dtourolle
9cba420fd5
A fixed 1408 tile is exact only in its centre, and Best keeps 896 of every 1408 it computes: 2.47 photosites of work for each one kept. The tiler now takes a network of any size as well as a square one, and plans the frame as the fewest equal tiles under the rung's limit -- one tile, the whole frame and its reflected border, whenever it fits. If the first call of a plan fails, as a GPU out of memory does, the kept centre is halved and the frame planned again. Each shipped network names its any-size sibling (mosaic-best.onnx beside mosaic-best-1408.onnx). OnnxNet::open takes it where the engine runs whole frames and the file is installed, and the 1408 tiles otherwise; open_tiled forces the tiles, and denoise_raw's DR_PLAN=tiles uses it to compare. The cache key stays on the fixed model: the output is the same network's. Tests hold any-size tiles, a grid of them and a plan rebuilt after a failure to the square tiles' answer in every Bayer phase.
538 lines
19 KiB
Rust
538 lines
19 KiB
Rust
//! TRACES: FR-DEV-3g
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//! The learned denoise in a develop session (docs/dev/denoise.md §7).
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//!
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//! The classical demosaic shows at once; when the photograph asks for the
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//! learned one, it is computed off the UI thread from the mosaic the
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//! session kept, and swapped in when it lands. Grain is a blend of the two
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//! results, made once per slider value by [`dr_gpu::GrainBlend`] and handed
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//! to the render as its source — the adjust pass never knows.
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//!
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//! `demosaiced` stays the classical result for the session's life: the raw
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//! histogram, the white balance picker, masks and segmentation measure the
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//! sensor data, and only [`DevelopSession::developed_source`] — what the
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//! render draws — changes.
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use std::sync::atomic::{AtomicBool, Ordering};
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use std::sync::mpsc;
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use std::sync::Arc;
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use dr_decode::RawImage;
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use dr_gpu::{DemosaicedImage, GrainBlend};
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use dr_pipeline::learned_denoise::Method;
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use super::session::DevelopSession;
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/// What the session holds for the learned denoise.
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#[derive(Default)]
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pub(crate) struct DenoiseState {
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/// The mosaic as decoded, kept only for a photograph that can take the
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/// learned stage. The job repairs a copy.
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mosaic: Option<Arc<RawImage>>,
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/// The file's `NoiseProfile` and ISO, read from the header at open.
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profile: Option<Vec<(f32, f32)>>,
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iso: Option<u32>,
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/// The network's result, once it has landed.
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result: Option<Arc<DemosaicedImage>>,
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/// The last grain blend made, and the grain it was made at.
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blended: Option<(f32, Arc<DemosaicedImage>)>,
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blend: Option<GrainBlend>,
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job: Option<Job>,
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/// Why the last attempt failed; not retried until the switch is
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/// toggled, so a photograph that cannot be denoised does not loop.
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failed: Option<String>,
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/// Where the noise figures came from, for the panel.
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source: Option<dr_denoise::Source>,
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/// The file's bytes, hashed for the on-disk cache, which keys each
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/// network's result on them and the model (`denoise_cache::FileHash`).
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file_hash: Option<super::denoise_cache::FileHash>,
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/// The method the result, the job and the failure above are for. A
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/// different one asked for discards them.
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method: Option<Method>,
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}
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struct Job {
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rx: mpsc::Receiver<Msg>,
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cancel: Arc<AtomicBool>,
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}
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enum Msg {
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Progress(usize, usize),
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Done(Result<Finished, String>),
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}
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struct Finished {
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rgb: Vec<f32>,
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width: u32,
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height: u32,
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source: dr_denoise::Source,
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rung: String,
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seconds: f64,
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}
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/// What a poll found, for the develop view's status line and redraw.
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#[derive(Debug, PartialEq)]
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pub enum DenoiseStatus {
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/// Nothing running and nothing new.
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Idle,
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/// Tiles done of tiles.
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Running(usize, usize),
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/// The result landed this poll: redraw.
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Landed,
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Failed(String),
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}
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impl DevelopSession {
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/// Keep the mosaic for the learned denoise, if it can take this frame.
