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gtsam/examples/SolverComparer.cpp

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file SolverComparer.cpp
* @brief Incremental and batch solving, timing, and accuracy comparisons
* @author Richard Roberts
* @date August, 2013
*
* Here is an example. Below, to run in batch mode, we first generate an initialization in incremental mode.
*
* Solve in incremental and write to file w_inc:
* ./SolverComparer --incremental -d w10000 -o w_inc
*
* You can then perturb that initialization to get batch something to optimize.
* Read in w_inc, perturb it with noise of stddev 0.6, and write to w_pert:
* ./SolverComparer --perturb 0.6 -i w_inc -o w_pert
*
* Then optimize with batch, read in w_pert, solve in batch, and write to w_batch:
* ./SolverComparer --batch -d w10000 -i w_pert -o w_batch
*
* And finally compare solutions in w_inc and w_batch to check that batch converged to the global minimum
* ./SolverComparer --compare w_inc w_batch
*
*/
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/sam/BearingRangeFactor.h>
#include <gtsam/slam/dataset.h>
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/geometry/Pose2.h>
#include <gtsam/nonlinear/ISAM2.h>
#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
#include <gtsam/nonlinear/Marginals.h>
#include <gtsam/linear/GaussianJunctionTree.h>
#include <gtsam/linear/GaussianEliminationTree.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/base/timing.h>
#include <gtsam/base/treeTraversal-inst.h>
#include <gtsam/config.h> // for GTSAM_USE_TBB
#include <boost/archive/binary_iarchive.hpp>
#include <boost/archive/binary_oarchive.hpp>
#include <boost/program_options.hpp>
#include <boost/range/algorithm/set_algorithm.hpp>
#include <boost/range/adaptor/reversed.hpp>
#include <boost/random.hpp>
#include <boost/serialization/export.hpp>
#include <fstream>
#include <iostream>
#ifdef GTSAM_USE_TBB
#include <tbb/tbb.h>
#undef max // TBB seems to include windows.h and we don't want these macros
#undef min
#endif
using namespace std;
using namespace gtsam;
using namespace gtsam::symbol_shorthand;
namespace po = boost::program_options;
namespace br = boost::range;
typedef Pose2 Pose;
typedef NoiseModelFactor1<Pose> NM1;
typedef NoiseModelFactor2<Pose,Pose> NM2;
typedef BearingRangeFactor<Pose,Point2> BR;
double chi2_red(const gtsam::NonlinearFactorGraph& graph, const gtsam::Values& config) {
// Compute degrees of freedom (observations - variables)
// In ocaml, +1 was added to the observations to account for the prior, but
// the factor graph already includes a factor for the prior/equality constraint.
// double dof = graph.size() - config.size();
int graph_dim = 0;
for(const boost::shared_ptr<gtsam::NonlinearFactor>& nlf: graph) {
graph_dim += (int)nlf->dim();
}
double dof = double(graph_dim) - double(config.dim()); // kaess: changed to dim
return 2. * graph.error(config) / dof; // kaess: added factor 2, graph.error returns half of actual error
}
// Global variables (these are only set once at program startup and never modified after)
string outputFile;
string inputFile;
string datasetName;
int firstStep;
int lastStep;
int nThreads;
int relinSkip;
bool incremental;
bool dogleg;
bool batch;
bool compare;
bool perturb;
bool stats;
double perturbationNoise;
string compareFile1, compareFile2;
Values initial;
NonlinearFactorGraph datasetMeasurements;
// Run functions for each mode
void runIncremental();
void runBatch();
void runCompare();
void runPerturb();
void runStats();
/* ************************************************************************* */
int main(int argc, char *argv[]) {
po::options_description desc("Available options");
desc.add_options()
("help", "Print help message")
