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//
// 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
ARMNN_NO_DEPRECATE_WARN_BEGIN
runtime->Execute(*memHandle.get(), {}, {{2, outputTensor1}}, importedInputVec /* pre-imported ids */);
ARMNN_NO_DEPRECATE_WARN_END
// 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});
ARMNN_NO_DEPRECATE_WARN_BEGIN
// Execute with Non-PreImported inputTensor1 as well as PreImported inputTensor2
runtime->Execute(*memHandle.get(), {{0, inputTensor1}}, {{2, outputTensor1}}, {1 /* pre-imported id */});
ARMNN_NO_DEPRECATE_WARN_END
// 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);
ARMNN_NO_DEPRECATE_WARN_BEGIN
// Execute with Non-PreImported inputTensor1, inputTensor2 and the PreImported outputTensor1
runtime->Execute(*memHandle.get(), {{0, inputTensor1}, {1, inputTensor2}}, {{2, outputTensor1}});
ARMNN_NO_DEPRECATE_WARN_END
std::cout << "Your number was " << outputData1.data()[0] << std::endl;
return 0;
}
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