mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 08:34:37 +08:00
Sameer Agarwal
2f946a582a
Refactor the core automatic and numeric differentiation internal helpers to utilize C++17 features for improved readability and maintainability: - Added IntegerSequenceTraits to provide a uniform compile-time interface for accessing head and tail of std::integer_sequence. - Eliminated recursive template meta-programming in autodiff.h, replacing it with fold expressions and std::index_sequence. - Simplified core helper functions like Make1stOrderPerturbation into unrolled loops. - Refactored EvaluateJacobianForParameterBlocks from a recursive struct to a recursive function template using if constexpr. - Updated NumericDiffCostFunction and NumericDiffFirstOrderFunction to use the modernized internal helper API. Change-Id: I934034f7434fc05a8855565b2b534d325f920584
256 lines
10 KiB
C++
256 lines
10 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2023 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: sameeragarwal@google.com (Sameer Agarwal)
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#ifndef CERES_PUBLIC_NUMERIC_DIFF_FIRST_ORDER_FUNCTION_H_
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#define CERES_PUBLIC_NUMERIC_DIFF_FIRST_ORDER_FUNCTION_H_
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#include <algorithm>
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#include <memory>
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#include <type_traits>
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#include <utility>
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#include "absl/container/fixed_array.h"
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#include "absl/log/check.h"
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#include "ceres/first_order_function.h"
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#include "ceres/internal/eigen.h"
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#include "ceres/internal/numeric_diff.h"
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#include "ceres/internal/parameter_dims.h"
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#include "ceres/internal/variadic_evaluate.h"
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#include "ceres/numeric_diff_options.h"
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#include "ceres/types.h"
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namespace ceres {
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// Creates FirstOrderFunctions as needed by the GradientProblem
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// framework, with gradients computed via numeric differentiation. For
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// more information on numeric differentiation, see the wikipedia
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// article at https://en.wikipedia.org/wiki/Numerical_differentiation
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//
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// To get an numerically differentiated cost function, you must define
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// a class with an operator() (a functor) that computes the cost.
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//
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// The function must write the computed value in the last argument
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// (the only non-const one) and return true to indicate success.
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//
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// For example, consider a scalar error e = x'y - a, where both x and y are
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// two-dimensional column vector parameters, the prime sign indicates
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// transposition, and a is a constant.
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//
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// To write an numerically-differentiable cost function for the above model,
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// first define the object
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//
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// class QuadraticCostFunctor {
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// public:
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// explicit QuadraticCostFunctor(double a) : a_(a) {}
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// bool operator()(const double* const xy, double* cost) const {
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// constexpr int kInputVectorLength = 2;
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// const double* const x = xy;
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// const double* const y = xy + kInputVectorLength;
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// *cost = x[0] * y[0] + x[1] * y[1] - a_;
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// return true;
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// }
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//
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// private:
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// double a_;
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// };
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//
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//
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// Note that in the declaration of operator() the input parameters xy
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// come first, and are passed as const pointers to array of
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// doubles. The output cost is the last parameter.
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//
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// Then given this class definition, the numerically differentiated
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// first order function with central differences used for computing the
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// derivative can be constructed as follows.
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//
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// std::unique_ptr<FirstOrderFunction> function
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// = std::make_unique<
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// NumericDiffFirstOrderFunction<MyScalarCostFunctor, CENTRAL, 4>>(
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// std::make_unique<QuadraticCostFunctor>(1.0)); ^ ^
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// | |
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// Finite Differencing Scheme -----+ |
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// Dimension of xy ----------------------+
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//
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//
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// In the instantiation above, the template parameters following
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// "QuadraticCostFunctor", "CENTRAL, 4", describe the finite
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// differencing scheme as "central differencing" and the functor as
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// computing its cost from a 4 dimensional input.
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//
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// If the size of the parameter vector is not known at compile time, then an
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// alternate construction syntax can be used:
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//
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// std::unique_ptr<FirstOrderFunction> function
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// = std::make_unique<NumericDiffFirstOrderFunction<MyScalarCostFunctor,
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// CENTRAL>>(
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// std::make_unique<QuadraticCostFunctor>(1.0), 4);
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//
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// Note that instead of passing 4 as a template argument, it is now passed as
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// the second argument to the constructor.
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template <typename FirstOrderFunctor,
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NumericDiffMethodType kMethod,
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int kNumParameters = DYNAMIC>
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class NumericDiffFirstOrderFunction final : public FirstOrderFunction {
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public:
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NumericDiffFirstOrderFunction(const NumericDiffFirstOrderFunction&) = delete;
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NumericDiffFirstOrderFunction& operator=(
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const NumericDiffFirstOrderFunction&) = delete;
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NumericDiffFirstOrderFunction(
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NumericDiffFirstOrderFunction&& other) noexcept = default;
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NumericDiffFirstOrderFunction& operator=(
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NumericDiffFirstOrderFunction&& other) noexcept = default;
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// Constructor for the case where the parameter size is known at compile time.
