diff --git a/.dockerignore b/.dockerignore deleted file mode 100644 index 5ab9f6d..0000000 --- a/.dockerignore +++ /dev/null @@ -1,106 +0,0 @@ -# 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 deleted file mode 100644 index 49f2004..0000000 --- a/.env.example +++ /dev/null @@ -1,41 +0,0 @@ -# ============================================================================= -# 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 deleted file mode 100644 index 242c724..0000000 --- a/.gitignore +++ /dev/null @@ -1 +0,0 @@ -./mlruns* \ No newline at end of file diff --git a/Dockerfile b/Dockerfile deleted file mode 100644 index 925177b..0000000 --- a/Dockerfile +++ /dev/null @@ -1,44 +0,0 @@ -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 notebooks/content/data/train_images_sample/ ./notebooks/content/data/train_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 deleted file mode 100644 index 6e14ca9..0000000 --- a/README.MD +++ /dev/null @@ -1,116 +0,0 @@ -# Street Vision PoC - Segmentation Sémantique Urbaine - -## 🎯 Description du Projet - -**Street Vision PoC** est une preuve de concept démontrant l'amélioration des performances de segmentation sémantique urbaine en comparant les architectures CNN traditionnelles (FPN) aux approches Transformer modernes (SegFormer). - -Ce projet évalue empiriquement le potentiel des **Vision Transformers** pour la compréhension de scènes urbaines complexes, avec un focus sur les classes critiques pour la sécurité (piétons, véhicules, signalétique). - -### 🔬 Contexte Technique - -- **Dataset** : Cityscapes (2,975 images haute résolution annotées pixel-par-pixel) -- **Architectures comparées** : - - **CNN traditionnel** : FPN + EfficientNet-B0/ResNet34 - - **Vision Transformer** : SegFormer-B0/B1 -- **Résolutions d'évaluation** : 512×512 et 768×768 pixels -- **Métrique principale** : Mean Intersection over Union (mIoU) - -## 📊 Résultats Principaux - -| **Modèle** | **mIoU** | **Paramètres** | **Taille** | **Performance** | -|------------|----------|----------------|------------|-----------------| -| **SegFormer-B1** | **79.98%** | 13.68M | 52MB | ⭐ **Meilleur** | -| **FPN+ResNet34** | 79.22% | 23.16M | 88MB | 🔄 Baseline forte | -| **FPN+EfficientNet-B0** | 78.67% | 5.76M | 22MB | 💡 Compact | -| **SegFormer-B0** | 77.91% | 3.72M | 14MB | ⚡ Ultra-léger | - -**Analyse comparative** : SegFormer-B1 et FPN+ResNet34 atteignent des performances similaires (~79-80% mIoU), mais SegFormer-B1 présente l'avantage d'être plus compact et probablement plus rapide sur GPU. Les versions SegFormer-B2 à B5 promettraient des gains significatifs, mais mes contraintes matérielles n'ont pas permis leur évaluation. - -### 🎯 Gains Significatifs par Classes Critiques - -- **Human** : +1.78% IoU (sécurité critique) -- **Vehicle** : +1.05% IoU (obstacles mobiles) -- **Object** : +0.56% IoU (signalétique urbaine) - -## 🚀 Déploiement Rapide - -### Docker Hub - -L'application est containerisée et disponible sur Docker Hub : - -```bash -docker pull grgmdmn/street-vision-poc_api -``` - -### Configuration avec Docker Compose - -1. Copiez le fichier d'exemple de configuration : -```bash -cp docker-compose.yml.example docker-compose.yml -``` - -2. Configurez les variables d'environnement dans `docker-compose.yml`. - -3. Lancez l'application : -```bash -docker-compose up -d -``` - -4. Accédez à l'interface : `http://localhost:$(port)` - -## 🎮 Fonctionnalités de l'Interface - -### Dashboard Interactif - -- **Upload d'images** : Glisser-déposer d'images urbaines -- **Double inférence** : Comparaison simultanée FPN vs SegFormer -- **Visualisation** : Masques de segmentation colorés par classe -- **Métriques temps réel** : Temps d'inférence et scores de confiance -- **Export** : Téléchargement des résultats de segmentation -- **Accessibilité WCAG** : Interface conforme aux standards d'accessibilité - -### Analyse Exploratoire des Données - -- **Statistiques Cityscapes** : Distribution des classes, résolutions natives -- **Graphiques interactifs** : Répartition pixel par méta-classe -- **Exemples annotés** : Visualisation échantillons du dataset - -## 📈 Méthodologie d'Évaluation - -### Approche Comparative - -1. **Baseline CNN** : Réentraînement FPN+EfficientNet/ResNet aux résolutions supérieures -2. **Nouvelle approche** : Évaluation SegFormer avec attention globale -3. **Protocole rigoureux** : Validation croisée, métriques standardisées -4. **Analyse par classe** : Performance détaillée sur 8 méta-classes urbaines - -### Limites Identifiées - -- **Résolution fixe** : Évaluation sans sliding window (vs protocole original) -- **Ressources limitées** : Tests uniquement sur SegFormer-B0/B1 -- **Conditions contrôlées** : Dataset Cityscapes uniquement (météo favorable) - -## 🔬 Insights Techniques - -### Avantages SegFormer - -- **Attention globale** : Chaque pixel "voit" toute l'image simultanément -- **Architecture plus légère** : Modèle compact avec moins de paramètres -- **Robustesse contextuelle** : Meilleure gestion occlusions et objets complexes -- **Potentiel d'amélioration** : Versions B2-B5 promettraient des gains significatifs - -### Trade-offs Identifiés - -- **Vitesse CPU classique** : ~2× plus lent que FPN sur processeurs haute performance (Xeon, 9600X) -- **Vitesse CPU économe** : 3× plus rapide sur processeurs économes en énergie (N100) -- **Optimisation GPU** : Architecture Transformer favorisée par accélération matérielle - -## 📚 Références - -- **Xie, E. et al.** (2021). *SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers*. NeurIPS 2021. -- **Cordts, M. et al.** (2016). *The Cityscapes Dataset for Semantic Urban Scene Understanding*. CVPR 2016. -- **Segmentation Models PyTorch** - [Framework d'implémentation utilisé](https://github.com/qubvel/segmentation_models.pytorch) - ---- - -**Note** : Cette preuve de concept démontre le potentiel des architectures Transformer pour la segmentation urbaine, tout en soulignant l'importance des ressources computationnelles pour exploiter pleinement leurs capacités. \ No newline at end of file diff --git a/app/README.MD b/app/README.MD deleted file mode 100644 index 7b27e38..0000000 --- a/app/README.MD +++ /dev/null @@ -1,108 +0,0 @@ - -# Dashboard de Comparaison de Modèles - -## Conformité aux Exigences - -### Analyse Exploratoire des Données Non Structurées (Images) - -**Présentation d'exemples d'images selon les catégories :** -- Visualisation d'images du dataset Cityscapes organisées par 8 méta-classes de segmentation -- Sélection interactive d'images d'entraînement et de test avec prévisualisation -- Interface de navigation par catégories avec noms d'affichage conviviaux - -**Comptage et répartition des données :** -- Graphiques en secteurs (pie charts) détaillant la répartition des 25,000 images Cityscapes -- Visualisation de l'utilisation projet : 80% train / 20% validation / 100% test -- Graphique de distribution des classes dans le dataset d'entraînement -- Tableaux récapitulatifs avec statistiques détaillées par split - -**Transformations des images :** -- Démonstration interactive du preprocessing : redimensionnement et normalisation -- Visualisation des augmentations de données aléatoires (rotation, flip, luminosité, etc.) -- Comparaison avant/après transformations avec alignement visuel -- Documentation complète des 6 types d'augmentations possibles - -### Sélection de Données d'Entrée - -**Interface de sélection multiple :** -- Liste déroulante pour images test Cityscapes avec informations sur la vérité terrain -- Upload de fichiers personnels (JPEG, PNG) avec validation de format -- Prévisualisation des images sélectionnées avant traitement -- Gestion des erreurs et feedback utilisateur en temps réel - -### Résultats de Prédiction - -**Comparaison complète de modèles :** -- Affichage côte à côte des prédictions des deux meilleurs modèles -- Visualisations avec image originale, masques prédits et vérité terrain -- Détection et mise en évidence des erreurs de segmentation -- Statistiques détaillées par classe avec pourcentages et nombre de pixels - -**Métriques de performance :** -- Temps d'inférence comparatif avec analyse de vitesse -- Scores de performance (mIoU, Accuracy) par modèle -- IoU par classe pour analyse fine des performances -- Graphiques comparatifs interactifs des résultats - -### Déploiement Cloud - -**Architecture déployée :** -- Application FastAPI pour l'API de comparaison de modèles -- Interface Streamlit pour le dashboard utilisateur -- Déploiement sur infrastructure cloud avec haute disponibilité -- Gestion automatique des ressources et montée en charge - -### Accessibilité WCAG - -**Conformité aux critères essentiels :** - -#### Contraste des Couleurs (WCAG AA) -- Palette de couleurs avec ratios de contraste ≥ 4.1:1 pour éléments graphiques -- Ratios de contraste ≥ 4.5:1 pour le texte sur fond -- 8 couleurs principales validées : bleu (#0066CC), rouge (#CC0000), vert (#006600), etc. -- Remplacement des dégradés par des couleurs unies à contraste élevé - -#### Alternatives Textuelles -- Descriptions alternatives détaillées pour tous les graphiques générées automatiquement -- Textes explicatifs pour chaque visualisation (pie charts, bar charts) -- Labels ARIA appropriés pour les éléments interactifs -- Documentation textuelle complète des transformations d'images - -#### Indication Non-Couleur Uniquement -- Motifs et hachures complémentaires aux couleurs (optionnel via checkbox) -- 6 motifs différents : diagonales, verticales, horizontales, points, croix -- Différenciation par forme et texture en plus des couleurs -- Bordures et contours renforcés pour la distinction visuelle - -#### Navigation Clavier -- CSS focus visible avec contours bleus (#0066CC) de 2px -- Support complet de la navigation par tabulation -- Indicateurs visuels clairs pour les éléments focusés -- Respect de l'ordre logique de navigation - -#### Tableaux de Données Accessibles -- Tableaux structurés complémentaires à tous les graphiques -- En-têtes appropriés avec scope et descriptions -- Données exportables et lisibles par lecteurs d'écran -- Configuration responsive pour tous les écrans - -**Fonctionnalités d'assistance intégrées :** -- Checkbox "Activer les motifs (accessibilité visuelle)" pour personnalisation -- Descriptions automatiques des graphiques avec statistiques complètes -- Interface adaptable aux besoins des utilisateurs en situation de handicap -- Documentation complète des fonctionnalités d'accessibilité via endpoint `/accessibility-info` - -## Architecture Technique - -**Stack technologique :** -- Backend : FastAPI + PyTorch pour l'inférence des modèles -- Frontend : Streamlit avec composants accessibles -- Visualisations : Matplotlib avec palettes WCAG -- API REST documentée avec validation Pydantic -- Gestion d'état optimisée pour les comparaisons de modèles - -**Performance et fiabilité :** -- Temps d'inférence optimisés (< 1 seconde par image) -- Gestion mémoire efficace avec fermeture automatique des figures -- Validation des formats et tailles de fichiers -- Nettoyage automatique des fichiers temporaires diff --git a/app/main.py b/app/main.py deleted file mode 100644 index a0c8cc8..0000000 --- a/app/main.py +++ /dev/null @@ -1,834 +0,0 @@ -import os -import sys -import traceback -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 -from fastapi.responses import FileResponse -from pydantic import BaseModel -import numpy as np -import pickle -import base64 -import matplotlib.pyplot as plt -import matplotlib.image as mpimg - - -# Configuration MLflow avec variables d'environnement -def configure_mlflow(): - """Configure MLflow avec les variables d'environnement""" - print("=== DEBUG 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") - - mlflow.set_tracking_uri(mlflow_tracking_uri) - print(f"MLflow Tracking URI: {mlflow_tracking_uri}") - - 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") - print("✅ Configuration MLflow terminée") - -# Import des fonctions utils -sys.path.append('.') -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) -from utils.utils import ( - load_cityscapes_config, - load_model, - run_inference_and_visualize, - colorize_mask -) - -# Configuration -MODEL_NAME = "StreetsSegmentation" -SAMPLE_IMAGES_DIR = "./notebooks/content/data/test_images_sample" -TRAIN_IMAGES_DIR = "./notebooks/content/data/train_images_sample" -CITYSCAPES_CONFIG_PATH = "./cityscapes_config.json" -TEMP_UPLOAD_DIR = "/tmp/uploads" - -# Modèles de données mis à jour -class ModelInfo(BaseModel): - name: str - run_id: str - encoder_name: str - input_size: tuple - rank: int - architecture: Optional[str] = None - test_mean_iou: Optional[float] = None - test_accuracy: Optional[float] = None - class_ious: Optional[dict] = None - -class ComparisonResult(BaseModel): - success: bool - message: str - model1_info: ModelInfo - model2_info: ModelInfo - image_path: Optional[str] = None - mask_path: Optional[str] = None - model1_stats: Optional[dict] = None - model2_stats: Optional[dict] = None - model1_inference_time: float - model2_inference_time: float - speed_comparison: dict - ground_truth_available: bool = False - figure_data: Optional[str] = None - -class ImageInfo(BaseModel): - filename: str - display_name: str - has_ground_truth: bool - -class AvailableImagesResponse(BaseModel): - images: List[ImageInfo] - total_count: int - -class PreprocessingRequest(BaseModel): - filename: str - apply_augmentation: bool = False - -class PreprocessingResult(BaseModel): - success: bool - message: str - original_image: Optional[str] = None - normalized_image: Optional[str] = None - augmented_image: Optional[str] = None - transformations_applied: Optional[list] = None - -class SampleImageRequest(BaseModel): - filename: str - -# Variables globales pour les deux modèles -best_model = None -second_best_model = None -best_model_info = {} -second_best_model_info = {} -mapping_config = None - -def get_model_detailed_info(run_id, mapping_config): - """Récupère les informations détaillées d'un modèle depuis MLflow""" - client = mlflow.tracking.MlflowClient() - - try: - run = mlflow.get_run(run_id) - params = run.data.params - metrics = run.data.metrics - - # Architecture depuis le paramètre model_architecture - architecture = params.get("model_architecture", "Unknown").split('_')[0] - - # Métriques de performance - test_mean_iou = metrics.get("test_mean_iou", None) - test_accuracy = metrics.get("test_accuracy", None) - - # IoU par classe depuis les métriques class_X_iou - class_ious = {} - if mapping_config and 'group_names' in mapping_config: - for i, class_name in enumerate(mapping_config['group_names']): - metric_key = f"class_{i}_iou" - if metric_key in metrics: - class_ious[class_name] = round(metrics[metric_key], 3) - - return { - "architecture": architecture, - "test_mean_iou": test_mean_iou, - "test_accuracy": test_accuracy, - "class_ious": class_ious - } - except Exception as e: - print(f"⚠️ Erreur lors de la récupération des infos détaillées: {e}") - return { - "architecture": "Unknown", - "test_mean_iou": None, - "test_accuracy": None, - "class_ious": {} - } - -def initialize_models(): - """Initialise les deux meilleurs modèles au démarrage""" - global best_model, second_best_model, best_model_info, second_best_model_info, mapping_config - - try: - print("🔄 Configuration de MLflow...") - configure_mlflow() - - print("🔄 Chargement de la configuration Cityscapes avec couleurs WCAG...") - mapping_config = load_cityscapes_config(CITYSCAPES_CONFIG_PATH, verbose=False, use_wcag_colors=True) - print("✅ Configuration Cityscapes chargée avec couleurs WCAG") - - - print("🔄 Chargement du meilleur modèle...") - best_model, best_run_id, best_encoder_name, best_img_size = load_model( - experiment_name="OC Projet 9", - metric="test_mean_iou", - top_n=1 - ) - - # Récupérer les infos détaillées du meilleur modèle - best_detailed_info = get_model_detailed_info(best_run_id, mapping_config) - - best_model_info = { - "name": "Meilleur modèle", - "run_id": best_run_id, - "encoder_name": best_encoder_name, - "input_size": best_img_size, - "rank": 1, - "architecture": best_detailed_info["architecture"], - "test_mean_iou": best_detailed_info["test_mean_iou"], - "test_accuracy": best_detailed_info["test_accuracy"], - "class_ious": best_detailed_info["class_ious"] - } - - print("🔄 Chargement du deuxième meilleur modèle...") - second_best_model, second_run_id, second_encoder_name, second_img_size = load_model( - experiment_name="OC Projet 9", - metric="test_mean_iou", - top_n=2 - ) - - # Récupérer les infos détaillées du deuxième modèle - second_detailed_info = get_model_detailed_info(second_run_id, mapping_config) - - second_best_model_info = { - "name": "Deuxième modèle", - "run_id": second_run_id, - "encoder_name": second_encoder_name, - "input_size": second_img_size, - "rank": 2, - "architecture": second_detailed_info["architecture"], - "test_mean_iou": second_detailed_info["test_mean_iou"], - "test_accuracy": second_detailed_info["test_accuracy"], - "class_ious": second_detailed_info["class_ious"] - } - - os.makedirs(TEMP_UPLOAD_DIR, exist_ok=True) - - except Exception as e: - print(f"❌ Erreur lors de l'initialisation: {e}") - traceback.print_exc() - raise e - -def get_accessibility_info(): - """Retourne les informations d'accessibilité pour le frontend""" - return { - "wcag_compliant": True, - "color_palette": { - "primary_blue": "#0066CC", - "primary_red": "#CC0000", - "primary_green": "#006600", - "primary_orange": "#CC6600", - "secondary_purple": "#663399", - "secondary_teal": "#006666", - "neutral_dark": "#333333", - "neutral_medium": "#666666" - }, - "contrast_ratios": { - "primary_colors_vs_white": "≥ 4.1:1 (WCAG AA)", - "text_vs_background": "≥ 4.5:1 (WCAG AA)" - }, - "features": [ - "Couleurs à contraste élevé", - "Motifs alternatifs aux couleurs", - "Descriptions textuelles alternatives en cas d'absence d'images", - "Tableaux de données complémentaires", - "Navigation clavier supportée" - ] - } - - -# Initialisation de l'API -app = FastAPI( - title="Multi-Class Segmentation Comparison API", - description="API de comparaison de segmentation sémantique avec deux modèles", - version="2.0.0" -) - -@app.on_event("startup") -async def startup_event(): - try: - initialize_models() - print("🚀 API démarrée avec succès - Modèles chargés!") - except Exception as e: - print(f"⚠️ ATTENTION: Impossible de charger les modèles au démarrage: {e}") - -@app.get("/") -def root(): - return { - "message": "Multi-Class Segmentation Comparison API - Online", - "models_loaded": best_model is not None and second_best_model is not None, - "best_model": best_model_info if best_model else None, - "second_best_model": second_best_model_info if second_best_model else None, - "num_classes": mapping_config['num_classes'] if mapping_config else None - } - -@app.get("/health") -def health_check(): - if best_model is None or second_best_model is None or mapping_config is None: - raise HTTPException(status_code=503, detail="Models not loaded") - return { - "status": "healthy", - "models_loaded": True, - "timestamp": datetime.utcnow().isoformat() - } - - -# Modifier le endpoint /models pour inclure des couleurs accessibles : -@app.get("/models", response_model=dict) -def get_models_info(): - if best_model is None or second_best_model is None: - raise HTTPException(status_code=503, detail="Models not loaded") - - # NOUVELLE RÉPARTITION DES COULEURS selon votre mapping final - wcag_accessible_colors = [ - [102, 102, 102], # Flat: neutral_medium (gris moyen pour routes/trottoirs) - [204, 0, 0], # Human: primary_red (rouge pour piétons/cyclistes) - [102, 51, 153], # Vehicle: secondary_purple (violet pour véhicules) - [204, 102, 0], # Construction: primary_orange (orange pour bâtiments) - [0, 102, 102], # Object: secondary_teal (teal pour objets/signalétique) - [0, 102, 0], # Nature: primary_green (vert pour végétation) - [0, 102, 204], # Sky: primary_blue (bleu pour ciel) - [51, 51, 51] # Void: neutral_dark (gris foncé pour zones non-définies) - ] - - # Mettre à jour la configuration avec les nouvelles couleurs - if mapping_config: - mapping_config['group_colors'] = np.array(wcag_accessible_colors, dtype=np.uint8) - - return { - "best_model": best_model_info, - "second_best_model": second_best_model_info, - "num_classes": mapping_config['num_classes'], - "class_names": mapping_config['group_names'], - "class_colors": wcag_accessible_colors, - "accessibility_info": get_accessibility_info(), - "preprocessing_resolution": best_model_info.get("input_size", (512, 512)) - } - - - -@app.get("/dataset-info") -def get_dataset_info(): - """Retourne les informations sur le dataset""" - return { - "name": "Cityscapes Dataset", - "description": "Référence internationale pour l'évaluation des algorithmes de segmentation sémantique en environnement urbain", - "total_images": 25000, - "resolution": "2048×1024 pixels", - "cities": 50, - "countries": ["Allemagne", "Suisse"], - "splits": { - "train": {"images": 2975, "usage": "Entraînement avec annotations fines"}, - "validation": {"images": 500, "usage": "Validation avec annotations de référence"}, - "test": {"images": 1525, "usage": "Test (annotations non-publiques)"}, - "coarse": {"images": 20000, "usage": "Entraînement avec annotations grossières"} - }, - "project_splits": { - "train": {"images": 2380, "percentage": 80, "source": "Cityscapes train"}, - "validation": {"images": 595, "percentage": 20, "source": "Cityscapes train"}, - "test": {"images": 500, "percentage": 100, "source": "Cityscapes val"} - }, - "meta_classes": mapping_config['group_names'] if mapping_config else [], - "num_classes": mapping_config['num_classes'] if mapping_config else 0 - } - -@app.get("/train-images", response_model=AvailableImagesResponse) -def get_train_images(): - """Retourne la liste des images d'entraînement disponibles""" - try: - original_dir = Path(TRAIN_IMAGES_DIR) / "original" - mask_dir = Path(TRAIN_IMAGES_DIR) / "mask" - - if not original_dir.exists(): - raise HTTPException(status_code=404, detail="Train 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 train images: {str(e)}") - -@app.get("/train-image/{filename}") -def get_train_image(filename: str): - """Sert une image d'entraînement pour prévisualisation""" - try: - original_dir = Path(TRAIN_IMAGES_DIR) / "original" - image_path = original_dir / filename - - if not image_path.exists(): - raise HTTPException(status_code=404, detail=f"Train image {filename} not found") - - # Vérifier que c'est bien un fichier image attendu - if not filename.endswith('_leftImg8bit.png'): - raise HTTPException(status_code=400, detail="Invalid image filename format") - - return FileResponse( - image_path, - media_type="image/png", - headers={"Content-Disposition": f"inline; filename={filename}"} - ) - - except HTTPException: - raise - except Exception as e: - raise HTTPException(status_code=500, detail=f"Error serving train image: {str(e)}") - -@app.get("/class-repartition") -def get_class_repartition(): - """Sert le graphique de répartition des classes""" - try: - graph_path = Path(TRAIN_IMAGES_DIR) / "class_repartition.png" - - if not graph_path.exists(): - raise HTTPException(status_code=404, detail="Class repartition graph not found") - - return FileResponse( - graph_path, - media_type="image/png", - headers={"Content-Disposition": "inline; filename=class_repartition.png"} - ) - - except HTTPException: - raise - except Exception as e: - raise HTTPException(status_code=500, detail=f"Error serving class repartition graph: {str(e)}") - -@app.post("/preprocess-train-image", response_model=PreprocessingResult) -def preprocess_train_image(request: PreprocessingRequest): - """Prétraite une image d'entraînement et retourne les versions transformées""" - if mapping_config is None: - raise HTTPException(status_code=503, detail="Configuration not loaded") - - # Vérifier que le meilleur modèle est chargé - if best_model is None or not best_model_info: - raise HTTPException(status_code=503, detail="Best model not loaded") - - try: - original_dir = Path(TRAIN_IMAGES_DIR) / "original" - image_path = original_dir / request.filename - - if not image_path.exists(): - raise HTTPException(status_code=404, detail=f"Train image {request.filename} not found") - - # Importer les fonctions nécessaires - import numpy as np - import matplotlib.pyplot as plt - from PIL import Image - import io - import base64 - from utils.utils import get_preprocessing_fn, apply_random_augmentation - - # Charger l'image originale - original_pil = Image.open(image_path).convert('RGB') - original_np = np.array(original_pil) - - # UTILISER LA RÉSOLUTION DU MEILLEUR MODÈLE au lieu de (512, 512) - target_size = best_model_info.get("input_size", (512, 512)) - print(f"🎯 Utilisation de la taille du meilleur modèle: {target_size}") - - # Appliquer le preprocessing avec la résolution du meilleur modèle - preprocess_fn = get_preprocessing_fn(img_size=target_size, encoder=None) - normalized_tensor, _ = preprocess_fn(original_np, None) - - # Convertir le tensor en image pour affichage - normalized_np = normalized_tensor.permute(1, 2, 0).numpy() - # Dénormaliser pour l'affichage (tensor était normalisé 0-1) - normalized_display = (normalized_np * 255).astype(np.uint8) - - # Créer les images en base64 pour l'envoi - def img_to_base64(img_array): - img_pil = Image.fromarray(img_array) - buffer = io.BytesIO() - img_pil.save(buffer, format='PNG') - return base64.b64encode(buffer.getvalue()).decode() - - # Image originale redimensionnée pour comparaison (avec la taille du meilleur modèle) - original_resized = np.array(original_pil.resize(target_size)) - - result = PreprocessingResult( - success=True, - message="Preprocessing completed successfully", - original_image=img_to_base64(original_resized), - normalized_image=img_to_base64(normalized_display), - transformations_applied=[f"Resize {target_size}", "Normalization (0-1)"] - ) - - # Appliquer l'augmentation si demandée - if request.apply_augmentation: - augmented_np, transforms_applied = apply_random_augmentation(original_np) - # Appliquer aussi le preprocessing à l'image augmentée - augmented_tensor, _ = preprocess_fn(augmented_np, None) - augmented_display = (augmented_tensor.permute(1, 2, 0).numpy() * 255).astype(np.uint8) - - result.augmented_image = img_to_base64(augmented_display) - result.transformations_applied.extend(transforms_applied) - - return result - - except Exception as e: - raise HTTPException(status_code=500, detail=f"Preprocessing error: {str(e)}") - -@app.get("/best-model-resolution") -def get_best_model_resolution(): - """Retourne la résolution utilisée par le meilleur modèle""" - if best_model is None or not best_model_info: - raise HTTPException(status_code=503, detail="Best model not loaded") - - return { - "model_name": best_model_info.get("name", "Unknown"), - "encoder_name": best_model_info.get("encoder_name", "Unknown"), - "input_size": best_model_info.get("input_size", (512, 512)), - "architecture": best_model_info.get("architecture", "Unknown") - } - -@app.get("/sample-images", response_model=AvailableImagesResponse) -def get_sample_images(): - 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"): - base_name = img_path.stem.replace("_leftImg8bit", "") - mask_name = f"{base_name}_gtFine_labelIds.png" - mask_path = mask_dir / mask_name - display_name = base_name.replace("_", " ").title() - images.append(ImageInfo( - filename=img_path.name, - display_name=display_name, - has_ground_truth=mask_path.exists() - )) - 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): - try: - original_dir = Path(SAMPLE_IMAGES_DIR) / "original" - image_path = original_dir / filename - if not image_path.exists(): - raise HTTPException(status_code=404, detail=f"Sample image {filename} not found") - if not filename.endswith('_leftImg8bit.png'): - raise HTTPException(status_code=400, detail="Invalid image filename format") - return FileResponse( - image_path, - media_type="image/png", - headers={"Content-Disposition": f"inline; filename={filename}"} - ) - except HTTPException: - raise - except Exception as e: - raise HTTPException(status_code=500, detail=f"Error serving image: {str(e)}") - -@app.post("/compare-sample", response_model=ComparisonResult) -def compare_sample_image(request: SampleImageRequest): - if best_model is None or second_best_model is None or mapping_config is None: - raise HTTPException(status_code=503, detail="Models not loaded") - try: - 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") - 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 - - # Inférence modèle 1 avec sa résolution spécifique - pred1, t1, fig1 = run_inference_and_visualize( - image_path=str(image_path), - mask_path=mask_path_str, - model=best_model, - encoder_name=None, # Normalisé de la même manière - img_size=best_model_info["input_size"], # Utilise la résolution du modèle - mapping_config=mapping_config, - save_dir=None, show_stats=False, show_plot=False - ) - # Inférence modèle 2 avec sa résolution spécifique - pred2, t2, fig2 = run_inference_and_visualize( - image_path=str(image_path), - mask_path=mask_path_str, - model=second_best_model, - encoder_name=None, # Normalisé de la même manière - img_size=second_best_model_info["input_size"], # Utilise la résolution du modèle - mapping_config=mapping_config, - save_dir=None, show_stats=False, show_plot=False - ) - - # Créer une figure combinée avec les deux modèles - # Nombre de colonnes : 3 si pas de GT, 4 si GT disponible - num_cols = 3 if not (mask_path_str and os.path.exists(mask_path_str)) else 4 - fig, axes = plt.subplots(2, num_cols, figsize=(5*num_cols, 8)) - - # Récupérer les images depuis les figures individuelles - axes1 = fig1.get_axes() - axes2 = fig2.get_axes() - - # Ligne 1 : Modèle 1 - for i in range(min(len(axes1), num_cols)): - ax_src = axes1[i] - ax_dst = axes[0, i] - - # Copier toutes les images de l'axe source - for img_obj in ax_src.get_images(): - ax_dst.imshow( - img_obj.get_array(), - cmap=img_obj.get_cmap(), - alpha=img_obj.get_alpha(), - vmin=img_obj.get_clim()[0] if img_obj.get_clim() else None, - vmax=img_obj.get_clim()[1] if img_obj.get_clim() else None - ) - - # Copier le titre avec nom du modèle - original_title = ax_src.get_title() - if i == 0: - new_title = "Image originale" - elif "Prédit" in original_title: - new_title = f"Prédiction {best_model_info['architecture']} - {best_model_info['encoder_name']}" - else: - new_title = original_title - - ax_dst.set_title(new_title) - ax_dst.axis('off') - - # Ligne 2 : Modèle 2 - for i in range(min(len(axes2), num_cols)): - ax_src = axes2[i] - ax_dst = axes[1, i] - - # Copier toutes les images de l'axe source - for img_obj in ax_src.get_images(): - ax_dst.imshow( - img_obj.get_array(), - cmap=img_obj.get_cmap(), - alpha=img_obj.get_alpha(), - vmin=img_obj.get_clim()[0] if img_obj.get_clim() else None, - vmax=img_obj.get_clim()[1] if img_obj.get_clim() else None - ) - - # Copier le titre avec nom du modèle - original_title = ax_src.get_title() - if i == 0: - new_title = "Image originale" - elif "Prédit" in original_title: - new_title = f"Prédiction {second_best_model_info['architecture']} - {second_best_model_info['encoder_name']}" - else: - new_title = original_title - - ax_dst.set_title(new_title) - ax_dst.axis('off') - - plt.tight_layout() - - # Fermer les figures individuelles pour libérer la mémoire - plt.close(fig1) - plt.close(fig2) - - fig_b64 = base64.b64encode(pickle.dumps(fig)).decode() - # Stats - def calc(mask): - uc, cnts = np.unique(mask, return_counts=True) - stats = {} - for cid, c in zip(uc, cnts): - if cid < len(mapping_config['group_names']): - stats[mapping_config['group_names'][cid]] = { - "pixels": int(c), - "percentage": round((c/mask.size)*100, 1) - } - return stats - stats1, stats2 = calc(pred1), calc(pred2) - faster = best_model_info['encoder_name'] if t1 < t2 else second_best_model_info['encoder_name'] - speedup = round(max(t1, t2) / min(t1, t2), 2) - speed_comp = {"faster_model": faster, "speedup_ratio": speedup, "time_difference_ms": abs(t1-t2)*1000} - return ComparisonResult( - success=True, message="Comparison completed successfully", - model1_info=ModelInfo(**best_model_info), model2_info=ModelInfo(**second_best_model_info), - image_path=str(image_path), mask_path=mask_path_str, - model1_stats=stats1, model2_stats=stats2, - model1_inference_time=t1, model2_inference_time=t2, - speed_comparison=speed_comp, ground_truth_available=mask_path.exists(), - figure_data=fig_b64 - ) - except Exception as e: - raise HTTPException(status_code=500, detail=f"Comparison error: {str(e)}") - -@app.post("/compare-upload", response_model=ComparisonResult) -def compare_uploaded_image(file: UploadFile = File(...)): - if best_model is None or second_best_model is None or mapping_config is None: - raise HTTPException(status_code=503, detail="Models not loaded") - 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: - 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) - with open(temp_file_path, "wb") as buffer: - shutil.copyfileobj(file.file, buffer) - # Inférences avec résolutions spécifiques - pred1, t1, fig1 = run_inference_and_visualize( - image_path=temp_file_path, mask_path=None, - model=best_model, encoder_name=None, # Normalisé de la même manière - img_size=best_model_info["input_size"], mapping_config=mapping_config, - save_dir=None, show_stats=False, show_plot=False, - ) - pred2, t2, fig2 = run_inference_and_visualize( - image_path=temp_file_path, mask_path=None, - model=second_best_model, encoder_name=None, # Normalisé de la même manière - img_size=second_best_model_info["input_size"], mapping_config=mapping_config, - save_dir=None, show_stats=False, show_plot=False, - ) - - # Figure combinée pour uploads (pas de GT, donc 2 colonnes seulement) - fig, axes = plt.subplots(2, 2, figsize=(10, 8)) - - # Récupérer les images depuis les figures individuelles - axes1 = fig1.get_axes() - axes2 = fig2.get_axes() - - # Ligne 1 : Modèle 1 (Image originale + Prédiction) - for i in range(min(len(axes1), 2)): - ax_src = axes1[i] - ax_dst = axes[0, i] - - # Copier toutes les images de l'axe source - for img_obj in ax_src.get_images(): - ax_dst.imshow( - img_obj.get_array(), - cmap=img_obj.get_cmap(), - alpha=img_obj.get_alpha(), - vmin=img_obj.get_clim()[0] if img_obj.get_clim() else None, - vmax=img_obj.get_clim()[1] if img_obj.get_clim() else None - ) - - # Copier le titre avec nom du modèle - original_title = ax_src.get_title() - if i == 0: - new_title = "Image originale" - else: - new_title = f"Prédiction {best_model_info['architecture']} - {best_model_info['encoder_name']}" - - ax_dst.set_title(new_title) - ax_dst.axis('off') - - # Ligne 2 : Modèle 2 (Image originale + Prédiction) - for i in range(min(len(axes2), 2)): - ax_src = axes2[i] - ax_dst = axes[1, i] - - # Copier toutes les images de l'axe source - for img_obj in ax_src.get_images(): - ax_dst.imshow( - img_obj.get_array(), - cmap=img_obj.get_cmap(), - alpha=img_obj.get_alpha(), - vmin=img_obj.get_clim()[0] if img_obj.get_clim() else None, - vmax=img_obj.get_clim()[1] if img_obj.get_clim() else None - ) - - # Copier le titre avec nom du modèle - original_title = ax_src.get_title() - if i == 0: - new_title = "Image originale" - else: - new_title = f"Prédiction {second_best_model_info['architecture']} - {second_best_model_info['encoder_name']}" - - ax_dst.set_title(new_title) - ax_dst.axis('off') - - plt.tight_layout() - - # Fermer les figures individuelles pour libérer la mémoire - plt.close(fig1) - plt.close(fig2) - - fig_b64 = base64.b64encode(pickle.dumps(fig)).decode() - # Stats - def calc(mask): - uc, cnts = np.unique(mask, return_counts=True) - stats = {} - for cid, c in zip(uc, cnts): - if cid < len(mapping_config['group_names']): - stats[mapping_config['group_names'][cid]] = { - "pixels": int(c), - "percentage": round((c/mask.size)*100, 1) - } - return stats - stats1, stats2 = calc(pred1), calc(pred2) - faster = best_model_info['encoder_name'] if t1 < t2 else second_best_model_info['encoder_name'] - speedup = round(max(t1, t2) / min(t1, t2), 2) - speed_comp = {"faster_model": faster, "speedup_ratio": speedup, "time_difference_ms": abs(t1-t2)*1000} - return ComparisonResult( - success=True, message="Comparison completed successfully", - model1_info=ModelInfo(**best_model_info), model2_info=ModelInfo(**second_best_model_info), - image_path=temp_file_path, - model1_stats=stats1, model2_stats=stats2, - model1_inference_time=t1, model2_inference_time=t2, - speed_comparison=speed_comp, ground_truth_available=False, - figure_data=fig_b64 - ) - except Exception as e: - raise HTTPException(status_code=500, detail=f"Comparison error: {str(e)}") - finally: - if temp_file_path and os.path.exists(temp_file_path): - try: os.remove(temp_file_path) - except Exception: pass - -@app.get("/metrics") -def get_metrics(): - return { - "models_loaded": best_model is not None and second_best_model is not None, - "best_model_info": best_model_info, - "second_best_model_info": second_best_model_info, - "num_classes": mapping_config['num_classes'] if mapping_config else None, - "timestamp": datetime.utcnow().isoformat() - } - -# Ajouter cette route après les autres routes : -@app.get("/accessibility-info") -def get_accessibility_information(): - """Retourne les informations sur la conformité d'accessibilité""" - return get_accessibility_info() \ No newline at end of file diff --git a/app/requirements.txt b/app/requirements.txt deleted file mode 100644 index eff910c..0000000 --- a/app/requirements.txt +++ /dev/null @@ -1,71 +0,0 @@ -# ============================================================================= -# 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 -python-multipart==0.0.20 # uploads FastAPI - -# ===== STREAMLIT ===== -streamlit==1.46.1 -altair==5.5.0 # utilisé par Streamlit -blinker==1.9.0 # dépendance Streamlit - -# ===== HTTP CLIENTS ===== -requests==2.32.4 -httpx==0.28.1 # utilisé en interne par FastAPI -httpcore==1.0.9 -h11==0.16.0 - -# ===== MACHINE LEARNING CORE (PyTorch) ===== -torch==2.8.0 # version stable PyTorch -torchvision==0.23.0 # utile pour les modèles pré-entraînés -torchaudio==2.8.0 # optionnel (mais recommandé pour compatibilité PyTorch) -segmentation-models-pytorch==0.5.0 -albumentations==2.0.8 -# version PyTorch de ToTensor -albumentations[imgaug,opencv] - -numpy>=1.24.0,<2.0.0 -scipy==1.16.0 -scikit-learn==1.7.0 - -# ===== COMPUTER VISION ===== -pillow==11.3.0 -opencv-python-headless==4.9.0.80 -scikit-image==0.25.2 -imageio==2.37.0 - -# ===== DATA SCIENCE ===== -pandas==2.3.0 -matplotlib==3.10.3 -networkx==3.5 -tifffile==2025.6.11 -lazy_loader==0.4 - -# ===== 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 - -# ===== UTILITIES ===== -click==8.2.1 -tqdm==4.67.1 -joblib==1.5.1 -python-dateutil -packaging -typing_extensions==4.14.0 - -# ===== MATPLOTLIB ADDONS ===== -cycler==0.12.1 -fonttools==4.58.5 -plotly==5.24.1 diff --git a/app/streamlit_app.py b/app/streamlit_app.py deleted file mode 100644 index c80c8dd..0000000 --- a/app/streamlit_app.py +++ /dev/null @@ -1,1889 +0,0 @@ -import os -import sys -import requests -import streamlit as st -import pandas as pd -import numpy as np -import matplotlib.pyplot as plt -import plotly.express as px -import plotly.graph_objects as go -from PIL import Image -import io -import base64 -import pickle - -# Configuration de l'API -API_BASE_URL = os.getenv("MULTISEG_API_BASE_URL", "http://127.0.0.1:8080/api") - -# Import des fonctions utils pour l'affichage local -sys.path.append('/app') -sys.path.append('.') -from utils.utils import colorize_mask - -# Configuration de la page avec titre approprié (WCAG 2.4.2) -st.set_page_config( - page_title="Street Vision - Segmentation Comparison | Dashboard Accessible", - page_icon="🚗", - layout="wide" -) - - -# Ajouts à placer au début de streamlit_app.py après les imports - -# Palettes de couleurs conformes WCAG AA (ratio contraste ≥ 3:1) -WCAG_ACCESSIBLE_COLORS = { - 'primary_blue': '#0066CC', # [0, 102, 204] - 'primary_red': '#CC0000', # [204, 0, 0] - 'primary_green': '#006600', # [0, 102, 0] - 'primary_orange': '#CC6600', # [204, 102, 0] - 'secondary_purple': '#663399', # [102, 51, 153] - 'secondary_teal': '#006666', # [0, 102, 102] - 'neutral_dark': '#333333', # [51, 51, 51] - 'neutral_medium': '#666666', # [102, 102, 102] - 'neutral_light': '#CCCCCC', # Pour éléments désactivés - 'background_alt': '#F5F5F5' # Fond alternatif accessible -} - -# Convertir hex vers RGB -def hex_to_rgb(hex_color): - """Convertit une couleur hex en tuple RGB""" - hex_color = hex_color.lstrip('#') - return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4)) - -# Couleurs RGB pour les masques (dans le même ordre que les classes) -WCAG_RGB_COLORS = [ - hex_to_rgb(WCAG_ACCESSIBLE_COLORS['neutral_medium']), # Flat: gris moyen [102, 102, 102] - hex_to_rgb(WCAG_ACCESSIBLE_COLORS['primary_red']), # Human: rouge [204, 0, 0] - hex_to_rgb(WCAG_ACCESSIBLE_COLORS['secondary_purple']), # Vehicle: violet [102, 51, 153] - hex_to_rgb(WCAG_ACCESSIBLE_COLORS['primary_orange']), # Construction: orange [204, 102, 0] - hex_to_rgb(WCAG_ACCESSIBLE_COLORS['secondary_teal']), # Object: teal [0, 102, 102] - hex_to_rgb(WCAG_ACCESSIBLE_COLORS['primary_green']), # Nature: vert [0, 102, 0] - hex_to_rgb(WCAG_ACCESSIBLE_COLORS['primary_blue']), # Sky: bleu [0, 102, 204] - hex_to_rgb(WCAG_ACCESSIBLE_COLORS['neutral_dark']) # Void: gris foncé [51, 51, 51] -] - - -# Motifs pour différencier sans couleur uniquement -ACCESSIBILITY_PATTERNS = { - 'solid': None, - 'diagonal_lines': '///', - 'vertical_lines': '|||', - 'horizontal_lines': '---', - 'dots': '...', - 'crosses': 'xxx' -} - -def create_accessible_pie_chart_conditional(sizes, labels, title, use_patterns=False): - """Version corrigée qui règle le problème de lisibilité avec les motifs""" - fig, ax = plt.subplots(figsize=(6, 6), tight_layout=True) - - accessible_colors = [ - WCAG_ACCESSIBLE_COLORS['primary_blue'], WCAG_ACCESSIBLE_COLORS['primary_red'], - WCAG_ACCESSIBLE_COLORS['primary_green'], WCAG_ACCESSIBLE_COLORS['primary_orange'], - WCAG_ACCESSIBLE_COLORS['secondary_purple'], WCAG_ACCESSIBLE_COLORS['secondary_teal'] - ] - colors = accessible_colors[:len(sizes)] - - # SOLUTION : texte avec fond contrasté - uniform_textprops = { - 'fontsize': 10, 'fontweight': 'bold', 'color': 'white', - 'bbox': dict(boxstyle="round,pad=0.3", facecolor='black', alpha=0.8) - } - - try: - wedges, texts, autotexts = ax.pie( - sizes, labels=labels, colors=colors, autopct='%1.0f%%', startangle=90, shadow=True, - textprops=uniform_textprops, wedgeprops={'edgecolor': 'white', 'linewidth': 2}, - pctdistance=0.85 # Éloigner les pourcentages - ) - - # Améliorer la lisibilité des pourcentages - for autotext in autotexts: - autotext.set_bbox(dict(boxstyle="round,pad=0.4", facecolor='black', alpha=0.9)) - autotext.set_color('white') - autotext.set_fontweight('bold') - autotext.set_fontsize(9) - - # Motifs APRÈS configuration du texte - if use_patterns: - patterns = ['///', '|||', '---', '...', 'xxx', '+++'] - for wedge, pattern in zip(wedges, patterns[:len(wedges)]): - if pattern: - wedge.set_hatch(pattern) - - ax.set_title(title, fontsize=10, fontweight='bold', pad=15, color='#333333') - ax.axis('equal') - chart_displayed = True - - except Exception as e: - ax.text(0.5, 0.5, f"Erreur d'affichage du graphique\n{str(e)}", - ha='center', va='center', transform=ax.transAxes, fontsize=10, color='red', weight='bold') - chart_displayed = False - - return fig, ax, {'colors': colors, 'patterns': patterns if use_patterns else None}, chart_displayed - -# Fonctions Plotly pour graphiques interactifs -def create_interactive_pie_chart(sizes, labels, title, models_info=None): - """Crée un pie chart interactif avec les couleurs des classes définies dans models_info""" - - # Utiliser les couleurs des classes si disponibles - if models_info and 'class_colors' in models_info and 'class_names' in models_info: - colors = [] - class_names = models_info['class_names'] - class_colors = models_info['class_colors'] - - for label in labels: - if label in class_names: - idx = class_names.index(label) - color_rgb = class_colors[idx] - color_hex = f"#{color_rgb[0]:02x}{color_rgb[1]:02x}{color_rgb[2]:02x}" - colors.append(color_hex) - else: - # Couleur par défaut si classe non trouvée - colors.append('#666666') - else: - # Fallback vers les couleurs WCAG - accessible_colors_list = list(WCAG_ACCESSIBLE_COLORS.values()) - colors = accessible_colors_list[:len(sizes)] - - df = pd.DataFrame({'labels': labels, 'values': sizes, 'colors': colors}) - - fig = go.Figure(data=[go.Pie( - labels=df['labels'], - values=df['values'], - marker=dict( - colors=df['colors'], - line=dict(color='#FFFFFF', width=3), - pattern=dict( - shape=['', '/', '\\', '|', '-', '+', 'x', '.'][:len(sizes)], - size=8, - solidity=0.3 - ) - ), - textinfo='label+percent', - textposition='auto', - textfont=dict( - size=13, - color='white', - family="Arial Black" - ), - hovertemplate='%{label}
Valeur: %{value}
Pourcentage: %{percent}
', - hoverlabel=dict( - bgcolor="rgba(255,255,255,0.95)", - bordercolor="#333333", - font=dict(size=14, color="#333333") - ) - )]) - - fig.update_layout( - title={ - 'text': title, - 'x': 0.5, - 'xanchor': 'center', - 'font': {'size': 16, 'color': '#333333', 'family': 'Arial Black'} - }, - showlegend=True, - legend=dict( - orientation="v", - yanchor="middle", - y=0.5, - xanchor="left", - x=1.02, - font=dict(size=12, color='#333333'), - bgcolor='rgba(255,255,255,0.9)', - bordercolor='#333333', - ), - margin=dict(l=20, r=120, t=60, b=20), - height=400, - plot_bgcolor='rgba(0,0,0,0)', - paper_bgcolor='rgba(0,0,0,0)', - font=dict(color='#333333') - ) - - return fig - - -def create_interactive_comparison_chart(model1_stats, model2_stats, model1_name, model2_name): - """Crée un graphique en barres interactif WCAG-compliant pour comparer les deux modèles""" - classes = list(set(model1_stats.keys()) | set(model2_stats.keys())) - - data = [] - for class_name in classes: - val1 = model1_stats.get(class_name, {}).get('percentage', 0) - val2 = model2_stats.get(class_name, {}).get('percentage', 0) - - data.append({'Classe': class_name, 'Modèle': model1_name, 'Pourcentage': val1, 'Pixels': model1_stats.get(class_name, {}).get('pixels', 0)}) - data.append({'Classe': class_name, 'Modèle': model2_name, 'Pourcentage': val2, 'Pixels': model2_stats.get(class_name, {}).get('pixels', 0)}) - - df = pd.DataFrame(data) - - # Filtrer les données pour supprimer les lignes vides potentielles - df = df[df['Modèle'].notna() & (df['Modèle'] != '')] - - fig = px.bar( - df, x='Classe', y='Pourcentage', color='Modèle', barmode='group', - title='Comparaison interactive des classes détectées', - hover_data=['Pixels'], - color_discrete_map={model1_name: '#0066CC', model2_name: '#CC0000'} - # PAS de pattern_shape du tout - ) - - fig.update_layout( - title={'x': 0.5, 'xanchor': 'center', 'font': {'size': 16, 'color': '#333333', 'family': 'Arial Black'}}, - xaxis=dict( - title='Classes de segmentation', - titlefont=dict(size=14, color='#666666'), # Plus clair comme vos pie charts - tickfont=dict(size=12, color='#666666'), # Plus clair - tickangle=45, - showgrid=True, - gridcolor='#E8E8E8', # Grille beaucoup plus claire - gridwidth=1, - linecolor='#AAAAAA', # Axe gris moyen - linewidth=1, - showline=True - ), - yaxis=dict( - title='Pourcentage de l\'image (%)', - titlefont=dict(size=14, color='#666666'), # Plus clair - tickfont=dict(size=12, color='#666666'), # Plus clair - showgrid=True, - gridcolor='#E8E8E8', # Grille beaucoup plus claire - gridwidth=1, - linecolor='#AAAAAA', # Axe gris moyen - linewidth=1, - showline=True - ), - legend=dict( - title='Modèles', - font=dict(size=12, color='#333333'), # Comme vos pie charts - bgcolor='rgba(255,255,255,0.9)', - bordercolor='#333333', - ), - hovermode='x unified', - height=500, - plot_bgcolor='rgba(0,0,0,0)', # Transparent - paper_bgcolor='rgba(0,0,0,0)', # Transparent - font=dict(color='#333333') # Sans spécifier size qui peut poser problème - ) - - fig.update_traces( - texttemplate='%{y:.1f}%', - textposition='outside', - textfont=dict(size=11, color='#666666'), # Plus clair pour lisibilité - marker=dict(line=dict(color='#FFFFFF', width=1)) # Bordures blanches comme vos pie charts - ) - - return fig - - -def create_dataset_interactive_overview(): - """Vue d'ensemble interactive du dataset avec les MÊMES couleurs que 'Visualisations accessibles'""" - - # RÉORGANISER les données pour que l'ordre soit logique et que les couleurs correspondent - dataset_data = { - 'Split': ['Train (notre projet)', 'Validation (notre projet)', 'Test final', 'Non utilisé'], - 'Images': [2380, 595, 500, 21525], - 'Utilisation': ['Entraînement', 'Validation', 'Test', 'Non utilisé'] - } - - df = pd.DataFrame(dataset_data) - - # Couleurs dans l'ordre exact des données - colors = [ - WCAG_ACCESSIBLE_COLORS['primary_blue'], # Train (notre projet) - bleu - WCAG_ACCESSIBLE_COLORS['primary_red'], # Validation (notre projet) - rouge - WCAG_ACCESSIBLE_COLORS['primary_orange'], # Test final - orange - WCAG_ACCESSIBLE_COLORS['neutral_dark'] # Non utilisé - gris foncé - ] - - # FORCER l'ordre avec category_orders - fig = px.pie( - df, - values='Images', - names='Split', - title='Répartition interactive du dataset Cityscapes', - color_discrete_sequence=colors, - hover_data=['Utilisation'], - category_orders={'Split': ['Train (notre projet)', 'Validation (notre projet)', 'Test final', 'Non utilisé']} - ) - - # FORCER la correspondance couleur-catégorie avec color_discrete_map - color_map = { - 'Train (notre projet)': WCAG_ACCESSIBLE_COLORS['primary_blue'], - 'Validation (notre projet)': WCAG_ACCESSIBLE_COLORS['primary_red'], - 'Test final': WCAG_ACCESSIBLE_COLORS['primary_orange'], - 'Non utilisé': WCAG_ACCESSIBLE_COLORS['neutral_dark'] - } - - fig.update_traces( - marker=dict( - colors=[color_map[split] for split in df['Split']], # Forcer les couleurs exactes - line=dict(color='#FFFFFF', width=3), - pattern=dict( - shape=['|', '/', '\\', ''], - size=8, - solidity=0.3 - ) - ), - textposition='auto', - textinfo='percent+label', - textfont=dict(size=12, color='white', family="Arial Black"), - hovertemplate='%{label}
Images: %{value}
Usage: %{customdata[0]}' - ) - - fig.update_layout( - title={'x': 0.5, 'xanchor': 'center', 'font': {'size': 16, 'color': '#333333'}}, - height=500, - legend=dict( - font=dict(size=12, color='#333333'), - bgcolor='rgba(255,255,255,0.9)', - bordercolor='#333333', - ), - font=dict(color='#333333') - ) - - return fig - - -def create_accessible_bar_chart_conditional(data, labels, title, colors=None, use_patterns=False): - """ - Crée un bar chart accessible - descriptions uniquement si erreur d'affichage - """ - import matplotlib.pyplot as plt - - fig, ax = plt.subplots(figsize=(10, 6)) - - if colors is None: - colors = [WCAG_ACCESSIBLE_COLORS['primary_blue']] * len(data) - - # Motifs pour différenciation - patterns = None - if use_patterns: - pattern_list = list(ACCESSIBILITY_PATTERNS.values()) - patterns = pattern_list[:len(data)] - - try: - bars = ax.bar( - labels, - data, - color=colors, - alpha=0.8, - edgecolor='#333333', - linewidth=1 - ) - - # Ajouter des motifs - if patterns: - for bar, pattern in zip(bars, patterns): - if pattern: - bar.set_hatch(pattern) - - # Style accessible - ax.set_ylabel('Valeur', fontweight='bold', color='#333333') - ax.set_xlabel('Catégories', fontweight='bold', color='#333333') - ax.set_title(title, fontsize=14, fontweight='bold', pad=20, color='#333333') - - # Grille pour faciliter la lecture - ax.grid(axis='y', alpha=0.3, linestyle='--', color='#666666') - ax.set_axisbelow(True) - - # Valeurs sur les barres pour accessibilité - for bar, value in zip(bars, data): - height = bar.get_height() - ax.text(bar.get_x() + bar.get_width()/2., height + max(data) * 0.01, - f'{value:.1f}' if isinstance(value, float) else str(value), - ha='center', va='bottom', fontsize=10, fontweight='bold', color='#333333') - - # Style des axes - ax.spines['top'].set_visible(False) - ax.spines['right'].set_visible(False) - ax.spines['left'].set_color('#666666') - ax.spines['bottom'].set_color('#666666') - - plt.xticks(rotation=45, ha='right', color='#333333') - plt.yticks(color='#333333') - - chart_displayed = True - - except Exception as e: - # En cas d'erreur, message d'erreur - ax.text(0.5, 0.5, f"Erreur d'affichage du graphique\n{str(e)}", - ha='center', va='center', transform=ax.transAxes, - fontsize=12, color='red', weight='bold') - chart_displayed = False - - return fig, ax, chart_displayed - -# Fonction pour générer du texte alternatif (WCAG 1.1.1) -def generate_alt_text_for_pie(sizes, labels, title): - """ - Génère un texte alternatif détaillé pour un pie chart - """ - total = sum(sizes) - descriptions = [] - - for size, label in zip(sizes, labels): - percentage = (size / total) * 100 - descriptions.append(f"{label}: {size} éléments ({percentage:.1f}%)") - - alt_text = f"Graphique en secteurs: {title}. " - alt_text += f"Total de {total} éléments répartis comme suit: " - alt_text += "; ".join(descriptions) + "." - - return alt_text - -# Fonction pour générer du texte alternatif (WCAG 1.1.1) -def generate_alt_text_for_images(image_type, model_name=None, stats=None): - """Génère un texte alternatif détaillé pour les images de segmentation""" - if image_type == "original": - return "Image originale en couleur montrant une scène urbaine typique du dataset Cityscapes" - elif image_type == "segmentation": - base_text = f"Masque de segmentation sémantique prédit par le modèle {model_name or 'IA'}" - if stats: - # Ajouter les statistiques principales - main_classes = sorted(stats.items(), key=lambda x: x[1]['percentage'], reverse=True)[:3] - stats_text = ". Classes principales détectées: " + ", ".join([ - f"{name} ({data['percentage']:.1f}%)" for name, data in main_classes - ]) - return base_text + stats_text - return base_text - elif image_type == "comparison": - return "Visualisation comparative des prédictions de deux modèles de segmentation sémantique" - else: - return "Image de segmentation sémantique" - -def generate_alt_text_for_bar(data, labels, title, unit=""): - """ - Génère un texte alternatif détaillé pour un bar chart - """ - descriptions = [] - - for value, label in zip(data, labels): - descriptions.append(f"{label}: {value}{unit}") - - alt_text = f"Graphique en barres: {title}. " - alt_text += "Valeurs: " + "; ".join(descriptions) + "." - - return alt_text - -def generate_comprehensive_alt_text(image_type, model_name=None, stats=None, comparison_data=None): - """Génère un texte alternatif complet selon WCAG 1.1.1""" - if image_type == "preprocessing_original": - return "Image urbaine originale redimensionnée à la résolution d'entraînement du modèle, montrant une scène typique du dataset Cityscapes avec véhicules, piétons et infrastructure urbaine" - - elif image_type == "preprocessing_normalized": - return "Même image après normalisation des pixels pour l'entraînement du modèle IA, les couleurs peuvent paraître légèrement différentes" - - elif image_type == "preprocessing_augmented": - return "Image avec augmentations de données appliquées (rotation, changement de luminosité, etc.) pour améliorer la robustesse du modèle" - - elif image_type == "segmentation_prediction": - base_text = f"Masque de segmentation sémantique coloré prédit par le modèle {model_name or 'IA'}" - if stats: - main_classes = sorted(stats.items(), key=lambda x: x[1]['percentage'], reverse=True)[:3] - stats_text = ". Classes principales détectées : " + ", ".join([ - f"{name} ({data['percentage']:.1f}% de l'image)" for name, data in main_classes - ]) - return base_text + stats_text - return base_text - - elif image_type == "comparison_grid": - return "Grille comparative montrant l'image originale et les prédictions de segmentation de deux modèles d'IA différents côte à côte pour évaluation" - - else: - return "Image de segmentation sémantique urbaine générée par intelligence artificielle" - -def create_data_table(data, columns, title, description=""): - """ - Crée un tableau de données accessible complémentaire aux graphiques - """ - import pandas as pd - - df = pd.DataFrame(data, columns=columns) - - st.markdown(f"**Tableau de données: {title}**") - if description: - st.markdown(f"*{description}*") - - # Configuration accessible du dataframe - st.dataframe( - df, - use_container_width=True, - hide_index=True, - column_config={ - col: st.column_config.TextColumn(col, width="medium") - for col in columns - } - ) - - return df - -# CSS amélioré pour l'accessibilité complète -ENHANCED_WCAG_CSS = """ - -""" - -st.markdown(ENHANCED_WCAG_CSS, unsafe_allow_html=True) - -# En-tête principal -st.markdown(""" -
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🚗 Street Vision - Comparaison de Modèles

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Évaluation comparative SegFormer vs FPN sur le dataset Cityscapes

-
-""", unsafe_allow_html=True) - -# Fonctions utilitaires -def get_models_info(): - """Récupère les informations des deux modèles""" - 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 modèles: {response.status_code}") - return None - except Exception as e: - st.error(f"Erreur de connexion à l'API: {e}") - return None - -def get_dataset_info(): - """Récupère les informations sur le dataset""" - try: - response = requests.get(f"{API_BASE_URL}/dataset-info") - if response.status_code == 200: - return response.json() - else: - st.error(f"Erreur lors de la récupération des infos dataset: {response.status_code}") - return None - except Exception as e: - st.error(f"Erreur de connexion à l'API: {e}") - return None - -def get_train_images(): - """Récupère la liste des images d'entraînement""" - try: - response = requests.get(f"{API_BASE_URL}/train-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 get_class_repartition(): - """Récupère le graphique de répartition des classes""" - try: - response = requests.get(f"{API_BASE_URL}/class-repartition", timeout=10) - if response.status_code == 200: - return response.content - else: - return None - except Exception as e: - st.warning(f"Graphique de répartition non disponible: {e}") - return None - -def preprocess_train_image(filename, apply_augmentation=False): - """Lance le preprocessing sur une image d'entraînement""" - try: - data = { - "filename": filename, - "apply_augmentation": apply_augmentation - } - response = requests.post( - f"{API_BASE_URL}/preprocess-train-image", - json=data, - headers={"Content-Type": "application/json"} - ) - - 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 du preprocessing: {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 compare_sample_image(filename): - """Lance une comparaison sur une image d'exemple""" - try: - data = {"filename": filename} - response = requests.post( - f"{API_BASE_URL}/compare-sample", - json=data, - headers={"Content-Type": "application/json"} - ) - - 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 comparaison: {e}") - return None - -def compare_uploaded_image(uploaded_file): - """Lance une comparaison sur une image uploadée""" - try: - files = {"file": (uploaded_file.name, uploaded_file.getvalue(), uploaded_file.type)} - response = requests.post(f"{API_BASE_URL}/compare-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 comparaison: {e}") - return None - -def display_comparison_results(result, models_info, context=""): - """Affiche les résultats de comparaison avec graphiques interactifs et accessibilité complète""" - if not result or not result['success']: - st.error("Aucun résultat de comparaison disponible") - return - - # Informations des modèles - st.markdown("### 🏆 Modèles Comparés") - - col1, col2 = st.columns(2) - - with col1: - model1 = result['model1_info'] - # Formater les métriques séparément - miou1 = f"{model1.get('test_mean_iou'):.4f}" if model1.get('test_mean_iou') is not None else 'N/A' - acc1 = f"{model1.get('test_accuracy'):.4f}" if model1.get('test_accuracy') is not None else 'N/A' - - st.markdown(f""" -
-

🥇 Modèle #1

- {model1.get('architecture', 'N/A')} - {model1['encoder_name']}
- 📐 Taille: {model1['input_size']}
- 📊 mIoU: {miou1}
- 🎯 Accuracy: {acc1} -
- """, unsafe_allow_html=True) - - with col2: - model2 = result['model2_info'] - # Formater les métriques séparément - miou2 = f"{model2.get('test_mean_iou'):.4f}" if model2.get('test_mean_iou') is not None else 'N/A' - acc2 = f"{model2.get('test_accuracy'):.4f}" if model2.get('test_accuracy') is not None else 'N/A' - - st.markdown(f""" -
-

🥈 Modèle #2

- {model2.get('architecture', 'N/A')} - {model2['encoder_name']}
- 📐 Taille: {model2['input_size']}
- 📊 mIoU: {miou2}
- 🎯 Accuracy: {acc2} -
- """, unsafe_allow_html=True) - - # Comparaison des temps d'inférence - st.markdown("### ⏱️ Comparaison des Performances") - - speed_comp = result['speed_comparison'] - model1_time = result['model1_inference_time'] * 1000 - model2_time = result['model2_inference_time'] * 1000 - - col1, col2, col3 = st.columns(3) - - with col1: - winner_class = "speed-winner" if speed_comp['faster_model'] == model1['encoder_name'] else "speed-slower" - st.markdown(f""" -
-
🥇 {model1.get('architecture', 'N/A')} - {model1['encoder_name']}
- {model1_time:.1f} ms -
- """, unsafe_allow_html=True) - - with col2: - st.markdown(f""" -
-
⚡ Comparaison
- {speed_comp['faster_model']}
- est {speed_comp['speedup_ratio']}x plus rapide
- Différence: {speed_comp['time_difference_ms']:.1f} ms -
- """, unsafe_allow_html=True) - - with col3: - winner_class = "speed-winner" if speed_comp['faster_model'] == model2['encoder_name'] else "speed-slower" - st.markdown(f""" -
-
🥈 {model2.get('architecture', 'N/A')} - {model2['encoder_name']}
- {model2_time:.1f} ms -
- """, unsafe_allow_html=True) - - # Affichage des segmentations avec descriptions alternatives CONDITIONNELLES - if result.get('figure_data'): - st.markdown("### 🎨 Résultats de Segmentation") - - try: - fig_bytes = base64.b64decode(result['figure_data']) - fig = pickle.loads(fig_bytes) - - st.pyplot(fig) - plt.close(fig) - - except Exception as e: - st.error(f"Erreur lors de l'affichage: {e}") - # Description alternative SEULEMENT en cas d'erreur - alt_text = generate_comprehensive_alt_text("comparison_grid") - st.warning(f"**Description alternative:** {alt_text}") - - # Section interactive (CE2 - Graphiques interactifs) - SEULEMENT 2 ONGLETS - st.markdown("### 📊 Analyse Interactive des Résultats") - - if result.get('model1_stats') and result.get('model2_stats'): - # SUPPRIMER l'onglet "Performances" - tab1, tab2 = st.tabs(["🥧 Répartitions", "📊 Comparaison"]) - - with tab1: - col1, col2 = st.columns(2) - - with col1: - st.markdown("#### Répartition Modèle 1") - sizes1 = [data['percentage'] for data in result['model1_stats'].values()] - labels1 = list(result['model1_stats'].keys()) - - fig_pie1 = create_interactive_pie_chart(sizes1, labels1, - f"Distribution - {result['model1_info'].get('architecture', 'N/A')} {result['model1_info']['encoder_name']}", - models_info) - # AJOUTER key unique pour éviter l'erreur Streamlit - st.plotly_chart(fig_pie1, use_container_width=True, key=f"pie_model1_{context}") - - with col2: - st.markdown("#### Répartition Modèle 2") - sizes2 = [data['percentage'] for data in result['model2_stats'].values()] - labels2 = list(result['model2_stats'].keys()) - - fig_pie2 = create_interactive_pie_chart(sizes2, labels2, - f"Distribution - {result['model2_info'].get('architecture', 'N/A')} {result['model2_info']['encoder_name']}", - models_info) - # AJOUTER key unique pour éviter l'erreur Streamlit - st.plotly_chart(fig_pie2, use_container_width=True, key=f"pie_model2_{context}") - - with tab2: - st.markdown("#### Comparaison Interactive des Classes") - - fig_comparison = create_interactive_comparison_chart( - result['model1_stats'], result['model2_stats'], - f"{result['model1_info'].get('architecture', 'N/A')} {result['model1_info']['encoder_name']}", - f"{result['model2_info'].get('architecture', 'N/A')} {result['model2_info']['encoder_name']}" - ) - # AJOUTER key unique pour éviter l'erreur Streamlit - st.plotly_chart(fig_comparison, use_container_width=True, key=f"comparison_chart_{context}") - - st.info("💡 **Interactivité :** Survolez les barres, cliquez sur la légende, zoomez en sélectionnant une zone.") - # SUPPRIMER la description systématique - - # Garder seulement le tableau détaillé (sans graphique redondant) - if result.get('model1_stats') and result.get('model2_stats'): - display_class_comparison(result['model1_stats'], result['model2_stats'], - result['model1_info'], result['model2_info'], models_info) - -# def display_model_stats_chart(stats, models_info, title): -# """Affiche les statistiques d'un modèle sous forme de graphique accessible""" -# if not stats: -# st.warning("Aucune statistique disponible") -# return - -# # Utiliser l'ordre des classes du modèle pour la cohérence -# if models_info and 'class_names' in models_info: -# ordered_class_names = [name for name in models_info['class_names'] if name in stats] -# else: -# ordered_class_names = sorted(stats.keys()) - -# percentages = [stats[name]['percentage'] for name in ordered_class_names] - -# # Couleurs accessibles WCAG -# accessible_colors = [ -# WCAG_ACCESSIBLE_COLORS['neutral_medium'], # Flat: gris moyen -# WCAG_ACCESSIBLE_COLORS['primary_red'], # Human: rouge -# WCAG_ACCESSIBLE_COLORS['secondary_purple'], # Vehicle: violet -# WCAG_ACCESSIBLE_COLORS['primary_orange'], # Construction: orange -# WCAG_ACCESSIBLE_COLORS['secondary_teal'], # Object: teal -# WCAG_ACCESSIBLE_COLORS['primary_green'], # Nature: vert -# WCAG_ACCESSIBLE_COLORS['primary_blue'], # Sky: bleu -# WCAG_ACCESSIBLE_COLORS['neutral_dark'] # Void: gris foncé -# ] - -# colors = accessible_colors[:len(ordered_class_names)] - -# # Créer le graphique accessible -# fig, ax, displayed = create_accessible_bar_chart_conditional( -# percentages, ordered_class_names, title, -# colors=colors, use_patterns=True -# ) - -# # Description alternative SEULEMENT si erreur d'affichage -# if not displayed: -# alt_text = generate_alt_text_for_bar( -# percentages, ordered_class_names, title, unit="%" -# ) -# st.warning(f"**Description du graphique:** {alt_text}") - -# st.pyplot(fig) -# plt.close(fig) - -# # Tableau de données complémentaire (toujours affiché) -# table_data = [] -# for name, percentage in zip(ordered_class_names, percentages): -# pixels = stats[name]['pixels'] -# table_data.append([name, f"{percentage:.1f}%", f"{pixels:,} pixels"]) - -# create_data_table( -# table_data, -# ["Classe", "Pourcentage", "Nombre de pixels"], -# f"Données - {title}", -# "Répartition détaillée des classes de segmentation" -# ) - -def display_class_comparison(stats1, stats2, model1, model2, models_info): - """Affiche uniquement le tableau détaillé de comparaison des classes (sans graphique redondant)""" - st.markdown("### 🔎 Tableau Détaillé des Comparaisons") - - # Préparer les données - if models_info and 'class_names' in models_info: - all_classes_in_stats = set(stats1.keys()) | set(stats2.keys()) - ordered_all_classes = [name for name in models_info['class_names'] if name in all_classes_in_stats] - else: - all_classes = set(stats1.keys()) | set(stats2.keys()) - ordered_all_classes = sorted(all_classes) - - class_names = ordered_all_classes - model1_values = [stats1.get(name, {}).get('percentage', 0) for name in class_names] - model2_values = [stats2.get(name, {}).get('percentage', 0) for name in class_names] - - # SUPPRIMER tout le code de génération du graphique matplotlib - # GARDER seulement le tableau - - table_data = [] - for i, name in enumerate(class_names): - val1 = model1_values[i] - val2 = model2_values[i] - diff = val1 - val2 - - if abs(diff) < 1: - category = "🟢 Similaire" - elif abs(diff) < 5: - category = "🟡 Différence modérée" - else: - category = "🔴 Différence importante" - - table_data.append([ - name, - f"{val1:.1f}%", - f"{val2:.1f}%", - f"{diff:+.1f}%", - category - ]) - - create_data_table( - table_data, - [ - "Classe", - f"🥇 {model1.get('architecture', 'N/A')[:15]} (%)", - f"🥈 {model2.get('architecture', 'N/A')[:15]} (%)", - "Différence", - "Évaluation" - ], - "Comparaison détaillée par classe", - "Analyse des écarts de performance entre les deux modèles pour chaque classe de segmentation" - ) - -# def display_class_comparison(stats1, stats2, model1, model2, models_info): -# """Affiche une comparaison accessible des classes entre les deux modèles""" -# st.markdown("### 🔍 Analyse Comparative par Classe") - -# # Préparer les données -# if models_info and 'class_names' in models_info: -# all_classes_in_stats = set(stats1.keys()) | set(stats2.keys()) -# ordered_all_classes = [name for name in models_info['class_names'] if name in all_classes_in_stats] -# else: -# all_classes = set(stats1.keys()) | set(stats2.keys()) -# ordered_all_classes = sorted(all_classes) - -# class_names = ordered_all_classes -# model1_values = [stats1.get(name, {}).get('percentage', 0) for name in class_names] -# model2_values = [stats2.get(name, {}).get('percentage', 0) for name in class_names] - -# # Couleurs accessibles avec motifs différents -# fig, ax = plt.subplots(figsize=(12, 7)) - -# x = np.arange(len(class_names)) -# width = 0.35 - -# # Couleurs accessibles -# color1 = WCAG_ACCESSIBLE_COLORS['primary_blue'] -# color2 = WCAG_ACCESSIBLE_COLORS['primary_red'] - -# bars1 = ax.bar(x - width/2, model1_values, width, -# label=f"{model1.get('architecture', 'N/A')} - {model1['encoder_name']}", -# color=color1, alpha=0.8, edgecolor='#333333') - -# bars2 = ax.bar(x + width/2, model2_values, width, -# label=f"{model2.get('architecture', 'N/A')} - {model2['encoder_name']}", -# color=color2, alpha=0.8, edgecolor='#333333', hatch='///') - -# # Style accessible -# ax.set_xlabel('Classes', fontsize=11, fontweight='bold', color='#333333') -# ax.set_ylabel('Pourcentage (%)', fontsize=11, fontweight='bold', color='#333333') -# ax.set_title('Comparaison des classes entre les deux modèles', -# fontsize=13, fontweight='bold', pad=15, color='#333333') -# ax.set_xticks(x) - -# if len(class_names) <= 4: -# ax.set_xticklabels(class_names, fontsize=10, color='#333333') -# elif len(class_names) <= 6: -# ax.set_xticklabels(class_names, rotation=30, ha='right', fontsize=9, color='#333333') -# else: -# ax.set_xticklabels(class_names, rotation=45, ha='right', fontsize=9, color='#333333') - -# ax.legend(loc='upper right', fontsize=10) -# ax.grid(axis='y', alpha=0.3, linestyle='--', color='#666666') -# ax.set_axisbelow(True) - -# # Style des axes -# ax.spines['top'].set_visible(False) -# ax.spines['right'].set_visible(False) -# ax.spines['left'].set_color('#666666') -# ax.spines['bottom'].set_color('#666666') - -# # Valeurs sur les barres -# for bar1, bar2, val1, val2 in zip(bars1, bars2, model1_values, model2_values): -# height1, height2 = bar1.get_height(), bar2.get_height() -# ax.text(bar1.get_x() + bar1.get_width()/2., height1 + 0.5, -# f'{val1:.1f}%', ha='center', va='bottom', fontsize=8, -# fontweight='bold', color='#333333') -# ax.text(bar2.get_x() + bar2.get_width()/2., height2 + 0.5, -# f'{val2:.1f}%', ha='center', va='bottom', fontsize=8, -# fontweight='bold', color='#333333') - -# plt.tight_layout() - -# # Description alternative -# alt_text = f"Graphique comparatif en barres des classes entre {model1['encoder_name']} et {model2['encoder_name']}. " -# comparisons = [] -# for name, val1, val2 in zip(class_names, model1_values, model2_values): -# comparisons.append(f"{name}: {val1:.1f}% vs {val2:.1f}%") -# alt_text += "Comparaisons: " + "; ".join(comparisons) - -# st.markdown(f"**Description du graphique:** {alt_text}") -# st.pyplot(fig) -# plt.close(fig) - -# # Tableau détaillé accessible -# st.markdown("**🔎 Tableau détaillé des comparaisons:**") - -# table_data = [] -# for i, name in enumerate(class_names): -# val1 = model1_values[i] -# val2 = model2_values[i] -# diff = val1 - val2 - -# if abs(diff) < 1: -# category = "🟢 Similaire" -# elif abs(diff) < 5: -# category = "🟡 Différence modérée" -# else: -# category = "🔴 Différence importante" - -# table_data.append([ -# name, -# f"{val1:.1f}%", -# f"{val2:.1f}%", -# f"{diff:+.1f}%", -# category -# ]) - -# create_data_table( -# table_data, -# [ -# "Classe", -# f"🥇 {model1.get('architecture', 'N/A')[:15]} (%)", -# f"🥈 {model2.get('architecture', 'N/A')[:15]} (%)", -# "Différence", -# "Évaluation" -# ], -# "Comparaison détaillée par classe", -# "Analyse des écarts de performance entre les deux modèles pour chaque classe de segmentation" -# ) - - -# Onglets principaux -main_tab1, main_tab2 = st.tabs(["📊 Présentation Jeu de données Cityscapes", "🔄 Comparaison de performances"]) - -# ===== ONGLET 1: PRÉSENTATION DATASET ===== -with main_tab1: - st.markdown("## 📊 Analyse Exploratoire - Dataset Cityscapes") - - # Informations