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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2019 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: mierle@gmail.com (Keir Mierle)
//
// WARNING WARNING WARNING
// WARNING WARNING WARNING Tiny solver is experimental and will change.
// WARNING WARNING WARNING
#ifndef CERES_PUBLIC_TINY_SOLVER_AUTODIFF_FUNCTION_H_
#define CERES_PUBLIC_TINY_SOLVER_AUTODIFF_FUNCTION_H_
#include <memory>
#include <type_traits>
#include "Eigen/Core"
#include "ceres/jet.h"
#include "ceres/types.h" // For kImpossibleValue.
namespace ceres {
// An adapter around autodiff-style CostFunctors to enable easier use of
// TinySolver. See the example below showing how to use it:
//
// // Example for cost functor with static residual size.
// // Same as an autodiff cost functor, but taking only 1 parameter.
// struct MyFunctor {
// template<typename T>
// bool operator()(const T* const parameters, T* residuals) const {
// const T& x = parameters[0];
// const T& y = parameters[1];
// const T& z = parameters[2];
// residuals[0] = x + 2.*y + 4.*z;
// residuals[1] = y * z;
// return true;
// }
// };
//
// typedef TinySolverAutoDiffFunction<MyFunctor, 2, 3>
// AutoDiffFunction;
//
// MyFunctor my_functor;
// AutoDiffFunction f(my_functor);
//
// Vec3 x = ...;
// TinySolver<AutoDiffFunction> solver;
// solver.Solve(f, &x);
//
// // Example for cost functor with dynamic residual size.
// // NumResiduals() supplies dynamic size of residuals.
// // Same functionality as in tiny_solver.h but with autodiff.
// struct MyFunctorWithDynamicResiduals {
// int NumResiduals() const {
// return 2;
// }
//
// template<typename T>
// bool operator()(const T* const parameters, T* residuals) const {
// const T& x = parameters[0];
// const T& y = parameters[1];
// const T& z = parameters[2];
// residuals[0] = x + static_cast<T>(2.)*y + static_cast<T>(4.)*z;
// residuals[1] = y * z;
// return true;
// }
// };
//
// typedef TinySolverAutoDiffFunction<MyFunctorWithDynamicResiduals,
// Eigen::Dynamic,
// 3>
// AutoDiffFunctionWithDynamicResiduals;
//
// MyFunctorWithDynamicResiduals my_functor_dyn;
// AutoDiffFunctionWithDynamicResiduals f(my_functor_dyn);
//
// Vec3 x = ...;
// TinySolver<AutoDiffFunctionWithDynamicResiduals> solver;
// solver.Solve(f, &x);
//
// WARNING: The cost function adapter is not thread safe.
template <typename CostFunctor,
int kNumResiduals,
int kNumParameters,
typename T = double>
class TinySolverAutoDiffFunction {
public:
// This class needs to have an Eigen aligned operator new as it contains
// as a member a Jet type, which itself has a fixed-size Eigen type as member.
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
TinySolverAutoDiffFunction(const CostFunctor& cost_functor)
: cost_functor_(cost_functor) {
Initialize<kNumResiduals>(cost_functor);
}
typedef T Scalar;
enum {
NUM_PARAMETERS = kNumParameters,
NUM_RESIDUALS = kNumResiduals,
};
// This is similar to AutoDifferentiate(), but since there is only one
// parameter block it is easier to inline to avoid overhead.
bool operator()(const T* parameters, T* residuals, T* jacobian) const {
if (jacobian == NULL) {
// No jacobian requested, so just directly call the cost function with
// doubles, skipping jets and derivatives.
return cost_functor_(parameters, residuals);
}
// Initialize the input jets with passed parameters.
for (int i = 0; i < kNumParameters; ++i) {
jet_parameters_[i].a = parameters[i]; // Scalar part.
jet_parameters_[i].v.setZero(); // Derivative part.
jet_parameters_[i].v[i] = T(1.0);
}
// Initialize the output jets such that we can detect user errors.
for (int i = 0; i < num_residuals_; ++i) {
jet_residuals_[i].a = kImpossibleValue;
jet_residuals_[i].v.setConstant(kImpossibleValue);
}
// Execute the cost function, but with jets to find the derivative.
if (!cost_functor_(jet_parameters_, jet_residuals_.data())) {
return false;
}
// Copy the jacobian out of the derivative part of the residual jets.
Eigen::Map<Eigen::Matrix<T, kNumResiduals, kNumParameters>> jacobian_matrix(
jacobian, num_residuals_, kNumParameters);
for (int r = 0; r < num_residuals_; ++r) {
residuals[r] = jet_residuals_[r].a;
// Note that while this looks like a fast vectorized write, in practice it
// unfortunately thrashes the cache since the writes to the column-major
// jacobian are strided (e.g. rows are non-contiguous).
jacobian_matrix.row(r) = jet_residuals_[r].v;
}
return true;
}
int NumResiduals() const {
return num_residuals_; // Set by Initialize.
}
private:
const CostFunctor& cost_functor_;
// The number of residuals at runtime.
// This will be overriden if NUM_RESIDUALS == Eigen::Dynamic.
int num_residuals_ = kNumResiduals;
// To evaluate the cost function with jets, temporary storage is needed. These
// are the buffers that are used during evaluation; parameters for the input,
// and jet_residuals_ are where the final cost and derivatives end up.
//
// Since this buffer is used for evaluation, the adapter is not thread safe.
using JetType = Jet<T, kNumParameters>;
mutable JetType jet_parameters_[kNumParameters];
// Eigen::Matrix serves as static or dynamic container.
mutable Eigen::Matrix<JetType, kNumResiduals, 1> jet_residuals_;
// The number of residuals is dynamically sized and the number of
// parameters is statically sized.
template <int R>
typename std::enable_if<(R == Eigen::Dynamic), void>::type Initialize(
const CostFunctor& function) {
jet_residuals_.resize(function.NumResiduals());
num_residuals_ = function.NumResiduals();
}
// The number of parameters and residuals are statically sized.
template <int R>
typename std::enable_if<(R != Eigen::Dynamic), void>::type Initialize(
const CostFunctor& /* function */) {
num_residuals_ = kNumResiduals;
}
};
} // namespace ceres
#endif // CERES_PUBLIC_TINY_SOLVER_AUTODIFF_FUNCTION_H_
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