31 const std::vector<TensorShape>& inputShapes =
37 unsigned int inputChannels = filterShape[1];
38 unsigned int filterWidth = filterShape[3];
39 unsigned int filterHeight = filterShape[2];
40 unsigned int depthMultiplier = filterShape[0];
42 fn(
"FilterWidth",std::to_string(filterWidth));
43 fn(
"FilterHeight",std::to_string(filterHeight));
44 fn(
"DepthMultiplier",std::to_string(depthMultiplier));
45 fn(
"InputChannels",std::to_string(inputChannels));
70 auto layer = CloneBase<DepthwiseConvolution2dLayer>(graph,
m_Param,
GetName());
73 if (layer->m_Param.m_BiasEnabled)
78 return std::move(layer);
81 std::vector<TensorShape>
88 ARMNN_ASSERT_MSG(inputShape.GetNumDimensions() == 4,
"Convolutions will always have 4D input.");
95 unsigned int inputBatchSize = inputShape[0];
96 unsigned int inputHeight = inputShape[dataLayoutIndex.
GetHeightIndex()];
97 unsigned int inputWidth = inputShape[dataLayoutIndex.
GetWidthIndex()];
102 unsigned int filterHeight = filterShape[1];
103 unsigned int dilatedFilterHeight = filterHeight + (
m_Param.
m_DilationY - 1) * (filterHeight - 1);
107 unsigned int filterWidth = filterShape[2];
108 unsigned int dilatedFilterWidth = filterWidth + (
m_Param.
m_DilationX - 1) * (filterWidth - 1);
112 unsigned int outputChannels = filterShape[3];
113 unsigned int outputBatchSize = inputBatchSize;
116 TensorShape{ outputBatchSize, outputHeight, outputWidth, outputChannels } :
117 TensorShape{ outputBatchSize, outputChannels, outputHeight, outputWidth };
119 return std::vector<TensorShape>{ tensorShape };
131 "DepthwiseConvolution2dLayer: Weights data should not be null.");
147 if (!tensors.empty())
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.
virtual std::unique_ptr< IWorkload > CreateWorkload(const IWorkloadFactory &factory) const override
Makes a workload for the DepthwiseConvolution2d type.
bool m_BiasEnabled
Enable/disable bias.
DepthwiseConvolution2dDescriptor m_Param
The parameters for the layer (not including tensor-valued weights etc.).
unsigned int GetWidthIndex() const
const TensorShape & GetShape() const
uint32_t m_PadBottom
Padding bottom value in the height dimension.
DepthwiseConvolution2dLayer * Clone(Graph &graph) const override
Creates a dynamically-allocated copy of this layer.
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
This layer represents a depthwise convolution 2d operation.
uint32_t GetNumInputs() const
Get the number of views/inputs.
uint32_t m_PadLeft
Padding left value in the width dimension.
ConstantTensors GetConstantTensorsByRef() override
Retrieve the handles to the constant values stored by the layer.
void SerializeLayerParameters(ParameterStringifyFunction &fn) const override
Helper to serialize the layer parameters to string (currently used in DotSerializer and company)...
void VerifyShapeInferenceType(const TensorShape &outputShape, ShapeInferenceMethod shapeInferenceMethod)
Copyright (c) 2021 ARM Limited and Contributors.
const DepthwiseConvolution2dDescriptor & GetParameters() const override
uint32_t m_DilationY
Dilation factor value for height dimension.
void ValidateAndCopyShape(const TensorShape &outputShape, const TensorShape &inferredShape, const ShapeInferenceMethod shapeInferenceMethod, const std::string &layerName, const unsigned int outputSlotIndex=0)
unsigned int GetHeightIndex() const
void ValidateTensorShapesFromInputs() override
Check if the input tensor shape(s) will lead to a valid configuration of DepthwiseConvolution2dLayer...
void SerializeLayerParameters(ParameterStringifyFunction &fn) const override
Helper to serialize the layer parameters to string.
void VerifyLayerConnections(unsigned int expectedConnections, const CheckLocation &location) const
const InputSlot & GetInputSlot(unsigned int index) const override
Get a const input slot handle by slot index.
uint32_t m_StrideX
Stride value when proceeding through input for the width dimension.
uint32_t GetNumInputs(bool biasEnabled)
std::vector< std::reference_wrapper< std::shared_ptr< ConstTensorHandle > >> ConstantTensors
uint32_t m_DilationX
Dilation factor value for width dimension.
uint32_t m_PadTop
Padding top value in the height dimension.
#define ARMNN_ASSERT_MSG(COND, MSG)
Provides access to the appropriate indexes for Channels, Height and Width based on DataLayout...
DepthwiseConvolution2dLayer(const DepthwiseConvolution2dDescriptor ¶m, const char *name)
Constructor to create a DepthwiseConvolution2dLayer.
std::shared_ptr< ConstTensorHandle > m_Bias
A unique pointer to store Bias values.
const ConstTensorHandle * m_Bias
#define ARMNN_ASSERT(COND)
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
const ConstTensorHandle * m_Weight
void SetAdditionalInfo(QueueDescriptor &descriptor) const
void ExecuteStrategy(IStrategy &strategy) const override
Apply a visitor to this layer.
uint32_t m_StrideY
Stride value when proceeding through input for the height dimension.
std::shared_ptr< ConstTensorHandle > m_Weight
A unique pointer to store Weight values.
Layer::ConstantTensors GetConnectedConstantAsInputTensors()
WorkloadInfo PrepInfoAndDesc(QueueDescriptor &descriptor) const
Helper function to reduce duplication in *Layer::CreateWorkload.
const OutputSlot & GetOutputSlot(unsigned int index=0) const override
Get the const output slot handle by slot index.
virtual const TensorInfo & GetTensorInfo() const =0
const char * GetName() const override
Returns the name of the layer.
std::function< void(const std::string &name, const std::string &value)> ParameterStringifyFunction
virtual std::unique_ptr< IWorkload > CreateWorkload(LayerType type, const QueueDescriptor &descriptor, const WorkloadInfo &info) const
const TensorInfo & GetTensorInfo() const override
ShapeInferenceMethod m_ShapeInferenceMethod
A DepthwiseConvolution2dDescriptor for the DepthwiseConvolution2dLayer.
Depthwise Convolution 2D layer workload data.
LayerType
When adding a new layer, adapt also the LastLayer enum value in the enum class LayerType below...
uint32_t m_PadRight
Padding right value in the width dimension.