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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2023 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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//
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// Author: sameeragarwal@google.com (Sameer Agarwal)
#ifndef CERES_PUBLIC_NUMERIC_DIFF_FIRST_ORDER_FUNCTION_H_
#define CERES_PUBLIC_NUMERIC_DIFF_FIRST_ORDER_FUNCTION_H_
#include <algorithm>
#include <memory>
#include <type_traits>
#include <utility>
#include "absl/container/fixed_array.h"
#include "absl/log/check.h"
#include "ceres/first_order_function.h"
#include "ceres/internal/eigen.h"
#include "ceres/internal/numeric_diff.h"
#include "ceres/internal/parameter_dims.h"
#include "ceres/internal/variadic_evaluate.h"
#include "ceres/numeric_diff_options.h"
#include "ceres/types.h"
namespace ceres {
// Creates FirstOrderFunctions as needed by the GradientProblem
// framework, with gradients computed via numeric differentiation. For
// more information on numeric differentiation, see the wikipedia
// article at https://en.wikipedia.org/wiki/Numerical_differentiation
//
// To get an numerically differentiated cost function, you must define
// a class with an operator() (a functor) that computes the cost.
//
// The function must write the computed value in the last argument
// (the only non-const one) and return true to indicate success.
//
// For example, consider a scalar error e = x'y - a, where both x and y are
// two-dimensional column vector parameters, the prime sign indicates
// transposition, and a is a constant.
//
// To write an numerically-differentiable cost function for the above model,
// first define the object
//
// class QuadraticCostFunctor {
// public:
// explicit QuadraticCostFunctor(double a) : a_(a) {}
// bool operator()(const double* const xy, double* cost) const {
// constexpr int kInputVectorLength = 2;
// const double* const x = xy;
// const double* const y = xy + kInputVectorLength;
// *cost = x[0] * y[0] + x[1] * y[1] - a_;
// return true;
// }
//
// private:
// double a_;
// };
//
//
// Note that in the declaration of operator() the input parameters xy
// come first, and are passed as const pointers to array of
// doubles. The output cost is the last parameter.
//
// Then given this class definition, the numerically differentiated
// first order function with central differences used for computing the
// derivative can be constructed as follows.
//
// std::unique_ptr<FirstOrderFunction> function
// = std::make_unique<
// NumericDiffFirstOrderFunction<MyScalarCostFunctor, CENTRAL, 4>>(
// std::make_unique<QuadraticCostFunctor>(1.0)); ^ ^
// | |
// Finite Differencing Scheme -----+ |
// Dimension of xy ----------------------+
//
//
// In the instantiation above, the template parameters following
// "QuadraticCostFunctor", "CENTRAL, 4", describe the finite
// differencing scheme as "central differencing" and the functor as
// computing its cost from a 4 dimensional input.
//
// If the size of the parameter vector is not known at compile time, then an
// alternate construction syntax can be used:
//
// std::unique_ptr<FirstOrderFunction> function
// = std::make_unique<NumericDiffFirstOrderFunction<MyScalarCostFunctor,
// CENTRAL>>(
// std::make_unique<QuadraticCostFunctor>(1.0), 4);
//
// Note that instead of passing 4 as a template argument, it is now passed as
// the second argument to the constructor.
template <typename FirstOrderFunctor,
NumericDiffMethodType kMethod,
int kNumParameters = DYNAMIC>
class NumericDiffFirstOrderFunction final : public FirstOrderFunction {
public:
NumericDiffFirstOrderFunction(const NumericDiffFirstOrderFunction&) = delete;
NumericDiffFirstOrderFunction& operator=(
const NumericDiffFirstOrderFunction&) = delete;
NumericDiffFirstOrderFunction(
NumericDiffFirstOrderFunction&& other) noexcept = default;
NumericDiffFirstOrderFunction& operator=(
NumericDiffFirstOrderFunction&& other) noexcept = default;
// Constructor for the case where the parameter size is known at compile time.
