SpaceToBatchNdLayer.cpp

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//
// Copyright © 2018-2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
 
#include "SpaceToBatchNdLayer.hpp"
#include "LayerCloneBase.hpp"
 
#include <armnn/backends/WorkloadData.hpp>
#include <armnn/backends/WorkloadFactory.hpp>
 
#include <numeric>
 
using namespace armnnUtils;
 
namespace armnn
{
 
SpaceToBatchNdLayer::SpaceToBatchNdLayer(const SpaceToBatchNdDescriptor param, const char* name)
    : LayerWithParameters(1, 1, LayerType::SpaceToBatchNd, param, name)
{}
 
std::unique_ptr<IWorkload> SpaceToBatchNdLayer::CreateWorkload(const IWorkloadFactory& factory) const
{
    SpaceToBatchNdQueueDescriptor descriptor;
    descriptor.m_Parameters.m_BlockShape = m_Param.m_BlockShape;
    descriptor.m_Parameters.m_PadList    = m_Param.m_PadList;
    SetAdditionalInfo(descriptor);
 
    return factory.CreateWorkload(LayerType::SpaceToBatchNd, descriptor, PrepInfoAndDesc(descriptor));
}
 
SpaceToBatchNdLayer* SpaceToBatchNdLayer::Clone(Graph& graph) const
{
    IgnoreUnused(graph);
    return CloneBase<SpaceToBatchNdLayer>(graph, m_Param, GetName());
}
 
std::vector<TensorShape> SpaceToBatchNdLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
{
    const TensorShape inputShape = inputShapes[0];
    TensorShape outputShape(inputShape);
 
    outputShape[0] = inputShape[0] * std::accumulate(m_Param.m_BlockShape.begin(),
                                                     m_Param.m_BlockShape.end(),
                                                     1U,
                                                     std::multiplies<>());
 
    // In a 4D tensor, there will be 2 spatialDimensions (H and W), and the for loop will run twice.
    // In a 3D tensor, there will be 1 spatialDimensions, and the for loop will run once.
    unsigned int firstSpatialDimension = m_Param.m_DataLayout == DataLayout::NCHW ? 2 : 1;
    for (unsigned int i = 0; i < m_Param.m_BlockShape.size(); ++i)
    {
        unsigned int spatialDimension = firstSpatialDimension + i;
        outputShape[spatialDimension] = 
          (inputShape[spatialDimension] + m_Param.m_PadList[i].first + m_Param.m_PadList[i].second)
          / m_Param.m_BlockShape[i];
    }
 
    return std::vector<TensorShape>({ outputShape });
}
 
void SpaceToBatchNdLayer::ValidateTensorShapesFromInputs()
{
    VerifyLayerConnections(1, CHECK_LOCATION());
 
    const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
 
    VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
 
    std::vector<TensorShape> 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, "SpaceToBatchNdLayer");
}
 
void SpaceToBatchNdLayer::ExecuteStrategy(IStrategy& strategy) const
{
    strategy.ExecuteStrategy(this, GetParameters(), {}, GetName());
}
 
} // namespace