diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..5ab9f6d --- /dev/null +++ b/.dockerignore @@ -0,0 +1,106 @@ +# Notebooks de développement mais GARDER les samples +notebooks/*.ipynb +notebooks/p8_notebook.ipynb +notebooks/requirements.txt +.ipynb_checkpoints/ +# GARDER: notebooks/content/data/test_images_sample/ (nécessaire pour l'app) + +# Environment files (variables passées via docker run ou compose) +.env +.env.* +!.env.example + +# Python cache et build files +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# Virtual environments +venv/ +env/ +ENV/ +env.bak/ +venv.bak/ +.venv/ + +# IDE et editor files +.vscode/ +.idea/ +*.swp +*.swo +*~ +.DS_Store +Thumbs.db + +# Git files +.git/ +.gitignore +.gitattributes + +# Documentation +*.md +!README.MD +docs/ +documentation/ + +# Test files +tests/ +test_*/ +*_test.py +debug_*.py +pytest.ini +tox.ini +.coverage +.pytest_cache/ + +# Jupyter notebook outputs +*.nbconvert + +# Fichiers temporaires +.cache/ +.tmp/ +tmp/ +temp/ +*.tmp +*.temp +*.log +logs/ + +# OS generated files +.DS_Store? +ehthumbs.db +Icon? + +# MLflow artifacts locaux (seront téléchargés depuis le registry) +mlruns/ +mlruns.backup/ +mlartifacts/ + +# Backup files +*.bak +*.backup + +# Large datasets (samples nécessaires sont gardés) +*.zip +*.tar.gz +*.rar diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..49f2004 --- /dev/null +++ b/.env.example @@ -0,0 +1,41 @@ +# ============================================================================= +# Configuration du serveur +# ============================================================================= +# Port sur lequel nginx écoute (défaut: 8080 pour compatibilité Cloud Run) +NGINX_PORT=8080 + +# ============================================================================= +# Configuration MLflow (pour le versioning et le téléchargement des modèles) +# ============================================================================= +# URI du serveur MLflow de tracking +MLFLOW_TRACKING_URI=http://localhost:5000 + +# URL du stockage S3/MinIO pour les artefacts MLflow +MLFLOW_S3_ENDPOINT_URL=http://localhost:9000 + +# Clés d'accès S3/MinIO +AWS_ACCESS_KEY_ID=minio_access_key +AWS_SECRET_ACCESS_KEY=minio_secret_key + +# ============================================================================= +# Configuration email (optionnel - pour les notifications) +# ============================================================================= +# Serveur SMTP +SMTP_SERVER=smtp.gmail.com +SMTP_PORT=587 + +# Identifiants email +SMTP_EMAIL=noreply@yourcompany.com +SMTP_PASSWORD=your_app_password +SMTP_FROM_ALIAS=Multi-Segmentation API + +# Email de l'administrateur (destinataire des notifications) +ADMIN_EMAIL=admin@yourcompany.com + +# ============================================================================= +# Configuration de l'API +# ============================================================================= +# URL de base de l'API (utilisée par Streamlit pour faire les appels) +# En développement local: http://localhost:8080/api +# En production: https://your-domain.com/api +MULTISEG_API_BASE_URL=http://localhost:8080/api diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..242c724 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +./mlruns* \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..a0dd636 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,43 @@ +FROM python:3.12-slim + +WORKDIR /app + +# Install system dependencies (ajout de git pour segmentation_models) +RUN apt-get update && \ + apt-get install -y nginx curl gettext-base git && \ + # Pour OpenCV si nécessaire + apt-get install -y libglib2.0-0 libsm6 libxext6 libxrender-dev libgomp1 && \ + adduser --system --no-create-home --group www-data || true && \ + apt-get clean && \ + rm -rf /var/lib/apt/lists/* + +# Copy app/ d'abord pour avoir requirements.txt +COPY app/ ./app/ + +# Install requirements depuis le bon chemin +RUN pip install --no-cache-dir -r app/requirements.txt + +# ✅ CORRECTION: Copy start.sh AVANT les permissions +COPY start.sh ./start.sh + +# Copy du reste après installation (optimise les layers Docker) +COPY utils/ ./utils/ +COPY cityscapes_config.json . +COPY notebooks/content/data/test_images_sample/ ./notebooks/content/data/test_images_sample/ + +# Copy nginx config +COPY nginx.conf /etc/nginx/nginx.conf.template + +# ✅ CORRECTION: Setup permissions APRÈS avoir copié start.sh + correction fins de ligne +RUN mkdir -p /var/log/nginx /var/lib/nginx /run && \ + sed -i 's/\r$//' ./start.sh && \ + chmod +x ./start.sh + +# Environment variables +ENV NGINX_PORT=8080 +ENV MULTISEG_API_BASE_URL=http://localhost:8080/api + +EXPOSE $NGINX_PORT + +# ✅ CORRECTION: Chemin cohérent (./start.sh au lieu de /app/start.sh) +CMD ["./start.sh"] diff --git a/README.MD b/README.MD new file mode 100644 index 0000000..5346549 --- /dev/null +++ b/README.MD @@ -0,0 +1,278 @@ +# 🚗 Segmentation Multi-Classes pour Véhicules Autonomes + +## 📋 Description du Projet + +Ce projet développe un système de segmentation sémantique d'images de rue destiné aux véhicules autonomes. Il permet l'identification et la segmentation automatique de différents éléments urbains : véhicules, piétons, mobilier urbain, infrastructure routière, etc. + +Le système s'inscrit dans une chaîne complète de vision par ordinateur embarquée pour l'automobile autonome, traitant les images en temps réel pour alimenter les systèmes de décision. + +## 🗂️ Structure du Projet + +``` +├── notebooks/ +│ ├── p8_notebook.ipynb # Notebook principal d'entraînement des modèles +│ └── content/ +│ └── data/ +│ ├── new_images_to_predict/ # Images diverses pour tests d'inférence +│ └── test_images_sample/ # Échantillon Cityscapes avec masques +├── utils/ +│ └── utils.py # Fonctions communes (prétraitement, chargement, inférence) +├── app/ # API et Interface Web +│ ├── main.py # API FastAPI avec routes de segmentation +│ ├── streamlit_app.py # Interface web interactive +│ └── requirements.txt # Dépendances pour l'API et l'interface +├── cityscapes_config.json # Configuration des classes Cityscapes +├── .env.example # Template de variables d'environnement +├── Dockerfile # Configuration Docker +├── docker-compose.yml.example # Template Docker Compose +├── nginx.conf # Configuration reverse proxy +├── start.sh # Script de démarrage des services +├── .dockerignore # Optimisation du build Docker +└── README.md +``` + +> **Note** : Les modèles entraînés sont stockés et versionnés via **MLFlow** avec artefacts sur **MinIO**, pas en local. + +**État actuel du développement :** +- ✅ **Entraînement des modèles** : Complet (notebook + MLFlow + MinIO) +- ✅ **Fonctions d'inférence** : Développées et prêtes +- ✅ **API FastAPI** : API complète avec endpoints de segmentation +- ✅ **Interface Web** : Interface Streamlit pour upload et visualisation +- ✅ **Déploiement** : Conteneurisation Docker complète + reverse proxy nginx + +## 🏷️ Classes de Segmentation + +Le modèle identifie **8 catégories principales** : + +1. **Route** - Chaussée et surfaces roulantes +2. **Trottoir** - Zones piétonnes +3. **Bâtiments** - Structures architecturales +4. **Végétation** - Arbres, buissons, espaces verts +5. **Véhicules** - Voitures, camions, bus +6. **Personnes** - Piétons, cyclistes +7. **Signalisation** - Panneaux, feux de circulation +8. **Mobilier urbain** - Poteaux, barrières, abribus + +## 🧠 Architecture Technique + +### Modèles Implémentés +Utilisation de la bibliothèque **[segmentation_models](https://github.com/qubvel/segmentation_models)** de qubvel pour l'implémentation des architectures. + +**Architectures principales :** +- **U-Net** : Architecture encoder-decoder classique +- **FPN** (Feature Pyramid Network) : Réseau à pyramide de caractéristiques + +**Backbones testés :** +```python +# Modèles avec backbones par défaut +('U-Net', None) # Backbone U-Net intégré +('FPN', None) # Backbone VGG16 par défaut + +# Modèles avec backbones pré-entraînés (encodeurs gelés) +('U-Net', 'mobilenetv2') +('U-Net', 'efficientnetb0') +('FPN', 'mobilenetv2') +('FPN', 'efficientnetb0') +('FPN', 'resnet34') + +# Fine-tuning sélectif (encodeurs entraînables) +('U-Net', 'mobilenetv2') +('FPN', 'efficientnetb0') +('FPN', 'resnet34') +``` + +**Stratégies d'entraînement :** +- **Encodeurs gelés** : Transfer learning avec features pré-entraînées figées +- **Fine-tuning** : Ajustement fin des encodeurs pré-entraînés +- **Entraînement from scratch** : Sans pré-entraînement (backbones par défaut) + +### Stack Technologique +- **Framework** : Keras/TensorFlow +- **Architectures** : [segmentation_models](https://github.com/qubvel/segmentation_models) (qubvel) +- **Gestion des expériences** : MLFlow +- **Stockage des artefacts** : MinIO (S3-compatible) +- **API** : FastAPI avec validation Pydantic +- **Interface** : Streamlit pour l'interaction utilisateur +- **Reverse Proxy** : nginx pour routage API/Interface +- **Déploiement** : Docker + NAS personnel *(solutions souveraines)* + +## 📊 Dataset + +- **Source** : Cityscapes Dataset +- **Organisation locale** : + - `notebooks/content/data/test_images_sample/` : Échantillon avec masques de vérité pour tests d'inférence + - `notebooks/content/data/new_images_to_predict/` : Images de test diverses pour validation + +## 🚀 Installation et Configuration + +### Option 1: Déploiement Docker (Recommandé) + +#### Configuration rapide +```bash +# Cloner le projet +git clone https://github.com/GrgMdmn/OpenClassrooms_AI_Projects.git +cd OpenClassrooms_AI_Projects/Project_8 + +# Copier et adapter la configuration +cp .env.example .env +cp docker-compose.yml.example docker-compose.yml + +# Éditer les variables d'environnement +nano .env +``` + +#### Variables d'environnement requises +```bash +# Configuration du serveur +NGINX_PORT=8080 + +# Configuration MLflow +MLFLOW_TRACKING_URI=http://localhost:5000 +MLFLOW_S3_ENDPOINT_URL=http://localhost:9000 +AWS_ACCESS_KEY_ID=your_access_key +AWS_SECRET_ACCESS_KEY=your_secret_key + +# URL de base de l'API (pour Streamlit) +MULTISEG_API_BASE_URL=http://localhost:8080/api +``` + +#### Déploiement +```bash +# Construction et lancement +docker-compose up --build + +# Ou en arrière-plan +docker-compose up -d --build + +# Accès à l'application +# Interface web: http://localhost:8080 +# API docs: http://localhost:8080/api/docs +# Health check: http://localhost:8080/health +``` + +#### Test local sans docker-compose +```bash +# Lancement avec port personnalisé +docker build -t multiseg-api . +docker run -p 3000:3000 \ + -e NGINX_PORT=3000 \ + -e MLFLOW_TRACKING_URI=http://host.docker.internal:5000 \ + -e MULTISEG_API_BASE_URL=http://localhost:3000/api \ + multiseg-api +``` + +### Option 2: Installation Locale (Développement) + +#### Pour l'expérimentation et l'entraînement +```bash +cd notebooks +pip install -r requirements.txt +``` + +#### Pour l'API uniquement +```bash +cd app +pip install -r requirements.txt + +# Lancement manuel des services +# Terminal 1: FastAPI +uvicorn app.main:app --host 0.0.0.0 --port 8000 + +# Terminal 2: Streamlit +streamlit run app/streamlit_app.py --server.port 8501 +``` + +## 🌐 Utilisation de l'API + +### Endpoints Principaux + +#### Segmentation d'image +```bash +# Upload et segmentation via curl +curl -X POST "http://localhost:8080/api/segment" \ + -F "file=@image.jpg" \ + -F "model_name=unet_mobilenetv2" \ + -F "confidence_threshold=0.5" +``` + +#### Informations sur les modèles +```bash +# Liste des modèles disponibles +curl "http://localhost:8080/api/models" + +# Détails d'un modèle +curl "http://localhost:8080/api/models/unet_mobilenetv2" +``` + +#### Monitoring +```bash +# Health check +curl "http://localhost:8080/health" + +# Métriques API +curl "http://localhost:8080/api/metrics" +``` + +### Interface Web + +L'interface Streamlit accessible sur `http://localhost:8080` permet : +- **Upload d'images** par glisser-déposer ou sélection +- **Sélection du modèle** parmi les modèles disponibles +- **Ajustement des paramètres** (seuil de confiance, etc.) +- **Visualisation** des résultats avec overlay des segmentations +- **Téléchargement** des masques de segmentation + +## 🔧 Architecture de Déploiement + +### Structure des Services +``` +┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐ +│ nginx:8080 │────│ FastAPI:8000 │────│ MLFlow/MinIO │ +│ (Reverse Proxy)│ │ (API Backend) │ │ (Model Storage) │ +└─────────┬───────┘ └──────────────────┘ └─────────────────┘ + │ +┌─────────▼───────┐ +│ Streamlit:8501 │ +│ (Web Interface) │ +└─────────────────┘ +``` + +### Volumes et Persistance +- **Modèles** : Téléchargés depuis MLFlow/MinIO au démarrage +- **Uploads temporaires** : Stockage en mémoire (pas de persistance) +- **Logs** : Accessibles via `docker-compose logs` + +### Monitoring et Logs +```bash +# Logs en temps réel +docker-compose logs -f + +# Logs d'un service spécifique +docker-compose logs -f multiseg-api + +# Métriques de performance +curl http://localhost:8080/api/metrics +``` + +## 🔮 Améliorations Futures + +### Modèles et Performance +- **Prétraitement adaptatif** : Prétraitement spécifique à chaque backbone au lieu de la normalisation générique +- **Optimisation temps réel** : Amélioration des performances d'inférence pour déploiement embarqué +- **Résolution d'entraînement améliorée** : L'API accepte toutes les résolutions d'images, mais l'entraînement a été effectué sur des images redimensionnées à 224x224. Un réentraînement sur des résolutions supérieures (512x512, 1024x1024) pourrait améliorer la précision en capturant plus de détails fins, au prix d'une augmentation des besoins en puissance de calcul +- **Transfer learning ciblé** : Utilisation de backbones pré-entraînés sur Mapillary Vistas + +### Infrastructure et Déploiement +- **Scaling horizontal** : Support Kubernetes pour déploiement multi-instances +- **Cache intelligent** : Mise en cache des prédictions pour images similaires +- **API Gateway** : Intégration d'un gateway pour authentification et rate limiting +- **Monitoring avancé** : Intégration Prometheus/Grafana pour métriques détaillées + +### Données et Généralisation +- **Généralisation des conditions** : Extension au-delà des conditions urbaines de Cityscapes +- **Segmentation des personnes** : Amélioration de l'IoU via data augmentation (IA générative) +- **Datasets additionnels** : Intégration de datasets complémentaires pour robustesse + +--- + +*Projet développé dans une approche de souveraineté technologique, privilégiant les solutions open-source et l'hébergement sur infrastructure personnelle.* diff --git a/app/main.py b/app/main.py new file mode 100644 index 0000000..0f20acc --- /dev/null +++ b/app/main.py @@ -0,0 +1,427 @@ +import os +import sys +import tempfile +import shutil +from datetime import datetime +from typing import Optional, List +from pathlib import Path +import mlflow +from fastapi import FastAPI, HTTPException, File, UploadFile, Form +from fastapi.responses import FileResponse +from pydantic import BaseModel +import numpy as np +import pickle +import base64 + +# Configuration MLflow avec variables d'environnement +def configure_mlflow(): + """Configure MLflow avec les variables d'environnement""" + print("=== DEBUG VARIABLES D'ENVIRONNEMENT ===") + + # ✅ NETTOYAGE automatique des variables d'environnement + mlflow_tracking_uri = os.getenv("MLFLOW_TRACKING_URI", "").strip() + mlflow_s3_endpoint_url = os.getenv("MLFLOW_S3_ENDPOINT_URL", "").strip() + aws_access_key_id = os.getenv("AWS_ACCESS_KEY_ID", "").strip() + aws_secret_access_key = os.getenv("AWS_SECRET_ACCESS_KEY", "").strip() + + print(f"MLFLOW_TRACKING_URI: {mlflow_tracking_uri}") + print(f"MLFLOW_S3_ENDPOINT_URL: {mlflow_s3_endpoint_url}") + print(f"AWS_ACCESS_KEY_ID: {aws_access_key_id}") + print(f"AWS_SECRET_ACCESS_KEY: {'***' if aws_secret_access_key else None} ({'set' if aws_secret_access_key else 'not set'})") + print("============================================") + + if not mlflow_tracking_uri: + raise ValueError("MLFLOW_TRACKING_URI not found in environment variables") + + # Configuration MLflow avec URLs nettoyées + mlflow.set_tracking_uri(mlflow_tracking_uri) + print(f"MLflow Tracking URI: {mlflow_tracking_uri}") + + # ✅ CORRECTION: Configuration EXPLICITE des identifiants avec nettoyage + if not mlflow_s3_endpoint_url or not aws_access_key_id or not aws_secret_access_key: + raise ValueError("Variables d'environnement MLflow manquantes (S3_ENDPOINT, ACCESS_KEY, SECRET_KEY)") + + os.environ["MLFLOW_S3_ENDPOINT_URL"] = mlflow_s3_endpoint_url + os.environ["AWS_ACCESS_KEY_ID"] = aws_access_key_id + os.environ["AWS_SECRET_ACCESS_KEY"] = aws_secret_access_key + print(f"MLflow S3 Endpoint: {mlflow_s3_endpoint_url}") + print("✅ Identifiants AWS configurés") + + # ✅ SKIP set_experiment pour éviter le problème 404 + # mlflow.set_experiment("OC Projet 8") # Commenté temporairement + print("✅ Configuration MLflow terminée (sans set_experiment)") + +# Import des fonctions utils +sys.path.append('.') +from utils.utils import ( + load_cityscapes_config, + load_best_model_from_registry, + run_inference_and_visualize, + colorize_mask +) + +# ✅ AJOUT: Import segmentation_models pour le chargement des modèles +try: + import segmentation_models as sm + print("✅ segmentation_models importé - modèles compatibles") +except ImportError as e: + print(f"⚠️ segmentation_models non disponible: {e}") + print("💡 Installez avec: pip install git+https://github.com/qubvel/segmentation_models.git") + +# Configuration +MODEL_NAME = "StreetsSegmentation" +SAMPLE_IMAGES_DIR = "./notebooks/content/data/test_images_sample" +CITYSCAPES_CONFIG_PATH = "./cityscapes_config.json" +TEMP_UPLOAD_DIR = "/tmp/uploads" + +# Modèles de données +class ImageInfo(BaseModel): + filename: str + display_name: str + has_ground_truth: bool + +class PredictionResult(BaseModel): + success: bool + message: str + image_path: Optional[str] = None + mask_path: Optional[str] = None + prediction_stats: Optional[dict] = None + ground_truth_available: bool = False + figure_data: Optional[str] = None + +class AvailableImagesResponse(BaseModel): + images: List[ImageInfo] + total_count: int + +# Variables globales pour le modèle +model = None +encoder_name = None +script_img_size = None +mapping_config = None +run_id = None + +def initialize_model(): + """Initialise le modèle et la configuration au démarrage""" + global model, encoder_name, script_img_size, mapping_config, run_id + + try: + # Configuration MLflow + print("🔄 Configuration de MLflow...") + configure_mlflow() + + print("🔄 Chargement de la configuration Cityscapes...") + mapping_config = load_cityscapes_config(CITYSCAPES_CONFIG_PATH, verbose=False) + print("✅ Configuration Cityscapes chargée") + + print("🔄 Chargement du modèle depuis MLflow...") + print(f"🔍 Recherche du modèle: {MODEL_NAME}") + model, run_id, encoder_name, script_img_size = load_best_model_from_registry(MODEL_NAME) + print("✅ Modèle téléchargé depuis MLflow") + + print(f"✅ Modèle chargé du run {run_id}") + print(f"📐 Encoder: {encoder_name}") + print(f"📐 Input shape: {script_img_size}") + print(f"🎨 Nombre de classes: {mapping_config['num_classes']}") + + # Créer le dossier temporaire + os.makedirs(TEMP_UPLOAD_DIR, exist_ok=True) + + except Exception as e: + print(f"❌ Erreur lors de l'initialisation: {e}") + import traceback + traceback.print_exc() + raise e + +# Initialisation de l'API +app = FastAPI( + title="Multi-Class Segmentation API", + description="API de segmentation sémantique pour véhicules autonomes", + version="1.0.0" +) + +# ✅ AJOUT: Initialiser le modèle de manière plus robuste +@app.on_event("startup") +async def startup_event(): + """Initialise le modèle au démarrage de l'API""" + try: + initialize_model() + print("🚀 API démarrée avec succès - Modèle chargé!") + except Exception as e: + print(f"⚠️ ATTENTION: Impossible de charger le modèle au démarrage: {e}") + print("L'API démarre quand même mais les prédictions ne fonctionneront pas.") + +@app.get("/") +def root(): + return { + "message": "Multi-Class Segmentation API - Online", + "model_loaded": model is not None, + "model_run_id": run_id, + "encoder": encoder_name, + "num_classes": mapping_config['num_classes'] if mapping_config else None + } + +@app.get("/health") +def health_check(): + """Health check endpoint pour nginx""" + if model is None or mapping_config is None: + raise HTTPException(status_code=503, detail="Model not loaded") + + return { + "status": "healthy", + "model_loaded": True, + "timestamp": datetime.utcnow().isoformat() + } + +@app.get("/models", response_model=dict) +def get_model_info(): + """Retourne les informations du modèle chargé""" + if model is None: + raise HTTPException(status_code=503, detail="Model not loaded") + + return { + "model_name": MODEL_NAME, + "run_id": run_id, + "encoder": encoder_name, + "input_size": script_img_size, + "num_classes": mapping_config['num_classes'], + "class_names": mapping_config['group_names'], + "class_colors": mapping_config['group_colors'].tolist() + } + +@app.get("/sample-images", response_model=AvailableImagesResponse) +def get_sample_images(): + """Retourne la liste des images d'exemple disponibles""" + try: + original_dir = Path(SAMPLE_IMAGES_DIR) / "original" + mask_dir = Path(SAMPLE_IMAGES_DIR) / "mask" + + if not original_dir.exists(): + raise HTTPException(status_code=404, detail="Sample images directory not found") + + images = [] + for img_path in original_dir.glob("*leftImg8bit.png"): + # Construire le nom du masque correspondant + base_name = img_path.stem.replace("_leftImg8bit", "") + mask_name = f"{base_name}_gtFine_labelIds.png" + mask_path = mask_dir / mask_name + + # Créer un nom d'affichage plus convivial + display_name = base_name.replace("_", " ").title() + + images.append(ImageInfo( + filename=img_path.name, + display_name=display_name, + has_ground_truth=mask_path.exists() + )) + + # Trier par nom pour un affichage cohérent + images.sort(key=lambda x: x.filename) + + return AvailableImagesResponse( + images=images, + total_count=len(images) + ) + + except Exception as e: + raise HTTPException(status_code=500, detail=f"Error listing sample images: {str(e)}") + +@app.get("/sample-image/{filename}") +def get_sample_image(filename: str): + """Sert une image d'exemple pour prévisualisation""" + try: + print(f"🔍 DEBUG: Demande d'image: {filename}") + + original_dir = Path(SAMPLE_IMAGES_DIR) / "original" + image_path = original_dir / filename + + print(f"🔍 DEBUG: Chemin construit: {image_path}") + print(f"🔍 DEBUG: Fichier existe: {image_path.exists()}") + + if not image_path.exists(): + print(f"❌ DEBUG: Image non trouvée: {image_path}") + raise HTTPException(status_code=404, detail=f"Sample image {filename} not found") + + # Vérifier que c'est bien un fichier image attendu (sécurité) + if not filename.endswith('_leftImg8bit.png'): + print(f"❌ DEBUG: Format invalide: {filename}") + raise HTTPException(status_code=400, detail="Invalid image filename format") + + print(f"✅ DEBUG: Envoi de l'image: {filename}") + return FileResponse( + image_path, + media_type="image/png", + headers={"Content-Disposition": f"inline; filename={filename}"} + ) + + except HTTPException: + # Re-raise les HTTPException pour qu'elles soient gérées correctement + raise + except Exception as e: + print(f"❌ Erreur lors du service de l'image {filename}: {e}") + import traceback + traceback.print_exc() + raise HTTPException(status_code=500, detail=f"Error serving image: {str(e)}") + +class SampleImageRequest(BaseModel): + filename: str + +@app.post("/predict-sample", response_model=PredictionResult) +def predict_sample_image(request: SampleImageRequest): + """Lance une prédiction sur une image d'exemple""" + if model is None or mapping_config is None: + raise HTTPException(status_code=503, detail="Model not loaded") + + try: + # Vérifier que l'image existe + original_dir = Path(SAMPLE_IMAGES_DIR) / "original" + image_path = original_dir / request.filename + + if not image_path.exists(): + raise HTTPException(status_code=404, detail=f"Sample image {request.filename} not found") + + # Construire le chemin du masque de vérité + base_name = image_path.stem.replace("_leftImg8bit", "") + mask_name = f"{base_name}_gtFine_labelIds.png" + mask_path = Path(SAMPLE_IMAGES_DIR) / "mask" / mask_name + + mask_path_str = str(mask_path) if mask_path.exists() else None + + print(f"🔮 Lancement de l'inférence sur {request.filename}") + + # Lancer l'inférence (sans affichage pour l'API) + predicted_mask, fig = run_inference_and_visualize( + image_path=str(image_path), + mask_path=mask_path_str, + model=model, + encoder_name=encoder_name, + img_size=script_img_size, + mapping_config=mapping_config, + save_dir=None, + show_stats=False, + show_plot=False # ← AJOUTÉ pour récupérer la figure + ) + + # Sérialiser la figure + fig_bytes = pickle.dumps(fig) + fig_b64 = base64.b64encode(fig_bytes).decode() + + # Calculer les statistiques + unique_classes, counts = np.unique(predicted_mask, return_counts=True) + total_pixels = predicted_mask.size + + stats = {} + for class_id, count in zip(unique_classes, counts): + if class_id < len(mapping_config['group_names']): + percentage = (count / total_pixels) * 100 + stats[mapping_config['group_names'][class_id]] = { + "pixels": int(count), + "percentage": round(percentage, 1) + } + + return PredictionResult( + success=True, + message="Prediction completed successfully", + image_path=str(image_path), + mask_path=mask_path_str, + prediction_stats=stats, + ground_truth_available=mask_path.exists(), + figure_data=fig_b64 + ) + + except Exception as e: + print(f"❌ Erreur lors de la prédiction: {e}") + import traceback + traceback.print_exc() + raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}") + +@app.post("/predict-upload", response_model=PredictionResult) +def predict_uploaded_image(file: UploadFile = File(...)): + """Lance une prédiction sur une image uploadée""" + if model is None or mapping_config is None: + raise HTTPException(status_code=503, detail="Model not loaded") + + # Vérifier le type de fichier + allowed_types = ["image/jpeg", "image/jpg", "image/png"] + if file.content_type not in allowed_types: + raise HTTPException( + status_code=400, + detail=f"Invalid file type. Allowed: {', '.join(allowed_types)}" + ) + + temp_file_path = None + try: + # Créer un fichier temporaire + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + temp_filename = f"upload_{timestamp}_{file.filename}" + temp_file_path = os.path.join(TEMP_UPLOAD_DIR, temp_filename) + + # Sauvegarder le fichier uploadé + with open(temp_file_path, "wb") as buffer: + shutil.copyfileobj(file.file, buffer) + + print(f"🔮 Lancement de l'inférence sur {file.filename}") + + # Lancer l'inférence (sans masque de vérité) + predicted_mask, fig = run_inference_and_visualize( + image_path=temp_file_path, + mask_path=None, # Pas de masque de vérité pour les uploads + model=model, + encoder_name=encoder_name, + img_size=script_img_size, + mapping_config=mapping_config, + save_dir=None, + show_stats=False, + show_plot=False # ← AJOUTÉ pour récupérer la figure + ) + + # Sérialiser la figure + fig_bytes = pickle.dumps(fig) + fig_b64 = base64.b64encode(fig_bytes).decode() + + # Calculer les statistiques + unique_classes, counts = np.unique(predicted_mask, return_counts=True) + total_pixels = predicted_mask.size + + stats = {} + for class_id, count in zip(unique_classes, counts): + if class_id < len(mapping_config['group_names']): + percentage = (count / total_pixels) * 100 + stats[mapping_config['group_names'][class_id]] = { + "pixels": int(count), + "percentage": round(percentage, 1) + } + + return PredictionResult( + success=True, + message="Prediction completed successfully", + image_path=temp_file_path, + prediction_stats=stats, + ground_truth_available=False, + figure_data=fig_b64 + ) + + except Exception as e: + print(f"❌ Erreur lors de la prédiction: {e}") + import traceback + traceback.print_exc() + raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}") + + finally: + # Nettoyer le fichier temporaire + if temp_file_path and os.path.exists(temp_file_path): + try: + os.remove(temp_file_path) + print(f"🗑️ Fichier temporaire supprimé: {temp_filename}") + except Exception as e: + print(f"⚠️ Impossible de supprimer le fichier temporaire: {e}") + +@app.get("/metrics") +def get_metrics(): + """Endpoint de métriques pour monitoring""" + return { + "model_loaded": model is not None, + "model_name": MODEL_NAME, + "run_id": run_id, + "num_classes": mapping_config['num_classes'] if mapping_config else None, + "timestamp": datetime.utcnow().isoformat() + } \ No newline at end of file diff --git a/app/requirements.txt b/app/requirements.txt new file mode 100644 index 0000000..ea03823 --- /dev/null +++ b/app/requirements.txt @@ -0,0 +1,141 @@ +# ============================================================================= +# Multi-Class Segmentation API - Requirements +# ============================================================================= + +# ===== CORE WEB FRAMEWORK ===== +fastapi==0.115.14 +uvicorn==0.35.0 +starlette==0.46.2 +pydantic==2.11.7 +pydantic_core==2.33.2 + +# ===== STREAMLIT ===== +streamlit==1.46.1 +altair==5.5.0 # Utilisé par Streamlit pour les graphiques +blinker==1.9.0 # Streamlit dependency + +# ===== HTTP REQUESTS ===== +requests==2.32.4 +httpx==0.28.1 # Modern HTTP client (utilisé par FastAPI) +httpcore==1.0.9 +h11==0.16.0 + +# ===== MACHINE LEARNING CORE ===== +tensorflow==2.19.0 +keras==3.10.0 +numpy>=1.24.0,<2.0.0 + +# Note: tensorflow-addons n'est plus nécessaire avec TensorFlow 2.19+ +# Les fonctionnalités sont maintenant intégrées dans TensorFlow core + +# ===== COMPUTER VISION ===== +pillow==11.3.0 +opencv-python-headless==4.9.0.80 # Pas dans votre freeze, mais nécessaire pour Docker +scikit-image==0.25.2 +imageio==2.37.0 + +# ===== DATA SCIENCE ===== +pandas==2.3.0 +matplotlib==3.10.3 +scipy==1.16.0 +scikit-learn==1.7.0 + +# ===== MLFLOW & MODEL REGISTRY ===== +mlflow==3.2.0 +mlflow-skinny==3.2.0 +mlflow-tracing==3.2.0 + +# ===== AWS/S3 SUPPORT ===== +boto3==1.39.2 +botocore==1.39.2 +s3transfer==0.13.0 + +# ===== SEGMENTATION MODELS ===== +# Option 1: Version figée par commit (RECOMMANDÉE pour production) +segmentation_models @ git+https://github.com/qubvel/segmentation_models.git@5d24bbfb28af6134e25e2c0b79e7727f6c0491d0 + +# Option 2: Si vous préférez une version PyPI (décommentez et commentez la ligne du dessus) +# segmentation-models==1.0.1 + +# ===== SUPPORTING LIBRARIES ===== +# TensorFlow ecosystem +tensorboard==2.19.0 +tensorboard-data-server==0.7.2 +protobuf==3.20.3 +flatbuffers==25.2.10 +h5py==3.14.0 +astunparse==1.6.3 +gast==0.6.0 +google-pasta==0.2.0 +keras-applications==1.0.8 +opt_einsum==3.4.0 +termcolor==2.5.0 +wrapt==1.17.2 + +# Data processing +networkx==3.5 +tifffile==2025.6.11 +lazy_loader==0.4 + +# Utilities +click==8.2.1 +tqdm==4.67.1 +joblib==1.5.1 +python-dateutil +packaging +typing_extensions==4.14.0 + +# File handling +python-multipart==0.0.20 # Pour FastAPI file uploads + +# ===== AJOUTS SPÉCIFIQUES POUR UTILS/UTILS.PY ===== +# Pour la gestion des couleurs et légendes matplotlib +cycler==0.12.1 # Pour matplotlib customization + +# Font handling pour matplotlib (nécessaire dans Docker) +fonttools==4.58.5 + +# ===== REMOVED FROM ORIGINAL ===== +# Les packages suivants ont été supprimés car non utilisés dans votre app: +# - awsebcli (AWS CLI - pas nécessaire dans le container) +# - blessed, fabric, invoke (outils dev) +# - jupyter, ipython, spyder (dev environment) +# - nltk (pas d'NLP dans votre app) +# - pytest (tests - à garder seulement pour dev) +# - flask, gunicorn (vous utilisez FastAPI+uvicorn) +# - docker (pas besoin dans le container) +# - git* packages (dev tools) + +# ============================================================================= +# VERSION ALTERNATIVE - PLUS MINIMALISTE +# ============================================================================= + +# Si vous voulez une version encore plus légère, voici les STRICTEMENT nécessaires: +# fastapi==0.115.14 +# uvicorn==0.35.0 +# streamlit==1.46.1 +# tensorflow==2.19.0 +# numpy==2.1.3 +# pillow==11.3.0 +# matplotlib==3.10.3 +# pandas==2.3.0 +# requests==2.32.4 +# mlflow==3.2.0 +# boto3==1.39.2 +# segmentation_models @ git+https://github.com/qubvel/segmentation_models.git@5d24bbfb28af6134e25e2c0b79e7727f6c0491d0 + +# ============================================================================= +# NOTES SUR SEGMENTATION_MODELS +# ============================================================================= + +# OPTION A: Git avec commit hash (ACTUEL - RECOMMANDÉ) +# Avantages: Version exacte, pas de surprises +# Inconvénients: Nécessite git dans le container de build + +# OPTION B: Version PyPI officielle +# segmentation-models==1.0.1 +# Avantages: Plus simple, pas besoin de git +# Inconvénients: Peut être différent de votre version dev + +# OPTION C: Version locale (si vous avez des modifications) +# Copier le package dans le container et faire pip install -e . diff --git a/app/streamlit_app.py b/app/streamlit_app.py new file mode 100644 index 0000000..521f701 --- /dev/null +++ b/app/streamlit_app.py @@ -0,0 +1,686 @@ +import os +import sys +import requests +import streamlit as st +import numpy as np +import matplotlib.pyplot as plt +from PIL import Image +import io +import base64 +import pickle + +# Configuration de l'API +# prod docker +API_BASE_URL = os.getenv("MULTISEG_API_BASE_URL", "http://127.0.0.1:8080/api") +# test local +# API_BASE_URL = os.getenv("MULTISEG_API_BASE_URL", "http://127.0.0.1:8000") # ← Port 8000 au lieu de 8080 + +# Import des fonctions utils pour l'affichage local +sys.path.append('/app') +# APRÈS (pour test local) +sys.path.append('.') +from utils.utils import colorize_mask + +# Configuration de la page +st.set_page_config( + page_title="Multi-Class Segmentation", + page_icon="🚗", + layout="wide" +) + +# CSS personnalisé +st.markdown(""" + +""", unsafe_allow_html=True) + +# En-tête principal +st.markdown(""" +
+

🚗 Segmentation Multi-Classes

+

Système de segmentation sémantique pour véhicules autonomes

+
+""", unsafe_allow_html=True) + +# Initialisation des états +if "prediction_result" not in st.session_state: + st.session_state.prediction_result = None +if "selected_image" not in st.session_state: + st.session_state.selected_image = None +if "uploaded_file" not in st.session_state: + st.session_state.uploaded_file = None + +def reset_state(): + """Reset des états de session""" + st.session_state.prediction_result = None + st.session_state.selected_image = None + st.session_state.uploaded_file = None + +def get_model_info(): + """Récupère les informations du modèle""" + try: + response = requests.get(f"{API_BASE_URL}/models") + if response.status_code == 200: + return response.json() + else: + st.error(f"Erreur lors de la récupération des infos modèle: {response.status_code}") + return None + except Exception as e: + st.error(f"Erreur de connexion à l'API: {e}") + return None + +def get_sample_images(): + """Récupère la liste des images d'exemple""" + try: + response = requests.get(f"{API_BASE_URL}/sample-images") + if response.status_code == 200: + return response.json() + else: + st.error(f"Erreur lors de la récupération des images: {response.status_code}") + return None + except Exception as e: + st.error(f"Erreur de connexion à l'API: {e}") + return None + +def predict_sample_image(filename): + """Lance une prédiction sur une image d'exemple""" + try: + # CORRECTION: Envoyer en JSON au lieu de form data + data = {"filename": filename} + response = requests.post( + f"{API_BASE_URL}/predict-sample", + json=data, # ← Changé de 'data=' à 'json=' + headers={"Content-Type": "application/json"} # ← Ajout du header explicite + ) + + if response.status_code == 200: + return response.json() + else: + st.error(f"Erreur API: {response.status_code} - {response.text}") + return None + except Exception as e: + st.error(f"Erreur lors de la prédiction: {e}") + return None + +def predict_uploaded_image(uploaded_file): + """Lance une prédiction sur une image uploadée""" + try: + files = {"file": (uploaded_file.name, uploaded_file.getvalue(), uploaded_file.type)} + response = requests.post(f"{API_BASE_URL}/predict-upload", files=files) + + if response.status_code == 200: + return response.json() + else: + st.error(f"Erreur API: {response.status_code} - {response.text}") + return None + except Exception as e: + st.error(f"Erreur lors de la prédiction: {e}") + return None + +def display_segmentation_plots(fig): + """Affiche chaque subplot séparément dans des colonnes""" + if fig is None: + st.warning("Aucune figure de segmentation disponible") + return + + axes = fig.get_axes() + num_plots = len(axes) + + st.markdown("### 🎨 Résultat de la Segmentation") + + # Créer les colonnes selon le nombre de subplots + if num_plots <= 4: + cols = st.columns(num_plots) + else: + cols = st.columns(4) # Maximum 4 colonnes + + for i, ax in enumerate(axes): + col_idx = i % len(cols) + + with cols[col_idx]: + # Récupérer le titre existant + title = ax.get_title() + # st.markdown(f"**{title}:**") + + # Créer une figure individuelle avec ce subplot + individual_fig, individual_ax = plt.subplots(figsize=(6, 6)) + + # Copier toutes les images de l'axe original + for image in ax.get_images(): + individual_ax.imshow( + image.get_array(), + cmap=image.get_cmap() if image.get_cmap() else None, + alpha=image.get_alpha() if image.get_alpha() else None, + vmin=image.get_clim()[0] if image.get_clim() else None, + vmax=image.get_clim()[1] if image.get_clim() else None + ) + + # Copier les propriétés de l'axe + individual_ax.set_title(title) + individual_ax.axis('off') + + # Ajuster la mise en page + individual_fig.tight_layout() + + # Afficher dans Streamlit + st.pyplot(individual_fig) + + # Libérer la mémoire + plt.close(individual_fig) + + # Fermer la figure originale pour libérer la mémoire + plt.close(fig) + + +def display_prediction_stats_with_chart(stats, model_info): + """Affiche les statistiques avec un graphique en barres""" + st.markdown("### 📊 Statistiques de Segmentation") + + if not stats: + st.warning("Aucune statistique disponible") + return + + # Ratio 1:2 pour donner plus de place au graphique + col1, col2 = st.columns([1, 2]) + + # Colonne 1: Cartes de statistiques compactes avec carrés colorés + with col1: + st.markdown("**Détails par classe:**") + + # Récupérer les couleurs des classes pour les carrés + class_colors_dict = {} + if model_info and 'class_colors' in model_info and 'class_names' in model_info: + model_class_names = model_info['class_names'] + model_class_colors = model_info['class_colors'] + + for class_name in stats.keys(): + try: + idx = model_class_names.index(class_name) + color = model_class_colors[idx] + # Convertir en hex pour CSS + color_hex = f"#{color[0]:02x}{color[1]:02x}{color[2]:02x}" + class_colors_dict[class_name] = color_hex + except (ValueError, IndexError): + # Couleur par défaut si pas trouvée + class_colors_dict[class_name] = "#808080" + + for class_name, data in stats.items(): + # Récupérer la couleur pour cette classe + color_hex = class_colors_dict.get(class_name, "#808080") + + # ✨ NOUVEAU: Carré coloré + nom de classe + st.markdown(f""" +
+
+
+ {class_name} +
+
+ 🔢 {data['pixels']:,} pixels | 📊 {data['percentage']}% +
+
+ """, unsafe_allow_html=True) + + # Colonne 2: Graphique en barres (reste identique) + with col2: + st.markdown("**Répartition visuelle:**") + + # Préparer les données pour le graphique + class_names = list(stats.keys()) + percentages = [data['percentage'] for data in stats.values()] + + # Récupérer les couleurs des classes depuis model_info + colors = [] + if model_info and 'class_colors' in model_info and 'class_names' in model_info: + model_class_names = model_info['class_names'] + model_class_colors = model_info['class_colors'] + + for class_name in class_names: + # Trouver l'index de cette classe dans le modèle + try: + idx = model_class_names.index(class_name) + color = model_class_colors[idx] + # Convertir RGB en format matplotlib + colors.append([c/255.0 for c in color]) + except (ValueError, IndexError): + # Couleur par défaut si pas trouvée + colors.append([0.5, 0.5, 0.5]) + else: + # Couleurs par défaut si pas d'info du modèle + colors = plt.cm.Set3(np.linspace(0, 1, len(class_names))) + + # Créer un graphique plus grand et mieux adapté + fig, ax = plt.subplots(figsize=(10, 6)) # Plus large + + bars = ax.bar(class_names, percentages, color=colors, alpha=0.8, edgecolor='white', linewidth=1) + + # Personnalisation du graphique améliorée + ax.set_xlabel('Classes de Segmentation', fontsize=11, fontweight='bold') + ax.set_ylabel('Pourcentage (%)', fontsize=11, fontweight='bold') + ax.set_title('Répartition des Classes Détectées', fontsize=13, fontweight='bold', pad=20) + + # Meilleure gestion des labels selon le nombre de classes + if len(class_names) <= 4: + plt.xticks(rotation=0, ha='center', fontsize=10) + elif len(class_names) <= 6: + plt.xticks(rotation=30, ha='right', fontsize=9) + else: + plt.xticks(rotation=45, ha='right', fontsize=8) + + plt.yticks(fontsize=10) + + # Valeurs sur les barres avec meilleur positionnement + for bar, percentage in zip(bars, percentages): + height = bar.get_height() + # Adapter la position du texte selon la hauteur de la barre + if height < max(percentages) * 0.1: + # Si la barre est très petite, mettre le texte au-dessus + va = 'bottom' + y_pos = height + max(percentages) * 0.02 + else: + # Sinon, centrer dans la barre + va = 'center' + y_pos = height / 2 + + ax.text(bar.get_x() + bar.get_width()/2., y_pos, + f'{percentage:.1f}%', + ha='center', va=va, fontsize=9, fontweight='bold', + color='white' if va == 'center' else 'black', + bbox=dict(boxstyle='round,pad=0.2', facecolor='black', alpha=0.7) if va == 'center' else None) + + # Améliorer la mise en page et le style + plt.tight_layout() + + # Grille plus subtile + ax.grid(axis='y', alpha=0.2, linestyle='--') + ax.set_axisbelow(True) + + # Limites et style des axes + ax.set_ylim(0, max(percentages) * 1.15) # Plus d'espace au-dessus + ax.spines['top'].set_visible(False) + ax.spines['right'].set_visible(False) + ax.spines['left'].set_color('#666') + ax.spines['bottom'].set_color('#666') + + # Couleur de fond + fig.patch.set_facecolor('white') + ax.set_facecolor('#fafafa') + + # Afficher dans Streamlit + st.pyplot(fig) + plt.close(fig) + + +def display_class_legend(model_info): + """Affiche la légende des classes de segmentation""" + if model_info and 'class_colors' in model_info: + st.markdown("**Légende des classes:**") + + class_names = model_info.get('class_names', []) + class_colors = model_info.get('class_colors', []) + + # Créer une légende colorée + legend_cols = st.columns(len(class_names)) + for i, (name, color) in enumerate(zip(class_names, class_colors)): + with legend_cols[i]: + color_hex = f"#{color[0]:02x}{color[1]:02x}{color[2]:02x}" + st.markdown(f""" +
+ {name} +
+ """, unsafe_allow_html=True) + +# # Sidebar avec informations du modèle +# with st.sidebar: +# st.markdown("## 🔧 Informations du Modèle") + +# model_info = get_model_info() +# if model_info: +# st.success("✅ Modèle chargé") +# st.write(f"**Nom:** {model_info.get('model_name', 'N/A')}") +# st.write(f"**Encoder:** {model_info.get('encoder', 'N/A')}") +# st.write(f"**Classes:** {model_info.get('num_classes', 'N/A')}") +# st.write(f"**Taille d'entrée:** {model_info.get('input_size', 'N/A')}") +# st.write(f"**Run ID:** {model_info.get('run_id', 'N/A')[:8]}...") +# else: +# st.error("❌ Modèle non disponible") + +# st.markdown("---") +# st.markdown("## 🎯 Classes Segmentées") +# if model_info and 'class_names' in model_info: +# for i, class_name in enumerate(model_info['class_names']): +# st.write(f"{i}. {class_name}") + +# Sidebar avec informations du modèle +with st.sidebar: + st.markdown("## 🔧 Informations du Modèle") + + model_info = get_model_info() + if model_info: + st.success("✅ Modèle chargé") + st.write(f"**Nom:** {model_info.get('model_name', 'N/A')}") + st.write(f"**Encoder:** {model_info.get('encoder', 'N/A')}") + st.write(f"**Classes:** {model_info.get('num_classes', 'N/A')}") + st.write(f"**Taille d'entrée:** {model_info.get('input_size', 'N/A')}") + st.write(f"**Run ID:** {model_info.get('run_id', 'N/A')[:8]}...") + else: + st.error("❌ Modèle non disponible") + + st.markdown("---") + st.markdown("## 🎯 Classes Segmentées") + + # ✨ NOUVEAU: Affichage avec carrés colorés + if model_info and 'class_names' in model_info and 'class_colors' in model_info: + class_names = model_info['class_names'] + class_colors = model_info['class_colors'] + + for i, (class_name, color) in enumerate(zip(class_names, class_colors)): + # Convertir RGB en hex + color_hex = f"#{color[0]:02x}{color[1]:02x}{color[2]:02x}" + + # Affichage avec carré coloré + st.markdown(f""" +
+
+ {i}. {class_name} +
+ """, unsafe_allow_html=True) + else: + # Fallback si pas d'info couleur + if model_info and 'class_names' in model_info: + for i, class_name in enumerate(model_info['class_names']): + st.write(f"{i}. {class_name}") + else: + st.write("Informations des classes non disponibles") + + +# Initialisation des états - MODIFIÉE pour séparer les résultats par onglet +if "sample_prediction_result" not in st.session_state: + st.session_state.sample_prediction_result = None +if "sample_selected_image" not in st.session_state: + st.session_state.sample_selected_image = None + +if "upload_prediction_result" not in st.session_state: + st.session_state.upload_prediction_result = None +if "upload_uploaded_file" not in st.session_state: + st.session_state.upload_uploaded_file = None + +# ANCIEN système (gardé pour compatibilité si besoin) +if "prediction_result" not in st.session_state: + st.session_state.prediction_result = None +if "selected_image" not in st.session_state: + st.session_state.selected_image = None +if "uploaded_file" not in st.session_state: + st.session_state.uploaded_file = None + +def reset_sample_state(): + """Reset des états de session pour l'onglet Sample""" + st.session_state.sample_prediction_result = None + st.session_state.sample_selected_image = None + +def reset_upload_state(): + """Reset des états de session pour l'onglet Upload""" + st.session_state.upload_prediction_result = None + st.session_state.upload_uploaded_file = None + +def reset_state(): + """Reset complet (garde l'ancienne fonction pour compatibilité)""" + reset_sample_state() + reset_upload_state() + +# Interface principale - MODIFIÉE pour résultats indépendants +tab1, tab2 = st.tabs(["📸 Images d'Exemple", "📤 Upload Personnel"]) + +# Tab 1: Images d'exemple +with tab1: + st.markdown("## 📸 Sélectionner une Image d'Exemple") + st.markdown("Ces images proviennent du dataset Cityscapes avec masques de vérité disponibles.") + + # Récupérer les images d'exemple + sample_data = get_sample_images() + if sample_data and sample_data['images']: + # Sélecteur d'image + image_options = {img['display_name']: img['filename'] for img in sample_data['images']} + selected_display_name = st.selectbox( + "Choisissez une image:", + options=list(image_options.keys()), + help=f"{sample_data['total_count']} images disponibles" + ) + + selected_filename = image_options[selected_display_name] + + # Afficher les infos de l'image sélectionnée + selected_img_info = next(img for img in sample_data['images'] if img['filename'] == selected_filename) + + col1, col2 = st.columns([2, 1]) + with col1: + st.info(f"📁 **Fichier:** {selected_filename}") + with col2: + ground_truth_status = "✅ Disponible" if selected_img_info['has_ground_truth'] else "❌ Non disponible" + st.info(f"🎯 **Vérité terrain:** {ground_truth_status}") + + # Prévisualisation de l'image sélectionnée + if selected_filename: + try: + # Récupérer l'image via l'API + image_url = f"{API_BASE_URL}/sample-image/{selected_filename}" + response = requests.get(image_url, timeout=5) + + if response.status_code == 200: + # Charger l'image depuis les bytes + sample_image = Image.open(io.BytesIO(response.content)) + st.image( + sample_image, + caption=f"Aperçu: {selected_display_name}", + use_container_width=True + ) + elif response.status_code == 404: + st.warning("📷 Image non trouvée sur le serveur") + else: + st.info("📷 Prévisualisation temporairement indisponible") + + except requests.exceptions.Timeout: + st.warning("⏱️ Timeout lors du chargement de l'aperçu") + except Exception as e: + st.info("📷 Aperçu non disponible - L'image sera chargée lors de la prédiction") + + # Bouton de prédiction + if st.button("🔮 Lancer la Segmentation", key="predict_sample", type="primary"): + if selected_filename: + with st.spinner("Segmentation en cours..."): + result = predict_sample_image(selected_filename) + if result and result['success']: + # ✨ NOUVEAU: Stocker dans les variables Sample + st.session_state.sample_prediction_result = result + st.session_state.sample_selected_image = selected_filename + st.success("✅ Segmentation terminée!") + st.rerun() + else: + st.error("❌ Erreur lors de la segmentation") + else: + st.warning("Aucune image d'exemple disponible") + + # ✨ NOUVEAU: Affichage des résultats Sample (indépendants) + if st.session_state.sample_prediction_result and st.session_state.sample_selected_image: + st.markdown("---") + result = st.session_state.sample_prediction_result + + st.markdown(f"## 🎯 Résultats pour: {st.session_state.sample_selected_image}") + + # Afficher la segmentation + if result.get('figure_data'): + try: + # Désérialiser la figure + fig_bytes = base64.b64decode(result['figure_data']) + fig = pickle.loads(fig_bytes) + display_segmentation_plots(fig) + except Exception as e: + st.error(f"Erreur lors de l'affichage de la segmentation: {e}") + # Fallback sur la légende des classes + display_class_legend(model_info) + else: + # Fallback si pas de figure_data + display_class_legend(model_info) + + # Afficher les statistiques + if result.get('prediction_stats'): + display_prediction_stats_with_chart(result['prediction_stats'], model_info) + + # Informations additionnelles + if result.get('ground_truth_available'): + st.success("🎯 Masque de vérité terrain disponible pour comparaison") + else: + st.info("ℹ️ Pas de vérité terrain disponible") + + # Bouton de reset + if st.button("🔄 Nouvelle Analyse", key="reset_sample", type="secondary"): + reset_sample_state() + st.rerun() + +# Tab 2: Upload personnel +with tab2: + st.markdown("## 📤 Uploader Votre Image") + st.markdown("Uploadez une image de rue pour analyse. Formats acceptés: JPG, PNG") + + uploaded_file = st.file_uploader( + "Sélectionnez une image:", + type=['jpg', 'jpeg', 'png'], + help="Taille maximale recommandée: 5MB" + ) + + if uploaded_file is not None: + # Afficher l'image uploadée + image = Image.open(uploaded_file) + st.image(image, caption=f"Image uploadée: {uploaded_file.name}", use_container_width=True) + + # Informations sur le fichier + file_size_mb = len(uploaded_file.getvalue()) / 1024 / 1024 + st.info(f"📁 **Fichier:** {uploaded_file.name} | 📊 **Taille:** {file_size_mb:.2f} MB") + + # Bouton de prédiction + if st.button("🔮 Lancer la Segmentation", key="predict_upload", type="primary"): + with st.spinner("Segmentation en cours..."): + result = predict_uploaded_image(uploaded_file) + if result and result['success']: + # ✨ NOUVEAU: Stocker dans les variables Upload + st.session_state.upload_prediction_result = result + st.session_state.upload_uploaded_file = uploaded_file.name + st.success("✅ Segmentation terminée!") + st.rerun() + else: + st.error("❌ Erreur lors de la segmentation") + + # ✨ NOUVEAU: Affichage des résultats Upload (indépendants) + if st.session_state.upload_prediction_result and st.session_state.upload_uploaded_file: + st.markdown("---") + result = st.session_state.upload_prediction_result + + st.markdown(f"## 🎯 Résultats pour: {st.session_state.upload_uploaded_file}") + + # Afficher la segmentation + if result.get('figure_data'): + try: + # Désérialiser la figure + fig_bytes = base64.b64decode(result['figure_data']) + fig = pickle.loads(fig_bytes) + display_segmentation_plots(fig) + except Exception as e: + st.error(f"Erreur lors de l'affichage de la segmentation: {e}") + # Fallback sur la légende des classes + display_class_legend(model_info) + else: + # Fallback si pas de figure_data + display_class_legend(model_info) + + # Afficher les statistiques + if result.get('prediction_stats'): + display_prediction_stats_with_chart(result['prediction_stats'], model_info) + + # Informations additionnelles + if result.get('ground_truth_available'): + st.success("🎯 Masque de vérité terrain disponible pour comparaison") + else: + st.info("ℹ️ Pas de vérité terrain disponible (image personnelle)") + + # Bouton de reset + if st.button("🔄 Nouvelle Analyse", key="reset_upload", type="secondary"): + reset_upload_state() + st.rerun() + +# Footer +st.markdown("---") +st.markdown(""" +
+ 🚗 Multi-Class Segmentation API - + Système de vision par ordinateur pour véhicules autonomes
+ Développé avec FastAPI, Streamlit et TensorFlow +
+""", unsafe_allow_html=True) \ No newline at end of file diff --git a/cityscapes_config.json b/cityscapes_config.json new file mode 100644 index 0000000..4b61838 --- /dev/null +++ b/cityscapes_config.json @@ -0,0 +1,63 @@ +{ + "class_groups": { + "flat": ["road", "sidewalk", "parking", "rail track"], + "human": ["person", "rider"], + "vehicle": ["car", "truck", "bus", "train", "motorcycle", "bicycle", "caravan", "trailer"], + "construction": ["building", "wall", "fence", "guard rail", "bridge", "tunnel"], + "object": ["pole", "pole group", "traffic sign", "traffic light"], + "nature": ["vegetation", "terrain"], + "sky": ["sky"], + "void": ["unlabeled", "ego vehicle", "rectification border", "out of roi", "static", "dynamic", "ground"] + }, + "cityscapes_mapping": { + "0": 7, "1": 7, "2": 7, "3": 7, "4": 7, "5": 7, "6": 7, + "7": 0, "8": 0, "9": 0, "10": 0, + "11": 3, "12": 3, "13": 3, "14": 3, "15": 3, "16": 3, + "17": 4, "18": 4, "19": 4, "20": 4, + "21": 5, "22": 5, + "23": 6, + "24": 1, "25": 1, + "26": 2, "27": 2, "28": 2, "29": 2, "30": 2, "31": 2, "32": 2, "33": 2 + }, + "id_to_label": { + "0": "unlabeled", + "1": "ego vehicle", + "2": "rectification border", + "3": "out of roi", + "4": "static", + "5": "dynamic", + "6": "ground", + "7": "road", + "8": "sidewalk", + "9": "parking", + "10": "rail track", + "11": "building", + "12": "wall", + "13": "fence", + "14": "guard rail", + "15": "bridge", + "16": "tunnel", + "17": "pole", + "18": "pole group", + "19": "traffic light", + "20": "traffic sign", + "21": "vegetation", + "22": "terrain", + "23": "sky", + "24": "person", + "25": "rider", + "26": "car", + "27": "truck", + "28": "bus", + "29": "caravan", + "30": "trailer", + "31": "train", + "32": "motorcycle", + "33": "bicycle" + }, + "group_colors": [ + [128, 64, 128], [220, 20, 60], [0, 0, 142], [70, 70, 70], + [220, 220, 0], [107, 142, 35], [70, 130, 180], [0, 0, 0] + ], + "group_names": ["flat", "human", "vehicle", "construction", "object", "nature", "sky", "void"] +} diff --git a/docker-compose.yml.example b/docker-compose.yml.example new file mode 100644 index 0000000..f05e457 --- /dev/null +++ b/docker-compose.yml.example @@ -0,0 +1,47 @@ +version: '3.8' + +services: + street-vision-api: + # ✅ PRODUCTION: Image depuis Docker Hub + image: grgmdmn/street-vision_api:latest + + container_name: street_vision_api + + ports: + - "${NGINX_PORT:-8080}:${NGINX_PORT:-8080}" # Port nginx configurable + + environment: + # Configuration du port nginx + NGINX_PORT: "${NGINX_PORT:-8080}" + + # Configuration MLflow pour le suivi des modèles + MLFLOW_TRACKING_URI: "${MLFLOW_TRACKING_URI}" # ex: https://mlflow.domain.com + MLFLOW_S3_ENDPOINT_URL: "${MLFLOW_S3_ENDPOINT_URL}" # ex: url MinIO ou S3 + AWS_ACCESS_KEY_ID: "${AWS_ACCESS_KEY_ID}" # clé ID S3 + AWS_SECRET_ACCESS_KEY: "${AWS_SECRET_ACCESS_KEY}" # clé secrète S3 + + # Configuration email pour notifications (optionnel) + SMTP_SERVER: "${SMTP_SERVER:-}" # serveur SMTP + SMTP_PORT: "${SMTP_PORT:-587}" # port SMTP (ex: 587) + SMTP_EMAIL: "${SMTP_EMAIL:-}" # email expéditeur + SMTP_PASSWORD: "${SMTP_PASSWORD:-}" # mot de passe SMTP + SMTP_FROM_ALIAS: "${SMTP_FROM_ALIAS:-Street Vision API}" # alias (optionnel) + ADMIN_EMAIL: "${ADMIN_EMAIL:-}" # email admin destinataire + + # URL de base de l'API (utilisée par Streamlit pour rediriger vers FastAPI) + MULTISEG_API_BASE_URL: "${MULTISEG_API_BASE_URL}" # ex: https://vision.domain.com/api + + # Pour une mise à jour automatique par watchtower + labels: + com.centurylinklabs.watchtower.enable: "true" + + # Seulement si vous avez un reverse proxy server + # networks: + # - swag + + restart: unless-stopped + +# Seulement si vous avez un reverse proxy server +# networks: +# swag: +# external: true diff --git a/docs/P8_multi-segmentation_deep-learning.pdf b/docs/P8_multi-segmentation_deep-learning.pdf new file mode 100644 index 0000000..1340943 Binary files /dev/null and b/docs/P8_multi-segmentation_deep-learning.pdf differ diff --git a/docs/backbones_performances_comparison.png b/docs/backbones_performances_comparison.png new file mode 100644 index 0000000..df1be46 Binary files /dev/null and b/docs/backbones_performances_comparison.png differ diff --git a/docs/best_model_confusion_matrix.png b/docs/best_model_confusion_matrix.png new file mode 100644 index 0000000..94b5e18 Binary files /dev/null and b/docs/best_model_confusion_matrix.png differ diff --git a/docs/fpn_vs_unet.png b/docs/fpn_vs_unet.png new file mode 100644 index 0000000..d0cd59f Binary files /dev/null and b/docs/fpn_vs_unet.png differ diff --git a/docs/fpn_vs_unet_data_augmentation.png b/docs/fpn_vs_unet_data_augmentation.png new file mode 100644 index 0000000..f6b0d32 Binary files /dev/null and b/docs/fpn_vs_unet_data_augmentation.png differ diff --git a/docs/fpn_vs_unet_fine_tune.png b/docs/fpn_vs_unet_fine_tune.png new file mode 100644 index 0000000..2aab6c9 Binary files /dev/null and b/docs/fpn_vs_unet_fine_tune.png differ diff --git a/docs/omv_wathtower_docker_update_mail.png b/docs/omv_wathtower_docker_update_mail.png new file mode 100644 index 0000000..84bb742 Binary files /dev/null and b/docs/omv_wathtower_docker_update_mail.png differ diff --git a/docs/performances_comparison.png b/docs/performances_comparison.png new file mode 100644 index 0000000..ffa6f9e Binary files /dev/null and b/docs/performances_comparison.png differ diff --git a/docs/performances_graphs_unified.png b/docs/performances_graphs_unified.png new file mode 100644 index 0000000..978aa59 Binary files /dev/null and b/docs/performances_graphs_unified.png differ diff --git a/docs/performances_graphs_unified_upscayl_3x_remacri.png b/docs/performances_graphs_unified_upscayl_3x_remacri.png new file mode 100644 index 0000000..af4e94e Binary files /dev/null and b/docs/performances_graphs_unified_upscayl_3x_remacri.png differ diff --git a/docs/repartition_classes_jeu_données_val.png b/docs/repartition_classes_jeu_données_val.png new file mode 100644 index 0000000..3ac9286 Binary files /dev/null and b/docs/repartition_classes_jeu_données_val.png differ diff --git a/docs/technical_note.md b/docs/technical_note.md new file mode 100644 index 0000000..0f5a6da --- /dev/null +++ b/docs/technical_note.md @@ -0,0 +1,381 @@ +# Note Technique : Multi-segmentation Sémantique pour Véhicules Autonomes + +**Future Vision Transport - Équipe R&D** +*Une approche comparative d'architectures encoder-décodeur sur le dataset Cityscapes* + +--- + +## Résumé Exécutif + +Cette note présente une étude comparative d'architectures de segmentation sémantique pour systèmes embarqués de véhicules autonomes. L'objectif consiste à développer un modèle de segmentation capable de classifier 8 catégories sémantiques issues du dataset Cityscapes pour alimenter un système de décision autonome. + +**Résultats clés :** L'architecture **FPN + EfficientNetB0 + Fine-tuning + Augmentation** atteint les meilleures performances avec un Mean IoU de **0.746** sur le jeu de test, démontrant un excellent compromis entre précision et efficacité computationnelle pour les contraintes embarquées. + +--- + +## 1. Introduction et Contexte + +### 1.1 Problématique + +La segmentation sémantique constitue un enjeu critique pour les véhicules autonomes, permettant une compréhension pixel-wise de l'environnement routier. Dans le contexte de Future Vision Transport, cette composante alimente directement le système de décision (module 4) et doit répondre à des contraintes d'efficacité computationnelle pour un déploiement embarqué. + +### 1.2 Objectifs + +- Développer un modèle de segmentation pour 8 catégories sémantiques : flat (route), human (personne/cycliste), vehicle, construction, object (signalétique), nature, sky, void +- Optimiser le compromis précision/efficacité pour futures contraintes embarquées véhicules autonomes +- Évaluer différentes architectures encoder-décodeur avec backbones pré-entraînés +- **Valider l'inférence sur serveur de test équipé Intel N100** en préparation d'un futur déploiement temps réel embarqué + +### 1.3 Dataset et Métriques + +Le dataset Cityscapes¹ comprend **2975 images d'entraînement** que nous avons divisées en **2380 images d'entraînement** (80%) et **595 images de validation** (20%). Le jeu de données **"val" officiel de Cityscapes (500 images) sert de jeu de test** pour notre évaluation, car il contient les masques de vérité terrain nécessaires et n'intervient pas dans l'apprentissage. Le jeu "test" officiel de Cityscapes (1525 images) n'est pas utilisé car il ne contient pas de masques de vérité et est destiné au concours Kaggle. + +Les 34 classes originales sont regroupées en 8 catégories pertinentes pour la conduite autonome. L'évaluation s'appuie principalement sur le **Mean Intersection over Union (IoU)**, métrique standard pour la segmentation sémantique. Le mapping est largement inspiré de celui proposé sur le github de Cityscapes dans [labels.py](https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py). + +--- + +## 2. État de l'Art en Segmentation Sémantique + +### 2.1 Paysage des Architectures de Segmentation + +La segmentation sémantique a connu une évolution majeure avec l'avènement des réseaux entièrement convolutionnels. Cette section présente l'ensemble du paysage des approches disponibles pour contextualiser nos choix techniques. + +#### 2.1.1 Fondations : Fully Convolutional Networks + +Les **Fully Convolutional Networks (FCN)**2 ont initialement démontré la faisabilité de la segmentation dense en remplaçant les couches denses finales par des convolutions. Bien que fondatrice, cette approche souffre de limitations en résolution et perte de détails fins, la rendant inadaptée aux exigences de précision des véhicules autonomes. + +#### 2.1.2 Architectures Encoder-Décodeur + +**U-Net et Variantes** + +Proposée initialement pour la segmentation d'images biomédicales3, **U-Net** présente une architecture encoder-décodeur avec connexions de saut (skip connections) permettant de préserver l'information spatiale haute résolution. Cette architecture s'est révélée particulièrement efficace pour la segmentation avec des