DeepTauId Class Reference

Inheritance diagram for DeepTauId:

Classes
struct	Output
struct	TauDiscInfo

Public Types
using	BasicDiscriminator = deep_tau::BasicDiscriminator
using	CandidateCollection = edm::View< reco::Candidate >
using	Cutter = deep_tau::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< deep_tau::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 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)
void	produce (edm::Event &event, const edm::EventSetup &es) override
Public Member Functions inherited from edm::stream::EDProducer< edm::GlobalCache< deep_tau::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)

Public Attributes
uint8_t	andPrediscriminants_
std::vector< TauDiscInfo< pat::PATTauDiscriminator > >	patPrediscriminants_
std::vector< TauDiscInfo< reco::PFTauDiscriminator > >	recoPrediscriminants_

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)
void	createOutputs (edm::Event &event, const tensorflow::Tensor &pred, edm::Handle< TauCollection > taus)
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)
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, const edm::EventNumber_t &eventnr, 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_
const deep_tau::DeepTauCache *	cache_
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_
const bool	is_online_
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_
OutputCollection	outputs_
edm::EDGetTokenT< CandidateCollection >	pfcandToken_
edm::EDGetTokenT< edm::AssociationVector< reco::PFTauRefProd, std::vector< reco::PFTauTransverseImpactParameterRef > > >	pfTauTransverseImpactParameters_token_
const std::vector< BasicDiscriminator >	requiredBasicDiscriminators_
const std::vector< BasicDiscriminator >	requiredBasicDiscriminatorsdR03_
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 std::map< BasicDiscriminator, std::string >	stringFromDiscriminator_
const unsigned	sub_version_
std::unique_ptr< tensorflow::Tensor >	tauBlockTensor_
std::vector< int >	tauInputs_indices_
edm::EDGetTokenT< TauCollection >	tausToken_
const unsigned	version_
edm::EDGetTokenT< reco::VertexCollection >	vtxToken_
std::map< std::string, WPList >	workingPoints_
const unsigned	year_
std::array< std::unique_ptr< tensorflow::Tensor >, 2 >	zeroOutputTensor_

Static Private Attributes
static constexpr float	pi = M_PI

Detailed Description

Definition at line 944 of file DeepTauId.cc.

Member Typedef Documentation

BasicDiscriminator

using DeepTauId::BasicDiscriminator = deep_tau::BasicDiscriminator

Definition at line 1009 of file DeepTauId.cc.

CandidateCollection

using DeepTauId::CandidateCollection = edm::View<reco::Candidate>

Definition at line 948 of file DeepTauId.cc.

Cutter

using DeepTauId::Cutter = deep_tau::TauWPThreshold

Definition at line 954 of file DeepTauId.cc.

CutterPtr

using DeepTauId::CutterPtr = std::unique_ptr<Cutter>

Definition at line 955 of file DeepTauId.cc.

ElectronCollection

using DeepTauId::ElectronCollection = pat::ElectronCollection

Definition at line 951 of file DeepTauId.cc.

LorentzVectorXYZ

using DeepTauId::LorentzVectorXYZ = ROOT::Math::LorentzVector<ROOT::Math::PxPyPzE4D<double> >

Definition at line 953 of file DeepTauId.cc.

MuonCollection

using DeepTauId::MuonCollection = pat::MuonCollection

Definition at line 952 of file DeepTauId.cc.

OutputCollection

using DeepTauId::OutputCollection = std::map<std::string, Output>

Definition at line 995 of file DeepTauId.cc.

TauCollection

using DeepTauId::TauCollection = edm::View<reco::BaseTau>

Definition at line 947 of file DeepTauId.cc.

TauDiscriminator

using DeepTauId::TauDiscriminator = reco::TauDiscriminatorContainer

Definition at line 946 of file DeepTauId.cc.

TauRef

using DeepTauId::TauRef = edm::Ref<TauCollection>

Definition at line 949 of file DeepTauId.cc.

