Convolution2dLayer Class Reference

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

#include <Convolution2dLayer.hpp>

Inheritance diagram for Convolution2dLayer:

Public Member Functions
virtual std::unique_ptr< IWorkload >	CreateWorkload (const IWorkloadFactory &factory) const override
	Makes a workload for the Convolution2d type. More...
Convolution2dLayer *	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 Convolution2dLayer. More...
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. More...
void	Accept (ILayerVisitor &visitor) const override
	Apply a visitor to this layer. More...
void	SerializeLayerParameters (ParameterStringifyFunction &fn) const override
	Helper to serialize the layer parameters to string. More...
Public Member Functions inherited from LayerWithParameters< Convolution2dDescriptor >
const Convolution2dDescriptor &	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)
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

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
	Convolution2dLayer (const Convolution2dDescriptor &param, const char *name)
	Constructor to create a Convolution2dLayer. More...
	~Convolution2dLayer ()=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< 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. 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
Protected Member Functions inherited from IConnectableLayer
	~IConnectableLayer ()
	Objects are not deletable via the handle. More...

Additional Inherited Members
Public Types inherited from LayerWithParameters< Convolution2dDescriptor >
using	DescriptorType = Convolution2dDescriptor
Protected Types inherited from Layer
using	ConstantTensors = std::vector< std::reference_wrapper< std::unique_ptr< ScopedCpuTensorHandle > >>
Protected Attributes inherited from LayerWithParameters< Convolution2dDescriptor >
Convolution2dDescriptor	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 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.

References armnn::Convolution2d.

    : LayerWithParameters(1, 1, LayerType::Convolution2d, param, name)
{

}

~Convolution2dLayer()

~Convolution2dLayer ( )

protecteddefault

Default destructor.

Member Function Documentation

Accept()

void Accept ( ILayerVisitor &  visitor ) const

overridevirtual

Apply a visitor to this layer.

Implements IConnectableLayer.

Definition at line 139 of file Convolution2dLayer.cpp.

References Layer::GetName(), LayerWithParameters< Convolution2dDescriptor >::GetParameters(), Convolution2dLayer::m_Bias, Convolution2dLayer::m_Weight, and ILayerVisitor::VisitConvolution2dLayer().

{
    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.VisitConvolution2dLayer(this, GetParameters(), weightsTensor, optionalBiasTensor, GetName());
}

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 66 of file Convolution2dLayer.cpp.

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

{
    auto layer = CloneBase<Convolution2dLayer>(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 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.

References IWorkloadFactory::CreateConvolution2d(), Convolution2dLayer::m_Bias, Convolution2dQueueDescriptor::m_Bias, Convolution2dDescriptor::m_BiasEnabled, LayerWithParameters< Convolution2dDescriptor >::m_Param, Convolution2dLayer::m_Weight, Convolution2dQueueDescriptor::m_Weight, and LayerWithParameters< Convolution2dDescriptor >::PrepInfoAndDesc().

{
    // on this level constant data should not be released..
    BOOST_ASSERT_MSG(m_Weight != nullptr, "Convolution2dLayer: Weights data should not be null.");

    Convolution2dQueueDescriptor descriptor;

    descriptor.m_Weight = m_Weight.get();

    if (m_Param.m_BiasEnabled)
    {
        BOOST_ASSERT_MSG(m_Bias != nullptr, "Convolution2dLayer: Bias data should not be null.");
        descriptor.m_Bias = m_Bias.get();
    }
    return factory.CreateConvolution2d(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 134 of file Convolution2dLayer.cpp.

References Convolution2dLayer::m_Bias, and Convolution2dLayer::m_Weight.

{
    return {m_Weight, m_Bias};
}

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 80 of file Convolution2dLayer.cpp.

References Convolution2dDescriptor::m_DataLayout, Convolution2dDescriptor::m_DilationX, Convolution2dDescriptor::m_DilationY, Convolution2dDescriptor::m_PadBottom, Convolution2dDescriptor::m_PadLeft, Convolution2dDescriptor::m_PadRight, Convolution2dDescriptor::m_PadTop, LayerWithParameters< Convolution2dDescriptor >::m_Param, Convolution2dDescriptor::m_StrideX, Convolution2dDescriptor::m_StrideY, and armnn::NHWC.

Referenced by Convolution2dInferOutputShapeTest(), and Convolution2dLayer::ValidateTensorShapesFromInputs().

{
    BOOST_ASSERT(inputShapes.size() == 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.
    BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Convolutions will always have 4D input.");

    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 });
}

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.

References InputSlot::GetConnection(), DataLayoutIndexed::GetHeightIndex(), Layer::GetInputSlot(), TensorInfo::GetShape(), IOutputSlot::GetTensorInfo(), DataLayoutIndexed::GetWidthIndex(), Convolution2dDescriptor::m_DataLayout, LayerWithParameters< Convolution2dDescriptor >::m_Param, Convolution2dLayer::m_Weight, and LayerWithParameters< Parameters >::SerializeLayerParameters().

{
    //using DescriptorType = Parameters;
    const std::vector<TensorShape>& inputShapes =
    {
        GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
        m_Weight->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);
}

ValidateTensorShapesFromInputs()

void ValidateTensorShapesFromInputs ( )

overridevirtual

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

Implements Layer.

Definition at line 115 of file Convolution2dLayer.cpp.

References CHECK_LOCATION, InputSlot::GetConnection(), Layer::GetInputSlot(), Layer::GetOutputSlot(), TensorInfo::GetShape(), IOutputSlot::GetTensorInfo(), OutputSlot::GetTensorInfo(), Convolution2dLayer::InferOutputShapes(), Convolution2dLayer::m_Weight, and Layer::VerifyLayerConnections().

{
    VerifyLayerConnections(1, CHECK_LOCATION());

    // check if we m_Weight data is not nullptr
    BOOST_ASSERT_MSG(m_Weight != nullptr, "Convolution2dLayer: Weights data should not be null.");

    auto inferredShapes = InferOutputShapes({
        GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
        m_Weight->GetTensorInfo().GetShape() });

    BOOST_ASSERT(inferredShapes.size() == 1);

    ConditionalThrowIfNotEqual<LayerValidationException>(
        "Convolution2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
        GetOutputSlot(0).GetTensorInfo().GetShape(),
        inferredShapes[0]);
}

Member Data Documentation

m_Bias

std::unique_ptr<ScopedCpuTensorHandle> m_Bias

A unique pointer to store Bias values.

Definition at line 22 of file Convolution2dLayer.hpp.

Referenced by Convolution2dLayer::Accept(), BOOST_AUTO_TEST_CASE(), Convolution2dLayer::Clone(), Convolution2dLayer::CreateWorkload(), and Convolution2dLayer::GetConstantTensorsByRef().

m_Weight

std::unique_ptr<ScopedCpuTensorHandle> m_Weight

A unique pointer to store Weight values.

Definition at line 20 of file Convolution2dLayer.hpp.

Referenced by Convolution2dLayer::Accept(), Network::AddConcatLayer(), BOOST_AUTO_TEST_CASE(), Convolution2dLayer::Clone(), CreateConvolution2dGraph(), Convolution2dLayer::CreateWorkload(), Convolution2dLayer::GetConstantTensorsByRef(), Convolution2dLayer::SerializeLayerParameters(), and Convolution2dLayer::ValidateTensorShapesFromInputs().

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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