générales sur le dataset - dataset_info = get_dataset_info() - - if dataset_info: - # Section méthodologique MODIFIÉE - st.markdown(""" - ### 🎯 Présentation générale du dataset Cityscapes - - #### Dataset retenu - - **Cityscapes Dataset : Référence en Segmentation Urbaine** - """) - - # Spécificités techniques - st.markdown(""" - ### ⚙️ Spécificités techniques - - - **Taille Datase**: 25 000 images haute résolution - - **Résolutions d'acquisition** : Données natives 2048×1024 pixels - - **Conditions d'acquisition** : Conditions météo bonnes à moyennes, pas de conditions adverses (pluie, neige) - - **Diversité géographique** : Villes allemandes et suisses (architectures variées) - - **Annotations pixel-perfect** : Masques de segmentation précis au pixel près (Train + Val) - - Ce dataset permet d'évaluer la capacité des modèles à comprendre les scènes urbaines complexes, élément fondamental pour les applications de conduite autonome et de surveillance urbaine intelligente. - """) - - # Composition du dataset avec pie charts NOUVEAU - st.markdown("### 🗂️ Composition et utilisation du dataset") - - # Version finale qui combine : tailles uniformes + couleurs WCAG + descriptions conditionnelles - if dataset_info.get('splits'): - splits_info = dataset_info['splits'] - project_splits = dataset_info.get('project_splits', {}) - - st.markdown("#### 📊 Visualisations accessibles") - - # Option pour activer les motifs (accessibilité) - use_patterns = st.checkbox( - "🎨 Activer les motifs (accessibilité visuelle)", - help="Active les motifs en plus des couleurs pour faciliter la distinction des données", - value=True - ) - - # Organiser en 1 ligne de 4 colonnes - col1, col2, col3, col4 = st.columns(4) - - # Configuration uniforme pour tous les graphiques (SOLUTION QUI MARCHAIT) - uniform_figsize = (6, 6) - uniform_textprops = {'fontsize': 8, 'fontweight': 'bold', 'color': '#333333'} - uniform_title_props = {'fontsize': 10, 'fontweight': 'bold', 'pad': 15} - - # Couleurs WCAG accessibles - accessible_colors = { - 'blue': WCAG_ACCESSIBLE_COLORS['primary_blue'], - 'red': WCAG_ACCESSIBLE_COLORS['primary_red'], - 'orange': WCAG_ACCESSIBLE_COLORS['primary_orange'], - 'gray': WCAG_ACCESSIBLE_COLORS['neutral_medium'], - 'light_gray': WCAG_ACCESSIBLE_COLORS['neutral_light'] - } - - # Motifs pour accessibilité - patterns = ['///', '|||', '---', '...', 'xxx', None] if use_patterns else [None] * 6 - - # Données pour le tableau récapitulatif - pie_data_for_table = [] - - with col1: - # Pie chart Train (avec répartition projet) - fig_train, ax_train = plt.subplots(figsize=uniform_figsize, tight_layout=True) - - train_total = splits_info['train']['images'] - train_our_train = project_splits['train']['images'] - train_our_val = project_splits['validation']['images'] - - sizes = [train_our_train, train_our_val] - labels = [f'Notre Entraînement\n{train_our_train} images (80%)', - f'Notre Validation\n{train_our_val} images (20%)'] - colors = [accessible_colors['blue'], accessible_colors['red']] - explode = (0.05, 0.05) - - try: - wedges, texts, autotexts = ax_train.pie( - sizes, labels=labels, colors=colors, autopct='%1.0f%%', - startangle=90, explode=explode, shadow=True, - textprops=uniform_textprops, - wedgeprops={'edgecolor': 'white', 'linewidth': 2} - ) - - # Ajouter des motifs si demandé - if use_patterns: - for wedge, pattern in zip(wedges, patterns[:2]): - if pattern: - wedge.set_hatch(pattern) - - ax_train.set_title(f'Split Train Original\n{train_total} images total', - **uniform_title_props, color='#333333') - ax_train.axis('equal') - - chart_displayed = True - - except Exception as e: - ax_train.text(0.5, 0.5, f"Erreur d'affichage\n{str(e)}", - ha='center', va='center', transform=ax_train.transAxes, - fontsize=10, color='red', weight='bold') - chart_displayed = False - - # Description SEULEMENT si erreur - if not chart_displayed: - alt_text = generate_alt_text_for_pie(sizes, labels, "Split Train") - st.warning(f"**Description du graphique:** {alt_text}") - - st.pyplot(fig_train) - plt.close(fig_train) - - pie_data_for_table.extend([ - ["Train - Entraînement", train_our_train, "80%", "Entraînement des modèles"], - ["Train - Validation", train_our_val, "20%", "Validation pendant entraînement"] - ]) - - with col2: - # Pie chart Validation (utilisation complète) - fig_val, ax_val = plt.subplots(figsize=uniform_figsize, tight_layout=True) - - val_total = splits_info['validation']['images'] - val_our_test = project_splits['test']['images'] - - sizes = [val_our_test] - labels = [f'Notre Test Final\n{val_our_test} images (100%)'] - colors = [accessible_colors['orange']] - - try: - wedges, texts, autotexts = ax_val.pie( - sizes, labels=labels, colors=colors, autopct='%1.0f%%', - startangle=90, shadow=True, textprops=uniform_textprops, - wedgeprops={'edgecolor': 'white', 'linewidth': 2} - ) - - if use_patterns and patterns[2]: - wedges[0].set_hatch(patterns[2]) - - ax_val.set_title(f'Split Validation Original\n{val_total} images total', - **uniform_title_props, color='#333333') - ax_val.axis('equal') - - chart_displayed = True - - except Exception as e: - ax_val.text(0.5, 0.5, f"Erreur d'affichage\n{str(e)}", - ha='center', va='center', transform=ax_val.transAxes, - fontsize=10, color='red', weight='bold') - chart_displayed = False - - if not chart_displayed: - alt_text = generate_alt_text_for_pie(sizes, labels, "Split Validation") - st.warning(f"**Description du graphique:** {alt_text}") - - st.pyplot(fig_val) - plt.close(fig_val) - - pie_data_for_table.append(["Validation - Test", val_our_test, "100%", "Test final des modèles"]) - - with col3: - # Pie chart Test (non utilisé) - fig_test, ax_test = plt.subplots(figsize=uniform_figsize, tight_layout=True) - - test_total = splits_info['test']['images'] - - sizes = [test_total] - labels = [f'Non utilisé dans notre projet\n{test_total} images\n(annotations privées)'] - colors = [accessible_colors['gray']] - - try: - wedges, texts, autotexts = ax_test.pie( - sizes, labels=labels, colors=colors, autopct='', - startangle=90, shadow=True, textprops=uniform_textprops, - wedgeprops={'edgecolor': 'white', 'linewidth': 2} - ) - - if use_patterns and patterns[3]: - wedges[0].set_hatch(patterns[3]) - - ax_test.set_title(f'Split Test Original\n{test_total} images total', - **uniform_title_props, color='#333333') - ax_test.axis('equal') - - chart_displayed = True - - except Exception as e: - ax_test.text(0.5, 0.5, f"Erreur d'affichage\n{str(e)}", - ha='center', va='center', transform=ax_test.transAxes, - fontsize=10, color='red', weight='bold') - chart_displayed = False - - if not chart_displayed: - alt_text = generate_alt_text_for_pie(sizes, labels, "Split Test") - st.warning(f"**Description du graphique:** {alt_text}") - - st.pyplot(fig_test) - plt.close(fig_test) - - pie_data_for_table.append(["Test", 0, "0%", "Non utilisé (annotations privées)"]) - - with col4: - # Pie chart Coarse (non utilisé) - fig_coarse, ax_coarse = plt.subplots(figsize=uniform_figsize, tight_layout=True) - - coarse_total = splits_info['coarse']['images'] - - sizes = [coarse_total] - labels = [f'Non utilisé dans notre projet\n{coarse_total} images\n(annotations grossières)'] - colors = [accessible_colors['light_gray']] - - try: - wedges, texts, autotexts = ax_coarse.pie( - sizes, labels=labels, colors=colors, autopct='', - startangle=90, shadow=True, textprops=uniform_textprops, - wedgeprops={'edgecolor': 'white', 'linewidth': 2} - ) - - if use_patterns and patterns[4]: - wedges[0].set_hatch(patterns[4]) - - ax_coarse.set_title(f'Split Coarse Original\n{coarse_total} images total', - **uniform_title_props, color='#333333') - ax_coarse.axis('equal') - - chart_displayed = True - - except Exception as e: - ax_coarse.text(0.5, 0.5, f"Erreur d'affichage\n{str(e)}", - ha='center', va='center', transform=ax_coarse.transAxes, - fontsize=10, color='red', weight='bold') - chart_displayed = False - - if not chart_displayed: - alt_text = generate_alt_text_for_pie(sizes, labels, "Split Coarse") - st.warning(f"**Description du graphique:** {alt_text}") - - st.pyplot(fig_coarse) - plt.close(fig_coarse) - - pie_data_for_table.append(["Coarse", 0, "0%", "Non utilisé (annotations grossières)"]) - - - - - st.markdown("### 📊 Exploration Interactive du Dataset") - - fig_dataset = create_dataset_interactive_overview() - - try: - st.plotly_chart(fig_dataset, use_container_width=True, key="dataset_overview") - except Exception as e: - st.warning("**Description :** Graphique circulaire interactif montrant la répartition de l'utilisation du dataset Cityscapes dans notre projet.") - st.info("💡 **Interactivité :** Survolez les sections pour voir les détails, cliquez sur la légende pour masquer/afficher des catégories.") - - # Tableau de données accessible (toujours affiché) - st.markdown("#### 📋 Tableau récapitulatif des données") - - create_data_table( - pie_data_for_table, - ["Split", "Images utilisées", "Pourcentage", "Utilisation"], - "Répartition du dataset Cityscapes", - "Détail de l'utilisation de chaque split du dataset original dans notre projet" - ) - - - - # Architecture des classes - st.markdown("### 🏗️ Architecture de classes hiérarchique") - st.markdown(""" - Le dataset organise 34 classes détaillées en **8 méta-classes** pour la segmentation : - - | Méta-classe | Description | Importance | - |-------------|-------------|------------| - | **Flat** | Route, trottoir, parkings | Navigation de base | - | **Human** | Piéton, cycliste | **Sécurité critique** | - | **Vehicle** | Voiture, camion, bus, train | Obstacles dynamiques | - | **Construction** | Bâtiment, mur, clôture, pont | Structure urbaine | - | **Object** | Poteau, feu, panneau | Signalétique | - | **Nature** | Végétation, terrain | Environnement | - | **Sky** | Ciel | Contexte | - | **Void** | Zones non-définies | Exclusion | - """) - - # Graphique de répartition des classes - st.markdown("### 📊 Répartition des classes dans le dataset d'entraînement") - - class_repartition_data = get_class_repartition() - if class_repartition_data: - try: - class_repartition_image = Image.open(io.BytesIO(class_repartition_data)) - st.image( - class_repartition_image, - caption="Distribution des classes dans les images d'entraînement Cityscapes", - use_container_width=True - ) - except Exception as e: - st.warning("Impossible d'afficher le graphique de répartition des classes") - else: - st.info("Graphique de répartition des classes non disponible") - - # Section preprocessing des images - st.markdown("---") - st.markdown("### 🔧 Exemple de Preprocessing d'Images") - - # Récupérer les images d'entraînement disponibles - train_data = get_train_images() - if train_data and train_data['images']: - # Sélecteur d'image - image_options = {img['display_name']: img['filename'] for img in train_data['images']} - selected_display_name = st.selectbox( - "Choisissez une image d'entraînement pour voir les transformations:", - options=list(image_options.keys()), - help=f"{train_data['total_count']} images disponibles", - key="train_image_selector" - ) - - selected_filename = image_options[selected_display_name] - - # Preview de l'image sélectionnée (avant preprocessing) - if selected_filename and "preprocessing_result" not in st.session_state: - try: - image_url = f"{API_BASE_URL}/train-image/{selected_filename}" - response = requests.get(image_url, timeout=5) - - if response.status_code == 200: - preview_image = Image.open(io.BytesIO(response.content)) - st.image( - preview_image, - caption=f"Aperçu de l'image sélectionnée : {selected_display_name}", - use_container_width=True - ) - except: - st.info("Aperçu non disponible") - - # Options de preprocessing - col1, col2 = st.columns(2) - with col1: - show_augmentation = st.checkbox( - "Inclure l'augmentation aléatoire", - help="Affiche un exemple d'augmentation de données utilisée pendant l'entraînement. Cliquez plusieurs fois sur 'Appliquer le Preprocessing' pour tester différentes augmentations aléatoires !", - key="augmentation_checkbox" - ) - - with col2: - if st.button("🔧 Appliquer le Preprocessing", type="primary", key="apply_preprocessing"): - with st.spinner("Application des transformations..."): - result = preprocess_train_image(selected_filename, show_augmentation) - - if result and result['success']: - st.session_state.preprocessing_result = result - st.success("Preprocessing terminé!") - st.rerun() - else: - st.error("Erreur lors du preprocessing") - - # Bouton pour revenir à l'aperçu - if "preprocessing_result" in st.session_state: - if st.button("🔄 Choisir une nouvelle image", type="secondary", key="reset_preprocessing"): - del st.session_state.preprocessing_result - st.rerun() - - # Affichage des résultats de preprocessing - if "preprocessing_result" in st.session_state: - result = st.session_state.preprocessing_result - - # Affichage des images transformées sur toute la largeur - st.markdown("#### Résultats des transformations :") - - if show_augmentation and result.get('augmented_image'): - cols = st.columns(3) - col_titles = ["Image Originale (redimensionnée)", "Après Normalisation", "Avec Augmentation Aléatoire"] - col_data = [result.get('original_image'), result.get('normalized_image'), result.get('augmented_image')] - else: - cols = st.columns(2) - col_titles = ["Image Originale (redimensionnée)", "Après Normalisation"] - col_data = [result.get('original_image'), result.get('normalized_image')] - - for i, (col, title, data) in enumerate(zip(cols, col_titles, col_data)): - with col: - st.markdown(f"**{title}**") - if data: - img_data = base64.b64decode(data) - img = Image.open(io.BytesIO(img_data)) - st.image(img, use_container_width=True) - - - # Explications des transformations appliquées - if result.get('transformations_applied'): - st.markdown("#### 📋 Transformations appliquées :") - - # Préparer les transformations pour l'alignement - transforms = result['transformations_applied'] - - # Extraire les transformations de base (toujours présentes) - base_transforms = [t for t in transforms if "Resize" in t or "Normalization" in t] - augmentation_transforms = [t for t in transforms if "Resize" not in t and "Normalization" not in t] - - if show_augmentation and result.get('augmented_image'): - # 3 colonnes quand augmentation active - col1, col2, col3 = st.columns(3) - - with col1: - st.markdown("**Image Originale (redimensionnée)**") - resize_transform = next((t for t in base_transforms if "Resize" in t), "1. Resize (512x512)") - st.write(f"• {resize_transform}") - - with col2: - st.markdown("**Après Normalisation**") - norm_transform = next((t for t in base_transforms if "Normalization" in t), "2. Normalization (0-1)") - st.write(f"• {norm_transform}") - - with col3: - st.markdown("**Avec Augmentation Aléatoire**") - if augmentation_transforms: - for i, transform in enumerate(augmentation_transforms, 3): - st.write(f"• {i}. {transform}") - else: - st.write("• 3. Aucune augmentation appliquée (aléatoire)") - - else: - # 2 colonnes quand pas d'augmentation - col1, col2 = st.columns(2) - - with col1: - st.markdown("**Image Originale (redimensionnée)**") - resize_transform = next((t for t in base_transforms if "Resize" in t), "1. Resize (512x512)") - st.write(f"• {resize_transform}") - - with col2: - st.markdown("**Après Normalisation**") - norm_transform = next((t for t in base_transforms if "Normalization" in t), "2. Normalization (0-1)") - st.write(f"• {norm_transform}") - - # Explications détaillées avec description complète des augmentations - with st.expander("ℹ️ Détails des transformations possibles"): - st.markdown(""" - **Normalisation standard (toujours appliquée) :** - - **Redimensionnement** : Toutes les images sont redimensionnées à 512×512 pixels - - **Normalisation** : Les valeurs de pixels sont normalisées de [0-255] vers [0-1] - - Cette normalisation uniforme est appliquée à tous les modèles (paramètre encoder non utilisé) - - **Augmentations de données possibles (si activées) :** - - Le système sélectionne aléatoirement entre 1 et 4 transformations parmi les suivantes : - - 1. **Rotation aléatoire** : Rotation de l'image entre -15° et +15° - 2. **Flip horizontal** : Miroir horizontal avec 50% de probabilité - 3. **Changement de luminosité/contraste** : Ajustement de ±20% de la luminosité et du contraste - 4. **Flou gaussien léger** : Application d'un flou gaussien subtil (blur 1-3 pixels) - 5. **Décalage couleur** : Modification de la teinte, saturation et valeur (±10-15%) - 6. **Zoom aléatoire** : Zoom de -10% à +10% (avec probabilité réduite) - - **Important :** Les augmentations sont **aléatoires** à chaque application. Cliquez plusieurs fois sur - "Appliquer le Preprocessing" pour voir différentes combinaisons ! Le résultat peut inclure une ou - plusieurs transformations, ou même aucune selon le hasard. - - Ces transformations permettent d'améliorer la robustesse et la généralisation du modèle pendant - l'entraînement en exposant le modèle à des variations réalistes des données. - """) - else: - st.warning("Aucune image d'entraînement disponible pour la démonstration") - -# ===== ONGLET 2: COMPARAISON DE PERFORMANCES ===== -with main_tab2: - # Variables de session pour les résultats - if "sample_comparison_result" not in st.session_state: - st.session_state.sample_comparison_result = None - if "upload_comparison_result" not in st.session_state: - st.session_state.upload_comparison_result = None - - def reset_sample_comparison(): - st.session_state.sample_comparison_result = None - - def reset_upload_comparison(): - st.session_state.upload_comparison_result = None - - # Sous-onglets pour la comparaison - sub_tab1, sub_tab2 = st.tabs(["🏙️ Images Test Cityscapes", "📤 Upload Personnel"]) - - # SUB-TAB 1: Images Test Cityscapes - with sub_tab1: - st.markdown("## 🏙️ Comparaison sur Images Test Cityscapes") - st.markdown("Comparez les performances des deux modèles sur des images Cityscapes avec masques de vérité.") - - # 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: - image_url = f"{API_BASE_URL}/sample-image/{selected_filename}" - response = requests.get(image_url, timeout=5) - - if response.status_code == 200: - sample_image = Image.open(io.BytesIO(response.content)) - st.image( - sample_image, - caption=f"Aperçu: {selected_display_name}", - use_container_width=True - ) - except: - st.info("📷 Aperçu non disponible - L'image sera chargée lors de la comparaison") - - # Bouton de comparaison - if st.button("🔄 Lancer la Comparaison", key="compare_sample", type="primary"): - if selected_filename: - with st.spinner("Comparaison en cours..."): - result = compare_sample_image(selected_filename) - if result and result['success']: - st.session_state.sample_comparison_result = result - st.success("✅ Comparaison terminée!") - st.rerun() - else: - st.error("❌ Erreur lors de la comparaison") - else: - st.warning("Aucune image d'exemple disponible") - - # Affichage des résultats de comparaison - if st.session_state.sample_comparison_result: - st.markdown("---") - models_info = get_models_info() - display_comparison_results(st.session_state.sample_comparison_result, models_info, "sample") - - # Bouton de reset - if st.button("🔄 Nouvelle Comparaison", key="reset_sample", type="secondary"): - reset_sample_comparison() - st.rerun() - - # SUB-TAB 2: Upload Personnel - with sub_tab2: - st.markdown("## 📤 Comparaison sur Image Personnelle") - st.markdown("Uploadez une image pour comparer les performances des deux modèles.") - - 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 comparaison - if st.button("🔄 Lancer la Comparaison", key="compare_upload", type="primary"): - with st.spinner("Comparaison en cours..."): - result = compare_uploaded_image(uploaded_file) - if result and result['success']: - st.session_state.upload_comparison_result = result - st.success("✅ Comparaison terminée!") - st.rerun() - else: - st.error("❌ Erreur lors de la comparaison") - - # Affichage des résultats de comparaison - if st.session_state.upload_comparison_result: - st.markdown("---") - models_info = get_models_info() - display_comparison_results(st.session_state.upload_comparison_result, models_info, "upload") - - # Bouton de reset - if st.button("🔄 Nouvelle Comparaison", key="reset_upload", type="secondary"): - reset_upload_comparison() - st.rerun() - -# Sidebar avec informations des modèles -with st.sidebar: - st.markdown("## 🔧 Informations des Modèles") - - models_info = get_models_info() - if models_info: - st.success("✅ Modèles chargés") - - # Modèle 1 - best_model = models_info.get('best_model', {}) - st.markdown("**🥇 Meilleur Modèle:**") - st.write(f"**Architecture:** {best_model.get('architecture', 'N/A')}") - st.write(f"**Encoder:** {best_model.get('encoder_name', 'N/A')}") - st.write(f"**Résolution:** {best_model.get('input_size', 'N/A')}") - - # Modèle 2 - second_model = models_info.get('second_best_model', {}) - st.markdown("**🥈 Deuxième Modèle:**") - st.write(f"**Architecture:** {second_model.get('architecture', 'N/A')}") - st.write(f"**Encoder:** {second_model.get('encoder_name', 'N/A')}") - st.write(f"**Résolution:** {second_model.get('input_size', 'N/A')}") - else: - st.error("❌ Modèles non disponibles") - - st.markdown("---") - st.markdown("## 🎯 Classes Segmentées (IoU)") - - # Affichage avec carrés colorés et IoU - if models_info and 'class_names' in models_info and 'class_colors' in models_info: - class_names = models_info['class_names'] - class_colors = models_info['class_colors'] - - # Récupérer les IoU des modèles - best_ious = best_model.get('class_ious', {}) if models_info else {} - second_ious = second_model.get('class_ious', {}) if models_info else {} - - for i, (class_name, color) in enumerate(zip(class_names, class_colors)): - color_hex = f"#{color[0]:02x}{color[1]:02x}{color[2]:02x}" - - # Construire la chaîne IoU - iou_str = "" - if best_ious.get(class_name) is not None and second_ious.get(class_name) is not None: - best_iou = best_ious[class_name] - second_iou = second_ious[class_name] - iou_str = f" (🥇{best_iou:.2f} - 🥈{second_iou:.2f})" - elif best_ious.get(class_name) is not None: - best_iou = best_ious[class_name] - iou_str = f" (🥇{best_iou:.2f})" - elif second_ious.get(class_name) is not None: - second_iou = second_ious[class_name] - iou_str = f" (🥈{second_iou:.2f})" - - st.markdown(f""" -
-
- {i}. {class_name}{iou_str} -
- """, unsafe_allow_html=True) - -# Footer -st.markdown("---") -st.markdown(""" -
- 🚗 Street Vision - Comparaison de Modèles - - Évaluation comparative de modèles de segmentation sémantique
- Développé avec FastAPI, Streamlit et PyTorch -
-""", unsafe_allow_html=True) - - -with st.sidebar: - st.markdown("---") - st.markdown("## ♿ Conformité WCAG 2.1") - - criteria_status = { - "1.1.1 Contenu non textuel": "✅", - "1.4.1 Utilisation de la couleur": "✅", - "1.4.3 Contraste (minimum)": "✅", - "1.4.4 Redimensionnement du texte": "✅", - "2.4.2 Titre de page": "✅" - } - - for criterion, status in criteria_status.items(): - st.write(f"{status} {criterion}") - - st.markdown("**Niveau :** AA | **Référence :** [WCAG 2.1](https://www.w3.org/WAI/WCAG21/quickref/)") - diff --git a/cityscapes_config.json b/cityscapes_config.json deleted file mode 100644 index 4b61838..0000000 --- a/cityscapes_config.json +++ /dev/null @@ -1,63 +0,0 @@ -{ - "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 deleted file mode 100644 index f05e457..0000000 --- a/docker-compose.yml.example +++ /dev/null @@ -1,47 +0,0 @@ -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/Cordts_The_Cityscapes_Dataset_CVPR_2016_paper.pdf b/docs/Cordts_The_Cityscapes_Dataset_CVPR_2016_paper.pdf deleted file mode 100644 index 5145822..0000000 --- a/docs/Cordts_The_Cityscapes_Dataset_CVPR_2016_paper.pdf +++ /dev/null @@ -1,21162 +0,0 @@ -%PDF-1.3 -1 0 obj -<< -/Kids [ 3 0 R 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R 12 0 R 13 0 R ] -/Type /Pages -/Count 11 ->> -endobj -2 0 obj -<< -/Title (The Cityscapes Dataset for Semantic Urban Scene Understanding) -/Producer (PyPDF2) -/Author (Marius Cordts\054 Mohamed Omran\054 Sebastian Ramos\054 Timo Rehfeld\054 Markus Enzweiler\054 Rodrigo Benenson\054 Uwe Franke\054 Stefan Roth\054 Bernt Schiele) -/Subject (2016 IEEE Conference on Computer Vision and Pattern 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(dataset) -369.98500 (to) -370.00400 (tr) 14.99010 (ain) -370 (and) -370.00200 (test) ] TJ -T* -[ (appr) 44.99370 (oac) 14.98340 (hes) -348.01400 (for) -346.98300 (pixel\055le) 14.98770 (vel) -347.98200 (and) -348.00600 (i) 0.98758 (nstance\055le) 14.99750 (vel) -347.98400 (semantic) -347.99600 (la\055) ] TJ -11.95590 TL -T* -[ (beling) 15.00300 (\056) -618.01200 (Cityscapes) -351.98300 (is) -352.98600 (comprised) -351.99200 (of) -352.98800 (a) -352.01500 (lar) 36.99140 (g) 10.00320 (e) 9.99343 (\054) -378.99300 (diver) 10.00810 (se) -352.01700 (set) -352.99000 (of) ] TJ -11.95510 TL -T* -[ (ster) 36.98950 (eo) -253.01600 (video) -251.98900 (sequences) -253.00700 (r) 37.01960 (ecor) 37.01590 (ded) -251.99100 (in) -253.00400 (str) 36.99380 (eets) -253.01200 (fr) 44.98510 (om) ] TJ -/R55 9.96260 Tf -190.55600 0 Td -(50) Tj -/R53 9.96260 Tf -12.47930 0 Td -[ (dif) 18.01660 (fer) 36.98400 (ent) ] TJ --203.03500 -11.95510 Td -(cities\056) Tj -/R55 9.96260 Tf -29.75590 0 Td -(5000) Tj -/R53 9.96260 Tf -23.46520 0 Td -[ (of) -354.98700 (these) -356.00900 (ima) 10.01300 (g) 10.00320 (es) -354.99800 (have) -354.99800 (high) -355.01700 (quality) -355.98700 (pixel\055le) 14.98770 (vel) ] TJ --53.22110 -11.95510 Td -(annotations\073) Tj -/R55 9.96260 Tf -54.44490 0 Td -[ (20) -166.98200 (000) ] TJ -/R53 9.96260 Tf -30.11410 0 Td -[ (additional) -356 (ima) 10.01300 (g) 10.00320 (es) -356.01700 (have) -356.01700 (coar) 10.01060 (se) -356.01700 (anno\055) ] TJ --84.55900 -11.95470 Td -[ (tations) -392.99000 (to) -392.98100 (enable) -394 (methods) -393.00500 (that) -393.00800 (le) 14.98030 (ver) 15.01470 (a) 10.00320 (g) 10.00320 (e) -392.99800 (lar) 36.99140 (g) 10.00320 (e) -392.99800 (volumes) -393.99600 (of) ] TJ -T* -[ (weakly\055labeled) -387.98400 (data\056) -722.99200 (Crucially) 55.01290 (\054) -422.00600 (our) -387.01800 (ef) 18 (fort) -387.98900 (e) 19.99180 (xceeds) -387.98200 (pr) 36.98650 (e) 15.01220 (vi\055) ] TJ -11.95590 TL -T* -[ (ous) -326.00800 (attempts) -325.98800 (in) -326.01200 (terms) -326.00600 (of) -326.01200 (dataset) -325.99300 (size) 10.01300 (\054) -344.99900 (annotation) -326.00700 (ric) 14.98770 (hness\054) ] TJ -11.95510 TL -T* -[ (scene) -325.00600 (variability) 55.01900 (\054) -344.99900 (and) -324.99000 (comple) 20.00770 (xity) 55.01660 (\056) -535.98700 (Our) -324.99000 (accompanying) -325.99200 (em\055) ] TJ -T* -[ (pirical) -381.00100 (study) -381.01500 (pr) 44.98390 (o) 10.00320 (vides) -380.00500 (an) -380.98500 (in\055depth) -381.00800 (analysis) -380.99800 (of) -380.98300 (the) -380.98800 (dataset) ] TJ -T* -[ (c) 15.01280 (har) 14.99380 (acteristics\054) -241.01600 (as) -239.00800 (well) -238.99600 (as) -239.00800 (a) -239.01400 (performance) -240.00900 (e) 15.01220 (valuation) -239.01600 (of) -239.00700 (se) 15.01960 (v\055) ] TJ -T* -[ (er) 15.01890 (al) -249.98500 (state\055of\055the\055art) -249.98600 (appr) 44.99370 (oac) 14.98400 (hes) -249.99000 (based) -249.98000 (on) -249.98800 (our) -249.98000 (benc) 15.01960 (hmark\056) ] TJ -/R38 11.95520 Tf -35.24380 TL -T* -[ (1\056) -249.99000 (Intr) 18.01460 (oduction) ] TJ -/R40 9.96260 Tf -11.95510 -18.92930 Td -[ (V) 59.99310 (isual) -338.98100 (scene) -337.98500 (understanding) -338.99700 (has) -338.01500 (mo) 14.99500 (v) 14.98280 (ed) -339.00200 (from) -338.01200 (an) -339.00200 (elusi) 24.99830 (v) 14.98280 (e) ] TJ --11.95510 -11.95470 Td -[ (goal) -411.01700 (to) -411.01700 (a) -410.99500 (focus) -410.00100 (of) -410.98900 (much) -411.01100 (recent) -410.99400 (research) -411.00300 (in) -411.01700 (computer) -411.00700 (vi\055) ] TJ -11.95510 TL -T* -[ (sion) -242.01500 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 72.44840 152.73100 Tm -(27) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 82.41090 152.73100 Tm -[ (\135\056) -307.98800 (Semantic) -242.00500 (reasoning) -242.01700 (about) -243 (the) -241.99100 (contents) -242.01100 (of) -241.99600 (a) -242.98400 (scene) ] TJ --32.29880 -11.95510 Td -[ (is) -459.98800 (thereby) -459.99100 (done) -459.99100 (on) -459.99200 (se) 25.01670 (v) 14.98280 (eral) -460.00700 (le) 25.01790 (v) 14.98280 (els) -459.99300 (of) -460 (abstraction\056) -940.01000 (Scene) ] TJ -11.95510 TL -T* -[ (recognition) -410.99500 (aims) -409.98900 (to) -411.01700 (determine) -411.00900 (the) -410.00300 (o) 14.98280 (v) 14.98280 (erall) -410.98800 (scene) -410.99300 (cate) 15.01220 (gory) ] TJ -11.95590 TL -T* -[ (by) -257.00600 (putting) -257.01300 (emphasis) -256.00300 (on) -257.00600 (understanding) -257.01100 (its) -256.99600 (global) -256.99100 (properties\054) ] TJ -/R53 9.96260 Tf -11.95510 TL -T* -[ (e) 15.01280 (\056g) ] TJ -/R40 9.96260 Tf -11.74610 0 Td -[ (\056) -360.99600 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 71.26290 104.91000 Tm -(46) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 81.22540 104.91000 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 85.74840 104.91000 Tm -(82) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 95.71090 104.91000 Tm -[ (\135\056) -642.01100 (Scene) -360.01200 (labeling) -361.01000 (methods\054) -388.99400 (on) -360.00900 (the) -360.99100 (other) -360.98900 (hand\054) ] TJ --45.59880 -11.95510 Td -[ (seek) -305.00500 (to) -304.01500 (identify) -305.01800 (the) -303.98100 (indi) 25 (vidual) -304.98300 (constituent) -303.98100 (parts) -304.99800 (of) -303.98600 (a) -305.01300 (whole) ] TJ -11.95510 TL -T* -[ (scene) -323.00700 (as) -323.98300 (well) -322.99300 (as) -323.00400 (their) -324.01200 (interrelations) -323.00600 (on) -324.01500 (a) -323.01000 (more) -322.99500 (local) -324.01500 (pix) 14.99500 (el\055) ] TJ -258.75000 400.87200 Td -[ (and) -295.98500 (instance\055le) 24.99340 (v) 14.98280 (el\054) ] TJ -/R53 9.96260 Tf -77.81800 0 Td -[ (e) 15.01220 (\056g) ] TJ -/R40 9.96260 Tf -11.74610 0 Td -[ (\056) -295.98700 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 407.18300 481.87200 Tm -(41) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 417.14600 481.87200 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 421.23000 481.87200 Tm -(71) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 431.19300 481.87200 Tm -[ (\135\056) -448.00400 (Specialized) -296.00500 (object\055centric) ] TJ --122.33100 -11.95510 Td -[ (methods) -275.01500 (f) 9.99588 (all) -273.98400 (some) 24.99580 (where) -275.01800 (in) -275.00300 (between) -275.00800 (by) -273.98400 (focusing) -274.98800 (on) -275.00300 (detect\055) ] TJ -11.95510 TL -T* -[ (ing) -231.98300 (a) -232.00500 (certain) -231.99800 (subset) -232.01500 (of) -231.99800 (\050mostly) -232 (dynamic\051) -232.01500 (scene) -232 (constituents\054) ] TJ -/R53 9.96260 Tf -T* -[ (e) 15.01220 (\056g) ] TJ -/R40 9.96260 Tf -11.74610 0 Td -[ (\056) -194.98500 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 328.35900 446.00700 Tm -(6) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 333.34000 446.00700 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 336.22000 446.00700 Tm -(12) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 346.18200 446.00700 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 349.06100 446.00700 Tm -(13) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 359.02400 446.00700 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 361.90300 446.00700 Tm -(15) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 371.86600 446.00700 Tm -[ (\135\056) -291.99000 (Despite) -195.98700 <7369676e690263616e74> -195.01600 (adv) 24.98110 (ances\054) -206.00500 (visual) -196.02100 (scene) ] TJ --63.00350 -11.95630 Td -[ (understanding) -323 (remains) -324.00500 (challenging\054) -341.00700 (particularly) -322.98000 (when) -324.01500 (tak\055) ] TJ -11.95510 TL -T* -[ (ing) -250.02000 (human) -250.01500 (performance) -249.99700 (as) -249.99700 (a) -250.00200 (reference\056) ] TJ -11.95510 -15.13090 Td -[ (The) -329 (resurrection) -330.01100 (of) -329.00100 (deep) -329.98400 (learning) -328.98400 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 463.37200 406.96500 Tm -(34) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 473.33400 406.96500 Tm -[ (\135) -329.00900 (has) -330.01800 (had) -329 (a) -329.98900 (major) ] TJ --164.47200 -11.95510 Td -[ (impact) -365.01500 (on) -364.00800 (the) -364.99100 (current) -364.99500 (state\055of\055the\055art) -364.00800 (in) -364.98800 (machine) -364.98300 (learning) ] TJ -11.95470 TL -T* -[ (and) -354.99500 (computer) -355.99700 (vision\056) -626.01600 (Man) 14.98770 (y) -354.99500 (top\055performing) -356.01900 (methods) -355.00400 (in) -356.00900 (a) ] TJ -T* -[ (v) 24.98110 (ariety) -231.99300 (of) -231.99800 (applications) -231.01800 (are) -232.01000 (no) 24.98600 (w) 10.00320 (adays) -231.99100 (b) 20.00160 (uilt) -232.01500 (around) -231.98600 (deep) -232 (neu\055) ] TJ -T* -[ (ral) -394.01300 (netw) 10.00810 (orks) -395.02000 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 366.97400 359.14500 Tm -(30) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 376.93700 359.14500 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 381.67900 359.14500 Tm -(41) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 391.64100 359.14500 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 396.39400 359.14500 Tm -(66) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 406.35600 359.14500 Tm -[ (\135\056) -743.01300 (A) -394.00800 (major) -395.01700 (contrib) 20.01630 (uting) -393.98300 (f) 9.99343 (actor) -394.99300 (to) ] TJ --97.49410 -11.95590 Td -[ (their) -203.01300 (success) -203.01500 (is) -201.99300 (the) -203.01800 (a) 19.99180 (v) 24.98110 (ailability) -202.98100 (of) -202.98600 (lar) 17.99700 (ge\055scale\054) -212.00400 (publicly) -202.98600 (a) 19.99180 (v) 24.98110 (ail\055) ] TJ -11.95510 TL -T* -[ (able) -376.01300 (datasets) -375.99600 (such) -376.00300 (as) ] TJ -/R53 9.96260 Tf -89.68400 0 Td -[ (Ima) 10.00320 (g) 10.00320 (eNet) ] TJ -/R40 9.96260 Tf -42.46090 0 Td -(\133) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 444.32500 335.23400 Tm -(59) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 454.28700 335.23400 Tm -(\135\054) Tj -/R53 9.96260 Tf -9.86914 0 Td -[ (P) 89.98990 (ASCAL) -376.01600 (V) 30 (OC) ] TJ -/R40 9.96260 Tf -61.86800 0 Td -(\133) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 529.34200 335.23400 Tm -(14) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 539.30400 335.23400 Tm -(\135\054) Tj -/R53 9.96260 Tf --230.44200 -11.95510 Td -[ (P) 89.98990 (ASCAL\055Conte) 19.98200 (xt) ] TJ -/R40 9.96260 Tf -72.48400 0 Td -(\133) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 384.66400 323.27900 Tm -(45) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 394.62600 323.27900 Tm -[ (\135\054) -396.99300 (and) ] TJ -/R53 9.96260 Tf -27.81210 0 Td -[ (Micr) 44.98270 (osoft) -367.98800 (COCO) ] TJ -/R40 9.96260 Tf -73.29690 0 Td -(\133) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 499.05300 323.27900 Tm -(38) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 509.01500 323.27900 Tm -[ (\135) -367.98300 (that) -366.98300 (al\055) ] TJ --200.15300 -11.95510 Td -[ (lo) 24.98850 (w) -249.99500 (deep) -249.99500 (neural) -249.99300 (netw) 10.00810 (orks) -249.98300 (to) -249.98800 (de) 25.01540 (v) 14.98280 (elop) -249.98300 (their) -249.98800 (full) -249.98300 (potential\056) ] TJ -11.95510 -15.13090 Td -[ (Despite) -369.99700 (the) 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Tf --35.16800 -14.23200 Td -(dynamic) Tj -/R107 4.98130 Tf -27.44880 2.50781 Td -[ (2) -0.59794 ] TJ -ET -Q -/R69 gs -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -0 1 -1 0 62.46720 638.43500 Tm -[ (number) -250.00600 (of) -249.99500 (pix) 14.99740 (els) ] TJ -1 0 0 1 115.47100 589.23500 Tm -[ (Figure) -249.98500 (1\056) -250 (Number) -250.01200 (of) -249.99300 <026e656c79> -250.01400 (annotated) -250.01900 (pix) 14.99470 (els) -249.98400 (\050y\055axis\051) -249.98400 (per) -250.01100 (class) -249.99700 (and) -249.99700 (their) -249.99500 (associated) -249.98400 (cate) 14.98110 (gories) -250 (\050x\055axis\051\056) ] TJ -/R40 9.96260 Tf --65.35900 -21.86600 Td -[ (tailored) -389.01400 (for) -390.01500 (autonomous) -388.98200 (dri) 24.98480 (ving) -389.99100 (in) -388.98200 (an) -388.99400 (urban) -390.00600 (en) 39.99330 (vironment) ] TJ -11.95510 TL -T* -[ (and) -237.99400 (in) 40.00560 (v) 20.00160 (olving) -238.98500 (a) -238.00400 (much) -239 (wider) -237.99200 (range) -238.00200 (of) -239.01800 (highly) -238.00400 (comple) 14.99750 (x) -239.01400 (inner) 20.00650 (\055) ] TJ -T* -[ (city) -326.98900 (street) -326.01500 (scenes) -327 (that) -326.99000 (were) -325.99300 (recorded) -327.00900 (in) -326.01200 (50) -326.99400 (dif) 24.98600 (ferent) -327.00700 (cities\056) ] TJ -11.95590 TL -T* -[ (Cityscapes) -206.98800 <7369676e690263616e746c79> -207.00300 (e) 15.01220 (xceeds) -206.98100 (pre) 25.01300 (vious) -206.99500 (ef) 25.00810 (forts) -207.01200 (in) -207.01200 (terms) -207.00700 (of) ] TJ -11.95510 TL -T* -[ (size\054) -293.98300 (annotation) -285.01200 (richness\054) -294.01000 (and\054) -293.98200 (more) -285.98200 (importantly) 64.98430 (\054) -293.98800 (re) 15.00980 (g) 4.98446 (arding) ] TJ -T* -[ (scene) -253.01800 (comple) 14.99870 (xity) -252.01200 (and) -253.01000 (v) 24.98110 (ariability) 65.00390 (\056) -317.98400 (W) 79.98790 (e) -252.00200 (go) -253.00700 (be) 15.01710 (yond) -252.99700 (pix) 14.99500 (el\055le) 25.01790 (v) 14.98280 (el) ] TJ -T* -[ (semantic) -210.01900 (labeling) -208.99600 (by) -209.99400 (also) -209.99100 (consider) 0.99616 (ing) -209.98700 (instance\055le) 24.99340 (v) 14.98280 (el) -210.00200 (seman\055) ] TJ -T* -[ (tic) -279.98300 (labeling) -278.98500 (in) -280.01900 (both) -280.01000 (our) -279.98500 (annotations) -279.00700 (and) -279.98800 (e) 25.01050 (v) 24.98110 (aluation) -280.00500 (metrics\056) ] TJ -T* -[ (T) 79.99030 (o) -338.01700 (f) 9.99527 (acilitate) -338.00400 (research) -337.99500 (on) -338.01200 (3D) -338.01500 (scene) -337.98500 (un) -1.00964 (de) 0.98023 (rstanding\054) -361.00300 (we) -337.98500 (also) ] TJ -11.95630 TL -T* -[ (pro) 14.98460 (vide) -249.98500 (depth) -250.01800 (information) -249.99400 (through) -250.00700 (stereo) -249.99000 (vision\056) ] TJ -11.95510 -11.95470 Td -[ (V) 111.00400 (ery) -287.99400 (recently) 65.01730 (\054) -287.98900 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 123.86500 447.81700 Tm -(75) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 133.82700 447.81700 Tm -[ (\135) -289.01400 (announced) -288.01300 (a) -287.99600 (ne) 25.01670 (w) -287.98900 (semantic) -288.98600 (scene) -287.99400 (la\055) ] TJ --83.71520 -11.95510 Td -[ (beling) -296.98400 (dataset) -296.99700 (for) -297.01000 (sub) 20.01260 (urban) -295.98200 (traf) 25.01180 <0263> -297 (scenes\056) -451.01500 (It) -297.00400 (pro) 14.98520 (vides) -296.99000 (tem\055) ] TJ -11.95510 TL -T* -[ (porally) -405.98900 (consistent) -406.99000 (3D) -406.00300 (semantic) -405.98600 (instance) -405.99400 (annotations) -406.98600 (with) ] TJ -T* -[ (2D) -269.00600 (annotations) -269.98900 (obtained) -268.98800 (through) -268.98500 (back\055projection\056) -367.98800 (W) 79.98660 (e) -270 (con\055) ] TJ -T* -[ (sider) -261.00500 (our) -261.00900 (ef) 25.00810 (forts) -261.00300 (to) -261.00300 (be) -261.01400 (complementary) -261.01600 (gi) 24.98850 (v) 14.98280 (en) -261.01500 (the) -261.00800 (dif) 24.98600 (ferences) ] TJ -11.95590 TL -T* -[ (in) -201.99300 (the) -203.01700 (w) 10.00140 (ay) -202.00500 (that) -201.99100 (semantic) -203 (annotations) -202 (are) -202.01800 (obtained\054) -212.01400 (and) -202 (in) -203.01300 (the) ] TJ -11.95510 TL -T* -[ (type) -302.99500 (of) -303.98700 (scenes) -303.00500 (considered\054) ] TJ -/R53 9.96260 Tf -109.19400 0 Td -(i\056e) Tj -/R40 9.96260 Tf -9.68320 0 Td -[ (\056) -303.00600 (sub) 20.01260 (urban) ] TJ -/R53 9.96260 Tf -44.85900 0 Td -(vs) Tj -/R40 9.96260 Tf -8.29883 0 Td -[ (\056) -303.00600 (inner) 20.00650 (\055city) -304.01500 (traf\055) ] TJ --172.03500 -11.95470 Td -[ <02632e> -499.00300 (T) 79.99030 (o) -313.00200 (maximize) -313.01500 (syner) 18.00560 (gies) -312.01300 (between) -313 (both) -312.98500 (datasets\054) -328.98200 (a) -313.01200 (com\055) ] TJ -T* -[ (mon) -232.01800 (label) -231.99000 <6465026e6974696f6e> -231.98800 (that) -231.98600 (allo) 24.99090 (ws) -233.01100 (for) -232.00200 (cross\055dataset) -231.98100 (e) 25.01050 (v) 24.98110 (aluation) ] TJ -T* -[ (has) -249.99100 (been) -249.99600 (mutually) -249.99300 (agreed) -250.00400 (upon) -250.01700 (and) -249.99100 (implemented\056) ] TJ -/R38 11.95520 Tf -23.50900 TL -T* -[ (2\056) -249.99000 (Dataset) ] TJ -/R40 9.96260 Tf -11.95470 -18.92890 Td -[ (Designing) -362.01800 (a) -363.00300 (lar) 17.99700 (ge\055scale) -361.98100 (dataset) -362.98600 (requires) -362.01100 (a) -361.98400 (multitude) -362.98600 (of) ] TJ --11.95470 -11.95510 Td -(decisions\054) Tj -/R53 9.96260 Tf -44.51870 0 Td -[ (e) 15.01280 (\056g) ] TJ -/R40 9.96260 Tf -11.74610 0 Td -[ (\056) -446.98200 (on) -447.99400 (t) 0.98758 (he) -448.00200 (modalities) -447.01500 (of) -446.98300 (data) -448.00100 (recording\054) -495.98100 (data) ] TJ --56.26480 -11.95510 Td -[ (preparation\054) -268.01700 (and) -263.98900 (the) -263.98800 (annotation) -265.01600 (protocol) 1.00718 (\056) -352.99700 (Our) -263.99000 (choices) -265.00500 (were) ] TJ -11.95510 TL -T* -[ (guided) -242.01500 (by) -241.98900 (the) -243.01100 (ultimate) -242.00100 (goal) -241.98700 (of) -241.99800 (enabling) -242.99400 <7369676e690263616e74> -241.98900 (progress) ] TJ -T* -[ (in) -249.98500 (the) -249.99000 <02656c64> -250.01500 (of) -249.99600 (semantic) -250.01200 (urban) -249.99000 (scene) -250 (understanding\056) ] TJ -/R38 10.95890 Tf -19.52420 TL -T* -[ (2\0561\056) -250.00500 (Data) -249.99000 <737065636902636174696f6e73> ] TJ -/R40 9.96260 Tf -11.95470 -17.93200 Td -[ (Our) -495.01100 (data) -495.99200 (recording) -495.00300 (and) -495.00900 (annotation) -495.01800 (methodology) -496.01800 (w) 10 (as) ] TJ --11.95470 -11.95510 Td -[ (carefully) -231.99600 (designed) -230.99600 (to) -231.98800 (capture) -232 (the) -231.99300 (high) -230.99800 (v) 24.98110 (ariability) -232.00500 (of) -231.99800 (outdoor) ] TJ -11.95590 TL -T* -[ (street) -205.01600 (scenes\056) -295.00300 (Se) 24.98600 (v) 14.98280 (eral) -206.01000 (hundreds) -205.00100 (of) -206.00400 (thousands) -204.98500 (of) -204.98300 (frames) -206.00200 (were) ] TJ -11.95510 TL -T* -[ (acquired) -297.98300 (from) -296.99800 (a) -297.99400 (mo) 14.99500 (ving) -296.98700 (v) 14.98280 (ehicle) -297.98300 (during) -296.98500 (the) -297.98200 (span) -296.99700 (of) -297.98700 (se) 25.01790 (v) 14.98280 (eral) ] TJ -T* -[ (months\054) -283.00800 (co) 15.01770 (v) 14.98280 (ering) -277.00300 (spring\054) -284.00400 (summer) 39.98230 (\054) -283.00900 (and) -277.00600 (f) 9.99588 (all) -277.00300 (in) ] TJ -/R55 9.96260 Tf -182.79900 0 Td -(50) Tj -/R40 9.96260 Tf -12.71990 0 Td -[ (cities\054) -282.99000 (pri\055) ] TJ --195.51900 -11.95470 Td -[ (marily) -335.01800 (in) -336.01000 (German) 14.98650 (y) -334.99800 (b) 20.00160 (ut) -336.01000 (also) -334.98900 (in) -336.01000 (neighboring) -335 (countries\056) -567.01600 (W) 79.98660 (e) ] TJ -T* -[ (deliberately) -355.01600 (did) -354.98200 (not) -354.00200 (record) -355.00400 (in) -354.98700 (adv) 14.98400 (erse) -355.00600 (weather) -355.00200 (conditions\054) ] TJ -11.95590 TL -T* -[ (such) -316.01400 (as) -315.00400 (hea) 19.99300 (vy) -316.01600 (rain) -315.98200 (or) -315.98500 (sno) 24.99220 (w) 65.01120 (\054) -331.98100 (as) -315.98500 (we) -315.00700 (belie) 24.98600 (v) 14.98280 (e) -315.99100 (such) -316.01300 (conditions) ] TJ -11.95510 TL -T* -[ (to) -249.98500 (require) -249.99600 (specialized) -249.98200 (techniques) -250.01000 (and) -249.99100 (datasets) -250.01700 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 237.14000 116.86500 Tm -(51) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 247.10200 116.86500 Tm -(\135\056) Tj --185.03600 -11.95510 Td -[ (Our) -323 (camera) -323.01300 (system) -323.01200 (and) -322.99900 (post\055processing) -324.01700 <726503656374> -323 (the) -322.99800 (cur) 19.99420 (\055) ] TJ --11.95470 -11.95510 Td -[ (rent) -507.00900 (state\055of\055the\055art) -507.98300 (in) -507.00100 (the) -507.00600 (automoti) 24.98110 (v) 14.98280 (e) -507.99900 (domain\056) -1081.98000 (Images) ] TJ -11.95510 TL -T* -[ (were) -468.99000 (recorded) -468.98500 (with) -470.01700 (an) -469.01900 (automoti) 24.98110 (v) 14.98280 (e\055grade) ] TJ -/R55 9.96260 Tf -174.57500 0 Td -[ (22) -166.98200 (c) -0.39944 (m) -0.30142 ] TJ -/R40 9.96260 Tf -29.02770 0 Td -(baseline) Tj -55.14730 486.36900 Td -[ (stereo) -354.99200 (camera) -355.98700 (using) ] TJ -/R113 6.97380 Tf -84.76600 2.04180 Td -[ (1) -0.50062 ] TJ -/R115 9.96260 Tf -2.86367 -2.04180 Td -(\075) Tj -/R113 6.97380 Tf -4.42812 0 Td -[ (3) -0.50062 ] TJ -/R55 9.96260 Tf -5.63203 0 Td -[ (i) -0.79889 (n) -0.60284 ] TJ -/R40 9.96260 Tf -11.84410 0 Td -(CMOS) Tj -/R55 9.96260 Tf -31.77380 0 Td -[ (2) -166.98700 (M) -0.70086 (P) -0.60284 ] TJ -/R40 9.96260 Tf -26.09610 0 Td -[ (sensors) -355.01900 (\050OnSemi) ] TJ --167.40400 -11.95510 Td -[ (AR0331\051) -458.00900 (with) -457 (rolling) -458.00700 (shutters) -458.01200 (at) -457.01700 (a) -458.00700 (frame\055rate) -458.01200 (of) ] TJ -/R55 9.96260 Tf -210.23600 0 Td -[ (17) -166.98200 (Hz) -0.39699 ] TJ -/R40 9.96260 Tf -23.52300 0 Td -(\056) Tj --233.75900 -11.95590 Td -[ (The) -249.01200 (sensors) -250.01700 (were) -248.98800 (mounted) -248.98300 (behind) -250.01200 (the) -249.00700 (windshield) -249.01200 (and) -249.99300 (yield) ] TJ -T* -[ (high) -205.00300 (dynamic\055range) -205.00500 (\050HDR\051) -205.01000 (images) -205.00500 (with) ] TJ -/R55 9.96260 Tf -161.28000 0 Td -[ (16) -166.98200 (b) -0.59794 (i) -0.80379 (t) -0.90181 (s) -0.40189 ] TJ -/R40 9.96260 Tf -29.77110 0 Td -[ (linear) -205.01500 (color) ] TJ --191.05100 -11.95510 Td -[ (depth\056) -419.00900 (Each) ] TJ -/R55 9.96260 Tf -51.56800 0 Td -[ (16) -166.98200 (b) -0.60039 (i) -0.79889 (t) -0.90181 ] TJ -/R40 9.96260 Tf -26.65200 0 Td -[ (stereo) -285.98700 (image) -286.99600 (pair) -285.98700 (w) 10.00320 (as) -285.99100 (subsequently) -285.98200 (de\055) ] TJ --78.21990 -11.95510 Td -[ (bayered) -339.00500 (and) -338.99700 <72656374690265642e> -576.00200 (W) 79.98660 (e) -339.00700 (relied) -338.99200 (on) -338.99200 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 461.53900 507.59300 Tm -(31) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 471.50200 507.59300 Tm -[ (\135) -339.00700 (for) -339.00200 (e) 15.01220 (xtrinsic) -339.01200 (and) ] TJ --162.63900 -11.95470 Td -[ (intrinsic) -291.01300 (calibration\056) -432.01600 (T) 79.99160 (o) -291.00500 (ensure) -289.98600 (calibration) -290.98100 (accurac) 14.99750 (y) -291.00500 (we) -291.01000 (re\055) ] TJ -11.95510 TL -T* -[ (calibrated) -249.98800 (on\055site) -250.01500 (before) -250.00200 (each) -250.00700 (recording) -249.98800 (session\056) ] TJ -11.95470 -12.83710 Td -[ (F) 14.99260 (or) -775.00500 (comparability) -775.98300 (and) -775.00300 (compatibility) -774.97900 (with) -776.00300 (e) 15.01220 (xisting) ] TJ --11.95470 -11.95510 Td -[ (datasets) -385.99400 (we) -386.01600 (also) -386 (pro) 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-/R40 9.96260 Tf -1 0 0 1 149.01000 429.09700 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 153.31400 429.09700 Tm -(80) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 163.27700 429.09700 Tm -[ (\135\056) -545.00800 (W) 79.98790 (e) -327.98900 (map) -328.99200 (those) -328.00400 (labels) -328.00700 (to) -328.99200 (our) ] TJ --113.16400 -11.95470 Td -[ (high\055le) 24.99830 (v) 14.98280 (el) -323.00200 (cate) 15.01100 (gories) -322.98900 (and) -322.99900 (analyze) -323.00500 (this) -323.01800 (consolidated) -323.00500 (set\056) -530.01700 (In) ] TJ -11.95510 TL -T* -[ (comparison\054) -371 (Cityscapes) -347.00300 (pro) 14.98520 (vides) -346.98300 <7369676e690263616e746c79> -347.01800 (more) -346.99100 (anno\055) ] TJ -11.95630 TL -T* -[ (tated) -302.99800 (images\054) ] TJ -/R53 9.96260 Tf -56.25590 0 Td -(i\056e) Tj -/R40 9.96260 Tf -9.68320 0 Td -(\056) Tj -/R55 9.96260 Tf -5.51172 0 Td -(5000) Tj -/R40 9.96260 Tf -22.94610 0 Td 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Td -[ (SC\051) -377.99100 (results) -377.01100 (in) -378.00300 (roughly) ] TJ -/R55 9.96260 Tf -95.31290 0 Td -[ (10) -166.98200 (\045) -0.30019 ] TJ -/R40 9.96260 Tf -23.68910 0 Td -(IoU) Tj -/R40 6.97380 Tf -15.49180 -1.49414 Td -(class) Tj -/R40 9.96260 Tf -14.05510 1.49414 Td -[ (\054) -410.00800 (as) -378.01300 (sho) 24.99340 (wn) -376.98900 (in) -378.00300 (T) 79.99160 (ab) 39.99330 (\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 278.89000 152.73100 Tm -(3) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 283.87100 152.73100 Tm -(\056) Tj --233.75900 -11.95510 Td -[ (These) -287.98500 (lo) 24.98850 (w) -287.00800 (scores) -287.98600 (emphasize) -288.01200 (t) 0.98758 (he) -287.99000 (high) -288.00800 (di) 24.98850 (v) 14.98280 (ersity) -286.99400 (of) -287.98900 (our) -287.98400 (data\056) ] TJ -11.95510 TL -T* -[ (SC) -310 (and) -311.00100 (SF) -309.99800 (ha) 19.99790 (ving) -310.98500 (similar) -309.99500 (performance) -311.00700 (indicates) -310.00200 (the) -311 (v) 24.98110 (alue) ] TJ -11.95590 TL -T* -[ (of) -386.99200 (our) -386.00700 (additional) -386.99300 (coarse) -387 (annotations\056) -719.99000 (Ev) 14.98770 (en) -386.01400 (if) -386.98900 (the) -386.98700 (ground) ] TJ -11.95510 TL -T* -[ (truth) -411.00900 (\050GT\051) -410.99000 (se) 15.01830 (gments) -410.99900 (are) -411.00300 <72652d636c61737369026564> -411.00700 (using) -411 (the) -412.00200 (most) -411 (fre\055) ] TJ -T* -[ (quent) -282.01400 (training) -280.98300 (label) -281.98100 (\050SF) -282.00600 (or) -281.99100 (SC\051) -280.98700 (within) -281.99700 (each) -282 (se) 15.01960 (gment) -282.00700 (mask\054) ] TJ -T* -[ (the) -249.99000 (performance) -249.99700 (does) -249.98500 (not) -250.02000 (notably) -250.00600 (increase\056) ] TJ -270.70500 629.03700 Td -[ (Secondly) 65.00140 (\054) -323.98300 (we) -309.99000 (re\055classify) -310.01700 (each) -309.01700 (ground) -310.00700 (truth) -308.98800 (se) 15.01710 (gment) -310.00200 (us\055) ] TJ --11.95470 -11.95510 Td -[ (ing) -377.01800 (FCN\0558s) -378.01500 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 363.98600 698.08200 Tm -(41) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 373.94800 698.08200 Tm -(\135\054) Tj -/R53 9.96260 Tf -9.88086 0 Td -(c\056f) Tj -/R40 9.96260 Tf -11.13830 0 Td -[ (\056) -376.99300 (Sec\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 421.85600 698.08200 Tm -(3\0564) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 434.30900 698.08200 Tm -[ (\056) -691.99700 (W) 79.98660 (e) -377 (compute) -377.99100 (the) -376.98600 (a) 19.99180 (v) 14.98280 (erage) ] TJ --125.44700 -11.95510 Td -[ (scores) -313.98200 (within) -313.01200 (each) -313.99400 (se) 15.01960 (gment) -314.00200 (and) -313.98200 (assign) -312.98200 (the) -314.01600 (maximizing) -314.01100 (la\055) ] TJ -11.95470 TL -T* -[ (bel\056) -499.00500 (The) -313.00200 (performance) -313.00700 (is) -312.99400 <7369676e690263616e746c79> -312.98700 (better) -312.99700 (than) -312.99200 (the) -312.99700 (static) ] TJ -T* -[ (predictors) -330.00400 (b) 20.00160 (ut) -330.01100 (still) -329.98900 (f) 9.99588 (ar) -329.99100 (from) ] TJ -/R55 9.96260 Tf -114.09400 0 Td -[ (100) -167.01600 (\045) -0.30387 ] TJ -/R40 9.96260 Tf -24.90700 0 Td -[ (\056) -549.98200 (W) 79.98660 (e) -329.98900 (conclude) -330.00900 (that) -330.00900 (it) -330.00900 (is) ] TJ --139.00100 -11.95630 Td -[ (necessary) -297.98700 (to) -298.01600 (optimize) -297.01400 (both) -298.00400 <636c6173736902636174696f6e> -297.98000 (and) -297.98500 (se) 15.01710 (gmentation) ] TJ -T* -[ (quality) -250.00500 (at) -249.99500 (the) -249.99000 (same) -249.98800 (time\056) ] TJ -11.95470 -12.62730 Td -[ (Thirdly) 64.99900 (\054) -559.98300 (we) -497.99800 (e) 25.01050 (v) 24.98110 (aluate) -497.98800 (the) -497.98600 (performance) -499.01500 (of) -497.99500 (subsampled) ] TJ --11.95470 -11.95470 Td -[ (ground) -371.01400 (truth) -371.99400 (annotations) -370.99200 (as) -370.99700 (predictors\056) -673.98100 (Subsampling) -371.98700 (w) 10.00320 (as) ] TJ -T* -[ (done) -425.99800 (by) -425.99800 (majority) -424.99000 (v) 20.00160 (oting) -426.01700 (of) -426.00700 (neighboring) -426.00200 (pix) 14.99750 (els\054) -469.99000 (follo) 24.99580 (wed) ] TJ -T* -[ (by) -360.00900 (resampling) -359.98200 (back) -360.01600 (to) -360.00600 (full) -360.00400 (resolution\056) -640.00600 (This) -361.01300 (yields) -359.99400 (an) -360.01800 (up\055) ] TJ -11.95630 TL -T* -[ (per) -327.98700 (bound) -329.01900 (on) -328.01400 (the) -328.01600 (performance) -329.00400 (at) -327.98000 (a) -327.98900 <0278> 14.99750 (ed) -327.98400 (output) -329.00900 (resolution) ] TJ -11.95470 TL -T* -[ (and) -428.00200 (is) -427.01500 (particularly) -427.98300 (rele) 25.01050 (v) 24.98110 (ant) -427.01700 (for) -428.00700 (deep) -426.98800 (learning) -427.98700 (approaches) ] TJ -T* -[ (that) -415.01400 (often) -416 (apply) -415.01200 (do) 24.98600 (wnscaling) -416.01900 (due) -414.98500 (to) -416 (constraints) -414.99000 (on) -416 (time\054) ] TJ -T* -[ (memory) 64.99650 (\054) -289.00800 (or) -281.00900 (the) -281.98500 (netw) 10.00810 (ork) -281.00500 (architecture) -281.00700 (itself\056) -403.98700 (Do) 24.98600 (wnsampling) ] TJ -T* -[ (f) 9.99588 (actors) ] TJ -/R55 9.96260 Tf -30.16680 0 Td -(2) Tj -/R40 9.96260 Tf -8.14023 0 Td -(and) Tj -/R55 9.96260 Tf -17.54490 0 Td -(4) Tj -/R40 9.96260 Tf -8.14102 0 Td -[ (correspond) -316.99100 (to) -316.99600 (the) -316.99600 (most) -317.01600 (common) -317.01100 (setting) -317.01100 (of) ] TJ --63.99300 -11.95590 Td -[ (our) -277.00500 (3) ] TJ -/R40 6.97380 Tf -21.02580 3.61602 Td -(rd) Tj -/R40 9.96260 Tf -6.30703 -3.61602 Td -[ (\055party) -277.00500 (baselines) -278.00800 (\050Sec\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 424.99200 518.08200 Tm -(3\0564) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 437.44500 518.08200 Tm -[ (\051\056) -392.99300 (Note) -277.00300 (that) -278.01700 (while) -276.99300 (subsam\055) ] TJ --128.58300 -11.95510 Td -[ (pling) -246.00800 (by) -245.00800 (a) -246.00300 (f) 9.99588 (actor) -245.99800 (of) ] TJ -/R55 9.96260 Tf -78.54180 0 Td -(2) Tj -/R40 9.96260 Tf -7.43008 0 Td -[ (hardly) -245.98300 (af) 25.00560 (fects) -245.01800 (the) -245.98800 (IoU) -245.99300 (score\054) -247.01800 (it) -245.00300 (clearly) ] TJ --85.97190 -11.95510 Td -[ (decreases) -317.01300 (the) -318.01800 (iIoU) -316.99400 (score) -317.98600 (gi) 24.98600 (v) 14.98280 (en) -317.00600 (its) -318.00600 (comparati) 24.99580 (v) 14.98280 (ely) -316.99600 (lar) 17.99700 (ge) -317.98600 (im\055) ] TJ -11.95510 TL -T* -[ (pact) -281.99000 (on) -281.98200 (small\054) -289.99800 (b) 20.00160 (ut) -281 (ne) 25.01540 (v) 14.98280 (ertheless) -282.01700 (important) -281.99200 (objects\056) -405.98600 (This) -282.00700 (un\055) ] TJ -T* -[ (derlines) -231 (the) -231.01300 (importance) -230.99600 (of) -232 (the) -231.01000 (separate) -230.98100 (instance\055normalized) ] TJ -T* -[ (e) 25.01050 (v) 24.98110 (aluation\056) -417 (The) -285.00600 (do) 24.98600 (wnsampling) -286.02100 (f) 9.99343 (actors) -285.98700 (of) ] TJ -/R55 9.96260 Tf -168.25200 0 Td -(8) Tj -/R40 9.96260 Tf -4.98086 0 Td -(\054) Tj -/R55 9.96260 Tf -5.42500 0 Td -(16) Tj -/R40 9.96260 Tf -9.96289 0 Td -[ (\054) -295.00500 (and) ] TJ -/R55 9.96260 Tf -22.65630 0 Td -(32) Tj -/R40 9.96260 Tf -12.80900 0 Td -(are) Tj --224.08600 -11.95590 Td -[ (moti) 25.00810 (v) 24.98110 (ated) -307.00500 (by) -308.01700 (the) -307 (corresponding) -307.99300 (strides) -306.98300 (of) -307.98800 (the) -306.99800 (FCN) -307.99800 (model\056) ] TJ -T* -[ (The) -215.01800 (performance) -214.98400 (of) -214.98100 (a) -214.98900 (GT) -215.01600 (do) 24.98600 (wnsampling) -215.01300 (by) -215.01300 (a) -214.98900 (f) 9.99343 (actor) -216.00300 (of) ] TJ -/R55 9.96260 Tf -217.50000 0 Td -(64) Tj -/R40 9.96260 Tf -12.10470 0 Td -(is) Tj --229.60500 -11.95510 Td -[ (comparable) -294.01500 (to) -294.99700 (the) -293.98300 (current) -295.01000 (state) -294.02000 (of) -293.99000 (the) -295 (art\054) -305.00800 (while) -294.99000 (do) 24.98600 (wnsam\055) ] TJ -T* -[ (pling) -282.00200 (by) -283.00200 (a) -281.99700 (f) 9.99588 (actor) -283.01200 (of) ] TJ -/R55 9.96260 Tf -80.36800 0 Td -(128) Tj -/R40 9.96260 Tf -17.75700 0 Td -[ (is) -282.01700 (the) -283.00200 (smallest) -281.99200 (\050po) 24.98110 (wer) -283.01700 (of) ] TJ -/R55 9.96260 Tf -101.80700 0 Td -(2) Tj -/R40 9.96260 Tf -4.98203 0 Td -[ (\051) -281.99700 (do) 24.98600 (wn\055) ] TJ --204.91400 -11.95510 Td -[ (sampling) -249.99300 (for) -250 (which) -249.98300 (all) -249.98800 (images) -250.01700 (ha) 19.99670 (v) 14.98280 (e) -250.00200 (a) -250.00200 (distinct) -249.99700 (labeling\056) ] TJ -11.95470 -12.62700 Td -[ (Lastly) 64.99410 (\054) -509.01100 (we) -458.00400 (emplo) 9.98363 (y) ] TJ -/R55 9.96260 Tf -82.34730 0 Td -(128) Tj -/R40 9.96260 Tf -14.94370 0 Td -[ (\055times) -458.01700 (subsampled) -457.01700 (annotations) ] TJ --109.24600 -11.95590 Td -[ (and) -313.00200 (retrie) 25.01540 (v) 14.98280 (e) -311.99200 (the) -313 (nearest) -313.00400 (training) -311.99700 (annotation) -313.00700 (in) -312.01700 (terms) -312.98700 (of) -313.00700 (the) ] TJ -T* -[ (Hamming) -224.00200 (distance\056) -300.98900 (The) -223.99700 (full) -223.98700 (resolution) -224.98200 (v) 14.98280 (ersion) -223.98700 (of) -224.00200 (this) -224.01700 (train\055) ] TJ -T* -[ (ing) -284.99400 (annotation) -285.01400 (is) -286.01800 (then) -285 (used) -285.00100 (as) -285.01100 (prediction\054) -293.99500 (resulting) -285.01600 (in) ] TJ -/R55 9.96260 Tf -216.32400 0 Td -[ (21) -166.98200 (\045) -0.30387 ] TJ -/R40 9.96260 Tf --216.32400 -11.95470 Td -(IoU) Tj -/R40 6.97380 Tf -15.49180 -1.49492 Td -(class) Tj -/R40 9.96260 Tf -14.05510 1.49492 Td -[ (\056) -844.01400 (While) -427.99200 (outperforming) 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-62.02700 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (7) -1336.92000 (8) -0.90126 (2) -0.90126 ] TJ -/R131 8.96640 Tf -25.80430 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (8) -1336.92000 (9) -0.90126 (2) -0.90126 ] TJ -/R131 8.96640 Tf -25.80430 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (1) -1336.92000 (8) -0.90126 (3) -0.90126 ] TJ -/R131 8.96640 Tf -25.80510 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55898 0 Td -[ (9) -0.90126 ] TJ -/R40 8.96640 Tf --219.66100 -10.95900 Td -[ (GT) -249.99300 (subsampled) -249.99100 (by) ] TJ -/R70 8.96640 Tf -69.98200 0 Td -[ (1) -0.89990 (6) -4862.88000 (8) -0.89990 (4) -0.89854 ] TJ -/R131 8.96640 Tf -62.02700 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (6) -1336.92000 (7) -0.90126 (0) -0.90126 ] TJ -/R131 8.96640 Tf -25.80430 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (8) -1336.92000 (8) -0.90126 (7) -0.90126 ] TJ -/R131 8.96640 Tf -25.80430 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (4) -1336.92000 (7) -0.90126 (2) -0.90126 ] TJ -/R131 8.96640 Tf -25.80510 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55898 0 Td -[ (9) -0.90126 ] TJ -/R40 8.96640 Tf --219.66100 -10.95780 Td -[ (GT) -249.99300 (subsampled) -249.99100 (by) ] TJ -/R70 8.96640 Tf -69.98200 0 Td -[ (3) -0.89990 (2) -4862.88000 (7) -0.89990 (5) -0.89854 ] TJ -/R131 8.96640 Tf -62.02700 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (4) -1336.92000 (5) -0.90126 (3) -0.90126 ] TJ -/R131 8.96640 Tf -25.80430 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (7) -1336.92000 (8) -0.90126 (0) -0.90126 ] TJ -/R131 8.96640 Tf -25.80430 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (2) -1336.92000 (5) -0.90126 (8) -0.90126 ] TJ -/R131 8.96640 Tf -25.80510 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55898 0 Td -[ (1) -0.90126 ] TJ -/R40 8.96640 Tf --219.66100 -10.95900 Td -[ (GT) -249.99300 (subsampled) -249.99100 (by) ] TJ -/R70 8.96640 Tf -69.98200 0 Td -[ (6) -0.89990 (4) -4862.88000 (6) -0.89990 (3) -0.89854 ] TJ -/R131 8.96640 Tf -62.02700 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (8) -1336.92000 (3) -0.90126 (5) -0.90126 ] TJ -/R131 8.96640 Tf -25.80430 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (1) -1336.92000 (7) -0.90126 (1) -0.90126 ] TJ -/R131 8.96640 Tf -25.80430 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (0) -1336.92000 (3) -0.90126 (9) -0.90126 ] TJ -/R131 8.96640 Tf -25.80390 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.56016 0 Td -[ (6) -0.90126 ] TJ -/R40 8.96640 Tf --219.66100 -10.95900 Td -[ (GT) -249.99300 (subsampled) -249.99100 (by) ] TJ -/R70 8.96640 Tf -69.98200 0 Td -[ (1) -0.89990 (2) -0.89854 (8) -4348.90000 (5) -0.89990 (0) -0.89854 ] TJ -/R131 8.96640 Tf -62.02700 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (6) -1336.92000 (2) -0.90126 (1) -0.90126 ] TJ -/R131 8.96640 Tf -25.80430 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (1) -1336.92000 (6) -0.90126 (0) -0.90126 ] TJ -/R131 8.96640 Tf -25.80430 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (6) -1336.92000 (2) -0.90126 (9) -0.90126 ] TJ -/R131 8.96640 Tf -25.80390 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.56016 0 Td -[ (9) -0.90126 ] TJ -/R40 8.96640 Tf --219.66100 -13.94800 Td -[ (nearest) -249.99200 (training) -249.98500 (neighbor) ] TJ -/R70 8.96640 Tf -122.79300 0 Td -[ (2) -0.89990 (1) -0.89854 ] TJ -/R131 8.96640 Tf -9.21602 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (3) -1593.91000 (5) -0.90126 ] TJ -/R131 8.96640 Tf -23.50000 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.56016 0 Td -[ (9) -1593.91000 (3) -0.90126 (9) -0.90126 ] TJ -/R131 8.96640 Tf -28.10820 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (7) -1336.92000 (1) -0.90126 (8) -0.90126 ] TJ -/R131 8.96640 Tf -25.80390 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.56016 0 Td -[ (6) -0.90126 ] TJ -ET -Q -7.97000 w -501.12100 5311.50000 m -2863.62000 5311.50000 l -S -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 50.11210 518.94800 Tm -[ (T) 79.98130 (able) -327.98900 (3\056) -328.98500 (Quantitati) 25.00380 (v) 14.98650 (e) -327.98000 (results) -328.01100 (of) -329.02100 (control) -328.02000 (e) 15.01100 (xperiments) -327.98200 (for) -327.98800 (semantic) ] TJ -10.95900 TL -T* -[ (labeling) -249.99000 (using) -250.01000 (the) -249.98800 (metrics) -249.99900 (presented) -249.99300 (in) -250.01400 (Sec\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 208.00200 507.98900 Tm -(3\0561) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 219.21000 507.98900 Tm -(\056) Tj -ET -Q -1 1 1 rg -501.12100 2830.88000 2362.50000 1962.42000 re -f -501.12100 4789.32000 m -2863.62000 4789.32000 l -S -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -0 1 -1 0 121.86600 452.97200 Tm -(train) Tj -12.45310 TL -T* -[ (v) 25.00660 (al) ] TJ -11.39880 TL -(coarse) ' -12.85590 TL -(sub) ' -1 0 0 1 181.62200 468.07500 Tm -[ (Classes) -3157.01000 (Cate) 15.01370 (gories) ] TJ -ET -Q -2.99000 w -1696.22000 4629.23000 m -2205.22000 4629.23000 l -S -2304.82000 4629.23000 m -2863.62000 4629.23000 l -S -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 172.88500 452.31500 Tm -[ (IoU) -1699.99000 (iIoU) -1699.98000 (IoU) -1698.99000 (iIoU) ] TJ -ET -Q -4.98000 w -501.12100 4470.63000 m -2863.62000 4470.63000 l -S -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 56.08980 436.35500 Tm -(FCN\05532s) Tj -/R276 8.96640 Tf -59.04530 0 Td -[ (X) -556.30200 (X) -0.30087 ] TJ -/R70 8.96640 Tf -56.52970 0 Td -[ (6) -0.89990 (1) -0.89854 ] TJ -/R131 8.96640 Tf -9.21523 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (3) -1567.90000 (3) -0.90126 (8) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55898 0 Td -[ (2) -1567.90000 (8) -0.90126 (2) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.56016 0 Td -[ (2) -1567.90000 (6) -0.90126 (5) -0.90126 ] TJ -/R131 8.96640 Tf -27.86990 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (4) -0.90126 ] TJ -/R40 8.96640 Tf --218.64100 -10.95900 Td -(FCN\05516s) Tj -/R276 8.96640 Tf -59.04530 0 Td -[ (X) -556.30200 (X) -0.30087 ] TJ -/R70 8.96640 Tf -56.52970 0 Td -[ (6) -0.89990 (4) -0.89854 ] TJ -/R131 8.96640 Tf -9.21523 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (3) -1567.90000 (4) -0.90126 (1) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55898 0 Td -[ (1) -1567.90000 (8) -0.90126 (4) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.56016 0 Td -[ (5) -1567.90000 (6) -0.90126 (9) -0.90126 ] TJ -/R131 8.96640 Tf -27.86990 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (2) -0.90126 ] TJ -/R40 8.96640 Tf --218.64100 -10.95900 Td -(FCN\0558s) Tj -/R276 8.96640 Tf -59.04530 0 Td -[ (X) -556.30200 (X) -0.30087 ] TJ -/R70 8.96640 Tf -56.52970 0 Td -[ (6) -0.89990 (5) -0.89854 ] TJ -/R131 8.96640 Tf -9.21523 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (3) -1567.90000 (4) -0.90126 (1) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55898 0 Td -[ (7) -1567.90000 (8) -0.90126 (5) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.56016 0 Td -[ (7) -1567.90000 (7) -0.90126 (0) -0.90126 ] TJ -/R131 8.96640 Tf -27.86990 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (1) -0.90126 ] TJ -/R40 8.96640 Tf --218.64100 -10.95900 Td -(FCN\0558s) Tj -/R276 8.96640 Tf -59.04530 0 Td -[ (X) -556.30200 (X) -0.30087 ] TJ -/R70 8.96640 Tf -38.14060 0 Td -[ (2) -1537.89000 (6) -0.89990 (1) -0.89854 ] TJ -/R131 8.96640 Tf -27.60430 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (9) -1567.90000 (3) -0.90126 (3) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55898 0 Td -[ (6) -1567.90000 (8) -0.90126 (1) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.56016 0 Td -[ (6) -1567.90000 (6) -0.90126 (0) -0.90126 ] TJ -/R131 8.96640 Tf -27.86990 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (9) -0.90126 ] TJ -/R40 8.96640 Tf --218.64100 -10.95900 Td -(FCN\0558s) Tj -/R276 8.96640 Tf -71.49920 0 Td -[ (X) -0.30087 ] TJ -/R70 8.96640 Tf -44.07580 0 Td -[ (5) -0.89990 (8) -0.89854 ] TJ -/R131 8.96640 Tf -9.21523 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (3) -1567.90000 (3) -0.90126 (7) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55898 0 Td -[ (4) -1567.90000 (8) -0.90126 (3) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.56016 0 Td -[ (4) -1567.90000 (6) -0.90126 (7) -0.90126 ] TJ -/R131 8.96640 Tf -27.86990 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (2) -0.90126 ] TJ -/R40 8.96640 Tf --218.64100 -10.95900 Td -(FCN\0558s) Tj -/R276 8.96640 Tf -83.95230 0 Td -[ (X) -0.30087 ] TJ -/R70 8.96640 Tf -31.62270 0 Td -[ (5) -0.89990 (8) -0.89854 ] TJ -/R131 8.96640 Tf -9.21523 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (0) -1567.90000 (3) -0.90126 (1) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55898 0 Td -[ (8) -1567.90000 (7) -0.90126 (8) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.56016 0 Td -[ (2) -1567.90000 (5) -0.90126 (8) -0.90126 ] TJ -/R131 8.96640 Tf -27.86990 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (4) -0.90126 ] TJ -ET -Q -501.12100 3763.08000 m -2863.62000 3763.08000 l -S -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 58.28980 365.60100 Tm -(\133) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 61.27580 365.60100 Tm -(4) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 65.75900 365.60100 Tm -[ (\135) -245.98500 (e) 15.01100 (xte) 0.99179 (nded) ] TJ -/R276 8.96640 Tf -49.37620 0 Td -[ (X) -0.30087 ] TJ -/R70 8.96640 Tf -38.14100 0 Td -[ (4) -1537.89000 (5) -0.89990 (6) -0.89854 ] TJ -/R131 8.96640 Tf -27.60390 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (1) -1567.90000 (3) -0.90126 (4) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55898 0 Td -[ (2) -1567.90000 (7) -0.90126 (9) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55898 0 Td -[ (8) -1567.90000 (6) -0.90126 (6) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (4) -0.90126 ] TJ -/R40 8.96640 Tf --216.40000 -10.95900 Td -(\133) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 61.31680 354.64200 Tm -(4) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 65.80000 354.64200 Tm -[ (\135) -249.99300 (basic) ] TJ -/R276 8.96640 Tf -49.33520 0 Td -[ (X) -0.30087 ] TJ -/R70 8.96640 Tf -38.14100 0 Td -[ (4) -1537.89000 (5) -0.89990 (7) -0.89854 ] TJ -/R131 8.96640 Tf -27.60390 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (0) -1567.90000 (3) -0.90126 (2) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55898 0 Td -[ (0) -1567.90000 (7) -0.90126 (9) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55898 0 Td -[ (1) -1567.90000 (6) -0.90126 (1) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.56016 0 Td -[ (9) -0.90126 ] TJ -/R40 8.96640 Tf --216.40000 -10.95900 Td -(\133) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 61.31720 343.68300 Tm -(40) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 70.28360 343.68300 Tm -(\135) Tj -/R276 8.96640 Tf -44.85160 0 Td -[ (X) -556.30200 (X) -555.30000 (X) -0.30087 ] TJ -/R70 8.96640 Tf -38.14100 0 Td -[ (3) -1537.89000 (5) -0.89990 (9) -0.89854 ] TJ -/R131 8.96640 Tf -27.60390 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.56016 0 Td -[ (1) -1567.90000 (2) -0.90126 (8) -0.90126 ] TJ -/R131 8.96640 Tf -27.86990 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (1) -1567.90000 (7) -0.90126 (9) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55898 0 Td -[ (5) -1567.90000 (5) -0.90126 (7) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.56016 0 Td -[ (9) -0.90126 ] TJ -/R40 8.96640 Tf --216.40000 -10.95900 Td -(\133) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 61.31720 332.72400 Tm -(81) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 70.28360 332.72400 Tm -(\135) Tj -/R276 8.96640 Tf -44.85160 0 Td -[ (X) -0.30087 ] TJ -/R70 8.96640 Tf -38.14100 0 Td -[ (2) -1537.89000 (6) -0.89990 (2) -0.89854 ] TJ -/R131 8.96640 Tf -27.60390 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.56016 0 Td -[ (5) -1567.90000 (3) -0.90126 (4) -0.90126 ] TJ -/R131 8.96640 Tf -27.86990 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (4) -1567.90000 (8) -0.90126 (2) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55898 0 Td -[ (7) -1567.90000 (6) -0.90126 (6) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55898 0 Td -[ (0) -0.90126 ] TJ -/R40 8.96640 Tf --216.39900 -10.95900 Td -(\133) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 61.31680 321.76500 Tm -(9) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 65.80000 321.76500 Tm -(\135) Tj -/R276 8.96640 Tf -49.33520 0 Td -[ (X) -556.30200 (X) -0.30087 ] TJ -/R70 8.96640 Tf -38.14100 0 Td -[ (2) -1537.89000 (6) -0.89990 (3) -0.89854 ] TJ -/R131 8.96640 Tf -27.60390 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (1) -1567.90000 (3) -0.90126 (4) -0.90126 ] TJ -/R131 8.96640 Tf -27.87030 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (5) -1567.90000 (8) -0.90126 (1) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55898 0 Td -[ (2) -1567.90000 (5) -0.90126 (8) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55898 0 Td -[ (7) -0.90126 ] TJ -/R40 8.96640 Tf --216.39900 -10.95900 Td -(\133) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 61.31680 310.80600 Tm -(48) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 70.28320 310.80600 Tm -(\135) Tj -/R276 8.96640 Tf -44.85200 0 Td -[ (X) -556.30200 (X) -555.30000 (X) -0.30087 ] TJ -/R70 8.96640 Tf -38.13980 0 Td -[ (2) -1537.89000 (6) -0.89990 (4) -0.89854 ] TJ -/R131 8.96640 Tf -27.60510 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (8) -1567.90000 (3) -0.90126 (4) -0.90126 ] TJ -/R131 8.96640 Tf -27.87030 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (9) -1567.90000 (8) -0.90126 (1) -0.90126 ] TJ -/R131 8.96640 Tf -27.86990 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.56016 0 Td -[ (3) -1567.90000 (5) -0.90126 (8) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55898 0 Td -[ (7) -0.90126 ] TJ -/R40 8.96640 Tf --216.39900 -10.95900 Td -(\133) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 61.31680 299.84700 Tm -(37) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 70.28320 299.84700 Tm -(\135) Tj -/R276 8.96640 Tf -44.85200 0 Td -[ (X) -0.30087 ] TJ -/R70 8.96640 Tf -56.52970 0 Td -[ (6) -0.89990 (6) -0.89854 ] TJ -/R131 8.96640 Tf -9.21523 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (4) -0.89990 ] TJ -/R147 8.96640 Tf -17.60900 0 Td -[ (4) -0.69977 (6) -0.69977 ] TJ -/R131 8.96640 Tf -10.61020 0 Td -[ (\072) -0.50100 ] TJ -/R147 8.96640 Tf -2.56016 0 Td -[ (7) -0.69977 ] TJ -/R70 8.96640 Tf -18.30590 0 Td -[ (8) -0.90126 (2) -0.90126 ] TJ -/R131 8.96640 Tf -9.21484 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.56016 0 Td -[ (8) -1567.90000 (6) -0.90126 (7) -0.90126 ] TJ -/R131 8.96640 Tf -27.87110 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55898 0 Td -[ (4) -0.90126 ] TJ -/R40 8.96640 Tf --216.39900 -10.95900 Td -(\133) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 61.31680 288.88800 Tm -(79) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 70.28320 288.88800 Tm -(\135) Tj -/R276 8.96640 Tf -44.85160 0 Td -[ (X) -0.30087 ] TJ -/R147 8.96640 Tf -55.48440 0 Td -[ (6) -0.69977 (7) -0.69977 ] TJ -/R131 8.96640 Tf -10.60980 0 Td -[ (\072) -0.50100 ] TJ -/R147 8.96640 Tf -2.56016 0 Td -[ (1) -0.69977 ] TJ -/R70 8.96640 Tf -18.30590 0 Td -[ (4) -0.90126 (2) -0.90126 ] TJ -/R131 8.96640 Tf -9.21523 0 Td -[ (\072) -0.50100 ] TJ -/R70 8.96640 Tf -2.55977 0 Td -[ (0) -0.90126 ] TJ -/R147 8.96640 Tf -17.60900 0 Td -[ (8) -0.69977 (6) -0.69977 ] TJ -/R131 8.96640 Tf -10.61020 0 Td -[ (\072) -0.50100 ] TJ -/R147 8.96640 Tf -2.55977 0 Td -[ (5) -1333.72000 (7) -0.69977 (1) -0.69977 ] TJ -/R131 8.96640 Tf -27.87030 0 Td -[ (\072) -0.50100 ] TJ -/R147 8.96640 Tf -2.55977 0 Td -[ (1) -0.69977 ] TJ -ET -Q -7.97000 w -501.12100 2834.87000 m -2863.62000 2834.87000 l -S -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 50.11210 271.28500 Tm -[ (T) 79.98130 (able) -247 (4\056) -246.99500 (Quantitati) 25.00380 (v) 14.98650 (e) -246.99100 (results) -246.97900 (of) -246.98700 (baselines) -247.00200 (for) -247 (semantic) -246.99700 (label) 0.98294 (ing) -247.00800 (us\055) ] TJ -10.95900 TL -T* -[ (ing) -306.99700 (t) 1.01085 (he) -306.98100 (metrics) -305.98000 (presented) -307.02100 (in) -305.99500 (Sec\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 171.93800 260.32600 Tm -(3\0561) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 183.14600 260.32600 Tm -[ (\056) -479.98200 (The) -305.98400 <02727374> -306.98900 (bl) 1.01017 (ock) -306.98100 (lists) -306.01200 (results) ] TJ --133.03400 -10.95940 Td -[ (from) -255.00800 (our) -254.00100 (o) 25.00660 (wn) -254.99900 (e) 15.01100 (xperiments\054) -254.98800 (the) -254.99800 (second) -254.02000 (from) -255.00700 (those) -253.99200 (pro) 15.01650 (vided) -255 (by) -254.98800 (3) ] TJ -/R40 5.97760 Tf -230.77300 3.80938 Td -(rd) Tj -/R40 8.96640 Tf --230.77300 -14.76840 Td -[ (parties\056) -308.01600 (All) -245.00400 (numbers) -244.98900 (are) -244.01800 (gi) 25.01470 (v) 14.98650 (en) -244.98700 (in) -245.00400 (percent) -244.98700 (and) -243.98500 (we) -244.99600 (indicate) -244.99800 (the) -244.02100 (used) ] TJ -10.95900 TL -T* -[ (training) -273.99000 (data) -274.01200 (for) -274.01200 (each) -273.99700 (method\054) ] TJ -/R53 8.96640 Tf -111.19800 0 Td -(i\056e) Tj -/R40 8.96640 Tf -8.71484 0 Td -[ (\056) -274.00500 (train) -273.99800 <026e652c> -280.01700 (v) 25.00660 (al) -274.99600 <026e652c> -280.01700 (coarse) -274.00500 (e) 15.01100 (xtra) ] TJ --119.91300 -10.95900 Td -[ (as) -234.00400 (well) -232.99800 (as) -234.00400 (a) -234.00900 (potential) -233.99300 (do) 25.00660 (wns) 0.99656 (caling) -234.01000 (f) 10.00640 (actor) -233.99000 (\050sub\051) -234.01400 (of) -233.00100 (the) -234 (input) -234.01700 (image\056) ] TJ -/R40 9.96260 Tf -33.14100 TL -T* -[ (of) -335.98100 (pix) 14.99500 (el\055wise) -334.99200 (semantic) -335.99800 (labels\054) -357.01900 (in) -334.99100 (particular) -336.01000 (through) -335.99300 (CNNs) ] TJ -11.95470 TL -(\133) ' -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 53.42970 171.39400 Tm -(4) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 58.41090 171.39400 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 61.63870 171.39400 Tm -(39) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 71.60120 171.39400 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 74.81910 171.39400 Tm -(44) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 84.78160 171.39400 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 88.00940 171.39400 Tm -(67) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 97.97190 171.39400 Tm -[ (\135) -216.00600 (or) -216.00200 (Recurrent) -215.99200 (Neural) -216 (Netw) 10.00570 (orks) -215.99100 (\050RNNs\051) -215.98400 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 262.38400 171.39400 Tm -(8) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 267.36600 171.39400 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 270.59300 171.39400 Tm -(52) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 280.55600 171.39400 Tm -(\135\056) Tj --230.44400 -11.95510 Td -[ (Furthermore\054) -285.99800 (a) -279.01700 (no) 14.98770 (v) 14.98280 (el) -279.01000 (C) 0.99371 (NN) -279.00600 (architecture) -279.01000 (e) 15.01220 (xplicitly) -279.00500 (designed) ] TJ -11.95510 TL -T* -[ (for) -249.99900 (dense) -249.99000 (prediction) -250.00700 (has) -249.99000 (been) -249.99600 (proposed) -250.01200 (recently) -249.99000 (by) -249.98800 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 258.71900 147.48400 Tm -(79) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 268.68100 147.48400 Tm -(\135\056) Tj --206.61400 -18.66290 Td -[ (Last) -217.01000 (b) 20.00160 (ut) -217.01000 (not) -217.98600 (least\054) -223.00700 (se) 25.01670 (v) 14.98280 (eral) -218.00800 (studies) -216.99100 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 197.10000 128.82100 Tm -(5) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 202.08200 128.82100 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 205.32000 128.82100 Tm -(11) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 215.28200 128.82100 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 218.53000 128.82100 Tm -(48) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 228.49200 128.82100 Tm -(\226) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 233.47300 128.82100 Tm -(50) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 243.43600 128.82100 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 246.67400 128.82100 Tm -(53) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 256.63600 128.82100 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 259.87400 128.82100 Tm -(74) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 269.83700 128.82100 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 273.08400 128.82100 Tm -(76) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 283.04700 128.82100 Tm -(\135) Tj --232.93500 -11.95630 Td -[ (lately) -371.01800 (ha) 19.99730 (v) 14.98280 (e) -371.98100 (e) 15.01220 (xplored) -371.01700 (dif) 24.98480 (ferent) -372.01900 (forms) -371.01900 (of) -370.99400 (weak) -372.01600 (supervision\054) ] TJ -11.95470 TL -T* -[ (such) -320.01300 (as) -319.98400 (bounding) -319.98300 (box) 14.99260 (es) -319.98500 (or) -319.98500 (image\055le) 24.99090 (v) 14.98280 (el) -319.98300 (labels\054) -338.00200 (for) -319.98800 (training) ] TJ -T* -[ (CNNs) -195.00100 (for) -196.00800 (pix) 14.99500 (el\055le) 25.01790 (v) 14.98280 (el) -194.98500 (semantic) -195.98200 (labeling\056) -291.02000 (W) 79.98660 (e) -196.01100 (hope) -195.01600 (our) -196 (coarse) ] TJ -T* -[ (annotations) -249.99300 (can) -250.00100 (further) -249.99400 (adv) 24.98240 (ance) -250.00600 (this) -250.01100 (area\056) ] TJ -/R38 10.95890 Tf -258.75000 629.03700 Td -[ (3\0564\056) -250.00400 (Baselines) ] TJ -/R40 9.96260 Tf -11.95510 -18.39610 Td -[ (Our) -253.99200 (o) 24.98110 (wn) -253.98900 (baseline) -255 (e) 15.01220 (xperiments) -254.00200 (\050T) 79.98660 (ab) 39.99330 (\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 467.17800 691.64100 Tm -(4) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 472.15900 691.64100 Tm -[ (\054) -255.01100 (top\051) -253.98200 (rely) -255.01100 (on) -253.98700 (fully) ] TJ --163.29700 -11.95510 Td -[ (con) 39.99820 (v) 20.00160 (olutional) -269.98900 (netw) 10.00810 (orks) -269.00200 (\050FCNs\051\054) -275.01800 (as) -269.99400 (the) 14.98770 (y) -269.98900 (are) -268.98500 (central) -269.98900 (to) -269.98400 (most) ] TJ -11.95510 TL -T* -[ (state\055of\055the\055art) -471.00700 (methods) -470.00200 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 414.51600 667.73100 Tm -(9) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 419.49700 667.73100 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 424.74700 667.73100 Tm -(37) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 434.71000 667.73100 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 439.97000 667.73100 Tm -(41) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 449.93200 667.73100 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 455.18300 667.73100 Tm -(63) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 465.14500 667.73100 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 470.39600 667.73100 Tm -(81) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 480.35800 667.73100 Tm -[ (\135\056) -971.99500 (W) 79.98660 (e) -470.98500 (adopted) ] TJ --171.49600 -11.95590 Td -[ (V) 14.98030 (GG16) -421.01300 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 347.76600 655.77500 Tm -(68) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 357.72900 655.77500 Tm -[ (\135) -422.01000 (and) -420.98400 (utilize) -421.98300 (the) -420.97900 (P) 92 (ASCAL\055conte) 14.98280 (xt) -421.01800 (setup) -421.98800 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 531.83500 655.77500 Tm -(41) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 541.79800 655.77500 Tm -(\135) Tj --232.93600 -11.95510 Td -[ (with) -375.99400 (a) -375.98100 <6d6f6469026564> -376.01100 (learning) -375.99600 (rate) -375.98100 (to) -376.00600 (match) -376 (our) -376.01100 (image) -376 (resolu\055) ] TJ -11.95510 TL -T* -[ (tion) -256.99100 (under) -257.98900 (an) -257.01600 (unnormalized) -256.98200 (loss\056) -331.99900 (According) -256.99600 (to) -257.00600 (the) -257.98600 (notation) ] TJ -T* -[ (in) -290.01800 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 322.82000 619.91000 Tm -(41) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 332.78200 619.91000 Tm -[ (\135\054) -299.98900 (we) -289.99300 (denote) -290.01500 (the) -289.98300 (dif) 24.98600 (ferent) -289.99600 (models) -291.02000 (as) ] TJ -/R53 9.96260 Tf -147.62100 0 Td -(FCN\05532s) Tj -/R40 9.96260 Tf -36.53320 0 Td -(\054) Tj -/R53 9.96260 Tf -5.48008 0 Td -(FCN\055) Tj --213.55500 -11.95470 Td -(16s) Tj -/R40 9.96260 Tf -13.83790 0 Td -[ (\054) -354.99700 (and) ] TJ -/R53 9.96260 Tf -23.74690 0 Td -(FCN\0558s) Tj -/R40 9.96260 Tf -31.55120 0 Td -[ (\054) -354.99700 (where) -335.00800 (the) -334.01300 (numbers) -333.99800 (are) -333.99300 (the) -334.99300 (stride) -334.00300 (of) -333.98300 (the) ] TJ --69.13590 -11.95630 Td -[ <026e657374> -250.98800 (heatmap\056) -310.98700 (Since) -251 (V) 14.98030 (GG16) -249.98300 (training) -250.98800 (on) ] TJ -/R55 9.96260 Tf -167.90600 0 Td -[ (2) -166.98700 (M) -0.70086 (P) -0.60284 ] TJ -/R40 9.96260 Tf -25.05120 0 Td -[ (images) -250.99700 (e) 15.01220 (x\055) ] TJ --192.95700 -11.95510 Td -[ (ceeds) -210.98700 (e) 25.01050 (v) 14.98280 (en) -210.98400 (the) -211.01600 (lar) 17.99700 (gest) -211.00900 (GPU) -209.98500 (memory) -211 (a) 19.99180 (v) 24.98110 (ailable\054) -219.00800 (we) -210.98400 (split) -210.98900 (each) ] TJ -11.95470 TL -T* -[ (image) -312.01200 (into) -310.98200 (tw) 10.00810 (o) -311.98200 (halv) 14.99010 (es) -311.00400 (with) -312.00200 (suf) 24.98600 <026369656e746c79> -311.00700 (lar) 17.99700 (ge) -311.98700 (o) 14.98280 (v) 14.98280 (erlap\056) -495.01100 (Ad\055) ] TJ -T* -[ (ditionally) 64.98180 (\054) -329.00100 (we) -313.00900 (trained) -312.99400 (a) -313.99200 (model) -312.98200 (on) -312.99700 (images) -312.98700 (do) 24.98600 (wnscaled) -312.99200 (by) -314.01600 (a) ] TJ -T* -[ (f) 9.99588 (actor) -236 (of) ] TJ -/R55 9.96260 Tf -36.13980 0 Td -(2) Tj -/R40 9.96260 Tf -4.98203 0 Td -[ (\056) -305.00500 (W) 79.98660 (e) -236.98500 <02727374> -236.01500 (train) -235.98800 (on) -237.00900 (our) -235.99500 (training) -236.01000 (set) -235.99000 (\050) ] TJ -/R53 9.96260 Tf -137.42900 0 Td -[ (tr) 14.99260 (ain) ] TJ -/R40 9.96260 Tf -19.22810 0 Td -[ (\051) -236.00500 (until) -236.99000 (the) ] TJ --197.77900 -11.95510 Td -[ (performance) -303.00800 (on) -302.99800 (our) -303 (v) 24.98110 (alidation) -303.01300 (set) -302.99800 (\050) ] TJ -/R53 9.96260 Tf -142.66700 0 Td -(val) Tj -/R40 9.96260 Tf -12.18400 0 Td -[ (\051) -303.01300 (saturates\054) -316.00600 (and) -303.00300 (then) ] TJ --154.85100 -11.95590 Td -[ (retrain) -249.99300 (on) ] TJ -/R53 9.96260 Tf -40.94570 0 Td -[ (tr) 14.99010 (ain\053val) ] TJ -/R40 9.96260 Tf -40.62730 0 Td -[ (with) -250.01500 (the) -249.98800 (same) -249.98800 (number) -250.01700 (of) -249.99700 (epochs\056) ] TJ --69.61800 -12.41800 Td -[ (T) 79.99160 (o) -487.01200 (obtain) -486.99200 (further) -487.99500 (baseline) -486.99700 (results\054) -546.00200 (we) -487.99700 (ask) 10.00320 (ed) -487.01700 (selected) ] TJ --11.95510 -11.95630 Td -[ (groups) -365.99500 (that) -366.98300 (ha) 19.99670 (v) 14.98280 (e) -365.98300 (proposed) -367.01200 (state\055of\055the\055art) -366.00800 (semantic) -367.01200 (label\055) ] TJ -T* -[ (ing) -415.99200 (approaches) -416.00400 (to) -415.99700 (optimize) -416.01400 (their) -416 (methods) -416.01400 (on) -416 (our) -416.00400 (dataset) ] TJ -T* -[ (and) -221.99700 (e) 25.01050 (v) 24.98110 (aluated) -221.01000 (their) -221.99000 (predictions) -220.98800 (on) -221.99200 (our) -221.01700 (test) -221.98700 (set\056) -300.01900 (The) -221.99700 (resulting) ] TJ -T* -[ (scores) -360.01300 (are) -359.99100 (gi) 24.98850 (v) 14.98280 (en) -360.01800 (in) -358.98700 (T) 79.99160 (ab) 39.99330 (\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 410.12300 464.02900 Tm -(4) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 418.69100 464.02900 Tm -[ (\050bottom\051) -359.98400 (and) -360.01300 (qualitati) 25.01540 (v) 14.98280 (e) -359.98400 (e) 15.01220 (xam\055) ] TJ --109.82900 -11.95510 Td -[ (ples) -233.98500 (of) -233.99800 (three) -234 (selected) -233.99300 (methods) -232.98600 (are) -234.01000 (sho) 24.99090 (wn) -233.99000 (in) -233.99000 (Fig\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 501.40900 452.07400 Tm -(5) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 506.39000 452.07400 Tm -[ (\056) -305.00300 (Interest\055) ] TJ --197.52800 -11.95510 Td -[ (ingly) -230.99100 (enough\054) -234.01700 (the) -231.01300 (perform) 0.99003 (ance) -230.99100 (ranking) -231 (in) -229.99100 (terms) -231 (of) -230 (the) -231.01000 (main) ] TJ -11.95590 TL -(IoU) ' -/R40 6.97380 Tf -15.49180 -1.49414 Td -(class) Tj -/R40 9.96260 Tf -17.34920 1.49414 Td -[ (score) -331.00400 (on) -330.01400 (Cityscapes) -331.00400 (is) -330.00900 (highly) -331.00800 (dif) 24.98600 (ferent) -331.00800 (from) -330.01400 (P) 92 (AS\055) ] TJ --32.84100 -11.95510 Td -[ (CAL) -359.01900 (V) 39.99580 (OC) -358.98200 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 359.89100 416.20800 Tm -(14) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 369.85300 416.20800 Tm -[ (\135\056) -636.01200 (While) -358.98400 (DPN) -359.01900 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 436.74200 416.20800 Tm -(40) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 446.70500 416.20800 Tm -[ (\135) -359.00400 (is) -358.98400 (the) -358.98900 (2) ] TJ -/R40 6.97380 Tf -37.84730 3.61602 Td -(nd) Tj -/R40 9.96260 Tf -11.04730 -3.61602 Td -[ (best) -358.98400 (method) ] TJ --186.73700 -11.95470 Td -[ (on) -364.00800 (P) 92 (ASCAL\054) -364.99800 (it) -364.00300 (is) -364.00500 (only) -365.01500 (the) -364.00800 (6) ] TJ -/R40 6.97380 Tf -118.56600 3.61484 Td -(th) Tj -/R40 9.96260 Tf -9.55312 -3.61484 Td -[ (best) -364.00300 (on) -364.98800 (Cityscapes\056) -652.98900 (FCN\055) ] TJ --128.11900 -11.95510 Td -[ (8s) -223.00900 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 323.25800 392.29800 Tm -(41) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 333.22100 392.29800 Tm -[ (\135) -222.00500 (is) -223.00900 (last) -221.98500 (on) -223.01200 (P) 92 (ASCAL\054) -222.00200 (b) 20.00160 (ut) -223.01200 (3) ] TJ -/R40 6.97380 Tf -104.34800 3.61484 Td -(rd) Tj -/R40 9.96260 Tf -8.52500 -3.61484 Td -[ (best) -223.00700 (on) -221.99200 (Cityscapes\056) -301.00900 (Ade\055) ] TJ --137.23200 -11.95510 Td -[ (laide) -283.98200 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 334.37600 380.34300 Tm -(37) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 344.33900 380.34300 Tm -[ (\135) -283.99400 (performs) -284.01600 (consistently) -284.01600 (well) -284.01600 (on) -283.98200 (both) -284.00700 (datasets) -284.01100 (with) ] TJ --35.47660 -11.95590 Td -(rank) Tj -/R55 9.96260 Tf -20.19410 0 Td -(1) Tj -/R40 9.96260 Tf -7.47187 0 Td -[ (on) -249.98800 (P) 92 (ASCAL) -250 (and) ] TJ -/R55 9.96260 Tf -69.10080 0 Td -(2) Tj -/R40 9.96260 Tf -7.47227 0 Td -[ (on) -249.98800 (Cityscapes\056) ] TJ --92.28400 -12.41800 Td -[ (From) -428.99700 (studying) -428 (these) -429.01700 (results\054) -472.99500 (we) -428.98700 (dra) 15.01220 (w) -428.00200 (se) 25.01540 (v) 14.98280 (eral) -428.99200 (conclu\055) ] TJ --11.95510 -11.95630 Td -[ (sions\072) -313.00900 (\0501\051) -252 (The) -251.99200 (amount) -250.98800 (of) -251.99500 (do) 24.98600 (wnscaling) -250.98800 (applied) -252.01700 (during) -252.01200 (train\055) ] TJ -11.95510 TL -T* -[ (ing) -344.98400 (and) -346.01600 (testing) -345.00600 (has) -346.01300 (a) -345.00600 (strong) -345.01600 (and) -346.01600 (consistent) -345.00100 (ne) 15.01710 (g) 4.98446 (ati) 24.98600 (v) 14.98280 (e) -345.98600 <696e03752d> ] TJ -T* -[ (ence) -325.99200 (on) -326.01400 (performance) -325.98500 (\050) ] TJ -/R53 9.96260 Tf -91.61800 0 Td -(c\056f) Tj -/R40 9.96260 Tf -11.13790 0 Td -(\056) Tj -/R53 9.96260 Tf -5.74023 0 Td -[ (FCN\0558s) -325.98500 (vs) ] TJ -/R40 9.96260 Tf -43.09880 0 Td -(\056) Tj -/R53 9.96260 Tf -5.73906 0 Td -(FCN\0558s) Tj -/R40 9.96260 Tf -34.80000 0 Td -[ (at) -325.98000 (half) -326.01900 (res\055) ] TJ --192.13400 -11.95510 Td -[ (olution\054) -364.01300 (as) -342.01900 (well) -340.99000 (as) -342.01900 (the) -340.99500 (2) ] TJ -/R40 6.97380 Tf -98.87380 3.61484 Td -(nd) Tj -/R40 9.96260 Tf -10.87420 -3.61484 Td -[ (half) -342.01600 (of) -341.00200 (the) -342.01200 (table\051\056) -584.01000 (The) -342.01600 (ranking) ] TJ --109.74800 -11.95510 Td -[ (according) -366.00800 (to) -366.98500 (IoU) ] TJ -/R40 6.97380 Tf -69.82810 -1.49492 Td -(class) Tj -/R40 9.96260 Tf -17.70590 1.49492 Td -[ (is) -366.98500 (st) 0.99493 (rictly) -367.01700 (consistent) -366.01700 (with) -367.01200 (the) -366.00800 (de) 15.01710 (gree) ] TJ --87.53400 -11.95510 Td -[ (of) -376.01300 (do) 24.98600 (wnscaling\056) -686.01000 (W) 79.98660 (e) -375.98100 (attrib) 20.01630 (ute) -376.00600 (this) -375.01100 (to) -376.00600 (the) -376.00600 (l) 0.98513 (ar) 17.98720 (ge) -376.01600 (scale) -376.01600 (v) 24.98110 (ari\055) ] TJ -11.95590 TL -T* -[ (ation) -361.99800 (present) -360.98600 (in) -362.00600 (our) -362.01100 (dataset\054) ] TJ -/R53 9.96260 Tf -118.18100 0 Td -(c\056f) Tj -/R40 9.96260 Tf -11.13830 0 Td -[ (\056) -362.01300 (Fig\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 463.65600 272.28200 Tm -(4) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 468.63700 272.28200 Tm -[ (\056) -646.00500 (This) -361.01300 (observ) 24.99090 (ation) ] TJ --159.77500 -11.95510 Td -[ (clearly) -319.00300 (indicates) -318.98100 (the) -318.99800 (demand) -318.98600 (for) 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-368.98500 (train) -368.98500 (models) -369.00600 (that) -368.00200 (are) -369.00900 (on) -368.98700 (a) -367.98300 (par) -369 (with) -369.01400 (or) -368.99500 (e) 25.01050 (v) 14.98280 (en) ] TJ -11.95590 TL -T* -[ (outperforming) -317.00200 (methods) -317.01000 (that) -316.01100 (are) -317.01800 <73706563690263616c6c79> -317.01100 (trained) -316.99400 (on) -316.99600 (an\055) ] TJ -11.95510 TL -T* -[ (other) -320.01500 (benchmark) -320.00900 (and) -320.01900 (specialized) -318.99000 (for) -319.98700 (its) -320.00500 (test) -320.00800 (data\056) -519.98700 (Further) 40.01290 (\054) ] TJ -T* -[ (our) -229.01400 (analysis) -228.99300 (sho) 24.99220 (ws) -229.01200 (that) -227.98600 (our) -229.01300 (ne) 25.01670 (w) -229.01800 (dataset) -229.00800 (inte) 14.99260 (grates) -229.01300 (well) -229.00800 (with) ] TJ -T* -[ (e) 15.01280 (xisting) -249.99400 (ones) -249.98600 (and) -249.99100 (allo) 24.99090 (ws) -249.98900 (for) -249.99900 (cross\055dataset) -250.01700 (research\056) ] TJ -/R38 11.95520 Tf -25.97770 TL -T* -[ (4\056) -249.99000 (Instance\055Le) 14.99230 (v) 9.98703 (el) -249.98800 (Semantic) -250.01100 (Labeling) ] TJ -/R40 9.96260 Tf -11.95470 -19.61800 Td -[ (The) -235.01500 (pix) 14.99500 (el\055le) 25.01790 (v) 14.98280 (el) -235.01700 (task\054) ] TJ -/R53 9.96260 Tf -83.15000 0 Td -(c\056f) Tj -/R40 9.96260 Tf -11.13830 0 Td -[ (\056) -235.01700 (Sec\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 180.40500 152.73100 Tm -(3) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 185.38600 152.73100 Tm -[ (\054) -237.99700 (does) -235.00700 (not) -235.00200 (aim) -235.00500 (to) -235.98800 (se) 15.01960 (gme) 0.99248 (n) -1.01454 (t) ] TJ --135.27400 -11.95510 Td -[ (indi) 25 (vidual) -429.00100 (object) -429.01100 (instances) 1.00351 (\056) -846.99300 (In) -428.98600 (contrast\054) -472.99700 (in) -429.01400 (the) -429.01900 (instance\055) ] TJ -11.95630 TL -T* -[ (le) 25.01850 (v) 14.98280 (el) -392.99000 (semantic) -391.98800 (labeling) -393.00400 (task\054) -427.99100 (we) -392.99400 (focus) -392.00600 (on) -392.98300 (simultaneously) ] TJ -11.95470 TL -T* -[ (detecting) -349.01700 (objects) -349.01800 (and) -348.99400 (se) 15.01830 (gmenting) -350.01800 (them\056) -607.01400 (This) -349.01800 (is) -350.00800 (an) -349 (e) 15.01220 (xten\055) ] TJ -T* -[ (sion) -298.00500 (to) -298.01600 (both) -298.00700 (traditional) -297.99000 (object) -298.01300 (detection\054) -310 (since) -298.01900 (per) 19.99420 (\055instance) ] TJ -T* -[ (se) 15.01890 (gments) -389.98200 (must) -389.98300 (be) -390.99200 (pro) 14.98520 (vided\054) -425.00600 (and) -390.00700 (semantic) -389.98900 (labeling\054) -426.01700 (since) ] TJ -T* -[ (each) -250.00500 (instance) -250.02000 (is) -249.98400 (treated) -249.99400 (as) -249.99600 (a) -250.00200 (separate) -250 (label\056) ] TJ -/R38 10.95890 Tf -258.75000 629.03700 Td -[ (4\0561\056) -250.00400 (T) 91.98300 (asks) -249.99300 (and) -250 (metrics) ] TJ -/R40 9.96260 Tf -11.95510 -17.93280 Td -[ (F) 14.99260 (or) -327.98200 (instance\055le) 24.99340 (v) 14.98280 (el) -329.00100 (semantic) -328 (labeling\054) -347.99100 (algorithms) -328.01400 (are) -329.01400 (re\055) ] TJ --11.95510 -11.95510 Td -[ (quired) -370.98200 (to) -370.98500 (deli) 24.99090 (v) 14.98280 (er) -371.00400 (a) -371.00200 (set) -370.98700 (of) -370.99700 (detections) -371.00700 (of) -370.99700 (traf) 25.01050 <0263> -370.98700 (participants) ] TJ -T* -[ (in) -352.00700 (the) -352.01200 (scene\054) -376.99600 (each) -351.98800 (associated) -352 (with) -351.99500 (a) -351.98500 <636f6e0264656e6365> -352.00500 (score) -351.98000 (and) ] TJ -T* -[ (a) -271.99900 (per) 19.99420 (\055instance) -272.01800 (se) 15.01960 (gmentation) -271.98900 (m) 0.99003 (ask\056) -376.00600 (T) 79.99160 (o) -271.98900 (assess) -272.01800 (instance\055le) 24.99090 (v) 14.98280 (el) ] TJ -T* -[ (performance\054) -327.00200 (we) -311.00900 (compute) -312.00200 (the) -310.99700 (a) 19.99180 (v) 14.98280 (erage) -311.01700 (precision) -312.00200 (on) -310.99700 (the) -312.01700 (re\055) ] TJ -11.95590 TL -T* -[ (gion) -314.98400 (le) 25.01790 (v) 14.98280 (el) -315.00400 (\050AP) -314.01100 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 374.31600 632.32800 Tm -(23) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 384.27900 632.32800 Tm -[ (\135\051) -315.01400 (for) -315.00900 (each) -315.01600 (class) -314.99600 (and) -313.98200 (a) 19.99180 (v) 14.98280 (erage) -315.01600 (it) -314.99200 (across) -315.00100 (a) ] TJ --75.41680 -11.95510 Td -[ (range) -331.98600 (of) -333 (o) 14.98280 (v) 14.98280 (erlap) -331.98400 (thresholds) -331.98400 (to) -332.99300 (a) 19.99180 (v) 20.00160 (oid) -332.00300 (a) -333.00800 (bias) -332.00800 (to) 24.98600 (w) 10.00320 (ards) -332.01800 (a) -333.00800 (spe\055) ] TJ -11.95510 TL -T* -[ <63690263> -221.99000 (v) 24.98110 (alue\056) -299.98400 <53706563690263616c6c79> 65.01370 (\054) -227.99900 (we) -222.00200 (follo) 24.99580 (w) -221.01700 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 451.30700 608.41800 Tm -(38) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 461.27000 608.41800 Tm -[ (\135) -222.00700 (and) -221.99700 (use) -221.99700 (10) -221.99200 (dif) 24.98600 (ferent) ] TJ --152.40800 -11.95510 Td -[ (o) 14.98280 (v) 14.98280 (erlaps) -396.98500 (ranging) -397.99200 (from) ] TJ -/R55 9.96260 Tf -95.13590 0 Td -(0) Tj -/R115 9.96260 Tf -4.98086 0 Td -[ (\072) -0.80379 ] TJ -/R55 9.96260 Tf -2.76797 0 Td -(5) Tj -/R40 9.96260 Tf -8.94102 0 Td -(to) Tj -/R55 9.96260 Tf -11.71020 0 Td -(0) Tj -/R115 9.96260 Tf -4.98203 0 Td -[ (\072) -0.80379 ] TJ -/R55 9.96260 Tf -2.76680 0 Td -(95) Tj -/R40 9.96260 Tf -13.92230 0 Td -[ (in) -397.02200 (steps) -397.99200 (of) ] TJ -/R55 9.96260 Tf -47.85390 0 Td -(0) Tj -/R115 9.96260 Tf -4.98086 0 Td -[ (\072) -0.80379 ] TJ -/R55 9.96260 Tf -2.76719 0 Td -(05) Tj -/R40 9.96260 Tf -9.96289 0 Td -[ (\056) -751.98700 (The) ] TJ --210.77200 -11.95470 Td -[ (o) 14.98280 (v) 14.98280 (erlap) -329.98400 (is) -330.01100 (computed) -329.99100 (at) -329.98200 (the) -330.01400 (re) 15.00730 (gion) -329.99900 (le) 25.02030 (v) 14.98280 (el\054) -350.99500 (making) -329.99400 (it) -330.00900 (equi) 24.99090 (v) 24.98110 (a\055) ] TJ -11.95510 TL -T* -[ (lent) -328.00900 (to) -326.99200 (the) -328.01600 (IoU) -328.01900 (of) -327.00200 (a) -327.98900 (single) -328 (inst) 1.00473 (ance\056) -543.01200 (W) 79.98660 (e) -327.98900 (penalize) -328.01900 (multiple) ] TJ -11.95630 TL -T* -[ (predictions) -271.99600 (of) -271.99100 (the) -273.00600 (same) -271.98400 (ground) -272.01300 (truth) -272.01300 (instance) -272.01300 (as) -273.01300 (f) 9.99343 (alse) -271.98900 (posi\055) ] TJ -11.95470 TL -T* -[ (ti) 24.99090 (v) 14.98280 (es\056) -340.01700 (T) 79.99160 (o) -259.01100 (obtain) -260.01100 (a) -260 (single\054) -262.00500 (easy) -259.99600 (to) -259.98600 (compare) -259.99100 (compound) -259.99600 (score\054) ] TJ -T* -[ (we) -271.01600 (report) -271.98600 (the) -271.00600 (mean) -271.98600 (a) 19.99180 (v) 14.98280 (erage) -270.98400 (precision) -272.00900 (AP\054) -276.99300 (obtained) -270.98900 (by) -271.98400 (also) ] TJ -T* -[ (a) 19.99180 (v) 14.98280 (eraging) -263.98500 (o) 14.98280 (v) 14.98280 (er) -264.00300 (the) -263.00800 (class) -263.98500 (label) -263.98500 (set\056) -350.99000 (As) -263.98500 (minor) -264.01000 (scores\054) -267.00400 (we) -263.99500 (add) ] TJ -(AP) ' -/R113 6.97380 Tf -12.73200 4.29102 Td -[ (5) -0.50062 (0) -0.50062 (\045) -0.49712 ] TJ -/R40 9.96260 Tf -17.45590 -4.29102 Td -[ (for) -248 (an) -247.99800 (o) 14.98280 (v) 14.98280 (erlap) -247.99800 (v) 24.98110 (alue) -247.99300 (of) ] TJ -/R55 9.96260 Tf -92.58090 0 Td -[ (50) -166.98200 (\045) -0.30387 ] TJ -/R40 9.96260 Tf -19.92620 0 Td -[ (\054) -247.99300 (as) -247.99800 (well) -247.98300 (as) -247.99800 (AP) ] TJ -/R113 6.97380 Tf -58.86090 4.29102 Td -[ (1) -0.49712 (0) -0.49712 (0) -0.49712 ] TJ -/R40 6.97380 Tf -11.91410 0 Td -(m) Tj -/R40 9.96260 Tf -8.39375 -4.29102 Td -(and) Tj --221.86400 -11.95510 Td -(AP) Tj -/R113 6.97380 Tf -12.73200 4.29102 Td -[ (5) -0.50062 (0) -0.50062 ] TJ -/R40 6.97380 Tf -7.94297 0 Td -(m) Tj -/R40 9.96260 Tf -9.29609 -4.29102 Td -[ (where) -337.98800 (the) -338.99500 (e) 25.01050 (v) 24.98110 (aluation) -337.99300 (is) -338.98800 (restricted) -338.00700 (to) -338.99200 (objects) -338.00200 (within) ] TJ -/R55 9.96260 Tf --29.97110 -11.95590 Td -[ (100) -167.01600 (m) -0.29897 ] TJ -/R40 9.96260 Tf -27.39800 0 Td -(and) Tj -/R55 9.96260 Tf -16.87620 0 Td -[ (50) -166.98200 (m) -0.29897 ] TJ -/R40 9.96260 Tf -22.41680 0 Td -[ (distance\054) -250.01700 (respecti) 24.98600 (v) 14.98280 (ely) 65.01860 (\056) ] TJ -/R38 10.95890 Tf --66.69100 -18.62810 Td -[ (4\0562\056) -250.00400 (State) -249.98600 (of) -249.98900 (the) -250.01100 (art) ] TJ -/R40 9.96260 Tf -11.95510 -17.93280 Td -[ (As) -402.00200 (detection) -403.00600 (results) -401.98700 (ha) 19.99670 (v) 14.98280 (e) -402.01600 (matured) -403.01100 (\050) ] TJ -/R55 9.96260 Tf -148.07000 0 Td -[ (70) -166.98200 (\045) -0.30387 ] TJ -/R40 9.96260 Tf -23.93520 0 Td -[ (mean) -402.00200 (AP) -403.01100 (on) ] TJ --183.96000 -11.95510 Td -[ (P) 92 (ASCAL) -337.98500 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 352.82700 440.35000 Tm -(14) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 362.78900 440.35000 Tm -(\054) Tj -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 367.16300 440.35000 Tm -(55) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 377.12500 440.35000 Tm -[ (\135\051\054) -359.98400 (the) -338.01500 (last) -338.00700 (years) -337.98300 (ha) 19.99670 (v) 14.98280 (e) -337.98800 (seen) -338.02200 (a) -339.00700 (rising) -337.99300 (inter) 20.00650 (\055) ] TJ --68.26330 -11.95510 Td -[ (est) -386.98400 (in) -386.98200 (more) -388.00400 (dif) 24.98600 <0263756c74> -387.00400 (settings\056) -721.01200 (Detections) -387.98400 (with) -387.00900 (pix) 14.99750 (el\055le) 25.01540 (v) 14.98280 (el) ] TJ -11.95510 TL -T* -[ (se) 15.01960 (gments) -323.01200 (rather) -323.00700 (than) -322.99300 (traditional) -323.00500 (bounding) -323 (box) 14.99260 (es) -323.98500 (pro) 14.98280 (vide) -322.99000 (a) ] TJ -T* -[ (richer) -363.98100 (output) -363.00300 (and) -364.01300 (allo) 24.99090 (w) -364.01500 (\050in) -364.00800 (principle\051) -363.01800 (for) -364.01800 (better) -364.00800 (occlusion) ] TJ -11.95630 TL -T* -[ (handling\056) -309.99000 (W) 79.98660 (e) -250.00200 (group) -249.98000 (e) 15.01220 (xisting) -249.99300 (methods) -250.00200 (into) -250.01200 (three) -249.99700 (cate) 15.01220 (gories\056) ] TJ -11.95510 -11.95510 Td -[ (The) -299.00400 <02727374> -298.98400 (encompasses) ] TJ -/R53 9.96260 Tf -91.94690 0 Td -[ (se) 39.99330 (gmentation\054) -310.99700 (then) -298.99400 (detection) ] TJ -/R40 9.96260 Tf -117.96200 0 Td -(and) Tj --221.86400 -11.95470 Td -[ (most) -214.01300 (prominently) -213.01100 (the) -213.99600 (R\055CNN) -214.01300 (detec) 1.01944 (tion) -214.01800 (frame) 25.01050 (w) 10.00320 (ork) -213.99900 (\133) ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 516.08400 368.61900 Tm -(21) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 526.04600 368.61900 Tm -[ (\135\054) -220.98300 (re\055) ] TJ --217.18500 -11.95510 Td -[ (lying) -311.99700 (on) -312.99700 (object) -312.01200 (proposals) -311.99700 (for) -313.00700 (gener) 1.01944 (ating) -312.98700 (detections\056) -496.98100 (Man) 14.98770 (y) ] TJ -11.95510 TL -T* -[ (of) -491.01400 (the) -491.00900 (commonly) -491.00900 (used) -491.00400 (bounding) -491.01100 (box) -491.00100 (proposal) -490.99200 (methods) ] TJ -(\133) ' -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 9.96260 Tf -1 0 0 1 312.17900 332.75400 Tm -(28) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT 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8.96640 Tf -1 0 0 1 229.65900 544.99100 Tm -(6) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 54.59490 532.90300 Tm -[ (\1336\135) -556.01000 (R\056) -422.98600 (Benenson\054) -467.02100 (M\056) -422.99400 (Mathias\054) -467.01200 (R\056) -422.98500 (T) 35.02120 (imofte\054) -467.00500 (and) -423.99800 (L\056) -423.00400 (V) 110.99400 (an) -422.99600 (Gool\056) ] TJ -15.43590 -10.95900 Td -[ (Pedestrian) -383.01400 (detection) -383.01400 (at) -381.99100 (100) -382.98500 (frames) -382.99300 (per) -383.01600 (second\056) -785.01900 (In) ] TJ -/R53 8.96640 Tf -191.67300 0 Td -(CVPR) Tj -/R40 8.96640 Tf -22.41600 0 Td -(\054) Tj --214.08900 -10.95900 Td -(2012\056) Tj -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 93.43320 510.98500 Tm -(1) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 54.59490 498.89700 Tm -[ (\1337\135) -556.01000 (G\056) -273.01300 (J\056) -272.99900 (Brosto) 25.01880 (w) 64.99010 (\054) -280.01700 (J\056) -272.99900 (F) 15.00420 (auqueur) 39.98350 (\054) -279.01500 (and) -274.00200 (R\056) -272.98900 (Cipolla\056) -435 (Semantic) -272.99200 (object) ] TJ -15.43590 -10.95900 Td -[ (classes) -397.00200 (in) -398.00600 (video\072) -604.99400 (A) -397.02100 <686967682d6465026e6974696f6e> -397.99500 (ground) -396.98400 (truth) -398.01100 (database\056) ] TJ -/R53 8.96640 Tf -10.95900 TL -T* -[ (P) 79.98130 (attern) -250.01900 (Reco) 10.02000 (gnition) -249.99600 (Letter) 9.97920 (s) ] TJ -/R40 8.96640 Tf -98.72810 0 Td -[ (\054) -250 (30\0502\051\07288\22697\054) -249.97800 (2009\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 245.45700 476.97900 Tm -(1) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 249.94100 476.97900 Tm -(\054) Tj -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 254.42400 476.97900 Tm -(3) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 258.90700 476.97900 Tm -(\054) Tj -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 263.39000 476.97900 Tm -(4) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 267.87300 476.97900 Tm -(\054) Tj -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 272.35700 476.97900 Tm -(7) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 54.59490 464.89200 Tm -[ (\1338\135) -556.01000 (W) 91.99110 (\056) -353.99100 (Byeon\054) -380.00600 (T) 74.01220 (\056) -353.99100 (M\056) -353.98700 (Breuel\054) -379.98600 (F) 80.00380 (\056) -354.99300 (Raue\054) -379.98000 (and) -353.98800 (M\056) -353.98800 (Liwicki\056) -692.98200 (Scene) ] TJ -15.43590 -10.95900 Td -[ (Labeling) -237.02100 (with) -236.98800 (LSTM) -235.98200 (Recurrent) -237.01500 (Neural) -237.00400 (Netw) 9.99010 (orks\056) -331 (In) ] TJ -/R53 8.96640 Tf -191.67300 0 Td -(CVPR) Tj -/R40 8.96640 Tf -22.41600 0 Td -(\054) Tj --214.08900 -10.95900 Td -(2015\056) Tj -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 93.43320 442.97400 Tm -(6) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 54.59490 430.88600 Tm -[ (\1339\135) -556.01000 (L\056\055C\056) -384.99400 (Chen\054) -420 (G\056) -386.02100 (P) 15.00420 (apandreou\054) -419.01900 (I\056) -385.98100 (K) 35.01710 (okkinos\054) -419.98200 (K\056) -386.02200 (Murph) 5 (y) 65 (\054) -418.99000 (and) ] TJ -15.43590 -10.95900 Td -[ (A\056) -311.00200 (L\056) -309.99500 (Y) 110.99500 (uille\056) -553.99200 (Semantic) -310.98200 (image) -309.98800 (se) 15.01510 (gmentation) -311.01600 (with) -310.00300 (deep) -311.00800 (con\055) ] TJ -10.95940 TL -T* -[ (v) 19.99650 (olutional) -236.02100 (nets) -236 (and) -237.01500 (fully) -235.99100 (connected) -236.01800 (CRFs\056) -329.98100 (In) ] TJ -/R53 8.96640 Tf -162.68100 0 Td -(ICLR) Tj -/R40 8.96640 Tf -19.43010 0 Td -[ (\054) -236.01600 (2015\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 279.63300 408.96800 Tm -(5) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 284.11600 408.96800 Tm -(\054) Tj -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 70.03090 398.00900 Tm -(6) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 74.51410 398.00900 Tm -(\054) Tj -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 78.99730 398.00900 Tm -(7) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 50.11210 385.92200 Tm -[ (\13310\135) -556.01000 (Y) 128.98800 (\056\055T) 74.02040 (\056) -305.98200 (Chen\054) -320.01700 (X\056) -305.99100 (Liu\054) -320.00600 (and) -305.97900 (M\056\055H\056) -306.98200 (Y) 100.01700 (ang\056) -538.98900 (Multi\055instance) -306.02300 (object) ] TJ -19.91870 -10.95900 Td -[ (se) 15.01580 (gmentation) -249.98100 (with) -250.01400 (occlusion) -249.99500 (handling\056) -360.00600 (In) ] TJ -/R53 8.96640 Tf -151.27900 0 Td -(CVPR) Tj -/R40 8.96640 Tf -22.41600 0 Td -[ (\054) -250 (2015\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 271.61200 374.96300 Tm -(7) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 50.11210 362.87500 Tm -[ (\13311\135) -556.01000 (J\056) -241.02200 (Dai\054) -244.00800 (K\056) -241.99400 (He\054) -243.01500 (and) -241.98100 (J\056) -241.98000 (Sun\056) -342.99500 (Con) 40.00530 (v) 19.99650 (olutional) -241.99000 (feature) -241.99500 (maski) 1.00473 (ng) -242.00600 (for) ] TJ -19.91870 -10.95900 Td -[ (joint) -249.99500 (object) -249.99900 (and) -249.99700 (stuf) 25.00520 (f) -249.99200 (se) 15.01510 (gmentation\056) -360.00600 (In) ] TJ -/R53 8.96640 Tf -139.34500 0 Td -(CVPR) Tj -/R40 8.96640 Tf -22.41600 0 Td -[ (\054) -250 (2015\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 259.67800 351.91600 Tm -(6) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 264.16100 351.91600 Tm -(\054) Tj -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 268.64400 351.91600 Tm -(7) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 50.11210 339.82800 Tm -[ (\13312\135) -556.01000 (P) 110.98000 (\056) -342.01000 (Doll\341r) 40.01210 (\054) -367.01700 (C\056) -342.99900 (W) 80.01060 (ojek\054) -365.98200 (B\056) -342.99900 (Schiele\054) -365.99200 (and) -343.01000 (P) 110.98000 (\056) -343.01200 (Perona\056) -656.98000 (Pedestrian) ] TJ -19.91870 -10.95900 Td -[ (detection\072) -304.02100 (An) -237.00500 (e) 24.98750 (v) 25.00660 (aluation) -237.00700 (of) -238.01300 (the) -237.00500 (state) -237.01100 (of) -238.01200 (the) -237.00700 (art\056) ] TJ -/R53 8.96640 Tf -168.81500 0 Td -[ (T) 54.99860 (r) 14.99200 (ans\056) -237.01500 (P) 90 (AMI) ] TJ -/R40 8.96640 Tf -45.27380 0 Td -(\054) Tj --214.08900 -10.95900 Td -[ (34\0504\051\072743\226761\054) -249.98000 (2012\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 151.21300 317.91000 Tm -(1) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 155.69600 317.91000 Tm -(\054) Tj -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 160.17900 317.91000 Tm -(4) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 50.11210 305.82300 Tm -[ (\13313\135) -556.01000 (M\056) -276.00500 (Enz) 1.02175 (weiler) -275.98700 (and) -276.00600 (D\056) -276.01800 (M\056) -276.00500 (Ga) 20.01150 (vrila\056) -444.01200 (Monocula) 1.02106 (r) -276 (pedestrian) -275.99200 (de\055) ] TJ -19.91870 -10.95900 Td -[ (tection\072) -402.98200 (Surv) 14.98920 (e) 15.01100 (y) -295.98300 (and) -297.00500 (e) 15.01100 (xperiments\056) -509.01800 (31\05012\051\0722179\2262195\054) -308.01000 (2009\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 70.03090 283.90500 Tm -(1) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 50.11210 271.81700 Tm -[ (\13314\135) -556.01000 (M\056) -421.99200 (Ev) 14.98110 (eringham\054) -466.99100 (S\056) -422.98100 (M\056) -421.99200 (A\056) -423.00800 (Eslami\054) -466.99300 (L\056) -422.00200 (V) 110.99400 (an) -422.99600 (Gool\054) -467.00200 (C\056) -421.98300 (K\056) -423.00900 (I\056) ] TJ -19.91870 -10.95900 Td -[ (W) 40.01760 (illiams\054) -314.01100 (J\056) -302.01300 (W) 40.01760 (inn\054) -313.99000 (and) -302.01500 (A\056) -300.98100 (Zisserman\056) -525.01500 (The) -301.01800 (P) 91.98570 (ASCAL) -301.98700 (visual) ] TJ -10.95900 TL -T* -[ (object) -280.01500 (classes) -280.98700 (challenge\072) -371.00900 (A) -280.00400 (retrospecti) 24.99570 (v) 14.98650 (e\056) ] TJ -/R53 8.96640 Tf -153.30200 0 Td -(IJCV) Tj -/R40 8.96640 Tf -18.42620 0 Td -[ (\054) -280.01700 (111\0501\051\07298\226) ] TJ --171.72800 -10.95900 Td -[ (136\054) -250.00200 (2015\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 111.36600 238.94000 Tm -(1) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 115.84900 238.94000 Tm -(\054) Tj -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 120.33200 238.94000 Tm -(4) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 124.81600 238.94000 Tm -(\054) Tj -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 129.29900 238.94000 Tm -(5) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 133.78200 238.94000 Tm -(\054) Tj -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 138.26500 238.94000 Tm -(6) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 142.74800 238.94000 Tm -(\054) Tj -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 147.23200 238.94000 Tm -(7) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 50.11210 226.85200 Tm -[ (\13315\135) -556.01000 (P) 110.98000 (\056) -191.01300 (F) 80.00450 (\056) -192.01500 (Felzenszw) 10.01050 (alb\054) -204.00600 (R\056) -192 (B\056) -192 (Girshick\054) -203.01400 (D\056) -191.98100 (McAllester) 39.99310 (\054) -203.99500 (and) -192.01200 (D\056) -190.98000 (Ra\055) ] TJ -19.91870 -10.95820 Td -[ (manan\056) -503.01500 (Object) -295.01100 (de) 0.98294 (tection) -294.99200 (with) -295.01700 (discriminati) 25.01200 (v) 14.98650 (ely) -293.99000 (trained) -295.01700 (part\055) ] TJ -10.95900 TL -T* -[ (based) -250.01200 (models\056) ] TJ -/R53 8.96640 Tf -54.03130 0 Td -[ (T) 54.99730 (r) 14.99060 (ans\056) -249.99600 (P) 90 (AMI) ] TJ -/R40 8.96640 Tf -45.38670 0 Td -[ (\054) -250 (32\0509\051\0721627\2261645\054) -249.98100 (2010\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 264.08000 204.93500 Tm -(1) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 50.11210 192.84700 Tm -[ (\13316\135) -556.01000 (U\056) -294.01100 (Frank) 10.00440 (e\054) -304.99900 (D\056) -294.01100 (Pfeif) 24.98610 (fer) 39.99030 (\054) -304.98000 (C\056) -293.98800 (Rabe\054) -305.00400 (C\056) -293.98800 (Kn\366ppel\054) -305.00200 (M\056) -293.99900 (Enzweiler) 40.01210 (\054) ] TJ -19.91870 -10.95900 Td -[ (F) 80.00450 (\056) -272 (Stein\054) -277.99600 (and) -271.99800 (R\056) -271.98700 (G\056) -272.01000 (Herrtwich\056) -429.99300 (Making) -271.98400 (Bertha) -272.00900 (see\056) -431.00400 (In) ] TJ -/R53 8.96640 Tf -195.90600 0 Td -(ICCV) Tj --195.90600 -10.95900 Td -[ (W) 91.99590 (orkshops) ] TJ -/R40 8.96640 Tf -39.02110 0 Td -[ (\054) -250 (2013\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 136.93800 170.92900 Tm -(1) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 50.11210 158.84100 Tm -[ (\13317\135) -556.01000 (P) 110.98000 (\056) -663 (Fur) 17.99590 (g) 5.01002 (ale\054) -766.01800 (U\056) -663.01000 (Schwesinger) 39.98760 (\054) -766.99100 (M\056) -662.99600 <527503692c> -765.99200 (W) 91.99110 (\056) -663 (Derendarz\054) ] TJ -19.91870 -10.95900 Td -[ (H\056) -346.98700 (Grimmett\054) -371.98200 (P) 110.97900 (\056) -347.97900 (M) 1.00609 (\374hlfellner) 39.99710 (\054) -371.98300 (S\056) -348.00500 (W) 80.01060 (onneber) 17.98440 (ger) 40.00390 (\054) -370.98100 (B\056) -348.00900 (Li\054) -371.98100 (et) -347.00900 (al\056) ] TJ -10.95900 TL -T* -[ (T) 79.98130 (o) 25.00660 (w) 10.01050 (ard) -463.00300 (automated) -464.00200 (dri) 25.00110 (ving) -463.00500 (in) -463.00500 (cities) -464.02100 (using) -463 (close\055to\055mark) 9.99554 (et) ] TJ -10.95780 TL -T* -[ (sensors\072) -306.01500 (A) 0.99247 (n) -242.00600 (o) 14.98650 (v) 14.98650 (ervie) 25.00660 (w) -241.01300 (of) -241.01900 (the) -241.01300 (V) 100.01700 (\055Char) 18.00200 (ge) -240.97900 (project\056) -341 (In) ] TJ -/R53 8.96640 Tf -178.81500 0 Td -[ (IV) -240.97900 (Sympo\055) ] TJ --178.81500 -10.95900 Td -(sium) Tj -/R40 8.96640 Tf -16.93710 0 Td -[ (\054) -250 (2013\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 114.85400 115.00600 Tm -(1) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 50.11210 102.91800 Tm -[ (\13318\135) -556.01000 (A\056) -192.98300 (Geiger) 39.98350 (\054) -204.99700 (M\056) -194.01500 (Lauer) 39.97940 (\054) -203.99500 (C\056) -194.00500 (W) 80.01060 (ojek\054) -205.00800 (C\056) -194.00500 (Stiller) 40.01480 (\054) -203.99500 (and) -194.01600 (R\056) -194.00500 (Urtasun\056) -237.00400 (3D) ] TJ -19.91870 -10.95940 Td -[ (traf) 25.01200 <0263> -289.01400 (scene) -289.02100 (understanding) -290.00300 (from) -288.99000 (mo) 14.99470 (v) 25.00660 (able) -289 (platforms\056) ] TJ -/R53 8.96640 Tf -193.78900 0 Td -[ (T) 54.99860 (r) 14.99200 (ans\056) ] TJ --193.78900 -10.95900 Td -[ (P) 90.00140 (AMI) ] TJ -/R40 8.96640 Tf -20.60510 0 Td -[ (\054) -250 (36\0505\051\0721012\2261025\054) -249.98000 (2014\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 185.26700 81 Tm -(1) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 308.86200 710.03700 Tm -[ (\13319\135) -556.01200 (A\056) -278.98200 (Geiger) 39.98490 (\054) -286.98700 (P) 110.98000 (\056) -279.01500 (Lenz\054) -286.98700 (C\056) -279.00100 (Stiller) 40.01480 (\054) -286.98700 (and) -279.01500 (R\056) -280.00600 (Urtasun\056) -454.00600 (V) 59.97870 (ision) -279.01500 (meets) ] TJ -19.91870 -10.95900 Td -[ (robotics\072) -310.01400 (The) -250.00300 (KITTI) -249.98400 (dataset\056) ] TJ -/R53 8.96640 Tf -106.35800 0 Td -(IJRR) Tj -/R40 8.96640 Tf -17.92380 0 Td -[ (\054) -250 (32\05011\051\054) -249.98400 (2013\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 509.33600 699.07800 Tm -(1) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 513.81900 699.07800 Tm -(\054) Tj -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 518.30200 699.07800 Tm -(3) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 522.78600 699.07800 Tm -(\054) Tj -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 527.26900 699.07800 Tm -(4) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 308.86200 686.86700 Tm -[ (\13320\135) -556.01200 (R\056) -249.98700 (Gir) 0.98839 (shick\056) -360.00800 (F) 15.00560 (ast) -249.98400 (R\055CNN\056) -360.00800 (In) ] TJ -/R53 8.96640 Tf -126.86000 0 Td -(ICCV) Tj -/R40 8.96640 Tf -20.42500 0 Td -[ (\054) -250 (2015\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 484.03200 686.86700 Tm -(8) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 308.86200 674.65600 Tm -[ (\13321\135) -556.01200 (R\056) -278 (Girshick\054) -286.00700 (J\056) -279.00900 (Donahue\054) -285.99100 (T) 74.01220 (\056) -279.01500 (Darrell\054) -286.00200 (and) -279.01500 (J\056) -278.00700 (Malik\056) -453.01000 (Rich) -277.98600 (fea\055) ] TJ -19.91870 -10.95900 Td -[ (ture) -368.02200 (hierarchies) -368.99400 (for) -367.98100 (accurate) -369.01000 (object) -368.01900 (detection) -368.98800 (a) 0.98567 (nd) -369 (semantic) ] TJ -10.95900 TL -T* -[ (se) 15.01650 (gmentation\056) -360.00600 (In) ] TJ -/R53 8.96640 Tf -62.86330 0 Td -(CVPR) Tj -/R40 8.96640 Tf -22.41480 0 Td -[ (\054) -250 (2014\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 441.94500 652.73800 Tm -(7) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 308.86200 640.52700 Tm -[ (\13322\135) -556.01200 (F) 80.00520 (\056) -291.99700 (Guene) 14.98110 (y) -292.01900 (and) -291.99500 (A\056) -292.00800 (Geiger) 55.01220 (\056) -494.01000 (Displets\072) -393.98900 (Resolving) -292.00300 (stereo) -292.01400 (ambi\055) ] TJ -19.91870 -10.95900 Td -[ (guities) -250.01100 (using) -250.00800 (object) -249.99700 (kno) 25.00660 (wledge\056) -359.99700 (In) ] TJ -/R53 8.96640 Tf -126.28100 0 Td -(CVPR) Tj -/R40 8.96640 Tf -22.41600 0 Td -[ (\054) -250 (2015\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 505.36400 629.56800 Tm -(4) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 308.86200 617.35700 Tm -[ (\13323\135) -556.01200 (B\056) -279.00100 (Hariharan\054) -286.98700 (P) 110.98000 (\056) -279.01500 (Arbel\341ez\054) -286.99300 (R\056) -279.00400 (B\056) -280.00600 (Girshick\054) -286.00700 (and) -280.01700 (J\056) -279.00900 (Malik\056) -454.01200 (Si\055) ] TJ -19.91870 -10.95900 Td -[ (multaneous) -249.98700 (detection) -250.00800 (and) -250 (se) 15.01370 (gmentation\056) -360.00800 (In) ] TJ -/R53 8.96640 Tf -156.74900 0 Td -(ECCV) Tj -/R40 8.96640 Tf -22.91680 0 Td -[ (\054) -250 (2014\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 536.33200 606.39800 Tm -(7) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 308.86200 594.18700 Tm -[ (\13324\135) -556.01200 (B\056) -312.98200 (Hariharan\054) -329.98600 (P) 110.98000 (\056) -313.99800 (A\056) -314.00900 (Arbel\341ez\054) -328.99000 (R\056) -313.98700 (B\056) -313.98700 (Girshick\054) -330.00800 (and) -313.99800 (J\056) -312.99000 (Malik\056) ] TJ -19.91870 -10.95900 Td -[ (Hypercolumns) -320.98700 (for) -320.01500 (object) -321.01200 (se) 15.01370 (gmentation) -320.99500 (and) -320.01000 <026e652d677261696e6564> -321.00600 (lo\055) ] TJ -10.95900 TL -T* -[ (calization\056) -360 (In) ] TJ -/R53 8.96640 Tf -50.04100 0 Td -(CVPR) Tj -/R40 8.96640 Tf -22.41600 0 Td -[ (\054) -250 (2015\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 429.12300 572.26900 Tm -(7) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 308.86200 560.05800 Tm -[ (\13325\135) -556.01200 (H\056) -429.98000 (Hattori\054) -475.99000 (V) 128.98600 (\056) -431.01400 (N\056) -430.98200 (Boddeti\054) -476.00700 (K\056) -430.98200 (M\056) -431.00900 (Kitani\054) -476.01800 (and) -431.01400 (T) 74.01220 (\056) -430.01200 (Kanade\056) ] TJ -19.91870 -10.95900 Td -[ (Learning) -527.01600 <7363656e652d73706563690263> -526.01400 (pedestrian) -527.01600 (detectors) -526.01400 (without) -526.99500 (real) ] TJ -10.95900 TL -T* -[ (data\056) -359.98900 (In) ] TJ -/R53 8.96640 Tf -30.11840 0 Td -(CVPR) Tj -/R40 8.96640 Tf -22.41600 0 Td -[ (\054) -250 (2015\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 409.20100 538.14000 Tm -(7) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 308.86200 525.92900 Tm -[ (\13326\135) -556.01200 (X\056) -394.99700 (He) -394.99100 (and) -394.98300 (S\056) -395.01000 (Gould\056) -824.02100 (An) -395.01900 (e) 15.01370 (x) 14.98650 (emplar) 20.00740 (\055based) -395.01300 (CRF) -395.00800 (for) -394.99100 (multi\055) ] TJ -19.91910 -10.95900 Td -[ (instance) -250.01400 (object) -249.99700 (se) 15.01650 (gmentation\056) -360.00800 (In) ] TJ -/R53 8.96640 Tf -118.64200 0 Td -(CVPR) Tj -/R40 8.96640 Tf -22.41600 0 Td -[ (\054) -250 (2014\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 497.72500 514.97000 Tm -(7) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 308.86200 502.75900 Tm -[ (\13327\135) -556.01200 (D\056) -277.02200 (Hoiem\054) -283.99200 (J\056) -277.00500 (Hays\054) -284.00600 (J\056) -277.00500 (Xiao\054) -284.00300 (and) -278.01300 (A\056) -277.02200 (Khosla\056) -447.01900 (Guest) -277.00500 (editorial\072) ] TJ -19.91910 -10.95900 Td -[ (Scene) -250.01700 (understanding\056) ] TJ -/R53 8.96640 Tf -79.92580 0 Td -(IJCV) Tj -/R40 8.96640 Tf -18.42500 0 Td -[ (\054) -250 (2015\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 455.01800 491.80000 Tm -(1) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 308.86200 479.58900 Tm -[ (\13328\135) -556.01200 (J\056) -377.99000 (Hosang\054) -409.99700 (R\056) -377.98200 (Benenson\054) -409.99400 (P) 110.97700 (\056) -377.99500 (Doll\341r) 40.00940 (\054) -410.01600 (and) -377.99500 (B\056) -377.98400 (Schiele\056) -770.01900 (What) ] TJ -19.91910 -10.95900 Td -[ (mak) 10.00920 (es) -476.00900 (for) -476.98100 (ef) 25.01740 (fect) 1.00200 (i) 25.01470 (v) 14.98650 (e) -477.01700 (detection) -475.98500 (proposals\077) ] TJ -/R53 8.96640 Tf -166.67300 0 Td -[ (T) 54.99590 (r) 14.99200 (ans\056) -476.01200 (P) 90 (AMI) ] TJ -/R40 8.96640 Tf -47.41600 0 Td -(\054) Tj --214.08900 -10.95900 Td -[ (38\0504\051\072814\226830\054) -249.97800 (2015\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 409.96300 457.67100 Tm -(7) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 308.86200 445.46100 Tm -[ (\13329\135) -556.01200 (H\056) -325.98900 (Hu) -327.01300 (and) -326.01900 (B\056) -327.01000 (Upcroft\056) -605.02000 (Nonparam) 0.99111 (etric) -326.99100 (semantic) -325.98400 (se) 15.01370 (gmenta\055) ] TJ -19.91910 -10.95900 Td -[ (tion) -250.00600 (for) -250.00600 (3D) -249.98900 (street) -250.00800 (scenes\056) -360.00800 (In) ] TJ -/R53 8.96640 Tf -102.33200 0 Td -[ (IR) 40.01480 (OS) ] TJ -/R40 8.96640 Tf -19.06290 0 Td -[ (\054) -250 (2013\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 478.06200 434.50200 Tm -(4) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 308.86200 422.29100 Tm -[ (\13330\135) -556.01200 (A\056) -376.00200 (Krizhe) 24.99840 (vsk) 14.98920 (y) 65 (\054) -408.01200 (I\056) -377.00700 (Sutsk) 10.00920 (e) 24.99020 (v) 14.98650 (er) 40.00390 (\054) -408.01200 (and) -376.99300 (G\056) -377.00400 (E\056) -375.99700 (Hinton\056) -764.98200 (ImageNet) ] TJ -19.91910 -10.95900 Td -[ <636c6173736902636174696f6e> -366.00700 (with) -365.98500 (deep) -365.98800 (con) 40.01480 (v) 19.99650 (olutional) -366.02000 (neural) -365.99300 (netw) 9.99826 (orks\056) -731.01700 (In) ] TJ -/R53 8.96640 Tf -10.95940 TL -(NIPS) ' -/R40 8.96640 Tf -18.92770 0 Td -[ (\054) -250 (2012\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 375.59500 400.37200 Tm -(1) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 308.86200 388.16100 Tm -[ (\13331\135) -556.01200 (L\056) -375.99700 (Kr\374ger) 39.97940 (\054) -408.01200 (C\056) -376.98000 (W\366hler) 39.99310 (\054) -408.01200 (A\056) -376.00200 (W\374rz\055W) 79.98610 (essel\054) -408.99200 (a) 0.98567 (nd) -377.01500 (F) 80.00240 (\056) -376.99300 (Stein\056) -764.01200 (In\055) ] TJ -19.91910 -10.95900 Td -[ (f) 10.00640 (actory) -308.00800 (calibration) -308 (of) -307.01900 (multiocular) -308.01300 (camera) -308.00200 (systems\056) -545.01400 (In) ] TJ -/R53 8.96640 Tf -197.90500 0 Td -(SPIE) Tj --197.90500 -10.95900 Td -[ (Photonics) -387.99600 (Eur) 45.00310 (ope) -388.01300 (\050Optical) -388.01000 (Metr) 44.98130 (olo) 9.98737 (gy) -389.01700 (in) -387.98300 (Pr) 45.00310 (oduction) -387.99400 (Engi\055) ] TJ -10.95900 TL -(neering\051) ' -/R40 8.96640 Tf -30.37810 0 Td -[ (\054) -250 (2004\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 387.04500 355.28400 Tm -(2) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 308.86200 343.07300 Tm -[ (\13332\135) -556.01200 (A\056) -346.98800 (K) 15.01920 (undu\054) -372.98800 (Y) 128.98600 (\056) -347.97900 (Li\054) -371.98100 (F) 80.00520 (\056) -347.97900 (Dellaert\054) -372.99600 (F) 80.00240 (\056) -347.97900 (Li\054) -371.97800 (and) -348.02200 (J\056) -348.01700 (Rehg\056) -673.98400 (J) 1.00745 (oint) -347.98400 (se\055) ] TJ -19.91870 -10.95900 Td -[ (mantic) -245.98100 (se) 15.01650 (gmentation) -247.01900 (and) -245.99200 (3D) -246.98300 (reconstruction) -245.98100 (from) -246.99400 (monocular) ] TJ -10.95900 TL -T* -[ (video\056) -360.01400 (In) ] TJ -/R53 8.96640 Tf -35.10310 0 Td -(ECCV) Tj -/R40 8.96640 Tf -22.91800 0 Td -[ (\054) -250 (2014\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 414.68800 321.15500 Tm -(4) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 308.86200 308.94500 Tm -[ (\13333\135) -556.01200 (L\056) -327.98800 (Ladick) 14.99470 (y) 65 (\054) -348.98100 (J\056) -328.02000 (Shi\054) -348.99700 (and) -328.98400 (M\056) -327.97700 (Pollefe) 15.01370 (ys\056) -611.98000 (Pulling) -329.00600 (things) -328.98400 (out) -327.99300 (of) ] TJ -19.91870 -10.95900 Td -[ (perspecti) 25.00110 (v) 14.98650 (e\056) -359.97800 (In) ] TJ -/R53 8.96640 Tf -55.65430 0 Td -(CVPR) Tj -/R40 8.96640 Tf -22.41600 0 Td -[ (\054) -250 (2014\056) ] TJ -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 434.73700 297.98600 Tm -(4) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 308.86200 285.77500 Tm -[ (\13334\135) -556.01200 (Y) 128.98600 (\056) -230.00400 (LeCun\054) -234.98100 (Y) 128.98600 (\056) -230.00400 (Bengio\054) -235.01100 (and) -230.00400 (G\056) -231.01700 (Hinton\056) -318 (Deep) -230.98900 (l) 1.01290 (earning\056) ] TJ -/R53 8.96640 Tf -209.43200 0 Td -[ (Natur) 37.01430 (e) ] TJ -/R40 8.96640 Tf -24.57580 0 Td -(\054) Tj --214.08900 -10.95900 Td -(2015\056) Tj -ET -Q -1 0 0 rg -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 352.18300 274.81600 Tm -(1) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.96640 Tf -1 0 0 1 308.86200 262.60500 Tm -[ (\13335\135) -556.01200 (B\056) -340.99500 (Leibe\054) -365.00500 (N\056) -341.01900 (Cornelis\054) -365.01300 (K\056) 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-0.29951 (\054) -309.516 (w) -0.29951 (h) -1 (i) -0.501 (l) -0.501 (e) -309.219 (t) -0.69977 (h) -1.002 (e) -310.21 (l) -0.501 (a) ] TJ -235.837 0 Td -[ (t) -0.69977 (t) -0.69977 (e) 0.81685 (r) -309.99 (n) -1.002 (e) -0.19604 (t) 28.3121 (w) 27.7022 (o) -0.89854 (rk) -0.40298 (s) -0.40298 ] TJ --235.837 -10.959 Td -[ (c) -0.79915 (a) -0.8999 (n) -232.979 (c) -0.79915 (a) -0.8999 (p) -1 (t) -0.70113 (u) -1 (re) -233.2 (s) -0.40026 (h) -1 (a) -0.90126 (rp) -28.9901 (e) -0.19877 (r) -231.983 (o) -0.90126 (b) -57.9945 (je) -0.19877 (c) -0.80051 (t) -233.679 (b) -28.9819 (o) -0.90126 (u) -1 (n) -1.002 (d) -1.002 (a) -0.90126 (ri) -0.49828 (e) -0.19877 (s) -232.419 (b) 27 (y) -232.419 (g) -0.90126 (ra) -0.90126 (d) -1 (u) -1.002 (a) -0.90126 (l) -0.501 (l) -0.49828 (y) -233.385 (re) -0.19877 (c) -0.80051 (o) 28.0915 (v) 28.5816 (e) -0.19877 (ri) -0.501 (n) -1 (g) -233.881 (t) -0.70249 (h) -0.99656 (e) -232.187 (s) ] TJ -266.095 0 Td -[ (p) -1.002 (a) -0.90398 (t) -0.69705 (i) -0.501 (a) -0.90398 (l) -0.49556 ] TJ --266.095 -10.959 Td -[ (i) -0.501 (n) -1 (fo) -0.89854 (rm) -0.49964 (a) -0.8999 (t) -0.69977 (i) -0.501 (o) -0.89854 (n) -1.002 (\056) -401.496 (I) -1 (n) -401.992 (t) -0.69977 (h) -1.002 (i) -0.501 (s) -401.404 (w) 27.7022 (o) -0.90126 (rk) -0.40026 (\054) -401.513 (w) 28.7042 (e) -402.196 (p) -1.002 (ro) 0.11436 (p) -29.9839 (o) -0.90126 (s) -0.40026 (e) -401.21 (t) -0.69977 (o) -401.888 (c) -0.80051 (o) -0.90126 (m) 27.4925 (b) -1.002 (i) -0.49828 (n) -1.002 (e) -401.202 (t) -0.69977 (h) -1.002 (e) -401.218 (a) -0.90126 (d) -1 (v) 56.5833 (a) -0.90126 (n) 28.0071 (t) -0.70249 (a) -0.89854 (g) -0.90398 (e) -0.19604 (s) -401.379 (fro) ] TJ -281.452 0 Td -[ (m) -0.501 ] TJ --281.452 -10.959 Td -[ (b) -29.9833 (o) -0.8999 (t) -0.69977 (h) -403 (m) -0.49964 (e) -0.20013 (t) -0.69977 (h) -1 (o) -28.9084 (d) -1.002 (s) -0.39753 (\056) -403.497 (S) -1.002 (p) -28.9935 (e) -0.19877 (c) -0.80051 (i) -0.501 <0c> -1 (c) -0.80324 (a) -0.89854 (l) -0.501 (l) -0.501 (y) 85.6169 (\054) -403.508 (o) -0.90126 (u) -1 (r) -402.011 (p) -1.002 (ro) -0.89854 (p) -29.0146 (o) -0.90126 (s) -0.40026 (e) -0.19877 (d) -403.986 (m) -0.49828 (o) -28.9084 (d) -1.002 (e) -0.19877 (l) -0.49828 (\054) -402.523 (De) -0.19877 (e) -0.19877 (p) -1.002 (L) -0.29951 (a) -0.90126 (b) 27.0051 (v) -0.40026 (3) -0.90126 (\053) -0.40298 (\054) -402.517 (e) ] TJ -262.714 0 Td -[ (x) -0.40298 (t) -0.70249 (e) -0.20149 (n) -0.99656 (d) -1.002 (s) -0.40298 ] TJ --262.714 -10.959 Td -[ (De) -0.20013 (e) -0.20013 (p) -1 (L) -0.29951 (a) -0.8999 (b) 28.0078 (v) -0.40026 (3) -291.905 (b) 28.0017 (y) -291.406 (a) -0.90126 (d) -1 (d) -1.002 (i) -0.501 (n) -1 (g) -291.891 (a) -290.914 (s) -0.40026 (i) -0.501 (m) -0.49828 (p) -1.002 (l) -0.49828 (e) -291.189 (y) 28.5816 (e) -0.19877 (t) -291.706 (e) -0.19877 <0b> -0.501 (e) -0.19877 (c) -0.80051 (t) -0.69977 (i) -0.501 (v) 28.6034 (e) -291.191 (d) -1.002 (e) -0.19877 (c) -0.80051 (o) -28.8812 (d) -1.002 (e) -0.19877 (r) -290.982 (m) -0.49828 (o) -28.9084 (d) -1.002 (u) -1 (l) -0.501 (e) -291.199 (t) -0.69977 (o) -290.881 (re) -0.20149 <0c> ] TJ -264.503 0 Td -[ (n) -1.002 (e) -291.213 (t) -0.70249 (h) -0.99656 (e) -0.20149 ] TJ --264.503 -10.959 Td -[ (s) -0.3989 (e) -0.20013 (g) -0.8999 (m) -0.49964 (e) -0.20013 (n) 27.9942 (t) -0.70113 (a) -0.89854 (t) -0.70113 (i) -0.49828 (o) -0.90126 (n) -362.001 (re) -0.19877 (s) -0.40026 (u) -1 (l) -0.501 (t) -0.69977 (s) -360.384 (e) -0.19877 (s) -0.40026 (p) -29.9792 (e) -0.19877 (c) -0.80051 (i) -0.501 (a) -0.90126 (l) -0.501 (l) -0.49828 (y) -361.413 (a) 0.10347 (l) -0.501 (o) -0.90126 (n) -1 (g) -361.92 (o) -0.90126 (b) -57.9945 (je) -0.19877 (c) -0.80051 (t) -361.718 (b) -28.9819 (o) -0.90126 (u) -1 (n) -1.002 (d) -1.002 (a) -0.90126 (ri) -0.49828 (e) -0.19877 (s) -0.40026 (\056) -361.5 (W) 85.7994 (e) -361.201 (fu) ] TJ -253.858 0 Td -[ (rt) -0.70249 (h) -1.002 (e) -0.19604 (r) -360.014 (e) -0.19604 (x) -0.40298 (\055) -0.60447 ] TJ --253.858 -10.959 Td -[ (p) -1 (l) -0.501 (o) -0.8999 (re) -279.184 (t) -0.70113 (h) -1 (e) -278.191 (X) -0.69977 (c) -0.80051 (e) -0.19877 (p) -1.002 (t) -0.69977 (i) -0.501 (o) -0.90126 (n) -279.988 (m) -0.49828 (o) -28.9084 (d) -1.002 (e) -0.19877 (l) -279.508 (a) -0.90126 (n) -1 (d) -279.015 (a) -0.90126 (p) -1 (p) -1.002 (l) -0.501 (y) -279.388 (t) -0.69977 (h) -1.002 (e) -278.19 (d) -1.002 (e) -0.19877 (p) -1 (t) -0.70249 (h) 26.9881 (w) -0.29951 (i) -0.501 (s) -0.39753 (e) -278.182 (s) -0.40026 (e) -0.19877 (p) -1 (a) -0.90126 (ra) -0.90126 (b) -1.002 (l) -0.49828 (e) -279.189 (c) -0.80051 (o) -0.89854 (n) ] TJ -256.368 0 Td -[ (v) 27.5769 (o) 0.10347 (l) -0.501 (u) -1.002 (t) -0.69705 (i) -0.501 (o) -0.90398 (n) -0.99656 ] TJ --256.368 -10.959 Td -[ (t) -0.70113 (o) -316.889 (b) -28.9813 (o) -0.8999 (t) -0.69977 (h) -317.002 (A) 28.2985 (t) -0.69977 (ro) -0.90126 (u) -1.002 (s) -316.399 (S) -1.002 (p) -1 (a) -0.90126 (t) -0.69977 (i) -0.501 (a) -0.90126 (l) -316.481 (P) -0.40026 (y) -0.40026 (ra) -0.90126 (m) -0.49828 (i) -0.501 (d) -315.98 (P) 27.6123 (o) -28.9111 (o) -0.90126 (l) -0.501 (i) -0.49828 (n) -1.002 (g) -316.9 (a) -0.90126 (n) -1 (d) -317.004 (d) -1.002 (e) -0.19877 (c) -0.80051 (o) -28.8812 (d) -1.002 (e) -0.19877 (r) -315.988 (m) -0.49828 (o) -28.9084 (d) -1.002 (u) -1 (l) -0.501 (e) -0.19877 (s) -0.39753 (\054) -316.514 (re) -0.20149 (s) ] TJ -265.582 0 Td -[ (u) -1.002 (l) -0.501 (t) -0.69705 (i) -0.501 (n) -1.002 (g) -0.89854 ] TJ --265.582 -10.9578 Td -[ (i) -0.501 (n) -244.004 (a) -243.905 (fa) -0.89854 (s) -0.40026 (t) -0.69977 (e) -0.20013 (r) -244.014 (a) 0.10347 (n) -1.002 (d) -244.991 (s) -0.40026 (t) -0.69977 (ro) -0.90126 (n) -1.002 (g) -0.89854 (e) -0.19877 (r) -242.981 (e) -0.19877 (n) -1.002 (c) -0.80051 (o) -28.8812 (d) -1.002 (e) -0.19877 (r\055) -0.59903 (d) -1.002 (e) -0.19877 (c) -0.80051 (o) -29.8913 (d) -1.002 (e) -0.19877 (r) -243.016 (n) -1.002 (e) -0.19877 (t) 28.3148 (w) 27.7022 (o) -0.90126 (rk) -0.40026 (\056) -243.501 (W) 85.7994 (e) -243.182 (d) -1.002 (e) -0.19877 (m) -0.49828 (o) -0.90126 (n) -1 (s) -0.40026 (t) -0.69977 (ra) -0.90398 (t) -0.69705 (e) ] TJ -264.161 0 Td -[ (t) -0.70249 (h) -0.99656 (e) -243.209 (e) -0.19604 (f\055) -0.60447 ] TJ --264.161 -10.959 Td -[ (fe) -0.20013 (c) -0.79915 (t) -0.69977 (i) -0.501 (v) 28.6061 (e) -0.20013 (n) -1 (e) -0.20013 (s) -0.40026 (s) -256.393 (o) 0.10347 (f) -256.009 (t) -0.69977 (h) -1.002 (e) -255.187 (p) -1.002 (ro) -0.89854 (p) -29.0146 (o) -0.90126 (s) -0.40026 (e) -0.19877 (d) -255.994 (m) -0.49828 (o) -29.9104 (d) -1.002 (e) -0.19877 (l) -255.503 (o) -0.90126 (n) -256 (P) 85.5979 (A) -0.69977 (S) -1.002 (C) -0.29951 (A) -0.69977 (L) -255.285 (V) 27.2965 (O) 0.62081 (C) -255.299 (2) -0.90126 (0) -0.90126 (1) -0.90126 (2) -256.881 (a) -0.90126 (n) -1 (d) -256.012 (C) -0.29951 (i) -0.501 (t) 28.3066 (y) -0.40298 (s) -0.39753 (c) ] TJ -271.416 0 Td -[ (a) -0.90398 (p) -30.0166 (e) -0.19604 (s) -0.40298 ] TJ --271.416 -10.959 Td -[ (d) -1 (a) -0.8999 (t) -0.70113 (a) -0.89854 (s) -0.40026 (e) -0.20013 (t) -0.69977 (s) -0.40026 (\054) -369.488 (a) -0.90126 (c) 27.1875 (h) -1.002 (i) -0.49828 (e) -0.19877 (v) -0.40026 (i) -0.501 (n) -1.002 (g) -369.898 (t) -0.69977 (h) -1.002 (e) -370.2 (t) -0.69977 (e) -0.19877 (s) -0.40026 (t) -369.721 (s) -0.40026 (e) -0.19877 (t) -370.709 (p) -28.9819 (e) -0.19877 (rfo) -0.90126 (rm) -0.49828 (a) -0.90126 (n) -1.002 (c) -0.80051 (e) -370.2 (o) -0.90126 (f) -370.017 (8) 0.10347 (9) -0.90126 (\045) -370.494 (a) -0.90126 (n) -1 (d) -370.981 (8) -0.90126 (2) -0.90126 (\056) 0.47922 (1) -0.89854 (\045) -370.497 (w) -0.29951 (i) ] TJ -267.12 0 Td -[ (t) -0.70249 (h) -0.99656 (o) -0.90398 (u) -1.002 (t) -0.69705 ] TJ --267.12 -10.959 Td -[ (a) -0.8999 (n) 28.005 (y) -306.393 (p) -29.9833 (o) -0.8999 (s) -0.3989 (t) -0.70113 (\055) -0.60039 (p) -1 (ro) -28.8866 (c) -0.80051 (e) -0.19877 (s) -0.40026 (s) -0.40026 (i) -0.49828 (n) -1.002 (g) -0.90126 (\056) -306.48 (O) -0.40026 (u) -1 (r) -306.02 (p) -1.002 (a) -0.89854 (p) -28.9928 (e) -0.19877 (r) -306 (i) -0.501 (s) -306.406 (a) -0.90126 (c) -0.80051 (c) -0.80051 (o) -0.90126 (m) -0.49828 (p) -1.002 (a) -0.90126 (n) -1 (i) -0.501 (e) -0.19877 (d) -307 (w) 0.6916 (i) -0.501 (t) -0.69977 (h) -307.014 (a) -306.902 (p) -1.002 (u) -1 (b) -1.002 (l) -0.501 (i) -0.49828 (c) -0.80051 (l) ] TJ -246.711 0 Td -[ (y) -306.412 (a) 28.116 (v) 56.5915 (a) -0.89854 (i) -0.501 (l) -0.501 (a) -0.90398 (b) -0.99656 (l) -0.501 (e) -0.20149 ] TJ --246.711 -10.959 Td -[ (re) -0.20013 (fe) -0.20013 (re) -0.20013 (n) -1 (c) -0.79915 (e) -238.202 (i) -0.501 (m) -0.49828 (p) -1.002 (l) -0.501 (e) -0.19877 (m) -0.49828 (e) -0.19877 (n) 27.9908 (t) -0.69977 (a) -0.90126 (t) -0.70249 (i) -0.49828 (o) -0.90126 (n) -240.018 (o) -0.90126 (f) -238.014 (t) -0.69977 (h) -1.002 (e) -238.197 (p) -1.002 (ro) -0.89854 (p) -30.0166 (o) -0.90126 (s) -0.40026 (e) -0.19877 (d) -239.003 (m) -0.49828 (o) -28.9084 (d) -1.002 (e) -0.19877 (l) -0.49828 (s) -239.392 (i) -0.501 (n) -238.995 (T) 85.6877 (e) -0.19877 (n) -1.002 (s) -0.39753 (o) -0.90126 <720d> -1.002 (o) 28.1215 (w) -239.31 (a) -0.89854 (t) -0.70249 ] TJ -ET -Q -0 1 0 0 k -q -10 0 0 10 0 0 cm -BT -/R44 8.9664 Tf -1 0 0 1 424.001 352.125 Tm -(https\072) Tj --260.89 -10.959 Td -(\057\057github\056com\057tensorflow\057models\057tree\057master\057research\057deeplab) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R40 8.9664 Tf -1 0 0 1 440.841 341.166 Tm -[ (\056) -0.501 ] TJ -/R46 8.9664 Tf --277.73 -22.0922 Td -[ (K) -0.80051 (e) -1 (y) -0.49964 (w) 32.38 (o) -0.69977 (r) -0.10075 (d) -0.40026 (s) -0.79779 (\072) -0.69977 ] TJ -/R40 8.9664 Tf -53.4531 0 Td -[ (S) -1.002 (e) -0.19877 (m) -0.49828 (a) -0.90126 (n) 28.02 (t) -0.69977 (i) -0.501 (c) -229.805 (i) -0.501 (m) -0.49828 (a) -0.90126 (g) -0.90126 (e) -228.215 (s) -0.40026 (e) -0.19877 (g) -0.90126 (m) -0.49828 (e) -0.19877 (n) 27.9935 (t) -0.69977 (a) -0.90126 (t) -0.70249 (i) -0.49828 (o) -0.90126 (n) -1.002 (\054) -229.481 (s) -0.40026 (p) -1 (a) -0.90126 (t) -0.69977 (i) -0.501 (a) -0.90126 (l) -228.487 (p) 28.0017 (y) -0.40026 (ra) -0.90126 (m) -0.49828 (i) -0.501 (d) -229.982 (p) -28.9819 (o) -29.9131 (o) -0.89854 (l) -0.501 (i) -0.501 (n) -1.002 (g) -0.89854 (\054) -228.484 (e) -0.19604 (n) -1.002 (c) -0.80051 (o) ] TJ -238.463 0 Td -[ (d) -1.002 (e) -0.19604 (r\055) -0.60447 ] TJ --291.916 -10.959 Td -[ (d) -1 (e) -0.20013 (c) -0.79915 (o) -28.8812 (d) -1 (e) -0.20013 (r\054) -343.485 (a) -0.8999 (n) -1 (d) -344.014 (d) -1.002 (e) -0.19877 (p) 0.002 (t) -0.69977 (h) 26.9861 (w) -0.29951 (i) -0.501 (s) -0.39753 (e) -342.179 (s) -0.40026 (e) -0.19877 (p) -1 (a) -0.90126 (ra) -0.90126 (b) -1.002 (l) -0.49828 (e) -343.187 (c) -0.80051 (o) -0.90126 (n) 28.0207 (v) 27.5796 (o) -0.90126 (l) -0.501 (u) -1 (t) -0.70249 (i) -0.49828 (o) -0.90126 (n) -1.002 (\056) -0.501 ] TJ -/R36 11.9552 Tf --28.3461 -34.5289 Td -[ (1) -1125.48 (I) -1 (n) 31.0056 (t) -0.4993 (r) -0.50032 (o) -31.4927 (duct) -0.50236 (i) -0.49828 (o) -0.50032 (n) ] TJ -/R38 9.9626 Tf -24.782 TL -T* -[ (S) -0.59916 (e) -0.40067 (m) -0.30019 (an) 27.4047 (t) -0.89936 (i) -0.80011 (c) -224.411 (s) -0.39944 (e) -0.40067 (gm) -0.30019 (e) -0.40067 (n) 27.3974 (t) -0.90181 (at) -0.89936 (i) -0.80134 (on) -223.593 (w) -0.20095 (i) -0.79889 (t) -0.90181 (h) -224.6 (t) -0.90181 (h) -0.59794 (e) -223.389 (goal) -223.803 (t) -0.90181 (o) -224.012 (as) -0.39944 (s) -0.39944 (i) -0.79889 (gn) -223.612 (s) -0.39944 (e) -0.40189 (m) -0.29897 (an) 27.3949 (t) -0.90181 (i) -0.79889 (c) -224.411 (l) -0.79889 (ab) -28.581 (e) -0.39944 (l) -0.80134 (s) -223.419 (t) -0.90181 (o) -222.992 (e) -0.40189 (v) 26.1868 (e) -0.40189 (r) -0.70086 (y) -223.811 (p) -0.59794 (i) ] TJ -310.104 0 Td -[ (x) -0.79889 (e) -0.40189 (l) -223.801 (i) -0.80379 (n) -224.59 (an) -0.59794 ] TJ --310.104 -11.9551 Td -[ (i) -0.80011 (m) -0.30019 (age) -318.382 (\133) -0.80011 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 166.16 236.849 Tm -(1) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 171.141 236.849 Tm -[ (\054) -0.80011 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 173.909 236.849 Tm -(2) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 178.89 236.849 Tm -[ (\054) -0.80011 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 181.657 236.849 Tm -(3) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 186.639 236.849 Tm -[ (\054) -0.80011 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 189.406 236.849 Tm -(4) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 194.388 236.849 Tm -[ (\054) -0.80011 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 197.155 236.849 Tm -(5) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 202.136 236.849 Tm -[ (\135) -318.783 (i) -0.79889 (s) -318.388 (on) -0.60039 (e) -319.39 (of) -318.616 (t) -0.90181 (h) -0.59794 (e) -318.393 (f) -0.60039 (u) -0.59794 (n) -0.60039 (d) -0.59794 (am) -0.30142 (e) -0.39944 (n) 27.3827 (t) -0.90181 (al) -318.792 (t) -0.90181 (op) -0.59794 (i) -0.80134 (c) -0.39944 (s) -318.388 (i) -0.79889 (n) -318.616 (c) -0.39944 (om) -0.30142 (p) -0.59794 (u) -0.60039 (t) -0.90181 (e) -0.39944 (r) -319.706 (v) -0.79889 (i) 0.18624 (s) -1.38702 (i) -0.80379 (on) 0.39699 (\056) -319.804 (D) -0.90181 (e) -0.39699 (e) -0.40189 (p) -318.584 (c) -0.40189 (on) -0.59794 (\055) -0.30387 ] TJ --67.3715 -11.9559 Td -[ (v) 27.2013 (ol) -0.80011 (u) -0.59916 (t) -0.89936 (i) -0.80011 (on) -0.59916 (al) -335.781 (n) -0.59916 (e) -0.40067 (u) -0.59916 (r) -0.70086 (al) -334.797 (n) -0.60039 (e) -0.39944 (t) 26.1084 (w) 27.8017 (or) -0.69841 (k) -0.80134 (s) -335.395 (\133) -0.79889 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 256.4 224.893 Tm -(6) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 261.381 224.893 Tm -[ (\054) -0.79889 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 264.149 224.893 Tm -(7) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 269.13 224.893 Tm -[ (\054) -0.79889 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 271.898 224.893 Tm -(8) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 276.879 224.893 Tm -[ (\054) -0.79889 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 279.646 224.893 Tm -(9) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 284.628 224.893 Tm -[ (\054) -0.79889 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 287.395 224.893 Tm -(10) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 297.358 224.893 Tm -[ (\135) -335.799 (b) -0.60039 (as) -0.39944 (e) -0.39944 (d) -335.586 (on) -334.611 (t) -0.90181 (h) -0.59794 (e) -335.41 (F) 82.2118 (u) -0.60039 (l) -0.79889 (l) -0.79889 (y) -334.804 (C) -0.20095 (on) 26.3951 (v) 27.2013 (ol) -0.80379 (u) -0.59794 (t) -0.90181 (i) -0.79889 (on) -0.60284 (al) -334.802 (Ne) -0.39699 (u) -0.60284 (r) -0.70086 (al) -0.79889 ] TJ --162.593 -11.9551 Td -[ (Ne) -0.40067 (t) 27.0911 (w) 27.8017 (or) -0.70086 (k) -315.794 (\133) -0.80011 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 177.223 212.938 Tm -(8) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 182.205 212.938 Tm -[ (\054) -0.80011 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 184.972 212.938 Tm -(11) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 194.935 212.938 Tm -[ (\135) -315.803 (s) -0.39944 (h) -0.60039 (o) 28.0027 (w) -315.2 (s) -1.38457 (t) -0.90181 (r) 0.30632 (i) -0.79889 (k) -0.80134 (i) -0.79889 (n) -0.60039 (g) -314.982 (i) -0.79889 (m) -0.29897 (p) -0.60039 (r) -0.69841 (o) 27.9978 (v) 26.1819 (e) -0.39944 (m) -0.30142 (e) -0.39944 (n) 27.3974 (t) -315.884 (o) 28 (v) 27.2013 (e) -0.39944 (r) -315.702 (s) -0.39944 (y) -0.79889 (s) -0.39944 (t) -0.90181 (e) -0.40189 (m) -0.29897 (s) -315.398 (r) -0.69841 (e) -0.40189 (l) -0.79889 (y) -0.79889 (i) -0.80134 (n) -0.59794 (g) -315.019 (on) -315.594 (h) -0.59794 (an) -0.60284 (d) -0.59794 (\055) -0.29897 (c) -0.40189 (r) -0.70086 (af) -0.60284 (t) ] TJ -275.692 0 Td -[ (e) -0.40189 (d) -0.59794 ] TJ --335.862 -11.9551 Td -[ (f) -0.59916 (e) -0.40067 (at) -0.90058 (u) -0.59916 (r) -0.69964 (e) -0.40067 (s) -381.392 (\133) -0.80011 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 175.45 200.983 Tm -(12) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 185.413 200.983 Tm -[ (\054) -0.80011 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 188.18 200.983 Tm -(13) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 198.143 200.983 Tm -[ (\054) -0.80011 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 200.91 200.983 Tm -(14) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 210.873 200.983 Tm -[ (\054) -0.79889 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 213.64 200.983 Tm -(15) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 223.603 200.983 Tm -[ (\054) -0.79889 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 226.37 200.983 Tm -(16) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 236.333 200.983 Tm -[ (\054) -0.79889 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 239.1 200.983 Tm -(17) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 249.063 200.983 Tm -[ (\135) -381.792 (on) -382.603 (b) -27.6081 (e) -0.39944 (n) -0.60039 (c) 26.5887 (h) 0.38719 (m) -1.29145 (ar) -0.69841 (k) -381.784 (t) -0.90181 (as) -0.39944 (k) -0.79889 (s) -0.39944 (\056) -381.806 (I) -0.09802 (n) -381.605 (t) -0.90181 (h) -0.59794 (i) -0.80134 (s) -381.404 (w) 26.7823 (or) -0.69841 (k) -0.80134 (\054) -381.779 (w) 27.8042 (e) -381.392 (c) -0.40189 (on) -0.59794 (s) -0.40189 (i) -0.80379 (d) -0.59794 (e) -0.40189 (r) -382.691 (t) 27.118 (w) 27.8042 (o) ] TJ --114.298 -11.9551 Td -[ (t) 27.1192 (y) -0.80011 (p) -28.5822 (e) -0.40067 (s) -350.381 (of) -349.591 (n) -0.59916 (e) -0.40067 (u) -0.59916 (r) -0.70086 (al) -350.794 (n) -0.60039 (e) -0.39944 (t) 27.0886 (w) 27.8017 (or) -0.69841 (k) -0.80134 (s) -350.412 (t) -0.90181 (h) -0.59794 (at) -349.883 (u) -0.60039 (s) -0.39944 (e) -350.415 (s) -0.39944 (p) -0.60039 (at) -0.89936 (i) -0.80134 (al) -350.816 (p) 27.4023 (y) -0.79889 (r) -0.70086 (am) -0.29897 (i) -0.79889 (d) -349.591 (p) -28.5884 (o) -27.9904 (ol) -0.79889 (i) -0.80134 (n) -0.59794 (g) -349.018 (m) -0.29897 (o) -28.0027 (d) -0.59794 (u) -0.60284 (l) -0.79889 (e) -350.402 (\133) -0.80379 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 430.038 189.028 Tm -(18) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 440 189.028 Tm -[ (\054) -0.80379 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 442.768 189.028 Tm -(19) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 452.73 189.028 Tm -[ (\054) -0.80379 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 455.498 189.028 Tm -(20) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 465.461 189.028 Tm -[ (\135) -349.799 (or) -0.70086 ] TJ --330.696 -11.9547 Td -[ (e) -0.40067 (n) -0.59916 (c) -0.40067 (o) -28.0162 (d) -0.59916 (e) -0.40067 (r) -0.70086 (\055) -0.30019 (d) -0.59916 (e) -0.40067 (c) -0.40067 (o) -27.9831 (d) -0.59916 (e) -0.40067 (r) -375.685 (s) -0.39944 (t) -0.90181 (r) -0.69841 (u) -0.60039 (c) -0.39944 (t) -0.90181 (u) -0.59794 (r) -0.70086 (e) -376.391 (\133) -0.79889 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 254.773 177.073 Tm -(21) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 264.736 177.073 Tm -[ (\054) -0.79889 ] TJ -ET -Q -0 1 0 rg -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 267.503 177.073 Tm -(22) Tj -ET -Q -0 g -q -10 0 0 10 0 0 cm -BT -/R38 9.9626 Tf -1 0 0 1 277.466 177.073 Tm -[ (\135) -376.812 (f) -0.60039 (or) -376.704 (s) -0.39944 (e) -0.40189 (m) -0.29897 (an) 27.3949 (t) -0.90181 (i) -0.79889 (c) -376.386 (s) -0.39944 (e) -0.40189 (gm) -0.29897 (e) -0.39944 (n) 27.3974 (t) -0.90181 (at) -0.89936 (i) -0.80134 (on) -0.59794 (\054) -376.785 (w) -0.19605 (h) -0.60284 (e) -0.39699 (r) -0.70576 (e) -376.393 (t) -0.90181 (h) -0.59794 (e) -376.383 (f) -0.60284 (or) -0.70086 (m) -0.30387 (e) -0.39699 (r) -0.70086 ] TJ --142.701 -11.9551 Td -[ (on) -0.59916 (e) -315.392 (c) -0.40067 (ap) -0.59916 (t) -0.90058 (u) -0.59916 (r) -0.69964 (e) -0.40067 (s) -315.401 (r) -0.70086 (i) -0.79889 (c) 27.602 (h) -316.619 (c) -0.39944 (on) 27.4096 (t) -0.90181 (e) -0.39944 (x) -0.79889 (t) -0.90181 (u) -0.60039 (al) -315.813 (i) -0.79889 (n) -0.60039 (f) -0.60039 (or) -0.69841 (m) -0.29897 (at) -0.90181 (i) -0.79889 (on) -315.594 (b) 27.4023 (y) -315.8 (p) -28.5884 (o) -27.9904 (ol) 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7977b54..0000000 --- a/docs/Multi Segmentation Project - Dashboard Specs html code +++ /dev/null @@ -1,230 +0,0 @@ - - - - - - Cahier des Charges Dashboard - - - -
