Mike Kelly | 8c1701a | 2019-02-11 17:01:27 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #include <armnn/ArmNN.hpp> |
| 7 | #include <armnn/INetwork.hpp> |
Aron Virginas-Tar | fc413c0 | 2019-02-13 15:41:52 +0000 | [diff] [blame] | 8 | |
Mike Kelly | 8c1701a | 2019-02-11 17:01:27 +0000 | [diff] [blame] | 9 | #include "../Serializer.hpp" |
Aron Virginas-Tar | fc413c0 | 2019-02-13 15:41:52 +0000 | [diff] [blame] | 10 | |
Derek Lamberti | 0028d1b | 2019-02-20 13:57:42 +0000 | [diff] [blame] | 11 | #include <armnnDeserializer/IDeserializer.hpp> |
Aron Virginas-Tar | fc413c0 | 2019-02-13 15:41:52 +0000 | [diff] [blame] | 12 | |
Aron Virginas-Tar | c04125f | 2019-02-19 16:31:08 +0000 | [diff] [blame] | 13 | #include <random> |
Mike Kelly | 8c1701a | 2019-02-11 17:01:27 +0000 | [diff] [blame] | 14 | #include <sstream> |
Aron Virginas-Tar | fc413c0 | 2019-02-13 15:41:52 +0000 | [diff] [blame] | 15 | #include <vector> |
| 16 | |
Mike Kelly | 8c1701a | 2019-02-11 17:01:27 +0000 | [diff] [blame] | 17 | #include <boost/test/unit_test.hpp> |
Aron Virginas-Tar | fc413c0 | 2019-02-13 15:41:52 +0000 | [diff] [blame] | 18 | #include <flatbuffers/idl.h> |
| 19 | |
Derek Lamberti | 0028d1b | 2019-02-20 13:57:42 +0000 | [diff] [blame] | 20 | using armnnDeserializer::IDeserializer; |
Mike Kelly | 8c1701a | 2019-02-11 17:01:27 +0000 | [diff] [blame] | 21 | |
Saoirse Stewart | 3166c3e | 2019-02-18 15:24:53 +0000 | [diff] [blame] | 22 | namespace |
| 23 | { |
| 24 | |
| 25 | armnn::INetworkPtr DeserializeNetwork(const std::string& serializerString) |
| 26 | { |
| 27 | std::vector<std::uint8_t> const serializerVector{serializerString.begin(), serializerString.end()}; |
Derek Lamberti | 0028d1b | 2019-02-20 13:57:42 +0000 | [diff] [blame] | 28 | return IDeserializer::Create()->CreateNetworkFromBinary(serializerVector); |
Saoirse Stewart | 3166c3e | 2019-02-18 15:24:53 +0000 | [diff] [blame] | 29 | } |
| 30 | |
| 31 | std::string SerializeNetwork(const armnn::INetwork& network) |
| 32 | { |
| 33 | armnnSerializer::Serializer serializer; |
| 34 | serializer.Serialize(network); |
| 35 | |
| 36 | std::stringstream stream; |
| 37 | serializer.SaveSerializedToStream(stream); |
| 38 | |
| 39 | std::string serializerString{stream.str()}; |
| 40 | return serializerString; |
| 41 | } |
| 42 | |
Aron Virginas-Tar | c04125f | 2019-02-19 16:31:08 +0000 | [diff] [blame] | 43 | template<typename DataType> |
| 44 | static std::vector<DataType> GenerateRandomData(size_t size) |
| 45 | { |
| 46 | constexpr bool isIntegerType = std::is_integral<DataType>::value; |
| 47 | using Distribution = |
| 48 | typename std::conditional<isIntegerType, |
| 49 | std::uniform_int_distribution<DataType>, |
| 50 | std::uniform_real_distribution<DataType>>::type; |
| 51 | |
| 52 | static constexpr DataType lowerLimit = std::numeric_limits<DataType>::min(); |
| 53 | static constexpr DataType upperLimit = std::numeric_limits<DataType>::max(); |
| 54 | |
| 55 | static Distribution distribution(lowerLimit, upperLimit); |
| 56 | static std::default_random_engine generator; |
| 57 | |
| 58 | std::vector<DataType> randomData(size); |
