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.

tools/quantise-models.py

@@ -41,7 +41,11 @@ TABLE = {
     "migan-512": dict(dir="inpaint", form="a16w16", feed="migan"),
     "xfeat-1024": dict(dir="keypoints", form="int8", feed="xfeat", rewrites=["unfold", "resize"]),
     "xfeat-768": dict(dir="keypoints", form="int8", feed="xfeat", rewrites=["unfold", "resize"]),
-    "mosaic-1408": dict(dir="denoise", form="a16w16", feed=None, rewrites=["bayer"]),
+    "mosaic-fast-1408": dict(dir="denoise", form="a16w16", feed=None, rewrites=["bayer"]),
+    "mosaic-medium-1408": dict(dir="denoise", form="a16w16", feed=None, rewrites=["bayer"]),
+    # Exported with its packs as SpaceToDepth already; the rewrite finds
+    # nothing to do.
+    "mosaic-best-1408": dict(dir="denoise", form="a16w16", feed=None, rewrites=["bayer"]),
 }