patch/23.02/_convolution2d_layer_8cpp_source.xhtml

 //
 // Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //

 #include "Convolution2dLayer.hpp"
 #include "LayerCloneBase.hpp"

 #include <armnn/TypesUtils.hpp>

 #include <armnnUtils/DataLayoutIndexed.hpp>

 #include <armnn/backends/TensorHandle.hpp>
 #include <armnn/backends/WorkloadFactory.hpp>

 #include <string>

 using namespace armnnUtils;

 namespace armnn
 {

 Convolution2dLayer::Convolution2dLayer(const Convolution2dDescriptor& param, const char* name)
     : LayerWithParameters(param.GetNumInputs(), 1, LayerType::Convolution2d, param, name)
 {

 }

 void Convolution2dLayer::SerializeLayerParameters(ParameterStringifyFunction& fn) const
 {
     //using DescriptorType = Parameters;
     const std::vector<TensorShape>& inputShapes =
     {
         GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
         GetInputSlot(1).GetConnection()->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);
 }

 std::unique_ptr<IWorkload> Convolution2dLayer::CreateWorkload(const IWorkloadFactory& factory) const
 {
     ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "Convolution2dLayer_CreateWorkload");
     Convolution2dQueueDescriptor descriptor;
     SetAdditionalInfo(descriptor);

     return factory.CreateWorkload(LayerType::Convolution2d, descriptor, PrepInfoAndDesc(descriptor));
 }

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

 std::vector<TensorShape> Convolution2dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
     ARMNN_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.
     ARMNN_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Convolutions will always have 4D input.");

     ARMNN_ASSERT( m_Param.m_StrideX > 0);
     ARMNN_ASSERT( m_Param.m_StrideY > 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 });
 }

 void Convolution2dLayer::ValidateTensorShapesFromInputs()
 {
     VerifyLayerConnections(m_Param.GetNumInputs(), CHECK_LOCATION());

     const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();

     VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);

     ARMNN_ASSERT_MSG(GetInputSlot(1).GetConnection(),
                      "Convolution2dLayer: Weights should be connected to input slot 1.");

     std::vector<TensorShape> inferredShapes = InferOutputShapes({
              GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
              GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape() });

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

     ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "Convolution2dLayer");
 }

 Layer::ConstantTensors Convolution2dLayer::GetConstantTensorsByRef()
 {
     Layer::ConstantTensors tensors = GetConnectedConstantAsInputTensors();
     return tensors;
 }

 void Convolution2dLayer::ExecuteStrategy(IStrategy& strategy) const
 {
     strategy.ExecuteStrategy(this, GetParameters(), {}, GetName());
 }

 } // namespace armnn
armnn::Convolution2dDescriptor::m_PadBottom
uint32_t m_PadBottom
Padding bottom value in the height dimension.
Definition: Descriptors.hpp:540

+armnn::Convolution2dDescriptor::m_DataLayout
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
Definition: Descriptors.hpp:552

+DataLayoutIndexed.hpp

+armnn::LayerWithParameters< Convolution2dDescriptor >::m_Param
Convolution2dDescriptor m_Param
The parameters for the layer (not including tensor-valued weights etc.).
Definition: LayerWithParameters.hpp:52

+armnnUtils::DataLayoutIndexed::GetWidthIndex
unsigned int GetWidthIndex() const
Definition: DataLayoutIndexed.hpp:25

+armnn::Convolution2dDescriptor::GetNumInputs
uint32_t GetNumInputs() const
Definition: Descriptors.cpp:443

+armnn::TensorInfo::GetShape
const TensorShape & GetShape() const
Definition: Tensor.hpp:191

+TensorHandle.hpp

+WorkloadFactory.hpp

+armnn::IWorkloadFactory
Definition: WorkloadFactory.hpp:22

+armnn::Convolution2dDescriptor
A Convolution2dDescriptor for the Convolution2dLayer.
Definition: Descriptors.hpp:502

+armnn::LayerWithParameters::SerializeLayerParameters
void SerializeLayerParameters(ParameterStringifyFunction &fn) const override
Helper to serialize the layer parameters to string (currently used in DotSerializer and company)...
Definition: LayerWithParameters.hpp:23

