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 | |
| 8 | #include <backendsCommon/test/DetectionPostProcessTestImpl.hpp> |
narpra01 | db2b160 | 2019-01-23 15:23:11 +0000 | [diff] [blame] | 9 | #include <backendsCommon/test/GatherEndToEndTestImpl.hpp> |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 10 | #include <backendsCommon/test/MergerTestImpl.hpp> |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 11 | #include <backendsCommon/test/ArithmeticTestImpl.hpp> |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 12 | |
| 13 | #include <boost/test/unit_test.hpp> |
Éanna Ó Catháin | 20e5880 | 2018-12-04 10:29:06 +0000 | [diff] [blame] | 14 | #include <boost/test/execution_monitor.hpp> |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 15 | |
| 16 | BOOST_AUTO_TEST_SUITE(RefEndToEnd) |
| 17 | |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 18 | std::vector<armnn::BackendId> defaultBackends = {armnn::Compute::CpuRef}; |
| 19 | |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 20 | BOOST_AUTO_TEST_CASE(ConstantUsage_Ref_Float32) |
| 21 | { |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 22 | BOOST_TEST(ConstantUsageFloat32Test(defaultBackends)); |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 23 | } |
| 24 | |
| 25 | BOOST_AUTO_TEST_CASE(ConstantUsage_Ref_Uint8) |
| 26 | { |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 27 | BOOST_TEST(ConstantUsageUint8Test(defaultBackends)); |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 28 | } |
| 29 | |
| 30 | BOOST_AUTO_TEST_CASE(Unsigned8) |
| 31 | { |
| 32 | using namespace armnn; |
| 33 | |
| 34 | // Create runtime in which test will run |
| 35 | armnn::IRuntime::CreationOptions options; |
| 36 | armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); |
| 37 | |
| 38 | // Builds up the structure of the network. |
| 39 | armnn::INetworkPtr net(INetwork::Create()); |
| 40 | |
| 41 | IConnectableLayer* input = net->AddInputLayer(0, "input"); |
| 42 | IConnectableLayer* softmax = net->AddSoftmaxLayer(SoftmaxDescriptor(), "softmax"); |
| 43 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 44 | |
| 45 | input->GetOutputSlot(0).Connect(softmax->GetInputSlot(0)); |
| 46 | softmax->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 47 | |
| 48 | // Sets the tensors in the network. |
| 49 | TensorInfo inputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8); |
| 50 | inputTensorInfo.SetQuantizationOffset(100); |
| 51 | inputTensorInfo.SetQuantizationScale(10000.0f); |
| 52 | input->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); |
| 53 | |
| 54 | TensorInfo outputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8); |
| 55 | outputTensorInfo.SetQuantizationOffset(0); |
| 56 | outputTensorInfo.SetQuantizationScale(1.0f/255.0f); |
| 57 | softmax->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 58 | |
| 59 | // optimize the network |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 60 | IOptimizedNetworkPtr optNet = Optimize(*net, defaultBackends, runtime->GetDeviceSpec()); |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 61 | |
| 62 | // Loads it into the runtime. |
| 63 | NetworkId netId; |
| 64 | auto error = runtime->LoadNetwork(netId, std::move(optNet)); |
| 65 | BOOST_TEST(error == Status::Success); |
| 66 | |
| 67 | // Creates structures for input & output. |
| 68 | std::vector<uint8_t> inputData |
| 69 | { |
| 70 | 1, 10, 3, 200, 5 // Some inputs - one of which is sufficiently larger than the others to saturate softmax. |
| 71 | }; |
| 72 | std::vector<uint8_t> outputData(5); |
| 73 | |
| 74 | armnn::InputTensors inputTensors |
| 75 | { |
| 76 | {0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())} |
| 77 | }; |
| 78 | armnn::OutputTensors outputTensors |
| 79 | { |
| 80 | {0, armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())} |
| 81 | }; |
| 82 | |
| 83 | // Does the inference. |
| 84 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 85 | |
| 86 | // Checks the results. |
| 87 | BOOST_TEST(outputData[0] == 0); |
