Test (#9)

* increase number of data to test multi-threaded sort

* test for cached_kdtree

* test for flat_voxelmap

include/small_gicp/ann/cached_kdtree.hpp

@@ -21,16 +21,18 @@ class CachedKdTree {
   /// @brief Constructor
   /// @param points     Input points
   /// @param leaf_size  Cache voxel resolution
-  CachedKdTree(const PointCloud& points, double leaf_size) : inv_leaf_size(1.0 / leaf_size), kdtree(points) {}
+  CachedKdTree(const std::shared_ptr<const PointCloud>& points, double leaf_size) : inv_leaf_size(1.0 / leaf_size), kdtree(points) {}
 
   size_t knn_search(const Eigen::Vector4d& pt, size_t k, size_t* k_indices, double* k_sq_dists) const {
     const Eigen::Vector3i coord = fast_floor(pt * inv_leaf_size).head<3>();
 
     CacheTable::const_accessor ca;
     if (cache.find(ca, coord)) {
-      std::copy(ca->second.first.begin(), ca->first.end(), k_indices);
-      std::copy(ca->second.second.begin(), ca->second.end(), k_sq_dists);
-      return ca->second.first.size();
+      const size_t n = std::min(ca->second.first.size(), k);
+
+      std::copy(ca->second.first.begin(), ca->second.first.begin() + n, k_indices);
+      std::copy(ca->second.second.begin(), ca->second.second.begin() + n, k_sq_dists);
+      return n;
     }
 
     const size_t n = kdtree.knn_search(pt, k, k_indices, k_sq_dists);
@@ -53,7 +55,7 @@ class CachedKdTree {
   using CacheTable = tbb::concurrent_hash_map<Eigen::Vector3i, KnnResult, XORVector3iHash>;
   mutable CacheTable cache;
 
-  UnsafeKdTree<PointCloud> kdtree;
+  KdTree<PointCloud> kdtree;
 };
 
 namespace traits {

include/small_gicp/ann/flat_voxelmap.hpp

@@ -35,9 +35,9 @@ struct FlatVoxelMap {
   using Ptr = std::shared_ptr<FlatVoxelMap>;
   using ConstPtr = std::shared_ptr<const FlatVoxelMap>;
 
-  FlatVoxelMap(double leaf_size, const PointCloud& points) : inv_leaf_size(1.0 / leaf_size), seek_count(2), points(points) {
+  FlatVoxelMap(const std::shared_ptr<const PointCloud>& points, double leaf_size) : inv_leaf_size(1.0 / leaf_size), seek_count(2), points(points) {
     set_offset_pattern(7);
-    create_table(points);
+    create_table(*points);
   }
   ~FlatVoxelMap() {}
 
@@ -101,7 +101,7 @@ struct FlatVoxelMap {
 
       const auto index_begin = indices.data() + voxel->index_loc;
       for (auto index_itr = index_begin; index_itr != index_begin + voxel->num_indices; index_itr++) {
-        const double sq_dist = (traits::point(points, *index_itr) - pt).squaredNorm();
+        const double sq_dist = (traits::point(*points, *index_itr) - pt).squaredNorm();
         if (queue.size() < k) {
           queue.push(IndexDistance{*index_itr, sq_dist});
         } else if (sq_dist < queue.top().distance) {
@@ -125,11 +125,16 @@ struct FlatVoxelMap {
 
 private:
   void create_table(const PointCloud& points) {
+    // Here, we assume that the data structure of std::atomic_int64_t is the same as that of std::int64_t.
+    // This is a dangerous assumption. If C++20 is available, should use std::atomic_ref<std::int64_t> instead.
+    static_assert(sizeof(std::atomic_int64_t) == sizeof(std::int64_t), "We assume that std::atomic_int64_t is the same as std::int64_t.");
+
     const double min_sq_dist_in_cell = 0.05 * 0.05;
     const int max_points_per_cell = 10;
 
