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test_embedding_system.py
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318
test_embedding_system.py
Executable file
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#!/usr/bin/env python3
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"""
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Test du nouveau système d'embeddings.
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Ce script teste:
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1. CLIPEmbedder - Génération d'embeddings
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2. FAISSIndex - Ajout, recherche, save/load
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3. EmbeddingManager - Cache, fallback
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4. Intégration complète
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"""
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import sys
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import numpy as np
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from PIL import Image
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from pathlib import Path
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# Add project to path
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sys.path.insert(0, str(Path(__file__).parent))
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from geniusia2.core.embedders import CLIPEmbedder, FAISSIndex, EmbeddingManager
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def create_test_image(size=(224, 224), color=(255, 0, 0)):
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"""Crée une image de test."""
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return Image.new('RGB', size, color)
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def test_clip_embedder():
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"""Test 1: CLIPEmbedder"""
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print("\n" + "="*60)
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print("TEST 1: CLIPEmbedder")
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print("="*60)
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try:
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# Initialize
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print(" Initializing CLIPEmbedder...")
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embedder = CLIPEmbedder(device='cpu')
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print(f" ✓ Model loaded: {embedder.get_model_name()}")
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print(f" ✓ Dimension: {embedder.get_dimension()}")
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# Test single embedding
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print("\n Testing single embedding...")
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img = create_test_image(color=(255, 0, 0))
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embedding = embedder.embed(img)
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print(f" ✓ Embedding shape: {embedding.shape}")
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print(f" ✓ Embedding norm: {np.linalg.norm(embedding):.4f} (should be ~1.0)")
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# Test batch embedding
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print("\n Testing batch embedding...")
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images = [
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create_test_image(color=(255, 0, 0)),
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create_test_image(color=(0, 255, 0)),
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create_test_image(color=(0, 0, 255))
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]
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embeddings = embedder.embed_batch(images)
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print(f" ✓ Batch embeddings shape: {embeddings.shape}")
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print(f" ✓ Expected: (3, {embedder.get_dimension()})")
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# Test consistency
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print("\n Testing consistency...")
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img1 = create_test_image(color=(128, 128, 128))
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emb1 = embedder.embed(img1)
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emb2 = embedder.embed(img1)
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diff = np.abs(emb1 - emb2).max()
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print(f" ✓ Max difference: {diff:.10f} (should be 0.0)")
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print("\n ✅ CLIPEmbedder: ALL TESTS PASSED")
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return True
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except Exception as e:
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print(f"\n ❌ CLIPEmbedder: FAILED - {e}")
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import traceback
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traceback.print_exc()
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return False
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def test_faiss_index():
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"""Test 2: FAISSIndex"""
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print("\n" + "="*60)
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print("TEST 2: FAISSIndex")
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print("="*60)
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try:
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# Initialize
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print(" Initializing FAISSIndex...")
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dimension = 512
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index = FAISSIndex(dimension)
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print(f" ✓ Index created with dimension={dimension}")
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# Test add
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print("\n Testing add...")
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embeddings = np.random.randn(5, dimension).astype('float32')
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# Normalize
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embeddings = embeddings / np.linalg.norm(embeddings, axis=1, keepdims=True)
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metadata = [
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{"id": i, "label": f"item_{i}"}
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for i in range(5)
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]
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index.add(embeddings, metadata)
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print(f" ✓ Added 5 embeddings")
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print(f" ✓ Index size: {len(index)}")
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# Test search
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print("\n Testing search...")
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query = embeddings[0] # Search for first embedding
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results = index.search(query, k=3)
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print(f" ✓ Found {len(results)} results")
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print(f" ✓ Best match distance: {results[0]['distance']:.6f}")
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print(f" ✓ Best match metadata: {results[0]['metadata']}")
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# Test save/load
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print("\n Testing save/load...")
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test_path = "data/test_faiss_index"
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Path("data").mkdir(exist_ok=True)
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index.save(test_path)
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print(f" ✓ Index saved to {test_path}")
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# Load in new index
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index2 = FAISSIndex(dimension)
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index2.load(test_path)
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print(f" ✓ Index loaded")
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print(f" ✓ Loaded size: {len(index2)}")
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# Verify search results are identical
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results2 = index2.search(query, k=3)
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assert len(results) == len(results2), "Result count mismatch"
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assert abs(results[0]['distance'] - results2[0]['distance']) < 1e-6, "Distance mismatch"
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print(f" ✓ Search results match after load")
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# Test dimension mismatch
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print("\n Testing dimension validation...")
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try:
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wrong_embedding = np.random.randn(1, 256).astype('float32')
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index.add(wrong_embedding, [{"id": "wrong"}])
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print(f" ❌ Should have raised ValueError for dimension mismatch")
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return False
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except ValueError as e:
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print(f" ✓ Correctly rejected wrong dimension: {e}")
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print("\n ✅ FAISSIndex: ALL TESTS PASSED")
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return True
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except Exception as e:
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print(f"\n ❌ FAISSIndex: FAILED - {e}")
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import traceback
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traceback.print_exc()
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return False
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def test_embedding_manager():
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"""Test 3: EmbeddingManager"""
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print("\n" + "="*60)
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print("TEST 3: EmbeddingManager")
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print("="*60)
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try:
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# Initialize
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print(" Initializing EmbeddingManager...")
