Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 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 | #include <string> |
| 22 | |
| 23 | namespace |
| 24 | { |
| 25 | |
| 26 | std::vector<char> CreateSplitTfLiteModel(tflite::TensorType tensorType, |
| 27 | std::vector<int32_t>& axisTensorShape, |
| 28 | std::vector<int32_t>& inputTensorShape, |
| 29 | const std::vector<std::vector<int32_t>>& outputTensorShapes, |
| 30 | std::vector<int32_t>& axisData, |
| 31 | const int32_t numSplits, |
| 32 | float quantScale = 1.0f, |
| 33 | int quantOffset = 0) |
| 34 | { |
| 35 | using namespace tflite; |
| 36 | flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| 37 | |
| 38 | std::array<flatbuffers::Offset<tflite::Buffer>, 2> buffers; |
| 39 | buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); |
| 40 | buffers[1] = CreateBuffer(flatBufferBuilder, |
| 41 | flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()), |
| 42 | sizeof(int32_t) * axisData.size())); |
| 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>, 4> tensors; |
| 52 | tensors[0] = CreateTensor(flatBufferBuilder, |
| 53 | flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(), |
| 54 | axisTensorShape.size()), |
| 55 | ::tflite::TensorType_INT32, |
| 56 | 1, |
| 57 | flatBufferBuilder.CreateString("axis"), |
| 58 | quantizationParameters); |
| 59 | tensors[1] = CreateTensor(flatBufferBuilder, |
| 60 | flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), |
| 61 | inputTensorShape.size()), |
| 62 | tensorType, |
| 63 | 0, |
| 64 | flatBufferBuilder.CreateString("input"), |
| 65 | quantizationParameters); |
| 66 | |
| 67 | // Create output tensor |
| 68 | for (unsigned int i = 0; i < outputTensorShapes.size(); ++i) |
| 69 | { |
| 70 | tensors[i + 2] = CreateTensor(flatBufferBuilder, |
| 71 | flatBufferBuilder.CreateVector<int32_t>(outputTensorShapes[i].data(), |
| 72 | outputTensorShapes[i].size()), |
| 73 | tensorType, |
| 74 | 0, |
| 75 | flatBufferBuilder.CreateString("output"), |
| 76 | quantizationParameters); |
| 77 | } |
| 78 | |
| 79 | // create operator. Mean uses ReducerOptions. |
| 80 | tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_SplitOptions; |
| 81 | flatbuffers::Offset<void> operatorBuiltinOptions = CreateSplitOptions(flatBufferBuilder, numSplits).Union(); |
| 82 | |
| 83 | const std::vector<int> operatorInputs{ {0, 1} }; |
| 84 | const std::vector<int> operatorOutputs{ {2, 3} }; |
| 85 | flatbuffers::Offset <Operator> controlOperator = |
| 86 | CreateOperator(flatBufferBuilder, |
| 87 | 0, |
| 88 | flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| 89 | flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| 90 | operatorBuiltinOptionsType, |
| 91 | operatorBuiltinOptions); |
| 92 | |
| 93 | const std::vector<int> subgraphInputs{ {0, 1} }; |
| 94 | const std::vector<int> subgraphOutputs{ {2, 3} }; |
| 95 | flatbuffers::Offset <SubGraph> subgraph = |
| 96 | CreateSubGraph(flatBufferBuilder, |
| 97 | flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| 98 | flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), |
| 99 | flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), |
| 100 | flatBufferBuilder.CreateVector(&controlOperator, 1)); |
| 101 | |
| 102 | flatbuffers::Offset <flatbuffers::String> modelDescription = |
| 103 | flatBufferBuilder.CreateString("ArmnnDelegate: SPLIT Operator Model"); |
| 104 | flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, BuiltinOperator_SPLIT); |
| 105 | |
| 106 | flatbuffers::Offset <Model> flatbufferModel = |
| 107 | CreateModel(flatBufferBuilder, |
| 108 | TFLITE_SCHEMA_VERSION, |
| 109 | flatBufferBuilder.CreateVector(&operatorCode, 1), |
