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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2022 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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// modification, are permitted provided that the following conditions are met:
//
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// 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
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// 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 {
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,
std::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.
std::map<ParameterBlock*, int> parameter_block_index;
std::map<int, std::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 std::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 std::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 =
std::min(options.num_threads, num_problems);
evaluator_options_.num_threads =
std::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();
const int old_state_offset = parameter_block->state_offset();
parameter_block->SetVarying();
parameter_block->set_index(0);
parameter_block->set_delta_offset(0);
parameter_block->set_state_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 + old_state_offset,
&inner_summary);
parameter_block->set_index(old_index);
parameter_block->set_delta_offset(old_delta_offset);
parameter_block->set_state_offset(old_state_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;
std::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,
std::string* message) {
const std::map<int, std::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