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File indexing completed on 2021-11-12 00:00:59

 #include "DQM/EcalMonitorClient/interface/MLClient.h"
 
 #include "DataFormats/EcalDetId/interface/EcalTrigTowerDetId.h"
 
 #include "CondFormats/EcalObjects/interface/EcalDQMStatusHelper.h"
 
 #include "DQM/EcalCommon/interface/EcalDQMCommonUtils.h"
 
 #include "FWCore/ParameterSet/interface/ParameterSet.h"
 
 #include "PhysicsTools/ONNXRuntime/interface/ONNXRuntime.h"
 
 #include "DQM/EcalCommon/interface/MESetNonObject.h"
 
 using namespace cms::Ort;
 
 namespace ecaldqm {
   MLClient::MLClient() : DQWorkerClient() { qualitySummaries_.insert("MLQualitySummary"); }
 
   void MLClient::setParams(edm::ParameterSet const& _params) {
     MLThreshold_ = _params.getUntrackedParameter<double>("MLThreshold");
     PUcorr_slope_ = _params.getUntrackedParameter<double>("PUcorr_slope");
     PUcorr_intercept_ = _params.getUntrackedParameter<double>("PUcorr_intercept");
     avgOcc_ = _params.getUntrackedParameter<std::vector<double>>("avgOcc");
     if (!onlineMode_) {
       MEs_.erase(std::string("MLQualitySummary"));
       MEs_.erase(std::string("EventsperMLImage"));
       sources_.erase(std::string("PU"));
       sources_.erase(std::string("NumEvents"));
       sources_.erase(std::string("DigiAllByLumi"));
       sources_.erase(std::string("AELoss"));
     }
   }
 
   void MLClient::producePlots(ProcessType) {
     if (!onlineMode_)
       return;
     using namespace std;
     MESet& meMLQualitySummary(MEs_.at("MLQualitySummary"));
     MESet& meEventsperMLImage(MEs_.at("EventsperMLImage"));
 
     MESetNonObject const& sPU(static_cast<MESetNonObject&>(sources_.at("PU")));
     MESetNonObject const& sNumEvents(static_cast<MESetNonObject&>(sources_.at("NumEvents")));
 
     //Get the no.of events and the PU per LS calculated in OccupancyTask
     int nEv = sNumEvents.getFloatValue();
     double pu = sPU.getFloatValue();
     //Do not compute ML quality if PU is non existent.
     if (pu < 0.) {
       return;
     }
     uint32_t mask(1 << EcalDQMStatusHelper::PEDESTAL_ONLINE_HIGH_GAIN_RMS_ERROR |
                   1 << EcalDQMStatusHelper::PHYSICS_BAD_CHANNEL_WARNING |
                   1 << EcalDQMStatusHelper::PHYSICS_BAD_CHANNEL_ERROR);
 
     //////////////// ML Data Preprocessing //////////////////////////////////
     //Inorder to feed the data into the ML model we apply some preprocessing.
     //We use the Digi Occupancy per Lumisection as the input source.
     //The model was trained on each occupancy plot having 500 events.
     //In apprehension of the low luminosity in the beginning of Run3, where in online DQM
     //the no.of events per LS could be lower than 500, we sum the occupancies over a fixed no.of lumisections as a running sum,
     //and require that the total no.of events on this summed occupancy to be atleast 200.
     //(This no.of LS and the no.of events are parameters which would require tuning later)
     //This summed occupancy is now the input image, which is then corrected for PileUp(PU) dependence and
     //change in no.of events, which are derived from training.
     //The input image is also padded by replicating the top and bottom rows so as to prevent the "edge effect"
     //wherein the ML model's learning degrades near the edge of the data set it sees.
     //This padding is then removed during inference on the model output.
 
