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Diffstat (limited to 'tests/ExecuteNetwork/TfliteExecutor.cpp')
| -rw-r--r-- | tests/ExecuteNetwork/TfliteExecutor.cpp | 251 |
1 files changed, 0 insertions, 251 deletions
diff --git a/tests/ExecuteNetwork/TfliteExecutor.cpp b/tests/ExecuteNetwork/TfliteExecutor.cpp deleted file mode 100644 index f7a3068d7b..0000000000 --- a/tests/ExecuteNetwork/TfliteExecutor.cpp +++ /dev/null @@ -1,251 +0,0 @@ -// -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "TfliteExecutor.hpp" - -TfLiteExecutor::TfLiteExecutor(const ExecuteNetworkParams& params) : m_Params(params) -{ - std::unique_ptr<tflite::FlatBufferModel> model = - tflite::FlatBufferModel::BuildFromFile(m_Params.m_ModelPath.c_str()); - - m_TfLiteInterpreter = std::make_unique<Interpreter>(); - tflite::ops::builtin::BuiltinOpResolver resolver; - - tflite::InterpreterBuilder builder(*model, resolver); - builder(&m_TfLiteInterpreter); - m_TfLiteInterpreter->AllocateTensors(); - - int status; - if (m_Params.m_TfLiteExecutor == ExecuteNetworkParams::TfLiteExecutor::ArmNNTfLiteDelegate) - { - // Create the Armnn Delegate - // Populate a DelegateOptions from the ExecuteNetworkParams. - armnnDelegate::DelegateOptions delegateOptions = m_Params.ToDelegateOptions(); - delegateOptions.SetExternalProfilingParams(delegateOptions.GetExternalProfilingParams()); - - std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> - theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), - armnnDelegate::TfLiteArmnnDelegateDelete); - // Register armnn_delegate to TfLiteInterpreter - status = m_TfLiteInterpreter->ModifyGraphWithDelegate(std::move(theArmnnDelegate)); - if (status == kTfLiteError) - { - LogAndThrow("Could not register ArmNN TfLite Delegate to TfLiteInterpreter"); - } - } - else - { - std::cout << "Running on TfLite without ArmNN delegate\n"; - } - - armnn::Optional<std::string> dataFile = m_Params.m_GenerateTensorData - ? armnn::EmptyOptional() - : armnn::MakeOptional<std::string>(m_Params.m_InputTensorDataFilePaths[0]); - - const size_t numInputs = m_Params.m_InputNames.size(); - - for(unsigned int inputIndex = 0; inputIndex < numInputs; ++inputIndex) - { - int input = m_TfLiteInterpreter->inputs()[inputIndex]; - - TfLiteIntArray* inputDims = m_TfLiteInterpreter->tensor(input)->dims; - - unsigned int inputSize = 1; - for (unsigned int dim = 0; dim < static_cast<unsigned int>(inputDims->size); ++dim) - { - inputSize *= inputDims->data[dim]; - } - - const auto& inputName = m_TfLiteInterpreter->input_tensor(input)->name; - const auto& dataType = m_TfLiteInterpreter->input_tensor(input)->type; - - switch (dataType) - { - case kTfLiteFloat32: - { - auto inputData = m_TfLiteInterpreter->typed_tensor<float>(input); - PopulateTensorWithData(inputData, inputSize, dataFile, inputName); - break; - } - case kTfLiteInt32: - { - auto inputData = m_TfLiteInterpreter->typed_tensor<int>(input); - PopulateTensorWithData(inputData, inputSize, dataFile, inputName); - break; - } - case kTfLiteUInt8: - { - auto inputData = m_TfLiteInterpreter->typed_tensor<uint8_t>(input); - PopulateTensorWithData(inputData, inputSize, dataFile, inputName); - break; - } - case kTfLiteInt16: - { - auto inputData = m_TfLiteInterpreter->typed_tensor<int16_t>(input); - PopulateTensorWithData(inputData, inputSize, dataFile, inputName); - break; - } - case kTfLiteInt8: - { - auto inputData = m_TfLiteInterpreter->typed_tensor<int8_t>(input); - PopulateTensorWithData(inputData, inputSize, dataFile, inputName); - break; - } - default: - { - LogAndThrow("Unsupported input tensor data type"); - } - } - } -} - -std::vector<const void *> TfLiteExecutor::Execute() -{ - int status = 0; - std::vector<const void*> results; - for (size_t x = 0; x < m_Params.m_Iterations; x++) - { - // Start timer to record inference time in milliseconds. - const auto start_time = armnn::GetTimeNow(); - // Run the