ArmNN
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NeonConvolution2dWorkload Class Reference

#include <NeonConvolution2dWorkload.hpp>

Inheritance diagram for NeonConvolution2dWorkload:
BaseWorkload< Convolution2dQueueDescriptor > IWorkload

Public Member Functions

 NeonConvolution2dWorkload (const Convolution2dQueueDescriptor &descriptor, const WorkloadInfo &info, std::shared_ptr< arm_compute::MemoryManagerOnDemand > &memoryManager, const bool isFastMathENabled=false)
 
void Execute () const override
 
arm_compute::ConvolutionMethod GetConvolutionMethod () const
 
- Public Member Functions inherited from BaseWorkload< Convolution2dQueueDescriptor >
 BaseWorkload (const Convolution2dQueueDescriptor &descriptor, const WorkloadInfo &info)
 
void ExecuteAsync (WorkingMemDescriptor &workingMemDescriptor) override
 
void PostAllocationConfigure () override
 
const Convolution2dQueueDescriptor & GetData () const
 
profiling::ProfilingGuid GetGuid () const final
 
- Public Member Functions inherited from IWorkload
virtual ~IWorkload ()
 
virtual void RegisterDebugCallback (const DebugCallbackFunction &)
 

Additional Inherited Members

- Protected Attributes inherited from BaseWorkload< Convolution2dQueueDescriptor >
Convolution2dQueueDescriptor m_Data
 
const profiling::ProfilingGuid m_Guid
 

Detailed Description

Definition at line 27 of file NeonConvolution2dWorkload.hpp.

Constructor & Destructor Documentation

◆ NeonConvolution2dWorkload()

NeonConvolution2dWorkload ( const Convolution2dQueueDescriptor &  descriptor,
const WorkloadInfo &  info,
std::shared_ptr< arm_compute::MemoryManagerOnDemand > &  memoryManager,
const bool  isFastMathENabled = false 
)

Definition at line 66 of file NeonConvolution2dWorkload.cpp.

References BaseWorkload< Convolution2dQueueDescriptor >::m_Data, QueueDescriptor::m_Inputs, QueueDescriptor::m_Outputs, and QueueDescriptor::ValidateInputsOutputs().

