foowaa

import contextlib
@contextlib.contextmanager
def open_func(file_name):
    # __enter__方法
    print('open file:', file_name, 'in __enter__')
    file_handler = open(file_name, 'r')

    yield file_handler

    # __exit__方法

foowaa

def collate_fn(data):
    """
    default_collate比较好地实现了对图片等的操作，但是并不支持对文字等不等长序列的操作
    dataloader的一个参数
    输入data是list of (x, y), list的长度是batch_size

    返回xs, ys, lens 
    (batch_size, x或y的size)的tensor
    """
    # Sort a data list 

foowaa

import torch.utils.data as data
class MyDataset(data.Dataset):
    '''
    Dataset must define __getitem__ and __len__
    '''
    def __init__(self, others):
        pass

    def __getitem__(self, index):
        """

foowaa

_registry_path = Path(__file__).parent / 'registry.json'
if _registry_path.exists():
    with _registry_path.open(encoding='utf-8') as f:
        _REGISTRY = json.load(f)
else:
    _REGISTRY = {}

def short_name(cls: type) -> str:
    """Returns just a class name (without package and module specification)."""
    return cls.__name__.split('.')[-1]

foowaa

import functools

def log1(func):
	@functools.warp(func)
	def wrapper(*args, **kw):
		print("call %s():", func.__name__)
		return func(*args, **kw)
	return wrapper

foowaa

import os
import torch.utils.data as data
from tqdm import tqdm

def train():
    # config saving
    model_path = './ckpt'
    if not os.path.exists(model_path):
        os.mkdir(model_path)
    save_step = 2

foowaa

import re
st_after = re.sub('\W', '',  st_before)

foowaa

class LogMixin(object):
    @property
    def logger(self):
        name = '.'.join([__name__, self.__class__.__name__])
        return logging.getLogger(name)

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for j in range(len(s)-1, -1, -1):
  pass

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def is_number(s):
    try:
        float(s)
        return True
    except ValueError:
        pass
    try:
        import unicodedata
        unicodedata.numeric(s)
        return True