| 59 | std::generate(randomData.begin(), randomData.end(), []() { return distribution(generator); }); |
| 60 | |
| 61 | return randomData; |
| 62 | } |
| 63 | |
| 64 | void CheckDeserializedNetworkAgainstOriginal(const armnn::INetwork& deserializedNetwork, |
| 65 | const armnn::INetwork& originalNetwork, |
| 66 | const armnn::TensorShape& inputShape, |
| 67 | const armnn::TensorShape& outputShape, |
| 68 | armnn::LayerBindingId inputBindingId = 0, |
| 69 | armnn::LayerBindingId outputBindingId = 0) |
| 70 | { |
| 71 | armnn::IRuntime::CreationOptions options; |
| 72 | armnn::IRuntimePtr runtime = armnn::IRuntime::Create(options); |
| 73 | |
| 74 | std::vector<armnn::BackendId> preferredBackends = { armnn::BackendId("CpuRef") }; |
| 75 | |
| 76 | // Optimize original network |
| 77 | armnn::IOptimizedNetworkPtr optimizedOriginalNetwork = |
| 78 | armnn::Optimize(originalNetwork, preferredBackends, runtime->GetDeviceSpec()); |
| 79 | BOOST_CHECK(optimizedOriginalNetwork); |
| 80 | |
| 81 | // Optimize deserialized network |
| 82 | armnn::IOptimizedNetworkPtr optimizedDeserializedNetwork = |
| 83 | armnn::Optimize(deserializedNetwork, preferredBackends, runtime->GetDeviceSpec()); |
| 84 | BOOST_CHECK(optimizedDeserializedNetwork); |
| 85 | |
| 86 | armnn::NetworkId networkId1; |
| 87 | armnn::NetworkId networkId2; |
| 88 | |
| 89 | // Load original and deserialized network |
| 90 | armnn::Status status1 = runtime->LoadNetwork(networkId1, std::move(optimizedOriginalNetwork)); |
| 91 | BOOST_CHECK(status1 == armnn::Status::Success); |
| 92 | |
| 93 | armnn::Status status2 = runtime->LoadNetwork(networkId2, std::move(optimizedDeserializedNetwork)); |
| 94 | BOOST_CHECK(status2 == armnn::Status::Success); |
| 95 | |
| 96 | // Generate some input data |
| 97 | std::vector<float> inputData = GenerateRandomData<float>(inputShape.GetNumElements()); |
| 98 | |
| 99 | armnn::InputTensors inputTensors1 |
| 100 | { |
| 101 | { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(networkId1, inputBindingId), inputData.data()) } |
| 102 | }; |
| 103 | |
| 104 | armnn::InputTensors inputTensors2 |
| 105 | { |
| 106 | { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(networkId2, inputBindingId), inputData.data()) } |
| 107 | }; |
| 108 | |
| 109 | std::vector<float> outputData1(outputShape.GetNumElements()); |
| 110 | std::vector<float> outputData2(outputShape.GetNumElements()); |
| 111 | |
| 112 | armnn::OutputTensors outputTensors1 |
| 113 | { |
| 114 | { 0, armnn::Tensor(runtime->GetOutputTensorInfo(networkId1, outputBindingId), outputData1.data()) } |
| 115 | }; |
| 116 | |
| 117 | armnn::OutputTensors outputTensors2 |
| 118 | { |
| 119 | { 0, armnn::Tensor(runtime->GetOutputTensorInfo(networkId2, outputBindingId), outputData2.data()) } |
| 120 | }; |
| 121 | |
| 122 | // Run original and deserialized network |
| 123 | runtime->EnqueueWorkload(networkId1, inputTensors1, outputTensors1); |
| 124 | runtime->EnqueueWorkload(networkId2, inputTensors2, outputTensors2); |
| 125 | |
| 126 | // Compare output data |
| 127 | BOOST_CHECK_EQUAL_COLLECTIONS(outputData1.begin(), outputData1.end(), |