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explicit NumericDiffFirstOrderFunction(
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std::unique_ptr<FirstOrderFunctor> functor,
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const NumericDiffOptions& options = NumericDiffOptions())
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: NumericDiffFirstOrderFunction(
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std::move(functor), TAKE_OWNERSHIP, kNumParameters, options) {
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static_assert(kNumParameters != DYNAMIC,
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"When kNumParameters is DYNAMIC, the number of parameters "
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"must be provided as a constructor argument.");
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}
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template <class... Args,
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typename = std::enable_if_t<
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(kNumParameters != DYNAMIC) &&
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std::is_constructible_v<FirstOrderFunctor, Args&&...>>>
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explicit NumericDiffFirstOrderFunction(Args&&... args)
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: NumericDiffFirstOrderFunction(
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std::make_unique<FirstOrderFunctor>(std::forward<Args>(args)...)) {}
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explicit NumericDiffFirstOrderFunction(FirstOrderFunctor* functor,
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Ownership ownership = TAKE_OWNERSHIP)
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: NumericDiffFirstOrderFunction(
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std::unique_ptr<FirstOrderFunctor>(functor),
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kNumParameters,
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ownership) {
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static_assert(kNumParameters != DYNAMIC,
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"When kNumParameters is DYNAMIC, the number of parameters "
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"must be provided as a constructor argument.");
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}
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// Constructor for the case where the parameter size is specified at run time.
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explicit NumericDiffFirstOrderFunction(
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std::unique_ptr<FirstOrderFunctor> functor,
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int num_parameters,
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Ownership ownership = TAKE_OWNERSHIP,
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const NumericDiffOptions& options = NumericDiffOptions())
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: NumericDiffFirstOrderFunction(
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std::move(functor), ownership, num_parameters, options) {
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if constexpr (kNumParameters != DYNAMIC) {
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DCHECK_EQ(num_parameters, kNumParameters);
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}
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}
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~NumericDiffFirstOrderFunction() override {
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if (ownership_ == DO_NOT_TAKE_OWNERSHIP) {
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functor_.release();
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}
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}
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bool Evaluate(const double* const parameters,
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double* cost,
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double* gradient) const override {
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// Get the function value (cost) at the point to evaluate.
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if (!(*functor_)(parameters, cost)) {
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return false;
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}
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if (gradient == nullptr) {
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return true;
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}
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// Create a copy of the parameters which will get mutated.
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absl::FixedArray<double> parameters_copy(num_parameters_);
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std::copy_n(parameters, num_parameters_, parameters_copy.data());
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double* parameters_ptr = parameters_copy.data();
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constexpr int kNumResiduals = 1;
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if constexpr (kNumParameters == DYNAMIC) {
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internal::FirstOrderFunctorAdapter<FirstOrderFunctor> fofa(*functor_);
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return internal::NumericDiff<
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internal::FirstOrderFunctorAdapter<FirstOrderFunctor>,
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kMethod,
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kNumResiduals,
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internal::DynamicParameterDims,
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0,
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DYNAMIC>::EvaluateJacobianForParameterBlock(&fofa,
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cost,
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options_,
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kNumResiduals,
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0,
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num_parameters_,
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¶meters_ptr,
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gradient);
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} else {
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using ParameterDims = internal::StaticParameterDims<kNumParameters>;
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return internal::EvaluateJacobianForParameterBlocks<kMethod,
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kNumResiduals,
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ParameterDims>(
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functor_.get(),
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cost,
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options_,
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kNumResiduals,
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¶meters_ptr,
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&gradient);
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}
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}
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int NumParameters() const override { return num_parameters_; }
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const FirstOrderFunctor& functor() const { return *functor_; }
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private:
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explicit NumericDiffFirstOrderFunction(
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std::unique_ptr<FirstOrderFunctor> functor,
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Ownership ownership,
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int num_parameters,
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const NumericDiffOptions& options)
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: functor_(std::move(functor)),
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num_parameters_(num_parameters),
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ownership_(ownership),
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options_(options) {
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if constexpr (kNumParameters == DYNAMIC) {
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DCHECK_GT(num_parameters, 0);
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} else {
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DCHECK_EQ(num_parameters, kNumParameters);
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}
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}
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std::unique_ptr<FirstOrderFunctor> functor_;
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int num_parameters_;
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Ownership ownership_;
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NumericDiffOptions options_;
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};
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} // namespace ceres
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#endif // CERES_PUBLIC_NUMERIC_DIFF_FIRST_ORDER_FUNCTION_H_
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