TMVA_SOFIE_RDataFrame.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 an ONNX model generated with the Python tutorial TMVA_SOFIE_PyTorch_HiggsModel.py You need to run that macro before to generate the trained PyTorch model and also the corresponding header file with SOFIE which can then be used for inference

Execute in this order:

python3 TMVA_SOFIE_PyTorch_HiggsModel.py
root TMVA_SOFIE_RDataFrame.C

 
// need to add the current directory (from where we are running this macro)
// to the include path for Cling
R__ADD_INCLUDE_PATH($PWD)
#include "HiggsModel.hxx"
 
#include <array>
#include <vector>
 
void TMVA_SOFIE_RDataFrame(int nthreads = 2){
 
   std::string inputFileName = "Higgs_data.root";
   std::string inputFile = std::string{gROOT->GetTutorialDir()} + "/machine_learning/data/" + inputFileName;
 
   ROOT::EnableImplicitMT(nthreads);
 
   ROOT::RDataFrame df1("sig_tree", inputFile);
   int nslots = df1.GetNSlots();
   std::cout << "Running using " << nslots << " threads" << std::endl;
 
   // A SOFIE Session holds the model weights and the intermediate buffers and is
   // not thread-safe: create one Session per RDataFrame processing slot and use
   // the slot number in the DefineSlot functor to dispatch to the right one.
   // The Session default constructor reads the weights from the default weight
   // file (HiggsModel.dat in this case).
   std::vector<TMVA_SOFIE_HiggsModel::Session> sessions(nslots);
 
   // The functor assembles the model input tensor from the RDataFrame columns
   // and evaluates the model. The column order must match the ordering of the
   // model input tensor.
   auto evalModel = [&sessions](unsigned int slot, float m_jj, float m_jjj, float m_lv, float m_jlv, float m_bb,
                                float m_wbb, float m_wwbb) {
      std::array<float, 7> input{m_jj, m_jjj, m_lv, m_jlv, m_bb, m_wbb, m_wwbb};
      auto result = sessions[slot].infer(input.data());
      return result[0];
   };
 
   auto h1 = df1.DefineSlot("DNN_Value", evalModel, {"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.DefineSlot("DNN_Value", evalModel, {"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();
 
}

Running using 2 threads

Author

Lorenzo Moneta

Definition in file TMVA_SOFIE_RDataFrame.C.