From 89b50e7679fb09ff6c859e930b13df6d61537ffc Mon Sep 17 00:00:00 2001 From: Richard Roberts Date: Tue, 2 Oct 2012 20:18:41 +0000 Subject: [PATCH] Renamed tic -> gttic and toc -> gttoc to avoid conflict with PCL tic/toc --- examples/UGM_chain.cpp | 8 +- gtsam/base/Matrix.cpp | 16 +- gtsam/base/cholesky.cpp | 12 +- gtsam/base/tests/timeTest.cpp | 24 +- gtsam/base/tests/timeVirtual.cpp | 52 ++-- gtsam/base/tests/timeVirtual2.cpp | 40 +-- gtsam/base/timing.cpp | 8 +- gtsam/base/timing.h | 12 +- gtsam/discrete/DiscreteFactorGraph.cpp | 12 +- gtsam/discrete/DiscreteSequentialSolver.cpp | 8 +- gtsam/inference/EliminationTree-inl.h | 24 +- gtsam/inference/JunctionTree-inl.h | 32 +-- gtsam/linear/GaussianBayesNet.cpp | 20 +- gtsam/linear/GaussianBayesTree.cpp | 20 +- gtsam/linear/GaussianFactorGraph.cpp | 60 ++--- gtsam/linear/GaussianJunctionTree.cpp | 12 +- gtsam/linear/GaussianMultifrontalSolver.cpp | 4 +- gtsam/linear/GaussianSequentialSolver.cpp | 8 +- gtsam/linear/HessianFactor.cpp | 44 ++-- gtsam/linear/JacobianFactor.cpp | 12 +- gtsam/linear/NoiseModel.cpp | 16 +- gtsam/nonlinear/DoglegOptimizerImpl.h | 40 +-- gtsam/nonlinear/ISAM2-impl.cpp | 32 +-- gtsam/nonlinear/ISAM2.cpp | 228 +++++++++--------- .../nonlinear/LevenbergMarquardtOptimizer.cpp | 14 +- gtsam/nonlinear/NonlinearFactorGraph.cpp | 12 +- .../SuccessiveLinearizationOptimizer.cpp | 2 +- gtsam_unstable/discrete/Scheduler.cpp | 12 +- .../discrete/examples/schedulingExample.cpp | 8 +- .../discrete/examples/schedulingQuals12.cpp | 12 +- .../discrete/tests/testScheduler.cpp | 4 +- tests/timeBatch.cpp | 8 +- tests/timeIncremental.cpp | 20 +- 33 files changed, 418 insertions(+), 418 deletions(-) diff --git a/examples/UGM_chain.cpp b/examples/UGM_chain.cpp index 58e4c14dc..0461c1869 100644 --- a/examples/UGM_chain.cpp +++ b/examples/UGM_chain.cpp @@ -79,7 +79,7 @@ int main(int argc, char** argv) { cout << "\nComputing Node Marginals ..(Sequential Elimination)" << endl; - tic_(Sequential); + gttic_(Sequential); for (vector::iterator itr = nodes.begin(); itr != nodes.end(); ++itr) { //Compute the marginal @@ -89,14 +89,14 @@ int main(int argc, char** argv) { cout << "Node#" << setw(4) << itr->first << " : "; print(margProbs); } - toc_(Sequential); + gttoc_(Sequential); // Here we'll make use of DiscreteMarginals class, which makes use of // bayes-tree based shortcut evaluation of marginals DiscreteMarginals marginals(graph); cout << "\nComputing Node Marginals ..(BayesTree based)" << endl; - tic_(Multifrontal); + gttic_(Multifrontal); for (vector::iterator itr = nodes.begin(); itr != nodes.end(); ++itr) { //Compute the marginal @@ -106,7 +106,7 @@ int main(int argc, char** argv) { cout << "Node#" << setw(4) << itr->first << " : "; print(margProbs); } - toc_(Multifrontal); + gttoc_(Multifrontal); tictoc_print_(); return 0; diff --git a/gtsam/base/Matrix.cpp b/gtsam/base/Matrix.cpp index 8b0a64588..d555d70ab 100644 --- a/gtsam/base/Matrix.cpp +++ b/gtsam/base/Matrix.cpp @@ -410,17 +410,17 @@ void householder_(Matrix& A, size_t k, bool copy_vectors) { double beta = houseInPlace(vjm); // do outer product update A(j:m,:) = (I-beta vv')*A = A - v*(beta*A'*v)' = A - v*w' - tic(householder_update); // bottleneck for system + gttic(householder_update); // bottleneck for system // don't touch old columns Vector w = beta * A.block(j,j,m-j,n-j).transpose() * vjm; A.block(j,j,m-j,n-j) -= vjm * w.transpose(); - toc(householder_update); + gttoc(householder_update); // the Householder vector is copied in the zeroed out part if (copy_vectors) { - tic(householder_vector_copy); + gttic(householder_vector_copy); A.col(j).segment(j+1, m-(j+1)) = vjm.segment(1, m-(j+1)); - toc(householder_vector_copy); + gttoc(householder_vector_copy); } } // column j } @@ -428,14 +428,14 @@ void householder_(Matrix& A, size_t k, bool copy_vectors) { /* ************************************************************************* */ void householder(Matrix& A, size_t k) { // version with zeros below diagonal - tic(householder_); + gttic(householder_); householder_(A,k,false); - toc(householder_); -// tic(householder_zero_fill); + gttoc(householder_); +// gttic(householder_zero_fill); // const size_t m = A.rows(), n = A.cols(), kprime = min(k,min(m,n)); // for(size_t j=0; j < kprime; j++) // A.col(j).segment(j+1, m-(j+1)).setZero(); -// toc(householder_zero_fill); +// gttoc(householder_zero_fill); } /* ************************************************************************* */ diff --git a/gtsam/base/cholesky.cpp b/gtsam/base/cholesky.cpp index 939594c56..15a8a9208 100644 --- a/gtsam/base/cholesky.cpp +++ b/gtsam/base/cholesky.cpp @@ -131,30 +131,30 @@ bool choleskyPartial(Matrix& ABC, size_t nFrontal) { const size_t n = ABC.rows(); // Compute Cholesky factorization of A, overwrites A. - tic(lld); + gttic(lld); Eigen::LLT llt = ABC.block(0,0,nFrontal,nFrontal).selfadjointView().llt(); ABC.block(0,0,nFrontal,nFrontal).triangularView() = llt.matrixU(); - toc(lld); + gttoc(lld); if(debug) cout << "R:\n" << Eigen::MatrixXd(ABC.topLeftCorner(nFrontal,nFrontal).triangularView()) << endl; // Compute S = inv(R') * B - tic(compute_S); + gttic(compute_S); if(n - nFrontal > 0) { ABC.topLeftCorner(nFrontal,nFrontal).triangularView().transpose().solveInPlace( ABC.topRightCorner(nFrontal, n-nFrontal)); } if(debug) cout << "S:\n" << ABC.topRightCorner(nFrontal, n-nFrontal) << endl; - toc(compute_S); + gttoc(compute_S); // Compute L = C - S' * S - tic(compute_L); + gttic(compute_L); if(debug) cout << "C:\n" << Eigen::MatrixXd(ABC.bottomRightCorner(n-nFrontal,n-nFrontal).selfadjointView()) << endl; if(n - nFrontal > 0) ABC.bottomRightCorner(n-nFrontal,n-nFrontal).selfadjointView().rankUpdate( ABC.topRightCorner(nFrontal, n-nFrontal).transpose(), -1.0); if(debug) cout << "L:\n" << Eigen::MatrixXd(ABC.bottomRightCorner(n-nFrontal,n-nFrontal).selfadjointView()) << endl; - toc(compute_L); + gttoc(compute_L); // Check last diagonal element - Eigen does not check it bool ok; diff --git a/gtsam/base/tests/timeTest.cpp b/gtsam/base/tests/timeTest.cpp index 9c1a93c18..807e95508 100644 --- a/gtsam/base/tests/timeTest.cpp +++ b/gtsam/base/tests/timeTest.cpp @@ -21,23 +21,23 @@ int main(int argc, char *argv[]) { ticPush_("1", "top 1"); ticPush_("1", "sub 1"); - tic_("sub sub a"); - toc_("sub sub a"); + gttic_("sub sub a"); + gttoc_("sub sub a"); ticPop_("1", "sub 1"); ticPush_("2", "sub 2"); - tic_("sub sub b"); - toc_("sub sub b"); + gttic_("sub sub b"); + gttoc_("sub sub b"); ticPop_("2", "sub 2"); ticPop_("1", "top 1"); ticPush_("2", "top 2"); ticPush_("1", "sub 1"); - tic_("sub sub a"); - toc_("sub sub a"); + gttic_("sub sub a"); + gttoc_("sub sub a"); ticPop_("1", "sub 1"); ticPush_("2", "sub 2"); - tic_("sub sub b"); - toc_("sub sub b"); + gttic_("sub sub b"); + gttoc_("sub sub b"); ticPop_("2", "sub 2"); ticPop_("2", "top 2"); @@ -49,10 +49,10 @@ int main(int argc, char *argv[]) { } for(size_t i=0; i<1000000; ++i) { - tic(overhead_a); - tic(overhead_b); - toc(overhead_b); - toc(overhead_a); + gttic(overhead_a); + gttic(overhead_b); + gttoc(overhead_b); + gttoc(overhead_a); } tictoc_print_(); diff --git a/gtsam/base/tests/timeVirtual.cpp b/gtsam/base/tests/timeVirtual.cpp index 697a219b1..38bfcec33 100644 --- a/gtsam/base/tests/timeVirtual.cpp +++ b/gtsam/base/tests/timeVirtual.cpp @@ -59,95 +59,95 @@ int main(int argc, char *argv[]) { size_t trials = 10000000; - tic_("heap plain alloc, dealloc"); + gttic_("heap plain alloc, dealloc"); for(size_t i=0; i obj(new Plain(i)); } - toc_("shared plain alloc, dealloc"); + gttoc_("shared plain alloc, dealloc"); - tic_("shared virtual alloc, dealloc"); + gttic_("shared virtual alloc, dealloc"); for(size_t i=0; i obj(new Virtual(i)); } - toc_("shared virtual alloc, dealloc"); + gttoc_("shared virtual alloc, dealloc"); - tic_("heap plain alloc, dealloc, call"); + gttic_("heap plain alloc, dealloc, call"); for(size_t i=0; isetData(i+1); delete obj; } - toc_("heap plain alloc, dealloc, call"); + gttoc_("heap plain alloc, dealloc, call"); - tic_("heap virtual alloc, dealloc, call"); + gttic_("heap virtual alloc, dealloc, call"); for(size_t i=0; isetData(i+1); delete obj; } - toc_("heap virtual alloc, dealloc, call"); + gttoc_("heap virtual alloc, dealloc, call"); - tic_("stack plain alloc, dealloc, call"); + gttic_("stack plain alloc, dealloc, call"); for(size_t i=0; i obj(new Plain(i)); obj->setData(i+1); } - toc_("shared plain alloc, dealloc, call"); + gttoc_("shared plain alloc, dealloc, call"); - tic_("shared virtual alloc, dealloc, call"); + gttic_("shared virtual alloc, dealloc, call"); for(size_t i=0; i obj(new Virtual(i)); obj->setData(i+1); } - toc_("shared virtual alloc, dealloc, call"); + gttoc_("shared virtual alloc, dealloc, call"); - tic_("intrusive virtual alloc, dealloc, call"); + gttic_("intrusive virtual alloc, dealloc, call"); for(size_t i=0; i obj(new VirtualCounted(i)); obj->setData(i+1); } - toc_("intrusive virtual alloc, dealloc, call"); + gttoc_("intrusive virtual alloc, dealloc, call"); tictoc_print_(); diff --git a/gtsam/base/tests/timeVirtual2.cpp b/gtsam/base/tests/timeVirtual2.cpp index c36ed9979..8ca1a448d 100644 --- a/gtsam/base/tests/timeVirtual2.cpp +++ b/gtsam/base/tests/timeVirtual2.cpp @@ -84,51 +84,51 @@ int main(int argc, char *argv[]) { { VirtualBase** b = new VirtualBase*[n]; - tic_(Virtual); - tic_(new); + gttic_(Virtual); + gttic_(new); for(int i=0; imethod(); - toc_(method); - tic_(dynamic_cast); + gttoc_(method); + gttic_(dynamic_cast); for(int