GCC Code Coverage Report

Directory: ./
File: unittest/test_problem.cpp
Date: 2025-01-16 08:47:40
Exec Total Coverage
Lines: 314 314 100.0%
Branches: 818 1582 51.7%
Line Branch Exec Source
1 ///////////////////////////////////////////////////////////////////////////////
2 // BSD 3-Clause License
3 //
4 // Copyright (C) 2020-2021, University of Edinburgh
5 // Copyright note valid unless otherwise stated in individual files.
6 // All rights reserved.
7 ///////////////////////////////////////////////////////////////////////////////
8
9 #define BOOST_TEST_NO_MAIN
10 #define BOOST_TEST_ALTERNATIVE_INIT_API
11
12 #include "crocoddyl/core/integrator/euler.hpp"
13 #include "crocoddyl/core/optctrl/shooting.hpp"
14 #include "factory/action.hpp"
15 #include "factory/diff_action.hpp"
16 #include "factory/integrator.hpp"
17 #include "unittest_common.hpp"
18
19 using namespace boost::unit_test;
20 using namespace crocoddyl::unittest;
21
22 //----------------------------------------------------------------------------//
23
24 7 void test_calc(ActionModelTypes::Type action_model_type) {
25 // create the model
26
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 7 ActionModelFactory factory;
27 const boost::shared_ptr<crocoddyl::ActionModelAbstract>& model =
28
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 7 factory.create(action_model_type);
29
30 // create two shooting problems (with and without data allocation)
31 7 std::size_t T = 20;
32
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 7 const Eigen::VectorXd& x0 = model->get_state()->rand();
33 std::vector<boost::shared_ptr<crocoddyl::ActionModelAbstract> > models(T,
34
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 7 model);
35
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 7 std::vector<boost::shared_ptr<crocoddyl::ActionDataAbstract> > datas(T);
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 147 for (std::size_t i = 0; i < T; ++i) {
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 140 datas[i] = model->createData();
38 }
39
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 7 crocoddyl::ShootingProblem problem1(x0, models, model);
40 crocoddyl::ShootingProblem problem2(x0, models, model, datas,
41
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 14 model->createData());
42
43 // Run the print function
44
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 7 std::ostringstream tmp;
45
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 7 tmp << problem1;
46
47 // create random trajectory
48
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 7 std::vector<Eigen::VectorXd> xs(T + 1);
49
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 7 std::vector<Eigen::VectorXd> us(T);
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 147 for (std::size_t i = 0; i < T; ++i) {
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 140 xs[i] = model->get_state()->rand();
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 140 us[i] = Eigen::VectorXd::Random(model->get_nu());
53 }
54
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 7 xs.back() = model->get_state()->rand();
55
56 // check the state and cost in each node
57
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 7 problem1.calc(xs, us);
58
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 7 problem2.calc(xs, us);
59
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 147 for (std::size_t i = 0; i < T; ++i) {
60 const boost::shared_ptr<crocoddyl::ActionDataAbstract>& data =
61
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 140 model->createData();
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 140 model->calc(data, xs[i], us[i]);
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 140 BOOST_CHECK(problem1.get_runningDatas()[i]->cost == data->cost);
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 140 BOOST_CHECK(problem2.get_runningDatas()[i]->cost == data->cost);
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 140 BOOST_CHECK(
66 (problem1.get_runningDatas()[i]->xnext - data->xnext).isZero(1e-9));
67
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 140 BOOST_CHECK(
68 (problem2.get_runningDatas()[i]->xnext - data->xnext).isZero(1e-9));
69 140 }
70 const boost::shared_ptr<crocoddyl::ActionDataAbstract>& data =
71
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 7 model->createData();
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 7 model->calc(data, xs.back());
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 7 BOOST_CHECK(problem1.get_terminalData()->cost == data->cost);
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 7 BOOST_CHECK(problem2.get_terminalData()->cost == data->cost);
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 7 BOOST_CHECK((problem1.get_terminalData()->xnext - data->xnext).isZero(1e-9));
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 7 BOOST_CHECK((problem2.get_terminalData()->xnext - data->xnext).isZero(1e-9));
77 7 }
78
79 92 void test_calc_diffAction(DifferentialActionModelTypes::Type action_model_type,
80 IntegratorTypes::Type integrator_type) {
81 // create the model
82
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 92 DifferentialActionModelFactory factory;
83 const boost::shared_ptr<crocoddyl::DifferentialActionModelAbstract>&
84
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 92 diffModel = factory.create(action_model_type);
85
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 92 IntegratorFactory factory_int;
