mediapipe/docs/framework_concepts/building_graphs_cpp.md

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default	Building Graphs in C++	Graphs	1

Building Graphs in C++

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1. TOC {:toc}

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C++ Graph Builder

C++ graph builder is a powerful tool for:

• Building complex graphs
• Parametrizing graphs (e.g. setting a delegate on InferenceCalculator, enabling/disabling parts of the graph)
• Deduplicating graphs (e.g. instead of CPU and GPU dedicated graphs in pbtxt you can have a single code that constructs required graphs, sharing as much as possible)
• Supporting optional graph inputs/outputs
• Customizing graphs per platform

Basic Usage

Let's see how C++ graph builder can be used for a simple graph:

// Graph inputs.
input_stream: "input_tensors"
input_side_packet: "model"

// Graph outputs.
output_stream: "output_tensors"

node {
  calculator: "InferenceCalculator"
  input_stream: "TENSORS:input_tensors"
  input_side_packet: "MODEL:model"
  output_stream: "TENSORS:output_tensors"
  node_options: {
    [type.googleapis.com/mediapipe.InferenceCalculatorOptions] {
      # Requesting GPU delegate.
      delegate { gpu {} }
    }
  }
}

Function to build the above CalculatorGraphConfig may look like:

CalculatorGraphConfig BuildGraph() {
  Graph graph;

  // Graph inputs.
  Stream<std::vector<Tensor>> input_tensors =
      graph.In(0).SetName("input_tensors").Cast<std::vector<Tensor>>();
  SidePacket<TfLiteModelPtr> model =
      graph.SideIn(0).SetName("model").Cast<TfLiteModelPtr>();

  auto& inference_node = graph.AddNode("InferenceCalculator");
  auto& inference_opts =
      inference_node.GetOptions<InferenceCalculatorOptions>();
  // Requesting GPU delegate.
  inference_opts.mutable_delegate()->mutable_gpu();
  input_tensors.ConnectTo(inference_node.In("TENSORS"));
  model.ConnectTo(inference_node.SideIn("MODEL"));
  Stream<std::vector<Tensor>> output_tensors =
      inference_node.Out("TENSORS").Cast<std::vector<Tensor>>();

  // Graph outputs.
  output_tensors.SetName("output_tensors").ConnectTo(graph.Out(0));

  // Get `CalculatorGraphConfig` to pass it into `CalculatorGraph`
  return graph.GetConfig();
}

Short summary:

• Use Graph::In/SideIn to get graph inputs as Stream/SidePacket
• Use Node::Out/SideOut to get node outputs as Stream/SidePacket
• Use Stream/SidePacket::ConnectTo to connect streams and side packets to node inputs (Node::In/SideIn) and graph outputs (Graph::Out/SideOut)
  • There's a "shortcut" operator >> that you can use instead of ConnectTo function (E.g. x >> node.In("IN")).
• Stream/SidePacket::Cast is used to cast stream or side packet of AnyType (E.g. Stream<AnyType> in = graph.In(0);) to a particular type
  • Using actual types instead of AnyType sets you on a better path for unleashing graph builder capabilities and improving your graphs readability.

Advanced Usage

Utility Functions

Let's extract inference construction code into a dedicated utility function to help for readability and code reuse:

// Updates graph to run inference.
Stream<std::vector<Tensor>> RunInference(
    Stream<std::vector<Tensor>> tensors, SidePacket<TfLiteModelPtr> model,
    const InferenceCalculatorOptions::Delegate& delegate, Graph& graph) {
  auto& inference_node = graph.AddNode("InferenceCalculator");
  auto& inference_opts =
      inference_node.GetOptions<InferenceCalculatorOptions>();
  *inference_opts.mutable_delegate() = delegate;
  tensors.ConnectTo(inference_node.In("TENSORS"));
  model.ConnectTo(inference_node.SideIn("MODEL"));
  return inference_node.Out("TENSORS").Cast<std::vector<Tensor>>();
}

CalculatorGraphConfig BuildGraph() {
  Graph graph;

  // Graph inputs.
  Stream<std::vector<Tensor>> input_tensors =
      graph.In(0).SetName("input_tensors").Cast<std::vector<Tensor>>();
  SidePacket<TfLiteModelPtr> model =
      graph.SideIn(0).SetName("model").Cast<TfLiteModelPtr>();

  InferenceCalculatorOptions::Delegate delegate;
  delegate.mutable_gpu();
  Stream<std::vector<Tensor>> output_tensors =
      RunInference(input_tensors, model, delegate, graph);

  // Graph outputs.
  output_tensors.SetName("output_tensors").ConnectTo(graph.Out(0));

  return graph.GetConfig();
}

As a result, RunInference provides a clear interface stating what are the inputs/outputs and their types.

