IVGCVSW-5284 Refactor ExecuteNetwork

 * Removed boost program options and replaced it with cxxopts
 * Unified adding, parsing and validation of program options
   into the struct ProgramOptions
 * Program options are now parsed directly into ExecuteNetworkParams
   which can be passed directly to MainImpl
 * Split NetworkExecutionUtils into header and source
 * Removed RunTest
 * Removed RunCsvTest
 * Removed RunClTuning
 * Moved MainImpl back to ExecuteNetwork.cpp
 * Added additional util functions
The functionality of ExecuteNetwork remains the same. Only
cl tuning runs need to be started separately
and there is no short option for fp16-turbo-mode because -h is
reserved in cxxopts to print help messages

Signed-off-by: Jan Eilers <jan.eilers@arm.com>
Change-Id: Ib9689375c81e1a184c17bb3ea66c3550430bbe09

2 files changed, 325 insertions, 901 deletions

diff --git a/tests/NetworkExecutionUtils/NetworkExecutionUtils.cpp b/tests/NetworkExecutionUtils/NetworkExecutionUtils.cpp
new file mode 100644
index 0000000000..3e7c87d653
--- /dev/null
+++ b/tests/NetworkExecutionUtils/NetworkExecutionUtils.cpp
@@ -0,0 +1,292 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "NetworkExecutionUtils.hpp"
+
+#include <Filesystem.hpp>
+#include <InferenceTest.hpp>
+#include <ResolveType.hpp>
+
+#if defined(ARMNN_SERIALIZER)
+#include "armnnDeserializer/IDeserializer.hpp"
+#endif
+#if defined(ARMNN_CAFFE_PARSER)
+#include "armnnCaffeParser/ICaffeParser.hpp"
+#endif
+#if defined(ARMNN_TF_PARSER)
+#include "armnnTfParser/ITfParser.hpp"
+#endif
+#if defined(ARMNN_TF_LITE_PARSER)
+#include "armnnTfLiteParser/ITfLiteParser.hpp"
+#endif
+#if defined(ARMNN_ONNX_PARSER)
+#include "armnnOnnxParser/IOnnxParser.hpp"
+#endif
+
+
+template<typename T, typename TParseElementFunc>
+std::vector<T> ParseArrayImpl(std::istream& stream, TParseElementFunc parseElementFunc, const char* chars = "\t ,:")
+{
+    std::vector<T> result;
+    // Processes line-by-line.
+    std::string line;
+    while (std::getline(stream, line))
+    {
+        std::vector<std::string> tokens = armnn::stringUtils::StringTokenizer(line, chars);
+        for (const std::string& token : tokens)
+        {
+            if (!token.empty()) // See https://stackoverflow.com/questions/10437406/
+            {
+                try
+                {
+                    result.push_back(parseElementFunc(token));
+                }
+                catch (const std::exception&)
+                {
+                    ARMNN_LOG(error) << "'" << token << "' is not a valid number. It has been ignored.";
+                }
+            }
+        }
+    }
+
+    return result;
+}
+
+
+template<armnn::DataType NonQuantizedType>
+auto ParseDataArray(std::istream& stream);
+
+template<armnn::DataType QuantizedType>
+auto ParseDataArray(std::istream& stream,
+                    const float& quantizationScale,
+                    const int32_t& quantizationOffset);
+
+template<>
+auto ParseDataArray<armnn::DataType::Float32>(std::istream& stream)
+{
+    return ParseArrayImpl<float>(stream, [](const std::string& s) { return std::stof(s); });
+}
+
+template<>
+auto ParseDataArray<armnn::DataType::Signed32>(std::istream& stream)
+{
+    return ParseArrayImpl<int>(stream, [](const std::string& s) { return std::stoi(s); });
+}
+
+template<>
+auto ParseDataArray<armnn::DataType::QAsymmU8>(std::istream& stream)
+{
+    return ParseArrayImpl<uint8_t>(stream,
+                                   [](const std::string& s) { return armnn::numeric_cast<uint8_t>(std::stoi(s)); });
+}
+
+template<>
+auto ParseDataArray<armnn::DataType::QAsymmU8>(std::istream& stream,
+                                               const float& quantizationScale,
+                                               const int32_t& quantizationOffset)
+{
+    return ParseArrayImpl<uint8_t>(stream,
+                                   [&quantizationScale, &quantizationOffset](const std::string& s)
+                                   {
+                                       return armnn::numeric_cast<uint8_t>(
+                                               armnn::Quantize<uint8_t>(std::stof(s),
+                                                                        quantizationScale,
+                                                                        quantizationOffset));
+                                   });
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+std::vector<T> GenerateDummyTensorData(unsigned int numElements)
+{
+    return std::vector<T>(numElements, static_cast<T>(0));
+}
+
+
+std::vector<unsigned int> ParseArray(std::istream& stream)
+{
+    return ParseArrayImpl<unsigned int>(
+            stream,
+            [](const std::string& s) { return armnn::numeric_cast<unsigned int>(std::stoi(s)); });
+}
+
+std::vector<std::string> ParseStringList(const std::string& inputString, const char* delimiter)
+{
+    std::stringstream stream(inputString);
+    return ParseArrayImpl<std::string>(stream, [](const std::string& s) {
+        return armnn::stringUtils::StringTrimCopy(s); }, delimiter);
+}
+
+
+TensorPrinter::TensorPrinter(const std::string& binding,
+                             const armnn::TensorInfo& info,
+                             const std::string& outputTensorFile,
+                             bool dequantizeOutput)
+                             : m_OutputBinding(binding)
+                             , m_Scale(info.GetQuantizationScale())
+                             , m_Offset(info.GetQuantizationOffset())
+                             , m_OutputTensorFile(outputTensorFile)
