| // |
| // Copyright © 2020 Samsung Electronics Co Ltd and Contributors. All rights reserved. |
| // Copyright © 2023-2024 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "ReduceLayer.hpp" |
| #include "LayerCloneBase.hpp" |
| |
| #include <armnn/TypesUtils.hpp> |
| |
| #include <armnn/backends/WorkloadData.hpp> |
| #include <armnn/backends/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; |
| descriptor.m_Parameters.m_vAxis = m_Param.m_vAxis; |
| descriptor.m_Parameters.m_KeepDims = m_Param.m_KeepDims; |
| descriptor.m_Parameters.m_ReduceOperation = m_Param.m_ReduceOperation; |
| SetAdditionalInfo(descriptor); |
| |
| return factory.CreateWorkload(LayerType::Reduce, descriptor, PrepInfoAndDesc(descriptor)); |
| } |
| |
| ReduceLayer* ReduceLayer::Clone(Graph& graph) const |
| { |
| auto layer = CloneBase<ReduceLayer>(graph, m_Param, GetName()); |
| layer->m_Param.m_vAxis = m_Param.m_vAxis; |
| layer->m_Param.m_KeepDims = m_Param.m_KeepDims; |
| layer->m_Param.m_ReduceOperation = m_Param.m_ReduceOperation; |
| |
| return std::move(layer); |
| } |
| |
| void ReduceLayer::ValidateTensorShapesFromInputs() |
| { |
| VerifyLayerConnections(1, CHECK_LOCATION()); |
| |
| const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape(); |
| |
| VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod); |
| |
| const TensorInfo& input = GetInputSlot(0).GetTensorInfo(); |
| |
| auto inputDims = input.GetNumDimensions(); |
| if (inputDims < 1 || inputDims > 4) |
| { |
| throw armnn::LayerValidationException("ReduceLayer: Reduce supports up to 4D input."); |
| } |
| |
| std::vector<TensorShape> inferredShapes = InferOutputShapes( {input.GetShape() }); |
| |
| ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "ReduceLayer"); |
| } |
| |
| std::vector<TensorShape> ReduceLayer::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& input = inputShapes[0]; |
| |
| auto inputDims = input.GetNumDimensions(); |
| if (inputDims < 1 || inputDims > 4) |
| { |
| throw armnn::Exception("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[i]); |
| ++outputIndex; |
| } |
| else if (m_Param.m_KeepDims) |
| { |
| dimSizes[outputIndex] = 1; |
| ++outputIndex; |
| } |
| } |
| } |
| return std::vector<TensorShape>({ TensorShape(outputRank, dimSizes.data()) }); |
| } |
| |
| void ReduceLayer::ExecuteStrategy(IStrategy& strategy) const |
| { |
| strategy.ExecuteStrategy(this, GetParameters(), {}, GetName()); |
| } |
| |
| } // namespace armnn |