("write-solution,o", po::value<string>(&outputFile)->default_value(""), "Write graph and solution to the specified file")
("read-solution,i", po::value<string>(&inputFile)->default_value(""), "Read graph and solution from the specified file")
("dataset,d", po::value<string>(&datasetName)->default_value(""), "Read a dataset file (if and only if --incremental is used)")
("first-step,f", po::value<int>(&firstStep)->default_value(0), "First step to process from the dataset file")
("last-step,l", po::value<int>(&lastStep)->default_value(-1), "Last step to process, or -1 to process until the end of the dataset")
("threads", po::value<int>(&nThreads)->default_value(-1), "Number of threads, or -1 to use all processors")
("relinSkip", po::value<int>(&relinSkip)->default_value(10), "Fluid relinearization check every arg steps")
("incremental", "Run in incremental mode using ISAM2 (default)")
("dogleg", "When in incremental mode, solve with Dogleg instead of Gauss-Newton in iSAM2")
("batch", "Run in batch mode, requires an initialization from --read-solution")
("compare", po::value<vector<string> >()->multitoken(), "Compare two solution files")
("perturb", po::value<double>(&perturbationNoise), "Perturb a solution file with the specified noise")
("stats", "Gather factorization statistics about the dataset, writes text-file histograms")
;
po::variables_map vm;
po::store(po::command_line_parser(argc, argv).options(desc).run(), vm);
po::notify(vm);
batch = (vm.count("batch") > 0);
compare = (vm.count("compare") > 0);
perturb = (vm.count("perturb") > 0);
stats = (vm.count("stats") > 0);
const int modesSpecified = int(batch) + int(compare) + int(perturb) + int(stats);
incremental = (vm.count("incremental") > 0 || modesSpecified == 0);
dogleg = (vm.count("dogleg") > 0);
if(compare) {
const vector<string>& compareFiles = vm["compare"].as<vector<string> >();
if(compareFiles.size() != 2) {
cout << "Must specify two files with --compare";
exit(1);
}
compareFile1 = compareFiles[0];
compareFile2 = compareFiles[1];
}
if(modesSpecified > 1) {
cout << "Only one of --incremental, --batch, --compare, --perturb, and --stats may be specified\n" << desc << endl;
exit(1);
}
if((incremental || batch) && datasetName.empty()) {
cout << "In incremental and batch modes, a dataset must be specified\n" << desc << endl;
exit(1);
}
if(!(incremental || batch || stats) && !datasetName.empty()) {
cout << "A dataset may only be specified in incremental or batch modes\n" << desc << endl;
exit(1);
}
if(batch && inputFile.empty()) {
cout << "In batch model, an input file must be specified\n" << desc << endl;
exit(1);
}
if(perturb && (inputFile.empty() || outputFile.empty())) {
cout << "In perturb mode, specify input and output files\n" << desc << endl;
exit(1);
}
if(stats && (datasetName.empty() || inputFile.empty())) {
cout << "In stats mode, specify dataset and input file\n" << desc << endl;
exit(1);
}
// Read input file
if(!inputFile.empty())
{
cout << "Reading input file " << inputFile << endl;
std::ifstream readerStream(inputFile.c_str(), ios::binary);
boost::archive::binary_iarchive reader(readerStream);
reader >> initial;
}
// Read dataset
if(!datasetName.empty())
{
cout << "Loading dataset " << datasetName << endl;
try {
string datasetFile = findExampleDataFile(datasetName);
std::pair<NonlinearFactorGraph::shared_ptr, Values::shared_ptr> data =
load2D(datasetFile);
datasetMeasurements = *data.first;
} catch(std::exception& e) {
cout << e.what() << endl;
exit(1);
}
}
#ifdef GTSAM_USE_TBB
std::auto_ptr<tbb::task_scheduler_init> init;
if(nThreads > 0) {
cout << "Using " << nThreads << " threads" << endl;
init.reset(new tbb::task_scheduler_init(nThreads));
} else
cout << "Using threads for all processors" << endl;
#else
if(nThreads > 0) {