TauRefProd

using DeepTauId::TauRefProd = edm::RefProd<TauCollection>

Definition at line 950 of file DeepTauId.cc.

WPList

using DeepTauId::WPList = std::vector<CutterPtr>

Definition at line 956 of file DeepTauId.cc.

Constructor & Destructor Documentation

DeepTauId()

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

inlineexplicit

Definition at line 1088 of file DeepTauId.cc.

References andPrediscriminants_, looper::cfg, convTensor_, DMR_cfg::cut, DeepTauId::TauDiscInfo< ConsumeType >::cut, DeepTauId::TauDiscInfo< ConsumeType >::disc_token, eGammaTensor_, nanoDQM_cfi::Electron, mps_fire::end, Exception, edm::ParameterSet::getParameter(), edm::ParameterSet::getParameterNamesForType(), getPartialPredictions(), hadronsTensor_, mps_fire::i, is_online_, label, DeepTauId::TauDiscInfo< ConsumeType >::label, dumpRecoGeometry_cfg::Muon, muonTensor_, dqmiodumpmetadata::n, outputs_, AlCaHLTBitMon_ParallelJobs::p, patPrediscriminants_, recoPrediscriminants_, scalingParamsMap_, deep_tau::Scaling::scalingParamsMap_PhaseIIv2p5, deep_tau::Scaling::scalingParamsMap_v2p1, deep_tau::Scaling::scalingParamsMap_v2p5, setCellConvFeatures(), findQualityFiles::size, jetUpdater_cfi::sort, AlCaHLTBitMon_QueryRunRegistry::string, sub_version_, tauBlockTensor_, tauInputs_indices_, HcalDetIdTransform::transform(), findQualityFiles::v, version_, workingPoints_, year_, and zeroOutputTensor_.

      : tausToken_(consumes<TauCollection>(cfg.getParameter<edm::InputTag>("taus"))),
        pfcandToken_(consumes<CandidateCollection>(cfg.getParameter<edm::InputTag>("pfcands"))),
        vtxToken_(consumes<reco::VertexCollection>(cfg.getParameter<edm::InputTag>("vertices"))),
        is_online_(cfg.getParameter<bool>("is_online")),
        outputs_(GetOutputs()),
        cache_(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"))),
        year_(cfg.getParameter<unsigned>("year")),
        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) {
    for (const auto& output_desc : outputs_) {
      produces<TauDiscriminator>(output_desc.first);
      const auto& cut_list = cfg.getParameter<std::vector<std::string>>(output_desc.first + "WP");
      for (const std::string& cut_str : cut_list) {
        workingPoints_[output_desc.first].push_back(std::make_unique<Cutter>(cut_str));
      }
    }

    // prediscriminant operator
    // require the tau to pass the following prediscriminants
    const edm::ParameterSet& prediscriminantConfig = cfg.getParameter<edm::ParameterSet>("Prediscriminants");

    // determine boolean operator used on the prediscriminants
    std::string pdBoolOperator = prediscriminantConfig.getParameter<std::string>("BooleanOperator");
    // convert string to lowercase
    transform(pdBoolOperator.begin(), pdBoolOperator.end(), pdBoolOperator.begin(), ::tolower);

    if (pdBoolOperator == "and") {
      andPrediscriminants_ = 0x1;  //use chars instead of bools so we can do a bitwise trick later
    } else if (pdBoolOperator == "or") {
      andPrediscriminants_ = 0x0;
    } else {
      throw cms::Exception("TauDiscriminationProducerBase")
          << "PrediscriminantBooleanOperator defined incorrectly, options are: AND,OR";
    }

