| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2010, 2011, 2012 Google Inc. All rights reserved. |
| // http://code.google.com/p/ceres-solver/ |
| // |
| // 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) |
| |
| #include "ceres/solver_impl.h" |
| |
| #include <iostream> // NOLINT |
| #include <numeric> |
| #include "ceres/evaluator.h" |
| #include "ceres/gradient_checking_cost_function.h" |
| #include "ceres/iteration_callback.h" |
| #include "ceres/levenberg_marquardt_strategy.h" |
| #include "ceres/linear_solver.h" |
| #include "ceres/map_util.h" |
| #include "ceres/minimizer.h" |
| #include "ceres/parameter_block.h" |
| #include "ceres/problem.h" |
| #include "ceres/problem_impl.h" |
| #include "ceres/program.h" |
| #include "ceres/residual_block.h" |
| #include "ceres/schur_ordering.h" |
| #include "ceres/stringprintf.h" |
| #include "ceres/trust_region_minimizer.h" |
| |
| namespace ceres { |
| namespace internal { |
| namespace { |
| |
| void EvaluateCostAndResiduals(ProblemImpl* problem_impl, |
| double* cost, |
| vector<double>* residuals) { |
| CHECK_NOTNULL(cost); |
| Program* program = CHECK_NOTNULL(problem_impl)->mutable_program(); |
| if (residuals != NULL) { |
| residuals->resize(program->NumResiduals()); |
| program->Evaluate(cost, &(*residuals)[0]); |
| } else { |
| program->Evaluate(cost, NULL); |
| } |
| } |
| |
| // Callback for updating the user's parameter blocks. Updates are only |
| // done if the step is successful. |
| class StateUpdatingCallback : public IterationCallback { |
| public: |
| StateUpdatingCallback(Program* program, double* parameters) |
| : program_(program), parameters_(parameters) {} |
| |
| CallbackReturnType operator()(const IterationSummary& summary) { |
| if (summary.step_is_successful) { |
| program_->StateVectorToParameterBlocks(parameters_); |
| program_->CopyParameterBlockStateToUserState(); |
| } |
| return SOLVER_CONTINUE; |
| } |
| |
| private: |
| Program* program_; |
| double* parameters_; |
| }; |
| |
| // Callback for logging the state of the minimizer to STDERR or STDOUT |
| // depending on the user's preferences and logging level. |
| class LoggingCallback : public IterationCallback { |
| public: |
| explicit LoggingCallback(bool log_to_stdout) |
| : log_to_stdout_(log_to_stdout) {} |
| |
| ~LoggingCallback() {} |
| |
| CallbackReturnType operator()(const IterationSummary& summary) { |
| const char* kReportRowFormat = |
| "% 4d: f:% 8e d:% 3.2e g:% 3.2e h:% 3.2e " |
| "rho:% 3.2e mu:% 3.2e li:% 3d"; |
| string output = StringPrintf(kReportRowFormat, |
| summary.iteration, |
| summary.cost, |
| summary.cost_change, |
| summary.gradient_max_norm, |
| summary.step_norm, |
| summary.relative_decrease, |
| summary.trust_region_radius, |
| summary.linear_solver_iterations); |
| if (log_to_stdout_) { |
| cout << output << endl; |
| } else { |
| VLOG(1) << output; |
| } |
| return SOLVER_CONTINUE; |
| } |
| |
| private: |
| const bool log_to_stdout_; |
| }; |
| |
| } // namespace |
| |
| void SolverImpl::Minimize(const Solver::Options& options, |
| Program* program, |
| Evaluator* evaluator, |
| LinearSolver* linear_solver, |
| double* parameters, |
| Solver::Summary* summary) { |
| Minimizer::Options minimizer_options(options); |
| LoggingCallback logging_callback(options.minimizer_progress_to_stdout); |
| if (options.logging_type != SILENT) { |
| minimizer_options.callbacks.push_back(&logging_callback); |
| } |
| |
| StateUpdatingCallback updating_callback(program, parameters); |
| if (options.update_state_every_iteration) { |
| minimizer_options.callbacks.push_back(&updating_callback); |
| } |
| |
| minimizer_options.evaluator = evaluator; |
| scoped_ptr<SparseMatrix> jacobian(evaluator->CreateJacobian()); |
