CaloMuonScoreTool.cxx

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/*
  Copyright (C) 2002-2023 CERN for the benefit of the ATLAS collaboration
*/
 
#include "CaloMuonScoreTool.h"
 
#include <cmath>
#include <iostream>
#include <map>
#include <string>
 
#include "CaloIdentifier/CaloCell_ID.h"
#include "FourMomUtils/xAODP4Helpers.h"
#include "GaudiKernel/SystemOfUnits.h"
#include "ParticleCaloExtension/ParticleCellAssociationCollection.h"
#include "PathResolver/PathResolver.h"
#include "TrkCaloExtension/CaloExtensionCollection.h"
#include "TrkParameters/TrackParameters.h"
#include "xAODCaloEvent/CaloCluster.h"
 
// CaloMuonScoreTool constructor
CaloMuonScoreTool::CaloMuonScoreTool(const std::string &type, const std::string &name, const IInterface *parent) :
    AthAlgTool(type, name, parent) {
    declareInterface<ICaloMuonScoreTool>(this);
}
 
// CaloMuonScoreTool::initialize
StatusCode CaloMuonScoreTool::initialize() {
    ATH_MSG_INFO("Initializing " << name());
 
    ATH_CHECK(m_svc.retrieve());
    ATH_CHECK(m_caloCellAssociationTool.retrieve());
 
    std::string model_file_name = PathResolverFindCalibFile(m_modelFileName);
 
    if (m_modelFileName.empty() || model_file_name.empty()) {
        ATH_MSG_FATAL("Could not find the requested ONNX model file: " << m_modelFileName);
        ATH_MSG_FATAL(
            "Please make sure it exists in the ATLAS calibration area (https://atlas-groupdata.web.cern.ch/atlas-groupdata/), and provide "
            "a model file name relative to the root of the calibration area.");
 
        return StatusCode::FAILURE;
    }
 
    // initialise session
    Ort::SessionOptions session_options;
    Ort::AllocatorWithDefaultOptions allocator;
    session_options.SetIntraOpNumThreads(1);
    session_options.SetGraphOptimizationLevel(ORT_ENABLE_BASIC);
 
    m_session = std::make_unique<Ort::Session>(m_svc->env(), model_file_name.c_str(), session_options);
 
    ATH_MSG_INFO("Created ONNX runtime session with model " << model_file_name);
 
    size_t num_input_nodes = m_session->GetInputCount();
    m_input_node_names.resize(num_input_nodes);
 
    for (std::size_t i = 0; i < num_input_nodes; i++) {
        // print input node names
        char* input_name = m_session->GetInputNameAllocated(i, allocator).release();
        ATH_MSG_INFO("Input " << i << " : "
                              << " name= " << input_name);
        m_input_node_names[i] = input_name;
        // print input node types
        Ort::TypeInfo type_info = m_session->GetInputTypeInfo(i);
        auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
        ONNXTensorElementDataType type = tensor_info.GetElementType();
        ATH_MSG_INFO("Input " << i << " : "
                              << " type= " << type);
 
        // print input shapes/dims
        m_input_node_dims = tensor_info.GetShape();
        ATH_MSG_INFO("Input " << i << " : num_dims= " << m_input_node_dims.size());
        for (std::size_t j = 0; j < m_input_node_dims.size(); j++) {
            if (m_input_node_dims[j] < 0) m_input_node_dims[j] = 1;
            ATH_MSG_INFO("Input " << i << " : dim " << j << "= " << m_input_node_dims[j]);
        }
    }
 
    // output nodes
    std::vector<int64_t> output_node_dims;
    size_t num_output_nodes = m_session->GetOutputCount();
    ATH_MSG_INFO("Have output nodes " << num_output_nodes);
    m_output_node_names.resize(num_output_nodes);
 
    for (std::size_t i = 0; i < num_output_nodes; i++) {
        // print output node names
        char* output_name = m_session->GetOutputNameAllocated(i, allocator).release();
        ATH_MSG_INFO("Output " << i << " : "
                               << " name= " << output_name);
        m_output_node_names[i] = output_name;
 
        Ort::TypeInfo type_info = m_session->GetOutputTypeInfo(i);
        auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
        ONNXTensorElementDataType type = tensor_info.GetElementType();
        ATH_MSG_INFO("Output " << i << " : "
                               << " type= " << type);
 
