NeonSpaceToBatchNdWorkload Class Reference

#include <NeonSpaceToBatchNdWorkload.hpp>

Inheritance diagram for NeonSpaceToBatchNdWorkload:

Collaboration diagram for NeonSpaceToBatchNdWorkload:

Public Member Functions
	NeonSpaceToBatchNdWorkload (const SpaceToBatchNdQueueDescriptor &descriptor, const WorkloadInfo &info)
virtual void	Execute () const override
Public Member Functions inherited from NeonBaseWorkload< SpaceToBatchNdQueueDescriptor >
	NeonBaseWorkload (const SpaceToBatchNdQueueDescriptor &descriptor, const WorkloadInfo &info)
void	ReplaceInputTensorHandle (ITensorHandle *tensorHandle, unsigned int slot) override
void	ReplaceOutputTensorHandle (ITensorHandle *tensorHandle, unsigned int slot) override
Public Member Functions inherited from BaseWorkload< SpaceToBatchNdQueueDescriptor >
	BaseWorkload (const SpaceToBatchNdQueueDescriptor &descriptor, const WorkloadInfo &info)
virtual const std::string &	GetName () const override
void	ExecuteAsync (ExecutionData &executionData) override
void	PostAllocationConfigure () override
const SpaceToBatchNdQueueDescriptor &	GetData () const
arm::pipe::ProfilingGuid	GetGuid () const final
virtual bool	SupportsTensorHandleReplacement () const override
Public Member Functions inherited from IWorkload
virtual	~IWorkload ()
virtual arm::pipe::ProfilingGuid	GetGuid () const =0
virtual bool	SupportsTensorHandleReplacement () const =0
virtual const std::string &	GetName () const =0
virtual void	RegisterDebugCallback (const DebugCallbackFunction &)
virtual armnn::Optional< armnn::MemoryRequirements >	GetMemoryRequirements ()

Additional Inherited Members
Protected Member Functions inherited from NeonBaseWorkload< SpaceToBatchNdQueueDescriptor >
virtual void	Reconfigure ()
Protected Attributes inherited from BaseWorkload< SpaceToBatchNdQueueDescriptor >
SpaceToBatchNdQueueDescriptor	m_Data
const arm::pipe::ProfilingGuid	m_Guid
const std::string	m_Name

Detailed Description

Definition at line 21 of file NeonSpaceToBatchNdWorkload.hpp.

Constructor & Destructor Documentation

NeonSpaceToBatchNdWorkload()

NeonSpaceToBatchNdWorkload	(	const SpaceToBatchNdQueueDescriptor &	descriptor,
		const WorkloadInfo &	info
	)

Definition at line 92 of file NeonSpaceToBatchNdWorkload.cpp.

        : NeonBaseWorkload<SpaceToBatchNdQueueDescriptor>(descriptor, info)
{
    // Report Profiling Details
    ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonSpaceToBatchNdWorkload_Construct",
                                         descriptor.m_Parameters,
                                         info,
                                         this->GetGuid());
 
    m_Data.ValidateInputsOutputs("NESpaceToBatchNdWorkload", 1, 1);
 
    arm_compute::ITensor& input  = PolymorphicPointerDowncast<IAclTensorHandle>(m_Data.m_Inputs[0])->GetTensor();
    arm_compute::ITensor& output = PolymorphicPointerDowncast<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);
 
    arm_compute::TensorInfo aclReshapeInputInfo = BuildArmComputeTensorInfo(info.m_InputTensorInfos[0],
                                                                            m_Data.m_Parameters.m_DataLayout);
    arm_compute::TensorInfo aclReshapeOutputInfo = BuildArmComputeTensorInfo(info.m_OutputTensorInfos[0],
                                                                             m_Data.m_Parameters.m_DataLayout);
 
    const unsigned int rank = info.m_InputTensorInfos[0].GetNumDimensions();
    if (rank == 3)
    {
        const arm_compute::TensorShape inputShape  = input.info()->tensor_shape();
        const arm_compute::TensorShape outputShape = output.info()->tensor_shape();
 
        // When a spacial dimension is missing set W to 1
        if (m_Data.m_Parameters.m_DataLayout == armnn::DataLayout::NHWC)
        {
            // In ACL dimensions are right to left: C, W, H, N
            aclReshapeInputInfo.set_tensor_shape({inputShape.x(), 1, inputShape.y(), inputShape.z()});
            aclReshapeOutputInfo.set_tensor_shape({outputShape.x(), 1, outputShape.y(), outputShape.z()});
        }
        else if (m_Data.m_Parameters.m_DataLayout == armnn::DataLayout::NCHW)
        {
            // In ACL dimensions are right to left: W, H, C, N
            aclReshapeInputInfo.set_tensor_shape({1, inputShape.x(), inputShape.y(), inputShape.z()});
            aclReshapeOutputInfo.set_tensor_shape({1, outputShape.x(), outputShape.y(), outputShape.z()});
        }
        else
        {
            throw InvalidArgumentException("Unsupported or unknown DataLayout", CHECK_LOCATION());
        }
 
        m_ReshapeInputTensor.allocator()->init(aclReshapeInputInfo);
        m_ReshapeOutputTensor.allocator()->init(aclReshapeOutputInfo);
 
        InitialiseArmComputeTensorEmpty(m_ReshapeInputTensor);
        InitialiseArmComputeTensorEmpty(m_ReshapeOutputTensor);
 
        m_LayerReshapeInput.reset(new arm_compute::NEReshapeLayer());
        m_LayerReshapeOutput.reset(new arm_compute::NEReshapeLayer());
 
        m_LayerReshapeInput->configure(&input, &m_ReshapeInputTensor);
        m_LayerReshapeOutput->configure(&m_ReshapeOutputTensor, &output);
    }
 
    // ArmNN blockShape is [H, W] ACl asks for W, H
    int32_t blockHeight = armnn::numeric_cast<int32_t>(m_Data.m_Parameters.m_BlockShape[0]);
    int32_t blockWidth = (rank == 3) ? 1: armnn::numeric_cast<int32_t>(descriptor.m_Parameters.m_BlockShape[1]);
 
    unsigned int padLeft  = (rank == 3) ? 0 : descriptor.m_Parameters.m_PadList[1].first;
    unsigned int padRight = (rank == 3) ? 0 : descriptor.m_Parameters.m_PadList[1].second;
    arm_compute::Size2D paddingLeftTop      = BuildArmComputeSize2D(padLeft,
                                                                    descriptor.m_Parameters.m_PadList[0].first);
    arm_compute::Size2D paddingRightBottom  = BuildArmComputeSize2D(padRight,
                                                                    descriptor.m_Parameters.m_PadList[0].second);
 
    m_Layer.reset(new arm_compute::NESpaceToBatchLayer());
    m_Layer->configure((rank == 3) ? &m_ReshapeInputTensor : &input,
                       blockWidth,
                       blockHeight,
                       paddingLeftTop,
                       paddingRightBottom,
                       (rank == 3) ? &m_ReshapeOutputTensor : &output);
    m_Layer->prepare();
}

References ARMNN_REPORT_PROFILING_WORKLOAD_DESC, armnn::info, BaseWorkload< SpaceToBatchNdQueueDescriptor >::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 174 of file NeonSpaceToBatchNdWorkload.cpp.

{
    ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID("NeonSpaceToBatchNdWorkload_Execute");
    if (m_LayerReshapeInput)
    {
        m_LayerReshapeInput->run();
    }
    if (m_Layer)
    {
        m_Layer->run();
    }
    if (m_LayerReshapeOutput)
    {
        m_LayerReshapeOutput->run();
    }
}

References ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID.

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

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