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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <armnn/Tensor.hpp>
#include <armnn/Types.hpp>
#include "Network.hpp"
#include "LayerFwd.hpp"
#include "Profiling.hpp"
#include <backends/reference/RefWorkloadFactory.hpp>
#include <backends/neon/NeonWorkloadFactory.hpp>
#include <backends/cl/ClWorkloadFactory.hpp>
#include <backends/Workload.hpp>
#include <backends/WorkloadFactory.hpp>
namespace cl
{
class Context;
class CommandQueue;
class Device;
}
namespace armnn
{
class LoadedNetwork
{
public:
TensorInfo GetInputTensorInfo(LayerBindingId layerId) const;
TensorInfo GetOutputTensorInfo(LayerBindingId layerId) const;
Status EnqueueWorkload(const InputTensors& inputTensors, const OutputTensors& outputTensors);
static std::unique_ptr<LoadedNetwork> MakeLoadedNetwork(std::unique_ptr<OptimizedNetwork> net,
std::string & errorMessage);
// NOTE we return by reference as the purpose of this method is only to provide
// access to the private m_Profiler and in theory we should not need to increment
// the shared_ptr's reference counter
const std::shared_ptr<Profiler>& GetProfiler() const { return m_Profiler; }
private:
LoadedNetwork(std::unique_ptr<OptimizedNetwork> net);
void EnqueueInput(const BindableLayer& layer, ITensorHandle* tensorHandle, const TensorInfo& tensorInfo);
void EnqueueOutput(const BindableLayer& layer, ITensorHandle* tensorHandle, const TensorInfo& tensorInfo);
bool Execute();
void TidyWorkloadQueue(size_t numInputs, size_t numOutputs);
const IWorkloadFactory& GetWorkloadFactory(const Layer& layer) const;
RefWorkloadFactory m_CpuRef;
NeonWorkloadFactory m_CpuAcc;
ClWorkloadFactory m_GpuAcc;
std::unique_ptr<OptimizedNetwork> m_OptimizedNetwork;
std::vector< std::unique_ptr<IWorkload> > m_WorkloadQueue;
std::shared_ptr<Profiler> m_Profiler;
};
}
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