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debug_embeddings.py
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225
debug_embeddings.py
Executable file
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#!/usr/bin/env python3
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"""
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Outil de debugging pour le système d'embeddings.
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Permet d'inspecter:
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- Embeddings d'une image
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- Résultats de recherche FAISS
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- Historique de fine-tuning
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- Statistiques du cache
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"""
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import sys
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import json
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from pathlib import Path
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from PIL import Image
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import numpy as np
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sys.path.insert(0, str(Path(__file__).parent))
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from geniusia2.core.embedders import (
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EmbeddingManager,
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FAISSIndex,
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LightweightFineTuner
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)
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def debug_embedding(image_path: str):
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"""Debug l'embedding d'une image."""
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print("\n" + "="*70)
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print(f"DEBUG EMBEDDING: {image_path}")
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print("="*70)
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# Load image
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try:
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img = Image.open(image_path)
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print(f"\n✓ Image chargée: {img.size} {img.mode}")
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except Exception as e:
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print(f"\n❌ Erreur de chargement: {e}")
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return 1
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# Generate embedding
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print("\nGénération de l'embedding...")
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manager = EmbeddingManager(model_name="clip")
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import time
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start = time.time()
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embedding = manager.embed(img)
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duration = time.time() - start
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print(f"✓ Embedding généré en {duration*1000:.1f}ms")
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print(f"\nDétails de l'embedding:")
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print(f" - Dimension: {embedding.shape[0]}")
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print(f" - Norme L2: {np.linalg.norm(embedding):.6f}")
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print(f" - Min: {embedding.min():.6f}")
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print(f" - Max: {embedding.max():.6f}")
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print(f" - Mean: {embedding.mean():.6f}")
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print(f" - Std: {embedding.std():.6f}")
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# Show first/last values
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print(f"\nPremières valeurs: {embedding[:5]}")
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print(f"Dernières valeurs: {embedding[-5:]}")
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# Cache stats
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stats = manager.get_stats()
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print(f"\nStatistiques du cache:")
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print(f" - Hit rate: {stats['cache_hit_rate']:.1%}")
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print(f" - Size: {stats['cache_size']}/{stats['cache_capacity']}")
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print(f" - Hits: {stats['cache_hits']}")
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print(f" - Misses: {stats['cache_misses']}")
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return 0
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def debug_faiss_search(image_path: str, index_path: str = "data/workflow_embeddings"):
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"""Debug la recherche FAISS."""
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print("\n" + "="*70)
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print(f"DEBUG FAISS SEARCH")
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print("="*70)
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# Load image
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try:
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img = Image.open(image_path)
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print(f"\n✓ Image chargée: {img.size}")
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except Exception as e:
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print(f"\n❌ Erreur de chargement: {e}")
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return 1
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# Load index
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print(f"\nChargement de l'index: {index_path}")
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try:
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manager = EmbeddingManager(model_name="clip")
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index = FAISSIndex(manager.get_dimension())
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index.load(index_path)
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print(f"✓ Index chargé: {len(index)} embeddings")
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except FileNotFoundError:
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print(f"❌ Index non trouvé: {index_path}")
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print(" Créez d'abord un index avec des workflows")
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return 1
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except Exception as e:
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print(f"❌ Erreur de chargement: {e}")
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return 1
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# Generate embedding
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print("\nGénération de l'embedding...")
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embedding = manager.embed(img)
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print(f"✓ Embedding généré")
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# Search
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print("\nRecherche des 5 plus similaires...")
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results = index.search(embedding, k=5)
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if not results:
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print("❌ Aucun résultat trouvé")
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return 1
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print(f"\n✓ Trouvé {len(results)} résultats:\n")
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for i, result in enumerate(results, 1):
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print(f"{i}. Similarity: {result['similarity']:.4f}")
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print(f" Distance: {result['distance']:.4f}")
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print(f" Metadata: {json.dumps(result['metadata'], indent=6)}")
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print()
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return 0
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def debug_fine_tuning(checkpoint_path: str = "data/fine_tuning/checkpoint.pkl"):
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"""Debug l'historique de fine-tuning."""
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print("\n" + "="*70)
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print(f"DEBUG FINE-TUNING")
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print("="*70)
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# Load checkpoint
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print(f"\nChargement du checkpoint: {checkpoint_path}")
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manager = EmbeddingManager(model_name="clip")
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fine_tuner = LightweightFineTuner(embedder=manager.embedder)
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if not fine_tuner.load_checkpoint("checkpoint"):
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print(f"❌ Checkpoint non trouvé")
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print(" Le fine-tuning n'a pas encore été exécuté")
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return 1
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stats = fine_tuner.get_stats()
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print(f"\n✓ Checkpoint chargé")
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print(f"\nStatistiques:")
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print(f" - Trainings effectués: {stats['training_count']}")
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print(f" - Exemples positifs: {stats['positive_examples']}")
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print(f" - Exemples négatifs: {stats['negative_examples']}")
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print(f" - Total exemples: {stats['total_examples']}")
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print(f" - En cours: {stats['is_training']}")
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if stats['last_training_time'] > 0:
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import time
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elapsed = time.time() - stats['last_training_time']
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print(f" - Dernier training: il y a {elapsed/60:.1f} minutes")
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# Show metrics history
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if stats['metrics_history']:
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print(f"\nHistorique des trainings ({len(stats['metrics_history'])}):\n")
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for metrics in stats['metrics_history'][-5:]: # Last 5
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print(f"Training #{metrics.get('training_number', '?')}:")
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print(f" - Loss: {metrics.get('loss', 'N/A'):.4f}")
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print(f" - Durée: {metrics.get('duration_seconds', 0):.1f}s")
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print(f" - Exemples: +{metrics.get('positive_count', 0)}, "
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f"-{metrics.get('negative_count', 0)}")
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print(f" - Timestamp: {metrics.get('timestamp', 0):.0f}")
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print()
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else:
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print("\n⚠️ Aucun historique de training")
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return 0
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def main():
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"""Main function."""
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import argparse
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parser = argparse.ArgumentParser(
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description='Outil de debugging pour les embeddings'
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)
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subparsers = parser.add_subparsers(dest='command', help='Commande à exécuter')
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# Embedding command
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embed_parser = subparsers.add_parser('embedding', help='Debug un embedding')
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embed_parser.add_argument('image', help='Chemin vers l\'image')
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# Search command
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search_parser = subparsers.add_parser('search', help='Debug une recherche FAISS')
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search_parser.add_argument('image', help='Chemin vers l\'image')
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search_parser.add_argument(
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'--index',
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default='data/workflow_embeddings',
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help='Chemin vers l\'index FAISS'
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)
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# Fine-tuning command
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ft_parser = subparsers.add_parser('finetuning', help='Debug le fine-tuning')
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ft_parser.add_argument(
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'--checkpoint',
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default='data/fine_tuning/checkpoint.pkl',
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help='Chemin vers le checkpoint'
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)
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args = parser.parse_args()
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if not args.command:
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parser.print_help()
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return 1
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if args.command == 'embedding':
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return debug_embedding(args.image)
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elif args.command == 'search':
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return debug_faiss_search(args.image, args.index)
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elif args.command == 'finetuning':
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return debug_fine_tuning(args.checkpoint)
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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