| minimizer_options.jacobian = jacobian.get(); |
| |
| TrustRegionStrategy::Options trust_region_strategy_options; |
| trust_region_strategy_options.linear_solver = linear_solver; |
| trust_region_strategy_options.initial_radius = |
| options.initial_trust_region_radius; |
| trust_region_strategy_options.max_radius = options.max_trust_region_radius; |
| trust_region_strategy_options.lm_min_diagonal = options.lm_min_diagonal; |
| trust_region_strategy_options.lm_max_diagonal = options.lm_max_diagonal; |
| trust_region_strategy_options.trust_region_strategy_type = |
| options.trust_region_strategy_type; |
| scoped_ptr<TrustRegionStrategy> strategy( |
| TrustRegionStrategy::Create(trust_region_strategy_options)); |
| minimizer_options.trust_region_strategy = strategy.get(); |
| |
| TrustRegionMinimizer minimizer; |
| time_t minimizer_start_time = time(NULL); |
| minimizer.Minimize(minimizer_options, parameters, summary); |
| summary->minimizer_time_in_seconds = time(NULL) - minimizer_start_time; |
| } |
| |
| void SolverImpl::Solve(const Solver::Options& original_options, |
| Problem* problem, |
| Solver::Summary* summary) { |
| Solver::Options options(original_options); |
| |
| #ifndef CERES_USE_OPENMP |
| if (options.num_threads > 1) { |
| LOG(WARNING) |
| << "OpenMP support is not compiled into this binary; " |
| << "only options.num_threads=1 is supported. Switching" |
| << "to single threaded mode."; |
| options.num_threads = 1; |
| } |
| if (options.num_linear_solver_threads > 1) { |
| LOG(WARNING) |
| << "OpenMP support is not compiled into this binary; " |
| << "only options.num_linear_solver_threads=1 is supported. Switching" |
| << "to single threaded mode."; |
| options.num_linear_solver_threads = 1; |
| } |
| #endif |
| |
| // Reset the summary object to its default values; |
| *CHECK_NOTNULL(summary) = Solver::Summary(); |
| summary->linear_solver_type_given = options.linear_solver_type; |
| summary->num_eliminate_blocks_given = original_options.num_eliminate_blocks; |
| summary->num_threads_given = original_options.num_threads; |
| summary->num_linear_solver_threads_given = |
| original_options.num_linear_solver_threads; |
| summary->ordering_type = original_options.ordering_type; |
| |
| ProblemImpl* problem_impl = CHECK_NOTNULL(problem)->problem_impl_.get(); |
| |
| summary->num_parameter_blocks = problem_impl->NumParameterBlocks(); |
| summary->num_parameters = problem_impl->NumParameters(); |
| summary->num_residual_blocks = problem_impl->NumResidualBlocks(); |
| summary->num_residuals = problem_impl->NumResiduals(); |
| |
| summary->num_threads_used = options.num_threads; |
| |
| // Evaluate the initial cost and residual vector (if needed). The |
| // initial cost needs to be computed on the original unpreprocessed |
| // problem, as it is used to determine the value of the "fixed" part |
| // of the objective function after the problem has undergone |
| // reduction. Also the initial residuals are in the order in which |
| // the user added the ResidualBlocks to the optimization problem. |
| EvaluateCostAndResiduals(problem_impl, |
| &summary->initial_cost, |
| options.return_initial_residuals |
| ? &summary->initial_residuals |
| : NULL); |
| |
| // If the user requests gradient checking, construct a new |
| // ProblemImpl by wrapping the CostFunctions of problem_impl inside |
| // GradientCheckingCostFunction and replacing problem_impl with |
| // gradient_checking_problem_impl. |
| scoped_ptr<ProblemImpl> gradient_checking_problem_impl; |
| if (options.check_gradients) { |
| VLOG(1) << "Checking Gradients"; |
| gradient_checking_problem_impl.reset( |
| CreateGradientCheckingProblemImpl( |
| problem_impl, |
| options.numeric_derivative_relative_step_size, |
