| // |
| // Copyright © 2017 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "NetworkExecutionUtils/NetworkExecutionUtils.hpp" |
| #include "ExecuteNetworkProgramOptions.hpp" |
| #include <armnn/IAsyncExecutionCallback.hpp> |
| #include <AsyncExecutionCallback.hpp> |
| |
| #include <armnn/Logging.hpp> |
| #include <armnnUtils/Filesystem.hpp> |
| #include <armnnUtils/TContainer.hpp> |
| #include <ProfilingOptionsConverter.hpp> |
| #include <InferenceTest.hpp> |
| |
| #if defined(ARMNN_SERIALIZER) |
| #include "armnnDeserializer/IDeserializer.hpp" |
| #endif |
| #if defined(ARMNN_TF_LITE_PARSER) |
| #include "armnnTfLiteParser/ITfLiteParser.hpp" |
| #endif |
| #if defined(ARMNN_ONNX_PARSER) |
| #include "armnnOnnxParser/IOnnxParser.hpp" |
| #endif |
| #if defined(ARMNN_TFLITE_DELEGATE) |
| #include <armnn_delegate.hpp> |
| #include <DelegateOptions.hpp> |
| |
| #include <tensorflow/lite/builtin_ops.h> |
| #include <tensorflow/lite/c/builtin_op_data.h> |
| #include <tensorflow/lite/c/common.h> |
| #include <tensorflow/lite/optional_debug_tools.h> |
| #include <tensorflow/lite/kernels/builtin_op_kernels.h> |
| #include <tensorflow/lite/interpreter.h> |
| #include <tensorflow/lite/kernels/register.h> |
| #endif |
| |
| #include <future> |
| |
| /** |
| * Given a measured duration and a threshold time tell the user whether we succeeded or not. |
| * |
| * @param duration the measured inference duration. |
| * @param thresholdTime the threshold time in milliseconds. |
| * @return false if the measured time exceeded the threshold. |
| */ |
| bool CheckInferenceTimeThreshold(const std::chrono::duration<double, std::milli>& duration, |
| const double& thresholdTime) |
| { |
| ARMNN_LOG(info) << "Inference time: " << std::setprecision(2) |
| << std::fixed << duration.count() << " ms\n"; |
| // If thresholdTime == 0.0 (default), then it hasn't been supplied at command line |
| if (thresholdTime != 0.0) |
| { |
| ARMNN_LOG(info) << "Threshold time: " << std::setprecision(2) |
| << std::fixed << thresholdTime << " ms"; |
| auto thresholdMinusInference = thresholdTime - 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; |
| return false; |
| } |
| } |
| return true; |
| } |
| |
| #if defined(ARMNN_TFLITE_DELEGATE) |
| int TfLiteDelegateMainImpl(const ExecuteNetworkParams& params, const armnn::IRuntime::CreationOptions runtimeOptions) |
| { |
| // Build model and corresponding interpreter |
| using namespace tflite; |
| |
| std::unique_ptr<tflite::FlatBufferModel> model = tflite::FlatBufferModel::BuildFromFile(params.m_ModelPath.c_str()); |
| |
| auto tfLiteInterpreter = std::make_unique<Interpreter>(); |
| tflite::ops::builtin::BuiltinOpResolver resolver; |
| |
| tflite::InterpreterBuilder builder(*model, resolver); |
| builder(&tfLiteInterpreter); |
| tfLiteInterpreter->AllocateTensors(); |
| |
| int status = 0; |
| |
| // Create & populate Armnn Delegate, then register it to TfLiteInterpreter |
| if (params.m_TfLiteExecutor == ExecuteNetworkParams::TfLiteExecutor::ArmNNTfLiteDelegate) |
| { |
| // Create the Armnn Delegate |
| // Populate a DelegateOptions from the ExecuteNetworkParams. |
| armnnDelegate::DelegateOptions delegateOptions = params.ToDelegateOptions(); |
| delegateOptions.SetExternalProfilingParams( |
| arm::pipe::ConvertExternalProfilingOptions(runtimeOptions.m_ProfilingOptions)); |
| |
| std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> |
| theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), |
| armnnDelegate::TfLiteArmnnDelegateDelete); |
| // Register armnn_delegate to TfLiteInterpreter |
| status = tfLiteInterpreter->ModifyGraphWithDelegate(std::move(theArmnnDelegate)); |
| if (status != kTfLiteOk) |
| { |
| ARMNN_LOG(fatal) << "Could not register ArmNN TfLite Delegate to TfLiteInterpreter!"; |
| return EXIT_FAILURE; |
| } |
| } |
| else |
| { |
| std::cout << "Running on TfLite without ArmNN delegate\n"; |
| } |
| |
| // Load (or generate) input data for inference |
