dtourolle
6aae4c3eb0
2d106det for the eye contours, OCEC for open or closed, SGC for sunglasses — all three pinned to a batch of one by the same script as the pair, and installed by every packager so the eyes-open filter works out of the box. The two classifiers are MIT, code and weights; the README records their provenance, SGC's undocumented training set, and the hashes as fetched and as shipped.
120 lines
4.7 KiB
Bash
Executable File
120 lines
4.7 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Freeze the input dimensions of the face models so tract can parse them.
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#
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# ./tools/fix-face-model-shapes.sh IN.onnx OUT.onnx --input NAME=1,3,640,640
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# ./tools/fix-face-model-shapes.sh IN.onnx OUT.onnx --dim NAME=1
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#
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# The two the face pipeline needs, verified 2026-08-26 (docs/faces.md §12 M1):
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#
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# ... det_500m.onnx scrfd_500m_640.onnx --input input.1=1,3,640,640
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# ... det_2.5g.onnx scrfd_2.5g_640.onnx --input input.1=1,3,640,640
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# ... det_10g.onnx scrfd_10g_640.onnx --input input.1=1,3,640,640
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# ... w600k_mbf.onnx arcface_mbf_b1.onnx --dim None=1
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#
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# And the three eye-state models, verified 2026-09-19 (docs/faces.md §17).
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# The landmark model's batch is the literal "None" like the embedder's; the
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# two classifiers' is a *named* dim_param "batch":
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#
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# ... 2d106det.onnx 2d106det_b1.onnx --dim None=1
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# ... ocec_s.onnx ocec_s_b1.onnx --dim batch=1
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# ... sgc_is_l_48x48.onnx sgc_l_48_b1.onnx --dim batch=1
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#
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# ## Why this exists
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#
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# The InsightFace exports declare dynamic input dimensions — SCRFD's H and W,
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# ArcFace's batch N. **tract cannot parse either graph in that form**, failing
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# at the input node and at the first Conv respectively:
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#
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# scrfd_500m_bnkps.onnx Translating node #0 "input.1" Source ToTypedTranslator
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# arcface_w600k_mbf.onnx Failed analyse for node #139 "Conv_0" ConvHir
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#
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# Both load cleanly once the dims are pinned. This is the same wall dr-segment
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# hit, which is why `tools/export-seg-model.sh` passes `dynamic=False`; here we
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# cannot re-export from PyTorch, because the weights are InsightFace's and the
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# training code is not in the loop, so the dims are rewritten in the ONNX file
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# instead.
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#
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# `make_dynamic_shape_fixed` only edits the declared dimension; it does not
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# retrain, requantise, or change a single weight. The output is numerically the
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# same graph with one shape pinned. SCRFD's *outputs* were already static — the
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# export was made at 640 and only its input forgot to say so — which is why 640
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# is not a free choice here.
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#
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# ## Why it is a script and not a build step
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#
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# Same reason as the segmentation export: the model is not a build input
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# (docs/faces.md §2.2 — the weights are never committed, because InsightFace's
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# grant is non-commercial). This runs once, wherever the user's model lives,
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# and the app loads the result. It exists so the transformation is reproducible
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# rather than a binary someone once produced and nobody can regenerate.
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#
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# Requires `uv`. Everything else is fetched into a throwaway venv, in /var/tmp
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# rather than /tmp — /tmp here is a tmpfs, and onnxruntime is not small.
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set -euo pipefail
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if [ "$#" -lt 4 ]; then
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sed -n '2,10p' "$0" >&2
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exit 2
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fi
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IN="$1"; shift
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OUT="$1"; shift
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[ -f "$IN" ] || { echo "no such model: $IN" >&2; exit 1; }
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WORK="$(mktemp -d -p /var/tmp fix-face-shapes.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="${WORK}/venv" uv pip install --quiet onnx onnxruntime
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# Two forms, because the two models need different ones — and which one a graph
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# needs is not a matter of taste:
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#
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# --dim NAME=VALUE for a *named* symbolic dimension.
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# --input NAME=D,D,D,D for a dimension that is dynamic but unnamed.
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#
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# ArcFace declares its batch as the literal dim_param "None", so `--dim` binds
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# it. SCRFD's H and W carry no dim_param at all, so there is no name to bind
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# and the whole input shape has to be restated. Reaching for `--dim` first and
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# getting a silent no-op is the half-hour worth skipping.
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CUR="$IN"
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STEP=0
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while [ "$#" -gt 0 ]; do
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FLAG="$1"; shift
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PAIR="${1:-}"; shift || true
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NAME="${PAIR%%=*}"
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VAL="${PAIR#*=}"
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STEP=$((STEP + 1))
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NEXT="${WORK}/step${STEP}.onnx"
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case "$FLAG" in
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--dim)
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echo "==> dim_param ${NAME} := ${VAL}"
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"${WORK}/venv/bin/python" -m onnxruntime.tools.make_dynamic_shape_fixed \
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--dim_param "${NAME}" --dim_value "${VAL}" "${CUR}" "${NEXT}"
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;;
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--input)
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echo "==> input ${NAME} := ${VAL}"
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"${WORK}/venv/bin/python" -m onnxruntime.tools.make_dynamic_shape_fixed \
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--input_name "${NAME}" --input_shape "${VAL}" "${CUR}" "${NEXT}"
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;;
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*)
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echo "unknown flag ${FLAG} (want --dim or --input)" >&2
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exit 2
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;;
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esac
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CUR="${NEXT}"
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done
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cp "${CUR}" "${OUT}"
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echo "==> wrote ${OUT}"
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"${WORK}/venv/bin/python" - "$OUT" <<'PY'
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import sys, onnx
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m = onnx.load(sys.argv[1])
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for vi in list(m.graph.input) + list(m.graph.output):
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dims = [d.dim_value if d.HasField("dim_value") else (d.dim_param or "?")
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for d in vi.type.tensor_type.shape.dim]
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print(f" {vi.name:<24} {dims}")
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PY
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