1 files changed, 190 insertions, 195 deletions

diff --git a/src/armnnSerializer/test/SerializerTests.cpp b/src/armnnSerializer/test/SerializerTests.cpp
index 31ef0455c3..a88193d842 100644
--- a/src/armnnSerializer/test/SerializerTests.cpp
+++ b/src/armnnSerializer/test/SerializerTests.cpp
@@ -10,7 +10,7 @@
 
 #include <armnnDeserializeParser/IDeserializeParser.hpp>
 
-#include <numeric>
+#include <random>
 #include <sstream>
 #include <vector>
 
@@ -40,113 +40,120 @@ std::string SerializeNetwork(const armnn::INetwork& network)
     return serializerString;
 }
 
-} // anonymous namespace
-
-BOOST_AUTO_TEST_SUITE(SerializerTests)
-
-BOOST_AUTO_TEST_CASE(SimpleNetworkSerialization)
+template<typename DataType>
+static std::vector<DataType> GenerateRandomData(size_t size)
 {
-    armnn::INetworkPtr network = armnn::INetwork::Create();
-    armnn::IConnectableLayer* const inputLayer0 = network->AddInputLayer(0);
-    armnn::IConnectableLayer* const inputLayer1 = network->AddInputLayer(1);
+    constexpr bool isIntegerType = std::is_integral<DataType>::value;
+    using Distribution =
+        typename std::conditional<isIntegerType,
+                                  std::uniform_int_distribution<DataType>,
+                                  std::uniform_real_distribution<DataType>>::type;
 
-    armnn::IConnectableLayer* const additionLayer0 = network->AddAdditionLayer();
-    inputLayer0->GetOutputSlot(0).Connect(additionLayer0->GetInputSlot(0));
-    inputLayer1->GetOutputSlot(0).Connect(additionLayer0->GetInputSlot(1));
+    static constexpr DataType lowerLimit = std::numeric_limits<DataType>::min();
+    static constexpr DataType upperLimit = std::numeric_limits<DataType>::max();
 
-    armnn::IConnectableLayer* const outputLayer0 = network->AddOutputLayer(0);
-    additionLayer0->GetOutputSlot(0).Connect(outputLayer0->GetInputSlot(0));
+    static Distribution distribution(lowerLimit, upperLimit);
+    static std::default_random_engine generator;
 
-    armnnSerializer::Serializer serializer;
-    serializer.Serialize(*network);
+    std::vector<DataType> randomData(size);
+    std::generate(randomData.begin(), randomData.end(), []() { return distribution(generator); });
 
-    std::stringstream stream;
-    serializer.SaveSerializedToStream(stream);
-    BOOST_TEST(stream.str().length() > 0);
+    return randomData;
 }
 
-BOOST_AUTO_TEST_CASE(Conv2dSerialization)
+void CheckDeserializedNetworkAgainstOriginal(const armnn::INetwork& deserializedNetwork,
+                                             const armnn::INetwork& originalNetwork,
+                                             const armnn::TensorShape& inputShape,
+                                             const armnn::TensorShape& outputShape,
+                                             armnn::LayerBindingId inputBindingId = 0,
+                                             armnn::LayerBindingId outputBindingId = 0)
 {
-    armnn::IRuntime::CreationOptions options; // default options
-    armnn::IRuntimePtr run = armnn::IRuntime::Create(options);
+    armnn::IRuntime::CreationOptions options;
+    armnn::IRuntimePtr runtime = armnn::IRuntime::Create(options);
 
-    armnnDeserializeParser::IDeserializeParserPtr parser = armnnDeserializeParser::IDeserializeParser::Create();
+    std::vector<armnn::BackendId> preferredBackends = { armnn::BackendId("CpuRef") };
 
