Module Independency & Standalone Usability

We should make sure the main avalanche modules are also usable as standalone libraries.

The Benchmarks and Extra modules already satisfy this requirement.

The Training one may be improved to work on any input data, making less assumptions as possible about the nature of the data processed.

The evaluation module and mostly the metrics are probably too difficult to use off-the-shelf at the moment as heavily integrated with the evaluation plugin. We should relax this!

What do you think?

[AntonioCarta]

Regarding the strategies, maybe there is a way to make plugins more independent from the rest of avalanche. We could restrict the plugin callbacks. Instead of taking the strategy as an argument, we could explicitly pass the needed arguments. For example:

    def adapt_train_dataset(self, strategy, **kwargs):
        """
        Expands the current training set with datapoint from
        the external memory before training.
        """
        pass

becomes:

    def adapt_train_dataset(self, current_data, **kwargs):
        """
        Expands the current training set with datapoint from
        the external memory before training.
        """
        return current_data

Notice that this change would restrict the plugin’s callbacks, because they would not have access to the entire strategy state at each point. However, it would make plugins relatively separated from the rest of avalanche. It seems a good tradeoff to me. Maybe it’s even better to have explicit arguments than having the entire strategy, because you remove the risk of plugins interfering with the strategy’s state. This will make it easier to implement new plugins.

Strategy classes (BaseStrategy, JointTraining) are basic avalanche components because they glue together plugins, data streams, and loggers. I don’t see how we could make them standalone.

@vlomonaco what do you think? If you agree I can work on this.

[AntonioCarta]

Ok, now I get it. Is this really a problem? StepInfo is a simple object, and we already provide a couple of methods to create scenarios given tensors. Isn’t it easier to convert the different data formats into scenarios instead of supporting every possible format inside the training module?

Maybe you want to add some other methods to make the transition between tensors and scenarios easier?