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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "BatchToSpaceNdLayer.hpp"
#include "LayerCloneBase.hpp"
#include "LayerWithParameters.hpp"
#include "BatchToSpaceNdLayer.hpp"
#include <armnn/TypesUtils.hpp>
#include <backendsCommon/CpuTensorHandle.hpp>
#include <backendsCommon/WorkloadData.hpp>
#include <backendsCommon/WorkloadFactory.hpp>
#include <DataLayoutIndexed.hpp>
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 Graph& graph,
const IWorkloadFactory& factory) const
{
BatchToSpaceNdQueueDescriptor descriptor;
return factory.CreateBatchToSpaceNd(descriptor, PrepInfoAndDesc(descriptor, graph));
}
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());
auto inferredShapes = InferOutputShapes({GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape()});
BOOST_ASSERT(inferredShapes.size() == 1);
ConditionalThrowIfNotEqual<LayerValidationException>(
"BatchToSpaceLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
GetOutputSlot(0).GetTensorInfo().GetShape(),inferredShapes[0]);
}
std::vector<TensorShape> BatchToSpaceNdLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
{
const DataLayoutIndexed dataLayout = m_Param.m_DataLayout;
const TensorShape& inputShape = inputShapes[0];
unsigned int inBatchSize = inputShape[0];
unsigned int channelSize = inputShape[dataLayout.GetChannelsIndex()];
std::vector<unsigned int> theBlockShape = m_Param.m_BlockShape;
unsigned int overallSize = inBatchSize * inputShape[dataLayout.GetHeightIndex()]
* inputShape[dataLayout.GetWidthIndex()];
std::vector<std::pair<unsigned int, unsigned int>> crops = m_Param.m_Crops;
std::pair<unsigned int, unsigned int> yCrops = crops[0];
std::pair<unsigned int, unsigned int> xCrops = crops[1];
unsigned int inputHeight = inputShape[dataLayout.GetHeightIndex()];
unsigned int outputHeight;
unsigned int yCropsTotal = yCrops.first + yCrops.second;
BOOST_ASSERT_MSG(yCropsTotal <= inputHeight,
"BatchToSpaceLayer: Overall height crop should be less than or equal to the input height.");
unsigned int croppedHeight = inputHeight - yCropsTotal;
if (theBlockShape.at(0) > 0)
{
outputHeight = theBlockShape.at(0) * croppedHeight;
}
else
{
outputHeight = croppedHeight;
}
unsigned int outputWidth;
unsigned int inputWidth = inputShape[dataLayout.GetWidthIndex()];
unsigned int xCropsTotal = xCrops.first + xCrops.second;
BOOST_ASSERT_MSG(xCropsTotal <= inputWidth,
"BatchToSpaceLayer: Overall width crop should be less than or equal to the input width.");
unsigned int croppedWidth = inputWidth - xCropsTotal;
if (theBlockShape.at(1) > 0)
{
outputWidth = theBlockShape.at(1) * croppedWidth;
}
else
{
outputWidth = croppedWidth;
}
unsigned int outputBatchSize = overallSize / (outputHeight * outputWidth);
if (dataLayout == DataLayout::NHWC)
{
return std::vector<TensorShape>({ TensorShape({ outputBatchSize, outputHeight, outputWidth, channelSize }) });
}
else
{
return std::vector<TensorShape>({ TensorShape({ outputBatchSize, channelSize, outputHeight, outputWidth }) });
}
}
void BatchToSpaceNdLayer::Accept(ILayerVisitor& visitor) const
{
visitor.VisitBatchToSpaceNdLayer(this, GetParameters(), GetName());
}
} // namespace armnn
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