Join: feature dataset

[JakobEliasWagner]

Feature/Cleanup: New Dataset Interface

Description

This pull request introduces an update to the datat-set handling mechanism.
It aims to leverage PyTorch's Dataset handling features to improve model's performance, scalability, ease of use, and maintainability.

Solves #31

Which issue does this PR tackle?

• Scalability of the data-handling system
• Efficiency of the data-handling system
• Ease of Use: Currently new features need to be manually added onto the set
• Integration with PyTorch Ecosystem

How does it solve the problem?

• Implements a DataSet class that is a child from Pytorch's DataSet.
  • Scalability of the data-handling system (https://pytorch.org/blog/efficient-pytorch-io-library-for-large-datasets-many-files-many-gpus/)
  • Efficiency of the data-handling system (https://pytorch.org/blog/efficient-pytorch-io-library-for-large-datasets-many-files-many-gpus/)
  • Ease of Use: Currently changing the batch size or specific transformations
  • Better integration with PyTorch Ecosystem
• Separation between the data-loader and data-set allows for greater flexibility.

How are the changes tested?

• The previously implemented adjusted unit-tests run without error.

Checklist for Contributors

• Scope: This PR tackles exactly one problem.
• Conventions: The branch follows the feature/title-slug convention.
• Conventions: The PR title follows the Bugfix: Title convention.
• Coding style: The code passes all pre-commit hooks.
• Documentation: All changes are well-documented.
• Tests: New features are tested and all tests pass successfully.
• Changelog: Updated CHANGELOG.md for new features or breaking changes.
• Review: A suitable reviewer has been assigned.

Checklist for Reviewers:

• The PR solves the issue it claims to solve and only this one.
• Changes are tested sufficiently and all tests pass.
• Documentation is complete and well-written.
• Changelog has been updated, if necessary.

[JakobEliasWagner]

@samuelburbulla added your suggested changes to the PR. Need some clarification on whether you think it is okay to use xi in basics.ipynb. Furthermore, I do not have access to the flame dataset.