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Version 3.6.0

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@danifranco danifranco released this 09 Jul 08:27
· 669 commits to master since this release
v3.6.0
36b6e1f

Major:

  • Transition to numpy 2 (#134)
  • Update notebooks by removing the cell that restarts the kernel, as it is no longer necessary after the transition to NumPy 2.
  • Changing default normalization to ‘zero_mean_unit_variance’
  • imgaug dependency removal by implementing the transformations we were using directly in BiaPy (#134)

Minor:

  • Renamed REMOVE_CLOSE_POST_POINTS_RADIUS_BY_MASK to REMOVE_CLOSE_POINTS_RADIUS_BY_MASK so it’s more general and to be used with pre points too
  • Add num_workers information for test generator
  • Improve classification message when expected classes and number of folder does not match (#124)
  • Change all os.walk functions to os_walk_clean so the hidden files/dirs are not listed (#125)
  • Add support to n5 files
  • Add MODEL.SKIP_UNMATCHED_LAYERS variable to allow finetune models without no SSL workflow pretraining (#132)
  • Add check in 3D data cropping to not allow padding greater than half of the patch size
  • Try to capture tiff metadata automatically to determine axis order
  • Limit zarr to be under 3.0 until it is more developed
  • Fix timm version to 1.0.14 as in 1.0.15 there are some errors when calling the ViT forward function. Check that test20 is working whenever we want to update the version
  • Add a script to repair the notebooks so they can be visualized in Github

Bugs fixed:

  • Avoid errors when no post-points are detected during synapse detection
  • Adapt histogram matching to new output from load_data_from_dir method
  • Add test data check for classification in check_configuration.py (#124)
  • Add the old AUGMENTOR.AFFINE_MODE and DATA.NORMALIZATION.CUSTOM_MODE key conversion to the current config version
  • Move model.eval() before BMZ input/output creation in test
  • Delete old DATA.NORMALIZATION.APPLICATION_MODE during cfg translation to the current version
  • Only write pre/post csv files if at least one point is found
  • Fix bug for semantic segmentation with more than 2 classes
  • Fix bug when loading prediction files when using MODEL.REUSE_PREDICTIONS, the stored prediction files are always of '.tif' extension (as created by the save_tif method).
  • Add div normalization in SR templates as the default normalization was changed it needs to be explicitly specified.
  • Add normalization instructions to classification workflow data load

Full Changelog: v3.5.13...v3.6.0

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