It can be easily reused, e.g. it's only a few lines if you want to run an extra model inference:

  // Run first inference.
  Stream<std::vector<Tensor>> output_tensors =
      RunInference(input_tensors, model, delegate, graph);
  // Run second inference on the output of the first one.
  Stream<std::vector<Tensor>> extra_output_tensors =
      RunInference(output_tensors, extra_model, delegate, graph);

And you don't need to duplicate names and tags (InferenceCalculator, TENSORS, MODEL) or introduce dedicated constants here and there - those details are localized to RunInference function.

Tip: extracting RunInference and similar functions to dedicated modules (e.g. inference.h/cc which depends on the inference calculator) enables reuse in graphs construction code and helps automatically pull in calculator dependencies (e.g. no need to manually add :inference_calculator dep, just let your IDE include inference.h and build cleaner pull in corresponding dependency).

Utility Classes

And surely, it's not only about functions, in some cases it's beneficial to introduce utility classes which can help making your graph construction code more readable and less error prone.

MediaPipe offers PassThroughCalculator calculator, which is simply passing through its inputs:

input_stream: "float_value"
input_stream: "int_value"
input_stream: "bool_value"

output_stream: "passed_float_value"
output_stream: "passed_int_value"
output_stream: "passed_bool_value"

node {
  calculator: "PassThroughCalculator"
  input_stream: "float_value"
  input_stream: "int_value"
  input_stream: "bool_value"
  // The order must be the same as for inputs (or you can use explicit indexes)
  output_stream: "passed_float_value"
  output_stream: "passed_int_value"
  output_stream: "passed_bool_value"
}

Let's see the straightforward C++ construction code to create the above graph:

CalculatorGraphConfig BuildGraph() {
  Graph graph;

  // Graph inputs.
  Stream<float> float_value = graph.In(0).SetName("float_value").Cast<float>();
  Stream<int> int_value = graph.In(1).SetName("int_value").Cast<int>();
  Stream<bool> bool_value = graph.In(2).SetName("bool_value").Cast<bool>();

  auto& pass_node = graph.AddNode("PassThroughCalculator");
  float_value.ConnectTo(pass_node.In("")[0]);
  int_value.ConnectTo(pass_node.In("")[1]);
  bool_value.ConnectTo(pass_node.In("")[2]);
  Stream<float> passed_float_value = pass_node.Out("")[0].Cast<float>();
  Stream<int> passed_int_value = pass_node.Out("")[1].Cast<int>();
  Stream<bool> passed_bool_value = pass_node.Out("")[2].Cast<bool>();

  // Graph outputs.
  passed_float_value.SetName("passed_float_value").ConnectTo(graph.Out(0));
  passed_int_value.SetName("passed_int_value").ConnectTo(graph.Out(1));
  passed_bool_value.SetName("passed_bool_value").ConnectTo(graph.Out(2));

  // Get `CalculatorGraphConfig` to pass it into `CalculatorGraph`
  return graph.GetConfig();
}

While pbtxt representation maybe error prone (when we have many inputs to pass through), C++ code looks even worse: repeated empty tags and Cast calls. Let's see how we can do better by introducing a PassThroughNodeBuilder:

class PassThroughNodeBuilder {
 public:
  explicit PassThroughNodeBuilder(Graph& graph)
      : node_(graph.AddNode("PassThroughCalculator")) {}

  template <typename T>
  Stream<T> PassThrough(Stream<T> stream) {
    stream.ConnectTo(node_.In(index_));
    return node_.Out(index_++).Cast<T>();
  }

 private:
  int index_ = 0;
  GenericNode& node_;
};

And now graph construction code can look like:

CalculatorGraphConfig BuildGraph() {
  Graph graph;

  // Graph inputs.
  Stream<float> float_value = graph.In(0).SetName("float_value").Cast<float>();
  Stream<int> int_value = graph.In(1).SetName("int_value").Cast<int>();
  Stream<bool> bool_value = graph.In(2).SetName("bool_value").Cast<bool>();

  PassThroughNodeBuilder pass_node_builder(graph);
  Stream<float> passed_float_value = pass_node_builder.PassThrough(float_value);
  Stream<int> passed_int_value = pass_node_builder.PassThrough(int_value);
  Stream<bool> passed_bool_value = pass_node_builder.PassThrough(bool_value);

  // Graph outputs.
  passed_float_value.SetName("passed_float_value").ConnectTo(graph.Out(0));
  passed_int_value.SetName("passed_int_value").ConnectTo(graph.Out(1));
  passed_bool_value.SetName("passed_bool_value").ConnectTo(graph.Out(2));

  // Get `CalculatorGraphConfig` to pass it into `CalculatorGraph`
  return graph.GetConfig();
}

Now you can't have incorrect order or index in your pass through construction code and save some typing by guessing the type for Cast from the PassThrough input.

Tip: the same as for the RunInference function, extracting PassThroughNodeBuilder and similar utility classes into dedicated modules enables reuse in graph construction code and helps to automatically pull in the corresponding calculator dependencies.