     const size_t buckets_size = traits::size(points);
-    std::vector<std::int64_t> assignment_table(max_points_per_cell * buckets_size, -1);
+    std::vector<std::atomic_int64_t> assignment_table(max_points_per_cell * buckets_size);
+    memset(assignment_table.data(), -1, sizeof(std::atomic_int64_t) * max_points_per_cell * buckets_size);
 
     std::vector<Eigen::Vector3i> coords(traits::size(points));
     tbb::parallel_for(static_cast<size_t>(0), static_cast<size_t>(traits::size(points)), [&](size_t i) {
@@ -140,10 +145,9 @@ struct FlatVoxelMap {
       const size_t hash = XORVector3iHash::hash(coord);
       for (size_t bucket_index = hash; bucket_index < hash + seek_count; bucket_index++) {
         auto slot_begin = assignment_table.data() + (bucket_index % buckets_size) * max_points_per_cell;
-        std::atomic_ref<std::int64_t> slot_begin_a(*slot_begin);
 
         std::int64_t expected = -1;
-        if (slot_begin_a.compare_exchange_strong(expected, static_cast<std::int64_t>(i))) {
+        if (slot_begin->compare_exchange_strong(expected, static_cast<std::int64_t>(i))) {
           // Succeeded to insert the point in the first slot
           break;
         }
@@ -160,8 +164,7 @@ struct FlatVoxelMap {
 
         for (auto slot = slot_begin + 1; slot != slot_begin + max_points_per_cell; slot++) {
           std::int64_t expected = -1;
-          std::atomic_ref<std::int64_t> slot_a(*slot);
-          if (slot_a.compare_exchange_strong(expected, static_cast<std::int64_t>(i))) {
+          if (slot->compare_exchange_strong(expected, static_cast<std::int64_t>(i))) {
             // Succeeded to insert the point
             break;
           }
@@ -206,7 +209,7 @@ struct FlatVoxelMap {
   const int seek_count;
   std::vector<Eigen::Vector3i> offsets;
 
-  const PointCloud& points;
+  std::shared_ptr<const PointCloud> points;
   std::vector<FlatVoxelInfo> voxels;
   std::vector<size_t> indices;
 };

src/test/kdtree_test.cpp

@@ -7,6 +7,8 @@
 #include <small_gicp/ann/kdtree.hpp>
 #include <small_gicp/ann/kdtree_omp.hpp>
 #include <small_gicp/ann/kdtree_tbb.hpp>
+#include <small_gicp/ann/cached_kdtree.hpp>
+#include <small_gicp/ann/flat_voxelmap.hpp>
 #include <small_gicp/util/downsampling.hpp>
 #include <small_gicp/points/point_cloud.hpp>
 #include <small_gicp/pcl/pcl_point_traits.hpp>
@@ -75,26 +77,32 @@ class KdTreeTest : public testing::Test, public testing::WithParamInterface<std:
   }
 
   template <typename PointCloud, typename KdTree>
-  void test_knn_(const PointCloud& points, const KdTree& tree) {
+  void test_knn_(const PointCloud& points, const KdTree& tree, bool strict = true) {
     for (size_t i = 0; i < queries.size(); i++) {
       // k-nearest neighbors search
       const auto& query = queries[i];
       std::vector<size_t> indices(k);
       std::vector<double> sq_dists(k);
       const size_t num_results = traits::knn_search(tree, query, k, indices.data(), sq_dists.data());
-      EXPECT_EQ(num_results, k) << "num_neighbors must be k";
-      for (size_t j = 0; j < k; j++) {
-        EXPECT_EQ(indices[j], k_indices[i][j]);
-        EXPECT_NEAR(sq_dists[j], k_sq_dists[i][j], 1e-3);
+
+      if (strict) {
+        EXPECT_EQ(num_results, k) << "num_neighbors must be k";
+        for (size_t j = 0; j < k; j++) {
+          EXPECT_EQ(indices[j], k_indices[i][j]);
+          EXPECT_NEAR(sq_dists[j], k_sq_dists[i][j], 1e-3);
+        }
       }
 
