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MYNT-EYE-S-SDK/3rdparty/ceres-solver-1.11.0/internal/ceres/solver.cc

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feat(3rdparty): add eigen and ceres
2019-01-03 10:25:18 +02:00
// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2015 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: keir@google.com (Keir Mierle)
// sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/solver.h"
#include <algorithm>
#include <sstream> // NOLINT
#include <vector>
#include "ceres/gradient_checking_cost_function.h"
#include "ceres/internal/port.h"
#include "ceres/parameter_block_ordering.h"
#include "ceres/preprocessor.h"
#include "ceres/problem.h"
#include "ceres/problem_impl.h"
#include "ceres/program.h"
#include "ceres/solver_utils.h"
#include "ceres/stringprintf.h"
#include "ceres/types.h"
#include "ceres/wall_time.h"
namespace ceres {
namespace {
using std::map;
using std::string;
using std::vector;
#define OPTION_OP(x, y, OP) \
if (!(options.x OP y)) { \
std::stringstream ss; \
ss << "Invalid configuration. "; \
ss << string("Solver::Options::" #x " = ") << options.x << ". "; \
ss << "Violated constraint: "; \
ss << string("Solver::Options::" #x " " #OP " "#y); \
*error = ss.str(); \
return false; \
}
#define OPTION_OP_OPTION(x, y, OP) \
if (!(options.x OP options.y)) { \
std::stringstream ss; \
ss << "Invalid configuration. "; \
ss << string("Solver::Options::" #x " = ") << options.x << ". "; \
ss << string("Solver::Options::" #y " = ") << options.y << ". "; \
ss << "Violated constraint: "; \
ss << string("Solver::Options::" #x); \
ss << string(#OP " Solver::Options::" #y "."); \
*error = ss.str(); \
return false; \
}
#define OPTION_GE(x, y) OPTION_OP(x, y, >=);
#define OPTION_GT(x, y) OPTION_OP(x, y, >);
#define OPTION_LE(x, y) OPTION_OP(x, y, <=);
#define OPTION_LT(x, y) OPTION_OP(x, y, <);
#define OPTION_LE_OPTION(x, y) OPTION_OP_OPTION(x, y, <=)
#define OPTION_LT_OPTION(x, y) OPTION_OP_OPTION(x, y, <)
bool CommonOptionsAreValid(const Solver::Options& options, string* error) {
OPTION_GE(max_num_iterations, 0);
OPTION_GE(max_solver_time_in_seconds, 0.0);
OPTION_GE(function_tolerance, 0.0);
OPTION_GE(gradient_tolerance, 0.0);
OPTION_GE(parameter_tolerance, 0.0);
OPTION_GT(num_threads, 0);
OPTION_GT(num_linear_solver_threads, 0);
if (options.check_gradients) {
OPTION_GT(gradient_check_relative_precision, 0.0);
OPTION_GT(numeric_derivative_relative_step_size, 0.0);
}
return true;
}
bool TrustRegionOptionsAreValid(const Solver::Options& options, string* error) {
OPTION_GT(initial_trust_region_radius, 0.0);
OPTION_GT(min_trust_region_radius, 0.0);
OPTION_GT(max_trust_region_radius, 0.0);
OPTION_LE_OPTION(min_trust_region_radius, max_trust_region_radius);
OPTION_LE_OPTION(min_trust_region_radius, initial_trust_region_radius);
OPTION_LE_OPTION(initial_trust_region_radius, max_trust_region_radius);
OPTION_GE(min_relative_decrease, 0.0);
OPTION_GE(min_lm_diagonal, 0.0);
OPTION_GE(max_lm_diagonal, 0.0);
OPTION_LE_OPTION(min_lm_diagonal, max_lm_diagonal);
OPTION_GE(max_num_consecutive_invalid_steps, 0);
OPTION_GT(eta, 0.0);
OPTION_GE(min_linear_solver_iterations, 0);
OPTION_GE(max_linear_solver_iterations, 1);
OPTION_LE_OPTION(min_linear_solver_iterations, max_linear_solver_iterations);
if (options.use_inner_iterations) {
OPTION_GE(inner_iteration_tolerance, 0.0);
}
if (options.use_nonmonotonic_steps) {
OPTION_GT(max_consecutive_nonmonotonic_steps, 0);
}
if (options.linear_solver_type == ITERATIVE_SCHUR &&
options.use_explicit_schur_complement &&
options.preconditioner_type != SCHUR_JACOBI) {
*error = "use_explicit_schur_complement only supports "
"SCHUR_JACOBI as the preconditioner.";
return false;
}
if (options.preconditioner_type == CLUSTER_JACOBI &&
options.sparse_linear_algebra_library_type != SUITE_SPARSE) {
*error = "CLUSTER_JACOBI requires "
"Solver::Options::sparse_linear_algebra_library_type to be "
"SUITE_SPARSE";
return false;
}
if (options.preconditioner_type == CLUSTER_TRIDIAGONAL &&
options.sparse_linear_algebra_library_type != SUITE_SPARSE) {
*error = "CLUSTER_TRIDIAGONAL requires "
"Solver::Options::sparse_linear_algebra_library_type to be "
"SUITE_SPARSE";
return false;
}
#ifdef CERES_NO_LAPACK
if (options.dense_linear_algebra_library_type == LAPACK) {
if (options.linear_solver_type == DENSE_NORMAL_CHOLESKY) {
*error = "Can't use DENSE_NORMAL_CHOLESKY with LAPACK because "
"LAPACK was not enabled when Ceres was built.";
return false;
} else if (options.linear_solver_type == DENSE_QR) {
*error = "Can't use DENSE_QR with LAPACK because "
"LAPACK was not enabled when Ceres was built.";
return false;
} else if (options.linear_solver_type == DENSE_SCHUR) {
*error = "Can't use DENSE_SCHUR with LAPACK because "
"LAPACK was not enabled when Ceres was built.";
return false;
}
}
#endif
#ifdef CERES_NO_SUITESPARSE
if (options.sparse_linear_algebra_library_type == SUITE_SPARSE) {
if (options.linear_solver_type == SPARSE_NORMAL_CHOLESKY) {
*error = "Can't use SPARSE_NORMAL_CHOLESKY with SUITESPARSE because "
"SuiteSparse was not enabled when Ceres was built.";
return false;
} else if (options.linear_solver_type == SPARSE_SCHUR) {
*error = "Can't use SPARSE_SCHUR with SUITESPARSE because "
"SuiteSparse was not enabled when Ceres was built.";
return false;
} else if (options.preconditioner_type == CLUSTER_JACOBI) {
*error = "CLUSTER_JACOBI preconditioner not supported. "
"SuiteSparse was not enabled when Ceres was built.";
return false;
} else if (options.preconditioner_type == CLUSTER_TRIDIAGONAL) {
*error = "CLUSTER_TRIDIAGONAL preconditioner not supported. "
"SuiteSparse was not enabled when Ceres was built.";
return false;
}
}
#endif
#ifdef CERES_NO_CXSPARSE
if (options.sparse_linear_algebra_library_type == CX_SPARSE) {
if (options.linear_solver_type == SPARSE_NORMAL_CHOLESKY) {
*error = "Can't use SPARSE_NORMAL_CHOLESKY with CX_SPARSE because "
"CXSparse was not enabled when Ceres was built.";
return false;
} else if (options.linear_solver_type == SPARSE_SCHUR) {
*error = "Can't use SPARSE_SCHUR with CX_SPARSE because "
"CXSparse was not enabled when Ceres was built.";
return false;
}
}
#endif
#ifndef CERES_USE_EIGEN_SPARSE
