Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2020 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #include <backendsCommon/test/CommonTestUtils.hpp> |
| 7 | #include <backendsCommon/test/mockBackend/MockImportBackend.hpp> |
| 8 | |
| 9 | #include <test/GraphUtils.hpp> |
| 10 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 11 | #include <doctest/doctest.h> |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 12 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 13 | TEST_SUITE("NeonFallback") |
| 14 | { |
| 15 | TEST_CASE("FallbackImportToCpuAcc") |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 16 | { |
| 17 | using namespace armnn; |
| 18 | |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 19 | // Create a mock backend objectN |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 20 | MockImportBackendInitialiser initialiser; // Register the Mock Backend |
| 21 | auto backendObjPtr = CreateBackendObject(MockImportBackendId()); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 22 | CHECK((backendObjPtr != nullptr)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 23 | |
| 24 | BackendIdSet backendIds = BackendRegistryInstance().GetBackendIds(); |
| 25 | if (backendIds.find("MockRef") == backendIds.end()) |
| 26 | { |
| 27 | std::string message = "Cannot load MockRef"; |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 28 | FAIL(message); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 29 | } |
| 30 | |
| 31 | // Create runtime in which test will run and allow fallback to CpuRef. |
| 32 | IRuntime::CreationOptions options; |
| 33 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 34 | |
| 35 | // Builds up the structure of the network. |
| 36 | INetworkPtr net(INetwork::Create()); |
| 37 | |
| 38 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 39 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 40 | IConnectableLayer* input2 = net->AddInputLayer(2, "input2"); |
| 41 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 42 | IConnectableLayer* sub = net->AddSubtractionLayer("sub"); |
| 43 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 44 | |
| 45 | input0->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 46 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 47 | input2->GetOutputSlot(0).Connect(sub->GetInputSlot(0)); |
| 48 | add->GetOutputSlot(0).Connect(sub->GetInputSlot(1)); |
| 49 | sub->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 50 | |
| 51 | TensorInfo info = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32); |
| 52 | |
| 53 | input0->GetOutputSlot(0).SetTensorInfo(info); |
| 54 | input1->GetOutputSlot(0).SetTensorInfo(info); |
| 55 | input2->GetOutputSlot(0).SetTensorInfo(info); |
| 56 | add->GetOutputSlot(0).SetTensorInfo(info); |
| 57 | sub->GetOutputSlot(0).SetTensorInfo(info); |
| 58 | |
| 59 | // optimize the network |
| 60 | std::vector<BackendId> backends = { "MockRef", Compute::CpuAcc }; |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 61 | OptimizerOptions optOptions; |
| 62 | optOptions.m_ImportEnabled = true; |
| 63 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec(), optOptions); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 64 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 65 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 66 | |
| 67 | armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); |
| 68 | armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); |
| 69 | armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "input2"); |
| 70 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "add"); |
| 71 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "[ add (0) -> sub (1) ]"); |
| 72 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "sub"); |
| 73 | armnn::Layer* const layer6 = GetFirstLayerWithName(graph, "output"); |
| 74 | |
| 75 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 76 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 77 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 78 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 79 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 80 | CHECK(CheckOrder(graph, layer4, layer5)); |
| 81 | CHECK(CheckOrder(graph, layer5, layer6)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 82 | |
| 83 | // Load it into the runtime. It should pass. |
| 84 | NetworkId netId; |
| 85 | std::string ignoredErrorMessage; |
Francis Murtagh | 73d3e2e | 2021-04-29 14:23:04 +0100 | [diff] [blame] | 86 | INetworkProperties networkProperties(false, MemorySource::Malloc, MemorySource::Malloc); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 87 | runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties); |
| 88 | |
| 89 | // Creates structures for input & output |
| 90 | std::vector<float> inputData0 |
| 91 | { |
| 92 | 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f |
| 93 | }; |
| 94 | std::vector<float> inputData1 |
| 95 | { |
| 96 | 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f |
| 97 | }; |
| 98 | std::vector<float> inputData2 |
| 99 | { |
| 100 | 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f |
| 101 | }; |
| 102 | |
| 103 | std::vector<float> outputData(12); |
| 104 | |
| 105 | std::vector<float> expectedOutput |
| 106 | { |
| 107 | 11.0f, 9.0f, 7.0f, 5.0f, 3.0f, 1.0f, -1.0f, -3.0f, -5.0f, -7.0f, -9.0f, -11.0f |
| 108 | }; |
| 109 | |
| 110 | InputTensors inputTensors |
| 111 | { |
| 112 | { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) }, |
| 113 | { 1, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) }, |
| 114 | { 2, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 2), inputData2.data()) } |
| 115 | }; |
| 116 | OutputTensors outputTensors |
| 117 | { |
| 118 | { 0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 119 | }; |
| 120 | |
| 121 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 122 | |
| 123 | // Do the inference |
| 124 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 125 | |
| 126 | // Retrieve the Profiler.Print() output to get the workload execution |
| 127 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 128 | std::stringstream ss; |
| 129 | profilerManager.GetProfiler()->Print(ss);; |
| 130 | std::string dump = ss.str(); |
| 131 | |
| 132 | // Contains ImportMemGeneric |
| 133 | std::size_t found = dump.find("ImportMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 134 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 135 | |
| 136 | // Contains SyncMemGeneric |
| 137 | found = dump.find("SyncMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 138 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 139 | |
| 140 | // Does not contain CopyMemGeneric |
| 141 | found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 142 | CHECK(found == std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 143 | |
| 144 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 145 | CHECK((layer4->GetType() == LayerType::MemImport)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 146 | |
| 147 | // Check output is as expected |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 148 | CHECK(outputData == expectedOutput); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 149 | } |
| 150 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 151 | TEST_CASE("FallbackPaddingCopyToCpuAcc") |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 152 | { |
| 153 | using namespace armnn; |
| 154 | |
| 155 | // Create a mock backend object |
| 156 | MockImportBackendInitialiser initialiser; // Register the Mock Backend |
| 157 | auto backendObjPtr = CreateBackendObject(MockImportBackendId()); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 158 | CHECK((backendObjPtr != nullptr)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 159 | |
