ArmNN
 22.08
DetectionPostProcessEndToEndTestImpl.hpp
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1 //
2 // Copyright © 2017 Arm Ltd. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5 
6 #pragma once
7 
8 #include <CommonTestUtils.hpp>
9 
10 #include <armnn/INetwork.hpp>
11 #include <ResolveType.hpp>
12 
13 #include <doctest/doctest.h>
14 
15 namespace{
16 
17 template<typename T>
18 armnn::INetworkPtr CreateDetectionPostProcessNetwork(const armnn::TensorInfo& boxEncodingsInfo,
19  const armnn::TensorInfo& scoresInfo,
20  const armnn::TensorInfo& anchorsInfo,
21  const std::vector<T>& anchors,
22  bool useRegularNms)
23 {
24  armnn::TensorInfo detectionBoxesInfo({ 1, 3, 4 }, armnn::DataType::Float32);
25  armnn::TensorInfo detectionScoresInfo({ 1, 3 }, armnn::DataType::Float32);
26  armnn::TensorInfo detectionClassesInfo({ 1, 3 }, armnn::DataType::Float32);
27  armnn::TensorInfo numDetectionInfo({ 1 }, armnn::DataType::Float32);
28 
29  armnn::DetectionPostProcessDescriptor desc;
30  desc.m_UseRegularNms = useRegularNms;
31  desc.m_MaxDetections = 3;
32  desc.m_MaxClassesPerDetection = 1;
33  desc.m_DetectionsPerClass =1;
34  desc.m_NmsScoreThreshold = 0.0;
35  desc.m_NmsIouThreshold = 0.5;
36  desc.m_NumClasses = 2;
37  desc.m_ScaleY = 10.0;
38  desc.m_ScaleX = 10.0;
39  desc.m_ScaleH = 5.0;
40  desc.m_ScaleW = 5.0;
41 
42  armnn::INetworkPtr net(armnn::INetwork::Create());
43 
44  armnn::IConnectableLayer* boxesLayer = net->AddInputLayer(0);
45  armnn::IConnectableLayer* scoresLayer = net->AddInputLayer(1);
46  armnn::ConstTensor anchorsTensor(anchorsInfo, anchors.data());
47  armnn::IConnectableLayer* detectionLayer = net->AddDetectionPostProcessLayer(desc, anchorsTensor,
48  "DetectionPostProcess");
49  armnn::IConnectableLayer* detectionBoxesLayer = net->AddOutputLayer(0, "detectionBoxes");
50  armnn::IConnectableLayer* detectionClassesLayer = net->AddOutputLayer(1, "detectionClasses");
51  armnn::IConnectableLayer* detectionScoresLayer = net->AddOutputLayer(2, "detectionScores");
52  armnn::IConnectableLayer* numDetectionLayer = net->AddOutputLayer(3, "numDetection");
53  Connect(boxesLayer, detectionLayer, boxEncodingsInfo, 0, 0);
54  Connect(scoresLayer, detectionLayer, scoresInfo, 0, 1);
55  Connect(detectionLayer, detectionBoxesLayer, detectionBoxesInfo, 0, 0);
56  Connect(detectionLayer, detectionClassesLayer, detectionClassesInfo, 1, 0);
57  Connect(detectionLayer, detectionScoresLayer, detectionScoresInfo, 2, 0);
58  Connect(detectionLayer, numDetectionLayer, numDetectionInfo, 3, 0);
59 
60  return net;
61 }
62 
63 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
64 void DetectionPostProcessEndToEnd(const std::vector<BackendId>& backends, bool useRegularNms,
65  const std::vector<T>& boxEncodings,
66  const std::vector<T>& scores,
67  const std::vector<T>& anchors,
68  const std::vector<float>& expectedDetectionBoxes,
69  const std::vector<float>& expectedDetectionClasses,
70  const std::vector<float>& expectedDetectionScores,
71  const std::vector<float>& expectedNumDetections,
72  float boxScale = 1.0f,
73  int32_t boxOffset = 0,
74  float scoreScale = 1.0f,
75  int32_t scoreOffset = 0,
76  float anchorScale = 1.0f,
77  int32_t anchorOffset = 0)
78 {
79  armnn::TensorInfo boxEncodingsInfo({ 1, 6, 4 }, ArmnnType);
80  armnn::TensorInfo scoresInfo({ 1, 6, 3}, ArmnnType);
81  armnn::TensorInfo anchorsInfo({ 6, 4 }, ArmnnType);
82 
83  boxEncodingsInfo.SetQuantizationScale(boxScale);
84  boxEncodingsInfo.SetQuantizationOffset(boxOffset);
85  boxEncodingsInfo.SetConstant(true);
86  scoresInfo.SetQuantizationScale(scoreScale);
87  scoresInfo.SetQuantizationOffset(scoreOffset);
88  scoresInfo.SetConstant(true);
89  anchorsInfo.SetQuantizationScale(anchorScale);
90  anchorsInfo.SetQuantizationOffset(anchorOffset);
91  anchorsInfo.SetConstant(true);
92 
93  // Builds up the structure of the network
94  armnn::INetworkPtr net = CreateDetectionPostProcessNetwork<T>(boxEncodingsInfo, scoresInfo,
