patch/20.02/_pooling2d_layer_8cpp_source.xhtml

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

 #include "Pooling2dLayer.hpp"

 #include "LayerCloneBase.hpp"

 #include <armnn/TypesUtils.hpp>

 #include <armnnUtils/DataLayoutIndexed.hpp>

 #include <backendsCommon/WorkloadData.hpp>
 #include <backendsCommon/WorkloadFactory.hpp>

 using namespace armnnUtils;

 namespace armnn
 {

 Pooling2dLayer::Pooling2dLayer(const Pooling2dDescriptor& param, const char* name)
     : LayerWithParameters(1, 1, LayerType::Pooling2d, param, name)
 {
 }

 std::unique_ptr<IWorkload> Pooling2dLayer::CreateWorkload(const IWorkloadFactory& factory) const
 {
     Pooling2dQueueDescriptor descriptor;
     return factory.CreatePooling2d(descriptor, PrepInfoAndDesc(descriptor));
 }

 Pooling2dLayer* Pooling2dLayer::Clone(Graph& graph) const
 {
     return CloneBase<Pooling2dLayer>(graph, m_Param, GetName());
 }

 std::vector<TensorShape> Pooling2dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
     BOOST_ASSERT(inputShapes.size() == 1);
     const TensorShape& inputShape = inputShapes[0];
     const DataLayoutIndexed dimensionIndices = m_Param.m_DataLayout;

     // If we support multiple batch dimensions in the future, then this assert will need to change.
     BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Pooling2dLayer will always have 4D input.");

     unsigned int inWidth = inputShape[dimensionIndices.GetWidthIndex()];
     unsigned int inHeight = inputShape[dimensionIndices.GetHeightIndex()];
     unsigned int inChannels = inputShape[dimensionIndices.GetChannelsIndex()];
     unsigned int inBatchSize = inputShape[0];

     bool isGlobalPooling = (m_Param.m_StrideX==0 && m_Param.m_StrideY==0);
     unsigned int outWidth = 1;
     unsigned int outHeight = 1;
     if (!isGlobalPooling)
     {
         BOOST_ASSERT_MSG(m_Param.m_StrideX!=0 && m_Param.m_StrideY!=0,
                          "Stride can only be zero when performing global pooling");

         auto CalcSize = [](auto inSize, auto lowPad, auto highPad, auto poolSize, auto stride, auto outputShapeRounding)
             {
                 unsigned int readSize = inSize + lowPad + highPad - poolSize;
                 float div = static_cast<float>(readSize) / static_cast<float>(stride);

                 unsigned int size = 0;
                 switch (outputShapeRounding)
                 {
                     case OutputShapeRounding::Ceiling:
                         size = static_cast<unsigned int>(ceil(div)) + 1;
                         break;
                     case OutputShapeRounding ::Floor:
                         size = static_cast<unsigned int>(floor(div)) + 1;
                         break;
                     default:
                         BOOST_ASSERT_MSG(false, "Unsupported Output Shape Rounding");
                 }

                 // MakeS sure that border operations will start from inside the input and not the padded area.
                 // This is what both Caffe and CL do...
                 if ((size - 1)*stride >= inSize + lowPad)
                 {
                     --size;
                 }

                 return size;
             };

         outWidth = CalcSize(inWidth, m_Param.m_PadLeft, m_Param.m_PadRight, m_Param.m_PoolWidth, m_Param.m_StrideX,
                             m_Param.m_OutputShapeRounding);
         outHeight = CalcSize(inHeight, m_Param.m_PadTop, m_Param.m_PadBottom, m_Param.m_PoolHeight, m_Param.m_StrideY,
                             m_Param.m_OutputShapeRounding);
     }
     unsigned int outChannels = inChannels;
     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 Pooling2dLayer::ValidateTensorShapesFromInputs()
 {
     VerifyLayerConnections(1, CHECK_LOCATION());

     auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });

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

     ConditionalThrowIfNotEqual<LayerValidationException>(
         "Pooling2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
         GetOutputSlot(0).GetTensorInfo().GetShape(),
         inferredShapes[0]);
 }

 void Pooling2dLayer::Accept(ILayerVisitor& visitor) const
 {
     visitor.VisitPooling2dLayer(this, GetParameters(), GetName());
 }

 } // namespace armnn
WorkloadData.hpp

+DataLayoutIndexed.hpp

+armnn::Pooling2dDescriptor::m_PadBottom
uint32_t m_PadBottom
Padding bottom value in the height dimension.
Definition: Descriptors.hpp:355

+armnn::LayerWithParameters< Pooling2dDescriptor >::m_Param
Pooling2dDescriptor m_Param
The parameters for the layer (not including tensor-valued weights etc.).
Definition: LayerWithParameters.hpp:50

+armnn::LayerWithParameters< Pooling2dDescriptor >::GetParameters
const Pooling2dDescriptor & GetParameters() const
Definition: LayerWithParameters.hpp:18

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

+armnn::TensorInfo::GetShape
const TensorShape & GetShape() const
Definition: Tensor.hpp:88

+armnn::OutputShapeRounding::Ceiling

+armnn::Pooling2dDescriptor::m_PadLeft
uint32_t m_PadLeft
Padding left value in the width dimension.
Definition: Descriptors.hpp:349

+armnn::OutputShapeRounding::Floor

+Pooling2dLayer.hpp

+WorkloadFactory.hpp

+armnn::IWorkloadFactory
Definition: WorkloadFactory.hpp:21

+armnn::Pooling2dLayer::Pooling2dLayer
Pooling2dLayer(const Pooling2dDescriptor &param, const char *name)
Constructor to create a Pooling2dLayer.
Definition: Pooling2dLayer.cpp:22

+armnn::Pooling2dDescriptor::m_PoolWidth
uint32_t m_PoolWidth
Pooling width value.
Definition: Descriptors.hpp:357

+armnn::Pooling2dDescriptor::m_PadTop
uint32_t m_PadTop
Padding top value in the height dimension.
Definition: Descriptors.hpp:353

+armnn
Copyright (c) 2020 ARM Limited.
Definition: 00_introduction.dox:25

+armnn::ILayerVisitor::VisitPooling2dLayer
virtual void VisitPooling2dLayer(const IConnectableLayer *layer, const Pooling2dDescriptor &pooling2dDescriptor, const char *name=nullptr)=0
Function that a pooling layer should call back to when its Accept(ILayerVisitor&) function is invoked...

