NeonNormalizationFloatWorkload Class Reference

#include <NeonNormalizationFloatWorkload.hpp>

Inheritance diagram for NeonNormalizationFloatWorkload:

Collaboration diagram for NeonNormalizationFloatWorkload:

Public Member Functions
	NeonNormalizationFloatWorkload (const NormalizationQueueDescriptor &descriptor, const WorkloadInfo &info, std::shared_ptr< arm_compute::MemoryManagerOnDemand > &memoryManager)
virtual void	Execute () const override
void	ReplaceInputTensorHandle (ITensorHandle *tensorHandle, unsigned int slot) override
void	ReplaceOutputTensorHandle (ITensorHandle *tensorHandle, unsigned int slot) override
Public Member Functions inherited from TypedWorkload< QueueDescriptor, DataTypes >
	TypedWorkload (const QueueDescriptor &descriptor, const WorkloadInfo &info)
Public Member Functions inherited from BaseWorkload< QueueDescriptor >
	BaseWorkload (const QueueDescriptor &descriptor, const WorkloadInfo &info)
virtual const std::string &	GetName () const override
void	ExecuteAsync (ExecutionData &executionData) override
void	PostAllocationConfigure () override
const QueueDescriptor &	GetData () const
arm::pipe::ProfilingGuid	GetGuid () const final
virtual bool	SupportsTensorHandleReplacement () const override
Public Member Functions inherited from IWorkload
virtual	~IWorkload ()
virtual void	RegisterDebugCallback (const DebugCallbackFunction &)
virtual armnn::Optional< armnn::MemoryRequirements >	GetMemoryRequirements ()

Additional Inherited Members
Protected Attributes inherited from BaseWorkload< QueueDescriptor >
QueueDescriptor	m_Data
const arm::pipe::ProfilingGuid	m_Guid
const std::string	m_Name

Detailed Description

Definition at line 23 of file NeonNormalizationFloatWorkload.hpp.

Constructor & Destructor Documentation

NeonNormalizationFloatWorkload()

NeonNormalizationFloatWorkload	(	const NormalizationQueueDescriptor &	descriptor,
		const WorkloadInfo &	info,
		std::shared_ptr< arm_compute::MemoryManagerOnDemand > &	memoryManager
	)

Definition at line 61 of file NeonNormalizationFloatWorkload.cpp.

    : FloatWorkload<NormalizationQueueDescriptor>(descriptor, info)
{
    // Report Profiling Details
    ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonNormalizationWorkload_Construct",
                                         descriptor.m_Parameters,
                                         info,
                                         this->GetGuid());
 
    m_Data.ValidateInputsOutputs("NeonNormalizationFloatWorkload", 1, 1);
    std::string reasonIfUnsupported;
    if (!IsNeonNormalizationDescriptorSupported(m_Data.m_Parameters, Optional<std::string&>(reasonIfUnsupported)))
    {
        throw UnimplementedException(reasonIfUnsupported);
    }
 
    // Input and output tensors have to have the same dimensionality.
    if (info.m_InputTensorInfos[0].GetShape()[1] != info.m_OutputTensorInfos[0].GetShape()[1]
        || info.m_InputTensorInfos[0].GetShape()[0] != info.m_OutputTensorInfos[0].GetShape()[0]
        || info.m_InputTensorInfos[0].GetShape()[3] != info.m_OutputTensorInfos[0].GetShape()[3]
        || info.m_InputTensorInfos[0].GetShape()[2] != info.m_OutputTensorInfos[0].GetShape()[2])
    {
        throw InvalidArgumentException("Normalization requires input and output tensors to have equal dimensionality.");
    }
 
    arm_compute::ITensor& input = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
    arm_compute::ITensor& output = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
    arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout);
    input.info()->set_data_layout(aclDataLayout);
    output.info()->set_data_layout(aclDataLayout);
 
    const arm_compute::NormType normType =
        ConvertNormalizationAlgorithmChannelToAclNormType(m_Data.m_Parameters.m_NormChannelType);
    arm_compute::NormalizationLayerInfo normalizationInfo(normType,
                                                          m_Data.m_Parameters.m_NormSize,
                                                          m_Data.m_Parameters.m_Alpha,
                                                          m_Data.m_Parameters.m_Beta,
                                                          m_Data.m_Parameters.m_K,
                                                          false);
    auto layer = std::make_unique<arm_compute::NENormalizationLayer>(memoryManager);
    layer->configure(&input, &output, normalizationInfo);
    m_NormalizationLayer.reset(layer.release());
}

References ARMNN_REPORT_PROFILING_WORKLOAD_DESC, armnn::info, BaseWorkload< QueueDescriptor >::m_Data, QueueDescriptor::m_Inputs, QueueDescriptor::m_Outputs, QueueDescriptorWithParameters< LayerDescriptor >::m_Parameters, and QueueDescriptor::ValidateInputsOutputs().

Member Function Documentation

Execute()

void Execute ( ) const

overridevirtual

Implements IWorkload.

Definition at line 107 of file NeonNormalizationFloatWorkload.cpp.

{
    ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID("NeonNormalizationFloatWorkload_Execute");
    m_NormalizationLayer->run();
}

References ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID.

ReplaceInputTensorHandle()

void ReplaceInputTensorHandle ( ITensorHandle *  tensorHandle, unsigned int  slot  )

overridevirtual

Reimplemented from BaseWorkload< QueueDescriptor >.

Definition at line 113 of file NeonNormalizationFloatWorkload.cpp.

{
    ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot];
    this->m_Data.m_Inputs[slot] = tensorHandle;
    try
    {
        Reconfigure();
    }
    catch(armnn::UnimplementedException& e)
    {
        // Cannot reconfigure, revert the slot back and throw the exception.
        this->m_Data.m_Inputs[slot] = backupHandle;
        throw e;
    }
}

References BaseWorkload< QueueDescriptor >::m_Data, and QueueDescriptor::m_Inputs.

ReplaceOutputTensorHandle()

void ReplaceOutputTensorHandle ( ITensorHandle *  tensorHandle, unsigned int  slot  )

overridevirtual

Reimplemented from BaseWorkload< QueueDescriptor >.

Definition at line 130 of file NeonNormalizationFloatWorkload.cpp.

{
    ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot];
    this->m_Data.m_Inputs[slot] = tensorHandle;
    try
    {
        Reconfigure();
    }
    catch(armnn::UnimplementedException& e)
    {
        // Cannot reconfigure, revert the slot back and throw the exception.
        this->m_Data.m_Inputs[slot] = backupHandle;
        throw e;
    }
}

References BaseWorkload< QueueDescriptor >::m_Data, and QueueDescriptor::m_Inputs.

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

• src/backends/neon/workloads/NeonNormalizationFloatWorkload.hpp
• src/backends/neon/workloads/NeonNormalizationFloatWorkload.cpp