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	configureInputOutputNames ()
	Set up input and output names and perform consistency checks.

Private Attributes
std::unique_ptr< ONNX::Session >	m_session
	The ONNX inference session wrapper.
ONNXOptions	m_specific_options
	ONNX specific options loaded from weightfile.
std::string	m_inputName
	Name of the input tensor (will be determined automatically)
std::string	m_outputName
	Name of the output tensor (will either be determined automatically or loaded from specific options)

Detailed Description

Expert for the ONNX MVA method.

Definition at line 437 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 130 of file ONNX.cc.

{
  auto nFeatures = testData.getNumberOfFeatures();
  auto nEvents = testData.getNumberOfEvents();
  auto input = Tensor<float>::make_shared({1, nFeatures});
  auto output = Tensor<float>::make_shared({1, 1});
  std::vector<float> result;
  result.reserve(nEvents);
  for (unsigned int iEvent = 0; iEvent < nEvents; ++iEvent) {
    testData.loadEvent(iEvent);
    input->setValues(testData.m_input);
    m_session->run({{m_inputName, input}}, {{m_outputName, output}});
    result.push_back(output->at(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 147 of file ONNX.cc.

{
  unsigned int nClasses = m_general_options.m_nClasses;
  auto nFeatures = testData.getNumberOfFeatures();
  auto nEvents = testData.getNumberOfEvents();
  auto input = Tensor<float>::make_shared({1, nFeatures});
  auto output = Tensor<float>::make_shared({1, nClasses});
  std::vector<std::vector<float>> result(nEvents, std::vector<float>(nClasses));
  for (unsigned int iEvent = 0; iEvent < nEvents; ++iEvent) {
    testData.loadEvent(iEvent);
    input->setValues(testData.m_input);
    m_session->run({{m_inputName, input}}, {{m_outputName, output}});
    for (unsigned int iClass = 0; iClass < nClasses; ++iClass) {
      result[iEvent][iClass] = output->at(iClass);
    }
  }
  return result;
}

configureInputOutputNames()

void configureInputOutputNames ( )

private

Set up input and output names and perform consistency checks.

Definition at line 72 of file ONNX.cc.

{
  const auto& inputNames = m_session->getOrtSession().GetInputNames();
  const auto& outputNames = m_session->getOrtSession().GetOutputNames();
 
  // Check if we have a single input model and set the input name to that
  if (inputNames.size() != 1) {
    std::stringstream msg;
    msg << "Model has multiple inputs: ";
    for (auto name : inputNames)
      msg << "\"" << name << "\" ";
    msg << "- only single-input models are supported.";
    B2FATAL(msg.str());
  }
  m_inputName = inputNames[0];
 
  m_outputName = m_specific_options.m_outputName;
 
  // For single-output models we just take the name of that single output
  if (outputNames.size() == 1) {
    if (!m_outputName.empty() && m_outputName != outputNames[0]) {
      B2INFO("Output name of the model is "
             << outputNames[0]
             << " - will use that despite the configured name being \""
             << m_outputName << "\"");
    }
    m_outputName = outputNames[0];
    return;
  }
 
  // Otherwise we have a multiple-output model and need to check if the
  // configured output name, or the fallback value "output", exists
  if (m_outputName.empty()) {
    m_outputName = "output";
  }
  auto outputFound = std::find(outputNames.begin(), outputNames.end(),
                               m_outputName) != outputNames.end();
  if (!outputFound) {
    std::stringstream msg;
    msg << "No output named \"" << m_outputName << "\" found. Instead got ";
    for (auto name : outputNames)
      msg << "\"" << name << "\" ";
    msg << "- either change your model to contain one named \"" << m_outputName
        << "\" or set `m_outputName` in the specific options to one of the available names.";
    B2FATAL(msg.str());
  }
}

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 120 of file ONNX.cc.

{
  std::string onnxModelFileName = weightfile.generateFileName();
  weightfile.getFile("ONNX_Modelfile", onnxModelFileName);
  weightfile.getOptions(m_general_options);
  weightfile.getOptions(m_specific_options);
  m_session = std::make_unique<Session>(onnxModelFileName.c_str());
  configureInputOutputNames();
}

Member Data Documentation

m_general_options

GeneralOptions m_general_options

protectedinherited

General options loaded from the weightfile.

Definition at line 70 of file Expert.h.

m_inputName

std::string m_inputName

private

Name of the input tensor (will be determined automatically)

Definition at line 476 of file ONNX.h.

m_outputName

std::string m_outputName

private

Name of the output tensor (will either be determined automatically or loaded from specific options)

Definition at line 482 of file ONNX.h.

m_session

std::unique_ptr<ONNX::Session> m_session

private

The ONNX inference session wrapper.

Definition at line 466 of file ONNX.h.

m_specific_options

ONNXOptions m_specific_options

private

ONNX specific options loaded from weightfile.

Definition at line 471 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