/usr/include/opengm/unittests/inferencetests/test_functions.hxx is in libopengm-dev 2.3.6-2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 | #pragma once
#ifndef OPENGM_TEST_TEST_FUNCTIONS_HXX
#define OPENGM_TEST_TEST_FUNCTIONS_HXX
#include <opengm/functions/potts.hxx>
#include <opengm/functions/pottsn.hxx>
#include <opengm/functions/pottsg.hxx>
#include <opengm/functions/absolute_difference.hxx>
#include <opengm/functions/squared_difference.hxx>
#include <opengm/functions/truncated_absolute_difference.hxx>
#include <opengm/functions/truncated_squared_difference.hxx>
/// \cond HIDDEN_SYMBOLS
namespace opengm {
namespace test{
/// \brief TestFunctions<INF>
/// Build a second order binary submodular 3x3-grid-model with several functions
/// and check if the algorithm has the correct behaviour.
/// This test helps to check the compatibility of the solver with opengm-functions empirically.
template <class INF>
class TestFunctions : public TestBase<INF>
{
public:
TestFunctions(TestBehaviour);
virtual void test(typename INF::Parameter);
private:
TestBehaviour behaviour_;
};
/// \brief TestMultiMode Constructor
/// \param behaviour expected behaviour of the algorithm
template <class INF>
TestFunctions<INF>::TestFunctions(TestBehaviour behaviour) : behaviour_(behaviour)
{;}
/// \brief test<INF> start test with algorithm INF
/// \param para parameters of algorithm
template <class INF>
void TestFunctions<INF>::test(typename INF::Parameter para) {
typedef typename INF::GraphicalModelType GraphicalModelType;
typedef typename GraphicalModelType::ValueType ValueType;
std::cout << " - FunctionTest ... " << std::flush;
std::vector<size_t> numberOfLabels(9, 2);
GraphicalModelType gm(numberOfLabels.begin(), numberOfLabels.end());
size_t var[2];
// construct 3x3 grid using various functions
// all pairwise potentials are (0,1,1,0) except edge (5,8) which is (0,2,2,0)
typename GraphicalModelType::ExplicitFunctionType fExplicit(numberOfLabels.begin(), numberOfLabels.begin() + 2, 0);
fExplicit(0, 1) = 1;
fExplicit(1, 0) = 1;
typename GraphicalModelType::FunctionIdentifier fExplicitId = gm.addFunction(fExplicit);
var[0] = 0;
var[1] = 1;
gm.addFactor(fExplicitId, var, var + 2);
opengm::PottsFunction<ValueType> fPotts(2, 2, 0, 1);
typename GraphicalModelType::FunctionIdentifier fPottsId = gm.addFunction(fPotts);
var[0] = 1;
var[1] = 2;
gm.addFactor(fPottsId, var, var + 2);
opengm::PottsNFunction<ValueType> fPottsN(numberOfLabels.begin(), numberOfLabels.begin() + 2, 0, 1);
typename GraphicalModelType::FunctionIdentifier fPottsNId = gm.addFunction(fPottsN);
var[0] = 3;
var[1] = 4;
gm.addFactor(fPottsNId, var, var + 2);
size_t values[2];
values[0] = 0;
values[1] = 1;
opengm::PottsGFunction<ValueType> fPottsG(numberOfLabels.begin(), numberOfLabels.begin() + 2, values);
typename GraphicalModelType::FunctionIdentifier fPottsGId = gm.addFunction(fPottsG);
var[0] = 4;
var[1] = 5;
gm.addFactor(fPottsGId, var, var + 2);
opengm::AbsoluteDifferenceFunction<ValueType> fAbsoluteDifference(numberOfLabels[0], numberOfLabels[1]);
