我正在对我使用类的代码应用一些并行化.我知道如果没有与 Python 提供的任何其他方法不同，就不可能选择一个类方法.我在这里找到了解决方案.在我的代码中，我必须使用类进行并行化的部分.在这里，我发布了一个非常简单的代码，仅代表我的结构(相同，但我删除了方法内容，这是很多数学演算，对我得到的输出来说微不足道).问题是因为我可以腌制一种方法(shepard_interpolation)，但使用另一种方法(calculate_orientation_uncertainty)我得到了腌制错误.我不知道为什么会这样，或者为什么会部分起作用.
I'm applying some parallelization to my code, in which I use classes. I knew that is not possible to pick a class method without any other approach different of what Python provides. I found a solution here. In my code, I have to parts that should be parallelized, both using class. Here, I'm posting a very simple code just representing the structure of mine (is the same, but I deleted the methods content, which was a lot of math calculus, insignificant for the output that I'm getting). The problem is 'cause I can pickle one method (shepard_interpolation), but with the other one (calculate_orientation_uncertainty) I got the pickle error. I don't know why this is happing, or why it works partly.
def _pickle_method(method):
func_name = method.im_func.__name__
obj = method.im_self
cls = method.im_class
if func_name.startswith('__') and not func_name.endswith('__'): #deal with mangled names
cls_name = cls.__name__.lstrip('_')
func_name = '_' + cls_name + func_name
print cls
return _unpickle_method, (func_name, obj, cls)
def _unpickle_method(func_name, obj, cls):
for cls in cls.__mro__:
try:
func = cls.__dict__[func_name]
except KeyError:
pass
else:
break
return func.__get__(obj, cls)
class ImageData(object):
def __init__(self, width=60, height=60):
self.width = width
self.height = height
self.data = []
for i in range(width):
self.data.append([0] * height)
def shepard_interpolation(self, seeds=20):
print "ImD - Sucess"
import copy_reg
import types
from itertools import product
from multiprocessing import Pool
copy_reg.pickle(types.MethodType, _pickle_method, _unpickle_method)
class VariabilityOfGradients(object):
def __init__(self):
pass
@staticmethod
def aux():
return "VoG - Sucess"
@staticmethod
def calculate_orientation_uncertainty():
results = []
pool = Pool()
for x, y in product(range(1, 5), range(1, 5)):
result = pool.apply_async(VariabilityOfGradients.aux)
results.append(result.get())
pool.close()
pool.join()
if __name__ == '__main__':
results = []
pool = Pool()
for _ in range(3):
result = pool.apply_async(ImageData.shepard_interpolation, args=[ImageData()])
results.append(result.get())
pool.close()
pool.join()
VariabilityOfGradients.calculate_orientation_uncertainty()
运行时，我得到PicklingError: Can't pickle : attribute lookup builtin.function failed".这与 here 几乎相同.我看到的唯一区别是我的方法是静态的.
When running, I got "PicklingError: Can't pickle : attribute lookup builtin.function failed". And this is almost the same found here. The only difference that I see is that my methods are static.
我注意到在我的 calculate_orientation_uncertainty 中，当我将函数调用为 result = pool.apply_async(VariabilityOfGradients.aux()) 时，即带有括号(在文档示例中我从未见过这个)，它似乎工作.但是，当我尝试获取结果时，我收到TypeError: 'int' object is not callable"...
I noticed that in my calculate_orientation_uncertainty, when I call the function as result = pool.apply_async(VariabilityOfGradients.aux()), i.e., with the parenthesis (in the doc examples I never saw this), it seems to work. But, when I try to get the result, I receive "TypeError: 'int' object is not callable"...
任何帮助将不胜感激.提前谢谢你.
Any help would be appreciated. Thank you in advance.
