TFModel Class Reference

#include <TFModel.h>

Public Member Functions
	TFModel (const std::string &savedir, const std::vector< InputConfigKeys > &scalarInputKeys, const std::vector< InputConfigKeys > &vectorInputKeys, const std::vector< std::string > &outputKeys)
void	ensure_initialized () const
std::vector< tensorflow::Tensor >	predict (const VarDict &vars) const

Private Member Functions
void	initTFSession () const

Static Private Member Functions
static tensorflow::Tensor	constructDummyVectorInput (const std::vector< std::string > &vars, float fillValue=0.0)
static tensorflow::Tensor	constructScalarInput (const std::unordered_map< std::string, double > &varMap, const std::vector< std::string > &vars)
static tensorflow::Tensor	constructVectorInput (const std::unordered_map< std::string, std::vector< double >> &varMap, const std::vector< std::string > &vars)

Private Attributes
ModelConfig	config
std::shared_ptr< tensorflow::Session >	tfSession
bool	initialized

Detailed Description

Definition at line 13 of file TFModel.h.

Constructor & Destructor Documentation

TFModel::TFModel	(	const std::string &	savedir,
		const std::vector< InputConfigKeys > &	scalarInputKeys,
		const std::vector< InputConfigKeys > &	vectorInputKeys,
		const std::vector< std::string > &	outputKeys
	)

Definition at line 17 of file TFModel.cxx.

  : config(savedir, scalarInputKeys, vectorInputKeys, outputKeys),
    tfSession(nullptr),
    initialized(false)
{ }

Member Function Documentation

Tensor TFModel::constructDummyVectorInput ( const std::vector< std::string > &  vars, float  fillValue = 0.0  )

staticprivate

Definition at line 27 of file TFModel.cxx.

{
    Tensor result(
        DT_FLOAT, TensorShape( {1, 1, asInt(vars.size())} )
    );

    auto resultData = result.tensor<float, 3>();

    for (int varIdx = 0; varIdx < asInt(vars.size()); varIdx++) {
        resultData(0, 0, varIdx) = fillValue;
    }

    return result;
}

Tensor TFModel::constructScalarInput ( const std::unordered_map< std::string, double > &  varMap, const std::vector< std::string > &  vars  )

staticprivate

Definition at line 44 of file TFModel.cxx.

{
    Tensor result(
        DT_FLOAT, TensorShape( {1, asInt(vars.size())} )
    );
    auto resultData = result.tensor<float, 2>();

    for (int varIdx = 0; varIdx < asInt(vars.size()); varIdx++) {
        resultData(0, varIdx) = varMap.at(vars[varIdx]);
    }

    return result;
}

tensorflow::Tensor TFModel::constructVectorInput ( const std::unordered_map< std::string, std::vector< double >> &  varMap, const std::vector< std::string > &  vars  )

staticprivate

Definition at line 61 of file TFModel.cxx.

{
    if (vars.empty()) {
        return constructDummyVectorInput(vars, 0.0);
    }

    const size_t vectorSize = (varMap.empty()) ? 0 : varMap.at(vars[0]).size();

    if (vectorSize == 0) {
        /*
         * NOTE: Fake tensor with vectorSize == 1 is needed, since otherwise
         * tensorflow fails to infer graph dimensions.
         */
        return constructDummyVectorInput(vars, 0.0);
    }

    Tensor result(
        DT_FLOAT, TensorShape({ 1, asInt(vectorSize), asInt(vars.size()) })
    );
    auto resultData = result.tensor<float, 3>();

    for (int varIdx = 0; varIdx < asInt(vars.size()); varIdx++)
    {
        const auto &values = varMap.at(vars[varIdx]);

        if (values.size() != vectorSize) {
            throw std::runtime_error("Vectors have different lengths");
        }

        for (int i = 0; i < asInt(vectorSize); i++) {
            resultData(0, i, varIdx) = values[i];
        }
    }

    return result;
}

void TFModel::ensure_initialized

(

)

const

Definition at line 135 of file TFModel.cxx.

{
    if (initialized) {
        return;
    }

    config.load();
    initTFSession();

    initialized = true;
}

void TFModel::initTFSession ( ) const

private

Definition at line 101 of file TFModel.cxx.

{
    GraphDef graph;
    Session *session = nullptr;

    /* TODO: Find NewSession version with safer memory management */
    auto status = NewSession(SessionOptions(), &session);

    if (! status.ok())
    {
        delete session;
        throw std::runtime_error(
            "Failed to initialize TF Session: " + status.ToString()
        );
    }

    tfSession.reset(session);
    session = nullptr;

    status = ReadBinaryProto(Env::Default(), config.getModelPath(), &graph);
    if (! status.ok()) {
        throw std::runtime_error(
            "Failed to load TF Graph: " + status.ToString()
        );
    }

    status = tfSession->Create(graph);
    if (! status.ok()) {
        throw std::runtime_error(
            "Failed to create TF Session: " + status.ToString()
        );
    }
}

std::vector< Tensor > TFModel::predict

(

const VarDict &

vars

)

const

Definition at line 147 of file TFModel.cxx.

{
    ensure_initialized();

    std::vector<std::pair<std::string, Tensor>> inputs;
    std::vector<Tensor>                         outputs;

    inputs.reserve(
        config.getScalarInputs().size() + config.getVectorInputs().size()
    );

    for (const auto &inputConfig : config.getScalarInputs()) {
        inputs.emplace_back(
            inputConfig.nodeName,
            constructScalarInput(vars.scalar, inputConfig.varNames)
        );
    }

    for (const auto &inputConfig : config.getVectorInputs()) {
        inputs.emplace_back(
            inputConfig.nodeName,
            constructVectorInput(vars.vector, inputConfig.varNames)
        );
    }

    auto status = tfSession->Run(
        inputs, config.getOutputNodes(), {}, &outputs
    );

    if (! status.ok()) {
        throw std::runtime_error(
            "Failed to run TF Session: " + status.ToString()
        );
    }

    return outputs;
}

Member Data Documentation

ModelConfig TFModel::config

mutableprivate

Definition at line 44 of file TFModel.h.

bool TFModel::initialized

mutableprivate

Definition at line 46 of file TFModel.h.

std::shared_ptr<tensorflow::Session> TFModel::tfSession

mutableprivate

Definition at line 45 of file TFModel.h.

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The documentation for this class was generated from the following files:

• dunereco/dunereco/VLNets/models/tf_model/TFModel.h
• dunereco/dunereco/VLNets/models/tf_model/TFModel.cxx