Tagging before major API overhaul

What's Changed

• Version update for development by @sarthakpati in #797
• Standardizing commenting style for major functions by @scap3yvt in #799
• Added a script to generate information useful for debugging by @scap3yvt in #803
• Update GitHub templates by @sarthakpati in #804
• Version update for dependency for new Google APIs by @scap3yvt in #809
• Add schedulers monai by @Ainesh06102004 in #807
• Add default line length for linting by @scap3yvt in #820
• Fix html rendering by @scap3yvt in #824
• Black trailing comma config by @szmazurek in #830
• Added readme for running tests locally by @VukW in #827
• Added black trailing comma style fix by @VukW in #832
• Fixed version dicom anonymizer by @VukW in #836
• Added ability to perform stratified data splits by @scap3yvt in #831
• Added ability to generate split CSVs using external script by @scap3yvt in #833
• Fix code issues reported by codacy by @benmalef in #808
• Removing double print of metrics by @scap3yvt in #838
• Version update for tagging a new release by @sarthakpati in #837

New Contributors

• @VukW made their first contribution in #827
• @benmalef made their first contribution in #808

Full Changelog: 0.0.18...0.0.19

A major release with multiple updated dependencies

What's Changed

• Updated version for development by @sarthakpati in #732
• Upgrade base python and numpy versions by @szmazurek in #730
• Updated docs for new python by @scap3yvt in #737
• Add error handling for brain age model by @1Pravi in #710
• Updated cosign installation by @scap3yvt in #739
• Upgrade base pandas version by @Ainesh06102004 in #740
• Making black version static by @scap3yvt in #744
• Revert to correct download location by @scap3yvt in #745
• Upgrade base torchmetrics version by @szmazurek in #742
• Consistent black across the codebase by @scap3yvt in #746
• Upgrade base PyTorch version by @szmazurek in #747
• Update installation link by @sarthakpati in #748
• Upgrade base torchio version by @scap3yvt in #749
• Update installation instructions by @scap3yvt in #750
• Updated the minimum and maximum python versions by @scap3yvt in #751
• Fixed the issue where a specific library prevents training to start by @scap3yvt in #753
• Minor documentation updates by @scap3yvt in #754
• Bump internal version by @dependabot in #759
• Fix for sample data download by @scap3yvt in #766
• Added documentation for extending optimizer and schedulers by @scap3yvt in #764
• Updated min devcontainer requirement by @scap3yvt in #769
• Ensure pip is available in environment path for devcontainers by @scap3yvt in #770
• Sync tests with openfl develop branch by @scap3yvt in #772
• Updated git command for speed by @scap3yvt in #773
• Consolidated a few checks by @scap3yvt in #774
• Added option to bias the patch extraction for label sampler by @scap3yvt in #771
• Added option to use different weights for sampler biasing and loss by @scap3yvt in #777
• Added option to not apply zero plane cropping for gandlf preprocess by @scap3yvt in #779
• Add optimizers monai by @Ainesh06102004 in #781
• Rename parseconfig for consistency by @scap3yvt in #784
• Upgrade pytorch and torchio versions by @scap3yvt in #786
• Added flowcharts for developers by @sarthakpati in #789
• Added an option for backwards compatibility for parseconfig by @scap3yvt in #791
• Updated openfl tests by @scap3yvt in #787
• Version updated for tagging by @sarthakpati in #796

New Contributors

• @szmazurek made their first contribution in #730
• @1Pravi made their first contribution in #710
• @Ainesh06102004 made their first contribution in #740