     //Get the histogram of the input digi occupancy per lumisection.
     TH2F* hEbDigiMap((sources_.at("DigiAllByLumi")).getME(1)->getTH2F());
 
     size_t nTowers = nEtaTowers * nPhiTowers;  //Each occupancy map is of size 34x72 towers
     std::vector<float> ebOccMap1dCumulPad;     //Vector to feed into the ML network
     std::valarray<float> ebOccMap1d(nTowers);  //Array to store occupancy map of size 34x72
     //Store the values from the input histogram into the array
     //to do preprocessing
     for (int i = 0; i < hEbDigiMap->GetNbinsY(); i++) {  //NbinsY = 34, NbinsX = 72
       for (int j = 0; j < hEbDigiMap->GetNbinsX(); j++) {
         int bin = hEbDigiMap->GetBin(j + 1, i + 1);
         int k = (i * nPhiTowers) + j;
         ebOccMap1d[k] = hEbDigiMap->GetBinContent(bin);
       }
     }
     ebOccMap1dQ.push_back(ebOccMap1d);  //Queue which stores input occupancy maps for nLS lumis
     NEventQ.push_back(nEv);             //Queue which stores the no.of events per LS for nLS lumis
 
     if (NEventQ.size() < nLS) {
       return;  //Should have nLS lumis to add the occupancy over.
     }
     if (NEventQ.size() > nLS) {
       NEventQ.pop_front();  //Keep only nLS consecutive LS. Pop the first one if size greater than nLS
     }
     if (ebOccMap1dQ.size() > nLS) {
       ebOccMap1dQ.pop_front();  //Same conditon for the input occupancy maps.
     }
 
     int TNum = 0;
     for (size_t i = 0; i < nLS; i++) {
       TNum += NEventQ[i];  //Total no.of events over nLS lumis
     }
     if (TNum < 200) {
       return;  //The total no.of events should be atleast 200 over nLS for meaningful statistics
     }
     //Fill the ME to monitor the trend of the total no.of events in each input image to the ML model
     meEventsperMLImage.fill(getEcalDQMSetupObjects(), EcalBarrel, double(timestamp_.iLumi), double(TNum));
 
     //Array to hold the sum of inputs, which make atleast 200 events.
     std::valarray<float> ebOccMap1dCumul(0., nTowers);
 
     for (size_t i = 0; i < ebOccMap1dQ.size(); i++) {
       ebOccMap1dCumul += ebOccMap1dQ[i];  //Sum the input arrays of N LS.
     }
     //Applying PU correction derived from training
     ebOccMap1dCumul = ebOccMap1dCumul / (PUcorr_slope_ * pu + PUcorr_intercept_);
 
     //Scaling up to match input dimensions. 36*72 used instead of 34*72 to accommodate the additional padding
     //of 2 rows to prevent the "edge effect" which is done below
     ebOccMap1dCumul = ebOccMap1dCumul * (nEtaTowersPad * nPhiTowers);
 
     //Correction for no.of events in each input image as originally model trained with 500 events per image
     ebOccMap1dCumul = ebOccMap1dCumul * (500. / TNum);
 
     //The pre-processed input is now fed into the input tensor vector which will go into the ML model
     ebOccMap1dCumulPad.assign(std::begin(ebOccMap1dCumul), std::end(ebOccMap1dCumul));
 
     //Replicate and pad with the first and last row to prevent the edge effect
     for (int k = 0; k < nPhiTowers; k++) {
       float val = ebOccMap1dCumulPad[nPhiTowers - 1];
       ebOccMap1dCumulPad.insert(ebOccMap1dCumulPad.begin(),
                                 val);  //padding in the beginning with the first row elements
     }
 
     int size = ebOccMap1dCumulPad.size();
     for (int k = (size - nPhiTowers); k < size; k++) {
       float val = ebOccMap1dCumulPad[k];
       ebOccMap1dCumulPad.push_back(val);  //padding at the end with the last row elements
     }
 
