diff options
Diffstat (limited to 'src/backends/backendsCommon/test')
| -rw-r--r-- | src/backends/backendsCommon/test/CommonTestUtils.hpp | 23 | ||||
| -rw-r--r-- | src/backends/backendsCommon/test/OptimizedNetworkTests.cpp | 129 |
2 files changed, 152 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/test/CommonTestUtils.hpp b/src/backends/backendsCommon/test/CommonTestUtils.hpp index 99412b9694..c7537f1eed 100644 --- a/src/backends/backendsCommon/test/CommonTestUtils.hpp +++ b/src/backends/backendsCommon/test/CommonTestUtils.hpp @@ -18,6 +18,8 @@ #include <test/TestUtils.hpp> #include <algorithm> +#include <random> +#include <vector> // Checks that two collections have the exact same contents (in any order) // The given collections do not have to contain duplicates @@ -94,3 +96,24 @@ armnn::TensorShape MakeTensorShape(unsigned int batches, unsigned int height, unsigned int width, armnn::DataLayout layout); + +template<typename DataType> +static std::vector<DataType> GenerateRandomData(size_t size) +{ + 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; + + static constexpr DataType lowerLimit = std::numeric_limits<DataType>::min(); + static constexpr DataType upperLimit = std::numeric_limits<DataType>::max(); + + static Distribution distribution(lowerLimit, upperLimit); + static std::default_random_engine generator; + + std::vector<DataType> randomData(size); + generate(randomData.begin(), randomData.end(), []() { return distribution(generator); }); + + return randomData; +} diff --git a/src/backends/backendsCommon/test/OptimizedNetworkTests.cpp b/src/backends/backendsCommon/test/OptimizedNetworkTests.cpp index 2c74690e6e..dcea9ef72e 100644 --- a/src/backends/backendsCommon/test/OptimizedNetworkTests.cpp +++ b/src/backends/backendsCommon/test/OptimizedNetworkTests.cpp @@ -3,6 +3,8 @@ // SPDX-License-Identifier: MIT // +#include "CommonTestUtils.hpp" + #include <Graph.hpp> #include <Network.hpp> @@ -358,4 +360,131 @@ TEST_CASE("OptimizeValidateWorkloadsDuplicateComputeDeviceWithFallback") } } +TEST_CASE("OptimizeNetworkCopy") +{ + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime = armnn::IRuntime::Create(options); + std::vector<armnn::NetworkId> networkIds; + + const std::string layerName("convolution2d"); + const armnn::TensorInfo inputInfo ({ 1, 5, 5, 1 }, armnn::DataType::Float32); + const armnn::TensorInfo outputInfo({ 1, 2, 2, 1 }, armnn::DataType::Float32); + + const armnn::TensorInfo weightsInfo({ 1, 3, 3, 1 }, armnn::DataType::Float32); + const armnn::TensorInfo biasesInfo ({ 1 }, armnn::DataType::Float32); + + 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); + + 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_DilationX = 2; + descriptor.m_DilationY = 2; + descriptor.m_BiasEnabled = true; + descriptor.m_DataLayout = armnn::DataLayout::NHWC; + + armnn::INetworkPtr network = armnn::INetwork::Create(); + armnn::IConnectableLayer* const inputLayer = network->AddInputLayer(0); + armnn::IConnectableLayer* const convLayer = + network->AddConvolution2dLayer(descriptor, + weights, + armnn::Optional<armnn::ConstTensor>(biases), + layerName.c_str()); + armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(0); + + inputLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(0)); + convLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); + + inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo); + convLayer->GetOutputSlot(0).SetTensorInfo(outputInfo); + + std::vector<armnn::BackendId> preferredBackends { "CpuRef" }; + armnn::ModelOptions modelOptions; + armnn::OptimizerOptions optimizerOptions(false, false, false, false, modelOptions); + std::vector<std::string> errorMessages; + + // optimize the network. + armnn::IOptimizedNetworkPtr optNet = Optimize(*network, + preferredBackends, + runtime->GetDeviceSpec(), + optimizerOptions, + armnn::Optional<std::vector<std::string>&>(errorMessages)); + + for (unsigned int i = 0; i < 2; ++i) + { + armnn::ModelOptions optimizedModelOptions; + auto copy = armnn::IOptimizedNetworkPtr(new armnn::IOptimizedNetwork(*optNet.get(), optimizedModelOptions), + &armnn::IOptimizedNetwork::Destroy); + + CHECK(copy); + + armnn::NetworkId netId; + std::string errorMessage; + + CHECK(armnn::Status::Success == runtime->LoadNetwork(netId, std::move(copy), errorMessage)); + + // Record the networkID for the loaded network + networkIds.emplace_back(netId); + } + armnn::NetworkId optNetId; + std::string errorMessage; + + // Load the original optNet + CHECK(armnn::Status::Success == runtime->LoadNetwork(optNetId, std::move(optNet), errorMessage)); + + std::vector<float> inputData = GenerateRandomData<float>(runtime->GetInputTensorInfo(optNetId, 0).GetNumElements()); + std::vector<float> outputData(runtime->GetOutputTensorInfo(optNetId, 0).GetNumElements()); + + armnn::InputTensors inputTensors + { + { + 0 ,armnn::ConstTensor(runtime->GetInputTensorInfo(optNetId, 0), inputData.data()) + } + }; + armnn::OutputTensors outputTensors + { + { + 0, armnn::Tensor(runtime->GetOutputTensorInfo(optNetId, 0), outputData.data()) + } + }; + runtime->EnqueueWorkload(optNetId, inputTensors, outputTensors); + runtime->UnloadNetwork(optNetId); + + // Record the networkID for the loaded network + for (unsigned int i = 0; i < networkIds.size(); ++i) + { + armnn::NetworkId netId = networkIds[i]; + std::vector<float> copyOutputData(runtime->GetOutputTensorInfo(netId, 0).GetNumElements()); + + armnn::InputTensors copyInputTensors + { + { + 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data()) + } + }; + armnn::OutputTensors copyOutputTensors + { + { + 0, armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), copyOutputData.data()) + } + }; + runtime->EnqueueWorkload(netId, copyInputTensors, copyOutputTensors); + runtime->UnloadNetwork(netId); + + // Check results are identical to "original" version + for (unsigned int j = 0; j < outputData.size(); ++j) + { + CHECK(outputData[j] == copyOutputData[j]); + } + } +} + } |