| options.gradient_check_relative_precision)); |
| |
| // From here on, problem_impl will point to the GradientChecking version. |
| problem_impl = gradient_checking_problem_impl.get(); |
| } |
| |
| // Create the three objects needed to minimize: the transformed program, the |
| // evaluator, and the linear solver. |
| |
| scoped_ptr<Program> reduced_program( |
| CreateReducedProgram(&options, problem_impl, &summary->error)); |
| if (reduced_program == NULL) { |
| return; |
| } |
| |
| summary->num_parameter_blocks_reduced = reduced_program->NumParameterBlocks(); |
| summary->num_parameters_reduced = reduced_program->NumParameters(); |
| summary->num_residual_blocks_reduced = reduced_program->NumResidualBlocks(); |
| summary->num_residuals_reduced = reduced_program->NumResiduals(); |
| |
| scoped_ptr<LinearSolver> |
| linear_solver(CreateLinearSolver(&options, &summary->error)); |
| summary->linear_solver_type_used = options.linear_solver_type; |
| summary->preconditioner_type = options.preconditioner_type; |
| summary->num_eliminate_blocks_used = options.num_eliminate_blocks; |
| summary->num_linear_solver_threads_used = options.num_linear_solver_threads; |
| |
| if (linear_solver == NULL) { |
| return; |
| } |
| |
| if (!MaybeReorderResidualBlocks(options, |
| reduced_program.get(), |
| &summary->error)) { |
| return; |
| } |
| |
| scoped_ptr<Evaluator> evaluator( |
| CreateEvaluator(options, reduced_program.get(), &summary->error)); |
| if (evaluator == NULL) { |
| return; |
| } |
| |
| // The optimizer works on contiguous parameter vectors; allocate some. |
| Vector parameters(reduced_program->NumParameters()); |
| |
| // Collect the discontiguous parameters into a contiguous state vector. |
| reduced_program->ParameterBlocksToStateVector(parameters.data()); |
| |
| // Run the optimization. |
| Minimize(options, |
| reduced_program.get(), |
| evaluator.get(), |
| linear_solver.get(), |
| parameters.data(), |
| summary); |
| |
| // If the user aborted mid-optimization or the optimization |
| // terminated because of a numerical failure, then return without |
| // updating user state. |
| if (summary->termination_type == USER_ABORT || |
| summary->termination_type == NUMERICAL_FAILURE) { |
| return; |
| } |
| |
| // Push the contiguous optimized parameters back to the user's parameters. |
| reduced_program->StateVectorToParameterBlocks(parameters.data()); |
| reduced_program->CopyParameterBlockStateToUserState(); |
| |
| // Return the final cost and residuals for the original problem. |
| EvaluateCostAndResiduals(problem->problem_impl_.get(), |
| &summary->final_cost, |
| options.return_final_residuals |
| ? &summary->final_residuals |
| : NULL); |
| |
| // Stick a fork in it, we're done. |
| return; |
| } |
| |
| // Strips varying parameters and residuals, maintaining order, and updating |
| // num_eliminate_blocks. |
| bool SolverImpl::RemoveFixedBlocksFromProgram(Program* program, |
| int* num_eliminate_blocks, |
| string* error) { |
| int original_num_eliminate_blocks = *num_eliminate_blocks; |
| vector<ParameterBlock*>* parameter_blocks = |
| program->mutable_parameter_blocks(); |
| |
| // Mark all the parameters as unused. Abuse the index member of the parameter |
| // blocks for the marking. |
| for (int i = 0; i < parameter_blocks->size(); ++i) { |
| (*parameter_blocks)[i]->set_index(-1); |
| } |
| |
| // Filter out residual that have all-constant parameters, and mark all the |
| // parameter blocks that appear in residuals. |
| { |
| vector<ResidualBlock*>* residual_blocks = |
| program->mutable_residual_blocks(); |
| int j = 0; |
| for (int i = 0; i < residual_blocks->size(); ++i) { |
| ResidualBlock* residual_block = (*residual_blocks)[i]; |
