Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2021 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #pragma once |
| 7 | |
| 8 | #include <ResolveType.hpp> |
| 9 | |
| 10 | #include <armnn/IWorkingMemHandle.hpp> |
| 11 | #include <armnn/INetwork.hpp> |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 12 | #include <armnn/IAsyncExecutionCallback.hpp> |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 13 | |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 14 | #include <AsyncExecutionCallback.hpp> |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 15 | #include <backendsCommon/test/CommonTestUtils.hpp> |
| 16 | |
| 17 | #include <boost/test/unit_test.hpp> |
| 18 | |
| 19 | #include <vector> |
| 20 | |
| 21 | namespace armnn |
| 22 | { |
| 23 | |
| 24 | namespace experimental |
| 25 | { |
| 26 | |
| 27 | template<DataType ArmnnIType, DataType ArmnnOType, |
| 28 | typename TInput = ResolveType <ArmnnIType>, typename TOutput = ResolveType <ArmnnOType>> |
Finn Williams | b8181f7 | 2021-04-07 10:23:21 +0100 | [diff] [blame] | 29 | void AsyncThreadedEndToEndTestImpl(INetworkPtr network, |
| 30 | const std::vector<std::map<int, std::vector<TInput>>>& inputTensorData, |
| 31 | const std::vector<std::map<int, std::vector<TOutput>>>& expectedOutputData, |
| 32 | std::vector<BackendId> backends, |
| 33 | const size_t numberOfInferences, |
| 34 | float tolerance = 0.000001f) |
| 35 | { |
| 36 | // Create Runtime in which test will run |
| 37 | IRuntime::CreationOptions options; |
| 38 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 39 | |
| 40 | // Optimize the Network |
| 41 | IOptimizedNetworkPtr optNet = Optimize(*network, backends, runtime->GetDeviceSpec()); |
| 42 | |
| 43 | |
| 44 | // Creates AsyncNetwork |
| 45 | NetworkId networkId = 0; |
| 46 | std::string errorMessage; |
Francis Murtagh | 73d3e2e | 2021-04-29 14:23:04 +0100 | [diff] [blame] | 47 | const INetworkProperties networkProperties(true, MemorySource::Undefined, MemorySource::Undefined); |
Finn Williams | b8181f7 | 2021-04-07 10:23:21 +0100 | [diff] [blame] | 48 | runtime->LoadNetwork(networkId, std::move(optNet), errorMessage, networkProperties); |
| 49 | |
| 50 | std::vector<InputTensors> inputTensorsVec; |
| 51 | std::vector<OutputTensors> outputTensorsVec; |
| 52 | std::vector<std::map<int, std::vector<TOutput>>> outputStorageVec; |
| 53 | std::vector<std::unique_ptr<IWorkingMemHandle>> workingMemHandles; |
| 54 | |
| 55 | for (unsigned int i = 0; i < numberOfInferences; ++i) |
| 56 | { |
| 57 | InputTensors inputTensors; |
| 58 | OutputTensors outputTensors; |
| 59 | outputStorageVec.emplace_back(std::map<int, std::vector<TOutput>>()); |
| 60 | |
| 61 | inputTensors.reserve(inputTensorData.size()); |
| 62 | for (auto&& it : inputTensorData[i]) |
| 63 | { |
| 64 | inputTensors.push_back({it.first, |
| 65 | ConstTensor(runtime->GetInputTensorInfo(networkId, it.first), it.second.data())}); |
| 66 | } |
| 67 | |
| 68 | outputTensors.reserve(expectedOutputData.size()); |
| 69 | for (auto&& it : expectedOutputData[i]) |
| 70 | { |
| 71 | std::vector<TOutput> out(it.second.size()); |
| 72 | outputStorageVec[i].emplace(it.first, out); |
| 73 | outputTensors.push_back({it.first, |
| 74 | Tensor(runtime->GetOutputTensorInfo(networkId, it.first), |
