Prototype.cxx

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#include <memory>
 
#include "TMVA/RModel.hxx"
#include "TMVA/RModelParser_ONNX.hxx"
 
#include <cctype>
#include <algorithm>
 
 
using namespace TMVA::Experimental::SOFIE;
 
int main(){
 
 
   RModelParser_ONNX parser;
   RModel model = parser.Parse("./Linear_64.onnx");
   RModel model2 = std::move(model);
   model2.PrintRequiredInputTensors();
   model2.PrintInitializedTensors();
   model2.HeadInitializedTensors("18bias");
   model2.HeadInitializedTensors("0weight");
 
   std::cout << "===" << std::endl;
 
   model2.Generate();
   //model2.PrintGenerated();
   //model2.Initialize();
   model2.PrintInitializedTensors();
   model2.HeadInitializedTensors("6bias", 100);
 
 
   std::cout << "===" << std::endl;
 
 
 
   //model2.PrintGenerated();
   model2.OutputGenerated();
   //model2.PrintIntermediateTensors();
/*
   std::cout << "===" << std::endl;
 
   RModel model3;
   model3.AddInputTensorInfo("1", ETensorType::FLOAT, {1,2,3,4});
   //auto op = std::make_unique<ROperator_Transpose<float>>({3,2,1,0}, "1", "2");
   std::unique_ptr<ROperator>op ( new ROperator_Transpose<float>({3,2,1,0}, "1", "2")) ;
   model3.AddOperator(std::move(op));
   //op->Initialize(model3);
   //std::cout << (op->Generate("1"));
 
   model3.AddInputTensorInfo("3", ETensorType::FLOAT, {2,3});
   model3.AddInputTensorInfo("4", ETensorType::FLOAT, {3,2});
   std::unique_ptr<ROperator> op2 (new ROperator_Gemm<float> (1.0, 1.0, 0, 0, "3", "4", "5"));
   model3.AddOperator(std::move(op2));
   std::unique_ptr<ROperator> op3 (new ROperator_Relu<float> ("5", "6"));
   model3.AddOperator(std::move(op3));
   //op2->Initialize(model3);
   //std::cout << (op2->Generate("2"));
 
   model3.Generate();
   model3.PrintGenerated();
*/
}