Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 1 | // |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 2 | // Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 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> |
Teresa Charlin | ad1b3d7 | 2023-03-14 12:10:28 +0000 | [diff] [blame] | 16 | #include <schema_generated.h> |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 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> CreateConcatTfLiteModel(tflite::BuiltinOperator controlOperatorCode, |
| 27 | tflite::TensorType tensorType, |
| 28 | std::vector<int32_t>& inputTensorShape, |
| 29 | const std::vector <int32_t>& outputTensorShape, |
| 30 | const int32_t inputTensorNum, |
| 31 | int32_t axis = 0, |
| 32 | float quantScale = 1.0f, |
| 33 | int quantOffset = 0) |
| 34 | { |
| 35 | using namespace tflite; |
| 36 | flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| 37 | |
| 38 | std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 39 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| 40 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| 41 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 42 | |
| 43 | auto quantizationParameters = |
| 44 | CreateQuantizationParameters(flatBufferBuilder, |
| 45 | 0, |
| 46 | 0, |
| 47 | flatBufferBuilder.CreateVector<float>({ quantScale }), |
| 48 | flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
| 49 | |
| 50 | std::vector<int32_t> operatorInputs{}; |
| 51 | const std::vector<int32_t> operatorOutputs{inputTensorNum}; |
| 52 | std::vector<int> subgraphInputs{}; |
| 53 | const std::vector<int> subgraphOutputs{inputTensorNum}; |
| 54 | |
| 55 | std::vector<flatbuffers::Offset<Tensor>> tensors(inputTensorNum + 1); |
| 56 | for (int i = 0; i < inputTensorNum; ++i) |
| 57 | { |
| 58 | tensors[i] = CreateTensor(flatBufferBuilder, |
| 59 | flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), |
| 60 | inputTensorShape.size()), |
| 61 | tensorType, |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 62 | 1, |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 63 | flatBufferBuilder.CreateString("input" + std::to_string(i)), |
| 64 | quantizationParameters); |
| 65 | |
| 66 | // Add number of inputs to vector. |
| 67 | operatorInputs.push_back(i); |
| 68 | subgraphInputs.push_back(i); |
| 69 | } |
| 70 | |
| 71 | // Create output tensor |
| 72 | tensors[inputTensorNum] = CreateTensor(flatBufferBuilder, |
| 73 | flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| 74 | outputTensorShape.size()), |
| 75 | tensorType, |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 76 | 2, |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 77 | flatBufferBuilder.CreateString("output"), |
| 78 | quantizationParameters); |
| 79 | |
| 80 | // create operator |
| 81 | tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_ConcatenationOptions; |
| 82 | flatbuffers::Offset<void> operatorBuiltinOptions = CreateConcatenationOptions(flatBufferBuilder, axis).Union(); |
| 83 | |
| 84 | flatbuffers::Offset <Operator> controlOperator = |
| 85 | CreateOperator(flatBufferBuilder, |
| 86 | 0, |
| 87 | flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| 88 | flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| 89 | operatorBuiltinOptionsType, |
| 90 | operatorBuiltinOptions); |
| 91 | |
| 92 | flatbuffers::Offset <SubGraph> subgraph = |
| 93 | CreateSubGraph(flatBufferBuilder, |
| 94 | flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| 95 | flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), |
| 96 | flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), |
| 97 | flatBufferBuilder.CreateVector(&controlOperator, 1)); |
| 98 | |
| 99 | flatbuffers::Offset <flatbuffers::String> modelDescription = |
| 100 | flatBufferBuilder.CreateString("ArmnnDelegate: Concatenation Operator Model"); |
| 101 | flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, controlOperatorCode); |
| 102 | |
| 103 | flatbuffers::Offset <Model> flatbufferModel = |
| 104 | CreateModel(flatBufferBuilder, |
| 105 | TFLITE_SCHEMA_VERSION, |
| 106 | flatBufferBuilder.CreateVector(&operatorCode, 1), |
| 107 | flatBufferBuilder.CreateVector(&subgraph, 1), |
| 108 | modelDescription, |
| 109 | flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| 110 | |
| 111 | flatBufferBuilder.Finish(flatbufferModel); |
| 112 | |
| 113 | return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| 114 | flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| 115 | } |
| 116 | |
| 117 | std::vector<char> CreateMeanTfLiteModel(tflite::BuiltinOperator controlOperatorCode, |
| 118 | tflite::TensorType tensorType, |
| 119 | std::vector<int32_t>& input0TensorShape, |
| 120 | std::vector<int32_t>& input1TensorShape, |
| 121 | const std::vector <int32_t>& outputTensorShape, |
