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 --- ..._normalization_float_workload_8cpp_source.xhtml | 143 +++++++++++++++++++++ 1 file changed, 143 insertions(+) create mode 100644 20.02/_neon_normalization_float_workload_8cpp_source.xhtml (limited to '20.02/_neon_normalization_float_workload_8cpp_source.xhtml') diff --git a/20.02/_neon_normalization_float_workload_8cpp_source.xhtml b/20.02/_neon_normalization_float_workload_8cpp_source.xhtml new file mode 100644 index 0000000000..e8b6b86500 --- /dev/null +++ b/20.02/_neon_normalization_float_workload_8cpp_source.xhtml @@ -0,0 +1,143 @@ + + + + + + + + + + + + + +ArmNN: src/backends/neon/workloads/NeonNormalizationFloatWorkload.cpp Source File + + + + + + + + + + + + + + + + +
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NeonNormalizationFloatWorkload.cpp
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+Go to the documentation of this file.
1 //
2 // Copyright © 2017 Arm Ltd. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5 
6 #include "NeonNormalizationFloatWorkload.hpp"
7 
8 #include "NeonWorkloadUtils.hpp"
9 #include <aclCommon/ArmComputeUtils.hpp>
10 #include <aclCommon/ArmComputeTensorUtils.hpp>
11 
12 #include <arm_compute/runtime/NEON/functions/NENormalizationLayer.h>
13 
14 using namespace armnn::armcomputetensorutils;
15 
16 namespace armnn
17 {
18 
19 namespace
20 {
21 
22 bool IsNeonNormalizationDescriptorSupported(const NormalizationDescriptor& parameters,
23  Optional<std::string&> reasonIfUnsupported)
24 {
25  if (parameters.m_NormMethodType != NormalizationAlgorithmMethod::LocalBrightness)
26  {
27  if (reasonIfUnsupported)
28  {
29  reasonIfUnsupported.value() = "Unsupported normalisation method type, only LocalBrightness is supported";
30  }
31  return false;
32  }
33  if (parameters.m_NormSize % 2 == 0)
34  {
35  if (reasonIfUnsupported)
36  {
37  reasonIfUnsupported.value() = "Normalization size must be an odd number.";
38  }
39  return false;
40  }
41 
42  return true;
43 }
44 
45 } // anonymous namespace
46 
47 arm_compute::Status NeonNormalizationWorkloadValidate(const TensorInfo& input,
48  const TensorInfo& output,
49  const NormalizationDescriptor& descriptor)
50 {
51  const arm_compute::TensorInfo aclInput = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
52  const arm_compute::TensorInfo aclOutput = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout);
53 
54  arm_compute::NormalizationLayerInfo normalizationInfo = BuildArmComputeNormalizationLayerInfo(descriptor);
55 
56  return arm_compute::NENormalizationLayer::validate(&aclInput, &aclOutput, normalizationInfo);
57 }
58 
59 NeonNormalizationFloatWorkload::NeonNormalizationFloatWorkload(const NormalizationQueueDescriptor& descriptor,
60  const WorkloadInfo& info,
61  std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager)
62  : FloatWorkload<NormalizationQueueDescriptor>(descriptor, info)
63 {
64  m_Data.ValidateInputsOutputs("NeonNormalizationFloatWorkload", 1, 1);
65  std::string reasonIfUnsupported;
66  if (!IsNeonNormalizationDescriptorSupported(m_Data.m_Parameters, Optional<std::string&>(reasonIfUnsupported)))
67  {
68  throw UnimplementedException(reasonIfUnsupported);
69  }
70 
71  // Input and output tensors have to have the same dimensionality.
