| // |
| // Copyright © 2017 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. 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; |
| |
| // 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::Warning); |
| |
| // 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()); |
| 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)); |
| |
| //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; |
| |
| } |