patch/21.02/_fully_connected_layer_8cpp_source.xhtml

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

 #include "LayerCloneBase.hpp"

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

 namespace armnn
 {

 FullyConnectedLayer::FullyConnectedLayer(const FullyConnectedDescriptor& param, const char* name)
     : LayerWithParameters(1, 1, LayerType::FullyConnected, param, name)
 {
 }

 std::unique_ptr<IWorkload> FullyConnectedLayer::CreateWorkload(const IWorkloadFactory& factory) const
 {
     // on this level constant data should not be released..
     ARMNN_ASSERT_MSG(m_Weight != nullptr, "FullyConnectedLayer: Weights data should not be null.");

     FullyConnectedQueueDescriptor descriptor;

     descriptor.m_Weight = m_Weight.get();
     if (m_Param.m_BiasEnabled)
     {
         ARMNN_ASSERT_MSG(m_Bias != nullptr, "FullyConnectedLayer: Bias data should not be null.");
         descriptor.m_Bias = m_Bias.get();
     }

     SetAdditionalInfo(descriptor);

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

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

     layer->m_Weight = m_Weight ? std::make_unique<ScopedCpuTensorHandle>(*m_Weight) : nullptr;
     if (layer->m_Param.m_BiasEnabled)
     {
         layer->m_Bias = m_Bias ? std::make_unique<ScopedCpuTensorHandle>(*m_Bias) : nullptr;
     }

     return std::move(layer);
 }

 std::vector<TensorShape> FullyConnectedLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
     ARMNN_ASSERT(inputShapes.size() == 2);
     const TensorShape& inputShape = inputShapes[0];
     const TensorShape weightShape = inputShapes[1];

     // Output for FC is [1, w[1]].
     unsigned int batches = inputShape[0];
     unsigned int dimIdx = m_Param.m_TransposeWeightMatrix ? 0 : 1;

     return std::vector<TensorShape>({ TensorShape({batches, weightShape[dimIdx]})});
 }

 void FullyConnectedLayer::ValidateTensorShapesFromInputs()
 {
     const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();

     VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);

     // check if we m_Weight data is not nullptr
     ARMNN_ASSERT_MSG(m_Weight != nullptr, "FullyConnectedLayer: Weights data should not be null.");

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

     ARMNN_ASSERT(inferredShapes.size() == 1);
     ARMNN_ASSERT(inferredShapes[0].GetDimensionality() == Dimensionality::Specified);

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

 Layer::ConstantTensors FullyConnectedLayer::GetConstantTensorsByRef()
 {
     return {m_Weight, m_Bias};
 }

 void FullyConnectedLayer::Accept(ILayerVisitor& visitor) const
 {
     ConstTensor weightsTensor(m_Weight->GetTensorInfo(), m_Weight->Map(true));
     Optional<ConstTensor> optionalBiasTensor = EmptyOptional();

     if (GetParameters().m_BiasEnabled)
     {
         ConstTensor biasTensor(m_Bias->GetTensorInfo(), m_Bias->GetConstTensor<void>());
         optionalBiasTensor = Optional<ConstTensor>(biasTensor);
     }

     visitor.VisitFullyConnectedLayer(this, GetParameters(), weightsTensor, optionalBiasTensor, GetName());
 }

 void FullyConnectedLayer::ExecuteStrategy(IStrategy& strategy) const
 {
     std::vector<armnn::ConstTensor> constTensors { {m_Weight->GetTensorInfo(), m_Weight->Map(true)} };

     if (GetParameters().m_BiasEnabled)
     {
         constTensors.emplace_back(ConstTensor(m_Bias->GetTensorInfo(), m_Bias->Map(true)));
     }

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

 } // namespace armnn
WorkloadData.hpp

+armnn::FullyConnectedLayer::Accept
void Accept(ILayerVisitor &visitor) const override
Apply a visitor to this layer.
Definition: FullyConnectedLayer.cpp:90

+armnn::FullyConnectedLayer::CreateWorkload
virtual std::unique_ptr< IWorkload > CreateWorkload(const IWorkloadFactory &factory) const override
Makes a workload for the FullyConnected type.
Definition: FullyConnectedLayer.cpp:22

+armnn::FullyConnectedLayer::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: FullyConnectedLayer.cpp:54

