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CHANGELOG.md

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Changelog

All notable changes to MONAI are documented in this file.

The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

Unreleased

0.3.0 - 2020-10-02

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

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

0.2.0 - 2020-07-02

Added

  • Overview document for feature highlights in v0.2.0
  • Type hints and static type analysis support
  • MONAI/research folder
  • monai.engine.workflow APIs for supervised training
  • monai.inferers APIs for validation and inference
  • 7 new tutorials and examples
  • 3 new loss functions
  • 4 new event handlers
  • 8 new layers, blocks, and networks
  • 12 new transforms, including post-processing transforms
  • monai.apps.datasets APIs, including MedNISTDataset and DecathlonDataset
  • Persistent caching, ZipDataset, and ArrayDataset in monai.data
  • Cross-platform CI tests supporting multiple Python versions
  • Optional import mechanism
  • Experimental features for third-party transforms integration

Changed

For more details please visit the project wiki

  • Core modules now require numpy >= 1.17
  • Categorized monai.transforms modules into crop and pad, intensity, IO, post-processing, spatial, and utility.
  • Most transforms are now implemented with PyTorch native APIs
  • Code style enforcement and automated formatting workflows now use autopep8 and black
  • Base Docker image upgraded to nvcr.io/nvidia/pytorch:20.03-py3 from nvcr.io/nvidia/pytorch:19.10-py3
  • Enhanced local testing tools
  • Documentation website domain changed to https://docs.monai.io

Removed

  • Support of Python < 3.6
  • Automatic installation of optional dependencies including pytorch-ignite, nibabel, tensorboard, pillow, scipy, scikit-image

Fixed

  • Various issues in type and argument names consistency
  • Various issues in docstring and documentation site
  • Various issues in unit and integration tests
  • Various issues in examples and notebooks

0.1.0 - 2020-04-17

Added

  • Public alpha source code release under the Apache 2.0 license (highlights)
  • Various tutorials and examples
    • Medical image classification and segmentation workflows
    • Spacing/orientation-aware preprocessing with CPU/GPU and caching
    • Flexible workflows with PyTorch Ignite and Lightning
  • Various GitHub Actions
    • CI/CD pipelines via self-hosted runners
    • Documentation publishing via readthedocs.org
    • PyPI package publishing
  • Contributing guidelines
  • A project logo and badges