Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +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. |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #pragma once |
| 7 | |
| 8 | #include "TestUtils.hpp" |
| 9 | |
| 10 | #include <armnn_delegate.hpp> |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 11 | #include <DelegateTestInterpreter.hpp> |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 12 | |
| 13 | #include <flatbuffers/flatbuffers.h> |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 14 | #include <tensorflow/lite/kernels/register.h> |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 15 | #include <tensorflow/lite/version.h> |
| 16 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 17 | #include <schema_generated.h> |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 18 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 19 | #include <doctest/doctest.h> |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 20 | |
| 21 | namespace |
| 22 | { |
| 23 | |
| 24 | std::vector<char> CreateSplitTfLiteModel(tflite::TensorType tensorType, |
| 25 | std::vector<int32_t>& axisTensorShape, |
| 26 | std::vector<int32_t>& inputTensorShape, |
| 27 | const std::vector<std::vector<int32_t>>& outputTensorShapes, |
| 28 | std::vector<int32_t>& axisData, |
| 29 | const int32_t numSplits, |
| 30 | float quantScale = 1.0f, |
| 31 | int quantOffset = 0) |
| 32 | { |
| 33 | using namespace tflite; |
| 34 | flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| 35 | |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 36 | std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; |
| 37 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| 38 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| 39 | buffers.push_back(CreateBuffer(flatBufferBuilder, |
| 40 | flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()), |
| 41 | sizeof(int32_t) * axisData.size()))); |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +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::array<flatbuffers::Offset<Tensor>, 4> tensors; |
| 51 | tensors[0] = CreateTensor(flatBufferBuilder, |
| 52 | flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(), |
| 53 | axisTensorShape.size()), |
| 54 | ::tflite::TensorType_INT32, |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 55 | 2, |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 56 | flatBufferBuilder.CreateString("axis"), |
| 57 | quantizationParameters); |
| 58 | tensors[1] = 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, |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 63 | flatBufferBuilder.CreateString("input"), |
| 64 | quantizationParameters); |
| 65 | |
| 66 | // Create output tensor |
| 67 | for (unsigned int i = 0; i < outputTensorShapes.size(); ++i) |
| 68 | { |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 69 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 70 | tensors[i + 2] = CreateTensor(flatBufferBuilder, |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 71 | flatBufferBuilder.CreateVector<int32_t>(outputTensorShapes[i].data(), |
| 72 | outputTensorShapes[i].size()), |
| 73 | tensorType, |
| 74 | (i+3), |
| 75 | flatBufferBuilder.CreateString("output"), |
| 76 | quantizationParameters); |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 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, |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 112 | flatBufferBuilder.CreateVector(buffers)); |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 113 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 114 | flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER); |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 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 | { |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 133 | using namespace delegateTestInterpreter; |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 134 | std::vector<char> modelBuffer = CreateSplitTfLiteModel(tensorType, |
| 135 | axisTensorShape, |
| 136 | inputTensorShape, |
| 137 | outputTensorShapes, |
| 138 | axisData, |
| 139 | numSplits, |
| 140 | quantScale, |
| 141 | quantOffset); |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 142 | // Setup interpreter with just TFLite Runtime. |
| 143 | auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| 144 | CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
| 145 | CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 1) == kTfLiteOk); |
| 146 | CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 147 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 148 | // Setup interpreter with Arm NN Delegate applied. |
| 149 | auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends); |
