kdtree_opencl.cpp
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1 /*
2 
3 Copyright (c) 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland
4 You can contact the author at <stephane at magnenat dot net>
5 
6 All rights reserved.
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30 */
31 
32 #ifdef HAVE_OPENCL
33 
34 #include "nabo_private.h"
35 #include "index_heap.h"
36 #include <iostream>
37 #include <sstream>
38 #include <fstream>
39 #include <stdexcept>
40 #include <limits>
41 #include <queue>
42 #include <algorithm>
43 // #include <map>
44 
45 
51 namespace cl
52 {
54  typedef std::vector<Device> Devices;
55 }
56 
57 namespace Nabo
58 {
60 
63  template<typename T, typename CloudType>
64  size_t argMax(const typename NearestNeighbourSearch<T, CloudType>::Vector& v)
65  {
66  T maxVal(0);
67  size_t maxIdx(0);
68  for (int i = 0; i < v.size(); ++i)
69  {
70  if (v[i] > maxVal)
71  {
72  maxVal = v[i];
73  maxIdx = i;
74  }
75  }
76  return maxIdx;
77  }
78 
80 
81 
83  #define MAX_K 32
84 
85  using namespace std;
86 
88  template<typename T, typename CloudType>
89  struct EnableCLTypeSupport {};
90 
92  template<typename CloudType>
93  struct EnableCLTypeSupport<float, CloudType>
94  {
96  static string code(const cl::Device& device)
97  {
98  return "typedef float T;\n";
99  }
100  };
101 
103  template<typename CloudType>
104  struct EnableCLTypeSupport<double, CloudType>
105  {
107 
108  static string code(const cl::Device& device)
109  {
110  string s;
111  const string& exts(device.getInfo<CL_DEVICE_EXTENSIONS>());
112  //cerr << "extensions: " << exts << endl;
113  // first try generic 64-bits fp, otherwise try to fall back on vendor-specific extensions
114  if (exts.find("cl_khr_fp64") != string::npos)
115  s += "#pragma OPENCL EXTENSION cl_khr_fp64 : enable\n";
116  else if (exts.find("cl_amd_fp64") != string::npos)
117  s += "#pragma OPENCL EXTENSION cl_amd_fp64 : enable\n";
118  else
119  throw runtime_error("The OpenCL platform does not support 64 bits double-precision floating-points scalars.");
120  s += "typedef double T;\n";
121  return s;
122  }
123  };
124 
126  struct SourceCacher
127  {
129  typedef std::vector<cl::Device> Devices;
131  typedef std::map<std::string, cl::Program> ProgramCache;
132 
133  cl::Context context;
134  Devices devices;
135  ProgramCache cachedPrograms;
136 
138  SourceCacher(const cl_device_type deviceType)
139  {
140  // looking for platforms, AMD drivers do not like the default for creating context
141  vector<cl::Platform> platforms;
142  cl::Platform::get(&platforms);
143  if (platforms.empty())
144  throw runtime_error("No OpenCL platform found");
145  //for(vector<cl::Platform>::iterator i = platforms.begin(); i != platforms.end(); ++i)
146  // cerr << "platform " << i - platforms.begin() << " is " << (*i).getInfo<CL_PLATFORM_VENDOR>() << endl;
147  cl::Platform platform = platforms[0];
148  const char *userDefinedPlatform(getenv("NABO_OPENCL_USE_PLATFORM"));
149  if (userDefinedPlatform)
150  {
151  size_t userDefinedPlatformId = atoi(userDefinedPlatform);
152  if (userDefinedPlatformId < platforms.size())
153  platform = platforms[userDefinedPlatformId];
154  }
155 
156  // create OpenCL contexts
157  cl_context_properties properties[] = { CL_CONTEXT_PLATFORM, (cl_context_properties)platform(), 0 };