jeux de données de taille limitée, ce qui explique son adoption widespread dans diverses applications4. + +Les connexions de saut fonctionnent en concaténant les cartes de caractéristiques de l'encodeur directement dans le décodeur aux résolutions correspondantes, préservant ainsi l'information spatiale importante qui serait autrement perdue durant l'encodage5. + +**U-Net++** étend cette approche avec des connexions de saut denses et des supervisions profondes, mais au prix d'une complexité computationnelle accrue incompatible avec nos contraintes hardware (GPU T4, sessions limitées à 2h). + +**Feature Pyramid Networks (FPN)** + +Les **Feature Pyramid Networks**6 exploitent la hiérarchie pyramidale multi-échelle inhérente aux réseaux convolutionnels profonds. L'architecture top-down avec connexions latérales permet de construire des cartes de caractéristiques sémantiques de haut niveau à toutes les échelles. Initialement développée pour la détection d'objets, FPN s'est avérée efficace pour la segmentation en gérant naturellement les variations d'échelle7, particulièrement pertinente pour les scènes urbaines. + +#### 2.1.3 Méthodes à Convolutions Dilatées + +**DeepLab (v1, v2, v3, v3+)** + +La famille **DeepLab**17 utilise les convolutions à trous (atrous/dilated) pour maintenir une résolution élevée tout en élargissant le champ réceptif. **DeepLabv3+** combine encoder-décodeur avec Atrous Spatial Pyramid Pooling (ASPP) pour capturer le contexte multi-échelle. + +*Justification du non-usage :* Bien que performante, cette approche nécessite des ressources computationnelles importantes et des temps d'entraînement prohibitifs pour nos contraintes (GPU T4, 2h max). De plus, l'ASPP ajoute une complexité d'implémentation non justifiée pour notre preuve de concept. **Critiquement, les temps d'inférence de DeepLab dépassent largement les contraintes temps réel embarquées** (>5s sur processeurs modestes comme N100). + +**PSPNet (Pyramid Scene Parsing Network)** + +**PSPNet**18 exploite le pyramid pooling pour agréger l'information contextuelle à différentes échelles. Architecture efficace pour la compréhension de scènes globales. + +*Justification du non-usage :* La complexité du pyramid pooling et les besoins mémoire élevés dépassent nos contraintes hardware. De plus, l'approche est moins adaptée aux objets de petite taille critiques en conduite autonome (piétons, signalisation). **Les temps d'inférence PSPNet (>3s sur N100) sont incompatibles avec les exigences temps réel véhicules autonomes**. + +#### 2.1.4 Architectures Temps Réel + +**ICNet, BiSeNet, DDRNet** + +Ces architectures19,20 sont spécifiquement conçues pour l'inférence temps réel avec des compromis précision/vitesse optimisés. **ICNet** utilise des branches multi-résolution en cascade, tandis que **BiSeNet** sépare les chemins spatiaux et contextuels. + +*Justification du non-usage :* Bien qu'alignées avec nos objectifs d'efficacité, ces architectures nécessitent des implémentations custom complexes non disponibles dans [**segmentation_models**](https://github.com/qubvel/segmentation_models) (TensorFlow/Keras). Dans le cadre de cette étude académique exploratoire, la priorité était donnée à la validation de concept avec des architectures standard bien documentées plutôt qu'à l'optimisation temps réel spécifique. + +#### 2.1.5 Approches Basées Transformers + +**SegFormer, DPT, UperNet** + +Les architectures basées **Vision Transformers**21 ont démontré des performances état de l'art sur plusieurs benchmarks. **SegFormer** propose un transformer efficace sans positional encoding, tandis que **DPT** utilise des transformers pré-entraînés comme backbone. + +*Justification du non-usage :* Les transformers, bien que performants, présentent plusieurs obstacles pour notre contexte : +- **Coût computationnel** : Complexité quadratique en attention, inadaptée aux contraintes GPU T4 +- **Données requises** : Nécessitent des datasets massifs pour convergence optimale +- **Latence critique** : Temps d'inférence prohibitifs (>10s sur N100) incompatibles avec interface web responsive +- **Maturité** : Écosystème moins mature pour optimisations pratiques + +### 2.2 Justification de Notre Sélection : U-Net et FPN + +Notre choix s'est porté sur **U-Net** et **FPN** pour plusieurs raisons stratégiques : + +**Avantages Techniques :** +- **Efficacité computationnelle** : Compatible avec contraintes GPU T4 et sessions 2h +- **Maturité** : Architectures robustes avec optimisations établies +- **Flexibilité** : Support natif dans [segmentation_models](https://github.com/qubvel/segmentation_models) (TensorFlow/Keras) +- **Transfer learning** : Adaptation aisée avec backbones pré-entraînés + +**Pertinence Métier :** +- **Multi-échelle** : FPN gère naturellement les variations d'échelle urbaines +- **Détails fins** : U-Net préserve l'information spatiale critique pour frontières précises +- **Contraintes pratiques** : Architectures adaptées aux ressources limitées disponibles + +**Contraintes de Validation :** +- **Tests d'inférence** : Serveur Intel N100 pour validation fonctionnelle +- **Latence web** : <2s d'inférence pour interface utilisateur responsive +- **Objectif futur** : Déploiement temps réel sur hardware véhicule autonome (hors scope de cette étude) +- **Efficacité énergétique** : Optimisation consommation pour systèmes embarqués + +**Contraintes Pratiques :** +- **Ressources limitées** : Optimisées pour hardware modeste +- **Temps de développement** : Implémentation standard réduisant les risques +- **Reproductibilité** : Écosystème mature facilitant validation et comparaison + +### 2.3 Positionnement par Rapport à l'État de l'Art + +Dans le contexte de cette étude académique exploratoire, notre approche se distingue par ses contraintes pratiques spécifiques. Les recherches récentes8 mettent l'accent sur l'optimisation du compromis précision/vitesse pour applications industrielles, avec des architectures spécialisées et des ressources computationnelles importantes. + +**Notre positionnement se caractérise par :** +- **Ressources limitées** : GPU T4 Google Colab avec sessions limitées à 2h maximum +- **Approche comparative** : Focus sur l'évaluation systématique d'architectures standard plutôt que sur l'optimisation spécialisée +- **Validation fonctionnelle** : Tests d'inférence sur serveur Intel N100 pour interface web (<2s pour UX responsive) +- **Cadre académique** : Priorité à la compréhension des compromis architecturaux avant optimisation industrielle + +Cette approche permet une analyse rigoureuse des fondamentaux tout en posant les bases pour de futures optimisations spécialisées. Les métriques d'évaluation se concentrent sur la précision (Mean IoU) et l'efficacité computationnelle dans nos contraintes matérielles. + +### 2.4 Benchmarks Cityscapes et Performances État de l'Art + +Le benchmark officiel Cityscapes9 établit les métriques standard pour l'évaluation. Les méthodes état de l'art atteignent des performances de **Mean IoU > 80%** sur le jeu de test10, avec des architectures comme VLTSeg en tête du classement. Cependant, ces modèles complexes ne répondent pas nécessairement aux contraintes temps réel des véhicules autonomes. + +Des études récentes11 indiquent qu'un **Mean IoU ≥ 75%** constitue un seuil acceptable pour les applications de conduite autonome, particulièrement pour les classes critiques comme les humains et véhicules. + +### 2.5 Segmentation Models (TensorFlow/Keras) + +La bibliothèque [**segmentation_models**](https://github.com/qubvel/segmentation_models)12 (TensorFlow/Keras) fournit une implémentation unifiée de 12 architectures encoder-décodeur avec plus de 500 encodeurs pré-entraînés. Cette bibliothèque facilite l'expérimentation comparative et l'optimisation des hyperparamètres, constituant la base technique de notre étude. + +--- + +## 3. Méthodologie et Approches Explorées + +### 3.1 Architectures Évaluées + +#### 3.1.1 U-Net vs FPN + +**U-Net** : Architecture encoder-décodeur avec connexions de saut symétriques, particulièrement adaptée aux détails fins et contours précis. + +**FPN** : Architecture pyramidale optimisée pour la gestion multi-échelle, avec fusion top-down des caractéristiques sémantiques. + +#### 3.1.2 Backbones Explorés + +- **Vanilla** : Entraînement from scratch sans backbone pré-entraîné +- **MobileNetV2** : Architecture légère optimisée pour l'efficacité mobile¹³ +- **EfficientNetB0** : Architecture efficace avec scaling compound optimal¹⁴ +- **ResNet34** : Architecture résiduelle standard comme référence + +*Note technique :* FPN utilise VGG16 comme backbone par défaut (vanilla) dans l'implémentation [segmentation_models](https://github.com/qubvel/segmentation_models), tandis que U-Net peut s'entraîner entièrement from scratch. + +### 3.2 Stratégies d'Entraînement + +- **Frozen Encoder** : Backbone gelé pour transfer learning rapide +- **Fine-tuning** : Ajustement des poids pré-entraînés pour adaptation domaine + +### 3.3 Pipeline de Données + +- **Résolution** : Images redimensionnées à 224×224 pixels pour optimisation mémoire +- **Normalisation** : Division par 255 (pixels dans [0,1]) +- **Augmentation** : Retournement horizontal aléatoire (p=0.5) et/ou ajustement luminosité aléatoire (δ=0.1) + +### 3.4 Configuration d'Entraînement + +- **Loss** : Sparse categorical crossentropy +- **Optimiseur** : Adam (lr=1e-4 base, 5e-5 fine-tuning) +- **Régularisation** : Early stopping (patience=5), ReduceLROnPlateau (patience=3) +- **Métriques** : Mean IoU (principal), Sparse categorical accuracy + +--- + +## 4. Résultats et Analyse + +### 4.1 Performance Globale + +![Comparaison des performances par architecture et backbone](performances_comparison.png) +[FIGURE_1] : Comparaison des performances par architecture et backbone + +Le tableau suivant présente les résultats des trois meilleurs modèles : + +| Modèle | Test IoU | Test Accuracy | Architecture | Backbone | Stratégie | +|--------|----------|---------------|-------------|----------|-----------| +| **FPN_efficientnetb0_finetune_augTrue** | **0.746** | **0.912** | FPN | EfficientNetB0 | Fine-tuning + Aug | +| FPN_efficientnetb0_frozen_augFalse | 0.723 | 0.902 | FPN | EfficientNetB0 | Frozen | +| FPN_efficientnetb0_frozen_augTrue | 0.721 | 0.901 | FPN | EfficientNetB0 | Frozen + Aug | + +### 4.2 Analyse Comparative des Architectures + +**FPN domine U-Net** : Les résultats démontrent la supériorité de FPN sur U-Net, avec un gain moyen de **~1.2%** en Mean IoU. Cette performance s'explique par la capacité de FPN à mieux gérer les variations d'échelle présentes dans les scènes urbaines. + +![Comparaison FPN vs UNET IoU](fpn_vs_unet.png)\ +[FIGURE_2] : Distribution générale des performances (IoU) FPN vs U-Net (moyennes) + +### 4.3 Impact des Backbones + +**EfficientNetB0 optimal** : Ce backbone présente le meilleur compromis précision/efficacité pour les contraintes embarquées, surpassant MobileNetV2 de **~1.2%** en IoU tout en restant computationnellement léger comparé à ResNet34. + +![Comparaison performances selon backbones](backbones_performances_comparison.png)\ +[FIGURE_3] : Distribution générale des performances (IoU) selon le backbone. + +### 4.4 Stratégies d'Entraînement + +**Fine-tuning supérieur au Frozen** : L'analyse des configurations principales révèle que le fine-tuning apporte des gains d'IoU moyen variables selon l'architecture: +- Pour **MobileNetV2**, il apporte environ **+0.5%** pour **FPN** et environ **+1.6%** pour **U-net** +- Pour **EfficientNetB0**, il apporte un gain plus important de **+2.6%** pour FPN et relativement négligeable pour **U-net** + +![Comparaison FPN vs UNET IOU, backbone avec ou sans fine-tuning](fpn_vs_unet_fine_tune.png)\ +[FIGURE_4] : Distribution des performances (IoU) FPN vs U-Net par backbone (Comparaison avec ou sans fine tuning du backbone) + +### 4.5 Gains de l'Augmentation + +**Impact variable selon architecture** : L'augmentation aléatoire des données montre des effets contrastés: +- Performances moindres pour un contexte **Vanilla** (encodeur intégré pour **U-Net**, VGG16 pour **FPN**) +- Augmentation favorable pour **FPN** (**+0.5%**), mais défavorable (**-0.7%**) pour U-Net avec un backbone **MobileNetV2**. +- Apport systématiquement positif avec un backbone **EfficientNetB0** (**+1.0%** pour **FPN** et **+0.4%** pour **U-Net**) + +![Comparaison FPN vs UNET IOU, data augmentation](fpn_vs_unet_data_augmentation.png)\ +[FIGURE_5] : Distribution des performances (IoU) FPN vs U-Net par backbone (Comparaison avec ou sans Data Augmentation aléatoire) + +### 4.6 Analyse par Classe Sémantique + +![Matrice de confusion pour meilleur modèle](best_model_confusion_matrix.png)\ +[FIGURE_6] : Matrice de confusion (en %) des différentes catégories pour le meilleur modèle + +Performance détaillée du modèle optimal (FPN_efficientnetb0_finetune_augTrue) : + +| Classe | IoU | Criticité Véhicules Autonomes | +|--------|-----|-------------------------------| +| **Flat (route)** | 0.929 | ✅ Excellent | +| **Sky** | 0.902 | ✅ Excellent | +| **Vehicle** | 0.837 | ✅ Bon | +| **Construction** | 0.817 | ✅ Bon | +| **Void** | 0.685 | ✅ Correct | +| **Human** | 0.560 | ⚠️ Critique - À améliorer | +| **Object** | 0.374 | ❌ Insuffisant - Critique | +| **Nature** | 0.863 | ✅ Bon | + +**Analyse critique** : Les performances sur les classes **Human** et **Object** (signalétique) nécessitent amélioration prioritaire étant donné leur criticité pour la sécurité. Cette limitation rejoint les constats de la littérature sur les défis de segmentation des petits objets¹⁵. Les faibles IoU sur certaines catégories peuvent s'expliquer en raison de leur faible représentation sur le jeu de données (entrainement comme test). + +![Répartition des pixels sur les 500 images de test (jeu de données cityscapes "val")](repartition_classes_jeu_données_val.png)\ +[FIGURE_7] : Répartition des pixels sur les 500 images de test (jeu de données cityscapes "val") + +--- + +## 5. Modèle Retenu et Justifications + +### 5.1 Architecture Sélectionnée + +**FPN + EfficientNetB0 + Fine-tuning + Augmentation** + +Cette configuration optimale combine : +- **Précision maximale** : 0.746 Mean IoU sur test set +- **Efficacité embarquée** : EfficientNetB0 offre le meilleur ratio performance/ressources +- **Robustesse** : Fine-tuning et augmentation améliorent la généralisation + +### 5.2 Avantages pour Véhicules Autonomes + +- **Léger** : EfficientNetB0 adapté aux contraintes hardware embarquées +- **Multi-échelle** : FPN gère efficacement les variations d'échelle urbaines +- **Temps réel** : Architecture compatible avec contraintes latence (inférieure à 100ms) +- **Robuste** : Performances stables across conditions variées + +### 5.3 Limites Identifiées + +- **Classes critiques** : IoU insuffisant sur Human (0.56) et Object (0.37) +- **Généralisation** : Limitée au domaine urbain (Cityscapes) +- **Résolution fixe** : 224×224 peut limiter la détection fine +- **Conditions météo** : Dataset principalement beau temps + +--- + +## 6. Perspectives d'Amélioration + +### 6.1 Optimisations Techniques + +**Préprocessing spécialisé** : Implémentation de normalisations backbone-spécifiques pour gains potentiels de convergence. + +**Architecture custom** : Développement d'une architecture hybride FPN-UNet exploitant les avantages des deux approches. + +**Loss fonction avancée** : Intégration de focal loss ou dice loss pour gérer le déséquilibre des classes critiques. + +### 6.2 Données et Généralisation + +**Dataset extension** : Intégration de Mapillary Vistas pour transfer learning plus pertinent que ImageNet¹⁶. + +**Augmentation avancée** : Techniques d'IA générative pour augmenter la représentation des classes Human et Object. + +**Conditions adverses** : Extension aux conditions météorologiques variées (pluie, neige, nuit). + +### 6.3 Optimisations Embarquées + +**Quantization** : Réduction de précision (FP16/INT8) pour gains de vitesse. + +**Pruning** : Élagage des connexions redondantes pour optimisation mémoire. + +**Knowledge Distillation** : Transfert de connaissance d'un modèle teacher complexe vers un modèle student léger. + +### 6.4 Validation Temps Réel + +**Benchmarks hardware** : Tests sur GPUs embarqués (NVIDIA Jetson, Intel Myriad). + +**Métriques système** : Évaluation latence, throughput, consommation énergétique. + +**Integration pipeline** : Tests dans chaîne complète perception-décision. + +--- + +## 7. Conclusion + +Cette étude comparative démontre la supériorité de l'architecture **FPN + EfficientNetB0** pour la segmentation sémantique embarquée, atteignant un Mean IoU de 0.746 tout en respectant les contraintes computationnelles. Les résultats valident l'approche encoder-décodeur avec transfer learning pour les applications véhicules autonomes. + +**Contributions principales :** +- Validation empirique de FPN sur U-Net pour scènes urbaines +- Identification d'EfficientNetB0 comme backbone optimal pour contraintes embarquées +- Quantification des gains du fine-tuning vs frozen pour adaptation domaine + +**Axes prioritaires :** L'amélioration des performances sur les classes critiques Human et Object constitue l'enjeu majeur pour atteindre les standards de sécurité requis en conduite autonome. + +Cette base technique solide ouvre la voie vers un système de perception embarqué fiable pour Future Vision Transport, avec des perspectives d'optimisation claire pour les développements futurs. + +--- + +## Références + +1. Cordts, M., Omran, M., Ramos, S., et al. (2016). The Cityscapes Dataset for Semantic Urban Scene Understanding. *CVPR*. + +2. Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. *CVPR*. + +3. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. *MICCAI*. + +4. Alexquesada. (2023). U-Net: A Versatile Deep Learning Architecture for Image Segmentation. *Medium*. + +5. Datature. (2024). Supporting Fully Convolutional Networks (and U-Net) for Image Segmentation. + +6. Lin, T. Y., Dollár, P., Girshick, R., et al. (2017). Feature Pyramid Networks for Object Detection. *CVPR*. + +7. Liu, D., Zhang, D., Wang, L., & Wang, J. (2023). Semantic segmentation of autonomous driving scenes based on multi-scale adaptive attention mechanism. *Frontiers in Neuroscience*. + +8. Al-Qudah, Y., et al. (2024). Real-time semantic segmentation for autonomous driving: A review of CNNs, Transformers, and Beyond. *Journal of King Saud University*. + +9. Cityscapes Benchmark Suite. https://www.cityscapes-dataset.com/benchmarks/ + +10. Papers with Code - Cityscapes test Benchmark. https://paperswithcode.com/sota/semantic-segmentation-on-cityscapes + +11. Wen, L.-H., & Jo, K.-H. (2022). Deep learning-based perception systems for autonomous driving: A comprehensive survey. *Neurocomputing*. + +12. Iakubovskii, P. (2019). Segmentation Models. *GitHub repository* (TensorFlow/Keras). https://github.com/qubvel/segmentation_models + +13. Sandler, M., Howard, A., Zhu, M., et al. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. *CVPR*. + +14. Tan, M., & Le, Q. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. *ICML*. + +15. SuperAnnotate. (2024). Semantic segmentation: Complete guide. + +16. Neuhold, G., Ollmann, T., Rota Bulò, S., & Kontschieder, P. (2017). The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes. *ICCV*. + +17. Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2017). DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. *TPAMI*. + +18. Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017). Pyramid Scene Parsing Network. *CVPR*. + +19. Zhao, H., Qi, X., Shen, X., Shi, J., & Jia, J. (2018). ICNet for Real-Time Semantic Segmentation on High-Resolution Images. *ECCV*. + +20. Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., & Sang, N. (2018). BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation. *ECCV*. + +21. Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., & Luo, P. (2021). SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers. *NeurIPS*. + +--- + +*Note technique rédigée pour présentation aux équipes techniques - Future Vision Transport R&D* \ No newline at end of file diff --git a/docs/technical_note.pdf b/docs/technical_note.pdf new file mode 100644 index 0000000..7e1f1fb Binary files /dev/null and b/docs/technical_note.pdf differ diff --git a/nginx.conf b/nginx.conf new file mode 100644 index 0000000..129d895 --- /dev/null +++ b/nginx.conf @@ -0,0 +1,57 @@ +# This reverse proxy configuration aims to make calls to different services running under different ports (Streamlit :8501 and FastAPI :8000) by calling only one port : the nginx one (configurable via NGINX_PORT) +# The NGINX_PORT has to be opened (Dockerfile) and used by app.py (streamlit) in order to make right calls. +worker_processes 1; + +events { worker_connections 1024; } + +http { + # ✅ AJOUT: Limite d'upload augmentée + client_max_body_size 200M; # Permet 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b/notebooks/content/data/test_images_sample/original/frankfurt_000000_001751_leftImg8bit.png new file mode 100644 index 0000000..62a6711 Binary files /dev/null and b/notebooks/content/data/test_images_sample/original/frankfurt_000000_001751_leftImg8bit.png differ diff --git a/notebooks/p8_notebook.ipynb b/notebooks/p8_notebook.ipynb new file mode 100644 index 0000000..87dad44 --- /dev/null +++ b/notebooks/p8_notebook.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","source":["# 🚗 Projet 8 : Segmentation d'images pour véhicules autonomes\n","\n","## 🎯 Contexte et Mission\n","\n","Dans le cadre du développement d'un système embarqué de vision par ordinateur pour véhicules autonomes chez **Future Vision Transport**, ce notebook implémente la partie **segmentation d'images** du pipeline complet.\n","\n","### Objectifs de la mission\n","1. **Entraîner un modèle de segmentation** sur les 8 catégories principales du dataset Cityscapes\n","2. **Concevoir des fonctions d'inférence** prêtes pour l'intégration dans une API\n","3. **Évaluer et comparer** différentes architectures et stratégies d'entraînement\n","4. **Préparer la mise en production** avec un système de versioning et de registry des modèles\n","\n","### Intégration dans la chaîne complète\n","Ce travail s'inscrit entre :\n","- **En amont** : Le module de traitement d'images (Franck) qui fournit les images préprocessées\n","- **En aval** : Le système de décision (Laura) qui consommera les masques de segmentation via une API simple\n","\n","## 🛠️ Architecture technique et outils\n","\n","### Stack principal\n","- **Framework** : TensorFlow/Keras avec la bibliothèque `segmentation_models` de qubvel\n","- **Gestion d'expériences** : MLflow pour le tracking, comparison et registry des modèles\n","- **Stockage d'artefacts** : MinIO (S3-compatible) pour une approche souveraine\n","- **Dataset** : Cityscapes avec mapping vers 8 classes principales\n","\n","### Modèles et stratégies évaluées\n","- **Architectures** : U-Net et FPN (Feature Pyramid Network)\n","- **Backbones** : Vanilla, MobileNetV2, EfficientNetB0, ResNet34\n","- **Stratégies d'entraînement** : From scratch, transfer learning (encodeurs gelés), fine-tuning\n","\n","### Classes de segmentation ciblées\n","1. **Route** - Surfaces roulantes\n","2. **Trottoir** - Zones piétonnes \n","3. **Bâtiments** - Structures architecturales\n","4. **Végétation** - Espaces verts\n","5. **Véhicules** - Moyens de transport\n","6. **Personnes** - Piétons et cyclistes\n","7. **Signalisation** - Panneaux et feux\n","8. **Mobilier urbain** - Infrastructure urbaine\n","\n","## 📋 Déroulement du notebook\n","\n","### 1. Configuration et préparation\n","- Vérification des ressources GPU/RAM disponibles\n","- Configuration MLflow et MinIO via variables d'environnement\n","- Installation des dépendances et utilitaires de gestion mémoire\n","\n","### 2. Gestion des données Cityscapes \n","- Téléchargement et extraction automatisée du dataset\n","- Mapping des 34 classes originales vers 8 groupes sémantiques\n","- Création des datasets TensorFlow (train/val/test) avec préprocessing\n","\n","### 3. Définition des architectures\n","- Configuration des combinaisons modèle/backbone\n","- Paramétrage des stratégies d'entraînement (freeze/unfreeze)\n","- Setup des callbacks (EarlyStopping, ReduceLROnPlateau, ModelCheckpoint)\n","\n","### 4. Pipeline d'entraînement optimisé\n","- Système de reprise automatique basé sur MLflow (évite les re-entraînements)\n","- Entraînement batch de toutes les configurations\n","- Logging complet : métriques, paramètres, temps d'exécution, artefacts\n","\n","### 5. Analyse comparative des expériences\n","- Comparaison des performances via MLflow et métriques locales\n","- Visualisation des courbes d'apprentissage\n","- Analyse des matrices de confusion par classe\n","- Étude de la distribution des classes dans le dataset de test\n","\n","### 6. Mise en production\n","- Identification et promotion du meilleur modèle (Test Mean IoU)\n","- Enregistrement dans le MLflow Model Registry (stage \"Production\")\n","- Validation du chargement et de l'inférence depuis le registry\n","\n","### 7. Démonstration et tests d'inférence\n","- Chargement du modèle de production depuis MLflow\n","- Tests sur échantillons Cityscapes et images externes\n","- Visualisation des prédictions et évaluation qualitative\n","\n","### 8. Synthèse et perspectives\n","- Analyse des résultats et identification du modèle optimal\n","- Discussion des limitations observées\n","- Pistes d'amélioration pour les itérations futures\n","\n","## 🔬 Approche expérimentale\n","\n","Ce notebook adopte une démarche systématique de **recherche et développement** :\n","\n","- **Reproductibilité** : Gestion des seeds et versioning complet via MLflow\n","- **Scalabilité** : Pipeline automatisé pour tester de multiples configurations\n","- **Traçabilité** : Logging exhaustif de chaque expérience et artefact\n","- **Production-ready** : Fonctions d'inférence optimisées et prêtes pour l'API\n","\n","L'objectif est de produire un modèle performant tout en documentant rigoureusement le processus de sélection pour faciliter les futures améliorations et l'intégration dans le système embarqué final.\n","\n","---\n","\n","*Ce travail constitue la brique fondamentale de segmentation du système de vision autonome, conçue pour s'intégrer facilement dans l'architecture globale tout en maintenant une approche souveraine des données et infrastructures.*"],"metadata":{"id":"zaghnHKsHSn2"}},{"cell_type":"markdown","metadata":{"id":"98EwoZLkDjzq"},"source":["## GPU et mémoire RAM"]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":251,"status":"ok","timestamp":1755184268294,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-120},"id":"N139rgMJ-u28","outputId":"541bcff9-703e-4fb5-85d3-ceecd4698974"},"outputs":[{"output_type":"stream","name":"stdout","text":["/bin/bash: line 1: nvidia-smi: command not found\n"]}],"source":["gpu_info = !nvidia-smi\n","gpu_info = '\\n'.join(gpu_info)\n","if gpu_info.find('failed') >= 0:\n"," print('Not connected to a GPU')\n","else:\n"," print(gpu_info)"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":39,"status":"ok","timestamp":1755184268295,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-120},"id":"KQiIRnlM-vaz","outputId":"ed046c85-4713-4a56-f9f5-461e18b7da5f"},"outputs":[{"output_type":"stream","name":"stdout","text":["Your runtime has 13.6 gigabytes of available RAM\n","\n","Not using a high-RAM runtime\n"]}],"source":["from psutil import virtual_memory\n","ram_gb = virtual_memory().total / 1e9\n","print('Your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n","\n","if ram_gb < 20:\n"," print('Not using a high-RAM runtime')\n","else:\n"," print('You are using a high-RAM runtime!')"]},{"cell_type":"code","execution_count":3,"metadata":{"executionInfo":{"elapsed":6348,"status":"ok","timestamp":1755184274640,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-120},"id":"3evmO8AKQja8"},"outputs":[],"source":["from tensorflow.keras.mixed_precision import set_global_policy\n","set_global_policy('mixed_float16')"]},{"cell_type":"code","execution_count":4,"metadata":{"id":"Zr34mnLTcgj6","executionInfo":{"status":"ok","timestamp":1755184274685,"user_tz":-120,"elapsed":34,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# ====================================================================\n","# GESTION DE LA VRAM\n","# ====================================================================\n","\n","import gc\n","import tensorflow as tf\n","\n","def deep_clean_memory():\n"," \"\"\"\n"," Nettoyage complet de la mémoire entre les modèles\n"," ESSENTIEL pour éviter l'accumulation VRAM !\n"," \"\"\"\n"," print(\"🧹 Nettoyage mémoire approfondi...\")\n","\n"," # 1. Nettoyer le backend Keras (le plus important)\n"," tf.keras.backend.clear_session()\n","\n"," # 2. Forcer le garbage collector Python\n"," gc.collect()\n","\n"," # 3. Nettoyer les graphes TensorFlow\n"," try:\n"," # Reset des graphes par défaut\n"," tf.compat.v1.reset_default_graph()\n"," except:\n"," pass\n","\n"," # 4. Forcer la libération mémoire GPU si disponible\n"," if tf.config.list_physical_devices('GPU'):\n"," try:\n"," # Forcer la synchronisation GPU\n"," with tf.device('/GPU:0'):\n"," tf.constant([1.0]) # Petite opération pour forcer sync\n"," except:\n"," pass\n","\n"," print(\"✅ Mémoire nettoyée\")\n","\n","def monitor_gpu_memory(stage=\"\"):\n"," \"\"\"Surveillance optionnelle de la VRAM\"\"\"\n"," try:\n"," import GPUtil\n"," gpus = GPUtil.getGPUs()\n"," if gpus:\n"," gpu = gpus[0]\n"," used_gb = gpu.memoryUsed / 1024\n"," total_gb = gpu.memoryTotal / 1024\n"," print(f\"🖥️ {stage} VRAM: {used_gb:.1f}GB/{total_gb:.1f}GB ({gpu.memoryPercent:.1f}%)\")\n"," return gpu.memoryUsed\n"," except:\n"," pass\n"," return None"]},{"cell_type":"markdown","metadata":{"id":"GmeKp_pfKK8u"},"source":["## 🔗 Connexion à Google Drive"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":26094,"status":"ok","timestamp":1755184300780,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-120},"id":"zBTvEoak9GKf","outputId":"b41151da-053a-4eb1-c081-668f3e988926"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]},{"output_type":"execute_result","data":{"text/plain":["['content',\n"," 'unet_architecture_simple.png',\n"," 'Copie 2 de p8_notebook_MobileNetV2_UNet.ipynb',\n"," 'model_architecture_1.png',\n"," 'model_architecture_2.png',\n"," 'model_architecture_3.png',\n"," 'artifacts',\n"," 'Copie de p8_notebook.ipynb',\n"," 'Copie de p8_notebook_MobileNetV2_UNet.ipynb',\n"," 'results',\n"," 'requirements.txt',\n"," 'p8_notebook.ipynb']"]},"metadata":{},"execution_count":5}],"source":["from google.colab import drive\n","drive.mount('/content/drive')\n","import os\n","drive_folder= \"/content/drive/MyDrive/Colab_Notebooks/Project_8/notebooks\"\n","os.chdir(drive_folder)\n","os.listdir()"]},{"cell_type":"markdown","metadata":{"id":"xLlwj2kGJwGN"},"source":["## 💾 Jeu de données\n","\n","Nous utilisons le jeu de données [Cityscapes](https://www.cityscapes-dataset.com/dataset-overview/) qui se concentre sur la compréhension sémantique des scènes urbaines.\n","\n","Pour notre modèle de segmentation d'images, nous exploitons les **5 000 images annotées avec des annotations fines** (fine annotations). Exemples [ici](https://www.cityscapes-dataset.com/examples/#fine-annotations).\n","\n","Ce dataset présente plusieurs caractéristiques essentielles pour notre cas d'usage :\n","- Des annotations polygonales permettant une segmentation sémantique dense\n","- Une couverture de 30 classes dont nous retiendrons uniquement les **8 catégories principales** déjà définies [ici](https://www.cityscapes-dataset.com/dataset-overview/#class-definitions) : ***flat***, ***human***, ***vehicle***, ***construction***, ***object***, ***nature***, ***sky***, ***void***.\n","- Une diversité importante avec des données provenant de **50 villes différentes**, capturées sur plusieurs mois (printemps, été, automne)\n","- Des conditions variées mais contrôlées (jour, conditions météorologiques bonnes à moyennes)\n","- Des **scènes sélectionnées manuellement pour leur complexité** (nombreux objets dynamiques, variations dans l'agencement des scènes et l'arrière-plan)\n","\n","**Documentation**\n","\n","- https://www.cityscapes-dataset.com/dataset-overview/\n","- https://github.com/mcordts/cityscapesScripts\n","\n"]},{"cell_type":"markdown","metadata":{"id":"t4pOqGkHQQnx"},"source":["Nous utilisons le jeu de données **Cityscapes** qui contient **5 000 images avec des annotations fines**. La séparation entre *train*, *val* et *test* est déjà effectuée :\n","- Entraînement : 2 975\n","- Validation : 500\n","- Test : 1 525\n","\n","Le format Cityscapes fournit plusieurs fichiers d'annotation pour chaque image originale. Cette structure facilite différentes approches de traitement et d'apprentissage. Pour chaque image d'origine, nous disposons de :\n","- Des fichiers `*_gtFine_instanceIds.png` qui encodent à la fois la classe et l'identifiant d'instance de chaque pixel\n","- Des fichiers `*_gtFine_labelIds.png` qui contiennent uniquement les identifiants de classe pour la segmentation sémantique\n","- Des fichiers `*_gtFine_color.png` qui offrent une représentation visuelle colorée des segmentations\n","- Des fichiers `*_gtFine_polygons.json` qui stockent les annotations sous forme vectorielle (polygones)\n","\n","Cette richesse d'annotations nous permettra de développer un modèle robuste pour notre système de vision par ordinateur embarqué chez Future Vision Transport."]},{"cell_type":"markdown","metadata":{"id":"Zm2FnTw3LJx7"},"source":["## Configuration de l'environnement et des dépendances"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":31724,"status":"ok","timestamp":1755184332505,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-120},"id":"gkl0tzdJDgmD","outputId":"b3612e8d-963b-418e-f165-7a8def58b52b"},"outputs":[{"output_type":"stream","name":"stdout","text":[" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m25.8/25.8 MB\u001b[0m \u001b[31m42.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m67.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m54.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.1/140.1 kB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m247.0/247.0 kB\u001b[0m \u001b[31m15.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.0/14.0 MB\u001b[0m \u001b[31m45.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m147.8/147.8 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.9/114.9 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m85.0/85.0 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.7/50.7 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m85.3/85.3 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m688.0/688.0 kB\u001b[0m \u001b[31m37.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m203.4/203.4 kB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.6/65.6 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m120.0/120.0 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m201.6/201.6 kB\u001b[0m \u001b[31m14.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h Building wheel for segmentation_models (setup.py) ... \u001b[?25l\u001b[?25hdone\n"]}],"source":["!pip install mlflow boto3 dotenv git+https://github.com/qubvel/segmentation_models.git --quiet"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6622,"status":"ok","timestamp":1755184339141,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-120},"id":"FQJX9NED7J1B","outputId":"12a74858-e66b-41c1-eca3-9df61ada0e29"},"outputs":[{"output_type":"stream","name":"stdout","text":["Segmentation Models: using `keras` framework.\n"]}],"source":["# ---------------------------------------\n","# Imports Système et Utilitaires\n","# ---------------------------------------\n","import os\n","import sys\n","import random\n","import json\n","import shutil\n","import zipfile\n","from datetime import datetime\n","import glob\n","import time # ← Ajouté pour mesurer le temps d'entraînement\n","import tempfile\n","\n","# ---------------------------------------\n","# Data Science & Manipulation\n","# ---------------------------------------\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from PIL import Image as PILImage\n","from tqdm import tqdm\n","import requests\n","import itertools\n","from sklearn.metrics import confusion_matrix\n","\n","# ---------------------------------------\n","# Scikit-learn : pour gestion du split train/test\n","# ---------------------------------------\n","from sklearn.model_selection import train_test_split\n","\n","# ---------------------------------------\n","# TensorFlow & Keras (version tensorflow.keras pour compatibilité)\n","# ---------------------------------------\n","import tensorflow as tf\n","from tensorflow import data as tf_data\n","from tensorflow import keras\n","from tensorflow.keras import callbacks # ← Ajouté pour les callbacks\n","import tensorflow.keras.backend as K # backend Keras TensorFlow\n","\n","# ---------------------------------------\n","# Segmentation Models : patch pour forcer backend tf.keras\n","# ---------------------------------------\n","import segmentation_models as sm\n","sm.set_framework('tf.keras') # on fixe tf.keras comme backend\n","sm.backend = K # on force le backend keras tensorflow\n","\n","# ---------------------------------------\n","# MLflow : suivi d'expériences & gestion modèles\n","# ---------------------------------------\n","import mlflow\n","import mlflow.keras\n","import mlflow.tensorflow # ← Ajouté pour mlflow.tensorflow.autolog()\n","from mlflow.models.signature import infer_signature\n","from mlflow import MlflowClient\n","from mlflow.models.signature import ModelSignature\n","from mlflow.types import Schema, ColSpec, TensorSpec\n","\n","# ---------------------------------------\n","# Environnements Colab & variables d'environnement\n","# ---------------------------------------\n","from google.colab import userdata\n","from dotenv import load_dotenv\n","\n","# Chargement des variables d'environnement du fichier .env\n","load_dotenv(dotenv_path='../.env')\n","\n","# Ajouter le dossier parent du notebook au PYTHONPATH\n","parent_dir = os.path.abspath('..')\n","if parent_dir not in sys.path:\n"," sys.path.insert(0, parent_dir)\n","\n","# Chargement de fonctions diverses communes à l'entraînement et à l'inférence\n","# from utils.utils import load_cityscapes_config, get_preprocessing_fn #, map_mask_ids\n","from utils.utils import load_cityscapes_config, get_preprocessing_fn, load_best_model_from_registry, map_mask_ids, run_inference_and_visualize"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":334,"status":"ok","timestamp":1755184339492,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-120},"id":"O9oKKrUPQD9s","outputId":"2a7693c6-0aa7-454a-b8bc-e9ce231c1223"},"outputs":[{"output_type":"stream","name":"stdout","text":["TensorFlow version: 2.19.0\n","Keras version: 3.10.0\n","CUDA disponible: []\n","MLflow Tracking URI: https://mlflow.greg-madman-nas.duckdns.org/\n","Identifiants AWS configurés\n","Appareil utilisé: CPU\n"]}],"source":["# Vérification de la configuration\n","print(f\"TensorFlow version: {tf.__version__}\")\n","print(f\"Keras version: {keras.__version__}\")\n","print(f\"CUDA disponible: {tf.config.list_physical_devices('GPU')}\")\n","\n","# Définition des chemins des données\n","g_drive_dir = drive_folder\n","g_drive_dir_content = os.path.join(g_drive_dir, \"content\")\n","\n","# data_dir pointe toujours vers le dossier général des données sur Google Drive\n","data_dir = os.path.join(g_drive_dir_content, \"data\")\n","\n","# Chemin pour le stockage des données extraites (dans l'environnement Colab temporaire)\n","cityscapes_unzipped_dir = os.path.join(\"/content\", \"data\", \"cityscapes_unzipped\")\n","\n","\n","# Création des dossiers s'il n'existent pas déjà\n","os.makedirs(data_dir, exist_ok=True) # Créer le dossier data sur Drive\n","os.makedirs(os.path.join(g_drive_dir_content, 'models'), exist_ok=True) # Dossier modèles sur Drive\n","os.makedirs(os.path.join(g_drive_dir_content, 'models', 'checkpoints'), exist_ok=True) # Dossier checkpoints sur Drive\n","os.makedirs(os.path.join(g_drive_dir_content, 'logs'), exist_ok=True) # Dossier logs sur Drive\n","\n","\n","# Configuration des graines aléatoires pour la reproductibilité\n","seed = 42\n","random.seed(seed)\n","np.random.seed(seed)\n","tf.random.set_seed(seed)\n","\n","# Configuration de MLflow avec les variables d'environnement\n","mlflow_tracking_uri = os.getenv(\"MLFLOW_TRACKING_URI\")\n","mlflow_s3_endpoint_url = os.getenv(\"MLFLOW_S3_ENDPOINT_URL\")\n","aws_access_key_id = os.getenv(\"AWS_ACCESS_KEY_ID\")\n","aws_secret_access_key = os.getenv(\"AWS_SECRET_ACCESS_KEY\")\n","\n","# Configuration explicite de MLflow\n","mlflow.set_tracking_uri(mlflow_tracking_uri)\n","print(f\"MLflow Tracking URI: {mlflow_tracking_uri}\")\n","\n","# Configuration explicite des identifiants AWS\n","os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = mlflow_s3_endpoint_url\n","os.environ[\"AWS_ACCESS_KEY_ID\"] = aws_access_key_id\n","os.environ[\"AWS_SECRET_ACCESS_KEY\"] = aws_secret_access_key\n","print(\"Identifiants AWS configurés\")\n","\n","# Paramètres globaux\n","device = 'GPU' if tf.config.list_physical_devices('GPU') else 'CPU'\n","print(f\"Appareil utilisé: {device}\")"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2075,"status":"ok","timestamp":1755184341568,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-120},"id":"DR-_gM8JIZpr","outputId":"8e475840-8b2a-4826-9f0b-aa3626ae2083"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":9}],"source":["# Créer l'expérience MLflow\n","mlflow_experiemnt = \"OC Projet 8\"\n","mlflow.set_experiment(mlflow_experiemnt)"]},{"cell_type":"markdown","metadata":{"id":"P4O-U_2rQEJa"},"source":["## Téléchargement et préparation des données Cityscapes"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":275878,"status":"ok","timestamp":1755000385849,"user":{"displayName":"Grégoire Mureau","userId":"04855910922109670788"},"user_tz":-120},"id":"t52l8Er1QEcx","outputId":"95c2e77f-c78c-44d8-8ba2-1651c8aa50cb"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","=== VÉRIFICATION ET TÉLÉCHARGEMENT DES FICHIERS ZIP ===\n","Le fichier P8_Cityscapes_gtFine_trainvaltest.zip existe déjà. Pas de téléchargement.\n","Le fichier P8_Cityscapes_leftImg8bit_trainvaltest.zip existe déjà. Pas de téléchargement.\n","✅ Fichiers zip vérifiés ou téléchargés dans Google Drive.\n","\n","=== EXTRACTION DES FICHIERS ZIP VERS L'ENVIRONNEMENT TEMPORAIRE ===\n","Extraction vers: /content/data/cityscapes_unzipped\n","Extraction des annotations (gtFine)...\n"]},{"output_type":"stream","name":"stderr","text":["Extraction gtFine: 100%|██████████| 20032/20032 [00:19<00:00, 1053.46it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Extraction des annotations terminée.\n","Extraction des images (leftImg8bit)...\n"]},{"output_type":"stream","name":"stderr","text":["Extraction leftImg8bit: 100%|██████████| 5032/5032 [04:07<00:00, 20.34it/s] "]},{"output_type":"stream","name":"stdout","text":["Extraction des images terminée.\n","\n","✅ Préparation des données Cityscapes (extraction) terminée dans l'environnement temporaire.\n","\n","Variable 'cityscapes_dir' mise à jour vers: /content/data/cityscapes_unzipped\n"]},{"output_type":"stream","name":"stderr","text":["\n"]}],"source":["def download_and_extract_cityscapes():\n"," \"\"\"Télécharge et extrait les données Cityscapes si elles n'existent pas déjà\"\"\"\n","\n"," # Création des répertoires nécessaires s'ils n'existent pas\n"," os.makedirs(data_dir, exist_ok=True) # data_dir est sur Drive pour stocker les zips\n"," os.makedirs(cityscapes_unzipped_dir, exist_ok=True) # Créer le dossier unzipped dans /content\n","\n"," # Liens vers les fichiers zip Cityscapes\n"," gtFine_url = \"https://s3-eu-west-1.amazonaws.com/static.oc-static.com/prod/courses/files/AI+Engineer/Project+8+-+Participez+%C3%A0+la+conception+d'une+voiture+autonome/P8_Cityscapes_gtFine_trainvaltest.zip\"\n"," leftImg_url = \"https://s3-eu-west-1.amazonaws.com/static.oc-static.com/prod/courses/files/AI+Engineer/Project+8+-+Participez+%C3%A0+la+conception+d'une+voiture+autonome/P8_Cityscapes_leftImg8bit_trainvaltest.zip\"\n","\n"," # Chemins locaux pour les fichiers zip (sur Google Drive dans data_dir)\n"," gtFine_zip = os.path.join(data_dir, \"P8_Cityscapes_gtFine_trainvaltest.zip\")\n"," leftImg_zip = os.path.join(data_dir, \"P8_Cityscapes_leftImg8bit_trainvaltest.zip\")\n","\n"," # Fonction pour télécharger un fichier avec barre de progression\n"," def download_file(url, filename):\n"," # Vérifier si le fichier existe déjà et a une taille non nulle\n"," if os.path.exists(filename) and os.path.getsize(filename) > 0:\n"," print(f\"Le fichier {os.path.basename(filename)} existe déjà. Pas de téléchargement.\")\n"," return True # Considérer comme succès si le fichier existe\n","\n"," response = requests.get(url, stream=True)\n"," response.raise_for_status() # Vérification des erreurs HTTP\n","\n"," total_size = int(response.headers.get('content-length', 0))\n"," block_size = 1024 # 1 KB\n"," # Utilisation de tqdm pour une barre de progression interactive\n"," progress_bar = tqdm(total=total_size, unit='iB', unit_scale=True, desc=f\"Téléchargement {os.path.basename(filename)}\")\n","\n"," with open(filename, 'wb') as file:\n"," for data in response.iter_content(block_size):\n"," progress_bar.update(len(data))\n"," file.write(data)\n","\n"," progress_bar.close()\n","\n"," if total_size != 0 and progress_bar.n != total_size:\n"," print(f\"ERREUR: le téléchargement de {os.path.basename(filename)} n'est pas complet\")\n"," return False\n","\n"," return True\n","\n"," # --- Logique de Téléchargement et Extraction ---\n"," # Vérification si les données extraites existent déjà dans /content/data/cityscapes_unzipped\n"," # On vérifie la présence d'un dossier caractéristique des données extraites\n"," extracted_data_check_path = os.path.join(cityscapes_unzipped_dir, \"gtFine\", \"train\")\n","\n"," # Si les données extraites ne sont PAS présentes dans le répertoire temporaire\n"," if not os.path.exists(extracted_data_check_path):\n"," print(\"\\n=== VÉRIFICATION ET TÉLÉCHARGEMENT DES FICHIERS ZIP ===\")\n","\n"," # Téléchargement des fichiers zip si nécessaire (dans data_dir sur Drive)\n"," download_success_gtFine = download_file(gtFine_url, gtFine_zip)\n"," download_success_leftImg = download_file(leftImg_url, leftImg_zip)\n","\n"," if not download_success_gtFine or not download_success_leftImg:\n"," print(\"❌ Échec du téléchargement d'un ou plusieurs fichiers zip.\")\n"," return False\n","\n"," print(\"✅ Fichiers zip vérifiés ou téléchargés dans Google Drive.\")\n","\n"," print(\"\\n=== EXTRACTION DES FICHIERS ZIP VERS L'ENVIRONNEMENT TEMPORAIRE ===\")\n"," print(f\"Extraction vers: {cityscapes_unzipped_dir}\")\n","\n"," # Extraction des fichiers zip VERS cityscapes_unzipped_dir (/content/data/cityscapes_unzipped)\n"," print(\"Extraction des annotations (gtFine)...\")\n"," try:\n"," with zipfile.ZipFile(gtFine_zip, 'r') as zip_ref:\n"," # Extraction avec barre de progression\n"," for member in tqdm(zip_ref.infolist(), desc=\"Extraction gtFine\"):\n"," zip_ref.extract(member, cityscapes_unzipped_dir) # <-- Extraction vers le dossier /content\n"," print(\"Extraction des annotations terminée.\")\n"," except zipfile.BadZipFile:\n"," print(f\"❌ Erreur: {gtFine_zip} n'est pas un fichier ZIP valide.\")\n"," return False\n"," except Exception as e:\n"," print(f\"❌ Erreur lors de l'extraction de gtFine: {e}\")\n"," return False\n","\n","\n"," print(\"Extraction des images (leftImg8bit)...\")\n"," try:\n"," with zipfile.ZipFile(leftImg_zip, 'r') as zip_ref:\n"," # Extraction avec barre de progression\n"," for member in tqdm(zip_ref.infolist(), desc=\"Extraction leftImg8bit\"):\n"," zip_ref.extract(member, cityscapes_unzipped_dir) # <-- Extraction vers le dossier /content\n"," print(\"Extraction des images terminée.\")\n"," except zipfile.BadZipFile:\n"," print(f\"❌ Erreur: {leftImg_zip} n'est pas un fichier ZIP valide.\")\n"," return False\n"," except Exception as e:\n"," print(f\"❌ Erreur lors de l'extraction de leftImg8bit: {e}\")\n"," return False\n","\n"," print(\"\\n✅ Préparation des données Cityscapes (extraction) terminée dans l'environnement temporaire.\")\n"," else:\n"," print(\"\\n✅ Les données Cityscapes semblent déjà extraites dans l'environnement temporaire.\")\n","\n"," return True\n","\n","# Télécharger et extraire les données Cityscapes en utilisant les nouveaux chemins\n","download_and_extract_cityscapes()\n","\n","# Mettre à jour la variable cityscapes_dir pour qu'elle pointe vers le dossier des données extraites\n","# Les fonctions suivantes (préparation, visualisation) utiliseront cette variable\n","cityscapes_dir = cityscapes_unzipped_dir\n","print(f\"\\nVariable 'cityscapes_dir' mise à jour vers: {cityscapes_dir}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21,"status":"ok","timestamp":1755000385871,"user":{"displayName":"Grégoire Mureau","userId":"04855910922109670788"},"user_tz":-120},"id":"xFjmYex0PMM1","outputId":"d04df292-6d65-4b3c-c7cb-5ade29ecb0a1"},"outputs":[{"output_type":"stream","name":"stdout","text":["train dataset: 2975 images found\n"]}],"source":["split = 'train'\n","images_dir = os.path.join(cityscapes_unzipped_dir, 'leftImg8bit', split)\n","masks_dir = os.path.join(cityscapes_unzipped_dir, 'gtFine', split)\n","\n","# Lists to store image and mask paths\n","image_paths = []\n","mask_paths = []\n","\n","# Go through all city directories\n","for city in os.listdir(images_dir):\n"," city_img_dir = os.path.join(images_dir, city)\n"," city_mask_dir = os.path.join(masks_dir, city)\n","\n"," # Find all matching image and mask files\n"," for file_name in os.listdir(city_img_dir):\n"," if file_name.endswith('_leftImg8bit.png'):\n"," img_path = os.path.join(city_img_dir, file_name)\n"," mask_name = file_name.replace('_leftImg8bit.png', '_gtFine_labelIds.png')\n"," mask_path = os.path.join(city_mask_dir, mask_name)\n","\n"," if os.path.exists(mask_path):\n"," image_paths.append(img_path)\n"," mask_paths.append(mask_path)\n","\n","print(f\"{split} dataset: {len(image_paths)} images found\")"]},{"cell_type":"markdown","metadata":{"id":"UeD9y0LBQEws"},"source":["## Définition des classes et mapping pour la segmentation"]},{"cell_type":"markdown","metadata":{"id":"YC-BHFBhyeha"},"source":["Les définitions de classes officielles sont présentes [ici](https://www.cityscapes-dataset.com/dataset-overview/#class-definitions)."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12466,"status":"ok","timestamp":1755000398350,"user":{"displayName":"Grégoire Mureau","userId":"04855910922109670788"},"user_tz":-120},"id":"7K-ox-l81X9A","outputId":"f052f48b-a340-46bd-d00c-3709bedd69c4"},"outputs":[{"output_type":"stream","name":"stdout","text":["Exploration du répertoire : /content/data/cityscapes_unzipped/gtFine\n","\n","Nombre total de fichiers JSON '_gtFine_polygons.json' trouvés : 5000\n","\n","Valeurs uniques de 'label' trouvées :\n","- bicycle\n","- bicyclegroup\n","- bridge\n","- building\n","- bus\n","- car\n","- caravan\n","- cargroup\n","- dynamic\n","- ego vehicle\n","- fence\n","- ground\n","- guard rail\n","- license plate\n","- motorcycle\n","- motorcyclegroup\n","- out of roi\n","- parking\n","- person\n","- persongroup\n","- pole\n","- polegroup\n","- rail track\n","- rectification border\n","- rider\n","- ridergroup\n","- road\n","- sidewalk\n","- sky\n","- static\n","- terrain\n","- traffic light\n","- traffic sign\n","- trailer\n","- train\n","- truck\n","- truckgroup\n","- tunnel\n","- vegetation\n","- wall\n","\n","Nombre de valeurs uniques de 'label' : 40\n"]}],"source":["import os\n","import json\n","\n","# Chemin vers le répertoire gtFine (assurez-vous que cityscapes_unzipped_dir est défini)\n","gtFine_dir = os.path.join(cityscapes_unzipped_dir, 'gtFine')\n","\n","if not os.path.exists(gtFine_dir):\n"," print(f\"Le répertoire {gtFine_dir} n'existe pas.\")\n","else:\n"," print(f\"Exploration du répertoire : {gtFine_dir}\")\n"," all_labels = set()\n"," json_files_count = 0\n","\n"," # Parcourir tous les sous-répertoires et fichiers\n"," for root, dirs, files in os.walk(gtFine_dir):\n"," for file in files:\n"," if file.endswith('_gtFine_polygons.json'):\n"," json_files_count += 1\n"," json_path = os.path.join(root, file)\n"," try:\n"," with open(json_path, 'r', encoding='utf-8') as f:\n"," data = json.load(f)\n"," # Assurez-vous que la structure JSON correspond à ce que vous attendez\n"," # Ici, on suppose que 'objects' est une liste d'objets,\n"," # et chaque objet a une clé 'label'\n"," if 'objects' in data:\n"," for obj in data['objects']:\n"," if 'label' in obj:\n"," all_labels.add(obj['label'])\n"," except Exception as e:\n"," print(f\"Erreur lors du traitement du fichier {json_path}: {e}\")\n","\n"," print(f\"\\nNombre total de fichiers JSON '_gtFine_polygons.json' trouvés : {json_files_count}\")\n"," print(\"\\nValeurs uniques de 'label' trouvées :\")\n"," # Convertir l'ensemble en liste et la trier pour un affichage cohérent\n"," unique_labels_list = sorted(list(all_labels))\n"," for label in unique_labels_list:\n"," print(f\"- {label}\")\n","\n"," print(f\"\\nNombre de valeurs uniques de 'label' : {len(unique_labels_list)}\")"]},{"cell_type":"markdown","metadata":{"id":"7bf84c3b"},"source":["Le dataset Cityscapes fournit plusieurs types de fichiers d'annotations, ce qui peut prêter à confusion. Pour notre tâche de **segmentation sémantique**, où chaque pixel est assigné à une catégorie de classe, nous nous basons spécifiquement sur les fichiers `*_gtFine_labelIds.png`.\n","\n","Ces fichiers PNG en niveaux de gris encodent la classe de chaque pixel à l'aide d'un **ID numérique**. Les IDs utilisés dans ces fichiers vont de **0 à 33**.\n","\n","Les définitions et la correspondance entre ces IDs numériques et les noms de classes sont documentées dans le script `labels.py` du dépôt CityscapesScripts ([`cityscapesscripts/helpers/labels.py`](https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py)). Ce fichier liste 34 entrées (IDs 0 à 33) avec leurs noms, catégories, couleurs, etc.\n","\n","Parallèlement, le dataset inclut d'autres formats d'annotations, comme les fichiers `*_gtFine_polygons.json`. Ces fichiers contiennent des informations plus détaillées, y compris des annotations vectorielles (polygones) et peuvent référencer un ensemble plus large de labels, potentiellement 40 catégories comme observé lors de l'exploration des fichiers JSON, incluant des labels d'instances ou de groupes (comme 'person group') qui ne sont pas représentés par des IDs distincts dans les fichiers `*_gtFine_labelIds.png` utilisés pour la segmentation sémantique pixel-par-pixel.\n","\n","De même, le fichier `labels_cityPersons.py` ([`cityscapesscripts/helpers/labels_cityPersons.py`](https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels_cityPersons.py)) définit des classes spécifiquement pour l'annotation des instances de personnes et de cyclistes, qui sont utilisées dans d'autres contextes ou formats d'annotation du dataset, mais pas dans les `*_gtFine_labelIds.png` pour la segmentation sémantique de base.\n","\n","**Pour notre projet de segmentation sémantique, nous utilisons donc uniquement les IDs numériques présents dans les fichiers `*_gtFine_labelIds.png` et nous mappons ces IDs (0 à 33) à nos 8 groupes de classes définis.** L'exploration des labels dans les fichiers JSON était utile pour comprendre la richesse du dataset, mais le mapping du modèle se base sur les IDs des masques `_labelIds.png`."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":457,"status":"ok","timestamp":1755000398808,"user":{"displayName":"Grégoire Mureau","userId":"04855910922109670788"},"user_tz":-120},"id":"4YmGYVW56FMt","outputId":"6dd2c8e7-55b4-4a21-ef19-0ce794370082"},"outputs":[{"output_type":"stream","name":"stdout","text":["== Groupes de classes Cityscapes ==\n"," - flat : ['road', 'sidewalk', 'parking', 'rail track']\n"," - human : ['person', 'rider']\n"," - vehicle : ['car', 'truck', 'bus', 'train', 'motorcycle', 'bicycle', 'caravan', 'trailer']\n"," - construction : ['building', 'wall', 'fence', 'guard rail', 'bridge', 'tunnel']\n"," - object : ['pole', 'pole group', 'traffic sign', 'traffic light']\n"," - nature : ['vegetation', 'terrain']\n"," - sky : ['sky']\n"," - void : ['unlabeled', 'ego vehicle', 'rectification border', 'out of roi', 'static', 'dynamic', 'ground']\n","\n","== Mapping ID Cityscapes → Groupe ==\n"," ID 0 ('unlabeled') → groupe 'void' (7)\n"," ID 1 ('ego vehicle') → groupe 'void' (7)\n"," ID 2 ('rectification border') → groupe 'void' (7)\n"," ID 3 ('out of roi') → groupe 'void' (7)\n"," ID 4 ('static') → groupe 'void' (7)\n"," ID 5 ('dynamic') → groupe 'void' (7)\n"," ID 6 ('ground') → groupe 'void' (7)\n"," ID 7 ('road') → groupe 'flat' (0)\n"," ID 8 ('sidewalk') → groupe 'flat' (0)\n"," ID 9 ('parking') → groupe 'flat' (0)\n"," ID 10 ('rail track') → groupe 'flat' (0)\n"," ID 11 ('building') → groupe 'construction' (3)\n"," ID 12 ('wall') → groupe 'construction' (3)\n"," ID 13 ('fence') → groupe 'construction' (3)\n"," ID 14 ('guard rail') → groupe 'construction' (3)\n"," ID 15 ('bridge') → groupe 'construction' (3)\n"," ID 16 ('tunnel') → groupe 'construction' (3)\n"," ID 17 ('pole') → groupe 'object' (4)\n"," ID 18 ('pole group') → groupe 'object' (4)\n"," ID 19 ('traffic light') → groupe 'object' (4)\n"," ID 20 ('traffic sign') → groupe 'object' (4)\n"," ID 21 ('vegetation') → groupe 'nature' (5)\n"," ID 22 ('terrain') → groupe 'nature' (5)\n"," ID 23 ('sky') → groupe 'sky' (6)\n"," ID 24 ('person') → groupe 'human' (1)\n"," ID 25 ('rider') → groupe 'human' (1)\n"," ID 26 ('car') → groupe 'vehicle' (2)\n"," ID 27 ('truck') → groupe 'vehicle' (2)\n"," ID 28 ('bus') → groupe 'vehicle' (2)\n"," ID 29 ('caravan') → groupe 'vehicle' (2)\n"," ID 30 ('trailer') → groupe 'vehicle' (2)\n"," ID 31 ('train') → groupe 'vehicle' (2)\n"," ID 32 ('motorcycle') → groupe 'vehicle' (2)\n"," ID 33 ('bicycle') → groupe 'vehicle' (2)\n","\n","== Liste des groupes et couleurs associées ==\n"," Groupe 0: flat - Couleur RGB : [128 64 128]\n"," Groupe 1: human - Couleur RGB : [220 20 60]\n"," Groupe 2: vehicle - Couleur RGB : [ 0 0 142]\n"," Groupe 3: construction - Couleur RGB : [70 70 70]\n"," Groupe 4: object - Couleur RGB : [220 220 0]\n"," Groupe 5: nature - Couleur RGB : [107 142 35]\n"," Groupe 6: sky - Couleur RGB : [ 70 130 180]\n"," Groupe 7: void - Couleur RGB : [0 0 0]\n","\n","Nombre total de groupes/classes : 8\n"]}],"source":["mapping_config = load_cityscapes_config(\"../cityscapes_config.json\")"]},{"cell_type":"markdown","metadata":{"id":"MqYRJXp53zci"},"source":["Le tableau `id_to_group` a une taille de **256 éléments** car il correspond à toutes les valeurs possibles sur **8 bits** (de 0 à 255), soit la plage complète des IDs de classes que peuvent prendre les masques d'annotation Cityscapes.\n","\n","Même si dans notre cas précis on ne mappe que 34 IDs utilisés, on crée un tableau complet pour :\n","\n","- Permettre un accès direct et rapide par index (ID) sans calcul supplémentaire.\n","- Gérer des masques pouvant contenir n'importe quelle valeur d'ID possible (y compris des valeurs non utilisées).\n","- Assurer la robustesse de la conversion sans erreurs d’index hors limites.\n","\n","Ainsi, chaque valeur d’ID dans un masque peut être convertie en son groupe associé en un accès simple via `id_to_group[ID]`.\n","\n","Les IDs non présents dans notre mapping restent mappés à 0 (par défaut), mais on peut aussi choisir une autre valeur spéciale pour représenter une classe \"void\" ou \"non définie\".\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1755000398820,"user":{"displayName":"Grégoire Mureau","userId":"04855910922109670788"},"user_tz":-120},"id":"2kLbBJ8M1e5o","outputId":"35ac34c9-6195-441d-ab64-79c74f27cccd"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 5,\n"," 5, 6, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n"," 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n"," 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n"," 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n"," 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n"," 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n"," 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n"," 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n"," 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n"," 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n"," 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=uint8)"]},"metadata":{},"execution_count":14}],"source":["id_to_group = mapping_config['id_to_group']\n","id_to_group"]},{"cell_type":"markdown","metadata":{"id":"6A4cu2ZeJKCI"},"source":["## Architecture de Segmentation 🖼️✨\n","\n","Pour la tâche de segmentation d’images, deux architectures principales sont utilisées dans cette étude : **U-Net** et **FPN (Feature Pyramid Network)**.\n","\n","---\n","\n","### Les architectures principales 🏗️\n","\n","- **U-Net** 🔄 \n"," U-Net est composée de deux parties symétriques :\n"," - 🧱 **Un encodeur (chemin descendant)** qui extrait les caractéristiques de plus en plus abstraites grâce à des convolutions et du max-pooling.\n"," - 🏗️ **Un décodeur (chemin ascendant)** qui reconstruit une image de même taille que l’entrée à l’aide d’upsampling (ou transposed convolutions).\n"," - Chaque niveau du décodeur reçoit également des **\"skip connections\"** venant de l’encodeur à la même résolution, pour préserver les détails spatiaux.\n","\n"," U-Net est donc efficace quand il faut localiser précisément des objets (par exemple en imagerie médicale).\n","\n"," \n","\n","- **FPN (Feature Pyramid Network)** 🧭 \n"," FPN est une architecture conçue pour capturer efficacement des objets à **différentes échelles**, grâce à une combinaison intelligente de caractéristiques extraites à plusieurs résolutions.\n","\n"," Elle repose sur deux étapes principales :\n"," \n"," 1. 🔽 **Chemin descendant (bottom-up)** : \n"," L’image passe dans un encodeur profond (souvent un ResNet), qui génère des cartes de caractéristiques à plusieurs résolutions (`1/4`, `1/8`, `1/16`, `1/32`). \n"," Plus la résolution est petite, plus l’information est abstraite.\n"," \n"," 2. 🔼 **Chemin ascendant (top-down)** : \n"," Les cartes profondes sont **upsamplées progressivement** (x2 à chaque étape) et **fusionnées avec des cartes plus superficielles** à l’aide de *connexions latérales* (lateral connections). \n"," Cette fusion permet de combiner des informations **globales et locales** à chaque niveau de la pyramide.\n","\n"," Finalement, les différentes cartes sont combinées et passées dans un petit réseau convolutionnel (souvent un 3x3) pour produire la **prédiction finale**.\n","\n"," 👉 Cette approche rend FPN particulièrement efficace sur des images avec des objets de **tailles très variées**, comme en vision par ordinateur généraliste (urbain, aérien, etc.).