Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2020 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
Sadik Armagan | a097d2a | 2021-11-24 15:47:28 +0000 | [diff] [blame] | 6 | #include <CommonTestUtils.hpp> |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 7 | |
Sadik Armagan | a097d2a | 2021-11-24 15:47:28 +0000 | [diff] [blame] | 8 | #include <GraphUtils.hpp> |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 9 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 10 | #include <doctest/doctest.h> |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 11 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 12 | TEST_SUITE("ClFallback") |
| 13 | { |
| 14 | TEST_CASE("ClImportEnabledFallbackToNeon") |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 15 | { |
| 16 | using namespace armnn; |
| 17 | |
| 18 | IRuntime::CreationOptions options; |
| 19 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 20 | |
| 21 | // Builds up the structure of the network. |
| 22 | INetworkPtr net(INetwork::Create()); |
| 23 | |
| 24 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 25 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 26 | IConnectableLayer* input2 = net->AddInputLayer(2, "input2"); |
| 27 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 28 | IConnectableLayer* sub = net->AddSubtractionLayer("sub"); |
| 29 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 30 | |
| 31 | input0->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 32 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 33 | input2->GetOutputSlot(0).Connect(sub->GetInputSlot(0)); |
| 34 | add->GetOutputSlot(0).Connect(sub->GetInputSlot(1)); |
| 35 | sub->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 36 | |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 37 | TensorInfo info = TensorInfo({ 1, 2, 4, 2 }, DataType::Float32); |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 38 | info.SetConstant(true); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 39 | |
| 40 | input0->GetOutputSlot(0).SetTensorInfo(info); |
| 41 | input1->GetOutputSlot(0).SetTensorInfo(info); |
| 42 | input2->GetOutputSlot(0).SetTensorInfo(info); |
| 43 | add->GetOutputSlot(0).SetTensorInfo(info); |
| 44 | sub->GetOutputSlot(0).SetTensorInfo(info); |
| 45 | |
| 46 | std::vector<BackendId> backends = { Compute::GpuAcc, Compute::CpuAcc }; |
| 47 | // Use BackendSelectionHint to specify CpuAcc for Subtraction layer |
| 48 | sub->BackendSelectionHint(backends[1]); |
| 49 | |
| 50 | // optimize the network |
| 51 | OptimizerOptions optOptions; |
| 52 | optOptions.m_ImportEnabled = true; |
| 53 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec(), optOptions); |
| 54 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 55 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 56 | |
| 57 | armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); |
| 58 | armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); |
| 59 | armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "input2"); |
| 60 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "add"); |
| 61 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "[ add (0) -> sub (1) ]"); |
| 62 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "sub"); |
| 63 | armnn::Layer* const layer6 = GetFirstLayerWithName(graph, "output"); |
| 64 | |
| 65 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 66 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 67 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 68 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 69 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 70 | CHECK(CheckOrder(graph, layer4, layer5)); |
| 71 | CHECK(CheckOrder(graph, layer5, layer6)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 72 | |
| 73 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 74 | CHECK((layer4->GetType() == LayerType::MemCopy)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 75 | |
| 76 | // Correctly use backend hint |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 77 | CHECK((layer5->GetBackendId() == Compute::CpuAcc )); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 78 | |
| 79 | // Load it into the runtime. It should pass. |
| 80 | NetworkId netId; |
| 81 | std::string ignoredErrorMessage; |
Francis Murtagh | 73d3e2e | 2021-04-29 14:23:04 +0100 | [diff] [blame] | 82 | INetworkProperties networkProperties(false, MemorySource::Malloc, MemorySource::Malloc); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 83 | runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties); |
| 84 | |
| 85 | // Creates structures for input & output |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 86 | std::vector<float> inputValue0 |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 87 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 88 | 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] | 89 | }; |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 90 | std::vector<float> inputValue1 |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 91 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 92 | 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] | 93 | }; |