| 128 | outputData2.begin(), outputData2.end()); |
| 129 | } |
| 130 | |
Saoirse Stewart | 3166c3e | 2019-02-18 15:24:53 +0000 | [diff] [blame] | 131 | } // anonymous namespace |
| 132 | |
| 133 | BOOST_AUTO_TEST_SUITE(SerializerTests) |
Aron Virginas-Tar | fc413c0 | 2019-02-13 15:41:52 +0000 | [diff] [blame] | 134 | |
Aron Virginas-Tar | c04125f | 2019-02-19 16:31:08 +0000 | [diff] [blame] | 135 | BOOST_AUTO_TEST_CASE(SerializeAddition) |
Mike Kelly | 8c1701a | 2019-02-11 17:01:27 +0000 | [diff] [blame] | 136 | { |
| 137 | armnn::INetworkPtr network = armnn::INetwork::Create(); |
| 138 | armnn::IConnectableLayer* const inputLayer0 = network->AddInputLayer(0); |
| 139 | armnn::IConnectableLayer* const inputLayer1 = network->AddInputLayer(1); |
| 140 | |
| 141 | armnn::IConnectableLayer* const additionLayer0 = network->AddAdditionLayer(); |
| 142 | inputLayer0->GetOutputSlot(0).Connect(additionLayer0->GetInputSlot(0)); |
| 143 | inputLayer1->GetOutputSlot(0).Connect(additionLayer0->GetInputSlot(1)); |
| 144 | |
| 145 | armnn::IConnectableLayer* const outputLayer0 = network->AddOutputLayer(0); |
| 146 | additionLayer0->GetOutputSlot(0).Connect(outputLayer0->GetInputSlot(0)); |
| 147 | |
| 148 | armnnSerializer::Serializer serializer; |
| 149 | serializer.Serialize(*network); |
| 150 | |
| 151 | std::stringstream stream; |
| 152 | serializer.SaveSerializedToStream(stream); |
| 153 | BOOST_TEST(stream.str().length() > 0); |
| 154 | } |
| 155 | |
Aron Virginas-Tar | c04125f | 2019-02-19 16:31:08 +0000 | [diff] [blame] | 156 | BOOST_AUTO_TEST_CASE(SerializeMultiplication) |
Sadik Armagan | 5f45027 | 2019-02-12 14:31:45 +0000 | [diff] [blame] | 157 | { |
| 158 | const armnn::TensorInfo info({ 1, 5, 2, 3 }, armnn::DataType::Float32); |
| 159 | |
| 160 | armnn::INetworkPtr network = armnn::INetwork::Create(); |
| 161 | armnn::IConnectableLayer* const inputLayer0 = network->AddInputLayer(0); |
| 162 | armnn::IConnectableLayer* const inputLayer1 = network->AddInputLayer(1); |
| 163 | |
| 164 | const char* multLayerName = "mult_0"; |
| 165 | |
| 166 | armnn::IConnectableLayer* const multiplicationLayer0 = network->AddMultiplicationLayer(multLayerName); |
| 167 | inputLayer0->GetOutputSlot(0).Connect(multiplicationLayer0->GetInputSlot(0)); |
| 168 | inputLayer1->GetOutputSlot(0).Connect(multiplicationLayer0->GetInputSlot(1)); |
| 169 | |
| 170 | armnn::IConnectableLayer* const outputLayer0 = network->AddOutputLayer(0); |
| 171 | multiplicationLayer0->GetOutputSlot(0).Connect(outputLayer0->GetInputSlot(0)); |
| 172 | |
| 173 | armnnSerializer::Serializer serializer; |
| 174 | serializer.Serialize(*network); |
| 175 | |
| 176 | std::stringstream stream; |
| 177 | serializer.SaveSerializedToStream(stream); |
| 178 | BOOST_TEST(stream.str().length() > 0); |
| 179 | BOOST_TEST(stream.str().find(multLayerName) != stream.str().npos); |
| 180 | } |
| 181 | |
Aron Virginas-Tar | c04125f | 2019-02-19 16:31:08 +0000 | [diff] [blame] | 182 | BOOST_AUTO_TEST_CASE(SerializeDeserializeConvolution2d) |
Saoirse Stewart | 263829c | 2019-02-19 15:54:14 +0000 | [diff] [blame] | 183 | { |
