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//
// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "ArmnnNetworkExecutor.hpp"
#include "Types.hpp"
#include <random>
#include <string>
namespace od
{
armnn::DataType ArmnnNetworkExecutor::GetInputDataType() const
{
return m_inputBindingInfo.second.GetDataType();
}
ArmnnNetworkExecutor::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;
}
//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();
InferenceResult oneLayerOutResult;
switch (dataType)
{
case armnn::DataType::Float32:
{
oneLayerOutResult.resize(tensorShape.GetNumElements(), 0);
break;
}
default:
{
errorMessage = "ArmnnNetworkExecutor: unsupported output tensor data type";
ARMNN_LOG(error) << errorMessage << " " << log_as_int(dataType);
throw armnn::Exception(errorMessage);
}
}
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())
));
}
}
}
void ArmnnNetworkExecutor::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)}};
}
bool ArmnnNetworkExecutor::Run(const void* inputData, const size_t dataBytes, InferenceResults& 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);
}
Size ArmnnNetworkExecutor::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 od
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