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https://github.com/ceres-solver/ceres-solver.git
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75dd30faed
- Don't trace residual -> Use input cost functor instead - Remove unnecessary copy from tmp jacobian array - Fix indent of generated Evaluate() function Change-Id: I23e09987d8ec30f7202a93eb253648505dcaaa4e
222 lines
9.6 KiB
C++
222 lines
9.6 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2019 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: darius.rueckert@fau.de (Darius Rueckert)
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//
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#ifndef CERES_PUBLIC_CODEGEN_AUTODIFF_H_
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#define CERES_PUBLIC_CODEGEN_AUTODIFF_H_
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#include "ceres/codegen/internal/code_generator.h"
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#include "ceres/codegen/internal/expression_graph.h"
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#include "ceres/codegen/internal/expression_ref.h"
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#include "ceres/internal/autodiff.h"
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#include "ceres/jet.h"
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namespace ceres {
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struct AutoDiffCodeGenOptions {};
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// TODO(darius): Documentation
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template <typename DerivedCostFunctor>
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std::vector<std::string> GenerateCodeForFunctor(
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const AutoDiffCodeGenOptions& options) {
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// Define some types and shortcuts to make the code below more readable.
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using ParameterDims = typename DerivedCostFunctor::ParameterDims;
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using Parameters = typename ParameterDims::Parameters;
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// Instead of using scalar Jets, we use Jets of ExpressionRef which record
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// their own operations during evaluation.
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using ExpressionRef = internal::ExpressionRef;
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using ExprJet = Jet<ExpressionRef, ParameterDims::kNumParameters>;
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constexpr int kNumResiduals = DerivedCostFunctor::kNumResiduals;
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constexpr int kNumParameters = ParameterDims::kNumParameters;
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constexpr int kNumParameterBlocks = ParameterDims::kNumParameterBlocks;
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// Create the cost functor using the default constructor.
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// Code is generated for the CostFunctor and not an instantiation of it. This
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// is different to AutoDiffCostFunction, which computes the derivatives for
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// a specific object.
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static_assert(std::is_default_constructible<DerivedCostFunctor>::value,
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"Cost functors used in code generation must have a default "
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"constructor. If you are using local variables, make sure to "
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"wrap them into the CERES_LOCAL_VARIABLE macro.");
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DerivedCostFunctor functor;
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// During recording phase all operations on ExpressionRefs are recorded to an
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// internal data structure, the ExpressionGraph. This ExpressionGraph is then
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// optimized and converted back into C++ code.
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internal::StartRecordingExpressions();
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// The Jet arrays are defined after StartRecordingExpressions, because Jets
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// are zero-initialized in the default constructor. This already creates
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// COMPILE_TIME_CONSTANT expressions.
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std::array<ExprJet, kNumParameters> all_parameters;
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std::array<ExprJet, kNumResiduals> residuals;
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std::array<ExprJet*, kNumParameterBlocks> unpacked_parameters =
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ParameterDims::GetUnpackedParameters(all_parameters.data());
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// Create input expressions that convert from the doubles passed from Ceres
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// into codegen Expressions. These inputs are assigned to the scalar part "a"
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// of the corresponding Jets.
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//
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// Example code generated by these expressions:
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// v_0 = parameters[0][0];
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// v_1 = parameters[0][1];
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// ...
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for (int i = 0; i < kNumParameterBlocks; ++i) {
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for (int j = 0; j < ParameterDims::GetDim(i); ++j) {
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ExprJet& parameter = unpacked_parameters[i][j];
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parameter.a = internal::MakeInputAssignment<ExpressionRef>(
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0.0,
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("parameters[" + std::to_string(i) + "][" + std::to_string(j) + "]")
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.c_str());
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}
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}
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// During the array initialization above, the derivative part of the Jets is
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// set to zero. Here, we set the correct element to 1.
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for (int i = 0; i < kNumParameters; ++i) {
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all_parameters[i].v(i) = ExpressionRef(1);
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}
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// Run the cost functor with Jets of ExpressionRefs.
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// Since we are still in recording mode, all operations of the cost functor
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// will be added to the graph.
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internal::VariadicEvaluate<ParameterDims>(
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functor, unpacked_parameters.data(), residuals.data());
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// At this point the Jets in 'residuals' contain references to the output
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// expressions. Here we add new expressions that assign the generated
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// temporaries to the actual residual array.