| 75 | outputStorageVec[i].at(it.first).data())}); |
| 76 | } |
| 77 | |
| 78 | inputTensorsVec.push_back(inputTensors); |
| 79 | outputTensorsVec.push_back(outputTensors); |
| 80 | |
| 81 | workingMemHandles.push_back(runtime->CreateWorkingMemHandle(networkId)); |
| 82 | } |
| 83 | |
| 84 | std::vector<std::thread> threads; |
| 85 | for (unsigned int i = 0; i < numberOfInferences; ++i) |
| 86 | { |
| 87 | // Access the vectors before we do anything multi-threaded |
| 88 | InputTensors& inputTensors = inputTensorsVec[i]; |
| 89 | OutputTensors& outputTensors = outputTensorsVec[i]; |
| 90 | IWorkingMemHandle& workingMemHandle = *workingMemHandles[i].get(); |
| 91 | |
| 92 | threads.emplace_back([&]() |
| 93 | { |
| 94 | // Run the async network |
| 95 | runtime->Execute(workingMemHandle, inputTensors, outputTensors); |
| 96 | }); |
| 97 | } |
| 98 | |
| 99 | for (unsigned int i = 0; i < numberOfInferences; ++i) |
| 100 | { |
| 101 | threads[i].join(); |
| 102 | } |
| 103 | |
| 104 | // Checks the results. |
| 105 | for (unsigned int i = 0; i < numberOfInferences; ++i) |
| 106 | { |
| 107 | for (auto &&it : expectedOutputData[i]) |
| 108 | { |
| 109 | std::vector<TOutput> out = outputStorageVec[i].at(it.first); |
| 110 | for (unsigned int j = 0; j < out.size(); ++j) |
| 111 | { |
| 112 | BOOST_CHECK(Compare<ArmnnOType>(it.second[j], out[j], tolerance) == true); |
| 113 | } |
| 114 | } |
| 115 | } |
| 116 | |
| 117 | } |
| 118 | |
Finn Williams | b8181f7 | 2021-04-07 10:23:21 +0100 | [diff] [blame] | 119 | template<DataType ArmnnIType, DataType ArmnnOType, |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 120 | typename TInput = ResolveType<ArmnnIType>, typename TOutput = ResolveType<ArmnnOType>> |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 121 | void AsyncEndToEndTestImpl(INetworkPtr network, |
| 122 | const std::map<int, std::vector<TInput>>& inputTensorData, |
| 123 | const std::map<int, std::vector<TOutput>>& expectedOutputData, |
| 124 | std::vector<BackendId> backends, |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 125 | float tolerance = 0.000001f, |
| 126 | size_t numThreads = 0) |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 127 | { |
| 128 | // Create Runtime in which test will run |
| 129 | IRuntime::CreationOptions options; |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 130 | IRuntimePtr runtime(IRuntime::Create(options)); |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 131 | |
| 132 | // Optimize the Network |
| 133 | IOptimizedNetworkPtr optNet = Optimize(*network, backends, runtime->GetDeviceSpec()); |
| 134 | |
| 135 | // Creates AsyncNetwork |
| 136 | NetworkId networkId = 0; |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 137 | |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 138 | std::string errorMessage; |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 139 | |
| 140 | const INetworkProperties networkProperties(true, MemorySource::Undefined, MemorySource::Undefined, numThreads); |
| 141 | |
Mike Kelly | 55a8ffd | 2021-04-07 20:10:49 +0100 | [diff] [blame] | 142 | runtime->LoadNetwork(networkId, std::move(optNet), errorMessage, networkProperties); |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 143 | |
| 144 | InputTensors inputTensors; |
| 145 | inputTensors.reserve(inputTensorData.size()); |