i=0; i(b[i]); if(d) d->method(); } - toc_(dynamic_cast); - tic_(delete); + gttoc_(dynamic_cast); + gttic_(delete); for(int i=0; imethod(); - toc_(method); - tic_(dynamic_cast (does nothing)); + gttoc_(method); + gttic_(dynamic_cast (does nothing)); for(int i=0; imethod(); - toc_(dynamic_cast (does nothing)); - tic_(delete); + gttoc_(dynamic_cast (does nothing)); + gttic_(delete); for(int i=0; i node = timingCurrent.lock()->child(id, label, timingCurrent); timingCurrent = node; node->ticInternal(); @@ -203,18 +203,18 @@ void TimingOutline::finishedIteration() { /* ************************************************************************* */ void tocInternal(size_t id, const char *label) { if(ISDEBUG("timing-verbose")) - std::cout << "toc(" << id << ", " << label << ")" << std::endl; + std::cout << "gttoc(" << id << ", " << label << ")" << std::endl; boost::shared_ptr current(timingCurrent.lock()); if(id != current->myId_) { timingRoot->print(); throw std::invalid_argument( - (boost::format("gtsam timing: Mismatched tic/toc: toc(\"%s\") called when last tic was \"%s\".") % + (boost::format("gtsam timing: Mismatched tic/toc: gttoc(\"%s\") called when last tic was \"%s\".") % label % current->label_).str()); } if(!current->parent_.lock()) { timingRoot->print(); throw std::invalid_argument( - (boost::format("gtsam timing: Mismatched tic/toc: extra toc(\"%s\"), already at the root") % + (boost::format("gtsam timing: Mismatched tic/toc: extra gttoc(\"%s\"), already at the root") % label).str()); } current->tocInternal(); diff --git a/gtsam/base/timing.h b/gtsam/base/timing.h index 62a2706e5..aeb127252 100644 --- a/gtsam/base/timing.h +++ b/gtsam/base/timing.h @@ -108,10 +108,10 @@ inline void tictoc_print2_() { } // Tic and toc functions using a string label -#define tic_(label) \ +#define gttic_(label) \ static const size_t label##_id_tic = ::gtsam::internal::getTicTocID(#label); \ ::gtsam::internal::AutoTicToc label##_obj = ::gtsam::internal::AutoTicToc(label##_id_tic, #label) -#define toc_(label) \ +#define gttoc_(label) \ label##_obj.stop() #define longtic_(label) \ static const size_t label##_id_tic = ::gtsam::internal::getTicTocID(#label); \ @@ -121,15 +121,15 @@ inline void tictoc_print2_() { ::gtsam::internal::tocInternal(label##_id_toc, #label) #ifdef ENABLE_TIMING -#define tic(label) tic_(label) -#define toc(label) toc_(label) +#define gttic(label) gttic_(label) +#define gttoc(label) gttoc_(label) #define longtic(label) longtic_(label) #define longtoc(label) longtoc_(label) #define tictoc_finishedIteration tictoc_finishedIteration_ #define tictoc_print tictoc_print_ #else -#define tic(label) ((void)0) -#define toc(label) ((void)0) +#define gttic(label) ((void)0) +#define gttoc(label) ((void)0) #define longtic(label) ((void)0) #define longtoc(label) ((void)0) inline void tictoc_finishedIteration() {} diff --git a/gtsam/discrete/DiscreteFactorGraph.cpp b/gtsam/discrete/DiscreteFactorGraph.cpp index d71939b00..d7e615606 100644 --- a/gtsam/discrete/DiscreteFactorGraph.cpp +++ b/gtsam/discrete/DiscreteFactorGraph.cpp @@ -98,23 +98,23 @@ namespace gtsam { EliminateDiscrete(const FactorGraph& factors, size_t num) { // PRODUCT: multiply all factors - tic(product); + gttic(product); DecisionTreeFactor product; BOOST_FOREACH(const DiscreteFactor::shared_ptr& factor, factors){ product = (*factor) * product; } - toc(product); + gttoc(product); // sum out frontals, this is the factor on the separator - tic(sum); + gttic(sum); DecisionTreeFactor::shared_ptr sum = product.sum(num); - toc(sum); + gttoc(sum); // now divide product/sum to get conditional - tic(divide); + gttic(divide); DiscreteConditional::shared_ptr cond(new DiscreteConditional(product, *sum)); - toc(divide); + gttoc(divide); return std::make_pair(cond, sum); } diff --git a/gtsam/discrete/DiscreteSequentialSolver.cpp b/gtsam/discrete/DiscreteSequentialSolver.cpp index b19b71dd5..6aeab58e3 100644 --- a/gtsam/discrete/DiscreteSequentialSolver.cpp +++ b/gtsam/discrete/DiscreteSequentialSolver.cpp @@ -35,18 +35,18 @@ namespace gtsam { "DiscreteSequentialSolver, elimination tree "); // Eliminate using the elimination tree - tic(eliminate); + gttic(eliminate); DiscreteBayesNet::shared_ptr bayesNet = eliminate(); - toc(eliminate); + gttoc(eliminate); if (debug) bayesNet->print("DiscreteSequentialSolver, Bayes net "); // Allocate the solution vector if it is not already allocated // Back-substitute - tic(optimize); + gttic(optimize); DiscreteFactor::sharedValues solution = gtsam::optimize(*bayesNet); - toc(optimize); + gttoc(optimize); if (debug) solution->print("DiscreteSequentialSolver, solution "); diff --git a/gtsam/inference/EliminationTree-inl.h b/gtsam/inference/EliminationTree-inl.h index c7ef125e0..fcbc66bd1 100644 --- a/gtsam/inference/EliminationTree-inl.h +++ b/gtsam/inference/EliminationTree-inl.h @@ -115,10 +115,10 @@ typename EliminationTree::shared_ptr EliminationTree::Create( static const bool debug = false; - tic(ET_ComputeParents); + gttic(ET_ComputeParents); // Compute the tree structure std::vector parents(ComputeParents(structure)); - toc(ET_ComputeParents); + gttoc(ET_ComputeParents); // Number of variables const size_t n = structure.size(); @@ -126,7 +126,7 @@ typename EliminationTree::shared_ptr EliminationTree::Create( static const Index none = std::numeric_limits::max(); // Create tree structure - tic(assemble_tree); + gttic(assemble_tree); std::vector trees(n); for (Index k = 1; k <= n; k++) { Index j = n - k; // Start at the last variable and loop down to 0 @@ -136,10 +136,10 @@ typename EliminationTree::shared_ptr EliminationTree::Create( else if(!structure[j].empty() && j != n - 1) // If a node other than the last has no parents, this is a forest throw DisconnectedGraphException(); } - toc(assemble_tree); + gttoc(assemble_tree); // Hang factors in right places - tic(hang_factors); + gttic(hang_factors); BOOST_FOREACH(const typename boost::shared_ptr& derivedFactor, factorGraph) { // Here we upwards-cast to the factor type of this EliminationTree. This // allows performing symbolic elimination on, for example, GaussianFactors. @@ -150,7 +150,7 @@ typename EliminationTree::shared_ptr EliminationTree::Create( trees[j]->add(factor); } } - toc(hang_factors); + gttoc(hang_factors); if(debug) trees.back()->print("ETree: "); @@ -165,9 +165,9 @@ typename EliminationTree::shared_ptr EliminationTree::Create(const FactorGraph& factorGraph) { // Build variable index - tic(ET_Create_variable_index); + gttic(ET_Create_variable_index); const VariableIndex variableIndex(factorGraph); - toc(ET_Create_variable_index); + gttoc(ET_Create_variable_index); // Build elimination tree return Create(factorGraph, variableIndex); @@ -205,21 +205,21 @@ typename EliminationTree::BayesNet::shared_ptr EliminationTree::eliminatePartial(typename EliminationTree::Eliminate function, size_t nrToEliminate) const { // call recursive routine - tic(ET_recursive_eliminate); + gttic(ET_recursive_eliminate); if(nrToEliminate > this->key_ + 1) throw std::invalid_argument("Requested that EliminationTree::eliminatePartial eliminate more variables than exist"); Conditionals conditionals(nrToEliminate); // reserve a vector of conditional shared pointers (void)eliminate_(function, conditionals); // modify in place - toc(ET_recursive_eliminate); + gttoc(ET_recursive_eliminate); // Add conditionals to BayesNet - tic(assemble_BayesNet); + gttic(assemble_BayesNet); typename BayesNet::shared_ptr bayesNet(new BayesNet); BOOST_FOREACH(const typename BayesNet::sharedConditional& conditional, conditionals) { if(conditional) bayesNet->push_back(conditional); } - toc(assemble_BayesNet); + gttoc(assemble_BayesNet); return bayesNet; } diff --git a/gtsam/inference/JunctionTree-inl.h b/gtsam/inference/JunctionTree-inl.h index 437b7fb89..43f58e777 100644 --- a/gtsam/inference/JunctionTree-inl.h +++ b/gtsam/inference/JunctionTree-inl.h @@ -31,29 +31,29 @@ namespace gtsam { /* ************************************************************************* */ template void JunctionTree::construct(const FG& fg, const VariableIndex& variableIndex) { - tic(JT_symbolic_ET); + gttic(JT_symbolic_ET); const typename EliminationTree::shared_ptr symETree = EliminationTree::Create(fg, variableIndex); - toc(JT_symbolic_ET); - tic(JT_symbolic_eliminate); + gttoc(JT_symbolic_ET); + gttic(JT_symbolic_eliminate); SymbolicBayesNet::shared_ptr sbn = symETree->eliminate(&EliminateSymbolic); - toc(JT_symbolic_eliminate); - tic(symbolic_BayesTree); + gttoc(JT_symbolic_eliminate); + gttic(symbolic_BayesTree); SymbolicBayesTree sbt(*sbn); - toc(symbolic_BayesTree); + gttoc(symbolic_BayesTree); // distribute factors - tic(distributeFactors); + gttic(distributeFactors); this->root_ = distributeFactors(fg, sbt.root()); - toc(distributeFactors); + gttoc(distributeFactors); } /* ************************************************************************* */ template JunctionTree::JunctionTree(const FG& fg) { - tic(VariableIndex); + gttic(VariableIndex); VariableIndex varIndex(fg); - toc(VariableIndex); + gttoc(VariableIndex); construct(fg, varIndex); } @@ -162,14 +162,14 @@ namespace gtsam { // Now that we know which factors and variables, and where variables // come from and go to, create and eliminate the new joint factor. - tic(CombineAndEliminate); + gttic(CombineAndEliminate); typename FG::EliminationResult eliminated(function(fg, current->frontal.size())); - toc(CombineAndEliminate); + gttoc(CombineAndEliminate); assert(std::equal(eliminated.second->begin(), eliminated.second->end(), current->separator.begin())); - tic(Update_tree); + gttic(Update_tree); // create