| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "MeanLayer.hpp" |
| #include "LayerCloneBase.hpp" |
| |
| #include <backendsCommon/CpuTensorHandle.hpp> |
| #include <backendsCommon/WorkloadData.hpp> |
| #include <backendsCommon/WorkloadFactory.hpp> |
| |
| #include <cstring> |
| |
| namespace armnn |
| { |
| |
| MeanLayer::MeanLayer(const armnn::MeanDescriptor& param, const char* name) |
| : LayerWithParameters(1, 1, LayerType::Mean, param, name) |
| {} |
| |
| std::unique_ptr<IWorkload> MeanLayer::CreateWorkload(const armnn::IWorkloadFactory& factory) const |
| { |
| MeanQueueDescriptor descriptor; |
| descriptor.m_Parameters.m_Axis = m_Param.m_Axis; |
| descriptor.m_Parameters.m_KeepDims = m_Param.m_KeepDims; |
| |
| return factory.CreateMean(descriptor, PrepInfoAndDesc(descriptor)); |
| } |
| |
| MeanLayer* MeanLayer::Clone(Graph& graph) const |
| { |
| auto layer = CloneBase<MeanLayer>(graph, m_Param, GetName()); |
| |
| layer->m_Param.m_Axis = m_Param.m_Axis; |
| layer->m_Param.m_KeepDims = m_Param.m_KeepDims; |
| |
| return std::move(layer); |
| } |
| |
| void MeanLayer::ValidateTensorShapesFromInputs(ShapeInferenceMethod shapeInferenceMethod) |
| { |
| IgnoreUnused(shapeInferenceMethod); |
| |
| VerifyLayerConnections(1, CHECK_LOCATION()); |
| |
| const TensorInfo& input = GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| |
| ARMNN_ASSERT_MSG(input.GetNumDimensions() > 0 && input.GetNumDimensions() <= 4, |
| "MeanLayer: Mean 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_Axis.empty()) |
| { |
| outputRank = 1; |
| } |
| else if (m_Param.m_Axis.size() > input.GetNumDimensions()) |
| { |
| throw LayerValidationException("MeanLayer: Dimensions to reduce can not be bigger than input dimensions"); |
| } |
| else |
| { |
| outputRank = input.GetNumDimensions() - boost::numeric_cast<unsigned int>(m_Param.m_Axis.size()); |
| if (outputRank == 0) |
| { |
| outputRank = 1; |
| } |
| } |
| |
| std::vector<unsigned int> dimSizes(outputRank, 1); |
| if (!m_Param.m_Axis.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_Axis.begin(), m_Param.m_Axis.end(), i) == m_Param.m_Axis.end()) |
| { |
| dimSizes[outputIndex] = boost::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()); |
| |
| ConditionalThrowIfNotEqual<LayerValidationException>( |
| "MeanLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.", |
| GetOutputSlot(0).GetTensorInfo().GetShape(), |
| inferredShape); |
| } |
| |
| void MeanLayer::Accept(ILayerVisitor& visitor) const |
| { |
| visitor.VisitMeanLayer(this, GetParameters(), GetName()); |
| } |
| |
| } // namespace armnn |