if (options.sparse_linear_algebra_library_type == EIGEN_SPARSE) {
if (options.linear_solver_type == SPARSE_NORMAL_CHOLESKY) {
*error = "Can't use SPARSE_NORMAL_CHOLESKY with EIGEN_SPARSE because "
"Eigen's sparse linear algebra was not enabled when Ceres was "
"built.";
return false;
} else if (options.linear_solver_type == SPARSE_SCHUR) {
*error = "Can't use SPARSE_SCHUR with EIGEN_SPARSE because "
"Eigen's sparse linear algebra was not enabled when Ceres was "
"built.";
return false;
}
}
#endif
if (options.sparse_linear_algebra_library_type == NO_SPARSE) {
if (options.linear_solver_type == SPARSE_NORMAL_CHOLESKY) {
*error = "Can't use SPARSE_NORMAL_CHOLESKY as "
"sparse_linear_algebra_library_type is NO_SPARSE.";
return false;
} else if (options.linear_solver_type == SPARSE_SCHUR) {
*error = "Can't use SPARSE_SCHUR as "
"sparse_linear_algebra_library_type is NO_SPARSE.";
return false;
}
}
if (options.trust_region_strategy_type == DOGLEG) {
if (options.linear_solver_type == ITERATIVE_SCHUR ||
options.linear_solver_type == CGNR) {
*error = "DOGLEG only supports exact factorization based linear "
"solvers. If you want to use an iterative solver please "
"use LEVENBERG_MARQUARDT as the trust_region_strategy_type";
return false;
}
}
if (options.trust_region_minimizer_iterations_to_dump.size() > 0 &&
options.trust_region_problem_dump_format_type != CONSOLE &&
options.trust_region_problem_dump_directory.empty()) {
*error = "Solver::Options::trust_region_problem_dump_directory is empty.";
return false;
}
if (options.dynamic_sparsity &&
options.linear_solver_type != SPARSE_NORMAL_CHOLESKY) {
*error = "Dynamic sparsity is only supported with SPARSE_NORMAL_CHOLESKY.";
return false;
}
return true;
}
bool LineSearchOptionsAreValid(const Solver::Options& options, string* error) {
OPTION_GT(max_lbfgs_rank, 0);
OPTION_GT(min_line_search_step_size, 0.0);
OPTION_GT(max_line_search_step_contraction, 0.0);
OPTION_LT(max_line_search_step_contraction, 1.0);
OPTION_LT_OPTION(max_line_search_step_contraction,
min_line_search_step_contraction);
OPTION_LE(min_line_search_step_contraction, 1.0);
OPTION_GT(max_num_line_search_step_size_iterations, 0);
OPTION_GT(line_search_sufficient_function_decrease, 0.0);
OPTION_LT_OPTION(line_search_sufficient_function_decrease,
line_search_sufficient_curvature_decrease);
OPTION_LT(line_search_sufficient_curvature_decrease, 1.0);
OPTION_GT(max_line_search_step_expansion, 1.0);
if ((options.line_search_direction_type == ceres::BFGS ||
options.line_search_direction_type == ceres::LBFGS) &&
options.line_search_type != ceres::WOLFE) {
*error =
string("Invalid configuration: Solver::Options::line_search_type = ")
+ string(LineSearchTypeToString(options.line_search_type))
+ string(". When using (L)BFGS, "
"Solver::Options::line_search_type must be set to WOLFE.");
return false;
}
// Warn user if they have requested BISECTION interpolation, but constraints
// on max/min step size change during line search prevent bisection scaling
// from occurring. Warn only, as this is likely a user mistake, but one which
// does not prevent us from continuing.
