这个解决方案只需要安装 dill 库,不需要其他的库,比如 pathos。
def apply_packed_function_for_map((dumped_function, item, args, kwargs),):
"""
Unpack dumped function as target function and call it with arguments.
:param (dumped_function, item, args, kwargs):
a tuple of dumped function and its arguments
:return:
result of target function
"""
target_function = dill.loads(dumped_function)
res = target_function(item, *args, **kwargs)
return res
def pack_function_for_map(target_function, items, *args, **kwargs):
"""
Pack function and arguments to object that can be sent from one
multiprocessing.Process to another. The main problem is:
«multiprocessing.Pool.map*» or «apply*»
cannot use class methods or closures.
It solves this problem with «dill».
It works with target function as argument, dumps it («with dill»)
and returns dumped function with arguments of target function.
For more performance we dump only target function itself
and don't dump its arguments.
How to use (pseudo-code):
~>>> import multiprocessing
~>>> images = [...]
~>>> pool = multiprocessing.Pool(100500)
~>>> features = pool.map(
~... *pack_function_for_map(
~... super(Extractor, self).extract_features,
~... images,
~... type='png'
~... **options,
~... )
~... )
~>>>
:param target_function:
function, that you want to execute like target_function(item, *args, **kwargs).
:param items:
list of items for map
:param args:
positional arguments for target_function(item, *args, **kwargs)
:param kwargs:
named arguments for target_function(item, *args, **kwargs)
:return: tuple(function_wrapper, dumped_items)
It returs a tuple with
* function wrapper, that unpack and call target function;
* list of packed target function and its' arguments.
"""
dumped_function = dill.dumps(target_function)
dumped_items = [(dumped_function, item, args, kwargs) for item in items]
return apply_packed_function_for_map, dumped_items
它也适用于numpy数组。