    // get the list of prediscriminants
    std::vector<std::string> prediscriminantsNames =
        prediscriminantConfig.getParameterNamesForType<edm::ParameterSet>();

    for (auto const& iDisc : prediscriminantsNames) {
      const edm::ParameterSet& iPredisc = prediscriminantConfig.getParameter<edm::ParameterSet>(iDisc);
      const edm::InputTag& label = iPredisc.getParameter<edm::InputTag>("Producer");
      double cut = iPredisc.getParameter<double>("cut");

      if (is_online_) {
        TauDiscInfo<reco::PFTauDiscriminator> thisDiscriminator;
        thisDiscriminator.label = label;
        thisDiscriminator.cut = cut;
        thisDiscriminator.disc_token = consumes<reco::PFTauDiscriminator>(label);
        recoPrediscriminants_.push_back(thisDiscriminator);
      } else {
        TauDiscInfo<pat::PATTauDiscriminator> thisDiscriminator;
        thisDiscriminator.label = label;
        thisDiscriminator.cut = cut;
        thisDiscriminator.disc_token = consumes<pat::PATTauDiscriminator>(label);
        patPrediscriminants_.push_back(thisDiscriminator);
      }
    }
    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())});
        if (year_ == 2026) {
          scalingParamsMap_ = &sc::scalingParamsMap_PhaseIIv2p5;
        } else {
          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 2585 of file DeepTauId.cc.

References default_value, hcalRecHitTable_cff::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 1349 of file DeepTauId.cc.

References hcalRecHitTable_cff::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 2679 of file DeepTauId.cc.

References funct::abs(), conifer::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 2701 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 1372 of file DeepTauId.cc.

References cms::cuda::assert(), gather_cfg::cout, debug_level, PVValHelper::eta, Exception, alignBH_cfg::fixed, nano_mu_digi_cff::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 1780 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 2017 of file DeepTauId.cc.

References funct::abs(), edm::View< T >::at(), calculateElectronClusterVarsV2(), nano_mu_local_reco_cff::chi2, HLT_2023v12_cff::dPhi, eGammaTensor_, nanoDQM_cfi::Electron, pwdgSkimBPark_cfi::electrons, f, nano_mu_digi_cff::float, heavyIonCSV_trainingSettings::idx, PixelMapPlotter::inputs, isInEcalCrack(), ndof, rho, IsoPhotonEBSkim_cff::rho, scalingParamsMap_, sub_version_, metsig::tau, runTauDisplay::tau_eta, runTauDisplay::tau_pt, and year_.

                                              {
    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);
      const bool mva_valid =
          (ele.mvaInput().earlyBrem > -2) ||
          (year_ !=
           2026);  // Known issue that input can be invalid in Phase2 samples (early/lateBrem==-2, hadEnergy==0, sigmaEtaEta/deltaEta==3.40282e+38). Unknown if also in Run2/3, so don't change there
      if (mva_valid) {
        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 2447 of file DeepTauId.cc.

References funct::abs(), edm::View< T >::at(), nano_mu_local_reco_cff::chi2, disable_hcalFraction_workaround_, HLT_2023v12_cff::dPhi, f, nano_mu_digi_cff::float, hadronsTensor_, heavyIonCSV_trainingSettings::idx, PixelMapPlotter::inputs, isInEcalCrack(), ndof, rho, IsoPhotonEBSkim_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 2296 of file DeepTauId.cc.

References funct::abs(), edm::View< T >::at(), nano_mu_local_reco_cff::chi2, MuonSubdetId::CSC, HLT_2023v12_cff::dPhi, MuonSubdetId::DT, f, nano_mu_digi_cff::float, heavyIonCSV_trainingSettings::idx, PixelMapPlotter::inputs, isInEcalCrack(), dumpRecoGeometry_cfg::Muon, PDWG_BPHSkim_cff::muons, muonTensor_, ndof, pat::Muon::PV2D, rho, IsoPhotonEBSkim_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);
        }
      }
    }
  }

createOutputs()

void DeepTauId::createOutputs ( edm::Event &  event, const tensorflow::Tensor &  pred, edm::Handle< TauCollection >  taus  )

inlineprivate

Definition at line 1314 of file DeepTauId.cc.