LOG_IF(WARNING,
(options.line_search_interpolation_type == ceres::BISECTION &&
(options.max_line_search_step_contraction > 0.5 ||
options.min_line_search_step_contraction < 0.5)))
<< "Line search interpolation type is BISECTION, but specified "
<< "max_line_search_step_contraction: "
<< options.max_line_search_step_contraction << ", and "
<< "min_line_search_step_contraction: "
<< options.min_line_search_step_contraction
<< ", prevent bisection (0.5) scaling, continuing with solve regardless.";
return true;
}
#undef OPTION_OP
#undef OPTION_OP_OPTION
#undef OPTION_GT
#undef OPTION_GE
#undef OPTION_LE
#undef OPTION_LT
#undef OPTION_LE_OPTION
#undef OPTION_LT_OPTION
void StringifyOrdering(const vector<int>& ordering, string* report) {
if (ordering.size() == 0) {
internal::StringAppendF(report, "AUTOMATIC");
return;
}
for (int i = 0; i < ordering.size() - 1; ++i) {
internal::StringAppendF(report, "%d, ", ordering[i]);
}
internal::StringAppendF(report, "%d", ordering.back());
}
void SummarizeGivenProgram(const internal::Program& program,
Solver::Summary* summary) {
summary->num_parameter_blocks = program.NumParameterBlocks();
summary->num_parameters = program.NumParameters();
summary->num_effective_parameters = program.NumEffectiveParameters();
summary->num_residual_blocks = program.NumResidualBlocks();
summary->num_residuals = program.NumResiduals();
}
void SummarizeReducedProgram(const internal::Program& program,
Solver::Summary* summary) {
summary->num_parameter_blocks_reduced = program.NumParameterBlocks();
summary->num_parameters_reduced = program.NumParameters();
summary->num_effective_parameters_reduced = program.NumEffectiveParameters();
summary->num_residual_blocks_reduced = program.NumResidualBlocks();
summary->num_residuals_reduced = program.NumResiduals();
}
void PreSolveSummarize(const Solver::Options& options,
const internal::ProblemImpl* problem,
Solver::Summary* summary) {
SummarizeGivenProgram(problem->program(), summary);
internal::OrderingToGroupSizes(options.linear_solver_ordering.get(),
&(summary->linear_solver_ordering_given));
internal::OrderingToGroupSizes(options.inner_iteration_ordering.get(),
&(summary->inner_iteration_ordering_given));
summary->dense_linear_algebra_library_type = options.dense_linear_algebra_library_type; // NOLINT
summary->dogleg_type = options.dogleg_type;
summary->inner_iteration_time_in_seconds = 0.0;
summary->line_search_cost_evaluation_time_in_seconds = 0.0;
summary->line_search_gradient_evaluation_time_in_seconds = 0.0;
summary->line_search_polynomial_minimization_time_in_seconds = 0.0;
summary->line_search_total_time_in_seconds = 0.0;
summary->inner_iterations_given = options.use_inner_iterations;
summary->line_search_direction_type = options.line_search_direction_type; // NOLINT
summary->line_search_interpolation_type = options.line_search_interpolation_type; // NOLINT
summary->line_search_type = options.line_search_type;
summary->linear_solver_type_given = options.linear_solver_type;
summary->max_lbfgs_rank = options.max_lbfgs_rank;
summary->minimizer_type = options.minimizer_type;
summary->nonlinear_conjugate_gradient_type = options.nonlinear_conjugate_gradient_type; // NOLINT
summary->num_linear_solver_threads_given = options.num_linear_solver_threads; // NOLINT
summary->num_threads_given = options.num_threads;
summary->preconditioner_type_given = options.preconditioner_type;
summary->sparse_linear_algebra_library_type = options.sparse_linear_algebra_library_type; // NOLINT
summary->trust_region_strategy_type = options.trust_region_strategy_type; // NOLINT
summary->visibility_clustering_type = options.visibility_clustering_type; // NOLINT
}
void PostSolveSummarize(const internal::PreprocessedProblem& pp,
Solver::Summary* summary) {
internal::OrderingToGroupSizes(pp.options.linear_solver_ordering.get(),
&(summary->linear_solver_ordering_used));
internal::OrderingToGroupSizes(pp.options.inner_iteration_ordering.get(),
&(summary->inner_iteration_ordering_used));
summary->inner_iterations_used = pp.inner_iteration_minimizer.get() != NULL; // NOLINT
summary->linear_solver_type_used = pp.options.linear_solver_type;
summary->num_linear_solver_threads_used = pp.options.num_linear_solver_threads; // NOLINT
summary->num_threads_used = pp.options.num_threads;
summary->preconditioner_type_used = pp.options.preconditioner_type; // NOLINT
internal::SetSummaryFinalCost(summary);
if (pp.reduced_program.get() != NULL) {
SummarizeReducedProgram(*pp.reduced_program, summary);
}
// It is possible that no evaluator was created. This would be the
// case if the preprocessor failed, or if the reduced problem did
// not contain any parameter blocks. Thus, only extract the
// evaluator statistics if one exists.
