ArmNN
 24.02
ElementwiseBinaryLayer.cpp
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1 //
2 // Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5 
7 
8 #include "LayerCloneBase.hpp"
9 
10 namespace armnn
11 {
12 
14  : LayerWithParameters(2, 1, LayerType::ElementwiseBinary, param, name)
15 {
16 }
17 
18 std::unique_ptr<IWorkload> ElementwiseBinaryLayer::CreateWorkload(const IWorkloadFactory& factory) const
19 {
21  SetAdditionalInfo(descriptor);
22 
23  return factory.CreateWorkload(LayerType::ElementwiseBinary, descriptor, PrepInfoAndDesc(descriptor));
24 }
25 
27 {
28  return CloneBase<ElementwiseBinaryLayer>(graph, m_Param, GetName());
29 }
30 
31 std::vector<TensorShape> ElementwiseBinaryLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
32 {
33  ARMNN_ASSERT(inputShapes.size() == 2);
34  TensorShape input0 = inputShapes[0];
35  TensorShape input1 = inputShapes[1];
36 
37  if (inputShapes[0].GetNumDimensions() < inputShapes[1].GetNumDimensions())
38  {
39  input1 = inputShapes[0];
40  input0 = inputShapes[1];
41  }
42 
43  unsigned int numDims = input0.GetNumDimensions();
44  unsigned int shiftedDims = input0.GetNumDimensions() - input1.GetNumDimensions();
45 
46  // Get the max of the inputs.
47  std::vector<unsigned int> dims(numDims);
48  for (unsigned int i = shiftedDims; i < numDims; i++)
49  {
50  unsigned int dim0 = input0[i];
51  unsigned int dim1 = input1[i - shiftedDims];
52 
53  // Validate inputs are broadcast compatible.
54  ARMNN_ASSERT_MSG(dim0 == dim1 || dim0 == 1 || dim1 == 1,
55  "Dimensions should either match or one should be of size 1.");
56 
57  dims[i] = std::max(dim0, dim1);
58  }
59 
60  // Fill in the rest of the shifted dimensions.
61  for (unsigned int i = 0; i < shiftedDims; i++)
62  {
63  dims[i] = input0[i];
64  }
65 
66  return std::vector<TensorShape>({ TensorShape(numDims, dims.data()) });
67 }
68 
70 {
72 
73  const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
74 
76 
77  auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetTensorInfo().GetShape(),
79 
80  ARMNN_ASSERT(inferredShapes.size() == 1);
81 
82  ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, GetLayerTypeAsCString(GetType()));
83 }
84 
86 {
87  strategy.ExecuteStrategy(this, GetParameters(), {}, GetName());
88 }
89 } // namespace armnn
ARMNN_ASSERT
#define ARMNN_ASSERT(COND)
Definition: Assert.hpp:14
ElementwiseBinaryLayer.hpp
armnn::GetLayerTypeAsCString
const char * GetLayerTypeAsCString(LayerType type)
Definition: InternalTypes.cpp:13
armnn::OutputSlot::GetTensorInfo
const TensorInfo & GetTensorInfo() const override
Definition: Layer.cpp:92
CHECK_LOCATION
#define CHECK_LOCATION()
Definition: Exceptions.hpp:203
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:435
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::ElementwiseBinaryLayer::InferOutputShapes
std::vector< TensorShape > InferOutputShapes(const std::vector< TensorShape > &inputShapes) const override
Returns inputShapes by default.
Definition: ElementwiseBinaryLayer.cpp:31
armnn::IStrategy
Definition: IStrategy.hpp:16
ARMNN_ASSERT_MSG
#define ARMNN_ASSERT_MSG(COND, MSG)
Definition: Assert.hpp:15
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< ElementwiseBinaryDescriptor >::GetParameters
const ElementwiseBinaryDescriptor & GetParameters() const override
Definition: LayerWithParameters.hpp:19
armnn::LayerType::ElementwiseBinary
@ ElementwiseBinary
armnn::LayerWithParameters
Definition: LayerWithParameters.hpp:14
armnn::Layer::GetName
const char * GetName() const override
Returns the name of the layer.