| 94 | std::vector<float> inputData2 |
| 95 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 96 | 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] | 97 | }; |
| 98 | |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 99 | std::vector<float> outputData(16); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 100 | |
| 101 | std::vector<float> expectedOutput |
| 102 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 103 | 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] | 104 | }; |
| 105 | |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 106 | // Prepare aligned data |
| 107 | unsigned int numElements = info.GetNumElements(); |
| 108 | size_t totalBytes = numElements * sizeof(float); |
| 109 | const size_t alignment = 64; |
| 110 | size_t space = totalBytes + alignment + alignment; |
| 111 | auto inputData0 = std::make_unique<uint8_t[]>(space); |
| 112 | void* alignedInputPtr0 = inputData0.get(); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 113 | CHECK(std::align(alignment, totalBytes, alignedInputPtr0, space)); |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 114 | |
| 115 | auto* intputPtr0 = reinterpret_cast<float*>(alignedInputPtr0); |
| 116 | std::copy(inputValue0.begin(), inputValue0.end(), intputPtr0); |
| 117 | |
| 118 | auto inputData1 = std::make_unique<uint8_t[]>(space); |
| 119 | void* alignedInputPtr1 = inputData1.get(); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 120 | CHECK(std::align(alignment, totalBytes, alignedInputPtr1, space)); |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 121 | |
| 122 | auto* intputPtr1 = reinterpret_cast<float*>(alignedInputPtr1); |
| 123 | std::copy(inputValue1.begin(), inputValue1.end(), intputPtr1); |
| 124 | |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 125 | InputTensors inputTensors |
| 126 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 127 | { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), alignedInputPtr0) }, |
| 128 | { 1, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 1), alignedInputPtr1) }, |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 129 | { 2, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 2), inputData2.data()) } |
| 130 | }; |
| 131 | OutputTensors outputTensors |
| 132 | { |
| 133 | { 0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 134 | }; |
| 135 | |
| 136 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 137 | |
| 138 | // Do the inference |
| 139 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 140 | |
| 141 | // Retrieve the Profiler.Print() output to get the workload execution |
| 142 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 143 | std::stringstream ss; |
| 144 | profilerManager.GetProfiler()->Print(ss);; |
| 145 | std::string dump = ss.str(); |
| 146 | |
| 147 | // Executed Subtraction using CpuAcc |
| 148 | std::size_t found = dump.find("NeonSubtractionWorkload_Execute"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 149 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 150 | |
| 151 | // Contain CopyMemGeneric |
| 152 | found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 153 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 154 | |
| 155 | // Check output is as expected |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 156 | CHECK(outputData == expectedOutput); |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 157 | |
| 158 | runtime->UnloadNetwork(netId); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 159 | } |
| 160 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 161 | TEST_CASE("ClImportDisabledFallbackToNeon") |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 162 | { |
| 163 | using namespace armnn; |
| 164 | |
| 165 | IRuntime::CreationOptions options; |
| 166 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 167 | |
| 168 | // Builds up the structure of the network. |
| 169 | INetworkPtr net(INetwork::Create()); |
| 170 | |
| 171 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 172 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 173 | IConnectableLayer* input2 = net->AddInputLayer(2, "input2"); |
| 174 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 175 | IConnectableLayer* sub = net->AddSubtractionLayer("sub"); |
| 176 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 177 | |
| 178 | input0->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 179 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 180 | input2->GetOutputSlot(0).Connect(sub->GetInputSlot(0)); |
| 181 | add->GetOutputSlot(0).Connect(sub->GetInputSlot(1)); |
| 182 | sub->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 183 | |
| 184 | TensorInfo info = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32); |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 185 | info.SetConstant(true); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 186 | |
| 187 | input0->GetOutputSlot(0).SetTensorInfo(info); |
| 188 | input1->GetOutputSlot(0).SetTensorInfo(info); |