| 88 | BOOST_TEST(outputData[1] == 0); |
| 89 | BOOST_TEST(outputData[2] == 0); |
| 90 | BOOST_TEST(outputData[3] == 255); // softmax has been saturated. |
| 91 | BOOST_TEST(outputData[4] == 0); |
| 92 | } |
| 93 | |
| 94 | BOOST_AUTO_TEST_CASE(TrivialAdd) |
| 95 | { |
| 96 | // This test was designed to match "AddTwo" in android nn/runtime/test/TestTrivialModel.cpp. |
| 97 | |
| 98 | using namespace armnn; |
| 99 | |
| 100 | // Create runtime in which test will run |
| 101 | armnn::IRuntime::CreationOptions options; |
| 102 | armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); |
| 103 | |
| 104 | // Builds up the structure of the network. |
| 105 | armnn::INetworkPtr net(INetwork::Create()); |
| 106 | |
| 107 | IConnectableLayer* input1 = net->AddInputLayer(0); |
| 108 | IConnectableLayer* input2 = net->AddInputLayer(1); |
| 109 | IConnectableLayer* add = net->AddAdditionLayer(); |
| 110 | IConnectableLayer* output = net->AddOutputLayer(0); |
| 111 | |
| 112 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 113 | input2->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 114 | add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 115 | |
| 116 | // Sets the tensors in the network. |
| 117 | TensorInfo tensorInfo(TensorShape({3, 4}), DataType::Float32); |
| 118 | input1->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 119 | input2->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 120 | add->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 121 | |
| 122 | // optimize the network |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 123 | IOptimizedNetworkPtr optNet = Optimize(*net, defaultBackends, runtime->GetDeviceSpec()); |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 124 | |
| 125 | // Loads it into the runtime. |
| 126 | NetworkId netId; |
| 127 | runtime->LoadNetwork(netId, std::move(optNet)); |
| 128 | |
| 129 | // Creates structures for input & output - matching android nn test. |
| 130 | std::vector<float> input1Data |
| 131 | { |
| 132 | 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f |
| 133 | }; |
| 134 | std::vector<float> input2Data |
| 135 | { |
| 136 | 100.f, 200.f, 300.f, 400.f, 500.f, 600.f, 700.f, 800.f, 900.f, 1000.f, 1100.f, 1200.f |
| 137 | }; |
| 138 | std::vector<float> outputData(12); |
| 139 | |
| 140 | InputTensors inputTensors |
| 141 | { |
| 142 | {0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input1Data.data())}, |
| 143 | {1,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input2Data.data())} |
| 144 | }; |
| 145 | OutputTensors outputTensors |
| 146 | { |
| 147 | {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())} |
| 148 | }; |
| 149 | |
| 150 | // Does the inference. |
| 151 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 152 | |
| 153 | // Checks the results |
| 154 | BOOST_TEST(outputData[0] == 101); |
| 155 | BOOST_TEST(outputData[1] == 202); |
| 156 | BOOST_TEST(outputData[2] == 303); |
| 157 | BOOST_TEST(outputData[3] == 404); |
| 158 | BOOST_TEST(outputData[4] == 505); |
| 159 | BOOST_TEST(outputData[5] == 606); |
| 160 | BOOST_TEST(outputData[6] == 707); |
| 161 | BOOST_TEST(outputData[7] == 808); |
| 162 | BOOST_TEST(outputData[8] == 909); |
| 163 | BOOST_TEST(outputData[9] == 1010); |
| 164 | BOOST_TEST(outputData[10] == 1111); |
| 165 | BOOST_TEST(outputData[11] == 1212); |
| 166 | } |
| 167 | |
| 168 | BOOST_AUTO_TEST_CASE(MultipleOutputs) |
| 169 | { |
| 170 | using namespace armnn; |
| 171 | |
| 172 | // Create runtime in which test will run |
| 173 | armnn::IRuntime::CreationOptions options; |
| 174 | armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); |
| 175 | |
| 176 | // Builds up the structure of the network. |
| 177 | INetworkPtr net(INetwork::Create()); |
| 178 | |
| 179 | IConnectableLayer* input = net->AddInputLayer(0); |
| 180 | |
| 181 | // ReLu1 |
| 182 | ActivationDescriptor activation1Descriptor; |
| 183 | activation1Descriptor.m_Function = ActivationFunction::BoundedReLu; |
