| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2017 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 <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: |
| // |
| // // 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); |
| // |
| // WARNING: The cost function adapter is not thread safe. |
| template<typename CostFunctor, |
| int kNumResiduals, |
| int kNumParameters, |
| typename T = double> |
| class TinySolverAutoDiffFunction { |
| public: |
| TinySolverAutoDiffFunction(const CostFunctor& cost_functor) |
| : cost_functor_(cost_functor) {} |
| |
| typedef T Scalar; |
| enum { |
| NUM_PARAMETERS = kNumParameters, |
| NUM_RESIDUALS = kNumResiduals, |
| }; |
| |
| // This is similar to AutoDiff::Differentiate(), 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 < kNumResiduals; ++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_)) { |
| return false; |
| } |
| |
| // Copy the jacobian out of the derivative part of the residual jets. |
| Eigen::Map<Eigen::Matrix<T, |
| kNumResiduals, |
| kNumParameters> > jacobian_matrix(jacobian); |
| for (int r = 0; r < kNumResiduals; ++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; |
| } |
| |
| private: |
| const CostFunctor& cost_functor_; |
| |
| // 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. |
| mutable Jet<T, kNumParameters> jet_parameters_[kNumParameters]; |
| mutable Jet<T, kNumParameters> jet_residuals_[kNumResiduals]; |
| }; |
| |
| } // namespace ceres |
| |
| #endif // CERES_PUBLIC_TINY_SOLVER_AUTODIFF_FUNCTION_H_ |