+armnn::Convolution2dDescriptor::m_PadRight
uint32_t m_PadRight
Padding right value in the width dimension.
Definition: Descriptors.hpp:536

+armnn::Convolution2dLayer::Convolution2dLayer
Convolution2dLayer(const Convolution2dDescriptor &param, const char *name)
Constructor to create a Convolution2dLayer.
Definition: Convolution2dLayer.cpp:23

+armnn::Layer::VerifyShapeInferenceType
void VerifyShapeInferenceType(const TensorShape &outputShape, ShapeInferenceMethod shapeInferenceMethod)
Definition: Layer.cpp:491

+armnn
Copyright (c) 2021 ARM Limited and Contributors.
Definition: 01_00_quick_start.dox:6

+armnn::LayerWithParameters< Convolution2dDescriptor >::GetParameters
const Convolution2dDescriptor & GetParameters() const override
Definition: LayerWithParameters.hpp:19

+armnn::TensorShape
Definition: Tensor.hpp:20

+armnn::InputSlot::GetConnection
const IOutputSlot * GetConnection() const override
Definition: Layer.hpp:206

+armnn::Convolution2dDescriptor::m_DilationY
uint32_t m_DilationY
Dilation along y axis.
Definition: Descriptors.hpp:548

+armnn::Layer::ValidateAndCopyShape
void ValidateAndCopyShape(const TensorShape &outputShape, const TensorShape &inferredShape, const ShapeInferenceMethod shapeInferenceMethod, const std::string &layerName, const unsigned int outputSlotIndex=0)
Definition: Layer.cpp:422

+ARMNN_SCOPED_PROFILING_EVENT
#define ARMNN_SCOPED_PROFILING_EVENT(backendId, name)
Definition: Profiling.hpp:220

+armnnUtils::DataLayoutIndexed::GetHeightIndex
unsigned int GetHeightIndex() const
Definition: DataLayoutIndexed.hpp:24

+armnn::IStrategy
Definition: IStrategy.hpp:16

+armnn::Layer::VerifyLayerConnections
void VerifyLayerConnections(unsigned int expectedConnections, const CheckLocation &location) const
Definition: Layer.cpp:378

+armnn::Convolution2dDescriptor::m_PadTop
uint32_t m_PadTop
Padding top value in the height dimension.
Definition: Descriptors.hpp:538

+armnn::Layer::GetInputSlot
const InputSlot & GetInputSlot(unsigned int index) const override
Get a const input slot handle by slot index.
Definition: Layer.hpp:324

+armnn::Convolution2dDescriptor::m_StrideX
uint32_t m_StrideX
Stride value when proceeding through input for the width dimension.
Definition: Descriptors.hpp:542

+armnn::Convolution2dLayer::ValidateTensorShapesFromInputs
void ValidateTensorShapesFromInputs() override
Check if the input tensor shape(s) will lead to a valid configuration of Convolution2dLayer.
Definition: Convolution2dLayer.cpp:102

+TypesUtils.hpp

+armnn::Convolution2dLayer::ExecuteStrategy
void ExecuteStrategy(IStrategy &strategy) const override
Apply a visitor to this layer.
Definition: Convolution2dLayer.cpp:128

+armnn::GetNumInputs
uint32_t GetNumInputs(bool biasEnabled)
Definition: Descriptors.cpp:428

+armnn::IConnectableLayer::ConstantTensors
std::vector< std::reference_wrapper< std::shared_ptr< ConstTensorHandle > >> ConstantTensors
Definition: INetwork.hpp:124

+ARMNN_ASSERT_MSG
#define ARMNN_ASSERT_MSG(COND, MSG)
Definition: Assert.hpp:15

+armnnUtils::DataLayoutIndexed
Provides access to the appropriate indexes for Channels, Height and Width based on DataLayout...
Definition: DataLayoutIndexed.hpp:17

+armnn::Compute::Undefined

+Convolution2dLayer.hpp

+armnn::LayerType::Convolution2d

+ARMNN_ASSERT
#define ARMNN_ASSERT(COND)
Definition: Assert.hpp:14

+armnn::Convolution2dLayer::Clone
Convolution2dLayer * Clone(Graph &graph) const override
Creates a dynamically-allocated copy of this layer.
Definition: Convolution2dLayer.cpp:58