std::cout << "GTSAM is not compiled with TBB, so threading is disabled and the --threads option cannot be used." << endl;
exit(1);
}
#endif
// Run mode
if(incremental)
runIncremental();
else if(batch)
runBatch();
else if(compare)
runCompare();
else if(perturb)
runPerturb();
else if(stats)
runStats();
return 0;
}
/* ************************************************************************* */
void runIncremental()
{
ISAM2Params params;
if(dogleg)
params.optimizationParams = ISAM2DoglegParams();
params.relinearizeSkip = relinSkip;
params.enablePartialRelinearizationCheck = true;
ISAM2 isam2(params);
// Look for the first measurement to use
cout << "Looking for first measurement from step " << firstStep << endl;
size_t nextMeasurement = 0;
bool havePreviousPose = false;
Key firstPose;
while(nextMeasurement < datasetMeasurements.size())
{
if(BetweenFactor<Pose>::shared_ptr measurement =
boost::dynamic_pointer_cast<BetweenFactor<Pose> >(datasetMeasurements[nextMeasurement]))
{
Key key1 = measurement->key1(), key2 = measurement->key2();
if(((int)key1 >= firstStep && key1 < key2) || ((int)key2 >= firstStep && key2 < key1)) {
// We found an odometry starting at firstStep
firstPose = std::min(key1, key2);
break;
}
if(((int)key2 >= firstStep && key1 < key2) || ((int)key1 >= firstStep && key2 < key1)) {
// We found an odometry joining firstStep with a previous pose
havePreviousPose = true;
firstPose = std::max(key1, key2);
break;
}
}
++ nextMeasurement;
}
if(nextMeasurement == datasetMeasurements.size()) {
cout << "The supplied first step is past the end of the dataset" << endl;
exit(1);
}
// If we didn't find an odometry linking to a previous pose, create a first pose and a prior
if(!havePreviousPose) {
cout << "Looks like " << firstPose << " is the first time step, so adding a prior on it" << endl;
NonlinearFactorGraph newFactors;
Values newVariables;
newFactors.push_back(boost::make_shared<PriorFactor<Pose> >(firstPose, Pose(), noiseModel::Unit::Create(3)));
newVariables.insert(firstPose, Pose());
isam2.update(newFactors, newVariables);
}
cout << "Playing forward time steps..." << endl;
for (size_t step = firstPose;
nextMeasurement < datasetMeasurements.size() && (lastStep == -1 || (int)step <= lastStep);
++step)
{
Values newVariables;
NonlinearFactorGraph newFactors;
// Collect measurements and new variables for the current step
gttic_(Collect_measurements);
while(nextMeasurement < datasetMeasurements.size()) {
NonlinearFactor::shared_ptr measurementf = datasetMeasurements[nextMeasurement];
if(BetweenFactor<Pose>::shared_ptr measurement =
boost::dynamic_pointer_cast<BetweenFactor<Pose> >(measurementf))
{
// Stop collecting measurements that are for future steps
if(measurement->key1() > step || measurement->key2() > step)
break;
// Require that one of the nodes is the current one
if(measurement->key1() != step && measurement->key2() != step)
throw runtime_error("Problem in data file, out-of-sequence measurements");
// Add a new factor
newFactors.push_back(measurement);
// Initialize the new variable
if(measurement->key1() > measurement->key2()) {
if(!newVariables.exists(measurement->key1())) { // Only need to check newVariables since loop closures come after odometry
if(step == 1)
newVariables.insert(measurement->key1(), measurement->measured().inverse());
else {
Pose prevPose = isam2.calculateEstimate<Pose>(measurement->key2());
newVariables.insert(measurement->key1(), prevPose * measurement->measured().inverse());
}
}
} else {
if(!newVariables.exists(measurement->key2())) { // Only need to check newVariables since loop closures come after odometry
if(step == 1)
newVariables.insert(measurement->key2(), measurement->measured());
else {
Pose prevPose = isam2.calculateEstimate<Pose>(measurement->key1());
