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Version 3.6.8

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@danifranco danifranco released this 13 Dec 15:05
· 109 commits to master since this release
v3.6.8
6eed2e1

Major:

  • Move to Pytorch 2.9.1
  • Add TEST.METRICS_IN_CPU option to calculate test metrics in CPU and make it default. Useful for large datasets such as MitoEM

Minor:

  • Update notebooks with version 3.6.7
  • Update some aux scripts
  • Add TEST.METRICS_IN_CPU option to calculate test metrics in CPU and make it default. Useful for large datasets such as MitoEM
  • Improve error message when an image couldn't be loaded
  • Add TEST.SAVE_MODEL_RAW_OUTPUT option to now save model raw outputs (related to #145)
  • Add TEST.POST_PROCESSING.MEASURE_PROPERTIES.EXTRA_PROPS to choose extra properties to be calculated for the predicted instances (related to #144)
  • Add SpineDL paper scripts and templates
  • Simplify HRNet configuration and delete hrnet2x20
  • Add ConvNeXtBlocks to be usable in HRNet
  • Add different possible heads to HRNet
  • Test checkpoint load process and reduce test14 experiment epochs, as it was taking 1h to complete
  • Remove DATA.EXTRACT_RANDOM_PATCH, DATA.REPLICATE, DATA.PROBABILITY_MAP, DATA.W_FOREGROUND, DATA.W_BACKGROUND
  • Update model references and improve BMZ documentation created
  • Change sigmoid to sofmax when classes are more than 2 for semantic seg
  • Add option to some models to add activations layers at the end, so no post-processing can be set during BMZ model creation, leading to more stable and reproducible results
  • More robust checkpoint capturation during model checkpoint load

Bugs fixed:

  • Change conda package action to be done after PyPI's action is done
  • Add minor fix to ensure unique_labels_fast functionality when labels are not integers
  • Fix edge case when creating the BMZ cover
  • Update BMZ model creation to support models that do not output an image with the same shape as the input as HRNet
  • Add all the variables used outside HRNet definition inside so it can be exported properly to BMZ
  • Make all the dependencies found by extract_model function be in a consistent order so all the dependencies are correctly found, i.e. aux functions/classes first
  • Resolve PATHS.CHECKPOINT_FILE variable bug (related to suggestions to overcome over segmentation)
  • Do not force class IoU calculation to be uint16 but uint8 in instance segmentation
  • Don't change patch size defined in the model yaml if only the channel axis is different
  • Solve critical bug in BMZ models, as they were not changed to .eval() when doing inference
  • Make metric calculation in denoising more robust

Full Changelog: v3.6.7...v3.6.8

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