71  : BaseWorkload<Convolution2dQueueDescriptor>(descriptor, info)
72 {
73  using arm_compute::NEConvolutionLayer;
74 
75  m_Data.ValidateInputsOutputs("NeonConvolution2dWorkload", 1, 1);
76 
77  arm_compute::ITensor& input = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
78  arm_compute::ITensor& output = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
79 
80  arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout);
81  input.info()->set_data_layout(aclDataLayout);
82  output.info()->set_data_layout(aclDataLayout);
83 
84  m_KernelTensor = std::make_unique<arm_compute::Tensor>();
85  BuildArmComputeTensor(*m_KernelTensor, m_Data.m_Weight->GetTensorInfo(), m_Data.m_Parameters.m_DataLayout);
86 
87  if (m_Data.m_Parameters.m_BiasEnabled)
88  {
89  m_BiasTensor = std::make_unique<arm_compute::Tensor>();
90  BuildArmComputeTensor(*m_BiasTensor, m_Data.m_Bias->GetTensorInfo(), m_Data.m_Parameters.m_DataLayout);
91  }
92 
93  arm_compute::PadStrideInfo padStrideInfo = BuildArmComputePadStrideInfo(m_Data.m_Parameters);
94 
95  const arm_compute::Size2D aclDilationInfo = BuildArmComputeSize2D(m_Data.m_Parameters.m_DilationX,
96  m_Data.m_Parameters.m_DilationY);
97 
98  const arm_compute::ActivationLayerInfo activationInfo = ConvertAdditionalInfoToAclActivationLayerInfo(descriptor);
99 
100  auto convolutionLayer = std::make_unique<arm_compute::NEConvolutionLayer>(memoryManager);
101  convolutionLayer->configure(&input,
102  m_KernelTensor.get(),
103  m_BiasTensor.get(),
104  &output,
105  padStrideInfo,
106  arm_compute::WeightsInfo(),
107  aclDilationInfo,
108  activationInfo,
109  isFastMathEnabled);
110 
111  m_ConvolutionMethod =
112  convolutionLayer->get_convolution_method(input.info(),
113  m_KernelTensor->info(),
114  output.info(),
115  padStrideInfo,
116  arm_compute::WeightsInfo(),
117  aclDilationInfo,
118  activationInfo,
119  isFastMathEnabled);
120 
121  // Add details for profiling output
122  WorkloadInfo detailsInfo;
123 
124  detailsInfo.m_InputTensorInfos = info.m_InputTensorInfos;
125  detailsInfo.m_OutputTensorInfos = info.m_OutputTensorInfos;
126  detailsInfo.m_WeightsTensorInfo = armnn::Optional<armnn::TensorInfo>(descriptor.m_Weight->GetTensorInfo());
127  detailsInfo.m_ConvolutionMethod = armnn::Optional<std::string>(GetConvolutionMethodString(m_ConvolutionMethod));
128  if (descriptor.m_Parameters.m_BiasEnabled)
129  {
130  detailsInfo.m_BiasTensorInfo = armnn::Optional<armnn::TensorInfo>(descriptor.m_Bias->GetTensorInfo());
131  }
132 
133  // Report Profiling Details
134  ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonConvolution2dWorkload_Construct",
135  descriptor.m_Parameters,
136  detailsInfo,
137  this->GetGuid());
138 
139  m_ConvolutionLayer.reset(convolutionLayer.release());
140 
141  ARMNN_ASSERT(m_ConvolutionLayer);
142 
143  InitializeArmComputeTensorData(*m_KernelTensor, m_Data.m_Weight);
144 
145  if (m_Data.m_Parameters.m_BiasEnabled)
146  {
147  InitializeArmComputeTensorData(*m_BiasTensor, m_Data.m_Bias);
148  }
149 
150  m_ConvolutionLayer->prepare();
151  FreeUnusedTensors();
152 }
armnn::Convolution2dDescriptor::m_BiasEnabled
bool m_BiasEnabled
Enable/disable bias.
Definition: Descriptors.hpp:466
armnn::Convolution2dDescriptor::m_DataLayout
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
Definition: Descriptors.hpp:468
armnn::DataLayout
DataLayout
Definition: Types.hpp:53
armnn::GetConvolutionMethodString
std::string GetConvolutionMethodString(arm_compute::ConvolutionMethod &convolutionMethod)
Definition: ClWorkloadUtils.hpp:37
armnn::ConvertAdditionalInfoToAclActivationLayerInfo
arm_compute::ActivationLayerInfo ConvertAdditionalInfoToAclActivationLayerInfo(const QueueDescriptor &queueDescriptor)
Definition: ArmComputeUtils.hpp:102
armnn::Convolution2dQueueDescriptor::m_Weight
const ConstTensorHandle * m_Weight
Definition: WorkloadData.hpp:205
armnn::Convolution2dQueueDescriptor::m_Bias
const ConstTensorHandle * m_Bias
Definition: WorkloadData.hpp:206
armnn::QueueDescriptor::ValidateInputsOutputs
void ValidateInputsOutputs(const std::string &descName, unsigned int numExpectedIn, unsigned int numExpectedOut) const
Definition: WorkloadData.cpp:452
armnn::Convolution2dDescriptor::m_DilationY
uint32_t m_DilationY
Dilation along y axis.
Definition: Descriptors.hpp:464
armnn::ConstTensorHandle::GetTensorInfo
const TensorInfo & GetTensorInfo() const
Definition: TensorHandle.hpp:37
ARMNN_ASSERT
#define ARMNN_ASSERT(COND)
Definition: Assert.hpp:14
armnn::Convolution2dDescriptor::m_DilationX
uint32_t m_DilationX
Dilation along x axis.
Definition: Descriptors.hpp:462
armnn::QueueDescriptor::m_Outputs
std::vector< ITensorHandle * > m_Outputs
Definition: WorkloadData.hpp:31
ARMNN_REPORT_PROFILING_WORKLOAD_DESC
#define ARMNN_REPORT_PROFILING_WORKLOAD_DESC(name, desc, infos, guid)
Definition: Profiling.hpp:226
armnn::InitializeArmComputeTensorData
void InitializeArmComputeTensorData(arm_compute::Tensor &tensor, const ConstTensorHandle *handle)
Definition: NeonWorkloadUtils.hpp:60
armnn::QueueDescriptor::m_Inputs
std::vector< ITensorHandle * > m_Inputs
Definition: WorkloadData.hpp:30

Member Function Documentation

◆ Execute()

void Execute ( ) const
overridevirtual

Implements IWorkload.

Definition at line 154 of file NeonConvolution2dWorkload.cpp.

References ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID, and BaseWorkload< Convolution2dQueueDescriptor >::GetGuid().

155 {
156  ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonConvolution2dWorkload_Execute", this->GetGuid());
157  m_ConvolutionLayer->run();
158 }
armnn::BaseWorkload< Convolution2dQueueDescriptor >::GetGuid
profiling::ProfilingGuid GetGuid() const final
Definition: Workload.hpp:55
ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID
#define ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID(name, guid)
Definition: NeonWorkloadUtils.hpp:24

◆ GetConvolutionMethod()

arm_compute::ConvolutionMethod GetConvolutionMethod ( ) const

Definition at line 160 of file NeonConvolution2dWorkload.cpp.

161 {
162  return m_ConvolutionMethod;
163 }
The documentation for this class was generated from the following files: