diff options
Diffstat (limited to 'src/backends/backendsCommon/test/StridedSliceAsyncEndToEndTest.hpp')
| -rw-r--r-- | src/backends/backendsCommon/test/StridedSliceAsyncEndToEndTest.hpp | 159 |
1 files changed, 146 insertions, 13 deletions
diff --git a/src/backends/backendsCommon/test/StridedSliceAsyncEndToEndTest.hpp b/src/backends/backendsCommon/test/StridedSliceAsyncEndToEndTest.hpp index b20ff4f142..87cade7dca 100644 --- a/src/backends/backendsCommon/test/StridedSliceAsyncEndToEndTest.hpp +++ b/src/backends/backendsCommon/test/StridedSliceAsyncEndToEndTest.hpp @@ -9,7 +9,9 @@ #include <armnn/IWorkingMemHandle.hpp> #include <armnn/INetwork.hpp> +#include <armnn/IAsyncExecutionCallback.hpp> +#include <AsyncExecutionCallback.hpp> #include <backendsCommon/test/CommonTestUtils.hpp> #include <boost/test/unit_test.hpp> @@ -114,27 +116,29 @@ void AsyncThreadedEndToEndTestImpl(INetworkPtr network, } - - template<DataType ArmnnIType, DataType ArmnnOType, - typename TInput = ResolveType <ArmnnIType>, typename TOutput = ResolveType <ArmnnOType>> + typename TInput = ResolveType<ArmnnIType>, typename TOutput = ResolveType<ArmnnOType>> void AsyncEndToEndTestImpl(INetworkPtr network, const std::map<int, std::vector<TInput>>& inputTensorData, const std::map<int, std::vector<TOutput>>& expectedOutputData, std::vector<BackendId> backends, - float tolerance = 0.000001f) + float tolerance = 0.000001f, + size_t numThreads = 0) { // Create Runtime in which test will run IRuntime::CreationOptions options; - IRuntimePtr runtime(IRuntime::Create(options)); + IRuntimePtr runtime(IRuntime::Create(options)); // Optimize the Network IOptimizedNetworkPtr optNet = Optimize(*network, backends, runtime->GetDeviceSpec()); // Creates AsyncNetwork NetworkId networkId = 0; + std::string errorMessage; - const INetworkProperties networkProperties(true, MemorySource::Undefined, MemorySource::Undefined); + + const INetworkProperties networkProperties(true, MemorySource::Undefined, MemorySource::Undefined, numThreads); + runtime->LoadNetwork(networkId, std::move(optNet), errorMessage, networkProperties); InputTensors inputTensors; @@ -157,17 +161,50 @@ void AsyncEndToEndTestImpl(INetworkPtr network, outputStorage.at(it.first).data())}); } - // Create WorkingMemHandle for this async network - std::unique_ptr<IWorkingMemHandle> workingMemHandle = runtime->CreateWorkingMemHandle(networkId); - IWorkingMemHandle& workingMemHandleRef = *workingMemHandle.get(); + if (numThreads == 0) + { + // Create WorkingMemHandle for this async network + std::unique_ptr<IWorkingMemHandle> workingMemHandle = runtime->CreateWorkingMemHandle(networkId); + IWorkingMemHandle& workingMemHandleRef = *workingMemHandle.get(); - // Run the async network - runtime->Execute(workingMemHandleRef, inputTensors, outputTensors); + // Run the async network + runtime->Execute(workingMemHandleRef, inputTensors, outputTensors); + } + else + { + std::vector<IAsyncExecutionCallbackPtr> callbacks; + + // Create 1000 callbacks that will be checked post scheduling + for (size_t i = 0; i < 1000; ++i) + { + callbacks.emplace_back(std::make_shared<AsyncExecutionCallback>()); + } + + // For the asyncronous execution, we are adding a pool of working memory handles (1 per thread) in the + // LoadedNetwork with a each scheduled inference having a spefic priority + for (IAsyncExecutionCallbackPtr cb : callbacks) + { + runtime->Schedule(networkId, + inputTensors, + outputTensors, + static_cast<QosExecPriority>(rand()%3), + cb); + } + + // Wait until the execution signals a notify + for (IAsyncExecutionCallbackPtr cb : callbacks) + { + cb->Wait(); + + // Checks the results. + BOOST_CHECK(cb->GetStatus() == Status::Success); + } + } - // Checks the results. for (auto&& it : expectedOutputData) { std::vector<TOutput> out = outputStorage.at(it.first); + for (unsigned int i = 0; i < out.size(); ++i) { BOOST_CHECK(Compare<ArmnnOType>(it.second[i], out[i], tolerance) == true); @@ -263,7 +300,103 @@ void StridedSlicedEndToEndTest(const std::vector<BackendId>& backends) std::map<int, std::vector<T>> inputTensorData = {{0, inputData}}; std::map<int, std::vector<T>> expectedOutputData = {{0, outputExpected}}; - AsyncEndToEndTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends, 1); + AsyncEndToEndTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends, 0.000001f); +} + +template<armnn::DataType ArmnnType> +void AsyncScheduledStridedSlicedEndToEndTest(const std::vector<BackendId>& backends) +{ + using namespace armnn; + using T = ResolveType<ArmnnType>; + + const TensorShape& inputShape = {3, 2, 3, 1}; + const TensorShape& outputShape = {1, 2, 3, 1}; + const std::vector<int>& beginData = {1, 0, 0, 0}; + const std::vector<int>& endData = {2, 2, 3, 1}; + const std::vector<int>& stridesData = {1, 1, 1, 1}; + int beginMask = 0; + int endMask = 0; + int shrinkAxisMask = 0; + int ellipsisMask = 0; + int newAxisMask = 0; + + // Builds up the structure of the network + INetworkPtr net = CreateStridedSliceNetwork<ArmnnType>(inputShape, + outputShape, + beginData, + endData, + stridesData, + beginMask, + endMask, + shrinkAxisMask, + ellipsisMask, + newAxisMask); + + // Creates structures for input & output. + std::vector<T> inputData{ + 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, + + 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, + + 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f + }; + + std::vector<T> outputExpected{ + 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f + }; + + std::map<int, std::vector<T>> inputTensorData = {{0, inputData}}; + std::map<int, std::vector<T>> expectedOutputData = {{0, outputExpected}}; + + AsyncEndToEndTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends, 0.000001f, 1); +} + +template<armnn::DataType ArmnnType> +void AsyncScheduledStridedSlicedMultiThreadedEndToEndTest(const std::vector<BackendId>& backends) +{ + using namespace armnn; + using T = ResolveType<ArmnnType>; + + const TensorShape& inputShape = {3, 2, 3, 1}; + const TensorShape& outputShape = {1, 2, 3, 1}; + const std::vector<int>& beginData = {1, 0, 0, 0}; + const std::vector<int>& endData = {2, 2, 3, 1}; + const std::vector<int>& stridesData = {1, 1, 1, 1}; + int beginMask = 0; + int endMask = 0; + int shrinkAxisMask = 0; + int ellipsisMask = 0; + int newAxisMask = 0; + + // Builds up the structure of the network + INetworkPtr net = CreateStridedSliceNetwork<ArmnnType>(inputShape, + outputShape, + beginData, + endData, + stridesData, + beginMask, + endMask, + shrinkAxisMask, + ellipsisMask, + newAxisMask); + + // Creates structures for input & output. + std::vector<T> inputData{ + 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, + + 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, + + 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f + }; + + std::vector<T> outputExpected{ + 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f + }; + + std::map<int, std::vector<T>> inputTensorData = {{0, inputData}}; + std::map<int, std::vector<T>> expectedOutputData = {{0, outputExpected}}; + + AsyncEndToEndTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends, 0.000001f, 3); } template<armnn::DataType ArmnnType> |