a new clique corresponding the combined factors typename BTClique::shared_ptr new_clique(BTClique::Create(eliminated)); new_clique->children_ = children; @@ -177,7 +177,7 @@ namespace gtsam { BOOST_FOREACH(typename BTClique::shared_ptr& childRoot, children) { childRoot->parent_ = new_clique; } - toc(Update_tree); + gttoc(Update_tree); return std::make_pair(new_clique, eliminated.second); } @@ -187,12 +187,12 @@ namespace gtsam { typename BTCLIQUE::shared_ptr JunctionTree::eliminate( typename FG::Eliminate function) const { if (this->root()) { - tic(JT_eliminate); + gttic(JT_eliminate); std::pair ret = this->eliminateOneClique(function, this->root()); if (ret.second->size() != 0) throw std::runtime_error( "JuntionTree::eliminate: elimination failed because of factors left over!"); - toc(JT_eliminate); + gttoc(JT_eliminate); return ret.first; } else return typename BTClique::shared_ptr(); diff --git a/gtsam/linear/GaussianBayesNet.cpp b/gtsam/linear/GaussianBayesNet.cpp index 52e65a3e4..8e1520235 100644 --- a/gtsam/linear/GaussianBayesNet.cpp +++ b/gtsam/linear/GaussianBayesNet.cpp @@ -142,9 +142,9 @@ VectorValues backSubstituteTranspose(const GaussianBayesNet& bn, /* ************************************************************************* */ VectorValues optimizeGradientSearch(const GaussianBayesNet& Rd) { - tic(Allocate_VectorValues); + gttic(Allocate_VectorValues); VectorValues grad = *allocateVectorValues(Rd); - toc(Allocate_VectorValues); + gttoc(Allocate_VectorValues); optimizeGradientSearchInPlace(Rd, grad); @@ -153,27 +153,27 @@ VectorValues optimizeGradientSearch(const GaussianBayesNet& Rd) { /* ************************************************************************* */ void optimizeGradientSearchInPlace(const GaussianBayesNet& Rd, VectorValues& grad) { - tic(Compute_Gradient); + gttic(Compute_Gradient); // Compute gradient (call gradientAtZero function, which is defined for various linear systems) gradientAtZero(Rd, grad); double gradientSqNorm = grad.dot(grad); - toc(Compute_Gradient); + gttoc(Compute_Gradient); - tic(Compute_Rg); + gttic(Compute_Rg); // Compute R * g FactorGraph Rd_jfg(Rd); Errors Rg = Rd_jfg * grad; - toc(Compute_Rg); + gttoc(Compute_Rg); - tic(Compute_minimizing_step_size); + gttic(Compute_minimizing_step_size); // Compute minimizing step size double step = -gradientSqNorm / dot(Rg, Rg); - toc(Compute_minimizing_step_size); + gttoc(Compute_minimizing_step_size); - tic(Compute_point); + gttic(Compute_point); // Compute steepest descent point scal(step, grad); - toc(Compute_point); + gttoc(Compute_point); } /* ************************************************************************* */ diff --git a/gtsam/linear/GaussianBayesTree.cpp b/gtsam/linear/GaussianBayesTree.cpp index 451e070bc..c9fbb375d 100644 --- a/gtsam/linear/GaussianBayesTree.cpp +++ b/gtsam/linear/GaussianBayesTree.cpp @@ -36,9 +36,9 @@ void optimizeInPlace(const GaussianBayesTree& bayesTree, VectorValues& result) { /* ************************************************************************* */ VectorValues optimizeGradientSearch(const GaussianBayesTree& bayesTree) { - tic(Allocate_VectorValues); + gttic(Allocate_VectorValues); VectorValues grad = *allocateVectorValues(bayesTree); - toc(Allocate_VectorValues); + gttoc(Allocate_VectorValues); optimizeGradientSearchInPlace(bayesTree, grad); @@ -47,27 +47,27 @@ VectorValues optimizeGradientSearch(const GaussianBayesTree& bayesTree) { /* ************************************************************************* */ void optimizeGradientSearchInPlace(const GaussianBayesTree& bayesTree, VectorValues& grad) { - tic(Compute_Gradient); + gttic(Compute_Gradient); // Compute gradient (call gradientAtZero function, which is defined for various linear systems) gradientAtZero(bayesTree, grad); double gradientSqNorm = grad.dot(grad); - toc(Compute_Gradient); + gttoc(Compute_Gradient); - tic(Compute_Rg); + gttic(Compute_Rg); // Compute R * g FactorGraph Rd_jfg(bayesTree); Errors Rg = Rd_jfg * grad; - toc(Compute_Rg); + gttoc(Compute_Rg); - tic(Compute_minimizing_step_size); + gttic(Compute_minimizing_step_size); // Compute minimizing step size double step = -gradientSqNorm / dot(Rg, Rg); - toc(Compute_minimizing_step_size); + gttoc(Compute_minimizing_step_size); - tic(Compute_point); + gttic(Compute_point); // Compute steepest descent point scal(step, grad); - toc(Compute_point); + gttoc(Compute_point); } /* ************************************************************************* */ diff --git a/gtsam/linear/GaussianFactorGraph.cpp b/gtsam/linear/GaussianFactorGraph.cpp index d8f9e7e44..cb6bfa6eb 100644 --- a/gtsam/linear/GaussianFactorGraph.cpp +++ b/gtsam/linear/GaussianFactorGraph.cpp @@ -253,7 +253,7 @@ break; if (debug) variableSlots.print(); if (debug) cout << "Determine dimensions" << endl; - tic(countDims); + gttic(countDims); vector varDims; size_t m, n; boost::tie(varDims, m, n) = countDims(factors, variableSlots); @@ -262,17 +262,17 @@ break; BOOST_FOREACH(const size_t dim, varDims) cout << dim << " "; cout << endl; } - toc(countDims); + gttoc(countDims); if (debug) cout << "Allocate new factor" << endl; - tic(allocate); + gttic(allocate); JacobianFactor::shared_ptr combined(new JacobianFactor()); combined->allocate(variableSlots, varDims, m); Vector sigmas(m); - toc(allocate); + gttoc(allocate); if (debug) cout << "Copy blocks" << endl; - tic(copy_blocks); + gttic(copy_blocks); // Loop over slots in combined factor Index combinedSlot = 0; BOOST_FOREACH(const VariableSlots::value_type& varslot, variableSlots) { @@ -293,10 +293,10 @@ break; } ++combinedSlot; } - toc(copy_blocks); + gttoc(copy_blocks); if (debug) cout << "Copy rhs (b) and sigma" << endl; - tic(copy_vectors); + gttic(copy_vectors); bool anyConstrained = false; // Loop over source factors size_t nextRow = 0; @@ -307,12 +307,12 @@ break; if (factors[factorI]->isConstrained()) anyConstrained = true; nextRow += sourceRows; } - toc(copy_vectors); + gttoc(copy_vectors); if (debug) cout << "Create noise model from sigmas" << endl; - tic(noise_model); + gttic(noise_model); combined->setModel(anyConstrained, sigmas); - toc(noise_model); + gttoc(noise_model); if (debug) cout << "Assert Invariants" << endl; combined->assertInvariants(); @@ -323,13 +323,13 @@ break; /* ************************************************************************* */ GaussianFactorGraph::EliminationResult EliminateJacobians(const FactorGraph< JacobianFactor>& factors, size_t nrFrontals) { - tic(Combine); + gttic(Combine); JacobianFactor::shared_ptr jointFactor = CombineJacobians(factors, VariableSlots(factors)); - toc(Combine); - tic(eliminate); + gttoc(Combine); + gttic(eliminate); GaussianConditional::shared_ptr gbn = jointFactor->eliminate(nrFrontals); - toc(eliminate); + gttoc(eliminate); return make_pair(gbn, jointFactor); } @@ -397,42 +397,42 @@ break; const bool debug = ISDEBUG("EliminateCholesky"); // Find the scatter and variable dimensions - tic(find_scatter); + gttic(find_scatter); Scatter scatter(findScatterAndDims(factors)); - toc(find_scatter); + gttoc(find_scatter); // Pull out keys and dimensions - tic(keys); + gttic(keys); vector dimensions(scatter.size() + 1); BOOST_FOREACH(const Scatter::value_type& var_slot, scatter) { dimensions[var_slot.second.slot] = var_slot.second.dimension; } // This is for the r.h.s. vector dimensions.back() = 1; - toc(keys); + gttoc(keys); // Form Ab' * Ab - tic(combine); + gttic(combine); HessianFactor::shared_ptr combinedFactor(new HessianFactor(factors, dimensions, scatter)); - toc(combine); + gttoc(combine); // Do Cholesky, note that after this, the lower triangle still contains // some untouched non-zeros that should be zero. We zero them while // extracting submatrices next. - tic(partial_Cholesky); + gttic(partial_Cholesky); combinedFactor->partialCholesky(nrFrontals); - toc(partial_Cholesky); + gttoc(partial_Cholesky); // Extract conditional and fill in details of the remaining factor - tic(split); + gttic(split); GaussianConditional::shared_ptr conditional = combinedFactor->splitEliminatedFactor(nrFrontals); if (debug) { conditional->print("Extracted conditional: "); combinedFactor->print("Eliminated factor (L piece): "); } - toc(split); + gttoc(split); combinedFactor->assertInvariants(); return make_pair(conditional, combinedFactor); @@ -482,15 +482,15 @@ break; // Convert all factors to the appropriate type and call the type-specific EliminateGaussian. if (debug) cout << "Using QR" << endl; - tic(convert_to_Jacobian); + gttic(convert_to_Jacobian); FactorGraph jacobians = convertToJacobians(factors); - toc(convert_to_Jacobian); + gttoc(convert_to_Jacobian); - tic(Jacobian_EliminateGaussian); + gttic(Jacobian_EliminateGaussian); GaussianConditional::shared_ptr conditional; GaussianFactor::shared_ptr factor; boost::tie(conditional, factor) = EliminateJacobians(jacobians, nrFrontals); - toc(Jacobian_EliminateGaussian); + gttoc(Jacobian_EliminateGaussian); return make_pair(conditional, factor); } // \EliminateQR @@ -522,9 +522,9 @@ break; return EliminateQR(factors, nrFrontals); else { GaussianFactorGraph::EliminationResult ret; - tic(EliminateCholesky); + gttic(EliminateCholesky); ret = EliminateCholesky(factors, nrFrontals); - toc(EliminateCholesky); + gttoc(EliminateCholesky); return ret; } diff --git a/gtsam/linear/GaussianJunctionTree.cpp b/gtsam/linear/GaussianJunctionTree.cpp index b93e8f921..381c35e3f 100644 --- a/gtsam/linear/GaussianJunctionTree.cpp +++ b/gtsam/linear/GaussianJunctionTree.cpp @@ -36,22 +36,22 @@ namespace gtsam { /* ************************************************************************* */ VectorValues GaussianJunctionTree::optimize(Eliminate function) const { - tic(GJT_eliminate); + gttic(GJT_eliminate); // eliminate from leaves to the root BTClique::shared_ptr rootClique(this->eliminate(function)); - toc(GJT_eliminate); + gttoc(GJT_eliminate); // Allocate solution vector and copy RHS - tic(allocate_VectorValues); + gttic(allocate_VectorValues); vector dims(rootClique->conditional()->back()+1, 0); countDims(rootClique, dims); VectorValues result(dims); - toc(allocate_VectorValues); + gttoc(allocate_VectorValues); // back-substitution - tic(backsubstitute); + gttic(backsubstitute); internal::optimizeInPlace(rootClique, result); - toc(backsubstitute); + gttoc(backsubstitute); return result; } diff --git a/gtsam/linear/GaussianMultifrontalSolver.cpp b/gtsam/linear/GaussianMultifrontalSolver.cpp index 89f03626f..97bfa118e 100644 --- a/gtsam/linear/GaussianMultifrontalSolver.cpp +++ b/gtsam/linear/GaussianMultifrontalSolver.cpp @@ -51,13 +51,13 @@ GaussianBayesTree::shared_ptr GaussianMultifrontalSolver::eliminate() const { /* ************************************************************************* */ VectorValues::shared_ptr GaussianMultifrontalSolver::optimize() const { - tic(optimize); + gttic(optimize); VectorValues::shared_ptr values; if (useQR_) values = VectorValues::shared_ptr(new VectorValues(junctionTree_->optimize(&EliminateQR))); else values= VectorValues::shared_ptr(new VectorValues(junctionTree_->optimize(&EliminatePreferCholesky))); - toc(optimize); + gttoc(optimize); return values; } diff --git a/gtsam/linear/GaussianSequentialSolver.cpp b/gtsam/linear/GaussianSequentialSolver.cpp index 75e67d94c..d631a2a63 100644 --- a/gtsam/linear/GaussianSequentialSolver.cpp +++ b/gtsam/linear/GaussianSequentialSolver.cpp @@ -64,21 +64,21 @@ VectorValues::shared_ptr GaussianSequentialSolver::optimize() const { if(debug) this->factors_->print("GaussianSequentialSolver, eliminating "); if(debug) this->eliminationTree_->print("GaussianSequentialSolver, elimination tree "); - tic(eliminate); + gttic(eliminate); // Eliminate using the elimination tree GaussianBayesNet::shared_ptr bayesNet(this->eliminate()); - toc(eliminate); + gttoc(eliminate); if(debug) bayesNet->print("GaussianSequentialSolver, Bayes net "); // Allocate the solution vector if it is not already allocated // VectorValues::shared_ptr solution = allocateVectorValues(*bayesNet); - tic(optimize); + gttic(optimize); // Back-substitute VectorValues::shared_ptr solution( new VectorValues(gtsam::optimize(*bayesNet))); - toc(optimize); + gttoc(optimize); if(debug) solution->print("GaussianSequentialSolver, solution "); diff --git a/gtsam/linear/HessianFactor.cpp b/gtsam/linear/HessianFactor.cpp index 6bf4ee423..ff34ca9f9 100644 --- a/gtsam/linear/HessianFactor.cpp +++ b/gtsam/linear/HessianFactor.cpp @@ -251,16 +251,16 @@ HessianFactor::HessianFactor(const FactorGraph& factors, const bool debug = ISDEBUG("EliminateCholesky"); // Form Ab' * Ab - tic(allocate); + gttic(allocate); info_.resize(dimensions.begin(), dimensions.end(), false); // Fill in keys keys_.resize(scatter.size()); std::transform(scatter.begin(), scatter.end(), keys_.begin(), boost::bind(&Scatter::value_type::first, ::_1)); - toc(allocate); - tic(zero); + gttoc(allocate); + gttic(zero); matrix_.noalias() = Matrix::Zero(matrix_.rows(),matrix_.cols()); - toc(zero); - tic(update); + gttoc(zero); + gttic(update); if (debug) cout << "Combining " << factors.size() << " factors" << endl; BOOST_FOREACH(const GaussianFactor::shared_ptr& factor, factors) { @@ -273,7 +273,7 @@ HessianFactor::HessianFactor(const FactorGraph& factors, throw invalid_argument("GaussianFactor is neither Hessian nor Jacobian"); } } - toc(update); + gttoc(update); if (debug) gtsam::print(matrix_, "Ab' * Ab: "); @@ -335,14 +335,14 @@ void HessianFactor::updateATA(const HessianFactor& update, const Scatter& scatte const bool debug = ISDEBUG("updateATA"); // First build an array of slots - tic(slots); + gttic(slots); size_t* slots = (size_t*)alloca(sizeof(size_t)*update.size()); // FIXME: alloca is bad, just ask Google. size_t slot = 0; BOOST_FOREACH(Index j, update) { slots[slot] = scatter.find(j)->second.slot; ++ slot; } - toc(slots); + gttoc(slots); if(debug) { this->print("Updating this: "); @@ -350,7 +350,7 @@ void HessianFactor::updateATA(const HessianFactor& update, const Scatter& scatte } // Apply updates to the upper triangle - tic(update); + gttic(update); for(size_t j2=0; j2info_.nBlocks()-1 : slots[j2]; for(size_t j1=0; j1<=j2; ++j1) { @@ -375,7 +375,7 @@ void HessianFactor::updateATA(const HessianFactor& update, const Scatter& scatte if(debug) this->print(); } } - toc(update); + gttoc(update); } /* ************************************************************************* */ @@ -388,16 +388,16 @@ void HessianFactor::updateATA(const JacobianFactor& update, const Scatter& scatt const bool debug = ISDEBUG("updateATA"); // First build an array of slots - tic(slots); + gttic(slots); size_t* slots = (size_t*)alloca(sizeof(size_t)*update.size()); // FIXME: alloca is bad, just ask Google. size_t slot = 0; BOOST_FOREACH(Index j, update) { slots[slot] = scatter.find(j)->second.slot; ++ slot; } - toc(slots); + gttoc(slots); - tic(form_ATA); + gttic(form_ATA); if(update.model_->isConstrained()) throw invalid_argument("Cannot update HessianFactor from JacobianFactor with constrained noise model"); @@ -423,10 +423,10 @@ void HessianFactor::updateATA(const JacobianFactor& update, const Scatter& scatt throw invalid_argument("In HessianFactor::updateATA, JacobianFactor noise model is neither Unit nor Diagonal"); } if (debug) cout << "updateInform: \n" << updateInform << endl; - toc(form_ATA); + gttoc(form_ATA); // Apply updates to the upper triangle - tic(update); + gttic(update); for(size_t j2=0; j2info_.nBlocks()-1 : slots[j2]; for(size_t j1=0; j1<=j2; ++j1) { @@ -452,7 +452,7 @@ void HessianFactor::updateATA(const JacobianFactor& update, const Scatter& scatt if(debug) this->print(); } } - toc(update); + gttoc(update); } /* ************************************************************************* */ @@ -467,7 +467,7 @@ GaussianConditional::shared_ptr HessianFactor::splitEliminatedFactor(size_t nrFr static const bool debug = false; // Extract conditionals - tic(extract_conditionals); + gttic(extract_conditionals); GaussianConditional::shared_ptr conditional(new GaussianConditional()); typedef VerticalBlockView BlockAb; BlockAb Ab(matrix_, info_); @@ -476,22 +476,22 @@ GaussianConditional::shared_ptr HessianFactor::splitEliminatedFactor(size_t nrFr Ab.rowEnd() = Ab.rowStart() + varDim; // Create one big conditionals with many frontal variables. - tic(construct_cond); + gttic(construct_cond); Vector sigmas = Vector::Ones(varDim); conditional = boost::make_shared(keys_.begin(), keys_.end(), nrFrontals, Ab, sigmas); - toc(construct_cond); + gttoc(construct_cond); if(debug) conditional->print("Extracted conditional: "); - toc(extract_conditionals); + gttoc(extract_conditionals); // Take lower-right block of Ab_ to get the new factor - tic(remaining_factor); + gttic(remaining_factor); info_.blockStart() = nrFrontals; // Assign the keys vector remainingKeys(keys_.size() - nrFrontals); remainingKeys.assign(keys_.begin() + nrFrontals, keys_.end()); keys_.swap(remainingKeys); - toc(remaining_factor); + gttoc(remaining_factor); return conditional; } diff --git a/gtsam/linear/JacobianFactor.cpp b/gtsam/linear/JacobianFactor.cpp index 416a56b1c..529df2454 100644 --- a/gtsam/linear/JacobianFactor.cpp +++ b/gtsam/linear/JacobianFactor.cpp @@ -388,7 +388,7 @@ namespace gtsam { throw IndeterminantLinearSystemException(this->keys().front()); // Extract conditional - tic(cond_Rd); + gttic(cond_Rd); // Restrict the matrix to be in the first nrFrontals variables Ab_.rowEnd() = Ab_.rowStart() + frontalDim; @@ -397,11 +397,11 @@ namespace gtsam { if(debug) conditional->print("Extracted conditional: "); Ab_.rowStart() += frontalDim; Ab_.firstBlock() += nrFrontals; - toc(cond_Rd); + gttoc(cond_Rd); if(debug) conditional->print("Extracted conditional: "); - tic(remaining_factor); + gttic(remaining_factor); // Take lower-right block of Ab to get the new factor Ab_.rowEnd() = model_->dim(); keys_.erase(begin(), begin() + nrFrontals); @@ -412,7 +412,7 @@ namespace gtsam { model_ = noiseModel::Diagonal::Sigmas(sub(model_->sigmas(), frontalDim, model_->dim())); if(debug) this->print("Eliminated factor: "); assert(Ab_.rows() <= Ab_.cols()-1); - toc(remaining_factor); + gttoc(remaining_factor); if(debug) print("Eliminated factor: "); @@ -439,9 +439,9 @@ namespace gtsam { if(debug) cout << "frontalDim = " << frontalDim << endl; // Use in-place QR dense Ab appropriate to NoiseModel - tic(QR); + gttic(QR); SharedDiagonal noiseModel = model_->QR(matrix_); - toc(QR); + gttoc(QR); // Zero the lower-left triangle. todo: not all of these entries actually // need to be zeroed if we are careful to start copying rows after the last diff --git a/gtsam/linear/NoiseModel.cpp b/gtsam/linear/NoiseModel.cpp index 63108dbbd..d41cec293 100644 --- a/gtsam/linear/NoiseModel.cpp +++ b/gtsam/linear/NoiseModel.cpp @@ -329,22 +329,22 @@ SharedDiagonal Constrained::QR(Matrix& Ab) const { Vector a = Ab.col(j); // Calculate weighted pseudo-inverse and corresponding precision - tic(constrained_QR_weightedPseudoinverse); + gttic(constrained_QR_weightedPseudoinverse); double