ArmNN
 24.08
NeonSpaceToBatchNdWorkload.cpp
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1 //
2 // Copyright © 2020-2023 Arm Ltd and Contributors. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5 
7 
9 
10 namespace armnn
11 {
12 
13 using namespace armcomputetensorutils;
14 
16  const TensorInfo& output,
17  const SpaceToBatchNdDescriptor& descriptor)
18 {
19  arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
20  arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout);
21 
22  arm_compute::Status statusSpaceToBatch = arm_compute::Status(arm_compute::ErrorCode::OK);
23  arm_compute::Status statusReshapeInput = arm_compute::Status(arm_compute::ErrorCode::OK);
24  arm_compute::Status statusReshapeOutput = arm_compute::Status(arm_compute::ErrorCode::OK);
25 
26  arm_compute::TensorInfo aclReshapeInputInfo = aclInputInfo;
27  arm_compute::TensorInfo aclReshapeOutputInfo = aclOutputInfo;
28 
29  // When a spacial dimension is missing (rank=3) set W to 1
30  const unsigned int rank = input.GetNumDimensions();
31  if (rank == 3)
32  {
33  const arm_compute::TensorShape inputShape = aclInputInfo.tensor_shape();
34  const arm_compute::TensorShape outputShape = aclOutputInfo.tensor_shape();
35 
36  if (descriptor.m_DataLayout == armnn::DataLayout::NHWC)
37  {
38  // In ACL dimensions are right to left: C, W, H, N
39  aclReshapeInputInfo.set_tensor_shape({inputShape.x(), 1, inputShape.y(), inputShape.z()});
40  aclReshapeOutputInfo.set_tensor_shape({outputShape.x(), 1, outputShape.y(), outputShape.z()});
41  }
42  else if (descriptor.m_DataLayout == armnn::DataLayout::NCHW)
43  {
44  // In ACL dimensions are right to left: W, H, C, N
45  aclReshapeInputInfo.set_tensor_shape({1, inputShape.x(), inputShape.y(), inputShape.z()});
46  aclReshapeOutputInfo.set_tensor_shape({1, outputShape.x(), outputShape.y(), outputShape.z()});
47  }
48  else
49  {
50  throw InvalidArgumentException("Unsupported or unknown DataLayout", CHECK_LOCATION());
51  }
52 
53  statusReshapeInput = arm_compute::NEReshapeLayer::validate(&aclInputInfo, &aclReshapeInputInfo);
54  statusReshapeOutput = arm_compute::NEReshapeLayer::validate(&aclReshapeOutputInfo, &aclOutputInfo);
55  }
56 
57  // ArmNN blockShape is [H, W] ACl asks for W, H
58  int32_t blockHeight = armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[0]);
59  int32_t blockWidth = (rank == 3) ? 1 : armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[1]);
60 
61  unsigned int padLeft = (rank == 3) ? 0 : descriptor.m_PadList[1].first;
62  unsigned int padRight = (rank == 3) ? 0 : descriptor.m_PadList[1].second;
63  arm_compute::Size2D paddingLeftTop = BuildArmComputeSize2D(padLeft,
64  descriptor.m_PadList[0].first);
65  arm_compute::Size2D paddingRightBottom = BuildArmComputeSize2D(padRight,
66  descriptor.m_PadList[0].second);
67 
68  statusSpaceToBatch = arm_compute::NESpaceToBatchLayer::validate(rank == 3 ? &aclReshapeInputInfo : &aclInputInfo,
69  blockWidth,
70  blockHeight,
71  paddingLeftTop,
72  paddingRightBottom,
73  rank == 3 ? &aclReshapeOutputInfo : &aclOutputInfo);
74 
75  if (statusReshapeInput.error_code() == arm_compute::ErrorCode::OK &&
76  statusReshapeOutput.error_code() == arm_compute::ErrorCode::OK &&
77  statusSpaceToBatch.error_code() == arm_compute::ErrorCode::OK)
78  {
79  return arm_compute::Status(arm_compute::ErrorCode::OK,
80  "All SpaceToBatch layers validate status OK.");
81  }
82  else
83  {
84  return arm_compute::Status(arm_compute::ErrorCode::RUNTIME_ERROR,
85  "SpaceToBatch layer validate status failed."
