MBOXNC4PCHIZUABVEZPMKUNMROGO4TH3N2EGB3OJXHROYWQPXDCQC pub fn init_with<B: Backend>(&self, record: ModelRecord<B>) -> Model<B> {Model {conv1: Conv2dConfig::new([1, 8], [3, 3]).init_with(record.conv1),conv2: Conv2dConfig::new([8, 16], [3, 3]).init_with(record.conv2),pool: AdaptiveAvgPool2dConfig::new([8, 8]).init(),activation: ReLU::new(),linear1: LinearConfig::new(16 * 8 * 8, self.hidden_size).init_with(record.linear1),linear2: LinearConfig::new(self.hidden_size, self.num_classes).init_with(record.linear2),dropout: DropoutConfig::new(self.dropout).init(),}}
};use clap::Parser;use crossterm::{event::{self, KeyCode, KeyEventKind},terminal::{disable_raw_mode, enable_raw_mode, EnterAlternateScreen, LeaveAlternateScreen},ExecutableCommand,};use image::GrayImage;use ratatui::{layout::{Constraint, Layout, Margin},prelude::{CrosstermBackend, Stylize, Terminal},widgets::{Block, Borders, Paragraph, Scrollbar, ScrollbarOrientation, ScrollbarState},Frame,
use ratatui_image::{picker::Picker, protocol::StatefulProtocol, StatefulImage};use std::io::stdout;struct App {should_close: bool,item: usize,results: Vec<(Box<dyn StatefulProtocol>, i32, u8)>,body_layout: Layout,scrollbar_state: ScrollbarState,}impl App {fn new(results: Vec<(Box<dyn StatefulProtocol>, i32, u8)>) -> Self {Self {should_close: false,item: 0,body_layout: Layout::vertical([Constraint::Length(1), Constraint::Min(1)]),scrollbar_state: ScrollbarState::new(results.len()),results,}}
use crate::model::ModelConfig;
fn draw(&mut self, frame: &mut Frame) {let area = frame.size();let [text_area, im_area] = self.body_layout.areas(area.inner(&Margin::new(1, 1)));let (ref mut im, predicted, label) = self.results[self.item];frame.render_widget(Paragraph::new(format!("Predicted: {predicted}, Expected: {label}")).white(),text_area,);let image = StatefulImage::new(None);frame.render_stateful_widget(image, im_area, im);frame.render_widget(Block::new().borders(Borders::RIGHT), area);let scrollbar = Scrollbar::new(ScrollbarOrientation::VerticalRight).begin_symbol(Some("↑")).end_symbol(Some("↓"));frame.render_stateful_widget(scrollbar,area.inner(&Margin {horizontal: 0,vertical: 1,}),&mut self.scrollbar_state,);}}#[derive(thiserror::Error, Debug)]#[error("{}", _0.lock().unwrap())]struct ThreadsafeBoxedError(std::sync::Arc<std::sync::Mutex<Box<dyn std::error::Error>>>);// We are thread-safe as we do *not* allow mutation (we must own the Box)// and all reads are (assumedly) idempotent and pure, so...bullshit the compilerunsafe impl Send for ThreadsafeBoxedError {}unsafe impl Sync for ThreadsafeBoxedError {}impl ThreadsafeBoxedError {fn new(boxed: Box<dyn std::error::Error>) -> Self {use std::sync::{Arc, Mutex};#[allow(clippy::arc_with_non_send_sync)]Self(Arc::new(Mutex::new(boxed)))}}
fn main() {type Backend = Wgpu<AutoGraphicsApi, f32, i32>;type AutodiffBackend = Autodiff<Backend>;
#[derive(clap::Parser, Debug)]struct Cli {#[command(subcommand)]mode: RunMode,}#[derive(clap::Subcommand, Debug)]enum RunMode {Train,Infer,}type Backend = Wgpu<AutoGraphicsApi, f32, i32>;type AutodiffBackend = Autodiff<Backend>;fn main() -> color_eyre::Result<()> {color_eyre::install()?;let cli = Cli::parse();match cli.mode {RunMode::Train => {train();Ok(())}RunMode::Infer => infer(),}}
fn infer() -> color_eyre::Result<()> {// Adjust the panic hook to leave terminal specialnesslet hook = std::panic::take_hook();std::panic::set_hook(Box::new(move |panic_info| {disable_raw_mode().unwrap();stdout().execute(LeaveAlternateScreen).unwrap();(hook)(panic_info);}));let device = WgpuDevice::default();let mnist_data = MNISTDataset::test();stdout().execute(EnterAlternateScreen)?;enable_raw_mode()?;let mut terminal = Terminal::new(CrosstermBackend::new(stdout()))?;terminal.clear()?;let mut picker = Picker::from_termios().map_err(ThreadsafeBoxedError::new)?;picker.guess_protocol();terminal.draw(|f| f.render_widget(Paragraph::new("Loading..."), f.size()))?;let results = (0..mnist_data.len()).map(|idx| {let item = mnist_data.get(idx).unwrap();let dyn_img = image::DynamicImage::ImageLuma8(GrayImage::from_raw(28,28,item.image.iter().flatten().map(|f| (255.0 * *f) as u8).collect(),).unwrap(),);let image = picker.new_resize_protocol(dyn_img);let (predicted, label) =inference::infer::<Backend>("/tmp/guide", device.clone(), item);(image, predicted, label)}).collect();let mut app = App::new(results);while !app.should_close {if event::poll(std::time::Duration::from_millis(16))? {if let event::Event::Key(key) = event::read()? {if key.kind == KeyEventKind::Press && key.code == KeyCode::Char('q') {break;}}}terminal.draw(|f| app.draw(f))?;}stdout().execute(LeaveAlternateScreen)?;disable_raw_mode()?;Ok(())}
use burn::{config::Config,data::{dataloader::batcher::Batcher, dataset::vision::MNISTItem},record::{CompactRecorder, Recorder},tensor::backend::Backend,};use crate::{data::MNISTBatcher, training::TrainingConfig};pub fn infer<B: Backend>(artifact_dir: &str,device: B::Device,item: MNISTItem,) -> (B::IntElem, u8) {let config = TrainingConfig::load(format!("{artifact_dir}/config.json")).expect("Config should exist for the model");let record = CompactRecorder::new().load(format!("{artifact_dir}/model").into(), &device).expect("Trained model should exist");let model = config.model.init_with::<B>(record);let label = item.label;let batcher = MNISTBatcher::new(device);let batch = batcher.batch(vec![item]);let output = model.forward(batch.images);let predicted = output.argmax(1).flatten::<1>(0, 1).into_scalar();(predicted, label)}
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