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// 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.
//
// Authors: keir@google.com (Keir Mierle),
// dgossow@google.com (David Gossow)
#include "ceres/gradient_checking_cost_function.h"
#include <algorithm>
#include <cmath>
#include <cstdint>
#include <memory>
#include <numeric>
#include <string>
#include <utility>
#include <vector>
#include "ceres/dynamic_numeric_diff_cost_function.h"
#include "ceres/gradient_checker.h"
#include "ceres/internal/eigen.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/stringprintf.h"
#include "ceres/types.h"
#include "glog/logging.h"
namespace ceres::internal {
namespace {
class GradientCheckingCostFunction final : public CostFunction {
public:
GradientCheckingCostFunction(const CostFunction* function,
const std::vector<const Manifold*>* manifolds,
const NumericDiffOptions& options,
double relative_precision,
std::string extra_info,
GradientCheckingIterationCallback* callback)
: function_(function),
gradient_checker_(function, manifolds, options),
relative_precision_(relative_precision),
extra_info_(std::move(extra_info)),
callback_(callback) {
CHECK(callback_ != nullptr);
const std::vector<int32_t>& parameter_block_sizes =
function->parameter_block_sizes();
*mutable_parameter_block_sizes() = parameter_block_sizes;
set_num_residuals(function->num_residuals());
}
bool Evaluate(double const* const* parameters,
double* residuals,
double** jacobians) const final {
if (!jacobians) {
// Nothing to check in this case; just forward.
return function_->Evaluate(parameters, residuals, nullptr);
}
GradientChecker::ProbeResults results;
bool okay =
gradient_checker_.Probe(parameters, relative_precision_, &results);
// If the cost function returned false, there's nothing we can say about
// the gradients.
if (results.return_value == false) {
return false;
}
// Copy the residuals.
const int num_residuals = function_->num_residuals();
MatrixRef(residuals, num_residuals, 1) = results.residuals;
// Copy the original jacobian blocks into the jacobians array.
const std::vector<int32_t>& block_sizes = function_->parameter_block_sizes();
for (int k = 0; k < block_sizes.size(); k++) {
if (jacobians[k] != nullptr) {
MatrixRef(jacobians[k],
results.jacobians[k].rows(),
results.jacobians[k].cols()) = results.jacobians[k];
}
}
if (!okay) {
std::string error_log =
"Gradient Error detected!\nExtra info for this residual: " +
extra_info_ + "\n" + results.error_log;
callback_->SetGradientErrorDetected(error_log);
}
return true;
}
private:
const CostFunction* function_;
GradientChecker gradient_checker_;
double relative_precision_;
std::string extra_info_;
GradientCheckingIterationCallback* callback_;
};
} // namespace
GradientCheckingIterationCallback::GradientCheckingIterationCallback()
: gradient_error_detected_(false) {}
CallbackReturnType GradientCheckingIterationCallback::operator()(
const IterationSummary& summary) {
if (gradient_error_detected_) {
LOG(ERROR) << "Gradient error detected. Terminating solver.";
return SOLVER_ABORT;
}
return SOLVER_CONTINUE;
}
void GradientCheckingIterationCallback::SetGradientErrorDetected(
std::string& error_log) {
std::lock_guard<std::mutex> l(mutex_);
gradient_error_detected_ = true;
error_log_ += "\n" + error_log;
}
std::unique_ptr<CostFunction> CreateGradientCheckingCostFunction(
const CostFunction* cost_function,
const std::vector<const Manifold*>* manifolds,
double relative_step_size,
double relative_precision,
const std::string& extra_info,
GradientCheckingIterationCallback* callback) {
NumericDiffOptions numeric_diff_options;
numeric_diff_options.relative_step_size = relative_step_size;
return std::make_unique<GradientCheckingCostFunction>(cost_function,
manifolds,
numeric_diff_options,
relative_precision,
extra_info,
callback);
}
std::unique_ptr<ProblemImpl> CreateGradientCheckingProblemImpl(
ProblemImpl* problem_impl,
double relative_step_size,
double relative_precision,
GradientCheckingIterationCallback* callback) {
CHECK(callback != nullptr);
// We create new CostFunctions by wrapping the original CostFunction in a
// gradient checking CostFunction. So its okay for the ProblemImpl to take
// ownership of it and destroy it. The LossFunctions and Manifolds are reused
// and since they are owned by problem_impl, gradient_checking_problem_impl
// should not take ownership of it.
