import random
import numpy as np
import torch
[docs]def set_global_seeds(seed):
r"""Set the seed for generating random numbers.
It sets the following dependencies with the given random seed:
1. PyTorch
2. Numpy
3. Python random
Args:
seed (int): a given seed.
"""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed) # both torch and torch.cuda internally
[docs]class Seeder(object):
r"""A random seed generator.
Given an initial seed, the seeder can be called continuously to sample a single
or a batch of random seeds.
.. note::
The seeder creates an independent RandomState to generate random
numbers. It does not affect the RandomState in ``np.random``.
Example::
>>> seeder = Seeder(init_seed=0)
>>> seeder(size=5)
[209652396, 398764591, 924231285, 1478610112, 441365315]
"""
[docs] def __init__(self, init_seed=0):
r"""Initialize the seeder.
Args:
init_seed (int, optional): Initial seed for generating random seeds. Default: ``0``.
"""
assert isinstance(init_seed, int) and init_seed >= 0, f'expected non-negative integer, got {init_seed}'
self.rng = np.random.RandomState(seed=init_seed)
# Upper bound for sampling new random seeds
self.max = np.iinfo(np.int32).max
[docs] def __call__(self, size=1):
r"""Return the sampled random seeds according to the given size.
Args:
size (int or list): The size of random seeds to sample.
Returns
-------
seeds : list
a list of sampled random seeds.
"""
seeds = self.rng.randint(low=0, high=self.max, size=size).tolist()
return seeds