From 6940dd720ebb6b3d1df8ca203ab696daefe58189 Mon Sep 17 00:00:00 2001 From: Jim Flynn Date: Fri, 20 Mar 2020 12:25:56 +0000 Subject: renamed Documentation folder 20.02 and added .nojekyll file Signed-off-by: Jim Flynn --- ...ssarmnn_1_1_ref_l2_normalization_workload.xhtml | 253 +++++++++++++++++++++ 1 file changed, 253 insertions(+) create mode 100644 20.02/classarmnn_1_1_ref_l2_normalization_workload.xhtml (limited to '20.02/classarmnn_1_1_ref_l2_normalization_workload.xhtml') diff --git a/20.02/classarmnn_1_1_ref_l2_normalization_workload.xhtml b/20.02/classarmnn_1_1_ref_l2_normalization_workload.xhtml new file mode 100644 index 0000000000..45b0d5d68b --- /dev/null +++ b/20.02/classarmnn_1_1_ref_l2_normalization_workload.xhtml @@ -0,0 +1,253 @@ + + + + + + + + + + + + + +ArmNN: RefL2NormalizationWorkload Class Reference + + + + + + + + + + + + + + + + +
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+ + + + ArmNN + + + +
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+  20.02 +
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+Public Member Functions | +List of all members
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RefL2NormalizationWorkload Class Reference
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#include <RefL2NormalizationWorkload.hpp>

+
+Inheritance diagram for RefL2NormalizationWorkload:
+
+
+ + +BaseWorkload< L2NormalizationQueueDescriptor > +IWorkload + +
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+Public Member Functions

 RefL2NormalizationWorkload (const L2NormalizationQueueDescriptor &descriptor, const WorkloadInfo &info)
 
void Execute () const override
 
- Public Member Functions inherited from BaseWorkload< L2NormalizationQueueDescriptor >
 BaseWorkload (const L2NormalizationQueueDescriptor &descriptor, const WorkloadInfo &info)
 
void PostAllocationConfigure () override
 
const L2NormalizationQueueDescriptor & 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< L2NormalizationQueueDescriptor >
const L2NormalizationQueueDescriptor m_Data
 
const profiling::ProfilingGuid m_Guid
 
+

Detailed Description

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+

Definition at line 14 of file RefL2NormalizationWorkload.hpp.

+

Constructor & Destructor Documentation

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◆ RefL2NormalizationWorkload()

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RefL2NormalizationWorkload (const L2NormalizationQueueDescriptor & descriptor,
const WorkloadInfo & info 
)
+ 		+explicit
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+ +

Definition at line 23 of file RefL2NormalizationWorkload.cpp.

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26  : BaseWorkload<L2NormalizationQueueDescriptor>(descriptor, info) {}
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Member Function Documentation

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◆ Execute()

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void Execute () const
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Implements IWorkload.

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Definition at line 28 of file RefL2NormalizationWorkload.cpp.

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References ARMNN_SCOPED_PROFILING_EVENT, armnn::CpuRef, TensorShape::GetNumDimensions(), armnn::GetTensorInfo(), BaseWorkload< L2NormalizationQueueDescriptor >::m_Data, L2NormalizationDescriptor::m_DataLayout, L2NormalizationDescriptor::m_Eps, QueueDescriptor::m_Inputs, QueueDescriptor::m_Outputs, QueueDescriptorWithParameters< LayerDescriptor >::m_Parameters, and armnn::numeric_cast().

