Spaces:
Running on Zero
Running on Zero
| """Determinism helpers. | |
| Reproducibility is a stated requirement of this project (a clean clone must | |
| reproduce the headline table), so seeding is not optional plumbing. | |
| """ | |
| from __future__ import annotations | |
| import os | |
| import random | |
| __all__ = ["seed_everything", "worker_init_fn"] | |
| def seed_everything(seed: int = 42, deterministic: bool = True) -> int: | |
| """Seed Python, NumPy and torch. Returns the seed for logging. | |
| `deterministic=True` also disables cuDNN autotuning. On MPS most kernels are | |
| deterministic already, but the flag is honoured where it applies. | |
| """ | |
| os.environ["PYTHONHASHSEED"] = str(seed) | |
| random.seed(seed) | |
| try: | |
| import numpy as np | |
| np.random.seed(seed) | |
| except ImportError: | |
| pass | |
| try: | |
| import torch | |
| torch.manual_seed(seed) | |
| if torch.cuda.is_available(): | |
| torch.cuda.manual_seed_all(seed) | |
| if deterministic: | |
| torch.backends.cudnn.deterministic = True | |
| torch.backends.cudnn.benchmark = False | |
| except ImportError: | |
| pass | |
| return seed | |
| def worker_init_fn(worker_id: int) -> None: | |
| """Give each DataLoader worker a distinct but reproducible seed. | |
| Without this, forked workers share the parent's NumPy state and every worker | |
| generates the *same* augmentation stream -- a subtle bug that silently | |
| reduces effective augmentation diversity. | |
| """ | |
| import numpy as np | |
| import torch | |
| seed = (torch.initial_seed() + worker_id) % 2**32 | |
| np.random.seed(seed) | |
| random.seed(seed) | |