SpaceToDepthQueueDescriptor Struct Reference

#include <WorkloadData.hpp>

Inheritance diagram for SpaceToDepthQueueDescriptor:

Public Member Functions
void	Validate (const WorkloadInfo &workloadInfo) const
Public Member Functions inherited from QueueDescriptorWithParameters< SpaceToDepthDescriptor >
virtual	~QueueDescriptorWithParameters ()=default
Public Member Functions inherited from QueueDescriptor
virtual	~QueueDescriptor ()=default
void	ValidateTensorNumDimensions (const TensorInfo &tensor, std::string const &descName, unsigned int numDimensions, std::string const &tensorName) const
void	ValidateTensorNumDimNumElem (const TensorInfo &tensorInfo, unsigned int numDimension, unsigned int numElements, std::string const &tensorName) const
void	ValidateInputsOutputs (const std::string &descName, unsigned int numExpectedIn, unsigned int numExpectedOut) const
template<typename T >
const T *	GetAdditionalInformation () const

Additional Inherited Members
Public Attributes inherited from QueueDescriptorWithParameters< SpaceToDepthDescriptor >
SpaceToDepthDescriptor	m_Parameters
Public Attributes inherited from QueueDescriptor
std::vector< ITensorHandle * >	m_Inputs
std::vector< ITensorHandle * >	m_Outputs
void *	m_AdditionalInfoObject
bool	m_AllowExpandedDims = false
Protected Member Functions inherited from QueueDescriptorWithParameters< SpaceToDepthDescriptor >
	QueueDescriptorWithParameters ()=default
	QueueDescriptorWithParameters (QueueDescriptorWithParameters const &)=default
QueueDescriptorWithParameters &	operator= (QueueDescriptorWithParameters const &)=default
Protected Member Functions inherited from QueueDescriptor
	QueueDescriptor ()
	QueueDescriptor (QueueDescriptor const &)=default
QueueDescriptor &	operator= (QueueDescriptor const &)=default

Detailed Description

Definition at line 422 of file WorkloadData.hpp.

Member Function Documentation

Validate()

void Validate

(

const WorkloadInfo &

workloadInfo

)

const

Definition at line 1925 of file WorkloadData.cpp.

References armnn::BFloat16, armnn::Float16, armnn::Float32, DataLayoutIndexed::GetChannelsIndex(), DataLayoutIndexed::GetHeightIndex(), TensorInfo::GetShape(), DataLayoutIndexed::GetWidthIndex(), WorkloadInfo::m_InputTensorInfos, WorkloadInfo::m_OutputTensorInfos, armnn::QAsymmS8, armnn::QAsymmU8, armnn::QSymmS16, and QueueDescriptor::ValidateTensorNumDimensions().

{
    const std::string descriptorName{"SpaceToDepthQueueDescriptor"};

    ValidateNumInputs(workloadInfo,  descriptorName, 1);
    ValidateNumOutputs(workloadInfo, descriptorName, 1);

    const TensorInfo& inputTensorInfo  = workloadInfo.m_InputTensorInfos[0];
    const TensorInfo& outputTensorInfo = workloadInfo.m_OutputTensorInfos[0];

    ValidateTensorNumDimensions(inputTensorInfo,  descriptorName, 4, "input");
    ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 4, "output");

    std::vector<DataType> supportedTypes =
    {
        DataType::BFloat16,
        DataType::Float32,
        DataType::Float16,
        DataType::QAsymmS8,
        DataType::QAsymmU8,
        DataType::QSymmS16
    };

    ValidateDataTypes(inputTensorInfo,  supportedTypes, descriptorName);
    ValidateDataTypes(outputTensorInfo, supportedTypes, descriptorName);

    ValidateTensorNumElementsMatch(inputTensorInfo, outputTensorInfo, descriptorName, "input", "output");

    if (m_Parameters.m_BlockSize == 0)
    {
        throw InvalidArgumentException(descriptorName + ": Block size cannot be 0.");
    }

    DataLayoutIndexed dimensionIndices(m_Parameters.m_DataLayout);
    const unsigned int wIndex = dimensionIndices.GetWidthIndex();
    const unsigned int hIndex = dimensionIndices.GetHeightIndex();
    const unsigned int cIndex = dimensionIndices.GetChannelsIndex();

    const TensorShape& inputShape = inputTensorInfo.GetShape();
    if (inputShape[hIndex] % m_Parameters.m_BlockSize != 0 || inputShape[wIndex]  % m_Parameters.m_BlockSize != 0)
    {
        throw InvalidArgumentException(descriptorName + ": Input shape must be divisible "
                                       "by block size in all spatial dimensions");
    }

    const TensorShape& outputShape = outputTensorInfo.GetShape();
    if (outputShape[cIndex] % (m_Parameters.m_BlockSize * m_Parameters.m_BlockSize) != 0)
    {
        throw InvalidArgumentException(descriptorName + ": The depth of the output tensor"
                                       "must be divisible by the square of block size." );
    }
}

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

• include/armnn/backends/WorkloadData.hpp
• src/backends/backendsCommon/WorkloadData.cpp