86 const boost::shared_ptr<crocoddyl::ActionModelAbstract>& model =
87
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 92 factory_int.create(integrator_type, diffModel);
88
89 // create two shooting problems (with and without data allocation)
90 92 std::size_t T = 20;
91
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 92 const Eigen::VectorXd& x0 = model->get_state()->rand();
92 std::vector<boost::shared_ptr<crocoddyl::ActionModelAbstract> > models(T,
93
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 92 model);
94
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 92 std::vector<boost::shared_ptr<crocoddyl::ActionDataAbstract> > datas(T);
95
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 1932 for (std::size_t i = 0; i < T; ++i) {
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 1840 datas[i] = model->createData();
97 }
98
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 92 crocoddyl::ShootingProblem problem1(x0, models, model);
99 crocoddyl::ShootingProblem problem2(x0, models, model, datas,
100
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 184 model->createData());
101
102 // create random trajectory
103
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 92 std::vector<Eigen::VectorXd> xs(T + 1);
104
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 92 std::vector<Eigen::VectorXd> us(T);
105
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 1932 for (std::size_t i = 0; i < T; ++i) {
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 1840 xs[i] = model->get_state()->rand();
107
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 1840 us[i] = Eigen::VectorXd::Random(model->get_nu());
108 }
109
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 92 xs.back() = model->get_state()->rand();
110
111 // check the state and cost in each node
112
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 92 problem1.calc(xs, us);
113
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 92 problem2.calc(xs, us);
114
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 1932 for (std::size_t i = 0; i < T; ++i) {
115 const boost::shared_ptr<crocoddyl::ActionDataAbstract>& data =
116
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 1840 model->createData();
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 1840 model->calc(data, xs[i], us[i]);
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 1840 BOOST_CHECK(problem1.get_runningDatas()[i]->cost == data->cost);
119
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 1840 BOOST_CHECK(problem2.get_runningDatas()[i]->cost == data->cost);
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 1840 BOOST_CHECK(
121 (problem1.get_runningDatas()[i]->xnext - data->xnext).isZero(1e-9));
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 1840 BOOST_CHECK(
123 (problem2.get_runningDatas()[i]->xnext - data->xnext).isZero(1e-9));
124 1840 }
125 const boost::shared_ptr<crocoddyl::ActionDataAbstract>& data =
126
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 92 model->createData();
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 92 model->calc(data, xs.back());
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 92 BOOST_CHECK(problem1.get_terminalData()->cost == data->cost);
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 92 BOOST_CHECK(problem2.get_terminalData()->cost == data->cost);
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 92 BOOST_CHECK((problem1.get_terminalData()->xnext - data->xnext).isZero(1e-9));
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 92 BOOST_CHECK((problem2.get_terminalData()->xnext - data->xnext).isZero(1e-9));
132 92 }
133
134 7 void test_calcDiff(ActionModelTypes::Type action_model_type) {
135 // create the model
136
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 7 ActionModelFactory factory;
137 const boost::shared_ptr<crocoddyl::ActionModelAbstract>& model =
138
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 7 factory.create(action_model_type);
139
140 // create two shooting problems (with and without data allocation)
141 7 std::size_t T = 20;
142
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 7 const Eigen::VectorXd& x0 = model->get_state()->rand();
143 std::vector<boost::shared_ptr<crocoddyl::ActionModelAbstract> > models(T,
144
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 7 model);
145
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 7 std::vector<boost::shared_ptr<crocoddyl::ActionDataAbstract> > datas(T);
146
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 147 for (std::size_t i = 0; i < T; ++i) {
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 140 datas[i] = model->createData();
148 }
149
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 7 crocoddyl::ShootingProblem problem1(x0, models, model);
150 crocoddyl::ShootingProblem problem2(x0, models, model, datas,
151
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 14 model->createData());
152
153 // create random trajectory
154
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 7 std::vector<Eigen::VectorXd> xs(T + 1);
155
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 7 std::vector<Eigen::VectorXd> us(T);
156
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 147 for (std::size_t i = 0; i < T; ++i) {
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 140 xs[i] = model->get_state()->rand();
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 140 us[i] = Eigen::VectorXd::Random(model->get_nu());
159 }
160
