ArmNN
 24.08
Convolution2dLayer.cpp
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1 //
2 // Copyright © 2017-2024 Arm Ltd and Contributors. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5 
6 #include "Convolution2dLayer.hpp"
7 #include "LayerCloneBase.hpp"
8 
9 #include <armnn/TypesUtils.hpp>
10 
12 
15 
16 #include <string>
17 
18 using namespace armnnUtils;
19 
20 namespace armnn
21 {
22 
24  : LayerWithParameters(param.GetNumInputs(), 1, LayerType::Convolution2d, param, name)
25 {
26 
27 }
28 
30 {
31  //using DescriptorType = Parameters;
32  const std::vector<TensorShape>& inputShapes =
33  {
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 }
48 
49 std::unique_ptr<IWorkload> Convolution2dLayer::CreateWorkload(const IWorkloadFactory& factory) const
50 {
51  ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "Convolution2dLayer_CreateWorkload");
53  SetAdditionalInfo(descriptor);
54 
55  return factory.CreateWorkload(LayerType::Convolution2d, descriptor, PrepInfoAndDesc(descriptor));
56 }
57 
59 {
60  auto layer = CloneBase<Convolution2dLayer>(graph, m_Param, GetName());
61  return std::move(layer);
62 }
63 
64 std::vector<TensorShape> Convolution2dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
65 {
66  if (inputShapes.size() != 2)
67  {
68  throw armnn::Exception("inputShapes' size is \"" + std::to_string(inputShapes.size()) +
69  "\" - should be \"2\".");
70  }
71 
72  const TensorShape& inputShape = inputShapes[0];
73  const TensorShape filterShape = inputShapes[1];
74 
75  // If we support multiple batch dimensions in the future, then this assert will need to change.
76  if (inputShape.GetNumDimensions() != 4)
77  {
78  throw armnn::Exception("Convolutions will always have 4D input.");
79  }
80 
81  if (m_Param.m_StrideX == 0)
82  {
83  throw armnn::Exception("m_StrideX cannot be 0.");
84  }
85 
86  if (m_Param.m_StrideY == 0)
87  {
88  throw armnn::Exception("m_StrideY cannot be 0.");
89  }
90 
91  DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout);
92 
93  unsigned int inWidth = inputShape[dataLayoutIndex.GetWidthIndex()];
94  unsigned int inHeight = inputShape[dataLayoutIndex.GetHeightIndex()];
95  unsigned int inBatchSize = inputShape[0];
96 
97  unsigned int filterWidth = filterShape[dataLayoutIndex.GetWidthIndex()];
98  unsigned int dilatedFilterWidth = filterWidth + (m_Param.m_DilationX - 1) * (filterWidth - 1);
99  unsigned int readWidth = (inWidth + m_Param.m_PadLeft + m_Param.m_PadRight) - dilatedFilterWidth;
100  unsigned int outWidth = 1 + (readWidth / m_Param.m_StrideX);
101 
102  unsigned int filterHeight = filterShape[dataLayoutIndex.GetHeightIndex()];
103  unsigned int dilatedFilterHeight = filterHeight + (m_Param.m_DilationY - 1) * (filterHeight - 1);
104  unsigned int readHeight = (inHeight + m_Param.m_PadTop + m_Param.m_PadBottom) - dilatedFilterHeight;
105  unsigned int outHeight = 1 + (readHeight / m_Param.m_StrideY);
106 
107  unsigned int outChannels = filterShape[0];
108  unsigned int outBatchSize = inBatchSize;
109 
111  TensorShape( { outBatchSize, outHeight, outWidth, outChannels } ) :
112  TensorShape( { outBatchSize, outChannels, outHeight, outWidth });
113 
114  return std::vector<TensorShape>({ tensorShape });
115 }
116 
118 {
120 
121  const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
122 
124 
125  if (!GetInputSlot(1).GetConnection())
126  {
127  throw armnn::NullPointerException("Convolution2dLayer: Weights should be connected to input slot 1.");
128  }
129 
130  std::vector<TensorShape> inferredShapes = InferOutputShapes({
133 
134  if (inferredShapes.size() != 1)
135  {
136  throw armnn::Exception("inferredShapes has "
137  + std::to_string(inferredShapes.size()) +
138  " elements - should only have 1.");
139  }
140 
141  ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "Convolution2dLayer");
142 }
143 
145 {
147  return tensors;
148 }
149 
151 {
152  strategy.ExecuteStrategy(this, GetParameters(), {}, GetName());
153 }
154 
155 } // namespace armnn
armnn::Convolution2dDescriptor::m_PadTop
uint32_t m_PadTop
Padding top value in the height dimension.