explicit NumericDiffFirstOrderFunction(
std::unique_ptr<FirstOrderFunctor> functor,
const NumericDiffOptions& options = NumericDiffOptions())
: NumericDiffFirstOrderFunction{std::move(functor),
kNumParameters,
TAKE_OWNERSHIP,
options,
FIXED_INIT} {}
template <class... Args,
bool kIsDynamic = kNumParameters == DYNAMIC,
std::enable_if_t<!kIsDynamic &&
std::is_constructible_v<FirstOrderFunctor,
Args&&...>>* = nullptr>
explicit NumericDiffFirstOrderFunction(Args&&... args)
: NumericDiffFirstOrderFunction{
std::make_unique<FirstOrderFunctor>(std::forward<Args>(args)...)} {}
// Constructor for the case where the parameter size is specified at run time.
explicit NumericDiffFirstOrderFunction(
std::unique_ptr<FirstOrderFunctor> functor,
int num_parameters,
Ownership ownership = TAKE_OWNERSHIP,
const NumericDiffOptions& options = NumericDiffOptions())
: NumericDiffFirstOrderFunction{std::move(functor),
num_parameters,
ownership,
options,
DYNAMIC_INIT} {}
~NumericDiffFirstOrderFunction() override {
if (ownership_ != TAKE_OWNERSHIP) {
functor_.release();
}
}
bool Evaluate(const double* const parameters,
double* cost,
double* gradient) const override {
// Get the function value (cost) at the point to evaluate.
if (!(*functor_)(parameters, cost)) {
return false;
}
if (gradient == nullptr) {
return true;
}
// Create a copy of the parameters which will get mutated.
absl::FixedArray<double> parameters_copy(num_parameters_);
std::copy_n(parameters, num_parameters_, parameters_copy.data());
double* parameters_ptr = parameters_copy.data();
constexpr int kNumResiduals = 1;
if constexpr (kNumParameters == DYNAMIC) {
internal::FirstOrderFunctorAdapter<FirstOrderFunctor> fofa(*functor_);
return internal::NumericDiff<
internal::FirstOrderFunctorAdapter<FirstOrderFunctor>,
kMethod,
kNumResiduals,
internal::DynamicParameterDims,
0,
DYNAMIC>::EvaluateJacobianForParameterBlock(&fofa,
cost,
options_,
kNumResiduals,
0,
num_parameters_,
&parameters_ptr,
gradient);
} else {
return internal::EvaluateJacobianForParameterBlocks<
internal::StaticParameterDims<kNumParameters>>::
template Apply<kMethod, 1>(functor_.get(),
cost,
options_,
kNumResiduals,
&parameters_ptr,
&gradient);
}
}
int NumParameters() const override { return num_parameters_; }
const FirstOrderFunctor& functor() const { return *functor_; }
private:
// Tags used to differentiate between dynamic and fixed size constructor
// delegate invocations.
static constexpr std::integral_constant<int, DYNAMIC> DYNAMIC_INIT{};
static constexpr std::integral_constant<int, kNumParameters> FIXED_INIT{};
template <class InitTag>
explicit NumericDiffFirstOrderFunction(
std::unique_ptr<FirstOrderFunctor> functor,
int num_parameters,
Ownership ownership,
const NumericDiffOptions& options,
InitTag /*unused*/)
: functor_(std::move(functor)),
num_parameters_(num_parameters),
ownership_(ownership),
options_(options) {
static_assert(
kNumParameters == FIXED_INIT,
"Template parameter must be DYNAMIC when using this constructor. If "
"you want to provide the number of parameters statically use the other "
"constructor.");
if constexpr (InitTag::value == DYNAMIC_INIT) {
CHECK_GT(num_parameters, 0);
}
}
std::unique_ptr<FirstOrderFunctor> functor_;
int num_parameters_;
Ownership ownership_;
NumericDiffOptions options_;
};
} // namespace ceres
#endif // CERES_PUBLIC_NUMERIC_DIFF_FIRST_ORDER_FUNCTION_H_