NetworkExecutionUtils.hpp File Reference

#include <armnn/ArmNN.hpp>
#include <armnn/TypesUtils.hpp>
#include <armnn/utility/Timer.hpp>
#include "CsvReader.hpp"
#include "../InferenceTest.hpp"
#include <Profiling.hpp>
#include <ResolveType.hpp>
#include <boost/program_options.hpp>
#include <boost/variant.hpp>
#include <iostream>
#include <fstream>
#include <functional>
#include <future>
#include <algorithm>
#include <iterator>

Go to the source code of this file.

Classes
struct	ExecuteNetworkParams

Functions
template<typename TParser , typename TDataType >
int	MainImpl (const ExecuteNetworkParams &params, const std::shared_ptr< armnn::IRuntime > &runtime=nullptr, size_t iterations=1)
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, const size_t iterations=1, const std::shared_ptr< armnn::IRuntime > &runtime=nullptr)
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)
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)

Variables
bool	generateTensorData = true

Function Documentation

MainImpl()

int MainImpl	(	const ExecuteNetworkParams &	params,
		const std::shared_ptr< armnn::IRuntime > &	runtime = nullptr,
		size_t	iterations = 1
	)

Definition at line 382 of file NetworkExecutionUtils.hpp.

References ARMNN_LOG, InferenceModel< IParser, TDataType >::GetInputQuantizationParams(), InferenceModel< IParser, TDataType >::GetInputSize(), InferenceModel< IParser, TDataType >::GetOutputBindingInfos(), InferenceModel< IParser, TDataType >::GetOutputSize(), Params::m_ComputeDevices, ExecuteNetworkParams::m_ComputeDevices, ExecuteNetworkParams::m_DequantizeOutput, Params::m_DynamicBackendsPath, ExecuteNetworkParams::m_DynamicBackendsPath, Params::m_EnableBf16TurboMode, ExecuteNetworkParams::m_EnableBf16TurboMode, Params::m_EnableFp16TurboMode, ExecuteNetworkParams::m_EnableFp16TurboMode, ExecuteNetworkParams::m_EnableLayerDetails, ExecuteNetworkParams::m_EnableProfiling, ExecuteNetworkParams::m_GenerateTensorData, Params::m_InferOutputShape, ExecuteNetworkParams::m_InferOutputShape, Params::m_InputBindings, ExecuteNetworkParams::m_InputNames, Params::m_InputShapes, ExecuteNetworkParams::m_InputTensorDataFilePaths, ExecuteNetworkParams::m_InputTensorShapes, ExecuteNetworkParams::m_InputTypes, Params::m_IsModelBinary, ExecuteNetworkParams::m_IsModelBinary, Params::m_ModelPath, ExecuteNetworkParams::m_ModelPath, Params::m_OutputBindings, ExecuteNetworkParams::m_OutputNames, ExecuteNetworkParams::m_OutputTensorFiles, ExecuteNetworkParams::m_OutputTypes, Params::m_ParseUnsupported, ExecuteNetworkParams::m_ParseUnsupported, ExecuteNetworkParams::m_PrintIntermediate, Params::m_PrintIntermediateLayers, ExecuteNetworkParams::m_QuantizeInput, Params::m_SubgraphId, ExecuteNetworkParams::m_SubgraphId, ExecuteNetworkParams::m_ThresholdTime, Params::m_VisualizePostOptimizationModel, InferenceModel< IParser, TDataType >::Run(), and Exception::what().

{
    using TContainer = boost::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;

        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);
                boost::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;
}

RunCLTuning()

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
	)

Definition at line 881 of file NetworkExecutionUtils.hpp.

References ARMNN_LOG, IRuntime::Create(), armnn::GetTimeDuration(), armnn::GetTimeNow(), IRuntime::CreationOptions::m_BackendOptions, and RunTest().

Referenced by main().

{
    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, 1, runtime);

    ARMNN_LOG(info) << "Tuning time: " << std::setprecision(2)
                    << std::fixed << armnn::GetTimeDuration(start_time).count() << " ms\n";

    return state;
}

RunCsvTest()

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
	)

Definition at line 753 of file NetworkExecutionUtils.hpp.

References ARMNN_ASSERT_MSG, ARMNN_LOG, armnn::BackendRegistryInstance(), BackendRegistry::GetBackendIdsAsString(), armnn::IgnoreUnused(), RunTest(), and CsvRow::values.

Referenced by main().

{
    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);
}

RunTest()

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,
		const size_t	iterations = 1,
		const std::shared_ptr< armnn::IRuntime > &	runtime = nullptr
	)

Definition at line 534 of file NetworkExecutionUtils.hpp.

References ARMNN_LOG, ExecuteNetworkParams::m_ComputeDevices, ExecuteNetworkParams::m_DequantizeOutput, ExecuteNetworkParams::m_DynamicBackendsPath, ExecuteNetworkParams::m_EnableBf16TurboMode, ExecuteNetworkParams::m_EnableFp16TurboMode, ExecuteNetworkParams::m_EnableLayerDetails, ExecuteNetworkParams::m_EnableProfiling, ExecuteNetworkParams::m_GenerateTensorData, ExecuteNetworkParams::m_InferOutputShape, ExecuteNetworkParams::m_InputNames, ExecuteNetworkParams::m_InputTensorDataFilePaths, ExecuteNetworkParams::m_InputTensorShapes, ExecuteNetworkParams::m_InputTypes, ExecuteNetworkParams::m_IsModelBinary, ExecuteNetworkParams::m_ModelPath, ExecuteNetworkParams::m_OutputNames, ExecuteNetworkParams::m_OutputTensorFiles, ExecuteNetworkParams::m_OutputTypes, ExecuteNetworkParams::m_ParseUnsupported, ExecuteNetworkParams::m_PrintIntermediate, ExecuteNetworkParams::m_QuantizeInput, ExecuteNetworkParams::m_SubgraphId, ExecuteNetworkParams::m_ThresholdTime, armnn::stringUtils::StringTrimCopy(), and Exception::what().

Referenced by BOOST_FIXTURE_TEST_CASE(), main(), RunCLTuning(), and RunCsvTest().

{
    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;

    // 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;
    }
}

Variable Documentation

generateTensorData

bool generateTensorData = true

Definition at line 350 of file NetworkExecutionUtils.hpp.