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> |
Narumol Prangnawarat | 6d302bf | 2019-02-04 11:46:26 +0000 | [diff] [blame] | 7 | |
Narumol Prangnawarat | 8c7324d | 2019-05-31 16:42:11 +0100 | [diff] [blame] | 8 | #include <backendsCommon/test/DequantizeEndToEndTestImpl.hpp> |
Narumol Prangnawarat | 6d302bf | 2019-02-04 11:46:26 +0000 | [diff] [blame] | 9 | #include <backendsCommon/test/DetectionPostProcessTestImpl.hpp> |
narpra01 | db2b160 | 2019-01-23 15:23:11 +0000 | [diff] [blame] | 10 | #include <backendsCommon/test/GatherEndToEndTestImpl.hpp> |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 11 | #include <backendsCommon/test/ConcatTestImpl.hpp> |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 12 | #include <backendsCommon/test/ArithmeticTestImpl.hpp> |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 13 | #include <backendsCommon/test/SplitterEndToEndTestImpl.hpp> |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 14 | |
| 15 | #include <boost/test/unit_test.hpp> |
Éanna Ó Catháin | 20e5880 | 2018-12-04 10:29:06 +0000 | [diff] [blame] | 16 | #include <boost/test/execution_monitor.hpp> |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 17 | |
| 18 | BOOST_AUTO_TEST_SUITE(RefEndToEnd) |
| 19 | |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 20 | std::vector<armnn::BackendId> defaultBackends = {armnn::Compute::CpuRef}; |
| 21 | |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 22 | BOOST_AUTO_TEST_CASE(ConstantUsage_Ref_Float32) |
| 23 | { |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 24 | BOOST_TEST(ConstantUsageFloat32Test(defaultBackends)); |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 25 | } |
| 26 | |
| 27 | BOOST_AUTO_TEST_CASE(ConstantUsage_Ref_Uint8) |
| 28 | { |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 29 | BOOST_TEST(ConstantUsageUint8Test(defaultBackends)); |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 30 | } |
| 31 | |
| 32 | BOOST_AUTO_TEST_CASE(Unsigned8) |
| 33 | { |
| 34 | using namespace armnn; |
| 35 | |
| 36 | // Create runtime in which test will run |
| 37 | armnn::IRuntime::CreationOptions options; |
| 38 | armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); |
| 39 | |
| 40 | // Builds up the structure of the network. |
| 41 | armnn::INetworkPtr net(INetwork::Create()); |
| 42 | |
| 43 | IConnectableLayer* input = net->AddInputLayer(0, "input"); |
| 44 | IConnectableLayer* softmax = net->AddSoftmaxLayer(SoftmaxDescriptor(), "softmax"); |
| 45 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 46 | |
| 47 | input->GetOutputSlot(0).Connect(softmax->GetInputSlot(0)); |
| 48 | softmax->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 49 | |
| 50 | // Sets the tensors in the network. |
| 51 | TensorInfo inputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8); |
| 52 | inputTensorInfo.SetQuantizationOffset(100); |
| 53 | inputTensorInfo.SetQuantizationScale(10000.0f); |
| 54 | input->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); |
| 55 | |
| 56 | TensorInfo outputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8); |
| 57 | outputTensorInfo.SetQuantizationOffset(0); |
| 58 | outputTensorInfo.SetQuantizationScale(1.0f/255.0f); |
| 59 | softmax->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 60 | |
| 61 | // optimize the network |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 62 | IOptimizedNetworkPtr optNet = Optimize(*net, defaultBackends, runtime->GetDeviceSpec()); |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 63 | |
| 64 | // Loads it into the runtime. |
| 65 | NetworkId netId; |
| 66 | auto error = runtime->LoadNetwork(netId, std::move(optNet)); |
| 67 | BOOST_TEST(error == Status::Success); |
| 68 | |
| 69 | // Creates structures for input & output. |
| 70 | std::vector<uint8_t> inputData |
| 71 | { |
| 72 | 1, 10, 3, 200, 5 // Some inputs - one of which is sufficiently larger than the others to saturate softmax. |
| 73 | }; |
| 74 | std::vector<uint8_t> outputData(5); |
| 75 | |
| 76 | armnn::InputTensors inputTensors |
| 77 | { |
| 78 | {0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())} |
| 79 | }; |
| 80 | armnn::OutputTensors outputTensors |
| 81 | { |
| 82 | {0, armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())} |
| 83 | }; |
| 84 | |
| 85 | // Does the inference. |
| 86 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 87 | |