| armnn::Optional<std::string> dataFile = params.m_GenerateTensorData |
| ? armnn::EmptyOptional() |
| : armnn::MakeOptional<std::string>(params.m_InputTensorDataFilePaths[0]); |
| |
| const size_t numInputs = params.m_InputNames.size(); |
| |
| // Populate input tensor of interpreter |
| for(unsigned int inputIndex = 0; inputIndex < numInputs; ++inputIndex) |
| { |
| int input = tfLiteInterpreter->inputs()[inputIndex]; |
| TfLiteIntArray* inputDims = tfLiteInterpreter->tensor(input)->dims; |
| |
| unsigned int inputSize = 1; |
| if (params.m_InputTensorShapes.size() > 0) |
| { |
| inputSize = params.m_InputTensorShapes[inputIndex]->GetNumElements(); |
| } |
| else |
| { |
| for (unsigned int dim = 0; dim < static_cast<unsigned int>(inputDims->size); ++dim) |
| { |
| inputSize *= inputDims->data[dim]; |
| } |
| } |
| |
| if (params.m_InputTypes[inputIndex].compare("float") == 0) |
| { |
| auto inputData = tfLiteInterpreter->typed_tensor<float>(input); |
| |
| if(inputData == NULL) |
| { |
| ARMNN_LOG(fatal) << "Input tensor is null, input type: " |
| "\"" << params.m_InputTypes[inputIndex] << "\" may be incorrect."; |
| return EXIT_FAILURE; |
| } |
| |
| std::vector<float> tensorData; |
| PopulateTensorWithDataGeneric<float>(tensorData, |
| inputSize, |
| dataFile, |
| [](const std::string& s) |
| { return std::stof(s); }); |
| |
| std::copy(tensorData.begin(), tensorData.end(), inputData); |
| } |
| else if (params.m_InputTypes[inputIndex].compare("qsymms8") == 0 || |
| params.m_InputTypes[inputIndex].compare("qasymms8") == 0) |
| { |
| auto inputData = tfLiteInterpreter->typed_tensor<int8_t>(input); |
| |
| if(inputData == NULL) |
| { |
| ARMNN_LOG(fatal) << "Input tensor is null, input type: " |
| "\"" << params.m_InputTypes[inputIndex] << "\" may be incorrect."; |
| return EXIT_FAILURE; |
| } |
| |
| std::vector<int8_t> tensorData; |
| PopulateTensorWithDataGeneric<int8_t>(tensorData, |
| inputSize, |
| dataFile, |
| [](const std::string& s) |
| { return armnn::numeric_cast<int8_t>(std::stoi(s)); }); |
| |
| std::copy(tensorData.begin(), tensorData.end(), inputData); |
| } |
| else if (params.m_InputTypes[inputIndex].compare("int") == 0) |
| { |
| auto inputData = tfLiteInterpreter->typed_tensor<int32_t>(input); |
| |
| if(inputData == NULL) |
| { |
| ARMNN_LOG(fatal) << "Input tensor is null, input type: " |
| "\"" << params.m_InputTypes[inputIndex] << "\" may be incorrect."; |
| return EXIT_FAILURE; |
| } |
| |
| std::vector<int32_t> tensorData; |
| PopulateTensorWithDataGeneric<int32_t>(tensorData, |
| inputSize, |
| dataFile, |
| [](const std::string& s) |
| { return std::stoi(s); }); |
| |
| std::copy(tensorData.begin(), tensorData.end(), inputData); |
| } |
| else if (params.m_InputTypes[inputIndex].compare("qasymm8") == 0 || |
| params.m_InputTypes[inputIndex].compare("qasymmu8") == 0) |
| { |
| auto inputData = tfLiteInterpreter->typed_tensor<uint8_t>(input); |
| |
| if(inputData == NULL) |
| { |
| ARMNN_LOG(fatal) << "Input tensor is null, input type: " |
| "\"" << params.m_InputTypes[inputIndex] << "\" may be incorrect."; |
| return EXIT_FAILURE; |
| } |
| |
| std::vector<uint8_t> tensorData; |
| PopulateTensorWithDataGeneric<uint8_t>(tensorData, |
| inputSize, |
| dataFile, |
| [](const std::string& s) |
| { return armnn::numeric_cast<uint8_t>(std::stoi(s)); }); |
| |
| std::copy(tensorData.begin(), tensorData.end(), inputData); |
| } |
| else |
| { |
| ARMNN_LOG(fatal) << "Unsupported input tensor data type \"" << params.m_InputTypes[inputIndex] << "\". "; |
| return EXIT_FAILURE; |
| } |
| } |
| |
| // Run inference, print the output of the inference |
| for (size_t x = 0; x < params.m_Iterations; x++) |
| { |
| // Start timer to record inference time in milliseconds. |
| const auto start_time = armnn::GetTimeNow(); |
| // Run the inference |
| status = tfLiteInterpreter->Invoke(); |
| const auto duration = armnn::GetTimeDuration(start_time); |
| |
| // The TFLite interpreter's outputs might be in a different order than the user inputted output names. |
