Initial commit of Ceres Solver.
diff --git a/internal/ceres/runtime_numeric_diff_cost_function.cc b/internal/ceres/runtime_numeric_diff_cost_function.cc
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+// 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 <glog/logging.h>
+#include "Eigen/Dense"
+#include "ceres/cost_function.h"
+#include "ceres/internal/scoped_ptr.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
