attempting to fix windows build memory error

csrc/cpu/radius_cpu.cpp

 #include "radius_cpu.h"
 #include <algorithm>
 #include "utils.h"
+#include <cstdint>
+
 
 torch::Tensor radius_cpu(torch::Tensor query, torch::Tensor support, 
-			 float radius, int max_num){
+			 double radius, int64_t max_num){
 
 	CHECK_CPU(query);
 	CHECK_CPU(support);
 
 	torch::Tensor out;
-	std::vector<long> neighbors_indices;
+	std::vector<size_t>* neighbors_indices = new std::vector<size_t>(); 
 	auto options = torch::TensorOptions().dtype(torch::kLong).device(torch::kCPU);
 	int max_count = 0;
 
@@ -28,9 +30,9 @@ torch::Tensor radius_cpu(torch::Tensor query, torch::Tensor support,
 
 	});
 
-	long* neighbors_indices_ptr = neighbors_indices.data();
+	size_t* neighbors_indices_ptr = neighbors_indices->data();
 
-	const long long tsize = static_cast<long long>(neighbors_indices.size()/2);
+	const long long tsize = static_cast<long long>(neighbors_indices->size()/2);
 	out = torch::from_blob(neighbors_indices_ptr, {tsize, 2}, options=options);
 	out = out.t();
 
@@ -60,7 +62,7 @@ torch::Tensor batch_radius_cpu(torch::Tensor query,
 			       torch::Tensor support,
 			       torch::Tensor query_batch,
 			       torch::Tensor support_batch,
-			       float radius, int max_num) {
+			       double radius, int64_t max_num) {
 
 	torch::Tensor out;
 	auto data_qb = query_batch.data_ptr<int64_t>();
@@ -71,7 +73,7 @@ torch::Tensor batch_radius_cpu(torch::Tensor query,
 	std::vector<long> support_batch_stl = std::vector<long>(data_sb, data_sb+support_batch.size(0));
 	std::vector<long> size_support_batch_stl;
 	get_size_batch(support_batch_stl, size_support_batch_stl);
-	std::vector<long> neighbors_indices;
+	std::vector<size_t>* neighbors_indices = new std::vector<size_t>(); 
 	auto options = torch::TensorOptions().dtype(torch::kLong).device(torch::kCPU);
 	int max_count = 0;
 
@@ -95,10 +97,10 @@ torch::Tensor batch_radius_cpu(torch::Tensor query,
 							    );
 	});
 
-	long* neighbors_indices_ptr = neighbors_indices.data();
+	size_t* neighbors_indices_ptr = neighbors_indices->data();
 
 
-	const long long tsize = static_cast<long long>(neighbors_indices.size()/2);
+	const long long tsize = static_cast<long long>(neighbors_indices->size()/2);
 	out = torch::from_blob(neighbors_indices_ptr, {tsize, 2}, options=options);
 	out = out.t();
 

csrc/cpu/radius_cpu.h

@@ -7,10 +7,10 @@
 #include "compat.h"
 
 torch::Tensor radius_cpu(torch::Tensor query, torch::Tensor support,
-			 			 float radius, int max_num);
+			 			 double radius, int64_t max_num);
 
 torch::Tensor batch_radius_cpu(torch::Tensor query,
 			       torch::Tensor support,
 			       torch::Tensor query_batch,
 			       torch::Tensor support_batch,
-			       float radius, int max_num);
+			       double radius, int64_t max_num);
\ No newline at end of file

csrc/cpu/utils/cloud.h

@@ -20,17 +20,17 @@
 template<typename scalar_t>
 struct PointCloud
 {
-	std::vector<std::vector<scalar_t>> pts;
+	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);
+		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(dim);
+				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];
+					(*point)[j]=new_pts[i+j];
 				}
 				temp[i/dim] = point;
 			}
@@ -38,12 +38,12 @@ struct PointCloud
 
 		pts = temp;
 	}
-	void set_batch(std::vector<scalar_t> new_pts, int begin, int size, int dim){
-		std::vector<std::vector<scalar_t>> temp(size);
-		for(int i=0; i < size; i++){
-			std::vector<scalar_t> point(dim);
+	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];
+					(*point)[j] = new_pts[dim*(begin+i)+j];
 			}
 
 			temp[i] = point;
@@ -58,7 +58,7 @@ struct PointCloud
 	// 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];
+		return (*pts[idx])[dim];
 	}
 
