Squashed 'third_party/ceres/' content from commit e51e9b4
Change-Id: I763587619d57e594d3fa158dc3a7fe0b89a1743b
git-subtree-dir: third_party/ceres
git-subtree-split: e51e9b46f6ca88ab8b2266d0e362771db6d98067
diff --git a/include/ceres/tiny_solver_autodiff_function.h b/include/ceres/tiny_solver_autodiff_function.h
new file mode 100644
index 0000000..cc93d73
--- /dev/null
+++ b/include/ceres/tiny_solver_autodiff_function.h
@@ -0,0 +1,206 @@
+// 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 <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:
+ 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_