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//
// Copyright © 2020 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <armnn/INetwork.hpp>
#include <armnn/IRuntime.hpp>
#include <armnn/Utils.hpp>
#include <armnn/Descriptors.hpp>
#include <iostream>
/// A simple example of using the ArmNN SDK API with the standalone sample dynamic backend.
/// In this example, an addition layer is used to add 2 input tensors to produce a result output tensor.
int main()
{
using namespace armnn;
// Construct ArmNN network
armnn::NetworkId networkIdentifier;
INetworkPtr myNetwork = INetwork::Create();
IConnectableLayer* input0 = myNetwork->AddInputLayer(0);
IConnectableLayer* input1 = myNetwork->AddInputLayer(1);
IConnectableLayer* add = myNetwork->AddAdditionLayer();
IConnectableLayer* output = myNetwork->AddOutputLayer(0);
input0->GetOutputSlot(0).Connect(add->GetInputSlot(0));
input1->GetOutputSlot(0).Connect(add->GetInputSlot(1));
add->GetOutputSlot(0).Connect(output->GetInputSlot(0));
TensorInfo tensorInfo(TensorShape({2, 1}), DataType::Float32);
input0->GetOutputSlot(0).SetTensorInfo(tensorInfo);
input1->GetOutputSlot(0).SetTensorInfo(tensorInfo);
add->GetOutputSlot(0).SetTensorInfo(tensorInfo);
// Create ArmNN runtime
IRuntime::CreationOptions options; // default options
armnn::IRuntimePtr run(armnn::IRuntime::Create(options));
// Optimise ArmNN network
armnn::IOptimizedNetworkPtr optNet = Optimize(*myNetwork, {"SampleDynamic"}, run->GetDeviceSpec());
if (!optNet)
{
// This shouldn't happen for this simple sample, with reference backend.
// But in general usage Optimize could fail if the hardware at runtime cannot
// support the model that has been provided.
std::cerr << "Error: Failed to optimise the input network." << std::endl;
return 1;
}
// Load graph into runtime
run->LoadNetwork(networkIdentifier, std::move(optNet));
// input data
std::vector<float> input0Data
{
5.0f, 3.0f
};
std::vector<float> input1Data
{
10.0f, 8.0f
};
std::vector<float> outputData(2);
InputTensors inputTensors
{
{0,armnn::ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), input0Data.data())},
{1,armnn::ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), input1Data.data())}
};
OutputTensors outputTensors
{
{0,armnn::Tensor(run->GetOutputTensorInfo(networkIdentifier, 0), outputData.data())}
};
// Execute network
run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);
std::cout << "Addition operator result is {" << outputData[0] << "," << outputData[1] << "}" << std::endl;
return 0;
}
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