src/armnn/layers/ComparisonLayer.cpp

//
// Copyright © 2019-2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//

#include "ComparisonLayer.hpp"

#include "LayerCloneBase.hpp"

#include <armnn/backends/WorkloadData.hpp>
#include <armnn/backends/WorkloadFactory.hpp>

#include <algorithm>

namespace armnn
{

ComparisonLayer::ComparisonLayer(const ComparisonDescriptor& param, const char* name)
    : LayerWithParameters(2, 1, LayerType::Comparison, param, name)
{
}

std::unique_ptr<IWorkload> ComparisonLayer::CreateWorkload(const IWorkloadFactory& factory) const
{
    ComparisonQueueDescriptor descriptor;
    SetAdditionalInfo(descriptor);

    return factory.CreateWorkload(LayerType::Comparison,descriptor, PrepInfoAndDesc(descriptor));
}

ComparisonLayer* ComparisonLayer::Clone(Graph& graph) const
{
    return CloneBase<ComparisonLayer>(graph, m_Param, GetName());
}

std::vector<TensorShape> ComparisonLayer::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\".");
    }

    TensorShape input0 = inputShapes[0];
    TensorShape input1 = inputShapes[1];

    if (inputShapes[0].GetNumDimensions() < inputShapes[1].GetNumDimensions())
    {
        input1 = inputShapes[0];
        input0 = inputShapes[1];
    }
    unsigned int numDims     = input0.GetNumDimensions();
    unsigned int shiftedDims = input0.GetNumDimensions() - input1.GetNumDimensions();

    // Get the max of the inputs.
    std::vector<unsigned int> dims(numDims);
    for (unsigned int i = shiftedDims; i < numDims; i++)
    {
        unsigned int dim0 = input0[i];
        unsigned int dim1 = input1[i - shiftedDims];

        // Validate inputs are broadcast compatible.
        if (dim0 != dim1 && dim0 != 1 && dim1 != 1)
        {
            throw armnn::Exception("Dimensions should either match or one should be of size 1.");
        }

        dims[i] = std::max(dim0, dim1);
    }

    // Fill in the rest of the shifted dimensions.
    for (unsigned int i = 0; i < shiftedDims; i++)
    {
        dims[i] = input0[i];
    }

    return std::vector<TensorShape>({ TensorShape(numDims, dims.data()) });
}

void ComparisonLayer::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, "ComparisonLayer");
}

void ComparisonLayer::ExecuteStrategy(IStrategy& strategy) const
{
    strategy.ExecuteStrategy(this, GetParameters(), {}, GetName());
}

} // namespace armnn