| // |
| // Copyright © 2021 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include <armnn/ArmNN.hpp> |
| |
| #include <iostream> |
| |
| // A simple example application to show the usage of Memory Management Pre Importing of Inputs and Outputs. In this |
| // sample, the users single input number is added to itself using an add layer and outputted to console as a number |
| // that is double the input. The code does not use EnqueueWorkload but instead uses runtime->Execute |
| |
| int main() |
| { |
| using namespace armnn; |
| |
| float number; |
| std::cout << "Please enter a number: " << std::endl; |
| std::cin >> number; |
| |
| // Turn on logging to standard output |
| // This is useful in this sample so that users can learn more about what is going on |
| armnn::ConfigureLogging(true, false, LogSeverity::Info); |
| |
| armnn::IRuntime::CreationOptions options; |
| armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); |
| |
| armnn::NetworkId networkIdentifier1 = 0; |
| |
| armnn::INetworkPtr testNetwork(armnn::INetwork::Create()); |
| auto inputLayer1 = testNetwork->AddInputLayer(0, "input 1 layer"); |
| auto inputLayer2 = testNetwork->AddInputLayer(1, "input 2 layer"); |
| ARMNN_NO_DEPRECATE_WARN_BEGIN |
| auto addLayer = testNetwork->AddAdditionLayer("add layer"); |
| ARMNN_NO_DEPRECATE_WARN_END |
| auto outputLayer = testNetwork->AddOutputLayer(2, "output layer"); |
| |
| // Set the tensors in the network. |
| TensorInfo tensorInfo{{4}, armnn::DataType::Float32}; |
| |
| inputLayer1->GetOutputSlot(0).Connect(addLayer->GetInputSlot(0)); |
| inputLayer1->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| inputLayer2->GetOutputSlot(0).Connect(addLayer->GetInputSlot(1)); |
| inputLayer2->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| |
| addLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); |
| addLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo); |
| |
| // Set preferred backend to CpuRef |
| std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; |
| |
| // To hold an eventual error message if loading the network fails |
| std::string er; |
| |
| // Initialize network properties with asyncEnabled and MemorySources != MemorySource::Undefined |
| armnn::INetworkProperties networkProperties(true, MemorySource::Malloc, MemorySource::Malloc); |
| |
| // Optimize and Load the network into runtime |
| runtime->LoadNetwork(networkIdentifier1, |
| Optimize(*testNetwork, backends, runtime->GetDeviceSpec()), |
| er, |
| networkProperties); |
| |
| // Create structures for input & output |
| std::vector<float> inputData1(4, number); |
| std::vector<float> inputData2(4, number); |
| ConstTensor inputTensor1(tensorInfo, inputData1.data()); |
| ConstTensor inputTensor2(tensorInfo, inputData2.data()); |
| |
| std::vector<float> outputData1(4); |
| Tensor outputTensor1{tensorInfo, outputData1.data()}; |
| |
| // ImportInputs separates the importing and mapping of InputTensors from network execution. |
| // Allowing for a set of InputTensors to be imported and mapped once, but used in execution many times. |
| // ImportInputs is not thread safe and must not be used while other threads are calling Execute(). |
| // Only compatible with AsyncEnabled networks |
| |
| // PreImport inputTensors giving pre-imported ids of 1 and 2 |
| std::vector<ImportedInputId> importedInputVec = runtime->ImportInputs(networkIdentifier1, |
| {{0, inputTensor1}, {1, inputTensor2}}); |
| |
| // Create a new unique WorkingMemHandle object. Create multiple handles if you wish to have |
| // overlapped Execution by calling this function from different threads. |
| auto memHandle = runtime->CreateWorkingMemHandle(networkIdentifier1); |
| |
| // Execute evaluates a network using input in inputTensors and outputs filled into outputTensors. |
| // This function performs a thread safe execution of the network. Returns once execution is complete. |
| // Will block until this and any other thread using the same workingMem object completes. |
| // Execute with PreImported inputTensor1 as well as Non-PreImported inputTensor2 |
| runtime->Execute(*memHandle.get(), {}, {{2, outputTensor1}}, importedInputVec /* pre-imported ids */); |
| |
| // ImportOutputs separates the importing and mapping of OutputTensors from network execution. |
| // Allowing for a set of OutputTensors to be imported and mapped once, but used in execution many times. |
| // This function is not thread safe and must not be used while other threads are calling Execute(). |
| // Only compatible with AsyncEnabled networks |
| // Provide layerBinding Id to outputTensor1 |
| std::pair<LayerBindingId, class Tensor> output1{2, outputTensor1}; |
| // PreImport outputTensor1 |
| std::vector<ImportedOutputId> importedOutputVec = runtime->ImportOutputs(networkIdentifier1, {output1}); |
| |
| // Execute with Non-PreImported inputTensor1 as well as PreImported inputTensor2 |
| runtime->Execute(*memHandle.get(), {{0, inputTensor1}}, {{2, outputTensor1}}, {1 /* pre-imported id */}); |
| |
| // Clear the previously PreImportedInput with the network Id and inputIds returned from ImportInputs() |
| // Note: This will happen automatically during destructor of armnn::LoadedNetwork |
| runtime->ClearImportedInputs(networkIdentifier1, importedInputVec); |
| |
| // Clear the previously PreImportedOutputs with the network Id and outputIds returned from ImportOutputs() |
| // Note: This will happen automatically during destructor of armnn::LoadedNetwork |
| runtime->ClearImportedOutputs(networkIdentifier1, importedOutputVec); |
| |
| // Execute with Non-PreImported inputTensor1, inputTensor2 and the PreImported outputTensor1 |
| runtime->Execute(*memHandle.get(), {{0, inputTensor1}, {1, inputTensor2}}, {{2, outputTensor1}}); |
| |
| std::cout << "Your number was " << outputData1.data()[0] << std::endl; |
| |
| return 0; |
| } |