95  anchorsInfo, anchors, useRegularNms);
96 
97  CHECK(net);
98 
99  std::map<int, std::vector<T>> inputTensorData = {{ 0, boxEncodings }, { 1, scores }};
100  std::map<int, std::vector<float>> expectedOutputData = {{ 0, expectedDetectionBoxes },
101  { 1, expectedDetectionClasses },
102  { 2, expectedDetectionScores },
103  { 3, expectedNumDetections }};
104 
105  EndToEndLayerTestImpl<ArmnnType, armnn::DataType::Float32>(
106  move(net), inputTensorData, expectedOutputData, backends);
107 }
108 
109 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
110 void DetectionPostProcessRegularNmsEndToEnd(const std::vector<BackendId>& backends,
111  const std::vector<T>& boxEncodings,
112  const std::vector<T>& scores,
113  const std::vector<T>& anchors,
114  float boxScale = 1.0f,
115  int32_t boxOffset = 0,
116  float scoreScale = 1.0f,
117  int32_t scoreOffset = 0,
118  float anchorScale = 1.0f,
119  int32_t anchorOffset = 0)
120 {
121  std::vector<float> expectedDetectionBoxes({
122  0.0f, 10.0f, 1.0f, 11.0f,
123  0.0f, 10.0f, 1.0f, 11.0f,
124  0.0f, 0.0f, 0.0f, 0.0f
125  });
126  std::vector<float> expectedDetectionScores({ 0.95f, 0.93f, 0.0f });
127  std::vector<float> expectedDetectionClasses({ 1.0f, 0.0f, 0.0f });
128  std::vector<float> expectedNumDetections({ 2.0f });
129 
130  DetectionPostProcessEndToEnd<ArmnnType>(backends, true, boxEncodings, scores, anchors,
131  expectedDetectionBoxes, expectedDetectionClasses,
132  expectedDetectionScores, expectedNumDetections,
133  boxScale, boxOffset, scoreScale, scoreOffset,
134  anchorScale, anchorOffset);
135 
136 };
137 
138 
139 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
140 void DetectionPostProcessFastNmsEndToEnd(const std::vector<BackendId>& backends,
141  const std::vector<T>& boxEncodings,
142  const std::vector<T>& scores,
143  const std::vector<T>& anchors,
144  float boxScale = 1.0f,
145  int32_t boxOffset = 0,
146  float scoreScale = 1.0f,
147  int32_t scoreOffset = 0,
148  float anchorScale = 1.0f,
149  int32_t anchorOffset = 0)
150 {
151  std::vector<float> expectedDetectionBoxes({
152  0.0f, 10.0f, 1.0f, 11.0f,
153  0.0f, 0.0f, 1.0f, 1.0f,
154  0.0f, 100.0f, 1.0f, 101.0f
155  });
156  std::vector<float> expectedDetectionScores({ 0.95f, 0.9f, 0.3f });
157  std::vector<float> expectedDetectionClasses({ 1.0f, 0.0f, 0.0f });
158  std::vector<float> expectedNumDetections({ 3.0f });
159 
160  DetectionPostProcessEndToEnd<ArmnnType>(backends, false, boxEncodings, scores, anchors,
161  expectedDetectionBoxes, expectedDetectionClasses,
162  expectedDetectionScores, expectedNumDetections,
163  boxScale, boxOffset, scoreScale, scoreOffset,
164  anchorScale, anchorOffset);
165 
166 };
167 
168 } // anonymous namespace
armnn::IConnectableLayer
Interface for a layer that is connectable to other layers via InputSlots and OutputSlots.
Definition: INetwork.hpp:68
ResolveType.hpp
armnn::ConstTensor
A tensor defined by a TensorInfo (shape and data type) and an immutable backing store.
Definition: Tensor.hpp:327
armnn::TensorInfo::SetQuantizationScale
void SetQuantizationScale(float scale)
Definition: Tensor.cpp:473
armnn::DetectionPostProcessDescriptor::m_UseRegularNms
bool m_UseRegularNms
Use Regular NMS.
Definition: Descriptors.hpp:718
INetwork.hpp
armnn::TensorInfo::SetConstant
void SetConstant(const bool IsConstant=true)
Marks the data corresponding to this tensor info as constant.
Definition: Tensor.cpp:514
armnn::TensorInfo::SetQuantizationOffset
void SetQuantizationOffset(int32_t offset)
Definition: Tensor.cpp:489
Connect
void Connect(armnn::IConnectableLayer *from, armnn::IConnectableLayer *to, const armnn::TensorInfo &tensorInfo, unsigned int fromIndex, unsigned int toIndex)
Definition: TestUtils.cpp:14
armnn::INetworkPtr
std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr
Definition: INetwork.hpp:238
armnn::DetectionPostProcessDescriptor
Definition: Descriptors.hpp:674
armnn::INetwork::Create
static INetworkPtr Create(NetworkOptions networkOptions={})
Definition: Network.cpp:475