| int num_parameter_blocks = residual_block->NumParameterBlocks(); |
| |
| // Determine if the residual block is fixed, and also mark varying |
| // parameters that appear in the residual block. |
| bool all_constant = true; |
| for (int k = 0; k < num_parameter_blocks; k++) { |
| ParameterBlock* parameter_block = residual_block->parameter_blocks()[k]; |
| if (!parameter_block->IsConstant()) { |
| all_constant = false; |
| parameter_block->set_index(1); |
| } |
| } |
| |
| if (!all_constant) { |
| (*residual_blocks)[j++] = (*residual_blocks)[i]; |
| } |
| } |
| residual_blocks->resize(j); |
| } |
| |
| // Filter out unused or fixed parameter blocks, and update |
| // num_eliminate_blocks as necessary. |
| { |
| vector<ParameterBlock*>* parameter_blocks = |
| program->mutable_parameter_blocks(); |
| int j = 0; |
| for (int i = 0; i < parameter_blocks->size(); ++i) { |
| ParameterBlock* parameter_block = (*parameter_blocks)[i]; |
| if (parameter_block->index() == 1) { |
| (*parameter_blocks)[j++] = parameter_block; |
| } else if (i < original_num_eliminate_blocks) { |
| (*num_eliminate_blocks)--; |
| } |
| } |
| parameter_blocks->resize(j); |
| } |
| |
| CHECK(((program->NumResidualBlocks() == 0) && |
| (program->NumParameterBlocks() == 0)) || |
| ((program->NumResidualBlocks() != 0) && |
| (program->NumParameterBlocks() != 0))) |
| << "Congratulations, you found a bug in Ceres. Please report it."; |
| return true; |
| } |
| |
| Program* SolverImpl::CreateReducedProgram(Solver::Options* options, |
| ProblemImpl* problem_impl, |
| string* error) { |
| Program* original_program = problem_impl->mutable_program(); |
| scoped_ptr<Program> transformed_program(new Program(*original_program)); |
| |
| if (options->ordering_type == USER && |
| !ApplyUserOrdering(*problem_impl, |
| options->ordering, |
| transformed_program.get(), |
| error)) { |
| return NULL; |
| } |
| |
| if (options->ordering_type == SCHUR && options->num_eliminate_blocks != 0) { |
| *error = "Can't specify SCHUR ordering and num_eliminate_blocks " |
| "at the same time; SCHUR ordering determines " |
| "num_eliminate_blocks automatically."; |
| return NULL; |
| } |
| |
| if (options->ordering_type == SCHUR && options->ordering.size() != 0) { |
| *error = "Can't specify SCHUR ordering type and the ordering " |
| "vector at the same time; SCHUR ordering determines " |
| "a suitable parameter ordering automatically."; |
| return NULL; |
| } |
| |
| int num_eliminate_blocks = options->num_eliminate_blocks; |
| |
| if (!RemoveFixedBlocksFromProgram(transformed_program.get(), |
| &num_eliminate_blocks, |
| error)) { |
| return NULL; |
| } |
| |
| if (transformed_program->NumParameterBlocks() == 0) { |
| LOG(WARNING) << "No varying parameter blocks to optimize; " |
| << "bailing early."; |
| return transformed_program.release(); |
| } |
| |
| if (options->ordering_type == SCHUR) { |
| vector<ParameterBlock*> schur_ordering; |
| num_eliminate_blocks = ComputeSchurOrdering(*transformed_program, |
| &schur_ordering); |
| CHECK_EQ(schur_ordering.size(), transformed_program->NumParameterBlocks()) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| |
| // Replace the transformed program's ordering with the schur ordering. |
| swap(*transformed_program->mutable_parameter_blocks(), schur_ordering); |
| } |
| options->num_eliminate_blocks = num_eliminate_blocks; |
| CHECK_GE(options->num_eliminate_blocks, 0) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| |
| // Since the transformed program is the "active" program, and it is mutated, |
| // update the parameter offsets and indices. |
| transformed_program->SetParameterOffsetsAndIndex(); |
| return transformed_program.release(); |
| } |
| |
