Datasets:
Replace obj/ and pcd/ with WebDataset train/test shards (float32/int32); old layout at tag v1-legacy
1e67656 verified | """Convert the obj/ + pcd/ + metadata.csv layout into WebDataset tar shards (train/, test/). | |
| Each sample key is the sample id, with members: | |
| {id}.vertices.npy (V, 3) float32 | |
| {id}.faces.npy (F, 3) int32 | |
| {id}.pcd.npy (8192, 3) float32 | |
| {id}.json {"id", "genus", "components", "sample_code"} | |
| """ | |
| import io, json, tarfile | |
| from multiprocessing import Pool | |
| from pathlib import Path | |
| import numpy as np | |
| import pandas as pd | |
| ROOT = Path(__file__).resolve().parents[1] | |
| PER_SHARD = 600 # ~1 GB per shard | |
| SEED = 0 | |
| meta = pd.read_csv(ROOT / "metadata.csv").set_index("id") | |
| obj_path = {p.stem: p for p in (ROOT / "obj").glob("*/*.npz")} | |
| pcd_path = {p.stem: p for p in (ROOT / "pcd").glob("*/*.npy")} | |
| def npy_bytes(a: np.ndarray) -> bytes: | |
| buf = io.BytesIO() | |
| np.save(buf, a) | |
| return buf.getvalue() | |
| def add(tar: tarfile.TarFile, name: str, data: bytes): | |
| info = tarfile.TarInfo(name) | |
| info.size = len(data) | |
| tar.addfile(info, io.BytesIO(data)) | |
| def write_shard(args): | |
| out, ids = args | |
| tmp = out.with_suffix(".tar.tmp") | |
| with tarfile.open(tmp, "w", format=tarfile.USTAR_FORMAT) as tar: | |
| for i in ids: | |
| z = np.load(obj_path[i]) | |
| row = meta.loc[i] | |
| add(tar, f"{i}.vertices.npy", npy_bytes(z["vertices"].astype(np.float32))) # (V, 3) | |
| add(tar, f"{i}.faces.npy", npy_bytes(z["faces"].astype(np.int32))) # (F, 3) | |
| add(tar, f"{i}.pcd.npy", npy_bytes(np.load(pcd_path[i]).astype(np.float32))) # (8192, 3) | |
| add(tar, f"{i}.json", json.dumps({ | |
| "id": i, "genus": int(row.genus), "components": int(row.components), | |
| "sample_code": json.loads(row.sample_code), | |
| }).encode()) | |
| tmp.rename(out) | |
| return out.name | |
| if __name__ == "__main__": | |
| jobs = [] | |
| for split in ["train", "test"]: | |
| ids = np.array((ROOT / "splits/full" / f"{split}.txt").read_text().split()) | |
| ids = ids[np.random.default_rng(SEED).permutation(len(ids))] # shuffle so shards are i.i.d. | |
| (ROOT / split).mkdir(exist_ok=True) | |
| for s, k in enumerate(range(0, len(ids), PER_SHARD)): | |
| out = ROOT / split / f"{split}-{s:05d}.tar" | |
| if not out.exists(): | |
| jobs.append((out, ids[k:k + PER_SHARD].tolist())) | |
| with Pool(8) as pool: | |
| for name in pool.imap_unordered(write_shard, jobs): | |
| print(name, flush=True) | |