This layer represents a 2D transpose convolution operation. More...

#include <TransposeConvolution2dLayer.hpp>

Inheritance diagram for TransposeConvolution2dLayer:

Public Member Functions
virtual std::unique_ptr< IWorkload >	CreateWorkload (const IWorkloadFactory &factory) const override
	Makes a workload for the TransposeConvolution2d type. More...

TransposeConvolution2dLayer *	Clone (Graph &graph) const override
	Creates a dynamically-allocated copy of this layer. More...

void	ValidateTensorShapesFromInputs () override
	Check if the input tensor shape(s) will lead to a valid configuration of TransposeConvolution2dLayer. More...

std::vector< TensorShape >	InferOutputShapes (const std::vector< TensorShape > &inputShapes) const override
	Infers the output shapes from given input shapes and layer properties. More...

void	Accept (ILayerVisitor &visitor) const override
	Apply a visitor to this layer. More...

Public Member Functions inherited from LayerWithParameters< TransposeConvolution2dDescriptor >
const TransposeConvolution2dDescriptor &	GetParameters () const

void	SerializeLayerParameters (ParameterStringifyFunction &fn) const override
	Helper to serialize the layer parameters to string (currently used in DotSerializer and company). More...

Public Member Functions inherited from Layer
	Layer (unsigned int numInputSlots, unsigned int numOutputSlots, LayerType type, const char *name)

	Layer (unsigned int numInputSlots, unsigned int numOutputSlots, LayerType type, DataLayout layout, const char *name)

const std::string &	GetNameStr () const

const OutputHandler &	GetOutputHandler (unsigned int i=0) const

OutputHandler &	GetOutputHandler (unsigned int i=0)

ShapeInferenceMethod	GetShapeInferenceMethod () const

const std::vector< InputSlot > &	GetInputSlots () const

const std::vector< OutputSlot > &	GetOutputSlots () const

std::vector< InputSlot >::iterator	BeginInputSlots ()

std::vector< InputSlot >::iterator	EndInputSlots ()

std::vector< OutputSlot >::iterator	BeginOutputSlots ()

std::vector< OutputSlot >::iterator	EndOutputSlots ()

bool	IsOutputUnconnected ()

void	ResetPriority () const

LayerPriority	GetPriority () const

LayerType	GetType () const

DataType	GetDataType () const

const BackendId &	GetBackendId () const

void	SetBackendId (const BackendId &id)

virtual void	CreateTensorHandles (const TensorHandleFactoryRegistry &registry, const IWorkloadFactory &factory, const bool IsMemoryManaged=true)

void	VerifyLayerConnections (unsigned int expectedConnections, const CheckLocation &location) const

virtual void	ReleaseConstantData ()

template<typename Op >
void	OperateOnConstantTensors (Op op)

const char *	GetName () const override
	Returns the name of the layer. More...

unsigned int	GetNumInputSlots () const override
	Returns the number of connectable input slots. More...

unsigned int	GetNumOutputSlots () const override
	Returns the number of connectable output slots. More...

const InputSlot &	GetInputSlot (unsigned int index) const override
	Get a const input slot handle by slot index. More...

InputSlot &	GetInputSlot (unsigned int index) override
	Get the input slot handle by slot index. More...

const OutputSlot &	GetOutputSlot (unsigned int index=0) const override
	Get the const output slot handle by slot index. More...

OutputSlot &	GetOutputSlot (unsigned int index=0) override
	Get the output slot handle by slot index. More...

void	SetGuid (LayerGuid guid)

LayerGuid	GetGuid () const final
	Returns the unique id of the layer. More...

void	AddRelatedLayerName (const std::string layerName)

const std::list< std::string > &	GetRelatedLayerNames ()

virtual void	Reparent (Graph &dest, std::list< Layer *>::const_iterator iterator)=0

void	BackendSelectionHint (Optional< BackendId > backend) final
	Provide a hint for the optimizer as to which backend to prefer for this layer. More...

Optional< BackendId >	GetBackendHint () const

void	SetShapeInferenceMethod (ShapeInferenceMethod shapeInferenceMethod)

template<typename T >
std::shared_ptr< T >	GetAdditionalInformation () const

void	SetAdditionalInfoForObject (const AdditionalInfoObjectPtr &additionalInfo)

Public Attributes
std::unique_ptr< ScopedCpuTensorHandle >	m_Weight
	A unique pointer to store weight values. More...

std::unique_ptr< ScopedCpuTensorHandle >	m_Bias
	A unique pointer to store bias values. More...