您可以在模块级别定义一个普通函数和一个静态方法.这保留了静态方法的调用语法、自省和可继承特性，同时避免了酸洗问题:
You could define a plain function at the module level and a staticmethod as well. This preserves the calling syntax, introspection and inheritability features of a staticmethod, while avoiding the pickling problem:
def aux():
return "VoG - Sucess"
class VariabilityOfGradients(object):
aux = staticmethod(aux)
<小时>
例如，
import copy_reg
import types
from itertools import product
import multiprocessing as mp
def _pickle_method(method):
"""
Author: Steven Bethard (author of argparse)
http://bytes.com/topic/python/answers/552476-why-cant-you-pickle-instancemethods
"""
func_name = method.im_func.__name__
obj = method.im_self
cls = method.im_class
cls_name = ''
if func_name.startswith('__') and not func_name.endswith('__'):
cls_name = cls.__name__.lstrip('_')
if cls_name:
func_name = '_' + cls_name + func_name
return _unpickle_method, (func_name, obj, cls)
def _unpickle_method(func_name, obj, cls):
"""
Author: Steven Bethard
http://bytes.com/topic/python/answers/552476-why-cant-you-pickle-instancemethods
"""
for cls in cls.mro():
try:
func = cls.__dict__[func_name]
except KeyError:
pass
else:
break
return func.__get__(obj, cls)
copy_reg.pickle(types.MethodType, _pickle_method, _unpickle_method)
class ImageData(object):
def __init__(self, width=60, height=60):
self.width = width
self.height = height
self.data = []
for i in range(width):
self.data.append([0] * height)
def shepard_interpolation(self, seeds=20):
print "ImD - Success"
def aux():
return "VoG - Sucess"
class VariabilityOfGradients(object):
aux = staticmethod(aux)
@staticmethod
def calculate_orientation_uncertainty():
pool = mp.Pool()
results = []
for x, y in product(range(1, 5), range(1, 5)):
# result = pool.apply_async(aux) # this works too
result = pool.apply_async(VariabilityOfGradients.aux, callback=results.append)
pool.close()
pool.join()
print(results)
if __name__ == '__main__':
results = []
pool = mp.Pool()
for _ in range(3):
result = pool.apply_async(ImageData.shepard_interpolation, args=[ImageData()])
results.append(result.get())
pool.close()
pool.join()
VariabilityOfGradients.calculate_orientation_uncertainty()
产量
ImD - Success
ImD - Success
ImD - Success
['VoG - Sucess', 'VoG - Sucess', 'VoG - Sucess', 'VoG - Sucess', 'VoG - Sucess', 'VoG - Sucess', 'VoG - Sucess', 'VoG - Sucess', 'VoG - Sucess', 'VoG - Sucess', 'VoG - Sucess', 'VoG - Sucess', 'VoG - Sucess', 'VoG - Sucess', 'VoG - Sucess', 'VoG - Sucess']
<小时>
顺便说一句，result.get() 会阻止调用过程，直到 pool.apply_async 调用的函数(例如 ImageData.shepard_interpolation)完成.所以
By the way, result.get() blocks the calling process until the function called by pool.apply_async (e.g. ImageData.shepard_interpolation) is completed. So
for _ in range(3):
result = pool.apply_async(ImageData.shepard_interpolation, args=[ImageData()])
results.append(result.get())
实际上是按顺序调用 ImageData.shepard_interpolation，违背了池的目的.
is really calling ImageData.shepard_interpolation sequentially, defeating the purpose of the pool.
你可以使用
for _ in range(3):
pool.apply_async(ImageData.shepard_interpolation, args=[ImageData()],
callback=results.append)
回调函数(例如results.append)在函数完成时在调用进程的线程中被调用.它被发送一个参数——函数的返回值.因此，没有什么能阻止快速进行三个 pool.apply_async 调用，并且三个调用 ImageData.shepard_interpolation 所做的工作将同时执行.
The callback function (e.g. results.append) is called in a thread of the calling process when the function is completed. It is sent one argument -- the return value of the function. Thus nothing blocks the three pool.apply_async calls from being made quickly, and the work done by the three calls to ImageData.shepard_interpolation will be performed concurrently.
或者，在此处使用 pool.map 可能更简单.
Alternatively, it might be simpler to just use pool.map here.
results = pool.map(ImageData.shepard_interpolation, [ImageData()]*3)
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