ArmNN
 20.02
Convolution2dLayer Class Reference

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

#include <Convolution2dLayer.hpp>

Inheritance diagram for Convolution2dLayer:
LayerWithParameters< Convolution2dDescriptor > Layer IConnectableLayer

Public Member Functions

virtual std::unique_ptr< IWorkloadCreateWorkload (const IWorkloadFactory &factory) const override
 Makes a workload for the Convolution2d type. More...
 
Convolution2dLayerClone (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< TensorShapeInferOutputShapes (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 Convolution2dDescriptorGetParameters () 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 OutputHandlerGetOutputHandler (unsigned int i=0) const
 
OutputHandlerGetOutputHandler (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 BackendIdGetBackendId () 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 InputSlotGetInputSlot (unsigned int index) const override
 Get a const input slot handle by slot index. More...
 
InputSlotGetInputSlot (unsigned int index) override
 Get the input slot handle by slot index. More...
 
const OutputSlotGetOutputSlot (unsigned int index=0) const override
 Get the const output slot handle by slot index. More...
 
OutputSlotGetOutputSlot (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< BackendIdGetBackendHint () const
 

Public Attributes

std::unique_ptr< ScopedCpuTensorHandlem_Weight
 A unique pointer to store Weight values. More...
 
std::unique_ptr< ScopedCpuTensorHandlem_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>
LayerTypeCloneBase (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< OutputHandlerm_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]paramConvolution2dDescriptor to configure the convolution2d operation.
[in]nameOptional name for the layer.

Definition at line 23 of file Convolution2dLayer.cpp.

References armnn::Convolution2d.

24  : LayerWithParameters(1, 1, LayerType::Convolution2d, param, name)
25 {
26 
27 }
LayerWithParameters(unsigned int numInputSlots, unsigned int numOutputSlots, LayerType type, const Convolution2dDescriptor &param, const char *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().

140 {
141  ConstTensor weightsTensor(m_Weight->GetTensorInfo(), m_Weight->Map(true)) ;
142  Optional<ConstTensor> optionalBiasTensor = EmptyOptional();
143 
144  if (GetParameters().m_BiasEnabled)
145  {
146  ConstTensor biasTensor(m_Bias->GetTensorInfo(), m_Bias->Map(true));
147  optionalBiasTensor = Optional<ConstTensor>(biasTensor);
148  }
149 
150  visitor.VisitConvolution2dLayer(this, GetParameters(), weightsTensor, optionalBiasTensor, GetName());
151 }
const Convolution2dDescriptor & GetParameters() const
std::unique_ptr< ScopedCpuTensorHandle > m_Bias
A unique pointer to store Bias values.
virtual void VisitConvolution2dLayer(const IConnectableLayer *layer, const Convolution2dDescriptor &convolution2dDescriptor, const ConstTensor &weights, const Optional< ConstTensor > &biases, const char *name=nullptr)=0
Function that a 2D convolution layer should call back to when its Accept(ILayerVisitor&) function is ...
std::unique_ptr< ScopedCpuTensorHandle > m_Weight
A unique pointer to store Weight values.
A tensor defined by a TensorInfo (shape and data type) and an immutable backing store.
Definition: Tensor.hpp:199
EmptyOptional is used to initialize the Optional class in case we want to have default value for an O...
Definition: Optional.hpp:32
const char * GetName() const override
Returns the name of the layer.
Definition: Layer.hpp:305

◆ Clone()

Convolution2dLayer * Clone ( Graph graph) const
overridevirtual

Creates a dynamically-allocated copy of this layer.

Parameters
[in]graphThe 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.