        // print output shapes/dims
        output_node_dims = tensor_info.GetShape();
        ATH_MSG_INFO("Output " << i << " : num_dims= " << output_node_dims.size());
        for (std::size_t j = 0; j < output_node_dims.size(); j++) {
            if (output_node_dims[j] < 0) output_node_dims[j] = 1;
            ATH_MSG_INFO("Output" << i << " : dim " << j << "= " << output_node_dims[j]);
        }
    }
 
    return StatusCode::SUCCESS;
}
 
// CaloMuonScoreTool::unwrapPhiAngles
std::vector<float> CaloMuonScoreTool::unwrapPhiAngles(const std::vector<float> &in) const {
    std::vector<float> out(in.size());
 
    out[0] = in[0];
 
    for (unsigned int i = 1; i < out.size(); i++) {
        float d = xAOD::P4Helpers::deltaPhi(in[i], in[i - 1]);
        out[i] = out[i - 1] + d;
    }
 
    return out;
}
 
// CaloMuonScoreTool::fillInputVectors
void CaloMuonScoreTool::fillInputVectors(std::unique_ptr<const Rec::ParticleCellAssociation> &association, std::vector<float> &eta,
                                         std::vector<float> &phi, std::vector<float> &energy, std::vector<int> &samplingId) const {
    int cell_count = 0;
 
    for (auto cluster : association->data()) {
        eta.push_back(cluster->eta());
        phi.push_back(cluster->phi());
        samplingId.push_back(cluster->caloDDE()->getSampling());
        energy.push_back(cluster->energy());
 
        cell_count++;
    }
 
    ATH_MSG_DEBUG("Iterated over " << cell_count << " calo cells");
 
    return;
}
 
// CaloMuonScoreTool::getMuonScore
float CaloMuonScoreTool::getMuonScore(const xAOD::TrackParticle *trk, const CaloCellContainer *cells,
                                      const CaloExtensionCollection *extensionCache) const {
    ATH_MSG_DEBUG("in CaloMuonScoreTool::getMuonScore()");
 
    double track_eta = trk->eta();
 
    // calculate muon score at all eta values
    if (std::abs(track_eta) > m_CaloMuonEtaCut) {
        ATH_MSG_DEBUG("Skip calculation of muon score for track particle due to failed eta cut of " << m_CaloMuonEtaCut
                                                                                                    << " (eta=" << track_eta << ")");
        return -1;
    }
 
    ATH_MSG_DEBUG("Calculating muon score for track particle with eta=" << track_eta);
 
    ATH_MSG_DEBUG("Finding calo cell association for track particle within cone of delta R=" << m_CaloCellAssociationConeSize);
 
    // - associate calocells to trackparticle
    std::unique_ptr<const Rec::ParticleCellAssociation> association =
        m_caloCellAssociationTool->particleCellAssociation(*trk, m_CaloCellAssociationConeSize, cells, extensionCache);
    if (!association) {
        ATH_MSG_VERBOSE("Could not get particleCellAssociation");
        return -1.;
    }
    ATH_MSG_VERBOSE(" particleCellAssociation done  " << association.get());
 
    // create input vectors from calo cell association
    std::vector<float> eta, phi, energy;
    std::vector<int> sampling;
 
    fillInputVectors(association, eta, phi, energy, sampling);
 
    // if any of the vectors are empty, return.
    // They are filled in the same loop in `fillInputVectors`, so it is enough to check one
    if (eta.empty()) {
        ATH_MSG_VERBOSE("Input vectors for CaloMuonScore are empty");
        return -1.;
    }
 
    // create tensor from vectors
    std::vector<float> inputTensor = getInputTensor(eta, phi, energy, sampling);
 
    // run inference on input tensor
    float outputScore = runOnnxInference(inputTensor);
    ATH_MSG_DEBUG("Computed CaloMuonScore: " << outputScore);
 
    return outputScore;
}
 
// CaloMuonScoreTool::runOnnxInference
float CaloMuonScoreTool::runOnnxInference(std::vector<float> &tensor) const {
    // create input tensor object from data values
    ATH_MSG_DEBUG("in CaloMuonScoreTool::runOnnxInference()");
 
    auto memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
    int input_tensor_size(m_etaBins * m_phiBins * m_nChannels);
    Ort::Value input_tensor =
        Ort::Value::CreateTensor<float>(memory_info, tensor.data(), input_tensor_size, m_input_node_dims.data(), m_input_node_dims.size());
 
