1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
|
//
// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "TestUtils.hpp"
#include <armnn_delegate.hpp>
#include <DelegateTestInterpreter.hpp>
#include <flatbuffers/flatbuffers.h>
#include <flatbuffers/flexbuffers.h>
#include <tensorflow/lite/kernels/register.h>
#include <tensorflow/lite/kernels/custom_ops_register.h>
#include <tensorflow/lite/version.h>
#include <schema_generated.h>
#include <doctest/doctest.h>
namespace
{
#if defined(ARMNN_POST_TFLITE_2_5)
std::vector<uint8_t> CreateCustomOptions(int, int, int, int, int, int, TfLitePadding);
std::vector<char> CreatePooling3dTfLiteModel(
std::string poolType,
tflite::TensorType tensorType,
const std::vector<int32_t>& inputTensorShape,
const std::vector<int32_t>& outputTensorShape,
TfLitePadding padding = kTfLitePaddingSame,
int32_t strideWidth = 0,
int32_t strideHeight = 0,
int32_t strideDepth = 0,
int32_t filterWidth = 0,
int32_t filterHeight = 0,
int32_t filterDepth = 0,
tflite::ActivationFunctionType fusedActivation = tflite::ActivationFunctionType_NONE,
float quantScale = 1.0f,
int quantOffset = 0)
{
using namespace tflite;
flatbuffers::FlatBufferBuilder flatBufferBuilder;
std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
buffers.push_back(CreateBuffer(flatBufferBuilder));
buffers.push_back(CreateBuffer(flatBufferBuilder));
buffers.push_back(CreateBuffer(flatBufferBuilder));
auto quantizationParameters =
CreateQuantizationParameters(flatBufferBuilder,
0,
0,
flatBufferBuilder.CreateVector<float>({ quantScale }),
flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
// Create the input and output tensors
std::array<flatbuffers::Offset<Tensor>, 2> tensors;
tensors[0] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
inputTensorShape.size()),
tensorType,
0,
flatBufferBuilder.CreateString("input"),
quantizationParameters);
tensors[1] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
outputTensorShape.size()),
tensorType,
0,
flatBufferBuilder.CreateString("output"),
quantizationParameters);
// Create the custom options from the function below
std::vector<uint8_t> customOperatorOptions = CreateCustomOptions(strideHeight, strideWidth, strideDepth,
filterHeight, filterWidth, filterDepth, padding);
// opCodeIndex is created as a uint8_t to avoid map lookup
uint8_t opCodeIndex = 0;
// Set the operator name based on the PoolType passed in from the test case
std::string opName = "";
if (poolType == "kMax")
{
opName = "MaxPool3D";
}
else
{
opName = "AveragePool3D";
}
// To create a custom operator code you pass in the builtin code for custom operators and the name of the custom op
flatbuffers::Offset<OperatorCode> operatorCode = CreateOperatorCodeDirect(flatBufferBuilder,
tflite::BuiltinOperator_CUSTOM,
opName.c_str());
// Create the Operator using the opCodeIndex and custom options. Also sets builtin options to none.
const std::vector<int32_t> operatorInputs{ 0 };
const std::vector<int32_t> operatorOutputs{ 1 };
flatbuffers::Offset<Operator> poolingOperator =
CreateOperator(flatBufferBuilder,
opCodeIndex,
flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
tflite::BuiltinOptions_NONE,
0,
flatBufferBuilder.CreateVector<uint8_t>(customOperatorOptions),
tflite::CustomOptionsFormat_FLEXBUFFERS);
// Create the subgraph using the operator created above.
const std::vector<int> subgraphInputs{ 0 };
const std::vector<int> subgraphOutputs{ 1 };
flatbuffers::Offset<SubGraph> subgraph =
CreateSubGraph(flatBufferBuilder,
flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
flatBufferBuilder.CreateVector(&poolingOperator, 1));
flatbuffers::Offset<flatbuffers::String> modelDescription =
flatBufferBuilder.CreateString("ArmnnDelegate: Pooling3d Operator Model");
// Create the model using operatorCode and the subgraph.
flatbuffers::Offset<Model> flatbufferModel =
CreateModel(flatBufferBuilder,
TFLITE_SCHEMA_VERSION,
flatBufferBuilder.CreateVector(&operatorCode, 1),
flatBufferBuilder.CreateVector(&subgraph, 1),
modelDescription,
flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);
return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
}
template<typename T>
void Pooling3dTest(std::string poolType,
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,
TfLitePadding padding = kTfLitePaddingSame,
int32_t strideWidth = 0,
int32_t strideHeight = 0,
int32_t strideDepth = 0,
int32_t filterWidth = 0,
int32_t filterHeight = 0,
int32_t filterDepth = 0,
tflite::ActivationFunctionType fusedActivation = tflite::ActivationFunctionType_NONE,
float quantScale = 1.0f,
int quantOffset = 0)
{
using namespace delegateTestInterpreter;
// Create the single op model buffer
std::vector<char> modelBuffer = CreatePooling3dTfLiteModel(poolType,
tensorType,
inputShape,
outputShape,
padding,
strideWidth,
strideHeight,
strideDepth,
filterWidth,
filterHeight,
filterDepth,
fusedActivation,
quantScale,
quantOffset);
std::string opType = "";
if (poolType == "kMax")
{
opType = "MaxPool3D";
}
else
{
opType = "AveragePool3D";
}
// Setup interpreter with just TFLite Runtime.
auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer, opType);
CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0);
std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0);
// Setup interpreter with Arm NN Delegate applied.
auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends, opType);
CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0);
std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputShape);
tfLiteInterpreter.Cleanup();
armnnInterpreter.Cleanup();
}
// Function to create the flexbuffer custom options for the custom pooling3d operator.
std::vector<uint8_t> CreateCustomOptions(int strideHeight, int strideWidth, int strideDepth,
int filterHeight, int filterWidth, int filterDepth, TfLitePadding padding)
{
auto flex_builder = std::make_unique<flexbuffers::Builder>();
size_t map_start = flex_builder->StartMap();
flex_builder->String("data_format", "NDHWC");
// Padding is created as a key and padding type. Only VALID and SAME supported
if (padding == kTfLitePaddingValid)
{
flex_builder->String("padding", "VALID");
}
else
{
flex_builder->String("padding", "SAME");
}
// Vector of filter dimensions in order ( 1, Depth, Height, Width, 1 )
auto start = flex_builder->StartVector("ksize");
flex_builder->Add(1);
flex_builder->Add(filterDepth);
flex_builder->Add(filterHeight);
flex_builder->Add(filterWidth);
flex_builder->Add(1);
// EndVector( start, bool typed, bool fixed)
flex_builder->EndVector(start, true, false);
// Vector of stride dimensions in order ( 1, Depth, Height, Width, 1 )
auto stridesStart = flex_builder->StartVector("strides");
flex_builder->Add(1);
flex_builder->Add(strideDepth);
flex_builder->Add(strideHeight);
flex_builder->Add(strideWidth);
flex_builder->Add(1);
// EndVector( stridesStart, bool typed, bool fixed)
flex_builder->EndVector(stridesStart, true, false);
flex_builder->EndMap(map_start);
flex_builder->Finish();
return flex_builder->GetBuffer();
}
#endif
} // anonymous namespace
|
