Hey,
I am interested in building a network having multiple inputs. I understand that when calling the forward function, only one Variable is taken in parameter. I have two possible use case here :

the same image at multiple resolutions is used
different images are used

I would like some advice to design a nn.Module in the same fashion as alexnet for example.
I have no idea how to :

give multiple inputs to the nn.Module

join the fc layers together

I am following the example of imagenet, which looks like this :
class SimpleConv(nn.Module):
    def __init__(self, num_classes):
        super(SimpleConv, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(64, 192, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(192, 384, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(384, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
        )
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(256 * 6 * 6, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), 256 * 6 * 6)
        x = self.classifier(x)
        return x


def simple_conv(pretrained=False, num_classes=140):
    model = SimpleConv(num_classes)
    # if pretrained:
    #     model.load_state_dict(model_zoo.load_url(model_urls['alexnet']))
    return model

I hope it’s clear 
Thanks