mirror of
https://github.com/azaion/ai-training.git
synced 2026-04-22 11:36:36 +00:00
Oleksandr Bezdieniezhnykh
142c6c4de8
- 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.
53 lines
2.1 KiB
Markdown
53 lines
2.1 KiB
Markdown
# Module: start_inference
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## Purpose
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Entry point for running inference on video files using a TensorRT engine. Downloads the encrypted model from the API/CDN, initializes the engine, and processes video.
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## Public Interface
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| Function | Signature | Returns | Description |
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|----------|-----------|---------|-------------|
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| `get_engine_filename` | `(device_id=0) -> str \| None` | Engine filename | Generates GPU-specific engine filename (duplicate of TensorRTEngine.get_engine_filename) |
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`__main__` block: Creates ApiClient, downloads encrypted TensorRT model (split big/small), initializes TensorRTEngine, runs Inference on a test video.
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## Internal Logic
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- **Model download flow**: ApiClient → `load_big_small_resource` → reassembles from local big part + API-downloaded small part → decrypts with model encryption key → raw engine bytes.
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- **Inference setup**: TensorRTEngine initialized from decrypted bytes, Inference configured with confidence_threshold=0.5, iou_threshold=0.3.
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- **Video source**: Hardcoded to `tests/ForAI_test.mp4`.
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- **get_engine_filename()**: Duplicates `TensorRTEngine.get_engine_filename()` — generates `azaion.cc_{major}.{minor}_sm_{sm_count}.engine` based on CUDA device compute capability and SM count.
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## Dependencies
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- `constants` — config file paths
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- `api_client` — ApiClient, ApiCredentials for model download
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- `cdn_manager` — CDNManager, CDNCredentials (imported but CDN managed by api_client)
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- `inference/inference` — Inference pipeline
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- `inference/tensorrt_engine` — TensorRTEngine
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- `security` — model encryption key
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- `utils` — Dotdict
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- `pycuda.driver` (external) — CUDA device queries
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- `yaml` (external)
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## Consumers
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None (entry point).
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## Data Models
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None.
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## Configuration
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- Confidence threshold: 0.5
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- IoU threshold: 0.3
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- Video path: `tests/ForAI_test.mp4` (hardcoded)
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## External Integrations
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- Azaion API + CDN for model download
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- TensorRT GPU inference
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- OpenCV video capture and display
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## Security
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- Model is downloaded encrypted (split big/small) and decrypted locally
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- Uses hardware-bound and model encryption keys
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## Tests
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None.
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