import torch import random import numpy as np from modules.utils import rng def deterministic(seed=0): random.seed(seed) np.random.seed(seed) torch_rn = rng.convert_np_to_torch(seed) torch.manual_seed(torch_rn) if torch.cuda.is_available(): torch.cuda.manual_seed_all(torch_rn) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False def is_numeric(obj): if isinstance(obj, str): try: float(obj) return True except ValueError: return False elif isinstance(obj, (np.integer, np.signedinteger, np.unsignedinteger)): return True elif isinstance(obj, np.floating): return True elif isinstance(obj, (int, float)): return True else: return False class SeedContext: def __init__(self, seed): assert is_numeric(seed), "Seed must be an number." try: self.seed = int(np.clip(int(seed), -1, 2**32 - 1, out=None, dtype=np.int64)) except Exception as e: raise ValueError(f"Seed must be an integer, but: {type(seed)}") self.seed = seed self.state = None if isinstance(seed, str) and seed.isdigit(): self.seed = int(seed) if isinstance(self.seed, float): self.seed = int(self.seed) if self.seed == -1: self.seed = random.randint(0, 2**32 - 1) def __enter__(self): self.state = (torch.get_rng_state(), random.getstate(), np.random.get_state()) try: deterministic(self.seed) except Exception as e: raise ValueError( f"Seed must be an integer, but: <{type(self.seed)}> {self.seed}" ) def __exit__(self, exc_type, exc_value, traceback): torch.set_rng_state(self.state[0]) random.setstate(self.state[1]) np.random.set_state(self.state[2]) if __name__ == "__main__": print(is_numeric("1234")) # True print(is_numeric("12.34")) # True print(is_numeric("-1234")) # True print(is_numeric("abc123")) # False print(is_numeric(np.int32(10))) # True print(is_numeric(np.float64(10.5))) # True print(is_numeric(10)) # True print(is_numeric(10.5)) # True print(is_numeric(np.int8(10))) # True print(is_numeric(np.uint64(10))) # True print(is_numeric(np.float16(10.5))) # True print(is_numeric([1, 2, 3])) # False