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

The RKNN conversion process typically involves setting up the Toolkit environment, writing a conversion script, running it, and then waiting to read a long list of logs. However, this process often uncovers issues such as dynamic batch dimensions, problematic operations, or unloved opsets before the conversion even begins.

To address this, RK Edge Doctor, a zero-dependency Python tool, has been enhanced with a "convert trio" feature. This tool does not perform the actual conversion but checks the model for three key aspects before attempting conversion: 1. Convert check - This pre-flight check analyzes the ONNX model's structure, including the opset version, input shapes, and operation histogram.

It flags dynamic input dimensions, control-flow operators, and known issues that may cause problems during quantization. The tool then categorizes the model as READY, RISKY, or LIKELY_TO_FAIL. 2. Convert recipe - After the initial check, this step generates a ready-to-run conversion script for the official RKNN Toolkit2. The script includes the target platform, int8 configuration, calibration dataset, and input size list derived from the ONNX inputs.

3. Convert verify - Finally, this step verifies the converted model post-conversion. It uses existing model diagnosis techniques, such as CPU fallback lists, PERF-counter latency, and ONNX output drift analysis, to evaluate the model's performance and compatibility with the target hardware.