Protected Member Functions
	TransposeConvolution2dLayer (const TransposeConvolution2dDescriptor &param, const char *name)
	Constructor to create a TransposeConvolution2dLayer. More...

	~TransposeConvolution2dLayer ()=default
	Default destructor. More...

ConstantTensors	GetConstantTensorsByRef () override
	Retrieve the handles to the constant values stored by the layer. More...

Protected Member Functions inherited from LayerWithParameters< TransposeConvolution2dDescriptor >
	LayerWithParameters (unsigned int numInputSlots, unsigned int numOutputSlots, LayerType type, const TransposeConvolution2dDescriptor &param, const char *name)

	~LayerWithParameters ()=default

WorkloadInfo	PrepInfoAndDesc (QueueDescriptor &descriptor) const
	Helper function to reduce duplication in *LayerCreateWorkload. More...

Protected Member Functions inherited from Layer
virtual	~Layer ()=default

template<typename QueueDescriptor >
void	CollectQueueDescriptorInputs (QueueDescriptor &descriptor, WorkloadInfo &info) const

template<typename QueueDescriptor >
void	CollectQueueDescriptorOutputs (QueueDescriptor &descriptor, WorkloadInfo &info) const

void	ValidateAndCopyShape (const TensorShape &outputShape, const TensorShape &inferredShape, const ShapeInferenceMethod shapeInferenceMethod, const std::string &layerName, const unsigned int outputSlotIndex=0)

void	VerifyShapeInferenceType (const TensorShape &outputShape, ShapeInferenceMethod shapeInferenceMethod)

template<typename QueueDescriptor >
WorkloadInfo	PrepInfoAndDesc (QueueDescriptor &descriptor) const
	Helper function to reduce duplication in *LayerCreateWorkload. More...

template<typename LayerType , typename ... Params>
LayerType *	CloneBase (Graph &graph, Params &&... params) const

void	SetAdditionalInfo (QueueDescriptor &descriptor) const

Protected Member Functions inherited from IConnectableLayer
	~IConnectableLayer ()
	Objects are not deletable via the handle. More...

Additional Inherited Members
Public Types inherited from LayerWithParameters< TransposeConvolution2dDescriptor >
using	DescriptorType = TransposeConvolution2dDescriptor

Protected Types inherited from Layer
using	ConstantTensors = std::vector< std::reference_wrapper< std::unique_ptr< ScopedCpuTensorHandle > >>

Protected Attributes inherited from LayerWithParameters< TransposeConvolution2dDescriptor >
TransposeConvolution2dDescriptor	m_Param
	The parameters for the layer (not including tensor-valued weights etc.). More...

Protected Attributes inherited from Layer
AdditionalInfoObjectPtr	m_AdditionalInfoObject

std::vector< OutputHandler >	m_OutputHandlers

ShapeInferenceMethod	m_ShapeInferenceMethod

Detailed Description

This layer represents a 2D transpose convolution operation.

Definition at line 15 of file TransposeConvolution2dLayer.hpp.

Constructor & Destructor Documentation

◆ TransposeConvolution2dLayer()

TransposeConvolution2dLayer	(	const TransposeConvolution2dDescriptor &	param,
		const char *	name
	)

protected

Constructor to create a TransposeConvolution2dLayer.

Parameters

[in]	param	TransposeConvolution2dDescriptor to configure the 2D transpose convolution operation.
[in]	name	Optional name for the layer.

Definition at line 19 of file TransposeConvolution2dLayer.cpp.

References armnn::TransposeConvolution2d.

     : LayerWithParameters(1, 1, LayerType::TransposeConvolution2d, param, name)
 {
 }

◆ ~TransposeConvolution2dLayer()

~TransposeConvolution2dLayer ( )

protecteddefault

Default destructor.

Member Function Documentation

◆ Accept()

void Accept ( ILayerVisitor & visitor ) const

overridevirtual

Apply a visitor to this layer.

Implements IConnectableLayer.

Definition at line 140 of file TransposeConvolution2dLayer.cpp.

References Layer::GetName(), LayerWithParameters< TransposeConvolution2dDescriptor >::GetParameters(), TransposeConvolution2dLayer::m_Bias, TransposeConvolution2dLayer::m_Weight, and ILayerVisitor::VisitTransposeConvolution2dLayer().