| 122 | std::vector<int32_t>& axisData, |
| 123 | const bool keepDims, |
| 124 | float quantScale = 1.0f, |
| 125 | int quantOffset = 0) |
| 126 | { |
| 127 | using namespace tflite; |
| 128 | flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| 129 | |
| 130 | std::array<flatbuffers::Offset<tflite::Buffer>, 2> buffers; |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 131 | buffers[0] = CreateBuffer(flatBufferBuilder); |
Matthew Sloyan | 91c4171 | 2020-11-13 09:47:35 +0000 | [diff] [blame] | 132 | buffers[1] = CreateBuffer(flatBufferBuilder, |
| 133 | flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()), |
| 134 | sizeof(int32_t) * axisData.size())); |
| 135 | |
| 136 | auto quantizationParameters = |
| 137 | CreateQuantizationParameters(flatBufferBuilder, |
| 138 | 0, |
| 139 | 0, |
| 140 | flatBufferBuilder.CreateVector<float>({ quantScale }), |
| 141 | flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
| 142 | |
| 143 | std::array<flatbuffers::Offset<Tensor>, 3> tensors; |
| 144 | tensors[0] = CreateTensor(flatBufferBuilder, |
| 145 | flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(), |
| 146 | input0TensorShape.size()), |
| 147 | tensorType, |
| 148 | 0, |
| 149 | flatBufferBuilder.CreateString("input"), |
| 150 | quantizationParameters); |
| 151 | |
| 152 | tensors[1] = CreateTensor(flatBufferBuilder, |
| 153 | flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(), |
| 154 | input1TensorShape.size()), |
| 155 | ::tflite::TensorType_INT32, |
| 156 | 1, |
| 157 | flatBufferBuilder.CreateString("axis"), |
| 158 | quantizationParameters); |
| 159 | |
| 160 | // Create output tensor |
| 161 | tensors[2] = CreateTensor(flatBufferBuilder, |
| 162 | flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| 163 | outputTensorShape.size()), |
| 164 | tensorType, |
| 165 | 0, |
| 166 | flatBufferBuilder.CreateString("output"), |
| 167 | quantizationParameters); |
| 168 | |
| 169 | // create operator. Mean uses ReducerOptions. |
| 170 | tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_ReducerOptions; |
| 171 | flatbuffers::Offset<void> operatorBuiltinOptions = CreateReducerOptions(flatBufferBuilder, keepDims).Union(); |
| 172 | |
| 173 | const std::vector<int> operatorInputs{ {0, 1} }; |
| 174 | const std::vector<int> operatorOutputs{ 2 }; |
| 175 | flatbuffers::Offset <Operator> controlOperator = |
| 176 | CreateOperator(flatBufferBuilder, |
| 177 | 0, |
| 178 | flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| 179 | flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| 180 | operatorBuiltinOptionsType, |
| 181 | operatorBuiltinOptions); |
| 182 | |
| 183 | const std::vector<int> subgraphInputs{ {0, 1} }; |
| 184 | const std::vector<int> subgraphOutputs{ 2 }; |
| 185 | flatbuffers::Offset <SubGraph> subgraph = |
| 186 | CreateSubGraph(flatBufferBuilder, |
| 187 | flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| 188 | flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), |
| 189 | flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), |
| 190 | flatBufferBuilder.CreateVector(&controlOperator, 1)); |
| 191 | |
| 192 | flatbuffers::Offset <flatbuffers::String> modelDescription = |
| 193 | flatBufferBuilder.CreateString("ArmnnDelegate: Mean Operator Model"); |
| 194 | flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, controlOperatorCode); |
| 195 | |
| 196 | flatbuffers::Offset <Model> flatbufferModel = |
| 197 | CreateModel(flatBufferBuilder, |
| 198 | TFLITE_SCHEMA_VERSION, |
| 199 | flatBufferBuilder.CreateVector(&operatorCode, 1), |
| 200 | flatBufferBuilder.CreateVector(&subgraph, 1), |
| 201 | modelDescription, |
| 202 | flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| 203 | |
| 204 | flatBufferBuilder.Finish(flatbufferModel); |
| 205 | |
| 206 | return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| 207 | flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| 208 | } |
| 209 | |
| 210 | template <typename T> |
| 211 | void ConcatenationTest(tflite::BuiltinOperator controlOperatorCode, |
| 212 | tflite::TensorType tensorType, |
| 213 | std::vector<armnn::BackendId>& backends, |
| 214 | std::vector<int32_t>& inputShapes, |
| 215 | std::vector<int32_t>& expectedOutputShape, |
| 216 | std::vector<std::vector<T>>& inputValues, |
| 217 | std::vector<T>& expectedOutputValues, |
| 218 | int32_t axis = 0, |
| 219 | float quantScale = 1.0f, |
| 220 | int quantOffset = 0) |
| 221 | { |
| 222 | using namespace tflite; |
| 223 | std::vector<char> modelBuffer = CreateConcatTfLiteModel(controlOperatorCode, |
| 224 | tensorType, |
| 225 | inputShapes, |
| 226 | expectedOutputShape, |
| 227 | inputValues.size(), |
| 228 | axis, |
| 229 | quantScale, |
| 230 | quantOffset); |
| 231 | |
| 232 | const Model* tfLiteModel = GetModel(modelBuffer.data()); |
| 233 | |
