| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2022 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: sameeragarwal@google.com (Sameer Agarwal) |
| |
| #include "ceres/coordinate_descent_minimizer.h" |
| |
| #include <algorithm> |
| #include <iterator> |
| #include <memory> |
| #include <numeric> |
| #include <vector> |
| |
| #include "ceres/evaluator.h" |
| #include "ceres/linear_solver.h" |
| #include "ceres/minimizer.h" |
| #include "ceres/parallel_for.h" |
| #include "ceres/parameter_block.h" |
| #include "ceres/parameter_block_ordering.h" |
| #include "ceres/problem_impl.h" |
| #include "ceres/program.h" |
| #include "ceres/residual_block.h" |
| #include "ceres/solver.h" |
| #include "ceres/trust_region_minimizer.h" |
| #include "ceres/trust_region_strategy.h" |
| |
| namespace ceres::internal { |
| |
| using std::map; |
| using std::max; |
| using std::min; |
| using std::set; |
| using std::string; |
| using std::vector; |
| |
| CoordinateDescentMinimizer::CoordinateDescentMinimizer(ContextImpl* context) |
| : context_(context) { |
| CHECK(context_ != nullptr); |
| } |
| |
| CoordinateDescentMinimizer::~CoordinateDescentMinimizer() = default; |
| |
| bool CoordinateDescentMinimizer::Init( |
| const Program& program, |
| const ProblemImpl::ParameterMap& parameter_map, |
| const ParameterBlockOrdering& ordering, |
| string* error) { |
| parameter_blocks_.clear(); |
| independent_set_offsets_.clear(); |
| independent_set_offsets_.push_back(0); |
| |
| // Serialize the OrderedGroups into a vector of parameter block |
| // offsets for parallel access. |
| map<ParameterBlock*, int> parameter_block_index; |
| map<int, set<double*>> group_to_elements = ordering.group_to_elements(); |
| for (const auto& g_t_e : group_to_elements) { |
| const auto& elements = g_t_e.second; |
| for (double* parameter_block : elements) { |
| parameter_blocks_.push_back(parameter_map.find(parameter_block)->second); |
| parameter_block_index[parameter_blocks_.back()] = |
| parameter_blocks_.size() - 1; |
| } |
| independent_set_offsets_.push_back(independent_set_offsets_.back() + |
| elements.size()); |
| } |
| |
| // The ordering does not have to contain all parameter blocks, so |
| // assign zero offsets/empty independent sets to these parameter |
| // blocks. |
| const vector<ParameterBlock*>& parameter_blocks = program.parameter_blocks(); |
| for (auto* parameter_block : parameter_blocks) { |
| if (!ordering.IsMember(parameter_block->mutable_user_state())) { |
| parameter_blocks_.push_back(parameter_block); |
| independent_set_offsets_.push_back(independent_set_offsets_.back()); |
| } |
| } |
| |
| // Compute the set of residual blocks that depend on each parameter |
| // block. |
| residual_blocks_.resize(parameter_block_index.size()); |
| const vector<ResidualBlock*>& residual_blocks = program.residual_blocks(); |
| for (auto* residual_block : residual_blocks) { |
| const int num_parameter_blocks = residual_block->NumParameterBlocks(); |
| for (int j = 0; j < num_parameter_blocks; ++j) { |
| ParameterBlock* parameter_block = residual_block->parameter_blocks()[j]; |
| const auto it = parameter_block_index.find(parameter_block); |
| if (it != parameter_block_index.end()) { |
| residual_blocks_[it->second].push_back(residual_block); |
| } |
| } |
| } |
| |
| evaluator_options_.linear_solver_type = DENSE_QR; |
| evaluator_options_.num_eliminate_blocks = 0; |
| evaluator_options_.num_threads = 1; |
| evaluator_options_.context = context_; |
| |
| return true; |
| } |
| |
| void CoordinateDescentMinimizer::Minimize(const Minimizer::Options& options, |
| double* parameters, |
| Solver::Summary* summary) { |
| // Set the state and mark all parameter blocks constant. |
| for (auto* parameter_block : parameter_blocks_) { |
| parameter_block->SetState(parameters + parameter_block->state_offset()); |
| parameter_block->SetConstant(); |
| } |
| |
| std::vector<std::unique_ptr<LinearSolver>> linear_solvers( |
| options.num_threads); |
| |
| LinearSolver::Options linear_solver_options; |
| linear_solver_options.type = DENSE_QR; |
| linear_solver_options.context = context_; |
| |
| for (int i = 0; i < options.num_threads; ++i) { |
| linear_solvers[i] = LinearSolver::Create(linear_solver_options); |
| } |
| |
| for (int i = 0; i < independent_set_offsets_.size() - 1; ++i) { |