| 146 | for (auto&& it : inputTensorData) |
| 147 | { |
| 148 | inputTensors.push_back({it.first, |
Mike Kelly | 55a8ffd | 2021-04-07 20:10:49 +0100 | [diff] [blame] | 149 | ConstTensor(runtime->GetInputTensorInfo(networkId, it.first), it.second.data())}); |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 150 | } |
| 151 | |
| 152 | OutputTensors outputTensors; |
| 153 | outputTensors.reserve(expectedOutputData.size()); |
| 154 | std::map<int, std::vector<TOutput>> outputStorage; |
| 155 | for (auto&& it : expectedOutputData) |
| 156 | { |
| 157 | std::vector<TOutput> out(it.second.size()); |
| 158 | outputStorage.emplace(it.first, out); |
| 159 | outputTensors.push_back({it.first, |
Mike Kelly | 55a8ffd | 2021-04-07 20:10:49 +0100 | [diff] [blame] | 160 | Tensor(runtime->GetOutputTensorInfo(networkId, it.first), |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 161 | outputStorage.at(it.first).data())}); |
| 162 | } |
| 163 | |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 164 | if (numThreads == 0) |
| 165 | { |
| 166 | // Create WorkingMemHandle for this async network |
| 167 | std::unique_ptr<IWorkingMemHandle> workingMemHandle = runtime->CreateWorkingMemHandle(networkId); |
| 168 | IWorkingMemHandle& workingMemHandleRef = *workingMemHandle.get(); |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 169 | |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 170 | // Run the async network |
| 171 | runtime->Execute(workingMemHandleRef, inputTensors, outputTensors); |
| 172 | } |
| 173 | else |
| 174 | { |
| 175 | std::vector<IAsyncExecutionCallbackPtr> callbacks; |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 176 | |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 177 | // Create 1000 callbacks that will be checked post scheduling |
| 178 | for (size_t i = 0; i < 1000; ++i) |
| 179 | { |
| 180 | callbacks.emplace_back(std::make_shared<AsyncExecutionCallback>()); |
| 181 | } |
| 182 | |
| 183 | // For the asyncronous execution, we are adding a pool of working memory handles (1 per thread) in the |
| 184 | // LoadedNetwork with a each scheduled inference having a spefic priority |
| 185 | for (IAsyncExecutionCallbackPtr cb : callbacks) |
| 186 | { |
| 187 | runtime->Schedule(networkId, |
| 188 | inputTensors, |
| 189 | outputTensors, |
| 190 | static_cast<QosExecPriority>(rand()%3), |
| 191 | cb); |
| 192 | } |
| 193 | |
| 194 | // Wait until the execution signals a notify |
| 195 | for (IAsyncExecutionCallbackPtr cb : callbacks) |
| 196 | { |
| 197 | cb->Wait(); |
| 198 | |
| 199 | // Checks the results. |
| 200 | BOOST_CHECK(cb->GetStatus() == Status::Success); |
| 201 | } |
| 202 | } |
| 203 | |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 204 | for (auto&& it : expectedOutputData) |
| 205 | { |
| 206 | std::vector<TOutput> out = outputStorage.at(it.first); |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 207 | |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 208 | for (unsigned int i = 0; i < out.size(); ++i) |
| 209 | { |
| 210 | BOOST_CHECK(Compare<ArmnnOType>(it.second[i], out[i], tolerance) == true); |
| 211 | } |
| 212 | } |
| 213 | } |
| 214 | |
| 215 | template<typename armnn::DataType DataType> |
| 216 | INetworkPtr CreateStridedSliceNetwork(const TensorShape& inputShape, |
