python中的pickling方法描述符对象

时间：2015-12-06 23:44:07

标签： python pickle

我正在尝试挑剔method_descriptor。


使用pickle或cloudpickle进行腌制失败：

Python 2.7.10 |Continuum Analytics, Inc.| (default, Oct 19 2015, 18:04:42) 
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://anaconda.org

>>> import pickle, cloudpickle

>>> pickle.dumps(set.union)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/pmd/anaconda3/envs/python2/lib/python2.7/pickle.py", line 1374, in dumps
    Pickler(file, protocol).dump(obj)
  File "/home/pmd/anaconda3/envs/python2/lib/python2.7/pickle.py", line 224, in dump
    self.save(obj)
  File "/home/pmd/anaconda3/envs/python2/lib/python2.7/pickle.py", line 306, in save
    rv = reduce(self.proto)
  File "/home/pmd/anaconda3/envs/python2/lib/python2.7/copy_reg.py", line 70, in _reduce_ex
    raise TypeError, "can't pickle %s objects" % base.__name__
TypeError: cannot pickle method_descriptor objects

>>> cloudpickle.dumps(set.union)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/pmd/anaconda3/envs/python2/lib/python2.7/site-packages/cloudpickle/cloudpickle.py", line 602, in dumps
    cp.dump(obj)
  File "/home/pmd/anaconda3/envs/python2/lib/python2.7/site-packages/cloudpickle/cloudpickle.py", line 111, in dump
    raise pickle.PicklingError(msg)
pickle.PicklingError: Could not pickle object as excessively deep recursion required.


以某种方式导入dill会使pickle正常工作，如下所示：

>>> import dill
>>> pickle.dumps(set.union)
'cdill.dill\n_getattr\np0\n(c__builtin__\nset\np1\nS\'union\'\np2\nS"<method \'union\' of \'set\' objects>"\np3\ntp4\nRp5\n.'

>>> f = pickle.loads(pickle.dumps(set.union))
>>> set.union(set([1,2]), set([3]))
set([1, 2, 3])
>>> f(set([1,2]), set([3]))
set([1, 2, 3])


cloudpickle导入后，dill中的问题仍然存在：

>>> cloudpickle.dumps(set.union)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/pmd/anaconda3/envs/python2/lib/python2.7/site-packages/cloudpickle/cloudpickle.py", line 602, in dumps
    cp.dump(obj)
  File "/home/pmd/anaconda3/envs/python2/lib/python2.7/site-packages/cloudpickle/cloudpickle.py", line 111, in dump
    raise pickle.PicklingError(msg)
pickle.PicklingError: Could not pickle object as excessively deep recursion required.


在我的应用程序中，我依靠cloudpickle来处理全局变量的函数。所以我的问题是，如何让cloudpickle在Python 2.7中为method_descriptor个对象工作？

编辑：我注意到Python 3.3中出现了同样的问题，但在Python 3.5中并不存在。

2 个答案:

答案 0 :(得分：2)

我是dill作者。当您执行import dill时，它会将dill的序列化注册表注入pickle（基本上，将copy_reg类型的所有dill类型知识放入pickle 1}}注册表）。 