References is_online_, eostools::move(), outputs_, mps_fire::result, Tau3MuMonitor_cff::taus, FWLite::working_points, and workingPoints_.

Referenced by produce().

                                                                                                   {
    for (const auto& output_desc : outputs_) {
      const WPList* working_points = nullptr;
      if (workingPoints_.find(output_desc.first) != workingPoints_.end()) {
        working_points = &workingPoints_.at(output_desc.first);
      }
      auto result = output_desc.second.get_value(taus, pred, working_points, is_online_);
      event.put(std::move(result), output_desc.first);
    }
  }

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 1872 of file DeepTauId.cc.

References funct::abs(), calculateGottfriedJacksonAngleDifference(), disable_dxy_pca_, reco::tau::eratio(), getValueLinear(), PixelMapPlotter::inputs, 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, IsoPhotonEBSkim_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 1043 of file DeepTauId.cc.

References edm::ConfigurationDescriptions::add(), edm::ParameterSetDescription::add(), edm::ParameterSetDescription::addOptional(), submitPVResolutionJobs::desc, ProducerED_cfi::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>("year", 2017);
    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 1719 of file DeepTauId.cc.

References reco::deltaPhi(), getInnerSignalConeRadius(), getRunAppsInfo::grid, dqmiodumpmetadata::n, getGTfromDQMFile::obj, conifer::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 2716 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 2671 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 999 of file DeepTauId.cc.

References GetRecoTauVFromDQM_MC_cff::Output, and 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 1755 of file DeepTauId.cc.

References 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  )

inlineprivate

Definition at line 1489 of file DeepTauId.cc.

References andPrediscriminants_, basicDiscrdR03IndexMap_, basicDiscrIndexMap_, HLT_2023v12_cff::basicTauDiscriminators, basicTauDiscriminators_inputToken_, HLT_2023v12_cff::basicTauDiscriminatorsdR03, basicTauDiscriminatorsdR03_inputToken_, discrIndicesMapped_, electrons_token_, Exception, f, is_online_, dqmdumpme::k, matchDiscriminatorIndices(), muons_token_, deep_tau::NumberOfOutputs, patPrediscriminants_, pfcandToken_, HLT_2023v12_cff::pfTauTransverseImpactParameters, pfTauTransverseImpactParameters_token_, recoPrediscriminants_, requiredBasicDiscriminators_, requiredBasicDiscriminatorsdR03_, rho, rho_token_, Tau3MuMonitor_cff::taus, version_, AlignmentTracksFromVertexSelector_cfi::vertices, and vtxToken_.

Referenced by produce().

                                                                                  {
    // 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);

    auto const& eventnr = event.id().event();

    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,
                                                             eventnr,
                                                             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,
                                                             eventnr,
                                                             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, const edm::EventNumber_t &  eventnr, std::vector< tensorflow::Tensor > &  pred_vector, TauFunc  tau_funcs  )

inlineprivate

Definition at line 1613 of file DeepTauId.cc.

References cms::cuda::assert(), 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, rho, tensorflow::run(), save_inputs_, saveInputs(), AlCaHLTBitMon_QueryRunRegistry::string, metsig::tau, tauBlockTensor_, and 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()
                << ", eventnr = " << eventnr << 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(eventnr) + "_" + std::to_string(tau_index) + ".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 1326 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 1331 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 1341 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 1295 of file DeepTauId.cc.

{}

initializeGlobalCache()

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

inlinestatic

Definition at line 1273 of file DeepTauId.cc.

References looper::cfg, mps_splice::entry, edm::FileInPath::fullPath(), HLT_2023v12_cff::graph_file, HLT_2023v12_cff::mem_mapped, and AlCaHLTBitMon_QueryRunRegistry::string.