Aron Virginas-Tar | c04125f | 2019-02-19 16:31:08 +0000 | [diff] [blame] | 184 | armnn::TensorInfo inputInfo ({ 1, 5, 5, 1 }, armnn::DataType::Float32); |
| 185 | armnn::TensorInfo outputInfo({ 1, 3, 3, 1 }, armnn::DataType::Float32); |
Saoirse Stewart | 263829c | 2019-02-19 15:54:14 +0000 | [diff] [blame] | 186 | |
Aron Virginas-Tar | c04125f | 2019-02-19 16:31:08 +0000 | [diff] [blame] | 187 | armnn::TensorInfo weightsInfo({ 1, 3, 3, 1 }, armnn::DataType::Float32); |
| 188 | armnn::TensorInfo biasesInfo ({ 1 }, armnn::DataType::Float32); |
| 189 | |
| 190 | // Construct network |
| 191 | armnn::INetworkPtr network = armnn::INetwork::Create(); |
| 192 | |
| 193 | armnn::Convolution2dDescriptor descriptor; |
| 194 | descriptor.m_PadLeft = 1; |
| 195 | descriptor.m_PadRight = 1; |
| 196 | descriptor.m_PadTop = 1; |
| 197 | descriptor.m_PadBottom = 1; |
| 198 | descriptor.m_StrideX = 2; |
| 199 | descriptor.m_StrideY = 2; |
| 200 | descriptor.m_BiasEnabled = true; |
| 201 | descriptor.m_DataLayout = armnn::DataLayout::NHWC; |
| 202 | |
| 203 | std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements()); |
| 204 | armnn::ConstTensor weights(weightsInfo, weightsData); |
| 205 | |
| 206 | std::vector<float> biasesData = GenerateRandomData<float>(biasesInfo.GetNumElements()); |
| 207 | armnn::ConstTensor biases(biasesInfo, biasesData); |
| 208 | |
| 209 | armnn::IConnectableLayer* const inputLayer = network->AddInputLayer(0, "input"); |
| 210 | armnn::IConnectableLayer* const convLayer = |
| 211 | network->AddConvolution2dLayer(descriptor, weights, biases, "convolution"); |
| 212 | armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(0, "output"); |
| 213 | |
| 214 | inputLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(0)); |
| 215 | inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo); |
| 216 | |
| 217 | convLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); |
| 218 | convLayer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| 219 | |
| 220 | armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network)); |
| 221 | BOOST_CHECK(deserializedNetwork); |
| 222 | |
| 223 | CheckDeserializedNetworkAgainstOriginal(*network, |
| 224 | *deserializedNetwork, |
| 225 | inputInfo.GetShape(), |
| 226 | outputInfo.GetShape()); |
| 227 | } |
| 228 | |
| 229 | BOOST_AUTO_TEST_CASE(SerializeDeserializeReshape) |
| 230 | { |
| 231 | unsigned int inputShape[] = { 1, 9 }; |
| 232 | unsigned int outputShape[] = { 3, 3 }; |
Saoirse Stewart | 263829c | 2019-02-19 15:54:14 +0000 | [diff] [blame] | 233 | |
| 234 | auto inputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::DataType::Float32); |
| 235 | auto outputTensorInfo = armnn::TensorInfo(2, outputShape, armnn::DataType::Float32); |
| 236 | auto reshapeOutputTensorInfo = armnn::TensorInfo(2, outputShape, armnn::DataType::Float32); |
| 237 | |
| 238 | armnn::ReshapeDescriptor reshapeDescriptor; |
| 239 | reshapeDescriptor.m_TargetShape = reshapeOutputTensorInfo.GetShape(); |
| 240 | |
| 241 | armnn::INetworkPtr network = armnn::INetwork::Create(); |
| 242 | armnn::IConnectableLayer *const inputLayer = network->AddInputLayer(0); |
