1 //
2 // Copyright © 2020, 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
24 template <typename T>
CreatePadTfLiteModel(tflite::BuiltinOperator padOperatorCode,tflite::TensorType tensorType,tflite::MirrorPadMode paddingMode,const std::vector<int32_t> & inputTensorShape,const std::vector<int32_t> & paddingTensorShape,const std::vector<int32_t> & outputTensorShape,const std::vector<int32_t> & paddingDim,const std::vector<T> paddingValue,float quantScale=1.0f,int quantOffset=0)25 std::vector<char> CreatePadTfLiteModel(
26 tflite::BuiltinOperator padOperatorCode,
27 tflite::TensorType tensorType,
28 tflite::MirrorPadMode paddingMode,
29 const std::vector<int32_t>& inputTensorShape,
30 const std::vector<int32_t>& paddingTensorShape,
31 const std::vector<int32_t>& outputTensorShape,
32 const std::vector<int32_t>& paddingDim,
33 const std::vector<T> paddingValue,
34 float quantScale = 1.0f,
35 int quantOffset = 0)
36 {
37 using namespace tflite;
38 flatbuffers::FlatBufferBuilder flatBufferBuilder;
39
40 auto quantizationParameters =
41 CreateQuantizationParameters(flatBufferBuilder,
42 0,
43 0,
44 flatBufferBuilder.CreateVector<float>({ quantScale }),
45 flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
46
47 auto inputTensor = CreateTensor(flatBufferBuilder,
48 flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
49 inputTensorShape.size()),
50 tensorType,
51 0,
52 flatBufferBuilder.CreateString("input"),
53 quantizationParameters);
54
55 auto paddingTensor = CreateTensor(flatBufferBuilder,
56 flatBufferBuilder.CreateVector<int32_t>(paddingTensorShape.data(),
57 paddingTensorShape.size()),
58 tflite::TensorType_INT32,
59 1,
60 flatBufferBuilder.CreateString("padding"));
61
62 auto outputTensor = CreateTensor(flatBufferBuilder,
63 flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
64 outputTensorShape.size()),
65 tensorType,
66 2,
67 flatBufferBuilder.CreateString("output"),
68 quantizationParameters);
69
70 std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, paddingTensor, outputTensor};
71
72 std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
73 buffers.push_back(CreateBuffer(flatBufferBuilder));
74 buffers.push_back(
75 CreateBuffer(flatBufferBuilder,
76 flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(paddingDim.data()),
77 sizeof(int32_t) * paddingDim.size())));
78 buffers.push_back(CreateBuffer(flatBufferBuilder));
79
80 std::vector<int32_t> operatorInputs;
81 std::vector<int> subgraphInputs;
82
83 tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_PadOptions;
84 flatbuffers::Offset<void> operatorBuiltinOptions;
85
86 if (padOperatorCode == tflite::BuiltinOperator_PAD)
87 {
88 operatorInputs = {{ 0, 1 }};
89 subgraphInputs = {{ 0, 1 }};
90 operatorBuiltinOptions = CreatePadOptions(flatBufferBuilder).Union();
91 }
92 else if(padOperatorCode == tflite::BuiltinOperator_MIRROR_PAD)
93 {
94 operatorInputs = {{ 0, 1 }};
95 subgraphInputs = {{ 0, 1 }};
96
97 operatorBuiltinOptionsType = BuiltinOptions_MirrorPadOptions;
98 operatorBuiltinOptions = CreateMirrorPadOptions(flatBufferBuilder, paddingMode).Union();
99 }
100 else if (padOperatorCode == tflite::BuiltinOperator_PADV2)
101 {
102 buffers.push_back(
103 CreateBuffer(flatBufferBuilder,
104 flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(paddingValue.data()),
105 sizeof(T))));
106
107 const std::vector<int32_t> shape = { 1 };
108 auto padValueTensor = CreateTensor(flatBufferBuilder,
109 flatBufferBuilder.CreateVector<int32_t>(shape.data(),
110 shape.size()),
111 tensorType,
112 3,
113 flatBufferBuilder.CreateString("paddingValue"),
114 quantizationParameters);
115
116 tensors.push_back(padValueTensor);
117
118 operatorInputs = {{ 0, 1, 3 }};
119 subgraphInputs = {{ 0, 1, 3 }};
120
121 operatorBuiltinOptionsType = BuiltinOptions_PadV2Options;
122 operatorBuiltinOptions = CreatePadV2Options(flatBufferBuilder).Union();