+                             , m_DequantizeOutput(dequantizeOutput) {}
+
+void TensorPrinter::operator()(const std::vector<float>& values)
+{
+    ForEachValue(values, [](float value)
+    {
+        printf("%f ", value);
+    });
+    WriteToFile(values);
+}
+
+void TensorPrinter::operator()(const std::vector<uint8_t>& values)
+{
+    if(m_DequantizeOutput)
+    {
+        auto& scale = m_Scale;
+        auto& offset = m_Offset;
+        std::vector<float> dequantizedValues;
+        ForEachValue(values, [&scale, &offset, &dequantizedValues](uint8_t value)
+        {
+            auto dequantizedValue = armnn::Dequantize(value, scale, offset);
+            printf("%f ", dequantizedValue);
+            dequantizedValues.push_back(dequantizedValue);
+        });
+        WriteToFile(dequantizedValues);
+    }
+    else
+    {
+        const std::vector<int> intValues(values.begin(), values.end());
+        operator()(intValues);
+    }
+}
+
+void TensorPrinter::operator()(const std::vector<int>& values)
+{
+    ForEachValue(values, [](int value)
+    {
+        printf("%d ", value);
+    });
+    WriteToFile(values);
+}
+
+template<typename Container, typename Delegate>
+void TensorPrinter::ForEachValue(const Container& c, Delegate delegate)
+{
+    std::cout << m_OutputBinding << ": ";
+    for (const auto& value : c)
+    {
+        delegate(value);
+    }
+    printf("\n");
+}
+
+template<typename T>
+void TensorPrinter::WriteToFile(const std::vector<T>& values)
+{
+    if (!m_OutputTensorFile.empty())
+    {
+        std::ofstream outputTensorFile;
+        outputTensorFile.open(m_OutputTensorFile, std::ofstream::out | std::ofstream::trunc);
+        if (outputTensorFile.is_open())
+        {
+            outputTensorFile << m_OutputBinding << ": ";
+            std::copy(values.begin(), values.end(), std::ostream_iterator<T>(outputTensorFile, " "));
+        }
+        else
+        {
+            ARMNN_LOG(info) << "Output Tensor File: " << m_OutputTensorFile << " could not be opened!";
+        }
+        outputTensorFile.close();
+    }
+}
+
+using TContainer         = mapbox::util::variant<std::vector<float>, std::vector<int>, std::vector<unsigned char>>;
+using QuantizationParams = std::pair<float, int32_t>;
+
+void PopulateTensorWithData(TContainer& tensorData,
+                            unsigned int numElements,
+                            const std::string& dataTypeStr,
+                            const armnn::Optional<QuantizationParams>& qParams,
+                            const armnn::Optional<std::string>& dataFile)
+{
+    const bool readFromFile = dataFile.has_value() && !dataFile.value().empty();
+    const bool quantizeData = qParams.has_value();
+
+    std::ifstream inputTensorFile;
+    if (readFromFile)
+    {
+        inputTensorFile = std::ifstream(dataFile.value());
+    }
+
+    if (dataTypeStr.compare("float") == 0)
+    {
+        if (quantizeData)
+        {
+            const float qScale  = qParams.value().first;
+            const int   qOffset = qParams.value().second;
+
+            tensorData = readFromFile ?
+                         ParseDataArray<armnn::DataType::QAsymmU8>(inputTensorFile, qScale, qOffset) :
+                         GenerateDummyTensorData<armnn::DataType::QAsymmU8>(numElements);
+        }
+        else
+        {
+            tensorData = readFromFile ?
+                         ParseDataArray<armnn::DataType::Float32>(inputTensorFile) :
+                         GenerateDummyTensorData<armnn::DataType::Float32>(numElements);
+        }
+    }
+    else if (dataTypeStr.compare("int") == 0)
+    {
+        tensorData = readFromFile ?
+                     ParseDataArray<armnn::DataType::Signed32>(inputTensorFile) :
+                     GenerateDummyTensorData<armnn::DataType::Signed32>(numElements);
+    }
+    else if (dataTypeStr.compare("qasymm8") == 0)
+    {
+        tensorData = readFromFile ?
+                     ParseDataArray<armnn::DataType::QAsymmU8>(inputTensorFile) :
+                     GenerateDummyTensorData<armnn::DataType::QAsymmU8>(numElements);
+    }
+    else
+    {
+        std::string errorMessage = "Unsupported tensor data type " + dataTypeStr;
+        ARMNN_LOG(fatal) << errorMessage;
+
+        inputTensorFile.close();
+        throw armnn::Exception(errorMessage);
+    }
+
+    inputTensorFile.close();
+}
+
+bool ValidatePath(const std::string& file, const bool expectFile)
+{
+    if (!fs::exists(file))
+    {
+        std::cerr << "Given file path '" << file << "' does not exist" << std::endl;
+        return false;
+    }
+    if (!fs::is_regular_file(file) && expectFile)
+    {
+        std::cerr << "Given file path '" << file << "' is not a regular file" << std::endl;
+        return false;
+    }
+    return true;
+}
+
+bool ValidatePaths(const std::vector<std::string>& fileVec, const bool expectFile)
+{
+    bool allPathsValid = true;
+    for (auto const& file : fileVec)
+    {
+        if(!ValidatePath(file, expectFile))
+        {
+            allPathsValid = false;
+        }
+    }
+    return allPathsValid;
+}
+
+
+
diff --git a/tests/NetworkExecutionUtils/NetworkExecutionUtils.hpp b/tests/NetworkExecutionUtils/NetworkExecutionUtils.hpp
index f79d630291..d101d4a23c 100644
--- a/tests/NetworkExecutionUtils/NetworkExecutionUtils.hpp
+++ b/tests/NetworkExecutionUtils/NetworkExecutionUtils.hpp
@@ -2,290 +2,50 @@
 // Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
-#include <armnn/ArmNN.hpp>
-#include <armnn/TypesUtils.hpp>
-#include <armnn/utility/NumericCast.hpp>
-#include <armnn/utility/Timer.hpp>
 