if (pp.evaluator.get() != NULL) {
const map<string, double>& evaluator_time_statistics =
pp.evaluator->TimeStatistics();
summary->residual_evaluation_time_in_seconds =
FindWithDefault(evaluator_time_statistics, "Evaluator::Residual", 0.0);
summary->jacobian_evaluation_time_in_seconds =
FindWithDefault(evaluator_time_statistics, "Evaluator::Jacobian", 0.0);
}
// Again, like the evaluator, there may or may not be a linear
// solver from which we can extract run time statistics. In
// particular the line search solver does not use a linear solver.
if (pp.linear_solver.get() != NULL) {
const map<string, double>& linear_solver_time_statistics =
pp.linear_solver->TimeStatistics();
summary->linear_solver_time_in_seconds =
FindWithDefault(linear_solver_time_statistics,
"LinearSolver::Solve",
0.0);
}
}
void Minimize(internal::PreprocessedProblem* pp,
Solver::Summary* summary) {
using internal::Program;
using internal::scoped_ptr;
using internal::Minimizer;
Program* program = pp->reduced_program.get();
if (pp->reduced_program->NumParameterBlocks() == 0) {
summary->message = "Function tolerance reached. "
"No non-constant parameter blocks found.";
summary->termination_type = CONVERGENCE;
VLOG_IF(1, pp->options.logging_type != SILENT) << summary->message;
summary->initial_cost = summary->fixed_cost;
summary->final_cost = summary->fixed_cost;
return;
}
scoped_ptr<Minimizer> minimizer(
Minimizer::Create(pp->options.minimizer_type));
minimizer->Minimize(pp->minimizer_options,
pp->reduced_parameters.data(),
summary);
if (summary->IsSolutionUsable()) {
program->StateVectorToParameterBlocks(pp->reduced_parameters.data());
program->CopyParameterBlockStateToUserState();
}
}
} // namespace
bool Solver::Options::IsValid(string* error) const {
if (!CommonOptionsAreValid(*this, error)) {
return false;
}
if (minimizer_type == TRUST_REGION &&
!TrustRegionOptionsAreValid(*this, error)) {
return false;
}
// We do not know if the problem is bounds constrained or not, if it
// is then the trust region solver will also use the line search
// solver to do a projection onto the box constraints, so make sure
// that the line search options are checked independent of what
// minimizer algorithm is being used.
return LineSearchOptionsAreValid(*this, error);
}
Solver::~Solver() {}
void Solver::Solve(const Solver::Options& options,
Problem* problem,
Solver::Summary* summary) {
using internal::PreprocessedProblem;
using internal::Preprocessor;
using internal::ProblemImpl;
using internal::Program;
using internal::scoped_ptr;
using internal::WallTimeInSeconds;
CHECK_NOTNULL(problem);
CHECK_NOTNULL(summary);
double start_time = WallTimeInSeconds();
*summary = Summary();
if (!options.IsValid(&summary->message)) {
LOG(ERROR) << "Terminating: " << summary->message;
return;
}
ProblemImpl* problem_impl = problem->problem_impl_.get();
Program* program = problem_impl->mutable_program();
PreSolveSummarize(options, problem_impl, summary);
// Make sure that all the parameter blocks states are set to the
// values provided by the user.
program->SetParameterBlockStatePtrsToUserStatePtrs();
scoped_ptr<internal::ProblemImpl> gradient_checking_problem;
if (options.check_gradients) {
gradient_checking_problem.reset(
CreateGradientCheckingProblemImpl(
problem_impl,
options.numeric_derivative_relative_step_size,
options.gradient_check_relative_precision));
problem_impl = gradient_checking_problem.get();
program = problem_impl->mutable_program();
}
scoped_ptr<Preprocessor> preprocessor(
Preprocessor::Create(options.minimizer_type));
PreprocessedProblem pp;
const bool status = preprocessor->Preprocess(options, problem_impl, &pp);
summary->fixed_cost = pp.fixed_cost;
summary->preprocessor_time_in_seconds = WallTimeInSeconds() - start_time;
if (status) {
const double minimizer_start_time = WallTimeInSeconds();
Minimize(&pp, summary);
summary->minimizer_time_in_seconds =
WallTimeInSeconds() - minimizer_start_time;
} else {
summary->message = pp.error;
}
const double postprocessor_start_time = WallTimeInSeconds();
problem_impl = problem->problem_impl_.get();
program = problem_impl->mutable_program();
// On exit, ensure that the parameter blocks again point at the user
// provided values and the parameter blocks are numbered according
// to their position in the original user provided program.