86  + statusSpaceToBatch.error_description()
87  + statusReshapeInput.error_description()
88  + statusReshapeOutput.error_description());
89  }
90 }
91 
93  const WorkloadInfo& info)
95 {
96  // Report Profiling Details
97  ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonSpaceToBatchNdWorkload_Construct",
98  descriptor.m_Parameters,
99  info,
100  this->GetGuid());
101 
102  m_Data.ValidateInputsOutputs("NESpaceToBatchNdWorkload", 1, 1);
103 
104  arm_compute::ITensor& input = PolymorphicPointerDowncast<IAclTensorHandle>(m_Data.m_Inputs[0])->GetTensor();
105  arm_compute::ITensor& output = PolymorphicPointerDowncast<IAclTensorHandle>(m_Data.m_Outputs[0])->GetTensor();
106 
107  arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout);
108  input.info()->set_data_layout(aclDataLayout);
109  output.info()->set_data_layout(aclDataLayout);
110 
111  arm_compute::TensorInfo aclReshapeInputInfo = BuildArmComputeTensorInfo(info.m_InputTensorInfos[0],
113  arm_compute::TensorInfo aclReshapeOutputInfo = BuildArmComputeTensorInfo(info.m_OutputTensorInfos[0],
115 
116  const unsigned int rank = info.m_InputTensorInfos[0].GetNumDimensions();
117  if (rank == 3)
118  {
119  const arm_compute::TensorShape inputShape = input.info()->tensor_shape();
120  const arm_compute::TensorShape outputShape = output.info()->tensor_shape();
121 
122  // When a spacial dimension is missing set W to 1
124  {
125  // In ACL dimensions are right to left: C, W, H, N
126  aclReshapeInputInfo.set_tensor_shape({inputShape.x(), 1, inputShape.y(), inputShape.z()});
127  aclReshapeOutputInfo.set_tensor_shape({outputShape.x(), 1, outputShape.y(), outputShape.z()});
128  }
130  {
131  // In ACL dimensions are right to left: W, H, C, N
132  aclReshapeInputInfo.set_tensor_shape({1, inputShape.x(), inputShape.y(), inputShape.z()});
133  aclReshapeOutputInfo.set_tensor_shape({1, outputShape.x(), outputShape.y(), outputShape.z()});
134  }
135  else
136  {
137  throw InvalidArgumentException("Unsupported or unknown DataLayout", CHECK_LOCATION());
138  }
139 
140  m_ReshapeInputTensor.allocator()->init(aclReshapeInputInfo);
141  m_ReshapeOutputTensor.allocator()->init(aclReshapeOutputInfo);
142 
143  InitialiseArmComputeTensorEmpty(m_ReshapeInputTensor);
144  InitialiseArmComputeTensorEmpty(m_ReshapeOutputTensor);
145 
146  m_LayerReshapeInput.reset(new arm_compute::NEReshapeLayer());
147  m_LayerReshapeOutput.reset(new arm_compute::NEReshapeLayer());
148 
149  m_LayerReshapeInput->configure(&input, &m_ReshapeInputTensor);
150  m_LayerReshapeOutput->configure(&m_ReshapeOutputTensor, &output);
151  }
152 
153  // ArmNN blockShape is [H, W] ACl asks for W, H
154  int32_t blockHeight = armnn::numeric_cast<int32_t>(m_Data.m_Parameters.m_BlockShape[0]);
155  int32_t blockWidth = (rank == 3) ? 1: armnn::numeric_cast<int32_t>(descriptor.m_Parameters.m_BlockShape[1]);
156 
157  unsigned int padLeft = (rank == 3) ? 0 : descriptor.m_Parameters.m_PadList[1].first;
158  unsigned int padRight = (rank == 3) ? 0 : descriptor.m_Parameters.m_PadList[1].second;
159  arm_compute::Size2D paddingLeftTop = BuildArmComputeSize2D(padLeft,
160  descriptor.m_Parameters.m_PadList[0].first);
161  arm_compute::Size2D paddingRightBottom = BuildArmComputeSize2D(padRight,
162  descriptor.m_Parameters.m_PadList[0].second);
163 
164  m_Layer.reset(new arm_compute::NESpaceToBatchLayer());
165  m_Layer->configure((rank == 3) ? &m_ReshapeInputTensor : &input,
166  blockWidth,
167  blockHeight,
168  paddingLeftTop,
169  paddingRightBottom,