| 189 | input2->GetOutputSlot(0).SetTensorInfo(info); |
| 190 | add->GetOutputSlot(0).SetTensorInfo(info); |
| 191 | sub->GetOutputSlot(0).SetTensorInfo(info); |
| 192 | |
| 193 | std::vector<BackendId> backends = { Compute::GpuAcc, Compute::CpuAcc }; |
| 194 | // Use BackendSelectionHint to specify CpuAcc for Subtraction layer |
| 195 | sub->BackendSelectionHint(backends[1]); |
| 196 | |
| 197 | // optimize the network |
| 198 | OptimizerOptions optOptions; |
| 199 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec(), optOptions); |
| 200 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 201 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [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, "input2"); |
| 206 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "add"); |
| 207 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "[ add (0) -> sub (1) ]"); |
| 208 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "sub"); |
| 209 | armnn::Layer* const layer6 = GetFirstLayerWithName(graph, "output"); |
| 210 | |
| 211 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 212 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 213 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 214 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 215 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 216 | CHECK(CheckOrder(graph, layer4, layer5)); |
| 217 | CHECK(CheckOrder(graph, layer5, layer6)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 218 | |
| 219 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 220 | CHECK((layer4->GetType() == LayerType::MemCopy)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 221 | |
| 222 | // Correctly use backend hint |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 223 | CHECK((layer5->GetBackendId() == Compute::CpuAcc )); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 224 | |
| 225 | // Load it into the runtime. It should pass. |
| 226 | NetworkId netId; |
| 227 | runtime->LoadNetwork(netId, std::move(optNet)); |
| 228 | |
| 229 | // Creates structures for input & output |
| 230 | std::vector<float> inputData0 |
| 231 | { |
| 232 | 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f |
| 233 | }; |
| 234 | std::vector<float> inputData1 |
| 235 | { |
| 236 | 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f |
| 237 | }; |
| 238 | std::vector<float> inputData2 |
| 239 | { |
| 240 | 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f |
| 241 | }; |
| 242 | |
| 243 | std::vector<float> outputData(12); |
| 244 | |
| 245 | std::vector<float> expectedOutput |
| 246 | { |
| 247 | 11.0f, 9.0f, 7.0f, 5.0f, 3.0f, 1.0f, -1.0f, -3.0f, -5.0f, -7.0f, -9.0f, -11.0f |
| 248 | }; |
| 249 | |
| 250 | InputTensors inputTensors |
| 251 | { |
| 252 | { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) }, |
| 253 | { 1, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) }, |
| 254 | { 2, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 2), inputData2.data()) } |
| 255 | }; |
| 256 | OutputTensors outputTensors |
| 257 | { |
| 258 | { 0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 259 | }; |
| 260 | |
| 261 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 262 | |
| 263 | // Do the inference |
| 264 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 265 | |
| 266 | // Retrieve the Profiler.Print() output to get the workload execution |
| 267 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 268 | std::stringstream ss; |
| 269 | profilerManager.GetProfiler()->Print(ss);; |
| 270 | std::string dump = ss.str(); |
| 271 | |
| 272 | // Executed Subtraction using CpuAcc |
| 273 | std::size_t found = dump.find("NeonSubtractionWorkload_Execute"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 274 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 275 | |
| 276 | // Contain CopyMemGeneric |
| 277 | found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 278 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 279 | |
| 280 | // Check output is as expected |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 281 | CHECK(outputData == expectedOutput); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 282 | } |
| 283 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 284 | TEST_CASE("ClImportEnabledFallbackSubgraphToNeon") |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 285 | { |
| 286 | using namespace armnn; |
| 287 | |
| 288 | IRuntime::CreationOptions options; |
| 289 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 290 | |
| 291 | // Builds up the structure of the network. |
| 292 | INetworkPtr net(INetwork::Create()); |
| 293 | |
| 294 | Pooling2dDescriptor desc; |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 295 | desc.m_PoolWidth = 2; |
| 296 | desc.m_PoolHeight = 2; |
| 297 | desc.m_StrideX = 2; |
| 298 | desc.m_StrideY = 2; |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 299 | |
| 300 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 301 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 302 | IConnectableLayer* input2 = net->AddInputLayer(2, "input2"); |
