Sketch the yolo wasm example. (#546)

* Sketch the yolo wasm example.

* Web ui.

* Get the web ui to work.

* UI tweaks.

* More UI tweaks.

* Use the natural width/height.

* Add a link to the hf space in the readme.
This commit is contained in:
Laurent Mazare
2023-08-22 11:56:43 +01:00
committed by GitHub
parent 44420d8ae1
commit 20ce3e9f39
13 changed files with 1260 additions and 5 deletions

View File

@ -0,0 +1,132 @@
use crate::model::{report, Bbox, Multiples, YoloV8};
use candle::{DType, Device, Result, Tensor};
use candle_nn::{Module, VarBuilder};
use serde::{Deserialize, Serialize};
use wasm_bindgen::prelude::*;
use yew_agent::{HandlerId, Public, WorkerLink};
#[wasm_bindgen]
extern "C" {
// Use `js_namespace` here to bind `console.log(..)` instead of just
// `log(..)`
#[wasm_bindgen(js_namespace = console)]
pub fn log(s: &str);
}
#[macro_export]
macro_rules! console_log {
// Note that this is using the `log` function imported above during
// `bare_bones`
($($t:tt)*) => ($crate::worker::log(&format_args!($($t)*).to_string()))
}
// Communication to the worker happens through bincode, the model weights and configs are fetched
// on the main thread and transfered via the following structure.
#[derive(Serialize, Deserialize)]
pub struct ModelData {
pub weights: Vec<u8>,
}
struct Model {
model: YoloV8,
}
impl Model {
fn run(
&self,
_link: &WorkerLink<Worker>,
_id: HandlerId,
image_data: Vec<u8>,
) -> Result<Vec<Vec<Bbox>>> {
console_log!("image data: {}", image_data.len());
let image_data = std::io::Cursor::new(image_data);
let original_image = image::io::Reader::new(image_data)
.with_guessed_format()?
.decode()
.map_err(candle::Error::wrap)?;
let image = {
let data = original_image
.resize_exact(640, 640, image::imageops::FilterType::Triangle)
.to_rgb8()
.into_raw();
Tensor::from_vec(data, (640, 640, 3), &Device::Cpu)?.permute((2, 0, 1))?
};
let image = (image.unsqueeze(0)?.to_dtype(DType::F32)? * (1. / 255.))?;
let predictions = self.model.forward(&image)?.squeeze(0)?;
console_log!("generated predictions {predictions:?}");
let bboxes = report(&predictions, original_image, 640, 640)?;
Ok(bboxes)
}
}
impl Model {
fn load(md: ModelData) -> Result<Self> {
let dev = &Device::Cpu;
let weights = safetensors::tensor::SafeTensors::deserialize(&md.weights)?;
let vb = VarBuilder::from_safetensors(vec![weights], DType::F32, dev);
let model = YoloV8::load(vb, Multiples::s(), 80)?;
Ok(Self { model })
}
}
pub struct Worker {
link: WorkerLink<Self>,
model: Option<Model>,
}
#[derive(Serialize, Deserialize)]
pub enum WorkerInput {
ModelData(ModelData),
Run(Vec<u8>),
}
#[derive(Serialize, Deserialize)]
pub enum WorkerOutput {
ProcessingDone(std::result::Result<Vec<Vec<Bbox>>, String>),
WeightsLoaded,
}
impl yew_agent::Worker for Worker {
type Input = WorkerInput;
type Message = ();
type Output = std::result::Result<WorkerOutput, String>;
type Reach = Public<Self>;
fn create(link: WorkerLink<Self>) -> Self {
Self { link, model: None }
}
fn update(&mut self, _msg: Self::Message) {
// no messaging
}
fn handle_input(&mut self, msg: Self::Input, id: HandlerId) {
let output = match msg {
WorkerInput::ModelData(md) => match Model::load(md) {
Ok(model) => {
self.model = Some(model);
Ok(WorkerOutput::WeightsLoaded)
}
Err(err) => Err(format!("model creation error {err:?}")),
},
WorkerInput::Run(image_data) => match &mut self.model {
None => Err("model has not been set yet".to_string()),
Some(model) => {
let result = model
.run(&self.link, id, image_data)
.map_err(|e| e.to_string());
Ok(WorkerOutput::ProcessingDone(result))
}
},
};
self.link.respond(id, output);
}
fn name_of_resource() -> &'static str {
"worker.js"
}
fn resource_path_is_relative() -> bool {
true
}
}