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

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

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

+armnn::FullyConnectedLayer::m_Weight
std::unique_ptr< ScopedCpuTensorHandle > m_Weight
A unique pointer to store Weight values.
Definition: FullyConnectedLayer.hpp:19

+armnn::Optional
Definition: Optional.hpp:270

+armnn::FullyConnectedQueueDescriptor::m_Weight
const ConstCpuTensorHandle * m_Weight
Definition: WorkloadData.hpp:173

+WorkloadFactory.hpp

+armnn::FullyConnectedLayer::ExecuteStrategy
void ExecuteStrategy(IStrategy &strategy) const override
Apply a visitor to this layer.
Definition: FullyConnectedLayer.cpp:104

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

+armnn::FullyConnectedDescriptor::m_TransposeWeightMatrix
bool m_TransposeWeightMatrix
Enable/disable transpose weight matrix.
Definition: Descriptors.hpp:404

+armnn::FullyConnectedLayer::GetConstantTensorsByRef
ConstantTensors GetConstantTensorsByRef() override
Retrieve the handles to the constant values stored by the layer.
Definition: FullyConnectedLayer.cpp:85

+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::FullyConnectedLayer::ValidateTensorShapesFromInputs
void ValidateTensorShapesFromInputs() override
Check if the input tensor shape(s) will lead to a valid configuration of FullyConnectedLayer.
Definition: FullyConnectedLayer.cpp:67

+armnn::FullyConnectedLayer::Clone
FullyConnectedLayer * Clone(Graph &graph) const override
Creates a dynamically-allocated copy of this layer.
Definition: FullyConnectedLayer.cpp:41

+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::FullyConnectedQueueDescriptor
Definition: WorkloadData.hpp:165

+armnn::FullyConnectedLayer::FullyConnectedLayer
FullyConnectedLayer(const FullyConnectedDescriptor &param, const char *name)
Constructor to create a FullyConnectedLayer.
Definition: FullyConnectedLayer.cpp:17

+armnn::Dimensionality::Specified

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

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

+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::FullyConnected
void FullyConnected(const TensorShape &rInputShape, Decoder< float > &rInputDecoder, const TensorShape &rOutputShape, Encoder< float > &rOutputEncoder, const TensorShape &rWeightsShape, Decoder< float > &rWeightDecoder, Decoder< float > &rBiasDecoder, const bool biasEnabled, const unsigned int K, const bool transposeWeights)
Performs a matrix multiplication and optionally adds a bias.
Definition: FullyConnected.cpp:13

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

+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::ILayerVisitor::VisitFullyConnectedLayer
virtual void VisitFullyConnectedLayer(const IConnectableLayer *layer, const FullyConnectedDescriptor &fullyConnectedDescriptor, const ConstTensor &weights, const Optional< ConstTensor > &biases, const char *name=nullptr)=0
Function that a fully connected layer should call back to when its Accept(ILayerVisitor&) function is...

+armnn::FullyConnectedLayer
This layer represents a fully connected operation.
Definition: FullyConnectedLayer.hpp:15

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

+armnn::FullyConnectedDescriptor
A FullyConnectedDescriptor for the FullyConnectedLayer.
Definition: Descriptors.hpp:389

+armnn::FullyConnectedDescriptor::m_BiasEnabled
bool m_BiasEnabled
Enable/disable bias.
Definition: Descriptors.hpp:402

+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::Graph
Definition: Graph.hpp:29

+CpuTensorHandle.hpp

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

+armnn::EmptyOptional
EmptyOptional is used to initialize the Optional class in case we want to have default value for an O...
Definition: Optional.hpp:32

+FullyConnectedLayer.hpp

+armnn::FullyConnectedLayer::m_Bias
std::unique_ptr< ScopedCpuTensorHandle > m_Bias
A unique pointer to store Bias values.
Definition: FullyConnectedLayer.hpp:21

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

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

+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::IWorkloadFactory::CreateFullyConnected
virtual std::unique_ptr< IWorkload > CreateFullyConnected(const FullyConnectedQueueDescriptor &descriptor, const WorkloadInfo &info) const
Definition: WorkloadFactory.cpp:1440

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

+armnn::FullyConnectedQueueDescriptor::m_Bias
const ConstCpuTensorHandle * m_Bias
Definition: WorkloadData.hpp:174

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

+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

+