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