v0.1.5

Wandb callback bug fixes.

v0.1.4

0.1.4 — 2022-10-06

Test

• Update loss tests

Fixes

• Add a conv block BasicConvOld to enable Dippa to cellseg conversion of models.

• Fix inst_key, aux_key bug in MultiTaskUnet

• Add a type_map > 0 masking for the inst_maps in post-processing

• Modify the optimizer adjustment utility function to adjust any optim/weight params.

• Modify lit SegmentationExperiment according to new changes.

Features

• Add optional spectral decoupliing to all losses

• Add optional Label smoothing to all losses

• Add optional Spatially varying label smoothing to all losses

• Add mse, ssim and iqi torchmetrics for metric logging.

• Add wandb per class metric callback for logging.

• Add from_yaml init classmethod to initialize from yaml files.

v0.1.3

0.1.3 — 2022-09-23

Test

- Update tests for Inferes and mask utils.

- Add tests for the benchmarkers.

Fixes

- init and typing fixes

Docs

- Typo fies in docs

Features

- Add numba parallellized median filter and majority voting for post-processing
- Add support for own semantic and type seg post-proc funcs in Inferers

- Add segmentation performance benchmarking helper class.
- Add segmentation latency benchmarking helper class.

v0.1.2

0.1.2 — 2022-09-09

Fixes

• datasets.writers: Update save2db & save2folder for optional type_map and sem_map args.
• datasets.writers: Pre-processing (pre-proc) callable arg for _get_tiles method. This enables the Lizard datamodule.
• inference: Fix- padding bug with sliding window inference.

Features

• datamodules: Lizard datamodule (https://arxiv.org/abs/2108.11195)

• models: Add a universal multi-task U-net model builder (experimental)

Test

• dataset: Update dataset tests.

• models: Update tests for multi-task U-Net

Type Hints

• models: Fix incorrect type hints.

Examples

• Add cellpose training with Lizard dataset notebook.

v0.1.1

First version of the library. Now in PyPi.