Track classifier using TensorFlow - continuation #32128
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| #ifndef RecoTracker_FinalTrackSelectors_TfGraphDefWrapper_h | ||
| #define RecoTracker_FinalTrackSelectors_TfGraphDefWrapper_h | ||
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| #include "PhysicsTools/TensorFlow/interface/TensorFlow.h" | ||
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| class TfGraphDefWrapper { | ||
| public: | ||
| TfGraphDefWrapper(tensorflow::Session*); | ||
| ~TfGraphDefWrapper() { tensorflow::closeSession(session_); }; | ||
| TfGraphDefWrapper(const TfGraphDefWrapper&) = delete; | ||
| TfGraphDefWrapper& operator=(const TfGraphDefWrapper&) = delete; | ||
| TfGraphDefWrapper(TfGraphDefWrapper&&) = delete; | ||
| TfGraphDefWrapper& operator=(TfGraphDefWrapper&&) = delete; | ||
| const tensorflow::Session* getSession() const; | ||
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| private: | ||
| tensorflow::Session* session_; | ||
| }; | ||
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| #endif | ||
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| // -*- C++ -*- | ||
| // | ||
| // Package: RecoTracker/FinalTrackSelectors | ||
| // Class: TFGraphDefProducer | ||
| // | ||
| /**\class TFGraphDefProducer | ||
| Description: Produces TfGraphRecord into the event containing a tensorflow GraphDef object that can be used for running inference on a pretrained network | ||
| */ | ||
| // | ||
| // Original Author: Joona Havukainen | ||
| // Created: Fri, 24 Jul 2020 08:04:00 GMT | ||
| // | ||
| // | ||
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| // system include files | ||
| #include <memory> | ||
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| // user include files | ||
| #include "FWCore/Framework/interface/ModuleFactory.h" | ||
| #include "FWCore/Framework/interface/ESProducer.h" | ||
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| #include "FWCore/Framework/interface/ESHandle.h" | ||
| #include "TrackingTools/Records/interface/TfGraphRecord.h" | ||
| #include "RecoTracker/FinalTrackSelectors/interface/TfGraphDefWrapper.h" | ||
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| // class declaration | ||
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| class TfGraphDefProducer : public edm::ESProducer { | ||
| public: | ||
| TfGraphDefProducer(const edm::ParameterSet&); | ||
| using ReturnType = std::unique_ptr<TfGraphDefWrapper>; | ||
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| ReturnType produce(const TfGraphRecord&); | ||
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| static void fillDescriptions(edm::ConfigurationDescriptions& descriptions); | ||
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| private: | ||
| const std::string filename_; | ||
| // ----------member data --------------------------- | ||
| }; | ||
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| TfGraphDefProducer::TfGraphDefProducer(const edm::ParameterSet& iConfig) | ||
| : filename_(iConfig.getParameter<edm::FileInPath>("FileName").fullPath()) { | ||
| auto componentName = iConfig.getParameter<std::string>("ComponentName"); | ||
| setWhatProduced(this, componentName); | ||
| } | ||
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| // ------------ method called to produce the data ------------ | ||
| std::unique_ptr<TfGraphDefWrapper> TfGraphDefProducer::produce(const TfGraphRecord& iRecord) { | ||
| return std::make_unique<TfGraphDefWrapper>(tensorflow::createSession(tensorflow::loadGraphDef(filename_), 1)); | ||
|
There was a problem hiding this comment. Does this actually leak the There was a problem hiding this comment. Thanks for pointing it out! Per the recent CMSSW ML documentation, we need to indeed delete the graphDef by hand after closeSession (for some reason I was under the impression closeSession deletes the associated graphs). I will open a PR to fix this. I don't seem to observe any detectable effect on the memory, though. |
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| } | ||
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| void TfGraphDefProducer::fillDescriptions(edm::ConfigurationDescriptions& descriptions) { | ||
| edm::ParameterSetDescription desc; | ||
| desc.add<std::string>("ComponentName", "tfGraphDef"); | ||
| desc.add<edm::FileInPath>("FileName"); | ||
| descriptions.add("tfGraphDefProducer", desc); | ||
| } | ||
