#include <LoadedNetwork.hpp>

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

Public Member Functions
	~LoadedNetwork ()

std::unique_ptr< IWorkingMemHandle >	CreateWorkingMemHandle (NetworkId networkId)
	Create a new unique WorkingMemHandle object. More...

TensorInfo	GetInputTensorInfo (LayerBindingId layerId) const

TensorInfo	GetOutputTensorInfo (LayerBindingId layerId) const

std::vector< ImportedInputId >	ImportInputs (const InputTensors &inputTensors, MemorySource forceImportMemorySource=MemorySource::Undefined)

std::vector< ImportedOutputId >	ImportOutputs (const OutputTensors &outputTensors, MemorySource forceImportMemorySource=MemorySource::Undefined)

void	ClearImportedInputs (const std::vector< ImportedInputId > inputIds)

void	ClearImportedOutputs (const std::vector< ImportedOutputId > outputIds)

Status	EnqueueWorkload (const InputTensors &inputTensors, const OutputTensors &outputTensors, std::vector< ImportedInputId > preImportedInputIds={}, std::vector< ImportedOutputId > preImportedOutputIds={})
	Single thread execution of the loaded network. More...

Status	Execute (const InputTensors &inputTensors, const OutputTensors &outputTensors, IWorkingMemHandle &workingMemHandle, std::vector< ImportedInputId > preImportedInputs={}, std::vector< ImportedOutputId > preImportedOutputs={})
	Thread safe execution of the loaded network. More...

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

void	FreeWorkingMemory ()

void	RegisterDebugCallback (const DebugCallbackFunction &func)

void	SendNetworkStructure (arm::pipe::IProfilingService &profilingService)

bool	IsAsyncEnabled ()

arm::pipe::ProfilingGuid	GetNetworkGuid ()

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

Detailed Description

Definition at line 42 of file LoadedNetwork.hpp.

Member Typedef Documentation

◆ WorkloadQueue

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

Definition at line 45 of file LoadedNetwork.hpp.

Constructor & Destructor Documentation

◆ ~LoadedNetwork()

~LoadedNetwork ( )

inline

Definition at line 47 of file LoadedNetwork.hpp.

     {
         FreeWorkingMemory();
     }

Member Function Documentation

◆ ClearImportedInputs()

void ClearImportedInputs ( const std::vector< ImportedInputId > inputIds )

Definition at line 1690 of file LoadedNetwork.cpp.

Referenced by RuntimeImpl::ClearImportedInputs().

 {
     for (auto id : inputIds)
     {
         if (id > m_PreImportedInputHandles.size())
         {
             throw InvalidArgumentException(fmt::format("ClearImportedInputs::Unknown ImportedInputId: {}", id));
         }
 
         auto& importedTensorHandle = m_PreImportedInputHandles[id].m_TensorHandle;
         if (!importedTensorHandle)
         {
             throw InvalidArgumentException(
                     fmt::format("ClearImportedInputs::ImportedInput with id: {} has already been deleted", id));
         }
         // Call Unimport then destroy the tensorHandle
         importedTensorHandle->Unimport();
         importedTensorHandle = {};
     }
 }

◆ ClearImportedOutputs()

void ClearImportedOutputs ( const std::vector< ImportedOutputId > outputIds )

Definition at line 1711 of file LoadedNetwork.cpp.

Referenced by RuntimeImpl::ClearImportedOutputs().

 {
     for (auto id : outputIds)
     {
         if (id > m_PreImportedOutputHandles.size())
         {
             throw InvalidArgumentException(fmt::format("ClearImportedOutputs::Unknown ImportedOutputId: {}", id));
         }
 
         auto& importedTensorHandle = m_PreImportedOutputHandles[id].m_TensorHandle;
         if (!importedTensorHandle)
         {
             throw InvalidArgumentException(
                     fmt::format("ClearImportedOutputs::ImportedOutput with id: {} has already been deleted", id));
         }
         // Call Unimport then destroy the tensorHandle
         importedTensorHandle->Unimport();
         importedTensorHandle = {};
     }
 }

◆ CreateWorkingMemHandle()

std::unique_ptr< IWorkingMemHandle > CreateWorkingMemHandle ( NetworkId networkId )

Create a new unique WorkingMemHandle object.

Create multiple handles if you wish to have overlapped Execution by calling this function from different threads.

Definition at line 1954 of file LoadedNetwork.cpp.

References ARMNN_ASSERT, ARMNN_NO_DEPRECATE_WARN_BEGIN, ARMNN_NO_DEPRECATE_WARN_END, ITensorHandleFactory::CreateTensorHandle(), Layer::GetBackendId(), and ITensorHandleFactory::LegacyFactoryId.

Referenced by RuntimeImpl::CreateWorkingMemHandle().

 {
     Graph& order = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph();
 
     // Tensors that will need to be allocated internally within armnn
     std::vector<std::unique_ptr<ITensorHandle>> managedTensorHandles;
     // Tensors that will be allocated externally by the user
     std::vector<std::unique_ptr<ITensorHandle>> unmanagedTensorHandles;
 
     std::vector<WorkingMemDescriptor> workingMemDescriptors;
     std::vector<std::pair<BackendId, ExecutionData>> executionDataVec;
 
     auto GetTensorHandle = [&](Layer* layer, const OutputSlot& outputSlot)
     {
         ITensorHandleFactory::FactoryId factoryId = outputSlot.GetTensorHandleFactoryId();
         const TensorInfo& tensorInfo = outputSlot.GetTensorInfo();
 
         if (factoryId == ITensorHandleFactory::LegacyFactoryId)
         {
             BackendId id = layer->GetBackendId();
             ARMNN_NO_DEPRECATE_WARN_BEGIN
             return m_WorkloadFactories.at(id)->CreateTensorHandle(tensorInfo, false);
             ARMNN_NO_DEPRECATE_WARN_END
         }
         else
         {
             ITensorHandleFactory* handleFactory = m_TensorHandleFactoryRegistry.GetFactory(factoryId);
             ARMNN_ASSERT(handleFactory);
             return handleFactory->CreateTensorHandle(tensorInfo, false);
         }
     };
 
     struct HandleInfo
     {
         ITensorHandle* m_TensorHandle;
 
         bool m_IsInputLayerHandle = false;
         bool m_IsOutputLayerHandle = false;
 
