I know that __call__ method in a class is triggered when the instance of a class is called. However, I have no idea when I can use this special method, because one can simply create a new method and perform the same operation done in __call__ method and instead of calling the instance, you can call the method.
I would really appreciate it if someone gives me a practical usage of this special method.
Django forms module uses __call__ method nicely to implement a consistent API for form validation. You can write your own validator for a form in Django as a function.
def custom_validator(value):
#your validation logic
Django has some default built-in validators such as email validators, url validators etc., which broadly fall under the umbrella of RegEx validators. To implement these cleanly, Django resorts to callable classes (instead of functions). It implements default Regex Validation logic in a RegexValidator and then extends these classes for other validations.
class RegexValidator(object):
def __call__(self, value):
# validation logic
class URLValidator(RegexValidator):
def __call__(self, value):
super(URLValidator, self).__call__(value)
#additional logic
class EmailValidator(RegexValidator):
# some logic
Now both your custom function and built-in EmailValidator can be called with the same syntax.
for v in [custom_validator, EmailValidator()]:
v(value) # <-----
As you can see, this implementation in Django is similar to what others have explained in their answers below. Can this be implemented in any other way? You could, but IMHO it will not be as readable or as easily extensible for a big framework like Django.
This example uses memoization, basically storing values in a table (dictionary in this case) so you can look them up later instead of recalculating them.
Here we use a simple class with a __call__ method to calculate factorials (through a callable object) instead of a factorial function that contains a static variable (as that's not possible in Python).
class Factorial:
def __init__(self):
self.cache = {}
def __call__(self, n):
if n not in self.cache:
if n == 0:
self.cache[n] = 1
else:
self.cache[n] = n * self.__call__(n-1)
return self.cache[n]
fact = Factorial()
Now you have a fact object which is callable, just like every other function. For example
for i in xrange(10):
print("{}! = {}".format(i, fact(i)))
# output
0! = 1
1! = 1
2! = 2
3! = 6
4! = 24
5! = 120
6! = 720
7! = 5040
8! = 40320
9! = 362880
And it is also stateful.
fact object that is indexable since your __call__ function is essentially an index. Also would use a list instead of a dict, but that's just me. –
Julie __call__, be simple and nothing more. –
Needle Django forms module uses __call__ method nicely to implement a consistent API for form validation. You can write your own validator for a form in Django as a function.
def custom_validator(value):
#your validation logic
Django has some default built-in validators such as email validators, url validators etc., which broadly fall under the umbrella of RegEx validators. To implement these cleanly, Django resorts to callable classes (instead of functions). It implements default Regex Validation logic in a RegexValidator and then extends these classes for other validations.
class RegexValidator(object):
def __call__(self, value):
# validation logic
class URLValidator(RegexValidator):
def __call__(self, value):
super(URLValidator, self).__call__(value)
#additional logic
class EmailValidator(RegexValidator):
# some logic
Now both your custom function and built-in EmailValidator can be called with the same syntax.
for v in [custom_validator, EmailValidator()]:
v(value) # <-----
As you can see, this implementation in Django is similar to what others have explained in their answers below. Can this be implemented in any other way? You could, but IMHO it will not be as readable or as easily extensible for a big framework like Django.
I find it useful because it allows me to create APIs that are easy to use (you have some callable object that requires some specific arguments), and are easy to implement because you can use Object Oriented practices.
The following is code I wrote yesterday that makes a version of the hashlib.foo methods that hash entire files rather than strings:
# filehash.py
import hashlib
class Hasher(object):
"""
A wrapper around the hashlib hash algorithms that allows an entire file to
be hashed in a chunked manner.
"""
def __init__(self, algorithm):
self.algorithm = algorithm
def __call__(self, file):
hash = self.algorithm()
with open(file, 'rb') as f:
for chunk in iter(lambda: f.read(4096), ''):
hash.update(chunk)
return hash.hexdigest()
md5 = Hasher(hashlib.md5)
sha1 = Hasher(hashlib.sha1)
sha224 = Hasher(hashlib.sha224)
sha256 = Hasher(hashlib.sha256)
sha384 = Hasher(hashlib.sha384)
sha512 = Hasher(hashlib.sha512)
This implementation allows me to use the functions in a similar fashion to the hashlib.foo functions:
from filehash import sha1
print sha1('somefile.txt')
Of course I could have implemented it a different way, but in this case it seemed like a simple approach.
