I'm applying some parallelization to my code, in which I use classes. I knew that is not possible to pickle a class method without any other approach different of what Python provides. I found a solution here.
In my code, I have two 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 while I can pickle one method (shepard_interpolation), 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()
When running, I got
PicklingError: Can't pickle <type 'function'>: 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.
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
How can I do this correctly?
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)
For example,
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()
yields
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']
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())
is really calling ImageData.shepard_interpolation sequentially, defeating the purpose of the pool.
Instead you could use
for _ in range(3):
pool.apply_async(ImageData.shepard_interpolation, args=[ImageData()],
callback=results.append)
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.
Alternatively, it might be simpler to just use pool.map here.
results = pool.map(ImageData.shepard_interpolation, [ImageData()]*3)