BiaPy

Major:

• Add I2I workflow notebooks

Minor:

• Allow I2I workflow to be operative for 3D images
• Add I2I YAML templates
• Add I2I 3D experiment in run_check.py

Fix:

• Correct models' activation check

Full Changelog: https://github.com/BiaPyX/BiaPy/compare/v3.4.3...v3.4.4

Minor:

• Improve the messaging of some errors to make them more comprehensible for the end user.
• Restrict TEST.POST_PROCESSING.REPARE_LARGE_BLOBS_SIZE usage to instance segmentation workflow and BP channels.
• Allow detection and denoising workflows use unetr and multiresunet models.
• Change affine AUGMENTOR.AFFINE_MODE to reflect by default.
• Now the grid in the aumented samples saved take into account the image size to alway create 5x5 grid.
• Adapt MAE's grid mask to be operative in 3D.

Bug fixes:

• 3D stack metric values.
• Fix errors in DATA.PROBABILITY_MAP.
• Prevent TEST.POST_PROCESSING.CLEAR_BORDER remove all instances in 2D.
• Fix minor bugs during some of the instance segmentation post-processing due image shape mismatch.
• Now TEST.POST_PROCESSING.CLEAR_BORDER and TEST.POST_PROCESSING.VORONOI_ON_MASK are above TEST.POST_PROCESSING.MEASURE_PROPERTIES.REMOVE_BY_PROPERTIES so the instances can be repaired before filtering and stats.
• Minor bugs in detection watershed
• Fix bug during detection mask creation for Zarr images with more than one channel
• Fix process_sample_by_chunks() function call in multi-gpu setting due to recent changes
• Fix errors in percentile clipping
• Avoid stuck processes to jump into inference phase in multi-gpu configuration when setting patience during training
• Fix cross-validation errors: 1) when using it in SR workflow due to its upsampling ; 2) in classification
• Fix bug in grid masking using mae in SSL workflow
• Add preprocessing function into classification
• Minor bug when C is not the last channel using Zarr inference

Full Changelog: https://github.com/BiaPyX/BiaPy/compare/v3.4.2...v3.4.3

Fix:

• Downgrade scikit-image version to avoid reloading the kernel in Colab notebooks
• I2I workflow inference edge case fixed when multiple images in multiple directories where found leading to an infinite loop.

Full Changelog: https://github.com/BiaPyX/BiaPy/compare/v3.4.1...v3.4.2

Minor:

• Add BiaPy version into YAML files

Fix:

• Update pyproject.toml specifiying a few libraries more to not be that restrictive and so BiaPy is smoothly installed in Colab

Full Changelog: https://github.com/BiaPyX/BiaPy/compare/v3.4.0...v3.4.1

Major:

• Allow to train with large Zarr/H5 files without loading data in memory
• Adapt by_chunks to multichannel data
• Add option to reuse predictions in test

Minor:

• Calculate detection metrics in by_chunks setting
• Add DATA.NORMALIZATION.CUSTOM_MODE option
• Add percentile normalization

Fix:

• Correct bug when discading samples from training when using TRAIN.MINIMUM_FOREGROUND_PER
• Fix PSNR calculation in I2I and SR workflows
• Update notebooks and correct errors related to gdown package
• Correct validation paths in generators when in memory is False

Full Changelog: https://github.com/BiaPyX/BiaPy/compare/v3.3.14...v3.4.0

Full Changelog: https://github.com/BiaPyX/BiaPy/compare/v3.3.14...v3.3.15

Full Changelog: https://github.com/BiaPyX/BiaPy/compare/v3.3.13...v3.3.14

Fix:

• Fix normalization issue during test image reconstruction in SSL and SR workflows

Full Changelog: https://github.com/BiaPyX/BiaPy/compare/v3.3.11...v3.3.12

Fix:

• Fix semantic segmentation multiclass edge error when a batch is compose by just one sample
• Correct minor error in SSL workflow using masking (MAE) during inference
• Fix some SR scale problems due to recent changes

Full Changelog: https://github.com/BiaPyX/BiaPy/compare/v3.3.11...v3.3.12

Fix:

• Correct few scale problems due to recent changes in SR workflow

Full Changelog: https://github.com/BiaPyX/BiaPy/compare/v3.3.10...v3.3.11

Quick patch:

• Fix identation error in some models.

Changes:

• AUGMENTOR.RANDOM_ROT and AUGMENTOR.ROT90 now are implemented in BiaPy and not done through imgaug.
• Add TRAIN.VERBOSE to visualize more or less info during each batch process print

Fixes:

• Fix 4 dims length Zarr data creation during TEST.BY_CHUNKS.
• Change slightly custom architectures (MODEL.SOURCE == biapy) so they can be converted into TorchScript via torch.jit.script() to create BMZ package.
• Fix U-Net like models for SR to depend on PROBLEM.SUPER_RESOLUTION.UPSCALING factor and allow MODEL.Z_DOWN in super-resolution workflow
• Limit number of workers per GPU for safety
• Fix crappify issues for SSL

Fix patch:

• Rename PROBLEM.NUM_CPUS to PROBLEM.NUM_WORKERS to clarify its usage.
• Speed up SSL workflow

Changes:

• Set TEST.DET_EXCLUDE_BORDER to False by default.
• Add TEST.DET_PEAK_LOCAL_MAX_MIN_DISTANCE.
• 3 int tuple for TEST.RESOLUTION in instance segmentation if TEST.ANALIZE_2D_IMGS_AS_3D_STACK.
• Prevent usage of EfficientNet architectures for 3D.
• Add PROBLEM.INSTANCE_SEG.WATERSHED_BY_2D_SLICE.

Fix:

• Prevent creating multiple processes to manage data if low samples are available.
• Solve EfficientNet issue with biapy backend as discussed here.
• Bug in instance seg when no labels are provided.
• Disable aug sample image generation if DA is disabled.
• Fix SSL bug during training due to recent changes.

Fixes:

• Change DATA.PREPROCESS.*.ACTIVATE to DATA.PREPROCESS.*.ENABLE as the rest of the variables.
• Separate per_image, full_image and as_3D_stack instance files in different folders.
• Separate instance segmentation metrics when multiple choices are selected. Before full_image and per_image metrics were mixed.
• Simplify inference by setting as default patch/merge reconstruction of the prediction. This implied to remove TEST.STATS and leave only FULL_IMG to be optional.
• TEST.FULL_IMG to False by default.