| 88 | // Checks the results. |
| 89 | BOOST_TEST(outputData[0] == 0); |
| 90 | BOOST_TEST(outputData[1] == 0); |
| 91 | BOOST_TEST(outputData[2] == 0); |
| 92 | BOOST_TEST(outputData[3] == 255); // softmax has been saturated. |
| 93 | BOOST_TEST(outputData[4] == 0); |
| 94 | } |
| 95 | |
| 96 | BOOST_AUTO_TEST_CASE(TrivialAdd) |
| 97 | { |
| 98 | // This test was designed to match "AddTwo" in android nn/runtime/test/TestTrivialModel.cpp. |
| 99 | |
| 100 | using namespace armnn; |
| 101 | |
| 102 | // Create runtime in which test will run |
| 103 | armnn::IRuntime::CreationOptions options; |
| 104 | armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); |
| 105 | |
| 106 | // Builds up the structure of the network. |
| 107 | armnn::INetworkPtr net(INetwork::Create()); |
| 108 | |
| 109 | IConnectableLayer* input1 = net->AddInputLayer(0); |
| 110 | IConnectableLayer* input2 = net->AddInputLayer(1); |
| 111 | IConnectableLayer* add = net->AddAdditionLayer(); |
| 112 | IConnectableLayer* output = net->AddOutputLayer(0); |
| 113 | |
| 114 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 115 | input2->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 116 | add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 117 | |
| 118 | // Sets the tensors in the network. |
| 119 | TensorInfo tensorInfo(TensorShape({3, 4}), DataType::Float32); |
| 120 | input1->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 121 | input2->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 122 | add->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 123 | |
| 124 | // optimize the network |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 125 | IOptimizedNetworkPtr optNet = Optimize(*net, defaultBackends, runtime->GetDeviceSpec()); |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 126 | |
| 127 | // Loads it into the runtime. |
| 128 | NetworkId netId; |
| 129 | runtime->LoadNetwork(netId, std::move(optNet)); |
| 130 | |
| 131 | // Creates structures for input & output - matching android nn test. |
| 132 | std::vector<float> input1Data |
| 133 | { |
| 134 | 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f |
| 135 | }; |
| 136 | std::vector<float> input2Data |
| 137 | { |
| 138 | 100.f, 200.f, 300.f, 400.f, 500.f, 600.f, 700.f, 800.f, 900.f, 1000.f, 1100.f, 1200.f |
| 139 | }; |
| 140 | std::vector<float> outputData(12); |
| 141 | |
| 142 | InputTensors inputTensors |
| 143 | { |
| 144 | {0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input1Data.data())}, |
| 145 | {1,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input2Data.data())} |
| 146 | }; |
| 147 | OutputTensors outputTensors |
| 148 | { |
| 149 | {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())} |
| 150 | }; |
| 151 | |
| 152 | // Does the inference. |
| 153 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 154 | |
| 155 | // Checks the results |
| 156 | BOOST_TEST(outputData[0] == 101); |
| 157 | BOOST_TEST(outputData[1] == 202); |
| 158 | BOOST_TEST(outputData[2] == 303); |
| 159 | BOOST_TEST(outputData[3] == 404); |
| 160 | BOOST_TEST(outputData[4] == 505); |
| 161 | BOOST_TEST(outputData[5] == 606); |
| 162 | BOOST_TEST(outputData[6] == 707); |
| 163 | BOOST_TEST(outputData[7] == 808); |
| 164 | BOOST_TEST(outputData[8] == 909); |
| 165 | BOOST_TEST(outputData[9] == 1010); |
| 166 | BOOST_TEST(outputData[10] == 1111); |
| 167 | BOOST_TEST(outputData[11] == 1212); |
| 168 | } |
| 169 | |
| 170 | BOOST_AUTO_TEST_CASE(MultipleOutputs) |
| 171 | { |
| 172 | using namespace armnn; |
| 173 | |
| 174 | // Create runtime in which test will run |
| 175 | armnn::IRuntime::CreationOptions options; |
| 176 | armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); |
| 177 | |
| 178 | // Builds up the structure of the network. |
| 179 | INetworkPtr net(INetwork::Create()); |
| 180 | |
| 181 | IConnectableLayer* input = net->AddInputLayer(0); |
| 182 | |
| 183 | // ReLu1 |
| 184 | ActivationDescriptor activation1Descriptor; |
| 185 | activation1Descriptor.m_Function = ActivationFunction::BoundedReLu; |
| 186 | activation1Descriptor.m_A = 1.f; |
| 187 | activation1Descriptor.m_B = -1.f; |
| 188 | IConnectableLayer* activation1 = net->AddActivationLayer(activation1Descriptor); |
| 189 | |
| 190 | // ReLu6 |
| 191 | ActivationDescriptor activation2Descriptor; |
| 192 | activation2Descriptor.m_Function = ActivationFunction::BoundedReLu; |
