| | |
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| | | parser.add_argument("--model", type=Path, required=True) |
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| | | parser.add_argument("--input", type=Path, required=True) |
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| | | parser.add_argument("--output", type=Path, required=True) |
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| | | parser.add_argument("--device", choices={"cpu", "cuda"}, default="cpu") |
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| | | parser.add_argument("--device", choices={"auto", "cpu", "cuda"}, default="auto") |
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| | | parser.add_argument("--batch-size", type=int, default=4096) |
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| | | args = parser.parse_args() |
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| | | if args.device == "cuda" and not torch.cuda.is_available(): |
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| | |
|---|
| | | raise SystemExit("Output directory must be new or empty.") |
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| | | |
|---|
| | | started = time.perf_counter() |
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| | | device = torch.device(args.device) |
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| | | device_name = "cuda" if args.device == "cuda" or (args.device == "auto" and torch.cuda.is_available()) else "cpu" |
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| | | device = torch.device(device_name) |
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| | | model, class_codes = load_model(args.model, device) |
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| | | xyz, rgb, source_las = load_cloud(args.input) |
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| | | center = xyz.mean(axis=0) |
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| | |
|---|
| | | summary = {"class_codes": class_codes, "class_counts": {str(code): counts[code] for code in class_codes}, "input_points": int(len(xyz)), "preview_points": int(len(preview_selection)), "preview_sampling": "deterministic class-aware cap; smaller predicted classes retained before the remaining budget is sampled", "input_has_rgb": True} |
|---|
| | | summary_path = args.output / "prediction-summary.json" |
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| | | summary_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8") |
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| | | metadata = {"capability": "05-3d-pointcloud", "classification": "B", "created_at": datetime.now(UTC).isoformat(), "model": {"path": str(args.model), "sha256": sha256(args.model), "architecture": "PointWiseNet shared MLP"}, "input": {"path": str(args.input), "sha256": sha256(args.input), "bytes": args.input.stat().st_size, "points": int(len(xyz)), "has_rgb": True}, "classes": {str(code): CLASS_SCHEMA[code] for code in class_codes}, "prediction": summary, "processing": {"device": args.device, "batch_size": args.batch_size, "normalization": {"method": "source-local per input", "xyz_center": center.tolist(), "xyz_scale": scale}}, "versions": {"python": sys.version.split()[0], "torch": torch.__version__, "open3d": o3d.__version__, "laspy": laspy.__version__}, "artifacts": {"preview": preview_path.name, "classified_las": classified_las.name, "class_counts": csv_path.name, "summary": summary_path.name}, "elapsed_seconds": round(time.perf_counter() - started, 3), "limitations": ["Predictions are model candidates, not asset inventory or inspection conclusions.", "This model requires observed RGB; it cannot infer labels for XYZ-only point clouds.", "Model metrics apply only to the labelled source spatial blocks. The current pole/tower class has high false-positive risk and requires review.", "New inputs are normalized with their own XYZ centre and scale to match the training feature definition; this preserves their coordinates but does not prove cross-site generalization."]} |
|---|
| | | metadata = {"capability": "05-3d-pointcloud", "classification": "B", "created_at": datetime.now(UTC).isoformat(), "model": {"path": str(args.model), "sha256": sha256(args.model), "architecture": "PointWiseNet shared MLP"}, "input": {"path": str(args.input), "sha256": sha256(args.input), "bytes": args.input.stat().st_size, "points": int(len(xyz)), "has_rgb": True}, "classes": {str(code): CLASS_SCHEMA[code] for code in class_codes}, "prediction": summary, "processing": {"requested_device": args.device, "device": device_name, "batch_size": args.batch_size, "normalization": {"method": "source-local per input", "xyz_center": center.tolist(), "xyz_scale": scale}}, "versions": {"python": sys.version.split()[0], "torch": torch.__version__, "open3d": o3d.__version__, "laspy": laspy.__version__}, "artifacts": {"preview": preview_path.name, "classified_las": classified_las.name, "class_counts": csv_path.name, "summary": summary_path.name}, "elapsed_seconds": round(time.perf_counter() - started, 3), "limitations": ["Predictions are model candidates, not asset inventory or inspection conclusions.", "This model requires observed RGB; it cannot infer labels for XYZ-only point clouds.", "Model metrics apply only to the labelled source spatial blocks. The current pole/tower class has high false-positive risk and requires review.", "New inputs are normalized with their own XYZ centre and scale to match the training feature definition; this preserves their coordinates but does not prove cross-site generalization."]} |
|---|
| | | (args.output / "run_metadata.json").write_text(json.dumps(metadata, ensure_ascii=False, indent=2), encoding="utf-8") |
|---|
| | | print(json.dumps(metadata, ensure_ascii=False)) |
|---|
| | | return 0 |
|---|