158  bool deviceFound = false;
159  try {
160  context = cl::Context(deviceType, properties);
161  deviceFound = true;
162  } catch (const cl::Error& e) {
163  cerr << "Cannot find device type " << deviceType << " for OpenCL, falling back to any device" << endl;
164  }
165  if (!deviceFound)
166  context = cl::Context(CL_DEVICE_TYPE_ALL, properties);
167  devices = context.getInfo<CL_CONTEXT_DEVICES>();
168  if (devices.empty())
169  throw runtime_error("No devices on OpenCL platform");
170  }
171 
173  ~SourceCacher()
174  {
175  cerr << "Destroying source cacher containing " << cachedPrograms.size() << " cached programs" << endl;
176  }
177 
179  bool contains(const std::string& source)
180  {
181  return cachedPrograms.find(source) != cachedPrograms.end();
182  }
183  };
184 
186  class ContextManager
187  {
188  public:
190  typedef std::map<cl_device_type, SourceCacher*> Devices;
191 
193  ~ContextManager()
194  {
195  cerr << "Destroying CL context manager, used " << devices.size() << " contexts" << endl;
196  for (Devices::iterator it(devices.begin()); it != devices.end(); ++it)
197  delete it->second;
198  }
200  cl::Context& createContext(const cl_device_type deviceType)
201  {
202  std::lock_guard lock(mutex);
203  Devices::iterator it(devices.find(deviceType));
204  if (it == devices.end())
205  {
206  it = devices.insert(
207  pair<cl_device_type, SourceCacher*>(deviceType, new SourceCacher(deviceType))
208  ).first;
209  }
210  return it->second->context;
211  }
213  SourceCacher* getSourceCacher(const cl_device_type deviceType)
214  {
215  std::lock_guard lock(mutex);
216  Devices::iterator it(devices.find(deviceType));
217  if (it == devices.end())
218  throw runtime_error("Attempt to get source cacher before creating a context");
219  return it->second;
220  }
221 
222  protected:
223  Devices devices;
224  std::mutex mutex;
225  };
226 
228  static ContextManager contextManager;
229 
230  template<typename T, typename CloudType>
231  OpenCLSearch<T, CloudType>::OpenCLSearch(const CloudType& cloud, const Index dim, const unsigned creationOptionFlags, const cl_device_type deviceType):
232  NearestNeighbourSearch<T, CloudType>::NearestNeighbourSearch(cloud, dim, creationOptionFlags),
233  deviceType(deviceType),
234  context(contextManager.createContext(deviceType))
235  {
236  }
237 
238  template<typename T, typename CloudType>
239  void OpenCLSearch<T, CloudType>::initOpenCL(const char* clFileName, const char* kernelName, const std::string& additionalDefines)
240  {
241  const bool collectStatistics(creationOptionFlags & NearestNeighbourSearch<T, CloudType>::TOUCH_STATISTICS);
242 
243  SourceCacher* sourceCacher(contextManager.getSourceCacher(deviceType));
244  SourceCacher::Devices& devices(sourceCacher->devices);
245 
246  // build and load source files
247  cl::Program::Sources sources;
248  // build defines
249  ostringstream oss;
250  oss << EnableCLTypeSupport<T, CloudType>::code(devices.back());
251  oss << "#define EPSILON " << numeric_limits<T>::epsilon() << "\n";
252  oss << "#define DIM_COUNT " << dim << "\n";
253  //oss << "#define CLOUD_POINT_COUNT " << cloud.cols() << "\n";
254  oss << "#define POINT_STRIDE " << cloud.stride() << "\n";
255  oss << "#define MAX_K " << MAX_K << "\n";
