From 639fb0437d1a5a8a6ea737fed5a16b554dfffead Mon Sep 17 00:00:00 2001 From: Aron Virginas-Tar Date: Thu, 20 Jun 2019 14:28:19 +0100 Subject: IVGCVSW-3319 Add frontend support for TransposeConvolution2d Layer Signed-off-by: Aron Virginas-Tar Change-Id: Ic06f63f1eff255e697facf319e2ac4c83d782e7c --- src/armnn/layers/TransposeConvolution2dLayer.cpp | 130 +++++++++++++++++++++++ src/armnn/layers/TransposeConvolution2dLayer.hpp | 59 ++++++++++ 2 files changed, 189 insertions(+) create mode 100644 src/armnn/layers/TransposeConvolution2dLayer.cpp create mode 100644 src/armnn/layers/TransposeConvolution2dLayer.hpp (limited to 'src/armnn/layers') diff --git a/src/armnn/layers/TransposeConvolution2dLayer.cpp b/src/armnn/layers/TransposeConvolution2dLayer.cpp new file mode 100644 index 0000000000..69f598d288 --- /dev/null +++ b/src/armnn/layers/TransposeConvolution2dLayer.cpp @@ -0,0 +1,130 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#include "TransposeConvolution2dLayer.hpp" + +#include "LayerCloneBase.hpp" + +#include + +#include +#include + +#include + +using namespace armnnUtils; + +namespace armnn +{ + +TransposeConvolution2dLayer::TransposeConvolution2dLayer(const TransposeConvolution2dDescriptor& param, + const char* name) + : LayerWithParameters(1, 1, LayerType::TransposeConvolution2d, param, name) +{ +} + +std::unique_ptr TransposeConvolution2dLayer::CreateWorkload(const Graph& graph, + const IWorkloadFactory& factory) const +{ + BOOST_ASSERT_MSG(m_Weight != nullptr, "TransposeConvolution2dLayer: Weights data should not be null."); + + TransposeConvolution2dQueueDescriptor descriptor; + descriptor.m_Weight = m_Weight.get(); + + if (m_Param.m_BiasEnabled) + { + BOOST_ASSERT_MSG(m_Bias != nullptr, "TransposeConvolution2dLayer: Bias data should not be null."); + descriptor.m_Bias = m_Bias.get(); + } + + return factory.CreateTransposeConvolution2d(descriptor, PrepInfoAndDesc(descriptor, graph)); +} + +TransposeConvolution2dLayer* TransposeConvolution2dLayer::Clone(Graph& graph) const +{ + auto layer = CloneBase(graph, m_Param, GetName()); + + layer->m_Weight = m_Weight ? std::make_unique(*m_Weight) : nullptr; + + if (layer->m_Param.m_BiasEnabled) + { + layer->m_Bias = m_Bias ? std::make_unique(*m_Bias) : nullptr; + } + + return std::move(layer); +} + +std::vector TransposeConvolution2dLayer::InferOutputShapes( + const std::vector& inputShapes) const +{ + BOOST_ASSERT(inputShapes.size() == 2); + const TensorShape& inputShape = inputShapes[0]; + const TensorShape& kernelShape = inputShapes[1]; + + BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Transpose convolutions will always have 4D input"); + + DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout); + + unsigned int inBatchSize = inputShape[0]; + unsigned int inWidth = inputShape[dataLayoutIndex.GetWidthIndex()]; + unsigned int inHeight = inputShape[dataLayoutIndex.GetHeightIndex()]; + unsigned int inChannels = inputShape[dataLayoutIndex.GetChannelsIndex()]; + + unsigned int kernelWidth = kernelShape[dataLayoutIndex.GetWidthIndex()]; + unsigned int kernelHeight = kernelShape[dataLayoutIndex.GetHeightIndex()]; + + unsigned int totalPaddingX = m_Param.m_PadLeft + m_Param.m_PadRight; + unsigned int totalPaddingY = m_Param.m_PadTop + m_Param.m_PadBottom; + + unsigned int outWidth = m_Param.m_StrideX * (inWidth + 1) - totalPaddingX + kernelWidth; + unsigned int outHeight = m_Param.m_StrideY * (inHeight + 1) - totalPaddingY + kernelHeight; + + unsigned int outChannels = inChannels; + 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 }); +} + +void