pytorch中如何设置随机种子

def seed_everything():
    '''
    设置整个开发环境的seed
    :param seed:
    :param device:
    :return:
    '''
    import os
    import random
    import numpy as np

    random.seed(seed)
    os.environ['pythonHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    
    # some cudnn methods can be random even after fixing the seed
    # unless you tell it to be deterministic
    torch.backends.cudnn.deterministic = True

import numpy as np
import random
import os
import torch
def seed_torch(seed=2021):
    random.seed(seed)
    os.environ['PythonHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed) # if you are using multi-GPU.
    torch.backends.cudnn.benchmark = False
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.enabled = False
seed_torch()

import tensorflow as tf
import os
import numpy as np
import random

SEED = 0

def set_seeds(seed=SEED):
    os.environ['PYTHONHASHSEED'] = str(seed)
    random.seed(seed)
    tf.random.set_seed(seed)
    np.random.seed(seed)

def set_global_determinism(seed=SEED):
    set_seeds(seed=seed)

    os.environ['TF_DETERMINISTIC_OPS'] = '1'
    os.environ['TF_CUDNN_DETERMINISTIC'] = '1'
    
    tf.config.threading.set_inter_op_parallelism_threads(1)
    tf.config.threading.set_intra_op_parallelism_threads(1)

# Call the above function with seed value
set_global_determinism(seed=SEED)