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pub(super) fn keep_mosaic(&mut self, raw: RawImage) {
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if dr_denoise::eligible(&raw) {
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self.denoise.mosaic = Some(Arc::new(raw));
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}
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self.graph
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.set_denoise_available(self.denoise.mosaic.is_some());
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}
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/// The header's part: the DNG's measured noise and the ISO.
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pub fn prepare_denoise(&mut self, bytes: &[u8], meta: &dr_decode::Metadata) {
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if self.denoise.mosaic.is_some() {
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self.denoise.profile = dr_decode::noise_profile(bytes);
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self.denoise.iso = meta.iso;
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// TRACES: FR-DEV-3g
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// The bytes are only here now, so they are hashed now: a
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// reopened or exported photograph finds its result on disk,
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// under whichever method it asks for.
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self.denoise.file_hash = Some(super::denoise_cache::FileHash::of(bytes));
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}
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}
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/// Bring what is computed in line with what the edit asks for: start the
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/// network when it is wanted and has not run, stop it when it is not.
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/// Cheap when nothing changed; the develop view calls it after every
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/// change to the edit, whatever made it — a slider, undo, a version.
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pub fn reconcile_denoise(&mut self) {
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self.forget_other_method();
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let wanted = self.graph.denoise_applied() && self.denoise.mosaic.is_some();
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if !wanted {
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if let Some(job) = self.denoise.job.take() {
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job.cancel.store(true, Ordering::Relaxed);
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}
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// Toggling off is how a failure is retried.
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self.denoise.failed = None;
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return;
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}
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if self.denoise.result.is_some()
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|| self.denoise.job.is_some()
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|| self.denoise.failed.is_some()
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{
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return;
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}
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let Some(work) = self.work(Arc::new(AtomicBool::new(false))) else {
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return;
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};
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let cancel = work.cancel.clone();
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let (tx, rx) = mpsc::channel();
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crate::executors::spawn(crate::executors::Executor::Decode, "denoise", move || {
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let result = work.run(&mut |done, total| {
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let _ = tx.send(Msg::Progress(done, total));
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});
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let _ = tx.send(Msg::Done(result));
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});
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self.denoise.job = Some(Job { rx, cancel });
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}
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/// Drop what was computed, or is being computed, for a network the edit
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/// no longer asks for — a choice in the panel, an undo, a version.
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/// Each network's result stays in the on-disk cache, so going back to
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/// one is a read, not a run. The classical demosaic asks for no network
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/// and drops nothing: the result is kept for the way back.
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fn forget_other_method(&mut self) {
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let asked = self.graph.denoise_method();
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if !asked.learned() || self.denoise.method == Some(asked) {
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return;
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}
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if self.denoise.method.is_none() {
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// The first network asked for: nothing computed is another's.
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self.denoise.method = Some(asked);
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return;
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}
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if let Some(job) = self.denoise.job.take() {
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job.cancel.store(true, Ordering::Relaxed);
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}
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self.denoise.result = None;
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self.denoise.blended = None;
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self.denoise.failed = None;
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self.denoise.source = None;
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self.denoise.method = Some(asked);
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}
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/// The job for the method the edit asks for, or `None` — with the reason
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/// kept as the failure — where its network is not installed.
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fn work(&mut self, cancel: Arc<AtomicBool>) -> Option<Work> {
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let raw = self.denoise.mosaic.clone()?;
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let Some((model, net)) = crate::library::denoise_model(self.graph.denoise_method()) else {
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self.denoise.failed = Some("the denoise model is not installed".into());
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return None;
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};
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Some(Work {
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ctx: self.ctx.clone(),
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raw,
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profile: self.denoise.profile.clone(),
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iso: self.denoise.iso,
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cache_key: self.denoise.file_hash.as_ref().map(|h| h.key(&model)),
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model,
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net,
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cancel,
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})
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}
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/// Collect what the job sent since the last poll.
