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83098e12e8b2252797a6570ba340c181eeddbd07
ceres-solver/internal/ceres/solver.cc
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Sameer Agarwal 83098e12e8 Two changes to how we manage user's state. 
1. When Solver::Options::update_state_every_iteration = true,
   the StateUpdatingCallback only updates the state on a
   successful iteration. This works fine but will not work
   if the user provides an EvaluationCallback, because
   every call to Evaluator::Evaluate will change the state
   visible to the user. This means that on unsuccessful
   iterations when the user's IterationCallback is called
   the state visible to the user would be the last evaluation
   which did not lead to an improved cost. This CL forces
   the StateUpdatingCallback to unconditionally update
   the user visible state.

2. When the minimizer terminates, we update the user visible
   state only if the solution is usable, otherwise the user's
   state will remain the same. To maintain this invariant
   we will now cache the user's state and make sure we
   use it to reset it upon return if the solver is not
   successful.

Change-Id: Ic1f8fa6cb10d2753130ea752c70ff3d6b2f1462f
2018-03-06 19:12:14 +00:00

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// 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/casts.h"
#include "ceres/context.h"
#include "ceres/context_impl.h"
#include "ceres/detect_structure.h"
#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/schur_templates.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);
if (options.check_gradients) {
OPTION_GT(gradient_check_relative_precision, 0.0);
OPTION_GT(gradient_check_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->num_line_search_steps = 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_threads;
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.linear_solver_options.type;
summary->num_linear_solver_threads_used = pp.options.num_threads;
summary->num_threads_used = pp.options.num_threads;
summary->preconditioner_type_used = pp.options.preconditioner_type;
internal::SetSummaryFinalCost(summary);
if (pp.reduced_program.get() != NULL) {
SummarizeReducedProgram(*pp.reduced_program, summary);
}
using internal::CallStatistics;
// 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, CallStatistics>& evaluator_statistics =
pp.evaluator->Statistics();
{
const CallStatistics& call_stats = FindWithDefault(
evaluator_statistics, "Evaluator::Residual", CallStatistics());
summary->residual_evaluation_time_in_seconds = call_stats.time;
summary->num_residual_evaluations = call_stats.calls;
}
{
const CallStatistics& call_stats = FindWithDefault(
evaluator_statistics, "Evaluator::Jacobian", CallStatistics());
summary->jacobian_evaluation_time_in_seconds = call_stats.time;
summary->num_jacobian_evaluations = call_stats.calls;
}
}
// 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, CallStatistics>& linear_solver_statistics =
pp.linear_solver->Statistics();
const CallStatistics& call_stats = FindWithDefault(
linear_solver_statistics, "LinearSolver::Solve", CallStatistics());
summary->num_linear_solves = call_stats.calls;
summary->linear_solver_time_in_seconds = call_stats.time;
}
}
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;
}
const Vector original_reduced_parameters = pp->reduced_parameters;
scoped_ptr<Minimizer> minimizer(
Minimizer::Create(pp->options.minimizer_type));
minimizer->Minimize(pp->minimizer_options,
pp->reduced_parameters.data(),
summary);
program->StateVectorToParameterBlocks(
summary->IsSolutionUsable()
? pp->reduced_parameters.data()
: original_reduced_parameters.data());
program->CopyParameterBlockStateToUserState();
}
std::string SchurStructureToString(const int row_block_size,
const int e_block_size,
const int f_block_size) {
const std::string row =
(row_block_size == Eigen::Dynamic)
? "d" : internal::StringPrintf("%d", row_block_size);
const std::string e =
(e_block_size == Eigen::Dynamic)
? "d" : internal::StringPrintf("%d", e_block_size);
const std::string f =
(f_block_size == Eigen::Dynamic)
? "d" : internal::StringPrintf("%d", f_block_size);
return internal::StringPrintf("%s,%s,%s", row.c_str(), e.c_str(), f.c_str());
}
} // 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);
// The main thread also does work so we only need to launch num_threads - 1.
problem_impl->context()->EnsureMinimumThreads(options.num_threads - 1);
// Make sure that all the parameter blocks states are set to the
// values provided by the user.
