This layer represents a QuantizedLstm operation. More...

#include <QuantizedLstmLayer.hpp>

Inheritance diagram for QuantizedLstmLayer:

Collaboration diagram for QuantizedLstmLayer:

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

QuantizedLstmLayer *	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 QuantizedLstmLayer.

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.

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

virtual void	SerializeLayerParameters (ParameterStringifyFunction &fn) const
	Helper to serialize the layer parameters to string.

virtual void	ReleaseConstantData ()

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)

virtual const BaseDescriptor &	GetParameters () const override
	If the layer has a descriptor return it.

Public Attributes
QuantizedLstmParameters	m_QuantizedLstmParameters

Protected Member Functions
	QuantizedLstmLayer (const char *name)
	Constructor to create a QuantizedLstmLayer.

	~QuantizedLstmLayer ()=default
	Default destructor.

Layer::ImmutableConstantTensors	GetConstantTensorsByRef () const override
	Retrieve the handles to the constant values stored by the layer.

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 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 Layer
AdditionalInfoObjectPtr	m_AdditionalInfoObject

std::vector< OutputHandler >	m_OutputHandlers

ShapeInferenceMethod	m_ShapeInferenceMethod

Detailed Description

This layer represents a QuantizedLstm operation.

Definition at line 45 of file QuantizedLstmLayer.hpp.

Constructor & Destructor Documentation

◆ QuantizedLstmLayer()

QuantizedLstmLayer ( const char * name )

protected

Constructor to create a QuantizedLstmLayer.

Parameters

[in] name Optional name for the layer.

Definition at line 17 of file QuantizedLstmLayer.cpp.

    : Layer(3, 2, LayerType::QuantizedLstm, name)
{
}

◆ ~QuantizedLstmLayer()

~QuantizedLstmLayer ( )

protecteddefault

Default destructor.

Member Function Documentation

◆ Clone()

QuantizedLstmLayer * 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 47 of file QuantizedLstmLayer.cpp.

{
    auto layer = CloneBase<QuantizedLstmLayer>(graph, GetName());
 
    layer->m_QuantizedLstmParameters.m_InputToInputWeights = m_QuantizedLstmParameters.m_InputToInputWeights ?
            m_QuantizedLstmParameters.m_InputToInputWeights : nullptr;
    layer->m_QuantizedLstmParameters.m_InputToForgetWeights = m_QuantizedLstmParameters.m_InputToForgetWeights ?
            m_QuantizedLstmParameters.m_InputToForgetWeights : nullptr;
    layer->m_QuantizedLstmParameters.m_InputToCellWeights = m_QuantizedLstmParameters.m_InputToCellWeights ?
            m_QuantizedLstmParameters.m_InputToCellWeights : nullptr;
    layer->m_QuantizedLstmParameters.m_InputToOutputWeights = m_QuantizedLstmParameters.m_InputToOutputWeights ?
            m_QuantizedLstmParameters.m_InputToOutputWeights : nullptr;
 
    layer->m_QuantizedLstmParameters.m_RecurrentToInputWeights = m_QuantizedLstmParameters.m_RecurrentToInputWeights ?
            m_QuantizedLstmParameters.m_RecurrentToInputWeights : nullptr;
    layer->m_QuantizedLstmParameters.m_RecurrentToForgetWeights = m_QuantizedLstmParameters.m_RecurrentToForgetWeights
            ? m_QuantizedLstmParameters.m_RecurrentToForgetWeights : nullptr;
    layer->m_QuantizedLstmParameters.m_RecurrentToCellWeights = m_QuantizedLstmParameters.m_RecurrentToCellWeights ?
            m_QuantizedLstmParameters.m_RecurrentToCellWeights : nullptr;
    layer->m_QuantizedLstmParameters.m_RecurrentToOutputWeights = m_QuantizedLstmParameters.m_RecurrentToOutputWeights
            ? m_QuantizedLstmParameters.m_RecurrentToOutputWeights : nullptr;
 