| 160 | BackendIdSet backendIds = BackendRegistryInstance().GetBackendIds(); |
| 161 | if (backendIds.find("MockRef") == backendIds.end()) |
| 162 | { |
| 163 | std::string message = "Cannot load MockRef"; |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 164 | FAIL(message); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 165 | } |
| 166 | |
| 167 | // Create runtime in which test will run and allow fallback to CpuRef. |
| 168 | IRuntime::CreationOptions options; |
| 169 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 170 | |
| 171 | // Builds up the structure of the network. |
| 172 | INetworkPtr net(INetwork::Create()); |
| 173 | |
| 174 | Pooling2dDescriptor desc; |
| 175 | |
| 176 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 177 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 178 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 179 | IConnectableLayer* pooling = net->AddPooling2dLayer(desc, "pooling"); |
| 180 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 181 | |
| 182 | input0->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 183 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 184 | add->GetOutputSlot(0).Connect(pooling->GetInputSlot(0)); |
| 185 | pooling->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 186 | |
| 187 | TensorInfo info = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32); |
| 188 | TensorInfo poolingInfo = TensorInfo({ 1, 2, 1, 1 }, DataType::Float32); |
| 189 | |
| 190 | input0->GetOutputSlot(0).SetTensorInfo(info); |
| 191 | input1->GetOutputSlot(0).SetTensorInfo(info); |
| 192 | add->GetOutputSlot(0).SetTensorInfo(info); |
| 193 | pooling->GetOutputSlot(0).SetTensorInfo(poolingInfo); |
| 194 | |
| 195 | // optimize the network |
| 196 | std::vector<BackendId> backends = { "MockRef", Compute::CpuAcc }; |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 197 | OptimizerOptions optOptions; |
| 198 | optOptions.m_ImportEnabled = true; |
| 199 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec(), optOptions); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 200 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 201 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 202 | |
| 203 | armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); |
| 204 | armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); |
| 205 | armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "add"); |
| 206 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "[ add (0) -> pooling (0) ]"); |
| 207 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "pooling"); |
| 208 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "output"); |
| 209 | |
| 210 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 211 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 212 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 213 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 214 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 215 | CHECK(CheckOrder(graph, layer4, layer5)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 216 | |
| 217 | // Load it into the runtime. It should pass. |
| 218 | NetworkId netId; |
| 219 | std::string ignoredErrorMessage; |
Francis Murtagh | 73d3e2e | 2021-04-29 14:23:04 +0100 | [diff] [blame] | 220 | INetworkProperties networkProperties(false, MemorySource::Malloc, MemorySource::Malloc); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 221 | |
| 222 | runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties); |
| 223 | |
| 224 | // Creates structures for input & output |
| 225 | std::vector<float> inputData0 |
| 226 | { |
| 227 | 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f |
| 228 | }; |
| 229 | std::vector<float> inputData1 |
| 230 | { |
| 231 | 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f |
| 232 | }; |
| 233 | |
| 234 | std::vector<float> outputData(2); |
| 235 | |
| 236 | std::vector<float> expectedOutput |
| 237 | { |
| 238 | 6.0f, 12.0f |
| 239 | }; |
| 240 | |
| 241 | InputTensors inputTensors |
| 242 | { |
| 243 | { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) }, |
| 244 | { 1, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) } |
| 245 | }; |
| 246 | OutputTensors outputTensors |
| 247 | { |
| 248 | { 0, armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 249 | }; |
| 250 | |
| 251 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 252 | |
| 253 | // Do the inference |
| 254 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 255 | |
| 256 | // Retrieve the Profiler.Print() output to get the workload execution |
| 257 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 258 | std::stringstream ss; |
| 259 | profilerManager.GetProfiler()->Print(ss);; |
| 260 | std::string dump = ss.str(); |
| 261 | |
| 262 | // Contains CopyMemGeneric between the backends |
| 263 | std::size_t found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 264 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 265 | |
| 266 | // Contains SyncMemGeneric for the output |
| 267 | found = dump.find("SyncMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 268 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 269 | |
| 270 | // Does not contain ImportMemGeneric |
| 271 | found = dump.find("ImportMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 272 | CHECK(found == std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 273 | |
| 274 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 275 | CHECK((layer3->GetType() == LayerType::MemCopy)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 276 | |
| 277 | // Check output is as expected |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 278 | CHECK(outputData == expectedOutput); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 279 | } |
| 280 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 281 | TEST_CASE("FallbackImportFromCpuAcc") |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 282 | { |
| 283 | using namespace armnn; |
| 284 | |
| 285 | // Create a mock backend object |
| 286 | MockImportBackendInitialiser initialiser; // Register the Mock Backend |
| 287 | auto backendObjPtr = CreateBackendObject(MockImportBackendId()); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 288 | CHECK((backendObjPtr != nullptr)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 289 | |
| 290 | BackendIdSet backendIds = BackendRegistryInstance().GetBackendIds(); |
| 291 | if (backendIds.find("MockRef") == backendIds.end()) |
| 292 | { |
| 293 | std::string message = "Cannot load MockRef"; |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 294 | FAIL(message); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 295 | } |
| 296 | |
| 297 | // Create runtime in which test will run and allow fallback to CpuRef. |
| 298 | IRuntime::CreationOptions options; |
| 299 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 300 | |
| 301 | // Builds up the structure of the network. |
| 302 | INetworkPtr net(INetwork::Create()); |
| 303 | |
| 304 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 305 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 306 | IConnectableLayer* input2 = net->AddInputLayer(2, "input2"); |
| 307 | IConnectableLayer* sub = net->AddSubtractionLayer("sub"); |
| 308 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 309 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 310 | |
| 311 | input0->GetOutputSlot(0).Connect(sub->GetInputSlot(0)); |
| 312 | input1->GetOutputSlot(0).Connect(sub->GetInputSlot(1)); |
| 313 | input2->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 314 | sub->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 315 | add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 316 | |
| 317 | TensorInfo info = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32); |
| 318 | |
| 319 | input0->GetOutputSlot(0).SetTensorInfo(info); |