| 234 | // Create TfLite Interpreters |
| 235 | std::unique_ptr<Interpreter> armnnDelegateInterpreter; |
| 236 | CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| 237 | (&armnnDelegateInterpreter) == kTfLiteOk); |
| 238 | CHECK(armnnDelegateInterpreter != nullptr); |
| 239 | CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); |
| 240 | |
| 241 | std::unique_ptr<Interpreter> tfLiteInterpreter; |
| 242 | CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| 243 | (&tfLiteInterpreter) == kTfLiteOk); |
| 244 | CHECK(tfLiteInterpreter != nullptr); |
| 245 | CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk); |
| 246 | |
| 247 | // Create the ArmNN Delegate |
| 248 | armnnDelegate::DelegateOptions delegateOptions(backends); |
| 249 | std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> |
| 250 | theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), |
| 251 | armnnDelegate::TfLiteArmnnDelegateDelete); |
| 252 | CHECK(theArmnnDelegate != nullptr); |
| 253 | |
| 254 | // Modify armnnDelegateInterpreter to use armnnDelegate |
| 255 | CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); |
| 256 | |
| 257 | // Set input data for all input tensors. |
| 258 | for (unsigned int i = 0; i < inputValues.size(); ++i) |
| 259 | { |
| 260 | // Get single input tensor and assign to interpreters. |
| 261 | auto inputTensorValues = inputValues[i]; |
| 262 | armnnDelegate::FillInput<T>(tfLiteInterpreter, i, inputTensorValues); |
| 263 | armnnDelegate::FillInput<T>(armnnDelegateInterpreter, i, inputTensorValues); |
| 264 | } |
| 265 | |
| 266 | // Run EnqueWorkload |
| 267 | CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); |
| 268 | CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); |
| 269 | |
| 270 | // Compare output data |
| 271 | armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, |
| 272 | armnnDelegateInterpreter, |
| 273 | expectedOutputShape, |
| 274 | expectedOutputValues); |
| 275 | |
| 276 | armnnDelegateInterpreter.reset(nullptr); |
| 277 | } |
| 278 | |
| 279 | template <typename T> |
| 280 | void MeanTest(tflite::BuiltinOperator controlOperatorCode, |
| 281 | tflite::TensorType tensorType, |
| 282 | std::vector<armnn::BackendId>& backends, |
| 283 | std::vector<int32_t>& input0Shape, |
| 284 | std::vector<int32_t>& input1Shape, |
| 285 | std::vector<int32_t>& expectedOutputShape, |
| 286 | std::vector<T>& input0Values, |
| 287 | std::vector<int32_t>& input1Values, |
| 288 | std::vector<T>& expectedOutputValues, |
| 289 | const bool keepDims, |
| 290 | float quantScale = 1.0f, |
| 291 | int quantOffset = 0) |
| 292 | { |
| 293 | using namespace tflite; |
| 294 | std::vector<char> modelBuffer = CreateMeanTfLiteModel(controlOperatorCode, |
| 295 | tensorType, |
| 296 | input0Shape, |
| 297 | input1Shape, |
| 298 | expectedOutputShape, |
| 299 | input1Values, |
| 300 | keepDims, |
| 301 | quantScale, |
| 302 | quantOffset); |
| 303 | |
| 304 | const Model* tfLiteModel = GetModel(modelBuffer.data()); |
| 305 | |
| 306 | // Create TfLite Interpreters |
| 307 | std::unique_ptr<Interpreter> armnnDelegateInterpreter; |
| 308 | CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| 309 | (&armnnDelegateInterpreter) == kTfLiteOk); |
| 310 | CHECK(armnnDelegateInterpreter != nullptr); |
| 311 | CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); |
| 312 | |
| 313 | std::unique_ptr<Interpreter> tfLiteInterpreter; |
| 314 | CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) |
| 315 | (&tfLiteInterpreter) == kTfLiteOk); |
| 316 | CHECK(tfLiteInterpreter != nullptr); |
| 317 | CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk); |
| 318 | |
| 319 | // Create the ArmNN Delegate |
| 320 | armnnDelegate::DelegateOptions delegateOptions(backends); |
| 321 | std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> |
| 322 | theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), |
| 323 | armnnDelegate::TfLiteArmnnDelegateDelete); |
| 324 | CHECK(theArmnnDelegate != nullptr); |
| 325 | |
| 326 | // Modify armnnDelegateInterpreter to use armnnDelegate |
| 327 | CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); |
| 328 | |
| 329 | // Set input data |
| 330 | armnnDelegate::FillInput<T>(tfLiteInterpreter, 0, input0Values); |
| 331 | armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, input0Values); |
| 332 | |
| 333 | // Run EnqueWorkload |
| 334 | CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); |
| 335 | CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); |
| 336 | |
| 337 | // Compare output data |
| 338 | armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, |
| 339 | armnnDelegateInterpreter, |
| 340 | expectedOutputShape, |
| 341 | expectedOutputValues); |
| 342 | |
| 343 | armnnDelegateInterpreter.reset(nullptr); |
| 344 | } |
| 345 | |
| 346 | } // anonymous namespace |