 	// Optional bounding-box computation: return false to default to a standard bbox computation loop.

csrc/cpu/utils/neighbors.cpp

@@ -4,8 +4,8 @@
 #include "neighbors.h"
 
 template<typename scalar_t>
-int nanoflann_neighbors(vector<scalar_t>& queries, vector<scalar_t>& supports,
-			vector<long>& neighbors_indices, float radius, int dim, int max_num){
+size_t nanoflann_neighbors(vector<scalar_t>& queries, vector<scalar_t>& supports,
+			vector<size_t>*& neighbors_indices, double radius, int dim, int64_t max_num){
 
 	const scalar_t search_radius = static_cast<scalar_t>(radius*radius);
 
@@ -37,13 +37,13 @@ int nanoflann_neighbors(vector<scalar_t>& queries, vector<scalar_t>& supports,
 	// search_params.sorted = true;
 	std::vector< std::vector<std::pair<size_t, scalar_t> > > list_matches(pcd_query.pts.size());
 
-	float eps = 0.000001;
+	double eps = 0.000001;
 
 	// indices
 	size_t i0 = 0;
 
-	for (auto& p0 : pcd_query.pts){
-
+	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); 
@@ -51,7 +51,7 @@ int nanoflann_neighbors(vector<scalar_t>& queries, vector<scalar_t>& supports,
 		list_matches[i0].reserve(max_count);
 		std::vector<std::pair<size_t, scalar_t> > ret_matches;
 
-		const size_t nMatches = index->radiusSearch(query_pt, search_radius+eps, ret_matches, search_params);
+		const size_t nMatches = index->radiusSearch(query_pt, (scalar_t)(search_radius+eps), ret_matches, search_params);
 		
 		list_matches[i0] = ret_matches;
 		if(max_count < nMatches) max_count = nMatches;
@@ -71,14 +71,14 @@ int nanoflann_neighbors(vector<scalar_t>& queries, vector<scalar_t>& supports,
 			size += max_count;
 	}
 
-	neighbors_indices.resize(size*2);
+	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;
+				(*neighbors_indices)[u] = inds[j].first;
+				(*neighbors_indices)[u + 1] = i1;
 				u += 2;
 			}
 		}
@@ -93,12 +93,12 @@ int nanoflann_neighbors(vector<scalar_t>& queries, vector<scalar_t>& supports,
 }
 
 template<typename scalar_t>
-int batch_nanoflann_neighbors (vector<scalar_t>& queries,
+size_t batch_nanoflann_neighbors (vector<scalar_t>& queries,
                                vector<scalar_t>& supports,
                                vector<long>& q_batches,
                                vector<long>& s_batches,
-                               vector<long>& neighbors_indices,
-                               float radius, int dim, int max_num){
+                               vector<size_t>*& neighbors_indices,
+                               double radius, int dim, int64_t max_num){
 
 
 // Initiate variables
@@ -117,7 +117,7 @@ int batch_nanoflann_neighbors (vector<scalar_t>& queries,
 	size_t sum_qb = 0;
 	size_t sum_sb = 0;
 
-	float eps = 0.000001;
+	double eps = 0.000001;
 	// Nanoflann related variables
 	// ***************************
 
@@ -145,7 +145,8 @@ int batch_nanoflann_neighbors (vector<scalar_t>& queries,
 	nanoflann::SearchParams search_params;
 	search_params.sorted = true;
 