| 320 | input1->GetOutputSlot(0).SetTensorInfo(info); |
| 321 | input2->GetOutputSlot(0).SetTensorInfo(info); |
| 322 | sub->GetOutputSlot(0).SetTensorInfo(info); |
| 323 | add->GetOutputSlot(0).SetTensorInfo(info); |
| 324 | |
| 325 | // optimize the network |
| 326 | std::vector<BackendId> backends = { "MockRef", Compute::CpuAcc }; |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 327 | OptimizerOptions optOptions; |
| 328 | optOptions.m_ImportEnabled = true; |
| 329 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec(), optOptions); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 330 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 331 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 332 | |
| 333 | armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); |
| 334 | armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); |
| 335 | armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "input2"); |
| 336 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "sub"); |
| 337 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "[ sub (0) -> add (1) ]"); |
| 338 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "add"); |
| 339 | armnn::Layer* const layer6 = GetFirstLayerWithName(graph, "output"); |
| 340 | |
| 341 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 342 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 343 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 344 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 345 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 346 | CHECK(CheckOrder(graph, layer4, layer5)); |
| 347 | CHECK(CheckOrder(graph, layer5, layer6)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 348 | |
| 349 | // Load it into the runtime. It should pass. |
| 350 | NetworkId netId; |
| 351 | std::string ignoredErrorMessage; |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 352 | |
Francis Murtagh | 73d3e2e | 2021-04-29 14:23:04 +0100 | [diff] [blame] | 353 | INetworkProperties networkProperties(false, MemorySource::Malloc, MemorySource::Malloc); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 354 | runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties); |
| 355 | |
| 356 | // Creates structures for input & output |
| 357 | std::vector<float> inputData0 |
| 358 | { |
| 359 | 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 0.0f |
| 360 | }; |
| 361 | std::vector<float> inputData1 |
| 362 | { |
| 363 | 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f |
| 364 | }; |
| 365 | std::vector<float> inputData2 |
| 366 | { |
| 367 | 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f |
| 368 | }; |
| 369 | |
| 370 | std::vector<float> outputData(12); |
| 371 | |
| 372 | std::vector<float> expectedOutput |
| 373 | { |
| 374 | 13.0f, 11.0f, 11.0f, 9.0f, 7.0f, 7.0f, 7.0f, 5.0f, 5.0f, 3.0f, 3.0f, -5.0f |
| 375 | }; |
| 376 | |
| 377 | InputTensors inputTensors |
| 378 | { |
| 379 | { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) }, |
| 380 | { 1, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) }, |
| 381 | { 2, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 2), inputData2.data()) } |
| 382 | }; |
| 383 | OutputTensors outputTensors |
| 384 | { |
| 385 | { 0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 386 | }; |
| 387 | |
| 388 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 389 | |
| 390 | // Do the inference |
| 391 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 392 | |
| 393 | // Retrieve the Profiler.Print() output to get the workload execution |
| 394 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 395 | std::stringstream ss; |
| 396 | profilerManager.GetProfiler()->Print(ss);; |
| 397 | std::string dump = ss.str(); |
| 398 | |
| 399 | // Contains ImportMemGeneric |
| 400 | std::size_t found = dump.find("ImportMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 401 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 402 | |
| 403 | // Contains SyncMemGeneric |
| 404 | found = dump.find("SyncMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 405 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 406 | |
| 407 | // Does not contain CopyMemGeneric |
| 408 | found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 409 | CHECK(found == std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 410 | |
| 411 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 412 | CHECK((layer4->GetType() == LayerType::MemImport)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 413 | |
| 414 | // Check output is as expected |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 415 | CHECK(outputData == expectedOutput); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 416 | } |
| 417 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 418 | TEST_CASE("FallbackPaddingCopyFromCpuAcc") |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 419 | { |
| 420 | using namespace armnn; |
| 421 | |
| 422 | // Create a mock backend object |
| 423 | MockImportBackendInitialiser initialiser; // Register the Mock Backend |
| 424 | auto backendObjPtr = CreateBackendObject(MockImportBackendId()); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 425 | CHECK((backendObjPtr != nullptr)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 426 | |
| 427 | BackendIdSet backendIds = BackendRegistryInstance().GetBackendIds(); |
| 428 | if (backendIds.find("MockRef") == backendIds.end()) |
| 429 | { |
| 430 | std::string message = "Cannot load MockRef"; |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 431 | FAIL(message); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 432 | } |
| 433 | |
| 434 | // Create runtime in which test will run and allow fallback to CpuRef. |
| 435 | IRuntime::CreationOptions options; |
| 436 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 437 | |
| 438 | // Builds up the structure of the network. |
| 439 | INetworkPtr net(INetwork::Create()); |
| 440 | |
| 441 | Pooling2dDescriptor desc; |
| 442 | |
| 443 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 444 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 445 | IConnectableLayer* pooling = net->AddPooling2dLayer(desc, "pooling"); |
| 446 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 447 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 448 | |
| 449 | input0->GetOutputSlot(0).Connect(pooling->GetInputSlot(0)); |
| 450 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 451 | pooling->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 452 | add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 453 | |
| 454 | TensorInfo inputInfo = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32); |
| 455 | TensorInfo poolingInfo = TensorInfo({ 1, 2, 1, 1 }, DataType::Float32); |
| 456 | |
| 457 | input0->GetOutputSlot(0).SetTensorInfo(inputInfo); |
| 458 | input1->GetOutputSlot(0).SetTensorInfo(poolingInfo); |
| 459 | pooling->GetOutputSlot(0).SetTensorInfo(poolingInfo); |
| 460 | add->GetOutputSlot(0).SetTensorInfo(poolingInfo); |
| 461 | |
| 462 | // optimize the network |
| 463 | std::vector<BackendId> backends = { "MockRef", Compute::CpuAcc }; |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 464 | OptimizerOptions optOptions; |
| 465 | optOptions.m_ImportEnabled = true; |
| 466 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec(), optOptions); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 467 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 468 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 469 | |
| 470 | armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); |
| 471 | armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); |
| 472 | armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "pooling"); |
| 473 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "[ pooling (0) -> add (0) ]"); |