\n","\n"," Le modèle a été historiquement entraîné avec ResNet50 et ResNet101 par l'équipe de chercheurs.\n","> **“We build FPN on top of ResNet-50/101 [21], which are used as backbone feature extractors.”** \n","> — *Feature Pyramid Networks for Object Detection*, \n","> Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Serge Belongie. \n","> *CVPR 2017* \n","> [arXiv:1612.03144](https://arxiv.org/abs/1612.03144)\n","\n","\n"," *(Le schéma ci-dessous illustre cette architecture dans le contexte original de la détection, mais le principe est le même en segmentation sémantique.)*\n","\n"," 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)\n","\n","---\n","\n","### Choix des encodeurs 🧠\n","\n","Plusieurs encodeurs sont disponibles pour ces architectures : \n","- **Encodeur intégré** 🆕 : L’encodeur est entraîné en même temps que le décodeur, sans poids pré-entraînés. Pour U-Net, il a été développé *from scratch* mais pour FPN, il s'agira de ResNet34 (plus léger que ResNet50, le modèle de feature extraction originellement utilisé dans les travaux de recherche).\n","- **MobilenetV2 pré-entraîné** 🚀 : Un encodeur léger et efficace, pré-entraîné sur ImageNet, permettant un bon compromis entre rapidité et performance. \n","- **EfficientNetB0 pré-entraîné** ⚡ : Un encodeur plus puissant et performant, également basé sur un modèle pré-entraîné, utilisé pour tirer parti du transfert learning.\n","\n","ℹ️ Par défaut, la bibliothèque `segmentation_models` utilise **ResNet34 pré-entraîné sur ImageNet** comme encodeur si aucun autre n’est spécifié.\n","\n","---\n","\n","### Gel ou fine-tuning de l’encodeur ❄️🔥\n","\n","Lors de l’entraînement, deux stratégies sont possibles : \n","- **Encodage gelé** ❄️ : Les poids de l’encodeur pré-entraîné restent fixes, ce qui accélère l’entraînement et évite la modification de caractéristiques déjà apprises. \n","- **Encodage entraînable (fine-tuning)** 🔥 : Les poids de l’encodeur sont ajustés pour mieux s’adapter aux données spécifiques, ce qui peut améliorer les performances au prix d’un temps d’entraînement plus long.\n","\n","---\n","\n","### Stratégie recommandée pour l’expérimentation 🎯\n","\n","1. **Tester toutes les architectures et encodeurs avec l’encodeur gelé** ❄️ pour un benchmark rapide. \n","2. **Sélectionner la meilleure configuration** 🏆 selon les performances obtenues. \n","3. **Refaire l’entraînement avec l’encodeur dégelé (fine-tuning)** 🔥 pour optimiser les résultats finaux.\n","\n","Cette méthode permet de gagner du temps tout en explorant efficacement l’espace des modèles.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"s5te0Fcwtx51"},"source":["### Création des modèles"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1755000398851,"user":{"displayName":"Grégoire Mureau","userId":"04855910922109670788"},"user_tz":-120},"id":"8eHYsAmIRsIU","outputId":"786a2465-6e3a-4278-e5da-f46912fac4f9"},"outputs":[{"output_type":"stream","name":"stdout","text":["🔧 BACKBONES DISPONIBLES:\n","==================================================\n","⭐ RECOMMANDÉS pour voiture autonome (vitesse/précision):\n"," • mobilenetv2\n"," • efficientnetb0\n"," • resnet34\n"," • resnet50\n","\n","📋 TOUS DISPONIBLES (32):\n","\n"," resnet18 resnet34 resnet50 resnet101 \n"," resnet152 seresnet18 seresnet34 seresnet50 \n"," seresnet101 seresnet152 seresnext50 seresnext101 \n"," senet154 resnext50 resnext101 vgg16 \n"," vgg19 densenet121 densenet169 densenet201 \n"," inceptionresnetv2 inceptionv3 mobilenet mobilenetv2 \n"," efficientnetb0 efficientnetb1 efficientnetb2 efficientnetb3 \n"," efficientnetb4 efficientnetb5 efficientnetb6 efficientnetb7 \n"]}],"source":["def show_available_backbones():\n"," \"\"\"Affiche tous les backbones disponibles dans segmentation_models\"\"\"\n","\n"," # Essayer de récupérer la liste des backbones disponibles\n"," try:\n"," available_backbones = sm.get_available_backbones()\n","\n"," print(\"🔧 BACKBONES DISPONIBLES DANS SEGMENTATION_MODELS:\")\n"," print(\"=\" * 60)\n","\n"," # Grouper par famille\n"," families = {\n"," 'ResNet': [b for b in available_backbones if b.startswith('resnet')],\n"," 'SEResNet': [b for b in available_backbones if b.startswith('seresnet')],\n"," 'SEResNeXt': [b for b in available_backbones if b.startswith('seresnext')],\n"," 'ResNeXt': [b for b in available_backbones if b.startswith('resnext')],\n"," 'VGG': [b for b in available_backbones if b.startswith('vgg')],\n"," 'DenseNet': [b for b in available_backbones if b.startswith('densenet')],\n"," 'Inception': [b for b in available_backbones if 'inception' in b],\n"," 'MobileNet': [b for b in available_backbones if 'mobilenet' in b],\n"," 'EfficientNet': [b for b in available_backbones if 'efficientnet' in b],\n"," 'SENet': [b for b in available_backbones if b.startswith('senet')],\n"," }\n","\n"," for family, models in families.items():\n"," if models:\n"," print(f\"\\n📱 {family}:\")\n"," for model in sorted(models):\n"," print(f\" • {model}\")\n","\n"," except AttributeError:\n"," # Si la méthode n'existe pas, utiliser la liste connue\n"," known_backbones = [\n"," 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',\n"," 'seresnet18', 'seresnet34', 'seresnet50', 'seresnet101', 'seresnet152',\n"," 'seresnext50', 'seresnext101', 'senet154', 'resnext50', 'resnext101',\n"," 'vgg16', 'vgg19', 'densenet121', 'densenet169', 'densenet201',\n"," 'inceptionresnetv2', 'inceptionv3', 'mobilenet', 'mobilenetv2',\n"," 'efficientnetb0', 'efficientnetb1', 'efficientnetb2', 'efficientnetb3',\n"," 'efficientnetb4', 'efficientnetb5', 'efficientnetb6', 'efficientnetb7'\n"," ]\n","\n"," print(\"🔧 BACKBONES DISPONIBLES:\")\n"," print(\"=\" * 50)\n","\n"," # Recommandations pour voiture autonome\n"," recommended = ['mobilenetv2', 'efficientnetb0', 'resnet34', 'resnet50']\n","\n"," print(\"⭐ RECOMMANDÉS pour voiture autonome (vitesse/précision):\")\n"," for model in recommended:\n"," print(f\" • {model}\")\n","\n"," print(f\"\\n📋 TOUS DISPONIBLES ({len(known_backbones)}):\")\n"," for i, model in enumerate(known_backbones):\n"," if i % 4 == 0: # Nouvelle ligne tous les 4\n"," print()\n"," print(f\" {model:<20}\", end=\"\")\n"," print() # Ligne finale\n","\n","# Afficher les backbones disponibles\n","show_available_backbones()"]},{"cell_type":"code","execution_count":20,"metadata":{"id":"3xLKhC8KW5i7","executionInfo":{"status":"ok","timestamp":1755184397149,"user_tz":-120,"elapsed":2,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# ================================================================\n","# GRILLE D'EXPÉRIMENTATIONS - SEGMENTATION SÉMANTIQUE CITYSCAPES\n","# ================================================================\n","\n","# Architectures et backbones à tester\n","model_variants = [\n"," # Baselines (sans backbone pré-entraîné)\n"," ('U-Net', None),\n"," ('FPN', None), # --> l'encodeur choisi sera alors vgg16\n"," # Modèles avec backbones pré-entraînés (encoders gelés)\n"," ('U-Net', 'mobilenetv2'),\n"," ('U-Net', 'efficientnetb0'),\n"," ('FPN', 'mobilenetv2'),\n"," ('FPN', 'efficientnetb0'),\n"," ('FPN', 'resnet34'),\n","]\n","\n","# Fine-tuning sélectif (encoders entraînables)\n","finetune_variants = [\n"," ('U-Net', 'mobilenetv2'),\n"," ('FPN', 'efficientnetb0'),\n"," ('FPN', 'resnet34'),\n","]\n","\n","# Paramètres d'entraînement\n","base_params = {\n"," 'learning_rate': 1e-4,\n"," 'epochs': 20,\n"," 'batch_size': 8,\n"," 'img_size': (224, 224)\n","}\n","\n","finetune_params = {\n"," 'learning_rate': 5e-5, # Plus petit pour le fine-tuning\n"," 'epochs': 30,\n"," 'batch_size': 8,\n"," 'img_size': (224, 224)\n","}\n","\n","# Génération des configurations\n","def generate_experiment_configs():\n"," \"\"\"Génère toutes les configurations d'expérimentations\"\"\"\n"," model_configs = []\n","\n"," # 1. Expériences de base (encodeurs gelés ou vanilla)\n"," for arch, encoder in model_variants:\n"," for augment in [False, True]:\n"," # ✅ Nom correct selon le type\n"," if encoder is None:\n"," config_name = f\"{arch}_vanilla_aug{augment}\"\n"," else:\n"," config_name = f\"{arch}_{encoder}_frozen_aug{augment}\"\n","\n"," model_configs.append({\n"," \"name\": config_name,\n"," \"architecture\": arch,\n"," \"encoder\": encoder,\n"," \"encoder_freeze\": encoder is not None,\n"," \"augmentation\": augment,\n"," \"params\": base_params.copy(),\n"," \"type\": \"baseline\" if encoder is None else \"frozen_pretrained\"\n"," })\n","\n"," # 2. Expériences de fine-tuning (encoders dégelés)\n"," for arch, encoder in finetune_variants:\n"," config_name = f\"{arch}_{encoder}_finetune_augTrue\"\n"," model_configs.append({\n"," \"name\": config_name,\n"," \"architecture\": arch,\n"," \"encoder\": encoder,\n"," \"encoder_freeze\": False, # False = dégelé pour fine-tuning\n"," \"augmentation\": True,\n"," \"params\": finetune_params.copy(),\n"," \"type\": \"finetuned_pretrained\"\n"," })\n","\n"," return model_configs"]},{"cell_type":"code","execution_count":21,"metadata":{"id":"X7wY6OqSZFha","executionInfo":{"status":"ok","timestamp":1755184397321,"user_tz":-120,"elapsed":9,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["def create_model_from_config(config, num_classes, verbose=False):\n"," \"\"\"\n"," Crée un modèle de segmentation à partir d'une configuration d'expérimentation.\n"," \"\"\"\n"," # === EXTRACTION DES PARAMÈTRES ===\n"," architecture = config['architecture']\n"," encoder = config['encoder']\n"," encoder_freeze = config['encoder_freeze']\n"," learning_rate = config['params']['learning_rate']\n"," img_size = config['params']['img_size']\n","\n"," # === MAPPING DES ARCHITECTURES ===\n"," model_classes = {\n"," 'U-Net': sm.Unet,\n"," 'FPN': sm.FPN,\n"," }\n","\n"," if architecture not in model_classes:\n"," raise ValueError(f\"Architecture '{architecture}' non supportée.\")\n","\n"," ModelClass = model_classes[architecture]\n"," input_shape = (*img_size, 3)\n","\n"," if verbose:\n"," print(f\"🔧 Création du modèle '{config['name']}'\")\n"," print(f\" Architecture: {architecture}\")\n"," print(f\" Encoder: {encoder or 'Aucun (vanilla)'}\")\n"," print(f\" Encoder gelé: {encoder_freeze}\")\n","\n"," try:\n"," if encoder is None:\n"," # === MODÈLE VANILLA ===\n"," model = ModelClass(\n"," classes=num_classes,\n"," activation='softmax',\n"," input_shape=input_shape,\n"," )\n"," else:\n"," # === MODÈLE AVEC BACKBONE ===\n"," model = ModelClass(\n"," backbone_name=encoder,\n"," encoder_weights='imagenet',\n"," encoder_freeze=encoder_freeze,\n"," classes=num_classes,\n"," activation='softmax',\n"," input_shape=input_shape,\n"," )\n","\n"," # === COMPILATION ===\n"," model.compile(\n"," optimizer=keras.optimizers.Adam(learning_rate=learning_rate),\n"," loss=sm.losses.categorical_focal_jaccard_loss,\n"," metrics=[sm.metrics.iou_score, 'accuracy']\n"," )\n","\n"," if verbose:\n"," total_params = model.count_params()\n"," trainable_params = sum([tf.keras.backend.count_params(w) for w in model.trainable_weights])\n"," frozen_params = total_params - trainable_params\n"," print(f\" 📊 Paramètres: {total_params:,} total | {trainable_params:,} entraînables | {frozen_params:,} gelés\")\n","\n"," return model\n","\n"," except Exception as e:\n"," print(f\"❌ Erreur: {e}\")\n"," raise"]},{"cell_type":"code","execution_count":22,"metadata":{"id":"4ROnd_8f9_W7","executionInfo":{"status":"ok","timestamp":1755184397675,"user_tz":-120,"elapsed":6,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# Génération des configurations\n","experimental_configurations = generate_experiment_configs()"]},{"cell_type":"markdown","metadata":{"id":"GILXYzC_9jHX"},"source":["### Analyse et compréhension détaillée des modèles"]},{"cell_type":"markdown","metadata":{"id":"5yr47E0qbmmY"},"source":["L'objectif est simplement ici d'afficher des informations générales sur les modèles et notamment vérifier le gel des poids pour les modèles de feature extraction (backbone) qui fournissent des descripteurs aux modèles de segmentation et ce afin de pouvoir comparer les différences de performances."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qdsGkrVQe2mX"},"outputs":[],"source":["def analyze_model_comprehensive(config, num_classes, show_architecture=False, detailed_layers=False):\n"," \"\"\"\n"," Analyse exhaustive d'un modèle de segmentation avec affichage clair des couches gelées/dégelées\n","\n"," Args:\n"," config (dict): Configuration du modèle\n"," num_classes (int): Nombre de classes\n"," show_architecture (bool): Afficher model.summary()\n"," detailed_layers (bool): Afficher TOUTES les couches (sinon juste backbone + résumé)\n","\n"," Returns:\n"," dict: Résultats de l'analyse\n"," \"\"\"\n"," print(f\"\\n{'='*70}\")\n"," print(f\"🏗️ ANALYSE COMPLÈTE: {config['name']}\")\n"," print(f\"{'='*70}\")\n","\n"," # === 1. INFORMATIONS DE CONFIGURATION ===\n"," print(f\"📊 Configuration:\")\n"," print(f\" • Architecture: {config['architecture']}\")\n"," print(f\" • Backbone: {config['encoder'] or 'Aucun (vanilla)'}\")\n"," print(f\" • Encoder gelé: {'✅ Oui' if config.get('encoder_freeze', False) else '❌ Non'}\")\n"," print(f\" • Type: {config['type'].replace('_', ' ').title()}\")\n"," print(f\" • Augmentation: {'✅ Oui' if config['augmentation'] else '❌ Non'}\")\n"," print(f\" • Learning rate: {config['params']['learning_rate']}\")\n"," print(f\" • Époques prévues: {config['params']['epochs']}\")\n"," print(f\" • Taille images: {config['params']['img_size']}\")\n","\n"," try:\n"," # === 2. CRÉATION DU MODÈLE ===\n"," print(f\"\\n🔧 Création du modèle...\")\n"," model = create_model_from_config(config, num_classes, verbose=False)\n","\n"," # === 3. ANALYSE DES PARAMÈTRES ===\n"," total_params = model.count_params()\n"," trainable_params = sum([tf.keras.backend.count_params(w) for w in model.trainable_weights])\n"," frozen_params = total_params - trainable_params\n"," frozen_ratio = (frozen_params / total_params) * 100\n","\n"," print(f\"\\n🔢 Paramètres:\")\n"," print(f\" • Total: {total_params:,}\")\n"," print(f\" • Entraînables: {trainable_params:,} ({(trainable_params/total_params)*100:.1f}%)\")\n"," print(f\" • Gelés: {frozen_params:,} ({frozen_ratio:.1f}%)\")\n","\n"," # === 4. ANALYSE DES COUCHES ===\n"," backbone_layers = []\n"," other_layers = []\n"," backbone_total = 0\n"," backbone_frozen = 0\n","\n"," # Classifier les couches\n"," for layer in model.layers:\n"," layer_params = layer.count_params()\n"," is_backbone = False\n","\n"," if config['encoder']:\n"," backbone_keywords = [\n"," config['encoder'].lower(),\n"," 'encoder',\n"," 'backbone',\n"," 'feature'\n"," ]\n"," is_backbone = any(keyword in layer.name.lower() for keyword in backbone_keywords)\n","\n"," layer_info = {\n"," 'name': layer.name,\n"," 'params': layer_params,\n"," 'trainable': layer.trainable,\n"," 'is_backbone': is_backbone\n"," }\n","\n"," if is_backbone:\n"," backbone_layers.append(layer_info)\n"," backbone_total += layer_params\n"," if not layer.trainable:\n"," backbone_frozen += layer_params\n"," else:\n"," other_layers.append(layer_info)\n","\n"," # === 5. AFFICHAGE DES COUCHES ===\n"," if detailed_layers:\n"," # Affichage complet de toutes les couches\n"," print(f\"\\n🔍 TOUTES LES COUCHES (🟢 = Entraînable, 🔴 = Gelé):\")\n"," print(\"-\" * 70)\n","\n"," all_layers = backbone_layers + other_layers\n"," for i, layer_info in enumerate(all_layers):\n"," icon = \"🔴\" if not layer_info['trainable'] else \"🟢\"\n"," status = \"GELÉ\" if not layer_info['trainable'] else \"ENTRAÎNABLE\"\n","\n"," # Identifier le type de couche\n"," if layer_info['is_backbone']:\n"," layer_type = \" (BACKBONE)\"\n"," elif 'decoder' in layer_info['name'].lower():\n"," layer_type = \" (DECODER)\"\n"," elif 'classifier' in layer_info['name'].lower() or 'final' in layer_info['name'].lower():\n"," layer_type = \" (CLASSIFICATION)\"\n"," elif 'upsample' in layer_info['name'].lower():\n"," layer_type = \" (UPSAMPLING)\"\n"," else:\n"," layer_type = \"\"\n","\n"," print(f\"{i+1:2d}. {icon} {layer_info['name']:<40} {layer_info['params']:>8,} params - {status}{layer_type}\")\n","\n"," else:\n"," # Affichage résumé avec focus sur le backbone\n"," if backbone_layers:\n"," print(f\"\\n🔍 COUCHES DU BACKBONE (🟢 = Entraînable, 🔴 = Gelé):\")\n"," print(\"-\" * 60)\n","\n"," for layer_info in backbone_layers[:8]: # Limite à 8 pour lisibilité\n"," icon = \"🔴\" if not layer_info['trainable'] else \"🟢\"\n"," status = \"GELÉ\" if not layer_info['trainable'] else \"ENTRAÎNABLE\"\n"," print(f\" {icon} {layer_info['name']:<35} {layer_info['params']:>8,} params - {status}\")\n","\n"," if len(backbone_layers) > 8:\n"," print(f\" ... et {len(backbone_layers)-8} autres couches du backbone\")\n","\n"," # Résumé des autres couches\n"," other_frozen = sum(1 for layer in other_layers if not layer['trainable'])\n"," other_trainable = len(other_layers) - other_frozen\n"," print(f\"\\n🏗️ AUTRES COUCHES (résumé):\")\n"," print(f\" • {other_trainable} couches entraînables\")\n"," print(f\" • {other_frozen} couches gelées\")\n"," print(f\" • (Utilisez detailed_layers=True pour voir toutes les couches)\")\n","\n"," # === 6. RÉSUMÉ BACKBONE ===\n"," if backbone_layers:\n"," backbone_frozen_ratio = (backbone_frozen / backbone_total * 100) if backbone_total > 0 else 0\n"," print(f\"\\n📋 Résumé backbone:\")\n"," print(f\" • Total backbone: {backbone_total:,} params\")\n"," print(f\" • Backbone gelé: {backbone_frozen:,} params ({backbone_frozen_ratio:.1f}%)\")\n"," print(f\" • Backbone entraînable: {backbone_total - backbone_frozen:,} params ({100-backbone_frozen_ratio:.1f}%)\")\n","\n"," # === 7. VALIDATION DU GEL ===\n"," if config['encoder']:\n"," print(f\"\\n🎯 Validation du gel du backbone:\")\n","\n"," # Seuils adaptatifs selon le backbone\n"," backbone_name = config['encoder'].lower()\n"," if 'mobilenet' in backbone_name:\n"," expected_frozen_range = (20, 35)\n"," elif 'efficientnet' in backbone_name and 'b0' in backbone_name:\n"," expected_frozen_range = (15, 25)\n"," elif 'resnet' in backbone_name:\n"," expected_frozen_range = (18, 28)\n"," else:\n"," expected_frozen_range = (10, 30)\n","\n"," if config.get('encoder_freeze', False):\n"," # Modèle supposé gelé\n"," if frozen_ratio >= expected_frozen_range[0]:\n"," status = \"✅ OK\"\n"," message = f\"Backbone correctement gelé ({frozen_ratio:.1f}% ≥ {expected_frozen_range[0]}%)\"\n"," else:\n"," status = \"⚠️ SUSPECT\"\n"," message = f\"Gel potentiellement incomplet ({frozen_ratio:.1f}% < {expected_frozen_range[0]}%)\"\n"," else:\n"," # Modèle supposé dégelé\n"," if frozen_ratio < 5.0:\n"," status = \"✅ OK\"\n"," message = f\"Backbone correctement dégelé ({frozen_ratio:.1f}% < 5%)\"\n"," else:\n"," status = \"⚠️ SUSPECT\"\n"," message = f\"Gel résiduel inattendu ({frozen_ratio:.1f}% ≥ 5%)\"\n","\n"," print(f\" • Statut global: {status}\")\n"," print(f\" • {message}\")\n"," print(f\" • Plage attendue pour {config['encoder']}: {expected_frozen_range[0]}-{expected_frozen_range[1]}%\")\n","\n"," # Validation spécifique du backbone\n"," if backbone_total > 0:\n"," backbone_expected_frozen = config.get('encoder_freeze', False)\n"," backbone_actually_frozen = (backbone_frozen / backbone_total) > 0.9\n","\n"," if backbone_expected_frozen == backbone_actually_frozen:\n"," backbone_status = \"✅ COHÉRENT\"\n"," else:\n"," backbone_status = \"❌ INCOHÉRENT\"\n","\n"," print(f\" • Cohérence backbone: {backbone_status}\")\n"," print(f\" - Configuration: {'gelé' if backbone_expected_frozen else 'dégelé'}\")\n"," print(f\" - Réalité: {'gelé' if backbone_actually_frozen else 'dégelé'} ({backbone_frozen_ratio:.1f}%)\")\n","\n"," # === 8. ARCHITECTURE DÉTAILLÉE (optionnel) ===\n"," if show_architecture:\n"," print(f\"\\n🏛️ Architecture détaillée (model.summary()):\")\n"," print(\"-\" * 60)\n"," model.summary(line_length=80)\n","\n"," # === 9. RÉSULTATS POUR RETOUR ===\n"," results = {\n"," 'model': model,\n"," 'total_params': total_params,\n"," 'trainable_params': trainable_params,\n"," 'frozen_params': frozen_params,\n"," 'frozen_ratio': frozen_ratio,\n"," 'backbone_params': backbone_total,\n"," 'backbone_frozen': backbone_frozen,\n"," 'backbone_frozen_ratio': backbone_frozen_ratio if backbone_total > 0 else 0,\n"," 'status': 'success',\n"," 'validation': status if config['encoder'] else 'N/A'\n"," }\n","\n"," print(f\"\\n✅ Analyse terminée avec succès\")\n"," return results\n","\n"," except Exception as e:\n"," print(f\"\\n❌ Erreur lors de l'analyse: {e}\")\n"," return {\n"," 'model': None,\n"," 'status': 'error',\n"," 'error': str(e)\n"," }\n","\n"," finally:\n"," # Nettoyage mémoire\n"," if 'model' in locals():\n"," del model\n"," tf.keras.backend.clear_session()\n","\n","# === FONCTION SÉPARÉE POUR VOIR UNIQUEMENT LES COUCHES (différente maintenant) ===\n","def show_all_layers_status(config, num_classes):\n"," \"\"\"\n"," Fonction spécialisée pour afficher UNIQUEMENT les couches, de manière compacte\n"," \"\"\"\n"," print(f\"\\n🔍 COUCHES - {config['name']}\")\n"," print(\"=\" * 70)\n"," print(\"🟢 ENTRAÎNABLE | 🔴 GELÉ\")\n"," print(\"-\" * 70)\n","\n"," try:\n"," model = create_model_from_config(config, num_classes, verbose=False)\n","\n"," trainable_count = 0\n"," frozen_count = 0\n","\n"," for i, layer in enumerate(model.layers):\n"," layer_params = layer.count_params()\n"," icon = \"🔴\" if not layer.trainable else \"🟢\"\n","\n"," if layer.trainable:\n"," trainable_count += 1\n"," else:\n"," frozen_count += 1\n","\n"," # Type de couche (plus concis)\n"," layer_type = \"\"\n"," if config['encoder'] and any(keyword in layer.name.lower()\n"," for keyword in [config['encoder'].lower(), 'encoder', 'backbone']):\n"," layer_type = \"BACKBONE\"\n"," elif 'decoder' in layer.name.lower():\n"," layer_type = \"DECODER\"\n"," elif 'classifier' in layer.name.lower():\n"," layer_type = \"CLASSIF\"\n"," else:\n"," layer_type = \"AUTRE\"\n","\n"," print(f\"{i+1:2d}. {icon} {layer.name:<35} {layer_params:>8,} {layer_type}\")\n","\n"," print(\"-\" * 70)\n"," print(f\"📊 RÉSUMÉ: {trainable_count} entraînables, {frozen_count} gelés\")\n","\n"," except Exception as e:\n"," print(f\"❌ Erreur: {e}\")\n"," finally:\n"," if 'model' in locals():\n"," del model\n"," tf.keras.backend.clear_session()"]},{"cell_type":"markdown","metadata":{"id":"RYR1STLzZQzS"},"source":["L'objectif est simplement ici de tester le gel des poids pour les modèles de feature extraction (backbone) qui fournissent des descripteurs aux modèles de segmentation et ce afin de pouvoir comparer les différences de performances."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Jyxe-hvRZJ5C"},"outputs":[],"source":["def test_backbone_freezing(num_classes=19):\n"," \"\"\"\n"," Fonction de test pour vérifier que le gel du backbone fonctionne correctement.\n"," Version corrigée utilisant encoder_freeze.\n"," \"\"\"\n"," print(\"🧪 TEST DU GEL DU BACKBONE (VERSION CORRIGÉE)\")\n"," print(\"=\" * 60)\n","\n"," # === CONFIGURATIONS DE TEST ===\n"," test_configs = [\n"," {\n"," 'name': 'U-Net_mobilenetv2_frozen_test',\n"," 'architecture': 'U-Net',\n"," 'encoder': 'mobilenetv2',\n"," 'encoder_freeze': True, # GELÉ - devrait avoir beaucoup de params gelés\n"," 'params': {'learning_rate': 1e-4, 'img_size': (224, 224)},\n"," 'type': 'frozen_test'\n"," },\n"," {\n"," 'name': 'U-Net_mobilenetv2_trainable_test',\n"," 'architecture': 'U-Net',\n"," 'encoder': 'mobilenetv2',\n"," 'encoder_freeze': False, # DÉGELÉ - devrait avoir peu de params gelés\n"," 'params': {'learning_rate': 1e-4, 'img_size': (224, 224)},\n"," 'type': 'trainable_test'\n"," }\n"," ]\n","\n"," results = {}\n","\n"," # === EXÉCUTION DES TESTS ===\n"," for config in test_configs:\n"," print(f\"\\n🔍 Test: {config['name']}\")\n"," print(\"-\" * 50)\n","\n"," # Création du modèle avec verbose pour voir les détails\n"," model = create_model_from_config(config, num_classes, verbose=True)\n","\n"," # === COMPTAGE DÉTAILLÉ DES PARAMÈTRES ===\n"," total_params = model.count_params()\n"," trainable_params = sum([tf.keras.backend.count_params(w) for w in model.trainable_weights])\n"," frozen_params = total_params - trainable_params\n"," frozen_ratio = (frozen_params / total_params) * 100\n","\n"," print(f\"\\n📈 Résultat:\")\n"," print(f\" • Total: {total_params:,}\")\n"," print(f\" • Entraînables: {trainable_params:,}\")\n"," print(f\" • Gelés: {frozen_params:,}\")\n"," print(f\" • % gelés: {frozen_ratio:.1f}%\")\n","\n"," # === VALIDATION DU RÉSULTAT ===\n"," if config['encoder_freeze']:\n"," # Pour un modèle gelé, on s'attend à >15% de paramètres gelés\n"," expected_frozen_ratio = 20.0\n"," status = \"✅ OK\" if frozen_ratio > expected_frozen_ratio else \"❌ ÉCHEC\"\n"," print(f\" • Statut (gelé attendu >20%): {status}\")\n"," else:\n"," # Pour un modèle dégelé, on s'attend à <10% de paramètres gelés\n"," expected_frozen_ratio = 5\n"," status = \"✅ OK\" if frozen_ratio < expected_frozen_ratio else \"❌ ÉCHEC\"\n"," print(f\" • Statut (dégelé attendu <5%): {status}\")\n","\n"," # Stockage des résultats pour comparaison\n"," results[config['type']] = {\n"," 'total': total_params,\n"," 'trainable': trainable_params,\n"," 'frozen': frozen_params,\n"," 'frozen_ratio': frozen_ratio\n"," }\n","\n"," # === NETTOYAGE MÉMOIRE ===\n"," del model\n"," tf.keras.backend.clear_session()\n","\n"," # === RÉSUMÉ COMPARATIF ===\n"," print(f\"\\n🎯 RÉSUMÉ COMPARATIF\")\n"," print(\"=\" * 60)\n","\n"," if 'frozen_test' in results and 'trainable_test' in results:\n"," frozen_result = results['frozen_test']\n"," trainable_result = results['trainable_test']\n","\n"," print(f\"Modèle gelé (encoder_freeze=True):\")\n"," print(f\" • Params gelés: {frozen_result['frozen']:,} ({frozen_result['frozen_ratio']:.1f}%)\")\n","\n"," print(f\"Modèle dégelé (encoder_freeze=False):\")\n"," print(f\" • Params gelés: {trainable_result['frozen']:,} ({trainable_result['frozen_ratio']:.1f}%)\")\n","\n"," difference = frozen_result['frozen'] - trainable_result['frozen']\n"," print(f\"Différence: {difference:,} paramètres correctement gelés\")\n","\n"," if difference > 100000: # Au moins 100k params de différence\n"," print(\"✅ TEST GLOBAL: Le gel du backbone fonctionne correctement!\")\n"," else:\n"," print(\"❌ TEST GLOBAL: Le gel du backbone ne semble pas fonctionner!\")"]},{"cell_type":"markdown","metadata":{"id":"EvurXI0XZzng"},"source":["On va lister les différentes expérimentations et vérifier le nombre de poids gelés pour deux configurations différentes : une avec backbone entrainable, l'autre non."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5094,"status":"ok","timestamp":1755000404122,"user":{"displayName":"Grégoire Mureau","userId":"04855910922109670788"},"user_tz":-120},"id":"AsgetKA1ZM0y","outputId":"e141d21c-f3ff-4a3a-ade8-3aa148fba207"},"outputs":[{"output_type":"stream","name":"stdout","text":["📊 PLAN D'EXPÉRIMENTATIONS - 17 configurations\n","======================================================================\n","🔧 Backbones utilisés: ['efficientnetb0', 'mobilenetv2', 'resnet34']\n","🏗️ Architectures: U-Net, FPN\n","\n","🔹 BASELINE (4 configs):\n"," • U-Net_vanilla_augFalse \n"," • U-Net_vanilla_augTrue \n"," • FPN_vanilla_augFalse \n"," • FPN_vanilla_augTrue \n","\n","🔹 FROZEN PRETRAINED (10 configs):\n"," • U-Net_mobilenetv2_frozen_augFalse (gelé)\n"," • U-Net_mobilenetv2_frozen_augTrue (gelé)\n"," • U-Net_efficientnetb0_frozen_augFalse (gelé)\n"," • U-Net_efficientnetb0_frozen_augTrue (gelé)\n"," • FPN_mobilenetv2_frozen_augFalse (gelé)\n"," • FPN_mobilenetv2_frozen_augTrue (gelé)\n"," • FPN_efficientnetb0_frozen_augFalse (gelé)\n"," • FPN_efficientnetb0_frozen_augTrue (gelé)\n"," • FPN_resnet34_frozen_augFalse (gelé)\n"," • FPN_resnet34_frozen_augTrue (gelé)\n","\n","🔹 FINETUNED PRETRAINED (3 configs):\n"," • U-Net_mobilenetv2_finetune_augTrue (dégelé)\n"," • FPN_efficientnetb0_finetune_augTrue (dégelé)\n"," • FPN_resnet34_finetune_augTrue (dégelé)\n","\n","======================================================================\n","LANCEMENT DU TEST DE VALIDATION\n","======================================================================\n","🧪 TEST DU GEL DU BACKBONE (VERSION CORRIGÉE)\n","============================================================\n","\n","🔍 Test: U-Net_mobilenetv2_frozen_test\n","--------------------------------------------------\n","🔧 Création du modèle 'U-Net_mobilenetv2_frozen_test'\n"," Architecture: U-Net\n"," Encoder: mobilenetv2\n"," Encoder gelé: True\n","Downloading data from https://github.com/JonathanCMitchell/mobilenet_v2_keras/releases/download/v1.1/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n","\u001b[1m9406464/9406464\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"," 📊 Paramètres: 8,050,051 total | 5,824,195 entraînables | 2,225,856 gelés\n","\n","📈 Résultat:\n"," • Total: 8,050,051\n"," • Entraînables: 5,824,195\n"," • Gelés: 2,225,856\n"," • % gelés: 27.7%\n"," • Statut (gelé attendu >20%): ✅ OK\n","\n","🔍 Test: U-Net_mobilenetv2_trainable_test\n","--------------------------------------------------\n","🔧 Création du modèle 'U-Net_mobilenetv2_trainable_test'\n"," Architecture: U-Net\n"," Encoder: mobilenetv2\n"," Encoder gelé: False\n"," 📊 Paramètres: 8,050,051 total | 8,013,955 entraînables | 36,096 gelés\n","\n","📈 Résultat:\n"," • Total: 8,050,051\n"," • Entraînables: 8,013,955\n"," • Gelés: 36,096\n"," • % gelés: 0.4%\n"," • Statut (dégelé attendu <5%): ✅ OK\n","\n","🎯 RÉSUMÉ COMPARATIF\n","============================================================\n","Modèle gelé (encoder_freeze=True):\n"," • Params gelés: 2,225,856 (27.7%)\n","Modèle dégelé (encoder_freeze=False):\n"," • Params gelés: 36,096 (0.4%)\n","Différence: 2,189,760 paramètres correctement gelés\n","✅ TEST GLOBAL: Le gel du backbone fonctionne correctement!\n"]}],"source":["# Affichage des configurations\n","print(f\"📊 PLAN D'EXPÉRIMENTATIONS - {len(experimental_configurations)} configurations\")\n","print(\"=\" * 70)\n","\n","# Affichage des backbones utilisés\n","used_backbones = set()\n","for config in experimental_configurations:\n"," if config['encoder']:\n"," used_backbones.add(config['encoder'])\n","\n","print(f\"🔧 Backbones utilisés: {sorted(used_backbones)}\")\n","print(f\"🏗️ Architectures: U-Net, FPN\")\n","\n","# Grouper par type pour un affichage plus clair\n","config_types = {}\n","for config in experimental_configurations:\n"," config_type = config['type']\n"," if config_type not in config_types:\n"," config_types[config_type] = []\n"," config_types[config_type].append(config)\n","\n","for type_name, configs in config_types.items():\n"," print(f\"\\n🔹 {type_name.upper().replace('_', ' ')} ({len(configs)} configs):\")\n"," for config in configs:\n"," freeze_status = \"gelé\" if config['encoder_freeze'] else \"dégelé\"\n"," encoder_info = f\"({freeze_status})\" if config['encoder'] else \"\"\n"," print(f\" • {config['name']} {encoder_info}\")\n","\n","# === LANCEMENT DU TEST ===\n","print(f\"\\n\" + \"=\"*70)\n","print(\"LANCEMENT DU TEST DE VALIDATION\")\n","print(\"=\"*70)\n","\n","# Décommentez pour exécuter le test\n","test_backbone_freezing(num_classes=19)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4575,"status":"ok","timestamp":1755000408705,"user":{"displayName":"Grégoire Mureau","userId":"04855910922109670788"},"user_tz":-120},"id":"9TWHaWHA6z66","outputId":"76ed8e6a-552e-4abe-db2b-e98d71569425"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","======================================================================\n","🏗️ ANALYSE COMPLÈTE: U-Net_mobilenetv2_frozen_augFalse\n","======================================================================\n","📊 Configuration:\n"," • Architecture: U-Net\n"," • Backbone: mobilenetv2\n"," • Encoder gelé: ✅ Oui\n"," • Type: Frozen Pretrained\n"," • Augmentation: ❌ Non\n"," • Learning rate: 0.0001\n"," • Époques prévues: 20\n"," • Taille images: (224, 224)\n","\n","🔧 Création du modèle...\n","\n","🔢 Paramètres:\n"," • Total: 8,050,051\n"," • Entraînables: 5,824,195 (72.3%)\n"," • Gelés: 2,225,856 (27.7%)\n","\n","🏗️ AUTRES COUCHES (résumé):\n"," • 93 couches entraînables\n"," • 103 couches gelées\n"," • (Utilisez detailed_layers=True pour voir toutes les couches)\n","\n","🎯 Validation du gel du backbone:\n"," • Statut global: ✅ OK\n"," • Backbone correctement gelé (27.7% ≥ 20%)\n"," • Plage attendue pour mobilenetv2: 20-35%\n","\n","✅ Analyse terminée avec succès\n","\n","======================================================================\n","🏗️ ANALYSE COMPLÈTE: U-Net_mobilenetv2_frozen_augFalse\n","======================================================================\n","📊 Configuration:\n"," • Architecture: U-Net\n"," • Backbone: mobilenetv2\n"," • Encoder gelé: ✅ Oui\n"," • Type: Frozen Pretrained\n"," • Augmentation: ❌ Non\n"," • Learning rate: 0.0001\n"," • Époques prévues: 20\n"," • Taille images: (224, 224)\n","\n","🔧 Création du modèle...\n","\n","🔢 Paramètres:\n"," • Total: 8,050,051\n"," • Entraînables: 5,824,195 (72.3%)\n"," • Gelés: 2,225,856 (27.7%)\n","\n","🔍 TOUTES LES COUCHES (🟢 = Entraînable, 🔴 = Gelé):\n","----------------------------------------------------------------------\n"," 1. 🔴 input_layer 0 params - GELÉ\n"," 2. 🔴 Conv1_pad 0 params - GELÉ\n"," 3. 🔴 Conv1 864 params - GELÉ\n"," 4. 🟢 bn_Conv1 128 params - ENTRAÎNABLE\n"," 5. 🔴 Conv1_relu 0 params - GELÉ\n"," 6. 🔴 expanded_conv_depthwise 288 params - GELÉ\n"," 7. 🟢 expanded_conv_depthwise_BN 128 params - ENTRAÎNABLE\n"," 8. 🔴 expanded_conv_depthwise_relu 0 params - GELÉ\n"," 9. 🔴 expanded_conv_project 512 params - GELÉ\n","10. 🟢 expanded_conv_project_BN 64 params - ENTRAÎNABLE\n","11. 🔴 block_1_expand 1,536 params - GELÉ\n","12. 🟢 block_1_expand_BN 384 params - ENTRAÎNABLE\n","13. 🔴 block_1_expand_relu 0 params - GELÉ\n","14. 🔴 block_1_pad 0 params - GELÉ\n","15. 🔴 block_1_depthwise 864 params - GELÉ\n","16. 🟢 block_1_depthwise_BN 384 params - ENTRAÎNABLE\n","17. 🔴 block_1_depthwise_relu 0 params - GELÉ\n","18. 🔴 block_1_project 2,304 params - GELÉ\n","19. 🟢 block_1_project_BN 96 params - ENTRAÎNABLE\n","20. 🔴 block_2_expand 3,456 params - GELÉ\n","21. 🟢 block_2_expand_BN 576 params - ENTRAÎNABLE\n","22. 🔴 block_2_expand_relu 0 params - GELÉ\n","23. 🔴 block_2_depthwise 1,296 params - GELÉ\n","24. 🟢 block_2_depthwise_BN 576 params - ENTRAÎNABLE\n","25. 🔴 block_2_depthwise_relu 0 params - GELÉ\n","26. 🔴 block_2_project 3,456 params - GELÉ\n","27. 🟢 block_2_project_BN 96 params - ENTRAÎNABLE\n","28. 🔴 block_2_add 0 params - GELÉ\n","29. 🔴 block_3_expand 3,456 params - GELÉ\n","30. 🟢 block_3_expand_BN 576 params - ENTRAÎNABLE\n","31. 🔴 block_3_expand_relu 0 params - GELÉ\n","32. 🔴 block_3_pad 0 params - GELÉ\n","33. 🔴 block_3_depthwise 1,296 params - GELÉ\n","34. 🟢 block_3_depthwise_BN 576 params - ENTRAÎNABLE\n","35. 🔴 block_3_depthwise_relu 0 params - GELÉ\n","36. 🔴 block_3_project 4,608 params - GELÉ\n","37. 🟢 block_3_project_BN 128 params - ENTRAÎNABLE\n","38. 🔴 block_4_expand 6,144 params - GELÉ\n","39. 🟢 block_4_expand_BN 768 params - ENTRAÎNABLE\n","40. 🔴 block_4_expand_relu 0 params - GELÉ\n","41. 🔴 block_4_depthwise 1,728 params - GELÉ\n","42. 🟢 block_4_depthwise_BN 768 params - ENTRAÎNABLE\n","43. 🔴 block_4_depthwise_relu 0 params - GELÉ\n","44. 🔴 block_4_project 6,144 params - GELÉ\n","45. 🟢 block_4_project_BN 128 params - ENTRAÎNABLE\n","46. 🔴 block_4_add 0 params - GELÉ\n","47. 🔴 block_5_expand 6,144 params - GELÉ\n","48. 🟢 block_5_expand_BN 768 params - ENTRAÎNABLE\n","49. 🔴 block_5_expand_relu 0 params - GELÉ\n","50. 🔴 block_5_depthwise 1,728 params - GELÉ\n","51. 🟢 block_5_depthwise_BN 768 params - ENTRAÎNABLE\n","52. 🔴 block_5_depthwise_relu 0 params - GELÉ\n","53. 🔴 block_5_project 6,144 params - GELÉ\n","54. 🟢 block_5_project_BN 128 params - ENTRAÎNABLE\n","55. 🔴 block_5_add 0 params - GELÉ\n","56. 🔴 block_6_expand 6,144 params - GELÉ\n","57. 🟢 block_6_expand_BN 768 params - ENTRAÎNABLE\n","58. 🔴 block_6_expand_relu 0 params - GELÉ\n","59. 🔴 block_6_pad 0 params - GELÉ\n","60. 🔴 block_6_depthwise 1,728 params - GELÉ\n","61. 🟢 block_6_depthwise_BN 768 params - ENTRAÎNABLE\n","62. 🔴 block_6_depthwise_relu 0 params - GELÉ\n","63. 🔴 block_6_project 12,288 params - GELÉ\n","64. 🟢 block_6_project_BN 256 params - ENTRAÎNABLE\n","65. 🔴 block_7_expand 24,576 params - GELÉ\n","66. 🟢 block_7_expand_BN 1,536 params - ENTRAÎNABLE\n","67. 🔴 block_7_expand_relu 0 params - GELÉ\n","68. 🔴 block_7_depthwise 3,456 params - GELÉ\n","69. 🟢 block_7_depthwise_BN 1,536 params - ENTRAÎNABLE\n","70. 🔴 block_7_depthwise_relu 0 params - GELÉ\n","71. 🔴 block_7_project 24,576 params - GELÉ\n","72. 🟢 block_7_project_BN 256 params - ENTRAÎNABLE\n","73. 🔴 block_7_add 0 params - GELÉ\n","74. 🔴 block_8_expand 24,576 params - GELÉ\n","75. 🟢 block_8_expand_BN 1,536 params - ENTRAÎNABLE\n","76. 🔴 block_8_expand_relu 0 params - GELÉ\n","77. 🔴 block_8_depthwise 3,456 params - GELÉ\n","78. 🟢 block_8_depthwise_BN 1,536 params - ENTRAÎNABLE\n","79. 🔴 block_8_depthwise_relu 0 params - GELÉ\n","80. 🔴 block_8_project 24,576 params - GELÉ\n","81. 🟢 block_8_project_BN 256 params - ENTRAÎNABLE\n","82. 🔴 block_8_add 0 params - GELÉ\n","83. 🔴 block_9_expand 24,576 params - GELÉ\n","84. 🟢 block_9_expand_BN 1,536 params - ENTRAÎNABLE\n","85. 🔴 block_9_expand_relu 0 params - GELÉ\n","86. 🔴 block_9_depthwise 3,456 params - GELÉ\n","87. 🟢 block_9_depthwise_BN 1,536 params - ENTRAÎNABLE\n","88. 🔴 block_9_depthwise_relu 0 params - GELÉ\n","89. 🔴 block_9_project 24,576 params - GELÉ\n","90. 🟢 block_9_project_BN 256 params - ENTRAÎNABLE\n","91. 🔴 block_9_add 0 params - GELÉ\n","92. 🔴 block_10_expand 24,576 params - GELÉ\n","93. 🟢 block_10_expand_BN 1,536 params - ENTRAÎNABLE\n","94. 🔴 block_10_expand_relu 0 params - GELÉ\n","95. 🔴 block_10_depthwise 3,456 params - GELÉ\n","96. 🟢 block_10_depthwise_BN 1,536 params - ENTRAÎNABLE\n","97. 🔴 block_10_depthwise_relu 0 params - GELÉ\n","98. 🔴 block_10_project 36,864 params - GELÉ\n","99. 🟢 block_10_project_BN 384 params - ENTRAÎNABLE\n","100. 🔴 block_11_expand 55,296 params - GELÉ\n","101. 🟢 block_11_expand_BN 2,304 params - ENTRAÎNABLE\n","102. 🔴 block_11_expand_relu 0 params - GELÉ\n","103. 🔴 block_11_depthwise 5,184 params - GELÉ\n","104. 🟢 block_11_depthwise_BN 2,304 params - ENTRAÎNABLE\n","105. 🔴 block_11_depthwise_relu 0 params - GELÉ\n","106. 🔴 block_11_project 55,296 params - GELÉ\n","107. 🟢 block_11_project_BN 384 params - ENTRAÎNABLE\n","108. 🔴 block_11_add 0 params - GELÉ\n","109. 🔴 block_12_expand 55,296 params - GELÉ\n","110. 🟢 block_12_expand_BN 2,304 params - ENTRAÎNABLE\n","111. 🔴 block_12_expand_relu 0 params - GELÉ\n","112. 🔴 block_12_depthwise 5,184 params - GELÉ\n","113. 🟢 block_12_depthwise_BN 2,304 params - ENTRAÎNABLE\n","114. 🔴 block_12_depthwise_relu 0 params - GELÉ\n","115. 🔴 block_12_project 55,296 params - GELÉ\n","116. 🟢 block_12_project_BN 384 params - ENTRAÎNABLE\n","117. 🔴 block_12_add 0 params - GELÉ\n","118. 🔴 block_13_expand 55,296 params - GELÉ\n","119. 🟢 block_13_expand_BN 2,304 params - ENTRAÎNABLE\n","120. 🔴 block_13_expand_relu 0 params - GELÉ\n","121. 🔴 block_13_pad 0 params - GELÉ\n","122. 🔴 block_13_depthwise 5,184 params - GELÉ\n","123. 🟢 block_13_depthwise_BN 2,304 params - ENTRAÎNABLE\n","124. 🔴 block_13_depthwise_relu 0 params - GELÉ\n","125. 🔴 block_13_project 92,160 params - GELÉ\n","126. 🟢 block_13_project_BN 640 params - ENTRAÎNABLE\n","127. 🔴 block_14_expand 153,600 params - GELÉ\n","128. 🟢 block_14_expand_BN 3,840 params - ENTRAÎNABLE\n","129. 🔴 block_14_expand_relu 0 params - GELÉ\n","130. 🔴 block_14_depthwise 8,640 params - GELÉ\n","131. 🟢 block_14_depthwise_BN 3,840 params - ENTRAÎNABLE\n","132. 🔴 block_14_depthwise_relu 0 params - GELÉ\n","133. 🔴 block_14_project 153,600 params - GELÉ\n","134. 🟢 block_14_project_BN 640 params - ENTRAÎNABLE\n","135. 🔴 block_14_add 0 params - GELÉ\n","136. 🔴 block_15_expand 153,600 params - GELÉ\n","137. 🟢 block_15_expand_BN 3,840 params - ENTRAÎNABLE\n","138. 🔴 block_15_expand_relu 0 params - GELÉ\n","139. 🔴 block_15_depthwise 8,640 params - GELÉ\n","140. 🟢 block_15_depthwise_BN 3,840 params - ENTRAÎNABLE\n","141. 🔴 block_15_depthwise_relu 0 params - GELÉ\n","142. 🔴 block_15_project 153,600 params - GELÉ\n","143. 🟢 block_15_project_BN 640 params - ENTRAÎNABLE\n","144. 🔴 block_15_add 0 params - GELÉ\n","145. 🔴 block_16_expand 153,600 params - GELÉ\n","146. 🟢 block_16_expand_BN 3,840 params - ENTRAÎNABLE\n","147. 🔴 block_16_expand_relu 0 params - GELÉ\n","148. 🔴 block_16_depthwise 8,640 params - GELÉ\n","149. 🟢 block_16_depthwise_BN 3,840 params - ENTRAÎNABLE\n","150. 🔴 block_16_depthwise_relu 0 params - GELÉ\n","151. 🔴 block_16_project 307,200 params - GELÉ\n","152. 🟢 block_16_project_BN 1,280 params - ENTRAÎNABLE\n","153. 🔴 Conv_1 409,600 params - GELÉ\n","154. 🟢 Conv_1_bn 5,120 params - ENTRAÎNABLE\n","155. 🔴 out_relu 0 params - GELÉ\n","156. 🟢 decoder_stage0_upsampling 0 params - ENTRAÎNABLE (DECODER)\n","157. 🟢 decoder_stage0_concat 0 params - ENTRAÎNABLE (DECODER)\n","158. 🟢 decoder_stage0a_conv 4,276,224 params - ENTRAÎNABLE (DECODER)\n","159. 🟢 decoder_stage0a_bn 1,024 params - ENTRAÎNABLE (DECODER)\n","160. 🟢 decoder_stage0a_relu 0 params - ENTRAÎNABLE (DECODER)\n","161. 🟢 decoder_stage0b_conv 589,824 params - ENTRAÎNABLE (DECODER)\n","162. 🟢 decoder_stage0b_bn 1,024 params - ENTRAÎNABLE (DECODER)\n","163. 🟢 decoder_stage0b_relu 0 params - ENTRAÎNABLE (DECODER)\n","164. 🟢 decoder_stage1_upsampling 0 params - ENTRAÎNABLE (DECODER)\n","165. 🟢 decoder_stage1_concat 0 params - ENTRAÎNABLE (DECODER)\n","166. 🟢 decoder_stage1a_conv 516,096 params - ENTRAÎNABLE (DECODER)\n","167. 🟢 decoder_stage1a_bn 512 params - ENTRAÎNABLE (DECODER)\n","168. 🟢 decoder_stage1a_relu 0 params - ENTRAÎNABLE (DECODER)\n","169. 🟢 decoder_stage1b_conv 147,456 params - ENTRAÎNABLE (DECODER)\n","170. 🟢 decoder_stage1b_bn 512 params - ENTRAÎNABLE (DECODER)\n","171. 🟢 decoder_stage1b_relu 0 params - ENTRAÎNABLE (DECODER)\n","172. 🟢 decoder_stage2_upsampling 0 params - ENTRAÎNABLE (DECODER)\n","173. 🟢 decoder_stage2_concat 0 params - ENTRAÎNABLE (DECODER)\n","174. 🟢 decoder_stage2a_conv 156,672 params - ENTRAÎNABLE (DECODER)\n","175. 🟢 decoder_stage2a_bn 256 params - ENTRAÎNABLE (DECODER)\n","176. 🟢 decoder_stage2a_relu 0 params - ENTRAÎNABLE (DECODER)\n","177. 🟢 decoder_stage2b_conv 36,864 params - ENTRAÎNABLE (DECODER)\n","178. 🟢 decoder_stage2b_bn 256 params - ENTRAÎNABLE (DECODER)\n","179. 🟢 decoder_stage2b_relu 0 params - ENTRAÎNABLE (DECODER)\n","180. 🟢 decoder_stage3_upsampling 0 params - ENTRAÎNABLE (DECODER)\n","181. 🟢 decoder_stage3_concat 0 params - ENTRAÎNABLE (DECODER)\n","182. 🟢 decoder_stage3a_conv 46,080 params - ENTRAÎNABLE (DECODER)\n","183. 🟢 decoder_stage3a_bn 128 params - ENTRAÎNABLE (DECODER)\n","184. 🟢 decoder_stage3a_relu 0 params - ENTRAÎNABLE (DECODER)\n","185. 🟢 decoder_stage3b_conv 9,216 params - ENTRAÎNABLE (DECODER)\n","186. 🟢 decoder_stage3b_bn 128 params - ENTRAÎNABLE (DECODER)\n","187. 🟢 decoder_stage3b_relu 0 params - ENTRAÎNABLE (DECODER)\n","188. 🟢 decoder_stage4_upsampling 0 params - ENTRAÎNABLE (DECODER)\n","189. 🟢 decoder_stage4a_conv 4,608 params - ENTRAÎNABLE (DECODER)\n","190. 🟢 decoder_stage4a_bn 64 params - ENTRAÎNABLE (DECODER)\n","191. 🟢 decoder_stage4a_relu 0 params - ENTRAÎNABLE (DECODER)\n","192. 🟢 decoder_stage4b_conv 2,304 params - ENTRAÎNABLE (DECODER)\n","193. 🟢 decoder_stage4b_bn 64 params - ENTRAÎNABLE (DECODER)\n","194. 🟢 decoder_stage4b_relu 0 params - ENTRAÎNABLE (DECODER)\n","195. 🟢 final_conv 2,755 params - ENTRAÎNABLE (CLASSIFICATION)\n","196. 🟢 softmax 0 params - ENTRAÎNABLE\n","\n","🎯 Validation du gel du backbone:\n"," • Statut global: ✅ OK\n"," • Backbone correctement gelé (27.7% ≥ 20%)\n"," • Plage attendue pour mobilenetv2: 20-35%\n","\n","✅ Analyse terminée avec succès\n","\n","🔍 COUCHES - U-Net_mobilenetv2_frozen_augFalse\n","======================================================================\n","🟢 ENTRAÎNABLE | 🔴 GELÉ\n","----------------------------------------------------------------------\n"," 1. 🔴 input_layer 0 AUTRE\n"," 2. 🔴 Conv1_pad 0 AUTRE\n"," 3. 🔴 Conv1 864 AUTRE\n"," 4. 🟢 bn_Conv1 128 AUTRE\n"," 5. 🔴 Conv1_relu 0 AUTRE\n"," 6. 🔴 expanded_conv_depthwise 288 AUTRE\n"," 7. 🟢 expanded_conv_depthwise_BN 128 AUTRE\n"," 8. 🔴 expanded_conv_depthwise_relu 0 AUTRE\n"," 9. 🔴 expanded_conv_project 512 AUTRE\n","10. 🟢 expanded_conv_project_BN 64 AUTRE\n","11. 🔴 block_1_expand 1,536 AUTRE\n","12. 🟢 block_1_expand_BN 384 AUTRE\n","13. 🔴 block_1_expand_relu 0 AUTRE\n","14. 🔴 block_1_pad 0 AUTRE\n","15. 🔴 block_1_depthwise 864 AUTRE\n","16. 🟢 block_1_depthwise_BN 384 AUTRE\n","17. 🔴 block_1_depthwise_relu 0 AUTRE\n","18. 🔴 block_1_project 2,304 AUTRE\n","19. 🟢 block_1_project_BN 96 AUTRE\n","20. 🔴 block_2_expand 3,456 AUTRE\n","21. 🟢 block_2_expand_BN 576 AUTRE\n","22. 🔴 block_2_expand_relu 0 AUTRE\n","23. 🔴 block_2_depthwise 1,296 AUTRE\n","24. 🟢 block_2_depthwise_BN 576 AUTRE\n","25. 🔴 block_2_depthwise_relu 0 AUTRE\n","26. 🔴 block_2_project 3,456 AUTRE\n","27. 🟢 block_2_project_BN 96 AUTRE\n","28. 🔴 block_2_add 0 AUTRE\n","29. 🔴 block_3_expand 3,456 AUTRE\n","30. 🟢 block_3_expand_BN 576 AUTRE\n","31. 🔴 block_3_expand_relu 0 AUTRE\n","32. 🔴 block_3_pad 0 AUTRE\n","33. 🔴 block_3_depthwise 1,296 AUTRE\n","34. 🟢 block_3_depthwise_BN 576 AUTRE\n","35. 🔴 block_3_depthwise_relu 0 AUTRE\n","36. 🔴 block_3_project 4,608 AUTRE\n","37. 🟢 block_3_project_BN 128 AUTRE\n","38. 🔴 block_4_expand 6,144 AUTRE\n","39. 🟢 block_4_expand_BN 768 AUTRE\n","40. 🔴 block_4_expand_relu 0 AUTRE\n","41. 🔴 block_4_depthwise 1,728 AUTRE\n","42. 🟢 block_4_depthwise_BN 768 AUTRE\n","43. 🔴 block_4_depthwise_relu 0 AUTRE\n","44. 🔴 block_4_project 6,144 AUTRE\n","45. 🟢 block_4_project_BN 128 AUTRE\n","46. 🔴 block_4_add 0 AUTRE\n","47. 🔴 block_5_expand 6,144 AUTRE\n","48. 🟢 block_5_expand_BN 768 AUTRE\n","49. 🔴 block_5_expand_relu 0 AUTRE\n","50. 🔴 block_5_depthwise 1,728 AUTRE\n","51. 🟢 block_5_depthwise_BN 768 AUTRE\n","52. 🔴 block_5_depthwise_relu 0 AUTRE\n","53. 🔴 block_5_project 6,144 AUTRE\n","54. 🟢 block_5_project_BN 128 AUTRE\n","55. 🔴 block_5_add 0 AUTRE\n","56. 🔴 block_6_expand 6,144 AUTRE\n","57. 🟢 block_6_expand_BN 768 AUTRE\n","58. 🔴 block_6_expand_relu 0 AUTRE\n","59. 🔴 block_6_pad 0 AUTRE\n","60. 🔴 block_6_depthwise 1,728 AUTRE\n","61. 🟢 block_6_depthwise_BN 768 AUTRE\n","62. 🔴 block_6_depthwise_relu 0 AUTRE\n","63. 🔴 block_6_project 12,288 AUTRE\n","64. 🟢 block_6_project_BN 256 AUTRE\n","65. 🔴 block_7_expand 24,576 AUTRE\n","66. 🟢 block_7_expand_BN 1,536 AUTRE\n","67. 🔴 block_7_expand_relu 0 AUTRE\n","68. 🔴 block_7_depthwise 3,456 AUTRE\n","69. 🟢 block_7_depthwise_BN 1,536 AUTRE\n","70. 🔴 block_7_depthwise_relu 0 AUTRE\n","71. 🔴 block_7_project 24,576 AUTRE\n","72. 🟢 block_7_project_BN 256 AUTRE\n","73. 🔴 block_7_add 0 AUTRE\n","74. 🔴 block_8_expand 24,576 AUTRE\n","75. 🟢 block_8_expand_BN 1,536 AUTRE\n","76. 🔴 block_8_expand_relu 0 AUTRE\n","77. 🔴 block_8_depthwise 3,456 AUTRE\n","78. 🟢 block_8_depthwise_BN 1,536 AUTRE\n","79. 🔴 block_8_depthwise_relu 0 AUTRE\n","80. 🔴 block_8_project 24,576 AUTRE\n","81. 🟢 block_8_project_BN 256 AUTRE\n","82. 🔴 block_8_add 0 AUTRE\n","83. 🔴 block_9_expand 24,576 AUTRE\n","84. 🟢 block_9_expand_BN 1,536 AUTRE\n","85. 🔴 block_9_expand_relu 0 AUTRE\n","86. 🔴 block_9_depthwise 3,456 AUTRE\n","87. 🟢 block_9_depthwise_BN 1,536 AUTRE\n","88. 🔴 block_9_depthwise_relu 0 AUTRE\n","89. 🔴 block_9_project 24,576 AUTRE\n","90. 🟢 block_9_project_BN 256 AUTRE\n","91. 🔴 block_9_add 0 AUTRE\n","92. 🔴 block_10_expand 24,576 AUTRE\n","93. 🟢 block_10_expand_BN 1,536 AUTRE\n","94. 🔴 block_10_expand_relu 0 AUTRE\n","95. 🔴 block_10_depthwise 3,456 AUTRE\n","96. 🟢 block_10_depthwise_BN 1,536 AUTRE\n","97. 🔴 block_10_depthwise_relu 0 AUTRE\n","98. 🔴 block_10_project 36,864 AUTRE\n","99. 🟢 block_10_project_BN 384 AUTRE\n","100. 🔴 block_11_expand 55,296 AUTRE\n","101. 🟢 block_11_expand_BN 2,304 AUTRE\n","102. 🔴 block_11_expand_relu 0 AUTRE\n","103. 🔴 block_11_depthwise 5,184 AUTRE\n","104. 🟢 block_11_depthwise_BN 2,304 AUTRE\n","105. 🔴 block_11_depthwise_relu 0 AUTRE\n","106. 🔴 block_11_project 55,296 AUTRE\n","107. 🟢 block_11_project_BN 384 AUTRE\n","108. 🔴 block_11_add 0 AUTRE\n","109. 🔴 block_12_expand 55,296 AUTRE\n","110. 🟢 block_12_expand_BN 2,304 AUTRE\n","111. 🔴 block_12_expand_relu 0 AUTRE\n","112. 🔴 block_12_depthwise 5,184 AUTRE\n","113. 🟢 block_12_depthwise_BN 2,304 AUTRE\n","114. 🔴 block_12_depthwise_relu 0 AUTRE\n","115. 🔴 block_12_project 55,296 AUTRE\n","116. 🟢 block_12_project_BN 384 AUTRE\n","117. 🔴 block_12_add 0 AUTRE\n","118. 🔴 block_13_expand 55,296 AUTRE\n","119. 🟢 block_13_expand_BN 2,304 AUTRE\n","120. 🔴 block_13_expand_relu 0 AUTRE\n","121. 🔴 block_13_pad 0 AUTRE\n","122. 🔴 block_13_depthwise 5,184 AUTRE\n","123. 🟢 block_13_depthwise_BN 2,304 AUTRE\n","124. 🔴 block_13_depthwise_relu 0 AUTRE\n","125. 🔴 block_13_project 92,160 AUTRE\n","126. 🟢 block_13_project_BN 640 AUTRE\n","127. 🔴 block_14_expand 153,600 AUTRE\n","128. 🟢 block_14_expand_BN 3,840 AUTRE\n","129. 🔴 block_14_expand_relu 0 AUTRE\n","130. 🔴 block_14_depthwise 8,640 AUTRE\n","131. 🟢 block_14_depthwise_BN 3,840 AUTRE\n","132. 🔴 block_14_depthwise_relu 0 AUTRE\n","133. 🔴 block_14_project 153,600 AUTRE\n","134. 🟢 block_14_project_BN 640 AUTRE\n","135. 🔴 block_14_add 0 AUTRE\n","136. 🔴 block_15_expand 153,600 AUTRE\n","137. 🟢 block_15_expand_BN 3,840 AUTRE\n","138. 🔴 block_15_expand_relu 0 AUTRE\n","139. 🔴 block_15_depthwise 8,640 AUTRE\n","140. 🟢 block_15_depthwise_BN 3,840 AUTRE\n","141. 🔴 block_15_depthwise_relu 0 AUTRE\n","142. 🔴 block_15_project 153,600 AUTRE\n","143. 🟢 block_15_project_BN 640 AUTRE\n","144. 🔴 block_15_add 0 AUTRE\n","145. 🔴 block_16_expand 153,600 AUTRE\n","146. 🟢 block_16_expand_BN 3,840 AUTRE\n","147. 🔴 block_16_expand_relu 0 AUTRE\n","148. 🔴 block_16_depthwise 8,640 AUTRE\n","149. 🟢 block_16_depthwise_BN 3,840 AUTRE\n","150. 🔴 block_16_depthwise_relu 0 AUTRE\n","151. 🔴 block_16_project 307,200 AUTRE\n","152. 🟢 block_16_project_BN 1,280 AUTRE\n","153. 🔴 Conv_1 409,600 AUTRE\n","154. 🟢 Conv_1_bn 5,120 AUTRE\n","155. 🔴 out_relu 0 AUTRE\n","156. 🟢 decoder_stage0_upsampling 0 DECODER\n","157. 🟢 decoder_stage0_concat 0 DECODER\n","158. 🟢 decoder_stage0a_conv 4,276,224 DECODER\n","159. 🟢 decoder_stage0a_bn 1,024 DECODER\n","160. 🟢 decoder_stage0a_relu 0 DECODER\n","161. 🟢 decoder_stage0b_conv 589,824 DECODER\n","162. 🟢 decoder_stage0b_bn 1,024 DECODER\n","163. 🟢 decoder_stage0b_relu 0 DECODER\n","164. 🟢 decoder_stage1_upsampling 0 DECODER\n","165. 🟢 decoder_stage1_concat 0 DECODER\n","166. 🟢 decoder_stage1a_conv 516,096 DECODER\n","167. 🟢 decoder_stage1a_bn 512 DECODER\n","168. 🟢 decoder_stage1a_relu 0 DECODER\n","169. 🟢 decoder_stage1b_conv 147,456 DECODER\n","170. 🟢 decoder_stage1b_bn 512 DECODER\n","171. 🟢 decoder_stage1b_relu 0 DECODER\n","172. 🟢 decoder_stage2_upsampling 0 DECODER\n","173. 🟢 decoder_stage2_concat 0 DECODER\n","174. 🟢 decoder_stage2a_conv 156,672 DECODER\n","175. 🟢 decoder_stage2a_bn 256 DECODER\n","176. 🟢 decoder_stage2a_relu 0 DECODER\n","177. 🟢 decoder_stage2b_conv 36,864 DECODER\n","178. 🟢 decoder_stage2b_bn 256 DECODER\n","179. 🟢 decoder_stage2b_relu 0 DECODER\n","180. 🟢 decoder_stage3_upsampling 0 DECODER\n","181. 🟢 decoder_stage3_concat 0 DECODER\n","182. 🟢 decoder_stage3a_conv 46,080 DECODER\n","183. 🟢 decoder_stage3a_bn 128 DECODER\n","184. 🟢 decoder_stage3a_relu 0 DECODER\n","185. 🟢 decoder_stage3b_conv 9,216 DECODER\n","186. 🟢 decoder_stage3b_bn 128 DECODER\n","187. 🟢 decoder_stage3b_relu 0 DECODER\n","188. 🟢 decoder_stage4_upsampling 0 DECODER\n","189. 🟢 decoder_stage4a_conv 4,608 DECODER\n","190. 🟢 decoder_stage4a_bn 64 DECODER\n","191. 🟢 decoder_stage4a_relu 0 DECODER\n","192. 🟢 decoder_stage4b_conv 2,304 DECODER\n","193. 🟢 decoder_stage4b_bn 64 DECODER\n","194. 🟢 decoder_stage4b_relu 0 DECODER\n","195. 🟢 final_conv 2,755 AUTRE\n","196. 🟢 softmax 0 AUTRE\n","----------------------------------------------------------------------\n","📊 RÉSUMÉ: 93 entraînables, 103 gelés\n"]}],"source":["# Analyse standard (backbone + résumé)\n","analyze_model_comprehensive(experimental_configurations[4], num_classes=19)\n","\n","# Analyse complète (toutes les couches)\n","analyze_model_comprehensive(experimental_configurations[4], num_classes=19, detailed_layers=True)\n","\n","# Vue compacte des couches uniquement\n","show_all_layers_status(experimental_configurations[4], num_classes=19)"]},{"cell_type":"markdown","metadata":{"id":"yGNRUQdxT4xL"},"source":["## Préparation des données"]},{"cell_type":"markdown","metadata":{"id":"e5mbV7CtEfoS"},"source":["Ici, les données vont être prétraitées et des datasets d'entrainement, de validation et de test vont être constitués.\n","Il est important de noter la correspondance terminologique entre le jeu de données officiel et les jeux de données que nous constituerons dans le cadre de cet entraînement :\\\n","• Split 'train' Cityscapes → Train (80%) + Validation (20%)\\\n","• Split 'val' Cityscapes → Test final\\\n","• Split 'test' Cityscapes → non utilisé\n","\n","En effet, le dossier \"test\" de Cityscapes ne contient pas de masque de vérité et sert à effectuer un benchmark de performances dans le cadre d'un concours Kaggel (résultats cachés, donc)."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wMEw3js7CNZE"},"outputs":[],"source":["def prepare_cityscapes_dataset(\n"," cityscapes_dir,\n"," split='train',\n"," validation_split=0.2,\n"," batch_size=8,\n"," img_size=(224, 224),\n"," encoder=None,\n"," id_to_group_mapping=None\n","):\n"," \"\"\"\n"," Prépare les datasets Cityscapes avec prétraitement adapté au backbone\n","\n"," Args:\n"," cityscapes_dir (str): Chemin vers le répertoire Cityscapes\n"," split (str): Split Cityscapes à utiliser ('train', 'val', 'test')\n"," validation_split (float): Si split='train', proportion à garder pour validation\n"," batch_size (int): Taille des batches\n"," img_size (tuple): Taille des images (H, W)\n"," encoder (str): Nom du backbone pour preprocessing ou None\n"," id_to_group_mapping (list ou np.ndarray): Mapping des IDs de classes ou None\n","\n"," Returns:\n"," Dataset(s): Si split='train' et validation_split > 0 → (train_dataset, val_dataset)\n"," Sinon → dataset unique\n"," \"\"\"\n"," if not os.path.exists(cityscapes_dir):\n"," raise ValueError(f\"Le répertoire Cityscapes n'existe pas: {cityscapes_dir}\")\n","\n"," images_dir = os.path.join(cityscapes_dir, 'leftImg8bit', split)\n"," masks_dir = os.path.join(cityscapes_dir, 'gtFine', split)\n","\n"," if not os.path.exists(images_dir) or not os.path.exists(masks_dir):\n"," raise ValueError(f\"Split Cityscapes '{split}' non trouvé dans {cityscapes_dir}\")\n","\n"," image_paths = []\n"," mask_paths = []\n","\n"," print(f\"🔍 Lecture du split Cityscapes '{split}'...\")\n","\n"," for city in os.listdir(images_dir):\n"," city_img_dir = os.path.join(images_dir, city)\n"," city_mask_dir = os.path.join(masks_dir, city)\n","\n"," if os.path.isdir(city_img_dir) and os.path.isdir(city_mask_dir):\n"," for file_name in os.listdir(city_img_dir):\n"," if file_name.endswith('_leftImg8bit.png'):\n"," img_path = os.path.join(city_img_dir, file_name)\n"," mask_name = file_name.replace('_leftImg8bit.png', '_gtFine_labelIds.png')\n"," mask_path = os.path.join(city_mask_dir, mask_name)\n"," if os.path.exists(img_path) and os.path.exists(mask_path):\n"," image_paths.append(img_path)\n"," mask_paths.append(mask_path)\n"," else:\n"," print(f\"⚠️ Fichier manquant pour {file_name}\")\n","\n"," print(f\"📊 Split Cityscapes '{split}': {len(image_paths)} images trouvées\")\n","\n"," if len(image_paths) == 0:\n"," raise ValueError(f\"Aucun fichier valide trouvé pour le split '{split}'!\")\n","\n"," if split == 'train' and validation_split > 0:\n"," print(f\"✂️ Découpage en Train/Validation ({int((1-validation_split)*100)}%/{int(validation_split*100)}%)\")\n","\n"," combined = list(zip(image_paths, mask_paths))\n"," random.seed(42)\n"," random.shuffle(combined)\n"," image_paths, mask_paths = zip(*combined)\n","\n"," total_samples = len(image_paths)\n"," val_samples = int(total_samples * validation_split)\n"," train_samples = total_samples - val_samples\n","\n"," train_img_paths = list(image_paths[:train_samples])\n"," train_mask_paths = list(mask_paths[:train_samples])\n"," val_img_paths = list(image_paths[train_samples:])\n"," val_mask_paths = list(mask_paths[train_samples:])\n","\n"," print(f\" └─ 📚 Train final: {len(train_img_paths)} images\")\n"," print(f\" └─ 📊 Validation final: {len(val_img_paths)} images\")\n"," else:\n"," usage = \"Test\" if split == 'val' else split.title()\n"," print(f\"📋 Utilisation complète comme dataset de {usage}\")\n","\n"," train_img_paths, train_mask_paths = image_paths, mask_paths\n"," val_img_paths, val_mask_paths = [], []\n","\n"," preprocessing_fn = get_preprocessing_fn(encoder)\n","\n"," def load_and_preprocess(img_path, mask_path):\n"," img = tf.io.read_file(img_path)\n"," img = tf.image.decode_png(img, channels=3)\n"," img = tf.image.resize(img, img_size, method='bilinear')\n"," img = tf.cast(img, tf.float32)\n"," img = preprocessing_fn(img)\n","\n"," mask = tf.io.read_file(mask_path)\n"," mask = tf.image.decode_png(mask, channels=1)\n"," mask = tf.image.resize(mask, img_size, method='nearest')\n"," mask = tf.cast(mask, tf.int32)\n","\n"," if id_to_group_mapping is not None:\n"," mask = map_mask_ids(mask, id_to_group_mapping)\n","\n"," mask = tf.squeeze(mask, axis=-1)\n"," return img, mask\n","\n"," def augment_data(image, mask):\n"," mask = tf.expand_dims(mask, axis=-1)\n","\n"," if tf.random.uniform(()) > 0.5:\n"," image = tf.image.flip_left_right(image)\n"," mask = tf.image.flip_left_right(mask)\n","\n"," image = tf.image.random_brightness(image, 0.1)\n"," image = tf.clip_by_value(image, 0, 1)\n","\n"," mask = tf.squeeze(mask, axis=-1)\n"," return image, mask\n","\n"," def create_dataset(image_paths, mask_paths, augment=False, shuffle_data=True):\n"," dataset = tf.data.Dataset.from_tensor_slices((image_paths, mask_paths))\n"," dataset = dataset.map(load_and_preprocess, num_parallel_calls=tf.data.AUTOTUNE)\n","\n"," if augment:\n"," dataset = dataset.map(augment_data, num_parallel_calls=tf.data.AUTOTUNE)\n","\n"," if shuffle_data:\n"," dataset = dataset.shuffle(buffer_size=1000)\n","\n"," dataset = dataset.batch(batch_size).prefetch(tf.data.AUTOTUNE)\n"," return dataset\n","\n"," if split == 'train' and validation_split > 0:\n"," train_dataset = create_dataset(train_img_paths, train_mask_paths, augment=True, shuffle_data=True)\n"," val_dataset = create_dataset(val_img_paths, val_mask_paths, augment=False, shuffle_data=False)\n"," return train_dataset, val_dataset\n"," else:\n"," dataset = create_dataset(train_img_paths, train_mask_paths, augment=False, shuffle_data=(split=='train'))\n"," return dataset\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":60423,"status":"ok","timestamp":1755000469150,"user":{"displayName":"Grégoire Mureau","userId":"04855910922109670788"},"user_tz":-120},"id":"VimLczDnCt7l","outputId":"e13908e6-ba98-4247-9884-6243aec44d75"},"outputs":[{"output_type":"stream","name":"stdout","text":["=== PRÉPARATION DES DATASETS CITYSCAPES ===\n","📊 Paramètres:\n"," • Taille image: (224, 224)\n"," • Taille batch: 8\n"," • Répertoire: /content/data/cityscapes_unzipped\n","\n","🗂️ Stratégie de découpage:\n"," • Split 'train' Cityscapes → Train (80%) + Validation (20%)\n"," • Split 'val' Cityscapes → Test final\n","\n","--- Découpage du split 'train' Cityscapes en Train/Validation ---\n","🔍 Lecture du split Cityscapes 'train'...\n","📊 Split Cityscapes 'train': 2975 images trouvées\n","✂️ Découpage en Train/Validation (80%/20%)\n"," └─ 📚 Train final: 2380 images\n"," └─ 📊 Validation final: 595 images\n","ℹ️ Modèle vanilla - pas de preprocessing\n","\n","--- Création du dataset de TEST à partir du split 'val' Cityscapes ---\n","🔍 Lecture du split Cityscapes 'val'...\n","📊 Split Cityscapes 'val': 500 images trouvées\n","📋 Utilisation complète comme dataset de Test\n","ℹ️ Modèle vanilla - pas de preprocessing\n","\n","✅ Datasets créés avec succès:\n"," 📚 Train: 2380 images (avec augmentation)\n"," 📊 Validation: 595 images (sans augmentation)\n"," 🧪 Test: 500 images (sans augmentation)\n","\n","📋 Récapitulatif:\n"," • Train dataset: pour l'entraînement des modèles\n"," • Validation dataset: pour la sélection des hyperparamètres\n"," • Test dataset: pour l'évaluation finale (données jamais vues)\n","\n","🔍 Vérification des formes:\n"," Train: images = (8, 224, 224, 3), masques = (8, 224, 224)\n"," Validation: images = (8, 224, 224, 3), masques = (8, 224, 224)\n"," Test: images = (8, 224, 224, 3), masques = (8, 224, 224)\n"]}],"source":["# === UTILISATION AVEC TERMINOLOGIE CLAIRE ===\n","# Définir les paramètres\n","BATCH_SIZE = 8\n","IMG_SIZE = (224, 224)\n","\n","# Vérifier si id_to_group existe, sinon définir None\n","try:\n"," id_mapping = id_to_group\n","except NameError:\n"," print(\"⚠️ Variable 'id_to_group' non définie, pas de mapping des IDs\")\n"," id_mapping = None\n","\n","print(\"=== PRÉPARATION DES DATASETS CITYSCAPES ===\")\n","print(f\"📊 Paramètres:\")\n","print(f\" • Taille image: {IMG_SIZE}\")\n","print(f\" • Taille batch: {BATCH_SIZE}\")\n","print(f\" • Répertoire: {cityscapes_dir}\")\n","\n","print(f\"\\n🗂️ Stratégie de découpage:\")\n","print(f\" • Split 'train' Cityscapes → Train (80%) + Validation (20%)\")\n","print(f\" • Split 'val' Cityscapes → Test final\")\n","\n","try:\n"," # 1. Créer TRAIN + VALIDATION à partir du split 'train' de Cityscapes\n"," print(f\"\\n--- Découpage du split 'train' Cityscapes en Train/Validation ---\")\n"," train_dataset, val_dataset = prepare_cityscapes_dataset(\n"," cityscapes_dir,\n"," split='train', # On utilise le split 'train' de Cityscapes\n"," batch_size=BATCH_SIZE,\n"," img_size=IMG_SIZE,\n"," validation_split=0.2, # 80% train, 20% validation\n"," id_to_group_mapping=id_mapping\n"," )\n","\n"," # 2. Créer TEST à partir du split 'val' de Cityscapes\n"," print(f\"\\n--- Création du dataset de TEST à partir du split 'val' Cityscapes ---\")\n"," test_dataset = prepare_cityscapes_dataset(\n"," cityscapes_dir,\n"," split='val', # On utilise le split 'val' de Cityscapes comme notre test\n"," batch_size=BATCH_SIZE,\n"," img_size=IMG_SIZE,\n"," id_to_group_mapping=id_mapping\n"," )\n","\n"," print(f\"\\n✅ Datasets créés avec succès:\")\n"," print(f\" 📚 Train: {2380} images (avec augmentation)\")\n"," print(f\" 📊 Validation: {595} images (sans augmentation)\")\n"," print(f\" 🧪 Test: {500} images (sans augmentation)\")\n","\n"," print(f\"\\n📋 Récapitulatif:\")\n"," print(f\" • Train dataset: pour l'entraînement des modèles\")\n"," print(f\" • Validation dataset: pour la sélection des hyperparamètres\")\n"," print(f\" • Test dataset: pour l'évaluation finale (données jamais vues)\")\n","\n"," # Vérification rapide\n"," print(f\"\\n🔍 Vérification des formes:\")\n"," for name, dataset in [(\"Train\", train_dataset), (\"Validation\", val_dataset), (\"Test\", test_dataset)]:\n"," sample = next(iter(dataset))\n"," print(f\" {name}: images = {sample[0].shape}, masques = {sample[1].shape}\")\n","\n","except Exception as e:\n"," print(f\"❌ Erreur: {e}\")\n"," print(\"Vérifications:\")\n"," print(\"1. Variable 'cityscapes_dir' définie ?\")\n"," print(\"2. Fichiers Cityscapes extraits ?\")\n"," print(\"3. Structure correcte (leftImg8bit/, gtFine/) ?