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pub fn poll_denoise(&mut self) -> DenoiseStatus {
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let Some(job) = &self.denoise.job else {
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return DenoiseStatus::Idle;
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};
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let mut last = None;
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let mut done = None;
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while let Ok(msg) = job.rx.try_recv() {
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match msg {
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Msg::Progress(d, t) => last = Some((d, t)),
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Msg::Done(r) => done = Some(r),
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}
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}
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match done {
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Some(Ok(f)) => {
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self.denoise.job = None;
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match self.land(f) {
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Ok(()) => DenoiseStatus::Landed,
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Err(e) => self.fail(e),
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}
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}
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Some(Err(e)) => {
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self.denoise.job = None;
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self.fail(e)
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}
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None => last.map_or(DenoiseStatus::Running(0, 1), |(d, t)| {
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DenoiseStatus::Running(d, t)
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}),
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}
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}
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/// Compute the result now, on this thread, if the edit wants it and it
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/// is not here: for an export, which must not write the classical
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/// picture of a photograph that asks for the learned one (§7.1).
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pub fn denoise_blocking(&mut self) -> Result<(), String> {
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if !self.graph.denoise_applied() || self.denoise.result.is_some() {
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return Ok(());
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}
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self.forget_other_method();
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if self.denoise.mosaic.is_none() {
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return Ok(());
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}
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// Already under way: wait for it rather than start again.
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if let Some(job) = self.denoise.job.take() {
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for msg in job.rx.iter() {
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if let Msg::Done(result) = msg {
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return result.and_then(|f| self.land(f));
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}
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}
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}
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let Some(work) = self.work(Arc::new(AtomicBool::new(false))) else {
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return Err(self.denoise.failed.clone().unwrap_or_default());
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};
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let finished = work.run(&mut |_, _| {})?;
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self.land(finished)
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}
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/// What the render draws: the learned result with the asked-for grain
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/// where there is one, else the classical demosaic.
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pub(super) fn developed_source(&mut self) -> Arc<DemosaicedImage> {
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if !self.graph.denoise_applied() {
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return self.demosaiced.clone();
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}
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let Some(result) = self.denoise.result.clone() else {
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return self.demosaiced.clone();
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};
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let grain = self.graph.denoise_grain();
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if grain <= 0.0 {
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return result;
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}
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if let Some((g, image)) = &self.denoise.blended {
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if (*g - grain).abs() < 1e-4 {
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return image.clone();
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}
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}
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let blend = self
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.denoise
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.blend
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.get_or_insert_with(|| GrainBlend::new(&self.ctx));
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match blend.blend(&result, &self.demosaiced, grain) {
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Ok(image) => {
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self.denoise.blended = Some((grain, image.clone()));
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image
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}
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Err(e) => {
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log::warn!("grain blend failed, showing the denoised result without grain: {e}");
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result
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}
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}
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}
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/// For the panel: where the noise figures came from, once computed.
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pub fn denoise_source(&self) -> Option<dr_denoise::Source> {
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self.denoise.source
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}
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pub fn denoise_failure(&self) -> Option<&str> {
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self.denoise.failed.as_deref()
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}
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fn land(&mut self, f: Finished) -> Result<(), String> {
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let image =
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DemosaicedImage::from_rgb_f32(&self.ctx, &self.demosaiced, f.width, f.height, &f.rgb)
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.map_err(|e| e.to_string())?;
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log::info!(
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"learned denoise: {}×{} on {} in {:.1} s, noise {}",
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f.width,
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f.height,
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f.rung,
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f.seconds,
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f.source.label()
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);
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self.denoise.result = Some(Arc::new(image));
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self.denoise.blended = None;
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self.denoise.source = Some(f.source);
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Ok(())
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}
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fn fail(&mut self, e: String) -> DenoiseStatus {
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log::warn!("learned denoise failed: {e}");
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self.denoise.failed = Some(e.clone());
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DenoiseStatus::Failed(e)
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}
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}
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/// Everything the job needs, owned, so it can leave the UI thread.
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struct Work {
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ctx: dr_gpu::GpuContext,
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raw: Arc<RawImage>,
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profile: Option<Vec<(f32, f32)>>,
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iso: Option<u32>,
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model: std::path::PathBuf,
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/// The network `model` is: its context and its whole-frame sibling.
