DeepTauId Class Reference

Inheritance diagram for DeepTauId:

Public Member Functions
	DeepTauId (const edm::ParameterSet &cfg, const deep_tau::DeepTauCache *cache)
const std::map< BasicDiscriminator, size_t >	matchDiscriminatorIndices (edm::Event &event, edm::EDGetTokenT< reco::TauDiscriminatorContainer > discriminatorContainerToken, std::vector< BasicDiscriminator > requiredDiscr)
Public Member Functions inherited from deep_tau::DeepTauBase
	DeepTauBase (const edm::ParameterSet &cfg, const OutputCollection &outputs, const DeepTauCache *cache)
void	produce (edm::Event &event, const edm::EventSetup &es) override
	~DeepTauBase () override
Public Member Functions inherited from edm::stream::EDProducer< edm::GlobalCache< DeepTauCache > >
	EDProducer ()=default
	EDProducer (const EDProducer &)=delete
bool	hasAbilityToProduceInBeginLumis () const final
bool	hasAbilityToProduceInBeginProcessBlocks () const final
bool	hasAbilityToProduceInBeginRuns () const final
bool	hasAbilityToProduceInEndLumis () const final
bool	hasAbilityToProduceInEndProcessBlocks () const final
bool	hasAbilityToProduceInEndRuns () const final
const EDProducer &	operator= (const EDProducer &)=delete

Static Public Member Functions
static void	fillDescriptions (edm::ConfigurationDescriptions &descriptions)
static const OutputCollection &	GetOutputs ()
static void	globalEndJob (const deep_tau::DeepTauCache *cache_)
static std::unique_ptr< deep_tau::DeepTauCache >	initializeGlobalCache (const edm::ParameterSet &cfg)
Static Public Member Functions inherited from deep_tau::DeepTauBase
static void	globalEndJob (const DeepTauCache *cache)
static std::unique_ptr< DeepTauCache >	initializeGlobalCache (const edm::ParameterSet &cfg)

Static Public Attributes
static constexpr float	default_value = -999.

Private Member Functions
void	checkInputs (const tensorflow::Tensor &inputs, const std::string &block_name, int n_inputs, const CellGrid *grid=nullptr) const
template<typename CandidateCastType , typename TauCastType >
void	createConvFeatures (const TauCastType &tau, const size_t tau_index, const edm::RefToBase< reco::BaseTau > tau_ref, const reco::Vertex &pv, double rho, const std::vector< pat::Electron > *electrons, const std::vector< pat::Muon > *muons, const edm::View< reco::Candidate > &pfCands, const CellGrid &grid, TauFunc tau_funcs, bool is_inner)
template<typename CandidateCastType , typename TauCastType >
void	createEgammaBlockInputs (unsigned idx, const TauCastType &tau, const size_t tau_index, const edm::RefToBase< reco::BaseTau > tau_ref, const reco::Vertex &pv, double rho, const std::vector< pat::Electron > *electrons, const edm::View< reco::Candidate > &pfCands, const Cell &cell_map, TauFunc tau_funcs, bool is_inner)
template<typename CandidateCastType , typename TauCastType >
void	createHadronsBlockInputs (unsigned idx, const TauCastType &tau, const size_t tau_index, const edm::RefToBase< reco::BaseTau > tau_ref, const reco::Vertex &pv, double rho, const edm::View< reco::Candidate > &pfCands, const Cell &cell_map, TauFunc tau_funcs, bool is_inner)
template<typename CandidateCastType , typename TauCastType >
void	createMuonBlockInputs (unsigned idx, const TauCastType &tau, const size_t tau_index, const edm::RefToBase< reco::BaseTau > tau_ref, const reco::Vertex &pv, double rho, const std::vector< pat::Muon > *muons, const edm::View< reco::Candidate > &pfCands, const Cell &cell_map, TauFunc tau_funcs, bool is_inner)
template<typename CandidateCastType , typename TauCastType >
void	createTauBlockInputs (const TauCastType &tau, const size_t &tau_index, const edm::RefToBase< reco::BaseTau > tau_ref, const reco::Vertex &pv, double rho, TauFunc tau_funcs)
template<typename Collection , typename TauCastType >
void	fillGrids (const TauCastType &tau, const Collection &objects, CellGrid &inner_grid, CellGrid &outer_grid)
tensorflow::Tensor	getPartialPredictions (bool is_inner)
tensorflow::Tensor	getPredictions (edm::Event &event, edm::Handle< TauCollection > taus) override
template<typename CandidateCastType , typename TauCastType >
void	getPredictionsV2 (TauCollection::const_reference &tau, const size_t tau_index, const edm::RefToBase< reco::BaseTau > tau_ref, const std::vector< pat::Electron > *electrons, const std::vector< pat::Muon > *muons, const edm::View< reco::Candidate > &pfCands, const reco::Vertex &pv, double rho, std::vector< tensorflow::Tensor > &pred_vector, TauFunc tau_funcs)
void	saveInputs (const tensorflow::Tensor &inputs, const std::string &block_name, int n_inputs, const CellGrid *grid=nullptr)
void	setCellConvFeatures (tensorflow::Tensor &convTensor, const tensorflow::Tensor &features, unsigned batch_idx, int eta_index, int phi_index)

Static Private Member Functions
static void	calculateElectronClusterVars (const pat::Electron *ele, float &elecEe, float &elecEgamma)
static bool	calculateElectronClusterVarsV2 (const pat::Electron &ele, float &cc_ele_energy, float &cc_gamma_energy, int &cc_n_gamma)
template<typename TauCastType >
static bool	calculateGottfriedJacksonAngleDifference (const TauCastType &tau, const size_t tau_index, double &gj_diff, TauFunc tau_funcs)
template<typename TauCastType >
static float	calculateGottfriedJacksonAngleDifference (const TauCastType &tau, const size_t tau_index, TauFunc tau_funcs)
template<typename TauCastType >
static const pat::Electron *	findMatchedElectron (const TauCastType &tau, const std::vector< pat::Electron > *electrons, double deltaR)
static double	getInnerSignalConeRadius (double pt)
template<typename T >
static float	getValue (T value)
template<typename T >
static float	getValueLinear (T value, float min_value, float max_value, bool positive)
template<typename T >
static float	getValueNorm (T value, float mean, float sigma, float n_sigmas_max=5)
static bool	isAbove (double value, double min)
static bool	isInEcalCrack (double eta)
template<typename CandidateCollection , typename TauCastType >
static void	processIsolationPFComponents (const TauCastType &tau, const CandidateCollection &candidates, LorentzVectorXYZ &p4, float &pt, float &d_eta, float &d_phi, float &m, float &n)
template<typename CandidateCollection , typename TauCastType >
static void	processSignalPFComponents (const TauCastType &tau, const CandidateCollection &candidates, LorentzVectorXYZ &p4_inner, LorentzVectorXYZ &p4_outer, float &pt_inner, float &dEta_inner, float &dPhi_inner, float &m_inner, float &pt_outer, float &dEta_outer, float &dPhi_outer, float &m_outer, float &n_inner, float &n_outer)

Private Attributes
std::map< BasicDiscriminator, size_t >	basicDiscrdR03IndexMap_
std::map< BasicDiscriminator, size_t >	basicDiscrIndexMap_
edm::EDGetTokenT< reco::TauDiscriminatorContainer >	basicTauDiscriminators_inputToken_
edm::EDGetTokenT< reco::TauDiscriminatorContainer >	basicTauDiscriminatorsdR03_inputToken_
std::array< std::unique_ptr< tensorflow::Tensor >, 2 >	convTensor_
const int	debug_level
const bool	disable_CellIndex_workaround_
const bool	disable_dxy_pca_
const bool	disable_hcalFraction_workaround_
bool	discrIndicesMapped_ = false
std::array< std::unique_ptr< tensorflow::Tensor >, 2 >	eGammaTensor_
edm::EDGetTokenT< std::vector< pat::Electron > >	electrons_token_
int	file_counter_
std::array< std::unique_ptr< tensorflow::Tensor >, 2 >	hadronsTensor_
std::string	input_layer_
bool	is_first_block_
std::ofstream *	json_file_
edm::EDGetTokenT< std::vector< pat::Muon > >	muons_token_
std::array< std::unique_ptr< tensorflow::Tensor >, 2 >	muonTensor_
std::string	output_layer_
edm::EDGetTokenT< edm::AssociationVector< reco::PFTauRefProd, std::vector< reco::PFTauTransverseImpactParameterRef > > >	pfTauTransverseImpactParameters_token_
edm::EDGetTokenT< double >	rho_token_
const bool	save_inputs_
const std::map< std::pair< deep_tau::Scaling::FeatureT, bool >, deep_tau::Scaling::ScalingParams > *	scalingParamsMap_
const unsigned	sub_version_
std::unique_ptr< tensorflow::Tensor >	tauBlockTensor_
std::vector< int >	tauInputs_indices_
const unsigned	version_
std::array< std::unique_ptr< tensorflow::Tensor >, 2 >	zeroOutputTensor_

Static Private Attributes
static constexpr float	pi = M_PI

Additional Inherited Members
Public Types inherited from deep_tau::DeepTauBase
enum	BasicDiscriminator {   ChargedIsoPtSum, NeutralIsoPtSum, NeutralIsoPtSumWeight, FootprintCorrection,   PhotonPtSumOutsideSignalCone, PUcorrPtSum }
using	CandidateCollection = edm::View< reco::Candidate >
using	Cutter = TauWPThreshold
using	CutterPtr = std::unique_ptr< Cutter >
using	ElectronCollection = pat::ElectronCollection
using	LorentzVectorXYZ = ROOT::Math::LorentzVector< ROOT::Math::PxPyPzE4D< double > >
using	MuonCollection = pat::MuonCollection
using	OutputCollection = std::map< std::string, Output >
using	TauCollection = edm::View< reco::BaseTau >
using	TauDiscriminator = reco::TauDiscriminatorContainer
using	TauRef = edm::Ref< TauCollection >
using	TauRefProd = edm::RefProd< TauCollection >
using	WPList = std::vector< CutterPtr >
Public Types inherited from edm::stream::EDProducer< edm::GlobalCache< DeepTauCache > >
using	CacheTypes = CacheContexts< T... >
using	GlobalCache = typename CacheTypes::GlobalCache
using	HasAbility = AbilityChecker< T... >
using	InputProcessBlockCache = typename CacheTypes::InputProcessBlockCache
using	LuminosityBlockCache = typename CacheTypes::LuminosityBlockCache
using	LuminosityBlockContext = LuminosityBlockContextT< LuminosityBlockCache, RunCache, GlobalCache >
using	LuminosityBlockSummaryCache = typename CacheTypes::LuminosityBlockSummaryCache
using	RunCache = typename CacheTypes::RunCache
using	RunContext = RunContextT< RunCache, GlobalCache >
using	RunSummaryCache = typename CacheTypes::RunSummaryCache
Public Attributes inherited from deep_tau::DeepTauBase
uint8_t	andPrediscriminants_
std::vector< TauDiscInfo< pat::PATTauDiscriminator > >	patPrediscriminants_
std::vector< TauDiscInfo< reco::PFTauDiscriminator > >	recoPrediscriminants_
Protected Attributes inherited from deep_tau::DeepTauBase
const DeepTauCache *	cache_
const bool	is_online_
OutputCollection	outputs_
edm::EDGetTokenT< CandidateCollection >	pfcandToken_
edm::EDGetTokenT< TauCollection >	tausToken_
edm::EDGetTokenT< reco::VertexCollection >	vtxToken_
std::map< std::string, WPList >	workingPoints_
Static Protected Attributes inherited from deep_tau::DeepTauBase
static const std::vector< BasicDiscriminator >	requiredBasicDiscriminators_
static const std::vector< BasicDiscriminator >	requiredBasicDiscriminatorsdR03_
static const std::map< BasicDiscriminator, std::string >	stringFromDiscriminator_

Detailed Description

Definition at line 827 of file DeepTauId.cc.

Constructor & Destructor Documentation

DeepTauId()

DeepTauId::DeepTauId ( const edm::ParameterSet &  cfg, const deep_tau::DeepTauCache *  cache  )

inlineexplicit

Definition at line 917 of file DeepTauId.cc.

References convTensor_, eGammaTensor_, nanoDQM_cff::Electron, mps_fire::end, Exception, getPartialPredictions(), hadronsTensor_, mps_fire::i, dumpRecoGeometry_cfg::Muon, muonTensor_, dqmiodumpmetadata::n, AlCaHLTBitMon_ParallelJobs::p, scalingParamsMap_, deep_tau::Scaling::scalingParamsMap_v2p1, deep_tau::Scaling::scalingParamsMap_v2p5, setCellConvFeatures(), findQualityFiles::size, jetUpdater_cfi::sort, sub_version_, tauBlockTensor_, tauInputs_indices_, findQualityFiles::v, version_, and zeroOutputTensor_.

      : DeepTauBase(cfg, GetOutputs(), cache),
        electrons_token_(consumes<std::vector<pat::Electron>>(cfg.getParameter<edm::InputTag>("electrons"))),
        muons_token_(consumes<std::vector<pat::Muon>>(cfg.getParameter<edm::InputTag>("muons"))),
        rho_token_(consumes<double>(cfg.getParameter<edm::InputTag>("rho"))),
        basicTauDiscriminators_inputToken_(consumes<reco::TauDiscriminatorContainer>(
            cfg.getUntrackedParameter<edm::InputTag>("basicTauDiscriminators"))),
        basicTauDiscriminatorsdR03_inputToken_(consumes<reco::TauDiscriminatorContainer>(
            cfg.getUntrackedParameter<edm::InputTag>("basicTauDiscriminatorsdR03"))),
        pfTauTransverseImpactParameters_token_(
            consumes<edm::AssociationVector<reco::PFTauRefProd, std::vector<reco::PFTauTransverseImpactParameterRef>>>(
                cfg.getParameter<edm::InputTag>("pfTauTransverseImpactParameters"))),
        version_(cfg.getParameter<unsigned>("version")),
        sub_version_(cfg.getParameter<unsigned>("sub_version")),
        debug_level(cfg.getParameter<int>("debug_level")),
        disable_dxy_pca_(cfg.getParameter<bool>("disable_dxy_pca")),
        disable_hcalFraction_workaround_(cfg.getParameter<bool>("disable_hcalFraction_workaround")),
        disable_CellIndex_workaround_(cfg.getParameter<bool>("disable_CellIndex_workaround")),
        save_inputs_(cfg.getParameter<bool>("save_inputs")),
        json_file_(nullptr),
        file_counter_(0) {
    if (version_ == 2) {
      using namespace dnn_inputs_v2;
      namespace sc = deep_tau::Scaling;
      tauInputs_indices_.resize(TauBlockInputs::NumberOfInputs);
      std::iota(std::begin(tauInputs_indices_), std::end(tauInputs_indices_), 0);

      if (sub_version_ == 1) {
        tauBlockTensor_ = std::make_unique<tensorflow::Tensor>(
            tensorflow::DT_FLOAT, tensorflow::TensorShape{1, TauBlockInputs::NumberOfInputs});
        scalingParamsMap_ = &sc::scalingParamsMap_v2p1;
      } else if (sub_version_ == 5) {
        std::sort(TauBlockInputs::varsToDrop.begin(), TauBlockInputs::varsToDrop.end());
        for (auto v : TauBlockInputs::varsToDrop) {
          tauInputs_indices_.at(v) = -1;  // set index to -1
          for (std::size_t i = v + 1; i < TauBlockInputs::NumberOfInputs; ++i)
            tauInputs_indices_.at(i) -= 1;  // shift all the following indices by 1
        }
        tauBlockTensor_ = std::make_unique<tensorflow::Tensor>(
            tensorflow::DT_FLOAT,
            tensorflow::TensorShape{1,
                                    static_cast<int>(TauBlockInputs::NumberOfInputs) -
                                        static_cast<int>(TauBlockInputs::varsToDrop.size())});
        scalingParamsMap_ = &sc::scalingParamsMap_v2p5;
      } else
        throw cms::Exception("DeepTauId") << "subversion " << sub_version_ << " is not supported.";

      std::map<std::vector<bool>, std::vector<sc::FeatureT>> GridFeatureTypes_map = {
          {{false}, {sc::FeatureT::TauFlat, sc::FeatureT::GridGlobal}},  // feature types without inner/outer grid split
          {{false, true},
           {sc::FeatureT::PfCand_electron,
            sc::FeatureT::PfCand_muon,  // feature types with inner/outer grid split
            sc::FeatureT::PfCand_chHad,
            sc::FeatureT::PfCand_nHad,
            sc::FeatureT::PfCand_gamma,
            sc::FeatureT::Electron,
            sc::FeatureT::Muon}}};

      // check that sizes of mean/std/lim_min/lim_max vectors are equal between each other
      for (const auto& p : GridFeatureTypes_map) {
        for (auto is_inner : p.first) {
          for (auto featureType : p.second) {
            const sc::ScalingParams& sp = scalingParamsMap_->at(std::make_pair(featureType, is_inner));
            if (!(sp.mean_.size() == sp.std_.size() && sp.mean_.size() == sp.lim_min_.size() &&
                  sp.mean_.size() == sp.lim_max_.size()))
              throw cms::Exception("DeepTauId") << "sizes of scaling parameter vectors do not match between each other";
          }
        }
      }

      for (size_t n = 0; n < 2; ++n) {
        const bool is_inner = n == 0;
        const auto n_cells = is_inner ? number_of_inner_cell : number_of_outer_cell;
        eGammaTensor_[is_inner] = std::make_unique<tensorflow::Tensor>(
            tensorflow::DT_FLOAT, tensorflow::TensorShape{1, 1, 1, EgammaBlockInputs::NumberOfInputs});
        muonTensor_[is_inner] = std::make_unique<tensorflow::Tensor>(
            tensorflow::DT_FLOAT, tensorflow::TensorShape{1, 1, 1, MuonBlockInputs::NumberOfInputs});
        hadronsTensor_[is_inner] = std::make_unique<tensorflow::Tensor>(
            tensorflow::DT_FLOAT, tensorflow::TensorShape{1, 1, 1, HadronBlockInputs::NumberOfInputs});
        convTensor_[is_inner] = std::make_unique<tensorflow::Tensor>(
            tensorflow::DT_FLOAT, tensorflow::TensorShape{1, n_cells, n_cells, number_of_conv_features});
        zeroOutputTensor_[is_inner] = std::make_unique<tensorflow::Tensor>(
            tensorflow::DT_FLOAT, tensorflow::TensorShape{1, 1, 1, number_of_conv_features});

        eGammaTensor_[is_inner]->flat<float>().setZero();
        muonTensor_[is_inner]->flat<float>().setZero();
        hadronsTensor_[is_inner]->flat<float>().setZero();

        setCellConvFeatures(*zeroOutputTensor_[is_inner], getPartialPredictions(is_inner), 0, 0, 0);
      }
    } else {
      throw cms::Exception("DeepTauId") << "version " << version_ << " is not supported.";
    }
  }

Member Function Documentation

calculateElectronClusterVars()

static void DeepTauId::calculateElectronClusterVars ( const pat::Electron *  ele, float &  elecEe, float &  elecEgamma  )

inlinestaticprivate

Definition at line 2269 of file DeepTauId.cc.

