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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// See LICENSE file in the project root for full license information.
//
#pragma once
#include "armnn/ArmNN.hpp"
#include "HeapProfiling.hpp"
#include <boost/exception/exception.hpp>
#include <boost/exception/diagnostic_information.hpp>
#include <boost/log/trivial.hpp>
#include <boost/format.hpp>
#include <boost/program_options.hpp>
#include <boost/filesystem.hpp>
#include <map>
#include <string>
#include <fstream>
template<typename TContainer>
inline armnn::InputTensors MakeInputTensors(const std::pair<armnn::LayerBindingId, armnn::TensorInfo>& input,
const TContainer& inputTensorData)
{
if (inputTensorData.size() != input.second.GetNumElements())
{
try
{
throw armnn::Exception(boost::str(boost::format("Input tensor has incorrect size. Expected %1% elements "
"but got %2%.") % input.second.GetNumElements() % inputTensorData.size()));
} catch (const boost::exception& e)
{
// Coverity fix: it should not be possible to get here but boost::str and boost::format can both
// throw uncaught exceptions - convert them to armnn exceptions and rethrow
throw armnn::Exception(diagnostic_information(e));
}
}
return { { input.first, armnn::ConstTensor(input.second, inputTensorData.data()) } };
}
template<typename TContainer>
inline armnn::OutputTensors MakeOutputTensors(const std::pair<armnn::LayerBindingId, armnn::TensorInfo>& output,
TContainer& outputTensorData)
{
if (outputTensorData.size() != output.second.GetNumElements())
{
throw armnn::Exception("Output tensor has incorrect size");
}
return { { output.first, armnn::Tensor(output.second, outputTensorData.data()) } };
}
template <typename IParser, typename TDataType>
class InferenceModel
{
public:
using DataType = TDataType;
struct CommandLineOptions
{
std::string m_ModelDir;
armnn::Compute m_ComputeDevice;
bool m_VisualizePostOptimizationModel;
};
static void AddCommandLineOptions(boost::program_options::options_description& desc, CommandLineOptions& options)
{
namespace po = boost::program_options;
desc.add_options()
("model-dir,m", po::value<std::string>(&options.m_ModelDir)->required(),
"Path to directory containing model files (.caffemodel/.prototxt)")
("compute,c", po::value<armnn::Compute>(&options.m_ComputeDevice)->default_value(armnn::Compute::CpuAcc),
"Which device to run layers on by default. Possible choices: CpuAcc, CpuRef, GpuAcc")
("visualize-optimized-model,v",
po::value<bool>(&options.m_VisualizePostOptimizationModel)->default_value(false),
"Produce a dot file useful for visualizing the graph post optimization."
"The file will have the same name as the model with the .dot extention.");
}
struct Params
{
std::string m_ModelPath;
std::string m_InputBinding;
std::string m_OutputBinding;
const armnn::TensorShape* m_InputTensorShape;
armnn::Compute m_ComputeDevice;
bool m_IsModelBinary;
bool m_VisualizePostOptimizationModel;
Params()
: m_InputTensorShape(nullptr)
, m_ComputeDevice(armnn::Compute::CpuRef)
, m_IsModelBinary(true)
, m_VisualizePostOptimizationModel(false)
{
}
};
InferenceModel(const Params& params)
: m_Runtime(armnn::IRuntime::Create(params.m_ComputeDevice))
{
const std::string& modelPath = params.m_ModelPath;
// Create a network from a file on disk
auto parser(IParser::Create());
std::map<std::string, armnn::TensorShape> inputShapes;
if (params.m_InputTensorShape)
{
inputShapes[params.m_InputBinding] = *params.m_InputTensorShape;
}
std::vector<std::string> requestedOutputs{ params.m_OutputBinding };
armnn::INetworkPtr network{nullptr, [](armnn::INetwork *){}};
{
ARMNN_SCOPED_HEAP_PROFILING("Parsing");
// Handle text and binary input differently by calling the corresponding parser function
network = (params.m_IsModelBinary ?
parser->CreateNetworkFromBinaryFile(modelPath.c_str(), inputShapes, requestedOutputs) :
parser->CreateNetworkFromTextFile(modelPath.c_str(), inputShapes, requestedOutputs));
}
m_InputBindingInfo = parser->GetNetworkInputBindingInfo(params.m_InputBinding);
m_OutputBindingInfo = parser->GetNetworkOutputBindingInfo(params.m_OutputBinding);
armnn::IOptimizedNetworkPtr optNet{nullptr, [](armnn::IOptimizedNetwork *){}};
{
ARMNN_SCOPED_HEAP_PROFILING("Optimizing");
optNet = armnn::Optimize(*network, m_Runtime->GetDeviceSpec());
}
if (params.m_VisualizePostOptimizationModel)
{
boost::filesystem::path filename = params.m_ModelPath;
filename.replace_extension("dot");
std::fstream file(filename.c_str(),file.out);
optNet->SerializeToDot(file);
}
armnn::Status ret;
{
ARMNN_SCOPED_HEAP_PROFILING("LoadNetwork");
ret = m_Runtime->LoadNetwork(m_NetworkIdentifier, std::move(optNet));
}
if (ret == armnn::Status::Failure)
{
throw armnn::Exception("IRuntime::LoadNetwork failed");
}
}
unsigned int GetOutputSize() const
{
return m_OutputBindingInfo.second.GetNumElements();
}
void Run(const std::vector<TDataType>& input, std::vector<TDataType>& output)
{
BOOST_ASSERT(output.size() == GetOutputSize());
armnn::Status ret = m_Runtime->EnqueueWorkload(m_NetworkIdentifier,
MakeInputTensors(input),
MakeOutputTensors(output));
if (ret == armnn::Status::Failure)
{
throw armnn::Exception("IRuntime::EnqueueWorkload failed");
}
}
private:
template<typename TContainer>
armnn::InputTensors MakeInputTensors(const TContainer& inputTensorData)
{
return ::MakeInputTensors(m_InputBindingInfo, inputTensorData);
}
template<typename TContainer>
armnn::OutputTensors MakeOutputTensors(TContainer& outputTensorData)
{
return ::MakeOutputTensors(m_OutputBindingInfo, outputTensorData);
}
armnn::NetworkId m_NetworkIdentifier;
armnn::IRuntimePtr m_Runtime;
std::pair<armnn::LayerBindingId, armnn::TensorInfo> m_InputBindingInfo;
std::pair<armnn::LayerBindingId, armnn::TensorInfo> m_OutputBindingInfo;
};
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