| 217 | const TensorShape& outputShape, |
| 218 | const std::vector<int>& beginData, |
| 219 | const std::vector<int>& endData, |
| 220 | const std::vector<int>& stridesData, |
| 221 | int beginMask = 0, |
| 222 | int endMask = 0, |
| 223 | int shrinkAxisMask = 0, |
| 224 | int ellipsisMask = 0, |
| 225 | int newAxisMask = 0, |
| 226 | const float qScale = 1.0f, |
| 227 | const int32_t qOffset = 0) |
| 228 | { |
| 229 | using namespace armnn; |
| 230 | // Builds up the structure of the network. |
| 231 | INetworkPtr net(INetwork::Create()); |
| 232 | |
| 233 | TensorInfo inputTensorInfo(inputShape, DataType, qScale, qOffset); |
| 234 | TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset); |
| 235 | |
| 236 | armnn::StridedSliceDescriptor stridedSliceDescriptor; |
| 237 | stridedSliceDescriptor.m_Begin = beginData; |
| 238 | stridedSliceDescriptor.m_End = endData; |
| 239 | stridedSliceDescriptor.m_Stride = stridesData; |
| 240 | stridedSliceDescriptor.m_BeginMask = beginMask; |
| 241 | stridedSliceDescriptor.m_EndMask = endMask; |
| 242 | stridedSliceDescriptor.m_ShrinkAxisMask = shrinkAxisMask; |
| 243 | stridedSliceDescriptor.m_EllipsisMask = ellipsisMask; |
| 244 | stridedSliceDescriptor.m_NewAxisMask = newAxisMask; |
| 245 | |
| 246 | IConnectableLayer* input = net->AddInputLayer(0, "Input_Layer"); |
| 247 | IConnectableLayer* stridedSlice = net->AddStridedSliceLayer(stridedSliceDescriptor, "splitter"); |
| 248 | IConnectableLayer* output = net->AddOutputLayer(0); |
| 249 | |
| 250 | Connect(input, stridedSlice, inputTensorInfo, 0, 0); |
| 251 | Connect(stridedSlice, output, outputTensorInfo, 0, 0); |
| 252 | |
| 253 | return net; |
| 254 | } |
| 255 | |
| 256 | template<armnn::DataType ArmnnType> |
| 257 | void StridedSlicedEndToEndTest(const std::vector<BackendId>& backends) |
| 258 | { |
| 259 | using namespace armnn; |
| 260 | using T = ResolveType<ArmnnType>; |
| 261 | |
| 262 | const TensorShape& inputShape = {3, 2, 3, 1}; |
| 263 | const TensorShape& outputShape = {1, 2, 3, 1}; |
| 264 | const std::vector<int>& beginData = {1, 0, 0, 0}; |
| 265 | const std::vector<int>& endData = {2, 2, 3, 1}; |
| 266 | const std::vector<int>& stridesData = {1, 1, 1, 1}; |
| 267 | int beginMask = 0; |
| 268 | int endMask = 0; |
| 269 | int shrinkAxisMask = 0; |
| 270 | int ellipsisMask = 0; |
| 271 | int newAxisMask = 0; |
| 272 | |
| 273 | // Builds up the structure of the network |
| 274 | INetworkPtr net = CreateStridedSliceNetwork<ArmnnType>(inputShape, |
| 275 | outputShape, |
| 276 | beginData, |
| 277 | endData, |
| 278 | stridesData, |
| 279 | beginMask, |
| 280 | endMask, |
| 281 | shrinkAxisMask, |
| 282 | ellipsisMask, |
| 283 | newAxisMask); |
| 284 | |
| 285 | BOOST_TEST_CHECKPOINT("create a network"); |
| 286 | |
| 287 | // Creates structures for input & output. |
| 288 | std::vector<T> inputData{ |
| 289 | 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, |
| 290 | |
| 291 | 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, |
| 292 | |
| 293 | 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f |
| 294 | }; |
| 295 | |
| 296 | std::vector<T> outputExpected{ |
| 297 | 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f |
| 298 | }; |
| 299 | |
| 300 | std::map<int, std::vector<T>> inputTensorData = {{0, inputData}}; |