| 184 | activation1Descriptor.m_A = 1.f; |
| 185 | activation1Descriptor.m_B = -1.f; |
| 186 | IConnectableLayer* activation1 = net->AddActivationLayer(activation1Descriptor); |
| 187 | |
| 188 | // ReLu6 |
| 189 | ActivationDescriptor activation2Descriptor; |
| 190 | activation2Descriptor.m_Function = ActivationFunction::BoundedReLu; |
| 191 | activation2Descriptor.m_A = 6.0f; |
| 192 | IConnectableLayer* activation2 = net->AddActivationLayer(activation2Descriptor); |
| 193 | |
| 194 | // BoundedReLu(min=2, max=5) |
| 195 | ActivationDescriptor activation3Descriptor; |
| 196 | activation3Descriptor.m_Function = ActivationFunction::BoundedReLu; |
| 197 | activation3Descriptor.m_A = 5.0f; |
| 198 | activation3Descriptor.m_B = 2.0f; |
| 199 | IConnectableLayer* activation3 = net->AddActivationLayer(activation3Descriptor); |
| 200 | |
| 201 | IConnectableLayer* output1 = net->AddOutputLayer(0); |
| 202 | IConnectableLayer* output2 = net->AddOutputLayer(1); |
| 203 | IConnectableLayer* output3 = net->AddOutputLayer(2); |
| 204 | |
| 205 | input->GetOutputSlot(0).Connect(activation1->GetInputSlot(0)); |
| 206 | input->GetOutputSlot(0).Connect(activation2->GetInputSlot(0)); |
| 207 | input->GetOutputSlot(0).Connect(activation3->GetInputSlot(0)); |
| 208 | |
| 209 | activation1->GetOutputSlot(0).Connect(output1->GetInputSlot(0)); |
| 210 | activation2->GetOutputSlot(0).Connect(output2->GetInputSlot(0)); |
| 211 | activation3->GetOutputSlot(0).Connect(output3->GetInputSlot(0)); |
| 212 | |
| 213 | // Sets the tensors in the network. |
| 214 | TensorInfo tensorInfo(TensorShape({ 10 }), DataType::Float32); |
| 215 | input->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 216 | activation1->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 217 | activation2->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 218 | activation3->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 219 | |
| 220 | // optimize the network |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 221 | IOptimizedNetworkPtr optNet = Optimize(*net, defaultBackends, runtime->GetDeviceSpec()); |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 222 | |
| 223 | // Loads it into the runtime. |
| 224 | NetworkId netId; |
| 225 | runtime->LoadNetwork(netId, std::move(optNet)); |
| 226 | |
| 227 | // Creates structures for input & output. |
| 228 | 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 }; |
| 229 | |
| 230 | std::vector<float> output1Data(inputData.size()); |
| 231 | std::vector<float> output2Data(inputData.size()); |
| 232 | std::vector<float> output3Data(inputData.size()); |
| 233 | |
| 234 | InputTensors inputTensors |
| 235 | { |
| 236 | {0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())} |
| 237 | }; |
| 238 | OutputTensors outputTensors |
| 239 | { |
| 240 | {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), output1Data.data())}, |
| 241 | {1,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 1), output2Data.data())}, |
| 242 | {2,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 2), output3Data.data())} |
| 243 | }; |
| 244 | |
| 245 | // Does the inference. |
| 246 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 247 | |
| 248 | // Checks the results. |
| 249 | 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 |
| 250 | 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 |
| 251 | 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] |
| 252 | } |
| 253 | |
Éanna Ó Catháin | 20e5880 | 2018-12-04 10:29:06 +0000 | [diff] [blame] | 254 | BOOST_AUTO_TEST_CASE(TrivialMin) |
| 255 | { |
| 256 | using namespace armnn; |
| 257 | |
| 258 | // Create runtime in which test will run |
| 259 | armnn::IRuntime::CreationOptions options; |
| 260 | armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); |
| 261 | |
| 262 | // Builds up the structure of the network. |
| 263 | armnn::INetworkPtr net(INetwork::Create()); |
| 264 | |
| 265 | IConnectableLayer* input1 = net->AddInputLayer(0); |
| 266 | IConnectableLayer* input2 = net->AddInputLayer(1); |
