to_target"`)
+* **num_workers** *(int)*: Number of workers to use for computation. Has no effect in case `batch` is not `None`, or the input lies on the GPU. (default: `1`)
+
 ```python
 import torch
 from torch_cluster import radius_graph
 
-x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]])
+x = torch.tensor([[-1., -1.], [-1., 1.], [1., -1.], [1., 1.]])
 batch = torch.tensor([0, 0, 0, 0])
 edge_index = radius_graph(x, r=1.5, batch=batch, loop=False)
 ```

csrc/cpu/knn_cpu.cpp

+#include "knn_cpu.h"
+
+#include "utils.h"
+#include "utils/neighbors.cpp"
+
+torch::Tensor knn_cpu(torch::Tensor x, torch::Tensor y,
+                      torch::optional<torch::Tensor> ptr_x,
+                      torch::optional<torch::Tensor> ptr_y, int64_t k,
+                      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 = new std::vector<size_t>();
+
+  AT_DISPATCH_ALL_TYPES(x.scalar_type(), "radius_cpu", [&] {
+    auto x_data = x.data_ptr<scalar_t>();
+    auto y_data = y.data_ptr<scalar_t>();
+    auto x_vec = std::vector<scalar_t>(x_data, x_data + x.numel());
+    auto y_vec = std::vector<scalar_t>(y_data, y_data + y.numel());
+
+    if (!ptr_x.has_value()) {
+      nanoflann_neighbors<scalar_t>(y_vec, x_vec, out_vec, 0, x.size(-1), 0,
+                                    num_workers, k, 0);
+    } else {
+      auto sx = (ptr_x.value().narrow(0, 1, ptr_x.value().numel() - 1) -
+                 ptr_x.value().narrow(0, 0, ptr_x.value().numel() - 1));
+      auto sy = (ptr_y.value().narrow(0, 1, ptr_y.value().numel() - 1) -
+                 ptr_y.value().narrow(0, 0, ptr_y.value().numel() - 1));
+      auto sx_data = sx.data_ptr<int64_t>();
+      auto sy_data = sy.data_ptr<int64_t>();
+      auto sx_vec = std::vector<long>(sx_data, sx_data + sx.numel());
+      auto sy_vec = std::vector<long>(sy_data, sy_data + sy.numel());
+      batch_nanoflann_neighbors<scalar_t>(y_vec, x_vec, sy_vec, sx_vec, out_vec,
+                                          k, x.size(-1), 0, k, 0);
+    }
+  });
+
+  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}));
+}

csrc/cpu/knn_cpu.h

+#pragma once
+
+#include <torch/extension.h>
+
+torch::Tensor knn_cpu(torch::Tensor x, torch::Tensor y,
+                      torch::optional<torch::Tensor> ptr_x,
+                      torch::optional<torch::Tensor> ptr_y, int64_t k,
+                      int64_t num_workers);

csrc/cpu/radius_cpu.cpp

+#include "radius_cpu.h"
+
+#include "utils.h"
+#include "utils/neighbors.cpp"
+
+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 = new std::vector<size_t>();
+
+  AT_DISPATCH_ALL_TYPES(x.scalar_type(), "radius_cpu", [&] {
+    auto x_data = x.data_ptr<scalar_t>();
+    auto y_data = y.data_ptr<scalar_t>();
+    auto x_vec = std::vector<scalar_t>(x_data, x_data + x.numel());
+    auto y_vec = std::vector<scalar_t>(y_data, y_data + y.numel());
+
+    if (!ptr_x.has_value()) {
+      nanoflann_neighbors<scalar_t>(y_vec, x_vec, out_vec, r, x.size(-1),
+                                    max_num_neighbors, num_workers, 0, 1);
+    } else {
+      auto sx = (ptr_x.value().narrow(0, 1, ptr_x.value().numel() - 1) -
+                 ptr_x.value().narrow(0, 0, ptr_x.value().numel() - 1));
+      auto sy = (ptr_y.value().narrow(0, 1, ptr_y.value().numel() - 1) -
+                 ptr_y.value().narrow(0, 0, ptr_y.value().numel() - 1));
+      auto sx_data = sx.data_ptr<int64_t>();
+      auto sy_data = sy.data_ptr<int64_t>();
+      auto sx_vec = std::vector<long>(sx_data, sx_data + sx.numel());
+      auto sy_vec = std::vector<long>(sy_data, sy_data + sy.numel());
+      batch_nanoflann_neighbors<scalar_t>(y_vec, x_vec, sy_vec, sx_vec, out_vec,
+                                          r, x.size(-1), max_num_neighbors, 0,
+                                          1);
+    }
+  });
+
+  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}));
+}

csrc/cpu/radius_cpu.h

+#pragma once
+
+#include <torch/extension.h>
+
+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);

csrc/cpu/utils/cloud.h

+#pragma once
+
+#include <ATen/ATen.h>
+#include <algorithm>
+#include <cmath>
+#include <iomanip>
+#include <iostream>
+#include <map>
+#include <numeric>
+#include <unordered_map>
+#include <vector>
+
+#include <time.h>
+
+template <typename scalar_t> struct PointCloud {
+  std::vector<std::vector<scalar_t> *> pts;
+
+  void set(std::vector<scalar_t> new_pts, int dim) {
+
+    std::vector<std::vector<scalar_t> *> temp(new_pts.size() / dim);
+    for (size_t i = 0; i < new_pts.size(); i++) {
+      if (i % dim == 0) {
+        std::vector<scalar_t> *point = new std::vector<scalar_t>(dim);
+
+        for (size_t j = 0; j < (size_t)dim; j++) {
+          (*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,
+                 int dim) {
+    std::vector<std::vector<scalar_t> *> temp(size);
+    for (size_t i = 0; i < (size_t)size; i++) {
+      std::vector<scalar_t> *point = new std::vector<scalar_t>(dim);
+      for (size_t j = 0; j < (size_t)dim; j++) {
+        (*point)[j] = new_pts[dim * (begin + i) + j];
+      }
+
+      temp[i] = point;
+    }
+    pts = temp;
+  }
+
+  // Must return the number of data points.
+  inline size_t kdtree_get_point_count() const { return pts.size(); }
+
+  // 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 {
+    return (*pts[idx])[dim];
+  }
+
+  // Optional bounding-box computation: return false to default to a standard
+  // 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
+  // the expected dimensionality (e.g. 2 or 3 for point clouds)
+  template <class BBOX> bool kdtree_get_bbox(BBOX & /* bb */) const {
+    return false;
+  }
+};

