Project-MONAI/MONAI: 0.6.0

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.transform