This layer represents a pooling 2d operation. More...

#include <Pooling2dLayer.hpp>

Inheritance diagram for Pooling2dLayer:

Public Member Functions
virtual std::unique_ptr< IWorkload >	CreateWorkload (const IWorkloadFactory &factory) const override

Pooling2dLayer *	Clone (Graph &graph) const override

void	ValidateTensorShapesFromInputs () override

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

void	Accept (ILayerVisitor &visitor) const override

Public Member Functions inherited from LayerWithParameters< Pooling2dDescriptor >
const Pooling2dDescriptor &	GetParameters () const

void	SerializeLayerParameters (ParameterStringifyFunction &fn) const override

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)

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

unsigned int	GetNumInputSlots () const override

unsigned int	GetNumOutputSlots () const override

const InputSlot &	GetInputSlot (unsigned int index) const override

InputSlot &	GetInputSlot (unsigned int index) override

const OutputSlot &	GetOutputSlot (unsigned int index=0) const override

OutputSlot &	GetOutputSlot (unsigned int index=0) override

void	SetGuid (LayerGuid guid)

LayerGuid	GetGuid () const final

void	AddRelatedLayerName (const std::string layerName)

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

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

Protected Member Functions
	Pooling2dLayer (const Pooling2dDescriptor &param, const char *name)

	~Pooling2dLayer ()=default
	Default destructor. More...

Protected Member Functions inherited from LayerWithParameters< Pooling2dDescriptor >
	LayerWithParameters (unsigned int numInputSlots, unsigned int numOutputSlots, LayerType type, const Pooling2dDescriptor &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

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

virtual ConstantTensors	GetConstantTensorsByRef ()

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

Additional Inherited Members
Public Types inherited from LayerWithParameters< Pooling2dDescriptor >
using	DescriptorType = Pooling2dDescriptor

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

Protected Attributes inherited from LayerWithParameters< Pooling2dDescriptor >
Pooling2dDescriptor	m_Param
	The parameters for the layer (not including tensor-valued weights etc.). More...

Protected Attributes inherited from Layer
std::vector< OutputHandler >	m_OutputHandlers

Detailed Description

This layer represents a pooling 2d operation.

Definition at line 13 of file Pooling2dLayer.hpp.

Constructor & Destructor Documentation

◆ Pooling2dLayer()

Pooling2dLayer	(	const Pooling2dDescriptor &	param,
		const char *	name
	)

protected

Constructor to create a Pooling2dLayer.

Parameters

[in]	param	Pooling2dDescriptor to configure the pooling2d operation.
[in]	name	Optional name for the layer.

Definition at line 22 of file Pooling2dLayer.cpp.

References armnn::Pooling2d().

     : LayerWithParameters(1, 1, LayerType::Pooling2d, param, name)
 {
 }

◆ ~Pooling2dLayer()

~Pooling2dLayer ( )

protecteddefault

Default destructor.

Member Function Documentation

◆ Accept()

void Accept ( ILayerVisitor & visitor ) const

overridevirtual

Implements IConnectableLayer.

Definition at line 117 of file Pooling2dLayer.cpp.

References Layer::GetName(), LayerWithParameters< Pooling2dDescriptor >::GetParameters(), and ILayerVisitor::VisitPooling2dLayer().

 {
     visitor.VisitPooling2dLayer(this, GetParameters(), GetName());
 }

◆ Clone()

Pooling2dLayer * 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 33 of file Pooling2dLayer.cpp.

References Layer::GetName(), and LayerWithParameters< Pooling2dDescriptor >::m_Param.

 {
     return CloneBase<Pooling2dLayer>(graph, m_Param, GetName());
 }

◆ CreateWorkload()

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

overridevirtual

Makes a workload for the Pooling2d 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 27 of file Pooling2dLayer.cpp.

References IWorkloadFactory::CreatePooling2d(), and LayerWithParameters< Pooling2dDescriptor >::PrepInfoAndDesc().

 {
     Pooling2dQueueDescriptor descriptor;
     return factory.CreatePooling2d(descriptor, PrepInfoAndDesc(descriptor));
 }

◆ InferOutputShapes()

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

overridevirtual

By default returns inputShapes if the number of inputs are equal to number of outputs, otherwise infers the output shapes from given input shapes and layer properties.

Parameters

[in] inputShapes The input shapes layer has.

Returns: A vector to the inferred output shape.

Reimplemented from Layer.