\")\n"," raise"]},{"cell_type":"markdown","metadata":{"id":"q4W-fiAxSwWR"},"source":["## Visualisation des données"]},{"cell_type":"markdown","metadata":{"id":"aSz8Oa3sS3_n"},"source":["Une visualisation de quelques images (train, val, test) sera proposée avec les masques de vérité dont on propose une colorisation tab10 puis une colorisation plus spécifique"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":98553,"status":"ok","timestamp":1755000567721,"user":{"displayName":"Grégoire Mureau","userId":"04855910922109670788"},"user_tz":-120},"id":"mWO3yOl3Oq12","outputId":"4f1e59cc-22c3-4255-be28-7a43e3e8a22f"},"outputs":[],"source":["def visualize_cityscapes_samples(\n"," datasets_dict=None,\n"," cityscapes_dir=None,\n"," num_samples=3,\n"," save_dir=None,\n"," show_stats=True,\n"," show_legend=True\n","):\n"," \"\"\"\n"," Visualise des échantillons Cityscapes avec options flexibles\n","\n"," Args:\n"," datasets_dict: {'train': train_dataset, 'val': val_dataset, 'test': test_dataset}\n"," cityscapes_dir: Chemin vers données brutes (si datasets_dict=None)\n"," num_samples: Nombre d'échantillons par dataset\n"," save_dir: Répertoire de sauvegarde (optionnel)\n"," show_stats: Afficher statistiques des classes\n"," show_legend: Créer légende des classes\n"," \"\"\"\n","\n"," # Mode 1: Depuis les datasets TensorFlow (préféré)\n"," if datasets_dict is not None:\n"," print(\"📊 Visualisation depuis les datasets TensorFlow...\")\n"," _visualize_from_datasets(datasets_dict, num_samples, save_dir, show_stats)\n","\n"," # Mode 2: Depuis les fichiers bruts (fallback)\n"," elif cityscapes_dir is not None:\n"," print(\"📁 Visualisation depuis les fichiers bruts...\")\n"," _visualize_from_files(cityscapes_dir, num_samples, save_dir, show_stats)\n","\n"," else:\n"," raise ValueError(\"Fournir soit datasets_dict soit cityscapes_dir\")\n","\n"," # Créer la légende si demandée\n"," if show_legend:\n"," _create_legend(save_dir)\n","\n","def _visualize_from_datasets(datasets_dict, num_samples, save_dir, show_stats):\n"," \"\"\"Visualise depuis les datasets TensorFlow (après preprocessing)\"\"\"\n","\n"," for split_name, dataset in datasets_dict.items():\n"," print(f\"\\n--- {split_name.title()} Dataset ---\")\n","\n"," sample_count = 0\n"," for batch in dataset.take(num_samples):\n"," images, masks = batch\n","\n"," # Prendre le premier échantillon du batch\n"," image = images[0]\n"," mask = masks[0]\n","\n"," # Créer la visualisation\n"," fig = _create_sample_plot(image.numpy(), mask.numpy(),\n"," f\"{split_name.title()} Sample {sample_count+1}\")\n","\n"," # Sauvegarder si demandé\n"," if save_dir:\n"," os.makedirs(save_dir, exist_ok=True)\n"," fig_path = os.path.join(save_dir, f'{split_name}_sample_{sample_count+1}.png')\n"," fig.savefig(fig_path, dpi=150, bbox_inches='tight')\n"," print(f\"💾 Sauvegardé: {fig_path}\")\n","\n"," plt.show()\n"," plt.close(fig)\n","\n"," # Afficher les stats si demandé\n"," if show_stats:\n"," _show_class_stats(mask.numpy())\n","\n"," sample_count += 1\n"," if sample_count >= num_samples:\n"," break\n","\n","def _visualize_from_files(cityscapes_dir, num_samples, save_dir, show_stats):\n"," \"\"\"Visualise depuis les fichiers bruts (fallback)\"\"\"\n","\n"," for split in ['train', 'val']:\n"," print(f\"\\n--- {split.title()} (Raw Files) ---\")\n","\n"," # Collecter les chemins (réutilise la logique de prepare_cityscapes_dataset)\n"," images_dir = os.path.join(cityscapes_dir, 'leftImg8bit', split)\n"," masks_dir = os.path.join(cityscapes_dir, 'gtFine', split)\n","\n"," if not os.path.exists(images_dir):\n"," print(f\"⚠️ Split '{split}' non trouvé, ignoré\")\n"," continue\n","\n"," image_paths, mask_paths = _collect_sample_paths(images_dir, masks_dir, num_samples)\n","\n"," for i, (img_path, mask_path) in enumerate(zip(image_paths, mask_paths)):\n"," # Charger avec PIL\n"," img = np.array(PILImage.open(img_path).resize((512, 256), PILImage.Resampling.BILINEAR))\n"," mask_raw = np.array(PILImage.open(mask_path).resize((512, 256), PILImage.Resampling.NEAREST))\n","\n"," # Appliquer le mapping (vérifier que id_to_group existe)\n"," if 'id_to_group' in globals():\n"," mask = id_to_group[mask_raw]\n"," else:\n"," print(\"⚠️ Variable 'id_to_group' non trouvée, utilisation brute\")\n"," mask = mask_raw\n","\n"," # Normaliser l'image pour l'affichage\n"," img_normalized = img.astype(np.float32) / 255.0\n","\n"," # Créer la visualisation\n"," fig = _create_sample_plot(img_normalized, mask, f\"{split.title()} Raw Sample {i+1}\")\n","\n"," # Sauvegarder si demandé\n"," if save_dir:\n"," os.makedirs(save_dir, exist_ok=True)\n"," fig_path = os.path.join(save_dir, f'{split}_raw_sample_{i+1}.png')\n"," fig.savefig(fig_path, dpi=150, bbox_inches='tight')\n"," print(f\"💾 Sauvegardé: {fig_path}\")\n","\n"," plt.show()\n"," plt.close(fig)\n","\n"," # Afficher les stats si demandé\n"," if show_stats:\n"," _show_class_stats(mask)\n","\n","def _create_sample_plot(image, mask, title):\n"," \"\"\"Crée le plot avec les 3 vues (factorisation du code de visualisation)\"\"\"\n","\n"," fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n"," fig.suptitle(title, fontsize=14)\n","\n"," # Vue 1: Image originale\n"," axes[0].imshow(image)\n"," axes[0].set_title(\"Image Originale\")\n"," axes[0].axis('off')\n","\n"," # Vue 2: Masque avec IDs (en tab10)\n"," im = axes[1].imshow(mask, cmap='tab10', vmin=0, vmax=mapping_config['num_classes']-1)\n"," axes[1].set_title(\"Masque (IDs de classes)\")\n"," axes[1].axis('off')\n","\n"," # Vue 3: Masque colorisé avec nos couleurs\n"," colored_mask = _colorize_mask(mask)\n"," axes[2].imshow(colored_mask)\n"," axes[2].set_title(\"Masque Segmenté (Coloré)\")\n"," axes[2].axis('off')\n","\n"," plt.tight_layout()\n"," return fig\n","\n","def _colorize_mask(mask):\n"," \"\"\"Applique la colorisation du masque (logique centralisée)\"\"\"\n"," colored_mask = np.zeros((mask.shape[0], mask.shape[1], 3), dtype=np.uint8)\n"," for group_id in range(mapping_config['num_classes']):\n"," colored_mask[mask == group_id] = mapping_config['group_colors'][group_id]\n"," return colored_mask\n","\n","def _show_class_stats(mask):\n"," \"\"\"Affiche les statistiques des classes (factorisation)\"\"\"\n"," unique_classes, counts = np.unique(mask, return_counts=True)\n"," print(\"\\n📊 Classes présentes dans cette image:\")\n"," for class_id, count in zip(unique_classes, counts):\n"," if class_id < len(mapping_config['group_names']): # Vérification bounds\n"," percentage = (count / mask.size) * 100\n"," print(f\" {mapping_config['group_names'][class_id]}: {count} pixels ({percentage:.1f}%)\")\n","\n","def _collect_sample_paths(images_dir, masks_dir, num_samples):\n"," \"\"\"Collecte aléatoirement des chemins d'images (factorisation)\"\"\"\n"," image_paths, mask_paths = [], []\n","\n"," for city in os.listdir(images_dir):\n"," city_img_dir = os.path.join(images_dir, city)\n"," city_mask_dir = os.path.join(masks_dir, city)\n","\n"," if os.path.isdir(city_img_dir) and os.path.isdir(city_mask_dir):\n"," for file_name in os.listdir(city_img_dir):\n"," if file_name.endswith('_leftImg8bit.png'):\n"," img_path = os.path.join(city_img_dir, file_name)\n"," mask_name = file_name.replace('_leftImg8bit.png', '_gtFine_labelIds.png')\n"," mask_path = os.path.join(city_mask_dir, mask_name)\n","\n"," if os.path.exists(mask_path):\n"," image_paths.append(img_path)\n"," mask_paths.append(mask_path)\n","\n"," # Sélection aléatoire\n"," if len(image_paths) > num_samples:\n"," selected_indices = np.random.choice(len(image_paths), size=num_samples, replace=False)\n"," image_paths = [image_paths[i] for i in selected_indices]\n"," mask_paths = [mask_paths[i] for i in selected_indices]\n","\n"," return image_paths, mask_paths\n","\n","def _create_legend(save_dir):\n"," \"\"\"Crée la légende des classes (factorisation)\"\"\"\n"," fig, ax = plt.subplots(figsize=(8, 5))\n"," fig.suptitle(\"Légende des classes de segmentation\", fontsize=14)\n","\n"," for i, (name, color) in enumerate(zip(mapping_config['group_names'], mapping_config['group_colors'])):\n"," ax.fill_between([0, 1], [i, i], [i+0.9, i+0.9], color=[c/255 for c in color])\n"," ax.text(1.1, i+0.5, name, va='center', fontsize=12)\n","\n"," ax.set_xlim(0, 2.5)\n"," ax.set_ylim(len(mapping_config['group_names'])-0.1, -0.1)\n"," ax.axis('off')\n","\n"," if save_dir:\n"," os.makedirs(save_dir, exist_ok=True)\n"," legend_path = os.path.join(save_dir, 'class_legend.png')\n"," fig.savefig(legend_path, dpi=150, bbox_inches='tight')\n"," print(f\"🏷️ Légende sauvegardée: {legend_path}\")\n","\n"," plt.show()\n"," plt.close(fig)\n","\n","# === UTILISATION SIMPLIFIÉE ===\n","print(\"=== VISUALISATION DES ÉCHANTILLONS CITYSCAPES ===\")\n","\n","# Méthode préférée: depuis les datasets TensorFlow\n","datasets_dict = {\n"," 'train': train_dataset,\n"," 'val': val_dataset,\n"," 'test': test_dataset\n","}\n","\n","# Créer le dossier de sauvegarde\n","viz_dir = os.path.join(data_dir, 'samples') if 'data_dir' in globals() else './visualizations'\n","\n","# Visualiser avec toutes les options\n","visualize_cityscapes_samples(\n"," datasets_dict=datasets_dict,\n"," num_samples=2, # 2 échantillons par dataset\n"," save_dir=viz_dir,\n"," show_stats=True,\n"," show_legend=True\n",")"]},{"cell_type":"markdown","metadata":{"id":"BcRXJIz6Ts6q"},"source":["## Entrainement du modèle"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":63,"status":"ok","timestamp":1755184341693,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-120},"id":"lUxAq504vmjH","outputId":"8cafb62e-4373-474c-daf6-f72303022b28"},"outputs":[{"output_type":"stream","name":"stdout","text":["🚀 MODE PRODUCTION ACTIVÉ\n","📊 Configuration globale: Device=/CPU:0, Max_Epochs=20\n"," Patience EarlyStopping=5, Patience LR=3\n"]}],"source":["# ====================================================================\n","# SCRIPT D'ENTRAÎNEMENT OPTIMISÉ POUR SEGMENTATION MULTI-CONFIGURATIONS\n","# AVEC SYSTÈME DE REPRISE AUTOMATIQUE\n","# ====================================================================\n","\n","# 🚨 PARAMÈTRE PRINCIPAL : Basculer entre TEST et PRODUCTION\n","TEST_MODE = False # Mettre False pour le mode production\n","\n","# Configuration selon le mode\n","if TEST_MODE:\n"," print(\"🧪 MODE TEST ACTIVÉ\")\n"," DEVICE_CONFIG = '/CPU:0'\n"," MAX_EPOCHS = 2 # Très rapide pour les tests\n"," EARLY_STOPPING_PATIENCE = 2 # Arrêt anticipé si pas d'amélioration pendant 2 époques\n"," LR_REDUCE_PATIENCE = 1 # Réduction LR si pas d'amélioration pendant 1 époque\n"," SUBSET_SIZE = 20\n"," # Taille fixe pour les tests (plus rapide)\n"," TEST_IMG_SIZE = (128, 128)\n"," TEST_BATCH_SIZE = 2\n"," TEST_MAX_CONFIGS = None # Nombre max de configs à tester\n"," # Expérience MLflow déjà définie en début de notebook\n","else:\n"," print(\"🚀 MODE PRODUCTION ACTIVÉ\")\n"," DEVICE_CONFIG = '/GPU:0' if tf.config.list_physical_devices('GPU') else '/CPU:0'\n"," MAX_EPOCHS = 20 # 20 époques max (beaucoup de modèles à tester)\n"," EARLY_STOPPING_PATIENCE = 5 # Arrêt anticipé si pas d'amélioration pendant 5 époques\n"," LR_REDUCE_PATIENCE = 3 # Réduction LR si pas d'amélioration pendant 3 époques\n"," SUBSET_SIZE = None\n"," # En production, utilise les paramètres de chaque configuration\n"," # Expérience MLflow déjà définie en début de notebook\n","\n","print(f\"📊 Configuration globale: Device={DEVICE_CONFIG}, Max_Epochs={MAX_EPOCHS}\")\n","print(f\" Patience EarlyStopping={EARLY_STOPPING_PATIENCE}, Patience LR={LR_REDUCE_PATIENCE}\")"]},{"cell_type":"code","execution_count":11,"metadata":{"id":"RqI-umcJvt9u","executionInfo":{"status":"ok","timestamp":1755184341707,"user_tz":-120,"elapsed":13,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# ====================================================================\n","# SYSTÈME DE REPRISE AUTOMATIQUE - VÉRIFICATION MLFLOW\n","# ====================================================================\n","\n","def get_completed_experiments(experiment_name=\"OC Projet 8\"):\n"," \"\"\"\n"," Récupère la liste des expériences déjà terminées dans MLflow\n"," Retourne un set des noms de runs terminés\n"," \"\"\"\n"," try:\n"," print(f\"🔍 Vérification des expériences déjà terminées dans '{experiment_name}'...\")\n","\n"," # Récupérer l'expérience par nom\n"," experiment = mlflow.get_experiment_by_name(experiment_name)\n"," if experiment is None:\n"," print(f\"⚠️ Expérience '{experiment_name}' non trouvée\")\n"," return set()\n","\n"," # Rechercher tous les runs de cette expérience\n"," runs = mlflow.search_runs(\n"," experiment_ids=[experiment.experiment_id],\n"," filter_string=\"status = 'FINISHED'\", # Seulement les runs terminés avec succès\n"," output_format=\"pandas\"\n"," )\n","\n"," if runs.empty:\n"," print(\"📊 Aucune expérience terminée trouvée\")\n"," return set()\n","\n"," # Extraire les noms des runs terminés\n"," completed_runs = set()\n"," for idx, run in runs.iterrows():\n"," run_name = run.get('tags.mlflow.runName', '')\n"," if run_name:\n"," completed_runs.add(run_name)\n","\n"," print(f\"✅ {len(completed_runs)} expériences terminées trouvées:\")\n"," for run_name in sorted(completed_runs):\n"," print(f\" - {run_name}\")\n","\n"," return completed_runs\n","\n"," except Exception as e:\n"," print(f\"⚠️ Erreur lors de la vérification MLflow: {e}\")\n"," print(\"🔄 Continuera sans vérification (tous les modèles seront entraînés)\")\n"," return set()\n","\n","\n","def generate_stable_run_name(config, test_mode=False):\n"," \"\"\"\n"," Génère un nom de run stable (sans date) basé sur la configuration\n"," Format: {config_name}_{mode}\n"," \"\"\"\n"," base_name = config['name']\n"," mode_suffix = \"test\" if test_mode else \"prod\"\n"," return f\"{base_name}_{mode_suffix}\"\n","\n","\n","def filter_configs_to_run(experimental_configurations, experiment_name=\"OC Projet 8\"):\n"," \"\"\"\n"," Filtre les configurations pour ne garder que celles non encore entraînées\n"," \"\"\"\n"," completed_runs = get_completed_experiments(experiment_name)\n","\n"," if not completed_runs:\n"," print(\"🚀 Aucune expérience terminée trouvée, tous les modèles seront entraînés\")\n"," return list(range(len(experimental_configurations)))\n","\n"," configs_to_run = []\n"," skipped_configs = []\n","\n"," for idx, config in enumerate(experimental_configurations):\n"," expected_run_name = generate_stable_run_name(config, TEST_MODE)\n","\n"," if expected_run_name in completed_runs:\n"," skipped_configs.append((idx, config['name'], expected_run_name))\n"," else:\n"," configs_to_run.append(idx)\n","\n"," # Rapport de filtrage\n"," print(f\"\\n📊 FILTRAGE DES CONFIGURATIONS:\")\n"," print(f\"✅ À entraîner: {len(configs_to_run)} configurations\")\n"," print(f\"⏭️ Déjà terminées: {len(skipped_configs)} configurations\")\n","\n"," if skipped_configs:\n"," print(f\"\\n⏭️ CONFIGURATIONS IGNORÉES (déjà terminées):\")\n"," for idx, config_name, run_name in skipped_configs:\n"," print(f\" [{idx}] {config_name} → {run_name}\")\n","\n"," if configs_to_run:\n"," print(f\"\\n🚀 CONFIGURATIONS À ENTRAÎNER:\")\n"," for idx in configs_to_run:\n"," config_name = experimental_configurations[idx]['name']\n"," expected_run_name = generate_stable_run_name(experimental_configurations[idx], TEST_MODE)\n"," print(f\" [{idx}] {config_name} → {expected_run_name}\")\n","\n"," return configs_to_run"]},{"cell_type":"code","execution_count":12,"metadata":{"id":"yXkQoZssvwTt","executionInfo":{"status":"ok","timestamp":1755184342020,"user_tz":-120,"elapsed":312,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# ====================================================================\n","# CALLBACK MLFLOW PERSONNALISÉ POUR LOGGING DÉTAILLÉ\n","# ====================================================================\n","\n","class ExperimentMLflowCallback(tf.keras.callbacks.Callback):\n"," \"\"\"Callback MLflow personnalisé pour logging complet des métriques\"\"\"\n","\n"," def __init__(self, config, run_id, batch_size, img_size):\n"," super().__init__()\n"," self.config = config\n"," self.run_id = run_id\n"," self.batch_size = batch_size\n"," self.img_size = img_size\n","\n"," def on_epoch_end(self, epoch, logs=None):\n"," \"\"\"Log des métriques à chaque époque (sans doublons)\"\"\"\n"," logs = logs or {}\n"," for name, value in logs.items():\n"," # Éviter les doublons : ignorer les métriques \"validation_\"\n"," if not name.startswith('validation_'):\n"," mlflow.log_metric(name, float(value), step=epoch)\n","\n"," def on_train_begin(self, logs=None):\n"," \"\"\"Log des paramètres au début de l'entraînement\"\"\"\n"," # Log config du modèle\n"," mlflow.log_param(\"model_architecture\", self.config[\"architecture\"])\n"," mlflow.log_param(\"model_name\", self.config[\"name\"])\n"," mlflow.log_param(\"encoder\", self.config.get(\"encoder\", \"None\"))\n"," mlflow.log_param(\"encoder_freeze\", self.config.get(\"encoder_freeze\", False))\n","\n"," # Log config du script (utilise les paramètres passés au callback)\n"," mlflow.log_param(\"script_batch_size\", self.batch_size)\n"," mlflow.log_param(\"script_img_size\", str(self.img_size))\n"," mlflow.log_param(\"test_mode\", TEST_MODE)\n"," mlflow.log_param(\"max_epochs\", MAX_EPOCHS)\n"," mlflow.log_param(\"device\", DEVICE_CONFIG)\n"," mlflow.log_param(\"subset_size\", SUBSET_SIZE)\n","\n","\n","class KerasModelCheckpoint(tf.keras.callbacks.Callback):\n"," \"\"\"Callback personnalisé pour sauvegarder en format .keras natif\"\"\"\n","\n"," def __init__(self, filepath, monitor='val_loss', save_best_only=True, mode='auto', verbose=1):\n"," super().__init__()\n"," self.filepath = filepath\n"," self.monitor = monitor\n"," self.save_best_only = save_best_only\n"," self.verbose = verbose\n","\n"," if mode == 'auto':\n"," if 'acc' in monitor or monitor.startswith('fmeasure'):\n"," self.mode = 'max'\n"," else:\n"," self.mode = 'min'\n"," else:\n"," self.mode = mode\n","\n"," if self.mode == 'min':\n"," self.best = float('inf')\n"," else:\n"," self.best = -float('inf')\n","\n"," def on_epoch_end(self, epoch, logs=None):\n"," logs = logs or {}\n"," current = logs.get(self.monitor)\n","\n"," if current is None:\n"," if self.verbose > 0:\n"," print(f'Warning: Metric {self.monitor} not found in logs.')\n"," return\n","\n"," if self.save_best_only:\n"," if (self.mode == 'min' and current < self.best) or (self.mode == 'max' and current > self.best):\n"," self.best = current\n","\n"," if self.verbose > 0:\n"," print(f'Epoch {epoch + 1}: {self.monitor} improved from {self.best} to {current:.5f}, saving model to {self.filepath}')\n","\n"," # SAUVEGARDE NATIVE KERAS - AUCUN WARNING !\n"," self.model.save(self.filepath, save_format='keras')\n"," else:\n"," if self.verbose > 0:\n"," print(f'Epoch {epoch + 1}: {self.monitor} did not improve from {self.best:.5f}')\n"," else:\n"," if self.verbose > 0:\n"," print(f'Epoch {epoch + 1}: saving model to {self.filepath}')\n"," self.model.save(self.filepath, save_format='keras')"]},{"cell_type":"code","source":["def calculate_detailed_metrics(model, test_dataset, num_classes, class_names=None):\n"," \"\"\"\n"," Calcule uniquement l'IoU par classe et les statistiques détaillées\n"," (Le mean_iou global est maintenant calculé par la métrique officielle)\n"," \"\"\"\n"," print(\" 📊 Analyse détaillée par classe...\")\n","\n"," all_true_masks = []\n"," all_pred_masks = []\n","\n"," # Collecter toutes les prédictions\n"," for batch_idx, (images, true_masks) in enumerate(test_dataset):\n"," predictions = model.predict(images, verbose=0)\n"," pred_masks = tf.argmax(predictions, axis=-1)\n","\n"," all_true_masks.extend(true_masks.numpy())\n"," all_pred_masks.extend(pred_masks.numpy())\n","\n"," if batch_idx % 10 == 0:\n"," print(f\" Batch {batch_idx + 1} traité...\")\n","\n"," all_true_masks = np.array(all_true_masks)\n"," all_pred_masks = np.array(all_pred_masks)\n","\n"," print(f\" Total d'images évaluées: {len(all_true_masks)}\")\n","\n"," # Calcul IoU par classe (pour analyse détaillée)\n"," class_ious = []\n"," class_stats = []\n","\n"," for class_id in range(num_classes):\n"," # Masques binaires pour cette classe\n"," true_binary = (all_true_masks == class_id)\n"," pred_binary = (all_pred_masks == class_id)\n","\n"," # Calcul IoU\n"," intersection = np.logical_and(true_binary, pred_binary).sum()\n"," union = np.logical_or(true_binary, pred_binary).sum()\n","\n"," iou = intersection / union if union > 0 else 0.0\n"," class_ious.append(iou)\n","\n"," # Statistiques de classe\n"," true_pixels = true_binary.sum()\n"," pred_pixels = pred_binary.sum()\n"," total_pixels = all_true_masks.size\n","\n"," class_name = class_names[class_id] if class_names else f\"Class_{class_id}\"\n","\n"," class_stats.append({\n"," 'class_id': class_id,\n"," 'class_name': class_name,\n"," 'iou': iou,\n"," 'true_pixels': true_pixels,\n"," 'pred_pixels': pred_pixels,\n"," 'true_percentage': 100 * true_pixels / total_pixels,\n"," 'pred_percentage': 100 * pred_pixels / total_pixels\n"," })\n","\n"," # Mean IoU calculé ici (pour cohérence avec l'affichage)\n"," mean_iou = np.mean(class_ious)\n","\n"," # Affichage des résultats détaillés\n"," print(f\"\\n📊 ANALYSE DÉTAILLÉE PAR CLASSE:\")\n"," print(\"-\" * 70)\n"," print(f\"{'Classe':<15} {'IoU':<8} {'Vrais %':<10} {'Prédits %':<12}\")\n"," print(\"-\" * 70)\n","\n"," for stats in class_stats:\n"," print(f\"{stats['class_name']:<15} {stats['iou']:<8.4f} {stats['true_percentage']:<10.2f} {stats['pred_percentage']:<12.2f}\")\n","\n"," print(\"-\" * 70)\n"," print(f\"{'MEAN IoU (calc)':<15} {mean_iou:<8.4f}\")\n"," print(\"💡 Le Mean IoU officiel (métrique Keras) fait référence\")\n","\n"," return {\n"," 'class_ious': class_ious,\n"," 'mean_iou': mean_iou, # Pour compatibilité avec le code existant\n"," 'class_stats': class_stats,\n"," 'all_true_masks': all_true_masks,\n"," 'all_pred_masks': all_pred_masks\n"," }\n"],"metadata":{"id":"9-rX20362Pz2","executionInfo":{"status":"ok","timestamp":1755184342020,"user_tz":-120,"elapsed":140,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":13,"outputs":[]},{"cell_type":"code","source":["def setup_training_components(config, run_id):\n"," \"\"\"\n"," Setup complet : paramètres, datasets, modèle, compilation, callbacks\n"," VERSION CORRIGÉE avec métriques officielles Keras\n"," \"\"\"\n"," # === EXTRACTION DES PARAMÈTRES DE LA CONFIG ===\n"," if TEST_MODE:\n"," batch_size = TEST_BATCH_SIZE\n"," img_size = TEST_IMG_SIZE\n"," print(f\"🧪 Mode test: batch_size={batch_size}, img_size={img_size}\")\n"," else:\n"," batch_size = config['params']['batch_size']\n"," img_size = config['params']['img_size']\n"," print(f\"🚀 Mode production: batch_size={batch_size}, img_size={img_size}\")\n","\n"," # === PRÉPARATION DES DONNÉES ===\n"," print(\"\\n📊 Préparation des datasets...\")\n"," try:\n"," id_mapping = id_to_group\n"," mlflow.log_param(\"class_mapping\", \"custom_id_to_group\")\n"," except NameError:\n"," print(\"⚠️ Variable 'id_to_group' non définie\")\n"," id_mapping = None\n"," mlflow.log_param(\"class_mapping\", \"none\")\n","\n"," # Créer les datasets avec les bons paramètres\n"," train_dataset, val_dataset = prepare_cityscapes_dataset(\n"," cityscapes_dir,\n"," split='train',\n"," batch_size=batch_size,\n"," img_size=img_size,\n"," validation_split=0.2,\n"," id_to_group_mapping=id_mapping\n"," )\n"," test_dataset = prepare_cityscapes_dataset(\n"," cityscapes_dir,\n"," split='val',\n"," batch_size=batch_size,\n"," img_size=img_size,\n"," id_to_group_mapping=id_mapping\n"," )\n","\n"," if TEST_MODE and SUBSET_SIZE:\n"," train_batches = SUBSET_SIZE // batch_size\n"," val_batches = SUBSET_SIZE // (batch_size * 4)\n"," test_batches = SUBSET_SIZE // (batch_size * 4)\n"," train_samples_used = train_batches * batch_size\n"," val_samples_used = val_batches * batch_size\n"," test_samples_used = test_batches * batch_size\n","\n"," print(f\"🧪 Mode test: limitation appliquée\")\n"," print(f\" └─ 📚 Train utilisé: {train_samples_used} images ({train_batches} batches)\")\n"," print(f\" └─ 📊 Validation utilisé: {val_samples_used} images ({val_batches} batches)\")\n"," print(f\" └─ 🧪 Test utilisé: {test_samples_used} images ({test_batches} batches)\")\n","\n"," train_dataset = train_dataset.take(train_batches)\n"," val_dataset = val_dataset.take(val_batches)\n"," test_dataset = test_dataset.take(test_batches)\n","\n"," # === CRÉATION DU MODÈLE ===\n"," print(f\"\\n🏗️ Création du modèle {config['name']}...\")\n"," if TEST_MODE:\n"," config_modified = config.copy()\n"," config_modified['params'] = config['params'].copy()\n"," config_modified['params']['img_size'] = img_size\n"," config_modified['params']['batch_size'] = batch_size\n"," model_config = config_modified\n"," else:\n"," model_config = config\n","\n"," with tf.device(DEVICE_CONFIG):\n"," model = create_model_from_config(model_config, mapping_config['num_classes'])\n"," num_classes_from_model = model.output_shape[-1]\n"," print(f\"📊 Nombre de classes: {num_classes_from_model}\")\n"," total_params = model.count_params()\n"," trainable_params = sum([tf.keras.backend.count_params(w) for w in model.trainable_weights])\n"," print(f\"📊 Paramètres: {total_params:,} total | {trainable_params:,} entraînables\")\n","\n"," # === COMPILATION AVEC MÉTRIQUES OFFICIELLES KERAS ===\n"," print(\"\\n⚙️ Compilation du modèle avec métriques officielles...\")\n","\n"," # ✅ MÉTRIQUES OFFICIELLES KERAS - AUCUNE MÉTRIQUE PERSONNALISÉE !\n"," metrics_list = [\n"," # Accuracy officielle pour classification sparse\n"," tf.keras.metrics.SparseCategoricalAccuracy(name='sparse_categorical_accuracy'),\n","\n"," # IoU officielle Keras - LA métrique de référence pour segmentation\n"," tf.keras.metrics.MeanIoU(\n"," num_classes=num_classes_from_model,\n"," name='mean_iou',\n"," sparse_y_true=True, # Les labels sont des entiers (pas one-hot)\n"," sparse_y_pred=False # Les prédictions sont des probabilités\n"," )\n"," ]\n","\n"," # Métrique principale pour les callbacks\n"," primary_metric = 'val_mean_iou'\n"," primary_mode = 'max'\n","\n"," print(\"🎯 MÉTRIQUES OFFICIELLES KERAS POUR SEGMENTATION SÉMANTIQUE:\")\n"," print(\" 🔥 LOSS: sparse_categorical_crossentropy\")\n"," print(\" └─ Optimise la classification pixel-par-pixel\")\n"," print(\" 📊 MÉTRIQUES (100% officielles Keras):\")\n"," print(\" ├─ SparseCategoricalAccuracy: % pixels correctement classifiés\")\n"," print(\" └─ MeanIoU: Intersection over Union (métrique gold standard)\")\n"," print(\" 🎛️ CALLBACKS:\")\n"," print(f\" └─ Monitor: {primary_metric} (IoU = métrique de référence)\")\n"," print(\"✅ Configuration 100% standard et fiable !\")\n","\n"," # Compilation du modèle\n"," model.compile(\n"," optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n"," loss='sparse_categorical_crossentropy',\n"," metrics=metrics_list\n"," )\n","\n"," # === CALLBACKS OPTIMISÉS ===\n"," print(f\"\\n📋 Configuration des callbacks...\")\n"," print(\"🎛️ CALLBACKS BASÉS SUR MEAN_IOU OFFICIEL:\")\n"," print(f\" ├─ EarlyStopping: Arrêt si {primary_metric} stagne {EARLY_STOPPING_PATIENCE} époques\")\n"," print(f\" ├─ ReduceLROnPlateau: Réduction LR si {primary_metric} stagne {LR_REDUCE_PATIENCE} époques\")\n"," print(f\" └─ ModelCheckpoint: Sauvegarde du meilleur modèle selon {primary_metric}\")\n","\n"," artifacts_dir = f\"artifacts/run_{run_id}\"\n"," os.makedirs(artifacts_dir, exist_ok=True)\n","\n"," mlflow_callback = ExperimentMLflowCallback(config, run_id, batch_size, img_size)\n","\n"," callbacks_list = [\n"," mlflow_callback,\n"," tf.keras.callbacks.ReduceLROnPlateau(\n"," monitor=primary_metric,\n"," factor=0.2,\n"," patience=LR_REDUCE_PATIENCE,\n"," verbose=1,\n"," min_lr=1e-6,\n"," mode=primary_mode\n"," ),\n"," tf.keras.callbacks.EarlyStopping(\n"," monitor=primary_metric,\n"," patience=EARLY_STOPPING_PATIENCE,\n"," restore_best_weights=True,\n"," verbose=1,\n"," mode=primary_mode\n"," ),\n"," KerasModelCheckpoint(\n"," filepath=os.path.join(artifacts_dir, 'best_model.keras'),\n"," monitor=primary_metric,\n"," save_best_only=True,\n"," mode=primary_mode,\n"," verbose=1\n"," ),\n"," tf.keras.callbacks.TensorBoard(\n"," log_dir=os.path.join(artifacts_dir, 'tensorboard'),\n"," histogram_freq=1\n"," )\n"," ]\n","\n"," return {\n"," 'model': model,\n"," 'train_dataset': train_dataset,\n"," 'val_dataset': val_dataset,\n"," 'test_dataset': test_dataset,\n"," 'callbacks_list': callbacks_list,\n"," 'artifacts_dir': artifacts_dir,\n"," 'num_classes_from_model': num_classes_from_model,\n"," 'batch_size': batch_size,\n"," 'img_size': img_size\n"," }\n","\n","def train_model_core(model, train_dataset, val_dataset, callbacks_list, max_epochs):\n"," \"\"\"\n"," Fonction pure d'entraînement - ne fait que l'entraînement\n"," \"\"\"\n"," print(f\"\\n🚀 Début de l'entraînement ({max_epochs} epochs max)...\")\n"," start_time = time.time()\n","\n"," with tf.device(DEVICE_CONFIG):\n"," history = model.fit(\n"," train_dataset,\n"," validation_data=val_dataset,\n"," epochs=max_epochs,\n"," callbacks=callbacks_list,\n"," verbose=1\n"," )\n","\n"," training_time = time.time() - start_time\n"," mlflow.log_metric(\"training_time_seconds\", training_time)\n"," print(f\"⏱️ Temps d'entraînement: {training_time:.2f}s\")\n","\n"," return history, training_time\n","\n","def evaluate_model_comprehensive(model, test_dataset, num_classes_from_model, artifacts_dir):\n"," \"\"\"\n"," Évaluation complète avec métriques officielles Keras + analyses détaillées\n"," \"\"\"\n"," # === ÉVALUATION AVEC MÉTRIQUES OFFICIELLES (même config que l'entraînement) ===\n"," print(\"\\n📊 Évaluation avec métriques officielles Keras...\")\n","\n"," # Créer exactement les mêmes métriques que pendant l'entraînement\n"," official_metrics = [\n"," tf.keras.metrics.SparseCategoricalAccuracy(name='sparse_categorical_accuracy'),\n"," tf.keras.metrics.MeanIoU(\n"," num_classes=num_classes_from_model,\n"," name='mean_iou',\n"," sparse_y_true=True,\n"," sparse_y_pred=False\n"," )\n"," ]\n","\n"," # Compiler temporairement avec les métriques officielles\n"," # (ça ne change pas les poids, juste les métriques)\n"," model.compile(\n"," optimizer=model.optimizer, # Garder l'optimizer existant\n"," loss=model.loss, # Garder la loss existante\n"," metrics=official_metrics\n"," )\n","\n"," # Évaluation officielle\n"," test_results = model.evaluate(test_dataset, verbose=1)\n","\n"," basic_test_metrics = {\n"," 'loss': test_results[0],\n"," 'sparse_categorical_accuracy': test_results[1],\n"," 'mean_iou': test_results[2]\n"," }\n","\n"," # Log dans MLflow\n"," for metric_name, value in basic_test_metrics.items():\n"," mlflow.log_metric(f\"test_{metric_name}\", value)\n"," print(f\"✅ Test {metric_name} (officiel): {value:.4f}\")\n","\n"," # === ÉVALUATION DÉTAILLÉE ADDITIONNELLE (pour analyse par classe) ===\n"," try:\n"," class_names = mapping_config['group_names'] if 'group_names' in mapping_config else None\n"," except:\n"," class_names = [f\"Class_{i}\" for i in range(num_classes_from_model)]\n","\n"," print(\"\\n🔍 Calcul des métriques détaillées par classe...\")\n"," detailed_metrics = calculate_detailed_metrics(\n"," model, test_dataset, num_classes_from_model, class_names\n"," )\n","\n"," # Log des métriques par classe (en complément)\n"," for i, iou in enumerate(detailed_metrics['class_ious']):\n"," mlflow.log_metric(f\"class_{i}_iou\", iou)\n","\n"," # === MATRICE DE CONFUSION ===\n"," print(\"\\n🔥 Génération de la matrice de confusion...\")\n"," confusion_matrix_data = generate_confusion_matrix(\n"," model, test_dataset, num_classes_from_model, class_names, artifacts_dir\n"," )\n","\n"," return basic_test_metrics, detailed_metrics, confusion_matrix_data, class_names\n","\n","def generate_confusion_matrix(model, test_dataset, num_classes, class_names, artifacts_dir):\n"," \"\"\"\n"," Génère la matrice de confusion sur le jeu de test et la sauvegarde (SANS AFFICHAGE)\n"," \"\"\"\n"," print(\"📊 Calcul de la matrice de confusion...\")\n","\n"," # Collecter toutes les prédictions\n"," y_true_all = []\n"," y_pred_all = []\n","\n"," for batch_images, batch_masks in test_dataset:\n"," # Prédictions du modèle\n"," predictions = model.predict(batch_images, verbose=0)\n"," pred_classes = tf.argmax(predictions, axis=-1)\n","\n"," # Aplatir les masques et prédictions\n"," true_flat = tf.reshape(batch_masks, [-1]).numpy()\n"," pred_flat = tf.reshape(pred_classes, [-1]).numpy()\n","\n"," y_true_all.extend(true_flat)\n"," y_pred_all.extend(pred_flat)\n","\n"," # Calcul de la matrice de confusion\n"," from sklearn.metrics import confusion_matrix\n"," import itertools\n","\n"," cm = confusion_matrix(y_true_all, y_pred_all, labels=range(num_classes))\n","\n"," # === CRÉATION DE LA VISUALISATION (SANS AFFICHAGE) ===\n"," plt.figure(figsize=(10, 8))\n"," plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n"," plt.title('Matrice de Confusion - Jeu de Test', fontsize=14)\n"," plt.colorbar()\n","\n"," # Définir les labels des axes\n"," if class_names:\n"," tick_marks = np.arange(len(class_names))\n"," plt.xticks(tick_marks, class_names, rotation=45, ha='right')\n"," plt.yticks(tick_marks, class_names)\n"," else:\n"," tick_marks = np.arange(num_classes)\n"," plt.xticks(tick_marks, [f\"Class_{i}\" for i in range(num_classes)])\n"," plt.yticks(tick_marks, [f\"Class_{i}\" for i in range(num_classes)])\n","\n"," # === AFFICHER LES VALEURS DANS CHAQUE CELLULE ===\n"," thresh = cm.max() / 2.0\n"," for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n"," plt.text(j, i, format(cm[i, j], 'd'),\n"," horizontalalignment=\"center\",\n"," verticalalignment=\"center\",\n"," color=\"white\" if cm[i, j] > thresh else \"black\",\n"," fontsize=10,\n"," fontweight='bold')\n","\n"," plt.ylabel('Vraie classe', fontsize=12)\n"," plt.xlabel('Classe prédite', fontsize=12)\n"," plt.tight_layout()\n","\n"," # Sauvegarder l'image (SANS AFFICHAGE)\n"," cm_image_path = os.path.join(artifacts_dir, 'confusion_matrix.png')\n"," plt.savefig(cm_image_path, dpi=300, bbox_inches='tight')\n"," plt.close() # ✅ FERMER LE PLOT - PAS D'AFFICHAGE\n","\n"," # Données JSON pour l'artefact\n"," confusion_matrix_data = {\n"," 'matrix': cm.tolist(), # Conversion numpy -> liste Python\n"," 'class_names': class_names if class_names else [f\"Class_{i}\" for i in range(num_classes)],\n"," 'total_samples': len(y_true_all),\n"," 'accuracy': float(np.trace(cm) / np.sum(cm)) # Accuracy globale\n"," }\n","\n"," print(f\"✅ Matrice de confusion sauvegardée : {cm_image_path}\")\n","\n"," return confusion_matrix_data\n","\n","def generate_training_visualizations(history, detailed_metrics, class_names, run_id, config, artifacts_dir):\n"," \"\"\"\n"," Génère toutes les visualisations : courbes d'entraînement + IoU par classe\n"," \"\"\"\n"," print(\"\\n📈 Création des visualisations...\")\n"," fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n"," fig.suptitle(f'Entraînement {config[\"name\"]} - Run {run_id[:8]}', fontsize=16)\n","\n"," # Loss\n"," axes[0, 0].plot(history.history['loss'], label='Train Loss')\n"," axes[0, 0].plot(history.history['val_loss'], label='Val Loss')\n"," axes[0, 0].set_title('Loss')\n"," axes[0, 0].legend()\n"," axes[0, 0].grid(True)\n","\n"," # Accuracy\n"," axes[0, 1].plot(history.history['sparse_categorical_accuracy'], label='Train Acc')\n"," axes[0, 1].plot(history.history['val_sparse_categorical_accuracy'], label='Val Acc')\n"," axes[0, 1].set_title('Accuracy')\n"," axes[0, 1].legend()\n"," axes[0, 1].grid(True)\n","\n"," # Mean IoU\n"," axes[1, 0].plot(history.history['mean_iou'], label='Train Mean IoU')\n"," axes[1, 0].plot(history.history['val_mean_iou'], label='Val Mean IoU')\n"," axes[1, 0].set_title('Mean IoU')\n"," axes[1, 0].legend()\n"," axes[1, 0].grid(True)\n","\n"," # IoU par classe (bar chart)\n"," if class_names and len(class_names) == len(detailed_metrics['class_ious']):\n"," axes[1, 1].bar(range(len(detailed_metrics['class_ious'])), detailed_metrics['class_ious'])\n"," axes[1, 1].set_title(f'IoU par classe sur jeu de test (Mean: {detailed_metrics[\"mean_iou\"]:.3f})')\n"," axes[1, 1].set_xticks(range(len(class_names)))\n"," axes[1, 1].set_xticklabels(class_names, rotation=45)\n"," axes[1, 1].grid(True, axis='y')\n","\n"," plt.tight_layout()\n"," training_curves_path = os.path.join(artifacts_dir, 'training_curves.png')\n"," plt.savefig(training_curves_path, dpi=300, bbox_inches='tight')\n"," plt.close()\n","\n","def save_training_artifacts(history, config, detailed_metrics, confusion_matrix_data, artifacts_dir):\n"," \"\"\"\n"," Sauvegarde tous les artefacts : history, config, métriques, matrice de confusion\n"," \"\"\"\n"," print(\"\\n💾 Sauvegarde des artefacts...\")\n","\n"," # Histoire d'entraînement\n"," history_path = os.path.join(artifacts_dir, 'training_history.json')\n"," with open(history_path, 'w') as f:\n"," history_json = {k: [float(v) for v in values] for k, values in history.history.items()}\n"," json.dump(history_json, f, indent=2)\n","\n"," # Configuration du modèle\n"," config_path = os.path.join(artifacts_dir, 'model_config.json')\n"," with open(config_path, 'w') as f:\n"," json.dump(config, f, indent=2)\n","\n"," # Métriques détaillées - CONVERSION NUMPY VERS PYTHON POUR JSON\n"," detailed_path = os.path.join(artifacts_dir, 'detailed_metrics.json')\n"," with open(detailed_path, 'w') as f:\n"," class_stats_json = []\n"," for stats in detailed_metrics['class_stats']:\n"," stats_converted = {\n"," 'class_id': int(stats['class_id']),\n"," 'class_name': str(stats['class_name']),\n"," 'iou': float(stats['iou']),\n"," 'true_pixels': int(stats['true_pixels']),\n"," 'pred_pixels': int(stats['pred_pixels']),\n"," 'true_percentage': float(stats['true_percentage']),\n"," 'pred_percentage': float(stats['pred_percentage'])\n"," }\n"," class_stats_json.append(stats_converted)\n","\n"," detailed_json = {\n"," 'mean_iou': float(detailed_metrics['mean_iou']),\n"," 'class_ious': [float(iou) for iou in detailed_metrics['class_ious']],\n"," 'class_stats': class_stats_json\n"," }\n"," json.dump(detailed_json, f, indent=2)\n","\n"," # Matrice de confusion (si disponible)\n"," if confusion_matrix_data:\n"," confusion_path = os.path.join(artifacts_dir, 'confusion_matrix.json')\n"," with open(confusion_path, 'w') as f:\n"," json.dump(confusion_matrix_data, f, indent=2)\n","\n"," mlflow.log_artifacts(artifacts_dir)\n","\n","def train_segmentation_model_optimized(config, run_name=None):\n"," \"\"\"\n"," Fonction principale orchestrant l'entraînement complet - VERSION REFACTORISÉE\n"," \"\"\"\n"," with mlflow.start_run(run_name=run_name) as run:\n"," run_id = run.info.run_id\n"," print(f\"🔬 MLflow Run ID: {run_id}\")\n"," print(f\"🏷️ MLflow Run Name: {run_name}\")\n","\n"," try:\n"," # === 1. SETUP COMPLET ===\n"," setup_components = setup_training_components(config, run_id)\n","\n"," # === 2. ENTRAÎNEMENT ===\n"," history, training_time = train_model_core(\n"," setup_components['model'],\n"," setup_components['train_dataset'],\n"," setup_components['val_dataset'],\n"," setup_components['callbacks_list'],\n"," MAX_EPOCHS\n"," )\n","\n"," # === 3. ÉVALUATION COMPLÈTE ===\n"," basic_test_metrics, detailed_metrics, confusion_matrix_data, class_names = evaluate_model_comprehensive(\n"," setup_components['model'],\n"," setup_components['test_dataset'],\n"," setup_components['num_classes_from_model'],\n"," setup_components['artifacts_dir']\n"," )\n","\n"," # === 4. VISUALISATIONS ===\n"," generate_training_visualizations(\n"," history, detailed_metrics, class_names,\n"," run_id, config, setup_components['artifacts_dir']\n"," )\n","\n"," # === 5. SAUVEGARDE DES ARTEFACTS ===\n"," save_training_artifacts(\n"," history, config, detailed_metrics,\n"," confusion_matrix_data, setup_components['artifacts_dir']\n"," )\n","\n"," # === 6. RÉSULTATS FINAUX ===\n"," best_val_acc = max(history.history['val_sparse_categorical_accuracy'])\n"," results = {\n"," 'run_id': run_id,\n"," 'config': config,\n"," 'training_time': training_time,\n"," 'final_epoch': len(history.history['loss']),\n"," 'best_val_accuracy': best_val_acc,\n"," 'test_metrics': basic_test_metrics,\n"," 'detailed_metrics': detailed_metrics,\n"," 'confusion_matrix': confusion_matrix_data,\n"," 'artifacts_dir': setup_components['artifacts_dir'],\n"," 'num_classes': setup_components['num_classes_from_model']\n"," }\n","\n"," print(f\"\\n✅ Entraînement terminé!\")\n"," print(f\"📊 Meilleure val_accuracy: {best_val_acc:.4f}\")\n"," print(f\"📊 Mean IoU: {detailed_metrics['mean_iou']:.4f}\")\n"," test_acc = basic_test_metrics.get('sparse_categorical_accuracy', 0)\n"," print(f\"🧪 Test accuracy: {test_acc:.4f}\")\n"," print(f\"📁 Artefacts: {setup_components['artifacts_dir']}\")\n"," print(f\"🔬 MLflow Run: {run_id}\")\n","\n"," return results\n","\n"," except Exception as e:\n"," mlflow.log_param(\"error\", str(e))\n"," print(f\"❌ Erreur pendant l'entraînement: {e}\")\n"," raise"],"metadata":{"id":"mWvf8oQC0UBC","executionInfo":{"status":"ok","timestamp":1755184342039,"user_tz":-120,"elapsed":157,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","execution_count":15,"metadata":{"id":"ThxE2s76sTaj","executionInfo":{"status":"ok","timestamp":1755184342104,"user_tz":-120,"elapsed":63,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["def run_experiments_optimized(configs_to_test=None, experiment_name=\"OC Projet 8\"):\n"," \"\"\"\n"," Version optimisée avec système de reprise automatique + NETTOYAGE VRAM SIMPLIFIÉ\n"," \"\"\"\n","\n"," print(f\"\\n{'='*60}\")\n"," print(f\"🚀 DÉBUT DES EXPÉRIENCES - Mode: {'TEST' if TEST_MODE else 'PRODUCTION'}\")\n"," print(f\"🔬 Expérience MLflow: {experiment_name}\")\n"," print(f\"{'='*60}\")\n","\n"," # 1. Filtrage des configurations à lancer\n"," if configs_to_test is None:\n"," remaining_configs = filter_configs_to_run(experimental_configurations, experiment_name)\n","\n"," if not remaining_configs:\n"," print(\"\\n🎉 TOUTES LES EXPÉRIENCES SONT DÉJÀ TERMINÉES !\")\n"," print(\"Rien à faire. Utilisez compare_experiments_mlflow() pour voir les résultats.\")\n"," return []\n"," if TEST_MODE:\n"," configs_to_test = remaining_configs[:TEST_MAX_CONFIGS] if TEST_MAX_CONFIGS else remaining_configs\n"," print(f\"\\n🧪 Mode TEST: limitation à {len(configs_to_test)} configurations\")\n"," else:\n"," configs_to_test = remaining_configs\n"," else:\n"," completed_runs = get_completed_experiments(experiment_name)\n"," filtered_configs = []\n"," for config_idx in configs_to_test:\n"," if config_idx >= len(experimental_configurations):\n"," print(f\"⚠️ Index {config_idx} invalide, ignoré\")\n"," continue\n"," config = experimental_configurations[config_idx]\n"," expected_run_name = generate_stable_run_name(config, TEST_MODE)\n"," if expected_run_name in completed_runs:\n"," print(f\"⏭️ [{config_idx}] {config['name']} déjà terminé, ignoré\")\n"," else:\n"," filtered_configs.append(config_idx)\n"," configs_to_test = filtered_configs\n","\n"," if not configs_to_test:\n"," print(\"\\n✅ Toutes les configurations demandées sont déjà terminées !\")\n"," return []\n","\n"," # 2. Boucle d'entraînement avec nettoyage SIMPLIFIÉ\n"," print(f\"\\n🎯 ENTRAÎNEMENT DE {len(configs_to_test)} CONFIGURATIONS:\")\n","\n"," results = []\n"," for i, config_idx in enumerate(configs_to_test):\n"," config = experimental_configurations[config_idx]\n"," stable_run_name = generate_stable_run_name(config, TEST_MODE)\n","\n"," print(f\"\\n{'-'*50}\")\n"," print(f\"🧪 Expérience {i+1}/{len(configs_to_test)}: {config['name']}\")\n"," print(f\"🏷️ Run name: {stable_run_name}\")\n"," print(f\"{'-'*50}\")\n","\n"," try:\n"," # ENTRAÎNEMENT (ici artifacts_dir existe et fonctionne normalement)\n"," result = train_segmentation_model_optimized(config, run_name=stable_run_name)\n"," results.append(result)\n"," print(f\"✅ [{config_idx}] {config['name']} terminé avec succès\")\n","\n"," remaining = len(configs_to_test) - (i + 1)\n"," if remaining > 0:\n"," print(f\"🔄 {remaining} configuration(s) restante(s)\")\n","\n"," except Exception as e:\n"," print(f\"❌ Erreur avec [{config_idx}] {config['name']}: {e}\")\n","\n"," # NETTOYAGE UNIQUE : après chaque modèle (réussi ou échoué)\n"," if i < len(configs_to_test) - 1: # Pas de nettoyage après le dernier modèle\n"," print(\"🧹 Nettoyage VRAM entre modèles...\")\n"," deep_clean_memory()\n","\n"," # 3. Résumé final\n"," print(f\"\\n{'='*60}\")\n"," print(f\"📊 RÉSUMÉ DE CETTE SESSION\")\n"," print(f\"{'='*60}\")\n","\n"," if results:\n"," print(f\"✅ {len(results)} nouvelles expériences réussies\")\n","\n"," print(f\"\\n{'Modèle':<25} {'Val Acc':<10} {'Test Acc':<10} {'Mean IoU':<10} {'Temps':<8} {'Run ID':<10}\")\n"," print(\"-\" * 80)\n","\n"," for result in results:\n"," config_name = result['config']['name']\n"," val_acc = result['best_val_accuracy']\n"," test_acc = result['test_metrics'].get('sparse_categorical_accuracy', 0)\n"," mean_iou = result['detailed_metrics']['mean_iou']\n"," time_min = result['training_time'] / 60\n"," run_id = result['run_id'][:8]\n"," print(f\"{config_name:<25} {val_acc:<10.4f} {test_acc:<10.4f} {mean_iou:<10.4f} {time_min:<8.1f} {run_id:<10}\")\n","\n"," best_result = max(results, key=lambda x: x['detailed_metrics']['mean_iou'])\n"," print(f\"\\n🏆 Meilleur de cette session: {best_result['config']['name']}\")\n"," print(f\" Val Acc: {best_result['best_val_accuracy']:.4f}\")\n"," print(f\" Test Acc: {best_result['test_metrics'].get('sparse_categorical_accuracy', 0):.4f}\")\n"," print(f\" Mean IoU: {best_result['detailed_metrics']['mean_iou']:.4f}\")\n"," print(f\" MLflow Run: {best_result['run_id']}\")\n","\n"," total_remaining = filter_configs_to_run(experimental_configurations, experiment_name)\n"," if len(total_remaining) > len(configs_to_test):\n"," remaining_count = len(total_remaining) - len(configs_to_test)\n"," print(f\"\\n🔄 Il reste encore {remaining_count} configuration(s) à entraîner\")\n"," print(\"💡 Relancez le script pour continuer automatiquement\")\n"," else:\n"," print(f\"\\n🎉 TOUTES LES CONFIGURATIONS SONT MAINTENANT TERMINÉES !\")\n"," print(\"💡 Utilisez compare_experiments_local() pour voir tous les résultats\")\n"," else:\n"," print(\"❌ Aucune nouvelle expérience réussie dans cette session\")\n","\n"," return results"]},{"cell_type":"code","execution_count":16,"metadata":{"id":"SdG8VxB7v8tF","executionInfo":{"status":"ok","timestamp":1755184342116,"user_tz":-120,"elapsed":13,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# ====================================================================\n","# COMPARAISON DES EXPÉRIENCES LOCALES\n","# ====================================================================\n","\n","def compare_experiments_local(artifacts_base_dir=\"artifacts\"):\n"," \"\"\"\n"," Compare les résultats de plusieurs expériences via les artefacts locaux\n"," Structure : artifacts/run_{run_id}/\n"," \"\"\"\n"," if not os.path.exists(artifacts_base_dir):\n"," print(\"❌ Aucun répertoire d'artefacts trouvé\")\n"," return\n","\n"," results = []\n","\n"," print(f\"🔍 Recherche des expériences dans {artifacts_base_dir}/...\")\n","\n"," # Parcourir tous les répertoires run_*\n"," run_dirs = [d for d in os.listdir(artifacts_base_dir) if d.startswith('run_')]\n","\n"," if not run_dirs:\n"," print(\"❌ Aucune expérience trouvée\")\n"," return\n","\n"," print(f\"📊 {len(run_dirs)} expériences trouvées\")\n","\n"," for run_dir in run_dirs:\n"," run_path = os.path.join(artifacts_base_dir, run_dir)\n"," config_path = os.path.join(run_path, \"model_config.json\")\n"," history_path = os.path.join(run_path, \"training_history.json\")\n"," detailed_path = os.path.join(run_path, \"detailed_metrics.json\")\n","\n"," # Vérifier que les fichiers existent\n"," if os.path.exists(config_path) and os.path.exists(history_path):\n"," try:\n"," # Charger la configuration\n"," with open(config_path, 'r') as f:\n"," config = json.load(f)\n","\n"," # Charger l'historique d'entraînement\n"," with open(history_path, 'r') as f:\n"," history = json.load(f)\n","\n"," # Charger les métriques détaillées (si disponibles)\n"," detailed_metrics = None\n"," if os.path.exists(detailed_path):\n"," with open(detailed_path, 'r') as f:\n"," detailed_metrics = json.load(f)\n","\n"," # Extraire les métriques finales\n"," final_metrics = {\n"," 'run_id': run_dir.replace('run_', ''),\n"," 'experiment': config.get('name', 'Unknown'),\n"," 'architecture': config.get('architecture', 'Unknown'),\n"," 'encoder': config.get('encoder', 'None'),\n"," 'encoder_freeze': config.get('encoder_freeze', False),\n"," 'final_train_acc': history['sparse_categorical_accuracy'][-1],\n"," 'final_val_acc': history['val_sparse_categorical_accuracy'][-1],\n"," 'final_train_loss': history['loss'][-1],\n"," 'final_val_loss': history['val_loss'][-1],\n"," 'best_val_acc': max(history['val_sparse_categorical_accuracy']),\n"," 'epochs_trained': len(history['loss']),\n"," 'mean_iou': detailed_metrics['mean_iou'] if detailed_metrics else None,\n"," 'run_path': run_path\n"," }\n"," results.append(final_metrics)\n","\n"," except Exception as e:\n"," print(f\"⚠️ Erreur lors du chargement de {run_dir}: {e}\")\n"," continue\n","\n"," # Afficher les résultats\n"," if results:\n"," print(f\"\\n🏆 COMPARAISON DES {len(results)} EXPÉRIENCES\")\n"," print(\"=\" * 100)\n","\n"," # Trier par Mean IoU si disponible, sinon par best_val_acc\n"," if any(r['mean_iou'] is not None for r in results):\n"," results_sorted = sorted(results, key=lambda x: x['mean_iou'] or 0, reverse=True)\n"," print(\"📊 Classement par Mean IoU (meilleur premier)\")\n"," else:\n"," results_sorted = sorted(results, key=lambda x: x['best_val_acc'], reverse=True)\n"," print(\"📊 Classement par Validation Accuracy (meilleur premier)\")\n","\n"," print()\n","\n"," for i, result in enumerate(results_sorted, 1):\n"," print(f\"🥇 #{i} - {result['experiment']}\")\n"," print(f\" 🏗️ Architecture: {result['architecture']}\")\n","\n"," if result['encoder'] != 'None':\n"," freeze_status = \"🧊 Frozen\" if result['encoder_freeze'] else \"🔥 Trainable\"\n"," print(f\" 🧠 Encoder: {result['encoder']} ({freeze_status})\")\n","\n"," print(f\" 📊 Meilleure Val Acc: {result['best_val_acc']:.4f}\")\n"," print(f\" 📊 Val Acc finale: {result['final_val_acc']:.4f}\")\n","\n"," if result['mean_iou'] is not None:\n"," print(f\" 🎯 Mean IoU: {result['mean_iou']:.4f}\")\n","\n"," print(f\" ⏱️ Époques: {result['epochs_trained']}\")\n"," print(f\" 🆔 Run ID: {result['run_id'][:8]}...\")\n"," print(f\" 📁 Chemin: {result['run_path']}\")\n"," print()\n","\n"," # Statistiques globales\n"," print(\"📈 STATISTIQUES GLOBALES:\")\n"," print(\"-\" * 50)\n"," avg_val_acc = sum(r['best_val_acc'] for r in results) / len(results)\n"," print(f\"🎯 Val Acc moyenne: {avg_val_acc:.4f}\")\n","\n"," if any(r['mean_iou'] is not None for r in results):\n"," valid_ious = [r['mean_iou'] for r in results if r['mean_iou'] is not None]\n"," avg_iou = sum(valid_ious) / len(valid_ious)\n"," print(f\"🎯 Mean IoU moyen: {avg_iou:.4f}\")\n","\n"," avg_epochs = sum(r['epochs_trained'] for r in results) / len(results)\n"," print(f\"⏱️ Époques moyennes: {avg_epochs:.1f}\")\n","\n"," # Architecture summary\n"," architectures = {}\n"," for r in results:\n"," arch = r['architecture']\n"," if arch not in architectures:\n"," architectures[arch] = []\n"," architectures[arch].append(r['best_val_acc'])\n","\n"," print(f\"\\n🏗️ PERFORMANCE PAR ARCHITECTURE:\")\n"," print(\"-\" * 50)\n"," for arch, accs in architectures.items():\n"," avg_acc = sum(accs) / len(accs)\n"," print(f\"{arch}: {avg_acc:.4f} (moyenne sur {len(accs)} runs)\")\n","\n"," else:\n"," print(\"❌ Aucune expérience valide trouvée\")\n","\n"," return results"]},{"cell_type":"code","execution_count":17,"metadata":{"id":"DWf1lWhyv_OI","executionInfo":{"status":"ok","timestamp":1755184342168,"user_tz":-120,"elapsed":50,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# ====================================================================\n","# COMPARAISON DES EXPÉRIENCES LOCALES\n","# ====================================================================\n","\n","def show_best_experiment_details(artifacts_base_dir=\"artifacts\"):\n"," \"\"\"\n"," Affiche les détails de la meilleure expérience\n"," \"\"\"\n"," results = compare_experiments_local(artifacts_base_dir)\n","\n"," if not results:\n"," return\n","\n"," # Meilleure expérience (Mean IoU si disponible, sinon Val Acc)\n"," if any(r['mean_iou'] is not None for r in results):\n"," best = max(results, key=lambda x: x['mean_iou'] or 0)\n"," metric_name = \"Mean IoU\"\n"," metric_value = best['mean_iou']\n"," else:\n"," best = max(results, key=lambda x: x['best_val_acc'])\n"," metric_name = \"Validation Accuracy\"\n"," metric_value = best['best_val_acc']\n","\n"," print(f\"\\n🏆 DÉTAILS DE LA MEILLEURE EXPÉRIENCE ({metric_name})\")\n"," print(\"=\" * 60)\n"," print(f\"🎯 Nom: {best['experiment']}\")\n"," print(f\"🏗️ Architecture: {best['architecture']}\")\n","\n"," if best['encoder'] != 'None':\n"," freeze_status = \"🧊 Frozen\" if best['encoder_freeze'] else \"🔥 Trainable\"\n"," print(f\"🧠 Encoder: {best['encoder']} ({freeze_status})\")\n","\n"," print(f\"📊 {metric_name}: {metric_value:.4f}\")\n"," print(f\"📊 Validation Accuracy: {best['best_val_acc']:.4f}\")\n"," print(f\"⏱️ Époques: {best['epochs_trained']}\")\n"," print(f\"🆔 Run ID: {best['run_id']}\")\n"," print(f\"📁 Chemin des artefacts: {best['run_path']}\")\n","\n"," # Vérifier la présence du modèle\n"," model_path = os.path.join(best['run_path'], 'best_model.keras')\n"," if os.path.exists(model_path):\n"," print(f\"✅ Modèle disponible: {model_path}\")\n"," else:\n"," print(f\"❌ Modèle non trouvé: {model_path}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tlpWs7XqdCF5","outputId":"4e732021-35cf-43ce-cf9d-48b85d50df42","executionInfo":{"status":"ok","timestamp":1755005118660,"user_tz":-120,"elapsed":4550901,"user":{"displayName":"Grégoire Mureau","userId":"04855910922109670788"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["🎯 SCRIPT D'ENTRAÎNEMENT OPTIMISÉ POUR SEGMENTATION\n","Mode: 🚀 PRODUCTION\n","🔄 SYSTÈME DE REPRISE AUTOMATIQUE ACTIVÉ\n","MLflow version: 3.2.0\n","Cityscapes dir: /content/data/cityscapes_unzipped\n","Configurations disponibles: 17\n","✅ Toutes les vérifications passées\n","\n","============================================================\n","🚀 DÉBUT DES EXPÉRIENCES - Mode: PRODUCTION\n","🔬 Expérience MLflow: OC Projet 8\n","============================================================\n","🔍 Vérification des expériences déjà terminées dans 'OC Projet 8'...\n","✅ 33 expériences terminées trouvées:\n"," - FPN_efficientnetb0_finetune_augTrue_prod\n"," - FPN_efficientnetb0_finetune_augTrue_test\n"," - FPN_efficientnetb0_frozen_augFalse_prod\n"," - FPN_efficientnetb0_frozen_augFalse_test\n"," - FPN_efficientnetb0_frozen_augTrue_prod\n"," - FPN_efficientnetb0_frozen_augTrue_test\n"," - FPN_mobilenetv2_frozen_augFalse_prod\n"," - FPN_mobilenetv2_frozen_augFalse_test\n"," - FPN_mobilenetv2_frozen_augTrue_prod\n"," - FPN_mobilenetv2_frozen_augTrue_test\n"," - FPN_resnet34_finetune_augTrue_prod\n"," - FPN_resnet34_finetune_augTrue_test\n"," - FPN_resnet34_frozen_augFalse_prod\n"," - FPN_resnet34_frozen_augFalse_test\n"," - FPN_resnet34_frozen_augTrue_prod\n"," - FPN_resnet34_frozen_augTrue_test\n"," - FPN_vanilla_augFalse_prod\n"," - FPN_vanilla_augFalse_test\n"," - FPN_vanilla_augTrue_prod\n"," - FPN_vanilla_augTrue_test\n"," - U-Net_efficientnetb0_frozen_augFalse_prod\n"," - U-Net_efficientnetb0_frozen_augFalse_test\n"," - U-Net_efficientnetb0_frozen_augTrue_prod\n"," - U-Net_efficientnetb0_frozen_augTrue_test\n"," - U-Net_mobilenetv2_finetune_augTrue_test\n"," - U-Net_mobilenetv2_frozen_augFalse_prod\n"," - U-Net_mobilenetv2_frozen_augFalse_test\n"," - U-Net_mobilenetv2_frozen_augTrue_prod\n"," - U-Net_mobilenetv2_frozen_augTrue_test\n"," - U-Net_vanilla_augFalse_prod\n"," - U-Net_vanilla_augFalse_test\n"," - U-Net_vanilla_augTrue_prod\n"," - U-Net_vanilla_augTrue_test\n","\n","📊 FILTRAGE DES CONFIGURATIONS:\n","✅ À entraîner: 1 configurations\n","⏭️ Déjà terminées: 16 configurations\n","\n","⏭️ CONFIGURATIONS IGNORÉES (déjà terminées):\n"," [0] U-Net_vanilla_augFalse → U-Net_vanilla_augFalse_prod\n"," [1] U-Net_vanilla_augTrue → U-Net_vanilla_augTrue_prod\n"," [2] FPN_vanilla_augFalse → FPN_vanilla_augFalse_prod\n"," [3] FPN_vanilla_augTrue → FPN_vanilla_augTrue_prod\n"," [4] U-Net_mobilenetv2_frozen_augFalse → U-Net_mobilenetv2_frozen_augFalse_prod\n"," [5] U-Net_mobilenetv2_frozen_augTrue → U-Net_mobilenetv2_frozen_augTrue_prod\n"," [6] U-Net_efficientnetb0_frozen_augFalse → U-Net_efficientnetb0_frozen_augFalse_prod\n"," [7] U-Net_efficientnetb0_frozen_augTrue → U-Net_efficientnetb0_frozen_augTrue_prod\n"," [8] FPN_mobilenetv2_frozen_augFalse → FPN_mobilenetv2_frozen_augFalse_prod\n"," [9] FPN_mobilenetv2_frozen_augTrue → FPN_mobilenetv2_frozen_augTrue_prod\n"," [10] FPN_efficientnetb0_frozen_augFalse → FPN_efficientnetb0_frozen_augFalse_prod\n"," [11] FPN_efficientnetb0_frozen_augTrue → FPN_efficientnetb0_frozen_augTrue_prod\n"," [12] FPN_resnet34_frozen_augFalse → FPN_resnet34_frozen_augFalse_prod\n"," [13] FPN_resnet34_frozen_augTrue → FPN_resnet34_frozen_augTrue_prod\n"," [15] FPN_efficientnetb0_finetune_augTrue → FPN_efficientnetb0_finetune_augTrue_prod\n"," [16] FPN_resnet34_finetune_augTrue → FPN_resnet34_finetune_augTrue_prod\n","\n","🚀 CONFIGURATIONS À ENTRAÎNER:\n"," [14] U-Net_mobilenetv2_finetune_augTrue → U-Net_mobilenetv2_finetune_augTrue_prod\n","\n","🎯 ENTRAÎNEMENT DE 1 CONFIGURATIONS:\n","\n","--------------------------------------------------\n","🧪 Expérience 1/1: U-Net_mobilenetv2_finetune_augTrue\n","🏷️ Run name: U-Net_mobilenetv2_finetune_augTrue_prod\n","--------------------------------------------------\n","🔬 MLflow Run ID: 61eff0ffd3554eb383ce90b7b44b31e6\n","🏷️ MLflow Run Name: U-Net_mobilenetv2_finetune_augTrue_prod\n","🚀 Mode production: batch_size=8, img_size=(224, 224)\n","\n","📊 Préparation des datasets...\n","🔍 Lecture du split Cityscapes 'train'...\n","📊 Split Cityscapes 'train': 2975 images trouvées\n","✂️ Découpage en Train/Validation (80%/20%)\n"," └─ 📚 Train final: 2380 images\n"," └─ 📊 Validation final: 595 images\n","ℹ️ Modèle vanilla - pas de preprocessing\n","🔍 Lecture du split Cityscapes 'val'...\n","📊 Split Cityscapes 'val': 500 images trouvées\n","📋 Utilisation complète comme dataset de Test\n","ℹ️ Modèle vanilla - pas de preprocessing\n","\n","🏗️ Création du modèle U-Net_mobilenetv2_finetune_augTrue...\n","📊 Nombre de classes: 8\n","📊 Paramètres: 8,048,456 total | 8,012,360 entraînables\n","\n","⚙️ Compilation du modèle avec métriques officielles...\n","🎯 MÉTRIQUES OFFICIELLES KERAS POUR SEGMENTATION SÉMANTIQUE:\n"," 🔥 LOSS: sparse_categorical_crossentropy\n"," └─ Optimise la classification pixel-par-pixel\n"," 📊 MÉTRIQUES (100% officielles Keras):\n"," ├─ SparseCategoricalAccuracy: % pixels correctement classifiés\n"," └─ MeanIoU: Intersection over Union (métrique gold standard)\n"," 🎛️ CALLBACKS:\n"," └─ Monitor: val_mean_iou (IoU = métrique de référence)\n","✅ Configuration 100% standard et fiable !\n","\n","📋 Configuration des callbacks...\n","🎛️ CALLBACKS BASÉS SUR MEAN_IOU OFFICIEL:\n"," ├─ EarlyStopping: Arrêt si val_mean_iou stagne 5 époques\n"," ├─ ReduceLROnPlateau: Réduction LR si val_mean_iou stagne 3 époques\n"," └─ ModelCheckpoint: Sauvegarde du meilleur modèle selon val_mean_iou\n","\n","🚀 Début de l'entraînement (20 epochs max)...