256  if (collectStatistics)
257  oss << "#define TOUCH_STATISTICS\n";
258  oss << additionalDefines;
259  //cerr << "params:\n" << oss.str() << endl;
260 
261  const std::string& source(oss.str());
262  if (!sourceCacher->contains(source))
263  {
264  const size_t defLen(source.length());
265  char *defContent(new char[defLen+1]);
266  strcpy(defContent, source.c_str());
267  sources.push_back(std::make_pair(defContent, defLen));
268  string sourceFileName(OPENCL_SOURCE_DIR);
269  sourceFileName += clFileName;
270  // load files
271  const char* files[] = {
272  OPENCL_SOURCE_DIR "structure.cl",
273  OPENCL_SOURCE_DIR "heap.cl",
274  sourceFileName.c_str(),
275  NULL
276  };
277  for (const char** file = files; *file != NULL; ++file)
278  {
279  std::ifstream stream(*file);
280  if (!stream.good())
281  throw runtime_error((string("cannot open file: ") + *file));
282 
283  stream.seekg(0, std::ios_base::end);
284  size_t size(stream.tellg());
285  stream.seekg(0, std::ios_base::beg);
286 
287  char* content(new char[size + 1]);
288  std::copy(std::istreambuf_iterator<char>(stream),
289  std::istreambuf_iterator<char>(), content);
290  content[size] = '\0';
291 
292  sources.push_back(std::make_pair(content, size));
293  }
294  sourceCacher->cachedPrograms[source] = cl::Program(context, sources);
295  cl::Program& program = sourceCacher->cachedPrograms[source];
296 
297  // build
298  cl::Error error(CL_SUCCESS);
299  try {
300  program.build(devices);
301  } catch (cl::Error e) {
302  error = e;
303  }
304 
305  // dump
306  for (cl::Devices::const_iterator it = devices.begin(); it != devices.end(); ++it)
307  {
308  cerr << "device : " << it->getInfo<CL_DEVICE_NAME>() << "\n";
309  cerr << "compilation log:\n" << program.getBuildInfo<CL_PROGRAM_BUILD_LOG>(*it) << endl;
310  }
311  // cleanup sources
312  for (cl::Program::Sources::iterator it = sources.begin(); it != sources.end(); ++it)
313  {
314  delete[] it->first;
315  }
316  sources.clear();
317 
318  // make sure to stop if compilation failed
319  if (error.err() != CL_SUCCESS)
320  throw error;
321  }
322  cl::Program& program = sourceCacher->cachedPrograms[source];
323 
324  // build kernel and command queue
325  knnKernel = cl::Kernel(program, kernelName);
326  queue = cl::CommandQueue(context, devices.back());
327 
328  // map cloud
329  if (!(cloud.Flags & Eigen::DirectAccessBit) || (cloud.Flags & Eigen::RowMajorBit))
330  throw runtime_error("wrong memory mapping in point cloud");
331  const size_t cloudCLSize(cloud.cols() * cloud.stride() * sizeof(T));
332  cloudCL = cl::Buffer(context, CL_MEM_READ_ONLY | CL_MEM_USE_HOST_PTR, cloudCLSize, const_cast<T*>(&cloud.coeff(0,0)));
333  knnKernel.setArg(0, sizeof(cl_mem), &cloudCL);
334  }
335 
336  template<typename T, typename CloudType>
337  unsigned long OpenCLSearch<T, CloudType>::knn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, const Index k, const T epsilon, const unsigned optionFlags, const T maxRadius) const
338  {
339  checkSizesKnn(query, indices, dists2, k, optionFlags);
340  const bool collectStatistics(creationOptionFlags & NearestNeighbourSearch<T, CloudType>::TOUCH_STATISTICS);
341 
342  // check K
343  if (k > MAX_K)
344  throw runtime_error("number of neighbors too large for OpenCL");
345 
346  // check consistency of query wrt cloud