Full Changelog: 0.0.17...0.0.18

New version before major dependency updates

What's Changed

• Version update for development by @sarthakpati in #648
• Added citation file by @sarthakpati in #654
• Added new optimizers by @AdiSir05 in #646
• Allow histology patches to be extracted without ground truth labels by @sarthakpati in #657
• Added metric calculation from CLI by @sarthakpati in #663
• Added a few segmentation metrics by @sarthakpati in #661
• Repository badges have been updated by @sarthakpati in #667
• Added instructions on creating new tutorials by @sarthakpati in #664
• Ensure parameters are built into the model dictionary by @sarthakpati in #673
• Calculating penalty after all compute objects are initialized by @sarthakpati in #675
• Add image similarity metrics by @sarthakpati in #669
• Allow the penalty and class weights in the config to be used by @sarthakpati in #677
• Added documentation related to OpenFL by @sarthakpati in #683
• Add MLCube wrapper for metrics API by @hasan7n in #681
• Adding mechanism to curate each extracted patch by @shubhaminnani in #653
• Added mask to SSIM function call by @FelixSteinbauer in #685
• Removed history file by @sarthakpati in #690
• Updated the metrics output by @sarthakpati in #687
• Update docker image name in workflow by @hasan7n in #692
• Fixed plotting function for final stats by @Geeks-Sid in #691
• Fixed import for collect stats by @sarthakpati in #694
• HED augmentations for digital pathology image by @Geeks-Sid in #649
• Added focal loss by @sarthakpati in #696
• Added a temporary fix for protobuf by @sarthakpati in #702
• Use torchmetric PSNR implementation and argument ordering by @FelixSteinbauer in #693
• Introduced percentile normalization for synthesis challenge metrics by @FelixSteinbauer in #700
• Upgrade openvino version to latest by @Geeks-Sid in #699
• Additional PSNR evaluations for the normalized synthesis case by @FelixSteinbauer in #703
• Improved formatting by @sarthakpati in #707
• Updated checkout version and test names for clarity by @sarthakpati in #708
• Updated default options for sgd by @sarthakpati in #709
• Added matthews correlation coefficient loss by @sarthakpati in #706
• Using tuples for PSNR datarange by @FelixSteinbauer in #712
• Deploy model entrypoint by @hasan7n in #711
• Added parameter to toggle NCC computation by @FelixSteinbauer in #717
• Adding second classification tutorial by @vavali08 in #698
• Minor code refactoring by @tosemml in #719
• Combined writing and temp file creation in a single step by @sarthakpati in #720
• Update usage information for anonymizer by @sanashah007 in #716
• Move unit testing data to the mlcommons storage by @sarthakpati in #722
• Fixed model saving when git repo not found by @scap3yvt in #729
• Removing dev from version for tagging by @scap3yvt in #731

New Contributors

• @AdiSir05 made their first contribution in #646
• @shubhaminnani made their first contribution in #653
• @FelixSteinbauer made their first contribution in #685
• @vavali08 made their first contribution in #698
• @tosemml made their first contribution in #719
• @sanashah007 made their first contribution in #716
• @scap3yvt made their first contribution in #729

Full Changelog: 0.0.16...0.0.17

Major release with the paper

What's Changed

• Implemented a common copyright message for CLI and updated links & email by @sarthakpati in #512
• Change python-test.yml to pass on docs-only by @AlexanderGetka-cbica in #517
• Making the flowchart a bit more generic to showcase inference by @sarthakpati in #514
• Updated install verification logic by @sarthakpati in #520
• Updated implementation for augmentations that need std-dev by @sarthakpati in #521
• Ability to generate multiple training configurations using a specific strategy by @sarthakpati in #525
• Updated patch divisibility logic by @sarthakpati in #523
• Added sanity check for norm_type for non-vgg network architectures by @sarthakpati in #528
• Fixing requirement for SimpleITK for a problematic version by @sarthakpati in #537
• Preprocessing is now working when a random set of metadata columns are present by @sarthakpati in #535
• Added ability to combine classification inference results from different architectures by @sarthakpati in #531
• Added ability to save initial and latest models in addition to best by @sarthakpati in #539
• Latest model is now saved correctly by @sarthakpati in #546
• Consideration of scaling_factor has been moved to loss/metrics by @sarthakpati in #541
• Added ability to specify testing data csv in main cli by @sarthakpati in #545
• added more information for #529 by @sarthakpati in #549
• Add docker docs by @AlexanderGetka-cbica in #550
• Added more details to documentation specifically related to training customization by @sarthakpati in #552
• Added more details about Docker by @sarthakpati in #554
• Separate modelDir and outputDir parameters for inference to cohesiveness by @sarthakpati in #557
• The logs now include metric headers from overall_stats by @sarthakpati in #558
• Fix parseConfig separately from my other PR by @AlexanderGetka-cbica in #561

Full Changelog: 0.0.5...0.0.16

Major release before dependency updates.

