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
anddirect predict
replace the corresponding python scripts (#151, #139) - Added baseline models (#122, #121, #123):
- 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- Outdated checkpoints are removed (#146)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 withRIM
(#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
Full Changelog: v0.2...v1.0.0