Adds "Building Graphs in C++" initial page and updates "Graph" page to link to that section showcasing alternative C++ graph representation.
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docs/framework_concepts/building_graphs_cpp.md
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docs/framework_concepts/building_graphs_cpp.md
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---
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layout: default
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title: Building Graphs in C++
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parent: Graphs
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nav_order: 1
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---
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# Building Graphs in C++
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{: .no_toc }
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1. TOC
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{:toc}
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---
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## C++ Graph Builder
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C++ graph builder is a powerful tool for:
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* Building complex graphs
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* Parametrizing graphs (e.g. setting a delegate on
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`InferenceCalculator`, enabling/disabling parts of the graph)
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* Deduplicating graphs (e.g. instead of CPU and GPU dedicated graphs in pbtxt
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you can have a single code that constructs required graphs, sharing as much
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as possible)
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* Supporting optional graph inputs/outputs
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* Customizing graphs per platform
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### Basic Usage
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Let's see how C++ graph builder can be used for a simple graph:
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```proto
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// Graph inputs.
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input_stream: "input_tensors"
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input_side_packet: "model"
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// Graph outputs.
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output_stream: "output_tensors"
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// Nodes.
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node {
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calculator: "InferenceCalculator"
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input_stream: "TENSORS:input_tensors"
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input_side_packet: "MODEL:model"
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output_stream: "TENSORS:output_tensors"
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node_options: {
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[type.googleapis.com/mediapipe.InferenceCalculatorOptions] {
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# Requesting GPU delegate.
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delegate { gpu {} }
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}
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}
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}
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```
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Function to build the above `CalculatorGraphConfig` may look like:
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```c++
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CalculatorGraphConfig BuildGraph() {
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Graph graph;
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// Graph inputs.
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Stream<std::vector<Tensor>> input_tensors =
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graph.In(0).SetName("input_tensors").Cast<std::vector<Tensor>>();
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SidePacket<TfLiteModelPtr> model =
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graph.SideIn(0).SetName("model").Cast<TfLiteModelPtr>();
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// Nodes.
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auto& inference_node = graph.AddNode("InferenceCalculator");
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auto& inference_opts =
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inference_node.GetOptions<InferenceCalculatorOptions>();
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// Requesting GPU delegate.
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inference_opts.mutable_delegate()->mutable_gpu();
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input_tensors.ConnectTo(inference_node.In("TENSORS"));
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model.ConnectTo(inference_node.SideIn("MODEL"));
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Stream<std::vector<Tensor>> output_tensors =
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inference_node.Out("TENSORS").Cast<std::vector<Tensor>>();
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// Graph outputs.
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output_tensors.SetName("output_tensors").ConnectTo(graph.Out(0));
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// Get `CalculatorGraphConfig` to pass it into `CalculatorGraph`
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return graph.GetConfig();
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}
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```
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Short summary:
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* Use `Graph::In/SideIn` to get graph inputs as `Stream/SidePacket`
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* Use `Node::Out/SideOut` to get node outputs as `Stream/SidePacket`
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* Use `Stream/SidePacket::ConnectTo` to connect streams and side packets to node
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inputs (`Node::In/SideIn`) and graph outputs (`Graph::Out/SideOut`)
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* There's a "shortcut" operator `>>` that you can use instead of
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`ConnectTo` function (E.g. `x >> node.In("IN")`).
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* `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
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* Using actual types instead of `AnyType` sets you on a better path for unleashing graph
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builder capabilities and improving your graphs readability.
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@ -36,6 +36,7 @@ passthrough calculators :
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# This graph named main_pass_throughcals_nosubgraph.pbtxt contains 4
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# passthrough calculators.
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input_stream: "in"
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output_stream: "out"
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node {
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calculator: "PassThroughCalculator"
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input_stream: "in"
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@ -54,10 +55,39 @@ node {
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node {
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calculator: "PassThroughCalculator"
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input_stream: "out3"
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output_stream: "out4"
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output_stream: "out"
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}
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```
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MediaPipe offers an alternative `C++` representation for complex graphs (e.g. ML pipelines, handling model metadata, optional nodes, etc.). The above graph may look like:
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```c++
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CalculatorGraphConfig BuildGraphConfig() {
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Graph graph;
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// Graph inputs
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Stream<AnyType> in = graph.In(0).SetName("in");
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auto pass_through_fn = [](Stream<AnyType> in,
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Graph& graph) -> Stream<AnyType> {
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auto& node = graph.AddNode("PassThroughCalculator");
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in.ConnectTo(node.In(0));
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return node.Out(0);
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};
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Stream<AnyType> out1 = pass_through_fn(in, graph);
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Stream<AnyType> out2 = pass_through_fn(out1, graph);
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Stream<AnyType> out3 = pass_through_fn(out2, graph);
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Stream<AnyType> out4 = pass_through_fn(out3, graph);
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// Graph outputs
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out4.SetName("out").ConnectTo(graph.Out(0));
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return graph.GetConfig();
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}
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```
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See more details in [Building Graphs in C++](building_graphs_cpp.md)
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## Subgraph
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To modularize a `CalculatorGraphConfig` into sub-modules and assist with re-use
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