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

+armnn::InputSlot::GetConnection
const IOutputSlot * GetConnection() const override
Definition: Layer.hpp:199

+armnn::Pooling2dDescriptor::m_StrideX
uint32_t m_StrideX
Stride value when proceeding through input for the width dimension.
Definition: Descriptors.hpp:361

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

+armnn::Pooling2dLayer::Accept
void Accept(ILayerVisitor &visitor) const override
Apply a visitor to this layer.
Definition: Pooling2dLayer.cpp:117

+armnn::Layer::VerifyLayerConnections
void VerifyLayerConnections(unsigned int expectedConnections, const CheckLocation &location) const
Definition: Layer.cpp:338

+armnn::Pooling2dDescriptor::m_PoolHeight
uint32_t m_PoolHeight
Pooling height value.
Definition: Descriptors.hpp:359

+armnn::Layer::GetInputSlot
const InputSlot & GetInputSlot(unsigned int index) const override
Get a const input slot handle by slot index.
Definition: Layer.hpp:310

+armnn::Pooling2dLayer::Clone
Pooling2dLayer * Clone(Graph &graph) const override
Creates a dynamically-allocated copy of this layer.
Definition: Pooling2dLayer.cpp:33

+TypesUtils.hpp

+armnn::Pooling2dDescriptor::m_PadRight
uint32_t m_PadRight
Padding right value in the width dimension.
Definition: Descriptors.hpp:351

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

+CHECK_LOCATION
#define CHECK_LOCATION()
Definition: Exceptions.hpp:192

+armnn::Graph
Definition: Graph.hpp:29

+armnn::Pooling2dLayer::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: Pooling2dLayer.cpp:38

+armnn::Pooling2dLayer
This layer represents a pooling 2d operation.
Definition: Pooling2dLayer.hpp:13

+armnn::Pooling2dDescriptor::m_DataLayout
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
Definition: Descriptors.hpp:369

+armnn::IWorkloadFactory::CreatePooling2d
virtual std::unique_ptr< IWorkload > CreatePooling2d(const Pooling2dQueueDescriptor &descriptor, const WorkloadInfo &info) const
Definition: WorkloadFactory.cpp:1340

+armnn::Pooling2dLayer::ValidateTensorShapesFromInputs
void ValidateTensorShapesFromInputs() override
Check if the input tensor shape(s) will lead to a valid configuration of Pooling2dLayer.
Definition: Pooling2dLayer.cpp:103

+armnn::Pooling2dDescriptor::m_OutputShapeRounding
OutputShapeRounding m_OutputShapeRounding
The rounding method for the output shape. (Floor, Ceiling).
Definition: Descriptors.hpp:365

+armnn::Pooling2dLayer::CreateWorkload
virtual std::unique_ptr< IWorkload > CreateWorkload(const IWorkloadFactory &factory) const override
Makes a workload for the Pooling2d type.
Definition: Pooling2dLayer.cpp:27

+armnn::LayerWithParameters< Pooling2dDescriptor >::PrepInfoAndDesc
WorkloadInfo PrepInfoAndDesc(QueueDescriptor &descriptor) const
Helper function to reduce duplication in *LayerCreateWorkload.
Definition: LayerWithParameters.hpp:43

+armnn::ILayerVisitor
Definition: ILayerVisitor.hpp:16

+armnnUtils
Definition: DataLayoutIndexed.hpp:13

+armnn::Layer::GetOutputSlot
const OutputSlot & GetOutputSlot(unsigned int index=0) const override
Get the const output slot handle by slot index.
Definition: Layer.hpp:312

+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:305

+armnn::LayerWithParameters
Definition: LayerWithParameters.hpp:13

+armnn::Pooling2dQueueDescriptor
Definition: WorkloadData.hpp:162

+armnn::Pooling2dDescriptor
A Pooling2dDescriptor for the Pooling2dLayer.
Definition: Descriptors.hpp:313

+armnn::Pooling2d
void Pooling2d(Decoder< float > &rInputDecoder, Encoder< float > &rOutputEncoder, const TensorInfo &inputInfo, const TensorInfo &outputInfo, const Pooling2dDescriptor &params)
Computes the Pooling2d operation.
Definition: Pooling2d.cpp:143

+armnn::OutputSlot::GetTensorInfo
const TensorInfo & GetTensorInfo() const override
Definition: Layer.cpp:63

+armnnUtils::DataLayoutIndexed::GetChannelsIndex
unsigned int GetChannelsIndex() const
Definition: DataLayoutIndexed.hpp:23

+armnn::Pooling2dDescriptor::m_StrideY
uint32_t m_StrideY
Stride value when proceeding through input for the height dimension.
Definition: Descriptors.hpp:363

+armnn::DataLayout::NHWC

+LayerCloneBase.hpp

+armnn::LayerType
LayerType
Definition: InternalTypes.hpp:14

+