csrc/cpu/utils/nanoflann.hpp

+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
+ * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
+ * Copyright 2011-2016  Jose Luis Blanco (joseluisblancoc@gmail.com).
+ *   All rights reserved.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+/** \mainpage nanoflann C++ API documentation
+ *  nanoflann is a C++ header-only library for building KD-Trees, mostly
+ *  optimized for 2D or 3D point clouds.
+ *
+ *  nanoflann does not require compiling or installing, just an
+ *  #include <nanoflann.hpp> in your code.
+ *
+ *  See:
+ *   - <a href="modules.html" >C++ API organized by modules</a>
+ *   - <a href="https://github.com/jlblancoc/nanoflann" >Online README</a>
+ *   - <a href="http://jlblancoc.github.io/nanoflann/" >Doxygen
+ * documentation</a>
+ */
+
+#ifndef NANOFLANN_HPP_
+#define NANOFLANN_HPP_
+
+#include <algorithm>
+#include <array>
+#include <cassert>
+#include <cmath>   // for abs()
+#include <cstdio>  // for fwrite()
+#include <cstdlib> // for abs()
+#include <functional>
+#include <limits> // std::reference_wrapper
+#include <stdexcept>
+#include <vector>
+
+/** Library version: 0xMmP (M=Major,m=minor,P=patch) */
+#define NANOFLANN_VERSION 0x130
+
+// Avoid conflicting declaration of min/max macros in windows headers
+#if !defined(NOMINMAX) &&                                                      \
+    (defined(_WIN32) || defined(_WIN32_) || defined(WIN32) || defined(_WIN64))
+#define NOMINMAX
+#ifdef max
+#undef max
+#undef min
+#endif
+#endif
+
+namespace nanoflann {
+/** @addtogroup nanoflann_grp nanoflann C++ library for ANN
+ *  @{ */
+
+/** the PI constant (required to avoid MSVC missing symbols) */
+template <typename T> T pi_const() {
+  return static_cast<T>(3.14159265358979323846);
+}
+
+/**
+ * Traits if object is resizable and assignable (typically has a resize | assign
+ * method)
+ */
+template <typename T, typename = int> struct has_resize : std::false_type {};
+
+template <typename T>
+struct has_resize<T, decltype((void)std::declval<T>().resize(1), 0)>
+    : std::true_type {};
+
+template <typename T, typename = int> struct has_assign : std::false_type {};
+
+template <typename T>
+struct has_assign<T, decltype((void)std::declval<T>().assign(1, 0), 0)>
+    : std::true_type {};
+
+/**
+ * Free function to resize a resizable object
+ */
+template <typename Container>
+inline typename std::enable_if<has_resize<Container>::value, void>::type
+resize(Container &c, const size_t nElements) {
+  c.resize(nElements);
+}
+
+/**
+ * Free function that has no effects on non resizable containers (e.g.
+ * std::array) It raises an exception if the expected size does not match
+ */
+template <typename Container>
+inline typename std::enable_if<!has_resize<Container>::value, void>::type
+resize(Container &c, const size_t nElements) {
+  if (nElements != c.size())
+    throw std::logic_error("Try to change the size of a std::array.");
+}
+
+/**
+ * Free function to assign to a container
+ */
+template <typename Container, typename T>
+inline typename std::enable_if<has_assign<Container>::value, void>::type
+assign(Container &c, const size_t nElements, const T &value) {
+  c.assign(nElements, value);
+}
+
+/**
+ * Free function to assign to a std::array
+ */
+template <typename Container, typename T>
+inline typename std::enable_if<!has_assign<Container>::value, void>::type
+assign(Container &c, const size_t nElements, const T &value) {
+  for (size_t i = 0; i < nElements; i++)
+    c[i] = value;
+}
+
+/** @addtogroup result_sets_grp Result set classes
+ *  @{ */
+template <typename _DistanceType, typename _IndexType = size_t,
+          typename _CountType = size_t>
+class KNNResultSet {
+public:
+  typedef _DistanceType DistanceType;
+  typedef _IndexType IndexType;
+  typedef _CountType CountType;
+
+private:
+  IndexType *indices;
+  DistanceType *dists;
+  CountType capacity;
+  CountType count;
+
+public:
+  inline KNNResultSet(CountType capacity_)
+      : indices(0), dists(0), capacity(capacity_), count(0) {}
+
+  inline void init(IndexType *indices_, DistanceType *dists_) {
+    indices = indices_;
+    dists = dists_;
+    count = 0;
+    if (capacity)
+      dists[capacity - 1] = (std::numeric_limits<DistanceType>::max)();
+  }
+
+  inline CountType size() const { return count; }
+
+  inline bool full() const { return count == capacity; }
+
+  /**
+   * Called during search to add an element matching the criteria.
+   * @return true if the search should be continued, false if the results are
+   * sufficient
+   */
+  inline bool addPoint(DistanceType dist, IndexType index) {
+    CountType i;
+    for (i = count; i > 0; --i) {
+#ifdef NANOFLANN_FIRST_MATCH // If defined and two points have the same
+                             // distance, the one with the lowest-index will be
+                             // returned first.
+      if ((dists[i - 1] > dist) ||
+          ((dist == dists[i - 1]) && (indices[i - 1] > index))) {
+#else
+      if (dists[i - 1] > dist) {
+#endif
+        if (i < capacity) {
+          dists[i] = dists[i - 1];
+          indices[i] = indices[i - 1];
+        }
+      } else
+        break;
+    }
+    if (i < capacity) {
+      dists[i] = dist;
+      indices[i] = index;
+    }
+    if (count < capacity)
+      count++;
+
+    // tell caller that the search shall continue
+    return true;
+  }
+
+  inline DistanceType worstDist() const { return dists[capacity - 1]; }
+};
+
+/** operator "<" for std::sort() */
+struct IndexDist_Sorter {
+  /** PairType will be typically: std::pair<IndexType,DistanceType> */
+  template <typename PairType>
+  inline bool operator()(const PairType &p1, const PairType &p2) const {
+    return p1.second < p2.second;
+  }
+};
+
+/**
+ * A result-set class used when performing a radius based search.
+ */
+template <typename _DistanceType, typename _IndexType = size_t>
+class RadiusResultSet {
+public:
+  typedef _DistanceType DistanceType;
+  typedef _IndexType IndexType;
+
+public:
+  const DistanceType radius;
+
+  std::vector<std::pair<IndexType, DistanceType>> &m_indices_dists;
+
+  inline RadiusResultSet(
+      DistanceType radius_,
+      std::vector<std::pair<IndexType, DistanceType>> &indices_dists)
+      : radius(radius_), m_indices_dists(indices_dists) {
+    init();
+  }
+
+  inline void init() { clear(); }
+  inline void clear() { m_indices_dists.clear(); }
+
+  inline size_t size() const { return m_indices_dists.size(); }
+
+  inline bool full() const { return true; }
+
+  /**
+   * Called during search to add an element matching the criteria.
+   * @return true if the search should be continued, false if the results are
+   * sufficient
+   */
+  inline bool addPoint(DistanceType dist, IndexType index) {
+    if (dist < radius)
+      m_indices_dists.push_back(std::make_pair(index, dist));
+    return true;
+  }
+
+  inline DistanceType worstDist() const { return radius; }
+
+  /**
+   * Find the worst result (furtherest neighbor) without copying or sorting
+   * Pre-conditions: size() > 0
+   */
+  std::pair<IndexType, DistanceType> worst_item() const {
+    if (m_indices_dists.empty())
+      throw std::runtime_error("Cannot invoke RadiusResultSet::worst_item() on "
+                               "an empty list of results.");
+    typedef
+        typename std::vector<std::pair<IndexType, DistanceType>>::const_iterator
+            DistIt;
+    DistIt it = std::max_element(m_indices_dists.begin(), m_indices_dists.end(),
+                                 IndexDist_Sorter());
+    return *it;
+  }
+};
+
+/** @} */
+
+/** @addtogroup loadsave_grp Load/save auxiliary functions
+ * @{ */
+template <typename T>
+void save_value(FILE *stream, const T &value, size_t count = 1) {
+  fwrite(&value, sizeof(value), count, stream);
+}
+
+template <typename T>
+void save_value(FILE *stream, const std::vector<T> &value) {
+  size_t size = value.size();
+  fwrite(&size, sizeof(size_t), 1, stream);
+  fwrite(&value[0], sizeof(T), size, stream);
+}
+
+template <typename T>
+void load_value(FILE *stream, T &value, size_t count = 1) {
+  size_t read_cnt = fread(&value, sizeof(value), count, stream);
+  if (read_cnt != count) {
+    throw std::runtime_error("Cannot read from file");
+  }
+}
+
+template <typename T> void load_value(FILE *stream, std::vector<T> &value) {
+  size_t size;
+  size_t read_cnt = fread(&size, sizeof(size_t), 1, stream);
+  if (read_cnt != 1) {
+    throw std::runtime_error("Cannot read from file");
+  }
+  value.resize(size);
+  read_cnt = fread(&value[0], sizeof(T), size, stream);
+  if (read_cnt != size) {
+    throw std::runtime_error("Cannot read from file");
+  }
+}
+/** @} */
+
+/** @addtogroup metric_grp Metric (distance) classes
+ * @{ */
+
+struct Metric {};
+
+/** Manhattan distance functor (generic version, optimized for
+ * high-dimensionality data sets). Corresponding distance traits:
+ * nanoflann::metric_L1 \tparam T Type of the elements (e.g. double, float,
+ * uint8_t) \tparam _DistanceType Type of distance variables (must be signed)
+ * (e.g. float, double, int64_t)
+ */
+template <class T, class DataSource, typename _DistanceType = T>
+struct L1_Adaptor {
+  typedef T ElementType;
+  typedef _DistanceType DistanceType;
+
+  const DataSource &data_source;
+
+  L1_Adaptor(const DataSource &_data_source) : data_source(_data_source) {}
+
+  inline DistanceType evalMetric(const T *a, const size_t b_idx, size_t size,
+                                 DistanceType worst_dist = -1) const {
+    DistanceType result = DistanceType();
+    const T *last = a + size;
+    const T *lastgroup = last - 3;
+    size_t d = 0;
+
+    /* Process 4 items with each loop for efficiency. */
+    while (a < lastgroup) {
+      const DistanceType diff0 =
+          std::abs(a[0] - data_source.kdtree_get_pt(b_idx, d++));
+      const DistanceType diff1 =
+          std::abs(a[1] - data_source.kdtree_get_pt(b_idx, d++));
+      const DistanceType diff2 =
+          std::abs(a[2] - data_source.kdtree_get_pt(b_idx, d++));
+      const DistanceType diff3 =
+          std::abs(a[3] - data_source.kdtree_get_pt(b_idx, d++));
+      result += diff0 + diff1 + diff2 + diff3;
+      a += 4;
+      if ((worst_dist > 0) && (result > worst_dist)) {
+        return result;
+      }
+    }
+    /* Process last 0-3 components.  Not needed for standard vector lengths. */
+    while (a < last) {
+      result += std::abs(*a++ - data_source.kdtree_get_pt(b_idx, d++));
+    }
+    return result;
+  }
+
+  template <typename U, typename V>
+  inline DistanceType accum_dist(const U a, const V b, const size_t) const {
+    return std::abs(a - b);
+  }
+};
+
+/** Squared Euclidean distance functor (generic version, optimized for
+ * high-dimensionality data sets). Corresponding distance traits:
+ * nanoflann::metric_L2 \tparam T Type of the elements (e.g. double, float,
+ * uint8_t) \tparam _DistanceType Type of distance variables (must be signed)
+ * (e.g. float, double, int64_t)
+ */
+template <class T, class DataSource, typename _DistanceType = T>
+struct L2_Adaptor {
+  typedef T ElementType;
+  typedef _DistanceType DistanceType;
+
+  const DataSource &data_source;
+
+  L2_Adaptor(const DataSource &_data_source) : data_source(_data_source) {}
+
+  inline DistanceType evalMetric(const T *a, const size_t b_idx, size_t size,
+                                 DistanceType worst_dist = -1) const {
+    DistanceType result = DistanceType();
+    const T *last = a + size;
+    const T *lastgroup = last - 3;
+    size_t d = 0;
+
+    /* Process 4 items with each loop for efficiency. */
+    while (a < lastgroup) {
+      const DistanceType diff0 = a[0] - data_source.kdtree_get_pt(b_idx, d++);
+      const DistanceType diff1 = a[1] - data_source.kdtree_get_pt(b_idx, d++);
+      const DistanceType diff2 = a[2] - data_source.kdtree_get_pt(b_idx, d++);
+      const DistanceType diff3 = a[3] - data_source.kdtree_get_pt(b_idx, d++);
+      result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
+      a += 4;
+      if ((worst_dist > 0) && (result > worst_dist)) {
+        return result;
+      }
+    }
+    /* Process last 0-3 components.  Not needed for standard vector lengths. */
+    while (a < last) {
+      const DistanceType diff0 = *a++ - data_source.kdtree_get_pt(b_idx, d++);
+      result += diff0 * diff0;
+    }
+    return result;
+  }
+
+  template <typename U, typename V>
+  inline DistanceType accum_dist(const U a, const V b, const size_t) const {
+    return (a - b) * (a - b);
+  }
+};
+
+/** Squared Euclidean (L2) distance functor (suitable for low-dimensionality
+ * datasets, like 2D or 3D point clouds) Corresponding distance traits:
+ * nanoflann::metric_L2_Simple \tparam T Type of the elements (e.g. double,
+ * float, uint8_t) \tparam _DistanceType Type of distance variables (must be
+ * signed) (e.g. float, double, int64_t)
+ */
+template <class T, class DataSource, typename _DistanceType = T>
+struct L2_Simple_Adaptor {
+  typedef T ElementType;
+  typedef _DistanceType DistanceType;
+
+  const DataSource &data_source;
+
+  L2_Simple_Adaptor(const DataSource &_data_source)
+      : data_source(_data_source) {}
+
+  inline DistanceType evalMetric(const T *a, const size_t b_idx,
+                                 size_t size) const {
+    DistanceType result = DistanceType();
+    for (size_t i = 0; i < size; ++i) {
+      const DistanceType diff = a[i] - data_source.kdtree_get_pt(b_idx, i);
+      result += diff * diff;
+    }
+    return result;
+  }
+
+  template <typename U, typename V>
+  inline DistanceType accum_dist(const U a, const V b, const size_t) const {
+    return (a - b) * (a - b);
+  }
+};
+
+/** SO2 distance functor
+ *  Corresponding distance traits: nanoflann::metric_SO2
+ * \tparam T Type of the elements (e.g. double, float)
+ * \tparam _DistanceType Type of distance variables (must be signed) (e.g.
+ * float, double) orientation is constrained to be in [-pi, pi]
+ */
+template <class T, class DataSource, typename _DistanceType = T>
+struct SO2_Adaptor {
+  typedef T ElementType;
+  typedef _DistanceType DistanceType;
+
+  const DataSource &data_source;
+
+  SO2_Adaptor(const DataSource &_data_source) : data_source(_data_source) {}
+
+  inline DistanceType evalMetric(const T *a, const size_t b_idx,
+                                 size_t size) const {
+    return accum_dist(a[size - 1], data_source.kdtree_get_pt(b_idx, size - 1),
+                      size - 1);
+  }
+
+  /** Note: this assumes that input angles are already in the range [-pi,pi] */
+  template <typename U, typename V>
