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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "ArmnnDriver.hpp"
#include "ArmnnDriverImpl.hpp"
#include "RequestThread.hpp"
#include <NeuralNetworks.h>
#include <armnn/ArmNN.hpp>
#include <armnn/Threadpool.hpp>
#include <string>
#include <vector>
namespace armnn_driver
{
using armnnExecuteCallback_1_0 = std::function<void(V1_0::ErrorStatus status, std::string callingFunction)>;
struct ArmnnCallback_1_0
{
armnnExecuteCallback_1_0 callback;
};
struct ExecutionContext_1_0 {};
using CallbackContext_1_0 = CallbackContext<armnnExecuteCallback_1_0, ExecutionContext_1_0>;
template <typename HalVersion>
class ArmnnPreparedModel : public V1_0::IPreparedModel
{
public:
using HalModel = typename HalVersion::Model;
ArmnnPreparedModel(armnn::NetworkId networkId,
armnn::IRuntime* runtime,
const HalModel& model,
const std::string& requestInputsAndOutputsDumpDir,
const bool gpuProfilingEnabled,
const bool asyncModelExecutionEnabled = false,
const unsigned int numberOfThreads = 1,
const bool importEnabled = false,
const bool exportEnabled = false);
virtual ~ArmnnPreparedModel();
virtual Return<V1_0::ErrorStatus> execute(const V1_0::Request& request,
const ::android::sp<V1_0::IExecutionCallback>& callback) override;
/// execute the graph prepared from the request
void ExecuteGraph(std::shared_ptr<std::vector<::android::nn::RunTimePoolInfo>>& pMemPools,
armnn::InputTensors& inputTensors,
armnn::OutputTensors& outputTensors,
CallbackContext_1_0 callback);
/// Executes this model with dummy inputs (e.g. all zeroes).
/// \return false on failure, otherwise true
bool ExecuteWithDummyInputs();
private:
template<typename CallbackContext>
class ArmnnThreadPoolCallback : public armnn::IAsyncExecutionCallback
{
public:
ArmnnThreadPoolCallback(ArmnnPreparedModel<HalVersion>* model,
std::shared_ptr<std::vector<::android::nn::RunTimePoolInfo>>& pMemPools,
std::shared_ptr<armnn::InputTensors>& inputTensors,
std::shared_ptr<armnn::OutputTensors>& outputTensors,
CallbackContext callbackContext) :
m_Model(model),
m_MemPools(pMemPools),
m_InputTensors(inputTensors),
m_OutputTensors(outputTensors),
m_CallbackContext(callbackContext)
{}
void Notify(armnn::Status status, armnn::InferenceTimingPair timeTaken) override;
ArmnnPreparedModel<HalVersion>* m_Model;
std::shared_ptr<std::vector<::android::nn::RunTimePoolInfo>> m_MemPools;
std::shared_ptr<armnn::InputTensors> m_InputTensors;
std::shared_ptr<armnn::OutputTensors> m_OutputTensors;
CallbackContext m_CallbackContext;
};
template <typename TensorBindingCollection>
void DumpTensorsIfRequired(char const* tensorNamePrefix, const TensorBindingCollection& tensorBindings);
/// schedule the graph prepared from the request for execution
template<typename CallbackContext>
void ScheduleGraphForExecution(
std::shared_ptr<std::vector<::android::nn::RunTimePoolInfo>>& pMemPools,
std::shared_ptr<armnn::InputTensors>& inputTensors,
std::shared_ptr<armnn::OutputTensors>& outputTensors,
CallbackContext m_CallbackContext);
armnn::NetworkId m_NetworkId;
armnn::IRuntime* m_Runtime;
HalModel m_Model;
// There must be a single RequestThread for all ArmnnPreparedModel objects to ensure serial execution of workloads
// It is specific to this class, so it is declared as static here
static RequestThread<ArmnnPreparedModel,
HalVersion,
CallbackContext_1_0> m_RequestThread;
uint32_t m_RequestCount;
const std::string& m_RequestInputsAndOutputsDumpDir;
const bool m_GpuProfilingEnabled;
// Static to allow sharing of threadpool between ArmnnPreparedModel instances
static std::unique_ptr<armnn::Threadpool> m_Threadpool;
std::shared_ptr<armnn::IWorkingMemHandle> m_WorkingMemHandle;
const bool m_AsyncModelExecutionEnabled;
const bool m_EnableImport;
const bool m_EnableExport;
};
}
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