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29 {
30  ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefL2NormalizationWorkload_Execute");
31 
32  const TensorInfo& inputInfo = GetTensorInfo(m_Data.m_Inputs[0]);
33  const TensorInfo& outputInfo = GetTensorInfo(m_Data.m_Outputs[0]);
34 
35  auto inputDecoder = MakeDecoder<float>(inputInfo, m_Data.m_Inputs[0]->Map());
36  auto outputEncoder = MakeEncoder<float>(outputInfo, m_Data.m_Outputs[0]->Map());
37 
38  DataLayoutIndexed dataLayout(m_Data.m_Parameters.m_DataLayout);
39 
40  const TensorShape& shape = inputInfo.GetShape();
41  unsigned int paddedShapeArray[4];
42  const int idxShift = 4 - boost::numeric_cast<int>(shape.GetNumDimensions());
43 
44  const unsigned int batches = (idxShift == 0) ? shape[0] : 1;
45  paddedShapeArray[0] = batches;
46 
47  const int channelsIdx = boost::numeric_cast<int>(dataLayout.GetChannelsIndex());
48  const unsigned int channels = (channelsIdx - idxShift >= 0)
49  ? shape[boost::numeric_cast<unsigned int>(channelsIdx - idxShift)]
50  : 1;
51  paddedShapeArray[channelsIdx] = channels;
52 
53  const int heightIdx = boost::numeric_cast<int>(dataLayout.GetHeightIndex());
54  const unsigned int height = (heightIdx - idxShift >= 0)
55  ? shape[boost::numeric_cast<unsigned int>(heightIdx - idxShift)]
56  : 1;
57  paddedShapeArray[heightIdx] = height;
58 
59  const int widthIdx = boost::numeric_cast<int>(dataLayout.GetWidthIndex());
60  const unsigned int width = (widthIdx - idxShift >= 0)
61  ? shape[boost::numeric_cast<unsigned int>(widthIdx - idxShift)]
62  : 1;
63  paddedShapeArray[widthIdx] = width;
64 
65  const TensorShape& paddedShape = TensorShape(4, paddedShapeArray);
66 
67  for (unsigned int n = 0; n < batches; ++n)
68  {
69  for (unsigned int c = 0; c < channels; ++c)
70  {
71  for (unsigned int h = 0; h < height; ++h)
72  {
73  for (unsigned int w = 0; w < width; ++w)
74  {
75  float reduction = 0.0;
76  for (unsigned int d = 0; d < channels; ++d)
77  {
78  unsigned int inputIndex = dataLayout.GetIndex(paddedShape, n, d, h, w);
79 
80  (*inputDecoder)[inputIndex];
81  const float value = inputDecoder->Get();
82  reduction += value * value;
83  }
84 
85  unsigned int index = dataLayout.GetIndex(paddedShape, n, c, h, w);
86 
87  float maximum = reduction < m_Data.m_Parameters.m_Eps ? m_Data.m_Parameters.m_Eps : reduction;
88 
89  const float scale = 1.0f / sqrtf(maximum);
90 
91  (*inputDecoder)[index];
92  (*outputEncoder)[index];
93  outputEncoder->Set(inputDecoder->Get() * scale);
94  }
95  }
96  }
97  }
98 }
armnn::L2NormalizationDescriptor::m_Eps
float m_Eps
Used to avoid dividing by zero.
Definition: Descriptors.hpp:604
+
armnn::Compute::CpuRef
CPU Execution: Reference C++ kernels.
+ +
armnn::BaseWorkload< L2NormalizationQueueDescriptor >::m_Data
const L2NormalizationQueueDescriptor m_Data
Definition: Workload.hpp:46
+
armnn::GetTensorInfo
const TensorInfo & GetTensorInfo(const ITensorHandle *tensorHandle)
float32 helpers
Definition: RefWorkloadUtils.hpp:25
+ + + +
ARMNN_SCOPED_PROFILING_EVENT
#define ARMNN_SCOPED_PROFILING_EVENT(backendId, name)
Definition: Profiling.hpp:169
+
armnn::L2NormalizationDescriptor::m_DataLayout
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
Definition: Descriptors.hpp:606
+
armnnUtils::DataLayoutIndexed
Provides access to the appropriate indexes for Channels, Height and Width based on DataLayout...
Definition: DataLayoutIndexed.hpp:17
+
armnn::numeric_cast
std::enable_if_t< std::is_unsigned< Source >::value &&std::is_unsigned< Dest >::value, Dest > numeric_cast(Source source)
Definition: NumericCast.hpp:33
+
armnn::QueueDescriptor::m_Outputs
std::vector< ITensorHandle * > m_Outputs
Definition: WorkloadData.hpp:31
+
armnn::TensorShape::GetNumDimensions
unsigned int GetNumDimensions() const
Definition: Tensor.hpp:43
+
armnn::QueueDescriptor::m_Inputs
std::vector< ITensorHandle * > m_Inputs
Definition: WorkloadData.hpp:30
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The documentation for this class was generated from the following files: +
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+ + + + -- cgit v1.2.1