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Add Capabilities/Utility Functions section in Building Graph in C++

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PiperOrigin-RevId: 508773788
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MediaPipe Team 2023-02-10 15:52:37 -08:00 committed by Copybara-Service
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commit 482ee8f96c
1 changed files with 74 additions and 9 deletions
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83
docs/framework_concepts/building_graphs_cpp.md
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@ -17,8 +17,8 @@ nav_order: 1
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)
* 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)
@ -37,7 +37,6 @@ input_side_packet: "model"
// Graph outputs.
output_stream: "output_tensors"
// Nodes.
node {
calculator: "InferenceCalculator"
input_stream: "TENSORS:input_tensors"
@ -64,7 +63,6 @@ CalculatorGraphConfig BuildGraph() {
SidePacket<TfLiteModelPtr> model =
graph.SideIn(0).SetName("model").Cast<TfLiteModelPtr>();
// Nodes.
auto& inference_node = graph.AddNode("InferenceCalculator");
auto& inference_opts =
inference_node.GetOptions<InferenceCalculatorOptions>();
@ -87,12 +85,79 @@ 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`)
* 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.
* `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:
```c++
// 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:
```c++
// 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 units (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).