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 | |
Aron Virginas-Tar | fe15eff | 2019-07-01 16:12:58 +0100 | [diff] [blame^] | 8 | #include <backendsCommon/test/ArithmeticTestImpl.hpp> |
Francis Murtagh | e24e3cd | 2019-06-25 14:41:55 +0100 | [diff] [blame] | 9 | #include <backendsCommon/test/BatchToSpaceNdEndToEndTestImpl.hpp> |
Aron Virginas-Tar | fe15eff | 2019-07-01 16:12:58 +0100 | [diff] [blame^] | 10 | #include <backendsCommon/test/ConcatTestImpl.hpp> |
Narumol Prangnawarat | 8c7324d | 2019-05-31 16:42:11 +0100 | [diff] [blame] | 11 | #include <backendsCommon/test/DequantizeEndToEndTestImpl.hpp> |
Narumol Prangnawarat | 6d302bf | 2019-02-04 11:46:26 +0000 | [diff] [blame] | 12 | #include <backendsCommon/test/DetectionPostProcessTestImpl.hpp> |
narpra01 | db2b160 | 2019-01-23 15:23:11 +0000 | [diff] [blame] | 13 | #include <backendsCommon/test/GatherEndToEndTestImpl.hpp> |
Aron Virginas-Tar | fe15eff | 2019-07-01 16:12:58 +0100 | [diff] [blame^] | 14 | #include <backendsCommon/test/ResizeEndToEndTestImpl.hpp> |
Keith Davis | 9515c7e | 2019-06-21 09:33:59 +0100 | [diff] [blame] | 15 | #include <backendsCommon/test/SpaceToDepthEndToEndTestImpl.hpp> |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 16 | #include <backendsCommon/test/SplitterEndToEndTestImpl.hpp> |
Aron Virginas-Tar | 98180ef | 2019-06-26 15:02:47 +0100 | [diff] [blame] | 17 | #include <backendsCommon/test/TransposeConvolution2dEndToEndTestImpl.hpp> |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 18 | |
| 19 | #include <boost/test/unit_test.hpp> |
Éanna Ó Catháin | 20e5880 | 2018-12-04 10:29:06 +0000 | [diff] [blame] | 20 | #include <boost/test/execution_monitor.hpp> |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 21 | |
| 22 | BOOST_AUTO_TEST_SUITE(RefEndToEnd) |
| 23 | |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 24 | std::vector<armnn::BackendId> defaultBackends = {armnn::Compute::CpuRef}; |
| 25 | |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 26 | BOOST_AUTO_TEST_CASE(ConstantUsage_Ref_Float32) |
| 27 | { |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 28 | BOOST_TEST(ConstantUsageFloat32Test(defaultBackends)); |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 29 | } |
| 30 | |
| 31 | BOOST_AUTO_TEST_CASE(ConstantUsage_Ref_Uint8) |
| 32 | { |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 33 | BOOST_TEST(ConstantUsageUint8Test(defaultBackends)); |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 34 | } |
| 35 | |
| 36 | BOOST_AUTO_TEST_CASE(Unsigned8) |
| 37 | { |
| 38 | using namespace armnn; |
| 39 | |
| 40 | // Create runtime in which test will run |
| 41 | armnn::IRuntime::CreationOptions options; |
| 42 | armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); |
| 43 | |
| 44 | // Builds up the structure of the network. |
| 45 | armnn::INetworkPtr net(INetwork::Create()); |
| 46 | |
| 47 | IConnectableLayer* input = net->AddInputLayer(0, "input"); |
| 48 | IConnectableLayer* softmax = net->AddSoftmaxLayer(SoftmaxDescriptor(), "softmax"); |
| 49 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 50 | |
| 51 | input->GetOutputSlot(0).Connect(softmax->GetInputSlot(0)); |
| 52 | softmax->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 53 | |
| 54 | // Sets the tensors in the network. |
| 55 | TensorInfo inputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8); |
| 56 | inputTensorInfo.SetQuantizationOffset(100); |
| 57 | inputTensorInfo.SetQuantizationScale(10000.0f); |
| 58 | input->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); |
| 59 | |
| 60 | TensorInfo outputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8); |
| 61 | outputTensorInfo.SetQuantizationOffset(0); |
| 62 | outputTensorInfo.SetQuantizationScale(1.0f/255.0f); |
| 63 | softmax->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 64 | |
| 65 | // optimize the network |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 66 | IOptimizedNetworkPtr optNet = Optimize(*net, defaultBackends, runtime->GetDeviceSpec()); |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 67 | |
| 68 | // Loads it into the runtime. |
| 69 | NetworkId netId; |
| 70 | auto error = runtime->LoadNetwork(netId, std::move(optNet)); |
| 71 | BOOST_TEST(error == Status::Success); |
| 72 | |
| 73 | // Creates structures for input & output. |
| 74 | std::vector<uint8_t> inputData |
| 75 | { |
| 76 | 1, 10, 3, 200, 5 // Some inputs - one of which is sufficiently larger than the others to saturate softmax. |
| 77 | }; |
| 78 | std::vector<uint8_t> outputData(5); |
| 79 | |
| 80 | armnn::InputTensors inputTensors |
| 81 | { |
| 82 | {0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())} |
| 83 | }; |
| 84 | armnn::OutputTensors outputTensors |
| 85 | { |
| 86 | {0, armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())} |
| 87 | }; |
| 88 | |
| 89 | // Does the inference. |
| 90 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 91 | |
| 92 | // Checks the results. |
| 93 | BOOST_TEST(outputData[0] == 0); |
| 94 | BOOST_TEST(outputData[1] == 0); |
| 95 | BOOST_TEST(outputData[2] == 0); |
| 96 | BOOST_TEST(outputData[3] == 255); // softmax has been saturated. |
| 97 | BOOST_TEST(outputData[4] == 0); |
| 98 | } |
| 99 | |
| 100 | BOOST_AUTO_TEST_CASE(TrivialAdd) |
| 101 | { |
| 102 | // This test was designed to match "AddTwo" in android nn/runtime/test/TestTrivialModel.cpp. |
| 103 | |
| 104 | using namespace armnn; |
| 105 | |
| 106 | // Create runtime in which test will run |
| 107 | armnn::IRuntime::CreationOptions options; |
| 108 | armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); |
| 109 | |
| 110 | // Builds up the structure of the network. |
| 111 | armnn::INetworkPtr net(INetwork::Create()); |
| 112 | |
| 113 | IConnectableLayer* input1 = net->AddInputLayer(0); |
| 114 | IConnectableLayer* input2 = net->AddInputLayer(1); |
| 115 | IConnectableLayer* add = net->AddAdditionLayer(); |
