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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <armnn/ArmNN.hpp>
#include <CpuExecutor.h>
#include <HalInterfaces.h>
#include <NeuralNetworks.h>
#include <boost/format.hpp>
#include <log/log.h>
#include <vector>
#include <string>
#include <fstream>
#include <iomanip>
namespace V1_0 = ::android::hardware::neuralnetworks::V1_0;
#ifdef ARMNN_ANDROID_NN_V1_2 // Using ::android::hardware::neuralnetworks::V1_2
namespace V1_2 = ::android::hardware::neuralnetworks::V1_2;
#endif
namespace armnn_driver
{
#ifdef ARMNN_ANDROID_R
using DataLocation = ::android::nn::hal::DataLocation;
#endif
extern const armnn::PermutationVector g_DontPermute;
template <typename OperandType>
class UnsupportedOperand: public std::runtime_error
{
public:
UnsupportedOperand(const OperandType type)
: std::runtime_error("Operand type is unsupported")
, m_type(type)
{}
OperandType m_type;
};
/// Swizzles tensor data in @a input according to the dimension mappings.
void SwizzleAndroidNn4dTensorToArmNn(const armnn::TensorInfo& tensor, const void* input, void* output,
const armnn::PermutationVector& mappings);
/// Returns a pointer to a specific location in a pool
void* GetMemoryFromPool(DataLocation location,
const std::vector<android::nn::RunTimePoolInfo>& memPools);
/// Can throw UnsupportedOperand
armnn::TensorInfo GetTensorInfoForOperand(const V1_0::Operand& operand);
#ifdef ARMNN_ANDROID_NN_V1_2 // Using ::android::hardware::neuralnetworks::V1_2
armnn::TensorInfo GetTensorInfoForOperand(const V1_2::Operand& operand);
#endif
std::string GetOperandSummary(const V1_0::Operand& operand);
#ifdef ARMNN_ANDROID_NN_V1_2 // Using ::android::hardware::neuralnetworks::V1_2
std::string GetOperandSummary(const V1_2::Operand& operand);
#endif
template <typename HalModel>
std::string GetModelSummary(const HalModel& model)
{
std::stringstream result;
result << model.inputIndexes.size() << " input(s), " << model.operations.size() << " operation(s), " <<
model.outputIndexes.size() << " output(s), " << model.operands.size() << " operand(s)" << std::endl;
result << "Inputs: ";
for (uint32_t i = 0; i < model.inputIndexes.size(); i++)
{
result << GetOperandSummary(model.operands[model.inputIndexes[i]]) << ", ";
}
result << std::endl;
result << "Operations: ";
for (uint32_t i = 0; i < model.operations.size(); i++)
{
result << toString(model.operations[i].type).c_str() << ", ";
}
result << std::endl;
result << "Outputs: ";
for (uint32_t i = 0; i < model.outputIndexes.size(); i++)
{
result << GetOperandSummary(model.operands[model.outputIndexes[i]]) << ", ";
}
result << std::endl;
return result.str();
}
void DumpTensor(const std::string& dumpDir,
const std::string& requestName,
const std::string& tensorName,
const armnn::ConstTensor& tensor);
void DumpJsonProfilingIfRequired(bool gpuProfilingEnabled,
const std::string& dumpDir,
armnn::NetworkId networkId,
const armnn::IProfiler* profiler);
std::string ExportNetworkGraphToDotFile(const armnn::IOptimizedNetwork& optimizedNetwork,
const std::string& dumpDir);
void RenameGraphDotFile(const std::string& oldName, const std::string& dumpDir, const armnn::NetworkId networkId);
/// Checks if a tensor info represents a dynamic tensor
bool IsDynamicTensor(const armnn::TensorInfo& outputInfo);
std::string GetFileTimestamp();
} // namespace armnn_driver
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