Crate tfmicro

TensorFlow + no_std + Rust = ❤️

The crate contains Rust bindings for the TensorFlow Lite for Microcontrollers project. TensorFlow Lite for Microcontrollers is a version of TensorFlow Lite designed to run without a standard library, for use on microcontrollers, wasm and more.

Thanks to Cargo and the CC crate, there's no porting required for new platforms - just drop tfmicro into your Cargo.toml and go. You will need a C++ compiler behind the scenes, including for cross-compiling targets, but in most cases this will be present already.

The aim of this implementation is to provide usability alongside all the usual Rust guarantees. Nonetheless this crate calls into a very significant quantity of C/C++ code, and you should employ the usual methods (static analysis, memory guards, fuzzing) as you would with any other legacy C/C++ codebase.

Currently pinned to TensorFlow 33689c48ad

Getting started

Add tfmicro in the dependencies section of your Cargo.toml

[dependencies]
tfmicro = 0.1.0

To understand how the TensorFlow Micro C examples map to idiomatic Rust code, see the Tests directory. Otherwise for a more general description see Usage.

Usage

Creating a model

Typically a model is exported from the TensorFlow training framework in a binary file format with extension .tflite. You can import this straight into Rust with the include_bytes! macro.

Then we can use the Model::from_buffer method to perform a zero-copy conversion into a Model.

let model_array = include_bytes!("../examples/models/hello_world.tflite");
let model = Model::from_buffer(&model_array[..]).unwrap();

Creating a tensor arena

The TensorFlow interpreter requires a working area called a "Tensor Arena". You can use an array on the stack for this, although it must remain in scope whilst you use it. Alternatively if you have a std or alloc environment, you can pass a heap-allocated Vec instead.

const TENSOR_ARENA_SIZE: usize = 4 * 1024;
let mut arena: [u8; TENSOR_ARENA_SIZE] = [0; TENSOR_ARENA_SIZE];

TensorFlow requires that the size of the arena is determined before creating the interpreter. However, once you have created the interpreter, you can get the number of bytes actually used by the model by calling arena_used_bytes.

Instantiating an Interpreter and Input Tensors

To run the model, an interpreter is built in much the same way as in the C API. Note that unlike the C API no error_reporter is required. Error reports from TensorFlow are always passed to the standard Rust log framework at the info log level. This allows any compatible log implementation to be used.

A op_resolver is required for the interpreter. The simplest option is to pass an AllOpResolver, but to save memory use a MutableOpResolver with the required operations only.

let op_resolver = AllOpResolver::new();

let mut interpreter =
    MicroInterpreter::new(&model, op_resolver, &mut arena[..]).unwrap();

interpreter.input(0, &[0.0]).unwrap(); // Input tensor of length 1

The input tensor is set with the input method. Simple models use only a single tensor, which can be specified with index 0.

Running the model and Output Tensors

The model is run by calling the invoke method on the interpreter. The resulting output tensor is available by calling the output method on the interpreter.

interpreter.invoke().unwrap();

dbg!(interpreter.output(0).as_data::<f32>());

And that's it for a minimal use case! See the Tests folder for more advanced use cases.

Developing

See DEVELOP.md

License (this crate)

Apache 2.0

Structs

AllOpResolver	An Op Resolver populated with all available operators
Frontend	Bindings for the audio "frontend" library for feature generation
MicroInterpreter	An interpreter for TensorFlow models
Model	A TensorFlow model
MutableOpResolver	An Op Resolver that has no operators by default, but can be added by calling methods in a builder pattern

Enums

Error	Error type for tfmicro
Status	The status resulting from a TensorFlow operation