WasmEdge Tensorflow Interface

A Rust library that provides Rust to WebAssembly developers with syntax for using tensorflow functionality when their Wasm is being executed on WasmEdge (formerly SSVM).

From a high-level overview here, we are essentially building a tensorflow interface that will allow the native operating system (which WasmEdge is running on) to play a part in the runtime execution. Specifically, play a part in using tensorflow with graphs and input and output tensors as part of Wasm execution.

How to use this library

Rust dependency

Developers will add the wasmedge_tensorflow_interface crate as a dependency to their Rust -> Wasm applications. For example, add the following line to the application's Cargo.toml file.

[dependencies]
wasmedge_tensorflow_interface = "^0.2.2"

Developers will bring the functions of wasmedge_tensorflow_interface into scope within their Rust -> Wasm application's code. For example, adding the following code to the top of their main.rs

use wasmedge_tensorflow_interface;

Image Loading And Conversion

let mut file_img = File::open("sample.jpg").unwrap();
let mut img_buf = Vec::new();
file_img.read_to_end(&mut img_buf).unwrap();
let flat_img = wasmedge_tensorflow_interface::load_jpg_image_to_rgb32f(&img_buf, 224, 224);
// The flat_img is a vec<f32> which contains normalized image in rgb32f format and resized to 224x224.

Create Session

// The mod_buf is a vec<u8> which contains model data.
let mut session = wasmedge_tensorflow_interface::Session::new(&mod_buf, wasmedge_tensorflow_interface::ModelType::TensorFlow);

Or use the wasmedge_tensorflow_interface::ModelType::TensorFlowLite to specify the tflite models.

Prepare Input Tensors

// The flat_img is a vec<f32> which contains normalized image in rgb32f format.
session.add_input("input", &flat_img, &[1, 224, 224, 3])
       .add_output("MobilenetV2/Predictions/Softmax");

Run TensorFlow Models

session.run();

Convert Output Tensors

let res_vec: Vec<f32> = session.get_output("MobilenetV2/Predictions/Softmax");

Build And Execution

$ cargo build --target=wasm32-wasi

The output WASM file will be at target/wasm32-wasi/debug/ or target/wasm32-wasi/release. Please refer to the WasmEdge tools with tensorflow extension for WASM execution.

Crates.io

The official crate is available at crates.io.