program->SetParameterBlockStatePtrsToUserStatePtrs();
program->SetParameterOffsetsAndIndex();
PostSolveSummarize(pp, summary);
summary->postprocessor_time_in_seconds =
WallTimeInSeconds() - postprocessor_start_time;
summary->total_time_in_seconds = WallTimeInSeconds() - start_time;
}
void Solve(const Solver::Options& options,
Problem* problem,
Solver::Summary* summary) {
Solver solver;
solver.Solve(options, problem, summary);
}
Solver::Summary::Summary()
// Invalid values for most fields, to ensure that we are not
// accidentally reporting default values.
: minimizer_type(TRUST_REGION),
termination_type(FAILURE),
message("ceres::Solve was not called."),
initial_cost(-1.0),
final_cost(-1.0),
fixed_cost(-1.0),
num_successful_steps(-1),
num_unsuccessful_steps(-1),
num_inner_iteration_steps(-1),
preprocessor_time_in_seconds(-1.0),
minimizer_time_in_seconds(-1.0),
postprocessor_time_in_seconds(-1.0),
total_time_in_seconds(-1.0),
linear_solver_time_in_seconds(-1.0),
residual_evaluation_time_in_seconds(-1.0),
jacobian_evaluation_time_in_seconds(-1.0),
inner_iteration_time_in_seconds(-1.0),
line_search_cost_evaluation_time_in_seconds(-1.0),
line_search_gradient_evaluation_time_in_seconds(-1.0),
line_search_polynomial_minimization_time_in_seconds(-1.0),
line_search_total_time_in_seconds(-1.0),
num_parameter_blocks(-1),
num_parameters(-1),
num_effective_parameters(-1),
num_residual_blocks(-1),
num_residuals(-1),
num_parameter_blocks_reduced(-1),
num_parameters_reduced(-1),
num_effective_parameters_reduced(-1),
num_residual_blocks_reduced(-1),
num_residuals_reduced(-1),
is_constrained(false),
num_threads_given(-1),
num_threads_used(-1),
num_linear_solver_threads_given(-1),
num_linear_solver_threads_used(-1),
linear_solver_type_given(SPARSE_NORMAL_CHOLESKY),
linear_solver_type_used(SPARSE_NORMAL_CHOLESKY),
inner_iterations_given(false),
inner_iterations_used(false),
preconditioner_type_given(IDENTITY),
preconditioner_type_used(IDENTITY),
visibility_clustering_type(CANONICAL_VIEWS),
trust_region_strategy_type(LEVENBERG_MARQUARDT),
dense_linear_algebra_library_type(EIGEN),
sparse_linear_algebra_library_type(SUITE_SPARSE),
line_search_direction_type(LBFGS),
line_search_type(ARMIJO),
line_search_interpolation_type(BISECTION),
nonlinear_conjugate_gradient_type(FLETCHER_REEVES),
max_lbfgs_rank(-1) {
}
using internal::StringAppendF;
using internal::StringPrintf;
string Solver::Summary::BriefReport() const {
return StringPrintf("Ceres Solver Report: "
"Iterations: %d, "
"Initial cost: %e, "
"Final cost: %e, "
"Termination: %s",
num_successful_steps + num_unsuccessful_steps,
initial_cost,
final_cost,
TerminationTypeToString(termination_type));
}
string Solver::Summary::FullReport() const {
using internal::VersionString;
string report = string("\nSolver Summary (v " + VersionString() + ")\n\n");
StringAppendF(&report, "%45s %21s\n", "Original", "Reduced");
StringAppendF(&report, "Parameter blocks % 25d% 25d\n",
num_parameter_blocks, num_parameter_blocks_reduced);
StringAppendF(&report, "Parameters % 25d% 25d\n",
num_parameters, num_parameters_reduced);
if (num_effective_parameters_reduced != num_parameters_reduced) {
StringAppendF(&report, "Effective parameters% 25d% 25d\n",
num_effective_parameters, num_effective_parameters_reduced);
}
StringAppendF(&report, "Residual blocks % 25d% 25d\n",