| 267 | IConnectableLayer* min = net->AddMinimumLayer(); |
| 268 | IConnectableLayer* output = net->AddOutputLayer(0); |
| 269 | |
| 270 | input1->GetOutputSlot(0).Connect(min->GetInputSlot(0)); |
| 271 | input2->GetOutputSlot(0).Connect(min->GetInputSlot(1)); |
| 272 | min->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 273 | |
| 274 | // Sets the tensors in the network. |
| 275 | TensorInfo tensorInfo(TensorShape({1, 1, 1, 4}), DataType::Float32); |
| 276 | input1->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 277 | input2->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 278 | min->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 279 | |
| 280 | // optimize the network |
| 281 | IOptimizedNetworkPtr optNet = Optimize(*net, defaultBackends, runtime->GetDeviceSpec()); |
| 282 | |
| 283 | // Loads it into the runtime. |
| 284 | NetworkId netId; |
| 285 | runtime->LoadNetwork(netId, std::move(optNet)); |
| 286 | |
| 287 | // Creates structures for input & output - matching android nn test. |
| 288 | std::vector<float> input1Data |
| 289 | { |
| 290 | 1.0f, 2.0f, 3.0f, 4.0f |
| 291 | }; |
| 292 | std::vector<float> input2Data |
| 293 | { |
| 294 | 2.0f, 1.0f, 5.0f, 2.0f |
| 295 | }; |
| 296 | std::vector<float> outputData(4); |
| 297 | |
| 298 | InputTensors inputTensors |
| 299 | { |
| 300 | {0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input1Data.data())}, |
| 301 | {1,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input2Data.data())} |
| 302 | }; |
| 303 | OutputTensors outputTensors |
| 304 | { |
| 305 | {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())} |
| 306 | }; |
| 307 | |
| 308 | // Does the inference. |
| 309 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 310 | |
| 311 | // Checks the results |
| 312 | BOOST_TEST(outputData[0] == 1); |
| 313 | BOOST_TEST(outputData[1] == 1); |
| 314 | BOOST_TEST(outputData[2] == 3); |
| 315 | BOOST_TEST(outputData[3] == 2); |
| 316 | } |
| 317 | |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 318 | BOOST_AUTO_TEST_CASE(RefEqualSimpleEndToEndTest) |
| 319 | { |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 320 | const std::vector<uint8_t> expectedOutput({ 1, 1, 1, 1, 0, 0, 0, 0, |
| 321 | 0, 0, 0, 0, 1, 1, 1, 1 }); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 322 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 323 | ArithmeticSimpleEndToEnd<armnn::DataType::Float32, armnn::DataType::Boolean>(defaultBackends, |
| 324 | LayerType::Equal, |
| 325 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 326 | } |
| 327 | |
| 328 | BOOST_AUTO_TEST_CASE(RefGreaterSimpleEndToEndTest) |
| 329 | { |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 330 | const std::vector<uint8_t> expectedOutput({ 0, 0, 0, 0, 1, 1, 1, 1, |
| 331 | 0, 0, 0, 0, 0, 0, 0, 0 }); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 332 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 333 | ArithmeticSimpleEndToEnd<armnn::DataType::Float32, armnn::DataType::Boolean>(defaultBackends, |
| 334 | LayerType::Greater, |
| 335 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 336 | } |
| 337 | |
| 338 | BOOST_AUTO_TEST_CASE(RefEqualSimpleEndToEndUint8Test) |
| 339 | { |
| 340 | const std::vector<uint8_t> expectedOutput({ 1, 1, 1, 1, 0, 0, 0, 0, |
| 341 | 0, 0, 0, 0, 1, 1, 1, 1 }); |
| 342 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 343 | ArithmeticSimpleEndToEnd<armnn::DataType::QuantisedAsymm8, armnn::DataType::Boolean>(defaultBackends, |
| 344 | LayerType::Equal, |
| 345 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 346 | } |
| 347 | |
| 348 | BOOST_AUTO_TEST_CASE(RefGreaterSimpleEndToEndUint8Test) |
| 349 | { |
| 350 | const std::vector<uint8_t> expectedOutput({ 0, 0, 0, 0, 1, 1, 1, 1, |
| 351 | 0, 0, 0, 0, 0, 0, 0, 0 }); |
| 352 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 353 | ArithmeticSimpleEndToEnd<armnn::DataType::QuantisedAsymm8, armnn::DataType::Boolean>(defaultBackends, |
| 354 | LayerType::Greater, |