     ///// Model Inference //////
     //An Autoencoder (AE) network with resnet architecture is used here which is trained on
     //certified good data (EB digi occupancy) from Run 2018 data.
     //On giving an input occupancy map, the encoder part of the AE compresses and reduces the input data, learning its features,
     //and the decoder reconstructs the data from the encoded form into a representation as close to the original input as possible.
     //We then compute the Mean squared error (MSE) between the input and output image, also called the Reconstruction Loss,
     //calculated at a tower by tower basis.
     //Thus, given an anomalous tower the loss should be significantly higher than the loss with respect to good towers, which the model
     //has already seen --> anomaly detection.
     //When calculating the loss we also apply a response correction by dividing each input and output image with the average occupancy from
     //all 2018 data (also to be tuned),to accommodate the difference in response of crystals in different regions of the Ecal Barrel
     //Further each loss map from each input image is then multiplied by the last N loss maps,
     ///so that real anomalies which persist with time are enhanced and fluctuations are suppressed.
     //A quality threshold is then applied on this time multiplied loss map, to mark them as GOOD or BAD,
     //after which it is stored as a quality summary ME.
 
     ///ONNX model running///
     std::string instanceName{"AE-DQM-inference"};
     std::string modelFilepath = edm::FileInPath("DQM/EcalMonitorClient/data/onnxModels/resnet.onnx").fullPath();
 
     Ort::SessionOptions sessionOptions;
     sessionOptions.SetIntraOpNumThreads(1);
     Ort::Env env(OrtLoggingLevel::ORT_LOGGING_LEVEL_WARNING, instanceName.c_str());
     Ort::Session session(env, modelFilepath.c_str(), sessionOptions);
 
     Ort::AllocatorWithDefaultOptions allocator;
 
     const char* inputName = session.GetInputName(0, allocator);
 
     Ort::TypeInfo inputTypeInfo = session.GetInputTypeInfo(0);
     auto inputTensorInfo = inputTypeInfo.GetTensorTypeAndShapeInfo();
 
     std::vector<int64_t> inputDims = inputTensorInfo.GetShape();
 
     const char* outputName = session.GetOutputName(0, allocator);
 
     Ort::TypeInfo outputTypeInfo = session.GetOutputTypeInfo(0);
     auto outputTensorInfo = outputTypeInfo.GetTensorTypeAndShapeInfo();
 
     std::vector<int64_t> outputDims = outputTensorInfo.GetShape();
 
     size_t TensorSize = nEtaTowersPad * nPhiTowers;
     std::vector<float> ebRecoOccMap1dPad(TensorSize);  //To store the output reconstructed occupancy
 
     std::vector<const char*> inputNames{inputName};
     std::vector<const char*> outputNames{outputName};
     std::vector<Ort::Value> inputTensors;
     std::vector<Ort::Value> outputTensors;
 
     Ort::MemoryInfo memoryInfo =
         Ort::MemoryInfo::CreateCpu(OrtAllocatorType::OrtArenaAllocator, OrtMemType::OrtMemTypeDefault);
     inputTensors.push_back(Ort::Value::CreateTensor<float>(
         memoryInfo, ebOccMap1dCumulPad.data(), TensorSize, inputDims.data(), inputDims.size()));
 
     outputTensors.push_back(Ort::Value::CreateTensor<float>(
         memoryInfo, ebRecoOccMap1dPad.data(), TensorSize, outputDims.data(), outputDims.size()));
 
     session.Run(Ort::RunOptions{nullptr},
                 inputNames.data(),
                 inputTensors.data(),
                 1,
                 outputNames.data(),
                 outputTensors.data(),
                 1);
 
     ///Inference on the output from the model///
     //2D Loss map to store tower by tower loss between the output (reconstructed) and input occupancies,
     //Have same dimensions as the occupancy plot
     std::valarray<std::valarray<float>> lossMap2d(std::valarray<float>(nPhiTowers), nEtaTowers);
 
