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Diffstat (limited to 'src/armnn/layers/ReduceLayer.cpp')
-rw-r--r-- | src/armnn/layers/ReduceLayer.cpp | 100 |
1 files changed, 100 insertions, 0 deletions
diff --git a/src/armnn/layers/ReduceLayer.cpp b/src/armnn/layers/ReduceLayer.cpp new file mode 100644 index 0000000000..b68cd2eabc --- /dev/null +++ b/src/armnn/layers/ReduceLayer.cpp @@ -0,0 +1,100 @@ +// +// Copyright © 2020 Samsung Electronics Co Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "ReduceLayer.hpp" +#include "LayerCloneBase.hpp" + +#include <armnn/TypesUtils.hpp> + +#include <backendsCommon/WorkloadData.hpp> +#include <backendsCommon/WorkloadFactory.hpp> + +namespace armnn +{ + +ReduceLayer::ReduceLayer(const ReduceDescriptor& param, const char* name) + : LayerWithParameters(1, 1, LayerType::Reduce, param, name) +{ +} + +std::unique_ptr<IWorkload> ReduceLayer::CreateWorkload(const IWorkloadFactory& factory) const +{ + ReduceQueueDescriptor descriptor; + return factory.CreateReduce(descriptor, PrepInfoAndDesc(descriptor)); +} + +ReduceLayer* ReduceLayer::Clone(Graph& graph) const +{ + return CloneBase<ReduceLayer>(graph, m_Param, GetName()); +} + +void ReduceLayer::ValidateTensorShapesFromInputs() +{ + VerifyLayerConnections(1, CHECK_LOCATION()); + + const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape(); + + VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod); + + const TensorInfo& input = GetInputSlot(0).GetConnection()->GetTensorInfo(); + + ARMNN_ASSERT_MSG(input.GetNumDimensions() > 0 && input.GetNumDimensions() <= 4, + "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_vAxis.empty()) + { + outputRank = 1; + } + else if (m_Param.m_vAxis.size() > input.GetNumDimensions()) + { + throw LayerValidationException("ReduceLayer: Dimensions to reduce can not be bigger than input dimensions"); + } + else + { + outputRank = input.GetNumDimensions() - armnn::numeric_cast<unsigned int>(m_Param.m_vAxis.size()); + if (outputRank == 0) + { + outputRank = 1; + } + } + + std::vector<unsigned int> dimSizes(outputRank, 1); + if (!m_Param.m_vAxis.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_vAxis.begin(), m_Param.m_vAxis.end(), i) == m_Param.m_vAxis.end()) + { + dimSizes[outputIndex] = armnn::numeric_cast<unsigned int>(input.GetShape()[i]); + ++outputIndex; + } + else if (m_Param.m_KeepDims) + { + dimSizes[outputIndex] = 1; + ++outputIndex; + } + } + } + const TensorShape& inferredShape = TensorShape(outputRank, dimSizes.data()); + + ValidateAndCopyShape(outputShape, inferredShape, m_ShapeInferenceMethod, "ReduceLayer"); +} + +void ReduceLayer::Accept(ILayerVisitor& visitor) const +{ + visitor.VisitReduceLayer(this, GetParameters(), GetName()); +} + +} // namespace armnn |