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> |
Finn Williams | f364d53 | 2021-06-09 17:07:33 +0100 | [diff] [blame] | 12 | #include <armnn/Threadpool.hpp> |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 13 | #include <armnn/IAsyncExecutionCallback.hpp> |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 14 | |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 15 | #include <AsyncExecutionCallback.hpp> |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 16 | #include <backendsCommon/test/CommonTestUtils.hpp> |
| 17 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 18 | #include <doctest/doctest.h> |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 19 | |
| 20 | #include <vector> |
| 21 | |
| 22 | namespace armnn |
| 23 | { |
| 24 | |
| 25 | namespace experimental |
| 26 | { |
| 27 | |
| 28 | template<DataType ArmnnIType, DataType ArmnnOType, |
| 29 | typename TInput = ResolveType <ArmnnIType>, typename TOutput = ResolveType <ArmnnOType>> |
Finn Williams | b8181f7 | 2021-04-07 10:23:21 +0100 | [diff] [blame] | 30 | void AsyncThreadedEndToEndTestImpl(INetworkPtr network, |
| 31 | const std::vector<std::map<int, std::vector<TInput>>>& inputTensorData, |
| 32 | const std::vector<std::map<int, std::vector<TOutput>>>& expectedOutputData, |
| 33 | std::vector<BackendId> backends, |
| 34 | const size_t numberOfInferences, |
| 35 | float tolerance = 0.000001f) |
| 36 | { |
| 37 | // Create Runtime in which test will run |
| 38 | IRuntime::CreationOptions options; |
| 39 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 40 | |
| 41 | // Optimize the Network |
| 42 | IOptimizedNetworkPtr optNet = Optimize(*network, backends, runtime->GetDeviceSpec()); |
| 43 | |
| 44 | |
| 45 | // Creates AsyncNetwork |
| 46 | NetworkId networkId = 0; |
| 47 | std::string errorMessage; |
Francis Murtagh | 73d3e2e | 2021-04-29 14:23:04 +0100 | [diff] [blame] | 48 | const INetworkProperties networkProperties(true, MemorySource::Undefined, MemorySource::Undefined); |
Finn Williams | b8181f7 | 2021-04-07 10:23:21 +0100 | [diff] [blame] | 49 | runtime->LoadNetwork(networkId, std::move(optNet), errorMessage, networkProperties); |
| 50 | |
| 51 | std::vector<InputTensors> inputTensorsVec; |
| 52 | std::vector<OutputTensors> outputTensorsVec; |
| 53 | std::vector<std::map<int, std::vector<TOutput>>> outputStorageVec; |
| 54 | std::vector<std::unique_ptr<IWorkingMemHandle>> workingMemHandles; |
| 55 | |
| 56 | for (unsigned int i = 0; i < numberOfInferences; ++i) |
| 57 | { |
| 58 | InputTensors inputTensors; |
| 59 | OutputTensors outputTensors; |
| 60 | outputStorageVec.emplace_back(std::map<int, std::vector<TOutput>>()); |
| 61 | |
| 62 | inputTensors.reserve(inputTensorData.size()); |
| 63 | for (auto&& it : inputTensorData[i]) |
| 64 | { |
| 65 | inputTensors.push_back({it.first, |
| 66 | ConstTensor(runtime->GetInputTensorInfo(networkId, it.first), it.second.data())}); |
| 67 | } |
| 68 | |
| 69 | outputTensors.reserve(expectedOutputData.size()); |
| 70 | for (auto&& it : expectedOutputData[i]) |
| 71 | { |
| 72 | std::vector<TOutput> out(it.second.size()); |
| 73 | outputStorageVec[i].emplace(it.first, out); |
| 74 | outputTensors.push_back({it.first, |
| 75 | Tensor(runtime->GetOutputTensorInfo(networkId, it.first), |
| 76 | outputStorageVec[i].at(it.first).data())}); |
| 77 | } |
| 78 | |
| 79 | inputTensorsVec.push_back(inputTensors); |
| 80 | outputTensorsVec.push_back(outputTensors); |
| 81 | |
| 82 | workingMemHandles.push_back(runtime->CreateWorkingMemHandle(networkId)); |
| 83 | } |
| 84 | |
| 85 | std::vector<std::thread> threads; |
