Dataset handling and refactoring

[kadri-nizam]

Is your feature request related to a problem or opportunity? Please describe.

• GriddedDataset requires a device parameter input which can be cumbersome.
• KFoldCrossValidator might benefit from inheriting from PyTorch data set where it will be easier to segment the boolean indices as different slices of a PyTorch dataset
• For multi-execution block/multi-GPU workflows, we might want to explore using DataLoader

Describe the solution you'd like
To stay idiomatic with PyTorch, we can implement a .to(device) method that encapsulates the process of transferring the required tensors to the correct device.

IC: Let's focus this issue specifically on the (potential) redesign of dataset-focused classes, like GriddedDataset or UVDataset and how inference or cross-validation loops will interact with them. Issue #154 will track progress related to the Gridder and methods to create a GriddedDataset in the first place.

[iancze]

#18 and #31 are relevant in the design for possible solutions that will also address spectral line datasets or image cubes with large numbers of channels. Closed issues #17 and #32 may provide additional things to think about in this context.

[iancze]

We have broken out the issues @kadri-nizam raised in the original post into more bite-sized issues.

Notes about splitting the imaging and averaging functions of the Gridder are now in #154, and have been implemented by #156, closing that issue.

Topics related to UVDataset are in #162, topics related to GriddedDataset are in #163. It probably makes more sense to try #162 first, since that will help us get a better grasp on the Pytorch dataset idioms.

[iancze]

Now that #163 and #157 and #154 are implemented, this issue is sufficiently diffuse that I don't think it warrants being open anymore. Issues related to UVDataset are now in #162 whereas issues related to Cross Validation are partially covered in places like #166 , #182 , #133 and #135 (and could probably be redeveloped/condensed).