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
CreateRedefineTfLiteModel(tflite::BuiltinOperator redefineOperatorCode,tflite::TensorType tensorType,const std::vector<int32_t> & inputTensorShape,const std::vector<int32_t> & outputTensorShape,const std::vector<int32_t> & targetShape,bool useOption=true,float quantScale=1.0f,int quantOffset=0)24 std::vector<char> CreateRedefineTfLiteModel(
25 tflite::BuiltinOperator redefineOperatorCode,
26 tflite::TensorType tensorType,
27 const std::vector<int32_t>& inputTensorShape,
28 const std::vector<int32_t>& outputTensorShape,
29 const std::vector<int32_t>& targetShape,
30 bool useOption = true,
31 float quantScale = 1.0f,
32 int quantOffset = 0)
33 {
34 using namespace tflite;
35 flatbuffers::FlatBufferBuilder flatBufferBuilder;
36 std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
37 buffers.push_back(CreateBuffer(flatBufferBuilder));
38 buffers.push_back(CreateBuffer(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 1,
52 flatBufferBuilder.CreateString("input"),
53 quantizationParameters);
54
55 std::vector<flatbuffers::Offset<Tensor>> tensors;
56 std::vector<int32_t> operatorInputs;
57 std::vector<int> subgraphInputs;
58 flatbuffers::Offset<void> operatorBuiltinOptions;
59
60 if (useOption)
61 {
62 buffers.push_back(CreateBuffer(flatBufferBuilder));
63 auto outputTensor = CreateTensor(flatBufferBuilder,
64 flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
65 outputTensorShape.size()),
66 tensorType,
67 2,
68 flatBufferBuilder.CreateString("output"),
69 quantizationParameters);
70 tensors = { inputTensor, outputTensor};
71 operatorInputs = {0};
72 subgraphInputs = {0};
73 operatorBuiltinOptions = CreateReshapeOptions(
74 flatBufferBuilder,
75 flatBufferBuilder.CreateVector(targetShape.data(), targetShape.size())).Union();
76 }
77 else
78 {
79 buffers.push_back(
80 CreateBuffer(flatBufferBuilder,
81 flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(targetShape.data()),
82 sizeof(int32_t) * targetShape.size())));
83 int32_t size = static_cast<int32_t>(targetShape.size());
84 auto shapeTensor = CreateTensor(flatBufferBuilder,
85 flatBufferBuilder.CreateVector<int32_t>( { size } ),
86 tflite::TensorType_INT32,
87 2,
88 flatBufferBuilder.CreateString("shape"));
89
90 buffers.push_back(CreateBuffer(flatBufferBuilder));
91 auto outputTensor = CreateTensor(flatBufferBuilder,
92 flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
93 outputTensorShape.size()),
94 tensorType,
95 3,
96 flatBufferBuilder.CreateString("output"),
97 quantizationParameters);
98
99 tensors = { inputTensor, outputTensor, shapeTensor };
100 operatorInputs = {0, 2};
101 subgraphInputs = {0, 2};
102 operatorBuiltinOptions = CreateReshapeOptions(flatBufferBuilder).Union();
103 }
104
105 // create operator
106 tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_ReshapeOptions;
107
108 const std::vector<int32_t> operatorOutputs{1};
109 flatbuffers::Offset <Operator> redefineOperator =
110 CreateOperator(flatBufferBuilder,
111 0,
112 flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
113 flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
114 operatorBuiltinOptionsType,
115 operatorBuiltinOptions);
116
117 const std::vector<int> subgraphOutputs{1};
118 flatbuffers::Offset <SubGraph> subgraph =
119 CreateSubGraph(flatBufferBuilder,
120 flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
121 flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
122 flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
123 flatBufferBuilder.CreateVector(&redefineOperator, 1));
124
125 flatbuffers::Offset <flatbuffers::String> modelDescription =
126 flatBufferBuilder.CreateString("ArmnnDelegate: Reshape Operator Model");
127 flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder,
128 redefineOperatorCode);
129
130 flatbuffers::Offset <Model> flatbufferModel =
131 CreateModel(flatBufferBuilder,
132 TFLITE_SCHEMA_VERSION,
133 flatBufferBuilder.CreateVector(&operatorCode, 1),
134 flatBufferBuilder.CreateVector(&subgraph, 1),
135 modelDescription,
136 flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
137
138 flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);
139
140 return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
141 flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
142 }
143
144 template <typename T>
RedefineTest(tflite::BuiltinOperator redefineOperatorCode,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,std::vector<int32_t> & targetShape,bool useOption=true,float quantScale=1.0f,int quantOffset=0)145 void RedefineTest(tflite::BuiltinOperator redefineOperatorCode,
146 tflite::TensorType tensorType,
147 const std::vector<armnn::BackendId>& backends,
148 const std::vector<int32_t>& inputShape,
149 std::vector<int32_t>& outputShape,
150 std::vector<T>& inputValues,
151 std::vector<T>& expectedOutputValues,
152 std::vector<int32_t>& targetShape,
153 bool useOption = true,
154 float quantScale = 1.0f,
155 int quantOffset = 0)
156 {
157 using namespace delegateTestInterpreter;
158 std::vector<char> modelBuffer = CreateRedefineTfLiteModel(redefineOperatorCode,
159 tensorType,
160 inputShape,
161 outputShape,
162 targetShape,
163 useOption,
164 quantScale,
165 quantOffset);
166
167 // Setup interpreter with just TFLite Runtime.
168 auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
169 CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
170 CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
171 CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
172 std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0);
173 std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0);
174
175 // Setup interpreter with Arm NN Delegate applied.
176 auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends);
177 CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
178 CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
179 CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
180 std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0);
181 std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
182
183 armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
184 armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputShape);
185
186 tfLiteInterpreter.Cleanup();
187 armnnInterpreter.Cleanup();
188 }
189
190 } // anonymous namespace