| // |
| // Copyright © 2017-2024 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "Convolution2dLayer.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 |
| { |
| |
| Convolution2dLayer::Convolution2dLayer(const Convolution2dDescriptor& param, const char* name) |
| : LayerWithParameters(param.GetNumInputs(), 1, LayerType::Convolution2d, param, name) |
| { |
| |
| } |
| |
| void Convolution2dLayer::SerializeLayerParameters(ParameterStringifyFunction& fn) const |
| { |
| //using DescriptorType = Parameters; |
| const std::vector<TensorShape>& inputShapes = |
| { |
| GetInputSlot(0).GetTensorInfo().GetShape(), |
| GetInputSlot(1).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 IWorkloadFactory& factory) const |
| { |
| ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "Convolution2dLayer_CreateWorkload"); |
| Convolution2dQueueDescriptor descriptor; |
| SetAdditionalInfo(descriptor); |
| |
| return factory.CreateWorkload(LayerType::Convolution2d, descriptor, PrepInfoAndDesc(descriptor)); |
| } |
| |
| Convolution2dLayer* Convolution2dLayer::Clone(Graph& graph) const |
| { |
| auto layer = CloneBase<Convolution2dLayer>(graph, m_Param, GetName()); |
| return std::move(layer); |
| } |
| |
| std::vector<TensorShape> Convolution2dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const |
| { |
| if (inputShapes.size() != 2) |
| { |
| throw armnn::Exception("inputShapes' size is \"" + std::to_string(inputShapes.size()) + |
| "\" - should be \"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. |
| if (inputShape.GetNumDimensions() != 4) |
| { |
| throw armnn::Exception("Convolutions will always have 4D input."); |
| } |
| |
| if (m_Param.m_StrideX == 0) |
| { |
| throw armnn::Exception("m_StrideX cannot be 0."); |
| } |
| |
| if (m_Param.m_StrideY == 0) |
| { |
| throw armnn::Exception("m_StrideY cannot be 0."); |
| } |
| |
| 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(m_Param.GetNumInputs(), CHECK_LOCATION()); |
| |
| const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape(); |
| |
| VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod); |
| |
| if (!GetInputSlot(1).GetConnection()) |
| { |
| throw armnn::NullPointerException("Convolution2dLayer: Weights should be connected to input slot 1."); |
| } |
| |
| std::vector<TensorShape> inferredShapes = InferOutputShapes({ |
| GetInputSlot(0).GetTensorInfo().GetShape(), |
| GetInputSlot(1).GetTensorInfo().GetShape() }); |
| |
| if (inferredShapes.size() != 1) |
| { |
| throw armnn::Exception("inferredShapes has " |
| + std::to_string(inferredShapes.size()) + |
| " elements - should only have 1."); |
| } |
| |
| ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "Convolution2dLayer"); |
| } |
| |
| Layer::ImmutableConstantTensors Convolution2dLayer::GetConstantTensorsByRef() const |
| { |
| Layer::ImmutableConstantTensors tensors = GetConnectedConstantAsInputTensors(); |
| return tensors; |
| } |
| |
| void Convolution2dLayer::ExecuteStrategy(IStrategy& strategy) const |
| { |
| strategy.ExecuteStrategy(this, GetParameters(), {}, GetName()); |
| } |
| |
| } // namespace armnn |