347  if (query.stride() != cloud.stride() ||
348  query.rows() != cloud.rows())
349  throw runtime_error("query is not of the same dimensionality as the point cloud");
350 
351  // map query
352  if (!(query.Flags & Eigen::DirectAccessBit) || (query.Flags & Eigen::RowMajorBit))
353  throw runtime_error("wrong memory mapping in query data");
354  const size_t queryCLSize(query.cols() * query.stride() * sizeof(T));
355  cl::Buffer queryCL(context, CL_MEM_READ_ONLY | CL_MEM_USE_HOST_PTR, queryCLSize, const_cast<T*>(&query.coeff(0,0)));
356  knnKernel.setArg(1, sizeof(cl_mem), &queryCL);
357  // map indices
358  assert((indices.Flags & Eigen::DirectAccessBit) && (!(indices.Flags & Eigen::RowMajorBit)));
359  const int indexStride(indices.stride());
360  const size_t indicesCLSize(indices.cols() * indexStride * sizeof(int));
361  cl::Buffer indicesCL(context, CL_MEM_WRITE_ONLY | CL_MEM_USE_HOST_PTR, indicesCLSize, &indices.coeffRef(0,0));
362  knnKernel.setArg(2, sizeof(cl_mem), &indicesCL);
363  // map dists2
364  assert((dists2.Flags & Eigen::DirectAccessBit) && (!(dists2.Flags & Eigen::RowMajorBit)));
365  const int dists2Stride(dists2.stride());
366  const size_t dists2CLSize(dists2.cols() * dists2Stride * sizeof(T));
367  cl::Buffer dists2CL(context, CL_MEM_WRITE_ONLY | CL_MEM_USE_HOST_PTR, dists2CLSize, &dists2.coeffRef(0,0));
368  knnKernel.setArg(3, sizeof(cl_mem), &dists2CL);
369 
370  // set resulting parameters
371  knnKernel.setArg(4, k);
372  knnKernel.setArg(5, (1 + epsilon)*(1 + epsilon));
373  knnKernel.setArg(6, maxRadius*maxRadius);
374  knnKernel.setArg(7, optionFlags);
375  knnKernel.setArg(8, indexStride);
376  knnKernel.setArg(9, dists2Stride);
377  knnKernel.setArg(10, cl_uint(cloud.cols()));
378 
379  // if required, map visit count
380  vector<cl_uint> visitCounts;
381  const size_t visitCountCLSize(query.cols() * sizeof(cl_uint));
382  cl::Buffer visitCountCL;
383  if (collectStatistics)
384  {
385  visitCounts.resize(query.cols());
386  visitCountCL = cl::Buffer(context, CL_MEM_READ_ONLY | CL_MEM_USE_HOST_PTR, visitCountCLSize, &visitCounts[0]);
387  knnKernel.setArg(11, sizeof(cl_mem), &visitCountCL);
388  }
389 
390  // execute query
391  queue.enqueueNDRangeKernel(knnKernel, cl::NullRange, cl::NDRange(query.cols()), cl::NullRange);
392  queue.enqueueMapBuffer(indicesCL, true, CL_MAP_READ, 0, indicesCLSize, 0, 0);
393  queue.enqueueMapBuffer(dists2CL, true, CL_MAP_READ, 0, dists2CLSize, 0, 0);
394  if (collectStatistics)
395  queue.enqueueMapBuffer(visitCountCL, true, CL_MAP_READ, 0, visitCountCLSize, 0, 0);
396  queue.finish();
397 
398  // if required, collect statistics
399  if (collectStatistics)
400  {
401  unsigned long totalVisitCounts(0);
402  for (size_t i = 0; i < visitCounts.size(); ++i)
403  totalVisitCounts += (unsigned long)visitCounts[i];
404  return totalVisitCounts;
405  }
406  else
407  return 0;
408  }
409 
410  template<typename T, typename CloudType>
411  BruteForceSearchOpenCL<T, CloudType>::BruteForceSearchOpenCL(const CloudType& cloud, const Index dim, const unsigned creationOptionFlags, const cl_device_type deviceType):
412  OpenCLSearch<T, CloudType>::OpenCLSearch(cloud, dim, creationOptionFlags, deviceType)
413  {
414 #ifdef EIGEN3_API
415  const_cast<Vector&>(this->minBound) = cloud.topRows(this->dim).rowwise().minCoeff();
