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