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Is your feature request related to a problem? Please describe.
I would like to implement a Solver for the new v0.1 version with multiple neural networks as models (for example a GAN) and multiple optimisers. Nevertheless, the solver interface takes only one model. If I want to define two models, I need to use the class Network in the GAN solver to do all the checks. Same story for optimisers. It could lead to high code redundancy.
Describe the solution you'd like
We can make in SolverInterface a list of models instead, and also a list of optimisers. So the __init__ would become:
class SolverInterface(pl.LightningModule, metaclass=ABCMeta):
""" Solver base class. """
def __init__(self, models, optimizers, problem, extra_features=None):
In this way when creating a new solver we can make all the checks in the SolverInterface class, avoiding repetition in the code for each solver.
Additional context
Add any other context or screenshots about the feature request here.
The text was updated successfully, but these errors were encountered:
Is your feature request related to a problem? Please describe.
I would like to implement a Solver for the new v0.1 version with multiple neural networks as models (for example a GAN) and multiple optimisers. Nevertheless, the solver interface takes only one model. If I want to define two models, I need to use the class Network in the GAN solver to do all the checks. Same story for optimisers. It could lead to high code redundancy.
Describe the solution you'd like
We can make in
SolverInterface
a list of models instead, and also a list of optimisers. So the__init__
would become:In this way when creating a new solver we can make all the checks in the SolverInterface class, avoiding repetition in the code for each solver.
Additional context
Add any other context or screenshots about the feature request here.
The text was updated successfully, but these errors were encountered: