dtourolle
2ea5737bbd
build_trt_engines.sh hardcoded 'input.1' for the ArcFace and SCRFD shape
profiles, which only matches arcface_w600k_{r50,mbf}. Building engines for
any other embedder failed with:
Cannot find input tensor with name "input.1" in the network inputs!
Input names differ per model: LVFace-B_Glint360K uses 'data', arcface_r18
uses 'input', arcface_w600k_{r50,mbf} use 'input.1'. This matters now that
LVFace-B is the default embedder (src/config.hpp), so ARCFACE_MODEL=<LVFace>
is the expected path.
Read the name from each model via onnxruntime at build time.
97 lines
3.5 KiB
Bash
Executable File
97 lines
3.5 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Pre-build TensorRT engines for ArcFace and SCRFD with the same shape profiles
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# the runtime nodes use. First-run ORT engine builds take 30–90 s per model and
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# block the pipeline; this script does it offline so cold starts are instant.
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#
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# Profiles must match src/arcface_embedder.hpp and src/scrfd_decoder.hpp:
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# ArcFace : min=1x3x112x112 opt=Nx3x112x112 max=Nx3x112x112 (N = embed batch)
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# SCRFD : 1x3x640x640 (fixed; we letterbox to this)
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# TransNetV2 : 1x100x27x48x3 (fixed; scene detector window)
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#
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# Input tensor names are read from each ONNX model at runtime rather than
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# hardcoded, since they differ between models (LVFace-B: "data", arcface_r18:
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# "input", arcface_w600k_{r50,mbf}: "input.1").
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#
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# These trtexec-built engines are *not* picked up by the ORT TRT EP cache —
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# ORT uses its own engine format. The point of this script is:
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# (a) sanity-check that the ONNX models build under TRT at all;
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# (b) measure pure inference latency without ORT overhead.
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# Run scene_analyze normally and ORT will populate ./trt_cache itself.
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set -euo pipefail
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ROOT="$(cd "$(dirname "$0")/.." && pwd)"
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MODELS="$ROOT/models"
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OUT="$ROOT/trt_cache"
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mkdir -p "$OUT"
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EMBED_BATCH="${EMBED_BATCH:-4}"
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ARCFACE_MODEL="${ARCFACE_MODEL:-$MODELS/arcface_w600k_r50.onnx}"
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SCRFD_MODEL="${SCRFD_MODEL:-$MODELS/scrfd_500m_bnkps.onnx}"
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SCENE_MODEL="${SCENE_MODEL:-$MODELS/transnetv2.onnx}"
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run() { echo "+ $*"; "$@"; }
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# The input tensor name is not the same across models — LVFace-B uses "data",
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# arcface_r18 uses "input", and arcface_w600k_{r50,mbf} use "input.1". A
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# hardcoded name makes trtexec fail with "Cannot find input tensor with name
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# ...", so read it from the model instead.
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input_name() {
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python3 - "$1" <<'PY'
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import sys
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try:
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import onnxruntime as ort
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except ImportError:
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sys.exit("onnxruntime is required to read the model's input name")
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sess = ort.InferenceSession(sys.argv[1], providers=["CPUExecutionProvider"])
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print(sess.get_inputs()[0].name)
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PY
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}
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ARCFACE_IN="$(input_name "$ARCFACE_MODEL")"
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SCRFD_IN="$(input_name "$SCRFD_MODEL")"
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echo "== ArcFace == (input tensor: $ARCFACE_IN)"
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run trtexec \
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--onnx="$ARCFACE_MODEL" \
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--fp16 \
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--minShapes="$ARCFACE_IN":1x3x112x112 \
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--optShapes="$ARCFACE_IN":${EMBED_BATCH}x3x112x112 \
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--maxShapes="$ARCFACE_IN":${EMBED_BATCH}x3x112x112 \
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--saveEngine="$OUT/arcface.$(basename "$ARCFACE_MODEL" .onnx).b${EMBED_BATCH}.fp16.engine" \
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--useCudaGraph
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echo
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echo "== SCRFD == (input tensor: $SCRFD_IN)"
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run trtexec \
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--onnx="$SCRFD_MODEL" \
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--fp16 \
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--minShapes="$SCRFD_IN":1x3x640x640 \
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--optShapes="$SCRFD_IN":1x3x640x640 \
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--maxShapes="$SCRFD_IN":1x3x640x640 \
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--saveEngine="$OUT/scrfd.$(basename "$SCRFD_MODEL" .onnx).640.fp16.engine" \
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--useCudaGraph
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if [[ -f "$SCENE_MODEL" ]]; then
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echo
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echo "== TransNetV2 (scene detector) =="
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# Fixed 1x100x27x48x3 window. The raw-TRT scene detector backend loads this
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# engine directly via --scene-detector-engine; the ORT-TRT EP builds its own.
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SCENE_IN="$(input_name "$SCENE_MODEL")"
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echo " (input tensor: $SCENE_IN)"
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run trtexec \
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--onnx="$SCENE_MODEL" \
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--fp16 \
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--minShapes="$SCENE_IN":1x100x27x48x3 \
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--optShapes="$SCENE_IN":1x100x27x48x3 \
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--maxShapes="$SCENE_IN":1x100x27x48x3 \
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--saveEngine="$OUT/transnetv2.100x27x48.fp16.engine" \
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--useCudaGraph
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else
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echo
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echo "== TransNetV2 skipped (no $SCENE_MODEL) =="
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fi
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echo
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echo "Engines saved under: $OUT"
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echo "Look for 'mean: ... ms' in each section for per-call latency."
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