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@github-actions github-actions released this 28 Feb 15:56
· 80 commits to main since this release
v0.4.0
73cdd3f
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Learn about vigilant mode.

0.4.0 - 2026-02-27

  • Added reusable WelfordAccumulator utility (cuvis_ai.utils.welford) for streaming mean/variance/covariance
  • Added resolve_reduce_dims() as shared module-level utility in binary_decider
  • Added TRAINABLE_BUFFERS class attribute — 5 nodes declare trainable buffers, base class handles buffer↔parameter conversion in freeze/unfreeze automatically
  • Added freeze() for LearnableChannelMixer matching existing unfreeze() override
  • Added ConcreteChannelMixer and LearnableChannelMixer exported from cuvis_ai.node
  • Added all 6 visualization nodes exported from cuvis_ai.node: AnomalyMask, RGBAnomalyMask, ScoreHeatmapVisualizer, CubeRGBVisualizer, PCAVisualization, PipelineComparisonVisualizer
  • Added insufficient-samples guard to RXGlobal and ScoreToLogit — raises early when training data has too few samples
  • Added plugin runtime smoke CI workflow (plugin-runtime-smoke.yml) with slow plugin tests
  • Added AdaCLIP standalone plugin manifest (configs/plugins/adaclip.yaml) and 6 example scripts
  • Added plugin contract, manifest sync, and runtime smoke test files
  • Added 8 new test files: test_welford, test_freeze_unfreeze, test_channel_selector_coverage, test_concrete_channel_mixer, test_pipeline_visualization, test_binary_decider, test_data_node, test_rx_per_batch
  • Added pytest markers (unit/integration/slow) on all 30 test files; session-scoped fixtures for expensive operations; pytest config consolidated in pytest.ini
  • Changed RXGlobal, ScoreToLogit, LADGlobal to use WelfordAccumulator instead of inline Welford implementations
  • Changed _compute_band_correlation_matrix to single-pass streaming with WelfordAccumulator
  • Changed TrainablePCA and LearnableChannelMixer to use streaming covariance + eigh instead of concat + SVD
  • Changed SoftChannelSelector variance init to use streaming WelfordAccumulator
  • Changed ZScoreNormalizerGlobal to use streaming WelfordAccumulator instead of concat + subsample
  • Changed supervised band selectors to use template method pattern, pulling shared forward() and statistical_initialization() into SupervisedSelectorBase
  • Changed YAML configs and docs to use new schema field names (hparams, class_name)
  • Changed EXECUTION_STAGE_VALIDATE references to VAL across gRPC docs
  • Changed .freezed references to .frozen in tests and docs (matches cuvis-ai-core rename)
  • Breaking: Reorganized channel selector and mixer nodes into separate files: band_selection.py + selector.py → channel_selector.py, concrete_selector.py + channel_mixer.py → channel_mixer.py, pca.py → dimensionality_reduction.py, visualizations.py + drcnn_tensorboard_viz.py → anomaly_visualization.py + pipeline_visualization.py
  • Breaking: Renamed 9 classes to reflect selector/mixer distinction: BandSelectorBase → ChannelSelectorBase, BaselineFalseRGBSelector → FixedWavelengthSelector, HighContrastBandSelector → HighContrastSelector, CIRFalseColorSelector → CIRSelector, SupervisedBandSelectorBase → SupervisedSelectorBase, SupervisedCIRBandSelector → SupervisedCIRSelector, SupervisedWindowedFalseRGBSelector → SupervisedWindowedSelector, SupervisedFullSpectrumBandSelector → SupervisedFullSpectrumSelector, ConcreteBandSelector → ConcreteChannelMixer, DRCNNTensorBoardViz → PipelineComparisonVisualizer
  • Breaking: Deleted old files — no deprecation stubs or re-exports
  • Removed redundant .to(device) calls from adaclip.py, anomaly_visualization.py, channel_selector.py — pipeline handles device placement
  • Changed pipeline configs reorganized into anomaly/ subdirectories (adaclip/, deep_svdd/, rx/)
  • Changed AdaCLIP pipeline node names and synced tuning values across 8 pipeline configs
  • Changed Deep SVDD configs, examples, and docs cleaned up for consistency
  • Changed CI workflows to install libgl1/libglib2.0-0 system dependencies for plugin imports
  • Updated 13 pipeline + 17 trainrun YAML configs with new class_name paths
  • Updated 11 example scripts with new import paths
  • Updated 19 documentation files with new class names, import paths, and new content for WelfordAccumulator and TRAINABLE_BUFFERS
  • Fixed pyproject.toml uv source field (develop to editable)
  • Fixed wavelength batching in supervised band selector _collect_training_data (flatten [B, C] to [C])
  • Fixed trainrun callback field name and channel_selector weights config
  • Fixed Werkzeug CVE-2026-27199 by bumping 3.1.5 → 3.1.6
  • Removed dead _quantile_threshold() and duplicate _resolve_reduce_dims() from TwoStageBinaryDecider
  • Removed frozen_nodes from pipeline configs and docs
Assets 5
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