67 {
68  auto layer = CloneBase<Convolution2dLayer>(graph, m_Param, GetName());
69 
70  layer->m_Weight = m_Weight ? std::make_unique<ScopedCpuTensorHandle>(*m_Weight) : nullptr;
71 
72  if (layer->m_Param.m_BiasEnabled)
73  {
74  layer->m_Bias = m_Bias ? std::make_unique<ScopedCpuTensorHandle>(*m_Bias) : nullptr;
75  }
76 
77  return std::move(layer);
78 }
Convolution2dDescriptor m_Param
The parameters for the layer (not including tensor-valued weights etc.).
std::unique_ptr< ScopedCpuTensorHandle > m_Bias
A unique pointer to store Bias values.
std::unique_ptr< ScopedCpuTensorHandle > m_Weight
A unique pointer to store Weight values.
const char * GetName() const override
Returns the name of the layer.
Definition: Layer.hpp:305

◆ CreateWorkload()

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

Makes a workload for the Convolution2d type.

Parameters
[in]graphThe graph where this layer can be found.
[in]factoryThe 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().

50 {
51  // on this level constant data should not be released..
52  BOOST_ASSERT_MSG(m_Weight != nullptr, "Convolution2dLayer: Weights data should not be null.");
53 
55 
56  descriptor.m_Weight = m_Weight.get();
57 
59  {
60  BOOST_ASSERT_MSG(m_Bias != nullptr, "Convolution2dLayer: Bias data should not be null.");
61  descriptor.m_Bias = m_Bias.get();
62  }
63  return factory.CreateConvolution2d(descriptor, PrepInfoAndDesc(descriptor));
64 }
bool m_BiasEnabled
Enable/disable bias.
const ConstCpuTensorHandle * m_Bias
Convolution2dDescriptor m_Param
The parameters for the layer (not including tensor-valued weights etc.).
std::unique_ptr< ScopedCpuTensorHandle > m_Bias
A unique pointer to store Bias values.
const ConstCpuTensorHandle * m_Weight
std::unique_ptr< ScopedCpuTensorHandle > m_Weight
A unique pointer to store Weight values.
WorkloadInfo PrepInfoAndDesc(QueueDescriptor &descriptor) const
Helper function to reduce duplication in *LayerCreateWorkload.
virtual std::unique_ptr< IWorkload > CreateConvolution2d(const Convolution2dQueueDescriptor &descriptor, const WorkloadInfo &info) const

◆ 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.

135 {
136  return {m_Weight, m_Bias};
137 }
std::unique_ptr< ScopedCpuTensorHandle > m_Bias
A unique pointer to store Bias values.
std::unique_ptr< ScopedCpuTensorHandle > m_Weight
A unique pointer to store Weight values.

◆ 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]inputShapesThe 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().

81 {
82  BOOST_ASSERT(inputShapes.size() == 2);
83  const TensorShape& inputShape = inputShapes[0];
84  const TensorShape filterShape = inputShapes[1];
85 
86  // If we support multiple batch dimensions in the future, then this assert will need to change.
87  BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Convolutions will always have 4D input.");
88 
89  DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout);
90 
91  unsigned int inWidth = inputShape[dataLayoutIndex.GetWidthIndex()];
92  unsigned int inHeight = inputShape[dataLayoutIndex.GetHeightIndex()];
93  unsigned int inBatchSize = inputShape[0];
94 
95  unsigned int filterWidth = filterShape[dataLayoutIndex.GetWidthIndex()];
96  unsigned int dilatedFilterWidth = filterWidth + (m_Param.m_DilationX - 1) * (filterWidth - 1);
97  unsigned int readWidth = (inWidth + m_Param.m_PadLeft + m_Param.m_PadRight) - dilatedFilterWidth;
98  unsigned int outWidth = 1 + (readWidth / m_Param.m_StrideX);
99 
100  unsigned int filterHeight = filterShape[dataLayoutIndex.GetHeightIndex()];
101  unsigned int dilatedFilterHeight = filterHeight + (m_Param.m_DilationY - 1) * (filterHeight - 1);
102  unsigned int readHeight = (inHeight + m_Param.m_PadTop + m_Param.m_PadBottom) - dilatedFilterHeight;
103  unsigned int outHeight = 1 + (readHeight / m_Param.m_StrideY);
104 
105  unsigned int outChannels = filterShape[0];
106  unsigned int outBatchSize = inBatchSize;
107 
109  TensorShape( { outBatchSize, outHeight, outWidth, outChannels } ) :
110  TensorShape( { outBatchSize, outChannels, outHeight, outWidth });
111 
112  return std::vector<TensorShape>({ tensorShape });
113 }
uint32_t m_PadBottom
Padding bottom value in the height dimension.
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
Convolution2dDescriptor m_Param
The parameters for the layer (not including tensor-valued weights etc.).
uint32_t m_PadRight
Padding right value in the width dimension.
uint32_t m_DilationY
Dilation along y axis.
uint32_t m_PadTop
Padding top value in the height dimension.
uint32_t m_StrideX
Stride value when proceeding through input for the width dimension.
Provides access to the appropriate indexes for Channels, Height and Width based on DataLayout...
uint32_t m_StrideY
Stride value when proceeding through input for the height dimension.
uint32_t m_DilationX
Dilation along x axis.
uint32_t m_PadLeft
Padding left value in the width dimension.