+  inline DistanceType accum_dist(const U a, const V b, const size_t) const {
+    DistanceType result = DistanceType(), PI = pi_const<DistanceType>();
+    result = b - a;
+    if (result > PI)
+      result -= 2 * PI;
+    else if (result < -PI)
+      result += 2 * PI;
+    return result;
+  }
+};
+
+/** SO3 distance functor (Uses L2_Simple)
+ *  Corresponding distance traits: nanoflann::metric_SO3
+ * \tparam T Type of the elements (e.g. double, float)
+ * \tparam _DistanceType Type of distance variables (must be signed) (e.g.
+ * float, double)
+ */
+template <class T, class DataSource, typename _DistanceType = T>
+struct SO3_Adaptor {
+  typedef T ElementType;
+  typedef _DistanceType DistanceType;
+
+  L2_Simple_Adaptor<T, DataSource> distance_L2_Simple;
+
+  SO3_Adaptor(const DataSource &_data_source)
+      : distance_L2_Simple(_data_source) {}
+
+  inline DistanceType evalMetric(const T *a, const size_t b_idx,
+                                 size_t size) const {
+    return distance_L2_Simple.evalMetric(a, b_idx, size);
+  }
+
+  template <typename U, typename V>
+  inline DistanceType accum_dist(const U a, const V b, const size_t idx) const {
+    return distance_L2_Simple.accum_dist(a, b, idx);
+  }
+};
+
+/** Metaprogramming helper traits class for the L1 (Manhattan) metric */
+struct metric_L1 : public Metric {
+  template <class T, class DataSource> struct traits {
+    typedef L1_Adaptor<T, DataSource> distance_t;
+  };
+};
+/** Metaprogramming helper traits class for the L2 (Euclidean) metric */
+struct metric_L2 : public Metric {
+  template <class T, class DataSource> struct traits {
+    typedef L2_Adaptor<T, DataSource> distance_t;
+  };
+};
+/** Metaprogramming helper traits class for the L2_simple (Euclidean) metric */
+struct metric_L2_Simple : public Metric {
+  template <class T, class DataSource> struct traits {
+    typedef L2_Simple_Adaptor<T, DataSource> distance_t;
+  };
+};
+/** Metaprogramming helper traits class for the SO3_InnerProdQuat metric */
+struct metric_SO2 : public Metric {
+  template <class T, class DataSource> struct traits {
+    typedef SO2_Adaptor<T, DataSource> distance_t;
+  };
+};
+/** Metaprogramming helper traits class for the SO3_InnerProdQuat metric */
+struct metric_SO3 : public Metric {
+  template <class T, class DataSource> struct traits {
+    typedef SO3_Adaptor<T, DataSource> distance_t;
+  };
+};
+
+/** @} */
+
+/** @addtogroup param_grp Parameter structs
+ * @{ */
+
+/**  Parameters (see README.md) */
+struct KDTreeSingleIndexAdaptorParams {
+  KDTreeSingleIndexAdaptorParams(size_t _leaf_max_size = 10)
+      : leaf_max_size(_leaf_max_size) {}
+
+  size_t leaf_max_size;
+};
+
+/** Search options for KDTreeSingleIndexAdaptor::findNeighbors() */
+struct SearchParams {
+  /** Note: The first argument (checks_IGNORED_) is ignored, but kept for
+   * compatibility with the FLANN interface */
+  SearchParams(int checks_IGNORED_ = 32, float eps_ = 0, bool sorted_ = true)
+      : checks(checks_IGNORED_), eps(eps_), sorted(sorted_) {}
+
+  int checks;  //!< Ignored parameter (Kept for compatibility with the FLANN
+               //!< interface).
+  float eps;   //!< search for eps-approximate neighbours (default: 0)
+  bool sorted; //!< only for radius search, require neighbours sorted by
+               //!< distance (default: true)
+};
+/** @} */
+
+/** @addtogroup memalloc_grp Memory allocation
+ * @{ */
+
+/**
+ * Allocates (using C's malloc) a generic type T.
+ *
+ * Params:
+ *     count = number of instances to allocate.
+ * Returns: pointer (of type T*) to memory buffer
+ */
+template <typename T> inline T *allocate(size_t count = 1) {
+  T *mem = static_cast<T *>(::malloc(sizeof(T) * count));
+  return mem;
+}
+
+/**
+ * Pooled storage allocator
+ *
+ * The following routines allow for the efficient allocation of storage in
+ * small chunks from a specified pool.  Rather than allowing each structure
+ * to be freed individually, an entire pool of storage is freed at once.
+ * This method has two advantages over just using malloc() and free().  First,
+ * it is far more efficient for allocating small objects, as there is
+ * no overhead for remembering all the information needed to free each
+ * object or consolidating fragmented memory.  Second, the decision about
+ * how long to keep an object is made at the time of allocation, and there
+ * is no need to track down all the objects to free them.
+ *
+ */
+
+const size_t WORDSIZE = 16;
+const size_t BLOCKSIZE = 8192;
+
+class PooledAllocator {
+  /* We maintain memory alignment to word boundaries by requiring that all
+      allocations be in multiples of the machine wordsize.  */
+  /* Size of machine word in bytes.  Must be power of 2. */
+  /* Minimum number of bytes requested at a time from	the system.  Must be
+   * multiple of WORDSIZE. */
+
+  size_t remaining; /* Number of bytes left in current block of storage. */
+  void *base;       /* Pointer to base of current block of storage. */
+  void *loc;        /* Current location in block to next allocate memory. */
+
+  void internal_init() {
+    remaining = 0;
+    base = NULL;
+    usedMemory = 0;
+    wastedMemory = 0;
+  }
+
+public:
+  size_t usedMemory;
+  size_t wastedMemory;
+
+  /**
+      Default constructor. Initializes a new pool.
+   */
+  PooledAllocator() { internal_init(); }
+
+  /**
+   * Destructor. Frees all the memory allocated in this pool.
+   */
+  ~PooledAllocator() { free_all(); }
+
+  /** Frees all allocated memory chunks */
+  void free_all() {
+    while (base != NULL) {
+      void *prev =
+          *(static_cast<void **>(base)); /* Get pointer to prev block. */
+      ::free(base);
+      base = prev;
+    }
+    internal_init();
+  }
+
+  /**
+   * Returns a pointer to a piece of new memory of the given size in bytes
+   * allocated from the pool.
+   */
+  void *malloc(const size_t req_size) {
+    /* Round size up to a multiple of wordsize.  The following expression
+        only works for WORDSIZE that is a power of 2, by masking last bits of
+        incremented size to zero.
+     */
+    const size_t size = (req_size + (WORDSIZE - 1)) & ~(WORDSIZE - 1);
+
+    /* Check whether a new block must be allocated.  Note that the first word
+        of a block is reserved for a pointer to the previous block.
+     */
+    if (size > remaining) {
+
+      wastedMemory += remaining;
+
+      /* Allocate new storage. */
+      const size_t blocksize =
+          (size + sizeof(void *) + (WORDSIZE - 1) > BLOCKSIZE)
+              ? size + sizeof(void *) + (WORDSIZE - 1)
+              : BLOCKSIZE;
+
+      // use the standard C malloc to allocate memory
+      void *m = ::malloc(blocksize);
+      if (!m) {
+        fprintf(stderr, "Failed to allocate memory.\n");
+        return NULL;
+      }
+
+      /* Fill first word of new block with pointer to previous block. */
+      static_cast<void **>(m)[0] = base;
+      base = m;
+
+      size_t shift = 0;
+      // int size_t = (WORDSIZE - ( (((size_t)m) + sizeof(void*)) &
+      // (WORDSIZE-1))) & (WORDSIZE-1);
+
+      remaining = blocksize - sizeof(void *) - shift;
+      loc = (static_cast<char *>(m) + sizeof(void *) + shift);
+    }
+    void *rloc = loc;
+    loc = static_cast<char *>(loc) + size;
+    remaining -= size;
+
+    usedMemory += size;
+
+    return rloc;
+  }
+
+  /**
+   * Allocates (using this pool) a generic type T.
+   *
+   * Params:
+   *     count = number of instances to allocate.
+   * Returns: pointer (of type T*) to memory buffer
+   */
+  template <typename T> T *allocate(const size_t count = 1) {
+    T *mem = static_cast<T *>(this->malloc(sizeof(T) * count));
+    return mem;
+  }
+};
+/** @} */
+
+/** @addtogroup nanoflann_metaprog_grp Auxiliary metaprogramming stuff
+ * @{ */
+
+/** Used to declare fixed-size arrays when DIM>0, dynamically-allocated vectors
+ * when DIM=-1. Fixed size version for a generic DIM:
+ */
+template <int DIM, typename T> struct array_or_vector_selector {
+  typedef std::array<T, DIM> container_t;
+};
+/** Dynamic size version */
+template <typename T> struct array_or_vector_selector<-1, T> {
+  typedef std::vector<T> container_t;
+};
+
+/** @} */
+
+/** kd-tree base-class
+ *
+ * Contains the member functions common to the classes KDTreeSingleIndexAdaptor
+ * and KDTreeSingleIndexDynamicAdaptor_.
+ *
+ * \tparam Derived The name of the class which inherits this class.
+ * \tparam DatasetAdaptor The user-provided adaptor (see comments above).
+ * \tparam Distance The distance metric to use, these are all classes derived
+ * from nanoflann::Metric \tparam DIM Dimensionality of data points (e.g. 3 for
+ * 3D points) \tparam IndexType Will be typically size_t or int
+ */
+
+template <class Derived, typename Distance, class DatasetAdaptor, int DIM = -1,
+          typename IndexType = size_t>
+class KDTreeBaseClass {
+
+public:
+  /** Frees the previously-built index. Automatically called within
+   * buildIndex(). */
+  void freeIndex(Derived &obj) {
+    obj.pool.free_all();
+    obj.root_node = NULL;
+    obj.m_size_at_index_build = 0;
+  }
+
+  typedef typename Distance::ElementType ElementType;
+  typedef typename Distance::DistanceType DistanceType;
+
+  /*--------------------- Internal Data Structures --------------------------*/
+  struct Node {
+    /** Union used because a node can be either a LEAF node or a non-leaf node,
+     * so both data fields are never used simultaneously */
+    union {
+      struct leaf {
+        IndexType left, right; //!< Indices of points in leaf node
+      } lr;
+      struct nonleaf {
+        int divfeat;                  //!< Dimension used for subdivision.
+        DistanceType divlow, divhigh; //!< The values used for subdivision.
+      } sub;
+    } node_type;
+    Node *child1, *child2; //!< Child nodes (both=NULL mean its a leaf node)
+  };
+
+  typedef Node *NodePtr;
+
+  struct Interval {
+    ElementType low, high;
+  };
+
+  /**
+   *  Array of indices to vectors in the dataset.
+   */
+  std::vector<IndexType> vind;
+
+  NodePtr root_node;
+
+  size_t m_leaf_max_size;
+
+  size_t m_size;                //!< Number of current points in the dataset
+  size_t m_size_at_index_build; //!< Number of points in the dataset when the
+                                //!< index was built
+  int dim;                      //!< Dimensionality of each data point
+
+  /** Define "BoundingBox" as a fixed-size or variable-size container depending
+   * on "DIM" */
+  typedef
+      typename array_or_vector_selector<DIM, Interval>::container_t BoundingBox;
+
+  /** Define "distance_vector_t" as a fixed-size or variable-size container
+   * depending on "DIM" */
+  typedef typename array_or_vector_selector<DIM, DistanceType>::container_t
+      distance_vector_t;
+
+  /** The KD-tree used to find neighbours */
+
+  BoundingBox root_bbox;
+
+  /**
+   * Pooled memory allocator.
+   *
+   * Using a pooled memory allocator is more efficient
+   * than allocating memory directly when there is a large
+   * number small of memory allocations.
+   */
+  PooledAllocator pool;
+
+  /** Returns number of points in dataset  */
+  size_t size(const Derived &obj) const { return obj.m_size; }
+
+  /** Returns the length of each point in the dataset */
+  size_t veclen(const Derived &obj) {
+    return static_cast<size_t>(DIM > 0 ? DIM : obj.dim);
+  }
+
+  /// Helper accessor to the dataset points:
+  inline ElementType dataset_get(const Derived &obj, size_t idx,
+                                 int component) const {
+    return obj.dataset.kdtree_get_pt(idx, component);
+  }
+
+  /**
+   * Computes the inde memory usage
+   * Returns: memory used by the index
+   */
+  size_t usedMemory(Derived &obj) {
+    return obj.pool.usedMemory + obj.pool.wastedMemory +
+           obj.dataset.kdtree_get_point_count() *
+               sizeof(IndexType); // pool memory and vind array memory
+  }
+
+  void computeMinMax(const Derived &obj, IndexType *ind, IndexType count,
+                     int element, ElementType &min_elem,
+                     ElementType &max_elem) {
+    min_elem = dataset_get(obj, ind[0], element);
+    max_elem = dataset_get(obj, ind[0], element);
+    for (IndexType i = 1; i < count; ++i) {
+      ElementType val = dataset_get(obj, ind[i], element);
+      if (val < min_elem)
+        min_elem = val;
+      if (val > max_elem)
+        max_elem = val;
+    }
+  }
+
+  /**
+   * Create a tree node that subdivides the list of vecs from vind[first]
+   * to vind[last].  The routine is called recursively on each sublist.
+   *
+   * @param left index of the first vector
+   * @param right index of the last vector
+   */
+  NodePtr divideTree(Derived &obj, const IndexType left, const IndexType right,
+                     BoundingBox &bbox) {
+    NodePtr node = obj.pool.template allocate<Node>(); // allocate memory
+
+    /* If too few exemplars remain, then make this a leaf node. */
+    if ((right - left) <= static_cast<IndexType>(obj.m_leaf_max_size)) {
+      node->child1 = node->child2 = NULL; /* Mark as leaf node. */
+      node->node_type.lr.left = left;
+      node->node_type.lr.right = right;
+
+      // compute bounding-box of leaf points
+      for (int i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
+        bbox[i].low = dataset_get(obj, obj.vind[left], i);
+        bbox[i].high = dataset_get(obj, obj.vind[left], i);
+      }
+      for (IndexType k = left + 1; k < right; ++k) {
+        for (int i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
+          if (bbox[i].low > dataset_get(obj, obj.vind[k], i))
+            bbox[i].low = dataset_get(obj, obj.vind[k], i);
+          if (bbox[i].high < dataset_get(obj, obj.vind[k], i))
+            bbox[i].high = dataset_get(obj, obj.vind[k], i);
+        }
+      }
+    } else {
+      IndexType idx;
+      int cutfeat;
+      DistanceType cutval;
+      middleSplit_(obj, &obj.vind[0] + left, right - left, idx, cutfeat, cutval,
+                   bbox);
+
+      node->node_type.sub.divfeat = cutfeat;
+
+      BoundingBox left_bbox(bbox);
+      left_bbox[cutfeat].high = cutval;
+      node->child1 = divideTree(obj, left, left + idx, left_bbox);
+
+      BoundingBox right_bbox(bbox);
+      right_bbox[cutfeat].low = cutval;
+      node->child2 = divideTree(obj, left + idx, right, right_bbox);
+
+      node->node_type.sub.divlow = left_bbox[cutfeat].high;
+      node->node_type.sub.divhigh = right_bbox[cutfeat].low;
+
+      for (int i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
+        bbox[i].low = std::min(left_bbox[i].low, right_bbox[i].low);