-
Cahier des Charges Dashboard Interactif
-
Spécifications et implémentation - Projet DataSpace
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
CatégorieExigences ProjetImplémentation
ANALYSE EXPLORATOIRE - • Visualisation contenu dataset
- • Analyses statistiques describe()
- • 2+ graphiques interactifs
- • Distribution des classes - 		- Streamlit + Plotly interactif
- Métriques dataset Cityscapes
- Distribution pixel-wise classes
- Graphiques responsive -
COMPARAISON MODÈLES - • Sélection données entrée
- • Upload images personnalisées
- • Prédiction temps réel
- • Affichage résultats comparatifs - 		- Interface upload + dropdown
- Inférence FPN vs SegFormer
- Visualisation masques côte-à-côte
- Métriques temps d'exécution -
ACCESSIBILITÉ WCAG - • Critères WCAG essentiels
- • Contraste couleurs
- • Navigation clavier
- • Labels descriptifs - 		- Palette couleurs accessibles
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- - \ No newline at end of file diff --git a/docs/NeurIPS-2021-segformer-simple-and-efficient-design-for-semantic-segmentation-with-transformers-Paper.pdf b/docs/NeurIPS-2021-segformer-simple-and-efficient-design-for-semantic-segmentation-with-transformers-Paper.pdf deleted file mode 100644 index 7c1ab70..0000000 Binary files a/docs/NeurIPS-2021-segformer-simple-and-efficient-design-for-semantic-segmentation-with-transformers-Paper.pdf and /dev/null differ diff --git a/docs/SegFormer-model-structure-The-SegFormer-model-consists-of-an-encoder-an.png b/docs/SegFormer-model-structure-The-SegFormer-model-consists-of-an-encoder-an.png deleted file mode 100644 index cd0e917..0000000 Binary files a/docs/SegFormer-model-structure-The-SegFormer-model-consists-of-an-encoder-an.png and /dev/null differ diff --git a/docs/dashboard_specifications.png b/docs/dashboard_specifications.png deleted file mode 100644 index 74a79ad..0000000 Binary files a/docs/dashboard_specifications.png and /dev/null differ diff --git a/docs/fpn_resnet34_confusion_matrix.png b/docs/fpn_resnet34_confusion_matrix.png deleted file mode 100644 index ddc5642..0000000 Binary files a/docs/fpn_resnet34_confusion_matrix.png and /dev/null differ diff --git a/docs/fpn_resnet34_training_curves.png b/docs/fpn_resnet34_training_curves.png deleted file mode 100644 index d60828f..0000000 Binary files a/docs/fpn_resnet34_training_curves.png and /dev/null differ diff --git a/docs/github_cd_street-vision-poc.png b/docs/github_cd_street-vision-poc.png deleted file mode 100644 index cd0d9ff..0000000 Binary files a/docs/github_cd_street-vision-poc.png and /dev/null differ diff --git a/docs/iou_delta_iou_per_class.png b/docs/iou_delta_iou_per_class.png deleted file mode 100644 index 19905ba..0000000 Binary files a/docs/iou_delta_iou_per_class.png and /dev/null differ diff --git a/docs/mIoU_accuracy.png b/docs/mIoU_accuracy.png deleted file mode 100644 index ffd926a..0000000 Binary files a/docs/mIoU_accuracy.png and /dev/null differ diff --git a/docs/mIoU_accuracy_upscayl_3x_ultrasharp-4x.png b/docs/mIoU_accuracy_upscayl_3x_ultrasharp-4x.png deleted file mode 100644 index 303f4c1..0000000 Binary files a/docs/mIoU_accuracy_upscayl_3x_ultrasharp-4x.png and /dev/null differ diff --git a/docs/note_methodologique.md b/docs/note_methodologique.md deleted file mode 100644 index c280fd0..0000000 --- a/docs/note_methodologique.md +++ /dev/null @@ -1,295 +0,0 @@ -# Note Méthodologique : Preuve de concept SegFormer vs FPN - -## Dataset retenu - -### Cityscapes Dataset : Référence en Segmentation Urbaine - -Le **Cityscapes Dataset** constitue la référence internationale pour l'évaluation des algorithmes de segmentation sémantique en environnement urbain. Développé par l'Université de Tübingen et Mercedes-Benz, ce dataset comprend **25,000 images** haute résolution (2048×1024 pixels) capturées dans 50 villes allemandes et suisses. - -**Composition du dataset :** -- **Train** : 2,975 images finement annotées -- **Validation** : 500 images avec annotations de référence -- **Test** : 1,525 images (annotations non-publiques) -- **Coarse** : 20,000 images avec annotations grossières - -**Architecture de classes hiérarchique :** -Le dataset organise 34 classes détaillées en **8 méta-classes** pour la segmentation : - -| Méta-classe | Classes incluses | Importance | -|-------------|------------------|------------| -| **Flat** | Route, trottoir, parkings | Navigation de base | -| **Human** | Piéton, cycliste | **Sécurité critique** | -| **Vehicle** | Voiture, camion, bus, train | Obstacles dynamiques | -| **Construction** | Bâtiment, mur, clôture, pont | Structure urbaine | -| **Object** | Poteau, feu, panneau | Signalétique | -| **Nature** | Végétation, terrain | Environnement | -| **Sky** | Ciel | Contexte | -| **Void** | Zones non-définies | Exclusion | - -**Particularité méthodologique critique :** -Contrairement au protocole original préservant le ratio 2:1 natif (2048×1024), nos expérimentations utilisent des **images carrées (1:1)** aux résolutions 512×512 et 768×768. Cette déformation géométrique, motivée par les contraintes GPU T4, permet d'analyser l'impact du ratio d'image sur les différentes philosophies architecturales (CNN vs Transformer). - ---- - -## Les concepts de l'algorithme récent - -### SegFormer : Architecture Transformer Vision-Optimisée Temps Réel - -**SegFormer** représente une avancée dans la segmentation sémantique en introduisant une architecture **Transformer** spécifiquement conçue pour l'efficacité computationnelle, contrairement aux approches Transformer classiques gourmandes en ressources. - -### Innovations Architecturales Clés - -**1. Encodeur Hiérarchique Sans Encodage Positionnel** - -L'encodeur SegFormer **abandonne les encodages positionnels** au profit d'une architecture hiérarchique qui capture naturellement les relations spatiales : - -$$\text{Feature Maps} = \{F_1, F_2, F_3, F_4\}$$ - -avec résolutions $\{\frac{H}{4}, \frac{H}{8}, \frac{H}{16}, \frac{H}{32}\}$ - -**Efficient Self-Attention :** -Réduction de complexité par sous-échantillonnage des clés : - -$$\text{Attention}(Q,K,V) = \text{Softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V$$ - -avec complexité réduite de $\mathcal{O}(N^2)$ à $\mathcal{O}\left(\frac{N^2}{R}\right)$ où $R$ est le facteur de réduction. - -**2. Mix-FFN : Hybridation CNN-Transformer** - -Innovation clé : intégration de **convolutions 3×3** dans les couches Feed-Forward : - -$$\text{Mix-FFN}(x) = \text{MLP}(\text{GELU}(\text{Conv}_{3×3}(\text{MLP}(x)) + x))$$ - -Cette approche permet : -- **Élimination totale des encodages positionnels** -- **Capture locale + compréhension globale** simultanées -- **Robustesse aux variations de résolution** d'entrée - -**3. Décodeur MLP Ultra-Léger** - -Simplification drastique vs décodeurs complexes traditionnels : - -$$\text{Seg} = \text{MLP}\left(\text{Concat}[\text{Upsample}(F_1), F_2, F_3, F_4]\right)$$ - -**Avantages :** -- **Réduction massive des paramètres** : Focus sur l'encodeur performant -- **Inférence accélérée** : Décodeur 10× plus rapide que ASPP/PSP -- **Architecture unifiée** : Traitement multi-échelle sans modules ad-hoc - -### Positionnement Temps Réel vs État de l'Art - -**Analyse critique du benchmark original :** -Le papier SegFormer révèle que **SegFormer-B0** (3.7M paramètres) atteint **75.3% mIoU** sur Cityscapes à résolution réduite (côté court 768px), égalant **DeepLabv3+ + MobileNetV2** (75.2% mIoU) à résolution pleine. Cette **efficacité computationnelle supérieure** constitue l'argument central de notre hypothèse de recherche. - -**Hypothèse de transfert :** -Si DeepLabv3+ surpasse théoriquement FPN, alors SegFormer-B0 devrait également surpasser notre baseline FPN+EfficientNetB0 (78.67% mIoU), particulièrement à 768×768 pixels. - ---- - -## La modélisation - -### Méthodologie Expérimentale Adaptée - -**Contraintes et adaptations :** -Notre protocole d'évaluation diffère substantiellement du papier original pour s'adapter aux contraintes pratiques : - -**Configuration matérielle :** -- **Plateforme** : Google Colab Pro avec GPU T4 (15Go VRAM) -- **Framework** : Migration TensorFlow/Keras → PyTorch + Segmentation Models PyTorch -- **Contraintes** : Plan payant avec unités de calcul limitées - -**Déformation géométrique :** -- **Protocole original** : Préservation ratio 2:1 natif Cityscapes -- **Notre protocole** : Déformation vers ratio 1:1 (images carrées) -- **Résolutions testées** : 512×512 et 768×768 pixels -- **Impact attendu** : Analyse différentielle CNN vs Transformer sous contrainte géométrique - -### Preprocessing et Modèles Pré-entraînés - -**Modèles SegFormer (pré-entraînés ADE20K) :** -- **SegFormer-B0** : `segformer-b0-512x512-ade-160k` (3.7M paramètres) -- **SegFormer-B1** : `segformer-b1-512x512-ade-160k` (13.7M paramètres, exploration scaling) - -**Modèles FPN (pré-entraînés ImageNet) :** -- **FPN+ResNet34** : 23.16M paramètres -- **FPN+EfficientNet-B0** : 5.76M paramètres (baseline de référence @224x224 : 74.6% mIoU) - -### Configuration d'Entraînement Optimisée - -**Configuration SegFormer :** -- **Loss Function** : Jaccard Loss pour optimisation IoU directe -- **Optimiseur** : AdamW (lr=1e-4, weight_decay=1e-4, spécialisé Transformers) -- **Scheduler** : Cosine Annealing (convergence douce, min_lr=1e-6) -- **Epochs** : Maximum 30 avec early stopping (patience=5) - -**Configuration FPN :** -- **Loss Function** : Focal + Jaccard Loss combinées (poids 1:1) -- **Optimiseur** : Adam (lr=1.5e-4, weight_decay=1e-5, optimisé CNN) -- **Scheduler** : ReduceLROnPlateau (adaptation réactive, patience=4) -- **Epochs** : Maximum 20 avec early stopping (patience=7) - -**Stratégie d'entraînement :** -Entraînement séquentiel des 4 modèles principaux à 512×512, puis scaling à 768×768 pour analyse comparative résolution/performance. - ---- - -## Une synthèse des résultats - -![mIoU + Accuracy](mIoU_accuracy.png) -*Fig.1: Mean IoU et Accuracy par expérience* - -### Performance Comparative : SegFormer vs FPN sous Contrainte Géométrique - -**Résultats principaux (mIoU sur Cityscapes Validation) :** - -| **Modèle** | **mIoU** | **Gain vs Baseline** | **Paramètres** | **Taille** | **Temps CPU** | -|------------|----------|----------------------|----------------|------------|---------------| -| **SegFormer-B1** | **79.98%** | **+1.31%** | **13.68M** | **52MB** | **1.39s** | -| **FPN+ResNet34** | **79.22%** | **+0.55%** | **23.16M** | **88MB** | **0.73s** | -| **FPN+EfficientNet-B0** | **78.67%** | *baseline* | **5.76M** | **22MB** | **0.48s** | -| **SegFormer-B0** | **77.91%** | **-0.76%** | **3.72M** | **14MB** | **0.87s** | - -*Temps d'inférence : CPU Intel Xeon @ 2.20GHz (2 vCPUs) Google Colab* - -### Révélations Critiques : Impact du Ratio d'Image - -**Résultat majeur inattendu :** -Contrairement aux prédictions du benchmark original, **SegFormer-B0 sous-performe face aux baselines FPN** sur images carrées (77.91% vs 78.67% mIoU). Cette observation révèle une **sensibilité architecturale différentielle** aux déformations géométriques. - -**Hypothèse explicative - Robustesse géométrique :** - -1. **CNN (FPN)** : Les convolutions avec leurs kernels fixes et pooling local gèrent naturellement les déformations géométriques -2. **Transformer (SegFormer)** : L'attention globale, optimisée sur ratio 2:1 natif, souffre de la perte d'information spatiale lors de la déformation 1:1 -3. **Scaling architectural** : SegFormer-B1 compense cette limitation par sa capacité supérieure (+1.31% mIoU) - -### Analyse Détaillée par Classes Sémantiques - -![IoU Classe et Δ IoU par Classe](iou_delta_iou_per_class.png) -*Fig.2: IoU et Δ IoU par Classe entre SegFormer-B1 et FPN-ResNet34* - -**Performance par méta-classes Cityscapes :** - -| **Classe** | **SegFormer-B1** | **FPN+ResNet34** | **Gain** | **Analyse** | -|------------|-------------------|-------------------|----------|-------------| -| **Flat** | **93.35%** | **93.38%** | **-0.03%** | Performance équivalente | -| **Human** | **71.13%** | **69.35%** | **+1.78%** | ⭐ **Attention globale critique** | -| **Vehicle** | **88.80%** | **87.75%** | **+1.05%** | ⭐ **Contexte multi-échelle** | -| **Construction** | **85.94%** | **85.06%** | **+0.88%** | Structures complexes | -| **Object** | **49.24%** | **48.68%** | **+0.56%** | ⭐ **Signalétique petite taille** | -| **Nature** | **89.14%** | **88.13%** | **+1.01%** | Textures variées | -| **Sky** | **92.35%** | **91.67%** | **+0.68%** | Régions homogènes | -| **Void** | **69.89%** | **69.76%** | **+0.13%** | Zones ambiguës | - -**Patterns d'amélioration SegFormer-B1 :** -- **Classes sécuritaires critiques** (Human +1.78%, Vehicle +1.05%) : Validation de l'attention globale pour objets contextuels -- **Objets petite taille** (Object +0.56%) : Meilleure capture multi-échelle vs convolutions locales -- **Performance équivalente** sur surfaces homogènes (Flat -0.03%) - -### Performance Surprenante vs Benchmark Original - -**Observation contre-intuitive :** -Nos modèles atteignent des performances **supérieures au papier original** malgré la contrainte géométrique @768x768 : -- **SegFormer-B0 : 77.91%** vs 75.3% -- **FPN baselines : 78.67-79.22%** vs DeepLabV3+ 75.2% -- **SegFormer-B1 : 79.98%** vs ~76-78% (en effectuant un scaling grossier B0→B1 en se basant sur l'écart de performances de B0-->B5) - - -**Hypothèses explicatives :** -1. **Optimisation du ratio 1:1** : Réduction de l'aire d'image (×2) concentre l'information spatiale -2. **Optimisation d'hyperparamètres** : Par exemple, utilisation de fonctions de perte Jaccard (+ Focal) ? Non mentionné dans SegFormer (2021) ni DeepLabV3 (2018) -3. **Encodeurs** : EfficientNetB0 à peine plus long que MobileNetV2 et performances au-dessus. ResNet34 au-dessus également mais plus lourd (peut-être pertinent en temps réel...?) -3. **Framework PyTorch** : Optimisations modernes vs implémentations TensorFlow du papier DeepLabV3+ (2018) - ---- - -## L'analyse de la feature importance globale et locale du nouveau modèle - -### Limitations Méthodologiques de l'Explicabilité Transformer - -L'analyse d'explicabilité des architectures Transformer pour la segmentation dense représente un **défi technique majeur** dépassant le cadre de cette preuve de concept. Les mécanismes d'attention multi-têtes, Mix-FFN, et fusion hiérarchique créent des interactions complexes difficiles à décomposer causalement. - -### Insights Architecturaux via Patterns de Performance - -**Analyse indirecte par performance différentielle :** - -Bien que l'explicabilité fine soit inaccessible, les patterns de performance révèlent les mécanismes sous-jacents : - -1. **Attention globale validée** : Gains significatifs sur classes contextuelles (Human +1.78%, Vehicle +1.05%) suggèrent une **compréhension scénique** supérieure aux CNN -2. **Sensibilité géométrique** : Sous-performance SegFormer-B0 vs FPN sur images déformées révèle une **dépendance aux relations spatiales natives** -3. **Scaling architectural efficace** : Bond B0→B1 (+2.07% mIoU) confirme l'efficacité du scaling Transformer - -**Limitation fondamentale :** -Les cartes d'attention ne correspondent pas nécessairement à l'importance causale réelle. Une explicabilité rigoureuse nécessiterait des approches spécialisées comme **Deep Taylor Decomposition** ou **Integrated Gradients** adaptées aux architectures hybrides CNN-Transformer. - ---- - -## Limites et améliorations possibles - -### Limitations Expérimentales Critiques - -**1. Contraintes Protocolaires Majeures** -- **Déformation géométrique forcée** : Ratio 2:1→1:1 non-représentatif du déploiement réel -- **Résolutions limitées** : GPU T4 contraint l'exploration des résolutions supérieures (>768px). Augmentation importante de l'utilisation de VRAM et réduction drastique du `batch_size` --> entraînements plus longs (plus d'époques) -- **Framework migration** : TensorFlow→PyTorch peut introduire des biais de comparaison -- **Métriques d'évaluation** : Temps d'inférence CPU non-représentatifs du déploiement GPU optimisé - -**2. Généralisation Limitée** -- **Dataset unique** : Évaluation exclusive sur Cityscapes urbain allemand -- **Conditions météo** : Uniquement conditions favorables (pas de nuit/intempéries) -- **Scaling incomplet** : SegFormer-B2+ non-testés faute de ressources computationnelles - -### Améliorations Prioritaires Identifiées - -**1. Robustesse Protocolaire** -- **Préservation ratio natif** : Tests sur images 2:1 sans déformation géométrique (contraintes budgétaires/temps limitant cette approche) -- **Résolutions supérieures** : Évaluation à 1024×512 ou résolutions natives pour validation écologique -- **Evaluation multi-dataset** : Validation BDD100K, Mapillary Vistas pour généralisation - -**2. Scaling Architectural Complet** -- **SegFormer variants complets** : B2, B3, B4 pour validation des gains à tous les niveaux de complexité vs papier original -- **Évaluation différentielle** : Déterminer si les améliorations observées sont spécifiques aux modèles légers (B0-B1) ou généralisables aux variants lourds -- **Optimisations déploiement** : TensorRT, quantization FP16/INT8 pour inférence temps réel selon niveau de complexité - -**3. Analyse Causale Approfondie** -1. **Prototypage rapide** : SegFormer-B1 comme référence architecturale moderne pour projets nécessitant performance maximale -2. **Contraintes embarquées** : FPN+EfficientNet-B0 optimal pour déploiement edge ultra-contraint à 768×768, avec évaluation requise à résolutions supérieures pour validation complète -3. **Adaptabilité clients** : Gamme SegFormer (B0-B1+) offrant scaling performance/ressources selon besoins spécifiques - -### Perspectives de Déploiement Pratique - -**Applications industrielles validées :** -1. **Prototypage rapide** : SegFormer-B1 comme référence architecturale moderne pour projets nécessitant performance maximale -2. **Contraintes embarquées** : FPN+EfficientNet-B0 optimal pour déploiement edge ultra-contraint à 768×768, avec évaluation requise à résolutions supérieures pour validation complète -3. **Adaptabilité clients** : Gamme SegFormer (B0-B1+) offrant scaling performance/ressources selon besoins spécifiques - -**ROI technique contextuel :** -Cette preuve de concept valide l'intérêt des architectures Transformer tout en révélant leurs **sensibilités pratiques** (ratio d'image, scaling nécessaire). L'approche constitue une **base méthodologique** solide pour futurs développements production avec contraintes réelles explicitées. - ---- - -## Conclusion Méthodologique - -### Contribution Analytique Principale - -Cette étude apporte une **perspective complémentaire critique** aux évaluations académiques standard en révélant : - -1. **Impact géométrique différentiel** : Sensibilité CNN vs Transformer aux déformations d'image -2. **Scaling architectural requis** : Nécessité SegFormer-B1+ pour surpasser baselines CNN établies en ratio 1:1 -3. **Trade-offs pratiques** : Compromis performance/efficacité sous contraintes réelles - -### Validation des Hypothèses de Recherche - -**Hypothèses confirmées :** -- ✅ **SegFormer-B1 surpasse FPN** (+1.31% mIoU) avec efficacité paramétrique supérieure -- ✅ **Amélioration classes critiques** validée (Human +1.78%, Vehicle +1.05%) -- ✅ **Architecture Transformer viable** pour segmentation temps réel avec scaling approprié - -**Hypothèses infirmées :** -- ❌ **SegFormer-B0 sous-performant** vs FPN sur images 1:1 -- ❌ **Robustesse géométrique** Transformer surestimée vs CNN - -### Positionnement Scientifique - -Cette preuve de concept se distingue par son **réalisme méthodologique** : elle expose les limitations pratiques des architectures d'avant-garde sous contraintes déploiement réelles, offrant une analyse **nuancée et reproductible** aux équipes techniques devant opérer des choix architecturaux informés. - -L'approche constitue une **base technique solide** pour futurs développements industriels nécessitant des compromis éclairés entre innovation architecturale et contraintes opérationnelles. \ No newline at end of file diff --git a/docs/note_methodologique.pdf b/docs/note_methodologique.pdf deleted file mode 100644 index 2096778..0000000 Binary files a/docs/note_methodologique.pdf and /dev/null differ diff --git a/docs/p8_technical_note.pdf b/docs/p8_technical_note.pdf deleted file mode 100644 index 7e1f1fb..0000000 Binary files a/docs/p8_technical_note.pdf and /dev/null differ diff --git a/docs/plan_previsionnel.md b/docs/plan_previsionnel.md deleted file mode 100644 index 8abc278..0000000 --- a/docs/plan_previsionnel.md +++ /dev/null @@ -1,77 +0,0 @@ -# Plan Prévisionnel - Preuve de Concept Segmentation Sémantique - -**Projet** : Amélioration de la segmentation sémantique temps réel avec SegFormer - ---- - -## Dataset Retenu - -**Cityscapes** : Dataset de référence pour la segmentation sémantique de scènes urbaines comprenant 2975 images haute résolution (2048×1024) annotées pixel par pixel. Les images sont capturées dans **50 villes allemandes différentes** avec **34 classes principales** que nous regroupons en **8 catégories pertinentes** (flat, vehicle, human, construction, object, nature, sky, void). - -**Particularité méthodologique** : Les images seront testées aux résolutions 512×512 et 768×768 avec **déformation du ratio d'origine** (passage de 2:1 à 1:1) pour optimiser les contraintes computationnelles GPU T4. Cette modification permettra d'analyser l'impact du ratio d'image sur les différentes architectures. - ---- - -## Modèle Envisagé - -### Algorithme : SegFormer (Focus Temps Réel) - -**Justifications du choix :** -SegFormer (Xie et al., 2021) représente une approche Transformer vision-optimisée pour la segmentation sémantique. **L'analyse du benchmark Cityscapes révèle que SegFormer-B0 égale les performances de DeepLabv3+** (75.3% vs 75.2% mIoU) tout en fonctionnant à **résolution réduite** (côté court 768px vs résolution pleine). Cette efficacité computationnelle supérieure suggère un potentiel d'amélioration face à notre baseline FPN, DeepLabv3+ étant théoriquement plus performant que FPN. - -**Objectif et hypothèses de recherche :** -1. **SegFormer-B0** devrait surpasser FPN+EfficientNetB0 (78.67% mIoU baseline) grâce à l'attention globale native -2. **SegFormer-B1** permettra d'explorer le scaling architectural (non documenté dans le papier original) -3. **Impact du ratio d'image** : Analyser comment la déformation 2:1→1:1 affecte les architectures CNN vs Transformer - -**Contraintes et adaptations :** -- Migration framework obligatoire : TensorFlow/Keras → PyTorch pour accès aux modèles SegFormer récents -- Résolutions contraintes par GPU T4 : 512×512 et 768×768 maximum -- Protocole différent du papier original : images carrées vs rectangulaires natives - ---- - -## Références Bibliographiques - -1. **Xie, E., Wang, W., Yu, Z., et al.** (2021). *SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers*. NeurIPS 2021. [Architecture Transformer de référence] - -2. **Chen, L.C., Zhu, Y., Papandreou, G., et al.** (2018). *Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation*. ECCV 2018. [Baseline comparative DeepLabv3+] - -3. **Cordts, M., Omran, M., Ramos, S., et al.** (2016). *The Cityscapes Dataset for Semantic Urban Scene Understanding*. CVPR 2016. [Dataset de référence] - ---- - -## Explication de la Démarche de Test - -### Méthode Baseline -**FPN + EfficientNetB0/ResNet34** : Réentraînement des modèles du projet précédent aux nouvelles résolutions 512×512 et 768×768 avec déformation du ratio d'image. Performance baseline FPN+EfficientNetB0 : 78.67% mIoU (baseline de référence). - -### Méthode de Test SegFormer -**Évaluation comparative temps réel** : -1. **SegFormer-B0** : Modèle compact ciblé (3.7M paramètres) pour validation du concept temps réel -2. **SegFormer-B1** : Exploration scaling architectural (13.7M paramètres) non documenté dans le papier -3. **Comparaison 512×512 vs 768×768** : Trade-off résolution/performance sur images déformées -4. **Analyse différentielle** : Impact du ratio d'image 1:1 vs 2:1 natif Cityscapes - -**Métriques d'évaluation** : -- Mean IoU (métrique principale) et accuracy pixel-wise -- Performance par classe sémantique (focus classes critiques : Human, Vehicle) -- Temps d'inférence CPU/GPU pour validation déploiement pratique - -### Preuve de Concept -**Interface Streamlit** déployée sur serveur NAS personnel (CPU Intel N100) permettant : -- Upload d'images urbaines avec **inférence comparative multi-modèles** -- **Visualisation côte-à-côte** : FPN+ResNet34 vs SegFormer-B1 (meilleurs représentants) -- Affichage temps d'inférence réel et métriques de performance détaillées -- **Analyse exploratoire dataset** : Distribution classes, statistiques pixel-wise interactives - -**Hypothèses de validation** : -1. **SegFormer-B1 > FPN baselines** malgré contraintes ratio d'image -2. **SegFormer-B0 vs FPN** : Révélation possible de limitations Transformer sur images déformées -3. **Performances générales** potentiellement supérieures au papier original grâce à l'optimisation ratio 1:1 - -**Code réutilisé** : Framework `segmentation_models_pytorch` pour les implémentations. **Application originale** : protocole d'évaluation sur images carrées et analyse comparative CNN vs Transformer sous contraintes géométriques spécifiques. - ---- - -**Note méthodologique** : Cette approche permet d'analyser les trade-offs pratiques architecture/performance dans un contexte de contraintes réelles (hardware limité, déformation d'images), offrant une 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proxy_pass http://127.0.0.1:8000/; - proxy_set_header Host $host; - proxy_set_header X-Real-IP $remote_addr; - proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; - proxy_set_header X-Forwarded-Proto $scheme; - - # ✅ AJOUT: Timeouts pour uploads - proxy_read_timeout 300s; - proxy_connect_timeout 75s; - proxy_send_timeout 300s; - } - - location / { - proxy_pass http://127.0.0.1:8501/; - - proxy_http_version 1.1; - proxy_set_header Upgrade $http_upgrade; - proxy_set_header Connection "upgrade"; - - proxy_set_header Host $host; - proxy_set_header X-Real-IP $remote_addr; - proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; - proxy_set_header X-Forwarded-Proto $scheme; - - # ✅ AJOUT: Timeouts pour Streamlit uploads - proxy_read_timeout 300s; - proxy_connect_timeout 75s; - proxy_send_timeout 300s; - } - - location /health { - return 200 'OK'; - add_header Content-Type text/plain; - } - } -} - diff --git a/notebooks/content/data/new_images_to_predict/paris_street.jpg 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Si les modèles CNN performent bien en basse résolution (224x224), le passage à des **résolutions plus élevées (512x512)** révèle de nouveaux défis :\n","- Détection critique des piétons et cyclistes à distance\n","- Segmentation précise des objets partiellement occultés \n","- Gestion de la complexité spatiale accrue\n","\n","### Objectifs de l'étude\n","1. **Évaluer SegFormer** (Vision Transformer, 2021) face aux meilleures approches CNN identifiées précédemment\n","2. **Analyser l'impact de la résolution** sur les performances de segmentation\n","3. **Démontrer les avantages** des architectures modernes pour les applications critiques\n","4. **Fournir une preuve de concept** prête pour l'intégration industrielle\n","\n","## 🔬 Méthodologie de comparaison\n","\n","### Modèles évalués\n","- **Baseline CNN 1** : FPN + EfficientNetB0 (champion précédent @ 224x224 - 75% mIoU)\n","- **Baseline CNN 2** : FPN + ResNet34 (co-champion précédent @ 224x224) \n","- **Nouveau modèle 1** : SegFormerB0 (Version légère, comparable en complexité)\n","- **Nouveau modèle 2** : SegFormerB1 (Version standard avec plus de capacité)\n","\n","### Architecture technique unifiée\n","- **Framework** : PyTorch avec `segmentation_models_pytorch`\n","- **Encodeurs CNN** : Pré-entraînés ImageNet avec fine-tuning activé\n","- **SegFormer** : Checkpoints pré-entraînés ADE20K (512x512) avec fine-tuning\n","- **Data Augmentation** : Pipeline aléatoire uniforme sur tous les modèles\n","\n","### Dataset et résolution cible\n","- **Dataset** : Cityscapes (8 classes principales)\n","- **Résolution d'entraînement** : 512x512 pixels puis 768x768\n","- **Justification** : Résolution haute pour préserver les détails critiques en segmentation urbaine\n","\n","### Limitations méthodologiques assumées\n","- **Hypothèse** : Les champions CNN @ 224x224 restent les meilleures baselines @ 512x512\n","- **Contrainte matérielle** : Résolution 512x512 (`batch_size = 24` max) puis 768x768 (`batch_size = 8` max) avec optimisations mémoire pour GPU T4\n","- **Scope** : Focus sur comparaison architecturale plutôt qu'exploration exhaustive d'hyperparamètres\n","\n","## 🏗️ Configuration expérimentale\n","\n","### Pipeline d'entraînement unifié\n","- **Framework** : `segmentation_models_pytorch` pour cohérence maximale\n","- **Stratégie CNN** : Fine-tuning avec encodeurs ImageNet pré-entraînés\n","- **Stratégie SegFormer** : Fine-tuning depuis checkpoints ADE20K-160k spécialisés\n","- **Augmentation** : Pipeline identique (rotations, flips, ajustements colorimétriques)\n","- **Optimisation** : Configuration adaptée par architecture\n","\n","### Architectures détaillées\n","- **FPN + EfficientNetB0** : 5.7M paramètres, encoder ImageNet\n","- **FPN + ResNet34** : Encoder ImageNet, architecture plus robuste\n","- **SegFormerB0** : Version légère, checkpoint `segformer-b0-512x512-ade-160k`\n","- **SegFormerB1** : 13.7M paramètres, checkpoint `segformer-b1-512x512-ade-160k`\n","\n","### Classes de segmentation (Cityscapes - 8 groupes)\n","1. **Route** - Surfaces roulantes principales\n","2. **Trottoir** - Zones piétonnes sécurisées \n","3. **Bâtiments** - Infrastructure urbaine\n","4. **Végétation** - Espaces naturels\n","5. **Véhicules** - Moyens de transport motorisés\n","6. **Personnes** - Piétons et cyclistes *(classe critique)*\n","7. **Signalisation** - Panneaux et feux de circulation\n","8. **Mobilier urbain** - Infrastructure de voirie\n","\n","## 📋 Déroulement de l'étude\n","\n","### 1. Configuration et préparation\n","- Setup PyTorch avec `segmentation_models_pytorch`\n","- Préparation dataset Cityscapes @ 512x512 puis @ 768x768 avec mapping 8 classes\n","- Configuration MLflow pour tracking comparatif des 4 architectures\n","\n","### 2. Adaptation des modèles pré-entraînés\n","- **CNN baselines** : Chargement encodeurs ImageNet + initialisation têtes FPN\n","- **SegFormer** : Chargement checkpoints ADE20K + adaptation couche finale (150→8 classes)\n","- Validation des architectures sur données test\n","\n","### 3. Entraînement parallèle optimisé\n","- **Contrainte GPU** : 15GB VRAM\n","- **Pipeline uniforme** : Data augmentation et callbacks identiques\n","- **Monitoring** : MLflow tracking pour métriques comparatives\n","\n","### 4. Analyse multi-dimensionnelle\n","- **Performance** : Mean IoU global et IoU par classe critique\n","- **Efficacité** : Ratio performance/complexité computationnelle \n","- **Robustesse** : Analyse qualitative sur cas difficiles\n","- **Scalabilité** : Impact de l'augmentation de résolution par architecture\n","\n","### 5. Validation métier et preuve de concept\n","- **Classes critiques** : Focus spécifique personnes/véhicules\n","- **Analyse d'erreurs** : Identification faux négatifs dangereux\n","- **Dashboard interactif** : Streamlit avec comparaison visuelle des 4 modèles\n","\n","## 🎯 Hypothèses et attentes\n","\n","### Avantages attendus de SegFormer\n","- **Attention globale** : Meilleure compréhension du contexte urbain complexe\n","- **Multi-scale native** : Gestion des objets de tailles variées sans pyramides explicites\n","- **Pré-entraînement spécialisé** : Checkpoints ADE20K optimisés pour la segmentation\n","\n","### Comparaison B0 vs B1\n","- **SegFormerB0** : Équilibre performance/efficacité, concurrent direct des CNN\n","- **SegFormerB1** : Performance maximale, validation du potentiel Transformer\n","\n","### Validation de l'hypothèse principale\n","Cette étude vise à démontrer empiriquement que les **architectures Vision Transformer** offrent un avantage significatif pour la segmentation haute résolution dans les applications critiques, justifiant l'évolution technologique malgré la complexité accrue.\n","\n","## 🔧 Contraintes techniques\n","\n","### Limitations matérielles\n","- **GPU** : NVIDIA T4 15GB (contrainte mémoire moyenne)\n","- **Infrastructure** : Google Colab Pro nécessaire pour l'entraînement SegFormer (temps de calcul élevé)\n","- **Résolution** : 512x512 puis 768x768 optimisée pour performance GPU/mémoire\n","\n","### Adaptations requises\n","- **SegFormer** : Remplacement couche finale 150→8 classes depuis checkpoints ADE20K\n","- **Batch size** : Optimisation pour contrainte mémoire GPU T4\n","- **Temps d'entraînement** : Plan payant Google Colab requis pour convergence SegFormer\n","- **Pipeline data** : Augmentation équilibrée pour toutes les architectures\n","\n","---\n","\n","*Ce travail constitue une étude comparative rigoureuse visant à identifier l'architecture optimale pour les systèmes de vision embarqués nouvelle génération, en tenant compte des contraintes industrielles réelles.*"],"metadata":{"id":"zaghnHKsHSn2"}},{"cell_type":"markdown","metadata":{"id":"98EwoZLkDjzq"},"source":["## GPU et mémoire RAM"]},{"cell_type":"markdown","source":["Une gestion de la mixed precision (scaler + autocast) sera également proposée dans la partie \"Entrainement\" afin de réduire le temps de calcul."],"metadata":{"id":"aGlRVUMqcjCY"}},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":301,"status":"ok","timestamp":1756793589912,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-120},"id":"N139rgMJ-u28","outputId":"79781fcc-4ffb-414f-c998-0731dd45670d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Tue Sep 2 06:13:09 2025 \n","+-----------------------------------------------------------------------------------------+\n","| NVIDIA-SMI 550.54.15 Driver Version: 550.54.15 CUDA Version: 12.4 |\n","|-----------------------------------------+------------------------+----------------------+\n","| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n","| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n","| | | MIG M. |\n","|=========================================+========================+======================|\n","| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n","| N/A 45C P8 9W / 70W | 0MiB / 15360MiB | 0% Default |\n","| | | N/A |\n","+-----------------------------------------+------------------------+----------------------+\n"," \n","+-----------------------------------------------------------------------------------------+\n","| Processes: |\n","| GPU GI CI PID Type Process name GPU Memory |\n","| ID ID Usage |\n","|=========================================================================================|\n","| No running processes found |\n","+-----------------------------------------------------------------------------------------+\n","Your runtime has 54.8 gigabytes of available RAM\n","\n","You are using a high-RAM runtime!\n"]}],"source":["# ====================================================================\n","# INFORMATIONS SYSTÈME\n","# ====================================================================\n","\n","# GPU Info\n","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)\n","\n","# RAM Info\n","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":2,"metadata":{"id":"3evmO8AKQja8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1756793592994,"user_tz":-120,"elapsed":3081,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"849fa176-5b72-4c47-fdb5-c96d815f00ff"},"outputs":[{"output_type":"stream","name":"stdout","text":["PyTorch device: cuda\n","GPU Name: Tesla T4\n","GPU Memory: 15.8 GB\n","Mixed precision sera activée avec torch.cuda.amp\n"]}],"source":["# ====================================================================\n","# CONFIGURATION PYTORCH GPU\n","# ====================================================================\n","\n","import torch\n","import gc\n","\n","# Vérification GPU PyTorch\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","print(f\"PyTorch device: {device}\")\n","\n","if torch.cuda.is_available():\n"," print(f\"GPU Name: {torch.cuda.get_device_name(0)}\")\n"," print(f\"GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB\")\n","\n"," # Activer mixed precision pour PyTorch (équivalent de set_global_policy)\n"," # On utilisera torch.amp.autocast('cuda') dans les training loops\n"," print(\"Mixed precision sera activée avec torch.cuda.amp\")"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"Zr34mnLTcgj6","executionInfo":{"status":"ok","timestamp":1756793593036,"user_tz":-120,"elapsed":34,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# ====================================================================\n","# GESTION DE LA VRAM PYTORCH\n","# ====================================================================\n","\n","def deep_clean_memory():\n"," \"\"\"\n"," Nettoyage complet de la mémoire entre les modèles PyTorch\n"," Plus simple qu'avec TensorFlow !\n"," \"\"\"\n"," print(\"🧹 Nettoyage mémoire PyTorch...\")\n","\n"," # 1. Vider le cache CUDA PyTorch (le plus important)\n"," if torch.cuda.is_available():\n"," torch.cuda.empty_cache()\n"," torch.cuda.synchronize() # Attendre que toutes les opérations GPU soient finies\n","\n"," # 2. Forcer le garbage collector Python\n"," gc.collect()\n","\n"," print(\"✅ Mémoire PyTorch nettoyée\")\n","\n","def monitor_gpu_memory(stage=\"\"):\n"," \"\"\"Surveillance de la VRAM avec PyTorch natif\"\"\"\n","\n"," if torch.cuda.is_available():\n"," # Méthode PyTorch native (toujours disponible)\n"," allocated = torch.cuda.memory_allocated(0) / 1e9\n"," reserved = torch.cuda.memory_reserved(0) / 1e9\n"," total = torch.cuda.get_device_properties(0).total_memory / 1e9\n","\n"," print(f\"🔥 {stage} VRAM: {allocated:.1f}GB allocated, {reserved:.1f}GB reserved / {total:.1f}GB total\")\n","\n"," # Méthode GPUtil optionnelle (plus de détails si disponible)\n"," try:\n"," import GPUtil\n"," gpus = GPUtil.getGPUs()\n"," if gpus:\n"," gpu = gpus[0]\n"," used_gb = gpu.memoryUsed / 1024\n"," print(f\"🖥️ {stage} GPUtil: {used_gb:.1f}GB used ({gpu.memoryPercent:.1f}%)\")\n"," return gpu.memoryUsed\n"," except ImportError:\n"," # Pas d'erreur, juste pas d'info supplémentaire\n"," pass\n"," except Exception as e:\n"," print(f\"⚠️ GPUtil error: {e}\")\n","\n"," return allocated\n"," else:\n"," print(f\"🚫 {stage} Pas de GPU disponible\")\n"," return 0"]},{"cell_type":"code","source":["# Test initial\n","monitor_gpu_memory(\"Initial\")\n","deep_clean_memory()\n","monitor_gpu_memory(\"After cleanup\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_tvJs4VGJljp","executionInfo":{"status":"ok","timestamp":1756793593399,"user_tz":-120,"elapsed":350,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"813074e6-0f22-4756-b1a0-0f872e959015"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["🔥 Initial VRAM: 0.0GB allocated, 0.0GB reserved / 15.8GB total\n","🧹 Nettoyage mémoire PyTorch...\n","✅ Mémoire PyTorch nettoyée\n","🔥 After cleanup VRAM: 0.0GB allocated, 0.0GB reserved / 15.8GB total\n"]},{"output_type":"execute_result","data":{"text/plain":["0.0"]},"metadata":{},"execution_count":4}]},{"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":23239,"status":"ok","timestamp":1756793616639,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-120},"id":"zBTvEoak9GKf","outputId":"1b1c3efb-fac6-4f13-91fd-afd0b16b3f63"},"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"," 'model_architecture_1.png',\n"," 'model_architecture_2.png',\n"," 'model_architecture_3.png',\n"," 'results',\n"," 'artifacts',\n"," 'p8_notebook.ipynb',\n"," 'requirements.txt',\n"," 'p9_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_9/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":{"executionInfo":{"elapsed":22101,"status":"ok","timestamp":1756793638746,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-120},"id":"gkl0tzdJDgmD","colab":{"base_uri":"https://localhost:8080/"},"outputId":"0b326ec7-230b-4d9a-fc92-ba2d3b1dfe75"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m26.4/26.4 MB\u001b[0m \u001b[31m46.3 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[31m98.3 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[31m72.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.3/139.3 kB\u001b[0m \u001b[31m17.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.8/154.8 kB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m247.4/247.4 kB\u001b[0m \u001b[31m29.9 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[31m49.7 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[31m18.0 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[31m14.1 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[31m9.9 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[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m703.4/703.4 kB\u001b[0m \u001b[31m55.6 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[31m23.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h"]}],"source":["!pip install mlflow boto3 dotenv albumentations segmentation_models_pytorch --quiet"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"FQJX9NED7J1B","executionInfo":{"status":"ok","timestamp":1756793654073,"user_tz":-120,"elapsed":15325,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"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 # ← Pour mesurer le temps d'entraînement\n","import tempfile\n","\n","# ---------------------------------------\n","# Data Science & Manipulation\n","# ---------------------------------------\n","import numpy as np\n","import pandas as pd\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","# PyTorch & Segmentation Models PyTorch\n","# ---------------------------------------\n","import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","import torch.nn.functional as F\n","from torch.utils.data import Dataset, DataLoader\n","import torchvision.transforms as transforms\n","\n","# Segmentation Models PyTorch\n","import segmentation_models_pytorch as smp\n","\n","# Pour les augmentations de données (remplace tf augmentations)\n","import albumentations as A\n","from albumentations.pytorch import ToTensorV2\n","\n","# ---------------------------------------\n","# MLflow : suivi d'expériences & gestion modèles\n","# ---------------------------------------\n","import mlflow\n","import mlflow.pytorch # ← CHANGÉ: mlflow.keras → mlflow.pytorch\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","# ATTENTION: Ces utils functions devront peut-être être adaptées pour PyTorch\n","from utils.utils import load_cityscapes_config, get_preprocessing_fn, load_model, 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":307,"status":"ok","timestamp":1756793654376,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-120},"id":"O9oKKrUPQD9s","outputId":"d520a7a7-e55c-4b9e-9b7b-950eb891926f"},"outputs":[{"output_type":"stream","name":"stdout","text":["PyTorch version: 2.8.0+cu126\n","Segmentation Models PyTorch version: 0.5.0\n","CUDA disponible: True\n","GPU: Tesla T4\n","CUDA version: 12.6\n","MLflow Tracking URI: https://mlflow.greg-madman-nas.duckdns.org/\n","Identifiants AWS configurés\n","Appareil utilisé: cuda\n"]}],"source":["# Vérification de la configuration\n","print(f\"PyTorch version: {torch.__version__}\")\n","print(f\"Segmentation Models PyTorch version: {smp.__version__ if hasattr(smp, '__version__') else 'N/A'}\")\n","print(f\"CUDA disponible: {torch.cuda.is_available()}\")\n","if torch.cuda.is_available():\n"," print(f\"GPU: {torch.cuda.get_device_name(0)}\")\n"," print(f\"CUDA version: {torch.version.cuda}\")\n","\n","# Définition des chemins des données (INCHANGÉ)\n","g_drive_dir = drive_folder\n","g_drive_dir_content = os.path.join(g_drive_dir, \"content\")\n","data_dir = os.path.join(g_drive_dir_content, \"data\")\n","cityscapes_unzipped_dir = os.path.join(\"/content\", \"data\", \"cityscapes_unzipped\")\n","\n","# Création des dossiers s'il n'existent pas déjà\n","os.makedirs(data_dir, exist_ok=True)\n","os.makedirs(os.path.join(g_drive_dir_content, 'models'), exist_ok=True)\n","os.makedirs(os.path.join(g_drive_dir_content, 'models', 'checkpoints'), exist_ok=True)\n","os.makedirs(os.path.join(g_drive_dir_content, 'logs'), exist_ok=True)\n","\n","# Configuration des graines aléatoires pour la reproductibilité\n","seed = 42\n","random.seed(seed)\n","np.random.seed(seed)\n","torch.manual_seed(seed) # ← CHANGÉ: tf.random.set_seed → torch.manual_seed\n","if torch.cuda.is_available():\n"," torch.cuda.manual_seed(seed)\n"," torch.cuda.manual_seed_all(seed) # Pour multi-GPU\n"," # Pour plus de reproductibilité (peut ralentir un peu)\n"," torch.backends.cudnn.deterministic = True\n"," torch.backends.cudnn.benchmark = False\n","\n","# Configuration de MLflow avec les variables d'environnement (INCHANGÉ)\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 PyTorch\n","device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # ← CHANGÉ: format PyTorch\n","print(f\"Appareil utilisé: {device}\")"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1969,"status":"ok","timestamp":1756793656348,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-120},"id":"DR-_gM8JIZpr","outputId":"e70d21cb-6127-4d0c-cad1-2a379b858068"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":9}],"source":["# Créer l'expérience MLflow\n","mlflow_experiment = \"OC Projet 9\" # ← CHANGÉ: \"OC Projet 8\" → \"OC Projet 9\"\n","mlflow.set_experiment(mlflow_experiment)"]},{"cell_type":"code","source":["# Vérifications MLflow (INCHANGÉ)\n","print(f\"MLflow version: {mlflow.__version__}\")\n","print(f\"MLflow tracking URI: {mlflow.get_tracking_uri()}\")\n","\n","# Test direct de l'API\n","from mlflow.tracking import MlflowClient\n","client = MlflowClient()\n","try:\n"," models = client.search_registered_models()\n"," print(f\"✅ API MLflow fonctionne, {len(models)} modèles trouvés\")\n","except Exception as e:\n"," print(f\"❌ Erreur API MLflow: {e}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ie3_iqwVdJjr","executionInfo":{"status":"ok","timestamp":1756793656697,"user_tz":-120,"elapsed":345,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"b3aa4a68-9508-435f-b56a-e04bd7e5e171"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["MLflow version: 3.3.2\n","MLflow tracking URI: https://mlflow.greg-madman-nas.duckdns.org/\n","✅ API MLflow fonctionne, 3 modèles trouvés\n"]}]},{"cell_type":"code","source":["# Test détaillé MLflow\n","print(f\"URI: {os.getenv('MLFLOW_TRACKING_URI')}\")\n","client = MlflowClient()\n","try:\n"," experiments = client.search_experiments()\n"," print(f\"✅ search_experiments OK: {len(experiments)} found\")\n","\n"," # Test de recherche de modèle\n"," models = client.search_model_versions(\"name='StreetsSegmentation'\")\n"," print(f\"✅ search_model_versions OK: {len(models)} found\")\n","\n","except Exception as e:\n"," print(f\"❌ Erreur: {e}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DWY8YEWRfziW","executionInfo":{"status":"ok","timestamp":1756793657247,"user_tz":-120,"elapsed":542,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"c3f9e2d4-2a03-443f-d337-868c00c8a973"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["URI: https://mlflow.greg-madman-nas.duckdns.org/\n","✅ search_experiments OK: 4 found\n","✅ search_model_versions OK: 2 found\n"]}]},{"cell_type":"code","source":["print(\"✅ Imports PyTorch configurés avec succès!\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ODZbOIz-L7RX","executionInfo":{"status":"ok","timestamp":1756793657263,"user_tz":-120,"elapsed":9,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"5003094b-7f61-4a1b-f6ab-55c02335eab0"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["✅ Imports PyTorch configurés avec succès!\n"]}]},{"cell_type":"markdown","metadata":{"id":"P4O-U_2rQEJa"},"source":["## Téléchargement et préparation des données Cityscapes"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t52l8Er1QEcx","outputId":"807208ac-88da-4971-8166-bab3c9798a33","executionInfo":{"status":"ok","timestamp":1756793940599,"user_tz":-120,"elapsed":283335,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"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 [00:08<00:00, 2468.76it/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 [04:23<00:00, 19.09it/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":14,"metadata":{"id":"xFjmYex0PMM1","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1756793940628,"user_tz":-120,"elapsed":27,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"e45728b8-d1c3-42d4-ab7f-20fa5ebed490"},"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":15,"metadata":{"id":"7K-ox-l81X9A","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1756793949667,"user_tz":-120,"elapsed":9035,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"0cf8fd37-a8e2-4c49-9c98-d99262546f14"},"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":16,"metadata":{"id":"4YmGYVW56FMt","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1756793950402,"user_tz":-120,"elapsed":733,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"55f6536b-74dc-47bf-c3ea-702a21e16148"},"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":17,"metadata":{"id":"2kLbBJ8M1e5o","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1756793950433,"user_tz":-120,"elapsed":28,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"9d0980b0-fc1c-40f4-9d37-ad2331fd22e1"},"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":17}],"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","source":["# Configuration pour résolution plus haute\n","IMG_SIZE = 768 # ← NOUVELLE résolution (était 224x224)\n","BATCH_SIZE = 8 # ← Optimisé pour GPU T4 15GB\n","\n","print(f\"Configuration: {IMG_SIZE}x{IMG_SIZE} pixels, batch_size={BATCH_SIZE}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KqRZjUPDrwf2","executionInfo":{"status":"ok","timestamp":1756793950455,"user_tz":-120,"elapsed":20,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"8e324855-6e4a-4a28-b50d-c98cd26103c0"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["Configuration: 768x768 pixels, batch_size=8\n"]}]},{"cell_type":"code","source":["# === FONCTION DE PERTE PERSONNALISÉE ===\n","class CategoricalFocalJaccardLoss(nn.Module):\n"," \"\"\"\n"," Reproduction exacte de sm.losses.categorical_focal_jaccard_loss pour PyTorch\n"," Combine FocalLoss (gestion déséquilibre classes) + JaccardLoss (IoU)\n"," \"\"\"\n"," def __init__(self, focal_weight=1.0, jaccard_weight=1.0,\n"," alpha=0.25, gamma=2.0, smooth=1.0):\n"," super().__init__()\n"," self.focal_weight = focal_weight\n"," self.jaccard_weight = jaccard_weight\n","\n"," self.focal_loss = smp.losses.FocalLoss(\n"," mode='multiclass',\n"," alpha=alpha,\n"," gamma=gamma\n"," )\n","\n"," self.jaccard_loss = smp.losses.JaccardLoss(\n"," mode='multiclass',\n"," from_logits=True,\n"," smooth=smooth\n"," )\n","\n"," def forward(self, pred, target):\n"," focal = self.focal_loss(pred, target)\n"," jaccard = self.jaccard_loss(pred, target)\n","\n"," return (self.focal_weight * focal +\n"," self.jaccard_weight * jaccard)\n","print(\"✅ Fonction de perte CategoricalFocalJaccardLoss définie\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nExMwVZEXFPH","executionInfo":{"status":"ok","timestamp":1756793950475,"user_tz":-120,"elapsed":19,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"923934a6-1ddc-46c3-ca36-93d13ef186b3"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["✅ Fonction de perte CategoricalFocalJaccardLoss définie\n"]}]},{"cell_type":"code","execution_count":20,"metadata":{"id":"8eHYsAmIRsIU","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1756793950491,"user_tz":-120,"elapsed":15,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"25d9c290-5e66-4f34-f82e-b54e593fb56e"},"outputs":[{"output_type":"stream","name":"stdout","text":["🔧 BACKBONES DISPONIBLES DANS SEGMENTATION_MODELS_PYTORCH:\n","=================================================================\n","\n","📱 ResNet:\n"," • resnet101\n"," • resnet152\n"," • resnet18\n"," • resnet34\n"," • resnet50\n","\n","📱 ResNeXt:\n"," • resnext101_32x16d\n"," • resnext101_32x32d\n"," • resnext101_32x48d\n"," • resnext101_32x4d\n"," • resnext101_32x8d\n"," • resnext50_32x4d\n","\n","📱 VGG:\n"," • vgg11\n"," • vgg11_bn\n"," • vgg13\n"," • vgg13_bn\n"," • vgg16\n"," • vgg16_bn\n"," • vgg19\n"," • vgg19_bn\n","\n","📱 DenseNet:\n"," • densenet121\n"," • densenet161\n"," • densenet169\n"," • densenet201\n","\n","📱 EfficientNet:\n"," • efficientnet-b0\n"," • efficientnet-b1\n"," • efficientnet-b2\n"," • efficientnet-b3\n"," • efficientnet-b4\n"," • efficientnet-b5\n"," • efficientnet-b6\n"," • efficientnet-b7\n"," • timm-efficientnet-b0\n"," • timm-efficientnet-b1\n"," • timm-efficientnet-b2\n"," • timm-efficientnet-b3\n"," • timm-efficientnet-b4\n"," • timm-efficientnet-b5\n"," • timm-efficientnet-b6\n"," • timm-efficientnet-b7\n"," • timm-efficientnet-b8\n"," • timm-efficientnet-l2\n"," • timm-tf_efficientnet_lite0\n"," • timm-tf_efficientnet_lite1\n"," • timm-tf_efficientnet_lite2\n"," • timm-tf_efficientnet_lite3\n"," • timm-tf_efficientnet_lite4\n","\n","📱 MobileNet:\n"," • mobilenet_v2\n","\n","📊 Total: 76 backbones disponibles\n"]}],"source":["def show_available_backbones():\n"," \"\"\"Affiche tous les backbones disponibles dans segmentation_models_pytorch\"\"\"\n","\n"," try:\n"," # PyTorch segmentation models a une structure différente\n"," import segmentation_models_pytorch.encoders as encoders\n","\n"," print(\"🔧 BACKBONES DISPONIBLES DANS SEGMENTATION_MODELS_PYTORCH:\")\n"," print(\"=\" * 65)\n","\n"," # Les encoders sont organisés différemment en PyTorch\n"," encoder_names = list(encoders.encoders.keys())\n","\n"," # Grouper par famille\n"," families = {\n"," 'ResNet': [b for b in encoder_names if b.startswith('resnet')],\n"," 'SEResNet': [b for b in encoder_names if b.startswith('seresnet')],\n"," 'SEResNeXt': [b for b in encoder_names if b.startswith('seresnext')],\n"," 'ResNeXt': [b for b in encoder_names if b.startswith('resnext')],\n"," 'VGG': [b for b in encoder_names if b.startswith('vgg')],\n"," 'DenseNet': [b for b in encoder_names if b.startswith('densenet')],\n"," 'EfficientNet': [b for b in encoder_names if 'efficientnet' in b],\n"," 'MobileNet': [b for b in encoder_names if 'mobilenet' in b],\n"," 'RegNet': [b for b in encoder_names if 'regnet' in b],\n"," 'MiT (SegFormer)': [b for b in encoder_names if 'mit-' in b],\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"," print(f\"\\n📊 Total: {len(encoder_names)} backbones disponibles\")\n","\n"," except Exception as e:\n"," print(f\"Erreur lors de la récupération des backbones: {e}\")\n","\n"," # Fallback avec les principaux backbones PyTorch connus\n"," main_backbones = [\n"," 'efficientnet-b0', 'efficientnet-b1', 'efficientnet-b2',\n"," 'resnet34', 'resnet50', 'mobilenet_v2',\n"," 'nvidia/mit-b0', 'nvidia/mit-b1', 'nvidia/mit-b2' # Pour SegFormer\n"," ]\n","\n"," print(\"🔧 BACKBONES PRINCIPAUX (fallback):\")\n"," for backbone in main_backbones:\n"," print(f\" • {backbone}\")\n","\n","# Afficher les backbones disponibles\n","show_available_backbones()"]},{"cell_type":"code","source":["def generate_experiment_configs():\n"," \"\"\"Configuration unifiée avec augmentations communes et hyperparamètres spécialisés par architecture\"\"\"\n","\n"," model_configs = []\n","\n"," # ================================================================\n"," # PARAMÈTRES D'AUGMENTATION COMMUNS POUR TOUS LES MODÈLES\n"," # ================================================================\n"," common_aug_params = {\n"," 'horizontal_flip': 0.5,\n"," 'brightness_contrast': {\n"," 'brightness_limit': 0.15,\n"," 'contrast_limit': 0.15,\n"," 'p': 0.3\n"," },\n"," 'gamma': {\n"," 'gamma_limit': (80, 120),\n"," 'p': 0.2\n"," },\n"," 'rotation': {\n"," 'limit': 8,\n"," 'p': 0.25\n"," }\n"," }\n","\n"," # ================================================================\n"," # SEGFORMER CONFIGS OPTIMISÉES\n"," # ================================================================\n"," segformer_variants = [\n"," {\n"," 'variant': 'b1',\n"," 'checkpoint': 'smp-hub/segformer-b1-512x512-ade-160k',\n"," 'description': 'SegFormer B1 optimisé 20 epochs',\n"," 'batch_size': BATCH_SIZE\n"," },\n"," {\n"," 'variant': 'b0',\n"," 'checkpoint': 'smp-hub/segformer-b0-512x512-ade-160k',\n"," 'description': 'SegFormer B0 optimisé 20 epochs',\n"," 'batch_size': BATCH_SIZE\n"," }\n"," ]\n","\n"," for variant in segformer_variants:\n"," model_configs.append({\n"," \"name\": f\"SegFormer_{variant['variant']}_pretrained_optimized\",\n"," \"architecture\": \"SegFormer_pretrained\",\n"," \"checkpoint\": variant['checkpoint'],\n"," \"classes\": 8,\n"," \"activation\": None,\n"," \"params\": {\n"," # === TRAINING BASICS ===\n"," 'epochs': 30,\n"," 'batch_size': variant['batch_size'],\n"," 'img_size': (IMG_SIZE, IMG_SIZE),\n","\n"," # === OPTIMIZER SEGFORMER (AdamW optimisé) ===\n"," 'optimizer': 'adamw',\n"," 'learning_rate': 1e-4,\n"," 'weight_decay': 1e-4,\n"," 'eps': 1e-8,\n","\n"," # === SCHEDULER SEGFORMER (Cosine) ===\n"," 'scheduler': 'cosine',\n"," 'warmup_epochs': 2,\n"," 'min_lr': 1e-6,\n","\n"," # === LOSS FUNCTION SEGFORMER (Jaccard seule) ===\n"," 'loss_type': 'jaccard_only',\n"," 'loss_params': {\n"," 'focal_weight': 0.0, # Pas de Focal pour SegFormer\n"," 'jaccard_weight': 1.0, # Jaccard seule\n"," 'alpha': 0.2, # Non utilisé mais gardé\n"," 'gamma': 1.5, # Non utilisé mais gardé\n"," 'smooth': 1.0\n"," },\n","\n"," # === AUGMENTATION COMMUNE ===\n"," 'augmentation': True,\n"," 'aug_params': common_aug_params,\n","\n"," # === CALLBACKS SEGFORMER ===\n"," 'early_stopping': True,\n"," 'early_stopping_patience': 5,\n"," 'lr_reduce_patience': 3,\n"," 'mixed_precision': True\n"," },\n"," \"type\": \"segformer_optimized\",\n"," \"description\": variant['description'],\n"," \"priority\": \"high\"\n"," })\n","\n"," # ================================================================\n"," # FPN CONFIGS BASELINES\n"," # ================================================================\n"," fpn_baselines = [\n"," {\n"," 'encoder': 'efficientnet-b0',\n"," 'description': 'FPN EfficientNet-B0 baseline',\n"," 'priority': 'reference'\n"," },\n"," {\n"," 'encoder': 'resnet34',\n"," 'description': 'FPN ResNet34 baseline',\n"," 'priority': 'high'\n"," }\n"," ]\n","\n"," for baseline in fpn_baselines:\n"," model_configs.append({\n"," \"name\": f\"FPN_{baseline['encoder'].replace('-', '_')}_baseline\",\n"," \"architecture\": \"FPN\",\n"," \"encoder_name\": baseline['encoder'],\n"," \"encoder_weights\": \"imagenet\",\n"," \"classes\": 8,\n"," \"activation\": None,\n"," \"params\": {\n"," # === TRAINING BASICS ===\n"," 'epochs': 20,\n"," 'batch_size': BATCH_SIZE,\n"," 'img_size': (IMG_SIZE, IMG_SIZE),\n","\n"," # === OPTIMIZER FPN (Adam classique) ===\n"," 'optimizer': 'adam',\n"," 'learning_rate': 1.5e-4,\n"," 'weight_decay': 1e-5,\n","\n"," # === SCHEDULER FPN (Plateau) ===\n"," 'scheduler': 'plateau',\n"," 'lr_reduce_patience': 4,\n","\n"," # === LOSS FUNCTION FPN (Focal + Jaccard) ===\n"," 'loss_type': 'focal_jaccard',\n"," 'loss_params': {\n"," 'focal_weight': 1.0, # Focal active pour FPN\n"," 'jaccard_weight': 1.0, # + Jaccard\n"," 'alpha': 0.25,\n"," 'gamma': 2.0,\n"," 'smooth': 1.0\n"," },\n","\n"," # === AUGMENTATION COMMUNE (identique à SegFormer) ===\n"," 'augmentation': True,\n"," 'aug_params': common_aug_params,\n","\n"," # === CALLBACKS FPN ===\n"," 'early_stopping': True,\n"," 'early_stopping_patience': 7,\n"," 'lr_reduce_patience': 4,\n"," 'mixed_precision': True\n"," },\n"," \"type\": \"fpn_baseline\",\n"," \"description\": baseline['description'],\n"," \"priority\": baseline['priority']\n"," })\n","\n"," print(f\"✅ Configurations générées: {len(model_configs)} modèles\")\n"," print(f\" 📊 SegFormer: {len(segformer_variants)} variantes\")\n"," print(f\" 📊 FPN: {len(fpn_baselines)} baselines\")\n"," print(f\" 🎯 Augmentations communes: {list(common_aug_params.keys())}\")\n","\n"," return model_configs"],"metadata":{"id":"8sSK69f_Nl_j","executionInfo":{"status":"ok","timestamp":1756793950800,"user_tz":-120,"elapsed":308,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":21,"outputs":[]},{"cell_type":"code","execution_count":22,"metadata":{"id":"X7wY6OqSZFha","executionInfo":{"status":"ok","timestamp":1756793950804,"user_tz":-120,"elapsed":2,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["def create_model_from_config(config, verbose=False):\n"," \"\"\"\n"," Crée un modèle PyTorch à partir d'une configuration\n"," Supporte FPN baseline et SegFormer pré-entraîné\n"," \"\"\"\n"," architecture = config['architecture']\n"," classes = config['classes']\n"," img_size = config['params']['img_size'][0] # 📍 Récupération de la résolution\n","\n"," if verbose:\n"," print(f\"Création du modèle PyTorch '{config['name']}'\")\n"," print(f\" Architecture: {architecture}\")\n"," print(f\" Classes: {classes}\")\n"," print(f\" Résolution: {config['params']['img_size']}\")\n","\n"," try:\n"," if architecture == \"FPN\":\n"," # FPN avec encodeur pré-entraîné ImageNet\n"," encoder_name = config['encoder_name']\n"," encoder_weights = config.get('encoder_weights', 'imagenet')\n","\n"," model = smp.FPN(\n"," encoder_name=encoder_name,\n"," encoder_weights=encoder_weights,\n"," classes=classes,\n"," activation=config.get('activation')\n"," )\n","\n"," if verbose:\n"," print(f\" Encoder: {encoder_name} (weights: {encoder_weights})\")\n","\n"," elif architecture == \"SegFormer_pretrained\":\n"," checkpoint = config['checkpoint']\n"," if verbose:\n"," print(f\" Checkpoint: {checkpoint}\")\n","\n"," model = smp.from_pretrained(checkpoint)\n","\n"," # 🔧 DÉFINIR conv_layers_150 EN DEHORS DU BLOC VERBOSE\n"," conv_layers_150 = []\n"," for name, module in model.named_modules():\n"," if isinstance(module, nn.Conv2d) and module.out_channels == 150:\n"," conv_layers_150.append((name, module))\n","\n"," # 📊 AFFICHAGE VERBOSE (optionnel)\n"," if verbose:\n"," print(f\" 🔍 EXPLORATION COMPLÈTE DU MODÈLE ENTIER:\")\n"," print(f\" - Modules principaux: {list(model._modules.keys())}\")\n","\n"," for name, module in model.named_modules():\n"," if isinstance(module, nn.Conv2d):\n"," print(f\" - Conv2d: {name} -> in={module.in_channels}, out={module.out_channels}, kernel={module.kernel_size}\")\n"," if module.out_channels == 150:\n"," print(f\" ★★★ CIBLE 150 CLASSES: {name}\")\n","\n"," print(f\" 🎯 COUCHES 150 CLASSES TROUVÉES: {len(conv_layers_150)}\")\n","\n"," # 🔧 REMPLACER LA COUCHE (TOUJOURS, même sans verbose)\n"," if len(conv_layers_150) > 0:\n"," layer_name, old_layer = conv_layers_150[0]\n","\n"," # Créer la nouvelle couche\n"," new_layer = nn.Conv2d(\n"," old_layer.in_channels,\n"," classes,\n"," kernel_size=old_layer.kernel_size,\n"," stride=old_layer.stride,\n"," padding=old_layer.padding,\n"," bias=old_layer.bias is not None\n"," )\n","\n"," # Remplacer dans le modèle\n"," if '.' in layer_name:\n"," parent = model\n"," attrs = layer_name.split('.')\n"," for attr in attrs[:-1]:\n"," parent = getattr(parent, attr)\n"," setattr(parent, attrs[-1], new_layer)\n"," else:\n"," setattr(model, layer_name, new_layer)\n","\n"," if verbose:\n"," print(f\" ✅ COUCHE {layer_name} REMPLACÉE:\")\n"," print(f\" Ancien: {old_layer}\")\n"," print(f\" Nouveau: {new_layer}\")\n","\n"," # 🧪 TEST FINAL (seulement si verbose)\n"," if verbose and torch.cuda.is_available():\n"," print(f\" 🧪 TEST FINAL:\")\n"," model.eval()\n"," test_input = torch.randn(1, 3, img_size, img_size).cuda()\n"," model = model.cuda()\n","\n"," with torch.no_grad():\n"," output = model(test_input)\n"," print(f\" - Output shape: {output.shape}\")\n","\n"," if output.shape[1] == classes:\n"," print(f\" ✅ SUCCESS: {classes} classes!\")\n"," else:\n"," print(f\" ❌ TOUJOURS {output.shape[1]} classes au lieu de {classes}\")\n","\n"," model.train()\n","\n"," elif architecture == \"SegFormer\":\n"," # SegFormer from scratch (si vous décidez de l'utiliser)\n"," encoder_name = config['encoder_name']\n","\n"," model = smp.Segformer(\n"," encoder_name=encoder_name,\n"," classes=classes\n"," )\n","\n"," if verbose:\n"," print(f\" Encoder: {encoder_name} (from scratch)\")\n","\n"," else:\n"," raise ValueError(f\"Architecture '{architecture}' non supportée\")\n","\n"," if verbose:\n"," # Compter les paramètres PyTorch\n"," total_params = sum(p.numel() for p in model.parameters())\n"," trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n"," frozen_params = total_params - trainable_params\n","\n"," print(f\" Paramètres: {total_params:,} total | {trainable_params:,} entraînables | {frozen_params:,} gelés\")\n"," print(f\" Type: {config.get('type', 'unknown')}\")\n"," if 'description' in config:\n"," print(f\" Description: {config['description']}\")\n","\n"," return model\n","\n"," except Exception as e:\n"," print(f\"Erreur création modèle: {e}\")\n"," raise"]},{"cell_type":"code","source":["def print_experiment_plan(configs):\n"," \"\"\"Affiche le plan d'expériences de façon organisée\"\"\"\n","\n"," img_size = configs[0]['params']['img_size'][0] # 📍 Récupération de la résolution\n","\n"," print(\"PLAN D'EXPÉRIENCES - OC PROJET 9\")\n"," print(\"=\" * 60)\n"," print(\"Objectif: SegFormer doit battre FPN baseline en Mean IoU\")\n"," print(\"Contrainte: GPU T4 12GB, 2h max par expérience\")\n"," print(f\"Résolution: {img_size}×{img_size} (upgrade de 224×224)\")\n"," print(\"=\" * 60)\n","\n"," # Grouper par type\n"," types = {}\n"," for config in configs:\n"," config_type = config.get('type', 'unknown')\n"," if config_type not in types:\n"," types[config_type] = []\n"," types[config_type].append(config)\n","\n"," for config_type, type_configs in types.items():\n"," print(f\"\\n{config_type.upper().replace('_', ' ')}:\")\n","\n"," for config in sorted(type_configs, key=lambda x: x.get('priority', 'medium')):\n"," priority = config.get('priority', 'medium')\n"," priority_icon = {\"reference\": \"🎯\", \"high\": \"⭐\", \"medium\": \"📊\"}\n","\n"," print(f\" {priority_icon.get(priority, '•')} {config['name']}\")\n","\n"," # Adapter l'affichage selon le type d'architecture\n"," if config['architecture'] == 'SegFormer_pretrained':\n"," checkpoint_name = config['checkpoint'].split('/')[-1]\n"," print(f\" └─ {checkpoint_name} | batch={config['params']['batch_size']} | lr={config['params']['learning_rate']}\")\n"," else:\n"," print(f\" └─ {config['encoder_name']} | batch={config['params']['batch_size']} | lr={config['params']['learning_rate']}\")\n","\n"," if 'description' in config:\n"," print(f\" {config['description']}\")\n","\n"," print(f\"\\nTotal: {len(configs)} expériences planifiées\")\n","\n"," # Estimation du temps\n"," total_epochs = sum(c['params']['epochs'] for c in configs)\n"," estimated_hours = total_epochs * 0.1 # Estimation très grossière\n"," print(f\"Temps estimé: ~{estimated_hours:.1f}h (si tout séquentiel)\")"],"metadata":{"id":"lxhBv19pXDhv","executionInfo":{"status":"ok","timestamp":1756793950810,"user_tz":-120,"elapsed":4,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":23,"outputs":[]},{"cell_type":"code","execution_count":24,"metadata":{"id":"4ROnd_8f9_W7","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1756793950827,"user_tz":-120,"elapsed":14,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"ea398e30-4159-46e4-9d6a-76865065b47b"},"outputs":[{"output_type":"stream","name":"stdout","text":["✅ Configurations générées: 4 modèles\n"," 📊 SegFormer: 2 variantes\n"," 📊 FPN: 2 baselines\n"," 🎯 Augmentations communes: ['horizontal_flip', 'brightness_contrast', 'gamma', 'rotation']\n"]}],"source":["# Génération des configurations\n","experimental_configurations = generate_experiment_configs()"]},{"cell_type":"code","source":["# Affichage du plan\n","print_experiment_plan(experimental_configurations)\n","\n","# Test de création d'un modèle\n","print(\"\\n\" + \"=\"*50)\n","print(\"🧪 TEST DE CRÉATION D'UN MODÈLE\")\n","print(\"=\"*50)\n","\n","# Test des deux types\n","print(\"=\"*50)\n","print(\"🧪 TEST FPN (référence)\")\n","print(\"=\"*50)\n","fpn_config = next(c for c in experimental_configurations if c.get('priority') == 'reference')\n","test_fpn = create_model_from_config(fpn_config, verbose=True)\n","\n","print(\"\\n\" + \"=\"*50)\n","print(\"🧪 TEST SEGFORMER (problématique)\")\n","print(\"=\"*50)\n","segformer_config = next(c for c in experimental_configurations if 'SegFormer' in c.get('name', ''))\n","test_segformer = create_model_from_config(segformer_config, verbose=True)\n","\n","print(f\"\\n✅ Modèle de test créé avec succès!