    layer->m_QuantizedLstmParameters.m_InputGateBias = m_QuantizedLstmParameters.m_InputGateBias ?
            m_QuantizedLstmParameters.m_InputGateBias : nullptr;
    layer->m_QuantizedLstmParameters.m_ForgetGateBias = m_QuantizedLstmParameters.m_ForgetGateBias ?
            m_QuantizedLstmParameters.m_ForgetGateBias : nullptr;
    layer->m_QuantizedLstmParameters.m_CellBias = m_QuantizedLstmParameters.m_CellBias ?
            m_QuantizedLstmParameters.m_CellBias : nullptr;
    layer->m_QuantizedLstmParameters.m_OutputGateBias = m_QuantizedLstmParameters.m_OutputGateBias ?
            m_QuantizedLstmParameters.m_OutputGateBias : nullptr;
 
    return std::move(layer);
}

◆ CreateWorkload()

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

overridevirtual

Makes a workload for the QuantizedLstm 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 22 of file QuantizedLstmLayer.cpp.

{
    QuantizedLstmQueueDescriptor descriptor;
 
    // QuantizedLstmLayer parameters - there are no optional params
    descriptor.m_InputToInputWeights  = m_QuantizedLstmParameters.m_InputToInputWeights.get();
    descriptor.m_InputToForgetWeights = m_QuantizedLstmParameters.m_InputToForgetWeights.get();
    descriptor.m_InputToCellWeights   = m_QuantizedLstmParameters.m_InputToCellWeights.get();
    descriptor.m_InputToOutputWeights = m_QuantizedLstmParameters.m_InputToOutputWeights.get();
 
    descriptor.m_RecurrentToInputWeights  = m_QuantizedLstmParameters.m_RecurrentToInputWeights.get();
    descriptor.m_RecurrentToForgetWeights = m_QuantizedLstmParameters.m_RecurrentToForgetWeights.get();
    descriptor.m_RecurrentToCellWeights   = m_QuantizedLstmParameters.m_RecurrentToCellWeights.get();
    descriptor.m_RecurrentToOutputWeights = m_QuantizedLstmParameters.m_RecurrentToOutputWeights.get();
 
    descriptor.m_InputGateBias  = m_QuantizedLstmParameters.m_InputGateBias.get();
    descriptor.m_ForgetGateBias = m_QuantizedLstmParameters.m_ForgetGateBias.get();
    descriptor.m_CellBias       = m_QuantizedLstmParameters.m_CellBias.get();
    descriptor.m_OutputGateBias = m_QuantizedLstmParameters.m_OutputGateBias.get();
 
    SetAdditionalInfo(descriptor);
 
    return factory.CreateWorkload(LayerType::QuantizedLstm, descriptor, PrepInfoAndDesc(descriptor));
}

◆ ExecuteStrategy()

void ExecuteStrategy ( IStrategy & strategy ) const

overridevirtual

Apply a visitor to this layer.

Reimplemented from Layer.

Definition at line 239 of file QuantizedLstmLayer.cpp.

{
    std::vector<ConstTensor> constTensors;
 
    ManagedConstTensorHandle managedInputToInputWeights(m_QuantizedLstmParameters.m_InputToInputWeights);
    ManagedConstTensorHandle managedInputToForgetWeights(m_QuantizedLstmParameters.m_InputToForgetWeights);
    ManagedConstTensorHandle managedInputToCellWeights(m_QuantizedLstmParameters.m_InputToCellWeights);
    ManagedConstTensorHandle managedInputToOutputWeights(m_QuantizedLstmParameters.m_InputToOutputWeights);
 