416  const_cast<Vector&>(this->maxBound) = cloud.topRows(this->dim).rowwise().maxCoeff();
417 #else // EIGEN3_API
418  // compute bounds
419  for (int i = 0; i < cloud.cols(); ++i)
420  {
421  const Vector& v(cloud.block(0,i,this->dim,1));
422  const_cast<Vector&>(this->minBound) = this->minBound.cwise().min(v);
423  const_cast<Vector&>(this->maxBound) = this->maxBound.cwise().max(v);
424  }
425 #endif // EIGEN3_API
426  // init openCL
427  initOpenCL("knn_bf.cl", "knnBruteForce");
428  }
429 
430  template struct BruteForceSearchOpenCL<float>;
431  template struct BruteForceSearchOpenCL<double>;
432  template struct BruteForceSearchOpenCL<float, Eigen::Matrix3Xf>;
433  template struct BruteForceSearchOpenCL<double, Eigen::Matrix3Xd>;
434  template struct BruteForceSearchOpenCL<float, Eigen::Map<const Eigen::Matrix3Xf, Eigen::Aligned> >;
435  template struct BruteForceSearchOpenCL<double, Eigen::Map<const Eigen::Matrix3Xd, Eigen::Aligned> >;
436 
437 
438 
439  template<typename T, typename CloudType>
440  size_t KDTreeBalancedPtInLeavesStackOpenCL<T, CloudType>::getTreeSize(size_t elCount) const
441  {
442  // FIXME: 64 bits safe stuff, only work for 2^32 elements right now
443  assert(elCount > 0);
444  elCount --;
445  size_t count = 0;
446  int i = 31;
447  for (; i >= 0; --i)
448  {
449  if (elCount & (1 << i))
450  break;
451  }
452  for (int j = 0; j <= i; ++j)
453  count |= (1 << j);
454  count <<= 1;
455  count |= 1;
456  return count;
457  }
458 
459  template<typename T, typename CloudType>
460  size_t KDTreeBalancedPtInLeavesStackOpenCL<T, CloudType>::getTreeDepth(size_t elCount) const
461  {
462  if (elCount <= 1)
463  return 0;
464  elCount --;
465  size_t i = 31;
466  for (; i >= 0; --i)
467  {
468  if (elCount & (1 << i))
469  break;
470  }
471  return i+1;
472  }
473 
474  template<typename T, typename CloudType>
475  void KDTreeBalancedPtInLeavesStackOpenCL<T, CloudType>::buildNodes(const BuildPointsIt first, const BuildPointsIt last, const size_t pos, const Vector minValues, const Vector maxValues)
476  {
477  const size_t count(last - first);
478  //cerr << count << endl;
479  if (count == 1)
480  {
481  const int d = -2-(first->index);
482  assert(pos < nodes.size());
483  nodes[pos] = Node(d);
484  return;
485  }
486 
487  // find the largest dimension of the box
488  size_t cutDim = argMax<T, CloudType>(maxValues - minValues);
489 
490  // compute number of elements
491  const size_t rightCount(count/2);
492  const size_t leftCount(count - rightCount);
493  assert(last - rightCount == first + leftCount);
494 
495  // sort
496  nth_element(first, first + leftCount, last, CompareDim(cutDim));
497 
498  // set node
499  const T cutVal((first+leftCount)->pos.coeff(cutDim));
500  nodes[pos] = Node(cutDim, cutVal);
501 
502  //cerr << pos << " cutting on " << cutDim << " at " << (first+leftCount)->pos[cutDim] << endl;
503 
504  // update bounds for left
505  Vector leftMaxValues(maxValues);
506  leftMaxValues[cutDim] = cutVal;
507  // update bounds for right
508  Vector rightMinValues(minValues);
509  rightMinValues[cutDim] = cutVal;
510 
511  // recurse
512  buildNodes(first, first + leftCount, childLeft(pos), minValues, leftMaxValues);
513  buildNodes(first + leftCount, last, childRight(pos), rightMinValues, maxValues);
