diff options
Diffstat (limited to 'src/armnn/layers')
-rw-r--r-- | src/armnn/layers/PreluLayer.cpp | 121 | ||||
-rw-r--r-- | src/armnn/layers/PreluLayer.hpp | 49 |
2 files changed, 170 insertions, 0 deletions
diff --git a/src/armnn/layers/PreluLayer.cpp b/src/armnn/layers/PreluLayer.cpp new file mode 100644 index 0000000000..6040248391 --- /dev/null +++ b/src/armnn/layers/PreluLayer.cpp @@ -0,0 +1,121 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "PreluLayer.hpp" + +#include "LayerCloneBase.hpp" + +#include <backendsCommon/WorkloadData.hpp> +#include <backendsCommon/WorkloadFactory.hpp> +#include <backendsCommon/CpuTensorHandle.hpp> + +namespace armnn +{ + +PreluLayer::PreluLayer(const char* name) + : Layer(2, 1, LayerType::Prelu, name) +{} + +std::unique_ptr<IWorkload> PreluLayer::CreateWorkload(const Graph& graph, + const IWorkloadFactory& factory) const +{ + PreluQueueDescriptor descriptor; + + return factory.CreatePrelu(descriptor, PrepInfoAndDesc(descriptor, graph)); +} + +PreluLayer* PreluLayer::Clone(Graph& graph) const +{ + auto layer = CloneBase<PreluLayer>(graph, GetName()); + + return std::move(layer); +} + +std::vector<TensorShape> PreluLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const +{ + BOOST_ASSERT(inputShapes.size() == 2); + + const TensorShape& inputShape = inputShapes[0]; + const TensorShape& alphaShape = inputShapes[1]; + + const unsigned int inputShapeDimensions = inputShape.GetNumDimensions(); + const unsigned int alphaShapeDimensions = alphaShape.GetNumDimensions(); + + BOOST_ASSERT(inputShapeDimensions > 0); + BOOST_ASSERT(alphaShapeDimensions > 0); + + // The size of the output is the maximum size along each dimension of the input operands, + // it starts with the trailing dimensions, and works its way forward + + unsigned int outputDimensions = std::max(inputShapeDimensions, alphaShapeDimensions); + + TensorShape outputShape(outputDimensions); + + int inputShapeIndex = boost::numeric_cast<int>(inputShapeDimensions) - 1; + int alphaShapeIndex = boost::numeric_cast<int>(alphaShapeDimensions) - 1; + unsigned int outputShapeIndex = outputDimensions - 1; + + // Loop backwards through the common part of the shapes + while (inputShapeIndex >= 0 && alphaShapeIndex >= 0) + { + unsigned int inputDimension = inputShape[boost::numeric_cast<unsigned int>(inputShapeIndex)]; + unsigned int alphaDimension = alphaShape[boost::numeric_cast<unsigned int>(alphaShapeIndex)]; + + // Check that the inputs are broadcast compatible + BOOST_ASSERT_MSG(inputDimension == alphaDimension || inputDimension == 1 || alphaDimension == 1, + "PreluLayer: Dimensions should either match or one should be of size 1"); + + outputShape[outputShapeIndex] = std::max(inputDimension, alphaDimension); + + inputShapeIndex--; + alphaShapeIndex--; + outputShapeIndex--; + } + + // Loop backwards through the remaing part of the input shape (if any) + while (inputShapeIndex >= 0) + { + outputShape[outputShapeIndex] = inputShape[boost::numeric_cast<unsigned int>(inputShapeIndex)]; + + inputShapeIndex--; + outputShapeIndex--; + } + + // Loop backwards through the remaing part of the alpha shape (if any) + while (alphaShapeIndex >= 0) + { + outputShape[outputShapeIndex] = alphaShape[boost::numeric_cast<unsigned int>(alphaShapeIndex)]; + + alphaShapeIndex--; + outputShapeIndex--; + } + + return { outputShape }; +} + +void PreluLayer::ValidateTensorShapesFromInputs() +{ + VerifyLayerConnections(2, CHECK_LOCATION()); + + std::vector<TensorShape> inferredShapes = InferOutputShapes( + { + GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(), + GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape() + }); + + BOOST_ASSERT(inferredShapes.size() == 1); + + ConditionalThrowIfNotEqual<LayerValidationException>( + "PreluLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.", + GetOutputSlot(0).GetTensorInfo().GetShape(), + inferredShapes[0]); +} + +void PreluLayer::Accept(ILayerVisitor& visitor) const +{ + visitor.VisitPreluLayer(this, GetName()); +} + +} // namespace armnn diff --git a/src/armnn/layers/PreluLayer.hpp b/src/armnn/layers/PreluLayer.hpp new file mode 100644 index 0000000000..54e57b22c1 --- /dev/null +++ b/src/armnn/layers/PreluLayer.hpp @@ -0,0 +1,49 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include "LayerWithParameters.hpp" + +namespace armnn +{ + +// This layer represents a PReLU activation operation. +class PreluLayer : public Layer +{ +public: + /// Makes a workload for the PReLU 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<IWorkload> 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. + PreluLayer* Clone(Graph& graph) const override; + + /// By default returns inputShapes if the number of inputs are equal to number of outputs, + /// otherwise infers the output shapes from given input shapes and layer properties. + /// @param [in] inputShapes The input shapes layer has. + /// @return A vector to the inferred output shape. + std::vector<TensorShape> InferOutputShapes(const std::vector<TensorShape>& inputShapes) const override; + + /// Check if the input tensor shape(s) + /// will lead to a valid configuration of @ref PreluLayer. + void ValidateTensorShapesFromInputs() override; + + void Accept(ILayerVisitor& visitor) const override; + +protected: + /// Constructor to create a PreluLayer. + /// @param [in] name Optional name for the layer. + PreluLayer(const char* name); + + /// Default destructor + ~PreluLayer() = default; +}; + +} // namespace armnn |