precision = weightedPseudoinverse(a, weights, pseudo); - toc(constrained_QR_weightedPseudoinverse); + gttoc(constrained_QR_weightedPseudoinverse); // If precision is zero, no information on this column // This is actually not limited to constraints, could happen in Gaussian::QR // In that case, we're probably hosed. TODO: make sure Householder is rank-revealing if (precision < 1e-8) continue; - tic(constrained_QR_create_rd); + gttic(constrained_QR_create_rd); // create solution [r d], rhs is automatically r(n) Vector rd(n+1); // uninitialized ! rd(j)=1.0; // put 1 on diagonal for (size_t j2=j+1; j2=maxRank) break; // update Ab, expensive, using outer product - tic(constrained_QR_update_Ab); + gttic(constrained_QR_update_Ab); Ab.middleCols(j+1,n-j) -= a * rd.segment(j+1, n-j).transpose(); - toc(constrained_QR_update_Ab); + gttoc(constrained_QR_update_Ab); } // Create storage for precisions Vector precisions(Rd.size()); - tic(constrained_QR_write_back_into_Ab); + gttic(constrained_QR_write_back_into_Ab); // Write back result in Ab, imperative as we are // TODO: test that is correct if a column was skipped !!!! size_t i = 0; // start with first row @@ -377,7 +377,7 @@ SharedDiagonal Constrained::QR(Matrix& Ab) const { Ab(i,j2) = rd(j2); i+=1; } - toc(constrained_QR_write_back_into_Ab); + gttoc(constrained_QR_write_back_into_Ab); // Must include mu, as the defaults might be higher, resulting in non-convergence return mixed ? Constrained::MixedPrecisions(mu_, precisions) : Diagonal::Precisions(precisions); diff --git a/gtsam/nonlinear/DoglegOptimizerImpl.h b/gtsam/nonlinear/DoglegOptimizerImpl.h index e6b747d96..340c32318 100644 --- a/gtsam/nonlinear/DoglegOptimizerImpl.h +++ b/gtsam/nonlinear/DoglegOptimizerImpl.h @@ -149,22 +149,22 @@ typename DoglegOptimizerImpl::IterationResult DoglegOptimizerImpl::Iterate( const F& f, const VALUES& x0, const Ordering& ordering, const double f_error, const bool verbose) { // Compute steepest descent and Newton's method points - tic(optimizeGradientSearch); - tic(allocateVectorValues); + gttic(optimizeGradientSearch); + gttic(allocateVectorValues); VectorValues dx_u = *allocateVectorValues(Rd); - toc(allocateVectorValues); - tic(optimizeGradientSearchInPlace); + gttoc(allocateVectorValues); + gttic(optimizeGradientSearchInPlace); optimizeGradientSearchInPlace(Rd, dx_u); - toc(optimizeGradientSearchInPlace); - toc(optimizeGradientSearch); - tic(optimizeInPlace); + gttoc(optimizeGradientSearchInPlace); + gttoc(optimizeGradientSearch); + gttic(optimizeInPlace); VectorValues dx_n(VectorValues::SameStructure(dx_u)); optimizeInPlace(Rd, dx_n); - toc(optimizeInPlace); - tic(jfg_error); + gttoc(optimizeInPlace); + gttic(jfg_error); const GaussianFactorGraph jfg(Rd); const double M_error = jfg.error(VectorValues::Zero(dx_u)); - toc(jfg_error); + gttoc(jfg_error); // Result to return IterationResult result; @@ -172,32 +172,32 @@ typename DoglegOptimizerImpl::IterationResult DoglegOptimizerImpl::Iterate( bool stay = true; enum { NONE, INCREASED_DELTA, DECREASED_DELTA } lastAction = NONE; // Used to prevent alternating between increasing and decreasing in one iteration while(stay) { - tic(Dog_leg_point); + gttic(Dog_leg_point); // Compute dog leg point result.dx_d = ComputeDoglegPoint(Delta, dx_u, dx_n, verbose); - toc(Dog_leg_point); + gttoc(Dog_leg_point); if(verbose) std::cout << "Delta = " << Delta << ", dx_d_norm = " << result.dx_d.vector().norm() << std::endl; - tic(retract); + gttic(retract); // Compute expmapped solution const VALUES x_d(x0.retract(result.dx_d, ordering)); - toc(retract); + gttoc(retract); - tic(decrease_in_f); + gttic(decrease_in_f); // Compute decrease in f result.f_error = f.error(x_d); - toc(decrease_in_f); + gttoc(decrease_in_f); - tic(decrease_in_M); + gttic(decrease_in_M); // Compute decrease in M const double new_M_error = jfg.error(result.dx_d); - toc(decrease_in_M); + gttoc(decrease_in_M); if(verbose) std::cout << std::setprecision(15) << "f error: " << f_error << " -> " << result.f_error << std::endl; if(verbose) std::cout << std::setprecision(15) << "M error: " << M_error << " -> " << new_M_error << std::endl; - tic(adjust_Delta); + gttic(adjust_Delta); // Compute gain ratio. Here we take advantage of the invariant that the // Bayes' net error at zero is equal to the nonlinear error const double rho = fabs(f_error - result.f_error) < 1e-15 || fabs(M_error - new_M_error) < 1e-15 ? @@ -266,7 +266,7 @@ typename DoglegOptimizerImpl::IterationResult DoglegOptimizerImpl::Iterate( stay = false; } } - toc(adjust_Delta); + gttoc(adjust_Delta); } // dx_d and f_error have already been filled in during the loop diff --git a/gtsam/nonlinear/ISAM2-impl.cpp b/gtsam/nonlinear/ISAM2-impl.cpp index 94ab6dc64..0bd608495 100644 --- a/gtsam/nonlinear/ISAM2-impl.cpp +++ b/gtsam/nonlinear/ISAM2-impl.cpp @@ -302,7 +302,7 @@ ISAM2::Impl::PartialSolve(GaussianFactorGraph& factors, PartialSolveResult result; - tic(select_affected_variables); + gttic(select_affected_variables); #ifndef NDEBUG // Debug check that all variables involved in the factors to be re-eliminated // are in affectedKeys, since we will use it to select a subset of variables. @@ -326,12 +326,12 @@ ISAM2::Impl::PartialSolve(GaussianFactorGraph& factors, if(debug) affectedKeysSelectorInverse.print("affectedKeysSelectorInverse: "); factors.permuteWithInverse(affectedKeysSelectorInverse); if(debug) factors.print("Factors to reorder/re-eliminate: "); - toc(select_affected_variables); - tic(variable_index); + gttoc(select_affected_variables); + gttic(variable_index); VariableIndex affectedFactorsIndex(factors); // Create a variable index for the factors to be re-eliminated if(debug) affectedFactorsIndex.print("affectedFactorsIndex: "); - toc(variable_index); - tic(ccolamd); + gttoc(variable_index); + gttic(ccolamd); vector cmember(affectedKeysSelector.size(), 0); if(reorderingMode.constrain == ReorderingMode::CONSTRAIN_LAST) { assert(reorderingMode.constrainedKeys); @@ -348,8 +348,8 @@ ISAM2::Impl::PartialSolve(GaussianFactorGraph& factors, } } Permutation::shared_ptr affectedColamd(inference::PermutationCOLAMD_(affectedFactorsIndex, cmember)); - toc(ccolamd); - tic(ccolamd_permutations); + gttoc(ccolamd); + gttic(ccolamd_permutations); Permutation::shared_ptr affectedColamdInverse(affectedColamd->inverse()); if(debug) affectedColamd->print("affectedColamd: "); if(debug) affectedColamdInverse->print("affectedColamdInverse: "); @@ -358,15 +358,15 @@ ISAM2::Impl::PartialSolve(GaussianFactorGraph& factors, result.fullReorderingInverse = *Permutation::Identity(reorderingMode.nFullSystemVars).partialPermutation(affectedKeysSelector, *affectedColamdInverse); if(debug) result.fullReordering.print("partialReordering: "); - toc(ccolamd_permutations); + gttoc(ccolamd_permutations); - tic(permute_affected_variable_index); + gttic(permute_affected_variable_index); affectedFactorsIndex.permuteInPlace(*affectedColamd); - toc(permute_affected_variable_index); + gttoc(permute_affected_variable_index); - tic(permute_affected_factors); + gttic(permute_affected_factors); factors.permuteWithInverse(*affectedColamdInverse); - toc(permute_affected_factors); + gttoc(permute_affected_factors); if(debug) factors.print("Colamd-ordered affected factors: "); @@ -376,15 +376,15 @@ ISAM2::Impl::PartialSolve(GaussianFactorGraph& factors, #endif // eliminate into a Bayes net - tic(eliminate); + gttic(eliminate); JunctionTree jt(factors, affectedFactorsIndex); if(!useQR) result.bayesTree = jt.eliminate(EliminatePreferCholesky); else result.bayesTree = jt.eliminate(EliminateQR); - toc(eliminate); + gttoc(eliminate); - tic(permute_eliminated); + gttic(permute_eliminated); if(result.bayesTree) result.bayesTree->permuteWithInverse(affectedKeysSelector); if(debug && result.bayesTree) { cout << "Full var-ordered eliminated BT:\n"; @@ -393,7 +393,7 @@ ISAM2::Impl::PartialSolve(GaussianFactorGraph& factors, // Undo permutation on our subset of cached factors, we must later permute *all* of the cached factors factors.permuteWithInverse(*affectedColamd); factors.permuteWithInverse(affectedKeysSelector); - toc(permute_eliminated); + gttoc(permute_eliminated); return result; } diff --git a/gtsam/nonlinear/ISAM2.cpp b/gtsam/nonlinear/ISAM2.cpp index e2b0a9694..d1d172cde 100644 --- a/gtsam/nonlinear/ISAM2.cpp +++ b/gtsam/nonlinear/ISAM2.cpp @@ -171,19 +171,19 @@ FastList ISAM2::getAffectedFactors(const FastList& keys) const { FactorGraph::shared_ptr ISAM2::relinearizeAffectedFactors(const FastList& affectedKeys, const FastSet& relinKeys) const { - tic(getAffectedFactors); + gttic(getAffectedFactors); FastList candidates = getAffectedFactors(affectedKeys); - toc(getAffectedFactors); + gttoc(getAffectedFactors); NonlinearFactorGraph nonlinearAffectedFactors; - tic(affectedKeysSet); + gttic(affectedKeysSet); // for fast lookup below FastSet affectedKeysSet; affectedKeysSet.insert(affectedKeys.begin(), affectedKeys.end()); - toc(affectedKeysSet); + gttoc(affectedKeysSet); - tic(check_candidates_and_linearize); + gttic(check_candidates_and_linearize); FactorGraph::shared_ptr linearized = boost::make_shared >(); BOOST_FOREACH(size_t idx, candidates) { bool inside = true; @@ -212,7 +212,7 @@ ISAM2::relinearizeAffectedFactors(const FastList& affectedKeys, const Fas } } } - toc(check_candidates_and_linearize); + gttoc(check_candidates_and_linearize); return linearized; } @@ -283,11 +283,11 @@ boost::shared_ptr > ISAM2::recalculate(const FastSet& mark // 1. Remove top of Bayes tree and convert to a