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//
// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "TileLayer.hpp"
#include <armnn/backends/WorkloadFactory.hpp>
#include "layers/LayerCloneBase.hpp"
namespace armnn
{
TileLayer::TileLayer(const TileDescriptor ¶m, const char *name)
: LayerWithParameters(1, 1, LayerType::Tile, param, name)
{}
std::unique_ptr<IWorkload> TileLayer::CreateWorkload(const IWorkloadFactory &factory) const
{
TileQueueDescriptor descriptor;
SetAdditionalInfo(descriptor);
return factory.CreateWorkload(LayerType::Tile, descriptor, PrepInfoAndDesc(descriptor));
}
TileLayer* TileLayer::Clone(armnn::Graph &graph) const
{
auto layer = CloneBase<TileLayer>(graph, m_Param, GetName());
return std::move(layer);
}
std::vector<TensorShape> TileLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
{
ARMNN_ASSERT(inputShapes.size() == 1);
const TensorShape& inputShape = inputShapes[0];
const std::vector<uint32_t> multipleShape = m_Param.m_Multiples;
std::vector<unsigned int> dimSizes;
// Check input shape and multiples have same length and multiply them together to get output shape
if(inputShape.GetNumDimensions() == multipleShape.size())
{
for(uint32_t i = 0; i < inputShape.GetNumDimensions(); ++i)
{
dimSizes.insert(dimSizes.begin(), inputShape[i] * multipleShape[i]);
}
}
else
{
throw LayerValidationException("TileLayer: input rank and length of multiples are different.");
}
return std::vector<TensorShape>({TensorShape({inputShape.GetNumElements(), dimSizes.data()})});
}
void TileLayer::ValidateTensorShapesFromInputs()
{
VerifyLayerConnections(1, CHECK_LOCATION());
const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetTensorInfo().GetShape() });
ARMNN_ASSERT(inferredShapes.size() == 1);
ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "TileLayer");
}
}
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