inference - status = m_TfLiteInterpreter->Invoke(); - const auto duration = armnn::GetTimeDuration(start_time); - - if (m_Params.m_DontPrintOutputs || m_Params.m_ReuseBuffers) - { - break; - } - // Print out the output - for (unsigned int outputIndex = 0; outputIndex < m_TfLiteInterpreter->outputs().size(); ++outputIndex) - { - auto tfLiteDelegateOutputId = m_TfLiteInterpreter->outputs()[outputIndex]; - TfLiteIntArray* outputDims = m_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 (!m_Params.m_OutputTensorFiles.empty()) - { - outputTensorFile = fopen(m_Params.m_OutputTensorFiles[outputIndex].c_str(), "w"); - if (outputTensorFile == NULL) - { - LogAndThrow("Specified output tensor file, \"" + m_Params.m_OutputTensorFiles[outputIndex] + - "\", cannot be created. Defaulting to stdout. Error was: " + std::strerror(errno)); - } - else - { - ARMNN_LOG(info) << "Writing output " << outputIndex << "' of iteration: " << x+1 << " to file: '" - << m_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 << m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->name << ": "; - results.push_back(m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->allocation); - - switch (m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->type) - { - - case kTfLiteFloat32: - { - auto tfLiteDelageOutputData = m_TfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId); - - for (int i = 0; i < outputSize; ++i) - { - fprintf(outputTensorFile, "%f ", tfLiteDelageOutputData[i]); - } - break; - } - case kTfLiteInt32: - { - auto tfLiteDelageOutputData = m_TfLiteInterpreter->typed_tensor<int32_t>(tfLiteDelegateOutputId); - for (int i = 0; i < outputSize; ++i) - { - fprintf(outputTensorFile, "%d ", tfLiteDelageOutputData[i]); - } - break; - } - case kTfLiteUInt8: - { - auto tfLiteDelageOutputData = m_TfLiteInterpreter->typed_tensor<uint8_t>(tfLiteDelegateOutputId); - for (int i = 0; i < outputSize; ++i) - { - fprintf(outputTensorFile, "%u ", tfLiteDelageOutputData[i]); - } - break; - } - case kTfLiteInt8: - { - auto tfLiteDelageOutputData = m_TfLiteInterpreter->typed_tensor<int8_t>(tfLiteDelegateOutputId); - for (int i = 0; i < outputSize; ++i) - { - fprintf(outputTensorFile, "%d ", tfLiteDelageOutputData[i]); - } - break; - } - default: - { - LogAndThrow("Unsupported output type"); - } - } - - std::cout << std::endl; - } - CheckInferenceTimeThreshold(duration, m_Params.m_ThresholdTime); - } - - std::cout << status; - return results; -} - -void TfLiteExecutor::CompareAndPrintResult(std::vector<const void*> otherOutput) -{ - for (unsigned int outputIndex = 0; outputIndex < m_TfLiteInterpreter->outputs().size(); ++outputIndex) - { - auto tfLiteDelegateOutputId = m_TfLiteInterpreter->outputs()[outputIndex]; - float result = 0; - switch (m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->type) - { - case kTfLiteFloat32: - { - result = ComputeRMSE<float>(m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->allocation, - otherOutput[outputIndex], - m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->bytes); - - break; - } - case kTfLiteInt32: - { - result = ComputeRMSE<int32_t>(m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->allocation, - otherOutput[outputIndex], - m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->bytes); - break; - } - case kTfLiteUInt8: - { - result = ComputeRMSE<uint8_t>(m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->allocation, - otherOutput[outputIndex], - m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->bytes); - break; - } - case kTfLiteInt8: - { - result = ComputeRMSE<int8_t>(m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->allocation, - otherOutput[outputIndex], - m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->bytes); - break; - } - default: - { - } - } - - std::cout << "RMSE of " - << m_TfLiteInterpreter->tensor(tfLiteDelegateOutputId)->name - << ": " << result << std::endl; - } -}; |