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name: export description: "Guides YOLOP ONNX export, ONNXRuntime inference validation, TensorRT .wts preparation, and TensorRT/ZED deployment constraints." disable-model-invocation: true metadata: disco-role: operating license: MIT

YOLOP Export and Deployment Preparation

Use this sub-skill when the task asks to export a YOLOP checkpoint to ONNX, run ONNXRuntime inference, inspect exported output names/shapes, produce TensorRT .wts weights, or understand the repo's C++ TensorRT/ZED deployment path.

Do not use this sub-skill for PyTorch demo inference (use inference), BDD100K data preparation (use data-preparation), or full training/evaluation (use training).

Read first

• references/workflows.md covers ONNX export, ONNXRuntime inference, output naming, and bundled helper usage.
• references/tensorrt-deployment.md distills the TensorRT/ZED deployment path and explains why it is not a CPU/Python smoke.
• references/troubleshooting.md covers export output mismatches, missing ONNX packages, checkpoint format issues, and TensorRT build blockers.
• scripts/export_onnx_model.py is the safe ONNX exporter with explicit output path.
• scripts/run_onnx_inference.py runs ONNXRuntime inference with explicit image/model/output paths.
• scripts/export_wts.py converts a PyTorch checkpoint to TensorRT .wts text format with explicit output or dry-run mode.

Quick route

# Export to a controlled location, using the export-specific MCnet wrapper
python sub-skills/export/scripts/export_onnx_model.py \
  --repo-root /path/to/YOLOP \
  --checkpoint /path/to/YOLOP/weights/End-to-end.pth \
  --output /tmp/yolop-640-640.onnx \
  --height 640 --width 640 --simplify --check

# Validate an ONNX model on a single image with ONNXRuntime CPU
python sub-skills/export/scripts/run_onnx_inference.py \
  --repo-root /path/to/YOLOP \
  --onnx /tmp/yolop-640-640.onnx \
  --image /path/to/YOLOP/test.jpg \
  --output-dir /tmp/yolop-onnx-output

# Inspect or write TensorRT .wts weights
python sub-skills/export/scripts/export_wts.py \
  --repo-root /path/to/YOLOP \
  --checkpoint /path/to/YOLOP/weights/End-to-end.pth \
  --dry-run

Key gotcha

Do not export lib.models.get_net(cfg) directly unless you inspect the resulting ONNX outputs. The active source model's eval detection head returns a nested tuple, and some PyTorch exporters flatten it into extra outputs. The source export_onnx.py defines an export-specific MCnet wrapper that returns exactly det_out, drive_area_seg, and lane_line_seg; the bundled exporter uses that wrapper.

Backend boundary

ONNX export and ONNXRuntime CPU inference can be validated in a CPU Python environment. TensorRT engine build and ZED-camera deployment require CUDA/TensorRT/ZED/OpenCV C++ prerequisites and are reference-only unless the user provides that environment.