blob: e98397aa2c2fb3b33c1547e8e0035f78566da98d [file] [log] [blame]
Keir Mierle8ebb0732012-04-30 23:09:08 -07001// 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: sameeragarwal@google.com (Sameer Agarwal)
30
31#include "ceres/autodiff_cost_function.h"
32
33#include <cstddef>
34
35#include "gtest/gtest.h"
36#include "ceres/cost_function.h"
37
38namespace ceres {
39namespace internal {
40
41class BinaryScalarCost {
42 public:
43 explicit BinaryScalarCost(double a): a_(a) {}
44 template <typename T>
45 bool operator()(const T* const x, const T* const y,
46 T* cost) const {
Keir Mierleefe7ac62012-06-24 22:25:28 -070047 cost[0] = x[0] * y[0] + x[1] * y[1] - T(a_);
Keir Mierle8ebb0732012-04-30 23:09:08 -070048 return true;
49 }
50 private:
51 double a_;
52};
53
Keir Mierlef1e67cc2012-10-19 10:50:02 -070054TEST(AutodiffCostFunction, BilinearDifferentiationTest) {
Keir Mierle8ebb0732012-04-30 23:09:08 -070055 CostFunction* cost_function =
56 new AutoDiffCostFunction<BinaryScalarCost, 1, 2, 2>(
57 new BinaryScalarCost(1.0));
58
59 double** parameters = new double*[2];
60 parameters[0] = new double[2];
61 parameters[1] = new double[2];
62
63 parameters[0][0] = 1;
64 parameters[0][1] = 2;
65
66 parameters[1][0] = 3;
67 parameters[1][1] = 4;
68
69 double** jacobians = new double*[2];
70 jacobians[0] = new double[2];
71 jacobians[1] = new double[2];
72
Keir Mierle8ebb0732012-04-30 23:09:08 -070073 double residuals = 0.0;
74
75 cost_function->Evaluate(parameters, &residuals, NULL);
Keir Mierlef1e67cc2012-10-19 10:50:02 -070076 EXPECT_EQ(10.0, residuals);
Keir Mierle8ebb0732012-04-30 23:09:08 -070077 cost_function->Evaluate(parameters, &residuals, jacobians);
78
Keir Mierlef1e67cc2012-10-19 10:50:02 -070079 EXPECT_EQ(3, jacobians[0][0]);
80 EXPECT_EQ(4, jacobians[0][1]);
81 EXPECT_EQ(1, jacobians[1][0]);
82 EXPECT_EQ(2, jacobians[1][1]);
Keir Mierle8ebb0732012-04-30 23:09:08 -070083
Keir Mierlef1e67cc2012-10-19 10:50:02 -070084 delete[] jacobians[0];
85 delete[] jacobians[1];
86 delete[] parameters[0];
87 delete[] parameters[1];
88 delete[] jacobians;
89 delete[] parameters;
90 delete cost_function;
91}
92
93struct TenParameterCost {
94 template <typename T>
95 bool operator()(const T* const x0,
96 const T* const x1,
97 const T* const x2,
98 const T* const x3,
99 const T* const x4,
100 const T* const x5,
101 const T* const x6,
102 const T* const x7,
103 const T* const x8,
104 const T* const x9,
105 T* cost) const {
106 cost[0] = *x0 + *x1 + *x2 + *x3 + *x4 + *x5 + *x6 + *x7 + *x8 + *x9;
107 return true;
108 }
109};
110
111TEST(AutodiffCostFunction, ManyParameterAutodiffInstantiates) {
112 CostFunction* cost_function =
113 new AutoDiffCostFunction<
114 TenParameterCost, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>(
115 new TenParameterCost);
116
117 double** parameters = new double*[10];
118 double** jacobians = new double*[10];
119 for (int i = 0; i < 10; ++i) {
120 parameters[i] = new double[1];
121 parameters[i][0] = i;
122 jacobians[i] = new double[1];
123 }
124
125 double residuals = 0.0;
126
127 cost_function->Evaluate(parameters, &residuals, NULL);
128 EXPECT_EQ(45.0, residuals);
129
130 cost_function->Evaluate(parameters, &residuals, jacobians);
131 EXPECT_EQ(residuals, 45.0);
132 for (int i = 0; i < 10; ++i) {
133 EXPECT_EQ(1.0, jacobians[i][0]);
134 }
135
136 for (int i = 0; i < 10; ++i) {
137 delete[] jacobians[i];
138 delete[] parameters[i];
139 }
140 delete[] jacobians;
141 delete[] parameters;
Keir Mierle8ebb0732012-04-30 23:09:08 -0700142 delete cost_function;
143}
144
145} // namespace internal
146} // namespace ceres