References default_value, HCALHighEnergyHPDFilter_cfi::energy, and pat::Electron::superCluster().

                                                                                                     {
    if (ele) {
      elecEe = elecEgamma = 0;
      auto superCluster = ele->superCluster();
      if (superCluster.isNonnull() && superCluster.isAvailable() && superCluster->clusters().isNonnull() &&
          superCluster->clusters().isAvailable()) {
        for (auto iter = superCluster->clustersBegin(); iter != superCluster->clustersEnd(); ++iter) {
          const double energy = (*iter)->energy();
          if (iter == superCluster->clustersBegin())
            elecEe += energy;
          else
            elecEgamma += energy;
        }
      }
    } else {
      elecEe = elecEgamma = default_value;
    }
  }

calculateElectronClusterVarsV2()

static bool DeepTauId::calculateElectronClusterVarsV2 ( const pat::Electron &  ele, float &  cc_ele_energy, float &  cc_gamma_energy, int &  cc_n_gamma  )

inlinestaticprivate

Definition at line 1045 of file DeepTauId.cc.

References HCALHighEnergyHPDFilter_cfi::energy, and pat::Electron::superCluster().

Referenced by createEgammaBlockInputs().

                                                              {
    cc_ele_energy = cc_gamma_energy = 0;
    cc_n_gamma = 0;
    const auto& superCluster = ele.superCluster();
    if (superCluster.isNonnull() && superCluster.isAvailable() && superCluster->clusters().isNonnull() &&
        superCluster->clusters().isAvailable()) {
      for (auto iter = superCluster->clustersBegin(); iter != superCluster->clustersEnd(); ++iter) {
        const float energy = static_cast<float>((*iter)->energy());
        if (iter == superCluster->clustersBegin())
          cc_ele_energy += energy;
        else {
          cc_gamma_energy += energy;
          ++cc_n_gamma;
        }
      }
      return true;
    } else
      return false;
  }

calculateGottfriedJacksonAngleDifference() [1/2]

template<typename TauCastType >

static bool DeepTauId::calculateGottfriedJacksonAngleDifference ( const TauCastType &  tau, const size_t  tau_index, double &  gj_diff, TauFunc  tau_funcs  )

inlinestaticprivate

Definition at line 2363 of file DeepTauId.cc.

References funct::abs(), funct::pow(), and metsig::tau.

Referenced by calculateGottfriedJacksonAngleDifference(), and createTauBlockInputs().

                                                                          {
    if (tau_funcs.getHasSecondaryVertex(tau, tau_index)) {
      static constexpr double mTau = 1.77682;
      const double mAOne = tau.p4().M();
      const double pAOneMag = tau.p();
      const double argumentThetaGJmax = (std::pow(mTau, 2) - std::pow(mAOne, 2)) / (2 * mTau * pAOneMag);
      const double argumentThetaGJmeasured = tau.p4().Vect().Dot(tau_funcs.getFlightLength(tau, tau_index)) /
                                             (pAOneMag * tau_funcs.getFlightLength(tau, tau_index).R());
      if (std::abs(argumentThetaGJmax) <= 1. && std::abs(argumentThetaGJmeasured) <= 1.) {
        double thetaGJmax = std::asin(argumentThetaGJmax);
        double thetaGJmeasured = std::acos(argumentThetaGJmeasured);
        gj_diff = thetaGJmeasured - thetaGJmax;
        return true;
      }
    }
    return false;
  }

calculateGottfriedJacksonAngleDifference() [2/2]

template<typename TauCastType >

static float DeepTauId::calculateGottfriedJacksonAngleDifference ( const TauCastType &  tau, const size_t  tau_index, TauFunc  tau_funcs  )

inlinestaticprivate

Definition at line 2385 of file DeepTauId.cc.

References calculateGottfriedJacksonAngleDifference(), default_value, and metsig::tau.

                                                                           {
    double gj_diff;
    if (calculateGottfriedJacksonAngleDifference(tau, tau_index, gj_diff, tau_funcs))
      return static_cast<float>(gj_diff);
    return default_value;
  }

checkInputs()

void DeepTauId::checkInputs ( const tensorflow::Tensor &  inputs, const std::string &  block_name, int  n_inputs, const CellGrid *  grid = nullptr  ) const

inlineprivate

Definition at line 1068 of file DeepTauId.cc.

References cms::cuda::assert(), gather_cfg::cout, debug_level, PVValHelper::eta, Exception, alignBH_cfg::fixed, dqmMemoryStats::float, getRunAppsInfo::grid, input, PixelMapPlotter::inputs, edm::isNotFinite(), and phi.

Referenced by getPredictionsV2().

                                                                {
    if (debug_level >= 1) {
      std::cout << "<checkInputs>: block_name = " << block_name << std::endl;
      if (block_name == "input_tau") {
        for (int input_index = 0; input_index < n_inputs; ++input_index) {
          float input = inputs.matrix<float>()(0, input_index);
          if (edm::isNotFinite(input)) {
            throw cms::Exception("DeepTauId")
                << "in the " << block_name
                << ", input is not finite, i.e. infinite or NaN, for input_index = " << input_index;
          }
          if (debug_level >= 2) {
            std::cout << block_name << "[var = " << input_index << "] = " << std::setprecision(5) << std::fixed << input
                      << std::endl;
          }
        }
      } else {
        assert(grid);
        int n_eta, n_phi;
        if (block_name.find("input_inner") != std::string::npos) {
          n_eta = 5;
          n_phi = 5;
        } else if (block_name.find("input_outer") != std::string::npos) {
          n_eta = 10;
          n_phi = 10;
        } else
          assert(0);
        int eta_phi_index = 0;
        for (int eta = -n_eta; eta <= n_eta; ++eta) {
          for (int phi = -n_phi; phi <= n_phi; ++phi) {
            const CellIndex cell_index{eta, phi};
            const auto cell_iter = grid->find(cell_index);
            if (cell_iter != grid->end()) {
              for (int input_index = 0; input_index < n_inputs; ++input_index) {
                float input = inputs.tensor<float, 4>()(eta_phi_index, 0, 0, input_index);
                if (edm::isNotFinite(input)) {
                  throw cms::Exception("DeepTauId")
                      << "in the " << block_name << ", input is not finite, i.e. infinite or NaN, for eta = " << eta
                      << ", phi = " << phi << ", input_index = " << input_index;
                }
                if (debug_level >= 2) {
                  std::cout << block_name << "[eta = " << eta << "][phi = " << phi << "][var = " << input_index
                            << "] = " << std::setprecision(5) << std::fixed << input << std::endl;
                }
              }
              eta_phi_index += 1;
            }
          }
        }
      }
    }
  }

createConvFeatures()

template<typename CandidateCastType , typename TauCastType >

void DeepTauId::createConvFeatures ( const TauCastType &  tau, const size_t  tau_index, const edm::RefToBase< reco::BaseTau >  tau_ref, const reco::Vertex &  pv, double  rho, const std::vector< pat::Electron > *  electrons, const std::vector< pat::Muon > *  muons, const edm::View< reco::Candidate > &  pfCands, const CellGrid &  grid, TauFunc  tau_funcs, bool  is_inner  )

inlineprivate

Definition at line 1470 of file DeepTauId.cc.

References convTensor_, gather_cfg::cout, debug_level, eGammaTensor_, pwdgSkimBPark_cfi::electrons, PVValHelper::eta, getPartialPredictions(), getRunAppsInfo::grid, hadronsTensor_, heavyIonCSV_trainingSettings::idx, createfilelist::int, PDWG_BPHSkim_cff::muons, muonTensor_, phi, MetAnalyzer::pv(), rho, setCellConvFeatures(), metsig::tau, and zeroOutputTensor_.

                                         {
    if (debug_level >= 2) {
      std::cout << "<DeepTauId::createConvFeatures (is_inner = " << is_inner << ")>:" << std::endl;
    }
    tensorflow::Tensor& convTensor = *convTensor_.at(is_inner);
    eGammaTensor_[is_inner] = std::make_unique<tensorflow::Tensor>(
        tensorflow::DT_FLOAT,
        tensorflow::TensorShape{
            (long long int)grid.num_valid_cells(), 1, 1, dnn_inputs_v2::EgammaBlockInputs::NumberOfInputs});
    muonTensor_[is_inner] = std::make_unique<tensorflow::Tensor>(
        tensorflow::DT_FLOAT,
        tensorflow::TensorShape{
            (long long int)grid.num_valid_cells(), 1, 1, dnn_inputs_v2::MuonBlockInputs::NumberOfInputs});
    hadronsTensor_[is_inner] = std::make_unique<tensorflow::Tensor>(
        tensorflow::DT_FLOAT,
        tensorflow::TensorShape{
            (long long int)grid.num_valid_cells(), 1, 1, dnn_inputs_v2::HadronBlockInputs::NumberOfInputs});

    eGammaTensor_[is_inner]->flat<float>().setZero();
    muonTensor_[is_inner]->flat<float>().setZero();
    hadronsTensor_[is_inner]->flat<float>().setZero();

    unsigned idx = 0;
    for (int eta = -grid.maxEtaIndex(); eta <= grid.maxEtaIndex(); ++eta) {
      for (int phi = -grid.maxPhiIndex(); phi <= grid.maxPhiIndex(); ++phi) {
        if (debug_level >= 2) {
          std::cout << "processing ( eta = " << eta << ", phi = " << phi << " )" << std::endl;
        }
        const CellIndex cell_index{eta, phi};
        const auto cell_iter = grid.find(cell_index);
        if (cell_iter != grid.end()) {
          if (debug_level >= 2) {
            std::cout << " creating inputs for ( eta = " << eta << ", phi = " << phi << " ): idx = " << idx
                      << std::endl;
          }
          const Cell& cell = cell_iter->second;
          createEgammaBlockInputs<CandidateCastType>(
              idx, tau, tau_index, tau_ref, pv, rho, electrons, pfCands, cell, tau_funcs, is_inner);
          createMuonBlockInputs<CandidateCastType>(
              idx, tau, tau_index, tau_ref, pv, rho, muons, pfCands, cell, tau_funcs, is_inner);
          createHadronsBlockInputs<CandidateCastType>(
              idx, tau, tau_index, tau_ref, pv, rho, pfCands, cell, tau_funcs, is_inner);
          idx += 1;
        } else {
          if (debug_level >= 2) {
            std::cout << " skipping creation of inputs, because ( eta = " << eta << ", phi = " << phi
                      << " ) is not in the grid !!" << std::endl;
          }
        }
      }
    }

    const auto predTensor = getPartialPredictions(is_inner);
    idx = 0;
    for (int eta = -grid.maxEtaIndex(); eta <= grid.maxEtaIndex(); ++eta) {
      for (int phi = -grid.maxPhiIndex(); phi <= grid.maxPhiIndex(); ++phi) {
        const CellIndex cell_index{eta, phi};
        const int eta_index = grid.getEtaTensorIndex(cell_index);
        const int phi_index = grid.getPhiTensorIndex(cell_index);

        const auto cell_iter = grid.find(cell_index);
        if (cell_iter != grid.end()) {
          setCellConvFeatures(convTensor, predTensor, idx, eta_index, phi_index);
          idx += 1;
        } else {
          setCellConvFeatures(convTensor, *zeroOutputTensor_[is_inner], 0, eta_index, phi_index);
        }
      }
    }
  }

createEgammaBlockInputs()

template<typename CandidateCastType , typename TauCastType >

void DeepTauId::createEgammaBlockInputs ( unsigned  idx, const TauCastType &  tau, const size_t  tau_index, const edm::RefToBase< reco::BaseTau >  tau_ref, const reco::Vertex &  pv, double  rho, const std::vector< pat::Electron > *  electrons, const edm::View< reco::Candidate > &  pfCands, const Cell &  cell_map, TauFunc  tau_funcs, bool  is_inner  )

inlineprivate

Definition at line 1707 of file DeepTauId.cc.

References funct::abs(), edm::View< T >::at(), calculateElectronClusterVarsV2(), hltPixelTracks_cff::chi2, HLT_2022v12_cff::dPhi, eGammaTensor_, nanoDQM_cff::Electron, pwdgSkimBPark_cfi::electrons, f, dqmMemoryStats::float, heavyIonCSV_trainingSettings::idx, PixelMapPlotter::inputs, isInEcalCrack(), ndof, MetAnalyzer::pv(), rho, ZElectronSkim_cff::rho, scalingParamsMap_, sub_version_, metsig::tau, runTauDisplay::tau_eta, and runTauDisplay::tau_pt.

