Merge pull request #19 from nicolas-chaulet/humanpose1-ball_query_cpu_memory_leak

Fix conflitcs

Author: nicolas-chaulet

cpu/include/cloud.h

@@ -37,31 +37,29 @@ struct PointCloud {
 
   std::vector<PointXYZ> pts;
 
-  void set(std::vector<scalar_t> new_pts) {
-    // pts = std::vector<PointXYZ>((PointXYZ*)new_pts,
-    // (PointXYZ*)new_pts+new_pts.size()/3);
-    std::vector<PointXYZ> temp(new_pts.size() / 3);
+  void set(const std::vector<scalar_t>& new_pts) {
+    pts.clear();
+		pts.resize(new_pts.size()/3);
     for (unsigned int i = 0; i < new_pts.size(); i++) {
       if (i % 3 == 0) {
         PointXYZ point;
         point.x = new_pts[i];
         point.y = new_pts[i + 1];
         point.z = new_pts[i + 2];
-        temp[i / 3] = point;
+        pts[i / 3] = point;
       }
     }
-    pts = temp;
   }
-  void set_batch(std::vector<scalar_t> new_pts, int begin, int size) {
-    std::vector<PointXYZ> temp(size);
+  void set_batch(const std::vector<scalar_t>& new_pts, int begin, int size) {
+    pts.clear();
+    pts.resize(size);
     for (int i = 0; i < size; i++) {
       PointXYZ point;
       point.x = new_pts[3 * (begin + i)];
       point.y = new_pts[3 * (begin + i) + 1];
       point.z = new_pts[3 * (begin + i) + 2];
-      temp[i] = point;
+      pts[i] = point;
     }
-    pts = temp;
   }
 
   // Must return the number of data points

cpu/src/neighbors.cpp

@@ -40,8 +40,7 @@ int nanoflann_neighbors(vector<scalar_t>& queries, vector<scalar_t>& supports,
       my_kd_tree_t;
 
   // Pointer to trees
-  my_kd_tree_t* index;
-  index = new my_kd_tree_t(3, pcd, tree_params);
+  std::unique_ptr<my_kd_tree_t> index(new my_kd_tree_t(3, pcd, tree_params));
   index->buildIndex();
   // Search neigbors indices
   // ***********************
@@ -156,11 +155,9 @@ int batch_nanoflann_neighbors(vector<scalar_t>& queries,
       PointCloud<scalar_t>, 3>
       my_kd_tree_t;
 
-  // Pointer to trees
-  my_kd_tree_t* index;
   // Build KDTree for the first batch element
-  current_cloud.set_batch(supports, s_batches[b], s_batches[b + 1]);
-  index = new my_kd_tree_t(3, current_cloud, tree_params);
+	current_cloud.set_batch(supports, s_batches[b], s_batches[b+1]);
+	std::unique_ptr<my_kd_tree_t> index(new my_kd_tree_t(3, current_cloud, tree_params));
   index->buildIndex();
   // Search neigbors indices
   // ***********************
@@ -177,10 +174,9 @@ int batch_nanoflann_neighbors(vector<scalar_t>& queries,
       current_cloud.pts.clear();
       if (s_batches[b] < s_batches[b + 1])
         current_cloud.set_batch(supports, s_batches[b], s_batches[b + 1]);
-      // Build KDTree of the current element of the batch
-      delete index;
 
-      index = new my_kd_tree_t(3, current_cloud, tree_params);
+      // Build KDTree of the current element of the batch
+      index.reset(new my_kd_tree_t(3, current_cloud, tree_params));
       index->buildIndex();
     }
 
@@ -233,7 +229,7 @@ int batch_nanoflann_neighbors(vector<scalar_t>& queries,
 
       i0++;
     }
-    delete index;
+    index.reset();
   } else if (mode == 1) {
     int size = 0;  // total number of edges
     for (auto& inds_dists : all_inds_dists) {

setup.py

@@ -44,7 +44,7 @@
 
 setup(
     name="torch_points",
-    version="0.2.1",
+    version="0.2.2",
     author="Nicolas Chaulet",
     packages=find_packages(),
     install_requires=requirements,