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 7 xs.back() = model->get_state()->rand();
161
162 // check the state and cost in each node
163
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 7 problem1.calc(xs, us);
164
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 7 problem2.calc(xs, us);
165
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 7 problem1.calcDiff(xs, us);
166
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 7 problem2.calcDiff(xs, us);
167
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 147 for (std::size_t i = 0; i < T; ++i) {
168 const boost::shared_ptr<crocoddyl::ActionDataAbstract>& data =
169
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 140 model->createData();
170
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 140 model->calc(data, xs[i], us[i]);
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 140 model->calcDiff(data, xs[i], us[i]);
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 140 BOOST_CHECK((problem1.get_runningDatas()[i]->Fx - data->Fx).isZero(1e-9));
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 140 BOOST_CHECK((problem2.get_runningDatas()[i]->Fx - data->Fx).isZero(1e-9));
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 140 BOOST_CHECK((problem1.get_runningDatas()[i]->Fu - data->Fu).isZero(1e-9));
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 140 BOOST_CHECK((problem2.get_runningDatas()[i]->Luu - data->Luu).isZero(1e-9));
186 140 }
187 const boost::shared_ptr<crocoddyl::ActionDataAbstract>& data =
188
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 7 BOOST_CHECK((problem1.get_terminalData()->Fx - data->Fx).isZero(1e-9));
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 7 BOOST_CHECK((problem1.get_terminalData()->Lx - data->Lx).isZero(1e-9));
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 7 BOOST_CHECK((problem2.get_terminalData()->Lx - data->Lx).isZero(1e-9));
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 7 BOOST_CHECK((problem1.get_terminalData()->Lxx - data->Lxx).isZero(1e-9));
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 7 BOOST_CHECK((problem2.get_terminalData()->Lxx - data->Lxx).isZero(1e-9));
197 7 }
198
199 92 void test_calcDiff_diffAction(
200 DifferentialActionModelTypes::Type action_model_type,
201 IntegratorTypes::Type integrator_type) {
202 // create the model
203
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 92 DifferentialActionModelFactory factory;
204 const boost::shared_ptr<crocoddyl::DifferentialActionModelAbstract>&
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 92 diffModel = factory.create(action_model_type);
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 92 IntegratorFactory factory_int;
207 const boost::shared_ptr<crocoddyl::ActionModelAbstract>& model =
208
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 92 factory_int.create(integrator_type, diffModel);
209
210 // create two shooting problems (with and without data allocation)
211 92 std::size_t T = 20;
212
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 92 const Eigen::VectorXd& x0 = model->get_state()->rand();
213 std::vector<boost::shared_ptr<crocoddyl::ActionModelAbstract> > models(T,
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 92 model);
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 92 std::vector<boost::shared_ptr<crocoddyl::ActionDataAbstract> > datas(T);
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 1932 for (std::size_t i = 0; i < T; ++i) {
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 1840 datas[i] = model->createData();
218 }
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 92 crocoddyl::ShootingProblem problem1(x0, models, model);
220 crocoddyl::ShootingProblem problem2(x0, models, model, datas,
221
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 184 model->createData());
222
223 // create random trajectory
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 92 std::vector<Eigen::VectorXd> xs(T + 1);
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 92 std::vector<Eigen::VectorXd> us(T);
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 1932 for (std::size_t i = 0; i < T; ++i) {
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 1840 xs[i] = model->get_state()->rand();
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 1840 us[i] = Eigen::VectorXd::Random(model->get_nu());
229 }
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 92 xs.back() = model->get_state()->rand();
231
232 // check the state and cost in each node
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 92 problem1.calc(xs, us);
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 92 problem2.calc(xs, us);
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 1932 for (std::size_t i = 0; i < T; ++i) {
238 const boost::shared_ptr<crocoddyl::ActionDataAbstract>& data =
239
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 1840 model->createData();
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 1840 model->calc(data, xs[i], us[i]);
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 1840 model->calcDiff(data, xs[i], us[i]);
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 1840 BOOST_CHECK((problem1.get_runningDatas()[i]->Fx - data->Fx).isZero(1e-7));
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 1840 BOOST_CHECK((problem2.get_runningDatas()[i]->Fx - data->Fx).isZero(1e-7));
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 1840 BOOST_CHECK((problem1.get_runningDatas()[i]->Fu - data->Fu).isZero(1e-7));
