patch/21.02/_batch_normalization_layer_8cpp_source.xhtml

 //
 // Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 #include "BatchNormalizationLayer.hpp"

 #include "LayerCloneBase.hpp"

 #include <armnn/TypesUtils.hpp>
 #include <backendsCommon/CpuTensorHandle.hpp>
 #include <backendsCommon/WorkloadFactory.hpp>

 namespace armnn
 {

 BatchNormalizationLayer::BatchNormalizationLayer(const armnn::BatchNormalizationDescriptor& param, const char* name)
     : LayerWithParameters(1, 1, LayerType::BatchNormalization, param, name)
 {
 }

 std::unique_ptr<IWorkload> BatchNormalizationLayer::CreateWorkload(const IWorkloadFactory& factory) const
 {
     // on this level constant data should not be released..
     ARMNN_ASSERT_MSG(m_Mean != nullptr, "BatchNormalizationLayer: Mean data should not be null.");
     ARMNN_ASSERT_MSG(m_Variance != nullptr, "BatchNormalizationLayer: Variance data should not be null.");
     ARMNN_ASSERT_MSG(m_Beta != nullptr, "BatchNormalizationLayer: Beta data should not be null.");
     ARMNN_ASSERT_MSG(m_Gamma != nullptr, "BatchNormalizationLayer: Gamma data should not be null.");

     BatchNormalizationQueueDescriptor descriptor;
     SetAdditionalInfo(descriptor);

     descriptor.m_Mean = m_Mean.get();
     descriptor.m_Variance = m_Variance.get();
     descriptor.m_Beta = m_Beta.get();
     descriptor.m_Gamma = m_Gamma.get();

     return factory.CreateBatchNormalization(descriptor, PrepInfoAndDesc(descriptor));
 }

 BatchNormalizationLayer* BatchNormalizationLayer::Clone(Graph& graph) const
 {
     auto layer = CloneBase<BatchNormalizationLayer>(graph, m_Param, GetName());

     layer->m_Mean = m_Mean ? std::make_unique<ScopedCpuTensorHandle>(*m_Mean) : nullptr;
     layer->m_Variance = m_Variance ? std::make_unique<ScopedCpuTensorHandle>(*m_Variance) : nullptr;
     layer->m_Beta = m_Beta ? std::make_unique<ScopedCpuTensorHandle>(*m_Beta) : nullptr;
     layer->m_Gamma = m_Gamma ? std::make_unique<ScopedCpuTensorHandle>(*m_Gamma) : nullptr;

     return std::move(layer);
 }

 void BatchNormalizationLayer::ValidateTensorShapesFromInputs()
 {
     VerifyLayerConnections(1, CHECK_LOCATION());

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

     VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);

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

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

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

 }

 Layer::ConstantTensors BatchNormalizationLayer::GetConstantTensorsByRef()
 {
     return {m_Mean, m_Variance, m_Beta, m_Gamma};
 }

 void BatchNormalizationLayer::Accept(ILayerVisitor& visitor) const
 {
     ConstTensor meanTensor(m_Mean->GetTensorInfo(), m_Mean->Map(true));
     ConstTensor varianceTensor(m_Variance->GetTensorInfo(), m_Variance->Map(true));
     ConstTensor betaTensor(m_Beta->GetTensorInfo(), m_Beta->Map(true));
     ConstTensor gammaTensor(m_Gamma->GetTensorInfo(), m_Gamma->Map(true));
     visitor.VisitBatchNormalizationLayer(
             this, GetParameters(), meanTensor, varianceTensor, betaTensor, gammaTensor, GetName());
 }

 void BatchNormalizationLayer::ExecuteStrategy(IStrategy& strategy) const
 {
     std::vector<armnn::ConstTensor> constTensors { {m_Mean->GetTensorInfo(), m_Mean->Map(true)},
                                                    {m_Variance->GetTensorInfo(), m_Variance->Map(true)},
                                                    {m_Beta->GetTensorInfo(), m_Beta->Map(true)},
                                                    {m_Gamma->GetTensorInfo(), m_Gamma->Map(true)} };

     strategy.ExecuteStrategy(this, GetParameters(), constTensors, GetName());
 }

 } // namespace armnn
armnn::IWorkloadFactory::CreateBatchNormalization
virtual std::unique_ptr< IWorkload > CreateBatchNormalization(const BatchNormalizationQueueDescriptor &descriptor, const WorkloadInfo &info) const
Definition: WorkloadFactory.cpp:1314

+armnn::BatchNormalizationQueueDescriptor::m_Gamma
const ConstCpuTensorHandle * m_Gamma
Definition: WorkloadData.hpp:306

+armnn::BatchNormalizationQueueDescriptor::m_Beta
const ConstCpuTensorHandle * m_Beta
Definition: WorkloadData.hpp:305

+armnn::BatchNormalizationLayer
This layer represents a batch normalization operation.
Definition: BatchNormalizationLayer.hpp:15

+armnn::BatchNormalizationLayer::CreateWorkload
virtual std::unique_ptr< IWorkload > CreateWorkload(const IWorkloadFactory &factory) const override
Makes a workload for the BatchNormalization type.
Definition: BatchNormalizationLayer.cpp:21

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

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

+armnn::BatchNormalizationLayer::Clone
BatchNormalizationLayer * Clone(Graph &graph) const override
Creates a dynamically-allocated copy of this layer.
Definition: BatchNormalizationLayer.cpp:40

