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[EventFiltering] Automatically deal with models that need float and double precision in input #1068

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Merged
fgrosa merged 2 commits into from
Jul 28, 2022
Merged

[EventFiltering] Automatically deal with models that need float and double precision in input #1068

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Automatically deal with models that need float and double precision i…
fgrosa Jul 28, 2022
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Automatically deal with models that need float and double precision i…
fgrosa Jul 28, 2022
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25 changes: 22 additions & 3 deletions EventFiltering/PWGHF/HFFilter.cxx
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Original file line number Diff line number Diff line change
Expand Up @@ -327,6 +327,7 @@ struct HfFilter { // Main struct for HF triggers
std::array<std::vector<std::vector<int64_t>>, kNCharmParticles> outputShapesML{};
std::array<std::shared_ptr<Ort::Experimental::Session>, kNCharmParticles> sessionML = {nullptr, nullptr, nullptr, nullptr, nullptr};
std::array<Ort::SessionOptions, kNCharmParticles> sessionOptions{};
std::array<int, kNCharmParticles> dataTypeML{};
std::array<Ort::Env, kNCharmParticles> env = {
Ort::Env{ORT_LOGGING_LEVEL_WARNING, "ml-model-d0-triggers"},
Ort::Env{ORT_LOGGING_LEVEL_WARNING, "ml-model-dplus-triggers"},
Expand Down Expand Up @@ -392,6 +393,10 @@ struct HfFilter { // Main struct for HF triggers
}
outputNamesML[iCharmPart] = sessionML[iCharmPart]->GetOutputNames();
outputShapesML[iCharmPart] = sessionML[iCharmPart]->GetOutputShapes();

Ort::TypeInfo typeInfo = sessionML[iCharmPart]->GetInputTypeInfo(0);
auto tensorInfo = typeInfo.GetTensorTypeAndShapeInfo();
dataTypeML[iCharmPart] = tensorInfo.GetElementType();
}
}
}
Expand Down Expand Up @@ -698,9 +703,16 @@ struct HfFilter { // Main struct for HF triggers
// apply ML models
if (applyML && onnxFiles[kD0] != "") {
// TODO: add more feature configurations
std::vector<float> inputFeaturesD0{trackPos.pt(), trackPos.dcaXY(), trackPos.dcaZ(), trackNeg.pt(), trackNeg.dcaXY(), trackNeg.dcaZ()};
std::vector<Ort::Value> inputTensorD0;
inputTensorD0.push_back(Ort::Experimental::Value::CreateTensor<float>(inputFeaturesD0.data(), inputFeaturesD0.size(), inputShapesML[kD0][0]));
std::vector<float> inputFeaturesD0{trackPos.pt(), trackPos.dcaXY(), trackPos.dcaZ(), trackNeg.pt(), trackNeg.dcaXY(), trackNeg.dcaZ()};
std::vector<double> inputFeaturesDoD0{trackPos.pt(), trackPos.dcaXY(), trackPos.dcaZ(), trackNeg.pt(), trackNeg.dcaXY(), trackNeg.dcaZ()};
if (dataTypeML[kD0] == 1) {
inputTensorD0.push_back(Ort::Experimental::Value::CreateTensor<float>(inputFeaturesD0.data(), inputFeaturesD0.size(), inputShapesML[kD0][0]));
} else if (dataTypeML[kD0] == 11) {
inputTensorD0.push_back(Ort::Experimental::Value::CreateTensor<double>(inputFeaturesDoD0.data(), inputFeaturesDoD0.size(), inputShapesML[kD0][0]));
} else {
LOG(fatal) << "Error running model inference: Unexpected input data type.";
}

// double-check the dimensions of the input tensor
if (inputTensorD0[0].GetTensorTypeAndShapeInfo().GetShape()[0] > 0) { // vectorial models can have negative shape if the shape is unknown
Expand Down Expand Up @@ -826,9 +838,16 @@ struct HfFilter { // Main struct for HF triggers
if (applyML) {
// TODO: add more feature configurations
std::vector<float> inputFeatures{trackFirst.pt(), trackFirst.dcaXY(), trackFirst.dcaZ(), trackSecond.pt(), trackSecond.dcaXY(), trackSecond.dcaZ(), trackThird.pt(), trackThird.dcaXY(), trackThird.dcaZ()};
std::vector<double> inputFeaturesD{trackFirst.pt(), trackFirst.dcaXY(), trackFirst.dcaZ(), trackSecond.pt(), trackSecond.dcaXY(), trackSecond.dcaZ(), trackThird.pt(), trackThird.dcaXY(), trackThird.dcaZ()};
for (auto iCharmPart{0}; (iCharmPart < kNCharmParticles - 1) && is3Prong[iCharmPart] && onnxFiles[iCharmPart + 1] != ""; ++iCharmPart) {
std::vector<Ort::Value> inputTensor;
inputTensor.push_back(Ort::Experimental::Value::CreateTensor<float>(inputFeatures.data(), inputFeatures.size(), inputShapesML[iCharmPart + 1][0]));
if (dataTypeML[iCharmPart + 1] == 1) {
inputTensor.push_back(Ort::Experimental::Value::CreateTensor<float>(inputFeatures.data(), inputFeatures.size(), inputShapesML[iCharmPart + 1][0]));
} else if (dataTypeML[iCharmPart + 1] == 11) {
inputTensor.push_back(Ort::Experimental::Value::CreateTensor<double>(inputFeaturesD.data(), inputFeaturesD.size(), inputShapesML[iCharmPart + 1][0]));
} else {
LOG(error) << "Error running model inference: Unexpected input data type.";
}

// double-check the dimensions of the input tensor
if (inputTensor[0].GetTensorTypeAndShapeInfo().GetShape()[0] > 0) { // vectorial models can have negative shape if the shape is unknown
Expand Down