What's Changed

• Python 3.8 fixes by @AlexanderGetka-cbica in #453
• Version updated for development by @sarthakpati in #452
• Fixing the stride_size check for histology inference by @sarthakpati in #455
• Update CI (PyTest to 3.8, and Docker Image creation) by @AlexanderGetka-cbica in #461
• Bump numpy from 1.21.0 to 1.22.0 by @dependabot in #460
• Add collectStats functionality for classification tasks by @garvm7 in #463
• Ensuring params are populated correctly even if only testing data is invoked by @sarthakpati in #466
• Added blending probability maps with original WSI by @sarthakpati in #465
• Update data type for stride size by @sarthakpati in #458
• Add FP16 option for model optimization by @junwenwu in #457
• Added option to not pick up pretrained weights for imagenet_vgg by @sarthakpati in #469
• Added ability to print model size and summary at run-time by @sarthakpati in #468
• Better handling of large WSIs by @sarthakpati in #472
• Added exception testing for histology classification by @sarthakpati in #477
• Fixing reported Codacy fixes by @sarthakpati in #474
• Formatting fixes for readability by @sarthakpati in #478
• Completely excising VIPS from GaNDLF by @sarthakpati in #480
• Update test workflow with file path filtering by @AlexanderGetka-cbica in #473
• Saving report for failure cases from pytest as a separate log by @sarthakpati in #482
• New metric added to calculated per-class accuracy by @sarthakpati in #486
• Single utility function to return array from tensor/image by @sarthakpati in #483
• Increasing unit test coverage by @sarthakpati in #487
• Fixed the order of coordinates in predictions.csv for WSI inference by @sarthakpati in #490
• Added ability to call specific post processing algorithms after reverse one-hot encoding by @sarthakpati in #494
• Adding Largest connected component analysis as a post-processing step by @Geeks-Sid in #467
• Dedicated rescaling preprocessing function added for increased flexibility by @sarthakpati in #496
• Fixes multi-batch issue for per_label_accuracy by @sarthakpati in #498
• Included classification-specific metrics using overall predictions and ground truths by @sarthakpati in #500
• version update for tagging by @sarthakpati in #501

