1 //
2 // Copyright © 2020 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
12 #include <flatbuffers/flatbuffers.h>
13 #include <tensorflow/lite/interpreter.h>
14 #include <tensorflow/lite/kernels/register.h>
15 #include <tensorflow/lite/model.h>
16 #include <tensorflow/lite/schema/schema_generated.h>
17 #include <tensorflow/lite/version.h>
18
19 #include <doctest/doctest.h>
20
21 namespace
22 {
23
CreatePooling2dTfLiteModel(tflite::BuiltinOperator poolingOperatorCode,tflite::TensorType tensorType,const std::vector<int32_t> & inputTensorShape,const std::vector<int32_t> & outputTensorShape,tflite::Padding padding=tflite::Padding_SAME,int32_t strideWidth=0,int32_t strideHeight=0,int32_t filterWidth=0,int32_t filterHeight=0,tflite::ActivationFunctionType fusedActivation=tflite::ActivationFunctionType_NONE,float quantScale=1.0f,int quantOffset=0)24 std::vector<char> CreatePooling2dTfLiteModel(
25 tflite::BuiltinOperator poolingOperatorCode,
26 tflite::TensorType tensorType,
27 const std::vector <int32_t>& inputTensorShape,
28 const std::vector <int32_t>& outputTensorShape,
29 tflite::Padding padding = tflite::Padding_SAME,
30 int32_t strideWidth = 0,
31 int32_t strideHeight = 0,
32 int32_t filterWidth = 0,
33 int32_t filterHeight = 0,
34 tflite::ActivationFunctionType fusedActivation = tflite::ActivationFunctionType_NONE,
35 float quantScale = 1.0f,
36 int quantOffset = 0)
37 {
38 using namespace tflite;
39 flatbuffers::FlatBufferBuilder flatBufferBuilder;
40
41 std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
42 buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})));
43
44 auto quantizationParameters =
45 CreateQuantizationParameters(flatBufferBuilder,
46 0,
47 0,
48 flatBufferBuilder.CreateVector<float>({ quantScale }),
49 flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
50
51 std::array<flatbuffers::Offset<Tensor>, 2> tensors;
52 tensors[0] = CreateTensor(flatBufferBuilder,
53 flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
54 inputTensorShape.size()),
55 tensorType,
56 0,
57 flatBufferBuilder.CreateString("input"),
58 quantizationParameters);
59
60 tensors[1] = CreateTensor(flatBufferBuilder,
61 flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
62 outputTensorShape.size()),
63 tensorType,
64 0,
65 flatBufferBuilder.CreateString("output"),
66 quantizationParameters);
67
68 // create operator
69 tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_Pool2DOptions;
70 flatbuffers::Offset<void> operatorBuiltinOptions = CreatePool2DOptions(flatBufferBuilder,
71 padding,
72 strideWidth,
73 strideHeight,
74 filterWidth,
75 filterHeight,
76 fusedActivation).Union();
77
78 const std::vector<int32_t> operatorInputs{{0}};
79 const std::vector<int32_t> operatorOutputs{{1}};
80 flatbuffers::Offset <Operator> poolingOperator =
81 CreateOperator(flatBufferBuilder,
82 0,
83 flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
84 flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
85 operatorBuiltinOptionsType,
86 operatorBuiltinOptions);
87
88 const std::vector<int> subgraphInputs{{0}};
89 const std::vector<int> subgraphOutputs{{1}};
90 flatbuffers::Offset <SubGraph> subgraph =
91 CreateSubGraph(flatBufferBuilder,
92 flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
93 flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
94 flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
95 flatBufferBuilder.CreateVector(&poolingOperator, 1));
96
97 flatbuffers::Offset <flatbuffers::String> modelDescription =
98 flatBufferBuilder.CreateString("ArmnnDelegate: Pooling2d Operator Model");
99 flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, poolingOperatorCode);
100
101 flatbuffers::Offset <Model> flatbufferModel =
102 CreateModel(flatBufferBuilder,
103 TFLITE_SCHEMA_VERSION,
104 flatBufferBuilder.CreateVector(&operatorCode, 1),
