Initiaized class member in pytorch module

When declaring a model in pytorch, having a model class member variable declared and initialized mysteriously prevents it from being populated in the constructor. Is this expected? If so, why?

Testing code below, with example models with a component member variable. The initialization value of the component (e.g. None, a number or a Tensor) does not change the behaviour.

import torch

class Lin1(torch.nn.Module):
    def __init__(self):
        super(Lin1, self).__init__()
        self.component = torch.nn.Linear(100,200)

class Lin2(torch.nn.Module):
    component = None
    def __init__(self):
        super(Lin2, self).__init__()
        self.component = torch.nn.Linear(100,200)

# instantiate and check member component
for cl in [Lin1, Lin2]:
    model = cl()
    print("nModel:", cl)
    print("Component:", model.component)
    print("It's None?: ", model.component is None)

Model: <class '__main__.Lin1'>
Component: Linear(in_features=100, out_features=200, bias=True)
It's None?:  False

Model: <class '__main__.Lin2'>
Component: None
It's None?:  True

Answer

This happens because nn.Module overwrites __getattr__, and it would only work as you expect if component was not in Lin2.__dict__ (nor in Lin2().__dict__). Since component is a class attribute, it is in Lin2.__dict__ and will be returned as it should.

When you write self.x = nn.Linear(...) or any other nn.Module (or even nn.Buffer or nn.Parameter), x is actually registered in a dictionary called _modules (or _buffers, etc.) In this way, when you ask for self.component, if component is already in the __dict__ of the class or the instance, Python will not call the custom nn.Module‘s __getattr__().

You can check the source-code of __getattr__ from nn.Module here. A similar discussion was done here. There was also a discussion about changing from __getattr__ to __getattribute__ in PyTorch, but as of now, this is a wontfix issue.