#include <WorkloadData.hpp>

Inheritance diagram for DepthwiseConvolution2dQueueDescriptor:

Public Member Functions
	DepthwiseConvolution2dQueueDescriptor ()

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

Public Attributes
const ConstCpuTensorHandle *	m_Weight

const ConstCpuTensorHandle *	m_Bias

Public Attributes inherited from QueueDescriptorWithParameters< DepthwiseConvolution2dDescriptor >
DepthwiseConvolution2dDescriptor	m_Parameters

Public Attributes inherited from QueueDescriptor
std::vector< ITensorHandle * >	m_Inputs

std::vector< ITensorHandle * >	m_Outputs

void *	m_AdditionalInfoObject

Additional Inherited Members
Protected Member Functions inherited from QueueDescriptorWithParameters< DepthwiseConvolution2dDescriptor >
	~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 207 of file WorkloadData.hpp.

Constructor & Destructor Documentation

◆ DepthwiseConvolution2dQueueDescriptor()

DepthwiseConvolution2dQueueDescriptor ( )

inline

Definition at line 209 of file WorkloadData.hpp.

         : m_Weight(nullptr)
         , m_Bias(nullptr)
     {
     }

Member Function Documentation

◆ Validate()

void Validate ( const WorkloadInfo & workloadInfo ) const

Definition at line 1305 of file WorkloadData.cpp.

References armnn::BFloat16, armnn::Float16, armnn::Float32, armnn::GetBiasDataType(), TensorInfo::GetDataType(), TensorInfo::GetShape(), WorkloadInfo::m_InputTensorInfos, WorkloadInfo::m_OutputTensorInfos, armnn::NCHW, armnn::QAsymmS8, armnn::QAsymmU8, armnn::QSymmS16, and OptionalReferenceSwitch< std::is_reference< T >::value, T >::value().

 {
     const std::string descriptorName{"DepthwiseConvolution2dQueueDescriptor"};
 
     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");
 
     ValidatePointer(m_Weight, descriptorName, "weight");
 
     const TensorInfo& weightTensorInfo = m_Weight->GetTensorInfo();
     ValidateTensorNumDimensions(weightTensorInfo, descriptorName, 4, "weight");
 
     if (m_Parameters.m_DilationX < 1 || m_Parameters.m_DilationY < 1 )
     {
         throw InvalidArgumentException(
             fmt::format("{}: dilationX (provided {}) and dilationY (provided {}) "
                         "cannot be smaller than 1.",
                         descriptorName, m_Parameters.m_DilationX, m_Parameters.m_DilationX));
     }
 
     if (m_Parameters.m_StrideX <= 0 || m_Parameters.m_StrideY <= 0  )
     {
         throw InvalidArgumentException(
             fmt::format("{}: strideX (provided {}) and strideY (provided {}) "
                         "cannot be either negative or 0.",
                         descriptorName, m_Parameters.m_StrideX, m_Parameters.m_StrideY));
     }
 
     const unsigned int channelIndex = (m_Parameters.m_DataLayout == DataLayout::NCHW) ? 1 : 3;
 
     // Expected weight shape: [ M, I, H, W ] - This shape does NOT depend on the data layout
     // inputChannels * channelMultiplier should be equal to outputChannels.
     const unsigned int numWeightChannelMultiplier = weightTensorInfo.GetShape()[0];
     const unsigned int numWeightInputChannels     = weightTensorInfo.GetShape()[1];
     const unsigned int numWeightOutputChannels    = outputTensorInfo.GetShape()[channelIndex];
     if (numWeightChannelMultiplier * numWeightInputChannels != numWeightOutputChannels)
     {
         throw InvalidArgumentException(fmt::format(
             "{0}: output_channels (provided {1}) should be equal to input_channels (provided {2}) "
             "multiplied by channel_multiplier (provided {3}).",
             descriptorName, numWeightOutputChannels, numWeightInputChannels, numWeightChannelMultiplier));
     }
 
     ValidateWeightDataType(inputTensorInfo, weightTensorInfo, descriptorName);
 
     Optional<TensorInfo> optionalBiasTensorInfo;
     if (m_Parameters.m_BiasEnabled)
     {
         ValidatePointer(m_Bias, descriptorName, "bias");
 
         optionalBiasTensorInfo = MakeOptional<TensorInfo>(m_Bias->GetTensorInfo());
         const TensorInfo& biasTensorInfo = optionalBiasTensorInfo.value();
 
         ValidateBiasTensorQuantization(biasTensorInfo, inputTensorInfo, weightTensorInfo, descriptorName);
         ValidateTensorDataType(biasTensorInfo, GetBiasDataType(inputTensorInfo.GetDataType()), descriptorName, "bias");
     }
     ValidatePerAxisQuantization(inputTensorInfo,
                                 outputTensorInfo,
                                 weightTensorInfo,
                                 optionalBiasTensorInfo,
                                 descriptorName);
 
     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");
 }