TransposeConvolution2dLayer::ValidateTensorShapesFromInputs() +{ + VerifyLayerConnections(1, CHECK_LOCATION()); + + BOOST_ASSERT_MSG(m_Weight != nullptr, "TransposeConvolution2dLayer: Weight data cannot be null."); + + auto inferredShapes = InferOutputShapes({ + GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(), + m_Weight->GetTensorInfo().GetShape() }); + + BOOST_ASSERT(inferredShapes.size() == 1); + + ConditionalThrowIfNotEqual( + "TransposeConvolution2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.", + GetOutputSlot(0).GetTensorInfo().GetShape(), + inferredShapes[0]); +} + +Layer::ConstantTensors TransposeConvolution2dLayer::GetConstantTensorsByRef() +{ + return {m_Weight, m_Bias}; +} + +void TransposeConvolution2dLayer::Accept(ILayerVisitor& visitor) const +{ + ConstTensor weightsTensor(m_Weight->GetTensorInfo(), m_Weight->Map(true)) ; + Optional optionalBiasTensor = EmptyOptional(); + + if (GetParameters().m_BiasEnabled) + { + ConstTensor biasTensor(m_Bias->GetTensorInfo(), m_Bias->Map(true)); + optionalBiasTensor = Optional(biasTensor); + } + + visitor.VisitTransposeConvolution2dLayer(this, GetParameters(), weightsTensor, optionalBiasTensor, GetName()); +} + +} // namespace armnn diff --git a/src/armnn/layers/TransposeConvolution2dLayer.hpp b/src/armnn/layers/TransposeConvolution2dLayer.hpp new file mode 100644 index 0000000000..4dc4644a3c --- /dev/null +++ b/src/armnn/layers/TransposeConvolution2dLayer.hpp @@ -0,0 +1,59 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include "LayerWithParameters.hpp" + +namespace armnn +{ + +class ScopedCpuTensorHandle; + +/// This layer represents a 2D transpose convolution operation. +class TransposeConvolution2dLayer : public LayerWithParameters +{ +public: + /// A unique pointer to store weight values. + std::unique_ptr m_Weight; + /// A unique pointer to store bias values. + std::unique_ptr m_Bias; + + /// Makes a workload for the TransposeConvolution2d type. + /// @param [in] graph The graph where this layer can be found. + /// @param [in] factory The workload factory which will create the workload. + /// @return A pointer to the created workload, or nullptr if not created. + virtual std::unique_ptr CreateWorkload(const Graph& graph, + const IWorkloadFactory& factory) const override; + + /// Creates a dynamically-allocated copy of this layer. + /// @param [in] graph The graph into which this layer is being cloned. + TransposeConvolution2dLayer* Clone(Graph& graph) const override; + + /// Check if the input tensor shape(s) + /// will lead to a valid configuration of @ref TransposeConvolution2dLayer. + void ValidateTensorShapesFromInputs() override; + + /// Infers the output shapes from given input shapes and layer properties. + /// @param [in] inputShapes The input shapes the layer has. + /// @return A vector of the inferred output shape. + std::vector InferOutputShapes(const std::vector& inputShapes) const override; + + void Accept(ILayerVisitor& visitor) const override; + +protected: + /// Constructor to create a TransposeConvolution2dLayer. + /// @param [in] param TransposeConvolution2dDescriptor to configure the 2D transpose convolution operation. + /// @param [in] name Optional name for the layer. + TransposeConvolution2dLayer(const TransposeConvolution2dDescriptor& param, const char* name); + + /// Default destructor + ~TransposeConvolution2dLayer() = default; + + /// Retrieve the handles to the constant values stored by the layer. + /// @return A vector of the constant tensors stored by this layer. + ConstantTensors GetConstantTensorsByRef() override; +}; + +} // namespace armnn -- cgit v1.2.1