Definition: Descriptors.hpp:570
armnn::Compute::Undefined
@ Undefined
armnn::OutputSlot::GetTensorInfo
const TensorInfo & GetTensorInfo() const override
Definition: Layer.cpp:100
armnn::DataLayout::NHWC
@ NHWC
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::Convolution2dLayer::SerializeLayerParameters
void SerializeLayerParameters(ParameterStringifyFunction &fn) const override
Helper to serialize the layer parameters to string.
Definition: Convolution2dLayer.cpp:29
TypesUtils.hpp
armnn::Convolution2dLayer::ValidateTensorShapesFromInputs
void ValidateTensorShapesFromInputs() override
Check if the input tensor shape(s) will lead to a valid configuration of Convolution2dLayer.
Definition: Convolution2dLayer.cpp:117
CHECK_LOCATION
#define CHECK_LOCATION()
Definition: Exceptions.hpp:203
armnnUtils::DataLayoutIndexed
Provides access to the appropriate indexes for Channels, Height and Width based on DataLayout.
Definition: DataLayoutIndexed.hpp:17
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:457
armnn::Layer::GetOutputSlot
const OutputSlot & GetOutputSlot(unsigned int index=0) const override
Get the const output slot handle by slot index.
Definition: Layer.hpp:339
armnn::Convolution2dDescriptor::m_StrideY
uint32_t m_StrideY
Stride value when proceeding through input for the height dimension.
Definition: Descriptors.hpp:576
armnn::IStrategy
Definition: IStrategy.hpp:16
armnn::Convolution2dDescriptor::m_PadLeft
uint32_t m_PadLeft
Padding left value in the width dimension.
Definition: Descriptors.hpp:566
armnn::Convolution2dDescriptor::m_DilationY
uint32_t m_DilationY
Dilation along y axis.
Definition: Descriptors.hpp:580
armnn::Layer::GetInputSlot
const InputSlot & GetInputSlot(unsigned int index) const override
Get a const input slot handle by slot index.
Definition: Layer.hpp:337
armnn::LayerWithParameters< Convolution2dDescriptor >::GetParameters
const Convolution2dDescriptor & GetParameters() const override
Definition: LayerWithParameters.hpp:19
WorkloadFactory.hpp
armnn::LayerWithParameters
Definition: LayerWithParameters.hpp:14
armnn::Layer::GetName
const char * GetName() const override
Returns the name of the layer.
Definition: Layer.hpp:332
armnnUtils::DataLayoutIndexed::GetHeightIndex
unsigned int GetHeightIndex() const
Definition: DataLayoutIndexed.hpp:24
armnn::Convolution2dLayer
This layer represents a convolution 2d operation.
Definition: Convolution2dLayer.hpp:15
Convolution2dLayer.hpp
armnn::IConnectableLayer::ImmutableConstantTensors
std::vector< std::reference_wrapper< const std::shared_ptr< ConstTensorHandle > >> ImmutableConstantTensors
Definition: INetwork.hpp:141
armnn::InputSlot::GetTensorInfo
const TensorInfo & GetTensorInfo() const override
Gets the TensorInfo for this InputSlot.
Definition: Layer.cpp:614
armnn::LayerWithParameters< Convolution2dDescriptor >::GetConnectedConstantAsInputTensors
Layer::ImmutableConstantTensors GetConnectedConstantAsInputTensors() const
Definition: LayerWithParameters.hpp:59
armnn::TensorShape
Definition: Tensor.hpp:20
armnn::LayerWithParameters< Convolution2dDescriptor >::m_Param
Convolution2dDescriptor m_Param
The parameters for the layer (not including tensor-valued weights etc.).
Definition: LayerWithParameters.hpp:52
armnn::Convolution2dDescriptor::GetNumInputs
uint32_t GetNumInputs() const
Definition: Descriptors.cpp:470
armnn::TensorShape::GetNumDimensions
unsigned int GetNumDimensions() const
Function that returns the tensor rank.
Definition: Tensor.cpp:174
armnn::Convolution2dQueueDescriptor
Definition: WorkloadData.hpp:210
ARMNN_SCOPED_PROFILING_EVENT
#define ARMNN_SCOPED_PROFILING_EVENT(backendId, name)
Definition: Profiling.hpp:220
armnn::LayerWithParameters< Convolution2dDescriptor >::PrepInfoAndDesc
WorkloadInfo PrepInfoAndDesc(QueueDescriptor &descriptor) const
Helper function to reduce duplication in *Layer::CreateWorkload.