         WorkingMemHandle::InputMemDescriptorCoords m_InputMemDescriptorCoords;
         WorkingMemHandle::OutputMemDescriptorCoords m_OutputMemDescriptorCoords;
     };
 
     std::unordered_map<const OutputSlot*, HandleInfo> outputToHandleInfoMap;
 
     unsigned int layerIndex = 0;
     for (auto&& layer : order)
     {
         // Constant layers execution and management is handled during loaded network construction
         if (layer->GetType() == LayerType::Constant)
         {
             continue;
         }
 
         WorkingMemDescriptor workingMemDescriptor;
 
         bool isMemoryManaged = true;
         bool isInputLayer = false;
         bool isOutputLayer = false;
         bool isConnectedToOutputLayer = false;
 
         if (layer->GetType() == LayerType::Input || layer->GetType() == LayerType::MemImport)
         {
             // Input layers/workloads will not be executed so the descriptor is not added to workingMemDescriptors
             // However we will still need to manage the tensorHandle
             isInputLayer = true;
             isMemoryManaged = !m_NetworkProperties.m_ImportEnabled;
         }
         else if (layer->GetType() == LayerType::Output)
         {
             isOutputLayer = true;
         }
 
         unsigned int slotIndex = 0;
         // Create a tensor handle for each output slot of a layer
         // Once we create it, we start managing its lifetime
         for (auto& slot : layer->GetOutputSlots())
         {
             for (unsigned int i = 0; i < slot.GetNumConnections(); ++i)
             {
                 if ((slot.GetConnection(i)->GetOwningLayer().GetType() == LayerType::Output))
                 {
                     if (!isConnectedToOutputLayer)
                     {
                         isConnectedToOutputLayer = true;
                         // If Export is enabled disable memory management, so we can export, otherwise we do a copy
                         isMemoryManaged = !m_NetworkProperties.m_ExportEnabled;
                     }
                     else
                     {
                         // Importing in this case would likely cause unexpected behaviour, so we disallow it.
                         ARMNN_LOG(warning) <<
                            fmt::format("Layer name: '{0}' guid: '{1}' has two or more OutputLayers connected to it. "
                                        "This will prevent importing on the connected OutputLayers.",
                                         layer->GetName(), layer->GetGuid());
                         isMemoryManaged = true;
                     }
                 }
             }
 
             ITensorHandle* tensorHandle;
             if (isMemoryManaged)
             {
                 managedTensorHandles.emplace_back(GetTensorHandle(layer, slot));
                 tensorHandle = managedTensorHandles.back().get();
             }
             else
             {
                 unmanagedTensorHandles.emplace_back(GetTensorHandle(layer, slot));
                 tensorHandle = unmanagedTensorHandles.back().get();
             }
 
             workingMemDescriptor.m_Outputs.push_back(tensorHandle);
 
             HandleInfo& handleInfo = outputToHandleInfoMap[&slot];
             handleInfo.m_TensorHandle = tensorHandle;
 
             // Store the coordinates of the current layer's OutputSlot that is connected to the OutputLayer
             if (isConnectedToOutputLayer)
             {
                 handleInfo.m_IsOutputLayerHandle = true;
                 handleInfo.m_OutputMemDescriptorCoords.m_OutputSlotCoords = {layerIndex, slotIndex};
             }
             // Store the LayerBindingId of the InputLayer
             if (isInputLayer)
             {
                 handleInfo.m_IsInputLayerHandle = true;
                 LayerBindingId bindingId = static_cast<BindableLayer*>(layer)->GetBindingId();
                 handleInfo.m_InputMemDescriptorCoords.m_LayerBindingId = bindingId;
             }
             slotIndex++;
         }
         // Loop through the input slots in the same layer and decrement the reference counter associated
         // to each tensor handle we encounter.
         // Once it reaches zero, the lifetime of the tensor handle has ended, and we mark its memory as available
         // so that the next tensor handle with a non overlapping lifetime can share its memory.
         for (auto& slot : layer->GetInputSlots())
         {
             ARMNN_ASSERT(slot.GetConnection());
             auto outputSlot = slot.GetConnectedOutputSlot();
             auto key = outputSlot->GetOwningLayer().GetGuid();
 
             // Constant layers execution and management is handled during loaded network construction
             auto found = m_ConstantTensorHandles.find(key);
             if (found != m_ConstantTensorHandles.end())
             {
                 ITensorHandle* tensorHandle = found->second;
                 workingMemDescriptor.m_Inputs.push_back(tensorHandle);
 
                 // Odd case where a constant layer is connected to an output layer
                 // We will need to create a HandleInfo to track it
                 if (isOutputLayer)
                 {
                     LayerBindingId bindingId = static_cast<BindableLayer*>(layer)->GetBindingId();
 
                     HandleInfo& handleInfo = outputToHandleInfoMap[outputSlot];
                     handleInfo.m_TensorHandle = tensorHandle;
                     handleInfo.m_IsOutputLayerHandle = true;
                     handleInfo.m_OutputMemDescriptorCoords.m_LayerBindingIds.push_back(bindingId);
                     handleInfo.m_OutputMemDescriptorCoords.m_InputSlotCoords.push_back({layerIndex, 0});
                 }
                 continue;
             }
 
             HandleInfo& handleInfo = outputToHandleInfoMap.at(outputSlot);
 
             ITensorHandle* inputTensorHandle = handleInfo.m_TensorHandle;
             workingMemDescriptor.m_Inputs.push_back(inputTensorHandle);
 