| std::map<unsigned int, int> paramToTfliteOutputIndex; |
| for (unsigned int paramIndex = 0; paramIndex < params.m_OutputNames.size(); ++paramIndex) |
| { |
| paramToTfliteOutputIndex[paramIndex] = -1; |
| for (unsigned int tfLiteIndex = 0; tfLiteIndex < tfLiteInterpreter->outputs().size(); ++tfLiteIndex) |
| { |
| if (params.m_OutputNames[paramIndex] == tfLiteInterpreter->GetOutputName(tfLiteIndex)) |
| { |
| paramToTfliteOutputIndex[paramIndex] = tfLiteIndex; |
| } |
| } |
| } |
| |
| // Print out the output |
| for (unsigned int paramOutputIndex = 0; paramOutputIndex < params.m_OutputNames.size(); ++paramOutputIndex) |
| { |
| int outputIndex = paramToTfliteOutputIndex[paramOutputIndex]; |
| if (outputIndex == -1) |
| { |
| std::cout << fmt::format("Output name: {} doesn't exist.", params.m_OutputNames[paramOutputIndex]) << |
| std::endl; |
| continue; |
| } |
| auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[outputIndex]; |
| TfLiteIntArray* outputDims = tfLiteInterpreter->tensor(tfLiteDelegateOutputId)->dims; |
| // If we've been asked to write to a file then set a file output stream. Otherwise use stdout. |
| FILE* outputTensorFile = stdout; |
| if (!params.m_OutputTensorFiles.empty()) |
| { |
| outputTensorFile = fopen(params.m_OutputTensorFiles[outputIndex].c_str(), "w"); |
| if (outputTensorFile == NULL) |
| { |
| ARMNN_LOG(fatal) << "Specified output tensor file, \"" << |
| params.m_OutputTensorFiles[outputIndex] << |
| "\", cannot be created. Defaulting to stdout. " << |
| "Error was: " << std::strerror(errno); |
| outputTensorFile = stdout; |
| } |
| else |
| { |
| ARMNN_LOG(info) << "Writing output " << outputIndex << "' of iteration: " << x+1 << " to file: '" |
| << params.m_OutputTensorFiles[outputIndex] << "'"; |
| } |
| } |
| long outputSize = 1; |
| for (unsigned int dim = 0; dim < static_cast<unsigned int>(outputDims->size); ++dim) |
| { |
| outputSize *= outputDims->data[dim]; |
| } |
| |
| std::cout << tfLiteInterpreter->GetOutputName(outputIndex) << ": "; |
| if (params.m_OutputTypes[paramOutputIndex].compare("float") == 0) |
| { |
| auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId); |
| if(tfLiteDelageOutputData == NULL) |
| { |
| ARMNN_LOG(fatal) << "Output tensor is null, output type: " |
| "\"" << params.m_OutputTypes[paramOutputIndex] << "\" may be incorrect."; |
| return EXIT_FAILURE; |
| } |
| |
| if (!params.m_DontPrintOutputs) |
| { |
| for (int i = 0; i < outputSize; ++i) |
| { |
| fprintf(outputTensorFile, "%f ", tfLiteDelageOutputData[i]); |
| } |
| } |
| } |
| else if (params.m_OutputTypes[paramOutputIndex].compare("int") == 0) |
| { |
| auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<int32_t>(tfLiteDelegateOutputId); |
| if(tfLiteDelageOutputData == NULL) |
| { |
| ARMNN_LOG(fatal) << "Output tensor is null, output type: " |
| "\"" << params.m_OutputTypes[paramOutputIndex] << "\" may be incorrect."; |
| return EXIT_FAILURE; |
| } |
| |
| if (!params.m_DontPrintOutputs) |
| { |
| for (int i = 0; i < outputSize; ++i) |
| { |
| fprintf(outputTensorFile, "%d ", tfLiteDelageOutputData[i]); |
| } |
| } |
| } |
| else if (params.m_OutputTypes[paramOutputIndex].compare("qsymms8") == 0 || |
| params.m_OutputTypes[paramOutputIndex].compare("qasymms8") == 0) |
| { |
| auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<int8_t>(tfLiteDelegateOutputId); |
| if(tfLiteDelageOutputData == NULL) |
| { |
| ARMNN_LOG(fatal) << "Output tensor is null, output type: " |
| "\"" << params.m_OutputTypes[paramOutputIndex] << "\" may be incorrect."; |
| return EXIT_FAILURE; |
| } |
| |
| if (!params.m_DontPrintOutputs) |
| { |
| for (int i = 0; i < outputSize; ++i) |
| { |
| fprintf(outputTensorFile, "%d ", tfLiteDelageOutputData[i]); |
| } |
| } |
| } |
| else if (params.m_OutputTypes[paramOutputIndex].compare("qasymm8") == 0 || |
| params.m_OutputTypes[paramOutputIndex].compare("qasymmu8") == 0) |
| { |
| auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<uint8_t>(tfLiteDelegateOutputId); |