| 301 | std::map<int, std::vector<T>> expectedOutputData = {{0, outputExpected}}; |
| 302 | |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 303 | AsyncEndToEndTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends, 0.000001f); |
| 304 | } |
| 305 | |
| 306 | template<armnn::DataType ArmnnType> |
| 307 | void AsyncScheduledStridedSlicedEndToEndTest(const std::vector<BackendId>& backends) |
| 308 | { |
| 309 | using namespace armnn; |
| 310 | using T = ResolveType<ArmnnType>; |
| 311 | |
| 312 | const TensorShape& inputShape = {3, 2, 3, 1}; |
| 313 | const TensorShape& outputShape = {1, 2, 3, 1}; |
| 314 | const std::vector<int>& beginData = {1, 0, 0, 0}; |
| 315 | const std::vector<int>& endData = {2, 2, 3, 1}; |
| 316 | const std::vector<int>& stridesData = {1, 1, 1, 1}; |
| 317 | int beginMask = 0; |
| 318 | int endMask = 0; |
| 319 | int shrinkAxisMask = 0; |
| 320 | int ellipsisMask = 0; |
| 321 | int newAxisMask = 0; |
| 322 | |
| 323 | // Builds up the structure of the network |
| 324 | INetworkPtr net = CreateStridedSliceNetwork<ArmnnType>(inputShape, |
| 325 | outputShape, |
| 326 | beginData, |
| 327 | endData, |
| 328 | stridesData, |
| 329 | beginMask, |
| 330 | endMask, |
| 331 | shrinkAxisMask, |
| 332 | ellipsisMask, |
| 333 | newAxisMask); |
| 334 | |
| 335 | // Creates structures for input & output. |
| 336 | std::vector<T> inputData{ |
| 337 | 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, |
| 338 | |
| 339 | 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, |
| 340 | |
| 341 | 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f |
| 342 | }; |
| 343 | |
| 344 | std::vector<T> outputExpected{ |
| 345 | 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f |
| 346 | }; |
| 347 | |
| 348 | std::map<int, std::vector<T>> inputTensorData = {{0, inputData}}; |
| 349 | std::map<int, std::vector<T>> expectedOutputData = {{0, outputExpected}}; |
| 350 | |
| 351 | AsyncEndToEndTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends, 0.000001f, 1); |
| 352 | } |
| 353 | |
| 354 | template<armnn::DataType ArmnnType> |
| 355 | void AsyncScheduledStridedSlicedMultiThreadedEndToEndTest(const std::vector<BackendId>& backends) |
| 356 | { |
| 357 | using namespace armnn; |
| 358 | using T = ResolveType<ArmnnType>; |
| 359 | |
| 360 | const TensorShape& inputShape = {3, 2, 3, 1}; |
| 361 | const TensorShape& outputShape = {1, 2, 3, 1}; |
| 362 | const std::vector<int>& beginData = {1, 0, 0, 0}; |
| 363 | const std::vector<int>& endData = {2, 2, 3, 1}; |
| 364 | const std::vector<int>& stridesData = {1, 1, 1, 1}; |
| 365 | int beginMask = 0; |
| 366 | int endMask = 0; |
| 367 | int shrinkAxisMask = 0; |
| 368 | int ellipsisMask = 0; |
| 369 | int newAxisMask = 0; |
| 370 | |
| 371 | // Builds up the structure of the network |
| 372 | INetworkPtr net = CreateStridedSliceNetwork<ArmnnType>(inputShape, |
| 373 | outputShape, |
| 374 | beginData, |
| 375 | endData, |
| 376 | stridesData, |
| 377 | beginMask, |
| 378 | endMask, |
| 379 | shrinkAxisMask, |
| 380 | ellipsisMask, |
| 381 | newAxisMask); |
| 382 | |
| 383 | // Creates structures for input & output. |
| 384 | std::vector<T> inputData{ |
| 385 | 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, |
| 386 | |
| 387 | 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, |