             // Store the LayerBindingId of the OutputLayer
             if (isOutputLayer)
             {
                 LayerBindingId bindingId = static_cast<BindableLayer*>(layer)->GetBindingId();
                 handleInfo.m_OutputMemDescriptorCoords.m_LayerBindingIds.push_back(bindingId);
                 handleInfo.m_OutputMemDescriptorCoords.m_InputSlotCoords.push_back({layerIndex, 0});
             }
             // In this case the layer is not an Output Layer but shares its input tensorhandle with an OutputLayer
             // It will need to be updated as well, if we swap out the tensorhandle
             else if (handleInfo.m_IsOutputLayerHandle)
             {
                 handleInfo.m_OutputMemDescriptorCoords.m_InputSlotCoords.push_back({layerIndex, slot.GetSlotIndex()});
             }
 
             // Store the coordinates of the InputSlots connected to the InputLayer
             // There can be more than one InputSlot connected to an InputLayer, so we use a vector
             if (handleInfo.m_IsInputLayerHandle)
             {
                 std::pair<LayerGuid, unsigned int> connectionLocation{layerIndex, slot.GetSlotIndex()};
                 handleInfo.m_InputMemDescriptorCoords.m_InputSlotCoords.emplace_back(connectionLocation);
             }
         }
 
         // Input/Output layers/workloads will not be executed, so the descriptor is not added to workingMemDescriptors
         // However we will still need to manage the tensorHandle
         if (!isInputLayer)
         {
             // Simply auto initialise ExecutionData here, so it's added only for the layer that require execution.
             // The memory and data will be allocated/assigned for the void* in WorkingMemHandle::Allocate.
             std::pair<BackendId, ExecutionData> dataPair;
             dataPair.first = layer->GetBackendId();
 
             executionDataVec.push_back(dataPair);
             workingMemDescriptors.push_back(workingMemDescriptor);
 
             layerIndex++;
         }
     }
 
     std::vector<std::pair<std::shared_ptr<TensorMemory>, MemorySource>> tensorMemory;
 
     auto externalMemoryManager = CreateExternalMemoryManger(tensorMemory);
 
     // Sort m_TensorMemory, so it's order matches the outputSlot order
     std::sort(tensorMemory.begin(), tensorMemory.end(),
               [](const std::pair<std::shared_ptr<TensorMemory>, MemorySource>& lhs,
                  const std::pair<std::shared_ptr<TensorMemory>, MemorySource>& rhs)
               {
                   return lhs.first->m_OutputSlotId < rhs.first->m_OutputSlotId;
               });
 
     std::vector<WorkingMemHandle::InputMemDescriptorCoords> inputConnectionsInfo;
     std::vector<WorkingMemHandle::OutputMemDescriptorCoords> outputConnectionsInfo;
 
     for (const auto& handleInfo: outputToHandleInfoMap)
     {
         if (handleInfo.second.m_IsOutputLayerHandle)
         {
             outputConnectionsInfo.emplace_back(handleInfo.second.m_OutputMemDescriptorCoords);
         }
 
         if (handleInfo.second.m_IsInputLayerHandle)
         {
             inputConnectionsInfo.emplace_back(handleInfo.second.m_InputMemDescriptorCoords);
         }
     }
 
     return std::make_unique<WorkingMemHandle>(networkId,
                                               inputConnectionsInfo,
                                               outputConnectionsInfo,
                                               workingMemDescriptors,
                                               std::move(externalMemoryManager),
                                               std::move(tensorMemory),
                                               std::move(managedTensorHandles),
                                               std::move(unmanagedTensorHandles),
                                               executionDataVec,
                                               &m_Backends);
 }

◆ EnqueueWorkload()

Status EnqueueWorkload	(	const InputTensors &	inputTensors,
		const OutputTensors &	outputTensors,
		std::vector< ImportedInputId >	preImportedInputIds = `{}`,
		std::vector< ImportedOutputId >	preImportedOutputIds = `{}`
	)

Single thread execution of the loaded network.

Definition at line 846 of file LoadedNetwork.cpp.

References ARMNN_ASSERT_MSG, ARMNN_LOG, ARMNN_SCOPED_PROFILING_EVENT, armnn::Failure, OutputHandler::GetData(), Graph::GetInputLayers(), Graph::GetNumInputs(), Graph::GetNumLayers(), Graph::GetNumOutputs(), Graph::GetOutputLayers(), armnn::info, QueueDescriptor::m_Inputs, WorkloadInfo::m_InputTensorInfos, armnn::Undefined, 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);
 
     // Input tensors can be provided as parameters or pre imported. Either way the number of
     // tensors should match the number of inputs.
     if (graph.GetNumInputs() != (inputTensors.size() + preImportedInputIds.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());
 
         unsigned int inputIndex = 0;
         unsigned int importedInputIdIndex = 0;
         std::sort(preImportedInputIds.begin(), preImportedInputIds.end());
         for (const BindableLayer* inputLayer : graph.GetInputLayers())
         {
             if (importedInputIdIndex < preImportedInputIds.size() &&
                 inputIndex == preImportedInputIds[importedInputIdIndex])
             {
                 // Only replace tensorhandles if they have not already been replaced
                 if (!m_IsInputImported[inputIndex])
                 {
                     auto outputTensorHandle = m_PreImportedInputHandles[inputIndex].m_TensorHandle.get();
 
                     for (const auto& workloadInfo: m_InputWorkloadSlotPairs[inputLayer->GetBindingId()])
                     {
                         auto workload = m_WorkloadQueue[workloadInfo.m_WorkloadIndex].get();
                         workload->ReplaceInputTensorHandle(outputTensorHandle, workloadInfo.m_SlotIndex);
                     }
                     m_IsInputImported[inputIndex] = true;
                 }
                 importedInputIdIndex++;
             }
             else
             {
                 if (m_IsInputImported[inputIndex])
                 {
                     OutputHandler& handler = const_cast<OutputHandler&>(inputLayer->GetOutputHandler(0));
 
                     for (const auto& workloadInfo: m_InputWorkloadSlotPairs[inputLayer->GetBindingId()])
                     {
                         auto workload = m_WorkloadQueue[workloadInfo.m_WorkloadIndex].get();
                         workload->ReplaceInputTensorHandle(handler.GetData(), workloadInfo.m_SlotIndex);
                     }
 