 {
     ConstTensor weightsTensor(m_Weight->GetTensorInfo(), m_Weight->Map(true)) ;
     Optional<ConstTensor> optionalBiasTensor = EmptyOptional();
 
     if (GetParameters().m_BiasEnabled)
     {
         ConstTensor biasTensor(m_Bias->GetTensorInfo(), m_Bias->Map(true));
         optionalBiasTensor = Optional<ConstTensor>(biasTensor);
     }
 
     visitor.VisitTransposeConvolution2dLayer(this, GetParameters(), weightsTensor, optionalBiasTensor, GetName());
 }

◆ Clone()

TransposeConvolution2dLayer * Clone ( Graph & graph ) const

overridevirtual

Creates a dynamically-allocated copy of this layer.

Parameters

[in] graph The graph into which this layer is being cloned.

Implements Layer.

Definition at line 43 of file TransposeConvolution2dLayer.cpp.

References Layer::GetName(), TransposeConvolution2dLayer::m_Bias, LayerWithParameters< TransposeConvolution2dDescriptor >::m_Param, and TransposeConvolution2dLayer::m_Weight.

 {
     auto layer = CloneBase<TransposeConvolution2dLayer>(graph, m_Param, GetName());
 
     layer->m_Weight = m_Weight ? std::make_unique<ScopedCpuTensorHandle>(*m_Weight) : nullptr;
 
     if (layer->m_Param.m_BiasEnabled)
     {
         layer->m_Bias = m_Bias ? std::make_unique<ScopedCpuTensorHandle>(*m_Bias) : nullptr;
     }
 
     return std::move(layer);
 }

◆ CreateWorkload()

std::unique_ptr< IWorkload > CreateWorkload ( const IWorkloadFactory & factory ) const

overridevirtual

Makes a workload for the TransposeConvolution2d type.

Parameters

[in]	graph	The graph where this layer can be found.
[in]	factory	The workload factory which will create the workload.

Returns: A pointer to the created workload, or nullptr if not created.

Implements Layer.

Definition at line 25 of file TransposeConvolution2dLayer.cpp.

References ARMNN_ASSERT_MSG, IWorkloadFactory::CreateTransposeConvolution2d(), TransposeConvolution2dLayer::m_Bias, TransposeConvolution2dQueueDescriptor::m_Bias, TransposeConvolution2dDescriptor::m_BiasEnabled, LayerWithParameters< TransposeConvolution2dDescriptor >::m_Param, TransposeConvolution2dLayer::m_Weight, TransposeConvolution2dQueueDescriptor::m_Weight, LayerWithParameters< TransposeConvolution2dDescriptor >::PrepInfoAndDesc(), and Layer::SetAdditionalInfo().

 {
     ARMNN_ASSERT_MSG(m_Weight != nullptr, "TransposeConvolution2dLayer: Weights data should not be null.");
 
     TransposeConvolution2dQueueDescriptor descriptor;
     descriptor.m_Weight = m_Weight.get();
 
     if (m_Param.m_BiasEnabled)
     {
         ARMNN_ASSERT_MSG(m_Bias != nullptr, "TransposeConvolution2dLayer: Bias data should not be null.");
         descriptor.m_Bias = m_Bias.get();
     }
 
     SetAdditionalInfo(descriptor);
 
     return factory.CreateTransposeConvolution2d(descriptor, PrepInfoAndDesc(descriptor));
 }

◆ GetConstantTensorsByRef()

Layer::ConstantTensors GetConstantTensorsByRef ( )

overrideprotectedvirtual

Retrieve the handles to the constant values stored by the layer.

Returns: A vector of the constant tensors stored by this layer.

Reimplemented from Layer.

Definition at line 135 of file TransposeConvolution2dLayer.cpp.

References TransposeConvolution2dLayer::m_Bias, and TransposeConvolution2dLayer::m_Weight.

 {
     return {m_Weight, m_Bias};
 }

◆ InferOutputShapes()

std::vector< TensorShape > InferOutputShapes ( const std::vector< TensorShape > & inputShapes ) const

overridevirtual

Infers the output shapes from given input shapes and layer properties.

Parameters

[in] inputShapes The input shapes the layer has.

Returns: A vector of the inferred output shape.

Reimplemented from Layer.

Definition at line 57 of file TransposeConvolution2dLayer.cpp.

References ARMNN_ASSERT, ARMNN_ASSERT_MSG, TransposeConvolution2dDescriptor::m_DataLayout, TransposeConvolution2dDescriptor::m_PadBottom, TransposeConvolution2dDescriptor::m_PadLeft, TransposeConvolution2dDescriptor::m_PadRight, TransposeConvolution2dDescriptor::m_PadTop, LayerWithParameters< TransposeConvolution2dDescriptor >::m_Param, TransposeConvolution2dDescriptor::m_StrideX, TransposeConvolution2dDescriptor::m_StrideY, and armnn::NHWC.