factor graph: // (a) For each affected variable, remove the corresponding clique and all parents up to the root. // (b) Store orphaned sub-trees \BayesTree_{O} of removed cliques. - tic(removetop); + gttic(removetop); Cliques orphans; BayesNet affectedBayesNet; this->removeTop(markedKeys, affectedBayesNet, orphans); - toc(removetop); + gttoc(removetop); if(debug) affectedBayesNet.print("Removed top: "); if(debug) orphans.print("Orphans: "); @@ -304,22 +304,22 @@ boost::shared_ptr > ISAM2::recalculate(const FastSet& mark // BEGIN OF COPIED CODE // ordering provides all keys in conditionals, there cannot be others because path to root included - tic(affectedKeys); + gttic(affectedKeys); FastList affectedKeys = affectedBayesNet.ordering(); - toc(affectedKeys); + gttoc(affectedKeys); boost::shared_ptr > affectedKeysSet(new FastSet()); // Will return this result if(affectedKeys.size() >= theta_.size() * batchThreshold) { - tic(batch); + gttic(batch); - tic(add_keys); + gttic(add_keys); BOOST_FOREACH(const Ordering::value_type& key_index, ordering_) { affectedKeysSet->insert(key_index.second); } - toc(add_keys); + gttoc(add_keys); - tic(reorder); - tic(CCOLAMD); + gttic(reorder); + gttic(CCOLAMD); // Do a batch step - reorder and relinearize all variables vector cmember(theta_.size(), 0); if(constrainKeys) { @@ -341,29 +341,29 @@ boost::shared_ptr > ISAM2::recalculate(const FastSet& mark } Permutation::shared_ptr colamd(inference::PermutationCOLAMD_(variableIndex_, cmember)); Permutation::shared_ptr colamdInverse(colamd->inverse()); - toc(CCOLAMD); + gttoc(CCOLAMD); // Reorder - tic(permute_global_variable_index); + gttic(permute_global_variable_index); variableIndex_.permuteInPlace(*colamd); - toc(permute_global_variable_index); - tic(permute_delta); + gttoc(permute_global_variable_index); + gttic(permute_delta); delta_ = delta_.permute(*colamd); deltaNewton_ = deltaNewton_.permute(*colamd); RgProd_ = RgProd_.permute(*colamd); - toc(permute_delta); - tic(permute_ordering); + gttoc(permute_delta); + gttic(permute_ordering); ordering_.permuteWithInverse(*colamdInverse); - toc(permute_ordering); - toc(reorder); + gttoc(permute_ordering); + gttoc(reorder); - tic(linearize); + gttic(linearize); GaussianFactorGraph linearized = *nonlinearFactors_.linearize(theta_, ordering_); if(params_.cacheLinearizedFactors) linearFactors_ = linearized; - toc(linearize); + gttoc(linearize); - tic(eliminate); + gttic(eliminate); JunctionTree jt(linearized, variableIndex_); sharedClique newRoot; if(params_.factorization == ISAM2Params::CHOLESKY) @@ -372,12 +372,12 @@ boost::shared_ptr > ISAM2::recalculate(const FastSet& mark newRoot = jt.eliminate(EliminateQR); else assert(false); if(debug) newRoot->print("Eliminated: "); - toc(eliminate); + gttoc(eliminate); - tic(insert); + gttic(insert); this->clear(); this->insert(newRoot); - toc(insert); + gttoc(insert); result.variablesReeliminated = affectedKeysSet->size(); @@ -392,20 +392,20 @@ boost::shared_ptr > ISAM2::recalculate(const FastSet& mark } } - toc(batch); + gttoc(batch); } else { - tic(incremental); + gttic(incremental); // 2. Add the new factors \Factors' into the resulting factor graph FastList affectedAndNewKeys; affectedAndNewKeys.insert(affectedAndNewKeys.end(), affectedKeys.begin(), affectedKeys.end()); affectedAndNewKeys.insert(affectedAndNewKeys.end(), observedKeys.begin(), observedKeys.end()); - tic(relinearizeAffected); + gttic(relinearizeAffected); GaussianFactorGraph factors(*relinearizeAffectedFactors(affectedAndNewKeys, relinKeys)); if(debug) factors.print("Relinearized factors: "); - toc(relinearizeAffected); + gttoc(relinearizeAffected); if(debug) { cout << "Affected keys: "; BOOST_FOREACH(const Index key, affectedKeys) { cout << key << " "; } cout << endl; } @@ -428,27 +428,27 @@ boost::shared_ptr > ISAM2::recalculate(const FastSet& mark << " avgCliqueSize: " << avgClique << " #Cliques: " << numCliques << " nnzR: " << nnzR << endl; #endif - tic(cached); + gttic(cached); // add the cached intermediate results from the boundary of the orphans ... GaussianFactorGraph cachedBoundary = getCachedBoundaryFactors(orphans); if(debug) cachedBoundary.print("Boundary factors: "); factors.push_back(cachedBoundary); - toc(cached); + gttoc(cached); // END OF COPIED CODE // 3. Re-order and eliminate the factor graph into a Bayes net (Algorithm [alg:eliminate]), and re-assemble into a new Bayes tree (Algorithm [alg:BayesTree]) - tic(reorder_and_eliminate); + gttic(reorder_and_eliminate); - tic(list_to_set); + gttic(list_to_set); // create a partial reordering for the new and contaminated factors // markedKeys are passed in: those variables will be forced to the end in the ordering affectedKeysSet->insert(markedKeys.begin(), markedKeys.end()); affectedKeysSet->insert(affectedKeys.begin(), affectedKeys.end()); - toc(list_to_set); + gttoc(list_to_set); - tic(PartialSolve); + gttic(PartialSolve); Impl::ReorderingMode reorderingMode; reorderingMode.nFullSystemVars = ordering_.nVars(); reorderingMode.algorithm = Impl::ReorderingMode::COLAMD; @@ -465,50 +465,50 @@ boost::shared_ptr > ISAM2::recalculate(const FastSet& mark } Impl::PartialSolveResult partialSolveResult = Impl::PartialSolve(factors, affectedUsedKeys, reorderingMode, (params_.factorization == ISAM2Params::QR)); - toc(PartialSolve); + gttoc(PartialSolve); // We now need to permute everything according this partial reordering: the // delta vector, the global ordering, and the factors we're about to // re-eliminate. The reordered variables are also mentioned in the // orphans and the leftover cached factors. - tic(permute_global_variable_index); + gttic(permute_global_variable_index); variableIndex_.permuteInPlace(partialSolveResult.fullReordering); - toc(permute_global_variable_index); - tic(permute_delta); + gttoc(permute_global_variable_index); + gttic(permute_delta); delta_ = delta_.permute(partialSolveResult.fullReordering); deltaNewton_ = deltaNewton_.permute(partialSolveResult.fullReordering); RgProd_ = RgProd_.permute(partialSolveResult.fullReordering); - toc(permute_delta); - tic(permute_ordering); + gttoc(permute_delta); + gttic(permute_ordering); ordering_.permuteWithInverse(partialSolveResult.fullReorderingInverse); - toc(permute_ordering); + gttoc(permute_ordering); if(params_.cacheLinearizedFactors) { - tic(permute_cached_linear); + gttic(permute_cached_linear); //linearFactors_.permuteWithInverse(partialSolveResult.fullReorderingInverse); FastList permuteLinearIndices = getAffectedFactors(affectedAndNewKeys); BOOST_FOREACH(size_t idx, permuteLinearIndices) { linearFactors_[idx]->permuteWithInverse(partialSolveResult.fullReorderingInverse); } - toc(permute_cached_linear); + gttoc(permute_cached_linear); } - toc(reorder_and_eliminate); + gttoc(reorder_and_eliminate); - tic(reassemble); + gttic(reassemble); if(partialSolveResult.bayesTree) { assert(!this->root_); this->insert(partialSolveResult.bayesTree); } - toc(reassemble); + gttoc(reassemble); // 4. Insert the orphans back into the new Bayes tree. - tic(orphans); - tic(permute); + gttic(orphans); + gttic(permute); BOOST_FOREACH(sharedClique orphan, orphans) { (void)orphan->permuteSeparatorWithInverse(partialSolveResult.fullReorderingInverse); } - toc(permute); - tic(insert); + gttoc(permute); + gttic(insert); // add orphans to the bottom of the new tree BOOST_FOREACH(sharedClique orphan, orphans) { // Because the affectedKeysSelector is sorted, the orphan separator keys @@ -520,10 +520,10 @@ boost::shared_ptr > ISAM2::recalculate(const FastSet& mark parent->children_ += orphan; orphan->parent_ = parent; // set new parent! } - toc(insert); - toc(orphans); + gttoc(insert); + gttoc(orphans); - toc(incremental); + gttoc(incremental); } // Root clique variables for detailed results @@ -565,12 +565,12 @@ ISAM2Result ISAM2::update( // Update delta if we need it to check relinearization later if(relinearizeThisStep) { - tic(updateDelta); + gttic(updateDelta); updateDelta(disableReordering); - toc(updateDelta); + gttoc(updateDelta); } - tic(push_back_factors); + gttic(push_back_factors); // Add the new factor indices to the result struct result.newFactorsIndices.resize(newFactors.size()); for(size_t i=0; ivariableStatus[key].isNew = true; } } - toc(add_new_variables); + gttoc(add_new_variables); - tic(evaluate_error_before); + gttic(evaluate_error_before); if(params_.evaluateNonlinearError) result.errorBefore.reset(nonlinearFactors_.error(calculateEstimate())); - toc(evaluate_error_before); + gttoc(evaluate_error_before); - tic(gather_involved_keys); + gttic(gather_involved_keys); // 3. Mark linear update FastSet markedKeys = Impl::IndicesFromFactors(ordering_, newFactors); // Get keys from new factors // Also mark keys involved in removed factors @@ -651,12 +651,12 @@ ISAM2Result ISAM2::update( if(unusedIndices.find(index) == unusedIndices.end()) // Only add if not unused observedKeys.push_back(index); // Make a copy of these, as we'll soon add to them } - toc(gather_involved_keys); + gttoc(gather_involved_keys); // Check relinearization if we're at the nth step, or we are using a looser loop relin threshold FastSet relinKeys; if (relinearizeThisStep) { - tic(gather_relinearize_keys); + gttic(gather_relinearize_keys); vector markedRelinMask(ordering_.nVars(), false); // 4. Mark keys in \Delta above threshold \beta: J=\{\Delta_{j}\in\Delta|\Delta_{j}\geq\beta\}. if(params_.enablePartialRelinearizationCheck) @@ -674,9 +674,9 @@ ISAM2Result ISAM2::update( // Add the variables being relinearized to the marked