Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
Aron Virginas-Tar | c9cc804 | 2018-11-01 16:15:57 +0000 | [diff] [blame] | 6 | #include <backendsCommon/test/EndToEndTestImpl.hpp> |
narpra01 | db2b160 | 2019-01-23 15:23:11 +0000 | [diff] [blame] | 7 | #include <backendsCommon/test/GatherEndToEndTestImpl.hpp> |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 8 | #include <backendsCommon/test/MergerTestImpl.hpp> |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 9 | #include <backendsCommon/test/ArithmeticTestImpl.hpp> |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 10 | |
| 11 | #include <boost/test/unit_test.hpp> |
Éanna Ó Catháin | 20e5880 | 2018-12-04 10:29:06 +0000 | [diff] [blame] | 12 | #include <boost/test/execution_monitor.hpp> |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 13 | |
| 14 | BOOST_AUTO_TEST_SUITE(RefEndToEnd) |
| 15 | |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 16 | std::vector<armnn::BackendId> defaultBackends = {armnn::Compute::CpuRef}; |
| 17 | |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 18 | BOOST_AUTO_TEST_CASE(ConstantUsage_Ref_Float32) |
| 19 | { |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 20 | BOOST_TEST(ConstantUsageFloat32Test(defaultBackends)); |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 21 | } |
| 22 | |
| 23 | BOOST_AUTO_TEST_CASE(ConstantUsage_Ref_Uint8) |
| 24 | { |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 25 | BOOST_TEST(ConstantUsageUint8Test(defaultBackends)); |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 26 | } |
| 27 | |
| 28 | BOOST_AUTO_TEST_CASE(Unsigned8) |
| 29 | { |
| 30 | using namespace armnn; |
| 31 | |
| 32 | // Create runtime in which test will run |
| 33 | armnn::IRuntime::CreationOptions options; |
| 34 | armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); |
| 35 | |
| 36 | // Builds up the structure of the network. |
| 37 | armnn::INetworkPtr net(INetwork::Create()); |
| 38 | |
| 39 | IConnectableLayer* input = net->AddInputLayer(0, "input"); |
| 40 | IConnectableLayer* softmax = net->AddSoftmaxLayer(SoftmaxDescriptor(), "softmax"); |
| 41 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 42 | |
| 43 | input->GetOutputSlot(0).Connect(softmax->GetInputSlot(0)); |
| 44 | softmax->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 45 | |
| 46 | // Sets the tensors in the network. |
| 47 | TensorInfo inputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8); |
| 48 | inputTensorInfo.SetQuantizationOffset(100); |
| 49 | inputTensorInfo.SetQuantizationScale(10000.0f); |
| 50 | input->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); |
| 51 | |
| 52 | TensorInfo outputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8); |
| 53 | outputTensorInfo.SetQuantizationOffset(0); |
| 54 | outputTensorInfo.SetQuantizationScale(1.0f/255.0f); |
| 55 | softmax->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 56 | |
| 57 | // optimize the network |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 58 | IOptimizedNetworkPtr optNet = Optimize(*net, defaultBackends, runtime->GetDeviceSpec()); |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 59 | |
| 60 | // Loads it into the runtime. |
| 61 | NetworkId netId; |
| 62 | auto error = runtime->LoadNetwork(netId, std::move(optNet)); |
| 63 | BOOST_TEST(error == Status::Success); |
| 64 | |
| 65 | // Creates structures for input & output. |
| 66 | std::vector<uint8_t> inputData |
| 67 | { |
| 68 | 1, 10, 3, 200, 5 // Some inputs - one of which is sufficiently larger than the others to saturate softmax. |
| 69 | }; |
| 70 | std::vector<uint8_t> outputData(5); |
| 71 | |
| 72 | armnn::InputTensors inputTensors |
| 73 | { |
| 74 | {0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())} |
| 75 | }; |
| 76 | armnn::OutputTensors outputTensors |
| 77 | { |
| 78 | {0, armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())} |
| 79 | }; |
| 80 | |
| 81 | // Does the inference. |
| 82 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 83 | |
| 84 | // Checks the results. |
| 85 | BOOST_TEST(outputData[0] == 0); |
| 86 | BOOST_TEST(outputData[1] == 0); |
| 87 | BOOST_TEST(outputData[2] == 0); |
| 88 | BOOST_TEST(outputData[3] == 255); // softmax has been saturated. |
