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