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ai-training/_docs/02_document/modules/utils.md
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Oleksandr Bezdieniezhnykh 142c6c4de8 Refactor constants management to use Pydantic BaseModel for configuration
- 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.
2026-03-27 18:18:30 +02:00

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Markdown

# Module: utils
## Purpose
Provides a dictionary subclass that supports dot-notation attribute access.
## Public Interface
| Name | Type | Signature |
|------|------|-----------|
| `Dotdict` | class (extends `dict`) | `Dotdict(dict)` |
`Dotdict` overrides `__getattr__`, `__setattr__`, `__delattr__` to delegate to `dict.get`, `dict.__setitem__`, `dict.__delitem__` respectively.
## Internal Logic
Single-class module. Allows `config.url` instead of `config["url"]` for YAML-loaded dicts.
## Dependencies
None (stdlib `dict` only).
## Consumers
exports, train, start_inference
## Data Models
None.
## Configuration
None.
## External Integrations
None.
## Security
None.
## Tests
None.