| 116 | IConnectableLayer* output = net->AddOutputLayer(0); |
| 117 | |
| 118 | input1->GetOutputSlot(0).Connect(add->GetInputSlot(0)); |
| 119 | input2->GetOutputSlot(0).Connect(add->GetInputSlot(1)); |
| 120 | add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 121 | |
| 122 | // Sets the tensors in the network. |
| 123 | TensorInfo tensorInfo(TensorShape({3, 4}), DataType::Float32); |
| 124 | input1->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 125 | input2->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 126 | add->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 127 | |
| 128 | // optimize the network |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 129 | IOptimizedNetworkPtr optNet = Optimize(*net, defaultBackends, runtime->GetDeviceSpec()); |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 130 | |
| 131 | // Loads it into the runtime. |
| 132 | NetworkId netId; |
| 133 | runtime->LoadNetwork(netId, std::move(optNet)); |
| 134 | |
| 135 | // Creates structures for input & output - matching android nn test. |
| 136 | std::vector<float> input1Data |
| 137 | { |
| 138 | 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f |
| 139 | }; |
| 140 | std::vector<float> input2Data |
| 141 | { |
| 142 | 100.f, 200.f, 300.f, 400.f, 500.f, 600.f, 700.f, 800.f, 900.f, 1000.f, 1100.f, 1200.f |
| 143 | }; |
| 144 | std::vector<float> outputData(12); |
| 145 | |
| 146 | InputTensors inputTensors |
| 147 | { |
| 148 | {0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input1Data.data())}, |
| 149 | {1,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input2Data.data())} |
| 150 | }; |
| 151 | OutputTensors outputTensors |
| 152 | { |
| 153 | {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())} |
| 154 | }; |
| 155 | |
| 156 | // Does the inference. |
| 157 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 158 | |
| 159 | // Checks the results |
| 160 | BOOST_TEST(outputData[0] == 101); |
| 161 | BOOST_TEST(outputData[1] == 202); |
| 162 | BOOST_TEST(outputData[2] == 303); |
| 163 | BOOST_TEST(outputData[3] == 404); |
| 164 | BOOST_TEST(outputData[4] == 505); |
| 165 | BOOST_TEST(outputData[5] == 606); |
| 166 | BOOST_TEST(outputData[6] == 707); |
| 167 | BOOST_TEST(outputData[7] == 808); |
| 168 | BOOST_TEST(outputData[8] == 909); |
| 169 | BOOST_TEST(outputData[9] == 1010); |
| 170 | BOOST_TEST(outputData[10] == 1111); |
| 171 | BOOST_TEST(outputData[11] == 1212); |
| 172 | } |
| 173 | |
| 174 | BOOST_AUTO_TEST_CASE(MultipleOutputs) |
| 175 | { |
| 176 | using namespace armnn; |
| 177 | |
| 178 | // Create runtime in which test will run |
| 179 | armnn::IRuntime::CreationOptions options; |
| 180 | armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); |
| 181 | |
| 182 | // Builds up the structure of the network. |
| 183 | INetworkPtr net(INetwork::Create()); |
| 184 | |
| 185 | IConnectableLayer* input = net->AddInputLayer(0); |
| 186 | |
| 187 | // ReLu1 |
| 188 | ActivationDescriptor activation1Descriptor; |
| 189 | activation1Descriptor.m_Function = ActivationFunction::BoundedReLu; |
| 190 | activation1Descriptor.m_A = 1.f; |
| 191 | activation1Descriptor.m_B = -1.f; |
| 192 | IConnectableLayer* activation1 = net->AddActivationLayer(activation1Descriptor); |
| 193 | |
| 194 | // ReLu6 |
| 195 | ActivationDescriptor activation2Descriptor; |
| 196 | activation2Descriptor.m_Function = ActivationFunction::BoundedReLu; |
| 197 | activation2Descriptor.m_A = 6.0f; |
| 198 | IConnectableLayer* activation2 = net->AddActivationLayer(activation2Descriptor); |
| 199 | |
| 200 | // BoundedReLu(min=2, max=5) |
| 201 | ActivationDescriptor activation3Descriptor; |
| 202 | activation3Descriptor.m_Function = ActivationFunction::BoundedReLu; |
| 203 | activation3Descriptor.m_A = 5.0f; |
| 204 | activation3Descriptor.m_B = 2.0f; |
| 205 | IConnectableLayer* activation3 = net->AddActivationLayer(activation3Descriptor); |
| 206 | |
| 207 | IConnectableLayer* output1 = net->AddOutputLayer(0); |
| 208 | IConnectableLayer* output2 = net->AddOutputLayer(1); |
| 209 | IConnectableLayer* output3 = net->AddOutputLayer(2); |
| 210 | |
| 211 | input->GetOutputSlot(0).Connect(activation1->GetInputSlot(0)); |
| 212 | input->GetOutputSlot(0).Connect(activation2->GetInputSlot(0)); |
| 213 | input->GetOutputSlot(0).Connect(activation3->GetInputSlot(0)); |
| 214 | |
| 215 | activation1->GetOutputSlot(0).Connect(output1->GetInputSlot(0)); |
| 216 | activation2->GetOutputSlot(0).Connect(output2->GetInputSlot(0)); |
| 217 | activation3->GetOutputSlot(0).Connect(output3->GetInputSlot(0)); |
| 218 | |
| 219 | // Sets the tensors in the network. |
| 220 | TensorInfo tensorInfo(TensorShape({ 10 }), DataType::Float32); |
| 221 | input->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 222 | activation1->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 223 | activation2->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 224 | activation3->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 225 | |
| 226 | // optimize the network |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 227 | IOptimizedNetworkPtr optNet = Optimize(*net, defaultBackends, runtime->GetDeviceSpec()); |
Aron Virginas-Tar | 7010400 | 2018-10-24 15:33:28 +0100 | [diff] [blame] | 228 | |
| 229 | // Loads it into the runtime. |
| 230 | NetworkId netId; |
| 231 | runtime->LoadNetwork(netId, std::move(optNet)); |
| 232 | |
| 233 | // Creates structures for input & output. |
| 234 | 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 }; |
| 235 | |
| 236 | std::vector<float> output1Data(inputData.size()); |
| 237 | std::vector<float> output2Data(inputData.size()); |
| 238 | std::vector<float> output3Data(inputData.size()); |
| 239 | |
| 240 | InputTensors inputTensors |
| 241 | { |
| 242 | {0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())} |
| 243 | }; |
| 244 | OutputTensors outputTensors |
| 245 | { |
