usls 0.1.11

A Rust library integrated with ONNXRuntime, providing a collection of ML models.
Documentation 
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use aksr::Builder;
use anyhow::Result;
use ndarray::Axis;

use crate::{elapsed_module, Config, Engine, Image, Mask, Ops, Processor, Xs, Y};

#[derive(Builder, Debug)]
pub struct MODNet {
    engine: Engine,
    height: usize,
    width: usize,
    batch: usize,
    spec: String,
    processor: Processor,
}

impl MODNet {
    pub fn new(config: Config) -> Result<Self> {
        let engine = Engine::try_from_config(&config.model)?;
        let spec = engine.spec().to_string();
        let (batch, height, width) = (
            engine.batch().opt(),
            engine.try_height().unwrap_or(&512.into()).opt(),
            engine.try_width().unwrap_or(&512.into()).opt(),
        );
        let processor = Processor::try_from_config(&config.processor)?
            .with_image_width(width as _)
            .with_image_height(height as _);

        Ok(Self {
            engine,
            height,
            width,
            batch,
            spec,
            processor,
        })
    }

    fn preprocess(&mut self, xs: &[Image]) -> Result<Xs> {
        Ok(self.processor.process_images(xs)?.into())
    }

    fn inference(&mut self, xs: Xs) -> Result<Xs> {
        self.engine.run(xs)
    }

    pub fn forward(&mut self, xs: &[Image]) -> Result<Vec<Y>> {
        let ys = elapsed_module!("MODNet", "preprocess", self.preprocess(xs)?);
        let ys = elapsed_module!("MODNet", "inference", self.inference(ys)?);
        let ys = elapsed_module!("MODNet", "postprocess", self.postprocess(ys)?);

        Ok(ys)
    }

    fn postprocess(&mut self, xs: Xs) -> Result<Vec<Y>> {
        let mut ys: Vec<Y> = Vec::new();
        for (idx, luma) in xs[0].axis_iter(Axis(0)).enumerate() {
            let (h1, w1) = (
                self.processor.images_transform_info[idx].height_src,
                self.processor.images_transform_info[idx].width_src,
            );

            let luma = luma.mapv(|x| (x * 255.0) as u8);
            let luma = Ops::resize_luma8_u8(
                &luma.into_raw_vec_and_offset().0,
                self.width as _,
                self.height as _,
                w1 as _,
                h1 as _,
                false,
                "Bilinear",
            )?;
            let luma: image::ImageBuffer<image::Luma<_>, Vec<_>> =
                match image::ImageBuffer::from_raw(w1 as _, h1 as _, luma) {
                    None => continue,
                    Some(x) => x,
                };
            ys.push(Y::default().with_masks(&[Mask::default().with_mask(luma)]));
        }

        Ok(ys)
    }
}