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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

0.5.3 - 2021-05-28

Changed

  • Project default branch renamed to dev from master
  • Base Docker image upgraded to nvcr.io/nvidia/pytorch:21.02-py3 from nvcr.io/nvidia/pytorch:21.04-py3
  • Enhanced type checks for the iteration_metric handler
  • Enhanced PersistentDataset to use tempfile during caching computation
  • Enhanced various info/error messages
  • Enhanced performance of RandAffine
  • Enhanced performance of SmartCacheDataset
  • Optionally requires cucim when the platform is Linux
  • Default device of TestTimeAugmentation changed to cpu

Fixed

  • Download utilities now provide better default parameters
  • Duplicated key_transforms in the patch-based transforms
  • A multi-GPU issue in ClassificationSaver
  • A default meta_data issue in SpacingD
  • Dataset caching issue with the persistent data loader workers
  • A memory issue in permutohedral_cuda
  • Dictionary key issue in CopyItemsd
  • box_start and box_end parameters for deepgrow SpatialCropForegroundd
  • Tissue mask array transpose issue in MaskedInferenceWSIDataset
  • Various type hint errors
  • Various docstring typos

Added

  • Support of to_tensor and device arguments for TransformInverter
  • Slicing options with SpatialCrop
  • Class name alias for the networks for backward compatibility
  • k_divisible option for CropForeground
  • map_items option for Compose
  • Warnings of inf and nan for surface distance computation
  • A print_log flag to the image savers
  • Basic testing pipelines for Python 3.9

0.5.0 - 2021-04-09

Added

  • Overview document for feature highlights in v0.5.0
  • Invertible spatial transforms
    • InvertibleTransform base APIs
    • Batch inverse and decollating APIs
    • Inverse of Compose
    • Batch inverse event handling
    • Test-time augmentation as an application
  • Initial support of learning-based image registration:
    • Bending energy, LNCC, and global mutual information loss
    • Fully convolutional architectures
    • Dense displacement field, dense velocity field computation
    • Warping with high-order interpolation with C++/CUDA implementations
  • Deepgrow modules for interactive segmentation:
    • Workflows with simulations of clicks
    • Distance-based transforms for guidance signals
  • Digital pathology support:
    • Efficient whole slide imaging IO and sampling with Nvidia cuCIM and SmartCache
    • FROC measurements for lesion
    • Probabilistic post-processing for lesion detection
    • TorchVision classification model adaptor for fully convolutional analysis
  • 12 new transforms, grid patch dataset, ThreadDataLoader, EfficientNets B0-B7
  • 4 iteration events for the engine for finer control of workflows
  • New C++/CUDA extensions:
    • Conditional random field
    • Fast bilateral filtering using the permutohedral lattice
  • Metrics summary reporting and saving APIs
  • DiceCELoss, DiceFocalLoss, a multi-scale wrapper for segmentation loss computation
  • Data loading utilities：
    • decollate_batch
    • PadListDataCollate with inverse support
  • Support of slicing syntax for Dataset
  • Initial Torchscript support for the loss modules
  • Learning rate finder
  • Allow for missing keys in the dictionary-based transforms
  • Support of checkpoint loading for transfer learning
  • Various summary and plotting utilities for Jupyter notebooks
  • Contributor Covenant Code of Conduct
  • Major CI/CD enhancements covering the tutorial repository
  • Fully compatible with PyTorch 1.8
  • Initial nightly CI/CD pipelines using Nvidia Blossom Infrastructure

Changed

  • Enhanced list_data_collate error handling
  • Unified iteration metric APIs
  • densenet* extensions are renamed to DenseNet*
  • se_res* network extensions are renamed to SERes*
  • Transform base APIs are rearranged into compose, inverse, and transform
  • _do_transform flag for the random augmentations is unified via RandomizableTransform
  • Decoupled post-processing steps, e.g. softmax, to_onehot_y, from the metrics computations
  • Moved the distributed samplers to monai.data.samplers from monai.data.utils
  • Engine's data loaders now accept generic iterables as input
  • Workflows now accept additional custom events and state properties
  • Various type hints according to Numpy 1.20
  • Refactored testing utility runtests.sh to have --unittest and --net (integration tests) options
  • Base Docker image upgraded to nvcr.io/nvidia/pytorch:21.02-py3 from nvcr.io/nvidia/pytorch:20.10-py3
  • Docker images are now built with self-hosted environments
  • Primary contact email updated to monai.contact@gmail.com
  • Now using GitHub Discussions as the primary communication forum