                                              {
    namespace dnn = dnn_inputs_v2::EgammaBlockInputs;
    namespace sc = deep_tau::Scaling;
    sc::FeatureT ft_global = sc::FeatureT::GridGlobal;
    sc::FeatureT ft_PFe = sc::FeatureT::PfCand_electron;
    sc::FeatureT ft_PFg = sc::FeatureT::PfCand_gamma;
    sc::FeatureT ft_e = sc::FeatureT::Electron;

    // needed to remap indices from scaling vectors to those from dnn_inputs_v2::EgammaBlockInputs
    int PFe_index_offset = scalingParamsMap_->at(std::make_pair(ft_global, false)).mean_.size();
    int e_index_offset = PFe_index_offset + scalingParamsMap_->at(std::make_pair(ft_PFe, false)).mean_.size();
    int PFg_index_offset = e_index_offset + scalingParamsMap_->at(std::make_pair(ft_e, false)).mean_.size();

    // to account for swapped order of PfCand_gamma and Electron blocks for v2p5 training w.r.t. v2p1
    int fill_index_offset_e = 0;
    int fill_index_offset_PFg = 0;
    if (sub_version_ == 5) {
      fill_index_offset_e =
          scalingParamsMap_->at(std::make_pair(ft_PFg, false)).mean_.size();  // size of PF gamma features
      fill_index_offset_PFg =
          -scalingParamsMap_->at(std::make_pair(ft_e, false)).mean_.size();  // size of Electron features
    }

    tensorflow::Tensor& inputs = *eGammaTensor_.at(is_inner);

    const auto& get = [&](int var_index) -> float& { return inputs.tensor<float, 4>()(idx, 0, 0, var_index); };

    const bool valid_index_pf_ele = cell_map.count(CellObjectType::PfCand_electron);
    const bool valid_index_pf_gamma = cell_map.count(CellObjectType::PfCand_gamma);
    const bool valid_index_ele = cell_map.count(CellObjectType::Electron);

    if (!cell_map.empty()) {
      const sc::ScalingParams& sp = scalingParamsMap_->at(std::make_pair(ft_global, false));
      get(dnn::rho) = sp.scale(rho, dnn::rho);
      get(dnn::tau_pt) = sp.scale(tau.polarP4().pt(), dnn::tau_pt);
      get(dnn::tau_eta) = sp.scale(tau.polarP4().eta(), dnn::tau_eta);
      get(dnn::tau_inside_ecal_crack) = sp.scale(isInEcalCrack(tau.polarP4().eta()), dnn::tau_inside_ecal_crack);
    }
    if (valid_index_pf_ele) {
      const sc::ScalingParams& sp = scalingParamsMap_->at(std::make_pair(ft_PFe, is_inner));
      size_t index_pf_ele = cell_map.at(CellObjectType::PfCand_electron);
      const auto& ele_cand = dynamic_cast<const CandidateCastType&>(pfCands.at(index_pf_ele));

      get(dnn::pfCand_ele_valid) = sp.scale(valid_index_pf_ele, dnn::pfCand_ele_valid - PFe_index_offset);
      get(dnn::pfCand_ele_rel_pt) =
          sp.scale(ele_cand.polarP4().pt() / tau.polarP4().pt(), dnn::pfCand_ele_rel_pt - PFe_index_offset);
      get(dnn::pfCand_ele_deta) =
          sp.scale(ele_cand.polarP4().eta() - tau.polarP4().eta(), dnn::pfCand_ele_deta - PFe_index_offset);
      get(dnn::pfCand_ele_dphi) =
          sp.scale(dPhi(tau.polarP4(), ele_cand.polarP4()), dnn::pfCand_ele_dphi - PFe_index_offset);
      get(dnn::pfCand_ele_pvAssociationQuality) = sp.scale<int>(
          candFunc::getPvAssocationQuality(ele_cand), dnn::pfCand_ele_pvAssociationQuality - PFe_index_offset);
      get(dnn::pfCand_ele_puppiWeight) = is_inner ? sp.scale(candFunc::getPuppiWeight(ele_cand, 0.9906834f),
                                                             dnn::pfCand_ele_puppiWeight - PFe_index_offset)
                                                  : sp.scale(candFunc::getPuppiWeight(ele_cand, 0.9669586f),
                                                             dnn::pfCand_ele_puppiWeight - PFe_index_offset);
      get(dnn::pfCand_ele_charge) = sp.scale(ele_cand.charge(), dnn::pfCand_ele_charge - PFe_index_offset);
      get(dnn::pfCand_ele_lostInnerHits) =
          sp.scale<int>(candFunc::getLostInnerHits(ele_cand, 0), dnn::pfCand_ele_lostInnerHits - PFe_index_offset);
      get(dnn::pfCand_ele_numberOfPixelHits) =
          sp.scale(candFunc::getNumberOfPixelHits(ele_cand, 0), dnn::pfCand_ele_numberOfPixelHits - PFe_index_offset);
      get(dnn::pfCand_ele_vertex_dx) =
          sp.scale(ele_cand.vertex().x() - pv.position().x(), dnn::pfCand_ele_vertex_dx - PFe_index_offset);
      get(dnn::pfCand_ele_vertex_dy) =
          sp.scale(ele_cand.vertex().y() - pv.position().y(), dnn::pfCand_ele_vertex_dy - PFe_index_offset);
      get(dnn::pfCand_ele_vertex_dz) =
          sp.scale(ele_cand.vertex().z() - pv.position().z(), dnn::pfCand_ele_vertex_dz - PFe_index_offset);
      get(dnn::pfCand_ele_vertex_dx_tauFL) =
          sp.scale(ele_cand.vertex().x() - pv.position().x() - tau_funcs.getFlightLength(tau, tau_index).x(),
                   dnn::pfCand_ele_vertex_dx_tauFL - PFe_index_offset);
      get(dnn::pfCand_ele_vertex_dy_tauFL) =
          sp.scale(ele_cand.vertex().y() - pv.position().y() - tau_funcs.getFlightLength(tau, tau_index).y(),
                   dnn::pfCand_ele_vertex_dy_tauFL - PFe_index_offset);
      get(dnn::pfCand_ele_vertex_dz_tauFL) =
          sp.scale(ele_cand.vertex().z() - pv.position().z() - tau_funcs.getFlightLength(tau, tau_index).z(),
                   dnn::pfCand_ele_vertex_dz_tauFL - PFe_index_offset);

      const bool hasTrackDetails = candFunc::getHasTrackDetails(ele_cand);
      if (hasTrackDetails) {
        get(dnn::pfCand_ele_hasTrackDetails) =
            sp.scale(hasTrackDetails, dnn::pfCand_ele_hasTrackDetails - PFe_index_offset);
        get(dnn::pfCand_ele_dxy) = sp.scale(candFunc::getTauDxy(ele_cand), dnn::pfCand_ele_dxy - PFe_index_offset);
        get(dnn::pfCand_ele_dxy_sig) = sp.scale(std::abs(candFunc::getTauDxy(ele_cand)) / ele_cand.dxyError(),
                                                dnn::pfCand_ele_dxy_sig - PFe_index_offset);
        get(dnn::pfCand_ele_dz) = sp.scale(candFunc::getTauDz(ele_cand), dnn::pfCand_ele_dz - PFe_index_offset);
        get(dnn::pfCand_ele_dz_sig) = sp.scale(std::abs(candFunc::getTauDz(ele_cand)) / ele_cand.dzError(),
                                               dnn::pfCand_ele_dz_sig - PFe_index_offset);
        get(dnn::pfCand_ele_track_chi2_ndof) =
            candFunc::getPseudoTrack(ele_cand).ndof() > 0
                ? sp.scale(candFunc::getPseudoTrack(ele_cand).chi2() / candFunc::getPseudoTrack(ele_cand).ndof(),
                           dnn::pfCand_ele_track_chi2_ndof - PFe_index_offset)
                : 0;
        get(dnn::pfCand_ele_track_ndof) =
            candFunc::getPseudoTrack(ele_cand).ndof() > 0
                ? sp.scale(candFunc::getPseudoTrack(ele_cand).ndof(), dnn::pfCand_ele_track_ndof - PFe_index_offset)
                : 0;
      }
    }
    if (valid_index_pf_gamma) {
      const sc::ScalingParams& sp = scalingParamsMap_->at(std::make_pair(ft_PFg, is_inner));
      size_t index_pf_gamma = cell_map.at(CellObjectType::PfCand_gamma);
      const auto& gamma_cand = dynamic_cast<const CandidateCastType&>(pfCands.at(index_pf_gamma));

      get(dnn::pfCand_gamma_valid + fill_index_offset_PFg) =
          sp.scale(valid_index_pf_gamma, dnn::pfCand_gamma_valid - PFg_index_offset);
      get(dnn::pfCand_gamma_rel_pt + fill_index_offset_PFg) =
          sp.scale(gamma_cand.polarP4().pt() / tau.polarP4().pt(), dnn::pfCand_gamma_rel_pt - PFg_index_offset);
      get(dnn::pfCand_gamma_deta + fill_index_offset_PFg) =
          sp.scale(gamma_cand.polarP4().eta() - tau.polarP4().eta(), dnn::pfCand_gamma_deta - PFg_index_offset);
      get(dnn::pfCand_gamma_dphi + fill_index_offset_PFg) =
          sp.scale(dPhi(tau.polarP4(), gamma_cand.polarP4()), dnn::pfCand_gamma_dphi - PFg_index_offset);
      get(dnn::pfCand_gamma_pvAssociationQuality + fill_index_offset_PFg) = sp.scale<int>(
          candFunc::getPvAssocationQuality(gamma_cand), dnn::pfCand_gamma_pvAssociationQuality - PFg_index_offset);
      get(dnn::pfCand_gamma_fromPV + fill_index_offset_PFg) =
          sp.scale<int>(candFunc::getFromPV(gamma_cand), dnn::pfCand_gamma_fromPV - PFg_index_offset);
      get(dnn::pfCand_gamma_puppiWeight + fill_index_offset_PFg) =
          is_inner ? sp.scale(candFunc::getPuppiWeight(gamma_cand, 0.9084110f),
                              dnn::pfCand_gamma_puppiWeight - PFg_index_offset)
                   : sp.scale(candFunc::getPuppiWeight(gamma_cand, 0.4211567f),
                              dnn::pfCand_gamma_puppiWeight - PFg_index_offset);
      get(dnn::pfCand_gamma_puppiWeightNoLep + fill_index_offset_PFg) =
          is_inner ? sp.scale(candFunc::getPuppiWeightNoLep(gamma_cand, 0.8857716f),
                              dnn::pfCand_gamma_puppiWeightNoLep - PFg_index_offset)
                   : sp.scale(candFunc::getPuppiWeightNoLep(gamma_cand, 0.3822604f),
                              dnn::pfCand_gamma_puppiWeightNoLep - PFg_index_offset);
      get(dnn::pfCand_gamma_lostInnerHits + fill_index_offset_PFg) =
          sp.scale<int>(candFunc::getLostInnerHits(gamma_cand, 0), dnn::pfCand_gamma_lostInnerHits - PFg_index_offset);
      get(dnn::pfCand_gamma_numberOfPixelHits + fill_index_offset_PFg) = sp.scale(
          candFunc::getNumberOfPixelHits(gamma_cand, 0), dnn::pfCand_gamma_numberOfPixelHits - PFg_index_offset);
      get(dnn::pfCand_gamma_vertex_dx + fill_index_offset_PFg) =
          sp.scale(gamma_cand.vertex().x() - pv.position().x(), dnn::pfCand_gamma_vertex_dx - PFg_index_offset);
      get(dnn::pfCand_gamma_vertex_dy + fill_index_offset_PFg) =
          sp.scale(gamma_cand.vertex().y() - pv.position().y(), dnn::pfCand_gamma_vertex_dy - PFg_index_offset);
      get(dnn::pfCand_gamma_vertex_dz + fill_index_offset_PFg) =
          sp.scale(gamma_cand.vertex().z() - pv.position().z(), dnn::pfCand_gamma_vertex_dz - PFg_index_offset);
      get(dnn::pfCand_gamma_vertex_dx_tauFL + fill_index_offset_PFg) =
          sp.scale(gamma_cand.vertex().x() - pv.position().x() - tau_funcs.getFlightLength(tau, tau_index).x(),
                   dnn::pfCand_gamma_vertex_dx_tauFL - PFg_index_offset);
      get(dnn::pfCand_gamma_vertex_dy_tauFL + fill_index_offset_PFg) =
          sp.scale(gamma_cand.vertex().y() - pv.position().y() - tau_funcs.getFlightLength(tau, tau_index).y(),
                   dnn::pfCand_gamma_vertex_dy_tauFL - PFg_index_offset);
      get(dnn::pfCand_gamma_vertex_dz_tauFL + fill_index_offset_PFg) =
          sp.scale(gamma_cand.vertex().z() - pv.position().z() - tau_funcs.getFlightLength(tau, tau_index).z(),
                   dnn::pfCand_gamma_vertex_dz_tauFL - PFg_index_offset);
      const bool hasTrackDetails = candFunc::getHasTrackDetails(gamma_cand);
      if (hasTrackDetails) {
        get(dnn::pfCand_gamma_hasTrackDetails + fill_index_offset_PFg) =
            sp.scale(hasTrackDetails, dnn::pfCand_gamma_hasTrackDetails - PFg_index_offset);
        get(dnn::pfCand_gamma_dxy + fill_index_offset_PFg) =
            sp.scale(candFunc::getTauDxy(gamma_cand), dnn::pfCand_gamma_dxy - PFg_index_offset);
        get(dnn::pfCand_gamma_dxy_sig + fill_index_offset_PFg) =
            sp.scale(std::abs(candFunc::getTauDxy(gamma_cand)) / gamma_cand.dxyError(),
                     dnn::pfCand_gamma_dxy_sig - PFg_index_offset);
        get(dnn::pfCand_gamma_dz + fill_index_offset_PFg) =
            sp.scale(candFunc::getTauDz(gamma_cand), dnn::pfCand_gamma_dz - PFg_index_offset);
        get(dnn::pfCand_gamma_dz_sig + fill_index_offset_PFg) =
            sp.scale(std::abs(candFunc::getTauDz(gamma_cand)) / gamma_cand.dzError(),
                     dnn::pfCand_gamma_dz_sig - PFg_index_offset);
        get(dnn::pfCand_gamma_track_chi2_ndof + fill_index_offset_PFg) =
            candFunc::getPseudoTrack(gamma_cand).ndof() > 0
                ? sp.scale(candFunc::getPseudoTrack(gamma_cand).chi2() / candFunc::getPseudoTrack(gamma_cand).ndof(),
                           dnn::pfCand_gamma_track_chi2_ndof - PFg_index_offset)
                : 0;
        get(dnn::pfCand_gamma_track_ndof + fill_index_offset_PFg) =
            candFunc::getPseudoTrack(gamma_cand).ndof() > 0
                ? sp.scale(candFunc::getPseudoTrack(gamma_cand).ndof(), dnn::pfCand_gamma_track_ndof - PFg_index_offset)
                : 0;
      }
    }
    if (valid_index_ele) {
      const sc::ScalingParams& sp = scalingParamsMap_->at(std::make_pair(ft_e, is_inner));
      size_t index_ele = cell_map.at(CellObjectType::Electron);
      const auto& ele = electrons->at(index_ele);

      get(dnn::ele_valid + fill_index_offset_e) = sp.scale(valid_index_ele, dnn::ele_valid - e_index_offset);
      get(dnn::ele_rel_pt + fill_index_offset_e) =
          sp.scale(ele.polarP4().pt() / tau.polarP4().pt(), dnn::ele_rel_pt - e_index_offset);
      get(dnn::ele_deta + fill_index_offset_e) =
          sp.scale(ele.polarP4().eta() - tau.polarP4().eta(), dnn::ele_deta - e_index_offset);
      get(dnn::ele_dphi + fill_index_offset_e) =
          sp.scale(dPhi(tau.polarP4(), ele.polarP4()), dnn::ele_dphi - e_index_offset);

