telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
David Beck | ecb56cd | 2018-09-05 12:52:57 +0100 | [diff] [blame] | 3 | // SPDX-License-Identifier: MIT |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 4 | // |
Matthew Bentham | 93b622f | 2020-01-17 09:36:49 +0000 | [diff] [blame] | 5 | #include <armnn/INetwork.hpp> |
| 6 | #include <armnn/IRuntime.hpp> |
| 7 | #include <armnn/Utils.hpp> |
| 8 | #include <armnn/Descriptors.hpp> |
| 9 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 10 | #include <iostream> |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 11 | |
| 12 | /// A simple example of using the ArmNN SDK API. In this sample, the users single input number is multiplied by 1.0f |
| 13 | /// using a fully connected layer with a single neuron to produce an output number that is the same as the input. |
| 14 | int main() |
| 15 | { |
| 16 | using namespace armnn; |
| 17 | |
| 18 | float number; |
| 19 | std::cout << "Please enter a number: " << std::endl; |
| 20 | std::cin >> number; |
| 21 | |
Matthew Bentham | 93b622f | 2020-01-17 09:36:49 +0000 | [diff] [blame] | 22 | // Turn on logging to standard output |
| 23 | // This is useful in this sample so that users can learn more about what is going on |
| 24 | armnn::ConfigureLogging(true, false, LogSeverity::Warning); |
| 25 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 26 | // Construct ArmNN network |
| 27 | armnn::NetworkId networkIdentifier; |
| 28 | INetworkPtr myNetwork = INetwork::Create(); |
| 29 | |
| 30 | armnn::FullyConnectedDescriptor fullyConnectedDesc; |
| 31 | float weightsData[] = {1.0f}; // Identity |
| 32 | TensorInfo weightsInfo(TensorShape({1, 1}), DataType::Float32); |
| 33 | armnn::ConstTensor weights(weightsInfo, weightsData); |
Matteo Martincigh | fc598e1 | 2019-05-14 10:36:13 +0100 | [diff] [blame] | 34 | IConnectableLayer *fullyConnected = myNetwork->AddFullyConnectedLayer(fullyConnectedDesc, |
| 35 | weights, |
| 36 | EmptyOptional(), |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 37 | "fully connected"); |
| 38 | |
| 39 | IConnectableLayer *InputLayer = myNetwork->AddInputLayer(0); |
| 40 | IConnectableLayer *OutputLayer = myNetwork->AddOutputLayer(0); |
| 41 | |
| 42 | InputLayer->GetOutputSlot(0).Connect(fullyConnected->GetInputSlot(0)); |
| 43 | fullyConnected->GetOutputSlot(0).Connect(OutputLayer->GetInputSlot(0)); |
| 44 | |
| 45 | // Create ArmNN runtime |
| 46 | IRuntime::CreationOptions options; // default options |
| 47 | IRuntimePtr run = IRuntime::Create(options); |
| 48 | |
| 49 | //Set the tensors in the network. |
| 50 | TensorInfo inputTensorInfo(TensorShape({1, 1}), DataType::Float32); |
| 51 | InputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); |
| 52 | |
| 53 | TensorInfo outputTensorInfo(TensorShape({1, 1}), DataType::Float32); |
| 54 | fullyConnected->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 55 | |
| 56 | // Optimise ArmNN network |
| 57 | armnn::IOptimizedNetworkPtr optNet = Optimize(*myNetwork, {Compute::CpuRef}, run->GetDeviceSpec()); |
Matthew Bentham | 93b622f | 2020-01-17 09:36:49 +0000 | [diff] [blame] | 58 | if (!optNet) |
| 59 | { |
| 60 | // This shouldn't happen for this simple sample, with reference backend. |
| 61 | // But in general usage Optimize could fail if the hardware at runtime cannot |
| 62 | // support the model that has been provided. |
| 63 | std::cerr << "Error: Failed to optimise the input network." << std::endl; |
| 64 | return 1; |
| 65 | } |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 66 | |
| 67 | // Load graph into runtime |
| 68 | run->LoadNetwork(networkIdentifier, std::move(optNet)); |
| 69 | |
| 70 | //Creates structures for inputs and outputs. |
| 71 | std::vector<float> inputData{number}; |
| 72 | std::vector<float> outputData(1); |
| 73 | |
| 74 | |
| 75 | armnn::InputTensors inputTensors{{0, armnn::ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), |
| 76 | inputData.data())}}; |
| 77 | armnn::OutputTensors outputTensors{{0, armnn::Tensor(run->GetOutputTensorInfo(networkIdentifier, 0), |
| 78 | outputData.data())}}; |
| 79 | |
| 80 | // Execute network |
| 81 | run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors); |
| 82 | |
| 83 | std::cout << "Your number was " << outputData[0] << std::endl; |
| 84 | return 0; |
| 85 | |
| 86 | } |