Before you convert that ONNX model to RKNN, check whether it can convert at all

The standard RKNN conversion experience goes like this: set up the Toolkit environment, write the conversion script, run it, wait, read a wall of log, and discover — forty minutes in — that your model has a dynamic batch dimension, or an op the converter places badly, or an opset the toolchain doesn't love. None of those are conversion problems. They're pre-flight problems, discovered at landing time.

So I added a convert trio to RK Edge Doctor (zero-dependency, Python stdlib only). It deliberately does not convert anything — the official RKNN Toolkit2 does that. It does the three things around the conversion:

1. convert check — pre-flight. A hand-rolled ONNX structure parser (raw protobuf wire format, no onnx package) reads opset, input shapes and the op histogram, then checks:

• dynamic input dimensions (fix them before quantization, not after)
• control-flow operators — Loop / If / Scan cannot become a static RKNN graph, verdict LIKELY_TO_FAIL
• operators known to need attention in Toolkit2 (NonMaxSuppression, Resize, GridSample, …) with the reason for each
• quantization-sensitive tails (Sigmoid/Softmax at the graph end under int8)

Verdict: READY / RISKY / LIKELY_TO_FAIL.

2. convert recipe — a script worth running. Generates a ready-to-run Toolkit2 conversion script: target platform, int8 config + calibration dataset if you asked, and input_size_list pinned from the ONNX inputs themselves. You run it in your own Toolkit environment.

3. convert verify — post-conversion acceptance. The existing model diagnosis (CPU fallback list, PERF-counter latency, ONNX output drift) framed as the verification step.

First contact with a real file was humbling in the right way. I tested the checker on the official Ultralytics YOLOv8n ONNX export (PyTorch, opset 20, 233 nodes) and it immediately failed — because my parser assumed the textbook ModelProto field layout, and real PyTorch exports arrange producer/opset fields differently. Fixed by identifying fields by content, not by faith, and the same file now gets an honest verdict:

$ rkdoctor convert check yolov8n.onnx --target rk3588
+ parse: 233 nodes (opset 20, producer pytorch)
! opset: 20 (outside the commonly used 11-19 range)
! caution_ops: Resize (some modes convert with restrictions)
+ dynamic_dims: static
+ weights: 12.78 MB
RISKY

That is the correct answer for that file, and it's the answer you want before the Toolkit run, not after.

With this release the tool's planned surface is complete: board (16 hardware checks), model (CPU-fallback diagnosis), rtsp (stream acceptance), and now convert. The model health table keeps growing weekly — custom conversions that check out RISKY, or run MIXED/CPU_HEAVY, are exactly the reports I want in issues.