72  if (info.m_InputTensorInfos[0].GetShape()[1] != info.m_OutputTensorInfos[0].GetShape()[1]
73  || info.m_InputTensorInfos[0].GetShape()[0] != info.m_OutputTensorInfos[0].GetShape()[0]
74  || info.m_InputTensorInfos[0].GetShape()[3] != info.m_OutputTensorInfos[0].GetShape()[3]
75  || info.m_InputTensorInfos[0].GetShape()[2] != info.m_OutputTensorInfos[0].GetShape()[2])
76  {
77  throw InvalidArgumentException("Normalization requires input and output tensors to have equal dimensionality.");
78  }
79 
80  arm_compute::ITensor& input = boost::polymorphic_downcast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
81  arm_compute::ITensor& output = boost::polymorphic_downcast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
82  arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout);
83  input.info()->set_data_layout(aclDataLayout);
84  output.info()->set_data_layout(aclDataLayout);
85 
86  const arm_compute::NormType normType =
87  ConvertNormalizationAlgorithmChannelToAclNormType(m_Data.m_Parameters.m_NormChannelType);
88  arm_compute::NormalizationLayerInfo normalizationInfo(normType,
89  m_Data.m_Parameters.m_NormSize,
90  m_Data.m_Parameters.m_Alpha,
91  m_Data.m_Parameters.m_Beta,
92  m_Data.m_Parameters.m_K,
93  false);
94  auto layer = std::make_unique<arm_compute::NENormalizationLayer>(memoryManager);
95  layer->configure(&input, &output, normalizationInfo);
96  m_NormalizationLayer.reset(layer.release());
97 }
98 
99 void NeonNormalizationFloatWorkload::Execute() const
100 {
101  ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonNormalizationFloatWorkload_Execute");
102  m_NormalizationLayer->run();
103 }
104 
105 } //namespace armnn
armnn::DataLayout
DataLayout
Definition: Types.hpp:49
+ +
ArmComputeUtils.hpp
+ +
armnn::BaseWorkload::m_Data
const QueueDescriptor m_Data
Definition: Workload.hpp:46
+
ARMNN_SCOPED_PROFILING_EVENT_NEON
#define ARMNN_SCOPED_PROFILING_EVENT_NEON(name)
Definition: NeonWorkloadUtils.hpp:17
+ +
ArmComputeTensorUtils.hpp
+
armnn::ConvertNormalizationAlgorithmChannelToAclNormType
arm_compute::NormType ConvertNormalizationAlgorithmChannelToAclNormType(NormalizationAlgorithmChannel channelType)
Definition: ArmComputeUtils.hpp:107
+
armnn::QueueDescriptor::ValidateInputsOutputs
void ValidateInputsOutputs(const std::string &descName, unsigned int numExpectedIn, unsigned int numExpectedOut) const
Definition: WorkloadData.cpp:461
+
armnn::NormalizationDescriptor::m_DataLayout
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
Definition: Descriptors.hpp:587
+
armnn
Copyright (c) 2020 ARM Limited.
Definition: 00_introduction.dox:25
+ +
armnn::WorkloadInfo::m_InputTensorInfos
std::vector< TensorInfo > m_InputTensorInfos
Definition: WorkloadInfo.hpp:18
+
armnn::Status
Status
enumeration
Definition: Types.hpp:26
+
NeonWorkloadUtils.hpp
+ +
armnn::WorkloadInfo::m_OutputTensorInfos
std::vector< TensorInfo > m_OutputTensorInfos
Definition: WorkloadInfo.hpp:19
+ +
armnn::NeonNormalizationWorkloadValidate
arm_compute::Status NeonNormalizationWorkloadValidate(const TensorInfo &input, const TensorInfo &output, const NormalizationDescriptor &descriptor)
Definition: NeonNormalizationFloatWorkload.cpp:47
+
NeonNormalizationFloatWorkload.hpp
+
armnn::QueueDescriptor::m_Outputs
std::vector< ITensorHandle * > m_Outputs
Definition: WorkloadData.hpp:31
+ +
armnn::NeonNormalizationFloatWorkload::NeonNormalizationFloatWorkload
NeonNormalizationFloatWorkload(const NormalizationQueueDescriptor &descriptor, const WorkloadInfo &info, std::shared_ptr< arm_compute::MemoryManagerOnDemand > &memoryManager)
Definition: NeonNormalizationFloatWorkload.cpp:59
+
armnn::WorkloadInfo
Contains information about inputs and outputs to a layer.
Definition: WorkloadInfo.hpp:16
+
armnn::QueueDescriptor::m_Inputs
std::vector< ITensorHandle * > m_Inputs
Definition: WorkloadData.hpp:30
+
armnn::NormalizationAlgorithmMethod::LocalBrightness
Krichevsky 2012: Local Brightness Normalization.
+
armnn::NormalizationDescriptor
A NormalizationDescriptor for the NormalizationLayer.
Definition: Descriptors.hpp:551
+ + +
+
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