Well, I am trying to work on NAS(Neural Architecture Search). And my prototype will choose different types of convolution layer when I create a new model. I need to save that information and find the best performance one.
I wrote an independent file which defines different convolution layers in it. And this function will return a random layer which have a random kernel size. And this function will be used in the definition file of the model. I mean, there is a loop to add the layers into the model and a special forward function which allows skipping connection between layers in the definition file.