170  (rank == 3) ? &m_ReshapeOutputTensor : &output);
171  m_Layer->prepare();
172 }
173 
175 {
176  ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID("NeonSpaceToBatchNdWorkload_Execute");
177  if (m_LayerReshapeInput)
178  {
179  m_LayerReshapeInput->run();
180  }
181  if (m_Layer)
182  {
183  m_Layer->run();
184  }
185  if (m_LayerReshapeOutput)
186  {
187  m_LayerReshapeOutput->run();
188  }
189 }
190 
191 } //namespace armnn
armnn::NeonSpaceToBatchNdWorkloadValidate
arm_compute::Status NeonSpaceToBatchNdWorkloadValidate(const TensorInfo &input, const TensorInfo &output, const SpaceToBatchNdDescriptor &descriptor)
Definition: NeonSpaceToBatchNdWorkload.cpp:15
armnn::DataLayout
DataLayout
Definition: Types.hpp:62
armnn::SpaceToBatchNdDescriptor::m_DataLayout
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
Definition: Descriptors.hpp:1071
armnn::DataLayout::NHWC
@ NHWC
armnn::QueueDescriptor::ValidateInputsOutputs
void ValidateInputsOutputs(const std::string &descName, unsigned int numExpectedIn, unsigned int numExpectedOut) const
Definition: WorkloadData.cpp:447
armnn::TensorInfo
Definition: Tensor.hpp:152
armnn::TensorInfo::GetNumDimensions
unsigned int GetNumDimensions() const
Definition: Tensor.hpp:197
CHECK_LOCATION
#define CHECK_LOCATION()
Definition: Exceptions.hpp:203
armnn::SpaceToBatchNdDescriptor::m_BlockShape
std::vector< unsigned int > m_BlockShape
Block shape value.
Definition: Descriptors.hpp:1066
armnn::SpaceToBatchNdDescriptor::m_PadList
std::vector< std::pair< unsigned int, unsigned int > > m_PadList
Specifies the padding values for the input dimension: heightPad{top, bottom} widthPad{left,...
Definition: Descriptors.hpp:1069
armnn::QueueDescriptorWithParameters::m_Parameters
LayerDescriptor m_Parameters
Definition: WorkloadData.hpp:66
armnn::WorkloadInfo
Contains information about TensorInfos of a layer.
Definition: WorkloadInfo.hpp:16
PolymorphicDowncast.hpp
armnn::InvalidArgumentException
Definition: Exceptions.hpp:80
armnn::SpaceToBatchNdDescriptor
A SpaceToBatchNdDescriptor for the SpaceToBatchNdLayer.
Definition: Descriptors.hpp:1043
armnn::BoostLogSeverityMapping::info
@ info
armnn::QueueDescriptor::m_Outputs
std::vector< ITensorHandle * > m_Outputs
Definition: WorkloadData.hpp:27
armnn::NeonSpaceToBatchNdWorkload::NeonSpaceToBatchNdWorkload
NeonSpaceToBatchNdWorkload(const SpaceToBatchNdQueueDescriptor &descriptor, const WorkloadInfo &info)
Definition: NeonSpaceToBatchNdWorkload.cpp:92
ARMNN_REPORT_PROFILING_WORKLOAD_DESC
#define ARMNN_REPORT_PROFILING_WORKLOAD_DESC(name, desc, infos, guid)
Definition: Profiling.hpp:227
armnn::NeonSpaceToBatchNdWorkload::Execute
virtual void Execute() const override
Definition: NeonSpaceToBatchNdWorkload.cpp:174
armnn::Status
Status
Definition: Types.hpp:42
armnn::BaseWorkload< SpaceToBatchNdQueueDescriptor >::m_Data
SpaceToBatchNdQueueDescriptor m_Data
Definition: Workload.hpp:89
NeonSpaceToBatchNdWorkload.hpp
armnn
Copyright (c) 2021 ARM Limited and Contributors.
Definition: 01_00_quick_start.dox:6
ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID
#define ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID(label)
Creates a profiling event that uses GetGuid() and GetName() from the calling class.
Definition: NeonWorkloadUtils.hpp:33
armnn::SpaceToBatchNdQueueDescriptor
Definition: WorkloadData.hpp:385
armnn::NeonBaseWorkload
Definition: NeonBaseWorkload.hpp:13
armnn::QueueDescriptor::m_Inputs
std::vector< ITensorHandle * > m_Inputs
Definition: WorkloadData.hpp:26
armnn::DataLayout::NCHW
@ NCHW