dtourolle
06422a07db
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.
40 lines
1.8 KiB
Bash
Executable File
40 lines
1.8 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Produce the Hexagon's form of each model (docs/dev/inference.md §1.5, §5).
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#
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# ./tools/quantise-models.sh PHOTO_DIR [MODEL ...]
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# ./tools/quantise-models.sh --ranges RANGES.json mosaic-medium-1408
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#
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# Writes `<stem>.<form>.onnx` beside each canonical file under models/: a QDQ
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# graph from QNN's own quantisation config, per-channel weights, in the form
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# the engine's `Rung::form` names for that role — A16W8, A16W16 or int8, each
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# the narrowest that held the model's accuracy on the tablet. The activation
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# ranges come from running the f32 model over the photographs in PHOTO_DIR,
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# fed exactly as the app feeds them (letterbox maths, pads, normalisation,
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# face crops through the app's own similarity), which is why this is a
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# release-time step and not something the device does. With no MODEL, every
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# model in the table.
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#
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# The denoiser is calibrated on noisy mosaics, not photographs: its ranges
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# come from darkroom-denoise's precision gate (`--ranges`), computed on a
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# smaller tile of the same network — activation ranges do not depend on the
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# tile's size, and the tensor names match.
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#
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# Then measure before shipping: a quantised form is a different network, and
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# the numbers in inference.md §1.5 are what each one had to hold.
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#
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# A venv per run, like fix-face-model-shapes.sh: the tools are not a build
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# input and nothing in the tree should have them on its path.
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set -euo pipefail
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if [ "$#" -lt 1 ]; then
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sed -n '2,27p' "$0" >&2
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exit 2
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fi
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WORK="$(mktemp -d -p /var/tmp quantise-models.XXXXXX)"
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trap 'rm -rf "${WORK}"' EXIT
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echo "==> venv in ${WORK}"
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uv venv --python 3.12 "${WORK}/venv" >/dev/null
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VIRTUAL_ENV="${WORK}/venv" uv pip install --quiet onnx onnxruntime pillow numpy sympy
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exec "${WORK}/venv/bin/python" "$(dirname "$0")/quantise-models.py" "$@"
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