745a64b342
- VegaModel CNN-FCNN 34.5M params, 82 isotopes, val acc 99.89% - Generation 50k spectres synthetiques 1D (12-24h durees) - Entrainement 100 epochs sur RTX 5060 Ti (CUDA 12.8, Blackwell) - Detection continue avec soustraction du background - Capture background 24h avec gestion deconnexion - Docker Compose : conteneur train (GPU) + detect (CPU/USB) - Modele entraite inclus (vega_best.pt, 395 Mo) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
94 lines
2.3 KiB
Python
94 lines
2.3 KiB
Python
#!/usr/bin/env python
|
|
"""
|
|
Run Vega Inference
|
|
|
|
Simple script to run inference with a trained Vega model.
|
|
"""
|
|
|
|
import sys
|
|
import argparse
|
|
from pathlib import Path
|
|
|
|
# Add project root to path
|
|
PROJECT_ROOT = Path(__file__).parent.parent
|
|
sys.path.insert(0, str(PROJECT_ROOT))
|
|
|
|
from inference.vega_inference import run_inference_demo, VegaInference
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser(
|
|
description="Run inference with trained Vega model"
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--model", "-m",
|
|
type=str,
|
|
default="models/vega_best.pt",
|
|
help="Path to model checkpoint"
|
|
)
|
|
parser.add_argument(
|
|
"--data", "-d",
|
|
type=str,
|
|
default="O:/master_data_collection/isotopev2",
|
|
help="Path to data directory with spectra"
|
|
)
|
|
parser.add_argument(
|
|
"--threshold", "-t",
|
|
type=float,
|
|
default=0.5,
|
|
help="Detection threshold (0-1)"
|
|
)
|
|
parser.add_argument(
|
|
"--spectrum", "-s",
|
|
type=str,
|
|
default=None,
|
|
help="Path to a specific spectrum file to analyze"
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
|
|
# Make paths absolute
|
|
model_path = Path(args.model)
|
|
if not model_path.is_absolute():
|
|
model_path = PROJECT_ROOT / model_path
|
|
|
|
if args.spectrum:
|
|
# Single spectrum inference
|
|
spectrum_path = Path(args.spectrum)
|
|
if not spectrum_path.is_absolute():
|
|
spectrum_path = PROJECT_ROOT / spectrum_path
|
|
|
|
print(f"\nLoading model from: {model_path}")
|
|
inference = VegaInference(str(model_path))
|
|
|
|
print(f"\nAnalyzing spectrum: {spectrum_path}")
|
|
prediction = inference.predict_from_file(
|
|
spectrum_path,
|
|
threshold=args.threshold
|
|
)
|
|
|
|
print("\n" + "=" * 60)
|
|
print("PREDICTION RESULTS")
|
|
print("=" * 60)
|
|
print(prediction.summary())
|
|
print("=" * 60)
|
|
|
|
else:
|
|
# Demo mode - analyze all spectra in data directory
|
|
data_path = Path(args.data)
|
|
if not data_path.is_absolute():
|
|
data_path = PROJECT_ROOT / data_path
|
|
|
|
run_inference_demo(
|
|
str(model_path),
|
|
str(data_path),
|
|
threshold=args.threshold
|
|
)
|
|
|
|
return 0
|
|
|
|
|
|
if __name__ == "__main__":
|
|
sys.exit(main())
|