Releases
0.6.0
wyli
released this
08 Jul 23:37
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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