1 //
2 // Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5
6 #pragma once
7
8 #include "TestUtils.hpp"
9
10 #include <armnn_delegate.hpp>
11 #include <DelegateTestInterpreter.hpp>
12
13 #include <flatbuffers/flatbuffers.h>
14 #include <tensorflow/lite/kernels/register.h>
15 #include <tensorflow/lite/version.h>
16
17 #include <schema_generated.h>
18
19 #include <doctest/doctest.h>
20
21 namespace
22 {
23
CreateNormalizationTfLiteModel(tflite::BuiltinOperator normalizationOperatorCode,tflite::TensorType tensorType,const std::vector<int32_t> & inputTensorShape,const std::vector<int32_t> & outputTensorShape,int32_t radius,float bias,float alpha,float beta,float quantScale=1.0f,int quantOffset=0)24 std::vector<char> CreateNormalizationTfLiteModel(tflite::BuiltinOperator normalizationOperatorCode,
25 tflite::TensorType tensorType,
26 const std::vector<int32_t>& inputTensorShape,
27 const std::vector<int32_t>& outputTensorShape,
28 int32_t radius,
29 float bias,
30 float alpha,
31 float beta,
32 float quantScale = 1.0f,
33 int quantOffset = 0)
34 {
35 using namespace tflite;
36 flatbuffers::FlatBufferBuilder flatBufferBuilder;
37
38 auto quantizationParameters =
39 CreateQuantizationParameters(flatBufferBuilder,
40 0,
41 0,
42 flatBufferBuilder.CreateVector<float>({ quantScale }),
43 flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
44
45 auto inputTensor = CreateTensor(flatBufferBuilder,
46 flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
47 inputTensorShape.size()),
48 tensorType,
49 1,
50 flatBufferBuilder.CreateString("input"),
51 quantizationParameters);
52
53 auto outputTensor = CreateTensor(flatBufferBuilder,
54 flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
55 outputTensorShape.size()),
56 tensorType,
57 2,
58 flatBufferBuilder.CreateString("output"),
59 quantizationParameters);
60
61 std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, outputTensor };
62
63 std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
64 buffers.push_back(CreateBuffer(flatBufferBuilder));
65 buffers.push_back(CreateBuffer(flatBufferBuilder));
66 buffers.push_back(CreateBuffer(flatBufferBuilder));
67
68 std::vector<int32_t> operatorInputs = { 0 };
69 std::vector<int> subgraphInputs = { 0 };
70
71 tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_L2NormOptions;
72 flatbuffers::Offset<void> operatorBuiltinOptions = CreateL2NormOptions(flatBufferBuilder,
73 tflite::ActivationFunctionType_NONE).Union();
74
75 if (normalizationOperatorCode == tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION)
76 {
77 operatorBuiltinOptionsType = BuiltinOptions_LocalResponseNormalizationOptions;
78 operatorBuiltinOptions =
79 CreateLocalResponseNormalizationOptions(flatBufferBuilder, radius, bias, alpha, beta).Union();
80 }
81
82 // create operator
83 const std::vector<int32_t> operatorOutputs{ 1 };
84 flatbuffers::Offset <Operator> normalizationOperator =
85 CreateOperator(flatBufferBuilder,
86 0,
87 flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
88 flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
89 operatorBuiltinOptionsType,
90 operatorBuiltinOptions);
91
92 const std::vector<int> subgraphOutputs{ 1 };
93 flatbuffers::Offset <SubGraph> subgraph =
94 CreateSubGraph(flatBufferBuilder,
95 flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
96 flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
97 flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
98 flatBufferBuilder.CreateVector(&normalizationOperator, 1));
99
100 flatbuffers::Offset <flatbuffers::String> modelDescription =
101 flatBufferBuilder.CreateString("ArmnnDelegate: Normalization Operator Model");
102 flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder,
103 normalizationOperatorCode);
104
105 flatbuffers::Offset <Model> flatbufferModel =
106 CreateModel(flatBufferBuilder,
107 TFLITE_SCHEMA_VERSION,
108 flatBufferBuilder.CreateVector(&operatorCode, 1),
109 flatBufferBuilder.CreateVector(&subgraph, 1),
110 modelDescription,
111 flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
112
113 flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);
114
115 return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
116 flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
117 }
118
119 template <typename T>
NormalizationTest(tflite::BuiltinOperator normalizationOperatorCode,tflite::TensorType tensorType,const std::vector<armnn::BackendId> & backends,const std::vector<int32_t> & inputShape,std::vector<int32_t> & outputShape,std::vector<T> & inputValues,std::vector<T> & expectedOutputValues,int32_t radius=0,float bias=0.f,float alpha=0.f,float beta=0.f,float quantScale=1.0f,int quantOffset=0)120 void NormalizationTest(tflite::BuiltinOperator normalizationOperatorCode,
121 tflite::TensorType tensorType,
122 const std::vector<armnn::BackendId>& backends,
123 const std::vector<int32_t>& inputShape,
124 std::vector<int32_t>& outputShape,
125 std::vector<T>& inputValues,
126 std::vector<T>& expectedOutputValues,
127 int32_t radius = 0,
128 float bias = 0.f,
129 float alpha = 0.f,
130 float beta = 0.f,
131 float quantScale = 1.0f,
132 int quantOffset = 0)
133 {
134 using namespace delegateTestInterpreter;
135 std::vector<char> modelBuffer = CreateNormalizationTfLiteModel(normalizationOperatorCode,
136 tensorType,
137 inputShape,
138 outputShape,
139 radius,
140 bias,
141 alpha,
142 beta,
143 quantScale,
144 quantOffset);
145
146 // Setup interpreter with just TFLite Runtime.