>>> import pickle
>>> pickle.Pickler.dispatch
{<type 'function'>: <function save_global at 0x105d0c7d0>, <type 'dict'>: <function save_dict at 0x105d0c668>, <type 'int'>: <function save_int at 0x105d0c230>, <type 'long'>: <function save_long at 0x105d0c2a8>, <type 'list'>: <function save_list at 0x105d0c578>, <type 'str'>: <function save_string at 0x105d0c398>, <type 'unicode'>: <function save_unicode at 0x105d0c410>, <type 'instance'>: <function save_inst at 0x105d0c758>, <type 'type'>: <function save_global at 0x105d0c7d0>, <type 'NoneType'>: <function save_none at 0x105d0c140>, <type 'bool'>: <function save_bool at 0x105d0c1b8>, <type 'tuple'>: <function save_tuple at 0x105d0c488>, <type 'float'>: <function save_float at 0x105d0c320>, <type 'classobj'>: <function save_global at 0x105d0c7d0>, <type 'builtin_function_or_method'>: <function save_global at 0x105d0c7d0>}
>>> import dill
>>> pickle.Pickler.dispatch
{<class '_pyio.BufferedReader'>: <function save_file at 0x106c8b848>, <class '_pyio.TextIOWrapper'>: <function save_file at 0x106c8b848>, <type 'operator.itemgetter'>: <function save_itemgetter at 0x106c8b578>, <type 'weakproxy'>: <function save_weakproxy at 0x106c8c050>, <type 'NoneType'>: <function save_none at 0x105d0c140>, <type 'str'>: <function save_string at 0x105d0c398>, <type 'file'>: <function save_file at 0x106c8b8c0>, <type 'classmethod'>: <function save_classmethod at 0x106c8c230>, <type 'float'>: <function save_float at 0x105d0c320>, <type 'instancemethod'>: <function save_instancemethod0 at 0x106c8ba28>, <type 'cell'>: <function save_cell at 0x106c8bb18>, <type 'member_descriptor'>: <function save_wrapper_descriptor at 0x106c8bc08>, <type 'slice'>: <function save_slice at 0x106c8bc80>, <type 'dict'>: <function save_module_dict at 0x106c8b410>, <type 'long'>: <function save_long at 0x105d0c2a8>, <type 'code'>: <function save_code at 0x106c8b320>, <type 'type'>: <function save_type at 0x106c8c0c8>, <type 'xrange'>: <function save_singleton at 0x106c8bde8>, <type 'builtin_function_or_method'>: <function save_builtin_method at 0x106c8b9b0>, <type 'classobj'>: <function save_classobj at 0x106c8b488>, <type 'weakref'>: <function save_weakref at 0x106c8bed8>, <type 'getset_descriptor'>: <function save_wrapper_descriptor at 0x106c8bc08>, <type 'weakcallableproxy'>: <function save_weakproxy at 0x106c8c050>, <class '_pyio.BufferedRandom'>: <function save_file at 0x106c8b848>, <type 'int'>: <function save_int at 0x105d0c230>, <type 'list'>: <function save_list at 0x105d0c578>, <type 'functools.partial'>: <function save_functor at 0x106c8b7d0>, <type 'bool'>: <function save_bool at 0x105d0c1b8>, <type 'function'>: <function save_function at 0x106c8b398>, <type 'thread.lock'>: <function save_lock at 0x106c8b500>, <type 'super'>: <function save_functor at 0x106c8b938>, <type 'staticmethod'>: <function save_classmethod at 0x106c8c230>, <type 'module'>: <function save_module at 0x106c8bf50>, <type 'method_descriptor'>: <function save_wrapper_descriptor at 0x106c8bc08>, <type 'operator.attrgetter'>: <function save_attrgetter at 0x106c8b5f0>, <type 'wrapper_descriptor'>: <function save_wrapper_descriptor at 0x106c8bc08>, <type 'numpy.ufunc'>: <function save_numpy_ufunc at 0x106c8bcf8>, <type 'method-wrapper'>: <function save_instancemethod at 0x106c8baa0>, <type 'instance'>: <function save_inst at 0x105d0c758>, <type 'cStringIO.StringI'>: <function save_stringi at 0x106c8b6e0>, <type 'unicode'>: <function save_unicode at 0x105d0c410>, <class '_pyio.BufferedWriter'>: <function save_file at 0x106c8b848>, <type 'property'>: <function save_property at 0x106c8c140>, <type 'ellipsis'>: <function save_singleton at 0x106c8bde8>, <type 'tuple'>: <function save_tuple at 0x105d0c488>, <type 'cStringIO.StringO'>: <function save_stringo at 0x106c8b758>, <type 'NotImplementedType'>: <function save_singleton at 0x106c8bde8>, <type 'dictproxy'>: <function save_dictproxy at 0x106c8bb90>}

cloudpickle具有（略微）不同于dill的酸洗功能，如果您使用的是cloudpickle，它会将自己的序列化功能推送到pickle注册表中。如果你想让cloudpickle为你工作，你可能能够解决一个解决方案...基本上在你的应用程序中安装一个import dill as cloudpickle的模块（很好的参考：http://blog.dscpl.com.au/2015/03/safely-applying-monkey-patches-in-python.html）...但是这将在您的应用程序上下文中用cloudpickle替换dill的整个用法。你也可以沿着这些方向尝试一个monkeypatch：

>>> #first import dill, which populates itself into pickle's dispatch
>>> import dill
>>> import pickle
>>> # save the MethodDescriptorType from dill
>>> MethodDescriptorType = type(type.__dict__['mro'])
>>> MethodDescriptorWrapper = pickle.Pickler.dispatch[MethodDescriptorType]
>>> # cloudpickle does the same, so let it update the dispatch table
>>> import cloudpickle
>>> # now, put the saved MethodDescriptorType back in
>>> pickle.Pickler.dispatch[MethodDescriptorWrapperType] = MethodDescriptorWrapper

请注意，如果您要直接使用cloudpickle.dumps，则必须通过在cloudpickle上执行上述monkeypatch直接重载cloudpickle.CloudPickler.dispatch中的注册表。

我不保证将工作，我也不保证它不会搞砸cloudpickle中的其他物品（基本上，我没有＆＃39） ; t尝试过），但它是用cloudpickle替换有问题的dill包装器的潜在途径。

如果您想要简短的答案，我会说（至少在这种情况下）使用dill。 ;）

编辑关于copyreg：

这里有dill中的内容：

def _getattr(objclass, name, repr_str):
    # hack to grab the reference directly
    try:
        attr = repr_str.split("'")[3]
        return eval(attr+'.__dict__["'+name+'"]')
    except:
        attr = getattr(objclass,name)
        if name == '__dict__':
            attr = attr[name]
        return attr

