blob: 0b60db28ee0b0c732d50177271c44b744cf0ca6d [file] [log] [blame]
//
// Copyright © 2019-2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "ResizeLayer.hpp"
#include "LayerCloneBase.hpp"
#include <armnn/TypesUtils.hpp>
#include <armnnUtils/DataLayoutIndexed.hpp>
#include <armnn/backends/WorkloadData.hpp>
#include <armnn/backends/WorkloadFactory.hpp>
using namespace armnnUtils;
namespace armnn
{
ResizeLayer::ResizeLayer(const ResizeDescriptor& param, const char* name)
: LayerWithParameters(1, 1, LayerType::Resize, param, name)
{
}
std::unique_ptr<IWorkload> ResizeLayer::CreateWorkload(const IWorkloadFactory& factory) const
{
ResizeQueueDescriptor descriptor;
SetAdditionalInfo(descriptor);
return factory.CreateWorkload(LayerType::Resize, descriptor, PrepInfoAndDesc(descriptor));
}
ResizeLayer* ResizeLayer::Clone(Graph& graph) const
{
return CloneBase<ResizeLayer>(graph, m_Param, GetName());
}
std::vector<TensorShape> ResizeLayer::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& inputShape = inputShapes[0];
const DataLayoutIndexed dimensionIndices = m_Param.m_DataLayout;
unsigned int outWidth = m_Param.m_TargetWidth;
unsigned int outHeight = m_Param.m_TargetHeight;
unsigned int outChannels = inputShape[dimensionIndices.GetChannelsIndex()];
unsigned int outBatch = inputShape[0];
TensorShape tensorShape = m_Param.m_DataLayout == armnn::DataLayout::NHWC ?
TensorShape( { outBatch, outHeight, outWidth, outChannels } ) :
TensorShape( { outBatch, outChannels, outHeight, outWidth });
if (m_Param.m_HalfPixelCenters && m_Param.m_AlignCorners)
{
throw LayerValidationException("ResizeLayer: AlignCorners cannot be true when HalfPixelCenters is true");
}
return std::vector<TensorShape>({ tensorShape });
}
void ResizeLayer::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, "ResizeLayer");
}
void ResizeLayer::ExecuteStrategy(IStrategy& strategy) const
{
strategy.ExecuteStrategy(this, GetParameters(), {}, GetName());
}
} // namespace armnn