3 releases
0.2.2 | Oct 19, 2021 |
---|---|
0.2.1 | Oct 19, 2021 |
0.2.0 | May 19, 2021 |
#784 in WebAssembly
28 downloads per month
19KB
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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.