| if(tfLiteDelageOutputData == NULL) |
| { |
| ARMNN_LOG(fatal) << "Output tensor is null, output type: " |
| "\"" << params.m_OutputTypes[paramOutputIndex] << "\" may be incorrect."; |
| return EXIT_FAILURE; |
| } |
| |
| if (!params.m_DontPrintOutputs) |
| { |
| for (int i = 0; i < outputSize; ++i) |
| { |
| fprintf(outputTensorFile, "%u ", tfLiteDelageOutputData[i]); |
| } |
| } |
| } |
| else |
| { |
| ARMNN_LOG(fatal) << "Output tensor is null, output type: " |
| "\"" << params.m_OutputTypes[paramOutputIndex] << |
| "\" may be incorrect. Output type can be specified with -z argument"; |
| return EXIT_FAILURE; |
| } |
| std::cout << std::endl; |
| } |
| CheckInferenceTimeThreshold(duration, params.m_ThresholdTime); |
| } |
| |
| return status; |
| } |
| #endif |
| template<typename TParser, typename TDataType> |
| int MainImpl(const ExecuteNetworkParams& params, |
| const std::shared_ptr<armnn::IRuntime>& runtime = nullptr) |
| { |
| using namespace std::chrono; |
| |
| std::vector<std::vector<armnnUtils::TContainer>> inputs; |
| std::vector<std::vector<armnnUtils::TContainer>> outputs; |
| |
| 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; |
| inferenceModelParams.m_SaveCachedNetwork = params.m_SaveCachedNetwork; |
| inferenceModelParams.m_CachedNetworkFilePath = params.m_CachedNetworkFilePath; |
| inferenceModelParams.m_NumberOfThreads = params.m_NumberOfThreads; |
| inferenceModelParams.m_MLGOTuningFilePath = params.m_MLGOTuningFilePath; |
| inferenceModelParams.m_AsyncEnabled = params.m_Concurrent; |
| inferenceModelParams.m_ThreadPoolSize = params.m_ThreadPoolSize; |
| inferenceModelParams.m_OutputDetailsToStdOut = params.m_OutputDetailsToStdOut; |
| inferenceModelParams.m_OutputDetailsOnlyToStdOut = params.m_OutputDetailsOnlyToStdOut; |
| inferenceModelParams.m_ImportInputsIfAligned = params.m_ImportInputsIfAligned; |
| |
| 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(); |
| |
| armnn::Optional<QuantizationParams> qParams = params.m_QuantizeInput ? |
| armnn::MakeOptional<QuantizationParams>( |
| model.GetInputQuantizationParams()) : |
| armnn::EmptyOptional(); |
| |
| if (params.m_InputTensorDataFilePaths.size() > numInputs) |
| { |
| ARMNN_LOG(info) << "Given network has " << numInputs << " input/s. One input-tensor-data file is required " |
| << "for each input. The user provided " |
| << params.m_InputTensorDataFilePaths.size() |
| << " input-tensor-data file/s which will be used to fill the input/s.\n"; |
| } |
| |
| for(unsigned int j = 0; j < params.m_Iterations ; ++j) |
| { |
| std::vector<armnnUtils::TContainer> inputDataContainers; |
| for(unsigned int i = 0; i < numInputs; ++i) |
| { |
| // If there are fewer input files given than required for the execution of |
| // params.m_Iterations we simply start with the first input file again |
| size_t inputFileIndex = j * numInputs + i; |
| if (!params.m_InputTensorDataFilePaths.empty()) |
| { |
| inputFileIndex = inputFileIndex % params.m_InputTensorDataFilePaths.size(); |
| } |
| |
| armnn::Optional<std::string> dataFile = params.m_GenerateTensorData ? |
| armnn::EmptyOptional() : |
| armnn::MakeOptional<std::string>( |
| params.m_InputTensorDataFilePaths.at(inputFileIndex)); |
| |
| 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(); |
| } |
| |
| armnnUtils::TContainer tensorData; |
| PopulateTensorWithData(tensorData, |
| numElements, |
| params.m_InputTypes[i], |
| qParams, |
| dataFile); |
| |
| inputDataContainers.push_back(tensorData); |
| } |
| inputs.push_back(inputDataContainers); |
| } |
| |
| const size_t numOutputs = inferenceModelParams.m_OutputBindings.size(); |
| |
| // The user is allowed to specify the data type of each output tensor. It is used here to construct the |
| // result tensors for each iteration. It is possible for the user to specify a type that does not match |
| // the data type of the corresponding model output. It may not make sense, but it is historically allowed. |