| 193 | activation2Descriptor.m_A = 6.0f; |
| 194 | IConnectableLayer* activation2 = net->AddActivationLayer(activation2Descriptor); |
| 195 | |
| 196 | // BoundedReLu(min=2, max=5) |
| 197 | ActivationDescriptor activation3Descriptor; |
| 198 | activation3Descriptor.m_Function = ActivationFunction::BoundedReLu; |
| 199 | activation3Descriptor.m_A = 5.0f; |
| 200 | activation3Descriptor.m_B = 2.0f; |
| 201 | IConnectableLayer* activation3 = net->AddActivationLayer(activation3Descriptor); |
| 202 | |
| 203 | IConnectableLayer* output1 = net->AddOutputLayer(0); |
| 204 | IConnectableLayer* output2 = net->AddOutputLayer(1); |
| 205 | IConnectableLayer* output3 = net->AddOutputLayer(2); |
| 206 | |
| 207 | input->GetOutputSlot(0).Connect(activation1->GetInputSlot(0)); |
| 208 | input->GetOutputSlot(0).Connect(activation2->GetInputSlot(0)); |
| 209 | input->GetOutputSlot(0).Connect(activation3->GetInputSlot(0)); |
| 210 | |
| 211 | activation1->GetOutputSlot(0).Connect(output1->GetInputSlot(0)); |
| 212 | activation2->GetOutputSlot(0).Connect(output2->GetInputSlot(0)); |
| 213 | activation3->GetOutputSlot(0).Connect(output3->GetInputSlot(0)); |
| 214 | |
| 215 | // Sets the tensors in the network. |
| 216 | TensorInfo tensorInfo(TensorShape({ 10 }), DataType::Float32); |
| 217 | input->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 218 | activation1->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 219 | activation2->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 220 | activation3->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 221 | |
| 222 | // optimize the network |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 223 | IOptimizedNetworkPtr optNet = Optimize(*net, defaultBackends, runtime->GetDeviceSpec()); |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 224 | |
| 225 | // Loads it into the runtime. |
| 226 | NetworkId netId; |
| 227 | runtime->LoadNetwork(netId, std::move(optNet)); |
| 228 | |
| 229 | // Creates structures for input & output. |
| 230 | 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 }; |
| 231 | |
| 232 | std::vector<float> output1Data(inputData.size()); |
| 233 | std::vector<float> output2Data(inputData.size()); |
| 234 | std::vector<float> output3Data(inputData.size()); |
| 235 | |
| 236 | InputTensors inputTensors |
| 237 | { |
| 238 | {0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())} |
| 239 | }; |
| 240 | OutputTensors outputTensors |
| 241 | { |
| 242 | {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), output1Data.data())}, |
| 243 | {1,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 1), output2Data.data())}, |
| 244 | {2,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 2), output3Data.data())} |
| 245 | }; |
| 246 | |
| 247 | // Does the inference. |
| 248 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 249 | |
| 250 | // Checks the results. |
| 251 | 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 |
| 252 | 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 |
| 253 | 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] |
| 254 | } |
| 255 | |
Éanna Ó Catháin | 20e5880 | 2018-12-04 10:29:06 +0000 | [diff] [blame] | 256 | BOOST_AUTO_TEST_CASE(TrivialMin) |
| 257 | { |
| 258 | using namespace armnn; |
| 259 | |
| 260 | // Create runtime in which test will run |
| 261 | armnn::IRuntime::CreationOptions options; |
| 262 | armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); |
| 263 | |
| 264 | // Builds up the structure of the network. |
| 265 | armnn::INetworkPtr net(INetwork::Create()); |
| 266 | |
| 267 | IConnectableLayer* input1 = net->AddInputLayer(0); |
| 268 | IConnectableLayer* input2 = net->AddInputLayer(1); |
| 269 | IConnectableLayer* min = net->AddMinimumLayer(); |
| 270 | IConnectableLayer* output = net->AddOutputLayer(0); |
| 271 | |
| 272 | input1->GetOutputSlot(0).Connect(min->GetInputSlot(0)); |
| 273 | input2->GetOutputSlot(0).Connect(min->GetInputSlot(1)); |
| 274 | min->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 275 | |
| 276 | // Sets the tensors in the network. |
| 277 | TensorInfo tensorInfo(TensorShape({1, 1, 1, 4}), DataType::Float32); |
| 278 | input1->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 279 | input2->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 280 | min->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 281 | |
| 282 | // optimize the network |
