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/*
* Software License Agreement (BSD License)
*
* Copyright (c) 2011, Willow Garage, Inc.
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * 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.
* * Neither the name of Willow Garage, Inc. nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "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
* COPYRIGHT OWNER OR CONTRIBUTORS 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.
*
* Author: Anatoly Baskeheev, Itseez Ltd, (myname.mysurname@mycompany.com)
*/
#include <gtest/gtest.h>
#include <iostream>
#include <fstream>
#include <numeric>
#if defined _MSC_VER
#pragma warning (disable: 4521)
#endif
#include <pcl/point_cloud.h>
#if defined _MSC_VER
#pragma warning (default: 4521)
#endif
#include <pcl/gpu/octree/octree.hpp>
#include <pcl/gpu/containers/device_array.h>
#include "data_source.hpp"
using namespace pcl::gpu;
//TEST(PCL_OctreeGPU, DISABLED_batchRadiusSearch)
TEST(PCL_OctreeGPU, batchRadiusSearch)
{
DataGenerator data;
data.data_size = 871000;
data.tests_num = 10000;
data.cube_size = 1024.f;
data.max_radius = data.cube_size/30.f;
data.shared_radius = data.cube_size/30.f;
data.printParams();
const int max_answers = 333;
//generate
data();
//prepare gpu cloud
pcl::gpu::Octree::PointCloud cloud_device;
cloud_device.upload(data.points);
//gpu build
pcl::gpu::Octree octree_device;
octree_device.setCloud(cloud_device);
octree_device.build();
//upload queries
pcl::gpu::Octree::Queries queries_device;
pcl::gpu::Octree::Radiuses radiuses_device;
queries_device.upload(data.queries);
radiuses_device.upload(data.radiuses);
//prepare output buffers on device
pcl::gpu::NeighborIndices result_device1(queries_device.size(), max_answers);
pcl::gpu::NeighborIndices result_device2(queries_device.size(), max_answers);
pcl::gpu::NeighborIndices result_device3(data.indices.size(), max_answers);
//prepare output buffers on host
std::vector< std::vector<int> > host_search1(data.tests_num);
std::vector< std::vector<int> > host_search2(data.tests_num);
for(std::size_t i = 0; i < data.tests_num; ++i)
{
host_search1[i].reserve(max_answers);
host_search2[i].reserve(max_answers);
}
//search GPU shared
octree_device.radiusSearch(queries_device, data.shared_radius, max_answers, result_device1);
//search GPU individual
octree_device.radiusSearch(queries_device, radiuses_device, max_answers, result_device2);
//search GPU shared with indices
pcl::gpu::Octree::Indices indices;
indices.upload(data.indices);
octree_device.radiusSearch(queries_device, indices, data.shared_radius, max_answers, result_device3);
//search CPU
octree_device.internalDownload();
for(std::size_t i = 0; i < data.tests_num; ++i)
{
octree_device.radiusSearchHost(data.queries[i], data.shared_radius, host_search1[i], max_answers);
octree_device.radiusSearchHost(data.queries[i], data.radiuses[i], host_search2[i], max_answers);
}
//download results
std::vector<int> sizes1;
std::vector<int> sizes2;
std::vector<int> sizes3;
result_device1.sizes.download(sizes1);
result_device2.sizes.download(sizes2);
result_device3.sizes.download(sizes3);
std::vector<int> downloaded_buffer1, downloaded_buffer2, downloaded_buffer3, results_batch;
result_device1.data.download(downloaded_buffer1);
result_device2.data.download(downloaded_buffer2);
result_device3.data.download(downloaded_buffer3);
//data.bruteForceSearch();
//verify results
for(std::size_t i = 0; i < data.tests_num; ++i)
{
std::vector<int>& results_host = host_search1[i];
int beg = i * max_answers;
int end = beg + sizes1[i];
results_batch.assign(downloaded_buffer1.begin() + beg, downloaded_buffer1.begin() + end);
std::sort(results_batch.begin(), results_batch.end());
std::sort(results_host.begin(), results_host.end());
if ((int)results_batch.size() == max_answers && results_batch.size() < results_host.size() && max_answers)
results_host.resize(max_answers);
ASSERT_EQ ( ( results_batch == results_host ), true );
//vector<int>& results_bf = data.bfresutls[i];
//ASSERT_EQ ( ( results_bf == results_batch), true );
//ASSERT_EQ ( ( results_bf == results_host ), true );
}
float avg_size1 = std::accumulate(sizes1.begin(), sizes1.end(), 0) * (1.f/sizes1.size());
std::cout << "avg_result_size1 = " << avg_size1 << std::endl;
ASSERT_GT(avg_size1, 5);
//verify results
for(std::size_t i = 0; i < data.tests_num; ++i)
{
std::vector<int>& results_host = host_search2[i];
int beg = i * max_answers;
int end = beg + sizes2[i];
results_batch.assign(downloaded_buffer2.begin() + beg, downloaded_buffer2.begin() + end);
std::sort(results_batch.begin(), results_batch.end());
std::sort(results_host.begin(), results_host.end());
if ((int)results_batch.size() == max_answers && results_batch.size() < results_host.size() && max_answers)
results_host.resize(max_answers);
ASSERT_EQ ( ( results_batch == results_host ), true );
//vector<int>& results_bf = data.bfresutls[i];
//ASSERT_EQ ( ( results_bf == results_batch), true );
//ASSERT_EQ ( ( results_bf == results_host ), true );
}
float avg_size2 = std::accumulate(sizes2.begin(), sizes2.end(), 0) * (1.f/sizes2.size());
std::cout << "avg_result_size2 = " << avg_size2 << std::endl;
ASSERT_GT(avg_size2, 5);
//verify results
for(std::size_t i = 0; i < data.tests_num; i+=2)
{
std::vector<int>& results_host = host_search1[i];
int beg = i/2 * max_answers;
int end = beg + sizes3[i/2];
results_batch.assign(downloaded_buffer3.begin() + beg, downloaded_buffer3.begin() + end);
std::sort(results_batch.begin(), results_batch.end());
std::sort(results_host.begin(), results_host.end());
if ((int)results_batch.size() == max_answers && results_batch.size() < results_host.size() && max_answers)
results_host.resize(max_answers);
ASSERT_EQ ( ( results_batch == results_host ), true );
//vector<int>& results_bf = data.bfresutls[i];
//ASSERT_EQ ( ( results_bf == results_batch), true );
//ASSERT_EQ ( ( results_bf == results_host ), true );
}
float avg_size3 = std::accumulate(sizes3.begin(), sizes3.end(), 0) * (1.f/sizes3.size());
std::cout << "avg_result_size3 = " << avg_size3 << std::endl;
ASSERT_GT(avg_size3, 5);
}
/* ---[ */
int
main (int argc, char** argv)
{
testing::InitGoogleTest (&argc, argv);
return (RUN_ALL_TESTS ());
}
/* ]--- */
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