0.3.0

Added

• Overview document for feature highlights in v0.3.0
• Automatic mixed precision support
• Multi-node, multi-GPU data parallel model training support
• 3 new evaluation metric functions
• 11 new network layers and blocks
• 6 new network architectures
• 14 new transforms, including an I/O adaptor
• Cross validation module for DecathlonDataset
• Smart Cache module in dataset
• monai.optimizers module
• monai.csrc module
• Experimental feature of ImageReader using ITK, Nibabel, Numpy, Pillow (PIL Fork)
• Experimental feature of differentiable image resampling in C++/CUDA
• Ensemble evaluator module
• GAN trainer module
• Initial cross-platform CI environment for C++/CUDA code
• Code style enforcement now includes isort and clang-format
• Progress bar with tqdm

Changed

• Now fully compatible with PyTorch 1.6
• Base Docker image upgraded to nvcr.io/nvidia/pytorch:20.08-py3 from nvcr.io/nvidia/pytorch:20.03-py3
• Code contributions now require signing off on the Developer Certificate of Origin (DCO)
• Major work in type hinting finished
• Remote datasets migrated to Open Data on AWS
• Optionally depend on PyTorch-Ignite v0.4.2 instead of v0.3.0
• Optionally depend on torchvision, ITK
• Enhanced CI tests with 8 new testing environments

Removed

• MONAI/examples folder (relocated into Project-MONAI/tutorials)
• MONAI/research folder (relocated to Project-MONAI/research-contributions)

Fixed

• dense_patch_slices incorrect indexing
• Data type issue in GeneralizedWassersteinDiceLoss
• ZipDataset return value inconsistencies
• sliding_window_inference indexing and device issues
• importing monai modules may cause namespace pollution
• Random data splits issue in DecathlonDataset
• Issue of randomising a Compose transform
• Various issues in function type hints
• Typos in docstring and documentation
• PersistentDataset issue with existing file folder
• Filename issue in the output writers