-	for (auto& p0 : query_pcd.pts){
+	for (auto& p : query_pcd.pts){
+		auto p0 = *p;
 // Check if we changed batch
 
 		scalar_t* query_pt = new scalar_t[dim];
@@ -193,7 +194,7 @@ int batch_nanoflann_neighbors (vector<scalar_t>& queries,
 		else
 			size += max_count;
 	}
-	neighbors_indices.resize(size * 2);
+	neighbors_indices->resize(size * 2);
 	i0 = 0;
 	sum_sb = 0;
 	sum_qb = 0;
@@ -207,8 +208,8 @@ int batch_nanoflann_neighbors (vector<scalar_t>& queries,
 		}
 		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;
+				(*neighbors_indices)[u] = inds_dists[j].first + sum_sb;
+				(*neighbors_indices)[u + 1] = i0;
 				u += 2;
 			}
 		}

csrc/cpu/utils/neighbors.h

@@ -11,7 +11,7 @@ using namespace std;
 
 template<typename scalar_t>
 int nanoflann_neighbors(vector<scalar_t>& queries, vector<scalar_t>& supports,
-			vector<long>& neighbors_indices, float radius, int dim, int max_num);
+			vector<long>& neighbors_indices, double radius, int dim, int64_t max_num);
 
 template<typename scalar_t>
 int batch_nanoflann_neighbors (vector<scalar_t>& queries,
@@ -19,4 +19,4 @@ int batch_nanoflann_neighbors (vector<scalar_t>& queries,
                                vector<long>& q_batches,
                                vector<long>& s_batches,
                                vector<long>& neighbors_indices,
-                               float radius, int dim, int max_num);
+                               double radius, int dim, int64_t max_num);
\ No newline at end of file

test/test_radius.py

@@ -3,7 +3,6 @@ from itertools import product
 import pytest
 import torch
 from torch_cluster import radius, radius_graph
-
 from .utils import grad_dtypes, devices, tensor
 
 
@@ -115,8 +114,8 @@ def test_radius_graph_pointnet_small(dtype, device):
 
     row, col = radius_graph(x, r=0.2, flow='source_to_target', batch=batch)
 
-    edges = set([(i, j) for (i, j) in zip(row.cpu().numpy(),
-                                          col.cpu().numpy())])
+    edges = set([(i, j) for (i, j) in zip(list(row.cpu().numpy()),
+                                          list(col.cpu().numpy()))])
 
     truth_row = [10, 11, 7, 9, 9, 1, 9, 1, 6, 7, 0, 11, 0, 10, 15, 12, 20, 16,
                  34, 31, 44, 43, 42, 41]
@@ -404,8 +403,8 @@ def test_radius_graph_pointnet_medium(dtype, device):
 
     row, col = radius_graph(x, r=0.2, flow='source_to_target', batch=batch)
 
-    edges = set([(i, j) for (i, j) in zip(row.cpu().numpy(),
-                                          col.cpu().numpy())])
+    edges = set([(i, j) for (i, j) in zip(list(row.cpu().numpy()),
+                                          list(col.cpu().numpy()))])
 
     truth_row = [6, 27, 17, 31, 3, 23, 62, 2, 14, 23, 36, 38, 62, 15, 0, 11,
                  27, 29, 50, 49, 54, 56, 12, 61, 16, 21, 24, 39, 6, 27, 29,
@@ -573,8 +572,8 @@ def test_radius_graph_ndim(dtype, device):
 
     row, col = radius_graph(x, r=4.4, flow='source_to_target', batch=batch)
 
-    edges = set([(i, j) for (i, j) in zip(row.cpu().numpy(),
-                                          col.cpu().numpy())])
+    edges = set([(i, j) for (i, j) in zip(list(row.cpu().numpy()),
+                                          list(col.cpu().numpy()))])
 
     truth_row = [2, 3, 2, 3, 0, 1, 3, 4, 0, 1, 2, 4, 2, 3, 6, 7, 9, 10, 5, 7,
                  8, 9, 10, 5, 6, 10, 6, 5, 6, 10, 5, 6, 7, 9, 13, 11, 16, 17,