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Diffstat (limited to 'internal/ceres/gradient_checker_test.cc')
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diff --git a/internal/ceres/gradient_checker_test.cc b/internal/ceres/gradient_checker_test.cc new file mode 100644 index 0000000..cf7ee20 --- /dev/null +++ b/internal/ceres/gradient_checker_test.cc @@ -0,0 +1,193 @@ +// 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: wjr@google.com (William Rucklidge) +// +// This file contains tests for the GradientChecker class. + +#include "ceres/gradient_checker.h" + +#include <cmath> +#include <cstdlib> +#include <glog/logging.h> +#include <vector> + +#include "ceres/cost_function.h" +#include "ceres/random.h" +#include "gtest/gtest.h" + +namespace ceres { +namespace internal { + +// We pick a (non-quadratic) function whose derivative are easy: +// +// f = exp(- a' x). +// df = - f a. +// +// where 'a' is a vector of the same size as 'x'. In the block +// version, they are both block vectors, of course. +class GoodTestTerm : public CostFunction { + public: + GoodTestTerm(int arity, int const *dim) : arity_(arity) { + // Make 'arity' random vectors. + a_.resize(arity_); + for (int j = 0; j < arity_; ++j) { + a_[j].resize(dim[j]); + for (int u = 0; u < dim[j]; ++u) { + a_[j][u] = 2.0 * RandDouble() - 1.0; + } + } + + for (int i = 0; i < arity_; i++) { + mutable_parameter_block_sizes()->push_back(dim[i]); + } + set_num_residuals(1); + } + + bool Evaluate(double const* const* parameters, + double* residuals, + double** jacobians) const { + // Compute a . x. + double ax = 0; + for (int j = 0; j < arity_; ++j) { + for (int u = 0; u < parameter_block_sizes()[j]; ++u) { + ax += a_[j][u] * parameters[j][u]; + } + } + + // This is the cost, but also appears as a factor + // in the derivatives. + double f = *residuals = exp(-ax); + + // Accumulate 1st order derivatives. + if (jacobians) { + for (int j = 0; j < arity_; ++j) { + if (jacobians[j]) { + for (int u = 0; u < parameter_block_sizes()[j]; ++u) { + // See comments before class. + jacobians[j][u] = - f * a_[j][u]; + } + } + } + } + + return true; + } + + private: + int arity_; + vector<vector<double> > a_; // our vectors. +}; + +class BadTestTerm : public CostFunction { + public: + BadTestTerm(int arity, int const *dim) : arity_(arity) { + // Make 'arity' random vectors. + a_.resize(arity_); + for (int j = 0; j < arity_; ++j) { + a_[j].resize(dim[j]); + for (int u = 0; u < dim[j]; ++u) { + a_[j][u] = 2.0 * RandDouble() - 1.0; + } + } + + for (int i = 0; i < arity_; i++) { + mutable_parameter_block_sizes()->push_back(dim[i]); + } + set_num_residuals(1); + } + + bool Evaluate(double const* const* parameters, + double* residuals, + double** jacobians) const { + // Compute a . x. + double ax = 0; + for (int j = 0; j < arity_; ++j) { + for (int u = 0; u < parameter_block_sizes()[j]; ++u) { + ax += a_[j][u] * parameters[j][u]; + } + } + + // This is the cost, but also appears as a factor + // in the derivatives. + double f = *residuals = exp(-ax); + + // Accumulate 1st order derivatives. + if (jacobians) { + for (int j = 0; j < arity_; ++j) { + if (jacobians[j]) { + for (int u = 0; u < parameter_block_sizes()[j]; ++u) { + // See comments before class. + jacobians[j][u] = - f * a_[j][u] + 0.001; + } + } + } + } + + return true; + } + + private: + int arity_; + vector<vector<double> > a_; // our vectors. +}; + +TEST(GradientChecker, SmokeTest) { + srand(5); + + // Test with 3 blocks of size 2, 3 and 4. + int const arity = 3; + int const dim[arity] = { 2, 3, 4 }; + + // Make a random set of blocks. + FixedArray<double*> parameters(arity); + for (int j = 0; j < arity; ++j) { + parameters[j] = new double[dim[j]]; + for (int u = 0; u < dim[j]; ++u) { + parameters[j][u] = 2.0 * RandDouble() - 1.0; + } + } + + // Make a term and probe it. + GoodTestTerm good_term(arity, dim); + typedef GradientChecker<GoodTestTerm, 1, 2, 3, 4> GoodTermGradientChecker; + EXPECT_TRUE(GoodTermGradientChecker::Probe( + parameters.get(), 1e-6, &good_term, NULL)); + + BadTestTerm bad_term(arity, dim); + typedef GradientChecker<BadTestTerm, 1, 2, 3, 4> BadTermGradientChecker; + EXPECT_FALSE(BadTermGradientChecker::Probe( + parameters.get(), 1e-6, &bad_term, NULL)); + + for (int j = 0; j < arity; j++) { + delete[] parameters[j]; + } +} + +} // namespace internal +} // namespace ceres |