| 355 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 356 | } |
| 357 | |
| 358 | BOOST_AUTO_TEST_CASE(RefEqualBroadcastEndToEndTest) |
| 359 | { |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 360 | const std::vector<uint8_t> expectedOutput({ 1, 0, 1, 1, 0, 0, |
| 361 | 0, 0, 0, 0, 0, 0 }); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 362 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 363 | ArithmeticBroadcastEndToEnd<armnn::DataType::Float32, armnn::DataType::Boolean>(defaultBackends, |
| 364 | LayerType::Equal, |
| 365 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 366 | } |
| 367 | |
| 368 | BOOST_AUTO_TEST_CASE(RefGreaterBroadcastEndToEndTest) |
| 369 | { |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 370 | const std::vector<uint8_t> expectedOutput({ 0, 1, 0, 0, 0, 1, |
| 371 | 1, 1, 1, 1, 1, 1 }); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 372 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 373 | ArithmeticBroadcastEndToEnd<armnn::DataType::Float32, armnn::DataType::Boolean>(defaultBackends, |
| 374 | LayerType::Greater, |
| 375 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 376 | } |
| 377 | |
| 378 | BOOST_AUTO_TEST_CASE(RefEqualBroadcastEndToEndUint8Test) |
| 379 | { |
| 380 | const std::vector<uint8_t > expectedOutput({ 1, 0, 1, 1, 0, 0, |
| 381 | 0, 0, 0, 0, 0, 0 }); |
| 382 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 383 | ArithmeticBroadcastEndToEnd<armnn::DataType::QuantisedAsymm8, armnn::DataType::Boolean>(defaultBackends, |
| 384 | LayerType::Equal, |
| 385 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 386 | } |
| 387 | |
| 388 | BOOST_AUTO_TEST_CASE(RefGreaterBroadcastEndToEndUint8Test) |
| 389 | { |
| 390 | const std::vector<uint8_t> expectedOutput({ 0, 1, 0, 0, 0, 1, |
| 391 | 1, 1, 1, 1, 1, 1 }); |
| 392 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 393 | ArithmeticBroadcastEndToEnd<armnn::DataType::QuantisedAsymm8, armnn::DataType::Boolean>(defaultBackends, |
| 394 | LayerType::Greater, |
| 395 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 396 | } |
Éanna Ó Catháin | 20e5880 | 2018-12-04 10:29:06 +0000 | [diff] [blame] | 397 | |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 398 | BOOST_AUTO_TEST_CASE(RefMergerEndToEndDim0Test) |
| 399 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 400 | MergerDim0EndToEnd<armnn::DataType::Float32>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 401 | } |
| 402 | |
| 403 | BOOST_AUTO_TEST_CASE(RefMergerEndToEndDim0Uint8Test) |
| 404 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 405 | MergerDim0EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 406 | } |
| 407 | |
| 408 | BOOST_AUTO_TEST_CASE(RefMergerEndToEndDim1Test) |
| 409 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 410 | MergerDim1EndToEnd<armnn::DataType::Float32>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 411 | } |
| 412 | |
| 413 | BOOST_AUTO_TEST_CASE(RefMergerEndToEndDim1Uint8Test) |
| 414 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 415 | MergerDim1EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 416 | } |
| 417 | |
| 418 | BOOST_AUTO_TEST_CASE(RefMergerEndToEndDim2Test) |
| 419 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 420 | MergerDim2EndToEnd<armnn::DataType::Float32>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 421 | } |
| 422 | |
| 423 | BOOST_AUTO_TEST_CASE(RefMergerEndToEndDim2Uint8Test) |
| 424 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 425 | MergerDim2EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 426 | } |
| 427 | |
| 428 | BOOST_AUTO_TEST_CASE(RefMergerEndToEndDim3Test) |
| 429 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 430 | MergerDim3EndToEnd<armnn::DataType::Float32>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 431 | } |
| 432 | |
| 433 | BOOST_AUTO_TEST_CASE(RefMergerEndToEndDim3Uint8Test) |
| 434 | { |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 435 | MergerDim3EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 436 | } |