147 auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
148 CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
149 CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
150 CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
151 std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0);
152 std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0);
153
154 // Setup interpreter with Arm NN Delegate applied.
155 auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends);
156 CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
157 CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
158 CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
159 std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0);
160 std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
161
162 armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
163 armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputShape);
164
165 tfLiteInterpreter.Cleanup();
166 armnnInterpreter.Cleanup();
167 }
168
L2NormalizationTest(std::vector<armnn::BackendId> & backends)169 void L2NormalizationTest(std::vector<armnn::BackendId>& backends)
170 {
171 // Set input data
172 std::vector<int32_t> inputShape { 1, 1, 1, 10 };
173 std::vector<int32_t> outputShape { 1, 1, 1, 10 };
174
175 std::vector<float> inputValues
176 {
177 1.0f,
178 2.0f,
179 3.0f,
180 4.0f,
181 5.0f,
182 6.0f,
183 7.0f,
184 8.0f,
185 9.0f,
186 10.0f
187 };
188
189 const float approxInvL2Norm = 0.050964719f;
190 std::vector<float> expectedOutputValues
191 {
192 1.0f * approxInvL2Norm,
193 2.0f * approxInvL2Norm,
194 3.0f * approxInvL2Norm,
195 4.0f * approxInvL2Norm,
196 5.0f * approxInvL2Norm,
197 6.0f * approxInvL2Norm,
198 7.0f * approxInvL2Norm,
199 8.0f * approxInvL2Norm,
200 9.0f * approxInvL2Norm,
201 10.0f * approxInvL2Norm
202 };
203
204 NormalizationTest<float>(tflite::BuiltinOperator_L2_NORMALIZATION,
205 ::tflite::TensorType_FLOAT32,
206 backends,
207 inputShape,
208 outputShape,
209 inputValues,
210 expectedOutputValues);
211 }
212
LocalResponseNormalizationTest(std::vector<armnn::BackendId> & backends,int32_t radius,float bias,float alpha,float beta)213 void LocalResponseNormalizationTest(std::vector<armnn::BackendId>& backends,
214 int32_t radius,
215 float bias,
216 float alpha,
217 float beta)
218 {
219 // Set input data
220 std::vector<int32_t> inputShape { 2, 2, 2, 1 };
221 std::vector<int32_t> outputShape { 2, 2, 2, 1 };
222
223 std::vector<float> inputValues
224 {
225 1.0f, 2.0f,
226 3.0f, 4.0f,
227 5.0f, 6.0f,
228 7.0f, 8.0f
229 };
230
231 std::vector<float> expectedOutputValues
232 {
233 0.5f, 0.400000006f, 0.300000012f, 0.235294119f,
234 0.192307696f, 0.16216217f, 0.140000001f, 0.123076923f
235 };
236
237 NormalizationTest<float>(tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION,
238 ::tflite::TensorType_FLOAT32,
239 backends,
240 inputShape,
241 outputShape,
242 inputValues,
243 expectedOutputValues,
244 radius,
245 bias,
246 alpha,
247 beta);
248 }
249
250 } // anonymous namespace