| 246 | {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), output1Data.data())}, |
| 247 | {1,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 1), output2Data.data())}, |
| 248 | {2,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 2), output3Data.data())} |
| 249 | }; |
| 250 | |
| 251 | // Does the inference. |
| 252 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 253 | |
| 254 | // Checks the results. |
| 255 | 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 |
| 256 | 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 |
| 257 | 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] |
| 258 | } |
| 259 | |
Éanna Ó Catháin | 20e5880 | 2018-12-04 10:29:06 +0000 | [diff] [blame] | 260 | BOOST_AUTO_TEST_CASE(TrivialMin) |
| 261 | { |
| 262 | using namespace armnn; |
| 263 | |
| 264 | // Create runtime in which test will run |
| 265 | armnn::IRuntime::CreationOptions options; |
| 266 | armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); |
| 267 | |
| 268 | // Builds up the structure of the network. |
| 269 | armnn::INetworkPtr net(INetwork::Create()); |
| 270 | |
| 271 | IConnectableLayer* input1 = net->AddInputLayer(0); |
| 272 | IConnectableLayer* input2 = net->AddInputLayer(1); |
| 273 | IConnectableLayer* min = net->AddMinimumLayer(); |
| 274 | IConnectableLayer* output = net->AddOutputLayer(0); |
| 275 | |
| 276 | input1->GetOutputSlot(0).Connect(min->GetInputSlot(0)); |
| 277 | input2->GetOutputSlot(0).Connect(min->GetInputSlot(1)); |
| 278 | min->GetOutputSlot(0).Connect(output->GetInputSlot(0)); |
| 279 | |
| 280 | // Sets the tensors in the network. |
| 281 | TensorInfo tensorInfo(TensorShape({1, 1, 1, 4}), DataType::Float32); |
| 282 | input1->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 283 | input2->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 284 | min->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 285 | |
| 286 | // optimize the network |
| 287 | IOptimizedNetworkPtr optNet = Optimize(*net, defaultBackends, runtime->GetDeviceSpec()); |
| 288 | |
| 289 | // Loads it into the runtime. |
| 290 | NetworkId netId; |
| 291 | runtime->LoadNetwork(netId, std::move(optNet)); |
| 292 | |
| 293 | // Creates structures for input & output - matching android nn test. |
| 294 | std::vector<float> input1Data |
| 295 | { |
| 296 | 1.0f, 2.0f, 3.0f, 4.0f |
| 297 | }; |
| 298 | std::vector<float> input2Data |
| 299 | { |
| 300 | 2.0f, 1.0f, 5.0f, 2.0f |
| 301 | }; |
| 302 | std::vector<float> outputData(4); |
| 303 | |
| 304 | InputTensors inputTensors |
| 305 | { |
| 306 | {0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input1Data.data())}, |
| 307 | {1,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input2Data.data())} |
| 308 | }; |
| 309 | OutputTensors outputTensors |
| 310 | { |
| 311 | {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())} |
| 312 | }; |
| 313 | |
| 314 | // Does the inference. |
| 315 | runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| 316 | |
| 317 | // Checks the results |
| 318 | BOOST_TEST(outputData[0] == 1); |
| 319 | BOOST_TEST(outputData[1] == 1); |
| 320 | BOOST_TEST(outputData[2] == 3); |
| 321 | BOOST_TEST(outputData[3] == 2); |
| 322 | } |
| 323 | |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 324 | BOOST_AUTO_TEST_CASE(RefEqualSimpleEndToEndTest) |
| 325 | { |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 326 | const std::vector<uint8_t> expectedOutput({ 1, 1, 1, 1, 0, 0, 0, 0, |
| 327 | 0, 0, 0, 0, 1, 1, 1, 1 }); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 328 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 329 | ArithmeticSimpleEndToEnd<armnn::DataType::Float32, armnn::DataType::Boolean>(defaultBackends, |
| 330 | LayerType::Equal, |
| 331 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 332 | } |
| 333 | |
| 334 | BOOST_AUTO_TEST_CASE(RefGreaterSimpleEndToEndTest) |
| 335 | { |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 336 | const std::vector<uint8_t> expectedOutput({ 0, 0, 0, 0, 1, 1, 1, 1, |
| 337 | 0, 0, 0, 0, 0, 0, 0, 0 }); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 338 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 339 | ArithmeticSimpleEndToEnd<armnn::DataType::Float32, armnn::DataType::Boolean>(defaultBackends, |
| 340 | LayerType::Greater, |
| 341 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 342 | } |
| 343 | |
| 344 | BOOST_AUTO_TEST_CASE(RefEqualSimpleEndToEndUint8Test) |
| 345 | { |
| 346 | const std::vector<uint8_t> expectedOutput({ 1, 1, 1, 1, 0, 0, 0, 0, |
| 347 | 0, 0, 0, 0, 1, 1, 1, 1 }); |
| 348 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 349 | ArithmeticSimpleEndToEnd<armnn::DataType::QuantisedAsymm8, armnn::DataType::Boolean>(defaultBackends, |
| 350 | LayerType::Equal, |
| 351 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 352 | } |
| 353 | |
| 354 | BOOST_AUTO_TEST_CASE(RefGreaterSimpleEndToEndUint8Test) |
| 355 | { |
| 356 | const std::vector<uint8_t> expectedOutput({ 0, 0, 0, 0, 1, 1, 1, 1, |
| 357 | 0, 0, 0, 0, 0, 0, 0, 0 }); |
| 358 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 359 | ArithmeticSimpleEndToEnd<armnn::DataType::QuantisedAsymm8, armnn::DataType::Boolean>(defaultBackends, |
| 360 | LayerType::Greater, |
| 361 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 362 | } |
| 363 | |
| 364 | BOOST_AUTO_TEST_CASE(RefEqualBroadcastEndToEndTest) |
| 365 | { |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 366 | const std::vector<uint8_t> expectedOutput({ 1, 0, 1, 1, 0, 0, |
| 367 | 0, 0, 0, 0, 0, 0 }); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 368 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 369 | ArithmeticBroadcastEndToEnd<armnn::DataType::Float32, armnn::DataType::Boolean>(defaultBackends, |
| 370 | LayerType::Equal, |
| 371 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 372 | } |
| 373 | |
| 374 | BOOST_AUTO_TEST_CASE(RefGreaterBroadcastEndToEndTest) |