| 110 | flatBufferBuilder.CreateVector(&subgraph, 1), |
| 111 | modelDescription, |
| 112 | flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| 113 | |
| 114 | flatBufferBuilder.Finish(flatbufferModel); |
| 115 | |
| 116 | return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| 117 | flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| 118 | } |
| 119 | |
| 120 | template <typename T> |
| 121 | void SplitTest(tflite::TensorType tensorType, |
| 122 | std::vector<armnn::BackendId>& backends, |
| 123 | std::vector<int32_t>& axisTensorShape, |
| 124 | std::vector<int32_t>& inputTensorShape, |
| 125 | std::vector<std::vector<int32_t>>& outputTensorShapes, |
| 126 | std::vector<int32_t>& axisData, |
| 127 | std::vector<T>& inputValues, |
| 128 | std::vector<std::vector<T>>& expectedOutputValues, |
| 129 | const int32_t numSplits, |
| 130 | float quantScale = 1.0f, |
| 131 | int quantOffset = 0) |
| 132 | { |
| 133 | using namespace tflite; |
| 134 | std::vector<char> modelBuffer = CreateSplitTfLiteModel(tensorType, |
| 135 | axisTensorShape, |
| 136 | inputTensorShape, |
| 137 | outputTensorShapes, |
| 138 | axisData, |
| 139 | numSplits, |
| 140 | quantScale, |
| 141 | quantOffset); |
| 142 | const Model* tfLiteModel = GetModel(modelBuffer.data()); |
| 143 | |
| 144 | // Create TfLite Interpreters |
| 145 | std::unique_ptr<Interpreter> armnnDelegate; |
| 146 | CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| 147 | (&armnnDelegate) == kTfLiteOk); |
| 148 | CHECK(armnnDelegate != nullptr); |
| 149 | CHECK(armnnDelegate->AllocateTensors() == kTfLiteOk); |
| 150 | |
| 151 | std::unique_ptr<Interpreter> tfLiteDelegate; |
| 152 | CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| 153 | (&tfLiteDelegate) == kTfLiteOk); |
| 154 | CHECK(tfLiteDelegate != nullptr); |
| 155 | CHECK(tfLiteDelegate->AllocateTensors() == kTfLiteOk); |
| 156 | |
| 157 | // Create the ArmNN Delegate |
| 158 | armnnDelegate::DelegateOptions delegateOptions(backends); |
| 159 | std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> |
| 160 | theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), |
| 161 | armnnDelegate::TfLiteArmnnDelegateDelete); |
| 162 | CHECK(theArmnnDelegate != nullptr); |
| 163 | |
| 164 | // Modify armnnDelegateInterpreter to use armnnDelegate |
| 165 | CHECK(armnnDelegate->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); |
| 166 | |
| 167 | // Set input data |
| 168 | armnnDelegate::FillInput<T>(tfLiteDelegate, 1, inputValues); |
| 169 | armnnDelegate::FillInput<T>(armnnDelegate, 1, inputValues); |
| 170 | |
| 171 | // Run EnqueWorkload |
| 172 | CHECK(tfLiteDelegate->Invoke() == kTfLiteOk); |
| 173 | CHECK(armnnDelegate->Invoke() == kTfLiteOk); |
| 174 | |
| 175 | // Compare output data |
| 176 | for (unsigned int i = 0; i < expectedOutputValues.size(); ++i) |
| 177 | { |
| 178 | armnnDelegate::CompareOutputData<T>(tfLiteDelegate, |
| 179 | armnnDelegate, |
| 180 | outputTensorShapes[i], |
| 181 | expectedOutputValues[i], |
| 182 | i); |
| 183 | } |
| 184 | |
| 185 | tfLiteDelegate.reset(nullptr); |
| 186 | armnnDelegate.reset(nullptr); |
| 187 | } // End of SPLIT Test |
| 188 | |
| 189 | std::vector<char> CreateSplitVTfLiteModel(tflite::TensorType tensorType, |
| 190 | std::vector<int32_t>& inputTensorShape, |
| 191 | std::vector<int32_t>& splitsTensorShape, |
| 192 | std::vector<int32_t>& axisTensorShape, |
| 193 | const std::vector<std::vector<int32_t>>& outputTensorShapes, |
| 194 | std::vector<int32_t>& splitsData, |
| 195 | std::vector<int32_t>& axisData, |
| 196 | const int32_t numSplits, |
| 197 | float quantScale = 1.0f, |
