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//
// Copyright © 2022-2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "GatherNdLayer.hpp"
#include "LayerCloneBase.hpp"
#include <armnn/TypesUtils.hpp>
#include <armnn/backends/WorkloadData.hpp>
#include <armnn/backends/WorkloadFactory.hpp>
namespace armnn
{
GatherNdLayer::GatherNdLayer(const char* name)
: Layer(2, 1, LayerType::GatherNd, name)
{
}
std::unique_ptr<IWorkload> GatherNdLayer::CreateWorkload(const armnn::IWorkloadFactory& factory) const
{
GatherNdQueueDescriptor descriptor;
SetAdditionalInfo(descriptor);
return factory.CreateWorkload(LayerType::GatherNd, descriptor, PrepInfoAndDesc(descriptor));
}
GatherNdLayer* GatherNdLayer::Clone(Graph& graph) const
{
return CloneBase<GatherNdLayer>(graph, GetName());
}
std::vector<TensorShape> GatherNdLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
{
if (inputShapes.size() != 2)
{
throw armnn::Exception("inputShapes' size is \"" + std::to_string(inputShapes.size()) +
"\" - should be \"2\".");
}
const TensorShape& params = inputShapes[0];
const TensorShape& indices = inputShapes[1];
if (indices.GetDimensionality() == Dimensionality::Scalar && indices.GetNumDimensions() == 1)
{
return std::vector<TensorShape>({ TensorShape(Dimensionality::Scalar)});
}
const unsigned int paramsDim = params.GetNumDimensions();
const unsigned int indicesDim = indices.GetNumDimensions();
// last dimension of indices
unsigned int index_depth = indices[indicesDim - 1];
if (index_depth > paramsDim)
{
throw armnn::Exception("index_depth must not be greater than paramsDim (\""
+ std::to_string(index_depth) +
"\" vs \""
+ std::to_string(paramsDim) + "\")");
}
// all but the last dimension of indices
std::vector<unsigned int> outer_shape;
outer_shape.reserve(indicesDim - 1);
for (unsigned int i = 0; i < indicesDim - 1; ++i)
{
outer_shape.emplace_back(indices[i]);
}
// elements after index_depth
std::vector<unsigned int> inner_shape;
inner_shape.reserve(paramsDim - index_depth);
for (unsigned int i = index_depth; i < paramsDim; ++i)
{
inner_shape.emplace_back(params[i]);
}
// concatenate outer_shape + inner_shape
std::vector<unsigned int> output_shape;
output_shape.reserve( outer_shape.size() + inner_shape.size() );
output_shape.insert( output_shape.end(), outer_shape.begin(), outer_shape.end() );
output_shape.insert( output_shape.end(), inner_shape.begin(), inner_shape.end() );
const auto outputDim = static_cast<unsigned int>(output_shape.size());
return std::vector<TensorShape>({ TensorShape({outputDim, output_shape.data()})});
}
void GatherNdLayer::ValidateTensorShapesFromInputs()
{
VerifyLayerConnections(2, CHECK_LOCATION());
const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
std::vector<TensorShape> inferredShapes = InferOutputShapes(
{GetInputSlot(0).GetTensorInfo().GetShape(),
GetInputSlot(1).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 &&
inferredShapes[0].GetDimensionality() != Dimensionality::Scalar)
{
throw armnn::LayerValidationException("inferredShapes' dimensionality is neither specified nor scalar.");
}
ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "GatherNdLayer");
}
} // namespace armnn
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