105 flatBufferBuilder.CreateVector(&subgraph, 1),
106 modelDescription,
107 flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
108
109 flatBufferBuilder.Finish(flatbufferModel);
110
111 return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
112 flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
113 }
114
115 template <typename T>
Pooling2dTest(tflite::BuiltinOperator poolingOperatorCode,tflite::TensorType tensorType,std::vector<armnn::BackendId> & backends,std::vector<int32_t> & inputShape,std::vector<int32_t> & outputShape,std::vector<T> & inputValues,std::vector<T> & expectedOutputValues,tflite::Padding padding=tflite::Padding_SAME,int32_t strideWidth=0,int32_t strideHeight=0,int32_t filterWidth=0,int32_t filterHeight=0,tflite::ActivationFunctionType fusedActivation=tflite::ActivationFunctionType_NONE,float quantScale=1.0f,int quantOffset=0)116 void Pooling2dTest(tflite::BuiltinOperator poolingOperatorCode,
117 tflite::TensorType tensorType,
118 std::vector<armnn::BackendId>& backends,
119 std::vector<int32_t>& inputShape,
120 std::vector<int32_t>& outputShape,
121 std::vector<T>& inputValues,
122 std::vector<T>& expectedOutputValues,
123 tflite::Padding padding = tflite::Padding_SAME,
124 int32_t strideWidth = 0,
125 int32_t strideHeight = 0,
126 int32_t filterWidth = 0,
127 int32_t filterHeight = 0,
128 tflite::ActivationFunctionType fusedActivation = tflite::ActivationFunctionType_NONE,
129 float quantScale = 1.0f,
130 int quantOffset = 0)
131 {
132 using namespace tflite;
133 std::vector<char> modelBuffer = CreatePooling2dTfLiteModel(poolingOperatorCode,
134 tensorType,
135 inputShape,
136 outputShape,
137 padding,
138 strideWidth,
139 strideHeight,
140 filterWidth,
141 filterHeight,
142 fusedActivation,
143 quantScale,
144 quantOffset);
145
146 const Model* tfLiteModel = GetModel(modelBuffer.data());
147 CHECK(tfLiteModel != nullptr);
148 // Create TfLite Interpreters
149 std::unique_ptr<Interpreter> armnnDelegateInterpreter;
150 CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
151 (&armnnDelegateInterpreter) == kTfLiteOk);
152 CHECK(armnnDelegateInterpreter != nullptr);
153 CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk);
154
155 std::unique_ptr<Interpreter> tfLiteInterpreter;
156 CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
157 (&tfLiteInterpreter) == kTfLiteOk);
158 CHECK(tfLiteInterpreter != nullptr);
159 CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk);
160
161 // Create the ArmNN Delegate
162 armnnDelegate::DelegateOptions delegateOptions(backends);
163 std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)>
164 theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions),
165 armnnDelegate::TfLiteArmnnDelegateDelete);
166 CHECK(theArmnnDelegate != nullptr);
167 // Modify armnnDelegateInterpreter to use armnnDelegate
168 CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk);
169
170 // Set input data
171 auto tfLiteDelegateInputId = tfLiteInterpreter->inputs()[0];
172 auto tfLiteDelegateInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInputId);
173 for (unsigned int i = 0; i < inputValues.size(); ++i)
174 {
175 tfLiteDelegateInputData[i] = inputValues[i];
176 }
177
178 auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0];
179 auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateInputId);
180 for (unsigned int i = 0; i < inputValues.size(); ++i)
181 {
182 armnnDelegateInputData[i] = inputValues[i];
183 }
184
185 // Run EnqueueWorkload
186 CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
187 CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
188
189 armnnDelegate::CompareOutputData(tfLiteInterpreter, armnnDelegateInterpreter, outputShape, expectedOutputValues);
190 }
191
192 } // anonymous namespace
193
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197