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D-FINE Export Instructions

Exporting ONNX Models with ONNXRuntime

To export D-FINE models to ONNX format, follow the steps below:

Repository

Peterande D-FINE Repository

Steps:

  1. Navigate to the D-FINE repository directory:

    cd D-FINE

  2. Define the model size you want to export (n, s, m, l, or x). For example:

    export model=l

  3. Run the export script:

    python tools/deployment/export_onnx.py --check -c configs/dfine/dfine_hgnetv2_${model}_coco.yml -r model.pth

Notes:

  • Ensure the batch size hardcoded in the export_onnx.py script is appropriate for your system's available RAM. If not, modify the batch size in the script to avoid out-of-memory errors.
  • Verify that model.pth corresponds to the correct pre-trained model checkpoint for the configuration file you're using.
  • The --check flag ensures that the exported ONNX model is validated after the export process.

Example:

To export the large model (l) with the corresponding configuration:

cd D-FINE
export model=l
python tools/deployment/export_onnx.py --check -c configs/dfine/dfine_hgnetv2_l_coco.yml -r model.pth

Convert ONNX Model to TensorRT

mkdir exports
docker run --rm -it --gpus=all -v $(pwd)/exports:/exports --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 -v $(pwd)/model.onnx:/workspace/model.onnx -w /workspace nvcr.io/nvidia/tensorrt:24.12-py3 /bin/bash -cx "trtexec --onnx="model.onnx" --saveEngine="model.engine" --fp16"