+        bbox[i].high = std::max(left_bbox[i].high, right_bbox[i].high);
+      }
+    }
+
+    return node;
+  }
+
+  void middleSplit_(Derived &obj, IndexType *ind, IndexType count,
+                    IndexType &index, int &cutfeat, DistanceType &cutval,
+                    const BoundingBox &bbox) {
+    const DistanceType EPS = static_cast<DistanceType>(0.00001);
+    ElementType max_span = bbox[0].high - bbox[0].low;
+    for (int i = 1; i < (DIM > 0 ? DIM : obj.dim); ++i) {
+      ElementType span = bbox[i].high - bbox[i].low;
+      if (span > max_span) {
+        max_span = span;
+      }
+    }
+    ElementType max_spread = -1;
+    cutfeat = 0;
+    for (int i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
+      ElementType span = bbox[i].high - bbox[i].low;
+      if (span > (1 - EPS) * max_span) {
+        ElementType min_elem, max_elem;
+        computeMinMax(obj, ind, count, i, min_elem, max_elem);
+        ElementType spread = max_elem - min_elem;
+        ;
+        if (spread > max_spread) {
+          cutfeat = i;
+          max_spread = spread;
+        }
+      }
+    }
+    // split in the middle
+    DistanceType split_val = (bbox[cutfeat].low + bbox[cutfeat].high) / 2;
+    ElementType min_elem, max_elem;
+    computeMinMax(obj, ind, count, cutfeat, min_elem, max_elem);
+
+    if (split_val < min_elem)
+      cutval = min_elem;
+    else if (split_val > max_elem)
+      cutval = max_elem;
+    else
+      cutval = split_val;
+
+    IndexType lim1, lim2;
+    planeSplit(obj, ind, count, cutfeat, cutval, lim1, lim2);
+
+    if (lim1 > count / 2)
+      index = lim1;
+    else if (lim2 < count / 2)
+      index = lim2;
+    else
+      index = count / 2;
+  }
+
+  /**
+   *  Subdivide the list of points by a plane perpendicular on axe corresponding
+   *  to the 'cutfeat' dimension at 'cutval' position.
+   *
+   *  On return:
+   *  dataset[ind[0..lim1-1]][cutfeat]<cutval
+   *  dataset[ind[lim1..lim2-1]][cutfeat]==cutval
+   *  dataset[ind[lim2..count]][cutfeat]>cutval
+   */
+  void planeSplit(Derived &obj, IndexType *ind, const IndexType count,
+                  int cutfeat, DistanceType &cutval, IndexType &lim1,
+                  IndexType &lim2) {
+    /* Move vector indices for left subtree to front of list. */
+    IndexType left = 0;
+    IndexType right = count - 1;
+    for (;;) {
+      while (left <= right && dataset_get(obj, ind[left], cutfeat) < cutval)
+        ++left;
+      while (right && left <= right &&
+             dataset_get(obj, ind[right], cutfeat) >= cutval)
+        --right;
+      if (left > right || !right)
+        break; // "!right" was added to support unsigned Index types
+      std::swap(ind[left], ind[right]);
+      ++left;
+      --right;
+    }
+    /* If either list is empty, it means that all remaining features
+     * are identical. Split in the middle to maintain a balanced tree.
+     */
+    lim1 = left;
+    right = count - 1;
+    for (;;) {
+      while (left <= right && dataset_get(obj, ind[left], cutfeat) <= cutval)
+        ++left;
+      while (right && left <= right &&
+             dataset_get(obj, ind[right], cutfeat) > cutval)
+        --right;
+      if (left > right || !right)
+        break; // "!right" was added to support unsigned Index types
+      std::swap(ind[left], ind[right]);
+      ++left;
+      --right;
+    }
+    lim2 = left;
+  }
+
+  DistanceType computeInitialDistances(const Derived &obj,
+                                       const ElementType *vec,
+                                       distance_vector_t &dists) const {
+    assert(vec);
+    DistanceType distsq = DistanceType();
+
+    for (int i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
+      if (vec[i] < obj.root_bbox[i].low) {
+        dists[i] = obj.distance.accum_dist(vec[i], obj.root_bbox[i].low, i);
+        distsq += dists[i];
+      }
+      if (vec[i] > obj.root_bbox[i].high) {
+        dists[i] = obj.distance.accum_dist(vec[i], obj.root_bbox[i].high, i);
+        distsq += dists[i];
+      }
+    }
+    return distsq;
+  }
+
+  void save_tree(Derived &obj, FILE *stream, NodePtr tree) {
+    save_value(stream, *tree);
+    if (tree->child1 != NULL) {
+      save_tree(obj, stream, tree->child1);
+    }
+    if (tree->child2 != NULL) {
+      save_tree(obj, stream, tree->child2);
+    }
+  }
+
+  void load_tree(Derived &obj, FILE *stream, NodePtr &tree) {
+    tree = obj.pool.template allocate<Node>();
+    load_value(stream, *tree);
+    if (tree->child1 != NULL) {
+      load_tree(obj, stream, tree->child1);
+    }
+    if (tree->child2 != NULL) {
+      load_tree(obj, stream, tree->child2);
+    }
+  }
+
+  /**  Stores the index in a binary file.
+   *   IMPORTANT NOTE: The set of data points is NOT stored in the file, so when
+   * loading the index object it must be constructed associated to the same
+   * source of data points used while building it. See the example:
+   * examples/saveload_example.cpp \sa loadIndex  */
+  void saveIndex_(Derived &obj, FILE *stream) {
+    save_value(stream, obj.m_size);
+    save_value(stream, obj.dim);
+    save_value(stream, obj.root_bbox);
+    save_value(stream, obj.m_leaf_max_size);
+    save_value(stream, obj.vind);
+    save_tree(obj, stream, obj.root_node);
+  }
+
+  /**  Loads a previous index from a binary file.
+   *   IMPORTANT NOTE: The set of data points is NOT stored in the file, so the
+   * index object must be constructed associated to the same source of data
+   * points used while building the index. See the example:
+   * examples/saveload_example.cpp \sa loadIndex  */
+  void loadIndex_(Derived &obj, FILE *stream) {
+    load_value(stream, obj.m_size);
+    load_value(stream, obj.dim);
+    load_value(stream, obj.root_bbox);
+    load_value(stream, obj.m_leaf_max_size);
+    load_value(stream, obj.vind);
+    load_tree(obj, stream, obj.root_node);
+  }
+};
+
+/** @addtogroup kdtrees_grp KD-tree classes and adaptors
+ * @{ */
+
+/** kd-tree static index
+ *
+ * Contains the k-d trees and other information for indexing a set of points
+ * for nearest-neighbor matching.
+ *
+ *  The class "DatasetAdaptor" must provide the following interface (can be
+ * non-virtual, inlined methods):
+ *
+ *  \code
+ *   // Must return the number of data poins
+ *   inline size_t kdtree_get_point_count() const { ... }
+ *
+ *
+ *   // Must return the dim'th component of the idx'th point in the class:
+ *   inline T kdtree_get_pt(const size_t idx, const size_t dim) const { ... }
+ *
+ *   // Optional bounding-box computation: return false to default to a standard
+ * 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 the expected dimensionality (e.g. 2 or 3
+ * for point clouds) template <class BBOX> bool kdtree_get_bbox(BBOX &bb) const
+ *   {
+ *      bb[0].low = ...; bb[0].high = ...;  // 0th dimension limits
+ *      bb[1].low = ...; bb[1].high = ...;  // 1st dimension limits
+ *      ...
+ *      return true;
+ *   }
+ *
+ *  \endcode
+ *
+ * \tparam DatasetAdaptor The user-provided adaptor (see comments above).
+ * \tparam Distance The distance metric to use: nanoflann::metric_L1,
+ * nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc. \tparam DIM
+ * Dimensionality of data points (e.g. 3 for 3D points) \tparam IndexType Will
+ * be typically size_t or int
+ */
+template <typename Distance, class DatasetAdaptor, int DIM = -1,
+          typename IndexType = size_t>
+class KDTreeSingleIndexAdaptor
+    : public KDTreeBaseClass<
+          KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM, IndexType>,
+          Distance, DatasetAdaptor, DIM, IndexType> {
+public:
+  /** Deleted copy constructor*/
+  KDTreeSingleIndexAdaptor(
+      const KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM, IndexType>
+          &) = delete;
+
+  /**
+   * The dataset used by this index
+   */
+  const DatasetAdaptor &dataset; //!< The source of our data
+
+  const KDTreeSingleIndexAdaptorParams index_params;
+
+  Distance distance;
+
+  typedef typename nanoflann::KDTreeBaseClass<
+      nanoflann::KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM,
+                                          IndexType>,
+      Distance, DatasetAdaptor, DIM, IndexType>
+      BaseClassRef;
+
+  typedef typename BaseClassRef::ElementType ElementType;
+  typedef typename BaseClassRef::DistanceType DistanceType;
+
+  typedef typename BaseClassRef::Node Node;
+  typedef Node *NodePtr;
+
+  typedef typename BaseClassRef::Interval Interval;
+  /** Define "BoundingBox" as a fixed-size or variable-size container depending
+   * on "DIM" */
+  typedef typename BaseClassRef::BoundingBox BoundingBox;
+
+  /** Define "distance_vector_t" as a fixed-size or variable-size container
+   * depending on "DIM" */
+  typedef typename BaseClassRef::distance_vector_t distance_vector_t;
+
+  /**
+   * KDTree constructor
+   *
+   * Refer to docs in README.md or online in
+   * https://github.com/jlblancoc/nanoflann
+   *
+   * The KD-Tree point dimension (the length of each point in the datase, e.g. 3
+   * for 3D points) is determined by means of:
+   *  - The \a DIM template parameter if >0 (highest priority)
+   *  - Otherwise, the \a dimensionality parameter of this constructor.
+   *
+   * @param inputData Dataset with the input features
+   * @param params Basically, the maximum leaf node size
+   */
+  KDTreeSingleIndexAdaptor(const int dimensionality,
+                           const DatasetAdaptor &inputData,
+                           const KDTreeSingleIndexAdaptorParams &params =
+                               KDTreeSingleIndexAdaptorParams())
+      : dataset(inputData), index_params(params), distance(inputData) {
+    BaseClassRef::root_node = NULL;
+    BaseClassRef::m_size = dataset.kdtree_get_point_count();
+    BaseClassRef::m_size_at_index_build = BaseClassRef::m_size;
+    BaseClassRef::dim = dimensionality;
+    if (DIM > 0)
+      BaseClassRef::dim = DIM;
+    BaseClassRef::m_leaf_max_size = params.leaf_max_size;
+
+    // Create a permutable array of indices to the input vectors.
+    init_vind();
+  }
+
+  /**
+   * Builds the index
+   */
+  void buildIndex() {
+    BaseClassRef::m_size = dataset.kdtree_get_point_count();
+    BaseClassRef::m_size_at_index_build = BaseClassRef::m_size;
+    init_vind();
+    this->freeIndex(*this);
+    BaseClassRef::m_size_at_index_build = BaseClassRef::m_size;
+    if (BaseClassRef::m_size == 0)
+      return;
+    computeBoundingBox(BaseClassRef::root_bbox);
+    BaseClassRef::root_node =
+        this->divideTree(*this, 0, BaseClassRef::m_size,
+                         BaseClassRef::root_bbox); // construct the tree
+  }
+
+  /** \name Query methods
+   * @{ */
+
+  /**
+   * Find set of nearest neighbors to vec[0:dim-1]. Their indices are stored
+   * inside the result object.
+   *
+   * Params:
+   *     result = the result object in which the indices of the
+   * nearest-neighbors are stored vec = the vector for which to search the
+   * nearest neighbors
+   *
+   * \tparam RESULTSET Should be any ResultSet<DistanceType>
+   * \return  True if the requested neighbors could be found.
+   * \sa knnSearch, radiusSearch
+   */
+  template <typename RESULTSET>
+  bool findNeighbors(RESULTSET &result, const ElementType *vec,
+                     const SearchParams &searchParams) const {
+    assert(vec);
+    if (this->size(*this) == 0)
+      return false;
+    if (!BaseClassRef::root_node)
+      throw std::runtime_error(
+          "[nanoflann] findNeighbors() called before building the index.");
+    float epsError = 1 + searchParams.eps;
+
+    distance_vector_t
+        dists; // fixed or variable-sized container (depending on DIM)
+    auto zero = static_cast<decltype(result.worstDist())>(0);
+    assign(dists, (DIM > 0 ? DIM : BaseClassRef::dim),
+           zero); // Fill it with zeros.
+    DistanceType distsq = this->computeInitialDistances(*this, vec, dists);
+
+    searchLevel(result, vec, BaseClassRef::root_node, distsq, dists,
+                epsError); // "count_leaf" parameter removed since was neither
+                           // used nor returned to the user.
+
+    return result.full();
+  }
+
+  /**
+   * Find the "num_closest" nearest neighbors to the \a query_point[0:dim-1].
+   * Their indices are stored inside the result object. \sa radiusSearch,
+   * findNeighbors \note nChecks_IGNORED is ignored but kept for compatibility
+   * with the original FLANN interface. \return Number `N` of valid points in
+   * the result set. Only the first `N` entries in `out_indices` and
+   * `out_distances_sq` will be valid. Return may be less than `num_closest`
+   * only if the number of elements in the tree is less than `num_closest`.
+   */
+  size_t knnSearch(const ElementType *query_point, const size_t num_closest,
+                   IndexType *out_indices, DistanceType *out_distances_sq,
+                   const int /* nChecks_IGNORED */ = 10) const {
+    nanoflann::KNNResultSet<DistanceType, IndexType> resultSet(num_closest);
+    resultSet.init(out_indices, out_distances_sq);
+    this->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
+    return resultSet.size();
+  }
+
+  /**
+   * Find all the neighbors to \a query_point[0:dim-1] within a maximum radius.
+   *  The output is given as a vector of pairs, of which the first element is a
+   * point index and the second the corresponding distance. Previous contents of
+   * \a IndicesDists are cleared.
+   *
+   *  If searchParams.sorted==true, the output list is sorted by ascending
+   * distances.
+   *
+   *  For a better performance, it is advisable to do a .reserve() on the vector
+   * if you have any wild guess about the number of expected matches.
+   *
+   *  \sa knnSearch, findNeighbors, radiusSearchCustomCallback
+   * \return The number of points within the given radius (i.e. indices.size()
+   * or dists.size() )
+   */
+  size_t
+  radiusSearch(const ElementType *query_point, const DistanceType &radius,
+               std::vector<std::pair<IndexType, DistanceType>> &IndicesDists,
+               const SearchParams &searchParams) const {
+    RadiusResultSet<DistanceType, IndexType> resultSet(radius, IndicesDists);
+    const size_t nFound =
+        radiusSearchCustomCallback(query_point, resultSet, searchParams);
+    if (searchParams.sorted)
+      std::sort(IndicesDists.begin(), IndicesDists.end(), IndexDist_Sorter());
+    return nFound;
+  }
+
+  /**
+   * Just like radiusSearch() but with a custom callback class for each point
+   * found in the radius of the query. See the source of RadiusResultSet<> as a
+   * start point for your own classes. \sa radiusSearch
