How to design my nn.module class

I made this simple class as following:

from vgg import VGG16
import torch.nn as nn

class NewNet(nn.Module):

    def __init__(self, n_classes,List,Input):
        super(NewNet, self).__init__()
        self.Input = Input
        self.n_classes = n_classes
        self.Base = VGG16()
        self.Loc = nn.Conv2d(self.Input.size(1), len(List) * 4, 3, padding=1)
        self.Conf = nn.Conv2d(self.Input.size(1), len(List) * (self.n_classes + 1), 3, padding=1)
        self.ConfMap = nn.Conv2d(self.Input.size(1), len(List), 3, padding=1)

    def forward(self):
        x= self.Input
        Out1 = self.Base(x)
        Loc_Out1 = self.Loc(Out1)
        return Loc_Out1

I am not sure why I get this error when I want to use it?

object is not iterable

Also I am newbie in terms of classes, so im not sure if I can do what i did in __init__ or not, if I can do it, how should i call my Class?

Can you give a more detailed stack trace along with how you are trying to instantiate the class?

are you sure that you wand to define function like: forward(self)? In that case, it won’t take any input outside and also impossible for you to iterate over the training date.

yeah you are right, i should have put input in the forward function :expressionless: