BatchToSpaceNdLayer.cpp

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//
// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
 
#include "BatchToSpaceNdLayer.hpp"
#include "LayerCloneBase.hpp"
#include "LayerWithParameters.hpp"
#include "BatchToSpaceNdLayer.hpp"
 
#include <armnn/TypesUtils.hpp>
 
#include <armnnUtils/DataLayoutIndexed.hpp>
 
#include <armnn/backends/TensorHandle.hpp>
#include <armnn/backends/WorkloadData.hpp>
#include <armnn/backends/WorkloadFactory.hpp>
 
#include <numeric>
 
using namespace armnnUtils;
 
namespace armnn
{
 
BatchToSpaceNdLayer::BatchToSpaceNdLayer(const armnn::BatchToSpaceNdDescriptor& param, const char* name)
    : LayerWithParameters(1, 1, LayerType::BatchToSpaceNd, param, name)
{
}
 
std::unique_ptr<IWorkload> BatchToSpaceNdLayer::CreateWorkload(const IWorkloadFactory& factory) const
{
    BatchToSpaceNdQueueDescriptor descriptor;
    SetAdditionalInfo(descriptor);
 
    return factory.CreateWorkload(LayerType::BatchToSpaceNd, descriptor, PrepInfoAndDesc(descriptor));
}
 
BatchToSpaceNdLayer* BatchToSpaceNdLayer::Clone(Graph& graph) const
{
    auto layer = CloneBase<BatchToSpaceNdLayer>(graph, m_Param, GetName());
    return std::move(layer);
}
 
void BatchToSpaceNdLayer::ValidateTensorShapesFromInputs()
{
    VerifyLayerConnections(1, CHECK_LOCATION());
 
    const TensorShape &outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
 
    VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
 
    auto inferredShapes = InferOutputShapes({GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape()});
 
    ARMNN_ASSERT(inferredShapes.size() == 1);
 
    ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "BatchToSpaceNdLayer");
}
 
std::vector<TensorShape> BatchToSpaceNdLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
{
    ARMNN_ASSERT(inputShapes.size() == 1);
 
    const TensorShape& inputShape = inputShapes[0];
    TensorShape outputShape(inputShape);
 
    unsigned int accumulatedBlockShape = std::accumulate(m_Param.m_BlockShape.begin(),
                                                         m_Param.m_BlockShape.end(),
                                                         1U,
                                                         std::multiplies<>());
 
    ARMNN_ASSERT(inputShape[0] % accumulatedBlockShape == 0);
 
    outputShape[0] = inputShape[0] / accumulatedBlockShape;
 
    DataLayoutIndexed dimensionIndices = m_Param.m_DataLayout;
    unsigned int heightIndex = dimensionIndices.GetHeightIndex();
    unsigned int widthIndex = dimensionIndices.GetWidthIndex();
 
    unsigned int heightCrop = m_Param.m_Crops[0].first + m_Param.m_Crops[0].second;
    unsigned int widthCrop = m_Param.m_Crops[1].first + m_Param.m_Crops[1].second;
 
    unsigned int outputHeight = inputShape[heightIndex] * m_Param.m_BlockShape[0];
    unsigned int outputWidth = inputShape[widthIndex] * m_Param.m_BlockShape[1];
 
    ARMNN_ASSERT_MSG(heightCrop <= outputHeight,
        "BatchToSpaceLayer: Overall height crop should be less than or equal to the uncropped output height.");
 
    ARMNN_ASSERT_MSG(widthCrop <= outputWidth,
        "BatchToSpaceLayer: Overall width crop should be less than or equal to the uncropped output width.");
 
    outputShape[heightIndex] = outputHeight - heightCrop;
    outputShape[widthIndex] = outputWidth - widthCrop;
 
    return std::vector<TensorShape>({ outputShape });
}
 
void BatchToSpaceNdLayer::ExecuteStrategy(IStrategy& strategy) const
{
    strategy.ExecuteStrategy(this, GetParameters(), {}, GetName());
}
 
} // namespace armnn