| 86 | for (unsigned int i = 0; i < numberOfInferences; ++i) |
| 87 | { |
| 88 | // Access the vectors before we do anything multi-threaded |
| 89 | InputTensors& inputTensors = inputTensorsVec[i]; |
| 90 | OutputTensors& outputTensors = outputTensorsVec[i]; |
| 91 | IWorkingMemHandle& workingMemHandle = *workingMemHandles[i].get(); |
| 92 | |
| 93 | threads.emplace_back([&]() |
| 94 | { |
| 95 | // Run the async network |
| 96 | runtime->Execute(workingMemHandle, inputTensors, outputTensors); |
| 97 | }); |
| 98 | } |
| 99 | |
| 100 | for (unsigned int i = 0; i < numberOfInferences; ++i) |
| 101 | { |
| 102 | threads[i].join(); |
| 103 | } |
| 104 | |
| 105 | // Checks the results. |
| 106 | for (unsigned int i = 0; i < numberOfInferences; ++i) |
| 107 | { |
| 108 | for (auto &&it : expectedOutputData[i]) |
| 109 | { |
| 110 | std::vector<TOutput> out = outputStorageVec[i].at(it.first); |
| 111 | for (unsigned int j = 0; j < out.size(); ++j) |
| 112 | { |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 113 | CHECK(Compare<ArmnnOType>(it.second[j], out[j], tolerance) == true); |
Finn Williams | b8181f7 | 2021-04-07 10:23:21 +0100 | [diff] [blame] | 114 | } |
| 115 | } |
| 116 | } |
| 117 | |
| 118 | } |
| 119 | |
Finn Williams | b8181f7 | 2021-04-07 10:23:21 +0100 | [diff] [blame] | 120 | template<DataType ArmnnIType, DataType ArmnnOType, |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 121 | typename TInput = ResolveType<ArmnnIType>, typename TOutput = ResolveType<ArmnnOType>> |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 122 | void AsyncEndToEndTestImpl(INetworkPtr network, |
| 123 | const std::map<int, std::vector<TInput>>& inputTensorData, |
| 124 | const std::map<int, std::vector<TOutput>>& expectedOutputData, |
| 125 | std::vector<BackendId> backends, |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 126 | float tolerance = 0.000001f, |
Kevin May | b4b3ac9 | 2021-05-21 16:42:21 +0100 | [diff] [blame] | 127 | size_t numThreads = 1) |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 128 | { |
| 129 | // Create Runtime in which test will run |
| 130 | IRuntime::CreationOptions options; |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 131 | IRuntimePtr runtime(IRuntime::Create(options)); |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 132 | |
| 133 | // Optimize the Network |
| 134 | IOptimizedNetworkPtr optNet = Optimize(*network, backends, runtime->GetDeviceSpec()); |
| 135 | |
| 136 | // Creates AsyncNetwork |
| 137 | NetworkId networkId = 0; |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 138 | |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 139 | std::string errorMessage; |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 140 | |
Finn Williams | f364d53 | 2021-06-09 17:07:33 +0100 | [diff] [blame] | 141 | const INetworkProperties networkProperties(true, MemorySource::Undefined, MemorySource::Undefined); |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 142 | |
Mike Kelly | 55a8ffd | 2021-04-07 20:10:49 +0100 | [diff] [blame] | 143 | runtime->LoadNetwork(networkId, std::move(optNet), errorMessage, networkProperties); |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 144 | |
| 145 | InputTensors inputTensors; |
| 146 | inputTensors.reserve(inputTensorData.size()); |
| 147 | for (auto&& it : inputTensorData) |
| 148 | { |
| 149 | inputTensors.push_back({it.first, |
Mike Kelly | 55a8ffd | 2021-04-07 20:10:49 +0100 | [diff] [blame] | 150 | ConstTensor(runtime->GetInputTensorInfo(networkId, it.first), it.second.data())}); |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 151 | } |
| 152 | |
| 153 | OutputTensors outputTensors; |
