Add DensePaddingDataLoader for flexible (padded) dense batching of BaseData objects

[amorehead]

🚀 The new functionality, motivation and pitch

• This PR adds a new DensePaddingDataLoader class that enables flexible dense batching of BaseData objects.
• Such functionality supports fast and direct conversion of PyG Data or HeteroData objects into CV/NLP-driven (dense-batching) model training pipelines.

Alternatives

No alternatives exist for this functionality, including PyG's DenseDataLoader which assumes each (sub)graph's node count must be identical to all other (sub)graphs.

Additional context

This new data loader enables one to easily experiment with message passing algorithms that operate on dense tensor representations of batched graph objects without having to redesign one's PyG-driven Dataset class. This idea was initiated per #8516 and raised as an issue in #8517.

[codecov]

Codecov Report

Attention: Patch coverage is 0% with 169 lines in your changes are missing coverage. Please review.

Project coverage is 89.08%. Comparing base (2fcd29d) to head (6cef254).
Report is 2 commits behind head on master.

Files	Patch %	Lines
...orch_geometric/loader/dense_padding_data_loader.py	0.00%	169 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #8518      +/-   ##
==========================================
- Coverage   90.02%   89.08%   -0.94%     
==========================================
  Files         470      471       +1     
  Lines       30164    30333     +169     
==========================================
- Hits        27154    27022     -132     
- Misses       3010     3311     +301     

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[rusty1s]

Thanks for the PR. It looks like we now have multiple places that dictate how to collate or pad attributes from data objects. I wonder if we can re-use existing code from there, e.g., we have the PadTransform, that already does most of what you have written here. Do you think there is a chance to re-use some of this logic for this PR?

[amorehead]

Hi, @rusty1s. It's possible we may be able to reuse existing assets here. I'm not too familiar with the PadTransform utility. Does it already support padding both float and non-float (e.g., int, bool) tensors?

[rusty1s]

Yes, that should be supported.