tract-core 0.2.1

Tiny, no-nonsense, self contained, TensorFlow and ONNX inference
Documentation
# Tract Tiny, no-nonsense, self contained, portable SharedTensor and ONNX inference. ## Example ``` # extern crate tract_core; # extern crate ndarray; # fn main() { use tract_core::*; use tract_core::model::*; use tract_core::model::dsl::*; // build a simple model that just add 3 to each input component let mut model = Model::default(); let input = model.add_source("input").unwrap(); let three = model.add_const("three".to_string(), 3f32.into()).unwrap(); let add = model.add_node("add".to_string(), Box::new(tract_core::ops::math::Add::default())).unwrap(); model.add_edge(OutletId::new(input, 0), InletId::new(add, 0)).unwrap(); model.add_edge(OutletId::new(three, 0), InletId::new(add, 1)).unwrap(); // we build an execution plan. default input and output are inferred from // the model graph let plan = SimplePlan::new(&model).unwrap(); // run the computation. let input = ndarray::arr1(&[1.0f32, 2.5, 5.0]); let mut outputs = plan.run(tvec![input.into()]).unwrap(); // take the first and only output tensor let mut tensor = outputs.pop().unwrap(); // unwrap it as array of f32 let tensor = tensor.to_array_view::().unwrap(); assert_eq!(tensor, ndarray::arr1(&[4.0, 5.5, 8.0]).into_dyn()); # } ``` While creating a model from Rust code is usefull for testing the library, real-life use-cases will usually load a SharedTensor or ONNX model using tract-tf or tract-onnx crates.