| 154 | outputTensors.reserve(expectedOutputData.size()); |
| 155 | std::map<int, std::vector<TOutput>> outputStorage; |
| 156 | for (auto&& it : expectedOutputData) |
| 157 | { |
| 158 | std::vector<TOutput> out(it.second.size()); |
| 159 | outputStorage.emplace(it.first, out); |
| 160 | outputTensors.push_back({it.first, |
Mike Kelly | 55a8ffd | 2021-04-07 20:10:49 +0100 | [diff] [blame] | 161 | Tensor(runtime->GetOutputTensorInfo(networkId, it.first), |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 162 | outputStorage.at(it.first).data())}); |
| 163 | } |
| 164 | |
Kevin May | b4b3ac9 | 2021-05-21 16:42:21 +0100 | [diff] [blame] | 165 | if (numThreads <= 1) |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 166 | { |
| 167 | // Create WorkingMemHandle for this async network |
| 168 | std::unique_ptr<IWorkingMemHandle> workingMemHandle = runtime->CreateWorkingMemHandle(networkId); |
| 169 | IWorkingMemHandle& workingMemHandleRef = *workingMemHandle.get(); |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 170 | |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 171 | // Run the async network |
| 172 | runtime->Execute(workingMemHandleRef, inputTensors, outputTensors); |
| 173 | } |
| 174 | else |
| 175 | { |
Finn Williams | f364d53 | 2021-06-09 17:07:33 +0100 | [diff] [blame] | 176 | std::vector<std::shared_ptr<IWorkingMemHandle>> memHandles; |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 177 | |
Finn Williams | f364d53 | 2021-06-09 17:07:33 +0100 | [diff] [blame] | 178 | for (size_t i = 0; i < numThreads; ++i) |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 179 | { |
Finn Williams | f364d53 | 2021-06-09 17:07:33 +0100 | [diff] [blame] | 180 | memHandles.emplace_back(runtime->CreateWorkingMemHandle(networkId)); |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 181 | } |
| 182 | |
Finn Williams | f364d53 | 2021-06-09 17:07:33 +0100 | [diff] [blame] | 183 | Threadpool threadpool(numThreads, runtime.get(), memHandles); |
| 184 | AsyncCallbackManager callbackManager; |
| 185 | |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 186 | // For the asyncronous execution, we are adding a pool of working memory handles (1 per thread) in the |
| 187 | // LoadedNetwork with a each scheduled inference having a spefic priority |
Finn Williams | f364d53 | 2021-06-09 17:07:33 +0100 | [diff] [blame] | 188 | for (size_t i = 0; i < 1000; ++i) |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 189 | { |
Finn Williams | f364d53 | 2021-06-09 17:07:33 +0100 | [diff] [blame] | 190 | threadpool.Schedule(networkId, |
| 191 | inputTensors, |
| 192 | outputTensors, |
| 193 | static_cast<QosExecPriority>(rand()%3), |
| 194 | callbackManager.GetNewCallback()); |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 195 | } |
| 196 | |
| 197 | // Wait until the execution signals a notify |
Finn Williams | f364d53 | 2021-06-09 17:07:33 +0100 | [diff] [blame] | 198 | for (size_t i = 0; i < 1000; ++i) |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 199 | { |
Finn Williams | f364d53 | 2021-06-09 17:07:33 +0100 | [diff] [blame] | 200 | auto cb = callbackManager.GetNotifiedCallback(); |
| 201 | |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 202 | // Checks the results. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 203 | CHECK(cb->GetStatus() == Status::Success); |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 204 | } |
| 205 | } |
| 206 | |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 207 | for (auto&& it : expectedOutputData) |
| 208 | { |
| 209 | std::vector<TOutput> out = outputStorage.at(it.first); |