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net: dr_denoise::Shipped,
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cancel: Arc<AtomicBool>,
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cache_key: Option<String>,
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}
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impl Work {
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fn run(self, progress: &mut dyn FnMut(usize, usize)) -> Result<Finished, String> {
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let started = std::time::Instant::now();
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// TRACES: FR-DEV-3g
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// A result computed before — this photograph opened earlier, or
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// developed and now exported — is read back rather than recomputed.
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let cache_dir = super::denoise_cache::dir();
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if let Some(hit) = self
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.cache_key
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.as_deref()
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.and_then(|key| super::denoise_cache::load(&cache_dir, key))
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{
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return Ok(Finished {
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rgb: hit.rgb,
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width: hit.width,
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height: hit.height,
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source: hit.source,
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rung: "the cache".into(),
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seconds: started.elapsed().as_secs_f64(),
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});
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}
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// The app's own hot-pixel pass, on a copy: the classical source was
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// repaired by the same pass inside `Demosaicer::run`.
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let mut raw = (*self.raw).clone();
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dr_gpu::Demosaicer::new(&self.ctx)
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.and_then(|d| d.repair_hot_pixels(&mut raw))
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.map_err(|e| e.to_string())?;
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let noise = dr_denoise::noise::for_frame_with(&raw, self.profile.as_deref(), self.iso)
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.ok_or("this photograph gives no way to measure its noise")?;
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let mut net =
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dr_denoise::onnx::OnnxNet::open(&self.model, self.net).map_err(|e| e.to_string())?;
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let rung = net
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.rung()
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.map(|r| r.label().to_string())
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.unwrap_or_default();
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let cancel = self.cancel;
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let rgb = dr_denoise::denoise(&raw, &noise, &mut net, &mut |done, total| {
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progress(done, total);
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!cancel.load(Ordering::Relaxed)
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})
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.map_err(|e| e.to_string())?
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.ok_or("stopped")?;
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let finished = Finished {
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rgb,
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width: raw.crop.width,
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height: raw.crop.height,
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source: noise.source,
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rung,
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seconds: started.elapsed().as_secs_f64(),
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};
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if let Some(key) = self.cache_key.as_deref() {
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let entry = super::denoise_cache::Entry {
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rgb: finished.rgb.clone(),
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width: finished.width,
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height: finished.height,
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source: finished.source,
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};
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super::denoise_cache::store(&cache_dir, key, &entry, super::denoise_cache::BUDGET);
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}
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Ok(finished)
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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use crate::develop::test_support::headless;
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use dr_pipeline::learned_denoise;
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fn bayer(w: u32, h: u32) -> RawImage {
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RawImage {
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width: w,
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height: h,
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data: vec![800; (w * h) as usize],
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cfa_pattern: dr_decode::CfaPattern::Rggb,
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black_level: [0; 4],
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white_level: 4095,
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wb_coeffs: [2.0, 1.0, 1.5, 1.0],
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color_matrix: Some([1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]),
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samples_per_pixel: 1,
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profile: None,
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profile_tables: None,
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baseline_exposure: 0.0,
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make: String::new(),
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model: String::new(),
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crop: dr_decode::CropRect {
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x: 0,
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y: 0,
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width: w,
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height: h,
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},
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}
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}
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fn landed(session: &mut DevelopSession) {
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let (w, h) = (64, 64);
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session
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.land(Finished {
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rgb: vec![0.2; (w * h * 3) as usize],
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width: w,
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height: h,
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source: dr_denoise::Source::Measured,
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rung: "test".into(),
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seconds: 0.0,
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})
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.expect("upload");
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}
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#[test]
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fn a_bayer_raw_offers_the_switch_and_an_rgb_image_does_not() {
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let Some(ctx) = headless() else { return };
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let raw =
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DevelopSession::open_owned(&ctx, bayer(64, 64), dr_types::Orientation::NORMAL).unwrap();
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assert!(raw
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.graph
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.capabilities()
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.iter()
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.any(|c| c.id == learned_denoise::ID));
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let rgba: Vec<u8> = (0..64 * 64).flat_map(|_| [128u8, 128, 128, 255]).collect();
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let jpeg =
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DevelopSession::open_rgb(&ctx, &rgba, 64, 64, dr_types::Orientation::NORMAL).unwrap();
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assert!(!jpeg
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.graph
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.capabilities()
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.iter()
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.any(|c| c.id == learned_denoise::ID));
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}
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#[test]
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fn the_render_draws_what_the_edit_asks_for() {
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let Some(ctx) = headless() else { return };
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let mut s =
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DevelopSession::open_owned(&ctx, bayer(64, 64), dr_types::Orientation::NORMAL).unwrap();
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let classical = s.demosaiced.clone();
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landed(&mut s);
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let result = s.denoise.result.clone().unwrap();
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let same = |a: &Arc<DemosaicedImage>, b: &Arc<DemosaicedImage>| Arc::ptr_eq(a, b);
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// On by default, at full strength: the network's result as it is.