                     m_IsInputImported[inputIndex] = false;
                 }
 
                 // InputTensorHandle is not imported yet, process to enqueue input
                 const TensorPin& pin = workloadData.GetInputTensorPin(inputLayer->GetBindingId());
                 EnqueueInput(*inputLayer, pin.GetTensorHandle(), pin.GetTensorInfo());
             }
             inputIndex++;
         }
     }
     // 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());
 
         if (preImportedOutputIds.size() > graph.GetNumOutputs())
         {
             throw InvalidArgumentException("Invalid number of preImportedOutputIds");
         }
 
         unsigned int outputIndex = 0;
         unsigned int importedOutputIdIndex = 0;
         std::sort(preImportedOutputIds.begin(), preImportedOutputIds.end());
         for (const BindableLayer* outputLayer : graph.GetOutputLayers())
         {
             if (importedOutputIdIndex < preImportedOutputIds.size() &&
                 outputIndex == preImportedOutputIds[importedOutputIdIndex])
             {
                 // Only replace tensorhandles if they have not already been replaced
                 ITensorHandle* inputTensorHandle = m_PreImportedOutputHandles[outputIndex].m_TensorHandle.get();
 
                 if (!m_IsOutputImported[outputIndex])
                 {
                     const auto bindingId = outputLayer->GetBindingId();
                     const auto& indices = m_OutputWorkloadSlotPairs[bindingId];
 
                     auto outputWorkload = m_WorkloadQueue[indices.m_OutputSlotIndices.m_WorkloadIndex].get();
 
                     outputWorkload->ReplaceOutputTensorHandle(inputTensorHandle,
                                                               indices.m_OutputSlotIndices.m_SlotIndex);
 
                     for (const auto& workloadInfo: indices.m_InputSlotIndices)
                     {
                         auto inputWorkload = m_WorkloadQueue[workloadInfo.m_WorkloadIndex].get();
                         inputWorkload->ReplaceInputTensorHandle(inputTensorHandle, workloadInfo.m_SlotIndex);
                     }
                     m_IsOutputImported[outputIndex] = true;
                 }
 
                 ARMNN_ASSERT_MSG(inputTensorHandle != nullptr, "Data should have been allocated.");
                 MemSyncQueueDescriptor syncDesc;
                 syncDesc.m_Inputs.push_back(inputTensorHandle);
                 WorkloadInfo info;
                 info.m_InputTensorInfos.push_back(
                         outputLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo());
                 auto syncWorkload = std::make_unique<SyncMemGenericWorkload>(syncDesc, info);
                 ARMNN_ASSERT_MSG(syncWorkload, "No sync workload created");
                 m_OutputQueue.push_back(move(syncWorkload));
                 importedOutputIdIndex++;
             }
             else
             {
                 if (m_IsOutputImported[outputIndex])
                 {
                     const auto bindingId = outputLayer->GetBindingId();
                     const auto& indices = m_OutputWorkloadSlotPairs[bindingId];
 
                     auto outputWorkload = m_WorkloadQueue[indices.m_OutputSlotIndices.m_WorkloadIndex].get();
                     const OutputHandler& outputHandler =
                             outputLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetOutputHandler();
 
                     outputWorkload->ReplaceOutputTensorHandle(
                             outputHandler.GetData(), indices.m_OutputSlotIndices.m_SlotIndex);
 
                     for (const auto& workloadInfo: indices.m_InputSlotIndices)
                     {
                         auto inputWorkload = m_WorkloadQueue[workloadInfo.m_WorkloadIndex].get();
                         inputWorkload->ReplaceInputTensorHandle(outputHandler.GetData(), workloadInfo.m_SlotIndex);
                     }
                     m_IsOutputImported[outputIndex] = false;
                 }
 
                 const TensorPin& pin = workloadData.GetOutputTensorPin(outputLayer->GetBindingId());
                 // OutputTensorHandle is not imported yet, process to enqueue Output
                 EnqueueOutput(*outputLayer, pin.GetTensorHandle(), pin.GetTensorInfo());
             }
             outputIndex++;
         }
     }
 
     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(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;
 }

◆ Execute()

Status Execute	(	const InputTensors &	inputTensors,
		const OutputTensors &	outputTensors,
		IWorkingMemHandle &	workingMemHandle,
		std::vector< ImportedInputId >	preImportedInputs = `{}`,
		std::vector< ImportedOutputId >	preImportedOutputs = `{}`
	)

Thread safe execution of the loaded network.

Definition at line 1732 of file LoadedNetwork.cpp.

References WorkingMemHandle::Allocate(), ARMNN_LOG, ARMNN_SCOPED_PROFILING_EVENT, armnn::CopyToOutputTensor(), armnn::error, armnn::Failure, WorkingMemHandle::GetBindingIdVector(), WorkingMemHandle::GetExecutionDataAt(), WorkingMemHandle::GetInputConnections(), WorkingMemHandle::GetInputHandle(), Graph::GetNumInputs(), Graph::GetNumOutputs(), WorkingMemHandle::GetOutputConnection(), WorkingMemHandle::GetOutputHandle(), WorkingMemHandle::IsAllocated(), WorkingMemHandle::MemSyncOutputs(), armnn::Success, armnn::Undefined, and WorkingMemHandle::ValidateBindingIds().

Referenced by RuntimeImpl::Execute(), and LoadedNetwork::FreeWorkingMemory().