                                                                                               {
    const auto graph_name_vector = cfg.getParameter<std::vector<std::string>>("graph_file");
    std::map<std::string, std::string> graph_names;
    for (const auto& entry : graph_name_vector) {
      const size_t sep_pos = entry.find(':');
      std::string entry_name, graph_file;
      if (sep_pos != std::string::npos) {
        entry_name = entry.substr(0, sep_pos);
        graph_file = entry.substr(sep_pos + 1);
      } else {
        entry_name = "";
        graph_file = entry;
      }
      graph_file = edm::FileInPath(graph_file).fullPath();
      if (graph_names.count(entry_name))
        throw cms::Exception("DeepTauCache") << "Duplicated graph entries";
      graph_names[entry_name] = graph_file;
    }
    bool mem_mapped = cfg.getParameter<bool>("mem_mapped");
    return std::make_unique<deep_tau::DeepTauCache>(graph_names, mem_mapped);
  }

isAbove()

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

inlinestaticprivate

Definition at line 1347 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 2710 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 1011 of file DeepTauId.cc.

References Exception, edm::ParameterSet::getParameter(), mps_fire::i, dqmiolumiharvest::j, edm::parameterSet(), AlCaHLTBitMon_QueryRunRegistry::string, and 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 2649 of file DeepTauId.cc.

References HLT_2023v12_cff::candidates, default_value, HLT_2023v12_cff::dEta, HLT_2023v12_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 2605 of file DeepTauId.cc.

References HLT_2023v12_cff::candidates, default_value, reco::deltaR(), HLT_2023v12_cff::dEta, HLT_2023v12_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;
  }

produce()

void DeepTauId::produce ( edm::Event &  event, const edm::EventSetup &  es  )

inlineoverride

Definition at line 1242 of file DeepTauId.cc.

References createOutputs(), Exception, getPredictions(), is_online_, patPrediscriminants_, recoPrediscriminants_, Tau3MuMonitor_cff::taus, and tausToken_.

                                                                  {
    edm::Handle<TauCollection> taus;
    event.getByToken(tausToken_, taus);
    edm::ProductID tauProductID = taus.id();

    // load prediscriminators
    size_t nPrediscriminants =
        patPrediscriminants_.empty() ? recoPrediscriminants_.size() : patPrediscriminants_.size();
    for (size_t iDisc = 0; iDisc < nPrediscriminants; ++iDisc) {
      edm::ProductID discKeyId;
      if (is_online_) {
        recoPrediscriminants_[iDisc].fill(event);
        discKeyId = recoPrediscriminants_[iDisc].handle->keyProduct().id();
      } else {
        patPrediscriminants_[iDisc].fill(event);
        discKeyId = patPrediscriminants_[iDisc].handle->keyProduct().id();
      }

      // Check to make sure the product is correct for the discriminator.
      // If not, throw a more informative exception.
      if (tauProductID != discKeyId) {
        throw cms::Exception("MisconfiguredPrediscriminant")
            << "The tau collection has product ID: " << tauProductID
            << " but the pre-discriminator is keyed with product ID: " << discKeyId << std::endl;
      }
    }

    const tensorflow::Tensor& pred = getPredictions(event, taus);
    createOutputs(event, pred, taus);
  }

saveInputs()

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

inlineprivate

Definition at line 1428 of file DeepTauId.cc.

References cms::cuda::assert(), gather_cfg::cout, debug_level, PVValHelper::eta, nano_mu_digi_cff::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 1861 of file DeepTauId.cc.

References lowptgsfeleseed::features(), nano_mu_digi_cff::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

andPrediscriminants_

uint8_t DeepTauId::andPrediscriminants_

Definition at line 1307 of file DeepTauId.cc.

Referenced by DeepTauId(), and getPredictions().

basicDiscrdR03IndexMap_

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

private

Definition at line 2785 of file DeepTauId.cc.

Referenced by getPredictions().

basicDiscrIndexMap_

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

private

Definition at line 2784 of file DeepTauId.cc.

Referenced by getPredictions().

basicTauDiscriminators_inputToken_

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

private

Definition at line 2760 of file DeepTauId.cc.