| 198 | int quantOffset = 0) |
| 199 | { |
| 200 | using namespace tflite; |
| 201 | flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| 202 | |
| 203 | std::array<flatbuffers::Offset<tflite::Buffer>, 3> buffers; |
| 204 | buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); |
| 205 | buffers[1] = CreateBuffer(flatBufferBuilder, |
| 206 | flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(splitsData.data()), |
| 207 | sizeof(int32_t) * splitsData.size())); |
| 208 | buffers[2] = CreateBuffer(flatBufferBuilder, |
| 209 | flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()), |
| 210 | sizeof(int32_t) * axisData.size())); |
| 211 | |
| 212 | auto quantizationParameters = |
| 213 | CreateQuantizationParameters(flatBufferBuilder, |
| 214 | 0, |
| 215 | 0, |
| 216 | flatBufferBuilder.CreateVector<float>({ quantScale }), |
| 217 | flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
| 218 | |
| 219 | std::array<flatbuffers::Offset<Tensor>, 5> tensors; |
| 220 | tensors[0] = CreateTensor(flatBufferBuilder, |
| 221 | flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), |
| 222 | inputTensorShape.size()), |
| 223 | tensorType, |
| 224 | 0, |
| 225 | flatBufferBuilder.CreateString("input"), |
| 226 | quantizationParameters); |
| 227 | tensors[1] = CreateTensor(flatBufferBuilder, |
| 228 | flatBufferBuilder.CreateVector<int32_t>(splitsTensorShape.data(), |
| 229 | splitsTensorShape.size()), |
| 230 | ::tflite::TensorType_INT32, |
| 231 | 1, |
| 232 | flatBufferBuilder.CreateString("splits"), |
| 233 | quantizationParameters); |
| 234 | tensors[2] = CreateTensor(flatBufferBuilder, |
| 235 | flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(), |
| 236 | axisTensorShape.size()), |
| 237 | ::tflite::TensorType_INT32, |
| 238 | 2, |
| 239 | flatBufferBuilder.CreateString("axis"), |
| 240 | quantizationParameters); |
| 241 | |
| 242 | // Create output tensor |
| 243 | for (unsigned int i = 0; i < outputTensorShapes.size(); ++i) |
| 244 | { |
| 245 | tensors[i + 3] = CreateTensor(flatBufferBuilder, |
| 246 | flatBufferBuilder.CreateVector<int32_t>(outputTensorShapes[i].data(), |
| 247 | outputTensorShapes[i].size()), |
| 248 | tensorType, |
| 249 | 0, |
| 250 | flatBufferBuilder.CreateString("output"), |
| 251 | quantizationParameters); |
| 252 | } |
| 253 | |
| 254 | // create operator. Mean uses ReducerOptions. |
| 255 | tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_SplitVOptions; |
| 256 | flatbuffers::Offset<void> operatorBuiltinOptions = CreateSplitVOptions(flatBufferBuilder, numSplits).Union(); |
| 257 | |
| 258 | const std::vector<int> operatorInputs{ {0, 1, 2} }; |
| 259 | const std::vector<int> operatorOutputs{ {3, 4} }; |
| 260 | flatbuffers::Offset <Operator> controlOperator = |
| 261 | CreateOperator(flatBufferBuilder, |
| 262 | 0, |
| 263 | flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| 264 | flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| 265 | operatorBuiltinOptionsType, |
| 266 | operatorBuiltinOptions); |
| 267 | |
| 268 | const std::vector<int> subgraphInputs{ {0, 1, 2} }; |
| 269 | const std::vector<int> subgraphOutputs{ {3, 4} }; |
| 270 | flatbuffers::Offset <SubGraph> subgraph = |
| 271 | CreateSubGraph(flatBufferBuilder, |
| 272 | flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| 273 | flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), |
| 274 | flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), |
| 275 | flatBufferBuilder.CreateVector(&controlOperator, 1)); |
| 276 | |
| 277 | flatbuffers::Offset <flatbuffers::String> modelDescription = |
| 278 | flatBufferBuilder.CreateString("ArmnnDelegate: SPLIT_V Operator Model"); |
