feat: get full evaluation back online
Very hacky, but this is enough to be finished with the graph IR for now.
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3c529c3a1a
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9 changed files with 105 additions and 26 deletions
2
.gitignore
vendored
2
.gitignore
vendored
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@ -4,3 +4,5 @@
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.pre-commit-config.yaml
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.pre-commit-config.yaml
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*.pdf
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*.pdf
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/docs/*.png
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/docs/*.png
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/testfiles/gen/*
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!/testfiles/gen/.gitkeep
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@ -1,31 +1,39 @@
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use std::mem;
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use ir::{
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use ir::{
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id,
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instruction::{Filter, Kind},
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instruction::{Filter, Kind},
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GraphIr, Instruction, InstructionRef,
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GraphIr, Instruction, Map,
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};
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};
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use crate::value::Dynamic;
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use crate::value::Variant;
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mod instr;
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mod instr;
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#[derive(Debug, Default)]
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#[derive(Debug, Default)]
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pub struct Evaluator {
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pub struct Evaluator {
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ir: GraphIr,
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ir: GraphIr,
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/// What the output of each individual streamer, and as result its output sockets, is.
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/// Grows larger as evaluation continues,
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/// as there's no mechanism for purging never-to-be-used-anymore instructions yet.
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evaluated: Map<id::Output, Variant>,
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}
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}
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impl crate::Evaluator for Evaluator {
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impl crate::Evaluator for Evaluator {
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fn feed(&mut self, ir: GraphIr) {
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fn feed(&mut self, ir: GraphIr) {
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// TODO: should add instead of replace, see note in Evaluator trait above this method
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// TODO: should add instead of replace, see note in Evaluator trait above this method
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self.ir = ir;
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self.ir = ir;
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self.evaluated.clear();
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}
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}
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fn eval_full(&mut self) {
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fn eval_full(&mut self) {
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// GraphIr::topological_sort returns InstructionRefs, which are mostly cool
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// but we'd like to have them owned, so we can call Self::step without lifetime hassle
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let queue: Vec<Instruction> = self
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let queue: Vec<Instruction> = self
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.ir
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.ir
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.topological_sort()
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.topological_sort()
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.into_iter()
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.into_iter()
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.map(Into::into)
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.map(Into::into)
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.collect();
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.collect();
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for instr in queue {
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for instr in queue {
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self.step(instr);
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self.step(instr);
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}
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}
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@ -35,23 +43,64 @@ impl crate::Evaluator for Evaluator {
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impl Evaluator {
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impl Evaluator {
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#[allow(clippy::needless_pass_by_value)]
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#[allow(clippy::needless_pass_by_value)]
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fn step(&mut self, instr: Instruction) {
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fn step(&mut self, instr: Instruction) {
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let _ = match instr.kind {
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// what inputs does this instr need? fetch them
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Kind::Read(details) => Some(Dynamic::Image(instr::read::read(details))),
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let inputs: Vec<_> = instr
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.input_sources()
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.iter()
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.map(|source| {
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let source_socket = source
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.as_ref()
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.expect("all inputs to be connected when an instruction is ran");
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self.evaluated
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.get(source_socket)
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.expect("toposort to yield later instrs only after previous ones")
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})
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.collect();
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// then actually do whatever the instruction should do
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// NOTE: makes heavy use of index slicing,
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// on the basis that ir::instruction::Kind::socket_count is correct
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// TODO: make this a more flexible dispatch-ish arch
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let output = match instr.kind {
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Kind::Read(details) => Some(Variant::Image(instr::read::read(details))),
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Kind::Write(details) => {
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Kind::Write(details) => {
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instr::write::write(details, todo!());
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#[allow(irrefutable_let_patterns)] // will necessarily change
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let Variant::Image(input) = inputs[0] else {
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panic!("cannot only write images, but received: `{:?}`", inputs[0]);
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};
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instr::write::write(details, input);
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None
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None
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}
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}
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Kind::Math(_) => todo!(),
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Kind::Math(_) => todo!(),
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Kind::Blend(_) => todo!(),
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Kind::Blend(_) => todo!(),
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Kind::Noise(_) => todo!(),
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Kind::Noise(_) => todo!(),
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Kind::Filter(filter_instruction) => match filter_instruction {
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Kind::Filter(filter_instruction) => match filter_instruction {
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Filter::Invert => Some(Dynamic::Image(instr::filters::invert::invert(
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Filter::Invert => {
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match todo!() {
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#[allow(irrefutable_let_patterns)]
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Some(Dynamic::Image(img)) => img,
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let Variant::Image(input) = inputs[0] else {
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_ => panic!("invalid value type for invert"),
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panic!(
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},
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"cannot only filter invert images, but received: `{:?}`",
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))),
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inputs[0]
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);
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};
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Some(Variant::Image(instr::filters::invert::invert(
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input.clone(),
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)))
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}
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},
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},
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};
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};
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if let Some(output) = output {
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// TODO: very inaccurate, actually respect individual instructions.
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// should be implied by a different arch
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// TODO: all of those should not be public, offer some methods to get this on
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// `Instruction` instead (can infer short-term based on Kind::socket_count)
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let socket = id::Output(id::Socket {
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belongs_to: instr.id,
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idx: id::SocketIdx(0),
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});
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self.evaluated.insert(socket, output);
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}
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}
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}
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}
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}
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use image::DynamicImage;
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use image::DynamicImage;
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pub enum Dynamic {
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/// Any runtime value that an instruction can input or output.
