PreluLayer.cpp

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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