-#if defined(ARMNN_SERIALIZER)
-#include "armnnDeserializer/IDeserializer.hpp"
-#endif
-#if defined(ARMNN_CAFFE_PARSER)
-#include "armnnCaffeParser/ICaffeParser.hpp"
-#endif
-#if defined(ARMNN_TF_PARSER)
-#include "armnnTfParser/ITfParser.hpp"
-#endif
-#if defined(ARMNN_TF_LITE_PARSER)
-#include "armnnTfLiteParser/ITfLiteParser.hpp"
-#endif
-#if defined(ARMNN_ONNX_PARSER)
-#include "armnnOnnxParser/IOnnxParser.hpp"
-#endif
-#include "CsvReader.hpp"
-#include "../InferenceTest.hpp"
+#pragma once
 
-#include <Profiling.hpp>
-#include <ResolveType.hpp>
+#include "CsvReader.hpp"
+#include <armnn/IRuntime.hpp>
+#include <armnn/Types.hpp>
 
-#include <boost/program_options.hpp>
 #include <mapbox/variant.hpp>
 
 #include <iostream>
-#include <fstream>
-#include <functional>
-#include <future>
-#include <algorithm>
-#include <iterator>
-
-namespace
-{
-
-// Configure boost::program_options for command-line parsing and validation.
-namespace po = boost::program_options;
-
-template<typename T, typename TParseElementFunc>
-std::vector<T> ParseArrayImpl(std::istream& stream, TParseElementFunc parseElementFunc, const char * chars = "\t ,:")
-{
-    std::vector<T> result;
-    // Processes line-by-line.
-    std::string line;
-    while (std::getline(stream, line))
-    {
-        std::vector<std::string> tokens = armnn::stringUtils::StringTokenizer(line, chars);
-        for (const std::string& token : tokens)
-        {
-            if (!token.empty()) // See https://stackoverflow.com/questions/10437406/
-            {
-                try
-                {
-                    result.push_back(parseElementFunc(token));
-                }
-                catch (const std::exception&)
-                {
-                    ARMNN_LOG(error) << "'" << token << "' is not a valid number. It has been ignored.";
-                }
-            }
-        }
-    }
 
-    return result;
-}
 
-bool CheckOption(const po::variables_map& vm,
-                 const char* option)
-{
-    // Check that the given option is valid.
-    if (option == nullptr)
-    {
-        return false;
-    }
-
-    // Check whether 'option' is provided.
-    return vm.find(option) != vm.end();
-}
-
-void CheckOptionDependency(const po::variables_map& vm,
-                           const char* option,
-                           const char* required)
-{
-    // Check that the given options are valid.
-    if (option == nullptr || required == nullptr)
-    {
-        throw po::error("Invalid option to check dependency for");
-    }
-
-    // Check that if 'option' is provided, 'required' is also provided.
-    if (CheckOption(vm, option) && !vm[option].defaulted())
-    {
-        if (CheckOption(vm, required) == 0 || vm[required].defaulted())
-        {
-            throw po::error(std::string("Option '") + option + "' requires option '" + required + "'.");
-        }
-    }
-}
-
-void CheckOptionDependencies(const po::variables_map& vm)
-{
-    CheckOptionDependency(vm, "model-path", "model-format");
-    CheckOptionDependency(vm, "model-path", "input-name");
-    CheckOptionDependency(vm, "model-path", "output-name");
-    CheckOptionDependency(vm, "input-tensor-shape", "model-path");
-}
+std::vector<unsigned int> ParseArray(std::istream& stream);
 
-template<armnn::DataType NonQuantizedType>
-auto ParseDataArray(std::istream & stream);
-
-template<armnn::DataType QuantizedType>
-auto ParseDataArray(std::istream& stream,
-                    const float& quantizationScale,
-                    const int32_t& quantizationOffset);
-
-template<>
-auto ParseDataArray<armnn::DataType::Float32>(std::istream & stream)
-{
-    return ParseArrayImpl<float>(stream, [](const std::string& s) { return std::stof(s); });
-}
-
-template<>
-auto ParseDataArray<armnn::DataType::Signed32>(std::istream & stream)
-{
-    return ParseArrayImpl<int>(stream, [](const std::string & s) { return std::stoi(s); });
-}
-
-template<>
-auto ParseDataArray<armnn::DataType::QAsymmU8>(std::istream& stream)
-{
-    return ParseArrayImpl<uint8_t>(stream,
-                                   [](const std::string& s) { return armnn::numeric_cast<uint8_t>(std::stoi(s)); });
-}
-
-template<>
-auto ParseDataArray<armnn::DataType::QAsymmU8>(std::istream& stream,
-                                                      const float& quantizationScale,
-                                                      const int32_t& quantizationOffset)
-{
-    return ParseArrayImpl<uint8_t>(stream,
-                                   [&quantizationScale, &quantizationOffset](const std::string & s)
-                                   {
-                                       return armnn::numeric_cast<uint8_t>(
-                                           armnn::Quantize<uint8_t>(std::stof(s),
-                                                                     quantizationScale,
-                                                                     quantizationOffset));
-                                   });
-}
-std::vector<unsigned int> ParseArray(std::istream& stream)
-{
-    return ParseArrayImpl<unsigned int>(stream,
-        [](const std::string& s) { return armnn::numeric_cast<unsigned int>(std::stoi(s)); });
-}
-
-std::vector<std::string> ParseStringList(const std::string & inputString, const char * delimiter)
-{
-    std::stringstream stream(inputString);
-    return ParseArrayImpl<std::string>(stream, [](const std::string& s) {
-           return armnn::stringUtils::StringTrimCopy(s); }, delimiter);
-}
-
-void RemoveDuplicateDevices(std::vector<armnn::BackendId>& computeDevices)
-{
-    // Mark the duplicate devices as 'Undefined'.
-    for (auto i = computeDevices.begin(); i != computeDevices.end(); ++i)
-    {
-        for (auto j = std::next(i); j != computeDevices.end(); ++j)
-        {
-            if (*j == *i)
-            {
-                *j = armnn::Compute::Undefined;
-            }
-        }
-    }
-
-    // Remove 'Undefined' devices.
-    computeDevices.erase(std::remove(computeDevices.begin(), computeDevices.end(), armnn::Compute::Undefined),
-                         computeDevices.end());
-}
+/// Splits a given string at every accurance of delimiter into a vector of string
+std::vector<std::string> ParseStringList(const std::string& inputString, const char* delimiter);
 
 struct TensorPrinter
 {
     TensorPrinter(const std::string& binding,
                   const armnn::TensorInfo& info,
                   const std::string& outputTensorFile,
-                  bool dequantizeOutput)
-        : m_OutputBinding(binding)
-        , m_Scale(info.GetQuantizationScale())
-        , m_Offset(info.GetQuantizationOffset())
-        , m_OutputTensorFile(outputTensorFile)
-        , m_DequantizeOutput(dequantizeOutput)
-    {}
+                  bool dequantizeOutput);
 
-    void operator()(const std::vector<float>& values)
-    {
-        ForEachValue(values, [](float value)
-            {
-                printf("%f ", value);
-            });
-        WriteToFile(values);
-    }
+    void operator()(const std::vector<float>& values);
 
-    void operator()(const std::vector<uint8_t>& values)
-    {
-        if(m_DequantizeOutput)
-        {
-            auto& scale = m_Scale;
-            auto& offset = m_Offset;
-            std::vector<float> dequantizedValues;
-            ForEachValue(values, [&scale, &offset, &dequantizedValues](uint8_t value)
-            {
-                auto dequantizedValue = armnn::Dequantize(value, scale, offset);
-                printf("%f ", dequantizedValue);
-                dequantizedValues.push_back(dequantizedValue);
-            });
-            WriteToFile(dequantizedValues);
-        }
-        else
-        {
-            const std::vector<int> intValues(values.begin(), values.end());
-            operator()(intValues);
-        }
-    }
+    void operator()(const std::vector<uint8_t>& values);
 