Definition: LayerWithParameters.hpp:44
armnn::Convolution2dLayer::GetConstantTensorsByRef
ImmutableConstantTensors GetConstantTensorsByRef() const override
Retrieve the handles to the constant values connected to the layer.
Definition: Convolution2dLayer.cpp:144
armnn::IWorkloadFactory
Definition: WorkloadFactory.hpp:22
armnnUtils
Definition: CompatibleTypes.hpp:10
armnn::ParameterStringifyFunction
std::function< void(const std::string &name, const std::string &value)> ParameterStringifyFunction
Definition: SerializeLayerParameters.hpp:14
armnn::Layer::VerifyShapeInferenceType
void VerifyShapeInferenceType(const TensorShape &outputShape, ShapeInferenceMethod shapeInferenceMethod)
Definition: Layer.cpp:526
armnn::Layer::SetAdditionalInfo
void SetAdditionalInfo(QueueDescriptor &descriptor) const
Definition: Layer.cpp:303
armnn::Convolution2dDescriptor::m_DataLayout
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
Definition: Descriptors.hpp:584
armnn::Convolution2dDescriptor::m_PadBottom
uint32_t m_PadBottom
Padding bottom value in the height dimension.
Definition: Descriptors.hpp:572
armnn::Exception
Base class for all ArmNN exceptions so that users can filter to just those.
Definition: Exceptions.hpp:46
armnnUtils::DataLayoutIndexed::GetWidthIndex
unsigned int GetWidthIndex() const
Definition: DataLayoutIndexed.hpp:25
armnn::Convolution2dDescriptor::m_StrideX
uint32_t m_StrideX
Stride value when proceeding through input for the width dimension.
Definition: Descriptors.hpp:574
armnn::Convolution2dLayer::Convolution2dLayer
Convolution2dLayer(const Convolution2dDescriptor &param, const char *name)
Constructor to create a Convolution2dLayer.
Definition: Convolution2dLayer.cpp:23
armnn::Convolution2dDescriptor::m_PadRight
uint32_t m_PadRight
Padding right value in the width dimension.
Definition: Descriptors.hpp:568
armnn::Convolution2dDescriptor
A Convolution2dDescriptor for the Convolution2dLayer.
Definition: Descriptors.hpp:534
armnn::GetNumInputs
uint32_t GetNumInputs(bool biasEnabled)
Definition: Descriptors.cpp:455
TensorHandle.hpp
armnn::TensorInfo::GetShape
const TensorShape & GetShape() const
Definition: Tensor.hpp:193
armnn::Convolution2dDescriptor::m_DilationX
uint32_t m_DilationX
Dilation along x axis.
Definition: Descriptors.hpp:578
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::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,...
Definition: Convolution2dLayer.cpp:64
armnn
Copyright (c) 2021 ARM Limited and Contributors.
Definition: 01_00_quick_start.dox:6
armnn::Convolution2dLayer::Clone
Convolution2dLayer * Clone(Graph &graph) const override
Creates a dynamically-allocated copy of this layer.
Definition: Convolution2dLayer.cpp:58
armnn::Layer::VerifyLayerConnections
void VerifyLayerConnections(unsigned int expectedConnections, const CheckLocation &location) const
Definition: Layer.cpp:410
armnn::LayerType::Convolution2d
@ Convolution2d
armnn::NullPointerException
Definition: Exceptions.hpp:146
armnn::Layer::m_ShapeInferenceMethod
ShapeInferenceMethod m_ShapeInferenceMethod
Definition: Layer.hpp:441
armnn::LayerType
LayerType
When adding a new layer, adapt also the LastLayer enum value in the enum class LayerType below.
Definition: Types.hpp:491
DataLayoutIndexed.hpp
armnn::Graph
Definition: Graph.hpp:30
armnn::IWorkloadFactory::CreateWorkload
virtual std::unique_ptr< IWorkload > CreateWorkload(LayerType type, const QueueDescriptor &descriptor, const WorkloadInfo &info) const =0
Backends should implement their own CreateWorkload function with a switch statement.
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::ExecuteStrategy
void ExecuteStrategy(IStrategy &strategy) const override
Apply a visitor to this layer.
Definition: Convolution2dLayer.cpp:150
LayerCloneBase.hpp