program->SetParameterBlockStatePtrsToUserStatePtrs();
// If gradient_checking is enabled, wrap all cost functions in a
// gradient checker and install a callback that terminates if any gradient
// error is detected.
scoped_ptr<internal::ProblemImpl> gradient_checking_problem;
internal::GradientCheckingIterationCallback gradient_checking_callback;
Solver::Options modified_options = options;
if (options.check_gradients) {
modified_options.callbacks.push_back(&gradient_checking_callback);
gradient_checking_problem.reset(
CreateGradientCheckingProblemImpl(
problem_impl,
options.gradient_check_numeric_derivative_relative_step_size,
options.gradient_check_relative_precision,
&gradient_checking_callback));
problem_impl = gradient_checking_problem.get();
program = problem_impl->mutable_program();
}
scoped_ptr<Preprocessor> preprocessor(
Preprocessor::Create(modified_options.minimizer_type));
PreprocessedProblem pp;
const bool status = preprocessor->Preprocess(modified_options, problem_impl, &pp);
// We check the linear_solver_options.type rather than
// modified_options.linear_solver_type because, depending on the
// lack of a Schur structure, the preprocessor may change the linear
// solver type.
if (IsSchurType(pp.linear_solver_options.type)) {
// TODO(sameeragarwal): We can likely eliminate the duplicate call
// to DetectStructure here and inside the linear solver, by
// calling this in the preprocessor.
int row_block_size;
int e_block_size;
int f_block_size;
DetectStructure(*static_cast<internal::BlockSparseMatrix*>(
pp.minimizer_options.jacobian.get())
->block_structure(),
pp.linear_solver_options.elimination_groups[0],
&row_block_size,
&e_block_size,
&f_block_size);
summary->schur_structure_given =
SchurStructureToString(row_block_size, e_block_size, f_block_size);
internal::GetBestSchurTemplateSpecialization(&row_block_size,
&e_block_size,
&f_block_size);
summary->schur_structure_used =
SchurStructureToString(row_block_size, e_block_size, f_block_size);
}
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;
// If the gradient checker reported an error, we want to report FAILURE
// instead of USER_FAILURE and provide the error log.
if (gradient_checking_callback.gradient_error_detected()) {
summary->termination_type = FAILURE;
summary->message = gradient_checking_callback.error_log();
}
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),
num_line_search_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),
num_linear_solves(-1),
residual_evaluation_time_in_seconds(-1.0),
num_residual_evaluations(-1),
jacobian_evaluation_time_in_seconds(-1.0),
num_jacobian_evaluations(-1),
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, "Residuals % 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);
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 (IsSchurType(linear_solver_type_used)) {
StringAppendF(&report,
"Schur structure %22s %24s\n",
schur_structure_given.c_str(),
schur_structure_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 line_search_used =
(minimizer_type == LINE_SEARCH ||
(minimizer_type == TRUST_REGION && is_constrained));
if (line_search_used) {
StringAppendF(&report, "Line search steps % 14d\n",
num_line_search_steps);
}
StringAppendF(&report, "\nTime (in seconds):\n");
StringAppendF(&report, "Preprocessor %25.6f\n",
preprocessor_time_in_seconds);
StringAppendF(&report, "\n Residual only evaluation %18.6f (%d)\n",
residual_evaluation_time_in_seconds, num_residual_evaluations);
if (line_search_used) {
StringAppendF(&report, " Line search cost evaluation %10.6f\n",
line_search_cost_evaluation_time_in_seconds);
}
StringAppendF(&report, " Jacobian & residual evaluation %12.6f (%d)\n",
jacobian_evaluation_time_in_seconds, num_jacobian_evaluations);
if (line_search_used) {
StringAppendF(&report, " Line search gradient evaluation %6.6f\n",
line_search_gradient_evaluation_time_in_seconds);
}
if (minimizer_type == TRUST_REGION) {
StringAppendF(&report, " Linear solver %23.6f (%d)\n",
linear_solver_time_in_seconds, num_linear_solves);
}
if (inner_iterations_used) {
StringAppendF(&report, " Inner iterations %23.6f\n",
inner_iteration_time_in_seconds);
}
if (line_search_used) {
StringAppendF(&report, " Line search polynomial minimization %.6f\n",
line_search_polynomial_minimization_time_in_seconds);
}
StringAppendF(&report, "Minimizer %25.6f\n\n",
minimizer_time_in_seconds);
StringAppendF(&report, "Postprocessor %24.6f\n",
postprocessor_time_in_seconds);
StringAppendF(&report, "Total %25.6f\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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