python 多处理中的笛卡尔积与 tqdm 进度条

Question

我找到了一些使用 tqdm 和 Python 多处理创建进度条的代码，它使用整数来更新进度条。我将其更改为将其用作文件循环，但 lambda 回调创建了一个带有文件路径的笛卡尔积，这让我的机器运行内存不足，并存有大量文件。我试图在其他问题中找到解决方案，但没有找到答案。我该怎么做才能避免 async_result 中的笛卡尔积（以及内存不足），但仍会创建进度条？

import glob
import jpylyzer
import multiprocessing as mp
from tqdm import tqdm
cores=2
src="/path/to/jp2/files"

def f_process_file(filename):
  now=time.strftime("%Y-%m-%d %H:%M:%S")
  try:
    result = jpylyzer.checkOneFile(filename)
    status=result.findtext('isValid')
  except Exception as ex:
    print("oopsie")
  return filename, status, now

# Find JP2 files in the source directory case insensitively
files = [f for f in glob.iglob(src + '/**/*.[jJ][pP]2', recursive=True)]
filecount=len(files)

# Start a multiprocessing pool
pool =  mp.Pool(processes = cores)

# Define a progress bar
pbar = tqdm(total=filecount)

# process all files asynchronously and do callback for the progress bar
async_result = [pool.map_async(f_process_file, files, callback=lambda _: pbar.update(1)) for file in files]

# magic for the progress barr
results = [p.get() for p in async_result]

pool.close()
pool.join()

for i in range(len(results)):
  if results[i][i][1] != 'True':
    print(results[i][i])

Answer 1

我通过从 async_result 中删除 [] 找到了答案，删除了 回调=lambda 并在动态启动之前为进度条声明一个全局变量 pbar

#!/usr/bin/env python3
import glob
from tqdm import tqdm
import time, sys


def f_process_file(filename):
  fnctn=sys._getframe().f_code.co_name
  now=time.strftime("%Y-%m-%d %H:%M:%S")
  try:
    # Do some stuff here
    result = 'isValid' # for testing purpose declare a value
    status = 'True' # for testing purpose declare a value
  except Exception as ex:
    print("failure in {}".format(fnctn))

  #update the progress bar
  time.sleep(0.005)
  pbar.update(1)
  return filename, status, now

def f_doall(src):
  files = [f for f in glob.iglob(src + '/**/*.[jJ][pP]2', recursive=True)]
  filecount=len(files)
  print(filecount)
  #Declare a global variable for the progress bar
  global pbar
  # Initiate the progree bar
  pbar  = tqdm(total=filecount)

  for f in files:
    f_process_file(f)

def main():
  src="/path/to/images"
  
  f_doall(src)

if __name__ == "__main__":
  main()

现在我可以扩展我的代码以使用多处理池

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