f4334277
 Hu Chunming
提交3rdparty
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// 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
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