    // score model & input tensor, get back output tensor
 
    // Ort::Session::Run is non-const.
    // However, the onxx authors claim that it is safe to call
    // from multiple threads:
    //  https://github.com/Microsoft/onnxruntime/issues/114
    Ort::Session* session ATLAS_THREAD_SAFE = m_session.get();
    auto output_tensors = session->Run(Ort::RunOptions{nullptr}, m_input_node_names.data(), &input_tensor, m_input_node_names.size(),
                                         m_output_node_names.data(), m_output_node_names.size());
 
    // Get pointer to output tensor float values
    float *output_score_array = output_tensors.front().GetTensorMutableData<float>();
 
    // Binary classification - the score is just the first element of the output tensor
    float output_score = output_score_array[0];
 
    return output_score;
}
 
// CaloMuonScoreTool::channelForSamplingId
int CaloMuonScoreTool::channelForSamplingId(int &samplingId) const {
    // List of 7 central calo sampling IDs: [0,1,2,3,12,13,14]
    switch (samplingId) {
        case 0: return 0;
        case 1: return 1;
        case 2: return 2;
        case 3: return 3;
        case 12: return 4;
        case 13: return 5;
        case 14: return 6;
        default: return -1;
    }
}
 
// CaloMuonScoreTool::getMedian
float CaloMuonScoreTool::getMedian(std::vector<float> v) const {
    if (v.empty()) return 0.0;
 
    int n = v.size() / 2;
    std::nth_element(v.begin(), v.begin() + n, v.end());
    float med = v[n];
 
    if (v.size() % 2 == 1) return med;
 
    auto max_it = std::max_element(v.begin(), v.begin() + n);
 
    return (*max_it + med) / 2.0;
}
 
int CaloMuonScoreTool::getBin(const float low_edge, const float up_edge, const int n_bins, float val) const {
    if (val < low_edge || val >= up_edge)
        return -1;
    const float bin_width = (up_edge - low_edge) / (n_bins - 1);
    float interval = val - low_edge;
    return std::ceil(interval / bin_width);
 
 
}
 
// CaloMuonScoreTool::getInputTensor
std::vector<float> CaloMuonScoreTool::getInputTensor(std::vector<float> &eta, std::vector<float> &phi, std::vector<float> &energy,
                                                     std::vector<int> &sampling) const {
    int n_cells = eta.size();
 
    // make sure the vector of phi values does not contain discontinuities around the
    // boundary between pi and -pi
    std::vector<float> unwrappedPhi = unwrapPhiAngles(phi);
 
    float median_eta = getMedian(eta);
    float median_phi = getMedian(unwrappedPhi);
 
    // initialise output matrix of zeros
    std::vector<float> tensor(m_etaBins * m_phiBins * m_nChannels, 0.);
 
    int skipped_cells = 0;
 
    for (int i = 0; i < n_cells; i++) {
        // take eta and phi values, and shift them by their repsective median
        float shifted_eta = eta[i] - median_eta;
        float shifted_phi = unwrappedPhi[i] - median_phi;
 
        int eta_bin = getBin(-m_etaCut, m_etaCut, m_etaBins, shifted_eta);
        int phi_bin = getBin(-m_phiCut, m_phiCut, m_phiBins, shifted_phi);
        // the cell lies outside the acceptable range
        if (eta_bin == -1 || phi_bin == -1) {
            skipped_cells++;
            ATH_MSG_DEBUG("Skipping cell because eta or phi bin lies outside of range. Eta bin: " << eta_bin << " phi bin: " << phi_bin);
            continue;
        }
 
        int channel = channelForSamplingId(sampling[i]);
 
        // this really should not happen, but let's skip this cell if it does
        if (channel == -1) {
            skipped_cells++;
            ATH_MSG_DEBUG("Skipping cell because sampling ID does not correspond to low-eta layers. Sampling ID: " << sampling[i]);
            continue;
        }
 
        // 3D array flattening in row-major style: https://en.wikipedia.org/wiki/Row-_and_column-major_order#Explanation_and_example
        int tensor_idx = eta_bin * m_phiBins * m_nChannels + phi_bin * m_nChannels + channel;
 
        tensor[tensor_idx] += energy[i];
    }
 
    ATH_MSG_DEBUG("Skipped " << skipped_cells << " out of " << n_cells << " cells");
 
    return tensor;
}