apps: high level medical domain specific deep learning applications.

auto3dseg: automated machine learning (AutoML) components for volumetric image analysis.

bundle: components to build the portable self-descriptive model bundle.

config: for system configuration and diagnostic output.

csrc: for C++/CUDA extensions.

data: for the datasets, readers/writers, and synthetic data.

engines: engine-derived classes for extending Ignite behaviour.

fl: federated learning components to allow pipeline integration with any federated learning framework.

handlers: defines handlers for implementing functionality at various stages in the training process.

inferers: defines model inference methods.

losses: classes defining loss functions, which follow the pattern of torch.nn.modules.loss.

metrics: defines metric tracking types.

networks: contains network definitions, component definitions, and Pytorch specific utilities.

optimizers: classes defining optimizers, which follow the pattern of torch.optim.

transforms: defines data transforms for preprocessing and postprocessing.

utils: generic utilities intended to be implemented in pure Python or using Numpy, and not with Pytorch, such as namespace aliasing, auto module loading.

visualize: utilities for data visualization.

_extensions: C++/CUDA extensions to be loaded in a just-in-time manner using torch.utils.cpp_extension.load.