File size: 5,485 Bytes
da6986a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | """Batch CLI: STEP folder -> Excel. Single process, models loaded once."""
from __future__ import annotations
import argparse
import sys
import tempfile
from datetime import datetime
from pathlib import Path
from typing import Dict, List
from openpyxl import Workbook
from heg_brep import (
DEFAULT_PASS1_MODEL, DEFAULT_ELBOW_MODEL, DEFAULT_TEE_MODEL, REPO_ROOT,
)
from heg_brep.inference import LoadedModel, TwoPassClassifier
from heg_brep.extraction import extract_folder_to_npz
COLUMNS = [
"file_name", "file_path", "stem", "status",
"pass1_argmax", "pass1_conf", "route",
"pass2_argmax", "pass2_predicted", "pass2_conf",
"final_label", "final_conf", "npz_path", "note",
]
def parse_args() -> argparse.Namespace:
ap = argparse.ArgumentParser(description=__doc__)
ap.add_argument("step_folder")
ap.add_argument("output_excel", nargs="?", default=None,
help="Defaults to ./brep_two_pass_<timestamp>.xlsx")
ap.add_argument("--pass1_model", default=str(DEFAULT_PASS1_MODEL))
ap.add_argument("--elbow_model", default=str(DEFAULT_ELBOW_MODEL))
ap.add_argument("--tee_model", default=str(DEFAULT_TEE_MODEL))
ap.add_argument("--device", default="cpu", choices=["cpu", "cuda"])
ap.add_argument("--pass2_min_conf", type=float, default=0.85)
ap.add_argument("--pass2_tau", type=float, default=0.0)
ap.add_argument("--num_workers", type=int, default=1)
ap.add_argument("--max_file_mb", type=float, default=None)
ap.add_argument("--npz_dir", default=None, help="Persist NPZs here (default: temp).")
return ap.parse_args()
def write_excel(rows: List[Dict[str, object]], output_excel: Path) -> None:
wb = Workbook()
ws = wb.active
ws.title = "brep_two_pass"
ws.append(COLUMNS)
for r in rows:
ws.append([r.get(c, "") for c in COLUMNS])
for col in ws.columns:
letter = col[0].column_letter
max_len = max((len(str(c.value)) if c.value is not None else 0 for c in col), default=0)
ws.column_dimensions[letter].width = min(max(12, max_len + 2), 70)
output_excel.parent.mkdir(parents=True, exist_ok=True)
wb.save(output_excel)
def main() -> int:
args = parse_args()
step_folder = Path(args.step_folder).expanduser().resolve()
if not step_folder.is_dir():
raise FileNotFoundError(f"step_folder not found: {step_folder}")
output_excel = (Path(args.output_excel).expanduser().resolve()
if args.output_excel
else REPO_ROOT / f"brep_two_pass_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx")
npz_dir = (Path(args.npz_dir).expanduser().resolve() if args.npz_dir
else Path(tempfile.mkdtemp(prefix="heg_brep_npz_")))
print(f"Extracting STEP files in {step_folder} -> {npz_dir}")
t0 = datetime.now()
summary = extract_folder_to_npz(
step_folder, output_dir=npz_dir,
num_workers=args.num_workers, max_file_mb=args.max_file_mb,
)
print(f" extraction took {(datetime.now() - t0).total_seconds():.1f}s "
f"({len(summary['ok_stems'])} OK, {len(summary['skipped'])} skipped)")
print(f"Loading models on {args.device}...")
t0 = datetime.now()
pass1 = LoadedModel(Path(args.pass1_model), device=args.device)
elbow = LoadedModel(Path(args.elbow_model), device=args.device) if Path(args.elbow_model).exists() else None
tee = LoadedModel(Path(args.tee_model), device=args.device) if Path(args.tee_model).exists() else None
clf = TwoPassClassifier(pass1=pass1, elbow=elbow, tee=tee,
pass2_min_conf=args.pass2_min_conf, pass2_tau=args.pass2_tau)
print(f" models loaded in {(datetime.now() - t0).total_seconds():.1f}s")
print(f"Classifying {len(summary['step_paths'])} files...")
t0 = datetime.now()
rows: List[Dict[str, object]] = []
ok = pass1_failed = pass2_failed = extraction_failed = 0
for sp in summary["step_paths"]:
stem = sp.stem
base = {c: "" for c in COLUMNS}
base.update({"file_name": sp.name, "file_path": str(sp), "stem": stem})
npz = npz_dir / f"{stem}.npz"
if not npz.exists():
base["status"] = "EXTRACTION_FAILED"
base["note"] = summary["skipped"].get(stem, "NPZ missing after extraction")
extraction_failed += 1
rows.append(base); continue
base["npz_path"] = str(npz)
try:
result = clf.classify_npz(npz)
except Exception as exc:
base["status"] = "INFERENCE_FAILED"
base["note"] = str(exc)[:500]
pass1_failed += 1
rows.append(base); continue
base.update({k: result.get(k, "") for k in result})
# Round confidences for the spreadsheet
for k in ("pass1_conf", "pass2_conf", "final_conf"):
v = base.get(k)
base[k] = round(float(v), 6) if isinstance(v, (int, float)) else v
base["status"] = "OK"
ok += 1
rows.append(base)
elapsed = (datetime.now() - t0).total_seconds()
print(f" inference took {elapsed:.1f}s ({elapsed / max(1, len(rows)):.2f}s/file)")
write_excel(rows, output_excel)
print(f"Saved Excel: {output_excel}")
print(f"Summary -> total: {len(rows)} | OK: {ok} | "
f"extraction_failed: {extraction_failed} | inference_failed: {pass1_failed}")
print(f"NPZ dir: {npz_dir}")
return 0
if __name__ == "__main__":
sys.exit(main())
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