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-rw-r--r--src/armnn/layers/PreluLayer.cpp121
1 files changed, 121 insertions, 0 deletions
diff --git a/src/armnn/layers/PreluLayer.cpp b/src/armnn/layers/PreluLayer.cpp
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+++ b/src/armnn/layers/PreluLayer.cpp
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+//
+// 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