inferencelayer 0.2.3

Kortexya's engine-native inference layer — LLM generation + embedding/encoder family on wgpu (WGSL kernels, any adapter) with a pure-Rust CPU fallback
Documentation 
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//! `tableformer` — Docling table-structure recognition on the engine, for manual A/B.
//!
//! Feeds a page image (rendered at scale 2.0) + a table bbox — or a standalone crop — through the
//! full port (`cv_resize` prep → resnet18-trunc encoder → tag transformer greedy OTSL decode →
//! bbox decoder + mergebboxes) and prints the OTSL tags + per-cell class/box as JSON.
//!
//!   tableformer <model-dir> --page <page.png> --bbox l,t,r,b
//!   tableformer <model-dir> --crop <table.png>        # bbox = whole image
//!
//! `<model-dir>` holds the pinned `tableformer_accurate.safetensors` (+ tm_config.json).
//! `OSFKB_TABLEFORMER_TRACE=1` prints per-stage wall times.

use std::path::PathBuf;

use anyhow::{Context, Result, bail};
use inferencelayer::tableformer::TableFormer;
use inferencelayer::vision::decode_rgb8;
use serde_json::json;

fn main() -> Result<()> {
    let usage = "usage: tableformer <model-dir> (--page <png> --bbox l,t,r,b | --crop <png>)";
    let mut args = std::env::args().skip(1);
    let dir = PathBuf::from(args.next().context(usage)?);

    let mut page: Option<PathBuf> = None;
    let mut crop: Option<PathBuf> = None;
    let mut bbox: Option<[f32; 4]> = None;
    while let Some(a) = args.next() {
        match a.as_str() {
            "--page" => page = Some(PathBuf::from(args.next().context("--page needs a path")?)),
            "--crop" => crop = Some(PathBuf::from(args.next().context("--crop needs a path")?)),
            "--bbox" => {
                let s = args.next().context("--bbox needs l,t,r,b")?;
                let v: Vec<f32> = s
                    .split(',')
                    .map(|x| x.trim().parse())
                    .collect::<Result<_, _>>()?;
                if v.len() != 4 {
                    bail!(
                        "--bbox needs exactly 4 comma-separated numbers, got {}",
                        v.len()
                    );
                }
                bbox = Some([v[0], v[1], v[2], v[3]]);
            }
            other => bail!("unknown arg {other}\n{usage}"),
        }
    }

    let model = TableFormer::load(&dir).context("load tableformer")?;

    let (rgb, w, h, table_bbox) = match (page, crop) {
        (Some(p), _) => {
            let (rgb, w, h) = decode_rgb8(&std::fs::read(&p)?)?;
            let bb = bbox.context("--page requires --bbox l,t,r,b")?;
            (rgb, w, h, bb)
        }
        (None, Some(c)) => {
            let (rgb, w, h) = decode_rgb8(&std::fs::read(&c)?)?;
            (rgb, w, h, [0.0, 0.0, w as f32, h as f32])
        }
        (None, None) => bail!("{usage}"),
    };

    let out = model.recognize(&rgb, w, h, table_bbox);
    let cells: Vec<_> = out
        .cell_classes
        .iter()
        .zip(&out.bboxes)
        .map(|(&class, b)| json!({ "class": class, "cxcywh": b }))
        .collect();
    let result = json!({
        "otsl": out.otsl_seq,
        "num_cells": out.bboxes.len(),
        "cells": cells,
    });
    println!("{}", serde_json::to_string_pretty(&result)?);
    Ok(())
}