◆ 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().

30 {
31  //using DescriptorType = Parameters;
32  const std::vector<TensorShape>& inputShapes =
33  {
35  m_Weight->GetTensorInfo().GetShape()
36  };
37  const TensorShape filterShape = inputShapes[1];
38  DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout);
39  unsigned int filterWidth = filterShape[dataLayoutIndex.GetWidthIndex()];
40  unsigned int filterHeight = filterShape[dataLayoutIndex.GetHeightIndex()];
41  unsigned int outChannels = filterShape[0];
42 
43  fn("OutputChannels",std::to_string(outChannels));
44  fn("FilterWidth",std::to_string(filterWidth));
45  fn("FilterHeight",std::to_string(filterHeight));
47 }
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
Convolution2dDescriptor m_Param
The parameters for the layer (not including tensor-valued weights etc.).
const TensorShape & GetShape() const
Definition: Tensor.hpp:88
void SerializeLayerParameters(ParameterStringifyFunction &fn) const override
Helper to serialize the layer parameters to string (currently used in DotSerializer and company)...
const IOutputSlot * GetConnection() const override
Definition: Layer.hpp:199
const InputSlot & GetInputSlot(unsigned int index) const override
Get a const input slot handle by slot index.
Definition: Layer.hpp:310
Provides access to the appropriate indexes for Channels, Height and Width based on DataLayout...
std::unique_ptr< ScopedCpuTensorHandle > m_Weight
A unique pointer to store Weight values.
virtual const TensorInfo & GetTensorInfo() const =0

◆ 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().

116 {
118 
119  // check if we m_Weight data is not nullptr
120  BOOST_ASSERT_MSG(m_Weight != nullptr, "Convolution2dLayer: Weights data should not be null.");
121 
122  auto inferredShapes = InferOutputShapes({
124  m_Weight->GetTensorInfo().GetShape() });
125 
126  BOOST_ASSERT(inferredShapes.size() == 1);
127 
128  ConditionalThrowIfNotEqual<LayerValidationException>(
129  "Convolution2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
131  inferredShapes[0]);
132 }
const TensorShape & GetShape() const
Definition: Tensor.hpp:88
const IOutputSlot * GetConnection() const override
Definition: Layer.hpp:199
void VerifyLayerConnections(unsigned int expectedConnections, const CheckLocation &location) const
Definition: Layer.cpp:338
const InputSlot & GetInputSlot(unsigned int index) const override
Get a const input slot handle by slot index.
Definition: Layer.hpp:310
std::unique_ptr< ScopedCpuTensorHandle > m_Weight
A unique pointer to store Weight values.
#define CHECK_LOCATION()
Definition: Exceptions.hpp:192
const OutputSlot & GetOutputSlot(unsigned int index=0) const override
Get the const output slot handle by slot index.
Definition: Layer.hpp:312
virtual const TensorInfo & GetTensorInfo() const =0
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.
const TensorInfo & GetTensorInfo() const override
Definition: Layer.cpp:63

Member Data Documentation

◆ m_Bias

◆ m_Weight


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