LoadedNetwork Class Reference

#include <LoadedNetwork.hpp>

Public Types
using	WorkloadQueue = std::vector< std::unique_ptr< IWorkload > >

Public Member Functions
	~LoadedNetwork ()
TensorInfo	GetInputTensorInfo (LayerBindingId layerId) const
TensorInfo	GetOutputTensorInfo (LayerBindingId layerId) const
Status	EnqueueWorkload (const InputTensors &inputTensors, const OutputTensors &outputTensors)
const std::shared_ptr< Profiler > &	GetProfiler () const
void	FreeWorkingMemory ()
void	RegisterDebugCallback (const DebugCallbackFunction &func)

Static Public Member Functions
static std::unique_ptr< LoadedNetwork >	MakeLoadedNetwork (std::unique_ptr< OptimizedNetwork > net, std::string &errorMessage, const INetworkProperties &networkProperties)

Detailed Description

Definition at line 33 of file LoadedNetwork.hpp.

Member Typedef Documentation

WorkloadQueue

using WorkloadQueue = std::vector< std::unique_ptr<IWorkload> >

Definition at line 36 of file LoadedNetwork.hpp.

Constructor & Destructor Documentation

~LoadedNetwork()

~LoadedNetwork ( )

inline

Definition at line 37 of file LoadedNetwork.hpp.

References armnn::GetInputTensorInfo().

{ FreeWorkingMemory(); }

Member Function Documentation

EnqueueWorkload()

Status EnqueueWorkload	(	const InputTensors &	inputTensors,
		const OutputTensors &	outputTensors
	)

Definition at line 412 of file LoadedNetwork.cpp.

References ARMNN_LOG, LabelsAndEventClasses::ARMNN_PROFILING_EOL_EVENT_CLASS, LabelsAndEventClasses::ARMNN_PROFILING_SOL_EVENT_CLASS, ARMNN_SCOPED_HEAP_PROFILING, ARMNN_SCOPED_PROFILING_EVENT, armnn::CheckFlag(), armnn::Failure, ITensorHandle::GetImportFlags(), Graph::GetInputLayers(), Layer::GetInputSlots(), Graph::GetNumInputs(), Layer::GetNumInputSlots(), Graph::GetNumLayers(), Graph::GetNumOutputs(), Layer::GetNumOutputSlots(), Layer::GetOutputHandler(), Graph::GetOutputLayers(), TimelineUtilityMethods::GetTimelineUtils(), Layer::GetType(), ITensorHandle::Import(), ProfilingService::IncrementCounterValue(), LabelsAndEventClasses::INFERENCE_GUID, armnn::info, armnn::Input, ProfilingService::Instance(), QueueDescriptor::m_Inputs, WorkloadInfo::m_InputTensorInfos, QueueDescriptor::m_Outputs, WorkloadInfo::m_OutputTensorInfos, armnn::Malloc, ITensorHandle::Map(), ProfilingService::NextGuid(), armnn::Output, armnn::profiling::RetentionLink, armnn::Success, armnn::Undefined, ITensorHandle::Unmap(), and armnn::warning.

Referenced by Runtime::EnqueueWorkload().

{
    ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "EnqueueWorkload");

    const Graph& graph = m_OptimizedNetwork->GetGraph();

    // Walk graph to determine the order of execution.
    if (graph.GetNumLayers() < 2)
    {
        ARMNN_LOG(warning) << "IRuntime::EnqueueWorkload()::Less than two nodes in graph";
        return Status::Failure;
    }

    // Data that must be kept alive for the entire execution of the workload.
    WorkloadData workloadData(inputTensors, outputTensors);

    if (graph.GetNumInputs() != inputTensors.size())
    {
        throw InvalidArgumentException("Number of inputs provided does not match network.");
    }

    // For each input to the network, call EnqueueInput with the data passed by the user.
    m_InputQueue.clear();
    m_InputQueue.reserve(graph.GetNumInputs());
    for (const BindableLayer* inputLayer : graph.GetInputLayers())
    {
        const TensorPin& pin = workloadData.GetInputTensorPin(inputLayer->GetBindingId());
        EnqueueInput(*inputLayer, pin.GetTensorHandle(), pin.GetTensorInfo());
    }

    // For each output to the network, call EnqueueOutput with the data passed by the user.
    m_OutputQueue.clear();
    m_OutputQueue.reserve(graph.GetNumOutputs());
    for (const BindableLayer* outputLayer : graph.GetOutputLayers())
    {
        const TensorPin& pin = workloadData.GetOutputTensorPin(outputLayer->GetBindingId());
        EnqueueOutput(*outputLayer, pin.GetTensorHandle(), pin.GetTensorInfo());
    }

    std::unique_ptr<TimelineUtilityMethods> timelineUtils = TimelineUtilityMethods::GetTimelineUtils();
    ProfilingGuid inferenceGuid = ProfilingService::Instance().NextGuid();
    if (timelineUtils)
    {
        // Add inference timeline trace if profiling is enabled.
        ProfilingGuid networkGuid = m_OptimizedNetwork->GetGuid();
        timelineUtils->CreateTypedEntity(inferenceGuid, LabelsAndEventClasses::INFERENCE_GUID);
        timelineUtils->CreateRelationship(ProfilingRelationshipType::RetentionLink, networkGuid, inferenceGuid);
        timelineUtils->RecordEvent(inferenceGuid, LabelsAndEventClasses::ARMNN_PROFILING_SOL_EVENT_CLASS);
    }

    bool executionSucceeded = true;

    {
        if (profiling::ProfilingService::Instance().IsProfilingEnabled())
        {
            profiling::ProfilingService::Instance().IncrementCounterValue(armnn::profiling::INFERENCES_RUN);
        }
        ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "Execute");
        ARMNN_SCOPED_HEAP_PROFILING("Executing");
        executionSucceeded = Execute(timelineUtils, inferenceGuid);
    }

    if (timelineUtils)
    {
        // Add end of life of the inference timeline if profiling is enabled.
        timelineUtils->RecordEvent(inferenceGuid, LabelsAndEventClasses::ARMNN_PROFILING_EOL_EVENT_CLASS);
        timelineUtils->Commit();
    }
    return executionSucceeded ? Status::Success : Status::Failure;
}

FreeWorkingMemory()

void FreeWorkingMemory

(

)

Definition at line 660 of file LoadedNetwork.cpp.