New Contributors

• @garvm7 made their first contribution in #463

Full Changelog: 0.0.14...0.0.15

Major release with lots of new functionality

What's Changed

• updated version for development by @sarthakpati in #259
• Fix for get_final_layer use in GANDLF.utils by @sarthakpati in #260
• Add functionality to save training patches by @sarthakpati in #263
• Updates to integrate new OPM APIs by @sarthakpati in #261
• Added option to print per-label segmentation scores by @sarthakpati in #264
• updated initialization for dicts by @sarthakpati in #267
• Motion artifact separated by @sarthakpati in #270
• added black and opencv-python as an explicit requirements by @sarthakpati in #271
• Bump numpy from 1.19.2 to 1.21.0 by @dependabot in #272
• Added capability to use InstanceNorm in DenseNet by @sarthakpati in #274
• Updated implementations of VGG and DenseNet to use ModelBase for consistency by @sarthakpati in #276
• Added git hash to model save by @sarthakpati in #277
• Added FAQ by @sarthakpati in #279
• Update to how git_hash is saved by @sarthakpati in #280
• Documentation updated for new module structure by @sarthakpati in #283
• Accuracy is now standardized from torchmetrics by @sarthakpati in #287
• Adding automated linting check for black by @sarthakpati in #284
• Post-processing module added by @sarthakpati in #289
• Added a GANDLF.anonymize module by @sarthakpati in #291
• Updated documentation (faq and usage) by @sarthakpati in #294
• Git submodule update is now integrate in setup by @sarthakpati in #296
• Ensure colorjitter works with defaults by @sarthakpati in #297
• More progress bars for better feedback by @sarthakpati in #301
• Update usage.md - plot final results by @anishshriram in #305
• Using Resample and Resize from TorchIO by @Geeks-Sid in #293
• Ensure hole filling output is int by @sarthakpati in #306
• Added instructions for dependency management by @sarthakpati in #307
• Minor fix for reset_prev by @sarthakpati in #310
• added captk and fets links by @sarthakpati in #311
• Updates documentation based on SB's notes by @sarthakpati in #313
• Added script to verify installation by @sarthakpati in #315
• Added ability to save preprocessed files with data augmentation by @sarthakpati in #317
• Added ResNet with multiple configurations by @Sofia-Mouchtaris in #319
• Bug fix in parameters for gandlf_collectStats by @dlang95 in #321
• Resample_Min added to ensure a baseline resolution for all images getting processed by @sarthakpati in #325
• Added final predictions for classification by @sarthakpati in #328
• Ensuring cropping zero planes are applied for preprocessing by @sarthakpati in #329
• Typo fixed for hd95 by @sarthakpati in #331
• Using TiffSlide instead of OpenSlide for a better deployment by @sarthakpati in #303
• Histology anonymization added by @Geeks-Sid in #281
• Bug fix for gandlf_collectStats by @sarthakpati in #335
• Adding resize_image as a separate option as compared to resize_patch by @sarthakpati in #337
• Sanity checking is now softer with a threshold by @sarthakpati in #340
• Added ability to resume with and without updating parameters and/or data by @sarthakpati in #344
• Reverse one-hot logic is now updated to output unique labels by @sarthakpati in #342
• Added EfficientNet with multiple configurations by @Sofia-Mouchtaris in #327
• Links added for current networks by @sarthakpati in #346
• Add Dockerfiles and Docker Hub CI by @AlexanderGetka-cbica in #353
• Add GitHub Packages upload step, refine Docker CI further by @AlexanderGetka-cbica in #355
• Added Docker Image builds by @sarthakpati in #354
• docker ci badge position change by @sarthakpati in #356
• Docker CI: Fix login requirement -- no need to log in for PRs (oops!) by @AlexanderGetka-cbica in #359
• Added some checks for inference mode by @sarthakpati in #358
• Fix for invalid classification accuracy in training by @orhunguley in #361
• Post-training optimization using OpenVINO added by @junwenwu in #312
• Enabling inference without having access to the ground truth by @sarthakpati in #363
• Ensure ONNX-related print only happens once during training by @sarthakpati in #364
• Fixed issue with balanced_accuracy parsing by @sarthakpati in #366
• Hard requirement of PyTorch to LTS by @sarthakpati in #369
• Added post-processing step to map predicted labels to new values by @sarthakpati in #367
• Fix Docker CI on PRs again (meta information missing causing a rebuild -> out of space) by @AlexanderGetka-cbica in #371
• Fixing docker builds by @sarthakpati in #372
• ROCm Docker builds are no longer valid since we are only supporting PyTorch LTS by @sarthakpati in #370
• Custom inference loop added for histo classification by @sarthakpati in #374
• UNet and Light UNet with varying depth by @Sofia-Mouchtaris in #357
• Create codacy-coverage-reporter.yml by @sarthakpati in #368
• added a comment about padding options by @sarthakpati in #376
• Improved penalty calculation and added ImageNet-specific VGG by @Geeks-Sid in #365
• Error checks added to ImageNet VGG by @sarthakpati in #377
• updated config comment for num_channels by @sarthakpati in #378
• Logic fixed for step scheduler by @sarthakpati in #379
• Add option to save model at every epoch by @sarthakpati in #381
• Added note for classification/regression tasks by @sarthakpati in #385
• Added RGBA to RGB conversion in preprocessing by @sarthakpati in #384
• Improved default step_size calculation by @sarthakpati in #386
• Added a key for exp scheduler by @sarthakpati in #387
• Documentation updates by @sarthakpati in #388
• Fixing tests on named venv by @sarthakpati in #382
• Fixing pytorch version dependency for macOS by @sarthakpati in #390
• Add option to strategize the final aggregation for segmentation by @sarthakpati in #392
• Fixing per-label metrics display by @sarthakpati in #393
• Added submodule to hand template-matching based normalization by @sarthakpati in #394
• Added RGB to RGBA conversion for preprocessing by @sarthakpati in #397
• A new transformer-based segmentation arch (UNetR) has been added by @Sofia-Mouchtaris in #398
• Fixing different device issue on multi-layer UNet and UNETR by @sarthakpati in #399
• Added sigmoid multiplier for UNet model output by @sarthakpati in #400
• Documentation update for installation by @sarthakpati in #403
• More inference mode options added by @sarthakpati in #405
• Update one_hot and reverse_one_hot logic for succinctness by @sarthakpati in #404
• moving get_final_layer method functionality to GANDLF.utils to avoid by @brandon-edwards in #407
• API updates to make GaNDLF's compute objects callable outside the tool by @sarthakpati in #401
• Added memory usage for cuda u...

Documentation updates and minor bugfixes

From History:

• Deep supervision added
• Documentation updated
• Model IO is now standardized

Multiple new features for training

From History:

• New Network: Light-UNet
• Misc bugfixes
• Automatic check-pointing of the model has been added
• Extending the codebase has been simplified
• New optimizers added
• New metrics added
• Affine augmentation can now be significantly fine-tuned
• Update logic for penalty calculation
• RGB-specific augmentation added
• Cropping added

New metric calculation mechanism

Changelog:

• Misc bugfixes for segmentation and classification
• DFU 2021 parameter file added
• Added SDNet for supervised learning - https://doi.org/10.1016/j.media.2019.101535
• Added option to re-orient all images to canonical
• Preprocessing and augmentation made into separate submodules

Updated feature set for segmentation and classification

Changelog:

• Half-time epoch loss and metric output added for increased information
• Gradient clipping added
• Per-epoch details in validation output added
• Different types of normalization layer options added
• Hausdorff as a validation metric has been added
• New option to save preprocessed data before the training starts