|
||
assert!(same(&s.developed_source(), &result));
|
||
// Bilinear: the classical demosaic, result or no result.
|
||
s.graph.set_param(
|
||
learned_denoise::ID,
|
||
learned_denoise::METHOD,
|
||
Method::Bilinear.index(),
|
||
);
|
||
assert!(same(&s.developed_source(), &classical));
|
||
s.graph.set_param(
|
||
learned_denoise::ID,
|
||
learned_denoise::METHOD,
|
||
Method::DEFAULT.index(),
|
||
);
|
||
// Less strength: a blend, made once per value and reused until it
|
||
// moves.
|
||
s.graph
|
||
.set_param(learned_denoise::ID, learned_denoise::STRENGTH, 60.0);
|
||
let blended = s.developed_source();
|
||
assert!(!same(&blended, &result) && !same(&blended, &classical));
|
||
assert!(
|
||
same(&s.developed_source(), &blended),
|
||
"the same grain must not blend again"
|
||
);
|
||
s.graph
|
||
.set_param(learned_denoise::ID, learned_denoise::STRENGTH, 40.0);
|
||
assert!(!same(&s.developed_source(), &blended));
|
||
// The sensor's own reading stays the classical one throughout.
|
||
assert!(same(&s.demosaiced, &classical));
|
||
}
|
||
|
||
#[test]
|
||
fn switching_off_stops_and_forgets_a_failure() {
|
||
let Some(ctx) = headless() else { return };
|
||
let mut s =
|
||
DevelopSession::open_owned(&ctx, bayer(64, 64), dr_types::Orientation::NORMAL).unwrap();
|
||
s.denoise.failed = Some("no model".into());
|
||
s.reconcile_denoise();
|
||
assert!(
|
||
s.denoise.job.is_none(),
|
||
"a failure is not retried while the method stays"
|
||
);
|
||
s.graph.set_param(
|
||
learned_denoise::ID,
|
||
learned_denoise::METHOD,
|
||
Method::Bilinear.index(),
|
||
);
|
||
s.reconcile_denoise();
|
||
assert!(
|
||
s.denoise.failed.is_none(),
|
||
"choosing bilinear is how a failure is retried"
|
||
);
|
||
}
|
||
|
||
#[test]
|
||
fn another_network_discards_the_result_and_bilinear_keeps_it() {
|
||
let Some(ctx) = headless() else { return };
|
||
let mut s =
|
||
DevelopSession::open_owned(&ctx, bayer(64, 64), dr_types::Orientation::NORMAL).unwrap();
|
||
s.denoise.method = Some(Method::DEFAULT);
|
||
landed(&mut s);
|
||
let to = |s: &mut DevelopSession, m: Method| {
|
||
s.graph
|
||
.set_param(learned_denoise::ID, learned_denoise::METHOD, m.index());
|
||
s.forget_other_method();
|
||
};
|
||
to(&mut s, Method::Bilinear);
|
||
assert!(s.denoise.result.is_some(), "kept for the way back");
|
||
to(&mut s, Method::DEFAULT);
|
||
assert!(s.denoise.result.is_some());
|
||
to(&mut s, Method::Fast);
|
||
assert!(s.denoise.result.is_none(), "another network's picture");
|
||
assert_eq!(s.denoise.method, Some(Method::Fast));
|
||
}
|
||
}
|