生成随机数列向量并保存到不同的log文件中

随机数列要求

• 长度 10
• 元素随机
• 每个数列中有两个相同的元素
• 生成到100个文本中
• 每个文本数列数量为 1000

 代码

import torch
import pickle
import numpy as np
import os
import shutil
import logging

torch.set_printoptions(precision=4,threshold=1000,edgeitems=3,linewidth=400,profile=None,sci_mode=False)
np.set_printoptions(precision=4,threshold=1000,edgeitems=3,linewidth=400)


def creat_logger(log_path,logging_name,suf_name):
    
    if not os.path.exists(log_path):
        os.makedirs(log_path)
    log_full_path = log_path + logging_name + suf_name
    
    logger = logging.getLogger(logging_name)
    logger.setLevel(level=logging.DEBUG)
    
    handler = logging.FileHandler(log_full_path, encoding='UTF-8',mode = 'w')
    handler.setLevel(logging.INFO)

    logger.addHandler(handler)
    return logger


def generate_tensor():
    tensor = torch.zeros(11)
    for i in range(10):
        tensor[i] = torch.randn(1)

    indices = torch.randperm(10)
    indices = indices[:2].clone()
    tensor[indices] = tensor[indices[0]].clone()
    
    tensor[10] = tensor[indices[0]].clone()
    
    return tensor

tensor = generate_tensor()
print(tensor)

num_tensors = 1000
path = './log/'
if os.path.exists(path):
    shutil.rmtree(path)

for j in range(100):
    log_trace = creat_logger(path,'j_loop'+str(j),'.txt')
    for i in range(num_tensors):
        tensor = generate_tensor()
        log_trace.info('['+','.join(str(tensor.numpy()).strip('[').strip(']').split())+']')
            
    for handler in list(log_trace.handlers):
        log_trace.removeHandler(handler)