Putting aside whether the use of isinstance is harmful, I have run into the following conundrum when trying to evaluate isinstance after serializing/deserializing an object via Pickle:
from __future__ import with_statement
import pickle
# Simple class definition
class myclass(object):
def __init__(self, data):
self.data = data
# Create an instance of the class
x = myclass(100)
# Pickle the instance to a file
with open("c:\\pickletest.dat", "wb") as f:
pickle.dump(x, f)
# Replace class with exact same definition
class myclass(object):
def __init__(self, data):
self.data = data
# Read an object from the pickled file
with open("c:\\pickletest.dat", "rb") as f:
x2 = pickle.load(f)
# The class names appear to match
print x.__class__
print x2.__class__
# Uh oh, this fails...(why?)
assert isinstance(x2, x.__class__)
Can anyone shed some light on why isinstance would fail in this situation? In other words, why does Python think these objects are of two different classes? When I remove the second class definition, isinstance
works fine.
Change your code to print the
id
ofx.__class__
andx2.__class__
and you'll see that they are different:The obvious answer, because its not the same class.
Its a similar class, but not the same.
This is how the unpickler works (site-packages/pickle.py):
To find and instantiate a class.
So of course if you replace a class with an identically named class, the
klass = getattr(mod, name)
will return the new class, and the instance will be of the new class, and so isinstance will fail.