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Diffstat (limited to 'internal/ceres/runtime_numeric_diff_cost_function.cc')
| -rw-r--r-- | internal/ceres/runtime_numeric_diff_cost_function.cc | 217 |
1 files changed, 217 insertions, 0 deletions
diff --git a/internal/ceres/runtime_numeric_diff_cost_function.cc b/internal/ceres/runtime_numeric_diff_cost_function.cc new file mode 100644 index 0000000..7af275c --- /dev/null +++ b/internal/ceres/runtime_numeric_diff_cost_function.cc @@ -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: keir@google.com (Keir Mierle) +// +// Based on the templated version in public/numeric_diff_cost_function.h. + +#include "ceres/runtime_numeric_diff_cost_function.h" + +#include <algorithm> +#include <numeric> +#include <vector> +#include "Eigen/Dense" +#include "ceres/cost_function.h" +#include "ceres/internal/scoped_ptr.h" +#include "glog/logging.h" + +namespace ceres { +namespace internal { +namespace { + +bool EvaluateJacobianForParameterBlock(const CostFunction* function, + int parameter_block_size, + int parameter_block, + RuntimeNumericDiffMethod method, + double relative_step_size, + double const* residuals_at_eval_point, + double** parameters, + double** jacobians) { + using Eigen::Map; + using Eigen::Matrix; + using Eigen::Dynamic; + using Eigen::RowMajor; + + typedef Matrix<double, Dynamic, 1> ResidualVector; + typedef Matrix<double, Dynamic, 1> ParameterVector; + typedef Matrix<double, Dynamic, Dynamic, RowMajor> JacobianMatrix; + + int num_residuals = function->num_residuals(); + + Map<JacobianMatrix> parameter_jacobian(jacobians[parameter_block], + num_residuals, + parameter_block_size); + + // Mutate one element at a time and then restore. + Map<ParameterVector> x_plus_delta(parameters[parameter_block], + parameter_block_size); + ParameterVector x(x_plus_delta); + ParameterVector step_size = x.array().abs() * relative_step_size; + + // To handle cases where a paremeter is exactly zero, instead use the mean + // step_size for the other dimensions. + double fallback_step_size = step_size.sum() / step_size.rows(); + if (fallback_step_size == 0.0) { + // If all the parameters are zero, there's no good answer. Use the given + // relative step_size as absolute step_size and hope for the best. + fallback_step_size = relative_step_size; + } + + // For each parameter in the parameter block, use finite differences to + // compute the derivative for that parameter. + for (int j = 0; j < parameter_block_size; ++j) { + if (step_size(j) == 0.0) { + // The parameter is exactly zero, so compromise and use the mean step_size + // from the other parameters. This can break in many cases, but it's hard + // to pick a good number without problem specific knowledge. + step_size(j) = fallback_step_size; + } + x_plus_delta(j) = x(j) + step_size(j); + + ResidualVector residuals(num_residuals); + if (!function->Evaluate(parameters, &residuals[0], NULL)) { + // Something went wrong; bail. + return false; + } + + // Compute this column of the jacobian in 3 steps: + // 1. Store residuals for the forward part. + // 2. Subtract residuals for the backward (or 0) part. + // 3. Divide out the run. + parameter_jacobian.col(j) = residuals; + + double one_over_h = 1 / step_size(j); + if (method == CENTRAL) { + // Compute the function on the other side of x(j). + x_plus_delta(j) = x(j) - step_size(j); + + if (!function->Evaluate(parameters, &residuals[0], NULL)) { + // Something went wrong; bail. + return false; + } + parameter_jacobian.col(j) -= residuals; + one_over_h /= 2; + } else { + // Forward difference only; reuse existing residuals evaluation. + parameter_jacobian.col(j) -= + Map<const ResidualVector>(residuals_at_eval_point, num_residuals); + } + x_plus_delta(j) = x(j); // Restore x_plus_delta. + + // Divide out the run to get slope. + parameter_jacobian.col(j) *= one_over_h; + } + return true; +} + +class RuntimeNumericDiffCostFunction : public CostFunction { + public: + RuntimeNumericDiffCostFunction(const CostFunction* function, + RuntimeNumericDiffMethod method, + double relative_step_size) + : function_(function), + method_(method), + relative_step_size_(relative_step_size) { + *mutable_parameter_block_sizes() = function->parameter_block_sizes(); + set_num_residuals(function->num_residuals()); + } + + virtual ~RuntimeNumericDiffCostFunction() { } + + virtual bool Evaluate(double const* const* parameters, + double* residuals, + double** jacobians) const { + // Get the function value (residuals) at the the point to evaluate. + bool success = function_->Evaluate(parameters, residuals, NULL); + if (!success) { + // Something went wrong; ignore the jacobian. + return false; + } + if (!jacobians) { + // Nothing to do; just forward. + return true; + } + + const vector<int16>& block_sizes = function_->parameter_block_sizes(); + CHECK(!block_sizes.empty()); + + // Create local space for a copy of the parameters which will get mutated. + int parameters_size = accumulate(block_sizes.begin(), block_sizes.end(), 0); + vector<double> parameters_copy(parameters_size); + vector<double*> parameters_references_copy(block_sizes.size()); + parameters_references_copy[0] = ¶meters_copy[0]; + for (int block = 1; block < block_sizes.size(); ++block) { + parameters_references_copy[block] = parameters_references_copy[block - 1] + + block_sizes[block - 1]; + } + + // Copy the parameters into the local temp space. + for (int block = 0; block < block_sizes.size(); ++block) { + memcpy(parameters_references_copy[block], + parameters[block], + block_sizes[block] * sizeof(*parameters[block])); + } + + for (int block = 0; block < block_sizes.size(); ++block) { + if (!jacobians[block]) { + // No jacobian requested for this parameter / residual pair. + continue; + } + if (!EvaluateJacobianForParameterBlock(function_, + block_sizes[block], + block, + method_, + relative_step_size_, + residuals, + ¶meters_references_copy[0], + jacobians)) { + return false; + } + } + return true; + } + + private: + const CostFunction* function_; + RuntimeNumericDiffMethod method_; + double relative_step_size_; +}; + +} // namespace + +CostFunction* CreateRuntimeNumericDiffCostFunction( + const CostFunction* cost_function, + RuntimeNumericDiffMethod method, + double relative_step_size) { + return new RuntimeNumericDiffCostFunction(cost_function, + method, + relative_step_size); +} + +} // namespace internal +} // namespace ceres |