Referenced by getPredictions().

basicTauDiscriminatorsdR03_inputToken_

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

private

Definition at line 2761 of file DeepTauId.cc.

Referenced by getPredictions().

cache_

const deep_tau::DeepTauCache* DeepTauId::cache_

private

Definition at line 2736 of file DeepTauId.cc.

Referenced by getPartialPredictions(), and getPredictionsV2().

convTensor_

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

private

Definition at line 2773 of file DeepTauId.cc.

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

debug_level

const int DeepTauId::debug_level

private

Definition at line 2768 of file DeepTauId.cc.

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

default_value

constexpr float DeepTauId::default_value = -999.

static

Definition at line 997 of file DeepTauId.cc.

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

disable_CellIndex_workaround_

const bool DeepTauId::disable_CellIndex_workaround_

private

Definition at line 2771 of file DeepTauId.cc.

Referenced by getPredictionsV2().

disable_dxy_pca_

const bool DeepTauId::disable_dxy_pca_

private

Definition at line 2769 of file DeepTauId.cc.

Referenced by createTauBlockInputs().

disable_hcalFraction_workaround_

const bool DeepTauId::disable_hcalFraction_workaround_

private

Definition at line 2770 of file DeepTauId.cc.

Referenced by createHadronsBlockInputs().

discrIndicesMapped_

bool DeepTauId::discrIndicesMapped_ = false

private

Definition at line 2783 of file DeepTauId.cc.

Referenced by getPredictions().

eGammaTensor_

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

private

Definition at line 2773 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 2757 of file DeepTauId.cc.

Referenced by getPredictions().

file_counter_

int DeepTauId::file_counter_

private

Definition at line 2779 of file DeepTauId.cc.

Referenced by getPredictionsV2().

hadronsTensor_

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

private

Definition at line 2773 of file DeepTauId.cc.

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

input_layer_

std::string DeepTauId::input_layer_

private

Definition at line 2764 of file DeepTauId.cc.

is_first_block_

bool DeepTauId::is_first_block_

private

Definition at line 2778 of file DeepTauId.cc.

Referenced by getPredictionsV2(), and saveInputs().

is_online_

const bool DeepTauId::is_online_

private

Definition at line 2734 of file DeepTauId.cc.

Referenced by createOutputs(), createTauBlockInputs(), DeepTauId(), getPredictions(), and produce().

json_file_

std::ofstream* DeepTauId::json_file_

private

Definition at line 2777 of file DeepTauId.cc.

Referenced by getPredictionsV2().

muons_token_

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

private

Definition at line 2758 of file DeepTauId.cc.

Referenced by getPredictions().

muonTensor_

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

private

Definition at line 2773 of file DeepTauId.cc.

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

output_layer_

std::string DeepTauId::output_layer_

private

Definition at line 2764 of file DeepTauId.cc.

outputs_

OutputCollection DeepTauId::outputs_

private

Definition at line 2735 of file DeepTauId.cc.

Referenced by createOutputs(), DeepTauId(), and GetOutputs().

patPrediscriminants_

std::vector<TauDiscInfo<pat::PATTauDiscriminator> > DeepTauId::patPrediscriminants_

Definition at line 1308 of file DeepTauId.cc.

Referenced by DeepTauId(), getPredictions(), and produce().

pfcandToken_

edm::EDGetTokenT<CandidateCollection> DeepTauId::pfcandToken_

private

Definition at line 2731 of file DeepTauId.cc.

Referenced by getPredictions().

pfTauTransverseImpactParameters_token_

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

private

Definition at line 2763 of file DeepTauId.cc.

Referenced by getPredictions().

pi

constexpr float DeepTauId::pi = M_PI

staticprivate

Definition at line 1312 of file DeepTauId.cc.

Referenced by createTauBlockInputs().

recoPrediscriminants_

std::vector<TauDiscInfo<reco::PFTauDiscriminator> > DeepTauId::recoPrediscriminants_

Definition at line 1309 of file DeepTauId.cc.

