diff options
Diffstat (limited to 'src/armnn/backends/NeonWorkloads/NeonFullyConnectedFloat32Workload.cpp')
-rw-r--r-- | src/armnn/backends/NeonWorkloads/NeonFullyConnectedFloat32Workload.cpp | 67 |
1 files changed, 55 insertions, 12 deletions
diff --git a/src/armnn/backends/NeonWorkloads/NeonFullyConnectedFloat32Workload.cpp b/src/armnn/backends/NeonWorkloads/NeonFullyConnectedFloat32Workload.cpp index e1c4448642..c3af41e20d 100644 --- a/src/armnn/backends/NeonWorkloads/NeonFullyConnectedFloat32Workload.cpp +++ b/src/armnn/backends/NeonWorkloads/NeonFullyConnectedFloat32Workload.cpp @@ -4,16 +4,47 @@ // #include "NeonFullyConnectedFloat32Workload.hpp" -#include "backends/CpuTensorHandle.hpp" + #include "backends/ArmComputeTensorUtils.hpp" +#include "backends/ArmComputeUtils.hpp" +#include "backends/CpuTensorHandle.hpp" namespace armnn { using namespace armcomputetensorutils; +arm_compute::Status NeonFullyConnectedWorkloadValidate(const TensorInfo& input, + const TensorInfo& output, + const TensorInfo& weights, + const TensorInfo& biases, + const FullyConnectedDescriptor& descriptor) +{ + const arm_compute::TensorInfo aclInput = BuildArmComputeTensorInfo(input); + const arm_compute::TensorInfo aclOutput = BuildArmComputeTensorInfo(output); + const arm_compute::TensorInfo aclWeights = BuildArmComputeTensorInfo(weights); + + arm_compute::TensorInfo aclBiases; + arm_compute::TensorInfo *optionalAclBiases = nullptr; + if (descriptor.m_BiasEnabled) + { + aclBiases = BuildArmComputeTensorInfo(biases); + optionalAclBiases = &aclBiases; + } + + const arm_compute::FullyConnectedLayerInfo fullyConnectedLayerInfo = + ConvertFullyConnectedDescriptorToAclFullyConnectedLayerInfo(descriptor); + + + return arm_compute::NEFullyConnectedLayer::validate(&aclInput, + &aclWeights, + optionalAclBiases, + &aclOutput, + fullyConnectedLayerInfo); +} + NeonFullyConnectedFloat32Workload::NeonFullyConnectedFloat32Workload(const FullyConnectedQueueDescriptor& descriptor, const WorkloadInfo& info, std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager) - : Float32Workload<FullyConnectedQueueDescriptor>(descriptor, info) + : FloatWorkload<FullyConnectedQueueDescriptor>(descriptor, info) , m_FullyConnectedLayer(memoryManager) { m_Data.ValidateInputsOutputs("NeonFullyConnectedFloat32Workload", 1, 1); @@ -21,33 +52,45 @@ NeonFullyConnectedFloat32Workload::NeonFullyConnectedFloat32Workload(const Fully arm_compute::ITensor& input = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); arm_compute::ITensor& output = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); - BuildArmComputeTensor(m_WeightsTensor, m_Data.m_Weight->GetTensorInfo()); + m_WeightsTensor = std::make_unique<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_WeightsTensor, m_Data.m_Weight->GetTensorInfo()); - arm_compute::Tensor* optionalBiasTensor = nullptr; if (m_Data.m_Parameters.m_BiasEnabled) { - BuildArmComputeTensor(m_BiasesTensor, m_Data.m_Bias->GetTensorInfo()); - optionalBiasTensor = &m_BiasesTensor; + m_BiasesTensor = std::make_unique<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_BiasesTensor, m_Data.m_Bias->GetTensorInfo()); } // Construct - m_FullyConnectedLayer.configure( - &input, &m_WeightsTensor, optionalBiasTensor, &output, m_Data.m_Parameters.m_TransposeWeightMatrix); + arm_compute::FullyConnectedLayerInfo fc_info; + fc_info.transpose_weights = m_Data.m_Parameters.m_TransposeWeightMatrix; + m_FullyConnectedLayer.configure(&input, m_WeightsTensor.get(), m_BiasesTensor.get(), &output, fc_info); // Allocate - InitialiseArmComputeTensorData(m_WeightsTensor, m_Data.m_Weight->GetConstTensor<float>()); + InitializeArmComputeTensorDataForFloatTypes(*m_WeightsTensor, m_Data.m_Weight); - if (optionalBiasTensor) + if (m_BiasesTensor) { - InitialiseArmComputeTensorData(*optionalBiasTensor, m_Data.m_Bias->GetConstTensor<float>()); + InitializeArmComputeTensorDataForFloatTypes(*m_BiasesTensor, m_Data.m_Bias); } + + // Force Compute Library to perform the necessary copying and reshaping, after which + // delete all the input tensors that will no longer be needed + m_FullyConnectedLayer.prepare(); + FreeUnusedTensors(); } void NeonFullyConnectedFloat32Workload::Execute() const { - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuAcc, "NeonFullyConnectedFloat32Workload_Execute"); + ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonFullyConnectedFloat32Workload_Execute"); m_FullyConnectedLayer.run(); } +void NeonFullyConnectedFloat32Workload::FreeUnusedTensors() +{ + FreeTensorIfUnused(m_WeightsTensor); + FreeTensorIfUnused(m_BiasesTensor); +} + } //namespace armnn |