| 474 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "add"); |
| 475 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "output"); |
| 476 | |
| 477 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 478 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 479 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 480 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 481 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 482 | CHECK(CheckOrder(graph, layer4, layer5)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 483 | |
| 484 | // Load it into the runtime. It should pass. |
| 485 | NetworkId netId; |
| 486 | std::string ignoredErrorMessage; |
Francis Murtagh | 73d3e2e | 2021-04-29 14:23:04 +0100 | [diff] [blame] | 487 | INetworkProperties networkProperties(false, MemorySource::Malloc, MemorySource::Malloc); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 488 | |
| 489 | runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties); |
| 490 | |
| 491 | // Creates structures for input & output |
| 492 | std::vector<float> inputData0 |
| 493 | { |
| 494 | 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f |
| 495 | }; |
| 496 | std::vector<float> inputData1 |
| 497 | { |
| 498 | -1.0f, 3.0f |
| 499 | }; |
| 500 | |
| 501 | std::vector<float> outputData(2); |
| 502 | |
| 503 | std::vector<float> expectedOutput |
| 504 | { |
| 505 | 5.0f, 15.0f |
| 506 | }; |
| 507 | |
| 508 | InputTensors inputTensors |
| 509 | { |
| 510 | { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) }, |
| 511 | { 1, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) } |
| 512 | }; |
| 513 | OutputTensors outputTensors |
| 514 | { |
| 515 | { 0, armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 516 | }; |
| 517 | |
| 518 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 519 | |
| 520 | // Do the inference |
| 521 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 522 | |
| 523 | // Retrieve the Profiler.Print() output to get the workload execution |
| 524 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 525 | std::stringstream ss; |
| 526 | profilerManager.GetProfiler()->Print(ss);; |
| 527 | std::string dump = ss.str(); |
| 528 | |
| 529 | // Contains CopyMemGeneric between the backends |
| 530 | std::size_t found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 531 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 532 | |
| 533 | // Contains SyncMemGeneric for the output |
| 534 | found = dump.find("SyncMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 535 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 536 | |
| 537 | // Does not contain ImportMemGeneric |
| 538 | found = dump.find("ImportMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 539 | CHECK(found == std::string::npos); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 540 | |
| 541 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 542 | CHECK((layer3->GetType() == LayerType::MemCopy)); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 543 | |
| 544 | // Check output is as expected |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 545 | CHECK(outputData == expectedOutput); |
Narumol Prangnawarat | b8d771a | 2020-08-14 11:51:12 +0100 | [diff] [blame] | 546 | } |
| 547 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 548 | TEST_CASE("FallbackDisableImportFromCpuAcc") |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 549 | { |
| 550 | using namespace armnn; |
| 551 | |
| 552 | // Create a mock backend object |
| 553 | MockImportBackendInitialiser initialiser; // Register the Mock Backend |
| 554 | auto backendObjPtr = CreateBackendObject(MockImportBackendId()); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 555 | CHECK((backendObjPtr != nullptr)); |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 556 | |
| 557 | BackendIdSet backendIds = BackendRegistryInstance().GetBackendIds(); |
| 558 | if (backendIds.find("MockRef") == backendIds.end()) |
| 559 | { |
| 560 | std::string message = "Cannot load MockRef"; |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 561 | FAIL(message); |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 562 | } |
| 563 | |
| 564 | // Create runtime in which test will run and allow fallback to CpuRef. |
| 565 | IRuntime::CreationOptions options; |
| 566 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 567 | |
| 568 | // Builds up the structure of the network. |
| 569 | INetworkPtr net(INetwork::Create()); |
| 570 | |
| 571 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 572 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 573 | IConnectableLayer* input2 = net->AddInputLayer(2, "input2"); |
| 574 | IConnectableLayer* sub = net->AddSubtractionLayer("sub"); |
| 575 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 576 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 577 | |
| 578 | input0->GetOutputSlot(0).Connect(sub->GetInputSlot(0)); |
| 579 | input1->GetOutputSlot(0).Connect(sub->GetInputSlot(1)); |
| 580 | input2->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 581 | sub->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 582 | add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 583 | |
| 584 | TensorInfo info = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32); |
| 585 | |
| 586 | input0->GetOutputSlot(0).SetTensorInfo(info); |
| 587 | input1->GetOutputSlot(0).SetTensorInfo(info); |
| 588 | input2->GetOutputSlot(0).SetTensorInfo(info); |
| 589 | sub->GetOutputSlot(0).SetTensorInfo(info); |
| 590 | add->GetOutputSlot(0).SetTensorInfo(info); |
| 591 | |
| 592 | // optimize the network |
| 593 | std::vector<BackendId> backends = { "MockRef", Compute::CpuAcc }; |
| 594 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec()); |
| 595 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 596 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 597 | |
| 598 | armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); |
| 599 | armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); |
| 600 | armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "input2"); |
| 601 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "sub"); |
| 602 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "[ sub (0) -> add (1) ]"); |
| 603 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "add"); |
| 604 | armnn::Layer* const layer6 = GetFirstLayerWithName(graph, "output"); |
| 605 | |
| 606 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 607 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 608 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 609 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 610 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 611 | CHECK(CheckOrder(graph, layer4, layer5)); |
| 612 | CHECK(CheckOrder(graph, layer5, layer6)); |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 613 | |
| 614 | // Load it into the runtime. It should pass. |
| 615 | NetworkId netId; |
| 616 | std::string ignoredErrorMessage; |
Francis Murtagh | 73d3e2e | 2021-04-29 14:23:04 +0100 | [diff] [blame] | 617 | INetworkProperties networkProperties(false, MemorySource::Undefined, MemorySource::Undefined); |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 618 | |
| 619 | runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties); |
| 620 | |
| 621 | // Creates structures for input & output |
| 622 | std::vector<float> inputData0 |
| 623 | { |
| 624 | 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 0.0f |
| 625 | }; |
| 626 | std::vector<float> inputData1 |
| 627 | { |
| 628 | 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f |
| 629 | }; |
| 630 | std::vector<float> inputData2 |
| 631 | { |
| 632 | 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f |
| 633 | }; |
| 634 | |
| 635 | std::vector<float> outputData(12); |
| 636 | |
| 637 | std::vector<float> expectedOutput |
| 638 | { |
| 639 | 13.0f, 11.0f, 11.0f, 9.0f, 7.0f, 7.0f, 7.0f, 5.0f, 5.0f, 3.0f, 3.0f, -5.0f |
| 640 | }; |
| 641 | |
| 642 | InputTensors inputTensors |
| 643 | { |
| 644 | { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) }, |
| 645 | { 1, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) }, |
| 646 | { 2, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 2), inputData2.data()) } |
| 647 | }; |
| 648 | OutputTensors outputTensors |
| 649 | { |
| 650 | { 0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 651 | }; |
| 652 | |
| 653 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 654 | |
| 655 | // Do the inference |
| 656 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 657 | |
| 658 | // Retrieve the Profiler.Print() output to get the workload execution |
| 659 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 660 | std::stringstream ss; |
| 661 | profilerManager.GetProfiler()->Print(ss);; |
| 662 | std::string dump = ss.str(); |
| 663 | |
| 664 | // Contains CopyMemGeneric between the backends |
| 665 | std::size_t found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 666 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 667 | |
| 668 | // Does not contain ImportMemGeneric |
| 669 | found = dump.find("ImportMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 670 | CHECK(found == std::string::npos); |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 671 | |
| 672 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 673 | CHECK((layer4->GetType() == LayerType::MemCopy)); |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 674 | |
| 675 | // Check output is as expected |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 676 | CHECK(outputData == expectedOutput); |
Narumol Prangnawarat | a2493a0 | 2020-08-19 14:39:07 +0100 | [diff] [blame] | 677 | } |
| 678 | |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 679 | #if defined(ARMCOMPUTECL_ENABLED) |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 680 | TEST_CASE("NeonImportEnabledFallbackToCl") |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 681 | { |
| 682 | using namespace armnn; |
| 683 | |
| 684 | IRuntime::CreationOptions options; |
| 685 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 686 | |
| 687 | // Builds up the structure of the network. |
| 688 | INetworkPtr net(INetwork::Create()); |
| 689 | |
| 690 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 691 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 692 | IConnectableLayer* input2 = net->AddInputLayer(2, "input2"); |
| 693 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 694 | IConnectableLayer* sub = net->AddSubtractionLayer("sub"); |
| 695 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 696 | |
| 697 | input0->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 698 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 699 | input2->GetOutputSlot(0).Connect(sub->GetInputSlot(0)); |
| 700 | add->GetOutputSlot(0).Connect(sub->GetInputSlot(1)); |
| 701 | sub->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 702 | |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 703 | TensorInfo info = TensorInfo({ 1, 2, 4, 2 }, DataType::Float32); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 704 | |
| 705 | input0->GetOutputSlot(0).SetTensorInfo(info); |
| 706 | input1->GetOutputSlot(0).SetTensorInfo(info); |
| 707 | input2->GetOutputSlot(0).SetTensorInfo(info); |
| 708 | add->GetOutputSlot(0).SetTensorInfo(info); |
| 709 | sub->GetOutputSlot(0).SetTensorInfo(info); |
| 710 | |
| 711 | std::vector<BackendId> backends = { Compute::CpuAcc, Compute::GpuAcc }; |
| 712 | // Use BackendSelectionHint to specify GpuAcc for Subtraction layer |
| 713 | sub->BackendSelectionHint(backends[1]); |
| 714 | |
| 715 | // optimize the network |
| 716 | OptimizerOptions optOptions; |
| 717 | optOptions.m_ImportEnabled = true; |
| 718 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec(), optOptions); |
| 719 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 720 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 721 | |
| 722 | armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); |
| 723 | armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); |
| 724 | armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "input2"); |
| 725 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "add"); |
| 726 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "[ add (0) -> sub (1) ]"); |
| 727 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "sub"); |
| 728 | armnn::Layer* const layer6 = GetFirstLayerWithName(graph, "output"); |
| 729 | |
| 730 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 731 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 732 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 733 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 734 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 735 | CHECK(CheckOrder(graph, layer4, layer5)); |
| 736 | CHECK(CheckOrder(graph, layer5, layer6)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 737 | |
| 738 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 739 | CHECK((layer4->GetType() == LayerType::MemCopy)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 740 | |
| 741 | // Correctly use backend hint |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 742 | CHECK((layer5->GetBackendId() == Compute::GpuAcc )); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 743 | |
| 744 | // Load it into the runtime. It should pass. |
| 745 | NetworkId netId; |
| 746 | std::string ignoredErrorMessage; |
Francis Murtagh | 73d3e2e | 2021-04-29 14:23:04 +0100 | [diff] [blame] | 747 | |
| 748 | INetworkProperties networkProperties(false, MemorySource::Malloc, MemorySource::Malloc); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 749 | |
| 750 | runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties); |
| 751 | |
| 752 | // Creates structures for input & output |
| 753 | std::vector<float> inputData0 |
| 754 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 755 | 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f, 1.0f, 1.0f, 2.0f, 2.0f |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 756 | }; |
| 757 | std::vector<float> inputData1 |
| 758 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 759 | 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 0.0f, 1.0f, 1.0f, 2.0f |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 760 | }; |
| 761 | std::vector<float> inputData2 |
| 762 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 763 | 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f, 12.0f, 11.0f, 10.0f, 9.0f |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 764 | }; |
| 765 | |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 766 | std::vector<float> outputData(16); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 767 | |
| 768 | std::vector<float> expectedOutput |
| 769 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 770 | 11.0f, 9.0f, 7.0f, 5.0f, 3.0f, 1.0f, -1.0f, -3.0f, -5.0f, -7.0f, -9.0f, -11.0f, 11.0f, 9.0f, 7.0f, 5.0f |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 771 | }; |
| 772 | |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 773 | // Creates structures for input & output |
| 774 | unsigned int numElements = info.GetNumElements(); |
| 775 | size_t totalBytes = numElements * sizeof(float); |
| 776 | |
| 777 | // Prepare aligned data |
| 778 | const size_t alignment = 64; |
| 779 | size_t space = totalBytes + alignment + alignment; |
| 780 | auto inputData = std::make_unique<uint8_t[]>(space); |
| 781 | void* alignedInputPtr = inputData.get(); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 782 | CHECK(std::align(alignment, totalBytes, alignedInputPtr, space)); |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 783 | |