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//
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// Example code generated by these expressions:
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// residuals[0] = v_200;
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// residuals[1] = v_201;
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// ...
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for (int i = 0; i < kNumResiduals; ++i) {
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auto& J = residuals[i];
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// Note: MakeOutput automatically adds the expression to the active graph.
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internal::MakeOutput(J.a, "residuals[" + std::to_string(i) + "]");
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}
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// Example code generated by these expressions:
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// jacobians[0][0] = v_351;
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// jacobians[0][1] = v_352;
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// ...
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for (int i = 0, total_param_id = 0; i < kNumParameterBlocks;
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total_param_id += ParameterDims::GetDim(i), ++i) {
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for (int r = 0; r < kNumResiduals; ++r) {
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for (int j = 0; j < ParameterDims::GetDim(i); ++j) {
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internal::MakeOutput(
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(residuals[r].v[total_param_id + j]),
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"jacobians[" + std::to_string(i) + "][" +
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std::to_string(r * ParameterDims::GetDim(i) + j) + "]");
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}
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}
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}
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// Stop recording and return the current active graph. Performing operations
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// of ExpressionRef after this line will result in an error.
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auto residual_and_jacobian_graph = internal::StopRecordingExpressions();
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// TODO(darius): Once the optimizer is in place, call it from
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// here to optimize the code before generating.
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// We have the optimized code of the cost functor stored in the
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// ExpressionGraphs. Now we generate C++ code for it and place it line-by-line
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// in this vector of strings.
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std::vector<std::string> output;
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output.emplace_back("// This file is generated with ceres::AutoDiffCodeGen.");
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output.emplace_back("// http://ceres-solver.org/");
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output.emplace_back("");
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{
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// Generate C++ code for the EvaluateResidualAndJacobian function and append
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// it to the output.
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internal::CodeGenerator::Options generator_options;
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generator_options.function_name =
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"void EvaluateResidualAndJacobian(double const* const* parameters, "
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"double* "
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"residuals, double** jacobians) const";
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internal::CodeGenerator gen(residual_and_jacobian_graph, generator_options);
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std::vector<std::string> code = gen.Generate();
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output.insert(output.end(), code.begin(), code.end());
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}
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output.emplace_back("");
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// Generate a generic combined function, which calls EvaluateResidual and
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// EvaluateResidualAndJacobian. This combined function is compatible to
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// CostFunction::Evaluate. Therefore the generated code can be directly used
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// in SizedCostFunctions.
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output.emplace_back("bool Evaluate(double const* const* parameters,");
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output.emplace_back(" double* residuals,");
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output.emplace_back(" double** jacobians) const {");
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output.emplace_back(" if (!jacobians) {");
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output.emplace_back(" // Use the input cost functor");
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output.emplace_back(" return (*this)(");
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for (int i = 0; i < kNumParameterBlocks; ++i) {
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output.emplace_back(" parameters[" + std::to_string(i) + "],");
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}
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output.emplace_back(" residuals");
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output.emplace_back(" );");
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output.emplace_back(" }");
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// Create a tmp array of all jacobians and use it for evaluation if the input
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// jacobian is null. The generated code for a <2,3,1,2> cost functor is:
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// double jacobians_data[6];
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// double* jacobians_ptrs[] = {
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// jacobians[0] ? jacobians[0] : jacobians_data + 0,
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// jacobians[1] ? jacobians[1] : jacobians_data + 6,
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// jacobians[2] ? jacobians[2] : jacobians_data + 8,
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// };
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output.emplace_back(" double jacobians_data[" +
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std::to_string(kNumParameters * kNumResiduals) + "];");
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output.emplace_back(" double* jacobians_ptrs[] = {");
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for (int i = 0, total_param_id = 0; i < kNumParameterBlocks;
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total_param_id += ParameterDims::GetDim(i), ++i) {
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output.emplace_back(" jacobians[" + std::to_string(i) +
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"] ? jacobians[" + std::to_string(i) +
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"] : jacobians_data + " +
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std::to_string(kNumResiduals * total_param_id) + ",");
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}
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output.emplace_back(" };");
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output.emplace_back(
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" EvaluateResidualAndJacobian(parameters, residuals, "
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"jacobians_ptrs);");
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output.emplace_back(" return true;");
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output.emplace_back("}");
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return output;
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}
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} // namespace ceres
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#endif // CERES_PUBLIC_CODEGEN_AUTODIFF_H_
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