| LinearSolver* SolverImpl::CreateLinearSolver(Solver::Options* options, |
| string* error) { |
| #ifdef CERES_NO_SUITESPARSE |
| if (options->linear_solver_type == SPARSE_NORMAL_CHOLESKY && |
| options->sparse_linear_algebra_library == SUITE_SPARSE) { |
| *error = "Can't use SPARSE_NORMAL_CHOLESKY with SUITESPARSE because " |
| "SuiteSparse was not enabled when Ceres was built."; |
| return NULL; |
| } |
| #endif |
| |
| #ifdef CERES_NO_CXSPARSE |
| if (options->linear_solver_type == SPARSE_NORMAL_CHOLESKY && |
| options->sparse_linear_algebra_library == CX_SPARSE) { |
| *error = "Can't use SPARSE_NORMAL_CHOLESKY with CXSPARSE because " |
| "CXSparse was not enabled when Ceres was built."; |
| return NULL; |
| } |
| #endif |
| |
| |
| if (options->linear_solver_max_num_iterations <= 0) { |
| *error = "Solver::Options::linear_solver_max_num_iterations is 0."; |
| return NULL; |
| } |
| if (options->linear_solver_min_num_iterations <= 0) { |
| *error = "Solver::Options::linear_solver_min_num_iterations is 0."; |
| return NULL; |
| } |
| if (options->linear_solver_min_num_iterations > |
| options->linear_solver_max_num_iterations) { |
| *error = "Solver::Options::linear_solver_min_num_iterations > " |
| "Solver::Options::linear_solver_max_num_iterations."; |
| return NULL; |
| } |
| |
| LinearSolver::Options linear_solver_options; |
| linear_solver_options.min_num_iterations = |
| options->linear_solver_min_num_iterations; |
| linear_solver_options.max_num_iterations = |
| options->linear_solver_max_num_iterations; |
| linear_solver_options.type = options->linear_solver_type; |
| linear_solver_options.preconditioner_type = options->preconditioner_type; |
| linear_solver_options.sparse_linear_algebra_library = |
| options->sparse_linear_algebra_library; |
| linear_solver_options.use_block_amd = options->use_block_amd; |
| |
| #ifdef CERES_NO_SUITESPARSE |
| if (linear_solver_options.preconditioner_type == SCHUR_JACOBI) { |
| *error = "SCHUR_JACOBI preconditioner not suppored. Please build Ceres " |
| "with SuiteSparse support."; |
| return NULL; |
| } |
| |
| if (linear_solver_options.preconditioner_type == CLUSTER_JACOBI) { |
| *error = "CLUSTER_JACOBI preconditioner not suppored. Please build Ceres " |
| "with SuiteSparse support."; |
| return NULL; |
| } |
| |
| if (linear_solver_options.preconditioner_type == CLUSTER_TRIDIAGONAL) { |
| *error = "CLUSTER_TRIDIAGONAL preconditioner not suppored. Please build " |
| "Ceres with SuiteSparse support."; |
| return NULL; |
| } |
| #endif |
| |
| linear_solver_options.num_threads = options->num_linear_solver_threads; |
| linear_solver_options.num_eliminate_blocks = |
| options->num_eliminate_blocks; |
| |
| if ((linear_solver_options.num_eliminate_blocks == 0) && |
| IsSchurType(linear_solver_options.type)) { |
| #if defined(CERES_NO_SUITESPARSE) && defined(CERES_NO_CXSPARSE) |
| LOG(INFO) << "No elimination block remaining switching to DENSE_QR."; |
| linear_solver_options.type = DENSE_QR; |
| #else |
| LOG(INFO) << "No elimination block remaining " |
| << "switching to SPARSE_NORMAL_CHOLESKY."; |
| linear_solver_options.type = SPARSE_NORMAL_CHOLESKY; |
| #endif |
| } |
| |
| #if defined(CERES_NO_SUITESPARSE) && defined(CERES_NO_CXSPARSE) |
| if (linear_solver_options.type == SPARSE_SCHUR) { |
| *error = "Can't use SPARSE_SCHUR because neither SuiteSparse nor" |
| "CXSparse was enabled when Ceres was compiled."; |
| return NULL; |
| } |
| #endif |
| |
| // The matrix used for storing the dense Schur complement has a |
| // single lock guarding the whole matrix. Running the |
| // SchurComplementSolver with multiple threads leads to maximum |
| // contention and slowdown. If the problem is large enough to |
| // benefit from a multithreaded schur eliminator, you should be |
