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

Classes
struct	ExecuteNetworkParams

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

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 enableProfiling, bool enableFp16TurboMode, const double &thresholdTime, bool printIntermediate, const size_t subgraphId, bool enableLayerDetails=false, bool parseUnsupported=false, 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 double &thresholdTime, const bool printIntermediate, bool enableLayerDetails=false, bool parseUnuspported=false)

Variables
bool	generateTensorData = true

Function Documentation

◆ MainImpl()

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

Definition at line 377 of file NetworkExecutionUtils.hpp.

 {
     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;
 
         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;
 
         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;
             }
         }
 
         // 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);
             boost::apply_visitor(printer, outputDataContainers[i]);
         }
 
         ARMNN_LOG(info) << "\nInference time: " << std::setprecision(2)
                                 << std::fixed << inference_duration.count() << " ms";
 
         // 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 (armnn::Exception const& e)
     {
         ARMNN_LOG(fatal) << "Armnn Error: " << e.what();
         return EXIT_FAILURE;
     }
 
     return EXIT_SUCCESS;
 }

◆ RunCsvTest()

int RunCsvTest	(	const armnnUtils::CsvRow &	csvRow,
		const std::shared_ptr< armnn::IRuntime > &	runtime,
		const bool	enableProfiling,
		const bool	enableFp16TurboMode,
		const double &	thresholdTime,
		const bool	printIntermediate,
		bool	enableLayerDetails = `false`,
		bool	parseUnuspported = `false`
	)

Definition at line 731 of file NetworkExecutionUtils.hpp.

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

Referenced by main().

 {
     boost::ignore_unused(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.")
         ("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.
         BOOST_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>();
 
     // 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,
                    enableProfiling, enableFp16TurboMode, thresholdTime, printIntermediate, subgraphId,
                    enableLayerDetails, parseUnuspported);
 }

◆ 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	enableProfiling,
		bool	enableFp16TurboMode,
		const double &	thresholdTime,
		bool	printIntermediate,
		const size_t	subgraphId,
		bool	enableLayerDetails = `false`,
		bool	parseUnsupported = `false`,
		const std::shared_ptr< armnn::IRuntime > &	runtime = `nullptr`
	)

Definition at line 520 of file NetworkExecutionUtils.hpp.

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

 {
     std::string modelFormat = boost::trim_copy(format);
     std::string modelPath = boost::trim_copy(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>(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_EnableProfiling          = enableProfiling;
     params.m_EnableFp16TurboMode      = enableFp16TurboMode;
     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;
 
     // 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);
 #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);
 #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);
 #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);
 #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);
 #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 348 of file NetworkExecutionUtils.hpp.

Classes