1 // Ceres Solver - A fast non-linear least squares minimizer 2 // Copyright 2010, 2011, 2012 Google Inc. All rights reserved. 3 // http://code.google.com/p/ceres-solver/ 4 // 5 // Redistribution and use in source and binary forms, with or without 6 // modification, are permitted provided that the following conditions are met: 7 // 8 // * Redistributions of source code must retain the above copyright notice, 9 // this list of conditions and the following disclaimer. 10 // * Redistributions in binary form must reproduce the above copyright notice, 11 // this list of conditions and the following disclaimer in the documentation 12 // and/or other materials provided with the distribution. 13 // * Neither the name of Google Inc. nor the names of its contributors may be 14 // used to endorse or promote products derived from this software without 15 // specific prior written permission. 16 // 17 // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 18 // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 19 // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE 20 // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE 21 // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR 22 // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF 23 // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS 24 // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN 25 // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) 26 // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 27 // POSSIBILITY OF SUCH DAMAGE. 28 // 29 // Author: wjr (at) google.com (William Rucklidge) 30 // 31 // This file contains tests for the GradientChecker class. 32 33 #include "ceres/gradient_checker.h" 34 35 #include <cmath> 36 #include <cstdlib> 37 #include <vector> 38 39 #include "ceres/cost_function.h" 40 #include "ceres/random.h" 41 #include "glog/logging.h" 42 #include "gtest/gtest.h" 43 44 namespace ceres { 45 namespace internal { 46 47 // We pick a (non-quadratic) function whose derivative are easy: 48 // 49 // f = exp(- a' x). 50 // df = - f a. 51 // 52 // where 'a' is a vector of the same size as 'x'. In the block 53 // version, they are both block vectors, of course. 54 class GoodTestTerm : public CostFunction { 55 public: 56 GoodTestTerm(int arity, int const *dim) : arity_(arity) { 57 // Make 'arity' random vectors. 58 a_.resize(arity_); 59 for (int j = 0; j < arity_; ++j) { 60 a_[j].resize(dim[j]); 61 for (int u = 0; u < dim[j]; ++u) { 62 a_[j][u] = 2.0 * RandDouble() - 1.0; 63 } 64 } 65 66 for (int i = 0; i < arity_; i++) { 67 mutable_parameter_block_sizes()->push_back(dim[i]); 68 } 69 set_num_residuals(1); 70 } 71 72 bool Evaluate(double const* const* parameters, 73 double* residuals, 74 double** jacobians) const { 75 // Compute a . x. 76 double ax = 0; 77 for (int j = 0; j < arity_; ++j) { 78 for (int u = 0; u < parameter_block_sizes()[j]; ++u) { 79 ax += a_[j][u] * parameters[j][u]; 80 } 81 } 82 83 // This is the cost, but also appears as a factor 84 // in the derivatives. 85 double f = *residuals = exp(-ax); 86 87 // Accumulate 1st order derivatives. 88 if (jacobians) { 89 for (int j = 0; j < arity_; ++j) { 90 if (jacobians[j]) { 91 for (int u = 0; u < parameter_block_sizes()[j]; ++u) { 92 // See comments before class. 93 jacobians[j][u] = - f * a_[j][u]; 94 } 95 } 96 } 97 } 98 99 return true; 100 } 101 102 private: 103 int arity_; 104 vector<vector<double> > a_; // our vectors. 105 }; 106 107 class BadTestTerm : public CostFunction { 108 public: 109 BadTestTerm(int arity, int const *dim) : arity_(arity) { 110 // Make 'arity' random vectors. 111 a_.resize(arity_); 112 for (int j = 0; j < arity_; ++j) { 113 a_[j].resize(dim[j]); 114 for (int u = 0; u < dim[j]; ++u) { 115 a_[j][u] = 2.0 * RandDouble() - 1.0; 116 } 117 } 118 119 for (int i = 0; i < arity_; i++) { 120 mutable_parameter_block_sizes()->push_back(dim[i]); 121 } 122 set_num_residuals(1); 123 } 124 125 bool Evaluate(double const* const* parameters, 126 double* residuals, 127 double** jacobians) const { 128 // Compute a . x. 129 double ax = 0; 130 for (int j = 0; j < arity_; ++j) { 131 for (int u = 0; u < parameter_block_sizes()[j]; ++u) { 132 ax += a_[j][u] * parameters[j][u]; 133 } 134 } 135 136 // This is the cost, but also appears as a factor 137 // in the derivatives. 138 double f = *residuals = exp(-ax); 139 140 // Accumulate 1st order derivatives. 141 if (jacobians) { 142 for (int j = 0; j < arity_; ++j) { 143 if (jacobians[j]) { 144 for (int u = 0; u < parameter_block_sizes()[j]; ++u) { 145 // See comments before class. 146 jacobians[j][u] = - f * a_[j][u] + 0.001; 147 } 148 } 149 } 150 } 151 152 return true; 153 } 154 155 private: 156 int arity_; 157 vector<vector<double> > a_; // our vectors. 158 }; 159 160 TEST(GradientChecker, SmokeTest) { 161 srand(5); 162 163 // Test with 3 blocks of size 2, 3 and 4. 164 int const arity = 3; 165 int const dim[arity] = { 2, 3, 4 }; 166 167 // Make a random set of blocks. 168 FixedArray<double*> parameters(arity); 169 for (int j = 0; j < arity; ++j) { 170 parameters[j] = new double[dim[j]]; 171 for (int u = 0; u < dim[j]; ++u) { 172 parameters[j][u] = 2.0 * RandDouble() - 1.0; 173 } 174 } 175 176 // Make a term and probe it. 177 GoodTestTerm good_term(arity, dim); 178 typedef GradientChecker<GoodTestTerm, 1, 2, 3, 4> GoodTermGradientChecker; 179 EXPECT_TRUE(GoodTermGradientChecker::Probe( 180 parameters.get(), 1e-6, &good_term, NULL)); 181 182 BadTestTerm bad_term(arity, dim); 183 typedef GradientChecker<BadTestTerm, 1, 2, 3, 4> BadTermGradientChecker; 184 EXPECT_FALSE(BadTermGradientChecker::Probe( 185 parameters.get(), 1e-6, &bad_term, NULL)); 186 187 for (int j = 0; j < arity; j++) { 188 delete[] parameters[j]; 189 } 190 } 191 192 } // namespace internal 193 } // namespace ceres 194