      float cc_ele_energy, cc_gamma_energy;
      int cc_n_gamma;
      const bool cc_valid = calculateElectronClusterVarsV2(ele, cc_ele_energy, cc_gamma_energy, cc_n_gamma);
      if (cc_valid) {
        get(dnn::ele_cc_valid + fill_index_offset_e) = sp.scale(cc_valid, dnn::ele_cc_valid - e_index_offset);
        get(dnn::ele_cc_ele_rel_energy + fill_index_offset_e) =
            sp.scale(cc_ele_energy / ele.polarP4().pt(), dnn::ele_cc_ele_rel_energy - e_index_offset);
        get(dnn::ele_cc_gamma_rel_energy + fill_index_offset_e) =
            sp.scale(cc_gamma_energy / cc_ele_energy, dnn::ele_cc_gamma_rel_energy - e_index_offset);
        get(dnn::ele_cc_n_gamma + fill_index_offset_e) = sp.scale(cc_n_gamma, dnn::ele_cc_n_gamma - e_index_offset);
      }
      get(dnn::ele_rel_trackMomentumAtVtx + fill_index_offset_e) =
          sp.scale(ele.trackMomentumAtVtx().R() / ele.polarP4().pt(), dnn::ele_rel_trackMomentumAtVtx - e_index_offset);
      get(dnn::ele_rel_trackMomentumAtCalo + fill_index_offset_e) = sp.scale(
          ele.trackMomentumAtCalo().R() / ele.polarP4().pt(), dnn::ele_rel_trackMomentumAtCalo - e_index_offset);
      get(dnn::ele_rel_trackMomentumOut + fill_index_offset_e) =
          sp.scale(ele.trackMomentumOut().R() / ele.polarP4().pt(), dnn::ele_rel_trackMomentumOut - e_index_offset);
      get(dnn::ele_rel_trackMomentumAtEleClus + fill_index_offset_e) = sp.scale(
          ele.trackMomentumAtEleClus().R() / ele.polarP4().pt(), dnn::ele_rel_trackMomentumAtEleClus - e_index_offset);
      get(dnn::ele_rel_trackMomentumAtVtxWithConstraint + fill_index_offset_e) =
          sp.scale(ele.trackMomentumAtVtxWithConstraint().R() / ele.polarP4().pt(),
                   dnn::ele_rel_trackMomentumAtVtxWithConstraint - e_index_offset);
      get(dnn::ele_rel_ecalEnergy + fill_index_offset_e) =
          sp.scale(ele.ecalEnergy() / ele.polarP4().pt(), dnn::ele_rel_ecalEnergy - e_index_offset);
      get(dnn::ele_ecalEnergy_sig + fill_index_offset_e) =
          sp.scale(ele.ecalEnergy() / ele.ecalEnergyError(), dnn::ele_ecalEnergy_sig - e_index_offset);
      get(dnn::ele_eSuperClusterOverP + fill_index_offset_e) =
          sp.scale(ele.eSuperClusterOverP(), dnn::ele_eSuperClusterOverP - e_index_offset);
      get(dnn::ele_eSeedClusterOverP + fill_index_offset_e) =
          sp.scale(ele.eSeedClusterOverP(), dnn::ele_eSeedClusterOverP - e_index_offset);
      get(dnn::ele_eSeedClusterOverPout + fill_index_offset_e) =
          sp.scale(ele.eSeedClusterOverPout(), dnn::ele_eSeedClusterOverPout - e_index_offset);
      get(dnn::ele_eEleClusterOverPout + fill_index_offset_e) =
          sp.scale(ele.eEleClusterOverPout(), dnn::ele_eEleClusterOverPout - e_index_offset);
      get(dnn::ele_deltaEtaSuperClusterTrackAtVtx + fill_index_offset_e) =
          sp.scale(ele.deltaEtaSuperClusterTrackAtVtx(), dnn::ele_deltaEtaSuperClusterTrackAtVtx - e_index_offset);
      get(dnn::ele_deltaEtaSeedClusterTrackAtCalo + fill_index_offset_e) =
          sp.scale(ele.deltaEtaSeedClusterTrackAtCalo(), dnn::ele_deltaEtaSeedClusterTrackAtCalo - e_index_offset);
      get(dnn::ele_deltaEtaEleClusterTrackAtCalo + fill_index_offset_e) =
          sp.scale(ele.deltaEtaEleClusterTrackAtCalo(), dnn::ele_deltaEtaEleClusterTrackAtCalo - e_index_offset);
      get(dnn::ele_deltaPhiEleClusterTrackAtCalo + fill_index_offset_e) =
          sp.scale(ele.deltaPhiEleClusterTrackAtCalo(), dnn::ele_deltaPhiEleClusterTrackAtCalo - e_index_offset);
      get(dnn::ele_deltaPhiSuperClusterTrackAtVtx + fill_index_offset_e) =
          sp.scale(ele.deltaPhiSuperClusterTrackAtVtx(), dnn::ele_deltaPhiSuperClusterTrackAtVtx - e_index_offset);
      get(dnn::ele_deltaPhiSeedClusterTrackAtCalo + fill_index_offset_e) =
          sp.scale(ele.deltaPhiSeedClusterTrackAtCalo(), dnn::ele_deltaPhiSeedClusterTrackAtCalo - e_index_offset);
      get(dnn::ele_mvaInput_earlyBrem + fill_index_offset_e) =
          sp.scale(ele.mvaInput().earlyBrem, dnn::ele_mvaInput_earlyBrem - e_index_offset);
      get(dnn::ele_mvaInput_lateBrem + fill_index_offset_e) =
          sp.scale(ele.mvaInput().lateBrem, dnn::ele_mvaInput_lateBrem - e_index_offset);
      get(dnn::ele_mvaInput_sigmaEtaEta + fill_index_offset_e) =
          sp.scale(ele.mvaInput().sigmaEtaEta, dnn::ele_mvaInput_sigmaEtaEta - e_index_offset);
      get(dnn::ele_mvaInput_hadEnergy + fill_index_offset_e) =
          sp.scale(ele.mvaInput().hadEnergy, dnn::ele_mvaInput_hadEnergy - e_index_offset);
      get(dnn::ele_mvaInput_deltaEta + fill_index_offset_e) =
          sp.scale(ele.mvaInput().deltaEta, dnn::ele_mvaInput_deltaEta - e_index_offset);
      const auto& gsfTrack = ele.gsfTrack();
      if (gsfTrack.isNonnull()) {
        get(dnn::ele_gsfTrack_normalizedChi2 + fill_index_offset_e) =
            sp.scale(gsfTrack->normalizedChi2(), dnn::ele_gsfTrack_normalizedChi2 - e_index_offset);
        get(dnn::ele_gsfTrack_numberOfValidHits + fill_index_offset_e) =
            sp.scale(gsfTrack->numberOfValidHits(), dnn::ele_gsfTrack_numberOfValidHits - e_index_offset);
        get(dnn::ele_rel_gsfTrack_pt + fill_index_offset_e) =
            sp.scale(gsfTrack->pt() / ele.polarP4().pt(), dnn::ele_rel_gsfTrack_pt - e_index_offset);
        get(dnn::ele_gsfTrack_pt_sig + fill_index_offset_e) =
            sp.scale(gsfTrack->pt() / gsfTrack->ptError(), dnn::ele_gsfTrack_pt_sig - e_index_offset);
      }
      const auto& closestCtfTrack = ele.closestCtfTrackRef();
      const bool has_closestCtfTrack = closestCtfTrack.isNonnull();
      if (has_closestCtfTrack) {
        get(dnn::ele_has_closestCtfTrack + fill_index_offset_e) =
            sp.scale(has_closestCtfTrack, dnn::ele_has_closestCtfTrack - e_index_offset);
        get(dnn::ele_closestCtfTrack_normalizedChi2 + fill_index_offset_e) =
            sp.scale(closestCtfTrack->normalizedChi2(), dnn::ele_closestCtfTrack_normalizedChi2 - e_index_offset);
        get(dnn::ele_closestCtfTrack_numberOfValidHits + fill_index_offset_e) =
            sp.scale(closestCtfTrack->numberOfValidHits(), dnn::ele_closestCtfTrack_numberOfValidHits - e_index_offset);
      }
    }
  }

createHadronsBlockInputs()

template<typename CandidateCastType , typename TauCastType >

void DeepTauId::createHadronsBlockInputs ( unsigned  idx, const TauCastType &  tau, const size_t  tau_index, const edm::RefToBase< reco::BaseTau >  tau_ref, const reco::Vertex &  pv, double  rho, const edm::View< reco::Candidate > &  pfCands, const Cell &  cell_map, TauFunc  tau_funcs, bool  is_inner  )

inlineprivate

Definition at line 2131 of file DeepTauId.cc.

References funct::abs(), edm::View< T >::at(), hltPixelTracks_cff::chi2, disable_hcalFraction_workaround_, HLT_2022v12_cff::dPhi, f, dqmMemoryStats::float, hadronsTensor_, heavyIonCSV_trainingSettings::idx, PixelMapPlotter::inputs, isInEcalCrack(), ndof, MetAnalyzer::pv(), rho, ZElectronSkim_cff::rho, scalingParamsMap_, metsig::tau, runTauDisplay::tau_eta, and runTauDisplay::tau_pt.

                                               {
    namespace dnn = dnn_inputs_v2::HadronBlockInputs;
    namespace sc = deep_tau::Scaling;
    sc::FeatureT ft_global = sc::FeatureT::GridGlobal;
    sc::FeatureT ft_PFchH = sc::FeatureT::PfCand_chHad;
    sc::FeatureT ft_PFnH = sc::FeatureT::PfCand_nHad;

    // needed to remap indices from scaling vectors to those from dnn_inputs_v2::HadronBlockInputs
    int PFchH_index_offset = scalingParamsMap_->at(std::make_pair(ft_global, false)).mean_.size();
    int PFnH_index_offset = PFchH_index_offset + scalingParamsMap_->at(std::make_pair(ft_PFchH, false)).mean_.size();

    tensorflow::Tensor& inputs = *hadronsTensor_.at(is_inner);

    const auto& get = [&](int var_index) -> float& { return inputs.tensor<float, 4>()(idx, 0, 0, var_index); };

    const bool valid_chH = cell_map.count(CellObjectType::PfCand_chargedHadron);
    const bool valid_nH = cell_map.count(CellObjectType::PfCand_neutralHadron);

    if (!cell_map.empty()) {
      const sc::ScalingParams& sp = scalingParamsMap_->at(std::make_pair(ft_global, false));
      get(dnn::rho) = sp.scale(rho, dnn::rho);
      get(dnn::tau_pt) = sp.scale(tau.polarP4().pt(), dnn::tau_pt);
      get(dnn::tau_eta) = sp.scale(tau.polarP4().eta(), dnn::tau_eta);
      get(dnn::tau_inside_ecal_crack) = sp.scale(isInEcalCrack(tau.polarP4().eta()), dnn::tau_inside_ecal_crack);
    }
    if (valid_chH) {
      const sc::ScalingParams& sp = scalingParamsMap_->at(std::make_pair(ft_PFchH, is_inner));
      size_t index_chH = cell_map.at(CellObjectType::PfCand_chargedHadron);
      const auto& chH_cand = dynamic_cast<const CandidateCastType&>(pfCands.at(index_chH));

      get(dnn::pfCand_chHad_valid) = sp.scale(valid_chH, dnn::pfCand_chHad_valid - PFchH_index_offset);
      get(dnn::pfCand_chHad_rel_pt) =
          sp.scale(chH_cand.polarP4().pt() / tau.polarP4().pt(), dnn::pfCand_chHad_rel_pt - PFchH_index_offset);
      get(dnn::pfCand_chHad_deta) =
          sp.scale(chH_cand.polarP4().eta() - tau.polarP4().eta(), dnn::pfCand_chHad_deta - PFchH_index_offset);
      get(dnn::pfCand_chHad_dphi) =
          sp.scale(dPhi(tau.polarP4(), chH_cand.polarP4()), dnn::pfCand_chHad_dphi - PFchH_index_offset);
      get(dnn::pfCand_chHad_leadChargedHadrCand) =
          sp.scale(&chH_cand == dynamic_cast<const CandidateCastType*>(tau.leadChargedHadrCand().get()),
                   dnn::pfCand_chHad_leadChargedHadrCand - PFchH_index_offset);
      get(dnn::pfCand_chHad_pvAssociationQuality) = sp.scale<int>(
          candFunc::getPvAssocationQuality(chH_cand), dnn::pfCand_chHad_pvAssociationQuality - PFchH_index_offset);
      get(dnn::pfCand_chHad_fromPV) =
          sp.scale<int>(candFunc::getFromPV(chH_cand), dnn::pfCand_chHad_fromPV - PFchH_index_offset);
      const float default_chH_pw_inner = 0.7614090f;
      const float default_chH_pw_outer = 0.1974930f;
      get(dnn::pfCand_chHad_puppiWeight) = is_inner ? sp.scale(candFunc::getPuppiWeight(chH_cand, default_chH_pw_inner),
                                                               dnn::pfCand_chHad_puppiWeight - PFchH_index_offset)
                                                    : sp.scale(candFunc::getPuppiWeight(chH_cand, default_chH_pw_outer),
                                                               dnn::pfCand_chHad_puppiWeight - PFchH_index_offset);
      get(dnn::pfCand_chHad_puppiWeightNoLep) =
          is_inner ? sp.scale(candFunc::getPuppiWeightNoLep(chH_cand, default_chH_pw_inner),
                              dnn::pfCand_chHad_puppiWeightNoLep - PFchH_index_offset)
                   : sp.scale(candFunc::getPuppiWeightNoLep(chH_cand, default_chH_pw_outer),
                              dnn::pfCand_chHad_puppiWeightNoLep - PFchH_index_offset);
      get(dnn::pfCand_chHad_charge) = sp.scale(chH_cand.charge(), dnn::pfCand_chHad_charge - PFchH_index_offset);
      get(dnn::pfCand_chHad_lostInnerHits) =
          sp.scale<int>(candFunc::getLostInnerHits(chH_cand, 0), dnn::pfCand_chHad_lostInnerHits - PFchH_index_offset);
      get(dnn::pfCand_chHad_numberOfPixelHits) = sp.scale(candFunc::getNumberOfPixelHits(chH_cand, 0),
                                                          dnn::pfCand_chHad_numberOfPixelHits - PFchH_index_offset);
      get(dnn::pfCand_chHad_vertex_dx) =
          sp.scale(chH_cand.vertex().x() - pv.position().x(), dnn::pfCand_chHad_vertex_dx - PFchH_index_offset);
      get(dnn::pfCand_chHad_vertex_dy) =
          sp.scale(chH_cand.vertex().y() - pv.position().y(), dnn::pfCand_chHad_vertex_dy - PFchH_index_offset);
      get(dnn::pfCand_chHad_vertex_dz) =
          sp.scale(chH_cand.vertex().z() - pv.position().z(), dnn::pfCand_chHad_vertex_dz - PFchH_index_offset);
      get(dnn::pfCand_chHad_vertex_dx_tauFL) =
          sp.scale(chH_cand.vertex().x() - pv.position().x() - tau_funcs.getFlightLength(tau, tau_index).x(),
                   dnn::pfCand_chHad_vertex_dx_tauFL - PFchH_index_offset);
      get(dnn::pfCand_chHad_vertex_dy_tauFL) =
          sp.scale(chH_cand.vertex().y() - pv.position().y() - tau_funcs.getFlightLength(tau, tau_index).y(),
                   dnn::pfCand_chHad_vertex_dy_tauFL - PFchH_index_offset);
      get(dnn::pfCand_chHad_vertex_dz_tauFL) =
          sp.scale(chH_cand.vertex().z() - pv.position().z() - tau_funcs.getFlightLength(tau, tau_index).z(),
                   dnn::pfCand_chHad_vertex_dz_tauFL - PFchH_index_offset);

      const bool hasTrackDetails = candFunc::getHasTrackDetails(chH_cand);
      if (hasTrackDetails) {
        get(dnn::pfCand_chHad_hasTrackDetails) =
            sp.scale(hasTrackDetails, dnn::pfCand_chHad_hasTrackDetails - PFchH_index_offset);
        get(dnn::pfCand_chHad_dxy) =
            sp.scale(candFunc::getTauDxy(chH_cand), dnn::pfCand_chHad_dxy - PFchH_index_offset);
        get(dnn::pfCand_chHad_dxy_sig) = sp.scale(std::abs(candFunc::getTauDxy(chH_cand)) / chH_cand.dxyError(),
                                                  dnn::pfCand_chHad_dxy_sig - PFchH_index_offset);
        get(dnn::pfCand_chHad_dz) = sp.scale(candFunc::getTauDz(chH_cand), dnn::pfCand_chHad_dz - PFchH_index_offset);
        get(dnn::pfCand_chHad_dz_sig) = sp.scale(std::abs(candFunc::getTauDz(chH_cand)) / chH_cand.dzError(),
                                                 dnn::pfCand_chHad_dz_sig - PFchH_index_offset);
        get(dnn::pfCand_chHad_track_chi2_ndof) =
            candFunc::getPseudoTrack(chH_cand).ndof() > 0
                ? sp.scale(candFunc::getPseudoTrack(chH_cand).chi2() / candFunc::getPseudoTrack(chH_cand).ndof(),
                           dnn::pfCand_chHad_track_chi2_ndof - PFchH_index_offset)
                : 0;
        get(dnn::pfCand_chHad_track_ndof) =
            candFunc::getPseudoTrack(chH_cand).ndof() > 0
                ? sp.scale(candFunc::getPseudoTrack(chH_cand).ndof(), dnn::pfCand_chHad_track_ndof - PFchH_index_offset)
                : 0;
      }
      float hcal_fraction = candFunc::getHCalFraction(chH_cand, disable_hcalFraction_workaround_);
      get(dnn::pfCand_chHad_hcalFraction) =
          sp.scale(hcal_fraction, dnn::pfCand_chHad_hcalFraction - PFchH_index_offset);
      get(dnn::pfCand_chHad_rawCaloFraction) =
          sp.scale(candFunc::getRawCaloFraction(chH_cand), dnn::pfCand_chHad_rawCaloFraction - PFchH_index_offset);
    }
    if (valid_nH) {
      const sc::ScalingParams& sp = scalingParamsMap_->at(std::make_pair(ft_PFnH, is_inner));
      size_t index_nH = cell_map.at(CellObjectType::PfCand_neutralHadron);
      const auto& nH_cand = dynamic_cast<const CandidateCastType&>(pfCands.at(index_nH));

      get(dnn::pfCand_nHad_valid) = sp.scale(valid_nH, dnn::pfCand_nHad_valid - PFnH_index_offset);
      get(dnn::pfCand_nHad_rel_pt) =
          sp.scale(nH_cand.polarP4().pt() / tau.polarP4().pt(), dnn::pfCand_nHad_rel_pt - PFnH_index_offset);
      get(dnn::pfCand_nHad_deta) =
          sp.scale(nH_cand.polarP4().eta() - tau.polarP4().eta(), dnn::pfCand_nHad_deta - PFnH_index_offset);
      get(dnn::pfCand_nHad_dphi) =
          sp.scale(dPhi(tau.polarP4(), nH_cand.polarP4()), dnn::pfCand_nHad_dphi - PFnH_index_offset);
      get(dnn::pfCand_nHad_puppiWeight) = is_inner ? sp.scale(candFunc::getPuppiWeight(nH_cand, 0.9798355f),
                                                              dnn::pfCand_nHad_puppiWeight - PFnH_index_offset)
                                                   : sp.scale(candFunc::getPuppiWeight(nH_cand, 0.7813260f),
                                                              dnn::pfCand_nHad_puppiWeight - PFnH_index_offset);
      get(dnn::pfCand_nHad_puppiWeightNoLep) = is_inner
                                                   ? sp.scale(candFunc::getPuppiWeightNoLep(nH_cand, 0.9046796f),
                                                              dnn::pfCand_nHad_puppiWeightNoLep - PFnH_index_offset)
                                                   : sp.scale(candFunc::getPuppiWeightNoLep(nH_cand, 0.6554860f),
                                                              dnn::pfCand_nHad_puppiWeightNoLep - PFnH_index_offset);
      float hcal_fraction = candFunc::getHCalFraction(nH_cand, disable_hcalFraction_workaround_);
      get(dnn::pfCand_nHad_hcalFraction) = sp.scale(hcal_fraction, dnn::pfCand_nHad_hcalFraction - PFnH_index_offset);
    }
  }

createMuonBlockInputs()

template<typename CandidateCastType , typename TauCastType >

void DeepTauId::createMuonBlockInputs ( unsigned  idx, const TauCastType &  tau, const size_t  tau_index, const edm::RefToBase< reco::BaseTau >  tau_ref, const reco::Vertex &  pv, double  rho, const std::vector< pat::Muon > *  muons, const edm::View< reco::Candidate > &  pfCands, const Cell &  cell_map, TauFunc  tau_funcs, bool  is_inner  )

inlineprivate

Definition at line 1980 of file DeepTauId.cc.

