Convolution2dLayer Class Reference

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

#include <Convolution2dLayer.hpp>

Inheritance diagram for Convolution2dLayer:

Collaboration diagram for Convolution2dLayer:

Public Member Functions
virtual std::unique_ptr< IWorkload >	CreateWorkload (const IWorkloadFactory &factory) const override
	Makes a workload for the Convolution2d type.
Convolution2dLayer *	Clone (Graph &graph) const override
	Creates a dynamically-allocated copy of this layer.
void	ValidateTensorShapesFromInputs () override
	Check if the input tensor shape(s) will lead to a valid configuration of Convolution2dLayer.
std::vector< TensorShape >	InferOutputShapes (const std::vector< TensorShape > &inputShapes) const override
	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.
void	ExecuteStrategy (IStrategy &strategy) const override
	Apply a visitor to this layer.
void	SerializeLayerParameters (ParameterStringifyFunction &fn) const override
	Helper to serialize the layer parameters to string.
void	ReleaseConstantData () override
	This layer does not have any data stored, weights and bias are now stored in constant layers.
Public Member Functions inherited from LayerWithParameters< Convolution2dDescriptor >
const Convolution2dDescriptor &	GetParameters () const override
	If the layer has a descriptor return it.
void	SerializeLayerParameters (ParameterStringifyFunction &fn) const override
	Helper to serialize the layer parameters to string (currently used in DotSerializer and company).
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
bool	GetAllowExpandedDims () 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 override
	Returns the armnn::LayerType of this layer.
DataType	GetDataType () const
const BackendId &	GetBackendId () const
void	SetBackendId (const BackendId &id) override
	Set the backend of the IConnectableLayer.
virtual void	CreateTensorHandles (const TensorHandleFactoryRegistry &registry, const IWorkloadFactory &factory, const bool IsMemoryManaged=true)
void	VerifyLayerConnections (unsigned int expectedConnections, const CheckLocation &location) const
std::vector< TensorShape >	InferOutputShapes (const std::vector< TensorShape > &inputShapes) const override
	Infer the shape of the output(s) based on the provided input shape(s)
template<typename Op>
void	OperateOnConstantTensors (Op op)
const char *	GetName () const override
	Returns the name of the layer.
unsigned int	GetNumInputSlots () const override
	Returns the number of connectable input slots.
unsigned int	GetNumOutputSlots () const override
	Returns the number of connectable output slots.
const InputSlot &	GetInputSlot (unsigned int index) const override
	Get a const input slot handle by slot index.
InputSlot &	GetInputSlot (unsigned int index) override
	Get the input slot handle by slot index.
const OutputSlot &	GetOutputSlot (unsigned int index=0) const override
	Get the const output slot handle by slot index.
OutputSlot &	GetOutputSlot (unsigned int index=0) override
	Get the output slot handle by slot index.
void	SetGuid (LayerGuid guid)
LayerGuid	GetGuid () const final
	Returns the unique id of the layer.
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.
Optional< BackendId >	GetBackendHint () const
void	SetShapeInferenceMethod (ShapeInferenceMethod shapeInferenceMethod)
void	SetAllowExpandedDims (bool allowExpandedDims)
template<typename T>
std::shared_ptr< T >	GetAdditionalInformation () const
void	SetAdditionalInfoForObject (const AdditionalInfoObjectPtr &additionalInfo)

Protected Member Functions
	Convolution2dLayer (const Convolution2dDescriptor &param, const char *name)
	Constructor to create a Convolution2dLayer.
	~Convolution2dLayer ()=default
	Default destructor.
ImmutableConstantTensors	GetConstantTensorsByRef () const override
	Retrieve the handles to the constant values connected to the layer.
Protected Member Functions inherited from LayerWithParameters< Convolution2dDescriptor >
	LayerWithParameters (unsigned int numInputSlots, unsigned int numOutputSlots, LayerType type, const Convolution2dDescriptor &param, const char *name)
	~LayerWithParameters ()=default
WorkloadInfo	PrepInfoAndDesc (QueueDescriptor &descriptor) const
	Helper function to reduce duplication in *LayerCreateWorkload.
void	ExecuteStrategy (IStrategy &strategy) const override
	Apply a visitor to this layer.
Layer::ImmutableConstantTensors	GetConnectedConstantAsInputTensors () const
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.
template<typename LayerType, typename ... Params>
LayerType *	CloneBase (Graph &graph, Params &&... params) const
virtual ConstantTensors	GetConstantTensorsByRef () override final
void	SetAdditionalInfo (QueueDescriptor &descriptor) const
Protected Member Functions inherited from IConnectableLayer
	~IConnectableLayer ()
	Objects are not deletable via the handle.

