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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <iostream>
#include "armnn/ArmNN.hpp"
/// A simple example of using the ArmNN SDK API. In this sample, the users single input number is multiplied by 1.0f
/// using a fully connected layer with a single neuron to produce an output number that is the same as the input.
int main()
{
using namespace armnn;
float number;
std::cout << "Please enter a number: " << std::endl;
std::cin >> number;
// Construct ArmNN network
armnn::NetworkId networkIdentifier;
INetworkPtr myNetwork = INetwork::Create();
armnn::FullyConnectedDescriptor fullyConnectedDesc;
float weightsData[] = {1.0f}; // Identity
TensorInfo weightsInfo(TensorShape({1, 1}), DataType::Float32);
armnn::ConstTensor weights(weightsInfo, weightsData);
IConnectableLayer *fullyConnected = myNetwork->AddFullyConnectedLayer(fullyConnectedDesc,
weights,
EmptyOptional(),
"fully connected");
IConnectableLayer *InputLayer = myNetwork->AddInputLayer(0);
IConnectableLayer *OutputLayer = myNetwork->AddOutputLayer(0);
InputLayer->GetOutputSlot(0).Connect(fullyConnected->GetInputSlot(0));
fullyConnected->GetOutputSlot(0).Connect(OutputLayer->GetInputSlot(0));
// Create ArmNN runtime
IRuntime::CreationOptions options; // default options
IRuntimePtr run = IRuntime::Create(options);
//Set the tensors in the network.
TensorInfo inputTensorInfo(TensorShape({1, 1}), DataType::Float32);
InputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
TensorInfo outputTensorInfo(TensorShape({1, 1}), DataType::Float32);
fullyConnected->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
// Optimise ArmNN network
armnn::IOptimizedNetworkPtr optNet = Optimize(*myNetwork, {Compute::CpuRef}, run->GetDeviceSpec());
// Load graph into runtime
run->LoadNetwork(networkIdentifier, std::move(optNet));
//Creates structures for inputs and outputs.
std::vector<float> inputData{number};
std::vector<float> outputData(1);
armnn::InputTensors inputTensors{{0, armnn::ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0),
inputData.data())}};
armnn::OutputTensors outputTensors{{0, armnn::Tensor(run->GetOutputTensorInfo(networkIdentifier, 0),
outputData.data())}};
// Execute network
run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);
std::cout << "Your number was " << outputData[0] << std::endl;
return 0;
}
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