References funct::abs(), edm::View< T >::at(), hltPixelTracks_cff::chi2, MuonSubdetId::CSC, HLT_2022v12_cff::dPhi, MuonSubdetId::DT, f, dqmMemoryStats::float, heavyIonCSV_trainingSettings::idx, PixelMapPlotter::inputs, isInEcalCrack(), dumpRecoGeometry_cfg::Muon, PDWG_BPHSkim_cff::muons, muonTensor_, ndof, MetAnalyzer::pv(), pat::Muon::PV2D, rho, ZElectronSkim_cff::rho, MuonSubdetId::RPC, scalingParamsMap_, relativeConstraints::station, metsig::tau, runTauDisplay::tau_eta, and runTauDisplay::tau_pt.

                                            {
    namespace dnn = dnn_inputs_v2::MuonBlockInputs;
    namespace sc = deep_tau::Scaling;
    sc::FeatureT ft_global = sc::FeatureT::GridGlobal;
    sc::FeatureT ft_PFmu = sc::FeatureT::PfCand_muon;
    sc::FeatureT ft_mu = sc::FeatureT::Muon;

    // needed to remap indices from scaling vectors to those from dnn_inputs_v2::MuonBlockInputs
    int PFmu_index_offset = scalingParamsMap_->at(std::make_pair(ft_global, false)).mean_.size();
    int mu_index_offset = PFmu_index_offset + scalingParamsMap_->at(std::make_pair(ft_PFmu, false)).mean_.size();

    tensorflow::Tensor& inputs = *muonTensor_.at(is_inner);

    const auto& get = [&](int var_index) -> float& { return inputs.tensor<float, 4>()(idx, 0, 0, var_index); };

    const bool valid_index_pf_muon = cell_map.count(CellObjectType::PfCand_muon);
    const bool valid_index_muon = cell_map.count(CellObjectType::Muon);

    if (!cell_map.empty()) {
      const sc::ScalingParams& sp = scalingParamsMap_->at(std::make_pair(ft_global, false));
      get(dnn::rho) = sp.scale(rho, dnn::rho);
      get(dnn::tau_pt) = sp.scale(tau.polarP4().pt(), dnn::tau_pt);
      get(dnn::tau_eta) = sp.scale(tau.polarP4().eta(), dnn::tau_eta);
      get(dnn::tau_inside_ecal_crack) = sp.scale(isInEcalCrack(tau.polarP4().eta()), dnn::tau_inside_ecal_crack);
    }
    if (valid_index_pf_muon) {
      const sc::ScalingParams& sp = scalingParamsMap_->at(std::make_pair(ft_PFmu, is_inner));
      size_t index_pf_muon = cell_map.at(CellObjectType::PfCand_muon);
      const auto& muon_cand = dynamic_cast<const CandidateCastType&>(pfCands.at(index_pf_muon));

      get(dnn::pfCand_muon_valid) = sp.scale(valid_index_pf_muon, dnn::pfCand_muon_valid - PFmu_index_offset);
      get(dnn::pfCand_muon_rel_pt) =
          sp.scale(muon_cand.polarP4().pt() / tau.polarP4().pt(), dnn::pfCand_muon_rel_pt - PFmu_index_offset);
      get(dnn::pfCand_muon_deta) =
          sp.scale(muon_cand.polarP4().eta() - tau.polarP4().eta(), dnn::pfCand_muon_deta - PFmu_index_offset);
      get(dnn::pfCand_muon_dphi) =
          sp.scale(dPhi(tau.polarP4(), muon_cand.polarP4()), dnn::pfCand_muon_dphi - PFmu_index_offset);
      get(dnn::pfCand_muon_pvAssociationQuality) = sp.scale<int>(
          candFunc::getPvAssocationQuality(muon_cand), dnn::pfCand_muon_pvAssociationQuality - PFmu_index_offset);
      get(dnn::pfCand_muon_fromPV) =
          sp.scale<int>(candFunc::getFromPV(muon_cand), dnn::pfCand_muon_fromPV - PFmu_index_offset);
      get(dnn::pfCand_muon_puppiWeight) = is_inner ? sp.scale(candFunc::getPuppiWeight(muon_cand, 0.9786588f),
                                                              dnn::pfCand_muon_puppiWeight - PFmu_index_offset)
                                                   : sp.scale(candFunc::getPuppiWeight(muon_cand, 0.8132477f),
                                                              dnn::pfCand_muon_puppiWeight - PFmu_index_offset);
      get(dnn::pfCand_muon_charge) = sp.scale(muon_cand.charge(), dnn::pfCand_muon_charge - PFmu_index_offset);
      get(dnn::pfCand_muon_lostInnerHits) =
          sp.scale<int>(candFunc::getLostInnerHits(muon_cand, 0), dnn::pfCand_muon_lostInnerHits - PFmu_index_offset);
      get(dnn::pfCand_muon_numberOfPixelHits) = sp.scale(candFunc::getNumberOfPixelHits(muon_cand, 0),
                                                         dnn::pfCand_muon_numberOfPixelHits - PFmu_index_offset);
      get(dnn::pfCand_muon_vertex_dx) =
          sp.scale(muon_cand.vertex().x() - pv.position().x(), dnn::pfCand_muon_vertex_dx - PFmu_index_offset);
      get(dnn::pfCand_muon_vertex_dy) =
          sp.scale(muon_cand.vertex().y() - pv.position().y(), dnn::pfCand_muon_vertex_dy - PFmu_index_offset);
      get(dnn::pfCand_muon_vertex_dz) =
          sp.scale(muon_cand.vertex().z() - pv.position().z(), dnn::pfCand_muon_vertex_dz - PFmu_index_offset);
      get(dnn::pfCand_muon_vertex_dx_tauFL) =
          sp.scale(muon_cand.vertex().x() - pv.position().x() - tau_funcs.getFlightLength(tau, tau_index).x(),
                   dnn::pfCand_muon_vertex_dx_tauFL - PFmu_index_offset);
      get(dnn::pfCand_muon_vertex_dy_tauFL) =
          sp.scale(muon_cand.vertex().y() - pv.position().y() - tau_funcs.getFlightLength(tau, tau_index).y(),
                   dnn::pfCand_muon_vertex_dy_tauFL - PFmu_index_offset);
      get(dnn::pfCand_muon_vertex_dz_tauFL) =
          sp.scale(muon_cand.vertex().z() - pv.position().z() - tau_funcs.getFlightLength(tau, tau_index).z(),
                   dnn::pfCand_muon_vertex_dz_tauFL - PFmu_index_offset);

      const bool hasTrackDetails = candFunc::getHasTrackDetails(muon_cand);
      if (hasTrackDetails) {
        get(dnn::pfCand_muon_hasTrackDetails) =
            sp.scale(hasTrackDetails, dnn::pfCand_muon_hasTrackDetails - PFmu_index_offset);
        get(dnn::pfCand_muon_dxy) = sp.scale(candFunc::getTauDxy(muon_cand), dnn::pfCand_muon_dxy - PFmu_index_offset);
        get(dnn::pfCand_muon_dxy_sig) = sp.scale(std::abs(candFunc::getTauDxy(muon_cand)) / muon_cand.dxyError(),
                                                 dnn::pfCand_muon_dxy_sig - PFmu_index_offset);
        get(dnn::pfCand_muon_dz) = sp.scale(candFunc::getTauDz(muon_cand), dnn::pfCand_muon_dz - PFmu_index_offset);
        get(dnn::pfCand_muon_dz_sig) = sp.scale(std::abs(candFunc::getTauDz(muon_cand)) / muon_cand.dzError(),
                                                dnn::pfCand_muon_dz_sig - PFmu_index_offset);
        get(dnn::pfCand_muon_track_chi2_ndof) =
            candFunc::getPseudoTrack(muon_cand).ndof() > 0
                ? sp.scale(candFunc::getPseudoTrack(muon_cand).chi2() / candFunc::getPseudoTrack(muon_cand).ndof(),
                           dnn::pfCand_muon_track_chi2_ndof - PFmu_index_offset)
                : 0;
        get(dnn::pfCand_muon_track_ndof) =
            candFunc::getPseudoTrack(muon_cand).ndof() > 0
                ? sp.scale(candFunc::getPseudoTrack(muon_cand).ndof(), dnn::pfCand_muon_track_ndof - PFmu_index_offset)
                : 0;
      }
    }
    if (valid_index_muon) {
      const sc::ScalingParams& sp = scalingParamsMap_->at(std::make_pair(ft_mu, is_inner));
      size_t index_muon = cell_map.at(CellObjectType::Muon);
      const auto& muon = muons->at(index_muon);

      get(dnn::muon_valid) = sp.scale(valid_index_muon, dnn::muon_valid - mu_index_offset);
      get(dnn::muon_rel_pt) = sp.scale(muon.polarP4().pt() / tau.polarP4().pt(), dnn::muon_rel_pt - mu_index_offset);
      get(dnn::muon_deta) = sp.scale(muon.polarP4().eta() - tau.polarP4().eta(), dnn::muon_deta - mu_index_offset);
      get(dnn::muon_dphi) = sp.scale(dPhi(tau.polarP4(), muon.polarP4()), dnn::muon_dphi - mu_index_offset);
      get(dnn::muon_dxy) = sp.scale(muon.dB(pat::Muon::PV2D), dnn::muon_dxy - mu_index_offset);
      get(dnn::muon_dxy_sig) =
          sp.scale(std::abs(muon.dB(pat::Muon::PV2D)) / muon.edB(pat::Muon::PV2D), dnn::muon_dxy_sig - mu_index_offset);

      const bool normalizedChi2_valid = muon.globalTrack().isNonnull() && muon.normChi2() >= 0;
      if (normalizedChi2_valid) {
        get(dnn::muon_normalizedChi2_valid) =
            sp.scale(normalizedChi2_valid, dnn::muon_normalizedChi2_valid - mu_index_offset);
        get(dnn::muon_normalizedChi2) = sp.scale(muon.normChi2(), dnn::muon_normalizedChi2 - mu_index_offset);
        if (muon.innerTrack().isNonnull())
          get(dnn::muon_numberOfValidHits) =
              sp.scale(muon.numberOfValidHits(), dnn::muon_numberOfValidHits - mu_index_offset);
      }
      get(dnn::muon_segmentCompatibility) =
          sp.scale(muon.segmentCompatibility(), dnn::muon_segmentCompatibility - mu_index_offset);
      get(dnn::muon_caloCompatibility) =
          sp.scale(muon.caloCompatibility(), dnn::muon_caloCompatibility - mu_index_offset);

      const bool pfEcalEnergy_valid = muon.pfEcalEnergy() >= 0;
      if (pfEcalEnergy_valid) {
        get(dnn::muon_pfEcalEnergy_valid) =
            sp.scale(pfEcalEnergy_valid, dnn::muon_pfEcalEnergy_valid - mu_index_offset);
        get(dnn::muon_rel_pfEcalEnergy) =
            sp.scale(muon.pfEcalEnergy() / muon.polarP4().pt(), dnn::muon_rel_pfEcalEnergy - mu_index_offset);
      }

      MuonHitMatchV2 hit_match(muon);
      static const std::map<int, std::pair<int, int>> muonMatchHitVars = {
          {MuonSubdetId::DT, {dnn::muon_n_matches_DT_1, dnn::muon_n_hits_DT_1}},
          {MuonSubdetId::CSC, {dnn::muon_n_matches_CSC_1, dnn::muon_n_hits_CSC_1}},
          {MuonSubdetId::RPC, {dnn::muon_n_matches_RPC_1, dnn::muon_n_hits_RPC_1}}};

      for (int subdet : hit_match.MuonHitMatchV2::consideredSubdets()) {
        const auto& matchHitVar = muonMatchHitVars.at(subdet);
        for (int station = MuonHitMatchV2::first_station_id; station <= MuonHitMatchV2::last_station_id; ++station) {
          const unsigned n_matches = hit_match.nMatches(subdet, station);
          const unsigned n_hits = hit_match.nHits(subdet, station);
          get(matchHitVar.first + station - 1) = sp.scale(n_matches, matchHitVar.first + station - 1 - mu_index_offset);
          get(matchHitVar.second + station - 1) = sp.scale(n_hits, matchHitVar.second + station - 1 - mu_index_offset);
        }
      }
    }
  }

createTauBlockInputs()

template<typename CandidateCastType , typename TauCastType >

void DeepTauId::createTauBlockInputs ( const TauCastType &  tau, const size_t &  tau_index, const edm::RefToBase< reco::BaseTau >  tau_ref, const reco::Vertex &  pv, double  rho, TauFunc  tau_funcs  )

inlineprivate

Definition at line 1562 of file DeepTauId.cc.

References funct::abs(), calculateGottfriedJacksonAngleDifference(), disable_dxy_pca_, reco::tau::eratio(), getValueLinear(), PixelMapPlotter::inputs, deep_tau::DeepTauBase::is_online_, isAbove(), isInEcalCrack(), reco::tau::n_photons_total(), pi, reco::tau::pt_weighted_deta_strip(), reco::tau::pt_weighted_dphi_strip(), reco::tau::pt_weighted_dr_iso(), reco::tau::pt_weighted_dr_signal(), rho, ZElectronSkim_cff::rho, scalingParamsMap_, sub_version_, metsig::tau, runTauDisplay::tau_eta, runTauDisplay::tau_mass, runTauDisplay::tau_phi, runTauDisplay::tau_pt, tauBlockTensor_, and tauInputs_indices_.