+   */
+  template <class SEARCH_CALLBACK>
+  size_t radiusSearchCustomCallback(
+      const ElementType *query_point, SEARCH_CALLBACK &resultSet,
+      const SearchParams &searchParams = SearchParams()) const {
+    this->findNeighbors(resultSet, query_point, searchParams);
+    return resultSet.size();
+  }
+
+  /** @} */
+
+public:
+  /** Make sure the auxiliary list \a vind has the same size than the current
+   * dataset, and re-generate if size has changed. */
+  void init_vind() {
+    // Create a permutable array of indices to the input vectors.
+    BaseClassRef::m_size = dataset.kdtree_get_point_count();
+    if (BaseClassRef::vind.size() != BaseClassRef::m_size)
+      BaseClassRef::vind.resize(BaseClassRef::m_size);
+    for (size_t i = 0; i < BaseClassRef::m_size; i++)
+      BaseClassRef::vind[i] = i;
+  }
+
+  void computeBoundingBox(BoundingBox &bbox) {
+    resize(bbox, (DIM > 0 ? DIM : BaseClassRef::dim));
+    if (dataset.kdtree_get_bbox(bbox)) {
+      // Done! It was implemented in derived class
+    } else {
+      const size_t N = dataset.kdtree_get_point_count();
+      if (!N)
+        throw std::runtime_error("[nanoflann] computeBoundingBox() called but "
+                                 "no data points found.");
+      for (int i = 0; i < (DIM > 0 ? DIM : BaseClassRef::dim); ++i) {
+        bbox[i].low = bbox[i].high = this->dataset_get(*this, 0, i);
+      }
+      for (size_t k = 1; k < N; ++k) {
+        for (int i = 0; i < (DIM > 0 ? DIM : BaseClassRef::dim); ++i) {
+          if (this->dataset_get(*this, k, i) < bbox[i].low)
+            bbox[i].low = this->dataset_get(*this, k, i);
+          if (this->dataset_get(*this, k, i) > bbox[i].high)
+            bbox[i].high = this->dataset_get(*this, k, i);
+        }
+      }
+    }
+  }
+
+  /**
+   * Performs an exact search in the tree starting from a node.
+   * \tparam RESULTSET Should be any ResultSet<DistanceType>
+   * \return true if the search should be continued, false if the results are
+   * sufficient
+   */
+  template <class RESULTSET>
+  bool searchLevel(RESULTSET &result_set, const ElementType *vec,
+                   const NodePtr node, DistanceType mindistsq,
+                   distance_vector_t &dists, const float epsError) const {
+    /* If this is a leaf node, then do check and return. */
+    if ((node->child1 == NULL) && (node->child2 == NULL)) {
+      // count_leaf += (node->lr.right-node->lr.left);  // Removed since was
+      // neither used nor returned to the user.
+      DistanceType worst_dist = result_set.worstDist();
+      for (IndexType i = node->node_type.lr.left; i < node->node_type.lr.right;
+           ++i) {
+        const IndexType index = BaseClassRef::vind[i]; // reorder... : i;
+        DistanceType dist = distance.evalMetric(
+            vec, index, (DIM > 0 ? DIM : BaseClassRef::dim));
+        if (dist < worst_dist) {
+          if (!result_set.addPoint(dist, BaseClassRef::vind[i])) {
+            // the resultset doesn't want to receive any more points, we're done
+            // searching!
+            return false;
+          }
+        }
+      }
+      return true;
+    }
+
+    /* Which child branch should be taken first? */
+    int idx = node->node_type.sub.divfeat;
+    ElementType val = vec[idx];
+    DistanceType diff1 = val - node->node_type.sub.divlow;
+    DistanceType diff2 = val - node->node_type.sub.divhigh;
+
+    NodePtr bestChild;
+    NodePtr otherChild;
+    DistanceType cut_dist;
+    if ((diff1 + diff2) < 0) {
+      bestChild = node->child1;
+      otherChild = node->child2;
+      cut_dist = distance.accum_dist(val, node->node_type.sub.divhigh, idx);
+    } else {
+      bestChild = node->child2;
+      otherChild = node->child1;
+      cut_dist = distance.accum_dist(val, node->node_type.sub.divlow, idx);
+    }
+
+    /* Call recursively to search next level down. */
+    if (!searchLevel(result_set, vec, bestChild, mindistsq, dists, epsError)) {
+      // the resultset doesn't want to receive any more points, we're done
+      // searching!
+      return false;
+    }
+
+    DistanceType dst = dists[idx];
+    mindistsq = mindistsq + cut_dist - dst;
+    dists[idx] = cut_dist;
+    if (mindistsq * epsError <= result_set.worstDist()) {
+      if (!searchLevel(result_set, vec, otherChild, mindistsq, dists,
+                       epsError)) {
+        // the resultset doesn't want to receive any more points, we're done
+        // searching!
+        return false;
+      }
+    }
+    dists[idx] = dst;
+    return true;
+  }
+
+public:
+  /**  Stores the index in a binary file.
+   *   IMPORTANT NOTE: The set of data points is NOT stored in the file, so when
+   * loading the index object it must be constructed associated to the same
+   * source of data points used while building it. See the example:
+   * examples/saveload_example.cpp \sa loadIndex  */
+  void saveIndex(FILE *stream) { this->saveIndex_(*this, stream); }
+
+  /**  Loads a previous index from a binary file.
+   *   IMPORTANT NOTE: The set of data points is NOT stored in the file, so the
+   * index object must be constructed associated to the same source of data
+   * points used while building the index. See the example:
+   * examples/saveload_example.cpp \sa loadIndex  */
+  void loadIndex(FILE *stream) { this->loadIndex_(*this, stream); }
+
+}; // class KDTree
+
+/** kd-tree dynamic index
+ *
+ * Contains the k-d trees and other information for indexing a set of points
+ * for nearest-neighbor matching.
+ *
+ *  The class "DatasetAdaptor" must provide the following interface (can be
+ * non-virtual, inlined methods):
+ *
+ *  \code
+ *   // Must return the number of data poins
+ *   inline size_t kdtree_get_point_count() const { ... }
+ *
+ *   // Must return the dim'th component of the idx'th point in the class:
+ *   inline T kdtree_get_pt(const size_t idx, const size_t dim) const { ... }
+ *
+ *   // Optional bounding-box computation: return false to default to a standard
+ * 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 the expected dimensionality (e.g. 2 or 3
+ * for point clouds) template <class BBOX> bool kdtree_get_bbox(BBOX &bb) const
+ *   {
+ *      bb[0].low = ...; bb[0].high = ...;  // 0th dimension limits
+ *      bb[1].low = ...; bb[1].high = ...;  // 1st dimension limits
+ *      ...
+ *      return true;
+ *   }
+ *
+ *  \endcode
+ *
+ * \tparam DatasetAdaptor The user-provided adaptor (see comments above).
+ * \tparam Distance The distance metric to use: nanoflann::metric_L1,
+ * nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc. \tparam DIM
+ * Dimensionality of data points (e.g. 3 for 3D points) \tparam IndexType Will
+ * be typically size_t or int
+ */
+template <typename Distance, class DatasetAdaptor, int DIM = -1,
+          typename IndexType = size_t>
+class KDTreeSingleIndexDynamicAdaptor_
+    : public KDTreeBaseClass<KDTreeSingleIndexDynamicAdaptor_<
+                                 Distance, DatasetAdaptor, DIM, IndexType>,
+                             Distance, DatasetAdaptor, DIM, IndexType> {
+public:
+  /**
+   * The dataset used by this index
+   */
+  const DatasetAdaptor &dataset; //!< The source of our data
+
+  KDTreeSingleIndexAdaptorParams index_params;
+
+  std::vector<int> &treeIndex;
+
+  Distance distance;
+
+  typedef typename nanoflann::KDTreeBaseClass<
+      nanoflann::KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM,
+                                                  IndexType>,
+      Distance, DatasetAdaptor, DIM, IndexType>
+      BaseClassRef;
+
+  typedef typename BaseClassRef::ElementType ElementType;
+  typedef typename BaseClassRef::DistanceType DistanceType;
+
+  typedef typename BaseClassRef::Node Node;
+  typedef Node *NodePtr;
+
+  typedef typename BaseClassRef::Interval Interval;
+  /** Define "BoundingBox" as a fixed-size or variable-size container depending
+   * on "DIM" */
+  typedef typename BaseClassRef::BoundingBox BoundingBox;
+
+  /** Define "distance_vector_t" as a fixed-size or variable-size container
+   * depending on "DIM" */
+  typedef typename BaseClassRef::distance_vector_t distance_vector_t;
+
+  /**
+   * KDTree constructor
+   *
+   * Refer to docs in README.md or online in
+   * https://github.com/jlblancoc/nanoflann
+   *
+   * The KD-Tree point dimension (the length of each point in the datase, e.g. 3
+   * for 3D points) is determined by means of:
+   *  - The \a DIM template parameter if >0 (highest priority)
+   *  - Otherwise, the \a dimensionality parameter of this constructor.
+   *
+   * @param inputData Dataset with the input features
+   * @param params Basically, the maximum leaf node size
+   */
+  KDTreeSingleIndexDynamicAdaptor_(
+      const int dimensionality, const DatasetAdaptor &inputData,
+      std::vector<int> &treeIndex_,
+      const KDTreeSingleIndexAdaptorParams &params =
+          KDTreeSingleIndexAdaptorParams())
+      : dataset(inputData), index_params(params), treeIndex(treeIndex_),
+        distance(inputData) {
+    BaseClassRef::root_node = NULL;
+    BaseClassRef::m_size = 0;
+    BaseClassRef::m_size_at_index_build = 0;
+    BaseClassRef::dim = dimensionality;
+    if (DIM > 0)
+      BaseClassRef::dim = DIM;
+    BaseClassRef::m_leaf_max_size = params.leaf_max_size;
+  }
+
+  /** Assignment operator definiton */
+  KDTreeSingleIndexDynamicAdaptor_
+  operator=(const KDTreeSingleIndexDynamicAdaptor_ &rhs) {
+    KDTreeSingleIndexDynamicAdaptor_ tmp(rhs);
+    std::swap(BaseClassRef::vind, tmp.BaseClassRef::vind);
+    std::swap(BaseClassRef::m_leaf_max_size, tmp.BaseClassRef::m_leaf_max_size);
+    std::swap(index_params, tmp.index_params);
+    std::swap(treeIndex, tmp.treeIndex);
+    std::swap(BaseClassRef::m_size, tmp.BaseClassRef::m_size);
+    std::swap(BaseClassRef::m_size_at_index_build,
+              tmp.BaseClassRef::m_size_at_index_build);
+    std::swap(BaseClassRef::root_node, tmp.BaseClassRef::root_node);
+    std::swap(BaseClassRef::root_bbox, tmp.BaseClassRef::root_bbox);
+    std::swap(BaseClassRef::pool, tmp.BaseClassRef::pool);
+    return *this;
+  }
+
+  /**
+   * Builds the index
+   */
+  void buildIndex() {
+    BaseClassRef::m_size = BaseClassRef::vind.size();
+    this->freeIndex(*this);
+    BaseClassRef::m_size_at_index_build = BaseClassRef::m_size;
+    if (BaseClassRef::m_size == 0)
+      return;
+    computeBoundingBox(BaseClassRef::root_bbox);
+    BaseClassRef::root_node =
+        this->divideTree(*this, 0, BaseClassRef::m_size,
+                         BaseClassRef::root_bbox); // construct the tree
+  }
+
+  /** \name Query methods
+   * @{ */
+
+  /**
+   * Find set of nearest neighbors to vec[0:dim-1]. Their indices are stored
+   * inside the result object.
+   *
+   * Params:
+   *     result = the result object in which the indices of the
+   * nearest-neighbors are stored vec = the vector for which to search the
+   * nearest neighbors
+   *
+   * \tparam RESULTSET Should be any ResultSet<DistanceType>
+   * \return  True if the requested neighbors could be found.
+   * \sa knnSearch, radiusSearch
+   */
+  template <typename RESULTSET>
+  bool findNeighbors(RESULTSET &result, const ElementType *vec,
+                     const SearchParams &searchParams) const {
+    assert(vec);
+    if (this->size(*this) == 0)
+      return false;
+    if (!BaseClassRef::root_node)
+      return false;
+    float epsError = 1 + searchParams.eps;
+
+    // fixed or variable-sized container (depending on DIM)
+    distance_vector_t dists;
+    // Fill it with zeros.
+    assign(dists, (DIM > 0 ? DIM : BaseClassRef::dim),
+           static_cast<typename distance_vector_t::value_type>(0));
+    DistanceType distsq = this->computeInitialDistances(*this, vec, dists);
+
+    searchLevel(result, vec, BaseClassRef::root_node, distsq, dists,
+                epsError); // "count_leaf" parameter removed since was neither
+                           // used nor returned to the user.
+
+    return result.full();
+  }
+
+  /**
+   * Find the "num_closest" nearest neighbors to the \a query_point[0:dim-1].
+   * Their indices are stored inside the result object. \sa radiusSearch,
+   * findNeighbors \note nChecks_IGNORED is ignored but kept for compatibility
+   * with the original FLANN interface. \return Number `N` of valid points in
+   * the result set. Only the first `N` entries in `out_indices` and
+   * `out_distances_sq` will be valid. Return may be less than `num_closest`
+   * only if the number of elements in the tree is less than `num_closest`.
+   */
+  size_t knnSearch(const ElementType *query_point, const size_t num_closest,
+                   IndexType *out_indices, DistanceType *out_distances_sq,
+                   const int /* nChecks_IGNORED */ = 10) const {
+    nanoflann::KNNResultSet<DistanceType, IndexType> resultSet(num_closest);
+    resultSet.init(out_indices, out_distances_sq);
+    this->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
+    return resultSet.size();
+  }
+
+  /**
+   * Find all the neighbors to \a query_point[0:dim-1] within a maximum radius.
+   *  The output is given as a vector of pairs, of which the first element is a
+   * point index and the second the corresponding distance. Previous contents of
+   * \a IndicesDists are cleared.
+   *
+   *  If searchParams.sorted==true, the output list is sorted by ascending
+   * distances.
+   *
+   *  For a better performance, it is advisable to do a .reserve() on the vector
+   * if you have any wild guess about the number of expected matches.
+   *
+   *  \sa knnSearch, findNeighbors, radiusSearchCustomCallback
+   * \return The number of points within the given radius (i.e. indices.size()
+   * or dists.size() )
+   */
+  size_t
+  radiusSearch(const ElementType *query_point, const DistanceType &radius,
+               std::vector<std::pair<IndexType, DistanceType>> &IndicesDists,
+               const SearchParams &searchParams) const {
+    RadiusResultSet<DistanceType, IndexType> resultSet(radius, IndicesDists);
+    const size_t nFound =
+        radiusSearchCustomCallback(query_point, resultSet, searchParams);
+    if (searchParams.sorted)
+      std::sort(IndicesDists.begin(), IndicesDists.end(), IndexDist_Sorter());
+    return nFound;
+  }
+
+  /**
+   * Just like radiusSearch() but with a custom callback class for each point
+   * found in the radius of the query. See the source of RadiusResultSet<> as a
+   * start point for your own classes. \sa radiusSearch
+   */
+  template <class SEARCH_CALLBACK>
+  size_t radiusSearchCustomCallback(