| 243 | armnn::IConnectableLayer *const reshapeLayer = network->AddReshapeLayer(reshapeDescriptor, "ReshapeLayer"); |
| 244 | armnn::IConnectableLayer *const outputLayer = network->AddOutputLayer(0); |
| 245 | |
| 246 | inputLayer->GetOutputSlot(0).Connect(reshapeLayer->GetInputSlot(0)); |
| 247 | inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); |
| 248 | reshapeLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); |
| 249 | reshapeLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 250 | |
Aron Virginas-Tar | c04125f | 2019-02-19 16:31:08 +0000 | [diff] [blame] | 251 | armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network)); |
| 252 | BOOST_CHECK(deserializedNetwork); |
Saoirse Stewart | 263829c | 2019-02-19 15:54:14 +0000 | [diff] [blame] | 253 | |
Aron Virginas-Tar | c04125f | 2019-02-19 16:31:08 +0000 | [diff] [blame] | 254 | CheckDeserializedNetworkAgainstOriginal(*network, |
| 255 | *deserializedNetwork, |
| 256 | inputTensorInfo.GetShape(), |
| 257 | outputTensorInfo.GetShape()); |
Saoirse Stewart | 263829c | 2019-02-19 15:54:14 +0000 | [diff] [blame] | 258 | } |
| 259 | |
Aron Virginas-Tar | c04125f | 2019-02-19 16:31:08 +0000 | [diff] [blame] | 260 | BOOST_AUTO_TEST_CASE(SerializeDeserializeDepthwiseConvolution2d) |
| 261 | { |
| 262 | armnn::TensorInfo inputInfo ({ 1, 5, 5, 3 }, armnn::DataType::Float32); |
| 263 | armnn::TensorInfo outputInfo({ 1, 3, 3, 3 }, armnn::DataType::Float32); |
| 264 | |
| 265 | armnn::TensorInfo weightsInfo({ 1, 3, 3, 3 }, armnn::DataType::Float32); |
| 266 | armnn::TensorInfo biasesInfo ({ 3 }, armnn::DataType::Float32); |
| 267 | |
| 268 | armnn::DepthwiseConvolution2dDescriptor descriptor; |
| 269 | descriptor.m_StrideX = 1; |
| 270 | descriptor.m_StrideY = 1; |
| 271 | descriptor.m_BiasEnabled = true; |
| 272 | descriptor.m_DataLayout = armnn::DataLayout::NHWC; |
| 273 | |
| 274 | std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements()); |
| 275 | armnn::ConstTensor weights(weightsInfo, weightsData); |
| 276 | |
| 277 | std::vector<int32_t> biasesData = GenerateRandomData<int32_t>(biasesInfo.GetNumElements()); |
| 278 | armnn::ConstTensor biases(biasesInfo, biasesData); |
| 279 | |
| 280 | armnn::INetworkPtr network = armnn::INetwork::Create(); |
| 281 | armnn::IConnectableLayer* const inputLayer = network->AddInputLayer(0); |
| 282 | armnn::IConnectableLayer* const depthwiseConvLayer = |
| 283 | network->AddDepthwiseConvolution2dLayer(descriptor, weights, biases, "depthwiseConv"); |
| 284 | armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(0); |
| 285 | |
| 286 | inputLayer->GetOutputSlot(0).Connect(depthwiseConvLayer->GetInputSlot(0)); |
| 287 | inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo); |
| 288 | depthwiseConvLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); |
| 289 | depthwiseConvLayer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
| 290 | |
| 291 | armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network)); |
| 292 | BOOST_CHECK(deserializedNetwork); |
| 293 | |
| 294 | CheckDeserializedNetworkAgainstOriginal(*network, |
| 295 | *deserializedNetwork, |