| 283 | IOptimizedNetworkPtr optNet = Optimize(*net, defaultBackends, runtime->GetDeviceSpec()); |
| 284 | |
| 285 | // Loads it into the runtime. |
| 286 | NetworkId netId; |
| 287 | runtime->LoadNetwork(netId, std::move(optNet)); |
| 288 | |
| 289 | // Creates structures for input & output - matching android nn test. |
| 290 | std::vector<float> input1Data |
| 291 | { |
| 292 | 1.0f, 2.0f, 3.0f, 4.0f |
| 293 | }; |
| 294 | std::vector<float> input2Data |
| 295 | { |
| 296 | 2.0f, 1.0f, 5.0f, 2.0f |
| 297 | }; |
| 298 | std::vector<float> outputData(4); |
| 299 | |
| 300 | InputTensors inputTensors |
| 301 | { |
| 302 | {0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input1Data.data())}, |
| 303 | {1,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input2Data.data())} |
| 304 | }; |
| 305 | OutputTensors outputTensors |
| 306 | { |
| 307 | {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())} |
| 308 | }; |
| 309 | |
| 310 | // Does the inference. |
| 311 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 312 | |
| 313 | // Checks the results |
| 314 | BOOST_TEST(outputData[0] == 1); |
| 315 | BOOST_TEST(outputData[1] == 1); |
| 316 | BOOST_TEST(outputData[2] == 3); |
| 317 | BOOST_TEST(outputData[3] == 2); |
| 318 | } |
| 319 | |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 320 | BOOST_AUTO_TEST_CASE(RefEqualSimpleEndToEndTest) |
| 321 | { |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 322 | const std::vector<uint8_t> expectedOutput({ 1, 1, 1, 1, 0, 0, 0, 0, |
| 323 | 0, 0, 0, 0, 1, 1, 1, 1 }); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 324 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 325 | ArithmeticSimpleEndToEnd<armnn::DataType::Float32, armnn::DataType::Boolean>(defaultBackends, |
| 326 | LayerType::Equal, |
| 327 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 328 | } |
| 329 | |
| 330 | BOOST_AUTO_TEST_CASE(RefGreaterSimpleEndToEndTest) |
| 331 | { |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 332 | const std::vector<uint8_t> expectedOutput({ 0, 0, 0, 0, 1, 1, 1, 1, |
| 333 | 0, 0, 0, 0, 0, 0, 0, 0 }); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 334 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 335 | ArithmeticSimpleEndToEnd<armnn::DataType::Float32, armnn::DataType::Boolean>(defaultBackends, |
| 336 | LayerType::Greater, |
| 337 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 338 | } |
| 339 | |
| 340 | BOOST_AUTO_TEST_CASE(RefEqualSimpleEndToEndUint8Test) |
| 341 | { |
| 342 | const std::vector<uint8_t> expectedOutput({ 1, 1, 1, 1, 0, 0, 0, 0, |
| 343 | 0, 0, 0, 0, 1, 1, 1, 1 }); |
| 344 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 345 | ArithmeticSimpleEndToEnd<armnn::DataType::QuantisedAsymm8, armnn::DataType::Boolean>(defaultBackends, |
| 346 | LayerType::Equal, |
| 347 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 348 | } |
| 349 | |
| 350 | BOOST_AUTO_TEST_CASE(RefGreaterSimpleEndToEndUint8Test) |
| 351 | { |
| 352 | const std::vector<uint8_t> expectedOutput({ 0, 0, 0, 0, 1, 1, 1, 1, |
| 353 | 0, 0, 0, 0, 0, 0, 0, 0 }); |
| 354 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 355 | ArithmeticSimpleEndToEnd<armnn::DataType::QuantisedAsymm8, armnn::DataType::Boolean>(defaultBackends, |
| 356 | LayerType::Greater, |
| 357 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 358 | } |
| 359 | |
| 360 | BOOST_AUTO_TEST_CASE(RefEqualBroadcastEndToEndTest) |
| 361 | { |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 362 | const std::vector<uint8_t> expectedOutput({ 1, 0, 1, 1, 0, 0, |
| 363 | 0, 0, 0, 0, 0, 0 }); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 364 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 365 | ArithmeticBroadcastEndToEnd<armnn::DataType::Float32, armnn::DataType::Boolean>(defaultBackends, |
| 366 | LayerType::Equal, |
| 367 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 368 | } |
| 369 | |
| 370 | BOOST_AUTO_TEST_CASE(RefGreaterBroadcastEndToEndTest) |
| 371 | { |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 372 | const std::vector<uint8_t> expectedOutput({ 0, 1, 0, 0, 0, 1, |
| 373 | 1, 1, 1, 1, 1, 1 }); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 374 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 375 | ArithmeticBroadcastEndToEnd<armnn::DataType::Float32, armnn::DataType::Boolean>(defaultBackends, |
| 376 | LayerType::Greater, |