num_residual_blocks, num_residual_blocks_reduced);
StringAppendF(&report, "Residual % 25d% 25d\n",
num_residuals, num_residuals_reduced);
if (minimizer_type == TRUST_REGION) {
// TRUST_SEARCH HEADER
StringAppendF(&report, "\nMinimizer %19s\n",
"TRUST_REGION");
if (linear_solver_type_used == DENSE_NORMAL_CHOLESKY ||
linear_solver_type_used == DENSE_SCHUR ||
linear_solver_type_used == DENSE_QR) {
StringAppendF(&report, "\nDense linear algebra library %15s\n",
DenseLinearAlgebraLibraryTypeToString(
dense_linear_algebra_library_type));
}
if (linear_solver_type_used == SPARSE_NORMAL_CHOLESKY ||
linear_solver_type_used == SPARSE_SCHUR ||
(linear_solver_type_used == ITERATIVE_SCHUR &&
(preconditioner_type_used == CLUSTER_JACOBI ||
preconditioner_type_used == CLUSTER_TRIDIAGONAL))) {
StringAppendF(&report, "\nSparse linear algebra library %15s\n",
SparseLinearAlgebraLibraryTypeToString(
sparse_linear_algebra_library_type));
}
StringAppendF(&report, "Trust region strategy %19s",
TrustRegionStrategyTypeToString(
trust_region_strategy_type));
if (trust_region_strategy_type == DOGLEG) {
if (dogleg_type == TRADITIONAL_DOGLEG) {
StringAppendF(&report, " (TRADITIONAL)");
} else {
StringAppendF(&report, " (SUBSPACE)");
}
}
StringAppendF(&report, "\n");
StringAppendF(&report, "\n");
StringAppendF(&report, "%45s %21s\n", "Given", "Used");
StringAppendF(&report, "Linear solver %25s%25s\n",
LinearSolverTypeToString(linear_solver_type_given),
LinearSolverTypeToString(linear_solver_type_used));
if (linear_solver_type_given == CGNR ||
linear_solver_type_given == ITERATIVE_SCHUR) {
StringAppendF(&report, "Preconditioner %25s%25s\n",
PreconditionerTypeToString(preconditioner_type_given),
PreconditionerTypeToString(preconditioner_type_used));
}
if (preconditioner_type_used == CLUSTER_JACOBI ||
preconditioner_type_used == CLUSTER_TRIDIAGONAL) {
StringAppendF(&report, "Visibility clustering%24s%25s\n",
VisibilityClusteringTypeToString(
visibility_clustering_type),
VisibilityClusteringTypeToString(
visibility_clustering_type));
}
StringAppendF(&report, "Threads % 25d% 25d\n",
num_threads_given, num_threads_used);
StringAppendF(&report, "Linear solver threads % 23d% 25d\n",
num_linear_solver_threads_given,
num_linear_solver_threads_used);
if (IsSchurType(linear_solver_type_used)) {
string given;
StringifyOrdering(linear_solver_ordering_given, &given);
string used;
StringifyOrdering(linear_solver_ordering_used, &used);
StringAppendF(&report,
"Linear solver ordering %22s %24s\n",
given.c_str(),
used.c_str());
}
if (inner_iterations_given) {
StringAppendF(&report,
"Use inner iterations %20s %20s\n",
inner_iterations_given ? "True" : "False",
inner_iterations_used ? "True" : "False");
}
if (inner_iterations_used) {
string given;
StringifyOrdering(inner_iteration_ordering_given, &given);
string used;
StringifyOrdering(inner_iteration_ordering_used, &used);
StringAppendF(&report,
"Inner iteration ordering %20s %24s\n",
given.c_str(),
used.c_str());
}
} else {
// LINE_SEARCH HEADER
StringAppendF(&report, "\nMinimizer %19s\n", "LINE_SEARCH");
string line_search_direction_string;
if (line_search_direction_type == LBFGS) {
line_search_direction_string = StringPrintf("LBFGS (%d)", max_lbfgs_rank);
} else if (line_search_direction_type == NONLINEAR_CONJUGATE_GRADIENT) {