| 296 | inputInfo.GetShape(), |
| 297 | outputInfo.GetShape()); |
| 298 | } |
| 299 | |
| 300 | BOOST_AUTO_TEST_CASE(SerializeDeserializeSoftmax) |
Aron Virginas-Tar | fc413c0 | 2019-02-13 15:41:52 +0000 | [diff] [blame] | 301 | { |
| 302 | armnn::TensorInfo tensorInfo({1, 10}, armnn::DataType::Float32); |
| 303 | |
| 304 | armnn::SoftmaxDescriptor descriptor; |
| 305 | descriptor.m_Beta = 1.0f; |
| 306 | |
Aron Virginas-Tar | fc413c0 | 2019-02-13 15:41:52 +0000 | [diff] [blame] | 307 | armnn::INetworkPtr network = armnn::INetwork::Create(); |
Saoirse Stewart | 3166c3e | 2019-02-18 15:24:53 +0000 | [diff] [blame] | 308 | armnn::IConnectableLayer* const inputLayer = network->AddInputLayer(0); |
| 309 | armnn::IConnectableLayer* const softmaxLayer = network->AddSoftmaxLayer(descriptor, "softmax"); |
| 310 | armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(0); |
Aron Virginas-Tar | fc413c0 | 2019-02-13 15:41:52 +0000 | [diff] [blame] | 311 | |
| 312 | inputLayer->GetOutputSlot(0).Connect(softmaxLayer->GetInputSlot(0)); |
| 313 | inputLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 314 | softmaxLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); |
| 315 | softmaxLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| 316 | |
Saoirse Stewart | 3166c3e | 2019-02-18 15:24:53 +0000 | [diff] [blame] | 317 | armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network)); |
Aron Virginas-Tar | fc413c0 | 2019-02-13 15:41:52 +0000 | [diff] [blame] | 318 | BOOST_CHECK(deserializedNetwork); |
| 319 | |
Aron Virginas-Tar | c04125f | 2019-02-19 16:31:08 +0000 | [diff] [blame] | 320 | CheckDeserializedNetworkAgainstOriginal(*network, |
| 321 | *deserializedNetwork, |
| 322 | tensorInfo.GetShape(), |
| 323 | tensorInfo.GetShape()); |
Aron Virginas-Tar | fc413c0 | 2019-02-13 15:41:52 +0000 | [diff] [blame] | 324 | } |
| 325 | |
Aron Virginas-Tar | c04125f | 2019-02-19 16:31:08 +0000 | [diff] [blame] | 326 | BOOST_AUTO_TEST_CASE(SerializeDeserializePooling2d) |
Saoirse Stewart | 3166c3e | 2019-02-18 15:24:53 +0000 | [diff] [blame] | 327 | { |
Saoirse Stewart | 3166c3e | 2019-02-18 15:24:53 +0000 | [diff] [blame] | 328 | unsigned int inputShape[] = {1, 2, 2, 1}; |
| 329 | unsigned int outputShape[] = {1, 1, 1, 1}; |
| 330 | |
Aron Virginas-Tar | c04125f | 2019-02-19 16:31:08 +0000 | [diff] [blame] | 331 | auto inputInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); |
| 332 | auto outputInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); |
Saoirse Stewart | 3166c3e | 2019-02-18 15:24:53 +0000 | [diff] [blame] | 333 | |
| 334 | armnn::Pooling2dDescriptor desc; |
| 335 | desc.m_DataLayout = armnn::DataLayout::NHWC; |
| 336 | desc.m_PadTop = 0; |
| 337 | desc.m_PadBottom = 0; |
| 338 | desc.m_PadLeft = 0; |
| 339 | desc.m_PadRight = 0; |
| 340 | desc.m_PoolType = armnn::PoolingAlgorithm::Average; |
| 341 | desc.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| 342 | desc.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| 343 | desc.m_PoolHeight = 2; |
| 344 | desc.m_PoolWidth = 2; |
| 345 | desc.m_StrideX = 2; |
| 346 | desc.m_StrideY = 2; |
| 347 | |
