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//
// Copyright © 2020 Samsung Electronics Co Ltd and Contributors. All rights reserved.
// Copyright © 2023-2024 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).GetTensorInfo();
auto inputDims = input.GetNumDimensions();
if (inputDims != std::clamp(inputDims, 1u, 4u))
{
throw armnn::LayerValidationException("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
{
if (inputShapes.size() != 1)
{
throw armnn::Exception("inputShapes' size is \"" + std::to_string(inputShapes.size()) +
"\" - should be \"1\".");
}
const TensorShape& input = inputShapes[0];
auto inputDims = input.GetNumDimensions();
if (inputDims != std::clamp(inputDims, 1u, 4u))
{
throw armnn::Exception("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
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