__call__ gives you a tool that lets you use OO techniques to solve problems. –
Petersen __call__, instead of e.g. using a different method name. Or (not mentioned in the question but the same question) why not use closures. (And on a side note, if someone's using Python and doesn't know closures, they should learn about them. I write code to be readable to people who know the language, and closures are a pretty fundamental concept, hardly exotic.) –
Smalt isinstance or something similar. –
Petersen __call__ method instead of a closure is when you're dealing with the multiprocessing module, which uses pickling to pass information between processes. You can't pickle a closure, but you can pickle an instance of a class. –
Stinkstone __call__ is also used to implement decorator classes in python. In this case the instance of the class is called when the method with the decorator is called.
class EnterExitParam(object):
def __init__(self, p1):
self.p1 = p1
def __call__(self, f):
def new_f():
print("Entering", f.__name__)
print("p1=", self.p1)
f()
print("Leaving", f.__name__)
return new_f
@EnterExitParam("foo bar")
def hello():
print("Hello")
if __name__ == "__main__":
hello()
program output:
Entering hello
p1= foo bar
Hello
Leaving hello
Yes, when you know you're dealing with objects, it's perfectly possible (and in many cases advisable) to use an explicit method call. However, sometimes you deal with code that expects callable objects - typically functions, but thanks to __call__ you can build more complex objects, with instance data and more methods to delegate repetitive tasks, etc. that are still callable.
Also, sometimes you're using both objects for complex tasks (where it makes sense to write a dedicated class) and objects for simple tasks (that already exist in functions, or are more easily written as functions). To have a common interface, you either have to write tiny classes wrapping those functions with the expected interface, or you keep the functions functions and make the more complex objects callable. Let's take threads as example. The Thread objects from the standard libary module threading want a callable as target argument (i.e. as action to be done in the new thread). With a callable object, you are not restricted to functions, you can pass other objects as well, such as a relatively complex worker that gets tasks to do from other threads and executes them sequentially:
class Worker(object):
def __init__(self, *args, **kwargs):
self.queue = queue.Queue()
self.args = args
self.kwargs = kwargs
def add_task(self, task):
self.queue.put(task)
def __call__(self):
while True:
next_action = self.queue.get()
success = next_action(*self.args, **self.kwargs)
if not success:
self.add_task(next_action)
This is just an example off the top of my head, but I think it is already complex enough to warrant the class. Doing this only with functions is hard, at least it requires returning two functions and that's slowly getting complex. One could rename __call__ to something else and pass a bound method, but that makes the code creating the thread slightly less obvious, and doesn't add any value.
__call__ used to use class instances (instead of functions) as WSGI applications. Here's an example from "The Definitive Guide to Pylons": Using Instances of Classes –
Unwritten Class-based decorators use __call__ to reference the wrapped function. E.g.:
class Deco(object):
def __init__(self,f):
self.f = f
def __call__(self, *args, **kwargs):
print args
print kwargs
self.f(*args, **kwargs)
There is a good description of the various options here at Artima.com
IMHO __call__ method and closures give us a natural way to create STRATEGY design pattern in Python. We define a family of algorithms, encapsulate each one, make them interchangeable and in the end we can execute a common set of steps and, for example, calculate a hash for a file.
I just stumbled upon a usage of __call__() in concert with __getattr__() which I think is beautiful. It allows you to hide multiple levels of a JSON/HTTP/(however_serialized) API inside an object.
The __getattr__() part takes care of iteratively returning a modified instance of the same class, filling in one more attribute at a time. Then, after all information has been exhausted, __call__() takes over with whatever arguments you passed in.
Using this model, you can for example make a call like api.v2.volumes.ssd.update(size=20), which ends up in a PUT request to https://some.tld/api/v2/volumes/ssd/update.
The particular code is a block storage driver for a certain volume backend in OpenStack, you can check it out here: https://github.com/openstack/cinder/blob/master/cinder/volume/drivers/nexenta/jsonrpc.py
EDIT: Updated the link to point to master revision.
I find a good place to use callable objects, those that define __call__(), is when using the functional programming capabilities in Python, such as map(), filter(), reduce().
The best time to use a callable object over a plain function or a lambda function is when the logic is complex and needs to retain some state or uses other info that in not passed to the __call__() function.
Here's some code that filters file names based upon their filename extension using a callable object and filter().