| 303 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 304 | IConnectableLayer* sub = net->AddSubtractionLayer("sub"); |
| 305 | IConnectableLayer* pooling = net->AddPooling2dLayer(desc, "pooling"); |
| 306 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 307 | |
| 308 | input0->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 309 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 310 | input2->GetOutputSlot(0).Connect(sub->GetInputSlot(0)); |
| 311 | add->GetOutputSlot(0).Connect(sub->GetInputSlot(1)); |
| 312 | sub->GetOutputSlot(0).Connect(pooling->GetInputSlot(0)); |
| 313 | pooling->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 314 | |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 315 | TensorInfo info = TensorInfo({ 1, 2, 4, 2 }, DataType::Float32); |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 316 | info.SetConstant(true); |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 317 | TensorInfo poolingInfo = TensorInfo({ 1, 2, 2, 1 }, DataType::Float32); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 318 | |
| 319 | input0->GetOutputSlot(0).SetTensorInfo(info); |
| 320 | input1->GetOutputSlot(0).SetTensorInfo(info); |
| 321 | input2->GetOutputSlot(0).SetTensorInfo(info); |
| 322 | add->GetOutputSlot(0).SetTensorInfo(info); |
| 323 | sub->GetOutputSlot(0).SetTensorInfo(info); |
| 324 | pooling->GetOutputSlot(0).SetTensorInfo(poolingInfo); |
| 325 | |
| 326 | std::vector<BackendId> backends = { Compute::GpuAcc, Compute::CpuAcc }; |
| 327 | // Use BackendSelectionHint to specify CpuAcc for Subtraction layer |
| 328 | sub->BackendSelectionHint(backends[1]); |
| 329 | |
| 330 | // optimize the network |
| 331 | OptimizerOptions optOptions; |
| 332 | optOptions.m_ImportEnabled = true; |
| 333 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec(), optOptions); |
| 334 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 335 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 336 | |
| 337 | armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); |
| 338 | armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); |
| 339 | armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "input2"); |
| 340 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "add"); |
| 341 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "[ add (0) -> sub (1) ]"); |
| 342 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "sub"); |
| 343 | armnn::Layer* const layer6 = GetFirstLayerWithName(graph, "[ sub (0) -> pooling (0) ]"); |
| 344 | armnn::Layer* const layer7 = GetFirstLayerWithName(graph, "pooling"); |
| 345 | armnn::Layer* const layer8 = GetFirstLayerWithName(graph, "output"); |
| 346 | |
| 347 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 348 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 349 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 350 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 351 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 352 | CHECK(CheckOrder(graph, layer4, layer5)); |
| 353 | CHECK(CheckOrder(graph, layer5, layer6)); |
| 354 | CHECK(CheckOrder(graph, layer6, layer7)); |
| 355 | CHECK(CheckOrder(graph, layer7, layer8)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 356 | |
| 357 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 358 | CHECK((layer4->GetType() == LayerType::MemCopy)); |
| 359 | CHECK((layer6->GetType() == LayerType::MemCopy)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 360 | |
| 361 | // Correctly use backend hint |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 362 | CHECK((layer5->GetBackendId() == Compute::CpuAcc )); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 363 | |
| 364 | // Load it into the runtime. It should pass. |
| 365 | NetworkId netId; |
| 366 | std::string ignoredErrorMessage; |
Francis Murtagh | 73d3e2e | 2021-04-29 14:23:04 +0100 | [diff] [blame] | 367 | INetworkProperties networkProperties(false, MemorySource::Malloc, MemorySource::Malloc); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 368 | runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties); |
| 369 | |
| 370 | // Creates structures for input & output |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 371 | std::vector<float> inputValue0 |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 372 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 373 | 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] | 374 | }; |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 375 | std::vector<float> inputValue1 |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 376 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 377 | 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] | 378 | }; |
| 379 | std::vector<float> inputData2 |
| 380 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 381 | 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] | 382 | }; |
| 383 | |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 384 | std::vector<float> outputData(4); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 385 | |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 386 | std::vector<float> expectedOutput{ 11.0f, 3.0f, -5.0f, 11.0f }; |