| 377 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 378 | } |
| 379 | |
| 380 | BOOST_AUTO_TEST_CASE(RefEqualBroadcastEndToEndUint8Test) |
| 381 | { |
| 382 | const std::vector<uint8_t > expectedOutput({ 1, 0, 1, 1, 0, 0, |
| 383 | 0, 0, 0, 0, 0, 0 }); |
| 384 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 385 | ArithmeticBroadcastEndToEnd<armnn::DataType::QuantisedAsymm8, armnn::DataType::Boolean>(defaultBackends, |
| 386 | LayerType::Equal, |
| 387 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 388 | } |
| 389 | |
| 390 | BOOST_AUTO_TEST_CASE(RefGreaterBroadcastEndToEndUint8Test) |
| 391 | { |
| 392 | const std::vector<uint8_t> expectedOutput({ 0, 1, 0, 0, 0, 1, |
| 393 | 1, 1, 1, 1, 1, 1 }); |
| 394 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 395 | ArithmeticBroadcastEndToEnd<armnn::DataType::QuantisedAsymm8, armnn::DataType::Boolean>(defaultBackends, |
| 396 | LayerType::Greater, |
| 397 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 398 | } |
Éanna Ó Catháin | 20e5880 | 2018-12-04 10:29:06 +0000 | [diff] [blame] | 399 | |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 400 | BOOST_AUTO_TEST_CASE(RefConcatEndToEndDim0Test) |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 401 | { |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 402 | ConcatDim0EndToEnd<armnn::DataType::Float32>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 403 | } |
| 404 | |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 405 | BOOST_AUTO_TEST_CASE(RefConcatEndToEndDim0Uint8Test) |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 406 | { |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 407 | ConcatDim0EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 408 | } |
| 409 | |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 410 | BOOST_AUTO_TEST_CASE(RefConcatEndToEndDim1Test) |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 411 | { |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 412 | ConcatDim1EndToEnd<armnn::DataType::Float32>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 413 | } |
| 414 | |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 415 | BOOST_AUTO_TEST_CASE(RefConcatEndToEndDim1Uint8Test) |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 416 | { |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 417 | ConcatDim1EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 418 | } |
| 419 | |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 420 | BOOST_AUTO_TEST_CASE(RefConcatEndToEndDim2Test) |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 421 | { |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 422 | ConcatDim2EndToEnd<armnn::DataType::Float32>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 423 | } |
| 424 | |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 425 | BOOST_AUTO_TEST_CASE(RefConcatEndToEndDim2Uint8Test) |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 426 | { |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 427 | ConcatDim2EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 428 | } |
| 429 | |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 430 | BOOST_AUTO_TEST_CASE(RefConcatEndToEndDim3Test) |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 431 | { |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 432 | ConcatDim3EndToEnd<armnn::DataType::Float32>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 433 | } |
| 434 | |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 435 | BOOST_AUTO_TEST_CASE(RefConcatEndToEndDim3Uint8Test) |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 436 | { |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 437 | ConcatDim3EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 438 | } |
| 439 | |
narpra01 | db2b160 | 2019-01-23 15:23:11 +0000 | [diff] [blame] | 440 | BOOST_AUTO_TEST_CASE(RefGatherFloatTest) |
| 441 | { |
| 442 | GatherEndToEnd<armnn::DataType::Float32>(defaultBackends); |
| 443 | } |
| 444 | |
| 445 | BOOST_AUTO_TEST_CASE(RefGatherUint8Test) |
| 446 | { |
| 447 | GatherEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 448 | } |
| 449 | |
| 450 | BOOST_AUTO_TEST_CASE(RefGatherMultiDimFloatTest) |
| 451 | { |
| 452 | GatherMultiDimEndToEnd<armnn::DataType::Float32>(defaultBackends); |
| 453 | } |
| 454 | |
| 455 | BOOST_AUTO_TEST_CASE(RefGatherMultiDimUint8Test) |
| 456 | { |
