RandomGeoSampler for Pre-chipped Datasets

[yichiac]

This PR replaces RandomBatchGeoSampler with RandomGeoSampler for Agrifieldnet, Sentinel2_CDL, Sentinel2_NCCM, Sentinel2_EuroCrops, and Sentinel2_SouthAmericaSoybean datamodules. RandomGeoSampler works better for the pre-chipped datasets because it returns the correct random bounding boxes.

[adamjstewart]

We should do this for all the datamodules you're using, not just these two.

[adamjstewart]

LGTM, is there anything else you wanted to add to this PR or can I merge?

[adamjstewart]

Tests fail because the fake data consists of a single file and 10% of 1 is 0.

[yichiac]

That's all I want to add to this PR. Thanks for reviewing the code, @adamjstewart!

[adamjstewart]

This PR adds 1K new files to git. Do we really need that many files?

[yichiac]

If we change the split portion from [0.8, 0.1, 0.1] to something like [0.6, 0.2, 0.2], we can reduce 50% of test images.
random_bbox_assignment(self.dataset, [0.8, 0.1, 0.1], generator=generator)
For the current split portion, we need at least 10 images to get at least 1 val/test image. For [0.6, 0.2, 0.2], we just need 5 images. The split portion requiring the minimum number of images would be [0.3, 0.3, 0.3], which only needs 3 images.

That being said, are 500 images too many to be added to git? We can manually change the split portion later using [0.8, 0.1, 0.1].

[adamjstewart]

If we change the split portion from [0.8, 0.1, 0.1] to something like [0.6, 0.2, 0.2], we can reduce 50% of test images.

We shouldn't change the default split just to make testing easier. If you want to make the split ratio an input parameter (like we do for many other data modules), then we could use [0.33, 0.33, 0.33] only in CI and use [0.8, 0.1, 0.1] by default.

we need at least 10 images

I'm fine with at least 10 images, I'm less fine with at least 1000 images.