StackQueueDescriptor Struct Reference

#include <WorkloadData.hpp>

Inheritance diagram for StackQueueDescriptor:

Public Member Functions
void	Validate (const WorkloadInfo &workloadInfo) const
Public Member Functions inherited from QueueDescriptor
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< StackDescriptor >
StackDescriptor	m_Parameters
Public Attributes inherited from QueueDescriptor
std::vector< ITensorHandle * >	m_Inputs
std::vector< ITensorHandle * >	m_Outputs
void *	m_AdditionalInfoObject
Protected Member Functions inherited from QueueDescriptorWithParameters< StackDescriptor >
	~QueueDescriptorWithParameters ()=default
	QueueDescriptorWithParameters ()=default
	QueueDescriptorWithParameters (QueueDescriptorWithParameters const &)=default
QueueDescriptorWithParameters &	operator= (QueueDescriptorWithParameters const &)=default
Protected Member Functions inherited from QueueDescriptor
	~QueueDescriptor ()=default
	QueueDescriptor ()
	QueueDescriptor (QueueDescriptor const &)=default
QueueDescriptor &	operator= (QueueDescriptor const &)=default

Detailed Description

Definition at line 142 of file WorkloadData.hpp.

Member Function Documentation

Validate()

void Validate

(

const WorkloadInfo &

workloadInfo

)

const

Definition at line 921 of file WorkloadData.cpp.

References armnn::BFloat16, armnn::Boolean, armnn::Float16, armnn::Float32, TensorShape::GetNumDimensions(), WorkloadInfo::m_InputTensorInfos, WorkloadInfo::m_OutputTensorInfos, armnn::QAsymmS8, armnn::QAsymmU8, armnn::QSymmS16, and armnn::Signed32.

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

    ValidateNumOutputs(workloadInfo, descriptorName, 1);

    if (m_Parameters.m_NumInputs != workloadInfo.m_InputTensorInfos.size())
    {
        throw InvalidArgumentException(descriptorName + ": Must have the defined number of input tensors.");
    }

    // All inputs must have the same shape, which is defined in parameters
    const TensorShape& inputShape = m_Parameters.m_InputShape;
    for (unsigned int i = 0; i < workloadInfo.m_InputTensorInfos.size(); ++i)
    {
        if (workloadInfo.m_InputTensorInfos[i].GetShape() != inputShape)
        {
            throw InvalidArgumentException(descriptorName + ": All input tensor shapes must match the defined shape.");
        }
    }

    if (inputShape.GetNumDimensions() > 4)
    {
        throw InvalidArgumentException(descriptorName + ": Input tensor may have up to 4 dimensions.");
    }

    // m_Axis is 0-based and may take values from 0 to the number of input dimensions (inclusive),
    // since the output tensor has an additional dimension.
    if (m_Parameters.m_Axis > inputShape.GetNumDimensions())
    {
        throw InvalidArgumentException(descriptorName + ": Axis may not be greater "
                                       "than the number of input dimensions.");
    }

    // Output shape must be as inferred from the input shape
    const TensorShape& outputShape = workloadInfo.m_OutputTensorInfos[0].GetShape();
    for (unsigned int i = 0; i < m_Parameters.m_Axis; ++i)
    {
        if (outputShape[i] != inputShape[i])
        {
            throw InvalidArgumentException(descriptorName + ": Output tensor must "
                                           "match shape inferred from input tensor.");
        }
    }

    if (outputShape[m_Parameters.m_Axis] != m_Parameters.m_NumInputs)
    {
        throw InvalidArgumentException(descriptorName + ": Output tensor must "
                                       "match shape inferred from input tensor.");
    }

    for (unsigned int i = m_Parameters.m_Axis + 1; i < inputShape.GetNumDimensions() + 1; ++i)
    {
        if (outputShape[i] != inputShape[i-1])
        {
            throw InvalidArgumentException(descriptorName + ": Output tensor must "
                                           "match shape inferred from input tensor.");
        }
    }

    if (outputShape.GetNumDimensions() > 5)
    {
        throw InvalidArgumentException(descriptorName + ": Output tensor may have up to 5 dimensions.");
    }

    // Check the supported data types
    std::vector<DataType> supportedTypes =
    {
        DataType::BFloat16,
        DataType::Float32,
        DataType::Float16,
        DataType::Boolean,
        DataType::Signed32,
        DataType::QAsymmS8,
        DataType::QAsymmU8,
        DataType::QSymmS16
    };

    ValidateDataTypes(workloadInfo.m_InputTensorInfos[0], supportedTypes, descriptorName);

    for (unsigned int i = 1ul; i < workloadInfo.m_InputTensorInfos.size(); ++i)
    {
        ValidateTensorDataTypesMatch(workloadInfo.m_InputTensorInfos[0],
                                     workloadInfo.m_InputTensorInfos[i],
                                     descriptorName,
                                     "input_0",
                                     "input_" + std::to_string(i));
    }

    ValidateTensorDataTypesMatch(workloadInfo.m_InputTensorInfos[0],
                                 workloadInfo.m_OutputTensorInfos[0],
                                 descriptorName,
                                 "input_0",
                                 "output");
}

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

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