| 150 | CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
| 151 | CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 1) == kTfLiteOk); |
| 152 | CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 153 | |
| 154 | // Compare output data |
| 155 | for (unsigned int i = 0; i < expectedOutputValues.size(); ++i) |
| 156 | { |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 157 | std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(i); |
| 158 | std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(i); |
| 159 | |
| 160 | std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(i); |
| 161 | std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(i); |
| 162 | |
| 163 | armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues[i]); |
| 164 | armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputTensorShapes[i]); |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 165 | } |
| 166 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 167 | tfLiteInterpreter.Cleanup(); |
| 168 | armnnInterpreter.Cleanup(); |
| 169 | |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 170 | } // End of SPLIT Test |
| 171 | |
| 172 | std::vector<char> CreateSplitVTfLiteModel(tflite::TensorType tensorType, |
| 173 | std::vector<int32_t>& inputTensorShape, |
| 174 | std::vector<int32_t>& splitsTensorShape, |
| 175 | std::vector<int32_t>& axisTensorShape, |
| 176 | const std::vector<std::vector<int32_t>>& outputTensorShapes, |
| 177 | std::vector<int32_t>& splitsData, |
| 178 | std::vector<int32_t>& axisData, |
| 179 | const int32_t numSplits, |
| 180 | float quantScale = 1.0f, |
| 181 | int quantOffset = 0) |
| 182 | { |
| 183 | using namespace tflite; |
| 184 | flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| 185 | |
| 186 | std::array<flatbuffers::Offset<tflite::Buffer>, 3> buffers; |
| 187 | buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); |
| 188 | buffers[1] = CreateBuffer(flatBufferBuilder, |
| 189 | flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(splitsData.data()), |
| 190 | sizeof(int32_t) * splitsData.size())); |
| 191 | buffers[2] = CreateBuffer(flatBufferBuilder, |
| 192 | flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()), |
| 193 | sizeof(int32_t) * axisData.size())); |
| 194 | |
| 195 | auto quantizationParameters = |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 196 | CreateQuantizationParameters(flatBufferBuilder, |
| 197 | 0, |
| 198 | 0, |
| 199 | flatBufferBuilder.CreateVector<float>({ quantScale }), |
| 200 | flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 201 | |
| 202 | std::array<flatbuffers::Offset<Tensor>, 5> tensors; |
| 203 | tensors[0] = CreateTensor(flatBufferBuilder, |
| 204 | flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), |
| 205 | inputTensorShape.size()), |
| 206 | tensorType, |
| 207 | 0, |
| 208 | flatBufferBuilder.CreateString("input"), |
| 209 | quantizationParameters); |
| 210 | tensors[1] = CreateTensor(flatBufferBuilder, |
| 211 | flatBufferBuilder.CreateVector<int32_t>(splitsTensorShape.data(), |
| 212 | splitsTensorShape.size()), |
| 213 | ::tflite::TensorType_INT32, |
| 214 | 1, |
| 215 | flatBufferBuilder.CreateString("splits"), |
| 216 | quantizationParameters); |
| 217 | tensors[2] = CreateTensor(flatBufferBuilder, |
| 218 | flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(), |
| 219 | axisTensorShape.size()), |
| 220 | ::tflite::TensorType_INT32, |
| 221 | 2, |
| 222 | flatBufferBuilder.CreateString("axis"), |
| 223 | quantizationParameters); |
| 224 | |
| 225 | // Create output tensor |
| 226 | for (unsigned int i = 0; i < outputTensorShapes.size(); ++i) |
| 227 | { |
| 228 | tensors[i + 3] = CreateTensor(flatBufferBuilder, |
| 229 | flatBufferBuilder.CreateVector<int32_t>(outputTensorShapes[i].data(), |
| 230 | outputTensorShapes[i].size()), |
| 231 | tensorType, |
| 232 | 0, |
| 233 | flatBufferBuilder.CreateString("output"), |
| 234 | quantizationParameters); |
| 235 | } |
| 236 | |
| 237 | // create operator. Mean uses ReducerOptions. |
| 238 | tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_SplitVOptions; |
| 239 | flatbuffers::Offset<void> operatorBuiltinOptions = CreateSplitVOptions(flatBufferBuilder, numSplits).Union(); |
| 240 | |
| 241 | const std::vector<int> operatorInputs{ {0, 1, 2} }; |
| 242 | const std::vector<int> operatorOutputs{ {3, 4} }; |
| 243 | flatbuffers::Offset <Operator> controlOperator = |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 244 | CreateOperator(flatBufferBuilder, |
| 245 | 0, |
| 246 | flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| 247 | flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| 248 | operatorBuiltinOptionsType, |
