| // |
| // Copyright © 2017 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "NeonNormalizationFloatWorkload.hpp" |
| |
| #include "NeonWorkloadUtils.hpp" |
| #include <aclCommon/ArmComputeUtils.hpp> |
| #include <aclCommon/ArmComputeTensorUtils.hpp> |
| #include <armnn/utility/PolymorphicDowncast.hpp> |
| |
| #include <arm_compute/runtime/NEON/functions/NENormalizationLayer.h> |
| |
| using namespace armnn::armcomputetensorutils; |
| |
| namespace armnn |
| { |
| |
| namespace |
| { |
| using ACLMemManagerOnDemand = std::shared_ptr<arm_compute::MemoryManagerOnDemand>; |
| |
| bool IsNeonNormalizationDescriptorSupported(const NormalizationDescriptor& parameters, |
| Optional<std::string&> reasonIfUnsupported) |
| { |
| if (parameters.m_NormMethodType != NormalizationAlgorithmMethod::LocalBrightness) |
| { |
| if (reasonIfUnsupported) |
| { |
| reasonIfUnsupported.value() = "Unsupported normalisation method type, only LocalBrightness is supported"; |
| } |
| return false; |
| } |
| if (parameters.m_NormSize % 2 == 0) |
| { |
| if (reasonIfUnsupported) |
| { |
| reasonIfUnsupported.value() = "Normalization size must be an odd number."; |
| } |
| return false; |
| } |
| |
| return true; |
| } |
| |
| } // anonymous namespace |
| |
| arm_compute::Status NeonNormalizationWorkloadValidate(const TensorInfo& input, |
| const TensorInfo& output, |
| const NormalizationDescriptor& descriptor) |
| { |
| const arm_compute::TensorInfo aclInput = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout); |
| const arm_compute::TensorInfo aclOutput = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout); |
| |
| arm_compute::NormalizationLayerInfo normalizationInfo = BuildArmComputeNormalizationLayerInfo(descriptor); |
| |
| return arm_compute::NENormalizationLayer::validate(&aclInput, &aclOutput, normalizationInfo); |
| } |
| |
| NeonNormalizationFloatWorkload::NeonNormalizationFloatWorkload(const NormalizationQueueDescriptor& descriptor, |
| const WorkloadInfo& info, |
| ACLMemManagerOnDemand& memoryManager) |
| : FloatWorkload<NormalizationQueueDescriptor>(descriptor, info) |
| { |
| // Report Profiling Details |
| ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonNormalizationWorkload_Construct", |
| descriptor.m_Parameters, |
| info, |
| this->GetGuid()); |
| |
| m_Data.ValidateInputsOutputs("NeonNormalizationFloatWorkload", 1, 1); |
| std::string reasonIfUnsupported; |
| if (!IsNeonNormalizationDescriptorSupported(m_Data.m_Parameters, Optional<std::string&>(reasonIfUnsupported))) |
| { |
| throw UnimplementedException(reasonIfUnsupported); |
| } |
| |
| // Input and output tensors have to have the same dimensionality. |
| if (info.m_InputTensorInfos[0].GetShape()[1] != info.m_OutputTensorInfos[0].GetShape()[1] |
| || info.m_InputTensorInfos[0].GetShape()[0] != info.m_OutputTensorInfos[0].GetShape()[0] |
| || info.m_InputTensorInfos[0].GetShape()[3] != info.m_OutputTensorInfos[0].GetShape()[3] |
| || info.m_InputTensorInfos[0].GetShape()[2] != info.m_OutputTensorInfos[0].GetShape()[2]) |
| { |
| throw InvalidArgumentException("Normalization requires input and output tensors to have equal dimensionality."); |
| } |
| |
| arm_compute::ITensor& input = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); |
| arm_compute::ITensor& output = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); |
| arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout); |
| input.info()->set_data_layout(aclDataLayout); |
| output.info()->set_data_layout(aclDataLayout); |
| |
| const arm_compute::NormType normType = |
| ConvertNormalizationAlgorithmChannelToAclNormType(m_Data.m_Parameters.m_NormChannelType); |
| arm_compute::NormalizationLayerInfo normalizationInfo(normType, |
| m_Data.m_Parameters.m_NormSize, |
| m_Data.m_Parameters.m_Alpha, |
| m_Data.m_Parameters.m_Beta, |
| m_Data.m_Parameters.m_K, |
| false); |
| auto layer = std::make_unique<arm_compute::NENormalizationLayer>(memoryManager); |
| layer->configure(&input, &output, normalizationInfo); |
| m_NormalizationLayer.reset(layer.release()); |
| } |
| |
| void NeonNormalizationFloatWorkload::Execute() const |
| { |
| ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonNormalizationFloatWorkload_Execute", this->GetGuid()); |
| m_NormalizationLayer->run(); |
| } |
| |
| } //namespace armnn |