| 375 | { |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 376 | const std::vector<uint8_t> expectedOutput({ 0, 1, 0, 0, 0, 1, |
| 377 | 1, 1, 1, 1, 1, 1 }); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 378 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 379 | ArithmeticBroadcastEndToEnd<armnn::DataType::Float32, armnn::DataType::Boolean>(defaultBackends, |
| 380 | LayerType::Greater, |
| 381 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 382 | } |
| 383 | |
| 384 | BOOST_AUTO_TEST_CASE(RefEqualBroadcastEndToEndUint8Test) |
| 385 | { |
| 386 | const std::vector<uint8_t > expectedOutput({ 1, 0, 1, 1, 0, 0, |
| 387 | 0, 0, 0, 0, 0, 0 }); |
| 388 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 389 | ArithmeticBroadcastEndToEnd<armnn::DataType::QuantisedAsymm8, armnn::DataType::Boolean>(defaultBackends, |
| 390 | LayerType::Equal, |
| 391 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 392 | } |
| 393 | |
| 394 | BOOST_AUTO_TEST_CASE(RefGreaterBroadcastEndToEndUint8Test) |
| 395 | { |
| 396 | const std::vector<uint8_t> expectedOutput({ 0, 1, 0, 0, 0, 1, |
| 397 | 1, 1, 1, 1, 1, 1 }); |
| 398 | |
kevmay01 | 2b4d88e | 2019-01-24 14:05:09 +0000 | [diff] [blame] | 399 | ArithmeticBroadcastEndToEnd<armnn::DataType::QuantisedAsymm8, armnn::DataType::Boolean>(defaultBackends, |
| 400 | LayerType::Greater, |
| 401 | expectedOutput); |
FrancisMurtagh | 2262bbd | 2018-12-20 16:09:45 +0000 | [diff] [blame] | 402 | } |
Éanna Ó Catháin | 20e5880 | 2018-12-04 10:29:06 +0000 | [diff] [blame] | 403 | |
Francis Murtagh | e24e3cd | 2019-06-25 14:41:55 +0100 | [diff] [blame] | 404 | BOOST_AUTO_TEST_CASE(RefBatchToSpaceNdEndToEndFloat32NHWCTest) |
| 405 | { |
| 406 | BatchToSpaceNdEndToEnd<armnn::DataType::Float32>(defaultBackends, armnn::DataLayout::NHWC); |
| 407 | } |
| 408 | |
| 409 | BOOST_AUTO_TEST_CASE(RefBatchToSpaceNdEndToEndUint8NHWCTest) |
| 410 | { |
| 411 | BatchToSpaceNdEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends, armnn::DataLayout::NHWC); |
| 412 | } |
| 413 | |
| 414 | BOOST_AUTO_TEST_CASE(RefBatchToSpaceNdEndToEndQSymm16NHWCTest) |
| 415 | { |
| 416 | BatchToSpaceNdEndToEnd<armnn::DataType::QuantisedSymm16>(defaultBackends, armnn::DataLayout::NHWC); |
| 417 | } |
| 418 | |
| 419 | BOOST_AUTO_TEST_CASE(RefBatchToSpaceNdEndToEndFloat32NCHWTest) |
| 420 | { |
| 421 | BatchToSpaceNdEndToEnd<armnn::DataType::Float32>(defaultBackends, armnn::DataLayout::NCHW); |
| 422 | } |
| 423 | |
| 424 | BOOST_AUTO_TEST_CASE(RefBatchToSpaceNdEndToEndUint8NCHWTest) |
| 425 | { |
| 426 | BatchToSpaceNdEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends, armnn::DataLayout::NCHW); |
| 427 | } |
| 428 | |
| 429 | BOOST_AUTO_TEST_CASE(RefBatchToSpaceNdEndToEndQSymm16NCHWTest) |
| 430 | { |
| 431 | BatchToSpaceNdEndToEnd<armnn::DataType::QuantisedSymm16>(defaultBackends, armnn::DataLayout::NCHW); |
| 432 | } |
| 433 | |
| 434 | BOOST_AUTO_TEST_CASE(RefBatchToSpaceNdEndToEndComplexFloat32NHWCTest) |
| 435 | { |
| 436 | BatchToSpaceNdComplexEndToEnd<armnn::DataType::Float32>(defaultBackends, armnn::DataLayout::NHWC); |
| 437 | } |
| 438 | |
| 439 | BOOST_AUTO_TEST_CASE(RefBatchToSpaceNdEndToEndComplexUint8NHWCTest) |
| 440 | { |
| 441 | BatchToSpaceNdComplexEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends, armnn::DataLayout::NHWC); |
| 442 | } |
| 443 | |
| 444 | BOOST_AUTO_TEST_CASE(RefBatchToSpaceNdEndToEndComplexQSymm16NHWCTest) |
| 445 | { |
| 446 | BatchToSpaceNdComplexEndToEnd<armnn::DataType::QuantisedSymm16>(defaultBackends, armnn::DataLayout::NHWC); |
| 447 | } |
| 448 | |
| 449 | BOOST_AUTO_TEST_CASE(RefBatchToSpaceNdEndToEndComplexFloat32NCHWTest) |
| 450 | { |
| 451 | BatchToSpaceNdComplexEndToEnd<armnn::DataType::Float32>(defaultBackends, armnn::DataLayout::NCHW); |
| 452 | } |
| 453 | |
| 454 | BOOST_AUTO_TEST_CASE(RefBatchToSpaceNdEndToEndComplexUint8NCHWTest) |
| 455 | { |
| 456 | BatchToSpaceNdComplexEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends, armnn::DataLayout::NCHW); |
| 457 | } |
| 458 | |
| 459 | BOOST_AUTO_TEST_CASE(RefBatchToSpaceNdEndToEndComplexQSymm16NCHWTest) |
| 460 | { |
| 461 | BatchToSpaceNdComplexEndToEnd<armnn::DataType::QuantisedSymm16>(defaultBackends, armnn::DataLayout::NCHW); |
| 462 | } |
| 463 | |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 464 | BOOST_AUTO_TEST_CASE(RefConcatEndToEndDim0Test) |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 465 | { |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 466 | ConcatDim0EndToEnd<armnn::DataType::Float32>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 467 | } |
| 468 | |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 469 | BOOST_AUTO_TEST_CASE(RefConcatEndToEndDim0Uint8Test) |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 470 | { |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 471 | ConcatDim0EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 472 | } |
| 473 | |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 474 | BOOST_AUTO_TEST_CASE(RefConcatEndToEndDim1Test) |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 475 | { |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 476 | ConcatDim1EndToEnd<armnn::DataType::Float32>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 477 | } |
| 478 | |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 479 | BOOST_AUTO_TEST_CASE(RefConcatEndToEndDim1Uint8Test) |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 480 | { |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 481 | ConcatDim1EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 482 | } |
| 483 | |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 484 | BOOST_AUTO_TEST_CASE(RefConcatEndToEndDim2Test) |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 485 | { |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 486 | ConcatDim2EndToEnd<armnn::DataType::Float32>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 487 | } |
