ONNXExpert Class Reference

Expert for the ONNX MVA method. More...

#include <ONNX.h>

Inheritance diagram for ONNXExpert:

Public Member Functions
virtual void	load (Weightfile &weightfile) override
	Load the expert from a Weightfile.
virtual std::vector< float >	apply (Dataset &testData) const override
	Apply this expert onto a dataset.
virtual std::vector< std::vector< float > >	applyMulticlass (Dataset &test_data) const override
	Apply this expert onto a dataset and return multiple outputs.

Protected Attributes
GeneralOptions	m_general_options
	General options loaded from the weightfile.

Private Member Functions
void	run (ONNXTensorView &view) const
	Run the current inputs through the onnx model Will retrieve and fill the buffers from the view.

Private Attributes
Ort::Env	m_env
	Environment object for ONNX session.
Ort::SessionOptions	m_sessionOptions
	ONNX session configuration.
std::unique_ptr< Ort::Session >	m_session
	The ONNX inference session.
Ort::RunOptions	m_runOptions
	Options to be passed to Ort::Session::Run.
const char *	m_inputNames [1] = {"input"}
	Input tensor names.
const char *	m_outputNames [1] = {"output"}
	Output tensor names.

Detailed Description

Expert for the ONNX MVA method.

Definition at line 149 of file ONNX.h.

Member Function Documentation

apply()

std::vector< float > apply ( Dataset & testData ) const

overridevirtual

Apply this expert onto a dataset.

Parameters

testData

dataset

Implements Expert.

Definition at line 45 of file ONNX.cc.

{
  auto view = ONNXTensorView(testData, 1);
  std::vector<float> result;
  result.reserve(testData.getNumberOfEvents());
  for (unsigned int iEvent = 0; iEvent < testData.getNumberOfEvents(); ++iEvent) {
    testData.loadEvent(iEvent);
    run(view);
    result.push_back(view.outputData()[0]);
  }
  return result;
}

applyMulticlass()

std::vector< std::vector< float > > applyMulticlass ( Dataset & test_data ) const

overridevirtual

Apply this expert onto a dataset and return multiple outputs.

Parameters

test_data

dataset

Reimplemented from Expert.

Definition at line 58 of file ONNX.cc.

{
  auto view = ONNXTensorView(testData, m_general_options.m_nClasses);
  std::vector<std::vector<float>> result(testData.getNumberOfEvents(),
                                         std::vector<float>(m_general_options.m_nClasses));
  for (unsigned int iEvent = 0; iEvent < testData.getNumberOfEvents(); ++iEvent) {
    testData.loadEvent(iEvent);
    run(view);
    auto outputs = view.outputData();
    for (unsigned int iClass = 0; iClass < m_general_options.m_nClasses; ++iClass) {
      result[iEvent][iClass] = outputs[iClass];
    }
  }
  return result;
}

load()

void load ( Weightfile & weightfile )

overridevirtual

Load the expert from a Weightfile.

Parameters

weightfile

containing all information necessary to build the expert

Implements Expert.

Definition at line 17 of file ONNX.cc.

{
  std::string onnxModelFileName = weightfile.generateFileName();
  weightfile.getFile("ONNX_Modelfile", onnxModelFileName);
  weightfile.getOptions(m_general_options);
 
  // Ensure single-threaded execution, see
  // https://onnxruntime.ai/docs/performance/tune-performance/threading.html
  //
  // InterOpNumThreads is probably optional (not used in ORT_SEQUENTIAL mode)
  // Also, with batch size 1 and ORT_SEQUENTIAL mode, MLP-like models will
  // always run single threaded, but maybe not e.g. graph networks which can run
  // in parallel on nodes. Here, setting IntraOpNumThreads to 1 is important to
  // ensure single-threaded execution.
  m_sessionOptions.SetIntraOpNumThreads(1);
  m_sessionOptions.SetInterOpNumThreads(1);
  m_sessionOptions.SetExecutionMode(ORT_SEQUENTIAL); // default, but make it explicit
 
  m_session = std::make_unique<Ort::Session>(m_env, onnxModelFileName.c_str(), m_sessionOptions);
}

run()

void run ( ONNXTensorView & view ) const

private

Run the current inputs through the onnx model Will retrieve and fill the buffers from the view.

Definition at line 38 of file ONNX.cc.

{
  m_session->Run(m_runOptions,
                 m_inputNames, view.inputTensor(), 1,
                 m_outputNames, view.outputTensor(), 1);
}

Member Data Documentation

m_env

Ort::Env m_env

private

Environment object for ONNX session.

Definition at line 179 of file ONNX.h.

m_general_options

GeneralOptions m_general_options

protectedinherited

General options loaded from the weightfile.

Definition at line 70 of file Expert.h.

m_inputNames

const char* m_inputNames[1] = {"input"}

private

Input tensor names.

Definition at line 199 of file ONNX.h.

{"input"};

m_outputNames

const char* m_outputNames[1] = {"output"}

private

Output tensor names.

Definition at line 204 of file ONNX.h.

{"output"};

m_runOptions

Ort::RunOptions m_runOptions

private

Options to be passed to Ort::Session::Run.

Definition at line 194 of file ONNX.h.

m_session

std::unique_ptr<Ort::Session> m_session

private

The ONNX inference session.

Definition at line 189 of file ONNX.h.

m_sessionOptions

Ort::SessionOptions m_sessionOptions

private

ONNX session configuration.

Definition at line 184 of file ONNX.h.

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The documentation for this class was generated from the following files:

• mva/methods/include/ONNX.h
• mva/methods/src/ONNX.cc