aterko-ConvNet_and_RNN_Benchmarks

root/tmva/tmva/CMakeLists.txt

@@ -9,13 +9,28 @@ if(ROOT_tmva_FOUND AND ROOT_tmva-cpu_FOUND AND ROOT_imt_FOUND)
    RB_ADD_GBENCHMARK(ConvNetCpuBenchmarks
       ConvNetCpuBenchmarks.cxx
       LABEL short
-      LIBRARIES Core Tree MathCore RIO Hist TMVA)
+      LIBRARIES Core Tree MathCore RIO Hist TMVA PyMVA)
 endif()
 
 if (ROOT_cuda_FOUND AND ROOT_tmva-gpu_FOUND)
    set(DNN_CUDA_LIBRARIES ${CUDA_CUBLAS_LIBRARIES} ${CUDNN_LIBRARIES})
    RB_ADD_GBENCHMARK(ConvNetCudaBenchmarks
       ConvNetCudaBenchmarks.cxx
       LABEL short
-      LIBRARIES Core Tree MathCore RIO Hist TMVA ${DNN_CUDA_LIBRARIES})
+      LIBRARIES Core Tree MathCore RIO Hist TMVA PyMVA ${DNN_CUDA_LIBRARIES})
+endif()
+
+if(ROOT_tmva_FOUND AND ROOT_tmva-cpu_FOUND AND ROOT_imt_FOUND)
+   RB_ADD_GBENCHMARK(RNNCpuBenchmarks
+      RNNCpuBenchmarks.cxx
+      LABEL short
+      LIBRARIES Core Tree MathCore Gpad RIO Hist TMVA PyMVA)
+endif()
+
+if (ROOT_cuda_FOUND AND ROOT_tmva-gpu_FOUND)
+   set(DNN_CUDA_LIBRARIES ${CUDA_CUBLAS_LIBRARIES} ${CUDNN_LIBRARIES})
+   RB_ADD_GBENCHMARK(RNNCudaBenchmarks
+      RNNCudaBenchmarks.cxx
+      LABEL short
+      LIBRARIES Core Tree MathCore Gpad RIO Hist TMVA PyMVA ${DNN_CUDA_LIBRARIES})
 endif()

root/tmva/tmva/ConvNetBenchmarks.h

@@ -1,9 +1,12 @@
 #include "TMVA/Factory.h"
 #include "TMVA/DataLoader.h"
 #include "TMVA/Config.h"
+#include "TMVA/PyMethodBase.h"
 #include "TMVA/DataSetInfo.h"
 #include "TFile.h"
 #include "TTree.h"
+#include "TMacro.h"
+#include "TSystem.h"
 #include "MakeImageData.h"
 
 void CNN_benchmark(TString archName) {
@@ -115,4 +118,120 @@ void CNN_benchmark(TString archName) {
    factory->TestAllMethods();
 