514  }
515 
516  template<typename T, typename CloudType>
517  KDTreeBalancedPtInLeavesStackOpenCL<T, CloudType>::KDTreeBalancedPtInLeavesStackOpenCL(const CloudType& cloud, const Index dim, const unsigned creationOptionFlags, const cl_device_type deviceType):
518  OpenCLSearch<T, CloudType>::OpenCLSearch(cloud, dim, creationOptionFlags, deviceType)
519  {
520  const bool collectStatistics(creationOptionFlags & NearestNeighbourSearch<T>::TOUCH_STATISTICS);
521 
522  // build point vector and compute bounds
523  BuildPoints buildPoints;
524  buildPoints.reserve(cloud.cols());
525  for (int i = 0; i < cloud.cols(); ++i)
526  {
527  const Vector& v(cloud.block(0,i,this->dim,1));
528  buildPoints.push_back(BuildPoint(v, i));
529 #ifdef EIGEN3_API
530  const_cast<Vector&>(minBound) = minBound.array().min(v.array());
531  const_cast<Vector&>(maxBound) = maxBound.array().max(v.array());
532 #else // EIGEN3_API
533  const_cast<Vector&>(minBound) = minBound.cwise().min(v);
534  const_cast<Vector&>(maxBound) = maxBound.cwise().max(v);
535 #endif // EIGEN3_API
536  }
537 
538  // create nodes
539  nodes.resize(getTreeSize(cloud.cols()));
540  buildNodes(buildPoints.begin(), buildPoints.end(), 0, minBound, maxBound);
541  const unsigned maxStackDepth(getTreeDepth(nodes.size()) + 1);
542 
543  // init openCL
544  initOpenCL("knn_kdtree_pt_in_leaves.cl", "knnKDTree", "#define MAX_STACK_DEPTH " + std::to_string(maxStackDepth) + "\n");
545 
546  // map nodes, for info about alignment, see sect 6.1.5
547  const size_t nodesCLSize(nodes.size() * sizeof(Node));
548  nodesCL = cl::Buffer(context, CL_MEM_READ_ONLY | CL_MEM_USE_HOST_PTR, nodesCLSize, &nodes[0]);
549  if (collectStatistics)
550  knnKernel.setArg(12, sizeof(cl_mem), &nodesCL);
551  else
552  knnKernel.setArg(11, sizeof(cl_mem), &nodesCL);
553  }
554 
555  template struct KDTreeBalancedPtInLeavesStackOpenCL<float>;
556  template struct KDTreeBalancedPtInLeavesStackOpenCL<double>;
557  template struct KDTreeBalancedPtInLeavesStackOpenCL<float, Eigen::Matrix3Xf>;
558  template struct KDTreeBalancedPtInLeavesStackOpenCL<double, Eigen::Matrix3Xd>;
559  template struct KDTreeBalancedPtInLeavesStackOpenCL<float, Eigen::Map<const Eigen::Matrix3Xf, Eigen::Aligned> >;
560  template struct KDTreeBalancedPtInLeavesStackOpenCL<double, Eigen::Map<const Eigen::Matrix3Xd, Eigen::Aligned> >;
561 
562 
563  template<typename T, typename CloudType>
564  size_t KDTreeBalancedPtInNodesStackOpenCL<T, CloudType>::getTreeSize(size_t elCount) const
565  {
566  // FIXME: 64 bits safe stuff, only work for 2^32 elements right now
567  size_t count = 0;
568  int i = 31;
569  for (; i >= 0; --i)
570  {
571  if (elCount & (1 << i))
572  break;
573  }
574  for (int j = 0; j <= i; ++j)
575  count |= (1 << j);
576  //cerr << "tree size " << count << " (" << elCount << " elements)\n";
577  return count;
578  }
579 
580  template<typename T, typename CloudType>
581  size_t KDTreeBalancedPtInNodesStackOpenCL<T, CloudType>::getTreeDepth(size_t elCount) const
582  {
583  // FIXME: 64 bits safe stuff, only work for 2^32 elements right now
584  int i = 31;
585  for (; i >= 0; --i)
586  {
587  if (elCount & (1 << i))
588  break;
589  }
590  return i + 1;
591  }
592 
593  template<typename T, typename CloudType>