Keith Davis | e813d67 | 2021-04-22 10:10:34 +0100 | [diff] [blame] | 210 | |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 211 | for (unsigned int i = 0; i < out.size(); ++i) |
| 212 | { |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 213 | CHECK(Compare<ArmnnOType>(it.second[i], out[i], tolerance) == true); |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 214 | } |
| 215 | } |
| 216 | } |
| 217 | |
| 218 | template<typename armnn::DataType DataType> |
| 219 | INetworkPtr CreateStridedSliceNetwork(const TensorShape& inputShape, |
| 220 | const TensorShape& outputShape, |
| 221 | const std::vector<int>& beginData, |
| 222 | const std::vector<int>& endData, |
| 223 | const std::vector<int>& stridesData, |
| 224 | int beginMask = 0, |
| 225 | int endMask = 0, |
| 226 | int shrinkAxisMask = 0, |
| 227 | int ellipsisMask = 0, |
| 228 | int newAxisMask = 0, |
| 229 | const float qScale = 1.0f, |
| 230 | const int32_t qOffset = 0) |
| 231 | { |
| 232 | using namespace armnn; |
| 233 | // Builds up the structure of the network. |
| 234 | INetworkPtr net(INetwork::Create()); |
| 235 | |
| 236 | TensorInfo inputTensorInfo(inputShape, DataType, qScale, qOffset); |
| 237 | TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset); |
| 238 | |
| 239 | armnn::StridedSliceDescriptor stridedSliceDescriptor; |
| 240 | stridedSliceDescriptor.m_Begin = beginData; |
| 241 | stridedSliceDescriptor.m_End = endData; |
| 242 | stridedSliceDescriptor.m_Stride = stridesData; |
| 243 | stridedSliceDescriptor.m_BeginMask = beginMask; |
| 244 | stridedSliceDescriptor.m_EndMask = endMask; |
| 245 | stridedSliceDescriptor.m_ShrinkAxisMask = shrinkAxisMask; |
| 246 | stridedSliceDescriptor.m_EllipsisMask = ellipsisMask; |
| 247 | stridedSliceDescriptor.m_NewAxisMask = newAxisMask; |
| 248 | |
| 249 | IConnectableLayer* input = net->AddInputLayer(0, "Input_Layer"); |
| 250 | IConnectableLayer* stridedSlice = net->AddStridedSliceLayer(stridedSliceDescriptor, "splitter"); |
| 251 | IConnectableLayer* output = net->AddOutputLayer(0); |
| 252 | |
| 253 | Connect(input, stridedSlice, inputTensorInfo, 0, 0); |
| 254 | Connect(stridedSlice, output, outputTensorInfo, 0, 0); |
| 255 | |
| 256 | return net; |
| 257 | } |
| 258 | |
| 259 | template<armnn::DataType ArmnnType> |
Kevin May | b4b3ac9 | 2021-05-21 16:42:21 +0100 | [diff] [blame] | 260 | void StridedSlicedEndToEndTest(const std::vector<BackendId>& backends, size_t numThreads) |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 261 | { |
| 262 | using namespace armnn; |
| 263 | using T = ResolveType<ArmnnType>; |
| 264 | |
| 265 | const TensorShape& inputShape = {3, 2, 3, 1}; |
| 266 | const TensorShape& outputShape = {1, 2, 3, 1}; |
| 267 | const std::vector<int>& beginData = {1, 0, 0, 0}; |
| 268 | const std::vector<int>& endData = {2, 2, 3, 1}; |
| 269 | const std::vector<int>& stridesData = {1, 1, 1, 1}; |
| 270 | int beginMask = 0; |
| 271 | int endMask = 0; |
| 272 | int shrinkAxisMask = 0; |
| 273 | int ellipsisMask = 0; |
| 274 | int newAxisMask = 0; |
| 275 | |
| 276 | // Builds up the structure of the network |
| 277 | INetworkPtr net = CreateStridedSliceNetwork<ArmnnType>(inputShape, |
| 278 | outputShape, |
| 279 | beginData, |
| 280 | endData, |
| 281 | stridesData, |
| 282 | beginMask, |
| 283 | endMask, |
| 284 | shrinkAxisMask, |
| 285 | ellipsisMask, |
| 286 | newAxisMask); |
| 287 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 288 | CHECK(net); |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 289 | // Creates structures for input & output. |
| 290 | std::vector<T> inputData{ |
| 291 | 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, |