Definition: Layer.hpp:332
armnn::InputSlot::GetTensorInfo
const TensorInfo & GetTensorInfo() const override
Gets the TensorInfo for this InputSlot.
Definition: Layer.cpp:592
armnn::ElementwiseBinaryDescriptor
A ElementwiseBinaryDescriptor for the ElementwiseBinaryLayer.
Definition: Descriptors.hpp:109
armnn::TensorShape
Definition: Tensor.hpp:20
armnn::ElementwiseBinaryQueueDescriptor
Definition: WorkloadData.hpp:671
armnn::LayerWithParameters< ElementwiseBinaryDescriptor >::m_Param
ElementwiseBinaryDescriptor m_Param
The parameters for the layer (not including tensor-valued weights etc.).
Definition: LayerWithParameters.hpp:52
armnn::ElementwiseBinaryLayer::ValidateTensorShapesFromInputs
void ValidateTensorShapesFromInputs() override
Check if the input tensor shape(s) will lead to a valid configuration of ElementwiseBinaryLayer.
Definition: ElementwiseBinaryLayer.cpp:69
armnn::TensorShape::GetNumDimensions
unsigned int GetNumDimensions() const
Function that returns the tensor rank.
Definition: Tensor.cpp:174
armnn::ElementwiseBinaryLayer::Clone
ElementwiseBinaryLayer * Clone(Graph &graph) const override
Creates a dynamically-allocated copy of this layer.
Definition: ElementwiseBinaryLayer.cpp:26
armnn::LayerWithParameters< ElementwiseBinaryDescriptor >::PrepInfoAndDesc
WorkloadInfo PrepInfoAndDesc(QueueDescriptor &descriptor) const
Helper function to reduce duplication in *Layer::CreateWorkload.
Definition: LayerWithParameters.hpp:44
armnn::ElementwiseBinaryLayer::CreateWorkload
virtual std::unique_ptr< IWorkload > CreateWorkload(const IWorkloadFactory &factory) const override
Makes a workload for the elementwiseBinary type.
Definition: ElementwiseBinaryLayer.cpp:18
armnn::IWorkloadFactory
Definition: WorkloadFactory.hpp:22
armnn::Layer::VerifyShapeInferenceType
void VerifyShapeInferenceType(const TensorShape &outputShape, ShapeInferenceMethod shapeInferenceMethod)
Definition: Layer.cpp:504
armnn::Layer::SetAdditionalInfo
void SetAdditionalInfo(QueueDescriptor &descriptor) const
Definition: Layer.cpp:287
armnn::ElementwiseBinaryLayer
This layer represents a elementwiseBinary operation.
Definition: ElementwiseBinaryLayer.hpp:14
armnn::Layer::GetType
LayerType GetType() const override
Returns the armnn::LayerType of this layer.
Definition: Layer.hpp:286
armnn::ElementwiseBinaryLayer::ExecuteStrategy
void ExecuteStrategy(IStrategy &strategy) const override
Apply a visitor to this layer.
Definition: ElementwiseBinaryLayer.cpp:85
armnn::TensorInfo::GetShape
const TensorShape & GetShape() const
Definition: Tensor.hpp:193
armnn
Copyright (c) 2021 ARM Limited and Contributors.
Definition: 01_00_quick_start.dox:6
armnn::Layer::VerifyLayerConnections
void VerifyLayerConnections(unsigned int expectedConnections, const CheckLocation &location) const
Definition: Layer.cpp:391
armnn::ElementwiseBinaryLayer::ElementwiseBinaryLayer
ElementwiseBinaryLayer(const ElementwiseBinaryDescriptor &param, const char *name)
Constructor to create a ElementwiseBinaryLayer.
Definition: ElementwiseBinaryLayer.cpp:13
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
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
LayerCloneBase.hpp