| 89 | BOOST_TEST(outputData[4] == 0); |
| 90 | } |
| 91 | |
| 92 | BOOST_AUTO_TEST_CASE(TrivialAdd) |
| 93 | { |
| 94 | // This test was designed to match "AddTwo" in android nn/runtime/test/TestTrivialModel.cpp. |
| 95 | |
| 96 | using namespace armnn; |
| 97 | |
| 98 | // Create runtime in which test will run |
| 99 | armnn::IRuntime::CreationOptions options; |
| 100 | armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); |
| 101 | |
| 102 | // Builds up the structure of the network. |
| 103 | armnn::INetworkPtr net(INetwork::Create()); |
| 104 | |
| 105 | IConnectableLayer* input1 = net->AddInputLayer(0); |
| 106 | IConnectableLayer* input2 = net->AddInputLayer(1); |
| 107 | IConnectableLayer* add = net->AddAdditionLayer(); |
| 108 | IConnectableLayer* output = net->AddOutputLayer(0); |
| 109 | |
| 110 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 111 | input2->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 112 | add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 113 | |
| 114 | // Sets the tensors in the network. |
| 115 | TensorInfo tensorInfo(TensorShape({3, 4}), DataType::Float32); |
| 116 | input1->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 117 | input2->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 118 | add->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 119 | |
| 120 | // optimize the network |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 121 | IOptimizedNetworkPtr optNet = Optimize(*net, defaultBackends, runtime->GetDeviceSpec()); |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 122 | |
| 123 | // Loads it into the runtime. |
| 124 | NetworkId netId; |
| 125 | runtime->LoadNetwork(netId, std::move(optNet)); |
| 126 | |
| 127 | // Creates structures for input & output - matching android nn test. |
| 128 | std::vector<float> input1Data |
| 129 | { |
| 130 | 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f |
| 131 | }; |
| 132 | std::vector<float> input2Data |
| 133 | { |
| 134 | 100.f, 200.f, 300.f, 400.f, 500.f, 600.f, 700.f, 800.f, 900.f, 1000.f, 1100.f, 1200.f |
| 135 | }; |
| 136 | std::vector<float> outputData(12); |
| 137 | |
| 138 | InputTensors inputTensors |
| 139 | { |
| 140 | {0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input1Data.data())}, |
| 141 | {1,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input2Data.data())} |
| 142 | }; |
| 143 | OutputTensors outputTensors |
| 144 | { |
| 145 | {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())} |
| 146 | }; |
| 147 | |
| 148 | // Does the inference. |
| 149 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 150 | |
| 151 | // Checks the results |
| 152 | BOOST_TEST(outputData[0] == 101); |
| 153 | BOOST_TEST(outputData[1] == 202); |
| 154 | BOOST_TEST(outputData[2] == 303); |
| 155 | BOOST_TEST(outputData[3] == 404); |
| 156 | BOOST_TEST(outputData[4] == 505); |
| 157 | BOOST_TEST(outputData[5] == 606); |
| 158 | BOOST_TEST(outputData[6] == 707); |
| 159 | BOOST_TEST(outputData[7] == 808); |
| 160 | BOOST_TEST(outputData[8] == 909); |
| 161 | BOOST_TEST(outputData[9] == 1010); |
| 162 | BOOST_TEST(outputData[10] == 1111); |
| 163 | BOOST_TEST(outputData[11] == 1212); |
| 164 | } |
| 165 | |
| 166 | BOOST_AUTO_TEST_CASE(MultipleOutputs) |
| 167 | { |
| 168 | using namespace armnn; |
| 169 | |
| 170 | // Create runtime in which test will run |
| 171 | armnn::IRuntime::CreationOptions options; |
| 172 | armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); |
| 173 | |
| 174 | // Builds up the structure of the network. |
| 175 | INetworkPtr net(INetwork::Create()); |
| 176 | |
| 177 | IConnectableLayer* input = net->AddInputLayer(0); |
| 178 | |
| 179 | // ReLu1 |
| 180 | ActivationDescriptor activation1Descriptor; |
| 181 | activation1Descriptor.m_Function = ActivationFunction::BoundedReLu; |
| 182 | activation1Descriptor.m_A = 1.f; |
| 183 | activation1Descriptor.m_B = -1.f; |
| 184 | IConnectableLayer* activation1 = net->AddActivationLayer(activation1Descriptor); |
| 185 | |
| 186 | // ReLu6 |
| 187 | ActivationDescriptor activation2Descriptor; |
| 188 | activation2Descriptor.m_Function = ActivationFunction::BoundedReLu; |
| 189 | activation2Descriptor.m_A = 6.0f; |