| // The potential problem here is a buffer overrun when a larger data type is written into the space for a |
| // smaller one. Issue a warning to highlight the potential problem. |
| for (unsigned int outputIdx = 0; outputIdx < model.GetOutputBindingInfos().size(); ++outputIdx) |
| { |
| armnn::DataType type = model.GetOutputBindingInfo(outputIdx).second.GetDataType(); |
| switch (type) |
| { |
| // --output-type only supports float, int, qasymms8 or qasymmu8. |
| case armnn::DataType::Float32: |
| if (params.m_OutputTypes[outputIdx].compare("float") != 0) |
| { |
| ARMNN_LOG(warning) << "Model output index: " << outputIdx << " has data type Float32. The " << |
| "corresponding --output-type is " << params.m_OutputTypes[outputIdx] << |
| ". This may cause unexpected problems or random failures."; |
| } |
| break; |
| case armnn::DataType::QAsymmU8: |
| if (params.m_OutputTypes[outputIdx].compare("qasymmu8") != 0) |
| { |
| ARMNN_LOG(warning) << "Model output index: " << outputIdx << " has data type QAsymmU8. The " << |
| "corresponding --output-type is " << params.m_OutputTypes[outputIdx] << |
| ". This may cause unexpected problemsor random failures."; |
| } |
| break; |
| case armnn::DataType::Signed32: |
| if (params.m_OutputTypes[outputIdx].compare("int") != 0) |
| { |
| ARMNN_LOG(warning) << "Model output index: " << outputIdx << " has data type Signed32. The " << |
| "corresponding --output-type is " << params.m_OutputTypes[outputIdx] << |
| ". This may cause unexpected problems or random failures."; |
| } |
| break; |
| case armnn::DataType::QAsymmS8: |
| if (params.m_OutputTypes[outputIdx].compare("qasymms8") != 0) |
| { |
| ARMNN_LOG(warning) << "Model output index: " << outputIdx << " has data type QAsymmS8. The " << |
| "corresponding --output-type is " << params.m_OutputTypes[outputIdx] << |
| ". This may cause unexpected problems or random failures."; |
| } |
| break; |
| default: |
| break; |
| } |
| } |
| for (unsigned int j = 0; j < params.m_Iterations; ++j) |
| { |
| std::vector <armnnUtils::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 || |
| params.m_OutputTypes[i].compare("qasymmu8") == 0) |
| { |
| outputDataContainers.push_back(std::vector<uint8_t>(model.GetOutputSize(i))); |
| } |
| else if (params.m_OutputTypes[i].compare("qasymms8") == 0) |
| { |
| outputDataContainers.push_back(std::vector<int8_t>(model.GetOutputSize(i))); |
| } else |
| { |
| ARMNN_LOG(fatal) << "Unsupported tensor data type \"" << params.m_OutputTypes[i] << "\". "; |
| return EXIT_FAILURE; |
| } |
| } |
| outputs.push_back(outputDataContainers); |
| } |
| |
| if (params.m_Iterations > 1) |
| { |
| std::stringstream msg; |
| msg << "Network will be executed " << params.m_Iterations; |
| if (params.m_Concurrent) |
| { |
| msg << " times in an asynchronous manner. "; |
| } |
| else |
| { |
| msg << " times successively. "; |
| } |
| msg << "The input-tensor-data files will be reused recursively if the user didn't provide enough to " |
| "cover each execution."; |
| ARMNN_LOG(info) << msg.str(); |
| } |
| |
| // Synchronous execution |
| if (!params.m_Concurrent) |
| { |
| for (size_t x = 0; x < params.m_Iterations; x++) |
| { |
| // model.Run returns the inference time elapsed in EnqueueWorkload (in milliseconds) |
| auto inference_duration = model.Run(inputs[x], outputs[x]); |
| |
| if (params.m_GenerateTensorData) |
| { |
| ARMNN_LOG(warning) << "The input data was generated, note that the output will not be useful"; |
| } |
| if (params.m_DontPrintOutputs) |
| { |
| ARMNN_LOG(info) << "Printing outputs to console is disabled."; |
| } |
| |
| // Print output tensors |
| const auto& infosOut = model.GetOutputBindingInfos(); |
| for (size_t i = 0; i < numOutputs; i++) |
| { |
| const armnn::TensorInfo& infoOut = infosOut[i].second; |
| |
| // We've made sure before that the number of output files either equals numOutputs, in which |
| // case we override those files when processing the results of each iteration (only the result |