| 388 | |
| 389 | 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f |
| 390 | }; |
| 391 | |
| 392 | std::vector<T> outputExpected{ |
| 393 | 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f |
| 394 | }; |
| 395 | |
| 396 | std::map<int, std::vector<T>> inputTensorData = {{0, inputData}}; |
| 397 | std::map<int, std::vector<T>> expectedOutputData = {{0, outputExpected}}; |
| 398 | |
| 399 | AsyncEndToEndTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends, 0.000001f, 3); |
Finn Williams | b8181f7 | 2021-04-07 10:23:21 +0100 | [diff] [blame] | 400 | } |
| 401 | |
| 402 | template<armnn::DataType ArmnnType> |
| 403 | void StridedSlicedMultiThreadedEndToEndTest(const std::vector<BackendId>& backends) |
| 404 | { |
| 405 | using namespace armnn; |
| 406 | using T = ResolveType<ArmnnType>; |
| 407 | |
| 408 | const TensorShape& inputShape = {3, 2, 3, 1}; |
| 409 | const TensorShape& outputShape = {1, 2, 3, 1}; |
| 410 | const std::vector<int>& beginData = {1, 0, 0, 0}; |
| 411 | const std::vector<int>& endData = {2, 2, 3, 1}; |
| 412 | const std::vector<int>& stridesData = {1, 1, 1, 1}; |
| 413 | int beginMask = 0; |
| 414 | int endMask = 0; |
| 415 | int shrinkAxisMask = 0; |
| 416 | int ellipsisMask = 0; |
| 417 | int newAxisMask = 0; |
| 418 | |
| 419 | // Builds up the structure of the network |
| 420 | INetworkPtr net = CreateStridedSliceNetwork<ArmnnType>(inputShape, |
| 421 | outputShape, |
| 422 | beginData, |
| 423 | endData, |
| 424 | stridesData, |
| 425 | beginMask, |
| 426 | endMask, |
| 427 | shrinkAxisMask, |
| 428 | ellipsisMask, |
| 429 | newAxisMask); |
| 430 | |
| 431 | BOOST_TEST_CHECKPOINT("create a network"); |
| 432 | |
| 433 | // Creates structures for input & output. |
| 434 | std::vector<T> inputData1{ |
| 435 | 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, |
| 436 | |
| 437 | 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, |
| 438 | |
| 439 | 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f |
| 440 | }; |
| 441 | |
| 442 | std::vector<T> outputExpected1{ 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f }; |
| 443 | |
| 444 | // Creates structures for input & output. |
| 445 | std::vector<T> inputData2{ |
| 446 | 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, |
| 447 | |
| 448 | 8.0f, 8.0f, 8.0f, 7.0f, 7.0f, 7.0f, |
| 449 | |
| 450 | 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f |
| 451 | }; |
| 452 | |
| 453 | std::vector<T> outputExpected2{ 8.0f, 8.0f, 8.0f, 7.0f, 7.0f, 7.0f }; |
| 454 | |
| 455 | std::vector<std::map<int, std::vector<T>>> inputTensors; |
| 456 | std::vector<std::map<int, std::vector<T>>> outputTensors; |
| 457 | |
| 458 | inputTensors.push_back(std::map<int, std::vector<T>> {{0, inputData1}}); |
| 459 | inputTensors.push_back(std::map<int, std::vector<T>> {{0, inputData2}}); |
| 460 | outputTensors.push_back(std::map<int, std::vector<T>> {{0, outputExpected1}}); |
| 461 | outputTensors.push_back(std::map<int, std::vector<T>> {{0, outputExpected2}}); |
| 462 | |
| 463 | AsyncThreadedEndToEndTestImpl<ArmnnType, ArmnnType>(move(net), inputTensors, outputTensors, backends, 2); |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 464 | } |
| 465 | |
| 466 | } // experimental namespace |
| 467 | |
| 468 | } // armnn namespace |
| 469 | |