-    armnn::TensorInfo inputInfo(armnn::TensorShape({1, 5, 5, 1}), armnn::DataType::Float32, 1.0f, 0);
-    armnn::TensorInfo outputInfo(armnn::TensorShape({1, 3, 3, 1}), armnn::DataType::Float32, 4.0f, 0);
+    // Optimize original network
+    armnn::IOptimizedNetworkPtr optimizedOriginalNetwork =
+        armnn::Optimize(originalNetwork, preferredBackends, runtime->GetDeviceSpec());
+    BOOST_CHECK(optimizedOriginalNetwork);
 
-    armnn::TensorInfo weightsInfo(armnn::TensorShape({1, 3, 3, 1}), armnn::DataType::Float32, 2.0f, 0);
+    // Optimize deserialized network
+    armnn::IOptimizedNetworkPtr optimizedDeserializedNetwork =
+        armnn::Optimize(deserializedNetwork, preferredBackends, runtime->GetDeviceSpec());
+    BOOST_CHECK(optimizedDeserializedNetwork);
 
-    std::vector<float> weightsData({4, 5, 6, 0, 0, 0, 3, 2, 1});
+    armnn::NetworkId networkId1;
+    armnn::NetworkId networkId2;
 
-    // Construct network
-    armnn::INetworkPtr network = armnn::INetwork::Create();
+    // Load original and deserialized network
+    armnn::Status status1 = runtime->LoadNetwork(networkId1, std::move(optimizedOriginalNetwork));
+    BOOST_CHECK(status1 == armnn::Status::Success);
 
-    armnn::Convolution2dDescriptor descriptor;
-    descriptor.m_PadLeft = 1;
-    descriptor.m_PadRight = 1;
-    descriptor.m_PadTop = 1;
-    descriptor.m_PadBottom = 1;
-    descriptor.m_StrideX = 2;
-    descriptor.m_StrideY = 2;
-    descriptor.m_BiasEnabled = false;
-    descriptor.m_DataLayout = armnn::DataLayout::NHWC;
+    armnn::Status status2 = runtime->LoadNetwork(networkId2, std::move(optimizedDeserializedNetwork));
+    BOOST_CHECK(status2 == armnn::Status::Success);
 
-    armnn::ConstTensor weights(weightsInfo, weightsData);
+    // Generate some input data
+    std::vector<float> inputData = GenerateRandomData<float>(inputShape.GetNumElements());
 
-    armnn::IConnectableLayer* const inputLayer  = network->AddInputLayer(0, "input");
-    armnn::IConnectableLayer* const convLayer   = network->AddConvolution2dLayer(descriptor, weights, "conv");
-    armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(0, "output");
+    armnn::InputTensors inputTensors1
+    {
+         { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(networkId1, inputBindingId), inputData.data()) }
+    };
 
-    inputLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(0));
-    inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
+    armnn::InputTensors inputTensors2
+    {
+         { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(networkId2, inputBindingId), inputData.data()) }
+    };
 
-    convLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
-    convLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
+    std::vector<float> outputData1(outputShape.GetNumElements());
+    std::vector<float> outputData2(outputShape.GetNumElements());
 
-    armnnSerializer::Serializer serializer;
-    serializer.Serialize(*network);
+    armnn::OutputTensors outputTensors1
+    {
+         { 0, armnn::Tensor(runtime->GetOutputTensorInfo(networkId1, outputBindingId), outputData1.data()) }
+    };
 
-    std::stringstream stream;
-    serializer.SaveSerializedToStream(stream);
+    armnn::OutputTensors outputTensors2
+    {
+         { 0, armnn::Tensor(runtime->GetOutputTensorInfo(networkId2, outputBindingId), outputData2.data()) }
+    };
 
-    std::string const serializerString{stream.str()};
-    std::vector<std::uint8_t> const serializerVector{serializerString.begin(), serializerString.end()};
+    // Run original and deserialized network
+    runtime->EnqueueWorkload(networkId1, inputTensors1, outputTensors1);
+    runtime->EnqueueWorkload(networkId2, inputTensors2, outputTensors2);
 
