| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| #include "Convolution2dLayer.hpp" |
| |
| #include "LayerCloneBase.hpp" |
| |
| #include <armnn/TypesUtils.hpp> |
| #include <backendsCommon/CpuTensorHandle.hpp> |
| #include <backendsCommon/WorkloadFactory.hpp> |
| #include <string> |
| #include <DataLayoutIndexed.hpp> |
| |
| using namespace armnnUtils; |
| |
| namespace armnn |
| { |
| |
| Convolution2dLayer::Convolution2dLayer(const Convolution2dDescriptor& param, const char* name) |
| : LayerWithParameters(1, 1, LayerType::Convolution2d, param, name) |
| { |
| |
| } |
| |
| void Convolution2dLayer::SerializeLayerParameters(ParameterStringifyFunction& fn) const |
| { |
| //using DescriptorType = Parameters; |
| const std::vector<TensorShape>& inputShapes = |
| { |
| GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(), |
| m_Weight->GetTensorInfo().GetShape() |
| }; |
| const TensorShape filterShape = inputShapes[1]; |
| DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout); |
| unsigned int filterWidth = filterShape[dataLayoutIndex.GetWidthIndex()]; |
| unsigned int filterHeight = filterShape[dataLayoutIndex.GetHeightIndex()]; |
| unsigned int outChannels = filterShape[0]; |
| |
| fn("OutputChannels",std::to_string(outChannels)); |
| fn("FilterWidth",std::to_string(filterWidth)); |
| fn("FilterHeight",std::to_string(filterHeight)); |
| LayerWithParameters<Convolution2dDescriptor>::SerializeLayerParameters(fn); |
| } |
| |
| std::unique_ptr<IWorkload> Convolution2dLayer::CreateWorkload(const Graph& graph, const IWorkloadFactory& factory) const |
| { |
| // on this level constant data should not be released.. |
| BOOST_ASSERT_MSG(m_Weight != nullptr, "Convolution2dLayer: Weights data should not be null."); |
| |
| Convolution2dQueueDescriptor descriptor; |
| |
| descriptor.m_Weight = m_Weight.get(); |
| |
| if (m_Param.m_BiasEnabled) |
| { |
| BOOST_ASSERT_MSG(m_Bias != nullptr, "Convolution2dLayer: Bias data should not be null."); |
| descriptor.m_Bias = m_Bias.get(); |
| } |
| return factory.CreateConvolution2d(descriptor, PrepInfoAndDesc(descriptor, graph)); |
| } |
| |
| Convolution2dLayer* Convolution2dLayer::Clone(Graph& graph) const |
| { |
| auto layer = CloneBase<Convolution2dLayer>(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> Convolution2dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const |
| { |
| BOOST_ASSERT(inputShapes.size() == 2); |
| const TensorShape& inputShape = inputShapes[0]; |
| const TensorShape filterShape = inputShapes[1]; |
| |
| // If we support multiple batch dimensions in the future, then this assert will need to change. |
| BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Convolutions will always have 4D input."); |
| |
| DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout); |
| |
| unsigned int inWidth = inputShape[dataLayoutIndex.GetWidthIndex()]; |
| unsigned int inHeight = inputShape[dataLayoutIndex.GetHeightIndex()]; |
| unsigned int inBatchSize = inputShape[0]; |
| |
| unsigned int filterWidth = filterShape[dataLayoutIndex.GetWidthIndex()]; |
| unsigned int dilatedFilterWidth = filterWidth + (m_Param.m_DilationX - 1) * (filterWidth - 1); |
| unsigned int readWidth = (inWidth + m_Param.m_PadLeft + m_Param.m_PadRight) - dilatedFilterWidth; |
| unsigned int outWidth = 1 + (readWidth / m_Param.m_StrideX); |
| |
| unsigned int filterHeight = filterShape[dataLayoutIndex.GetHeightIndex()]; |
| unsigned int dilatedFilterHeight = filterHeight + (m_Param.m_DilationY - 1) * (filterHeight - 1); |
| unsigned int readHeight = (inHeight + m_Param.m_PadTop + m_Param.m_PadBottom) - dilatedFilterHeight; |
| unsigned int outHeight = 1 + (readHeight / m_Param.m_StrideY); |
| |
| unsigned int outChannels = filterShape[0]; |
| unsigned int outBatchSize = inBatchSize; |
| |
| TensorShape tensorShape = m_Param.m_DataLayout == armnn::DataLayout::NHWC ? |
| TensorShape( { outBatchSize, outHeight, outWidth, outChannels } ) : |
| TensorShape( { outBatchSize, outChannels, outHeight, outWidth }); |
| |
| return std::vector<TensorShape>({ tensorShape }); |
| } |
| |
| void Convolution2dLayer::ValidateTensorShapesFromInputs() |
| { |
| VerifyLayerConnections(1, CHECK_LOCATION()); |
| |
| // check if we m_Weight data is not nullptr |
| BOOST_ASSERT_MSG(m_Weight != nullptr, "Convolution2dLayer: 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>( |
| "Convolution2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.", |
| GetOutputSlot(0).GetTensorInfo().GetShape(), |
| inferredShapes[0]); |
| } |
| |
| Layer::ConstantTensors Convolution2dLayer::GetConstantTensorsByRef() |
| { |
| return {m_Weight, m_Bias}; |
| } |
| |
| void Convolution2dLayer::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->Map(true)); |
| optionalBiasTensor = Optional<ConstTensor>(biasTensor); |
| } |
| |
| visitor.VisitConvolution2dLayer(this, GetParameters(), weightsTensor, optionalBiasTensor, GetName()); |
| } |
| |
| } // namespace armnn |