| // |
| // Copyright © 2017 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "DepthwiseConvolution2dLayer.hpp" |
| #include "LayerCloneBase.hpp" |
| |
| #include <armnn/TypesUtils.hpp> |
| |
| #include <armnnUtils/DataLayoutIndexed.hpp> |
| |
| #include <armnn/backends/TensorHandle.hpp> |
| #include <armnn/backends/WorkloadFactory.hpp> |
| |
| #include <string> |
| |
| using namespace armnnUtils; |
| |
| namespace armnn |
| { |
| |
| DepthwiseConvolution2dLayer::DepthwiseConvolution2dLayer(const DepthwiseConvolution2dDescriptor& param, |
| const char* name) |
| : LayerWithParameters(param.GetNumInputs(), 1, LayerType::DepthwiseConvolution2d, param, name) |
| { |
| } |
| |
| void DepthwiseConvolution2dLayer::SerializeLayerParameters(ParameterStringifyFunction& fn) const |
| { |
| const std::vector<TensorShape>& inputShapes = |
| { |
| GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(), |
| GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape() |
| }; |
| const TensorShape filterShape = inputShapes[1]; |
| unsigned int inputChannels = filterShape[1]; |
| unsigned int filterWidth = filterShape[3]; |
| unsigned int filterHeight = filterShape[2]; |
| unsigned int depthMultiplier = filterShape[0]; |
| |
| fn("FilterWidth",std::to_string(filterWidth)); |
| fn("FilterHeight",std::to_string(filterHeight)); |
| fn("DepthMultiplier",std::to_string(depthMultiplier)); |
| fn("InputChannels",std::to_string(inputChannels)); |
| |
| LayerWithParameters<DepthwiseConvolution2dDescriptor>::SerializeLayerParameters(fn); |
| } |
| |
| std::unique_ptr<IWorkload> DepthwiseConvolution2dLayer::CreateWorkload(const IWorkloadFactory& factory) const |
| { |
| DepthwiseConvolution2dQueueDescriptor descriptor; |
| |
| if (m_Weight) |
| { |
| descriptor.m_Weight = m_Weight.get(); |
| } |
| if (m_Param.m_BiasEnabled && m_Bias) |
| { |
| descriptor.m_Bias = m_Bias.get(); |
| } |
| |
| SetAdditionalInfo(descriptor); |
| |
| return factory.CreateWorkload(LayerType::DepthwiseConvolution2d, descriptor, PrepInfoAndDesc(descriptor)); |
| } |
| |
| DepthwiseConvolution2dLayer* DepthwiseConvolution2dLayer::Clone(Graph& graph) const |
| { |
| auto layer = CloneBase<DepthwiseConvolution2dLayer>(graph, m_Param, GetName()); |
| layer->m_Weight = m_Weight ? m_Weight : nullptr; |
| |
| if (layer->m_Param.m_BiasEnabled) |
| { |
| layer->m_Bias = m_Bias ? m_Bias : nullptr; |
| } |
| |
| return std::move(layer); |
| } |
| |
| std::vector<TensorShape> |
| DepthwiseConvolution2dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const |
| { |
| ARMNN_ASSERT(inputShapes.size() == 2); |
| const TensorShape& inputShape = inputShapes[0]; |
| const TensorShape& filterShape = inputShapes[1]; |
| |
| ARMNN_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Convolutions will always have 4D input."); |
| |
| ARMNN_ASSERT( m_Param.m_StrideX > 0); |
| ARMNN_ASSERT( m_Param.m_StrideY > 0); |
| |
| DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout); |
| |
| unsigned int inputBatchSize = inputShape[0]; |
| unsigned int inputHeight = inputShape[dataLayoutIndex.GetHeightIndex()]; |
| unsigned int inputWidth = inputShape[dataLayoutIndex.GetWidthIndex()]; |
| |
| // Expected filter shape: [ 1, H, W, O ] - This shape does NOT depend on the data layout |
| // Namely: [ 1, filter height, filter width, output channels ] |
| |
| unsigned int filterHeight = filterShape[1]; |
| unsigned int dilatedFilterHeight = filterHeight + (m_Param.m_DilationY - 1) * (filterHeight - 1); |
| unsigned int readHeight = (inputHeight + m_Param.m_PadTop + m_Param.m_PadBottom) - dilatedFilterHeight; |
| unsigned int outputHeight = 1 + (readHeight / m_Param.m_StrideY); |
| |
| unsigned int filterWidth = filterShape[2]; |
| unsigned int dilatedFilterWidth = filterWidth + (m_Param.m_DilationX - 1) * (filterWidth - 1); |
| unsigned int readWidth = (inputWidth + m_Param.m_PadLeft + m_Param.m_PadRight) - dilatedFilterWidth; |
| unsigned int outputWidth = 1 + (readWidth / m_Param.m_StrideX); |
| |
| unsigned int outputChannels = filterShape[3]; |
| unsigned int outputBatchSize = inputBatchSize; |
| |
| TensorShape tensorShape = m_Param.m_DataLayout == armnn::DataLayout::NHWC ? |
| TensorShape{ outputBatchSize, outputHeight, outputWidth, outputChannels } : |
| TensorShape{ outputBatchSize, outputChannels, outputHeight, outputWidth }; |
| |
| return std::vector<TensorShape>{ tensorShape }; |
| } |
| |
| void DepthwiseConvolution2dLayer::ValidateTensorShapesFromInputs() |
| { |
| VerifyLayerConnections(m_Param.GetNumInputs(), CHECK_LOCATION()); |
| |
| const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape(); |
| |
| VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod); |
| |
| ARMNN_ASSERT_MSG(GetInputSlot(1).GetConnection(), |
| "DepthwiseConvolution2dLayer: Weights data should not be null."); |
| |
| auto inferredShapes = InferOutputShapes({ |
| GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(), |
| GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape() |
| }); |
| |
| ARMNN_ASSERT(inferredShapes.size() == 1); |
| |
| ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "DepthwiseConvolution2dLayer"); |
| } |
| |
| Layer::ConstantTensors DepthwiseConvolution2dLayer::GetConstantTensorsByRef() |
| { |
| Layer::ConstantTensors tensors = GetConnectedConstantAsInputTensors(); |
| |
| if (!tensors.empty()) |
| { |
| return tensors; |
| } |
| |
| // For API stability DO NOT ALTER order and add new members to the end of vector |
| return {m_Weight, m_Bias}; |
| } |
| |
| void DepthwiseConvolution2dLayer::ExecuteStrategy(IStrategy& strategy) const |
| { |
| strategy.ExecuteStrategy(this, GetParameters(), {}, GetName()); |
| } |
| |
| } // namespace armnn |