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ml: refactor ML_ANN test

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This commit is contained in:
Alexander Alekhin 2018-02-19 19:45:04 +03:00
parent 88b689bcf1
commit 12d2bd4adb
1 changed files with 51 additions and 26 deletions
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77
modules/ml/test/test_mltests2.cpp
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@ -252,31 +252,35 @@ TEST(ML_ANN, ActivationFunction)
} }
} }
TEST(ML_ANN, Method) 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 folder = string(cvtest::TS::ptr()->get_data_path());
String original_path = folder + "waveform.data"; String original_path = folder + "waveform.data";
String dataname = folder + "waveform"; String dataname = folder + "waveform" + '_' + methodName;
Ptr<TrainData> tdata2 = TrainData::loadFromCSV(original_path, 0); Ptr<TrainData> tdata2 = TrainData::loadFromCSV(original_path, 0);
Mat responses(tdata2->getResponses().rows, 3, CV_32FC1, Scalar(0)); Mat samples = tdata2->getSamples()(Range(0, N), Range::all());
for (int i = 0; i<tdata2->getResponses().rows; i++) 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; responses.at<float>(i, static_cast<int>(tdata2->getResponses().at<float>(i, 0))) = 1;
Ptr<TrainData> tdata = TrainData::create(tdata2->getSamples(), ml::ROW_SAMPLE, responses); Ptr<TrainData> tdata = TrainData::create(samples, ml::ROW_SAMPLE, responses);
ASSERT_FALSE(tdata.empty()) << "Could not find test data file : " << original_path; ASSERT_FALSE(tdata.empty()) << "Could not find test data file : " << original_path;
RNG& rng = theRNG(); RNG& rng = theRNG();
rng.state = 0; rng.state = 0;
tdata->setTrainTestSplitRatio(0.8); tdata->setTrainTestSplitRatio(0.8);
vector<int> methodType; Mat testSamples = tdata->getTestSamples();
methodType.push_back(ml::ANN_MLP::RPROP);
methodType.push_back(ml::ANN_MLP::ANNEAL);
// methodType.push_back(ml::ANN_MLP::BACKPROP); -----> NO BACKPROP TEST
vector<String> methodName;
methodName.push_back("_rprop");
methodName.push_back("_anneal");
// methodName.push_back("_backprop"); -----> NO BACKPROP TEST
#ifdef GENERATE_TESTDATA #ifdef GENERATE_TESTDATA
{ {
Ptr<ml::ANN_MLP> xx = ml::ANN_MLP_ANNEAL::create(); Ptr<ml::ANN_MLP> xx = ml::ANN_MLP_ANNEAL::create();
@ -296,14 +300,13 @@ TEST(ML_ANN, Method)
fs.release(); fs.release();
} }
#endif #endif
for (size_t i = 0; i < methodType.size(); i++)
{ {
FileStorage fs; FileStorage fs;
fs.open(dataname + "_init_weight.yml.gz", FileStorage::READ + FileStorage::BASE64); fs.open(dataname + "_init_weight.yml.gz", FileStorage::READ);
Ptr<ml::ANN_MLP> x = ml::ANN_MLP_ANNEAL::create(); Ptr<ml::ANN_MLP> x = ml::ANN_MLP_ANNEAL::create();
x->read(fs.root()); x->read(fs.root());
x->setTrainMethod(methodType[i]); x->setTrainMethod(methodType);
if (methodType[i] == ml::ANN_MLP::ANNEAL) if (methodType == ml::ANN_MLP::ANNEAL)
{ {
x->setAnnealEnergyRNG(RNG(CV_BIG_INT(0xffffffff))); x->setAnnealEnergyRNG(RNG(CV_BIG_INT(0xffffffff)));
x->setAnnealInitialT(12); x->setAnnealInitialT(12);
@ -313,28 +316,50 @@ TEST(ML_ANN, Method)
} }
x->setTermCriteria(TermCriteria(TermCriteria::COUNT, 100, 0.01)); 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); x->train(tdata, ml::ANN_MLP::NO_OUTPUT_SCALE + ml::ANN_MLP::NO_INPUT_SCALE + ml::ANN_MLP::UPDATE_WEIGHTS);
ASSERT_TRUE(x->isTrained()) << "Could not train networks with " << methodName[i]; ASSERT_TRUE(x->isTrained()) << "Could not train networks with " << methodName;
string filename = dataname + ".yml.gz";
Mat r_gold;
#ifdef GENERATE_TESTDATA #ifdef GENERATE_TESTDATA
x->save(dataname + methodName[i] + ".yml.gz"); x->save(filename);
x->predict(testSamples, r_gold);
{
FileStorage fs_response(dataname + "_response.yml.gz", FileStorage::WRITE + FileStorage::BASE64);
fs_response << "response" << r_gold;
}
#else
{
FileStorage fs_response(dataname + "_response.yml.gz", FileStorage::READ);
fs_response["response"] >> r_gold;
}
#endif #endif
Ptr<ml::ANN_MLP> y = Algorithm::load<ANN_MLP>(dataname + methodName[i] + ".yml.gz"); ASSERT_FALSE(r_gold.empty());
ASSERT_TRUE(y != NULL) << "Could not load " << dataname + methodName[i] + ".yml"; Ptr<ml::ANN_MLP> y = Algorithm::load<ANN_MLP>(filename);
Mat testSamples = tdata->getTestSamples(); ASSERT_TRUE(y != NULL) << "Could not load " << filename;
Mat rx, ry, dst; Mat rx, ry;
for (int j = 0; j < 4; j++) for (int j = 0; j < 4; j++)
{ {
rx = x->getWeights(j); rx = x->getWeights(j);
ry = y->getWeights(j); ry = y->getWeights(j);
double n = cvtest::norm(rx, ry, NORM_INF); double n = cvtest::norm(rx, ry, NORM_INF);
EXPECT_LT(n, FLT_EPSILON) << "Weights are not equal for " << dataname + methodName[i] + ".yml and " << methodName[i] << " layer : " << j; EXPECT_LT(n, FLT_EPSILON) << "Weights are not equal for layer: " << j;
} }
x->predict(testSamples, rx); x->predict(testSamples, rx);
y->predict(testSamples, ry); y->predict(testSamples, ry);
double n = cvtest::norm(rx, ry, NORM_INF); double n = cvtest::norm(ry, rx, NORM_INF);
EXPECT_LT(n, FLT_EPSILON) << "Predict are not equal for " << dataname + methodName[i] + ".yml and " << methodName[i]; EXPECT_LT(n, FLT_EPSILON) << "Predict are not equal to result of the saved model";
n = cvtest::norm(r_gold, rx, NORM_INF);
EXPECT_LT(n, FLT_EPSILON) << "Predict are not equal to 'gold' response";
} }
} }
INSTANTIATE_TEST_CASE_P(/*none*/, ML_ANN_METHOD,
testing::Values(
make_tuple<ANN_MLP_METHOD, string, int>(ml::ANN_MLP::RPROP, "rprop", 5000),
make_tuple<ANN_MLP_METHOD, string, int>(ml::ANN_MLP::ANNEAL, "anneal", 1000)
//make_pair<ANN_MLP_METHOD, string>(ml::ANN_MLP::BACKPROP, "backprop", 5000); -----> NO BACKPROP TEST
)
);
// 6. dtree // 6. dtree
// 7. boost // 7. boost