 {
     const Graph& graph = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph();
 
     if (inputTensors.size() + preImportedInputs.size() != graph.GetNumInputs())
     {
         if (preImportedInputs.empty())
         {
             throw InvalidArgumentException("LoadedNetwork::Execute: Number of inputs provided does not match network.");
         }
         else
         {
             throw InvalidArgumentException("LoadedNetwork::Execute: "
                                            "Number of inputs + preImportedInputs provided does not match network.");
         }
     }
 
     if (outputTensors.size() + preImportedOutputs.size() != graph.GetNumOutputs())
     {
         if (preImportedOutputs.empty())
         {
             throw InvalidArgumentException("LoadedNetwork::Execute: "
                                            "Number of outputs provided does not match network.");
         }
         else
         {
             throw InvalidArgumentException("LoadedNetwork::Execute: "
                                            "Number of outputs + preImportedOutputs provided does not match network.");
         }
     }
 
     WorkingMemHandle& workingMemHandle = dynamic_cast<WorkingMemHandle&>(iWorkingMemHandle);
     // Collect all the given LayerBindingIds and check them for duplicates and unknowns.
     std::vector<LayerBindingId>& bindingIds = workingMemHandle.GetBindingIdVector();
     unsigned int index = 0;
     for (auto pair : inputTensors)
     {
         bindingIds[index++] = pair.first;
     }
     for (ImportedInputId id : preImportedInputs)
     {
         bindingIds[index++] = ValidateImportedInputID(id);
     }
     for (auto pair : outputTensors)
     {
         bindingIds[index++] = pair.first;
     }
     for (ImportedOutputId id : preImportedOutputs)
     {
         bindingIds[index++] = ValidateImportedOutputID(id);
     }
 
     workingMemHandle.ValidateBindingIds();
 
     auto resetMemHandle = [&]()
     {
         for (ImportedInputId id: preImportedInputs)
         {
             const LayerBindingId layerBindingId = m_PreImportedInputHandles[id].m_LayerBindingId;
 
             auto inputHandle = workingMemHandle.GetInputHandle(layerBindingId);
             auto inputConnections = workingMemHandle.GetInputConnections(layerBindingId);
             for (auto it : inputConnections)
             {
                 *it = inputHandle;
             }
         }
 
         for (ImportedOutputId id: preImportedOutputs)
         {
             const LayerBindingId layerBindingId = m_PreImportedOutputHandles[id].m_LayerBindingId;
 
             auto outputHandle = workingMemHandle.GetOutputHandle(layerBindingId);
             auto outputConnections = workingMemHandle.GetOutputConnection(layerBindingId);
 
             for (auto it : outputConnections)
             {
                 *it = outputHandle;
             }
         }
     };
 
     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 (timelineUtils)
     {
         // Add end of life of the inference timeline if profiling is enabled.
         timelineUtils->RecordEvent(inferenceGuid,LabelsAndEventClasses::ARMNN_PROFILING_EOL_EVENT_CLASS);
         timelineUtils->Commit();
     }
 
     if (!workingMemHandle.IsAllocated())
     {
         workingMemHandle.Allocate();
     }
 
     {
         ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "PrepareInputs");
         for (auto pair : inputTensors)
         {
             EnqueueInput(pair.second, workingMemHandle.GetInputHandle(pair.first));
         }
 
         // Swap in the pre-imported inputs if any
         for (ImportedInputId id : preImportedInputs)
         {
             const ImportedTensorHandlePin& importedInputPin = m_PreImportedInputHandles[id];
             const LayerBindingId layerBindingId = m_PreImportedInputHandles[id].m_LayerBindingId;
             const auto& preimportedHandle = importedInputPin.m_TensorHandle;
 
             auto inputConnections = workingMemHandle.GetInputConnections(layerBindingId);
             for (auto it : inputConnections)
             {
                 *it = preimportedHandle.get();
             }
         }
     }
     {
         ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "PrepareOutputs");
         if (m_NetworkProperties.m_ExportEnabled)
         {
             for (auto pair: outputTensors)
             {
                 ImportOutputTensor(pair.second, workingMemHandle.GetOutputHandle(pair.first));
             }
         }
 
         for (ImportedOutputId id : preImportedOutputs)
         {
             const ImportedTensorHandlePin& importedOutputPin = m_PreImportedOutputHandles[id];
             const LayerBindingId layerBindingId = m_PreImportedOutputHandles[id].m_LayerBindingId;
             const auto& preimportedHandle = importedOutputPin.m_TensorHandle;
 
             auto outputConnections = workingMemHandle.GetOutputConnection(layerBindingId);
             for (auto it : outputConnections)
             {
                 *it = preimportedHandle.get();
             }
         }
     }
 
     auto Fail = [&](const std::exception& error)
     {
         ARMNN_LOG(error) << "An error occurred attempting to execute a workload: " << error.what();
         executionSucceeded = false;
     };
     ProfilingDynamicGuid workloadInferenceID(0);
 
     try
     {
         for (unsigned int i = 0; i < m_WorkloadQueue.size(); ++i)
         {
             auto& workload = m_WorkloadQueue[i];
             if (timelineUtils)
             {
                 workloadInferenceID = timelineUtils->RecordWorkloadInferenceAndStartOfLifeEvent(workload->GetGuid(),
                                                                                                 inferenceGuid);
             }
 
             workload->ExecuteAsync(workingMemHandle.GetExecutionDataAt(i).second);
 
             if (timelineUtils)
             {
                 timelineUtils->RecordEndOfLifeEvent(workloadInferenceID);
             }
         }
     }
     catch (const RuntimeException& error)
     {
         resetMemHandle();
         Fail(error);
     }
     catch (const std::runtime_error& error)
     {
         resetMemHandle();
         Fail(error);
     }
     catch (...)
     {
         resetMemHandle();
         throw;
     }
 
     if (!m_NetworkProperties.m_ExportEnabled)
     {
         for (auto pair: outputTensors)
         {
             CopyToOutputTensor(pair.second, workingMemHandle.GetOutputHandle(pair.first));
         }
     }
     else
     {
        ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "SyncMemGeneric_Execute");
        workingMemHandle.MemSyncOutputs();
     }
 
     resetMemHandle();
 
     return executionSucceeded ? Status::Success : Status::Failure;
 }

◆ FreeWorkingMemory()

void FreeWorkingMemory ( )

Definition at line 1229 of file LoadedNetwork.cpp.

References ARMNN_ASSERT_MSG, ARMNN_LOG, armnn::CheckFlag(), armnn::CopyTensorContentsGeneric(), armnn::error, LoadedNetwork::Execute(), ITensorHandle::GetImportFlags(), BaseTensor< MemoryType >::GetInfo(), BaseTensor< MemoryType >::GetMemoryArea(), ITensorHandle::Import(), and ITensorHandle::Map().

