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//
// Copyright © 2017,2019-2024 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
{
if (inputShapes.size() != 2)
{
throw armnn::Exception("inputShapes' size is \"" + std::to_string(inputShapes.size()) +
"\" - should be \"2\".");
}
const TensorShape& inputShape = inputShapes[0];
const TensorShape& alphaShape = inputShapes[1];
const unsigned int inputShapeDimensions = inputShape.GetNumDimensions();
const unsigned int alphaShapeDimensions = alphaShape.GetNumDimensions();
if (inputShapeDimensions == 0)
{
throw armnn::Exception("inputShapeDimensions must be greater than 0.");
}
if (alphaShapeDimensions == 0)
{
throw armnn::Exception("alphaShapeDimensions must be not be zero (\""
+ std::to_string(alphaShapeDimensions) + "\")");
}
// 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
if (inputDimension != alphaDimension && inputDimension != 1 && alphaDimension != 1)
{
throw armnn::Exception("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()
});
if (inferredShapes.size() != 1)
{
throw armnn::LayerValidationException("inferredShapes has "
+ std::to_string(inferredShapes.size()) +
" elements - should only have 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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