| 488 | |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 489 | BOOST_AUTO_TEST_CASE(RefConcatEndToEndDim2Uint8Test) |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 490 | { |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 491 | ConcatDim2EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 492 | } |
| 493 | |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 494 | BOOST_AUTO_TEST_CASE(RefConcatEndToEndDim3Test) |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 495 | { |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 496 | ConcatDim3EndToEnd<armnn::DataType::Float32>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 497 | } |
| 498 | |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 499 | BOOST_AUTO_TEST_CASE(RefConcatEndToEndDim3Uint8Test) |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 500 | { |
Jim Flynn | e242f2d | 2019-05-22 14:24:13 +0100 | [diff] [blame] | 501 | ConcatDim3EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
narpra01 | b9546cf | 2018-11-20 15:21:28 +0000 | [diff] [blame] | 502 | } |
| 503 | |
narpra01 | db2b160 | 2019-01-23 15:23:11 +0000 | [diff] [blame] | 504 | BOOST_AUTO_TEST_CASE(RefGatherFloatTest) |
| 505 | { |
| 506 | GatherEndToEnd<armnn::DataType::Float32>(defaultBackends); |
| 507 | } |
| 508 | |
| 509 | BOOST_AUTO_TEST_CASE(RefGatherUint8Test) |
| 510 | { |
| 511 | GatherEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 512 | } |
| 513 | |
Ellen Norris-Thompson | e0dbedf | 2019-06-24 09:23:38 +0100 | [diff] [blame] | 514 | BOOST_AUTO_TEST_CASE(RefGatherInt16Test) |
| 515 | { |
| 516 | GatherEndToEnd<armnn::DataType::QuantisedSymm16>(defaultBackends); |
| 517 | } |
| 518 | |
narpra01 | db2b160 | 2019-01-23 15:23:11 +0000 | [diff] [blame] | 519 | BOOST_AUTO_TEST_CASE(RefGatherMultiDimFloatTest) |
| 520 | { |
| 521 | GatherMultiDimEndToEnd<armnn::DataType::Float32>(defaultBackends); |
| 522 | } |
| 523 | |
| 524 | BOOST_AUTO_TEST_CASE(RefGatherMultiDimUint8Test) |
| 525 | { |
| 526 | GatherMultiDimEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 527 | } |
| 528 | |
Ellen Norris-Thompson | e0dbedf | 2019-06-24 09:23:38 +0100 | [diff] [blame] | 529 | BOOST_AUTO_TEST_CASE(RefGatherMultiDimInt16Test) |
| 530 | { |
| 531 | GatherMultiDimEndToEnd<armnn::DataType::QuantisedSymm16>(defaultBackends); |
| 532 | } |
| 533 | |
Narumol Prangnawarat | 8c7324d | 2019-05-31 16:42:11 +0100 | [diff] [blame] | 534 | BOOST_AUTO_TEST_CASE(DequantizeEndToEndSimpleTest) |
| 535 | { |
| 536 | DequantizeEndToEndSimple<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 537 | } |
| 538 | |
| 539 | BOOST_AUTO_TEST_CASE(DequantizeEndToEndOffsetTest) |
| 540 | { |
| 541 | DequantizeEndToEndOffset<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 542 | } |
| 543 | |
Narumol Prangnawarat | b6441e4 | 2019-06-04 11:22:00 +0100 | [diff] [blame] | 544 | BOOST_AUTO_TEST_CASE(DequantizeEndToEndSimpleInt16Test) |
| 545 | { |
| 546 | DequantizeEndToEndSimple<armnn::DataType::QuantisedSymm16>(defaultBackends); |
| 547 | } |
| 548 | |
| 549 | BOOST_AUTO_TEST_CASE(DequantizeEndToEndOffsetInt16Test) |
| 550 | { |
| 551 | DequantizeEndToEndOffset<armnn::DataType::QuantisedSymm16>(defaultBackends); |
| 552 | } |
| 553 | |
Narumol Prangnawarat | 6d302bf | 2019-02-04 11:46:26 +0000 | [diff] [blame] | 554 | BOOST_AUTO_TEST_CASE(RefDetectionPostProcessRegularNmsTest) |
| 555 | { |
| 556 | std::vector<float> boxEncodings({ |
| 557 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 558 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 559 | 0.0f, -1.0f, 0.0f, 0.0f, |
| 560 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 561 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 562 | 0.0f, 0.0f, 0.0f, 0.0f |
| 563 | }); |
| 564 | std::vector<float> scores({ |
| 565 | 0.0f, 0.9f, 0.8f, |
| 566 | 0.0f, 0.75f, 0.72f, |
| 567 | 0.0f, 0.6f, 0.5f, |
| 568 | 0.0f, 0.93f, 0.95f, |
| 569 | 0.0f, 0.5f, 0.4f, |
| 570 | 0.0f, 0.3f, 0.2f |
| 571 | }); |
| 572 | std::vector<float> anchors({ |
| 573 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 574 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 575 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 576 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 577 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 578 | 0.5f, 100.5f, 1.0f, 1.0f |
| 579 | }); |
| 580 | DetectionPostProcessRegularNmsEndToEnd<armnn::DataType::Float32>(defaultBackends, boxEncodings, scores, anchors); |
| 581 | } |
| 582 | |
| 583 | inline void QuantizeData(uint8_t* quant, const float* dequant, const TensorInfo& info) |
| 584 | { |
| 585 | for (size_t i = 0; i < info.GetNumElements(); i++) |
| 586 | { |
| 587 | quant[i] = armnn::Quantize<uint8_t>(dequant[i], info.GetQuantizationScale(), info.GetQuantizationOffset()); |
| 588 | } |
| 589 | } |
| 590 | |
| 591 | BOOST_AUTO_TEST_CASE(RefDetectionPostProcessRegularNmsUint8Test) |
| 592 | { |
| 593 | armnn::TensorInfo boxEncodingsInfo({ 1, 6, 4 }, armnn::DataType::Float32); |
| 594 | armnn::TensorInfo scoresInfo({ 1, 6, 3 }, armnn::DataType::Float32); |
| 595 | armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::Float32); |
| 596 | |
| 597 | boxEncodingsInfo.SetQuantizationScale(1.0f); |
| 598 | boxEncodingsInfo.SetQuantizationOffset(1); |
| 599 | scoresInfo.SetQuantizationScale(0.01f); |
| 600 | scoresInfo.SetQuantizationOffset(0); |
| 601 | anchorsInfo.SetQuantizationScale(0.5f); |
| 602 | anchorsInfo.SetQuantizationOffset(0); |
| 603 | |
| 604 | std::vector<float> boxEncodings({ |
| 605 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 606 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 607 | 0.0f, -1.0f, 0.0f, 0.0f, |
| 608 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 609 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 610 | 0.0f, 0.0f, 0.0f, 0.0f |
| 611 | }); |
| 612 | std::vector<float> scores({ |
| 613 | 0.0f, 0.9f, 0.8f, |
| 614 | 0.0f, 0.75f, 0.72f, |