-    armnn::INetworkPtr deserializedNetwork = parser->CreateNetworkFromBinary(serializerVector);
+    // Compare output data
+    BOOST_CHECK_EQUAL_COLLECTIONS(outputData1.begin(), outputData1.end(),
+                                  outputData2.begin(), outputData2.end());
+}
 
-    auto deserializedOptimized = Optimize(*deserializedNetwork, {armnn::Compute::CpuRef}, run->GetDeviceSpec());
+} // anonymous namespace
 
-    armnn::NetworkId networkIdentifier;
+BOOST_AUTO_TEST_SUITE(SerializerTests)
 
-    // Load graph into runtime
-    run->LoadNetwork(networkIdentifier, std::move(deserializedOptimized));
+BOOST_AUTO_TEST_CASE(SerializeAddition)
+{
+    armnn::INetworkPtr network = armnn::INetwork::Create();
+    armnn::IConnectableLayer* const inputLayer0 = network->AddInputLayer(0);
+    armnn::IConnectableLayer* const inputLayer1 = network->AddInputLayer(1);
 
-    std::vector<float> inputData
-    {
-            1, 5, 2, 3, 5, 8, 7, 3, 6, 3, 3, 3, 9, 1, 9, 4, 1, 8, 1, 3, 6, 8, 1, 9, 2
-    };
-    armnn::InputTensors inputTensors
-    {
-            {0, armnn::ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), inputData.data())}
-    };
+    armnn::IConnectableLayer* const additionLayer0 = network->AddAdditionLayer();
+    inputLayer0->GetOutputSlot(0).Connect(additionLayer0->GetInputSlot(0));
+    inputLayer1->GetOutputSlot(0).Connect(additionLayer0->GetInputSlot(1));
 
-    std::vector<float> expectedOutputData
-    {
-            23, 33, 24, 91, 99, 48, 26, 50, 19
-    };
+    armnn::IConnectableLayer* const outputLayer0 = network->AddOutputLayer(0);
+    additionLayer0->GetOutputSlot(0).Connect(outputLayer0->GetInputSlot(0));
 
-    std::vector<float> outputData(9);
-    armnn::OutputTensors outputTensors
-    {
-            {0, armnn::Tensor(run->GetOutputTensorInfo(networkIdentifier, 0), outputData.data())}
-    };
-    run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);
-    BOOST_CHECK_EQUAL_COLLECTIONS(outputData.begin(), outputData.end(),
-                                  expectedOutputData.begin(), expectedOutputData.end());
+    armnnSerializer::Serializer serializer;
+    serializer.Serialize(*network);
+
+    std::stringstream stream;
+    serializer.SaveSerializedToStream(stream);
+    BOOST_TEST(stream.str().length() > 0);
 }
 
-BOOST_AUTO_TEST_CASE(SimpleNetworkWithMultiplicationSerialization)
+BOOST_AUTO_TEST_CASE(SerializeMultiplication)
 {
     const armnn::TensorInfo info({ 1, 5, 2, 3 }, armnn::DataType::Float32);
 
@@ -172,14 +179,57 @@ BOOST_AUTO_TEST_CASE(SimpleNetworkWithMultiplicationSerialization)
     BOOST_TEST(stream.str().find(multLayerName) != stream.str().npos);
 }
 