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| //define this as a plug-in | ||
| #include "FWCore/PluginManager/interface/ModuleDef.h" | ||
| #include "FWCore/Framework/interface/MakerMacros.h" | ||
| DEFINE_FWK_EVENTSETUP_MODULE(TfGraphDefProducer); | ||
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| #include "RecoTracker/FinalTrackSelectors/interface/TrackMVAClassifier.h" | ||
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| #include "FWCore/Framework/interface/EventSetup.h" | ||
| #include "FWCore/Framework/interface/global/EDProducer.h" | ||
| #include "DataFormats/TrackReco/interface/Track.h" | ||
| #include "DataFormats/VertexReco/interface/Vertex.h" | ||
| #include "FWCore/Framework/interface/ConsumesCollector.h" | ||
| #include "getBestVertex.h" | ||
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| #include "TrackingTools/Records/interface/TfGraphRecord.h" | ||
| #include "PhysicsTools/TensorFlow/interface/TensorFlow.h" | ||
| #include "RecoTracker/FinalTrackSelectors/interface/TfGraphDefWrapper.h" | ||
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| namespace { | ||
| class TfDnn { | ||
| public: | ||
| TfDnn(const edm::ParameterSet& cfg) | ||
| : tfDnnLabel_(cfg.getParameter<std::string>("tfDnnLabel")), | ||
| session_(nullptr) | ||
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| {} | ||
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| static const char* name() { return "TrackTfClassifier"; } | ||
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| static void fillDescriptions(edm::ParameterSetDescription& desc) { | ||
| desc.add<std::string>("tfDnnLabel", "trackSelectionTf"); | ||
| } | ||
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| void beginStream() {} | ||
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| void initEvent(const edm::EventSetup& es) { | ||
| if (session_ == nullptr) { | ||
| edm::ESHandle<TfGraphDefWrapper> tfDnnHandle; | ||
| es.get<TfGraphRecord>().get(tfDnnLabel_, tfDnnHandle); | ||
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you can see this PR for an example: https://github.com/cms-sw/cmssw/pull/32246/files There was a problem hiding this comment. Hello, it seems like I need to include the new header file for esConsumes(), but I am not sure which header is supposed to used in here. There was a problem hiding this comment. esConsumes needs to come from the EDProducer, or passed via ConsumesCollector. This will require a few changes to the other Track MVA classes. So that this PR here doesn't get stuck or expand in scope too much, let's defer the ESGetToken update for a follow-up. |
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| session_ = tfDnnHandle.product()->getSession(); | ||
| } | ||
| } | ||
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| float operator()(reco::Track const& trk, | ||
| reco::BeamSpot const& beamSpot, | ||
| reco::VertexCollection const& vertices) const { | ||
| const auto& bestVertex = getBestVertex(trk, vertices); | ||
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| tensorflow::Tensor input1(tensorflow::DT_FLOAT, {1, 29}); | ||
| tensorflow::Tensor input2(tensorflow::DT_FLOAT, {1, 1}); | ||
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| input1.matrix<float>()(0, 0) = trk.pt(); | ||
| input1.matrix<float>()(0, 1) = trk.innerMomentum().x(); | ||
| input1.matrix<float>()(0, 2) = trk.innerMomentum().y(); | ||
| input1.matrix<float>()(0, 3) = trk.innerMomentum().z(); | ||
| input1.matrix<float>()(0, 4) = trk.innerMomentum().rho(); | ||
| input1.matrix<float>()(0, 5) = trk.outerMomentum().x(); | ||
| input1.matrix<float>()(0, 6) = trk.outerMomentum().y(); | ||
| input1.matrix<float>()(0, 7) = trk.outerMomentum().z(); | ||
| input1.matrix<float>()(0, 8) = trk.outerMomentum().rho(); | ||
| input1.matrix<float>()(0, 9) = trk.ptError(); | ||
| input1.matrix<float>()(0, 10) = trk.dxy(bestVertex); | ||
| input1.matrix<float>()(0, 11) = trk.dz(bestVertex); | ||
| input1.matrix<float>()(0, 12) = trk.dxy(beamSpot.position()); | ||
| input1.matrix<float>()(0, 13) = trk.dz(beamSpot.position()); | ||
| input1.matrix<float>()(0, 14) = trk.dxyError(); | ||
| input1.matrix<float>()(0, 15) = trk.dzError(); | ||
| input1.matrix<float>()(0, 16) = trk.normalizedChi2(); | ||
| input1.matrix<float>()(0, 17) = trk.eta(); | ||
| input1.matrix<float>()(0, 18) = trk.phi(); | ||
| input1.matrix<float>()(0, 19) = trk.etaError(); | ||
| input1.matrix<float>()(0, 20) = trk.phiError(); | ||