| 615 | 0.0f, 0.6f, 0.5f, |
| 616 | 0.0f, 0.93f, 0.95f, |
| 617 | 0.0f, 0.5f, 0.4f, |
| 618 | 0.0f, 0.3f, 0.2f |
| 619 | }); |
| 620 | std::vector<float> anchors({ |
| 621 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 622 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 623 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 624 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 625 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 626 | 0.5f, 100.5f, 1.0f, 1.0f |
| 627 | }); |
| 628 | |
| 629 | std::vector<uint8_t> qBoxEncodings(boxEncodings.size(), 0); |
| 630 | std::vector<uint8_t> qScores(scores.size(), 0); |
| 631 | std::vector<uint8_t> qAnchors(anchors.size(), 0); |
| 632 | QuantizeData(qBoxEncodings.data(), boxEncodings.data(), boxEncodingsInfo); |
| 633 | QuantizeData(qScores.data(), scores.data(), scoresInfo); |
| 634 | QuantizeData(qAnchors.data(), anchors.data(), anchorsInfo); |
| 635 | DetectionPostProcessRegularNmsEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends, qBoxEncodings, |
| 636 | qScores, qAnchors, |
| 637 | 1.0f, 1, 0.01f, 0, 0.5f, 0); |
| 638 | } |
| 639 | |
| 640 | BOOST_AUTO_TEST_CASE(RefDetectionPostProcessFastNmsTest) |
| 641 | { |
| 642 | std::vector<float> boxEncodings({ |
| 643 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 644 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 645 | 0.0f, -1.0f, 0.0f, 0.0f, |
| 646 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 647 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 648 | 0.0f, 0.0f, 0.0f, 0.0f |
| 649 | }); |
| 650 | std::vector<float> scores({ |
| 651 | 0.0f, 0.9f, 0.8f, |
| 652 | 0.0f, 0.75f, 0.72f, |
| 653 | 0.0f, 0.6f, 0.5f, |
| 654 | 0.0f, 0.93f, 0.95f, |
| 655 | 0.0f, 0.5f, 0.4f, |
| 656 | 0.0f, 0.3f, 0.2f |
| 657 | }); |
| 658 | std::vector<float> anchors({ |
| 659 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 660 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 661 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 662 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 663 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 664 | 0.5f, 100.5f, 1.0f, 1.0f |
| 665 | }); |
| 666 | DetectionPostProcessFastNmsEndToEnd<armnn::DataType::Float32>(defaultBackends, boxEncodings, scores, anchors); |
| 667 | } |
| 668 | |
| 669 | BOOST_AUTO_TEST_CASE(RefDetectionPostProcessFastNmsUint8Test) |
| 670 | { |
| 671 | armnn::TensorInfo boxEncodingsInfo({ 1, 6, 4 }, armnn::DataType::Float32); |
| 672 | armnn::TensorInfo scoresInfo({ 1, 6, 3 }, armnn::DataType::Float32); |
| 673 | armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::Float32); |
| 674 | |
| 675 | boxEncodingsInfo.SetQuantizationScale(1.0f); |
| 676 | boxEncodingsInfo.SetQuantizationOffset(1); |
| 677 | scoresInfo.SetQuantizationScale(0.01f); |
| 678 | scoresInfo.SetQuantizationOffset(0); |
| 679 | anchorsInfo.SetQuantizationScale(0.5f); |
| 680 | anchorsInfo.SetQuantizationOffset(0); |
| 681 | |
| 682 | std::vector<float> boxEncodings({ |
| 683 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 684 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 685 | 0.0f, -1.0f, 0.0f, 0.0f, |
| 686 | 0.0f, 0.0f, 0.0f, 0.0f, |
| 687 | 0.0f, 1.0f, 0.0f, 0.0f, |
| 688 | 0.0f, 0.0f, 0.0f, 0.0f |
| 689 | }); |
| 690 | std::vector<float> scores({ |
| 691 | 0.0f, 0.9f, 0.8f, |
| 692 | 0.0f, 0.75f, 0.72f, |
| 693 | 0.0f, 0.6f, 0.5f, |
| 694 | 0.0f, 0.93f, 0.95f, |
| 695 | 0.0f, 0.5f, 0.4f, |
| 696 | 0.0f, 0.3f, 0.2f |
| 697 | }); |
| 698 | std::vector<float> anchors({ |
| 699 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 700 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 701 | 0.5f, 0.5f, 1.0f, 1.0f, |
| 702 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 703 | 0.5f, 10.5f, 1.0f, 1.0f, |
| 704 | 0.5f, 100.5f, 1.0f, 1.0f |
| 705 | }); |
| 706 | |
| 707 | std::vector<uint8_t> qBoxEncodings(boxEncodings.size(), 0); |
| 708 | std::vector<uint8_t> qScores(scores.size(), 0); |
| 709 | std::vector<uint8_t> qAnchors(anchors.size(), 0); |
| 710 | QuantizeData(qBoxEncodings.data(), boxEncodings.data(), boxEncodingsInfo); |
| 711 | QuantizeData(qScores.data(), scores.data(), scoresInfo); |
| 712 | QuantizeData(qAnchors.data(), anchors.data(), anchorsInfo); |
| 713 | DetectionPostProcessFastNmsEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends, qBoxEncodings, |
| 714 | qScores, qAnchors, |
| 715 | 1.0f, 1, 0.01f, 0, 0.5f, 0); |
| 716 | } |
| 717 | |
Keith Davis | 9515c7e | 2019-06-21 09:33:59 +0100 | [diff] [blame] | 718 | BOOST_AUTO_TEST_CASE(RefSpaceToDepthNHWCEndToEndTest1) |
| 719 | { |
| 720 | const unsigned int blockSize = 2; |
| 721 | |
| 722 | armnn::TensorShape inputShape{1, 2, 2, 1}; |
| 723 | armnn::TensorInfo inputTensorInfo(inputShape, armnn::DataType::Float32); |
| 724 | |
| 725 | armnn::TensorShape outputShape{1, 1, 1, 4}; |
| 726 | armnn::TensorInfo outputTensorInfo(outputShape, armnn::DataType::Float32); |
| 727 | |
| 728 | std::vector<float> inputData = std::vector<float>( |
| 729 | { |
| 730 | 1.0f, 2.0f, 3.0f, 4.0f |
| 731 | }); |
| 732 | |
| 733 | std::vector<float> expectedOutputData = std::vector<float>( |
| 734 | { |
| 735 | 1.0f, 2.0f, 3.0f, 4.0f |
| 736 | }); |
| 737 | |
| 738 | SpaceToDepthEndToEnd(defaultBackends, |
| 739 | armnn::DataLayout::NHWC, |
| 740 | inputTensorInfo, |
| 741 | outputTensorInfo, |
| 742 | inputData, |
| 743 | expectedOutputData, |
| 744 | blockSize); |
| 745 | } |
| 746 | |
| 747 | BOOST_AUTO_TEST_CASE(RefSpaceToDepthNCHWEndToEndTest1) |
| 748 | { |
| 749 | const unsigned int blockSize = 2; |
| 750 | |
| 751 | armnn::TensorShape inputShape{1, 2, 2, 1}; |
| 752 | armnn::TensorInfo inputTensorInfo(inputShape, armnn::DataType::Float32); |
| 753 | |
| 754 | armnn::TensorShape outputShape{1, 1, 1, 4}; |
| 755 | armnn::TensorInfo outputTensorInfo(outputShape, armnn::DataType::Float32); |
| 756 | |
| 757 | std::vector<float> inputData = std::vector<float>( |
