// // Copyright © 2017 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // #include "PadLayer.hpp" #include "LayerCloneBase.hpp" #include #include #include #include namespace armnn { PadLayer::PadLayer(const armnn::PadDescriptor& param, const char* name) : LayerWithParameters(1, 1, LayerType::Pad, param, name) {} std::unique_ptr PadLayer::CreateWorkload(const armnn::IWorkloadFactory& factory) const { PadQueueDescriptor descriptor; descriptor.m_Parameters.m_PadList = m_Param.m_PadList; return factory.CreatePad(descriptor, PrepInfoAndDesc(descriptor)); } PadLayer* PadLayer::Clone(Graph& graph) const { auto layer = CloneBase(graph, m_Param, GetName()); layer->m_Param.m_PadList = m_Param.m_PadList; return std::move(layer); } std::vector PadLayer::InferOutputShapes(const std::vector& inputShapes) const { ARMNN_ASSERT(inputShapes.size() == 1); const TensorShape& inputShape = inputShapes[0]; unsigned int rank = inputShape.GetNumDimensions(); ARMNN_ASSERT(m_Param.m_PadList.size() == rank); ARMNN_ASSERT(rank != 0); std::vector outputDimensionSizes; outputDimensionSizes.reserve(rank); for (unsigned int i = 0; i < rank; ++i) { outputDimensionSizes[i] = inputShape[i] + m_Param.m_PadList[i].first + m_Param.m_PadList[i].second; } TensorShape tensorShape = TensorShape( rank, outputDimensionSizes.data()); return std::vector({ tensorShape }); } void PadLayer::ValidateTensorShapesFromInputs() { VerifyLayerConnections(1, CHECK_LOCATION()); const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape(); VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod); auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() }); ARMNN_ASSERT(inferredShapes.size() == 1); ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "PadLayer"); } void PadLayer::Accept(ILayerVisitor& visitor) const { visitor.VisitPadLayer(this, GetParameters(), GetName()); } } // namespace armnn