                                               {
    namespace dnn = dnn_inputs_v2::TauBlockInputs;
    namespace sc = deep_tau::Scaling;
    sc::FeatureT ft = sc::FeatureT::TauFlat;
    const sc::ScalingParams& sp = scalingParamsMap_->at(std::make_pair(ft, false));

    tensorflow::Tensor& inputs = *tauBlockTensor_;
    inputs.flat<float>().setZero();

    const auto& get = [&](int var_index) -> float& {
      return inputs.matrix<float>()(0, tauInputs_indices_.at(var_index));
    };

    auto leadChargedHadrCand = dynamic_cast<const CandidateCastType*>(tau.leadChargedHadrCand().get());

    get(dnn::rho) = sp.scale(rho, tauInputs_indices_[dnn::rho]);
    get(dnn::tau_pt) = sp.scale(tau.polarP4().pt(), tauInputs_indices_[dnn::tau_pt]);
    get(dnn::tau_eta) = sp.scale(tau.polarP4().eta(), tauInputs_indices_[dnn::tau_eta]);
    if (sub_version_ == 1) {
      get(dnn::tau_phi) = getValueLinear(tau.polarP4().phi(), -pi, pi, false);
    }
    get(dnn::tau_mass) = sp.scale(tau.polarP4().mass(), tauInputs_indices_[dnn::tau_mass]);
    get(dnn::tau_E_over_pt) = sp.scale(tau.p4().energy() / tau.p4().pt(), tauInputs_indices_[dnn::tau_E_over_pt]);
    get(dnn::tau_charge) = sp.scale(tau.charge(), tauInputs_indices_[dnn::tau_charge]);
    get(dnn::tau_n_charged_prongs) = sp.scale(tau.decayMode() / 5 + 1, tauInputs_indices_[dnn::tau_n_charged_prongs]);
    get(dnn::tau_n_neutral_prongs) = sp.scale(tau.decayMode() % 5, tauInputs_indices_[dnn::tau_n_neutral_prongs]);
    get(dnn::chargedIsoPtSum) =
        sp.scale(tau_funcs.getChargedIsoPtSum(tau, tau_ref), tauInputs_indices_[dnn::chargedIsoPtSum]);
    get(dnn::chargedIsoPtSumdR03_over_dR05) =
        sp.scale(tau_funcs.getChargedIsoPtSumdR03(tau, tau_ref) / tau_funcs.getChargedIsoPtSum(tau, tau_ref),
                 tauInputs_indices_[dnn::chargedIsoPtSumdR03_over_dR05]);
    if (sub_version_ == 1)
      get(dnn::footprintCorrection) =
          sp.scale(tau_funcs.getFootprintCorrectiondR03(tau, tau_ref), tauInputs_indices_[dnn::footprintCorrection]);
    else if (sub_version_ == 5)
      get(dnn::footprintCorrection) =
          sp.scale(tau_funcs.getFootprintCorrection(tau, tau_ref), tauInputs_indices_[dnn::footprintCorrection]);

    get(dnn::neutralIsoPtSum) =
        sp.scale(tau_funcs.getNeutralIsoPtSum(tau, tau_ref), tauInputs_indices_[dnn::neutralIsoPtSum]);
    get(dnn::neutralIsoPtSumWeight_over_neutralIsoPtSum) =
        sp.scale(tau_funcs.getNeutralIsoPtSumWeight(tau, tau_ref) / tau_funcs.getNeutralIsoPtSum(tau, tau_ref),
                 tauInputs_indices_[dnn::neutralIsoPtSumWeight_over_neutralIsoPtSum]);
    get(dnn::neutralIsoPtSumWeightdR03_over_neutralIsoPtSum) =
        sp.scale(tau_funcs.getNeutralIsoPtSumdR03Weight(tau, tau_ref) / tau_funcs.getNeutralIsoPtSum(tau, tau_ref),
                 tauInputs_indices_[dnn::neutralIsoPtSumWeightdR03_over_neutralIsoPtSum]);
    get(dnn::neutralIsoPtSumdR03_over_dR05) =
        sp.scale(tau_funcs.getNeutralIsoPtSumdR03(tau, tau_ref) / tau_funcs.getNeutralIsoPtSum(tau, tau_ref),
                 tauInputs_indices_[dnn::neutralIsoPtSumdR03_over_dR05]);
    get(dnn::photonPtSumOutsideSignalCone) = sp.scale(tau_funcs.getPhotonPtSumOutsideSignalCone(tau, tau_ref),
                                                      tauInputs_indices_[dnn::photonPtSumOutsideSignalCone]);
    get(dnn::puCorrPtSum) = sp.scale(tau_funcs.getPuCorrPtSum(tau, tau_ref), tauInputs_indices_[dnn::puCorrPtSum]);
    // The global PCA coordinates were used as inputs during the NN training, but it was decided to disable
    // them for the inference, because modeling of dxy_PCA in MC poorly describes the data, and x and y coordinates
    // in data results outside of the expected 5 std. dev. input validity range. On the other hand,
    // these coordinates are strongly era-dependent. Kept as comment to document what NN expects.
    if (sub_version_ == 1) {
      if (!disable_dxy_pca_) {
        auto const pca = tau_funcs.getdxyPCA(tau, tau_index);
        get(dnn::tau_dxy_pca_x) = sp.scale(pca.x(), tauInputs_indices_[dnn::tau_dxy_pca_x]);
        get(dnn::tau_dxy_pca_y) = sp.scale(pca.y(), tauInputs_indices_[dnn::tau_dxy_pca_y]);
        get(dnn::tau_dxy_pca_z) = sp.scale(pca.z(), tauInputs_indices_[dnn::tau_dxy_pca_z]);
      } else {
        get(dnn::tau_dxy_pca_x) = 0;
        get(dnn::tau_dxy_pca_y) = 0;
        get(dnn::tau_dxy_pca_z) = 0;
      }
    }

    const bool tau_dxy_valid =
        isAbove(tau_funcs.getdxy(tau, tau_index), -10) && isAbove(tau_funcs.getdxyError(tau, tau_index), 0);
    if (tau_dxy_valid) {
      get(dnn::tau_dxy_valid) = sp.scale(tau_dxy_valid, tauInputs_indices_[dnn::tau_dxy_valid]);
      get(dnn::tau_dxy) = sp.scale(tau_funcs.getdxy(tau, tau_index), tauInputs_indices_[dnn::tau_dxy]);
      get(dnn::tau_dxy_sig) =
          sp.scale(std::abs(tau_funcs.getdxy(tau, tau_index)) / tau_funcs.getdxyError(tau, tau_index),
                   tauInputs_indices_[dnn::tau_dxy_sig]);
    }
    const bool tau_ip3d_valid =
        isAbove(tau_funcs.getip3d(tau, tau_index), -10) && isAbove(tau_funcs.getip3dError(tau, tau_index), 0);
    if (tau_ip3d_valid) {
      get(dnn::tau_ip3d_valid) = sp.scale(tau_ip3d_valid, tauInputs_indices_[dnn::tau_ip3d_valid]);
      get(dnn::tau_ip3d) = sp.scale(tau_funcs.getip3d(tau, tau_index), tauInputs_indices_[dnn::tau_ip3d]);
      get(dnn::tau_ip3d_sig) =
          sp.scale(std::abs(tau_funcs.getip3d(tau, tau_index)) / tau_funcs.getip3dError(tau, tau_index),
                   tauInputs_indices_[dnn::tau_ip3d_sig]);
    }
    if (leadChargedHadrCand) {
      const bool hasTrackDetails = candFunc::getHasTrackDetails(*leadChargedHadrCand);
      const float tau_dz = (is_online_ && !hasTrackDetails) ? 0 : candFunc::getTauDz(*leadChargedHadrCand);
      get(dnn::tau_dz) = sp.scale(tau_dz, tauInputs_indices_[dnn::tau_dz]);
      get(dnn::tau_dz_sig_valid) =
          sp.scale(candFunc::getTauDZSigValid(*leadChargedHadrCand), tauInputs_indices_[dnn::tau_dz_sig_valid]);
      const double dzError = hasTrackDetails ? leadChargedHadrCand->dzError() : -999.;
      get(dnn::tau_dz_sig) = sp.scale(std::abs(tau_dz) / dzError, tauInputs_indices_[dnn::tau_dz_sig]);
    }
    get(dnn::tau_flightLength_x) =
        sp.scale(tau_funcs.getFlightLength(tau, tau_index).x(), tauInputs_indices_[dnn::tau_flightLength_x]);
    get(dnn::tau_flightLength_y) =
        sp.scale(tau_funcs.getFlightLength(tau, tau_index).y(), tauInputs_indices_[dnn::tau_flightLength_y]);
    get(dnn::tau_flightLength_z) =
        sp.scale(tau_funcs.getFlightLength(tau, tau_index).z(), tauInputs_indices_[dnn::tau_flightLength_z]);
    if (sub_version_ == 1)
      get(dnn::tau_flightLength_sig) = 0.55756444;  //This value is set due to a bug in the training
    else if (sub_version_ == 5)
      get(dnn::tau_flightLength_sig) =
          sp.scale(tau_funcs.getFlightLengthSig(tau, tau_index), tauInputs_indices_[dnn::tau_flightLength_sig]);

    get(dnn::tau_pt_weighted_deta_strip) = sp.scale(reco::tau::pt_weighted_deta_strip(tau, tau.decayMode()),
                                                    tauInputs_indices_[dnn::tau_pt_weighted_deta_strip]);

    get(dnn::tau_pt_weighted_dphi_strip) = sp.scale(reco::tau::pt_weighted_dphi_strip(tau, tau.decayMode()),
                                                    tauInputs_indices_[dnn::tau_pt_weighted_dphi_strip]);
    get(dnn::tau_pt_weighted_dr_signal) = sp.scale(reco::tau::pt_weighted_dr_signal(tau, tau.decayMode()),
                                                   tauInputs_indices_[dnn::tau_pt_weighted_dr_signal]);
    get(dnn::tau_pt_weighted_dr_iso) =
        sp.scale(reco::tau::pt_weighted_dr_iso(tau, tau.decayMode()), tauInputs_indices_[dnn::tau_pt_weighted_dr_iso]);
    get(dnn::tau_leadingTrackNormChi2) =
        sp.scale(tau_funcs.getLeadingTrackNormChi2(tau), tauInputs_indices_[dnn::tau_leadingTrackNormChi2]);
    const auto eratio = reco::tau::eratio(tau);
    const bool tau_e_ratio_valid = std::isnormal(eratio) && eratio > 0.f;
    get(dnn::tau_e_ratio_valid) = sp.scale(tau_e_ratio_valid, tauInputs_indices_[dnn::tau_e_ratio_valid]);
    get(dnn::tau_e_ratio) = tau_e_ratio_valid ? sp.scale(eratio, tauInputs_indices_[dnn::tau_e_ratio]) : 0.f;
    const double gj_angle_diff = calculateGottfriedJacksonAngleDifference(tau, tau_index, tau_funcs);
    const bool tau_gj_angle_diff_valid = (std::isnormal(gj_angle_diff) || gj_angle_diff == 0) && gj_angle_diff >= 0;
    get(dnn::tau_gj_angle_diff_valid) =
        sp.scale(tau_gj_angle_diff_valid, tauInputs_indices_[dnn::tau_gj_angle_diff_valid]);
    get(dnn::tau_gj_angle_diff) =
        tau_gj_angle_diff_valid ? sp.scale(gj_angle_diff, tauInputs_indices_[dnn::tau_gj_angle_diff]) : 0;
    get(dnn::tau_n_photons) = sp.scale(reco::tau::n_photons_total(tau), tauInputs_indices_[dnn::tau_n_photons]);
    get(dnn::tau_emFraction) = sp.scale(tau_funcs.getEmFraction(tau), tauInputs_indices_[dnn::tau_emFraction]);

    get(dnn::tau_inside_ecal_crack) =
        sp.scale(isInEcalCrack(tau.p4().eta()), tauInputs_indices_[dnn::tau_inside_ecal_crack]);
    get(dnn::leadChargedCand_etaAtEcalEntrance_minus_tau_eta) =
        sp.scale(tau_funcs.getEtaAtEcalEntrance(tau) - tau.p4().eta(),
                 tauInputs_indices_[dnn::leadChargedCand_etaAtEcalEntrance_minus_tau_eta]);
  }

fillDescriptions()

static void DeepTauId::fillDescriptions ( edm::ConfigurationDescriptions &  descriptions )

inlinestatic

Definition at line 873 of file DeepTauId.cc.

References edm::ConfigurationDescriptions::add(), edm::ParameterSetDescription::add(), edm::ParameterSetDescription::addOptional(), submitPVResolutionJobs::desc, HLT_2022v12_cff::InputTag, and AlCaHLTBitMon_QueryRunRegistry::string.

                                                                           {
    edm::ParameterSetDescription desc;
    desc.add<edm::InputTag>("electrons", edm::InputTag("slimmedElectrons"));
    desc.add<edm::InputTag>("muons", edm::InputTag("slimmedMuons"));
    desc.add<edm::InputTag>("taus", edm::InputTag("slimmedTaus"));
    desc.add<edm::InputTag>("pfcands", edm::InputTag("packedPFCandidates"));
    desc.add<edm::InputTag>("vertices", edm::InputTag("offlineSlimmedPrimaryVertices"));
    desc.add<edm::InputTag>("rho", edm::InputTag("fixedGridRhoAll"));
    desc.add<std::vector<std::string>>("graph_file",
                                       {"RecoTauTag/TrainingFiles/data/DeepTauId/deepTau_2017v2p6_e6.pb"});
    desc.add<bool>("mem_mapped", false);
    desc.add<unsigned>("version", 2);
    desc.add<unsigned>("sub_version", 1);
    desc.add<int>("debug_level", 0);
    desc.add<bool>("disable_dxy_pca", false);
    desc.add<bool>("disable_hcalFraction_workaround", false);
    desc.add<bool>("disable_CellIndex_workaround", false);
    desc.add<bool>("save_inputs", false);
    desc.add<bool>("is_online", false);

    desc.add<std::vector<std::string>>("VSeWP", {"-1."});
    desc.add<std::vector<std::string>>("VSmuWP", {"-1."});
    desc.add<std::vector<std::string>>("VSjetWP", {"-1."});

    desc.addUntracked<edm::InputTag>("basicTauDiscriminators", edm::InputTag("basicTauDiscriminators"));
    desc.addUntracked<edm::InputTag>("basicTauDiscriminatorsdR03", edm::InputTag("basicTauDiscriminatorsdR03"));
    desc.add<edm::InputTag>("pfTauTransverseImpactParameters", edm::InputTag("hpsPFTauTransverseImpactParameters"));

    {
      edm::ParameterSetDescription pset_Prediscriminants;
      pset_Prediscriminants.add<std::string>("BooleanOperator", "and");
      {
        edm::ParameterSetDescription psd1;
        psd1.add<double>("cut");
        psd1.add<edm::InputTag>("Producer");
        pset_Prediscriminants.addOptional<edm::ParameterSetDescription>("decayMode", psd1);
      }
      desc.add<edm::ParameterSetDescription>("Prediscriminants", pset_Prediscriminants);
    }

    descriptions.add("DeepTau", desc);
  }

fillGrids()

template<typename Collection , typename TauCastType >

void DeepTauId::fillGrids ( const TauCastType &  tau, const Collection &  objects, CellGrid &  inner_grid, CellGrid &  outer_grid  )

inlineprivate

Definition at line 1409 of file DeepTauId.cc.

References reco::deltaPhi(), getInnerSignalConeRadius(), getRunAppsInfo::grid, dqmiodumpmetadata::n, getGTfromDQMFile::obj, funct::pow(), and metsig::tau.

Referenced by getPredictionsV2().

                                                                                                                {
    static constexpr double outer_dR2 = 0.25;  //0.5^2
    const double inner_radius = getInnerSignalConeRadius(tau.polarP4().pt());
    const double inner_dR2 = std::pow(inner_radius, 2);

    const auto addObject = [&](size_t n, double deta, double dphi, CellGrid& grid) {
      const auto& obj = objects.at(n);
      const CellObjectType obj_type = GetCellObjectType(obj);
      if (obj_type == CellObjectType::Other)
        return;
      CellIndex cell_index;
      if (grid.tryGetCellIndex(deta, dphi, cell_index)) {
        Cell& cell = grid[cell_index];
        auto iter = cell.find(obj_type);
        if (iter != cell.end()) {
          const auto& prev_obj = objects.at(iter->second);
          if (obj.polarP4().pt() > prev_obj.polarP4().pt())
            iter->second = n;
        } else {
          cell[obj_type] = n;
        }
      }
    };

    for (size_t n = 0; n < objects.size(); ++n) {
      const auto& obj = objects.at(n);
      const double deta = obj.polarP4().eta() - tau.polarP4().eta();
      const double dphi = reco::deltaPhi(obj.polarP4().phi(), tau.polarP4().phi());
      const double dR2 = std::pow(deta, 2) + std::pow(dphi, 2);
      if (dR2 < inner_dR2)
        addObject(n, deta, dphi, inner_grid);
      if (dR2 < outer_dR2)
        addObject(n, deta, dphi, outer_grid);
    }
  }

findMatchedElectron()

template<typename TauCastType >

static const pat::Electron* DeepTauId::findMatchedElectron ( const TauCastType &  tau, const std::vector< pat::Electron > *  electrons, double  deltaR  )

inlinestaticprivate

Definition at line 2400 of file DeepTauId.cc.

References PbPb_ZMuSkimMuonDPG_cff::deltaR, reco::deltaR2(), pwdgSkimBPark_cfi::electrons, reco::LeafCandidate::pt(), and metsig::tau.

                                                                 {
    const double dR2 = deltaR * deltaR;
    const pat::Electron* matched_ele = nullptr;
    for (const auto& ele : *electrons) {
      if (reco::deltaR2(tau.p4(), ele.p4()) < dR2 && (!matched_ele || matched_ele->pt() < ele.pt())) {
        matched_ele = &ele;
      }
    }
    return matched_ele;
  }

getInnerSignalConeRadius()

static double DeepTauId::getInnerSignalConeRadius ( double  pt )

inlinestaticprivate

Definition at line 2355 of file DeepTauId.cc.

References SiStripPI::max, cosmictrackSelector_cfi::min_pt, and DiDispStaMuonMonitor_cfi::pt.

Referenced by fillGrids(), and processSignalPFComponents().

                                                    {
    static constexpr double min_pt = 30., min_radius = 0.05, cone_opening_coef = 3.;
    // This is equivalent of the original formula (std::max(std::min(0.1, 3.0/pt), 0.05)
    return std::max(cone_opening_coef / std::max(pt, min_pt), min_radius);
  }

GetOutputs()

static const OutputCollection& DeepTauId::GetOutputs ( )

inlinestatic

Definition at line 831 of file DeepTauId.cc.