| 758 | { |
| 759 | 1.0f, 2.0f, 3.0f, 4.0f |
| 760 | }); |
| 761 | |
| 762 | std::vector<float> expectedOutputData = std::vector<float>( |
| 763 | { |
| 764 | 1.0f, 2.0f, 3.0f, 4.0f |
| 765 | }); |
| 766 | |
| 767 | SpaceToDepthEndToEnd(defaultBackends, |
| 768 | armnn::DataLayout::NCHW, |
| 769 | inputTensorInfo, |
| 770 | outputTensorInfo, |
| 771 | inputData, |
| 772 | expectedOutputData, |
| 773 | blockSize); |
| 774 | } |
| 775 | |
| 776 | BOOST_AUTO_TEST_CASE(RefSpaceToDepthNHWCEndToEndTest2) |
| 777 | { |
| 778 | const unsigned int blockSize = 2; |
| 779 | |
| 780 | armnn::TensorShape inputShape{1, 2, 2, 2}; |
| 781 | armnn::TensorShape outputShape{1, 1, 1, 8}; |
| 782 | |
| 783 | armnn::TensorInfo outputTensorInfo(outputShape, armnn::DataType::Float32); |
| 784 | armnn::TensorInfo inputTensorInfo(inputShape, armnn::DataType::Float32); |
| 785 | |
| 786 | std::vector<float> inputData = std::vector<float>( |
| 787 | { |
| 788 | 1.4f, 2.3f, 3.2f, 4.1f, 5.4f, 6.3f, 7.2f, 8.1f |
| 789 | }); |
| 790 | |
| 791 | std::vector<float> expectedOutputData = std::vector<float>( |
| 792 | { |
| 793 | 1.4f, 2.3f, 3.2f, 4.1f, 5.4f, 6.3f, 7.2f, 8.1f |
| 794 | }); |
| 795 | |
| 796 | SpaceToDepthEndToEnd(defaultBackends, |
| 797 | armnn::DataLayout::NHWC, |
| 798 | inputTensorInfo, |
| 799 | outputTensorInfo, |
| 800 | inputData, |
| 801 | expectedOutputData, |
| 802 | blockSize); |
| 803 | } |
| 804 | |
| 805 | BOOST_AUTO_TEST_CASE(RefSpaceToDepthNCHWEndToEndTest2) |
| 806 | { |
| 807 | const unsigned int blockSize = 2; |
| 808 | |
| 809 | armnn::TensorShape inputShape{1, 2, 2, 2}; |
| 810 | armnn::TensorShape outputShape{1, 1, 1, 8}; |
| 811 | |
| 812 | armnn::TensorInfo inputTensorInfo(inputShape, armnn::DataType::Float32); |
| 813 | armnn::TensorInfo outputTensorInfo(outputShape, armnn::DataType::Float32); |
| 814 | |
| 815 | |
| 816 | std::vector<float> inputData = std::vector<float>( |
| 817 | { |
| 818 | 1.4f, 2.3f, 3.2f, 4.1f, 5.4f, 6.3f, 7.2f, 8.1f |
| 819 | }); |
| 820 | |
| 821 | std::vector<float> expectedOutputData = std::vector<float>( |
| 822 | { |
| 823 | 1.4f, 2.3f, 3.2f, 4.1f, 5.4f, 6.3f, 7.2f, 8.1f |
| 824 | }); |
| 825 | |
| 826 | SpaceToDepthEndToEnd(defaultBackends, |
| 827 | armnn::DataLayout::NCHW, |
| 828 | inputTensorInfo, |
| 829 | outputTensorInfo, |
| 830 | inputData, |
| 831 | expectedOutputData, |
| 832 | blockSize); |
| 833 | } |
| 834 | |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 835 | BOOST_AUTO_TEST_CASE(RefSplitter1dEndToEndTest) |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 836 | { |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 837 | Splitter1dEndToEnd<armnn::DataType::Float32>(defaultBackends); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 838 | } |
| 839 | |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 840 | BOOST_AUTO_TEST_CASE(RefSplitter1dEndToEndUint8Test) |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 841 | { |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 842 | Splitter1dEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 843 | } |
| 844 | |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 845 | BOOST_AUTO_TEST_CASE(RefSplitter2dDim0EndToEndTest) |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 846 | { |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 847 | Splitter2dDim0EndToEnd<armnn::DataType::Float32>(defaultBackends); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 848 | } |
| 849 | |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 850 | BOOST_AUTO_TEST_CASE(RefSplitter2dDim1EndToEndTest) |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 851 | { |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 852 | Splitter2dDim1EndToEnd<armnn::DataType::Float32>(defaultBackends); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 853 | } |
| 854 | |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 855 | BOOST_AUTO_TEST_CASE(RefSplitter2dDim0EndToEndUint8Test) |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 856 | { |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 857 | Splitter2dDim0EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 858 | } |
| 859 | |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 860 | BOOST_AUTO_TEST_CASE(RefSplitter2dDim1EndToEndUint8Test) |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 861 | { |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 862 | Splitter2dDim1EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 863 | } |
| 864 | |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 865 | BOOST_AUTO_TEST_CASE(RefSplitter3dDim0EndToEndTest) |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 866 | { |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 867 | Splitter3dDim0EndToEnd<armnn::DataType::Float32>(defaultBackends); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 868 | } |
| 869 | |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 870 | BOOST_AUTO_TEST_CASE(RefSplitter3dDim1EndToEndTest) |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 871 | { |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 872 | Splitter3dDim1EndToEnd<armnn::DataType::Float32>(defaultBackends); |
| 873 | } |
| 874 | |
| 875 | BOOST_AUTO_TEST_CASE(RefSplitter3dDim2EndToEndTest) |
| 876 | { |
| 877 | Splitter3dDim2EndToEnd<armnn::DataType::Float32>(defaultBackends); |
| 878 | } |
| 879 | |
| 880 | BOOST_AUTO_TEST_CASE(RefSplitter3dDim0EndToEndUint8Test) |
| 881 | { |
| 882 | Splitter3dDim0EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 883 | } |
| 884 | |
| 885 | BOOST_AUTO_TEST_CASE(RefSplitter3dDim1EndToEndUint8Test) |
| 886 | { |
| 887 | Splitter3dDim1EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 888 | } |
| 889 | |
| 890 | BOOST_AUTO_TEST_CASE(RefSplitter3dDim2EndToEndUint8Test) |
| 891 | { |
| 892 | Splitter3dDim2EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 893 | } |
| 894 | |