| // of the last iteration will be stored), or there are enough |
| // output files for each output of each iteration. |
| size_t outputFileIndex = x * numOutputs + i; |
| if (!params.m_OutputTensorFiles.empty()) |
| { |
| outputFileIndex = outputFileIndex % params.m_OutputTensorFiles.size(); |
| ARMNN_LOG(info) << "Writing output " << i << " named: '" |
| << inferenceModelParams.m_OutputBindings[i] |
| << "' of iteration: " << x+1 << " to file: '" |
| << params.m_OutputTensorFiles[outputFileIndex] << "'"; |
| } |
| auto outputTensorFile = params.m_OutputTensorFiles.empty() |
| ? "" |
| : params.m_OutputTensorFiles[outputFileIndex]; |
| |
| TensorPrinter printer(inferenceModelParams.m_OutputBindings[i], |
| infoOut, |
| outputTensorFile, |
| params.m_DequantizeOutput, |
| !params.m_DontPrintOutputs); |
| mapbox::util::apply_visitor(printer, outputs[x][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; |
| } |
| } |
| } |
| } |
| // Asynchronous execution using the Arm NN thread pool |
| else if (params.m_ThreadPoolSize >= 1) |
| { |
| try |
| { |
| ARMNN_LOG(info) << "Asynchronous execution with Arm NN thread pool... \n"; |
| armnn::AsyncCallbackManager callbackManager; |
| std::unordered_map<armnn::InferenceId, std::vector<armnnUtils::TContainer>&> inferenceOutputMap; |
| |
| // Declare the latest and earliest inference times here to be used when calculating overall time |
| std::chrono::high_resolution_clock::time_point earliestStartTime; |
| std::chrono::high_resolution_clock::time_point latestEndTime = |
| std::chrono::high_resolution_clock::now(); |
| |
| // For the asynchronous execution, we are adding a pool of working memory handles (1 per thread) in the |
| // LoadedNetwork with each scheduled inference having a specific priority |
| for (size_t i = 0; i < params.m_Iterations; ++i) |
| { |
| std::shared_ptr<armnn::AsyncExecutionCallback> cb = callbackManager.GetNewCallback(); |
| inferenceOutputMap.insert({cb->GetInferenceId(), outputs[i]}); |
| model.RunAsync(inputs[i], outputs[i], cb); |
| } |
| |
| // Check the results |
| unsigned int j = 0; |
| for (size_t iteration = 0; iteration < params.m_Iterations; ++iteration) |
| { |
| auto cb = callbackManager.GetNotifiedCallback(); |
| |
| // Get the results |
| auto endTime = time_point_cast<std::chrono::milliseconds>(cb->GetEndTime()); |
| auto startTime = time_point_cast<std::chrono::milliseconds>(cb->GetStartTime()); |
| auto inferenceDuration = endTime - startTime; |
| |
| if (latestEndTime < cb->GetEndTime()) |
| { |
| latestEndTime = cb->GetEndTime(); |
| } |
| |
| if (earliestStartTime.time_since_epoch().count() == 0) |
| { |
| earliestStartTime = cb->GetStartTime(); |
| } |
| else if (earliestStartTime > cb->GetStartTime()) |
| { |
| earliestStartTime = cb->GetStartTime(); |
| } |
| |
| if (params.m_GenerateTensorData) |
| { |
| ARMNN_LOG(warning) << "The input data was generated, note that the output will not be useful"; |
| } |
| if (params.m_DontPrintOutputs) |
| { |
| ARMNN_LOG(info) << "Printing outputs to console is disabled."; |
| } |
| |
| // Print output tensors |
| const auto& infosOut = model.GetOutputBindingInfos(); |
| for (size_t i = 0; i < numOutputs; i++) |
| { |
| // We've made sure before that the number of output files either equals numOutputs, in which |
| // case we override those files when processing the results of each iteration (only the |
| // result of the last iteration will be stored), or there are enough |
| // output files for each output of each iteration. |
| size_t outputFileIndex = iteration * numOutputs + i; |
| if (!params.m_OutputTensorFiles.empty()) |
| { |
| outputFileIndex = outputFileIndex % params.m_OutputTensorFiles.size(); |
| ARMNN_LOG(info) << "Writing output " << i << " named: '" |
| << inferenceModelParams.m_OutputBindings[i] |
| << "' of iteration: " << iteration+1 << " to file: '" |
| << params.m_OutputTensorFiles[outputFileIndex] << "'"; |
| } |
| |
| const armnn::TensorInfo& infoOut = infosOut[i].second; |