+      const ElementType *query_point, SEARCH_CALLBACK &resultSet,
+      const SearchParams &searchParams = SearchParams()) const {
+    this->findNeighbors(resultSet, query_point, searchParams);
+    return resultSet.size();
+  }
+
+  /** @} */
+
+public:
+  void computeBoundingBox(BoundingBox &bbox) {
+    resize(bbox, (DIM > 0 ? DIM : BaseClassRef::dim));
+
+    if (dataset.kdtree_get_bbox(bbox)) {
+      // Done! It was implemented in derived class
+    } else {
+      const size_t N = BaseClassRef::m_size;
+      if (!N)
+        throw std::runtime_error("[nanoflann] computeBoundingBox() called but "
+                                 "no data points found.");
+      for (int i = 0; i < (DIM > 0 ? DIM : BaseClassRef::dim); ++i) {
+        bbox[i].low = bbox[i].high =
+            this->dataset_get(*this, BaseClassRef::vind[0], i);
+      }
+      for (size_t k = 1; k < N; ++k) {
+        for (int i = 0; i < (DIM > 0 ? DIM : BaseClassRef::dim); ++i) {
+          if (this->dataset_get(*this, BaseClassRef::vind[k], i) < bbox[i].low)
+            bbox[i].low = this->dataset_get(*this, BaseClassRef::vind[k], i);
+          if (this->dataset_get(*this, BaseClassRef::vind[k], i) > bbox[i].high)
+            bbox[i].high = this->dataset_get(*this, BaseClassRef::vind[k], i);
+        }
+      }
+    }
+  }
+
+  /**
+   * Performs an exact search in the tree starting from a node.
+   * \tparam RESULTSET Should be any ResultSet<DistanceType>
+   */
+  template <class RESULTSET>
+  void searchLevel(RESULTSET &result_set, const ElementType *vec,
+                   const NodePtr node, DistanceType mindistsq,
+                   distance_vector_t &dists, const float epsError) const {
+    /* If this is a leaf node, then do check and return. */
+    if ((node->child1 == NULL) && (node->child2 == NULL)) {
+      // count_leaf += (node->lr.right-node->lr.left);  // Removed since was
+      // neither used nor returned to the user.
+      DistanceType worst_dist = result_set.worstDist();
+      for (IndexType i = node->node_type.lr.left; i < node->node_type.lr.right;
+           ++i) {
+        const IndexType index = BaseClassRef::vind[i]; // reorder... : i;
+        if (treeIndex[index] == -1)
+          continue;
+        DistanceType dist = distance.evalMetric(
+            vec, index, (DIM > 0 ? DIM : BaseClassRef::dim));
+        if (dist < worst_dist) {
+          if (!result_set.addPoint(
+                  static_cast<typename RESULTSET::DistanceType>(dist),
+                  static_cast<typename RESULTSET::IndexType>(
+                      BaseClassRef::vind[i]))) {
+            // the resultset doesn't want to receive any more points, we're done
+            // searching!
+            return; // false;
+          }
+        }
+      }
+      return;
+    }
+
+    /* Which child branch should be taken first? */
+    int idx = node->node_type.sub.divfeat;
+    ElementType val = vec[idx];
+    DistanceType diff1 = val - node->node_type.sub.divlow;
+    DistanceType diff2 = val - node->node_type.sub.divhigh;
+
+    NodePtr bestChild;
+    NodePtr otherChild;
+    DistanceType cut_dist;
+    if ((diff1 + diff2) < 0) {
+      bestChild = node->child1;
+      otherChild = node->child2;
+      cut_dist = distance.accum_dist(val, node->node_type.sub.divhigh, idx);
+    } else {
+      bestChild = node->child2;
+      otherChild = node->child1;
+      cut_dist = distance.accum_dist(val, node->node_type.sub.divlow, idx);
+    }
+
+    /* Call recursively to search next level down. */
+    searchLevel(result_set, vec, bestChild, mindistsq, dists, epsError);
+
+    DistanceType dst = dists[idx];
+    mindistsq = mindistsq + cut_dist - dst;
+    dists[idx] = cut_dist;
+    if (mindistsq * epsError <= result_set.worstDist()) {
+      searchLevel(result_set, vec, otherChild, mindistsq, dists, epsError);
+    }
+    dists[idx] = dst;
+  }
+
+public:
+  /**  Stores the index in a binary file.
+   *   IMPORTANT NOTE: The set of data points is NOT stored in the file, so when
+   * loading the index object it must be constructed associated to the same
+   * source of data points used while building it. See the example:
+   * examples/saveload_example.cpp \sa loadIndex  */
+  void saveIndex(FILE *stream) { this->saveIndex_(*this, stream); }
+
+  /**  Loads a previous index from a binary file.
+   *   IMPORTANT NOTE: The set of data points is NOT stored in the file, so the
+   * index object must be constructed associated to the same source of data
+   * points used while building the index. See the example:
+   * examples/saveload_example.cpp \sa loadIndex  */
+  void loadIndex(FILE *stream) { this->loadIndex_(*this, stream); }
+};
+
+/** kd-tree dynaimic index
+ *
+ * class to create multiple static index and merge their results to behave as
+ * single dynamic index as proposed in Logarithmic Approach.
+ *
+ *  Example of usage:
+ *  examples/dynamic_pointcloud_example.cpp
+ *
+ * \tparam DatasetAdaptor The user-provided adaptor (see comments above).
+ * \tparam Distance The distance metric to use: nanoflann::metric_L1,
+ * nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc. \tparam DIM
+ * Dimensionality of data points (e.g. 3 for 3D points) \tparam IndexType Will
+ * be typically size_t or int
+ */
+template <typename Distance, class DatasetAdaptor, int DIM = -1,
+          typename IndexType = size_t>
+class KDTreeSingleIndexDynamicAdaptor {
+public:
+  typedef typename Distance::ElementType ElementType;
+  typedef typename Distance::DistanceType DistanceType;
+
+protected:
+  size_t m_leaf_max_size;
+  size_t treeCount;
+  size_t pointCount;
+
+  /**
+   * The dataset used by this index
+   */
+  const DatasetAdaptor &dataset; //!< The source of our data
+
+  std::vector<int> treeIndex; //!< treeIndex[idx] is the index of tree in which
+                              //!< point at idx is stored. treeIndex[idx]=-1
+                              //!< means that point has been removed.
+
+  KDTreeSingleIndexAdaptorParams index_params;
+
+  int dim; //!< Dimensionality of each data point
+
+  typedef KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM>
+      index_container_t;
+  std::vector<index_container_t> index;
+
+public:
+  /** Get a const ref to the internal list of indices; the number of indices is
+   * adapted dynamically as the dataset grows in size. */
+  const std::vector<index_container_t> &getAllIndices() const { return index; }
+
+private:
+  /** finds position of least significant unset bit */
+  int First0Bit(IndexType num) {
+    int pos = 0;
+    while (num & 1) {
+      num = num >> 1;
+      pos++;
+    }
+    return pos;
+  }
+
+  /** Creates multiple empty trees to handle dynamic support */
+  void init() {
+    typedef KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM>
+        my_kd_tree_t;
+    std::vector<my_kd_tree_t> index_(
+        treeCount, my_kd_tree_t(dim /*dim*/, dataset, treeIndex, index_params));
+    index = index_;
+  }
+
+public:
+  Distance distance;
+
+  /**
+   * KDTree constructor
+   *
+   * Refer to docs in README.md or online in
+   * https://github.com/jlblancoc/nanoflann
+   *
+   * The KD-Tree point dimension (the length of each point in the datase, e.g. 3
+   * for 3D points) is determined by means of:
+   *  - The \a DIM template parameter if >0 (highest priority)
+   *  - Otherwise, the \a dimensionality parameter of this constructor.
+   *
+   * @param inputData Dataset with the input features
+   * @param params Basically, the maximum leaf node size
+   */
+  KDTreeSingleIndexDynamicAdaptor(const int dimensionality,
+                                  const DatasetAdaptor &inputData,
+                                  const KDTreeSingleIndexAdaptorParams &params =
+                                      KDTreeSingleIndexAdaptorParams(),
+                                  const size_t maximumPointCount = 1000000000U)
+      : dataset(inputData), index_params(params), distance(inputData) {
+    treeCount = static_cast<size_t>(std::log2(maximumPointCount));
+    pointCount = 0U;
+    dim = dimensionality;
+    treeIndex.clear();
+    if (DIM > 0)
+      dim = DIM;
+    m_leaf_max_size = params.leaf_max_size;
+    init();
+    const size_t num_initial_points = dataset.kdtree_get_point_count();
+    if (num_initial_points > 0) {
+      addPoints(0, num_initial_points - 1);
+    }
+  }
+
+  /** Deleted copy constructor*/
+  KDTreeSingleIndexDynamicAdaptor(
+      const KDTreeSingleIndexDynamicAdaptor<Distance, DatasetAdaptor, DIM,
+                                            IndexType> &) = delete;
+
+  /** Add points to the set, Inserts all points from [start, end] */
+  void addPoints(IndexType start, IndexType end) {
+    size_t count = end - start + 1;
+    treeIndex.resize(treeIndex.size() + count);
+    for (IndexType idx = start; idx <= end; idx++) {
+      int pos = First0Bit(pointCount);
+      index[pos].vind.clear();
+      treeIndex[pointCount] = pos;
+      for (int i = 0; i < pos; i++) {
+        for (int j = 0; j < static_cast<int>(index[i].vind.size()); j++) {
+          index[pos].vind.push_back(index[i].vind[j]);
+          if (treeIndex[index[i].vind[j]] != -1)
+            treeIndex[index[i].vind[j]] = pos;
+        }
+        index[i].vind.clear();
+        index[i].freeIndex(index[i]);
+      }
+      index[pos].vind.push_back(idx);
+      index[pos].buildIndex();
+      pointCount++;
+    }
+  }
+
+  /** Remove a point from the set (Lazy Deletion) */
+  void removePoint(size_t idx) {
+    if (idx >= pointCount)
+      return;
+    treeIndex[idx] = -1;
+  }
+
+  /**
+   * Find set of nearest neighbors to vec[0:dim-1]. Their indices are stored
+   * inside the result object.
+   *
+   * Params:
+   *     result = the result object in which the indices of the
+   * nearest-neighbors are stored vec = the vector for which to search the
+   * nearest neighbors
+   *
+   * \tparam RESULTSET Should be any ResultSet<DistanceType>
+   * \return  True if the requested neighbors could be found.
+   * \sa knnSearch, radiusSearch
+   */
+  template <typename RESULTSET>
+  bool findNeighbors(RESULTSET &result, const ElementType *vec,
+                     const SearchParams &searchParams) const {
+    for (size_t i = 0; i < treeCount; i++) {
+      index[i].findNeighbors(result, &vec[0], searchParams);
+    }
+    return result.full();
+  }
+};
+
+/** An L2-metric KD-tree adaptor for working with data directly stored in an
+ * Eigen Matrix, without duplicating the data storage. Each row in the matrix
+ * represents a point in the state space.
+ *
+ *  Example of usage:
+ * \code
+ * 	Eigen::Matrix<num_t,Dynamic,Dynamic>  mat;
+ * 	// Fill out "mat"...
+ *
+ * 	typedef KDTreeEigenMatrixAdaptor< Eigen::Matrix<num_t,Dynamic,Dynamic> >
+ * my_kd_tree_t; const int max_leaf = 10; my_kd_tree_t   mat_index(mat, max_leaf
+ * ); mat_index.index->buildIndex(); mat_index.index->... \endcode
+ *
+ *  \tparam DIM If set to >0, it specifies a compile-time fixed dimensionality
+ * for the points in the data set, allowing more compiler optimizations. \tparam
+ * Distance The distance metric to use: nanoflann::metric_L1,
+ * nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc.
+ */
+template <class MatrixType, int DIM = -1, class Distance = nanoflann::metric_L2>
+struct KDTreeEigenMatrixAdaptor {
+  typedef KDTreeEigenMatrixAdaptor<MatrixType, DIM, Distance> self_t;
+  typedef typename MatrixType::Scalar num_t;
+  typedef typename MatrixType::Index IndexType;
+  typedef
+      typename Distance::template traits<num_t, self_t>::distance_t metric_t;
+  typedef KDTreeSingleIndexAdaptor<metric_t, self_t,
+                                   MatrixType::ColsAtCompileTime, IndexType>
+      index_t;
+
+  index_t *index; //! The kd-tree index for the user to call its methods as
+                  //! usual with any other FLANN index.
+
+  /// Constructor: takes a const ref to the matrix object with the data points
+  KDTreeEigenMatrixAdaptor(const size_t dimensionality,
+                           const std::reference_wrapper<const MatrixType> &mat,
+                           const int leaf_max_size = 10)
+      : m_data_matrix(mat) {
+    const auto dims = mat.get().cols();
+    if (size_t(dims) != dimensionality)
+      throw std::runtime_error(
+          "Error: 'dimensionality' must match column count in data matrix");
+    if (DIM > 0 && int(dims) != DIM)
+      throw std::runtime_error(
+          "Data set dimensionality does not match the 'DIM' template argument");
+    index =
+        new index_t(static_cast<int>(dims), *this /* adaptor */,
+                    nanoflann::KDTreeSingleIndexAdaptorParams(leaf_max_size));
+    index->buildIndex();
+  }
+
+public:
+  /** Deleted copy constructor */
+  KDTreeEigenMatrixAdaptor(const self_t &) = delete;
+
+  ~KDTreeEigenMatrixAdaptor() { delete index; }
+
+  const std::reference_wrapper<const MatrixType> m_data_matrix;
+
+  /** Query for the \a num_closest closest points to a given point (entered as
+   * query_point[0:dim-1]). Note that this is a short-cut method for
+   * index->findNeighbors(). The user can also call index->... methods as
+   * desired. \note nChecks_IGNORED is ignored but kept for compatibility with
+   * the original FLANN interface.
+   */
+  inline void query(const num_t *query_point, const size_t num_closest,
+                    IndexType *out_indices, num_t *out_distances_sq,
+                    const int /* nChecks_IGNORED */ = 10) const {
+    nanoflann::KNNResultSet<num_t, IndexType> resultSet(num_closest);
+    resultSet.init(out_indices, out_distances_sq);
+    index->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
+  }
+
+  /** @name Interface expected by KDTreeSingleIndexAdaptor
+   * @{ */
+
+  const self_t &derived() const { return *this; }
+  self_t &derived() { return *this; }
+
+  // Must return the number of data points
+  inline size_t kdtree_get_point_count() const {
+    return m_data_matrix.get().rows();
+  }
+
+  // Returns the dim'th component of the idx'th point in the class:
+  inline num_t kdtree_get_pt(const IndexType idx, size_t dim) const {
+    return m_data_matrix.get().coeff(idx, IndexType(dim));
+  }
+
+  // Optional bounding-box computation: return false to default to a standard
+  // 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
+  //   the expected dimensionality (e.g. 2 or 3 for point clouds)
+  template <class BBOX> bool kdtree_get_bbox(BBOX & /*bb*/) const {
+    return false;
+  }
+
+  /** @} */
+
+}; // end of KDTreeEigenMatrixAdaptor
+   /** @} */
+
+/** @} */ // end of grouping
+} // namespace nanoflann
+
+#endif /* NANOFLANN_HPP_ */