    ManagedConstTensorHandle managedRecurrentToInputWeights(m_QuantizedLstmParameters.m_RecurrentToInputWeights);
    ManagedConstTensorHandle managedRecurrentToForgetWeights(m_QuantizedLstmParameters.m_RecurrentToForgetWeights);
    ManagedConstTensorHandle managedRecurrentToCellWeights(m_QuantizedLstmParameters.m_RecurrentToCellWeights);
    ManagedConstTensorHandle managedRecurrentToOutputWeights(m_QuantizedLstmParameters.m_RecurrentToOutputWeights);
 
    ManagedConstTensorHandle managedInputGateBias(m_QuantizedLstmParameters.m_InputGateBias);
    ManagedConstTensorHandle managedForgetGateBias(m_QuantizedLstmParameters.m_ForgetGateBias);
    ManagedConstTensorHandle managedCellBias(m_QuantizedLstmParameters.m_CellBias);
    ManagedConstTensorHandle managedOutputGateBias(m_QuantizedLstmParameters.m_OutputGateBias);
 
    // InputToX weight tensors
    if (m_QuantizedLstmParameters.m_InputToInputWeights != nullptr)
    {
        constTensors.emplace_back(ConstTensor(managedInputToInputWeights.GetTensorInfo(),
                                              managedInputToInputWeights.Map()));
    }
 
    if (m_QuantizedLstmParameters.m_InputToForgetWeights != nullptr)
    {
        constTensors.emplace_back(ConstTensor(managedInputToForgetWeights.GetTensorInfo(),
                                              managedInputToForgetWeights.Map()));
    }
 
    if (m_QuantizedLstmParameters.m_InputToCellWeights != nullptr)
    {
        constTensors.emplace_back(ConstTensor(managedInputToCellWeights.GetTensorInfo(),
                                              managedInputToCellWeights.Map()));
    }
 
    if (m_QuantizedLstmParameters.m_InputToOutputWeights != nullptr)
    {
        constTensors.emplace_back(ConstTensor(managedInputToOutputWeights.GetTensorInfo(),
                                              managedInputToOutputWeights.Map()));
    }
 
    // RecurrentToX weight tensors
    if (m_QuantizedLstmParameters.m_RecurrentToInputWeights != nullptr)
    {
        constTensors.emplace_back(ConstTensor(
                managedRecurrentToInputWeights.GetTensorInfo(),
                managedRecurrentToInputWeights.Map()));
    }
 
    if (m_QuantizedLstmParameters.m_RecurrentToForgetWeights != nullptr)
    {
        constTensors.emplace_back(ConstTensor(
                managedRecurrentToForgetWeights.GetTensorInfo(),
                managedRecurrentToForgetWeights.Map()));
    }
 
    if (m_QuantizedLstmParameters.m_RecurrentToCellWeights != nullptr)
    {
        constTensors.emplace_back(ConstTensor(
                managedRecurrentToCellWeights.GetTensorInfo(),
                managedRecurrentToCellWeights.Map()));
    }
 
    if (m_QuantizedLstmParameters.m_RecurrentToOutputWeights != nullptr)
    {
        constTensors.emplace_back(ConstTensor(
                managedRecurrentToOutputWeights.GetTensorInfo(),
                managedRecurrentToOutputWeights.Map()));
    }
 
    // Bias tensors
    if (m_QuantizedLstmParameters.m_InputGateBias != nullptr)
    {
        constTensors.emplace_back(ConstTensor(managedInputGateBias.GetTensorInfo(),
                                              managedInputGateBias.Map()));
    }
 
    if (m_QuantizedLstmParameters.m_ForgetGateBias != nullptr)
    {
        constTensors.emplace_back(ConstTensor(managedForgetGateBias.GetTensorInfo(),
                                              managedForgetGateBias.Map()));
    }
 
    if (m_QuantizedLstmParameters.m_CellBias != nullptr)
    {
        constTensors.emplace_back(ConstTensor(managedCellBias.GetTensorInfo(),
                                              managedCellBias.Map()));
    }
 
    if (m_QuantizedLstmParameters.m_OutputGateBias != nullptr)
    {
        constTensors.emplace_back(ConstTensor(managedOutputGateBias.GetTensorInfo(),
                                              managedOutputGateBias.Map()));
    }
 
 
    strategy.ExecuteStrategy(this, BaseDescriptor(), constTensors, GetName());
}

◆ GetConstantTensorsByRef()

Layer::ImmutableConstantTensors GetConstantTensorsByRef ( ) const

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 217 of file QuantizedLstmLayer.cpp.