| 190 | IConnectableLayer* activation2 = net->AddActivationLayer(activation2Descriptor); |
| 191 | |
| 192 | // BoundedReLu(min=2, max=5) |
| 193 | ActivationDescriptor activation3Descriptor; |
| 194 | activation3Descriptor.m_Function = ActivationFunction::BoundedReLu; |
| 195 | activation3Descriptor.m_A = 5.0f; |
| 196 | activation3Descriptor.m_B = 2.0f; |
| 197 | IConnectableLayer* activation3 = net->AddActivationLayer(activation3Descriptor); |
| 198 | |
| 199 | IConnectableLayer* output1 = net->AddOutputLayer(0); |
| 200 | IConnectableLayer* output2 = net->AddOutputLayer(1); |
| 201 | IConnectableLayer* output3 = net->AddOutputLayer(2); |
| 202 | |
| 203 | input->GetOutputSlot(0).Connect(activation1->GetInputSlot(0)); |
| 204 | input->GetOutputSlot(0).Connect(activation2->GetInputSlot(0)); |
| 205 | input->GetOutputSlot(0).Connect(activation3->GetInputSlot(0)); |
| 206 | |
| 207 | activation1->GetOutputSlot(0).Connect(output1->GetInputSlot(0)); |
| 208 | activation2->GetOutputSlot(0).Connect(output2->GetInputSlot(0)); |
| 209 | activation3->GetOutputSlot(0).Connect(output3->GetInputSlot(0)); |
| 210 | |
| 211 | // Sets the tensors in the network. |
| 212 | TensorInfo tensorInfo(TensorShape({ 10 }), DataType::Float32); |
| 213 | input->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 214 | activation1->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 215 | activation2->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 216 | activation3->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 217 | |
| 218 | // optimize the network |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 219 | IOptimizedNetworkPtr optNet = Optimize(*net, defaultBackends, runtime->GetDeviceSpec()); |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 220 | |
| 221 | // Loads it into the runtime. |
| 222 | NetworkId netId; |
| 223 | runtime->LoadNetwork(netId, std::move(optNet)); |
| 224 | |
| 225 | // Creates structures for input & output. |
| 226 | const std::vector<float> inputData{ 3.f, 5.f, 2.f, 3.f, 7.f, 0.f, -2.f, -1.f, 3.f, 3.f }; |
| 227 | |
| 228 | std::vector<float> output1Data(inputData.size()); |
| 229 | std::vector<float> output2Data(inputData.size()); |
| 230 | std::vector<float> output3Data(inputData.size()); |
| 231 | |
| 232 | InputTensors inputTensors |
| 233 | { |
| 234 | {0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())} |
| 235 | }; |
| 236 | OutputTensors outputTensors |
| 237 | { |
| 238 | {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), output1Data.data())}, |
| 239 | {1,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 1), output2Data.data())}, |
| 240 | {2,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 2), output3Data.data())} |
| 241 | }; |
| 242 | |
| 243 | // Does the inference. |
| 244 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 245 | |
| 246 | // Checks the results. |
| 247 | BOOST_TEST(output1Data == std::vector<float>({ 1.f, 1.f, 1.f, 1.f, 1.f, 0.f, -1.f, -1.f, 1.f, 1.f })); // ReLu1 |
| 248 | BOOST_TEST(output2Data == std::vector<float>({ 3.f, 5.f, 2.f, 3.f, 6.f, 0.f, 0.f, 0.f, 3.f, 3.f })); // ReLu6 |
| 249 | BOOST_TEST(output3Data == std::vector<float>({ 3.f, 5.f, 2.f, 3.f, 5.f, 2.f, 2.f, 2.f, 3.f, 3.f })); // [2, 5] |
| 250 | } |
| 251 | |
Éanna Ó Catháin | 20e5880 | 2018-12-04 10:29:06 +0000 | [diff] [blame] | 252 | BOOST_AUTO_TEST_CASE(TrivialMin) |
| 253 | { |
| 254 | using namespace armnn; |
| 255 | |
| 256 | // Create runtime in which test will run |
| 257 | armnn::IRuntime::CreationOptions options; |
| 258 | armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); |
| 259 | |
| 260 | // Builds up the structure of the network. |
| 261 | armnn::INetworkPtr net(INetwork::Create()); |
| 262 | |
| 263 | IConnectableLayer* input1 = net->AddInputLayer(0); |
| 264 | IConnectableLayer* input2 = net->AddInputLayer(1); |
| 265 | IConnectableLayer* min = net->AddMinimumLayer(); |
| 266 | IConnectableLayer* output = net->AddOutputLayer(0); |
| 267 | |
| 268 | input1->GetOutputSlot(0).Connect(min->GetInputSlot(0)); |
| 269 | input2->GetOutputSlot(0).Connect(min->GetInputSlot(1)); |
| 270 | min->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 271 | |
| 272 | // Sets the tensors in the network. |
