v0.5.0
======

Updates / New Features
----------------------

CI

* Updated notebooks CI workflow to include notebook data caching.

Documentation

* Added text discussing black box methods to ``introduction.rst``.

* Added a section to ``introduction.rst`` that describes the links between saliency algorithms and implementations.

* Edited all text.

* Update top-level ``README.md`` file to have more useful content.

* Update misc. doc on local SonarQube scanning.

Examples

* Add example notebook for saliency on Atari deep RL agent, including updates
  on top of the original work to normalize saliency maps and conform to our
  API standards.

* Add example demonstrating saliency map generation for COCO formatted
  serialized detections.

* Updated examples to all use a common data sub-directory when downloading or
  saving generated data.

Implementations

* Add ``SquaredDifferenceScoring`` implementation of the ``GenerateClassifierConfidenceSaliency``
  interface that uses squared difference.

* Add ``RandomGrid`` implementation of ``PerturbImage``. This generates masks
  of randomly occluded cells with a given size in pixels.

Utilities

* Add ``gen_coco_sal`` function to compute saliency maps for detections in a
  ``kwcoco`` dataset, with accompanying cli script ``sal-on-coco-dets`` which
  does this on a COCO formatted json file and writes saliency maps to disk.

* Add multi-threaded functionality to ``occlude_image_batch`` utility.

Containerization

* Added Dockerfile and compose file that create base xaitk_saliency image.

Fixes
-----

Build

* Fix incorrect specification of actually-optional ``papermill`` in relation to
  its intended inclusion in the ``example_deps`` extra.

* Update patch version of Pillow transitive dependency locked in the
  ``poetry.lock`` file to address CVE-2021-23437.

* Update the developer dependency and locked version of ipython to address a
  security vulnerability.

Implementations

* Fix incorrect cosine similarity computation and mask inversion in implementation of
  ``DRISEScoring`` detector saliency.

Examples

* Updated example Jupyter notebooks with more consistent dependency checks and
  also fixed minor header formatting issues.

Tests

* Fix deprecation warnings around the use of ``numpy.random.random_integers``.

Utilities

* Fix ``xaitk_saliency.utils.detection.format_detection`` to not upcast the
  data type output when ``objectness is None``.

* Fix ``xaitk_saliency.utils.masking.weight_regions_by_scalar`` to not upcast
  the data type output when ``inv_masks is True``.

* Update ``xaitk_saliency.utils.masking.weight_regions_by_scalar`` to not use
  fully vectorized operation which significantly improves efficiency.