Referenced by RuntimeImpl::CreateWorkingMemHandle(), and RuntimeImpl::EnqueueWorkload().

 {
 #if !defined(ARMNN_DISABLE_THREADS)
     std::lock_guard<std::mutex> lockGuard(m_WorkingMemMutex);
 #endif
 
     if (!m_IsWorkingMemAllocated)
     {
         return;
     }
 
     if (m_ExternalMemoryManager)
     {
         m_ExternalMemoryManager->Deallocate();
     }
 
     // Informs the memory managers to release memory in its respective memory group
     for (auto&& memoryManager : m_BackendMemoryMangers)
     {
         if (memoryManager)
         {
             memoryManager->Release();
         }
     }
     m_TensorHandleFactoryRegistry.ReleaseMemory();
     m_IsWorkingMemAllocated = false;
 }

◆ GetInputTensorInfo()

TensorInfo GetInputTensorInfo ( LayerBindingId layerId ) const

Definition at line 697 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()

ProfilingGuid GetNetworkGuid ( )

Definition at line 692 of file LoadedNetwork.cpp.

 {
     return m_OptimizedNetwork->GetGuid();
 }

◆ GetOutputTensorInfo()

TensorInfo GetOutputTensorInfo ( LayerBindingId layerId ) const

Definition at line 711 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 87 of file LoadedNetwork.hpp.

Referenced by RuntimeImpl::CreateWorkingMemHandle(), RuntimeImpl::EnqueueWorkload(), and RuntimeImpl::Execute().

87 { return m_OptimizedNetwork->GetProfiler(); }

◆ ImportInputs()

std::vector< ImportedInputId > ImportInputs	(	const InputTensors &	inputTensors,
		MemorySource	forceImportMemorySource = `MemorySource::Undefined`
	)

Definition at line 1423 of file LoadedNetwork.cpp.

References ARMNN_ASSERT, ARMNN_LOG, Graph::InputLayersAccessor::begin(), ITensorHandle::CanBeImported(), armnn::CheckFlag(), ITensorHandleFactory::CreateTensorHandle(), Graph::InputLayersAccessor::end(), Layer::GetBackendId(), BindableLayer::GetBindingId(), ITensorHandle::GetImportFlags(), BaseTensor< MemoryType >::GetInfo(), Graph::GetInputLayers(), BaseTensor< MemoryType >::GetMemoryArea(), Layer::GetOutputSlots(), OutputSlot::GetTensorHandleFactoryId(), OutputSlot::GetTensorInfo(), Layer::GetType(), armnn::HasCapability(), ITensorHandle::Import(), armnn::Input, Graph::TopologicalSort(), armnn::Undefined, and Exception::what().

Referenced by RuntimeImpl::ImportInputs().

 {
     if (!m_NetworkProperties.m_AsyncEnabled)
     {
         // Cannot import if import is not enabled and forceImportMemorySource is undefined
         if (forceImportMemorySource == MemorySource::Undefined)
         {
             throw MemoryImportException("ImportInputs: Memory Import failed, NetworkProperties.m_ImportEnabled");
         }
         // The number of pre imported tensors should not exceed the number of inputs.
         if (inputTensors.size() > m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().GetNumInputs())
         {
             throw MemoryImportException("ImportInputs: The number of tensors provided exceeds the number of inputs.");
         }
 
         std::vector<ImportedInputId> importedInputs;
         Graph& graph = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().TopologicalSort();
         unsigned int inputIndex = 0;
         for (const BindableLayer* inputLayer : graph.GetInputLayers())
         {
             auto outputTensorHandle = m_PreImportedInputHandles[inputIndex].m_TensorHandle.get();
 
             if (!outputTensorHandle)
             {
                 inputIndex++;
                 continue;
             }
 
             auto layerBindingId = inputLayer->GetBindingId();
             auto it = std::find_if(inputTensors.begin(), inputTensors.end(), [=](const auto& inputTensor)
             {
                 return inputTensor.first == layerBindingId;
             });
 
             if (it == inputTensors.end())
             {
                 inputIndex++;
                 continue;
             }
 
             const auto& inputTensor = *it;
             std::unique_ptr<ITensorHandle> passThroughTensorHandle =
                     std::make_unique<ConstPassthroughTensorHandle>(inputTensor.second.GetInfo(),
                                                                    inputTensor.second.GetMemoryArea());
 
             try
             {
                 if (outputTensorHandle->CanBeImported(passThroughTensorHandle->Map(), forceImportMemorySource)
                     && (outputTensorHandle->Import(passThroughTensorHandle->Map(), forceImportMemorySource)))
                 {
                     importedInputs.push_back(inputIndex);
                 }
                 passThroughTensorHandle->Unmap();
             }
             catch(const MemoryImportException& exception)
             {
                 ARMNN_LOG(error) << "An error occurred attempting to import input_"
                                            << inputIndex << " : " << exception.what();
                 passThroughTensorHandle->Unmap();
             }
             inputIndex++;
         }
 
         return importedInputs;
     }
     else
     {
         // Import when the import of network properties is enabled
         std::vector<ImportedInputId> importedInputs;
         Graph& graph = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().TopologicalSort();
 
         for (auto inputTensor : inputTensors)
         {
             auto layerBindingId = inputTensor.first;
             auto it = std::find_if(graph.GetInputLayers().begin(), graph.GetInputLayers().end(), [=](auto* layer)
             {
                 return layer->GetBindingId() == layerBindingId;
             });
 
             if (it == graph.GetInputLayers().end())
             {
                 throw MemoryImportException(fmt::format(
                     "ImportInputs: Memory Import failed, unknown LayerBindingId: {}", layerBindingId));
             }
 
             const Layer* layer = *it;
             if (layer->GetType() != LayerType::Input)
             {
                 throw InvalidArgumentException("ImportInputs: given layer not an InputLayer");
             }
 
             auto& backend = m_Backends.at(layer->GetBackendId());
             if (!HasCapability(BackendOptions::BackendOption{"PreImportIOTensors", true}, backend->GetCapabilities()))
             {
                 std::string er = backend->GetId();
                 er += " does not have PreImportIOTensors capability";
                 throw BackendCapabilityException(er);
             }
 
             const OutputSlot& outputSlot = layer->GetOutputSlots()[0];
 
