I cannot reproduce this issue by using an input tensor of [batch_size, 3, 416, 416] and am running into a shape mismatch error before.
Changing x.view(-1, 2048) to x.view(x.size(0), -1) and adapting the expected in_features in self.linear works:
num_classes = 20
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
                
        self.inc = models.inception_v3(pretrained=True)
        self.inc.aux_logits = False

        for child in list(self.inc.children())[:-5]:
            for param in child.parameters():
                param.requires_grad = False

        self.inc.fc = nn.Sequential()
                    
        self.dens121 = models.densenet121(pretrained=True)

        for child in list(self.dens121.children())[:-6]:
            for param in child.parameters():
                param.requires_grad = False

        self.dens121 = nn.Sequential(*list(self.dens121.children())[:-1])
           
        self.SiLU = nn.SiLU()      
        self.linear = nn.Linear(175104, num_classes)
        self.dropout = nn.Dropout(0.2)
        
    def forward(self, x):
        x1 = self.SiLU(self.dens121(x))
        x1 = x1.view(x.size(0), -1)
        
        x2 = self.inc(x).view(x.size(0), -1)
        x = torch.cat([x1,x2],1)

        return self.linear(self.dropout(x))

model = Net()
x = torch.randn(2, 3, 416, 416)
out = model(x)