| 457 | GatherMultiDimEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 458 | } |
| 459 | |
Narumol Prangnawarat | 8c7324d | 2019-05-31 16:42:11 +0100 | [diff] [blame] | 460 | BOOST_AUTO_TEST_CASE(DequantizeEndToEndSimpleTest) |
| 461 | { |
| 462 | DequantizeEndToEndSimple<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 463 | } |
| 464 | |
| 465 | BOOST_AUTO_TEST_CASE(DequantizeEndToEndOffsetTest) |
| 466 | { |
| 467 | DequantizeEndToEndOffset<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 468 | } |
| 469 | |
Narumol Prangnawarat | b6441e4 | 2019-06-04 11:22:00 +0100 | [diff] [blame] | 470 | BOOST_AUTO_TEST_CASE(DequantizeEndToEndSimpleInt16Test) |
| 471 | { |
| 472 | DequantizeEndToEndSimple<armnn::DataType::QuantisedSymm16>(defaultBackends); |
| 473 | } |
| 474 | |
| 475 | BOOST_AUTO_TEST_CASE(DequantizeEndToEndOffsetInt16Test) |
| 476 | { |
| 477 | DequantizeEndToEndOffset<armnn::DataType::QuantisedSymm16>(defaultBackends); |
| 478 | } |
| 479 | |
Narumol Prangnawarat | 6d302bf | 2019-02-04 11:46:26 +0000 | [diff] [blame] | 480 | BOOST_AUTO_TEST_CASE(RefDetectionPostProcessRegularNmsTest) |
| 481 | { |
| 482 | std::vector<float> boxEncodings({ |
| 483 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 484 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 485 | 0.0f, -1.0f, 0.0f, 0.0f, |
| 486 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 487 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 488 | 0.0f, 0.0f, 0.0f, 0.0f |
| 489 | }); |
| 490 | std::vector<float> scores({ |
| 491 | 0.0f, 0.9f, 0.8f, |
| 492 | 0.0f, 0.75f, 0.72f, |
| 493 | 0.0f, 0.6f, 0.5f, |
| 494 | 0.0f, 0.93f, 0.95f, |
| 495 | 0.0f, 0.5f, 0.4f, |
| 496 | 0.0f, 0.3f, 0.2f |
| 497 | }); |
| 498 | std::vector<float> anchors({ |
| 499 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 500 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 501 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 502 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 503 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 504 | 0.5f, 100.5f, 1.0f, 1.0f |
| 505 | }); |
| 506 | DetectionPostProcessRegularNmsEndToEnd<armnn::DataType::Float32>(defaultBackends, boxEncodings, scores, anchors); |
| 507 | } |
| 508 | |
| 509 | inline void QuantizeData(uint8_t* quant, const float* dequant, const TensorInfo& info) |
| 510 | { |
| 511 | for (size_t i = 0; i < info.GetNumElements(); i++) |
| 512 | { |
| 513 | quant[i] = armnn::Quantize<uint8_t>(dequant[i], info.GetQuantizationScale(), info.GetQuantizationOffset()); |
| 514 | } |
| 515 | } |
| 516 | |
| 517 | BOOST_AUTO_TEST_CASE(RefDetectionPostProcessRegularNmsUint8Test) |
| 518 | { |
| 519 | armnn::TensorInfo boxEncodingsInfo({ 1, 6, 4 }, armnn::DataType::Float32); |
| 520 | armnn::TensorInfo scoresInfo({ 1, 6, 3 }, armnn::DataType::Float32); |
| 521 | armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::Float32); |
| 522 | |
| 523 | boxEncodingsInfo.SetQuantizationScale(1.0f); |
| 524 | boxEncodingsInfo.SetQuantizationOffset(1); |
| 525 | scoresInfo.SetQuantizationScale(0.01f); |
| 526 | scoresInfo.SetQuantizationOffset(0); |
| 527 | anchorsInfo.SetQuantizationScale(0.5f); |
| 528 | anchorsInfo.SetQuantizationOffset(0); |
| 529 | |
| 530 | std::vector<float> boxEncodings({ |
| 531 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 532 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 533 | 0.0f, -1.0f, 0.0f, 0.0f, |
| 534 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 535 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 536 | 0.0f, 0.0f, 0.0f, 0.0f |
| 537 | }); |
| 538 | std::vector<float> scores({ |
| 539 | 0.0f, 0.9f, 0.8f, |
| 540 | 0.0f, 0.75f, 0.72f, |
| 541 | 0.0f, 0.6f, 0.5f, |
| 542 | 0.0f, 0.93f, 0.95f, |
| 543 | 0.0f, 0.5f, 0.4f, |
| 544 | 0.0f, 0.3f, 0.2f |
| 545 | }); |
| 546 | std::vector<float> anchors({ |
| 547 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 548 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 549 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 550 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 551 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 552 | 0.5f, 100.5f, 1.0f, 1.0f |
| 553 | }); |
| 554 | |
| 555 | std::vector<uint8_t> qBoxEncodings(boxEncodings.size(), 0); |
| 556 | std::vector<uint8_t> qScores(scores.size(), 0); |
| 557 | std::vector<uint8_t> qAnchors(anchors.size(), 0); |