       // Nearest neighbor search
       size_t k_index;
       double k_sq_dist;
       const size_t found = traits::nearest_neighbor_search(tree, query, &k_index, &k_sq_dist);
-      EXPECT_EQ(found, 1) << "num_neighbors must be 1";
-      EXPECT_EQ(k_index, k_indices[i][0]);
-      EXPECT_NEAR(k_sq_dist, k_sq_dists[i][0], 1e-3);
+
+      if (strict) {
+        EXPECT_EQ(found, 1) << "num_neighbors must be 1";
+        EXPECT_EQ(k_index, k_indices[i][0]);
+        EXPECT_NEAR(k_sq_dist, k_sq_dists[i][0], 1e-3);
+      }
     }
   }
 
@@ -123,7 +131,7 @@ TEST_F(KdTreeTest, LoadCheck) {
   }
 }
 
-INSTANTIATE_TEST_SUITE_P(KdTreeTest, KdTreeTest, testing::Values("SMALL", "TBB", "OMP"), [](const auto& info) { return info.param; });
+INSTANTIATE_TEST_SUITE_P(KdTreeTest, KdTreeTest, testing::Values("SMALL", "TBB", "OMP", "CACHED", "FLAT"), [](const auto& info) { return info.param; });
 
 // Check if kdtree works correctly for empty points
 TEST_P(KdTreeTest, EmptyTest) {
@@ -155,6 +163,28 @@ TEST_P(KdTreeTest, KnnTest) {
 
     auto kdtree_pcl = std::make_shared<KdTreeOMP<pcl::PointCloud<pcl::PointXYZ>>>(points_pcl, 4);
     test_knn_(*points_pcl, *kdtree_pcl);
+  } else if (method == "CACHED") {
+    // This is approximated and no guarantee about the search result
+    const bool strict = false;
+
+    auto kdtree = std::make_shared<CachedKdTree<PointCloud>>(points, 0.025);
+    test_knn_(*points, *kdtree, strict);
+
+    auto kdtree_pcl = std::make_shared<CachedKdTree<pcl::PointCloud<pcl::PointXYZ>>>(points_pcl, 0.025);
+    test_knn_(*points_pcl, *kdtree_pcl, strict);
+  } else if (method == "FLAT") {
+    // This is approximated and no guarantee about the search result
+    const bool strict = false;
+
+    auto kdtree = std::make_shared<FlatVoxelMap<PointCloud>>(points, 0.5);
+    kdtree->set_offset_pattern(1);
+    kdtree->set_offset_pattern(7);
+    kdtree->set_offset_pattern(27);
+    test_knn_(*points, *kdtree, strict);
+
+    auto kdtree_pcl = std::make_shared<FlatVoxelMap<pcl::PointCloud<pcl::PointXYZ>>>(points_pcl, 0.5);
+    kdtree_pcl->set_offset_pattern(27);
+    test_knn_(*points_pcl, *kdtree_pcl, strict);
   } else {
     throw std::runtime_error("Invalid method: " + method);
   }

src/test/sort_omp_test.cpp

@@ -26,7 +26,7 @@ bool identical(const std::vector<T>& arr1, const std::vector<T>& arr2) {
 TEST(SortOMP, MergeSortTest) {
   std::mt19937 mt;
 
-  std::uniform_int_distribution<> size_dist(0, 1024);
+  std::uniform_int_distribution<> size_dist(0, 8192);
 
   // int
   for (int i = 0; i < 100; i++) {
@@ -68,7 +68,7 @@ TEST(SortOMP, MergeSortTest) {
 TEST(SortOMP, QuickSortTest) {
   std::mt19937 mt;
 
-  std::uniform_int_distribution<> size_dist(0, 1024);
+  std::uniform_int_distribution<> size_dist(0, 8192);
 
   // int
   for (int i = 0; i < 100; i++) {