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 {
CreateRoundTfLiteModel(tflite::BuiltinOperator roundOperatorCode,tflite::TensorType tensorType,const std::vector<int32_t> & tensorShape,float quantScale=1.0f,int quantOffset=0)23 std::vector<char> CreateRoundTfLiteModel(tflite::BuiltinOperator roundOperatorCode,
24 tflite::TensorType tensorType,
25 const std::vector <int32_t>& tensorShape,
26 float quantScale = 1.0f,
27 int quantOffset = 0)
28 {
29 using namespace tflite;
30 flatbuffers::FlatBufferBuilder flatBufferBuilder;
31
32 std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
33 buffers.push_back(CreateBuffer(flatBufferBuilder));
34 buffers.push_back(CreateBuffer(flatBufferBuilder));
35 buffers.push_back(CreateBuffer(flatBufferBuilder));
36
37 auto quantizationParameters =
38 CreateQuantizationParameters(flatBufferBuilder,
39 0,
40 0,
41 flatBufferBuilder.CreateVector<float>({quantScale}),
42 flatBufferBuilder.CreateVector<int64_t>({quantOffset}));
43
44 std::array<flatbuffers::Offset<Tensor>, 2> tensors;
45 tensors[0] = CreateTensor(flatBufferBuilder,
46 flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
47 tensorShape.size()),
48 tensorType,
49 1,
50 flatBufferBuilder.CreateString("input"),
51 quantizationParameters);
52 tensors[1] = CreateTensor(flatBufferBuilder,
53 flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
54 tensorShape.size()),
55 tensorType,
56 2,
57 flatBufferBuilder.CreateString("output"),
58 quantizationParameters);
59
60 const std::vector<int32_t> operatorInputs({0});
61 const std::vector<int32_t> operatorOutputs({1});
62
63 flatbuffers::Offset<Operator> roundOperator;
64 flatbuffers::Offset<flatbuffers::String> modelDescription;
65 flatbuffers::Offset<OperatorCode> operatorCode;
66
67 switch (roundOperatorCode)
68 {
69 case tflite::BuiltinOperator_FLOOR:
70 default:
71 roundOperator =
72 CreateOperator(flatBufferBuilder,
73 0,
74 flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
75 flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()));
76 modelDescription = flatBufferBuilder.CreateString("ArmnnDelegate: Floor Operator Model");
77 operatorCode = CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_FLOOR);
78 break;
79 }
80 const std::vector<int32_t> subgraphInputs({0});
81 const std::vector<int32_t> subgraphOutputs({1});
82 flatbuffers::Offset<SubGraph> subgraph =
83 CreateSubGraph(flatBufferBuilder,
84 flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
85 flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
86 flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
87 flatBufferBuilder.CreateVector(&roundOperator, 1));
88
89 flatbuffers::Offset<Model> flatbufferModel =
90 CreateModel(flatBufferBuilder,
91 TFLITE_SCHEMA_VERSION,
92 flatBufferBuilder.CreateVector(&operatorCode, 1),
93 flatBufferBuilder.CreateVector(&subgraph, 1),
94 modelDescription,
95 flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
96
97 flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);
98 return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
99 flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
100 }
101
102 template<typename T>
RoundTest(tflite::BuiltinOperator roundOperatorCode,tflite::TensorType tensorType,std::vector<armnn::BackendId> & backends,std::vector<int32_t> & shape,std::vector<T> & inputValues,std::vector<T> & expectedOutputValues,float quantScale=1.0f,int quantOffset=0)103 void RoundTest(tflite::BuiltinOperator roundOperatorCode,
104 tflite::TensorType tensorType,
105 std::vector<armnn::BackendId>& backends,
106 std::vector<int32_t>& shape,
107 std::vector<T>& inputValues,
108 std::vector<T>& expectedOutputValues,
109 float quantScale = 1.0f,
110 int quantOffset = 0)
111 {
112 using namespace delegateTestInterpreter;
113 std::vector<char> modelBuffer = CreateRoundTfLiteModel(roundOperatorCode,
114 tensorType,
115 shape,
116 quantScale,
117 quantOffset);
118
119 // Setup interpreter with just TFLite Runtime.
120 auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
121 CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
122 CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
123 CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
124 std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0);
125 std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0);
126
127 // Setup interpreter with Arm NN Delegate applied.
128 auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends);
129 CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
130 CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
131 CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
132 std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0);
133 std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
134
135 armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
136 armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, shape);
137
138 tfLiteInterpreter.Cleanup();
139 armnnInterpreter.Cleanup();
140 }
141
142 } // anonymous namespace
143