1 //
2 // Copyright © 2017 Arm Ltd and Contributors. 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 #include <armnn/utility/PolymorphicDowncast.hpp>
12
13 #include <arm_compute/runtime/NEON/functions/NENormalizationLayer.h>
14
15 using namespace armnn::armcomputetensorutils;
16
17 namespace armnn
18 {
19
20 namespace
21 {
22 using ACLMemManagerOnDemand = std::shared_ptr<arm_compute::MemoryManagerOnDemand>;
23
IsNeonNormalizationDescriptorSupported(const NormalizationDescriptor & parameters,Optional<std::string &> reasonIfUnsupported)24 bool IsNeonNormalizationDescriptorSupported(const NormalizationDescriptor& parameters,
25 Optional<std::string&> reasonIfUnsupported)
26 {
27 if (parameters.m_NormMethodType != NormalizationAlgorithmMethod::LocalBrightness)
28 {
29 if (reasonIfUnsupported)
30 {
31 reasonIfUnsupported.value() = "Unsupported normalisation method type, only LocalBrightness is supported";
32 }
33 return false;
34 }
35 if (parameters.m_NormSize % 2 == 0)
36 {
37 if (reasonIfUnsupported)
38 {
39 reasonIfUnsupported.value() = "Normalization size must be an odd number.";
40 }
41 return false;
42 }
43
44 return true;
45 }
46
47 } // anonymous namespace
48
NeonNormalizationWorkloadValidate(const TensorInfo & input,const TensorInfo & output,const NormalizationDescriptor & descriptor)49 arm_compute::Status NeonNormalizationWorkloadValidate(const TensorInfo& input,
50 const TensorInfo& output,
51 const NormalizationDescriptor& descriptor)
52 {
53 const arm_compute::TensorInfo aclInput = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
54 const arm_compute::TensorInfo aclOutput = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout);
55
56 arm_compute::NormalizationLayerInfo normalizationInfo = BuildArmComputeNormalizationLayerInfo(descriptor);
57
58 return arm_compute::NENormalizationLayer::validate(&aclInput, &aclOutput, normalizationInfo);
59 }
60
NeonNormalizationFloatWorkload(const NormalizationQueueDescriptor & descriptor,const WorkloadInfo & info,ACLMemManagerOnDemand & memoryManager)61 NeonNormalizationFloatWorkload::NeonNormalizationFloatWorkload(const NormalizationQueueDescriptor& descriptor,
62 const WorkloadInfo& info,
63 ACLMemManagerOnDemand& memoryManager)
64 : FloatWorkload<NormalizationQueueDescriptor>(descriptor, info)
65 {
66 // Report Profiling Details
67 ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonNormalizationWorkload_Construct",
68 descriptor.m_Parameters,
69 info,
70 this->GetGuid());
71
72 m_Data.ValidateInputsOutputs("NeonNormalizationFloatWorkload", 1, 1);
73 std::string reasonIfUnsupported;
74 if (!IsNeonNormalizationDescriptorSupported(m_Data.m_Parameters, Optional<std::string&>(reasonIfUnsupported)))
75 {
76 throw UnimplementedException(reasonIfUnsupported);
77 }
78
79 // Input and output tensors have to have the same dimensionality.
80 if (info.m_InputTensorInfos[0].GetShape()[1] != info.m_OutputTensorInfos[0].GetShape()[1]
81 || info.m_InputTensorInfos[0].GetShape()[0] != info.m_OutputTensorInfos[0].GetShape()[0]
82 || info.m_InputTensorInfos[0].GetShape()[3] != info.m_OutputTensorInfos[0].GetShape()[3]
83 || info.m_InputTensorInfos[0].GetShape()[2] != info.m_OutputTensorInfos[0].GetShape()[2])
84 {
85 throw InvalidArgumentException("Normalization requires input and output tensors to have equal dimensionality.");
86 }
87
88 arm_compute::ITensor& input = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
89 arm_compute::ITensor& output = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
90 arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout);
91 input.info()->set_data_layout(aclDataLayout);
92 output.info()->set_data_layout(aclDataLayout);
93
94 const arm_compute::NormType normType =
95 ConvertNormalizationAlgorithmChannelToAclNormType(m_Data.m_Parameters.m_NormChannelType);
96 arm_compute::NormalizationLayerInfo normalizationInfo(normType,
97 m_Data.m_Parameters.m_NormSize,
98 m_Data.m_Parameters.m_Alpha,
99 m_Data.m_Parameters.m_Beta,
100 m_Data.m_Parameters.m_K,
101 false);
102 auto layer = std::make_unique<arm_compute::NENormalizationLayer>(memoryManager);
103 layer->configure(&input, &output, normalizationInfo);
104 m_NormalizationLayer.reset(layer.release());
105 }
106
Execute() const107 void NeonNormalizationFloatWorkload::Execute() const
108 {
109 ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonNormalizationFloatWorkload_Execute", this->GetGuid());
110 m_NormalizationLayer->run();
111 }
112
ReplaceInputTensorHandle(ITensorHandle * tensorHandle,unsigned int slot)113 void NeonNormalizationFloatWorkload::ReplaceInputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot)
114 {
115 ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot];
116 this->m_Data.m_Inputs[slot] = tensorHandle;
117 try
118 {
119 Reconfigure();
120 }
121 catch(armnn::UnimplementedException& e)
122 {
123 // Cannot reconfigure, revert the slot back and throw the exception.
124 this->m_Data.m_Inputs[slot] = backupHandle;
125 throw e;
126 }
127 }
128
129 // Replace output tensor handle with the given TensorHandle
ReplaceOutputTensorHandle(ITensorHandle * tensorHandle,unsigned int slot)130 void NeonNormalizationFloatWorkload::ReplaceOutputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot)
131 {
132 ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot];
133 this->m_Data.m_Inputs[slot] = tensorHandle;
134 try
135 {
136 Reconfigure();
137 }
138 catch(armnn::UnimplementedException& e)
139 {
140 // Cannot reconfigure, revert the slot back and throw the exception.
141 this->m_Data.m_Inputs[slot] = backupHandle;
142 throw e;
143 }
144 }
145
Reconfigure()146 void NeonNormalizationFloatWorkload::Reconfigure()
147 {
148 throw armnn::UnimplementedException("Reconfigure not implemented for this workload");
149 }
150
151 } //namespace armnn
152