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