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.
| 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 | |
| 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 | ConfigureLogging(true, false, LogSeverity::Warning); |
| 25 | |
| 26 | // Construct ArmNN network |
| 27 | NetworkId networkIdentifier; |
| 28 | INetworkPtr myNetwork = INetwork::Create(); |
| 29 | |
| 30 | float weightsData[] = {1.0f}; // Identity |
| 31 | TensorInfo weightsInfo(TensorShape({1, 1}), DataType::Float32, 0.0f, 0, true); |
| 32 | weightsInfo.SetConstant(); |
| 33 | ConstTensor weights(weightsInfo, weightsData); |
| 34 | |
| 35 | // Constant layer that now holds weights data for FullyConnected |
| 36 | IConnectableLayer* const constantWeightsLayer = myNetwork->AddConstantLayer(weights, "const weights"); |
| 37 | |
| 38 | FullyConnectedDescriptor fullyConnectedDesc; |
| 39 | IConnectableLayer* const fullyConnectedLayer = myNetwork->AddFullyConnectedLayer(fullyConnectedDesc, |
| 40 | "fully connected"); |
| 41 | IConnectableLayer* InputLayer = myNetwork->AddInputLayer(0); |
| 42 | IConnectableLayer* OutputLayer = myNetwork->AddOutputLayer(0); |
| 43 | |
| 44 | InputLayer->GetOutputSlot(0).Connect(fullyConnectedLayer->GetInputSlot(0)); |
| 45 | constantWeightsLayer->GetOutputSlot(0).Connect(fullyConnectedLayer->GetInputSlot(1)); |
| 46 | fullyConnectedLayer->GetOutputSlot(0).Connect(OutputLayer->GetInputSlot(0)); |
| 47 | |
| 48 | // Create ArmNN runtime |
| 49 | IRuntime::CreationOptions options; // default options |
| 50 | IRuntimePtr run = IRuntime::Create(options); |
| 51 | |
| 52 | //Set the tensors in the network. |
| 53 | TensorInfo inputTensorInfo(TensorShape({1, 1}), DataType::Float32); |
| 54 | InputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); |
| 55 | |
| 56 | TensorInfo outputTensorInfo(TensorShape({1, 1}), DataType::Float32); |
| 57 | fullyConnectedLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 58 | constantWeightsLayer->GetOutputSlot(0).SetTensorInfo(weightsInfo); |
| 59 | |
| 60 | // Optimise ArmNN network |
| 61 | IOptimizedNetworkPtr optNet = Optimize(*myNetwork, {Compute::CpuRef}, run->GetDeviceSpec()); |
| 62 | if (!optNet) |
| 63 | { |
| 64 | // This shouldn't happen for this simple sample, with reference backend. |
| 65 | // But in general usage Optimize could fail if the hardware at runtime cannot |
| 66 | // support the model that has been provided. |
| 67 | std::cerr << "Error: Failed to optimise the input network." << std::endl; |
| 68 | return 1; |
| 69 | } |
| 70 | |
| 71 | // Load graph into runtime |
nothing calls this directly
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