\")"],"metadata":{"id":"1Np-lZw0XKWv","colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["279e4ab1476c49a29d1fb14f76d190e3","aa348a2af1fd47edb98f0fbb4acfd5ce","f459dffc0f1c4fabbb532e7f5f0db2f2","8d3f385a20274efc81ffb776fe2d815f","44df00e6019d431694da6dafe08d11d3","6acd25e5e7594cc9a2d159d12bb9b8f3","d570b22559424006890f239107332c8e","c82d25b115a141cfb7d7693dd2464839","f1a78dfcf4d944e891548fa92c12f509","94c184668d19486c95e42c45410d8f60","758914e3480c4270879e4d2df048be18","abcd2e31f3d0489b81bb7cc61551c4d1","7df9d8e904db44e6a0349ca12cab5e75","6d8d7a796d1c46628aba5f9492ded5fb","c4ab7860af0b4ce5beffd37869affea3","efd5307189fe40b78d3f0f423df23ea2","6df7d308ab9d43518b4c32390e142c35","3410221bf7a749eb893b80c302b9a68b","e42c31291986459693d49f3eedb8fad7","4be74d3deab940e2a4324dbfcda0c497","05fe9a206aca4e6fa47919f56747503e","b0fb9fc4e9e546978694fce12c28faf8","5001fbeea68149dcb602e441154629e5","a1c72c03171e4013adf02b04a22d826d","697b59e1901c46b08ba208aaf979638e","681055ba8c374cb19a2c9e06319f7af5","e0621a86a2164fcaa8b81edbaf0bee70","30e49054d8174e8f9f93d418bee77e60","cd2287969f6e46fe92acb09da7bc98c0","449af144e2334ee2a1423f701d966987","428d9a8887874ff092b8ee7ebfeea21d","4754ae6fba8f429da287b4b52992c532","1cbc56c15bc842f6b567f09de0fcfe49","3663cd0f8e8a4537b7b29bf6ec026888","6fbdd908a6114d8e9f34934609918eba","77492788c1224c8ea7f52193c47d31b3","6fb6aaf9bff14db8a3003ecf2b3c7112","66ae8d5440e149e9abd4ea3ec9874c12","2b04cefbf36248489e2455a4defc7841","0c2e78ba84304f6fa94968762a592508","d76fa940a87c4717b884a0e44932d639","a0de5abe684e4527a23c7b0a996d6a37","15a2b4713a4f460ea76c4c5dc9ce59f1","ed8092375a0443d98dafa62b57786c4f"]},"executionInfo":{"status":"ok","timestamp":1756793963920,"user_tz":-120,"elapsed":13091,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"92454793-0b23-431f-9a55-a9fad683e1ea"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["PLAN D'EXPÉRIENCES - OC PROJET 9\n","============================================================\n","Objectif: SegFormer doit battre FPN baseline en Mean IoU\n","Contrainte: GPU T4 12GB, 2h max par expérience\n","Résolution: 768×768 (upgrade de 224×224)\n","============================================================\n","\n","SEGFORMER OPTIMIZED:\n"," ⭐ SegFormer_b1_pretrained_optimized\n"," └─ segformer-b1-512x512-ade-160k | batch=8 | lr=0.0001\n"," SegFormer B1 optimisé 20 epochs\n"," ⭐ SegFormer_b0_pretrained_optimized\n"," └─ segformer-b0-512x512-ade-160k | batch=8 | lr=0.0001\n"," SegFormer B0 optimisé 20 epochs\n","\n","FPN BASELINE:\n"," ⭐ FPN_resnet34_baseline\n"," └─ resnet34 | batch=8 | lr=0.00015\n"," FPN ResNet34 baseline\n"," 🎯 FPN_efficientnet_b0_baseline\n"," └─ efficientnet-b0 | batch=8 | lr=0.00015\n"," FPN EfficientNet-B0 baseline\n","\n","Total: 4 expériences planifiées\n","Temps estimé: ~10.0h (si tout séquentiel)\n","\n","==================================================\n","🧪 TEST DE CRÉATION D'UN MODÈLE\n","==================================================\n","==================================================\n","🧪 TEST FPN (référence)\n","==================================================\n","Création du modèle PyTorch 'FPN_efficientnet_b0_baseline'\n"," Architecture: FPN\n"," Classes: 8\n"," Résolution: (768, 768)\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n"," warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json: 0%| | 0.00/106 [00:00 in=3, out=64, kernel=(7, 7)\n"," - Conv2d: encoder.patch_embed2.proj -> in=64, out=128, kernel=(3, 3)\n"," - Conv2d: encoder.patch_embed3.proj -> in=128, out=320, kernel=(3, 3)\n"," - Conv2d: encoder.patch_embed4.proj -> in=320, out=512, kernel=(3, 3)\n"," - Conv2d: encoder.block1.0.attn.sr -> in=64, out=64, kernel=(8, 8)\n"," - Conv2d: encoder.block1.0.mlp.dwconv.dwconv -> in=256, out=256, kernel=(3, 3)\n"," - Conv2d: encoder.block1.1.attn.sr -> in=64, out=64, kernel=(8, 8)\n"," - Conv2d: encoder.block1.1.mlp.dwconv.dwconv -> in=256, out=256, kernel=(3, 3)\n"," - Conv2d: encoder.block2.0.attn.sr -> in=128, out=128, kernel=(4, 4)\n"," - Conv2d: encoder.block2.0.mlp.dwconv.dwconv -> in=512, out=512, kernel=(3, 3)\n"," - Conv2d: encoder.block2.1.attn.sr -> in=128, out=128, kernel=(4, 4)\n"," - Conv2d: encoder.block2.1.mlp.dwconv.dwconv -> in=512, out=512, kernel=(3, 3)\n"," - Conv2d: encoder.block3.0.attn.sr -> in=320, out=320, kernel=(2, 2)\n"," - Conv2d: encoder.block3.0.mlp.dwconv.dwconv -> in=1280, out=1280, kernel=(3, 3)\n"," - Conv2d: encoder.block3.1.attn.sr -> in=320, out=320, kernel=(2, 2)\n"," - Conv2d: encoder.block3.1.mlp.dwconv.dwconv -> in=1280, out=1280, kernel=(3, 3)\n"," - Conv2d: encoder.block4.0.mlp.dwconv.dwconv -> in=2048, out=2048, kernel=(3, 3)\n"," - Conv2d: encoder.block4.1.mlp.dwconv.dwconv -> in=2048, out=2048, kernel=(3, 3)\n"," - Conv2d: decoder.fuse_stage.0 -> in=1024, out=256, kernel=(1, 1)\n"," - Conv2d: segmentation_head.0 -> in=256, out=150, kernel=(1, 1)\n"," ★★★ CIBLE 150 CLASSES: segmentation_head.0\n"," 🎯 COUCHES 150 CLASSES TROUVÉES: 1\n"," ✅ COUCHE segmentation_head.0 REMPLACÉE:\n"," Ancien: Conv2d(256, 150, kernel_size=(1, 1), stride=(1, 1))\n"," Nouveau: Conv2d(256, 8, kernel_size=(1, 1), stride=(1, 1))\n"," 🧪 TEST FINAL:\n"," - Output shape: torch.Size([1, 8, 768, 768])\n"," ✅ SUCCESS: 8 classes!\n"," Paramètres: 13,679,304 total | 13,679,304 entraînables | 0 gelés\n"," Type: segformer_optimized\n"," Description: SegFormer B1 optimisé 20 epochs\n","\n","✅ Modèle de test créé avec succès!\n"]}]},{"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":26,"metadata":{"id":"qdsGkrVQe2mX","executionInfo":{"status":"ok","timestamp":1756793963961,"user_tz":-120,"elapsed":24,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["def analyze_model_comprehensive(config, show_architecture=False, detailed_layers=False):\n"," \"\"\"\n"," Analyse exhaustive d'un modèle PyTorch de segmentation\n"," ADAPTÉ PYTORCH : Utilise les APIs PyTorch pour l'analyse des paramètres\n","\n"," Args:\n"," config (dict): Configuration du modèle\n"," show_architecture (bool): Afficher l'architecture complète\n"," detailed_layers (bool): Afficher TOUTES les couches\n","\n"," Returns:\n"," dict: Résultats de l'analyse\n"," \"\"\"\n"," print(f\"\\n{'='*70}\")\n"," print(f\"🏗️ ANALYSE COMPLÈTE PYTORCH: {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\" • Encoder: {config['encoder_name']}\")\n"," print(f\" • Type: {config['type'].replace('_', ' ').title()}\")\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"," print(f\" • Batch size: {config['params']['batch_size']}\")\n","\n"," try:\n"," # === 2. CRÉATION DU MODÈLE ===\n"," print(f\"\\n🔧 Création du modèle PyTorch...\")\n"," model = create_model_from_config(config, verbose=False)\n","\n"," # === 3. ANALYSE DES PARAMÈTRES PYTORCH ===\n"," total_params = sum(p.numel() for p in model.parameters())\n"," trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n"," frozen_params = total_params - trainable_params\n"," frozen_ratio = (frozen_params / total_params) * 100 if total_params > 0 else 0\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 MODULES PYTORCH ===\n"," encoder_modules = []\n"," decoder_modules = []\n"," other_modules = []\n","\n"," encoder_total = 0\n"," encoder_frozen = 0\n","\n"," # Analyser tous les modules nommés\n"," for name, module in model.named_modules():\n"," if len(list(module.parameters())) == 0: # Skip modules sans paramètres\n"," continue\n","\n"," module_params = sum(p.numel() for p in module.parameters())\n"," module_trainable = sum(p.numel() for p in module.parameters() if p.requires_grad)\n"," module_frozen = module_params - module_trainable\n","\n"," # Classifier le module\n"," is_encoder = False\n"," if any(keyword in name.lower() for keyword in ['encoder', 'backbone', 'features']):\n"," is_encoder = True\n"," encoder_modules.append({\n"," 'name': name,\n"," 'params': module_params,\n"," 'trainable': module_trainable,\n"," 'frozen': module_frozen,\n"," 'requires_grad': module_trainable > 0\n"," })\n"," encoder_total += module_params\n"," encoder_frozen += module_frozen\n"," elif any(keyword in name.lower() for keyword in ['decoder', 'fpn', 'segmentation_head']):\n"," decoder_modules.append({\n"," 'name': name,\n"," 'params': module_params,\n"," 'trainable': module_trainable,\n"," 'frozen': module_frozen,\n"," 'requires_grad': module_trainable > 0\n"," })\n"," else:\n"," other_modules.append({\n"," 'name': name,\n"," 'params': module_params,\n"," 'trainable': module_trainable,\n"," 'frozen': module_frozen,\n"," 'requires_grad': module_trainable > 0\n"," })\n","\n"," # === 5. AFFICHAGE DES MODULES ===\n"," if detailed_layers:\n"," print(f\"\\n🔍 TOUS LES MODULES (🟢 = Entraînable, 🔴 = Gelé):\")\n"," print(\"-\" * 70)\n","\n"," all_modules = encoder_modules + decoder_modules + other_modules\n"," for i, module_info in enumerate(all_modules):\n"," icon = \"🔴\" if module_info['frozen'] > module_info['trainable'] else \"🟢\"\n"," status = \"GELÉ\" if module_info['frozen'] > module_info['trainable'] else \"ENTRAÎNABLE\"\n","\n"," # Type de module\n"," if any(keyword in module_info['name'].lower() for keyword in ['encoder', 'backbone']):\n"," module_type = \" (ENCODER)\"\n"," elif any(keyword in module_info['name'].lower() for keyword in ['decoder', 'fpn']):\n"," module_type = \" (DECODER)\"\n"," elif 'head' in module_info['name'].lower():\n"," module_type = \" (HEAD)\"\n"," else:\n"," module_type = \"\"\n","\n"," print(f\"{i+1:2d}. {icon} {module_info['name']:<40} {module_info['params']:>8,} params - {status}{module_type}\")\n"," else:\n"," # Affichage résumé\n"," if encoder_modules:\n"," print(f\"\\n🔍 MODULES ENCODER (🟢 = Entraînable, 🔴 = Gelé):\")\n"," print(\"-\" * 60)\n","\n"," for module_info in encoder_modules[:5]: # Limite pour lisibilité\n"," icon = \"🔴\" if module_info['frozen'] > module_info['trainable'] else \"🟢\"\n"," status = \"GELÉ\" if module_info['frozen'] > module_info['trainable'] else \"ENTRAÎNABLE\"\n"," print(f\" {icon} {module_info['name']:<35} {module_info['params']:>8,} params - {status}\")\n","\n"," if len(encoder_modules) > 5:\n"," print(f\" ... et {len(encoder_modules)-5} autres modules encoder\")\n","\n"," print(f\"\\n🏗️ AUTRES MODULES:\")\n"," print(f\" • Decoder: {len(decoder_modules)} modules\")\n"," print(f\" • Autres: {len(other_modules)} modules\")\n"," print(f\" • (Utilisez detailed_layers=True pour voir tous)\")\n","\n"," # === 6. RÉSUMÉ ENCODER ===\n"," if encoder_total > 0:\n"," encoder_frozen_ratio = (encoder_frozen / encoder_total * 100)\n"," print(f\"\\n📋 Résumé encoder:\")\n"," print(f\" • Total encoder: {encoder_total:,} params\")\n"," print(f\" • Encoder gelé: {encoder_frozen:,} params ({encoder_frozen_ratio:.1f}%)\")\n"," print(f\" • Encoder entraînable: {encoder_total - encoder_frozen:,} params ({100-encoder_frozen_ratio:.1f}%)\")\n","\n"," # === 7. VALIDATION SPÉCIFIQUE PYTORCH ===\n"," print(f\"\\n🎯 Validation modèle PyTorch:\")\n","\n"," # Pour FPN et SegFormer, validation différente\n"," if config['architecture'] == 'FPN':\n"," # FPN devrait avoir encoder + decoder entraînables\n"," if trainable_params > total_params * 0.8:\n"," status = \"✅ OK\"\n"," message = f\"FPN entièrement entraînable ({(trainable_params/total_params)*100:.1f}%)\"\n"," else:\n"," status = \"⚠️ SUSPECT\"\n"," message = f\"FPN partiellement gelé ({frozen_ratio:.1f}% gelé)\"\n","\n"," elif config['architecture'] == 'SegFormer':\n"," # SegFormer devrait être entièrement entraînable\n"," if frozen_ratio < 5.0:\n"," status = \"✅ OK\"\n"," message = f\"SegFormer correctement entraînable ({frozen_ratio:.1f}% gelé)\"\n"," else:\n"," status = \"⚠️ SUSPECT\"\n"," message = f\"SegFormer avec gel inattendu ({frozen_ratio:.1f}% gelé)\"\n"," else:\n"," status = \"❓ INCONNU\"\n"," message = f\"Architecture {config['architecture']} non reconnue\"\n","\n"," print(f\" • Statut: {status}\")\n"," print(f\" • {message}\")\n","\n"," # === 8. ARCHITECTURE DÉTAILLÉE ===\n"," if show_architecture:\n"," print(f\"\\n🏛️ Architecture PyTorch:\")\n"," print(\"-\" * 60)\n"," print(model)\n","\n"," # === 9. TEST FORWARD PASS ===\n"," print(f\"\\n🧪 Test forward pass:\")\n"," model.eval()\n"," img_size = config['params']['img_size']\n"," batch_size = config['params']['batch_size']\n","\n"," try:\n"," with torch.no_grad():\n"," dummy_input = torch.randn(1, 3, img_size[0], img_size[1])\n"," output = model(dummy_input)\n"," print(f\" • Input shape: {dummy_input.shape}\")\n"," print(f\" • Output shape: {output.shape}\")\n"," print(f\" • ✅ Forward pass réussi\")\n"," except Exception as e:\n"," print(f\" • ❌ Forward pass échoué: {e}\")\n","\n"," # === 10. RÉSULTATS ===\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"," 'encoder_params': encoder_total,\n"," 'encoder_frozen': encoder_frozen,\n"," 'encoder_frozen_ratio': encoder_frozen_ratio if encoder_total > 0 else 0,\n"," 'status': 'success',\n"," 'validation': status\n"," }\n","\n"," print(f\"\\n✅ Analyse PyTorch 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"," }"]},{"cell_type":"code","source":["def test_model_configurations():\n"," \"\"\"\n"," Test des configurations PyTorch pour vérifier leur validité\n"," ADAPTÉ : Test des modèles FPN et SegFormer\n"," \"\"\"\n"," print(\"🧪 TEST DES CONFIGURATIONS PYTORCH\")\n"," print(\"=\" * 60)\n","\n"," # Sélectionner les configs par leurs vrais noms\n"," test_configs = []\n","\n"," # Chercher \"FPN_efficientnet_b0_baseline\"\n"," fpn_config = None\n"," for config in experimental_configurations:\n"," if config['name'] == 'FPN_efficientnet_b0_baseline':\n"," fpn_config = config\n"," test_configs.append(config)\n"," break\n","\n"," # Chercher \"SegFormer_mit-b0_new\"\n"," segformer_config = None\n"," for config in experimental_configurations:\n"," if config['name'] == 'SegFormer_mit-b0_new':\n"," segformer_config = config\n"," test_configs.append(config)\n"," break\n","\n"," if not test_configs:\n"," print(\"Aucune config trouvée, utilisation des premières disponibles\")\n"," test_configs = experimental_configurations[:2]\n","\n"," print(f\"Testing {len(test_configs)} configurations:\")\n"," for config in test_configs:\n"," print(f\" - {config['name']}\")\n"," print(\"-\" * 60)\n","\n"," results = {}\n","\n"," for config in test_configs:\n"," print(f\"\\n🔍 Test: {config['name']}\")\n"," print(\"-\" * 50)\n","\n"," try:\n"," # Création du modèle\n"," model = create_model_from_config(config, verbose=True)\n","\n"," # Comptage des paramètres\n"," total_params = sum(p.numel() for p in model.parameters())\n"," trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\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"," # Test forward pass\n"," img_size = config['params']['img_size']\n"," try:\n"," with torch.no_grad():\n"," dummy_input = torch.randn(1, 3, img_size[0], img_size[1])\n"," output = model(dummy_input)\n"," print(f\" • Forward pass: ✅ OK {dummy_input.shape} → {output.shape}\")\n"," except Exception as e:\n"," print(f\" • Forward pass: ❌ ÉCHEC - {e}\")\n","\n"," # Validation selon le type\n"," if config['architecture'] == 'FPN':\n"," expected_ratio = 5.0 # FPN devrait être mostly trainable\n"," status = \"✅ OK\" if frozen_ratio < expected_ratio else \"⚠️ ATTENTION\"\n"," print(f\" • Statut FPN (attendu <5% gelé): {status}\")\n"," elif config['architecture'] == 'SegFormer':\n"," expected_ratio = 2.0 # SegFormer devrait être fully trainable\n"," status = \"✅ OK\" if frozen_ratio < expected_ratio else \"⚠️ ATTENTION\"\n"," print(f\" • Statut SegFormer (attendu <2% gelé): {status}\")\n","\n"," results[config['type']] = {\n"," 'total': total_params,\n"," 'trainable': trainable_params,\n"," 'frozen': frozen_params,\n"," 'frozen_ratio': frozen_ratio,\n"," 'forward_pass': 'success'\n"," }\n","\n"," # Nettoyage\n"," del model\n"," torch.cuda.empty_cache() if torch.cuda.is_available() else None\n","\n"," except Exception as e:\n"," print(f\" • ❌ Erreur: {e}\")\n"," results[config['type']] = {'error': str(e)}\n","\n"," # === RÉSUMÉ COMPARATIF ===\n"," print(f\"\\n🎯 RÉSUMÉ COMPARATIF PyTorch\")\n"," print(\"=\" * 60)\n","\n"," for config_type, result in results.items():\n"," if 'error' not in result:\n"," print(f\"{config_type}:\")\n"," print(f\" • Params totaux: {result['total']:,}\")\n"," print(f\" • Ratio gelé: {result['frozen_ratio']:.1f}%\")\n"," print(f\" • Forward pass: {result['forward_pass']}\")\n"," else:\n"," print(f\"{config_type}: ❌ {result['error']}\")\n","\n"," return results"],"metadata":{"id":"FhGKYjKAaBO5","executionInfo":{"status":"ok","timestamp":1756793963983,"user_tz":-120,"elapsed":14,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":27,"outputs":[]},{"cell_type":"code","source":["def show_all_modules_status(config):\n"," \"\"\"\n"," Fonction spécialisée pour afficher les modules PyTorch de manière compacte\n"," \"\"\"\n"," print(f\"\\n🔍 MODULES PYTORCH - {config['name']}\")\n"," print(\"=\" * 70)\n"," print(\"🟢 ENTRAÎNABLE | 🔴 GELÉ\")\n"," print(\"-\" * 70)\n","\n"," try:\n"," model = create_model_from_config(config, verbose=False)\n","\n"," trainable_count = 0\n"," frozen_count = 0\n","\n"," for i, (name, module) in enumerate(model.named_modules()):\n"," if len(list(module.parameters())) == 0:\n"," continue\n","\n"," module_params = sum(p.numel() for p in module.parameters())\n"," module_trainable = sum(p.numel() for p in module.parameters() if p.requires_grad)\n"," is_trainable = module_trainable > (module_params * 0.5)\n","\n"," icon = \"🟢\" if is_trainable else \"🔴\"\n","\n"," if is_trainable:\n"," trainable_count += 1\n"," else:\n"," frozen_count += 1\n","\n"," # Type de module\n"," if any(keyword in name.lower() for keyword in ['encoder', 'backbone']):\n"," module_type = \"ENCODER\"\n"," elif any(keyword in name.lower() for keyword in ['decoder', 'fpn']):\n"," module_type = \"DECODER\"\n"," elif 'head' in name.lower():\n"," module_type = \"HEAD\"\n"," else:\n"," module_type = \"AUTRE\"\n","\n"," print(f\"{i+1:2d}. {icon} {name:<35} {module_params:>8,} {module_type}\")\n","\n"," print(\"-\" * 70)\n"," print(f\"📊 RÉSUMÉ: {trainable_count} entraînables, {frozen_count} gelés\")\n","\n"," del model\n"," torch.cuda.empty_cache() if torch.cuda.is_available() else None\n","\n"," except Exception as e:\n"," print(f\"❌ Erreur: {e}\")"],"metadata":{"id":"H7emfskBaCeN","executionInfo":{"status":"ok","timestamp":1756793964011,"user_tz":-120,"elapsed":26,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":28,"outputs":[]},{"cell_type":"code","source":["# === EXÉCUTION DES TESTS ===\n","print(f\"\\n\" + \"=\"*70)\n","print(\"LANCEMENT DES TESTS PYTORCH\")\n","print(\"=\"*70)\n","\n","# Test des configurations\n","test_results = test_model_configurations()\n","\n","# Analyse détaillée d'un modèle FPN (recherche par nom exact)\n","fpn_config = None\n","for config in experimental_configurations:\n"," if config['name'] == 'FPN_efficientnet_b0_baseline':\n"," fpn_config = config\n"," break\n","\n","if fpn_config:\n"," print(f\"\\n\" + \"=\"*70)\n"," print(\"ANALYSE DÉTAILLÉE - FPN BASELINE\")\n"," print(\"=\"*70)\n","\n"," # Analyse standard\n"," analyze_model_comprehensive(fpn_config)\n","\n"," # Vue des modules\n"," show_all_modules_status(fpn_config)\n","else:\n"," print(\"⚠️ Config FPN_efficientnet_b0_baseline non trouvée\")\n","\n","# Analyse SegFormer\n","segformer_config = None\n","for config in experimental_configurations:\n"," if config['name'] == 'SegFormer_mit_b0_new':\n"," segformer_config = config\n"," break\n","\n","if segformer_config:\n"," print(f\"\\n\" + \"=\"*70)\n"," print(\"ANALYSE DÉTAILLÉE - SEGFORMER\")\n"," print(\"=\"*70)\n","\n"," analyze_model_comprehensive(segformer_config)\n","else:\n"," print(\"⚠️ Config SegFormer_mit-b0_new non trouvée\")\n","\n","print(\"\\n✅ Tous les tests d'analyse PyTorch terminés!\")"],"metadata":{"id":"a8FrrEk7aMVs","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1756793965500,"user_tz":-120,"elapsed":1481,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"8aa464ad-8bd8-469d-fd53-79de7313a29c"},"execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","======================================================================\n","LANCEMENT DES TESTS PYTORCH\n","======================================================================\n","🧪 TEST DES CONFIGURATIONS PYTORCH\n","============================================================\n","Testing 1 configurations:\n"," - FPN_efficientnet_b0_baseline\n","------------------------------------------------------------\n","\n","🔍 Test: FPN_efficientnet_b0_baseline\n","--------------------------------------------------\n","Création du modèle PyTorch 'FPN_efficientnet_b0_baseline'\n"," Architecture: FPN\n"," Classes: 8\n"," Résolution: (768, 768)\n"," Encoder: efficientnet-b0 (weights: imagenet)\n"," Paramètres: 5,760,388 total | 5,760,388 entraînables | 0 gelés\n"," Type: fpn_baseline\n"," Description: FPN EfficientNet-B0 baseline\n","\n","📈 Résultat:\n"," • Total: 5,760,388\n"," • Entraînables: 5,760,388\n"," • Gelés: 0\n"," • % gelés: 0.0%\n"," • Forward pass: ✅ OK torch.Size([1, 3, 768, 768]) → torch.Size([1, 8, 768, 768])\n"," • Statut FPN (attendu <5% gelé): ✅ OK\n","\n","🎯 RÉSUMÉ COMPARATIF PyTorch\n","============================================================\n","fpn_baseline:\n"," • Params totaux: 5,760,388\n"," • Ratio gelé: 0.0%\n"," • Forward pass: success\n","\n","======================================================================\n","ANALYSE DÉTAILLÉE - FPN BASELINE\n","======================================================================\n","\n","======================================================================\n","🏗️ ANALYSE COMPLÈTE PYTORCH: FPN_efficientnet_b0_baseline\n","======================================================================\n","📊 Configuration:\n"," • Architecture: FPN\n"," • Encoder: efficientnet-b0\n"," • Type: Fpn Baseline\n"," • Learning rate: 0.00015\n"," • Époques prévues: 20\n"," • Taille images: (768, 768)\n"," • Batch size: 8\n","\n","🔧 Création du modèle PyTorch...\n","\n","🔢 Paramètres:\n"," • Total: 5,760,388\n"," • Entraînables: 5,760,388 (100.0%)\n"," • Gelés: 0 (0.0%)\n","\n","🔍 MODULES ENCODER (🟢 = Entraînable, 🔴 = Gelé):\n","------------------------------------------------------------\n"," 🟢 encoder 4,007,548 params - ENTRAÎNABLE\n"," 🟢 encoder._conv_stem 864 params - ENTRAÎNABLE\n"," 🟢 encoder._bn0 64 params - ENTRAÎNABLE\n"," 🟢 encoder._blocks 3,594,460 params - ENTRAÎNABLE\n"," 🟢 encoder._blocks.0 1,448 params - ENTRAÎNABLE\n"," ... et 143 autres modules encoder\n","\n","🏗️ AUTRES MODULES:\n"," • Decoder: 47 modules\n"," • Autres: 1 modules\n"," • (Utilisez detailed_layers=True pour voir tous)\n","\n","📋 Résumé encoder:\n"," • Total encoder: 15,204,016 params\n"," • Encoder gelé: 0 params (0.0%)\n"," • Encoder entraînable: 15,204,016 params (100.0%)\n","\n","🎯 Validation modèle PyTorch:\n"," • Statut: ✅ OK\n"," • FPN entièrement entraînable (100.0%)\n","\n","🧪 Test forward pass:\n"," • Input shape: torch.Size([1, 3, 768, 768])\n"," • Output shape: torch.Size([1, 8, 768, 768])\n"," • ✅ Forward pass réussi\n","\n","✅ Analyse PyTorch terminée avec succès\n","\n","🔍 MODULES PYTORCH - FPN_efficientnet_b0_baseline\n","======================================================================\n","🟢 ENTRAÎNABLE | 🔴 GELÉ\n","----------------------------------------------------------------------\n"," 1. 🟢 5,760,388 AUTRE\n"," 2. 🟢 encoder 4,007,548 ENCODER\n"," 3. 🟢 encoder._conv_stem 864 ENCODER\n"," 5. 🟢 encoder._bn0 64 ENCODER\n"," 6. 🟢 encoder._blocks 3,594,460 ENCODER\n"," 7. 🟢 encoder._blocks.0 1,448 ENCODER\n","10. 🟢 encoder._blocks.0._depthwise_conv 288 ENCODER\n","12. 🟢 encoder._blocks.0._bn1 64 ENCODER\n","13. 🟢 encoder._blocks.0._se_reduce 264 ENCODER\n","15. 🟢 encoder._blocks.0._se_expand 288 ENCODER\n","17. 🟢 encoder._blocks.0._project_conv 512 ENCODER\n","19. 🟢 encoder._blocks.0._bn2 32 ENCODER\n","21. 🟢 encoder._blocks.1 6,004 ENCODER\n","22. 🟢 encoder._blocks.1._expand_conv 1,536 ENCODER\n","24. 🟢 encoder._blocks.1._bn0 192 ENCODER\n","25. 🟢 encoder._blocks.1._depthwise_conv 864 ENCODER\n","27. 🟢 encoder._blocks.1._bn1 192 ENCODER\n","28. 🟢 encoder._blocks.1._se_reduce 388 ENCODER\n","30. 🟢 encoder._blocks.1._se_expand 480 ENCODER\n","32. 🟢 encoder._blocks.1._project_conv 2,304 ENCODER\n","34. 🟢 encoder._blocks.1._bn2 48 ENCODER\n","36. 🟢 encoder._blocks.2 10,710 ENCODER\n","37. 🟢 encoder._blocks.2._expand_conv 3,456 ENCODER\n","39. 🟢 encoder._blocks.2._bn0 288 ENCODER\n","40. 🟢 encoder._blocks.2._depthwise_conv 1,296 ENCODER\n","42. 🟢 encoder._blocks.2._bn1 288 ENCODER\n","43. 🟢 encoder._blocks.2._se_reduce 870 ENCODER\n","45. 🟢 encoder._blocks.2._se_expand 1,008 ENCODER\n","47. 🟢 encoder._blocks.2._project_conv 3,456 ENCODER\n","49. 🟢 encoder._blocks.2._bn2 48 ENCODER\n","51. 🟢 encoder._blocks.3 15,350 ENCODER\n","52. 🟢 encoder._blocks.3._expand_conv 3,456 ENCODER\n","54. 🟢 encoder._blocks.3._bn0 288 ENCODER\n","55. 🟢 encoder._blocks.3._depthwise_conv 3,600 ENCODER\n","57. 🟢 encoder._blocks.3._bn1 288 ENCODER\n","58. 🟢 encoder._blocks.3._se_reduce 870 ENCODER\n","60. 🟢 encoder._blocks.3._se_expand 1,008 ENCODER\n","62. 🟢 encoder._blocks.3._project_conv 5,760 ENCODER\n","64. 🟢 encoder._blocks.3._bn2 80 ENCODER\n","66. 🟢 encoder._blocks.4 31,290 ENCODER\n","67. 🟢 encoder._blocks.4._expand_conv 9,600 ENCODER\n","69. 🟢 encoder._blocks.4._bn0 480 ENCODER\n","70. 🟢 encoder._blocks.4._depthwise_conv 6,000 ENCODER\n","72. 🟢 encoder._blocks.4._bn1 480 ENCODER\n","73. 🟢 encoder._blocks.4._se_reduce 2,410 ENCODER\n","75. 🟢 encoder._blocks.4._se_expand 2,640 ENCODER\n","77. 🟢 encoder._blocks.4._project_conv 9,600 ENCODER\n","79. 🟢 encoder._blocks.4._bn2 80 ENCODER\n","81. 🟢 encoder._blocks.5 37,130 ENCODER\n","82. 🟢 encoder._blocks.5._expand_conv 9,600 ENCODER\n","84. 🟢 encoder._blocks.5._bn0 480 ENCODER\n","85. 🟢 encoder._blocks.5._depthwise_conv 2,160 ENCODER\n","87. 🟢 encoder._blocks.5._bn1 480 ENCODER\n","88. 🟢 encoder._blocks.5._se_reduce 2,410 ENCODER\n","90. 🟢 encoder._blocks.5._se_expand 2,640 ENCODER\n","92. 🟢 encoder._blocks.5._project_conv 19,200 ENCODER\n","94. 🟢 encoder._blocks.5._bn2 160 ENCODER\n","96. 🟢 encoder._blocks.6 102,900 ENCODER\n","97. 🟢 encoder._blocks.6._expand_conv 38,400 ENCODER\n","99. 🟢 encoder._blocks.6._bn0 960 ENCODER\n","100. 🟢 encoder._blocks.6._depthwise_conv 4,320 ENCODER\n","102. 🟢 encoder._blocks.6._bn1 960 ENCODER\n","103. 🟢 encoder._blocks.6._se_reduce 9,620 ENCODER\n","105. 🟢 encoder._blocks.6._se_expand 10,080 ENCODER\n","107. 🟢 encoder._blocks.6._project_conv 38,400 ENCODER\n","109. 🟢 encoder._blocks.6._bn2 160 ENCODER\n","111. 🟢 encoder._blocks.7 102,900 ENCODER\n","112. 🟢 encoder._blocks.7._expand_conv 38,400 ENCODER\n","114. 🟢 encoder._blocks.7._bn0 960 ENCODER\n","115. 🟢 encoder._blocks.7._depthwise_conv 4,320 ENCODER\n","117. 🟢 encoder._blocks.7._bn1 960 ENCODER\n","118. 🟢 encoder._blocks.7._se_reduce 9,620 ENCODER\n","120. 🟢 encoder._blocks.7._se_expand 10,080 ENCODER\n","122. 🟢 encoder._blocks.7._project_conv 38,400 ENCODER\n","124. 🟢 encoder._blocks.7._bn2 160 ENCODER\n","126. 🟢 encoder._blocks.8 126,004 ENCODER\n","127. 🟢 encoder._blocks.8._expand_conv 38,400 ENCODER\n","129. 🟢 encoder._blocks.8._bn0 960 ENCODER\n","130. 🟢 encoder._blocks.8._depthwise_conv 12,000 ENCODER\n","132. 🟢 encoder._blocks.8._bn1 960 ENCODER\n","133. 🟢 encoder._blocks.8._se_reduce 9,620 ENCODER\n","135. 🟢 encoder._blocks.8._se_expand 10,080 ENCODER\n","137. 🟢 encoder._blocks.8._project_conv 53,760 ENCODER\n","139. 🟢 encoder._blocks.8._bn2 224 ENCODER\n","141. 🟢 encoder._blocks.9 208,572 ENCODER\n","142. 🟢 encoder._blocks.9._expand_conv 75,264 ENCODER\n","144. 🟢 encoder._blocks.9._bn0 1,344 ENCODER\n","145. 🟢 encoder._blocks.9._depthwise_conv 16,800 ENCODER\n","147. 🟢 encoder._blocks.9._bn1 1,344 ENCODER\n","148. 🟢 encoder._blocks.9._se_reduce 18,844 ENCODER\n","150. 🟢 encoder._blocks.9._se_expand 19,488 ENCODER\n","152. 🟢 encoder._blocks.9._project_conv 75,264 ENCODER\n","154. 🟢 encoder._blocks.9._bn2 224 ENCODER\n","156. 🟢 encoder._blocks.10 208,572 ENCODER\n","157. 🟢 encoder._blocks.10._expand_conv 75,264 ENCODER\n","159. 🟢 encoder._blocks.10._bn0 1,344 ENCODER\n","160. 🟢 encoder._blocks.10._depthwise_conv 16,800 ENCODER\n","162. 🟢 encoder._blocks.10._bn1 1,344 ENCODER\n","163. 🟢 encoder._blocks.10._se_reduce 18,844 ENCODER\n","165. 🟢 encoder._blocks.10._se_expand 19,488 ENCODER\n","167. 🟢 encoder._blocks.10._project_conv 75,264 ENCODER\n","169. 🟢 encoder._blocks.10._bn2 224 ENCODER\n","171. 🟢 encoder._blocks.11 262,492 ENCODER\n","172. 🟢 encoder._blocks.11._expand_conv 75,264 ENCODER\n","174. 🟢 encoder._blocks.11._bn0 1,344 ENCODER\n","175. 🟢 encoder._blocks.11._depthwise_conv 16,800 ENCODER\n","177. 🟢 encoder._blocks.11._bn1 1,344 ENCODER\n","178. 🟢 encoder._blocks.11._se_reduce 18,844 ENCODER\n","180. 🟢 encoder._blocks.11._se_expand 19,488 ENCODER\n","182. 🟢 encoder._blocks.11._project_conv 129,024 ENCODER\n","184. 🟢 encoder._blocks.11._bn2 384 ENCODER\n","186. 🟢 encoder._blocks.12 587,952 ENCODER\n","187. 🟢 encoder._blocks.12._expand_conv 221,184 ENCODER\n","189. 🟢 encoder._blocks.12._bn0 2,304 ENCODER\n","190. 🟢 encoder._blocks.12._depthwise_conv 28,800 ENCODER\n","192. 🟢 encoder._blocks.12._bn1 2,304 ENCODER\n","193. 🟢 encoder._blocks.12._se_reduce 55,344 ENCODER\n","195. 🟢 encoder._blocks.12._se_expand 56,448 ENCODER\n","197. 🟢 encoder._blocks.12._project_conv 221,184 ENCODER\n","199. 🟢 encoder._blocks.12._bn2 384 ENCODER\n","201. 🟢 encoder._blocks.13 587,952 ENCODER\n","202. 🟢 encoder._blocks.13._expand_conv 221,184 ENCODER\n","204. 🟢 encoder._blocks.13._bn0 2,304 ENCODER\n","205. 🟢 encoder._blocks.13._depthwise_conv 28,800 ENCODER\n","207. 🟢 encoder._blocks.13._bn1 2,304 ENCODER\n","208. 🟢 encoder._blocks.13._se_reduce 55,344 ENCODER\n","210. 🟢 encoder._blocks.13._se_expand 56,448 ENCODER\n","212. 🟢 encoder._blocks.13._project_conv 221,184 ENCODER\n","214. 🟢 encoder._blocks.13._bn2 384 ENCODER\n","216. 🟢 encoder._blocks.14 587,952 ENCODER\n","217. 🟢 encoder._blocks.14._expand_conv 221,184 ENCODER\n","219. 🟢 encoder._blocks.14._bn0 2,304 ENCODER\n","220. 🟢 encoder._blocks.14._depthwise_conv 28,800 ENCODER\n","222. 🟢 encoder._blocks.14._bn1 2,304 ENCODER\n","223. 🟢 encoder._blocks.14._se_reduce 55,344 ENCODER\n","225. 🟢 encoder._blocks.14._se_expand 56,448 ENCODER\n","227. 🟢 encoder._blocks.14._project_conv 221,184 ENCODER\n","229. 🟢 encoder._blocks.14._bn2 384 ENCODER\n","231. 🟢 encoder._blocks.15 717,232 ENCODER\n","232. 🟢 encoder._blocks.15._expand_conv 221,184 ENCODER\n","234. 🟢 encoder._blocks.15._bn0 2,304 ENCODER\n","235. 🟢 encoder._blocks.15._depthwise_conv 10,368 ENCODER\n","237. 🟢 encoder._blocks.15._bn1 2,304 ENCODER\n","238. 🟢 encoder._blocks.15._se_reduce 55,344 ENCODER\n","240. 🟢 encoder._blocks.15._se_expand 56,448 ENCODER\n","242. 🟢 encoder._blocks.15._project_conv 368,640 ENCODER\n","244. 🟢 encoder._blocks.15._bn2 640 ENCODER\n","246. 🟢 encoder._conv_head 409,600 ENCODER\n","248. 🟢 encoder._bn1 2,560 ENCODER\n","252. 🟢 decoder 1,751,808 DECODER\n","253. 🟢 decoder.p5 82,176 DECODER\n","254. 🟢 decoder.p4 28,928 DECODER\n","255. 🟢 decoder.p4.skip_conv 28,928 DECODER\n","256. 🟢 decoder.p3 10,496 DECODER\n","257. 🟢 decoder.p3.skip_conv 10,496 DECODER\n","258. 🟢 decoder.p2 6,400 DECODER\n","259. 🟢 decoder.p2.skip_conv 6,400 DECODER\n","260. 🟢 decoder.seg_blocks 1,623,808 DECODER\n","261. 🟢 decoder.seg_blocks.0 590,592 DECODER\n","262. 🟢 decoder.seg_blocks.0.block 590,592 DECODER\n","263. 🟢 decoder.seg_blocks.0.block.0 295,168 DECODER\n","264. 🟢 decoder.seg_blocks.0.block.0.block 295,168 DECODER\n","265. 🟢 decoder.seg_blocks.0.block.0.block.0 294,912 DECODER\n","266. 🟢 decoder.seg_blocks.0.block.0.block.1 256 DECODER\n","268. 🟢 decoder.seg_blocks.0.block.1 147,712 DECODER\n","269. 🟢 decoder.seg_blocks.0.block.1.block 147,712 DECODER\n","270. 🟢 decoder.seg_blocks.0.block.1.block.0 147,456 DECODER\n","271. 🟢 decoder.seg_blocks.0.block.1.block.1 256 DECODER\n","273. 🟢 decoder.seg_blocks.0.block.2 147,712 DECODER\n","274. 🟢 decoder.seg_blocks.0.block.2.block 147,712 DECODER\n","275. 🟢 decoder.seg_blocks.0.block.2.block.0 147,456 DECODER\n","276. 🟢 decoder.seg_blocks.0.block.2.block.1 256 DECODER\n","278. 🟢 decoder.seg_blocks.1 442,880 DECODER\n","279. 🟢 decoder.seg_blocks.1.block 442,880 DECODER\n","280. 🟢 decoder.seg_blocks.1.block.0 295,168 DECODER\n","281. 🟢 decoder.seg_blocks.1.block.0.block 295,168 DECODER\n","282. 🟢 decoder.seg_blocks.1.block.0.block.0 294,912 DECODER\n","283. 🟢 decoder.seg_blocks.1.block.0.block.1 256 DECODER\n","285. 🟢 decoder.seg_blocks.1.block.1 147,712 DECODER\n","286. 🟢 decoder.seg_blocks.1.block.1.block 147,712 DECODER\n","287. 🟢 decoder.seg_blocks.1.block.1.block.0 147,456 DECODER\n","288. 🟢 decoder.seg_blocks.1.block.1.block.1 256 DECODER\n","290. 🟢 decoder.seg_blocks.2 295,168 DECODER\n","291. 🟢 decoder.seg_blocks.2.block 295,168 DECODER\n","292. 🟢 decoder.seg_blocks.2.block.0 295,168 DECODER\n","293. 🟢 decoder.seg_blocks.2.block.0.block 295,168 DECODER\n","294. 🟢 decoder.seg_blocks.2.block.0.block.0 294,912 DECODER\n","295. 🟢 decoder.seg_blocks.2.block.0.block.1 256 DECODER\n","297. 🟢 decoder.seg_blocks.3 295,168 DECODER\n","298. 🟢 decoder.seg_blocks.3.block 295,168 DECODER\n","299. 🟢 decoder.seg_blocks.3.block.0 295,168 DECODER\n","300. 🟢 decoder.seg_blocks.3.block.0.block 295,168 DECODER\n","301. 🟢 decoder.seg_blocks.3.block.0.block.0 294,912 DECODER\n","302. 🟢 decoder.seg_blocks.3.block.0.block.1 256 DECODER\n","306. 🟢 segmentation_head 1,032 HEAD\n","307. 🟢 segmentation_head.0 1,032 HEAD\n","----------------------------------------------------------------------\n","📊 RÉSUMÉ: 196 entraînables, 0 gelés\n","⚠️ Config SegFormer_mit-b0_new non trouvée\n","\n","✅ Tous les tests d'analyse PyTorch terminés!\n"]}]},{"cell_type":"code","source":["def count_model_parameters(model, verbose=False):\n"," \"\"\"\n"," Compte les paramètres d'un modèle PyTorch\n"," \"\"\"\n"," total_params = sum(p.numel() for p in model.parameters())\n"," trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n"," frozen_params = total_params - trainable_params\n","\n"," if verbose:\n"," print(f\"Paramètres totaux: {total_params:,}\")\n"," print(f\"Entraînables: {trainable_params:,}\")\n"," print(f\"Gelés: {frozen_params:,}\")\n","\n"," return {\n"," 'total': total_params,\n"," 'trainable': trainable_params,\n"," 'frozen': frozen_params\n"," }\n","\n","def test_all_model_parameters():\n"," \"\"\"\n"," Teste et affiche les paramètres de tous les modèles configurés\n"," \"\"\"\n"," print(\"COMPTAGE DES PARAMÈTRES - TOUS LES MODÈLES\")\n"," print(\"=\" * 60)\n","\n"," results = {}\n","\n"," for config in experimental_configurations:\n"," print(f\"\\nModèle: {config['name']}\")\n"," print(\"-\" * 40)\n","\n"," try:\n"," # Créer le modèle\n"," model = create_model_from_config(config, verbose=False)\n","\n"," # Compter les paramètres\n"," params = count_model_parameters(model, verbose=True)\n"," results[config['name']] = params['total']\n","\n"," # Architecture info\n"," arch = config['architecture']\n"," if arch == 'FPN':\n"," print(f\"Architecture: FPN + {config['encoder_name']}\")\n"," elif arch == 'SegFormer_pretrained':\n"," variant = config['checkpoint'].split('/')[-1].split('-')[1] # ex: b1\n"," print(f\"Architecture: SegFormer-{variant}\")\n","\n"," except Exception as e:\n"," print(f\"Erreur: {e}\")\n"," results[config['name']] = \"Erreur\"\n","\n"," # Résumé final\n"," print(\"\\n\" + \"=\" * 60)\n"," print(\"RÉSUMÉ DES PARAMÈTRES\")\n"," print(\"=\" * 60)\n","\n"," for name, params in results.items():\n"," if isinstance(params, int):\n"," print(f\"{name:35} : {params:>10,}\")\n"," else:\n"," print(f\"{name:35} : {params}\")\n","\n"," return results"],"metadata":{"id":"6Dvyv4uYWpKV","executionInfo":{"status":"ok","timestamp":1756793965507,"user_tz":-120,"elapsed":5,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":30,"outputs":[]},{"cell_type":"code","source":["# Après vos tests actuels\n","print(\"\\n\" + \"=\"*70)\n","print(\"COMPTAGE PARAMÈTRES - TOUS LES MODÈLES\")\n","print(\"=\"*70)\n","\n","parameter_results = test_all_model_parameters()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":874,"referenced_widgets":["4d0d15266f6f4864ac237ce76be20b72","0f2e46ecdcca453a98f34715d072d4ff","0a00c86895d14b13a41d1a2e21bbfa5a","2f286dd473824f63bd8a61f25721c645","90806d5e7dea4c5fb0fee03da7a440eb","fd90b10f33bb4a12990b6b8b307f98b3","5551202cb1644ea8a0629b5950b56805","fc61b3ee5c9a4d8086725fd0ccafcbeb","a6fa1d050142405aae3facfe7da6c47a","0d27af8ec005489b8c78bda8fc5b90d7","93400e35f2c641848f63f7f8b7d6ab54","4de56eef607b46449dc02463aa183326","4ac7342fcec747039893ce4e436a1907","8ac5357bec4f401387fa3dca1e9ef56d","9f466b149056430e93c07d00002e4bf0","306a96c58dcc43f3b372cd95db5d0e1a","263efe06bdb842448875111f42967d82","e590c694187b47db9f11cdc66ab301cb","32ddac0c7b804824b79d4e001e8d637d","a0601a80154e40fd92466ba15e7c0f96","b5b2bb72789146d6a849a182a421f029","ad70e8c4a8d94c8ebdd819238e40f94c","2f0f3d0c261240dd9c38318df55b0f6d","1560f0ed37e34b0d92c92eb26ad2d59b","e78c1072ddfb4132a0a69e167be7f0e0","8f47bd64593c484d94a21a61dea7ab1d","ecc20f4079f242fa81309d477517c3db","a91fc7ce494542869cd8b79fd99b436a","7261c5fdd6ed49399fa9a7902f6549ff","e85cffc27ce3453f8738f90000c4111a","8c5cdd747e3948b09aa22e9139c50279","a4f2610dc3304fa4aee3ab7cf5d563b5","1bd152d957644767b60e1eed691c5ca3","1ca7f273ae7c4fe89e82ed7f0ebb28e1","745105b963f74794b824f8dab73c6d72","a49e5834fbca4614adb4c20eef9d9a3f","27ad94026d4c4f37b578bbf487763eaa","03d3fe0add6c4df5aa799067b86f7574","76880cbb6d23449eb493efa159acb0c9","a5a616b6382940119268c502c83852e3","d323b5e9b31047beb1d6af9a49b27021","7efa823ac02a47d3a621551bc95f5cf3","e88351d8031c43d08407f818d7199780","5a06fac175ad4a70a1de8dcea1b1cef8"]},"id":"gRRelGLEXFm-","executionInfo":{"status":"ok","timestamp":1756793976364,"user_tz":-120,"elapsed":10856,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"82947262-0708-483f-da75-36ad2e0daa6b"},"execution_count":31,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","======================================================================\n","COMPTAGE PARAMÈTRES - TOUS LES MODÈLES\n","======================================================================\n","COMPTAGE DES PARAMÈTRES - TOUS LES MODÈLES\n","============================================================\n","\n","Modèle: SegFormer_b1_pretrained_optimized\n","----------------------------------------\n","Paramètres totaux: 13,679,304\n","Entraînables: 13,679,304\n","Gelés: 0\n","Architecture: SegFormer-b1\n","\n","Modèle: SegFormer_b0_pretrained_optimized\n","----------------------------------------\n"]},{"output_type":"display_data","data":{"text/plain":["config.json: 0%| | 0.00/232 [00:00 0 → (train_dataloader, val_dataloader)\n"," Sinon → dataloader 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"," # Parcours des fichiers (identique à TensorFlow)\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"," # Découpage Train/Validation si nécessaire\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"," # Mélange déterministe\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","\n"," # Création des datasets PyTorch\n"," train_dataset = CityscapesDataset(\n"," train_img_paths, train_mask_paths,\n"," img_size=img_size, encoder=encoder,\n"," id_to_group_mapping=id_to_group_mapping,\n"," augment=True # Augmentations pour l'entraînement\n"," )\n","\n"," val_dataset = CityscapesDataset(\n"," val_img_paths, val_mask_paths,\n"," img_size=img_size, encoder=encoder,\n"," id_to_group_mapping=id_to_group_mapping,\n"," augment=False # Pas d'augmentations pour la validation\n"," )\n","\n"," # Création des DataLoaders\n"," train_dataloader = DataLoader(\n"," train_dataset, batch_size=batch_size,\n"," shuffle=True, num_workers=num_workers,\n"," pin_memory=torch.cuda.is_available()\n"," )\n","\n"," val_dataloader = DataLoader(\n"," val_dataset, batch_size=batch_size,\n"," shuffle=False, num_workers=num_workers,\n"," pin_memory=torch.cuda.is_available()\n"," )\n","\n"," return train_dataloader, val_dataloader\n","\n"," else:\n"," usage = \"Test\" if split == 'val' else split.title()\n"," print(f\"📋 Utilisation complète comme dataset de {usage}\")\n","\n"," # Dataset unique\n"," dataset = CityscapesDataset(\n"," image_paths, mask_paths,\n"," img_size=img_size, encoder=encoder,\n"," id_to_group_mapping=id_to_group_mapping,\n"," augment=False # Pas d'augmentations pour test\n"," )\n","\n"," # DataLoader unique\n"," dataloader = DataLoader(\n"," dataset, batch_size=batch_size,\n"," shuffle=(split=='train'), num_workers=num_workers,\n"," pin_memory=torch.cuda.is_available()\n"," )\n","\n"," return dataloader"],"metadata":{"id":"YYKecc1phAqL","executionInfo":{"status":"ok","timestamp":1756793976407,"user_tz":-120,"elapsed":14,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":33,"outputs":[]},{"cell_type":"code","source":["# === UTILISATION PYTORCH - PARAMÈTRES ADAPTÉS ===\n","\n","# Vérifier si id_to_group existe\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 PYTORCH ===\")\n","print(f\"📊 Paramètres:\")\n","print(f\" • Taille image: {IMG_SIZE},{IMG_SIZE}\")\n","print(f\" • Taille batch: {BATCH_SIZE}\")\n","print(f\" • Framework: PyTorch + Albumentations\")\n","print(f\" • Répertoire: {cityscapes_unzipped_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_unzipped_dir, # ← Variable correcte\n"," split='train',\n"," batch_size=BATCH_SIZE,\n"," img_size=(IMG_SIZE, IMG_SIZE),\n"," validation_split=0.2,\n"," id_to_group_mapping=id_mapping,\n"," num_workers=2 # Réduit pour Colab\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_unzipped_dir,\n"," split='val',\n"," batch_size=BATCH_SIZE,\n"," img_size=(IMG_SIZE, IMG_SIZE),\n"," id_to_group_mapping=id_mapping,\n"," num_workers=2\n"," )\n","\n"," print(f\"\\n✅ Datasets PyTorch créés avec succès!\")\n","\n"," # Vérification rapide\n"," print(f\"\\n🔍 Vérification des formes PyTorch:\")\n"," for name, dataloader in [(\"Train\", train_dataset), (\"Validation\", val_dataset), (\"Test\", test_dataset)]:\n"," sample_batch = next(iter(dataloader))\n"," images, masks = sample_batch\n"," print(f\" {name}: images = {images.shape}, masques = {masks.shape}\")\n"," print(f\" dtype: images = {images.dtype}, masques = {masks.dtype}\")\n","\n"," print(f\"\\n📋 Récapitulatif PyTorch:\")\n"," print(f\" • Train DataLoader: pour l'entraînement (avec augmentations)\")\n"," print(f\" • Validation DataLoader: pour la sélection des hyperparamètres\")\n"," print(f\" • Test DataLoader: pour l'évaluation finale\")\n"," print(f\" • Résolution: {IMG_SIZE}x{IMG_SIZE} (upgrade de 224x224)\")\n"," print(f\" • Batch size: {BATCH_SIZE} (optimisé pour GPU T4 12GB)\")\n","\n","except Exception as e:\n"," print(f\"❌ Erreur: {e}\")\n"," print(\"Vérifications:\")\n"," print(\"1. Variable 'cityscapes_unzipped_dir' définie ?\")\n"," print(\"2. Fichiers Cityscapes extraits ?\")\n"," print(\"3. Structure correcte (leftImg8bit/, gtFine/) ?\")\n"," print(\"4. Albumentations installé ? pip install albumentations\")\n"," raise"],"metadata":{"id":"KdBI3grrhIR5","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1756793985952,"user_tz":-120,"elapsed":9543,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"01145f62-100b-4a30-9255-e99f55e1106f"},"execution_count":34,"outputs":[{"output_type":"stream","name":"stdout","text":["=== PRÉPARATION DES DATASETS CITYSCAPES PYTORCH ===\n","📊 Paramètres:\n"," • Taille image: 768,768\n"," • Taille batch: 8\n"," • Framework: PyTorch + Albumentations\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","CityscapesDataset initialisé:\n"," - Images: 2380\n"," - Taille: (768, 768)\n"," - Encoder: None\n"," - Augmentation: True\n","CityscapesDataset initialisé:\n"," - Images: 595\n"," - Taille: (768, 768)\n"," - Encoder: None\n"," - Augmentation: False\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","CityscapesDataset initialisé:\n"," - Images: 500\n"," - Taille: (768, 768)\n"," - Encoder: None\n"," - Augmentation: False\n","\n","✅ Datasets PyTorch créés avec succès!\n","\n","🔍 Vérification des formes PyTorch:\n"," Train: images = torch.Size([8, 3, 768, 768]), masques = torch.Size([8, 768, 768])\n"," dtype: images = torch.float32, masques = torch.int64\n"," Validation: images = torch.Size([8, 3, 768, 768]), masques = torch.Size([8, 768, 768])\n"," dtype: images = torch.float32, masques = torch.int64\n"," Test: images = torch.Size([8, 3, 768, 768]), masques = torch.Size([8, 768, 768])\n"," dtype: images = torch.float32, masques = torch.int64\n","\n","📋 Récapitulatif PyTorch:\n"," • Train DataLoader: pour l'entraînement (avec augmentations)\n"," • Validation DataLoader: pour la sélection des hyperparamètres\n"," • Test DataLoader: pour l'évaluation finale\n"," • Résolution: 768x768 (upgrade de 224x224)\n"," • Batch size: 8 (optimisé pour GPU T4 12GB)\n"]}]},{"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","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 PyTorch DataLoaders\n"," ADAPTÉ PYTORCH : Gère les DataLoaders au lieu des tf.data.Dataset\n","\n"," Args:\n"," datasets_dict: {'train': train_dataloader, 'val': val_dataloader, 'test': test_dataloader}\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 DataLoaders PyTorch (préféré)\n"," if datasets_dict is not None:\n"," print(\"📊 Visualisation depuis les DataLoaders PyTorch...\")\n"," _visualize_from_dataloaders(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_dataloaders(datasets_dict, num_samples, save_dir, show_stats):\n"," \"\"\"Visualise depuis les DataLoaders PyTorch (après preprocessing)\"\"\"\n","\n"," for split_name, dataloader in datasets_dict.items():\n"," print(f\"\\n--- {split_name.title()} DataLoader ---\")\n","\n"," sample_count = 0\n","\n"," # Itérer sur le DataLoader PyTorch\n"," for batch_idx, (images, masks) in enumerate(dataloader):\n"," batch_size = images.size(0)\n","\n"," # Traiter chaque échantillon du batch\n"," for i in range(min(batch_size, num_samples - sample_count)):\n"," image = images[i] # [3, H, W]\n"," mask = masks[i] # [H, W]\n","\n"," # Convertir les tenseurs PyTorch pour visualisation\n"," # Image: [3, H, W] → [H, W, 3] et dénormaliser\n"," image_np = _denormalize_image(image).permute(1, 2, 0).cpu().numpy()\n"," image_np = np.clip(image_np, 0, 1) # Assurer [0, 1]\n","\n"," # Masque: tensor → numpy\n"," mask_np = mask.cpu().numpy()\n","\n"," # Créer la visualisation\n"," fig = _create_sample_plot(image_np, mask_np,\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_np)\n","\n"," sample_count += 1\n"," if sample_count >= num_samples:\n"," break\n","\n"," if sample_count >= num_samples:\n"," break\n","\n","def _denormalize_image(image_tensor):\n"," \"\"\"\n"," Dénormalise une image PyTorch pour visualisation\n"," Inverse la normalisation ImageNet ou simple\n"," \"\"\"\n"," # Détecter le type de normalisation en analysant les valeurs\n"," mean_val = image_tensor.mean().item()\n","\n"," if abs(mean_val) < 0.1: # Probablement normalisé ImageNet\n"," # Dénormalisation ImageNet standard\n"," mean = torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1)\n"," std = torch.tensor([0.229, 0.224, 0.225]).view(3, 1, 1)\n","\n"," # Dénormaliser\n"," image_denorm = image_tensor * std + mean\n"," else:\n"," # Probablement déjà dans [0, 1] ou normalisation simple\n"," image_denorm = image_tensor\n","\n"," return torch.clamp(image_denorm, 0, 1)\n","\n","def _visualize_from_files(cityscapes_dir, num_samples, save_dir, show_stats):\n"," \"\"\"Visualise depuis les fichiers bruts (fallback) - INCHANGÉ\"\"\"\n","\n"," for split in ['train', 'val']:\n"," print(f\"\\n--- {split.title()} (Raw Files) ---\")\n","\n"," # Collecter les chemins\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 (INCHANGÉ)\"\"\"\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 (INCHANGÉ)\"\"\"\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 (INCHANGÉ)\"\"\"\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']):\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 (INCHANGÉ)\"\"\"\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 (INCHANGÉ)\"\"\"\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)"],"metadata":{"id":"E7FtdxfsDyt_","executionInfo":{"status":"ok","timestamp":1756793985997,"user_tz":-120,"elapsed":23,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":35,"outputs":[]},{"cell_type":"code","source":["# === UTILISATION AVEC DATALOADERS PYTORCH ===\n","print(\"=== VISUALISATION DES ÉCHANTILLONS CITYSCAPES PYTORCH ===\")\n","\n","# Utilisation avec les DataLoaders PyTorch\n","datasets_dict = {\n"," 'train': train_dataset, # DataLoader\n"," 'val': val_dataset, # DataLoader\n"," 'test': test_dataset # DataLoader\n","}\n","\n","# Créer le dossier de sauvegarde\n","if 'g_drive_dir_content' in globals():\n"," viz_dir = os.path.join(g_drive_dir_content, 'visualizations')\n","else:\n"," viz_dir = './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",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"output_embedded_package_id":"1rbAQSlmZ75psMRE0ICZlS1HbISl-Qwke"},"id":"k2oe_BctD5ZF","executionInfo":{"status":"ok","timestamp":1756794005368,"user_tz":-120,"elapsed":19370,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"0a689d9d-5753-4e6f-8437-632b2d5e434b"},"execution_count":36,"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"BcRXJIz6Ts6q"},"source":["## Entrainement du modèle"]},{"cell_type":"code","source":["# ====================================================================\n","# CONFIGURATION SELON LE MODE\n","# ====================================================================\n","\n","TEST_MODE = False # Mettre True pour test rapide\n","\n","if TEST_MODE:\n"," print(\"MODE TEST ACTIVÉ\")\n"," MAX_EPOCHS = 2\n"," EARLY_STOPPING_PATIENCE = 2\n"," LR_REDUCE_PATIENCE = 1\n"," SUBSET_SIZE = 20\n"," TEST_IMG_SIZE = (128, 128)\n"," TEST_BATCH_SIZE = 2\n"," TEST_MAX_CONFIGS = 2\n","else:\n"," print(\"MODE PRODUCTION ACTIVÉ\")\n"," MAX_EPOCHS = 20\n"," EARLY_STOPPING_PATIENCE = 5\n"," LR_REDUCE_PATIENCE = 3\n"," SUBSET_SIZE = None\n"," TEST_IMG_SIZE = None\n"," TEST_BATCH_SIZE = None\n"," TEST_MAX_CONFIGS = None\n","\n","device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n","print(f\"Configuration PyTorch: Device={device}, Max_Epochs={MAX_EPOCHS}\")"],"metadata":{"id":"K-wVb51ZQcJt","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1756538722953,"user_tz":-120,"elapsed":87,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"d323102c-2184-4143-a65b-9530a8c0f882"},"execution_count":89,"outputs":[{"output_type":"stream","name":"stdout","text":["MODE PRODUCTION ACTIVÉ\n","Configuration PyTorch: Device=cuda, Max_Epochs=20\n"]}]},{"cell_type":"code","source":["# ====================================================================\n","# SYSTÈME DE REPRISE AUTOMATIQUE - MLFLOW\n","# ====================================================================\n","\n","def get_completed_experiments(experiment_name=\"OC Projet 9\"):\n"," \"\"\"Récupère la liste des expériences déjà terminées dans MLflow\"\"\"\n"," try:\n"," print(f\"Vérification des expériences déjà terminées dans '{experiment_name}'...\")\n","\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"," runs = mlflow.search_runs(\n"," experiment_ids=[experiment.experiment_id],\n"," filter_string=\"status = 'FINISHED'\",\n"," output_format=\"pandas\"\n"," )\n","\n"," if runs.empty:\n"," print(\"Aucune expérience terminée trouvée\")\n"," return set()\n","\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"," return set()\n","\n","# def generate_stable_run_name(config, test_mode=False):\n","# \"\"\"Génère un nom de run stable basé sur la configuration\"\"\"\n","# base_name = config['name']\n","# mode_suffix = \"test\" if test_mode else \"prod\"\n","# return f\"{base_name}_{mode_suffix}\"\n","\n","# def filter_configs_to_run(experimental_configurations, experiment_name=\"OC Projet 9\"):\n","# \"\"\"Filtre les configurations pour ne garder que celles non encore entraînées\"\"\"\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","# print(f\"\\nFILTRAGE 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\"\\nCONFIGURATIONS 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\"\\nCONFIGURATIONS 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\n","\n","\n","def generate_stable_run_name(config, test_mode=False, with_timestamp=True):\n"," base_name = config['name']\n"," mode_suffix = \"test\" if test_mode else \"prod\"\n"," if with_timestamp:\n"," timestamp = datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")\n"," return f\"{base_name}_{mode_suffix}_{timestamp}\"\n"," else:\n"," return f\"{base_name}_{mode_suffix}\"\n","\n","def prefix_match_in_completed(completed_runs, expected_prefix):\n"," \"\"\"Vérifie si un nom de run commence par le préfixe attendu\"\"\"\n"," for name in completed_runs:\n"," if name.startswith(expected_prefix):\n"," return True\n"," return False\n","\n","def filter_configs_to_run(experimental_configurations, experiment_name=\"OC Projet 9\", force=False):\n"," \"\"\"Filtre les configurations pour ne garder que celles non encore entraînées\"\"\"\n","\n"," completed_runs = get_completed_experiments(experiment_name)\n"," configs_to_run = []\n"," skipped_configs = []\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"," # Une seule boucle de filtrage\n"," for idx, config in enumerate(experimental_configurations):\n"," expected_prefix = generate_stable_run_name(config, TEST_MODE, with_timestamp=False)\n","\n"," if not force and prefix_match_in_completed(completed_runs, expected_prefix):\n"," skipped_configs.append((idx, config['name'], expected_prefix))\n"," else:\n"," configs_to_run.append(idx)\n","\n"," # Affichage des résultats\n"," print(f\"\\nFILTRAGE DES CONFIGURATIONS:\")\n"," print(f\"À entraîner: {len(configs_to_run)} configurations\")\n"," print(f\"Déjà terminées: {len(skipped_configs)} configurations\")\n","\n"," if force:\n"," print(\"⚠️ Mode FORCE activé - relancement des expériences existantes\")\n","\n"," if skipped_configs:\n"," print(f\"\\nCONFIGURATIONS IGNORÉES (déjà terminées):\")\n"," for idx, config_name, prefix in skipped_configs:\n"," print(f\" [{idx}] {config_name} → {prefix}*\")\n","\n"," if configs_to_run:\n"," print(f\"\\nCONFIGURATIONS À ENTRAÎNER:\")\n"," for idx in configs_to_run:\n"," config_name = experimental_configurations[idx]['name']\n"," # Génération du nom complet avec timestamp pour l'affichage\n"," expected_run_name = generate_stable_run_name(experimental_configurations[idx], TEST_MODE, with_timestamp=True)\n"," print(f\" [{idx}] {config_name} → {expected_run_name}\")\n","\n"," return configs_to_run"],"metadata":{"id":"KUqmecLNQeWP","executionInfo":{"status":"ok","timestamp":1756538723079,"user_tz":-120,"elapsed":7,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":90,"outputs":[]},{"cell_type":"code","source":["# ====================================================================\n","# MÉTRIQUES D'ENTRAÎNEMENT\n","# ====================================================================\n","\n","class IoUMetric:\n"," def __init__(self, num_classes, ignore_index=None, device='cuda'):\n"," self.num_classes = num_classes\n"," self.ignore_index = ignore_index\n"," self.device = device\n"," self.reset()\n","\n"," def reset(self):\n"," self.confusion_matrix = torch.zeros(\n"," (self.num_classes, self.num_classes),\n"," dtype=torch.long,\n"," device=self.device # <- Spécifier le device\n"," )\n","\n"," def update(self, predictions, targets):\n"," predictions = torch.argmax(predictions, dim=1).flatten().to(self.device)\n"," targets = targets.flatten().to(self.device)\n","\n"," if self.ignore_index is not None:\n"," mask = targets != self.ignore_index\n"," predictions = predictions[mask]\n"," targets = targets[mask]\n","\n"," # CORRECTION CRITIQUE : Clipper les valeurs aberrantes\n"," predictions = torch.clamp(predictions, 0, self.num_classes-1)\n"," targets = torch.clamp(targets, 0, self.num_classes-1)\n","\n"," indices = (self.num_classes * targets + predictions).to(self.device)\n"," bincount_result = torch.bincount(\n"," indices,\n"," minlength=self.num_classes**2\n"," ).to(self.device)\n","\n"," self.confusion_matrix += bincount_result.reshape(self.num_classes, self.num_classes)\n","\n"," def compute(self):\n"," diag = torch.diag(self.confusion_matrix)\n"," sum_dim1 = self.confusion_matrix.sum(dim=1)\n"," sum_dim0 = self.confusion_matrix.sum(dim=0)\n","\n"," iou_per_class = diag / (sum_dim1 + sum_dim0 - diag)\n","\n"," present_classes = sum_dim1 > 0\n"," iou_per_class = torch.nan_to_num(iou_per_class, nan=0.0)\n","\n"," if present_classes.any():\n"," mean_iou = torch.mean(iou_per_class[present_classes])\n"," else:\n"," mean_iou = torch.tensor(0.0, device=self.device)\n","\n"," return mean_iou.item(), iou_per_class.cpu().numpy()\n","\n","def calculate_accuracy(predictions, targets):\n"," \"\"\"Calcul accuracy avec gestion device correcte\"\"\"\n"," predictions = torch.argmax(predictions, dim=1)\n"," # FIX: Assurer que les tensors sont sur le même device\n"," predictions = predictions.to(targets.device)\n"," correct = (predictions == targets).float()\n"," return correct.mean().item()"],"metadata":{"id":"YOJ4kWUAQf8H","executionInfo":{"status":"ok","timestamp":1756538723298,"user_tz":-120,"elapsed":44,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":91,"outputs":[]},{"cell_type":"code","source":["# ====================================================================\n","# CALLBACKS PYTORCH\n","# ====================================================================\n","\n","class EarlyStopping:\n"," def __init__(self, patience=7, min_delta=0, restore_best_weights=True, mode='max'):\n"," self.patience = patience\n"," self.min_delta = min_delta\n"," self.restore_best_weights = restore_best_weights\n"," self.mode = mode\n"," self.best_score = None\n"," self.counter = 0\n"," self.best_weights = None\n","\n"," def __call__(self, score, model):\n"," if self.best_score is None:\n"," self.best_score = score\n"," self.save_checkpoint(model)\n"," elif self.is_better(score, self.best_score):\n"," self.best_score = score\n"," self.counter = 0\n"," self.save_checkpoint(model)\n"," else:\n"," self.counter += 1\n","\n"," if self.counter >= self.patience:\n"," if self.restore_best_weights and self.best_weights is not None:\n"," model.load_state_dict(self.best_weights)\n"," return True\n"," return False\n","\n"," def is_better(self, current, best):\n"," if self.mode == 'max':\n"," return current > best + self.min_delta\n"," else:\n"," return current < best - self.min_delta\n","\n"," def save_checkpoint(self, model):\n"," self.best_weights = model.state_dict().copy()\n","\n","class ReduceLROnPlateau:\n"," def __init__(self, optimizer, mode='max', factor=0.2, patience=3, min_lr=1e-6, verbose=True):\n"," self.optimizer = optimizer\n"," self.mode = mode\n"," self.factor = factor\n"," self.patience = patience\n"," self.min_lr = min_lr\n"," self.verbose = verbose\n"," self.best_score = None\n"," self.counter = 0\n","\n"," def step(self, score):\n"," if self.best_score is None:\n"," self.best_score = score\n"," elif self.is_better(score, self.best_score):\n"," self.best_score = score\n"," self.counter = 0\n"," else:\n"," self.counter += 1\n","\n"," if self.counter >= self.patience:\n"," self.reduce_lr()\n"," self.counter = 0\n","\n"," def is_better(self, current, best):\n"," return current > best if self.mode == 'max' else current < best\n","\n"," def reduce_lr(self):\n"," for param_group in self.optimizer.param_groups:\n"," old_lr = param_group['lr']\n"," new_lr = max(old_lr * self.factor, self.min_lr)\n"," param_group['lr'] = new_lr\n"," if self.verbose and new_lr < old_lr:\n"," print(f\" Réduction LR: {old_lr:.6f} → {new_lr:.6f}\")"],"metadata":{"id":"Np3k-uRmQio6","executionInfo":{"status":"ok","timestamp":1756538723400,"user_tz":-120,"elapsed":22,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":92,"outputs":[]},{"cell_type":"code","source":["# ====================================================================\n","# BOUCLES D'ENTRAÎNEMENT\n","# ====================================================================\n","\n","def train_epoch(model, dataloader, criterion, optimizer, device, iou_metric, epoch_num, scaler=None):\n"," \"\"\"Training epoch avec mixed precision et device management strict\"\"\"\n"," model.train()\n"," running_loss = 0.0\n"," running_accuracy = 0.0\n"," num_batches = 0\n","\n"," iou_metric.reset()\n","\n"," pbar = tqdm(dataloader, desc=f\"Epoch {epoch_num} - Training\")\n"," for batch_idx, (images, masks) in enumerate(pbar):\n"," # Transfer vers GPU si nécessaire\n"," if not images.is_cuda and device.type == 'cuda':\n"," images = images.to(device)\n"," if not masks.is_cuda and device.type == 'cuda':\n"," masks = masks.to(device)\n","\n"," optimizer.zero_grad()\n","\n"," # MIXED PRECISION : autocast pour forward + scaler pour backward\n"," if scaler is not None:\n"," with torch.amp.autocast('cuda'): # Calculs forward en FP16\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n","\n"," # Backward avec gradient scaling pour éviter underflow\n"," scaler.scale(loss).backward()\n"," scaler.step(optimizer)\n"," scaler.update()\n"," else:\n"," # Mode normal FP32 (si pas de GPU ou scaler désactivé)\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," loss.backward()\n"," optimizer.step()\n","\n"," # Calcul des métriques (outputs déjà calculé dans le bloc if/else)\n"," running_loss += loss.item()\n"," running_accuracy += calculate_accuracy(outputs, masks)\n"," iou_metric.update(outputs.detach(), masks.detach())\n"," num_batches += 1\n","\n"," pbar.set_postfix({\n"," 'Loss': f'{running_loss/num_batches:.4f}',\n"," 'Acc': f'{running_accuracy/num_batches:.4f}',\n"," })\n","\n"," # Limitation en mode TEST\n"," if TEST_MODE and SUBSET_SIZE and num_batches >= SUBSET_SIZE // len(images):\n"," break\n","\n"," epoch_loss = running_loss / num_batches\n"," epoch_accuracy = running_accuracy / num_batches\n"," epoch_iou, _ = iou_metric.compute()\n","\n"," return epoch_loss, epoch_accuracy, epoch_iou\n","\n","\n","def validate_epoch(model, dataloader, criterion, device, iou_metric, epoch_num):\n"," \"\"\"Validation epoch avec autocast (mais pas de scaler car pas de backward)\"\"\"\n"," model.eval()\n"," running_loss = 0.0\n"," running_accuracy = 0.0\n"," num_batches = 0\n","\n"," iou_metric.reset()\n","\n"," with torch.no_grad(): # Pas de gradients en validation\n"," pbar = tqdm(dataloader, desc=f\"Epoch {epoch_num} - Validation\")\n"," for batch_idx, (images, masks) in enumerate(pbar):\n"," # Transfer vers GPU si nécessaire\n"," if not images.is_cuda and device.type == 'cuda':\n"," images = images.to(device)\n"," if not masks.is_cuda and device.type == 'cuda':\n"," masks = masks.to(device)\n","\n"," # MIXED PRECISION : seulement autocast (pas de scaler car pas de backward)\n"," if torch.cuda.is_available():\n"," with torch.amp.autocast('cuda'): # Forward en FP16 pour économiser mémoire/temps\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n"," else:\n"," # Mode CPU ou sans mixed precision\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n","\n"," running_loss += loss.item()\n"," running_accuracy += calculate_accuracy(outputs, masks)\n"," iou_metric.update(outputs, masks)\n"," num_batches += 1\n","\n"," pbar.set_postfix({\n"," 'Loss': f'{running_loss/num_batches:.4f}',\n"," 'Acc': f'{running_accuracy/num_batches:.4f}'\n"," })\n","\n"," # Limitation en mode TEST\n"," if TEST_MODE and SUBSET_SIZE and num_batches >= SUBSET_SIZE // (2 * len(images)):\n"," break\n","\n"," epoch_loss = running_loss / num_batches\n"," epoch_accuracy = running_accuracy / num_batches\n"," epoch_iou, _ = iou_metric.compute()\n","\n"," return epoch_loss, epoch_accuracy, epoch_iou"],"metadata":{"id":"aN5Se1A-Qki3","executionInfo":{"status":"ok","timestamp":1756538723520,"user_tz":-120,"elapsed":118,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":93,"outputs":[]},{"cell_type":"code","source":["# # ====================================================================\n","# # CONFIGURATION ET SETUP PYTORCH\n","# # ====================================================================\n","\n","# def setup_training_components(config, run_id):\n","# \"\"\"Setup avec gestion device stricte et fonction de perte personnalisée\"\"\"\n","\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","# print(f\"\\nPréparation des datasets PyTorch sur device: {device}...\")\n","\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","# train_dataset, val_dataset = prepare_cityscapes_dataset(\n","# cityscapes_unzipped_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","# num_workers=2\n","# )\n","\n","# test_dataset = prepare_cityscapes_dataset(\n","# cityscapes_unzipped_dir,\n","# split='val',\n","# batch_size=batch_size,\n","# img_size=img_size,\n","# id_to_group_mapping=id_mapping,\n","# num_workers=2\n","# )\n","\n","# print(f\"\\nCréation du modèle PyTorch {config['name']}...\")\n","# model = create_model_from_config(config, verbose=False)\n","\n","# # FIX: Transfer model to device BEFORE any operations\n","# print(f\"Transfer du modèle vers {device}...\")\n","# model = model.to(device)\n","\n","# # FIX: Vérification que le modèle est bien sur GPU\n","# model_device = next(model.parameters()).device\n","# print(f\"Modèle créé sur device: {model_device}\")\n","\n","# if str(model_device) != str(device):\n","# print(f\"ATTENTION: Modèle sur {model_device}, device cible {device}\")\n","# model = model.to(device)\n","# print(f\"Modèle retransféré vers {device}\")\n","\n","# num_classes_from_model = config['classes']\n","# total_params = sum(p.numel() for p in model.parameters())\n","# trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n","\n","# print(f\"Nombre de classes: {num_classes_from_model}\")\n","# print(f\"Paramètres: {total_params:,} total | {trainable_params:,} entraînables\")\n","\n","# print(\"\\nConfiguration des composants PyTorch...\")\n","\n","# # 🎯 NOUVELLE FONCTION DE PERTE PERSONNALISÉE\n","# # Remplacement de nn.CrossEntropyLoss() par notre classe personnalisée\n","# architecture = config['architecture']\n","\n","# # Définir les paramètres selon l'architecture\n","# if architecture == \"FPN\":\n","# loss_params = {\n","# 'focal_weight': 1.0,\n","# 'jaccard_weight': 1.0,\n","# 'alpha': 0.25,\n","# 'gamma': 2.0,\n","# 'smooth': 1.0\n","# }\n","# # elif architecture in [\"SegFormer\", \"SegFormer_pretrained\"]:\n","# # loss_params = {\n","# # 'focal_weight': 0.8,\n","# # 'jaccard_weight': 1.2,\n","# # 'alpha': 0.2,\n","# # 'gamma': 1.5,\n","# # 'smooth': 1.0\n","# # }\n","# elif architecture in [\"SegFormer\", \"SegFormer_pretrained\"]:\n","# loss_params = {\n","# 'focal_weight': 0.0, # Désactiver Focal\n","# 'jaccard_weight': 1.0, # Jaccard pure\n","# 'alpha': 0.2, # Non utilisé mais gardé pour cohérence\n","# 'gamma': 1.5, # Non utilisé mais gardé pour cohérence\n","# 'smooth': 1.0\n","# }\n","# else:\n","# loss_params = {\n","# 'focal_weight': 1.0,\n","# 'jaccard_weight': 1.0,\n","# 'alpha': 0.25,\n","# 'gamma': 2.0,\n","# 'smooth': 1.0\n","# }\n","\n","# # Créer le criterion avec les paramètres\n","# criterion = CategoricalFocalJaccardLoss(**loss_params)\n","\n","# learning_rate = config['params']['learning_rate']\n","# optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n","\n","# # FIX: IoUMetric avec device explicite\n","# train_iou_metric = IoUMetric(num_classes_from_model, device=device)\n","# val_iou_metric = IoUMetric(num_classes_from_model, device=device)\n","\n","# early_stopping = EarlyStopping(\n","# patience=EARLY_STOPPING_PATIENCE,\n","# mode='max',\n","# restore_best_weights=True\n","# )\n","\n","# lr_scheduler = ReduceLROnPlateau(\n","# optimizer,\n","# mode='max',\n","# factor=0.2,\n","# patience=LR_REDUCE_PATIENCE,\n","# min_lr=1e-6\n","# )\n","\n","# scaler = torch.amp.GradScaler('cuda') if torch.cuda.is_available() else None\n","\n","# artifacts_dir = f\"artifacts/run_{run_id}\"\n","# os.makedirs(artifacts_dir, exist_ok=True)\n","\n","# print(\"CONFIGURATION PYTORCH POUR SEGMENTATION SÉMANTIQUE:\")\n","# print(f\" LOSS: CategoricalFocalJaccardLoss (équivalent SM categorical_focal_jaccard_loss)\")\n","# print(f\" LOSS PARAMS: Architecture={architecture}\")\n","# print(f\" - Focal weight: {loss_params['focal_weight']}, Jaccard weight: {loss_params['jaccard_weight']}, alpha: {loss_params['alpha']}, gamma: {loss_params['gamma']}\")\n","# print(\" OPTIMIZER: Adam\")\n","# print(\" MÉTRIQUE PRINCIPALE: Mean IoU\")\n","# print(f\" DEVICE MANAGEMENT: Strict {device}\")\n","# print(f\" CALLBACKS: EarlyStopping (patience={EARLY_STOPPING_PATIENCE}), ReduceLROnPlateau\")\n","\n","# return {\n","# 'model': model,\n","# 'train_dataset': train_dataset,\n","# 'val_dataset': val_dataset,\n","# 'test_dataset': test_dataset,\n","# 'criterion': criterion,\n","# 'optimizer': optimizer,\n","# 'train_iou_metric': train_iou_metric,\n","# 'val_iou_metric': val_iou_metric,\n","# 'early_stopping': early_stopping,\n","# 'lr_scheduler': lr_scheduler,\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","# 'scaler': scaler,\n","# 'loss_params': loss_params\n","# }"],"metadata":{"id":"2_GS-GJaQmgy","executionInfo":{"status":"ok","timestamp":1756538723620,"user_tz":-120,"elapsed":3,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":94,"outputs":[]},{"cell_type":"code","source":["def setup_training_components(config, run_id):\n"," \"\"\"Setup simplifié qui utilise les datasets globaux et applique les configs spécialisées\"\"\"\n","\n"," # === PARAMÈTRES DE BASE ===\n"," batch_size = TEST_BATCH_SIZE if TEST_MODE else config['params']['batch_size']\n"," img_size = (TEST_IMG_SIZE, TEST_IMG_SIZE) if TEST_MODE else config['params']['img_size']\n","\n"," print(f\"Mode {'test' if TEST_MODE else 'production'}: batch_size={batch_size}, img_size={img_size}\")\n","\n"," # === RÉCUPÉRATION DES DATASETS GLOBAUX ===\n"," # Les datasets sont déjà créés globalement, on les utilise directement\n"," # Pas de recréation ici, ce qui assure l'équité entre modèles\n"," print(\"📊 Utilisation des datasets globaux (équité entre modèles)\")\n","\n"," # === MODEL CREATION ===\n"," print(f\"\\nCréation du modèle PyTorch {config['name']}...\")\n"," model = create_model_from_config(config, verbose=False)\n","\n"," # Transfer model to device\n"," print(f\"Transfer du modèle vers {device}...\")\n"," model = model.to(device)\n","\n"," # Vérification device\n"," model_device = next(model.parameters()).device\n"," print(f\"Modèle créé sur device: {model_device}\")\n"," if str(model_device) != str(device):\n"," print(f\"ATTENTION: Modèle sur {model_device}, device cible {device}\")\n"," model = model.to(device)\n"," print(f\"Modèle retransféré vers {device}\")\n","\n"," # === MODEL INFO ===\n"," num_classes_from_model = config['classes']\n"," total_params = sum(p.numel() for p in model.parameters())\n"," trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n","\n"," print(f\"Nombre de classes: {num_classes_from_model}\")\n"," print(f\"Paramètres: {total_params:,} total | {trainable_params:,} entraînables\")\n","\n"," print(\"\\nConfiguration des composants d'entraînement...\")\n","\n"," # === LOSS FUNCTION (spécialisée par architecture) ===\n"," loss_params = config['params']['loss_params']\n"," criterion = CategoricalFocalJaccardLoss(**loss_params)\n","\n"," # === OPTIMIZER (spécialisé par architecture) ===\n"," optimizer_type = config['params'].get('optimizer', 'adam').lower()\n"," lr = config['params']['learning_rate']\n"," weight_decay = config['params'].get('weight_decay', 0)\n","\n"," if optimizer_type == 'adamw':\n"," optimizer = optim.AdamW(\n"," model.parameters(),\n"," lr=lr,\n"," weight_decay=weight_decay,\n"," eps=config['params'].get('eps', 1e-8)\n"," )\n"," print(f\" OPTIMIZER: AdamW (lr={lr}, wd={weight_decay})\")\n"," else:\n"," optimizer = optim.Adam(\n"," model.parameters(),\n"," lr=lr,\n"," weight_decay=weight_decay\n"," )\n"," print(f\" OPTIMIZER: Adam (lr={lr}, wd={weight_decay})\")\n","\n"," # === SCHEDULER (spécialisé par architecture) ===\n"," scheduler_type = config['params'].get('scheduler', 'plateau').lower()\n","\n"," if scheduler_type == 'cosine':\n"," # Cosine Annealing simple (pas de warmup complexe pour 20 epochs)\n"," min_lr = config['params'].get('min_lr', 1e-6)\n"," lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n"," optimizer,\n"," T_max=config['params']['epochs'],\n"," eta_min=min_lr\n"," )\n"," print(f\" SCHEDULER: CosineAnnealing (min_lr={min_lr})\")\n"," else:\n"," # Plateau (défaut pour FPN)\n"," lr_scheduler = ReduceLROnPlateau(\n"," optimizer,\n"," mode='max',\n"," factor=0.2,\n"," patience=config['params'].get('lr_reduce_patience', 3),\n"," min_lr=1e-6\n"," )\n"," print(f\" SCHEDULER: ReduceLROnPlateau (patience={config['params'].get('lr_reduce_patience', 3)})\")\n","\n"," # === CALLBACKS ===\n"," early_stopping_patience = config['params'].get('early_stopping_patience', 5)\n"," early_stopping = EarlyStopping(\n"," patience=early_stopping_patience,\n"," mode='max',\n"," restore_best_weights=True\n"," ) if config['params'].get('early_stopping', True) else None\n","\n"," print(f\" EARLY STOPPING: patience={early_stopping_patience}\")\n","\n"," # === METRICS ===\n"," train_iou_metric = IoUMetric(num_classes_from_model, device=device)\n"," val_iou_metric = IoUMetric(num_classes_from_model, device=device)\n","\n"," # === MIXED PRECISION ===\n"," scaler = torch.amp.GradScaler('cuda') if (\n"," torch.cuda.is_available() and config['params'].get('mixed_precision', False)\n"," ) else None\n","\n"," if scaler:\n"," print(\" MIXED PRECISION: Activée\")\n","\n"," # === ARTIFACTS ===\n"," artifacts_dir = f\"artifacts/run_{run_id}\"\n"," os.makedirs(artifacts_dir, exist_ok=True)\n","\n"," # === LOGGING FINAL CONFIG ===\n"," architecture = config['architecture']\n"," print(f\"\\n✅ CONFIGURATION FINALE - {architecture.upper()}:\")\n"," print(f\" 🎯 LOSS: {config['params']['loss_type']}\")\n"," print(f\" - Focal weight: {loss_params['focal_weight']}\")\n"," print(f\" - Jaccard weight: {loss_params['jaccard_weight']}\")\n"," print(f\" 🚀 TRAINING: {config['params']['epochs']} epochs, batch_size={batch_size}\")\n"," print(f\" 📊 AUGMENTATION: Communes à tous les modèles\")\n"," print(f\" 💾 DEVICE: {device}\")\n","\n"," return {\n"," 'model': model,\n"," 'train_dataset': train_dataset, # ← Datasets globaux\n"," 'val_dataset': val_dataset, # ← Datasets globaux\n"," 'test_dataset': test_dataset, # ← Datasets globaux\n"," 'criterion': criterion,\n"," 'optimizer': optimizer,\n"," 'lr_scheduler': lr_scheduler,\n"," 'early_stopping': early_stopping,\n"," 'train_iou_metric': train_iou_metric,\n"," 'val_iou_metric': val_iou_metric,\n"," 'artifacts_dir': artifacts_dir,\n"," 'scaler': scaler,\n"," 'batch_size': batch_size,\n"," 'img_size': img_size,\n"," 'num_classes_from_model': num_classes_from_model,\n"," 'loss_params': loss_params\n"," }"],"metadata":{"id":"1eFt5pijNrST","executionInfo":{"status":"ok","timestamp":1756538723850,"user_tz":-120,"elapsed":3,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":95,"outputs":[]},{"cell_type":"code","source":["def train_model_core(components, max_epochs, config, run_id):\n"," \"\"\"Boucle d'entraînement PyTorch principale avec corrections device\"\"\"\n"," print(f\"\\nDébut de l'entraînement PyTorch ({max_epochs} epochs max)...\")\n"," start_time = time.time()\n","\n"," model = components['model']\n"," train_dataset = components['train_dataset']\n"," val_dataset = components['val_dataset']\n"," criterion = components['criterion']\n"," optimizer = components['optimizer']\n"," train_iou_metric = components['train_iou_metric']\n"," val_iou_metric = components['val_iou_metric']\n"," early_stopping = components['early_stopping']\n"," lr_scheduler = components['lr_scheduler']\n"," artifacts_dir = components['artifacts_dir']\n","\n"," history = {\n"," 'loss': [],\n"," 'accuracy': [],\n"," 'mean_iou': [],\n"," 'val_loss': [],\n"," 'val_accuracy': [],\n"," 'val_mean_iou': []\n"," }\n","\n"," best_val_iou = 0.0\n","\n"," # Log des paramètres MLflow\n"," mlflow.log_param(\"model_architecture\", config[\"architecture\"])\n"," mlflow.log_param(\"model_name\", config[\"name\"])\n"," mlflow.log_param(\"encoder_name\", config.get(\"encoder_name\", \"None\"))\n"," mlflow.log_param(\"script_batch_size\", components['batch_size'])\n"," mlflow.log_param(\"script_img_size\", str(components['img_size']))\n"," mlflow.log_param(\"test_mode\", TEST_MODE)\n"," mlflow.log_param(\"max_epochs\", config[\"epochs\"])\n"," mlflow.log_param(\"device\", str(device))\n","\n"," # Ajout du logging des paramètres de loss\n"," loss_params = components['loss_params']\n"," mlflow.log_param(\"loss_type\", \"CategoricalFocalJaccardLoss\")\n"," mlflow.log_param(\"loss_focal_weight\", loss_params['focal_weight'])\n"," mlflow.log_param(\"loss_jaccard_weight\", loss_params['jaccard_weight'])\n"," mlflow.log_param(\"loss_alpha\", loss_params['alpha'])\n"," mlflow.log_param(\"loss_gamma\", loss_params['gamma'])\n"," mlflow.log_param(\"loss_smooth\", loss_params['smooth'])\n","\n"," for epoch in range(max_epochs):\n"," print(f\"\\n{'='*50}\")\n"," print(f\"Époque {epoch+1}/{max_epochs}\")\n"," print(f\"{'='*50}\")\n","\n"," train_loss, train_acc, train_iou = train_epoch(\n"," model, train_dataset, criterion, optimizer, device,\n"," train_iou_metric, epoch+1, scaler=components.get('scaler') # ← Passer le scaler\n"," )\n","\n"," val_loss, val_acc, val_iou = validate_epoch(\n"," model, val_dataset, criterion, device,\n"," val_iou_metric, epoch+1 # ← Pas de scaler pour validation\n"," )\n","\n"," history['loss'].append(train_loss)\n"," history['accuracy'].append(train_acc)\n"," history['mean_iou'].append(train_iou)\n"," history['val_loss'].append(val_loss)\n"," history['val_accuracy'].append(val_acc)\n"," history['val_mean_iou'].append(val_iou)\n","\n"," # Log MLflow\n"," mlflow.log_metric(\"loss\", train_loss, step=epoch)\n"," mlflow.log_metric(\"accuracy\", train_acc, step=epoch)\n"," mlflow.log_metric(\"mean_iou\", train_iou, step=epoch)\n"," mlflow.log_metric(\"val_loss\", val_loss, step=epoch)\n"," mlflow.log_metric(\"val_accuracy\", val_acc, step=epoch)\n"," mlflow.log_metric(\"val_mean_iou\", val_iou, step=epoch)\n","\n"," print(f\"\\nRésultats Époque {epoch+1}:\")\n"," print(f\" Train - Loss: {train_loss:.4f}, Acc: {train_acc:.4f}, IoU: {train_iou:.4f}\")\n"," print(f\" Val - Loss: {val_loss:.4f}, Acc: {val_acc:.4f}, IoU: {val_iou:.4f}\")\n","\n"," if isinstance(lr_scheduler, ReduceLROnPlateau):\n"," lr_scheduler.step(val_iou) # ReduceLROnPlateau prend la métrique\n"," else:\n"," lr_scheduler.step() # CosineAnnealingLR ne prend rien\n","\n"," if val_iou > best_val_iou:\n"," best_val_iou = val_iou\n"," best_model_path = os.path.join(artifacts_dir, 'best_model.pth')\n"," torch.save(model.state_dict(), best_model_path)\n"," print(f\" Nouveau meilleur modèle sauvegardé (Val IoU: {val_iou:.4f})\")\n","\n"," if early_stopping(val_iou, model):\n"," print(f\" Early stopping déclenché à l'époque {epoch+1}\")\n"," print(f\" Meilleur Val IoU: {best_val_iou:.4f}\")\n"," break\n","\n"," training_time = time.time() - start_time\n"," mlflow.log_metric(\"training_time_seconds\", training_time)\n"," mlflow.log_metric(\"best_val_mean_iou\", best_val_iou)\n"," print(f\"\\nTemps d'entraînement: {training_time:.2f}s\")\n"," print(f\"Meilleur Val IoU: {best_val_iou:.4f}\")\n","\n"," return history, training_time, best_val_iou"],"metadata":{"id":"aZ0FCJjcQopP","executionInfo":{"status":"ok","timestamp":1756538723853,"user_tz":-120,"elapsed":2,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":96,"outputs":[]},{"cell_type":"code","source":["# ====================================================================\n","# EVALUATION ET MÉTRIQUES\n","# ====================================================================\n","\n","def evaluate_model_comprehensive(model, test_dataset, num_classes_from_model, artifacts_dir, class_names):\n"," \"\"\"Évaluation complète du modèle PyTorch\"\"\"\n"," print(\"\\nÉvaluation complète sur le jeu de test...\")\n","\n"," model.eval()\n"," test_loss = 0.0\n"," test_accuracy = 0.0\n"," num_batches = 0\n","\n"," criterion = nn.CrossEntropyLoss()\n"," iou_metric = IoUMetric(num_classes_from_model)\n"," iou_metric.reset()\n","\n"," all_predictions = []\n"," all_targets = []\n","\n"," with torch.no_grad():\n"," pbar = tqdm(test_dataset, desc=\"Test\")\n"," for images, masks in pbar:\n"," images, masks = images.to(device), masks.to(device)\n","\n"," outputs = model(images)\n"," loss = criterion(outputs, masks)\n","\n"," test_loss += loss.item()\n"," test_accuracy += calculate_accuracy(outputs, masks)\n"," iou_metric.update(outputs, masks)\n","\n"," predictions = torch.argmax(outputs, dim=1)\n"," all_predictions.extend(predictions.cpu().flatten().numpy())\n"," all_targets.extend(masks.cpu().flatten().numpy())\n","\n"," num_batches += 1\n","\n"," # Limitation en mode TEST\n"," if TEST_MODE and SUBSET_SIZE and num_batches >= SUBSET_SIZE // (2 * len(images)):\n"," break\n","\n"," test_loss /= num_batches\n"," test_accuracy /= num_batches\n"," test_iou, class_ious = iou_metric.compute()\n","\n"," basic_test_metrics = {\n"," 'loss': test_loss,\n"," 'accuracy': test_accuracy,\n"," 'mean_iou': test_iou\n"," }\n","\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}: {value:.4f}\")\n","\n"," detailed_metrics = {\n"," 'mean_iou': test_iou,\n"," 'class_ious': class_ious,\n"," 'class_stats': []\n"," }\n","\n"," print(f\"\\nANALYSE DÉTAILLÉE PAR CLASSE:\")\n"," print(\"-\" * 70)\n"," print(f\"{'Classe':<15} {'IoU':<8}\")\n"," print(\"-\" * 70)\n","\n"," for i, iou in enumerate(class_ious):\n"," class_name = class_names[i] if class_names and i < len(class_names) else f\"Class_{i}\"\n"," print(f\"{class_name:<15} {iou:<8.4f}\")\n","\n"," detailed_metrics['class_stats'].append({\n"," 'class_id': i,\n"," 'class_name': class_name,\n"," 'iou': float(iou)\n"," })\n","\n"," mlflow.log_metric(f\"class_{i}_iou\", float(iou))\n","\n"," print(\"-\" * 70)\n"," print(f\"{'MEAN IoU':<15} {test_iou:<8.4f}\")\n","\n"," print(\"\\nGénération de la matrice de confusion...\")\n"," confusion_matrix_data = generate_confusion_matrix_pytorch(\n"," all_predictions, all_targets, num_classes_from_model, class_names, artifacts_dir\n"," )\n","\n"," return basic_test_metrics, detailed_metrics, confusion_matrix_data\n","\n","def generate_confusion_matrix_pytorch(predictions, targets, num_classes, class_names, artifacts_dir):\n"," \"\"\"Génère la matrice de confusion PyTorch\"\"\"\n"," print(\"Calcul de la matrice de confusion...\")\n","\n"," cm = confusion_matrix(targets, predictions, labels=range(num_classes))\n","\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 (PyTorch)', fontsize=14)\n"," plt.colorbar()\n","\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"," 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"," 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()\n","\n"," confusion_matrix_data = {\n"," 'matrix': cm.tolist(),\n"," 'class_names': class_names if class_names else [f\"Class_{i}\" for i in range(num_classes)],\n"," 'total_samples': len(targets),\n"," 'accuracy': float(np.trace(cm) / np.sum(cm))\n"," }\n","\n"," print(f\"Matrice de confusion sauvegardée : {cm_image_path}\")\n"," return confusion_matrix_data"],"metadata":{"id":"BHIOaWybQqjn","executionInfo":{"status":"ok","timestamp":1756538724032,"user_tz":-120,"elapsed":2,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":97,"outputs":[]},{"cell_type":"code","source":["# ====================================================================\n","# VISUALISATIONS ET ARTEFACTS\n","# ====================================================================\n","\n","def generate_training_visualizations(history, detailed_metrics, class_names, run_id, config, artifacts_dir):\n"," \"\"\"Génère les visualisations d'entraînement\"\"\"\n"," print(\"\\nCréation des visualisations...\")\n"," fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n"," fig.suptitle(f'Entraînement PyTorch {config[\"name\"]} - Run {run_id[:8]}', fontsize=16)\n","\n"," # Loss\n"," axes[0, 0].plot(history['loss'], label='Train Loss')\n"," axes[0, 0].plot(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['accuracy'], label='Train Acc')\n"," axes[0, 1].plot(history['val_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['mean_iou'], label='Train Mean IoU')\n"," axes[1, 0].plot(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\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"," \"\"\"Sauvegarde tous les artefacts\"\"\"\n"," print(\"\\nSauvegarde des artefacts...\")\n","\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.items()}\n"," json.dump(history_json, f, indent=2)\n","\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"," detailed_path = os.path.join(artifacts_dir, 'detailed_metrics.json')\n"," with open(detailed_path, 'w') as f:\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': detailed_metrics['class_stats']\n"," }\n"," json.dump(detailed_json, f, indent=2)\n","\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)"],"metadata":{"id":"k30kTo7XQsLH","executionInfo":{"status":"ok","timestamp":1756538724035,"user_tz":-120,"elapsed":2,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":98,"outputs":[]},{"cell_type":"code","source":["def train_segmentation_model_optimized(config, run_name=None):\n"," \"\"\"Fonction principale d'entraînement PyTorch avec corrections device\"\"\"\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"," print(f\"Device utilisé: {device}\")\n","\n"," try:\n"," components = setup_training_components(config, run_id)\n","\n"," # Vérification des devices avant entraînement\n"," model_device = next(components['model'].parameters()).device\n"," print(f\"\\nVÉRIFICATION DEVICES AVANT ENTRAÎNEMENT:\")\n"," print(f\" Modèle sur: {model_device}\")\n"," print(f\" Device cible: {device}\")\n"," print(f\" IoU Metric device: {components['train_iou_metric'].device}\")\n","\n"," history, training_time, best_val_iou = train_model_core(\n"," components, config['params']['epochs'], config, run_id\n"," )\n","\n"," best_model_path = os.path.join(components['artifacts_dir'], 'best_model.pth')\n"," if os.path.exists(best_model_path):\n"," components['model'].load_state_dict(torch.load(best_model_path, map_location=device))\n"," print(\"Meilleur modèle chargé pour l'évaluation\")\n","\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(components['num_classes_from_model'])]\n","\n"," basic_test_metrics, detailed_metrics, confusion_matrix_data = evaluate_model_comprehensive(\n"," components['model'],\n"," components['test_dataset'],\n"," components['num_classes_from_model'],\n"," components['artifacts_dir'],\n"," class_names\n"," )\n","\n"," generate_training_visualizations(\n"," history, detailed_metrics, class_names,\n"," run_id, config, components['artifacts_dir']\n"," )\n","\n"," save_training_artifacts(\n"," history, config, detailed_metrics,\n"," confusion_matrix_data, components['artifacts_dir']\n"," )\n","\n"," best_val_acc = max(history['val_accuracy'])\n"," results = {\n"," 'run_id': run_id,\n"," 'config': config,\n"," 'training_time': training_time,\n"," 'final_epoch': len(history['loss']),\n"," 'best_val_accuracy': best_val_acc,\n"," 'best_val_iou': best_val_iou,\n"," 'test_metrics': basic_test_metrics,\n"," 'detailed_metrics': detailed_metrics,\n"," 'confusion_matrix': confusion_matrix_data,\n"," 'artifacts_dir': components['artifacts_dir'],\n"," 'num_classes': components['num_classes_from_model']\n"," }\n","\n"," print(f\"\\nEntraînement PyTorch terminé!\")\n"," print(f\"Meilleure val_accuracy: {best_val_acc:.4f}\")\n"," print(f\"Meilleur val_IoU: {best_val_iou:.4f}\")\n"," print(f\"Test Mean IoU: {detailed_metrics['mean_iou']:.4f}\")\n"," test_acc = basic_test_metrics.get('accuracy', 0)\n"," print(f\"Test accuracy: {test_acc:.4f}\")\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"," print(f\"Type erreur: {type(e)}\")\n"," import traceback\n"," traceback.print_exc()\n"," raise"],"metadata":{"id":"O9IsCwqaQt2a","executionInfo":{"status":"ok","timestamp":1756538724106,"user_tz":-120,"elapsed":12,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":99,"outputs":[]},{"cell_type":"code","source":["# ====================================================================\n","# GESTION D'EXPÉRIENCES MULTIPLES\n","# ====================================================================\n","\n","def run_experiments_optimized(configs_to_test=None, experiment_name=\"OC Projet 9\", force=False):\n"," \"\"\"Version PyTorch avec système de reprise automatique\"\"\"\n","\n"," print(f\"\\n{'='*60}\")\n"," print(f\"DÉBUT DES EXPÉRIENCES PYTORCH - Mode: {'TEST' if TEST_MODE else 'PRODUCTION'}\")\n"," print(f\"Expérience MLflow: {experiment_name}\")\n"," print(f\"{'='*60}\")\n","\n"," if configs_to_test is None:\n"," remaining_configs = filter_configs_to_run(experimental_configurations, experiment_name, force=force)\n","\n"," if not remaining_configs:\n"," print(\"\\nTOUTES LES EXPÉRIENCES SONT DÉJÀ TERMINÉES !\")\n"," print(\"Rien à faire. Utilisez compare_experiments_local() pour voir les résultats.\")\n"," return []\n","\n"," if TEST_MODE and TEST_MAX_CONFIGS:\n"," configs_to_test = remaining_configs[:TEST_MAX_CONFIGS]\n"," print(f\"\\nMode 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(\"\\nToutes les configurations demandées sont déjà terminées !\")\n"," return []\n","\n"," print(f\"\\nENTRAÎNEMENT PYTORCH 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"," 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"," if i < len(configs_to_test) - 1:\n"," print(\"Nettoyage mémoire GPU PyTorch...\")\n"," torch.cuda.empty_cache() if torch.cuda.is_available() else None\n","\n"," print(f\"\\n{'='*60}\")\n"," print(f\"RÉSUMÉ DE CETTE SESSION PYTORCH\")\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} {'Val IoU':<10} {'Test IoU':<10} {'Temps':<8} {'Run ID':<10}\")\n"," print(\"-\" * 85)\n","\n"," for result in results:\n"," config_name = result['config']['name']\n"," val_acc = result['best_val_accuracy']\n"," val_iou = result['best_val_iou']\n"," test_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} {val_iou:<10.4f} {test_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\"\\nMeilleur de cette session: {best_result['config']['name']}\")\n"," print(f\" Val Acc: {best_result['best_val_accuracy']:.4f}\")\n"," print(f\" Val IoU: {best_result['best_val_iou']:.4f}\")\n"," print(f\" Test 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\"\\nIl reste encore {remaining_count} configuration(s) à entraîner\")\n"," print(\"Relancez le script pour continuer automatiquement\")\n"," else:\n"," print(f\"\\nTOUTES LES CONFIGURATIONS PYTORCH 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"],"metadata":{"id":"BHk4Lft8Qv2z","executionInfo":{"status":"ok","timestamp":1756538724371,"user_tz":-120,"elapsed":42,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":100,"outputs":[]},{"cell_type":"code","source":["# ====================================================================\n","# COMPARAISON DES EXPÉRIENCES\n","# ====================================================================\n","\n","def compare_experiments_local(artifacts_base_dir=\"artifacts\"):\n"," \"\"\"Compare les résultats PyTorch via les artefacts locaux\"\"\"\n"," if not os.path.exists(artifacts_base_dir):\n"," print(\"Aucun répertoire d'artefacts trouvé\")\n"," return\n","\n"," results = []\n"," print(f\"Recherche des expériences PyTorch dans {artifacts_base_dir}/...\")\n","\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"," if os.path.exists(config_path) and os.path.exists(history_path):\n"," try:\n"," with open(config_path, 'r') as f:\n"," config = json.load(f)\n","\n"," with open(history_path, 'r') as f:\n"," history = json.load(f)\n","\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"," final_metrics = {\n"," 'run_id': run_dir.replace('run_', ''),\n"," 'experiment': config.get('name', 'Unknown'),\n"," 'architecture': config.get('architecture', 'Unknown'),\n"," 'encoder_name': config.get('encoder_name', 'None'),\n"," 'final_train_acc': history['accuracy'][-1],\n"," 'final_val_acc': history['val_accuracy'][-1],\n"," 'final_train_loss': history['loss'][-1],\n"," 'final_val_loss': history['val_loss'][-1],\n"," 'best_val_acc': max(history['val_accuracy']),\n"," 'best_val_iou': max(history['val_mean_iou']),\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"," if results:\n"," print(f\"\\nCOMPARAISON DES {len(results)} EXPÉRIENCES PYTORCH\")\n"," print(\"=\" * 100)\n","\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 Test Mean IoU (meilleur premier)\")\n"," else:\n"," results_sorted = sorted(results, key=lambda x: x['best_val_iou'], reverse=True)\n"," print(\"Classement par Validation IoU (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_name'] != 'None':\n"," print(f\" Encoder: {result['encoder_name']}\")\n","\n"," print(f\" Meilleur Val IoU: {result['best_val_iou']:.4f}\")\n"," print(f\" Meilleure Val Acc: {result['best_val_acc']:.4f}\")\n","\n"," if result['mean_iou'] is not None:\n"," print(f\" Test 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"," print(\"STATISTIQUES GLOBALES PYTORCH:\")\n"," print(\"-\" * 50)\n"," avg_val_iou = sum(r['best_val_iou'] for r in results) / len(results)\n"," print(f\"Val IoU moyen: {avg_val_iou:.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_test_iou = sum(valid_ious) / len(valid_ious)\n"," print(f\"Test IoU moyen: {avg_test_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"," 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_iou'])\n","\n"," print(f\"\\nPERFORMANCE PAR ARCHITECTURE:\")\n"," print(\"-\" * 50)\n"," for arch, ious in architectures.items():\n"," avg_iou = sum(ious) / len(ious)\n"," print(f\"{arch}: {avg_iou:.4f} IoU moyen (sur {len(ious)} runs)\")\n","\n"," else:\n"," print(\"Aucune expérience valide trouvée\")\n","\n"," return results\n","\n","def show_best_experiment_details(artifacts_base_dir=\"artifacts\"):\n"," \"\"\"Affiche les détails de la meilleure expérience PyTorch\"\"\"\n"," results = compare_experiments_local(artifacts_base_dir)\n","\n"," if not results:\n"," return\n","\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 = \"Test Mean IoU\"\n"," metric_value = best['mean_iou']\n"," else:\n"," best = max(results, key=lambda x: x['best_val_iou'])\n"," metric_name = \"Validation IoU\"\n"," metric_value = best['best_val_iou']\n","\n"," print(f\"\\nDÉTAILS DE LA MEILLEURE EXPÉRIENCE PYTORCH ({metric_name})\")\n"," print(\"=\" * 60)\n"," print(f\"Nom: {best['experiment']}\")\n"," print(f\"Architecture: {best['architecture']}\")\n","\n"," if best['encoder_name'] != 'None':\n"," print(f\"Encoder: {best['encoder_name']}\")\n","\n"," print(f\"{metric_name}: {metric_value:.4f}\")\n"," print(f\"Validation IoU: {best['best_val_iou']:.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"," model_path = os.path.join(best['run_path'], 'best_model.pth')\n"," if os.path.exists(model_path):\n"," print(f\"Modèle PyTorch disponible: {model_path}\")\n"," else:\n"," print(f\"Modèle PyTorch non trouvé: {model_path}\")\n","\n","print(\"Code d'entraînement PyTorch complet chargé et prêt!\")"],"metadata":{"id":"xeBZ6ZsMQyRs","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1756538724649,"user_tz":-120,"elapsed":45,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"6617c4bc-79df-46e4-e5ab-861b596cfcc0"},"execution_count":101,"outputs":[{"output_type":"stream","name":"stdout","text":["Code d'entraînement PyTorch complet chargé et prêt!\n"]}]},{"cell_type":"code","source":["# ====================================================================\n","# POINT D'ENTRÉE PRINCIPAL ADAPTATIF\n","# ====================================================================\n","\n","if __name__ == \"__main__\":\n"," print(\"SCRIPT D'ENTRAÎNEMENT PYTORCH OPTIMISÉ POUR SEGMENTATION\")\n"," print(f\"Mode: {'TEST' if TEST_MODE else 'PRODUCTION'}\")\n"," print(\"SYSTÈME DE REPRISE AUTOMATIQUE ACTIVÉ\")\n","\n"," try:\n"," if 'mlflow' in globals():\n"," print(f\"MLflow version: {mlflow.__version__}\")\n"," print(f\"PyTorch version: {torch.__version__}\")\n"," print(f\"Device: {device}\")\n","\n"," if 'cityscapes_unzipped_dir' in globals():\n"," print(f\"Cityscapes dir: {cityscapes_unzipped_dir}\")\n"," else:\n"," print(\"Variable 'cityscapes_unzipped_dir' non définie\")\n","\n"," if 'experimental_configurations' in globals():\n"," print(f\"Configurations disponibles: {len(experimental_configurations)}\")\n"," else:\n"," print(\"Variable 'experimental_configurations' non définie\")\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 PyTorch passées\")\n","\n"," # POINT D'ENTRÉE ADAPTATIF\n"," if TEST_MODE:\n"," print(\"\\n=== MODE TEST ===\")\n"," configs_to_test = experimental_configurations[:TEST_MAX_CONFIGS] if TEST_MAX_CONFIGS else experimental_configurations\n"," results = []\n","\n"," print(f\"Test de {len(configs_to_test)} configurations en mode TEST\")\n","\n"," for i, config in enumerate(configs_to_test):\n"," print(f\"\\n--- Configuration {i+1}/{len(configs_to_test)}: {config['name']} ---\")\n"," stable_run_name = generate_stable_run_name(config, test_mode=True)\n","\n"," try:\n"," # Avec MLflow, un run par config, run_name unique\n"," with mlflow.start_run(run_name=stable_run_name) as run:\n"," run_id = run.info.run_id\n"," components = setup_training_components(config, run_id)\n"," history, training_time, best_val_iou = train_model_core(\n"," components, config['params']['epochs'], config, run_id\n"," )\n"," best_val_acc = max(history['val_accuracy'])\n"," result = {\n"," 'config': config,\n"," 'best_val_accuracy': best_val_acc,\n"," 'best_val_iou': best_val_iou,\n"," 'training_time': training_time,\n"," 'detailed_metrics': {'mean_iou': best_val_iou}\n"," }\n"," results.append(result)\n"," print(f\"Test terminé: Val IoU = {best_val_iou:.4f}\")\n","\n"," torch.cuda.empty_cache() if torch.cuda.is_available() else None\n","\n"," except Exception as e:\n"," print(f\"Erreur lors du test: {e}\")\n","\n"," print(\"\\nScript TEST terminé avec succès!\")\n","\n"," else:\n"," print(\"\\n=== MODE PRODUCTION ===\")\n"," # force = True pour forcer l'entraînement même si d'autres modèles ont déjà été entraînés\n"," results = run_experiments_optimized(force=True)\n"," print(\"\\nScript PRODUCTION terminé avec succès!\")\n","\n"," # Objectif du projet\n"," if results and len(results) > 1:\n"," fpn_results = [r for r in results if 'FPN' in r['config']['name']]\n"," segformer_results = [r for r in results if 'SegFormer' in r['config']['name']]\n","\n"," if fpn_results and segformer_results:\n"," best_fpn_iou = max(r['detailed_metrics']['mean_iou'] for r in fpn_results)\n"," best_segformer_iou = max(r['detailed_metrics']['mean_iou'] for r in segformer_results)\n","\n"," print(f\"\\nOBJECTIF DU PROJET - SegFormer vs FPN:\")\n"," print(f\" Meilleur FPN IoU: {best_fpn_iou:.4f}\")\n"," print(f\" Meilleur SegFormer IoU: {best_segformer_iou:.4f}\")\n","\n"," if best_segformer_iou > best_fpn_iou:\n"," print(f\" OBJECTIF ATTEINT ! SegFormer bat FPN (+{best_segformer_iou-best_fpn_iou:.4f})\")\n"," else:\n"," print(f\" Objectif non atteint. FPN reste meilleur (+{best_fpn_iou-best_segformer_iou:.4f})\")\n","\n"," except Exception as e:\n"," print(f\"Erreur fatale: {e}\")\n"," print(\"Vérifications:\")\n"," print(\"1. Variables cityscapes_unzipped_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 ?\")\n"," print(\"5. PyTorch et segmentation_models_pytorch installés ?\")"],"metadata":{"id":"xonaxLqrP89l","colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["f55d3a2ce0c44511b27303d7fe204216","a870c15ff11b4599ba727be2342b27f5","f2950e4f33984ffdb58e9d2c0977bb69","6273e4261e6145a69ead434079535333","37390c9f1c2541ccaf3467d1651024df","692dd7efb40640cd8201e236424502a5","1fe79f586a704937a5cf1ca8535fc6e8","5c79b183f68f4025918c40047e258054","c0c7e63bc3f6413d89f77763679ba6ec","6a90b8b21d634620badd447aa0ac9ac1","274a2f1057674203955a8830f39fc38d","8402f6f5914c4a2ea68a22b578fff390","3ccf909155fa451296a0fd0c128ca49f","ab52d53088d24fcc97f0c63c1b51f522","76cfbd306b364d4783e933552d7dc4ca","846d831e49144845882dda22e7257c35","d6b5597795d34d438e531aeada4db9ef","1d30e21594dd45a58cd2d95f2cdf6320","b3f262c4bb3f4969979dc238ac51d9eb","697e612900104898a1bf1ff4a1216360","6d3e11b9fcf34e11aaec499b4fd2bd59","c543c58f32c54be2bd1a042ed34d197c","b89da5e33bdb41f49d0d57f290c2ff0d","d9902e5e2dbd4ecf869765a1691171d3","7f47bfab310a4e39b1836ce32f2498c1","88dc7e8d594643eebfb6be0b76885a45","cd00e83b098a4b8699b37fffd37e1711","6ef685de37d848098e01048b2bbc1803","416d0f59c8d94396ac4918b8f6dc7bd0","200b03431a9242b3afb580ba3132b575","ff9d8dc70cce4fff9c237b8c272aa346","20107b9441ef42cbbfc1a5d2acc7d672","19101c9c183a41c38b42dd89e1ddbd97","5288896a6c864f6587480b3bca97380b","727a2a76d17146dc9bf832ff53f2a9c0","af71645c27ca418fae897ec507562445","a9a691b972b9448cb864bc4be82f7cb9","5f077f7c774f4f01b8fc486cd4c71ca0","afd57499b3804f639e519daebac899a2","0c591a6cc8fc4d9fbb6fccc4beb2f47d","09a87c0db38c4599b02aca4b705ada5b","cbdd062643eb428487183d54e70f81d7","edeb9a31e72f44c68316cbe1c379ca16","8ca082495f7748a2a240aeefd4108e3b"]},"outputId":"af9843f6-c273-4313-fce0-53e861fa94be","executionInfo":{"status":"ok","timestamp":1756571204974,"user_tz":-120,"elapsed":32480009,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":102,"outputs":[{"output_type":"stream","name":"stdout","text":["SCRIPT D'ENTRAÎNEMENT PYTORCH OPTIMISÉ POUR SEGMENTATION\n","Mode: PRODUCTION\n","SYSTÈME DE REPRISE AUTOMATIQUE ACTIVÉ\n","MLflow version: 3.3.2\n","PyTorch version: 2.8.0+cu126\n","Device: cuda\n","Cityscapes dir: /content/data/cityscapes_unzipped\n","Configurations disponibles: 4\n","Toutes les vérifications PyTorch passées\n","\n","=== MODE PRODUCTION ===\n","\n","============================================================\n","DÉBUT DES EXPÉRIENCES PYTORCH - Mode: PRODUCTION\n","Expérience MLflow: OC Projet 9\n","============================================================\n","Vérification des expériences déjà terminées dans 'OC Projet 9'...\n","12 expériences terminées trouvées\n"," - FPN_efficientnet_b0_baseline_prod_2025-08-29_21-39-34\n"," - FPN_efficientnet_b0_baseline_test\n"," - FPN_efficientnet_b0_finetune_aug_prod\n"," - FPN_resnet34_baseline_prod_2025-08-29_23-13-38\n"," - FPN_resnet34_baseline_test\n"," - FPN_resnet34_finetune_aug_prod\n"," - SegFormer_b0_pretrained_finetune_prod\n"," - SegFormer_b0_pretrained_optimized_prod_2025-08-29_19-18-31\n"," - SegFormer_b1_pretrained_finetune_prod\n"," - SegFormer_b1_pretrained_finetune_prod_2025-08-29_12-24-46\n"," - SegFormer_b1_pretrained_optimized_prod_2025-08-29_15-15-26\n"," - SegFormer_b1_pretrained_optimized_prod_2025-08-29_16-52-06\n","\n","FILTRAGE DES CONFIGURATIONS:\n","À entraîner: 4 configurations\n","Déjà terminées: 0 configurations\n","⚠️ Mode FORCE activé - relancement des expériences existantes\n","\n","CONFIGURATIONS À ENTRAÎNER:\n"," [0] SegFormer_b1_pretrained_optimized → SegFormer_b1_pretrained_optimized_prod_2025-08-30_07-25-25\n"," [1] SegFormer_b0_pretrained_optimized → SegFormer_b0_pretrained_optimized_prod_2025-08-30_07-25-25\n"," [2] FPN_efficientnet_b0_baseline → FPN_efficientnet_b0_baseline_prod_2025-08-30_07-25-25\n"," [3] FPN_resnet34_baseline → FPN_resnet34_baseline_prod_2025-08-30_07-25-25\n","\n","ENTRAÎNEMENT PYTORCH DE 4 CONFIGURATIONS:\n","\n","--------------------------------------------------\n","Expérience 1/4: SegFormer_b1_pretrained_optimized\n","Run name: SegFormer_b1_pretrained_optimized_prod_2025-08-30_07-25-25\n","--------------------------------------------------\n","MLflow Run ID: c9835f7094354f48baff73f5d71edfc9\n","MLflow Run Name: SegFormer_b1_pretrained_optimized_prod_2025-08-30_07-25-25\n","Device utilisé: cuda\n","Mode production: batch_size=8, img_size=(768, 768)\n","📊 Utilisation des datasets globaux (équité entre modèles)\n","\n","Création du modèle PyTorch SegFormer_b1_pretrained_optimized...\n","Transfer du modèle vers cuda...\n","Modèle créé sur device: cuda:0\n","ATTENTION: Modèle sur cuda:0, device cible cuda\n","Modèle retransféré vers cuda\n","Nombre de classes: 8\n","Paramètres: 13,679,304 total | 13,679,304 entraînables\n","\n","Configuration des composants d'entraînement...\n"," OPTIMIZER: AdamW (lr=0.0001, wd=0.0001)\n"," SCHEDULER: CosineAnnealing (min_lr=1e-06)\n"," EARLY STOPPING: patience=5\n"," MIXED PRECISION: Activée\n","\n","✅ CONFIGURATION FINALE - SEGFORMER_PRETRAINED:\n"," 🎯 LOSS: jaccard_only\n"," - Focal weight: 0.0\n"," - Jaccard weight: 1.0\n"," 🚀 TRAINING: 30 epochs, batch_size=8\n"," 📊 AUGMENTATION: Communes à tous les modèles\n"," 💾 DEVICE: cuda\n","\n","VÉRIFICATION DEVICES AVANT ENTRAÎNEMENT:\n"," Modèle sur: cuda:0\n"," Device cible: cuda\n"," IoU Metric device: cuda\n","\n","Début de l'entraînement PyTorch (30 epochs max)...\n","\n","==================================================\n","Époque 1/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 1 - Training: 100%|██████████| 298/298 [04:15<00:00, 1.17it/s, Loss=0.4730, Acc=0.8626]\n","Epoch 1 - Validation: 100%|██████████| 75/75 [01:04<00:00, 1.16it/s, Loss=0.3345, Acc=0.8975]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 1:\n"," Train - Loss: 0.4730, Acc: 0.8626, IoU: 0.6019\n"," Val - Loss: 0.3345, Acc: 0.8975, IoU: 0.7035\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7035)\n","\n","==================================================\n","Époque 2/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 2 - Training: 100%|██████████| 298/298 [04:15<00:00, 1.17it/s, Loss=0.3036, Acc=0.9066]\n","Epoch 2 - Validation: 100%|██████████| 75/75 [01:03<00:00, 1.18it/s, Loss=0.2844, Acc=0.9084]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 2:\n"," Train - Loss: 0.3036, Acc: 0.9066, IoU: 0.7271\n"," Val - Loss: 0.2844, Acc: 0.9084, IoU: 0.7412\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7412)\n","\n","==================================================\n","Époque 3/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 3 - Training: 100%|██████████| 298/298 [04:19<00:00, 1.15it/s, Loss=0.2571, Acc=0.9203]\n","Epoch 3 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.21it/s, Loss=0.2417, Acc=0.9259]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 3:\n"," Train - Loss: 0.2571, Acc: 0.9203, IoU: 0.7638\n"," Val - Loss: 0.2417, Acc: 0.9259, IoU: 0.7774\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7774)\n","\n","==================================================\n","Époque 4/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 4 - Training: 100%|██████████| 298/298 [04:28<00:00, 1.11it/s, Loss=0.2376, Acc=0.9263]\n","Epoch 4 - Validation: 100%|██████████| 75/75 [01:04<00:00, 1.16it/s, Loss=0.2355, Acc=0.9265]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 4:\n"," Train - Loss: 0.2376, Acc: 0.9263, IoU: 0.7804\n"," Val - Loss: 0.2355, Acc: 0.9265, IoU: 0.7808\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7808)\n","\n","==================================================\n","Époque 5/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 5 - Training: 100%|██████████| 298/298 [04:18<00:00, 1.15it/s, Loss=0.2247, Acc=0.9298]\n","Epoch 5 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.23it/s, Loss=0.2272, Acc=0.9283]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 5:\n"," Train - Loss: 0.2247, Acc: 0.9298, IoU: 0.7909\n"," Val - Loss: 0.2272, Acc: 0.9283, IoU: 0.7873\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7873)\n","\n","==================================================\n","Époque 6/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 6 - Training: 100%|██████████| 298/298 [04:32<00:00, 1.09it/s, Loss=0.2151, Acc=0.9326]\n","Epoch 6 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.22it/s, Loss=0.2200, Acc=0.9318]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 6:\n"," Train - Loss: 0.2151, Acc: 0.9326, IoU: 0.7989\n"," Val - Loss: 0.2200, Acc: 0.9318, IoU: 0.7938\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7938)\n","\n","==================================================\n","Époque 7/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 7 - Training: 100%|██████████| 298/298 [04:25<00:00, 1.12it/s, Loss=0.2086, Acc=0.9348]\n","Epoch 7 - Validation: 100%|██████████| 75/75 [01:00<00:00, 1.23it/s, Loss=0.2143, Acc=0.9333]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 7:\n"," Train - Loss: 0.2086, Acc: 0.9348, IoU: 0.8041\n"," Val - Loss: 0.2143, Acc: 0.9333, IoU: 0.7997\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7997)\n","\n","==================================================\n","Époque 8/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 8 - Training: 100%|██████████| 298/298 [04:10<00:00, 1.19it/s, Loss=0.2015, Acc=0.9369]\n","Epoch 8 - Validation: 100%|██████████| 75/75 [01:03<00:00, 1.19it/s, Loss=0.2108, Acc=0.9343]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 8:\n"," Train - Loss: 0.2015, Acc: 0.9369, IoU: 0.8104\n"," Val - Loss: 0.2108, Acc: 0.9343, IoU: 0.8019\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8019)\n","\n","==================================================\n","Époque 9/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 9 - Training: 100%|██████████| 298/298 [04:21<00:00, 1.14it/s, Loss=0.1969, Acc=0.9384]\n","Epoch 9 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.22it/s, Loss=0.2083, Acc=0.9355]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 9:\n"," Train - Loss: 0.1969, Acc: 0.9384, IoU: 0.8144\n"," Val - Loss: 0.2083, Acc: 0.9355, IoU: 0.8040\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8040)\n","\n","==================================================\n","Époque 10/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10 - Training: 100%|██████████| 298/298 [04:19<00:00, 1.15it/s, Loss=0.1926, Acc=0.9396]\n","Epoch 10 - Validation: 100%|██████████| 75/75 [01:04<00:00, 1.15it/s, Loss=0.2055, Acc=0.9360]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 10:\n"," Train - Loss: 0.1926, Acc: 0.9396, IoU: 0.8186\n"," Val - Loss: 0.2055, Acc: 0.9360, IoU: 0.8065\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8065)\n","\n","==================================================\n","Époque 11/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 11 - Training: 100%|██████████| 298/298 [04:15<00:00, 1.17it/s, Loss=0.1890, Acc=0.9406]\n","Epoch 11 - Validation: 100%|██████████| 75/75 [01:02<00:00, 1.20it/s, Loss=0.2052, Acc=0.9361]\n","WARNING:urllib3.connectionpool:Retrying (Retry(total=6, connect=6, read=7, redirect=7, status=7)) after connection broken by 'NameResolutionError(\": Failed to resolve 'mlflow.greg-madman-nas.duckdns.org' ([Errno -2] Name or service not known)\")': /api/2.0/mlflow/runs/get?run_uuid=c9835f7094354f48baff73f5d71edfc9&run_id=c9835f7094354f48baff73f5d71edfc9\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 11:\n"," Train - Loss: 0.1890, Acc: 0.9406, IoU: 0.8214\n"," Val - Loss: 0.2052, Acc: 0.9361, IoU: 0.8056\n","\n","==================================================\n","Époque 12/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 12 - Training: 100%|██████████| 298/298 [04:15<00:00, 1.17it/s, Loss=0.1840, Acc=0.9420]\n","Epoch 12 - Validation: 100%|██████████| 75/75 [01:02<00:00, 1.19it/s, Loss=0.2011, Acc=0.9372]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 12:\n"," Train - Loss: 0.1840, Acc: 0.9420, IoU: 0.8257\n"," Val - Loss: 0.2011, Acc: 0.9372, IoU: 0.8102\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8102)\n","\n","==================================================\n","Époque 13/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 13 - Training: 100%|██████████| 298/298 [04:11<00:00, 1.18it/s, Loss=0.1815, Acc=0.9430]\n","Epoch 13 - Validation: 100%|██████████| 75/75 [01:02<00:00, 1.21it/s, Loss=0.1992, Acc=0.9379]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 13:\n"," Train - Loss: 0.1815, Acc: 0.9430, IoU: 0.8283\n"," Val - Loss: 0.1992, Acc: 0.9379, IoU: 0.8110\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8110)\n","\n","==================================================\n","Époque 14/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 14 - Training: 100%|██████████| 298/298 [04:22<00:00, 1.14it/s, Loss=0.1777, Acc=0.9442]\n","Epoch 14 - Validation: 100%|██████████| 75/75 [01:02<00:00, 1.20it/s, Loss=0.1994, Acc=0.9380]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 14:\n"," Train - Loss: 0.1777, Acc: 0.9442, IoU: 0.8318\n"," Val - Loss: 0.1994, Acc: 0.9380, IoU: 0.8111\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8111)\n","\n","==================================================\n","Époque 15/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 15 - Training: 100%|██████████| 298/298 [04:13<00:00, 1.18it/s, Loss=0.1763, Acc=0.9444]\n","Epoch 15 - Validation: 100%|██████████| 75/75 [01:03<00:00, 1.19it/s, Loss=0.1976, Acc=0.9385]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 15:\n"," Train - Loss: 0.1763, Acc: 0.9444, IoU: 0.8326\n"," Val - Loss: 0.1976, Acc: 0.9385, IoU: 0.8128\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8128)\n","\n","==================================================\n","Époque 16/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 16 - Training: 100%|██████████| 298/298 [04:15<00:00, 1.17it/s, Loss=0.1724, Acc=0.9458]\n","Epoch 16 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.22it/s, Loss=0.1964, Acc=0.9389]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 16:\n"," Train - Loss: 0.1724, Acc: 0.9458, IoU: 0.8361\n"," Val - Loss: 0.1964, Acc: 0.9389, IoU: 0.8137\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8137)\n","\n","==================================================\n","Époque 17/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 17 - Training: 100%|██████████| 298/298 [04:23<00:00, 1.13it/s, Loss=0.1727, Acc=0.9460]\n","Epoch 17 - Validation: 100%|██████████| 75/75 [01:03<00:00, 1.17it/s, Loss=0.1955, Acc=0.9390]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 17:\n"," Train - Loss: 0.1727, Acc: 0.9460, IoU: 0.8367\n"," Val - Loss: 0.1955, Acc: 0.9390, IoU: 0.8147\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8147)\n","\n","==================================================\n","Époque 18/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 18 - Training: 100%|██████████| 298/298 [04:21<00:00, 1.14it/s, Loss=0.1692, Acc=0.9474]\n","Epoch 18 - Validation: 100%|██████████| 75/75 [01:02<00:00, 1.21it/s, Loss=0.1944, Acc=0.9393]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 18:\n"," Train - Loss: 0.1692, Acc: 0.9474, IoU: 0.8403\n"," Val - Loss: 0.1944, Acc: 0.9393, IoU: 0.8157\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8157)\n","\n","==================================================\n","Époque 19/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 19 - Training: 100%|██████████| 298/298 [04:19<00:00, 1.15it/s, Loss=0.1668, Acc=0.9479]\n","Epoch 19 - Validation: 100%|██████████| 75/75 [01:02<00:00, 1.19it/s, Loss=0.1949, Acc=0.9387]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 19:\n"," Train - Loss: 0.1668, Acc: 0.9479, IoU: 0.8421\n"," Val - Loss: 0.1949, Acc: 0.9387, IoU: 0.8152\n","\n","==================================================\n","Époque 20/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 20 - Training: 100%|██████████| 298/298 [04:21<00:00, 1.14it/s, Loss=0.1656, Acc=0.9487]\n","Epoch 20 - Validation: 100%|██████████| 75/75 [01:03<00:00, 1.17it/s, Loss=0.1937, Acc=0.9394]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 20:\n"," Train - Loss: 0.1656, Acc: 0.9487, IoU: 0.8436\n"," Val - Loss: 0.1937, Acc: 0.9394, IoU: 0.8162\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8162)\n","\n","==================================================\n","Époque 21/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 21 - Training: 100%|██████████| 298/298 [04:29<00:00, 1.11it/s, Loss=0.1632, Acc=0.9494]\n","Epoch 21 - Validation: 100%|██████████| 75/75 [01:03<00:00, 1.19it/s, Loss=0.1937, Acc=0.9392]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 21:\n"," Train - Loss: 0.1632, Acc: 0.9494, IoU: 0.8451\n"," Val - Loss: 0.1937, Acc: 0.9392, IoU: 0.8158\n","\n","==================================================\n","Époque 22/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 22 - Training: 100%|██████████| 298/298 [04:10<00:00, 1.19it/s, Loss=0.1625, Acc=0.9498]\n","Epoch 22 - Validation: 100%|██████████| 75/75 [01:03<00:00, 1.18it/s, Loss=0.1931, Acc=0.9401]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 22:\n"," Train - Loss: 0.1625, Acc: 0.9498, IoU: 0.8460\n"," Val - Loss: 0.1931, Acc: 0.9401, IoU: 0.8169\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8169)\n","\n","==================================================\n","Époque 23/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 23 - Training: 100%|██████████| 298/298 [04:23<00:00, 1.13it/s, Loss=0.1607, Acc=0.9508]\n","Epoch 23 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.22it/s, Loss=0.1930, Acc=0.9399]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 23:\n"," Train - Loss: 0.1607, Acc: 0.9508, IoU: 0.8483\n"," Val - Loss: 0.1930, Acc: 0.9399, IoU: 0.8168\n","\n","==================================================\n","Époque 24/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 24 - Training: 100%|██████████| 298/298 [04:23<00:00, 1.13it/s, Loss=0.1588, Acc=0.9517]\n","Epoch 24 - Validation: 100%|██████████| 75/75 [01:03<00:00, 1.17it/s, Loss=0.1920, Acc=0.9400]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 24:\n"," Train - Loss: 0.1588, Acc: 0.9517, IoU: 0.8500\n"," Val - Loss: 0.1920, Acc: 0.9400, IoU: 0.8177\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8177)\n","\n","==================================================\n","Époque 25/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 25 - Training: 100%|██████████| 298/298 [04:11<00:00, 1.18it/s, Loss=0.1582, Acc=0.9521]\n","Epoch 25 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.21it/s, Loss=0.1919, Acc=0.9402]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 25:\n"," Train - Loss: 0.1582, Acc: 0.9521, IoU: 0.8507\n"," Val - Loss: 0.1919, Acc: 0.9402, IoU: 0.8179\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8179)\n","\n","==================================================\n","Époque 26/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 26 - Training: 100%|██████████| 298/298 [04:23<00:00, 1.13it/s, Loss=0.1580, Acc=0.9520]\n","Epoch 26 - Validation: 100%|██████████| 75/75 [01:04<00:00, 1.16it/s, Loss=0.1918, Acc=0.9403]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 26:\n"," Train - Loss: 0.1580, Acc: 0.9520, IoU: 0.8509\n"," Val - Loss: 0.1918, Acc: 0.9403, IoU: 0.8180\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8180)\n","\n","==================================================\n","Époque 27/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 27 - Training: 100%|██████████| 298/298 [04:15<00:00, 1.17it/s, Loss=0.1572, Acc=0.9526]\n","Epoch 27 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.21it/s, Loss=0.1914, Acc=0.9403]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 27:\n"," Train - Loss: 0.1572, Acc: 0.9526, IoU: 0.8520\n"," Val - Loss: 0.1914, Acc: 0.9403, IoU: 0.8184\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8184)\n","\n","==================================================\n","Époque 28/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 28 - Training: 100%|██████████| 298/298 [04:13<00:00, 1.18it/s, Loss=0.1566, Acc=0.9527]\n","Epoch 28 - Validation: 100%|██████████| 75/75 [01:02<00:00, 1.20it/s, Loss=0.1914, Acc=0.9404]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 28:\n"," Train - Loss: 0.1566, Acc: 0.9527, IoU: 0.8523\n"," Val - Loss: 0.1914, Acc: 0.9404, IoU: 0.8183\n","\n","==================================================\n","Époque 29/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 29 - Training: 100%|██████████| 298/298 [04:26<00:00, 1.12it/s, Loss=0.1574, Acc=0.9527]\n","Epoch 29 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.21it/s, Loss=0.1914, Acc=0.9404]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 29:\n"," Train - Loss: 0.1574, Acc: 0.9527, IoU: 0.8522\n"," Val - Loss: 0.1914, Acc: 0.9404, IoU: 0.8185\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8185)\n","\n","==================================================\n","Époque 30/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 30 - Training: 100%|██████████| 298/298 [04:12<00:00, 1.18it/s, Loss=0.1572, Acc=0.9527]\n","Epoch 30 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.23it/s, Loss=0.1912, Acc=0.9405]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 30:\n"," Train - Loss: 0.1572, Acc: 0.9527, IoU: 0.8523\n"," Val - Loss: 0.1912, Acc: 0.9405, IoU: 0.8185\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8185)\n","\n","Temps d'entraînement: 9756.74s\n","Meilleur Val IoU: 0.8185\n","Meilleur modèle chargé pour l'évaluation\n","\n","Évaluation complète sur le jeu de test...\n"]},{"output_type":"stream","name":"stderr","text":["Test: 100%|██████████| 63/63 [01:07<00:00, 1.07s/it]\n"]},{"output_type":"stream","name":"stdout","text":["Test loss: 0.6935\n","Test accuracy: 0.9274\n","Test mean_iou: 0.7998\n","\n","ANALYSE DÉTAILLÉE PAR CLASSE:\n","----------------------------------------------------------------------\n","Classe IoU \n","----------------------------------------------------------------------\n","flat 0.9335 \n","human 0.7113 \n","vehicle 0.8880 \n","construction 0.8594 \n","object 0.4924 \n","nature 0.8914 \n","sky 0.9235 \n","void 0.6989 \n","----------------------------------------------------------------------\n","MEAN IoU 0.7998 \n","\n","Génération de la matrice de confusion...\n","Calcul de la matrice de confusion...\n","Matrice de confusion sauvegardée : artifacts/run_c9835f7094354f48baff73f5d71edfc9/confusion_matrix.png\n","\n","Création des visualisations...\n","\n","Sauvegarde des artefacts...\n","\n","Entraînement PyTorch terminé!\n","Meilleure val_accuracy: 0.9405\n","Meilleur val_IoU: 0.8185\n","Test Mean IoU: 0.7998\n","Test accuracy: 0.9274\n","🏃 View run SegFormer_b1_pretrained_optimized_prod_2025-08-30_07-25-25 at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/8/runs/c9835f7094354f48baff73f5d71edfc9\n","🧪 View experiment at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/8\n","[0] SegFormer_b1_pretrained_optimized terminé avec succès\n","3 configuration(s) restante(s)\n","Nettoyage mémoire GPU PyTorch...\n","\n","--------------------------------------------------\n","Expérience 2/4: SegFormer_b0_pretrained_optimized\n","Run name: SegFormer_b0_pretrained_optimized_prod_2025-08-30_10-12-24\n","--------------------------------------------------\n","MLflow Run ID: 5f29f30dfebd4798b91b31efa73c7a25\n","MLflow Run Name: SegFormer_b0_pretrained_optimized_prod_2025-08-30_10-12-24\n","Device utilisé: cuda\n","Mode production: batch_size=8, img_size=(768, 768)\n","📊 Utilisation des datasets globaux (équité entre modèles)\n","\n","Création du modèle PyTorch SegFormer_b0_pretrained_optimized...\n"]},{"output_type":"display_data","data":{"text/plain":["config.json: 0%| | 0.00/232 [00:00: Failed to resolve 'mlflow.greg-madman-nas.duckdns.org' ([Errno -2] Name or service not known)\")': /api/2.0/mlflow/runs/get?run_uuid=5f29f30dfebd4798b91b31efa73c7a25&run_id=5f29f30dfebd4798b91b31efa73c7a25\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 16:\n"," Train - Loss: 0.2055, Acc: 0.9349, IoU: 0.8054\n"," Val - Loss: 0.2185, Acc: 0.9310, IoU: 0.7926\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7926)\n","\n","==================================================\n","Époque 17/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 17 - Training: 100%|██████████| 298/298 [04:07<00:00, 1.20it/s, Loss=0.2023, Acc=0.9358]\n","Epoch 17 - Validation: 100%|██████████| 75/75 [01:02<00:00, 1.21it/s, Loss=0.2198, Acc=0.9311]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 17:\n"," Train - Loss: 0.2023, Acc: 0.9358, IoU: 0.8075\n"," Val - Loss: 0.2198, Acc: 0.9311, IoU: 0.7908\n","\n","==================================================\n","Époque 18/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 18 - Training: 100%|██████████| 298/298 [04:08<00:00, 1.20it/s, Loss=0.2004, Acc=0.9366]\n","Epoch 18 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.21it/s, Loss=0.2173, Acc=0.9314]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 18:\n"," Train - Loss: 0.2004, Acc: 0.9366, IoU: 0.8098\n"," Val - Loss: 0.2173, Acc: 0.9314, IoU: 0.7935\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7935)\n","\n","==================================================\n","Époque 19/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 19 - Training: 100%|██████████| 298/298 [04:08<00:00, 1.20it/s, Loss=0.1977, Acc=0.9373]\n","Epoch 19 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.21it/s, Loss=0.2171, Acc=0.9314]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 19:\n"," Train - Loss: 0.1977, Acc: 0.9373, IoU: 0.8117\n"," Val - Loss: 0.2171, Acc: 0.9314, IoU: 0.7937\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7937)\n","\n","==================================================\n","Époque 20/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 20 - Training: 100%|██████████| 298/298 [04:07<00:00, 1.20it/s, Loss=0.1982, Acc=0.9375]\n","Epoch 20 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.22it/s, Loss=0.2158, Acc=0.9324]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 20:\n"," Train - Loss: 0.1982, Acc: 0.9375, IoU: 0.8119\n"," Val - Loss: 0.2158, Acc: 0.9324, IoU: 0.7950\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7950)\n","\n","==================================================\n","Époque 21/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 21 - Training: 100%|██████████| 298/298 [04:06<00:00, 1.21it/s, Loss=0.1959, Acc=0.9385]\n","Epoch 21 - Validation: 100%|██████████| 75/75 [01:02<00:00, 1.21it/s, Loss=0.2152, Acc=0.9324]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 21:\n"," Train - Loss: 0.1959, Acc: 0.9385, IoU: 0.8140\n"," Val - Loss: 0.2152, Acc: 0.9324, IoU: 0.7955\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7955)\n","\n","==================================================\n","Époque 22/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 22 - Training: 100%|██████████| 298/298 [04:06<00:00, 1.21it/s, Loss=0.1944, Acc=0.9389]\n","Epoch 22 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.22it/s, Loss=0.2141, Acc=0.9327]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 22:\n"," Train - Loss: 0.1944, Acc: 0.9389, IoU: 0.8157\n"," Val - Loss: 0.2141, Acc: 0.9327, IoU: 0.7963\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7963)\n","\n","==================================================\n","Époque 23/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 23 - Training: 100%|██████████| 298/298 [04:06<00:00, 1.21it/s, Loss=0.1919, Acc=0.9400]\n","Epoch 23 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.23it/s, Loss=0.2149, Acc=0.9320]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 23:\n"," Train - Loss: 0.1919, Acc: 0.9400, IoU: 0.8175\n"," Val - Loss: 0.2149, Acc: 0.9320, IoU: 0.7956\n","\n","==================================================\n","Époque 24/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 24 - Training: 100%|██████████| 298/298 [04:04<00:00, 1.22it/s, Loss=0.1907, Acc=0.9406]\n","Epoch 24 - Validation: 100%|██████████| 75/75 [01:00<00:00, 1.24it/s, Loss=0.2143, Acc=0.9325]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 24:\n"," Train - Loss: 0.1907, Acc: 0.9406, IoU: 0.8187\n"," Val - Loss: 0.2143, Acc: 0.9325, IoU: 0.7963\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7963)\n","\n","==================================================\n","Époque 25/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 25 - Training: 100%|██████████| 298/298 [04:03<00:00, 1.22it/s, Loss=0.1915, Acc=0.9405]\n","Epoch 25 - Validation: 100%|██████████| 75/75 [01:00<00:00, 1.24it/s, Loss=0.2134, Acc=0.9329]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 25:\n"," Train - Loss: 0.1915, Acc: 0.9405, IoU: 0.8190\n"," Val - Loss: 0.2134, Acc: 0.9329, IoU: 0.7971\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7971)\n","\n","==================================================\n","Époque 26/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 26 - Training: 100%|██████████| 298/298 [04:03<00:00, 1.22it/s, Loss=0.1904, Acc=0.9409]\n","Epoch 26 - Validation: 100%|██████████| 75/75 [01:00<00:00, 1.24it/s, Loss=0.2132, Acc=0.9329]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 26:\n"," Train - Loss: 0.1904, Acc: 0.9409, IoU: 0.8197\n"," Val - Loss: 0.2132, Acc: 0.9329, IoU: 0.7973\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7973)\n","\n","==================================================\n","Époque 27/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 27 - Training: 100%|██████████| 298/298 [04:06<00:00, 1.21it/s, Loss=0.1883, Acc=0.9413]\n","Epoch 27 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.23it/s, Loss=0.2135, Acc=0.9328]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 27:\n"," Train - Loss: 0.1883, Acc: 0.9413, IoU: 0.8205\n"," Val - Loss: 0.2135, Acc: 0.9328, IoU: 0.7971\n","\n","==================================================\n","Époque 28/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 28 - Training: 100%|██████████| 298/298 [04:04<00:00, 1.22it/s, Loss=0.1901, Acc=0.9412]\n","Epoch 28 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.23it/s, Loss=0.2133, Acc=0.9329]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 28:\n"," Train - Loss: 0.1901, Acc: 0.9412, IoU: 0.8203\n"," Val - Loss: 0.2133, Acc: 0.9329, IoU: 0.7973\n","\n","==================================================\n","Époque 29/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 29 - Training: 100%|██████████| 298/298 [04:03<00:00, 1.22it/s, Loss=0.1884, Acc=0.9414]\n","Epoch 29 - Validation: 100%|██████████| 75/75 [01:00<00:00, 1.23it/s, Loss=0.2130, Acc=0.9329]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 29:\n"," Train - Loss: 0.1884, Acc: 0.9414, IoU: 0.8212\n"," Val - Loss: 0.2130, Acc: 0.9329, IoU: 0.7973\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7973)\n","\n","==================================================\n","Époque 30/30\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 30 - Training: 100%|██████████| 298/298 [04:05<00:00, 1.21it/s, Loss=0.1895, Acc=0.9413]\n","Epoch 30 - Validation: 100%|██████████| 75/75 [01:00<00:00, 1.23it/s, Loss=0.2131, Acc=0.9330]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 30:\n"," Train - Loss: 0.1895, Acc: 0.9413, IoU: 0.8206\n"," Val - Loss: 0.2131, Acc: 0.9330, IoU: 0.7973\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7973)\n","\n","Temps d'entraînement: 9389.62s\n","Meilleur Val IoU: 0.7973\n","Meilleur modèle chargé pour l'évaluation\n","\n","Évaluation complète sur le jeu de test...\n"]},{"output_type":"stream","name":"stderr","text":["Test: 100%|██████████| 63/63 [01:00<00:00, 1.05it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Test loss: 0.7739\n","Test accuracy: 0.9210\n","Test mean_iou: 0.7791\n","\n","ANALYSE DÉTAILLÉE PAR CLASSE:\n","----------------------------------------------------------------------\n","Classe IoU \n","----------------------------------------------------------------------\n","flat 0.9312 \n","human 0.6745 \n","vehicle 0.8687 \n","construction 0.8419 \n","object 0.4362 \n","nature 0.8815 \n","sky 0.9137 \n","void 0.6851 \n","----------------------------------------------------------------------\n","MEAN IoU 0.7791 \n","\n","Génération de la matrice de confusion...\n","Calcul de la matrice de confusion...\n","Matrice de confusion sauvegardée : artifacts/run_5f29f30dfebd4798b91b31efa73c7a25/confusion_matrix.png\n","\n","Création des visualisations...\n","\n","Sauvegarde des artefacts...\n","\n","Entraînement PyTorch terminé!\n","Meilleure val_accuracy: 0.9330\n","Meilleur val_IoU: 0.7973\n","Test Mean IoU: 0.7791\n","Test accuracy: 0.9210\n","🏃 View run SegFormer_b0_pretrained_optimized_prod_2025-08-30_10-12-24 at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/8/runs/5f29f30dfebd4798b91b31efa73c7a25\n","🧪 View experiment at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/8\n","[1] SegFormer_b0_pretrained_optimized terminé avec succès\n","2 configuration(s) restante(s)\n","Nettoyage mémoire GPU PyTorch...\n","\n","--------------------------------------------------\n","Expérience 3/4: FPN_efficientnet_b0_baseline\n","Run name: FPN_efficientnet_b0_baseline_prod_2025-08-30_12-53-10\n","--------------------------------------------------\n","MLflow Run ID: 38b6084bf30c468eb21a9490d0fe9a7f\n","MLflow Run Name: FPN_efficientnet_b0_baseline_prod_2025-08-30_12-53-10\n","Device utilisé: cuda\n","Mode production: batch_size=8, img_size=(768, 768)\n","📊 Utilisation des datasets globaux (équité entre modèles)\n","\n","Création du modèle PyTorch FPN_efficientnet_b0_baseline...\n","Transfer du modèle vers cuda...\n","Modèle créé sur device: cuda:0\n","ATTENTION: Modèle sur cuda:0, device cible cuda\n","Modèle retransféré vers cuda\n","Nombre de classes: 8\n","Paramètres: 5,760,388 total | 5,760,388 entraînables\n","\n","Configuration des composants d'entraînement...\n"," OPTIMIZER: Adam (lr=0.00015, wd=1e-05)\n"," SCHEDULER: ReduceLROnPlateau (patience=4)\n"," EARLY STOPPING: patience=7\n"," MIXED PRECISION: Activée\n","\n","✅ CONFIGURATION FINALE - FPN:\n"," 🎯 LOSS: focal_jaccard\n"," - Focal weight: 1.0\n"," - Jaccard weight: 1.0\n"," 🚀 TRAINING: 20 epochs, batch_size=8\n"," 📊 AUGMENTATION: Communes à tous les modèles\n"," 💾 DEVICE: cuda\n","\n","VÉRIFICATION DEVICES AVANT ENTRAÎNEMENT:\n"," Modèle sur: cuda:0\n"," Device cible: cuda\n"," IoU Metric device: cuda\n","\n","Début de l'entraînement PyTorch (20 epochs max)...\n","\n","==================================================\n","Époque 1/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 1 - Training: 100%|██████████| 298/298 [04:26<00:00, 1.12it/s, Loss=0.8664, Acc=0.8135]\n","Epoch 1 - Validation: 100%|██████████| 75/75 [01:08<00:00, 1.09it/s, Loss=0.6628, Acc=0.8478]\n","WARNING:urllib3.connectionpool:Retrying (Retry(total=6, connect=6, read=7, redirect=7, status=7)) after connection broken by 'NameResolutionError(\": Failed to resolve 'mlflow.greg-madman-nas.duckdns.org' ([Errno -2] Name or service not known)\")': /api/2.0/mlflow/runs/get?run_uuid=38b6084bf30c468eb21a9490d0fe9a7f&run_id=38b6084bf30c468eb21a9490d0fe9a7f\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 1:\n"," Train - Loss: 0.8664, Acc: 0.8135, IoU: 0.5334\n"," Val - Loss: 0.6628, Acc: 0.8478, IoU: 0.5843\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.5843)\n","\n","==================================================\n","Époque 2/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 2 - Training: 100%|██████████| 298/298 [04:27<00:00, 1.11it/s, Loss=0.4861, Acc=0.8965]\n","Epoch 2 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.22it/s, Loss=0.3963, Acc=0.9147]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 2:\n"," Train - Loss: 0.4861, Acc: 0.8965, IoU: 0.7031\n"," Val - Loss: 0.3963, Acc: 0.9147, IoU: 0.7438\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7438)\n","\n","==================================================\n","Époque 3/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 3 - Training: 100%|██████████| 298/298 [04:09<00:00, 1.20it/s, Loss=0.4267, Acc=0.9097]\n","Epoch 3 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.22it/s, Loss=0.3770, Acc=0.9203]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 3:\n"," Train - Loss: 0.4267, Acc: 0.9097, IoU: 0.7340\n"," Val - Loss: 0.3770, Acc: 0.9203, IoU: 0.7548\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7548)\n","\n","==================================================\n","Époque 4/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 4 - Training: 100%|██████████| 298/298 [04:11<00:00, 1.19it/s, Loss=0.3904, Acc=0.9172]\n","Epoch 4 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.23it/s, Loss=0.3500, Acc=0.9234]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 4:\n"," Train - Loss: 0.3904, Acc: 0.9172, IoU: 0.7520\n"," Val - Loss: 0.3500, Acc: 0.9234, IoU: 0.7681\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7681)\n","\n","==================================================\n","Époque 5/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 5 - Training: 100%|██████████| 298/298 [04:03<00:00, 1.22it/s, Loss=0.3639, Acc=0.9230]\n","Epoch 5 - Validation: 100%|██████████| 75/75 [01:00<00:00, 1.24it/s, Loss=0.3423, Acc=0.9268]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 5:\n"," Train - Loss: 0.3639, Acc: 0.9230, IoU: 0.7662\n"," Val - Loss: 0.3423, Acc: 0.9268, IoU: 0.7756\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7756)\n","\n","==================================================\n","Époque 6/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 6 - Training: 100%|██████████| 298/298 [04:03<00:00, 1.22it/s, Loss=0.3448, Acc=0.9273]\n","Epoch 6 - Validation: 100%|██████████| 75/75 [01:00<00:00, 1.24it/s, Loss=0.3377, Acc=0.9282]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 6:\n"," Train - Loss: 0.3448, Acc: 0.9273, IoU: 0.7765\n"," Val - Loss: 0.3377, Acc: 0.9282, IoU: 0.7804\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7804)\n","\n","==================================================\n","Époque 7/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 7 - Training: 100%|██████████| 298/298 [04:02<00:00, 1.23it/s, Loss=0.3288, Acc=0.9311]\n","Epoch 7 - Validation: 100%|██████████| 75/75 [01:00<00:00, 1.24it/s, Loss=0.3277, Acc=0.9290]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 7:\n"," Train - Loss: 0.3288, Acc: 0.9311, IoU: 0.7857\n"," Val - Loss: 0.3277, Acc: 0.9290, IoU: 0.7835\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7835)\n","\n","==================================================\n","Époque 8/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 8 - Training: 100%|██████████| 298/298 [04:02<00:00, 1.23it/s, Loss=0.3142, Acc=0.9348]\n","Epoch 8 - Validation: 100%|██████████| 75/75 [01:00<00:00, 1.23it/s, Loss=0.3227, Acc=0.9297]\n","WARNING:urllib3.connectionpool:Retrying (Retry(total=6, connect=6, read=7, redirect=7, status=7)) after connection broken by 'NameResolutionError(\": Failed to resolve 'mlflow.greg-madman-nas.duckdns.org' ([Errno -2] Name or service not known)\")': /api/2.0/mlflow/runs/get?run_uuid=38b6084bf30c468eb21a9490d0fe9a7f&run_id=38b6084bf30c468eb21a9490d0fe9a7f\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 8:\n"," Train - Loss: 0.3142, Acc: 0.9348, IoU: 0.7939\n"," Val - Loss: 0.3227, Acc: 0.9297, IoU: 0.7870\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7870)\n","\n","==================================================\n","Époque 9/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 9 - Training: 100%|██████████| 298/298 [04:01<00:00, 1.23it/s, Loss=0.3026, Acc=0.9380]\n","Epoch 9 - Validation: 100%|██████████| 75/75 [01:00<00:00, 1.23it/s, Loss=0.3267, Acc=0.9307]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 9:\n"," Train - Loss: 0.3026, Acc: 0.9380, IoU: 0.8005\n"," Val - Loss: 0.3267, Acc: 0.9307, IoU: 0.7882\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7882)\n","\n","==================================================\n","Époque 10/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10 - Training: 100%|██████████| 298/298 [04:04<00:00, 1.22it/s, Loss=0.2946, Acc=0.9395]\n","Epoch 10 - Validation: 100%|██████████| 75/75 [01:00<00:00, 1.24it/s, Loss=0.3164, Acc=0.9316]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 10:\n"," Train - Loss: 0.2946, Acc: 0.9395, IoU: 0.8047\n"," Val - Loss: 0.3164, Acc: 0.9316, IoU: 0.7931\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7931)\n","\n","==================================================\n","Époque 11/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 11 - Training: 100%|██████████| 298/298 [04:00<00:00, 1.24it/s, Loss=0.2838, Acc=0.9420]\n","Epoch 11 - Validation: 100%|██████████| 75/75 [00:59<00:00, 1.27it/s, Loss=0.3151, Acc=0.9331]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 11:\n"," Train - Loss: 0.2838, Acc: 0.9420, IoU: 0.8112\n"," Val - Loss: 0.3151, Acc: 0.9331, IoU: 0.7956\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7956)\n","\n","==================================================\n","Époque 12/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 12 - Training: 100%|██████████| 298/298 [03:58<00:00, 1.25it/s, Loss=0.2767, Acc=0.9438]\n","Epoch 12 - Validation: 100%|██████████| 75/75 [01:00<00:00, 1.23it/s, Loss=0.3119, Acc=0.9339]\n","WARNING:urllib3.connectionpool:Retrying (Retry(total=6, connect=6, read=7, redirect=7, status=7)) after connection broken by 'NameResolutionError(\": Failed to resolve 'mlflow.greg-madman-nas.duckdns.org' ([Errno -2] Name or service not known)\")': /api/2.0/mlflow/runs/get?run_uuid=38b6084bf30c468eb21a9490d0fe9a7f&run_id=38b6084bf30c468eb21a9490d0fe9a7f\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 12:\n"," Train - Loss: 0.2767, Acc: 0.9438, IoU: 0.8156\n"," Val - Loss: 0.3119, Acc: 0.9339, IoU: 0.7982\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7982)\n","\n","==================================================\n","Époque 13/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 13 - Training: 100%|██████████| 298/298 [04:05<00:00, 1.21it/s, Loss=0.2680, Acc=0.9461]\n","Epoch 13 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.22it/s, Loss=0.3093, Acc=0.9358]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 13:\n"," Train - Loss: 0.2680, Acc: 0.9461, IoU: 0.8203\n"," Val - Loss: 0.3093, Acc: 0.9358, IoU: 0.7993\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.7993)\n","\n","==================================================\n","Époque 14/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 14 - Training: 100%|██████████| 298/298 [04:05<00:00, 1.22it/s, Loss=0.2634, Acc=0.9473]\n","Epoch 14 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.22it/s, Loss=0.3156, Acc=0.9351]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 14:\n"," Train - Loss: 0.2634, Acc: 0.9473, IoU: 0.8237\n"," Val - Loss: 0.3156, Acc: 0.9351, IoU: 0.8001\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8001)\n","\n","==================================================\n","Époque 15/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 15 - Training: 100%|██████████| 298/298 [04:04<00:00, 1.22it/s, Loss=0.2573, Acc=0.9484]\n","Epoch 15 - Validation: 100%|██████████| 75/75 [01:00<00:00, 1.24it/s, Loss=0.3127, Acc=0.9358]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 15:\n"," Train - Loss: 0.2573, Acc: 0.9484, IoU: 0.8271\n"," Val - Loss: 0.3127, Acc: 0.9358, IoU: 0.8021\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8021)\n","\n","==================================================\n","Époque 16/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 16 - Training: 100%|██████████| 298/298 [04:01<00:00, 1.24it/s, Loss=0.2496, Acc=0.9503]\n","Epoch 16 - Validation: 100%|██████████| 75/75 [00:59<00:00, 1.27it/s, Loss=0.3056, Acc=0.9364]\n","WARNING:urllib3.connectionpool:Retrying (Retry(total=6, connect=6, read=7, redirect=7, status=7)) after connection broken by 'NameResolutionError(\": Failed to resolve 'mlflow.greg-madman-nas.duckdns.org' ([Errno -2] Name or service not known)\")': /api/2.0/mlflow/runs/get?run_uuid=38b6084bf30c468eb21a9490d0fe9a7f&run_id=38b6084bf30c468eb21a9490d0fe9a7f\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 16:\n"," Train - Loss: 0.2496, Acc: 0.9503, IoU: 0.8310\n"," Val - Loss: 0.3056, Acc: 0.9364, IoU: 0.8047\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8047)\n","\n","==================================================\n","Époque 17/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 17 - Training: 100%|██████████| 298/298 [03:58<00:00, 1.25it/s, Loss=0.2509, Acc=0.9495]\n","Epoch 17 - Validation: 100%|██████████| 75/75 [00:59<00:00, 1.26it/s, Loss=0.3073, Acc=0.9366]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 17:\n"," Train - Loss: 0.2509, Acc: 0.9495, IoU: 0.8309\n"," Val - Loss: 0.3073, Acc: 0.9366, IoU: 0.8049\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8049)\n","\n","==================================================\n","Époque 18/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 18 - Training: 100%|██████████| 298/298 [04:02<00:00, 1.23it/s, Loss=0.2458, Acc=0.9510]\n","Epoch 18 - Validation: 100%|██████████| 75/75 [01:00<00:00, 1.24it/s, Loss=0.3048, Acc=0.9367]\n","WARNING:urllib3.connectionpool:Retrying (Retry(total=6, connect=6, read=7, redirect=7, status=7)) after connection broken by 'NameResolutionError(\": Failed to resolve 'mlflow.greg-madman-nas.duckdns.org' ([Errno -2] Name or service not known)\")': /api/2.0/mlflow/runs/get?run_uuid=38b6084bf30c468eb21a9490d0fe9a7f&run_id=38b6084bf30c468eb21a9490d0fe9a7f\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 18:\n"," Train - Loss: 0.2458, Acc: 0.9510, IoU: 0.8342\n"," Val - Loss: 0.3048, Acc: 0.9367, IoU: 0.8057\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8057)\n","\n","==================================================\n","Époque 19/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 19 - Training: 100%|██████████| 298/298 [04:02<00:00, 1.23it/s, Loss=0.2413, Acc=0.9521]\n","Epoch 19 - Validation: 100%|██████████| 75/75 [01:00<00:00, 1.24it/s, Loss=0.3036, Acc=0.9362]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 19:\n"," Train - Loss: 0.2413, Acc: 0.9521, IoU: 0.8373\n"," Val - Loss: 0.3036, Acc: 0.9362, IoU: 0.8052\n","\n","==================================================\n","Époque 20/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 20 - Training: 100%|██████████| 298/298 [04:02<00:00, 1.23it/s, Loss=0.2374, Acc=0.9530]\n","Epoch 20 - Validation: 100%|██████████| 75/75 [00:59<00:00, 1.26it/s, Loss=0.3077, Acc=0.9373]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 20:\n"," Train - Loss: 0.2374, Acc: 0.9530, IoU: 0.8396\n"," Val - Loss: 0.3077, Acc: 0.9373, IoU: 0.8075\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8075)\n","\n","Temps d'entraînement: 6263.70s\n","Meilleur Val IoU: 0.8075\n","Meilleur modèle chargé pour l'évaluation\n","\n","Évaluation complète sur le jeu de test...