| 895 | BOOST_AUTO_TEST_CASE(RefSplitter4dDim0EndToEndTest) |
| 896 | { |
| 897 | Splitter4dDim0EndToEnd<armnn::DataType::Float32>(defaultBackends); |
| 898 | } |
| 899 | |
| 900 | BOOST_AUTO_TEST_CASE(RefSplitter4dDim1EndToEndTest) |
| 901 | { |
| 902 | Splitter4dDim1EndToEnd<armnn::DataType::Float32>(defaultBackends); |
| 903 | } |
| 904 | |
| 905 | BOOST_AUTO_TEST_CASE(RefSplitter4dDim2EndToEndTest) |
| 906 | { |
| 907 | Splitter4dDim2EndToEnd<armnn::DataType::Float32>(defaultBackends); |
| 908 | } |
| 909 | |
| 910 | BOOST_AUTO_TEST_CASE(RefSplitter4dDim3EndToEndTest) |
| 911 | { |
| 912 | Splitter4dDim3EndToEnd<armnn::DataType::Float32>(defaultBackends); |
| 913 | } |
| 914 | |
| 915 | BOOST_AUTO_TEST_CASE(RefSplitter4dDim0EndToEndUint8Test) |
| 916 | { |
| 917 | Splitter4dDim0EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 918 | } |
| 919 | |
| 920 | BOOST_AUTO_TEST_CASE(RefSplitter4dDim1EndToEndUint8Test) |
| 921 | { |
| 922 | Splitter4dDim1EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 923 | } |
| 924 | |
| 925 | BOOST_AUTO_TEST_CASE(RefSplitter4dDim2EndToEndUint8Test) |
| 926 | { |
| 927 | Splitter4dDim2EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
| 928 | } |
| 929 | |
| 930 | BOOST_AUTO_TEST_CASE(RefSplitter4dDim3EndToEndUint8Test) |
| 931 | { |
| 932 | Splitter4dDim3EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 933 | } |
| 934 | |
Aron Virginas-Tar | 98180ef | 2019-06-26 15:02:47 +0100 | [diff] [blame] | 935 | // TransposeConvolution2d |
| 936 | BOOST_AUTO_TEST_CASE(RefTransposeConvolution2dEndToEndFloatNchwTest) |
| 937 | { |
| 938 | TransposeConvolution2dEndToEnd<armnn::DataType::Float32, armnn::DataType::Float32>( |
| 939 | defaultBackends, armnn::DataLayout::NCHW); |
| 940 | } |
| 941 | |
| 942 | BOOST_AUTO_TEST_CASE(RefTransposeConvolution2dEndToEndUint8NchwTest) |
| 943 | { |
| 944 | TransposeConvolution2dEndToEnd<armnn::DataType::QuantisedAsymm8, armnn::DataType::Signed32>( |
| 945 | defaultBackends, armnn::DataLayout::NCHW); |
| 946 | } |
| 947 | |
| 948 | BOOST_AUTO_TEST_CASE(RefTransposeConvolution2dEndToEndInt16NchwTest) |
| 949 | { |
| 950 | TransposeConvolution2dEndToEnd<armnn::DataType::QuantisedSymm16, armnn::DataType::Signed32>( |
| 951 | defaultBackends, armnn::DataLayout::NCHW); |
| 952 | } |
| 953 | |
| 954 | BOOST_AUTO_TEST_CASE(RefTransposeConvolution2dEndToEndFloatNhwcTest) |
| 955 | { |
| 956 | TransposeConvolution2dEndToEnd<armnn::DataType::Float32, armnn::DataType::Float32>( |
| 957 | defaultBackends, armnn::DataLayout::NHWC); |
| 958 | } |
| 959 | |
| 960 | BOOST_AUTO_TEST_CASE(RefTransposeConvolution2dEndToEndUint8NhwcTest) |
| 961 | { |
| 962 | TransposeConvolution2dEndToEnd<armnn::DataType::QuantisedAsymm8, armnn::DataType::Signed32>( |
| 963 | defaultBackends, armnn::DataLayout::NHWC); |
| 964 | } |
| 965 | |
| 966 | BOOST_AUTO_TEST_CASE(RefTransposeConvolution2dEndToEndInt16NhwcTest) |
| 967 | { |
| 968 | TransposeConvolution2dEndToEnd<armnn::DataType::QuantisedSymm16, armnn::DataType::Signed32>( |
| 969 | defaultBackends, armnn::DataLayout::NHWC); |
| 970 | } |
| 971 | |
Aron Virginas-Tar | fe15eff | 2019-07-01 16:12:58 +0100 | [diff] [blame^] | 972 | // Resize Bilinear |
| 973 | BOOST_AUTO_TEST_CASE(RefResizeBilinearEndToEndFloatNchwTest) |
| 974 | { |
| 975 | ResizeBilinearEndToEnd<armnn::DataType::Float32>(defaultBackends, armnn::DataLayout::NCHW); |
| 976 | } |
| 977 | |
| 978 | BOOST_AUTO_TEST_CASE(RefResizeBilinearEndToEndUint8NchwTest) |
| 979 | { |
| 980 | ResizeBilinearEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends, armnn::DataLayout::NCHW); |
| 981 | } |
| 982 | |
| 983 | BOOST_AUTO_TEST_CASE(RefResizeBilinearEndToEndInt16NchwTest) |
| 984 | { |
| 985 | ResizeBilinearEndToEnd<armnn::DataType::QuantisedSymm16>(defaultBackends, armnn::DataLayout::NCHW); |
| 986 | } |
| 987 | |
| 988 | BOOST_AUTO_TEST_CASE(RefResizeBilinearEndToEndFloatNhwcTest) |
| 989 | { |
| 990 | ResizeBilinearEndToEnd<armnn::DataType::Float32>(defaultBackends, armnn::DataLayout::NHWC); |
| 991 | } |
| 992 | |
| 993 | BOOST_AUTO_TEST_CASE(RefResizeBilinearEndToEndUint8NhwcTest) |
| 994 | { |
| 995 | ResizeBilinearEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends, armnn::DataLayout::NHWC); |
| 996 | } |
| 997 | |
| 998 | BOOST_AUTO_TEST_CASE(RefResizeBilinearEndToEndInt16NhwcTest) |
| 999 | { |
| 1000 | ResizeBilinearEndToEnd<armnn::DataType::QuantisedSymm16>(defaultBackends, armnn::DataLayout::NHWC); |
| 1001 | } |
| 1002 | |
| 1003 | // Resize NearestNeighbor |
| 1004 | BOOST_AUTO_TEST_CASE(RefResizeNearestNeighborEndToEndFloatNchwTest) |
| 1005 | { |
| 1006 | ResizeNearestNeighborEndToEnd<armnn::DataType::Float32>(defaultBackends, armnn::DataLayout::NCHW); |
| 1007 | } |
| 1008 | |
| 1009 | BOOST_AUTO_TEST_CASE(RefResizeNearestNeighborEndToEndUint8NchwTest) |
| 1010 | { |
| 1011 | ResizeNearestNeighborEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends, armnn::DataLayout::NCHW); |
| 1012 | } |
| 1013 | |
| 1014 | BOOST_AUTO_TEST_CASE(RefResizeNearestNeighborEndToEndInt16NchwTest) |
| 1015 | { |
| 1016 | ResizeNearestNeighborEndToEnd<armnn::DataType::QuantisedSymm16>(defaultBackends, armnn::DataLayout::NCHW); |
| 1017 | } |
| 1018 | |
| 1019 | BOOST_AUTO_TEST_CASE(RefResizeNearestNeighborEndToEndFloatNhwcTest) |
| 1020 | { |
| 1021 | ResizeNearestNeighborEndToEnd<armnn::DataType::Float32>(defaultBackends, armnn::DataLayout::NHWC); |
| 1022 | } |
| 1023 | |
| 1024 | BOOST_AUTO_TEST_CASE(RefResizeNearestNeighborEndToEndUint8NhwcTest) |
| 1025 | { |
| 1026 | ResizeNearestNeighborEndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends, armnn::DataLayout::NHWC); |
| 1027 | } |
| 1028 | |
| 1029 | BOOST_AUTO_TEST_CASE(RefResizeNearestNeighborEndToEndInt16NhwcTest) |
| 1030 | { |
| 1031 | ResizeNearestNeighborEndToEnd<armnn::DataType::QuantisedSymm16>(defaultBackends, armnn::DataLayout::NHWC); |
| 1032 | } |
| 1033 | |
| 1034 | BOOST_AUTO_TEST_SUITE_END() |