用于注册具有较低级别reduce函数的函数（直接在pickler实例上）。 obj是腌制的对象。

pickler.save_reduce(_getattr, (obj.__objclass__, obj.__name__, obj.__repr__()), obj=obj)

我相信这会转换为reduce方法（直接在copyreg.pickle中使用），如下所示：

def _reduce_method_descriptor(obj):
    return _getattr, (obj.__objclass__, obj.__name__, obj.__repr__())

答案 1 :(得分：1)

经过多次搞乱后，我想我找到了一个适用于Python 2.7和3.3的问题的简洁答案。请注意，Python 3.5没有问题可以开始。

在我展示我的调查结果之前，我想归功于while True: d = defer.Deferred() d.delay = reactor.callLater(sys.maxint, d.callback, None) yield d 模块，这是我获得使这项工作的代码的要点。

在下文中，我将使用multiprocessing.forking作为set.union的示例。

Python 3.5：方法描述符开箱即用

<class 'method_descriptor'>


Python 3.3：使用copyreg为Python 3.5.0 |Continuum Analytics, Inc.| (default, Oct 19 2015, 21:57:25) [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import pickle >>> pickle.dumps(set.union) b'\x80\x03cbuiltins\ngetattr\nq\x00cbuiltins\nset\nq\x01X\x05\x00\x00\x00unionq\x02\x86q\x03Rq\x04.' >>> f = pickle.loads(pickle.dumps(set.union)) >>> f({1, 2, 3}, {5}) {1, 2, 3, 5} >>> 提供pickle

的方式 
method_descriptor

Python 3.3.5 |Continuum Analytics, Inc.| (default, Jun 4 2015, 15:22:11) [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import pickle >>> pickle.dumps(set.union) Traceback (most recent call last): File "<stdin>", line 1, in <module> _pickle.PicklingError: Can't pickle <class 'method_descriptor'>: attribute lookup builtins.method_descriptor failed 的类型为set.union：

method_descriptor

我们为method_descriptor定义reduce函数并将其注册到>>> type(set.union) <class 'method_descriptor'> ：

copyreg

成功：

>>> def _reduce_method_descriptor(m):
...     return getattr, (m.__objclass__, m.__name__)
... 
>>> import copyreg
>>> copyreg.pickle(type(set.union), _reduce_method_descriptor)

如果我们现在导入>>> pickle.dumps(set.union) b'\x80\x03cbuiltins\ngetattr\nq\x00cbuiltins\nset\nq\x01X\x05\x00\x00\x00unionq\x02\x86q\x03Rq\x04.' >>> f = pickle.loads(pickle.dumps(set.union)) >>> f({1, 2, 3}, {5}) {1, 2, 3, 5} ，注册的酸洗功能仍然有效：

cloudpickle


Python 2.7：使用copy_reg为>>> import cloudpickle >>> cloudpickle.dumps(set.union) b'\x80\x02c__builtin__\ngetattr\nq\x00c__builtin__\nset\nq\x01X\x05\x00\x00\x00unionq\x02\x86q\x03Rq\x04.' >>> f = pickle.loads(pickle.dumps(set.union)) >>> f({1, 2, 3}, {5}) {1, 2, 3, 5} >>> 提供pickle

的方式

在Python 2.7中，注册pickle支持函数的模块称为method_descriptor。 

copy_reg

Python 2.7.10 |Continuum Analytics, Inc.| (default, Oct 19 2015, 18:04:42) [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2 Type "help", "copyright", "credits" or "license" for more information. Anaconda is brought to you by Continuum Analytics. Please check out: http://continuum.io/thanks and https://anaconda.org >>> import pickle >>> pickle.dumps(set.union) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/pmd/anaconda3/envs/python2/lib/python2.7/pickle.py", line 1374, in dumps Pickler(file, protocol).dump(obj) File "/home/pmd/anaconda3/envs/python2/lib/python2.7/pickle.py", line 224, in dump self.save(obj) File "/home/pmd/anaconda3/envs/python2/lib/python2.7/pickle.py", line 306, in save rv = reduce(self.proto) File "/home/pmd/anaconda3/envs/python2/lib/python2.7/copy_reg.py", line 70, in _reduce_ex raise TypeError, "can't pickle %s objects" % base.__name__ TypeError: can't pickle method_descriptor objects 的类型为set.union：

method_descriptor

我们为method_descriptor定义reduce函数并将其注册到>>> type(set.union) <type 'method_descriptor'> ：

copyreg

成功：

>>> def _reduce_method_descriptor(m):
...     return getattr, (m.__objclass__, m.__name__)
... 
>>> import copy_reg
>>> copy_reg.pickle(type(set.union), _reduce_method_descriptor)
>>> pickle.dumps(set.union)
"c__builtin__\ngetattr\np0\n(c__builtin__\nset\np1\nS'union'\np2\ntp3\nRp4\n."

也适用于>>> f = pickle.loads(pickle.dumps(set.union)) >>> f(set([1, 2, 3]), set([5])) ：

cloudpickle
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