+armnn::TensorInfo::GetShape
const TensorShape & GetShape() const
Definition: Tensor.hpp:187

+armnn::BatchNormalizationLayer::ExecuteStrategy
void ExecuteStrategy(IStrategy &strategy) const override
Apply a visitor to this layer.
Definition: BatchNormalizationLayer.cpp:83

+armnn::Layer::InferOutputShapes
std::vector< TensorShape > InferOutputShapes(const std::vector< TensorShape > &inputShapes) const override
Infer the shape of the output(s) based on the provided input shape(s)
Definition: Layer.cpp:367

+WorkloadFactory.hpp

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

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

+armnn::BatchNormalizationQueueDescriptor::m_Mean
const ConstCpuTensorHandle * m_Mean
Definition: WorkloadData.hpp:303

+armnn::BatchNormalizationLayer::m_Gamma
std::unique_ptr< ScopedCpuTensorHandle > m_Gamma
A unique pointer to store Gamma values.
Definition: BatchNormalizationLayer.hpp:25

+armnn::BatchNormalizationQueueDescriptor::m_Variance
const ConstCpuTensorHandle * m_Variance
Definition: WorkloadData.hpp:304

+armnn::BatchNormalizationLayer::m_Variance
std::unique_ptr< ScopedCpuTensorHandle > m_Variance
A unique pointer to store Variance values.
Definition: BatchNormalizationLayer.hpp:21

+armnn::Layer::VerifyShapeInferenceType
void VerifyShapeInferenceType(const TensorShape &outputShape, ShapeInferenceMethod shapeInferenceMethod)
Definition: Layer.cpp:432

+armnn
Copyright (c) 2021 ARM Limited and Contributors.
Definition: 01_00_software_tools.dox:6

+armnn::BatchNormalizationLayer::Accept
void Accept(ILayerVisitor &visitor) const override
Apply a visitor to this layer.
Definition: BatchNormalizationLayer.cpp:73

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

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

+armnn::ILayerVisitor::VisitBatchNormalizationLayer
virtual void VisitBatchNormalizationLayer(const IConnectableLayer *layer, const BatchNormalizationDescriptor &desc, const ConstTensor &mean, const ConstTensor &variance, const ConstTensor &beta, const ConstTensor &gamma, const char *name=nullptr)=0
Function that a batch normalization layer should call back to when its Accept(ILayerVisitor&) functio...

+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:392

+armnn::BatchNormalizationLayer::m_Beta
std::unique_ptr< ScopedCpuTensorHandle > m_Beta
A unique pointer to store Beta values.
Definition: BatchNormalizationLayer.hpp:23

+armnn::IStrategy
Definition: IStrategy.hpp:13

+armnn::Layer::VerifyLayerConnections
void VerifyLayerConnections(unsigned int expectedConnections, const CheckLocation &location) const
Definition: Layer.cpp:348

+armnn::Layer::GetInputSlot
const InputSlot & GetInputSlot(unsigned int index) const override
Get a const input slot handle by slot index.
Definition: Layer.hpp:316

+TypesUtils.hpp

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

+armnn::BatchNormalizationLayer::m_Mean
std::unique_ptr< ScopedCpuTensorHandle > m_Mean
A unique pointer to store Mean values.
Definition: BatchNormalizationLayer.hpp:19

+armnn::ConstTensor
A tensor defined by a TensorInfo (shape and data type) and an immutable backing store.
Definition: Tensor.hpp:314

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

+armnn::BatchNormalizationLayer::ValidateTensorShapesFromInputs
void ValidateTensorShapesFromInputs() override
Check if the input tensor shape(s) will lead to a valid configuration of BatchNormalizationLayer.
Definition: BatchNormalizationLayer.cpp:52

+CHECK_LOCATION
#define CHECK_LOCATION()
Definition: Exceptions.hpp:197

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

+BatchNormalizationLayer.hpp

+CpuTensorHandle.hpp

+armnn::Layer::SetAdditionalInfo
void SetAdditionalInfo(QueueDescriptor &descriptor) const
Definition: Layer.cpp:245

+armnn::BatchNormalizationLayer::GetConstantTensorsByRef
ConstantTensors GetConstantTensorsByRef() override
Retrieve the handles to the constant values stored by the layer.
Definition: BatchNormalizationLayer.cpp:68

+armnn::BatchNormalizationQueueDescriptor
Definition: WorkloadData.hpp:293

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

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

+armnn::LayerType::BatchNormalization

+armnn::Layer::GetOutputSlot
const OutputSlot & GetOutputSlot(unsigned int index=0) const override
Get the const output slot handle by slot index.
Definition: Layer.hpp:318

+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:311

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

+armnn::Layer::ConstantTensors
std::vector< std::reference_wrapper< std::unique_ptr< ScopedCpuTensorHandle > >> ConstantTensors
Definition: Layer.hpp:393

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

+armnn::BatchNormalizationLayer::BatchNormalizationLayer
BatchNormalizationLayer(const BatchNormalizationDescriptor &param, const char *name)
Constructor to create a BatchNormalizationLayer.
Definition: BatchNormalizationLayer.cpp:16

+armnn::Layer::m_ShapeInferenceMethod
ShapeInferenceMethod m_ShapeInferenceMethod
Definition: Layer.hpp:408

+armnn::BatchNormalizationDescriptor
A BatchNormalizationDescriptor for the BatchNormalizationLayer.
Definition: Descriptors.hpp:626

+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:419

+