line_search_direction_string =
NonlinearConjugateGradientTypeToString(
nonlinear_conjugate_gradient_type);
} else {
line_search_direction_string =
LineSearchDirectionTypeToString(line_search_direction_type);
}
StringAppendF(&report, "Line search direction %19s\n",
line_search_direction_string.c_str());
const string line_search_type_string =
StringPrintf("%s %s",
LineSearchInterpolationTypeToString(
line_search_interpolation_type),
LineSearchTypeToString(line_search_type));
StringAppendF(&report, "Line search type %19s\n",
line_search_type_string.c_str());
StringAppendF(&report, "\n");
StringAppendF(&report, "%45s %21s\n", "Given", "Used");
StringAppendF(&report, "Threads % 25d% 25d\n",
num_threads_given, num_threads_used);
}
StringAppendF(&report, "\nCost:\n");
StringAppendF(&report, "Initial % 30e\n", initial_cost);
if (termination_type != FAILURE &&
termination_type != USER_FAILURE) {
StringAppendF(&report, "Final % 30e\n", final_cost);
StringAppendF(&report, "Change % 30e\n",
initial_cost - final_cost);
}
StringAppendF(&report, "\nMinimizer iterations % 16d\n",
num_successful_steps + num_unsuccessful_steps);
// Successful/Unsuccessful steps only matter in the case of the
// trust region solver. Line search terminates when it encounters
// the first unsuccessful step.
if (minimizer_type == TRUST_REGION) {
StringAppendF(&report, "Successful steps % 14d\n",
num_successful_steps);
StringAppendF(&report, "Unsuccessful steps % 14d\n",
num_unsuccessful_steps);
}
if (inner_iterations_used) {
StringAppendF(&report, "Steps with inner iterations % 14d\n",
num_inner_iteration_steps);
}
const bool print_line_search_timing_information =
minimizer_type == LINE_SEARCH ||
(minimizer_type == TRUST_REGION && is_constrained);
StringAppendF(&report, "\nTime (in seconds):\n");
StringAppendF(&report, "Preprocessor %25.4f\n",
preprocessor_time_in_seconds);
StringAppendF(&report, "\n Residual evaluation %23.4f\n",
residual_evaluation_time_in_seconds);
if (print_line_search_timing_information) {
StringAppendF(&report, " Line search cost evaluation %10.4f\n",
line_search_cost_evaluation_time_in_seconds);
}
StringAppendF(&report, " Jacobian evaluation %23.4f\n",
jacobian_evaluation_time_in_seconds);
if (print_line_search_timing_information) {
StringAppendF(&report, " Line search gradient evaluation %6.4f\n",
line_search_gradient_evaluation_time_in_seconds);
}
if (minimizer_type == TRUST_REGION) {
StringAppendF(&report, " Linear solver %23.4f\n",
linear_solver_time_in_seconds);
}
if (inner_iterations_used) {
StringAppendF(&report, " Inner iterations %23.4f\n",
inner_iteration_time_in_seconds);
}
if (print_line_search_timing_information) {
StringAppendF(&report, " Line search polynomial minimization %.4f\n",
line_search_polynomial_minimization_time_in_seconds);
}
StringAppendF(&report, "Minimizer %25.4f\n\n",
minimizer_time_in_seconds);
StringAppendF(&report, "Postprocessor %24.4f\n",
postprocessor_time_in_seconds);
StringAppendF(&report, "Total %25.4f\n\n",
total_time_in_seconds);
StringAppendF(&report, "Termination: %25s (%s)\n",
TerminationTypeToString(termination_type), message.c_str());
return report;
}
bool Solver::Summary::IsSolutionUsable() const {
return internal::IsSolutionUsable(*this);
}
} // namespace ceres
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