| 437 | |
narpra01 | db2b160 | 2019-01-23 15:23:11 +0000 | [diff] [blame] | 438 | BOOST_AUTO_TEST_CASE(RefGatherFloatTest) |
| 439 | { |
| 440 | GatherEndToEnd<armnn::DataType::Float32>(defaultBackends); |
| 441 | } |
| 442 | |
| 443 | BOOST_AUTO_TEST_CASE(RefGatherUint8Test) |
| 444 | { |
| 445 | GatherEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 446 | } |
| 447 | |
| 448 | BOOST_AUTO_TEST_CASE(RefGatherMultiDimFloatTest) |
| 449 | { |
| 450 | GatherMultiDimEndToEnd<armnn::DataType::Float32>(defaultBackends); |
| 451 | } |
| 452 | |
| 453 | BOOST_AUTO_TEST_CASE(RefGatherMultiDimUint8Test) |
| 454 | { |
| 455 | GatherMultiDimEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 456 | } |
| 457 | |
Narumol Prangnawarat | 6d302bf | 2019-02-04 11:46:26 +0000 | [diff] [blame] | 458 | BOOST_AUTO_TEST_CASE(RefDetectionPostProcessRegularNmsTest) |
| 459 | { |
| 460 | std::vector<float> boxEncodings({ |
| 461 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 462 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 463 | 0.0f, -1.0f, 0.0f, 0.0f, |
| 464 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 465 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 466 | 0.0f, 0.0f, 0.0f, 0.0f |
| 467 | }); |
| 468 | std::vector<float> scores({ |
| 469 | 0.0f, 0.9f, 0.8f, |
| 470 | 0.0f, 0.75f, 0.72f, |
| 471 | 0.0f, 0.6f, 0.5f, |
| 472 | 0.0f, 0.93f, 0.95f, |
| 473 | 0.0f, 0.5f, 0.4f, |
| 474 | 0.0f, 0.3f, 0.2f |
| 475 | }); |
| 476 | std::vector<float> anchors({ |
| 477 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 478 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 479 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 480 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 481 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 482 | 0.5f, 100.5f, 1.0f, 1.0f |
| 483 | }); |
| 484 | DetectionPostProcessRegularNmsEndToEnd<armnn::DataType::Float32>(defaultBackends, boxEncodings, scores, anchors); |
| 485 | } |
| 486 | |
| 487 | inline void QuantizeData(uint8_t* quant, const float* dequant, const TensorInfo& info) |
| 488 | { |
| 489 | for (size_t i = 0; i < info.GetNumElements(); i++) |
| 490 | { |
| 491 | quant[i] = armnn::Quantize<uint8_t>(dequant[i], info.GetQuantizationScale(), info.GetQuantizationOffset()); |
| 492 | } |
| 493 | } |
| 494 | |
| 495 | BOOST_AUTO_TEST_CASE(RefDetectionPostProcessRegularNmsUint8Test) |
| 496 | { |
| 497 | armnn::TensorInfo boxEncodingsInfo({ 1, 6, 4 }, armnn::DataType::Float32); |
| 498 | armnn::TensorInfo scoresInfo({ 1, 6, 3 }, armnn::DataType::Float32); |
| 499 | armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::Float32); |
| 500 | |
| 501 | boxEncodingsInfo.SetQuantizationScale(1.0f); |
| 502 | boxEncodingsInfo.SetQuantizationOffset(1); |
| 503 | scoresInfo.SetQuantizationScale(0.01f); |
| 504 | scoresInfo.SetQuantizationOffset(0); |
| 505 | anchorsInfo.SetQuantizationScale(0.5f); |
| 506 | anchorsInfo.SetQuantizationOffset(0); |
| 507 | |
| 508 | std::vector<float> boxEncodings({ |
| 509 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 510 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 511 | 0.0f, -1.0f, 0.0f, 0.0f, |
| 512 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 513 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 514 | 0.0f, 0.0f, 0.0f, 0.0f |
| 515 | }); |
| 516 | std::vector<float> scores({ |
| 517 | 0.0f, 0.9f, 0.8f, |
| 518 | 0.0f, 0.75f, 0.72f, |
| 519 | 0.0f, 0.6f, 0.5f, |
| 520 | 0.0f, 0.93f, 0.95f, |
| 521 | 0.0f, 0.5f, 0.4f, |
| 522 | 0.0f, 0.3f, 0.2f |
| 523 | }); |
| 524 | std::vector<float> anchors({ |
| 525 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 526 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 527 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 528 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 529 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 530 | 0.5f, 100.5f, 1.0f, 1.0f |
| 531 | }); |
| 532 | |
| 533 | std::vector<uint8_t> qBoxEncodings(boxEncodings.size(), 0); |