             ITensorHandleFactory::FactoryId factoryId = outputSlot.GetTensorHandleFactoryId();
             const TensorInfo& tensorInfo = outputSlot.GetTensorInfo();
 
             ITensorHandleFactory* handleFactory = m_TensorHandleFactoryRegistry.GetFactory(factoryId);
             ARMNN_ASSERT(handleFactory);
 
             ImportedTensorHandlePin importedTensorHandlePin{layerBindingId,
                                                             handleFactory->CreateTensorHandle(tensorInfo, false)};
 
             ITensorHandle* tensorHandle = importedTensorHandlePin.m_TensorHandle.get();
 
             if (!CheckFlag(tensorHandle->GetImportFlags(), forceImportMemorySource))
             {
                 throw MemoryImportException(
                     fmt::format("ImportInputs: Memory Import failed, backend: "
                                 "{} does not support importing from source {}"
                                 , factoryId, m_NetworkProperties.m_InputSource));
             }
 
             std::unique_ptr<ITensorHandle> passThroughTensorHandle =
                     std::make_unique<ConstPassthroughTensorHandle>(inputTensor.second.GetInfo(),
                                                                    inputTensor.second.GetMemoryArea());
 
             if (tensorHandle->Import(passThroughTensorHandle->Map(), forceImportMemorySource))
             {
                 importedInputs.push_back(m_CurImportedInputId++);
                 passThroughTensorHandle->Unmap();
             }
             else
             {
                 passThroughTensorHandle->Unmap();
                 throw MemoryImportException("ImportInputs: Memory Import failed");
             }
 
             m_PreImportedInputHandles.push_back(std::move(importedTensorHandlePin));
         }
         return importedInputs;
     }
 }

◆ ImportOutputs()

std::vector< ImportedOutputId > ImportOutputs	(	const OutputTensors &	outputTensors,
		MemorySource	forceImportMemorySource = `MemorySource::Undefined`
	)

Definition at line 1565 of file LoadedNetwork.cpp.

References ARMNN_ASSERT, ARMNN_LOG, Graph::OutputLayersAccessor::begin(), ITensorHandle::CanBeImported(), armnn::CheckFlag(), ITensorHandleFactory::CreateTensorHandle(), Graph::OutputLayersAccessor::end(), Layer::GetBackendId(), BindableLayer::GetBindingId(), InputSlot::GetConnectedOutputSlot(), ITensorHandle::GetImportFlags(), Layer::GetInputSlots(), BaseTensor< MemoryType >::GetMemoryArea(), Graph::GetOutputLayers(), OutputSlot::GetTensorHandleFactoryId(), OutputSlot::GetTensorInfo(), Layer::GetType(), armnn::HasCapability(), ITensorHandle::Import(), armnn::Output, Graph::TopologicalSort(), armnn::Undefined, and Exception::what().

Referenced by RuntimeImpl::ImportOutputs().

 {
     if (!m_NetworkProperties.m_AsyncEnabled)
     {
         // Cannot import if import is not enabled and forceImportMemorySource is undefined
         if (forceImportMemorySource == MemorySource::Undefined)
         {
             throw MemoryImportException("ImportOutputs: Memory Import failed, NetworkProperties.m_ImportEnabled");
         }
         // If forceImportMemorySource is defined, try import if memory is aligned
         if (outputTensors.size() != m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().GetNumOutputs())
         {
             throw MemoryImportException("ImportOutputs: Force Import failed, incorrect number of tensors");
         }
         std::vector<ImportedOutputId> importedOutputs;
         Graph& graph = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().TopologicalSort();
 
         unsigned int outputIndex = 0;
         for (const BindableLayer* const outputLayer : graph.GetOutputLayers())
         {
             auto inputTensorHandle = m_PreImportedOutputHandles[outputIndex].m_TensorHandle.get();
             if (!inputTensorHandle)
             {
                 outputIndex++;
                 continue;
             }
 
             auto layerBindingId = outputLayer->GetBindingId();
             auto it = std::find_if(outputTensors.begin(), outputTensors.end(), [=] (const auto& outputTensor)
             {
                 return outputTensor.first == layerBindingId;
             });
 
             if (it == outputTensors.end())
             {
                 outputIndex++;
                 continue;
             }
 
             const auto outputTensor = *it;
             try
             {
                 // Check if the output memory can be imported
                 if (inputTensorHandle->CanBeImported(outputTensor.second.GetMemoryArea(), forceImportMemorySource)
                     && inputTensorHandle->Import(outputTensor.second.GetMemoryArea(), forceImportMemorySource))
                 {
                     importedOutputs.push_back(outputIndex);
                 }
             }
             catch(const MemoryImportException& exception)
             {
                 ARMNN_LOG(error) << "An error occurred attempting to import output_"
                                  << outputIndex << " : " << exception.what();
             }
             outputIndex++;
         }
         return importedOutputs;
     }
 
     std::vector<ImportedOutputId> importedOutputs;
     Graph& graph = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().TopologicalSort();
 
     for (const auto& outputTensor : outputTensors)
     {
         auto layerBindingId = outputTensor.first;
         auto it = std::find_if(graph.GetOutputLayers().begin(), graph.GetOutputLayers().end(), [=](auto* layer)
         {
             return layer->GetBindingId() == layerBindingId;
         });
 
         if (it == graph.GetOutputLayers().end())
         {
             throw MemoryImportException(fmt::format("ImportOutputs: Memory Import failed, unknown LayerBindingId: {}",
                                                      layerBindingId));
         }
 
         const Layer* layer = *it;
         if (layer->GetType() != LayerType::Output)
         {
             throw InvalidArgumentException("ImportOutputs: given layer not an OutputLayer");
         }
 
         auto& backend = m_Backends.at(layer->GetBackendId());
         if (!HasCapability(BackendOptions::BackendOption{"PreImportIOTensors", true}, backend->GetCapabilities()))
         {
             std::string er = backend->GetId();
             er += " does not have PreImportIOTensors capability";
             throw BackendCapabilityException(er);
         }
 
         const InputSlot& inputSlot = layer->GetInputSlots()[0];
         ITensorHandleFactory::FactoryId factoryId = inputSlot.GetConnectedOutputSlot()->GetTensorHandleFactoryId();
         const TensorInfo& tensorInfo = inputSlot.GetConnectedOutputSlot()->GetTensorInfo();
 
