Feature request: e2cnn equivalent to torch.nn.ModuleList

[drewm1980]

As a minor convenience, it would be nice to have an equivariant equivalent to torch.nn.ModuleList.

The main use cases:

• Same as ModuleList, contain modules that are (elsewhere) hooked up in some arbitrary DAG, rather than Sequential, and still have them get registered properly.
• Support .export() to a torch.nn.ModuleList containing .exported() children.
• Aid mechanical 1:1 porting from pytorch to e2cnn equivalents.

It would not subclass EquivariantModule, since it can't meaningfully conform to most of that API, but it would probably get used mostly in EquivariantModule subclass implementations.

I'm currently trying out the following pattern in my code:

# Inside a bigger class that is itself implementing .export()... with self.up_path also a ModuleList...
def export(self):
        self.eval()
        up_path_exported = torch.nn.ModuleList()
        for module in self.up_path:
            up_path_exported.append(module.export())

If the EquivariantModules were already living in a e2cnn.nn.ModuleList, it would just be:

def export(self):
    self.eval()
    up_path_exported = self.up_path.export()

So a couple lines of boilerplate would be saved, but the discovery aspect is probably worth more... "just build everything using e2cnn 1:1 equivalents, train, then .export()"

[Gabri95]

Hi @drewm1980 ,

I see your point here.
I usually define another export method in the class defining the full model, where then I loop over self.named_modules() to find all equivariant modules to export.
Having an EquivariantModuleList would mostly automate this.

I think it is a nice idea, thanks for suggesting it!
I will add it later today or tomorrow.

Thanks again!
Gabriele

[Gabri95]

I implemented it as a subclass of torch.nn.ModuleList to reuse its functionalities.
The only difference with it is that it should only accept EquivariantModules.

Let me know if this works well for you.

P.S.: I have not pushed the new code on pypi yet, so you should intall it using pip install git+https://github.com/QUVA-Lab/e2cnn

Best,
Gabriele