Summary of our code

1. Creating the hdf5: see dedicated doc page.

2. The trainer abstract :

   • Uses a model-dependent child implementation of the BatchSamplerAbstract:

     • For instance, the BatchSequenceSampler creates batches of streamline ids that will be used for each batch iteratively through __iter__. Those chosen streamlines can then be loaded and processed with load_batch, which also uses data augmentation.

     • For instance, the BatchSequencesSamplerOneInputVolume then uses the generated streamlines to load one underlying input for each timestep of each streamline.

     • The BatchSampler uses the MultiSubjectDataset (or lazy)

       • Creates a list of subjects. Using self.load_data(), it loops on all subjects (for either the training set or the validation set) and loads the data from the hdf5 (lazily or not).

       • The list is a DataListForTorch (or lazy). It contains the subjects but also common parameters such as the feature sizes of each input.

       • The elements are SubjectData (or lazy)

         • They contain both the volumes and the streamlines, with the subject ID.

         • Volumes are MRIData (or lazy). They contain the data and affine.

         • Streamlines are SFTData (or lazy). They contain all information necessary to create a Stateful Tractogram from the streamlines. In the lazy version, streamlines are not loaded all together but read when needed from the LazyStreamlinesGetter.