keys BOOST_FOREACH(const Index j, relinKeys) { markedRelinMask[j] = true; } markedKeys.insert(relinKeys.begin(), relinKeys.end()); - toc(gather_relinearize_keys); + gttoc(gather_relinearize_keys); - tic(fluid_find_all); + gttic(fluid_find_all); // 5. Mark all cliques that involve marked variables \Theta_{J} and all their ancestors. if (!relinKeys.empty() && this->root()) { // add other cliques that have the marked ones in the separator @@ -692,38 +692,38 @@ ISAM2Result ISAM2::update( result.detail->variableStatus[inverseOrdering_->at(index)].isRelinearized = true; } } } } - toc(fluid_find_all); + gttoc(fluid_find_all); - tic(expmap); + gttic(expmap); // 6. Update linearization point for marked variables: \Theta_{J}:=\Theta_{J}+\Delta_{J}. if (!relinKeys.empty()) Impl::ExpmapMasked(theta_, delta_, ordering_, markedRelinMask, delta_); - toc(expmap); + gttoc(expmap); result.variablesRelinearized = markedKeys.size(); } else { result.variablesRelinearized = 0; } - tic(linearize_new); + gttic(linearize_new); // 7. Linearize new factors if(params_.cacheLinearizedFactors) { - tic(linearize); + gttic(linearize); FactorGraph::shared_ptr linearFactors = newFactors.linearize(theta_, ordering_); linearFactors_.push_back(*linearFactors); assert(nonlinearFactors_.size() == linearFactors_.size()); - toc(linearize); + gttoc(linearize); - tic(augment_VI); + gttic(augment_VI); // Augment the variable index with the new factors variableIndex_.augment(*linearFactors); - toc(augment_VI); + gttoc(augment_VI); } else { variableIndex_.augment(*newFactors.symbolic(ordering_)); } - toc(linearize_new); + gttoc(linearize_new); - tic(recalculate); + gttic(recalculate); // 8. Redo top of Bayes tree // Convert constrained symbols to indices boost::optional > constrainedIndices; @@ -742,25 +742,25 @@ ISAM2Result ISAM2::update( if(replacedKeys) { BOOST_FOREACH(const Index var, *replacedKeys) { deltaReplacedMask_[var] = true; } } - toc(recalculate); + gttoc(recalculate); // After the top of the tree has been redone and may have index gaps from // unused keys, condense the indices to remove gaps by rearranging indices // in all data structures. if(!unusedKeys.empty()) { - tic(remove_variables); + gttic(remove_variables); Impl::RemoveVariables(unusedKeys, root_, theta_, variableIndex_, delta_, deltaNewton_, RgProd_, deltaReplacedMask_, ordering_, Base::nodes_, linearFactors_); - toc(remove_variables); + gttoc(remove_variables); } result.cliques = this->nodes().size(); deltaDoglegUptodate_ = false; deltaUptodate_ = false; - tic(evaluate_error_after); + gttic(evaluate_error_after); if(params_.evaluateNonlinearError) result.errorAfter.reset(nonlinearFactors_.error(calculateEstimate())); - toc(evaluate_error_after); + gttoc(evaluate_error_after); return result; } @@ -773,9 +773,9 @@ void ISAM2::updateDelta(bool forceFullSolve) const { const ISAM2GaussNewtonParams& gaussNewtonParams = boost::get(params_.optimizationParams); const double effectiveWildfireThreshold = forceFullSolve ? 0.0 : gaussNewtonParams.wildfireThreshold; - tic(Wildfire_update); + gttic(Wildfire_update); lastBacksubVariableCount = Impl::UpdateDelta(this->root(), deltaReplacedMask_, delta_, effectiveWildfireThreshold); - toc(Wildfire_update); + gttoc(Wildfire_update); } else if(params_.optimizationParams.type() == typeid(ISAM2DoglegParams)) { // If using Dogleg, do a Dogleg step @@ -783,16 +783,16 @@ void ISAM2::updateDelta(bool forceFullSolve) const { boost::get(params_.optimizationParams); // Do one Dogleg iteration - tic(Dogleg_Iterate); + gttic(Dogleg_Iterate); DoglegOptimizerImpl::IterationResult doglegResult(DoglegOptimizerImpl::Iterate( *doglegDelta_, doglegParams.adaptationMode, *this, nonlinearFactors_, theta_, ordering_, nonlinearFactors_.error(theta_), doglegParams.verbose)); - toc(Dogleg_Iterate); + gttoc(Dogleg_Iterate); - tic(Copy_dx_d); + gttic(Copy_dx_d); // Update Delta and linear step doglegDelta_ = doglegResult.Delta; delta_ = doglegResult.dx_d; // Copy the VectorValues containing with the linear solution - toc(Copy_dx_d); + gttoc(Copy_dx_d); } deltaUptodate_ = true; @@ -802,16 +802,16 @@ void ISAM2::updateDelta(bool forceFullSolve) const { Values ISAM2::calculateEstimate() const { // We use ExpmapMasked here instead of regular expmap because the former // handles Permuted - tic(Copy_Values); + gttic(Copy_Values); Values ret(theta_); - toc(Copy_Values); - tic(getDelta); + gttoc(Copy_Values); + gttic(getDelta); const VectorValues& delta(getDelta()); - toc(getDelta); - tic(Expmap); + gttoc(getDelta); + gttic(Expmap); vector mask(ordering_.nVars(), true); Impl::ExpmapMasked(ret, delta, ordering_, mask); - toc(Expmap); + gttoc(Expmap); return ret; } @@ -831,9 +831,9 @@ const VectorValues& ISAM2::getDelta() const { /* ************************************************************************* */ VectorValues optimize(const ISAM2& isam) { - tic(allocateVectorValues); + gttic(allocateVectorValues); VectorValues delta = *allocateVectorValues(isam); - toc(allocateVectorValues); + gttoc(allocateVectorValues); optimizeInPlace(isam, delta); return delta; } @@ -842,7 +842,7 @@ VectorValues optimize(const ISAM2& isam) { void optimizeInPlace(const ISAM2& isam, VectorValues& delta) { // We may need to update the solution calcaulations if(!isam.deltaDoglegUptodate_) { - tic(UpdateDoglegDeltas); + gttic(UpdateDoglegDeltas); double wildfireThreshold = 0.0; if(isam.params().optimizationParams.type() == typeid(ISAM2GaussNewtonParams)) wildfireThreshold = boost::get(isam.params().optimizationParams).wildfireThreshold; @@ -852,19 +852,19 @@ void optimizeInPlace(const ISAM2& isam, VectorValues& delta) { assert(false); ISAM2::Impl::UpdateDoglegDeltas(isam, wildfireThreshold, isam.deltaReplacedMask_, isam.deltaNewton_, isam.RgProd_); isam.deltaDoglegUptodate_ = true; - toc(UpdateDoglegDeltas); + gttoc(UpdateDoglegDeltas); } - tic(copy_delta); + gttic(copy_delta); delta = isam.deltaNewton_; - toc(copy_delta); + gttoc(copy_delta); } /* ************************************************************************* */ VectorValues optimizeGradientSearch(const ISAM2& isam) { - tic(Allocate_VectorValues); + gttic(Allocate_VectorValues); VectorValues grad = *allocateVectorValues(isam); - toc(Allocate_VectorValues); + gttoc(Allocate_VectorValues); optimizeGradientSearchInPlace(isam, grad); @@ -875,7 +875,7 @@ VectorValues optimizeGradientSearch(const ISAM2& isam) { void optimizeGradientSearchInPlace(const ISAM2& isam, VectorValues& grad) { // We may need to update the solution calcaulations if(!isam.deltaDoglegUptodate_) { - tic(UpdateDoglegDeltas); + gttic(UpdateDoglegDeltas); double wildfireThreshold = 0.0; if(isam.params().optimizationParams.type() == typeid(ISAM2GaussNewtonParams)) wildfireThreshold = boost::get(isam.params().optimizationParams).wildfireThreshold; @@ -885,25 +885,25 @@ void optimizeGradientSearchInPlace(const ISAM2& isam, VectorValues& grad) { assert(false); ISAM2::Impl::UpdateDoglegDeltas(isam, wildfireThreshold, isam.deltaReplacedMask_, isam.deltaNewton_, isam.RgProd_); isam.deltaDoglegUptodate_ = true; - toc(UpdateDoglegDeltas); + gttoc(UpdateDoglegDeltas); } - tic(Compute_Gradient); + gttic(Compute_Gradient); // Compute gradient (call gradientAtZero function, which is defined for various linear systems) gradientAtZero(isam, grad); double gradientSqNorm = grad.dot(grad); - toc(Compute_Gradient); + gttoc(Compute_Gradient); - tic(Compute_minimizing_step_size); + gttic(Compute_minimizing_step_size); // Compute minimizing step size double RgNormSq = isam.RgProd_.vector().squaredNorm(); double step = -gradientSqNorm / RgNormSq; - toc(Compute_minimizing_step_size); + gttoc(Compute_minimizing_step_size); - tic(Compute_point); + gttic(Compute_point); // Compute steepest descent point grad.vector() *= step; - toc(Compute_point); + gttoc(Compute_point); } /* ************************************************************************* */ diff --git a/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp b/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp index d22509db6..f9dec6874 100644 --- a/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp +++ b/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp @@ -71,7 +71,7 @@ void LevenbergMarquardtParams::print(const std::string& str) const { /* ************************************************************************* */ void LevenbergMarquardtOptimizer::iterate() { - tic(LM_iterate); + gttic(LM_iterate); // Linearize graph GaussianFactorGraph::shared_ptr linear = graph_.linearize(state_.values, *params_.ordering); @@ -87,7 +87,7 @@ void LevenbergMarquardtOptimizer::iterate() { // Add prior-factors // TODO: replace this dampening with a backsubstitution approach - tic(damp); + gttic(damp); GaussianFactorGraph dampedSystem(*linear); { double sigma = 1.0 / std::sqrt(state_.lambda); @@ -102,7 +102,7 @@ void LevenbergMarquardtOptimizer::iterate() { dampedSystem.push_back(prior); } } - toc(damp); + gttoc(damp); if (lmVerbosity >= LevenbergMarquardtParams::DAMPED) dampedSystem.print("damped"); // Try solving @@ -114,14 +114,14 @@ void LevenbergMarquardtOptimizer::iterate() { if (lmVerbosity >= LevenbergMarquardtParams::TRYDELTA) delta.print("delta"); // update values - tic(retract); + gttic(retract); Values newValues = state_.values.retract(delta, *params_.ordering); - toc(retract); + gttoc(retract); // create new optimization state with more adventurous lambda - tic(compute_error); + gttic(compute_error); double error = graph_.error(newValues); - toc(compute_error); + gttoc(compute_error); if (lmVerbosity >= LevenbergMarquardtParams::TRYLAMBDA) cout << "next error = " << error << endl; diff --git a/gtsam/nonlinear/NonlinearFactorGraph.cpp b/gtsam/nonlinear/NonlinearFactorGraph.cpp index b2b9f035a..0b9ae35d5 100644 --- a/gtsam/nonlinear/NonlinearFactorGraph.cpp +++ b/gtsam/nonlinear/NonlinearFactorGraph.cpp @@ -44,7 +44,7 @@ void NonlinearFactorGraph::print(const std::string& str, const KeyFormatter& key /* ************************************************************************* */ double NonlinearFactorGraph::error(const Values& c) const { - tic(NonlinearFactorGraph_error); + gttic(NonlinearFactorGraph_error); double total_error = 0.; // iterate over all the factors_ to accumulate the log probabilities BOOST_FOREACH(const sharedFactor& factor, this->factors_) { @@ -68,7 +68,7 @@ FastSet NonlinearFactorGraph::keys() const { Ordering::shared_ptr NonlinearFactorGraph::orderingCOLAMD( const Values& config) const { - tic(NonlinearFactorGraph_orderingCOLAMD); + gttic(NonlinearFactorGraph_orderingCOLAMD); // Create symbolic graph and initial (iterator) ordering SymbolicFactorGraph::shared_ptr symbolic; @@ -93,7 +93,7 @@ Ordering::shared_ptr NonlinearFactorGraph::orderingCOLAMD( Ordering::shared_ptr NonlinearFactorGraph::orderingCOLAMDConstrained(const Values& config, const std::map& constraints) const { - tic(NonlinearFactorGraph_orderingCOLAMDConstrained); + gttic(NonlinearFactorGraph_orderingCOLAMDConstrained); // Create symbolic graph and initial (iterator) ordering SymbolicFactorGraph::shared_ptr symbolic; @@ -122,7 +122,7 @@ Ordering::shared_ptr NonlinearFactorGraph::orderingCOLAMDConstrained(const Value /* ************************************************************************* */ SymbolicFactorGraph::shared_ptr NonlinearFactorGraph::symbolic(const Ordering& ordering) const { - tic(NonlinearFactorGraph_symbolic_from_Ordering); + gttic(NonlinearFactorGraph_symbolic_from_Ordering); // Generate the symbolic factor graph SymbolicFactorGraph::shared_ptr symbolicfg(new SymbolicFactorGraph); @@ -142,7 +142,7 @@ SymbolicFactorGraph::shared_ptr NonlinearFactorGraph::symbolic(const Ordering& o pair NonlinearFactorGraph::symbolic( const Values& config) const { - tic(NonlinearFactorGraph_symbolic_from_Values); + gttic(NonlinearFactorGraph_symbolic_from_Values); // Generate an initial key ordering in iterator order Ordering::shared_ptr ordering(config.orderingArbitrary()); @@ -153,7 +153,7 @@ pair NonlinearFactorGraph GaussianFactorGraph::shared_ptr NonlinearFactorGraph::linearize( const Values& config, const Ordering& ordering) const { - tic(NonlinearFactorGraph_linearize); + gttic(NonlinearFactorGraph_linearize); // create an empty linear FG GaussianFactorGraph::shared_ptr linearFG(new GaussianFactorGraph); diff --git a/gtsam/nonlinear/SuccessiveLinearizationOptimizer.cpp b/gtsam/nonlinear/SuccessiveLinearizationOptimizer.cpp index 0bd719f22..0e8e4d748 100644 --- a/gtsam/nonlinear/SuccessiveLinearizationOptimizer.cpp +++ b/gtsam/nonlinear/SuccessiveLinearizationOptimizer.cpp @@ -54,7 +54,7 @@ void SuccessiveLinearizationParams::print(const std::string& str) const { } VectorValues solveGaussianFactorGraph(const GaussianFactorGraph &gfg, const SuccessiveLinearizationParams ¶ms) { - tic(solveGaussianFactorGraph); + gttic(solveGaussianFactorGraph); VectorValues delta; if (params.isMultifrontal()) { delta = GaussianJunctionTree(gfg).optimize(params.getEliminationFunction()); diff --git a/gtsam_unstable/discrete/Scheduler.cpp b/gtsam_unstable/discrete/Scheduler.cpp index c075e2a2d..ff092bf8d 100644 --- a/gtsam_unstable/discrete/Scheduler.cpp +++ b/gtsam_unstable/discrete/Scheduler.cpp @@ -253,12 +253,12 @@ namespace gtsam { /** Eliminate, return a Bayes net */ DiscreteBayesNet::shared_ptr Scheduler::eliminate() const { - tic(my_solver); + gttic(my_solver); DiscreteSequentialSolver solver(*this); - toc(my_solver); - tic(my_eliminate); + gttoc(my_solver); + gttic(my_eliminate); DiscreteBayesNet::shared_ptr chordal = solver.eliminate(); - toc(my_eliminate); + gttoc(my_eliminate); return chordal; } @@ -273,9 +273,9 @@ namespace gtsam { (*it)->print(student.name_); } - tic(my_optimize); + gttic(my_optimize); sharedValues mpe = optimize(*chordal); - toc(my_optimize); + gttoc(my_optimize); return mpe; } diff --git a/gtsam_unstable/discrete/examples/schedulingExample.cpp b/gtsam_unstable/discrete/examples/schedulingExample.cpp index 4ca18dab1..60b8a197d 100644 --- a/gtsam_unstable/discrete/examples/schedulingExample.cpp +++ b/gtsam_unstable/discrete/examples/schedulingExample.cpp @@ -117,9 +117,9 @@ void runLargeExample() { // Do exact inference // SETDEBUG("timing-verbose", true); SETDEBUG("DiscreteConditional::DiscreteConditional", true); - tic(large); + gttic(large); DiscreteFactor::sharedValues MPE = scheduler.optimalAssignment(); - toc(large); + gttoc(large); tictoc_finishedIteration(); tictoc_print(); scheduler.printAssignment(MPE); @@ -151,9 +151,9 @@ void solveStaged(size_t addMutex = 2) { scheduler.buildGraph(addMutex); // Do EXACT INFERENCE - tic_(eliminate); + gttic_(eliminate); DiscreteBayesNet::shared_ptr chordal = scheduler.eliminate(); - toc_(eliminate); + gttoc_(eliminate); // find root node DiscreteConditional::shared_ptr root = *(chordal->rbegin()); diff --git a/gtsam_unstable/discrete/examples/schedulingQuals12.cpp b/gtsam_unstable/discrete/examples/schedulingQuals12.cpp index af62e04a0..66c5b9bfc 100644 --- a/gtsam_unstable/discrete/examples/schedulingQuals12.cpp +++ b/gtsam_unstable/discrete/examples/schedulingQuals12.cpp @@ -124,9 +124,9 @@ void runLargeExample() { SETDEBUG("DiscreteConditional::DiscreteConditional", true); #define SAMPLE #ifdef SAMPLE - tic(large); + gttic(large); DiscreteBayesNet::shared_ptr chordal = scheduler.eliminate(); - toc(large); + gttoc(large); tictoc_finishedIteration(); tictoc_print(); for (size_t i=0;i<100;i++) { @@ -143,9 +143,9 @@ void runLargeExample() { } } #else - tic(large); + gttic(large); DiscreteFactor::sharedValues MPE = scheduler.optimalAssignment(); - toc(large); + gttoc(large); tictoc_finishedIteration(); tictoc_print(); scheduler.printAssignment(MPE); @@ -178,9 +178,9 @@ void solveStaged(size_t addMutex = 2) { scheduler.buildGraph(addMutex); // Do EXACT INFERENCE - tic_(eliminate); + gttic_(eliminate); DiscreteBayesNet::shared_ptr chordal = scheduler.eliminate(); - toc_(eliminate); + gttoc_(eliminate); // find root node DiscreteConditional::shared_ptr root = *(chordal->rbegin()); diff --git a/gtsam_unstable/discrete/tests/testScheduler.cpp b/gtsam_unstable/discrete/tests/testScheduler.cpp index ff5974547..406ca0c8e 100644 --- a/gtsam_unstable/discrete/tests/testScheduler.cpp +++ b/gtsam_unstable/discrete/tests/testScheduler.cpp @@ -125,9 +125,9 @@ TEST( schedulingExample, test) //product.dot("scheduling", false); // Do exact inference - tic(small); + gttic(small); DiscreteFactor::sharedValues MPE = s.optimalAssignment(); - toc(small); + gttoc(small); // print MPE, commented out as unit tests don't print // s.printAssignment(MPE); diff --git a/tests/timeBatch.cpp b/tests/timeBatch.cpp index f94210a90..a8d2d9e82 100644 --- a/tests/timeBatch.cpp +++ b/tests/timeBatch.cpp @@ -34,15 +34,15 @@ int main(int argc, char *argv[]) { cout << "Optimizing..." << endl; - tic_(Create_optimizer); + gttic_(Create_optimizer); LevenbergMarquardtOptimizer optimizer(graph, initial); - toc_(Create_optimizer); + gttoc_(Create_optimizer); tictoc_print_(); double lastError = optimizer.error(); do { - tic_(Iterate_optimizer); + gttic_(Iterate_optimizer); optimizer.iterate(); - toc_(Iterate_optimizer); + gttoc_(Iterate_optimizer); tictoc_finishedIteration_(); tictoc_print_(); cout << "Error: " << optimizer.error() << ", lambda: " << optimizer.lambda() << endl; diff --git a/tests/timeIncremental.cpp b/tests/timeIncremental.cpp index f1a3c5e0f..15ff8acd7 100644 --- a/tests/timeIncremental.cpp +++ b/tests/timeIncremental.cpp @@ -46,11 +46,11 @@ int main(int argc, char *argv[]) { cout << "Loading data..." << endl; - tic_(Find_datafile); + gttic_(Find_datafile); string datasetFile = findExampleDataFile("w10000-odom"); std::pair data = load2D(datasetFile); - toc_(Find_datafile); + gttoc_(Find_datafile); NonlinearFactorGraph measurements = *data.first; Values initial = *data.second; @@ -66,7 +66,7 @@ int main(int argc, char *argv[]) { NonlinearFactorGraph newFactors; // Collect measurements and new variables for the current step - tic_(Collect_measurements); + gttic_(Collect_measurements); if(step == 1) { // cout << "Initializing " << 0 << endl; newVariables.insert(0, Pose()); @@ -114,19 +114,19 @@ int main(int argc, char *argv[]) { } ++ nextMeasurement; } - toc_(Collect_measurements); + gttoc_(Collect_measurements); // Update iSAM2 - tic_(Update_ISAM2); + gttic_(Update_ISAM2); isam2.update(newFactors, newVariables); - toc_(Update_ISAM2); + gttoc_(Update_ISAM2); if(step % 100 == 0) { - tic_(chi2); + gttic_(chi2); Values estimate(isam2.calculateEstimate()); double chi2 = chi2_red(isam2.getFactorsUnsafe(), estimate); cout << "chi2 = " << chi2 << endl; - toc_(chi2); + gttoc_(chi2); } tictoc_finishedIteration_(); @@ -141,9 +141,9 @@ int main(int argc, char *argv[]) { Marginals marginals(isam2.getFactorsUnsafe(), isam2.calculateEstimate()); int i=0; BOOST_FOREACH(Key key, initial.keys()) { - tic_(marginalInformation); + gttic_(marginalInformation); Matrix info = marginals.marginalInformation(key); - toc_(marginalInformation); + gttoc_(marginalInformation); tictoc_finishedIteration_(); if(i % 1000 == 0) tictoc_print_();