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 1840 BOOST_CHECK((problem2.get_runningDatas()[i]->Fu - data->Fu).isZero(1e-7));
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 1840 BOOST_CHECK((problem1.get_runningDatas()[i]->Lx - data->Lx).isZero(1e-7));
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 1840 BOOST_CHECK((problem2.get_runningDatas()[i]->Lx - data->Lx).isZero(1e-7));
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 1840 BOOST_CHECK((problem1.get_runningDatas()[i]->Lu - data->Lu).isZero(1e-7));
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 1840 BOOST_CHECK((problem2.get_runningDatas()[i]->Lu - data->Lu).isZero(1e-7));
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 1840 BOOST_CHECK((problem1.get_runningDatas()[i]->Lxx - data->Lxx).isZero(1e-7));
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 1840 BOOST_CHECK((problem2.get_runningDatas()[i]->Lxx - data->Lxx).isZero(1e-7));
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 1840 BOOST_CHECK((problem1.get_runningDatas()[i]->Lxu - data->Lxu).isZero(1e-7));
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 1840 BOOST_CHECK((problem2.get_runningDatas()[i]->Lxu - data->Lxu).isZero(1e-7));
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 1840 BOOST_CHECK((problem1.get_runningDatas()[i]->Luu - data->Luu).isZero(1e-7));
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 1840 BOOST_CHECK((problem2.get_runningDatas()[i]->Luu - data->Luu).isZero(1e-7));
256 1840 }
257 const boost::shared_ptr<crocoddyl::ActionDataAbstract>& data =
258
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 92 model->calc(data, xs.back());
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 92 model->calcDiff(data, xs.back());
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 92 BOOST_CHECK((problem1.get_terminalData()->Fx - data->Fx).isZero(1e-7));
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 92 BOOST_CHECK((problem2.get_terminalData()->Fx - data->Fx).isZero(1e-7));
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 92 BOOST_CHECK((problem1.get_terminalData()->Lx - data->Lx).isZero(1e-7));
264
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 92 BOOST_CHECK((problem2.get_terminalData()->Lx - data->Lx).isZero(1e-7));
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 92 BOOST_CHECK((problem1.get_terminalData()->Lxx - data->Lxx).isZero(1e-7));
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 92 BOOST_CHECK((problem2.get_terminalData()->Lxx - data->Lxx).isZero(1e-7));
267 92 }
268
269 7 void test_rollout(ActionModelTypes::Type action_model_type) {
270 // create the model
271
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272 const boost::shared_ptr<crocoddyl::ActionModelAbstract>& model =
273
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 7 factory.create(action_model_type);
274
275 // create the shooting problem
276 7 std::size_t T = 20;
277
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278 std::vector<boost::shared_ptr<crocoddyl::ActionModelAbstract> > models(T,
279
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 7 model);
280
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 7 crocoddyl::ShootingProblem problem(x0, models, model);
281
282 // create random trajectory
283
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284
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285
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 147 for (std::size_t i = 0; i < T; ++i) {
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 140 xs[i] = model->get_state()->zero();
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 140 us[i] = Eigen::VectorXd::Random(model->get_nu());
288 }
289
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 7 xs.back() = model->get_state()->zero();
290
291 // check the state and cost in each node
292
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293
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 147 for (std::size_t i = 0; i < T; ++i) {
294 const boost::shared_ptr<crocoddyl::ActionDataAbstract>& data =
295
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 140 model->createData();
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 140 model->calc(data, xs[i], us[i]);
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 140 BOOST_CHECK((xs[i + 1] - data->xnext).isZero(1e-7));
298 140 }
299 7 }
300
301 92 void test_rollout_diffAction(
302 DifferentialActionModelTypes::Type action_model_type,
303 IntegratorTypes::Type integrator_type) {
304 // create the model
305
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306 const boost::shared_ptr<crocoddyl::DifferentialActionModelAbstract>&
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309 const boost::shared_ptr<crocoddyl::ActionModelAbstract>& model =
310
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 92 factory_int.create(integrator_type, diffModel);
311
312 // create the shooting problem
313 92 std::size_t T = 20;
314
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 92 const Eigen::VectorXd& x0 = model->get_state()->rand();
315 std::vector<boost::shared_ptr<crocoddyl::ActionModelAbstract> > models(T,
316
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 92 model);
317
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 92 crocoddyl::ShootingProblem problem(x0, models, model);
318
319 // create random trajectory
320
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 92 std::vector<Eigen::VectorXd> xs(T + 1);
321
