Notable changes between 0.1.0 and 0.2.0

Summary

This page briefly summaries the API changes between v0.1.0 and v0.2.0.

0.2.0 - 2020-07-02

Added

• MedNIST and Decathlon public datasets
• highlight for v0.2.0
• MONAI for PyTorch users tutorial
• Support of spatial size fallback to default image size in the transforms, sliding window inferer
• monai.utils.enums
• monai.utils.fall_back_tuple
• Label to contour transforms
• Lambda transforms
• spatial transform padding mode defaults to nearest or reflection

Changed

• Various documentation updates
• Documentation is now available at https://docs.monai.io
• Revised utility's alias, for example monai.utils has all the submodule's class names
• process_bar is renamed to progress_bar

0.2.0rc1+13.g379c959 - 2020-06-26

Added

• Image padding transforms: divisible padding and border padding
• Type hint enhancements
• CI tests for MONAI core (minimal external dependencies)
• Multi-gpu inference example
• GAN networks and examples
• RandSpatialCropSamples for random window sampling

Changed

• MeanDice handler is now implemented with DiceMetric class, instead of a functional (non-breaking)
• Focal loss now supports one-hot labels
• RandCropByPosNegLabeld supports one-hot sampling mask label_key
• Improved optional import error messages
• In RandCropByPosNegLabeld , size is renamed to spatial_size
• In NiftiSaver and write_nifti, output_shape renamed to output_spatial_size
• in PNGSaver and write_png, output_shape is renamed to output_spatial_size
• Rand2DElasticd, Rand3DElasticd support spatial_size with -1, indicating adaptive size from the original input
• Arugment interp_order is renamed to mode in all cases, to be consistent with the Pytorch APIs
• Arugment mode is renamed to padding_mode in all cases, to be consistent with the Pytorch APIs

0.1.0+153.gc98da0d - 2020-06-18

Added

generic

• Support of Pytorch v1.4 and v1.5
• Unit tests for Windows latest and MacOS latest (without GPUs)
• Cachedataset with multi-thread and persistent cache support
• Code quality and usability improvements in runtests.sh
• New options to run static type and code style checks in runtests.sh
• Automatic code formatting with psf/Black
• New type definitions for type hinting: KeyCollection and IndexSelection
• Tutorials on using third-party libraries with MONAI
• Installation guide https://monai.readthedocs.io/en/latest/installation.html
• MONAI/research folder for research demonstrations and prototypes
• requirements-dev.txt for setting up MONAI development environment

monai.losses

• TverskyLoss in monai.losses
• FocalLoss in monai.losses
• MaskedDiceLoss as an extension of DiceLoss with additional mask inputs.

monai.data

• Saving output as PNG format images (optionally with intensity clipping and spatial resampling)
• ZipDataset and ArrayDataset in monai.data for flexible data loading with transforms
• trainer and evaluator workflow interfaces and utilities in monai.engines (extending pytorch-ignite)
• Medical segmentation decathlon data list loader in monai.data
• monai.data.DataLoader with improved default values for torch.utils.data.DataLoader
• worker_init_fn utility in monai.data.utils to enhance the DataLoader's default behavior when loading with randomized transforms

utilities

• set_determinism method in monai.utils

monai.handlers

• Learning rate schedule handler in monai.handlers
• CheckpointSaver and CheckpointLoader event handers in monai.handlers

monai.transforms

• SimulateDelay/SimulateDelayd Transforms for improved usability
• DataStats/DataStatsd Transforms for improved usability
• SplitChannel/SplitChanneld Transforms in monai.transforms
• Activation/Activationd Transforms in monai.transforms
• AsDiscrete/AsDiscreted Transforms in monai.transforms
• SqueezeDim/SqueezeDimd Transforms in monai.transforms
• KeepLargestConnectedComponent/KeepLargestConnectedComponent Transforms in monai.transforms
• Identity/Identityd Transforms in monai.transforms
• Various new spatial padding and cropping transforms in monai.transforms
• Various new post-processing transforms in monai.transforms

monai.application

• MedNISTDataset in monai.application with automatic downloading and decompressing

monai.networks

• AffineTransforms in monai.networks.layers for trainable affine transformations
• Squeeze and excitation layers in monai.networks.blocks
• Upsample and MaxAvgDownsample layers in monai.networks.blocks
• Simplified atrous spatial pyramid pooling (ASPP) in monai.networks.blocks
• Various non-linear activations in monai.networks.layers.LayerFactory

Changed

generic

• Base Docker image upgraded to nvcr.io/nvidia/pytorch:20.03-py3 from nvcr.io/nvidia/pytorch:19.10-py3
• Drop the support of Python < 3.6 (now MONAI requires Python >= 3.6)
• Unified boolean options in all modules: add_sigmoid/do_sigmoid to sigmoid, add_softmax/do_softmax to softmax
• Contributing guideline to have more coding style instructions
• Enabled type hinting and static type checks
• Improved, consistent Python code style via psf/Black
• Coding style: prefer Python f-string over the "format string" whenever possible
• Optional import -- MONAI requires Numpy >= 1.17 and Pytorch >= 1.4, all other external packages are not required/installed by default

monai.transforms

• Restructured monai.transforms.transforms into sub-modules, such as monai.transforms.intensity
• image_only option to LoadPNG and LoadPNGd transforms (to track the original metadata such as filename)
• Dropped the dtype= option in intensity and spatial transforms in monai.transforms
• Default padding mode to nearest or edge in various transforms (instead of padding with Constant)
• Zoom/Zoomd defaults to keep_size=True (instead of keep_size=False)
• monai.transforms.spatial is now implemented with Pytorch native interfaces (instead of scipy, scikit-image)
• monai.transforms.spatial interpolation order and padding mode options changed to accept only Pytorch supported choices
• monai.transforms.util.apply_transform accepts new argument map_iterms: bool (defaults to True)
• Dictionary-level transforms now by default works with nested dict: {"image": array, "image_meta_dict": {"affine": array, "original_affine": array}}
• DeleteKeys/Deletekesd Transforms renamed to DeleteItems/DeleteItemsd in monai.transforms
• Adds padding mode and interpolation order options to spatial transforms' __call__, in addtion to those in __init__.
• Moved monai.data.nifti_reader.load_nifti to monai.data.transforms.io

monai.networks

• GaussianFilter in monai.networks.layers now accepts anisotropic sigmas

monai.metrics

• one_hot implementation changed for better performance
• one_hot accepts new optional argument dtype, defaults to torch.float
• Support of customized overlap ratio and blending_mode in sliding window inference in monai.inferer
• Function compute_meandice is refactored to DiceMetric with a callable interface in monai.metrics

monai.data

• Moved monai.data.sliding_window_inferences to monai.inferers
• Randomized synthetic image generation in monai.data.synthetic accepts new optional argument random_state (defaults to None)

monai.handlers

• By default not importing monai.handlers modules at first time importing monai
• monai.config.print_confg() now prints optional dependency information

utilities

• monai.utils.misc.ensure_tuple now converts string as a single element (ensure_tuple("test") -> ("test",) instead of ("t", "e", "s", "t")

Fixed

• Various issues in docstring and the documentation website
• Various issues in unit and integration tests

Removed

• Automatic installation of optional dependencies including pytorch-ignite==0.3.0, nibabel, tensorboard, pillow, scipy, scikit-image