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name: inference-export description: "Run D-FINE inference, export, deployment, benchmark, visualization, and EMA checkpoint workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

D-FINE Inference and Export Router

Use this sub-skill when the user needs D-FINE deployment or inference commands, backend prerequisites, generated result filenames, benchmark commands, or EMA-only checkpoint extraction.

Route by task

• PyTorch image/video inference: collect a D-FINE config, checkpoint, input image/video, and device; then use references/inference-and-export.md or scripts/dfine_inference_command.py --backend torch.
• ONNX export or ONNX Runtime inference: collect config, checkpoint, ONNX model path, input image/video, and optional check/simplify requirements; then use references/inference-and-export.md and scripts/dfine_export_command.py or scripts/dfine_inference_command.py --backend onnx.
• TensorRT engine build, inference, or latency: collect ONNX model, engine output/input, CUDA device, image directory for latency, and TensorRT toolchain availability; then use references/deployment-and-benchmarks.md, scripts/dfine_export_command.py --build-trt, scripts/dfine_inference_command.py --backend trt, or scripts/dfine_benchmark_command.py --benchmark trt.
• OpenVINO inference: collect an OpenVINO IR/XML model and image path; then use references/inference-and-export.md or scripts/dfine_inference_command.py --backend openvino.
• FLOPs/MACs/params or FiftyOne visualization: use references/deployment-and-benchmarks.md and scripts/dfine_benchmark_command.py --benchmark flops for static model info.
• EMA checkpoint extraction: use scripts/extract_ema_checkpoint.py when a checkpoint has ema.module and the user needs a {model: ...} checkpoint for simpler loading/export.

Required inputs to ask for

• Config file for config-bound workflows, for example a COCO, Objects365, CrowdHuman, or custom D-FINE YAML.
• Checkpoint for PyTorch inference, ONNX export, FiftyOne, or EMA extraction.
• ONNX model for ONNX Runtime inference or TensorRT engine creation.
• TensorRT engine for TensorRT inference/latency; OpenVINO IR/XML model for OpenVINO inference.
• Image/video path for visualization inference; image directory for TensorRT latency benchmark.
• Device string where the native script accepts one, such as cpu, cuda:0, or TensorRT CUDA device.
• Expected output filename: PyTorch uses torch_results.jpg or torch_results.mp4; ONNX Runtime uses onnx_result.jpg or onnx_result.mp4; TensorRT uses trt_result.jpg or trt_result.mp4; OpenVINO writes openvino_result.jpg.

Boundaries

• This sub-skill covers inference, export, deployment conversion, benchmark commands, optional backend prerequisites, output files, and deployment-oriented checkpoint conversion.
• Route training, resume, tuning, evaluation, checkpoint creation, and long experiment launch decisions to the training/evaluation sub-skill.
• Route dataset layout and data config editing to the data/config sub-skill.
• Route architecture internals to the architecture/API sub-skill, except for deploy-mode facts needed for export and inference.

Failure handling

Start with references/troubleshooting.md for missing checkpoint keys, HGNetv2.pretrained, ONNX opset/check/simplify, TensorRT/trtexec, OpenVINO IR/device, preprocessing, shifted boxes, and output filename mismatches.