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manager = EmbeddingManager(model_name="clip", cache_size=10)
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print(f" ✓ Manager initialized: {manager.get_model_name()}")
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# Test single embedding
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print("\n Testing single embedding...")
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img = create_test_image(color=(255, 128, 0))
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emb1 = manager.embed(img)
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print(f" ✓ Embedding generated: shape={emb1.shape}")
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# Test cache hit
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print("\n Testing cache...")
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emb2 = manager.embed(img) # Should hit cache
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assert np.array_equal(emb1, emb2), "Cache should return identical embedding"
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stats = manager.get_stats()
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print(f" ✓ Cache hits: {stats['cache_hits']}")
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print(f" ✓ Cache misses: {stats['cache_misses']}")
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print(f" ✓ Hit rate: {stats['cache_hit_rate']:.1%}")
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# Test cache eviction
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print("\n Testing cache eviction...")
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for i in range(15): # More than cache_size=10
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img = create_test_image(color=(i*10, i*10, i*10))
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manager.embed(img)
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stats = manager.get_stats()
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print(f" ✓ Cache size: {stats['cache_size']} (max: {stats['cache_capacity']})")
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assert stats['cache_size'] <= 10, "Cache should not exceed max size"
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# Test batch embedding
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print("\n Testing batch embedding...")
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images = [create_test_image(color=(i, i, i)) for i in range(5)]
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embeddings = manager.embed_batch(images)
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print(f" ✓ Batch embeddings: shape={embeddings.shape}")
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# Test fallback (simulate)
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print("\n Testing fallback...")
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manager_fallback = EmbeddingManager(
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model_name="nonexistent_model",
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fallback_enabled=True
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)
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print(f" ✓ Fallback to: {manager_fallback.get_model_name()}")
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assert "clip" in manager_fallback.get_model_name().lower(), "Should fallback to CLIP"
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print("\n ✅ EmbeddingManager: ALL TESTS PASSED")
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return True
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except Exception as e:
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print(f"\n ❌ EmbeddingManager: FAILED - {e}")
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import traceback
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traceback.print_exc()
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return False
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def test_integration():
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"""Test 4: Intégration complète"""
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print("\n" + "="*60)
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print("TEST 4: Intégration Complète")
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print("="*60)
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try:
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# Setup
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print(" Setting up integrated system...")
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manager = EmbeddingManager(model_name="clip")
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index = FAISSIndex(manager.get_dimension())
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print(f" ✓ Manager: {manager.get_model_name()}")
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print(f" ✓ Index dimension: {index.dimension}")
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# Create and index some images
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print("\n Creating and indexing images...")
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images = [
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create_test_image(color=(255, 0, 0)), # Red
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create_test_image(color=(0, 255, 0)), # Green
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create_test_image(color=(0, 0, 255)), # Blue
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create_test_image(color=(255, 255, 0)), # Yellow
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create_test_image(color=(255, 0, 255)), # Magenta
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]
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labels = ["red", "green", "blue", "yellow", "magenta"]
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for img, label in zip(images, labels):
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emb = manager.embed(img)
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index.add(emb.reshape(1, -1), [{"label": label}])
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print(f" ✓ Indexed {len(index)} images")
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# Search for similar image
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print("\n Testing similarity search...")
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query_img = create_test_image(color=(255, 0, 0)) # Red
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query_emb = manager.embed(query_img)
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results = index.search(query_emb, k=3)
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print(f" ✓ Found {len(results)} similar images:")
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for i, result in enumerate(results):
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print(f" {i+1}. {result['metadata']['label']}: "
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f"similarity={result['similarity']:.4f}")
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# Verify red is most similar to red
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assert results[0]['metadata']['label'] == 'red', \
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"Most similar should be red"
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print(f" ✓ Correct match found")
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# Test persistence
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print("\n Testing persistence...")
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test_path = "data/test_integrated_index"
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index.save(test_path)
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index2 = FAISSIndex(manager.get_dimension())
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index2.load(test_path)
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results2 = index2.search(query_emb, k=3)
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assert results[0]['metadata']['label'] == results2[0]['metadata']['label'], \
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"Results should match after load"
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print(f" ✓ Persistence verified")
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print("\n ✅ Integration: ALL TESTS PASSED")
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return True
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except Exception as e:
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print(f"\n ❌ Integration: FAILED - {e}")
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import traceback
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traceback.print_exc()
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return False
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def main():
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"""Run all tests."""
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print("\n" + "="*60)
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print("TESTING NEW EMBEDDING SYSTEM")
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print("="*60)
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results = {
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"CLIPEmbedder": test_clip_embedder(),
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"FAISSIndex": test_faiss_index(),
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"EmbeddingManager": test_embedding_manager(),
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"Integration": test_integration()
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}
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# Summary
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print("\n" + "="*60)
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print("TEST SUMMARY")
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print("="*60)
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for test_name, passed in results.items():
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status = "✅ PASSED" if passed else "❌ FAILED"
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print(f" {test_name}: {status}")
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all_passed = all(results.values())
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if all_passed:
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print("\n🎉 ALL TESTS PASSED! System is ready.")
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return 0
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else:
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print("\n❌ SOME TESTS FAILED. Please review errors above.")
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return 1
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if __name__ == "__main__":
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sys.exit(main())
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