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
187
188
189
190
191
192
193
194
195
196
197
198
199
200
|
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
// #define GENERATE_TESTDATA
namespace opencv_test { namespace {
struct Activation
{
int id;
const char * name;
};
void PrintTo(const Activation &a, std::ostream *os) { *os << a.name; }
Activation activation_list[] =
{
{ ml::ANN_MLP::IDENTITY, "identity" },
{ ml::ANN_MLP::SIGMOID_SYM, "sigmoid_sym" },
{ ml::ANN_MLP::GAUSSIAN, "gaussian" },
{ ml::ANN_MLP::RELU, "relu" },
{ ml::ANN_MLP::LEAKYRELU, "leakyrelu" },
};
typedef testing::TestWithParam< Activation > ML_ANN_Params;
TEST_P(ML_ANN_Params, ActivationFunction)
{
const Activation &activation = GetParam();
const string dataname = "waveform";
const string data_path = findDataFile(dataname + ".data");
const string model_name = dataname + "_" + activation.name + ".yml";
Ptr<TrainData> tdata = TrainData::loadFromCSV(data_path, 0);
ASSERT_FALSE(tdata.empty());
// hack?
const uint64 old_state = theRNG().state;
theRNG().state = 1027401484159173092;
tdata->setTrainTestSplit(500);
theRNG().state = old_state;
Mat_<int> layerSizes(1, 4);
layerSizes(0, 0) = tdata->getNVars();
layerSizes(0, 1) = 100;
layerSizes(0, 2) = 100;
layerSizes(0, 3) = tdata->getResponses().cols;
Mat testSamples = tdata->getTestSamples();
Mat rx, ry;
{
Ptr<ml::ANN_MLP> x = ml::ANN_MLP::create();
x->setActivationFunction(activation.id);
x->setLayerSizes(layerSizes);
x->setTrainMethod(ml::ANN_MLP::RPROP, 0.01, 0.1);
x->setTermCriteria(TermCriteria(TermCriteria::COUNT, 300, 0.01));
x->train(tdata, ml::ANN_MLP::NO_OUTPUT_SCALE);
ASSERT_TRUE(x->isTrained());
x->predict(testSamples, rx);
#ifdef GENERATE_TESTDATA
x->save(cvtest::TS::ptr()->get_data_path() + model_name);
#endif
}
{
const string model_path = findDataFile(model_name);
Ptr<ml::ANN_MLP> y = Algorithm::load<ANN_MLP>(model_path);
ASSERT_TRUE(y);
y->predict(testSamples, ry);
EXPECT_MAT_NEAR(rx, ry, FLT_EPSILON);
}
}
INSTANTIATE_TEST_CASE_P(/**/, ML_ANN_Params, testing::ValuesIn(activation_list));
//==================================================================================================
CV_ENUM(ANN_MLP_METHOD, ANN_MLP::RPROP, ANN_MLP::ANNEAL)
typedef tuple<ANN_MLP_METHOD, string, int> ML_ANN_METHOD_Params;
typedef TestWithParam<ML_ANN_METHOD_Params> ML_ANN_METHOD;
TEST_P(ML_ANN_METHOD, Test)
{
int methodType = get<0>(GetParam());
string methodName = get<1>(GetParam());
int N = get<2>(GetParam());
String folder = string(cvtest::TS::ptr()->get_data_path());
String original_path = findDataFile("waveform.data");
string dataname = "waveform_" + methodName;
string weight_name = dataname + "_init_weight.yml.gz";
string model_name = dataname + ".yml.gz";
string response_name = dataname + "_response.yml.gz";
Ptr<TrainData> tdata2 = TrainData::loadFromCSV(original_path, 0);
ASSERT_FALSE(tdata2.empty());
Mat samples = tdata2->getSamples()(Range(0, N), Range::all());
Mat responses(N, 3, CV_32FC1, Scalar(0));
for (int i = 0; i < N; i++)
responses.at<float>(i, static_cast<int>(tdata2->getResponses().at<float>(i, 0))) = 1;
Ptr<TrainData> tdata = TrainData::create(samples, ml::ROW_SAMPLE, responses);
ASSERT_FALSE(tdata.empty());
// hack?
