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diff --git a/include/ceres/autodiff_cost_function.h b/include/ceres/autodiff_cost_function.h new file mode 100644 index 0000000..bb08d64 --- /dev/null +++ b/include/ceres/autodiff_cost_function.h @@ -0,0 +1,217 @@ +// Ceres Solver - A fast non-linear least squares minimizer +// Copyright 2010, 2011, 2012 Google Inc. All rights reserved. +// http://code.google.com/p/ceres-solver/ +// +// 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: sameeragarwal@google.com (Sameer Agarwal) +// +// Helpers for making CostFunctions as needed by the least squares framework, +// with Jacobians computed via automatic differentiation. For more information +// on automatic differentation, see the wikipedia article at +// http://en.wikipedia.org/wiki/Automatic_differentiation +// +// To get an auto differentiated cost function, you must define a class with a +// templated operator() (a functor) that computes the cost function in terms of +// the template parameter T. The autodiff framework substitutes appropriate +// "jet" objects for T in order to compute the derivative when necessary, but +// this is hidden, and you should write the function as if T were a scalar type +// (e.g. a double-precision floating point number). +// +// 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 = k - x'y, where both x and y are +// two-dimensional column vector parameters, the prime sign indicates +// transposition, and k is a constant. The form of this error, which is the +// difference between a constant and an expression, is a common pattern in least +// squares problems. For example, the value x'y might be the model expectation +// for a series of measurements, where there is an instance of the cost function +// for each measurement k. +// +// The actual cost added to the total problem is e^2, or (k - x'k)^2; however, +// the squaring is implicitly done by the optimization framework. +// +// To write an auto-differentiable cost function for the above model, first +// define the object +// +// class MyScalarCostFunction { +// MyScalarCostFunction(double k): k_(k) {} +// +// template <typename T> +// bool operator()(const T* const x , const T* const y, T* e) const { +// e[0] = T(k_) - x[0] * y[0] + x[1] * y[1]; +// return true; +// } +// +// private: +// double k_; +// }; +// +// Note that in the declaration of operator() the input parameters x and y come +// first, and are passed as const pointers to arrays of T. If there were three +// input parameters, then the third input parameter would come after y. The +// output is always the last parameter, and is also a pointer to an array. In +// the example above, e is a scalar, so only e[0] is set. +// +// Then given this class definition, the auto differentiated cost function for +// it can be constructed as follows. +// +// CostFunction* cost_function +// = new AutoDiffCostFunction<MyScalarCostFunction, 1, 2, 2>( +// new MyScalarCostFunction(1.0)); ^ ^ ^ +// | | | +// Dimension of residual ------+ | | +// Dimension of x ----------------+ | +// Dimension of y -------------------+ +// +// In this example, there is usually an instance for each measumerent of k. +// +// In the instantiation above, the template parameters following +// "MyScalarCostFunction", "1, 2, 2", describe the functor as computing a +// 1-dimensional output from two arguments, both 2-dimensional. +// +// The autodiff cost function also supports cost functions with a +// runtime-determined number of residuals. For example: +// +// CostFunction* cost_function +// = new AutoDiffCostFunction<MyScalarCostFunction, DYNAMIC, 2, 2>( +// new CostFunctionWithDynamicNumResiduals(1.0), ^ ^ ^ +// runtime_number_of_residuals); <----+ | | | +// | | | | +// | | | | +// Actual number of residuals ------+ | | | +// Indicate dynamic number of residuals ---------+ | | +// Dimension of x -------------------------------------+ | +// Dimension of y ----------------------------------------+ +// +// The framework can currently accommodate cost functions of up to 6 independent +// variables, and there is no limit on the dimensionality of each of them. +// +// WARNING #1: Since the functor will get instantiated with different types for +// T, you must to convert from other numeric types to T before mixing +// computations with other variables of type T. In the example above, this is +// seen where instead of using k_ directly, k_ is wrapped with T(k_). +// +// WARNING #2: A common beginner's error when first using autodiff cost +// functions is to get the sizing wrong. In particular, there is a tendency to +// set the template parameters to (dimension of residual, number of parameters) +// instead of passing a dimension parameter for *every parameter*. In the +// example above, that would be <MyScalarCostFunction, 1, 2>, which is missing +// the last '2' argument. Please be careful when setting the size parameters. + +#ifndef CERES_PUBLIC_AUTODIFF_COST_FUNCTION_H_ +#define CERES_PUBLIC_AUTODIFF_COST_FUNCTION_H_ + +#include <glog/logging.h> +#include "ceres/internal/autodiff.h" +#include "ceres/internal/scoped_ptr.h" +#include "ceres/sized_cost_function.h" +#include "ceres/types.h" + +namespace ceres { + +// A cost function which computes the derivative of the cost with respect to +// the parameters (a.k.a. the jacobian) using an autodifferentiation framework. +// The first template argument is the functor object, described in the header +// comment. The second argument is the dimension of the residual (or +// ceres::DYNAMIC to indicate it will be set at runtime), and subsequent +// arguments describe the size of the Nth parameter, one per parameter. +// +// The constructors take ownership of the cost functor. +// +// If the number of residuals (argument "M" below) is ceres::DYNAMIC, then the +// two-argument constructor must be used. The second constructor takes a number +// of residuals (in addition to the templated number of residuals). This allows +// for varying the number of residuals for a single autodiff cost function at +// runtime. +template <typename CostFunctor, + int M, // Number of residuals, or ceres::DYNAMIC. + int N0, // Number of parameters in block 0. + int N1 = 0, // Number of parameters in block 1. + int N2 = 0, // Number of parameters in block 2. + int N3 = 0, // Number of parameters in block 3. + int N4 = 0, // Number of parameters in block 4. + int N5 = 0, // Number of parameters in block 5. + int N6 = 0, // Number of parameters in block 6. + int N7 = 0, // Number of parameters in block 7. + int N8 = 0, // Number of parameters in block 8. + int N9 = 0> // Number of parameters in block 9. +class AutoDiffCostFunction : + public SizedCostFunction<M, N0, N1, N2, N3, N4, N5, N6, N7, N8, N9> { + public: + // Takes ownership of functor. Uses the template-provided value for the + // number of residuals ("M"). + explicit AutoDiffCostFunction(CostFunctor* functor) + : functor_(functor) { + CHECK_NE(M, DYNAMIC) << "Can't run the fixed-size constructor if the " + << "number of residuals is set to ceres::DYNAMIC."; + } + + // Takes ownership of functor. Ignores the template-provided number of + // residuals ("M") in favor of the "num_residuals" argument provided. + // + // This allows for having autodiff cost functions which return varying + // numbers of residuals at runtime. + AutoDiffCostFunction(CostFunctor* functor, int num_residuals) + : functor_(functor) { + CHECK_EQ(M, DYNAMIC) << "Can't run the dynamic-size constructor if the " + << "number of residuals is not ceres::DYNAMIC."; + SizedCostFunction<M, N0, N1, N2, N3, N4, N5, N6, N7, N8, N9> + ::set_num_residuals(num_residuals); + } + + virtual ~AutoDiffCostFunction() {} + + // Implementation details follow; clients of the autodiff cost function should + // not have to examine below here. + // + // To handle varardic cost functions, some template magic is needed. It's + // mostly hidden inside autodiff.h. + virtual bool Evaluate(double const* const* parameters, + double* residuals, + double** jacobians) const { + if (!jacobians) { + return internal::VariadicEvaluate< + CostFunctor, double, N0, N1, N2, N3, N4, N5, N6, N7, N8, N9> + ::Call(*functor_, parameters, residuals); + } + return internal::AutoDiff<CostFunctor, double, + N0, N1, N2, N3, N4, N5, N6, N7, N8, N9>::Differentiate( + *functor_, + parameters, + SizedCostFunction<M, N0, N1, N2, N3, N4, N5, N6, N7, N8, N9> + ::num_residuals(), + residuals, + jacobians); + } + + private: + internal::scoped_ptr<CostFunctor> functor_; +}; + +} // namespace ceres + +#endif // CERES_PUBLIC_AUTODIFF_COST_FUNCTION_H_ |