-    void operator()(const std::vector<int>& values)
-    {
-        ForEachValue(values, [](int value)
-            {
-                printf("%d ", value);
-            });
-        WriteToFile(values);
-    }
+    void operator()(const std::vector<int>& values);
 
 private:
     template<typename Container, typename Delegate>
-    void ForEachValue(const Container& c, Delegate delegate)
-    {
-        std::cout << m_OutputBinding << ": ";
-        for (const auto& value : c)
-        {
-            delegate(value);
-        }
-        printf("\n");
-    }
+    void ForEachValue(const Container& c, Delegate delegate);
 
     template<typename T>
-    void WriteToFile(const std::vector<T>& values)
-    {
-        if (!m_OutputTensorFile.empty())
-        {
-            std::ofstream outputTensorFile;
-            outputTensorFile.open(m_OutputTensorFile, std::ofstream::out | std::ofstream::trunc);
-            if (outputTensorFile.is_open())
-            {
-                outputTensorFile << m_OutputBinding << ": ";
-                std::copy(values.begin(), values.end(), std::ostream_iterator<T>(outputTensorFile, " "));
-            }
-            else
-            {
-                ARMNN_LOG(info) << "Output Tensor File: " << m_OutputTensorFile << " could not be opened!";
-            }
-            outputTensorFile.close();
-        }
-    }
+    void WriteToFile(const std::vector<T>& values);
 
     std::string m_OutputBinding;
-    float m_Scale=0.0f;
-    int m_Offset=0;
+    float m_Scale;
+    int m_Offset;
     std::string m_OutputTensorFile;
-    bool m_DequantizeOutput = false;
+    bool m_DequantizeOutput;
 };
 
-
-
-template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
-std::vector<T> GenerateDummyTensorData(unsigned int numElements)
-{
-    return std::vector<T>(numElements, static_cast<T>(0));
-}
-
 using TContainer         = mapbox::util::variant<std::vector<float>, std::vector<int>, std::vector<unsigned char>>;
 using QuantizationParams = std::pair<float, int32_t>;
 