| 292 | |
| 293 | 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, |
| 294 | |
| 295 | 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f |
| 296 | }; |
| 297 | |
| 298 | std::vector<T> outputExpected{ |
| 299 | 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f |
| 300 | }; |
| 301 | |
| 302 | std::map<int, std::vector<T>> inputTensorData = {{0, inputData}}; |
| 303 | std::map<int, std::vector<T>> expectedOutputData = {{0, outputExpected}}; |
| 304 | |
Kevin May | b4b3ac9 | 2021-05-21 16:42:21 +0100 | [diff] [blame] | 305 | AsyncEndToEndTestImpl<ArmnnType, ArmnnType>(move(net), |
| 306 | inputTensorData, |
| 307 | expectedOutputData, |
| 308 | backends, |
| 309 | 0.000001f, |
| 310 | numThreads); |
Finn Williams | b8181f7 | 2021-04-07 10:23:21 +0100 | [diff] [blame] | 311 | } |
| 312 | |
| 313 | template<armnn::DataType ArmnnType> |
| 314 | void StridedSlicedMultiThreadedEndToEndTest(const std::vector<BackendId>& backends) |
| 315 | { |
| 316 | using namespace armnn; |
| 317 | using T = ResolveType<ArmnnType>; |
| 318 | |
| 319 | const TensorShape& inputShape = {3, 2, 3, 1}; |
| 320 | const TensorShape& outputShape = {1, 2, 3, 1}; |
| 321 | const std::vector<int>& beginData = {1, 0, 0, 0}; |
| 322 | const std::vector<int>& endData = {2, 2, 3, 1}; |
| 323 | const std::vector<int>& stridesData = {1, 1, 1, 1}; |
| 324 | int beginMask = 0; |
| 325 | int endMask = 0; |
| 326 | int shrinkAxisMask = 0; |
| 327 | int ellipsisMask = 0; |
| 328 | int newAxisMask = 0; |
| 329 | |
| 330 | // Builds up the structure of the network |
| 331 | INetworkPtr net = CreateStridedSliceNetwork<ArmnnType>(inputShape, |
| 332 | outputShape, |
| 333 | beginData, |
| 334 | endData, |
| 335 | stridesData, |
| 336 | beginMask, |
| 337 | endMask, |
| 338 | shrinkAxisMask, |
| 339 | ellipsisMask, |
| 340 | newAxisMask); |
| 341 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 342 | CHECK(net); |
Finn Williams | b8181f7 | 2021-04-07 10:23:21 +0100 | [diff] [blame] | 343 | |
| 344 | // Creates structures for input & output. |
| 345 | std::vector<T> inputData1{ |
| 346 | 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, |
| 347 | |
| 348 | 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, |
| 349 | |
| 350 | 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f |
| 351 | }; |
| 352 | |
| 353 | std::vector<T> outputExpected1{ 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f }; |
| 354 | |
| 355 | // Creates structures for input & output. |
| 356 | std::vector<T> inputData2{ |
| 357 | 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, |
| 358 | |
| 359 | 8.0f, 8.0f, 8.0f, 7.0f, 7.0f, 7.0f, |
| 360 | |
| 361 | 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f |
| 362 | }; |
| 363 | |
| 364 | std::vector<T> outputExpected2{ 8.0f, 8.0f, 8.0f, 7.0f, 7.0f, 7.0f }; |
| 365 | |
| 366 | std::vector<std::map<int, std::vector<T>>> inputTensors; |
| 367 | std::vector<std::map<int, std::vector<T>>> outputTensors; |
| 368 | |
| 369 | inputTensors.push_back(std::map<int, std::vector<T>> {{0, inputData1}}); |
| 370 | inputTensors.push_back(std::map<int, std::vector<T>> {{0, inputData2}}); |
| 371 | outputTensors.push_back(std::map<int, std::vector<T>> {{0, outputExpected1}}); |
| 372 | outputTensors.push_back(std::map<int, std::vector<T>> {{0, outputExpected2}}); |
| 373 | |
| 374 | AsyncThreadedEndToEndTestImpl<ArmnnType, ArmnnType>(move(net), inputTensors, outputTensors, backends, 2); |
Mike Kelly | 386ff1a | 2021-03-29 15:04:50 +0100 | [diff] [blame] | 375 | } |
| 376 | |
| 377 | } // experimental namespace |
| 378 | |
| 379 | } // armnn namespace |
| 380 | |