Structure of the library

Our implementation is based on PyTorch and is inspired by the architecture of Pythae. The implementations of the models are collected in the module multivae.models. Each model class is accompanied by a configuration dataclass gathering the collection of any relevant hyperparameter which enables them to be saved and loaded straightforwardly. The models are implemented in a unified way, so that they can be easily integrated within the multivae.trainers. Trainers are also accompanied by a training configuration dataclass used to specify any training-related hyperparameters (number of epochs, optimizers, schedulers, etc..). Models that have a multistage training (JNF, TELBO) benefit from their dedicated trainer that makes them as straightforward to use as other models. Partially observed datasets can be conveniently handled using the IncompleteDataset class that contains masks informing on missing or corrupted modalities in each sample. For Data Augmentation purposes the module multivae.samplers regroups different ways generating fully synthetic data. Finally, the MultiVae library also integrates an evaluation pipeline for all models with common metrics such as likelihoods, coherences, FID scores and visualizations implemented in the module multivae.metrics.