add ONNX sanitize required for Ultralytics

examples/meter-detection/README.md

@@ -227,6 +227,23 @@ The command downloads the job's `model.tar.gz`, finds `model.onnx`, uploads it t
 compilation, validation, and profiling. The uploaded source model uses the configured
 `aihub.model_name`.
 
+The training example sanitizes the Ultralytics ONNX export before saving `model.onnx`. This removes graph input or
+output names, such as `output0`, that are duplicated in the ONNX `value_info` metadata and rejected by AI Hub.
+
+For a model already downloaded by a failed upload attempt, sanitize the extracted ONNX file and retry using the local
+model. Replace the job name in both paths:
+
+```bash
+uv run --with onnx python examples/meter-detection/source/sanitize_onnx.py \
+  build/qai-hub/meter-detection/qc-cli-YYYYMMDD-HHMMSS/source/extracted/model.onnx \
+  --output build/qai-hub/meter-detection/model.aihub.onnx
+
+qc-cli ai-hub upload \
+  examples/meter-detection/data/aihub_calibration \
+  examples/meter-detection/data/inputs.npz \
+  --onnx-path build/qai-hub/meter-detection/model.aihub.onnx
+```
+
 If the meter-detection job is still the last training job in `.qc-cli.json`, `--from-job` can be omitted. Keeping it
 explicit prevents accidentally uploading an artifact from a different training run.
 

examples/meter-detection/source/sanitize_onnx.py

@@ -0,0 +1,38 @@
+#!/usr/bin/env python3
+"""Remove ONNX value_info entries that duplicate graph inputs or outputs."""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+import onnx  # type: ignore[reportMissingImports]
+
+
+def sanitize_onnx(path: Path, output_path: Path | None = None) -> Path:
+    model = onnx.load(path)
+    io_names = {value.name for value in (*model.graph.input, *model.graph.output)}
+    retained_value_info = [value for value in model.graph.value_info if value.name not in io_names]
+
+    destination = output_path or path
+    if len(retained_value_info) != len(model.graph.value_info):
+        del model.graph.value_info[:]
+        model.graph.value_info.extend(retained_value_info)
+
+    destination.parent.mkdir(parents=True, exist_ok=True)
+    onnx.save(model, destination)
+    return destination
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("onnx_path", type=Path)
+    parser.add_argument("--output", type=Path)
+    args = parser.parse_args()
+
+    written = sanitize_onnx(args.onnx_path, args.output)
+    print(f"Saved sanitized ONNX model to {written}")
+
+
+if __name__ == "__main__":
+    main()

examples/meter-detection/source/train.py

@@ -11,6 +11,7 @@ from pathlib import Path
 from typing import Any
 
 import yaml
+from sanitize_onnx import sanitize_onnx
 from ultralytics import YOLO  # type: ignore[reportMissingImports]
 
 
@@ -103,7 +104,8 @@ def main() -> None:
     copy_if_exists(trained_weights, model_dir / "best.pt")
     trained_model = YOLO(str(trained_weights))
     onnx_path = Path(trained_model.export(format="onnx", imgsz=args.imgsz))
-    copy_if_exists(onnx_path, model_dir / "model.onnx")
+    saved_onnx_path = sanitize_onnx(onnx_path, model_dir / "model.onnx")
+    print(f"Saved {saved_onnx_path}")
 
     metrics = {
         "model": args.model,
@@ -114,7 +116,7 @@ def main() -> None:
         "patience": args.patience,
         "data_yaml": str(data_yaml),
         "weights": str(trained_weights),
-        "onnx": str(onnx_path),
+        "onnx": str(saved_onnx_path),
     }
     (model_dir / "metrics.json").write_text(json.dumps(metrics, indent=2), encoding="utf-8")
     print(f"Saved model artifacts to {model_dir}")