| auto outputTensorFile = params.m_OutputTensorFiles.empty() |
| ? "" |
| : params.m_OutputTensorFiles[outputFileIndex]; |
| |
| TensorPrinter printer(inferenceModelParams.m_OutputBindings[i], |
| infoOut, |
| outputTensorFile, |
| params.m_DequantizeOutput, |
| !params.m_DontPrintOutputs); |
| mapbox::util::apply_visitor(printer, inferenceOutputMap.at(cb->GetInferenceId())[i]); |
| } |
| |
| CheckInferenceTimeThreshold(inferenceDuration, params.m_ThresholdTime); |
| ++j; |
| } |
| //print duration difference between overallStartTime and overallEndTime |
| auto overallEndTime = time_point_cast<std::chrono::milliseconds>(latestEndTime); |
| auto overallStartTime = time_point_cast<std::chrono::milliseconds>(earliestStartTime); |
| auto totalInferenceDuration = overallEndTime - overallStartTime; |
| ARMNN_LOG(info) << "\nOverall Inference time: " << std::setprecision(2) |
| << std::fixed << totalInferenceDuration.count() << " ms\n"; |
| } |
| catch (const armnn::Exception& e) |
| { |
| ARMNN_LOG(fatal) << "Armnn Error: " << e.what(); |
| return EXIT_FAILURE; |
| } |
| } |
| // Asynchronous execution using std::launch::async |
| else |
| { |
| try |
| { |
| ARMNN_LOG(info) << "Asynchronous Execution with std::launch:async... \n"; |
| std::vector<std::future<std::tuple<unsigned int, |
| std::chrono::duration<double, std::milli>>>> inferenceResults; |
| inferenceResults.reserve(params.m_Iterations); |
| |
| // Create WorkingMemHandles for each inference |
| std::vector<std::unique_ptr<armnn::experimental::IWorkingMemHandle>> workingMemHandles; |
| workingMemHandles.reserve(params.m_Iterations); |
| for (unsigned int i = 0; i < params.m_Iterations; ++i) |
| { |
| workingMemHandles.push_back(model.CreateWorkingMemHandle()); |
| } |
| |
| // Run each inference in its own thread |
| // start a timer |
| const auto start_time = armnn::GetTimeNow(); |
| for (unsigned int i = 0; i < params.m_Iterations; ++i) |
| { |
| armnn::experimental::IWorkingMemHandle& workingMemHandleRef = *workingMemHandles[i].get(); |
| |
| inferenceResults.push_back(std::async( |
| std::launch::async, [&model, &workingMemHandleRef, &inputs, &outputs, i]() { |
| return model.RunAsync(workingMemHandleRef, inputs[i], outputs[i], i); |
| } |
| )); |
| } |
| |
| // Check the results |
| for (unsigned int j = 0; j < inferenceResults.size(); ++j) |
| { |
| // Get the results |
| auto inferenceResult = inferenceResults[j].get(); |
| auto inferenceDuration = std::get<1>(inferenceResult); |
| auto inferenceID = std::get<0>(inferenceResult); |
| |
| if (params.m_GenerateTensorData) |
| { |
| ARMNN_LOG(warning) << "The input data was generated, note that the output will not be useful"; |
| } |
| if (params.m_DontPrintOutputs) |
| { |
| ARMNN_LOG(info) << "Printing outputs to console is disabled."; |
| } |
| |
| // Print output tensors |
| const auto& infosOut = model.GetOutputBindingInfos(); |
| for (size_t i = 0; i < numOutputs; i++) |
| { |
| // We've made sure before that the number of output files either equals numOutputs, in which |
| // case we override those files when processing the results of each iteration (only the |
| // result of the last iteration will be stored), or there are enough |
| // output files for each output of each iteration. |
| size_t outputFileIndex = j * numOutputs + i; |
| if (!params.m_OutputTensorFiles.empty()) |
| { |
| outputFileIndex = outputFileIndex % params.m_OutputTensorFiles.size(); |
| ARMNN_LOG(info) << "Writing output " << i << " named: '" |
| << inferenceModelParams.m_OutputBindings[i] |
| << "' of iteration: " << j+1 << " to file: '" |
| << params.m_OutputTensorFiles[outputFileIndex] << "'"; |
| } |
| const armnn::TensorInfo& infoOut = infosOut[i].second; |
| auto outputTensorFile = params.m_OutputTensorFiles.empty() |
| ? "" |
| : params.m_OutputTensorFiles[outputFileIndex]; |
| |
| TensorPrinter printer(inferenceModelParams.m_OutputBindings[i], |
| infoOut, |
| outputTensorFile, |
| params.m_DequantizeOutput, |
| !params.m_DontPrintOutputs); |