| input1.matrix<float>()(0, 21) = trk.hitPattern().numberOfValidPixelHits(); | ||
| input1.matrix<float>()(0, 22) = trk.hitPattern().numberOfValidStripHits(); | ||
| input1.matrix<float>()(0, 23) = trk.ndof(); | ||
| input1.matrix<float>()(0, 24) = trk.hitPattern().numberOfLostTrackerHits(reco::HitPattern::MISSING_INNER_HITS); | ||
| input1.matrix<float>()(0, 25) = trk.hitPattern().numberOfLostTrackerHits(reco::HitPattern::MISSING_OUTER_HITS); | ||
| input1.matrix<float>()(0, 26) = | ||
| trk.hitPattern().trackerLayersTotallyOffOrBad(reco::HitPattern::MISSING_INNER_HITS); | ||
| input1.matrix<float>()(0, 27) = | ||
| trk.hitPattern().trackerLayersTotallyOffOrBad(reco::HitPattern::MISSING_OUTER_HITS); | ||
| input1.matrix<float>()(0, 28) = trk.hitPattern().trackerLayersWithoutMeasurement(reco::HitPattern::TRACK_HITS); | ||
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| //Original algo as its own input, it will enter the graph so that it gets one-hot encoded, as is the preferred | ||
| //format for categorical inputs, where the labels do not have any metric amongst them | ||
| input2.matrix<float>()(0, 0) = trk.originalAlgo(); | ||
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| //The names for the input tensors get locked when freezing the trained tensorflow model. The NamedTensors must | ||
| //match those names | ||
| tensorflow::NamedTensorList inputs; | ||
| inputs.resize(2); | ||
| inputs[0] = tensorflow::NamedTensor("x", input1); | ||
| inputs[1] = tensorflow::NamedTensor("y", input2); | ||
| std::vector<tensorflow::Tensor> outputs; | ||
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| //evaluate the input | ||
| tensorflow::run(const_cast<tensorflow::Session*>(session_), inputs, {"Identity"}, &outputs); | ||
| //scale output to be [-1, 1] due to convention | ||
| float output = 2.0 * outputs[0].matrix<float>()(0, 0) - 1.0; | ||
| return output; | ||
| } | ||
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| const std::string tfDnnLabel_; | ||
| const tensorflow::Session* session_; | ||
| }; | ||
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| using TrackTfClassifier = TrackMVAClassifier<TfDnn>; | ||
| } // namespace | ||
| #include "FWCore/PluginManager/interface/ModuleDef.h" | ||
| #include "FWCore/Framework/interface/MakerMacros.h" | ||
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| DEFINE_FWK_MODULE(TrackTfClassifier); | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,5 @@ | ||
| from RecoTracker.FinalTrackSelectors.tfGraphDefProducer_cfi import tfGraphDefProducer as _tfGraphDefProducer | ||
| trackSelectionTf = _tfGraphDefProducer.clone( | ||
| ComponentName = "trackSelectionTf", | ||
| FileName = "RecoTracker/FinalTrackSelectors/data/TrackTfClassifier/QCDFlatPU_QCDHighPt_ZEE_DisplacedSUSY_2020.pb" | ||
| ) |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,4 @@ | ||
| #include "RecoTracker/FinalTrackSelectors/interface/TfGraphDefWrapper.h" | ||
| #include "FWCore/Utilities/interface/typelookup.h" | ||
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| TYPELOOKUP_DATA_REG(TfGraphDefWrapper); |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,6 @@ | ||
| #include "RecoTracker/FinalTrackSelectors/interface/TfGraphDefWrapper.h" | ||
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| TfGraphDefWrapper::TfGraphDefWrapper(tensorflow::Session* session) : session_(session) {} | ||
| const tensorflow::Session* TfGraphDefWrapper::getSession() const { | ||
| return const_cast<const tensorflow::Session*>(session_); | ||
| } |
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Given the destructor, it would be safer to make this class non-copyable and non-movable. In principle movability could be achieved, but that would require changes in the destructor. (currently an accidental copy of
TfGraphDefWrappercould lead to premature closing of the Session)There was a problem hiding this comment.
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Could we have some suggestions to implement it? Maybe try something like ‘TfGraphDefWrapper & operator=(TfGraphDefWrapper&) = delete’ ?
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Thanks Matti for catching this!
@minxiyang per the rule of five https://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines#Rc-five, if you define a custom destructor, you should explicitly state what happens to the move and copy constructors&operators. In this case, they should be deleted: