File size: 3,639 Bytes
a207665 | 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 | #!/usr/bin/env python3
"""Validate ShapeNetCar mlcfd_data structure and readable arrays."""
from __future__ import annotations
import argparse
import sys
from pathlib import Path
import numpy as np
STATS = {
"mean_in.npy": (7,),
"std_in.npy": (7,),
"mean_out.npy": (4,),
"std_out.npy": (4,),
}
TRAINING_FILES = ("Cd.npy", "I1.npy", "I2.npy", "Press.npy", "Velo.npy")
PREPROCESSED_FILES = ("x.npy", "y.npy", "pos.npy", "surf.npy", "edge_index.npy")
def fail(message: str) -> None:
print(f"[FAIL] {message}", file=sys.stderr)
raise SystemExit(1)
def load(path: Path) -> np.ndarray:
try:
return np.load(path, allow_pickle=False)
except Exception as exc: # pragma: no cover - diagnostic path
fail(f"cannot read {path}: {exc}")
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--data-root", default="data/mlcfd_data")
args = parser.parse_args()
root = Path(args.data_root)
if not root.is_dir():
fail(f"data root not found: {root}")
for subdir in ("training_data", "preprocessed_data", "stats"):
if not (root / subdir).is_dir():
fail(f"missing directory: {root / subdir}")
for filename, shape in STATS.items():
arr = load(root / "stats" / filename)
if arr.shape != shape:
fail(f"stats shape mismatch for {filename}: expected {shape}, got {arr.shape}")
if not np.issubdtype(arr.dtype, np.floating):
fail(f"stats dtype mismatch for {filename}: got {arr.dtype}")
train_param_dirs = sorted(p for p in (root / "training_data").glob("param*") if p.is_dir())
if not train_param_dirs:
fail("no training_data/param* directories found")
for param_dir in train_param_dirs:
for filename in TRAINING_FILES:
path = param_dir / filename
if not path.is_file():
fail(f"missing training file: {path}")
arr = load(path)
if arr.size == 0:
fail(f"empty training array: {path}")
sample_dirs = sorted(p for p in (root / "preprocessed_data").glob("param*/*") if p.is_dir())
if not sample_dirs:
fail("no preprocessed sample directories found")
sample = sample_dirs[0]
arrays = {name: load(sample / name) for name in PREPROCESSED_FILES}
if arrays["x.npy"].ndim != 2 or arrays["x.npy"].shape[1] != 7:
fail(f"x.npy schema mismatch in {sample}: {arrays['x.npy'].shape}")
if arrays["y.npy"].ndim != 2 or arrays["y.npy"].shape[1] != 4:
fail(f"y.npy schema mismatch in {sample}: {arrays['y.npy'].shape}")
if arrays["pos.npy"].ndim != 2 or arrays["pos.npy"].shape[1] != 3:
fail(f"pos.npy schema mismatch in {sample}: {arrays['pos.npy'].shape}")
if arrays["surf.npy"].ndim != 1:
fail(f"surf.npy schema mismatch in {sample}: {arrays['surf.npy'].shape}")
if arrays["edge_index.npy"].ndim != 2 or arrays["edge_index.npy"].shape[0] != 2:
fail(f"edge_index.npy schema mismatch in {sample}: {arrays['edge_index.npy'].shape}")
node_count = arrays["x.npy"].shape[0]
if arrays["y.npy"].shape[0] != node_count or arrays["pos.npy"].shape[0] != node_count:
fail(f"node count mismatch in {sample}")
if arrays["surf.npy"].shape[0] != node_count:
fail(f"surface mask length mismatch in {sample}")
print("[OK] ShapeNetCar data validation passed")
print(f"[OK] training param dirs: {len(train_param_dirs)}")
print(f"[OK] preprocessed samples: {len(sample_dirs)}")
print(f"[OK] checked sample: {sample.relative_to(root)}")
if __name__ == "__main__":
main()
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