// // Copyright © 2017 Arm Ltd. All rights reserved. // SPDX-License-Identifier: MIT // #include "RefFullyConnectedWorkload.hpp" #include "FullyConnected.hpp" #include "RefWorkloadUtils.hpp" #include "Profiling.hpp" namespace armnn { RefFullyConnectedWorkload::RefFullyConnectedWorkload( const FullyConnectedQueueDescriptor& descriptor, const WorkloadInfo& info) : BaseWorkload(descriptor, info), m_Weight(std::make_unique(*(descriptor.m_Weight))) { const TensorInfo& rWeightInfo = m_Weight->GetTensorInfo(); m_WeightShape = rWeightInfo.GetShape(); m_WeightDecoder = MakeDecoder(rWeightInfo, m_Weight->Map(true)); if (descriptor.m_Parameters.m_BiasEnabled) { m_Bias = std::make_unique(*(descriptor.m_Bias)); const TensorInfo& biasInfo = m_Bias->GetTensorInfo(); m_BiasDecoder = MakeDecoder(biasInfo, m_Bias->Map(true)); } } void RefFullyConnectedWorkload::PostAllocationConfigure() { const TensorInfo& inputInfo = GetTensorInfo(m_Data.m_Inputs[0]); BOOST_ASSERT(inputInfo.GetNumDimensions() > 1); m_InputShape = inputInfo.GetShape(); m_InputDecoder = MakeDecoder(inputInfo); const TensorInfo& outputInfo = GetTensorInfo(m_Data.m_Outputs[0]); m_OutputShape = outputInfo.GetShape(); m_OutputEncoder = MakeEncoder(outputInfo); m_NumActivations = 1; // Total number of activations in the input. for (unsigned int i = 1; i < inputInfo.GetNumDimensions(); i++) { m_NumActivations *= inputInfo.GetShape()[i]; } } void RefFullyConnectedWorkload::Execute() const { ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefFullyConnectedWorkload_Execute"); m_InputDecoder->Reset(m_Data.m_Inputs[0]->Map()); m_OutputEncoder->Reset(m_Data.m_Outputs[0]->Map()); FullyConnected(m_InputShape, *m_InputDecoder, m_OutputShape, *m_OutputEncoder, *m_WeightDecoder, *m_BiasDecoder, m_Data.m_Parameters.m_BiasEnabled, m_NumActivations, m_Data.m_Parameters.m_TransposeWeightMatrix); } } //namespace armnn