| 249 | operatorBuiltinOptions); |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 250 | |
| 251 | const std::vector<int> subgraphInputs{ {0, 1, 2} }; |
| 252 | const std::vector<int> subgraphOutputs{ {3, 4} }; |
| 253 | flatbuffers::Offset <SubGraph> subgraph = |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 254 | CreateSubGraph(flatBufferBuilder, |
| 255 | flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| 256 | flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), |
| 257 | flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), |
| 258 | flatBufferBuilder.CreateVector(&controlOperator, 1)); |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 259 | |
| 260 | flatbuffers::Offset <flatbuffers::String> modelDescription = |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 261 | flatBufferBuilder.CreateString("ArmnnDelegate: SPLIT_V Operator Model"); |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 262 | flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, BuiltinOperator_SPLIT_V); |
| 263 | |
| 264 | flatbuffers::Offset <Model> flatbufferModel = |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 265 | CreateModel(flatBufferBuilder, |
| 266 | TFLITE_SCHEMA_VERSION, |
| 267 | flatBufferBuilder.CreateVector(&operatorCode, 1), |
| 268 | flatBufferBuilder.CreateVector(&subgraph, 1), |
| 269 | modelDescription, |
| 270 | flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 271 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 272 | flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER); |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 273 | |
| 274 | return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| 275 | flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| 276 | } |
| 277 | |
| 278 | template <typename T> |
| 279 | void SplitVTest(tflite::TensorType tensorType, |
| 280 | std::vector<armnn::BackendId>& backends, |
| 281 | std::vector<int32_t>& inputTensorShape, |
| 282 | std::vector<int32_t>& splitsTensorShape, |
| 283 | std::vector<int32_t>& axisTensorShape, |
| 284 | std::vector<std::vector<int32_t>>& outputTensorShapes, |
| 285 | std::vector<T>& inputValues, |
| 286 | std::vector<int32_t>& splitsData, |
| 287 | std::vector<int32_t>& axisData, |
| 288 | std::vector<std::vector<T>>& expectedOutputValues, |
| 289 | const int32_t numSplits, |
| 290 | float quantScale = 1.0f, |
| 291 | int quantOffset = 0) |
| 292 | { |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 293 | using namespace delegateTestInterpreter; |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 294 | std::vector<char> modelBuffer = CreateSplitVTfLiteModel(tensorType, |
| 295 | inputTensorShape, |
| 296 | splitsTensorShape, |
| 297 | axisTensorShape, |
| 298 | outputTensorShapes, |
| 299 | splitsData, |
| 300 | axisData, |
| 301 | numSplits, |
| 302 | quantScale, |
| 303 | quantOffset); |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 304 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 305 | // Setup interpreter with just TFLite Runtime. |
| 306 | auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| 307 | CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
| 308 | CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk); |
| 309 | CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 310 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 311 | // Setup interpreter with Arm NN Delegate applied. |
| 312 | auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends); |
| 313 | CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
| 314 | CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk); |
| 315 | CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 316 | |
| 317 | // Compare output data |
| 318 | for (unsigned int i = 0; i < expectedOutputValues.size(); ++i) |
| 319 | { |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 320 | std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(i); |
| 321 | std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(i); |
| 322 | |
| 323 | std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(i); |
| 324 | std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(i); |
| 325 | |
| 326 | armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues[i]); |
| 327 | armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputTensorShapes[i]); |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 328 | } |
| 329 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 330 | tfLiteInterpreter.Cleanup(); |
| 331 | armnnInterpreter.Cleanup(); |
Sadik Armagan | 34fa1bd | 2020-11-27 12:40:52 +0000 | [diff] [blame] | 332 | } // End of SPLIT_V Test |
| 333 | |
| 334 | } // anonymous namespace |