| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "NeonNormalizationFloatWorkload.hpp" |
| #include <backends/NeonLayerSupport.hpp> |
| #include <backends/aclCommon/ArmComputeUtils.hpp> |
| #include <backends/aclCommon/ArmComputeTensorUtils.hpp> |
| |
| namespace armnn |
| { |
| |
| arm_compute::Status NeonNormalizationWorkloadValidate(const TensorInfo& input, |
| const TensorInfo& output, |
| const NormalizationDescriptor& descriptor) |
| { |
| const arm_compute::TensorInfo aclInput = armcomputetensorutils::BuildArmComputeTensorInfo(input); |
| const arm_compute::TensorInfo aclOutput = armcomputetensorutils::BuildArmComputeTensorInfo(output); |
| |
| arm_compute::NormalizationLayerInfo normalizationInfo = |
| armcomputetensorutils::BuildArmComputeNormalizationLayerInfo(descriptor); |
| |
| return arm_compute::NENormalizationLayer::validate(&aclInput, &aclOutput, normalizationInfo); |
| } |
| |
| NeonNormalizationFloatWorkload::NeonNormalizationFloatWorkload(const NormalizationQueueDescriptor& descriptor, |
| const WorkloadInfo& info, |
| std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager) |
| : FloatWorkload<NormalizationQueueDescriptor>(descriptor, info) |
| , m_NormalizationLayer(memoryManager) |
| { |
| m_Data.ValidateInputsOutputs("NeonNormalizationFloatWorkload", 1, 1); |
| std::string reasonIfUnsupported; |
| if (!IsNeonNormalizationDescParamsSupported(&reasonIfUnsupported, m_Data.m_Parameters)) |
| { |
| 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 = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); |
| arm_compute::ITensor& output = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); |
| |
| 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); |
| |
| m_NormalizationLayer.configure(&input, &output, normalizationInfo); |
| } |
| |
| void NeonNormalizationFloatWorkload::Execute() const |
| { |
| ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonNormalizationFloatWorkload_Execute"); |
| m_NormalizationLayer.run(); |
| } |
| |
| } //namespace armnn |