@@ -293,648 +53,20 @@ void PopulateTensorWithData(TContainer& tensorData,
                             unsigned int numElements,
                             const std::string& dataTypeStr,
                             const armnn::Optional<QuantizationParams>& qParams,
-                            const armnn::Optional<std::string>& dataFile)
-{
-    const bool readFromFile = dataFile.has_value() && !dataFile.value().empty();
-    const bool quantizeData = qParams.has_value();
-
-    std::ifstream inputTensorFile;
-    if (readFromFile)
-    {
-        inputTensorFile = std::ifstream(dataFile.value());
-    }
-
-    if (dataTypeStr.compare("float") == 0)
-    {
-        if (quantizeData)
-        {
-            const float qScale  = qParams.value().first;
-            const int   qOffset = qParams.value().second;
-
-            tensorData = readFromFile ?
-                ParseDataArray<armnn::DataType::QAsymmU8>(inputTensorFile, qScale, qOffset) :
-                GenerateDummyTensorData<armnn::DataType::QAsymmU8>(numElements);
-        }
-        else
-        {
-            tensorData = readFromFile ?
-                ParseDataArray<armnn::DataType::Float32>(inputTensorFile) :
-                GenerateDummyTensorData<armnn::DataType::Float32>(numElements);
-        }
-    }
-    else if (dataTypeStr.compare("int") == 0)
-    {
-        tensorData = readFromFile ?
-            ParseDataArray<armnn::DataType::Signed32>(inputTensorFile) :
-            GenerateDummyTensorData<armnn::DataType::Signed32>(numElements);
-    }
-    else if (dataTypeStr.compare("qasymm8") == 0)
-    {
-         tensorData = readFromFile ?
-            ParseDataArray<armnn::DataType::QAsymmU8>(inputTensorFile) :
-            GenerateDummyTensorData<armnn::DataType::QAsymmU8>(numElements);
-    }
-    else
-    {
-        std::string errorMessage = "Unsupported tensor data type " + dataTypeStr;
-        ARMNN_LOG(fatal) << errorMessage;
-
-        inputTensorFile.close();
-        throw armnn::Exception(errorMessage);
-    }
-
-    inputTensorFile.close();
-}
-
-} // anonymous namespace
-
-bool generateTensorData = true;
-
-struct ExecuteNetworkParams
-{
-    using TensorShapePtr = std::unique_ptr<armnn::TensorShape>;
-
-    const char*                   m_ModelPath;
-    bool                          m_IsModelBinary;
-    std::vector<armnn::BackendId> m_ComputeDevices;
-    std::string                   m_DynamicBackendsPath;
-    std::vector<string>           m_InputNames;
-    std::vector<TensorShapePtr>   m_InputTensorShapes;
-    std::vector<string>           m_InputTensorDataFilePaths;
-    std::vector<string>           m_InputTypes;
-    bool                          m_QuantizeInput;
-    std::vector<string>           m_OutputTypes;
-    std::vector<string>           m_OutputNames;
-    std::vector<string>           m_OutputTensorFiles;
-    bool                          m_DequantizeOutput;
-    bool                          m_EnableProfiling;
-    bool                          m_EnableFp16TurboMode;
-    bool                          m_EnableBf16TurboMode;
-    double                        m_ThresholdTime;
-    bool                          m_PrintIntermediate;
-    size_t                        m_SubgraphId;
-    bool                          m_EnableLayerDetails = false;
-    bool                          m_GenerateTensorData;
-    bool                          m_ParseUnsupported = false;
-    bool                          m_InferOutputShape = false;
-    bool                          m_EnableFastMath   = false;
-};
-
-template<typename TParser, typename TDataType>
-int MainImpl(const ExecuteNetworkParams& params,
-             const std::shared_ptr<armnn::IRuntime>& runtime = nullptr,
-             size_t iterations = 1)
-{
-    using TContainer = mapbox::util::variant<std::vector<float>, std::vector<int>, std::vector<unsigned char>>;
-
-    std::vector<TContainer> inputDataContainers;
-
-    try
-    {
-        // Creates an InferenceModel, which will parse the model and load it into an IRuntime.
-        typename InferenceModel<TParser, TDataType>::Params inferenceModelParams;
-        inferenceModelParams.m_ModelPath                      = params.m_ModelPath;
-        inferenceModelParams.m_IsModelBinary                  = params.m_IsModelBinary;
-        inferenceModelParams.m_ComputeDevices                 = params.m_ComputeDevices;
-        inferenceModelParams.m_DynamicBackendsPath            = params.m_DynamicBackendsPath;
-        inferenceModelParams.m_PrintIntermediateLayers        = params.m_PrintIntermediate;
-        inferenceModelParams.m_VisualizePostOptimizationModel = params.m_EnableLayerDetails;
-        inferenceModelParams.m_ParseUnsupported               = params.m_ParseUnsupported;
-        inferenceModelParams.m_InferOutputShape               = params.m_InferOutputShape;
-        inferenceModelParams.m_EnableFastMath                 = params.m_EnableFastMath;
-
-        for(const std::string& inputName: params.m_InputNames)
-        {
-            inferenceModelParams.m_InputBindings.push_back(inputName);
-        }
-
-        for(unsigned int i = 0; i < params.m_InputTensorShapes.size(); ++i)
-        {
-            inferenceModelParams.m_InputShapes.push_back(*params.m_InputTensorShapes[i]);
-        }
-
-        for(const std::string& outputName: params.m_OutputNames)
-        {
-            inferenceModelParams.m_OutputBindings.push_back(outputName);
-        }
-
-        inferenceModelParams.m_SubgraphId          = params.m_SubgraphId;
-        inferenceModelParams.m_EnableFp16TurboMode = params.m_EnableFp16TurboMode;
-        inferenceModelParams.m_EnableBf16TurboMode = params.m_EnableBf16TurboMode;
-
-        InferenceModel<TParser, TDataType> model(inferenceModelParams,
-                                                 params.m_EnableProfiling,
-                                                 params.m_DynamicBackendsPath,
-                                                 runtime);
-
-        const size_t numInputs = inferenceModelParams.m_InputBindings.size();
-        for(unsigned int i = 0; i < numInputs; ++i)
-        {
-            armnn::Optional<QuantizationParams> qParams = params.m_QuantizeInput ?
-                armnn::MakeOptional<QuantizationParams>(model.GetInputQuantizationParams()) :
-                armnn::EmptyOptional();
-
-            armnn::Optional<std::string> dataFile = params.m_GenerateTensorData ?
-                armnn::EmptyOptional() :
-                armnn::MakeOptional<std::string>(params.m_InputTensorDataFilePaths[i]);
-
-            unsigned int numElements = model.GetInputSize(i);
-            if (params.m_InputTensorShapes.size() > i && params.m_InputTensorShapes[i])
-            {
-                // If the user has provided a tensor shape for the current input,
-                // override numElements
-                numElements = params.m_InputTensorShapes[i]->GetNumElements();
-            }
-
-            TContainer tensorData;
-            PopulateTensorWithData(tensorData,
-                                   numElements,
-                                   params.m_InputTypes[i],
-                                   qParams,
-                                   dataFile);
-
-            inputDataContainers.push_back(tensorData);
-        }
-
-        const size_t numOutputs = inferenceModelParams.m_OutputBindings.size();
-        std::vector<TContainer> outputDataContainers;