| 273 | TensorInfo tensorInfo(TensorShape({1, 1, 1, 4}), DataType::Float32); |
| 274 | input1->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 275 | input2->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 276 | min->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 277 | |
| 278 | // optimize the network |
| 279 | IOptimizedNetworkPtr optNet = Optimize(*net, defaultBackends, runtime->GetDeviceSpec()); |
| 280 | |
| 281 | // Loads it into the runtime. |
| 282 | NetworkId netId; |
| 283 | runtime->LoadNetwork(netId, std::move(optNet)); |
| 284 | |
| 285 | // Creates structures for input & output - matching android nn test. |
| 286 | std::vector<float> input1Data |
| 287 | { |
| 288 | 1.0f, 2.0f, 3.0f, 4.0f |
| 289 | }; |
| 290 | std::vector<float> input2Data |
| 291 | { |
| 292 | 2.0f, 1.0f, 5.0f, 2.0f |
| 293 | }; |
| 294 | std::vector<float> outputData(4); |
| 295 | |
| 296 | InputTensors inputTensors |
| 297 | { |
| 298 | {0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input1Data.data())}, |
| 299 | {1,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input2Data.data())} |
| 300 | }; |
| 301 | OutputTensors outputTensors |
| 302 | { |
| 303 | {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())} |
| 304 | }; |
| 305 | |
| 306 | // Does the inference. |
| 307 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 308 | |
| 309 | // Checks the results |
| 310 | BOOST_TEST(outputData[0] == 1); |
| 311 | BOOST_TEST(outputData[1] == 1); |
| 312 | BOOST_TEST(outputData[2] == 3); |
| 313 | BOOST_TEST(outputData[3] == 2); |
| 314 | } |
| 315 | |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 316 | BOOST_AUTO_TEST_CASE(RefEqualSimpleEndToEndTest) |
| 317 | { |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 318 | const std::vector<uint8_t> expectedOutput({ 1, 1, 1, 1, 0, 0, 0, 0, |
| 319 | 0, 0, 0, 0, 1, 1, 1, 1 }); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 320 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 321 | ArithmeticSimpleEndToEnd<armnn::DataType::Float32, armnn::DataType::Boolean>(defaultBackends, |
| 322 | LayerType::Equal, |
| 323 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 324 | } |
| 325 | |
| 326 | BOOST_AUTO_TEST_CASE(RefGreaterSimpleEndToEndTest) |
| 327 | { |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 328 | const std::vector<uint8_t> expectedOutput({ 0, 0, 0, 0, 1, 1, 1, 1, |
| 329 | 0, 0, 0, 0, 0, 0, 0, 0 }); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 330 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 331 | ArithmeticSimpleEndToEnd<armnn::DataType::Float32, armnn::DataType::Boolean>(defaultBackends, |
| 332 | LayerType::Greater, |
| 333 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 334 | } |
| 335 | |
| 336 | BOOST_AUTO_TEST_CASE(RefEqualSimpleEndToEndUint8Test) |
| 337 | { |
| 338 | const std::vector<uint8_t> expectedOutput({ 1, 1, 1, 1, 0, 0, 0, 0, |
| 339 | 0, 0, 0, 0, 1, 1, 1, 1 }); |
| 340 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 341 | ArithmeticSimpleEndToEnd<armnn::DataType::QuantisedAsymm8, armnn::DataType::Boolean>(defaultBackends, |
| 342 | LayerType::Equal, |
| 343 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 344 | } |
| 345 | |
| 346 | BOOST_AUTO_TEST_CASE(RefGreaterSimpleEndToEndUint8Test) |
| 347 | { |
| 348 | const std::vector<uint8_t> expectedOutput({ 0, 0, 0, 0, 1, 1, 1, 1, |
| 349 | 0, 0, 0, 0, 0, 0, 0, 0 }); |
| 350 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 351 | ArithmeticSimpleEndToEnd<armnn::DataType::QuantisedAsymm8, armnn::DataType::Boolean>(defaultBackends, |
| 352 | LayerType::Greater, |
| 353 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 354 | } |
| 355 | |
| 356 | BOOST_AUTO_TEST_CASE(RefEqualBroadcastEndToEndTest) |
| 357 | { |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 358 | const std::vector<uint8_t> expectedOutput({ 1, 0, 1, 1, 0, 0, |
| 359 | 0, 0, 0, 0, 0, 0 }); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 360 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 361 | ArithmeticBroadcastEndToEnd<armnn::DataType::Float32, armnn::DataType::Boolean>(defaultBackends, |
| 362 | LayerType::Equal, |
| 363 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 364 | } |
