TensorRT plans its memory for the profile's largest shape, and up to a
whole 6D frame with Best's border (4608 x 6656) it asked for 4.9-5.9 GB
and would not build on the RTX 3050. The profile now ends at 4608 x 3328
(15 MP), tuned for 4160 x 3248, and the tiler cuts a 6D frame into two
such tiles: 27 MP of work for 20 MP kept, against 49 MP in 1408 tiles.
The engine's directory names the profile, so a later range never loads
an engine built for this one.
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.
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.
A 20 MP frame spent 0.32 s outside the network: each tile's mosaic and
sigma gathered on one thread, then its 24 MB output copied out of the
runtime and back into the frame, all in series with the device. Tiles are
now gathered on every core by a producer thread one tile ahead, so the
gather overlaps the run; the centre is written back across cores; and the
tile interface hands its inputs over and lends its output, so neither
side is copied. With a stand-in network that does nothing, the tiler's own
time falls to 0.14 s at the 1408 tile and 0.09 s at 2048. The exactness
and Bayer-phase tests are unchanged and pass.
The noise model takes the best source the frame has: the body's measured
table (the Canon EOS 6D's, from the library), the DNG's NoiseProfile, or
the frame itself — read, row and column noise from its masked border, and
only the shot gain estimated, from the quietest flat patches. Checked on
130 6D frames, the estimate is within 10 % from ISO 1000 up; the network
loses under 0.3 dB for a sigma off by 15-20 %, so every Bayer body is
eligible.
Tiles of 1408 keep their central 1024 behind a 192-photosite halo, past the
185-photosite receptive field, and the frame is extended by reflection,
which keeps every photosite's colour; a pattern that starts on another
colour is read from one photosite up or left so the network sees RGGB, and
nothing is cropped. The tests run every Bayer phase, tiled against whole,
with a stand-in network of known reach.
The model ships as models/denoise/mosaic-1408.onnx (LFS), trained in
darkroom-denoise on the maintainer's own photographs, GPL like the code.
denoise_raw runs a file end to end: on a 6D frame at ISO 8000 the result
matches the training repository's own path to 2.5e-4 at worst, and takes
3.1 s on TensorRT fp16 (75 dB from f32) or 14.4 s on the CPU.
dtourolle