| // |
| // 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, |
| "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; |
| |
| } |