mirror of
https://github.com/azaion/gps-denied-onboard.git
synced 2026-04-22 22:06:37 +00:00
fix: post-audit — runtime bugs, functional gaps, docs, hardening
Phase A — Runtime bugs: - SSE: add push_event() method to SSEEventStreamer (was missing, masked by mocks) - MAVLink: satellites_visible=10 (was 0, triggers ArduPilot failsafe) - MAVLink: horiz_accuracy=sqrt(P[0,0]+P[1,1]) per spec (was sqrt(avg)) - MAVLink: MEDIUM confidence → fix_type=3 per solution.md (was 2) Phase B — Functional gaps: - handle_user_fix() injects operator GPS into ESKF with noise=500m - app.py uses create_vo_backend() factory (was hardcoded SequentialVO) - ESKF: Mahalanobis gating on satellite updates (rejects outliers >5σ) - ESKF: public accessors (position, quaternion, covariance, last_timestamp) - Processor: no more private ESKF field access Phase C — Documentation: - README: correct API endpoints, CLI command, 40+ env vars documented - Dockerfile: ENV prefixes match pydantic-settings (DB_, SATELLITE_, MAVLINK_) - tech_stack.md marked ARCHIVED (contradicts solution.md) Phase D — Hardening: - JWT auth middleware (AUTH_ENABLED=false default, verify_token on /flights) - TLS config env vars (AUTH_SSL_CERTFILE, AUTH_SSL_KEYFILE) - SHA-256 tile manifest verification in SatelliteDataManager - AuthConfig, ESKFSettings, MAVLinkConfig, SatelliteConfig in config.py Also: conftest.py shared fixtures, download_tiles.py, convert_to_trt.py scripts, config wiring into app.py lifespan, config-driven ESKF, calculate_precise_angle fix. Tests: 196 passed / 8 skipped. Ruff clean. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Yuzviak
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#!/usr/bin/env python3
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"""TensorRT engine conversion script.
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Converts ONNX models to TensorRT FP16 .engine files for Jetson deployment.
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Wraps trtexec CLI (available on Jetson with JetPack installed).
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Usage:
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# Convert a single model
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python scripts/convert_to_trt.py --onnx weights/litesam.onnx --output /opt/engines/litesam.engine
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# Convert all known models from weights/ to engines/
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python scripts/convert_to_trt.py --all --onnx-dir weights/ --engine-dir /opt/engines/
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# Dry run (check trtexec availability)
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python scripts/convert_to_trt.py --check
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Requires:
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- NVIDIA TensorRT (trtexec in PATH) — available on Jetson with JetPack
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- NOT available on dev/CI machines — script exits cleanly with a message
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"""
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from __future__ import annotations
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import argparse
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import os
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import shutil
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import subprocess
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import sys
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# Known models and their expected ONNX filenames
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_MODELS = {
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"superpoint": "superpoint.onnx",
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"lightglue": "lightglue.onnx",
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"xfeat": "xfeat.onnx",
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"dinov2": "dinov2.onnx",
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"litesam": "litesam.onnx",
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}
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def find_trtexec() -> str | None:
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"""Find trtexec binary in PATH or common Jetson locations."""
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path = shutil.which("trtexec")
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if path:
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return path
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# Common Jetson paths
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for candidate in [
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"/usr/src/tensorrt/bin/trtexec",
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"/usr/local/cuda/bin/trtexec",
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]:
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if os.path.isfile(candidate) and os.access(candidate, os.X_OK):
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return candidate
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return None
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def convert_onnx_to_engine(
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onnx_path: str,
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engine_path: str,
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fp16: bool = True,
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workspace_mb: int = 1024,
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trtexec_path: str | None = None,
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) -> bool:
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"""Run trtexec to convert ONNX → TensorRT engine.
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Returns True on success, False on failure.
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"""
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trtexec = trtexec_path or find_trtexec()
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if not trtexec:
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print("ERROR: trtexec not found. Install TensorRT or run on Jetson with JetPack.")
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return False
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if not os.path.isfile(onnx_path):
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print(f"ERROR: ONNX file not found: {onnx_path}")
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return False
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os.makedirs(os.path.dirname(engine_path) or ".", exist_ok=True)
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cmd = [
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trtexec,
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f"--onnx={onnx_path}",
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f"--saveEngine={engine_path}",
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f"--workspace={workspace_mb}",
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]
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if fp16:
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cmd.append("--fp16")
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print(f" Converting: {onnx_path} → {engine_path}")
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print(f" Command: {' '.join(cmd)}")
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try:
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result = subprocess.run(cmd, capture_output=True, text=True, timeout=600)
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if result.returncode == 0:
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size_mb = os.path.getsize(engine_path) / (1024 * 1024)
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print(f" OK: {engine_path} ({size_mb:.1f} MB)")
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return True
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else:
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print(f" FAIL (exit {result.returncode})")
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if result.stderr:
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print(f" stderr: {result.stderr[:500]}")
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return False
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except subprocess.TimeoutExpired:
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print(" FAIL: trtexec timed out (>600s)")
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return False
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except FileNotFoundError:
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print(f" FAIL: trtexec not found at {trtexec}")
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return False
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def main() -> int:
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parser = argparse.ArgumentParser(description="Convert ONNX models to TensorRT FP16 engines")
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parser.add_argument("--onnx", help="Input ONNX model path")
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parser.add_argument("--output", help="Output .engine path")
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parser.add_argument("--all", action="store_true", help="Convert all known models")
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parser.add_argument("--onnx-dir", default="weights", help="Directory with ONNX files")
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parser.add_argument("--engine-dir", default="/opt/engines", help="Output engine directory")
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parser.add_argument("--no-fp16", action="store_true", help="Disable FP16 (use FP32)")
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parser.add_argument("--workspace", type=int, default=1024, help="TRT workspace (MB)")
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parser.add_argument("--check", action="store_true", help="Check trtexec availability only")
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args = parser.parse_args()
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# Check mode
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if args.check:
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trtexec = find_trtexec()
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if trtexec:
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print(f"trtexec found: {trtexec}")
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return 0
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else:
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print("trtexec not found. Not on Jetson or TensorRT not installed.")
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return 1
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fp16 = not args.no_fp16
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# Single model conversion
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if args.onnx and args.output:
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ok = convert_onnx_to_engine(args.onnx, args.output, fp16=fp16, workspace_mb=args.workspace)
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return 0 if ok else 1
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# Batch conversion
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if args.all:
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trtexec = find_trtexec()
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if not trtexec:
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print("trtexec not found. Conversion requires Jetson with JetPack installed.")
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return 1
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print(f"Converting all known models from {args.onnx_dir}/ → {args.engine_dir}/")
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success = 0
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fail = 0
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for model_name, onnx_file in _MODELS.items():
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onnx_path = os.path.join(args.onnx_dir, onnx_file)
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engine_path = os.path.join(args.engine_dir, f"{model_name}.engine")
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if not os.path.isfile(onnx_path):
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print(f" SKIP {model_name}: {onnx_path} not found")
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continue
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ok = convert_onnx_to_engine(
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onnx_path, engine_path,
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fp16=fp16, workspace_mb=args.workspace, trtexec_path=trtexec,
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)
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if ok:
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success += 1
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else:
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fail += 1
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print(f"\nDone: {success} converted, {fail} failed")
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return 1 if fail > 0 else 0
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parser.print_help()
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return 1
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if __name__ == "__main__":
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sys.exit(main())
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