| 784 | auto* intputPtr = reinterpret_cast<float*>(alignedInputPtr); |
| 785 | std::copy(inputData2.begin(), inputData2.end(), intputPtr); |
| 786 | |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 787 | InputTensors inputTensors |
| 788 | { |
| 789 | { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) }, |
| 790 | { 1, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) }, |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 791 | { 2, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 2), alignedInputPtr) } |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 792 | }; |
| 793 | OutputTensors outputTensors |
| 794 | { |
| 795 | { 0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 796 | }; |
| 797 | |
| 798 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 799 | |
| 800 | // Do the inference |
| 801 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 802 | |
| 803 | // Retrieve the Profiler.Print() output to get the workload execution |
| 804 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 805 | std::stringstream ss; |
| 806 | profilerManager.GetProfiler()->Print(ss);; |
| 807 | std::string dump = ss.str(); |
| 808 | |
| 809 | // Executed Subtraction using GpuAcc |
| 810 | std::size_t found = dump.find("ClSubtractionWorkload_Execute"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 811 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 812 | |
| 813 | // Contain CopyMemGeneric |
| 814 | found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 815 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 816 | |
| 817 | // Check output is as expected |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 818 | for(unsigned int i = 0; i < numElements; ++i) |
| 819 | { |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 820 | CHECK(outputData[i] == expectedOutput[i]); |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 821 | } |
| 822 | runtime->UnloadNetwork(netId); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 823 | } |
| 824 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 825 | TEST_CASE("NeonImportDisabledFallbackToCl") |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 826 | { |
| 827 | using namespace armnn; |
| 828 | |
| 829 | IRuntime::CreationOptions options; |
| 830 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 831 | |
| 832 | // Builds up the structure of the network. |
| 833 | INetworkPtr net(INetwork::Create()); |
| 834 | |
| 835 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 836 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 837 | IConnectableLayer* input2 = net->AddInputLayer(2, "input2"); |
| 838 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 839 | IConnectableLayer* sub = net->AddSubtractionLayer("sub"); |
| 840 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 841 | |
| 842 | input0->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 843 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 844 | input2->GetOutputSlot(0).Connect(sub->GetInputSlot(0)); |
| 845 | add->GetOutputSlot(0).Connect(sub->GetInputSlot(1)); |
| 846 | sub->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 847 | |
| 848 | TensorInfo info = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32); |
| 849 | |
| 850 | input0->GetOutputSlot(0).SetTensorInfo(info); |
| 851 | input1->GetOutputSlot(0).SetTensorInfo(info); |
| 852 | input2->GetOutputSlot(0).SetTensorInfo(info); |
| 853 | add->GetOutputSlot(0).SetTensorInfo(info); |
| 854 | sub->GetOutputSlot(0).SetTensorInfo(info); |
| 855 | |
| 856 | std::vector<BackendId> backends = { Compute::CpuAcc, Compute::GpuAcc }; |
| 857 | // Use BackendSelectionHint to specify GpuAcc for Subtraction layer |
| 858 | sub->BackendSelectionHint(backends[1]); |
| 859 | |
| 860 | // optimize the network |
| 861 | OptimizerOptions optOptions; |
| 862 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec(), optOptions); |
| 863 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 864 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 865 | |
| 866 | armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); |
| 867 | armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); |
| 868 | armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "input2"); |
| 869 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "add"); |
| 870 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "[ add (0) -> sub (1) ]"); |
| 871 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "sub"); |
| 872 | armnn::Layer* const layer6 = GetFirstLayerWithName(graph, "output"); |
| 873 | |
| 874 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 875 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 876 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 877 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 878 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 879 | CHECK(CheckOrder(graph, layer4, layer5)); |
| 880 | CHECK(CheckOrder(graph, layer5, layer6)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 881 | |
| 882 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 883 | CHECK((layer4->GetType() == LayerType::MemCopy)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 884 | |
| 885 | // Correctly use backend hint |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 886 | CHECK((layer5->GetBackendId() == Compute::GpuAcc )); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 887 | |
| 888 | // Load it into the runtime. It should pass. |
| 889 | NetworkId netId; |
| 890 | runtime->LoadNetwork(netId, std::move(optNet)); |
| 891 | |
| 892 | // Creates structures for input & output |
| 893 | std::vector<float> inputData0 |
| 894 | { |
| 895 | 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f |
| 896 | }; |
| 897 | std::vector<float> inputData1 |
| 898 | { |
| 899 | 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f |
| 900 | }; |
| 901 | std::vector<float> inputData2 |
| 902 | { |
| 903 | 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f |
| 904 | }; |
| 905 | |
| 906 | std::vector<float> outputData(12); |
| 907 | |
| 908 | std::vector<float> expectedOutput |
| 909 | { |
| 910 | 11.0f, 9.0f, 7.0f, 5.0f, 3.0f, 1.0f, -1.0f, -3.0f, -5.0f, -7.0f, -9.0f, -11.0f |
| 911 | }; |
| 912 | |
| 913 | InputTensors inputTensors |
| 914 | { |
| 915 | { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) }, |
| 916 | { 1, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) }, |
| 917 | { 2, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 2), inputData2.data()) } |
| 918 | }; |
| 919 | OutputTensors outputTensors |
| 920 | { |
| 921 | { 0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 922 | }; |
| 923 | |
| 924 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 925 | |
| 926 | // Do the inference |
| 927 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 928 | |
| 929 | // Retrieve the Profiler.Print() output to get the workload execution |
| 930 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 931 | std::stringstream ss; |
| 932 | profilerManager.GetProfiler()->Print(ss);; |
| 933 | std::string dump = ss.str(); |
| 934 | |
| 935 | // Executed Subtraction using GpuAcc |
| 936 | std::size_t found = dump.find("ClSubtractionWorkload_Execute"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 937 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 938 | |
| 939 | // Contain CopyMemGeneric |
| 940 | found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 941 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 942 | |
| 943 | // Check output is as expected |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 944 | CHECK(outputData == expectedOutput); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 945 | } |
