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Refactor constants management to use Pydantic BaseModel for configuration

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- Replaced module-level path variables in constants.py with a structured Pydantic Config class.
- Updated all relevant modules (train.py, augmentation.py, exports.py, dataset-visualiser.py, manual_run.py) to access paths through the new config structure.
- Fixed bugs related to image processing and model saving.
- Enhanced test infrastructure to accommodate the new configuration approach.

This refactor improves code maintainability and clarity by centralizing configuration management.
This commit is contained in:
Oleksandr Bezdieniezhnykh
2026-03-27 18:18:30 +02:00
parent b68c07b540
commit 142c6c4de8
106 changed files with 5706 additions and 654 deletions
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tests/test_annotation_classes.py
+6 -2
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@@ -42,9 +42,13 @@ def data_yaml_text(monkeypatch, tmp_path, fixture_classes_json):
_stub_train_imports()
import train
monkeypatch.setattr(train, "today_dataset", str(tmp_path))
import constants as c
monkeypatch.setattr(c, "config", c.Config(dirs=c.DirsConfig(root=str(tmp_path))))
monkeypatch.setattr(train, "today_folder", "")
from pathlib import Path
Path(c.config.datasets_dir).mkdir(parents=True, exist_ok=True)
train.create_yaml()
return (tmp_path / "data.yaml").read_text(encoding="utf-8")
return (Path(c.config.datasets_dir) / "data.yaml").read_text(encoding="utf-8")
def test_bt_cls_01_base_classes(fixture_classes_json):