| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| #include "DepthwiseConvolution2dLayer.hpp" |
| |
| #include "LayerCloneBase.hpp" |
| |
| #include <armnn/TypesUtils.hpp> |
| |
| #include <backendsCommon/CpuTensorHandle.hpp> |
| #include <backendsCommon/WorkloadFactory.hpp> |
| |
| #include <DataLayoutIndexed.hpp> |
| |
| using namespace armnnUtils; |
| |
| namespace armnn |
| { |
| |
| DepthwiseConvolution2dLayer::DepthwiseConvolution2dLayer(const DepthwiseConvolution2dDescriptor& param, |
| const char* name) |
| : LayerWithParameters(1, 1, LayerType::DepthwiseConvolution2d, param, name) |
| { |
| } |
| |
| std::unique_ptr<IWorkload> DepthwiseConvolution2dLayer::CreateWorkload(const Graph& graph, |
| const IWorkloadFactory& factory) const |
| { |
| // on this level constant data should not be released.. |
| BOOST_ASSERT_MSG(m_Weight != nullptr, "DepthwiseConvolution2dLayer: Weights data should not be null."); |
| |
| DepthwiseConvolution2dQueueDescriptor descriptor; |
| |
| descriptor.m_Weight = m_Weight.get(); |
| |
| if (m_Param.m_BiasEnabled) |
| { |
| BOOST_ASSERT_MSG(m_Bias != nullptr, "DepthwiseConvolution2dLayer: Bias data should not be null."); |
| descriptor.m_Bias = m_Bias.get(); |
| } |
| return factory.CreateDepthwiseConvolution2d(descriptor, PrepInfoAndDesc(descriptor, graph)); |
| } |
| |
| DepthwiseConvolution2dLayer* DepthwiseConvolution2dLayer::Clone(Graph& graph) const |
| { |
| auto layer = CloneBase<DepthwiseConvolution2dLayer>(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> |
| DepthwiseConvolution2dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const |
| { |
| BOOST_ASSERT(inputShapes.size() == 2); |
| const TensorShape& inputShape = inputShapes[0]; |
| const TensorShape& filterShape = inputShapes[1]; |
| |
| BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Convolutions will always have 4D input."); |
| |
| DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout); |
| |
| unsigned int inputBatchSize = inputShape[0]; |
| unsigned int inputHeight = inputShape[dataLayoutIndex.GetHeightIndex()]; |
| unsigned int inputWidth = inputShape[dataLayoutIndex.GetWidthIndex()]; |
| unsigned int inputChannels = inputShape[dataLayoutIndex.GetChannelsIndex()]; |
| |
| // Expected filter shape: [ M, I, H, W ] - This shape does NOT depend on the data layout |
| // Namely: [ depth multiplier, input channels, filter height, filter width ] |
| // Output channels = input channels * depthMultiplier |
| |
| unsigned int depthMultiplier = filterShape[0]; |
| |
| unsigned int filterHeight = filterShape[2]; |
| unsigned int readHeight = (inputHeight + m_Param.m_PadTop + m_Param.m_PadBottom) - filterHeight; |
| unsigned int outputHeight = 1 + (readHeight / m_Param.m_StrideY); |
| |
| unsigned int filterWidth = filterShape[3]; |
| unsigned int readWidth = (inputWidth + m_Param.m_PadLeft + m_Param.m_PadRight) - filterWidth; |
| unsigned int outputWidth = 1 + (readWidth / m_Param.m_StrideX); |
| |
| unsigned int outputChannels = inputChannels * depthMultiplier; |
| 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(1, CHECK_LOCATION()); |
| |
| // on this level constant data should not be released.. |
| BOOST_ASSERT_MSG(m_Weight != nullptr, "DepthwiseConvolution2dLayer: Weights data should not be null."); |
| |
| auto inferredShapes = InferOutputShapes({ |
| GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(), |
| m_Weight->GetTensorInfo().GetShape() |
| }); |
| |
| BOOST_ASSERT(inferredShapes.size() == 1); |
| |
| ConditionalThrowIfNotEqual<LayerValidationException>( |
| "DepthwiseConvolution2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.", |
| GetOutputSlot(0).GetTensorInfo().GetShape(), |
| inferredShapes[0]); |
| } |
| |
| Layer::ConstantTensors DepthwiseConvolution2dLayer::GetConstantTensorsByRef() |
| { |
| return {m_Weight, m_Bias}; |
| } |
| |
| void DepthwiseConvolution2dLayer::Accept(ILayerVisitor& visitor) const |
| { |
| ConstTensor weightsTensor(m_Weight->GetTensorInfo(), m_Weight->GetTensor<void*>()) ; |
| ConstTensor biasTensor(m_Bias->GetTensorInfo(), m_Bias->GetConstTensor<void*>()); |
| if (GetParameters().m_BiasEnabled) |
| { |
| visitor.VisitDepthwiseConvolution2dLayer(this, GetParameters(), weightsTensor, biasTensor, GetName()); |
| } |
| else |
| { |
| visitor.VisitDepthwiseConvolution2dLayer(this, GetParameters(), weightsTensor, GetName()); |
| } |
| } |
| |
| } // namespace armnn |