Problem::Options gradient_checking_problem_options;
gradient_checking_problem_options.cost_function_ownership = TAKE_OWNERSHIP;
gradient_checking_problem_options.loss_function_ownership =
DO_NOT_TAKE_OWNERSHIP;
gradient_checking_problem_options.manifold_ownership = DO_NOT_TAKE_OWNERSHIP;
gradient_checking_problem_options.context = problem_impl->context();
NumericDiffOptions numeric_diff_options;
numeric_diff_options.relative_step_size = relative_step_size;
auto gradient_checking_problem_impl =
std::make_unique<ProblemImpl>(gradient_checking_problem_options);
Program* program = problem_impl->mutable_program();
// For every ParameterBlock in problem_impl, create a new parameter block with
// the same manifold and constancy.
const std::vector<ParameterBlock*>& parameter_blocks = program->parameter_blocks();
for (auto* parameter_block : parameter_blocks) {
gradient_checking_problem_impl->AddParameterBlock(
parameter_block->mutable_user_state(),
parameter_block->Size(),
parameter_block->mutable_manifold());
if (parameter_block->IsConstant()) {
gradient_checking_problem_impl->SetParameterBlockConstant(
parameter_block->mutable_user_state());
}
for (int i = 0; i < parameter_block->Size(); ++i) {
gradient_checking_problem_impl->SetParameterUpperBound(
parameter_block->mutable_user_state(),
i,
parameter_block->UpperBound(i));
gradient_checking_problem_impl->SetParameterLowerBound(
parameter_block->mutable_user_state(),
i,
parameter_block->LowerBound(i));
}
}
// For every ResidualBlock in problem_impl, create a new
// ResidualBlock by wrapping its CostFunction inside a
// GradientCheckingCostFunction.
const std::vector<ResidualBlock*>& residual_blocks = program->residual_blocks();
for (int i = 0; i < residual_blocks.size(); ++i) {
ResidualBlock* residual_block = residual_blocks[i];
// Build a human readable string which identifies the
// ResidualBlock. This is used by the GradientCheckingCostFunction
// when logging debugging information.
std::string extra_info =
StringPrintf("Residual block id %d; depends on parameters [", i);
std::vector<double*> parameter_blocks;
std::vector<const Manifold*> manifolds;
parameter_blocks.reserve(residual_block->NumParameterBlocks());
manifolds.reserve(residual_block->NumParameterBlocks());
for (int j = 0; j < residual_block->NumParameterBlocks(); ++j) {
ParameterBlock* parameter_block = residual_block->parameter_blocks()[j];
parameter_blocks.push_back(parameter_block->mutable_user_state());
StringAppendF(&extra_info, "%p", parameter_block->mutable_user_state());
extra_info += (j < residual_block->NumParameterBlocks() - 1) ? ", " : "]";
manifolds.push_back(
problem_impl->GetManifold(parameter_block->mutable_user_state()));
}
// Wrap the original CostFunction in a GradientCheckingCostFunction.
CostFunction* gradient_checking_cost_function =
new GradientCheckingCostFunction(residual_block->cost_function(),
&manifolds,
numeric_diff_options,
relative_precision,
extra_info,
callback);
// The const_cast is necessary because
// ProblemImpl::AddResidualBlock can potentially take ownership of
// the LossFunction, but in this case we are guaranteed that this
// will not be the case, so this const_cast is harmless.
gradient_checking_problem_impl->AddResidualBlock(
gradient_checking_cost_function,
const_cast<LossFunction*>(residual_block->loss_function()),
parameter_blocks.data(),
static_cast<int>(parameter_blocks.size()));
}
// Normally, when a problem is given to the solver, we guarantee
// that the state pointers for each parameter block point to the
// user provided data. Since we are creating this new problem from a
// problem given to us at an arbitrary stage of the solve, we cannot
// depend on this being the case, so we explicitly call
// SetParameterBlockStatePtrsToUserStatePtrs to ensure that this is
// the case.
gradient_checking_problem_impl->mutable_program()
->SetParameterBlockStatePtrsToUserStatePtrs();
return gradient_checking_problem_impl;
}
} // namespace ceres::internal