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 92 std::vector<Eigen::VectorXd> us(T);
322
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 1932 for (std::size_t i = 0; i < T; ++i) {
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 1840 xs[i] = model->get_state()->zero();
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 1840 us[i] = Eigen::VectorXd::Random(model->get_nu());
325 }
326
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 92 xs.back() = model->get_state()->zero();
327
328 // check the state and cost in each node
329
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 92 problem.rollout(us, xs);
330
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 1932 for (std::size_t i = 0; i < T; ++i) {
331 const boost::shared_ptr<crocoddyl::ActionDataAbstract>& data =
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 1840 model->createData();
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 1840 model->calc(data, xs[i], us[i]);
334
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 1840 BOOST_CHECK((xs[i + 1] - data->xnext).isZero(1e-7));
335 1840 }
336 92 }
337
338 7 void test_quasiStatic(ActionModelTypes::Type action_model_type) {
339 // create the model
340
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341 const boost::shared_ptr<crocoddyl::ActionModelAbstract>& model =
342
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 7 factory.create(action_model_type);
343
344 // create two shooting problems (with and without data allocation)
345 7 std::size_t T = 20;
346
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 7 const Eigen::VectorXd& x0 = model->get_state()->rand();
347 std::vector<boost::shared_ptr<crocoddyl::ActionModelAbstract> > models(T,
348
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 7 model);
349
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 7 std::vector<boost::shared_ptr<crocoddyl::ActionDataAbstract> > datas(T);
350
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 140 datas[i] = model->createData();
352 }
353
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 7 crocoddyl::ShootingProblem problem1(x0, models, model);
354 crocoddyl::ShootingProblem problem2(x0, models, model, datas,
355
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 14 model->createData());
356
357 // create random trajectory
358
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359
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360
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 140 xs[i] = model->get_state()->rand();
362
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 140 xs[i].tail(model->get_state()->get_nv()) *= 0;
363
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 140 us[i] = Eigen::VectorXd::Zero(model->get_nu());
364 }
365
366 // check the state and cost in each node
367
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368
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369 const boost::shared_ptr<crocoddyl::ActionDataAbstract>& data =
370
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371
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 140 Eigen::VectorXd u = Eigen::VectorXd::Zero(model->get_nu());
372
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 140 model->quasiStatic(data, u, xs[i]);
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 140 BOOST_CHECK((u - us[i]).isZero(1e-7));
374 140 }
375
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376
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 147 for (std::size_t i = 0; i < T; ++i) {
377 const boost::shared_ptr<crocoddyl::ActionDataAbstract>& data =
378
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379
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 140 Eigen::VectorXd u = Eigen::VectorXd::Zero(model->get_nu());
380
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 140 model->quasiStatic(data, u, xs[i]);
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 140 BOOST_CHECK((u - us[i]).isZero(1e-7));
382 140 }
383 7 }
384
385 92 void test_quasiStatic_diffAction(
386 DifferentialActionModelTypes::Type action_model_type,
387 IntegratorTypes::Type integrator_type) {
388 // create the model
389
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 92 DifferentialActionModelFactory factory;
390 const boost::shared_ptr<crocoddyl::DifferentialActionModelAbstract>&
391
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 92 diffModel = factory.create(action_model_type);
392
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 92 IntegratorFactory factory_int;
393 const boost::shared_ptr<crocoddyl::ActionModelAbstract>& model =
394
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 92 factory_int.create(integrator_type, diffModel);
395
396 // create two shooting problems (with and without data allocation)
397 92 std::size_t T = 20;
398
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 92 const Eigen::VectorXd& x0 = model->get_state()->rand();
399 std::vector<boost::shared_ptr<crocoddyl::ActionModelAbstract> > models(T,
400
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 92 model);
401
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 92 std::vector<boost::shared_ptr<crocoddyl::ActionDataAbstract> > datas(T);
402
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 1932 for (std::size_t i = 0; i < T; ++i) {
403
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 1840 datas[i] = model->createData();
404 }
405
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 92 crocoddyl::ShootingProblem problem1(x0, models, model);