Additional Inherited Members
Public Types inherited from LayerWithParameters< Convolution2dDescriptor >
using	DescriptorType
Public Types inherited from IConnectableLayer
using	ConstantTensors = std::vector<std::reference_wrapper<std::shared_ptr<ConstTensorHandle>>>
using	ImmutableConstantTensors = std::vector<std::reference_wrapper<const std::shared_ptr<ConstTensorHandle>>>
Protected Attributes inherited from LayerWithParameters< Convolution2dDescriptor >
Convolution2dDescriptor	m_Param
	The parameters for the layer (not including tensor-valued weights etc.).
Protected Attributes inherited from Layer
AdditionalInfoObjectPtr	m_AdditionalInfoObject
std::vector< OutputHandler >	m_OutputHandlers
ShapeInferenceMethod	m_ShapeInferenceMethod

Detailed Description

This layer represents a convolution 2d operation.

Definition at line 15 of file Convolution2dLayer.hpp.

Constructor & Destructor Documentation

Convolution2dLayer()

Convolution2dLayer ( const Convolution2dDescriptor & param, const char * name )

protected

Constructor to create a Convolution2dLayer.

Parameters

[in]	param	Convolution2dDescriptor to configure the convolution2d operation.
[in]	name	Optional name for the layer.

Definition at line 23 of file Convolution2dLayer.cpp.

    : LayerWithParameters(param.GetNumInputs(), 1, LayerType::Convolution2d, param, name)
{
 
}

References armnn::Convolution2d, armnn::GetNumInputs(), and LayerWithParameters< Convolution2dDescriptor >::LayerWithParameters().

Referenced by Clone().

~Convolution2dLayer()

~Convolution2dLayer ( )

protecteddefault

Default destructor.

Member Function Documentation

Clone()

Convolution2dLayer * 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 58 of file Convolution2dLayer.cpp.

{
    auto layer = CloneBase<Convolution2dLayer>(graph, m_Param, GetName());
    return std::move(layer);
}

References Layer::CloneBase(), Convolution2dLayer(), Layer::GetName(), and LayerWithParameters< Convolution2dDescriptor >::m_Param.

CreateWorkload()

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

overridevirtual

Makes a workload for the Convolution2d 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 49 of file Convolution2dLayer.cpp.

{
    ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "Convolution2dLayer_CreateWorkload");
    Convolution2dQueueDescriptor descriptor;
    SetAdditionalInfo(descriptor);
 
    return factory.CreateWorkload(LayerType::Convolution2d, descriptor, PrepInfoAndDesc(descriptor));
}

References ARMNN_SCOPED_PROFILING_EVENT, armnn::Convolution2d, IWorkloadFactory::CreateWorkload(), LayerWithParameters< Convolution2dDescriptor >::PrepInfoAndDesc(), Layer::SetAdditionalInfo(), and armnn::Undefined.

ExecuteStrategy()

void ExecuteStrategy ( IStrategy & strategy ) const

overridevirtual

Apply a visitor to this layer.

Implements IConnectableLayer.

Definition at line 150 of file Convolution2dLayer.cpp.

{
    strategy.ExecuteStrategy(this, GetParameters(), {}, GetName());
}

References IStrategy::ExecuteStrategy(), Layer::GetName(), and LayerWithParameters< Convolution2dDescriptor >::GetParameters().

GetConstantTensorsByRef()

Layer::ImmutableConstantTensors GetConstantTensorsByRef ( ) const

overrideprotectedvirtual

Retrieve the handles to the constant values connected to the layer.

Returns

A vector of the constant tensors connected to the layer.

Reimplemented from Layer.

Definition at line 144 of file Convolution2dLayer.cpp.

{
    Layer::ImmutableConstantTensors tensors = GetConnectedConstantAsInputTensors();
    return tensors;
}

References LayerWithParameters< Convolution2dDescriptor >::GetConnectedConstantAsInputTensors().

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.

Implements IConnectableLayer.

Definition at line 64 of file Convolution2dLayer.cpp.

{
    if (inputShapes.size() != 2)
    {
        throw armnn::Exception("inputShapes' size is \"" + std::to_string(inputShapes.size()) +
                               "\" - should be \"2\".");
    }
 
    const TensorShape& inputShape = inputShapes[0];
    const TensorShape filterShape = inputShapes[1];
 
    // If we support multiple batch dimensions in the future, then this assert will need to change.
    if (inputShape.GetNumDimensions() != 4)
    {
        throw armnn::Exception("Convolutions will always have 4D input.");
    }
 
    if (m_Param.m_StrideX == 0)
    {
        throw armnn::Exception("m_StrideX cannot be 0.");
    }
 
    if (m_Param.m_StrideY == 0)
    {
        throw armnn::Exception("m_StrideY cannot be 0.");
    }
 
    DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout);
 
    unsigned int inWidth = inputShape[dataLayoutIndex.GetWidthIndex()];
    unsigned int inHeight = inputShape[dataLayoutIndex.GetHeightIndex()];
    unsigned int inBatchSize = inputShape[0];
 
    unsigned int filterWidth = filterShape[dataLayoutIndex.GetWidthIndex()];
    unsigned int dilatedFilterWidth = filterWidth + (m_Param.m_DilationX - 1) * (filterWidth - 1);
    unsigned int readWidth = (inWidth + m_Param.m_PadLeft + m_Param.m_PadRight) - dilatedFilterWidth;
    unsigned int outWidth = 1 + (readWidth / m_Param.m_StrideX);
 
    unsigned int filterHeight = filterShape[dataLayoutIndex.GetHeightIndex()];
    unsigned int dilatedFilterHeight = filterHeight + (m_Param.m_DilationY - 1) * (filterHeight - 1);
    unsigned int readHeight = (inHeight + m_Param.m_PadTop + m_Param.m_PadBottom) - dilatedFilterHeight;
    unsigned int outHeight = 1 + (readHeight / m_Param.m_StrideY);
 
    unsigned int outChannels = filterShape[0];
    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 });
}

References DataLayoutIndexed::GetHeightIndex(), TensorShape::GetNumDimensions(), DataLayoutIndexed::GetWidthIndex(), LayerWithParameters< Convolution2dDescriptor >::m_Param, and armnn::NHWC.

Referenced by ValidateTensorShapesFromInputs().

ReleaseConstantData()

void ReleaseConstantData ( )

inlineoverridevirtual

This layer does not have any data stored, weights and bias are now stored in constant layers.

We do not want to release the data in the constant layer, that is why we override with an empty function.

Reimplemented from Layer.

Definition at line 46 of file Convolution2dLayer.hpp.

{}

SerializeLayerParameters()

void SerializeLayerParameters ( ParameterStringifyFunction & fn ) const

overridevirtual

Helper to serialize the layer parameters to string.

(currently used in DotSerializer and company).

Reimplemented from Layer.

Definition at line 29 of file Convolution2dLayer.cpp.

{
    //using DescriptorType = Parameters;
    const std::vector<TensorShape>& inputShapes =
    {
        GetInputSlot(0).GetTensorInfo().GetShape(),
        GetInputSlot(1).GetTensorInfo().GetShape()
    };
    const TensorShape filterShape = inputShapes[1];
    DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout);
    unsigned int filterWidth = filterShape[dataLayoutIndex.GetWidthIndex()];
    unsigned int filterHeight = filterShape[dataLayoutIndex.GetHeightIndex()];
    unsigned int outChannels = filterShape[0];
 
    fn("OutputChannels",std::to_string(outChannels));
    fn("FilterWidth",std::to_string(filterWidth));
    fn("FilterHeight",std::to_string(filterHeight));
    LayerWithParameters<Convolution2dDescriptor>::SerializeLayerParameters(fn);
}

References DataLayoutIndexed::GetHeightIndex(), Layer::GetInputSlot(), TensorInfo::GetShape(), InputSlot::GetTensorInfo(), DataLayoutIndexed::GetWidthIndex(), LayerWithParameters< Convolution2dDescriptor >::m_Param, and LayerWithParameters< Parameters >::SerializeLayerParameters().

ValidateTensorShapesFromInputs()

void ValidateTensorShapesFromInputs ( )

overridevirtual

Check if the input tensor shape(s) will lead to a valid configuration of Convolution2dLayer.

Parameters

[in]

shapeInferenceMethod

Indicates if output shape shall be overwritten or just validated.

Implements Layer.

Definition at line 117 of file Convolution2dLayer.cpp.

{
    VerifyLayerConnections(m_Param.GetNumInputs(), CHECK_LOCATION());
 
    const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
 
    VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
 
    if (!GetInputSlot(1).GetConnection())
    {
        throw armnn::NullPointerException("Convolution2dLayer: Weights should be connected to input slot 1.");
    }
 
    std::vector<TensorShape> inferredShapes = InferOutputShapes({
             GetInputSlot(0).GetTensorInfo().GetShape(),
             GetInputSlot(1).GetTensorInfo().GetShape() });
 
    if (inferredShapes.size() != 1)
    {
        throw armnn::Exception("inferredShapes has "
                               + std::to_string(inferredShapes.size()) +
                               " elements - should only have 1.");
    }
 
    ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "Convolution2dLayer");
}

References CHECK_LOCATION, Layer::GetInputSlot(), Layer::GetOutputSlot(), TensorInfo::GetShape(), InputSlot::GetTensorInfo(), OutputSlot::GetTensorInfo(), InferOutputShapes(), LayerWithParameters< Convolution2dDescriptor >::m_Param, Layer::m_ShapeInferenceMethod, Layer::ValidateAndCopyShape(), Layer::VerifyLayerConnections(), and Layer::VerifyShapeInferenceType().

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

• src/armnn/layers/Convolution2dLayer.hpp
• src/armnn/layers/Convolution2dLayer.cpp