    outputFile->Close();
-}
+}
+
+
+void CNN_Keras_benchmark(TString archName) {
+
+	int ntrainEvts = 500;
+	int ntestEvts =  500;
+
+	size_t nx = 32;
+	size_t ny = 32;
+
+	ROOT::EnableImplicitMT(0);
+	TMVA::Config::Instance();
+
+	// for using Keras
+	if (archName == "CPU") gSystem->Setenv("CUDA_VISIBLE_DEVICES","-1");
+	gSystem->Setenv("KERAS_BACKEND", "tensorflow");
+	TMVA::PyMethodBase::PyInitialize();
+
+	// Load the input data
+	TString fname = "imagesData.root";
+	TString fopt = "CACHEREAD";
+
+	// Make some Gaussian Images.
+	makeImages(ntrainEvts + ntestEvts, nx, ny);
+
+	auto input = TFile::Open(fname, fopt);
+
+	R__ASSERT(input);
+
+	std::cout << "--- Classification  : Using input file: " << input->GetName() << std::endl;
+
+	// Create a ROOT output file where TMVA will store ntuples, histograms, etc.
+	TString outfileName( "output.root" );
+	TFile* outputFile = TFile::Open(outfileName, "RECREATE");
+
+	// Creating the factory object
+	TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
+		                                       "!Correlations:!V:!Silent:Color:DrawProgressBar:"
+		                                       "AnalysisType=Classification:!ModelPersistence:Transformations=None" );
+	TMVA::DataLoader *dataloader = new TMVA::DataLoader("dataset_cnn_ecal");
+
+	TTree *signalTree = (TTree*)input->Get("sgn");
+	TTree *background = (TTree*)input->Get("bkg");
+
+	// add variables (time zero and time 1)
+	for (size_t j = 0; j < nx * ny; ++j) {
+	  TString varName = TString::Format("var%zu", j);
+	  dataloader->AddVariable(varName, 'F');
+	}
+
+	dataloader->AddSignalTree    ( signalTree, 1.0 );
+	dataloader->AddBackgroundTree( background, 1.0 );
+
+	// check given input
+	auto & datainfo = dataloader->GetDataSetInfo();
+	auto vars = datainfo.GetListOfVariables();
+	std::cout << "number of variables is " << vars.size() << std::endl;
+
+	TString trainAndTestOpt = TString::Format("nTrain_Signal=%d:nTrain_Background=%d:nTest_Signal=%d:nTest_Background=%d:"
+		                                     "SplitMode=Random:NormMode=NumEvents:!V",
+		                                     ntrainEvts, ntrainEvts, ntestEvts, ntestEvts);
+	TCut mycuts = "";
+	TCut mycutb = "";
+	dataloader->PrepareTrainingAndTestTree(mycuts, mycutb, trainAndTestOpt);
+
+	TMVA::MsgLogger::InhibitOutput();
+	dataloader->GetDefaultDataSetInfo().GetDataSet();
+	TMVA::MsgLogger::EnableOutput();
+
+	std::cout << "prepared DATA LOADER " << std::endl;
+
+	Info("TMVA_CNN_Classification", "Building convolutional keras model");
+
+	TMacro m;
+	m.AddLine("import tensorflow");
+	m.AddLine("from tensorflow.keras.models import Sequential");
+	m.AddLine("from tensorflow.keras.optimizers import Adam");
+	m.AddLine("from tensorflow.keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPooling2D, Reshape");
+	m.AddLine("");
+	m.AddLine("model = Sequential() ");
+	m.AddLine("model.add(Reshape((32, 32, 1), input_shape = (1024, )))");
+	m.AddLine("model.add(Conv2D(12, kernel_size = (3, 3), kernel_initializer = 'glorot_normal',activation = "
+		"'relu', padding = 'same'))");
+	m.AddLine("model.add(Conv2D(12, kernel_size = (3, 3), kernel_initializer = 'glorot_normal',activation = "
+		"'relu', padding = 'same'))");
+	m.AddLine("model.add(MaxPooling2D(pool_size = (2, 2), strides = (2,2))) ");
+
+	m.AddLine("model.add(Conv2D(12, kernel_size = (3, 3), kernel_initializer = 'glorot_normal',activation = "
+		"'relu', padding = 'same'))");
+	m.AddLine("model.add(Conv2D(12, kernel_size = (3, 3), kernel_initializer = 'glorot_normal',activation = "
+		"'relu', padding = 'same'))");
+	m.AddLine("model.add(MaxPooling2D(pool_size = (2, 2), strides = (2,2))) ");
+
+	m.AddLine("model.add(Flatten())");
+	m.AddLine("model.add(Dense(64, activation = 'relu')) ");
+	m.AddLine("model.add(Dense(32, activation = 'relu')) ");
+	m.AddLine("model.add(Dense(1, activation = 'linear')) ");
+
+	m.AddLine("model.compile(loss = 'binary_crossentropy', optimizer = Adam(lr = 0.001), metrics = ['accuracy'])");
+	m.AddLine("model.save('model_cnn.h5')");
+	m.AddLine("model.summary()");
+
+	m.SaveSource("make_cnn_model.py");
+	gSystem->Exec("python make_cnn_model.py");
+
+	factory->BookMethod(
+	dataloader, TMVA::Types::kPyKeras, "PyKeras",
+	"H:!V:VarTransform=None:FilenameModel=model_cnn.h5:tf.keras:"
+	"FilenameTrainedModel=trained_model_cnn.h5:NumEpochs=10:BatchSize=32:"
+	"GpuOptions=allow_growth=True"); 
+
+	factory->TrainAllMethods();
+	factory->TestAllMethods();
+
+	outputFile->Close();
+}

root/tmva/tmva/ConvNetCpuBenchmarks.cxx

@@ -12,4 +12,15 @@ static void BM_ConvolutionalNetwork_CPU(benchmark::State &state)
 }
 BENCHMARK(BM_ConvolutionalNetwork_CPU);
 
-BENCHMARK_MAIN();
+static void BM_ConvolutionalNetwork_Keras_CPU(benchmark::State &state)
+{
+    TString architecture("CPU");
+
+    // Benchmarking
+    for (auto _ : state) {
+        CNN_Keras_benchmark(architecture);
+    }
+}
+BENCHMARK(BM_ConvolutionalNetwork_Keras_CPU);
+
+BENCHMARK_MAIN();

root/tmva/tmva/ConvNetCudaBenchmarks.cxx

@@ -12,4 +12,15 @@ static void BM_ConvolutionalNetwork_CUDA(benchmark::State &state)
 }
 BENCHMARK(BM_ConvolutionalNetwork_CUDA);
 
+static void BM_ConvolutionalNetwork_Keras_CUDA(benchmark::State &state)
+{
+    TString architecture("GPU");
+
+    // Benchmarking
+    for (auto _ : state) {
+        CNN_Keras_benchmark(architecture);
+    }
+}
+BENCHMARK(BM_ConvolutionalNetwork_Keras_CUDA);
+
 BENCHMARK_MAIN();