References ARMNN_LOG, and armnn::error.

Referenced by Runtime::EnqueueWorkload().

{
    std::lock_guard<std::mutex> lockGuard(m_WorkingMemMutex);
    if (!m_IsWorkingMemAllocated)
    {
        return;
    }
    // Informs the memory managers to release memory in it's respective memory group
    for (auto&& workloadFactory : m_WorkloadFactories)
    {
        IBackendInternal::IMemoryManagerSharedPtr memoryManager = workloadFactory.second.second;
        if (memoryManager)
        {
            memoryManager->Release();
        }
    }
    m_TensorHandleFactoryRegistry.ReleaseMemory();
    m_IsWorkingMemAllocated = false;
}

GetInputTensorInfo()

TensorInfo GetInputTensorInfo

(

LayerBindingId

layerId

)

const

Definition at line 263 of file LoadedNetwork.cpp.

Referenced by Runtime::GetInputTensorInfo().

{
    for (auto&& inputLayer : m_OptimizedNetwork->GetGraph().GetInputLayers())
    {
        BOOST_ASSERT_MSG(inputLayer->GetNumOutputSlots() == 1, "Input layer should have exactly 1 output slot");
        if (inputLayer->GetBindingId() == layerId)
        {
            return inputLayer->GetOutputSlot(0).GetTensorInfo();
        }
    }

    throw InvalidArgumentException(boost::str(boost::format("No input layer is associated with id %1%") % layerId));
}

GetOutputTensorInfo()

TensorInfo GetOutputTensorInfo

(

LayerBindingId

layerId

)

const

Definition at line 277 of file LoadedNetwork.cpp.

References CHECK_LOCATION, BackendId::Get(), Layer::GetBackendId(), Layer::GetNameStr(), armnn::IgnoreUnused(), armnn::info, and IWorkloadFactory::IsLayerSupported().

Referenced by Runtime::GetOutputTensorInfo().

{
    for (auto&& outputLayer : m_OptimizedNetwork->GetGraph().GetOutputLayers())
    {
        BOOST_ASSERT_MSG(outputLayer->GetNumInputSlots() == 1, "Output layer should have exactly 1 input slot");
        BOOST_ASSERT_MSG(outputLayer->GetInputSlot(0).GetConnection(), "Input slot on Output layer must be connected");
        if (outputLayer->GetBindingId() == layerId)
        {
            return outputLayer->GetInputSlot(0).GetConnection()->GetTensorInfo();
        }
    }

    throw InvalidArgumentException(boost::str(boost::format("No output layer is associated with id %1%") % layerId));
}

GetProfiler()

const std::shared_ptr<Profiler>& GetProfiler ( ) const

inline

Definition at line 51 of file LoadedNetwork.hpp.

{ return m_Profiler; }

MakeLoadedNetwork()

std::unique_ptr< LoadedNetwork > MakeLoadedNetwork ( std::unique_ptr< OptimizedNetwork >  net, std::string &  errorMessage, const INetworkProperties &  networkProperties  )

static

Definition at line 85 of file LoadedNetwork.cpp.

References ARMNN_LOG, armnn::BackendRegistryInstance(), IBackendInternal::CreateMemoryManager(), IBackendInternal::CreateWorkloadFactory(), armnn::error, BackendRegistry::GetFactory(), ProfilerManager::GetInstance(), TimelineUtilityMethods::GetTimelineUtils(), armnn::Input, INetworkProperties::m_ExportEnabled, INetworkProperties::m_ImportEnabled, LabelsAndEventClasses::NETWORK_GUID, armnn::Output, ProfilerManager::RegisterProfiler(), IBackendInternal::RegisterTensorHandleFactories(), IBackendInternal::SupportsTensorAllocatorAPI(), and Graph::TopologicalSort().

Referenced by Runtime::LoadNetwork().

{
    std::unique_ptr<LoadedNetwork> loadedNetwork;

    auto Fail = [&](const std::exception& error) -> std::unique_ptr<LoadedNetwork>
    {
        errorMessage = ToErrorMessage("An error occurred when preparing the network workloads: ", error);
        ARMNN_LOG(error) << errorMessage;

        return std::unique_ptr<LoadedNetwork>();
    };

    try
    {
        loadedNetwork.reset(new LoadedNetwork(std::move(net), networkProperties));
    }
    catch (const armnn::RuntimeException& error)
    {
        return Fail(error);
    }
    catch (const armnn::Exception& error)
    {
        return Fail(error);
    }
    catch (const std::runtime_error& error)
    {
        return Fail(error);
    }

    return loadedNetwork;
}

RegisterDebugCallback()

void RegisterDebugCallback

(

const DebugCallbackFunction &

func

)

Definition at line 750 of file LoadedNetwork.cpp.

Referenced by Runtime::RegisterDebugCallback().

{
    for (auto&& workloadPtr: m_WorkloadQueue)
    {
        workloadPtr.get()->RegisterDebugCallback(func);
    }
}

---

The documentation for this class was generated from the following files:

• src/armnn/LoadedNetwork.hpp
• src/armnn/LoadedNetwork.cpp