31 const std::vector<TensorShape>& inputShapes =
38 unsigned int inputChannels = filterShape[1];
39 unsigned int filterWidth = filterShape[3];
40 unsigned int filterHeight = filterShape[2];
41 unsigned int depthMultiplier = filterShape[0];
43 fn(
"FilterWidth",std::to_string(filterWidth));
44 fn(
"FilterHeight",std::to_string(filterHeight));
45 fn(
"DepthMultiplier",std::to_string(depthMultiplier));
46 fn(
"InputChannels",std::to_string(inputChannels));
54 BOOST_ASSERT_MSG(
m_Weight !=
nullptr,
"DepthwiseConvolution2dLayer: Weights data should not be null.");
62 BOOST_ASSERT_MSG(
m_Bias !=
nullptr,
"DepthwiseConvolution2dLayer: Bias data should not be null.");
70 auto layer = CloneBase<DepthwiseConvolution2dLayer>(graph,
m_Param,
GetName());
71 layer->
m_Weight =
m_Weight ? std::make_unique<ScopedCpuTensorHandle>(*m_Weight) :
nullptr;
73 if (layer->m_Param.m_BiasEnabled)
75 layer->m_Bias =
m_Bias ? std::make_unique<ScopedCpuTensorHandle>(*m_Bias) :
nullptr;
78 return std::move(layer);
81 std::vector<TensorShape>
84 BOOST_ASSERT(inputShapes.size() == 2);
88 BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4,
"Convolutions will always have 4D input.");
92 unsigned int inputBatchSize = inputShape[0];
93 unsigned int inputHeight = inputShape[dataLayoutIndex.GetHeightIndex()];
94 unsigned int inputWidth = inputShape[dataLayoutIndex.GetWidthIndex()];
95 unsigned int inputChannels = inputShape[dataLayoutIndex.GetChannelsIndex()];
100 unsigned int depthMultiplier = filterShape[0];
102 unsigned int filterHeight = filterShape[2];
103 unsigned int dilatedFilterHeight = filterHeight + (
m_Param.
m_DilationY - 1) * (filterHeight - 1);
107 unsigned int filterWidth = filterShape[3];
108 unsigned int dilatedFilterWidth = filterWidth + (
m_Param.
m_DilationX - 1) * (filterWidth - 1);
112 unsigned int outputChannels = inputChannels * depthMultiplier;
113 unsigned int outputBatchSize = inputBatchSize;
116 TensorShape{ outputBatchSize, outputHeight, outputWidth, outputChannels } :
117 TensorShape{ outputBatchSize, outputChannels, outputHeight, outputWidth };
119 return std::vector<TensorShape>{ tensorShape };
127 BOOST_ASSERT_MSG(
m_Weight !=
nullptr,
"DepthwiseConvolution2dLayer: Weights data should not be null.");
131 m_Weight->GetTensorInfo().GetShape()
134 BOOST_ASSERT(inferredShapes.size() == 1);
136 ConditionalThrowIfNotEqual<LayerValidationException>(
137 "DepthwiseConvolution2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
virtual std::unique_ptr< IWorkload > CreateDepthwiseConvolution2d(const DepthwiseConvolution2dQueueDescriptor &descriptor, const WorkloadInfo &info) const
uint32_t m_StrideX
Stride value when proceeding through input for the width dimension.
uint32_t m_PadBottom
Padding bottom value in the height dimension.
void Accept(ILayerVisitor &visitor) const override
void SerializeLayerParameters(ParameterStringifyFunction &fn) const override
const char * GetName() const override
DepthwiseConvolution2dLayer * Clone(Graph &graph) const override
uint32_t m_DilationX
Dilation factor value for width dimension.
A tensor defined by a TensorInfo (shape and data type) and an immutable backing store.
virtual const TensorInfo & GetTensorInfo() const =0
uint32_t m_PadLeft
Padding left value in the width dimension.
This layer represents a depthwise convolution 2d operation.
std::unique_ptr< ScopedCpuTensorHandle > m_Bias
A unique pointer to store Bias values.
virtual void VisitDepthwiseConvolution2dLayer(const IConnectableLayer *layer, const DepthwiseConvolution2dDescriptor &convolution2dDescriptor, const ConstTensor &weights, const Optional< ConstTensor > &biases, const char *name=nullptr)=0
const ConstCpuTensorHandle * m_Bias
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
uint32_t m_PadTop
Padding top value in the height dimension.
void ValidateTensorShapesFromInputs() override
std::unique_ptr< ScopedCpuTensorHandle > m_Weight
A unique pointer to store Weight values.
DepthwiseConvolution2dLayer(const DepthwiseConvolution2dDescriptor ¶m, const char *name)
std::vector< std::reference_wrapper< std::unique_ptr< ScopedCpuTensorHandle > >> ConstantTensors
const ConstCpuTensorHandle * m_Weight
const DepthwiseConvolution2dDescriptor & GetParameters() const
DepthwiseConvolution2dDescriptor m_Param
The parameters for the layer (not including tensor-valued weights etc.).
void VerifyLayerConnections(unsigned int expectedConnections, const CheckLocation &location) const
void SerializeLayerParameters(ParameterStringifyFunction &fn) const override
WorkloadInfo PrepInfoAndDesc(QueueDescriptor &descriptor) const
Helper function to reduce duplication in *LayerCreateWorkload.
ConstantTensors GetConstantTensorsByRef() override
std::vector< TensorShape > InferOutputShapes(const std::vector< TensorShape > &inputShapes) const override
bool m_BiasEnabled
Enable/disable bias.
std::function< void(const std::string &name, const std::string &value)> ParameterStringifyFunction
virtual std::unique_ptr< IWorkload > CreateWorkload(const IWorkloadFactory &factory) const override
A DepthwiseConvolution2dDescriptor for the DepthwiseConvolution2dLayer.
const TensorShape & GetShape() const
const TensorInfo & GetTensorInfo() const override
uint32_t m_StrideY
Stride value when proceeding through input for the height dimension.
const OutputSlot & GetOutputSlot(unsigned int index=0) const override
uint32_t m_DilationY
Dilation factor value for height dimension.
const InputSlot & GetInputSlot(unsigned int index) const override
uint32_t m_PadRight
Padding right value in the width dimension.