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Diffstat (limited to 'samples/common/include/ArmnnUtils/ArmnnNetworkExecutor.hpp')
| -rw-r--r-- | samples/common/include/ArmnnUtils/ArmnnNetworkExecutor.hpp | 214 |
1 files changed, 214 insertions, 0 deletions
diff --git a/samples/common/include/ArmnnUtils/ArmnnNetworkExecutor.hpp b/samples/common/include/ArmnnUtils/ArmnnNetworkExecutor.hpp new file mode 100644 index 0000000000..96cc1d0184 --- /dev/null +++ b/samples/common/include/ArmnnUtils/ArmnnNetworkExecutor.hpp @@ -0,0 +1,214 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include "Types.hpp" + +#include "armnn/ArmNN.hpp" +#include "armnnTfLiteParser/ITfLiteParser.hpp" +#include "armnnUtils/DataLayoutIndexed.hpp" +#include <armnn/Logging.hpp> + +#include <string> +#include <vector> + +namespace common +{ +/** +* @brief Used to load in a network through ArmNN and run inference on it against a given backend. +* +*/ +template <class Tout> +class ArmnnNetworkExecutor +{ +private: + armnn::IRuntimePtr m_Runtime; + armnn::NetworkId m_NetId{}; + mutable InferenceResults<Tout> m_OutputBuffer; + armnn::InputTensors m_InputTensors; + armnn::OutputTensors m_OutputTensors; + std::vector<armnnTfLiteParser::BindingPointInfo> m_outputBindingInfo; + + std::vector<std::string> m_outputLayerNamesList; + + armnnTfLiteParser::BindingPointInfo m_inputBindingInfo; + + void PrepareTensors(const void* inputData, const size_t dataBytes); + + template <typename Enumeration> + auto log_as_int(Enumeration value) + -> typename std::underlying_type<Enumeration>::type + { + return static_cast<typename std::underlying_type<Enumeration>::type>(value); + } + +public: + ArmnnNetworkExecutor() = delete; + + /** + * @brief Initializes the network with the given input data. Parsed through TfLiteParser and optimized for a + * given backend. + * + * Note that the output layers names order in m_outputLayerNamesList affects the order of the feature vectors + * in output of the Run method. + * + * * @param[in] modelPath - Relative path to the model file + * * @param[in] backends - The list of preferred backends to run inference on + */ + ArmnnNetworkExecutor(std::string& modelPath, + std::vector<armnn::BackendId>& backends); + + /** + * @brief Returns the aspect ratio of the associated model in the order of width, height. + */ + Size GetImageAspectRatio(); + + armnn::DataType GetInputDataType() const; + + float GetQuantizationScale(); + + int GetQuantizationOffset(); + + /** + * @brief Runs inference on the provided input data, and stores the results in the provided InferenceResults object. + * + * @param[in] inputData - input frame data + * @param[in] dataBytes - input data size in bytes + * @param[out] results - Vector of DetectionResult objects used to store the output result. + */ + bool Run(const void* inputData, const size_t dataBytes, common::InferenceResults<Tout>& outResults); + +}; + +template <class Tout> +ArmnnNetworkExecutor<Tout>::ArmnnNetworkExecutor(std::string& modelPath, + std::vector<armnn::BackendId>& preferredBackends) + : m_Runtime(armnn::IRuntime::Create(armnn::IRuntime::CreationOptions())) +{ + // Import the TensorFlow lite model. + armnnTfLiteParser::ITfLiteParserPtr parser = armnnTfLiteParser::ITfLiteParser::Create(); + armnn::INetworkPtr network = parser->CreateNetworkFromBinaryFile(modelPath.c_str()); + + std::vector<std::string> inputNames = parser->GetSubgraphInputTensorNames(0); + + m_inputBindingInfo = parser->GetNetworkInputBindingInfo(0, inputNames[0]); + + m_outputLayerNamesList = parser->GetSubgraphOutputTensorNames(0); + + std::vector<armnn::BindingPointInfo> outputBindings; + for(const