Definition at line 38 of file Pooling2dLayer.cpp.

Referenced by Pooling2dLayer::ValidateTensorShapesFromInputs().

 {
     BOOST_ASSERT(inputShapes.size() == 1);
     const TensorShape& inputShape = inputShapes[0];
     const DataLayoutIndexed dimensionIndices = m_Param.m_DataLayout;
 
     // If we support multiple batch dimensions in the future, then this assert will need to change.
     BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Pooling2dLayer will always have 4D input.");
 
     unsigned int inWidth = inputShape[dimensionIndices.GetWidthIndex()];
     unsigned int inHeight = inputShape[dimensionIndices.GetHeightIndex()];
     unsigned int inChannels = inputShape[dimensionIndices.GetChannelsIndex()];
     unsigned int inBatchSize = inputShape[0];
 
     bool isGlobalPooling = (m_Param.m_StrideX==0 && m_Param.m_StrideY==0);
     unsigned int outWidth = 1;
     unsigned int outHeight = 1;
     if (!isGlobalPooling)
     {
         BOOST_ASSERT_MSG(m_Param.m_StrideX!=0 && m_Param.m_StrideY!=0,
                          "Stride can only be zero when performing global pooling");
 
         auto CalcSize = [](auto inSize, auto lowPad, auto highPad, auto poolSize, auto stride, auto outputShapeRounding)
             {
                 unsigned int readSize = inSize + lowPad + highPad - poolSize;
                 float div = static_cast<float>(readSize) / static_cast<float>(stride);
 
                 unsigned int size = 0;
                 switch (outputShapeRounding)
                 {
                     case OutputShapeRounding::Ceiling:
                         size = static_cast<unsigned int>(ceil(div)) + 1;
                         break;
                     case OutputShapeRounding ::Floor:
                         size = static_cast<unsigned int>(floor(div)) + 1;
                         break;
                     default:
                         BOOST_ASSERT_MSG(false, "Unsupported Output Shape Rounding");
                 }
 
                 // MakeS sure that border operations will start from inside the input and not the padded area.
                 // This is what both Caffe and CL do...
                 if ((size - 1)*stride >= inSize + lowPad)
                 {
                     --size;
                 }
 
                 return size;
             };
 
         outWidth = CalcSize(inWidth, m_Param.m_PadLeft, m_Param.m_PadRight, m_Param.m_PoolWidth, m_Param.m_StrideX,
                             m_Param.m_OutputShapeRounding);
         outHeight = CalcSize(inHeight, m_Param.m_PadTop, m_Param.m_PadBottom, m_Param.m_PoolHeight, m_Param.m_StrideY,
                             m_Param.m_OutputShapeRounding);
     }
     unsigned int outChannels = inChannels;
     unsigned int outBatchSize = inBatchSize;
 
     TensorShape tensorShape = m_Param.m_DataLayout == armnn::DataLayout::NHWC ?
         TensorShape( { outBatchSize, outHeight, outWidth, outChannels } ) :
         TensorShape( { outBatchSize, outChannels, outHeight, outWidth });
 
     return std::vector<TensorShape>({ tensorShape });
 }

◆ ValidateTensorShapesFromInputs()

void ValidateTensorShapesFromInputs ( )

overridevirtual

Check if the input tensor shape(s) will lead to a valid configuration of Pooling2dLayer.

Implements Layer.

Definition at line 103 of file Pooling2dLayer.cpp.

References CHECK_LOCATION, InputSlot::GetConnection(), Layer::GetInputSlot(), Layer::GetOutputSlot(), TensorInfo::GetShape(), IOutputSlot::GetTensorInfo(), OutputSlot::GetTensorInfo(), Pooling2dLayer::InferOutputShapes(), and Layer::VerifyLayerConnections().

 {
     VerifyLayerConnections(1, CHECK_LOCATION());
 
     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
 
     BOOST_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "Pooling2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
         GetOutputSlot(0).GetTensorInfo().GetShape(),
         inferredShapes[0]);
 }

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

src/armnn/layers/Pooling2dLayer.hpp
src/armnn/layers/Pooling2dLayer.cpp

Public Member Functions

Protected Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ Pooling2dLayer()

◆ ~Pooling2dLayer()

Member Function Documentation

◆ Accept()

◆ Clone()

◆ CreateWorkload()

◆ InferOutputShapes()

◆ ValidateTensorShapesFromInputs()