Batch loader

These classes define how batches of streamlines are loaded from a MultiSubjectDataset and how data augmentation is applied. Two main types of batch loaders are implemented:

DWIMLStreamlinesBatchLoader

Loads augmented streamlines only (no MRI volumes). Loads streamlines from a dataset, applies optional data augmentation (resampling, cutting, reversing, noise), and returns them in voxel/corner space.

Methods:

• set_context: Sets whether the loader operates on training or validation data. Also configures which noise augmentation applies.

• load_batch_streamlines(streamline_ids_per_subj): Loads the streamlines for each subject, applies: resampling or compression, splitting, reversing, conversion to voxel + corner coordinates

DWIMLBatchLoaderOneInput

Child class of DWIMLStreamlinesBatchLoader. Additionnally communicates with the model to prepare input volume(s) under each point of each streamline and performs trilinear interpolation, and, optionnally, neighborhood extraction.

Methods:

• load_batch_inputs(batch_streamlines, ids_per_subj)

Note: Must be used with a model with inputs, uses the model’s method: prepare_batch_one_input().