| // using a SPARSE_SCHUR solver anyways. |
| if ((linear_solver_options.num_threads > 1) && |
| (linear_solver_options.type == DENSE_SCHUR)) { |
| LOG(WARNING) << "Warning: Solver::Options::num_linear_solver_threads = " |
| << options->num_linear_solver_threads |
| << " with DENSE_SCHUR will result in poor performance; " |
| << "switching to single-threaded."; |
| linear_solver_options.num_threads = 1; |
| } |
| |
| options->linear_solver_type = linear_solver_options.type; |
| options->num_linear_solver_threads = linear_solver_options.num_threads; |
| |
| return LinearSolver::Create(linear_solver_options); |
| } |
| |
| bool SolverImpl::ApplyUserOrdering(const ProblemImpl& problem_impl, |
| vector<double*>& ordering, |
| Program* program, |
| string* error) { |
| if (ordering.size() != program->NumParameterBlocks()) { |
| *error = StringPrintf("User specified ordering does not have the same " |
| "number of parameters as the problem. The problem" |
| "has %d blocks while the ordering has %ld blocks.", |
| program->NumParameterBlocks(), |
| ordering.size()); |
| return false; |
| } |
| |
| // Ensure that there are no duplicates in the user's ordering. |
| { |
| vector<double*> ordering_copy(ordering); |
| sort(ordering_copy.begin(), ordering_copy.end()); |
| if (unique(ordering_copy.begin(), ordering_copy.end()) |
| != ordering_copy.end()) { |
| *error = "User specified ordering contains duplicates."; |
| return false; |
| } |
| } |
| |
| vector<ParameterBlock*>* parameter_blocks = |
| program->mutable_parameter_blocks(); |
| |
| fill(parameter_blocks->begin(), |
| parameter_blocks->end(), |
| static_cast<ParameterBlock*>(NULL)); |
| |
| const ProblemImpl::ParameterMap& parameter_map = problem_impl.parameter_map(); |
| for (int i = 0; i < ordering.size(); ++i) { |
| ProblemImpl::ParameterMap::const_iterator it = |
| parameter_map.find(ordering[i]); |
| if (it == parameter_map.end()) { |
| *error = StringPrintf("User specified ordering contains a pointer " |
| "to a double that is not a parameter block in the " |
| "problem. The invalid double is at position %d " |
| " in options.ordering.", i); |
| return false; |
| } |
| (*parameter_blocks)[i] = it->second; |
| } |
| return true; |
| } |
| |
| // Find the minimum index of any parameter block to the given residual. |
| // Parameter blocks that have indices greater than num_eliminate_blocks are |
| // considered to have an index equal to num_eliminate_blocks. |
| int MinParameterBlock(const ResidualBlock* residual_block, |
| int num_eliminate_blocks) { |
| int min_parameter_block_position = num_eliminate_blocks; |
| for (int i = 0; i < residual_block->NumParameterBlocks(); ++i) { |
| ParameterBlock* parameter_block = residual_block->parameter_blocks()[i]; |
| if (!parameter_block->IsConstant()) { |
| CHECK_NE(parameter_block->index(), -1) |
| << "Did you forget to call Program::SetParameterOffsetsAndIndex()? " |
| << "This is a Ceres bug; please contact the developers!"; |
| min_parameter_block_position = std::min(parameter_block->index(), |
| min_parameter_block_position); |
| } |
| } |
| return min_parameter_block_position; |
| } |
| |
| // Reorder the residuals for program, if necessary, so that the residuals |
| // involving each E block occur together. This is a necessary condition for the |
| // Schur eliminator, which works on these "row blocks" in the jacobian. |
| bool SolverImpl::MaybeReorderResidualBlocks(const Solver::Options& options, |
| Program* program, |
| string* error) { |
| // Only Schur types require the lexicographic reordering. |
| if (!IsSchurType(options.linear_solver_type)) { |
| return true; |
| } |
| |
| CHECK_NE(0, options.num_eliminate_blocks) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| |
