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//
// Copyright © 2017,2019-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "PreluLayer.hpp"
#include "LayerCloneBase.hpp"
#include <armnn/utility/NumericCast.hpp>
#include <armnn/backends/TensorHandle.hpp>
#include <armnn/backends/WorkloadData.hpp>
#include <armnn/backends/WorkloadFactory.hpp>
namespace armnn
{
PreluLayer::PreluLayer(const char* name)
: Layer(2, 1, LayerType::Prelu, name)
{}
std::unique_ptr<IWorkload> PreluLayer::CreateWorkload(const IWorkloadFactory& factory) const
{
PreluQueueDescriptor descriptor;
SetAdditionalInfo(descriptor);
return factory.CreateWorkload(LayerType::Prelu, descriptor, PrepInfoAndDesc(descriptor));
}
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
{
ARMNN_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();
ARMNN_ASSERT(inputShapeDimensions > 0);
ARMNN_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 = armnn::numeric_cast<int>(inputShapeDimensions) - 1;
int alphaShapeIndex = armnn::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[armnn::numeric_cast<unsigned int>(inputShapeIndex)];
unsigned int alphaDimension = alphaShape[armnn::numeric_cast<unsigned int>(alphaShapeIndex)];
// Check that the inputs are broadcast compatible
ARMNN_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[armnn::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[armnn::numeric_cast<unsigned int>(alphaShapeIndex)];
alphaShapeIndex--;
outputShapeIndex--;
}
return { outputShape };
}
void PreluLayer::ValidateTensorShapesFromInputs()
{
VerifyLayerConnections(2, CHECK_LOCATION());
const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
std::vector<TensorShape> inferredShapes = InferOutputShapes(
{
GetInputSlot(0).GetTensorInfo().GetShape(),
GetInputSlot(1).GetTensorInfo().GetShape()
});
ARMNN_ASSERT(inferredShapes.size() == 1);
ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "PreluLayer");
}
void PreluLayer::ExecuteStrategy(IStrategy& strategy) const
{
strategy.ExecuteStrategy(this, GetParameters(), {}, GetName());
}
} // namespace armnn
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