| 534 | std::vector<uint8_t> qScores(scores.size(), 0); |
| 535 | std::vector<uint8_t> qAnchors(anchors.size(), 0); |
| 536 | QuantizeData(qBoxEncodings.data(), boxEncodings.data(), boxEncodingsInfo); |
| 537 | QuantizeData(qScores.data(), scores.data(), scoresInfo); |
| 538 | QuantizeData(qAnchors.data(), anchors.data(), anchorsInfo); |
| 539 | DetectionPostProcessRegularNmsEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends, qBoxEncodings, |
| 540 | qScores, qAnchors, |
| 541 | 1.0f, 1, 0.01f, 0, 0.5f, 0); |
| 542 | } |
| 543 | |
| 544 | BOOST_AUTO_TEST_CASE(RefDetectionPostProcessFastNmsTest) |
| 545 | { |
| 546 | std::vector<float> boxEncodings({ |
| 547 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 548 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 549 | 0.0f, -1.0f, 0.0f, 0.0f, |
| 550 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 551 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 552 | 0.0f, 0.0f, 0.0f, 0.0f |
| 553 | }); |
| 554 | std::vector<float> scores({ |
| 555 | 0.0f, 0.9f, 0.8f, |
| 556 | 0.0f, 0.75f, 0.72f, |
| 557 | 0.0f, 0.6f, 0.5f, |
| 558 | 0.0f, 0.93f, 0.95f, |
| 559 | 0.0f, 0.5f, 0.4f, |
| 560 | 0.0f, 0.3f, 0.2f |
| 561 | }); |
| 562 | std::vector<float> anchors({ |
| 563 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 564 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 565 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 566 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 567 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 568 | 0.5f, 100.5f, 1.0f, 1.0f |
| 569 | }); |
| 570 | DetectionPostProcessFastNmsEndToEnd<armnn::DataType::Float32>(defaultBackends, boxEncodings, scores, anchors); |
| 571 | } |
| 572 | |
| 573 | BOOST_AUTO_TEST_CASE(RefDetectionPostProcessFastNmsUint8Test) |
| 574 | { |
| 575 | armnn::TensorInfo boxEncodingsInfo({ 1, 6, 4 }, armnn::DataType::Float32); |
| 576 | armnn::TensorInfo scoresInfo({ 1, 6, 3 }, armnn::DataType::Float32); |
| 577 | armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::Float32); |
| 578 | |
| 579 | boxEncodingsInfo.SetQuantizationScale(1.0f); |
| 580 | boxEncodingsInfo.SetQuantizationOffset(1); |
| 581 | scoresInfo.SetQuantizationScale(0.01f); |
| 582 | scoresInfo.SetQuantizationOffset(0); |
| 583 | anchorsInfo.SetQuantizationScale(0.5f); |
| 584 | anchorsInfo.SetQuantizationOffset(0); |
| 585 | |
| 586 | std::vector<float> boxEncodings({ |
| 587 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 588 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 589 | 0.0f, -1.0f, 0.0f, 0.0f, |
| 590 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 591 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 592 | 0.0f, 0.0f, 0.0f, 0.0f |
| 593 | }); |
| 594 | std::vector<float> scores({ |
| 595 | 0.0f, 0.9f, 0.8f, |
| 596 | 0.0f, 0.75f, 0.72f, |
| 597 | 0.0f, 0.6f, 0.5f, |
| 598 | 0.0f, 0.93f, 0.95f, |
| 599 | 0.0f, 0.5f, 0.4f, |
| 600 | 0.0f, 0.3f, 0.2f |
| 601 | }); |
| 602 | std::vector<float> anchors({ |
| 603 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 604 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 605 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 606 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 607 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 608 | 0.5f, 100.5f, 1.0f, 1.0f |
| 609 | }); |
| 610 | |
| 611 | std::vector<uint8_t> qBoxEncodings(boxEncodings.size(), 0); |
| 612 | std::vector<uint8_t> qScores(scores.size(), 0); |
| 613 | std::vector<uint8_t> qAnchors(anchors.size(), 0); |
| 614 | QuantizeData(qBoxEncodings.data(), boxEncodings.data(), boxEncodingsInfo); |
| 615 | QuantizeData(qScores.data(), scores.data(), scoresInfo); |
| 616 | QuantizeData(qAnchors.data(), anchors.data(), anchorsInfo); |
| 617 | DetectionPostProcessFastNmsEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends, qBoxEncodings, |
| 618 | qScores, qAnchors, |
| 619 | 1.0f, 1, 0.01f, 0, 0.5f, 0); |
| 620 | } |
| 621 | |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 622 | BOOST_AUTO_TEST_SUITE_END() |