| 946 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 947 | TEST_CASE("NeonImportEnabledFallbackSubgraphToCl") |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 948 | { |
| 949 | using namespace armnn; |
| 950 | |
| 951 | IRuntime::CreationOptions options; |
| 952 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 953 | |
| 954 | // Builds up the structure of the network. |
| 955 | INetworkPtr net(INetwork::Create()); |
| 956 | |
| 957 | Pooling2dDescriptor desc; |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 958 | desc.m_PoolWidth = 2; |
| 959 | desc.m_PoolHeight = 2; |
| 960 | desc.m_StrideX = 2; |
| 961 | desc.m_StrideY = 2; |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 962 | |
| 963 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 964 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 965 | IConnectableLayer* input2 = net->AddInputLayer(2, "input2"); |
| 966 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 967 | IConnectableLayer* sub = net->AddSubtractionLayer("sub"); |
| 968 | IConnectableLayer* pooling = net->AddPooling2dLayer(desc, "pooling"); |
| 969 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 970 | |
| 971 | input0->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 972 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 973 | input2->GetOutputSlot(0).Connect(sub->GetInputSlot(0)); |
| 974 | add->GetOutputSlot(0).Connect(sub->GetInputSlot(1)); |
| 975 | sub->GetOutputSlot(0).Connect(pooling->GetInputSlot(0)); |
| 976 | pooling->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 977 | |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 978 | TensorInfo info = TensorInfo({ 1, 2, 4, 2 }, DataType::Float32); |
| 979 | TensorInfo poolingInfo = TensorInfo({ 1, 2, 2, 1 }, DataType::Float32); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 980 | |
| 981 | input0->GetOutputSlot(0).SetTensorInfo(info); |
| 982 | input1->GetOutputSlot(0).SetTensorInfo(info); |
| 983 | input2->GetOutputSlot(0).SetTensorInfo(info); |
| 984 | add->GetOutputSlot(0).SetTensorInfo(info); |
| 985 | sub->GetOutputSlot(0).SetTensorInfo(info); |
| 986 | pooling->GetOutputSlot(0).SetTensorInfo(poolingInfo); |
| 987 | |
| 988 | std::vector<BackendId> backends = { Compute::CpuAcc, Compute::GpuAcc }; |
| 989 | // Use BackendSelectionHint to specify GpuAcc for Subtraction layer |
| 990 | sub->BackendSelectionHint(backends[1]); |
| 991 | |
| 992 | // optimize the network |
| 993 | OptimizerOptions optOptions; |
| 994 | optOptions.m_ImportEnabled = true; |
| 995 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec(), optOptions); |
| 996 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 997 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 998 | |
| 999 | armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); |
| 1000 | armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); |
| 1001 | armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "input2"); |
| 1002 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "add"); |
| 1003 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "[ add (0) -> sub (1) ]"); |
| 1004 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "sub"); |
| 1005 | armnn::Layer* const layer6 = GetFirstLayerWithName(graph, "[ sub (0) -> pooling (0) ]"); |
| 1006 | armnn::Layer* const layer7 = GetFirstLayerWithName(graph, "pooling"); |
| 1007 | armnn::Layer* const layer8 = GetFirstLayerWithName(graph, "output"); |
| 1008 | |
| 1009 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1010 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 1011 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 1012 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 1013 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 1014 | CHECK(CheckOrder(graph, layer4, layer5)); |
| 1015 | CHECK(CheckOrder(graph, layer5, layer6)); |
| 1016 | CHECK(CheckOrder(graph, layer6, layer7)); |
| 1017 | CHECK(CheckOrder(graph, layer7, layer8)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1018 | |
| 1019 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1020 | CHECK((layer4->GetType() == LayerType::MemCopy)); |
| 1021 | CHECK((layer6->GetType() == LayerType::MemCopy)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1022 | |
| 1023 | // Correctly use backend hint |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1024 | CHECK((layer5->GetBackendId() == Compute::GpuAcc )); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1025 | |
| 1026 | // Load it into the runtime. It should pass. |
| 1027 | NetworkId netId; |
| 1028 | std::string ignoredErrorMessage; |
Francis Murtagh | 73d3e2e | 2021-04-29 14:23:04 +0100 | [diff] [blame] | 1029 | |
| 1030 | INetworkProperties networkProperties(false, MemorySource::Malloc, MemorySource::Malloc); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1031 | |
| 1032 | runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties); |
| 1033 | |
| 1034 | // Creates structures for input & output |
| 1035 | std::vector<float> inputData0 |
| 1036 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 1037 | 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f, 1.0f, 1.0f, 2.0f, 2.0f |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1038 | }; |
| 1039 | std::vector<float> inputData1 |
| 1040 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 1041 | 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 0.0f, 1.0f, 1.0f, 2.0f |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1042 | }; |
| 1043 | std::vector<float> inputData2 |
| 1044 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 1045 | 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f, 12.0f, 11.0f, 10.0f, 9.0f |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1046 | }; |
| 1047 | |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 1048 | std::vector<float> outputData(4); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1049 | |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 1050 | std::vector<float> expectedOutput{ 11.0f, 3.0f, -5.0f, 11.0f }; |
| 1051 | |
| 1052 | // Prepare aligned data |
| 1053 | unsigned int numElements = info.GetNumElements(); |
| 1054 | size_t totalBytes = numElements * sizeof(float); |
| 1055 | const size_t alignment = 64; |
| 1056 | size_t space = totalBytes + alignment + alignment; |
| 1057 | auto inputData = std::make_unique<uint8_t[]>(space); |
| 1058 | void* alignedInputPtr = inputData.get(); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1059 | CHECK(std::align(alignment, totalBytes, alignedInputPtr, space)); |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 1060 | |
| 1061 | auto* intputPtr = reinterpret_cast<float*>(alignedInputPtr); |
| 1062 | std::copy(inputData2.begin(), inputData2.end(), intputPtr); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1063 | |
| 1064 | InputTensors inputTensors |
| 1065 | { |
| 1066 | { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) }, |
| 1067 | { 1, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) }, |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 1068 | { 2, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 2), alignedInputPtr) } |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1069 | }; |
| 1070 | OutputTensors outputTensors |
| 1071 | { |
| 1072 | { 0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 1073 | }; |
| 1074 | |
| 1075 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 1076 | |
| 1077 | // Do the inference |
| 1078 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 1079 | |
| 1080 | // Retrieve the Profiler.Print() output to get the workload execution |
| 1081 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 1082 | std::stringstream ss; |
| 1083 | profilerManager.GetProfiler()->Print(ss);; |
| 1084 | std::string dump = ss.str(); |
| 1085 | |
| 1086 | // Executed Subtraction using GpuAcc |
