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@wyli wyli released this 08 Jul 23:37
· 2051 commits to dev since this release
0.6.0
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Learn about vigilant mode.

Added

  • Overview document for feature highlights in v0.6
  • 10 new transforms, a masked loss wrapper, and a NetAdapter for transfer learning
  • APIs to load networks and pre-trained weights from Clara Train Medical Model ARchives (MMARs)
  • Base metric and cumulative metric APIs, 4 new regression metrics
  • Initial CSV dataset support
  • Decollating mini-batch as the default first postprocessing step
  • Initial backward compatibility support via monai.utils.deprecated
  • Attention-based vision modules and UNETR for segmentation
  • Generic module loaders and Gaussian mixture models using the PyTorch JIT compilation
  • Inverse of image patch sampling transforms
  • Network block utilities get_[norm, act, dropout, pool]_layer
  • unpack_items mode for apply_transform and Compose
  • New event INNER_ITERATION_STARTED in the deepgrow interactive workflow
  • set_data API for cache-based datasets to dynamically update the dataset content
  • Fully compatible with PyTorch 1.9
  • --disttests and --min options for runtests.sh
  • Initial support of pre-merge tests with Nvidia Blossom system

Changed

  • Base Docker image upgraded to nvcr.io/nvidia/pytorch:21.06-py3 from
    nvcr.io/nvidia/pytorch:21.04-py3
  • Optionally depend on PyTorch-Ignite v0.4.5 instead of v0.4.4
  • Unified the demo, tutorial, testing data to the project shared drive, and
    Project-MONAI/MONAI-extra-test-data
  • Unified the terms: post_transform is renamed to postprocessing, pre_transform is renamed to preprocessing
  • Unified the postprocessing transforms and event handlers to accept the "channel-first" data format
  • evenly_divisible_all_gather and string_list_all_gather moved to monai.utils.dist

Removed

  • Support of 'batched' input for postprocessing transforms and event handlers
  • TorchVisionFullyConvModel
  • set_visible_devices utility function
  • SegmentationSaver and TransformsInverter handlers

Fixed

  • Issue of handling big-endian image headers
  • Multi-thread issue for non-random transforms in the cache-based datasets
  • Persistent dataset issue when multiple processes sharing a non-exist cache location
  • Typing issue with Numpy 1.21.0
  • Loading checkpoint with both model and optmizier using CheckpointLoader when strict_shape=False
  • SplitChannel has different behaviour depending on numpy/torch inputs
  • Transform pickling issue caused by the Lambda functions
  • Issue of filtering by name in generate_param_groups
  • Inconsistencies in the return value types of class_activation_maps
  • Various docstring typos
  • Various usability enhancements in monai.transforms
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