References GetRecoTauVFromDQM_MC_cff::Output, and deep_tau::DeepTauBase::outputs_.

                                              {
    static constexpr size_t e_index = 0, mu_index = 1, tau_index = 2, jet_index = 3;
    static const OutputCollection outputs_ = {
        {"VSe", Output({tau_index}, {e_index, tau_index})},
        {"VSmu", Output({tau_index}, {mu_index, tau_index})},
        {"VSjet", Output({tau_index}, {jet_index, tau_index})},
    };
    return outputs_;
  }

getPartialPredictions()

tensorflow::Tensor DeepTauId::getPartialPredictions ( bool  is_inner )

inlineprivate

Definition at line 1445 of file DeepTauId.cc.

References deep_tau::DeepTauBase::cache_, eGammaTensor_, deep_tau::DeepTauCache::getSession(), hadronsTensor_, muonTensor_, and tensorflow::run().

Referenced by createConvFeatures(), and DeepTauId().

                                                        {
    std::vector<tensorflow::Tensor> pred_vector;
    if (is_inner) {
      tensorflow::run(&(cache_->getSession("inner")),
                      {
                          {"input_inner_egamma", *eGammaTensor_.at(is_inner)},
                          {"input_inner_muon", *muonTensor_.at(is_inner)},
                          {"input_inner_hadrons", *hadronsTensor_.at(is_inner)},
                      },
                      {"inner_all_dropout_4/Identity"},
                      &pred_vector);
    } else {
      tensorflow::run(&(cache_->getSession("outer")),
                      {
                          {"input_outer_egamma", *eGammaTensor_.at(is_inner)},
                          {"input_outer_muon", *muonTensor_.at(is_inner)},
                          {"input_outer_hadrons", *hadronsTensor_.at(is_inner)},
                      },
                      {"outer_all_dropout_4/Identity"},
                      &pred_vector);
    }
    return pred_vector.at(0);
  }

getPredictions()

tensorflow::Tensor DeepTauId::getPredictions ( edm::Event &  event, edm::Handle< TauCollection >  taus  )

inlineoverrideprivatevirtual

Implements deep_tau::DeepTauBase.

Definition at line 1185 of file DeepTauId.cc.

References deep_tau::DeepTauBase::andPrediscriminants_, basicDiscrdR03IndexMap_, basicDiscrIndexMap_, HLT_2022v12_cff::basicTauDiscriminators, basicTauDiscriminators_inputToken_, HLT_2022v12_cff::basicTauDiscriminatorsdR03, basicTauDiscriminatorsdR03_inputToken_, discrIndicesMapped_, electrons_token_, Exception, f, deep_tau::DeepTauBase::is_online_, dqmdumpme::k, matchDiscriminatorIndices(), muons_token_, deep_tau::NumberOfOutputs, deep_tau::DeepTauBase::patPrediscriminants_, deep_tau::DeepTauBase::pfcandToken_, HLT_2022v12_cff::pfTauTransverseImpactParameters, pfTauTransverseImpactParameters_token_, deep_tau::DeepTauBase::recoPrediscriminants_, deep_tau::DeepTauBase::requiredBasicDiscriminators_, deep_tau::DeepTauBase::requiredBasicDiscriminatorsdR03_, rho, rho_token_, Tau3MuMonitor_cff::taus, version_, AlignmentTracksFromVertexSelector_cfi::vertices, and deep_tau::DeepTauBase::vtxToken_.

                                                                                           {
    // Empty dummy vectors
    const std::vector<pat::Electron> electron_collection_default;
    const std::vector<pat::Muon> muon_collection_default;
    const reco::TauDiscriminatorContainer basicTauDiscriminators_default;
    const reco::TauDiscriminatorContainer basicTauDiscriminatorsdR03_default;
    const edm::AssociationVector<reco::PFTauRefProd, std::vector<reco::PFTauTransverseImpactParameterRef>>
        pfTauTransverseImpactParameters_default;

    const std::vector<pat::Electron>* electron_collection;
    const std::vector<pat::Muon>* muon_collection;
    const reco::TauDiscriminatorContainer* basicTauDiscriminators;
    const reco::TauDiscriminatorContainer* basicTauDiscriminatorsdR03;
    const edm::AssociationVector<reco::PFTauRefProd, std::vector<reco::PFTauTransverseImpactParameterRef>>*
        pfTauTransverseImpactParameters;

    if (!is_online_) {
      electron_collection = &event.get(electrons_token_);
      muon_collection = &event.get(muons_token_);
      pfTauTransverseImpactParameters = &pfTauTransverseImpactParameters_default;
      basicTauDiscriminators = &basicTauDiscriminators_default;
      basicTauDiscriminatorsdR03 = &basicTauDiscriminatorsdR03_default;
    } else {
      electron_collection = &electron_collection_default;
      muon_collection = &muon_collection_default;
      pfTauTransverseImpactParameters = &event.get(pfTauTransverseImpactParameters_token_);
      basicTauDiscriminators = &event.get(basicTauDiscriminators_inputToken_);
      basicTauDiscriminatorsdR03 = &event.get(basicTauDiscriminatorsdR03_inputToken_);

      // Get indices for discriminators
      if (!discrIndicesMapped_) {
        basicDiscrIndexMap_ =
            matchDiscriminatorIndices(event, basicTauDiscriminators_inputToken_, requiredBasicDiscriminators_);
        basicDiscrdR03IndexMap_ =
            matchDiscriminatorIndices(event, basicTauDiscriminatorsdR03_inputToken_, requiredBasicDiscriminatorsdR03_);
        discrIndicesMapped_ = true;
      }
    }

    TauFunc tauIDs = {basicTauDiscriminators,
                      basicTauDiscriminatorsdR03,
                      pfTauTransverseImpactParameters,
                      basicDiscrIndexMap_,
                      basicDiscrdR03IndexMap_};

    edm::Handle<edm::View<reco::Candidate>> pfCands;
    event.getByToken(pfcandToken_, pfCands);

    edm::Handle<reco::VertexCollection> vertices;
    event.getByToken(vtxToken_, vertices);

    edm::Handle<double> rho;
    event.getByToken(rho_token_, rho);

    tensorflow::Tensor predictions(tensorflow::DT_FLOAT, {static_cast<int>(taus->size()), deep_tau::NumberOfOutputs});

    for (size_t tau_index = 0; tau_index < taus->size(); ++tau_index) {
      const edm::RefToBase<reco::BaseTau> tauRef = taus->refAt(tau_index);

      std::vector<tensorflow::Tensor> pred_vector;

      bool passesPrediscriminants;
      if (is_online_) {
        passesPrediscriminants = tauIDs.passPrediscriminants<std::vector<TauDiscInfo<reco::PFTauDiscriminator>>>(
            recoPrediscriminants_, andPrediscriminants_, tauRef);
      } else {
        passesPrediscriminants = tauIDs.passPrediscriminants<std::vector<TauDiscInfo<pat::PATTauDiscriminator>>>(
            patPrediscriminants_, andPrediscriminants_, tauRef);
      }

      if (passesPrediscriminants) {
        if (version_ == 2) {
          if (is_online_) {
            getPredictionsV2<reco::PFCandidate, reco::PFTau>(taus->at(tau_index),
                                                             tau_index,
                                                             tauRef,
                                                             electron_collection,
                                                             muon_collection,
                                                             *pfCands,
                                                             vertices->at(0),
                                                             *rho,
                                                             pred_vector,
                                                             tauIDs);
          } else
            getPredictionsV2<pat::PackedCandidate, pat::Tau>(taus->at(tau_index),
                                                             tau_index,
                                                             tauRef,
                                                             electron_collection,
                                                             muon_collection,
                                                             *pfCands,
                                                             vertices->at(0),
                                                             *rho,
                                                             pred_vector,
                                                             tauIDs);
        } else {
          throw cms::Exception("DeepTauId") << "version " << version_ << " is not supported.";
        }

        for (int k = 0; k < deep_tau::NumberOfOutputs; ++k) {
          const float pred = pred_vector[0].flat<float>()(k);
          if (!(pred >= 0 && pred <= 1))
            throw cms::Exception("DeepTauId")
                << "invalid prediction = " << pred << " for tau_index = " << tau_index << ", pred_index = " << k;
          predictions.matrix<float>()(tau_index, k) = pred;
        }
      } else {
        // This else statement was added as a part of the DeepTau@HLT development. It does not affect the current state
        // of offline DeepTauId code as there the preselection is not used (it was added in the DeepTau@HLT). It returns
        // default values for deepTau score if the preselection failed. Before this statement the values given for this tau
        // were random. k == 2 corresponds to the tau score and all other k values to e, mu and jets. By defining in this way
        // the final score is -1.
        for (int k = 0; k < deep_tau::NumberOfOutputs; ++k) {
          predictions.matrix<float>()(tau_index, k) = (k == 2) ? -1.f : 2.f;
        }
      }
    }
    return predictions;
  }

getPredictionsV2()

template<typename CandidateCastType , typename TauCastType >

void DeepTauId::getPredictionsV2 ( TauCollection::const_reference &  tau, const size_t  tau_index, const edm::RefToBase< reco::BaseTau >  tau_ref, const std::vector< pat::Electron > *  electrons, const std::vector< pat::Muon > *  muons, const edm::View< reco::Candidate > &  pfCands, const reco::Vertex &  pv, double  rho, std::vector< tensorflow::Tensor > &  pred_vector, TauFunc  tau_funcs  )

inlineprivate

Definition at line 1305 of file DeepTauId.cc.

References cms::cuda::assert(), deep_tau::DeepTauBase::cache_, checkInputs(), convTensor_, gather_cfg::cout, debug_level, disable_CellIndex_workaround_, eGammaTensor_, pwdgSkimBPark_cfi::electrons, file_counter_, fillGrids(), deep_tau::DeepTauCache::getSession(), hadronsTensor_, heavyIonCSV_trainingSettings::idx, is_first_block_, json_file_, label, PDWG_BPHSkim_cff::muons, muonTensor_, deep_tau::NumberOfOutputs, MetAnalyzer::pv(), rho, tensorflow::run(), save_inputs_, saveInputs(), AlCaHLTBitMon_QueryRunRegistry::string, metsig::tau, tauBlockTensor_, and cond::impl::to_string().

                                           {
    using namespace dnn_inputs_v2;
    if (debug_level >= 2) {
      std::cout << "<DeepTauId::getPredictionsV2 (moduleLabel = " << moduleDescription().moduleLabel()
                << ")>:" << std::endl;
      std::cout << " tau: pT = " << tau.pt() << ", eta = " << tau.eta() << ", phi = " << tau.phi() << std::endl;
    }
    CellGrid inner_grid(number_of_inner_cell, number_of_inner_cell, 0.02, 0.02, disable_CellIndex_workaround_);
    CellGrid outer_grid(number_of_outer_cell, number_of_outer_cell, 0.05, 0.05, disable_CellIndex_workaround_);
    fillGrids(dynamic_cast<const TauCastType&>(tau), *electrons, inner_grid, outer_grid);
    fillGrids(dynamic_cast<const TauCastType&>(tau), *muons, inner_grid, outer_grid);
    fillGrids(dynamic_cast<const TauCastType&>(tau), pfCands, inner_grid, outer_grid);

    createTauBlockInputs<CandidateCastType>(
        dynamic_cast<const TauCastType&>(tau), tau_index, tau_ref, pv, rho, tau_funcs);
    checkInputs(*tauBlockTensor_, "input_tau", static_cast<int>(tauBlockTensor_->shape().dim_size(1)));
    createConvFeatures<CandidateCastType>(dynamic_cast<const TauCastType&>(tau),
                                          tau_index,
                                          tau_ref,
                                          pv,
                                          rho,
                                          electrons,
                                          muons,
                                          pfCands,
                                          inner_grid,
                                          tau_funcs,
                                          true);
    checkInputs(*eGammaTensor_[true], "input_inner_egamma", EgammaBlockInputs::NumberOfInputs, &inner_grid);
    checkInputs(*muonTensor_[true], "input_inner_muon", MuonBlockInputs::NumberOfInputs, &inner_grid);
    checkInputs(*hadronsTensor_[true], "input_inner_hadrons", HadronBlockInputs::NumberOfInputs, &inner_grid);
    createConvFeatures<CandidateCastType>(dynamic_cast<const TauCastType&>(tau),
                                          tau_index,
                                          tau_ref,
                                          pv,
                                          rho,
                                          electrons,
                                          muons,
                                          pfCands,
                                          outer_grid,
                                          tau_funcs,
                                          false);
    checkInputs(*eGammaTensor_[false], "input_outer_egamma", EgammaBlockInputs::NumberOfInputs, &outer_grid);
    checkInputs(*muonTensor_[false], "input_outer_muon", MuonBlockInputs::NumberOfInputs, &outer_grid);
    checkInputs(*hadronsTensor_[false], "input_outer_hadrons", HadronBlockInputs::NumberOfInputs, &outer_grid);

    if (save_inputs_) {
      std::string json_file_name = "DeepTauId_" + std::to_string(file_counter_) + ".json";
      json_file_ = new std::ofstream(json_file_name.data());
      is_first_block_ = true;
      (*json_file_) << "{";
      saveInputs(*tauBlockTensor_, "input_tau", static_cast<int>(tauBlockTensor_->shape().dim_size(1)));
      saveInputs(
          *eGammaTensor_[true], "input_inner_egamma", dnn_inputs_v2::EgammaBlockInputs::NumberOfInputs, &inner_grid);
      saveInputs(*muonTensor_[true], "input_inner_muon", dnn_inputs_v2::MuonBlockInputs::NumberOfInputs, &inner_grid);
      saveInputs(
          *hadronsTensor_[true], "input_inner_hadrons", dnn_inputs_v2::HadronBlockInputs::NumberOfInputs, &inner_grid);
      saveInputs(
          *eGammaTensor_[false], "input_outer_egamma", dnn_inputs_v2::EgammaBlockInputs::NumberOfInputs, &outer_grid);
      saveInputs(*muonTensor_[false], "input_outer_muon", dnn_inputs_v2::MuonBlockInputs::NumberOfInputs, &outer_grid);
      saveInputs(
          *hadronsTensor_[false], "input_outer_hadrons", dnn_inputs_v2::HadronBlockInputs::NumberOfInputs, &outer_grid);
      (*json_file_) << "}";
      delete json_file_;
      ++file_counter_;
    }

    tensorflow::run(&(cache_->getSession("core")),
                    {{"input_tau", *tauBlockTensor_},
                     {"input_inner", *convTensor_.at(true)},
                     {"input_outer", *convTensor_.at(false)}},
                    {"main_output/Softmax"},
                    &pred_vector);
    if (debug_level >= 1) {
      std::cout << "output = { ";
      for (int idx = 0; idx < deep_tau::NumberOfOutputs; ++idx) {
        if (idx > 0)
          std::cout << ", ";
        std::string label;
        if (idx == 0)
          label = "e";
        else if (idx == 1)
          label = "mu";
        else if (idx == 2)
          label = "tau";
        else if (idx == 3)
          label = "jet";
        else
          assert(0);
        std::cout << label << " = " << pred_vector[0].flat<float>()(idx);
      }
      std::cout << " }" << std::endl;
    }
  }

getValue()

template<typename T >

static float DeepTauId::getValue ( T  value )

inlinestaticprivate

Definition at line 1022 of file DeepTauId.cc.

References f, and relativeConstraints::value.

Referenced by getValueLinear(), and getValueNorm().

                                 {
    return std::isnormal(value) ? static_cast<float>(value) : 0.f;
  }

getValueLinear()

template<typename T >

static float DeepTauId::getValueLinear ( T  value, float  min_value, float  max_value, bool  positive  )

inlinestaticprivate

Definition at line 1027 of file DeepTauId.cc.

References getValue(), hcaldqm::quantity::max_value, and hcaldqm::quantity::min_value.

Referenced by createTauBlockInputs().

                                                                                        {
    const float fixed_value = getValue(value);
    const float clamped_value = std::clamp(fixed_value, min_value, max_value);
    float transformed_value = (clamped_value - min_value) / (max_value - min_value);
    if (!positive)
      transformed_value = transformed_value * 2 - 1;
    return transformed_value;
  }

getValueNorm()

template<typename T >

static float DeepTauId::getValueNorm ( T  value, float  mean, float  sigma, float  n_sigmas_max = 5  )

inlinestaticprivate

Definition at line 1037 of file DeepTauId.cc.

References getValue(), and SiStripPI::mean.

                                                                                      {
    const float fixed_value = getValue(value);
    const float norm_value = (fixed_value - mean) / sigma;
    return std::clamp(norm_value, -n_sigmas_max, n_sigmas_max);
  }

globalEndJob()

static void DeepTauId::globalEndJob ( const deep_tau::DeepTauCache *  cache_ )

inlinestatic

Definition at line 1016 of file DeepTauId.cc.

