ArmNN
 20.05
DeserializeNormalization.cpp
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1 //
2 // Copyright © 2017 Arm Ltd. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5 
6 #include <boost/test/unit_test.hpp>
8 #include "../Deserializer.hpp"
9 
10 #include <string>
11 #include <iostream>
12 
13 BOOST_AUTO_TEST_SUITE(Deserializer)
14 
15 struct NormalizationFixture : public ParserFlatbuffersSerializeFixture
16 {
17  explicit NormalizationFixture(const std::string &inputShape,
18  const std::string & outputShape,
19  const std::string &dataType,
20  const std::string &normAlgorithmChannel,
21  const std::string &normAlgorithmMethod,
22  const std::string &dataLayout)
23  {
24  m_JsonString = R"(
25  {
26  inputIds: [0],
27  outputIds: [2],
28  layers: [{
29  layer_type: "InputLayer",
30  layer: {
31  base: {
32  layerBindingId: 0,
33  base: {
34  index: 0,
35  layerName: "InputLayer",
36  layerType: "Input",
37  inputSlots: [{
38  index: 0,
39  connection: {sourceLayerIndex:0, outputSlotIndex:0 },
40  }],
41  outputSlots: [{
42  index: 0,
43  tensorInfo: {
44  dimensions: )" + inputShape + R"(,
45  dataType: )" + dataType + R"(,
46  quantizationScale: 0.5,
47  quantizationOffset: 0
48  },
49  }]
50  },
51  }
52  },
53  },
54  {
55  layer_type: "NormalizationLayer",
56  layer : {
57  base: {
58  index:1,
59  layerName: "NormalizationLayer",
60  layerType: "Normalization",
61  inputSlots: [{
62  index: 0,
63  connection: {sourceLayerIndex:0, outputSlotIndex:0 },
64  }],
65  outputSlots: [{
66  index: 0,
67  tensorInfo: {
68  dimensions: )" + outputShape + R"(,
69  dataType: )" + dataType + R"(
70  },
71  }],
72  },
73  descriptor: {
74  normChannelType: )" + normAlgorithmChannel + R"(,
75  normMethodType: )" + normAlgorithmMethod + R"(,
76  normSize: 3,
77  alpha: 1,
78  beta: 1,
79  k: 1,
80  dataLayout: )" + dataLayout + R"(
81  }
82  },
83  },
84  {
85  layer_type: "OutputLayer",
86  layer: {
87  base:{
88  layerBindingId: 0,
89  base: {
90  index: 2,
91  layerName: "OutputLayer",
92  layerType: "Output",
93  inputSlots: [{
94  index: 0,
95  connection: {sourceLayerIndex:1, outputSlotIndex:0 },
96  }],
97  outputSlots: [ {
98  index: 0,
99  tensorInfo: {
100  dimensions: )" + outputShape + R"(,
101  dataType: )" + dataType + R"(
102  },
103  }],
104  }
105  }},
106  }]
107  }
108  )";
109  SetupSingleInputSingleOutput("InputLayer", "OutputLayer");
110  }
111 };
112 
113 struct FloatNhwcLocalBrightnessAcrossNormalizationFixture : NormalizationFixture
114 {
115  FloatNhwcLocalBrightnessAcrossNormalizationFixture() : NormalizationFixture("[ 2, 2, 2, 1 ]", "[ 2, 2, 2, 1 ]",
116  "Float32", "0", "0", "NHWC") {}
117 };
118 
119 
120 BOOST_FIXTURE_TEST_CASE(Float32NormalizationNhwcDataLayout, FloatNhwcLocalBrightnessAcrossNormalizationFixture)
121 {
122  RunTest<4, armnn::DataType::Float32>(0, { 1.0f, 2.0f, 3.0f, 4.0f,
123  5.0f, 6.0f, 7.0f, 8.0f },
124  { 0.5f, 0.400000006f, 0.300000012f, 0.235294119f,
125  0.192307696f, 0.16216217f, 0.140000001f, 0.123076923f });
126 }
127 
128 struct FloatNchwLocalBrightnessWithinNormalizationFixture : NormalizationFixture
129 {
130  FloatNchwLocalBrightnessWithinNormalizationFixture() : NormalizationFixture("[ 2, 1, 2, 2 ]", "[ 2, 1, 2, 2 ]",
131  "Float32", "1", "0", "NCHW") {}
132 };
133 
134 BOOST_FIXTURE_TEST_CASE(Float32NormalizationNchwDataLayout, FloatNchwLocalBrightnessWithinNormalizationFixture)
135 {
136  RunTest<4, armnn::DataType::Float32>(0, { 1.0f, 2.0f, 3.0f, 4.0f,
137  5.0f, 6.0f, 7.0f, 8.0f },
138  { 0.0322581f, 0.0645161f, 0.0967742f, 0.1290323f,
139  0.0285714f, 0.0342857f, 0.04f, 0.0457143f });
140 }
141 
142 
BOOST_AUTO_TEST_SUITE(TensorflowLiteParser)
void SetupSingleInputSingleOutput(const std::string &inputName, const std::string &outputName)
BOOST_FIXTURE_TEST_CASE(Float32NormalizationNhwcDataLayout, FloatNhwcLocalBrightnessAcrossNormalizationFixture)
BOOST_AUTO_TEST_SUITE_END()