Offer three denoise networks and a method to choose between them

AI Denoise's Apply switch becomes Method: Bilinear, Fast, Medium, Best,
default Best, so an untouched raw writes nothing and develops through the
mixture. `apply` is still read and never written: 0 is Bilinear, 1 keeps
a network already chosen.

- Best is the mixture of a flat and an edge expert with a learned gate;
  Medium and Fast are students distilled from it. 2.48 s, 0.79 s and
  0.57 s for a 20 MP frame on TensorRT fp16.
- Each network carries its own tile border (256 for the mixture, 192 for
  the students) through `dr_denoise::Shipped` and `TileNet::halo`.
- The file is hashed once at open and each network keys its own cached
  result; Bilinear keeps the result in memory for the way back.
- Each has an .a16w16 sibling for the Hexagon: 0.00 dB on the 6D gate,
  at most 0.11 dB with the noise scaled x0.5 to x4.
- APK BUNDLED 19 -> 23; the PKGBUILD installs all three.

core/dr-denoise/src/onnx.rs

@@ -21,15 +21,19 @@ pub const TILE: usize = 1408;
 pub struct OnnxNet {
     model: Model,
     tile: usize,
+    halo: usize,
 }
 
 impl OnnxNet {
-    pub fn from_path(path: &std::path::Path) -> Result<Self, DenoiseError> {
+    /// The network at `path`, which needs `halo` photosites of context
+    /// ([`crate::Shipped::halo`]).
+    pub fn from_path(path: &std::path::Path, halo: usize) -> Result<Self, DenoiseError> {
         let (path, form) = dr_inference_engine::resolve_model(Role::Denoiser, path);
         let bytes = std::fs::read(&path)?;
         Ok(OnnxNet {
             model: dr_inference_engine::open(Role::Denoiser, form, &bytes)?,
             tile: TILE,
+            halo,
         })
     }
 
@@ -44,6 +48,10 @@ impl TileNet for OnnxNet {
         self.tile
     }
 
+    fn halo(&self) -> usize {
+        self.halo
+    }
+
     fn run(
         &mut self,
         mosaic: Vec<f32>,