Callable:
import os
class FileAcceptor(object):
def __init__(self, accepted_extensions):
self.accepted_extensions = accepted_extensions
def __call__(self, filename):
base, ext = os.path.splitext(filename)
return ext in self.accepted_extensions
class ImageFileAcceptor(FileAcceptor):
def __init__(self):
image_extensions = ('.jpg', '.jpeg', '.gif', '.bmp')
super(ImageFileAcceptor, self).__init__(image_extensions)
Usage:
filenames = [
'me.jpg',
'me.txt',
'friend1.jpg',
'friend2.bmp',
'you.jpeg',
'you.xml']
acceptor = ImageFileAcceptor()
image_filenames = filter(acceptor, filenames)
print image_filenames
Output:
['me.jpg', 'friend1.jpg', 'friend2.bmp', 'you.jpeg']
I'm a novice, but here is my take: having __call__ makes composition easier to code. If f, g are instance of a class Function that has a method eval(self,x), then with __call___ one could write f(g(x)) as opposed to f.eval(g.eval(x)).
A neural network can be composed from smaller neural networks, and in pytorch we have a __call__ in the Module class:
Here's an example of where __call__ is used in practice: in pytorch, when defining a neural network (call it class MyNN(nn.Module) for example) as a subclass of torch.nn.module.Module, one typically defines a forward method for the class, but typically when applying an input tensor x to an instance model=MyNN() we just write model(x) as opposed to model.forward(x) but both give the same answer. If you dig into the source for torch.nn.module.Module here
https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module
and search for __call__ one can eventually trace it back - at least in some cases - to a call to self.forward
import torch.nn as nn
import torch.nn.functional as F
class MyNN(nn.Module):
def __init__(self):
super().__init__()
self.layer1 = nn.Linear(784, 200)
def forward(self, x):
return F.ReLU(self.layer1(x))
x=torch.rand(10,784)
model=MyNN()
print(model(x))
print(model.forward(x))
will print the same values in the last two lines as the Module class has implemented __call___ so that's what I believe Python turns to when it sees model(x), and the __call__ which in turn eventually calls model.forward(x))
Specify a __metaclass__ and override the __call__ method, and have the specified meta classes' __new__ method return an instance of the class, viola you have a "function" with methods.
We can use __call__ method to use other class methods as static methods.
class _Callable:
def __init__(self, anycallable):
self.__call__ = anycallable
class Model:
def get_instance(conn, table_name):
""" do something"""
get_instance = _Callable(get_instance)
provs_fac = Model.get_instance(connection, "users")
One common example is the __call__ in functools.partial, here is a simplified version (with Python >= 3.5):
class partial:
"""New function with partial application of the given arguments and keywords."""
def __new__(cls, func, *args, **kwargs):
if not callable(func):
raise TypeError("the first argument must be callable")
self = super().__new__(cls)
self.func = func
self.args = args
self.kwargs = kwargs
return self
def __call__(self, *args, **kwargs):
return self.func(*self.args, *args, **self.kwargs, **kwargs)
Usage:
def add(x, y):
return x + y
inc = partial(add, y=1)
print(inc(41)) # 42
This is too late but I'm giving an example. Imagine you have a Vector class and a Point class. Both take x, y as positional args. Let's imagine you want to create a function that moves the point to be put on the vector.
put_point_on_vec(point, vec)
Make it a method on the vector class. e.g my_vec.put_point(point)
Point class. my_point.put_on_vec(vec)Vector implements __call__, So you can use it like my_vec_instance(point)This is actually part of some examples I'm working on for a guide for dunder methods explained with Maths that I'm gonna release sooner or later.
I left the logic of moving the point itself because this is not what this question is about
The function call operator.
class Foo:
def __call__(self, a, b, c):
# do something
x = Foo()
x(1, 2, 3)
The __call__ method can be used to redefined/re-initialize the same object. It also facilitates the use of instances/objects of a class as functions by passing arguments to the objects.
import random
class Bingo:
def __init__(self,items):
self._items=list(items)
random.shuffle(self._items,random=None)
def pick(self):
try:
return self._items.pop()
except IndexError:
raise LookupError('It is empty now!')
def __call__(self):
return self.pick()
b= Bingo(range(3))
print(b.pick())
print(b())
print(callable(b))
Now the output can be..(As first two answer keeps shuffling between [0,3])
2
1
True
You can see that Class Bingo implements _call_ method, an easy way to create a function like objects that have an internal state that must be kept across invocations like remaining items in Bingo. Another good use case of _call_ is Decorators. Decorators must be callable and it is sometimes convenient to "remember" something between calls of decorators. (i.e., memoization- caching the results of expensive computation for later use.)
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__call__is hidden in plain view; it's how you instantiate a class:x = Foo()is reallyx = type(Foo).__call__(Foo), where__call__is defined by the metaclass ofFoo. – Omeara