| const int num_problems = |
| independent_set_offsets_[i + 1] - independent_set_offsets_[i]; |
| // Avoid parallelization overhead call if the set is empty. |
| if (num_problems == 0) { |
| continue; |
| } |
| |
| const int num_inner_iteration_threads = |
| min(options.num_threads, num_problems); |
| evaluator_options_.num_threads = |
| max(1, options.num_threads / num_inner_iteration_threads); |
| |
| // The parameter blocks in each independent set can be optimized |
| // in parallel, since they do not co-occur in any residual block. |
| ParallelFor( |
| context_, |
| independent_set_offsets_[i], |
| independent_set_offsets_[i + 1], |
| num_inner_iteration_threads, |
| [&](int thread_id, int j) { |
| ParameterBlock* parameter_block = parameter_blocks_[j]; |
| const int old_index = parameter_block->index(); |
| const int old_delta_offset = parameter_block->delta_offset(); |
| parameter_block->SetVarying(); |
| parameter_block->set_index(0); |
| parameter_block->set_delta_offset(0); |
| |
| Program inner_program; |
| inner_program.mutable_parameter_blocks()->push_back(parameter_block); |
| *inner_program.mutable_residual_blocks() = residual_blocks_[j]; |
| |
| // TODO(sameeragarwal): Better error handling. Right now we |
| // assume that this is not going to lead to problems of any |
| // sort. Basically we should be checking for numerical failure |
| // of some sort. |
| // |
| // On the other hand, if the optimization is a failure, that in |
| // some ways is fine, since it won't change the parameters and |
| // we are fine. |
| Solver::Summary inner_summary; |
| Solve(&inner_program, |
| linear_solvers[thread_id].get(), |
| parameters + parameter_block->state_offset(), |
| &inner_summary); |
| |
| parameter_block->set_index(old_index); |
| parameter_block->set_delta_offset(old_delta_offset); |
| parameter_block->SetState(parameters + |
| parameter_block->state_offset()); |
| parameter_block->SetConstant(); |
| }); |
| } |
| |
| for (auto* parameter_block : parameter_blocks_) { |
| parameter_block->SetVarying(); |
| } |
| } |
| |
| // Solve the optimization problem for one parameter block. |
| void CoordinateDescentMinimizer::Solve(Program* program, |
| LinearSolver* linear_solver, |
| double* parameter, |
| Solver::Summary* summary) { |
| *summary = Solver::Summary(); |
| summary->initial_cost = 0.0; |
| summary->fixed_cost = 0.0; |
| summary->final_cost = 0.0; |
| string error; |
| |
| Minimizer::Options minimizer_options; |
| minimizer_options.evaluator = |
| Evaluator::Create(evaluator_options_, program, &error); |
| CHECK(minimizer_options.evaluator != nullptr); |
| minimizer_options.jacobian = minimizer_options.evaluator->CreateJacobian(); |
| CHECK(minimizer_options.jacobian != nullptr); |
| |
| TrustRegionStrategy::Options trs_options; |
| trs_options.linear_solver = linear_solver; |
| minimizer_options.trust_region_strategy = |
| TrustRegionStrategy::Create(trs_options); |
| CHECK(minimizer_options.trust_region_strategy != nullptr); |
| minimizer_options.is_silent = true; |
| |
| TrustRegionMinimizer minimizer; |
| minimizer.Minimize(minimizer_options, parameter, summary); |
| } |
| |
| bool CoordinateDescentMinimizer::IsOrderingValid( |
| const Program& program, |
| const ParameterBlockOrdering& ordering, |
| string* message) { |
| const map<int, set<double*>>& group_to_elements = |
| ordering.group_to_elements(); |
| |
| // Verify that each group is an independent set |
| for (const auto& g_t_e : group_to_elements) { |
| if (!program.IsParameterBlockSetIndependent(g_t_e.second)) { |
| *message = StringPrintf( |
| "The user-provided parameter_blocks_for_inner_iterations does not " |
| "form an independent set. Group Id: %d", |
| g_t_e.first); |
| return false; |
| } |
| } |
| return true; |
| } |
| |
| // Find a recursive decomposition of the Hessian matrix as a set |
| // of independent sets of decreasing size and invert it. This |
| // seems to work better in practice, i.e., Cameras before |
| // points. |
| std::shared_ptr<ParameterBlockOrdering> |
| CoordinateDescentMinimizer::CreateOrdering(const Program& program) { |
| auto ordering = std::make_shared<ParameterBlockOrdering>(); |
| ComputeRecursiveIndependentSetOrdering(program, ordering.get()); |
| ordering->Reverse(); |
| return ordering; |
| } |
| |
| } // namespace ceres::internal |