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https://github.com/ceres-solver/ceres-solver.git
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Let NumericDiffFirstOrderFunction take a dynamically sized parameter vector
Also fix a template naming lint along the way. Change-Id: Iabb98aeec2ff9609a19c3778b9ea2da37771c985
This commit is contained in:
committed by
Sameer Agarwal
parent
6c27ac6d50
commit
4cd257cf4a
@@ -76,7 +76,7 @@ namespace ceres {
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// cost_function.AddParameterBlock(5);
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// cost_function.AddParameterBlock(10);
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// cost_function.SetNumResiduals(21);
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template <typename CostFunctor, NumericDiffMethodType method = CENTRAL>
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template <typename CostFunctor, NumericDiffMethodType kMethod = CENTRAL>
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class DynamicNumericDiffCostFunction final : public DynamicCostFunction {
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public:
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explicit DynamicNumericDiffCostFunction(
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@@ -134,7 +134,7 @@ class DynamicNumericDiffCostFunction final : public DynamicCostFunction {
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for (size_t block = 0; block < block_sizes.size(); ++block) {
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if (jacobians[block] != nullptr &&
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!NumericDiff<CostFunctor,
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method,
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kMethod,
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ceres::DYNAMIC,
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internal::DynamicParameterDims,
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ceres::DYNAMIC,
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@@ -106,6 +106,29 @@ inline bool VariadicEvaluate(const Functor& functor,
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return VariadicEvaluateImpl<ParameterDims>(functor, input, output, &functor);
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}
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// When differentiating dynamically sized CostFunctions, VariadicEvaluate
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// expects a functor with the signature:
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//
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// bool operator()(double const* const* parameters, double* cost) const
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//
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// However for NumericDiffFirstOrderFunction, the functor has the signature
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//
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// bool operator()(double const* parameters, double* cost) const
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//
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// This thin wrapper adapts the latter to the former.
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template <typename Functor>
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class FirstOrderFunctorAdapter {
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public:
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explicit FirstOrderFunctorAdapter(const Functor& functor)
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: functor_(functor) {}
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bool operator()(double const* const* parameters, double* cost) const {
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return functor_(*parameters, cost);
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}
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private:
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const Functor& functor_;
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};
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} // namespace ceres::internal
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#endif // CERES_PUBLIC_INTERNAL_VARIADIC_EVALUATE_H_
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@@ -176,7 +176,7 @@
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namespace ceres {
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template <typename CostFunctor,
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NumericDiffMethodType method = CENTRAL,
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NumericDiffMethodType kMethod = CENTRAL,
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int kNumResiduals = 0, // Number of residuals, or ceres::DYNAMIC
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int... Ns> // Parameters dimensions for each block.
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class NumericDiffCostFunction final
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@@ -236,7 +236,7 @@ class NumericDiffCostFunction final
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}
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internal::EvaluateJacobianForParameterBlocks<ParameterDims>::
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template Apply<method, kNumResiduals>(
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template Apply<kMethod, kNumResiduals>(
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functor_.get(),
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residuals,
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options_,
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@@ -42,6 +42,7 @@
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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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#include "glog/logging.h"
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namespace ceres {
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@@ -99,19 +100,55 @@ namespace ceres {
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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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// FirstOrderFunction* function
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// = new NumericDiffFirstOrderFunction<MyScalarCostFunctor, CENTRAL>(
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// new 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 method,
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int kNumParameters>
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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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// Constructor for the case where the parameter size is known at compile time.
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explicit NumericDiffFirstOrderFunction(
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FirstOrderFunctor* functor,
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Ownership ownership = TAKE_OWNERSHIP,
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const NumericDiffOptions& options = NumericDiffOptions())
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: functor_(functor), ownership_(ownership), options_(options) {
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: functor_(functor),
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num_parameters_(kNumParameters),
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ownership_(ownership),
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options_(options) {
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static_assert(kNumParameters != DYNAMIC,
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"Number of parameters must be static when defined via the "
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"template parameter. Use the other constructor for "
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"dynamically sized functions.");
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static_assert(kNumParameters > 0, "kNumParameters must be positive");
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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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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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: functor_(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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static_assert(
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kNumParameters == DYNAMIC,
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"Template parameter must be DYNAMIC when using this constructor. If "
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"you want to provide the number of parameters statically use the other "
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"constructor.");
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CHECK_GT(num_parameters, 0);
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}
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~NumericDiffFirstOrderFunction() override {
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if (ownership_ != TAKE_OWNERSHIP) {
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functor_.release();
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@@ -121,12 +158,8 @@ class NumericDiffFirstOrderFunction final : public FirstOrderFunction {
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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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using ParameterDims = internal::StaticParameterDims<kNumParameters>;
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constexpr int kNumResiduals = 1;
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// Get the function value (cost) at the the point to evaluate.
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if (!internal::VariadicEvaluate<ParameterDims>(
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*functor_, ¶meters, cost)) {
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if (!(*functor_)(parameters, cost)) {
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return false;
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}
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@@ -135,27 +168,47 @@ class NumericDiffFirstOrderFunction final : public FirstOrderFunction {
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}
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// Create a copy of the parameters which will get mutated.
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internal::FixedArray<double, 32> parameters_copy(kNumParameters);
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std::copy_n(parameters, kNumParameters, parameters_copy.data());
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internal::FixedArray<double, 32> 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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internal::EvaluateJacobianForParameterBlocks<
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ParameterDims>::template Apply<method, kNumResiduals>(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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return true;
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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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return internal::EvaluateJacobianForParameterBlocks<
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internal::StaticParameterDims<kNumParameters>>::
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template Apply<kMethod, 1>(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 kNumParameters; }
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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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std::unique_ptr<FirstOrderFunctor> functor_;
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Ownership ownership_;
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NumericDiffOptions options_;
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const int num_parameters_;
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const Ownership ownership_;
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const NumericDiffOptions options_;
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};
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} // namespace ceres
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