Referenced by TransposeConvolution2dInferOutputShapeTest(), and TransposeConvolution2dLayer::ValidateTensorShapesFromInputs().

 {
     ARMNN_ASSERT(inputShapes.size() == 2);
     const TensorShape& inputShape  = inputShapes[0];
     const TensorShape& kernelShape = inputShapes[1];
 
     ARMNN_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Transpose convolutions will always have 4D input");
 
     DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout);
 
     const unsigned int batches = inputShape[0];
 
     const unsigned int wInput = inputShape[dataLayoutIndex.GetWidthIndex()];
     const unsigned int hInput = inputShape[dataLayoutIndex.GetHeightIndex()];
 
     const unsigned int wKernel = kernelShape[dataLayoutIndex.GetWidthIndex()];
     const unsigned int hKernel = kernelShape[dataLayoutIndex.GetHeightIndex()];
 
     unsigned int wPadding = m_Param.m_PadLeft + m_Param.m_PadRight;
     unsigned int hPadding = m_Param.m_PadTop + m_Param.m_PadBottom;
 
     unsigned int wOutput = (wInput - 1) * m_Param.m_StrideX + wKernel - wPadding;
     unsigned int hOutput = (hInput - 1) * m_Param.m_StrideY + hKernel - hPadding;
 
     unsigned int kernelElements = kernelShape[0] * kernelShape[dataLayoutIndex.GetChannelsIndex()];
     unsigned int inputElements  = batches * inputShape[dataLayoutIndex.GetChannelsIndex()];
 
     ARMNN_ASSERT_MSG(inputElements != 0, "Invalid number of input elements");
 
     unsigned int channels;
     if (kernelElements >= inputElements)
     {
         ARMNN_ASSERT_MSG(kernelElements % inputElements == 0 , "Invalid number of elements");
         channels = kernelElements / inputElements;
     }
     else
     {
         ARMNN_ASSERT_MSG(inputElements % kernelElements == 0 , "Invalid number of elements");
         channels = kernelShape[0];
     }
 
     TensorShape tensorShape = m_Param.m_DataLayout == armnn::DataLayout::NHWC ?
          TensorShape( { batches, hOutput, wOutput, channels } ) :
          TensorShape( { batches, channels, hOutput, wOutput });
 
     return std::vector<TensorShape>({ tensorShape });
 }

◆ ValidateTensorShapesFromInputs()

void ValidateTensorShapesFromInputs ( )

overridevirtual

Check if the input tensor shape(s) will lead to a valid configuration of TransposeConvolution2dLayer.

Parameters

[in] shapeInferenceMethod Indicates if output shape shall be overwritten or just validated.

Implements Layer.

Definition at line 106 of file TransposeConvolution2dLayer.cpp.

References ARMNN_ASSERT, ARMNN_ASSERT_MSG, CHECK_LOCATION, InputSlot::GetConnection(), Layer::GetInputSlot(), Layer::GetOutputSlot(), TensorInfo::GetShape(), IOutputSlot::GetTensorInfo(), OutputSlot::GetTensorInfo(), TransposeConvolution2dLayer::InferOutputShapes(), TransposeConvolution2dDescriptor::m_OutputShape, TransposeConvolution2dDescriptor::m_OutputShapeEnabled, LayerWithParameters< TransposeConvolution2dDescriptor >::m_Param, Layer::m_ShapeInferenceMethod, TransposeConvolution2dLayer::m_Weight, Layer::ValidateAndCopyShape(), Layer::VerifyLayerConnections(), and Layer::VerifyShapeInferenceType().

 {
     VerifyLayerConnections(1, CHECK_LOCATION());
 
     const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
 
     VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
 
     ARMNN_ASSERT_MSG(m_Weight != nullptr, "TransposeConvolution2dLayer: Weight data cannot be null.");
 
     std::vector<TensorShape> expectedOutputShape;
     // If output_shape was specified then use it rather than calculate an inferred output shape.
     if (m_Param.m_OutputShapeEnabled)
     {
         TensorShape shapeAsTensorShape(static_cast<unsigned int>(m_Param.m_OutputShape.size()),
             m_Param.m_OutputShape.data());
         expectedOutputShape.push_back(shapeAsTensorShape);
     }
     else
     {
         expectedOutputShape = InferOutputShapes({GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
                                                  m_Weight->GetTensorInfo().GetShape() });
     }
 
     ARMNN_ASSERT(expectedOutputShape.size() == 1);
 
     ValidateAndCopyShape(outputShape, expectedOutputShape[0], m_ShapeInferenceMethod, "TransposeConvolution2dLayer");
 }