std::string& name : m_outputLayerNamesList) + { + m_outputBindingInfo.push_back(std::move(parser->GetNetworkOutputBindingInfo(0, name))); + } + std::vector<std::string> errorMessages; + // optimize the network. + armnn::IOptimizedNetworkPtr optNet = Optimize(*network, + preferredBackends, + m_Runtime->GetDeviceSpec(), + armnn::OptimizerOptions(), + armnn::Optional<std::vector<std::string>&>(errorMessages)); + + if (!optNet) + { + const std::string errorMessage{"ArmnnNetworkExecutor: Failed to optimize network"}; + ARMNN_LOG(error) << errorMessage; + throw armnn::Exception(errorMessage); + } + + // Load the optimized network onto the m_Runtime device + std::string errorMessage; + if (armnn::Status::Success != m_Runtime->LoadNetwork(m_NetId, std::move(optNet), errorMessage)) + { + ARMNN_LOG(error) << errorMessage; + throw armnn::Exception(errorMessage); + } + + //pre-allocate memory for output (the size of it never changes) + for (int it = 0; it < m_outputLayerNamesList.size(); ++it) + { + const armnn::DataType dataType = m_outputBindingInfo[it].second.GetDataType(); + const armnn::TensorShape& tensorShape = m_outputBindingInfo[it].second.GetShape(); + + std::vector<Tout> oneLayerOutResult; + oneLayerOutResult.resize(tensorShape.GetNumElements(), 0); + m_OutputBuffer.emplace_back(oneLayerOutResult); + + // Make ArmNN output tensors + m_OutputTensors.reserve(m_OutputBuffer.size()); + for (size_t it = 0; it < m_OutputBuffer.size(); ++it) + { + m_OutputTensors.emplace_back(std::make_pair( + m_outputBindingInfo[it].first, + armnn::Tensor(m_outputBindingInfo[it].second, + m_OutputBuffer.at(it).data()) + )); + } + } + +} + +template <class Tout> +armnn::DataType ArmnnNetworkExecutor<Tout>::GetInputDataType() const +{ + return m_inputBindingInfo.second.GetDataType(); +} + +template <class Tout> +void ArmnnNetworkExecutor<Tout>::PrepareTensors(const void* inputData, const size_t dataBytes) +{ + assert(m_inputBindingInfo.second.GetNumBytes() >= dataBytes); + m_InputTensors.clear(); + m_InputTensors = {{ m_inputBindingInfo.first, armnn::ConstTensor(m_inputBindingInfo.second, inputData)}}; +} + +template <class Tout> +bool ArmnnNetworkExecutor<Tout>::Run(const void* inputData, const size_t dataBytes, InferenceResults<Tout>& outResults) +{ + /* Prepare tensors if they are not ready */ + ARMNN_LOG(debug) << "Preparing tensors..."; + this->PrepareTensors(inputData, dataBytes); + ARMNN_LOG(trace) << "Running inference..."; + + armnn::Status ret = m_Runtime->EnqueueWorkload(m_NetId, m_InputTensors, m_OutputTensors); + + std::stringstream inferenceFinished; + inferenceFinished << "Inference finished with code {" << log_as_int(ret) << "}\n"; + + ARMNN_LOG(trace) << inferenceFinished.str(); + + if (ret == armnn::Status::Failure) + { + ARMNN_LOG(error) << "Failed to perform inference."; + } + + outResults.reserve(m_outputLayerNamesList.size()); + outResults = m_OutputBuffer; + + return (armnn::Status::Success == ret); +} + +template <class Tout> +float ArmnnNetworkExecutor<Tout>::GetQuantizationScale() +{ + return this->m_inputBindingInfo.second.GetQuantizationScale(); +} + +template <class Tout> +int ArmnnNetworkExecutor<Tout>::GetQuantizationOffset() +{ + return this->m_inputBindingInfo.second.GetQuantizationOffset(); +} + +template <class Tout> +Size ArmnnNetworkExecutor<Tout>::GetImageAspectRatio() +{ + const auto shape = m_inputBindingInfo.second.GetShape(); + assert(shape.GetNumDimensions() == 4); + armnnUtils::DataLayoutIndexed nhwc(armnn::DataLayout::NHWC); + return Size(shape[nhwc.GetWidthIndex()], + shape[nhwc.GetHeightIndex()]); +} +}// namespace common
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