594  void KDTreeBalancedPtInNodesStackOpenCL<T, CloudType>::buildNodes(const BuildPointsIt first, const BuildPointsIt last, const size_t pos, const Vector minValues, const Vector maxValues)
595  {
596  const size_t count(last - first);
597  //cerr << count << endl;
598  if (count == 1)
599  {
600  nodes[pos] = Node(-1, *first);
601  return;
602  }
603 
604  // find the largest dimension of the box
605  const size_t cutDim = argMax<T, CloudType>(maxValues - minValues);
606 
607  // compute number of elements
608  const size_t recurseCount(count-1);
609  const size_t rightCount(recurseCount/2);
610  const size_t leftCount(recurseCount-rightCount);
611  assert(last - rightCount == first + leftCount + 1);
612 
613  // sort
614  nth_element(first, first + leftCount, last, CompareDim(cloud, cutDim));
615 
616  // set node
617  const Index index(*(first+leftCount));
618  const T cutVal(cloud.coeff(cutDim, index));
619  nodes[pos] = Node(cutDim, index);
620 
621  //cerr << pos << " cutting on " << cutDim << " at " << (first+leftCount)->pos[cutDim] << endl;
622 
623  // update bounds for left
624  Vector leftMaxValues(maxValues);
625  leftMaxValues[cutDim] = cutVal;
626  // update bounds for right
627  Vector rightMinValues(minValues);
628  rightMinValues[cutDim] = cutVal;
629 
630  // recurse
631  if (count > 2)
632  {
633  buildNodes(first, first + leftCount, childLeft(pos), minValues, leftMaxValues);
634  buildNodes(first + leftCount + 1, last, childRight(pos), rightMinValues, maxValues);
635  }
636  else
637  {
638  nodes[childLeft(pos)] = Node(-1, *first);
639  nodes[childRight(pos)] = Node(-2, 0);
640  }
641  }
642 
643  template<typename T, typename CloudType>
644  KDTreeBalancedPtInNodesStackOpenCL<T, CloudType>::KDTreeBalancedPtInNodesStackOpenCL(const CloudType& cloud, const Index dim, const unsigned creationOptionFlags, const cl_device_type deviceType):
645  OpenCLSearch<T, CloudType>::OpenCLSearch(cloud, dim, creationOptionFlags, deviceType)
646  {
647  const bool collectStatistics(creationOptionFlags & NearestNeighbourSearch<T, CloudType>::TOUCH_STATISTICS);
648 
649  // build point vector and compute bounds
650  BuildPoints buildPoints;
651  buildPoints.reserve(cloud.cols());
652  for (int i = 0; i < cloud.cols(); ++i)
653  {
654  buildPoints.push_back(i);
655  const Vector& v(cloud.block(0,i,this->dim,1));
656 #ifdef EIGEN3_API
657  const_cast<Vector&>(minBound) = minBound.array().min(v.array());
658  const_cast<Vector&>(maxBound) = maxBound.array().max(v.array());
659 #else // EIGEN3_API
660  const_cast<Vector&>(minBound) = minBound.cwise().min(v);
661  const_cast<Vector&>(maxBound) = maxBound.cwise().max(v);
662 #endif // EIGEN3_API
663  }
664 
665  // create nodes
666  nodes.resize(getTreeSize(cloud.cols()));
667  buildNodes(buildPoints.begin(), buildPoints.end(), 0, minBound, maxBound);
668  const unsigned maxStackDepth(getTreeDepth(nodes.size()) + 1);
669 
670  // init openCL
671  initOpenCL("knn_kdtree_pt_in_nodes.cl", "knnKDTree", "#define MAX_STACK_DEPTH " + std::to_string(maxStackDepth) + "\n");
672 
673  // map nodes, for info about alignment, see sect 6.1.5
674  const size_t nodesCLSize(nodes.size() * sizeof(Node));
675  nodesCL = cl::Buffer(context, CL_MEM_READ_ONLY | CL_MEM_USE_HOST_PTR, nodesCLSize, &nodes[0]);