const uint64 old_state = theRNG().state;
theRNG().state = 0;
tdata->setTrainTestSplitRatio(0.8);
theRNG().state = old_state;
Mat testSamples = tdata->getTestSamples();
// train 1st stage
Ptr<ml::ANN_MLP> xx = ml::ANN_MLP::create();
Mat_<int> layerSizes(1, 4);
layerSizes(0, 0) = tdata->getNVars();
layerSizes(0, 1) = 30;
layerSizes(0, 2) = 30;
layerSizes(0, 3) = tdata->getResponses().cols;
xx->setLayerSizes(layerSizes);
xx->setActivationFunction(ml::ANN_MLP::SIGMOID_SYM);
xx->setTrainMethod(ml::ANN_MLP::RPROP);
xx->setTermCriteria(TermCriteria(TermCriteria::COUNT, 1, 0.01));
xx->train(tdata, ml::ANN_MLP::NO_OUTPUT_SCALE + ml::ANN_MLP::NO_INPUT_SCALE);
#ifdef GENERATE_TESTDATA
{
FileStorage fs;
fs.open(cvtest::TS::ptr()->get_data_path() + weight_name, FileStorage::WRITE + FileStorage::BASE64);
xx->write(fs);
}
#endif
// train 2nd stage
Mat r_gold;
Ptr<ml::ANN_MLP> x = ml::ANN_MLP::create();
{
const string weight_file = findDataFile(weight_name);
FileStorage fs;
fs.open(weight_file, FileStorage::READ);
x->read(fs.root());
}
x->setTrainMethod(methodType);
if (methodType == ml::ANN_MLP::ANNEAL)
{
x->setAnnealEnergyRNG(RNG(CV_BIG_INT(0xffffffff)));
x->setAnnealInitialT(12);
x->setAnnealFinalT(0.15);
x->setAnnealCoolingRatio(0.96);
x->setAnnealItePerStep(11);
}
x->setTermCriteria(TermCriteria(TermCriteria::COUNT, 100, 0.01));
x->train(tdata, ml::ANN_MLP::NO_OUTPUT_SCALE + ml::ANN_MLP::NO_INPUT_SCALE + ml::ANN_MLP::UPDATE_WEIGHTS);
ASSERT_TRUE(x->isTrained());
#ifdef GENERATE_TESTDATA
x->save(cvtest::TS::ptr()->get_data_path() + model_name);
x->predict(testSamples, r_gold);
{
FileStorage fs_response(cvtest::TS::ptr()->get_data_path() + response_name, FileStorage::WRITE + FileStorage::BASE64);
fs_response << "response" << r_gold;
}
#endif
{
const string response_file = findDataFile(response_name);
FileStorage fs_response(response_file, FileStorage::READ);
fs_response["response"] >> r_gold;
}
ASSERT_FALSE(r_gold.empty());
// verify
const string model_file = findDataFile(model_name);
Ptr<ml::ANN_MLP> y = Algorithm::load<ANN_MLP>(model_file);
ASSERT_TRUE(y);
Mat rx, ry;
for (int j = 0; j < 4; j++)
{
rx = x->getWeights(j);
ry = y->getWeights(j);
EXPECT_MAT_NEAR(rx, ry, FLT_EPSILON) << "Weights are not equal for layer: " << j;
}
x->predict(testSamples, rx);
y->predict(testSamples, ry);
EXPECT_MAT_NEAR(ry, rx, FLT_EPSILON) << "Predict are not equal to result of the saved model";
EXPECT_MAT_NEAR(r_gold, rx, FLT_EPSILON) << "Predict are not equal to 'gold' response";
}
INSTANTIATE_TEST_CASE_P(/*none*/, ML_ANN_METHOD,
testing::Values(
ML_ANN_METHOD_Params(ml::ANN_MLP::RPROP, "rprop", 5000),
ML_ANN_METHOD_Params(ml::ANN_MLP::ANNEAL, "anneal", 1000)
// ML_ANN_METHOD_Params(ml::ANN_MLP::BACKPROP, "backprop", 500) -----> NO BACKPROP TEST
)
);
}} // namespace
|