{
    // For API stability DO NOT ALTER order and add new members to the end of vector
    return
    {
        m_QuantizedLstmParameters.m_InputToInputWeights,
        m_QuantizedLstmParameters.m_InputToForgetWeights,
        m_QuantizedLstmParameters.m_InputToCellWeights,
        m_QuantizedLstmParameters.m_InputToOutputWeights,
 
        m_QuantizedLstmParameters.m_RecurrentToInputWeights,
        m_QuantizedLstmParameters.m_RecurrentToForgetWeights,
        m_QuantizedLstmParameters.m_RecurrentToCellWeights,
        m_QuantizedLstmParameters.m_RecurrentToOutputWeights,
 
        m_QuantizedLstmParameters.m_InputGateBias,
        m_QuantizedLstmParameters.m_ForgetGateBias,
        m_QuantizedLstmParameters.m_CellBias,
        m_QuantizedLstmParameters.m_OutputGateBias
    };
}

◆ 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 81 of file QuantizedLstmLayer.cpp.

{
    if (inputShapes.size() != 3)
    {
        throw armnn::Exception("inputShapes' size is \"" + std::to_string(inputShapes.size()) +
                               "\" - should be \"3\".");
    }
 
    // Get input values for validation
    unsigned int numBatches = inputShapes[0][0];
    unsigned int outputSize = inputShapes[1][1];
 
    std::vector<TensorShape> outShapes;
    outShapes.push_back(TensorShape({numBatches, outputSize})); // cellStateOut
    outShapes.push_back(TensorShape({numBatches, outputSize})); // output
 
    return outShapes;
}

Referenced by QuantizedLstmLayer::ValidateTensorShapesFromInputs().

◆ ValidateTensorShapesFromInputs()

void ValidateTensorShapesFromInputs ( )

overridevirtual

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

Parameters

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

Implements Layer.

Definition at line 100 of file QuantizedLstmLayer.cpp.

{
    VerifyLayerConnections(3, CHECK_LOCATION());
 
    const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
 
    VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
 
    auto inferredShapes = InferOutputShapes(
    {
        GetInputSlot(0).GetTensorInfo().GetShape(), // input
        GetInputSlot(1).GetTensorInfo().GetShape(), // previousCellStateIn
        GetInputSlot(2).GetTensorInfo().GetShape()  // previousOutputIn
    });
 
    if (inferredShapes.size() != 2)
    {
        throw armnn::LayerValidationException("inferredShapes has "
                                              + std::to_string(inferredShapes.size()) +
                                              " element(s) - should only have 2.");
    }
 
    // Check weights and bias for nullptr
    if (!m_QuantizedLstmParameters.m_InputToInputWeights)
    {
        throw armnn::LayerValidationException("QuantizedLstmLayer: "
                                              "m_QuantizedLstmParameters.m_InputToInputWeights "
                                              "should not be null.");
    }
 
    if (!m_QuantizedLstmParameters.m_InputToForgetWeights)
    {
        throw armnn::LayerValidationException("QuantizedLstmLayer: "
                                              "m_QuantizedLstmParameters.m_InputToForgetWeights "
                                              "should not be null.");
    }
 
    if (!m_QuantizedLstmParameters.m_InputToCellWeights)
    {
        throw armnn::LayerValidationException("QuantizedLstmLayer: "
                                              "m_QuantizedLstmParameters.m_InputToCellWeights "
                                              "should not be null.");
    }
 
