samples/SimpleSample.cpp

   1 //
   2 // Copyright © 2017 Arm Ltd. All rights reserved.
   3 // SPDX-License-Identifier: MIT
   4 //
   5 #include <armnn/INetwork.hpp>
   6 #include <armnn/IRuntime.hpp>
   7 #include <armnn/Utils.hpp>
   8 #include <armnn/Descriptors.hpp>
   9
  10 #include <iostream>
  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
  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
  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);
  34     IConnectableLayer *fullyConnected = myNetwork->AddFullyConnectedLayer(fullyConnectedDesc,
  35                                                                           weights,
  36                                                                           EmptyOptional(),
  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());
  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     }
  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 }