123 }
124
125 // create operator
126 const std::vector<int32_t> operatorOutputs{ 2 };
127 flatbuffers::Offset <Operator> paddingOperator =
128 CreateOperator(flatBufferBuilder,
129 0,
130 flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
131 flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
132 operatorBuiltinOptionsType,
133 operatorBuiltinOptions);
134
135 const std::vector<int> subgraphOutputs{ 2 };
136 flatbuffers::Offset <SubGraph> subgraph =
137 CreateSubGraph(flatBufferBuilder,
138 flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
139 flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
140 flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
141 flatBufferBuilder.CreateVector(&paddingOperator, 1));
142
143 flatbuffers::Offset <flatbuffers::String> modelDescription =
144 flatBufferBuilder.CreateString("ArmnnDelegate: Pad Operator Model");
145 flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder,
146 padOperatorCode);
147
148 flatbuffers::Offset <Model> flatbufferModel =
149 CreateModel(flatBufferBuilder,
150 TFLITE_SCHEMA_VERSION,
151 flatBufferBuilder.CreateVector(&operatorCode, 1),
152 flatBufferBuilder.CreateVector(&subgraph, 1),
153 modelDescription,
154 flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
155
156 flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);
157
158 return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
159 flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
160 }
161
162 template <typename T>
PadTest(tflite::BuiltinOperator padOperatorCode,tflite::TensorType tensorType,const std::vector<armnn::BackendId> & backends,const std::vector<int32_t> & inputShape,const std::vector<int32_t> & paddingShape,std::vector<int32_t> & outputShape,std::vector<T> & inputValues,std::vector<int32_t> & paddingDim,std::vector<T> & expectedOutputValues,T paddingValue,float quantScale=1.0f,int quantOffset=0,tflite::MirrorPadMode paddingMode=tflite::MirrorPadMode_SYMMETRIC)163 void PadTest(tflite::BuiltinOperator padOperatorCode,
164 tflite::TensorType tensorType,
165 const std::vector<armnn::BackendId>& backends,
166 const std::vector<int32_t>& inputShape,
167 const std::vector<int32_t>& paddingShape,
168 std::vector<int32_t>& outputShape,
169 std::vector<T>& inputValues,
170 std::vector<int32_t>& paddingDim,
171 std::vector<T>& expectedOutputValues,
172 T paddingValue,
173 float quantScale = 1.0f,
174 int quantOffset = 0,
175 tflite::MirrorPadMode paddingMode = tflite::MirrorPadMode_SYMMETRIC)
176 {
177 using namespace delegateTestInterpreter;
178 std::vector<char> modelBuffer = CreatePadTfLiteModel<T>(padOperatorCode,
179 tensorType,
180 paddingMode,
181 inputShape,
182 paddingShape,
183 outputShape,
184 paddingDim,
185 {paddingValue},
186 quantScale,
187 quantOffset);
188
189 // Setup interpreter with just TFLite Runtime.
190 auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
191 CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
192 CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
193 CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
194 std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0);
195 std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0);
196
197 // Setup interpreter with Arm NN Delegate applied.
198 auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends);
199 CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
200 CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
201 CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
202 std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0);
203 std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
204
205 armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
206 armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputShape);
207
208 tfLiteInterpreter.Cleanup();
209 armnnInterpreter.Cleanup();
210 }
211
212 } // anonymous namespace
213