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< IProfiler > &	GetProfiler () const
void	FreeWorkingMemory ()
void	RegisterDebugCallback (const DebugCallbackFunction &func)
void	SendNetworkStructure ()
profiling::ProfilingGuid	GetNetworkGuid ()

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

Detailed Description

Definition at line 34 of file LoadedNetwork.hpp.

Member Typedef Documentation

WorkloadQueue

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

Definition at line 37 of file LoadedNetwork.hpp.

Constructor & Destructor Documentation

~LoadedNetwork()

~LoadedNetwork ( )

inline

Definition at line 38 of file LoadedNetwork.hpp.

References armnn::GetInputTensorInfo().

{ FreeWorkingMemory(); }

Member Function Documentation

EnqueueWorkload()

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

Definition at line 470 of file LoadedNetwork.cpp.

References ARMNN_ASSERT_MSG, ARMNN_LOG, LabelsAndEventClasses::ARMNN_PROFILING_EOL_EVENT_CLASS, LabelsAndEventClasses::ARMNN_PROFILING_SOL_EVENT_CLASS, ARMNN_SCOPED_HEAP_PROFILING, ARMNN_SCOPED_PROFILING_EVENT, armnn::CheckFlag(), LabelsAndEventClasses::EXECUTION_OF_GUID, 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(), armnn::IgnoreUnused(), ITensorHandle::Import(), LabelsAndEventClasses::INFERENCE_GUID, armnn::info, armnn::Input, QueueDescriptor::m_Inputs, WorkloadInfo::m_InputTensorInfos, QueueDescriptor::m_Outputs, WorkloadInfo::m_OutputTensorInfos, armnn::Malloc, ITensorHandle::Map(), armnn::Output, armnn::Success, armnn::Undefined, ITensorHandle::Unmap(), and armnn::warning.

Referenced by RuntimeImpl::EnqueueWorkload().

{
    const Graph& graph = m_OptimizedNetwork->pOptimizedNetworkImpl->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.
    {
        ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "PrepareInputs");
        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.
    {
        ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "PrepareOutputs");
        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(m_ProfilingService);
    ProfilingGuid inferenceGuid = m_ProfilingService.GetNextGuid();
    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,
                                          LabelsAndEventClasses::EXECUTION_OF_GUID);
        timelineUtils->RecordEvent(inferenceGuid, LabelsAndEventClasses::ARMNN_PROFILING_SOL_EVENT_CLASS);
    }

    bool executionSucceeded = true;

    {
        if (m_ProfilingService.IsProfilingEnabled())
        {
            m_ProfilingService.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 725 of file LoadedNetwork.cpp.

References ARMNN_LOG, and armnn::error.

Referenced by RuntimeImpl::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 321 of file LoadedNetwork.cpp.

References ARMNN_ASSERT_MSG.

Referenced by RuntimeImpl::GetInputTensorInfo().

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

    throw InvalidArgumentException(fmt::format("No input layer is associated with id {}", layerId));
}

GetNetworkGuid()

profiling::ProfilingGuid GetNetworkGuid

(

)

Definition at line 316 of file LoadedNetwork.cpp.

{
    return m_OptimizedNetwork->GetGuid();
}

GetOutputTensorInfo()

TensorInfo GetOutputTensorInfo

(

LayerBindingId

layerId

)

const

Definition at line 335 of file LoadedNetwork.cpp.

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

Referenced by RuntimeImpl::GetOutputTensorInfo().

{
    for (auto&& outputLayer : m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().GetOutputLayers())
    {
        ARMNN_ASSERT_MSG(outputLayer->GetNumInputSlots() == 1, "Output layer should have exactly 1 input slot");
        ARMNN_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(fmt::format("No output layer is associated with id {}", layerId));
}

GetProfiler()

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

inline

Definition at line 53 of file LoadedNetwork.hpp.

Referenced by RuntimeImpl::EnqueueWorkload().

{ return m_Profiler; }

MakeLoadedNetwork()

std::unique_ptr< LoadedNetwork > MakeLoadedNetwork ( std::unique_ptr< IOptimizedNetwork >  net, std::string &  errorMessage, const INetworkProperties &  networkProperties, profiling::ProfilingService &  profilingService  )

static

Definition at line 83 of file LoadedNetwork.cpp.

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

Referenced by RuntimeImpl::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, profilingService));
    }
    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 795 of file LoadedNetwork.cpp.

Referenced by RuntimeImpl::RegisterDebugCallback().

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

SendNetworkStructure()

void SendNetworkStructure

(

)

Definition at line 279 of file LoadedNetwork.cpp.

References TimelineUtilityMethods::GetTimelineUtils(), armnn::Input, LabelsAndEventClasses::NETWORK_GUID, armnn::Output, and Graph::TopologicalSort().

{
    Graph& order = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().TopologicalSort();
    ProfilingGuid networkGuid = m_OptimizedNetwork->GetGuid();

    std::unique_ptr<TimelineUtilityMethods> timelineUtils =
                        TimelineUtilityMethods::GetTimelineUtils(m_ProfilingService);

    timelineUtils->CreateTypedEntity(networkGuid, LabelsAndEventClasses::NETWORK_GUID);

    for (auto&& layer : order)
    {
        // Add layer to the post-optimisation network structure
        AddLayerStructure(timelineUtils, *layer, networkGuid);
        switch (layer->GetType())
        {
        case LayerType::Input:
        case LayerType::Output:
        {
            // Inputs and outputs are treated in a special way - see EnqueueInput() and EnqueueOutput().
            break;
        }
        default:
            {
            for (auto& workload : m_WorkloadQueue)
            {
                // Add workload to the post-optimisation network structure
                AddWorkloadStructure(timelineUtils, workload, *layer);
            }
            break;
            }
        }
    }
    // Commit to send the post-optimisation network structure
    timelineUtils->Commit();
}

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

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