diff options
Diffstat (limited to 'src/backends/cl/test/ClImportTensorHandleTests.cpp')
| -rw-r--r-- | src/backends/cl/test/ClImportTensorHandleTests.cpp | 153 |
1 files changed, 153 insertions, 0 deletions
diff --git a/src/backends/cl/test/ClImportTensorHandleTests.cpp b/src/backends/cl/test/ClImportTensorHandleTests.cpp index 3d702642aa..161765484d 100644 --- a/src/backends/cl/test/ClImportTensorHandleTests.cpp +++ b/src/backends/cl/test/ClImportTensorHandleTests.cpp @@ -274,4 +274,157 @@ TEST_CASE("ClCanBeImportedAlignedMemory") // we can be confident that it will be successfully imported. All other cases will need to be handled by the user. } +TEST_CASE_FIXTURE(ClContextControlFixture, "ClForceImportConv2dEndToEnd") +{ + // Create runtime in which test will run + IRuntime::CreationOptions options; + IRuntimePtr runtime(armnn::IRuntime::Create(options)); + + // build up the structure of the network + INetworkPtr network(INetwork::Create()); + + armnn::TensorInfo inputInfo({ 1, 3, 4, 1 }, DataType::Float32); + armnn::TensorInfo kernelInfo({ 1, 3, 3, 1 }, DataType::Float32); + armnn::TensorInfo outputInfo({ 1, 3, 4, 1 }, DataType::Float32); + + kernelInfo.SetConstant(true); + + std::vector<float> kernel = + { + 4, 5, 6, + 0, 0, 0, + 3, 2, 1 + }; + + const std::vector<float> expectedOutput = + { + 23, 41, 33, 21, + 44, 65, 76, 52, + 82, 85, 79, 42 + }; + + unsigned int numElements = inputInfo.GetNumElements(); + size_t totalBytes = numElements * sizeof(float); + + IConnectableLayer* const inputLayer = network->AddInputLayer(0, "input"); + ARMNN_ASSERT(inputLayer); + + armnn::ConstTensor weights(kernelInfo, kernel); + + armnn::Convolution2dDescriptor convDesc2d; + convDesc2d.m_StrideX = 1; + convDesc2d.m_StrideY = 1; + convDesc2d.m_PadLeft = 1; + convDesc2d.m_PadRight = 1; + convDesc2d.m_PadTop = 1; + convDesc2d.m_PadBottom = 1; + convDesc2d.m_DataLayout = DataLayout::NHWC; + armnn::IConnectableLayer* const convLayer = network->AddConvolution2dLayer(convDesc2d, + weights, + armnn::EmptyOptional(), + "conv"); + ARMNN_ASSERT(convLayer); + + inputLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(0)); + inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo); + + IConnectableLayer* output = network->AddOutputLayer(0, "output"); + convLayer->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + convLayer->GetOutputSlot(0).SetTensorInfo(outputInfo); + + // Optimize the network + OptimizerOptions optOptions; + optOptions.m_ImportEnabled = false; + std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; + IOptimizedNetworkPtr optNet = Optimize(*network, backends, runtime->GetDeviceSpec(), optOptions); + CHECK(optNet); + + // Loads it into the runtime. + NetworkId netId; + std::string ignoredErrorMessage; + // Enable Importing + INetworkProperties networkProperties(false, MemorySource::Undefined, MemorySource::Undefined); + runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties); + + // Creates structures for input & output + const size_t alignment = + arm_compute::CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE>(); + size_t space = totalBytes + alignment + alignment; + auto inputData = std::make_unique<uint8_t[]>(space); + void* alignedInputPtr = inputData.get(); + CHECK(std::align(alignment, totalBytes, alignedInputPtr, space)); + + // Input with negative values + auto* inputPtr = reinterpret_cast<float*>(alignedInputPtr); + inputPtr[0] = 1; + inputPtr[1] = 5; + inputPtr[2] = 2; + inputPtr[3] = 3; + inputPtr[4] = 8; + inputPtr[5] = 7; + inputPtr[6] = 3; + inputPtr[7] = 6; + inputPtr[8] = 3; + inputPtr[9] = 3; + inputPtr[10] = 9; + inputPtr[11] = 1; + + + auto outputData = std::make_unique<uint8_t[]>(space); + void* alignedOutputPtr = outputData.get(); + CHECK(std::align(alignment, totalBytes, alignedOutputPtr, space)); + auto* outputPtr = reinterpret_cast<float*>(alignedOutputPtr); + std::fill_n(outputPtr, numElements, -10.0f); + + TensorInfo inputTensorInfo = runtime->GetInputTensorInfo(netId, 0); + inputTensorInfo.SetConstant(true); + InputTensors inputTensors + { + {0,armnn::ConstTensor(inputTensorInfo, alignedInputPtr)}, + }; + OutputTensors outputTensors + { + {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), alignedOutputPtr)} + }; + + runtime->GetProfiler(netId)->EnableProfiling(true); + + INFO("Run ImportInputs"); + std::vector<ImportedInputId> importedInputIds = + runtime->ImportInputs(netId, inputTensors, MemorySource::Malloc); + std::vector<ImportedOutputId> importedOutputIds = + runtime->ImportOutputs(netId, outputTensors, MemorySource::Malloc); + + // Do the inference + runtime->EnqueueWorkload(netId, inputTensors, outputTensors, importedInputIds, importedOutputIds); + + // Retrieve the Profiler.Print() output to get the workload execution + ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance(); + std::stringstream ss; + profilerManager.GetProfiler()->Print(ss);; + std::string dump = ss.str(); + + // Contains Convolution2dWorkload + std::size_t found = dump.find("Convolution2dWorkload"); + CHECK(found != std::string::npos); + + // Contains SyncMemGeneric + found = dump.find("SyncMemGeneric"); + CHECK(found != std::string::npos); + + // Does not contain CopyMemGeneric + found = dump.find("CopyMemGeneric"); + CHECK(found == std::string::npos); + + runtime->UnloadNetwork(netId); + + // Check output is as expected + // Validate result by checking that the output has no negative values + auto* outputResult = reinterpret_cast<float*>(alignedOutputPtr); + CHECK(outputResult); + + // Check the output is correct + CHECK(std::equal(outputResult, outputResult + numElements, expectedOutput.begin(), expectedOutput.end())); +} + } |