| |
| |
| """Quickstart: the model card's Python snippet on the shipped sample, on one Blackhole p150. |
| |
| pip install -e . # once, from the repo root, on top of an environment that has ttnn (tt-metal) |
| python examples/quickstart.py [input] [--out-dir examples/output] |
| |
| Writes <out-dir>/quickstart.json (the same JSON as POST /predict). The input is a sample manifest (the camera images |
| and their calibration preset, ``samples/*.json`` of the package, read with ``load_sample``); the default is found |
| relative to this file (runs from any directory), an input given on the command line is relative to the current |
| directory. |
| """ |
| import argparse |
| import json |
| from pathlib import Path |
|
|
| REPO = Path(__file__).resolve().parents[1] |
| ap = argparse.ArgumentParser() |
| ap.add_argument("input", nargs="?", default=str(REPO / "code" / "tt_streampetr" / "samples" / "synthetic_5cam.json")) |
| ap.add_argument("--out-dir", default=str(REPO / "examples" / "output")) |
| ap.add_argument("--device-id", type=int, default=0) |
| args = ap.parse_args() |
| out_dir = Path(args.out_dir) |
| out_dir.mkdir(parents=True, exist_ok=True) |
|
|
| |
| from tt_streampetr import StreamPETR, load_sample |
|
|
| with StreamPETR.from_pretrained(device_id=args.device_id) as model: |
| out = model(**load_sample(args.input)) |
|
|
| for d in out.to_dicts()[:10]: |
| print(d) |
| |
|
|
| (out_dir / "quickstart.json").write_text(json.dumps(out.to_dict(), indent=1)) |
| print(f"{len(out)} results -> {out_dir / 'quickstart.json'} timing_ms={ {k: round(v, 2) for k, v in out.timing_ms.items()} }") |
|
|