| 387 | |
| 388 | unsigned int numElements = info.GetNumElements(); |
| 389 | size_t totalBytes = numElements * sizeof(float); |
| 390 | const size_t alignment = 64; |
| 391 | size_t space = totalBytes + alignment + alignment; |
| 392 | auto inputData0 = std::make_unique<uint8_t[]>(space); |
| 393 | void* alignedInputPtr0 = inputData0.get(); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 394 | CHECK(std::align(alignment, totalBytes, alignedInputPtr0, space)); |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 395 | |
| 396 | auto* intputPtr0 = reinterpret_cast<float*>(alignedInputPtr0); |
| 397 | std::copy(inputValue0.begin(), inputValue0.end(), intputPtr0); |
| 398 | |
| 399 | auto inputData1 = std::make_unique<uint8_t[]>(space); |
| 400 | void* alignedInputPtr1 = inputData1.get(); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 401 | CHECK(std::align(alignment, totalBytes, alignedInputPtr1, space)); |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 402 | |
| 403 | auto* intputPtr1 = reinterpret_cast<float*>(alignedInputPtr1); |
| 404 | std::copy(inputValue1.begin(), inputValue1.end(), intputPtr1); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 405 | |
| 406 | InputTensors inputTensors |
| 407 | { |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 408 | { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), alignedInputPtr0) }, |
| 409 | { 1, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 1), alignedInputPtr1) }, |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 410 | { 2, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 2), inputData2.data()) } |
| 411 | }; |
| 412 | OutputTensors outputTensors |
| 413 | { |
| 414 | { 0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 415 | }; |
| 416 | |
| 417 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 418 | |
| 419 | // Do the inference |
| 420 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 421 | |
| 422 | // Retrieve the Profiler.Print() output to get the workload execution |
| 423 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 424 | std::stringstream ss; |
| 425 | profilerManager.GetProfiler()->Print(ss);; |
| 426 | std::string dump = ss.str(); |
| 427 | |
| 428 | // Executed Subtraction using CpuAcc |
| 429 | std::size_t found = dump.find("NeonSubtractionWorkload_Execute"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 430 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 431 | |
| 432 | // Correctly switch back to GpuAcc |
| 433 | found = dump.find("ClPooling2dWorkload_Execute"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 434 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 435 | |
| 436 | // Contain CopyMemGeneric |
| 437 | found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 438 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 439 | |
| 440 | // Check output is as expected |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 441 | CHECK(outputData == expectedOutput); |
Narumol Prangnawarat | e5f0b24 | 2021-05-07 17:52:36 +0100 | [diff] [blame] | 442 | |
| 443 | runtime->UnloadNetwork(netId); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 444 | } |
| 445 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 446 | TEST_CASE("ClImportDisableFallbackSubgraphToNeon") |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 447 | { |
| 448 | using namespace armnn; |
| 449 | |
| 450 | IRuntime::CreationOptions options; |
| 451 | IRuntimePtr runtime(IRuntime::Create(options)); |
| 452 | |
| 453 | // Builds up the structure of the network. |
| 454 | INetworkPtr net(INetwork::Create()); |
| 455 | |
| 456 | Pooling2dDescriptor desc; |
| 457 | |
| 458 | IConnectableLayer* input0 = net->AddInputLayer(0, "input0"); |
| 459 | IConnectableLayer* input1 = net->AddInputLayer(1, "input1"); |
| 460 | IConnectableLayer* input2 = net->AddInputLayer(2, "input2"); |
| 461 | IConnectableLayer* add = net->AddAdditionLayer("add"); |
| 462 | IConnectableLayer* sub = net->AddSubtractionLayer("sub"); |
| 463 | IConnectableLayer* pooling = net->AddPooling2dLayer(desc, "pooling"); |
| 464 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 465 | |
| 466 | input0->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 467 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 468 | input2->GetOutputSlot(0).Connect(sub->GetInputSlot(0)); |
| 469 | add->GetOutputSlot(0).Connect(sub->GetInputSlot(1)); |
| 470 | sub->GetOutputSlot(0).Connect(pooling->GetInputSlot(0)); |
| 471 | pooling->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 472 | |
| 473 | TensorInfo info = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32); |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 474 | info.SetConstant(true); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 475 | TensorInfo poolingInfo = TensorInfo({ 1, 2, 1, 1 }, DataType::Float32); |
| 476 | |
| 477 | input0->GetOutputSlot(0).SetTensorInfo(info); |
| 478 | input1->GetOutputSlot(0).SetTensorInfo(info); |
| 479 | input2->GetOutputSlot(0).SetTensorInfo(info); |
| 480 | add->GetOutputSlot(0).SetTensorInfo(info); |