-BOOST_AUTO_TEST_CASE(SimpleReshapeIntegration)
+BOOST_AUTO_TEST_CASE(SerializeDeserializeConvolution2d)
 {
-    armnn::NetworkId networkIdentifier;
-    armnn::IRuntime::CreationOptions options; // default options
-    armnn::IRuntimePtr run = armnn::IRuntime::Create(options);
+    armnn::TensorInfo inputInfo ({ 1, 5, 5, 1 }, armnn::DataType::Float32);
+    armnn::TensorInfo outputInfo({ 1, 3, 3, 1 }, armnn::DataType::Float32);
+
+    armnn::TensorInfo weightsInfo({ 1, 3, 3, 1 }, armnn::DataType::Float32);
+    armnn::TensorInfo biasesInfo ({ 1 }, armnn::DataType::Float32);
+
+    // Construct network
+    armnn::INetworkPtr network = armnn::INetwork::Create();
+
+    armnn::Convolution2dDescriptor descriptor;
+    descriptor.m_PadLeft     = 1;
+    descriptor.m_PadRight    = 1;
+    descriptor.m_PadTop      = 1;
+    descriptor.m_PadBottom   = 1;
+    descriptor.m_StrideX     = 2;
+    descriptor.m_StrideY     = 2;
+    descriptor.m_BiasEnabled = true;
+    descriptor.m_DataLayout  = armnn::DataLayout::NHWC;
+
+    std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements());
+    armnn::ConstTensor weights(weightsInfo, weightsData);
+
+    std::vector<float> biasesData = GenerateRandomData<float>(biasesInfo.GetNumElements());
+    armnn::ConstTensor biases(biasesInfo, biasesData);
 
-    unsigned int inputShape[] = {1, 9};
-    unsigned int outputShape[] = {3, 3};
+    armnn::IConnectableLayer* const inputLayer  = network->AddInputLayer(0, "input");
+    armnn::IConnectableLayer* const convLayer   =
+        network->AddConvolution2dLayer(descriptor, weights, biases, "convolution");
+    armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(0, "output");
+
+    inputLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(0));
+    inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
+
+    convLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
+    convLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
+
+    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
+    BOOST_CHECK(deserializedNetwork);
+
+    CheckDeserializedNetworkAgainstOriginal(*network,
+                                            *deserializedNetwork,
+                                            inputInfo.GetShape(),
+                                            outputInfo.GetShape());
+}
+
+BOOST_AUTO_TEST_CASE(SerializeDeserializeReshape)
+{
+    unsigned int inputShape[]  = { 1, 9 };
+    unsigned int outputShape[] = { 3, 3 };
 
     auto inputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::DataType::Float32);
     auto outputTensorInfo = armnn::TensorInfo(2, outputShape, armnn::DataType::Float32);
@@ -198,49 +248,62 @@ BOOST_AUTO_TEST_CASE(SimpleReshapeIntegration)
     reshapeLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
     reshapeLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
 
-    armnnSerializer::Serializer serializer;
-    serializer.Serialize(*network);
-    std::stringstream stream;
-    serializer.SaveSerializedToStream(stream);
-    std::string const serializerString{stream.str()};
+    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
+    BOOST_CHECK(deserializedNetwork);
 
-    //Deserialize network.
-    auto deserializedNetwork = DeserializeNetwork(serializerString);
+    CheckDeserializedNetworkAgainstOriginal(*network,
+                                            *deserializedNetwork,
+                                            inputTensorInfo.GetShape(),
+                                            outputTensorInfo.GetShape());
+}
 
-    //Optimize the deserialized network
-    auto deserializedOptimized = Optimize(*deserializedNetwork, {armnn::Compute::CpuRef},
-                                          run->GetDeviceSpec());
+BOOST_AUTO_TEST_CASE(SerializeDeserializeDepthwiseConvolution2d)
+{
+    armnn::TensorInfo inputInfo ({ 1, 5, 5, 3 }, armnn::DataType::Float32);
+    armnn::TensorInfo outputInfo({ 1, 3, 3, 3 }, armnn::DataType::Float32);
 
-    // Load graph into runtime
-    run->LoadNetwork(networkIdentifier, std::move(deserializedOptimized));
+    armnn::TensorInfo weightsInfo({ 1, 3, 3, 3 }, armnn::DataType::Float32);
+    armnn::TensorInfo biasesInfo ({ 3 }, armnn::DataType::Float32);
 
-    std::vector<float> input1Data(inputTensorInfo.GetNumElements());
-    std::iota(input1Data.begin(), input1Data.end(), 8);
+    armnn::DepthwiseConvolution2dDescriptor descriptor;
+    descriptor.m_StrideX     = 1;
+    descriptor.m_StrideY     = 1;
+    descriptor.m_BiasEnabled = true;
+    descriptor.m_DataLayout  = armnn::DataLayout::NHWC;
 
-    armnn::InputTensors inputTensors
-    {
-         {0, armnn::ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), input1Data.data())}
-    };
+    std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements());
+    armnn::ConstTensor weights(weightsInfo, weightsData);
 
-    std::vector<float> outputData(input1Data.size());
-    armnn::OutputTensors outputTensors
-    {
-         {0,armnn::Tensor(run->GetOutputTensorInfo(networkIdentifier, 0), outputData.data())}
-    };
+    std::vector<int32_t> biasesData = GenerateRandomData<int32_t>(biasesInfo.GetNumElements());
+    armnn::ConstTensor biases(biasesInfo, biasesData);
 
-    run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);
+    armnn::INetworkPtr network = armnn::INetwork::Create();
+    armnn::IConnectableLayer* const inputLayer = network->AddInputLayer(0);
+    armnn::IConnectableLayer* const depthwiseConvLayer =
+        network->AddDepthwiseConvolution2dLayer(descriptor, weights, biases, "depthwiseConv");
+    armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(0);
 
-    BOOST_CHECK_EQUAL_COLLECTIONS(outputData.begin(),outputData.end(), input1Data.begin(),input1Data.end());
+    inputLayer->GetOutputSlot(0).Connect(depthwiseConvLayer->GetInputSlot(0));
+    inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
+    depthwiseConvLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
+    depthwiseConvLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
+
+    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
+    BOOST_CHECK(deserializedNetwork);
+
+    CheckDeserializedNetworkAgainstOriginal(*network,
+                                            *deserializedNetwork,
+                                            inputInfo.GetShape(),
+                                            outputInfo.GetShape());
 }
 
-BOOST_AUTO_TEST_CASE(SimpleSoftmaxIntegration)
+BOOST_AUTO_TEST_CASE(SerializeDeserializeSoftmax)
 {
     armnn::TensorInfo tensorInfo({1, 10}, armnn::DataType::Float32);
 
     armnn::SoftmaxDescriptor descriptor;
     descriptor.m_Beta = 1.0f;
 
-    // Create test network
     armnn::INetworkPtr network = armnn::INetwork::Create();
     armnn::IConnectableLayer* const inputLayer   = network->AddInputLayer(0);
     armnn::IConnectableLayer* const softmaxLayer = network->AddSoftmaxLayer(descriptor, "softmax");
@@ -251,71 +314,22 @@ BOOST_AUTO_TEST_CASE(SimpleSoftmaxIntegration)
     softmaxLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
     softmaxLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo);
 