References deep_tau::DeepTauBase::cache_.

{ return DeepTauBase::globalEndJob(cache_); }

initializeGlobalCache()

static std::unique_ptr<deep_tau::DeepTauCache> DeepTauId::initializeGlobalCache ( const edm::ParameterSet &  cfg )

inlinestatic

Definition at line 1012 of file DeepTauId.cc.

References looper::cfg.

                                                                                               {
    return DeepTauBase::initializeGlobalCache(cfg);
  }

isAbove()

static bool DeepTauId::isAbove ( double  value, double  min  )

inlinestaticprivate

Definition at line 1043 of file DeepTauId.cc.

References SiStripPI::min.

Referenced by createTauBlockInputs().

{ return std::isnormal(value) && value > min; }

isInEcalCrack()

static bool DeepTauId::isInEcalCrack ( double  eta )

inlinestaticprivate

Definition at line 2394 of file DeepTauId.cc.

References funct::abs(), and PVValHelper::eta.

Referenced by createEgammaBlockInputs(), createHadronsBlockInputs(), createMuonBlockInputs(), and createTauBlockInputs().

                                        {
    const double abs_eta = std::abs(eta);
    return abs_eta > 1.46 && abs_eta < 1.558;
  }

matchDiscriminatorIndices()

const std::map<BasicDiscriminator, size_t> DeepTauId::matchDiscriminatorIndices ( edm::Event &  event, edm::EDGetTokenT< reco::TauDiscriminatorContainer >  discriminatorContainerToken, std::vector< BasicDiscriminator >  requiredDiscr  )

inline

Definition at line 841 of file DeepTauId.cc.

References Exception, edm::ParameterSet::getParameter(), mps_fire::i, dqmiolumiharvest::j, edm::parameterSet(), AlCaHLTBitMon_QueryRunRegistry::string, and deep_tau::DeepTauBase::stringFromDiscriminator_.

Referenced by getPredictions().

                                                   {
    std::map<std::string, size_t> discrIndexMapStr;
    auto const aHandle = event.getHandle(discriminatorContainerToken);
    auto const aProv = aHandle.provenance();
    if (aProv == nullptr)
      aHandle.whyFailed()->raise();
    const auto& psetsFromProvenance = edm::parameterSet(aProv->stable(), event.processHistory());
    auto const idlist = psetsFromProvenance.getParameter<std::vector<edm::ParameterSet>>("IDdefinitions");
    for (size_t j = 0; j < idlist.size(); ++j) {
      std::string idname = idlist[j].getParameter<std::string>("IDname");
      if (discrIndexMapStr.count(idname)) {
        throw cms::Exception("DeepTauId")
            << "basic discriminator " << idname << " appears more than once in the input.";
      }
      discrIndexMapStr[idname] = j;
    }

    //translate to a map of <BasicDiscriminator, index> and check if all discriminators are present
    std::map<BasicDiscriminator, size_t> discrIndexMap;
    for (size_t i = 0; i < requiredDiscr.size(); i++) {
      if (discrIndexMapStr.find(stringFromDiscriminator_.at(requiredDiscr[i])) == discrIndexMapStr.end())
        throw cms::Exception("DeepTauId") << "Basic Discriminator " << stringFromDiscriminator_.at(requiredDiscr[i])
                                          << " was not provided in the config file.";
      else
        discrIndexMap[requiredDiscr[i]] = discrIndexMapStr[stringFromDiscriminator_.at(requiredDiscr[i])];
    }
    return discrIndexMap;
  }

processIsolationPFComponents()

template<typename CandidateCollection , typename TauCastType >

static void DeepTauId::processIsolationPFComponents ( const TauCastType &  tau, const CandidateCollection &  candidates, LorentzVectorXYZ &  p4, float &  pt, float &  d_eta, float &  d_phi, float &  m, float &  n  )

inlinestaticprivate

Definition at line 2333 of file DeepTauId.cc.

References HLT_2022v12_cff::candidates, default_value, HLT_2022v12_cff::dEta, HLT_2022v12_cff::dPhi, visualization-live-secondInstance_cfg::m, dqmiodumpmetadata::n, DiDispStaMuonMonitor_cfi::pt, and metsig::tau.

                                                     {
    p4 = LorentzVectorXYZ(0, 0, 0, 0);
    n = 0;

    for (const auto& cand : candidates) {
      p4 += cand->p4();
      ++n;
    }

    pt = n != 0 ? p4.Pt() : default_value;
    d_eta = n != 0 ? dEta(p4, tau.p4()) : default_value;
    d_phi = n != 0 ? dPhi(p4, tau.p4()) : default_value;
    m = n != 0 ? p4.mass() : default_value;
  }

processSignalPFComponents()

template<typename CandidateCollection , typename TauCastType >

static void DeepTauId::processSignalPFComponents ( const TauCastType &  tau, const CandidateCollection &  candidates, LorentzVectorXYZ &  p4_inner, LorentzVectorXYZ &  p4_outer, float &  pt_inner, float &  dEta_inner, float &  dPhi_inner, float &  m_inner, float &  pt_outer, float &  dEta_outer, float &  dPhi_outer, float &  m_outer, float &  n_inner, float &  n_outer  )

inlinestaticprivate

Definition at line 2289 of file DeepTauId.cc.

References HLT_2022v12_cff::candidates, default_value, reco::deltaR(), HLT_2022v12_cff::dEta, HLT_2022v12_cff::dPhi, HGC3DClusterGenMatchSelector_cfi::dR, getInnerSignalConeRadius(), and metsig::tau.

                                                        {
    p4_inner = LorentzVectorXYZ(0, 0, 0, 0);
    p4_outer = LorentzVectorXYZ(0, 0, 0, 0);
    n_inner = 0;
    n_outer = 0;

    const double innerSigCone_radius = getInnerSignalConeRadius(tau.pt());
    for (const auto& cand : candidates) {
      const double dR = reco::deltaR(cand->p4(), tau.leadChargedHadrCand()->p4());
      const bool isInside_innerSigCone = dR < innerSigCone_radius;
      if (isInside_innerSigCone) {
        p4_inner += cand->p4();
        ++n_inner;
      } else {
        p4_outer += cand->p4();
        ++n_outer;
      }
    }

    pt_inner = n_inner != 0 ? p4_inner.Pt() : default_value;
    dEta_inner = n_inner != 0 ? dEta(p4_inner, tau.p4()) : default_value;
    dPhi_inner = n_inner != 0 ? dPhi(p4_inner, tau.p4()) : default_value;
    m_inner = n_inner != 0 ? p4_inner.mass() : default_value;

    pt_outer = n_outer != 0 ? p4_outer.Pt() : default_value;
    dEta_outer = n_outer != 0 ? dEta(p4_outer, tau.p4()) : default_value;
    dPhi_outer = n_outer != 0 ? dPhi(p4_outer, tau.p4()) : default_value;
    m_outer = n_outer != 0 ? p4_outer.mass() : default_value;
  }

saveInputs()

void DeepTauId::saveInputs ( const tensorflow::Tensor &  inputs, const std::string &  block_name, int  n_inputs, const CellGrid *  grid = nullptr  )

inlineprivate

Definition at line 1124 of file DeepTauId.cc.

References cms::cuda::assert(), gather_cfg::cout, debug_level, PVValHelper::eta, dqmMemoryStats::float, getRunAppsInfo::grid, input, PixelMapPlotter::inputs, is_first_block_, and phi.

Referenced by getPredictionsV2().

                                                         {
    if (debug_level >= 1) {
      std::cout << "<saveInputs>: block_name = " << block_name << std::endl;
    }
    if (!is_first_block_)
      (*json_file_) << ", ";
    (*json_file_) << "\"" << block_name << "\": [";
    if (block_name == "input_tau") {
      for (int input_index = 0; input_index < n_inputs; ++input_index) {
        float input = inputs.matrix<float>()(0, input_index);
        if (input_index != 0)
          (*json_file_) << ", ";
        (*json_file_) << input;
      }
    } else {
      assert(grid);
      int n_eta, n_phi;
      if (block_name.find("input_inner") != std::string::npos) {
        n_eta = 5;
        n_phi = 5;
      } else if (block_name.find("input_outer") != std::string::npos) {
        n_eta = 10;
        n_phi = 10;
      } else
        assert(0);
      int eta_phi_index = 0;
      for (int eta = -n_eta; eta <= n_eta; ++eta) {
        if (eta != -n_eta)
          (*json_file_) << ", ";
        (*json_file_) << "[";
        for (int phi = -n_phi; phi <= n_phi; ++phi) {
          if (phi != -n_phi)
            (*json_file_) << ", ";
          (*json_file_) << "[";
          const CellIndex cell_index{eta, phi};
          const auto cell_iter = grid->find(cell_index);
          for (int input_index = 0; input_index < n_inputs; ++input_index) {
            float input = 0.;
            if (cell_iter != grid->end()) {
              input = inputs.tensor<float, 4>()(eta_phi_index, 0, 0, input_index);
            }
            if (input_index != 0)
              (*json_file_) << ", ";
            (*json_file_) << input;
          }
          if (cell_iter != grid->end()) {
            eta_phi_index += 1;
          }
          (*json_file_) << "]";
        }
        (*json_file_) << "]";
      }
    }
    (*json_file_) << "]";
    is_first_block_ = false;
  }

setCellConvFeatures()

void DeepTauId::setCellConvFeatures ( tensorflow::Tensor &  convTensor, const tensorflow::Tensor &  features, unsigned  batch_idx, int  eta_index, int  phi_index  )

inlineprivate

Definition at line 1551 of file DeepTauId.cc.

References lowptgsfeleseed::features(), dqmMemoryStats::float, and dqmiodumpmetadata::n.

Referenced by createConvFeatures(), and DeepTauId().

                                          {
    for (int n = 0; n < dnn_inputs_v2::number_of_conv_features; ++n) {
      convTensor.tensor<float, 4>()(0, eta_index, phi_index, n) = features.tensor<float, 4>()(batch_idx, 0, 0, n);
    }
  }

Member Data Documentation

basicDiscrdR03IndexMap_

std::map<BasicDiscriminator, size_t> DeepTauId::basicDiscrdR03IndexMap_

private

Definition at line 2441 of file DeepTauId.cc.

Referenced by getPredictions().

basicDiscrIndexMap_

std::map<BasicDiscriminator, size_t> DeepTauId::basicDiscrIndexMap_

private

Definition at line 2440 of file DeepTauId.cc.

Referenced by getPredictions().

basicTauDiscriminators_inputToken_

edm::EDGetTokenT<reco::TauDiscriminatorContainer> DeepTauId::basicTauDiscriminators_inputToken_

private

Definition at line 2417 of file DeepTauId.cc.

Referenced by getPredictions().

basicTauDiscriminatorsdR03_inputToken_

edm::EDGetTokenT<reco::TauDiscriminatorContainer> DeepTauId::basicTauDiscriminatorsdR03_inputToken_

private

Definition at line 2418 of file DeepTauId.cc.

Referenced by getPredictions().

convTensor_

std::array<std::unique_ptr<tensorflow::Tensor>, 2> DeepTauId::convTensor_

private

Definition at line 2429 of file DeepTauId.cc.

Referenced by createConvFeatures(), DeepTauId(), and getPredictionsV2().

debug_level

const int DeepTauId::debug_level

private

Definition at line 2424 of file DeepTauId.cc.

Referenced by checkInputs(), createConvFeatures(), getPredictionsV2(), and saveInputs().

default_value

constexpr float DeepTauId::default_value = -999.

static

Definition at line 829 of file DeepTauId.cc.

Referenced by calculateElectronClusterVars(), calculateGottfriedJacksonAngleDifference(), processIsolationPFComponents(), and processSignalPFComponents().

disable_CellIndex_workaround_

const bool DeepTauId::disable_CellIndex_workaround_

private

Definition at line 2427 of file DeepTauId.cc.

Referenced by getPredictionsV2().

disable_dxy_pca_

const bool DeepTauId::disable_dxy_pca_

private

Definition at line 2425 of file DeepTauId.cc.

Referenced by createTauBlockInputs().

disable_hcalFraction_workaround_

const bool DeepTauId::disable_hcalFraction_workaround_

private

Definition at line 2426 of file DeepTauId.cc.

Referenced by createHadronsBlockInputs().

discrIndicesMapped_

bool DeepTauId::discrIndicesMapped_ = false

private

Definition at line 2439 of file DeepTauId.cc.

Referenced by getPredictions().

eGammaTensor_

std::array<std::unique_ptr<tensorflow::Tensor>, 2> DeepTauId::eGammaTensor_

private

Definition at line 2429 of file DeepTauId.cc.

Referenced by createConvFeatures(), createEgammaBlockInputs(), DeepTauId(), getPartialPredictions(), and getPredictionsV2().

electrons_token_

edm::EDGetTokenT<std::vector<pat::Electron> > DeepTauId::electrons_token_

private

Definition at line 2414 of file DeepTauId.cc.

Referenced by getPredictions().

file_counter_

int DeepTauId::file_counter_

private

Definition at line 2435 of file DeepTauId.cc.

Referenced by getPredictionsV2().

hadronsTensor_

std::array<std::unique_ptr<tensorflow::Tensor>, 2> DeepTauId::hadronsTensor_

private

Definition at line 2429 of file DeepTauId.cc.

Referenced by createConvFeatures(), createHadronsBlockInputs(), DeepTauId(), getPartialPredictions(), and getPredictionsV2().

input_layer_

std::string DeepTauId::input_layer_

private

Definition at line 2421 of file DeepTauId.cc.

is_first_block_

bool DeepTauId::is_first_block_

private

Definition at line 2434 of file DeepTauId.cc.

Referenced by getPredictionsV2(), and saveInputs().

json_file_

std::ofstream* DeepTauId::json_file_

private

Definition at line 2433 of file DeepTauId.cc.

Referenced by getPredictionsV2().

muons_token_

edm::EDGetTokenT<std::vector<pat::Muon> > DeepTauId::muons_token_

private

Definition at line 2415 of file DeepTauId.cc.

Referenced by getPredictions().

muonTensor_

std::array<std::unique_ptr<tensorflow::Tensor>, 2> DeepTauId::muonTensor_

private

Definition at line 2429 of file DeepTauId.cc.

Referenced by createConvFeatures(), createMuonBlockInputs(), DeepTauId(), getPartialPredictions(), and getPredictionsV2().

output_layer_

std::string DeepTauId::output_layer_

private

Definition at line 2421 of file DeepTauId.cc.

pfTauTransverseImpactParameters_token_

edm::EDGetTokenT<edm::AssociationVector<reco::PFTauRefProd, std::vector<reco::PFTauTransverseImpactParameterRef> > > DeepTauId::pfTauTransverseImpactParameters_token_

private

Definition at line 2420 of file DeepTauId.cc.

Referenced by getPredictions().

pi

constexpr float DeepTauId::pi = M_PI

staticprivate

Definition at line 1019 of file DeepTauId.cc.

Referenced by createTauBlockInputs().

rho_token_

edm::EDGetTokenT<double> DeepTauId::rho_token_

private

Definition at line 2416 of file DeepTauId.cc.

Referenced by getPredictions().

save_inputs_

const bool DeepTauId::save_inputs_

private

Definition at line 2432 of file DeepTauId.cc.

Referenced by getPredictionsV2().

scalingParamsMap_

const std::map<std::pair<deep_tau::Scaling::FeatureT, bool>, deep_tau::Scaling::ScalingParams>* DeepTauId::scalingParamsMap_

private

Definition at line 2431 of file DeepTauId.cc.

Referenced by createEgammaBlockInputs(), createHadronsBlockInputs(), createMuonBlockInputs(), createTauBlockInputs(), and DeepTauId().

sub_version_

const unsigned DeepTauId::sub_version_

private

Definition at line 2423 of file DeepTauId.cc.

Referenced by createEgammaBlockInputs(), createTauBlockInputs(), and DeepTauId().

tauBlockTensor_

std::unique_ptr<tensorflow::Tensor> DeepTauId::tauBlockTensor_

private

Definition at line 2428 of file DeepTauId.cc.

Referenced by createTauBlockInputs(), DeepTauId(), and getPredictionsV2().

tauInputs_indices_

std::vector<int> DeepTauId::tauInputs_indices_

private

Definition at line 2436 of file DeepTauId.cc.

Referenced by createTauBlockInputs(), and DeepTauId().

version_

const unsigned DeepTauId::version_

private

Definition at line 2422 of file DeepTauId.cc.

Referenced by DeepTauId(), and getPredictions().

zeroOutputTensor_

std::array<std::unique_ptr<tensorflow::Tensor>, 2> DeepTauId::zeroOutputTensor_

private

Definition at line 2429 of file DeepTauId.cc.

Referenced by createConvFeatures(), and DeepTauId().