ReduceLayer.cpp

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//
// Copyright © 2020 Samsung Electronics Co Ltd and Contributors. All rights reserved.
// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
 
#include "ReduceLayer.hpp"
#include "LayerCloneBase.hpp"
 
#include <armnn/TypesUtils.hpp>
 
#include <armnn/backends/WorkloadData.hpp>
#include <armnn/backends/WorkloadFactory.hpp>
 
namespace armnn
{
 
ReduceLayer::ReduceLayer(const ReduceDescriptor& param, const char* name)
    : LayerWithParameters(1, 1, LayerType::Reduce, param, name)
{
}
 
std::unique_ptr<IWorkload> ReduceLayer::CreateWorkload(const IWorkloadFactory& factory) const
{
    ReduceQueueDescriptor descriptor;
    descriptor.m_Parameters.m_vAxis           = m_Param.m_vAxis;
    descriptor.m_Parameters.m_KeepDims        = m_Param.m_KeepDims;
    descriptor.m_Parameters.m_ReduceOperation = m_Param.m_ReduceOperation;
    SetAdditionalInfo(descriptor);
 
    return factory.CreateWorkload(LayerType::Reduce, descriptor, PrepInfoAndDesc(descriptor));
}
 
ReduceLayer* ReduceLayer::Clone(Graph& graph) const
{
    auto layer =  CloneBase<ReduceLayer>(graph, m_Param, GetName());
    layer->m_Param.m_vAxis           = m_Param.m_vAxis;
    layer->m_Param.m_KeepDims        = m_Param.m_KeepDims;
    layer->m_Param.m_ReduceOperation = m_Param.m_ReduceOperation;
 
    return std::move(layer);
}
 
void ReduceLayer::ValidateTensorShapesFromInputs()
{
    VerifyLayerConnections(1, CHECK_LOCATION());
 
    const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
 
    VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
 
    const TensorInfo& input = GetInputSlot(0).GetConnection()->GetTensorInfo();
 
    ARMNN_ASSERT_MSG(input.GetNumDimensions() > 0 && input.GetNumDimensions() <= 4,
                     "ReduceLayer: Reduce supports up to 4D input.");
 
    std::vector<TensorShape> inferredShapes = InferOutputShapes( {input.GetShape() });
 
    ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "ReduceLayer");
}
 
std::vector<TensorShape> ReduceLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
{
    ARMNN_ASSERT(inputShapes.size() == 1);
    const TensorShape& input = inputShapes[0];
 
    ARMNN_ASSERT_MSG(input.GetNumDimensions() > 0 && input.GetNumDimensions() <= 4,
                     "ReduceLayer: Reduce supports up to 4D input.");
 
    unsigned int rank = input.GetNumDimensions();
    unsigned int outputRank = 0;
 
    // Calculate output dimension
    if (m_Param.m_KeepDims)
    {
        outputRank = rank;
    }
    else if (m_Param.m_vAxis.empty())
    {
        outputRank = 1;
    }
    else if (m_Param.m_vAxis.size() > input.GetNumDimensions())
    {
        throw LayerValidationException("ReduceLayer: Dimensions to reduce can not be bigger than input dimensions");
    }
    else
    {
        outputRank = input.GetNumDimensions() - armnn::numeric_cast<unsigned int>(m_Param.m_vAxis.size());
        if (outputRank == 0)
        {
            outputRank = 1;
        }
    }
 
    std::vector<unsigned int> dimSizes(outputRank, 1);
    if (!m_Param.m_vAxis.empty())
    {
        // Skip the dimension that has been reduced unless keepDims is true.
        unsigned int outputIndex = 0;
        for (unsigned int i = 0; i < input.GetNumDimensions(); ++i)
        {
            if (std::find(m_Param.m_vAxis.begin(), m_Param.m_vAxis.end(), i) == m_Param.m_vAxis.end())
            {
                dimSizes[outputIndex] = armnn::numeric_cast<unsigned int>(input[i]);
                ++outputIndex;
            }
            else if (m_Param.m_KeepDims)
            {
                dimSizes[outputIndex] = 1;
                ++outputIndex;
            }
        }
    }
    return std::vector<TensorShape>({ TensorShape(outputRank, dimSizes.data()) });
}
 
void ReduceLayer::ExecuteStrategy(IStrategy& strategy) const
{
    strategy.ExecuteStrategy(this, GetParameters(), {}, GetName());
}
 
} // namespace armnn