newVariables.insert(measurement->key2(), prevPose * measurement->measured());
}
}
}
}
else if(BearingRangeFactor<Pose, Point2>::shared_ptr measurement =
boost::dynamic_pointer_cast<BearingRangeFactor<Pose, Point2> >(measurementf))
{
Key poseKey = measurement->keys()[0], lmKey = measurement->keys()[1];
// Stop collecting measurements that are for future steps
if(poseKey > step)
throw runtime_error("Problem in data file, out-of-sequence measurements");
// Add new factor
newFactors.push_back(measurement);
// Initialize new landmark
if(!isam2.getLinearizationPoint().exists(lmKey))
{
Pose pose;
if(isam2.getLinearizationPoint().exists(poseKey))
pose = isam2.calculateEstimate<Pose>(poseKey);
else
pose = newVariables.at<Pose>(poseKey);
Rot2 measuredBearing = measurement->measured().first;
double measuredRange = measurement->measured().second;
newVariables.insert(lmKey,
pose.transform_from(measuredBearing.rotate(Point2(measuredRange, 0.0))));
}
}
else
{
throw std::runtime_error("Unknown factor type read from data file");
}
++ nextMeasurement;
}
gttoc_(Collect_measurements);
// Update iSAM2
try {
gttic_(Update_ISAM2);
isam2.update(newFactors, newVariables);
gttoc_(Update_ISAM2);
} catch(std::exception& e) {
cout << e.what() << endl;
exit(1);
}
if((step - firstPose) % 1000 == 0) {
try {
gttic_(chi2);
Values estimate(isam2.calculateEstimate());
double chi2 = chi2_red(isam2.getFactorsUnsafe(), estimate);
cout << "chi2 = " << chi2 << endl;
gttoc_(chi2);
} catch(std::exception& e) {
cout << e.what() << endl;
exit(1);
}
}
tictoc_finishedIteration_();
if((step - firstPose) % 1000 == 0) {
cout << "Step " << step << endl;
tictoc_print_();
}
}
if(!outputFile.empty())
{
try {
cout << "Writing output file " << outputFile << endl;
std::ofstream writerStream(outputFile.c_str(), ios::binary);
boost::archive::binary_oarchive writer(writerStream);
Values estimates = isam2.calculateEstimate();
writer << estimates;
} catch(std::exception& e) {
cout << e.what() << endl;
exit(1);
}
}
tictoc_print_();
// Compute marginals
//try {
// Marginals marginals(graph, values);
// int i=0;
// for (Key key1: boost::adaptors::reverse(values.keys())) {
// int j=0;
// for (Key key12: boost::adaptors::reverse(values.keys())) {
// if(i != j) {
// gttic_(jointMarginalInformation);
// std::vector<Key> keys(2);
// keys[0] = key1;
// keys[1] = key2;
// JointMarginal info = marginals.jointMarginalInformation(keys);
// gttoc_(jointMarginalInformation);
// tictoc_finishedIteration_();
// }
// ++j;
// if(j >= 50)
// break;
// }
// ++i;
// if(i >= 50)
// break;
// }
// tictoc_print_();
// for(Key key: values.keys()) {
// gttic_(marginalInformation);
// Matrix info = marginals.marginalInformation(key);
// gttoc_(marginalInformation);
// tictoc_finishedIteration_();
// ++i;
// }
//} catch(std::exception& e) {
// cout << e.what() << endl;
//}
//tictoc_print_();
}
/* ************************************************************************* */
void runBatch()
{
cout << "Creating batch optimizer..." << endl;
NonlinearFactorGraph measurements = datasetMeasurements;
measurements.push_back(boost::make_shared<PriorFactor<Pose> >(0, Pose(), noiseModel::Unit::Create(3)));
gttic_(Create_optimizer);
GaussNewtonParams params;
params.linearSolverType = NonlinearOptimizerParams::MULTIFRONTAL_CHOLESKY;
GaussNewtonOptimizer optimizer(measurements, initial, params);
gttoc_(Create_optimizer);
double lastError;
do {
lastError = optimizer.error();
gttic_(Iterate_optimizer);
optimizer.iterate();
gttoc_(Iterate_optimizer);
cout << "Error: " << lastError << " -> " << optimizer.error() /*<< ", lambda: " << optimizer.lambda()*/ << endl;
gttic_(chi2);
double chi2 = chi2_red(measurements, optimizer.values());
cout << "chi2 = " << chi2 << endl;
gttoc_(chi2);
