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//
// Copyright © 2017-2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "MeanLayer.hpp"
#include "LayerCloneBase.hpp"
#include <armnn/utility/NumericCast.hpp>
#include <armnn/backends/TensorHandle.hpp>
#include <armnn/backends/WorkloadData.hpp>
#include <armnn/backends/WorkloadFactory.hpp>
#include <cstring>
namespace armnn
{
MeanLayer::MeanLayer(const armnn::MeanDescriptor& param, const char* name)
: LayerWithParameters(1, 1, LayerType::Mean, param, name)
{}
std::unique_ptr<IWorkload> MeanLayer::CreateWorkload(const armnn::IWorkloadFactory& factory) const
{
MeanQueueDescriptor descriptor;
descriptor.m_Parameters.m_Axis = m_Param.m_Axis;
descriptor.m_Parameters.m_KeepDims = m_Param.m_KeepDims;
SetAdditionalInfo(descriptor);
return factory.CreateWorkload(LayerType::Mean, descriptor, PrepInfoAndDesc(descriptor));
}
MeanLayer* MeanLayer::Clone(Graph& graph) const
{
auto layer = CloneBase<MeanLayer>(graph, m_Param, GetName());
layer->m_Param.m_Axis = m_Param.m_Axis;
layer->m_Param.m_KeepDims = m_Param.m_KeepDims;
return std::move(layer);
}
void MeanLayer::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.");
}
if (inferredShapes[0].GetDimensionality() != Dimensionality::Specified)
{
throw armnn::LayerValidationException("inferredShapes' dimensionality has not been specified.");
}
ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "MeanLayer");
}
std::vector<TensorShape> MeanLayer::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];
if (auto inputDims = input.GetNumDimensions(); 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_Axis.empty())
{
outputRank = 1;
}
else if (m_Param.m_Axis.size() > input.GetNumDimensions())
{
throw LayerValidationException("MeanLayer: Dimensions to reduce can not be bigger than input dimensions");
}
else
{
outputRank = input.GetNumDimensions() - armnn::numeric_cast<unsigned int>(m_Param.m_Axis.size());
if (outputRank == 0)
{
outputRank = 1;
}
}
std::vector<unsigned int> dimSizes(outputRank, 1);
if (!m_Param.m_Axis.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_Axis.begin(), m_Param.m_Axis.end(), i) == m_Param.m_Axis.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 MeanLayer::ExecuteStrategy(IStrategy& strategy) const
{
strategy.ExecuteStrategy(this, GetParameters(), {}, GetName());
}
} // namespace armnn
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