Migration to structured and validated configs - #3198

…onverter_config.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

- reading breaks for corrupted yaml
- added tests for prefered behavior that is currently not implemented (yaml safeloading and validating config keys)

This mixin provides methods for:
    - Loading configurations from dictionaries or YAML files
    - Validating configuration data against pydantic models
    - Converting configurations to dictionaries
    - Pretty printing configuration data

add imports from utils

- Introduced new configuration classes for inference, logging, model, pose, project, runner, and training settings.
- Refactored data loading mechanisms to utilize new configuration structures.
- Moved the multithreading and compilation options in inference configuration to the config module.
- Typed configuration for logging.
- Updated dataset loaders to accept model configurations directly or via file paths.

(The fields are kept identical to old multianimal project configs for now)

move ProjectConfig to deeplabcut/core/config

…correct TypeAdapter usage)

The return value should be the dictionary, not the instantiated transforms

…nfig

Apply a set of small fixes and cleanup changes across multiple modules:
- fix Path/string handling in weight_init
- remove many redundant `pass` stubs in abstract classes and placeholders
- adjust loop/range usages (camera calibration, tracklets, DataFrame index)
- improve legacy-argument handling in Loader (DeprecationWarning with stacklevel, error messages) and infer model config path
- reorder and add/remove imports where appropriate
- avoid silently swallowing exceptions after printing (materialize, make_labeled_video)
- use tuple form for startswith check in auxiliary functions
- tweak test random.sample range; and other minor formatting/consistency tweaks. These changes are intended to improve correctness, clarity, and maintainability without altering core behavior.
- Prevent notes on missing fields.
- Fix yaml comments for nested config fields.
- fix validation for None-type read_config_as_dict.
- VersionedConfigs use default factories for PrivateAttrs.
- add missing snapshot_prefix to runner (SA inference requires it).
- fix some faulty default values for modelzoo config templates

1. Refactor PoseConfig: separate modules and add canonical build method.

- Split enums into separate file
- Split PoseMetadata into separate file
- Split PafParamters into separate file
- Add canonical build methods for structured configs: e.g. build from project config

2. Refactor make_pose_config:

- build entire default dict for net_type from the yaml file, before validation.
- deprecate `make_pytorch_test_config`, `make_pytorch_pose_config` and `make_basic_project_config`.

3. Refactor modelzoo - superanimal configs construction via build method

1. Add tests for centralized config edge cases

Introduce tests/core/config/test_config_breakage.py covering pathological cases for the centralized config model. Tests exercise in-place nested mutation validation (xfail), dirty-state isolation between instances, change-note handling and alias-to-canonical mapping, nested YAML comments (xfail), and normalization/serialization of nested models containing Path and Enum values. Uses ProjectConfig, DLCBaseConfig and DLCVersionedConfig to assert expected dirty-tracking, logging, validation, and YAML output behaviors. add test for cross-field validation with bulk updates

2. fix invalid test imaug (faulty fixture) see DeepLabCut/UnitTestData#4

3. Update testscript_pytorch_multi_animal: ctd_conditions required for ctd shuffle.

The documented primary workflow is: set conditions at shuffle / dataset creation, not defer until inference. This was not reflected in the test, and surfaced with the new structured configs.

This commit makes the test in line with recommended workflow in API docstring; Config docs; BUCTD COLAB notebook; and GUI. -> ctd_conditions are passed at dataset creation time. making the shuffle a fully complete artefact, rather than somthing that requires patching in later API calls.

4. fix testscript multianimal transfer learning pass net_type when creating training dataset

5. add tests:  resume training from existing snapshot

add tests for pose config creation

refactor test_pose_config_creation (test cases for migration to typed)

update xfail for cross fields overrides

remove comment tests

fix test_apis_training

make migrations fixtures to fix registry pollution

use tmp instead of tempfile

tests/core/config: catch expected DeprecationWarnings for aliases

move TEST_DATA_DIR to fixture

use centralized yaml loader instead of pyyaml

update test_modify_train_test_cfg_sets_expected_values

update documentation and tests for write_project_config

update test_pose_config_creation copy dict instead of mutate

update test_pose_config_creation, use centralized yaml loader

add test for ProjectConfig bodypartslist

update test_pose_config_creation separate testing of saving

expose get_yaml_loader get_yaml_dumper in core.config

…old API

- export.py
- deprecate update_config_by_dotpath and update_config
- simplified schema DetectorDataConfig instead of DataConfig

use centralized read_config in trainingsetmanipulation

hook up trainsetmanipulation.py to typed ProjectConfig

update callers of edit_config to use typed updates instead

Notable:
revert  default `box_score_thresh` in `DetectorModelConfig`
currently a different box_score_thresh is used in inference (0.01) and evaluation (0.6), putting a default value in the config would change behavior.

move deprecation.py from utils to core