src/backends/neon/workloads/NeonNormalizationFloatWorkload.cpp

   1 //
   2 // Copyright © 2017 Arm Ltd. All rights reserved.
   3 // SPDX-License-Identifier: MIT
   4 //
   5
   6 #include "NeonNormalizationFloatWorkload.hpp"
   7 #include <backends/neon/NeonLayerSupport.hpp>
   8 #include <backends/aclCommon/ArmComputeUtils.hpp>
   9 #include <backends/aclCommon/ArmComputeTensorUtils.hpp>
  10
  11 using namespace armnn::armcomputetensorutils;
  12
  13 namespace armnn
  14 {
  15
  16 arm_compute::Status NeonNormalizationWorkloadValidate(const TensorInfo& input,
  17                                                       const TensorInfo& output,
  18                                                       const NormalizationDescriptor& descriptor)
  19 {
  20     const arm_compute::TensorInfo aclInput = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
  21     const arm_compute::TensorInfo aclOutput = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout);
  22
  23     arm_compute::NormalizationLayerInfo normalizationInfo = BuildArmComputeNormalizationLayerInfo(descriptor);
  24
  25     return arm_compute::NENormalizationLayer::validate(&aclInput, &aclOutput, normalizationInfo);
  26 }
  27
  28 NeonNormalizationFloatWorkload::NeonNormalizationFloatWorkload(const NormalizationQueueDescriptor& descriptor,
  29                                                    const WorkloadInfo& info,
  30                                                    std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager)
  31     : FloatWorkload<NormalizationQueueDescriptor>(descriptor, info)
  32     , m_NormalizationLayer(memoryManager)
  33 {
  34     m_Data.ValidateInputsOutputs("NeonNormalizationFloatWorkload", 1, 1);
  35     std::string reasonIfUnsupported;
  36     if (!IsNeonNormalizationDescParamsSupported(&reasonIfUnsupported, m_Data.m_Parameters))
  37     {
  38         throw UnimplementedException(reasonIfUnsupported);
  39     }
  40
  41     // Input and output tensors have to have the same dimensionality.
  42     if (info.m_InputTensorInfos[0].GetShape()[1] != info.m_OutputTensorInfos[0].GetShape()[1]
  43         || info.m_InputTensorInfos[0].GetShape()[0] != info.m_OutputTensorInfos[0].GetShape()[0]
  44         || info.m_InputTensorInfos[0].GetShape()[3] != info.m_OutputTensorInfos[0].GetShape()[3]
  45         || info.m_InputTensorInfos[0].GetShape()[2] != info.m_OutputTensorInfos[0].GetShape()[2])
  46     {
  47         throw InvalidArgumentException("Normalization requires input and output tensors to have equal dimensionality.");
  48     }
  49
  50     arm_compute::ITensor& input = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
  51     arm_compute::ITensor& output = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
  52
  53     const arm_compute::NormType normType =
  54         ConvertNormalizationAlgorithmChannelToAclNormType(m_Data.m_Parameters.m_NormChannelType);
  55     arm_compute::NormalizationLayerInfo normalizationInfo(normType,
  56                                                           m_Data.m_Parameters.m_NormSize,
  57                                                           m_Data.m_Parameters.m_Alpha,
  58                                                           m_Data.m_Parameters.m_Beta,
  59                                                           m_Data.m_Parameters.m_K,
  60                                                           false);
  61
  62     m_NormalizationLayer.configure(&input, &output, normalizationInfo);
  63 }
  64
  65 void NeonNormalizationFloatWorkload::Execute() const
  66 {
  67     ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonNormalizationFloatWorkload_Execute");
  68     m_NormalizationLayer.run();
  69 }
  70
  71 } //namespace armnn