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Python util.set_random_seed方法代码示例

本文整理汇总了Python中utils.util.set_random_seed方法的典型用法代码示例。如果您正苦于以下问题:Python util.set_random_seed方法的具体用法?Python util.set_random_seed怎么用?Python util.set_random_seed使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在utils.util的用法示例。


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示例1: main

# 需要导入模块: from utils import util [as 别名]
# 或者: from utils.util import set_random_seed [as 别名]
def main():
    #### setup options of three networks
    parser = argparse.ArgumentParser()
    parser.add_argument('-opt_P', type=str, help='Path to option YMAL file of Predictor.')
    parser.add_argument('-opt_C', type=str, help='Path to option YMAL file of Corrector.')
    parser.add_argument('-opt_F', type=str, help='Path to option YMAL file of SFTMD_Net.')
    parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
                        help='job launcher')
    parser.add_argument('--local_rank', type=int, default=0)
    args = parser.parse_args()
    opt_P = option.parse(args.opt_P, is_train=True)
    opt_C = option.parse(args.opt_C, is_train=True)
    opt_F = option.parse(args.opt_F, is_train=True)

    # convert to NoneDict, which returns None for missing keys
    opt_P = option.dict_to_nonedict(opt_P)
    opt_C = option.dict_to_nonedict(opt_C)
    opt_F = option.dict_to_nonedict(opt_F)

    #### random seed
    seed = opt_P['train']['manual_seed']
    if seed is None:
        seed = random.randint(1, 10000)
    util.set_random_seed(seed)

    # create PCA matrix of enough kernel
    batch_ker = util.random_batch_kernel(batch=30000, l=opt_P['kernel_size'], sig_min=0.2, sig_max=4.0, rate_iso=1.0, scaling=3, tensor=False)
    print('batch kernel shape: {}'.format(batch_ker.shape))
    b = np.size(batch_ker, 0)
    batch_ker = batch_ker.reshape((b, -1))
    pca_matrix = util.PCA(batch_ker, k=opt_P['code_length']).float()
    print('PCA matrix shape: {}'.format(pca_matrix.shape))

    #### distributed training settings
    if args.launcher == 'none':  # disabled distributed training
        opt_P['dist'] = False
        opt_F['dist'] = False
        opt_C['dist'] = False
        rank = -1
        print('Disabled distributed training.')
    else:
        opt_P['dist'] = True
        opt_F['dist'] = True
        opt_C['dist'] = True
        init_dist()
        world_size = torch.distributed.get_world_size() #Returns the number of processes in the current process group
        rank = torch.distributed.get_rank() #Returns the rank of current process group

    torch.backends.cudnn.benchmark = True
    # torch.backends.cudnn.deterministic = True

    ###### SFTMD train ######
    SFTMD_train(opt_F, rank, world_size, pca_matrix)
   
    # choose small opt for SFTMD test
    opt_F = opt_F['sftmd']

    ###### Predictor&Corrector train ######
    IKC_train(opt_P, opt_C, opt_F, rank, world_size, pca_matrix) 
开发者ID:yuanjunchai,项目名称:IKC,代码行数:61,代码来源:train.py


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