| 1087 | std::size_t found = dump.find("ClSubtractionWorkload_Execute"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1088 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1089 | |
| 1090 | // Correctly switch back to CpuAcc |
| 1091 | found = dump.find("NeonPooling2dWorkload_Execute"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1092 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1093 | |
| 1094 | // Contain CopyMemGeneric |
| 1095 | found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1096 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1097 | |
| 1098 | // Contains SyncMemGeneric for output |
| 1099 | found = dump.find("SyncMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1100 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1101 | |
| 1102 | // Check output is as expected |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1103 | CHECK(outputData == expectedOutput); |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 1104 | runtime->UnloadNetwork(netId); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1105 | } |
| 1106 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1107 | TEST_CASE("NeonImportDisableFallbackSubgraphToCl") |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1108 | { |
| 1109 | using namespace armnn; |
| 1110 | |
| 1111 | IRuntime::CreationOptions options; |
| 1112 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 1113 | |
| 1114 | // Builds up the structure of the network. |
| 1115 | INetworkPtr net(INetwork::Create()); |
| 1116 | |
| 1117 | Pooling2dDescriptor desc; |
| 1118 | |
| 1119 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 1120 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 1121 | IConnectableLayer* input2 = net->AddInputLayer(2, "input2"); |
| 1122 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 1123 | IConnectableLayer* sub = net->AddSubtractionLayer("sub"); |
| 1124 | IConnectableLayer* pooling = net->AddPooling2dLayer(desc, "pooling"); |
| 1125 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 1126 | |
| 1127 | input0->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 1128 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 1129 | input2->GetOutputSlot(0).Connect(sub->GetInputSlot(0)); |
| 1130 | add->GetOutputSlot(0).Connect(sub->GetInputSlot(1)); |
| 1131 | sub->GetOutputSlot(0).Connect(pooling->GetInputSlot(0)); |
| 1132 | pooling->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 1133 | |
| 1134 | TensorInfo info = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32); |
| 1135 | TensorInfo poolingInfo = TensorInfo({ 1, 2, 1, 1 }, DataType::Float32); |
| 1136 | |
| 1137 | input0->GetOutputSlot(0).SetTensorInfo(info); |
| 1138 | input1->GetOutputSlot(0).SetTensorInfo(info); |
| 1139 | input2->GetOutputSlot(0).SetTensorInfo(info); |
| 1140 | add->GetOutputSlot(0).SetTensorInfo(info); |
| 1141 | sub->GetOutputSlot(0).SetTensorInfo(info); |
| 1142 | pooling->GetOutputSlot(0).SetTensorInfo(poolingInfo); |
| 1143 | |
| 1144 | std::vector<BackendId> backends = { Compute::CpuAcc, Compute::GpuAcc }; |
| 1145 | // Use BackendSelectionHint to specify GpuAcc for Subtraction layer |
| 1146 | sub->BackendSelectionHint(backends[1]); |
| 1147 | |
| 1148 | // optimize the network |
| 1149 | OptimizerOptions optOptions; |
| 1150 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec(), optOptions); |
| 1151 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 1152 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1153 | |
| 1154 | armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); |
| 1155 | armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); |
| 1156 | armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "input2"); |
| 1157 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "add"); |
| 1158 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "[ add (0) -> sub (1) ]"); |
| 1159 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "sub"); |
| 1160 | armnn::Layer* const layer6 = GetFirstLayerWithName(graph, "[ sub (0) -> pooling (0) ]"); |
| 1161 | armnn::Layer* const layer7 = GetFirstLayerWithName(graph, "pooling"); |
| 1162 | armnn::Layer* const layer8 = GetFirstLayerWithName(graph, "output"); |
| 1163 | |
| 1164 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1165 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 1166 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 1167 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 1168 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 1169 | CHECK(CheckOrder(graph, layer4, layer5)); |
| 1170 | CHECK(CheckOrder(graph, layer5, layer6)); |
| 1171 | CHECK(CheckOrder(graph, layer6, layer7)); |
| 1172 | CHECK(CheckOrder(graph, layer7, layer8)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1173 | |
| 1174 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1175 | CHECK((layer4->GetType() == LayerType::MemCopy)); |
| 1176 | CHECK((layer6->GetType() == LayerType::MemCopy)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1177 | |
| 1178 | // Correctly use backend hint |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1179 | CHECK((layer5->GetBackendId() == Compute::GpuAcc )); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1180 | |
| 1181 | // Load it into the runtime. It should pass. |
| 1182 | NetworkId netId; |
| 1183 | runtime->LoadNetwork(netId, std::move(optNet)); |
| 1184 | |
| 1185 | // Creates structures for input & output |
| 1186 | std::vector<float> inputData0 |
| 1187 | { |
| 1188 | 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f |
| 1189 | }; |
| 1190 | std::vector<float> inputData1 |
| 1191 | { |
| 1192 | 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f |
| 1193 | }; |
| 1194 | std::vector<float> inputData2 |
| 1195 | { |
| 1196 | 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f |
| 1197 | }; |
| 1198 | |
| 1199 | std::vector<float> outputData(2); |
| 1200 | |
| 1201 | std::vector<float> expectedOutput{ 11.0f, -1.0f }; |
| 1202 | |
| 1203 | InputTensors inputTensors |
| 1204 | { |
| 1205 | { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) }, |
| 1206 | { 1, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) }, |
| 1207 | { 2, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 2), inputData2.data()) } |
| 1208 | }; |
| 1209 | OutputTensors outputTensors |
| 1210 | { |
| 1211 | { 0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 1212 | }; |
| 1213 | |
| 1214 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 1215 | |
| 1216 | // Do the inference |
| 1217 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 1218 | |
| 1219 | // Retrieve the Profiler.Print() output to get the workload execution |
| 1220 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 1221 | std::stringstream ss; |
| 1222 | profilerManager.GetProfiler()->Print(ss);; |
| 1223 | std::string dump = ss.str(); |
| 1224 | |
| 1225 | // Executed Subtraction using GpuAcc |
| 1226 | std::size_t found = dump.find("ClSubtractionWorkload_Execute"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1227 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1228 | |
| 1229 | // Correctly switch back to CpuAcc |
| 1230 | found = dump.find("NeonPooling2dWorkload_Execute"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1231 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1232 | |
| 1233 | // Contain CopyMemGeneric |
| 1234 | found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1235 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1236 | |
| 1237 | // Check output is as expected |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1238 | CHECK(outputData == expectedOutput); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1239 | } |
| 1240 | #endif |
| 1241 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 1242 | } |