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///
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/// The name is taken from [Godot's `Variant` type],
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/// which is very similar to this one.
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///
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/// [Godot's `Variant` type]: https://docs.godotengine.org/en/stable/classes/class_variant.html
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#[derive(Clone, Debug)]
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pub enum Variant {
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Image(DynamicImage),
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Image(DynamicImage),
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}
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}
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@ -49,7 +49,7 @@ impl Input {
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///
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///
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/// In contrast to [`Input`]s, [`Output`]s may be used or unused.
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/// In contrast to [`Input`]s, [`Output`]s may be used or unused.
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#[derive(Clone, Debug, PartialEq, Eq, PartialOrd, Ord, Hash, Serialize, Deserialize)]
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#[derive(Clone, Debug, PartialEq, Eq, PartialOrd, Ord, Hash, Serialize, Deserialize)]
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pub struct Output(pub(super) Socket);
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pub struct Output(pub Socket); // TODO: Restrict publicness to super
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impl Output {
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impl Output {
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#[must_use]
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#[must_use]
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/// This really only serves for denoting where a socket is,
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/// This really only serves for denoting where a socket is,
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/// when it's already clear which instruction is referred to.
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/// when it's already clear which instruction is referred to.
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#[derive(Clone, PartialEq, Eq, PartialOrd, Ord, Hash, Serialize, Deserialize)]
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#[derive(Clone, PartialEq, Eq, PartialOrd, Ord, Hash, Serialize, Deserialize)]
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pub struct SocketIdx(pub u16);
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pub struct SocketIdx(pub u16); // TODO: Restrict publicness to super
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impl fmt::Debug for SocketIdx {
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impl fmt::Debug for SocketIdx {
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fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
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fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
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/// The toplevel representation of a whole pipeline.
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/// The toplevel representation of a whole pipeline.
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///
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///
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/// # DAGs
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///
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/// Pipelines may not be fully linear. They may branch out and recombine later on.
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/// Pipelines may not be fully linear. They may branch out and recombine later on.
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/// As such, the representation for them which is currently used is a
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/// As such, the representation for them which is currently used is a
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/// [**D**irected **A**cyclic **G**raph](https://en.wikipedia.org/wiki/Directed_acyclic_graph).
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/// [**D**irected **A**cyclic **G**raph](https://en.wikipedia.org/wiki/Directed_acyclic_graph).
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/// So the vertices of the DAG are the **sockets**
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/// So the vertices of the DAG are the **sockets**
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/// (which are either [`id::Input`] or [`id::Output`] depending on the direction),
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/// (which are either [`id::Input`] or [`id::Output`] depending on the direction),
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/// and each **socket** in turn belongs to an **instruction**.
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/// and each **socket** in turn belongs to an **instruction**.
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///
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/// # Usage
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///
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/// - If you want to build one from scratch,
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/// add a few helper methods like
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/// constructing an empty one,
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/// adding instructions and
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/// adding edges
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/// - If you want to construct one from an existing repr,
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/// maybe you want to use [`semi_human::GraphIr`].
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///
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/// # Storing additional data
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///
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/// Chances are the graph IR seems somewhat fit to put metadata in it.
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/// However, most likely you're interacting in context of some other system,
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/// and also want to manage and index that data on your own.
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///
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/// As such, consider using _secondary_ maps instead.
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/// That is, store in a data structure _you_ own a mapping
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/// from [`id`]s
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/// to whatever data you need.
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#[derive(Clone, Debug, Default, PartialEq, Eq, Deserialize, Serialize)]
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#[derive(Clone, Debug, Default, PartialEq, Eq, Deserialize, Serialize)]
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pub struct GraphIr {
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pub struct GraphIr {
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/// "Backbone" storage of all **instruction** IDs to
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/// "Backbone" storage of all **instruction** IDs to
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pub enum DynamicValue {
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Image(DynamicImage),
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}
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(
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(
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instructions: {
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instructions: {
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0: Read((
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0: Read((
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source: File("testfiles/juan.jpg"),
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source: File("testfiles/rails.png"),
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format: Jpeg,
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format: Png,
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)),
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)),
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1: Write((
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1: Write((
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target: File("testfiles/out.png"),
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target: File("testfiles/gen/out.jpg"),
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format: Png,
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format: Jpeg,
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)),
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)),
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},
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},
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edges: {
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edges: {
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1
testfiles/gen/.gitkeep
Normal file
1
testfiles/gen/.gitkeep
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@ -0,0 +1 @@
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the testfile scripts will place generated images and media here. thank you for your understanding.
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(
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(
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instructions: {
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instructions: {
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0: Read((
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0: Read((
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source: File("testfiles/juan.jpg"),
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source: File("testfiles/rails.png"),
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format: Jpeg,
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format: Png,
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)),
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)),
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1: Filter(Invert),
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1: Filter(Invert),
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2: Write((
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2: Write((
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target: File("testfiles/inverted.png"),
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target: File("testfiles/gen/inverted.png"),
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format: Png,
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format: Png,
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)),
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)),
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},
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},
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