Removed

  • Compatibility tests for PyTorch 1.5.x
  • Format specific loaders, e.g. LoadNifti, NiftiDataset
  • Assert statements from non-test files
  • from module import * statements, addressed flake8 F403

Fixed

  • Uses American English spelling for code, as per PyTorch
  • Code coverage now takes multiprocessing runs into account
  • SmartCache with initial shuffling
  • ConvertToMultiChannelBasedOnBratsClasses now supports channel-first inputs
  • Checkpoint handler to save with non-root permissions
  • Fixed an issue for exiting the distributed unit tests
  • Unified DynUNet to have single tensor output w/o deep supervision
  • SegmentationSaver now supports user-specified data types and a squeeze_end_dims flag
  • Fixed *Saver event handlers output filenames with a data_root_dir option
  • Load image functions now ensure little-endian
  • Fixed the test runner to support regex-based test case matching
  • Usability issues in the event handlers

0.4.0 - 2020-12-15

Added

  • Overview document for feature highlights in v0.4.0
  • Torchscript support for the net modules
  • New networks and layers:
    • Discrete Gaussian kernels
    • Hilbert transform and envelope detection
    • Swish and mish activation
    • Acti-norm-dropout block
    • Upsampling layer
    • Autoencoder, Variational autoencoder
    • FCNet
  • Support of initialisation from pretrained weights for densenet, senet, multichannel AHNet
  • Layer-wise learning rate API
  • New model metrics and event handlers based on occlusion sensitivity, confusion matrix, surface distance
  • CAM/GradCAM/GradCAM++
  • File format-agnostic image loader APIs with Nibabel, ITK readers
  • Enhancements for dataset partition, cross-validation APIs
  • New data APIs:
    • LMDB-based caching dataset
    • Cache-N-transforms dataset
    • Iterable dataset
    • Patch dataset
  • Weekly PyPI release
  • Fully compatible with PyTorch 1.7
  • CI/CD enhancements:
    • Skipping, speed up, fail fast, timed, quick tests
    • Distributed training tests
    • Performance profiling utilities
  • New tutorials and demos:
    • Autoencoder, VAE tutorial
    • Cross-validation demo
    • Model interpretability tutorial
    • COVID-19 Lung CT segmentation challenge open-source baseline
    • Threadbuffer demo
    • Dataset partitioning tutorial
    • Layer-wise learning rate demo
    • MONAI Bootcamp 2020

Changed

  • Base Docker image upgraded to nvcr.io/nvidia/pytorch:20.10-py3 from nvcr.io/nvidia/pytorch:20.08-py3

Backwards Incompatible Changes

  • monai.apps.CVDecathlonDataset is extended to a generic monai.apps.CrossValidation with an dataset_cls option
  • Cache dataset now requires a monai.transforms.Compose instance as the transform argument
  • Model checkpoint file name extensions changed from .pth to .pt
  • Readers' get_spatial_shape returns a numpy array instead of list
  • Decoupled postprocessing steps such as sigmoid, to_onehot_y, mutually_exclusive, logit_thresh from metrics and event handlers, the postprocessing steps should be used before calling the metrics methods
  • ConfusionMatrixMetric and DiceMetric computation now returns an additional not_nans flag to indicate valid results
  • UpSample optional mode now supports "deconv", "nontrainable", "pixelshuffle"; interp_mode is only used when mode is "nontrainable"
  • SegResNet optional upsample_mode now supports "deconv", "nontrainable", "pixelshuffle"
  • monai.transforms.Compose class inherits monai.transforms.Transform
  • In Rotate, Rotated, RandRotate, RandRotated transforms, the angle related parameters are interpreted as angles in radians instead of degrees.
  • SplitChannel and SplitChanneld moved from transforms.post to transforms.utility

Removed

  • Support of PyTorch 1.4

Fixed

  • Enhanced loss functions for stability and flexibility
  • Sliding window inference memory and device issues
  • Revised transforms:
    • Normalize intensity datatype and normalizer types
    • Padding modes for zoom
    • Crop returns coordinates
    • Select items transform
    • Weighted patch sampling
    • Option to keep aspect ratio for zoom
  • Various CI/CD issues

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