-    // Serialize & deserialize network
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    armnn::IRuntime::CreationOptions options;
-    armnn::IRuntimePtr runtime = armnn::IRuntime::Create(options);
-
-    armnn::IOptimizedNetworkPtr optimizedNetwork =
-        armnn::Optimize(*network, {armnn::Compute::CpuRef}, runtime->GetDeviceSpec());
-    BOOST_CHECK(optimizedNetwork);
-
-    armnn::IOptimizedNetworkPtr deserializedOptimizedNetwork =
-        armnn::Optimize(*deserializedNetwork, {armnn::Compute::CpuRef}, runtime->GetDeviceSpec());
-    BOOST_CHECK(deserializedOptimizedNetwork);
-
-    armnn::NetworkId networkId1;
-    armnn::NetworkId networkId2;
-
-    runtime->LoadNetwork(networkId1, std::move(optimizedNetwork));
-    runtime->LoadNetwork(networkId2, std::move(deserializedOptimizedNetwork));
-
-    std::vector<float> inputData(tensorInfo.GetNumElements());
-    std::iota(inputData.begin(), inputData.end(), 0);
-
-    armnn::InputTensors inputTensors1
-    {
-         {0, armnn::ConstTensor(runtime->GetInputTensorInfo(networkId1, 0), inputData.data())}
-    };
-
-    armnn::InputTensors inputTensors2
-    {
-         {0, armnn::ConstTensor(runtime->GetInputTensorInfo(networkId2, 0), inputData.data())}
-    };
-
-    std::vector<float> outputData1(inputData.size());
-    std::vector<float> outputData2(inputData.size());
-
-    armnn::OutputTensors outputTensors1
-    {
-         {0, armnn::Tensor(runtime->GetOutputTensorInfo(networkId1, 0), outputData1.data())}
-    };
-
-    armnn::OutputTensors outputTensors2
-    {
-         {0, armnn::Tensor(runtime->GetOutputTensorInfo(networkId2, 0), outputData2.data())}
-    };
-
-    runtime->EnqueueWorkload(networkId1, inputTensors1, outputTensors1);
-    runtime->EnqueueWorkload(networkId2, inputTensors2, outputTensors2);
-
-    BOOST_CHECK_EQUAL_COLLECTIONS(outputData1.begin(), outputData1.end(),
-                                  outputData2.begin(), outputData2.end());
+    CheckDeserializedNetworkAgainstOriginal(*network,
+                                            *deserializedNetwork,
+                                            tensorInfo.GetShape(),
+                                            tensorInfo.GetShape());
 }
 
-BOOST_AUTO_TEST_CASE(SimplePooling2dIntegration)
+BOOST_AUTO_TEST_CASE(SerializeDeserializePooling2d)
 {
-    armnn::NetworkId networkIdentifier;
-    armnn::IRuntime::CreationOptions options; // default options
-    armnn::IRuntimePtr runtime = armnn::IRuntime::Create(options);
-
     unsigned int inputShape[]  = {1, 2, 2, 1};
     unsigned int outputShape[] = {1, 1, 1, 1};
 
-    auto inputTensorInfo  = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    auto outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
+    auto inputInfo  = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
+    auto outputInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
 
     armnn::Pooling2dDescriptor desc;
     desc.m_DataLayout          = armnn::DataLayout::NHWC;
@@ -337,36 +351,17 @@ BOOST_AUTO_TEST_CASE(SimplePooling2dIntegration)
     armnn::IConnectableLayer *const outputLayer = network->AddOutputLayer(0);
 
     inputLayer->GetOutputSlot(0).Connect(pooling2dLayer->GetInputSlot(0));
-    inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
+    inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
     pooling2dLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
-    pooling2dLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
-
-    auto deserializeNetwork = DeserializeNetwork(SerializeNetwork(*network));
-
-    //Optimize the deserialized network
-    auto deserializedOptimized = Optimize(*deserializeNetwork, {armnn::Compute::CpuRef},
-                                          runtime->GetDeviceSpec());
+    pooling2dLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
 
-    // Load graph into runtime
-    runtime->LoadNetwork(networkIdentifier, std::move(deserializedOptimized));
-
-    std::vector<float> input1Data(inputTensorInfo.GetNumElements());
-    std::iota(input1Data.begin(), input1Data.end(), 4);
-
-    armnn::InputTensors inputTensors
-    {
-          {0, armnn::ConstTensor(runtime->GetInputTensorInfo(networkIdentifier, 0), input1Data.data())}
-    };
-
-    std::vector<float> outputData(input1Data.size());
-    armnn::OutputTensors outputTensors
-    {
-           {0, armnn::Tensor(runtime->GetOutputTensorInfo(networkIdentifier, 0), outputData.data())}
-    };
-
-    runtime->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);
+    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
+    BOOST_CHECK(deserializedNetwork);
 
-    BOOST_CHECK_EQUAL(outputData[0], 5.5);
+    CheckDeserializedNetworkAgainstOriginal(*network,
+                                            *deserializedNetwork,
+                                            inputInfo.GetShape(),
+                                            outputInfo.GetShape());
 }
 
-BOOST_AUTO_TEST_SUITE_END()
+BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file