TMVA_SOFIE_RDataFrame_JIT.C File Reference

Detailed Description

This macro provides an example of using a trained model with PyTorch and make inference using SOFIE and RDataFrame This macro uses as input the SOFIE header generated from the ONNX model with the TMVA_SOFIE_PyTorch_HiggsModel.py tutorial You need to run that macro before this one.

In this case we are parsing the input file and then run the inference in the same macro making use of the ROOT JITing capability

 
using namespace TMVA::Experimental;
 
/// function to compile the generated model and the declaration of the SofieFunctor
/// used by RDF.
/// Assume that the model name as in the header file
void CompileModelForRDF(const std::string & headerModelFile, unsigned int ninputs, unsigned int nslots=0) {
 
   std::string modelName = headerModelFile.substr(0,headerModelFile.find(".hxx"));
   std::string cmd = std::string("#include \"") + headerModelFile + std::string("\"");
   auto ret = gInterpreter->Declare(cmd.c_str());
   if (!ret)
    throw std::runtime_error("Error compiling : " + cmd);
   std::cout << "compiled : " << cmd << std::endl;
 
   cmd = "auto sofie_functor = TMVA::Experimental::SofieFunctor<" + std::to_string(ninputs) + ",TMVA_SOFIE_" +
    modelName + "::Session>(" + std::to_string(nslots) + ");";
   ret = gInterpreter->Declare(cmd.c_str());
   if (!ret)
    throw std::runtime_error("Error compiling : " + cmd);
   std::cout << "compiled : " << cmd << std::endl;
   std::cout << "Model is ready to be evaluated" << std::endl;
   return;
}
 
void TMVA_SOFIE_RDataFrame_JIT(std::string modelName = "HiggsModel"){
 
    // check if the input file exists
    std::string modelHeaderFile = modelName + ".hxx";
    if (gSystem->AccessPathName(modelHeaderFile.c_str())) {
       Info("TMVA_SOFIE_RDataFrame", "You need to run TMVA_SOFIE_PyTorch_HiggsModel.py to generate the SOFIE header "
                                     "for the PyTorch trained model");
       return;
    }
 
    // check that also weigh file exists
    std::string modelWeightFile = modelName + std::string(".dat");
    if (gSystem->AccessPathName(modelWeightFile.c_str())) {
        Error("TMVA_SOFIE_RDataFrame","Generated weight file is missing");
        return;
    }
 
    // now compile using ROOT JIT trained model (see function above)
    CompileModelForRDF(modelHeaderFile,7);
 
    std::string inputFileName = "Higgs_data.root";
    std::string inputFile = std::string{gROOT->GetTutorialDir()} + "/machine_learning/data/" + inputFileName;
 
    ROOT::RDataFrame df1("sig_tree", inputFile);
    auto h1 = df1.Define("DNN_Value", "sofie_functor(rdfslot_,m_jj, m_jjj, m_lv, m_jlv, m_bb, m_wbb, m_wwbb)")
                   .Histo1D({"h_sig", "", 100, 0, 1},"DNN_Value");
 
    ROOT::RDataFrame df2("bkg_tree", inputFile);
    auto h2 = df2.Define("DNN_Value", "sofie_functor(rdfslot_,m_jj, m_jjj, m_lv, m_jlv, m_bb, m_wbb, m_wwbb)")
                   .Histo1D({"h_bkg", "", 100, 0, 1},"DNN_Value");
 
    h1->SetLineColor(kRed);
    h2->SetLineColor(kBlue);
 
    auto c1 = new TCanvas();
    gStyle->SetOptStat(0);
 
    h2->DrawClone();
    h1->DrawClone("SAME");
    c1->BuildLegend();
 
 
}

compiled : #include "HiggsModel.hxx"
compiled : auto sofie_functor = TMVA::Experimental::SofieFunctor<7,TMVA_SOFIE_HiggsModel::Session>(0);
Model is ready to be evaluated

Author

Lorenzo Moneta

Definition in file TMVA_SOFIE_RDataFrame_JIT.C.