| 348 | armnn::INetworkPtr network = armnn::INetwork::Create(); |
| 349 | armnn::IConnectableLayer *const inputLayer = network->AddInputLayer(0); |
| 350 | armnn::IConnectableLayer *const pooling2dLayer = network->AddPooling2dLayer(desc, "ReshapeLayer"); |
| 351 | armnn::IConnectableLayer *const outputLayer = network->AddOutputLayer(0); |
| 352 | |
| 353 | inputLayer->GetOutputSlot(0).Connect(pooling2dLayer->GetInputSlot(0)); |
Aron Virginas-Tar | c04125f | 2019-02-19 16:31:08 +0000 | [diff] [blame] | 354 | inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo); |
Saoirse Stewart | 3166c3e | 2019-02-18 15:24:53 +0000 | [diff] [blame] | 355 | pooling2dLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); |
Aron Virginas-Tar | c04125f | 2019-02-19 16:31:08 +0000 | [diff] [blame] | 356 | pooling2dLayer->GetOutputSlot(0).SetTensorInfo(outputInfo); |
Saoirse Stewart | 3166c3e | 2019-02-18 15:24:53 +0000 | [diff] [blame] | 357 | |
Aron Virginas-Tar | c04125f | 2019-02-19 16:31:08 +0000 | [diff] [blame] | 358 | armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network)); |
| 359 | BOOST_CHECK(deserializedNetwork); |
Saoirse Stewart | 3166c3e | 2019-02-18 15:24:53 +0000 | [diff] [blame] | 360 | |
Aron Virginas-Tar | c04125f | 2019-02-19 16:31:08 +0000 | [diff] [blame] | 361 | CheckDeserializedNetworkAgainstOriginal(*network, |
| 362 | *deserializedNetwork, |
| 363 | inputInfo.GetShape(), |
| 364 | outputInfo.GetShape()); |
Saoirse Stewart | 3166c3e | 2019-02-18 15:24:53 +0000 | [diff] [blame] | 365 | } |
| 366 | |
Nattapat Chaimanowong | 30b0020 | 2019-02-20 17:31:34 +0000 | [diff] [blame^] | 367 | BOOST_AUTO_TEST_CASE(SerializeDeserializePermute) |
| 368 | { |
| 369 | unsigned int inputShape[] = { 4, 3, 2, 1 }; |
| 370 | unsigned int outputShape[] = { 1, 2, 3, 4 }; |
| 371 | unsigned int dimsMapping[] = { 3, 2, 1, 0 }; |
| 372 | |
| 373 | auto inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); |
| 374 | auto outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); |
| 375 | |
| 376 | armnn::PermuteDescriptor permuteDescriptor(armnn::PermutationVector(dimsMapping, 4)); |
| 377 | |
| 378 | armnn::INetworkPtr network = armnn::INetwork::Create(); |
| 379 | armnn::IConnectableLayer *const inputLayer = network->AddInputLayer(0); |
| 380 | armnn::IConnectableLayer *const permuteLayer = network->AddPermuteLayer(permuteDescriptor, "PermuteLayer"); |
| 381 | armnn::IConnectableLayer *const outputLayer = network->AddOutputLayer(0); |
| 382 | |
| 383 | inputLayer->GetOutputSlot(0).Connect(permuteLayer->GetInputSlot(0)); |
| 384 | inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); |
| 385 | permuteLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); |
| 386 | permuteLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 387 | |
| 388 | armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network)); |
| 389 | BOOST_CHECK(deserializedNetwork); |
| 390 | |
| 391 | CheckDeserializedNetworkAgainstOriginal(*network, |
| 392 | *deserializedNetwork, |
| 393 | inputTensorInfo.GetShape(), |
| 394 | outputTensorInfo.GetShape()); |
| 395 | } |
| 396 | |
| 397 | BOOST_AUTO_TEST_SUITE_END() |