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//
// Copyright © 2017,2019-2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "ArgMinMaxLayer.hpp"
#include "LayerCloneBase.hpp"
#include <armnn/TypesUtils.hpp>
#include <armnnUtils/TensorUtils.hpp>
#include <armnn/backends/WorkloadData.hpp>
#include <armnn/backends/WorkloadFactory.hpp>
namespace armnn
{
ArgMinMaxLayer::ArgMinMaxLayer(const ArgMinMaxDescriptor& param, const char* name)
: LayerWithParameters(1, 1, LayerType::ArgMinMax, param, name)
{
}
std::unique_ptr<IWorkload> ArgMinMaxLayer::CreateWorkload(const IWorkloadFactory& factory) const
{
ArgMinMaxQueueDescriptor descriptor;
SetAdditionalInfo(descriptor);
return factory.CreateWorkload(LayerType::ArgMinMax, descriptor, PrepInfoAndDesc(descriptor));
}
ArgMinMaxLayer* ArgMinMaxLayer::Clone(Graph& graph) const
{
return CloneBase<ArgMinMaxLayer>(graph, m_Param, GetName());
}
std::vector<TensorShape> ArgMinMaxLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
{
if (inputShapes.size() != 1)
{
throw armnn::LayerValidationException("inputShapes' size is \"" + std::to_string(inputShapes.size()) +
"\" - should be \"1\".");
}
TensorShape inputShape = inputShapes[0];
auto inputNumDimensions = inputShape.GetNumDimensions();
auto axis = m_Param.m_Axis;
auto unsignedAxis = armnnUtils::GetUnsignedAxis(inputNumDimensions, axis);
if (unsignedAxis > inputNumDimensions)
{
throw armnn::LayerValidationException("Axis must not be greater than number of input dimensions (\""
+ std::to_string(unsignedAxis) +
"\" vs \""
+ std::to_string(inputNumDimensions) + "\").");
}
// 1D input shape results in scalar output
if (inputShape.GetNumDimensions() == 1)
{
std::vector<unsigned int> tensorDimensions(1, 1);
TensorShape outputShape(1, tensorDimensions.data());
return std::vector<TensorShape>({ outputShape });
}
std::vector<unsigned int> tensorDimensions(inputNumDimensions - 1, 0);
for (unsigned int i = 0; i < unsignedAxis; ++i)
{
tensorDimensions[i] = inputShape[i];
}
for (unsigned int i = unsignedAxis + 1; i < inputNumDimensions; ++i)
{
tensorDimensions[i - 1] = inputShape[i];
}
TensorShape outputShape = TensorShape(inputNumDimensions - 1, tensorDimensions.data());
return std::vector<TensorShape>({ outputShape });
}
void ArgMinMaxLayer::ValidateTensorShapesFromInputs()
{
VerifyLayerConnections(1, CHECK_LOCATION());
const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
auto 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, "ArgMinMaxLayer");
}
void ArgMinMaxLayer::ExecuteStrategy(IStrategy& strategy) const
{
strategy.ExecuteStrategy(this, GetParameters(), {}, GetName());
}
} // namespace armnn
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