-
-        for (unsigned int i = 0; i < numOutputs; ++i)
-        {
-            if (params.m_OutputTypes[i].compare("float") == 0)
-            {
-                outputDataContainers.push_back(std::vector<float>(model.GetOutputSize(i)));
-            }
-            else if (params.m_OutputTypes[i].compare("int") == 0)
-            {
-                outputDataContainers.push_back(std::vector<int>(model.GetOutputSize(i)));
-            }
-            else if (params.m_OutputTypes[i].compare("qasymm8") == 0)
-            {
-                outputDataContainers.push_back(std::vector<uint8_t>(model.GetOutputSize(i)));
-            }
-            else
-            {
-                ARMNN_LOG(fatal) << "Unsupported tensor data type \"" << params.m_OutputTypes[i] << "\". ";
-                return EXIT_FAILURE;
-            }
-        }
-
-        for (size_t x = 0; x < iterations; x++)
-        {
-            // model.Run returns the inference time elapsed in EnqueueWorkload (in milliseconds)
-            auto inference_duration = model.Run(inputDataContainers, outputDataContainers);
-
-            if (params.m_GenerateTensorData)
-            {
-                ARMNN_LOG(warning) << "The input data was generated, note that the output will not be useful";
-            }
-
-            // Print output tensors
-            const auto& infosOut = model.GetOutputBindingInfos();
-            for (size_t i = 0; i < numOutputs; i++)
-            {
-                const armnn::TensorInfo& infoOut = infosOut[i].second;
-                auto outputTensorFile = params.m_OutputTensorFiles.empty() ? "" : params.m_OutputTensorFiles[i];
-
-                TensorPrinter printer(inferenceModelParams.m_OutputBindings[i],
-                                    infoOut,
-                                    outputTensorFile,
-                                    params.m_DequantizeOutput);
-                mapbox::util::apply_visitor(printer, outputDataContainers[i]);
-            }
-
-            ARMNN_LOG(info) << "\nInference time: " << std::setprecision(2)
-                                    << std::fixed << inference_duration.count() << " ms\n";
-
-            // If thresholdTime == 0.0 (default), then it hasn't been supplied at command line
-            if (params.m_ThresholdTime != 0.0)
-            {
-                ARMNN_LOG(info) << "Threshold time: " << std::setprecision(2)
-                                        << std::fixed << params.m_ThresholdTime << " ms";
-                auto thresholdMinusInference = params.m_ThresholdTime - inference_duration.count();
-                ARMNN_LOG(info) << "Threshold time - Inference time: " << std::setprecision(2)
-                                        << std::fixed << thresholdMinusInference << " ms" << "\n";
-
-                if (thresholdMinusInference < 0)
-                {
-                    std::string errorMessage = "Elapsed inference time is greater than provided threshold time.";
-                    ARMNN_LOG(fatal) << errorMessage;
-                }
-            }
-        }
-    }
-    catch (const armnn::Exception& e)
-    {
-        ARMNN_LOG(fatal) << "Armnn Error: " << e.what();
-        return EXIT_FAILURE;
-    }
-
-    return EXIT_SUCCESS;
-}
-
-// This will run a test
-int RunTest(const std::string& format,
-            const std::string& inputTensorShapesStr,
-            const vector<armnn::BackendId>& computeDevices,
-            const std::string& dynamicBackendsPath,
-            const std::string& path,
-            const std::string& inputNames,
-            const std::string& inputTensorDataFilePaths,
-            const std::string& inputTypes,
-            bool quantizeInput,
-            const std::string& outputTypes,
-            const std::string& outputNames,
-            const std::string& outputTensorFiles,
-            bool dequantizeOuput,
-            bool enableProfiling,
-            bool enableFp16TurboMode,
-            bool enableBf16TurboMode,
-            const double& thresholdTime,
-            bool printIntermediate,
-            const size_t subgraphId,
-            bool enableLayerDetails = false,
-            bool parseUnsupported = false,
-            bool inferOutputShape = false,
-            bool enableFastMath   = false,
-            const size_t iterations = 1,
-            const std::shared_ptr<armnn::IRuntime>& runtime = nullptr)
-{
-    std::string modelFormat = armnn::stringUtils::StringTrimCopy(format);
-    std::string modelPath = armnn::stringUtils::StringTrimCopy(path);
-    std::vector<std::string> inputNamesVector = ParseStringList(inputNames, ",");
-    std::vector<std::string> inputTensorShapesVector = ParseStringList(inputTensorShapesStr, ":");
-    std::vector<std::string> inputTensorDataFilePathsVector = ParseStringList(
-        inputTensorDataFilePaths, ",");
-    std::vector<std::string> outputNamesVector = ParseStringList(outputNames, ",");
-    std::vector<std::string> inputTypesVector = ParseStringList(inputTypes, ",");
-    std::vector<std::string> outputTypesVector = ParseStringList(outputTypes, ",");
-    std::vector<std::string> outputTensorFilesVector = ParseStringList(outputTensorFiles, ",");
-
-    // Parse model binary flag from the model-format string we got from the command-line
-    bool isModelBinary;
-    if (modelFormat.find("bin") != std::string::npos)
-    {
-        isModelBinary = true;
-    }
-    else if (modelFormat.find("txt") != std::string::npos || modelFormat.find("text") != std::string::npos)
-    {
-        isModelBinary = false;
-    }
-    else
-    {
-        ARMNN_LOG(fatal) << "Unknown model format: '" << modelFormat << "'. Please include 'binary' or 'text'";
-        return EXIT_FAILURE;
-    }
-
-    if ((inputTensorShapesVector.size() != 0) && (inputTensorShapesVector.size() != inputNamesVector.size()))
-    {
-        ARMNN_LOG(fatal) << "input-name and input-tensor-shape must have the same amount of elements.";
-        return EXIT_FAILURE;
-    }
-
-    if ((inputTensorDataFilePathsVector.size() != 0) &&
-        (inputTensorDataFilePathsVector.size() != inputNamesVector.size()))
-    {
-        ARMNN_LOG(fatal) << "input-name and input-tensor-data must have the same amount of elements.";
-        return EXIT_FAILURE;
-    }
-
-    if ((outputTensorFilesVector.size() != 0) &&
-        (outputTensorFilesVector.size() != outputNamesVector.size()))
-    {
-        ARMNN_LOG(fatal) << "output-name and write-outputs-to-file must have the same amount of elements.";
-        return EXIT_FAILURE;
-    }
-
-    if (inputTypesVector.size() == 0)
-    {
-        //Defaults the value of all inputs to "float"
-        inputTypesVector.assign(inputNamesVector.size(), "float");
-    }
-    else if ((inputTypesVector.size() != 0) && (inputTypesVector.size() != inputNamesVector.size()))
-    {
-        ARMNN_LOG(fatal) << "input-name and input-type must have the same amount of elements.";
-        return EXIT_FAILURE;
-    }
-
-    if (outputTypesVector.size() == 0)
-    {
-        //Defaults the value of all outputs to "float"
-        outputTypesVector.assign(outputNamesVector.size(), "float");
-    }
-    else if ((outputTypesVector.size() != 0) && (outputTypesVector.size() != outputNamesVector.size()))
-    {
-        ARMNN_LOG(fatal) << "output-name and output-type must have the same amount of elements.";
-        return EXIT_FAILURE;
-    }
-
-    // Parse input tensor shape from the string we got from the command-line.