406 crocoddyl::ShootingProblem problem2(x0, models, model, datas,
407
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 184 model->createData());
408
409 // create random trajectory
410
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 92 std::vector<Eigen::VectorXd> xs(T);
411
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 92 std::vector<Eigen::VectorXd> us(T);
412
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 1932 for (std::size_t i = 0; i < T; ++i) {
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 1840 xs[i] = model->get_state()->rand();
414
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 1840 xs[i].tail(model->get_state()->get_nv()) *= 0;
415
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 1840 us[i] = Eigen::VectorXd::Zero(model->get_nu());
416 }
417
418 // check the state and cost in each node
419
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 92 problem1.quasiStatic(us, xs);
420
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 1932 for (std::size_t i = 0; i < T; ++i) {
421 const boost::shared_ptr<crocoddyl::ActionDataAbstract>& data =
422
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 1840 model->createData();
423
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 1840 Eigen::VectorXd u = Eigen::VectorXd::Zero(model->get_nu());
424
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 1840 model->quasiStatic(data, u, xs[i]);
425
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 1840 BOOST_CHECK((u - us[i]).isZero(1e-7));
426 1840 }
427
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 92 problem2.quasiStatic(us, xs);
428
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 1932 for (std::size_t i = 0; i < T; ++i) {
429 const boost::shared_ptr<crocoddyl::ActionDataAbstract>& data =
430
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 1840 model->createData();
431
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 1840 Eigen::VectorXd u = Eigen::VectorXd::Zero(model->get_nu());
432
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 1840 model->quasiStatic(data, u, xs[i]);
433
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 1840 BOOST_CHECK((u - us[i]).isZero(1e-7));
434 1840 }
435 92 }
436
437 //----------------------------------------------------------------------------//
438
439 7 void register_action_model_unit_tests(
440 ActionModelTypes::Type action_model_type) {
441
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442
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 7 std::cout << "Running " << test_name.str() << std::endl;
444
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 7 test_suite* ts = BOOST_TEST_SUITE(test_name.str());
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 7 ts->add(BOOST_TEST_CASE(boost::bind(&test_calc, action_model_type)));
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 7 ts->add(BOOST_TEST_CASE(boost::bind(&test_calcDiff, action_model_type)));
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 7 ts->add(BOOST_TEST_CASE(boost::bind(&test_quasiStatic, action_model_type)));
448
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 7 ts->add(BOOST_TEST_CASE(boost::bind(&test_rollout, action_model_type)));
449
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 7 framework::master_test_suite().add(ts);
450 7 }
451
452 92 void register_diff_action_model_unit_tests(
453 DifferentialActionModelTypes::Type action_model_type,
454 IntegratorTypes::Type integrator_type) {
455
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 92 boost::test_tools::output_test_stream test_name;
456
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 92 test_name << "test_" << action_model_type << "_" << integrator_type;
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 92 std::cout << "Running " << test_name.str() << std::endl;
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 92 test_suite* ts = BOOST_TEST_SUITE(test_name.str());
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 92 ts->add(BOOST_TEST_CASE(
460 boost::bind(&test_calc_diffAction, action_model_type, integrator_type)));
461
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 92 ts->add(BOOST_TEST_CASE(boost::bind(&test_calcDiff_diffAction,
462 action_model_type, integrator_type)));
463
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 92 ts->add(BOOST_TEST_CASE(boost::bind(&test_quasiStatic_diffAction,
464 action_model_type, integrator_type)));
465
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 92 ts->add(BOOST_TEST_CASE(boost::bind(&test_rollout_diffAction,
466 action_model_type, integrator_type)));
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 92 framework::master_test_suite().add(ts);
468 92 }
469
470 1 bool init_function() {
471
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 8 for (size_t i = 0; i < ActionModelTypes::all.size(); ++i) {
472 7 register_action_model_unit_tests(ActionModelTypes::all[i]);
473 }
474
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 24 for (size_t i = 0; i < DifferentialActionModelTypes::all.size(); ++i) {
475
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 115 for (size_t j = 0; j < IntegratorTypes::all.size(); ++j) {
476 92 register_diff_action_model_unit_tests(
477 92 DifferentialActionModelTypes::all[i], IntegratorTypes::all[j]);
478 }
479 }
480 1 return true;
481 }
482
483 1 int main(int argc, char** argv) {
484 1 return ::boost::unit_test::unit_test_main(&init_function, argc, argv);
485 }
486