| 558 | QuantizeData(qBoxEncodings.data(), boxEncodings.data(), boxEncodingsInfo); |
| 559 | QuantizeData(qScores.data(), scores.data(), scoresInfo); |
| 560 | QuantizeData(qAnchors.data(), anchors.data(), anchorsInfo); |
| 561 | DetectionPostProcessRegularNmsEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends, qBoxEncodings, |
| 562 | qScores, qAnchors, |
| 563 | 1.0f, 1, 0.01f, 0, 0.5f, 0); |
| 564 | } |
| 565 | |
| 566 | BOOST_AUTO_TEST_CASE(RefDetectionPostProcessFastNmsTest) |
| 567 | { |
| 568 | std::vector<float> boxEncodings({ |
| 569 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 570 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 571 | 0.0f, -1.0f, 0.0f, 0.0f, |
| 572 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 573 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 574 | 0.0f, 0.0f, 0.0f, 0.0f |
| 575 | }); |
| 576 | std::vector<float> scores({ |
| 577 | 0.0f, 0.9f, 0.8f, |
| 578 | 0.0f, 0.75f, 0.72f, |
| 579 | 0.0f, 0.6f, 0.5f, |
| 580 | 0.0f, 0.93f, 0.95f, |
| 581 | 0.0f, 0.5f, 0.4f, |
| 582 | 0.0f, 0.3f, 0.2f |
| 583 | }); |
| 584 | std::vector<float> anchors({ |
| 585 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 586 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 587 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 588 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 589 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 590 | 0.5f, 100.5f, 1.0f, 1.0f |
| 591 | }); |
| 592 | DetectionPostProcessFastNmsEndToEnd<armnn::DataType::Float32>(defaultBackends, boxEncodings, scores, anchors); |
| 593 | } |
| 594 | |
| 595 | BOOST_AUTO_TEST_CASE(RefDetectionPostProcessFastNmsUint8Test) |
| 596 | { |
| 597 | armnn::TensorInfo boxEncodingsInfo({ 1, 6, 4 }, armnn::DataType::Float32); |
| 598 | armnn::TensorInfo scoresInfo({ 1, 6, 3 }, armnn::DataType::Float32); |
| 599 | armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::Float32); |
| 600 | |
| 601 | boxEncodingsInfo.SetQuantizationScale(1.0f); |
| 602 | boxEncodingsInfo.SetQuantizationOffset(1); |
| 603 | scoresInfo.SetQuantizationScale(0.01f); |
| 604 | scoresInfo.SetQuantizationOffset(0); |
| 605 | anchorsInfo.SetQuantizationScale(0.5f); |
| 606 | anchorsInfo.SetQuantizationOffset(0); |
| 607 | |
| 608 | std::vector<float> boxEncodings({ |
| 609 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 610 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 611 | 0.0f, -1.0f, 0.0f, 0.0f, |
| 612 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 613 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 614 | 0.0f, 0.0f, 0.0f, 0.0f |
| 615 | }); |
| 616 | std::vector<float> scores({ |
| 617 | 0.0f, 0.9f, 0.8f, |
| 618 | 0.0f, 0.75f, 0.72f, |
| 619 | 0.0f, 0.6f, 0.5f, |
| 620 | 0.0f, 0.93f, 0.95f, |
| 621 | 0.0f, 0.5f, 0.4f, |
| 622 | 0.0f, 0.3f, 0.2f |
| 623 | }); |
| 624 | std::vector<float> anchors({ |
| 625 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 626 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 627 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 628 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 629 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 630 | 0.5f, 100.5f, 1.0f, 1.0f |
| 631 | }); |
| 632 | |
| 633 | std::vector<uint8_t> qBoxEncodings(boxEncodings.size(), 0); |
| 634 | std::vector<uint8_t> qScores(scores.size(), 0); |
| 635 | std::vector<uint8_t> qAnchors(anchors.size(), 0); |
| 636 | QuantizeData(qBoxEncodings.data(), boxEncodings.data(), boxEncodingsInfo); |
| 637 | QuantizeData(qScores.data(), scores.data(), scoresInfo); |
| 638 | QuantizeData(qAnchors.data(), anchors.data(), anchorsInfo); |
| 639 | DetectionPostProcessFastNmsEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends, qBoxEncodings, |
| 640 | qScores, qAnchors, |
| 641 | 1.0f, 1, 0.01f, 0, 0.5f, 0); |
| 642 | } |
| 643 | |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 644 | BOOST_AUTO_TEST_CASE(RefSplitter1dEndToEndTest) |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 645 | { |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 646 | Splitter1dEndToEnd<armnn::DataType::Float32>(defaultBackends); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 647 | } |
| 648 | |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 649 | BOOST_AUTO_TEST_CASE(RefSplitter1dEndToEndUint8Test) |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 650 | { |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 651 | Splitter1dEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 652 | } |
