SpaceToBatchNdQueueDescriptor Struct Reference

#include <WorkloadData.hpp>

Inheritance diagram for SpaceToBatchNdQueueDescriptor:

Collaboration diagram for SpaceToBatchNdQueueDescriptor:

Public Member Functions
void	Validate (const WorkloadInfo &workloadInfo) const
Public Member Functions inherited from QueueDescriptorWithParameters< SpaceToBatchNdDescriptor >
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< SpaceToBatchNdDescriptor >
SpaceToBatchNdDescriptor	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< SpaceToBatchNdDescriptor >
	QueueDescriptorWithParameters ()=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 385 of file WorkloadData.hpp.

Member Function Documentation

Validate()

void Validate

(

const WorkloadInfo &

workloadInfo

)

const

Definition at line 1856 of file WorkloadData.cpp.

{
    const std::string descriptorName{"SpaceToBatchNdQueueDescriptor"};
 
    ValidateNumInputs(workloadInfo,  descriptorName, 1);
    ValidateNumOutputs(workloadInfo, descriptorName, 1);
 
    const TensorInfo& inputTensorInfo  = workloadInfo.m_InputTensorInfos[0];
    const TensorInfo& outputTensorInfo = workloadInfo.m_OutputTensorInfos[0];
 
    if (m_Parameters.m_BlockShape.size() != m_Parameters.m_PadList.size())
    {
        throw InvalidArgumentException(descriptorName + ": Pad List must contain the same number of "
                                       "dimensions as Block Shape.");
    }
 
    if (m_Parameters.m_BlockShape.size() == 2)
    {
        ValidateTensorNumDimensions(inputTensorInfo,  descriptorName, 4, "input");
        ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 4, "output");
    }
    else if (m_Parameters.m_BlockShape.size() == 1)
    {
        ValidateTensorNumDimensions(inputTensorInfo,  descriptorName, 3, "input");
        ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 3, "output");
    }
    else
    {
        throw InvalidArgumentException(descriptorName + ": Invalid Block and Crops size.");
    }
 
    // Check input + padding and output have the same number of elements
    DataLayoutIndexed dimensionIndices(m_Parameters.m_DataLayout);
    const unsigned int inputHeight = inputTensorInfo.GetShape()[dimensionIndices.GetHeightIndex()] +
                                     m_Parameters.m_PadList[0].first + m_Parameters.m_PadList[0].second;
    const unsigned int inputWidth = (inputTensorInfo.GetNumDimensions() == 3) ? 1 :
                                    inputTensorInfo.GetShape()[dimensionIndices.GetWidthIndex()] +
                                    m_Parameters.m_PadList[1].first + m_Parameters.m_PadList[1].second;
 
    const int channelsIndex_int = (m_Parameters.m_DataLayout == DataLayout::NCHW) ? 1 : -1;
    const unsigned int channelsIndex = channelsIndex_int < 0 ?
            static_cast<unsigned int>(channelsIndex_int) + inputTensorInfo.GetNumDimensions()
            : static_cast<unsigned int>(channelsIndex_int);
 
    const unsigned int numInputElements = inputTensorInfo.GetShape()[0] *
                                          inputHeight *
                                          inputWidth *
                                          inputTensorInfo.GetShape()[channelsIndex];
 
    if (outputTensorInfo.GetNumElements() != numInputElements)
    {
        throw InvalidArgumentException(descriptorName + ": Input tensor has " +
                                       to_string(numInputElements) + " after padding but output tensor has " +
                                       to_string(outputTensorInfo.GetNumElements()) + " elements.");
    }
 
    // In a 4D tensor, there will be 2 spatialDimensions (H and W), and the for loop will run twice.
    // In a 3D tensor, there will be 1 spatialDimensions, and the for loop will run once.
    unsigned int firstSpatialDimension = m_Parameters.m_DataLayout == DataLayout::NCHW ? 2 : 1;
    for (unsigned int i = 0; i < m_Parameters.m_BlockShape.size(); ++i)
    {
        unsigned int spatialDimension = firstSpatialDimension + i;
        auto inputSize = inputTensorInfo.GetShape()[spatialDimension] +
                         m_Parameters.m_PadList[i].first +
                         m_Parameters.m_PadList[i].second;
        if (inputSize % m_Parameters.m_BlockShape[i] != 0)
        {
            throw InvalidArgumentException(descriptorName + ": Input dimension size after padding must be "
                                        "divisible by Block Shape in dimension: " + to_string(spatialDimension) + ".");
        }
    }
 
    std::vector<DataType> supportedTypes =
    {
        DataType::BFloat16,
        DataType::Float16,
        DataType::Float32,
        DataType::QAsymmS8,
        DataType::QAsymmU8,
        DataType::QSymmS16
    };
 
    ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);
    ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, "input", "output");
}

References armnn::BFloat16, armnn::Float16, armnn::Float32, DataLayoutIndexed::GetHeightIndex(), TensorInfo::GetNumDimensions(), TensorInfo::GetNumElements(), TensorInfo::GetShape(), DataLayoutIndexed::GetWidthIndex(), WorkloadInfo::m_InputTensorInfos, WorkloadInfo::m_OutputTensorInfos, QueueDescriptorWithParameters< SpaceToBatchNdDescriptor >::m_Parameters, armnn::NCHW, armnn::QAsymmS8, armnn::QAsymmU8, armnn::QSymmS16, and QueueDescriptor::ValidateTensorNumDimensions().

---

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

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