676  if (collectStatistics)
677  knnKernel.setArg(12, sizeof(cl_mem), &nodesCL);
678  else
679  knnKernel.setArg(11, sizeof(cl_mem), &nodesCL);
680  }
681 
682  template struct KDTreeBalancedPtInNodesStackOpenCL<float>;
683  template struct KDTreeBalancedPtInNodesStackOpenCL<double>;
684  template struct KDTreeBalancedPtInNodesStackOpenCL<float, Eigen::Matrix3Xf>;
685  template struct KDTreeBalancedPtInNodesStackOpenCL<double, Eigen::Matrix3Xd>;
686  template struct KDTreeBalancedPtInNodesStackOpenCL<float, Eigen::Map<const Eigen::Matrix3Xf, Eigen::Aligned> >;
687  template struct KDTreeBalancedPtInNodesStackOpenCL<double, Eigen::Map<const Eigen::Matrix3Xd, Eigen::Aligned> >;
688 
690 }
691 
692 #endif // HAVE_OPENCL
693 /* vim: set ts=8 sw=8 tw=0 noexpandtab cindent softtabstop=8 :*/
Nabo
Namespace for Nabo.
Definition: experimental/kdtree_cpu.cpp:40
index_heap.h
implementation of index heaps
nabo_private.h
header for implementation
Nabo::argMax
size_t argMax(const typename NearestNeighbourSearch< T, CloudType >::Vector &v)
Return the index of the maximum value of a vector of positive values.
Definition: experimental/kdtree_cpu.cpp:45
Nabo::NearestNeighbourSearch< T, Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic > >::maxBound
const Vector maxBound
the high bound of the search space (axis-aligned bounding box)
Definition: nabo.h:282
s
XmlRpcServer s
Index
NNSNabo::Index Index
Definition: python/nabo.cpp:11
Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt::nodes
Nodes nodes
search nodes
Definition: nabo_private.h:174
Nabo::NearestNeighbourSearch< T, Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic > >::cloud
const CloudType & cloud
the reference to the data-point cloud, which must remain valid during the lifetime of the NearestNeig...
Definition: nabo.h:274
Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt::Vector
NearestNeighbourSearch< T, CloudType >::Vector Vector
Definition: nabo_private.h:96
Nabo::NearestNeighbourSearch< T, Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic > >::creationOptionFlags
const unsigned creationOptionFlags
creation options
Definition: nabo.h:278
Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt::BuildPoints
std::vector< Index > BuildPoints
indices of points during kd-tree construction
Definition: nabo_private.h:111
testing::internal::string
::std::string string
Definition: gtest.h:1979
PointMatcherSupport::contains
bool contains(const M &m, const typename M::key_type &k)
Definition: Bibliography.cpp:50
d
d
Nabo::NearestNeighbourSearch< T, Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic > >::minBound
const Vector minBound
the low bound of the search space (axis-aligned bounding box)
Definition: nabo.h:280
std
PointMatcherSupport::get
const M::mapped_type & get(const M &m, const typename M::key_type &k)
Definition: Bibliography.cpp:57
Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt::buildNodes
unsigned buildNodes(const BuildPointsIt first, const BuildPointsIt last, const Vector minValues, const Vector maxValues)
construct nodes for points [first..last[ inside the hyperrectangle [minValues..maxValues]
Definition: nabo/kdtree_cpu.cpp:110
NearestNeighbourSearch
Definition: python/nabo.cpp:95

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autogenerated on Fri Dec 20 2024 03:45:59