First stable release including baselines

In the first stable release, we provide the implementation of baselines, including trained models on the publicly available datasets.

New features

• Command-line interface direct train and direct predict replace the corresponding python scripts (#151, #139)
• Added baseline models (#122, #121, #123):
  • End-to-end Variational Network (VarNet)
  • Learned Primal Dual Network (LDPNet)
  • X-Primal Dual Network (XPDNet), KIKI-Net
  • Joint-ICNet
  • AIRS Medical model modification (MultiDomainNet)
  • U-Net / Normalized U-Net baselines
• Updated documentation
• Checkpoints and configs can now be loaded from remote URL (#133, #127, #135), and training configuration now supports the ability to initialize from a URL (#141)
• New sampling masks (radial and spiral for Cartesian data) (#140)
• Implement recon models by (#123)
• Add recurrentvarnet implementation (#131)
• Added direct train CLI interface (Closes #109, #139)

Code quality

• Removed experimental named tensor feature, enabling the update to pytorch 1.9 (PR #103)
• Remove large files from the repository and store these in an S3 bucket and are downloaded when used.
• Code coverage checks and badges are added (#153)
• Add several tests, code coverage is now to 73% (#144)
• Tests are now in a separate folder (#142)
• Outdated checkpoints are removed (#146)
• New models are added, requiring that MRIReconstruction is merged with RIM (#113)
• Allow reading checkpoints from S3 storage (#133, Closes #135)
• Allow for remote config files (#133, Closes #135)

Documentation

Internal changes

• Experimental named tensors are removed (PR #103)
• Pytorch 1.9 and Python 3.8 are now required.

Bugfixes

• Evaluation function had a problem where the last volume sometimes was dropped (#111)
• Checkpointer tried to load state_dict if key is of the format __<>__ (#144 closes #143)
• Fixed crash when validation set is empty (#125)

New Contributors

• @georgeyiasemis

Full Changelog: v0.2...v1.0.0