} while(!checkConvergence(optimizer.params().relativeErrorTol,
optimizer.params().absoluteErrorTol, optimizer.params().errorTol,
lastError, optimizer.error(), optimizer.params().verbosity));
tictoc_finishedIteration_();
tictoc_print_();
if(!outputFile.empty())
{
try {
cout << "Writing output file " << outputFile << endl;
std::ofstream writerStream(outputFile.c_str(), ios::binary);
boost::archive::binary_oarchive writer(writerStream);
writer << optimizer.values();
} catch(std::exception& e) {
cout << e.what() << endl;
exit(1);
}
}
}
/* ************************************************************************* */
void runCompare()
{
Values soln1, soln2;
cout << "Reading solution file " << compareFile1 << endl;
{
std::ifstream readerStream(compareFile1.c_str(), ios::binary);
boost::archive::binary_iarchive reader(readerStream);
reader >> soln1;
}
cout << "Reading solution file " << compareFile2 << endl;
{
std::ifstream readerStream(compareFile2.c_str(), ios::binary);
boost::archive::binary_iarchive reader(readerStream);
reader >> soln2;
}
// Check solution for equality
cout << "Comparing solutions..." << endl;
vector<Key> missingKeys;
br::set_symmetric_difference(soln1.keys(), soln2.keys(), std::back_inserter(missingKeys));
if(!missingKeys.empty()) {
cout << " Keys unique to one solution file: ";
for(size_t i = 0; i < missingKeys.size(); ++i) {
cout << DefaultKeyFormatter(missingKeys[i]);
if(i != missingKeys.size() - 1)
cout << ", ";
}
cout << endl;
}
vector<Key> commonKeys;
br::set_intersection(soln1.keys(), soln2.keys(), std::back_inserter(commonKeys));
double maxDiff = 0.0;
for(Key j: commonKeys)
maxDiff = std::max(maxDiff, soln1.at(j).localCoordinates_(soln2.at(j)).norm());
cout << " Maximum solution difference (norm of logmap): " << maxDiff << endl;
}
/* ************************************************************************* */
void runPerturb()
{
// Set up random number generator
boost::mt19937 rng;
boost::normal_distribution<double> normal(0.0, perturbationNoise);
// Perturb values
VectorValues noise;
for(const Values::KeyValuePair& key_val: initial)
{
Vector noisev(key_val.value.dim());
for(Vector::Index i = 0; i < noisev.size(); ++i)
noisev(i) = normal(rng);
noise.insert(key_val.key, noisev);
}
Values perturbed = initial.retract(noise);
// Write results
try {
cout << "Writing output file " << outputFile << endl;
std::ofstream writerStream(outputFile.c_str(), ios::binary);
boost::archive::binary_oarchive writer(writerStream);
writer << perturbed;
} catch(std::exception& e) {
cout << e.what() << endl;
exit(1);
}
}
/* ************************************************************************* */
void runStats()
{
cout << "Gathering statistics..." << endl;
GaussianFactorGraph linear = *datasetMeasurements.linearize(initial);
GaussianJunctionTree jt(GaussianEliminationTree(linear, Ordering::Colamd(linear)));
treeTraversal::ForestStatistics statistics = treeTraversal::GatherStatistics(jt);
ofstream file;
cout << "Writing SolverComparer_Stats_problemSizeHistogram.txt..." << endl;
file.open("SolverComparer_Stats_problemSizeHistogram.txt");
treeTraversal::ForestStatistics::Write(file, statistics.problemSizeHistogram);
file.close();
cout << "Writing SolverComparer_Stats_numberOfChildrenHistogram.txt..." << endl;
file.open("SolverComparer_Stats_numberOfChildrenHistogram.txt");
treeTraversal::ForestStatistics::Write(file, statistics.numberOfChildrenHistogram);
file.close();
cout << "Writing SolverComparer_Stats_problemSizeOfSecondLargestChildHistogram.txt..." << endl;
file.open("SolverComparer_Stats_problemSizeOfSecondLargestChildHistogram.txt");
treeTraversal::ForestStatistics::Write(file, statistics.problemSizeOfSecondLargestChildHistogram);
file.close();
}
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