| 365 | |
| 366 | BOOST_AUTO_TEST_CASE(RefGreaterBroadcastEndToEndTest) |
| 367 | { |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 368 | const std::vector<uint8_t> expectedOutput({ 0, 1, 0, 0, 0, 1, |
| 369 | 1, 1, 1, 1, 1, 1 }); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 370 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 371 | ArithmeticBroadcastEndToEnd<armnn::DataType::Float32, armnn::DataType::Boolean>(defaultBackends, |
| 372 | LayerType::Greater, |
| 373 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 374 | } |
| 375 | |
| 376 | BOOST_AUTO_TEST_CASE(RefEqualBroadcastEndToEndUint8Test) |
| 377 | { |
| 378 | const std::vector<uint8_t > expectedOutput({ 1, 0, 1, 1, 0, 0, |
| 379 | 0, 0, 0, 0, 0, 0 }); |
| 380 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 381 | ArithmeticBroadcastEndToEnd<armnn::DataType::QuantisedAsymm8, armnn::DataType::Boolean>(defaultBackends, |
| 382 | LayerType::Equal, |
| 383 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 384 | } |
| 385 | |
| 386 | BOOST_AUTO_TEST_CASE(RefGreaterBroadcastEndToEndUint8Test) |
| 387 | { |
| 388 | const std::vector<uint8_t> expectedOutput({ 0, 1, 0, 0, 0, 1, |
| 389 | 1, 1, 1, 1, 1, 1 }); |
| 390 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 391 | ArithmeticBroadcastEndToEnd<armnn::DataType::QuantisedAsymm8, armnn::DataType::Boolean>(defaultBackends, |
| 392 | LayerType::Greater, |
| 393 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 394 | } |
Éanna Ó Catháin | 20e5880 | 2018-12-04 10:29:06 +0000 | [diff] [blame] | 395 | |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 396 | BOOST_AUTO_TEST_CASE(RefMergerEndToEndDim0Test) |
| 397 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 398 | MergerDim0EndToEnd<armnn::DataType::Float32>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 399 | } |
| 400 | |
| 401 | BOOST_AUTO_TEST_CASE(RefMergerEndToEndDim0Uint8Test) |
| 402 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 403 | MergerDim0EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 404 | } |
| 405 | |
| 406 | BOOST_AUTO_TEST_CASE(RefMergerEndToEndDim1Test) |
| 407 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 408 | MergerDim1EndToEnd<armnn::DataType::Float32>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 409 | } |
| 410 | |
| 411 | BOOST_AUTO_TEST_CASE(RefMergerEndToEndDim1Uint8Test) |
| 412 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 413 | MergerDim1EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 414 | } |
| 415 | |
| 416 | BOOST_AUTO_TEST_CASE(RefMergerEndToEndDim2Test) |
| 417 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 418 | MergerDim2EndToEnd<armnn::DataType::Float32>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 419 | } |
| 420 | |
| 421 | BOOST_AUTO_TEST_CASE(RefMergerEndToEndDim2Uint8Test) |
| 422 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 423 | MergerDim2EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 424 | } |
| 425 | |
| 426 | BOOST_AUTO_TEST_CASE(RefMergerEndToEndDim3Test) |
| 427 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 428 | MergerDim3EndToEnd<armnn::DataType::Float32>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 429 | } |
| 430 | |
| 431 | BOOST_AUTO_TEST_CASE(RefMergerEndToEndDim3Uint8Test) |
| 432 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 433 | MergerDim3EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 434 | } |
| 435 | |
narpra01 | db2b160 | 2019-01-23 15:23:11 +0000 | [diff] [blame] | 436 | BOOST_AUTO_TEST_CASE(RefGatherFloatTest) |
| 437 | { |
| 438 | GatherEndToEnd<armnn::DataType::Float32>(defaultBackends); |
| 439 | } |
| 440 | |
| 441 | BOOST_AUTO_TEST_CASE(RefGatherUint8Test) |
| 442 | { |
| 443 | GatherEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 444 | } |
| 445 | |
| 446 | BOOST_AUTO_TEST_CASE(RefGatherMultiDimFloatTest) |
| 447 | { |
| 448 | GatherMultiDimEndToEnd<armnn::DataType::Float32>(defaultBackends); |
| 449 | } |
| 450 | |
| 451 | BOOST_AUTO_TEST_CASE(RefGatherMultiDimUint8Test) |
| 452 | { |
| 453 | GatherMultiDimEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 454 | } |
| 455 | |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 456 | BOOST_AUTO_TEST_SUITE_END() |