How to understand the layer object in the forward function

Hello Everybody,

I’m new to PyTorch and I have a question about how to understand the layer object in the forward function. Here is an example:

class Net(torch.nn.Module): 
    def __init__(self, n_feature, n_hidden, n_output):
        super(Net, self).__init__()  
        self.hidden = torch.nn.Linear(n_feature, n_hidden)  
        self.predict = torch.nn.Linear(n_hidden, n_output)  

    def forward(self, x):   
        x = F.relu(self.hidden(x))
        x = self.predict(x)           
        return x

net = Net(n_feature=1, n_hidden=10, n_output=1)

The self.hidden is defined as a Linear class object in the init(). Why this object (self.hidden) can accept x (self.hidden(x)) as an input? It’s not a function but an object! Is the forward() function in the Linear class called automatically here?
Thank you very much!

nn.Linear is also an nn.Module just like your Net.
Internally when you call Net(x) or self.hidden(x) the __call__ method is called, which registers hooks etc., and finally calls forward. You can check it out in the source file.


Thank you so much for your timely answer! It’s clear to me now.