ArgMinMaxLayer.cpp

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//
// Copyright © 2017,2019-2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
 
#include "ArgMinMaxLayer.hpp"
#include "LayerCloneBase.hpp"
 
#include <armnn/TypesUtils.hpp>
 
#include <armnnUtils/TensorUtils.hpp>
 
#include <armnn/backends/WorkloadData.hpp>
#include <armnn/backends/WorkloadFactory.hpp>
 
namespace armnn
{
 
ArgMinMaxLayer::ArgMinMaxLayer(const ArgMinMaxDescriptor& param, const char* name)
    : LayerWithParameters(1, 1, LayerType::ArgMinMax, param, name)
{
}
 
std::unique_ptr<IWorkload> ArgMinMaxLayer::CreateWorkload(const IWorkloadFactory& factory) const
{
    ArgMinMaxQueueDescriptor descriptor;
    SetAdditionalInfo(descriptor);
 
    return factory.CreateWorkload(LayerType::ArgMinMax, descriptor, PrepInfoAndDesc(descriptor));
}
 
ArgMinMaxLayer* ArgMinMaxLayer::Clone(Graph& graph) const
{
    return CloneBase<ArgMinMaxLayer>(graph, m_Param, GetName());
}
 
std::vector<TensorShape> ArgMinMaxLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
{
    if (inputShapes.size() != 1)
    {
        throw armnn::LayerValidationException("inputShapes' size is \"" + std::to_string(inputShapes.size()) +
                                              "\" - should be \"1\".");
    }
 
    TensorShape inputShape = inputShapes[0];
    auto inputNumDimensions = inputShape.GetNumDimensions();
 
    auto axis = m_Param.m_Axis;
    auto unsignedAxis = armnnUtils::GetUnsignedAxis(inputNumDimensions, axis);
 
    if (unsignedAxis > inputNumDimensions)
    {
        throw armnn::LayerValidationException("Axis must not be greater than number of input dimensions (\""
                                              + std::to_string(unsignedAxis) +
                                              "\" vs \""
                                              + std::to_string(inputNumDimensions) + "\").");
    }
 
    // 1D input shape results in scalar output
    if (inputShape.GetNumDimensions() == 1)
    {
        std::vector<unsigned int> tensorDimensions(1, 1);
        TensorShape outputShape(1, tensorDimensions.data());
 
        return std::vector<TensorShape>({ outputShape });
    }
 
    std::vector<unsigned int> tensorDimensions(inputNumDimensions - 1, 0);
    for (unsigned int i = 0; i < unsignedAxis; ++i)
    {
        tensorDimensions[i] = inputShape[i];
    }
 
    for (unsigned int i = unsignedAxis + 1; i < inputNumDimensions; ++i)
    {
        tensorDimensions[i - 1] = inputShape[i];
    }
 
    TensorShape outputShape = TensorShape(inputNumDimensions - 1, tensorDimensions.data());
 
    return std::vector<TensorShape>({ outputShape });
}
 
void ArgMinMaxLayer::ValidateTensorShapesFromInputs()
{
    VerifyLayerConnections(1, CHECK_LOCATION());
 
    const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
 
    VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
 
    auto inferredShapes = InferOutputShapes({ GetInputSlot(0).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, "ArgMinMaxLayer");
}
 
void ArgMinMaxLayer::ExecuteStrategy(IStrategy& strategy) const
{
    strategy.ExecuteStrategy(this, GetParameters(), {}, GetName());
}
 
} // namespace armnn