| 279 | flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, BuiltinOperator_SPLIT_V); |
| 280 | |
| 281 | flatbuffers::Offset <Model> flatbufferModel = |
| 282 | CreateModel(flatBufferBuilder, |
| 283 | TFLITE_SCHEMA_VERSION, |
| 284 | flatBufferBuilder.CreateVector(&operatorCode, 1), |
| 285 | flatBufferBuilder.CreateVector(&subgraph, 1), |
| 286 | modelDescription, |
| 287 | flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| 288 | |
| 289 | flatBufferBuilder.Finish(flatbufferModel); |
| 290 | |
| 291 | return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| 292 | flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| 293 | } |
| 294 | |
| 295 | template <typename T> |
| 296 | void SplitVTest(tflite::TensorType tensorType, |
| 297 | std::vector<armnn::BackendId>& backends, |
| 298 | std::vector<int32_t>& inputTensorShape, |
| 299 | std::vector<int32_t>& splitsTensorShape, |
| 300 | std::vector<int32_t>& axisTensorShape, |
| 301 | std::vector<std::vector<int32_t>>& outputTensorShapes, |
| 302 | std::vector<T>& inputValues, |
| 303 | std::vector<int32_t>& splitsData, |
| 304 | std::vector<int32_t>& axisData, |
| 305 | std::vector<std::vector<T>>& expectedOutputValues, |
| 306 | const int32_t numSplits, |
| 307 | float quantScale = 1.0f, |
| 308 | int quantOffset = 0) |
| 309 | { |
| 310 | using namespace tflite; |
| 311 | std::vector<char> modelBuffer = CreateSplitVTfLiteModel(tensorType, |
| 312 | inputTensorShape, |
| 313 | splitsTensorShape, |
| 314 | axisTensorShape, |
| 315 | outputTensorShapes, |
| 316 | splitsData, |
| 317 | axisData, |
| 318 | numSplits, |
| 319 | quantScale, |
| 320 | quantOffset); |
| 321 | const Model* tfLiteModel = GetModel(modelBuffer.data()); |
| 322 | |
| 323 | // Create TfLite Interpreters |
| 324 | std::unique_ptr<Interpreter> armnnDelegate; |
| 325 | CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| 326 | (&armnnDelegate) == kTfLiteOk); |
| 327 | CHECK(armnnDelegate != nullptr); |
| 328 | CHECK(armnnDelegate->AllocateTensors() == kTfLiteOk); |
| 329 | |
| 330 | std::unique_ptr<Interpreter> tfLiteDelegate; |
| 331 | CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| 332 | (&tfLiteDelegate) == kTfLiteOk); |
| 333 | CHECK(tfLiteDelegate != nullptr); |
| 334 | CHECK(tfLiteDelegate->AllocateTensors() == kTfLiteOk); |
| 335 | |
| 336 | // Create the ArmNN Delegate |
| 337 | armnnDelegate::DelegateOptions delegateOptions(backends); |
| 338 | std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> |
| 339 | theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), |
| 340 | armnnDelegate::TfLiteArmnnDelegateDelete); |
| 341 | CHECK(theArmnnDelegate != nullptr); |
| 342 | |
| 343 | // Modify armnnDelegateInterpreter to use armnnDelegate |
| 344 | CHECK(armnnDelegate->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); |
| 345 | |
| 346 | // Set input data |
| 347 | armnnDelegate::FillInput<T>(tfLiteDelegate, 0, inputValues); |
| 348 | armnnDelegate::FillInput<T>(armnnDelegate, 0, inputValues); |
| 349 | |
| 350 | // Run EnqueWorkload |
| 351 | CHECK(tfLiteDelegate->Invoke() == kTfLiteOk); |
| 352 | CHECK(armnnDelegate->Invoke() == kTfLiteOk); |
| 353 | |
| 354 | // Compare output data |
| 355 | for (unsigned int i = 0; i < expectedOutputValues.size(); ++i) |
| 356 | { |
| 357 | armnnDelegate::CompareOutputData<T>(tfLiteDelegate, |
| 358 | armnnDelegate, |
| 359 | outputTensorShapes[i], |
| 360 | expectedOutputValues[i], |
| 361 | i); |
| 362 | } |
| 363 | |
| 364 | tfLiteDelegate.reset(nullptr); |
| 365 | armnnDelegate.reset(nullptr); |
| 366 | } // End of SPLIT_V Test |
| 367 | |
| 368 | } // anonymous namespace |