Easier way to use Data Processing steps outside of datamodule about torchgeo HOT 4 OPEN

If you pass a datamodule, it will only select the predefined validation loader and validate on that, but maybe I would like to validate on the train set and the validation set, for example when taking a pre-trained model and checking performance without training. Might also be relevant if you try something like cross validation, where you split your train/val sets. In my case, I am trying conformal prediction, where you need to take a subset of the validation set to create a separate calibration set and use the the model with that, so you need to control "which" split dataloader to apply validation to.

from torchgeo.

I can understand why you would want to be able to use a dataset if a data module doesn't exist, but why would you want to use a dataset if a data module does exist?

from torchgeo.

In order to do trainer.validate(model, dataloaders=datamodule.val_dataloader()) but not having to implement my own normalization scheme as a collate fn for every dataloader from a datamodule I want to use. So for example say I train one model and want to validate it on a bunch of datasets, then I could pass multiple dataloaders from different datasets or datamodules to trainer.validate()

from torchgeo.

But why not use trainer.validate(model, datamodule=datamodule) for all data modules?

from torchgeo.