for file in allfiles: itemPath = os.path.join(filePath, file) if not os.path.isdir(itemPath): # 获取文件的大小 fileSize = os.path.getsize(itemPath) if fileSize > 200000: print(f'该文件的大小为{fileSize}字节,路径为{itemPath}') else: find_big_File(itemPath) if __name__ == ...
def findBigFile(folder,size): bigFileAbs=[] for foldername,subfolders,filenames in os.walk(folder): #对文件进行遍历 for filename in filenames: #.getsize(path)必须是完整路径 fileAbs=os.path.join(foldername,filename) if os.path.getsize(fileAbs)>size and len(bigFileAbs)<100: #fileAbs=...
deffindBigFile(folder,size): bigFileAbs=[] forfoldername,subfolders,filenamesinos.walk(folder): #对文件进行遍历 forfilenameinfilenames: #.getsize(path)必须是完整路径 fileAbs=os.path.join(foldername,filename) ifos.path.getsize(fileAbs)>sizeandlen(bigFileAbs)<100: #fileAbs=os.path.join(fo...
然后可以发现程序运行过程中的显存变化(第一行是载入前的显存,最后一行是载入后的显存): At __main__ <module>: line 13 Total Used Memory:472.2 Mb + | 1 * Size:(128, 64, 3, 3) | Memory: 0.2949 M | <class 'torch.nn.parameter.Parameter'> + | 1 * Size:(256, 128, 3, 3) | Memo...
'__sizeof__', '__str__', '__subclasshook__', '_formatter_field_name_split', '_formatter_parser', 'capitalize', 'center', 'count', 'decode', 'encode', 'endswith', 'expandtabs', 'find', 'format', 'index', 'isalnum', 'isalpha', 'isdigit', 'islower', 'isspace', 'istitle'...
find是linux系统中的一种实时查找工具,通过遍历指定起始路径下文件系统层级结构完成对文件的查找。 find命令的这种查找机制决定了它的一些工作特性: 查找速度略慢(只是略慢) 精准查找 实时查找 名称及简要说明 find - search for files in a directory hierarchy ...
open("document.docx", "rb") as docx_file: result = mammoth.convert_to_html(docx_file) ...
read(size),每次读取size个字节的内容,适合于未知文件大小的读取; readline( ),每次读取一行内容; readlines( ),一次性读取所有内容,并按行返回list,适用于配置文件的读取。 file-like Object:像open()函数返回的这种有个read()方法的对象,在Python中统称为file-like Object。除了file外,还可以是内存的字节流,网...
Usually, the file size is measured in Bytes (B), Kilobytes (KB), Megabytes (MB), Gigabytes (GB), Terabytes (TB), and so on. The file sizes can be measured using a binary system (where kilo means 1024) or metric system (kilo means 1000). We can follow different approaches to get ...
使用 lru_cache 实现缓存/记忆 我在之前的博客中介绍过这一技巧，但我认为它值得用一个简单例子再次进行说明： import functools import time # caching up to 12 different results @functools.lru_cache(maxsize=12) def slow_func(x): time.sleep(2) # Simulate long computation return x slow_func(1) #...