    if (!m_QuantizedLstmParameters.m_InputToOutputWeights)
    {
        throw armnn::LayerValidationException("QuantizedLstmLayer: "
                                              "m_QuantizedLstmParameters.m_InputToOutputWeights "
                                              "should not be null.");
    }
 
    if (!m_QuantizedLstmParameters.m_RecurrentToInputWeights)
    {
        throw armnn::LayerValidationException("QuantizedLstmLayer: "
                                              "m_QuantizedLstmParameters.m_RecurrentToInputWeights "
                                              "should not be null.");
    }
 
    if (!m_QuantizedLstmParameters.m_RecurrentToForgetWeights)
    {
        throw armnn::LayerValidationException("QuantizedLstmLayer: "
                                              "m_QuantizedLstmParameters.m_RecurrentToForgetWeights "
                                              "should not be null.");
    }
 
    if (!m_QuantizedLstmParameters.m_RecurrentToCellWeights)
    {
        throw armnn::LayerValidationException("QuantizedLstmLayer: "
                                              "m_QuantizedLstmParameters.m_RecurrentToCellWeights "
                                              "should not be null.");
    }
 
    if (!m_QuantizedLstmParameters.m_RecurrentToOutputWeights)
    {
        throw armnn::LayerValidationException("QuantizedLstmLayer: "
                                              "m_QuantizedLstmParameters.m_RecurrentToOutputWeights "
                                              "should not be null.");
    }
 
    if (!m_QuantizedLstmParameters.m_InputGateBias)
    {
        throw armnn::LayerValidationException("QuantizedLstmLayer: "
                                              "m_QuantizedLstmParameters.m_InputGateBias "
                                              "should not be null.");
    }
 
    if (!m_QuantizedLstmParameters.m_ForgetGateBias)
    {
        throw armnn::LayerValidationException("QuantizedLstmLayer: "
                                              "m_QuantizedLstmParameters.m_ForgetGateBias "
                                              "should not be null.");
    }
 
    if (!m_QuantizedLstmParameters.m_CellBias)
    {
        throw armnn::LayerValidationException("QuantizedLstmLayer: "
                                              "m_QuantizedLstmParameters.m_CellBias "
                                              "should not be null.");
    }
 
    if (!m_QuantizedLstmParameters.m_OutputGateBias)
    {
        throw armnn::LayerValidationException("QuantizedLstmLayer: "
                                              "m_QuantizedLstmParameters.m_OutputGateBias "
                                              "should not be null.");
    }
 
    // Check output TensorShape(s) match inferred shape
    ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "QuantizedLstmLayer");
 
    ValidateAndCopyShape(GetOutputSlot(1).GetTensorInfo().GetShape(),
                         inferredShapes[1],
                         m_ShapeInferenceMethod,
                         "QuantizedLstmLayer",
                         1);
}

Member Data Documentation

◆ m_QuantizedLstmParameters

QuantizedLstmParameters m_QuantizedLstmParameters

Definition at line 49 of file QuantizedLstmLayer.hpp.

Referenced by NetworkImpl::AddQuantizedLstmLayer(), QuantizedLstmLayer::Clone(), QuantizedLstmLayer::CreateWorkload(), QuantizedLstmLayer::ExecuteStrategy(), QuantizedLstmLayer::GetConstantTensorsByRef(), and QuantizedLstmLayer::ValidateTensorShapesFromInputs().

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

src/armnn/layers/QuantizedLstmLayer.hpp
src/armnn/layers/QuantizedLstmLayer.cpp

Public Member Functions

Public Attributes

Protected Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ QuantizedLstmLayer()

◆ ~QuantizedLstmLayer()

Member Function Documentation

◆ Clone()

◆ CreateWorkload()

◆ ExecuteStrategy()

◆ GetConstantTensorsByRef()

◆ InferOutputShapes()

◆ ValidateTensorShapesFromInputs()

Member Data Documentation

◆ m_QuantizedLstmParameters