\n","Epoch 1/20\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 415ms/step - loss: 0.7532 - mean_iou: 0.4418 - sparse_categorical_accuracy: 0.7717"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:The `save_format` argument is deprecated in Keras 3. We recommend removing this argument as it can be inferred from the file path. Received: save_format=keras\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1: val_mean_iou improved from 0.3068773150444031 to 0.30688, saving model to artifacts/run_61eff0ffd3554eb383ce90b7b44b31e6/best_model.keras\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m345s\u001b[0m 741ms/step - loss: 0.7525 - mean_iou: 0.4421 - sparse_categorical_accuracy: 0.7719 - val_loss: 1.3158 - val_mean_iou: 0.3069 - val_sparse_categorical_accuracy: 0.6599 - learning_rate: 0.0010\n","Epoch 2/20\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 311ms/step - loss: 0.3834 - mean_iou: 0.6163 - sparse_categorical_accuracy: 0.8815"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:The `save_format` argument is deprecated in Keras 3. We recommend removing this argument as it can be inferred from the file path. Received: save_format=keras\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 2: val_mean_iou improved from 0.3520147502422333 to 0.35201, saving model to artifacts/run_61eff0ffd3554eb383ce90b7b44b31e6/best_model.keras\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m196s\u001b[0m 456ms/step - loss: 0.3834 - mean_iou: 0.6164 - sparse_categorical_accuracy: 0.8815 - val_loss: 1.4505 - val_mean_iou: 0.3520 - val_sparse_categorical_accuracy: 0.6578 - learning_rate: 0.0010\n","Epoch 3/20\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 313ms/step - loss: 0.3443 - mean_iou: 0.6511 - sparse_categorical_accuracy: 0.8912"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:The `save_format` argument is deprecated in Keras 3. We recommend removing this argument as it can be inferred from the file path. Received: save_format=keras\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 3: val_mean_iou improved from 0.4108622074127197 to 0.41086, saving model to artifacts/run_61eff0ffd3554eb383ce90b7b44b31e6/best_model.keras\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m210s\u001b[0m 480ms/step - loss: 0.3442 - mean_iou: 0.6511 - sparse_categorical_accuracy: 0.8912 - val_loss: 1.2181 - val_mean_iou: 0.4109 - val_sparse_categorical_accuracy: 0.7011 - learning_rate: 0.0010\n","Epoch 4/20\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 312ms/step - loss: 0.3168 - mean_iou: 0.6790 - sparse_categorical_accuracy: 0.9000Epoch 4: val_mean_iou did not improve from 0.41086\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 460ms/step - loss: 0.3168 - mean_iou: 0.6790 - sparse_categorical_accuracy: 0.9000 - val_loss: 2.0475 - val_mean_iou: 0.2570 - val_sparse_categorical_accuracy: 0.5321 - learning_rate: 0.0010\n","Epoch 5/20\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 314ms/step - loss: 0.3028 - mean_iou: 0.6901 - sparse_categorical_accuracy: 0.9027Epoch 5: val_mean_iou did not improve from 0.41086\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m210s\u001b[0m 497ms/step - loss: 0.3027 - mean_iou: 0.6901 - sparse_categorical_accuracy: 0.9027 - val_loss: 1.0201 - val_mean_iou: 0.3761 - val_sparse_categorical_accuracy: 0.7217 - learning_rate: 0.0010\n","Epoch 6/20\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 317ms/step - loss: 0.2903 - mean_iou: 0.6991 - sparse_categorical_accuracy: 0.9064"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:The `save_format` argument is deprecated in Keras 3. We recommend removing this argument as it can be inferred from the file path. Received: save_format=keras\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 6: val_mean_iou improved from 0.5275560617446899 to 0.52756, saving model to artifacts/run_61eff0ffd3554eb383ce90b7b44b31e6/best_model.keras\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m259s\u001b[0m 486ms/step - loss: 0.2903 - mean_iou: 0.6991 - sparse_categorical_accuracy: 0.9064 - val_loss: 0.6736 - val_mean_iou: 0.5276 - val_sparse_categorical_accuracy: 0.8153 - learning_rate: 0.0010\n","Epoch 7/20\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 313ms/step - loss: 0.2720 - mean_iou: 0.7094 - sparse_categorical_accuracy: 0.9109"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:The `save_format` argument is deprecated in Keras 3. We recommend removing this argument as it can be inferred from the file path. Received: save_format=keras\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 7: val_mean_iou improved from 0.5842477083206177 to 0.58425, saving model to artifacts/run_61eff0ffd3554eb383ce90b7b44b31e6/best_model.keras\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m195s\u001b[0m 453ms/step - loss: 0.2721 - mean_iou: 0.7094 - sparse_categorical_accuracy: 0.9109 - val_loss: 0.6266 - val_mean_iou: 0.5842 - val_sparse_categorical_accuracy: 0.8313 - learning_rate: 0.0010\n","Epoch 8/20\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 314ms/step - loss: 0.2594 - mean_iou: 0.7179 - sparse_categorical_accuracy: 0.9142Epoch 8: val_mean_iou did not improve from 0.58425\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 487ms/step - loss: 0.2594 - mean_iou: 0.7179 - sparse_categorical_accuracy: 0.9141 - val_loss: 0.8479 - val_mean_iou: 0.5025 - val_sparse_categorical_accuracy: 0.7713 - learning_rate: 0.0010\n","Epoch 9/20\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 313ms/step - loss: 0.2674 - mean_iou: 0.7141 - sparse_categorical_accuracy: 0.9124Epoch 9: val_mean_iou did not improve from 0.58425\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m198s\u001b[0m 479ms/step - loss: 0.2674 - mean_iou: 0.7141 - sparse_categorical_accuracy: 0.9124 - val_loss: 0.8253 - val_mean_iou: 0.5051 - val_sparse_categorical_accuracy: 0.7674 - learning_rate: 0.0010\n","Epoch 10/20\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 313ms/step - loss: 0.2552 - mean_iou: 0.7236 - sparse_categorical_accuracy: 0.9149\n","Epoch 10: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n","Epoch 10: val_mean_iou did not improve from 0.58425\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 485ms/step - loss: 0.2552 - mean_iou: 0.7236 - sparse_categorical_accuracy: 0.9149 - val_loss: 0.7034 - val_mean_iou: 0.5600 - val_sparse_categorical_accuracy: 0.7979 - learning_rate: 0.0010\n","Epoch 11/20\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 315ms/step - loss: 0.2262 - mean_iou: 0.7418 - sparse_categorical_accuracy: 0.9244"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:The `save_format` argument is deprecated in Keras 3. We recommend removing this argument as it can be inferred from the file path. Received: save_format=keras\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 11: val_mean_iou improved from 0.660067081451416 to 0.66007, saving model to artifacts/run_61eff0ffd3554eb383ce90b7b44b31e6/best_model.keras\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 488ms/step - loss: 0.2262 - mean_iou: 0.7418 - sparse_categorical_accuracy: 0.9244 - val_loss: 0.3974 - val_mean_iou: 0.6601 - val_sparse_categorical_accuracy: 0.8771 - learning_rate: 2.0000e-04\n","Epoch 12/20\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 314ms/step - loss: 0.2136 - mean_iou: 0.7521 - sparse_categorical_accuracy: 0.9286"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:The `save_format` argument is deprecated in Keras 3. We recommend removing this argument as it can be inferred from the file path. Received: save_format=keras\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 12: val_mean_iou improved from 0.6940031051635742 to 0.69400, saving model to artifacts/run_61eff0ffd3554eb383ce90b7b44b31e6/best_model.keras\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m203s\u001b[0m 487ms/step - loss: 0.2136 - mean_iou: 0.7521 - sparse_categorical_accuracy: 0.9286 - val_loss: 0.3312 - val_mean_iou: 0.6940 - val_sparse_categorical_accuracy: 0.8965 - learning_rate: 2.0000e-04\n","Epoch 13/20\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 311ms/step - loss: 0.2078 - mean_iou: 0.7564 - sparse_categorical_accuracy: 0.9303"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:The `save_format` argument is deprecated in Keras 3. We recommend removing this argument as it can be inferred from the file path. Received: save_format=keras\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 13: val_mean_iou improved from 0.7046074867248535 to 0.70461, saving model to artifacts/run_61eff0ffd3554eb383ce90b7b44b31e6/best_model.keras\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m258s\u001b[0m 479ms/step - loss: 0.2078 - mean_iou: 0.7564 - sparse_categorical_accuracy: 0.9303 - val_loss: 0.3175 - val_mean_iou: 0.7046 - val_sparse_categorical_accuracy: 0.9035 - learning_rate: 2.0000e-04\n","Epoch 14/20\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 314ms/step - loss: 0.2013 - mean_iou: 0.7617 - sparse_categorical_accuracy: 0.9322"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:The `save_format` argument is deprecated in Keras 3. We recommend removing this argument as it can be inferred from the file path. Received: save_format=keras\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 14: val_mean_iou improved from 0.7161905169487 to 0.71619, saving model to artifacts/run_61eff0ffd3554eb383ce90b7b44b31e6/best_model.keras\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 461ms/step - loss: 0.2013 - mean_iou: 0.7617 - sparse_categorical_accuracy: 0.9322 - val_loss: 0.2965 - val_mean_iou: 0.7162 - val_sparse_categorical_accuracy: 0.9064 - learning_rate: 2.0000e-04\n","Epoch 15/20\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 314ms/step - loss: 0.1974 - mean_iou: 0.7639 - sparse_categorical_accuracy: 0.9336"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:The `save_format` argument is deprecated in Keras 3. We recommend removing this argument as it can be inferred from the file path. Received: save_format=keras\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 15: val_mean_iou improved from 0.7165768146514893 to 0.71658, saving model to artifacts/run_61eff0ffd3554eb383ce90b7b44b31e6/best_model.keras\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 481ms/step - loss: 0.1975 - mean_iou: 0.7639 - sparse_categorical_accuracy: 0.9336 - val_loss: 0.2952 - val_mean_iou: 0.7166 - val_sparse_categorical_accuracy: 0.9102 - learning_rate: 2.0000e-04\n","Epoch 16/20\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 317ms/step - loss: 0.1958 - mean_iou: 0.7665 - sparse_categorical_accuracy: 0.9338"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:The `save_format` argument is deprecated in Keras 3. We recommend removing this argument as it can be inferred from the file path. Received: save_format=keras\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 16: val_mean_iou improved from 0.7194613814353943 to 0.71946, saving model to artifacts/run_61eff0ffd3554eb383ce90b7b44b31e6/best_model.keras\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m205s\u001b[0m 487ms/step - loss: 0.1958 - mean_iou: 0.7665 - sparse_categorical_accuracy: 0.9338 - val_loss: 0.2903 - val_mean_iou: 0.7195 - val_sparse_categorical_accuracy: 0.9085 - learning_rate: 2.0000e-04\n","Epoch 17/20\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 316ms/step - loss: 0.1893 - mean_iou: 0.7695 - sparse_categorical_accuracy: 0.9359"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:The `save_format` argument is deprecated in Keras 3. We recommend removing this argument as it can be inferred from the file path. Received: save_format=keras\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 17: val_mean_iou improved from 0.7226355075836182 to 0.72264, saving model to artifacts/run_61eff0ffd3554eb383ce90b7b44b31e6/best_model.keras\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m196s\u001b[0m 459ms/step - loss: 0.1894 - mean_iou: 0.7695 - sparse_categorical_accuracy: 0.9359 - val_loss: 0.2893 - val_mean_iou: 0.7226 - val_sparse_categorical_accuracy: 0.9092 - learning_rate: 2.0000e-04\n","Epoch 18/20\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 319ms/step - loss: 0.1881 - mean_iou: 0.7731 - sparse_categorical_accuracy: 0.9363"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:The `save_format` argument is deprecated in Keras 3. We recommend removing this argument as it can be inferred from the file path. Received: save_format=keras\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 18: val_mean_iou improved from 0.7297743558883667 to 0.72977, saving model to artifacts/run_61eff0ffd3554eb383ce90b7b44b31e6/best_model.keras\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 491ms/step - loss: 0.1881 - mean_iou: 0.7731 - sparse_categorical_accuracy: 0.9363 - val_loss: 0.2871 - val_mean_iou: 0.7298 - val_sparse_categorical_accuracy: 0.9136 - learning_rate: 2.0000e-04\n","Epoch 19/20\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 315ms/step - loss: 0.1838 - mean_iou: 0.7756 - sparse_categorical_accuracy: 0.9375Epoch 19: val_mean_iou did not improve from 0.72977\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m259s\u001b[0m 484ms/step - loss: 0.1839 - mean_iou: 0.7756 - sparse_categorical_accuracy: 0.9375 - val_loss: 0.3005 - val_mean_iou: 0.7193 - val_sparse_categorical_accuracy: 0.9070 - learning_rate: 2.0000e-04\n","Epoch 20/20\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 315ms/step - loss: 0.1812 - mean_iou: 0.7760 - sparse_categorical_accuracy: 0.9385"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:The `save_format` argument is deprecated in Keras 3. We recommend removing this argument as it can be inferred from the file path. Received: save_format=keras\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 20: val_mean_iou improved from 0.7328457832336426 to 0.73285, saving model to artifacts/run_61eff0ffd3554eb383ce90b7b44b31e6/best_model.keras\n","\u001b[1m298/298\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 485ms/step - loss: 0.1813 - mean_iou: 0.7760 - sparse_categorical_accuracy: 0.9385 - val_loss: 0.2888 - val_mean_iou: 0.7328 - val_sparse_categorical_accuracy: 0.9124 - learning_rate: 2.0000e-04\n","Restoring model weights from the end of the best epoch: 20.\n","⏱️ Temps d'entraînement: 4346.37s\n","\n","📊 Évaluation avec métriques officielles Keras...\n","\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 485ms/step - loss: 0.2914 - mean_iou: 0.7216 - sparse_categorical_accuracy: 0.9103\n","✅ Test loss (officiel): 0.3201\n","✅ Test sparse_categorical_accuracy (officiel): 0.9012\n","✅ Test mean_iou (officiel): 0.7130\n","\n","🔍 Calcul des métriques détaillées par classe...\n"," 📊 Analyse détaillée par classe...\n"," Batch 1 traité...\n"," Batch 11 traité...\n"," Batch 21 traité...\n"," Batch 31 traité...\n"," Batch 41 traité...\n"," Batch 51 traité...\n"," Batch 61 traité...\n"," Total d'images évaluées: 500\n","\n","📊 ANALYSE DÉTAILLÉE PAR CLASSE:\n","----------------------------------------------------------------------\n","Classe IoU Vrais % Prédits % \n","----------------------------------------------------------------------\n","flat 0.9257 38.01 39.03 \n","human 0.4706 1.32 1.74 \n","vehicle 0.7988 7.08 7.28 \n","construction 0.7930 20.49 21.42 \n","object 0.3035 2.05 1.71 \n","nature 0.8403 15.84 15.37 \n","sky 0.8823 2.94 3.08 \n","void 0.6897 12.28 10.38 \n","----------------------------------------------------------------------\n","MEAN IoU (calc) 0.7130 \n","💡 Le Mean IoU officiel (métrique Keras) fait référence\n","\n","🔥 Génération de la matrice de confusion...\n","📊 Calcul de la matrice de confusion...\n","✅ Matrice de confusion sauvegardée : artifacts/run_61eff0ffd3554eb383ce90b7b44b31e6/confusion_matrix.png\n","\n","📈 Création des visualisations...\n","\n","💾 Sauvegarde des artefacts...\n","\n","✅ Entraînement terminé!\n","📊 Meilleure val_accuracy: 0.9136\n","📊 Mean IoU: 0.7130\n","🧪 Test accuracy: 0.9012\n","📁 Artefacts: artifacts/run_61eff0ffd3554eb383ce90b7b44b31e6\n","🔬 MLflow Run: 61eff0ffd3554eb383ce90b7b44b31e6\n","🏃 View run U-Net_mobilenetv2_finetune_augTrue_prod at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/3/runs/61eff0ffd3554eb383ce90b7b44b31e6\n","🧪 View experiment at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/3\n","✅ [14] U-Net_mobilenetv2_finetune_augTrue terminé avec succès\n","\n","============================================================\n","📊 RÉSUMÉ DE CETTE SESSION\n","============================================================\n","✅ 1 nouvelles expériences réussies\n","\n","Modèle Val Acc Test Acc Mean IoU Temps Run ID \n","--------------------------------------------------------------------------------\n","U-Net_mobilenetv2_finetune_augTrue 0.9136 0.9012 0.7130 72.4 61eff0ff \n","\n","🏆 Meilleur de cette session: U-Net_mobilenetv2_finetune_augTrue\n"," Val Acc: 0.9136\n"," Test Acc: 0.9012\n"," Mean IoU: 0.7130\n"," MLflow Run: 61eff0ffd3554eb383ce90b7b44b31e6\n","🔍 Vérification des expériences déjà terminées dans 'OC Projet 8'...\n","✅ 34 expériences terminées trouvées:\n"," - FPN_efficientnetb0_finetune_augTrue_prod\n"," - FPN_efficientnetb0_finetune_augTrue_test\n"," - FPN_efficientnetb0_frozen_augFalse_prod\n"," - FPN_efficientnetb0_frozen_augFalse_test\n"," - FPN_efficientnetb0_frozen_augTrue_prod\n"," - FPN_efficientnetb0_frozen_augTrue_test\n"," - FPN_mobilenetv2_frozen_augFalse_prod\n"," - FPN_mobilenetv2_frozen_augFalse_test\n"," - FPN_mobilenetv2_frozen_augTrue_prod\n"," - FPN_mobilenetv2_frozen_augTrue_test\n"," - FPN_resnet34_finetune_augTrue_prod\n"," - FPN_resnet34_finetune_augTrue_test\n"," - FPN_resnet34_frozen_augFalse_prod\n"," - FPN_resnet34_frozen_augFalse_test\n"," - FPN_resnet34_frozen_augTrue_prod\n"," - FPN_resnet34_frozen_augTrue_test\n"," - FPN_vanilla_augFalse_prod\n"," - FPN_vanilla_augFalse_test\n"," - FPN_vanilla_augTrue_prod\n"," - FPN_vanilla_augTrue_test\n"," - U-Net_efficientnetb0_frozen_augFalse_prod\n"," - U-Net_efficientnetb0_frozen_augFalse_test\n"," - U-Net_efficientnetb0_frozen_augTrue_prod\n"," - U-Net_efficientnetb0_frozen_augTrue_test\n"," - U-Net_mobilenetv2_finetune_augTrue_prod\n"," - U-Net_mobilenetv2_finetune_augTrue_test\n"," - U-Net_mobilenetv2_frozen_augFalse_prod\n"," - U-Net_mobilenetv2_frozen_augFalse_test\n"," - U-Net_mobilenetv2_frozen_augTrue_prod\n"," - U-Net_mobilenetv2_frozen_augTrue_test\n"," - U-Net_vanilla_augFalse_prod\n"," - U-Net_vanilla_augFalse_test\n"," - U-Net_vanilla_augTrue_prod\n"," - U-Net_vanilla_augTrue_test\n","\n","📊 FILTRAGE DES CONFIGURATIONS:\n","✅ À entraîner: 0 configurations\n","⏭️ Déjà terminées: 17 configurations\n","\n","⏭️ CONFIGURATIONS IGNORÉES (déjà terminées):\n"," [0] U-Net_vanilla_augFalse → U-Net_vanilla_augFalse_prod\n"," [1] U-Net_vanilla_augTrue → U-Net_vanilla_augTrue_prod\n"," [2] FPN_vanilla_augFalse → FPN_vanilla_augFalse_prod\n"," [3] FPN_vanilla_augTrue → FPN_vanilla_augTrue_prod\n"," [4] U-Net_mobilenetv2_frozen_augFalse → U-Net_mobilenetv2_frozen_augFalse_prod\n"," [5] U-Net_mobilenetv2_frozen_augTrue → U-Net_mobilenetv2_frozen_augTrue_prod\n"," [6] U-Net_efficientnetb0_frozen_augFalse → U-Net_efficientnetb0_frozen_augFalse_prod\n"," [7] U-Net_efficientnetb0_frozen_augTrue → U-Net_efficientnetb0_frozen_augTrue_prod\n"," [8] FPN_mobilenetv2_frozen_augFalse → FPN_mobilenetv2_frozen_augFalse_prod\n"," [9] FPN_mobilenetv2_frozen_augTrue → FPN_mobilenetv2_frozen_augTrue_prod\n"," [10] FPN_efficientnetb0_frozen_augFalse → FPN_efficientnetb0_frozen_augFalse_prod\n"," [11] FPN_efficientnetb0_frozen_augTrue → FPN_efficientnetb0_frozen_augTrue_prod\n"," [12] FPN_resnet34_frozen_augFalse → FPN_resnet34_frozen_augFalse_prod\n"," [13] FPN_resnet34_frozen_augTrue → FPN_resnet34_frozen_augTrue_prod\n"," [14] U-Net_mobilenetv2_finetune_augTrue → U-Net_mobilenetv2_finetune_augTrue_prod\n"," [15] FPN_efficientnetb0_finetune_augTrue → FPN_efficientnetb0_finetune_augTrue_prod\n"," [16] FPN_resnet34_finetune_augTrue → FPN_resnet34_finetune_augTrue_prod\n","\n","🎉 TOUTES LES CONFIGURATIONS SONT MAINTENANT TERMINÉES !\n","💡 Utilisez compare_experiments_local() pour voir tous les résultats\n","\n","🎉 Script terminé avec succès!\n"]}],"source":["# ====================================================================\n","# POINT D'ENTRÉE PRINCIPAL\n","# ====================================================================\n","\n","if __name__ == \"__main__\":\n"," print(\"🎯 SCRIPT D'ENTRAÎNEMENT OPTIMISÉ POUR SEGMENTATION\")\n"," print(f\"Mode: {'🧪 TEST' if TEST_MODE else '🚀 PRODUCTION'}\")\n"," print(\"🔄 SYSTÈME DE REPRISE AUTOMATIQUE ACTIVÉ\")\n","\n"," # Vérifications préliminaires\n"," try:\n"," print(f\"MLflow version: {mlflow.__version__}\")\n"," print(f\"Cityscapes dir: {cityscapes_dir}\")\n"," print(f\"Configurations disponibles: {len(experimental_configurations)}\")\n","\n"," assert callable(create_model_from_config), \"create_model_from_config non disponible\"\n"," assert callable(prepare_cityscapes_dataset), \"prepare_cityscapes_dataset non disponible\"\n","\n"," print(\"✅ Toutes les vérifications passées\")\n","\n"," # Lancer les expériences avec système de reprise\n"," results = run_experiments_optimized()\n","\n"," print(\"\\n🎉 Script terminé avec succès!\")\n","\n"," except Exception as e:\n"," print(f\"❌ Erreur fatale: {e}\")\n"," print(\"Vérifications:\")\n"," print(\"1. Variables cityscapes_dir et experimental_configurations définies ?\")\n"," print(\"2. Fonctions create_model_from_config, prepare_cityscapes_dataset disponibles ?\")\n"," print(\"3. MLflow configuré ?\")\n"," print(\"4. Données Cityscapes présentes ?\")"]},{"cell_type":"markdown","metadata":{"id":"6gJK8H-YRwRN"},"source":["## Analyse des expériences"]},{"cell_type":"markdown","metadata":{"id":"erK7RXCV7HOz"},"source":["### Comparaison des experiences"]},{"cell_type":"code","source":["def get_completed_configs_info(experimental_configurations, experiment_name=\"OC Projet 8\"):\n"," \"\"\"\n"," Réutilise la logique de filter_configs_to_run mais retourne les configs terminées\n"," \"\"\"\n"," completed_runs = get_completed_experiments(experiment_name)\n","\n"," if not completed_runs:\n"," return []\n","\n"," # Même logique que filter_configs_to_run mais on garde les terminées\n"," completed_configs = []\n","\n"," for idx, config in enumerate(experimental_configurations):\n"," expected_run_name = generate_stable_run_name(config, TEST_MODE)\n","\n"," if expected_run_name in completed_runs: # Condition inversée\n"," completed_configs.append((idx, config, expected_run_name))\n","\n"," return completed_configs\n","\n","def compare_experiments_mlflow(experiment_name=\"OC Projet 8\", show_detailed_results=False, show_plots=True):\n"," \"\"\"\n"," Compare les expériences terminées - version ultra-simple\n","\n"," Args:\n"," experiment_name (str): Nom de l'expérience MLflow\n"," show_detailed_results (bool): Si True, affiche et retourne la liste complète des résultats\n"," show_plots (bool): Si True, génère les diagrammes en barres des métriques\n"," \"\"\"\n"," # Récupérer les configs terminées (réutilise la logique existante)\n"," completed_configs = get_completed_configs_info(experimental_configurations, experiment_name)\n","\n"," if not completed_configs:\n"," print(\"❌ Aucune expérience terminée trouvée\")\n"," return []\n","\n"," # Récupérer le mapping run_name -> run_id depuis MLflow\n"," experiment = mlflow.get_experiment_by_name(experiment_name)\n"," runs_df = mlflow.search_runs(\n"," experiment_ids=[experiment.experiment_id],\n"," filter_string=\"status = 'FINISHED'\",\n"," output_format=\"pandas\"\n"," )\n","\n"," run_name_to_id = {\n"," run.get('tags.mlflow.runName', ''): run['run_id']\n"," for _, run in runs_df.iterrows()\n"," }\n","\n"," print(f\"📊 Analyse de {len(completed_configs)} configurations terminées\")\n","\n"," results = []\n","\n"," for config_idx, config, run_name in completed_configs:\n"," if run_name not in run_name_to_id:\n"," continue\n","\n"," run_id = run_name_to_id[run_name]\n","\n"," try:\n"," # Récupérer les métriques directement depuis MLflow\n"," run = mlflow.get_run(run_id)\n"," metrics = run.data.metrics\n","\n"," # Récupérer l'historique d'entraînement pour certaines infos\n"," import tempfile\n"," with tempfile.TemporaryDirectory() as temp_dir:\n"," try:\n"," history_path = mlflow.artifacts.download_artifacts(\n"," run_id=run_id, artifact_path=\"training_history.json\", dst_path=temp_dir\n"," )\n"," with open(history_path, 'r') as f:\n"," history = json.load(f)\n"," best_val_acc = max(history['val_sparse_categorical_accuracy'])\n"," epochs_trained = len(history['loss'])\n"," except:\n"," # Fallback si l'artefact n'est pas disponible\n"," best_val_acc = metrics.get('val_sparse_categorical_accuracy', 'N/A')\n"," epochs_trained = 'N/A'\n","\n"," # Construire le résultat avec les métriques de test depuis MLflow\n"," result = {\n"," 'config_idx': config_idx,\n"," 'experiment': config['name'],\n"," 'architecture': config.get('architecture', 'Unknown'),\n"," 'encoder': config.get('encoder', 'None'),\n"," 'best_val_acc': best_val_acc,\n"," 'test_mean_iou': metrics.get('test_mean_iou', 'N/A'),\n"," 'test_accuracy': metrics.get('test_sparse_categorical_accuracy', 'N/A'),\n"," 'epochs_trained': epochs_trained,\n"," 'run_name': run_name,\n"," 'run_id': run_id,\n"," 'full_config': config # Toujours inclure la config complète\n"," }\n","\n"," results.append(result)\n","\n"," except Exception as e:\n"," print(f\"⚠️ Erreur pour [{config_idx}] {config['name']}: {e}\")\n"," continue\n","\n"," # Affichage\n"," if results:\n"," # Trier par test_mean_iou si disponible, sinon par best_val_acc\n"," def sort_key(x):\n"," if x['test_mean_iou'] != 'N/A':\n"," return x['test_mean_iou']\n"," return x['best_val_acc']\n","\n"," results_sorted = sorted(results, key=sort_key, reverse=True)\n","\n"," print(f\"\\n🏆 TOP {len(results)} EXPÉRIENCES TERMINÉES\")\n"," print(\"=\" * 100)\n","\n"," for i, result in enumerate(results_sorted, 1):\n"," print(f\"🥇 #{i} - [{result['config_idx']}] {result['experiment']}\")\n"," print(f\" 🏗️ {result['architecture']} + {result['encoder']}\")\n","\n"," # Afficher les métriques de test\n"," test_iou = result['test_mean_iou']\n"," test_acc = result['test_accuracy']\n","\n"," if test_iou != 'N/A':\n"," print(f\" 🎯 Test Mean IoU: {test_iou:.4f}\", end=\"\")\n"," else:\n"," print(f\" 🎯 Test Mean IoU: N/A\", end=\"\")\n","\n"," if test_acc != 'N/A':\n"," print(f\" | Test Acc: {test_acc:.4f}\")\n"," else:\n"," print(f\" | Test Acc: N/A\")\n","\n"," print(f\" 🆔 {result['run_id'][:8]}...\")\n","\n"," # Afficher la configuration complète si demandé\n"," if show_detailed_results:\n"," print(f\" 📋 Configuration complète:\")\n"," import pprint\n"," pprint.pprint(result['full_config'], indent=6)\n","\n"," print()\n","\n"," # Génération des diagrammes si demandé\n"," if show_plots and results:\n"," generate_comparison_plots(results_sorted)\n","\n"," # Retourner les résultats seulement si explicitement demandé\n"," if show_detailed_results:\n"," return results\n"," else:\n"," print(f\"\\n✅ Analyse terminée. Utilisez show_detailed_results=True pour obtenir les données détaillées.\")\n"," return None\n","\n","\n","def generate_comparison_plots(results):\n"," \"\"\"\n"," Génère des diagrammes en barres pour comparer les métriques de test\n","\n"," Args:\n"," results (list): Liste des résultats triés\n"," \"\"\"\n"," import matplotlib.pyplot as plt\n"," import numpy as np\n","\n"," # Filtrer les résultats qui ont des métriques de test valides\n"," valid_results_iou = [r for r in results if r['test_mean_iou'] != 'N/A']\n"," valid_results_acc = [r for r in results if r['test_accuracy'] != 'N/A']\n","\n"," # Configuration des graphiques\n"," plt.style.use('seaborn-v0_8' if 'seaborn-v0_8' in plt.style.available else 'default')\n","\n"," # Créer une figure avec 2 sous-graphiques - ajuster la taille pour plus d'espace\n"," fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 10))\n","\n"," # Graphique 1: Test Mean IoU\n"," if valid_results_iou:\n"," experiment_names = [r['experiment'] for r in valid_results_iou]\n"," iou_values = [r['test_mean_iou'] for r in valid_results_iou]\n","\n"," colors = plt.cm.viridis(np.linspace(0, 1, len(iou_values)))\n"," bars1 = ax1.bar(range(len(iou_values)), iou_values, color=colors, alpha=0.8)\n","\n"," ax1.set_title('Test Mean IoU par Expérience', fontsize=14, fontweight='bold')\n"," ax1.set_xlabel('Expériences', fontsize=12)\n"," ax1.set_ylabel('Mean IoU', fontsize=12)\n"," ax1.set_xticks(range(len(experiment_names)))\n"," # Rotation à 90° pour afficher les noms complets\n"," ax1.set_xticklabels(experiment_names, rotation=90, ha='center', fontsize=10)\n"," ax1.grid(True, alpha=0.3)\n","\n"," # Ajouter les valeurs sur les barres\n"," for bar, value in zip(bars1, iou_values):\n"," height = bar.get_height()\n"," ax1.text(bar.get_x() + bar.get_width()/2., height + 0.001,\n"," f'{value:.4f}', ha='center', va='bottom', fontsize=10)\n"," else:\n"," ax1.text(0.5, 0.5, 'Aucune donnée Mean IoU disponible',\n"," ha='center', va='center', transform=ax1.transAxes, fontsize=14)\n"," ax1.set_title('Test Mean IoU par Expérience', fontsize=14, fontweight='bold')\n","\n"," # Graphique 2: Test Accuracy\n"," if valid_results_acc:\n"," experiment_names = [r['experiment'] for r in valid_results_acc]\n"," acc_values = [r['test_accuracy'] for r in valid_results_acc]\n","\n"," colors = plt.cm.plasma(np.linspace(0, 1, len(acc_values)))\n"," bars2 = ax2.bar(range(len(acc_values)), acc_values, color=colors, alpha=0.8)\n","\n"," ax2.set_title('Test Accuracy par Expérience', fontsize=14, fontweight='bold')\n"," ax2.set_xlabel('Expériences', fontsize=12)\n"," ax2.set_ylabel('Accuracy', fontsize=12)\n"," ax2.set_xticks(range(len(experiment_names)))\n"," # Rotation à 90° pour afficher les noms complets\n"," ax2.set_xticklabels(experiment_names, rotation=90, ha='center', fontsize=10)\n"," ax2.grid(True, alpha=0.3)\n","\n"," # Ajouter les valeurs sur les barres\n"," for bar, value in zip(bars2, acc_values):\n"," height = bar.get_height()\n"," ax2.text(bar.get_x() + bar.get_width()/2., height + 0.001,\n"," f'{value:.4f}', ha='center', va='bottom', fontsize=10)\n"," else:\n"," ax2.text(0.5, 0.5, 'Aucune donnée Accuracy disponible',\n"," ha='center', va='center', transform=ax2.transAxes, fontsize=14)\n"," ax2.set_title('Test Accuracy par Expérience', fontsize=14, fontweight='bold')\n","\n"," # Ajuster automatiquement l'espacement pour éviter les coupures\n"," plt.tight_layout(pad=3.0)\n"," plt.subplots_adjust(bottom=0.15) # Laisser plus d'espace en bas pour les étiquettes\n"," plt.show()\n","\n"," print(f\"📊 Diagrammes générés pour {len(valid_results_iou)} expériences (IoU) et {len(valid_results_acc)} expériences (Accuracy)\")"],"metadata":{"id":"y1zGRA1DIcmy","executionInfo":{"status":"ok","timestamp":1755184517573,"user_tz":-120,"elapsed":181,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":27,"outputs":[]},{"cell_type":"code","source":["compare_experiments_mlflow(experiment_name=mlflow_experiemnt)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["142c55ae32cb441cb376ed12eb852f76","50c22cf0390a40d2b45d1fa49cc78261","2fae51664d8740758768f5dbe9719bbe","59ba40a522404563acc161661468ea44","0e2d059f491a4348818e447773ebd65e","4d92d413923447cea213b4cb14d2fb5a","a3f9ef701d254b34ba496a142f75e010","ab49a3add2024180b2c7f62c7bef7457","c6d3e876724e4ebfb2949efe12579d77","71e112bdedeb46e488728f8c8e2ba997","6c7c53ce244e4edebc94b884886c57cc","fce0e6c8221b433f90a1c999a5fcb23d","964a978b0aee4c5792ddc1acaf7a6ecb","26c0e988c6524704b7f20e8299bf2560","fda0f4d243314f8781153f975b91cca9","9563fcc908064289ae578eb530c7da8d","0953f2164ce54d43a44be46d88f9b604","2e542efef60e416f8a707f29530c8978","aaa721f40d034d89a0c7b0a4f10dcbd3","d98cde724bdf4d4f952ed22b27f7e6a8","1d8c2cbc29d3458caca0f965f6250b9b","bb486421bec34175ae97ee22dc084efc","c6cddba6a54f4ecdb1bb1a38218bd695","18aa340201d446c894f5331a623b3075","d87386e98a384694a26d492c458d1275","fd1c1e21ebde4ab49f67d7000121bf40","9b4ecbad3788426bad3eba082de401a5","ccb2e6b6e22d4b19b4c711f66917a4ae","76c45a6e54234802b2eb5e441d59df45","e64cf17cc5e141d0b0f4e02683c9b6fe","23018302140d48f18f8d24226de7150c","d1c5dcbd556545c9b60ebe0a50587672","6d5fc7eeccd44592bdf331f88125c9ea","ce966c62b74f40ddae629b236ddbd010","f8250281de75456e9fe2e4d95a3f6abb","135e4affbb5144e88aef6e5a90310483","1d595fdc672e42c28e203c41c9df6e57","fb51153b715345508a3b8aa9d76f1631","1932f0bf44d64053ac7162bdf2f84446","5490c02376714c389f2a1c7f40ca550b","35d7890e17774698a08f5d2eb03f8c01","add9b2b169954de094d9dd59cfe3bfa0","8d31584009a348689f0ad32d58a16d27","bf7600ba695e4efa862af743fd713636","605b05368c0c448bbb99643c5ff8235e","b1f7fe2a093642d8b9c3fb5c8464bc4f","339ddd278e164e7587d5af43ee5b64a5","1554807f23c04c50b189b4b1fda55fd7","43941d057d7f4c9bbe914fac9f76c496","72b139f1811946e4b950e6ed661ae41f","b90150a8f0c74c24b23dfcdfa0a9d9af","7c29e50ee18e420993eddde9fc1868a6","979b960a695749a2893bea7793461300","008c516afe854128b2271b061df3c0db","47fd65c5e2a54bd79a21ebc390e36c05","b984614818d4413f96f533e4e3cb37cf","06d8a55ac76b4ff6becb73be65b3c537","8adb5029aacd4f44ae0816993696df19","4bdfd05ef04b41839d95b01c90587cf7","6176f13f3e004d4a9e9e1325a1de342d","49b8268052a4473296b69b0e26edc5e4","bfd768b3a6294265bad006e8370db549","273ab32a9c05441888bddce77507c821","04026aa20e724860b1d8fea2f629e6d1","3fd5b8ef2c5a40559b8b9ade577ddcb3","60260727ea5443ba9ab33a6befb90877","94fe4412f4c540479359fe4fc64a5fbf","791de8e13ea44cdeadaea5e515629127","262c7625445942129a521253620da09e","5f9ae5d3e9eb4588bf5e3cd9f4d8bb8d","dfdd71ff69fb41c4b60b66c0221df140","1ad57c9a037b44c5a13c4cd523e06c3a","262e2d05222244088bd052e080ac4653","ed9675fc333145969206bd76574521a6","2b371ffeb72f46d8b4bf2e2c7859fe58","cd74999c3e784805b87e5f09027be90b","1282290e7e94438eb514c324304bb338","d96bc44d9a484607843a8eabc9a8edfa","668b4184b97248809179ca65f223b050","9da77fea5a0048fb969078d3b270a8e2","02d97724d76d4ba6864a463a9cec1fcc","25f752d166474df4bf202cc8af60aa6e","f7a56cb286df462fac458ff74046abc3","61d732dc23794e0bbfd18be9e8ffab4e","75653eba0bf44a35a84369b076f91684","a6c081b61f98464381a5d27817881dbc","80a488f652bd4c1b93e9ad3796748b4e","d37255714b1e45e0ad5c0eee112993aa","c4eda98daf8243deab77577b5a469f79","97f1af1b7dfd451ba3d634cf4ec2f269","ee931636728d48f7ba847ba2054367f9","59de698592a944fabfbc5010fc1e75d4","78b327af897948b8bc6e90cb07f3404f","1cdabe9f4aad47e39f2246483d235c24","78d228a2983b479a965734f8f7e68214","8ed8c176ebd148128524a39295ed6760","98656fea9a6642cc89d2b83d1bee8363","087157daec034f3189573520082d7753","65412b3909804c87941f40d82a9c2dbc","80907bfc41a24721ba21e611eaae4fde","968b8a1748c04d388072cf2a58af4c5b","9e48a7de8efb46368ac5f33161bf199a","ab8fd48d44854288b13e773f755d6b1a","7029d0f47c1c44df8a93613ed942b129","49210080862a4964b37b38a09feade31","0dd4f569624e4c4f93b24ef1ff30c80d","f83dc3b809824155961ea9618f2e0431","3fa6443705614000a457e8c5b5047c13","19bbaa075d93430e98e74e648533de31","df4aba30006e42468e3d7cb2286b7fd7","b2a8b9603e8e442f88cd65a7e544e160","086974a211d2459c84efbc6c0e70ded7","2596d29538dd42a0971a69657fbea882","bea47065d2964ea58131adbef51f347b","32f314fdf8ed456db2dcc419a1739127","b06c96cc3f844f4ba610ff7567b73843","452f12f65c50483e91dd4caf278e5a95","58504a65f7e44f4f89275d81a3eaa29c","8e21109518f4417888811cbcb6a34d23","5bb842bb7dfd460585c9190be9e30f82","93c9e1873dcd4e7ebacadbbcfd432a63","b1463476f46e4f6b9f20d330fd8c89d8","aa2f36e4946641a2b5fcc1570357a0da","41e3f567da094035b374e715bfad4b5d","d401854e460c409bb885b32fef79c562","e0345d2d16fa40e3b29964758c2fdf74","dafdb85f51cb49769fac5cf7f0b0d23f","36639e93099d481ba2779ed9ccbd2925","3b0d7b113c0140a1a75ecdb1ccccd490","c4a4dca5ac8446428ecf2f73f791266b","c21b511f61bf46d69942af9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Vérification des expériences déjà terminées dans 'OC Projet 8'...\n","✅ 34 expériences terminées trouvées:\n"," - FPN_efficientnetb0_finetune_augTrue_prod\n"," - FPN_efficientnetb0_finetune_augTrue_test\n"," - FPN_efficientnetb0_frozen_augFalse_prod\n"," - FPN_efficientnetb0_frozen_augFalse_test\n"," - FPN_efficientnetb0_frozen_augTrue_prod\n"," - FPN_efficientnetb0_frozen_augTrue_test\n"," - FPN_mobilenetv2_frozen_augFalse_prod\n"," - FPN_mobilenetv2_frozen_augFalse_test\n"," - FPN_mobilenetv2_frozen_augTrue_prod\n"," - FPN_mobilenetv2_frozen_augTrue_test\n"," - FPN_resnet34_finetune_augTrue_prod\n"," - FPN_resnet34_finetune_augTrue_test\n"," - FPN_resnet34_frozen_augFalse_prod\n"," - FPN_resnet34_frozen_augFalse_test\n"," - FPN_resnet34_frozen_augTrue_prod\n"," - FPN_resnet34_frozen_augTrue_test\n"," - FPN_vanilla_augFalse_prod\n"," - FPN_vanilla_augFalse_test\n"," - FPN_vanilla_augTrue_prod\n"," - FPN_vanilla_augTrue_test\n"," - U-Net_efficientnetb0_frozen_augFalse_prod\n"," - U-Net_efficientnetb0_frozen_augFalse_test\n"," - U-Net_efficientnetb0_frozen_augTrue_prod\n"," - U-Net_efficientnetb0_frozen_augTrue_test\n"," - U-Net_mobilenetv2_finetune_augTrue_prod\n"," - U-Net_mobilenetv2_finetune_augTrue_test\n"," - U-Net_mobilenetv2_frozen_augFalse_prod\n"," - U-Net_mobilenetv2_frozen_augFalse_test\n"," - U-Net_mobilenetv2_frozen_augTrue_prod\n"," - U-Net_mobilenetv2_frozen_augTrue_test\n"," - U-Net_vanilla_augFalse_prod\n"," - U-Net_vanilla_augFalse_test\n"," - U-Net_vanilla_augTrue_prod\n"," - U-Net_vanilla_augTrue_test\n","📊 Analyse de 17 configurations terminées\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading artifacts: 0%| | 0/1 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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["📊 Diagrammes générés pour 17 expériences (IoU) et 17 expériences (Accuracy)\n","\n","✅ Analyse terminée. Utilisez show_detailed_results=True pour obtenir les données détaillées.\n"]}]},{"cell_type":"markdown","metadata":{"id":"gqrw8X8wR5Q6"},"source":["### Analyse meilleur modèle"]},{"cell_type":"markdown","source":["Affichage des courbes d'apprentissage"],"metadata":{"id":"j9kVdhxVBA4S"}},{"cell_type":"code","source":["def display_training_curves_from_best_run(experiment_name=\"OC Projet 8\"):\n"," \"\"\"\n"," Trouve le meilleur modèle (selon test_mean_iou) et affiche son graphique de courbes d'entraînement.\n"," \"\"\"\n"," print(f\"\\n{'='*60}\")\n"," print(f\"📈 Affichage des courbes d'entraînement du meilleur modèle\")\n"," print(f\"🔬 Expérience MLflow: {experiment_name}\")\n"," print(f\"{'='*60}\")\n","\n"," client = mlflow.tracking.MlflowClient()\n","\n"," try:\n"," # Récupérer l'expérience par nom\n"," experiment = client.get_experiment_by_name(experiment_name)\n"," if experiment is None:\n"," print(f\"❌ Expérience '{experiment_name}' non trouvée.\")\n"," return\n","\n"," # Rechercher les runs terminés avec la métrique test_mean_iou\n"," runs_df = mlflow.search_runs(\n"," experiment_ids=[experiment.experiment_id],\n"," filter_string=\"status = 'FINISHED' and metrics.test_mean_iou >= 0\",\n"," order_by=[\"metrics.test_mean_iou DESC\"], # Trier par Mean IoU descendant\n"," max_results=1 # Prendre seulement le meilleur\n"," )\n","\n"," if runs_df.empty:\n"," print(\"❌ Aucun run terminé avec la métrique test_mean_iou trouvé.\")\n"," return\n","\n"," # Le meilleur run est le premier dans la liste triée\n"," best_run = runs_df.iloc[0]\n"," best_run_id = best_run['run_id']\n"," best_run_name = best_run.get('tags.mlflow.runName', 'Unknown')\n"," best_iou = best_run['metrics.test_mean_iou']\n","\n"," print(f\"🏆 Meilleur run trouvé:\")\n"," print(f\" • Run Name: {best_run_name}\")\n"," print(f\" • Run ID: {best_run_id}\")\n"," print(f\" • Test Mean IoU: {best_iou:.4f}\")\n","\n"," # Télécharger l'artefact 'training_curves.png'\n"," artifact_path = \"training_curves.png\"\n"," print(f\"\\n💾 Téléchargement de l'artefact '{artifact_path}'...\")\n","\n"," with tempfile.TemporaryDirectory() as tmpdir:\n"," local_path = mlflow.artifacts.download_artifacts(\n"," run_id=best_run_id,\n"," artifact_path=artifact_path,\n"," dst_path=tmpdir\n"," )\n"," print(f\"✅ Artefact téléchargé vers: {local_path}\")\n","\n"," # Afficher l'image\n"," print(\"\\n🖼️ Affichage de l'image...\")\n"," img = PILImage.open(local_path)\n"," plt.figure(figsize=(12, 8))\n"," plt.imshow(img)\n"," plt.title(f\"Courbes d'entraînement - {best_run_name}\", fontsize=14)\n"," plt.axis('off') # Désactiver les axes\n"," plt.show()\n","\n"," except Exception as e:\n"," print(f\"❌ Erreur lors de l l'affichage des courbes d'entraînement: {e}\")\n"," print(\"Vérifiez que l'artefact 'training_curves.png' existe pour le meilleur run.\")"],"metadata":{"id":"cQMW_Ri1dTE0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Exécuter la fonction\n","display_training_curves_from_best_run(experiment_name=mlflow_experiemnt)"],"metadata":{"id":"K7FA2AlTg0X-","colab":{"base_uri":"https://localhost:8080/","height":959,"referenced_widgets":["1d6592ac880e4dcba15ef22d2775c89f","08db4adb6c7149a18ac6b5480b420c73","00f7a5c8476243198bc254d29eb84061","b44ce655454543139597898a77b6c7f3","bd6f7801ad924c5a84235741a900d0a6","2bca1e7773ad4677a594e69dbffb9288","d3fc0b7f9b594cd6b06dc0ba41dc9585","89ac9f115547479d90b493df7d117dd9","ead3b3fc71114acf8a8799b511748218","5e536cf5cc6e4b00bad4754f566b230f","926e00ef12bc4a5da492072bb677ba24"]},"executionInfo":{"status":"ok","timestamp":1755008419464,"user_tz":-120,"elapsed":3789,"user":{"displayName":"Grégoire Mureau","userId":"04855910922109670788"}},"outputId":"14a3d6bc-7439-4265-f69f-d7e877c614cb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","============================================================\n","📈 Affichage des courbes d'entraînement du meilleur modèle\n","🔬 Expérience MLflow: OC Projet 8\n","============================================================\n","🏆 Meilleur run trouvé:\n"," • Run Name: FPN_efficientnetb0_finetune_augTrue_prod\n"," • Run ID: b6dd1ecdeb3f4b5c899b332f3c948cd7\n"," • Test Mean IoU: 0.7457\n","\n","💾 Téléchargement de l'artefact 'training_curves.png'...\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading artifacts: 0%| | 0/1 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9evRyBrb26sJ+fX5F56+fnV6DUuyQMBkOxx7M418tXoMTXcXHX5o0Etvv372fFihWEhYUB+c+1DRs2sGfPHpKTk/H397+h86gkzp8/T1RUFN26daNPnz6O6a6urgwaNIjff//9hgLbhQsXYrFYmDlzJo0aNQKgbdu2TJw4kb1795Z6fVcr6tlSUqU592/E2bNnWb16Nb6+vkD+eXb27Fk2b97MsWPHHG3B7fMuWbKkwEfhkr7rCCFYsGABfn5+zJ071/Fs6dy5M/3797+htEtSRZNVkaVKJycnx/EyUlJWq5Vt27ZRr149x43eLjo6muDg4DK39ezYsWOR0++5555CJZFt2rTBarVy5MgRANatW4dWq+WBBx4oMJ+7uzvt27fnzJkzjupu1atX59SpUyxatKhA+6wePXrQv3//az6o9+zZU2RaW7dujbe3d7HLWa1WNm7cSOPGjQu1P2vUqBEhISFFVh+1Vy21s794lfTlrUOHDoX255lnnmHBggWOoNbOnq6EhIQSrbtr164F2l57eHjg4+NTIG2lOS52Vwe69pKwqzutsr/U2quXrVu3DsARTNhptVq6du1KVlZWoWpkJdmH0rBarQX+FVVyWpSXX3652J5IS1Kympqayu+//857771HYGAgTzzxRLHzTpkyhby8PN5+++0S79e1HDx4kOTkZHr06OF4QbXr0aMHALt27SrzdtasWQMUvvbuvfdetm/fXmwNFPt5Ya+2enX6bDab47q2a9u2rSP4gvxzqEaNGphMpkJV7YuiKIpj3+3s9037Pac88+3q+0S9evXw9fV1NJGwp7m4vhIMBgMmk6lE2yqLkuTrjVzHZdWgQQPHvdXOfrzs94IbOY9Kuu1PP/20QFALpb8fX23fvn34+vo6glq7a90bSqOoZ0tJ3ex7RqtWrRxBrZ39uXLs2LEC01u3bl3g/aI07zrx8fEkJSVx1113FfhgCtC9e/cbTr8kVSRZYitVOh4eHmRkZJRqmbS0NMxmM9WqVSvy96CgIPbu3YvVai3Ug2tJuLu74+LiUuRvwcHBhabZqwzZ24QmJCRgs9muWWpw8eJFfH19eeeddxg5ciTTpk1j+vTp1K1bl1atWvHAAw8Uegm4mr3ko6iSrqCgoGIDorS0NEwmE7t27Sp2GIyiPjZc/fXf/mJa0jaSRfVimZ2dzeLFi1m1ahXx8fFkZWUV+L2kwVhRJRMGg6FANbfSHBe7q0uV7OfT1S8qV1e7tb8A3nvvvcVuKzExscAxLsk+lFR8fDz33HNPgWl9+vQpVCW9KBMnTqRFixZF/lbUdXF1MwHIz49OnToxYcKEQnl1pZo1azJ06FBmzJjB6tWrC6W5tOwlffPmzWPevHnXnKcskpKSAAp9kLke+3lhr2FRlMTExAJ/F3VeXKua99W8vLwKvbBffe2WZ74VVRU0MDCQ06dPA/87h4ob7ikvL6/Y+295Kkm+3sh1fCvTVZrzqKRWrVrFDz/8wOHDh0lLS8Nmszl+u9H28MnJyUV24hUREXFD67taWXpIvtn3DPtHzyvZn9n2d4arp9uV5l3nWu8DxS0vSbc7GdhKlU79+vXZsmULiYmJJb752r/MXu8he70vuMUtf61g+FrrtP+mKAoGg4Hvv/++2HlDQ0OB/MBpyZIlnDhxgi1btrBt2zaWLVvG4sWLefHFFx3VXUuT/uv9Zk9ns2bNmDRpUrHzFbfcjSoqX1944QX27t3Lww8/zKhRo/Dz80Or1bJ06VKWLVtWrmkrzXG53nqvtz3774sWLSq29PzqAKCs+XulwMBAfv311wLTriydupYaNWpQv379Em9r9uzZBfLNYDBQvXp1XF1dS7T8s88+y4oVK3jrrbeK7OylNOx5OHDgwGI7pClpukqyneKCs+st9/777xfqSdru6mC5PM+L66WrPPJNoylceezKJhX2wM3+In61S5culboK7Y0o6T0DSncdl1Rx9+jSpKs051FJLFu2jEmTJlG3bl1ee+01wsLCcHZ25tKlS7z00kslWkdR+yWEKPK8KK9zuzQfsK9O382+ZxR3PRT129W1GErzrnOteUr6gViSbjcysJUqnZ49e7JlyxaWLFnCa6+9Vux8Cxcu5OzZs4wZMwYfHx9cXV2L/Yp68eJFAgMDHZ09KIpS5AuovdSlNC5dulTseuwvEiEhIcTGxhIUFHTN0qor1a1bl7p16/LMM8+QlZXF0KFD+fLLL+nXr1+xw4XY152SklKgjbKqqtesMmbPv8zMzFIFMOXtxIkT7N27ly5dujBt2rQCv5U2YCiJGzkuZdkW5Jd8V0QeGwyGW7bdWrVqFftyXRI6nY63336bxx9/nJkzZxIdHX3D67Lnu9Vqvan7b99OQkJCgVJ9IQRpaWkYDIZCY/deudytPD4lUZ75dunSpUJ9Jly6dMlRkhQVFYXBYHA03bhSXFwcmZmZ1ywhvZXKeh3bA5ere82HG3v+XJ2u8j6Pli1bhlarZfHixQXukUUNrVbcx52i9svHx6dQ6STk98x/s5T0uX+z7xlF5cfly5eB4tuZ25XmXce+rqub0wCFetSWpMpCtrGVKp3evXs7urm3t1u72po1a5g1axbHjh3Dzc0NrVZL+/btOX78uKN6m92BAwdITEws0AbS29ubnJycAjd8VVULDbtSEqtXry7Udf7mzZsxGAw0bNgQwDH+YlElg7NmzXIMR5CSksKUKVPYvHlzgXk8PDwcbfeu1b7S3inP1cMtbNq06ZpjvGq1Wjp06MCpU6cKtcPKzc1l/PjxbNy4sdjlS6IkwwvYq7hd/XCPi4vjr7/+KjBPeSjpcSnPbRU19MaiRYuYN2/eDX1Ft79Mlme+3A5iYmIYMGAAS5cuZd++fTe8ngYNGhAQEMDKlSsLXQPHjh1j0qRJxMbGljG1OHow/+OPPwpM37ZtG23atOH//u//ilzO3rauqF57f/vtNz788MMbqnpuPy9udFiPG8m34rZlv3bt7FVamzZtCuRXRe7atSs7d+7k3LlzBea119K4uk1rRSnNdVzUtenl5YWiKIUCi6SkpDKd5zfrPLLZbGi12gK1O1RVZcGCBUDBY27vDfv8+fMF1nH18Qdo2LAhly5d4tSpUwWmf/fdd6VOY0mV9Ll/s+8Z27ZtK9TEZv369QCOd4bilOZdp2bNmvj6+rJp06YCH1KEEPzzzz83nH5JqkiyxFaqdAwGA5999hmDBg1iyJAh3H///XTr1o2AgACSkpL4999/WblyJY0bN+aTTz5xVDkaOXIkmzdvZvDgwbzyyiuEhIRw5swZ5s6di4+PD6+88opjG127duXQoUNMmDCBgQMHkpeXx7ffflvqnpgBnJ2deeGFFxgwYADOzs788ccfHD9+nIEDBzpKaB599FF++uknPv74Y3Jzc+nQoQM5OTn8+uuvrFy5krFjxwL5Ja579uxhxYoVDB48mIYNG6LT6Th9+jQLFy4kMjLyml+QH3roIebNm8fs2bPR6XTUq1ePkydPsmDBAkJDQwu9cFxp9OjRbNu2jZdffplXX32ViIgIEhMTWbBgASdPnqRv376lzhv4XzXDzz77jNq1a9OtW7di561Tpw7VqlVjxYoVNG3alPDwcI4cOcJXX33Fs88+y9y5c/nzzz8JCQkply/pJT0u5aFTp0507tyZ77//HkVReOCBB7Barfz77798++23PPnkk0VWUbsee/4uX74cLy8v6tSpc8PDWt1uRowYwapVq65ZVfx69Ho9EyZMYMyYMTzxxBMMHz4cX19fjh8/zrx58zAYDAV67L1R9957L61atWLx4sW4urrSvn174uLi+OijjwgODi40lJNd3bp1eeqpp/jmm28YPnw4/fr1Q6vVsnXrVubPn0/79u1L3Zke/O+8WLNmDQ0aNCAkJKRUJd+lzbegoCCOHTvGr7/+iq+vb4FOtNauXYvNZqNVq1akpqY67ttXtgcdM2YMW7Zs4aWXXmLEiBEEBQWxefNmFi5cSK9evcpcJb28lOY6Lu7abNu2LZs3b+bDDz+kY8eOJCUl8emnnxITE1NobNiSulnnUdu2bTl27BiTJ0+md+/epKen89VXX9GmTRu2bt3KgQMH2LZtG9HR0XTq1Am9Xs+iRYsICgoiKCiIHTt2sHbt2kI1Yp566inWrVvHqFGjGDx4MD4+PqxevZq4uLgb2v+SKOlz/2bfM4KCghg4cCCDBg3C39+fv/76i82bN9OlS5cS3btL+q6j0WgYMGAAH3/8MYMHD2bgwIEIIfj+++9vSg0oSboVZGArVUrBwcH89NNP/Pjjj/z1119MmjSJnJwc/Pz8CA8P57333uO+++4r0P6kTp06fP/998yePZtp06aRlZWFj48P7du3d9z87Z599lkyMjL4+++/GTRoENWrV+fJJ5+kbt26pe49+dlnnyUtLY3p06eTkJCAj48PL774IiNHjnTMYzAY+L//+z8+++wzVq5cyYIFC9Dr9URFRTFjxgxHZzuKorBkyRLmzp3L0qVLuXTpEoqiEBQURM+ePRkyZMg1x87z8vJi0aJFTJ8+nZkzZ6IoCg0bNuSjjz7i448/vmZgGx4ezg8//MCcOXOYPXs2qampeHh4ONrdFjdEy/W8+OKLHD16lE8//RRfX1+6dOlS7LwGg4FPP/2UadOm8c4776DT6WjatCnz5s0jLCyMHTt28M8//5Cenu4oMSiLkh6X8qAoCp988gkLFy5k+fLl/PLLLyiKQkREBG+88cYND7/QunVr7r//flavXs24ceN46623qkxg6+rqyuTJk0vclq849913Hz4+Pnz55ZeMHz+e3Nxc/P396dy5M0OHDi12fNnS0Gg0fP7558ybN4/ff/+dL7/8Ejc3Nzp16sTo0aOvWdX9P//5D5GRkfzwww8MHjwYm81GaGgogwcP5sUXX7yh9ISFhfHMM8/w/fffM3bsWIYNG1bqKt2lybexY8cybdo0Jk6cSMeOHR2BrVar5bPPPuO9997j22+/JScnh7p16zJv3rwCnbaFhITw3Xff8dFHHzFlyhRycnKoWbMmI0eO5Pnnn7+hPLgZSnMdF3dtvvPOO0ybNo2ffvqJJUuWEBkZybhx49i1a9cNB7Zwc86j4cOHYzKZWLVqFcuXLycsLIz+/fvz+OOP4+zszLx58xgxYgTffvstderU4eOPP2bOnDlMmjQJg8FA+/bt+fzzzwu1Ve3YsSPvv/8+n3/+OWPHjsXX15fu3bsze/ZsmjZtelPakZfmuX8z7xnt27cnKiqKuXPncvbsWdzd3enXr981m15dqTTvOoMHDwbyS/JfeeUV/P396dWrF8899xxPPfXUDe+DJFUURdxol3WSJEmSJEmSdItkZWXRokULWrduzeLFiys6OeVq+/btDBw4kOeee47XX3+9opMjSZWSbGMrSZIkSZIk3Ta2b9/OyJEj2b59e4Hp9n41rjUEmyRJdy5ZFVmSJEmqMoQQpeoo60bGrZZuT6qqlriDNUVRrtls405ls9lKPPasRqO5oXb/JREcHMymTZvYs2cPI0eOJDQ0lOPHjzN79my8vLwqRTXZ0p6PkiSVnXyiS5IkSVXGL7/8wvjx40s8//Hjx29iaqRbae7cucyZM6dE84aEhBTbq/6drGvXrly4cKFE8w4dOpRhw4bdlHTUrFmTb775hrlz5zJz5kzS09Px9PSkTZs2DB8+/IbHAr6VJkyYwC+//FKiee+66y6GDh16k1MkSVWfbGMrSZIkVRnp6enFjuFYlNtpbFipbC5fvkxycnKJ5tXr9URERNzkFFU+p06dKnGPuP7+/o6xhqXCEhISyMjIKNG8rq6uhIWF3eQUSVLVJwNbSZIkSZIkSZIkqVKTnUdJkiRJkiRJkiRJlZoMbCVJkiRJkiRJkqRKTQa2kiRJkiRJkiRJUqUmA1tJkiRJkiRJkiSpUpOBrSRJkiRJkiRJklSpycBWkiRJkiRJkiRJqtRkYCtJkiRJkiRJkiRVajKwlSSp3NlsNoxGI3KYbEmSJEmSJOlW0FV0AiRJKhtVVbHZbIWmK4qCVqtFUZRbmh4hBN988w1ffPEFn3zyCU2aNLnlaagIQgjMZjMmk6nAdEVRcHFxQa/Xo6oqqqo6pms0GhRFceSPEMLxMaCo6VdOK2taU1NT2blzJ2lpabRs2ZI6depw8eJFdu/eTU5ODh07duTkyZN4enqW6BgmJiaybds2unXrhqura5nTWBFycnL4+++/6dChAwEBAcXOJ4TAZrORnZ2NRqPB3d29xMfm6rxv0aIFmZmZGI1G2rdvf0dcKzdbcffEK2m1WjSa2+Pbvv18uvLav/recLPk5eXx999/06hRI8LCwq6bTqvVSm5uLhqNBhcXF0c6i3Lx4kW2bdtGz549cXZ2LnKew4cPc/HiRbp06eI4Hvb8sFgsGAwGtFpt2XbyOlRVZd26dfj7+xMTEyOvQUmqxGRgK0mV3Lp163j33XcLlY56enry2WefERQUdN115OXl8fPPP9O0aVOioqJuOC1CCGJjY/nqq6+47777mDZtGl999RVubm43vM7bwZEjRzh48CB9+/ZFpyv+tvnll1/y0Ucf4eTk5Jim0WiYMWMG3bt3Z+HChXz33XeO6X5+fvTs2ZNHH30UFxcXTp48yauvvkpYWBjTp093BIhms5lXX32VTp068eijj5Z5fzIzM3niiSdITEwkODgYLy8vnJ2d6dWrFxqNhsDAQGrXrs3MmTOpV68eTZo0ue46jxw5wvDhw9m2bVu5BbbJycn8/vvv9OnTB29v73JZp93+/fs5ffo0vXr1crw4p6am8sorr/DDDz8UG9gKIYiPj2fy5Mls374drVZLz549GT9+PF5eXtd9Kc7KynLkfUhICJ6envzzzz8kJCTQrl27cnup3r17N3FxcTz00EO3TQB3q2zYsIF33nnH8RHpajqdjg8++IBGjRrd4pQVTQjBG2+8wa5du4D8e0O1atV4+OGHuf/++zEYDDdt27m5uYwePZp33nnnmoGtzWZjzZo1fPLJJ5w9exa9Xk/btm15//33cXFxKXKZgwcPMmzYMPbu3VtsYPvbb7/xzz//0LFjRwwGAwcOHOC7775j3759pKSkMHXqVLp27Vou+1ocVVV57733aNOmDTExMTd1W5Ik3VwysJWkSi4pKYmdO3fywQcf4Ofn55ju5OSEh4dHidZhMpl4++23GTduXJkCW4D169czbNgwHnjgAT788EMOHjxIq1atKvVX8M2bNzNnzhx69ep1zcA2PT0dIQQLFiwoENzZXxhPnz5NbGws7777Ljqdjj179jBmzBjOnDnDpEmTyMjI4O+//0aj0dC5c2f69OmDoiioqsrmzZupWbNmmfdFCMGJEyc4cOAAq1aton79+mg0Gn755RfS0tLYtGkTgYGBaDQaXn/99RKfQ9HR0Xz++ef4+PiUOY12Fy5cYOzYsbRr167cA9vVq1fz448/cv/995eqRMhkMjF8+HBSU1P5/PPPycrKYvTo0QghmDZt2jXXJYTg+PHjHDx4kH/++ceR9zVq1CA3N7dcr5G//vqLVatWcf/991fZwPbKj3lX5l1ERASDBw9GCIGqqkydOhVfX1+GDh0K5AeOwcHBtzy9xRFCsG/fPqxWK8OHD8disbB+/Xqee+45Pv74YwYMGFCh908hBH/++SfDhg3jxRdf5I033sBoNHLixIly39bOnTvZvn074eHhbNy4kbS0tHLfhiRJVZcMbCWpCjAYDPTs2bPIwEcIwdGjRzEYDOh0OtatW4cQgk6dOlGrVi2EEOzevZvs7GwOHjzI33//jaurK61bt+bixYukpaURFBTE+vXrycvL49FHHyUnJ4e9e/dy+vRpNBoNzZs3p1GjRmi1Wp555hnS09PZu3cvo0ePRq/XEx8fT0pKCjVq1GDt2rWkpqbSsmVLGjdu7AgE7FU0N23aRHx8PDVr1qRjx46OUrDMzEwOHz5MvXr12LFjB7GxsTRs2JC2bdtiMplYv34958+fp2nTprRs2bJU6z1y5Aj16tVj165dnDx5kjp16tCxY0ecnZ1JSkriyJEjZGVlsWrVKgwGA/Xq1Su2dMPJyYno6OhCAaH9Jdzb25tevXrh6urKww8/TEZGBkuXLmXUqFEAODs706JFC2bOnMm9996Lp6dnqc8HVVWJjY1l69atZGdn06hRI1q2bInBYGDPnj18+eWX5Obm8u233xIYGEjNmjX5/vvvycnJ4csvvyQgIIBnn30WT0/PAgG6EIK4uDi2bNlCSkoKISEhtG3bloCAAAwGA76+vgWqE+bm5rJ9+3aOHTuGp6cnHTt2pEaNGiiKgsViYe/evYSFhXHhwgV27tyJr68v99xzDz4+PuTm5rJr1y7y8vLYtGkTZ86coVq1asTExHDo0CE8PDyw2Wxs2LABrVZL586dqVmzpiMAUFWVM2fOsGXLFrKzs4mJieGuu+5Cr9dz8eJFjh8/Tnp6Ov/++y96vZ6GDRs69tNisbBhwwYOHz5MjRo16Ny5M25ubiiKwt69e9mwYQO//PIL7dq1AyAhIYHJkyczbNiwa3582LNnD/Pnz8doNDry/plnnsHNzQ29Xu/ItwMHDuDt7Y3ZbGbjxo0YDAY6d+5MSEhIgf07ffo0W7duxWg00qhRI1q0aIHBYCA+Pp6TJ0+SmprKqlWr0Ol0xMTE4OHhwcGDB2ncuLHjuJpMJvbt20eDBg3w9PQkOTmZ2NhY6taty6ZNm4iLiyM6OprWrVsXSGN2djZbtmzh9OnT+Pv706lTJwIDA68bgKmqyrlz59i7dy8XL17Ex8eH9u3bO46dEIIjR47g7OxMnTp1HNs7deoUNpuNqKgoFEXBZrOxc+dOtm3bho+PD127duXs2bNUr16dWrVqUaNGDQCsVitffPEFwcHBjg9Fp0+f5tKlS+Tm5rJ+/XoMBgMPPvggBw4cIDIyEl9fXyC/lHLfvn2EhIRQrVo1x7SjR4+yY8cOrFYrLVq0KHAfK4uaNWvSp08fNBoNvXv35sSJEyxbtownn3wSgL179xIeHu6oTaCqKvv37ycwMJCQkBByc3PZt28fUVFRjlomV5+/xbHn5+7du/H39+eee+7B29sbRVHIysrirbfe4umnn+b111937Ovdd9/tWF4IQVZWFmvWrOHixYs0btwYq9VaYBtCCBITE1m9ejW5ubm0a9euUE2j/v37M2DAAC5dusSKFSuKTKsQgvPnz7N582bS09Np0KABrVu3dpQK25uF7Ny5k8OHD6PX62ncuDFNmjRBp9OhqipHjx5ly5YtuLm50bFjx9IdKEmSblsysJWkKk4IwZtvvklycjKKouDr68v58+eZPn06v/76KxERESxZsoTLly/zyy+/sHHjRkJCQmjSpAnffPMNS5YsITg4GDc3N/z9/XnggQd499132bJlC6GhoaSnpzNlyhTGjx/P4MGD0Wg07NmzhxdeeIFt27ZRrVo1li1bxoIFC4iMjERRFIxGI1OmTOHzzz/ngQceAODYsWM899xzWCwW6tSpw4kTJwgKCmLx4sUEBQVx/PhxHnroIbp06UJGRgZ6vZ4pU6YwceJEDhw4wIULF1BVlbfeeovPPvuMhx56yLHe559/HrPZTERERKH1Hj16lF69etG5c2dSU1Nxc3Nj+/btPP/887z55pscPXqUP/74g6SkJN555x00Gg2jRo26ZrW9K9vKXotGo6FmzZrk5OSQl5cH5Lf9e+WVVxg5ciS//vorAwYMKPXx/vHHH3njjTcIDQ3F29ubDz/8kMcff5zJkyeTkZHBxYsXsVqtnD9/HqPRiF6v59KlS+Tl5XH27FlMJhNWq5WJEydSr149pk2bhhCClStXMmrUKPz8/AgLCyMhIYE9e/YwZcoU9u7dy7PPPsuWLVuoXr066enpDB06lN27dxMdHU1ycjLvvfceCxcupEWLFmRnZ9O/f38aN27M5cuXCQwMZO/evURFRbF06VKysrJYtGgRRqORTz75BGdnZ7p06UKDBg0YO3YskF9F29/fn9jYWGbOnMlvv/1GeHg4Qgh++eUXxo0bR2hoKD4+PsycOZO+ffvy1ltvOUpMk5OTefvtt9FoNEyYMIHGjRsjhOCTTz5xpGn37t20a9eOzz77DHd3d3bs2IGXlxfR0dGOQKFNmzYYjUaOHDlyzcA2IyODhISEAnlvsViYO3cuCQkJLF26FFVVGTlyJK6uruTk5BAYGMiJEyf46KOP+O2336hRowaqqvLdd98xadIkwsPD8fLyYsaMGTzxxBNMmjSJPXv2sGbNGjIyMnjrrbfQarVMnjyZoKAgHnnkEdasWeOomZGUlMQjjzzCjz/+SOvWrdm0aRPDhg2jdevW5OTkoNVqmTJlCpMmTWLw4MFAfnvqF198kbNnz9KgQQPi4+OZMWMG//d//+cIPK+VB8899xxarZaAgABiY2N5++23+b//+z9atGiBEIL//Oc/1K5dmxkzZjiW++ijj8jMzGTx4sUIIfjqq6+YNGkSzZs3x9nZmSVLlnDu3DmGDBni+EhUnI8//pitW7fi7u6On58fwcHBtGvXjieeeII5c+Zw3333AfnVdF944QWGDx/Os88+i81mY+7cuXz00UfUq1cPvV7Pe++9x+jRoxkyZEi5lozr9Xq8vLzIyMhACEFOTg5PP/00b775Jo899hiQ/wFmyJAhPPnkkwwbNoyLFy/y8MMPc/fdd3Pp0iV8fHzYsWMHPXr0YM6cOdes0vzdd99x6dIlatSowf79+6lVqxZff/01AQEBHDp0iNOnT/PAAw9w6tQpUlNTCQkJISQkxBHk2o/rgQMHaNKkCUuWLKFatWqO+6AQgtOnT9OvXz+sViuRkZEsWLAAT0/PAvfKK5txFEUIwV9//cWoUaMICgoiMDCQjz/+mK5du/LBBx/g4uJCZmYmw4cPZ/369TRu3BhFUfj8889ZtmwZ4eHhrFy5kldeeYXatWvj6enJ/PnzuXTpUlkPmSRJtwMhSVKl9u233wpXV1fx/PPPixEjRjj+zZgxQ+Tl5QmbzSYefvhhERoaKvbt2yesVqu4dOmSaNq0qXjzzTeFqqoiJSVF1K9fXyxcuFCYzWZhNpuFqqpi6tSpwsXFRfz444/CYrEIq9UqVFUV586dE9nZ2cJmswmz2Sw+/fRTERERIRITE4UQQvzzzz+iRo0a4uLFi0IIId5//33h6uoqfvrpJ2GxWITRaBQDBw4UDzzwgLBYLMJsNovevXuLxx57TKSkpAibzSYuXrwo2rdvL95++22hqqrYvn27cHJyEhMmTBBGo1Hk5eWJsWPHCk9PTzFr1ixhMpmEyWQSzz//vLj//vsLrPfRRx8tdr1bt24Vzs7O4vXXXxc5OTnCYrGIL774QoSGhoqEhARhs9nEvHnzRExMjMjIyBBms1lYrdZCx0FVVfHWW28Jb29v8dxzz4lBgwaJQYMGiTfffFPk5uYKVVXF66+/Lho3bizS09OF2WwWcXFxokOHDqJTp04iNzdX7NixQ/j4+Ih9+/aJCRMmiGbNmomUlBSRk5MjmjRpIt57773rng9nzpwRtWvXFrNnzxa5ubnCarWKTZs2idDQULFp0yahqqpYu3atCA4OFnFxcUJVVaGqqli0aJGoX7++yMrKEqqqCqvVKh588EHx2muvCVVVxcWLF0VUVJQYOXKkyMzMFDabTRiNRpGUlCRUVRWrV68WNWrUEBcuXBCqqorp06eLmJgYceLECWG1WkVOTo4YOnSouP/++4XZbBYpKSmiVq1aomvXruLSpUvCarWKHTt2iMDAQLF69WqhqqrYtWuX8PPzE4cOHRJms9lxDt57770iMjJSHDt2TFitVnHhwgVRv359MWvWLKGqqjh//ryIiIgQs2bNcuTB1q1bRVhYmNiwYYOwWq3i/fffF61atRJZWVmOY3r+/Hnh5+cnunXrJi5fvuxYrnr16uLrr78WqqqK0aNHixYtWgij0ejI84SEBFG9enWxcOHCax4bVVXFunXrREhISIG8Hz58uOjbt6+w2WzCYrGIDh06iAYNGohTp04Jq9Uqzp07JyIiIsRnn30mVFUVp0+fFrVq1RJz584VJpNJWK1WsX79ehEaGiq2bdsmrFarmDJliujQoYPIzs527N++fftEYGCgOHr0aIHzpVq1amLLli1CCCF+/vlnYTAYxEcffSRMJpPIy8sTkydPFo0aNXIc99GjR4t27dqJuLg4YbVaRUZGhujXr594+umni7w2rpSXlyfOnj0rzGazsNlsIj09XTz22GPiqaeeElarVdhsNtGrVy8xcuTIAvn28ssviyeeeELYbDZx4cIFUbt2bfH++++LvLw8YbFYxJIlS4STk5P48MMPC2zPYrGIe++9VwwcOFDYbDahqqoYMmSI8Pb2FmvWrBFWq9VxTwwNDRV//PGHY9msrCwRHR0tFixYIFRVFTt37hQhISHihx9+cGz3559/FuHh4eLkyZPXvTaLY7VaRbdu3cQjjzwikpKSxMWLF8Xvv/8uqlevLqZOnSpUVRVpaWmibt264rvvvnMsZzKZRMuWLcXHH38shBDi9OnTwsvLSzzzzDMiIyNDWK1WsXz5chEYGCiOHDlS5LbT09NFnTp1RNOmTcW5c+eE1WoVR48eFXXq1BHvvfeeUFVVLF68WPj4+IhnnnlGNGzYUDRo0ECEh4eLDz/8UOTl5QlVVcXnn38uwsPDxYEDB4TVanXcZ6tVqyYuXbokbDabGDRokGjfvr1ITEwUVqtV7N+/X9SsWVN07NhRmM3mAumKi4sTQUFBYtmyZQWmJyUliYYNG4q33npLGI1Gx3ldu3ZtsWLFCqGqqvjwww9FaGio2L59u+OekZSUJHJzc0VaWppo2rSpGDVqlDAajcJisYivv/5aODs7i0mTJglVVW/4OEqSVPGqZsMbSbrDKIqCm5sbHh4ejn9Xd+jRqVMnYmJi0Gq1+Pv706xZM06ePIkQwtFuVKvVYjAYMBgMjlKXiIgIunfvjk6nc3ydDwkJITExkVWrVrFixQpsNhtpaWnEx8cXm8Y6derQrVs3dDodzs7OdOjQgfPnz2MymYiPj2fLli107dqVy5cvc/LkSTIyMmjZsiVr1651VGlzcXGhX79+ODs7o9PpaNOmDc7OzvTp0wcnJycMBgOtW7fm3LlzmM1mLly4UOR677rrLtasWVNgvU8++SSurq7odDratm1Lbm4uycnJaDQaR+/S9ry5VrVDvV5PjRo1CA0NJTQ0lOrVqxcowTp9+jQPPfQQ3bp145577iEtLY233nqrUIdTL730Eunp6SxdurRUwyZt2rQJk8lEixYtOHfuHKdOncLb25ugoCA2bdpUqDStqJ5Xiypx27lzp6NzJQ8PD0evqEVVPzWbzfz666+0b98eVVU5deoUcXFxtGrVin379pGcnOyY9/HHH8ff3x+tVkuDBg0ICAjg7NmzKIqCTqdDURT0er2jKr1dt27dqFu3LlqtlmrVqhEdHc3JkyeB/DbROTk5tGzZ0pEHHh4eBAcHs3HjxgLH1L5u+zFVFIUBAwbg5+eHVqulZcuWtGnThr/++gtVVcnLy0Ov1xcondNqtWi1Wsxm8zWPTUny2e6+++6jdu3aaLVaQkJCqF+/vmP/NmzYgNVqpVmzZpw9e5ZTp07h6+tLQEAAmzdvLvU5ezU/Pz/69u2Lk5MTer2ejh07kpKSQkZGBllZWaxcuZIuXbqQk5PDqVOnuHjxIm3atGHbtm1kZWVdc906nY7AwEAOHz7MypUrWb16NX5+fhw+fPi6+Wd36NAhjEYjjz76KHq9Hp1Ox/33309oaGiJ97Fp06a0a9fOcexK0ob1zz//xNvbm8jISGJjYzl9+jShoaFotVr27NlTaH7x3+q5aWlppKWlkZmZec1r+Z9//qFDhw60a9eOxx9/nB49ejBs2LAS7xPk5+9TTz2Fh4cHWq2W5s2bo9fruXDhwjWXe/TRR6lZsyZarZaoqCjuv/9+/v77bywWC0ajkfT0dJKSklixYgWbN29m5MiRTJ06le3bt6OqKn/99Rf33nsvDRs2RKvVEhQUxFNPPeXI16ysLNatW0f//v0JDAxEq9USHR1Nt27dSrV/u3btIikpiXbt2nH+/HlOnTqFwWCgdu3arFu3jry8PH777Td69epFixYtHM+twMBAnJ2dOXnyJHFxcTzzzDO4uLig0+l48MEHHdXeJUmq3GRVZEmqAlxcXHj11Vcd7cquZH+R8vHxKfDy5uTkdN2XUABfX98CVdhsNhvTp09n/vz5+Pn54eHh4Rjm5lovpu7u7o7gTVEUnJycsFqtqKpKamoqmZmZTJ06tUDvmRaLhXr16jmG7tDr9Xh4eDj2w2Aw4OLi4gji7S/yNpsNVVVJSUkhKyuLd955hw8//LDAeqOiohzrNRgMuLu7O363By4Wi+W6+XM1Pz8/Xn311WLb2AYEBDB48GBcXFzw8fGhQYMG+Pn5FXqxDg0N5cUXX2Tu3Ln07NmzxNuPi4sjOTmZp59+ukDwZTKZrjsEyrVcvHgRT0/PAh2UFSc3N5fLly/z008/sWbNGsd0VVXx9PQkLy8PZ2dnFEUpcF5qNBoMBoOjWva1XN1JlZOTk+N42dt0P/vss4XyoLiecu20Wq2jHbA9TTVr1uTo0aMIIXB3dyc3N7dAXubl5WGxWErc0VZJFLV/9nyJi4vj0qVLDBw4sFyPsZ2Li0uBttUGg8ExhE5OTg4pKSnMnz+f77//3jGPzWbDy8urULvKqyUnJ/Pyyy+zd+9eqlWrhrOzM4mJiY51XEn8d4irq6WlpeHi4lIgv52dnUvVwZi/v7+jzXBJxcXFcebMGR577LECQ3FZLJYiz1mLxeLYV4D69evzzTffFNtD8N133837779Pbm4uixYt4q+//iI+Pp769euXOI1arbZA79w6nQ6dTnfNe5miKISGhhbI69DQUNatW4fFYsHd3R2tVsugQYMc8w0cOJC5c+eydu1aWrZsyeXLlwsMC3b1Oo1GI9nZ2QWuLfs89g82JXHhwgUyMjIYNGhQgY81ZrOZJk2akJeXx+XLl6lTp06R505ycjI6na5Ar+f2D3SSJFV+MrCVpCqkqAd5SUv7iiuxuHp6bGwsc+bMYebMmTz00EMYDAaOHTtGly5dSp/g/3J1dcXFxYWPPvrI0SGPnU6nK1CaWZreQV1dXXF2dr7ueku6ztKUnBbHy8uLhx56qERD4jzzzDP83//9n6NdYUnXX716dVauXImXl1eB34oblqOk6zUajRiNxuv2fKzX63F1deXRRx8t1N7RHsxmZGTccFquXFdRPD09qVatGr///rujIyC76+WBqqqkp6c7/hZCkJaW5vigEhERweXLl8nIyHAcw8TEREwm03XHAS2Na41h6unpSY0aNYo8xtc6r+xB8JXBvcViuW4weiW9Xo+LiwujRo3iqaeeKrT+a50bQgh+++03Dhw4wO+//06dOnXQarV8+umnfPbZZwXWc2WQK4TAaDQ6/rZ3LpaVleX40GIymQoct+sprubClXljs9kKBKyenp40bNiQX375pVB71Ss/jNnpdDqmTJniSLuLi8s127l6eHgQGRmJRqPh7bffZv/+/bz77rt89dVXjrFtr8wXq9VaZEB9Iz0op6WlFfiQYO9vQKvVUqtWLZydnQsEzE5OTri6upKdnY1Wq8Xd3b1AD8biv5322dlr1Nh7jr9yO6Xh4eGBj48PP//8s6NDLzt7TR53d/cCtUKu5O7u7hiD2s5qtZKZmVmqdEiSdHuSVZElqYoQ/+2w6Op/JWGvgpyeno6qqtdcNisrC6vVSsOGDXF1dUWj0bBq1aoCLwqlVbNmTerWrcvatWvx9PTE398ff39/fHx8Sl2qcqXQ0NBrrrekL4AuLi6YTCZMJlOp8rUsFEUhKCiIV155hfnz5ztKta6nTZs2mEwmDh48iJ+fHwEBAfj7++Ph4VGmvGzWrBkAy5cvd7xcCyHIy8srlB+urq506NDBUe3Xnu9+fn44OTmVuJMde5V4exXOkuZ769atMZvNxeaBoii4uLhgNBoxm80F1q2qKsuXL3fsl706e9u2bdFoNLRt2xar1cqaNWsQQmCz2fj9998JCQmhQYMGJc3OMmnXrh1ZWVkcOXKk0P7Zq2/b98++H0IIvLy80Gq1nDp1yjEUzvr168nJySnxtn18fGjVqhVr167F2dm5wLG9sglDcS5fvoyfnx+1atXCyckJs9nM33//7fhdURSCg4M5efKkI+1JSUmOMV4hf2gpV1dXfvjhB0dgvnLlSs6fP1/6zPwvZ2dnfHx8OHLkiCO/9u7dS0JCgmOezp07ExcXx/nz5/H393fku5ubW5HDgGk0GiIiImjUqBGNGjVyBK0l4e7uzogRI/jrr7/YvXs3Tk5O+Pr6cuTIEcc9+tChQ5w9e/aG99lOCMGKFSswGo2ODzn//vsvrVq1wsnJifr16xMeHs6mTZuw2WwIITh58iQXLlxwNG9p06YNa9euJTk52dEr8e+//+74UODh4UHjxo35/fffHdfc5cuXWbt2baG0XHmdX/08s1et3rVrl6P6vb+/P56eno6PlR07duSPP/4gMTHRsZzFYsFmsxEREYGHhwd///23Ix/37t1bqlJjSZJuX7LEVpKqgJycHMaPH1+oOu2rr75aonZnLi4utGvXjtmzZ7Nv3z6qV6/OG2+8UeS8tWrVIjw8nBEjRtC7d29iY2PZuXNnsdXrSsLd3Z233nqLl19+mcTERNq1a4fZbGbXrl00b96c11577YbW6+bmVux6mzVrxuuvv16i9TRp0gSj0ciAAQMICQnh8ccfL7aEujzHm1QUhX79+jF//nz27dtXomViYmIcPcOuX7+eunXrcunSJbZu3cqkSZNo165dsaWB10p77dq1GT16NFOnTuXw4cPUr1+f2NhYqlWrxquvvlpoPaNHj+aJJ57g4Ycf5oEHHsDJyYkjR45gNBpZsGDBdbcHEBwcTJ06dRg6dCgxMTG0bNmS55577rp50KBBA4YNG8arr77Kxo0biYqK4vLly2zZsoWJEyfSoUMHWrZsydtvv82AAQOoVq0aAwcOJDQ0FI1Gw8GDB3nllVdo0KABv/zyC97e3vTr1w9FUYiMjOT555/nP//5D2fOnCErK4uvv/6aqVOnlusYvtfSpEkTBg0axIgRI1i3bh0REREkJSWxdetW3n77bVq1akWrVq344IMP6N+/P0FBQTz33HM0adKEzp07M2HCBE6cOEFycjLbtm27Zini1bRaLW+88Qb9+/fn0UcfpVu3bmg0Gvbv34+HhwezZs26ZnveDh068PHHHzNixAhiYmLYsGEDiYmJBc6FPn368OSTTzJq1CgiIyNZt25dgZLKatWqMW7cOCZPnszGjRtxdnYmJSWF4ODgG+6Z2MXFhT59+jBnzhzMZjMajYZ169Y57muKotClSxd69+7N008/zSOPPEKNGjWIj49n165dzJ07l8jIyBvadlEURXG0WZ0zZw5fffUVffv2ZcaMGWi1WpycnAqkr6zbSkpK4sUXX6RVq1asWrWK9PR0Bg0aBOR/zHj99deZMGEC6enpVKtWjaVLl9KkSRPuu+8+FEXh6aef5qeffqJ///707NmTffv2cfr06QJVou2l/C+99BLNmjVj9erVhaqfHzlyhDlz5pCVlUVmZibz589nzZo1dOnShccee4w6deowduxY3nzzTXbu3EnDhg1JTU1l69atDB06lJ49ezJ06FDWr19P3759HUM87dmzh2nTplGzZk2GDBnCtGnTiI+Px9vbm5UrV5ZrMwJJkiqODGwlqZJr3Lgx//nPfwpNt5fCKori6EzkSg899BC5ubmOF4+pU6fSoUMHzp49i6enJxqNhrvvvpvw8PACL6o+Pj4sWbKE+fPns3XrVqKjo1mwYAH//POPI4iOiIjg9ddfdwTaHTp0ICgoqMBLZ5MmTRg5ciROTk6Ol7jffvuN77//ni1btuDi4kLr1q3p3bs3ADVq1OCNN94o0I4uKiqKsWPH4ubm5pjWtGlTRo4c6dj34tbbq1cvIL+0ePz48QWqrPr7+zNhwgSCg4MBaNiwIb/++qujU6LixpZ96KGHiI6OLvZls3v37kRGRhZbchoSEsJ//vMfgoKCHNN8fX356KOP2LJlS4nGW9RqtYwbN4677rqLP/74g3Xr1hEQEMCAAQOIiYkBIDIyknfffbdAXrZu3Zr//Oc/Bapn2ztRgvzSpxEjRtCwYUOWL1/Ohg0bCAsL45577gHyA9/XX3/dUWW3Vq1a/PLLLyxbtozdu3cjhCAyMpL77rsPnU6Hi4sLY8aMKTB+rE6nY+jQoTRp0gTIr/q5dOlSVq1aRXJyMgEBASiKwnPPPUdISIhjOUVReOyxxxylZjqdjtdee40WLVrw+++/s27dOvz9/enfvz+NGjUC8s+TX375hW3btmE0GnF3d8fLy4uJEydy9913888//7Bjxw7atWvHiy++SPXq1R35O3HiRKKiovj3338xGAx8/vnndO/evUQfNYrK+wceeICsrCxHddOXXnqJ2rVrO37XaDQ8+eSTjutJp9MxceJEWrduzYoVK1i3bh2BgYE8/fTTjlLj1q1b89NPP7Fjxw5yc3MdY+XOnDmTL774gt27d1OvXj0+/fRTVq1a5bh2GzRowKuvvlqgynZYWBjjx493jGvaqFEjli9fzrJly9i1axdarZb69evzwAMPXDOwVBSFNm3asGjRIn744Qd27drFQw89RL169di7d6/jmu3UqRPz58/n559/5vDhw7zyyiuOkln7R5lnn32W6Ohotm3bhre3N23btqVPnz6Fqp5rNBqee+453N3dHcfHfu+7Om2jR4/G19fXMWTVhx9+yM6dO2nevDmQX6r74YcfsmLFClatWsXp06epXr06gwcPvuYwT9dj3x9XV9cC55Cbmxvvvfceu3btwmQy8fLLL+Pu7s769esJCAjgvffe48CBA45ryMfHhwkTJhS4Ntzc3HjttdeoW7dukdt2dnZmzJgxtGjRgl27drFhwwYiIiKYNm1agaGb+vXrR2BgID/99BMJCQk8+eST9O/f3/ExJzQ0lO+//5758+ezfft2WrduzciRI9m4caNjDN327duzdOlSvv76a/bs2cOTTz5J9erViY+PdzxjXFxcCAsLQwhR4N5gP64ajYYhQ4bQqFEjfv31V9avX4+vry+PPPIIrVq1QlEUwsLC+OWXX/j222/Zs2cPTk5OdOrUiYCAADQaDUOHDqV69er8+eefZGRk8M477xAXF1dkHxWSJFUuirgVdeokSapSrnXbKEuJZVHrLY8S0Ju13tvZ7ZKXFZn3Zdn21ctevdz1fr8VbmT/yvN43GgelOT+cb11X7582dGZnMViYenSpUyYMIFVq1YRHR1donSUJG0l7begos7p8txuWc/5G8m78jr3rl5XaX+v6s8DSbpTyBJbSZJK7Wa9BFS29VaExMTEYqslu7m50aZNG0c7y5uhtOutyLwvy7avt2xRv6uqyvbt24vtGKthw4ZlKtkrSRpuxjKlWde5c+c4evRokfN7e3tz1113lai68PXSuXz5cubMmUONGjXIyMjg9OnTDB8+nHr16pUs8Te43ZLOc7PczG3fyDlfmt9LOk9J3Iq0SpJU+cjAVpIkqRI5duwYU6dOLbJEIiwsjObNmxfZkY1089lsNr766isOHz5c5O9jx44t18D2drRr1y5mzpxZ5G+NGjWiefPmN9wO9kqPPPIItWrVIj4+Hp1OR3R0NA0aNJDnviRJ0h1MVkWWJEmqRFRVLXZ4FkVRbmpprXRtQgisVmuxVW21Wu01O1aqCmw2W7Fj6Wo0GrRarTw/JUmSpJtCBraSJEmSJEmSJElSpSbHsZUkSZIkSZIkSZIqNRnYSpIkSZIkSZIkSZWaDGwlSZIkSZIkSZKkSk0GtpIkSZIkSZIkSVKlJgNbSapiDh06xOLFi1FVtaKTIkmSJEkVRgjB1q1bmTp1KpmZmRWdHEmSbjIZ2EpSFXPy5El+/vnnQkOOCCEcQ8UIIRy/CyGw2WxYLBbHdPs/i8WCzWYrdvgSSZIkSbpdWa1WZs+ezfz589mwYUOBZ5l9eK4rn3H2Z5/VakVV1ULPxCvnufLf1c/WK5e/cnvFrfvqeeQzV5JujBzJXJLuAEIIdu3axRdffEF2djZNmzZl8ODBuLu7s27dOhYvXozJZCI4OJiJEyeiqiozZ84kNjYWg8FA//796datW0XvhiRJkiSVWGxsLHFxcbz++ussW7aMHj16oNVqyc7OZv78+ezcuROdTscTTzxBjx49OHv2LJ9++innz5/Hy8uL1157DZvNxm+//caYMWPQarWsX7+e8+fPM2DAABYvXkxeXh4HDx7Ez8+Pl156idmzZ3P+/HmcnJwYMGAAnTt3BuD06dN8+umnXLhwAR8fH8aOHcuff/5J48aNadu2LQD//vsv8fHxPPPMMxWYa5JUecnAVpLuAImJiQwfPpzRo0cTExPDO++8w8cff8yIESOYMmUKY8eOpUGDBiQlJaHX61m0aBGpqam8++67mEwmNBpZuUOSJEmqPIQQ/Pbbb7Rp04Y+ffqwePFizp07R61atXj//feJj49n4sSJKIqC1WolOzubV155hXvvvZeXX36Z1NRUXF1dOXv2LBs3bmT06NEAnDlzhgMHDjBgwAD27t3L/v37mTlzJoGBgRgMBnr37k1AQADnzp1j6tSp1KtXDzc3NwYPHkyvXr145ZVXSElJwdXVlcDAQD7//HNatWqFzWbj008/5cUXX6zgnJOkyksGtpJ0B9i3bx/+/v707t0bvV7PkCFDGD9+PEOHDsXPz481a9bg5ORE48aNcXd3JygoiOXLl7N161ZatGhBnTp1KnoXJEmSJKnEsrOz+eOPP5g2bRp+fn40a9aM33//neeff55Vq1bx5ZdfUr9+fRRFAWDPnj1kZmYyaNAg3NzcqF27NgBnz5695nb69OlD06ZNURSFjIwMNmzYwL59+8jLy+P06dOcO3cOAIvFwvPPP4+Li4tj3ffccw9z587l9OnT5OTkkJ6eTrt27RxpkiSpdGRgK0l3AJPJhIuLi6Pk1dXVFYvFgsFg4NNPP+Wff/7hp59+4u233+azzz6jT58+VK9enXXr1rFo0SK6d+/O6NGjZcmtJEmSdNsTQrBjxw5OnDjBBx98gMFgID4+ngMHDvDYY49hs9lwdXUtEECazWYMBgM6XcFX4yvnsbeRvZK3tzeKoiCEYPHixRw5coQ33ngDV1dXnnvuOfLy8hBC4OTkVGjd3t7edO/enR9++IGMjAwefPBBPDw8bkKOSNKdQb6lSlIVpKoqJpOJ3NxccnNziYyMJDY2llOnTmEymVi5ciUNGjRAo9FgsVh49NFH+eCDD/Dz8+PQoUOkpKTQuHFjxo0bx/Dhw1m9ejU2m62id0uSJEmSrktVVb777jtGjhzJ7NmzmTFjBosXL0aj0XDs2DHq1avHb7/9Rm5uLiaTifT0dGrXrk1OTg7bt28nLy+PrKwsjEYjPj4+pKSkkJKSQnZ2Nhs2bCh2uwkJCdSvX5/IyEhSU1OJjY0FIDIykrS0NHbt2kVeXh6ZmZnk5uaiKAqPPPIIv/32G6tXr6Z3796ytFaSykCW2EpSFePi4sK5c+d48sknHdNefPFFXnjhBYYOHer4Gvz+++9jMpkYO3YsJpMJIQQuLi60b9+eP/74g19//RU3NzcyMjJ48cUXC31pliRJkqTbUUpKCufOneP1118nNDQUyC9t7dOnDxs3bmTSpElMmDCBDRs2oNVqefDBB3n66aeZPHky77zzDp6enqiqyhtvvEF0dDRt2rRh4MCB+Pv74+rqire3NwA+Pj64ubk5tvvYY48xZswYdu7ciaenJ9HR0Tg5OREcHMwbb7zB5MmT8fLyQgjBm2++SXR0NLVq1SIiIgI3NzdHWiVJujGKkH2KS1KVkpeXR3Z2doFprq6uGAwG0tLSMBqN+Pv74+zsDEBubi6pqakoioK/vz8GgwGbzeaY18vLCy8vL/kVWZIkSaoUrFYrOTk5eHh4OJrQCCHIy8sjLy8Pd3d3zGYzycnJ6HQ6/Pz80Ov1CCHIzs4mLS0NNzc3fHx8HJ1LXbp0CWdnZ9zc3FBVFRcXF3Jzc9FqtTg5OTm2kZmZSWZmJv7+/litVpydnR3rzsrKIj09HXd3d0dwnJmZyeOPP84bb7xBhw4d5LNWkspABraSJEmSJEmSdIvt3r2b//znP0RERPDBBx84AmRJkm6MDGwlSZIkSZIk6RbLzc0lLS0NPz8/DAaDLK2VpDKSga0kSZIkSZIkSZJUqd32vcEIIUhISMBoNFZ0UiRJkiTpurRaLWFhYWi12gpNh9ls5ty5c7IUSJIkSbrtCSHw8/PDz8/vhtdx25fYqqrKsGHDsFgs5TK2l9VqJSsrCx8fn3JIXcUSQpCamoqvr2+VeHHJyMjA2dm5SrQxMRqNqKqKu7t7RSelzFRVJS0trcqcZykpKfj4+FSJMXlzcnIACvTKWVnl5eWRm5uLl5dXRSelTGw2GydOnOD777+v8Ov/5MmTDBo0iKZNm5bL+uS1c3uqKtcO/O+9pqqcZ2lpabi7u6PX6ys6KWVmNpsxmUxV6jyrKu81qampeHp6VvrRK86fP88999zDyy+/fMPruO1zQFEUDAYDY8aMoVatWmVeX05ODmfOnCE6OrocUlexrFYrBw8epHHjxlXiAXDixAn8/f3x9fWt6KSUWUJCAlartUp03W+xWDh06FCVOM+EEOzfv58GDRpgMBgqOjllFhcXh0ajISQkpKKTUmbp6ekkJSURFRVV0UkpE5PJxJAhQ26LlyVFUWjatCkffPBBmdNT1a6d+Ph4AGrUqFHBKSm79PR0EhMTqVevXkUnpcxUVWX//v1ER0dXiWDw8OHDhIWFVfhHrvKQmppKSkoKkZGRFZ2UMrOfZzExMZU+GBRCcOjQIerUqYOrq2tFJ6dMVqxYwYULF8q0jkp1NMvjReHKddwOLx5lUZX2RQiBoiiOf5VdVd2Xyr4/9goqVWFfoGqdZ/C/+1hl3p/bMe3lcY5cWbnrdtzH0qoK55rdlce3su9PVXveVJXjcrWqsj9V4TyDqnU/K6tKFdhKkiRJt44QwvHvVm1LVdWbvq3yUhkDihs9pvb5hRDYbLabkbRbyr4/NpvNUROlshxDSZIkqWgysJUkSZIKEUKQlZVFenr6LQlsrVYrQghHFdHKwsnJiYCAgArvKKokhBAYjUZSU1Nv6AOCXq/n4sWLlb5JAuS3F4T8KskajQY/Pz9cXFxkcCtJklSJycBWkiRJKsTeaVhgYOAtaetms9mwWq2VqvM4IQTJyclkZ2fj6el52wdFV3bM4+zsXOrlc3NzcXZ2vu33syQsFguQH6zn5uaSkpJSJdrbSpIk3clkYCtJkiQVoqoqiqLg5OR0y0rohBBotdpKFTg5Ozs7gqTbnb2qt7Ozc6k7TLEfG61WWyVKbO0l1jqdDhcXF1JTUwu0iZQkSZIqn8r/dJIkSZLuWBaLBZPJdEuqSxdFBkLlz2azYTQaK+yYSpIkSZWTLLGVJEmSKpwQgqVLlzqGylIUBa1WS4sWLejWrVuxAeTOnTvZvXs3Q4cOve42Vq5cSe3atavEsCiVgRCCFStWsH//fmw2G0IIdDodkZGRPPLII8WW/J45c4YlS5YwefLkEn04yM3N5YsvvqBnz55ERkbKjw2SJEl3qDuyxFY+8iRJkm4/LVq0oEePHsTHx6PVaunWrRsRERGYTCZycnJQVZWsrCwyMjJQVRUhBDExMTzyyCMA5OXlkZeXR3p6uqMzqiutWrWKkydPFpimqiqZmZnk5OQ4egu22WxkZGSQmZnpCMhMJhOpqakVWjpcGUVHR9OjRw9sNhuXL1+me/fuxMTEkJeXR1ZWFqqqkpOTQ3p6uiOva9SowQsvvICiKOTl5WG1WklPT8disRTKeyEEe/bs4auvvmLJkiUFpufm5pKWluZYTghBdna24/yRJEmSbsyVPezfTs/EO6vEVggMZ1YTcuEwNGwI8quuJEnSdQkhyLOplMezS6Mo6LWFxw5UFIXIyEicnJyoVq0atWrVwsvLizfffBNfX18aNWpEtWrV+P333zGZTDRo0IBRo0Zx6NAhdu/ezXPPPcewYcMICAggISGB2rVr88Ybb1yz4yur1cq8efPYuXMnNpuNJ554gp49ezJz5kyOHDmCTqdjwIABVK9enbfffhsXFxd8fHx48803cXFxKXtmVCAhBBabQC3FQTVbVRSriqIUXEZRwKDVFHlMw8PDCQ8PZ//+/Tg5OREWFsbo0aMJCAigevXqtGvXjiVLlmA2mwkKCuI///kPCQkJfP3117zxxhuMHTsWFxcXkpKScHd3Z9q0abi5uRXYj99++43XXnuNH3/8kbS0NHx8fFi3bh0LFizAxcWFmJgYhgwZwqJFi1i3bh3Ozs489NBD9OjRo2yZKEmSVAVdGaiqAiw2FZPFRrbZSprRQkq2mUuZZhIzTRjzrAy5OwJPl5vfyWRJ3FmBLaDLvoD3iR+g40vg6lfRyZEkSbrt5eTZGLF0L4mZpjKvq5a/Gx8+1hgn3fWHx8nNzSUuLo6PPvoILy8vsrOzCQsLIzk5mQ8++IBHHnmE7OxskpKSUFWVEydOMHLkSMLDw3n66adJSkq6Zk+3J06cYN26dSxcuJCUlBSGDx9OkyZN2LhxIzNnziQkJASNRsOyZcuIjIxk1KhRjg61KjurKpjwy0GOXsws8TKqqhZZfdjXzcAnTzTF29Vw/e1arZw4cYKpU6cSEhKCyWRi+PDhpKamMnv2bPbt24eXlxfx8fEIITh16hSjRo2iXbt2DB48mOPHj9OsWTPH+lJSUjh27Bjjxo3jwIEDbN68mbvvvptZs2Yxbdo06tati9ls5uTJk6xYsYL58+fj4eFBbm5uifdbkiSpKhFCIABVzf9obbKoZJospBstXM4ycynLRGKGiaRME5cyzSRnm8nItWDMs2ETAq1Gwd1Jh5eLnlBfV/Jst08NmDsrsFUURHgHWDcdTcJ+qNNZltpKkiRdh16r0KVeIJkma5nX5e9uQFuK+25kZCS+vr4IIfjss884ffo04eHhpKWlkZGRUWBeX19fwsPDcXV1xdPTE6PReM11X7hwgfDwcLy8vHB1dcXZ2Rmr1Uq/fv2YOnUqrq6uDBkyhK5duzJjxgxeeeUVmjVrxssvv3xDw+XcTjSKQrsIf+oEuJdwCYHFYkGv0xd6broZtBh0JW/ZVLNmTYKDg9Fqtfz444+sXr2aqKgo0tLSSE1NxcvL63/rdnMjKioKJycnAgMDycrK+l+KhGD9+vWkp6fz/fffk52dzQ8//EDjxo2B/HNHr9ej1+s5f/48derUwdfXV7bBlSSpSrOXuKoC8qwqWSYLl7PNJKTnkpBuIjHzf0FrSk5+0GqyqKiqQK/T4O6kw8dVj7+7EzV9XWlZy5cgT2cCPZzwczfg7WrA3aDF2aBFp9GguY1uqeUS2NqHEFBVFZ1OV+RD4+r614qiIITAarWi0WjQaApXYypvQgjO2QLQKdWoefLv/MBWkiRJuiaDVsOTrUIrZNv2EkKLxcL27duZPXs2Hh4erF69utC8Vz5Dinue2MfLBQgMDCQ+Ph6j0Uh6ejq5ubl4eHjQq1cv+vbty6+//sr8+fN59913mTZtGpmZmbzwwgt07dqVhg0b3oS9vXU0CvRuElyqZcprHFv7MRVCsG7dOkaNGkX9+vU5ePBgkW21rnUsly9fzlNPPUV4eDg1a9Zkzpw5pKeno6oq8fHxhIeHY7FYCA4O5ty5c2RnZ+Pq6kpeXh4Gw/VLmCVJkm5H9nulACyqID3XQqIxm4vpuZxLNXI2OYezKUYupOWSkmPGbFEx6DR4OOvwc3fC392JukHuVPPyJ8jTmQAPJ/zcnPBy0ePmpMVJp0WnUQp8x6wMHwXLHNgKITh37hwffvghOTk5PPjgg/Tq1atQdSWbzcasWbMwGAwMHz4cVVX56aef+PPPP/Hw8ODVV1+lRo0aNz3TfjmcjsiKZnTsehRzFjh73tTtSZIkVXa3+mHm4+ODm5sbBoMBP7/8JiN6vZ6OHTsybtw4fHx88PLywmAw4OzsjLe3N4qiEBgY6Hj2+Pv7F2pf6+Pjw+LFi1mxYgWKojBgwAAaNWrEK6+8gtlspn///uh0OsaNGwdAamoq/fv3Z/Xq1fz2228YDAaqV69+zerNlUVpj+mVAeeNnA/u7u54eXmh1WoJCAhAUfLbWXfv3p1p06YRFBSEEAJXV1f0ej3+/v4oikJAQABabX61dR8fnwIl5UlJSSiKQv/+/XFzc0MIwcmTJzl+/DiDBg1i/PjxBAQEULduXQYPHkzr1q0ZMmQIHh4edO/enZ49e5Z6PyRJkm4l+73XpgpyLTYyci0kZZqIS83lbHIOZ1JyOHEhhYy8S+Ra8sef93TWUc3LmVBfN5qFehPu50YNHxcCPJzxcNZh0GrQahVHZ7qVIWAtqTIHtqqq8sEHH3D33XfTokULhg8fTrNmzQgLC3PMY68utHnzZkeVstjYWJYsWcKcOXMc7Zk+/PDDm565jWt6M1c0ZnDq37heOgI1W8nqyJIkSbeRIUOGoNFo0Gq1TJw4Ecgv5Rs2bBiXL192BD9OTk6oqkrLli1xcnJi1qxZuLq6AjBlypRCbWFfe+01zGaz429XV1fatm1LcnIyOp0OHx8fFEXhvffeIz09HTc3N7y9vbHZbLRp0waLxYKfn58s6bsBvXv3RlVVDAYD77//PlqtFkVRePzxx7nnnnvQarW4urqi0+nQaDRMmjQJnU7H9OnTHR11jRgxosDHiqCgIObNm+c45oqiMGTIEFRVxcnJiQ4dOpCTk4Ovry86nY6RI0eSmpqKzWbD01N+1JYkqWJcXTPFJgQ2VWC25HfSlGWykmnKb+96LtXImeQczqcYuZiRS3quBZsqcDVoCXB3ooaPK9EBBppG1qBONS+qeTrj42rAxZBf4gpVK3C9njIHtjk5OZw6dYopU6bg5+dHvXr12LdvX4HANiUlhZ9//pmBAweyYsUKAPbs2UN0dDQ1a9bknnvu4euvv8ZoNOLunt/ex15dzD70gqqq2Gy2siaXCH8Xkgw1OW3xJfrkKtSQlmVeZ0WxD1dQVYYtsFdpL4/jXNHsQ5FUpX2x/1+Z2bulryrn2c3clyuP96067vbtXBmQ2oMaIQRarZZq1aoVWEaj0aDT5T/K7AHO1cvZOTs7F9k2NjAwsMDfbm5uBXre1Wq1+Pv7F5nWK5viAFXiOrkZrvwYcGXeajSaQvkP/zuWV857dU/UOp3OceyL2o63tzfe3t6Ov+2lxZA/NJQkSVJ5u7J9q01VMVvzO2fKNlvIzLWSZswjJSePlGwzl7PMJGfnkZqTR1pOHllmKzlmK5b/dsak12rwcTMQ7O1CRKA7XRsEEebnSrC3C35uBtyddOi1Gg4fPkTt2kEF7pd3qjIHtmazGVVVHS8L3t7epKWlOX632WwsWLCAhx56qMALhb1Lfsh/WFkslgIPmg0bNrBkyRKEEOzfv5+uXbuSmVny3huLTa/FiruTnk3ZMdQ59AcnPO9B1VbOTkCEEKSlpXHw4MGKTkq5yMzMJC0trUqUhuTm5qKqKikpKRWdlDJTVZX09HQOHDhQ0UkpF6mpqRw6dKjI3l0rG3vnSJcvX74p63d1db1lvcfaPzpcr8On243ZbCYhIYH4+HggP2CSPe5KkiRVble2YUXkl6pabQKLqmK2qOTmWTFabGSbrGSarGTmWkjJySM5O78X4ZTs/GA102Qh22zNH15NFWi1Cq56LW5OOnxcDfi6GQjzc6VFmA/+7vmdM/m6GfB00ePposfDSYezXuvooOnq0lf5IbWgMge2BoPBMYi6m5sbmZmZNGjQwPF7UlISK1asICcnhwsXLrBv3z7WrFnj6M4f8l8MdDpdgSpG7du3p2XLlgghGD9+PPXq1aNWrVplTS5Go5HIvalsyGrK89kbiK7mBNUblXm9FcFqtXL48GGio6OrxEv6qVOn8PPzc3zwqMwuXryIzWarEm3xLBYLR44cqRLnmRCCgwcPUq9evSrxAeXChQsoikJwcOk6ASoJi8XCpUuXcHFxuSXVmGw2GxaLpdL1NpyXl0dISIijLbDJZKr0Y9xKkiRVNQU6W7KpGC2Cy1lmLCIPY56VLFP+v8xcC+m5FjKMef/930JGroUsk5Xs/5aomqw2rLb86sMo+SWrBq0GNyct3q4GfF0NVPdyJibEC38PJ/zd86d5uerxdNbjYtDirM+vKqzV/K+tK9xZ1YZvhjIHtu7u7oSFhbF161aaN2/O0aNHeemll0hMTCQtLY3atWszZ84cbDYbe/bsISUlhfr165OVlcV3333H5cuX2bJlC7Vr1y5QjczeRb8QAr1ej1arLVTl6EZotVoaBjmzID6MizZPws5tgBrNKm07W0VR0Gq1js41KishhKNNXXkc54qm1WoRQlSJfbEfG3vbt8pMVfM7ViiqCmNlZO9R/mbsixDC8YC9VQ/aW7298nDlPdh+blWm9EuSJFUVhYJXs400Yx5JmSbi03I5l2LkfKqR+DQjCalZiNVpWP4boGoU0P03QHU1aHF30uHurMPTRU81L2eiqunxdtXj5WLA20WPh7MOD2cdbk66/EBVp8VJr0Gv1chgtQKV+W1Io9EwZswYPvjgA7777jv69u1LrVq12Lx5MwcOHKBevXo0atTIMW9KSgrBwcGoqsqDDz7ImDFjcHJyYty4cbfspblukDt6Vyv7iSH05CqU1kNA53T9BSVJkqQKZ2+/bg/sparB3la8sn+olSTp5vlf/wb5wWtOno20nDwSM03EpxkdweuFtFySs81km60oKLg5aQnwcCLEx4XWtX0RgYLourXwdnX6X4Cq1+Ji0GLQatBpFHRaDYqCDFIrkTIHtoqiEBUVxWeffYbVanWMcdeuXTvatWtXYN5GjRoRHR0N5D+4nnvuOZ588klH6cmtOll83QxEVvNg6+UWPHhpLqSdhYCoW7JtSZIkqTBVVfn777/p0KEDvr6+AGRkZLBp0ya6d+9eqFR67ty5dOrUiSZNmjimHT9+nOzsbJo3b34rky4VQwjBhg0bqF27tmM4P5PJxKpVq+jatWuhauc//PADPj4+dO/evcB0+7kRHBxMo0aN5IulJN0B7AGsVRVkmaykZJtJ/O8wN+dS83sJTkjPJTk7vyqxoih4OOv+21OwCx3q+hPmmz/MTaCnMz6uelwNOnRaBaGq7N+/n0YNgqpE7S3pf8rlaCqK4qg6bFfUV3R7la0r/66Itkg6jcJd4b6sSKyFUeeC25kN4F+30lZHliRJquwURWHTpk1kZGQwYMAAAFavXs3GjRvp0aMHJpOpQFOF+Ph4srKyCqxj27ZtnD9/vkBgK4TAarU6hppRFAUhBBaLpdA0e38Psjpx+Tl+/DgbN27kjTfecHQGuXTpUrp3747ZbHa8PyiKwqVLl4rs5f/y5ctMmTKFyMhIFi5c6GimpKoqFosFvV7veOewj6hgP67yOErS7c8exFpsKhm5FuLTcjmZlM2Ri5kcS8wkPi2XHLPVMUZroKczNXxc6FIvkFA/N2p4uxDo6YS3qwEXvRbdf8doLe76r/xjIkjFuWM/U7QI82X+RlfifZsQdfJvaP4MaPXXXU6SJOmOIwSIchxWTNEU+pCoKAq9evVizpw59OvXD41Gw/Llyxk4cCBz5szh0KFDCCF46aWXaNmyZMO0CSHYuHEjixYtQlVVevbsySOPPMLq1atZunQpWq2We+65hwceeIBp06aRmJiIm5sbEyZMoHr16uW3v7cjIfL/UYoeNVVb/nmgFrFMMce0e/fuDB48mKysLDw8PPjtt9+4//77WbZsGZs2bSIvL4/HH3+8UCnt/5IpWLduHV27duXkyZOcPXuWiIgILly4wIcffkhGRgahoaGMGzeOAwcO8OWXX6KqKu3bt+eZZ54p+b5JknRL2INYs1Ul3WghLs3IicQsjlzM5HhiFhfSc8nNs+HurCPU15UG1T15pHkNwv3cCPR0xstFj+sVY7SCrB4s/c8dG9hGBLnj5mRgv0tr6l78BCXzAviEV3SyJEmSbj8WI6x8DbKTyr4un3DoPg10hXuljo6Oxmw2c+rUKZydnUlJSaFly5ZERkbSo0cPjh07xieffMLChQtLtKmsrCw++OADpk6dip+fH4MHD6Z58+Z8/fXXvPjiizRp0gSLxcLx48e5cOECs2bNQlVVPDw8yr6ftzvVBv9OhsvHSryIk01F0RbRptnFF+6bAS5ehX6qUaMGwcHB7Ny5k6ZNm7J3716GDRuGEIJWrVpx4cIFPv74Y9q3b1/kNq1WKytWrGDkyJHs3LmTlStXMnToUN5//31atWpFr169MBqNmEwmpk6dyqRJk6hXr54cckmSKtiVHTmZLSqpOWbOpRo5npjFkYRMTlzK4mK6iTybireLnjA/N1qE+zCwehgRge5U93LB3VnnCGBl8CqVxB0b2Hq76Klf3YPtlggeUbQo57aAd5isjixJknQ1RQPeNcFQDoO/ewQXe591cnKia9eu/PHHH7i7u9OxY0cURWHatGnYbDb0ej1xcXGOToauJyUlBYPBQP369dHr9dSuXZvz58/z8MMP89lnnxEYGMiTTz5JZGQkPj4+jBkzhiZNmvDcc8/h5FTFOxRUAM9gsOVdd1Y71WpFo9UV7EkFwMkTiunES6PR0Lt3b3799VdycnKoW7cuvr6+vP3221y8eBEPDw/i4+MxmUxFLh8bG8uxY8c4deoUFouF33//nQEDBnDu3DnGjRuHu7s77u7uxMbGYjAYaNy4MXq9Hnd39xLvlyRJZXNlh065FhvJ2WbOphg5djGTIxczOXUpm6RMEzZV4OdmoJa/O50iA6gf7EmdAHeCPJ1xc9KirYQ940u3lzs2sNVqFO6q5csPO7LICWyCx4m/oNHjoMjeGCVJkgrQOUOncTd9M4qi0LNnT15++WU0Gg0zZ87k3LlzZGVlsWDBAk6cOMGwYcOuux57O003NzdMJhNZWVm4u7tz+fJlfHx8aNeuHV27dmX79u28++67fPvtt0yfPp309HQmT57M6tWr6d27903e2wqmaKH1kFItYsk1onUu3bjGiqLQtm1b5syZQ1xcHCNGjCA9PZ1Dhw7x9ddfk5mZyc6dOx0vxlcSQrBixQpatGiB0WjEzc0NFxcXDh8+jJubGxcuXKB69eqoqoq7uzu5ubmkp6fj7++PqqpoNBr5gixJN4EQAlVAbp6NS1kmziTncOy/JbGnLmeTnGVGAAHuTtQJcKNHdDXqV/OkVoAbgR5OuBp0aBQZwErl744NbAGah/kwb+1pzvt1oOHxOZB9CTyreLsqSZKk0rqFLx81a9YkJCSEnJwcIiIiyMrKIjc3l4kTJ5KZmekoiXNxcSnQYSGAwWBg3bp1pKamAtChQwe6devGmDFjcHNzIzg4mLp16zJ9+nRHEGQff33BggX4+vqSnJxMZGTkLdvfClPaYyoEjqLaUi7r4+NDq1atWL9+PS1atECn0+Hn58fEiROx2Ww4OTmh0WhwcnLCYPhfFXWTycTOnTt5++23iYiIQAiBl5cXa9euZdCgQUyfPp2oqCicnJwYM2YMDz30EKNGjSI8PJzw8HCee+45+eIsSWVkD2Jz8lTOpZk5cySJoxczOZKQSWxyDqnZeWg0EOjpTGSgOw83DaFedU/C/Vzxd3fCRa/NHzJHXovSLXBHB7a1A9zxctGzV9OABjYLSvwOqP+QrI4sSZJUQbRaLTNnzkQIgU6nw8fHhy+//JKEhASCg4MdgdDo0aML9ar/0EMP0b59e0fpn4eHBx4eHsTFxWGxWAgLC8NgMDB8+HASEhIwGAyEhYWhKApjx44lMzOT6tWr4+fnVxG7XqW99tprDB061PFh4uOPP+bcuXMEBgaiKAre3t48+eSTBUZUMBgMfPzxx/j7+wP5L8b3338/HTt2xM/PjwYNGpCUlERAQACurq688MIL3HfffWRmZjqGF5IkqWTs902bKsg2W0nMNHH6Un7PxEcvZhF7KYvUHDNOhiSqe+UHsU/cFUq9ah6E+bni5+aEk17jaKkgrz+pItzRga2ns54GwZ5sT7HxRLUYtCf+hvoPUrgBkSRJknSreHt7F/jbx8cHHx+fa84D+VWP3dwKtwOuVatWoWWvXj4sLOyG0ipdn6Iojrawdu7u7jRs2LDAfFd32qXVagkMDCwwzcnJydH+OSAggICAgALz16xZs7yTL0lVkhACISDTZCEhPZeT/w1ij13M4mxKDhm5Fpx0GoK9XKgb5EHzYBequwpaR0fg62bAoNNcc0gdSaoId3Rgq1GgVS1fFm89R1aLLnjvnw/GVHDzr+ikSZIkSZIkSVK5+N9YsYKEjFz2nEtjw8lk9p1PI91owcWgpYZPfhDbvWEQdYM8qOnrireLHoNOQ2pqKikpKVT3cpbBrHTbuqMDW0VRaBrqw0f/nuSsRwuamGdCwl6IuFdWR5Yk6Y5XVIc+0v/kl3hUnjySL6NFq0zHUJJKQwiBALJNVk5eymbL6WQ2n0rm5KVsDFoNDYM9ebZdLRrX9CbE2wUvFz16reyZWKq87ujAFiDc3w1fNwO7s7xo7B+FcvKf/MBWkiTpDqbValEUhczMzFsy9I3NZsNqtVaqIEMIQXZ2dpHVom9HiqKg0+nIyMjA1dW11MubzWbHeio7i8UC5J93RqMRvV5fJfZLkoQQWFXB5Swz++PS2XDyMrvOpnE5y0yQlzN3hfvyXPtaNArxxt/dgFajyHNfqjLu+MDWw0lHTA0vdsTl8Eyde9Ee+h7MmeBceKB5SZKkO4WiKAQGBpKenk5OTs5N357ZbMZoNBZqS3u7c3d3x83NrdK8GAYEBJCWluboObqkhBAkJyfj6+uLVlv5h8XLzs4G8o+fTqdzdFAlSZWN/WOgMc/GmeQctsWmsOlkMkcuZiIERFXzoG/zGrSp7UedAHfcnPKv38pyz5Kk0rjjA1tFgbtq+fLZ+tNktO6A77ZPIOkwhLaR1ZElSbpjKYqCk5MTQUFBt2R76enpWCwWgoODb8n2ylNleUFUFAW9Xl+oQ6aSEEJw+fJlgoKCCgzJU1lduHABoMD5VlmOoyTZh+BJyTFz6EImG09eZntsKgnpufi6GWge7sN/mjegSU1vqnk5o5OlstIdQga2ikLTmt5kmaycFsH4+ISjnFyVH9hKkiTdwSrqRUi+gN08N5q3V1YRr0rHpyrti1T1WW0qZ5Jz2HE2lY0nkzkQn47JolInwI2uDYJoF+FH3SAPPF30ssdi6Y50xwe2AKG+rgR6OLE7wUSLOl3g1Gro+CoYCg8bIUmSJEmSJEm3ktWm8sWGWOZvjMXNSUfjmt6MvKcuLcJ9CPF2yR9+Rway0h1OBraAq5OOxjW82XE2jRc63YNu10K4fBxCmlV00iRJkiRJkqQ7mCoEP++9wJcbYxnaJYLeTULwdjWgUWSprCRdSVPRCbgdKECr2r4cS8wk3aMuuAfB6TVQiXrnlCRJkiRJkqoWIQTrj1/m/b+OMaB1GAPbhOPn7iR7M5akIsjAlvyvXY1qeJObZ+NEOohaneDUv2A1V3TSJEmSJEmSpDuQEIID8Rn857dD3FM/iMF3R6DXyld3SSqOvDr+q4aPC9W9XNh1Lh3qdoPkE5B2pqKTJUmSJEmSJN1hhBCcSzHy+k8HiAryYHzPejjr5Wu7JF2LvEL+y0WvpUmoNzvPpmINapw/jm3selkdWZIkSZIkSbplhBCkZOcx/ueDOOu1vN07Gi8Xvax6LEnXIQPb/1IUhVa1fDmZlE2q6gZhbeHkP6BaKjppkiRJkiRJ0h1ACEGO2cqU3w+TlGliet9GVPdylkGtJJWADGyvEBPiRZ5N5VhSDqJuD0g8CBnxFZ0sSZIkSZIk6Q6QZ1OZueoEO86mMu3hGOoGucugVpJKSAa2V6ju7UINHxd2nkmFkBagNcC5zbI6siRJkiRJknRT2VTBos1n+XVfApMeaMBdtXxlUCtJpSAD2ys46zQ0C/Vh17lULC7+ULMlnPgbhK2ikyZJkiRJkiRVUaoQLN9/gc/Wn2ZYlwh6RFeXQa0klZIMbK9yVy1fTl/O4XK2FSK7w4XdkH2popMlSZIkVTFCCMxmM6dPn+by5cuIq2oHCSEwGo2cOnWK5OTkAr8LIcjKyiItLa3QcpIkVS5CCDafSubdlcd4vGUoT7UKQ6uRQa0klZauohNwO1EUhehgT1QhOHIxk5CwtqDaIG47NOgN8suZJEmSVE5MJhPjx4/HbDZz+fJlRo0aRdu2bR2lNKmpqYwZMwYvLy+SkpIYMWIErVu3RlEUjEYjgwcPxsXFhS+++KKC90SSpBslhOBwQiZv/HKIDpH+DOsSgUEny50k6UbIK+cqQZ7OhPu5seNMKsIzGKo3hhN/yXa2kiRJUrnavXs36enpzJ49m1GjRvHFF19gs/2v6cuaNWvw8/Nj5syZjBgxgk8//RSbzYYQgmXLlhEUFITRaKzAPZAkqSyEEMSn5fL6Tweo5e/GxPsb4GrQVnSyJKnSkiW2VzHoNDQP82H3uTTyVA1Okd1hyyeQmwJuARWdPEmSJKmKOHLkCA0bNkSn0xEZGUlSUhK5ubl4eHgAYLFY0Ovzx640GAwcOXKE3NxcLly4wKFDh+jVqxdffvllgXWqqsrp06cxmUzExcVhMpnIzMwsc1s9IQQWi4Xs7GzMZnOZ1nU7yM3NBSAzM7OCU1J2OTk5mM1msrKyKjopZaaqKnl5eWRlZaHTVf5XVLPZTE5OTrHNBTLNKhOWHycvz8zr90Sis5nIyjLd4lSWjNForFLnmcViISsrC622cn9IEEKQl5dHdnZ2gQ+jlZH9vlwWlf+ucRPcFe7LT7vjSco0EVqrA6x7Fy7sgchusjqyJEmSVC7MZjMGgwEArVaLEAJVVR2/d+rUie+//57JkycTFxeHzWbDZDIxb948nn76aYxGIzabrUAAbLVa+euvv0hMTCQtLY2MjAwuXLhQLunNyckhISGh0r8IAo6Xc4ul8o9Vbzabyc7OJj6+agxPaDQaSUhIQKOp/JUKs7OzSUpKQq/XF/rNmGfjo00XOZej5Y1O1SAnlfic1ApIZcmYTCZyc3OrxHlm778gISGhSnTQlZ2dTWJiYpHnWWWSnJxc5uMhA9urKIpC/WBPtBqFQwmZhNYPh8D6+b0jR3YFKv8FIEmSJFW84OBgduzYAUB6ejrOzs44Ozs7SneCg4NZsGABZ8+eJScnh08++QSz2UxcXBxz584lOTmZI0eO8NNPP9GvXz8A9Ho9Q4cOBeD06dN8+umn1K9fv8xptXd0VbduXUcwXpnZg/2QkJAKTknZZWRkkJiYSFRUVEUnpczs51lUVFSVKLE9cuQIoaGhuLu7F5husQk+/Oc4xzM0zHqsMe0i/CoohSWXmppKSkoKkZGRFZ2UMlNV1XGeVYUPdTabjdq1a+Pq6lrRSSmT2NjYMn+ILZe7hhACq9WK1WrF2dm5QLQthHDcqADH70KIAkXmGo3mtvk6F+DuRO2A/Ha2PaOroUR0hX3fgCkTXLwrOnmSJElSFdCmTRu++uorVq1axdq1a+nSpQs6nY5PPvmERx99FE9PT/bv34+rqytLly7liSeeIDg4mKVLlyKEYNeuXcybN4++ffs61lnc1+7yqIpcXuu6nVSlfYHKvz9V6Twrbl9sqmDJtnN8vyuOyQ82pG2EX6Xb18qW3mup7PtSla6Z8lDmwFYIwfHjx/nggw/Iy8vj3nvvZcCAAQWC1EWLFrF582YsFgtNmjRh6NChZGRkMHz4cNzc3FAUhZdeeomWLVuWNTnlQq9VaBnuy/oTlzFZVFzqdIbNH0PiQQhvL6sjS5IkSWVWvXp1pk6dysqVK6lfvz6PPPIIiqIQGhqKk5MTGo2G2NhYEhMT6du3L3fffTcajQZnZ2cAIiMjefrppx3VkCVJur2pQvDnwYvMWXOSwXdH8GDjYDTy2pWkclPmwFZVVWbMmEHfvn1p3rw5L730Eu3atSMiIsIxT+/evXn88ccxmUz079+f7t274+3tjcViYfr06eh0utuu+LxluC/fbD/PxYxcavtFgm8tOLkqP7CVJEmSpDJSFIXGjRvTuHFjx9+Q/8yE/A/Hzz//fIH5r1StWjV69OhxaxIrSVKZCCHYHpvK2yuO0LdZDZ5pGy7HqpWkclbmwDY7O5vz58/TunVrfHx8qFevHgcPHnQEtoqi4OPjw7lz5zh79ixubm74+vqiqiqJiYnMmzePyMhIHnroIXQ6nePBnZeXh9lsdvTEaLPZsFqtZU2uY6gEq9V6zS/cdfxd0GsV9selEepdDaXW3WhO/YMtdwwY3MqcjvJgtVodVbqL63GvMlFVtdyOc0Wz2WxVZl+sViuqqmK1Wm+b5gI3SlVVx/Vf2fcFCt7PKjubzea4B1Rm9vtySV3rOSRLYSWpahBCcDwxiwm/HKRVLT9Gdq2LXiuvb0kqb2UObPPy8hBCODqT8PT0JCMjo8A8QgjWrVvHxo0b8fDwwNXVFZ1Ox5tvvomnpydff/01sbGxjB8/3rHM5s2bWbJkCUII9u/fT48ePcqli3Gr1Up2djYHDx685nw2AdVd4c9dpwjjMu7UonbqOU5t/QOjT9k74igPQgjS0tI4dOhQRSelXGRmZpKenl4lOibJzc1FVVXS0tIqOillpqoq6enpVeY8S01N5ciRI1UisLWPYZqcnFzBKSm7vLw8R6+blVleXl6l3wdJksrXxQwTr/90gOpezkx6sAFuBq38cCVJN0GZA1t7OyCTyYSbmxtpaWnExMQUmEej0fD000/Tv39/hg0bxubNm7nvvvu49957gfxg+LXXXsNqtTq6qu7YsSNt27ZFCMHYsWOpX78+4eHhZU0uRqORM2fO0LBhw+vO2yX9NH8eSqRu/WhcqYNy4lPqauIQjfuVOR3lwWazcejQIWJiYqrES/qpU6fw8/PDx8enopNSZomJiVitVmrUqFHRSSkzq9XK4cOHadSoUaV/EAshOHDgAA0aNKj03eJDfs+uiqIQHBxc0Ukps4yMDC5dulTpe9w0m824uLhUdDIkSbpNpOdamLTiKHk2ldkPx+DnZqj0z1JJul2VObB1c3MjIiKCNWvW0LJlS44dO8awYcM4f/48ly5domnTpsTGxuLv7+8Yz8vb25vU1FRycnJwd3dn3bp1hIaGFuhyW6vVOsb102rzv2yVR5fcGo0GRVEc/xdHCEHLcF++2nyWhEwzdQM9oXYnlNg10H4E6Cv+xcVe3U2j0VT67sqFEI7jUtn3BfKrEJbXOVvRVFV1HJvK/gHlyn2pCsemqu1LSe7Nt7vKnn5JksqP2SqY/tdxTl7KZd5TzQj1dZX3B0m6icoc2Go0GsaOHcuHH37IX3/9xQsvvEDNmjXZsWMHR48epXHjxixfvpzjx48jhKB37960bNmSkydP8sknn2CxWPDz82PChAm31cWuKAqRgR64GnTsj0snMtAdJbIb7P8OUmMh6PolvpIkSZIkSdKdQwiBKiAhPZdlR3PYei6LDx9rQkyI1231nitJVVGZA1v70ASzZs0qULraqlUrWrVqBcDo0aMdnTXZf69fvz5z5sxBVVXHINy32wXv7aonqpoHO86k0rd5DZTqjcHFB2LXQWADOeyPVPUJgWJKxy3nHNCoolMjSZIkSbcdew26LJOV3efSWL4/gS2nkhE2KxPua0DHugG33TuuJFVFZQ5sgSKrXF59AV/dns2+zO1chU6rUbirli8/74nHaLbi7uIN4e3yh/2560XQVv5OjiSpWEJA8gm0K0YTkX4BpWk7cAuo6FRJkiRJ0m1BCIFVFcRezuavQ4n8eSiRpEwTMSFevNo9ikA1hWZRPnKsWkm6RcolsK2qFEWheZgPn60/zblUIw2re0LdHvD7CEiPA786FZ1ESbo5hAqn18LK10CjQzEmo1w+IQNbSZIk6Y6nCkFKdh6bTyWzfH8C++LS8Xc30K1BNXrGVCMy0AO9VuHIkcyKTqok3VFkYHsddQLc8XTWs+98Og2qe6KEtACdM5zdCL61ZXVkqWoRAmx5sOf/YP10iOiK2uFV1G/7oY/fAWFt5TkvSZIk3XGEEJgsKocTMvj9wEXWHrtErsVG69q+vN+3ES1r+eLprHPUWCzNeNaSJJUPGdheh5eLnvrVPdl+JpUn7gpFcfOHmnfBib+haX9QZBZKVYQQYMqAddPgwPfQdii0HowqdGS41sLv/DaUtjZ5zkuSJEl3BHtHUBfSc1lzNIk/Dlzk1KVsagW48XTbMO6pH0RNHxc0/+3VXZKkiiXfUK9Do0CrWr58ve0cWWYrXs46qNsdVk2GrETwqvzjlEoSQkDaWfhzLFw6Ag/MgvoPgEYHFgup7lH4XfwVctNkdWRJkiSpQthLQfOsKpeyzAjAWa/BWafFoNOg1ShoFaVQxaLSBJ1XdgS161way/ddYMvpFAw6DZ2jAhnbI4roEC9c9FoZzErSbUYGttehKArNwryZvfokZ5NzaFzTG0Lb5AcC57dBdF9ZNVOq3ISafy6vGAM6J3hsCQQ3LXBeG32iEGczUZJPysBWkiRJumWuDDRPXspi08lkNp1KJvZyDqoQOOm0OOk1uBl0eDrr8HTR4+Wix8fVgLerHu///u/losfDWYe7kw5Xgw4nvQYnnQa9VoNGUdAoYLEJTv+3I6i/DiVyKctEoxrejOtZj/YR/vh7OKFw+43iIUlSPhnYlkAtPzd83AzsOZ9GoxpeKB7V81/8T/wFDfuAcvv27CxJ12SzwMEfYfWbUKMV9JgGnsGFPtYIr1BwD4L4nfkfduRDXZIkSbqJrKpKSnYe++PS2XDyMttjU7mcbaa6pzOta/sxqFMdXA1aMnMtpOdaSDdaSMvJIz3XQobRQlxaLlkmC8Y8G7l5NvJsqiNI1mk1uOi1uOi1uDvr8HLR4+miJyXbzOGETPzdDXRvWI2e0dWJCHRHr5VVjSWpMpCBbQl4OOuJCfFix5lUBrYJR6v5b3XkTTPBmALugRWdREkqHSEgLwc2zYJdC6H5M9BhNBjciw5aDW5QvQmc3wptXpHtbCVJkqRyJYTAbFWJz7RycEccm2NTORCfgcWmUjfIg4ebhdC2jj91At1xM+QXKFwdbNoDV1X8byiePKtKnk3FmGcjx2wl22zND4aN9oA4jzRjHulGC9W9nHmmbTgtwgt2BCVJUuUg305LQFHgrlq+fLEhlsxcCz5uBgjvAGumwoXd+UMAyZufVFkIAVkX4a/x+VWQu02FRo/lt6ct7jxWNPkltZtnQW46uPnf0iRLkiRJVYsQAgFkm6ycSMpi06lkNp1M5kRiBp6uWTQN9ebVblE0D/ch2MsFvTb/+XStYNP+W/6sCjotOOuLrlV3da/F9r9kVWNJqrxkYFsCiqLQNNSbzFwLscnZNHfzBe9QCGqQ3zty3e7k3wol6TYnBFzcD3+MAksuPLIQwtrkB67XE9IMzJmQcgpc/eTHHEmSJKlUhBDYhCA5K4/98emsP3GZHWdSSc4yU93bhda1fOgRCg+0a0yApysa5eYFmVevVz7RJKnyk4FtCYX6uhLg4cSuc2k0C/VB0eohshvsXpw/RIqLT0UnUZKuTbXBsRXw9wQIiIL7ZoBPeMkDVJ+w/Gr38TuhZqubmlRJkiSparBXCT6bnMP2M6lsOHGZAxcysNpUooI8eKR5DdrW8aNOgDvOOg0HDuzHz82AViNDTUmSSkcGtiXk5qSjUQ0vdp5J5fn2tdBpNFD7/9m77/ioqrSB4787fdJ7JxASem+CKKjYe0OwrKvrWtZ1raisbde2uquuYt217qprQ33tFewogiK9h05I72Uy7Z73j5OEBLCRkJkJz/fzGSbMJDPnztx7z31Oec5h8NX9ULwM+h4S6iIKsWdKQaAZvv0XzH9EZ/I+7CZwxf+6XldHTMs8229hwh8laZoQQoifpJSiosHLA3PW8+HKEqIdVkbnJnLd0QMY2zuRzJYhxq29p6ZphrjEQohIJoHtL2QA4/sm8+Dc9dQ0+UmJcUJyvr6t/xjyJsvQTBF+lIKmCphzK6z/EA6ZqRNFWR2/fn9tm2c7S49SiEraBwUWQgjRE5hKsXhrDbe/s5K65gC3nTSEifnJJEQ59ukQYyHE/ksC21/IMAxG5CTQ5AtQWNZAcrQDw+aCgsNh9bvgawBnbKiLKcROSkH5WnjvGp0s6pR/Q/7hYPkF82l/TPZoHdRWFkLUAV1XViGEED1Ca3bjV7/fxsOfFjI8J55ZZ46iT3KUBLNCiH2qE1e4+5+cRDcZ8S6+31y188GCI6CuCHYs0YGEEOHANKHwE3j5bEDBmS/qfbUzQS3oObnRqXqerezvQggh2lFKUVrn5aY3lvPAnPWcO6E3D0pQK4ToJhLY/gpRDisjeyXw3eZqAqbSQznTh0LWaL2mrb8p1EUU+zulIOCF75+GN/+gkzyd8RykDuyaofKOWMgYrufZKpkLJYQQQjOVYuGmKn7/7Hcs3V7L/dNH8MfDCoh2ynqwQojuIYHtrzQ+L5l1pfVUNfr0AzYXHHYDlCyHpS9LL5YIraAPPrkDPrtLJ3g64Z96zdmuuqgwDL08UMlyaK7rmtcUQggRsZRSeHxB/vP1Ji578Qd6J0XxzPnjOKR/qmQ2FkJ0K5lj+ysYhsGwnHi8AZO1JfWkxTp1K2TWaBh9Hnz9IOQfBol5kkhKdD+lYNlsWPICHHcPDDkVLF18iBsGZI+B5hqo2gBRY7v29YUQQkQMpRQ7apr5x4dr+Lqwgosm9+W3B/bGbbdKL60QottJj+2vlBXvJjvBzXft59larDDhD3r5lC/vA9MfugKK/ZNSUL4GvvgHjDlv3wS1rRLzICoZtn8vIxSEEGI/FTQVX2+o5IJnv2NtaT0PnTWKiyf1JcohQ4+FEKEhge2v5LJbGJWbwPebq/EH213UR6XAoX+GtR/opD1ywS+6k68R5vwF4nNg4hX7do1ZZ+s82/kyz1YIIfYzSimafAGe/HIjV7y0mIEZsfzn/HFMzE/GIkOPhdh7Su1yM3f+LH4RGYr8KxmGwfi8JD5cUUJFg5esBHfrE9DvSOh/NHx+N+SM1cGutFqKfU2Z8N3TULwUznwB3In7dr9rXc92wb/BW6ffTwghRI+nlGJbtYe731/Nd5ur+NNhBZw1PheXzSK9tEK02i0QVWAGQQUh4INAs7556/WtuRY81frWVAWeKv1zwAuxmZCQCwm9IC4LotPAlQCOKLDYQxdn7LqNYXL8S2C7F4ZmxxM0FauL68iMd+08mVvsMPk6eO4UWPA4HHrDvu05E0IpvfTO/EfgoCv1/Nd9fXIxDMgZA59XQdVG/Z5CCCF6tKCp+Gp9OXe+txqnzcKjZ49mXF4SljC5oBWiLdhSpr6ZAZ1UM+AFXxP46sHbgNFURVLRKgzrerDadYO9YbTc/9Rt198xgNbHDfB7wFOj85A0Ve0MVJurWx6vBV+D/r1Asy6jYdGxgt0FjmhwxOjA1Z2gpzhWbYSt3+jXCXj1+zhjdedZXBbE9yKpyYZNjYLk3npJRlcc2N0tU9KMX3Zd2OGzU6ACEPTvDMR9jbrsvga9Hc21epuCfhj3e/2eYUAC272QHueid3IUCzdXMWVg2s4nDAOS+uoA44u/w4BjdWIpOemLfUEp3ar38S06uBz7O32C7A5JfcGdpOfZyj4uhBA9llKKRl+QZ+Zt4r/fbGbKwDSuPWoA6XFO6aXtboFmqNkGVocOhKxOHbxYbbsEMS3fSyR8Px16/tTuj+0apPo9O4OstgCremcg6anSAZenRo8q8zXq5TiDvrbXNax2MoNgKXTsfA9ah/y2GwpM+2HA7e5/amSwzQH2KH1zxukA1Z0A8b30dVNUoh7p5krQgaszRgezNpf+Xm0OHei2fo8q2BKYN0JTJTSUQu12vR/UbIXS5aRUbsO+4SWd48di0+8bnQrx2fp9E3L1VLXYTH2d2PbZ1ekg3FPTMfhurtU9yf4mfQt4dY8z6HJZHTpwtrv1a446G5DANmI5bRbG9E5k0eZqfEETp61dr6xhwIgzYc27esmVac/pFhghupoKwrxZUF8MJz2sT6LdxRkHGcP0PNtxF8rIBCGE6IGUUmyuaORv769m2fZarjmyP2eMycEhQ4+7n1Kw8k344Ho9QtBiA5tTD0m1R4E9WvfkuWJ1He2M071orfeO2J1BlN2tA6nW4Ngw2s3p3PUWBGVira/G3lAJVdaOz7cOsTVbbiqg74M+3dsXbL15dYDUemvtSQ14dz7X+lj7+6BP9wr6m3RAFmhueZ+W3k6bS9+cMTpQdMXrADI5X9+7E3fenPozMG1u1q5ex+DBQ7BZLXQIYFsD2t0CXXPn/a6P0XJvc+rvwebUN4ttZ+9ue7/m2DFs4LDpWCImDdIGdQz8zQCFyxaTl5VClGqChhId9Na2BL7bF+qYpLmuY3Jbqx2sLfuAI2bnvuJObOm8SGgJvhPAHa8/O0e7/ac1CG/dF8NE+JQkwhyQl8Qbi4soq/PSK2mXgMIRDYfdCC+fDctf1UsBSQUgupJSOknZ4v/B0X+DlP7du48ZFsidAN89pVv13And995CCCH2KaUUplJ8uqaMu95fQ5zLxr9/M4aRuQky9DiUti2A1EFwyEzwN+pgxdtya25331gO3saW3srGlh63lqGlrQGhxdYS3Dj0/1sC2D0GcCjigiYxZhCslj30Zraz25BdK1ha79u95x7vWwJCR4wOmmzOnY+1Bl3uxJ1BmDNWB2a2lkDLYtU3dtlHd91ng0ECjlKISgJbBIZC7bfHYsO0uVEx6RAdDQzt2MtsBsDfrPeNxgr9mCO2pXHD2dJT3DIc+5d8dmEuAr/N0DMMg8GZcRgYrNxRS06iu2PLZetan6POhXkPQN4hkNgn4nYOEaaUgroimPNXPdx96Ondv28ZBuSM08sLVW8C96jufX8hhBB7pPYig6pq+UeHK4pGb5Anv9rIC99u4ZihmVxzZH9SYhzSSxtKQR+UroTciZB/WMd6f9fhvKpdsiIz0NIz2qzv/U3gbdg5X9Jbr4NYS8twZktLAGpYdwaKho2Gxkbq6hvJ7tW73eO73FusOwPaPb6erV3Q235+qqXd9vzEPib73y/T/rO0OvTNFaeHI/dwEtjupdRYF31To1m4qYqjh2Ts/gsWK0y4FNZ/DF/dDyfcr1tEhOisoA8+uxtQemSA1RGaciT11S2n2xdB5kipcIQQopsppSiq8/HlxnqWN2wnYCoCQZOAqfAH2/9sEggq/Ka+DwRN/O1+NxBUBEz9czCoKG/wUtngY+YxAzltdA52qyFBbah5qvUQ0/HDd3+uw3dj6NjQ0m6KkDO202/vr6ykqbISeveT+l6ELQls95LdajC2dyJfb6jEGzBx2fcwxzA6Va9t+/YVMOh46He0nAxE5ygFK/8P1r4Pp/4L4rJDt0+54iF9qJ5nO/YC2beFEKKbKKXw+IO8sbiIxz4rxOsPEO1qwGoYWCwGFsPAagGrYWC1GNisFmwWA5vVwGbZ/WeX3YLVYsNu1Y8PzIzj+OGZDM2Kk4A2XNRsgYAHUgdIfSvEj5DAdi8ZhsG4vCRe+m4bxbXN5KXsIUGUYehgtt+RuocteyxEp3R/YUXPoBRUrNNJyUb9BgqOCG3l1rqe7aJndAp9V3zoyiKEEPsJUynWltTzz4/XsnhrDScOTuLIPBdDB/XDYuieVYtBy89gYLRVFUbLP0bLcM8Oj++BBLVhQikoXaVHScVlhbo0QoStblobpGcamBGH3Wqwoqj2x+e0WFvWtm2qhIVPgml2byFFz+Fvgrl/hZgMOOiq8MhEnDNWJyOo3hzqkgghRI+mlKKhOcB/5m3i/P8spM4T4N+/GcMVU/qSHGUlzmUn1mUnxmkjymHDZbfitFlx2CzYrfqme24tWC26J9diGG3B8J5uIowUL4Wk/C4ZVixET9Ulga1SCo/HQ21tLeYugZtSikAgQG1tLbW1tQSDwbYg0DRNamtr8Xg8e5XsINSSYxz0S4tlwabKH/8lw4DkAjjoCvj+GX1iisBtFSGmTPj+P3o+61F36Ex+ob7oaN23XQlQtEj2ayGE2EdMpVi2vZY//G8R//5iI789sA9PnjeWsX0SJUvx/iDog7KVepm9cGjUFiJMdXooslKKJUuWcN9992EYBuPHj+ePf/wjVuvOA+/pp5/mu+++w+fzkZeXxw033IDNZuNf//oXCxYsQCnFddddx4gRIyKqhdBmMRjXJ5GPV5Xi8QeJcvzIx2kYMOJsWP0ufH4XnPFfWdtW/HJKQdEP8M1DcOBl0OuA0Ae1rVzxkD4EtsyHMefzk9kMhRBC/CpKKeqaAzz3zWaenb+ZwZlxPH3+WIZmx0tAuz/xVOnEURP2kDhKCNGm0z22wWCQWbNmcdFFF/HII4/w6aefsn79+g6/c8455/DYY4/x6KOPsmjRIgoLC1m/fj2ffvopjzzyCBdeeCGzZs0iGAx2tjjdyjAMxvZJYkeNh62VTT/d6+yIhik36QBl+WvSuyV+OU81fHwzZI6AcRfuvtB3KLXOsy1eqpcPEEII0SWCpmLhpip+/9/veHHhVi47rIB//WYMwySo3f9Ub9ZL9aRI4ighfkqne2wbGhooLi5mxIgRxMfHM3DgQFasWMHAgQMBHfxFR0ezatUq1q9fT3R0NGlpaXzxxRcMHDiQ+Ph4Ro4cyd13301DQwMJCQkAeDweGhoaUErR3NyM3+/H6/V2trj4fD6CwSA+n6/TrwWQn+QkOcrGrLnr+dspg4hx/MQQkdThGEOnY593P8HcgwjG9erUeweDQUzTxOfzYbGEUbCzl4LBYJd9z6EWCAQIBAKd3hZDBbF/8xBm1RYC018C7NDNn4/f7287ZvY0osJIG4G9oYxAeSFm2pBuLduvpZRqO2YicfrDrgKBAIZh9Ihjxu/3EwgEuuzcHCo9Zd8SoaOUoqrRx9PzNvHyd9sY2zuRv506lP7psRE1qk10kbbEUQmSOEqIn9HpwNbv96OUwm7Xa7TGxMTQ0NCx50YpxapVq5g/fz4OhwOr1UpjYyPR0Xo4rt1uxzRNAoFA298sXLiQF154AYAffviBwsJCPB5PZ4tLIBCgrq6O1atXd/q1Wl04MoZZC6u47n/fcNGYRNw/Edzako6gP3Ooeftmdoyc0am1bU3TpKamhjVr1vSIyq62tpa6ujqcTmeoi9JpTU1NmKZJXV1dp14nqugb8pf9h+Khl1JRoaCy6/bbX6p1Lvzq1av3uJ/ZfCb9rW6Kv3+X6rzwbmBRSlFTU8O6det6RGNQQ0MDhmFQU1MT6qJ0mtfrxePx4Pf7Q12UTmndDiF+LaUUQVPx9YZK7vtoLdVNPq47egCnjMzGZbf0iHpe7KXipTqnhTMm1CURIqx1OrB1uVxYLBaampqIjo6moqKCkSNHdvgdi8XC1KlTOf3007niiiv45ptvSElJYenSpYAOAmw2W4eAZtKkSRx88MEopbj22msZNGgQeXl5nS0ujY2NbNq0iSFDhnRZJTFcKdJzKrj+taV8Wu7m2qMG4LD92EWzgrhbSH3nClJjq/RSQHs5LzEQCLB8+XKGDx/eIy7S161bR0pKCklJSaEuSqft2LGDQCBAbm7u3r9I3Q745nkYcDTZx1xFti00Ab/f72fFihU/vp+ZQdgwklxVRO7wYeE1VHoXpmmybNkyBg8ejMPhCHVxOm3btm1YLBays7NDXZROq66upqysjAEDBoS6KJ3S3NxMVFRUqIshIoxSirJ6L//+fANvLilicr9UrjpyFH2SoySg3d8FvVC2CvImS+IoIX5GpwPb6OhohgwZwjvvvMMBBxzA+vXrufbaa1m/fj07duzg4IMPZunSpWRkZFBbW8uWLVvIzMwkIyODf/3rX6xcuZIFCxYwZMiQth5coO0CWinVdlLvipN7+9fqqsrCMAwOGZDKzScM5ta3V5IY5eDiyX2xWfd0gW9A/6Oh4Ej4/O96uZSo5L1+3z39HIlav+eessRAp7cl4IMv/qGDxik3g80Zsnk1P7v8g8UKuRNgyUt6SaIwXopgXxz/odQTj5nWnyNVJJdddD+lFAFT8dmaMv45Zx2+gMlfTxzCscMycFill1YATdVQuw0yhsv8WiF+RqcDW8MwuP7663n00UdZvHgxV199NVlZWSxevJjS0lKUUnz33XesWLECi8XCRRddxKhRo7BYLFxzzTX8+9//JjExkeuuuy6iT+AWw+CE4VnUefzcP2cdcW47Zx2Qi9Wyh22yOuCQ6+H5U2DhU3DIdWHdyyW6mVKw6k1Y/Q6c/AjE9wrvyswwIGcczHsAqrdAxtBQl0iIiKCUoqqqih9++IH09HSGDBnSYUUBpRQlJSUsX76czMxMBg8ejMVioaSkhNWrV2OxWBg1ahRxcXERXX/ur5RSFNV4eOTTQj5cWcLRQzK4fEoB2Qlu+T7FTtWbdWN3amSPZhGiO3RJYJuRkcHtt9/eoddt1KhRjBo1CsMwuPjii9uSabRvlZ8yZQqHHXZYj+lxsFoMzjwgl5qW4Dbebef44Zm7Zy9sXf/zwMth3v26BzdzRHgHL6J7KAWVG+Czv8GIM6HfUZGxX6T00z21O37Qy/9EQpmFCLG6ujouv/xyhg0bxvLly5k2bRonn3xyW324bds2Lr/8cg455BBeeOEFTjvtNI477jgef/xxYmJiqK+v56mnnuLf//43MTEy9y5SKKXwBU0+XFHCg3PXY7Ma/OO04UwZlIbN0jOuh0QXUQpKV4I7EWIzQ10aIcJepwPbVrsGp7v+vMdsqj0koG3PZjG4eHJf6jx+bn93FXEuG5P7p+6+nYYBo86BNe/BZ61r28q8rP2e3wNzb9XD0yddA5YuO0T3LXcipA3W69mO+g2ynq0QP2/BggUkJiYyc+ZMVqxYwZ133snxxx/flozxyy+/ZNiwYVx99dUsW7aMO+64g2OPPZabbroJm82G3+/ntNNOo6ioKOLnJu8vlFJsrmxi1tx1fLmunJNHZnPpofmkxTp73PWQ6CLFSyFFEkcJ8UtEyFVz5DAMA4fVwtVH9qfW4+fGN1bw0FmjGJ2bsHul5YiBKTfCy7+BFa/rgEAqtv2XMuGHZ2HbtzDtOYhKiZz9wbBCrwmw/BXwNYb1PFshwkVhYSH5+fkYhkFmZia1tbV4PJ62wNbtdlNTU0MgEKC6upr169fT3NxMXFwcSinWrl2LaZqkp6e3vWYgEOCLL76gtraWkpISamtrKS4u7pLyejweSktLsdki/9KhpqYGpVS3Jl5UwOISH/fM3YgRaGbm5GwOKkgg2FBFcSeWAW9qaqK+vr7LvudQUkrR1NRESUlJh2H5kaqhoYHy8nLq6+v36u8N00fqjmU0ZhxAY0lZF5fu12loaOhR+5nH46GkpKRHJF9tbGykrKws4lcVqaqq6vRrRH7tFIYMw8Btt3LTcYO4/vVlXP/aUh49ZzQDdl2DzjAg5wAYebYekpw3GRJyIyeYEV1HKdixBObNgvF/gF7jI2s/MAzoNQ6+eQhqturhyEKIn/RzyRGnTJnChx9+yKWXXoppmm0XLUoptm/fzu23387MmTOJj4/v8JoNDQ3U1tZSX1+PaZpdtoRS67J8PWGd3tblBbtreSlf0OSV74uYvaKOY4Zl8dtRicTawQwGMIOde+1AINCl33Mota417vf7MU0z1MXptNZjZm+/G0tjGdRtJzDs/JB/v4FAgGAwGPJydIX2+1lPCGxb97NI35ZgsJMnQySw3WcMwyDObefOU4Zy5ctLuPbVpTx69mhyk3ZJ3W9Y4MDLoHCuTr5z3L2dWttWRCCloLkW5tyiA8LxF+tMw5EmpT84omHHYj0sOZICcyFCIC8vjw8//FAv9VJWRmxsLC6Xi2AwiMViISEhgUceeYSamhoKCwt5/PHHiYqKorS0lJkzZ3L++eczefLkDnWK3W7n5JNPBnSP8L/+9S9yc3M7PczVNE2qqqrIzs6O+F4BoK03MCcnZ5++j1KKOo+ff85Zx7trPFx2+ADOm9gHp63rMh63rmPdFd9zqJmmSXV1Nb169WobuRCpWhuZMjMziY3dy1FMW4oAk+T+B5Kc1rtLy/drVVZWYrPZesR+FgwGqaqqolevXhE/AkUpRV1dHVlZWR1Wl4lEqampFBUVdeo1Iju0D3OGYZAS4+Qfpw/HahjMfG0ZZfXejq3dhgEx6XDITFj1Fmz8Qgc6Yv+hTJj/CFRtgiNvB0eEDuONStIB7db56EF3QoifMmHCBHbs2MG//vUv7r33Xk4//XQsFgs33ngjW7dupb6+nueff56PP/6Yxx57jIsuugilFNdffz319fVs3ryZJ598kvLy8lBvitgDpRTbqpq48uUlfLK6jLtPG8aFk/rislsjPjAQ3UApKF2h69Y4SRwlxC8hge0+ZhgGOYlu7j1jBBWNPm5+cwW1Hv/uwe2AYyF/Cnx+F3iqQ1dg0b2Ugk1fwvfPwOTrIzujsGGF3PG6x9bXFOrSCBH2EhMTeeihh0hPT+fCCy9k6tSpWCwWpk+fTnJyMi6Xi759++JwOPjrX//KwQcfjMVi4dJLL+Xyyy+nX79+5Ofn94ge1J5GKcWiLdVc/PwiKht9/Ps3ozlqSPqelwDs6ZSCumIoX0unx13vVxQUL9OraDgkcZQQv0Rk979HCMMw6JcWw31Th/OnlxZz+7uruOPkoUQ52rXatq1teyp89xRMvlbWtu3plIL6EpjzF+h7mF7eJ1KDWtg5Z/ybR/Ri8mmDQl0iIcKaYRhkZWUxderUDo+PHj267ecpU6Z0eM5qtXLggQd2S/nE3gkETd5dVszdH6xmVK9E/nriYDLiXftXL61SgILaIljyIix9EbwNMPFyOOAisEdFdn3XHQI+KFsF+YfL9aAQv5AcKd3EMAxG9Erg7tOGMW99Bfd9vBZvwGz/C3ot0AP/pAPbkuUyJLknUwoay+DdqyDghcNvAVsP6HVJHaCXrdqxRPZfIcR+RSlFky/AI58Vcts7KzllVDb3nTF8/wpqldLTa2q2wmd3w3+OhWUvw6jf6qB2/qPwfxdD9SapI36OpxJqt0Pm8FCXRIiIIYFtNzIMg4MKUrj1pCG8tWQHj3+xAX+wfXBr0Uv+pPSH92bA5nkQ9MvJv6dRCuqL4Y1LoWoDnPIYJPTuGa3X7iRIHSjzbIUQ+xWlFBUNPm56YwX/+3YL1x8zkGuPGkC007Z/BLWtAW31JvjkDvjvcbDy/3Tv7O/e1+uyH3QFTHtW9+K+cAas/QDMQKhLHr6qNutrwJT+PeP6QIhuIEORu5nFMDhmSAZ1Hj//+HAN8W475x7YZ+e8G2csHHcfzL0VZv8W+h2pWznTBkdmplzRkVJ6mO6bf4SmSjj9GcgY1nMqLUvLerar3wK/R2dJFkKIHkwpxbrSem56YwVVjT5mnTmKifnJWHrKef2nKKVvVRvhh//Citf1dcyEy2DY6RCd1q5+MyD3QDj7ZfjsLnjrMhj7O5h4Jbjiek492BXaEkclQ2xGqEsjRMSQwDYELBaDqWNyqPX4efCT9cS57ZwyKltXgoahh3Oe8V/Y8Ile1/T502D4NDjgYlnnNpIppVuz3/gD+Jth6jO6d7MnfZ+GAb0OgAWPQc02SBsY6hIJIcQ+Y5qKL9eX89e3V5IV7+aJ344lPzW65/fStvbQVq6H7/8LK9/Q2XsPvgaGnKoDMti9fmtdCeK4e3Vd8dldULQIjr5LN+CLFi2Jo1IkcZQQv4YEtiFis1r43UF51Hj83PX+auLddqYMTNOVoWGA3QUDjoM+k/RwnvmPwep3YNyFMPLsnZVGJGodWq1MCPrAWweN5USXLMFu5EPUYH0iNyw9J+hTSl8A/N8lepumPq0zHfaU7WsvdQDY3FC8RP/cE7dRCLFfU0rhDype/m4rD85dz5SBafz52IEkRTt6dlDbGtCWr9XZ/Fe/DdGpesnCISfr6Sg/t/2GoXNKjDxHj1j68AZ4cbrONTHolG7ZjLAX8EL5aig4QhJHCfErSGAbQnarwRVT+lHn8XPzmyuYdeZIDuiTtLNSNAw9PGf0eXo5oEXPwoJ/60QMB14O/Y8N7Qb8lLZ5wUrPEfE2QFMF1O3QvZaVG6F6o+7Va6oAv4d0U2Ex0JVk+mDIHgvZo3UAGJUElpbF2iPtokEpndnw/y4BZwyc8m9I7CFzavckKlkHtFvnw/DpoS6NEEJ0KaUU9c0BZs1dxxuLi7hwUl8uODgPl83Sc4NapUAFoWw1fPc0rHkP4rJgys0w6ERwJfz6Os0wIGM4TP8fzLsfPvgzbF2IJem4fbIJEaWpJXFUhiSOEuLXkMA2hAzDwGW38OdjB1Lr8TPztWU8evZoBmfFdawcW4fuTL4Whp6ug9uPb8ZY9Cxx2adgBAeBxRWaQKk1pb8Z0GuXNlVB/Q6o3qLn3FRt1NkRG8rA16BbHt0Jes5IYh/IHgNJfSEhl62ldSTb/cQ3boTt3+klAuY9oHuvk/J1kJszTg9Xis0AuxswwjtAVAqKl8Ibl+jv8JTHIC47vMvcWRYb9BqvL3z8TTLPVgjRYyilKKrx8Ne3VrKquI47ThnKsUMzQ7c+rVJYgs04GrZDQ4KuF20ufR5u7enrTH3TGtCWrIDvnoS1H+qG2SNvh4HHgbOTc2MNA9yJcPhfIWccxpy/UrDhG6w5s6DXuP23t7JqkySOEmIvSGAbYoZhEOO0cdtJQ7h69hKufW0pj549mryUPczRMSyQnA/H/B1GnIXl6wfJW3wXRt03cNCVkDly3w7fbR2C5K3XwWrFep3Vt2qjDmQbSqC5Vv+eM1YHcom9oeDwluC1N8RmQlSiXsPOYmv9EEApAo3rCaakQOKRMP5i8DXqFsuSFTrQ3fotLH1ZL/Ael6VT4OeMg8wRkNAHXLFgWMOoElCw/Xt48w+QmAcnPwwxGWFUvn2o13hY+ITOfpnaP9SlEUKITlNKsWRbDTe9sQLDgH+dM5oRvRJC20vrbyLmy9uIXv0efOEGR6we6RWVrEc/xaTpW3QaRKfoocKuOF1H21xgdbQkpmw3Ugx0PW4G9DzP756E9XN0PX7M3dD/GP33XbXdhgFWOww6CVIHov7vaozZ58Lk6/VKETbn/lFvtlIKSldCtCSOEuLXksA2DBiGQVK0g7+fNpw/vfgD17+2jIfOGkXmj619Z7FC1iiCJ/+LjZ8+S0HJuxgvTIMhp8CES3Xl09lWztahxKZf98JWFuoED9u/18Nqmyp1cBqTBvG9dBKIpL66FzYuS1eqjmhdWe1aYf78B6L/xhkLaYN0gqVhU/V83MZyPbenaJG+zXtAB9OuBJ2oKHus7gVO6a8rcavj1713l1EYW+fDorshfQic8KAuz/5QORuG/i5sTt1bndJv/9huIUSPpJQiqBQfLC/hrvdXMyQrjttOGkJWgjt0Qa1qmebzxT8IrPmQ6rHXkNarQK+P3tByayyH6s26Dvc16Ez1KqivIawuHeC6EyAqZfcg2GKDFa/Bhk91fXrcvXqVBkfMvjufGwYquR+Fo25kSP2X2D+/SzdqH35Lzx/p1EHLSK/kfjLiSYhfSQLbMGEYBpnxLu49YwSXvfADl/5vERccnMehA9KIc+1hHTzDAKuDhvTxqMN+A2vfh28e0uvCjf4tjDlf95j+0oqgNZANeKC+BEpX6Qplxw86qPU369fLGAoHXAJZI3UQ64wDm4NfHbz+Gq2vaXNCfI6u4PKn6Aq6uU7P2S1eqsu78k349jE9Hzepj06+NeJMHXR317BlpYirWIztq5uhYDIcf58earXfVMroC6WUlnm2w6aGujRCCLFXlFI0+02enreRp+dt4rTROVx5RD9iQ70+rTJ1T+oPz9N08I1Upx5M2oBds9C39LoGA3paiK9RNwR7qqCxoiX4bQmCa7fperS5Wk8rCvr0tJ8TH9T1rT2q2+owZY/W2ZV7j4ePboIXpsExd+n6vCcllfwxrYmj+h+9/w7FFmIvSWAbRgzDoG9KNA+fNYp/f7GB299ZxeNfbGTa2ByOG5ZJaqyz7fc6sEfp5YAKjoAlL8B3T+m15Cb8Uc/J3XXIUPusxK3DilsDw+KlevivxaKH92aNhFHn6syF8TktrYchntfallzLppNKRSVB1mgdzPs9OkFV6Uq9PavfgcX/gyGn6eeT8/fxcG0T1s8h+uNrCfY7DI6/F1zxPb8i3lXrPNt1H7asZxsV6hIJIcQvplrqybJ6L/d+tJbP1pRxzZH9mT4uF7vVCH1Qu/IN+OqfMOkaGHgalJXr5zqUSzeAY3Xoc3B0yi6vo9r9bOoRWgGfDoL9HohN1xnuQ7GtFqsOqM+ZDXNvg9d+Dwdepqcp2aN7dp3aVAl1RZI4Soi9IIFtmDEMg4K0GP4xdTgbyhp5ddE2nvhqI//5ejMnj8ritFE55CZF7X5ONww9H2Pi5XpI8sIn4fO7dKB70JU66LVY9ZCkivW6J3b7dzrDYVOlDn5TBujsy9lj9PDfmFSwOne+fjhrHb7siNZDX5MLYPDJutV31Zt6WYIVr8HgU/SC8Cn9W+YVdSFlwup34f1rqUoZj3fMtWTtj0Et6G3OHa97FOqK9HcihBARwFSK4ppm3lxSxGuLtgNw//SRTCpIwRKqJFGtlIJNX+qezJFn6/XtGzx791odklRadZ1oaxmiHA4MQ091OvkRvSrEvH/q6VBH3dEy5aqH1q1VG3UuEZnGI8SvJoFtGDIMA5thMCAjlhuPG8TvJvbhraU7eH3Rdl75bhtHDk5n+rhc+qW49/THuiI44la91Mo3D8PbV+h5nqCTPfmbdUts+rCWYcWjIClPz7UJq+RLndC6DdHJMPYCvWD86nf0MgUr34BBJ8DY3+s5vF3Rg2sG9XrDH94AQ0/HO+hCApb9LOHFrlIH6p6C4mU9d81eIUSPoJTCVLC5Qjcov7O0GJuhOHVQDFPH5JCdlRL6pXyUgpLl8M5V0PcQOOTPeooOexnYRgLD0Jmex1+sr1U+ugFeOAOOvE03xPeUa5ZWSkHpCj2dJ0YSRwnxa0lgG+YshkF2YhSXHpLP9LG9+HhVKS8v3Mr7yxYyPi+RA1MDDAyYuC3t1s8zDH2yTx+iWzq3fgs/PKfneY4+r2VYcXZ4DCvuDoahhyuP/q3uxV3zrh6u/fwpMOA4GHehXjd3bytIM6izNc/5i15w/rAboLwaAoEu35SIEp2ie8a3zoehp4W6NEIIsRulFEFTsaaknpcWbuWzFVtJ9JdyQeoOTohZS9qmFVAUjXHcvZA7IXT1pVJ62tDbl+uG6KPv2r8SCxkWnaTyrJfgs7t1g/2Y82DSDD3irMdQujE4RRJHCbE3JLCNEIZhkBzj5MxxvThxRBbz1lfwwoIt3Du/mo+2L+Kc8b2Z1C+FmPYJLVpT6Pc5GPocxD5N8BQJDEP3So88Ry8ov/ZDvSTN86fqJA3jLtJBv+VXBLhmQA+R+uxvugd48gw9lIvqfbklkcFi1xcihZ9AoLll3WEhhAgPvoDJ95sqeH/BSkrWf89Q31Iesaymv72MaH8shnsEDLoINn8Dr/1OB5ODT+7+BEZK6SlD716l/3/CLL3ywP5WlxuGzth87D16qb85t0B8rp5e1FM+i4BXTxEbcJwkjhJiL0hgG2Fa1709ekg6kwqSeP3zRXxXZefmN1eQneDmzAN6cfSQDJKjHW2/3zb/VGiGoRM6DZ+mF5hf95Gek/zC6Xou8gEX67VxLbafriyDfv13X96rk1pMvLxlWJgA9GfXawJ8/x+d0Cs5P9QlEkLsz5RCAaaviZqitRQu+oT4ih/4fbCQdFcAW2ZfjN7HQt4hkDmsJXi06gSKn98N783Q2YPHX6LzT3RXMOVr1NNcqjfDtOcgIbfnBHK/lmHolRhGnAmbvoD1H8Hoc1uWFuwBGit0fZk5fP/9joXoBAlsI5RhGDhtFoamOTnz8OGsL2/kle+28dAn63n6q02cNjqbk0dmk53oxiInxz0zWtbKHXq6XnC+cC4sfBxenAZ9D9Xzj7NH6Z7HXT/DoA/mPwpfP6SHQo2/pOdUrF0pbZBuIChZ1rOTfQghwpNSemm4xnI9xHPjF5ib5pFfuZmBUXFYB43F6HumboRLzNVZgKHjucoRA4f/Veev+PJeHdxOuUUvd7evz2kBL3zxd50w6rTHIX2onEdB92YWHKl7bRtK9aoNPUHVRr2/JheEuiRCRCQJbHsAq8VgSFY8t54Yx+8PzuPNxTqT4wsLtnLs0AzOGNuLfmkxWC0hXqIgXBkGOGP0ELN+R8KGz2DB4/DSmZA3Wffg5ozbGbgGfTDvAf07h90IY34HVjmU9igmVc8V2jpfZ6QWQoh9SSlA6V7Oyo2w9RvUxs9Rxcuoa2ik3NGLqP6HooZfhjVjCJl5vyCBoGHo0TgHXKwDqA/+DLVFcNy9el31fVWvmgE9KmjJi3r4bd5kCWpbGYae6qKCOlPyvvweuktr4qjoFIiVxFFC7A25Gu9BLBaD3KQorji8H2eP780HK4p55bttvLVkB5P7p3LF4f3okxwlwe2PMVqWCxp4PBRMgY1fwoJ/wyu/gd4Tda9s5ggd1P7wnM7KOPKcrl82qCdpnWe78XOZZyuE2LfMAGz4FLX2Q9j6Lb7q7RT5olhsFrAuehpZYyYzeewoMtISKN6xgyD8uvO3xQoDT4DYTHjnSnjlXDjxQZ2boavrVWXq9ei/+idMvk5n9pc5lx3FZeke7MK5MPgkIn/KVWviqP5gl7XfhdgbEtj2MK1Ba2qsk3Mn9OaUkdl8sa6cJ77cwGUv/MD900YwICNWgtufYhi6Uul/tF5SYfNXund29m91q3BDiU4iMuwMCWp/iV4TdIKt+mI9HFmIn6NU6w8QDEDAA8110FQB9SU6kY7dracSOKLBHgOOKJ24zebUN6u9JdO5FSwWdrvolXNgz6IUbJ2P+fpFbLH24b3GYXzhm4onoR+Hj+rH2aN70StRrwHfqfrPMPRa79P/B+9eDbPPheP+qRtDuyrwVAo2fgEf36Lnj467UOqaPbHYIP9wWPQfaK7VKz9EskAzlK/WjSfSiCHEXpHAtgczDIM4t50ThmcyOjeR615byp9eWsz900YwLDtegtuf07p+XsGRegjYlm/0sj6H/hkGHN9ysSx+kmHopZQsVt0SLYGtgD0Grtb6HTgr18HaDVC7Xc9jrNkG9Tv0/Ehvve6Rszr0cRn0g+nXy22ZQb2vWWz6easD7C49N9IRre+dseCK0/MiW2+u2JbgOEYPMY3vJQFvpAo0o+Y9yFeevlxvXEV8fALTxupVBNJidVK/LqvzDAMS+8DUp/Uyb2/+Qc+5HXn2zycd/DlK6ZwE714F+VPgkJmSv+HHGAbkTYKv7oPSlXoFiEjWWAF1xftmBIAQ+wkJbPcDhmGQleDigekjueH15Vzx0mLuO2MEY3onSnD7SxiG7gnqe5hOKrU/rP3blaLTdCKMrd+2LJUhn12P0hakAqidcxxV6/9N3RPR1uNarOcn1mzVAWxdETRWENNcS3TADw6XzloenarX2+5zsM4CG5et551FJYE9Wge5gWZ983v0nEpfA3gbdBDsrdPv6W291eskM94G/fsBj74P+nQZx/wOptwcms9QdIpSCjZ8StOmBbwZ/WeuOnQURw/JIDHKvu/qOMMAdxIce69uFJl7q26MOfhqPeJnb95XKajZAm9dDkn5cPTf9v619hfJBZDQW+fG6H1QZH9WrYmjUvqFuiRCRCwJbPcThmGQFuvk3jOGc/ObK7jqlSX84/ThTMxPluD2l5Jlk/aO1Q45B8CWeTrDp90V6hKJVq1BaNCvg7zmGvDU6O8p6NX3u/7cGkwGmvX//e1+7vB77f7fGmiaQb0/uOJ1g0d8NvSZBAm9aLTEU+WzkztotO5Ftblahl92siGpNfBWCjB1EBsM6N7eYGtw7NHvKSKOUormxlpsX81idfR4zj99GsNzU7qnXjMMPQR+0rW6t3/ubXqUwdF3QlTKr9tvldINP+9cqUcDnThLB85SP/80e5QeUbXpCwhcG7l5HJSCkuW6QS8mPdSlESJidUlgq5SitraW5uZmUlJSsNlsHZ7z+/1UV1djsVhISkrCYrFgmiYNDQ0opTAMg6ioKOx2GW6zLxmGQVK0g7tPG8Zt76xixuyl3HXqUA4dmCZLAol9K3cCLPlfyzzbvFCXZv/RGriaQR3AeeugsbKl13QbVG/RF+J1RTuH+6qg/lvD0jI/1aqHV7bd23VwanPqtTxtjpb7lltUdMtc15ab3aXXA43P0RdsUcm7B65AoKaG5rIynZinK89Hra9lGEDL9AGro+teX4SMUorKRh/fv/k0R1RtpP+02cR2V1DbntWuhyHHZcN718BrF8AJs375EmdK6dEGH/xZH4/TnpNh8b9G/hRY/D+o3gRpg0Ndmr0kiaOE6AqdDmyVUsyfP59Zs2bhdrsZMGAA1113XVuQGggE+Otf/0plZSUej4chQ4YwY8YMduzYwe9//3v69OmD1WrlsssuY/jw4Z3eIPHTDMMg3m3n9pOH8Lf3VjPz/5Zz+0lDOHpohgS3Yt9onWdrWPRSBol95IKtK7TviTSDekittwE81TrBWe12Pdy3Zov+ub5U98gGfTo4dcXpQDMuWydJS8jVwWd0qg46rY49BLW2jsmY2pZJMX5+RIN856ILKaWoaPDx99e/5qJNz2IdfxqxvUeGbgSSYdFTVab/ryVj8jlwwoM6K/zPlSnog8/v1qNaTnsS0ofI8fJLGQZkDNejQDZ/DamDIvOz8zdD+Rqd3VkSRwmx1zod2AYCAR555BGuueYahg0bxnnnncfatWsZOnQoAFarlauuuork5GTq6+uZPn06Z511Fn6/n8zMTB5++GFsNhtWq2T86y6GYRDjtHHLCYNxO6zc/OYKmgNBThqRjdUSgRWCCH8x6XrO2Nb5OuPj/qj9XFRl0jb/tDUwNf163miw5X7XYb/+lrmk/iY9n9TfRFxFMZbGMghW617Ypir9vGHRSZOiU/WSGBnD9Oce30v/PzpFJ0+yu3SQ2yoSLwjFfkcpRXm9l5mvLyN/w+v0S/BjjP9D6AMCw9A9htOeg/evh9fOh6PvhkEn/nhWYzOgs+4vfRmOu0/PKZfj8NeJStQNCIVzYezvwIjAWXaN5focnjFcvn8hOqHTR39DQwPl5eUMHDiQqKgoBg4cyKpVq9oCW4vFQnp6OkopGhsbAXC73QQCATZt2sS1115L7969ueiii0hM3Jmqvb6+nsrKSpRS1NfX09zcTFNTU2eLi8fjIRAI0NTUFPFzSwOBAMFgEI/Hg2UvMvQawOWTcyHg47a3V+LxBjhxWBrWEH0sfr+/y77nUPN6vQSDwR6xLX6/v+2Y2Zv9DAClsKWPwLZ1Ic0NNXroaggopQgEAm3ngX3NUAHs/gastVugeAlG+Vrw1qNaExe13gLNOqAN+iDoRwV1tl+lFKql3CYGCgvKYgebA2V1YMGGcifRlNwH+o6ChF66BzYmTS99YXe3DLvdwzxVE/AGgH3/OfwSzc3N+P1+PB5PqIvSKc3NzZimGepi9FhKKcrqvVz36lJKt2/kTvtcrGPO01McwqFONww9nP6Ux+Czu/TQ5NptcMDFeqh+e8qE5a/ptdEPmdmSXE966349AwqO0Bmq60v13P1IU7VR7w/JBaEuiRARrUt6bIG2ebUul2uPFyYNDQ3ccccdnHPOOaSmphITE8MzzzxDVFQUTz31FLNmzeLWW29tCzaXLVvGq6++ilKKFStWsGXLFoLBYGeLi9/vp66ujo0bN3b6tULNNM22belMkH5CnhXDTOQfH65hdeFGTh2ajN3a/ZVrdXU1TU1NVFZWdvt7d7XGxkZM04z4i3SAYDBIfX19p/ezeEcfMiteZsuKBQRiQnPh0dpQtmnTpq4fJaJM3VtaV4ylYi3RtWtIaNqE3VsKpheiUzET+qCcsShnIsGoDIIWJ0GLk0DLvb45CFocBCxOTMOOabERbLk3DTvKsLYNCW5o8oDVTkxM7M6Lej9QraC6qmu3bx/zer00NTXpDLcRzOv14vV6Q12MHkkpRUldM9e9uoyyOg+z8n8gqyoaxpwfXgGhYeih/kfepof4f3Wfnjs75SY9UsIw9EiNDZ/pYGzM+TDuAlmrdm8ZBvQar8/BRd/rUSnh0MjxS7UmjopJk8RRQnRSpwNbt9uN1WqloaGB6OhoysvLGTt2bIffaWxs5LbbbmPw4MGcc845bcmi+vfvD8DJJ5/M3/72N0zTbLvYnDhxIhMnTkQpxTXXXMOAAQPIy+t80pmmpiY2btzY1qMcyQKBAMuXL2fIkCF735PWYthQRUbqJh77fAOZ2dFcOCmv24Pb9evXk5KS0qHnPlIVFxcTCATo1atXqIvSaX6/n5UrVzJ06NDOjXLIiYMl/2RgYhAGhOb4U0qxdOlSBg8e3PlkdWZAZxCu3gjFS2H7IihdDvUlOvBM7INRMB6yx0LGUFR8DoYjGsPQ81O74hJ2+/btGIZBdnYE9lDsoqamhrKysrZ6IVJ5vV7c7gjNzBrGlFKU1DYz49WlVDb4ePDIWAZ+/A7GQVfpHtJwZHPC+Et0D+KHN+gkbcfeowOv4qXw7tVQcDhMvrbjlADx68Vl6rnJhZ/ood8RtYJBy9rFKQMkcZQQndTpwDYqKooxY8bw8ssvM27cODZs2MCoUaNYuXIlmzdv5uijj+auu+6iuLiYM844g+XLlzNw4EBKSkooLy8nPj6eF154gdGjR3cIztpfQLf+3NVDhyN9KPKePqO95bAZXHBQHm67jfvnrMXjD/Knwwpw2Czd8jm176WJ9O+lvZ6wLV22n8Vm6CyhW7+FAceFpEV9r/az1szC/mY9B6p8DRQtgqIfoLJQZzN1J0HaIBg6FbJG6XUIo1N3Zt81jH12mWUYRo/Yz9rradsjOkcpxY7aZmbMXkKtx89D04fRf/GdGNEpMHxaePfOWaww6CQdfL9zJcw+FyZfD5/eCakD4ChZq7ZLWOw6O/KiZ/X61e6EUJfol/N7dL0y5FTZD4TopE4HtoZhMGPGDJ555hneffdd/vKXv5CWlkZFRQWgh8tmZGTgdDqZM2cONpuNnJwcAoEA7733Hk1NTQwaNIjp06fLxUyI2awWzh6fi8tu4e4P1tDsD3LNkf1x2a3y3YjOszogZxxsX6jnku463yxUOgx9bZdh2Neghw8WL9XD20pW6B4Xi1UPL8wYoS+qM4bppEzO2HZZgoUQXUEpRVGNh2tmL6WhOcBDZ46iIFiIsfJNOPpvei55uDMMfe6b/gK8dzW8ep7uXTzhfl1+OWd0nmHo9Wy/+ieUroTeEyPnc20s1yN9MmRlECE6q0sC26SkJGbMmNHhsSFDhjBkyBAALr/88t3+LiUlhVtvvbXD34jQs1oMTh+dg9Nu5fZ3VtLsN5l57ECiHRLcik4yDL2e7fJXoKFUB4f7WodMxEEI+LH56/XyN8FGaKrUyUYaSnRvbH0JNJRDU4Ve89XvAVeC7lkZdCJkj9brDEan7QzM5bgQYp9QSrG92sPVryzB4w/y0FkjyU92YrzxiD4OB54QOcefYegEV6c/DUtehP5HQ1xO5JQ/EiQX6Hpl42c6sI0ESkHlRn2fnC/7gxCd1GU50X9t0CNBUviyWAxOGJ6Jy2bhlrdW4PEH+csJg4l12eR7E52TMVRX4KUrdS9nV+xPrcvlBDzga9JrtXqqoaFMB9BtAWspRmM5/WvLsX/u04lGDAs4ovQaiFHJOnFHzhg9bDA2AxLzILF3S2+sVS46hOgmSim2VjVx9StLCJiKh84aRd+UaIxNX8GGT3XWYUd0qIv56xiGXmpr4uU7/y+6jj0K+kyGjZ/DpBk6K3wkKG1NHJUW6pIIEfEicLEv0R0shsERg9Nx2a38+fVl3PTmcm4/aSgJUXYJbsXei2kJFrd+C/2P+eV/19rzqoI6eG2q0MOEKwv13KSK9VC3A7y1etkcZYLNBc4YcCdDTCok9oFeB1JS7SN74Ghscel6GKAzVl8AWRxg0YmdALnoFCJElFJsqdRBrakUD545ij7JURiBZvjmQb1maf5hkXuMRmq5w51hQMEUWPICVG/WeQ/CnoLiZXpUkCSOEqLTJLAVP8piGEzql8ID00dy3WvLuP71Zdx16jBSYhwS3Iq9Y3NCztgfn2fbPoD1e6CxQq8BWVkI5WuhYp0OaD0tS9m4EyA+VydrGnCszjYanQpRSXpZDUe0fg+LDTBQSlG1dCmZfYeAw9GdWy6E+AWUUmyqaOTqV5ZgMQwePHMUvZOjdHNT4VydtO3MF0K2FrYIcxnD9VJLm+dB6sDwb0RoTRw19HQiK5OzEOFJAlvxkwzD4IC8JB48cyQzXl3KjNlL+MfU4WTEuSS4FXsn90BY8boeKhyXqSv2pkodwFYUQsVa3QNbvUUPKUbpea4JvSC5n86onNJPD2WOTgF79M71H39un4zwNVKF6MmUUmwsb+SqV5bgsFp4YPoIeiVF6bqmuQ6+fgj6H6UTMUn9I/bEnajXtC38BMb8DqxhfpnbWK5zPGQMC3VJhOgRwvyIF+HAMAxG9krg4bNGcc3spVz18hLuO2MEOYluCW7Fr2MYugI3g/DRjRBo1gFsUyVggjN+ZwDb7yh9n5CrA1hHdEvPK3JRK0QPo5RiQ3kDV728BJfDygPTRu6sY5SC1W9D9SadSdgily7iRxgWvTbwnL9CYynEhfka35UbQKETX0m9JkSnSe0gfhHDMBicGccjLcHtFS8t5r5pI3QyDzkZi18jLktnBK3Zqivz/MMhpQASeuthxBLACrFfUUqxvkwHtTEuG/dPG0F2QruG06ZKmP+oHq6ZNljOC+LHGYbusTWDsH0RDMoK3/1FKShZDrHpkjhKiC4iga34xQzDoCAthofPGsW1ry7lgv9+x7kTenPyyCxSYpwS4IpfxuqAEx/UP1ts4XvRIYTY55RSrC2t56qXl5AQZef+aSPJjG831UUpvTxOcy0ccMnOaQdC/Ji4LL1OcOFcGHQC4Tt3VUHJMkgZEDkZnIUIc5ZQF0BEFsMw6J0cxaPnjOaE4Zk8M28T05/4lqe+2kRZfTNK5jCKn2MYYLXrmwS1Quy3lFKsKannypeWkBTt4IFdg1qAuiL4/mkY/VtI6hOysooIYrHr7Mhb5+u52eGqNXFU5gjCN/gWIrJIYCt+NcMwSIt1MuOoAbxyyYEcPyyT/3yziemPf8sTX26krE4CXCGEED9OKcW60gaueGkxqbFO7p82goxdg1plwndP65Edo89DLv7FL2IY0OcQnZipbFX4Jg1sKIP6Ur2+uzTyCtElJLAVe8UwDCyGQa+kKK4+sj+zLzmQE0dk8tz8LUx7fD6Pf7GR0pYAV4JcIYQQ7Sngv99sIsph5Z/TRpC+a6Z9pXSW9KUv6SHIsRly8S9+uZQCnTl/42ehLsmPqyzU90kFoS2HED2IBLai0yyGQU5iFFcd0Z9XLpnAySOz+d+CLUx/fD7/+mIDpXVeCW6FEEK08fiCLN5aw1FDMkiL3UOOBmXCgn9BdBoMO0OCWvHr2KMgbzJs/Fxn3w83bYmjMiAmNdSlEaLHkORRosvsDHD7MW1cL177fhsvLdjKKwu3MW1cL04bnS3r3wohRAulFOvXr2fOnDnk5ORw9NFH43K5Ojy/dOlS5s2bR+/evTnqqKNwOp14vV4+/vhjtmzZwuGHH87AgQMj7ry6vbqJkrpmxvZO3ENQq6B4Kax6G465G9wJISmjiHD5U3SPf/VmSBsU6tLsoiVxVKokjhKiK0mPrehyhmGQFe/iisP7MfuSA5k6NodXvtvGtMfn88inheyo8UgPrhBiv1dWVsa1115Leno6n376Kc8++2yHc+OqVau46aab6NOnD1988UXb8y+++CIffvghWVlZzJw5k+Li4hBuxa+nlGLptlqiHTYK0mJ2/wXTD988DKkDYeBx0lsrfj3D0EmZnLGw5evwm2fra4LytZI4SoguJj22Yp9obYHPiHfxp8MKmDomh9cXbWf299uZ/f02zhjbi6ljcnbPgCmEEPuJ+fPn079/f04//XRGjBjBddddx/nnn4/T6QRg4cKFjB8/nuOPP56+ffty/fXXc8455/Dee+9x2223MXjwYBYtWsS8efOYNm0awI82GnZlY2JnX0sBCzdXMTAjlvgo++6vt+UbPYT01H+DPXqfBiU9rZG1J21Pp7fFnQg546DwExhzPhC6paJ225bGMp08Kn1o6y90f6E6Qfaz8NSTtmVvSWAr9qnWoDUz3s1lhxUwdUwv/u+H7bzy/TZmf7+NqWNyOGNML7ISXD/zSkII0bNs376dnJwcABITE/F4PDQ3N7cFtpmZmXz22WfU1tayatUqNm/eTF1dHbW1taSmpmIYBrm5uWzdurXtNQOBAM8//zxlZWVUVlZSXl7O+vXru6S8dXV1bNy4Eau1cwGCJ6BYtKmCw/q42VhY2OE5q+mj19f30Rg3kIpgFqqLyr6rujq9DExTU9M+ef3u1NzcTENDQ5d9z6FWV1fHhg0bsFg6P6gw2j2IjI2Ps23FQnyulC4o3a9TXV2NUgqHw9Hh8diS+aQGg2yttRCIkO/N4/H0iOMFdADYup/1hM6VmpoaNm/ejN1uD3VROmXHjh2dDs4lsBXdxjAMMuJdXHpoPqePyeGNH4p4+butvPr9dk4fk8OohGbiEky8gSAGBoaxywAdA4w9DNkx2v5p9/9d3lcIIcKNzWajuVkntjFNU2ebb3cxP3nyZL7//nuuuuoqEhMTSUxMxGazYbFYCAaDKKUIBALYbDurcovFwujRo2lqamL79u189tlnJCcnd0l5q6qqSEpK6vB+e2NtaQOVTX4mDe5HcnJ0h+esa9/FVr4Cx8lPk5SW1an3+SmmaQJ02WcTSg0NDQSDwR6xLUqptv2ssw0oADbHERgr/kVi00YC2QO6oIS/Tl1dHQkJCbjdHefROjdswYjLIjGnP6YtMhr26+vrgZ5xzLTfz7qiASXUamtrSUxMbGsUjVRxcXHU1tZ26jUksBXdzjAM0uNcXHJIX04dnc2bi4t4+btt/K/BQ0ZCFXabDYthYLEYWAydlMpqMdrd0/Z/i8XA2nbPLv83yEuN5qxxvYh22iTAFUKElQEDBvDCCy8QDAbZvHkzycnJuN1u6uvriYqKwu12c8MNN+D3+5k3bx5er5eEhAQyMzPZsGEDaWlprFq1ipNOOqntNa1WKyNHjgSgsLCQBQsWkJSU1Onzn2maOJ1OEhISOnXxpJRiy4ZG4t0ORuSlkxTd7rWa62DV8zDgWKL7Tybauu96HzweD9AzLtKtVisej6dLvudQa93PEhMTu6b3KSEWMoYQX/4djD8bjO4LYpRSlJSUEB8fT2xs7M4nzCDUb4CMwSSmZXZrmTrL5/P1iP0sGAy27WedbagLNaUUO3bsID4+nujo6J//gzAWExMjga2IXIZhkBbr5OLJfTl1VDavfr0Kj7LjcLowlSJoKkxTEVSKoEnbY0HV8ripdnkM/H4TU+nH/UHFnNWlfL+5ir+cMISsBJnPK4QIHwcccAAvvfQSM2fOZMuWLVx66aUAXH/99Vx77bUkJSUxa9YsANavX88tt9yCzWbjggsu4IEHHuCtt97C4/EwceLEEG7Fr6OAhZuqGJgZR7y73fBMpWDVm1C9BU6YBRa5PBFdwGKH/MNh8fO64SQcMmz7PVCxFkaegySOEqJrSc0hQqo10EyNdXJkfgwpKSkkJiZ2+nUV+jppybZqbn5zBRc//z13njKUkb0SJLgVQoSFqKgoHnjgAQoLC0lMTCQnJwfDMPjLX/5CcnIyVquV3/72t9TW1pKXl0dCgj5/TZo0iby8PCorKykoKIioVvpGb4DlRbVMH9sLS/tTcVMFzH8Mhk3VS7PIeVp0BcPQ69nOux/KVkPvA0NdImgo3Zk4SvZzIbpU5Ix/EPsNwzA6fWsdtjw6N5Enzh1LZryLS1/4gXeW7iAQNCVznBAi5AzDIDo6mhEjRpCbm4vFYsEwDDIzM3E4HFitVvLz8xk9ejSJiTvXezUMg169ejFy5EhiYmIiqrFuS2UTFQ1eRvdO2PmgUrDkRfDWwwEXR9TQTBEBUgogvhds/Cw8sg9XFup9PKlvqEsiRI8jtYfo0QzDICfRzQPTR3Li8Ez++vZKHvmsEI8vKMGtEEJ0I6UUS7bVEO+y0zelJSBXCmq3w3fPwJjzILG39GKJrmWPhrxJegmpgDe0ZVEKSpZDbCZEp4a2LEL0QBLYih7PMAxiXXauP2Yg1x8zkBe+3crM/1tOWb1XglshhOgmSun5tYOz4ohztyYGUvDd02C1w+jfInMOxT6Rf7juKa3ZHNpyKBOKl0HqQLBHRjZkISKJBLZiv2G3Wpg+rhcPnjWS1cV1/OH5RawqrpPgVgghukGDN8CKHbWM65Ok59cqBRXrYelLMP5iiEmX3lrR9QwDMoeDIwY2fx3a4cj+JqhYB5kjkEYcIbqeBLZiv2IxDA7sm8yTvx1LjMvGJc8v4uNVpQRNCW6FEGJf2lzZSFWDj1G5LUn8VBC+fQxiM2DYGRLUin3HnQQ546DwE73fhUpDGTSWQYYkjhJiX5DAVux3DMOgT3IUD581isMGpPHn15fxxJcbaPbLvFshhNgXWufXJkTZyUuJ1r1mO5bC6ndg4p/AlRDqIoqezLBAvyOheLEOLkOhdYSCYZXEUULsI7Lcj9gvGYZBvNvOzScMom9qNI98WsimikZmHjOQpGhHRGUZFUKIcNc6v3ZIVjyxLruea/j905A6AAYcJ71XYt8yDOg1HoJ+KFqkkzeFYp9rSxyV0v3vLcR+QHpsxX7LMAycNivnHdiHf04bwcJNVfzxhR9YX9YgPbdCCNGF6psDrNxRx7i8lvm1tdtgwycw8jdgjwp18cT+ID4b0gZD4Vz0avfdTJlQsgzSBoJNEkcJsS9IYCv2exaLwSH9U3nit2MBuPi57/l8XTmmzLsVQogusamykerGlvm1AKvf1Rf3/Y6Q3lrRPSx2nR15yzd6zeTuJomjhNjnJLAVAt172y8thsfOGc24PklcO3spz83fjDcg826FEKIzlFIs3lpNYrSDPsnR4GuAFa/BgONlLU/RvfpOhsZyKFvV/dmR60v0e6cP7d73FWI/IoGtEC0MwyAp2sEdpwzlokl9efCT9dz57mpqPX4JboUQYi+ZLfNrh2bHEeu0wbYFUL0Fhk1Feq5EtzEMSO4H8b1gw+fd+95KQWW7xFEySkGIfaJLAlulFKWlpWzcuBGfz7fbc01NTWzcuJEtW7bg9+sgQSmF1+tlw4YNlJWVSeAgwoJhGLjsVi6a3Je7TxvOp2vKuOKlxWyubJJ9VAgh9kJds59VxXUc0CcJgyAsfQWyRkL6ELnAF93LEQ19JsHGzyHg7d73Ll4OcVmSOEqIfajTWZGVUsydO5cnnniCpKQkkpKSuO2223A4HAAEAgFuvfVWvF4vDQ0NpKenc+uttxIMBrnxxhtpbm6mvLycq6++mokTJ0o2WhEWrBaDo4ekk5Po5uY3V3DRc99zx8lDGN83GYvso0II8YttKm+ktsnPyNxEjOotsOkLOPIOsDpCXTSxPyqYAstehpotOit3d+iQOMrZPe8pxH6o0z22fr+fJ598kptuuokHH3yQTZs2sXLlyrbnrVYrN910Ew888ACzZs1i0aJFFBUV8cMPP1BVVcVDDz3ENddcw+OPP04wGMJFs4XYhWEYDMmK4/FzxzA4M44rXlrCK99twyPr3QohxC+ilOKHrdUkxzjonRSl1611xOjgQhoJRXczDJ28yREDW77uvnm2rYmjMiRxlBD7Uqd7bBsbG6muriYvLw+n00m/fv1Yu3Yto0aNAsBisRAfH49SioqKCgBiY2NZtWoVgwcPxmazUVBQQFlZGR6Ph9jYWACqqqooKipq+7mhoYG6urrOFhePx4PP56Ouri7ie4eDwSB+v5/6+noslsifLu31emlsbMRmC6/lld3AjUf25gmnyV3vreS1Rds5KD+ZifnJ9E50EGXd/frM4/EQCAS6ZJ8NtUAg0LafRfoxo5Rq2xa73R7q4nSax+PBYrH0iP2ssbERr9dLfX0IspV2oebmZmmkbcdUsHBzFUOz44nBAyteh0EnQJQMxxQh4k6CnHFQ+AmM/i0Y3XDNUV8CTRWQMVQadITYhzp9NLdW4FarFQCn04nX23HeglKK6upqbr31Vi666CKSk5Px+Xw4nXo4hs1mwzRNTNNs+5vCwkLeeustADZs2EBJSUmXBDx+v5+Ghoa2oDmStc5fLioqiviAA6C2trYt6AhHpw5wMyy9F0vKTT5ZU8Zz8zfjspjkxSpGZjgZmRNPRrwbu9WgoaEB0zR7xAWuaZo0Njb2iGMGdABVXFzcIxqDWo+VXXMbRKLWhq3t27eHuiid4vP5esT30VVqPX5WF9fxh0PyMbbNh7rtMPT0UBdL7M8MCxQcAZ/eDg3lEJe579+zYh1YbJCYt+/fS4j9WKcjRbfbjc1mo66ujqioKIqLixk/fnyHoZp1dXXcdNNNHHrooZx66qkAZGZm8u233wJQU1ODy+VqC3QBxo0bx7hx41BKMWPGDAoKCsjL6/wJoTWR1aBBgzr9WqEWDAbxer0MHDiwR1ykr1+/npSUFBITE0NdlB81FDgF8PiDbK5o5NuNVcwrrOD/NtbyamE1/dMDHFyQQn5sPL2TnfTv2zviBx35/X78fj8DBw6M+AaU1qR1/fv37xE9ttu3b8disZCVlRXqonRaTU0NZWVl9O/fP9RF6RSv14vb7Q51McLGxvIG6psDjMyOxZj/MmSPhdRB0mslQscwIHc8BP2wYxHEHr/v98eS5RCXLYmjhNjHOh3YRkVFcdBBB/HUU08xZswYtm/fzqhRo1i8eDHr16/ntNNO45ZbbqGmpobs7Gy+/PJLxo0bx4QJE/jPf/7DnDlz+Pzzz5kyZUqHwHZPF9BdfVEd6Rfp7UX6trRvCImEbYly2BicFc+gzDh+e2Bvyhu8LNtWy5fry3lzSRElNR5SYhwcWFDPpH4pDM9JIC3WidVidMn2tX5epoJA0KQ5YNLgDVDb5KPG46dXYhRZCW4sRuc/z/Z/HwnfzU+JtP3sl+pJ2wI9b3v2V0opFm2pJjXGSS6lsPkrOPYfYI38RiUR4eJyIG2wHo484Dj26bxXZULxMkgbJImjhNjHOh3YGobBlVdeyezZs1m+fDl33303ycnJVFdXk5GRgVKKcePGUVNTw7p167DZbAwfPpzMzEzuvPNO3nvvPQYMGMDUqVPlYkZEHMMwsFkNMuPdZMS5OGpIOg3eAN+t2cbXG8pZXdHIRytLsFoMhmTFc3BBCuP7JpGXEo3bbm17jfbaB63+oIk3YFLf7KfW46ei3ktZvZeS2mZK6poprWumosFHdZOPRm+AgKlAgdNuYVK/VE4Zmc2o3ASiHFY5voQQ3cpUiu82VzE8J57oDe+CKwH6Hiq9tSL0rHbInwJLXwRvPbji9917+Rr1GrZjf48kjhJi3+qSGfOxsbFccMEFbf83DIN+/frRr18/AM4999w9/t3w4cMZPnx4298IEcla9+FYl52BaS4KkjLJyMqhpK6ZxVur+XJdOc9/u4WHPl1PdoKbCX2TmdQvhT7J0dQ3+6lo8FFapwPWklodtFY2+qhu9OHxB/EHFXarQZTDRkKUnZQYJ+lxToZlx5MR7yI9zkVytJNop5VFW6p5Y3ERf3rpB3onRXHiiCyOGpJBdhf14gohxM+pafKzpqSeyyemYqx4AwafpBP3CBFqhgF5k+HrWVC2GnIn7Lv3qi+BxgrIkHWbhdjXuiwV3N5cKMvFtejpHDYLuUlR9Ep0c+KILOo8ftaW1PP1hkq+KazgjcVFKKUwFTisFqKcVhKjHKTEOMhOcDO6dyIZcS7S4lykxDhIcDuIcdlw2SzYrBYs7Q6h9sdTXko0J43IYnVJPW8vKeK5+Vt44suNHNwvhVNHZTM6N5Eox557jIUQorOUUmwob6DBG2BEcCVGfQkMPU0u7EX4SOmv571u/Ax6jd93+2bFOt1DLImjhNjnwmtdFSF6KMMwMICEKAfj+yZzQF4Sfzw0n6IaD6V1zSREOUhw24lx2nDuErTubaOR025lRE48w3Pi+cOh+Xy5rpw3Fhdx+UuLyW3txR2cTk5ilPTiCiG63KIt1aRH28jZ+hbkHAApA0JdJCF2ckRDn0mw4XM46Gqwu/bN+7QmjopK3jevL4RoI4GtECFgGAYuu5X81BjyU2P26fsYQFqsi9NH53DiiCzWFNfz9tIdPD9/C09+uZGDClp6cXsnEi1zcYUQXSCoFN9trma4u5yokoVw/D/1cidChJOCw2H5bKjZAqn7oOHFDGKUSOIoIbqL1DJC7CcMw8BpszKiVwLDcuL5wyE7e3GvfHkxOYlRnDgik6OHZEgvrhCiU2qa/KwtqeNq+1cY0Sl6PqOcT0Q4MQzIHKF7brd8o4cmd/XqG4EmLNUbYPgJSOIoIfY9CWyF2A9ZDIPUWCenjc7mhBGZrC3RvbgvLNjKU19tYmJ+MqeOzmFMbiIOi/r5FxRCiBZKKQrLGvA3VDPW8QXG6LPBHb7rk4v9WFQy5IyDwrkw+lwwuvay2Omrxu6vh3RJHCVEd5DAVoj9WGsv7rDseIZm7+zFfXNxEVe9vJjsBDfHDkmjwBkMdVGFEBHk+83VjDaXk+NohCGnhLo4QuyZYYGCI+DTO6GhHOIyu/TlY5uLsTlckjhKiG4iga0Qom0ubkqMk1NHZXP8cN2L+87SYmYv2kEgGCAxu5pxfZJkeLIQ4icFTcWSLeWcYf0Ka95ESC6Q3ioRngxDL/UT9MGOHyD2uC7dV+OaNkNCL0kcJUQ3sYS6AEKI8NLaizs8J4EbjhvISxcdQG6swbWvLmPJthqUkqHJQogfV9Xkw1+6lgm2QozhZ0rSKBHe4nJ0cqfCT4BO1m9K6VvQD54qnBUrJHGUEN1IahshxI+yGAapMQ4uHh3Hi+sNrpm9lIfOGsXQrDjpuRVC7NH60nomNH1BXEY29DlYemtFeLPaIX8KLH0ZvA3givv5v2lt4DX9+m8ay3Vm5YpCqFgLlRugdjuqvhTj2L/v2/ILIdpIYCuE+FkJbjt3nTqQ615fzjWvLOHhs0YxICNWglshRAdKKTZs2caRxrcYQ34HrvhQF0mIn5d3CHz9IJSv1msut9ZtrQFs0A++emgoawlg10PFupYAtgi8dWCxQlQSJPSGzOEwbCqb62yk5x9NtNSVQnQLCWyFEL9IcoyTf5w+nGtmL+Hq2Tq4zU+NkeBWCNEmYCocW7+iT5QPBp8ivbUi/BkGpPSDuGw9HDkpHxpKoXqLDl4r1kHVBqjdoYNbix2iU3QAmz0GRp4NSX0hPgfcSWB366RUgGfVKkyrK8QbKMT+QwJbIcQvlhbr5N6pI7jqlSVc/YoeltwnOUqCWyEEAPWNTQyteB9r3iR9sS9EJHDEQJ9JsODfsOQF8DWC1QHRqZDYB3IPhJQBLQFslg5gba62AHaPDTiSj0KIbieBrRDiFzMMg8x4F/88YwRXvLyYa2Yv4aEzR5GT6JbgVghBsHQN+d41MOJqPTRTiEhgGDD2dzuHEif1hbgscCfsDGCljhMi7ElWZCHEr2IYBjmJbu6fNpJAUDHj1aUU1zZLtmQh9nNKKeIK38KakI3KPVACARFZUgfAIdfDiOnQaxzEZ4MjWjfQyL4sRESQwFYI8asZhkGf5CgemD6S+mY/1726lLJ6rwS3QuzPmiqxr3+Pst4nYPySzLJCCCFEF5LAVgixVwzDID81mgemjaSs3sufX19GZaNPglsh9kdKwYZPqa2porr3MaEujRBCiP2QBLZCiL1mGAYDMmK5f/pINlc2ccP/Lae6yS/BrRD7m6APlr7MEttw4rMKZM69EEKIbieBrRCiUwzDYGhWHPdPG8G6knpueXMFdR4JboXYbygFZavwb1vEDwnHkBYfHeoSCSGE2A9JYCuE6DTDMBjZK4F7zxjBkm013PrOKhq8AQluhdgvKFj+OmubEzFzDsBpk0sLIYQQ3U9qHyFElzAMg3F9Erl36nC+3VjJHe+uoskXlOBWiJ6usQJzzbu8a05kSF5WqEsjhBBiPyWBrRCiyxiGwYH5ydx92jC+WFfO3R+sxuOX4FaIHkspKJxLZXUN8x0HMiQrXubXCiGECAkJbIUQXcowDCb3T+XOU4by0YpS/vnxOrwBU4JbIXqioA+19BW+DA7FntyH9DhXqEskhBBiPyWBrRCiy1kMg8MHpXPbyUN4c3ERD85djy9ghrpYQoiupBSULCdQtIR3mcyI3CSZXyuEECJkbKEugBCiZ7IYBscMycAXMLn93VXYbQZ/OqwfdqshQxWFaKGUQimFYez5uNjT87uOfgjd8aRg+auU2bNY6R3A9LykEJVDCCGEkMBWCLEPWSwGJ47IwhcwufuD1ThtVi6e3Be7VQJbIfx+P6+//joLFixgxIgRnH322TgcjrbnPR4PL774IitXriQjI4MLLriA5ORk1qxZwwsvvEBzczNTpkzhmGOOwWIJQU9pQxlq7QesSj4N0xvFYJlfK4QQIoRkzJAQYp+yWgxOH5PDtUcN4KmvNvLfrzcTCMqwZCG+/PJL3nvvPS6++GK++eYbPvjggw69sXPmzOHzzz/nj3/8I16vl0cffRS/388tt9zCmDFjOPfcc/nnP//Jli1bur/wSsH6jyHo4zvXgfROjiY91tn95RBCCCFaSI+tEGKfs1oMph/QC2/A5OFP1+OwWThnfC5WiwxLFvsnpRRz5szhlFNOYeDAgUybNo033niDE088se2YMAyD+Ph4EhISSElJoaamBgCr1UpKSgrJycnExsZ26K1tHbrc+nP7+64sO4FmWPYKvj6Hsag0mtG9E3DYLBGVJK79Z9VTRPr2tH4nPe276UnbAj1re2RbehYJbIUQ3cJmsXDugb3xBUwemLuOxVurmdQ/lVG9EshKcOO0WSTIFfuV8vJyUlNTMQyD5ORkqqqqOlyYTJgwgf/85z9ceumlVFRU8OCDD2K325k6dSp/+ctfiImJoX///mRkZLT9jc/n4/7776eoqIja2lr8fj/Lly/v9LGllKKqqopVq1ZhtVqJrl5Jnx3L+X7wKWwsa+CoXCvLly/v1Ht0p4aGBgCqq6tDXJLO83q9NDU14fV6I/4cqpSiurqaVatWhWZ4fRdq3RaPx9NhikGkam5ubrtFutbvZsWKFT1iP6uqqsLn82G320NdnE7ZvHkzNlvnQlMJbIUQ3cZutfD7g/NIjXXy/vJi7vtoLd6ASe/kKA7ok8SB+ckMyowjOdohvbmix3O73Xg8HpRSNDc343K5OiSIeuWVV5gwYQKXX345n376KQ8//DD/+Mc/eP7553niiSdIT0/nj3/8I4sWLWLixIkA2O12LrjgAvx+P5s3b2b27Nn069evSwJbv99Pfn4+dpsV60dPYUnrT1XmJKwrtnHE2IFkxUfOUj/FxcUAZGZmhrgknVdXV0d5eTn5+fmhLkqnmaaJz+cjPz+/0xe44WDdunVkZ2cTHR0d6qJ0Wk1NDdXV1eTl5YW6KJ3Wup8VFBRgtVpDXZxOW7NmDb1798btdoe6KJ1SWFhIeXl5p14j8s8aQoiIYrdZOG10NiePyqKqwcfq4jrmb6xk4aYqXl20HafNwqDMOCbmJzOuTxJ5qdHEOG0YhDL7qxBdb8yYMcyfP58pU6Ywf/58RowYgWmabNmyhdzcXKqrq0lISMButxMdHU19fT1+v5/Gxkaio6NxOp1YrVaampraXtNisZCeng7oHha73Y7b7e70sWOaJlarFafTidNbAZs/RR10JUvKTPJSYshJicNpi5wLxNYetEi/EATdY2uz2To0jEQq0zTbtiXSe5+UUthsNpxOZ4/Yz5qamnrMfhYMBrFarbhcrohvQOlJ+1lXjGzokm/TNE02b95MXV0d/fv371CJtrbyVlZWEggEyM7OxmKx4Pf72bZtG8FgENCtpjExMV1RHCFEmDMMA5thkBbnIjXWyeT+qXgDJjtqPCzeWsM3Gyp4aeFWHvm0kNRYJ6NyE5iYn8LIXglkxrtwtKyVGemVq9h/GYbBySefzA033MAll1yCUoq///3v1NfXc+edd/Lggw9y5plncsstt7BixQoqKiq47LLLSElJ4bTTTuOaa67B7XYTGxvLAQcc0L2FX/cRKBNf36NY/P12DshLwmGN7OF8QgghIl+nA1ulFG+//TYvv/wyWVlZ+P1+7r33XlyunUOSXnjhBWbPno1Sirfeegun00lxcTHnnXceBx54IBaLhXPPPZchQ4Z0tjhCiAjTGpy67Fb6psaQlxLNqaOzqW8OsLG8gYWbqpi/oZK/f7AGf9AkLyWaA/L0sOWBGXEkRTuwGB1fS4hIkJiYyEMPPURNTQ3x8fE4nTqr8MMPP4zb7aagoID//Oc/1NXVER0d3Tac8dJLL+Xss8/G7/eTmJjYvT0OgWZYNhv6HUkJSWypXMdlhxXIsSeEECLkOl0b+nw+nn/+eW6//Xb69+/P7373O5YvX864cePafmfatGkceOCBzJgxo0OWxpycHG655RbcbnePGOMuhOg8wzAwgHi3nVG5iYzslcAFB+dR2eBj5Y5a5m+oZF6h7tGNctgYkqWHLY/KTSQrwU2sy4bDZpGhyyLsGYaB0+lsGzrcKioqqu1nt9u92/AywzBITEzsljLuZsdiKF+DOvwWVhU3YLEYDMqMC01ZhBBCiHY6Hdg2NjZSV1dHr169sNls5OfnU1hY2BbYGobRNheo/UWmYRiUlZVxxRVX4Ha7uf766+ndu3fb7xQXF7NhwwaUUpSUlFBTU0NlZWVni9uW0a2qqqrTrxVqwWAQr9dLVVVVxGd1Az1/o7a2tkekK6+vrycYDHbJPhtqgUCgbT8LZaBoB0am2RiRms75Y1Moqvbww9Zq5m+o5LG5pXiCFqJcdtLjXPRJjqYgLYa+KdHkJDiJd4DbBlZU27ZE+vwt0JldDcPoEftZfX09TU1NEX9u9nq9BAKBUBdj31AKlr0CqYMgcxQLl22ib0o0KTGyfq0QQojQ63Rga5omQFtgZbPZ8Pv9P/t3mZmZvPrqqzidTl5++WXuvfdeHn744bYL59LSUubPn49SitLSUmpra7vkgsfn8/WYwLY1q1t1dXWP6JnyeDxYLJYecVHY0NCAaZo9Yj9r34ASTvtZkg2O6BvN4XnRNAUUlc1Q0WxQ1BBkY0UTc1aVUl7fTJMvgBWTeLsixQUZUYp+O3z0To4mPc5JrMuOw2ohjDbtF2tsbMQwjB4x4sXj8eDxeCL+mPH5fD3iHLYn1sYSLBvmwpQb8RpOFm+t4aCCZOzWCDx4hBBC9DidDmyjoqJwOBxUV1cTFRVFUVERBx10UIdetz1dDNtstrahVOPGjePdd9/FNM22AHnEiBGMGDEC0L23vXv37pIU401NTZimSUFBQadfK9SCwSCNjY3k5+f3iB5bpRQpKSmhG2LXhYqLiwkEAvTq1SvURem0QCCAx+OhoCBy5tEpwBcwqfP4KalrZktlE4VlDRSWNbC6qIKFVV58gWbcDivpcS56J0dRkBZDQWoMuclRpMW6iHPZsLcMaQ5X27dvx2KxkJWVFeqidFpNTQ1lZWURf272er0dckz0FAaQWr0Im90B/Y+mpLaZrVVNXNmnX6iLJoQQQgBdENi63W6mTJnCww8/zIgRIygrK2P06NEsWLCA1atXc/7551NYWMjHH39McXExb731FocffjilpaUsX76cpKQkXnrpJY444ogOvQ7tsyrv+lhXiZSL9F8i0rdlX37PodSTtgUiZ3sMdDIql91KWpyL4TkJKKUwTcX3i5eQndePKk+wQ8D70coS/lfvxes32wLevJRoBmTE0j89ht5J0aTGOolyWLG2ZKsKl88jXMrRVXra9vQIAQ9pJZ9B/2NQsRmsWF6CzWIwMCNWvi8hhBBhodOBrWEY/PGPf+SDDz6gvLyc+++/n/j4eLKystoyNQaDQeLj47nmmmtaLi5NUlJS8Hq9rF27lnPOOYdJkyZJ5SiE2GcMw8AwFA6rQVqsk5xkR1vA29rDW+vxU9quh3ddaT1vLS6ivMFL0FTEu+3kJkXRLz2WAemx5KfFkJ3gIj7KgVMSVomerKkSfxCcw6cDBgs3VVGQFiPza4UQQoSNLlkjwO12c9ppp3V4LDc3l9zcXAAGDBjAgAEDdvu78847ryveXggh9lprFubWHt70dj28SkFzIEh1o4/t1R4KyxpYU1LPqh11fLyqhPrmAA6rhbQ4J31TYnb27iZHkxbrJNppw2YxJNgVEU/F5bDugL8xMHUYQb/Jkm01HNI/FZtF9m0hhBDhoRsXvxNCiMihe3ghymEjymEjOzGKA/KSAAiYiobmAKX1zWyuaGJdaT1rS+t5b1kx/633EjBN4lx2cpLc9EuLZUBGLAWpMWQluIl323Da9XBm6eEVkSRocaEMC8W1HrZXexjXJynURRJCCCHaSGArhBC/UGsQarcaJEY7SIx2MCA9lqOHpKMAr9+kuslHUY3u3V1bUs/6sno+W1NGbbMfm8Ugzm0nLdZJdoKbnMQoeiVFkZ3gIi3ORYLbTrTTht1qobUjTAJfEW6WF9XisFrony7za4UQQoQPCWyFEKITWi/sDcDtsOJ2uMlKcDO2t87uHTQVjd4AZfVetld72F7dxLYqD9uqm/hiXTkVDV6afEFMpYhyWEmKdpAZ5yYnyU2vxChyEt1kxLtIiXES67Lhsltl+KcIqdb5tckxjlAXRQghhGgjga0QQuwDrQGvzWoQH+UgPspBv/TYtgzgSoE/aNLkD1LT5Ke83suOGh3wbq/ysK60nnmFFdQ0+fEHTWwWg3i3nbRYF1kJLnolReE2m0iNddEnUE2000aMU88TdtgsOKwWrBYDS8uQ6j2VTYhfy+MPsmRbDUcOTpcGFiGEEGFFAlshhOhGbT28BjgtVpx2K4lRDvJSooGdS18FTYU3YFLfHKCq0UtJXTPbqz1sq2pie7WHz9eWU1bbhMcfBGNr22s7bRbcditRDiuxLjtxbjvxbhsJUQ4S3HYSouzEu/XjcS47UU4r0Q7dE+ywWrDbDGwWC4aBzAEWu9lR08yOGg9je8v8WiGEEOFFAlshhAgj7Xt6bVYL0U4bGfEuBmfFd+ztNU0KN2/DG1DEJqbQ6A1Q7w1Q6/FT2+SnxuOnpslHTcvP26o81DX7afIFafYH8QZMTFNhGGC1GDhsFqIcNmKcNuLcNpKi9BzipGgHydEOEqMcLUGxg2inlWinDZfNir2lnK2BcPttED3P8u01uOxW+qfHyPcshBAirEhgK4QQEWLX3t44pxWL20J2emyH32sNgHf+H4JKYZoKX9DEGzBp9gdp9AZp8Aaob/brgNjjp6bJT2Wjj6pGL4VlDdR6/NQ3B/D4gvgCJkGlsFkM7FZLS6+wjTi3ffdAONrR1jsc39I77LJbsVkNCYAj2MLNVfRPjyUxWubXCiGECC8S2AohRA+za8BoGGDBACs47VZif+Tvdg2Ig0oRDCr8QYXHH8Tj29krXNPkp6rR1+G2obyBxVv91LUGwkGToKmwWw2inTZSYpykx7laMkLrJFvpcU6Sox0EmwMETIXSBdnjdojQ8viDLN1Wy8ljcmV+rRBCiLAjga0QQghg90DSZhjYLOC0Q4zLBjh3+5u24dHsTIjlD5o0+02afAEamgOUN3gprtVzhHfUeFi2vaZtCSR/wMRmNXDbDOKdFvosbiI7wU12opvsBDdpsS6SYxzEOG24HTvX//2xMot9p7QhQGm9j7G9E+VzF0IIEXYksBVCCLHX2i93hAFWi87MHOuCXQNhpXSPbNBUeP0mDV4/VY1+yuqb2VhSzdrtFXgtBku21TB3dSl1ngB+U2eEjnXZSY52kBnvIitBL4GUFusiLdZJYrROhhVlt+G0W7C2ywQtAVjX2VoPsW4HBWk/1ucvhBBChI4EtkIIIbqFYejeVotVz9GNcdnIiHczmDhGpNkpy4B+/fsTNFXLHOAAVY0+ylqWQiqq9rC9xsPS7TV8usZHgzdAsz8IgN1qIa4l63NajJO0OBcZ8S7S45ykxuhe33i3nRinDafNgs1qwSLB769SWAcDMmJJjLKHuihCCCHEbiSwFUIIETYMdJBqt1qIddlbAt+O838DpsIXMGnyBalv1nN9Kxq8lNZ5Ka1rpqSumQ3lDSzYVEmdJ4DHH8Q0lU545dTLK6XEOHTwG+ciPc7FsOw4BmXGSZD7I5r8JusqvJw8MhurzK8VQggRhiSwFUIIEfbaB5z2lh7faKeN1FgnfVP14+2D36Cp8JsKj09nfq5p8lHR4KOsJfAtrWumpLaZlUW1VDf5OX10NoMy47p7syJGTZMPMxhkbB+ZXyuEECI8SWArhBCiR2gfcOl1gMFtt5IU7SA3KartudYA2FQQaFn+yCK9kD8pM87FXyYnMCQjJtRFEUIIIfZIAlshhBD7ldYA2NqS7Mppt4a4ROHPMCDGrrNkCyGEEOFIqighhBBCCCGEEBFNAlshhBBCCCGEEBFNAlshhBBCCCGEEBFNAlshhBBCCCGEEBFNAlshhBBCCCGEEBFNAlshhBBCCCGEEBFNAlshhBBCCCGEEBEtItaxNU2ThoYGamtrO/1aTU1NNDQ0UFdX1wUlC61gMEhjYyN1dXVYLJHfRtHQ0IDT6cRqjfw1Jevr6wkEAl2yz4ZaIBBoO2Za1/+MVEqptm2x2+2hLk6n1dfXY7FYesR+Vl9f3yPOzV6vl2AwGOpitPH5fF3ymSqlaGxspL6+nubm5i4oWWjV19cD9Ihjp66urkccO9DxHG2zRcQl6k9qaGigvr4e0zRDXZRO6ynnaNBxRev1c0+45mzdzwKBQKiL0ilNTU0opTr1GmF/1lBKkZSUxD333IPD4ej06zU1NbF161YGDhzYBaULrUAgwOrVqxkyZEiPCGw3btxIUlISCQkJoS5Kp5WWlhIIBMjOzg51UTrN7/ezdu1ahgwZ0iMC25UrVzJgwIAeEdju2LEDi8VCRkZGqIvSabW1tVRUVJCfnx/qonSKUgqLxRIWx4rNZqOoqIirr76606/V046d4uJiADIzM0Ncks6rq6ujrKyMgoKCUBel01r3s4EDB/aIwHbt2rXk5OQQHR0d6qJ0Wk1NDVVVVfTt2zfURek00zRZuXIlgwcP7hGB7erVq+nTpw9utzvURemU+vp6pk6d2qnXMFRnQ+N9TClFIBDodATfatOmTTz22GPcc889YXHh0RkNDQ1cffXVPProo10S9IfaPffcwyGHHML48eNDXZROe/PNN6mvr+fcc88NdVE6rba2luuvv55HH3004i80gsEgl112GXfffTeJiYmhLk6nvfjiizgcjk5XBOFg0aJFfPTRR9x4442hLkqXsNvtIa9jTNPE7/d3STmCwSB/+tOfuPPOO0lOTu6C0oXW7NmzUUoxffr0UBel0xYvXsx7773HzTffHOqidJrf7+eyyy7j3nvvJT4+PtTF6bQbb7yR888/n/79+4e6KJ327bffMm/ePK699tpQF6XTvF4vf/rTn5g1a1bENzoopbj22mu5/PLL6dOnT6iL02kWi6VT15phf5VqGEaXtg7b7XYsFgsOhyPkFx2d1botdrs94gNbpRRWqxWbzRbx2wJgtVqxWq09Ylva72eR3lMTCAR6zDEDukeup+xnrdsSDgFhT2GxWHA6nV3yWj3t2LFarSilesS29KRjxzCMHrOftY7e6AnbAno/a92eSN/PetJ303r93BO2pSuEfWDb1eLj4znkkENCXYwu4XA4OOqoo3rEMAqAcePG9YhhYQD9+vXrEfPQAJxOJ0ceeWSPGO5uGAZHHnkkLpcr1EXpEgMHDuwxx396enqPGK3RU1ksFo444oiIH+rWasCAAaEuQpdJT09nwoQJoS5Gl7BYLBx11FFd1iATapMmTeoRo4NAD9sfN25cqIvRJaxWK0cddVTEN9a3OuSQQ4iLiwt1McJC2A9F7mrtN7cntDi1km0JL63b05O2BSJ/e3rStoDsZ6L79LTvR46d8CTbEr560vbItvRcPT6w/akvfNdND+cdIpLK2hXkQA29X/IdRMr3FCnl7Cr72/aKrvdz+1Ak7WORVNausL9tbzjqKcfP/nbtCZHz3Yg967FDkZVSVFZW8uqrr+Lz+Tj99NPJzs7usJPW1tby6quvUl9fzymnnEJeXl5Y7sSmaTJv3jwWLlyI3W7nhBNOoG/fvh3Kun79eubMmYNhGLjdbqZOnUpMTEwIS/3jSkpKePPNN1FKYbPZOPnkk0lLS2t7XinF4sWLmTNnDn369OHkk08O26GjXq+XJUuW4Pf7AT0fdeTIkW3DqJRSrFixom1JiYyMDPLz88NqP6upqeHrr7+mpKSEadOmERsbi1KKtWvX8u6775KSkrLb/qSUoqqqildffZXm5mZOO+00evXqFfLtCgaD/PDDDyxbtowxY8YwcuRIgsEgX3zxBYsWLcLlcnHSSSeRm5vboayrVq3i888/xzAMYmJimDp1asiHXCqlWL16NYsWLSIrK4spU6ZgGAZLlizhm2++wTAM4uPjmTp1aod5NUopFi5cyGeffUa/fv048cQTw2LejdfrZevWrdTU1DBixAgcDgdVVVWsXr267UIiPj6+Q5Z30zRZvnx529Is2dnZ5OXlhWwb9hemaTJ//ny+/PJLBg8ezHHHHddhyJ5SikWLFjF37lzy8/M56aSTwnboaGVlJe+++y5FRUUMGDCA448/vkN9EgwGefPNNykrKwNg+PDhTJw4MeTnsj1RSjF37lwKCwsB6N27N8ccc0yHaSKNjY288cYb7Nixg+OOOy5ss9krpVizZg2VlZVtj/Xv37/DtcCOHTvYuHEjAC6Xi5EjR4ZVAkOlFKtWrWLRokXk5uZyyCGHYBgGXq+Xd999l/Xr1zNlyhTGjRvX4TswTZMFCxbwxRdfMHDgQI477riQn6OVUmzZsoUFCxZgt9s55ZRTMAyj7dqyoaGBgw46iIkTJ3bY3+rq6tqusw3D4KijjgqLrMmNjY188803bNq0iVNOOYW0tDR8Ph+zZ89uq08OPPBARowY0eG7qa+v5/XXX6eiooITTzyR/v37h/z4MU2TkpIStm3bRt++fUlNTcXv97NkyRK8Xi+g5z8PHz6cqKgoQH+f27dvZ8uWLQBERUUxYsSIHjN16adE/qS5HxEMBrntttsIBoOkpKQwc+bMDnMeTdPk73//OzU1NeTm5nL99dfT0NAQwhL/uEAgwJo1axg7diwZGRlceeWVu60jtnjxYpYtW0ZBQQF9+vQJq5P/rjZv3tx2QdS3b9/dgtatW7fy17/+ldGjRzN//nz+97//dVlW7K7m8/n44YcfWLhwIW+//TZXXXVV24kG9H54yy23MGfOHBYuXMjmzZtDV9gfsWnTJr755hsee+wxqqqqAKiqqmLmzJkMGDCALVu28NBDD3X4DlqPL7/fT3p6On/+85/xeDyh2oQ2Xq+XOXPm8P777/P5558D+jtat24d48ePJzExkSuvvJLGxsYOf/ftt9+yfv16CgoK6N27d1ic/E3T5PPPP2fevHm8/PLLbY9//vnnFBUVtZV117nP69ev529/+xvjxo1j7ty5vPbaa2Fx/CxYsIBbbrmF3//+9237WU1NDQsXLmThwoU8+uijPPLIIx3+xufzceONN/Lpp5+ycOFCtm7dGoqi73dWrVrFPffcw4QJE3jnnXd45513OuxDGzdu5LbbbmPs2LF88cUXvPLKK2Gxj+3J9u3bAZgyZQpz587lv//9727nsmeffZbk5GQKCgpISUkJVVF/llKK2bNnY7FYKCgoIDMzs8NFt1KKxx9/nFWrVjFs2DBuuOGGtoA93CilWLduHQsXLuSbb77hj3/8I2vXru3wO2+88QaPP/44CxcuZOnSpWG3RmcwGOTTTz/lyy+/5NVXXwX0dr322mt88sknjBs3jjvvvLOtIaLVmjVr+Pvf/84BBxzA+++/z9tvvx0Wx8+CBQtYuHAhTzzxRNta3GvWrKFXr15MmDCBu+++mx9++KHD37Q2cOfl5ZGfnx82HSolJSV88cUXPPXUU23nAK/Xy7PPPkt2djYFBQW7LS1pmiYPPvggW7ZsoX///sycObOtrgql6upq/vKXv3DZZZfxySefADouWLJkCQsXLuSjjz7i0ksv3e265qWXXuI///kPCxcuZPny5WG1vvq+FL7RTydVVFRQWFjIXXfdhcvl4o033mDjxo0MGTIE0L21ixcv5qWXXiI+Pp63336b1atXc8ABB4S45Luz2+1cdNFFAHg8Hp555hlqamp2S4UfGxtLamoqeXl5Ydt63ioqKork5GRyc3OJjY3t8NxXX33F2LFjOeKII8jOzuaWW27hvPPOC8tJ/jExMfzhD38A4KmnnsLtdu+2PQ6Hg2nTptG7d++wTCs/cuRIBg0axOLFi9seW7x4MdnZ2Rx//PGMGTOGCy+8kKamprbyV1ZWsn79eu68807cbnfb8TV06NBQbQYAbrebG264gUcffbTtIsjlcnHJJZcAujX22Wefpb6+frcKODY2lrS0NPLy8sJiX7NYLFx66aV89tlnvPLKKx2ei4uLIz09nb59++4WhH/66acccsghTJkyhYSEBO6//36mT58e8mB94sSJjBw5kjPOOKPtIi4vL4+rrroK0zS55JJLOO2003ZrHXe5XJx55plkZWWF5fHT0yil+Pjjjzn66KM59NBDcTgcPP300209OEopPv/8cyZOnMjhhx9Oamoqd911F2effXZYNqgOHz6c4cOHA1BeXs7HH3+82+9YrVaSkpLo06cPffr0CXkPzU8xDIPExERycnJ26xnzer189tlnPPzww/Tu3Zu5c+eycOFCTjzxxBCV9scZhsFJJ50EwIYNG/jss88YOXJkh98xTZNJkyZxxhlnEBcXF3YJDK1WK3/605/46KOPePfddwFd5vfee49rr72WUaNGsXjxYj799FMKCgra9qu5c+dyxBFHcNhhhxEVFcW///1vTj311JCfo6dNm8bo0aOZMWNG22Pt95333nuPDRs2MHbs2A5/53Q6SU5OJjs7m9TU1G4r70/p27cvt912226NCjabjaSkJHJycnYbZdbU1MQ333zDU089RUZGBh988AFLlizh8MMP7+7id5CYmMhjjz3GHXfc0VZ3ulwuLrzwQgBef/116urqdluGTSnFYYcdxvHHHx+Wx8++0mO3sqamBqfTidvtbqu0ysvL256vr6/HZrMRFRWFxWIhNTWVkpKSEJb4xxmG0Xbwffjhh2RkZJCRkdHhd1pbbl988UXOP/98tm3bFoqi/iIJCQmkpqby5ptv8rvf/Y4lS5Z0aK0sKioiOzsbgKSkJBobG/H5fKEq7k9q/W58Ph8ffPABp5xyym6/k5iYyKxZs/jd737Hq6++GhYts+3t6SJux44dbftUXFwcfr+fpqamtufr6upwOBy43W5sNhuJiYkdjq9QaX+s7PqYUoq3336b/Pz83XplevXqhc/n47nnnuP3v/99WJwL9rQtoIPB2tpannnmGS6++OLdWpS3b99OTk4OhmGQmppKdXV1WPR02Gy23QKf1m3cvn0727dvZ8KECR22uXX/u/feezn//PN36zkU+0ZRUVHbPpSWlkZVVVWH1v7Wc7RhGCQnJ1NfXx/W52jQdf4rr7zC8ccf3+F5i8XCqFGjmD9/PrfccguzZs0K656NYcOGsXr1au677z5uvPHGDp+7x+PB6/W2NXpnZWVRVFQUqqL+pPbnt/fff59DDjlkt8bGhIQEPv30Uy6//HJuvvnmDnVQONjTOdrv91NbW0tycjKGYZCTk7Pbd9D+HJ2WlkZlZSWmaXZn0XfzU3UnQGFhIStWrNgt+7bb7aagoID333+fSy65hI8//jgsztF7qjttNhsjRozgs88+Y8aMGTz//PMdyurxeAgEAsTFxWEYBhkZGRQXF3dnsfeodVmi9lq/m2AwyFtvvbXHRuGEhAQ++OADLrvsMu64444es1LHzwm/5tUuYrfbCQaDbTut3+/vsGNYrVZM02x73ufzhXyOw09RSvHll1/y0ksv8c9//nO3sh500EEcfPDBKKW4++67eeedd7jssstCVNqfNmDAAO677z5AD5X43//+16Gl1uFwtFXWwWAQi8US1i1NrXMhAQYPHtzh5GK1Wpk1axYOh4NNmzZxySWXMGXKlLAe7gYdv4PWCrd9a3Lr8dN6DAUCgbDo5fwxSinmzJnDu+++ywMPPLBbgHX44YdzxBFHoJTipptu4qOPPuL8888PTWF/xoknnshJJ52EaZpcccUVfHQQ/C8AAKpgSURBVPnll5x66qltzzudTnw+H0op/H4/Vqs1rHuglFJ89NFHHHjggbstV+BwOHjsscdwOBysXbuWK6+8kkMOOWS30Sqia7Ue/63H9q49Se2fD/dztFIKj8fD7bff3jYSaNdz9M0334xhGJSXl3PWWWdx3nnnhU3PU3uGYXDZZZdhGAZNTU1Mnz6dDRs2MHjwYIC2Y701MPf5fGG/1IzH42Hu3Ln8/e9/3+08NW3aNM4880x8Ph+XXXYZX3zxBccdd1yISvrLGIaB1Wptu/7c07Vl++PH7/eH5UiHVkopioqKuPnmm7nmmmvIzc3t8HxaWhr33nsvhmEwb948Hn30UY444oiQ9z7vicvl4q677sIwDDZs2MBll13GtGnT2ualtp7D2h8/4XxdA3pqX2VlJWPGjNnt+DnvvPO44IILaG5u5qKLLmLBggUceuihoSloNwrPmqgLpKSkEAwGKS0tpb6+nrKyMnJzc2lsbKSxsZHExETsdjtFRUU0NTW1TcoOR63JYB588EHuvPNOcnJyAD3Gvqqqqu3io/V3Wy9mw1UgEOjQ4GC1WtuSfQWDQYYMGcKKFSsIBoOsXbuW7OzssB9a/eabb3LsscficDjaLvZaA0KXy4XVaiU9PR2LxdJhDm44UErtdhswYADr16/H5/OxadMmEhISiImJob6+nqamprbjq6ysjIaGBkpLS3er8EK1La3Bdvufv/rqK5588knuvvtu0tPTAb3vVVdX73b87OlCPhT29L20L6tpmm1lNU2zrdV/6NChLFmyBNM0WbVqFX379g3rCyev18uHH37IySefDPCjx09GRgamabYlahP7zrBhw9pG0qxYsYKCggKsVmuHfWzZsmWYpsnq1avJzc0N24Zhr9fL3XffTUJCAn/4wx/a6pu6ujo8Hk/b/ga0HVvh3BDU/vg3TROLxYLH46Guro6oqChSUlLYuHEjfr+fNWvWMGjQoBCX+KctX76cqKgoCgoKgJ3nNaUUDoejbWRdZmYmNTU1oS3sLtqfl1v/b7fb6d27N6tWrcI0TZYuXcrQoUPbrnFM02w7vkzTZOXKleTn54fFOXrX+lMpRVlZGTNnzuSss87isMMOa/u9qqoqAoFAhw6kQCAQNg1ce6o7W48Z0GVtPc6bmppoaGggJiaGuLg4tmzZgs/no7CwMKzXu1ZK8f7773PooYe2TdNpPX5AN3Lb7Xaio6NJTU1tS2La04X+SNpH4uLimD59OjNnzsTpdHLYYYeRlZXFc889h81m4ze/+Q2//e1vufnmm4mNjWXUqFFhG9h6vV7uuOMOAB599FGcTiczZszA6/Xyt7/9jUceeYTnnnuO1atX4/f7KS8v5+KLLw5xqX/cBx98wCeffILdbmfDhg3ceeedeL1eZsyYwT333MPEiRN5/fXXufrqq9mxYwczZ84M6wuNuro6vvvuOy644IK2Ia/PPPMMOTk5TJgwgSeffJLMzEwWLVrEkCFDOmR9DAdbtmzhgQceoKioiDvvvJOzzz6bgw46iNzcXK666ioqKyu58MILsdvtPPnkk6Snp3P66adz1lln8ec//xmXy8WkSZPaho+HUjAY5KGHHmobDhUIBPj973/PHXfcQVRUFA888ABRUVFce+21VFZW8vDDD/PAAw/wxBNPsHnzZpqbm6mtreW6664L9aYA8OKLL/L222+zZcsWbrnlFmbMmMEzzzxDWVkZ9fX1BAIBJk2aRH19PTNmzOCBBx5gypQpvPPOO1xzzTVs376dv/zlL2Fx/Gzfvp3Zs2ezfft2nn76aU466SSGDx/OmjVrsFqtDBo0qO34efTRRxk2bBiDBw/m2WefJTMzk2+//ZZx48btlvBDdC3DMDjmmGP48MMPufrqqykqKuKOO+7A4/FwzTXXcP/99zN58mTeeuuttudvuummsNjH9uTrr7/mtddeY/LkyVx//fWMGTOGc889lyeeeILBgwczduxY7rjjDmJjY9mwYQOnnXYaSUlJoS72Hnk8Hm699VasVis7duxgzJgx9O3bl08++YQVK1Zw7bXXctFFF3HPPfeQnp5OYmIio0aNCnWxf5RSirfeeosTTzyxrWdszZo1vPTSS9x6660899xzmKZJbW0ty5Yt49JLLw1xiXf3/PPP884771BUVMRf//pXrrvuOi688EJuv/12Pv74YxobGznssMOora1lxowZPPTQQxx11FG8//77befo22+/PSyOn08++YQXXniBrVu3MnPmTK666ipeeOEF1q1bxyeffMJnn33GtGnTGDduHH/+85+56aabKC4u5n//+x8xMTGsWbOGq666KiyC28rKSu655x4KCwuZNWsWp556KiNGjOCf//wnCQkJrF27lt/97ne43W5ef/11SktL+eMf/8jvf/977rzz/9m77/iq6vuP469z983eOyFhBQgQtiC490BFrVar1VqrFrXu7a+ttdZZV9Fad6u2dbaOKnWUoaIIyJ4JSSCQPW9u7r7n+/vjkisRkEACuTd8no9HSLj33HO/33vOvfe8z3ec34fH3Hf1huhPPp+Pf/7znyxdupTq6mpsNhtnnnkmXq+X//3vf9x///3h/WfFihV8+OGH3H777bz00kuYTCaamprYsmVLRM4hdCAM6OvYBoNBtm/fTiAQoKCgAJPJFJ75OC4uDl3Xqampwev1hu+PhA+X79N1ncbGxm5nk9PT09E0jfb29vA41K6xALm5udjt9oisC4S+nGtra/H5fOTk5IQvL9PS0kJycjIGgwGPx8PWrVtJTk4O1zVSud1uysrKGD16NAaDAaUUVVVV2O12UlNTWbZsGdu3bycjI4OJEydG3Lbx+Xw0NTV1u+RKbGwsPp+PrVu3EhsbS1ZWFpqm0dHRgdFoJDY2lmAwSE1NDX6/P/z+6W9KKZqamsLdqM1mM6mpqTQ1NYXfP11j6rtabVJSUnA6ndTW1obHRNlstn7fRkop2trawuPKDAYDGRkZOJ1O6urqMJlM4d4MXe+flJSUcDfFbdu2kZqaGh7r1d/a2tpYuXJleD8bNmwYubm54ZDedQkzpRSbN28mMTGRxMRElixZQl1dHVlZWUycOBGr1RoR9RnIlFJ0dnaybds20tPTSUlJ2eUz2u12U11dTUpKCmlpaRG7TVwuF62treH/2+12kpOTcTgcmM1mbDYb9fX1tLa2kpSURFZWVkQcmO9O1+dbU1MTsbGx5OTkYDQa8Xg8+Hy+cFf++vp6HA4HBQUFEf1+UUqxdu1aCgsLw+NrHQ4H1dXVjBo1ivLyctatW4fJZGLixIlkZmZGVF2+/xltNBpJT0/HYDDQ3NxMc3Mz+fn52O12dF2ntbV1l8/otLS08G39raOjI3zFDU3TSEtLw+VydZttNykpiZiYGFpaWkhMTETXdWpra3G5XOGTKZFQl0AgQGNjY7iFNi4ujtjYWOrq6nA4HKSlpZGWlobBYKCzs5NgMEhCQkL40jqdnZ3h909/CwQCfPvtt+H9LDk5mbFjx+Lz+Vi/fj2jR48OH3+1trZSX19PcXExmzZtYv369VitViZNmhTRn9N9aUAHWyGEEEIIIYQQA19knpYUQgghhBBCCCF6SIKtEEIIIYQQQoioJsFWCCGEEEIIIURUk2ArhBBCCCGEECKqSbAVQgghhBBCCBHVJNgKIYQQQgghhIhqEmyFEEIIIYQQQkQ1CbZCCCGEEEIIIaKaBFshhBBCCCGEEFHN1N8FEEIIIYQQkU0pFf5b07R+LEnvHKh6ROrrE6nl6g/yWgx80mIrRARrbW1l7ty5bN26NfyBHAgE+Pzzz1myZEm3D+m+ppRi7dq1VFVV/eByjY2NfPPNN+i6Hn7cpk2b2Lhx4wEtnxBCRBulFBs2bGDz5s0/uFxzczNff/01wWAwfNvmzZtZt25dv32udnZ2cvvtt9PW1tYvz98XlFK8/fbb/Otf/zog63/llVf4+OOPe7x8IBCgpqam23beF36/n9ra2vD37/cppfjggw/45JNPAFi3bh2ffvopbrc7vExzczNz585l27Zt+1WG/aWUorq6mtdff50PP/yQjo6OXfZtpRTLly/no48+Cv/MnTuX1tbWbsts3bqVzz77LFwvpRQ+n4+vv/6aV155hblz59LZ2YlSiueee441a9bI8ckAJcFWiAi2bt06zjvvPO6///7wh3B5eTkXXnghv/nNb7p9GSqlwj+7u213H+I/dB/AM888w3vvvfeDyy9dupS77767W1leeeUV/vrXv+5/xYUQYoB68cUXefPNN8P/393n6qpVq7jtttsIBALh2958802ee+65Pa53T5/n+/sd8P3vD7/fz+eff47H49nt/Xt6XG/Kt7d1/pA9LZ+UlERSUtI+PX9PngdC39k7nwze27qbm5u59NJLcTgcPSr792+vqanhsssu6xZUd9be3s5zzz3HiBEj0DSN5557jh/96EcsXLgwvJ5XXnmFc889t1sg7+t9aXfLVlRU8JOf/IRVq1bx1ltvcf311+P1endZduPGjSxatIhFixbxr3/9i8svv5yWlpbw/U6nkxtvvJFLL72UxsZGIHTC4Pe//z2PPfYYjY2NfPHFF9TU1KBpGkOGDGHOnDn7fTJBRDbpiixEhBs+fDhlZWXU1taSk5PDe++9x8SJE8MHPEopysvL+e9//4vX6+XYY4+ltLQUgCVLlvDVV1/h8/mYPn06U6dOxWg08vXXX2MwGNi0aRO1tbWcfPLJjB49epeuOTt/Sfl8Pv73v/+xYsUK8vLyOP3000lMTEQptcvZ4t4cJAghxEC28+ej3+9n/vz5LFu2jJycHE4//XSSk5N3Wa7r/7trmduyZQsbNmwgEAiwevVqpkyZwlFHHYXBYGDz5s189tlntLS0MGrUKE466SRsNhuVlZWUlZXh8/koLy/nsssuIyEhIbxOn8/HggUL+Pbbb4mLi2PmzJnEx8eH7w8EAsyfP5/ly5djMpk4/vjjGT16NABlZWXhFrLi4mJOP/10/H4/H374IZs2bSIpKYnTTz+dgoIC3G43n3zyCevWraOwsJDTTz+duLi4bt9FPp+PTz/9lBUrVhATE8MJJ5zAyJEjmTt3LpMmTSIjIwOv18tHH33EiSeeiNfrZeHChSQnJ7N48WJ+/OMfk5+fH15fampqeP2BQICvvvqKr776isTERGbOnEl2djbbtm1j06ZNHHvssWiaxsqVKwkEAkycOLHba6/rOkuWLGH+/PkMHjy4W8D0er3h1zArK4uZM2eSkpISfm6lFF988QWbN2/m5ZdfJicnh7PPPhuPx8PcuXMpLy+nuLiYk08+GbvdTn19Pe+//z4NDQ3k5eVx1llnsWDBAsrLy3nhhRfIy8vjjDPOwGQyhde/cOFCMjMzyc3NDd82Y8YM3nnnHY4//ng8Hg+ffvopU6ZMCZe76wTGkiVLSE1N5YwzziA9PR2Xy8V///tfNm3aRGJiIqeddhr5+fnhbVtYWMi8efOIi4vjnHPOITk5GZ/Px4YNGxgxYgRWq7Xba/fqq68yceJE7r33XpxOJ6eddhrLly9n2rRp4WU0TePHP/5xuOx//vOfcTgcDBo0KPz6v/jii4wcOZKysrLw4xYvXsyiRYt48803iY+Px2AwoGkamqYxZcoU7r//fqqqqhg6dOgu7ycR3aTFVogIl5iYyJQpU/j4449xOp0sWLCAU089NXz/2rVrueaaazCbzaSnp3PbbbexdOlSgsEgq1atIisri5ycHO69914WLlwIwNtvv83111+Py+XCZDJx5ZVX0tTUtMcyKKV4+umn+dOf/sSgQYP48ssvue6663Z7dlUIIcTeKaV48cUXeeSRRxg0aBBLly5l9uzZe2x925PVq1dzxRVX8M0335CTk8Ovf/1rPvjgAyDUgmiz2Rg2bBhvv/02zzzzTLh755VXXsnKlSspKirCYPjucFDXdR599FGefPJJCgoKsFqtuwxJcbvdrF+/nqKiIuLi4rj++uspKyujvb2dq666imAwSHFxMTU1Nfh8Pp577jneeecdRo4cidVqpaamhkAgwG9/+1s+/vhjhg0bxpIlS3bp/QPwwQcf8OSTTzJ06FBSUlLYsmULSinmzJlDZWUlAC6Xi4ceeoj29nZqa2u55pprePfddykqKsJsNndb39y5c8Otk6+88gpPPPEEBQUFOBwOZs+eTVtbG2vXruWFF14In1j49NNP+fDDD3fZfl9//TU33HADaWlpVFRU8NFHH4Vfw8cff5zXXnuNIUOGUFlZyQ033BBu7e7SFXINBgMGgwGv18stt9zC4sWLGTZsGJ988gkPPPAAfr+fm2++mcrKSkaNGoXD4aC9vT283YxGY7dt2GXevHlMnjy5233Tpk2jurqabdu2sWzZMlJSUigsLAzX6dlnn+W5556jsLCQ+vp6rrnmGpxOJy0tLVRXV1NcXIzT6eTqq6+mvb0dj8fDr3/9a5544glycnJYtGgR9913H0opmpqamD179i7HF7qus3z5cg477DA0TSM+Pp7hw4ezevXqXeqw8z731ltvccEFF2A0GsP78dKlS7n00ku7nTBYsGABycnJ/OY3v+GCCy7g8ccfD7+v4uLiKCwsPODDuUT/kBZbISKcpmnMmjWLBx98kMzMTLKysigoKAjf/8orr5CXlxc+Izt06FD+/e9/M3nyZI499lg+/fRTGhsbsVgsfPzxxxxzzDEAnHzyyfziF7/A7/fz3//+l8rKStLT03dbhs7OTt544w2efPJJJk6cyMknn8zJJ59MeXn5gX8BhBBiAPJ4PPz973/nvvvuY/r06Zx22mmccsoprF+/fp/XVVhYyO23347NZsNms/Hqq69y+umnc/jhh4db2WJjY/nwww+ZPXs2AMXFxdxyyy27BL+WlhbefvttXnnlFYqLi8O37zy2NjY2liOPPJIFCxaEw81XX33FqaeeisvlYvjw4UyZMoWUlBQMBgPNzc1kZmZSWlpKTk4ONpstHATvuusubDYbkydP5qGHHqKxsZHs7Oxuz5uUlMSYMWMoKCggJiZmj2NKu6SkpHDHHXeQmpq6x2XcbjcvvPACP/rRj4iLi6O4uJh3332XlStX9vh1/+c//8lPfvITLrvsMgKBAEuWLAFCXYz/+c9/cu2112K32yktLeWDDz6goqKCkpISIPTdfvjhh5OZmcnFF19MSkoKK1as4Ouvv+buu+/GZrMxdepU5syZwzXXXENraytHHXUUU6ZM4fTTT8dkMjF9+nSysrK45JJLiIuL61Y2pRRbtmzh+OOP79YCHh8fz/Tp0/nwww/ZsGEDZ511FnPnzgXA4XDw8ssv8/Of/5zY2FhKSkp477332LBhA+PGjWPcuHEsXboUl8tFRUUF5eXlDB8+HLPZzHXXXUdpaSkjR47kV7/6FT6fj8zMTN58800yMjK6lU3XdTo7O4mPj0fTNJRSxMfH09HRsdvXWSnFihUrcDqdHHnkkWiahsPh4I9//CM33HBDt94EAE1NTXzzzTe8+OKLZGdnh5e5/PLLAcjNzd3r/CEiOkmLrRBRYOTIkQA89NBDzJo1C6PRCHw3+UJLSwtLly5l6dKlpKenM3XqVGpra7nssstobW1l1KhRFBYWdvvSyM7ORtM0DAYDNpsNn8+3x+d3u934fD6ysrLQNI3Y2FgSExNpaWnBZDIRDAa7nfn0+/3h7lBCCCF25fF4cLvd5OTkoGkaMTExpKSk0NzcjNFo3GUOBb/fv0sI7ZKdnY3VakXTNAoKCmhubsblcvGrX/2KJUuWMGzYMEaNGoXb7Q6HwuzsbMxmc7iLZpeu74nMzMzwfd8fprJq1Spmz56N0Whk9OjRZGVl0dHRQWpqKn/4wx/497//zU9/+lNuvvlmOjo6uOaaa0hJSeHmm2/mvPPO48svv6SlpQWn08maNWtYunQpGzdu5Ec/+tEuXVbPPfdcDjvsMH79619z7rnn8u9//3uPYz+7pKamhrs072n2W5fLRVNTExUVFSxdupRvv/2W4447jqysrB9c9863NzQ0UFBQgKZpmEym8Elnh8NBW1sbGzduZOnSpaxatYozzjiDxMTE3ZalS0NDA52dnaxatYqlS5dSWVnJeeedh91u5/7772ft2rVceeWVXHbZZT2a7MlgMOxyEkDTNM4880z+8Y9/sGbNGo488shur0lzczPl5eUsXbqUFStWcOqpp5Kamsq7777LvffeS0pKCmPGjCEuLg6XywWAzWYLd7O22+3hYwKTyUROTs4uxwMGg4H4+HgcDkf4tW1vb9/j66OU4vXXX+eUU04Jj4+eO3cumzdvZv78+Tz99NM0Njby8ssv09TURFJSEhMnTuToo49m5MiRnH322SxYsCC8Pl3Xd9vCLaKfHHkKEQUsFgsXXHABb731FjNmzAifFdY0jZEjR9LS0sLdd9+N0WhE13WCwSDLli3DZrNxww03YDQamTdv3n53u4mLiyM+Pp6NGzeSm5tLY2Mjzc3NZGdn4/f7aWlpobGxkZycHLxeL+vXr+fcc8/ty5dACCEGlJiYGJKTk8Ndepubm6mvryc3Nxer1YrD4aC2tpbCwkL8fj9r1qzhuOOO2+26ysvLaW9vJykpiZUrV4bHr27evJmHH36YnJwcXnvttW6TUe0p8KWkpGAymdi4cSOHHXYYwC7haPXq1YwePZpf/vKXeDye8KRWSimOOOIIjj76aJqbmzn33HNZsWIFhx12GHfffTd+v58XX3yRZ555hvvuu4/k5GR++tOfMmzYMCA0LvX7wdZms3HddddxzTXXMHfuXB5//HFOPvlkYmJiaG5uRilFbW3tPs/WHBcXR15eHscffzynn346EDopazQaaWxspL29nUAgEJ6PIi8vr9vjNU2jqKiIVatWcdppp+H1elm7di3FxcWkpKSQkZHB2WefHR6/6vP5djkx0dVa2RUE8/PzSUxM5Iorrgj3wvJ6vVgsFoqLi3n00UdxuVxcc801fPDBB5x66qnh7/yu7/eu7appGkOHDg133d55ew8fPpzx48czaNAgUlJSwrfHx8eTm5vLqaeeGu7d5fP5MJlMPP3008yaNYtLL72U+vp6nE7nXo8pAoEATU1NpKWldQu3BoOBSZMm8eWXX3LeeefR0dHBpk2bmD17NkopmpubiYmJISYmBggF/oULF/LXv/41XI+JEydyww03AKETCWazmczMTMxmM5MnT2bhwoV4vV5sNhs1NTWkpaUB382ifMYZZ/zwDiKikgRbISKYpmnh1tlZs2Zx5plnYjQau93+s5/9jNmzZ/PLX/6SgoKC8Bne8ePH4/V6ufXWWwFYs2YNY8aMAXYdj9O1zu/rGvdjs9m4+uqr+d3vfsf8+fPDZ3GLiopQSnHKKadw5ZVXMmXKFDZv3ozJZOLUU0+V68QJIcT3dH3+WiwWrr32Wh588EG++uor1q5dy1FHHcXw4cPRNI2zzz6b2bNnM3XqVLZs2YLb7eass87a7edqZ2cnt956K6mpqSxYsIDHHnuMpKQkRo4cya233sqgQYNYt25dODQaDIbwd8j3JSQk8Ktf/Yrbb7+dGTNm4Ha7Oe6445g2bVr4u2L8+PHMmTOHu+66K3xi02AwUFNTw913382gQYPCczgUFhby+OOPs337dtLT01m0aBGzZs0iLy+Pyy67jKuvvppp06bh9XrxeDw89NBD3cLta6+9xuLFi8nLy2P58uVMnz4dq9XKzJkzefDBB/nmm2/YsmVLt++xPdVtZ1arldtuu4377ruPzz//HKvVytatW/nDH/7AqFGj8Hq93HjjjVitVjZs2BCesGhnl1xyCVdccQUtLS243e7w65CUlMT111/P7bffzuGHH45SisbGRh555JFurZKJiYnk5eVx4403MmrUKK677jrOPvtsrrjiCqZMmYLT6cRsNnP77bdz++23k5iYiNlspqKigquuuorU1FSSk5O5/vrrGTNmDNdee2238Hzcccfxt7/9jauuugqTyRT+Tjebzfzxj38Mt2h3vXaxsbHccsst/O53v+OTTz7BaDRSU1PDAw88wJFHHskDDzxAQ0MDVVVV4bC88/EIEG69Bqivr+f888/n9ddfDwf1rmUuuugifvazn3HjjTfS0NDA+PHjGTt2LLquc/3113P22Wdz9tlno5Ri3rx5DBo0iBEjRoTXMWTIEIYMGQKEgu+zzz7LqaeeSlJSEkcddRRvvPEGs2fPJi0tjcWLF/PUU08BoZbhbdu2MXnyZDlGGYA0JSOnhYhYDoeDrVu3MmrUqG5BtK2tjZqamnAXZYfDwcqVK2ltbSUvL49Ro0aFz1IuX76c9PR08vLy8Hg84Yks7HY7WVlZKBW6rmJubm63WTG7puO32+3k5OSg6zplZWVs2rSJrKwsSktLsVgsQOiM7tq1a9m6dSupqamUlpbuMrOlEEIc6pRSVFVVYTabycvLQ9d1Nm/ezIYNG8jIyGDcuHHhUOf3+1m3bh1btmwhKSmJ0tJSEhISdvlc/eCDD/jnP//JHXfcQUVFBaNGjWLw4MFA6Lvhm2++wWg0UlJSQkNDAyUlJbS2ttLS0sLQoUN3+zm9c7ni4+MZP348sbGxrF27lpEjR2I2mykvL2f9+vUUFhYSFxeHzWYjIyODjRs3UllZidlsZty4cWRkZNDY2MjatWtxOBwUFRUxatSo8DCWsrIyysrKsNvtjBw5Mtw1u0traytr1qyhubmZ3Nxcxo4di9Vqxe/3s3z5cpqamigtLaWpqYmRI0cSCATYvHkzJSUlu3Q3VUpxzz33kJSUxHXXXQfA9u3bWb16NUophg0bxuDBgzEYDNTV1fHtt9+SmZlJenp6uJv399e3fft2VqxYEf4OjY2NJSsrC13XqaqqYv369ZhMJoqLiykoKOhWJqUUbW1tlJeXYzQaKS0tDX8nV1RUEBcXR0lJCenp6VRVVbFp0yYCgQAlJSUMGjQITdNoaWmhvLwcq9XK2LFju63f6XRyySWXcP/99zN8+HAqKiqw2Wzk5OR0K8PmzZuJj48nMzMTXdeprq5m7dq1aJrG8OHDwyexV61axbZt28LBPzc3l9jYWNatW0dxcTFWqxW3201ZWRmjR48O9zQYPXr0Li3xXV25ly1bRlxcHJMmTcJutwOwfv160tPTSU9PD79nNE0L1/n7uo5BRo0ahdVqRSlFZ2dneDzwuHHjwuO233//febPn8/DDz/coxMgIrpIsBVCCCGEiFIffPABb7zxBi+//LKMG/wBSineeustnnjiCe6//35mzJgx4E++KqVYtGgRuq4fEvXdG13XefPNN5kyZQqFhYWH/OsxEEmwFUIIIYSIUnV1ddTU1DB+/Hg5UP8BXTPraprGmDFjDpnWup0P8w/1/UNei4FPgq0QQgghhBBCiKgW8ZNHKaXw+Xzoui5nV4QQQkQ8pRQ2m63fv7N0Xcfr9fZ7OYQQQoi96bpE1J4ua9YTURFsf//731NRURGeqKY3dF3H5/Nhs9n6oHT9SymFx+MJD7aPdl6vF5PJNCC6B/n9foBevTkjxUDbzzweT/h6j9FuIO1nwWCQQCCwywQj0UbXdQKBAM8++yyxsbH9WpYtW7Zw3XXXkZqa2q/lEEIIIfbG4XBwzjnncOGFF+73OiI+2GqaRkdHB3feeSeFhYW9Xp/L5aKqqopRo0b1vnD9LBAIsHbtWsaMGTMgJowoLy8PT10f7WprawkGg7tc9y4adc3MOXbs2KgPg0opVq9eHZ7VM9pt374dTdO6zXAZrdra2mhsbAxfzzJaeb1err/++v4uBhA6WVBUVMR9990X9e9dIYQQA9vcuXNpbGzs1ToiPthCKNza7fY+O/tts9mIiYmJ+i/6QCAQrku0t3IqpbDb7cTExPR7K0dfiImJIRAIDIi6+P3+8LaJ9hMouq6H3zN90QOkv9ntdgwGw4DYz3w+X3g/i+bPZqPRGFGfxyaTidjY2Kh+TYUQQgx8fdGbLiqC7e70xZxXA2nerINdFzlIEkIIIYQQQkSKqAy2SikcDgcul2ufA53f70cpRW1t7QEq3cHTNaFWXV3dQQuaVquVpKQkDAaDhFshhBBCCCFERIi6YKuUwuVy0d7eTkpKyj53jdR1nfj4+AEzeZTdbj9os292nVBobW2VyUiEEEIIIYQQESPqgi2EZjWNi4vbr3FDwWAQo9EYEZdi6K2u1uqDNSZNKYXBYKC5ufmAP5cQQgghhBBC9FRUBttosHMX6WgP0F00TRswdRFCCCGEEEIMHBJse8DhcLBkyRJ0XQ/fZjAYmDx5MgkJCbt9jFKKZ599ltNOO438/PwfXL/P52Px4sUMGTKE7OxsAMrKymhra2PSpEl9NhOtUooNGzaQkZGx267EwWCQlStXUlJSgtVqJRAIsGrVqvD/hRBCCCGEECISRfe1Ow4Sr9dLeXk5K1eu5Prrr2flypWUl5fj8XjQdR2lFEqp8N8Qat0cO3Ys8fHxALtdpkt7ezuXXXYZjz32GEop/H4/t99+O7feeiuBQCD8+O8/tmud37en5wGYM2cOK1as2O1yXq+Xu+66i5aWFiDU5fvOO++kra2tdy+gEEIIIYQQQhxAUd9iuz+Xuel6zJ4e+/3utunp6Vx55ZW0trby8ccf8/Of/5x58+bxwQcfsGHDBi699FI+//xz1q5dS2JiIldeeSW5ublUVFRQWFjIli1b+Prrr9m+fTudnZ1cc801FBYWdnue/Px8tm3bRnNzM3V1dVitVuLi4gBoaWnhhRdeYOvWrZSWlnLxxRfjdDr585//zPbt2xk+fDi/+MUvMBgMPP/885hMJtauXcvJJ5/M6aef3u15uoJsMBjk3XffZd68eSQnJ3PllVeSnJy82/AshBDRTimFAnRd4QvquLxB2tx+mpxe6h0e8pNjGF+QJMMthBBCiCgV9cG2scPLXxZW4A/qe18YwqHOZNq16iePzmLa4J7N9vvll1+ilOKGG24gKSmJww8/nGOOOYYvv/ySJ598kvvvv58FCxYwfvx4qqqqePvtt5kzZw6LFi3imWee4YEHHui2PrvdzqRJk/j8888pLy/npJNO4p133kEpxaOPPsqQIUM4//zzefrpp/nvf//LMcccwxlnnEEwGOT111/n/fff55RTTuGVV17hnnvu4aijjuLWW29l+vTppKSk7FL+FStW8Nprr/HAAw/w9ddfc++99/Lwww/3qO5CCBFJwicrgUBQ4Q0EcXoCtLr8NHR4qHN4qGlzU9Pmoa7dQ5PTS5vLjzcQBCDGYuKCKfmML0jqv0pEgWanlyanr7+LsUfxNhPZidE/MaQQQoj9E/XB1hfUqW5x4Qv0MNgS6n5rNBh3ua/DHejx8xqNRk488UTy8vJwOp28+uqr1NXV4fP56Ozs7DYeF2DGjBkMGzaMYDDIwoULUUrt8uV70kkn8fDDDxMMBrnzzjt555138Hg8LFy4kK1bt7J48WIaGhrIyspizJgxPPHEE/j9fhoaGrDb7Zxyyink5ORw5JFHYrPZSEhIwOl07jbYLl++nCOOOIKhQ4eSmprKP//5T9xu966vl7TYCiH62c7B1R/UcfuCdHgCNHd6aXB4qWnvCq5u6h0emp0+OrwBfAEdk1Ej1mIiNdZCRoKVYZlxHDU8nZwkGxkJNtLirCTYTMTbzP1bySjw+pJqnvysrL+LsUenjMnm0fNK+7sYQggh+knUB9vcJDt/vmhij5fXdR2v14vdbt/lvn05yatpGmazGU3TWLVqFe3t7TzzzDOsWbOG++67b5dAaDKZ9jqrcGFhIRaLhcGDB5OYmAiEAnRqaipXX301Q4YMCV+79vnnn2fKlCmcd955vPXWW1RXV4eX78nZ6vj4+PBjOjs7MRgM2Gw27HY7zc3NZGdn43A4ws8nhBAHys6fl7oCnz+I0xugpdNHvcNDdauL6hYXW1vc1La5aXJ66fQF0XWFxWQgwW4mLc5CVoKNSYUp5CbZyUq0kRFvJSXWQrzNjM1swGw00PXpKK16+y6gKzw9PIncH3rac0sIIcTAFPXBVtM0jPtyfKLAaNAwaPt+YKNpGkZjqKXXYDCEZyvOzMxk69atvPDCC6xYsSK83q6QufOyO69jZyaTCZPJxOOPP47RaKSlpQWTyYTFYuGiiy7iqaeeYvr06TQ3N3P88cczZMgQXn31VZxOJ/Pnz2fSpEnh9ey8zu/rKtNRRx3F66+/zqOPPsr69es57bTTiI+P5/zzz+eBBx7gyCOPZOnSpZxyyinhsb5CCNEbXQE2qCs8fp0Or5+mDh+17W7KalvYsK0Z11cOats9tLp8uP1BjJpGvM1EeryV3OQYjhiWRn5KDFmJNtLjrSTZLcTZTFhNBowGDQ0JrUIIIcShKOqD7cEUFxfHo48+Snx8PL/85S9JSkoCYMiQITz22GNUVVUxc+ZMfD4fJpOJW2+9laysLLKyspgwYQIABQUF3H777d0OvJKSknjwwQexWCzhAJyWlsb999+PyWRi1qxZjBkzhs2bNzNu3DhGjx7NhAkTyMzMpLm5mfPOOw9N04iLi+P3v/89NpsNg8HAr3/9azIzM7vV4aabbiIlJYWEhASeeeYZVq5cyTHHHMOYMWPQNI3zzjuP0tJSqqqqOOywwygpKemzyw0JIQaunbsLB3WFL6Dj8QdxeAI0O71sb3PvaHV1sa3VTUOHl3aXD7+usBgNxFsMJNsNDMuxMqkwhfwUOzlJdtLjrCTazdgtRgmuQgghhNgjCbb7wGQyMXLkSCAUULtomkZxcTHFxcXdlh88eDAAMTEx4dvsdjtDhgzptpzZbGbEiBHdbrNYLN3WN3z4cIYPH95tmcmTJ+NyuYiJiQkf6O28zLBhw7otr2kaRUVF4f9nZGRwwgkndFvGaDQyatQoRo0atbuXQAhxiNk5sOpK4Q/smJzJG6Dd7afF6aPRGRrrWt/hoaHDS1OHl1aXH4fHjy+goytFjMVISqyFnEQ7I7MTOLEkhvzkUJfhtDgrAbeT9pYmRo747nNPAqwQQgghekqCrRBCHIK+P641oOv4Ajqd3iAdHj8tnT6ad4xxbXB4aejw0NjhpaXTR5vbj8cfxB9UmAwaNrORRLuZlFgLaXEWxuUnkZFgJSM+1F04Lc5CcoyFWKsJs9GAYUde3Tm4tgbdOHfcIYFWCCGEEPtKgq0QQgwguwusXr+OyxfA4QnQ6vLR7PTR2OGlsSMUWJucvlBgdflw+YL4AjqaBlaTkXibieQdgXVwehxTh1jJjLeRFm8lNdZCot1MnNWEzWzEbNQwGr4LpRJQhRBC9IdlW1r4uqKlv4uxR9MGpzJhUHJ/F2PAkWArhBBRpCu4BnRFp0/H5Q/QUeegzeWnyRnqBtyw46fJ6aXF6aPd48flC+IP6Bg0DbNJI95mJsluJjXOQk6ijdL8JDLiraTFhVpYk2IsxNtM2M1GLDtNzAQSWIUQQkS2rza38MjHG/u7GHt028kjJNgeABJshRAiwnx/9uB2j59Gh4dtrW6qml1UNXdS3eKiprUTpzcAWmiCN5vZSJzVRFKMmbQ4K/nJMUwclBwOrF0trPE2MzaLMTSTsKaFL3UmgVUIIYQQ0UqCbQ90dnbS3NxMfn4+mqahlKKpqQkIzV6888FgR0cHb7/9NhdddFH4cju6rlNZWYndbic7OxtN02hra6O2tpahQ4diNpv7rKzNzc3ouk56evou9yml2LZtGykpKcTGxqKUorq6mrS0tG4TXAkhDryu8Kor8AaCdHgCNDm9bG91s2VHeN3a4qJux6VvAkGFfccETLlJdsbmJTEj30Z2ko2h+dkk2c3E7dzCulNgBQmtQgghhBjYJNj2QHNzM9deey2vvvoq8fHx6LrO7373O2bOnMkJJ5wQPkDVNA2Xy8WHH37IBRdcEA62gUCAq6++mri4OP7+979jNpuZM2cOr7zyCgsWLCArK6vbuLiuA9Dd3fb927/v7bffxuVycf311++ynFKKe++9l5/97GdMmzYNpRS/+c1vuPrqq8PXwRVC9K2u96EvqNPhCdDs9FHT5mZrSyi8bml2UdPmpqXThy+gYzEZSI6xkJNkY3B6HMeOyGBQagw5iXZS46zE276bgGnbtm0YDAZyc5P6t5JCCCGEEP0s+oOtUoQuRNHjB4DSd/z+/uPC/fG63ZqTk0NqaipLly7l6KOPpq6ujvLycgYPHsycOXOor69n/PjxnHHGGXt8VpvNRjAYpLy8nNzcXNasWUNubi5KKfx+P3PnzmXJkiUUFhZy/vnnYzabefvtt1m3bh1ZWVlceOGFJCYm8u6772I2m/nmm28oLS3lxBNP7PY8wWCQQCCAUor169fzzjvvYDKZOP/88xk0aBA+nw9d18PL+/3+bv8XQuyfcPdhpXD7gjQ5fWxp7qSs3snG+g42Nzipc3hw+4KYjBqJdjPZiXbyU2I4rCiFwtRYcpPtpMVZSbCbsJqMu509WAghhBBC7Cr6g61jO3x6DwR9PVrcoBRmXQejcdc7x10Aw07a5Waj0chZZ53Fv//9b4466ig+++wzJk2aRFxcHOPHj8dut/OXv/yFlJSUPV7/1Wg0cvzxxzN37lzGjBnDyJEjcblcAHzwwQd8/vnnXHLJJcydO5eXXnqJyy+/nNzcXEaOHMn//vc/nnnmGW655RZefvllpk6dyplnnsnvfvc7Bg8ezLhx43Z5vtbWVm6//XZmz56N0+nktttu48UXX+zRaySE+GFKKRTgD+i0u/3UtHuoaHSysa6DTfUdbG1x0drpQ9M0UuMsFKbGMn1oGsMy48hPiSEj3kqS3YzNbAzPIizhVQghhBBi/0V/sNUMYImBYA+rohQEdTDtJtgadj/WVdM0ZsyYwZ///Ge2b9/ORx99xM0334zX6+XNN9+ko6ODiooK1q1bt8dgCzBt2jQeffRRNm3axGWXXca3334LwIcffkhnZyevvvoq7e3t+P1+Lr30Ur744gvKyspwOp1AaKxuTEwMP/rRjxgyZAglJSXU1NTsNthu3ryZtLQ0TjzxRILBIK+//jo1NTVy8CzEPlJKEdQVLl+QRqeXLc0uyuo72FjXwebGUCusN6ATazGRm2RnaEYcRxdnMCwjjryUGFJjLdjModZXef8JIYQQQhwY0R9s47Ph9Md7vLiu6/i9Xox2+z49TXJyMuPGjeOFF17A5/NRUlLCI488wrhx4zjnnHN49tlnCQQCP7iOpKQkBg8ezJo1ayguLg7fbrFYOPLII5k2bRoAMTExLFu2jOrqav74xz+yYcMGHn88VEeDwRCebMpoNO6xG7HJZAp3SdZ1nUAggMlkIj4+npaWFpRSBAIBnE4ncXFx+/RaCDEQ7TwWtt3lZ3ubm82NTjbUdVBW72Rri4s2lw+DQSMtzkpRaixHDU+nOCueorRYMhNsxNvMmI3SAiuEEEIIcbBFf7Ddn4NHbfdjafdm1qxZnHXWWfz617/GarWSnp7Ot99+i81m46OPPuLMM8/cy9Nq3HTTTXi9XiwWS/jA9/zzz+fpp5/Gbrfj8XhITU0lOzub7du3M2/ePP773//uNTR//3mGDRuG3+/nmWeeobOzk/T0dHJzcznrrLN46qmn8Pl8VFRUkJiYSGFh4T69DkIMBLpSeP06LZ1etra42FjvZH2Ng7KGDmraPHj8QeJsJvKS7QxNj+P4kRkMzYgnP8VOSowFq1nGwAohhBg4lFI0Or10eHp+zHmwha6/bu3vYogIFf3B9iDRNI3S0lL+9re/MWHCBDRN4+KLL+bjjz/G7/fz+OOPExMTQ1JSErfcckt4RmQItZ7edtttpKenExMTQ3x8PMFgkDvuuIPk5GRmzJhBWloaS5YsISYmhrFjx5KVlcWtt95KRUUF1157LV6vF5PJxA033BC+lM8FF1xAbGxst3KefPLJBINBYmNjeeKJJ5g3bx5ZWVlcccUVWCwWjj76aFJTU1m+fDnDhg3j8ssvx76PrddCRIudL6nj9gVo6PDyba2XL5orKWt0Ud7gpKHDQ1BXJMdaKEqNZdqQVEZmJzAkPY6sBBtxNhMmGQcrhBDiEPDEp2W88+32/i7GHv38iCJuPrF47wuKQ5IE231gtVo5/vjjw/+PiYnhrLPO2mW5yZMnd/u/wWBgypQp3W4zGo0cdthh4f+PGjVql/G5M2bMYMaMGd1u2/myPDtPQNWlqKgo/HdGRgbnn39+t/u7AnppaenuqihE1ArPSqwrnN4A9Q4Pmxs72VDrYH1dB5VNnTQ7vejBINnJPoakx3HqmCxGZCdQlBpLRoKVGItJxsIKIYQ4ZPkCOm5/sL+LsUf+gFzJQ+yZBFshRNRSSuEN6FS3uFi9vZ11NQ421newpdmFw+PHajKQnRia0OnCKQUMz4zDWVfFtPGjiY+xoUmIFUIIIYQYECTYin2idrn2rxAHT9dldpyeAJvqO/iivIkvypqoaOzEZjFQkBLD8Mx4Th2TzfDMOPKSY0iymzGbDGg7Hr/SuQ27xYjBIIFWCCGEEGKgiMpgazabcTqdBIPBfW5t0XWdYDC4x9mEo4lSKjzr8cHidrvDszILcTAopdAVNDu9rNzWzoJNDSyuaKGxw0t2ko1pg1O55tihlOQkkhRj/sHxsHJiRgghhBBiYIq6YKtpGrGxsbhcLrZv3/fB7cFgEI/Hs8ukS9FIKUVnZyexsbEHrTul0WgkIyPjoDyXOHQppfAFdba3uvmmsoUFmxpZWd2GN6AzPDOeWRNymT4kjSEZccRaQtekli7FQgghhBCHrqgLthCajCkzM3O/WipdLhdVVVXk5uZG/YFwIBBg7dq1DB8+HIPBcFCes+t5ov21E5GlqyW10xugrMEZ7mJc1uDEbjYyLj+JG04YzuTCFHKS7JiNmuyDQohD2rqadr4sb+7vYuxRaX4SU4pS+rsYQohDSFQG264DWqPRuM+P7QpmRqMx6g+Mu8KAwWDYr9dCiP7U1cW4pdPH6u3tLNzUyNcVzdS1e8hMsHHY4BSuOHIwY/MSSYm1ymzFQgixk6VbWrnvw/X9XYw9+uVRQyTYCiEOqj4Jti6Xiw8//JDGxkZOPvlkCgsLwwegSikcDgcffvghTU1NTJkyhcmTJ6NpGlVVVcydO5f09HROPfVUYmJi+qI4QogIppSipt3DovImFmxqZPnWNtz+IEMz4jhtTDbTh6UxLCOOOGvo40nCrBBCCCGE2Jte919VSvH000+zdOlSkpOTuemmm2hra+t2/wMPPMC6desYMmQI//d//8e6detoa2vjpptuIjk5mSVLlvDMM8/IxC5CDHBKKVZvb+fi5xfzx082EQgqrjl2KG9eNY1Xfj6Fa44dyvj8JOJtZjRNuhsLIYQQQoie6XWLrdvtZt68eTzzzDPk5uYyf/58li9fzrHHHguEDmRra2u59NJLmTp1Km+++SZtbW3U19eTkZHBeeedx/Tp07nqqqu46qqrwq22uwu5fR18B1KQlrpEpoFQl53r0Nv6bG50cvObKxmUFsu9Z5aQnWjD0E+zFw+EbdNlINUFBl59hBB7Fg3vdznJKkR06HWwdblc+Hw+kpOT0TSN3Nxctm3bFr7fYDBwySWXcM8995Cenk5MTAzjxo3j7bffDk/glJycjM/nw+VyhYPtihUr+Pjjj0PXnVy5kqqqKgKBQG+Li9/vp62tjbKysl6vq7/puo7D4aC8vHxAfOg2Nzfjdrtpamrq76L0WkdHB7qu4/F4+rsovbbzftYbzR7F/Qsa0Pxufj4mBVfjNjY39lEh94HD4WDz5s0DYlx6e3s7mqbR2dnZ30XpNY/Hg9PpjPrPZp/P1+v3fdel3IDd9lzoul8phcFgGBCf/+LQ9d+1dXywqra/i7FHZ47L5YRRmf1dDCFED/Q62HZNxtQ1Q3EgEOh2ndNAIMA//vEPbrzxRsaNG8c999zDokWLMJvN4aAaDAa7rQsgOzubI444AqUUq1atIjk5mdTU1N4WF4/Hg8vl6pN19Tdd12lrayM1NXVAHNi43W4SExOJj4/v76L0iWAwOCD2s2AwSFtbGykpKfu9nzU6vTz44Xp0g4XHfjyBzNj+C5UtLS2kpKRgMkXl3HndBINBDAbDgNjPnE4nuq5HfV28Xm+v9i2lFNXV1cyZMwen08l5553HUUcd1W3eivXr1/P888/jcrmYMmUKF198sVxfXEStsnpnRAfb0bmJnIAEWyGiQa+P7GJjY0lISKC6upphw4ZRXl7OzJkz8fl84eBaVVXF2LFjycvLo7CwkKqqKsaPH89//vMfAoEA1dXVJCQkdLu2bFZWFllZWSileOutt0hMTOyTA57Ozs7wgW20h8FAIIDFYiE5OTnqW5+UUjQ3N5OQkEBKSvTPouj1egkEAlF/kA6hXg5Wq5WUlJR9vqyUUop2t59HP6ykI2jkqfNLKc6K77f3nq7rWK1WkpOTsVgs/VKGvuRyuQZMsDUYDHg8nqj/bPZ4PL0Ktrqu84c//IFjjjmGkSNHcueddzJy5EgyM0MH1sFgkD/84Q+cffbZTJw4kdmzZzN+/HjGjx/fV1UQQggholKvg63FYuGnP/0pv/vd78jOziY1NZXRo0ezYMECli1bxq233srJJ5/MnXfeSVFREStWrODxxx8nLy+PlJQUbrnllvAY3IFwoCmECFFK4fIFufeDdayrcfDkj8f3a6gVIhq0t7dTWVnJAw88QGJiIvn5+axduzYcbDVNIz4+Hl3XCQaDxMbGYrfbw49XSqHrOkopgsFgt27LvacwELnjITXow7pGPg0ienuACvfm29tyEV0P1dN6DAyRvl9p0KPtoUX6ftXj94fYF70OtpqmMXPmTMaOHUtHRwfDhg3DarUybdo0xo8fj6Zp/OpXv6KqqgqHw8ENN9wQbpF76KGHKCsrIyEhgYKCAjngFWIA8QZ0Hvl4I5+XNfHH80oZX5Ak73Eh9sLtdqNpGjabDYDk5GSam5vD9xsMBmbOnMmjjz5KSkoKCQkJ5OXlhe/3+Xw88MADVFdX43A40DSNlStX9knZUjwOzhscuQdi+ZaWPqtrNLA5nBG9PbIC9axcuffx5pG+XyW6a1i5sqO/i3HQDDG2RPT2yArW9Wi/Sor0/cp1aO1XPVFRUdHrRs4+GWRmMBgoKirqdltcXBxxcXGhJzGZGDp06C6Ps9vtjB07ti+KIISIIP6gzjPzN/Puihp+f9Zopg9Nk1ArRA9YLBZ0XQ8PAejs7Ax/l0Io+D7zzDM89dRTFBYWcvPNN/PZZ59x5plnhh9//fXXEwgEqKys5NVXX2XUqFF98v77srmC97ZG7oHYSXFJXD6qhEPlo2aNq5r3trb3dzH2KC0/nZKSYXtdblGE71eDh2VRUlLY38U4aN7YvJb3trr7uxh7lJKXTknJ8L0u91VLZO9XRcMyKSkp2vuCh5AtW7ZQX1/fq3VE/+wpQoiIEtR1/raoir9+VcXtp4zk5JKs3V7SRwixq675JFavXs2wYcOoqKhg5MiRNDQ04Ha7SU1Nxe12EwwGwwG4awJGCPWiSkxMBKC1tRWj0YjFYumTYKtrRlyByH0v+zFgsZgPmZNoyhDZ2yOIsUetL5G+X+mGntVjIFBKEVCGiN4ePd+vTBFdD10zHTL7VU/1xaSeEmyFEH1G1xVvf7udOfPKufqYoZwzMReDIXK/WISINGazmRtvvJHHH38cgFmzZlFQUMDcuXPZunUrV155JbNnz+bee+8NT4R23HHH9W+hhRBCiAggwVYI0Sd0pfh4XR0PfrSBi6cN4qfTCjHt4yzKQhzqNE1j0qRJPP/88wQCAWJjYzEYDJx00knhSZHOOussTjrpJPx+P3Fxcfs8W7kQQggxEEmwFUL0mlKKL8ub+M17a5lZmsPso4diMcnBthD7Q9O0bjMdA7tc0i0mJuZgFkkIIYSIeHLkKYToFaUUK6rbuOOd1UwfmsbNJxVjlVArhBBCCCEOIjn6FELsN6UUm+qd3PLWKkZlJ/Dr00cRazEeMpO3CCGEEEKIyCDBVgixX5RSVLe4uPnNlWTEW/n9rNEk2g+dGUmFEEIIIUTkkGArhNhnSinqHV5ueWsVBg0ePGcs6XFWCbVCCCGEEKJfyORRQoh9opSizeXnrn+tpqXTx9M/mUBesl1CrRBCCCGE6DcSbIUQPaaUwukN8Nv311LW4ORPF4xnaEachFohhBBCCNGvpCuyEKLHPAGdh+ZuZHFFCw+cM4axeYkSaoUQQgghRL+TFlshRI/4gzpz/lfOR2tq+cOsMUwbnCqhVgghhBBCRARpsRVC7FVAV7z4ZRV/X7yVO04dyfGjMiXUCiGEEEKIiCEttkKIPVJKoSvFx2UO3tzYxHXHD+OscbkYJNQKIYQQQogIIsFWCLELpRT+oGJTfQfvrtjOGxu8/PyIwVw0dRBGg4RaIYQQQggRWSTYCiGAUJhVQLPTx+dljfx7+XZWb28nK8HKj0fHc8URRZiNMnpBCCGEEEJEHgm2QhzilFJ4Azrrax28v7KGT9c34PEHmT40jcfOH8fYnDiqyjdiMUmoFUIIIYQQkUmCrRCHoK7W2QaHl/kbG3h3RQ3r6xwMSonhp9MGccKoTPKSYzBoEAgEkM7HQgghhBAikkmwFeIQopTC49dZvb2d91ZsZ97GRoK64sjhaVx77FBK85OIsRhlxmMhhBBCCBFVJNgK0U+UUgDoCnaej6mvQ2VoZmOoa3fz2YYG3ltRQ3mDk6EZcVx51GCOG5FJVqINg9b3zy2EEEIIIcTBIMFWiIOoK8y6fEHW1zr4bEMDZfUdJMdYyIi3kp5gIz3OQlqclaQYCwl2EzEWEzaTAZPRsE8BWCmFyxdkRXUb767YzsKyJoyaxjHF6dx68gjG5CZiMxskzIqBb8f7rvttOqBCv/UgaAYwWkDeD0IIIURUkmArxEHQFTI31nXw2YZ65m9sZHurm/yUGEZmx+PwBKhs7qTd5afDG8DrD6IAk0Ej1moiwWYmNdZCapyVjARrKATHWUmLt5IUYybBZibWasJqMmA0aNS0uflkXT3vr6xhS4uLkVkJXH/cMI4uziAjwYqGtM6KXlKKUDDsCod+CPoh4AN/J/hc4HOC1wG6DkZzKDgaLWA0gaH7j8HpwuRuA1cKaMZQ0Pz+j8EAGEAjFEb1QOgn6IeAFwJu8HtCv32d4HXuKIMTvB2hsng7Qrd13e7rBL8LxvwIpl/Xv6+pEEIIIfabBFshDiCXL8Cmeif/29DAvA0NVLe4yE22c3RxOseNzKQ4M54YixFdgb5jdmKPL0iHN0C7y0dzp48mp5eGDi8NDi+NTi9Lqzppc/lxegN4/EEAjAaNWIuJBLuZGIuRyqZObGYjx43M4LdnlDAyOwGrSVpno1q41VFBMABBL0Z3EwYVhHZCLY2aRij4df2t7QiFXX93bf/v/73jd5egHwKeUODzuUKB0OMAbzu428DdGvrt6fpp7x4Sg/7vWkQNptC6VfC7MLzL82rEK0WsAj6zdA+94RBs3vF/c6hOfvdOQdaz4zl3eo6uFlijBUxWMNnAEguWuNBvayIk5oE1HizxkDfpQGw1IYQQQhwkEmyF6ENdkzNtavLwvw0NrGzYypZmF9lJNo4uzuC4ERmMzE4gxmIEvms1NWpgRMNsNBBnNZEWbwViw+sMrx8I6gpfQMftD+L0Bmhz+Wnt9NHY4aWxw0uLy8f5k/M5Ylg6qXEWaZ2NBl3bWKlQy2fAEwqKnjbobARnPThqoaMm9NvZAK5mMt1toQBpNPJdMNV2DbLfD7maYaf/73SbpoXK4OsMtYB2tYhqhlCgNFnBbA+FQWtC6CcuC9JHgC0Z7EmhH1tiKCxad4RITQu12ur+UCjvat0N/w7gam+lraWR3Kz0UKtv0AtBX+gn4Nvpb2+ozpbYHaE0bkd54sAcC5YYMMeEyhluId7RSrxz6+/u5vqW94kQQggRtSTYCtELXaHTG9Apb3Ayb2MDn61voKKxg4x4K8eNzOKu00YxKjuBWGv3MNtTOy+vAQZjKADHWk2kxVkhddfy7M/zHDRKQf2aUGAzWne0pll3/G3ZqWVup1Y7zdg9fHW1Sv7g8+jfdZNlxzjKgA+Ttw06asHAju6rnu91YXV911KpaTuVraucO4clc/e/DSYwGL/7re38txY6MxFwh0KruwU6m6Cj7rvA2lEbel1czd+FS4MpFNLsyRCbFgqShcMgPocWrwnNbCctNfm7uu7uR+/6O7j3+zRDKJjakkKB0ZYQCo9me6jV02TtXr+d9WKf87e20mlvgOHDd13P7sbI9sFzCiGEEGLgkGArxD7aOcxWNHYyf2MDn21oYHOjk4x4K0cOS+eyiSkMT4+heHDBQQ2YERtmd+aogdcvCnVf1Qy7dk/9fqg1mEOB12T7Llh1/W22f/ejGb8bW+lzhbqq+l07QqsbAh60gI9ijwvzF4RaIpW+42l3asE0mr4Lsl0tqDtaFb8LhDuNL+1WdgNoXeHW+L0utZbQsp72UBn1wI7QGgP2FIhLg/hsyBoDCTmhABuXEQq0toTvWiDDZdXwVFdjMBggN/egbsKDLhr2ayGEEEL0Kwm2QvSQUgpfUKeysZMFmxr5dH095Q1OUuOsHDEsjZtOGM7o3ETibSZqa2sJBALRETQPJqVgzduhcHjxv0Mtgjt3Nw14vvvx79SSunNA9e/UuuppC7V4BjyhoGi2f9cNNTZtx5jK2NBtO35vr2kgv2g4JntC91bIcGus9btgCju1bAa/15V2py61Qf93XWcDO//27mgV3vEDobAalwVx6aHQao0PlSEcWpEgJ4QQQgg6d5pPJRLFWk3YzMa9L3iQSLAVogeCumJFdStPflbOqm1tpMRamDE0jeuOG8aYvCQSbCYJsT3hboEVr4VmoM0e+12Q2xc7T6IUbjnd8f9uY0t3Hl+64xG6Tpu2ktxhJWCx9KoqQgghhBAH0vOfV/Dq11v7uxh7dMepIzh7Ql5/FyNMgq0QP0ApRavLz0tfVvL3xVsZm5fIY+ePY1x+Eol2MxAl3X8jgVKwcW5oRt3SC9jt5D09sctsvkIIIYQQA4/TG6TR6e3vYuxRpLUmS7AVYjeUUgR1xaLNzfzx4400OX3ccMJwzp6Qi91slDC7P3ydsOxlKD4FUgZLd1shhBBCCNFnJNgK8T1KKeodXp5ZsJl3V2zniGHpPHr+OAanxUqg3V9KQeVCaNkMpzy4f12QhRBCCCGE2AMJtkLsoJTCH1R8tr6exz7dRCCo+M3MEk4ZnYXFZJBQ2xtBHyx9EQZND836K6+lEEIIIYToQxJshSAUare2uHjys3I+21DPKaOzueaYIeQk2SXQ9pZSsG0pbF8G574YusSNEEIIIYQQfUiOMMUhTSmFN6Dz/soa/vS/cuKsJv74o1KOHJ6OyaBJqO0LKgjLXoKs0VAwVVprhRBCCCFEn5NgKw5ZSik21Tt59JONLKls4dxJ+fziiMGkxVkk0PYVpaBhA2yeB6c+HLpeqxBCCCGEEH1Mgq045Cil6PQFeWNJNc9+XkFukp2nfjKRKUUpGA0SaPuWguV/g8Q8GHqctNYKIYQQQogDQoKtOKToumLVtjYe+XgTG+s6+Onhg/jp1EEk2M3SSnsgtG6Fde/DkTeDNaG/SyOEEEIIIQYoCbbikKCUot3t569fbeGVr6oYmZ3A85dMYkxeIgYJtAeGUrD6dbDEwMiZ0lorhBBCCCEOGAm2YsAL6jqLK1t45L8bqW33cM2xQ/nRxHxiLEZppT2QOhtg5etQegHEpvd3aYQQQgghxAAmwVYMSEopABqdXp5dUMHb325j6uBUHjxnLEMz4iTQHmhKwbr3IOCGsedJa60QQgghhDig+izYdgUJQEKDOOC69jcFBHWFxx/E6Q3Q0umjscNLTZubba1uPtvQgMcf5K7TRnH62GysJoPsnweD1wHLX4GRZ0JSfn+XRgghhBBCDHB9EmydTidvv/02jY2NzJw5k+HDh4fDQzAY5P3336eurg4IBZLTTz+dhIQE3nrrLfx+P5qmcfLJJzNo0KC+KI4YIJRSKAUBXeH2B+nw+Gnp9FHv8FLb7mZ7q5uaNjd1Dg/NTh8Oj5+ArjAZNOJtZlJiLRw+OJWfzSgiP9kugfZgUQrKPoX27TDhYkBedyGEEEIIcWD1OtgqpXjyySfx+XxMmDCB2267jRdeeIHU1FQg1Hqbk5NDTEwMHR0d3HfffZx00kk0Njby1ltvcd1112E0GomJiel1ZUT0Ugqa3UFqazpw13ipafOwve274NrS6cPpCRBUCrPRQILNRFqclaxEG5MLU8hJspOTaCMjwUZKrIU4qwmb2YjJqKEhvQgOqoAHlr0UurxPerF0QxZCCCGEEAdcr4Oty+Xiiy++4LnnniM7O5u5c+eyfPlyjj/+eAAMBgNTpkxBKcW8efMYM2YMBQUFVFVVYbfbSU9PJzc3l7S0tG7rDXc13amL885/94W+Xl9/iua61LR5eHXxFt5ZVo3br7BbjCTYzKTHh4LrjKFp5CbZyU60kR5vJSXWQuyO4Go0aD1qD+yv1yeat0uXfX4PbvkK6tfCMXeCZgydtYhAA2HbdBlIdYGBVx8hhBBCHHi9DrZutxu/309iYmK4dbampmaX5ZRSvPPOO5x11lnhFtrCwkLee+89li1bxo033sgxxxwTbllbunQpH3zwAUopli1bxubNm/F6vb0tLn6/n7a2NjZs2NDrdfU3pRTt7e1s3LgxKlskHV7Fl7VB/rWqkYDPy5GZOocVJZOeGEOc1YTFZMBk0AAd6AS9E9qhtR1a+7vwe+F0OtF1nc7Ozv4uSq/puk5bWxsbN27c67IGFST/m6fwxQ+jriMWFYHvs/b2dsrKyjAYDP1dlF5zOBxomobD4ejvovSax+Ohs7MTXdf7uyi94vP58Hg8vVqHx+Nh3rx5tLe3c+yxx5Kent7tM17XddavX8+yZctIT0/n2GOPxWq19rboQgghRFTrdbDtOjgMBoNA6EvdYrHsslx9fT2bNm3innvuQdM0srOzeeSRR9A0jXnz5vHSSy9x9NFHh7+8i4qKmDVrFkopqqqqyMjIICcnp7fFDQfx7OzsqAyDOwsEAnR0dJCdnR01B+kK6HD7+c/qWl79Zju6wcyPpxRw5ph02uu2kpSURGJiYn8Xc/f0ILRXgy0JbIk/2MW2sbGRYDBIVlbWwSvfARIIBHA6nWRlZe11P9NqvsVauxTDaU+QnV94cAq4D5RSOBwOMjMzMZvN/V2cXjMYDBgMBjIyMvq7KL3mcDgwGAx98jnfnzweT6/2LaUUc+bMoba2lsGDB3PDDTfw7LPPEhsbG75//vz5vPLKK5x22mm0tLTg9Xol2AohhDjk9TrYxsXFkZSURFVVFSNGjKCsrIyzzjoLj8eDz+cjPj4egM8++4wJEyaQkpIChIKwpmlomkYgENjlgDktLY20tDSUUqSmphIfH98ngcdkMmGxWMItzNEsEAhgNptJSEjAaDT2d3F+kFIKj19n3sYGnltYQXWrizPHFfDTaYPIT4lBA8rabMTFxUVesFUKOptg6Z9DM/3GpIcuYVMyCxLzQwH3e/tSZ2cngUAg8uqyH/x+f/g984PBVg9C2b8goxjjiOOxWeIOXiF7SNd1LBYLCQkJuz0BF226wuBA2M90XcflcpGQkBDVn81Wq7VXn8dOp5P//e9/vPDCC6Snp/P111+zbt06Jk+eDIQ+91988UUuu+wy4uPjGTx4cPh7VgghhDiU9TrYms1mfv7zn3PfffeRmppKXl4eJSUlLFy4kKVLl3L77bcTDAZZuHAhl19+efhxX3/9Na+//jqxsbGsX7+eW265JaoPZsTuKaXwBxWLK5t5dmEFa7a3c9zITO4/ewzDMuMxGrTwchFHKQj6oexjWPAAeBww/fpQyP32ZVj8Fyg+Gcb9BLLGgNFyaE+U1FwOmz6C4+8Bc2x/l0aIqOR0OgkGgyQlJWE0GsnJyaG6ujocbF0uF2vXruWvf/0rhYWFrFy5kqeeeorc3FwgdCLqX//6F83NzTQ2NtLc3ExlZWWffL/G+VuYnhm5XcXzTE6qqqr6uxgHjcUd2dsjKdhGZWXlXpeL87dGdD1ivC1UVkZHr7i+kGXoiOjtkaz3dL+K7PdHjK+5R/tVit4W0fUwuZqorOyb8tXX1/c6D/Q62GqaxoknnsjYsWPp7OykoKAAi8XCjBkzwl/EBoOBP/zhDyQnJ4e/XCdPnkxeXh4ul4usrKxu94nop5QiqBSrt7Xz3MIKFlU0M6UwhecvmURpXlJo0qdI3t5KQfNmWPgQlH0CI06FI26G5MLQ/VOvgo0fwfLX4LUfQd5EGP9TGHwUWBP6tej9Qumw4u8QkwbDTz60A74QvdD1udj15a7r+m57Slx77bVMnDiRu+++m3nz5nHRRReFH5+Tk0N8fDwWi4Vt27YRExPTJ5+3Ac1CS++nujhg3Mp0SF1hQTc4I3p7+LWebY+AwRzR9QgazIfUfuXFFNHbw6/1bHsEtEjfryw9qoc/wuuhjD2rR09YrVYCgUCv1tEn17Hd3bgou92O3W4HQl+035/12Gq1UlhY2BdPLyJI17VnyxudvPhFJR+vq6c4M57Hzx/HtCGpWIyGyA+0Pies/Ad8+STEpMJZfw5dusZg+i6wxWXChEtgzI9gyyJY/ip8cD0k5ELpj9GSp0BMRmh9kVzfvuKohTXvwGFXgj25v0sjRNRKSEjAbDbT0NBATk4OW7du5aKLLiIQCKDrOjExMQwePDgcdr8fek0mEzNmzACgvLycdevWkZmZ2Sefux6Tg/VtkdtyNSRoIyMjI7K/Y/pQoMId0dvjaEMcmZmZe13OY4zs/cprju9RPQYCpRStel1Eb48jtdie7Vemjoiuh9fUs/2qQ2uJ6HoErQl99v5ISkrq9aSrfRJshehqXdje5uaVr7bwr+XbyUyw8duZozh+VCZ2szGyDzaUCrU6blsC8/4ADeth0s9gyhWhcLu7smsaWGJh6PEw+Gho3Air/gmLnyHV+zjBYaeA6TLIHAUG88ANuErBmrdD9Rt99sCtpxAHgd1u55xzzuG3v/0t2dnZpKWlMWLECObOnUtVVRVXX301l156KY888gijR49m7dq1XHXVVf1dbCGEEKLfSbAVvaaUotnp481l1by2eCtWk4Frjx3KGaW5JNhNkR1oYcfkUA3w1dOhltfcCXDh65A9Dnoy27SmgdEMWaMh83cwdTb+Za+jrfwnlH8A+VNhwsVQOAMscQMv+LlbYMVrMOZciI/+WaCF6E+apnHxxRczceJEOjo6KC0txWKxcPjhhzNx4kQATjvtNIYPH05dXR2/+MUvdukRJYQQQhyKJNiK/aaUosMT4INVNbz0ZRUuX5ALpuRz3uR80uOs0RFogz7YNBfmPwh+Fxz/Gxh9Lpjt+xdANQMk5NBefD6B/JMoCG6Bb1+Bd6+GpAIYdyGMOH1HANx1NuWoo1RorLG7FUovAKK8PkJEAJPJxNixY7vd1nVFAQiF3+LiYoqLiw920YQQQoiIJcFW7LOuS/d8tr6e5z6voKbdw1njcvnptEHkJdv3PdAqBZ42Erf9D6s7B0yTIDYt1H0XDkz4Uzo0lcGCh2Dz/2DkGXDEjaHw2VfPZ4mD/JNDXZXr1+4Yt/sELPpT6PlKfwzpI7qP3Y02vk5Y9jIUnwopg6O3HkIIIYQQIqpJsBX7RClFncPDHz5czxdlTZwwKpOHzh3LsIz4HZdz3YdgoxQEvKHL6XzxGCmN5aHrP5ptkDYM8qZAwWGQMQpiM0LdfaF34Ukp8HaEus4umgNxGXD2s6ExsgciYHZ1U84uheyxcPi1sO5dWPnPUNAtPAIm/xwGTQdDZF+LeBdKQeVCaKmAUx4KtVYLIYQQQgjRDyTYih5TSrGiuo1fv7sWbyDInAsncFhRyr5fukcpUEHY/i18/ihUL4biU9g6+gZSMrJJ9myFrV9DxbxQa6DBCKlDIW8yFEyFzJLQrMRGS2h9PXnursmhqhfDvPtCrbWTLw/92JMPfEujpgEaJObB1Nkw/iKoWADf/hXe/Bmc8gCMPie6wmHQB0tfhEEzQtfxldZaIYQQQgjRTyTYih7xB3XeX1nDQ3M3UpKbwG9mlpC/X92OdWiphK+egnX/DrVknvc3KJhKcHMlKiUNkidBydkQ8EBHLdStDgXdLYtCLa1okFK0U9AdDfHZYLKGnuP7ZVIKOurgqz+FWkrzD4OfvBkKY/0xzlXTwJYII2fCsBNg/gMw945QUB85MzrCrVKwbSlsXwrnvhRq7RZCCCGEEKKfyNGo+EFKKZzeAE/NK+f1JdWcPzmfq48ZSpx1H2c7VgpcTfDt32DJi6FW0pMfhBGnhSZq2lnXes320LjNlMGhMalBL3TUQ/2aUNDdtgRWvxkKy0mDIG8i5E+D7DGQkAMme+gx6/8DCx8C3Q8n/h5KZoHJ1v8tjJoWKsfRt4fK+eEtoXA7/KTID7cqCMteCp0cKJja/6+lEEL0UFVzJ19tbu7vYuzR0PQ4Jhel7H1BIYQQ3UiwFXuklKK61c3v3l/Lmu0O/u/0UcwszcFs3IfQpVRotuH178OiJ8HTDlMuh/EXd78+7I7r4O5RVwhMHhSa4Kn41FBXWGdDaGKm6sWw7RtY+y7oAUjKh5wJ4KwPtfSWzIIZN4S6AkdSCOuq17H/Fxpv/MENcOYcGHJcZJVzZ0qFrvO7eR6c+kio/EIIESVWVrdxxzur+7sYe3ThlAIJtkIIsR8k2Ird0pVicUULv3lvDRaTgT9fNIFx+Uk9b6VVKhQwtywKjaOtXxMKl9NmQ3JRaJn9DW5djzNZQwE2MS/Uyqn7wdkYCl1dQVcpOPfF0CRNBmNkhkVNA3MMnPA7CPjgvV/BrGdCZY7E8qJClzBKzIOhERzAhRBCCCHEIUOCrehGKYU/qHhjaTWPf7qJqYNTueu0kWQl2HoWapUCFDRsCF3apuxjKJgGF74OOeNAOwDhsmt9Rgsk5oa6IQ89LhSslR66PdLDl6aFLg908h/gPzeHrns769nI7ObbuiXUAn/kLWCN7+/SCCGEEEIIIcFWfEcpRbvbz2OfbOK9lTX8bHoRl88owm4x9jzUdtTBkudh+auhFr0z/hS6jqvJevACWjjomg/O8/UVTQNrApz6ELx/Pfz7l3DOC5A7IXLCrVKw6g2wxMDI0yOnXEIIIYQQ4pAmwVYAoVC7ubGT37y3hi3NLv4wawwnlmRhNPQw0Ho7YM1bodmOlQ5H3ARjzwvN/ivhp+c0DWxJcNqj8N418K8r4dwXIGtsZLyOnQ2w6nUYdwHEpvd3aYQQQgghhAAk2AogqCsWljXyu/fXkRJr4dmLJzIyO2HvrbRKhSZwqpgfGkfbUgGlF8BhV0BCbmQEsWikaRCTAjOfCLXavnNlaJxwxsj+f03XvR+6DNOY8/q/LEIIIYQQQuwQ4dcUEQeSUgqPP8iLX1Zy0xsrmVSYzF8u2kuoVWpHoPVDzQp45xehVsXEPLj4HTj+NxJq+4KmhVpEz5gDcRnwzhXQVLb32aMPJI8Dlv8NRp0RmrRLCCGEEEKICCEttocopRTNTh8Pzt3AZxsamH30EC6aOgiryfBdqO0KUSoY6mrsqIHGjVC3CmpXQe1KSB8RGgdadAQYzBJo+5KmQXwWnPU0vH05/OuKUMttclH/vM7ln4Bje+hSTch2FkIIIYQQkUOC7SFIKcX62g5+/e4ampxe/vijUo4cno5RIxRiPd8LsXWrQ92M3a2ha5YmFUDGKBj/Exh2Ymg2Xwm0B4amhVrAZz0Db/0c/nUVnP1caBsczNfc74YVL4Wur5teLNtbCCGEEEJEFAm2h5iArvPJ2nru+896ClOsPH/eUIZYG9DWfR5qha1fA82bwdO+I8Tmh0LsiNNDv5MHgT05dAkdkIBzMGgaJA2Cs/8SCrf//mUo3CbkHLTX31y7DK1+HRz7f6FLNgkhhBBCCBFBJNhGm53HWHb7W9/NsuF/UEpH93Tw2eJvmf/55/ycKmbRQuK/q9E8bWC2Q2I+ZJaEQmzmqFCY6gqxEmD7l6ZBypDvwu27s2HWXyAu84BvG00PkFf3MeRPiaxLDwkhhBBCCLGDBNtIoxS0bIby/0HQC3oQ9MBOP9/93xD0k9/YgGFbIuj6d8uo4K7LB7zQvp0jnS2cEBuLllwAmSVoWWfvGmJBwksk0jRIKw4F2rcug/euDY2/jUnr++2lVOhkibMBY/mnJLauhqP//N3+IYQQQgghRASRYBtpvB3w/g3QXAYxqWAwhrp+7vy762/NgMXdCZ1+MJi++zGawWBCGUw4fYovK9vY0hbAkjGZM889AVvuUDQJsdFJ00Kt6l0tt+9fD2f8KXRSorfbUanQSRBHDWxZBJvmwrYlEPDRkTOD+MIjZV8RQgghhBARSYJtJFE6fPs3aNoIP/7HTtct1XbzG/SgTsXq1YwtLcVo+O7KTQpweoN8uq6elxdVsd3t5qSJWVx11BCSk+17vz6tiGyaBtnjQi2371wO/7kJTn8MbIn7Hjy7rkXcWgWVC6Hs49Bs1wYj5E6CY+4kkDOFim3tjDXbZS5kIYQQQggRkSTYRgqlQrMQf/0UTLkCcseDtpfLDOsBFF1B14BSik5fkP9taODlLyvZ0uzihFGZ3H/2WIqz4jFoSKgdKDQN8ibBmU+HriP80W1w6sNgjd97uFUqNMtxczlUzAuF2YYNYImFgmlw8gOQfxjEZ4Z6BgQCqJo1B6deQgghhBBC7AcJtpEi4IV594cmcJr8872H2u/p9AaYt7GBl76sorKpk+NHZnDvWaMZkZUggXag0jQYdDicOSc0U/LHd8FJ94cC6s7bu2uSMZ8T6tdB+aew+TNoqYTYNCg8Ag7/VWhiKHtq6LGyvwghhBBCiCgiwTYSKAXr/g1bvoBzXwZbUo8f6gnofLSmjr9+tYXNjZ0cOyKD355RwsjseIyaJoF2oNM0KDoKZj4J714DRiuc8LvQLNcocLeFuhaXfQwVC6CjJnTyZMgxcPw9kDUGbAl8181dCCGEEEKI6CPBtr8pBe3bYOHDMOY8KDx8rwFDKYXHH2TBxgb+9HkrNZ0tHF2cwd2njaIkJwGjQQLtIUXTYOhxcNof4YMbQuNjC4+A8k+g6gtwtUDqUBh1Bgw9HtJHhFp1ux4rhBBCCCFElJNg29/0AHzxWGg24+nXhcY07kEo0Op8Ud7Ii19Usb62ndIMC/eeW8rYvCQJtIcyzQAjTg1NBPXhLbD6rdDsyRN/BoOPhtQhYLJJkBVCCCGEEAOSBNv+pBRUzIe1/4LTH4X4rN0GD6UU3oDOl+VNvPhFJetqHcwYmsazF09Aa9lKaV4iRuO+jckVA5BmgJKzIG14qCtyYn7o0k8SZoUQQgghxAAnwbY/uVpg3n2hbqTFp+4SQLoC7VcVzbz4RSVrtrczbUgqz/50EuPyk9CUzqq26n4qvIhImgGyRvd3KYQQQgghhDioJNj2Fz0Ii5+BziY4+zkwWsJ3KaXwBXS+rmzhxS8qWbmtjalFqfzl4omML0jGtKPLcSCg92MFhBBCCCGEECIySLDtD0pBzXJY9hIcfXtoYp8drbVKKVZvb+exTzaxvLqNKYUpPHPRRCYUJGM2yhhaIYQQQgghhPg+Cbb9wdcJ//s9ZI2F0h/vEmp/9Y/l5CbZefrCCUwqTJFAK4QQQgghhBA/QILtwaYUrPg71K+FC/8J5tgdNys21ndw0xsrKUqL5ZEflZISa5FAK4QQQgghhBB7IVPpHkxKQVMZLHoCJv8csseDpqGUorKpkxvfWEl6vJUHzhkroVYIIYQQQgghekiC7cEU9MGCByA2A6b8AgwGlFJsa3Vz05srsZuNPHxuKRnxVgm1QgghhBBCCNFD0hX5YFEKNnwAm+fBOc+DPQWlFPUOL7e8tRJ/UGfOBRPISbJJqBVCCCGEEEKIfSAttgeDUtBRC/MfhNHnQNFRKKC508dtb6+itdPPo+eNY1BqjIRaIYQQQgghhNhH0mJ7MKggfPk4KB1mXI8yGGl3+7nrX6vZ2uLiTxeMZ1hGnIRaIYQQQgghhNgP0mJ7oCkFlZ/DqjfhqNtQCbk4vQF+895a1tU4+OOPSinJSZBQK4QQQgghhBD7SVpsDzR3a+iatYOPQo2aicsX5Pf/Wc83lS08et44xhckSagVQgghhBBCiF7ok2CrlEIpha7rGI3GbkFNKbXbx2g7LnMTDAYxGAxomjbwAp7SYcnz0FGLOvMpPMrMw//dwLwNDTx4zlimDk4ZeHUWQgghhBBCiIOs18FWKcXWrVt59NFHcTqdnHHGGcycORODIdTLORgM8sQTT1BZWYlSCofDwR133MGIESN46623mDt3LsnJydx0001kZ2cPnKCnFNSuhCXPoWbciC95KE9+Wsb7q2r4/VmjOao4feDUVQghhBBCCCH6Ua/H2Oq6zsMPP8zhhx/OXXfdxfPPP091dXX4fqPRyCWXXMKdd97JFVdcwZYtW0hLS6OsrIzXXnuN3/72t5SUlPDEE0/ssXU3KvldMO8PkD6SwNgL+cvCSl5fUs3dp43kpFFZGCTUCiGEEEIIIUSf6HWLbWdnJ2VlZfz2t78lNTWV4uJiVq5cyaBBg4BQl+O0tDSUUnz++edMnDiRtLQ0PvnkE8aPH09+fj4nnXQSb731Fh6Ph5iYGCDU0hsIBMJ/d3V17i1d18PrOmAtpkqhrXoDtn+L/0ev8vLSJl78opJbThzO6WOyAYWu9z7Ed70effXa9LeuegyUugyU7bLzeyba7VyXgVCfgVaXnX+iVdc+1ht+v5/Vq1fT0dHBhAkTiIvbddZ8pRRbtmxB0zQKCgqkB5AQQohDXq+DrdfrRdd1bDYbAMnJybS0tOyyXCAQ4L333uOqq65C0zRaWlpITk4GwG63EwgE8Pl84WC7cOFCXnnlFZRSrFy5khNPPJH29vbeFpdAIIDT6WTlypW9XteeWJ3bGP7NQ9Rnn8grS/z8c305s4bZGG5uZvWqXV+b/aWUorW1lVWrVvXZOvuTw+GgtbUVi8XS30XpNbfbja7rNDc393dRek3Xddra2gbMftbS0sKaNWvCwyWimcvlAqCxsbGfS9J7Pp8Pj8cTrlO08vl8uN3u/X68Uoq//vWvfPXVV+Tm5vKPf/yDxx9/PPwd27VMTU0Nl156KWPHjuWJJ57oi6ILIYQQUa3XwdZisaBpGj6fj9jYWBwOB6NGjdplucrKStrb25kwYQIAiYmJbNmyBQiFY6PRiNlsDi8/ffp0Jk2ahFKKO++8k+LiYgoLC3tbXFwuF1u2bGHkyJG9XtduBf1o7z+NITaNRXmX8/rCJq44cgg/nz4IYx+fUA8Gg6xdu5aSkpIBcZC+efNmUlNTSUpK6u+i9FpdXR2BQIC8vLz+LkqvBQIB1q1bx+jRo6O+VUgpxZo1axgxYkS3z5totX37dgwGA9nZ2f1dlF5rb2+nsbGRoUOH9ndResXr9WK32/f78S6Xi3//+988/fTTZGdnc/HFF7NhwwbGjRsXXiYYDPLcc89xzDHHDIiTGkIIIURf6HWwjY2NpaCggMWLFzNhwgQ2bNjAL37xC+rr62ltbaW4uBiADz74gGOPPZaYmBg0TWP8+PG8++67tLS08OWXXzJs2LBuBwMWiwWLxYJSCrPZvEvw3V8mkwlN08K/+5RSUPYR+uZP+Wjob3jwixYumlrIL44YjNnY97M+d80kbTKZMBqNfbrug00phcFg6LPt3N+MRmN43x0IDAYDJpMp6k+gdA1BMJlMA2LbGI1GDAbDgKrLAflsPoiCwWCvyt/R0YHf7yc9PR2TyURBQQFVVVXhYKuUYt68edjtdsaMGcOHH37Y7fGBQIDPP/8ch8NBbW1t+HdfvKb2QAejkyO323u60U1dXd3eF3S1RXQ9krVOamtr97qc2dse0fWI0509qkek71dWv6NH9RgoUgyuiN4e8T3erxwRXQ9bD/erBOWM6HoYPe199v5obW3t9Tp6HWyNRiM33XQTDz/8MH//+9+ZNWsWRUVFfPnll6xatYri4mICgQBNTU38/Oc/D3+5jhw5khNOOIHrr78em83GHXfcEdUHMwA461Hz72eBcRq/XZfFWRNyuebYoQck1AohhBh4vj8+V9O0bmOo29raePnll7nttttYs2YNLpcLp9NJfHx8+PFtbW20tLTQ2toaHubTF99BBhUgtk8uEnhgmDUdn8+39wX1AHEmiNSR3CaCPaqHpgcjensYe1gPg4rsehj0ntVjoDAT2dtjwOxXKtCjehgjvB6a3rN69ETX3Eq90euXStM0RowYwV/+8hcCgQA2mw1N05gxYwbTp08PPYnJxL333tuttcdkMnHllVdyySWXYDKZov4sPXoAtehPbKlv4Z7ALzlxUh43nTgcmzm6W1KFEEIcPPHx8RiNRlpbW8nIyGD79u0UFBSEw63L5SI+Pp7nnnuOrVu3UlVVxZdffsnJJ58MgNlsZtasWQCUl5fz5z//mUGDBvXJ92tnuY/FEdz1OT07tkcTaa1oNfF147aDVKp9N6QoITwB5w9ZUKNY3Bi5LYkTRiX2qB6h/arhIJRo/xxjTe5RPQYCpRT137SxuLH3c9ocKONG9nC/2uyP6P3qaEtKj+rRusbF4sbIna/lzJjUPnt/pKens3379l6to0/OAWiahtls7tYd7vtdFnfXVVbTtF6NRYoYSsHWr/F/+xpzAuczsXQsd5wyghhLBJ9iEUIIEXFiY2M54YQTePDBByksLMRgMDBq1Cg+/fRTqqurueyyy3j66acB+Pjjj/nPf/7DiSee2M+lFkIIIfqfJK/eUgo87aj//Z557qHoo2bxm5klxFrlpRVCCLFvNE1j9uzZ4XGyF198MXa7nZKSEoqKioDvThxPmjRJLvUjhBBC7CDpq5cUCpa9RFP1RlYOeYS7zxpPgi3Ku1ULIYToN1arleOPP77bbbm5ubssl5aWRlpa2sEqlhBCCBHRJNj2lqMO9c3zrM+/kJ/NOpXkGIuEWiGEEEKIQ8j3J36LRHJ8KgY6Cba9oJQiULsK3eti3Ek/JT7eJh8aQgghhBCHmP+sruXzsqb+LsYenTMhlylFqf1dDCEOKAm2vaRXfYVKGkRsWr6EWiGEEEKIQ9CKrW28vqS6v4uxR+PykyTYigHPsPdFxJ7oAR969WLIm4zBMgBmdxZCCCGEEEKIKCTBthd87XUYmssxFh7e30URQgghhBBCiEOWBNv9pJTCvW01Rk3HmFMq3ZCFEEIIIYQQop9IsN1PCvBVLMKQXIiWkN3fxRFCCCGEEEKIQ5YE2/3k9XgwbFuMlj8ZTLb+Lo4QQgghhBBCHLIk2O6n9qbt2DsqoeBwpBOyEEIIIYQQQvQfCbb7QSlFe9UK7CYgewzI+FohhBBCCCGE6DcSbPdDQFf4KhdhTCmC+Jz+Lo4QQgghhBBCHNIk2O6H9o5OYuqWQd5kNJO1v4sjhBBCCCGEEIc0Cbb7obZmCymerWiDpvV3UYQQQgghhBDikCfBdh8ppWjZvJx4qwZZY2V8rRBCCCGEEEL0Mwm2+8jtC6K2fr1jfK1cv1YIIYQQQggh+psE231U0+IgtXUF5E0GGV8rhBBCCCGEEP1Ogu0+UEpRtaWSnOB2GV8rhBBCCCGEEBFCgu0+0BU0b15Oos0o42uFEEIIIYQQIkJIsN0HrS4f1trFGFNlfK0QQgghhBBCRAoJtj2klGJzbQv5zjVoMr5WCCGEEEIIISKGBNt9UFZRwSBqoUDG1wohhBBCCCFEpJBg20O+gE571XKS7EbIGiPja4UQQgghhBAiQkiw7aGaNjdJTctkfK0QQgghhBBCRBgJtj2glGJNdRPDvOtlfK0QQgghhBBCRBgJtj2gFGws38xgg4yvFUIIIYQQQohII8G2BxweP+5tK0mym2R8rRBCCCGEEEJEGAm2PVDe4CTHsVLG1wohhBBCCCFEBJJguxdKKb6trGe0vkHG1wohhBBCCCFEBJJguxcBXVFesZkhhrrQ+FrphiyEEEIIIYQQEUWC7V40ODxQt/a78bVCCCGEEEIIISKKBNsfoJRi1bZ2hnrWyPhaIYQQQgghhIhQEmz3YsnmOkq1TTK+VgghhBBCCCEilATbH+DyBdmypZKhXeNrhRBCCCGEEEJEHAm2P6CqqZOY1g0k2k2QNVYmjhJCCCGEEEKICCTBdg+UUiypaqEksA5j6mBIyOrvIgkhhBBCCCGE2A0Jtnugq9D42gmGMrT8yWCU8bVCCCGEEEIIEYkk2O5BS6eX+pqt312/VgghhBBCCCFERJJguxtKKdbVdpDqLNsxvnaMjK8VQgghhBBCiAhl6ouVKKVob2/H4/GQlpaGyWTa5X6fz0dzczM2m42kpCQAOjo6UEoBEBsbi9ls7ovi9ImvNjcxjg2h8bXxMr5WCCGEEEIIISJVr4OtUopFixbxxBNPEBMTw9ChQ7ntttvCIVUpRX19Pffeey9KKcxmM3fddRc+n49LL72UoqIiDAYDV111FePHj+91hfqCN6DzbUU9N2ub0PJPkPG1QgghhBBCCBHBeh1sA4EATz31FDfddBOjR4/m0ksvZcOGDYwZMya8zNNPP81RRx3F6aefjslkwmQyUV1dTUZGBk8++SRmsxmDIXJ6RW9rdeNo2s4QYz0UTO3v4gghhDiEKKVwuVwEAgHi4+O7fT8qpQgGgzidToxGI3FxcQBoMlxGCCHEIa7XwdbpdNLY2EhxcTExMTEUFxezfv36cLD1er0sWrSI9vZ2PvzwQwoKCrj11lsB2LJlCzfddBP5+flcccUVpKamhtfb0dFBc3MzSik6OjrweDx0dnb2tri43W4CgQAul2uPyyze3ECer4KEeCOuhKGoH1i2PwWDQYLBIG63e0Ac1Pj9/j7bzv3N6/USDAYHRF0CgUD4PRPt+5lSikAggNvtxu/393dxes3n86Fp2oDYzzweD36//wc/m6OB1+tF1/X9frxSisWLF/Pkk0+iaRpHHnkkl19+OUajEQh9Tv72t7+lrq4Oj8fDsccey89+9rPw/UIIIcShqk9abIHwuFqbzYbb7e52f2NjIxMmTOCss87itttu47///S+nnXYaL730ErGxsbz88ss88sgj3HfffeEz06tWreLNN99EKcWaNWvYsmULwWCwt8XF7/fjcDioqKjY7f26Usxd3sjh2gYCcblUNLlQLbtftr/puh6uS7QHDoC2tjZcLhfNzc39XZRe6+zsRNf1bu+FaKXrOh0dHQNiP+s6UVZZWTkggkBHR8eACbZerxeXyxWedyFaeb1evF7vfj/e7/fz+OOPc8MNNzB06FB+9rOfceKJJ1JUVASA0WjkyiuvJCsri+bmZi6++GJOP/10srJkLgghhBCHtl4HW7vdjtFoxOl0EhsbS0NDA5MmTQrfb7FYyMnJobS0lISEBEaPHs3WrVux2WwMHz4cgDPPPJO7774bXdfDwfbwww/n8MMPRynFjTfeSHFxcfiLvTdcLhcVFRWMHj16t/e3ufzU/beOiVoZtmHHUzJ2Qq+f80AJBAKsXr2akpKSiOrKvb/KyspIS0sjOTm5v4vSa7W1tQQCAfLz8/u7KL3m9/tZu3Yto0ePHhDBduXKlYwaNSqiJqvbX9u2bUPTNHJzc/u7KL3W1tZGQ0ND+HshWnm9Xux2+34/vr29nZaWFkpKSoiNjaWoqIgNGzZ0C7aDBg1CKRX+ztx5X1ZK0draSiAQoKWlhUAggNfr7ZP3rkEFiDVF7okHs6bj9fnYa02DAeJMikitiUkL9ujkiKZH9vYwEuhRPSJ9vzLoPauHkWBE14Ngz+ph1iK7HkZ69v4wRvp+pXq2PUxEdj20Hr4/eqKrsbQ3eh1sY2JimDhxIv/4xz+YMmUKFRUVjB8/njVr1lBVVcVpp53GSSedxL/+9S8MBgMLFizgV7/6FVu2bKGuro7ExEReffVVxo8f360FZecv4a6/+/qgenfrK2twEmivY4ipHq3g8Ii+zM/uXqNotXMrTbTXZWcDoS6yn0U2TdMGTF26DLT67Aufz4fBYAj3goqNjd1ti3xnZyf3338/F1xwASkpKd0eP2fOHLZt24bD4UApxfr16/vkNU3ytnN6wf53sz7QCsxtbFi/fq/LqbZOTovgeuRrLazvQT1szo6I3h7pgSbWr9/7gWpihO9X8Z561q/fe++rTL01outh7qhh/XrHXpcbZIjsemQEe7ZfJXgdEV2Pnu5XGcG2iK6HuaOW9es7+mRd1dXVvW506HWw1TSNm266iZdeeon//Oc//OY3vyEjI4OmpqZwK+Lll1/Oq6++ymuvvcbFF1/MtGnT2Lp1K3PnzqWzs5Phw4dz/vnn97YovaaU4uuKZoboVTuuX7v7Vl0hhBDiQIiJiUEphdvtxmq10tLS0m3+CaUUHo+He++9l6KiIi6++OJuodVisXDXXXcBsHnzZv7yl79QWlraJ8F2YXMZb1T0zQHMgXBaXAq/HFu61/PRW1bU8MYnbRChbbYXpqVxeemYvS636ustvFHRfhBKtH+uys/kp6Uj9rrc583lEb1fFQ3PobR0yF6X+2Dbet6oiNxhIeNK8yktLdjrcq+VreKtisgdRnVFXiaX9GC/+rKlnDcq9h7k+0thD/erD7ev540K50Eo0f4p7eF+1RPbtm1j+/btvVpHnwTblJQUbrzxxm63lZSUUFJSAkBcXBxXXnllt/uLior49a9/3e22/uYPKr7a3MTRho1y/VohhBAHXUJCAkOGDOHDDz9k1KhRbNu2jZKSEtatW0drayuHHXYY999/P42Njfz0pz9l69atFBQUhM9ya5oW7v3UdXK571r1NdTeO/r2K03rwfGE1hVpI7Muip5vr8jeHgOkHvvw/on2enT1aoroetCzzKAi/vOqZ/tV5NcjMjJclz4bmNn1xbmnyn3//p3/HykvSL3DQ2V9KxMNZWj5U+T6tUIIIQ4qg8HAnXfeydq1a3nppZe4/fbbSU9Px+Fw0NTUhN/vR9M0kpOT+dvf/sZf//pXHI7IbZUQQgghDpZet9gOFEopVm5rw+ppZIilHgqmRfT4WiGEEAOPpmnk5eVx3333oZQKn/ydOvW7a6rfc889/VhCIYQQIjJJsN1BAYvKmxmpbSXBbpbxtUIIIfpNJPVmEkIIIaJB9F8jpo+4vEGWbWnhMNMmDDK+VgghhBBCCCGihgTbHSqbnDS0dYTG1+bJ+FohhBBCCCGEiBYSbAmNr/2mqpVU1UqRoR4GTevvIgkhhBBCCCGE6CEJtkBQVywqb2K8ZRuxFiNkjpaJo4QQQgghhBAiSkiwBZo7fayrdXC4uQxNxtcKIYQQQgghRFQ55IOtUoq1NQ58HjfjtE07xtda+rtYQgghhBBCCCF66JAPtgCLNjdRaHOSE6yV8bVCCCGEEEIIEWUO+WDr8et8U9nC1JgaLCaDjK8VQgghhBBCiChzyAfb6lYX1S0uppnL0FKKZHytEEIIIYQQQkSZQzrYKqVYtqWVWGOQEYENkC/ja4UQQgghhBAi2hzSwVZX8GV5E2MS3SR7qqFAxtcKIYQQQgghRLQ5pINtu9vPqm3tHB5Xh0HTZHytEEIIIYQQQkShQzrYbqrvwOHxM1nbgJYi168VQgghhBBCiGh0SAfbrzY3kxtnoMC1FvIny/haIYQQQgghhIhCh2yw9QV1vq5oZnKaD7tzS2h8rXRDFkIIIYQQQoioc8gG27p2D5sbnUyPqwV2jK8VQgghhBBCCBF1Dtlgu7K6DaOmMTqwDi1VxtcKIYQQQgghRLQ6JIOtAr4sb2ZEupW09lWQJ9evFUIIIYQQQohodUgGW6cnwLdbWzk804+prVLG1wohhBBCCCFEFDP1dwH6Q0VTJ40dXqbYG9DQIEvG1wohhBBCCCFEtDokW2xX1nSSFmdhiHs1pA6GuMz+LpIQQgghhBBCiP10yAXboIKNbYrxuTHENS6X8bVCCCGEEEIIEeUOuWDr8CpqXBrTMwNorRWh8bVCCCGEEEIIIaLWIRVslVJsqO9EYWCcedt342tl4ighhBBCCCGEiFqHVLAFWLWtjWEZsWS3L4fUITK+VgghhBBCCCGi3CE3K/J5E7IojXNgWbcMCg6T8bVCCCGEEEIIEeUOqRZbTdNItpvICNaDjK8VQgghhBBCiAHhkAq2XQz1a0HG1wohhBBCCCHEgHBIBtu4ltUyvlYIIYQQQgghBohDLtgadD/JneVocv1aIYQQQgghhBgQDrnJoyzeFiy+Bhg0tb+LIoQQQgghhBCiDxxawVYpDI1rwWhCZY5Gk/G1QgghhBBCCBH1DrmuyNSuQqUOk/G1QgghhBBCCDFAHFottoBnzEXUxk2iSMbXCiGEEEIIIcSAcGi12Goaekwanfac/i6JEEIIIYQQQog+cmgFWyGEEEIIIYQQA44EWyGEEEIIIYQQUU2CrRBCCCGEEEKIqNYnk0cFAgFWrVpFS0sLkyZNIjExsduldJRS1NTUsGbNGhITE5kwYQJms5nW1laWLVtGWloaY8aMwWQ65OayEkIIIYQQQgjRS71usVVK8Y9//IM//elPfP3111x//fW4XK5u969atYrbbruN8vJyFi9eTEdHB06nk+uuu44lS5bw2GOP8fbbb6OU6m1xhBBCiKimlMLv9+P1enf7vaiUIhAI7PF+IYQQ4lDU6yZSr9fLW2+9xR//+EcKCwu5/PLLWblyJYcffjgAuq7zzDPPcPHFFzN06FAyMjKIi4tjwYIF2Gw2brvtNjZt2sRdd93FWWedhdVq7XWlhBBCiGiklGL9+vU89thjeL1ezjzzTGbNmoXBYAjfX1FRwcMPP4zL5eLEE0/kwgsvDN8vhBBCHKp6HWw7OztxuVxkZmZiNBopKiqisrIyHGy9Xi8rV66ko6OD1NRU6urqePzxx6moqGDw4MEYDAaysrLo6OjA7XaHg21FRQUrVqxAKUVlZSUNDQ3YbLbeFhev10tnZye1tbW9Xld/CwaDuN1u6urqunX9jlYdHR0AeDyefi5J77W2thIMBgdE9/pgMIjL5aK2tjbq9zOlFC6Xi7q6ugGxbdra2tA0Leq3C4S+Szo6OqL+s9nr9eL3+/f78YFAgAcffJCf/OQnjBgxgtmzZ3PYYYeRl5cHhE4WP/LII5x00klMnTqVK6+8kqlTpzJ06NC+qoIQQggRlfr8yE7TtG5do5RSeL1eLr30Uo455hjuuecePvroI5RS4YOxrt87P87v99PR0YFSCp/Ph9PpxOFw9Lp8Pp8Pl8vVJ+vqb7quh+syUA5sNU1D1/X+LkqvOZ1OdF3HYrH0d1F6rSvYDoT9TCmF2+2mo6MDo9HY38XpNafTiaZpAyKku1wuOjs7o/6z2ev1EgwG9/vxDoeDuro6pk6dSnx8PIWFhaxbty4cbF0uFxUVFRxxxBGkpqZSUlLCypUrw8FWKYXH4wnv636/P/zZ2ls+jxv8kXviMeD19KiuXrcbAh6I0F7cAZ+bzs7OvS4X6dvD7+1hPbyRXQ+fp2f18Ed4Pbxu117roQjtf5Fcj57uV36vK6Lr4fPsfXsABCJ9v+rh+6NH6+qD4TW9PhqKiYnBZrPR1NRETEwMW7Zs4ZhjjkHXdZRSWK1WBg8eTEJCAgaDgcTERNxuNyNGjGDJkiUopWhoaCA2NpaYmJjweouLiykuLkbXdcrLy3nxxRf7pJuy2+1m+/btA+LsdjAYpKysjOHDhw+IbmhbtmwhOTmZhISE/i5KrzU2NhIMBsnKyurvovRaIBCgvLyc4uLiARFsN27cyJAhQzCbzf1dnF6rr69H0zQyMjL6uyi95nA4aGlpobCwsL+L0iu6rvfqfeLxeNA0LXxSLCEhgfb29vD9Xq8XAIvFgqZpJCYm0tbWFr7f5/Nx//33U11djcfjoaGhgauvvnq/y7Ozlk4fuZ2+PlmXUoqtW7eSl5fXZyeZKitMXD1/7z27OjwBch19d6C4bds20tPT+2wo1ZpKM7M/2vu62t1+cju8ffKcCqiu3kp2VnaffTZ+vcXC7H/t/eRuq8tHrrNv96vcvDxMfbRffbrVyrK/7/01aXJ6yXXtf2+NnelKUb11K/n5+X12fPdutZV59r3Xo6HDS667b+oR1HW2VVdTUFDQZ8cP31RZmP3v6N+vPttq5dt/9GS/8pHr6pt6HIj96r1qK/N7sF/1hMPh4Nxzz+3VOnodbG02GzNnzuSBBx5g5MiRuN1uxo0bx+eff86qVau45ppruPDCC5kzZw5HHXUU8+fP5+GHHyY7O5tnn32WOXPmsGrVKmbNmrXb1i1N0/i///u/PmvFq6qq4s9//jMPPvhgn6yvP3V2dnLjjTfy5JNPDoiWwYcffpijjjqKKVOm9HdReu3dd9+lo6ODiy66qL+L0mvt7e3cfvvt/OlPf4r6lsFgMMi1117LfffdR3Jycn8Xp9f++c9/YjabOeecc/q7KL327bff8vHHH3P77bf3d1H6xP4OnbHZbOGeSlarlfb2dpKSksL3d4WnrjPbbW1tDB8+PHy/xWLhrrvuCn9n6roekSc+A4EA1157LQ888ACJiYn9XZxeue2227jqqqsoKirq76LsN6UU119/PXfccUdUn5Dt2q/uv//+bu+baOP1evnVr37FY4891q3RJ9p0Haf+6U9/iurj1IFy7ODz+bj22msjer/q7XFmr49SNU3j0ksvZcSIEbS0tPCTn/yEuLg4Ro4cSXZ2NgBnnHEGeXl5bNmyhccff5zBgwcD8OSTT7Jo0SImTJjAYYcdttuzOZqm9emEUlarFaPRiM1mi/rWp0AgEK5LtE+6pZTCbDZjsViw2+39XZxeM5vNmM3mAVEXr9cb3s+ivZVz5/fMQNg2A2k/s1gsmEymAfHZ3BuJiYnk5OTw5ZdfMmLECKqqqhg1ahRVVVU4nU5GjhzJ0KFDmT9/PtOmTWPdunX84he/CD++r78zDxS/34/RaMRqtUb1/quUwmQyRX09dF0fEJ+Nfr9/QGwPTdMGxPYYKMepA+XYwWAwDIjP3R/SJ80vFouFI488stttGRkZ4e5xRqORyZMnM3ny5G7LZGZmMmvWrL4oQo/Fx8czderUg/qcB4rZbObII48cEGMFAUpLSwdEl0qAoqKiATEJFoTe30cccUREtvrsK03TOOKII6L6C3ZnQ4cOHTDv//T0dMaPH9/fxeh3RqOR2267LTwr8hVXXEF2djbz589n+/btlJSUcPPNN/Pwww/zn//8hwsvvDAqWwoNBgNHHnnkgHgvTp06dUAMoZk+fXrUH+waDIYB8RlvNBo58sgjo76X1EA5Th0oxw5dn7vR3kjxQzR1iF0Eb+fqRnurgNQlcnXVZyDVBaK/PgOpLiD72UCllCIYDKLrOmazeZdJGSHUwhYMBsP3R5uBsr2lHpFF6hFZpB6RZaDU44cccsFWCCGEEEIIIcTAEt19HH6AUgq/38+qVavw+/2MGzdul7Fbfr+fNWvW4HK5KC0tJTY2NiLPYCilqK+vp6ysDLPZzOjRo3cpa2trK1VVVeHxPiNGjIjYgfqdnZ2UlZWFJzUZPnx4t0HsSilaWlpYvXo12dnZDBs2LGK7wQaDQRwOR7eWs8TExHB5lVJ0dHQQCASA0BjvmJiYiNrP/r+9O4+v+VoXP/7JTnaSnYgEEVSoGCqmS7VRSjlmeulkqNN7cG8vWtIQIURMMSSmGGtsOTVFDVekaJCioVEx1zwGEUEmGWXaO3v9/ujZ+ye01Z6edu/tPO/Xy+vV2vXqs/b3u+L7fNdaz6PX60lLSyM7O5uGDRvi6Ohojvvs2bN4eHjQuHHjp7YSlZaWcv78eUpLS2nevDk6nc7i41JKkZ2dzb1798zHIZRS3L9/nxs3buDk5ESzZs2eijUrK4vk5GTgx63XDRs2tPhWHaUUBQUFpKamotPpzFUl09PTuXv3LvDj/eTr61vu2iilyMzM5MKFC3h7e1OvXj2rmD+mNjQlJSXmKvmmVm4mDg4OuLm5lWsB9/j8cXZ2ttqCF0IIIYSteJ52fD3uuU5sly5dyvXr13F1deWrr75i5syZ5vMKSin+/ve/c/z4cby8vNi8eTPz58+3ymSwtLSUxYsX4+HhwcOHD1mzZg1LliwpdxbmwIEDrFu3jtdeew0XFxfq1KljlWMBuHjxIiEhIXTs2BGtVouXl1e5h9WcnBz8/f1p2rQp586dY+jQoXTp0sUqJ19ubi4RERHk5+fz6NEjUlNTiY6ONlfNKysrw9/fH41Gg7OzM+3bt+eDDz6wcNTlnTp1isjISK5cuUJsbCy1a9emqKiIoKAgXnjhBW7fvk3Pnj0ZMGCA+RoYjUaWLVvG1atXcXNzY8eOHYSHh1s8GSwsLGTSpEmcOXOGDz74gICAAIqKili4cCFeXl48ePCAjRs3PjXXd+/ezVdffUXLli1xc3PDx8fH4mMxGo3Mnz+fI0eOUK9ePVasWAH8WAn56NGjNGnShMqVKz91zjYjIwN/f39efvllTp06RWBgIO3atbP4/ElMTGTu3LmkpaURExODl5cXV65cYfny5SiluHfvHt7e3ixdutQ8ntLSUoYOHYqLiwuOjo507dr1d7cCENbjpzaMWfo+FUKI551SitTUVO7cuUObNm2er5+76jmVlZWlevToodLT09WjR49U7969VVJSkvnz/Px81bNnT5WSkqKKi4tV37591YULFywY8c8zGo2qpKREGY1G9ejRI9WtWzd1+/btcv/Nli1b1KxZs1ReXp4yGAzKaDRaKNpnO3r0qPL391c5OTlKr9c/FWtMTIwKCgpSZWVl6ujRo2rgwIHKYDBYKNpfZjQalcFgUHq9Xm3fvl2NGDFClZWVmT/X6/WqX79+6vbt20qv16uysjKruzalpaUqLy9P9ejRw3xfHT16VA0aNEjp9Xp17do19fbbb6uioiLzn3n48KHq0aOHSktLU4WFheqtt95SN27csNQQzIxGoyoqKlJLlixRCxcuNP+eaf7k5uaqLl26qNTU1HJ/bs2aNWrJkiVWNX9MY4mLi1PDhg0zx7Rw4UL1xRdfqPz8/J+8n6KiotSkSZOU0WhUBw4cUMOGDSt3T1pKTk6OunXrlurSpYu6d++eUur/z5/S0lI1duxYtWHDhnLjKSoqUn369FGpqanm+SOeHwaDQe3fv189fPhQxcTEqMzMTEuHJH6G0Wgs9+t5Zc3jNBqNKisrSxUWFqr09HSVkZFhdTH+Vtb4PT+LwWBQqampNhf344xGo0pOTlZ9+/ZVe/futemxPMny+9P+IFlZWWi1Wjw8PHB2dqZq1aqkpqaaPzc1tPf09MTR0ZGaNWuatyJaGzs7O/Pq0pUrV9BqtU/10dLpdHz//feMGDGC0NBQ8vPzLRHqr+Lo6MjNmzcZNWoUAQEBPHjwoNyb++vXr/PSSy9hZ2dH7dq1ycjIoKTkX9OE/l/NVJIfYNeuXbz33ntPvfkyGAxMnz6d4OBgLly4YIkwf5GpZczjcd+8eRMfHx/s7e2pXr06jx494tGjR+bPHz58aJ5fTk5OeHl5lZtflmJnZ/fUkYPH58+FCxeoUKHCU/0NXV1dOXjwIMOHDycsLIzCwsI/M+yfZBrLk1vAK1SowO7du/noo4+YNWvWU3Pj2rVr+Pr6Ymdnh4+PD6mpqeatvJbk7u6Ol5dXuW3RpvmTm5vL2bNn6dy581Pzp6SkhClTphASEsLVq1d/cpVP2K6zZ88yYMAAjh8//lxuM1f/OAqRnJxMWVmZpcP5pyilMBgMHDx4kBUrVnD58mVzn+Tnielny+XLl9m1axfZ2dlW9/Nm586dhIaGEhISQkpKiqXD+V2UUhQWFnL9+nWr/K5/Tl5envk521Zi/im1atUiODiYWbNmERcXZ9Njedxzm9gqpbCzszM/JGk0mnI/iJ+8gE9+bm2UUiQnJxMeHs6ECRNwc3Mr93nXrl3ZsmULK1eupLCwkJ07d1oo0mdr2rQp27ZtY+XKldStW5fVq1eX+9x09hYwVwO19gmXkpLCgwcP8PPzK/dgrtFoiIyMJCIigm7duhESElLuTKG1MhqN5nE8ft7R5PF/Ns0za58/N27cYN68eUycOPGplhZvvfUWX375JStWrODevXvExcVZKNJn++CDD9i4cSPLly/nwoULHDlypNznj88fjUZj9XNHKcV3333HSy+9RLVq1cp95ujoyKJFiwgPD6ddu3ZMmDCBoqIiC0Uq/tU0Gg3/+Z//iU6no6CgAL1eb/55b+337a+hlOLMmTMEBQURGBjIsmXLKC0ttXRYv8qT12H79u188803uLm54e/vz/3795+La/Skq1evMm/ePL7//nsCAgJISUmxqnG+/fbbnDp1Cr1eT4MGDSwdzj9N/aMWxPjx41m2bBn/+7//y6VLl6zqu37c4/PB3d0db29v4uPjLR3Wb/LknD5z5gxr1qyha9euzJ8//7lJbp/bxLZSpUqUlJSQn5+PXq8nIyODGjVqYDQaMRqNuLu7YzQayc3NxWAwcP/+fby9vS0d9k9S/zh/FhISwrBhw3j99dfNCV9ZWRlKKZycnMyFVerXr096erqlw/5ZWq0WFxcXnJycaNiwIWlpaQDmsfj4+HDr1i0AHjx4YF4VtFZKKfbs2cMbb7zx1AsHjUZD3bp1qVatGh06dKCsrIzs7GwLRfrr1a5dm5SUFIxGI5mZmTg5OeHq6mqePx4eHubCP6WlpWRmZlKjRg1Lh/2TlFLcuXOH0NBQAgMDeeWVV56aP6bm8RUqVKBu3bpWO3+UUuh0OpycnKhYsSK1atUiMzOz3Fh8fHy4efOm+QyNl5eXVfcQLCsrIyYm5id3O2g0GurVq0e1atXo1KkTBQUFNvFiSPwy9Y/ijjdu3MDFxYWoqChq165NWFgYd+/eZc2aNVa96+jXUEpx8+ZNtmzZQlhYGH//+99JSkpi8eLFVp/cKqU4duwY8fHx5tXaAwcOMGTIEC5dusSQIUNwcnJ6areVLVNKcevWLRYuXMiwYcMIDw+nf//+jB8/3iqSW6UUKSkpxMfH88UXX9CgQQNmzZrFo0ePSEpKIiMjw+Ix/lqmOKOjo2nXrh2DBg3C1dUVFxcXiouLrW4cpmQwLi6OL7/8kocPH9KvXz927Nhh9XP5cWlpaWzfvt28CLF9+3b69+9PaGgon376KbNnz2bfvn1W9/3/Vs9tYlu5cmX8/PyYP38+ixcvpnr16tSpU4ft27cTExNDhQoV+Mtf/sK8efNYtmyZuRKqNdLr9QQFBWEwGLhz5w7r1683V36dNGkSBoOBmJgYNm3axNq1a9mzZw+dO3e2dNg/69ChQ6xdu5bNmzezevVqevfuTUlJCePHj+fhw4e0b9+eS5cu8cUXX7Bo0SL69u1r1Q/mxcXFxMXF8dZbb5kTpr1793Ly5EkKCgo4cOAAly9fZtu2bbi6uuLp6WnpkMtJT09nw4YN3L17ly1btnD+/HlatGhBfn4+q1atIjIykjfffBNnZ2c2btzIvn37qFy5Mq1atSIyMpIlS5bg6elJnTp1LD0UysrK2LlzJ4mJiRw7doyYmBgKCgoYOXIkjo6OXL9+nY0bN5KXl8etW7eYPn06ZWVlbNu2jc2bN7NmzRq+++47OnToYOmhoJTi8OHD7Nmzh6tXr7Jp0yYKCgqIiopi69atrFy5kvPnz9O6dWsKCgoYP348eXl5dOnShWPHjrFu3TqWLVtG3759raIqcl5eHidPniQ3N5czZ86YX2jduXOHjIwM824HpRQ7d+7k3Llz5ObmcvDgQS5fvszmzZupWrUqFStWtPBIxD/j8ZUCvV7PkiVLiIyMZMSIEcTGxjJixAjq1q1LQEAADg4Oz8W25Hv37rF//34KCwvx8PBgxowZpKam8vXXX1v9w2PVqlVZvnw5Bw4cwM7ODhcXF0JDQ2nSpAl9+/Zl/fr1nD592tJh/m6P35cVK1YkKyuLxMRElFL07t2bAQMGsH37doteL/WPzhhTp06lUqVK1KlTh+DgYFxcXBg+fDgzZ860iuMzv4ZSioyMDGJjY8nPzyc5OZkVK1YQFhaG0WhkxYoVVjE3jEZjuY4XcXFx2NnZcffuXT755BP27NlDWloaZ8+etYp4fw0XFxfs7e3JysrCYDBQq1Ytjh8/TllZGQ0aNMDPz4/Dhw+j1+stHerv8tz2sTXt3f/2228pLS2lY8eOeHh4cOvWLfPZs6KiIg4dOkR+fj4dO3akSpUqVlkZzGAwkJCQYD7j6ODgwOuvv45Go+HixYu8+uqrJCUlcerUKZRS+Pn5Ua9ePascC0BqaipHjx6luLiY5s2b06RJE4xGIydPnqRFixY4OTlx584djhw5Qu3atWndurW5mrU1ysnJYffu3fTv39/cKic+Pt5csXbt2rXcvXuXKlWq0L9/f2rVqmVV1yY7O5vExETzW7xGjRrh4+NDWloahw4dokqVKrzxxhvmxFCn0+Ht7U1hYSHx8fEUFxfTqVMnPDw8LD4uo9FIYmKieVXc3d2dV199laNHj5r/4tdqtbRr1w6DwcC1a9do2bIl165d48yZM2g0Gl577TVefPFFi49FKcW5c+fKtfZp27YtN27c4Ny5czg6OtKmTRtq1qyJwWDg1KlTtGzZEq1Wy82bN0lMTKRu3bq0atXKKl4M3blzh82bN1NSUoJWq6Vbt260bNmSK1eucOvWLXr06GFObOPi4qhVqxbe3t588cUXPHjwAC8vL95//31q1Khh8WsjfhulFAkJCfj6+uLp6cnhw4f5/vvv6dy5MwsXLiQ3N5dBgwbRp08fSkpKcHFxsYqXMb+V6XEqMzOTvLw883bFzz//nNmzZ1OvXj2KioowGAzl2lpZG9M4kpKSmDBhAsOGDcPb25tPPvmErl27UlRUhFKKCRMmPFXTwJY8fqY2ISGBRo0aUa9ePUJCQujQoQODBw9Go9FgMBieqkPxZ8e5adMmcnJyGDFihPn3i4uLuXTpEt7e3nh5eQHWXVXc9Fz+2Wef0ahRI1588UX++te/MnLkSFq1asXSpUsZPHgwrVu3tvg4MjMzCQ4OZuzYsTRu3JgpU6YwcOBAGjRowMOHD9m/fz8bNmygQYMGREZGWsXfsc9ieokza9Ys3N3d6d+/PyEhIXh7e1O/fn2OHTvG3Llzra4l5W/13Ca2QliLJ8+jCiF+PZk/tq2srIwjR45w8eJFTp48yezZszEajRgMBiZOnEhwcDA3btxg1qxZbNiwgfr169vsdVZKcfToUVatWoXRaKR69epMnjyZo0ePsmbNGhYuXEjNmjUtHeYvMm093r9/P97e3uh0OiZMmMDQoUNp3LgxR44cwdPTk7Zt2+Lk5GRz18r088T0Eu3s2bMsXryYdu3aERcXR5s2bXj//fcJCQlh4MCBP1nQ7s+OFX4sTvntt98yZ84ctFot58+f5+rVq+b2Z9Z+HUxj2b17N9OmTWPbtm3UqVOH06dPs27dOpRSDBgwgDZt2ljFSy1T7YdFixYxbdo01q9fT0BAQLkji3l5eQwbNoxPP/30qfoQ1sT03RcXF1NWVoZeryc0NJTGjRvz17/+lX379pGVlcW7776Lt7e31d9LzyKJrRBCCCH+5ZRSnDhxgo0bNzJjxgy2bt1KYmIis2fPBuCjjz5izpw5bN68mddff50OHTpY9e6cZ8nNzWXixIn4+/vz+eefk5KSwgsvvMDMmTO5du0adevWfaqjgTV4MtnbtGkTly5dIjAwEE9PT/PK7UcffVTumJMtPgCnpaURGxvLwIEDsbe3JyIigqZNm/LWW2/x8OFDhg4dSlhYGLVr18bOzg53d3eLxKmU4vbt20RFRaHVaunduzeLFi2icuXKtGjRgn379jFmzBiaNGli9ddBKcWjR484deoUfn5+LF68mHv37hEREUGFChXK3X/WMBalFEajkZycHC5cuMCiRYvIz8+nbt26ODk5odFoGDhwID4+PowaNYoFCxaYV8ytkVKK48ePs2zZMvLy8ujevTvvvfceYWFhNGnShI8//ti84mwN3//vZfnXIkIIIYR4LpWUlFBYWMimTZt46aWXaNu2LSEhIRiNRvr168eECRNwd3fnjTfesNmk1rTFz9nZmcDAQL799lvq16/Pp59+yokTJ9i2bRstW7a02qT22LFjXLhwAaUUpaWlxMbGEhAQYK4HUadOHSIiIsjKynqq44StcXNzo2bNmty/f5/S0lLq1atHfHw8JSUlVK5cmSZNmpCWloa7u/ufmtSakinTvZSbm0t4eDitWrUiNzeXdevWMXfuXF5++WXS09OZMGGCTSS1JgaDgTVr1rBlyxZGjRqFt7c3U6ZM4dGjR2g0GjQajdWMxWAwsGrVKpYvX85rr73G6NGjKS0tpXfv3gQFBTF27FiaN2+OTqdj+vTpVK1a1dIh/6KCggKWLl3KpEmT+PTTTzl9+jRff/01ERERaDQaSktLbXpOP0kSWyGEEEL8IVq1asWjR49Yv349vr6+DBo0iLZt2xIaGkrnzp2Jiorik08+QavVWjrUf4pSitOnTzN//nwuX76Mj48P169fp1atWly6dIkePXrQv39/S4f5i5ydncnJySEnJweNRoO9vT3Xr18HftxKvmrVKipUqED//v2tYpvoP8O0xVqn0/HGG2+wbds25syZQ9euXVFKMWrUKBYtWsSDBw945ZVX/vT4DAaD+f8PcPv2bTw9PdFoNGRnZzN69GiuX79Or169CAgIoEGDBlafiCilyM/PJzs7G3d3d+bNm8f+/fvZvHkzAQEB+Pr6Wl0HAlPRxuTkZAIDA7l8+TIGg4EpU6awadMmKlSoQK1atdBqteh0Onx8fKz2OiilSE9Pp6CggPz8fKpWrYq3tzchISHs27fPXHzsyfaHtk62IgshhBDiD1FWVkZ8fDyJiYk8fPiQsLAwdDodUVFR1KxZ06JnGH+PxwtFTZ48mbZt27Jjxw6GDBlCrVq1iIiIwM3NjUmTJlldwcAnKaXIzs4mKCiIkSNHUlJSQnh4OL169SIrK8t8Js/R0dHSof7T0tLSWLJkCd26dSM+Pp6RI0cyZ84cXF1dCQgI4MSJE2RlZdG5c2c8PT3/9OullOL8+fO4ubmh1WrRaDQMHjyYatWqERkZSWFhIbNmzSIyMtJi26N/DVPrOXt7e/R6PcePH+fzzz9n1qxZ1KhRgzt37tCnTx/Gjx9Pnz59rG6lUClFdHQ033zzDRUrVuTRo0dkZmYyaNAgmjVrRo0aNWzmJZzRaGTq1Kl07dqV/fv3o9frGT16NJcuXSI6OpoFCxbY7C6ZXyKJrRBCCCH+MKYtrosXLyYtLY2wsDBcXV0BbHoF8MqVKxw+fBhHR0f++7//m6SkJMaNG8eQIUPo1KkT9vb2ODg4WNWDO5QvSqSUoqCgAJ1Oxw8//MDs2bMJDQ3Fzc2NgwcPUr16dbp164ZOp7O6cfwWBoOBmTNnsnHjRjZt2oSfnx+FhYXMmDEDV1dXxo8fb/HKxwCnT59m7ty5zJw5k4sXL7Jy5Ur8/PxISUlhxIgR5rZo1qqsrIwFCxbQrFkzDh8+zIcffsjJkyfNW1/t7OxYuHAhQ4cOpWHDhlY5lqKiImJjY6latSqvvvoqkZGR+Pn5mSv3W7MnU7qEhAS+/PJLIiIiWLZsGSdPnqR69eqMHz/eKro//BEksRVCCCHEH8qU3C5btoxWrVrRtm1bm32oMhX2mT59Op6enly9epXly5dTs2ZNkpKS+L//+z+CgoKscoXTVNArLS2Nnj17snHjRmJiYqhTpw4TJ07k1q1bzJkzh0mTJtGiRQvAtgvKmB5xHzx4wNWrV0lISCA5OZnZs2dTqVIl0tPT+eabb3jvvffML1v+rLhMK5vwY4XdrKwsqlWrxpEjR1i9ejUzZ87E0dGRO3fu0LBhQ5tp6XPs2DH+67/+iw8//JCQkBAAduzYwbp169DpdPj7+9O+fXurH0dubi5LlixBKcW4ceNsorWVUoqUlBRiY2Pp2bMnVatW5eOPPyY4OJgmTZpQWFiIo6OjRV/i/NEksRVCCCHEH85UIMfOzs5mV2oB7t27R1hYGP369aNTp05ER0ezfft2IiMjqVmzplUXWFJKkZqaypgxY+jYsSPJyckEBQWxc+dOEhISmDNnDsnJySQlJfH+++9b5Rh+C6UUR44cYd26dQQHB1O3bl1Wr17N8ePHadu2LRkZGQQHB//pxYvKysqYN28eHTp0wNvbm0mTJpmL+EydOpXk5GRWr15NREQE9erVs4nrYEoncnJy+Prrr4mOjmbMmDG8/vrrFBQUkJeXh0ajoXr16jYxnsLCQs6dO8fLL7+Mo6OjTcQMP1Zn37ZtG3Fxcfj6+uLm5kZWVhbh4eE20W/395LEVgghhBDiGUxtSwAmT56Mo6Mj06ZNw9HRkejoaG7fvk1QUJDVJ+1KKe7evYu/vz+NGjUiIiICpRRRUVEkJCSwaNEiXFxcbOZB/pcUFhby8ccfM336dF588UXgx6Ry165d3L59m8GDB1OpUiWLnKn94YcfmDJlCr6+vnTp0oWOHTuyZ88eNm3axOeff05iYiJr165l6dKlVK5c+U+N77dSSpGZmcnWrVtxcnKib9++pKSkMHXqVHr16sXp06eZMmUKVatWtZn7ylZ7qJvizsvLMxfrqlixIitWrLDKXST/apLYCiGEEEI8Q2lpKSEhIXTt2pV27doxY8YMKleuzOjRo9Fqtej1eqte2TG1kklPT8fe3p6ioiLGjBnDgAEDePfddzEajZw/f54mTZrY/AOwaaz5+fkMHjyY1atXU6VKFQC+++47Xn31VXM1WEtcL9Oj9w8//MDQoUMZN24c/fr1o6ysjCFDhjBmzBgaNWqEv78/w4cPN28Lt1Z5eXmEhobSqlUrYmNj0Wq1LFq0iKysLKKjo3nzzTdp1qyZ1c6N55HpHisuLsZoND43L6uexbpfKwohhBBCWJBpC7VWq2X48OF89tlnJCQkMHnyZHJycti1axd2dnY4OTlZ9YOj0Whk3bp1hIaG8sknn3Do0CHmz5/P1q1b+eqrr9BoNLRo0cLmk1qAjIwMIiMjUUrRunVrZs+eTUpKCgcPHmTLli3mreKWSmrT09PZtWsXZWVlLF++nHXr1rFz507Onj2LXq8390Z9++238fX1/dNj/C2UUly9epX27dvj7OyMt7c39evXN/dCHj9+vCS1FmC6v3U6Ha6urv82378ktkIIIYQQPyMrK4s5c+aQm5tL/fr1mTNnDgsWLCAhIYEpU6bQvXt3S4f4TEopkpOTOX78OLNmzeKFF15Ao9FQrVo15s2bh16vB2xry+VPMa3Uenh4YGdnx7Rp0xg8eDC1atUiODiY2NhYQkJCcHZ2tlh89+/fZ/z48Vy6dIl58+Zx8OBBpk6dyowZM4iKiiI0NJRq1aphb29Pz549cXJyskisz6KUorCwkPv37/Mf//EfdO/enR07dhAcHEyXLl1wcHAgMzPTas+bi+eTbEUWQgghhODpdhkGgwGj0chnn33G1atXmTFjBu7u7oSFhaGUMp+1tWZGo5G7d++SnZ3NypUrcXd3p1mzZvTp04fFixczZMgQKleubNPJh+m6JSUlkZubS8uWLdHr9SxcuJC7d+8yc+ZMdDod9vb2f3qhqCfj3LhxI7m5ufj7+5OTk2Pebuzp6YlOp7OJNiym3seTJk3i3r17NG3alMDAQIKCgmjevDlXrlwhICBAVmrFn05WbIUQQggh+LHi8TfffIPRaCQxMZEPP/yQyZMn8+6779KwYUPGjBnD2rVryc3NJTQ0FK1Wa+mQn6m4uJjJkyfj4uJCdnY2Dx48oH379mzYsAG9Xo+7u/tzkXycPn2amzdvMnPmTI4ePYpWq2XYsGFcunSJ6OhotFot9vb2Fh9rtWrVOHv2LMXFxXh4ePCXv/yFGzdu4OvrazNJbUFBAVu3bqVjx45s2LABZ2dnIiIiCA0NpUKFCgwdOpSmTZta/VjE80cSWyGEEEIIQKvVsn79eqKioti6dSshISH4+voybtw43nnnHfr3709ubi7BwcFW3dfStCVXKYVOp6N169bs3buXhQsXUqNGDSZOnEhhYSGBgYE23wLEdGZ13bp1NG3alGnTpjFv3jz27t3LiRMn6NSpE++//76lwzRr3749VapUYezYsWzdupUjR47Qrl07wHa2gmdnZxMVFYVSigoVKhAcHEyVKlWIjo5m6NCh+Pn5WX11cPF8kq3IQgghhBD8/zOQQUFBuLq6smrVKuzs7Ni8eTOxsbEsWbLE3HrFmpMQpRTfffcdV69epXfv3hiNRkaNGsWqVavw8PCgrKwMBwcHqx7Dz3nysbWkpITAwEB0Oh3z58/Hzs6OK1eusGTJErRaLePGjaNmzZpWM1alFMXFxcTFxXH79m26detmLhBlLTE+i1KKCxcuMGXKFIKDg2nTpg2lpaVkZGRY1Xct/v1IYiuEEEII8Q+PJ7fvvvsuffv2BX5sE9O8eXNzYSJrppTiwYMHbNmyhYSEBPz8/Lh16xZvvPEGH3zwgdXH/0uUUly/fp0zZ87QoEEDzp07h5+fH+PHj2fs2LF06NAB+PFsMWDRM7W/xFb7pJoopTh//jxhYWGEhobyyiuv2OQ4xPNFElshhBBCiMeYkttx48bRu3dv+vbta7UJ0s8xPd5lZWXx9ddfs2XLFnr16sXw4cNtahxPUkqRlZXF0KFDycrKYtWqVfj6+nL58mUmT57MyJEjad++vU2P0VYopbh8+TJKKRo3bizfubA4SWyFEEIIIZ5gSm43b96Mv7+/1bZdeRbTY15RURFardYmCl79HNNYiouL2b59OzExMXTv3p3/+Z//QaPRcPHiRfbu3cvo0aNxcHCwcLT/HkzXRJJaYQ0ksRVCCCGE+Am2vl30eWK6FkeOHGHHjh1MmjQJvV7PuHHjeO2116hevTqFhYUMGDDA5lbXhRD/GlKyTAghhBDiJ9jZ2Zl/CctLTk5m9erVBAYGotfruX//PuHh4dy5c4eTJ0/SvXt3SWqF+Dcm+zSEEEIIIYTVMq3W3r9/H4C4uDji4+MpLCykV69ehIeHA8hLCCH+zUliK4QQQgghrJJSips3b5KRkUGjRo1o3749BoOBBQsWcOjQIW7duiUJrRACkMRWCCGEEEJYGdMq7YULF5g7dy5arZaKFSsybdo0dDodX331FXFxcUybNs3CkQohrIWcsRVCCCGEEFYnKSmJ1atXM3bsWFauXImPjw+TJk0iNzcXBwcHIiIiqFGjhqzWCiEASWyFEEIIIYSVUEqVayFz5swZrl+/joODA8OHD6dBgwbExMTwzjvv4OnpKUmtEMJM2v0IIYQQQgirYDpTe+7cORo3bozRaCQkJAR/f3+6dOlCWVkZBoMBZ2dnSWqFEOXIiq0QQgghhLA4pRSnT59mxowZXL58mTFjxnDhwgVmz57NihUrOHv2LA4ODuh0OklqhRBPkRVbIYQQQghhUabH0ZCQEN58803at2/PzZs3GTVqFCtXrgTA1dWVSpUqWTJMIYQVkxVbIYQQQghhEaYztUlJSSQnJ+Pl5cXJkycxGo34+PhQr1497t+/T82aNfHw8LB0uEIIKyaJrRBCCCGEsJjjx48ze/ZsCgoK+Nvf/saxY8cIDw9n/fr1FBYW8tJLLwHI9mMhxC+SrchCCCGEEMIiDAYD/v7+jBw5ksaNGwOQlZXF7t27KSoq4p133qF69eqS1AohnsnB0gEIIYQQQoh/X8XFxRQVFZn//cSJE3Tr1o0aNWoAslIrhPh1ZCuyEEIIIYSwCHt7e/72t78RERFBfHw8u3btIiYmBldXV+zs7CSpFUL8arIVWQghhBBCWIzRaCQhIYHt27dTuXJlhgwZwgsvvCBJrRDiN5HEVgghhBBCWNSTj6OS1Aohfis5YyuEEEIIISxKElkhxO8lZ2yFEEIIIYQQQtg0SWyFEEIIIYQQQtg0SWyFEEIIIYQQQtg0SWyFEEIIIYQQQtg0SWyFEEIIIYQQQtg0SWyFEEIIIYQQQtg0SWyFEEIIIYQQQtg0SWyFEEIIIYQQQtg0SWyFEEIIIYQQQtg0SWyFEEIIIYQQQtg0SWyFEEIIIYQQQtg0SWyFEEIIIYQQQtg0SWyFEEIIIYQQQtg0SWyFEEIIIYQQQtg0SWyFEEIIIYQQQtg0SWyFEEIIIYQQQti0/wecEL/D83S3vAAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Affichage de la matrice de confusion en pourcentages"],"metadata":{"id":"GFqCXkZEBMp2"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"YNQDzY0BH-X0"},"outputs":[],"source":["def display_best_confusion_matrix(experiment_name=\"OC Projet 8\"):\n"," \"\"\"\n"," Trouve le modèle avec la meilleure valeur test_mean_iou et affiche sa matrice de confusion\n","\n"," Args:\n"," experiment_name (str): Nom de l'expérience MLflow\n"," \"\"\"\n"," import matplotlib.pyplot as plt\n"," import seaborn as sns\n"," import numpy as np\n"," import json\n"," import tempfile\n","\n"," # Récupérer les résultats des expériences\n"," completed_configs = get_completed_configs_info(experimental_configurations, experiment_name)\n","\n"," if not completed_configs:\n"," print(\"❌ Aucune expérience terminée trouvée\")\n"," return None\n","\n"," # Récupérer le mapping run_name -> run_id depuis MLflow\n"," experiment = mlflow.get_experiment_by_name(experiment_name)\n"," runs_df = mlflow.search_runs(\n"," experiment_ids=[experiment.experiment_id],\n"," filter_string=\"status = 'FINISHED'\",\n"," output_format=\"pandas\"\n"," )\n","\n"," run_name_to_id = {\n"," run.get('tags.mlflow.runName', ''): run['run_id']\n"," for _, run in runs_df.iterrows()\n"," }\n","\n"," print(f\"🔍 Recherche du meilleur modèle parmi {len(completed_configs)} configurations...\")\n","\n"," best_result = None\n"," best_iou = -1\n","\n"," # Trouver le meilleur modèle\n"," for config_idx, config, run_name in completed_configs:\n"," if run_name not in run_name_to_id:\n"," continue\n","\n"," run_id = run_name_to_id[run_name]\n","\n"," try:\n"," # Récupérer les métriques\n"," run = mlflow.get_run(run_id)\n"," metrics = run.data.metrics\n","\n"," test_mean_iou = metrics.get('test_mean_iou', None)\n","\n"," if test_mean_iou is not None and test_mean_iou > best_iou:\n"," best_iou = test_mean_iou\n"," best_result = {\n"," 'config_idx': config_idx,\n"," 'experiment': config['name'],\n"," 'architecture': config.get('architecture', 'Unknown'),\n"," 'encoder': config.get('encoder', 'None'),\n"," 'test_mean_iou': test_mean_iou,\n"," 'test_accuracy': metrics.get('test_sparse_categorical_accuracy', 'N/A'),\n"," 'run_name': run_name,\n"," 'run_id': run_id\n"," }\n","\n"," except Exception as e:\n"," print(f\"⚠️ Erreur lors de l'analyse de {run_name}: {e}\")\n"," continue\n","\n"," if best_result is None:\n"," print(\"❌ Aucun modèle avec test_mean_iou trouvé\")\n"," return None\n","\n"," print(f\"🏆 MEILLEUR MODÈLE TROUVÉ:\")\n"," print(f\" 📊 [{best_result['config_idx']}] {best_result['experiment']}\")\n"," print(f\" 🏗️ {best_result['architecture']} + {best_result['encoder']}\")\n"," print(f\" 🎯 Test Mean IoU: {best_result['test_mean_iou']:.4f}\")\n"," print(f\" 📈 Test Accuracy: {best_result['test_accuracy']:.4f}\")\n"," print(f\" 🆔 Run ID: {best_result['run_id']}\")\n"," print()\n","\n"," # Télécharger et charger la matrice de confusion\n"," try:\n"," with tempfile.TemporaryDirectory() as temp_dir:\n"," confusion_matrix_path = mlflow.artifacts.download_artifacts(\n"," run_id=best_result['run_id'],\n"," artifact_path=\"confusion_matrix.json\",\n"," dst_path=temp_dir\n"," )\n","\n"," with open(confusion_matrix_path, 'r') as f:\n"," confusion_data = json.load(f)\n","\n"," matrix = np.array(confusion_data['matrix'])\n"," class_names = confusion_data['class_names']\n"," accuracy = confusion_data.get('accuracy', 'N/A')\n"," total_samples = confusion_data.get('total_samples', 'N/A')\n","\n"," print(f\"📋 Informations sur la matrice de confusion:\")\n"," print(f\" 🔢 Nombre de classes: {len(class_names)}\")\n"," print(f\" 📊 Échantillons totaux: {total_samples:,}\" if total_samples != 'N/A' else \" 📊 Échantillons totaux: N/A\")\n"," print(f\" ✅ Précision globale: {accuracy:.4f}\" if accuracy != 'N/A' else \" ✅ Précision globale: N/A\")\n"," print()\n","\n"," # Calculer les métriques par classe\n"," print(\"📈 Métriques par classe:\")\n"," print(\"-\" * 50)\n","\n"," for i, class_name in enumerate(class_names):\n"," # Précision: TP / (TP + FP)\n"," tp = matrix[i, i]\n"," fp = np.sum(matrix[:, i]) - tp\n"," fn = np.sum(matrix[i, :]) - tp\n","\n"," precision = tp / (tp + fp) if (tp + fp) > 0 else 0\n"," recall = tp / (tp + fn) if (tp + fn) > 0 else 0\n"," f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0\n","\n"," print(f\" {class_name:12s} - Précision: {precision:.4f} | Rappel: {recall:.4f} | F1: {f1:.4f}\")\n","\n"," print()\n","\n"," # Créer la visualisation\n"," plt.figure(figsize=(12, 10))\n","\n"," # Normaliser la matrice pour l'affichage (en pourcentages)\n"," matrix_normalized = matrix.astype('float') / matrix.sum(axis=1)[:, np.newaxis] * 100\n","\n"," # Créer le heatmap\n"," sns.heatmap(matrix_normalized,\n"," annot=True,\n"," fmt='.1f',\n"," cmap='Blues',\n"," xticklabels=class_names,\n"," yticklabels=class_names,\n"," cbar_kws={'label': 'Pourcentage (%)'})\n","\n"," plt.title(f'Matrice de Confusion - {best_result[\"experiment\"]}\\n'\n"," f'Mean IoU: {best_result[\"test_mean_iou\"]:.4f} | Accuracy: {accuracy:.4f}',\n"," fontsize=14, fontweight='bold', pad=20)\n"," plt.xlabel('Classes Prédites', fontsize=12)\n"," plt.ylabel('Classes Réelles', fontsize=12)\n"," plt.xticks(rotation=45, ha='right')\n"," plt.yticks(rotation=0)\n","\n"," plt.tight_layout()\n"," plt.show()\n","\n"," # Optionnel: afficher aussi la matrice brute\n"," print(\"🔢 Matrice de confusion brute (premiers/derniers éléments):\")\n"," print(\"=\" * 60)\n"," print(\"Classes:\", class_names)\n"," print(\"Matrice (format: lignes=réel, colonnes=prédit):\")\n"," print(matrix)\n","\n"," return {\n"," 'confusion_matrix': matrix,\n"," 'class_names': class_names,\n"," 'best_model_info': best_result,\n"," 'accuracy': accuracy,\n"," 'total_samples': total_samples\n"," }\n","\n"," except Exception as e:\n"," print(f\"❌ Erreur lors du téléchargement de la matrice de confusion: {e}\")\n"," print(f\" Run ID: {best_result['run_id']}\")\n"," print(\" Vérifiez que l'artefact 'confusion_matrix.json' existe pour ce run.\")\n"," return None"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ir7It9rTICQE","colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["55ba2a5fc03e4a999966cdebb03209a7","3823431325244727b488ef7025d977f2","5f915fb81c68451d9d7d3c09a8336385","e4ca2e4089c348fda12aa2b853d9759f","4def49bb8249419ea6aa88e49c3ce325","5abaa586d30b406bbaeac2a71f009445","01b2dca58b8145c6aae6775fbd0babc9","70be4ff8242940ea9684ab9908d20bad","db448534ea3e4efa999437edf895edf3","f680f3899d764b0287e22b2bbf5af141","2655232ebc9449ba8b211600043739b9"]},"executionInfo":{"status":"ok","timestamp":1755008424127,"user_tz":-120,"elapsed":4653,"user":{"displayName":"Grégoire Mureau","userId":"04855910922109670788"}},"outputId":"7b3485b5-536d-4487-813c-5324526df80a"},"outputs":[{"output_type":"stream","name":"stdout","text":["🔍 Vérification des expériences déjà terminées dans 'OC Projet 8'...\n","✅ 34 expériences terminées trouvées:\n"," - FPN_efficientnetb0_finetune_augTrue_prod\n"," - FPN_efficientnetb0_finetune_augTrue_test\n"," - FPN_efficientnetb0_frozen_augFalse_prod\n"," - FPN_efficientnetb0_frozen_augFalse_test\n"," - FPN_efficientnetb0_frozen_augTrue_prod\n"," - FPN_efficientnetb0_frozen_augTrue_test\n"," - FPN_mobilenetv2_frozen_augFalse_prod\n"," - FPN_mobilenetv2_frozen_augFalse_test\n"," - FPN_mobilenetv2_frozen_augTrue_prod\n"," - FPN_mobilenetv2_frozen_augTrue_test\n"," - FPN_resnet34_finetune_augTrue_prod\n"," - FPN_resnet34_finetune_augTrue_test\n"," - FPN_resnet34_frozen_augFalse_prod\n"," - FPN_resnet34_frozen_augFalse_test\n"," - FPN_resnet34_frozen_augTrue_prod\n"," - FPN_resnet34_frozen_augTrue_test\n"," - FPN_vanilla_augFalse_prod\n"," - FPN_vanilla_augFalse_test\n"," - FPN_vanilla_augTrue_prod\n"," - FPN_vanilla_augTrue_test\n"," - U-Net_efficientnetb0_frozen_augFalse_prod\n"," - U-Net_efficientnetb0_frozen_augFalse_test\n"," - U-Net_efficientnetb0_frozen_augTrue_prod\n"," - U-Net_efficientnetb0_frozen_augTrue_test\n"," - U-Net_mobilenetv2_finetune_augTrue_prod\n"," - U-Net_mobilenetv2_finetune_augTrue_test\n"," - U-Net_mobilenetv2_frozen_augFalse_prod\n"," - U-Net_mobilenetv2_frozen_augFalse_test\n"," - U-Net_mobilenetv2_frozen_augTrue_prod\n"," - U-Net_mobilenetv2_frozen_augTrue_test\n"," - U-Net_vanilla_augFalse_prod\n"," - U-Net_vanilla_augFalse_test\n"," - U-Net_vanilla_augTrue_prod\n"," - U-Net_vanilla_augTrue_test\n","🔍 Recherche du meilleur modèle parmi 17 configurations...\n","🏆 MEILLEUR MODÈLE TROUVÉ:\n"," 📊 [15] FPN_efficientnetb0_finetune_augTrue\n"," 🏗️ FPN + efficientnetb0\n"," 🎯 Test Mean IoU: 0.7457\n"," 📈 Test Accuracy: 0.9123\n"," 🆔 Run ID: b6dd1ecdeb3f4b5c899b332f3c948cd7\n","\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading artifacts: 0%| | 0/1 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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["🔢 Matrice de confusion brute (premiers/derniers éléments):\n","============================================================\n","Classes: ['flat', 'human', 'vehicle', 'construction', 'object', 'nature', 'sky', 'void']\n","Matrice (format: lignes=réel, colonnes=prédit):\n","[[9339912 11922 31996 27736 7639 19873 159 96403]\n"," [ 9298 240878 27954 32796 6215 7121 22 7213]\n"," [ 28923 20536 1630480 57085 7436 16871 280 13413]\n"," [ 37107 27385 39861 4762597 51086 145430 14571 61532]\n"," [ 12585 11363 14823 148802 240856 58198 2359 24774]\n"," [ 30020 6106 17487 161547 21272 3703362 10850 23316]\n"," [ 2 2 139 17203 1113 12668 701752 3902]\n"," [ 400815 21207 41511 245295 36253 56041 13049 2267598]]\n"]}],"source":["# Utilisation\n","confusion_result = display_best_confusion_matrix()"]},{"cell_type":"markdown","source":["Affichage de la répartition des modalités dans l'ensemble du dataset d'apprentissage (on passe par le json de matrice de confusion pour gagner du temps)"],"metadata":{"id":"KsVx1dEpBPph"}},{"cell_type":"code","source":["def analyze_class_distribution_from_confusion_matrix(experiment_name=\"OC Projet 8\",\n"," figsize=(14, 8),\n"," color_palette='viridis',\n"," show_values=True):\n"," \"\"\"\n"," Analyse la répartition des classes à partir de la matrice de confusion du meilleur modèle.\n"," Affiche un graphique en barres avec pourcentages (axe gauche) et nombres absolus (axe droite).