typename GraphicalModelType::FunctionIdentifier fAbsoluteDifferenceId = gm.addFunction(fAbsoluteDifference);
var[0] = 6;
var[1] = 7;
gm.addFactor(fAbsoluteDifferenceId, var, var + 2);
opengm::SquaredDifferenceFunction<ValueType> fSquaredDifference(numberOfLabels[0], numberOfLabels[1]);
typename GraphicalModelType::FunctionIdentifier fSquaredDifferenceId = gm.addFunction(fSquaredDifference);
var[0] = 7;
var[1] = 8;
gm.addFactor(fSquaredDifferenceId, var, var + 2);
opengm::TruncatedAbsoluteDifferenceFunction<ValueType> fTruncatedAbsoluteDifference(numberOfLabels[0], numberOfLabels[1], 1, 1);
typename GraphicalModelType::FunctionIdentifier fTruncatedAbsoluteDifferenceId = gm.addFunction(fTruncatedAbsoluteDifference);
var[0] = 0;
var[1] = 3;
gm.addFactor(fTruncatedAbsoluteDifferenceId, var, var + 2);
opengm::TruncatedSquaredDifferenceFunction<ValueType> fTruncatedSquaredDifference(numberOfLabels[0], numberOfLabels[1], 1, 1);
typename GraphicalModelType::FunctionIdentifier fTruncatedSquaredDifferenceId = gm.addFunction(fTruncatedSquaredDifference);
var[0] = 3;
var[1] = 6;
gm.addFactor(fTruncatedSquaredDifferenceId, var, var + 2);
var[0] = 1;
var[1] = 4;
gm.addFactor(fExplicitId, var, var + 2);
var[0] = 4;
var[1] = 7;
gm.addFactor(fExplicitId, var, var + 2);
var[0] = 2;
var[1] = 5;
gm.addFactor(fExplicitId, var, var + 2);
fExplicit(0, 0) = 0;
fExplicit(0, 1) = 2;
fExplicit(1, 0) = 2;
fExplicit(1, 1) = 0;
typename GraphicalModelType::FunctionIdentifier fExplicitId2 = gm.addFunction(fExplicit);
var[0] = 5;
var[1] = 8;
gm.addFactor(fExplicitId2, var, var + 2);
// add some unary potentials to nodes 0, 4, 6 and 8
typename GraphicalModelType::ExplicitFunctionType fUnary(numberOfLabels.begin(), numberOfLabels.begin() + 1);
fUnary(0) = 0;
fUnary(1) = 2;
typename GraphicalModelType::FunctionIdentifier fUnaryId = gm.addFunction(fUnary);
var[0] = 0;
gm.addFactor(fUnaryId, var, var + 1);
fUnary(0) = 6;
fUnary(1) = 0;
typename GraphicalModelType::FunctionIdentifier fUnaryId2 = gm.addFunction(fUnary);
var[0] = 4;
gm.addFactor(fUnaryId2, var, var + 1);
fUnary(0) = 1;
fUnary(1) = 0;
typename GraphicalModelType::FunctionIdentifier fUnaryId3 = gm.addFunction(fUnary);
var[0] = 6;
gm.addFactor(fUnaryId3, var, var + 1);
fUnary(0) = 0;
fUnary(1) = 2;
typename GraphicalModelType::FunctionIdentifier fUnaryId4 = gm.addFunction(fUnary);
var[0] = 8;
gm.addFactor(fUnaryId4, var, var + 1);
bool fail = false;
std::vector<size_t> sol;
try {
INF inf(gm);
inf.infer(para);
inf.arg(sol);
bool optTest = (sol[0] == 0) && (sol[1] == 0) && (sol[2] == 0)
&& (sol[3] == 1) && (sol[4] == 1) && (sol[5] == 0)
&& (sol[6] == 1) && (sol[7] == 1) && (sol[8] == 0);
OPENGM_TEST(state.size() == gm_.numberOfVariables());
if(behaviour_ == opengm::test::OPTIMAL) {
OPENGM_TEST(optTest)
}
}
catch (std::exception& error) {
std::cout << error.what() << std::endl;
fail = true;
}
// Check if exception has been thrown
if(behaviour_ == opengm::test::FAIL) {
OPENGM_TEST(fail);
}else{
OPENGM_TEST(!fail);
}
std::cout <<"done!"<<std::endl;
}
}
}
/// \endcond
#endif
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