| mapbox::util::apply_visitor(printer, outputs[j][i]); |
| } |
| CheckInferenceTimeThreshold(inferenceDuration, params.m_ThresholdTime); |
| ARMNN_LOG(info) << "Asynchronous Execution is finished for Inference ID: " << inferenceID << " \n"; |
| } |
| // finish timer |
| const auto duration = armnn::GetTimeDuration(start_time); |
| ARMNN_LOG(info) << "\nOverall Inference time: " << std::setprecision(2) |
| << std::fixed << duration.count() << " ms\n"; |
| } |
| catch (const armnn::Exception& e) |
| { |
| ARMNN_LOG(fatal) << "Armnn Error: " << e.what(); |
| return EXIT_FAILURE; |
| } |
| } |
| } |
| catch (const armnn::Exception& e) |
| { |
| ARMNN_LOG(fatal) << "Armnn Error: " << e.what(); |
| return EXIT_FAILURE; |
| } |
| |
| return EXIT_SUCCESS; |
| } |
| |
| // MAIN |
| int main(int argc, const char* argv[]) |
| { |
| // Configures logging for both the ARMNN library and this test program. |
| #ifdef NDEBUG |
| armnn::LogSeverity level = armnn::LogSeverity::Info; |
| #else |
| armnn::LogSeverity level = armnn::LogSeverity::Debug; |
| #endif |
| armnn::ConfigureLogging(true, true, level); |
| |
| |
| // Get ExecuteNetwork parameters and runtime options from command line |
| // This might throw an InvalidArgumentException if the user provided invalid inputs |
| ProgramOptions ProgramOptions; |
| try { |
| ProgramOptions.ParseOptions(argc, argv); |
| } catch (const std::exception &e){ |
| ARMNN_LOG(fatal) << e.what(); |
| return EXIT_FAILURE; |
| } |
| |
| if ((ProgramOptions.m_ExNetParams.m_OutputDetailsToStdOut || |
| ProgramOptions.m_ExNetParams.m_OutputDetailsOnlyToStdOut) |
| && !ProgramOptions.m_ExNetParams.m_EnableProfiling) |
| { |
| ARMNN_LOG(fatal) << "You must enable profiling if you would like to output layer details"; |
| return EXIT_FAILURE; |
| } |
| |
| std::string modelFormat = ProgramOptions.m_ExNetParams.m_ModelFormat; |
| |
| // Forward to implementation based on the parser type |
| if (modelFormat.find("armnn") != std::string::npos) |
| { |
| #if defined(ARMNN_SERIALIZER) |
| std::shared_ptr<armnn::IRuntime> runtime(armnn::IRuntime::Create(ProgramOptions.m_RuntimeOptions)); |
| return MainImpl<armnnDeserializer::IDeserializer, float>(ProgramOptions.m_ExNetParams, runtime); |
| #else |
| ARMNN_LOG(fatal) << "Not built with serialization support."; |
| return EXIT_FAILURE; |
| #endif |
| } |
| else if (modelFormat.find("onnx") != std::string::npos) |
| { |
| #if defined(ARMNN_ONNX_PARSER) |
| std::shared_ptr<armnn::IRuntime> runtime(armnn::IRuntime::Create(ProgramOptions.m_RuntimeOptions)); |
| return MainImpl<armnnOnnxParser::IOnnxParser, float>(ProgramOptions.m_ExNetParams, runtime); |
| #else |
| ARMNN_LOG(fatal) << "Not built with Onnx parser support."; |
| return EXIT_FAILURE; |
| #endif |
| } |
| else if(modelFormat.find("tflite") != std::string::npos) |
| { |
| if (ProgramOptions.m_ExNetParams.m_TfLiteExecutor == ExecuteNetworkParams::TfLiteExecutor::ArmNNTfLiteParser) |
| { |
| #if defined(ARMNN_TF_LITE_PARSER) |
| std::shared_ptr<armnn::IRuntime> runtime(armnn::IRuntime::Create(ProgramOptions.m_RuntimeOptions)); |
| return MainImpl<armnnTfLiteParser::ITfLiteParser, float>(ProgramOptions.m_ExNetParams, runtime); |
| #else |
| ARMNN_LOG(fatal) << "Not built with Tensorflow-Lite parser support."; |
| return EXIT_FAILURE; |
| #endif |
| } |
| else if (ProgramOptions.m_ExNetParams.m_TfLiteExecutor == |
| ExecuteNetworkParams::TfLiteExecutor::ArmNNTfLiteDelegate || |
| ProgramOptions.m_ExNetParams.m_TfLiteExecutor == |
| ExecuteNetworkParams::TfLiteExecutor::TfliteInterpreter) |
| { |
| #if defined(ARMNN_TF_LITE_DELEGATE) |
| return TfLiteDelegateMainImpl(ProgramOptions.m_ExNetParams, ProgramOptions.m_RuntimeOptions); |
| #else |
| ARMNN_LOG(fatal) << "Not built with Arm NN Tensorflow-Lite delegate support."; |
| return EXIT_FAILURE; |
| #endif |
| } |
| } |
| else |
| { |
| ARMNN_LOG(fatal) << "Unknown model format: '" << modelFormat |
| << "'. Please include 'tflite' or 'onnx'"; |
| return EXIT_FAILURE; |
| } |
| } |