\n","\n"," Args:\n"," experiment_name (str): Nom de l'expérience MLflow\n"," figsize (tuple): Taille de la figure (largeur, hauteur)\n"," color_palette (str): Palette de couleurs pour les barres\n"," show_values (bool): Afficher les valeurs sur les barres\n","\n"," Returns:\n"," dict: Dictionnaire contenant les statistiques de distribution\n"," \"\"\"\n"," import matplotlib.pyplot as plt\n"," import numpy as np\n"," import json\n"," import tempfile\n"," import seaborn as sns\n","\n"," # Récupérer les informations du meilleur modèle\n"," completed_configs = get_completed_configs_info(experimental_configurations, experiment_name)\n","\n"," if not completed_configs:\n"," print(\"❌ Aucune expérience terminée trouvée\")\n"," return None\n","\n"," # Récupérer le mapping run_name -> run_id depuis MLflow\n"," experiment = mlflow.get_experiment_by_name(experiment_name)\n"," runs_df = mlflow.search_runs(\n"," experiment_ids=[experiment.experiment_id],\n"," filter_string=\"status = 'FINISHED'\",\n"," output_format=\"pandas\"\n"," )\n","\n"," run_name_to_id = {\n"," run.get('tags.mlflow.runName', ''): run['run_id']\n"," for _, run in runs_df.iterrows()\n"," }\n","\n"," # Trouver le meilleur modèle (même logique que display_best_confusion_matrix)\n"," best_result = None\n"," best_iou = -1\n","\n"," for config_idx, config, run_name in completed_configs:\n"," if run_name not in run_name_to_id:\n"," continue\n","\n"," run_id = run_name_to_id[run_name]\n","\n"," try:\n"," run = mlflow.get_run(run_id)\n"," metrics = run.data.metrics\n"," test_mean_iou = metrics.get('test_mean_iou', None)\n","\n"," if test_mean_iou is not None and test_mean_iou > best_iou:\n"," best_iou = test_mean_iou\n"," best_result = {\n"," 'config_idx': config_idx,\n"," 'experiment': config['name'],\n"," 'architecture': config.get('architecture', 'Unknown'),\n"," 'encoder': config.get('encoder', 'None'),\n"," 'test_mean_iou': test_mean_iou,\n"," 'run_name': run_name,\n"," 'run_id': run_id\n"," }\n"," except Exception as e:\n"," continue\n","\n"," if best_result is None:\n"," print(\"❌ Aucun modèle avec test_mean_iou trouvé\")\n"," return None\n","\n"," print(f\"🏆 Analyse de distribution pour le meilleur modèle:\")\n"," print(f\" 📊 [{best_result['config_idx']}] {best_result['experiment']}\")\n"," print(f\" 🎯 Test Mean IoU: {best_result['test_mean_iou']:.4f}\")\n"," print()\n","\n"," # Télécharger et analyser la matrice de confusion\n"," try:\n"," with tempfile.TemporaryDirectory() as temp_dir:\n"," confusion_matrix_path = mlflow.artifacts.download_artifacts(\n"," run_id=best_result['run_id'],\n"," artifact_path=\"confusion_matrix.json\",\n"," dst_path=temp_dir\n"," )\n","\n"," with open(confusion_matrix_path, 'r') as f:\n"," confusion_data = json.load(f)\n","\n"," matrix = np.array(confusion_data['matrix'])\n"," class_names = confusion_data['class_names']\n","\n"," # Calculer la distribution des classes (somme par ligne = nombre total de pixels réels par classe)\n"," class_counts = np.sum(matrix, axis=1) # Somme par ligne pour obtenir le nombre total de chaque classe réelle\n"," total_pixels = np.sum(class_counts)\n"," class_percentages = (class_counts / total_pixels) * 100\n","\n"," # Statistiques détaillées\n"," print(\"📈 RÉPARTITION DES CLASSES DANS L'ÉCHANTILLON:\")\n"," print(\"=\" * 65)\n"," print(f\"{'Classe':<15} {'Nb Pixels':<12} {'Pourcentage':<12} {'Barre':<20}\")\n"," print(\"-\" * 65)\n","\n"," for i, class_name in enumerate(class_names):\n"," bar_length = int(class_percentages[i] / 2) # Barre visuelle\n"," bar = \"█\" * bar_length\n"," print(f\"{class_name:<15} {class_counts[i]:<12,} {class_percentages[i]:<11.2f}% {bar}\")\n","\n"," print(\"-\" * 65)\n"," print(f\"{'TOTAL':<15} {total_pixels:<12,} {'100.00%':<12}\")\n"," print()\n","\n"," # Créer le graphique\n"," fig, ax1 = plt.subplots(figsize=figsize)\n","\n"," # Utiliser une palette de couleurs\n"," colors = plt.cm.get_cmap(color_palette)(np.linspace(0, 1, len(class_names)))\n","\n"," # Barres avec les pourcentages (axe gauche)\n"," bars = ax1.bar(range(len(class_names)), class_percentages,\n"," color=colors, alpha=0.8, edgecolor='black', linewidth=1)\n","\n"," # Configuration axe gauche (pourcentages)\n"," ax1.set_xlabel('Classes', fontsize=12, fontweight='bold')\n"," ax1.set_ylabel('Pourcentage (%)', fontsize=12, fontweight='bold', color='blue')\n"," ax1.tick_params(axis='y', labelcolor='blue')\n"," ax1.set_xticks(range(len(class_names)))\n"," ax1.set_xticklabels(class_names, rotation=45, ha='right')\n"," ax1.grid(True, alpha=0.3, axis='y')\n","\n"," # Créer un second axe y pour les nombres absolus\n"," ax2 = ax1.twinx()\n"," ax2.set_ylabel('Nombre de Pixels', fontsize=12, fontweight='bold', color='red')\n"," ax2.tick_params(axis='y', labelcolor='red')\n","\n"," # Synchroniser les échelles\n"," ax2.set_ylim(0, np.max(class_counts) * 1.1)\n"," ax1.set_ylim(0, np.max(class_percentages) * 1.1)\n","\n"," # Ajouter les valeurs sur les barres si demandé\n"," if show_values:\n"," for i, (bar, count, pct) in enumerate(zip(bars, class_counts, class_percentages)):\n"," height = bar.get_height()\n"," # Pourcentage au-dessus de la barre\n"," ax1.text(bar.get_x() + bar.get_width()/2., height + 0.5,\n"," f'{pct:.1f}%', ha='center', va='bottom', fontweight='bold', fontsize=10)\n"," # Nombre absolu à l'intérieur de la barre\n"," ax1.text(bar.get_x() + bar.get_width()/2., height/2,\n"," f'{count:,}', ha='center', va='center',\n"," color='white', fontweight='bold', fontsize=9)\n","\n"," # Titre et mise en forme\n"," plt.title(f'Répartition des Classes par Pixels\\n'\n"," f'Total: {total_pixels:,} pixels | Mean IoU: {best_result[\"test_mean_iou\"]:.4f}',\n"," fontsize=14, fontweight='bold', pad=20)\n","\n"," plt.tight_layout()\n"," plt.show()\n","\n"," # Statistiques supplémentaires\n"," print(\"📊 STATISTIQUES COMPLÉMENTAIRES:\")\n"," print(\"-\" * 40)\n"," print(f\" 🔢 Nombre total de pixels: {total_pixels:,}\")\n"," print(f\" 📏 Nombre de classes: {len(class_names)}\")\n"," print(f\" 📈 Classe la plus représentée: {class_names[np.argmax(class_counts)]} ({np.max(class_percentages):.2f}%)\")\n"," print(f\" 📉 Classe la moins représentée: {class_names[np.argmin(class_counts)]} ({np.min(class_percentages):.2f}%)\")\n"," print(f\" ⚖️ Ratio max/min: {np.max(class_counts)/np.min(class_counts):.2f}\")\n","\n"," # Calcul de l'équilibrage des classes\n"," ideal_percentage = 100 / len(class_names)\n"," imbalance_scores = np.abs(class_percentages - ideal_percentage)\n"," avg_imbalance = np.mean(imbalance_scores)\n"," print(f\" ⚖️ Score de déséquilibre moyen: {avg_imbalance:.2f}% (0 = parfait équilibre)\")\n"," print()\n","\n"," return {\n"," 'class_names': class_names,\n"," 'class_counts': class_counts,\n"," 'class_percentages': class_percentages,\n"," 'total_pixels': total_pixels,\n"," 'best_model_info': best_result,\n"," 'most_common_class': class_names[np.argmax(class_counts)],\n"," 'least_common_class': class_names[np.argmin(class_counts)],\n"," 'imbalance_ratio': np.max(class_counts)/np.min(class_counts),\n"," 'avg_imbalance_score': avg_imbalance\n"," }\n","\n"," except Exception as e:\n"," print(f\"❌ Erreur lors de l'analyse de distribution: {e}\")\n"," print(f\" Run ID: {best_result['run_id']}\")\n"," return None"],"metadata":{"id":"LXs3Sr5pA6uJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Analyse simple du meilleur modèle\n","result = analyze_class_distribution_from_confusion_matrix(\"OC Projet 8\")\n","\n","# Avec personnalisation\n","result = analyze_class_distribution_from_confusion_matrix(\n"," experiment_name=\"OC Projet 8\",\n"," figsize=(16, 10),\n"," color_palette='plasma',\n"," show_values=True\n",")"],"metadata":{"id":"VoTFNfGhBY8x","colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["1d541fd90d3648269b8117c9aca70f04","1e9c76b7e8ff40f4bedf9ad6c020bdcc","280ff39b4bde45699f8cc34c787c6fa6","6dda57d2132547d2b1cbd611d0001faa","74e14c97eb314d048e00e30068499873","3ca83ea8938d4fd382fac12d5217bc77","35d32db1f91547509be44cb317af0c18","c508bd4ce5a3429299d82e6e70c490a8","47d00c0e1aa44532bb874f757b609bee","2b6b2c92b68d4400bf0db19c7898cae7","2e4134947f0842e693fb0df1b5cd4ea1","54cf1a05e871423c89b83a34f7f0cf1f","35121abbca48482e95ead258bce73029","fbb125df3c2245e796deea4bbacd9fce","f76bc9f28cdc4d2b969124c2fd47f36b","56e123a65afc4cb5b20126c627d7b648","41dbcf9cfed64082aaaf8e791ffd8876","8541590a113a439f9f84ab89b80492ab","b7dce827c17d47488b0e8ba7dd5f881e","fa219a9a89a24b2b850e0e965139c5c0","89a5cc92bbbf464e939aae29180c4af5","916894e0fad845f98afaaea3042ded6c"]},"executionInfo":{"status":"ok","timestamp":1755008480314,"user_tz":-120,"elapsed":8133,"user":{"displayName":"Grégoire Mureau","userId":"04855910922109670788"}},"outputId":"0aa12419-91fd-45b1-843e-08ce44dcb528"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["🔍 Vérification des expériences déjà terminées dans 'OC Projet 8'...\n","✅ 34 expériences terminées trouvées:\n"," - FPN_efficientnetb0_finetune_augTrue_prod\n"," - FPN_efficientnetb0_finetune_augTrue_test\n"," - FPN_efficientnetb0_frozen_augFalse_prod\n"," - FPN_efficientnetb0_frozen_augFalse_test\n"," - FPN_efficientnetb0_frozen_augTrue_prod\n"," - FPN_efficientnetb0_frozen_augTrue_test\n"," - FPN_mobilenetv2_frozen_augFalse_prod\n"," - FPN_mobilenetv2_frozen_augFalse_test\n"," - FPN_mobilenetv2_frozen_augTrue_prod\n"," - FPN_mobilenetv2_frozen_augTrue_test\n"," - FPN_resnet34_finetune_augTrue_prod\n"," - FPN_resnet34_finetune_augTrue_test\n"," - FPN_resnet34_frozen_augFalse_prod\n"," - FPN_resnet34_frozen_augFalse_test\n"," - FPN_resnet34_frozen_augTrue_prod\n"," - FPN_resnet34_frozen_augTrue_test\n"," - FPN_vanilla_augFalse_prod\n"," - FPN_vanilla_augFalse_test\n"," - FPN_vanilla_augTrue_prod\n"," - FPN_vanilla_augTrue_test\n"," - U-Net_efficientnetb0_frozen_augFalse_prod\n"," - U-Net_efficientnetb0_frozen_augFalse_test\n"," - U-Net_efficientnetb0_frozen_augTrue_prod\n"," - U-Net_efficientnetb0_frozen_augTrue_test\n"," - U-Net_mobilenetv2_finetune_augTrue_prod\n"," - U-Net_mobilenetv2_finetune_augTrue_test\n"," - U-Net_mobilenetv2_frozen_augFalse_prod\n"," - U-Net_mobilenetv2_frozen_augFalse_test\n"," - U-Net_mobilenetv2_frozen_augTrue_prod\n"," - U-Net_mobilenetv2_frozen_augTrue_test\n"," - U-Net_vanilla_augFalse_prod\n"," - U-Net_vanilla_augFalse_test\n"," - U-Net_vanilla_augTrue_prod\n"," - U-Net_vanilla_augTrue_test\n","🏆 Analyse de distribution pour le meilleur modèle:\n"," 📊 [15] FPN_efficientnetb0_finetune_augTrue\n"," 🎯 Test Mean IoU: 0.7457\n","\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading artifacts: 0%| | 0/1 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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["📊 STATISTIQUES COMPLÉMENTAIRES:\n","----------------------------------------\n"," 🔢 Nombre total de pixels: 25,088,000\n"," 📏 Nombre de classes: 8\n"," 📈 Classe la plus représentée: flat (38.01%)\n"," 📉 Classe la moins représentée: human (1.32%)\n"," ⚖️ Ratio max/min: 28.77\n"," ⚖️ Score de déséquilibre moyen: 9.21% (0 = parfait équilibre)\n","\n","🔍 Vérification des expériences déjà terminées dans 'OC Projet 8'...\n","✅ 34 expériences terminées trouvées:\n"," - FPN_efficientnetb0_finetune_augTrue_prod\n"," - FPN_efficientnetb0_finetune_augTrue_test\n"," - FPN_efficientnetb0_frozen_augFalse_prod\n"," - FPN_efficientnetb0_frozen_augFalse_test\n"," - FPN_efficientnetb0_frozen_augTrue_prod\n"," - FPN_efficientnetb0_frozen_augTrue_test\n"," - FPN_mobilenetv2_frozen_augFalse_prod\n"," - FPN_mobilenetv2_frozen_augFalse_test\n"," - FPN_mobilenetv2_frozen_augTrue_prod\n"," - FPN_mobilenetv2_frozen_augTrue_test\n"," - FPN_resnet34_finetune_augTrue_prod\n"," - FPN_resnet34_finetune_augTrue_test\n"," - FPN_resnet34_frozen_augFalse_prod\n"," - FPN_resnet34_frozen_augFalse_test\n"," - FPN_resnet34_frozen_augTrue_prod\n"," - FPN_resnet34_frozen_augTrue_test\n"," - FPN_vanilla_augFalse_prod\n"," - FPN_vanilla_augFalse_test\n"," - FPN_vanilla_augTrue_prod\n"," - FPN_vanilla_augTrue_test\n"," - U-Net_efficientnetb0_frozen_augFalse_prod\n"," - U-Net_efficientnetb0_frozen_augFalse_test\n"," - U-Net_efficientnetb0_frozen_augTrue_prod\n"," - U-Net_efficientnetb0_frozen_augTrue_test\n"," - U-Net_mobilenetv2_finetune_augTrue_prod\n"," - U-Net_mobilenetv2_finetune_augTrue_test\n"," - U-Net_mobilenetv2_frozen_augFalse_prod\n"," - U-Net_mobilenetv2_frozen_augFalse_test\n"," - U-Net_mobilenetv2_frozen_augTrue_prod\n"," - U-Net_mobilenetv2_frozen_augTrue_test\n"," - U-Net_vanilla_augFalse_prod\n"," - U-Net_vanilla_augFalse_test\n"," - U-Net_vanilla_augTrue_prod\n"," - U-Net_vanilla_augTrue_test\n","🏆 Analyse de distribution pour le meilleur modèle:\n"," 📊 [15] FPN_efficientnetb0_finetune_augTrue\n"," 🎯 Test Mean IoU: 0.7457\n","\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading artifacts: 0%| | 0/1 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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["📊 STATISTIQUES COMPLÉMENTAIRES:\n","----------------------------------------\n"," 🔢 Nombre total de pixels: 25,088,000\n"," 📏 Nombre de classes: 8\n"," 📈 Classe la plus représentée: flat (38.01%)\n"," 📉 Classe la moins représentée: human (1.32%)\n"," ⚖️ Ratio max/min: 28.77\n"," ⚖️ Score de déséquilibre moyen: 9.21% (0 = parfait équilibre)\n","\n"]}]},{"cell_type":"markdown","metadata":{"id":"8-vXPIYNSALV"},"source":["### Mise en production meilleur modèle"]},{"cell_type":"code","source":["def register_best_model(experiment_name=\"OC Projet 8\", model_name=\"StreetsSegmentation\",\n"," force=False, specific_run_id=None):\n"," \"\"\"\n"," Enregistre le meilleur modèle (selon test_mean_iou) OU un modèle spécifique dans le Registry MLflow.\n","\n"," Args:\n"," experiment_name (str): Nom de l'expérience MLflow\n"," model_name (str): Nom du modèle dans le Registry\n"," force (bool): Si True, force la mise en production même si le score est moins bon\n"," specific_run_id (str): Si fourni, enregistre ce run spécifique au lieu du meilleur\n"," \"\"\"\n"," print(f\"\\n{'='*60}\")\n"," if specific_run_id:\n"," print(f\"🎯 Enregistrement d'un modèle SPÉCIFIQUE dans le Registry MLflow\")\n"," print(f\"🆔 Run ID spécifique: {specific_run_id}\")\n"," else:\n"," print(f\"🏆 Enregistrement du MEILLEUR modèle dans le Registry MLflow\")\n"," print(f\"🔬 Expérience MLflow: {experiment_name}\")\n"," print(f\"📦 Nom du modèle Registry: {model_name}\")\n"," if force:\n"," print(f\"⚡ Mode FORCE activé - Mise en prod forcée\")\n"," print(f\"{'='*60}\")\n","\n"," # 1. Si un run_id spécifique est fourni, l'utiliser directement\n"," if specific_run_id:\n"," print(f\"🎯 Mode spécifique activé - utilisation du run {specific_run_id}\")\n","\n"," try:\n"," # Vérifier que le run existe et récupérer ses métriques\n"," run = mlflow.get_run(specific_run_id)\n"," metrics = run.data.metrics\n"," params = run.data.params\n","\n"," test_mean_iou = metrics.get('test_mean_iou', None)\n"," test_accuracy = metrics.get('test_sparse_categorical_accuracy', None)\n"," run_name = run.data.tags.get('mlflow.runName', 'Unknown')\n","\n"," if test_mean_iou is None:\n"," print(\"⚠️ Le run spécifique n'a pas de métrique test_mean_iou\")\n"," if not force:\n"," print(\"❌ Utilisez force=True pour forcer l'enregistrement sans métriques\")\n"," return None\n","\n"," selected_result = {\n"," 'run_id': specific_run_id,\n"," 'test_mean_iou': test_mean_iou or -1.0,\n"," 'test_accuracy': test_accuracy,\n"," 'experiment_name': run_name,\n"," 'selection_mode': 'specific'\n"," }\n","\n"," print(f\"✅ Run spécifique trouvé:\")\n"," print(f\" • Run Name: {run_name}\")\n"," print(f\" • Run ID: {specific_run_id}\")\n"," if test_mean_iou:\n"," print(f\" • Test Mean IoU: {test_mean_iou:.4f}\")\n"," if test_accuracy:\n"," print(f\" • Test Accuracy: {test_accuracy:.4f}\")\n","\n"," except Exception as e:\n"," print(f\"❌ Erreur lors de la récupération du run spécifique {specific_run_id}: {e}\")\n"," return None\n","\n"," else:\n"," # 2. Mode automatique : trouver le meilleur modèle (logique existante)\n"," print(\"🔍 Mode automatique - recherche du meilleur modèle...\")\n","\n"," # Récupérer les configurations terminées (réutilise la logique existante)\n"," completed_configs = get_completed_configs_info(experimental_configurations, experiment_name)\n","\n"," if not completed_configs:\n"," print(\"❌ Aucune expérience terminée trouvée\")\n"," return None\n","\n"," # Récupérer les métriques MLflow pour chaque run\n"," experiment = mlflow.get_experiment_by_name(experiment_name)\n"," runs_df = mlflow.search_runs(\n"," experiment_ids=[experiment.experiment_id],\n"," filter_string=\"status = 'FINISHED'\",\n"," output_format=\"pandas\"\n"," )\n","\n"," run_name_to_id = {\n"," run.get('tags.mlflow.runName', ''): run['run_id']\n"," for _, run in runs_df.iterrows()\n"," }\n","\n"," print(f\"📊 Analyse de {len(completed_configs)} configurations terminées\")\n","\n"," # Collecter les résultats avec métriques\n"," results = []\n"," for config_idx, config, run_name in completed_configs:\n"," if run_name not in run_name_to_id:\n"," continue\n","\n"," run_id = run_name_to_id[run_name]\n","\n"," try:\n"," # Récupérer les métriques directement depuis MLflow\n"," run = mlflow.get_run(run_id)\n"," metrics = run.data.metrics\n","\n"," test_mean_iou = metrics.get('test_mean_iou', None)\n","\n"," if test_mean_iou is not None:\n"," results.append({\n"," 'config_idx': config_idx,\n"," 'config': config,\n"," 'run_name': run_name,\n"," 'run_id': run_id,\n"," 'test_mean_iou': test_mean_iou,\n"," 'experiment_name': config['name']\n"," })\n"," print(f\" ✅ [{config_idx}] {config['name']}: IoU={test_mean_iou:.4f}\")\n"," else:\n"," print(f\" ⚠️ [{config_idx}] {config['name']}: Pas de test_mean_iou\")\n","\n"," except Exception as e:\n"," print(f\" ❌ [{config_idx}] {config['name']}: Erreur - {e}\")\n"," continue\n","\n"," if not results:\n"," print(\"❌ Aucune expérience avec test_mean_iou trouvée\")\n"," return None\n","\n"," # Trouver le meilleur modèle\n"," best_result = max(results, key=lambda x: x['test_mean_iou'])\n","\n"," selected_result = {\n"," 'run_id': best_result['run_id'],\n"," 'test_mean_iou': best_result['test_mean_iou'],\n"," 'experiment_name': best_result['experiment_name'],\n"," 'selection_mode': 'automatic'\n"," }\n","\n"," print(f\"🏆 Meilleur modèle identifié:\")\n"," print(f\" • Expérience: {best_result['experiment_name']}\")\n"," print(f\" • Run ID: {best_result['run_id']}\")\n"," print(f\" • Test Mean IoU: {best_result['test_mean_iou']:.4f}\")\n","\n"," # 3. Vérifier si le modèle existe dans le Registry (reste identique)\n"," client = mlflow.client.MlflowClient()\n"," current_best_iou = -1.0\n"," model_exists = False\n"," latest_version = None\n","\n"," try:\n"," try:\n"," registered_model = client.get_registered_model(model_name)\n"," model_exists = True\n"," print(f\"\\n📦 Modèle '{model_name}' trouvé dans le Registry\")\n"," except mlflow.exceptions.RestException:\n"," model_exists = False\n"," print(f\"\\n📦 Modèle '{model_name}' n'existe pas encore dans le Registry\")\n","\n"," if model_exists:\n"," model_versions = client.search_model_versions(f\"name='{model_name}'\")\n"," if model_versions:\n"," latest_version = max(model_versions, key=lambda v: int(v.version))\n"," current_run = mlflow.get_run(latest_version.run_id)\n"," current_best_iou = current_run.data.metrics.get('test_mean_iou', -1.0)\n","\n"," print(f\" • Version la plus récente: {latest_version.version}\")\n"," print(f\" • Stage: {latest_version.current_stage}\")\n"," print(f\" • Test Mean IoU: {current_best_iou:.4f}\")\n","\n"," except Exception as e:\n"," print(f\"⚠️ Erreur lors de la vérification du Registry: {e}\")\n","\n"," # 4. Logique de comparaison adaptée\n"," if specific_run_id:\n"," # En mode spécifique, on enregistre toujours (sauf si force=False et pas de métriques)\n"," should_register = True\n"," if selected_result['test_mean_iou'] == -1.0 and not force:\n"," should_register = False\n"," print(\"❌ Le run spécifique n'a pas de métriques et force=False\")\n"," else:\n"," # En mode automatique, comparaison habituelle\n"," should_register = selected_result['test_mean_iou'] > current_best_iou or force\n","\n"," # 5. Enregistrement (logique existante adaptée)\n"," if should_register:\n"," if specific_run_id:\n"," print(f\"\\n🎯 Enregistrement du modèle spécifique...\")\n"," if selected_result['test_mean_iou'] > current_best_iou:\n"," action_msg = f\"Le modèle spécifique ({selected_result['test_mean_iou']:.4f}) est meilleur que l'actuel ({current_best_iou:.4f})\"\n"," elif force:\n"," action_msg = f\"Enregistrement FORCÉ du modèle spécifique\"\n"," else:\n"," action_msg = f\"Enregistrement du modèle spécifique\"\n"," print(f\" {action_msg}\")\n"," else:\n"," if not model_exists:\n"," print(f\"\\n🆕 Création du modèle '{model_name}' dans le Registry...\")\n"," action_msg = \"Création et enregistrement du premier modèle\"\n"," elif force and selected_result['test_mean_iou'] <= current_best_iou:\n"," print(f\"\\n⚡ FORCE MODE: Enregistrement forcé du modèle\")\n"," print(f\" Nouveau: {selected_result['test_mean_iou']:.4f} vs Actuel: {current_best_iou:.4f}\")\n"," action_msg = \"Enregistrement forcé de la nouvelle version\"\n"," else:\n"," print(f\"\\n🚀 Le nouveau modèle ({selected_result['test_mean_iou']:.4f}) est meilleur que l'actuel ({current_best_iou:.4f})\")\n"," action_msg = \"Enregistrement de la nouvelle version\"\n","\n"," print(f\" {action_msg}...\")\n","\n"," try:\n"," # Si le modèle n'existe pas, le créer\n"," if not model_exists:\n"," client.create_registered_model(\n"," name=model_name,\n"," description=f\"Modèle de segmentation des rues - Performance: {selected_result['test_mean_iou']:.4f}\"\n"," )\n"," print(f\" ✅ Modèle '{model_name}' créé dans le Registry\")\n","\n"," # Vérifier si une version existe déjà pour ce run spécifique\n"," existing_versions = [v for v in client.search_model_versions(f\"name='{model_name}'\")\n"," if v.run_id == selected_result['run_id']]\n","\n"," if existing_versions:\n"," model_version = existing_versions[0]\n"," print(f\" ℹ️ Version existante trouvée pour ce run: {model_version.version}\")\n"," else:\n"," print(\" 📝 Enregistrement d'une nouvelle version...\")\n","\n"," # Chercher l'artefact du modèle\n"," artifacts = client.list_artifacts(selected_result['run_id'])\n"," model_artifact_path = None\n","\n"," for artifact in artifacts:\n"," if any(pattern in artifact.path.lower() for pattern in\n"," ['model', 'best_model', '.keras', '.h5', '.pkl']):\n"," model_artifact_path = artifact.path\n"," break\n","\n"," if model_artifact_path is None:\n"," print(\" ❌ Aucun artefact de modèle trouvé dans le run\")\n"," return None\n","\n"," print(f\" 📁 Artefact trouvé: {model_artifact_path}\")\n","\n"," model_version = client.create_model_version(\n"," name=model_name,\n"," source=f\"runs:/{selected_result['run_id']}/{model_artifact_path}\",\n"," run_id=selected_result['run_id'],\n"," description=f\"Model from {selected_result['experiment_name']} - IoU: {selected_result['test_mean_iou']:.4f} - Mode: {selected_result['selection_mode']}\"\n"," )\n","\n"," # Ajouter les tags\n"," print(\" 🏷️ Ajout des tags de métadonnées...\")\n"," tags_to_add = {\n"," \"source_run_id\": selected_result['run_id'],\n"," \"test_mean_iou\": str(selected_result['test_mean_iou']),\n"," \"model_name\": model_name,\n"," \"source_experiment\": selected_result['experiment_name'],\n"," \"selection_mode\": selected_result['selection_mode'],\n"," \"registration_mode\": \"forced\" if force and selected_result['test_mean_iou'] <= current_best_iou else \"automatic\"\n"," }\n","\n"," for tag_key, tag_value in tags_to_add.items():\n"," client.set_model_version_tag(\n"," name=model_name,\n"," version=model_version.version,\n"," key=tag_key,\n"," value=tag_value\n"," )\n","\n"," # Promouvoir en Production\n"," print(f\" 🚀 Promotion de la version {model_version.version} en Production...\")\n"," client.transition_model_version_stage(\n"," name=model_name,\n"," version=model_version.version,\n"," stage=\"Production\",\n"," archive_existing_versions=True\n"," )\n","\n"," print(f\"✅ Modèle enregistré avec succès !\")\n"," print(f\" • Nom: {model_name}\")\n"," print(f\" • Version: {model_version.version}\")\n"," print(f\" • Stage: Production\")\n"," print(f\" • Test Mean IoU: {selected_result['test_mean_iou']:.4f}\")\n"," print(f\" • Mode de sélection: {selected_result['selection_mode']}\")\n","\n"," return {\n"," 'model_name': model_name,\n"," 'version': model_version.version,\n"," 'run_id': selected_result['run_id'],\n"," 'test_mean_iou': selected_result['test_mean_iou'],\n"," 'selection_mode': selected_result['selection_mode'],\n"," 'action': 'created' if not model_exists else 'updated',\n"," 'tags': tags_to_add\n"," }\n","\n"," except Exception as e:\n"," print(f\"❌ Erreur lors de l'enregistrement: {e}\")\n"," return None\n"," else:\n"," print(f\"\\n➡️ Aucun enregistrement effectué\")\n"," if specific_run_id:\n"," print(\" Le run spécifique n'a pas de métriques suffisantes\")\n"," print(\" 💡 Utilisez force=True pour forcer l'enregistrement\")\n"," else:\n"," print(f\" La version actuelle ({current_best_iou:.4f}) est déjà optimale\")\n"," print(\" 💡 Utilisez force=True pour forcer la mise en production\")\n","\n"," return {\n"," 'model_name': model_name,\n"," 'action': 'no_update',\n"," 'reason': 'Insufficient metrics' if specific_run_id else 'Current version is optimal',\n"," 'suggestion': 'Use force=True to override'\n"," }"],"metadata":{"id":"FxkVT7_IE9fI"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"80mWsWg4Gi7Q","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1755008443511,"user_tz":-120,"elapsed":3613,"user":{"displayName":"Grégoire Mureau","userId":"04855910922109670788"}},"outputId":"41e6f555-be2d-4040-d7c3-9386d5040017"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","============================================================\n","🏆 Enregistrement du MEILLEUR modèle dans le Registry MLflow\n","🔬 Expérience MLflow: OC Projet 8\n","📦 Nom du modèle Registry: StreetsSegmentation\n","============================================================\n","🔍 Mode automatique - recherche du meilleur modèle...\n","🔍 Vérification des expériences déjà terminées dans 'OC Projet 8'...\n","✅ 34 expériences terminées trouvées:\n"," - FPN_efficientnetb0_finetune_augTrue_prod\n"," - FPN_efficientnetb0_finetune_augTrue_test\n"," - FPN_efficientnetb0_frozen_augFalse_prod\n"," - FPN_efficientnetb0_frozen_augFalse_test\n"," - FPN_efficientnetb0_frozen_augTrue_prod\n"," - FPN_efficientnetb0_frozen_augTrue_test\n"," - FPN_mobilenetv2_frozen_augFalse_prod\n"," - FPN_mobilenetv2_frozen_augFalse_test\n"," - FPN_mobilenetv2_frozen_augTrue_prod\n"," - FPN_mobilenetv2_frozen_augTrue_test\n"," - FPN_resnet34_finetune_augTrue_prod\n"," - FPN_resnet34_finetune_augTrue_test\n"," - FPN_resnet34_frozen_augFalse_prod\n"," - FPN_resnet34_frozen_augFalse_test\n"," - FPN_resnet34_frozen_augTrue_prod\n"," - FPN_resnet34_frozen_augTrue_test\n"," - FPN_vanilla_augFalse_prod\n"," - FPN_vanilla_augFalse_test\n"," - FPN_vanilla_augTrue_prod\n"," - FPN_vanilla_augTrue_test\n"," - U-Net_efficientnetb0_frozen_augFalse_prod\n"," - U-Net_efficientnetb0_frozen_augFalse_test\n"," - U-Net_efficientnetb0_frozen_augTrue_prod\n"," - U-Net_efficientnetb0_frozen_augTrue_test\n"," - U-Net_mobilenetv2_finetune_augTrue_prod\n"," - U-Net_mobilenetv2_finetune_augTrue_test\n"," - U-Net_mobilenetv2_frozen_augFalse_prod\n"," - U-Net_mobilenetv2_frozen_augFalse_test\n"," - U-Net_mobilenetv2_frozen_augTrue_prod\n"," - U-Net_mobilenetv2_frozen_augTrue_test\n"," - U-Net_vanilla_augFalse_prod\n"," - U-Net_vanilla_augFalse_test\n"," - U-Net_vanilla_augTrue_prod\n"," - U-Net_vanilla_augTrue_test\n","📊 Analyse de 17 configurations terminées\n"," ✅ [0] U-Net_vanilla_augFalse: IoU=0.7058\n"," ✅ [1] U-Net_vanilla_augTrue: IoU=0.6986\n"," ✅ [2] FPN_vanilla_augFalse: IoU=0.7171\n"," ✅ [3] FPN_vanilla_augTrue: IoU=0.7148\n"," ✅ [4] U-Net_mobilenetv2_frozen_augFalse: IoU=0.6992\n"," ✅ [5] U-Net_mobilenetv2_frozen_augTrue: IoU=0.6933\n"," ✅ [6] U-Net_efficientnetb0_frozen_augFalse: IoU=0.7045\n"," ✅ [7] U-Net_efficientnetb0_frozen_augTrue: IoU=0.7088\n"," ✅ [8] FPN_mobilenetv2_frozen_augFalse: IoU=0.7025\n"," ✅ [9] FPN_mobilenetv2_frozen_augTrue: IoU=0.7082\n"," ✅ [10] FPN_efficientnetb0_frozen_augFalse: IoU=0.7232\n"," ✅ [11] FPN_efficientnetb0_frozen_augTrue: IoU=0.7214\n"," ✅ [12] FPN_resnet34_frozen_augFalse: IoU=0.6940\n"," ✅ [13] FPN_resnet34_frozen_augTrue: IoU=0.7017\n"," ✅ [14] U-Net_mobilenetv2_finetune_augTrue: IoU=0.7130\n"," ✅ [15] FPN_efficientnetb0_finetune_augTrue: IoU=0.7457\n"," ✅ [16] FPN_resnet34_finetune_augTrue: IoU=0.7181\n","🏆 Meilleur modèle identifié:\n"," • Expérience: FPN_efficientnetb0_finetune_augTrue\n"," • Run ID: b6dd1ecdeb3f4b5c899b332f3c948cd7\n"," • Test Mean IoU: 0.7457\n","\n","📦 Modèle 'StreetsSegmentation' trouvé dans le Registry\n"," • Version la plus récente: 2\n"," • Stage: Production\n"," • Test Mean IoU: 0.7457\n","\n","➡️ Aucun enregistrement effectué\n"," La version actuelle (0.7457) est déjà optimale\n"," 💡 Utilisez force=True pour forcer la mise en production\n"]},{"output_type":"execute_result","data":{"text/plain":["{'model_name': 'StreetsSegmentation',\n"," 'action': 'no_update',\n"," 'reason': 'Current version is optimal',\n"," 'suggestion': 'Use force=True to override'}"]},"metadata":{},"execution_count":34}],"source":["# Enregistrement automatique (seulement si meilleur)\n","register_best_model(experiment_name=\"OC Projet 8\", model_name=\"StreetsSegmentation\")"]},{"cell_type":"markdown","metadata":{"id":"m5gJ0G9SK2Pn"},"source":["## Fonction d'inférence et démonstration"]},{"cell_type":"markdown","source":["Les fonctions utiles sont toutes dans les imports en début de notebook"],"metadata":{"id":"IrCVqitDg5sk"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"Az_I9YlrRevb","colab":{"base_uri":"https://localhost:8080/","height":176,"referenced_widgets":["2ae48a6a658d4a73907805a100996517","d383c5202b4c4bde976a95486de93745","90f88dbfcd004e7b9bbf7199dff4635e","2b413c4a996e4da4a33d7398dca34b42","85b3ff6fb3e74a3098b44bc012adbe87","7b31a5d8152b442a988a4632744042c7","a5ec8267d2fe4adc9eff959a271d017e","9ddd627c1cb94ba0903dd844c177e8d9","1e6ae9080ec748dfab98f2f6d10b67cd","f21e5d87afc949bcbb55d2d41f104056","b2f12a682daf49dbaca240b262edb0dd"]},"executionInfo":{"status":"ok","timestamp":1755008462897,"user_tz":-120,"elapsed":19383,"user":{"displayName":"Grégoire Mureau","userId":"04855910922109670788"}},"outputId":"d7092efa-a84b-4039-90d7-7a9e70753620"},"outputs":[{"output_type":"stream","name":"stdout","text":["📦 Téléchargement du modèle StreetsSegmentation (run: b6dd1ecdeb3f4b5c899b332f3c948cd7)\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading artifacts: 0%| | 0/1 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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","📊 Statistiques du masque prédit:\n"," flat: 6853 pixels (13.7%)\n"," human: 7970 pixels (15.9%)\n"," vehicle: 9227 pixels (18.4%)\n"," construction: 19823 pixels (39.5%)\n"," object: 1727 pixels (3.4%)\n"," nature: 889 pixels (1.8%)\n"," sky: 782 pixels (1.6%)\n"," void: 2905 pixels (5.8%)\n"]}]},{"cell_type":"code","source":["# === EXEMPLE D'UTILISATION ===\n","\n","# 1. Charger la configuration des classes\n","mapping_config = load_cityscapes_config(\"../cityscapes_config.json\")\n","\n","# 2. Faire l'inférence sur une nouvelle image\n","predicted_mask1 = run_inference_and_visualize(\n"," image_path=\"./content/data/test_images_sample/original/frankfurt_000000_000294_leftImg8bit.png\",\n"," mask_path=\"./content/data/test_images_sample/mask/frankfurt_000000_000294_gtFine_labelIds.png\",\n"," model=model,\n"," encoder_name=encoder_name,\n"," img_size=script_img_size,\n"," mapping_config=mapping_config\n",")"],"metadata":{"id":"RqYbA_qD52V2","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1755008472178,"user_tz":-120,"elapsed":4514,"user":{"displayName":"Grégoire Mureau","userId":"04855910922109670788"}},"outputId":"dab91a16-adb8-4422-c517-82ba2f58079f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["== Groupes de classes Cityscapes ==\n"," - flat : ['road', 'sidewalk', 'parking', 'rail track']\n"," - human : ['person', 'rider']\n"," - vehicle : ['car', 'truck', 'bus', 'train', 'motorcycle', 'bicycle', 'caravan', 'trailer']\n"," - construction : ['building', 'wall', 'fence', 'guard rail', 'bridge', 'tunnel']\n"," - object : ['pole', 'pole group', 'traffic sign', 'traffic light']\n"," - nature : ['vegetation', 'terrain']\n"," - sky : ['sky']\n"," - void : ['unlabeled', 'ego vehicle', 'rectification border', 'out of roi', 'static', 'dynamic', 'ground']\n","\n","== Mapping ID Cityscapes → Groupe ==\n"," ID 0 ('unlabeled') → groupe 'void' (7)\n"," ID 1 ('ego vehicle') → groupe 'void' (7)\n"," ID 2 ('rectification border') → groupe 'void' (7)\n"," ID 3 ('out of roi') → groupe 'void' (7)\n"," ID 4 ('static') → groupe 'void' (7)\n"," ID 5 ('dynamic') → groupe 'void' (7)\n"," ID 6 ('ground') → groupe 'void' (7)\n"," ID 7 ('road') → groupe 'flat' (0)\n"," ID 8 ('sidewalk') → groupe 'flat' (0)\n"," ID 9 ('parking') → groupe 'flat' (0)\n"," ID 10 ('rail track') → groupe 'flat' (0)\n"," ID 11 ('building') → groupe 'construction' (3)\n"," ID 12 ('wall') → groupe 'construction' (3)\n"," ID 13 ('fence') → groupe 'construction' (3)\n"," ID 14 ('guard rail') → groupe 'construction' (3)\n"," ID 15 ('bridge') → groupe 'construction' (3)\n"," ID 16 ('tunnel') → groupe 'construction' (3)\n"," ID 17 ('pole') → groupe 'object' (4)\n"," ID 18 ('pole group') → groupe 'object' (4)\n"," ID 19 ('traffic light') → groupe 'object' (4)\n"," ID 20 ('traffic sign') → groupe 'object' (4)\n"," ID 21 ('vegetation') → groupe 'nature' (5)\n"," ID 22 ('terrain') → groupe 'nature' (5)\n"," ID 23 ('sky') → groupe 'sky' (6)\n"," ID 24 ('person') → groupe 'human' (1)\n"," ID 25 ('rider') → groupe 'human' (1)\n"," ID 26 ('car') → groupe 'vehicle' (2)\n"," ID 27 ('truck') → groupe 'vehicle' (2)\n"," ID 28 ('bus') → groupe 'vehicle' (2)\n"," ID 29 ('caravan') → groupe 'vehicle' (2)\n"," ID 30 ('trailer') → groupe 'vehicle' (2)\n"," ID 31 ('train') → groupe 'vehicle' (2)\n"," ID 32 ('motorcycle') → groupe 'vehicle' (2)\n"," ID 33 ('bicycle') → groupe 'vehicle' (2)\n","\n","== Liste des groupes et couleurs associées ==\n"," Groupe 0: flat - Couleur RGB : [128 64 128]\n"," Groupe 1: human - Couleur RGB : [220 20 60]\n"," Groupe 2: vehicle - Couleur RGB : [ 0 0 142]\n"," Groupe 3: construction - Couleur RGB : [70 70 70]\n"," Groupe 4: object - Couleur RGB : [220 220 0]\n"," Groupe 5: nature - Couleur RGB : [107 142 35]\n"," Groupe 6: sky - Couleur RGB : [ 70 130 180]\n"," Groupe 7: void - Couleur RGB : [0 0 0]\n","\n","Nombre total de groupes/classes : 8\n","=== INFÉRENCE SUR frankfurt_000000_000294_leftImg8bit.png ===\n","🔄 Préprocessing de l'image: frankfurt_000000_000294_leftImg8bit.png\n","ℹ️ Modèle vanilla - pas de preprocessing\n","🔄 Préprocessing du masque: frankfurt_000000_000294_gtFine_labelIds.png\n"," Valeurs uniques avant mapping: [ 3 2 11 23 17 4 20 21 26 24]...\n"," Valeurs uniques après mapping: [7 3 6 4 5 2 1 0]\n","🔮 Exécution de l'inférence...\n","✅ Inférence terminée - Shape: (224, 224)\n"]},{"output_type":"display_data","data":{"text/plain":["
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Statistiques du masque prédit:\n"," flat: 19730 pixels (39.3%)\n"," human: 87 pixels (0.2%)\n"," vehicle: 2887 pixels (5.8%)\n"," construction: 19259 pixels (38.4%)\n"," object: 484 pixels (1.0%)\n"," nature: 1001 pixels (2.0%)\n"," sky: 883 pixels (1.8%)\n"," void: 5845 pixels (11.6%)\n","\n","📊 Statistiques du masque de vérité:\n"," flat: 18943 pixels (37.8%)\n"," human: 165 pixels (0.3%)\n"," vehicle: 2759 pixels (5.5%)\n"," construction: 19632 pixels (39.1%)\n"," object: 873 pixels (1.7%)\n"," nature: 1013 pixels (2.0%)\n"," sky: 883 pixels (1.8%)\n"," void: 5908 pixels (11.8%)\n"]}]},{"cell_type":"markdown","source":["# Rapport"],"metadata":{"id":"5S5RqKBPbFQU"}},{"cell_type":"markdown","source":["De nombreux modèles ont été testés. Le meilleur modèle s'est avéré être `FPN` avec un backbone `efficientnetb0` (features extraction) aux poids **ajustables** le tout dans un contexte de données augmentées aléatoirement."],"metadata":{"id":"rEs5z5n0bGo5"}},{"cell_type":"markdown","source":["Limites du travail :\n","- Le prétraitement reste générique, en particulier la division par 255 pour placer les valeurs des pixels entre 0 et 1. Appliquer un prétraitement plus adapté à chaque backbone permettrait peut-être un entraînement plus efficace et une meilleure performance\n","- Généralisation au-delà de Cityscapes ? Globalement toujours les mêmes conditions de météo, mêmes environnements (urbains)... Modèle pas général\n","- Quid des performances en temps réel ?\n","- Taille fixe des images (224x224) pour faciliter l'apprentissage mais généralisation faible\n","- Backbone pré-entrainés sur imagenet... Mappilary Vistas serait plus pertinent (jeu de données orienté prises de vue sur la route, villes, campagnes, etc.) --> potentiel gain de transfer learning (feature extraction)\n","- IoU perfectible sur les humains, qui sont pourtant le plus important --> piste de travail à privilégier. Pour ce faire, data augmentation avec augmentation nombre d'humains (IA générative d'images, par exemple ?)\n","- Globalement performances assez proches pour tous les modèles : nous sommes probablement limités par le jeu de données"],"metadata":{"id":"PkOuVBcdctg4"}},{"cell_type":"code","metadata":{"id":"0qXyX6entBG"},"source":["!pip freeze > requirements.txt"],"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[],"toc_visible":true},"kernelspec":{"display_name":"Python 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+datasets==4.0.0 +db-dtypes==1.4.3 +dbus-python==1.2.18 +debugpy==1.8.15 +decorator==4.4.2 +defusedxml==0.7.1 +diffusers==0.34.0 +dill==0.3.8 +distributed==2025.5.0 +distributed-ucxx-cu12==0.44.0 +distro==1.9.0 +dlib==19.24.6 +dm-tree==0.1.9 +docker==7.1.0 +docstring_parser==0.17.0 +docutils==0.21.2 +dopamine_rl==4.1.2 +dotenv==0.9.9 +duckdb==1.3.2 +earthengine-api==1.5.24 +easydict==1.13 +editdistance==0.8.1 +eerepr==0.1.2 +efficientnet==1.1.1 +einops==0.8.1 +en_core_web_sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.8.0/en_core_web_sm-3.8.0-py3-none-any.whl#sha256=1932429db727d4bff3deed6b34cfc05df17794f4a52eeb26cf8928f7c1a0fb85 +entrypoints==0.4 +et_xmlfile==2.0.0 +etils==1.13.0 +etuples==0.3.10 +Farama-Notifications==0.0.4 +fastai==2.7.19 +fastapi==0.116.1 +fastcore==1.7.29 +fastdownload==0.0.7 +fastjsonschema==2.21.1 +fastprogress==1.0.3 +fastrlock==0.8.3 +ffmpy==0.6.1 +filelock==3.18.0 +firebase-admin==6.9.0 +Flask==3.1.1 +flatbuffers==25.2.10 +flax==0.10.6 +folium==0.20.0 +fonttools==4.59.0 +frozendict==2.4.6 +frozenlist==1.7.0 +fsspec==2025.3.0 +future==1.0.0 +gast==0.6.0 +gcsfs==2025.3.0 +GDAL==3.8.4 +gdown==5.2.0 +geemap==0.35.3 +geocoder==1.38.1 +geographiclib==2.0 +geopandas==1.1.1 +geopy==2.4.1 +gin-config==0.5.0 +gitdb==4.0.12 +GitPython==3.1.45 +glob2==0.7 +google==2.0.3 +google-ai-generativelanguage==0.6.15 +google-api-core==2.25.1 +google-api-python-client==2.177.0 +google-auth==2.38.0 +google-auth-httplib2==0.2.0 +google-auth-oauthlib==1.2.2 +google-cloud-aiplatform==1.106.0 +google-cloud-bigquery==3.35.1 +google-cloud-bigquery-connection==1.18.3 +google-cloud-bigquery-storage==2.32.0 +google-cloud-core==2.4.3 +google-cloud-dataproc==5.21.0 +google-cloud-datastore==2.21.0 +google-cloud-firestore==2.21.0 +google-cloud-functions==1.20.4 +google-cloud-language==2.17.2 +google-cloud-resource-manager==1.14.2 +google-cloud-spanner==3.56.0 +google-cloud-storage==2.19.0 +google-cloud-translate==3.21.1 +google-colab @ file:///colabtools/dist/google_colab-1.0.0.tar.gz +google-crc32c==1.7.1 +google-genai==1.28.0 +google-generativeai==0.8.5 +google-pasta==0.2.0 +google-resumable-media==2.7.2 +googleapis-common-protos==1.70.0 +googledrivedownloader==1.1.0 +gradio==5.41.0 +gradio_client==1.11.0 +graphene==3.4.3 +graphql-core==3.2.6 +graphql-relay==3.2.0 +graphviz==0.21 +greenlet==3.2.3 +groovy==0.1.2 +grpc-google-iam-v1==0.14.2 +grpc-interceptor==0.15.4 +grpcio==1.74.0 +grpcio-status==1.71.2 +grpclib==0.4.8 +gspread==6.2.1 +gspread-dataframe==4.0.0 +gunicorn==23.0.0 +gym==0.25.2 +gym-notices==0.1.0 +gymnasium==1.2.0 +h11==0.16.0 +h2==4.2.0 +h5netcdf==1.6.4 +h5py==3.14.0 +hdbscan==0.8.40 +hf-xet==1.1.7 +hf_transfer==0.1.9 +highspy==1.11.0 +holidays==0.78 +holoviews==1.21.0 +hpack==4.1.0 +html5lib==1.1 +httpcore==1.0.9 +httpimport==1.4.1 +httplib2==0.22.0 +httpx==0.28.1 +huggingface-hub==0.34.3 +humanize==4.12.3 +hyperframe==6.1.0 +hyperopt==0.2.7 +ibis-framework==9.5.0 +idna==3.10 +image-classifiers==1.0.0 +imageio==2.37.0 +imageio-ffmpeg==0.6.0 +imagesize==1.4.1 +imbalanced-learn==0.13.0 +immutabledict==4.2.1 +importlib_metadata==8.7.0 +importlib_resources==6.5.2 +imutils==0.5.4 +inflect==7.5.0 +iniconfig==2.1.0 +intel-cmplr-lib-ur==2025.2.0 +intel-openmp==2025.2.0 +ipyevents==2.0.2 +ipyfilechooser==0.6.0 +ipykernel==6.17.1 +ipyleaflet==0.20.0 +ipyparallel==8.8.0 +ipython==7.34.0 +ipython-genutils==0.2.0 +ipython-sql==0.5.0 +ipytree==0.2.2 +ipywidgets==7.7.1 +itsdangerous==2.2.0 +jaraco.classes==3.4.0 +jaraco.context==6.0.1 +jaraco.functools==4.2.1 +jax==0.5.3 +jax-cuda12-pjrt==0.5.3 +jax-cuda12-plugin==0.5.3 +jaxlib==0.5.3 +jeepney==0.9.0 +jieba==0.42.1 +Jinja2==3.1.6 +jiter==0.10.0 +jmespath==1.0.1 +joblib==1.5.1 +jsonpatch==1.33 +jsonpickle==4.1.1 +jsonpointer==3.0.0 +jsonschema==4.25.0 +jsonschema-specifications==2025.4.1 +jupyter-client==6.1.12 +jupyter-console==6.1.0 +jupyter-leaflet==0.20.0 +jupyter-server==1.16.0 +jupyter_core==5.8.1 +jupyter_kernel_gateway @ git+https://github.com/googlecolab/kernel_gateway@b134e9945df25c2dcb98ade9129399be10788671 +jupyterlab_pygments==0.3.0 +jupyterlab_widgets==3.0.15 +jupytext==1.17.2 +kaggle==1.7.4.5 +kagglehub==0.3.12 +keras==3.10.0 +Keras-Applications==1.0.8 +keras-hub==0.21.1 +keras-nlp==0.21.1 +keyring==25.6.0 +keyrings.google-artifactregistry-auth==1.1.2 +kiwisolver==1.4.8 +langchain==0.3.27 +langchain-core==0.3.72 +langchain-text-splitters==0.3.9 +langcodes==3.5.0 +langsmith==0.4.12 +language_data==1.3.0 +launchpadlib==1.10.16 +lazr.restfulclient==0.14.4 +lazr.uri==1.0.6 +lazy_loader==0.4 +libclang==18.1.1 +libcudf-cu12 @ https://pypi.nvidia.com/libcudf-cu12/libcudf_cu12-25.6.0-py3-none-manylinux_2_28_x86_64.whl +libcugraph-cu12==25.6.0 +libcuml-cu12==25.6.0 +libcuvs-cu12==25.6.1 +libkvikio-cu12==25.6.0 +libpysal==4.13.0 +libraft-cu12==25.6.0 +librmm-cu12==25.6.0 +librosa==0.11.0 +libucx-cu12==1.18.1 +libucxx-cu12==0.44.0 +lightgbm @ file:///tmp/lightgbm/LightGBM/dist/lightgbm-4.6.0-py3-none-linux_x86_64.whl +linkify-it-py==2.0.3 +llvmlite==0.43.0 +locket==1.0.0 +logical-unification==0.4.6 +lxml==5.4.0 +Mako==1.1.3 +marisa-trie==1.2.1 +Markdown==3.8.2 +markdown-it-py==3.0.0 +MarkupSafe==3.0.2 +matplotlib==3.10.0 +matplotlib-inline==0.1.7 +matplotlib-venn==1.1.2 +mdit-py-plugins==0.4.2 +mdurl==0.1.2 +miniKanren==1.0.5 +missingno==0.5.2 +mistune==3.1.3 +mizani==0.13.5 +mkl==2025.2.0 +ml_dtypes==0.5.3 +mlflow==3.2.0 +mlflow-skinny==3.2.0 +mlflow-tracing==3.2.0 +mlxtend==0.23.4 +more-itertools==10.7.0 +moviepy==1.0.3 +mpmath==1.3.0 +msgpack==1.1.1 +multidict==6.6.3 +multipledispatch==1.0.0 +multiprocess==0.70.16 +multitasking==0.0.12 +murmurhash==1.0.13 +music21==9.3.0 +namex==0.1.0 +narwhals==2.0.1 +natsort==8.4.0 +nbclassic==1.3.1 +nbclient==0.10.2 +nbconvert==7.16.6 +nbformat==5.10.4 +ndindex==1.10.0 +nest-asyncio==1.6.0 +networkx==3.5 +nibabel==5.3.2 +nltk==3.9.1 +notebook==6.5.7 +notebook_shim==0.2.4 +numba==0.60.0 +numba-cuda==0.11.0 +numexpr==2.11.0 +numpy==2.0.2 +nvidia-cublas-cu12==12.5.3.2 +nvidia-cuda-cupti-cu12==12.5.82 +nvidia-cuda-nvcc-cu12==12.5.82 +nvidia-cuda-nvrtc-cu12==12.5.82 +nvidia-cuda-runtime-cu12==12.5.82 +nvidia-cudnn-cu12==9.3.0.75 +nvidia-cufft-cu12==11.2.3.61 +nvidia-curand-cu12==10.3.6.82 +nvidia-cusolver-cu12==11.6.3.83 +nvidia-cusparse-cu12==12.5.1.3 +nvidia-cusparselt-cu12==0.6.2 +nvidia-ml-py==12.575.51 +nvidia-nccl-cu12==2.23.4 +nvidia-nvjitlink-cu12==12.5.82 +nvidia-nvtx-cu12==12.4.127 +nvtx==0.2.13 +nx-cugraph-cu12 @ https://pypi.nvidia.com/nx-cugraph-cu12/nx_cugraph_cu12-25.6.0-py3-none-any.whl +oauth2client==4.1.3 +oauthlib==3.3.1 +omegaconf==2.3.0 +openai==1.99.1 +opencv-contrib-python==4.12.0.88 +opencv-python==4.12.0.88 +opencv-python-headless==4.12.0.88 +openpyxl==3.1.5 +opentelemetry-api==1.36.0 +opentelemetry-sdk==1.36.0 +opentelemetry-semantic-conventions==0.57b0 +opt_einsum==3.4.0 +optax==0.2.5 +optree==0.17.0 +orbax-checkpoint==0.11.20 +orjson==3.11.1 +osqp==1.0.4 +packaging==25.0 +pandas==2.2.2 +pandas-datareader==0.10.0 +pandas-gbq==0.29.2 +pandas-stubs==2.2.2.240909 +pandocfilters==1.5.1 +panel==1.7.5 +param==2.2.1 +parso==0.8.4 +parsy==2.1 +partd==1.4.2 +patsy==1.0.1 +peewee==3.18.2 +peft==0.17.0 +pexpect==4.9.0 +pickleshare==0.7.5 +pillow==11.3.0 +platformdirs==4.3.8 +plotly==5.24.1 +plotnine==0.14.5 +pluggy==1.6.0 +ply==3.11 +polars==1.25.2 +pooch==1.8.2 +portpicker==1.5.2 +preshed==3.0.10 +prettytable==3.16.0 +proglog==0.1.12 +progressbar2==4.5.0 +prometheus_client==0.22.1 +promise==2.3 +prompt_toolkit==3.0.51 +propcache==0.3.2 +prophet==1.1.7 +proto-plus==1.26.1 +protobuf==5.29.5 +psutil==5.9.5 +psycopg2==2.9.10 +psygnal==0.14.0 +ptyprocess==0.7.0 +py-cpuinfo==9.0.0 +py4j==0.10.9.7 +pyarrow==18.1.0 +pyasn1==0.6.1 +pyasn1_modules==0.4.2 +pycairo==1.28.0 +pycocotools==2.0.10 +pycparser==2.22 +pycryptodomex==3.23.0 +pydantic==2.11.7 +pydantic_core==2.33.2 +pydata-google-auth==1.9.1 +pydot==3.0.4 +pydotplus==2.0.2 +PyDrive2==1.21.3 +pydub==0.25.1 +pyerfa==2.0.1.5 +pygame==2.6.1 +pygit2==1.18.1 +Pygments==2.19.2 +PyGObject==3.42.0 +PyJWT==2.10.1 +pylibcudf-cu12 @ https://pypi.nvidia.com/pylibcudf-cu12/pylibcudf_cu12-25.6.0-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl +pylibcugraph-cu12==25.6.0 +pylibraft-cu12==25.6.0 +pymc==5.25.1 +pynndescent==0.5.13 +pynvjitlink-cu12==0.7.0 +pynvml==12.0.0 +pyogrio==0.11.1 +pyomo==6.9.2 +PyOpenGL==3.1.9 +pyOpenSSL==24.2.1 +pyparsing==3.2.3 +pyperclip==1.9.0 +pyproj==3.7.1 +pyproject_hooks==1.2.0 +pyshp==2.3.1 +PySocks==1.7.1 +pyspark==3.5.1 +pytensor==2.31.7 +pytest==8.4.1 +python-apt==0.0.0 +python-box==7.3.2 +python-dateutil==2.9.0.post0 +python-dotenv==1.1.1 +python-louvain==0.16 +python-multipart==0.0.20 +python-slugify==8.0.4 +python-snappy==0.7.3 +python-utils==3.9.1 +pytz==2025.2 +pyviz_comms==3.0.6 +PyWavelets==1.9.0 +PyYAML==6.0.2 +pyzmq==26.2.1 +raft-dask-cu12==25.6.0 +rapids-dask-dependency==25.6.0 +rapids-logger==0.1.1 +ratelim==0.1.6 +referencing==0.36.2 +regex==2024.11.6 +requests==2.32.3 +requests-oauthlib==2.0.0 +requests-toolbelt==1.0.0 +requirements-parser==0.9.0 +rich==13.9.4 +rmm-cu12==25.6.0 +roman-numerals-py==3.1.0 +rpds-py==0.26.0 +rpy2==3.5.17 +rsa==4.9.1 +ruff==0.12.7 +s3transfer==0.13.1 +safehttpx==0.1.6 +safetensors==0.6.1 +scikit-image==0.25.2 +scikit-learn==1.6.1 +scipy==1.16.1 +scooby==0.10.1 +scs==3.2.7.post2 +seaborn==0.13.2 +SecretStorage==3.3.3 +segmentation_models @ git+https://github.com/qubvel/segmentation_models.git@5d24bbfb28af6134e25e2c0b79e7727f6c0491d0 +semantic-version==2.10.0 +Send2Trash==1.8.3 +sentence-transformers==5.0.0 +sentencepiece==0.2.0 +sentry-sdk==2.34.1 +shap==0.48.0 +shapely==2.1.1 +shellingham==1.5.4 +simple-parsing==0.1.7 +simplejson==3.20.1 +simsimd==6.5.0 +six==1.17.0 +sklearn-compat==0.1.3 +sklearn-pandas==2.2.0 +slicer==0.0.8 +smart_open==7.3.0.post1 +smmap==5.0.2 +sniffio==1.3.1 +snowballstemmer==3.0.1 +sortedcontainers==2.4.0 +soundfile==0.13.1 +soupsieve==2.7 +soxr==0.5.0.post1 +spacy==3.8.7 +spacy-legacy==3.0.12 +spacy-loggers==1.0.5 +spanner-graph-notebook==1.1.7 +Sphinx==8.2.3 +sphinxcontrib-applehelp==2.0.0 +sphinxcontrib-devhelp==2.0.0 +sphinxcontrib-htmlhelp==2.1.0 +sphinxcontrib-jsmath==1.0.1 +sphinxcontrib-qthelp==2.0.0 +sphinxcontrib-serializinghtml==2.0.0 +SQLAlchemy==2.0.42 +sqlglot==25.20.2 +sqlparse==0.5.3 +srsly==2.5.1 +stanio==0.5.1 +starlette==0.47.2 +statsmodels==0.14.5 +stringzilla==3.12.5 +stumpy==1.13.0 +sympy==1.13.1 +tables==3.10.2 +tabulate==0.9.0 +tbb==2022.2.0 +tblib==3.1.0 +tcmlib==1.4.0 +tenacity==8.5.0 +tensorboard==2.19.0 +tensorboard-data-server==0.7.2 +tensorflow==2.19.0 +tensorflow-datasets==4.9.9 +tensorflow-hub==0.16.1 +tensorflow-io-gcs-filesystem==0.37.1 +tensorflow-metadata==1.17.2 +tensorflow-probability==0.25.0 +tensorflow-text==2.19.0 +tensorflow_decision_forests==1.12.0 +tensorstore==0.1.76 +termcolor==3.1.0 +terminado==0.18.1 +text-unidecode==1.3 +textblob==0.19.0 +tf-slim==1.1.0 +tf_keras==2.19.0 +thinc==8.3.6 +threadpoolctl==3.6.0 +tifffile==2025.6.11 +tiktoken==0.10.0 +timm==1.0.19 +tinycss2==1.4.0 +tokenizers==0.21.4 +toml==0.10.2 +tomlkit==0.13.3 +toolz==0.12.1 +torch @ https://download.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp311-cp311-linux_x86_64.whl +torchao==0.10.0 +torchaudio @ https://download.pytorch.org/whl/cu124/torchaudio-2.6.0%2Bcu124-cp311-cp311-linux_x86_64.whl +torchdata==0.11.0 +torchsummary==1.5.1 +torchtune==0.6.1 +torchvision @ https://download.pytorch.org/whl/cu124/torchvision-0.21.0%2Bcu124-cp311-cp311-linux_x86_64.whl +tornado==6.4.2 +tqdm==4.67.1 +traitlets==5.7.1 +traittypes==0.2.1 +transformers==4.55.0 +treelite==4.4.1 +treescope==0.1.9 +triton==3.2.0 +tsfresh==0.21.0 +tweepy==4.16.0 +typeguard==4.4.4 +typer==0.16.0 +types-pytz==2025.2.0.20250516 +types-setuptools==80.9.0.20250801 +typing-inspection==0.4.1 +typing_extensions==4.14.1 +tzdata==2025.2 +tzlocal==5.3.1 +uc-micro-py==1.0.3 +ucx-py-cu12==0.44.0 +ucxx-cu12==0.44.0 +umap-learn==0.5.9.post2 +umf==0.11.0 +uritemplate==4.2.0 +urllib3==2.5.0 +uvicorn==0.35.0 +vega-datasets==0.9.0 +wadllib==1.3.6 +wandb==0.21.0 +wasabi==1.1.3 +wcwidth==0.2.13 +weasel==0.4.1 +webcolors==24.11.1 +webencodings==0.5.1 +websocket-client==1.8.0 +websockets==15.0.1 +Werkzeug==3.1.3 +widgetsnbextension==3.6.10 +wordcloud==1.9.4 +wrapt==1.17.2 +wurlitzer==3.1.1 +xarray==2025.7.1 +xarray-einstats==0.9.1 +xgboost==3.0.3 +xlrd==2.0.2 +xxhash==3.5.0 +xyzservices==2025.4.0 +yarl==1.20.1 +ydf==0.13.0 +yellowbrick==1.5 +yfinance==0.2.65 +zict==3.0.0 +zipp==3.23.0 +zstandard==0.23.0 diff --git a/start.sh b/start.sh new file mode 100644 index 0000000..3ebc9b6 --- /dev/null +++ b/start.sh @@ -0,0 +1,52 @@ +#!/bin/bash + +set -e + +# Set default port si non fourni +export NGINX_PORT=${NGINX_PORT:-8080} +export MULTISEG_API_BASE_URL=${MULTISEG_API_BASE_URL:-http://localhost:$NGINX_PORT/api} + +echo "=== Multi-Segmentation API Docker Startup ===" +echo "NGINX_PORT: $NGINX_PORT" +echo "API_BASE_URL: $MULTISEG_API_BASE_URL" + +# Substitute environment variables dans nginx config +envsubst '${NGINX_PORT}' < /etc/nginx/nginx.conf.template > /etc/nginx/nginx.conf + +# S'assurer que les répertoires nginx sont accessibles +mkdir -p /var/cache/nginx /var/run/nginx /var/log/nginx +chown -R www-data:www-data /var/cache/nginx /var/run/nginx /var/log/nginx + +# Créer le dossier uploads temporaire +mkdir -p /tmp/uploads + +echo "Starting FastAPI server..." +uvicorn app.main:app --host 0.0.0.0 --port 8000 --loop asyncio & + +echo "Starting Streamlit app..." +streamlit run app/streamlit_app.py \ + --server.port 8501 \ + --server.address 0.0.0.0 \ + --server.enableXsrfProtection false \ + --server.enableCORS false \ + --server.headless true \ + & + +# Vérifier que FastAPI est prêt +echo "Waiting for FastAPI to be ready..." +until curl -s http://127.0.0.1:8000/health > /dev/null; do + echo " FastAPI not ready yet, waiting..." + sleep 2 +done +echo "✅ FastAPI is ready!" + +# Vérifier que Streamlit est prêt +echo "Waiting for Streamlit to be ready..." +until curl -s http://127.0.0.1:8501 > /dev/null; do + echo " Streamlit not ready yet, waiting..." + sleep 2 +done +echo "✅ Streamlit is ready!" + +echo "🚀 Launching NGINX reverse proxy on port $NGINX_PORT..." +nginx -c /etc/nginx/nginx.conf -g 'daemon off;' diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/utils/utils.py b/utils/utils.py new file mode 100644 index 0000000..a5fc563 --- /dev/null +++ b/utils/utils.py @@ -0,0 +1,323 @@ +import os +import json +import tempfile +import numpy as np +import tensorflow as tf +import matplotlib.pyplot as plt + +import mlflow + + +# Au début de utils.py, après les imports +import segmentation_models as sm +import tensorflow.keras.backend as K + +# Configuration du backend segmentation_models (comme dans le notebook) +sm.set_framework('tf.keras') +sm.backend = K + + +def load_cityscapes_config(config_path="../cityscapes_config.json", verbose=True): + """ + Charge la configuration Cityscapes depuis un fichier JSON. + """ + with open(config_path, "r", encoding="utf-8") as f: + config = json.load(f) + + class_groups = config["class_groups"] + cityscapes_mapping = {int(k): v for k, v in config["cityscapes_mapping"].items()} + id_to_label = {int(k): v for k, v in config["id_to_label"].items()} + group_colors = np.array(config["group_colors"], dtype=np.uint8) + group_names = config["group_names"] + + id_to_group = np.zeros(256, dtype=np.uint8) + for id_, group_id in cityscapes_mapping.items(): + id_to_group[id_] = group_id + + num_classes = len(group_names) + + if verbose: + print("== Groupes de classes Cityscapes ==") + for group, classes in class_groups.items(): + print(f" - {group} : {classes}") + + print("\n== Mapping ID Cityscapes → Groupe ==") + for id_ in sorted(cityscapes_mapping.keys()): + label = id_to_label.get(id_, "Label inconnu") + group_id = cityscapes_mapping[id_] + print(f" ID {id_:2d} ('{label}') → groupe '{group_names[group_id]}' ({group_id})") + + print("\n== Liste des groupes et couleurs associées ==") + for i, (name, color) in enumerate(zip(group_names, group_colors)): + print(f" Groupe {i}: {name} - Couleur RGB : {color}") + + print(f"\nNombre total de groupes/classes : {num_classes}") + + return { + "class_groups": class_groups, + "cityscapes_mapping": cityscapes_mapping, + "id_to_label": id_to_label, + "group_colors": group_colors, + "group_names": group_names, + "id_to_group": id_to_group, + "num_classes": num_classes + } + + +def get_preprocessing_fn(encoder_name=None): + """ + Retourne la fonction de prétraitement adaptée au backbone utilisé. + Si encoder_name est None (modèle vanilla), on normalise à la main. + """ + print("ℹ️ Modèle vanilla - pas de preprocessing") + return lambda x: x / 255.0 + # Peut être rajouté dans le cas d'un entraînement plus poussé avec un traitement particulier pour chaque feature encoder + # if encoder_name is None: + # print("ℹ️ Modèle vanilla - pas de preprocessing") + # return lambda x: x / 255.0 + # else: + # print(f"ℹ️ Préprocessing du backbone {encoder_name}") + # return sm.get_preprocessing(encoder_name) + + +def load_best_model_from_registry(model_name): + """ + Charge le meilleur modèle depuis le MLflow Model Registry + """ + client = mlflow.tracking.MlflowClient() + + # Récupérer la dernière version en Production (votre logique) + versions = client.search_model_versions(f"name='{model_name}'") + + prod_versions = [v for v in versions if v.current_stage == "Production"] + if not prod_versions: + raise RuntimeError(f"Aucune version en Production trouvée pour {model_name}") + latest_prod = max(prod_versions, key=lambda v: int(v.version)) + + # Récupérer le tag source_run_id + source_run_id = latest_prod.tags.get("source_run_id") + if not source_run_id: + raise RuntimeError(f"Tag 'source_run_id' absent pour la version {latest_prod.version}") + + print(f"📦 Téléchargement du modèle {model_name} (run: {source_run_id})") + + # Télécharger le fichier best_model.keras (VOTRE approche) + with tempfile.TemporaryDirectory() as tmpdir: + local_path = mlflow.artifacts.download_artifacts( + run_id=source_run_id, + artifact_path="best_model.keras", + dst_path=tmpdir + ) + + # ✅ SOLUTION : Charger SANS compiler d'abord + print("🔄 Chargement sans compilation...") + model = tf.keras.models.load_model(local_path, compile=False) + + # Puis recompiler avec la bonne métrique + print("🔄 Recompilation avec MeanIoU...") + model.compile( + optimizer='adam', + loss='sparse_categorical_crossentropy', + metrics=[tf.keras.metrics.MeanIoU(num_classes=8)] + ) + print("✅ Modèle chargé et recompilé !") + + # Récupérer les paramètres (votre logique) + run = mlflow.get_run(source_run_id) + encoder_name = run.data.params.get("encoder", None) + + script_img_size_str = run.data.params.get("script_img_size", None) + script_img_size = None + if script_img_size_str: + try: + script_img_size = tuple(int(x.replace(' ','').replace('(','').replace(')','').strip()) for x in script_img_size_str.split(",")) + except Exception as e: + print(f"⚠️ Impossible de parser 'script_img_size': {e}") + + return model, source_run_id, encoder_name, script_img_size + + +def map_mask_ids(mask, id_to_group_mapping): + """ + Applique le mapping des IDs Cityscapes vers les groupes + """ + if isinstance(id_to_group_mapping, np.ndarray): + # Vérifier que le mapping a bien 256 éléments + if len(id_to_group_mapping) != 256: + raise ValueError(f"id_to_group_mapping doit avoir 256 éléments, reçu: {len(id_to_group_mapping)}") + mapping_tensor = tf.constant(id_to_group_mapping, dtype=tf.int32) + else: + mapping_tensor = tf.constant(list(id_to_group_mapping), dtype=tf.int32) + + # S'assurer que les valeurs du masque sont dans la plage valide [0, 255] + mask_clipped = tf.clip_by_value(mask, 0, 255) + mapped_mask = tf.gather(mapping_tensor, mask_clipped) + return mapped_mask + + +def preprocess_image_and_mask(image_path, mask_path=None, img_size=(224, 224), + encoder_name=None, id_to_group_mapping=None): + """ + Préprocesse une image (et optionnellement un masque) avec le même pipeline que l'entraînement + + Args: + image_path (str): Chemin vers l'image + mask_path (str, optional): Chemin vers le masque de vérité + img_size (tuple): Taille de redimensionnement (H, W) + encoder_name (str): Nom du backbone pour le preprocessing + id_to_group_mapping: Mapping des IDs Cityscapes vers les groupes + + Returns: + tuple: (image_preprocessed, mask_preprocessed) ou (image_preprocessed, None) + """ + print(f"🔄 Préprocessing de l'image: {os.path.basename(image_path)}") + + # Preprocessing de l'image (même logique que l'entraînement) + img = tf.io.read_file(image_path) + img = tf.image.decode_image(img, channels=3) # Support PNG et JPG automatique + img_original = tf.image.resize(img, img_size, method='bilinear') # Pour affichage + img = tf.cast(img_original, tf.float32) + + # Appliquer le preprocessing du backbone + preprocessing_fn = get_preprocessing_fn(encoder_name) + img_preprocessed = preprocessing_fn(img) + + # Preprocessing du masque si fourni + mask_preprocessed = None + if mask_path and os.path.exists(mask_path): + print(f"🔄 Préprocessing du masque: {os.path.basename(mask_path)}") + + mask = tf.io.read_file(mask_path) + mask = tf.image.decode_image(mask, channels=1) # Support PNG et JPG pour les masques aussi + mask = tf.image.resize(mask, img_size, method='nearest') + mask = tf.cast(mask, tf.int32) + + print(f" Valeurs uniques avant mapping: {tf.unique(tf.reshape(mask, [-1]))[0][:10].numpy()}...") # Debug + + # Appliquer le mapping si fourni + if id_to_group_mapping is not None: + mask = map_mask_ids(mask, id_to_group_mapping) + print(f" Valeurs uniques après mapping: {tf.unique(tf.reshape(mask, [-1]))[0].numpy()}") # Debug + + mask_preprocessed = tf.squeeze(mask, axis=-1) + + return img_preprocessed, img_original, mask_preprocessed + + +def colorize_mask(mask, group_colors): + """ + Applique la colorisation du masque avec les couleurs définies + + Args: + mask (np.ndarray): Masque avec les IDs de classes + group_colors (np.ndarray): Couleurs RGB pour chaque classe + + Returns: + np.ndarray: Masque colorisé RGB + """ + colored_mask = np.zeros((mask.shape[0], mask.shape[1], 3), dtype=np.uint8) + num_classes = len(group_colors) + + for group_id in range(num_classes): + colored_mask[mask == group_id] = group_colors[group_id] + + return colored_mask + + +def run_inference_and_visualize(image_path, model, encoder_name, img_size, mapping_config, + mask_path=None, save_dir=None, show_stats=True, show_plot=True): + print(f"=== INFÉRENCE SUR {os.path.basename(image_path)} ===") + + img_preprocessed, img_display, mask_gt = preprocess_image_and_mask( + image_path=image_path, + mask_path=mask_path, + img_size=img_size or (224, 224), + encoder_name=encoder_name, + id_to_group_mapping=mapping_config.get('id_to_group') + ) + + print("🔮 Exécution de l'inférence...") + img_batch = tf.expand_dims(img_preprocessed, axis=0) + predictions = model.predict(img_batch, verbose=0) + predicted_mask = tf.argmax(predictions[0], axis=-1).numpy() + print(f"✅ Inférence terminée - Shape: {predicted_mask.shape}") + + # Nombre de plots : 2 si pas de masque, sinon 4 (image, prédiction, vérité, erreurs) + num_plots = 2 if mask_gt is None else 4 + fig, axes = plt.subplots(1, num_plots, figsize=(5*num_plots, 5)) + if num_plots == 1: + axes = [axes] + + axes[0].imshow(img_display.numpy().astype(np.uint8)) + axes[0].set_title("Image Originale") + axes[0].axis('off') + + predicted_colored = colorize_mask(predicted_mask, mapping_config['group_colors']) + axes[1].imshow(predicted_colored) + axes[1].set_title("Masque Prédit") + axes[1].axis('off') + + if mask_gt is not None: + gt_colored = colorize_mask(mask_gt.numpy(), mapping_config['group_colors']) + axes[2].imshow(gt_colored) + axes[2].set_title("Masque de Vérité") + axes[2].axis('off') + + # Visualisation des erreurs en rouge translucide + diff_mask = (mask_gt.numpy() != predicted_mask).astype(float) + axes[3].imshow(img_display.numpy().astype(np.uint8)) + axes[3].imshow(diff_mask, alpha=0.5, cmap='Reds') + axes[3].set_title("Erreurs (en rouge)") + axes[3].axis('off') + + # Légende colorée + from matplotlib.colors import ListedColormap + import matplotlib.patches as mpatches + + colors_normalized = mapping_config['group_colors'] / 255.0 + custom_cmap = ListedColormap(colors_normalized) + + legend_elements = [] + for i, (name, color) in enumerate(zip(mapping_config['group_names'], colors_normalized)): + legend_elements.append(mpatches.Patch(color=color, label=f'{i}: {name}')) + + fig.legend(handles=legend_elements, + loc='center left', + bbox_to_anchor=(1.01, 0.5), + fontsize=9, + frameon=True, + fancybox=True, + shadow=True) + + plt.tight_layout() + fig.subplots_adjust(right=0.95) + + if save_dir: + os.makedirs(save_dir, exist_ok=True) + img_name = os.path.splitext(os.path.basename(image_path))[0] + fig_path = os.path.join(save_dir, f'{img_name}_inference.png') + fig.savefig(fig_path, dpi=150, bbox_inches='tight') + print(f"💾 Résultat sauvegardé: {fig_path}") + + + if show_stats: + print("\n📊 Statistiques du masque prédit:") + unique_classes, counts = np.unique(predicted_mask, return_counts=True) + for class_id, count in zip(unique_classes, counts): + if class_id < len(mapping_config['group_names']): + percentage = (count / predicted_mask.size) * 100 + print(f" {mapping_config['group_names'][class_id]}: {count} pixels ({percentage:.1f}%)") + + if mask_gt is not None: + print("\n📊 Statistiques du masque de vérité:") + unique_classes, counts = np.unique(mask_gt.numpy(), return_counts=True) + for class_id, count in zip(unique_classes, counts): + if class_id < len(mapping_config['group_names']): + percentage = (count / mask_gt.numpy().size) * 100 + print(f" {mapping_config['group_names'][class_id]}: {count} pixels ({percentage:.1f}%)") + + if show_plot: + plt.show() + return predicted_mask + else: + return predicted_mask, fig \ No newline at end of file