15 conv fwd

if module_type == "convolutional" or module_type == "upsample":
   x = self.module_list[i](x)
   outputs[i] = x

elif module_type == "route":
   layers = module["layers"]
   layers = [int(a) for a in layers]
   if len(layers) == 1:
      x = outputs[layers[0]]
   if len(layers) > 1:
       map1 = outputs[layers[0]]
       map2 = outputs[layers[1]]
       x = torch.cat((map1,map2),1)
   outputs[i] = x
                
elif  module_type == "shortcut":
   from_ = int(module["from"])
   # just adding outputs for residual network
   x = outputs[i-1] + outputs[i+from_]  
   outputs[i] = x