-    std::vector<std::unique_ptr<armnn::TensorShape>> inputTensorShapes;
-
-    if (!inputTensorShapesVector.empty())
-    {
-        inputTensorShapes.reserve(inputTensorShapesVector.size());
-
-        for(const std::string& shape : inputTensorShapesVector)
-        {
-            std::stringstream ss(shape);
-            std::vector<unsigned int> dims = ParseArray(ss);
-
-            try
-            {
-                // Coverity fix: An exception of type armnn::InvalidArgumentException is thrown and never caught.
-                inputTensorShapes.push_back(
-                    std::make_unique<armnn::TensorShape>(static_cast<unsigned int>(dims.size()), dims.data()));
-            }
-            catch (const armnn::InvalidArgumentException& e)
-            {
-                ARMNN_LOG(fatal) << "Cannot create tensor shape: " << e.what();
-                return EXIT_FAILURE;
-            }
-        }
-    }
-
-    // Check that threshold time is not less than zero
-    if (thresholdTime < 0)
-    {
-        ARMNN_LOG(fatal) << "Threshold time supplied as a command line argument is less than zero.";
-        return EXIT_FAILURE;
-    }
-
-    ExecuteNetworkParams params;
-    params.m_ModelPath                = modelPath.c_str();
-    params.m_IsModelBinary            = isModelBinary;
-    params.m_ComputeDevices           = computeDevices;
-    params.m_DynamicBackendsPath      = dynamicBackendsPath;
-    params.m_InputNames               = inputNamesVector;
-    params.m_InputTensorShapes        = std::move(inputTensorShapes);
-    params.m_InputTensorDataFilePaths = inputTensorDataFilePathsVector;
-    params.m_InputTypes               = inputTypesVector;
-    params.m_QuantizeInput            = quantizeInput;
-    params.m_OutputTypes              = outputTypesVector;
-    params.m_OutputNames              = outputNamesVector;
-    params.m_OutputTensorFiles        = outputTensorFilesVector;
-    params.m_DequantizeOutput         = dequantizeOuput;
-    params.m_EnableProfiling          = enableProfiling;
-    params.m_EnableFp16TurboMode      = enableFp16TurboMode;
-    params.m_EnableBf16TurboMode      = enableBf16TurboMode;
-    params.m_ThresholdTime            = thresholdTime;
-    params.m_PrintIntermediate        = printIntermediate;
-    params.m_SubgraphId               = subgraphId;
-    params.m_EnableLayerDetails       = enableLayerDetails;
-    params.m_GenerateTensorData       = inputTensorDataFilePathsVector.empty();
-    params.m_ParseUnsupported         = parseUnsupported;
-    params.m_InferOutputShape         = inferOutputShape;
-    params.m_EnableFastMath           = enableFastMath;
-
-    // Warn if ExecuteNetwork will generate dummy input data
-    if (params.m_GenerateTensorData)
-    {
-        ARMNN_LOG(warning) << "No input files provided, input tensors will be filled with 0s.";
-    }
-
-    // Forward to implementation based on the parser type
-    if (modelFormat.find("armnn") != std::string::npos)
-    {
-#if defined(ARMNN_SERIALIZER)
-        return MainImpl<armnnDeserializer::IDeserializer, float>(params, runtime, iterations);
-#else
-        ARMNN_LOG(fatal) << "Not built with serialization support.";
-        return EXIT_FAILURE;
-#endif
-    }
-    else if (modelFormat.find("caffe") != std::string::npos)
-    {
-#if defined(ARMNN_CAFFE_PARSER)
-        return MainImpl<armnnCaffeParser::ICaffeParser, float>(params, runtime, iterations);
-#else
-        ARMNN_LOG(fatal) << "Not built with Caffe parser support.";
-        return EXIT_FAILURE;
-#endif
-    }
-    else if (modelFormat.find("onnx") != std::string::npos)
-    {
-#if defined(ARMNN_ONNX_PARSER)
-        return MainImpl<armnnOnnxParser::IOnnxParser, float>(params, runtime, iterations);
-#else
-        ARMNN_LOG(fatal) << "Not built with Onnx parser support.";
-        return EXIT_FAILURE;
-#endif
-    }
-    else if (modelFormat.find("tensorflow") != std::string::npos)
-    {
-#if defined(ARMNN_TF_PARSER)
-        return MainImpl<armnnTfParser::ITfParser, float>(params, runtime, iterations);
-#else
-        ARMNN_LOG(fatal) << "Not built with Tensorflow parser support.";
-        return EXIT_FAILURE;
-#endif
-    }
-    else if(modelFormat.find("tflite") != std::string::npos)
-    {
-#if defined(ARMNN_TF_LITE_PARSER)
-        if (! isModelBinary)
-        {
-            ARMNN_LOG(fatal) << "Unknown model format: '" << modelFormat
-                << "'. Only 'binary' format supported for tflite files";
-            return EXIT_FAILURE;
-        }
-        return MainImpl<armnnTfLiteParser::ITfLiteParser, float>(params, runtime, iterations);
-#else
-        ARMNN_LOG(fatal) << "Unknown model format: '" << modelFormat
-            << "'. Please include 'caffe', 'tensorflow', 'tflite' or 'onnx'";
-        return EXIT_FAILURE;
-#endif
-    }
-    else
-    {
-        ARMNN_LOG(fatal) << "Unknown model format: '" << modelFormat
-            << "'. Please include 'caffe', 'tensorflow', 'tflite' or 'onnx'";
-        return EXIT_FAILURE;
-    }
-}
-
-int RunCsvTest(const armnnUtils::CsvRow &csvRow, const std::shared_ptr<armnn::IRuntime>& runtime,
-               const bool enableProfiling, const bool enableFp16TurboMode, const bool enableBf16TurboMode,
-               const double& thresholdTime, const bool printIntermediate, bool enableLayerDetails = false,
-               bool parseUnuspported = false, bool inferOutputShape = false, bool enableFastMath = false)
-{
-    IgnoreUnused(runtime);
-    std::string modelFormat;
-    std::string modelPath;
-    std::string inputNames;
-    std::string inputTensorShapes;
-    std::string inputTensorDataFilePaths;
-    std::string outputNames;
-    std::string inputTypes;
-    std::string outputTypes;
-    std::string dynamicBackendsPath;
-    std::string outputTensorFiles;
-
-    size_t subgraphId = 0;
-
-    const std::string backendsMessage = std::string("The preferred order of devices to run layers on by default. ")
-                                      + std::string("Possible choices: ")
-                                      + armnn::BackendRegistryInstance().GetBackendIdsAsString();
-
-    po::options_description desc("Options");
-    try
-    {
-        desc.add_options()
-        ("model-format,f", po::value(&modelFormat),
-         "armnn-binary, caffe-binary, caffe-text, tflite-binary, onnx-binary, onnx-text, tensorflow-binary or "
-         "tensorflow-text.")
-        ("model-path,m", po::value(&modelPath), "Path to model file, e.g. .armnn, .caffemodel, .prototxt, "
-         ".tflite, .onnx")
-        ("compute,c", po::value<std::vector<armnn::BackendId>>()->multitoken(),
-         backendsMessage.c_str())
-        ("dynamic-backends-path,b", po::value(&dynamicBackendsPath),
-         "Path where to load any available dynamic backend from. "
-         "If left empty (the default), dynamic backends will not be used.")
-        ("input-name,i", po::value(&inputNames), "Identifier of the input tensors in the network separated by comma.")
-        ("subgraph-number,n", po::value<size_t>(&subgraphId)->default_value(0), "Id of the subgraph to be "
-         "executed. Defaults to 0.")
-        ("input-tensor-shape,s", po::value(&inputTensorShapes),
-         "The shape of the input tensors in the network as a flat array of integers separated by comma. "
-         "Several shapes can be passed separating them by semicolon. "
-         "This parameter is optional, depending on the network.")
-        ("input-tensor-data,d", po::value(&inputTensorDataFilePaths)->default_value(""),
-         "Path to files containing the input data as a flat array separated by whitespace. "
-         "Several paths can be passed separating them by comma. If not specified, the network will be run with dummy "
-         "data (useful for profiling).")
-        ("input-type,y",po::value(&inputTypes), "The type of the input tensors in the network separated by comma. "
-         "If unset, defaults to \"float\" for all defined inputs. "
-         "Accepted values (float, int or qasymm8).")
-        ("quantize-input,q",po::bool_switch()->default_value(false),
-         "If this option is enabled, all float inputs will be quantized to qasymm8. "
-         "If unset, default to not quantized. "
-         "Accepted values (true or false)")
-        ("output-type,z",po::value(&outputTypes), "The type of the output tensors in the network separated by comma. "
-         "If unset, defaults to \"float\" for all defined outputs. "
-         "Accepted values (float, int or qasymm8).")
-        ("output-name,o", po::value(&outputNames),
-         "Identifier of the output tensors in the network separated by comma.")
-        ("dequantize-output,l",po::bool_switch()->default_value(false),
-         "If this option is enabled, all quantized outputs will be dequantized to float. "
-         "If unset, default to not get dequantized. "
-         "Accepted values (true or false)")
-        ("write-outputs-to-file,w", po::value(&outputTensorFiles),
-         "Comma-separated list of output file paths keyed with the binding-id of the output slot. "
-         "If left empty (the default), the output tensors will not be written to a file.");
-    }
-    catch (const std::exception& e)
-    {
-        // Coverity points out that default_value(...) can throw a bad_lexical_cast,
-        // and that desc.add_options() can throw boost::io::too_few_args.
-        // They really won't in any of these cases.
-        ARMNN_ASSERT_MSG(false, "Caught unexpected exception");
-        ARMNN_LOG(fatal) << "Fatal internal error: " << e.what();
-        return EXIT_FAILURE;
-    }
-
-    std::vector<const char*> clOptions;
-    clOptions.reserve(csvRow.values.size());
-    for (const std::string& value : csvRow.values)
-    {
-        clOptions.push_back(value.c_str());
-    }
-
-    po::variables_map vm;
-    try
-    {
-        po::store(po::parse_command_line(static_cast<int>(clOptions.size()), clOptions.data(), desc), vm);
-
-        po::notify(vm);
-
-        CheckOptionDependencies(vm);
-    }
-    catch (const po::error& e)
-    {
-        std::cerr << e.what() << std::endl << std::endl;
-        std::cerr << desc << std::endl;
-        return EXIT_FAILURE;
-    }
-
-    // Get the value of the switch arguments.
-    bool quantizeInput = vm["quantize-input"].as<bool>();
-    bool dequantizeOutput = vm["dequantize-output"].as<bool>();
-
-    // Get the preferred order of compute devices.
-    std::vector<armnn::BackendId> computeDevices = vm["compute"].as<std::vector<armnn::BackendId>>();
-
-    // Remove duplicates from the list of compute devices.
-    RemoveDuplicateDevices(computeDevices);
-
-    // Check that the specified compute devices are valid.
-    std::string invalidBackends;
-    if (!CheckRequestedBackendsAreValid(computeDevices, armnn::Optional<std::string&>(invalidBackends)))
-    {
-        ARMNN_LOG(fatal) << "The list of preferred devices contains invalid backend IDs: "
-                                 << invalidBackends;
-        return EXIT_FAILURE;
-    }
-
-    return RunTest(modelFormat, inputTensorShapes, computeDevices, dynamicBackendsPath, modelPath, inputNames,
-                   inputTensorDataFilePaths, inputTypes, quantizeInput, outputTypes, outputNames, outputTensorFiles,
-                   dequantizeOutput, enableProfiling, enableFp16TurboMode, enableBf16TurboMode,
-                   thresholdTime, printIntermediate, subgraphId, enableLayerDetails, parseUnuspported,
-                   inferOutputShape, enableFastMath);
-}
-
-#if defined(ARMCOMPUTECL_ENABLED)
-int RunCLTuning(const std::string& tuningPath,
-            const int tuningLevel,
-            const std::string& modelFormat,
-            const std::string& inputTensorShapes,
-            const vector<armnn::BackendId>& computeDevices,
-            const std::string& dynamicBackendsPath,
-            const std::string& modelPath,
-            const std::string& inputNames,
-            const std::string& inputTensorDataFilePaths,
-            const std::string& inputTypes,
-            bool quantizeInput,
-            const std::string& outputTypes,
-            const std::string& outputNames,
-            const std::string& outputTensorFiles,
-            bool dequantizeOutput,
-            bool enableProfiling,
-            bool enableFp16TurboMode,
-            bool enableBf16TurboMode,
-            const double& thresholdTime,
-            bool printIntermediate,
-            const size_t subgraphId,
-            bool enableLayerDetails = false,
-            bool parseUnsupported = false,
-            bool inferOutputShape = false,
-            bool enableFastMath = false)
-{
-    armnn::IRuntime::CreationOptions options;
-    options.m_BackendOptions.emplace_back(
-        armnn::BackendOptions
-        {
-            "GpuAcc",
-            {
-                {"TuningLevel", tuningLevel},
-                {"TuningFile", tuningPath.c_str()},
-                {"KernelProfilingEnabled", enableProfiling}
-            }
-        }
-    );
-
-    std::shared_ptr<armnn::IRuntime> runtime(armnn::IRuntime::Create(options));
-    const auto start_time = armnn::GetTimeNow();
-
-    ARMNN_LOG(info) << "Tuning run...\n";
-    int state = RunTest(modelFormat, inputTensorShapes, computeDevices, dynamicBackendsPath, modelPath, inputNames,
-                        inputTensorDataFilePaths, inputTypes, quantizeInput, outputTypes, outputNames,
-                        outputTensorFiles, dequantizeOutput, enableProfiling, enableFp16TurboMode, enableBf16TurboMode,
-                        thresholdTime, printIntermediate, subgraphId, enableLayerDetails, parseUnsupported,
-                        inferOutputShape, enableFastMath, 1, runtime);
-
-    ARMNN_LOG(info) << "Tuning time: " << std::setprecision(2)
-                    << std::fixed << armnn::GetTimeDuration(start_time).count() << " ms\n";
-
-    return state;
-}
-#endif
\ No newline at end of file
+                            const armnn::Optional<std::string>& dataFile);
+
+/**
+ * Verifies if the given string is a valid path. Reports invalid paths to std::err.
+ * @param file string - A string containing the path to check
+ * @param expectFile bool - If true, checks for a regular file.
+ * @return bool - True if given string is a valid path., false otherwise.
+ * */
+bool ValidatePath(const std::string& file, const bool expectFile);
+
+/**
+ * Verifies if a given vector of strings are valid paths. Reports invalid paths to std::err.
+ * @param fileVec vector of string - A vector of string containing the paths to check
+ * @param expectFile bool - If true, checks for a regular file.
+ * @return bool - True if all given strings are valid paths., false otherwise.
+ * */
+bool ValidatePaths(const std::vector<std::string>& fileVec, const bool expectFile);
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