Referenced by DeepTauId(), getPredictions(), and produce().

requiredBasicDiscriminators_

const std::vector<BasicDiscriminator> DeepTauId::requiredBasicDiscriminators_

private

Initial value:

{BasicDiscriminator::ChargedIsoPtSum,
                                                                     BasicDiscriminator::NeutralIsoPtSum,
                                                                     BasicDiscriminator::NeutralIsoPtSumWeight,
                                                                     BasicDiscriminator::PhotonPtSumOutsideSignalCone,
                                                                     BasicDiscriminator::PUcorrPtSum}

Definition at line 2745 of file DeepTauId.cc.

Referenced by getPredictions().

requiredBasicDiscriminatorsdR03_

const std::vector<BasicDiscriminator> DeepTauId::requiredBasicDiscriminatorsdR03_

private

Initial value:

{
      BasicDiscriminator::ChargedIsoPtSum,
      BasicDiscriminator::NeutralIsoPtSum,
      BasicDiscriminator::NeutralIsoPtSumWeight,
      BasicDiscriminator::PhotonPtSumOutsideSignalCone,
      BasicDiscriminator::FootprintCorrection}

Definition at line 2750 of file DeepTauId.cc.

Referenced by getPredictions().

rho_token_

edm::EDGetTokenT<double> DeepTauId::rho_token_

private

Definition at line 2759 of file DeepTauId.cc.

Referenced by getPredictions().

save_inputs_

const bool DeepTauId::save_inputs_

private

Definition at line 2776 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 2775 of file DeepTauId.cc.

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

stringFromDiscriminator_

const std::map<BasicDiscriminator, std::string> DeepTauId::stringFromDiscriminator_

private

Initial value:

{
      {BasicDiscriminator::ChargedIsoPtSum, "ChargedIsoPtSum"},
      {BasicDiscriminator::NeutralIsoPtSum, "NeutralIsoPtSum"},
      {BasicDiscriminator::NeutralIsoPtSumWeight, "NeutralIsoPtSumWeight"},
      {BasicDiscriminator::FootprintCorrection, "TauFootprintCorrection"},
      {BasicDiscriminator::PhotonPtSumOutsideSignalCone, "PhotonPtSumOutsideSignalCone"},
      {BasicDiscriminator::PUcorrPtSum, "PUcorrPtSum"}}

Definition at line 2738 of file DeepTauId.cc.

Referenced by matchDiscriminatorIndices().

sub_version_

const unsigned DeepTauId::sub_version_

private

Definition at line 2767 of file DeepTauId.cc.

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

tauBlockTensor_

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

private

Definition at line 2772 of file DeepTauId.cc.

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

tauInputs_indices_

std::vector<int> DeepTauId::tauInputs_indices_

private

Definition at line 2780 of file DeepTauId.cc.

Referenced by createTauBlockInputs(), and DeepTauId().

tausToken_

edm::EDGetTokenT<TauCollection> DeepTauId::tausToken_

private

Definition at line 2730 of file DeepTauId.cc.

Referenced by produce().

version_

const unsigned DeepTauId::version_

private

Definition at line 2766 of file DeepTauId.cc.

Referenced by DeepTauId(), and getPredictions().

vtxToken_

edm::EDGetTokenT<reco::VertexCollection> DeepTauId::vtxToken_

private

Definition at line 2732 of file DeepTauId.cc.

Referenced by getPredictions().

workingPoints_

std::map<std::string, WPList> DeepTauId::workingPoints_

private

Definition at line 2733 of file DeepTauId.cc.

Referenced by createOutputs(), and DeepTauId().

year_

const unsigned DeepTauId::year_

private

Definition at line 2765 of file DeepTauId.cc.

Referenced by createEgammaBlockInputs(), and DeepTauId().

zeroOutputTensor_

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

private

Definition at line 2773 of file DeepTauId.cc.

Referenced by createConvFeatures(), and DeepTauId().