+armnn::IStrategy::ExecuteStrategy
virtual void ExecuteStrategy(const IConnectableLayer *layer, const armnn::BaseDescriptor &descriptor, const std::vector< armnn::ConstTensor > &constants, const char *name, const armnn::LayerBindingId id=0)=0

+armnn::Convolution2dLayer::GetConstantTensorsByRef
ConstantTensors GetConstantTensorsByRef() override
Retrieve the handles to the constant values connected to the layer.
Definition: Convolution2dLayer.cpp:122

+CHECK_LOCATION
#define CHECK_LOCATION()
Definition: Exceptions.hpp:203

+armnn::Convolution2dDescriptor::m_StrideY
uint32_t m_StrideY
Stride value when proceeding through input for the height dimension.
Definition: Descriptors.hpp:544

+armnn::Graph
Definition: Graph.hpp:30

+armnn::Layer::SetAdditionalInfo
void SetAdditionalInfo(QueueDescriptor &descriptor) const
Definition: Layer.cpp:274

+armnn::Convolution2dQueueDescriptor
Definition: WorkloadData.hpp:205

+armnn::Convolution2dDescriptor::m_DilationX
uint32_t m_DilationX
Dilation along x axis.
Definition: Descriptors.hpp:546

+armnn::Convolution2dLayer::CreateWorkload
virtual std::unique_ptr< IWorkload > CreateWorkload(const IWorkloadFactory &factory) const override
Makes a workload for the Convolution2d type.
Definition: Convolution2dLayer.cpp:49

+armnn::Convolution2dLayer::SerializeLayerParameters
void SerializeLayerParameters(ParameterStringifyFunction &fn) const override
Helper to serialize the layer parameters to string.
Definition: Convolution2dLayer.cpp:29

+armnn::LayerWithParameters< Convolution2dDescriptor >::GetConnectedConstantAsInputTensors
Layer::ConstantTensors GetConnectedConstantAsInputTensors()
Definition: LayerWithParameters.hpp:59

+armnn::LayerWithParameters< Convolution2dDescriptor >::PrepInfoAndDesc
WorkloadInfo PrepInfoAndDesc(QueueDescriptor &descriptor) const
Helper function to reduce duplication in *Layer::CreateWorkload.
Definition: LayerWithParameters.hpp:44

+armnnUtils
Definition: CompatibleTypes.hpp:10

+armnn::Layer::GetOutputSlot
const OutputSlot & GetOutputSlot(unsigned int index=0) const override
Get the const output slot handle by slot index.
Definition: Layer.hpp:326

+armnn::IOutputSlot::GetTensorInfo
virtual const TensorInfo & GetTensorInfo() const =0

+armnn::Layer::GetName
const char * GetName() const override
Returns the name of the layer.
Definition: Layer.hpp:319

+armnn::Convolution2dLayer
This layer represents a convolution 2d operation.
Definition: Convolution2dLayer.hpp:15

+armnn::LayerWithParameters
Definition: LayerWithParameters.hpp:14

+armnn::Convolution2dLayer::InferOutputShapes
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.
Definition: Convolution2dLayer.cpp:64

+armnn::ParameterStringifyFunction
std::function< void(const std::string &name, const std::string &value)> ParameterStringifyFunction
Definition: SerializeLayerParameters.hpp:14

+armnn::IWorkloadFactory::CreateWorkload
virtual std::unique_ptr< IWorkload > CreateWorkload(LayerType type, const QueueDescriptor &descriptor, const WorkloadInfo &info) const
Definition: WorkloadFactory.cpp:1561

+armnn::OutputSlot::GetTensorInfo
const TensorInfo & GetTensorInfo() const override
Definition: Layer.cpp:92

+armnn::Layer::m_ShapeInferenceMethod
ShapeInferenceMethod m_ShapeInferenceMethod
Definition: Layer.hpp:423

+armnn::Convolution2dDescriptor::m_PadLeft
uint32_t m_PadLeft
Padding left value in the width dimension.
Definition: Descriptors.hpp:534

+armnn::DataLayout::NHWC

+LayerCloneBase.hpp

+armnn::LayerType
LayerType
When adding a new layer, adapt also the LastLayer enum value in the enum class LayerType below...
Definition: Types.hpp:466

+