         ITensorHandleFactory* handleFactory = m_TensorHandleFactoryRegistry.GetFactory(factoryId);
         ARMNN_ASSERT(handleFactory);
 
         ImportedTensorHandlePin importedTensorHandlePin{layerBindingId,
                                                         handleFactory->CreateTensorHandle(tensorInfo, false)};
 
         ITensorHandle* tensorHandle = importedTensorHandlePin.m_TensorHandle.get();
 
         if (!CheckFlag(tensorHandle->GetImportFlags(), forceImportMemorySource))
         {
             throw MemoryImportException(fmt::format("ImportInputs: Memory Import failed, backend: "
                                                     "{} does not support importing from source {}"
                                                     , factoryId, forceImportMemorySource));
         }
 
         if (tensorHandle->Import(outputTensor.second.GetMemoryArea(), forceImportMemorySource))
         {
             importedOutputs.push_back(m_CurImportedOutputId++);
         }
         else
         {
             throw MemoryImportException("ImportInputs: Memory Import failed");
         }
 
         m_PreImportedOutputHandles.push_back(std::move(importedTensorHandlePin));
     }
 
     return importedOutputs;
 }

◆ IsAsyncEnabled()

bool IsAsyncEnabled ( )

inline

Definition at line 95 of file LoadedNetwork.hpp.

Referenced by RuntimeImpl::CreateWorkingMemHandle(), RuntimeImpl::EnqueueWorkload(), and RuntimeImpl::Execute().

     {
         return m_NetworkProperties.m_AsyncEnabled;
     }

◆ MakeLoadedNetwork()

std::unique_ptr< LoadedNetwork > MakeLoadedNetwork	(	std::unique_ptr< IOptimizedNetwork >	net,
		std::string &	errorMessage,
		const INetworkProperties &	networkProperties,
		arm::pipe::IProfilingService *	profilingService
	)

static

Definition at line 170 of file LoadedNetwork.cpp.

References ITensorHandle::Allocate(), ARMNN_ASSERT, ARMNN_LOG, ARMNN_SCOPED_PROFILING_EVENT, armnn::BackendRegistryInstance(), armnn::Constant, IBackendInternal::CreateMemoryManager(), ITensorHandleFactory::CreateTensorHandle(), IBackendInternal::CreateWorkloadFactory(), armnn::error, IBackendInternal::GetCapabilities(), BackendRegistry::GetFactory(), IBackend::GetId(), ProfilerManager::GetInstance(), BackendRegistry::GetMemoryOptimizerStrategies(), Graph::GetNumInputs(), Graph::GetNumOutputs(), armnn::HasCapability(), armnn::Input, ITensorHandleFactory::LegacyFactoryId, INetworkProperties::m_AsyncEnabled, INetworkProperties::m_OutputNetworkDetailsMethod, WorkingMemDescriptor::m_Outputs, INetworkProperties::m_ProfilingEnabled, armnn::MemImport, armnn::numeric_cast(), armnn::Output, ProfilerManager::RegisterProfiler(), Graph::SetLayersOutOfOrder(), IBackendInternal::SupportsTensorAllocatorAPI(), Graph::TopologicalSort(), armnn::Undefined, and armnn::ValidateSourcesMatchOptimizedNetwork().

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 2202 of file LoadedNetwork.cpp.

References armnn::BackendRegistryInstance(), armnn::Constant, BackendRegistry::GetAllocators(), Layer::GetBackendId(), InputSlot::GetConnectedOutputSlot(), Layer::GetInputSlot(), Layer::GetInputSlots(), OutputSlot::GetNumConnections(), Layer::GetOutputSlots(), ITensorHandle::GetParent(), Layer::GetType(), armnn::Input, BufferStorage::m_BufferSize, TensorMemory::m_Offset, BufferStorage::m_TensorMemoryVector, armnn::Output, Graph::TopologicalSort(), and armnn::Undefined.

Referenced by RuntimeImpl::RegisterDebugCallback().

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

◆ SendNetworkStructure()

void SendNetworkStructure ( arm::pipe::IProfilingService & profilingService )

Definition at line 654 of file LoadedNetwork.cpp.

References ARMNN_SCOPED_PROFILING_EVENT, armnn::Input, armnn::Output, Graph::TopologicalSort(), and armnn::Undefined.

 {
     ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "LoadNetwork_SendNetworkStructure");
     Graph& order = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().TopologicalSort();
     ProfilingGuid networkGuid = m_OptimizedNetwork->GetGuid();
 
     std::unique_ptr<TimelineUtilityMethods> timelineUtils =
         TimelineUtilityMethods::GetTimelineUtils(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();
 }

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

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

Public Types

Public Member Functions

Static Public Member Functions

Detailed Description

Member Typedef Documentation

◆ WorkloadQueue

Constructor & Destructor Documentation

◆ ~LoadedNetwork()

Member Function Documentation

◆ ClearImportedInputs()

◆ ClearImportedOutputs()

◆ CreateWorkingMemHandle()

◆ EnqueueWorkload()

◆ Execute()

◆ FreeWorkingMemory()

◆ GetInputTensorInfo()

◆ GetNetworkGuid()

◆ GetOutputTensorInfo()

◆ GetProfiler()

◆ ImportInputs()

◆ ImportOutputs()

◆ IsAsyncEnabled()

◆ MakeLoadedNetwork()

◆ RegisterDebugCallback()

◆ SendNetworkStructure()