csrc/cpu/utils/neighbors.cpp

+#include "cloud.h"
+#include "nanoflann.hpp"
+#include <cstdint>
+#include <iostream>
+#include <set>
+#include <thread>
+
+typedef struct thread_struct {
+  void *kd_tree;
+  void *matches;
+  void *queries;
+  size_t *max_count;
+  std::mutex *ct_m;
+  std::mutex *tree_m;
+  size_t start;
+  size_t end;
+  double search_radius;
+  bool small;
+  bool option;
+  size_t k;
+} thread_args;
+
+template <typename scalar_t> void thread_routine(thread_args *targs) {
+  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;
+  my_kd_tree_t *index = (my_kd_tree_t *)targs->kd_tree;
+  kd_pair *matches = (kd_pair *)targs->matches;
+  PointCloud<scalar_t> *pcd_query = (PointCloud<scalar_t> *)targs->queries;
+  size_t *max_count = targs->max_count;
+  std::mutex *ct_m = targs->ct_m;
+  std::mutex *tree_m = targs->tree_m;
+  double eps;
+  if (targs->small) {
+    eps = 0.000001;
+  } else {
+    eps = 0;
+  }
+  double search_radius = (double)targs->search_radius;
+  size_t start = targs->start;
+  size_t end = targs->end;
+  auto k = targs->k;
+  for (size_t i = start; i < end; 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);
+    (*matches)[i].reserve(*max_count);
+    std::vector<std::pair<size_t, scalar_t>> ret_matches;
+    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();
+
+    size_t nMatches;
+    if (targs->option) {
+      nMatches = index->radiusSearch(query_pt, (scalar_t)(search_radius + eps),
+                                     ret_matches, nanoflann::SearchParams());
+    } else {
+      nMatches = index->knnSearch(query_pt, k, &(*knn_ret_matches)[0],
+                                  &(*knn_dist_matches)[0]);
+      auto temp = new std::vector<std::pair<size_t, scalar_t>>(
+          (*knn_dist_matches).size());
+      for (size_t j = 0; j < (*knn_ret_matches).size(); j++) {
+        (*temp)[j] =
+            std::make_pair((*knn_ret_matches)[j], (*knn_dist_matches)[j]);
+      }
+      ret_matches = *temp;
+    }
+    tree_m->unlock();
+
+    (*matches)[i] = ret_matches;
+
+    ct_m->lock();
+    if (*max_count < nMatches) {
+      *max_count = nMatches;
+    }
+    ct_m->unlock();
+  }
+}
+
+template <typename scalar_t>
+size_t nanoflann_neighbors(std::vector<scalar_t> &queries,
+                           std::vector<scalar_t> &supports,
+                           std::vector<size_t> *&neighbors_indices,
+                           double radius, int dim, int64_t max_num,
+                           int64_t n_threads, int64_t k, int option) {
+
+  const scalar_t search_radius = static_cast<scalar_t>(radius * radius);
+
+  // Counting vector
+  size_t *max_count = new size_t();
+  *max_count = 1;
+
+  size_t ssize = supports.size();
+  // CLoud variable
+  PointCloud<scalar_t> pcd;
+  pcd.set(supports, dim);
+  // Cloud query
+  PointCloud<scalar_t> *pcd_query = new PointCloud<scalar_t>();
+  (*pcd_query).set(queries, dim);
+
+  // Tree parameters
+  nanoflann::KDTreeSingleIndexAdaptorParams tree_params(15 /* max leaf */);
+
+  // KDTree type definition
+  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;
+
+  // Pointer to trees
+  my_kd_tree_t *index;
+  index = new my_kd_tree_t(dim, pcd, tree_params);
+  index->buildIndex();
+  // Search neigbors indices
+
+  // Search params
+  nanoflann::SearchParams search_params;
+  // search_params.sorted = true;
+  kd_pair *list_matches = new kd_pair((*pcd_query).pts.size());
+
+  // single threaded routine
+  if (n_threads == 1) {
+    size_t i0 = 0;
+    double eps;
+    if (ssize < 10) {
+      eps = 0.000001;
+    } else {
+      eps = 0;
+    }
+
+    for (auto &p : (*pcd_query).pts) {
+      auto p0 = *p;
+      // Find neighbors
+      scalar_t *query_pt = new scalar_t[dim];
+      std::copy(p0.begin(), p0.end(), query_pt);
+
+      (*list_matches)[i0].reserve(*max_count);
+      std::vector<std::pair<size_t, scalar_t>> ret_matches;
+      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);
+
+      size_t nMatches;
+      if (!!(option)) {
+        nMatches =
+            index->radiusSearch(query_pt, (scalar_t)(search_radius + eps),
+                                ret_matches, search_params);
+      } else {
+        nMatches = index->knnSearch(query_pt, (size_t)k, &(*knn_ret_matches)[0],
+                                    &(*knn_dist_matches)[0]);
+        auto temp = new std::vector<std::pair<size_t, scalar_t>>(
+            (*knn_dist_matches).size());
+        for (size_t j = 0; j < (*knn_ret_matches).size(); j++) {
+          (*temp)[j] =
+              std::make_pair((*knn_ret_matches)[j], (*knn_dist_matches)[j]);
+        }
+        ret_matches = *temp;
+      }
+      (*list_matches)[i0] = ret_matches;
+      if (*max_count < nMatches)
+        *max_count = nMatches;
+      i0++;
+    }
+  } else { // Multi-threaded routine
+    std::mutex *mtx = new std::mutex();
+    std::mutex *mtx_tree = new std::mutex();
+
+    size_t n_queries = (*pcd_query).pts.size();
+    size_t actual_threads =
+        std::min((long long)n_threads, (long long)n_queries);
+
+    std::vector<std::thread *> tid(actual_threads);
+
+    size_t start, end;
+    size_t length;
+    if (n_queries) {
+      length = 1;
+    } else {
+      auto res = std::lldiv((long long)n_queries, (long long)n_threads);
+      length = (size_t)res.quot;
+    }
+    for (size_t t = 0; t < actual_threads; t++) {
+      start = t * length;
+      if (t == actual_threads - 1) {
+        end = n_queries;
+      } else {
+        end = (t + 1) * length;
+      }
+      thread_args *targs = new thread_args();
+      targs->kd_tree = index;
+      targs->matches = list_matches;
+      targs->max_count = max_count;
+      targs->ct_m = mtx;
+      targs->tree_m = mtx_tree;
+      targs->search_radius = search_radius;
+      targs->queries = pcd_query;
+      targs->start = start;
+      targs->end = end;
+      if (ssize < 10) {
+        targs->small = true;
+      } else {
+        targs->small = false;
+      }
+      targs->option = !!(option);
+      targs->k = k;
+      std::thread *temp = new std::thread(thread_routine<scalar_t>, targs);
+      tid[t] = temp;
+    }
+
+    for (size_t t = 0; t < actual_threads; t++) {
+      tid[t]->join();
+    }
+  }
+
+  // Reserve the memory
+  if (max_num > 0) {
+    *max_count = max_num;
+  }
+
+  size_t size = 0; // total number of edges
+  for (auto &inds : *list_matches) {
+    if (inds.size() <= *max_count)
+      size += inds.size();
+    else
+      size += *max_count;
+  }
+
+  neighbors_indices->resize(size * 2);
+  size_t i1 = 0; // index of the query points
+  size_t u = 0;  // curent index of the neighbors_indices
+  for (auto &inds : *list_matches) {
+    for (size_t j = 0; j < *max_count; j++) {
+      if (j < inds.size()) {
+        (*neighbors_indices)[u] = inds[j].first;
+        (*neighbors_indices)[u + 1] = i1;
+        u += 2;
+      }
+    }
+    i1++;
+  }
+
+  return *max_count;
+}
+
+template <typename scalar_t>
+size_t batch_nanoflann_neighbors(std::vector<scalar_t> &queries,
+                                 std::vector<scalar_t> &supports,
+                                 std::vector<long> &q_batches,
+                                 std::vector<long> &s_batches,
+                                 std::vector<size_t> *&neighbors_indices,
+                                 double radius, int dim, int64_t max_num,
+                                 int64_t k, int option) {
+
+  // Indices.
+  size_t i0 = 0;
+
+  // Square radius.
+  const scalar_t r2 = static_cast<scalar_t>(radius * radius);
+
+  // Counting vector.
+  size_t max_count = 0;
+
+  // Batch index.
+  size_t b = 0;
+  size_t sum_qb = 0;
+  size_t sum_sb = 0;
+
+  double eps;
+  if (supports.size() < 10) {
+    eps = 0.000001;
+  } else {
+    eps = 0;
+  }
+  // Nanoflann related variables.
+
+  // Cloud variable.
+  PointCloud<scalar_t> current_cloud;
+  PointCloud<scalar_t> query_pcd;
+  query_pcd.set(queries, dim);
+  std::vector<std::vector<std::pair<size_t, scalar_t>>> all_inds_dists(
+      query_pcd.pts.size());
+
+  // Tree parameters.
+  nanoflann::KDTreeSingleIndexAdaptorParams tree_params(10 /* max leaf */);
+
+  // KDTree type definition.
+  typedef nanoflann::KDTreeSingleIndexAdaptor<
+      nanoflann::L2_Adaptor<scalar_t, PointCloud<scalar_t>>,
+      PointCloud<scalar_t>>
+      my_kd_tree_t;
+
+  // Pointer to trees.
+  my_kd_tree_t *index;
+  // Build KDTree for the first batch element.
+  current_cloud.set_batch(supports, sum_sb, s_batches[b], dim);
+  index = new my_kd_tree_t(dim, current_cloud, tree_params);
+  index->buildIndex();
+  // Search neigbors indices.
+  // Search params.
+  nanoflann::SearchParams search_params;
+  search_params.sorted = true;
+
+  for (auto &p : query_pcd.pts) {
+    auto p0 = *p;
+    // Check if we changed batch.
+
+    scalar_t *query_pt = new scalar_t[dim];
+    std::copy(p0.begin(), p0.end(), query_pt);
+
+    if (i0 == sum_qb + q_batches[b]) {
+      sum_qb += q_batches[b];
+      sum_sb += s_batches[b];
+      b++;
+
+      // Change the points.
+      current_cloud.pts.clear();
+      current_cloud.set_batch(supports, sum_sb, s_batches[b], dim);
+      // Build KDTree of the current element of the batch.
+      delete index;
+      index = new my_kd_tree_t(dim, current_cloud, tree_params);
+      index->buildIndex();
+    }
+    // Initial guess of neighbors size.
+    all_inds_dists[i0].reserve(max_count);
+
+    // Find neighbors.
+    size_t nMatches;
+    if (!!option) {
+      nMatches = index->radiusSearch(query_pt, r2 + eps, all_inds_dists[i0],
+                                     search_params);
+      // Update max count.
+    } else {
+      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);
+      nMatches = index->knnSearch(query_pt, (size_t)k, &(*knn_ret_matches)[0],
+                                  &(*knn_dist_matches)[0]);
+      auto temp = new std::vector<std::pair<size_t, scalar_t>>(
+          (*knn_dist_matches).size());
+      for (size_t j = 0; j < (*knn_ret_matches).size(); j++) {
+        (*temp)[j] =
+            std::make_pair((*knn_ret_matches)[j], (*knn_dist_matches)[j]);
+      }
+      all_inds_dists[i0] = *temp;
+    }
+    if (nMatches > max_count)
+      max_count = nMatches;
+    i0++;
+  }
+
+  // How many neighbors do we keep.
+  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 += inds_dists.size();
+    else
+      size += max_count;
+  }
+
+  neighbors_indices->resize(size * 2);
+  i0 = 0;
+  sum_sb = 0;
+  sum_qb = 0;
+  b = 0;
+  size_t u = 0;
+  for (auto &inds_dists : all_inds_dists) {
+    if (i0 == sum_qb + q_batches[b]) {
+      sum_qb += q_batches[b];
+      sum_sb += s_batches[b];
+      b++;
+    }
+    for (size_t j = 0; j < max_count; j++) {
+      if (j < inds_dists.size()) {
+        (*neighbors_indices)[u] = inds_dists[j].first + sum_sb;
+        (*neighbors_indices)[u + 1] = i0;
+        u += 2;
+      }
+    }
+    i0++;
+  }
+
+  return max_count;
+}

csrc/cuda/knn_cuda.cu

@@ -75,26 +75,42 @@ __global__ void knn_kernel(const scalar_t *x, const scalar_t *y,
   }
 }
 
-torch::Tensor knn_cuda(torch::Tensor x, torch::Tensor y, torch::Tensor ptr_x,
-                       torch::Tensor ptr_y, int64_t k, bool cosine) {
+torch::Tensor knn_cuda(torch::Tensor x, torch::Tensor y,
+                       torch::optional<torch::Tensor> ptr_x,
+                       torch::optional<torch::Tensor> ptr_y, int64_t k,
+                       bool cosine) {
+
   CHECK_CUDA(x);
+  CHECK_INPUT(x.dim() == 2);
   CHECK_CUDA(y);
-  CHECK_CUDA(ptr_x);
-  CHECK_CUDA(ptr_y);
+  CHECK_INPUT(y.dim() == 2);
   cudaSetDevice(x.get_device());
 
-  x = x.view({x.size(0), -1}).contiguous();
-  y = y.view({y.size(0), -1}).contiguous();
+  if (ptr_x.has_value()) {
+    CHECK_CUDA(ptr_x.value());
+    CHECK_INPUT(ptr_x.value().dim() == 1);
+  } else {
+    ptr_x = torch::arange(0, x.size(0) + 1, x.size(0),
+                          x.options().dtype(torch::kLong));
+  }
+  if (ptr_y.has_value()) {
+    CHECK_CUDA(ptr_y.value());
+    CHECK_INPUT(ptr_y.value().dim() == 1);
+  } else {
+    ptr_y = torch::arange(0, y.size(0) + 1, y.size(0),
+                          y.options().dtype(torch::kLong));
+  }
+  CHECK_INPUT(ptr_x.value().numel() == ptr_y.value().numel());
 
   auto dist = torch::full(y.size(0) * k, 1e38, y.options());
-  auto row = torch::empty(y.size(0) * k, ptr_y.options());
-  auto col = torch::full(y.size(0) * k, -1, ptr_y.options());
+  auto row = torch::empty(y.size(0) * k, ptr_y.value().options());
+  auto col = torch::full(y.size(0) * k, -1, ptr_y.value().options());
 
   auto stream = at::cuda::getCurrentCUDAStream();
   AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "knn_kernel", [&] {
-    knn_kernel<scalar_t><<<ptr_x.size(0) - 1, THREADS, 0, stream>>>(
+    knn_kernel<scalar_t><<<ptr_x.value().size(0) - 1, THREADS, 0, stream>>>(
         x.data_ptr<scalar_t>(), y.data_ptr<scalar_t>(),
-        ptr_x.data_ptr<int64_t>(), ptr_y.data_ptr<int64_t>(),
+        ptr_x.value().data_ptr<int64_t>(), ptr_y.value().data_ptr<int64_t>(),
         dist.data_ptr<scalar_t>(), row.data_ptr<int64_t>(),
         col.data_ptr<int64_t>(), k, x.size(1), cosine);
   });

csrc/cuda/knn_cuda.h

@@ -2,5 +2,7 @@
 
 #include <torch/extension.h>
 
-torch::Tensor knn_cuda(torch::Tensor x, torch::Tensor y, torch::Tensor ptr_x,
-                       torch::Tensor ptr_y, int64_t k, bool cosine);
+torch::Tensor knn_cuda(torch::Tensor x, torch::Tensor y,
+                       torch::optional<torch::Tensor> ptr_x,
+                       torch::optional<torch::Tensor> ptr_y, int64_t k,
+                       bool cosine);

csrc/cuda/radius_cuda.cu

@@ -44,26 +44,42 @@ __global__ void radius_kernel(const scalar_t *x, const scalar_t *y,
   }
 }
 
