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ai-training/_docs/02_document/tests/resource-limit-tests.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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Resource Limit Test Scenarios

RL-AUG-01: Augmentation output count bounded

  • Input: 1 image
  • Action: Run augment_inner()
  • Expected: Returns exactly 8 outputs (never more, even with retries)
  • Traces: AC: 8× augmentation ratio (1 original + 7 augmented)

RL-DSF-01: Dataset split ratios sum to 100%

  • Input: Any number of images
  • Action: Check train_set + valid_set + test_set
  • Expected: Equals 100
  • Traces: AC: 70/20/10 split

RL-DSF-02: No data duplication across splits

  • Input: 100 images
  • Action: Run form_dataset(), collect all filenames across train/valid/test
  • Expected: No filename appears in more than one split
  • Traces: AC: Dataset integrity

RL-ENC-01: Encrypted output size bounded

  • Input: N bytes plaintext
  • Action: Encrypt
  • Expected: Ciphertext size ≤ N + 32 bytes (16 IV + up to 16 padding)
  • Traces: Restriction: AES-256-CBC overhead

RL-CLS-01: Total class count is exactly 80

  • Input: classes.json
  • Action: Generate class list for YAML
  • Expected: Exactly 80 entries (17 named × 3 weather + 29 placeholders = 80)
  • Traces: AC: 80 total class slots