     //1D val arrays to store row wise information corresponding to the reconstructed, input and average occupancies, and loss.
     //and to do element wise (tower wise) operations on them to calculate the MSE loss between the reco and input occupancy.
     std::valarray<float> recoOcc1d(0., nPhiTowers);
     std::valarray<float> inputOcc1d(0., nPhiTowers);
     std::valarray<float> avgOcc1d(0., nPhiTowers);
     std::valarray<float> loss_;
 
     //Loss calculation
     //Ignore the top and bottom replicated padded rows when doing inference
     //by making index i run over (1,35) instead of (0,36)
     for (int i = 1; i < 35; i++) {
       for (int j = 0; j < nPhiTowers; j++) {
         int k = (i * nPhiTowers) + j;
         recoOcc1d[j] = ebRecoOccMap1dPad[k];
         inputOcc1d[j] = ebOccMap1dCumulPad[k];
         avgOcc1d[j] = avgOcc_[k];
       }
       //Calculate the MSE loss = (output-input)^2, with avg response correction
       loss_ = std::pow((recoOcc1d / avgOcc1d - inputOcc1d / avgOcc1d), 2);
       lossMap2d[i - 1] = (loss_);
     }
 
     lossMap2dQ.push_back(lossMap2d);  //Store each loss map from the output in the queue
     if (lossMap2dQ.size() > nLSloss) {
       lossMap2dQ.pop_front();  //Keep exactly nLSloss loss maps to multiply
     }
     if (lossMap2dQ.size() < nLSloss) {  //Exit if there are not nLSloss loss maps
       return;
     }
     //To hold the final multiplied loss
     std::valarray<std::valarray<float>> lossMap2dMult(std::valarray<float>(1., nPhiTowers), nEtaTowers);
 
     //Multiply together the last nLSloss loss maps
     //So that real anomalies which persist with time are enhanced and fluctuations are suppressed.
     for (size_t i = 0; i < lossMap2dQ.size(); i++) {
       lossMap2dMult *= lossMap2dQ[i];
     }
 
     //Fill the AELoss ME with the values of this time multiplied loss map
     MESet const& sAELoss(sources_.at("AELoss"));
     TH2F* hLossMap2dMult(sAELoss.getME(1)->getTH2F());
     for (int i = 0; i < hLossMap2dMult->GetNbinsY(); i++) {
       for (int j = 0; j < hLossMap2dMult->GetNbinsX(); j++) {
         int bin_ = hLossMap2dMult->GetBin(j + 1, i + 1);
         double content = lossMap2dMult[i][j];
         hLossMap2dMult->SetBinContent(bin_, content);
       }
     }
     ///////////////////// ML Quality Summary /////////////////////
     //Apply the quality threshold on the time multiplied loss map stored in the ME AELoss
     //If anomalous, the tower entry will have a large loss value. If good, the value will be close to zero.
 
     MESet::const_iterator dAEnd(sAELoss.end(GetElectronicsMap()));
     for (MESet::const_iterator dItr(sAELoss.beginChannel(GetElectronicsMap())); dItr != dAEnd;
          dItr.toNextChannel(GetElectronicsMap())) {
       DetId id(dItr->getId());
 
       bool doMaskML(meMLQualitySummary.maskMatches(id, mask, statusManager_, GetTrigTowerMap()));
 
       float entries(dItr->getBinContent());
       int quality(doMaskML ? kMGood : kGood);
       //If a trigger tower entry is greater than the ML threshold, set it to Bad quality, otherwise Good.
       if (entries > MLThreshold_) {
         quality = doMaskML ? kMBad : kBad;
       }
       //Fill the quality summary with the quality of the given tower id.
       meMLQualitySummary.setBinContent(getEcalDQMSetupObjects(), id, double(quality));
     }  // ML Quality Summary
   }    // producePlots()
 
   DEFINE_ECALDQM_WORKER(MLClient);
 }  // namespace ecaldqm

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