| 481 | sub->GetOutputSlot(0).SetTensorInfo(info); |
| 482 | pooling->GetOutputSlot(0).SetTensorInfo(poolingInfo); |
| 483 | |
| 484 | std::vector<BackendId> backends = { Compute::GpuAcc, Compute::CpuAcc }; |
| 485 | // Use BackendSelectionHint to specify CpuAcc for Subtraction layer |
| 486 | sub->BackendSelectionHint(backends[1]); |
| 487 | |
| 488 | // optimize the network |
| 489 | OptimizerOptions optOptions; |
| 490 | IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec(), optOptions); |
| 491 | |
Francis Murtagh | 3d2b4b2 | 2021-02-15 18:23:17 +0000 | [diff] [blame] | 492 | Graph& graph = GetGraphForTesting(optNet.get()); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 493 | |
| 494 | armnn::Layer* const layer0 = GetFirstLayerWithName(graph, "input0"); |
| 495 | armnn::Layer* const layer1 = GetFirstLayerWithName(graph, "input1"); |
| 496 | armnn::Layer* const layer2 = GetFirstLayerWithName(graph, "input2"); |
| 497 | armnn::Layer* const layer3 = GetFirstLayerWithName(graph, "add"); |
| 498 | armnn::Layer* const layer4 = GetFirstLayerWithName(graph, "[ add (0) -> sub (1) ]"); |
| 499 | armnn::Layer* const layer5 = GetFirstLayerWithName(graph, "sub"); |
| 500 | armnn::Layer* const layer6 = GetFirstLayerWithName(graph, "[ sub (0) -> pooling (0) ]"); |
| 501 | armnn::Layer* const layer7 = GetFirstLayerWithName(graph, "pooling"); |
| 502 | armnn::Layer* const layer8 = GetFirstLayerWithName(graph, "output"); |
| 503 | |
| 504 | // Checks order is valid. |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 505 | CHECK(CheckOrder(graph, layer0, layer1)); |
| 506 | CHECK(CheckOrder(graph, layer1, layer2)); |
| 507 | CHECK(CheckOrder(graph, layer2, layer3)); |
| 508 | CHECK(CheckOrder(graph, layer3, layer4)); |
| 509 | CHECK(CheckOrder(graph, layer4, layer5)); |
| 510 | CHECK(CheckOrder(graph, layer5, layer6)); |
| 511 | CHECK(CheckOrder(graph, layer6, layer7)); |
| 512 | CHECK(CheckOrder(graph, layer7, layer8)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 513 | |
| 514 | // Use memory import between backends |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 515 | CHECK((layer4->GetType() == LayerType::MemCopy)); |
| 516 | CHECK((layer6->GetType() == LayerType::MemCopy)); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 517 | |
| 518 | // Correctly use backend hint |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 519 | CHECK((layer5->GetBackendId() == Compute::CpuAcc )); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 520 | |
| 521 | // Load it into the runtime. It should pass. |
| 522 | NetworkId netId; |
| 523 | runtime->LoadNetwork(netId, std::move(optNet)); |
| 524 | |
| 525 | // Creates structures for input & output |
| 526 | std::vector<float> inputData0 |
| 527 | { |
| 528 | 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f |
| 529 | }; |
| 530 | std::vector<float> inputData1 |
| 531 | { |
| 532 | 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f |
| 533 | }; |
| 534 | std::vector<float> inputData2 |
| 535 | { |
| 536 | 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f |
| 537 | }; |
| 538 | |
| 539 | std::vector<float> outputData(2); |
| 540 | |
| 541 | std::vector<float> expectedOutput{ 11.0f, -1.0f }; |
| 542 | |
| 543 | InputTensors inputTensors |
| 544 | { |
| 545 | { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) }, |
| 546 | { 1, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) }, |
| 547 | { 2, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 2), inputData2.data()) } |
| 548 | }; |
| 549 | OutputTensors outputTensors |
| 550 | { |
| 551 | { 0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data()) } |
| 552 | }; |
| 553 | |
| 554 | runtime->GetProfiler(netId)->EnableProfiling(true); |
| 555 | |
| 556 | // Do the inference |
| 557 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 558 | |
| 559 | // Retrieve the Profiler.Print() output to get the workload execution |
| 560 | ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); |
| 561 | std::stringstream ss; |
| 562 | profilerManager.GetProfiler()->Print(ss);; |
| 563 | std::string dump = ss.str(); |
| 564 | |
| 565 | // Executed Subtraction using CpuAcc |
| 566 | std::size_t found = dump.find("NeonSubtractionWorkload_Execute"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 567 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 568 | |
| 569 | // Correctly switch back to GpuAcc |
| 570 | found = dump.find("ClPooling2dWorkload_Execute"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 571 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 572 | |
| 573 | // Contain CopyMemGeneric |
| 574 | found = dump.find("CopyMemGeneric"); |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 575 | CHECK(found != std::string::npos); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 576 | |
| 577 | // Check output is as expected |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 578 | CHECK(outputData == expectedOutput); |
Narumol Prangnawarat | 265e53e | 2020-10-30 16:06:55 +0000 | [diff] [blame] | 579 | } |
| 580 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 581 | } |