NeonNormalizationFloatWorkload Class Reference

#include <NeonNormalizationFloatWorkload.hpp>

Inheritance 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
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)
void	PostAllocationConfigure () override
const QueueDescriptor &	GetData () const
profiling::ProfilingGuid	GetGuid () const final
Public Member Functions inherited from IWorkload
virtual	~IWorkload ()
virtual void	RegisterDebugCallback (const DebugCallbackFunction &)

Additional Inherited Members
Protected Attributes inherited from BaseWorkload< QueueDescriptor >
const QueueDescriptor	m_Data
const profiling::ProfilingGuid	m_Guid

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 60 of file NeonNormalizationFloatWorkload.cpp.

References BaseWorkload< QueueDescriptor >::m_Data, QueueDescriptor::m_Inputs, WorkloadInfo::m_InputTensorInfos, QueueDescriptor::m_Outputs, WorkloadInfo::m_OutputTensorInfos, and QueueDescriptor::ValidateInputsOutputs().

    : FloatWorkload<NormalizationQueueDescriptor>(descriptor, info)
{
    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());
}

Member Function Documentation

Execute()

void Execute ( ) const

overridevirtual

Implements IWorkload.

Definition at line 100 of file NeonNormalizationFloatWorkload.cpp.

References ARMNN_SCOPED_PROFILING_EVENT_NEON.

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

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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