-torch::Tensor radius_cuda(torch::Tensor x, torch::Tensor y, torch::Tensor ptr_x,
-                          torch::Tensor ptr_y, double r,
+torch::Tensor radius_cuda(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) {
   CHECK_CUDA(x);
+  CHECK_INPUT(x.dim() == 2);
   CHECK_CUDA(y);
-  CHECK_CUDA(ptr_x);
-  CHECK_CUDA(ptr_y);
+  CHECK_INPUT(y.dim() == 2);
   cudaSetDevice(x.get_device());
 
-  x = x.view({x.size(0), -1}).contiguous();
-  y = y.view({y.size(0), -1}).contiguous();
+  if (ptr_x.has_value()) {
+    CHECK_CUDA(ptr_x.value());
+    CHECK_INPUT(ptr_x.value().dim() == 1);
+  } else {
+    ptr_x = torch::arange(0, x.size(0) + 1, x.size(0),
+                          x.options().dtype(torch::kLong));
+  }
+  if (ptr_y.has_value()) {
+    CHECK_CUDA(ptr_y.value());
+    CHECK_INPUT(ptr_y.value().dim() == 1);
+  } else {
+    ptr_y = torch::arange(0, y.size(0) + 1, y.size(0),
+                          y.options().dtype(torch::kLong));
+  }
+  CHECK_INPUT(ptr_x.value().numel() == ptr_y.value().numel());
 
-  auto row = torch::full(y.size(0) * max_num_neighbors, -1, ptr_y.options());
-  auto col = torch::full(y.size(0) * max_num_neighbors, -1, ptr_y.options());
+  auto row =
+      torch::full(y.size(0) * max_num_neighbors, -1, ptr_y.value().options());
+  auto col =
+      torch::full(y.size(0) * max_num_neighbors, -1, ptr_y.value().options());
 
   auto stream = at::cuda::getCurrentCUDAStream();
   AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "radius_kernel", [&] {
-    radius_kernel<scalar_t><<<ptr_x.size(0) - 1, THREADS, 0, stream>>>(
+    radius_kernel<scalar_t><<<ptr_x.value().size(0) - 1, THREADS, 0, stream>>>(
         x.data_ptr<scalar_t>(), y.data_ptr<scalar_t>(),
-        ptr_x.data_ptr<int64_t>(), ptr_y.data_ptr<int64_t>(),
+        ptr_x.value().data_ptr<int64_t>(), ptr_y.value().data_ptr<int64_t>(),
         row.data_ptr<int64_t>(), col.data_ptr<int64_t>(), r, max_num_neighbors,
         x.size(1));
   });

csrc/cuda/radius_cuda.h

@@ -2,6 +2,7 @@
 
 #include <torch/extension.h>
 
-torch::Tensor radius_cuda(torch::Tensor x, torch::Tensor y, torch::Tensor ptr_x,
-                          torch::Tensor ptr_y, double r,
+torch::Tensor radius_cuda(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);

csrc/knn.cpp

 #include <Python.h>
 #include <torch/script.h>
 
+#include "cpu/knn_cpu.h"
+
 #ifdef WITH_CUDA
 #include "cuda/knn_cuda.h"
 #endif
@@ -9,8 +11,10 @@
 PyMODINIT_FUNC PyInit__knn(void) { return NULL; }
 #endif
 
-torch::Tensor knn(torch::Tensor x, torch::Tensor y, torch::Tensor ptr_x,
-                  torch::Tensor ptr_y, int64_t k, bool cosine) {
+torch::Tensor knn(torch::Tensor x, torch::Tensor y,
+                  torch::optional<torch::Tensor> ptr_x,
+                  torch::optional<torch::Tensor> ptr_y, int64_t k, bool cosine,
+                  int64_t num_workers) {
   if (x.device().is_cuda()) {
 #ifdef WITH_CUDA
     return knn_cuda(x, y, ptr_x, ptr_y, k, cosine);
@@ -18,7 +22,9 @@ torch::Tensor knn(torch::Tensor x, torch::Tensor y, torch::Tensor ptr_x,
     AT_ERROR("Not compiled with CUDA support");
 #endif
   } else {
-    AT_ERROR("No CPU version supported");
+    if (cosine)
+      AT_ERROR("`cosine` argument not supported on CPU");
+    return knn_cpu(x, y, ptr_x, ptr_y, k, num_workers);
   }
 }
 

csrc/radius.cpp

 #include <Python.h>
 #include <torch/script.h>
 
+#include "cpu/radius_cpu.h"
+
 #ifdef WITH_CUDA
 #include "cuda/radius_cuda.h"
 #endif
@@ -9,8 +11,10 @@
 PyMODINIT_FUNC PyInit__radius(void) { return NULL; }
 #endif
 
-torch::Tensor radius(torch::Tensor x, torch::Tensor y, torch::Tensor ptr_x,
-                     torch::Tensor ptr_y, double r, int64_t max_num_neighbors) {
+torch::Tensor radius(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) {
   if (x.device().is_cuda()) {
 #ifdef WITH_CUDA
     return radius_cuda(x, y, ptr_x, ptr_y, r, max_num_neighbors);
@@ -18,7 +22,7 @@ torch::Tensor radius(torch::Tensor x, torch::Tensor y, torch::Tensor ptr_x,
     AT_ERROR("Not compiled with CUDA support");
 #endif
   } else {
-    AT_ERROR("No CPU version supported");
+    return radius_cpu(x, y, ptr_x, ptr_y, r, max_num_neighbors, num_workers);
   }
 }
 

setup.py

@@ -57,13 +57,13 @@ def get_extensions():
     return extensions
 
 
-install_requires = ['scipy']
+install_requires = []
 setup_requires = ['pytest-runner']
-tests_require = ['pytest', 'pytest-cov']
+tests_require = ['pytest', 'pytest-cov', 'scipy']
 
 setup(
     name='torch_cluster',
-    version='1.5.4',
+    version='1.5.5',
     author='Matthias Fey',
     author_email='matthias.fey@tu-dortmund.de',
     url='https://github.com/rusty1s/pytorch_cluster',
@@ -80,6 +80,7 @@ setup(
     install_requires=install_requires,
     setup_requires=setup_requires,
     tests_require=tests_require,
+    extras_require={'test': tests_require},
     ext_modules=get_extensions() if not BUILD_DOCS else [],
     cmdclass={
         'build_ext':

test/test_knn.py

@@ -2,11 +2,16 @@ from itertools import product
 
 import pytest
 import torch
+import scipy.spatial
 from torch_cluster import knn, knn_graph
 
 from .utils import grad_dtypes, devices, tensor
 
 
+def to_set(edge_index):
+    return set([(i, j) for i, j in edge_index.t().tolist()])
+
+
 @pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
 def test_knn(dtype, device):
     x = tensor([
@@ -27,16 +32,15 @@ def test_knn(dtype, device):
     batch_x = tensor([0, 0, 0, 0, 1, 1, 1, 1], torch.long, device)
     batch_y = tensor([0, 1], torch.long, device)
 
-    row, col = knn(x, y, 2, batch_x, batch_y)
-    col = col.view(-1, 2).sort(dim=-1)[0].view(-1)
+    edge_index = knn(x, y, 2)
+    assert to_set(edge_index) == set([(0, 2), (0, 3), (1, 0), (1, 1)])
 
-    assert row.tolist() == [0, 0, 1, 1]
-    assert col.tolist() == [2, 3, 4, 5]
+    edge_index = knn(x, y, 2, batch_x, batch_y)
+    assert to_set(edge_index) == set([(0, 2), (0, 3), (1, 4), (1, 5)])
 
     if x.is_cuda:
-        row, col = knn(x, y, 2, batch_x, batch_y, cosine=True)
-        assert row.tolist() == [0, 0, 1, 1]
-        assert col.tolist() == [0, 1, 4, 5]
+        edge_index = knn(x, y, 2, batch_x, batch_y, cosine=True)
+        assert to_set(edge_index) == set([(0, 0), (0, 1), (1, 4), (1, 5)])
 
 
 @pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
@@ -48,12 +52,24 @@ def test_knn_graph(dtype, device):
         [+1, -1],
     ], dtype, device)
 
-    row, col = knn_graph(x, k=2, flow='target_to_source')
-    col = col.view(-1, 2).sort(dim=-1)[0].view(-1)
-    assert row.tolist() == [0, 0, 1, 1, 2, 2, 3, 3]
-    assert col.tolist() == [1, 3, 0, 2, 1, 3, 0, 2]
+    edge_index = knn_graph(x, k=2, flow='target_to_source')
+    assert to_set(edge_index) == set([(0, 1), (0, 3), (1, 0), (1, 2), (2, 1),
+                                      (2, 3), (3, 0), (3, 2)])
+
+    edge_index = knn_graph(x, k=2, flow='source_to_target')
+    assert to_set(edge_index) == set([(1, 0), (3, 0), (0, 1), (2, 1), (1, 2),
+                                      (3, 2), (0, 3), (2, 3)])
+
+
+@pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
+def test_knn_graph_large(dtype, device):
+    x = torch.randn(1000, 3)
+
+    edge_index = knn_graph(x, k=5, flow='target_to_source', loop=True,
+                           num_workers=6)
+
+    tree = scipy.spatial.cKDTree(x.numpy())
+    _, col = tree.query(x.cpu(), k=5)
+    truth = set([(i, j) for i, ns in enumerate(col) for j in ns])
 
-    row, col = knn_graph(x, k=2, flow='source_to_target')
-    row = row.view(-1, 2).sort(dim=-1)[0].view(-1)
-    assert row.tolist() == [1, 3, 0, 2, 1, 3, 0, 2]
-    assert col.tolist() == [0, 0, 1, 1, 2, 2, 3, 3]
+    assert to_set(edge_index) == truth

test/test_radius.py

@@ -2,16 +2,14 @@ from itertools import product
 
 import pytest
 import torch
+import scipy.spatial
 from torch_cluster import radius, radius_graph
 
 from .utils import grad_dtypes, devices, tensor
 
 
-def coalesce(index):
-    N = index.max().item() + 1
-    tensor = torch.sparse_coo_tensor(index, index.new_ones(index.size(1)),
-                                     torch.Size([N, N]))
-    return tensor.coalesce().indices()
+def to_set(edge_index):
+    return set([(i, j) for i, j in edge_index.t().tolist()])
 
 
 @pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
@@ -34,8 +32,13 @@ def test_radius(dtype, device):
     batch_x = tensor([0, 0, 0, 0, 1, 1, 1, 1], torch.long, device)
     batch_y = tensor([0, 1], torch.long, device)
 
-    out = radius(x, y, 2, batch_x, batch_y, max_num_neighbors=4)
-    assert coalesce(out).tolist() == [[0, 0, 0, 0, 1, 1], [0, 1, 2, 3, 5, 6]]
+    edge_index = radius(x, y, 2, max_num_neighbors=4)
+    assert to_set(edge_index) == set([(0, 0), (0, 1), (0, 2), (0, 3), (1, 1),
+                                      (1, 2), (1, 5), (1, 6)])
+
+    edge_index = radius(x, y, 2, batch_x, batch_y, max_num_neighbors=4)
+    assert to_set(edge_index) == set([(0, 0), (0, 1), (0, 2), (0, 3), (1, 5),
+                                      (1, 6)])
 
 
 @pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
@@ -47,12 +50,24 @@ def test_radius_graph(dtype, device):
         [+1, -1],
     ], dtype, device)
 
-    row, col = radius_graph(x, r=2, flow='target_to_source')
-    col = col.view(-1, 2).sort(dim=-1)[0].view(-1)
-    assert row.tolist() == [0, 0, 1, 1, 2, 2, 3, 3]
-    assert col.tolist() == [1, 3, 0, 2, 1, 3, 0, 2]
+    edge_index = radius_graph(x, r=2, flow='target_to_source')
+    assert to_set(edge_index) == set([(0, 1), (0, 3), (1, 0), (1, 2), (2, 1),
+                                      (2, 3), (3, 0), (3, 2)])
+
+    edge_index = radius_graph(x, r=2, flow='source_to_target')
+    assert to_set(edge_index) == set([(1, 0), (3, 0), (0, 1), (2, 1), (1, 2),
+                                      (3, 2), (0, 3), (2, 3)])
+
+
+@pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
+def test_radius_graph_large(dtype, device):
+    x = torch.randn(1000, 3)
+
+    edge_index = radius_graph(x, r=0.5, flow='target_to_source', loop=True,
+                              max_num_neighbors=1000, num_workers=6)
+
+    tree = scipy.spatial.cKDTree(x.numpy())
+    col = tree.query_ball_point(x.cpu(), r=0.5)
+    truth = set([(i, j) for i, ns in enumerate(col) for j in ns])
 
-    row, col = radius_graph(x, r=2, flow='source_to_target')
-    row = row.view(-1, 2).sort(dim=-1)[0].view(-1)
-    assert row.tolist() == [1, 3, 0, 2, 1, 3, 0, 2]
-    assert col.tolist() == [0, 0, 1, 1, 2, 2, 3, 3]
+    assert to_set(edge_index) == truth

test/test_rw.py

@@ -17,6 +17,6 @@ def test_rw(device):
 
     for n in range(start.size(0)):
         cur = start[n].item()
-        for l in range(1, walk_length):
-            assert out[n, l].item() in col[row == cur].tolist()
-            cur = out[n, l].item()
+        for i in range(1, walk_length):
+            assert out[n, i].item() in col[row == cur].tolist()
+            cur = out[n, i].item()