| // Create a histogram of the number of residuals for each E block. There is an |
| // extra bucket at the end to catch all non-eliminated F blocks. |
| vector<int> residual_blocks_per_e_block(options.num_eliminate_blocks + 1); |
| vector<ResidualBlock*>* residual_blocks = program->mutable_residual_blocks(); |
| vector<int> min_position_per_residual(residual_blocks->size()); |
| for (int i = 0; i < residual_blocks->size(); ++i) { |
| ResidualBlock* residual_block = (*residual_blocks)[i]; |
| int position = MinParameterBlock(residual_block, |
| options.num_eliminate_blocks); |
| min_position_per_residual[i] = position; |
| DCHECK_LE(position, options.num_eliminate_blocks); |
| residual_blocks_per_e_block[position]++; |
| } |
| |
| // Run a cumulative sum on the histogram, to obtain offsets to the start of |
| // each histogram bucket (where each bucket is for the residuals for that |
| // E-block). |
| vector<int> offsets(options.num_eliminate_blocks + 1); |
| std::partial_sum(residual_blocks_per_e_block.begin(), |
| residual_blocks_per_e_block.end(), |
| offsets.begin()); |
| CHECK_EQ(offsets.back(), residual_blocks->size()) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| |
| CHECK(find(residual_blocks_per_e_block.begin(), |
| residual_blocks_per_e_block.end() - 1, 0) != |
| residual_blocks_per_e_block.end()) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| |
| // Fill in each bucket with the residual blocks for its corresponding E block. |
| // Each bucket is individually filled from the back of the bucket to the front |
| // of the bucket. The filling order among the buckets is dictated by the |
| // residual blocks. This loop uses the offsets as counters; subtracting one |
| // from each offset as a residual block is placed in the bucket. When the |
| // filling is finished, the offset pointerts should have shifted down one |
| // entry (this is verified below). |
| vector<ResidualBlock*> reordered_residual_blocks( |
| (*residual_blocks).size(), static_cast<ResidualBlock*>(NULL)); |
| for (int i = 0; i < residual_blocks->size(); ++i) { |
| int bucket = min_position_per_residual[i]; |
| |
| // Decrement the cursor, which should now point at the next empty position. |
| offsets[bucket]--; |
| |
| // Sanity. |
| CHECK(reordered_residual_blocks[offsets[bucket]] == NULL) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| |
| reordered_residual_blocks[offsets[bucket]] = (*residual_blocks)[i]; |
| } |
| |
| // Sanity check #1: The difference in bucket offsets should match the |
| // histogram sizes. |
| for (int i = 0; i < options.num_eliminate_blocks; ++i) { |
| CHECK_EQ(residual_blocks_per_e_block[i], offsets[i + 1] - offsets[i]) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| } |
| // Sanity check #2: No NULL's left behind. |
| for (int i = 0; i < reordered_residual_blocks.size(); ++i) { |
| CHECK(reordered_residual_blocks[i] != NULL) |
| << "Congratulations, you found a Ceres bug! Please report this error " |
| << "to the developers."; |
| } |
| |
| // Now that the residuals are collected by E block, swap them in place. |
| swap(*program->mutable_residual_blocks(), reordered_residual_blocks); |
| return true; |
| } |
| |
| Evaluator* SolverImpl::CreateEvaluator(const Solver::Options& options, |
| Program* program, |
| string* error) { |
| Evaluator::Options evaluator_options; |
| evaluator_options.linear_solver_type = options.linear_solver_type; |
| evaluator_options.num_eliminate_blocks = options.num_eliminate_blocks; |
| evaluator_options.num_threads = options.num_threads; |
| return Evaluator::Create(evaluator_options, program, error); |
| } |
| |
| } // namespace internal |
| } // namespace ceres |
