Tract

小巧、实用、独立，可移植的 TensorFlow 和 ONNX 推理。

示例

use tract_core::internal::*;

// build a simple model that just add 3 to each input component
let mut model = TypedModel::default();

let input_fact = f32::fact(&[3]);
let input = model.add_source("input", input_fact).unwrap();
let three = model.add_const("three".to_string(), tensor1(&[3f32])).unwrap();
let add = model.wire_node("add".to_string(),
    tract_core::ops::math::add(),
    [input, three].as_ref()
    ).unwrap();

model.auto_outputs().unwrap();

// We build an execution plan. Default inputs and outputs are inferred from
// the model graph.
let plan = SimplePlan::new(&model).unwrap();

// run the computation.
let input = tensor1(&[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();

assert_eq!(tensor, tensor1(&[4.0f32, 5.5, 8.0]).into());

虽然从 Rust 代码创建模型对测试库很有用，但实际应用通常使用 tract-tensorflow 或 tract-onnx crate 加载 TensorFlow 或 ONNX 模型。