| 653 | |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 654 | BOOST_AUTO_TEST_CASE(RefSplitter2dDim0EndToEndTest) |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 655 | { |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 656 | Splitter2dDim0EndToEnd<armnn::DataType::Float32>(defaultBackends); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 657 | } |
| 658 | |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 659 | BOOST_AUTO_TEST_CASE(RefSplitter2dDim1EndToEndTest) |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 660 | { |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 661 | Splitter2dDim1EndToEnd<armnn::DataType::Float32>(defaultBackends); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 662 | } |
| 663 | |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 664 | BOOST_AUTO_TEST_CASE(RefSplitter2dDim0EndToEndUint8Test) |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 665 | { |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 666 | Splitter2dDim0EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 667 | } |
| 668 | |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 669 | BOOST_AUTO_TEST_CASE(RefSplitter2dDim1EndToEndUint8Test) |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 670 | { |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 671 | Splitter2dDim1EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 672 | } |
| 673 | |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 674 | BOOST_AUTO_TEST_CASE(RefSplitter3dDim0EndToEndTest) |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 675 | { |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 676 | Splitter3dDim0EndToEnd<armnn::DataType::Float32>(defaultBackends); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 677 | } |
| 678 | |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 679 | BOOST_AUTO_TEST_CASE(RefSplitter3dDim1EndToEndTest) |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 680 | { |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 681 | Splitter3dDim1EndToEnd<armnn::DataType::Float32>(defaultBackends); |
| 682 | } |
| 683 | |
| 684 | BOOST_AUTO_TEST_CASE(RefSplitter3dDim2EndToEndTest) |
| 685 | { |
| 686 | Splitter3dDim2EndToEnd<armnn::DataType::Float32>(defaultBackends); |
| 687 | } |
| 688 | |
| 689 | BOOST_AUTO_TEST_CASE(RefSplitter3dDim0EndToEndUint8Test) |
| 690 | { |
| 691 | Splitter3dDim0EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 692 | } |
| 693 | |
| 694 | BOOST_AUTO_TEST_CASE(RefSplitter3dDim1EndToEndUint8Test) |
| 695 | { |
| 696 | Splitter3dDim1EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 697 | } |
| 698 | |
| 699 | BOOST_AUTO_TEST_CASE(RefSplitter3dDim2EndToEndUint8Test) |
| 700 | { |
| 701 | Splitter3dDim2EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 702 | } |
| 703 | |
| 704 | BOOST_AUTO_TEST_CASE(RefSplitter4dDim0EndToEndTest) |
| 705 | { |
| 706 | Splitter4dDim0EndToEnd<armnn::DataType::Float32>(defaultBackends); |
| 707 | } |
| 708 | |
| 709 | BOOST_AUTO_TEST_CASE(RefSplitter4dDim1EndToEndTest) |
| 710 | { |
| 711 | Splitter4dDim1EndToEnd<armnn::DataType::Float32>(defaultBackends); |
| 712 | } |
| 713 | |
| 714 | BOOST_AUTO_TEST_CASE(RefSplitter4dDim2EndToEndTest) |
| 715 | { |
| 716 | Splitter4dDim2EndToEnd<armnn::DataType::Float32>(defaultBackends); |
| 717 | } |
| 718 | |
| 719 | BOOST_AUTO_TEST_CASE(RefSplitter4dDim3EndToEndTest) |
| 720 | { |
| 721 | Splitter4dDim3EndToEnd<armnn::DataType::Float32>(defaultBackends); |
| 722 | } |
| 723 | |
| 724 | BOOST_AUTO_TEST_CASE(RefSplitter4dDim0EndToEndUint8Test) |
| 725 | { |
| 726 | Splitter4dDim0EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 727 | } |
| 728 | |
| 729 | BOOST_AUTO_TEST_CASE(RefSplitter4dDim1EndToEndUint8Test) |
| 730 | { |
| 731 | Splitter4dDim1EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 732 | } |
| 733 | |
| 734 | BOOST_AUTO_TEST_CASE(RefSplitter4dDim2EndToEndUint8Test) |
| 735 | { |
| 736 | Splitter4dDim2EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 737 | } |
| 738 | |
| 739 | BOOST_AUTO_TEST_CASE(RefSplitter4dDim3EndToEndUint8Test) |
| 740 | { |
| 741 | Splitter4dDim3EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 742 | } |
| 743 | |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 744 | BOOST_AUTO_TEST_SUITE_END() |