Pooling2dLayer.cpp

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//
// Copyright © 2017-2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
 
#include "Pooling2dLayer.hpp"
 
#include "LayerCloneBase.hpp"
 
#include <armnn/TypesUtils.hpp>
 
#include <armnnUtils/DataLayoutIndexed.hpp>
 
#include <armnn/backends/WorkloadData.hpp>
#include <armnn/backends/WorkloadFactory.hpp>
 
using namespace armnnUtils;
 
namespace armnn
{
 
Pooling2dLayer::Pooling2dLayer(const Pooling2dDescriptor& param, const char* name)
    : LayerWithParameters(1, 1, LayerType::Pooling2d, param, name)
{
}
 
std::unique_ptr<IWorkload> Pooling2dLayer::CreateWorkload(const IWorkloadFactory& factory) const
{
    Pooling2dQueueDescriptor descriptor;
    SetAdditionalInfo(descriptor);
 
    return factory.CreateWorkload(LayerType::Pooling2d, descriptor, PrepInfoAndDesc(descriptor));
}
 
Pooling2dLayer* Pooling2dLayer::Clone(Graph& graph) const
{
    return CloneBase<Pooling2dLayer>(graph, m_Param, GetName());
}
 
std::vector<TensorShape> Pooling2dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
{
    if (inputShapes.size() != 1)
    {
        throw armnn::Exception("inputShapes' size is \"" + std::to_string(inputShapes.size()) +
                               "\" - should be \"1\".");
    }
 
    const TensorShape& inputShape = inputShapes[0];
    const DataLayoutIndexed dimensionIndices = m_Param.m_DataLayout;
 
    // If we support multiple batch dimensions in the future, then this assert will need to change.
    if (inputShape.GetNumDimensions() != 4)
    {
        throw armnn::Exception("Pooling2dLayer will always have 4D input.");
    }
 
    unsigned int inWidth = inputShape[dimensionIndices.GetWidthIndex()];
    unsigned int inHeight = inputShape[dimensionIndices.GetHeightIndex()];
    unsigned int inChannels = inputShape[dimensionIndices.GetChannelsIndex()];
    unsigned int inBatchSize = inputShape[0];
 
    bool isGlobalPooling = (m_Param.m_StrideX==0 && m_Param.m_StrideY==0);
    unsigned int outWidth = 1;
    unsigned int outHeight = 1;
    if (!isGlobalPooling)
    {
        if (!m_Param.m_StrideX || !m_Param.m_StrideY)
        {
            throw armnn::Exception("Stride can only be zero when performing global pooling");
        }
 
        auto CalcSize = [](auto inSize, auto lowPad, auto highPad, auto poolSize, auto stride, auto outputShapeRounding)
            {
                unsigned int readSize = inSize + lowPad + highPad - poolSize;
                float div = static_cast<float>(readSize) / static_cast<float>(stride);
 
                unsigned int size = 0;
                switch (outputShapeRounding)
                {
                    case OutputShapeRounding::Ceiling:
                        size = static_cast<unsigned int>(ceil(div)) + 1;
                        break;
                    case OutputShapeRounding ::Floor:
                        size = static_cast<unsigned int>(floor(div)) + 1;
                        break;
                    default:
                        throw armnn::Exception("Unsupported Output Shape Rounding");
                }
 
                // MakeS sure that border operations will start from inside the input and not the padded area.
                // This is what CL does...
                if ((size - 1)*stride >= inSize + lowPad)
                {
                    --size;
                }
 
                return size;
            };
 
        outWidth = CalcSize(inWidth, m_Param.m_PadLeft, m_Param.m_PadRight, m_Param.m_PoolWidth, m_Param.m_StrideX,
                            m_Param.m_OutputShapeRounding);
        outHeight = CalcSize(inHeight, m_Param.m_PadTop, m_Param.m_PadBottom, m_Param.m_PoolHeight, m_Param.m_StrideY,
                            m_Param.m_OutputShapeRounding);
    }
    unsigned int outChannels = inChannels;
    unsigned int outBatchSize = inBatchSize;
 
    TensorShape tensorShape = m_Param.m_DataLayout == armnn::DataLayout::NHWC ?
        TensorShape( { outBatchSize, outHeight, outWidth, outChannels } ) :
        TensorShape( { outBatchSize, outChannels, outHeight, outWidth });
 
    return std::vector<TensorShape>({ tensorShape });
}
 
void Pooling2dLayer::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, "Pooling2dLayer");
}
 
void Pooling2dLayer::ExecuteStrategy(IStrategy& strategy) const
{
    strategy.ExecuteStrategy(this, GetParameters(), {}, GetName());
}
 
} // namespace armnn