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apps: high level medical domain specific deep learning applications.
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config: for system configuration and diagnostic output.
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csrc: for C++/CUDA extensions.
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data: for the datasets, readers/writers, and synthetic data.
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engines: engine-derived classes for extending Ignite behaviour.
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handlers: defines handlers for implementing functionality at various stages in the training process.
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inferers: defines model inference methods.
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losses: classes defining loss functions, which follow the pattern of
torch.nn.modules.loss
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metrics: defines metric tracking types.
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networks: contains network definitions, component definitions, and Pytorch specific utilities.
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optimizers: classes defining optimizers, which follow the pattern of
torch.optim
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transforms: defines data transforms for preprocessing and postprocessing.
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utils: generic utilities intended to be implemented in pure Python or using Numpy, and not with Pytorch, such as namespace aliasing, auto module loading.
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visualize: utilities for data visualization.
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_extensions: C++/CUDA extensions to be loaded in a just-in-time manner using
torch.utils.cpp_extension.load
.
monai
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