CVPR experiments, SheppLogan datasets, New training command

New features

• Normalised ConvGRU model (NormConv2dGRU) following the implementation of NormUnet2d (#176)
• Shepp Logan Datasets based on "2D & 3D Shepp-Logan phantom standards for MRI", 2008 19th International Conference on Systems Engineering. IEEE, 2008. (#202):
  • SheppLoganProtonDataset
  • SheppLoganT1Dataset
  • SheppLoganT2Dataset
• Sensitivity map simulator by producing Gaussian distributions with number of centers = number of desired coils (#202)
• Documentation updates (#180, #183, #196)
• Experiments for our CVPR 2022 paper "Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction" as shown in the paper (#180)
• Tutorials/examples for Calgary Campinas Dataset and Google Colab added (#199)

Code quality

• Remove unambiguous complex assertions (#194)
• modulus_if_complex function removed, modulus needs to specify axis (#194)
• Added tests/end-to-end tests. Coverage to 81% (#196)
• Improve typing (#196)
• mypy and pylint fixes (#196)
• Docker image updated (#204)
• Refactored direct train, direct predict and python3 projects/predict_val.py to not necessarily require path to data as some datasets don't require it (e.g. SheppLogan Datasets) - build_dataset_from_input relies on **kwargs now. Refactored configs and docs to comply with the above. (#202)

  • Train command example:

    direct train <experiment_directory> --num-gpus <number_of_gpus> --cfg <path_or_url_to_yaml_file> \ [--training-root <training_data_root> --validation-root <validation_data_root>] [--other-flags]

Bufixes

• Minor update in Normalize transform due to new version dependancy (#177)
• Minor bug fixes (#196)

Contributors

• @georgeyiasemis

Full Changelog: v1.0.1...v1.0.2