AgentFEM-Material-Loading-Memory / src /verify_t2_download.py
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Add multiaxial OOD v2 data, six neural models, and FE validation
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"""Verify a downloaded T2 release without importing AgentFEM."""
from __future__ import annotations
import argparse
import hashlib
import json
from collections import Counter
from pathlib import Path
import h5py
import numpy as np
EXPECTED_SPLITS = {"train": 768, "validation": 120, "test": 120}
EXPECTED_MODELS = {"j2_linear_isotropic": 504, "chaboche_combined": 504}
EXPECTED_PATH_COUNT = 168
def sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as stream:
for block in iter(lambda: stream.read(1024 * 1024), b""):
digest.update(block)
return digest.hexdigest()
def resolve_shard(root: Path, relative_path: str) -> Path:
local = root / "shards" / Path(relative_path).name
if local.exists():
return local
packaged = root.parents[1] / relative_path
if packaged.exists():
return packaged
raise FileNotFoundError(f"Missing shard: {relative_path}")
def verify(root: Path) -> dict[str, object]:
manifest = json.loads((root / "manifest.json").read_text(encoding="utf-8"))
ids: list[str] = []
splits: Counter[str] = Counter()
models: Counter[str] = Counter()
paths: Counter[str] = Counter()
for shard in manifest["shards"]:
path = resolve_shard(root, shard["path"])
observed_hash = sha256(path)
if observed_hash != shard["sha256"]:
raise RuntimeError(f"SHA-256 mismatch: {path}")
with h5py.File(path, "r") as h5:
shard_ids = sorted(h5.keys())
if len(shard_ids) != shard["sample_count"]:
raise RuntimeError(f"Sample-count mismatch: {path}")
if shard_ids[0] != shard["first_id"] or shard_ids[-1] != shard["last_id"]:
raise RuntimeError(f"ID-range mismatch: {path}")
for sample_id in shard_ids:
group = h5[sample_id]
ids.append(sample_id)
splits[str(group.attrs["split"])] += 1
models[str(group.attrs["material_model"])] += 1
paths[str(group.attrs["path_family"])] += 1
for name in group.keys():
if not np.isfinite(group[name][:]).all():
raise RuntimeError(f"Non-finite values: {path}:{sample_id}/{name}")
expected_ids = [f"{index:05d}" for index in range(manifest["sample_count"])]
if ids != expected_ids:
raise RuntimeError("Trajectory IDs are incomplete, duplicated or out of order.")
if dict(splits) != EXPECTED_SPLITS:
raise RuntimeError(f"Unexpected split counts: {dict(splits)}")
if dict(models) != EXPECTED_MODELS:
raise RuntimeError(f"Unexpected material-model counts: {dict(models)}")
if set(paths.values()) != {EXPECTED_PATH_COUNT} or len(paths) != 6:
raise RuntimeError(f"Unexpected path-family counts: {dict(paths)}")
result = {
"status": "verified",
"sample_count": len(ids),
"shard_count": len(manifest["shards"]),
"splits": dict(sorted(splits.items())),
"material_models": dict(sorted(models.items())),
"path_families": dict(sorted(paths.items())),
"agentfem_commit": manifest["agentfem_commit"],
}
print(json.dumps(result, indent=2, sort_keys=True))
return result
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"dataset_root",
nargs="?",
default="data/t2_material_loading_memory_v1",
)
args = parser.parse_args()
verify(Path(args.dataset_root))
if __name__ == "__main__":
main()