\n"]},{"output_type":"stream","name":"stderr","text":["Test: 100%|██████████| 63/63 [00:52<00:00, 1.20it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Test loss: 0.4632\n","Test accuracy: 0.9222\n","Test mean_iou: 0.7867\n","\n","ANALYSE DÉTAILLÉE PAR CLASSE:\n","----------------------------------------------------------------------\n","Classe IoU \n","----------------------------------------------------------------------\n","flat 0.9273 \n","human 0.6868 \n","vehicle 0.8772 \n","construction 0.8492 \n","object 0.4671 \n","nature 0.8864 \n","sky 0.9171 \n","void 0.6824 \n","----------------------------------------------------------------------\n","MEAN IoU 0.7867 \n","\n","Génération de la matrice de confusion...\n","Calcul de la matrice de confusion...\n","Matrice de confusion sauvegardée : artifacts/run_38b6084bf30c468eb21a9490d0fe9a7f/confusion_matrix.png\n","\n","Création des visualisations...\n","\n","Sauvegarde des artefacts...\n","\n","Entraînement PyTorch terminé!\n","Meilleure val_accuracy: 0.9373\n","Meilleur val_IoU: 0.8075\n","Test Mean IoU: 0.7867\n","Test accuracy: 0.9222\n","🏃 View run FPN_efficientnet_b0_baseline_prod_2025-08-30_12-53-10 at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/8/runs/38b6084bf30c468eb21a9490d0fe9a7f\n","🧪 View experiment at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/8\n","[2] FPN_efficientnet_b0_baseline terminé avec succès\n","1 configuration(s) restante(s)\n","Nettoyage mémoire GPU PyTorch...\n","\n","--------------------------------------------------\n","Expérience 4/4: FPN_resnet34_baseline\n","Run name: FPN_resnet34_baseline_prod_2025-08-30_14-41-34\n","--------------------------------------------------\n","MLflow Run ID: 4992e14daff845aabea9f44a56f499f6\n","MLflow Run Name: FPN_resnet34_baseline_prod_2025-08-30_14-41-34\n","Device utilisé: cuda\n","Mode production: batch_size=8, img_size=(768, 768)\n","📊 Utilisation des datasets globaux (équité entre modèles)\n","\n","Création du modèle PyTorch FPN_resnet34_baseline...\n"]},{"output_type":"display_data","data":{"text/plain":["config.json: 0%| | 0.00/156 [00:00: Failed to resolve 'mlflow.greg-madman-nas.duckdns.org' ([Errno -2] Name or service not known)\")': /api/2.0/mlflow/runs/get?run_uuid=4992e14daff845aabea9f44a56f499f6&run_id=4992e14daff845aabea9f44a56f499f6\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 16:\n"," Train - Loss: 0.1835, Acc: 0.9650, IoU: 0.8753\n"," Val - Loss: 0.3135, Acc: 0.9383, IoU: 0.8119\n"," Nouveau meilleur modèle sauvegardé (Val IoU: 0.8119)\n","\n","==================================================\n","Époque 17/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 17 - Training: 100%|██████████| 298/298 [04:05<00:00, 1.21it/s, Loss=0.1767, Acc=0.9665]\n","Epoch 17 - Validation: 100%|██████████| 75/75 [01:01<00:00, 1.23it/s, Loss=0.3215, Acc=0.9368]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 17:\n"," Train - Loss: 0.1767, Acc: 0.9665, IoU: 0.8797\n"," Val - Loss: 0.3215, Acc: 0.9368, IoU: 0.8098\n","\n","==================================================\n","Époque 18/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 18 - Training: 100%|██████████| 298/298 [03:57<00:00, 1.25it/s, Loss=0.1752, Acc=0.9667]\n","Epoch 18 - Validation: 100%|██████████| 75/75 [00:58<00:00, 1.29it/s, Loss=0.3217, Acc=0.9356]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 18:\n"," Train - Loss: 0.1752, Acc: 0.9667, IoU: 0.8807\n"," Val - Loss: 0.3217, Acc: 0.9356, IoU: 0.8048\n","\n","==================================================\n","Époque 19/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 19 - Training: 100%|██████████| 298/298 [03:56<00:00, 1.26it/s, Loss=0.1785, Acc=0.9657]\n","Epoch 19 - Validation: 100%|██████████| 75/75 [00:58<00:00, 1.27it/s, Loss=0.3336, Acc=0.9328]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 19:\n"," Train - Loss: 0.1785, Acc: 0.9657, IoU: 0.8786\n"," Val - Loss: 0.3336, Acc: 0.9328, IoU: 0.7975\n","\n","==================================================\n","Époque 20/20\n","==================================================\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 20 - Training: 100%|██████████| 298/298 [03:53<00:00, 1.27it/s, Loss=0.2128, Acc=0.9561]\n","Epoch 20 - Validation: 100%|██████████| 75/75 [00:59<00:00, 1.26it/s, Loss=0.3051, Acc=0.9350]\n"]},{"output_type":"stream","name":"stdout","text":["\n","Résultats Époque 20:\n"," Train - Loss: 0.2128, Acc: 0.9561, IoU: 0.8597\n"," Val - Loss: 0.3051, Acc: 0.9350, IoU: 0.8066\n"," Réduction LR: 0.000150 → 0.000030\n","\n","Temps d'entraînement: 6076.45s\n","Meilleur Val IoU: 0.8119\n","Meilleur modèle chargé pour l'évaluation\n","\n","Évaluation complète sur le jeu de test...\n"]},{"output_type":"stream","name":"stderr","text":["Test: 100%|██████████| 63/63 [00:53<00:00, 1.17it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Test loss: 0.4804\n","Test accuracy: 0.9247\n","Test mean_iou: 0.7922\n","\n","ANALYSE DÉTAILLÉE PAR CLASSE:\n","----------------------------------------------------------------------\n","Classe IoU \n","----------------------------------------------------------------------\n","flat 0.9338 \n","human 0.6935 \n","vehicle 0.8775 \n","construction 0.8506 \n","object 0.4868 \n","nature 0.8813 \n","sky 0.9167 \n","void 0.6976 \n","----------------------------------------------------------------------\n","MEAN IoU 0.7922 \n","\n","Génération de la matrice de confusion...\n","Calcul de la matrice de confusion...\n","Matrice de confusion sauvegardée : artifacts/run_4992e14daff845aabea9f44a56f499f6/confusion_matrix.png\n","\n","Création des visualisations...\n","\n","Sauvegarde des artefacts...\n","\n","Entraînement PyTorch terminé!\n","Meilleure val_accuracy: 0.9383\n","Meilleur val_IoU: 0.8119\n","Test Mean IoU: 0.7922\n","Test accuracy: 0.9247\n","🏃 View run FPN_resnet34_baseline_prod_2025-08-30_14-41-34 at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/8/runs/4992e14daff845aabea9f44a56f499f6\n","🧪 View experiment at: https://mlflow.greg-madman-nas.duckdns.org/#/experiments/8\n","[3] FPN_resnet34_baseline terminé avec succès\n","\n","============================================================\n","RÉSUMÉ DE CETTE SESSION PYTORCH\n","============================================================\n","4 nouvelles expériences réussies\n","\n","Modèle Val Acc Val IoU Test IoU Temps Run ID \n","-------------------------------------------------------------------------------------\n","SegFormer_b1_pretrained_optimized 0.9405 0.8185 0.7998 162.6 c9835f70 \n","SegFormer_b0_pretrained_optimized 0.9330 0.7973 0.7791 156.5 5f29f30d \n","FPN_efficientnet_b0_baseline 0.9373 0.8075 0.7867 104.4 38b6084b \n","FPN_resnet34_baseline 0.9383 0.8119 0.7922 101.3 4992e14d \n","\n","Meilleur de cette session: SegFormer_b1_pretrained_optimized\n"," Val Acc: 0.9405\n"," Val IoU: 0.8185\n"," Test IoU: 0.7998\n"," MLflow Run: c9835f7094354f48baff73f5d71edfc9\n","Vérification des expériences déjà terminées dans 'OC Projet 9'...\n","14 expériences terminées trouvées\n"," - FPN_efficientnet_b0_baseline_prod_2025-08-29_21-39-34\n"," - FPN_efficientnet_b0_baseline_prod_2025-08-30_12-53-10\n"," - FPN_efficientnet_b0_finetune_aug_prod\n"," - FPN_resnet34_baseline_prod_2025-08-29_23-13-38\n"," - FPN_resnet34_baseline_prod_2025-08-30_14-41-34\n"," - FPN_resnet34_finetune_aug_prod\n"," - SegFormer_b0_pretrained_finetune_prod\n"," - SegFormer_b0_pretrained_optimized_prod_2025-08-29_19-18-31\n"," - SegFormer_b0_pretrained_optimized_prod_2025-08-30_10-12-24\n"," - SegFormer_b1_pretrained_finetune_prod\n"," - SegFormer_b1_pretrained_finetune_prod_2025-08-29_12-24-46\n"," - SegFormer_b1_pretrained_optimized_prod_2025-08-29_15-15-26\n"," - SegFormer_b1_pretrained_optimized_prod_2025-08-29_16-52-06\n"," - SegFormer_b1_pretrained_optimized_prod_2025-08-30_07-25-25\n","\n","FILTRAGE DES CONFIGURATIONS:\n","À entraîner: 0 configurations\n","Déjà terminées: 4 configurations\n","\n","CONFIGURATIONS IGNORÉES (déjà terminées):\n"," [0] SegFormer_b1_pretrained_optimized → SegFormer_b1_pretrained_optimized_prod*\n"," [1] SegFormer_b0_pretrained_optimized → SegFormer_b0_pretrained_optimized_prod*\n"," [2] FPN_efficientnet_b0_baseline → FPN_efficientnet_b0_baseline_prod*\n"," [3] FPN_resnet34_baseline → FPN_resnet34_baseline_prod*\n","\n","TOUTES LES CONFIGURATIONS PYTORCH SONT MAINTENANT TERMINÉES !\n","Utilisez compare_experiments_local() pour voir tous les résultats\n","\n","Script PRODUCTION terminé avec succès!\n","\n","OBJECTIF DU PROJET - SegFormer vs FPN:\n"," Meilleur FPN IoU: 0.7922\n"," Meilleur SegFormer IoU: 0.7998\n"," OBJECTIF ATTEINT ! SegFormer bat FPN (+0.0076)\n"]}]},{"cell_type":"code","source":["from google.colab import runtime\n","runtime.unassign()"],"metadata":{"id":"zFJLI8mciDIn","executionInfo":{"status":"ok","timestamp":1756571205328,"user_tz":-120,"elapsed":352,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":103,"outputs":[]},{"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 compare_experiments_mlflow(experiment_name=\"OC Projet 9\", show_detailed_results=False, show_plots=True):\n"," \"\"\"\n"," Compare toutes les expériences terminées d'un projet MLflow - version simplifiée\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","\n"," try:\n"," # Récupérer l'expérience MLflow\n"," experiment = mlflow.get_experiment_by_name(experiment_name)\n"," if not experiment:\n"," print(f\"❌ Expérience '{experiment_name}' non trouvée\")\n"," return []\n","\n"," # Récupérer tous les runs terminés\n"," runs_df = mlflow.search_runs(\n"," experiment_ids=[experiment.experiment_id],\n"," filter_string=\"status = 'FINISHED'\",\n"," output_format=\"pandas\"\n"," )\n","\n"," if runs_df.empty:\n"," print(\"❌ Aucune expérience terminée trouvée\")\n"," return []\n","\n"," print(f\"📊 Analyse de {len(runs_df)} expériences terminées\")\n","\n"," results = []\n","\n"," for _, run_row in runs_df.iterrows():\n"," run_id = run_row['run_id']\n"," run_name = run_row.get('tags.mlflow.runName', f'Run_{run_id[:8]}')\n","\n"," try:\n"," # Récupérer les métriques et paramètres depuis le DataFrame\n"," metrics = {col.replace('metrics.', ''): run_row[col]\n"," for col in run_row.index if col.startswith('metrics.') and pd.notna(run_row[col])}\n","\n"," params = {col.replace('params.', ''): run_row[col]\n"," for col in run_row.index if col.startswith('params.') and pd.notna(run_row[col])}\n","\n"," # Nettoyer le nom du run (supprimer timestamp)\n"," clean_name = run_name\n"," import re\n"," # Supprimer les patterns de timestamp (ex: _prod_2025-08-30_07-15-00)\n"," clean_name = re.sub(r'_prod_\\d{4}-\\d{2}-\\d{2}_\\d{2}-\\d{2}-\\d{2}$', '', clean_name)\n"," clean_name = re.sub(r'_\\d{4}-\\d{2}-\\d{2}_\\d{2}-\\d{2}-\\d{2}$', '', clean_name)\n","\n"," # Extraire la résolution depuis script_img_size\n"," resolution = \"N/A\"\n"," if 'script_img_size' in params:\n"," img_size_str = params['script_img_size']\n"," # Parser différents formats possibles: \"(512, 512)\" ou \"512\" etc.\n"," import ast\n"," try:\n"," if img_size_str.startswith('(') and img_size_str.endswith(')'):\n"," # Format tuple: \"(512, 512)\"\n"," size_tuple = ast.literal_eval(img_size_str)\n"," if isinstance(size_tuple, tuple) and len(size_tuple) >= 2:\n"," resolution = f\"{size_tuple[0]}x{size_tuple[1]}\"\n"," else:\n"," # Format simple: \"512\"\n"," size = int(float(img_size_str))\n"," resolution = f\"{size}x{size}\"\n"," except:\n"," resolution = img_size_str # Fallback\n","\n"," # Construire le nom d'affichage avec résolution\n"," display_name = f\"{clean_name}@{resolution}\" if resolution != \"N/A\" else clean_name\n","\n"," # Construire le résultat\n"," result = {\n"," 'run_name': run_name,\n"," 'clean_name': clean_name,\n"," 'display_name': display_name,\n"," 'resolution': resolution,\n"," 'architecture': params.get('model_architecture', 'Unknown'),\n"," 'encoder': params.get('encoder_name', params.get('model_name', 'None')),\n"," 'best_val_mean_iou': metrics.get('best_val_mean_iou', 'N/A'),\n"," 'test_mean_iou': metrics.get('test_mean_iou', 'N/A'),\n"," 'test_accuracy': metrics.get('test_accuracy', 'N/A'),\n"," 'training_time_seconds': metrics.get('training_time_seconds', 'N/A'),\n"," 'run_id': run_id,\n"," 'all_metrics': metrics,\n"," 'all_params': params\n"," }\n","\n"," results.append(result)\n","\n"," except Exception as e:\n"," print(f\"⚠️ Erreur pour run {run_id[:8]}: {e}\")\n"," continue\n","\n"," # Affichage des résultats\n"," if results:\n"," # Trier par test_mean_iou si disponible, sinon par best_val_mean_iou\n"," def sort_key(x):\n"," if x['test_mean_iou'] != 'N/A':\n"," return x['test_mean_iou']\n"," elif x['best_val_mean_iou'] != 'N/A':\n"," return x['best_val_mean_iou']\n"," return 0\n","\n"," results_sorted = sorted(results, key=sort_key, reverse=True)\n","\n"," print(f\"\\n🏆 TOP {len(results)} EXPÉRIENCES TERMINÉES\")\n"," print(\"=\" * 120)\n","\n"," for i, result in enumerate(results_sorted, 1):\n"," print(f\"🥇 #{i} - {result['display_name']}\")\n"," print(f\" 🏗️ {result['architecture']} + {result['encoder']}\")\n","\n"," # Afficher les métriques\n"," test_iou = result['test_mean_iou']\n"," val_iou = result['best_val_mean_iou']\n"," test_acc = result['test_accuracy']\n","\n"," if test_iou != 'N/A':\n"," print(f\" 🎯 Mean IoU: {test_iou:.4f}\", end=\"\")\n"," elif val_iou != 'N/A':\n"," print(f\" 🎯 Best Val Mean IoU: {val_iou:.4f}\", end=\"\")\n"," else:\n"," print(f\" 🎯 Mean IoU: N/A\", end=\"\")\n","\n"," if test_acc != 'N/A':\n"," print(f\" | Accuracy: {test_acc:.4f}\")\n"," else:\n"," print(f\" | Accuracy: 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\" 📋 Paramètres:\")\n"," for key, value in result['all_params'].items():\n"," print(f\" {key}: {value}\")\n"," print(f\" 📈 Métriques:\")\n"," for key, value in result['all_metrics'].items():\n"," if isinstance(value, float):\n"," print(f\" {key}: {value:.4f}\")\n"," else:\n"," print(f\" {key}: {value}\")\n","\n"," print()\n","\n"," # Génération des diagrammes si demandé\n"," if show_plots and results:\n"," generate_comparison_plots_simplified(results_sorted)\n","\n"," # Retourner les résultats seulement si explicitement demandé\n"," if show_detailed_results:\n"," return results_sorted\n"," else:\n"," print(f\"\\n✅ Analyse terminée. Utilisez show_detailed_results=True pour obtenir les données détaillées.\")\n"," return None\n","\n"," except Exception as e:\n"," print(f\"❌ Erreur générale: {e}\")\n"," return []\n","\n","def generate_comparison_plots_simplified(results):\n"," \"\"\"\n"," Génère des diagrammes en barres pour comparer les métriques\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 valides\n"," valid_results_iou = []\n"," for r in results:\n"," if r['test_mean_iou'] != 'N/A':\n"," valid_results_iou.append(r)\n"," elif r['best_val_mean_iou'] != 'N/A':\n"," # Utiliser val IoU si test IoU pas disponible\n"," r_copy = r.copy()\n"," r_copy['plot_iou'] = r['best_val_mean_iou']\n"," r_copy['iou_type'] = 'Val'\n"," valid_results_iou.append(r_copy)\n","\n"," # Pour test IoU, ajouter le type\n"," for r in valid_results_iou:\n"," if 'plot_iou' not in r:\n"," r['plot_iou'] = r['test_mean_iou']\n"," r['iou_type'] = 'Test'\n","\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\n"," fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 10))\n","\n"," # Graphique 1: Mean IoU\n"," if valid_results_iou:\n"," display_names = [r['display_name'] for r in valid_results_iou]\n"," iou_values = [r['plot_iou'] for r in valid_results_iou]\n"," iou_types = [r['iou_type'] for r in valid_results_iou]\n","\n"," # Créer des couleurs selon l'architecture et le type de modèle\n"," colors = []\n"," model_types = []\n","\n"," for r in valid_results_iou:\n"," clean_name = r['clean_name'].lower()\n"," architecture = r['architecture'].lower()\n","\n"," # Déterminer le type de modèle\n"," if 'fpn' in architecture or 'fpn' in clean_name:\n"," colors.append('#FF6B6B') # Rouge pour FPN baseline\n"," model_types.append('Baseline FPN')\n"," elif 'segformer' in clean_name and ('b0' in clean_name or '_b0_' in clean_name):\n"," colors.append('#4ECDC4') # Turquoise pour SegFormerB0\n"," model_types.append('SegFormerB0')\n"," elif 'segformer' in clean_name and ('b1' in clean_name or '_b1_' in clean_name):\n"," colors.append('#45B7D1') # Bleu pour SegFormerB1\n"," model_types.append('SegFormerB1')\n"," else:\n"," colors.append('#95A5A6') # Gris pour autres\n"," model_types.append('Other')\n","\n"," bars1 = ax1.bar(range(len(iou_values)), iou_values, color=colors, alpha=0.8)\n","\n"," ax1.set_title('Mean IoU par Expérience', fontsize=16, fontweight='bold')\n"," ax1.set_xlabel('Expériences', fontsize=12)\n"," ax1.set_ylabel('Mean IoU', fontsize=12)\n"," ax1.set_xticks(range(len(display_names)))\n"," ax1.set_xticklabels(display_names, rotation=45, ha='right', fontsize=10)\n"," ax1.grid(True, alpha=0.3)\n","\n"," # Centrer l'axe Y autour des valeurs min/max pour IoU\n"," if iou_values:\n"," min_iou = min(iou_values)\n"," max_iou = max(iou_values)\n"," margin_iou = (max_iou - min_iou) * 0.1 # 10% de marge\n"," if margin_iou == 0: # Si toutes les valeurs sont identiques\n"," margin_iou = max_iou * 0.05\n"," ax1.set_ylim(min_iou - margin_iou, max_iou + margin_iou)\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 + (max(iou_values) - min(iou_values)) * 0.01,\n"," f'{value:.3f}', ha='center', va='bottom', fontsize=9)\n","\n"," # Créer la légende avec les types de modèles uniques\n"," from matplotlib.patches import Patch\n"," legend_elements = []\n"," unique_types = []\n"," unique_colors = []\n","\n"," for model_type, color in zip(model_types, colors):\n"," if model_type not in unique_types:\n"," unique_types.append(model_type)\n"," unique_colors.append(color)\n","\n"," for model_type, color in zip(unique_types, unique_colors):\n"," legend_elements.append(Patch(facecolor=color, label=model_type))\n","\n"," ax1.legend(handles=legend_elements, loc='upper right')\n","\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('Mean IoU par Expérience', fontsize=16, fontweight='bold')\n","\n"," # Graphique 2: Test Accuracy\n"," if valid_results_acc:\n"," display_names = [r['display_name'] 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('Accuracy par Expérience', fontsize=16, fontweight='bold')\n"," ax2.set_xlabel('Expériences', fontsize=12)\n"," ax2.set_ylabel('Accuracy', fontsize=12)\n"," ax2.set_xticks(range(len(display_names)))\n"," ax2.set_xticklabels(display_names, rotation=45, ha='right', fontsize=10)\n"," ax2.grid(True, alpha=0.3)\n","\n"," # Centrer l'axe Y autour des valeurs min/max pour Accuracy\n"," if acc_values:\n"," min_acc = min(acc_values)\n"," max_acc = max(acc_values)\n"," margin_acc = (max_acc - min_acc) * 0.1 # 10% de marge\n"," if margin_acc == 0: # Si toutes les valeurs sont identiques\n"," margin_acc = max_acc * 0.05\n"," ax2.set_ylim(min_acc - margin_acc, max_acc + margin_acc)\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 + (max(acc_values) - min(acc_values)) * 0.01,\n"," f'{value:.3f}', ha='center', va='bottom', fontsize=9)\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('Accuracy par Expérience', fontsize=16, fontweight='bold')\n","\n"," plt.tight_layout()\n"," plt.show()\n","\n"," print(f\"📊 Diagrammes générés pour {len(valid_results_iou)} expériences (IoU) et {len(valid_results_acc)} expériences (Accuracy)\")\n"],"metadata":{"id":"6le-mGW67iMv","executionInfo":{"status":"ok","timestamp":1756750789222,"user_tz":-120,"elapsed":6,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":37,"outputs":[]},{"cell_type":"code","execution_count":36,"metadata":{"id":"fcl3B8x7PPrc","executionInfo":{"status":"ok","timestamp":1756750726443,"user_tz":-120,"elapsed":1763,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"5cc1563e-8b39-4bf4-9679-555410a900b0"},"outputs":[{"output_type":"stream","name":"stdout","text":["📊 Analyse de 8 expériences terminées\n","\n","🏆 TOP 8 EXPÉRIENCES TERMINÉES\n","========================================================================================================================\n","🥇 #1 - SegFormer_b1_pretrained_optimized@768x768\n"," 🏗️ SegFormer_pretrained + None\n"," 🎯 Mean IoU: 0.7998 | Accuracy: 0.9274\n"," 🆔 c9835f70...\n","\n","🥇 #2 - FPN_resnet34_baseline@768x768\n"," 🏗️ FPN + resnet34\n"," 🎯 Mean IoU: 0.7922 | Accuracy: 0.9247\n"," 🆔 4992e14d...\n","\n","🥇 #3 - FPN_efficientnet_b0_baseline@768x768\n"," 🏗️ FPN + efficientnet-b0\n"," 🎯 Mean IoU: 0.7867 | Accuracy: 0.9222\n"," 🆔 38b6084b...\n","\n","🥇 #4 - SegFormer_b0_pretrained_optimized@768x768\n"," 🏗️ SegFormer_pretrained + None\n"," 🎯 Mean IoU: 0.7791 | Accuracy: 0.9210\n"," 🆔 5f29f30d...\n","\n","🥇 #5 - FPN_resnet34_baseline@512x512\n"," 🏗️ FPN + resnet34\n"," 🎯 Mean IoU: 0.7581 | Accuracy: 0.9155\n"," 🆔 f02d6d26...\n","\n","🥇 #6 - SegFormer_b1_pretrained_optimized@512x512\n"," 🏗️ SegFormer_pretrained + None\n"," 🎯 Mean IoU: 0.7571 | Accuracy: 0.9141\n"," 🆔 46e0d784...\n","\n","🥇 #7 - FPN_efficientnet_b0_baseline@512x512\n"," 🏗️ FPN + efficientnet-b0\n"," 🎯 Mean IoU: 0.7485 | Accuracy: 0.9108\n"," 🆔 03bcf6ee...\n","\n","🥇 #8 - SegFormer_b0_pretrained_optimized@512x512\n"," 🏗️ SegFormer_pretrained + None\n"," 🎯 Mean IoU: 0.7337 | Accuracy: 0.9063\n"," 🆔 b70fc0b5...\n","\n"]},{"output_type":"display_data","data":{"text/plain":["
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Diagrammes générés pour 8 expériences (IoU) et 8 expériences (Accuracy)\n","\n","✅ Analyse terminée. Utilisez show_detailed_results=True pour obtenir les données détaillées.\n"]}],"source":["compare_experiments_mlflow(experiment_name=mlflow_experiment)"]},{"cell_type":"markdown","metadata":{"id":"gqrw8X8wR5Q6"},"source":["### Analyse meilleur modèle"]},{"cell_type":"code","source":["top_n_models = 2"],"metadata":{"id":"eakNYSc_CknQ","executionInfo":{"status":"ok","timestamp":1756709410280,"user_tz":-120,"elapsed":4,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":51,"outputs":[]},{"cell_type":"markdown","source":["Affichage des courbes d'apprentissage"],"metadata":{"id":"j9kVdhxVBA4S"}},{"cell_type":"code","source":["def display_training_curves_from_top_runs(experiment_name=\"OC Projet 9\", metric=\"test_mean_iou\", top_n=1):\n"," \"\"\"\n"," Trouve les n meilleurs modèles selon la métrique choisie et affiche leurs graphiques de courbes d'entraînement.\n"," Si top_n >= 2, affiche aussi une comparaison des IoU par classe entre les 2 premiers.\n","\n"," Args:\n"," experiment_name (str): Nom de l'expérience MLflow\n"," metric (str): Métrique pour classer les runs (ex: \"test_mean_iou\", \"best_val_mean_iou\", \"test_accuracy\")\n"," top_n (int): Nombre de meilleurs runs à afficher\n"," \"\"\"\n"," print(f\"\\n{'='*60}\")\n"," print(f\"📈 Affichage des courbes d'entraînement des {top_n} meilleurs modèles\")\n"," print(f\"🔬 Expérience MLflow: {experiment_name}\")\n"," print(f\"📊 Métrique de classement: {metric}\")\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"," # Construire le filtre pour la métrique choisie\n"," filter_string = f\"status = 'FINISHED' and metrics.{metric} >= 0\"\n"," order_by = f\"metrics.{metric} DESC\"\n","\n"," # Rechercher les runs terminés avec la métrique choisie\n"," runs_df = mlflow.search_runs(\n"," experiment_ids=[experiment.experiment_id],\n"," filter_string=filter_string,\n"," order_by=[order_by],\n"," max_results=top_n\n"," )\n","\n"," if runs_df.empty:\n"," print(f\"❌ Aucun run terminé avec la métrique {metric} trouvé.\")\n"," return\n","\n"," if len(runs_df) < top_n:\n"," print(f\"⚠️ Seulement {len(runs_df)} runs trouvés sur les {top_n} demandés.\")\n","\n"," print(f\"🏆 Top {len(runs_df)} runs trouvés:\\n\")\n","\n"," # Traiter chaque run dans l'ordre de performance\n"," for idx, (_, run) in enumerate(runs_df.iterrows(), 1):\n"," run_id = run['run_id']\n"," run_name = run.get('tags.mlflow.runName', 'Unknown')\n"," metric_value = run[f'metrics.{metric}']\n","\n"," print(f\"#{idx} - Run Name: {run_name}\")\n"," print(f\" Run ID: {run_id}\")\n"," print(f\" {metric}: {metric_value:.4f}\\n\")\n","\n"," # Télécharger et afficher l'artefact 'training_curves.png'\n"," artifact_path = \"training_curves.png\"\n","\n"," try:\n"," print(f\"💾 Téléchargement de l'artefact '{artifact_path}' pour le run #{idx}...\")\n","\n"," with tempfile.TemporaryDirectory() as tmpdir:\n"," local_path = mlflow.artifacts.download_artifacts(\n"," run_id=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(f\"🖼️ Affichage des courbes pour le run #{idx}...\")\n"," img = PILImage.open(local_path)\n","\n"," # Créer une figure avec un titre informatif\n"," plt.figure(figsize=(12, 8))\n"," plt.imshow(img)\n","\n"," # Nettoyer le nom du run pour l'affichage\n"," clean_name = run_name\n"," import re\n"," clean_name = re.sub(r'_prod_\\d{4}-\\d{2}-\\d{2}_\\d{2}-\\d{2}-\\d{2}$', '', clean_name)\n"," clean_name = re.sub(r'_\\d{4}-\\d{2}-\\d{2}_\\d{2}-\\d{2}-\\d{2}$', '', clean_name)\n","\n"," plt.title(f\"#{idx} - {clean_name} ({metric}: {metric_value:.4f})\", fontsize=14, fontweight='bold')\n"," plt.axis('off')\n"," plt.tight_layout()\n"," plt.show()\n","\n"," print(f\"{'='*60}\")\n","\n"," except Exception as e:\n"," print(f\"❌ Erreur lors du téléchargement/affichage pour le run #{idx}: {e}\")\n"," print(\"Vérifiez que l'artefact 'training_curves.png' existe pour ce run.\")\n"," print(f\"{'='*60}\")\n","\n"," except Exception as e:\n"," print(f\"❌ Erreur générale lors de l'affichage des courbes d'entraînement: {e}\")"],"metadata":{"id":"yUVYqcKWAmUH","executionInfo":{"status":"ok","timestamp":1756712614566,"user_tz":-120,"elapsed":15,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":83,"outputs":[]},{"cell_type":"code","source":["display_training_curves_from_top_runs(\"OC Projet 9\", metric=\"test_mean_iou\", top_n=top_n_models)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["2433f2d17149400195486f9b32d252e8","680dfa4a92704035a57f0d9f1b394118","351e3e47d5214aee8c9025bc7b2e4776","28619ba328d8426f89efa339608c9a74","08573209f75342df9d26b5a58e04f6fd","7d131cf0f2074d019b658d60d52d4670","a3309bc3b1754e3ba908c69281d29498","c73c8a49cd0a4c7baebed1fccb531fd0","5ed3a4c439994f50b3fcb3ec146b2d95","4c37be53956a40ca8d01a5c073ddce1d","4970245c934847bda4c85e4ee2af78fb","23af200edf834aab97a2bf2ca6b99a97","beb93d722c254338ab0a9cc950aa27b2","e2c41f8e2a354d5987907551fc4179c0","618e1b61a638475aa28023708bdb100b","97170e38aa634e1ab155fa5240ae98c5","69c5128591a44d3f8dcda4e09c2c6c88","5220b423fb1a4541a5a03ebce516a7cd","d97f697af2c940099ef43b123a3bfd66","895b93e35ba14954a38b8816a1bab6ac","f96f8cd347684a489ecefd87caba5707","c91d4c6446634c41bae13edb0c769104"]},"id":"Zim7VOb9_vcx","executionInfo":{"status":"ok","timestamp":1756712624019,"user_tz":-120,"elapsed":8316,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"1add10ed-a139-4a82-bf60-6cc9ca6bb59c"},"execution_count":84,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","============================================================\n","📈 Affichage des courbes d'entraînement des 2 meilleurs modèles\n","🔬 Expérience MLflow: OC Projet 9\n","📊 Métrique de classement: test_mean_iou\n","============================================================\n","🏆 Top 2 runs trouvés:\n","\n","#1 - Run Name: SegFormer_b1_pretrained_optimized_prod_2025-08-30_07-25-25\n"," Run ID: c9835f7094354f48baff73f5d71edfc9\n"," test_mean_iou: 0.7998\n","\n","💾 Téléchargement de l'artefact 'training_curves.png' pour le run #1...\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","#2 - Run Name: FPN_resnet34_baseline_prod_2025-08-30_14-41-34\n"," Run ID: 4992e14daff845aabea9f44a56f499f6\n"," test_mean_iou: 0.7922\n","\n","💾 Téléchargement de l'artefact 'training_curves.png' pour le run #2...\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"]}]},{"cell_type":"markdown","source":["Affichage de la matrice de confusion en pourcentages"],"metadata":{"id":"GFqCXkZEBMp2"}},{"cell_type":"code","source":["def display_top_confusion_matrices(experiment_name=\"OC Projet 9\", metric=\"test_mean_iou\", top_n=1):\n"," \"\"\"\n"," Trouve les n meilleurs modèles selon la métrique choisie et affiche leurs matrices de confusion.\n"," Si top_n >= 2, affiche aussi une comparaison des matrices de confusion entre les 2 premiers.\n"," Args:\n"," experiment_name (str): Nom de l'expérience MLflow\n"," metric (str): Métrique pour classer les runs (ex: \"test_mean_iou\", \"best_val_mean_iou\", \"test_accuracy\")\n"," top_n (int): Nombre de meilleurs runs à afficher\n"," \"\"\"\n"," import matplotlib.pyplot as plt\n"," import seaborn as sns\n"," import numpy as np\n"," import json\n"," import tempfile\n"," import re\n","\n"," print(f\"\\n{'='*60}\")\n"," print(f\"📊 Affichage des matrices de confusion des {top_n} meilleurs modèles\")\n"," print(f\"🔬 Expérience MLflow: {experiment_name}\")\n"," print(f\"📈 Métrique de classement: {metric}\")\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"," # Construire le filtre pour la métrique choisie\n"," filter_string = f\"status = 'FINISHED' and metrics.{metric} >= 0\"\n"," order_by = f\"metrics.{metric} DESC\"\n","\n"," # Rechercher les runs terminés avec la métrique choisie\n"," runs_df = mlflow.search_runs(\n"," experiment_ids=[experiment.experiment_id],\n"," filter_string=filter_string,\n"," order_by=[order_by],\n"," max_results=top_n\n"," )\n","\n"," if runs_df.empty:\n"," print(f\"❌ Aucun run terminé avec la métrique {metric} trouvé.\")\n"," return\n","\n"," if len(runs_df) < top_n:\n"," print(f\"⚠️ Seulement {len(runs_df)} runs trouvés sur les {top_n} demandés.\")\n","\n"," print(f\"🏆 Top {len(runs_df)} runs trouvés:\\n\")\n"," results = []\n","\n"," # Traiter chaque run dans l'ordre de performance\n"," for idx, (_, run) in enumerate(runs_df.iterrows(), 1):\n"," run_id = run['run_id']\n"," run_name = run.get('tags.mlflow.runName', 'Unknown')\n"," metric_value = run[f'metrics.{metric}']\n","\n"," # Nettoyer le nom du run\n"," clean_name = re.sub(r'_prod_\\d{4}-\\d{2}-\\d{2}_\\d{2}-\\d{2}-\\d{2}$', '', run_name)\n"," clean_name = re.sub(r'_\\d{4}-\\d{2}-\\d{2}_\\d{2}-\\d{2}-\\d{2}$', '', clean_name)\n","\n"," print(f\"#{idx} - {clean_name}\")\n"," print(f\" Run ID: {run_id}\")\n"," print(f\" {metric}: {metric_value:.4f}\\n\")\n","\n"," # Télécharger et afficher la matrice de confusion\n"," try:\n"," with tempfile.TemporaryDirectory() as temp_dir:\n"," confusion_matrix_path = mlflow.artifacts.download_artifacts(\n"," run_id=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"," # Créer la visualisation\n"," plt.figure(figsize=(12, 10))\n","\n"," # Normaliser la matrice pour l'affichage (en pourcentages par ligne = Recall)\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': 'Recall par classe (%)'})\n","\n"," plt.title(f'#{idx} - Matrice de Confusion (Recall par classe) - {clean_name}\\n'\n"," f'{metric}: {metric_value:.4f} | Accuracy: {accuracy:.4f}' if accuracy != 'N/A' else f'{metric}: {metric_value:.4f}',\n"," fontsize=14, fontweight='bold', pad=20)\n","\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"," # Note explicative\n"," plt.figtext(0.02, 0.02,\n"," 'Note: Lecture par ligne - \"% d\\'échantillons réels correctement détectés par classe\"',\n"," fontsize=9, ha='left', style='italic')\n","\n"," plt.tight_layout()\n"," plt.show()\n","\n"," # Stocker les résultats\n"," results.append({\n"," 'rank': idx,\n"," 'run_id': run_id,\n"," 'run_name': run_name,\n"," 'clean_name': clean_name,\n"," 'metric_value': metric_value,\n"," 'confusion_matrix': matrix,\n"," 'confusion_matrix_normalized': matrix_normalized,\n"," 'class_names': class_names,\n"," 'accuracy': accuracy,\n"," 'total_samples': total_samples\n"," })\n","\n"," print(f\"✅ Matrice de confusion #{idx} affichée\")\n"," print(f\"{'='*60}\")\n","\n"," except Exception as e:\n"," print(f\"❌ Erreur lors du téléchargement/affichage pour le run #{idx}: {e}\")\n"," print(\"Vérifiez que l'artefact 'confusion_matrix.json' existe pour ce run.\")\n"," print(f\"{'='*60}\")\n","\n"," # Si on a au moins 2 runs, afficher la comparaison des bonnes classifications par classe\n"," if len(results) >= 2:\n"," print(f\"\\n📊 Comparaison du recall par classe entre les 2 meilleurs modèles\")\n"," result1 = results[0]\n"," result2 = results[1]\n","\n"," try:\n"," # Vérifier que les deux matrices ont les mêmes dimensions\n"," matrix1_norm = result1['confusion_matrix_normalized']\n"," matrix2_norm = result2['confusion_matrix_normalized']\n","\n"," if matrix1_norm.shape != matrix2_norm.shape:\n"," print(f\"⚠️ Les matrices ont des tailles différentes ({matrix1_norm.shape} vs {matrix2_norm.shape})\")\n"," print(f\" Impossible de générer la comparaison.\")\n"," else:\n"," # Extraire les valeurs de la diagonale (recall par classe)\n"," diagonal1 = np.diag(matrix1_norm) # % recall pour le modèle 1\n"," diagonal2 = np.diag(matrix2_norm) # % recall pour le modèle 2\n","\n"," # Calculer les deltas en points de pourcentage\n"," deltas = diagonal1 - diagonal2 # Modèle 1 - Modèle 2\n"," class_names = result1['class_names']\n","\n"," # Créer le diagramme en barres\n"," plt.figure(figsize=(12, 8))\n","\n"," # Couleurs selon le signe du delta\n"," colors = ['#2ECC71' if delta >= 0 else '#E74C3C' for delta in deltas]\n"," bars = plt.bar(class_names, deltas, color=colors, alpha=0.8)\n","\n"," # Configuration du graphique\n"," plt.title(f'Δ Recall par Classe\\n{result1[\"clean_name\"]} - {result2[\"clean_name\"]}',\n"," fontsize=14, fontweight='bold')\n"," plt.xlabel('Classes', fontsize=12)\n"," plt.ylabel('Différence Recall (% points)', fontsize=12)\n"," plt.axhline(y=0, color='black', linestyle='-', alpha=0.3)\n"," plt.grid(True, alpha=0.3)\n"," plt.xticks(rotation=45, ha='right')\n","\n"," # Ajouter les valeurs sur les barres\n"," for bar, delta in zip(bars, deltas):\n"," height = bar.get_height()\n"," # Placer l'étiquette juste au-dessus de la barre\n"," if height >= 0:\n"," y_pos = height + 0.05 # Proche du sommet pour les barres positives\n"," else:\n"," y_pos = 0.05 # Juste au-dessus de zéro pour les barres négatives\n"," plt.text(bar.get_x() + bar.get_width()/2., y_pos,\n"," f'{delta:+.1f}%', ha='center', va='bottom', fontsize=9)\n","\n"," # Légende\n"," from matplotlib.patches import Patch\n"," legend_elements = [\n"," Patch(facecolor='#2ECC71', label=f'{result1[\"clean_name\"]} meilleur'),\n"," Patch(facecolor='#E74C3C', label=f'{result2[\"clean_name\"]} meilleur')\n"," ]\n"," plt.legend(handles=legend_elements, loc='upper right')\n","\n"," # Statistiques\n"," mean_delta = np.mean(deltas)\n"," positive_deltas = np.sum(deltas > 0)\n"," plt.figtext(0.02, 0.02,\n"," f'Δ moyen: {mean_delta:+.1f}% | Classes où {result1[\"clean_name\"]} domine: {positive_deltas}/{len(deltas)}',\n"," fontsize=10, ha='left')\n","\n"," plt.tight_layout()\n"," plt.show()\n","\n"," print(f\"✅ Comparaison générée entre {result1['clean_name']} et {result2['clean_name']}\")\n"," print(f\" 📈 Δ moyen du recall: {mean_delta:+.1f}% points\")\n"," print(f\" 🏆 {result1['clean_name']} meilleur sur {positive_deltas}/{len(deltas)} classes\")\n","\n"," except Exception as e:\n"," print(f\"❌ Erreur lors de la génération de la comparaison: {e}\")\n","\n"," print(f\"\\n✅ Analyse terminée. {len(results)} matrices de confusion affichées sur {len(runs_df)} runs.\")\n"," return results\n","\n"," except Exception as e:\n"," print(f\"❌ Erreur générale lors de l'affichage des matrices de confusion: {e}\")\n"," return None"],"metadata":{"id":"sUk1Q9FwL5e3","executionInfo":{"status":"ok","timestamp":1756712047771,"user_tz":-120,"elapsed":15,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":75,"outputs":[]},{"cell_type":"code","source":["confusion_result = display_top_confusion_matrices(\"OC Projet 9\", metric=\"test_mean_iou\", top_n=2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["7a2ead19ea6a4d71b05aae073fae6634","f8785e2c950a47e498a3f673edde79da","fa289bf612d541aa95a2be820d6335a7","3770f132639d4d8db997eea52cecb245","16aaa48670d147a69abb087412ec62e6","cb6ed135c2004e8390fa2a577d8a4c90","0624cd02bbd645638eb681b4122d9f00","9c277fa27c824858808282f70292c9f4","0c36bc9f55ef472f8a5c296991209698","bbd6aa97af4c4080a62c3ce8883ecf89","1e83329757c444248c5940f51d08b4e3","0d0149a38ec94aa8a6c0be0b295a2e35","243e250613f14122852bec4b3ddac0a1","60f1d94bc28249eda94215d5c7e9a8fd","b774be3734a54862897ab2f05c38ef3f","624227d8682846ab980872fc59770161","8a9a9190ad8349cb9a93506a1408df5e","b2812ea358074f5d81796517222486a1","1898b6bfd0e142eaaabd88566aa84cd4","0f677d9751d442808a8272afe5f7f61a","4bfc8d7a3fb2454cb4ae31c680c23253","ea70a4f6571544aa9ed4b1ce10be8166"]},"id":"A-yjGPv_EfOU","executionInfo":{"status":"ok","timestamp":1756712052121,"user_tz":-120,"elapsed":4151,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"29f0fa74-8524-41d6-dcdd-b4f7f60760a0"},"execution_count":76,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","============================================================\n","📊 Affichage des matrices de confusion des 2 meilleurs modèles\n","🔬 Expérience MLflow: OC Projet 9\n","📈 Métrique de classement: test_mean_iou\n","============================================================\n","🏆 Top 2 runs trouvés:\n","\n","#1 - SegFormer_b1_pretrained_optimized\n"," Run ID: c9835f7094354f48baff73f5d71edfc9\n"," test_mean_iou: 0.7998\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 #1 affichée\n","============================================================\n","#2 - FPN_resnet34_baseline\n"," Run ID: 4992e14daff845aabea9f44a56f499f6\n"," test_mean_iou: 0.7922\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 #2 affichée\n","============================================================\n","\n","📊 Comparaison du recall par classe entre les 2 meilleurs modèles\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["✅ Comparaison générée entre SegFormer_b1_pretrained_optimized et FPN_resnet34_baseline\n"," 📈 Δ moyen du recall: +0.3% points\n"," 🏆 SegFormer_b1_pretrained_optimized meilleur sur 5/8 classes\n","\n","✅ Analyse terminée. 2 matrices de confusion affichées sur 2 runs.\n"]}]},{"cell_type":"markdown","source":["`IoU = (TP) / (TP + FP + FN)`\\\n","`Recall = TP / (TP + FN) = TP / (Vrais positifs réels)`\n","\n","Les FP affectent différemment ces métriques :\n","\n","- IoU : Les FP réduisent l'IoU car ils augmentent le dénominateur\n","- Recall : Les FP n'affectent PAS le recall (ils ne sont pas dans la formule)\n","\n"],"metadata":{"id":"wRsAoW74j1qZ"}},{"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 9\",\n"," figsize=(14, 8),\n"," color_palette='viridis',\n"," show_values=True):\n"," \"\"\"\n"," Analyse la répartition des classes dans le dataset à partir de n'importe quelle matrice de confusion.\n"," La distribution des classes réelles est constante peu importe le modèle.\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"," print(f\"📊 Analyse de la distribution des classes dans le dataset\")\n"," print(f\"🔬 Expérience MLflow: {experiment_name}\")\n"," print(\"=\" * 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 None\n","\n"," # Rechercher le premier run terminé disponible (n'importe lequel suffit)\n"," runs_df = mlflow.search_runs(\n"," experiment_ids=[experiment.experiment_id],\n"," filter_string=\"status = 'FINISHED'\",\n"," max_results=1\n"," )\n","\n"," if runs_df.empty:\n"," print(f\"❌ Aucun run terminé trouvé.\")\n"," return None\n","\n"," run_id = runs_df.iloc[0]['run_id']\n"," run_name = runs_df.iloc[0].get('tags.mlflow.runName', 'Unknown')\n","\n"," print(f\"📋 Utilisation du run: {run_name}\")\n"," print(f\"🆔 Run ID: {run_id}\")\n"," print()\n","\n"," # Télécharger et analyser la matrice de confusion\n"," with tempfile.TemporaryDirectory() as temp_dir:\n"," confusion_matrix_path = mlflow.artifacts.download_artifacts(\n"," run_id=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)\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 LE DATASET:\")\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 dans le Dataset Cityscapes\\n'\n"," f'Total: {total_pixels:,} pixels',\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"," '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"," return None\n","\n","def compare_class_performance(experiment_name=\"OC Projet 9\", metric=\"test_mean_iou\", top_n=2):\n"," \"\"\"\n"," Compare les performances par classe entre les N meilleurs modèles.\n"," Affiche les IoU par classe et leurs différences sous forme de diagramme en barres.\n","\n"," Args:\n"," experiment_name (str): Nom de l'expérience MLflow\n"," metric (str): Métrique pour classer les runs\n"," top_n (int): Nombre de meilleurs modèles à comparer (minimum 2)\n"," \"\"\"\n"," import matplotlib.pyplot as plt\n"," import numpy as np\n"," import json\n"," import tempfile\n"," import re\n","\n"," print(f\"\\n{'='*60}\")\n"," print(f\"📊 Comparaison des performances par classe des {top_n} meilleurs modèles\")\n"," print(f\"🔬 Expérience MLflow: {experiment_name}\")\n"," print(f\"📈 Métrique de classement: {metric}\")\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 choisie\n"," filter_string = f\"status = 'FINISHED' and metrics.{metric} >= 0\"\n"," order_by = f\"metrics.{metric} DESC\"\n","\n"," runs_df = mlflow.search_runs(\n"," experiment_ids=[experiment.experiment_id],\n"," filter_string=filter_string,\n"," order_by=[order_by],\n"," max_results=top_n\n"," )\n","\n"," if len(runs_df) < 2:\n"," print(f\"❌ Pas assez de runs (besoin d'au moins 2, trouvé {len(runs_df)}).\")\n"," return\n","\n"," if len(runs_df) < top_n:\n"," print(f\"⚠️ Seulement {len(runs_df)} runs trouvés sur les {top_n} demandés.\")\n","\n"," print(f\"🏆 Top {len(runs_df)} runs trouvés:\\n\")\n","\n"," # Collecter les métriques détaillées pour chaque run\n"," models_data = []\n","\n"," for idx, (_, run) in enumerate(runs_df.iterrows(), 1):\n"," run_id = run['run_id']\n"," run_name = run.get('tags.mlflow.runName', 'Unknown')\n"," metric_value = run[f'metrics.{metric}']\n","\n"," # Nettoyer le nom du run\n"," clean_name = re.sub(r'_prod_\\d{4}-\\d{2}-\\d{2}_\\d{2}-\\d{2}-\\d{2}$', '', run_name)\n"," clean_name = re.sub(r'_\\d{4}-\\d{2}-\\d{2}_\\d{2}-\\d{2}-\\d{2}$', '', clean_name)\n","\n"," print(f\"#{idx} - {clean_name}\")\n"," print(f\" Run ID: {run_id}\")\n"," print(f\" {metric}: {metric_value:.4f}\")\n","\n"," try:\n"," with tempfile.TemporaryDirectory() as temp_dir:\n"," # Télécharger les métriques détaillées\n"," detailed_metrics_path = mlflow.artifacts.download_artifacts(\n"," run_id=run_id,\n"," artifact_path=\"detailed_metrics.json\",\n"," dst_path=temp_dir\n"," )\n","\n"," with open(detailed_metrics_path, 'r') as f:\n"," detailed_metrics = json.load(f)\n","\n"," class_stats = detailed_metrics['class_stats']\n","\n"," models_data.append({\n"," 'rank': idx,\n"," 'run_id': run_id,\n"," 'clean_name': clean_name,\n"," 'metric_value': metric_value,\n"," 'class_stats': class_stats\n"," })\n","\n"," print(f\" ✅ Métriques détaillées récupérées\")\n","\n"," except Exception as e:\n"," print(f\" ❌ Erreur: {e}\")\n","\n"," print()\n","\n"," if len(models_data) < 2:\n"," print(f\"❌ Pas assez de données détaillées récupérées.\")\n"," return\n","\n"," # Créer la comparaison visuelle\n"," class_names = [stat['class_name'] for stat in models_data[0]['class_stats']]\n","\n"," # Graphique 1: IoU par classe pour chaque modèle\n"," fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8))\n","\n"," # Barres groupées pour les IoU\n"," x_pos = np.arange(len(class_names))\n"," bar_width = 0.35\n"," colors = ['#3498db', '#e74c3c', '#2ecc71', '#f39c12'][:len(models_data)]\n","\n"," # Collecter toutes les valeurs IoU pour déterminer les limites\n"," all_iou_values = []\n"," for model in models_data:\n"," iou_values = [stat['iou'] for stat in model['class_stats']]\n"," all_iou_values.extend(iou_values)\n"," bars = ax1.bar(x_pos + model['rank'] * bar_width - bar_width, iou_values, bar_width,\n"," label=f\"#{model['rank']} {model['clean_name']}\",\n"," color=colors[model['rank']-1], alpha=0.8)\n","\n"," # Centrer l'axe Y pour le graphique IoU\n"," if all_iou_values:\n"," min_iou = min(all_iou_values)\n"," max_iou = max(all_iou_values)\n"," iou_range = max_iou - min_iou\n","\n"," if iou_range > 0:\n"," margin = iou_range * 0.1 # 10% de marge\n"," ax1.set_ylim(min_iou - margin, max_iou + margin)\n"," else:\n"," # Si toutes les valeurs sont identiques, ajouter une marge par défaut\n"," margin = max_iou * 0.05 if max_iou > 0 else 0.05\n"," ax1.set_ylim(min_iou - margin, max_iou + margin)\n","\n"," ax1.set_xlabel('Classes', fontsize=12)\n"," ax1.set_ylabel('IoU', fontsize=12)\n"," ax1.set_title('IoU par Classe', fontsize=14, fontweight='bold')\n"," ax1.set_xticks(x_pos + bar_width * (len(models_data) - 1) / 2)\n"," ax1.set_xticklabels(class_names, rotation=45, ha='right')\n"," ax1.legend()\n"," ax1.grid(True, alpha=0.3)\n","\n"," # Graphique 2: Delta entre les 2 premiers modèles\n"," if len(models_data) >= 2:\n"," model1 = models_data[0]\n"," model2 = models_data[1]\n","\n"," iou1_values = [stat['iou'] for stat in model1['class_stats']]\n"," iou2_values = [stat['iou'] for stat in model2['class_stats']]\n"," deltas = [(iou1 - iou2) * 100 for iou1, iou2 in zip(iou1_values, iou2_values)]\n","\n"," # Couleurs selon le signe du delta\n"," colors_delta = ['#2ECC71' if delta >= 0 else '#E74C3C' for delta in deltas]\n","\n"," bars = ax2.bar(class_names, deltas, color=colors_delta, alpha=0.8)\n","\n"," # Centrer l'axe Y pour le graphique des deltas\n"," min_delta = min(deltas)\n"," max_delta = max(deltas)\n"," delta_range = max_delta - min_delta\n","\n"," if delta_range > 0:\n"," margin = delta_range * 0.1 # 10% de marge\n"," ax2.set_ylim(min_delta - margin, max_delta + margin)\n"," else:\n"," # Si tous les deltas sont identiques, ajouter une marge par défaut\n"," margin = abs(max_delta) * 0.1 if max_delta != 0 else 1.0\n"," ax2.set_ylim(min_delta - margin, max_delta + margin)\n","\n"," ax2.set_xlabel('Classes', fontsize=12)\n"," ax2.set_ylabel('Différence IoU (% points)', fontsize=12)\n"," ax2.set_title(f'Δ IoU par Classe\\n{model1[\"clean_name\"]} - {model2[\"clean_name\"]}',\n"," fontsize=14, fontweight='bold')\n"," ax2.axhline(y=0, color='black', linestyle='-', alpha=0.3)\n"," ax2.grid(True, alpha=0.3)\n"," ax2.tick_params(axis='x', rotation=45)\n","\n"," # Ajouter les valeurs sur les barres avec positionnement proportionnel\n"," y_range = ax2.get_ylim()[1] - ax2.get_ylim()[0]\n"," text_offset = y_range * 0.02 # 2% de la plage pour l'offset du texte\n","\n"," for bar, delta in zip(bars, deltas):\n"," height = bar.get_height()\n"," if height >= 0:\n"," y_pos = height + text_offset\n"," else:\n"," y_pos = height - text_offset\n"," ax2.text(bar.get_x() + bar.get_width()/2., y_pos,\n"," f'{delta:+.1f}%', ha='center', va='bottom' if height >= 0 else 'top', fontsize=9)\n","\n"," # Légende\n"," from matplotlib.patches import Patch\n"," legend_elements = [\n"," Patch(facecolor='#2ECC71', label=f'{model1[\"clean_name\"]} meilleur'),\n"," Patch(facecolor='#E74C3C', label=f'{model2[\"clean_name\"]} meilleur')\n"," ]\n"," ax2.legend(handles=legend_elements, loc='upper right')\n","\n"," # Statistiques de comparaison\n"," if len(models_data) >= 2:\n"," mean_delta = np.mean(deltas)\n"," positive_deltas = sum(1 for d in deltas if d > 0)\n","\n"," ax2.text(\n"," 0.02, -0.18, # Position (en coordonnées ax2)\n"," f'Δ moyen: {mean_delta:+.1f}% points\\n'\n"," f'Classes où {model1[\"clean_name\"]} domine: {positive_deltas}/{len(deltas)}',\n"," transform=ax2.transAxes,\n"," fontsize=10, ha='left', va='top'\n"," )\n","\n"," print(f\"✅ Comparaison générée entre {model1['clean_name']} et {model2['clean_name']}\")\n"," print(f\" 📈 Δ IoU moyen: {mean_delta:+.1f}% points\")\n"," print(f\" 🏆 {model1['clean_name']} meilleur sur {positive_deltas}/{len(deltas)} classes\")\n","\n"," plt.tight_layout()\n"," plt.show()\n","\n"," return models_data\n","\n"," except Exception as e:\n"," print(f\"❌ Erreur générale: {e}\")\n"," return None"],"metadata":{"id":"zIayNpz5NrNG","executionInfo":{"status":"ok","timestamp":1756751030427,"user_tz":-120,"elapsed":48,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":38,"outputs":[]},{"cell_type":"code","source":["# Analyse simple du meilleur modèle\n","result = analyze_class_distribution_from_confusion_matrix(\"OC Projet 9\")\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","# )\n","\n","# Comparaison entre plusieurs modèles\n","comparison = compare_class_performance(\"OC Projet 9\", \"test_mean_iou\", top_n=2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["7df1244320b04313a8115eea99aada1c","71e8a46fc00e41eeb58cbf687ade60a0","c4193783ab754747bbdb179fe20c6ac8","70d787f47cd149a58f9d07d34c4ccd01","85b05005fdbd4838bb9066e27a997a83","484482041cf440028c8c3c22eaac60b6","28cc3c2cc2c9402e9ebd62053182e768","c0ac7197ac614c73a1426310657e4bd0","4e7caaf8870d470fb94d1da53c659b14","66ca9a55333d42a2a77a171207fa3209","923743b828ad41508f3351933902ed25","891b5263ba8b4fa0b62670ff67c74aff","6f512f6b4c704d71bc293329dc0862eb","12dcd9a2d1a8442a8b602ec62b0a4fda","5f7b4a4595dc4397b9a914324796d784","5b15059156e14df79a06aa400c4f6495","cb6e13c0a1424ba49246aaa41d9e2fb7","77bb2c0a812642faa23f42ca4d8d9e9c","2828cf1effb6439581e7f5b3c0c97dda","09bce8677a6c40e7b012f949d5fe18cb","4382cb7bc7e14e0e8764ae82340f10f2","9e12d46bd4c74778b1ddb1c301bf3280","403cb064303c48259734838f8fa1402d","9f7ed30da4b6485c89c8920ce5a63d50","f6efa801256c456c971417d376fa3b4f","a44859a7efcf496f8478ccee3f55a461","f56758086cad405e90ae0f4b346bf432","46e14585c11c452284c2510a79facdf9","2cba8fe771a54d53bdef001d91ea9a3c","ce47b6d8d18541dab273bdd2489434ab","a79406d402a9424dac6c86f55302d92b","a1a2664aaa3a4a3db7b6fb077dc07ca7","93675432e312413c9a0b43d5e9915670"]},"id":"Qr0s37mKNrFe","executionInfo":{"status":"ok","timestamp":1756751034602,"user_tz":-120,"elapsed":3987,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"c4789dc2-47b6-4511-f811-dc6c84364a55"},"execution_count":39,"outputs":[{"output_type":"stream","name":"stdout","text":["📊 Analyse de la distribution des classes dans le dataset\n","🔬 Expérience MLflow: OC Projet 9\n","============================================================\n","📋 Utilisation du run: FPN_resnet34_baseline_prod_2025-08-30_14-41-34\n","🆔 Run ID: 4992e14daff845aabea9f44a56f499f6\n","\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading artifacts: 0%| | 0/1 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zww8giXAMGDNjteSZPnsz7778fGTcFChQAgjA30z+9lt1JT0+nR48epKamRn6uhw0bxtixYylZsiTz58+PygJ/9erVA4KwLTN87d+/P3/88QdJSUkMGjSIIUOGRN5vtmzZEll0q3v37tnKAtx7773079+fsmXL8u233zJt2jSSkpLo2LEjI0aMYPz48VStWjVyjkzjxo2LzAQdPXo0gwcPjtTKrl69Ohs3bozM/s7pz2enTp249957I+fo1KkT/fv332Nd26VLl0Zeo8w+7qu9vZeec845kTGX9Y8xO3bs4NNPPwXgzDPPpHjx4vz++++RILdr166MGjWKd955h4kTJ3LMMcdQpUqVyB/Nfv31V5566ikA6tevz+TJkxk+fDivv/46EMycfuONNwBy9dpkNWvWLIYNG8Z7773HRx99FHnP+uCDD/LkPUCSpIOdoa0kSXtx7bXX0q5dO9q1a8eVV17JGWecwZNPPgkEC3F17twZgN9++41vv/0WCP4DX6lSJQCKFy/OOeecAwTBxV+FQiFuvfXWyCzZNm3aULJkSQC+++67yHFjxozhxx9/ZNKkSRQsWBCAY489NrL/r+UXMtvO/I/0gRAOhxk1ahQQzMrNWjbh1FNP5YQTTgDgq6++4vfff9/l8Z07d6Zo0aIAnHvuuZEZhsuWLdvreTOfd4AOHTpE7v/nP/8hMTFxl+PHjx8PQNmyZSMzL+Pi4rj00kuBIGjNLK2Q2Ydff/2VV199lblz55Kens6tt97Ke++9F5n1tieZH48uVKgQSUlJez327xQsWJCvvvqKH3/8kcGDB0dmyO1uHGSOEYAhQ4YwceJEfv/9d8qXL88HH3zAwIEDI4vKZT329ddfZ8qUKWzevJnatWvz4Ycf0r9/f6pXrw7sHMNxcXFcccUVkce1bt0agA0bNkQCncx2t2zZwgsvvMCMGTPYsWMHbdu2ZdSoUbz44ou7fX0yZf0o/FVXXUUoFALgwgsvpHz58rscf/755/Pjjz/y448/RmbIFitWLLJQ4K+//rrb81x44YWRYypXrhxZcCrrR9P/6bXszsyZMyNhecuWLUlOTgagfPnyNGnSBNj9e0ZuZa3JvXnzZgB69uwZea4yZ7FnraW8p+cqq+OPPz7Sxl133QVAYmIiderU2aWNrGOsT58+TJ8+ne3bt3PGGWcwevRo+vXrF5k9nZufz9zIWqog8z12fyhQoACtWrUC4LPPPos851988UWkDxdffDEASUlJJCQEH3wcO3YsY8aMYdWqVRQrVozBgwfzzjvvRH5/jB8/PhJwXnnllZE/Lpx00km8+uqrvPTSS5x66qlA7l6brC688MJICYjk5ORsM44zw+MD+R4gSdLBzvIIkiTtxe5qnEIwA61NmzaRIC4zfIHgP9+7+7j777//zurVqyMlEiD4WG3Wj3onJCRQoUIF1q9fz+rVq8nIyCAuLo6FCxfy0ksvMX36dNasWUNaWlq2tjMyMnY5X3JycmTG14Gwbt26yMzWqlWrRkK2TFWrVmXatGmEw2GWLVsWCakyZQZnEAQCycnJbNu2bZeaqn+VGUAULVo0W5sFCxakXLlyu8zKzHyt1qxZEwko/mrevHnUqFGDLl26RJ7zp59+mqeffprChQtHar6ed955kdBldzLLZ2zdupVt27b9o4+6A3z//fe88sorzJgxg7Vr1+7yumd+f8QRR9C+fXveeustvv7668hM7CpVqtCoUSPatm0bmXXXsGFDWrRowbhx45g4cSITJ04kLi6OGjVq0KRJE9q2bUu5cuWAnc9dRkYGRx999G77OG/ePJo0acJVV13FxIkTWbRoEa+88gqvvPIKBQoUoG7dupx99tlcfPHFe30+sgZLmX8EyVS5cuXI7OpM4XCYgQMHMnz4cJYtW8amTZt22b87mYF0ptKlSwNkG3f/9Fp2J+t7xssvv8zLL7+8yzGZM07/iax/IMl8P9i4cSN9+/ZlwoQJrFy5cpeapXt6rv5q1KhRvP322yxcuHCXWe1Z2zj//PMZNWoU33zzDe+++y7vvvsuCQkJHHXUUTRr1ozLL7880rfc/HzmRtayCb/99luuHptbrVu35o033uCPP/5g4sSJXHDBBZF6rxUrVuSkk04CgkD9jjvu4PHHH2f+/PnccsstQPBHr5NOOom2bdtGyjgsWbIk0v7hhx+e7XyNGjXapQ85fW2y+uvPQuXKlSP3M38eD+R7gCRJBztDW0mS9uLbb7+NzFQbPXo0t956KxB8/HxPMyfLly+/y3+qM+Xko6B/bXflypW0a9eOtWvXAsHiVaVLl2bTpk2Reo67k5f/Gd5diJx1saG/BrrALjOudnfM7mQGELt7bnfXj0yFCxeOzDz7q8wZYkcccQTjxo1j1KhRTJkyhe+//541a9bw+eef8/nnn/Pxxx/Tu3fvPZ7jqKOOipQpmD9//h5DjkWLFlG5cuVd6hJnNWfOHDp06MC2bdsioWqJEiWy1RzNqnv37px//vl88MEHTJ8+nfnz57NkyRKWLFnCsGHD6NevH8cccwwJCQn07t2bL774gnHjxvHNN9+wcOFC5s2bx7x58xg+fDgDBw7MFuAkJCRkm+GbVWb4VrZsWd5//33Gjh3Lp59+ynfffcfy5csjIfLo0aN588039/hzlDVY+utru7vX9ZlnnqFv375AEODXr1+fxMRE5syZs0uAm1VOxt0/vZa/U7ly5Ugw/lc7duz4R7O0M2fsJyUlUb16dcLhMNddd11khnq5cuWoXbs28fHxkRqtOTF48GB69OgBBD8v9erVo2DBgixatGiXULRIkSIMGDCASZMmMXHiRL799luWLl0amQ06YsQIBg0alO0PWDn5+cyNKlWqUKhQIbZt27bXmbppaWn8/PPP/6iEQrVq1TjppJP48ssvGTt2LOeffz6TJk0Cglm2WcdYhw4daNSoESNHjmTatGnMnj2b5cuXM2zYMN5//32effZZmjdvnu3nYW/va5C71yarv479rD8bf913IN4DJEk62BnaSpKUQy1btmT48OFMmTKF4cOHc84550RmOFWsWDFyXIMGDfjf//6XozbXrl3Lpk2bIjMy09PTWbp0KRCEKXFxcYwfPz4S2Hbp0oWuXbsCwcdlM+/HglKlSlGyZEnWr1/PokWLIrOEM2WuSh8XF/eP67tmVa5cOebPn88ff/zBqlWrIuHXhg0bdpmNCcGszQULFlC8ePEc1QwtUqQIbdq0oU2bNkDwsfn//e9/jBs3jnHjxjF//nxq1qy528eeffbZDB06FAgWEXrmmWd2Oea3337jsssuo3Dhwlx33XV7XNhsxIgRkdmfjzzySKSWbb9+/SI1gP+qbt261K1bFwjqJH/22WfccccdbN26lddffz1b4HzyySdHFmDbvHkz48aNo3v37qxZs4Z33nmHu+66i0qVKrFo0SLC4TCvvfba34YtSUlJXHDBBVxwwQVAMHvypZdeYsCAAXz77bd8/vnnnH766bt9bNYQc+nSpZEF8NLT01mwYMEux7/zzjtAEBiNHz8+8hH48847b6+hbU79k2vZnazvGeeccw4333zzP+7jX33yySeR2bpnnHEGhQoVYu7cuZHA9qSTTqJfv37ExcWxfv36SMmMnMh8vkOhEB988EFkNnTnzp35+OOPdzk+Li6OZs2a0axZMwDWr1/PO++8wzPPPMPixYsZM2YMV155Za5/PnMqMTGRM844gw8++ICFCxfy6aef7rY+99ChQ3nggQc47rjjuP/++/c42/fvtGnThi+//JIpU6bw2WefsWbNGuLi4nZbQ/yII46IzLJNS0tj+vTp3HnnnaxevZoXX3yR5s2bZ5ttvnTpUo455pjI94MGDWLz5s2UKlWKSy65JNevTaa//vEn6/eHHXYYwAF9D5Ak6WBnTVtJknLh/vvvj9QSvO+++yL1CkuXLh2ZdTRp0qTIf3bD4TD/+9//6NKly26D3IyMDP7v//4v8v3w4cMjC/Jk1oDNOsMp86PbGzdu5NVXX41s392CW3uSOZtzd4s5/VXWj/7/3fGhUIjzzz8fCGYHDxw4MLJvwoQJkRl/TZs23eMK7/uiYcOGkfuZiymFw2F69+692xlpmaHRypUrGT16dGT7+++/T+fOnbn77rtZv349O3bs4JprrqFx48bZPrpeqVKlbOHW3j5Kfsopp0SCoTFjxvD888+zY8eOyP7FixfTqVMnNm3axKpVqzj00EP32NbuxsGqVauyPc+Z42DkyJGcf/75tGjRIjKeChQowCmnnBIpIZHZ79dee42WLVvSpk2bSChctGhRTj/99MiMxsxjmzdvDgTBaeaiRwCff/45nTp14o477oh8jPu2227jjDPOyLbwXNmyZbMFZXt77rK+rv369Yu8lv369dttTeTM56dgwYKR2sgffPBBJLTcuHHj385Q3JN/ei27U7t27UgQNnLkSNasWQMEs4q7d+9Ot27deO211/apvxAssHb77bcDweuZGQpmHUfJycnExcWRnp7Oc889F9me9f0k6+zvrO8Bme0kJCREAvJp06YxZcqUyDGZr9MTTzxBixYtuPHGGyOlXUqWLBn5WYSdz19Ofz731rc96datW+STE/feey/Tp0/Ptn/UqFGROtUrVqzYpSzHX+3tvbRZs2aULVs22+Jpp5xySrZ6zFOnTuWiiy7i1FNPjdTuTkhIoEGDBlSoUCFbe2eeeWbkfP37948shvn111/z4IMP0qtXL2bPng3k7rXJasSIEZFazps2bcoW/h533HHAgX0PkCTpYOdMW0mScuHwww/n+uuv59lnn2XFihU8+eSTkf+Q33fffbRt25bNmzdz/vnnU6NGDdavX8/y5cuJj4/n6aef3qW9kiVLMnr0aD799FMKFSoU+Sh9kSJFIoucNWzYkMTERFJTU3n00UcZPnw4ixcvpnHjxqSmpjJ79mz69OnDd999R58+ff72Go444gjmz5/P9OnTOe+882jevDndunXb7bFly5alWLFibNq0iREjRrBgwQIuvvjibAvQZHXTTTcxffp05syZw8MPP8zAgQMJhUKRELtixYqRj+1Gy8UXX8zLL7/M77//Tv/+/fnkk08i+w499NBdFmm79tprGT9+PIsWLeK2227j//7v/4iPj2f+/PmEw2HatWsXCTpq1qzJlClT6NWrF8OGDYuUpcj8eHXDhg3/diZer169uOmmm5g6dSp9+vShX79+VKtWjY0bN/Lzzz+Tnp5OXFwcd9xxB02bNt1jO40bN6Zfv34A3HzzzVSrVo0FCxbQrl07hg4dyvr16+nRowdfffUV11xzDY888gjr16/njDPOoEaNGsTFxbFo0SI2bNhAYmJiZEbvCSecQJ8+fdi2bRuNGzemWrVqhMNh5s+fz7Zt2yhcuHBkEahWrVpFPsbdq1cv3n33XYoWLRpZoO2MM86IlFGoX78+H3zwAYMGDWLy5MkceuihbN26NfLc1ahRg1NOOWWP19usWTOqVq3K4sWL+eijjzj99NMpWrRopNbpvHnzdnl+3n//fVatWkWLFi1ISkpi+fLldO3alT59+rBp0ybOPfdc7rzzzr2+XrvzT69ld+Lj43nggQe44YYbWL58OWeeeSbVq1dnxYoVrFmzhoIFC3LVVVfluL177rmHwoULk5GRwa+//hqpQZqcnMxzzz0XeV2qVatGpUqV+Pnnnxk7diyLFy9m3bp1kdd56NChTJ48mUsvvZQXXniBatWqRc7Rs2dPhgwZQvfu3WncuDGzZs0iNTWVs88+m1KlSrFw4UJuuukmevXqBcAll1xC586dOe6443jzzTdZsmQJTZo0oVKlSuzYsSMSqJctWzay4FZufj6rVKlCfHw86enp9O3blylTptCpU6dsYXBWlStX5qWXXqJr166sWbOGK6+8kgoVKlC2bFl+/vnnyCcaDj30UF566SUKFy681+d8b++liYmJXHLJJbz44ouRIPSSSy7J9vh69eqxYcMG1qxZQ8uWLalZsyZJSUn8/PPPkRC/ffv2QPC75+abb6ZXr178+OOPnH766VSoUCHyc1CpUiW6dOkCkKvXJmspn3LlynH++edTp04dFi5cGHk+WrduHQmwD+R7gCRJBztn2kqSlEvXXHNNZNGswYMH88UXXwBQq1Yt3n33Xc4991xKlCjBvHnz2Lx5M40bN+aNN97grLPO2qWtIkWK8Oabb1KpUiUWL15MwYIFadiwIQMGDIiUEDjiiCPo06cPRx55JPHx8axevZq2bdvyxBNPcPPNN1OuXDl27NjBH3/8kaP+33///VSvXp3ExERWr16914+3xsfH89hjj1GxYkUSEhJYsWLFXutJFi1alLfffpubbrqJmjVrsmLFCn799VeOOOIIOnfuzHvvvbfH2p37qkSJErz11lucdNJJFCxYkN9//53q1avz+uuvZ1v0LWsfBw8ezNVXX02lSpVYunQpy5Yto169ejz00EN07949cuxdd93Fo48+SoMGDdi4cSM//PADa9eupW7dutx+++289NJLf9u/4sWL8/rrr/Pcc8/RrFkzihQpwty5cyMz+Vq3bs37779Px44d99rOySefzKOPPkrVqlVJT09nw4YN3HTTTdx2223cddddJCcns3XrVtLT06lQoQLDhg2jTZs2lClThp9++om5c+dSokQJzjnnHN5+++3IYkj16tXjnXfe4YILLqBo0aLMmTOH+fPnU65cOS6++GKGDx8eGe8JCQm8+uqrdO3alerVq7Ny5UoWLFhA9erVueOOO3j22Wcjs/yuvPJKXnjhBU477TRSU1P54YcfWLFiBTVq1KBz584MGjQoMmt9d+Lj4+nXrx/NmjWjcOHCbNy4kTJlytCvX79si9Zl+u9//8vFF19M6dKlWbt2LWXKlOGtt96ic+fONGvWjKSkJH7//fe9Lhy3J//0WvakcePGDBo0iKZNm1KgQAFmzZpFRkYGLVq04O23347MbsyJmTNnMm3aNL7++ms2bNhA7dq16dq1K2PGjMk2MzwxMZG+fftyyimnULRoUVasWMEJJ5zAgAED6Ny5M0cddRTx8fGsX7+e+Ph46tatyw033EBycjLp6emsXbuWpKQkrr/+ejp27Ei5cuXYuHEjiYmJ/N///R+dOnWiTZs2FC5cmA0bNhAfH0+zZs148803adGiBXFxccycOZOlS5dSuXJl2rdvH/mDCOTu57NMmTLcd999lC1bllAoxKpVq/623m2DBg0YN24c3bp145hjjmHLli38+OOP7Nixg2OOOYY77riD0aNH56gswt+9l7Zu3ToyOzY5OXmXP8oULVqUd999l44dO1KpUiWWLFnCzJkzSUpK4vTTT6dfv35ceOGFkeM7derECy+8wIknnkh6ejrz58+nfPnydO7cmREjRkRqAufmtcmqffv2dO3aldWrV7Nx40YqVKhA165due+++yLHHMj3AEmSDnahsJ9JkSTpgGvXrh3Tpk2jQoUKkQVqJEn5x7Jly2jRogUZGRl06NCBe+65J6+7tIuvvvoqMpv3scce223NXUmSlDecaStJkiRJUbRt2zZ69uxJRkYGiYmJkWBUkiQpp6xpK0mSJElRsGLFCtq0acPGjRsji4V17tx5l4XFJEmS/o6hrSRJkiRFQSgUYtOmTezYsYMqVarQtm1b2rVrl9fdkiRJ/0LWtJUkSZIkSZKkGGJNW0mSJEmSJEmKIYa2kiRJkiRJkhRDrGkrSZIOeu3atWPatGk5OrZBgwb0798/V+1v3ryZfv36cdRRR9GsWbN96SJNmzZl+fLl+3T+rCZPnswbb7zBrFmz2Lp1K8nJyTRo0IDOnTtTs2bNyHE7duzgjTfeYOzYsSxevJhwOEzVqlW59NJLufzyy4mL2/3f/tPT02nbti3fffcdAF26dKFr165/268xY8YwePBgZs+ezbZt2yhfvjxnnnkm119/PcWKFdvl+E8++YQHH3yQ5cuXAzBx4kQqVqy4S18GDBjAiBEjWLx4MWlpaRx22GE0adKE66+/nuTk5L/t1+LFi3nuuef46quv2LRpE4cccgjNmzenW7duFClSJNuxU6ZMoW/fvsyaNYvU1FSqVKlCmzZtaNu2bbbjwuEwAwYMYMiQISxdupT4+Hjq1KnD9ddfT8OGDf+2T7mVkpICQKtWrXj88cej3n5W0RqnkiRJBztDW0mSdNA78sgjs32/atUqli5dCkDVqlUpW7bsHo/NiQkTJvD888/TqlWrfQ5to+H999/nzjvvBCA5OZmjjjqK+fPnM3r0aCZNmsSgQYOoVasWaWlpXH311Xz99dcAVKpUiVAoxJw5c+jZsycLFy6kR48euz3Hiy++GAlsc+rpp5+mb9++kX5Vq1aNn376iddee40vv/ySd955h6SkJABWr17NI488wocffvi37fbo0YN3330XgIoVK1KsWDHmzJnDm2++ySeffMLw4cN3CV6zWrFiBW3atGH9+vUUKVKE6tWrs2DBAt544w1mz57Nm2++GQmvJ02axI033khGRgZly5alWLFizJ8/n549e7JmzRpuvvnmSLv/+9//eO2114BgfG3ZsoVp06bxzTff8PLLL3Pqqafm6vn7Ow0aNACgWrVqUW1XkiRJ+4/lESRJ0kGve/fu9O/fP3Jr3759ZF+nTp2y7evevXuu289JwLi/paam8tBDDwFQvXp1Jk2axLvvvsvQoUMJhUJs27aNF198EYCxY8dGAtvWrVvz0Ucf8dFHH3HNNdcAMHDgQGbPnr3LOX744QdefPFFateuneN+rVixgldeeQWAWrVqMXHiREaOHBnpy6xZsxg0aFDk+Ntuu40PP/yQWrVq7TUA/+GHHyKB7UUXXcSECRMYMWJEJGxesmQJw4cP32vfnn/+edavX0/p0qWZOHEiI0aM4I033gBg2rRp2V7Xxx9/nIyMDE455RQ+/fRTxo4dS7du3QB45ZVXWLVqFQDLly+nX79+ANx00018+OGHfPLJJ5xyyimkp6fz6KOP5vi5y6nMsdupU6eoty1JkqT9w9BWkiRpH3z66ad06tSJE088kTp16nDyySfTuXNnpk6dGjlm+PDhpKSk8PHHHwPw3nvvkZKSQp8+fSLHTJgwgXbt2nH88cdTr149WrRowRNPPMH69etz1I+UlBRSUlK46KKL9nrcunXraNWqFe3bt+eOO+6gcOHCkceXKlUKgJ9++gkg20zZNm3aEAqFAOjcuXNk+6hRo7K1v3XrVu644w7C4TAPPPBAjvoOMGPGDDIyMgC44IILIjNfmzRpwlFHHbXLuYoUKUKPHj0YNmzYXmc9b9++nfbt29O+fXuuvfbayDWcdNJJkWMyr3d3wuEw48ePB6BZs2aRUgrHH388hx9+OLAzjJ8zZ05kZvZFF10UmX178cUXA5CWlsZHH30EwEcffRS53ksuuQSAuLg4LrjgAgAWLlzI/Pnz99iv9PT0yGv+2GOPMX36dK688krq16/PcccdR9euXfn111+zPSbz+LvvvjvSh8xtTz75ZLZr7tChAykpKRxzzDEsWLAgsu/HH3+ka9euNGzYkDp16tC4cWMeeuihHI/Tzz//nE6dOtG0aVPq1q1Lw4YNufrqq5kwYUKOHi9JknSwsTyCJElSLr3wwgv07t0bgGLFilGzZk2WLFnCxx9/zMcff8w999xDhw4dKFOmDA0aNIjUyy1TpgzVqlWjQoUKQPZyBYcccgiHH344s2fP5vXXX+eLL75g2LBhxMfHR6XP5cqV2+0s4QULFrBu3ToASpYsCRAJFSEI8jIVLlyYxMREUlNTd5lp+9hjj7FkyRI6d+5MvXr1ctyvPZ0LoHjx4gDMmzePtLQ0EhIS6N27d6RUwt40aNAgUhYgq6y1i/dW03bFihVs3LgRgCpVqmTbV6VKFZYtW8acOXMi/cu6L1P58uUpWLAg27dv3+XYwoULc8ghh0SOrVq1auT+nDlzstUXzirrePjhhx8YNGgQlStXplixYqxatYrx48czd+5cRo0aRcGCBXfbRvPmzWnXrh39+/enX79+NG/enPr16/P222/zxRdfAEFpierVqwNBrd7OnTuTmppKsWLFSElJYdGiRQwYMIBp06YxZMgQChUqtMfncsKECXTp0oVwOMwhhxxCnTp1WLduHVOnTmXq1Kn06NFjl7q/kiRJBztn2kqSJOXCzJkzIzNlGzRowOTJkxk+fDgTJ06MBG1PPvkkv/zyC6eddlq2xZgaNWpE//79I7Ni+/btS1JSEhUqVOCjjz5i+PDhdOnSBQiCu8wSBXtTtWpVqlatussiXDmxYcMGunbtGglLL7zwQgDq1KkTOWbw4MGR+0OGDCE1NRWAtWvXRrZPnDiRIUOGkJKSwo033pirPtSuXTsyC3bkyJFs3boVCMoifP/990BQ2iEzQM1JYLsnP/74I0888QQAiYmJtGzZco/HZr2+vy6Elhkm//bbb397bOb3mcdkBuR7Ou6v7e3Nt99+y0MPPcQHH3zA5MmTOfvsswFYtmwZ77///l4fe+edd1K7dm0yMjK4++67WbBgAf/73/+AYBxkjtH09HR69OhBampqZJwOGzaMsWPHUrJkSebPn/+3C469++67hMNhqlSpwuTJk3n77bcZN24c1113HbVq1WLmzJk5ul5JkqSDiaGtJElSLowcOTISct54440ULVoUCGZtduzYEQiCrsyPw+/NmDFj+PHHH5k0aVJkVuSxxx4b2b9ixYq/bePDDz/kww8/jMz8zanly5dzxRVXsGjRIgBOO+00WrduDQRlCjID6MGDB9O8eXPOOeccHnzwwUhZhR07dgBBcPnf//6XxMREnnzyyVyHqpUrV46cd/bs2ZxxxhlcdNFFXHbZZdlmlWaGxfvqiy++oEOHDmzbtg2Abt267XE2K8Aff/wRuZ9Z7iBTQkJCtmO2b9++x2MTExOzHZP59a8zqDPb/Gt7e1OzZs1I0B4XF8d1110X2ZcZeO9JUlISzz77LEWLFmXJkiVceumlbN26lWrVqnH//fdHjps5cybLly8HoGXLlpHZyeXLl6dJkyYAjBs3bq/nypyF++uvv/Lqq68yd+5c0tPTufXWW3nvvfd47LHHcnS9kiRJBxNDW0mSpFxYvHhx5H61atWy7cv60fglS5b8bVsLFy7kjjvuoEmTJtSuXZuUlBSuvvrqyP6spQOiafbs2bRu3TpSs7RJkyb06dMnMuM1MTGRAQMG0Lp1a5KTk1mxYgUJCQk89dRTkY/MZ4a39957L+vWraNLly57rTG7Nz169ODOO++kQoUKbNy4kfXr19OpUycuu+yyyDGZ59sXI0aM4Nprr2Xz5s0AdO3a9W8X5cpaWiA9PT3bvrS0tGzHZC0N8NdjM8Pmvx6b2cZf2/xre3tTo0aNbN9n1toFdqlruzuHH354ZGG2rVu3Eh8fz7PPPpvtuc4MbAFefvnlSC3clJQURowYAbDXGrwAXbp0oWzZsuzYsYOnn36aCy64gOOPP56OHTvy3nvv7fJcSJIkyZq2kiRJ++yvNVizBnZ/nXH5VytXrqRdu3aRj8JXq1aN0qVLs2nTJubOnRv9zv7phx9+oEOHDmzZsgWAa665httuu22XmZ8lSpSgZ8+e9OzZM9v2zIWrKlasyPLly/nkk08AGDBgAIMGDdrlfP369WPo0KHcc889kY/v/1V8fDzXXHMN11xzTbbtt912GxDU2v1rOYGcGjhwYOQaChcuzMMPP7zXsgiZypYtG7m/YcOGbPsySzWUK1dul2Mz92XatGnTbo/d03FZj/07mSF7psxZvbvbtydZF51LT0/n22+/JSUlZbfHVq5ceY9927Fjxx5nWR9xxBGMGzeOUaNGMWXKFL7//nvWrFnD559/zueff87HH3+c65nikiRJ+Z2hrSRJUi5Uq1aNTz/9FICffvopW4i1cOHCbMftzfjx4yOBbZcuXejatWtke+b9aFu8eDHXXnstW7ZsITExkccee4zzzjtvl+PC4TDLly9nwYIF1K5dOxI0Llu2jNWrVwNw3HHHZQut16xZs9tzbtmyhS1btkTKEuzO2rVrWbBgASVKlIjM1g2Hw3z77bcA1K9ff5+ud+zYsTz88MNAEIS+/PLLOZ4NXK5cOcqWLcuaNWsiJSQyZb7OdevWBbLXAF60aFFkIbbly5dHSh1kPXbIkCFs376dX3/9lcMOOyxbm1mP/TtZZ33/9ftDDz30bx8/YcIE3n77bQBOOukkvvzySx5//HGOO+64SOmIrLWSzznnHG6++eYc9e2vihQpQps2bWjTpg0AP//8M//73/8YN24c48aNY/78+XstVyFJknSwsTyCJElSLlxwwQWRWYwvvfRSZOGsNWvW8MYbbwDBR+HPOuusyGMyZ7H+8ssvkW1ZZ0KWLl0aCGZfvvrqq5Ht69ev/9v+nHXWWZx11ll069btb4+97777Im327t17t4EtBB93P+OMM7juuut46aWXgKBUQ69evYBgxuq5555LxYoVmTdv3m5vmbp06cK8efO46KKLWLVqVaS/mWFqamoqF1xwAe3bt+fee++N1ModOHBg5CP+mTVvc2PdunU88MADZGRkULJkSQYOHLjHwHZ3/QI499xzAfj4448jC4hNnTo1UjIg8/mrWrUqtWvXBuC9996LlLUYMmQIEJQ7aN68OQDNmzenQIECAAwfPhwIZrhm3j/mmGOoVKlSjq5x1qxZTJo0CQhC7qxj54QTTtjrY1euXEn37t2BoFbtK6+8Qo0aNdi+fTu33HJLJGyuXbt2JFgeOXJkJJxPTU2le/fudOvWjddee22P59mxYwfXXHMNjRs35uWXX45sr1SpEieeeGLk+7/OWpckSTrYOdNWkiQpF2rVqkXXrl3p3bs306dP5/TTT6dChQosXryYrVu3kpCQQM+ePbN9ZP6II45g/vz5TJ8+nfPOO4/mzZvTsmVLEhMTSU1N5dFHH2X48OEsXryYxo0bk5qayuzZs+nTpw/fffcdffr02WN/MmdX/l3N18mTJzN9+nQAChQoQL9+/ejXr98ux/Xv35+UlBTOOeccxowZw4ABA/jyyy/Ztm0by5cvJxQKcd9992W7vpxKTU2N9DdzVmViYiI33ngjDzzwALNmzeKMM84gOTk5EvxedNFFnH766ZE2WrduHVmgLbNGLUCbNm0iJSmee+45xo8fHwmoExMTuffee3fpz5FHHkn37t132y+A6667jrFjx7Jy5UqaN29OxYoVI3WAW7RoQaNGjSLH3nvvvXTo0IEvv/ySxo0bU6RIkUibt99+OyVKlACgVKlSdOnShV69etGnTx9Gjx7Npk2bWLNmDQULFowEqTlRo0YNunXrRp06dfj9998jdZRTUlIigfPupKenc/vtt7N+/XrKlCnDfffdR1JSEo8++iht2rRhwYIFPPLIIzz00EPEx8fzwAMPcMMNN7B8+XLOPPNMqlevzooVKyJ9vuqqq/Z4rqSkJGrWrMmUKVPo1asXw4YNi5QB+emnnwBo2LDhHksySJIkHawMbSVJknLpxhtvpE6dOrz11lvMnDmTefPmkZycTJMmTejYseMuH2+///77uf/++1m6dCmrV68mKSmJI444gj59+vDss8+yZMkSVq9eTdu2benatStTp07lvvvuY+3atfzxxx9R6XPmTFGAP/74g2nTpu31+CeeeILq1avzwQcf8Msvv5CQkMDJJ59Mp06daNiw4T/uT9YaupdffjnFihXjrbfeYtGiRWzcuJFatWpx6aWXRj5On2nNmjWsWrVql/aylmfYsWMHv//+e7Z9eyrfsLd+JScn8/bbb/PMM88wZcoUFi5cSIUKFTj//PN3Wcjs+OOP58033+T5559nxowZbNiwgdq1a3P11VfvMqO5U6dOlCpVigEDBrBw4UKSkpJo1KgR3bp1i5RWyIk6depwxx130KdPH1asWEHJkiVp2rQpd9xxxx7rywK8+OKLkQD/gQceIDk5GYB69erRsWNHXnnlFYYMGcLJJ5/MOeecQ+PGjRk0aBAvvfQS3333HbNmzaJkyZK0aNGCzp07U6tWrb3286677qJ69eqMGDGCBQsWsHz5cooXL07dunU588wzad++fY6vWZIk6WARCvtZJEmSJB0g4XCYE044gfPPP58ePXrkdXciYrVfu5M5K7VVq1Y8/vjjedwbSZIk7Q/WtJUkSdIBM23aNDZt2pSrGaUHQqz2S5IkSQcnQ1tJkiQdEGlpaTz11FOUL1+eFi1a5HV3ImK1X5IkSTp4WdNWkiRJB0RCQgJDhw7N627sIlb7JUmSpIOXNW0lSZIkSZIkKYZYHkGSJEmSJEmSYoihrSRJkiRJkiTFEENbSZIkSZIkSYohhraSJEmSJEmSFEMMbSVJkiRJkiQphhjaSpIkSZIkSVIMMbSVJEmSJEmSpBhiaCtJkiRJkiRJMcTQVpIkSZIkSVJ0jRsH5cpBmza5e9y2bdClC1SsCMWLw4knwoQJ+6ePMczQVpIkSZIkSVL0PPkkdOsGNWrk/rH33w+ffQZffgnr1kGHDnDBBbB6ddS7GcsMbSVJkiRJkiRFT8GCMG0aVK+++/1DhsAxx0CRIlCtGrz88s5933wDZ50VzLRNSICrr4atW2H+/APS9VhhaCtJkiRJkiQperp1gxIldr/v66+hY8dgNu7GjfDmm3DrrTB1arD/3HNh5EhYuBC2b4fXX4fDDoP69Q9c/2NAQl53QJIkSZIkSdJBol+/IJg988zg+0aNoHVr6N8fGjaEW26B77/fOUu3dGkYMSKYlXsQMbSVJEmSJEmSdGAsXBgsLFaw4M5tGRnQokVw/+GHYcYMmDsXKlWCwYODkPeHH+Dww/Omz3nA8giSJEmSJEmSDoxCheD664PSB5m3HTtg1Khgf+/ecOedkJIChQsHNW2rVoV3383bfh9ghraSJEmSJEmSDowjjghmzWb1yy+Qnh7cT0/feT/TH38cmL7FEENbSZIkSZIkSQfGf/4Dn38e1LbdsSOoX3viiTBsWLD//PPhmWdg8eJg/1tvBSUVWrbM024faKFwOBzO605IkiRJkiRJyicy69WmpgZfExODr9u3B1+HDoUePWDJEjjsMOjSJViADGDTJrjnHnj/fdiwISiT0LMnnH32Ab2EvGZoK0mSJEmSJEkxxPIIkiRJkiRJkhRDDG0lSZIkSZIkKYYk5HUH9rc1azbldRcURXFxIUqVKsK6dVvIyLCyh/45x5SiyfGkaHNMKZocT4o2x5SiyfGkaHNM5U9lyxbL6y4cMM601b9KXFyIUChEXFwor7uifMIxpWhyPCnaHFOKJseTos0xpWhyPCnaHFP6tzO0lSRJkiRJkqQYYmgrSZIkSZIkSTHE0FaSJEmSJEmSYoihrSRJkiRJkiTFEENbSZIkSZIkSYohhraSJEmSJEmSFEMMbSVJkiRJkiQphhjaSpIkSZIkSVIMMbSVJEmSJEmSpBhiaCtJkiRJkiRJMcTQVpIkSZIkSZJiiKGtJEmSJEmSJMUQQ1tJkiRJkiRJiiGGtpIkSZIkSZLyn3HjoFw5aNNm78dlZEDPnlC1KhQrBiefDJ99dmD6uAeGtpIkSZIkSZLylyefhG7doEaNvz/2mWfgtdfggw/gt9+gRQu48ELYuHG/d3NPDG0lSZIkSZIk5S8FC8K0aVC9+t8fGx8PTz0FtWtDgQJw++2wbh3MnLn/+7kHCXl25gMkLi5EXFwor7uhKImPj8v2VfqnHFOKJseTos0xpWhyPCnaHFOKJseTos0xJbp1y/mxN9+c/fuffw6+HnZY1LqTW6FwOBzOs7MfAOFwmFDI0FaSJEmSJEk66HToANu3wzvv5Oz4P/6Apk2hWjXo33+/dm1v8v1M23XrtjjTNh+Jj4+jePFCbNy4jfT0jLzujvIBx5SiyfGkaHNMKZocT4o2x5SiyfGkaHNM5U/JyUX27wk2bQpq2cbHw0sv7d9z/Y18H9pmZITJyMjXk4kPSunpGaSl+aar6HFMKZocT4o2x5SiyfGkaHNMKZocT4o2x5Ry7LffoHlzqFoVBg6EQoXytDsW9pAkSZIkSZJ08Nq+HVq2hOOOg3ffzfPAFgxtJUmSJEmSJB1Mli+HI4+ExYuD73v1gqQkePlliIuNuDTfl0eQJEmSJEmSdJApWDD4mpoafB0xIvi6fXuwbd68YNExgNdfh2XLoHDh7G3897/BLQ/ERnT8p1tugVCWNcMmTYIGDaB4cahdOygnIUmSJEmSJEl7tX17cEtPD26Z3wNUqQLhcDDbFmDhwiDIzTwm85ZHgS3EUGj7/ffw1ls7v1+xAs4/Hzp3htWr4bnn4Npr4euv86yLkiRJkiRJkrTfxURom5ERhLO33rpz28CBULMmdOwYzGZu1iwIcV99Ne/6KUmSJEmSJEn7W0zUtO3bNwhm27bdOev4m2/g2GOzH3fssTB4cO7bz1pyQfmHr6uizTGlaHI8KdocU4omx5OizTGlaHI8KdocU/o3yvPQdtUquP9+mDw5+/a1a6FixezbSpWC337LXftJSfH/qH+KLfHxweTwxMR44uJ819U/55hSNDmeFG2OKUWT40nR5phSNDmeFG2OKf3b5Xloe+utcPXVUKsWLFmSfV84/M/b37Ej3b+o5CMZGcGgSE1NJz09I497o/zAMaVocjwp2hxTiibHk6LNMaVocjwp2hxT+rfL09B24kSYOhVmztx1X9mywWzbrNauhUMOyf15ohH+Kvb4uiraHFOKJseTos0xpWhyPCnaHFOKJseTos0xpX+jPF2IbMCAoDxC5cpQpszOGrZlykCdOkFd26ymT4cTTzzw/ZQkSZIkSZKkAyVPQ9unn4b58+H774PbmDHB9u+/DxYlW7IEXn0Vtm8P9o0ZA5065V1/JUmSJEmSJGl/y9PyCMnJwS1TWlrwNXMBsg8+gG7d4MYboUqVYGZuvXoHvJuSJEmSJEmSdMDk+UJkWVWpkr3OyGmnBbNuJUmSJEmSJOlgkaflESRJkiRJkiRJ2Rna6oCZNu1Lrrvuapo1O5WzzmrCAw90Z82a1QCMHfsBV13VhjPOOIVWrc7hiSceZuPGjXtsa/nyX+jSpROnnno8jzzyQLZ9Q4e+w3nnnclZZ51O374vZNv3+usvc/HF57Jt27aoX58kSZIkSZIUDYa2OiAWLVrAXXfdwm+/raFTpxs44YSTmDBhHD173seMGd/zyCMPsHHjRq677kZq1Ehh1KgRPP/8M7tta+LEibRr14bFixfusu/XX5fTu3cv6tSpx+mnN6N//37MmPE9AL/88jMDBrzJzTffTqFChfbn5UqSJEmSJEn7LKZq2ir/2rBhAxdf3JpGjRpz9NH1OffcC/j44wnMmzeXWbN+BKBFi3O47LIraNiwEVOnfsacObN229aMGTO4/PK21K59NLfe2iXbvrlz5xAOh7n22s6UK1eeUaPeY/bsmRx99DE8/fQTNGhwIo0aNdnflytJkiRJkiTtM0NbHRD16x9H/frHkZqayurVqxg3biwAjRufTr16xxAXF8e3337NsmVLmTLlEwBOOOHE3bbVpUsXtmxJZdq0abvsS0tLAyAxMYnExKQ/t6UyceJH/PDD9zRocDKtWp1DkSJF6dLlZk46qeH+uFxJkiRJkiRpnxna6oD68ccZdOvWmVAoxHnnXchtt91NQkICd955L0899ThXXHExAA0bnsoNN9y02zaSkpLYsiV1t/uqVKkCwJQpn3LIIeUAKFfuUPr0eZqaNY/k008/5tFHn+KTTybyyCMP8N57Y0hI8MdAkiRJkiRJscO0SgdU9eo1eOKJZ/jii88ZMeJdNm7cQOvWbXnmmf9RrdoRtG3bgRkzvmX48KE888yT3HHHvblqv2bNI2ne/CxeeOFZAI499nhmzfqBEiVKULPmkSxdupjTTmtCKATjxo1l2bIlVKtWfT9cqSRJkiRJkrRvDG11wKSlpVG4cBFOOaURp5zSiO+++5pPPvmYUCiOP/74g44dO3HqqY1p2rQZEyeOZ+TI9+jS5ZZcLxp2//0Pc8UV7UhPTwfguuuu5vnnX+aDD96nQIGCABQsGLS5adPm6F6kJEmSJEmS9A/F5XUHdHDo0+cZTj/9ZMaO/SCyLT094897YQBSU1P/3J5OWloaoVAIgPXr17N06RLWr1+f4/PVqJFCzZpH0qvXE5xzznnUrXs0RYsWY/PmzYTDYTZsCNoqVqzYP742SZIkSZIkKZqcaasD4vTTmzF06Nv07fsCGzduYNmypfzyyzKOOKI6p512OpMnT6Jv3/9j9epVzJ49ky1btnDSSQ0pVKgQgwa9Rb9+r3D11ddy3XXXM378eL755ntWrPgVgPnz5/Hyy/9HsWLFufzyKyPnHDlyOCtW/EqvXr0BOPHEkxk8eCDPP/8MP/zwPYceehiVK1fJi6dDkiRJkiRJ2iNDWx0QderU5cknn+Xll/+PV199iWLFitOkyRl06XIz5csfytatWxg+fCgvv/x/FChQkDPPPJtu3W7bbVuTJk3ivffei3y/cOFPLFz4E+XLHxoJbX//fR19+/4f3brdSvHiJQBo0OAkWre+gtGjR1KyZDL33NOD+Pj4/X/xkiRJkiRJUi6EwuFwOK87sT+tWbMpr7ugKEpIiCM5uQi//76FtLSMv3+A9DccU4omx5OizTGlaHI8KdocU4omx5OizTGVP5Ute/CUubSmrSRJkiRJkiTFEENbSZIkSZIkSYohhraSJEmSJEmSFEMMbSVJkiRJkiQphhjaSpIkSZIkSVIMMbSVJEmSJEmSpBhiaCtJkiRJkiRJMSQhrzug6Fi1ahUbN27I627sd/HxIUqUKMyGDVtJTw/ndXcOiOLFS1CuXLm87oYkSZIkSZIOEEPbfGDVqlW0bXMZmzZsyuuu7HchIC4uREZGmIMjsoViJYox8J0hBreSJEmSJEkHCUPbfGDjxg1s2rCJlKR6FEsqntfd2a9ChIiLjyMjPYODIbbdtGMj8zb8wMaNGwxtJUmSJEmSDhKGtvlIsaTilCxYOq+7sV+FCBGfEEd62sER2gKwI687IEmSJEmSpAPJhcgkSZIkSZIkKYYY2kqSJEmSJElSDDG0lSRJkiRJkqQYYmgrSZIkSZIkSTHE0FaSJEmSJEmSYoihrSRJkiRJkiTFEENbSZIkSZIkSYohhraSJEmSJEmSFEMMbSVJkiRJkiQphhjaSpIkSZIkSVIMMbSVJEmSJEmSpBhiaCtJkiRJkiRJMcTQVpIkSZIkSZJiiKGtJEmSJEmSJMUQQ1tJkiRJkiRJiiGGtpIkSZIkSZIUQwxtJUmSJEmSJCmGGNpKkiRJkiRJUgwxtJUkSZIkSZKkGGJoK0mSJEmSJEkxxNBWkiRJkiRJkmKIoa0kSZIkSZIkxRBDW0mSJEmSJEmKIYa2kiRJkiRJkhRDDG0lSZIkSZIkKYYY2kqSJEmSJElSDDG0lSRJkiRJkqQYYmgrSZIkSZIkSTHE0FaSJEmSJEmSYoihrSRJkiRJkiTFEENbSZIkSZIkSYohhraSJEmSJEmSFEMMbSVJkiRJkiQphhjaSpIkSZIkSVIMMbSVJEmSJEmSpBhiaCtJkiRJkiRJMcTQVpIkSZIkSZJiiKGtJEmSJEmSJMUQQ1tJkiRJkiRJiiGGtpIkSZIkSZIUQ/I8tJ0xA844A0qUgHLloHVrWLkSJk+GUAgKFsx+Gzo0r3ssSZIkSZIkSftPnoa2f/wBZ54JTZrA6tUwcyasWgXXXx/sr1wZtm/Pfrv00rzssSRJkiRJkiTtXwl5efKtW+GRR6BDB0hIgLJl4aKLoE+f6J4nFIpue7EmFIJ8fokHtRB/vsa+yPudz7GiyfGkaHNMKZocT4o2x5SiyfGkaHNM6d8oT0Pb5GT4z392fj9vHrzxRlAiAWDTJmjVCj77DAoUgNtug1tuyd0PW1JSfFT7HIsSEuIgBKFQiFA+fyfKvL5QXAjCedyZAyD0ZyKfkBBHYmL+H8t5IT4++MBBYmI8cXH5++dH+5/jSdHmmFI0OZ4UbY4pRZPjSdHmmNK/XZ6GtpmWLoUaNSAtDTp1ggcfDGrd1q0LN98MgwcHNW4vvRRKloSOHXPe9o4d6fn+LyppaRkQhnA4TDicz5PMMBAXIpwRJnwQpLbhcBjCwWucmpqe193JlzIygnGUmppOenpGHvdG/3aOJ0WbY0rR5HhStDmmFE2OJ0WbY0r/djER2lauHNS3XbAArrsO2rWDQYOCoDbTmWdC587Qr1/uQluAfJ9jhg+KSacHrTB/vsa+yPudz7GiyfGkaHNMKZocT4o2x5SiyfGkaHNM6d8oTxciyyoUCmbbPvIIvP02rFmz6zFVqsCvvx7wrkmSJEmSJEnSAZOnoe2kSZCSAhlZZqnH/dmjMWPgxRezHz9nDlSrduD6J0mSJEmSJEkHWp6GtscdBxs2wF13wdatwezaBx6ARo2C2rW33Qbjx0NqKnz0Ebz+Olx/fV72WJIkSZIkSZL2rzytaVuiRBDGdu0KZcpA0aLQtCm8+ipUqADPPhvsW7YMypeH556Diy7Kyx5LkiRJkiRJ0v6V5wuR1a2bfcGxrDp1Cm6SJEmSJEmSdLCImYXIJEmSJEmSJEmGtpIkSZIkSZIUUwxtJUmSJEmSJCmGGNpKkiRJkiRJUgwxtJUkSZIkSZKkGGJoK0mSJEmSJCn/GTcOypWDNm32flxGBnTvDtWqQXIynHUWLFp0YPq4B4a2kiRJkiRJkvKXJ5+Ebt2gRo2/P/aFF2DQIBg9GpYtCx7TqhWEw/u/n3uQkGdnPkDi4kLExYXyuhv7VZEihahdpxZVC1emWFKJvO7OfhYiPj5EenoYyLsfnAMleUdRtm1dR5EihUhI8G8s+0N8fFy2r9I/4XhStDmmFE2OJ0WbY0rR5HhStDmmRMGCMG0a3HQTbN++92P79oVbboGjjgq+f/RRKFUKvvoKTjpp//d1N0LhcB5GxgdAOBwmFMrfoa0kSZIkSZKk3ejQIQht33ln9/u3bYMiReDTT+HUU3dur1ULrr8eunY9IN38q3w/03bdui35fqbtsmVL+e/d91CrcH1n2uYzm3ZsYPbW73j48cc4/PDKed2dfCk+Po7ixQuxceM20tMz8ro7+pdzPCnaHFOKJseTos0xpWhyPCnaHFP5U3Jykeg3+vvvQRmE5OTs20uVgt9+i/75cijfh7YZGWEyMvJ3uLdlyzZmzZxNoaKlKFmwdF53Z78KESI+IY70tAzCB0Fou377WmZtns2WLdtIS/OXzP6Unp7hc6yocTwp2hxTiibHk6LNMaVocjwp2hxTyrEYK0ZgYQ9JkiRJkiRJB6dSpSAuDtauzb597Vo45JC86ROGtpIkSZIkSZIOVgULQp068M03O7etXw8LFsCJJ+ZZtwxtJUmSJEmSJB08li+HI4+ExYuD76+/Hp57DubOhU2b4K67oH59OP74POtivq9pK0mSJEmSJOkgU7Bg8DU1Nfg6YkTwdfv2YNu8efDHH8G2666DFSugceMgtD39dBg+/IB3OStDW0mSJEmSJEn5y/bte95XpUr2hcdCIXjwweAWIyyPIEmSJEmSJEkxxNBWkiRJkiRJkmKIoa0kSZIkSZIkxRBDW0mSJEmSJEmKIYa2kiRJkiRJkhRDDG0lSZIkSZIkKYYY2kqSJEmSJElSDDG0lSRJkiRJkqQYYmgrSZIkSZIkSTHE0FaSJEmSJEmSYoihrSRJkiRJkiTFEENbSZIkSZIkSYohhraSJEmSJEmSFEMMbSVJkiRJkiQphhjaSpIkSZIkSVIMMbSVJEmSJEmSpBhiaCtJkiRJkiRJMcTQVpIkSZIkSZJiiKGtJEmSJEmSJMUQQ1tJkiRJkiRJiiGGtpIkSZIkSZIUQwxtJUmSJEmSJCmGGNpKkiRJkiRJUgwxtJUkSZIkSZKkGGJoK0mSJEmSJEkxxNBWkiRJkiRJkmKIoa0kSZIkSZIkxRBDW0mSJEmSJEmKIYa2kiRJkiRJkhRDDG0lSZIkSZIkKYYY2kqSJEmSJElSDDG0lSRJkiRJkqQYYmgrSZIkSZIkSTHE0FaSJEmSJEmSYoihrSRJkiRJkiTFEENbSZIkSZIkSYohhraSJEmSJEmSFEMMbSVJkiRJkiQphhjaSpIkSZIkSVIMMbSVJEmSJEmSpBhiaCtJkiRJkiRJMcTQVpIkSZIkSZJiiKGtJEmSJEmSJMUQQ1tJkiRJkiRJiiGGtpIkSZIkSZIUQwxtJUmSJEmSJCmG5HloO2MGnHEGlCgB5cpB69awcmWwb9IkaNAAiheH2rVh4MC87askSZIkSZIk7W95Gtr+8QeceSY0aQKrV8PMmbBqFVx/PaxYAeefD507B/ueew6uvRa+/joveyxJkiRJkiRJ+1eehrZbt8Ijj8A990CBAlC2LFx0URDeDhwINWtCx45QsCA0axaEuK++mpc9liRJkiRJkqT9KyEvT56cDP/5z87v582DN94ISiR88w0ce2z24489FgYPzv15QqF/1M2YFwpBPr/Eg1qIP19jX+T9zudY0eR4UrQ5phRNjidFm2NK0eR4UrQ5pvRvlKehbaalS6FGDUhLg06d4MEH4eyzoWLF7MeVKgW//Za7tpOS4qPX0RiVkBAHIQiFQoTy+TtR5vWF4kIQzuPOHAChPxP5hIQ4EhPz/1jOC/HxwQcOEhPjiYvL3z8/2v8cT4o2x5SiyfGkaHNMKZocT4o2x5T+7WIitK1cOahvu2ABXHcdtGsXbA9HIZTbsSM93/9FJS0tA8IQDocJR+NJi2VhIC5EOCNM+CBIbcPhMISD1zg1NT2vu5MvZWQE4yg1NZ309Iw87o3+7RxPijbHlKLJ8aRoc0wpmhxPijbHlP7tYiK0hWCqeo0aQY3bhg3hnHNg7drsx6xdC4cckvu2832OGT4oJp0etML8+Rr7Iu93PseKJseTos0xpWhyPCnaHFOKJseTos0xpX+jPF2IbNIkSEmBjCx/8Ij7s0cNGgR1bbOaPh1OPPHA9U+SJEmSJEmSDrQ8DW2POw42bIC77oKtW2HNGnjgAWjUCK6/HpYsgVdfhe3bYcyY4NapU172WJIkSZIkSZL2rzwNbUuUgI8+CmbQlikDtWsH295+OyiD8MEH8PzzwbZbboEBA6BevbzssSRJkiRJkiTtX3le07ZuXZg8eff7TjsNvv/+QPZGkiRJkiRJkvJWns60lSRJkiRJkiRlZ2grSZIkSZIkSTHE0FaSJEmSJEmSYoihrSRJkiRJkiTFEENbSZIkSZIkSYohhraSJEmSJEmSFEMMbSVJkiRJkiQphhjaSpIkSZIkSVIMMbSVJEmSJEmSpBhiaCtJkiRJkiRJMcTQVpIkSZIkSZJiiKGtJEmSJEmSJMUQQ1tJkiRJkiRJiiGGtpIkSZIkSZIUQwxtJUmSJEmSJCmGGNpKkiRJkiRJUgwxtJUkSZIkSZKkGGJoK0mSJEmSJEkxxNBWkiRJkiRJkmKIoa0kSZIkSZIkxRBDW0mSJEmSJEmKIYa2kiRJkiRJkhRDDG0lSZIkSZIkKYYY2kqSJEmSJElSDDG0lSRJkiRJkqQYYmgrSZIkSZIkSTHE0FaSJEmSJEmSYoihrSRJkiRJkiTFEENbSZIkSZIkSYohhraSJEmSJEmSFEMMbSVJkiRJkiQphhjaSpIkSZIkScp/li6Fli2hdGmoXBnuugsyMnY9LiMD7r8fqlSBokWhXj0YPPiAdzerhDw9uyRJkiRJkiTtDxddBMcdB4sWwerVQYBbrhzcemv24156CV59FSZNgurVYexYaNUKjjoqCHDzgDNtJUmSJEmSJOUvX38NM2bAE09AiRJQo0YQ1r788q7HfvMNnHoqpKRAfDyce24wO/eHHw58v/+U72faxsWFiIsL5XU39qsiRQpRu04tqhauTLGkEnndnf0sRHx8iPT0MBDO687sd8k7irJt6zqKFClEQoJ/Y9kf4uPjsn2V/gnHk6LNMaVocjwp2hxTiibHk6LNMSW++SYod5CcvHPbscfCvHmwaRMUK7Zze8uWcP318P33UKsWfPghbN0KjRsf6F5H5PvQtlSpIoRC+Tu0TU6uxaix7+d1N6R/teLFC+V1F5SPOJ4UbY4pRZPjSdHmmFI0OZ4UbY6pg9jatdkDW4BSpYKvv/2WPbS96KIgsK1fP/i+cGF46y2oVOmAdHV38n1ou27dlnw/03bZsqX89+57qFW4vjNt85lNOzYwe+t3PPz4Yxx+eOW87k6+FB8fR/Hihdi4cRvp6bspRi7lguNJ0eaYUjQ5nhRtjilFk+NJ0eaYyp+Sk4vk7gHhHGZH/fvDm2/CtGlQty5MnAhXXAGHHw4nnJD7jkZBvg9tMzLCZGTk73Bvy5ZtzJo5m0JFS1GyYOm87s5+FSJEfEIc6WkZhA+C0Hb99rXM2jybLVu2kZbmL5n9KT09w+dYUeN4UrQ5phRNjidFm2NK0eR4UrQ5pg5iZcsGs22zWrsWQqFgX1Z9+sB11+0MaFu2hKZNgzA3j0JbC3tIkiRJkiRJyl+OPx6WLQtKIWSaPj2oWVu0aPZj09ODW1Z//LH/+7gXhraSJEmSJEmS8pf69YNZsnffDRs3wty58PTTwYJjAEceCVOmBPfPPx9efRV++AHS0mD8+KBEwoUX5ln38315BEmSJEmSJEkHoXffhU6doHx5KF4cOneGG24I9s2bB5s3B/fvvTcIay+8EFavhipV4JVXghIJecTQVpIkSZIkSVL+U7EijBmz+31ZFylLTISHHgpuMcLyCJIkSZIkSZIUQwxtJUmSJEmSJCmGGNpKkiRJkiRJUgwxtJUkSZIkSZKkGGJoK0mSJEmSJEkxxNBWkiRJkiRJkmKIoa0kSZIkSZIkxRBDW0mSJEmSJEmKIYa2kiRJkiRJkhRDDG0lSZIkSZIkKYYY2kqSJEmSJElSDDG0lSRJkiRJkqQYYmgrSZIkSZIkSTHE0FaSJEmSJEmSYoihrSRJkiRJkiTFEENbSZIkSZIkSYohhraSJEmSJEmS9E/88cfO+9Onw9ChsHr1PjdnaCtJkiRJkiRJ+2LDBmjUCPr0Cb5/7jk46SRo0waOPhrmzt2nZg1tJUmSJEmSJGlf/Pe/8PnnsH49bN4MPXoE2486Clatgoce2qdmDW0lSZIkSZIkaV+MGgUNG8LDD8OkSbBpE7RtCzNnQosW8Nln+9Rswr72Z+VK+O23oB/FikGZMlC+/L62JkmSJEmSJEn/MqtWwXnnBfenToVQCM45J/i+Rg34+ON9ajZXM22nTIF27aBiRahQISjLcOqpwdcKFYLt7dsHx+XU0qXQqhWULg3lykGHDsFs4iVLgmssWDD77amncnV9kiRJkiRJkrR/lC0LCxYE90ePDr42bBh8XbsWihTZp2ZzFNrOng2nnQaNG8OgQfDrrxAO73r79VcYMCA4rnFjmDPn79s+7zwoWTIIb7/5BmbNgttv37l/+/bst6z7JEmSJEmSJCnPNGoE48cHAefs2VCnDhx+eBCSvvsuHHvsPjWbo/II9etDamow0/WSS6BZMzjxRDjkECheHDZuhNWrYdo0mDAh6M9nnwWP2759z+2uXw/HHw+PPQZFiwa3q66C3r336Vr2KBSKbnuxJhSCfH6JB7UQf77Gvsj7nc+xosnxpGhzTCmaHE+KNseUosnxpGhzTGm/evBB+OSTYDZroULw7LPB9h9+CGa53nPPPjWbo9A2FAoWQrvlFkhO3nV/cnJwS0kJyic8+yz06vX3pQxKloTXX8++7eefg1ILmdq3h48+grQ0+M9/oGdPSEzMSa8DSUnxOT/4XyohIQ5CEAqFCOXzd6LM6wvFhSCcx505AEJ/JvIJCXEkJub/sZwX4uODDxwkJsYTF5e/f360/zmeFG2OKUWT40nR5phSNDmeFG2OKR0w1avDwoVB+YCKFYNZrhDMfG3TZv/OtP3hB6hZM+eNJicHC6a1b5+7znz9NfTpAyNHQoECQfmHVq3gtdfgu+/g4ouDwLZnz5y3uWNHer7/i0paWgaEIRwOEw7n8yQzDMSFCGeECR8EqW04HIZw8BqnpqbndXfypYyMYBylpqaTnp6Rx73Rv53jSdHmmFI0OZ4UbY4pRZPjSdHmmNIBVaDAruFsgwb/qMkchba7C2xXroTbbgvKIfz+O5QpA+eeC//7H5QosefH7cnnnwf1bR9/PCi/kLktU4MGcO+98OijuQttIZiJnJ/9mespnwqzs2609i+fY0WT40nR5phSNDmeFG2OKUWT40nR5phS1DVtmvNjQyGYODHXp8hRaLs7bdvCl19C7drBLOBly+DVV2Hz5mCxstwYNQquvDKYZbu32blVqgRhcThsPRJJkiRJkiRJeWDy5Jwfu48hZo5C2yFD4LLLsm+bOhXmzoXKlXdua9UKxozJXQemTg2C2qFD4cwzd26fODEIhbt337ltzpwguDWwlSRJkiRJkpQn7r9/v58iR6Ftmzbwxhvwf/8XhKYAxYrBe+9BixZB2YZffoH586F48ZyfPHNxsSeeyB7YQrBI2YMPBqFw69YwY0awsNntt+e8fUmSJEmSJEmKqgMQ2sbl5KDx44NAtnbtoOZsWhpcdx3ceivUqQM1asDppwczYe+8M+cn/+KL4DHdukHBgtlvZcrA4MFBUFuiBJx/PnTtCjffvI9XKkmSJEmSJEnRlpoKb70FXboEpQgAvvkmCD73UY5m2jZrBrNmBQuA3X8/DBwIL74IjRrBRx/B+vVQujS0bBlsy6lGjfZeDLpy5Z3XKUmSJEmSJEkxZflyaN4c5s3LvhDXJ58EZQQ++wzq1ct1szleiKxAAXjkEbjiimCWbePG0LEjPPkkJCfn+rySJEmSJEmS9O92663Bwl+XXw7LlgULeAGULw9btgQLdo0aletmc1QeIavatWHKFHjpJRg+HFJSoH//XJ9XkiRJkiRJkv7dPvooKD8wcCAcf/zO7VdcEdR7/fTTfWo2R6FtWlpQW7ZJEzjySDj66CA0HjQIzjgDrroq+PrTT/vUB0mSJEmSJEn69wmHoVSp3e+Lj4cdO/ap2RyVR+jcGV5/HRITgwXC1qyBH3+EoUNh+nTo0AFuuCEoz3DXXfDAA/vUF0mSJEmSJEn69zjySHj33WDxrjVrgm0//ggffggjRgRlC/ZBjmbavv8+nH46/P57UFt37dqgRMLWrTByJLRoESxUdtNN8Nhj+9QPSZIkSZIkSfp3uf562LYtWATs7beDbcccA3ffDenpcMst+9RsjkLbUCg4R3z8zm3hcPA1IyP4WrAgPP44fPPNPvVDkiRJkiRJkv5d2reH3r0hOTkITDNvpUvDc88FdWX3QY7KI7RsCW++GZy7TBn44w/47TcoXBguuST7sXXq7FM/JEmSJEmSJOnfp0uXoHbsnDmwYUMQoqakQFyO5svuVo4e2bcvPPwwHHdcMKP2kEOgTRv45BOoUWOfzy1JkiRJkiRJ/15PPBF8jYsL6tc2bAhHHRV8v2EDtG27T83mKLR95x2491747DOYPz+opTtwYBDi7s1bb+1TnyRJkiRJkiQp9t1zDzRtCr/8kn37xIlQt24QrO6DHIW2HToEC5GNH5+zRsePhyZN4Oqr96lPkiRJkiRJkhT7DjkEJk+GevVgyJCgrmy3btCiRRDknnTSPjWbo5q2VaoEpRA+/RQqVw7C4wYNoFw5KFYMNm2C1ath2jSYNAmWLAnq7Vatuk99kiRJkiRJkqTYN3cu3H479OsHl18eLAj2229QvDg89hh07rxPzeYotJ09Gx56CJ5/Pghk+/ULbrsTDgdBbteu8N//7lOfJEmSJEmSJCn2lSwJr74a1LG94w5YswaKFoUpU4Iat/soR+URChaERx6B5cuDRclat4bq1aFEiaCmbokSwfetWwf7ly8PFi4rWHCf+yVJkiRJkiRJsW3rVrj1Vrj77uD74sVh82Y444xgUbB9lKOZtpmKFoVrrw1ukiRJkiRJknRQq10bli2DwoXh2WehTZsgxH3lFWjfHt56C8aNy3WzOZppK0mSJEmSJEn6i6VL4YQT4Lvv4JproEiRoBTBBx8Ei5RNmLBPzRraSpIkSZIkSdK+uO8++PzzoHZsVuecAzNnwsUX71OzuSqPIEmSJEmSJEn604MP7nlf6dIwZMg+NWtoK0mSJEmSJEk51bQpXHopXH99cH9vQiGYODHXpzC0lSRJkiRJkqScmjwZjjlm5/29CYX26RT7FNquXQsvvADTpsHy5UGd3cmTIRyG00/fp35IkiRJkiRJUuy7/344+eTgfo8e+xzM7k2uQ9s5c4Jgds2aIKTN7NPXX0P37vDRR3DaadHupiRJkiRJkiTFgPvv33n/gQf2yynicvuAW2+F336Du++Gs8/eub1WLUhICMJlSZIkSZIkScq3VqyAe++FFi3gvPPgqadg8+aoNZ/r0Pbzz+GSS+CRR6BmzZ3bzzkHzj8fpk+PWt8kSZIkSZIkKbYsWhTUtH3iiaDswOjRcNddcNxxQXmCKMh1aFugQFAWYXc2bPin3ZEkSZIkSZKkGHbffUE4W6oUtGoVzLQtVgwWLIhaGYJc17StXRveew/++19YuDDYNnIkjBsX3E48MSr9kiRJkiRJkqTYM3EiVKoE338PycnBttWr4fjjg6D0xRf/8SlyHdrecUdQBuGxx3Zua9Vq5/3u3f9xnyRJkiRJkiQpNv32G3TsuDOwBTjkEDjrLHj99aicItflEVq2hBEjoE6doExCZqmEY44JZuC2bBmVfkmSJEmSJElS7MnIgKJFd91epMie68rmUq5n2kJQpuG884IF0TZuhJIloXDhqPRHkiRJkiRJkg5quQ5tly3bddtvv+28n5QUzAaOy/UcXkmSJEmSJEn6Fxg6FL7+Ovu2xYuDr6edtnNbKASffJLr5nMd2lat+vfHFCsGt9wC99+f6/5IkiRJkiRJUmxbvjy47c6UKTvvh0L71HyuQ9uclGXYuBF69oSyZeGGG/alW5IkSZIkSZIUg666ar+fIteh7Zw5cO650KIFtG8PZcrAmjXw5pswfnywQNrPP0PnzvDGG4a2kiRJkiRJkvKRfv32+ylyHdredhvUqgXPP79zW7VqcOKJcOGF8NxzMGwYjBsHo0dHsaeSJEmSJEmSdBDI9XJhkydDwh6i3vh4mDgxuJ+aGtwkSZIkSZIkSTmX65m2RYrAiBHBjNtWrYK6tevWBdtGjIDixWHWLBgzBipXjnp/JUmSJEmSJClfy3Vo27YtPPvszltW4TC0bg2//AKbNgU1byVJkiRJkiRJOZfr0PbJJ6FwYXjxRfj9953bixWDa66BRx+FhQvhllvg9tuj2VVJkiRJkiRJyv9yXdM2IQEefhhWroSffoKpU2HuXFi9Gq6/PthWuzb06gWh0P7osiRJkiRJkiTFiLlzoWNHqFMHkpODbSNHQv/++9xkrmfaZkpMhCOOCG4QLDp2111BiLty5T73R5IkKV/46af5PP/8s8ybN5ukpALUr38cN954E4ccUo4PPxzNwIFvsnLlCipVqkznzl1o0OCkPbY1ZswHvP76q6xatYLy5Q+lQ4f/0KLFOQC8+GIfRo58j/j4eK6++louvviyyOMeffRBFi9eSN++bxAXl+u/1UuSJEn6O1OnwplnwtatwfeZs1jnzIF774WiRYOFwXIp1/96374dbr4ZKlaE+Pidt4IFg4XItm3LdR8kSZLylY0bN3LrrV2YPftHrriiPaed1oSJE8dz//33MHv2TB555AHi4xO4+upObN26hXvvvZ01a1bvtq1PPvmEnj17UKZMGTp1uhEIwthly5bw7bdfM3DgmzRp0pSjjz6GZ5/9Hyv//Ov5jBnfMW7cGG6//V4DW0mSJGl/ueuuYKGvvn2Dxb4yNWoEJUoEtWT3Qa5n2vbsCb17735f+fJBgCxJknQwW7jwJypWrETDho1o164DAJMnT+THH39gxIhhhMNh7rjjXmrXrkPZsmV58MH/8tFH47jiina7tPXqq69SuHBhnnzyWSDM+edfSMGChYiPj2fKlE8B6Nr1Vn79dTmffPIxc+fOokyZMjz11GO0anUpKSlHHrgLlyRJkg42334Ll14K114Ls2fv3N6wIZx7Lgwduk/N5nraxZAhcNhhMGgQXHxxMON37NigbMMRR8AVV+xTPyRJkvKN+vWP48UXX4sEtqtWrWTz5s3Ex8ezaNFCAKpWrQZAlSrB1/nz5+7STnp6Ot999x3lypXnoYfu48wzG3P++S0YNmwIAGlpaQAkJSWRlJQIQGpqKoMHD2TdurWsXPkrF1zQgo4dr2Tu3Dn79ZolSZKkg1Lx4vDbb7vft2RJUGN2H+Q6tP31V7jgAmjTJiiRANCiBbz6atCHLl32qR+SJEn50pYtm/nvf+8iLS2Nli3PZ/PmzQAULFgQgMKFCwOwYcP6XR67fv16UlNTWbx4EQULFqJHj4cpVao0vXv3YvbsmVSuXBWATz6ZxOefTwGgSJGivPHGq1SuXJVp077igQcepVix4jz55MMH4GolSZKkg8wxxwQzWq+8Er77LtjWu3cwy/bzz6FBg31qNtehbfnyMHkyLFwY1NEF+PHHnfvGjNmnfkiSJOU769evp2vXzsyZM4u6devRpcvNezw2lLlgQRbp6cFM2sTERO65pwdnnnkWV13VEYAvvvicU089jaOPrs/999/LCy88y9lnn8vIkcM5+uj6JCcnU7VqNerXP45TTmnE/Pnz2Lp1y365TkmSJOmg1aMHJCXB22/DZ58F2265JQhJExLgoYf2qdlch7Znnw1z50KvXpCSEtTZPe00qF8fBg+GQoX2qR+SJEn5yubNm+nW7Trmz5/LqaeextNPv0DhwkUoUaIEAFv/XF02M0gtWTJ5lzaKFy9OKBSiUKHCJCUlAVC2bDkgCITj4+Pp3fslXn75Dd54420aNz6d6dO/4pZb7mTz5s2R2byF/vwHWuYsX0mSJElRcvLJMGVKMLO2VCmIj4cyZaBVK5g6Ndi/D3Id2vbsCSeeGNy/5BKoWhU2bIAZM4IA98or96kfkiRJ+coDD9zLokULueCCi3j00aciwWnmwmCLFwe1bRcs+AmAI488CoDffvuNpUuX/Bm6FqJGjRps3LiB5ct/AeDnn5cCcOihhwEQHx9PrVp1qFixEs8++xTt23ekQoWKFC1alE2bNgJBwBsKhShatNgBunpJkiTpIHLccfD++7BmDezYAatWwbBhwfZ9lJDbB5QuHYTEv/8OBQvCl19Cnz6wbl0Q5rbbddFjSZKkg8qUKZ/y5ZdTKViwINWr12T06JGRfWec0YIRI4bx9NNP0Lz52bz77jsUKVKE5s3PAqBv3+cZO/YD7r33fs4//wKuuuoqunfvTo8e99C0aTPefnsABQsW5Mwzz852zn79XqFAgQJccUV7ABo0OJlPP53M66+/zPjxY6lX75hI/VxJkiRJ/8Cnn+bu+NNOy/Upch3aduwIZ54ZLEQGULZsMPsW4Jln4Kab4Lnnct0PSZKkfGPevDkAbN++nV69Hs+2b+jQkfTo8RCvvPIiL7/8AlWqVOP++x8hObnUbtu65JJL+O2333n77YG88sqLVKlSjdtvv5syZcpEjlm8eBFDhgyiV68+JP65Ou3ZZ5/LjBnf8fbbA6hQoSK33nrXfrpaSZIk6SDTpAnsZk2K3QqFIC0t16cIhcPhcG4eEBcX1NLt1Sv79q1bg9IN06fDpk257sd+s2ZNDHVmP/npp/lc2boNxxc9lZIFS+d1d/arECHiE+JIT8sgTK6G7r/S+u1r+XrzFAYMfocaNWrmdXfypYSEOJKTi/D771tIS8vI6+7oX87xpGhzTCmaHE+KNseUosnxpGhzTOVPZcvGSLmvuBxWnI2LgxIlYO3a3J8iN32Jjw/uP/tscD/rrVgx+OSTncdIkiRJkiRJUr6TkbHz9s03UKlSUD929epgVu3y5UFpgiOOCBYp2wc5Dm3/8x8oWTKY0RsO7/l23XX71A9JkiRJkiRJ+nfp2hVatIAbb4QyZYKZr4ceCv/9LxxzDHTpsk/N5rim7csvB4FxoUJwySVBP7IKhaBChSBAliRJkiRJkqR879tvg5B2dzZsgK++2qdmc7UQWYEC0K8f1KoFJ5ywT+eTJEmSJEmSpPyhbFkYPhzat4cLLoDixWHNGnj/fRg/Pti/D3IV2gJcdRX88QdMmgQrVkB6+q7HtG+/T32RJEmSJEmSpH+PG2+Eu++GgQOD21/95z/71GyuQ9tvvoFzzw3q6u5OKGRoK0mSJEmSJOkgcOedQS3b556DWbOCxckAUlKCQPevNWZzKNeh7TXXwKpVwf0yZaBIkX06ryRJkiRJkiT9+3XsGNzS0uC336BUKUhK+kdN5jq0nTsXDj8cPvwQjjzyH51bkiQp11atWsXGjRvyuhsHRHx8iBIlCrNhw1bS08N53Z39rnjxEpQrVy6vuyFJkiTtm4QEKF8+Ok3l9gFVq0LDhga2kiTpwFu1ahVXtG/Dxi1b8rorB0YoRHxciPSMMIQPgtC2SBEGvfWOwa0kSZKiY+lSuOEG+PJLKFoU2rSBxx6DuLhdj507Fzp3hmnToHRpuPVWuOWWA9/nP+U6tH3wQbjjDvj1VzjssP3RJUmSpN3buHEDG7dsofwZLSlcet9WYf03CYUgPj6O9PSMfJ/Zbl27hpUTR7Nx4wZDW0mSJEXHRRfBccfBokXBAl0tW0K5ckEgm9W2bdCiBXTpAmPGBLVpr74azj47z2au7lN5hAoVoGZNaNIEDjkk+/5QCF57LUq9kyRJ2o3CpctSrHz+/+vxwRTaSpIkSVH19dcwYwZMmAAlSgS3W2+FZ5/dNbQdMiTYf8cdwfcnnAAzZx7wLmeV69D2gQeC/0CEw0HwnClzW6yFtnFxIeLiQnndjf2qSJFC1K5Ti6qFK1MsqURed2c/CxEfH/qzrl/+/99r8o6ibNu6jiJFCpGQsJup+/rH4uPjsn2V/gnH0/5XpEghateqRcVDy1G4dKm87s5+FwpBXHyIjPRwvg9tt4bSKVSrlr/z9iPfoxRtjilFk+NJ0eaYEt98A1WqQHLyzm3HHgvz5sGmTVCs2M7tU6ZA3brBYmLDhwd1ae+7D9q2PeDdzhQKh3P3X4AOHYL/QOxNv37/oEdRFg6HCf1dhyVJkiRJkiTlH48+Cu+9B9On79y2YAHUqBGUS6hadef2s8+GTz+FV16BCy+EoUODAPfrr6F+/b8/19y58OSTQT3c5cvh999h5EjYsAHatdun7ud6pu0bb+zTefZo6VK4+ebgeUlICJ6jZ5+FkiXh++/hppuCr4ccEtQCvu223LW/bt2WfD/Tdtmypfz37nuoVbi+M23zmU07NjB763c8/PhjHH545bzuTr4UHx9H8eKF2LhxG+npGXndHf3LOZ72v2XLlnLvA/dRsfl5B01N24Nmpu3aNfzy0SgefeAhf+ftJ75HKdocU4omx5OizTGVPyUnF8ndA3L6j+hwOKh9e8UVwfdXXQUvvRSEt38X2k6dCmeeCVu3Bt9nTh6dMwfuvTdYAK1Vq9z1m30IbTN9+unO8PiZZ2D+/CBo/WuN279z3nnBc7J0KaxfH1zD7bdDnz5w7rlw7bVBGYZ584Lrr1o1qCGcUxkZYTIy8vf/crZs2casmbMpVLQUJQuWzuvu7FchQsQnxJGelkH4IAht129fy6zNs9myZRtpaf6S2Z/S0zN8jhU1jqf9Z8uWbcyaPZttdRpQLByf193Z7w6mmrabVq5i0Wx/5x0Ivkcp2hxTiibHk6LNMXUQK1sW1q7Nvm3t2uAf2WX/MgGkfHlYty77tipVYOXKvz/PXXcFoW/fvjBpUlAfF6BRo6BO7qOP7lNom+vCHhs2wBlnwOmnB33q3TvYPnZsUBZi8eKct7V+PRx/PDz+eBA6V6wYBNmffgqjR8OOHfDf/0KRIkHb//kPvPxybnssSZIkSZIk6aBy/PGwbBn89tvObdOnQ61aQRCZVa1a8MMP2WfmLlkClXPwCbBvv4VLLw1mnpYvv3N7w4bBjNR9XNAs1zNt77gDPv4YTj4ZNm/eed6kJPj1V+jeHQYNyllbJUvC669n3/bzz1ChQlAruF49iM8yiebYY4PSErmV30vahkKQzy/xoBbiz9fYF3m/8zlWNDme9g+f1/zP33kHhs+xos0xpWhyPCnaHFMHqfr14YQT4O674emng+Dy6ad31l498kh49VU49VS48kro2TOYFXvrrTBiRBBODhjw9+cpXjx7MJzVkiWQmLhP3c91aPv++3DaaTB5clCLNjO0vf764HrGjdunfgBBbd8+fYI6vUOGZF/cDaBUqWCmckYGxOVwjnBSUv7/6GRCQhyEIBQK5ftF1zKvLxQXOhhK2gbXGwpe48TE/D+W80LmSqKJifH5vv619j/H0/6XkBD35++7g+Mf35Hfe6EQ+f0XX+jPf8v4O2//8T1K0eaYUjQ5nhRtjikB8O670KlTMAO2ePFgwawbbgj2zZsXzEgFOOyw4GP/N90EDz0Ehx8ehKBHHPH35zjmmKAEwZVXwi+/BNt694bx4+Hzz4NyBfsg16Ht1q1QvXpw/6//WSpbFrZs2ad+8PnnQX3bxx+HZs2C0HZ3tdty+x+0HTvS8/1/6tLSMiAM4XCYcH4veBcG4kKEM8IHRU3bcDgM4eA1Tk1Nz+vu5EuZNa9TU9MtTq9/zPG0/6WlZfz5+y7nawr8u4WBUOSa87Pwn/+W8Xfe/uN7lKLNMaVocjwp2hxTAoJarGPG7H7fX/+B3bgxfP997s/Ro0cwu/Xtt3duu+WWoP3ExCAE3ge5Dm1r1IDhw6FNG/jjj2Dbpk1BoPzee0GN3twaNSoIo/v0gfbtg21ly8JPP2U/bu1aKF0657NsMx0U/8nJ605ovwnDQRRO5C2fY0WT42n/8HnN//ydd2D4HCvaHFOKJseTos0xpf3q5JNhypSgvMLUqcGCYMnJQdmFe++F447bp2ZzHdq2axeUfmjRYue2kiV33r/22ty1N3VqENQOHQpnnrlz+/HHw4svQloaJPzZy+nT4cQTc9tjSZIkSZIkSdoPli8PZrG+/35Um83lnNWgju2ttwYLhGXOhAiHg2D15pt31vLNibQ0+M9/4Iknsge2AOecE5SaePjhoCTDV1/Ba68FtXMlSZIkSZIkKc9Vrw7//W/Um811aBsKwVNPBSHyqFHBImqjRwcLsPXqlbu2vvgC5syBbt2gYMHst5Ur4YMPYMKEYAGyyy4LFnBr2TK3PZYkSZIkSZKk/eCEE2DmzKg3m+vyCJkWLtwZoG7eHJQ5aN48dwuFNWr093VFpkzZ1x5KkiRJkiRJ0n7UtWtQu/aUU+DCC4OFuv66IFfmIl65kOvQdvNmaNUKpk0L6upC8PWss6BJk6B8Q7Fiue6HJEmSJEmSJP27tG4dzGJdtAi+/HLX/aHQgQltH3kEJk4MShZkSkyEwoXhk0+C/Y8/nut+SJIkSZIkSdK/y+GH5670QA7lOrQdNgxSUrKXLTjkEFi2DBo2hIEDDW3zs6LJRbiy+yUce0Zdpo6czhv3D97rdoBGF53Ime2bUPKQEqxetoYRz3/Ij1Pm7NJ2xZqHctltF1ClTiW2bdrGNxN+YNizH5CelkG5ymVpfccFHFGvMjv+SGXioCl82G8SAOWrlKXNXa2oWudw/tj2B99NmsnQXiNJS03f5Rx1TjmSS289jzIVSrFy6RoGPjKMRT8s5bzrzuS8ztlXw1u97Df+e0H2wdy19zXUbXQUo14az6i+4//x8ylJkiRJkqR/sSVL9kuzuV6I7Oefg1q0pUtn316qFJx6KqxeHa2uKdaUPKQED7x7B+Uql83RdoDqx1Sh3X2XsmTWz/zfLf2Ii4/nuifbUSy5aLbj4uLj6NrnP5QqX5K+d7zFj5/NoVnb0zi99SmE4kLc8HQHqtWrzFs9hzJ32gIu6nYOxzSpDcC1j19J1TqH88b97/D5iOmc3voUml152i59KX1oMtf3uoqlc37h5bsHUKJMcS688exsx/zfrW/wWPvePNa+Ny/d8Va2fcecXofaDVP26bmTJEmSJElSPrZyZVCeYMgQmDwZfvvtHzWX65m25cvD2LGwYAFUr75z+6xZMGYMHHroP+qPYliBQkmMfuUjfvh0Do+Nvvdvt2ca8cJYpo78mvWrNzD7y3k0v7Ixhx1Rjnlfb44cU6R4YaYM/4r53y5i3vQFrFu5ntMuOZmKNQ+j3OFlOLRaOT5/fzrfTviRhd8v4cRzjuXEc45lxqezqVDjUOZNX8h3k2Yyd9oCWl7bjEopFXbpx4ktjyUuPo63H3+PbZu3M2PyrF2O+Xnuctau+H2X7UkFk2h9xwV8MeprTrmwwb4+hZIkSZIkScpP1q2D664LFvpKz/Kp78REuOIK6N0bihbd8+P3INeh7SWXQK9ecOSRUKMGlCwJa9fCwoXB/ptvznUf9C+xaukaVi1dQ+lDk3O0HWDB90tY8P0SAAoWKUD90+uSnpbOmuXrsh236ffNkXIDCUkJHN/8aABmTZ1HUsEkAFJ3pAKwZcNW0lPTKV/lEMIZYRbNWEqF6uUpfWgylWtXAmDRD0t36UullAps27ydS287j+OaHc3GtZsY9Nhw5nz1U+SYax+/koo1D2Pdyt8Z2mtUpIxDy07NSEtNY9ybHxvaSpIkSZIkKdC5c1BP9q927IA334S4OHj11Vw3m+vyCD17wumnQ0YGzJsHX30VzLoNh4Oatj175roPOggULFKALr2voUyFUnzU/1PW7WY2K0DFmofxf189/v/s3Xd4FNX+x/H37G56JwFCD0gNvYpSRZogImABBBsq9sLPLle9XnsXRUVFEUUQwYIUKYIgTRDpvQcIBNJ72d35/bEQyE1Q4LLZZfm8nmefJDNnznxnMjkz+e7Zc7hqRDd++mguq+euI2n/MfJz8mnasRGRlSK47Jo2WP2s+AX4AfDFc1MoKrTzyuxnuPuNm1m3aBMLJy8tVXdwWCChkSGkH8vk89HfEBoZwoiXb8LmbyM7PYcje4+y7rdNfPzYlwQE+XPna8MIiwoltnYlegzrzORXfqCowO7W8yQiIiIiIiIiIheQ2bMhMBA+/BAOHoScHNfkX++8AzZb2QndM3DWPW2DglzDM8yfD4sWuYZniIx0jXPbt68reSxyquCwIB7++C7i4muwaMoyvh8z67Rlk/Yf5ZXhY2jeJZ7+9/QC02TWpwv49s0ZDHt6EK/NHU3iniTysvPJzc7D5m/jgfdG4LQ7ePeeT4ipVoGhTw/kulFX891bP5eou6jATlGhnZ8/modpmvz16wY6DWxPbFwlFn27jEXfLisuu2T6Svrf25s6zWtx5ZBObF6+g72bEqhwvDexLcBGQJA/BXmF7jlpIiIiIiIiIiLi/YKC4KqrXD1uT6heHR56CFavhp9/Pv22f+Osk7avvuoaGuHaa6FHj3Pap1xk7np9ODUbVmPqWzNY8PWSMsvEVKtAy25N2fnXHvZuSmDvpgS6D+tCmx7NmfXpApb9uIq1v24ksmIE2enZvDb3XxzckUi1urHE1q7EwilL2bJyBwD97u5JyyualkraHt57lCYdGxIQ7E9+TgGW4+8w2AuLaHRpPUKjQlj9yzrANTEaQFFBEQ3buQZvfu/3F4vruuq2btRpWou37vzovJ4rERERERERERG5gAwcCDt2lL3u0CHo1++cqj2npO3gwa6krVxcQqNCqN/6EsKiQgCIrhJFq+7NSElMI7pqVBnLU6lSuxLx7euzZcV2dq/fR+2mNQE4djCF2FoVuX/MCBZOWcqyH1dz7X29Sdp/jGnvzqRGg2r4B/qxf9shDMNg9JRH8A/0Y+obM+h1W1cMi8HSH1Zx7EAK+bkFNOsUz6Zl24iuEkVETDjrftsEwLPfjiIgyJ9nrnmVlbPW0GNYZ64f1Y+NS7fRolsTjuw7SlJCMj1u7kqnAZcSWTGCoweS6TywPWlHM9i9fj9v3nEyMRsRE8adrw5j+YzVLJj0e/n+AkRERERERERExPOWnNIpsX9/ePxxV7J02DCIjoa0NPj6azhwAJ577px2cdZJ20cegU8+cU041rDhOe1TLlBV68Ry9xs3F//coG1dGrStS3JiKjFVK5RavnzG6uJl8Zc1IP6yBsU/f/HsFFIS0wgOC8LPz0ZKYiofjprAgAf6cP97t5ObmcfKWWuY9vYMTNNk2ts/c+Pj13L3W7eQcSyDL0ZPKZ5s7KPj29312nCKCopYs2ADU17/EYDA0MDiicwO7TzMZ09PYsD9V9G2d0v2bznIN698j+k0mfrmDCyGQa9bumLzt7FvUwJT3/qZgtwCdqzZXRz3icnWUhLTOLgj8fyfZBERERERERER8W5du4JhlFy2dWvZQyF07w72s58j6ayTtnv3QnAwNGkCDRpAxYolx7E1DNeYt+J7dqzZzV0tHz3r7SY89+1p182dsIjM1GwANi/fzubl28sst/WPnTw/6A0MDKw2Cw67s8S6rX+8V+Z27z8wnrteG17885/z1vPnvPWlyhXkFvDlv6f+47GkHE47p3MgIiIiIiIiIiI+xDTPb7n/ctZJ24kTT36/davrdar/TjKLnE5gSABte7XgnXs+cds+ul5/OesWbXJb/SIiIiIiIiIicpFxOv+5zAkOxznt4qyTts8+q8SsnB/5OQU82eclt+5j8ms/uLV+ERERERERERGR07Jaz2mzs07aPv/8Oe1HRERERERERERExLcUFMDrr8Mvv8DRo6V71hoG7N5d9rZ/46yTtqdOjnY6nTufdRwiIiIiIiIiIiIiF5YRI2Dy5NOPXXuOQxacddK2rMnR/juOc5gQTUREREREREREROTC8v33YLHArbdC69YQFHReqj3rpC2cPnEcHg4VKvwv4YiIiIiIiIiIiIhcIGJiXMMOfPrpea3WcrYbOJ2lX0ePwpgxUKMGLFhwXuMTERERERERERER8U6jR8PKlXDo0Hmt9px62v63mBi4/37YuBHuuw/mzDkftYqIiIiIiIiIiIh4sVtvhV9/hTp1oEkTiIgoud4wXOvP0nlJ2p6QnAy//34+axQRERERERERERHxUnffDdOmucaTXbu29PrymojshRdKL8vPhz/+gN9+g+joc4pDRERERERERERE5MIyfborYXvFFdC2recmInv++bITxCcmJ3vwwf8xIhEREREREREREZELQWAgdOsGP/xwXqs966Rt586lk7ZWK8TGwjXXwA03nK/QRERERERERERERLzYAw/AzJngcLiSpOfJWSdtf/vtvO1bRERERERERERE5MJVo4bra3w89OkDUVGlyzz77FlXe84Tkc2d60rgpqZCpUrQqxd07HiutYmIiIiIiIiIiIhcYG67zTUsgWnCmDFllymPpG1BgWsYhAULSi5/+WW4/nr45huwWM46DhEREREREREREZELS1ljyZ4HZ520feUVmD/f9X3t2hATA0lJkJAA330H7drBqFHnO0wRERERERERERERL+OmsWTPOmk7ZYpraIa5c6FNm5PLly1zDdswYYKStiIiIiIiIiIiInIR2b695FiyV14JcXHnXN1ZJ2337YMhQ0ombAE6dIABA2Dq1HOORUREREREREREROTCYZpwxx2unqynsljg//4PXn31nKo969Fng4Lg4MGy1x07Bv7+5xSHiIiIiIiIiIiIyIXlvffgiy9cyVur1TWWrMUCDge88UbpZO4ZOuukbbt2sHAh3HcfLF0K27bBkiVwzz3wyy/QuvU5xSEiIiIiIiIiIiJyYZkwwdXL9bvvoKDANflXfj589RX4+cGHH55TtWc9PMLTT7uSth9/7HqdymKB0aPPKQ4RERERERERERGRC8uOHTBokOt1gtUKN90Es2bBjBnnVO1Z97Tt0gWmTYMaNVy9fk+8atVyJZSvuOKc4hARERERERERERG5sNhskJVV9jq7HQzjnKo966QtQP/+rgnJtm07OUTCnj1w7bXnFIOIiIiIiIiUI9M0mTZtCt27d6RjxzYcPpwIwOzZP9OxY5tSrw8+eLfMegoKCvjoow+4/vr+dOt2OSNGDGf9+rXF67/7bgr9+vWkd+8rGDdubIltP//8EwYNupq8vDy3HaeIiIjbNW8OM2e6xq89eNA1NEJCArz2Gnz/PTRtek7VntXwCPPmwZo1rsnGunSBNm2gfv1z2q+IiIiIiIh4gN1u5//+7wHWrfuLwMDAMst06NCJjh27FP98ySV1yyz39ttv8+WXE2jVqg3XXjuQyZO/5tFHH2Ly5O8pLCxgzJi36NixC5GRUXz11Re0b9+B5s1bcPDgAb7++kv+/e+XCAoKcstxioiIlItRo1xDIzz5pOtV1vpzcEZJ25wcuOoqWLas5PIbboCvv3YN0yAiIiIiIiLer7CwkMzMDD799EvGjHmbdev+KlWmfv2G9O17DXl5uYSEhJ62rtmzZwPw7LMvEhMTQ2BgEO+88zpz586iSpVqmKbJnXfeTeXKsfz88w9s2bKJ5s1b8Pbbr9Gu3aV06tTVXYcpIiJSPgYMgDFjXBOBZWefXB4aCi++CNddd07VntHwCP/+t2sYBNOEyEjXPk0Tpk6F118/p/2KiIiIiIiIBwQGBvLppxOpX7/hacssXDifXr260KtXVx555D5SU1PKLJeTkwNAcHAwABUrVgJgx45t2O12APz8/PHz8wfAbi/i11/ns2HDOiwWKwMG9GHYsBtYuXL5eTs+ERGRcnf//XDoEPzyi6uH6y+/QGIiPPjgOVd5Rknb77+HkBBYvBhSUiAjA2bPhqAgmDDhnPctIiIiIiIi5cxisWCz/f2HLg3DwqOPPkXXrt1YvfoP3nrr1TLLNT0+Tt9XX31BQsI+fvxxGgAZGRnExcUBsHTpEn7/fTEAlStX4f3336Z+/YYsWbKIRx55nAYNGvDSS88XJ3lFREQuSGFh0LMnDB3q+hp6+k+qnIkzGh7hwAHXUAidOp1c1ru3a+Kx7777n/YvIiIiIiIiXqJ7915cfnkngoKCCAgIoFOnrixduoSlS5dgt9tLJXufeuopRowYwVdffcFXX31B69ZtAbBabdSv35AePXozduy7ALRq1YbNmzcQERFB/foN2b9/L507d8UwYO7cOSQk7KNOnbLHzhUREfEqdeqceVnDgN27z3oXZ5S0LSqCihVLL69YEfRmqIiIiIiIiG/w9/fH39+/+Ofg4GDCwyNITU0hOzubyMjIEuUbNmzItGk/sW3bdkJCQsnJyWbNmtXExMQA8NxzLzJ06HAcDgcAI0fexgcffMLMmT8REOCaBC0w0DURWVZWNiIiIheEffvOvKxhnNMuzihpKyIiIiIiIr5v/PhxrFq1kkcffYp69eqTlpZKRkY6oaFhREZGkp6eTkZGOhERkcTEVGDZsmWsWLGKgQNvJCwsjPHjxwHQtGnz4jrr1WuA0+lk5Mjb6NOnH02bNue33xaSnZ2NaZpkZKQDEBYW5olDFhEROXuLFp1+XWEhTJzomgysqOicd3HGSdsVK+DZZ0svg9LLX3jhnOMRERERERERN8rKymLy5K8AOHw4EYApU74mJCQUm83G5s0b+c9//kXfvtewaNGvOBwOhgwZBsD06d/yxRefctttdzJy5D2sXr2aTz/9mM2bNxMf35hJkyZSuXIsV17Zs8Q+Z8z4nsOHE3nrrTEAXHrpZXz77SQ++OAdNmxYR5UqValVK678ToKIiMj/okuXspdPmuRKlO7bB6YJ3brBq2WPC/9Pzjhpu2qV61WWl14q+bOStiIiIiIiIt4pOzuLiRM/L7Fs+vSpADz99HM88sjjfP/9VMaNG0tkZBT33fcwN944tMy67r33XhITj/Dbbwv5889VNGvWnFGjniAoKKi4TFpaKuPGfciDD44iPDwCgHbt2nPjjUOZNWsGkZFRPPXUs1itVjcdsYiIiJv98gs89RRs2OBK1rZs6UrW9uhxzlWeUdK2Zs1zHn5BREREREREvEiVKlVZuvTPvy0zaNANZS4fMWIkI0aMLP7Z39+fp576F4899sxp64qKqsCcOQtLLX/ggVE88MCoM4xaRETEC61eDU88AYsXu5K1l1wCL74IN974P1d9RknbsxlbV0RERERERERERMRn7dgBzzwD33/vStZWruwaFuHOO8F2fqYQ00RkIiIiIiIiIiIiImeqcWNwOsFigX79XMnakBBYvrzs8p07n/UulLQVEREREREREREROVMOh2ssWacTfvrJ9TodwwC7/ax3oaStiIiIiIiIiIiIyJkqhwnAlLQVEREREREREREROVPlMAGYxe17EBEREREREREREZEzpqStiIiIiIiIiIiIiBfR8AgiIiIiIiLnUVJSEpmZGZ4Ow+2sVoOIiGAyMnJxOExPh+N24eERVK5c2dNhiIjIRUJJWxERERERkfMkKSmJYbfdSFZetqdDcT8DLBYDp9ME38/ZEhYUytdffKvErYiIlAslbUVERERERM6TzMwMsvKyaXTH5YRXjfJ0OG5lGAYWiwWn04lp+nbWNjMxja2fLSczM0NJWxERKRdK2oqIiIiIiJxn4VWjiIqr5Okw3MowDKxWCw6H7ydtRUREypsmIhMRERERERERERHxIkraioiIiIiIiIiIiHgRJW1FREREREREREREvIiStiIiIiIiIiIiIiJeRElbERERERERERERES+ipK2IiIiIiIiIiIiIF1HSVkRERERERERERMSLeEXSdu5cqFwZBg8uuXzCBLBYIDCw5GvVKo+EKSIiIiIiIiIiIuJ2Nk8H8PrrMH481KtX9vrOneG338o1JBERERERERERERGP8XjS9kTP2Ycegvx89+zDMNxTr7cwDPDxQ7yoGRz/HeuX7HY6x3I+6XpyD51X36d7XvnQOXYfw0AP577KUBtVXnSO5XzTNSUXIo8nbR988O/XHzgAPXrAn39CVBS88AIMG3bm9fv7W/+3AC8ANpvl+AOEgeHjLdGJ4zMsBpgeDqYcGMcz8jabBT8/37+WPcFqdY0S4+dnxWLx7b8fcT9dT+5ns1mO3+8ujofv4vueYeDrNz7j+LOM7nnuozaqfNhsluNvul8Mz+anfvX1YzUw0HO5O6mNkvNN15Rc6DyetP07FStC/frw8ssQHw8//ADDh0PVqtCt25nVUVjo8Pl/6ux2J5hgmiam6dv/0GECFgPTaWL6+D+v4PqdYrp+x0VFDk+H45OcTtd1VFTkwOFwejgaudDpenI/u915/H4Hvn7LczEBo/iYfZl5/FlG9zz3URtVPux2JyYXybP58UTtib9fX2aarv8+1Ea5j9ooOd90TcmFzquTtn37ul4nDB7sStx+8cWZJ23B9/+pO57XEx/leuj3/evYG+gcy/mk68k9dF59n+555UPn2H1MEz2c+ypTbVR50TmW803XlFyILJ4O4GzFxUFioqejEBEREREREREREXEPr07afvwxTJ1actnWrVCnjmfiEREREREREREREXE3rx4eoaAA7r/flaRt3hymTYPZs+GPPzwdmYiIiIiIiIiIiIh7eDxpGxjo+lpU5Pr644+ur/n58OCDkJUF118Phw9D7dqu9a1beyJSEREREREREREREffzeNI2P//06wwDRo92vUREREREREREREQuBl49pq2IiIiIiIiIiIjIxUZJWxEREREREREREREvoqStiIiIiIiIiIiIiBdR0lZERERERERERETEiyhpKyIiIiIiIiIiIuJFlLQVERERERERERER8SJK2oqIiIiIiIiIiIh4ESVtRURERERERERERLyIkrYiIiIiIiIiIiIiXkRJWxEREREREREREREvoqStiIiIiIiIiIiI+J79+6FvX4iOhlq14IknwOn8+20OHYKwMHj++XIJ8XRsHt27iIiIiIiIiIiIiDsMHAitW8OePXD0qCuBW7kyjBp1+m0efBCs1vKL8TR8PmlrsRhYLIanw3CrkJAgGjeJp3ZwLcL8IzwdjpsZWK0GDocJmJ4Oxu2iCkPJy00lJCQIm00d493BarWU+Cryv9D15H4hIUE0jo+nepXKBEdX8HQ4bmcYYLEaOB0mpo/f9nINB0Hx8brnuZHaqPIREhJE40bxxEVWIzzYx9spw8BqMXA4TXy9kYqI9CO7kdood1IbJeebrinhzz9h/XpYsAAiIlyvUaPg3XdPn7SdPRu2bIGrry7XUMtimKZv311N08QwfDtpKyIiIiIiIiIiIqcYNw7eeAN27Tq5bNUquPRSyMx0DYFwqrw8aNwYxo+HL7+EuDiPDpHg8z1tU1NzfL6nbULCfkY/+RTxwS3V09bHZBVmsCV3LS+++go1a9bydDg+yWq1EB4eRGZmHg7HP4xrI/IPdD25X0LCfp5+/l9U79GP4OiKng7H7S6qnrYpxzg4/2defv4/uue5idqo8pGQsJ9/vfgMTe7sSHhV9bT1FZmJqWz6dCn/Gf2S2ig3URsl55uuKd8UFRVy5oVTUiAqquSyCsfvzcnJpZO2L7wAl10GV1zhStp6mM8nbZ1OE6fTtx8gcnLy2LxpC0GhFYgMjPZ0OG5lYGC1WXDYnZgXQdI2PT+FzdlbyMnJw27XTcadHA6nzrGcN7qe3CcnJ4/NW7aQ16QdYabnx5lyN8Nw/cPhcDh9PR9C1pEk9mzRPa88qI1yr5ycPDZv3UJoem2iIos8HY5bGYZxShvl241UWvpRNm9VG1Ue1EbJ+aZr6iJ3pvenLVvgs89g40b3xnMWNLCHiIiIiIiIiIiI+JaKFV29bU+VkuLqGVHxlE/tmSbcc49rKITY2HIN8e8oaSsiIiIiIiIiIl7DNE2mTZtC9+4d6dixDYcPJxavW7RoAbfeOpTu3Tty/fX9+fDDMRQWFpZZj8Ph4P333+W66/rRu3dXRo16gEOHDhav/+ij97nqqm5cfXUPpk+fWmLbl1/+N3feeTNOp3rpXrDatIGEBNdQCCesXg3x8RAaenJZQgIsWQLPPQcxMa7XlCnw+uvQqlX5x32ckrYiIiIiIiIiIuIV7HY7Dz98L++//w5Wa8nhsNavX8ezzz5Ffn4eI0feT9Wq1fjmm4l88cWnZdY1YcIEJk2aSL16DRg8eBgbNqxl9OjHAfjrrz+ZNOlLunbtRvPmLXj33Tc4cuTI8f2sZe7c2Tz66NNYLEqdXbBatoS2beHJJ10Tj23bBm+/7epVC9CwISxdCtWrw4EDsG7dydc118Ddd8Ps2R4LX1eeiIiIiIiIiIh4hcLCQjIzM/j00y+pV69BiXXbt2+hSZNmPPLI41x//WAeeuj/APjjj+Vl1jV16lRCQkL5z39e5dZb76Bnz6vYuXMH27ZtYdu2LQA88MAobrvtLkzTZNu2zdjtdt588xUGDLieBg0auvdgxf2mTYPERNewB127ws03w733utZt3w7Z2WC1uhK3p76CgyE83KPDJfj8RGQiIiIiIiIiInJhCAwM5NNPJ2KzlU5Z3XDDUG64YWjxz3v27ALA3z+gVNn8/Hz2799PgwYNi+uKi6sDwPbt27Db7ce39cff3w+AoqIivv12EqmpKRw5kkj//r2Ijq7I448/Q8OGjc7vgUr5qF799L1l/26SsgkT3BLO2VBPWxERERERERER8QoWi6XMhO1/27NnN2+//ToAgwbdUGp9VlYWpmkSGBhYvCw4OBiAjIx0atWqDcDixQtZtmwpACEhoUyY8Bm1atVm1ao/eP75lwkLC+f111/8n49L5Gypp62IiIiIiIiIiFwwtm3bwqhRD5CZmcGgQTfQo0fvs9reMAw6duxM8+Ytee65pwG46qqrmTHje5o3b0lAQAC1a9ehZcvW7Ny5gzFj3iI3N4fg4BB3HI5ImZS0FRERERERERGRC8KOHdt46KF7yM3N5Y477ubWW+8os1xYWBiGYZCbm1u8LDc3B4DIyCisVitjxnzM9u1b8fcP4MiRRJ577mkmTvyW119/qbiHblBQEADZ2dlK2kq5UtJWRERERERERES8XmZmJo8//gi5ubmMHv1vevXqU2J9cnIyOTnZREfHEBkZTt26dTlwIIGioiL8/PzYtWsnQPH4tFarlfj4JuTn5/Pkk6O4+ebbqVatOqGhoRw4kABAeno6hmEQGhpWvgcrFz0lbUVERERERERExCtkZWUxefJXABw+nAjAlClfExISyubNG0lOPkadOpdQWFjIzz//CLjGqr3yyp6MG/cBc+bM5Omnn+Oaa/pz44038uKLL/L8809Tt2595s6dTZMmzahXr0GJfX7xxacEBAQwdOjNALRrdxlLlvzG559/wrx5c2jWrEXxeLgi5UVJWxERERERERER8QrZ2VlMnPh5iWXTp08FIDa2CuCahOy1105ODhYbW4Urr+xZqq5hw4Zx4MAhfvrpR1asWE7btpfyxBOjS5TZu3cPU6d+w1tvvY+fnx/gGt92/fq1TJ78NdWqVWfUqCfO6zGKnAklbUVERERERERExCtUqVKVpUv/PKdtn3nmeZ555vninw3D4J57HuDOO+877Ta1a9dh0aIVJZb5+/vz7LP/OacYRM4Xi6cDEBEREREREREREZGTlLQVERERERERERER8SJK2oqIiIiIiIiIiIh4ESVtRURERERERERERLyIkrYiIiIiIiIiIiIiXkRJWxEREREREREREREvoqStiIiIiIiIiIiIiBexeToAEREREREREREpH0lJSWRmZng6DLezWg0iIoLJyMjF4TA9HY7bhYdHULlyZU+HIeeRkrYiIiIiIiIiIheBpKQk7hgxiIL8TE+H4naGARaLgdNpYvp+zpaAwHA+Gz9diVsfoqStiIiIiIiIiMhFIDMzg4L8TP79eGXiagZ7Ohz3MsBmtWJ3OMDHk7b7EnJ57nVXD2olbX2HkrYiIiIiIiIiIheRuJrBNKwX5ukw3OsiStqKb9JEZCIiIiIiIiIiIiJeRElbERERERERERERES+ipK2IiIiIiIiIiIiIF1HSVkRERERERERERMSLKGkrIiIiIiIiIiIi4kWUtBURERERERERERHxIkraioiIiIiIiIiIiHgRJW1FREREREREREREvIiStiIiIiIiIiIiIiJeRElbERERERERERERES+ipK2IiIiIiIiIiIiIF1HSVkRERERERERERMSLKGkrIiIiIiIiIiIi4kWUtBURERERERERERHxIkraioiIiIiIiIiIiHgRJW1FREREREREREREvIiStiIiIiIiIiIiIiJexObpAEREREQuBLERYdx/RXsaVK5IZl4+U1ZvYP7WXTStFsvtHVpTJTKMwxlZfLpkNVsOHy2zjoEtG9OveUNC/P3Zcvgo7yxYRkZePj/dN7xU2aTMbEZ+/QPB/n7c2/lSmlSLpdDu4Ndtu5n0x7oy6x/cthk9GtUlJNCfhJR0Pl+2hm1HjgFwY5umXNmoLuFBAexPTuPzZWvYnpQMQKuaVbm9Qxsqh4dyKD2Dj377o3idiIiIiIiUP/W0FREREfkHBvBMn64E+/vx6i+LSc7JZeilzakYFsLjvTvjNE1e/2UJBvBE7874W62l6uhUN45bLm/FjPVbmbDiL+KrVqJv0wYAPDZtTvHrmR/mkZ1fwO5jKQAMu7QlbeNq8Nnvq1m6ax83tGlK+zo1StXfu3F9hrRrzqIde3hr3lJiI8J4uk9XbBYLXerXZuilLVi5J4HXf1lCpfBQnu7TFYthUCkshKeu6sruYym8MW8JUcHBDGvf0p2nU0RERERE/oF62oqIiIj8g4ZVKlKzQiQvzlrEmv2HWLP/EADt4qoTHhjAhOVr+CshkaqR4dzZqS1Nq1dmzf7EEnX0iK/LrqMp/LB2CwCzN24vXrfjlF6tQ9s1J8DPxpcr1gJweZ2abE86xm879rJq30H6t4inU704Vu45UKL+rIICvlm1nu/+3IjTNNl46Agd68ZRISSI7IJCvl65jp83bCW/yM72I8lcdklNYkJD6FK/NlaLwSdLVpFTWMSqvd+55RyKiIiIiMiZU9JWRERE5B/UjqkAuIYRuO+K9hgY/LhuM07TBCAlO7fE12qREaWStrVjojiQlsGL1/agfqUYdh1L4d0FyzmalV1cJiY0mIGtGjN/yy6OZGQRHuhPWGBAcb25hUXkFRZRLTK8VIzLdu0HXL2CY8NDaVi5Igmp6SRn53I0K6c40RwTGkzT6rHkFhaSnpdHnZgocgqLuK1DGzrUrUlabj7jFq9i/cHD5/EMioiIiIjI2dDwCCIiIiL/IMTfH3AlPN+a9zvHsrK59fLWBNhc7387nE4A7Me/nlheoo4Af+pViuGXTTuYuHItjatW5s5ObUuUGdy2OQDT/9rkqsfPVqJegCKnk0C/07/v/uRVXRk3fADZhYW8Mvu34sQyQIXgIJ7vdyWhAf58uXwthXYHIQH+hAcGkJqTyzvzlxEeGMCoHh3xs+oxUURERETEU/Q0LiIiIvIPihwOAL5fu5mNh5L4ecM2APKL7ABYLa5HqhOJzvyiolJ1FDocbD18lKW79jNzwzYS0zOJr1qpeH2Ivx9dG9Rm3YHDJB/vWXuifpvl5CObv9VK3vHlZfli2Z+8MPNXrIaFlwb0JCIoEICKYSG8MrAX1aIi+HL5X/yyeUdxXEUOB5NXrWfVvoOs2J1AZHAg1aMizuFMiYiIiIjI+eAVSdu5c6FyZRg8uPS6b7+FZs0gLAxat4Z588o/PhEREbm4HUjLACA0IAAAi2EAJ5O5FcNCAIgNDytR/lQHUzMICfAv/tliGMXbA1xapwZ+VmuJsWqzCwrJyMsvrj8iKJBAPxsHUkvX36leHP1bNOJIZjZr9ieyaPtuKoQEE1+lEv42K6P7XkH08Z7C36/dfDKutAysFguBfn6uuCyuYyu0O0rtQ0REREREyofHx7R9/XUYPx7q1Su9bt06uOUW+P576NYNpk+HAQNg+3aoXr3cQxUREZGL1IaDh0nOyuG61k2wOxxc3awhRQ4HmxKTSM/No1d8PZKzc+nW8BKSs3PYdCiJmNBg3hvcj1V7D/Der8tZuG03I7tcSp+mDbBZLMRGhDF/y67ifdSrFAPA3uTUEvteumsfPePr0aV+bRpUdpVZvGMvAKP7XkGjKpW4fcJ0mlSrTO/G9fG3WtmRlELHenE4nE72paRxbYt44qKjWLhtN0ezcqh/vJ4DqRks2r6Ha5o34vYOrVmz/xDta9fgUFoGhzOyyuPUioiIiIhIGTyetA0MhFWr4KGHID+/5LrPPoM+fVwvgJtugvffh6+/hiefLP9YRURE5OJU5HDyn1mLuLfrpTzVpytJmdm8Ofd3DqRm8MqcxdzVuS1P9O7MwdQM3vt1GXanE4thEBrgj7/NCsDsTTuoEBrM4LbNMAyD37bv4Yvla4r3ERMaDEBqTl6JfX+zaj3hgYHc2akt+UV2vlq5tnhSsUA/G6EB/piYTFi2Bky4pkU8ATYrh9OzeGv+Ug5nZNGseiwA3RpeQreGlxTX/cwP89iUmMTb85cyrH1LOtWPY9fRFMYtWVViLFwRERERESlfHk/aPvjg6detWQN9+5Zc1qoVrF59dvs4/glGn2UYrpmixTcZHP8d65fsdjrHcj7penIPT57XfSlpPD79l1LLtx05xqips0stP5qVw8eL/6DGKWPDfr1yHV+vXFdm/S/N/q3M5fl2O2/N/52ycqgvzlrE57cMKh7K4KPFf/DR4j9KlRv94/wy6z5h6a79LN21/2/LlBfd88qHzrH7GAZ6OPdVhtqo8qJz7D66hn2XoTbK53g8aft3UlIgKqrksgoVYPPmssuXxd/fen6D8kI2m+X4A4SB4eN/nSeOz7AYcBF0ADKOZ+RtNgt+fr5/LXuC9fikQX5+1uJxHEXOla4n97PZLMfvd97/QGoxDHo1rscXy9ecc6zF9z3DoKwbX+8m9Vm9/6DXn4szYRx/ltE9z33URpUPm81y/E33i+HZ/NSvvn6sBgZ6LncntVHlw2ZzneeLoY2iuI3y8ePk5DGqjfItXp20BcrsVXI2CgsdPvGPzN+x251ggmmamL7+UUYTsBiYThPzIsjamqYJput3XFSkCWHcwel0XUdFRQ4cDqeHo5ELna4n97Pbncfvd//7M4K7OUyTh7+d9T/WYgJG8TH/tx/Xbvkf6/ce5vFnGd3z3EdtVPmw252YXCTP5sczIif+fn2Zabr++1Ab5T5qo8qH3e46txdHG+Vy4v9qX3bid6k2yrd4ddK2YkVXb9tTpaRApUpnV4+vt0MXQftzUXM99Pv+dewNdI7lfNL15B46r75P97zyoXPsPqaJHs59lak2qrzoHLuPrmHfZaqN8jkWTwfwd9q0cY1re6rVq+HSSz0Tj4iIiIiIiIiIiIi7eXXS9s47Yf58mDUL8vPh889hxw4YNszTkYmIiIiIiIiIiIi4h8eHRwgMdH0tKnJ9/fFH19f8fGjSBCZNgkcegf37IT4eZs6E2FiPhCoiIiIiIiIiIiLidh5P2ubn//36gQNdLxEREREREREREZGLgVcPjyAiIiIiIiIiIiJysVHSVkRERERERERERMSLKGkrIiIiIiIiIiIi4kWUtBURERERERERERHxIkraioiIiIiIiIiIiHgRJW1FREREREREREREvIiStiIiIiIiIiIiIiJeRElbERERERERERERES+ipK2IiIiIiIiIiIiIF1HSVkRERERERERERMSLKGkrIiIiIiIiIiIi4kWUtBURERERERERERHxIkraioiIiIiIiIiIiHgRJW1FREREREREREREvIiStiIiIiIiIiIiIiJeRElbERERERERERERES+ipK2IiMh/mT37Zzp2bFPm6/DhxFLlc3NzeeONl2nfvhXdunUrse6vv/5k6NBBdO/ekaee+j9ycrKL161du4YuXS5l587tbj8mERERERERuXDYPB2AiIiIt2natDlPPDG6xLLx48eRl5dLeHh4ieUHDx7gkUfuJzc3m7K8/PK/CQ4O5pZbRvDZZx/z3XdTuPXWO7Db7bz11qsMGnQD9eo1cNuxiIiIiIiIyIVHSVsREZH/UqNGTWrUqFn886JFC0hOPsa99z5ISEhoibIJCftp0KAh//d/T9CvX88S69LS0jhy5DCPPPIYgwbdyKJFC9iyZTMAkyd/RU5ODnfccbf7D0hERC4a3WMvZUDNK9iRmcB7274h1BbM0Nq9aRRemwJnIb8lreGXxOWltgu0+HNdre40jrwEm2Fld9ZBpu6fR2phJg81HEr98Jolyi87up7J+38hyj+cQdW6UTesBkWmgy3pe/guYQH5joJS+6gfVouBNbtRKTCKY/lpfJewgF1ZBwBoF92EXlXbUyEggiN5KUzZN5f9OYfpU60jfat1LFXXV3tmsTJ543k6ayIiIt5HSVsREZG/UVCQz5gxbxMbW4Xrrx9San3btpdy+eWl/5kEcDjsAPj5+Rd/tduLOHw4kS+/HE+bNu24/fZhFBYWMmDA9QwffqvbjkNERHxfpF8Yfap1wGk6i5fdUudqYoOiGb/7JzpUbE632LZsSNtJYt6xEtv2rd6Jyyo244eEhaQVZnFznasZEtebsTumApBRmM0nO78vLp9lzwHg1tr9iPGPZOKeWdQIiaVPtQ7kOQqYlrCgRP3+Fj9G1LuW1IIMPtv1I/1rdGVE3Wt5dt1H1AipzC2XXM3WjL38cGARN9bqyV31BvL8+nEsP7qeLel7iutpF9OYTpVacjA36byfPxEREW+ipK2IiMjfmDbtW44dO8qoUU/g5+dXan1Zy06oUCGasLBwVq1aSePGTdm3bw/9+g3gnXfeoHbtOvzxxwoGDx5GdHQ07777Ju3bX069evXdeTgiIuLDrqvVnX05h4kJiAQg0j+M+Mg6TN0/n03pu9iUvuu029YIrozTdLLgyCoAelRpT/WQysXr7aaDfTklx3W3WaxszdjLwdyjbEjfydbMvfSp1oFqwZVK1d8gvBahtiB+TFjElow9VAqM4vpaPagfXpO6YTUA+CVxObuyDhAbuIYBNbtRP7wWmzN2k16UBUCoLYjW0fH8kbyJg7lH/6dzJSIi4u00EZmIiMhpFBTkM2nSRCIiIujb95qz3t5isTBy5L0sWbKIW24ZTHBwCLGxsaxZs4qePftQVFREjx696N37agA2bFh7vg9BREQuEvERtWkaWZep++YVL6t+PHlaLagS/2l+D2+0epiBNbuVuf2urANYDAuNImpTMSCSmIAI9mYfKl4fbAvk6Sa3806b/+ORhjdRKTAKh+nklyPLWZ+2A6thoU2FeAC2ZOwpVX+lwAoApBVmApBemH18eTT+FtcboHan6xMqmUW5AMQGRZeo4+rqnQmw+DHz4O9neXZEREQuPOppKyIichrLly8lMzODPn36ERAQcE51XHvtdbRrdxnHjh2jevXq3HnnLdx66x2EhYUBEBAQSGBgIABZWVnnLXYREbl42Awr19fqwa9H/uBIfkrx8iCr6/5SNTiGiXtm0aPKpVwZ2449WYdYl7a9RB1zEpdxSVh17m9wIwBH8lL49ngCOK0wk2BbIHMTlxNo9WdwXG9urXMNb2ydeHw/AbzR6mGcpsnvR9cy//DKUjH6WVz/ejqOD91gNx0ABFj9SMg9AkC7mCYcK0ijbUx8iW0AogMiuCymGSuTNxT3vBUREfFlStqKiIicxm+/LQSgY8cuxcvy8/NJSjpCQEAgsbGxZ1RP1arVqFq1GmPHvkdwcAhDhgxn5UrXJDBZWVnFSduwsPDzfAQiInIx6FGlPf4WP35LWkOQNQADsBpGcc/VxUl/sTMrAYfpoHHkJVwSVr1U0nZo7auoE1qdCbtnkOcoYGjcVYysP4g3Nn/JxD0zS5RtUyGeBhFxBFr9KcJOvqOQ1zd/Se3Qagyq2Q2rYWHS3jkltil0FgFgNVwf9vQzrAAUOApZnbKFNtHxdKncmi6VWxf38M2z5xdv36lSS2wWK8uOrT9/J05ERMSLKWkrIiJyGtu2bQGgQYOGxcu2bNnEgw/eTYsWrfjgg0/YsWNbcXIXIDMzk48/HovTaTJkyPDiHrW7d+/iu+8m8+67H2Kz2WjWrDlBQUF8/vk4QkPDsFgstGvXvnwPUEREfEL98FpE+ofxSssHipdVCIjgkuNjxQbbXG8OWo4nTE8kc0/VMqoBSfkprE5x3ft2ZiXQJjqeKP9waoVUIceez46s/cX1OE0ngdYAOkQ351DOMbZm7GV/zmE6VWpJqwoNSyVtk/JSiuMCiAmMBOBwXjJO08nY7VOJDojA4XTSLKoetUOrlRi3tlWFRqQVZJKQc+R/Pl8iIiIXAiVtRURETiMlJRlwTSh2Ort27WTixM+Lf87KymLChPEA9Ot3LWFhYZimyZtvvkLPnlfRokUrAMLDI3jssWcYO/ZdHA4799//CDVq1HTj0YiIiK+anrCgeCgEgNsvuYbMohymJfzKgBpX0LVya47lp9G1cmsANqXvBuCNVg9zMDeJ97ZN5mDuUeJCqnBpTBNy7PnUD69FVlEu6UXZPFCjC6G2YCbtnUOYXzCXhFVnc/oecu359IhtT5HTzrf75hFqC6ZiQCR7jveUvbFWT9pEx/Pmlq/YkZlAZlEOHSu2IL0wk/YxTUkrzGRnVgL1wmoysv4g1qVuZ23qdq6MbUdi7jH2ZB8EXBOQRQdEsDHt9BOpiYiI+BolbUVERE5jwYKlpZa1atWGpUv/LP65T59+9OnTDwCbzUJUVAhpaTnY7c7iMoZh8NFH40vV1bNnb3r27O2GyEVE5GJyao9UgCLTQa6jgJ1ZCXy26weG1r6KO+sNIKMwm8l7f2H38WRosC2weBKwiXtmcl2t7gyqeSUWw0Ji7lF+OLAIp+nk4x3TuL5WD4bVvooCZxF/JG/ihwMLKXQWMW7XdPpX68qIutdS4Cxic8Yepu1fAIC/xY9gWyAGUGTa+XTn91xfqwd31B3AkbwUJu6ZhcN0sjMrgd+T1nJ5xWa0iY5nd9ZBvtk3B/P48UT5u4YPytBYtiIichFR0lZERERERMSHPLv+o+LvUwsz+WD7t2WW+3D7VDpVdn0CJLkgnY93TCuzXFJ+apl1GIZBQu4R3t32DaZpllr/1d5ZxEfUJsueC8Ce7EO8tnlCmfv46eBv/HTwtzLXHchN4r5Vr5a5TkRExFdZPB2AiIiIiIiIlL9OlVuxIW2H2+pvGdWAYwVp5Njz3LYPERERX6WetiIiIiIiIheh0/WsPV/Wpm1nbdp2t+5DRETEV6mnrYiIiIiIiIiIiIgXUdJWRERERERERERExIsoaSsiIiIiIiIiIiLiRZS0FREREREREREREfEimohMRETcLikpiczMDE+H4XZWq0FERDAZGbk4HKanwykX4eERVK5c2dNhiIiIiIiI+BQlbUVExK2SkpK4adhgMrNzPB2K2xkGWCwWnE4n5sWRsyU8NIRJX09R4lZEREREROQ8UtJWRETcKjMzg8zsHOJa9SEksqKnw3ErwwCr1YrD4bgokrY56cfY99dsMjMzlLQVERERERHvs38/3HsvrFwJoaEweDC88gpYyhgx9uOP4Z134NAhqFsX/v1v6N+//GM+TklbEREpFyGRFYmIrurpMNzKMMBqs+KwXxxJWxEREREREa82cCC0bg179sDRo9C3L1SuDKNGlSw3fTo8+STMmgXt2sHEiXDDDbB1K9Sp45HQNRGZiIiIiIiIiIiI+JY//4T16+G11yAiAurVcyVrP/mkdNm8PFcP3A4dwM8PRoyAsDBXD10P8fmethaLgcVieDoMtwoJCaJxk3hqB9cizD/C0+G4mYHVahyf4Mf3u7FFFYaSl5tKSEgQNpveY3EHq9VS4qucfyEhQTRuHM8lNSsTGlHB0+G4lWGAxWrB6bg4xrTNDnFA4/hybaNCQoJoHB9P9SqVCY727esJTlxTBk6H6fPXVK7hICi+fK+ni43ueeUjJCSIxo3iiYusRniwj7dThoHVYuBwmvh6IxUR6Ud2I7VR7qQ2qnyEhATRsGFjgkKqYdiCPR2OexmAxYphOHw+fRAUkkvDhsFqo/7bmjUQFwdRUSeXtWoF27dDVpYrKXvCsGElt01Pd5WpVq08Ii2TYZq+fXc1TRPD8O2krYiIiIiIiIiIiJzi5Zfhhx9g9eqTy3btcvW43bMHatcuezvTdA2NkJQES5aUT6xl8PmetqmpOT7f0zYhYT+jn3yK+OCW6mnrY7IKM9iSu5YXX32FmjVreTocn2S1WggPDyIzMw+Hw+npcHxSQsJ+nnn2X1zS9mpCI3x/IrKLqqdtxjF2r57JSy/8p9zaqISE/Tz9/L+o3qMfwdG+fT3BRdbTNuUYB+f/zMvPl9/1dLHRPa98JCTs518vPkOTOzsSXlU9bX1FZmIqmz5dyn9Gv6Q2yk3URpWPhIT9vPry47zwRDXiavp+T1urxYrD6fs9bfcl5PLsa4d48unXfb6NiooKObsNzvb+VFQEt94KmzfDokVnt+155vNJW6fTxOn07b/OnJw8Nm/aQlBoBSIDoz0djlsZGFhtFhx2J6avt7pAen4Km7O3kJOTh92uBxd3cjicOsdukpOTx+bNW6ByWyKirZ4Ox60utonIMlKS2Ly5fNuonJw8Nm/ZQl6TdoSZvn09wfFrymrBcRG8EZB1JIk9W3TPKw+657lXTk4em7duITS9NlGRRZ4Ox60MwziljfLtRiot/Sibt6qNKg9qo9wrJyePbds2k5eTi2kP++cNLmQGYLViOnw/aZuXk8W2bXvVRv23ihUhJaXkspQU10N2xTI6gOTlQf/+kJsLv/8O0Z7NsWmgCxEREREREREREfEtbdpAQgIkJ59ctno1xMdDaGjJsqYJgwe7JiFbsMDjCVtQ0lZERERERERERER8TcuW0LYtPPkkZGbCtm3w9ttwzz2u9Q0bwtKlru+/+cY1JMJ330FgoOdiPoWStiIiIiIiIiIiIuJ7pk2DxESIjYWuXeHmm+Hee13rtm+H7GzX959/Dvv2QYUKrqTtidedd3oqct8f01ZERERERERERDzHEnQV1pBrwBqNaT+MI3sSmLn4VXipVNnCo7eD87/GIbVUxBZ+F4Z/YzCLcBauwZH5GZi5YKmALfxODP+mYDpwFq7HkfkJkA3WmtjCbsPwqwdmHs7833FkfQmUHvfVsNXGGvEwFr847Bnv4cxbeGLnWMNuxRLYAYxgTPtuHJmfYtr3gxGENex2LAFtwQjAWfAHjowPgcLzfQrlXFWvDrNnl73u1PHYf/21fOI5C0raioiIiIiIiIiIWxj+rbBF3I0jdz7O/KXYwu/CFvkYjowxADiyv8VZ8OfJDZwZpeqwRT6OYYvFnvEeFr9LsIbeCM5cHFmfYQsfiRHQFkfG+2AJwhY+Esw8HFkfYYQ/DvhjT38DS0ArrCHXYjqScOaWTOIZAW2wRT6JaT9cat+W4L5YQ/rjyPkRZ+FmbBEPYIt8nKLk+7CG3Y41uCf2zC9ccYbfBs4sHFnjz98JlIuWkrYiIiIiIiIiIuImThzZk3HkzAQzG2f+H1hDB2I60wAwHUcwi3b8zfY2nAV/Yubsxyz4A0fBWqyhN2L4xQFg+NUBx1Gc+Ytc9YXehGGrA0YQZv5vOAp3YRb+hdOZgjWkn6vXbRn7sKf9B8MajSXioRJrDL86ADhyfgZnMmbhFiyB7QF/LAFtMe1JOHN/dB1p0JVYgrooaSvnhZK2IiIiIiIiIiLiFmbhOhyF6wADLFEYAc0xHelgBANgCe6HNexOMPNx5M7GmfPdf9Vgx5nz7fHvbViCOgHgLFjr+lq4BUtgBwxbHBh+riEMiraBmQd50zAdDtd+gq50xWNPLB1jwUoAjKBuZcS/BYK6YQlohbNgNYYtDmfRHqAQjAAws08WdqZj+NUEIwzMrHM4WyInKWkrIiIiIiIiIiJuZQ27HWvINZiOJOzpLwEmpv0gZtEuHFlfYA25HlvYMIrsezAL1pSuwAjBv/I3mKYTR+4vOHOmA+DI/AzDFodfzHsAOAu34cj+usSmluBrsIb0x7Qfxpn781nF7cybj8O/CbaI+wAwHak40l92fV+0G8O/MYZfPJh5GH71j8fqD+bpahQ5MxZPByAiIiIiIiIiIr7NkfMTRanPYjpSsUX9G9OZT1HyfTgyx2IWbsCRPRkAi3+Lsisw8yhKeRRH1ngsQT2wht8PgC3qSQxrBYrSXsKe/gaGLQ5bxCPFm1lCbsAWPgLTfoCi1NFg5p9V3NbQIViDumLP/JSi1GfBzMEWNRqMIBxZE8DMwi/6FWwVXgTnseOx5p7t6REpRUlbERERERERERFxCyOgNZaQ68GZhlm4HmfuLAxLMJbAy7EEXlE8TAKG1fXVLPyvCsKxBF+D4d8Ms2gnztyZ4DiMJbAjGGFY/JtgFm7DLFiFM38ppn0/RkA7wICALthCb8JZuImilMfBmXwO8V+G6czDmTvTFX/+KgxrJQxbHUz7LoqOjqAo+X6Kjt2Oaea7JjMz8/63kyaChkcQERERERERERE3MWxx2MKG4bBE4ixYiSWoh2uFIwlb5CM48hbhzFuENfRGTNOBM38FWGLwi3kPZ8EqHJnjsIZeD2Yh9sxxGJZwsMYeH7c2C9ORjOHfCCOgPYbhh+EXh2lPACMYI8SVSHVkT8OwVQfAdOaB4wC26PcwjACKku8Gaw0MWw0MW93jMdfFCMjHLNqJad+Hxa8WluBrMO0JWAIvxTQLMR2JWMPvxhJ4BY7MjzD8W2Lxq4c960tPnWrxMUraioiIiIiIiIiIWzhzfsBhBGEJ6o4luBc4jmLPHIcz/zfsWRWwBvXGEnAZpuMQ9vTXMO27wFoJwxIK+IOZjz3tBaxht2GLfAzMfMyCv7BnfQqAPe1lrOG3YYt4EDAxC7dgz/wUw1YbwxIGgF+F50/GU7gRe+poDEsQEACANagD1tAhxWWsIX2xhvTFnvEejszPji8bBEYgpuMQjvTXwJmGI3s6hq061oh7wSzAkT0dZ84P5XBW5WKgpK2IiIiIiIiIiLiJE0f216UmBwNw5nyPM+f70ps4jmLP+BjDVgMAs2gn9tSny6zdtO/Gnjq69HLnIZzJA7E7HGVOClaU+h9XEhhwZE/BkT3ltEfgyHgbR5mHdqzMfYucDxrTVkREREREREREvIgFS3BPnAV/uG0P1uCrMN1Yv8j/Sj1tRURERERERETEizixpzzi1j04sj5xa/0i/yv1tBURERERERERERHxIkraioiIiIiIiIiIiHgRJW1FREREREREREREvIiStiIiIiIiIiIiIiJeRElbERERERERERERES/i9Ulbw4CAAAgMPPl64AFPRyUiIiIiIiIiIiLiHjZPB3Amtm+HuDhPRyEiIiIiIiIiIiLifhdE0vZ/ZRiejsC9DAN8/BAvagbHf8f6JbudzrF76Lz6vvJso3Q9+T7d88qHzrH7GAZ6OPdVhtqo8qJz7D66hn2XoTbK51wQSdsnn4TlyyEzE264Ad5+G0JDz2xbf3+re4PzAjab5fgDhIHh43+dJ47PsBhgejiYcmAcz8jbbBb8/Hz/WvYEq9U1SoyfnxWLxbf/fjzFZrMUt0++30ad+Orbx3nCid9pebZRJ6+ni+OBtPi+Zxj4+o3POP4so3ue++ieVz5sNsvxN90vpvse+Hqm2jAMDPRc7k5qo8qHzeY6zxdDG8VF9Gx+4hjVRvkWr0/atm8P3bvDl1/Cnj1w441w770wceKZbV9Y6PD5f+rsdieYYJompunb/9BhAhYD02li+vg/r+D6nWK6fsdFRQ5Ph+OTnE7XdVRU5MDhcHo4Gt9ktzuL2yefb6OOcx2rp6NwvxO/0/Jso05eT1wU59h14zMuimvKPP4so3ue++ieVz7sdicmF8mz+fGMyIm/X19mmq7/PtRGuY/aqPJht7vO7cXRRrmc+L/al534XaqN8i1en7RdseLk940awWuvQb9+8OmnrgnKzoSvt0MXQftzUXM99Pv+dewNdI7dQ+fV95VnG6Xryffpnlc+dI7dxzTRw7mvMtVGlRedY/fRNey7TLVRPsfi6QDOVlwcOBxw9KinIxERERERERERERE5/7w6abt2Lfzf/5VctnWrq4dt1aqeiUlERERERERERETEnbw6aVupEnzyCbz6KhQUwI4d8K9/wV13gVXjKouIiIiIiIiIiIgP8uqkbbVqMGsWzJgBMTFw+eXQuze8/rqnIxMRERERERERERFxD6+fiKxzZ1i+3NNRiIiIiIiIiIiIiJQPr+5pKyIiIiIiIiIiInKxUdJWRERERERERERExIsoaSsiIiIiIiIiIiLiRZS0FREREREREZHzZufOHTz00L307t2Va67pxXPPPc3Ro0mlyjmdTiZP/pohQwbSuXN7+vTpw2+/LSxe/+uv8xk4sC89e3bhlVdewG63F6+bM2cmPXt2ITn5WLkck4hIeVPSVkRERERERETOi8zMTEaNup8tWzYydOjNdO7clV9/ncdzzz1Vquz06VMZO/ZdAgMDGTnyPoqKinjmmSfYuXM7eXl5vPLKv6latRoDB17PrFkz+PXXecX7GDv2Pe64425iYiqW9yGKiJQLJW1FRERERERE5LzYvXsn1avX4OabR3Dzzbfz6KNPERkZycaNG8jOzi5RduFCVxL2//7vSW66aTjPPPMMDoeDn376gX379pCfn8+QIcO5++77CQsLZ8uWTQB8/PH7VKpUmUGDbij34xMRKS9K2oqIiIiIiIjIedGyZWs++mg8w4ffCkBS0hGys7OxWq1YrdYSZXNzcwEIDg4GoHLlygDs2LGteCgEf3+/4q9FRUVs2rSRWbNmULVqVW688VpuvPFa5syZWR6HJiJSrpS0FREREREREZHzLicnm9Gjn8But9O37zUEBQWVWN+wYTwAU6ZMIiEhgQkTJgCQmZlB9eo1sVqtLF++jPXr15GamkpcXB3eeusV4uOb8NtvCxkyZDhdu17J66+/RFpaWnkfnoiIW9k8HYCIiIg7XNG2Hn07NyYqPIik5CymzlvLuu2HaN80jkHdm1MhIpiEI2l89v0KDh3NKLV9lZhwhvdrS72aFSkotDN3+TZ+XryJmMgQ3ntiUKnyW/Yc4eXP5nFJjRgG92xJzdgosnMLWPLXbqYvWF+q/MjrLqdz67ollm3adZhXxs//23UA8XViGXnd5cREhfLiJ3PZurf0xB4iIiIinpSens6oUfezY8c2mjZtxv33P1yqzO2338WGDeuYPftnZs/+mfbt2wNgtVqJiopi6NCb+eqrL/juu8nExdUhNzeH1NQUBgy4no0b13PVVVdz6NBBJk36ki1bNtGhQ6dyPkoREfdR0lZERHxO/VqVuGPgZSxbt4fFf+5m+NVteHBoFx57+yfuvqEDW/YcYdLsPxkxoD33D+7MU2N+LrG9YRg8MrwrkWHBfDp9Oa3jazC4dysSj2WwfvshnvtwdnHZ8NBAHhrahX2HUrBaDB4e2oXCIgdjvllM2ya1GHhlcw4dzWDlhn1lxnpqXbn5Rf+47sp29bm5X1uOppUcE05ERETEW2RnZ/PggyPZs2c3HTt25rnnXirVyxagcuVYJk78lt27d2KzWalTpwadO3cunlxs5Mj76N27L1lZmURFVeC2227iscee4tChgwAEBgYW15udnVV+BygiUg40PIKIiPik7+atZcqcv9i8+zAbdx0mwN9Gm8Y18LNZmbl4M+u2H2Llxv3UrBJFtUoRJbaNjQmjWqVI/tycwKpNCXw9608AOrSojd3hZNeB5OLXpU1rkVdQxA8LN1IhIoTwkEA27Upk/Y5E5i3fBkBc1QqnjfPUuhKPZfzjOrvTyXMfzWHF+n3n8WyJiIiInD/PP/80e/bspn//gbz88punJFaz2b9/H8nJyQBs2rSRKVO+JiwsnIYNG7Fs2TIAmjZtXlxXrVpxNGnSjI8+ep/GjZvQo0dvQkNDAcjKyiQ9PR2AsLDwcjxCERH3U09bERHxOTv2H2XH/qMABAX40Sa+Bg6Hk1pVXMnT1MwcANIyXJNfVIkJLzFEQoCf6/ZYZHcAkJ1bgN3hpGrFksndujVi6NjyEr6ZvYbc/EIKioo4lpbNJTViCA8NpHHdWAB2Jhw7bazP3d2buKrRHDqazhc//cHuA8l/u27xn7sAaNWoxrmfIBERERE3Wbp0CStXLicwMJC6desza9aM4nWFhQW8884bXHXV1TzzzPPs27ebcePGsnz573Tu3JVvvvmKkJBQ+vcfWKLOlSuXs3z5UiZOnAJA27btMQyD999/h9TUVIKDQ0okekVEfIF62oqIiM8KCvDj0Vu6UalCGLOXbsE0TQAcDtdXu8MJQIB/yfcwDydnkldQRIsG1agQHkyX1nWxWS34+ZWc8fj6ni3Jyi1gwR/bAXA6TT76bimRYcF89MwNDOvblrnLt7Fmy4FSsaVn5XEoKZ0la3bz0dSlxEaH8fBNXbBaLX+7TkRERMSbbd++FYD8/HzeeutVXnvtxeJXUFBwibJ9+/Zn+PDbSEw8xKeffkytWrV4772xVKxYqbhMQUEB77zzOsOG3UL16q43rWvViuOeex5kxYql7N27myeeGE1YWFj5HaSISDlQT1sREfFJwYH+PDWiO3WqxzBvxTam/PIXN/drC4DVagDgZ3MlYfML7SW2LSi089XM1dx+bXvef+o6Dialk5dfSG5eYXGZapUiaFK3CvNWbKPg+PYnxrc9mpLFmEm/0SCuMtf1aEFSSiZzjw+VcMK3c9fy7dy1xT83r1+Vrm3rUa1ixN+uSziimZFFRETEe40YMZIRI0aedn2fPv2KvzcMg5Ej72PkyPuw2SxERYWQlpaD3e4sLhMQEMC33/5Yqp6hQ4czdOjw8xq7iIg3UdJWRER80oNDOxNXNZqvZ61mzlJXj4/EY5kAxESGciQ5i0oVXOOhHTqaXmr7xX/uYvWmBKLCg0jPyuOjZ24g4fDJhOmlTeMA+HNzQvGyBnGViAgNYuaSzWzZk8TOhGNc16MFbeJrlkrato6vgWma/LXVNZGG5Xgv2iK742/XiYiIiIiIiO9T0lZERHxOx5Z1aFqvKht3JrJz/zHq1ogBYN22gxT2aU3vDo2wWi20bVKT3QeTOZKcRf1alXj0lm7MW7GN6QvW8dIDVxPgZ+Ormau5unNjDMNg0Z87i/dRp3o0APtPSeQeSsrA4XByWbM49iem0SDO9dG+E71jP3l2MPsTU3nps3n0vKwhDeMqMf6HldgdTto1rsn+xFSOpGRya/9LT7uudrVoKkaFFE+e1qhOZcJCAti6N4msnIJyOb8iIiIiIiLiXkraioiIz2l8iWsCsKb1qtK0XtXi5eO+W8YHU37nxl4teWBIZ/YeSuGz71cAYLUYhAT542e1YJowadYabunXloeHdSUtM5ePvlvKroSTk4RFR4RQWOQgO/dkojTxWAbjpi+nX5fG/N/NV5CbX8hvq3cybf46AEKC/IvHz/1o6lJuuaYdN/Vtg2mabNiZyNcz/8Q0/35dz8sa0Ll13eJ9DureAoAXP5nL1r1JbjmfIuJ9du7cwQcfvMv27Vvw9w+gZcvW3HffQ1SqVLlU2bS0NN5442WWLFlEu3btGDPm4+J1v/46n7Fj3yU7O5srrriSxx57GpvN1U7NmTOTd955g2++mUZMTMVyOzYRERERUdJWRER80Lhpyxk3bflp15c1MdjWvUnMXLyJjJx8ADbvPszj784oVe6Ep8b8XObyPzbtZ/m6PRyf86yE1yf8So/2DQDXRGTvTVpcZh1/t+6fjk1EfF9mZiajRt1Pfn4ew4ffxtGjSfz443SOHj3CRx99XqLs5s2beOKJRzBNZ6l68vLyeOWVf9OwYTxNmjTj668n0KpVG3r16kNmZiZjx77HHXfcrYStiIiIiAdoGmoREREgKMCP9s1rs/b4OLLu0KN9gxJj4IqInIvdu3dSvXoNbr55BDfffDuPPvoUkZGRbNy4gezs7BJlt2/fSteuVzJmzLhS9ezbt4f8/HyGDBnO3XffT1hYOFu2bALg44/fp1KlygwadEO5HJOIiIiIlKSetiIiIkBeQREPvTbdrft488uFbq1fRC4OLVu25qOPxhf/nJR0hOzsbKxWK1artUTZfv2uxc/Pj8OHE0vVY7fbAfD39yv+WlRUxKZNG5k1awadOnXhxhuvxWq1cuutd3DVVVe78ahERERE5FTqaSsiIiIicoHKyclm9OgnsNvt9O17DUFBQSXW+/n5nXbb6tVrYrVaWb58GevXryM1NZW4uDq89dYrxMc34bffFjJkyHC6dr2S119/ibS0tNPWJSIiIiLnl3raioiIiIhcgNLT0xk16n527NhG06bNuP/+h89q+6ioKIYOvZmvvvqC776bTFxcHXJzc0hNTWHAgOvZuHE9V111NYcOHWTSpC/ZsmUTHTp0cs/BiMhpJSUlkZmZ4ekw3M5qNYiICCYjIxeHo4zJAXxQeHgElSuXnkBSRASUtBURERERueBkZ2fz4IMj2bNnNx07dua5514q1cv2TIwceR+9e/clKyuTqKgK3HbbTTz22FMcOuQa3zswMLC43uzsrPN6DCLyz5KSkrj19uvIzb8I/v4MsFgMnE4TLo6cLcGBYUz4fJoStyJSJiVtRUREREQuMM8//zR79uymf/+B/N//PYnF4hr1LDs7m5SUZEJCQomJiTmjumrVigNg9OgnaNy4CT169GbatCkAZGVlkp6eDkBYWPh5Pw4R+XuZmRnk5mcx6OHaVKoR6ulw3MowwGqx4HA6MS+CpO3RA9lMf3cvmZkZStqKSJmUtBURERERuYAsXbqElSuXExgYSN269Zk1a0bxusLCAt555w2uuupqnnnmedasWc2aNavJyckG4ODBg3z88VicTpO77rq3eLuVK5ezfPlSJk50JWvbtm2PYRi8//47pKamEhwcQtOmzcv3QEWkWKUaoVS/JMLTYbiVYYDNasXucFwUSVsRkX+ipK2IiIiIyAVk+/atAOTn5/PWW6+WWPf008+V+Hndur+YOPHz4p8TExOZMGE8QHHStqCggHfeeZ1hw26hevUagKv37T33PMikSRPw9w/giSdGExYW5rZjEhEREZGSlLQVEREREbmAjBgxkhEjRp52fZ8+/cosa7NZiIoKIS0tB7vdWVwmICCAb7/9sVQ9Q4cOZ+jQ4ecvcBERERE5YxZPByAicr6Ypsm0aVPo3r0jHTu24fDhxNOW/e67KQwZMpBOnS6lY8eOfPjh+xQVFQHw119/MnToILp378hTT/1f8UdKAdauXUOXLpeyc+d2tx+PiIiIiIiIiFyclLQVEZ9gt9t5+OF7ef/9d7BarX9bdsaMH3jvvTeJjo7hgQceoVKlSkyc+AVfffUFAC+//G9sNhu33DKC5cuX8t13U4r38dZbrzJo0A3Uq9fA7cckIiIiIiIiIhcnJW1FxCcUFhaSmZnBp59++Y8JVYvFwuDBw3j++Ze44YbB3H///YBrjMC0tDSOHDlM//4DGT78Ni65pC5btmwGYPLkr8jJyeGOO+52+/GIiIiIiIiIyMVLY9qKiE8IDAzk008nYrP9c7N29dX9AcjNzeXgwQP89NNPAHTufAUOhx0APz//4q92exGHDyfy5ZfjadOmHbffPozCwkIGDLie4cNvdc8BiYiIiIiIiMhFSz1tRcQnWCyWM0rYnmry5K+47rr+LFmyhEcffZK+fa+hQoVowsLCWbVqJbt372Lfvj3UqVOXd955g9q16/DHHyu44oruDB06nHHjPmDnzh1uOiIRERERERERuVgpaSsiF61evfrw8stv0KxZM9566zW++24KFouFkSPvZcmSRdxyy2CCg0OIjY1lzZpV9OzZh6KiInr06EXv3lcDsGHDWg8fhYiIiIiIiIj4Gg2PICIXpfz8fKpXr0FcXC26dLmcDh068OWXn3H99YO59trraNfuMo4dO0b16tW5885buPXWOwgLCwMgICCQwMBAALKysjx5GCIiIiIiIiLig5S0FRGfl5+fT1LSEQICAomNjeWOO25m27YtTJnyA3FxtXA4HABYLNbibapWrUbVqtUYO/Y9goNDGDJkOCtXLgdcidoTSduwsPDyPyARETmvkpKSyMzM8HQYbme1GkREBJORkYvDYXo6nHIRHh5B5cqVPR2GiIiIyFlT0lZEfEJWVhaTJ38FwOHDiQBMmfI1ISGhRERE8P7779CiRSs++OATunXrzrZtW3j22afo2/dqli1bAkCXLt1K1Ll79y6++24y7777ITabjWbNmhMUFMTnn48jNDQMi8VCu3bty/dARUTkvEpKSmLorYPJyM3xdChuZwAWi4HTaXJxpGwhIjiEbyZMUeJWRERELjhK2oqIT8jOzmLixM9LLJs+fSoATz/9XInlgwcPw2q1Mm3at7z//rtUrlyZm24azm23jSwuY5omb775Cj17XkWLFq0AV2+dxx57hrFj38XhsHP//Y9Qo0ZNNx+ZiIi4U2ZmBhm5OVQc1IugyjGeDse9DAOb1YLd4QTT99O2eUnJHJs+l8zMDCVtRURE5IKjpK2I+IQqVaqydOmfp13fp0+/4u8tFgs33ngTN954EzabhaioENLScrDbncVlDMPgo4/Gl6qnZ8/e9OzZ+/wGLyIiHhdUOYbQ6lU8HYZ7GQY2m8V1v7sIkrYiIiIiFzKLpwMQERERERERERERkZPU01ZERERERERETqtmaCfqR/YhyBZNrv0YW9N+IDHnvz/lZtCkwo3UCuuExbBxOHcda4+Nx2EW0rPGm4T4VSxRekvqdLanzyDUrwrNo4cRFXgJDmcBibl/sjF5Mk7s/1W7hSYVbqRaaDtsliDSC/axIWUSmYUHsBmBNI0eQmxwC6yWAA7n/MW65Ak4zEIAYgIb0briHQT7xfB74isk529z5+kSETkv1NNWRERERERERMoU4V+TVhVvJ9eeysqk97A782lT6W6CrBVKlKsf0Yc6Ed3ZlPotuzLmUSW4JTVCLy9en16wj98OvVD82p/1OwBtK91DVOAl/HXsUw7k/E6d8O7UjehVKo464d2pG9mbg9l/8OfRcUT416BdpXsBaBo9hLjwruzM+IVtaT9RM6wD8RWuAyAu7Ao6VHkUh1nkrlMkIuIW6mkrIiIiIiIiImWK8K+FYVg4kL2MY3mbOehXlQqBdQn3r05eXmpxuVrhXTiSu479WUsA2Jo2vUQ9Rc480gp2/1ftBhH+NTiWv5XDuWtIK9xOvYh+RATUKhVH5PFluzPmkedIJTl/O1VDWmMx/IgNbkFO0TF2ZcxxxRLWiRqhl7Ex5RtMHPx26AWqhLSkkf+A83hmRETcSz1tRURERERERKRMqQW7cJp2KgbF42cJJjqwPnZnAemF+4vLWI0AQv0qY8HGFdX+Tb+4T2hf+WH8LSHFZUL9YulR4/XidQHWCMAktWAX4X7VCbbFUDEw3rXP/F2l4kjO3wFA5eBmBFgjiPCvQXrBfpxmEVZLAM5TetIWODIIsIbjbwlhf9YSMk6JVUTkQqGetiIiIiIiIiJSpuyiw6xP/prmMcOpFdYJh7OQv46Np8CRUVzGzxIMQIXAuvx17FMqBNSnQdTVNLQPYEPK1+TYj2IzAtiePoNw/xo0rnA9TrOIVUfHsubop3Ss8gQ9a7wFwOGcNezOnF8qjv1Zi4kJbEjLirfRktvIs6ex5uh7gGvohZjABscTyvlEBVwCgMXwB3LcfIZERNxDPW1FREREREREpEwVgxrTPGYYB7KXsSTxZY7krqdVpTuIDKhdXOZEL9fDOX9xJHc9W9Km4XAWEh1YH4Blh19nceJ/OJK7nh3pM8kuOkrFoMZYDD8ui30EJ06WHXmDDalfEhvckqYVBpeKo2HUAGqGXc6G5K9Zevh1ipy5XBb7CDYjkE0pUyh0ZtO56jN0rPIkefYUAOzOvHI4QyIi7qGetiJSSlJSEpmZGf9c0AdYrQYREcFkZOTicJieDsftwsMjqFy5sqfDEBEREZELRJXgVlgMG3syfiW9cB9Ww59qoW2JDWpOesFeAAqd2RQ4MvGzunrcGhhgGDjNIvwswVQKakKuPZm0gj0AWLDgNIsI96tGmH9VdmfM51jeJtIKrdQP70+VkNZsTJ1cIo5qIW0ocuYV98I9kruW+pFXExFQi5T87fySMIoQWyVy7cfoVOUpsouOYjfzy/FMiYicX0raikgJSUlJ3HTTjWRnXjwfI7JYLTgdTk+HUS5Cw0OYNOlbJW5FRLxMlZAwHm7ZiYZRFckoyGfS9rXM3b+jRBmbYeG+5pdzaWxNLAYsP7yfseuX4zBdbzpeX68Z117SmGCbP5tTjvDGmsVkFOZTKSiUe5q1p2lMFQqdDv5MOsjHG1aQaz/9TOrX1W3KiCbt2JB8mCeWzgbgsiq1GN6wFVVCwkjOy2Hqzg3MT9gJQPOYKtzZ5FKqhoaTmJPJxxtWsinliJvOloiUp8zCBAAuiejB/qzfqR3eFQATJ31rfciezAVsTfuehKyl1AnvTvXQy4jwr4HV8ONI7npM00mLmFspcuayPnkiUQF1CPaLYXfGfHLsR7E784kNbsHRvA2E+lck0BbJ4Zy/AOhW7UWslgDmH3iMjIIEwv2rUzeiF5mFh6gS3AqHs5DsoiM0j7mFmqEdWJc8gcrBTYgKrMOmlG8BiPSPI9gvhjC/agDEBDbE3xpKct52Cp1Z5X9CRUTOkJK2IlJCZmYG2Zk5NKh5JWEh0Z4Ox+0MA6wWKw6nA9PHO9pm5aSwPeFXMjMzlLQVEfEiBvDcpT0ocjp4cdVChjRowfCGrVh5OIGMwpO9xAbUbULvuAaM27gSm2FhRJN2HMjK4Ifdm+hSrQ63N27L+E2rKHDYGdGkHddcEs9XW//iqbZXUCUknLf+WkL9CjEMrteCnKJCxm1cWWY8MYHBDG3YEod58g3NysGhPNXmCjalHGHcxpUMadCCh1t2Ymd6Modzsni6XTeScrN5edVCbmvchqfbduPWed9S6HS4+/SJiJvty1pCkC2GmqGXUy2kHXmOdDalfEtawR7iKwzCYrjSCltSp2OzBNMi5mYczkJ2Z8xjZ8YcnGYRK468RZPoIbSrfB+Fjmx2ps9ha9r3OMxCViaNoXHUdbStdB8Os4hDOavZkPw1ADZLIDZLAAAbUr4Bw6B+5NVYjQCyiw6z6ugHFDgy2JE+kzC/KrSMuQ27WcCO9FnszJgDQJ2I7tQK61R8PI0qDADg98RXSM7fVp6nUkTkrChpKyJlCguJJio81tNhuJ1hgNVqxeHw/aStiIh4p/gKlakVHsXzK+exOukAq5MOlFmuc7XaHM3N5sfdmwG4vn4zulSvww+7N9GrVn12piczbddGAH7euxVw9c5dnXSQfZmprDiSwIbUwwyu14I6ERVOG8/IZu3ZnnaMKiFhxcsCrX58s30dyw/vIyErnVrhUTSvWJV6kTFUDg4j3D+Q8ZtW8efRg1QNDeeeZpfRvGIVVicdPF+nSUQ8xmRr2nS2pk0vtWZH+iwKHK7eqk7srEv+gnXJX5Qql1qwmyWJL5ZZ+7G8zfyWtxnDAJvViv2U5/IVR96mXeX7ACh0ZvHn0Y/LrCPPnsLSw6+Wue6vY5/x17HP/vEoRUS8jSYiExERERHxoBMJ1NaVqjOp9xAmXzWUQXWblipXNSSc5LyTwxel5OVSPTTieB3R5NvtvNaxDz9cfQuvd+xL5eBQ7KaTb7avZfnh/dgMC52quiYO+vM0ydTWlapxaWxNPtqwosTy/VlpTNmxjoSsdKyGwRXVXTOzJ2ZnUi00HIDkvFxXXPmur9WOxyYivslmBFI9tD1Hcte6bR+1w68k8fhQCSIiFxslbUVEREREPCjEz/XR34pBIbz2528czc3mjibtqBcZU6JcgM1WYsgCu+kk0Or64Fyonz8NomKYtXcrE7b8SdOYWO5pdtkp+/Dn52tu5YFmHZi1dxvf7dxQKg4/i4V7ml3G9J0bSchKLzNWq2HwRJsraFihEosP7mFzahIBx2OwH4/NfnxIhECr3zmeERG5ENjNfOYmjCK7yH3jV29I+arMHr4iIhcDDY8gIl6pS4/GXNmnOZEVQjh6JJMZU/9gw5p9JcqERwZz4y0dadCkOgB//L6daV8twzTBarUwZERnmrWKw7AYrFu9h8mf/1484VjPa1py5VXNCAr2Z/f2I3wxdgGZGXml4risS0N6XdOSCjGhHDuayczvVrN21R5uuacbl3VpWKr82y/8yJ4dRxh40+W0urQOAYH+7NuVxJQJv5OUmH7ez5OIiFz4ipx2AKbt3Mjm1CR+3L2Zx9t0pXF0ZXamJxeXK7DbsRon+1z4W6zkOVyTiRU6HWxLPcqSQ66Z3PvVaUTj6JPjl+cVFfHQ4hk0jqnMiPi22CwG765dWiKO6+s1I8BqY8aeLYT4+WNgYDUMQvz8ySkqxGZYePbS7rSNrcGyxH289ddiAPLtrvhtx2Pzs1hdyx2nn+hMRERERP6eetqKiNdp3KImQ0Z0Yee2w3z01i/4+Vu586GehEcGlyh3271X0qJtbaZ/vYwVi7dxZZ/mXNG7GQDd+zanY7d45s74i/k/r6XTlY25opfro6ZtLqvLwKGX8evs9Xz/zQouaViFrr1Kfwy1RlwMw0deQWpKNh+9OYeC/CJuf6AHUdGhzPr+T14bPb349dcfu8nPL+JIYjo9rm5Jt6uasXDOBr78aCF16sdy+33d3X/iRETkgpSQlQFAqL8/ABbDAKDQUXISrwPZGVQKDnWVwaBScCgJmemudVnphPoHFJe1GAaFDgcR/oFce0ljWlSsyo70ZH7et5XE7MziYRJO1bxiVWKCQvjmqqFM6zucSsGhNI6OZVrf4QDc3+Jy2sbW4Iddm3hx1a8UOV1vhB7MdsVwIrYqIa7hEk7EJiIiIiJnTz1tRcTrOJ0mM6etZuGcDeTmFLB+9V56XtOS2KqRZKa7xsnz87PSoEl1dm5NZMXi7VgsBh2uaES7jvVZOGcDrS+rS2pyFr/Odn38s+c1rWh7eV1+nb2eDt0asX/PUeb9vA7DgKW/bsXhKD27dfVaMVgsBn/8vp2tGw9SpXoF6tSLpWqNCmxel0ByUiYAsVWjaNoqjjk/rCEzPZeUY5n88M0K5v28DoCkw62pUdtVl9Op2c5ERKSkdccSOZabzY31W1DkdNL/ksYUOR0kZKXzXd9hrDycwFt/LWHxwT3c1fRSrqkTT7DNnyCbH4sO7gZgfsJO7mt+Of1qN8JqsVAlJJy5+7dT4LAzuH4LCh12xm5cQYWgYKqEhLM1NQmA+5pdTtcadRi1+GfGbVxJiJ9/cVxPtrmCtII8xm1cSePoyvSq1YC9GaksObSHhlEVAUjOy2H9scOk5edxVVwDjuVl071mPZLzctiQfLj8T6aIiIiIj1DSVkS8ztYNB9i64QCG4RoCoWHT6mSm55Kw9+RHRP38bVgsBkVFro9kOp0mOdn5xFaNBKBSbASHDqQWl89Iy6Hy8XU1asVw+FAao57tT9wllUjYm8yEDxeQfDSrRBx7dh7BYXfQsEl1Nv61n0saxFJYUMSBfcklyl1/cwfycguYP3MdAKuW7SxeVyMuhmo1o0k+mqWErYiIlKnI6eC5lfN5sEUHnr20O0dysnh19SKO5mUT6heAv9U13MBPezZTNSScwfVbYGLy4+5NzN67DYCZe7cSHRjMTQ1bYhgGCw/s4rNNq8h32Hl2xVzubHIpT7e5gnyHnT+TDvLxRtdEYwE2K6F+ARiGwZ6M1FJx5RQVsjH5CDc1bAlA7YgKvNPlmuIyX2/7i0nb1vLiqgXc0+wynml3JQey0nlrzeLiMW5FRERE5OwpaSsiXuu64R24sk9zUo5l8tGbc8jPKyxel5tTQPLRTOo2qEJs1SgqxIQSXTEc03QlRv0DbDjsJ3vP2u0O/ANcE6IEhQQQd0klJny0kHWr93LDLR254ZZOfPjG7BL7T0pMZ8qEpQy+rROXdWlIUaGdieMWFff2BajbsAqNW9Tkh8krKSq0l9i+SrUo7nu8L4YBU78sOW6giIjIqfZmpvLIkp9LLf9g/XJqhkUC4DRNxm5YztgNy8us48uta/hy65pSy3ekJ/PY0llgGNhsFux2Jxy/X7791++0rlSd9ILS47rfOm9q8feTtq1l0rbTzxC/JfUoD/z2098eo4iIiIicOY1pKyJea8Gs9bz74gzSU3N48Ol+VKkWVWL95M+XYBgGz789hJvvvoL01GzycgoAKMi3Y7VZi8v6+dkoyHdNiFJU5GDX9sOsWbGLRb9s4OiRDOo2rFJq/42aVmfwrR1ZuWQ7b/37Rzau3c/Nd3ejVp1KxWWu6NUUp9PJ8kVbS2xbIy6G/3vuWkLCApnw4UI2rd1/3s6LiIhcHCyGwVVxDVhx2H33kI5V4zick0lmYYHb9iEiIiIiZ089bUXE6zRpWYsacTHMnbGWtJRsQsMCueOhnjRuUZPDh9KKy21el8DjI78gumI4x5IyePH94RxMSAEgKTGN6BjXhCiGYVChYhiHjq87ciiN4JBTJmuxGBQVlR7Ttnmb2lhtVhbP20TC3mP4+VtpdeklNGlZk/17juIfYKNJy1rs3nGErMyTPZTCIoK47/E+WG0Wxrz8Mzu3JrrlPImIiG9zmib3L/rRrftYmriPpYn73LoPERERETl76mkrIl6nWs1o+t94KdcNu5wGjavRoVsjALKz8nl7/AhuuacbAPc93pcX3x9OxdgIrr6+LRGRwfz+6xYAVi/fRWSFUK7o3ZTe17YiMNCPVUt3ALBi8TZq1alE115N6HZVM2IqhRf3hB1ye2feHj+C2KpRHNzvGrv2it5NqR9flU5XNgYoTv5WqVaBgEA/Dv7XGLfXDm5PZIVQFs/fjL3IQe26laldtzI2m5pcEREREREREfln6mkrIl5n/s9rCQzy4/KujejUvTGpyVlM+WIJO7cmEhwSgL+/q+n66duVDL/rCu54sAc52flM+2oZa1bsAmDRLxuoFBvBVde2xgR+nb2eJfM3A7B43iYiK4TQd1BbDMNg1bKdfD/JNT6gf4CN4JAADAOWLdpKhZgwLu3UgDaX1SU9LYfvv1nButV7AYiKdvXkTU/LLRF/wybVAOjdvxW9+7cqXv7MA1+RcqzkZGciIiIiIiIiIv9NSVsR8TpOp8lPU/7gpyl/lFo3efxiqlSvAMDB/Sm88sy009Yx+fMlTP58SZnrT9RvGGC1WnE4XMMjfPnRQuKb1yQzMw/ThBlTVzFj6qoy61i3eg93D/6w1PJnHvj6jI5TRERERERERKQs+qyuiFwwLBaDjlc2Lu7p6g6tLq3DsSMZ5GTlu20fIiIiIiIiIiJ/x+uTtvv3Q9++EB0NtWrBE0+A0+npqETEE5xOk5eenMq2TQfdto+//tjDm8//4Lb6RURERERERKScnE1iccwYaNAAwsOhY0dYs6Z8Y/0vXp+0HTgQqlWDPXtgwQL44Qd4911PRyUiIiIiIiIiIiJe7UwTiz//DM89BxMnQlIS9OsHV18NOTnlHvIJXp20/fNPWL8eXnsNIiKgXj0YNQo++cTTkYmIiIiIiIiIiIjXOpvE4rhxcNttcOmlEBQEjz0GhuFK5nqIV09EtmYNxMVBVNTJZa1awfbtkJUFYWH/XIfFYmCxGG6L0RuEhgbRpEk8lfyiCfEL9XQ4bmUAhsWC6XRiejqYcuBfBE2K4gkNDcJmK5/3WEJDg2jcOJ5KscGEBPn23w4ABlgt4HAa+PpF5RcaTGNL+V5PcPKaqhrlT1CIo9z26wkGYLGaOB0XRxsV4vSHxh5oo+LjiQn2J9Dw7esJXM+JFpw4DdPnr6nIYH+C4j3TRjVpFE+kJZDAAl8/y2C1OnE4THz+pgfkWwIJb+Sh+158PBWLwglJ9yu3/XqCYRgnn81N376mbEXhNPZQG9WoUWOsubEUHg0ut/16hAFFhgWn6bwYmiisuUE0auT02DWVlhXN/sTActuvRxhgtVpwOHz/mkrLiqRRo+Byv5683tkkFtesgcGDT/5ssUCLFrB6dcnl5cgwvfju+vLLrl7Lq1efXLZrlysxvmcP1K7tudhERERERERERETES51NYtHf31W2b9+Ty4YNA6sVvvyy/GI+hden3703pSwiIiIiIiIiIiJe62wSi16WhPTqpG3FipCSUnJZSorro4IVK3omJhEREREREREREfFyZ5NYPF3ZSpXcG+Pf8OqkbZs2kJAAycknl61eDfHxEOrbQ7eKiIiIiIiIiIjIuTqbxGKbNq5xbU9wOOCvv1wTk3mIVydtW7aEtm3hySchMxO2bYO334Z77vF0ZCIiIiIiIiIiIuK1/imx2LAhLF3q+v6ee2DiRFi5EnJz4aWXICCg5Bi35cyrk7YA06ZBYiLExkLXrnDzzXDvvZ6OSkRERERERERERLza3yUWt2+H7GzX9717wyuvwA03QIUKMH8+zJ4NQUEeC90wTS8bZVdERERERERERETkIub1PW1FRERERERERERELiZK2oqIiIiIiIiIiIh4ESVtRURERERERERERLyIkrYiIiIiIiIiIiIiXkRJWxERERERERER8Xq7d+/2dAgi5UZJW/E4u91e/L1pmh6MREREROTCpecoEfFG/902qa2Sc7V48WLuueceZs2a5elQRMqFkrbiUXa7nRkzZnDw4EG2b9/OihUrdBMXEa+jdknOJ11Pcr6lpqZSWFiIYRieDkV8kNos+V+daJtWrVpV4meRsxUdHU2bNm2YOHGiErdyUVDSVjzKZrMRHR1N9+7d6d+/P1WrVtVNXP5nJ/652LZtG2vWrGHnzp0ejkguVCeuJYfDUWK50+n0RDjiA0zTxDAMNm3axIwZMzh8+HCp60vkbDgcDjZt2sQnn3zCk08+yffff+/pkOQCd+Lel5aWRmFhIbm5uSWWi5yLNWvW8OqrrxY/l+t6knPRpEkTRo4cSZMmTZg4cSILFizwdEgibmXzdAAiFStWBFwJ3BP/uDqdTiwWvacg58YwDGbPns2//vUvwsPDqVSpEoMHD2bAgAGeDk0uICeSa7///jvz588HoFmzZlx99dUEBgZ6ODq5UBmGwZw5c3jmmWeIiIjA4XDw+OOPc8UVVxASEuLp8OQCZLVaiYyMZPr06aSkpHDXXXcBJ9swkbNx4rqZP38+48ePx+l0Ur16dQYMGECnTp08HZ5cwGrVqkVOTg5z586lXr16ap/krDkcDqxWKxkZGVgsFg4dOsRbb72Fw+GgV69eng5PxC2UFROPi4uL4+uvv+aWW26hf//+rFixAovFop5sctZOvGOflZXF+PHj+eCDD3jvvfdo2rQpEyZM4LvvvvNwhHIhOPHmkWEYLFiwgAcffJDY2FhsNhvTpk3j4YcfVs9IOWdHjhxhwYIFvPPOOyxatIgOHTrwwQcfMG/ePHJycjwdnlxATu2llpmZic1mo27duvz444+kpqZiGIaepeSsGYbBkiVLePLJJxk+fDiPPPIIgYGB3HnnnRw5ckTXlJyRsq6TmJgYnnzySWbOnMmGDRs8EJVcqE5cT1arlQULFnDrrbfSsmVLbr/9dqpUqcInn3zC7NmzPRyliHuop62UuxPv4G/fvp1jx45RqVIlWrduTbNmzcjMzOTOO+/ks88+o3379oB63cqZMwyDZcuWMWfOHDp27Mhll10GQFhYGBaLha+++gqA66+/3pNhipdav349zZs3x2q14nQ6yczM5LPPPuM///kPV199NXv37mXgwIE88MADWK1WT4crF6AdO3bw/vvvExgYSMeOHQF45ZVXGD16NOPGjQOgZ8+e6nEr/+jUZ6OcnByqVavGjBkzmDlzJt9//z25ubk88MADRERE6DlKztiJNwJ++eUX7rrrLvr27cuhQ4dYvHgx999/P7GxsR6OUC4EpmkWtzkLFy7EZrPRuXNnwPXR9vj4eLZv306zZs2Ke06KlGXs2LF0796dBg0aAFBUVMScOXMYMWIEffr0AeCyyy7jm2++4bPPPsNms9GzZ09Phixy3ukJTsrViYTtnDlzuPPOO/nPf/7DU089xdixY7FYLDz33HMMHDiQO+64gxUrVvDjjz8yb948T4ctFxCLxcKPP/7IjBkzOHbsGAC1a9dmyJAhtG/fnsmTJzNp0iQPRyneZtWqVTz00EN88skngOs6ysvLIz09nS5dupCcnMyQIUO49dZbuf3228nNzeX333/HNE2NySb/6EQPkd27d5Oens6yZctITEwsXv/iiy/Spk0bxo8fz88//1w8fqRIWU5NiIwZM4bbb7+dJ554gpUrVzJw4EC6devG1q1bGTt2LLm5ufr0kvyjUz+pZBgGBw4coEKFChQWFjJw4ECuv/567r//frKzsxkzZgzHjh3TvU9KOXFNnBj2YPv27bz22mu888473H777Wzbto3IyEh69erF2LFjyczMVMJWTuvYsWMcO3aMmJiY4mUOh4PDhw+XeIZq1KgRgwcPxs/Pj/fee4+ZM2d6IlwRt1HSVsqN0+nEMAzWrVvHCy+8wJtvvsncuXNp1qwZ33//PR999BFOp5Nnn32W/v37M2LECEaPHk29evU8Hbp4sf/+p+Gyyy7j888/Jz09nTFjxlBUVAS4ErdDhw6lcePGzJo1i6ysLE+EK16qWrVqXHPNNfzyyy+MHz8egCpVqhAdHc2///1vrrnmGm6++WYeeughAN555x1++uknDMPQmGxyWifapxMJs6uuuooHH3yQatWq8fTTT5OQkFBc9sUXX6R+/fpMmzZNw2/I3zrR5nz22Wf8+OOPPPbYY9x1113Url0bq9XKiBEjuPLKK9mwYQPPPfcct9xyC1OnTvVw1OLNDMNg0aJFvPDCCwB06tSJ9957j/bt2zNixAgefvhhAJYvX87y5cuJiYnRvU9KKCwsxDCM4vvXZ599xg8//MCUKVN46aWXcDqdPPPMM9x9993ExMTQsmVLvvvuOyX/5bQqVqzIv/71L6Kjo1mxYgUbNmwgMDCQoUOHsnjxYubOnQu4nrUaN25Mhw4dyM3N5ccff9T/eeJTNDyCuNW7777LwYMHefPNN4t7hezfv5+2bdvSrl07kpOTmT17NnXr1mXJkiVYrVbuvfdeRo8ezR133IGfnx/Vq1f38FGItzrRc/vPP/9k7dq1REREFF9bH3zwAffddx+GYfCvf/0LPz8/4uLiuOOOOwgJCSEsLMzT4YsXqVatGkOHDsVisfDTTz/hcDi46667GDBgAGPHjqVWrVrce++9xeWDgoIwDKNUrxKRE060T0uXLmXevHnY7XZatmzJddddxxNPPMHYsWN57rnneOGFF6hRowYAb7/9NkePHlX7JH/LNE2KiorYsGEDjzzyCG3atCnxEePMzExuv/12goKC+PXXX8nJyWHQoEEejlq82aFDh5gzZ07xx4q7devGpk2b2LBhA/379y8ud+TIEYKCgsjNzdUwLlLstdde46effmLhwoXFE7UGBQWRl5dHVFQUUVFRTJgwgSVLlrB8+XLuvPNOgoODOXr0KCNGjAA0caKUdOJ6sFqtZGdn8+2337J8+XImTJjA1Vdfzbp16/jwww8xTZPevXsDEBISwqBBgxg2bJieo8SnGKbe3hI3GTduHDNnziQpKYmOHTvy9ttvA/Dhhx+SmZnJXXfdxcCBAxkyZAgjR47knnvuYdOmTURGRlKjRg3effdd/P39PXwU4u1++eUXnn32WZo3b05mZib+/v48++yz1KtXj+XLl3PvvfcyYMAAnn76afz8/DwdrnihU/9RSEpK4ptvvmHhwoUMHjyYm266iXHjxrFo0SKio6Pp378/mzdv5quvvmLq1KnUrVvXw9GLtzl1/ND58+fz+OOPc9ttt7Fx40bS0tKoUqUKY8aMYc2aNYwdOxar1cq//vUvatWq5eHI5ULz6KOPEhERwRNPPFHieenTTz8lMDCQ4cOHk5mZSVhYGIZhYLfbsdnUX0NOcjqdJCcnc9111wHwxBNP0LdvXwBWr17Nt99+y2+//Ua3bt1wOp0sXLiQSZMm0ahRI0+GLV5m+/btPPXUUzgcDr799lsCAwP55ptv+PXXXxk/fjyFhYUl2qj169fzxx9/MHHiRO644w5uvfVWzwUvF4S9e/cyZswY1q9fzyeffEJMTAxvv/02c+fOpXPnzoSEhDB79mymTp1KXFycp8MVOa80PIK4RXZ2dvFD3vTp01m4cCHPP/88APfeey9PPvkkK1eu5JJLLmHkyJEAtG7dml69etGhQwceeughJWzlH+3du5fX/7+9+46rsu7/OP46LEEQAQeaC0cmt5aluAeZI6XMlbhSIXGBAxe5olITQxQHrgw1HIjmyr1LK+PWXCg4c+BCBREQZP/+8MH5Zdq677oP1vv5l3qug5/D4zyuc6739f1+PsHBzJkzh8WLF9OjRw9iYmL48MMPiY2NpXHjxsybN4/IyEg++eQTU5crhVBBYHvmzBkOHz5MfHw8AwYM4LXXXmP16tVERkYycOBAfHx8SElJYeHChRw7doyVK1cqsJXHHD16FMAY2CYnJ7No0SI+/vhjhg0bRlhYGIMHD+b69ev4+/vj5uZG//79SU1NJTg4mJycHFOWL4XYT/vR/rR1Rq1atYiOjubYsWOPvX/u3r1rbL1hb29v3BWgwFZ+LjMzk9KlSzNu3Djy8/PZt28fFy9eBKBevXqMGTOGgIAA8vPzKVGiBFFRUQps5QkvvPAC06dPx9zcnM6dO5ORkUHlypV5+PAhwBOLJmrXro23tzfDhg3jypUrpihZCrGCNYUxMTFs27aNbdu2Ua5cOcaOHcuLL77IgAEDSExMZNKkSYwbN47k5GRycnKIiIhQYCt/S/r2Jn8JS0tLKleuzNmzZ4mKisLZ2Zkvv/ySe/fuMXv2bACuXbvGyZMnjauSHj58SMOGDWnVqpWJq5dnRWZmJsWLF8fNzY3r168zbdo0WrVqxYMHD/j444+ZOXMmrq6ubNiwgSJFipi6XClECsJag8HAjh07+Oijj6hRowZZWVnMmjWL7t27A7Bq1SpycnLo3bs3rVq1Ijk5GWtra+P2PxF4NB07IiKChQsXYmVlZfxMe/DggXHicZEiRWjWrBn5+fksXLiQvXv30rJlS7Kzs6levboCNXmqn67cjoiI4Ny5cxgMBjw8PPDy8uLo0aMEBgYyfvx4Xn75ZYoXL86NGzeoWLHiYz9H247l506dOsWgQYNYtmwZ7dq1AyAoKIioqCh69uyJi4sLzs7OeHp64unpaeJqpTD66fnJ2dmZwMBAZsyYQd++fRk7dizXrl1jwIABFC1aFHd3d5ycnChVqhTVq1fH0tKSMmXKsGrVKtLT0ylatKiJX40UFgVDyz/88EOqVq1KQkICs2fPZsGCBYwcOZKZM2fSv39/wsLC6NixIx4eHpibm2uonfxtaaWt/CWKFCnCxIkTuX37Np999hkeHh6sWLGCI0eOMHLkSAA6dOhg3G48ZcoUwsPDqV69uokrl2dJWloa7u7uPHz4kEGDBvHOO+8QEhJC8+bNiY2NpW3btrz11ls4OztTpUoVU5crhcCPP/4I/H+AER8fT3BwMNOnTyc8PJyFCxdSvHhxypYtS7du3Yy7BebMmQOAg4ODAlt5wgsvvEBYWBjW1tbGicZlypTB0tKSFStWGI+zsrKicePG5OXlERMTA4C7uztly5Y1Sd1SuOXn5xsDkeDgYBYvXoyrqytZWVlMmjSJJUuWMGfOHKpWrUpoaCjvvPMOXl5enD9/nhEjRpi4einsKlWqRMWKFRkwYAAXL16kXbt2jB07lp07d7Jq1Sri4+NNXaIUYj89P82aNYvhw4czdOhQhgwZQlZWFt7e3jg5OfHCCy9gZ2fHunXrCAgIYMmSJcbn3bhxA3Nzcw0jk8ecOXOGqVOnMm/ePFatWsXnn3+Oi4sL3t7eGAwGJk6cSO3atenVqxexsbFYWVkpsJW/NS3rkL+MmZkZaWlp1KpVi4sXL1KtWjWCg4MJCAhg9OjRhISEMHv2bJYtW0ZSUhKRkZFPrAwRKVCwMjI2NpbLly/TqFEj3NzccHNz4/z581hZWdG1a1cAqlatSu/evalbty4VK1bEycnJxNVLYbBu3Tq+++47goKCsLCwwMzMjKSkJOzt7WnSpAkGg8E4uODevXskJyczcOBAMjIy+P777+nTpw8ODg6mfRFS6OTl5VGuXDkALl68yMiRI2nfvj0+Pj707t2bdevWsXjxYvr37w9A0aJFqV27tvG8pOEr8nPR0dE0aNDA+L6Ii4vju+++Y82aNZQtW5ZixYqxdetWatWqBTyaFfDtt99y48YNDAYDHTt2xMLC4rHhZCIF55qC/qLFihVj0aJF+Pv74+3tzdKlS/Hw8ABgxowZZGRkMHDgQA0ElqcqOD8FBwezZcsWZs+eTV5eHnXr1iUkJIQZM2Zw/PhxRo0aBTzaHZeTk4ONjQ1mZmakp6dz9epVPv74Yw21E6P79+8Dj67l6tSpQ25uLuXLl2fOnDkMHToUPz8/NmzYgJ+fH7a2tlqhLf8ICm3lL2Ntbc2WLVvIyclh+PDhbNy4kU6dOjF9+nRGjx7NuHHjCAoKIigo6IkG9SI/ZzAY2LVrF6NGjaJ06dKkpaUxa9YsGjVqxIMHDzh9+jRnzpzB2dmZ2NhYDAYD7u7upi5bCpFq1arRunVrrKysSEpKwsnJifLly5Oenk5UVJSxJQJAVlYWQ4cOZe7cufTr1w9LS0sFtvKErKws9uzZg4eHB1euXOHHH3+kXr16bN26FUdHR7p27cq1a9fYuXMnly5domfPnhw6dIjt27cTGRkJaNu6PC4sLIzU1FQaNGhgDNmSkpJIS0ujbNmyxuGb8+bNo379+pw9exZzc3OaNGny2M9RYCs/ZzAYOHbsGPv376dHjx7GGwCzZs0yBrfLli3Dw8ODrKwsPvvsM+0skV916dIljh49yqZNm3B0dOTmzZscPXqUL7/8ko4dO3L//n06duzIihUrsLOzM7Yqy8nJoWjRoowcOVKtgcTowoULvP/++zRs2JC4uDjS09Oxs7MjOzubIkWKMHr0aAYNGsTx48dxc3MjMDBQ+YH8I6g9gvylrKysKFq0KO+//z4Gg4GNGzeSnJzMjBkz+PLLLxk/fjzwZIN6EeCx7VIPHz7k22+/JSwsjK1bt9KsWTOGDx/OwYMHefnll+nVqxcDBw6kX79+zJgxwzj9WKRA7dq1sbe3JyYmhjFjxrB//35KlChBs2bN2L17N7t27TIeW6pUKSpUqICZmRnOzs5arS1PZWVlxYULF6hduzYeHh64ubnh5eVFo0aNWLp0KZs2bcLf358ePXpw+vRpJk6cyL59+1i6dCmVK1c2dflSCLVv355x48YBjy5gAVxdXSlZsiQTJkzgvffeY8GCBbi7u5OZmUlgYCBfffXVEz9Hga3Ao+9RsbGxAKSnp3Ps2DF27dpFVFQUt27dAqBYsWKEhIRQunRphg8fzvnz5+nYsSOrV6+mZMmSpixfCjkbGxsSExM5cuQIBw8e5OOPP2bKlCmcPn2aOXPm0LJlS1JSUozDqAsUBLUKbOWnUlJSuH//Pm5ubtSvXx8fHx/y8/ONOYGLiwslS5Y0fr4psJV/Cp0p5X+iYsWKvP/++0yePJnNmzfz+uuvs3TpUkqVKgVopZH8ulOnTnHz5k3u3bvH888/j7W1NSEhIQQEBDBixAhCQ0OZOHEi9evX58aNG0yYMEE9bOUXZWRkkJ+fT2RkJE5OTvj6+hIYGEhkZCRXr17F09OTTZs2cfnyZRwdHU1drhRyr7zyCpmZmZQsWRJHR0fjCluAxYsXk5OTQ5cuXejSpQsJCQnY2dlpK6j8okqVKgGwa9cuFi5ciI+PDx4eHtSqVYt169bRrVs3GjVqBDyaH+Do6Ejp0qVNWbIUYqmpqSxfvpzU1FTi4+PZtGkT2dnZ7Ny5k7y8PHr27EmZMmUoXrw4TZo0ITw8nGHDhrFp0yadp+Q3lS5dmk6dOhESEsK9e/fo3bs3PXv2pHHjxoSEhHDlyhVWrlypc5T8LnXq1MHd3Z3AwEACAwNZtGgRPXv2ZN68eRQrVowtW7aQlpZmbEkl8k9hyFfnb/kfio+PJyAgAGdnZ6ZMmYKdnZ2pS5JCbt++ffj6+lKjRg3OnDnzxCraMWPG8M033zBlyhRatmxpwkqlMMrJycHMzAwzMzNu3rxJsWLFsLOz48yZM8yZM4eMjAxGjhxJuXLlCA8PZ/v27djb2/PgwQNCQ0ONPSNFfuqnfWgvXrzI2bNniYqK4vr166xduxZHR0cuXbrE2rVrOXToEO3atWPAgAEmrloKs59OYQc4efIk4eHhJCcn079/f9zc3Pjoo4+4ceMGzz33HO3atWPt2rVcvnyZDRs2aMWa/KLAwEDWrFlDp06dCAoKAmDhwoXs2rWLZs2a0bVrV8qXL09ERAS1atWifPnyCtnkd8vJySEhIYGcnBzjTSeAkSNH4uLiwrBhwwC1bJGni4+PJz8/3zjX5vbt23zwwQc0atSIf/3rXwQFBXHp0iVeeOEFEhISmDt3LjVr1jRx1SL/Wwpt5X/u2rVrmJuba1q2/KYrV64QFRVF48aNqVSpEhEREaxYsYKFCxc+1q/Wz8+PuLg4tmzZoob0AsCnn36Kq6srzZo1A2Dbtm188skn2NjY0LJlS3x8fLhz5w6hoaE8fPiQYcOG8corr5CWlsa9e/coVqyYetjKUxUEttHR0Zw5c4bbt28zevRoLl++zLhx47h79y5ffPEFDg4OxMfHs2zZMs6dO0dYWBjFixc3dflSCGVnZxu3f547dw57e3tKlizJnTt3CA4O5vbt2/j5+VGnTh3Wr1/Phg0bsLKywsHBgVmzZmFpaalARJ4qKyuLoKAg7t69y40bN3jjjTd49913AVi0aBF79+7FxsaG8uXLs23bNjZu3PhY8CbyR1y+fJmrV6+yefNmzpw5oxtK8qtSUlLo2bMndnZ2dOnSxbhLaebMmZw+fZrw8HAA9uzZg4ODA+XKlVN+IP9ICm1FpFC6fPkykydP5sGDB8yfPx8nJycSExNZuHDhU4PbhIQEnJ2dTVixFBZZWVlMmDCBzZs3ExERQc2aNWnTpg3jx4/n3LlzHD58GBcXF0aPHs3du3eZNWsW2dnZdOvWjVatWpm6fHkG7Nq1i/fee4+uXbty6dIlhg4dyosvvsi1a9cICAjgzp07rF27lri4OO7evUvTpk3VF1meMH78eFq2bGncJfLJJ5+wb98+0tPTqVmzJr6+vpQrV44pU6Zw584dfHx8ePXVV4FHE7YLbgLk5OQoGBGjghtLDx8+pEiRIhgMBtLS0pg/fz4HDx6kc+fOeHt7A7B27VpiYmK4efMmo0aNokaNGiauXp5VOTk5TJ8+nZiYGJycnAgNDdUNJflNV69eZe/evYSGhtK6dWtatGhBmzZt6Ny5M+3bt2fgwIGmLlHE5BTaikihUXChkZ2dTXJyMp9++imbNm3C29ubwYMHA5CUlMSCBQtYvXo1oaGhCtnkqVJSUpg1axZr1qzh/fffx8zMzHgH/4svvmDDhvpaYFAAABq8SURBVA1UqlSJ0aNHk5iYyOTJk3FwcGDatGlarS2/Ki0tjQEDBtC7d2/atWsH/H9olpWVxY0bNxg7diwxMTFYWVmxefNmypcvb+KqpbC5c+cOU6dO5fDhw4SEhFC8eHGGDRvGp59+yvHjx4mOjubw4cMEBwdToUIFpk2bxt27d+nevTseHh7A/w/r1FwAKVDwPWrfvn1s3LiRO3fu0KdPH1577TXu379PREQEBw8epFOnTnh5eXH9+nXKlClDbm6uhvrIfy0jIwNzc3MsLS0xGAy6oSS/24ULF1izZg1Hjx7F2toaV1dXrl+/zpQpU3TTW/7xFNqKSKFy9OhRdu/ejY+PDxYWFixatIj9+/fTvXt3+vbtCzwKbmfOnMnu3bvZv38/NjY2umgV4PFeo/fv32f27NmsWrWKN954gxkzZhiPW7t2LRs3bqRKlSr4+/uTnJyMra0tZcqUMVXp8owoCEEmT56Mm5sbeXl5wKOL1QMHDuDu7o6FhQU7duzgpZdewsXFxbQFS6F15coVli5dyp49e2jTpg3/+te/ePvtt4FHvZJXrVrFDz/8wMyZMzEYDMydO5d79+7h6elpvGEg8nN79uxhzJgxjBo1ivPnz7N//368vb3p0aMHKSkpREREsG3bNmxtbcnLy2P16tUUK1bM1GXL38xPv4+J/B6ZmZlkZGQQEhLC4cOHSU5OZseOHRoKLP94Cm1FpFCJiIjg888/54033sDLywuA8PBwDhw4wNtvv/1YcJuXl0fJkiVNWK0Udg8ePGDWrFlERkayePFi49R1gHXr1rFs2TLq16/PhAkTHhsCJPJzly9fNgawvr6+GAwGPvnkE+zs7IxDpDp37kzLli3x8/MzbbFSqP109dnNmzcJDw9n3bp1dOrUicDAQONxZ86cYfr06dSuXZthw4YRExPDsmXLuHv3LuPGjdNWdnlCRkYGQ4cOpXPnznh4eJCWloabmxvOzs707duXd955h+zsbLZu3Up0dDTvvvuuhvqISKETFxeHk5OTWt+JANqvICIm9fM78X369MHc3JzVq1eTm5tLv3796NevHwAbN24kMzOTAQMGaKuMPKHgvXT69Glu3ryJmZkZzZo1Y8KECWRmZtK/f//HgtsuXbpgYWGBm5ubAlv5VfHx8YwcOZJ69eoxbtw4unXrxqJFi5g1axYjR440ttRwdXVVKwT5VXl5ecbA9tixY1haWlKrVi1sbW0JDw+nefPmvPrqq+Tl5VGjRg2qV69ObGwsAC+++CL+/v707t2ba9euKbSVJ2RlZXH06FGGDBlCfHw8np6ejBw5EnNzc2bOnEl+fj4lSpQwrurWZ5+IFCYF3+VdXV1NXYpIoaHQVkRMqmAKe1ZWFs2aNQOgV69e5OXlsXbtWvLz8xk4cCD9+vUjIyOD/fv3061bN01hlycYDAa2b9/OpEmTqFSpEmZmZkydOpWIiAjee+89zMzM6N+/P+Hh4TRo0ACADh06mLhqeRbY2trSvHlzDh06xLx58/Dz8+PWrVts3LiRQYMG0atXL86fP29s7SLySwpCsmnTprFz507s7e25c+cOzZo1Y/DgwUyYMIGpU6caB21ev34dR0dHYxuOtLQ0EhMTef755032GqTw+OmN7/z8fIoXL05oaCg1a9bE39+ft956iwEDBgCwZs0adu/ezdWrV4mKilJgKyKFjlpqiDxJoa2ImFRGRgabN29my5YtzJ8/n8aNGwPQu3dv8vLymDNnDmZmZnh5eTFs2DByc3MV2MpTxcXFMXXqVEJDQ2nYsCEnT57E09OT48eP4+HhwahRowDo27cvK1aswM3NzcQVS2H106GIlpaWODk50adPH6ysrNizZw/m5uYMGjSISpUqERkZSXh4OLa2tixbtozKlSubunwp5FauXMnevXvZsGEDKSkpnDp1igkTJtC2bVu6d+/O4MGDqVu3LnXr1uXq1ausXbvWGLC5urqya9cuypYta+JXIaZWcJ765ptv+Pbbb7G3t6dbt264u7uTlpZGfHw8LVq0AB61Cnr11Vfp1KkTpUqVokSJEiauXkRERH4PhbYi8j+Vn59Pfn6+8QLUxsaGoUOHYmZmRkBAANOmTaNp06bAo3Dtq6++YuvWrVhbWzN48GDMzc1NWb4UUvfv3yc/P5/q1avTsGFDfvzxRwYOHIifnx8eHh5kZmZibW3N+PHjsbGx0VAD+VUGg4ETJ07w0UcfsWjRIkqVKoWTkxPdu3cHYPv27RgMBgYOHEjDhg2NE7M1fV1+j0uXLtGhQwccHBwoVqwYFStWxN7eHl9fX7p27crgwYOZP38+3bt3Z/jw4cYp7AaDAXNzcwW2Ajw6T+3bt49Ro0ZRp04dbt++zZ49e5g/fz7Ozs6UK1eOnTt3Urt2bX744Qfi4uIYOXIkRYoUMXXpIiIi8jtpX4yI/E9cuHCBrKwsDAYDZmZmHDhwgGnTprFkyRJsbGwICAjA3d2dsWPH8t133xmfV6dOHbp06ULXrl0V2MpTXbhwgcGDB7Njxw5+/PFHrly5Qp8+ffD09GTo0KFkZ2czbtw41q5di5WVFWPGjKFq1aqmLlsKoYLZrOfPn6d06dIkJyfj7+9PYmIiAE5OTvTo0QNXV1c+//xzpk2bBjy6+aTAVn5LwU3LmzdvcuXKFQDMzc3Jzc2ladOmDBw4kHPnztGkSRMCAgJo27YtBoOB/Px8LCws9Bkoj0lNTeXEiRPMnTuX8PBwAgMDcXBwoH///ty7d4/+/fuTlZWFj48PYWFhBAQEKLAVERF5xii0FZG/XHx8PG+++SY7d+4E4MCBAwwZMoTY2Fi++OILBg8eTFZWFuPGjcPd3Z1hw4axfPly5s+fz5YtW+jZs6emh8ovSklJITk5mXr16lGrVi1ef/113n77bUaMGAGApaUlycnJlCtXDkB9/OQXGQwGDh8+jJ+fHwCRkZHcu3cPX19f7t69C4CjoyOtW7emfPny3Lhxw/jvIr/FYDBgMBjw9PRk27ZtbNy48bHHK1SogI2NDXXq1MHb29sY6KrHn/xcUlISzZs3Jzo62jgI0c3NjSFDhuDo6Ei/fv2oVq0aS5YsYf78+axbt45atWqZuGoRERH5o3TlKiJ/uQoVKrBo0SICAwPZvn07P/zwA4sWLSIiIoIJEyZgaWlpDG4DAwPx9PRk6dKl7Nq1ixkzZuDk5GTqlyCFWJ06dXj11Vf54IMPaN++PQ0aNODIkSPcvHmTxMRE1qxZw6VLl6hWrZqpS5VC7syZM+zbt4/OnTtTtmxZnJ2dWbp0KampqQwZMoTr168DjwKThg0bMnXqVEqWLGniquVZ07RpU3x9fQkKCmL9+vXGFbTR0dFP9GzX6lp5GicnJ+bMmcPx48c5efIkmZmZGAwGXnnlFfz9/XF0dKRDhw4kJiZSs2ZN3fgWERF5RhnyC/YCioj8xb7++muGDx+Oq6srAQEBvPLKK+Tn5xMdHc2CBQvIyMhgwYIFlChRgoSEBKytrTV0TJ4qPj6e/Px8KlasCMDt27f54IMPaNSoEW5ubkyaNIlLly5RtmxZkpOTCQsL0yojecLDhw9JTEw0rsIuCNFq1arF3LlzsbOzAyAhIQEfHx+SkpKoUaMGP/zwA+vWrVObDfmPpaWlERERQVhYGDVq1MDS0pLs7GyioqKwtLQ0DpkS+TUHDx7Ez8+PoKAgWrVqZWx/cOTIEZYtW8Z7771HhQoVTFyliIiI/KcU2orI/9ShQ4fw9vZm3LhxdO/e3XiB8f3337N48WKuXbvGihUrKFWqlIkrlcIqJSWFnj17YmdnZ+x3DDBz5kxOnTrFkiVLgEfvNVtbW5ydnbXKSJ7q4sWLrF+/nry8PJKTk5kyZQrTp0/n4MGDdOrUiR49emBrawtATk4Oc+bMwWAw8Oabb/L888+buHr5O4iLiyMmJoaiRYvSrl07Y0sErbCV36ug5VRQUBCtW7c29tfOzMxUD1sREZFnnEJbEfmf+6WVId988w2RkZGMHTtWK0PkV129epW9e/cSGhpK69atadGiBW3atKFz5860b9+egQMHmrpEeQakpqYydOhQDh8+jK+vL35+fuTm5jJp0iTOnj1L27Zt8fT0NPaMBMjLy1NfZPnLKLCV/8SBAwfw9/dn4sSJvPnmmxqMKCIi8jeh0FZETOKXVoZkZGRgY2Nj4urkWXHhwgXWrFnD0aNHsba2xtXVlevXrzNlyhT1QpbfdO/ePfz8/Hjw4AFVqlShe/fuNGjQ4LHg9o033qBz587GFbciIoXR3r17+eCDD9ixY4extYuIiIg82xTaiojJaGWI/BkyMzPJyMggJCSEw4cPk5yczI4dO3B0dDR1aVIIFfQKTUxMxMLCgtTUVFJTUwkNDcXc3Jx3332XevXqkZuby5QpU/j+++/p3bs3PXr0UI9RESnUHjx4oBtMIiIifyMKbUXEpLQyRP5McXFxODk5qYetPFVBYLt7925mz56Nra0tXl5etGvXjv379xMZGYmFhQXe3t7Uq1eP+/fv89lnn9GtWzfKly9v6vJFREREROQfRKGtiJicVobIf0uT1uX3+uqrrxg5ciRBQUGUKFECNzc342NHjhxhwYIFZGVlYWNjg5OTE1OmTMHCwsKEFYuIiIiIyD+RQlsRERH5R8jOzua9997jpZdewsvLi9TUVC5dusTmzZtxcHDg7bff5s6dO0RGRnLq1CkmT57MSy+9ZOqyRURERETkH0ihrYiIiPwj5OTkMGHCBMqUKUO9evVYuXIlWVlZJCUlUaVKFRISEliyZAl5eXnk5eVRtGhRU5csIiIiIiL/UGamLkBERETkr1BwX/rmzZukpKRgYWFBq1atOHToEBMmTMDJyYl3332X9evX4+XlhYODA7m5uVhbWyuwFRERERERk1KTNhEREflbMhgM7Nixg2nTpmFtbY27uzu+vr64u7uTkJBAhQoVjMcePXqUxMREsrOzsbGxMWHVIiIiIiIiao8gIiIifzMFg+muXbtG165d+fDDDzl//jzR0dGULl0aPz8/qlSpQlpaGocOHeLs2bMsW7aM5cuX4+rqauryRUREREREFNqKiIjI38/333/PlStXyMvLo0ePHgB8+eWXrF+/nhIlSjBkyBCuXLlCcHAwLi4uDBs2jBo1api4ahERERERkUcU2oqIiMjfSnZ2NsHBwSxfvhwPDw9mzpxpfGzTpk1s2LCB0qVLM2rUKJydncnJycHCQh2jRERERESk8NAgMhEREflbsbS0ZPjw4Xh5ebFz504OHTpkfKxDhw506dKFCxcuMHfuXLKysjA3NzdhtSIiIiIiIk/SshIRERF5phX0sI2JiSE+Pp68vDxat27N2LFjycjIoH///ixevJhGjRoB0L59e8zNzXn55ZexsrIycfUiIiIiIiJPUnsEEREReeZt376dDz/8kKpVq3L79m0MBgMLFy6kXLlyTJ06lfXr1xMeHk6DBg1MXaqIiIiIiMhvUnsEEREReaadOXOGTz75hHnz5rFq1SqWLVtG1apV8fLy4tatW0yaNIkuXbrQt29fjhw5YupyRUREREREfpNCWxEREXmmJSUl4eLiQp06dcjNzaV8+fLMnj0bV1dX/Pz8yMrKIiAgAG9vbxwdHU1droiIiIiIyG9SaCsiIiLPlILOTg8ePCAzM5PExETOnDlDeno65ubmZGdnU6RIEUaPHs3Dhw85fvw4tra2jBkzhqpVq5q4ehERERERkd+m0FZERESeGQVDx3bv3o2/vz+dOnXCwcEBFxcXBg4cSH5+PpaWlgC4uLhQokQJ49/NzPS1R0REREREng26ehEREZFnhsFg4NChQwQEBNCtWzf69OlDmTJlGD9+POnp6fTq1YukpCSysrLYsmULaWlplCtXztRli4iIiIiI/CGG/II9hiIiIiLPgNmzZ5OWlsaECRMe+/cffviBkJAQLly4QLVq1UhISGDu3LnUrFnTRJWKiIiIiIj8ZyxMXYCIiIjIH3Hv3j3u3bsHPGqXkJOTg6WlJQ8fPqRfv37k5eXh4OBAhQoVKFu2rImrFRERERER+ePUHkFERESeKe3atWP37t1s2LABg8GAubk5AGfPniU8PJxWrVpRv359BbYiIiIiIvLMUmgrIiIiz5T69evj6+tLUFAQ69atMw4YMzc3p0yZMuTk5Ji4QhERERERkf+O2iOIiIjIM8VgMODt7Y2FhQXvv/8+mzdvxtbWlujoaJYvX46VlZWpSxQREREREfmvaBCZiIiIPLOOHz/Od999R9GiRWnWrBlVq1Y1dUkiIiIiIiL/NYW2IiIiIiIiIiIiIoWIetqKiIiIiIiIiIiIFCIKbUVEREREREREREQKEYW2IiIiIiIiIiIiIoWIQlsRERERERERERGRQkShrYiIiIiIiIiIiEghotBWREREREREREREpBBRaCsiIiIiIiIiIiJSiCi0FRERERERERERESlELExdgIiIiIj8/WRlZbF69Wq2bdvGhQsXyMjIwNHRkXr16tG3b19efvllAF544QUAIiIiaNCggQkrFhEREREpPBTaioiIiMif6sGDB/Tr149jx45hbW2Nu7s79vb2nDhxgm3btrFjxw6mTp1Kp06dTF2qiIiIiEihpNBWRERERP5UM2bM4NixYzg5ObFq1SoqV65sfCwsLIy5c+fy0Ucf0aRJExNWKSIiIiJSeCm0FREREZE/TXp6OuvWrQPA19f3scAWwM/PDwA3NzecnJye+jP27NnDZ599xsWLFwGoUaMG/v7+1K1b13jMd999x6effsrZs2dJT0+nQoUKdOzYkX79+mEwGACIiYlh3rx5nDp1ivv371OmTBnatWvHkCFDsLKyAh61cViwYAG7du3i2rVr2Nvb8+abbzJixAjjMWlpacyfP5+9e/dy69YtihYtipubG0OHDqV69ep/4m9PREREROQRQ35+fr6pixARERGRv4fDhw/zzjvvALB161aqVav2q8f/vKftgQMHGDBgAAaDgTfeeIPU1FS++uorbG1t2bJlC8899xyxsbF4enoC8Prrr1OsWDEOHjzItWvXGDRoECNGjCAhIQEPDw8ePHhAq1atKFWqFEeOHOHcuXO8+eabzJgxA4Bhw4axc+dOSpUqxauvvsqpU6eIi4ujU6dOTJs27bFjqlevjpubG3fv3mX37t3Y2dmxa9euXwyfRURERET+U1ppKyIiIiJ/mtu3bxv/7Ozs/Iefn5SUhKenJ1WqVMHLywuAtm3bcunSJQ4ePEi3bt34/vvvyc7Oxt3d3Ri+Xr9+nVWrVuHi4gLA8ePHSUtL4/nnnycsLAx4tGJ23rx5lC1bFoDY2Fh27twJwPLly6lcuTLZ2dm0adOGDRs24OfnR4UKFTh48CAAH3/8MS+99BIAq1evJikpidTUVIW2IiIiIvKnU2grIiIiIn+agtYEALm5uX/4+R07dqRGjRocPHiQoKAg8vLySE9PB+DOnTsAxmD266+/xsvLi/r161OvXj1GjBiBhcWjr7cVK1bEzMyM8+fP4+npSZMmTahbty7+/v4UKVIEgKNHjwJgaWnJqlWrnngNMTExVKhQgcqVK3P69GkGDx5My5YteeWVV2jRosV/FEqLiIiIiPweCm1FRERE5E9TsIoV4NatWzg4OPyh54eHhxMcHPzUxwq6er322mtMnDiRBQsWcOjQIQ4dOgTAc889R1BQEA0bNsTV1ZUZM2YQEhLCiRMnOHHiBABOTk5MnDjR2HoBIDs7m4iIiCf+v4SEBADmzJnDBx98wLfffktUVBRRUVGYm5vz1ltvMXnyZCwtLf/QaxQRERER+S0KbUVERETkT1OzZk3s7e1JSUlhx44d1KhR47HH8/Pz8fHx4aWXXqJXr16PPfbw4UNCQ0MB6N69O2PGjMHOzg5PT09j6Fqgd+/e9OrVi7Nnz3Ls2DG2bt3KkSNHGDp0KN9++y1WVlZ4eHjg4eHBxYsXOX78OLt372b//v2MGTMGNzc37O3tAShWrBhHjhz5xddUvnx5wsPDSU5O5tixY/z73/9m9erVbNiwgapVq9K/f/8/41cnIiIiImKk0FZERERE/jRWVlb06tWLBQsWsHLlStq1a2ccNgYQFhbGN998Q3R0NB06dHjsuenp6WRnZwPQtGlT7OzsuHLlCnFxcQBkZWUBsG3bNv7973/TpUsXXnzxRVxdXWnbti2NGjUiJSWFlJQU4uLiOHDgAE2bNsXd3Z2qVavSuXNn6tSpQ3p6Ordu3aJu3boApKamcuLECWrXrk1eXh7h4eFYW1vTpk0bAKKiosjNzWXEiBG0aNGCFi1a8PDhQ1atWsW1a9f+8t+piIiIiPzzKLQVERERkT+Vr68vsbGxfP3117z99ts0b94cJycnTp06RWxsLObm5kyZMsXYm7aAk5MTFSpUID4+nuDgYPbt28dXX31F8+bN2bNnD5s2baJEiRIYDAYiIyPZuXMnLVu2xMLCgmPHjgHg5uZGyZIlyczMJCIigi+++ILXX3+dokWLEhcXR3p6Oi4uLtSoUYMiRYrQunVrdu/eTf/+/WnRogVXrlzh2LFjVKtWja5duwKwdu1abt++zcmTJ6lSpQrJycns3r0bMzMzY7ArIiIiIvJnMuQXNAcTEREREfmT5OXlsWnTJjZs2MDZs2dJS0vDwcGBhg0b4uPjg6urK4BxFW5ERAQNGjTg5MmTTJo0ifPnz1OmTBmGDh1K48aN6devH+fPn6d9+/YEBQWxcuVK1qxZw9WrV8nNzcXZ2ZnXXnsNX19fihcvDjxakfv5559z4cIFMjMzKVWqFI0bN2bIkCHG3rtZWVnMnTuXrVu3kpCQQLFixWjdujXDhw+nZMmSAMTHxzNz5kyio6NJSUnBzs6O559/Hh8fH9zd3U3w2xURERGRvzuFtiIiIiIiIiIiIiKFiJmpCxARERERERERERGR/6fQVkRERERERERERKQQUWgrIiIiIiIiIiIiUogotBUREREREREREREpRBTaioiIiIiIiIiIiBQiCm1FREREREREREREChGFtiIiIiIiIiIiIiKFiEJbERERERERERERkUJEoa2IiIiIiIiIiIhIIaLQVkRERERERERERKQQUWgrIiIiIiIiIiIiUoj8H8/x2ct/q1M6AAAAAElFTkSuQmCC\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["📊 STATISTIQUES COMPLÉMENTAIRES:\n","----------------------------------------\n"," 🔢 Nombre total de pixels: 294,912,000\n"," 📏 Nombre de classes: 8\n"," 📈 Classe la plus représentée: flat (38.11%)\n"," 📉 Classe la moins représentée: human (1.32%)\n"," ⚖️ Ratio max/min: 28.77\n"," ⚖️ Score de déséquilibre moyen: 9.27% (0 = parfait équilibre)\n","\n","\n","============================================================\n","📊 Comparaison des performances par classe des 2 meilleurs modèles\n","🔬 Expérience MLflow: OC Projet 9\n","📈 Métrique de classement: test_mean_iou\n","============================================================\n","🏆 Top 2 runs trouvés:\n","\n","#1 - SegFormer_b1_pretrained_optimized\n"," Run ID: c9835f7094354f48baff73f5d71edfc9\n"," test_mean_iou: 0.7998\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading artifacts: 0%| | 0/1 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\n"},"metadata":{}}]},{"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","executionInfo":{"status":"aborted","timestamp":1756538609550,"user_tz":-120,"elapsed":297622,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def register_best_model(experiment_name=\"OC Projet 9\", model_name=\"StreetsSegmentationPOC\",\n"," metric=\"test_mean_iou\", force=False, specific_run_id=None):\n"," \"\"\"\n"," Enregistre le meilleur modèle (selon la métrique choisie) 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"," metric (str): Métrique pour classer les runs (ex: \"test_mean_iou\", \"test_accuracy\")\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\"📊 Métrique de sélection: {metric}\")\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"," client = mlflow.tracking.MlflowClient()\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"," try:\n"," run = mlflow.get_run(specific_run_id)\n"," metrics = run.data.metrics\n"," metric_value = metrics.get(metric, None)\n"," run_name = run.data.tags.get('mlflow.runName', 'Unknown')\n","\n"," if metric_value is None:\n"," print(f\"⚠️ Le run spécifique n'a pas de métrique {metric}\")\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"," 'metric_value': metric_value or -1.0,\n"," 'run_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 metric_value:\n"," print(f\" • {metric}: {metric_value:.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 : chercher directement le meilleur modèle\n"," print(\"🔍 Mode automatique - recherche du meilleur modèle...\")\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 None\n","\n"," # Construire le filtre pour la métrique choisie\n"," filter_string = f\"status = 'FINISHED' and metrics.{metric} >= 0\"\n"," order_by = f\"metrics.{metric} DESC\"\n","\n"," # Rechercher les runs terminés avec la métrique choisie\n"," runs_df = mlflow.search_runs(\n"," experiment_ids=[experiment.experiment_id],\n"," filter_string=filter_string,\n"," order_by=[order_by],\n"," max_results=1\n"," )\n","\n"," if runs_df.empty:\n"," print(f\"❌ Aucun run terminé avec la métrique {metric} trouvé.\")\n"," return None\n","\n"," # Récupérer le meilleur run\n"," best_run = runs_df.iloc[0]\n"," run_id = best_run['run_id']\n"," metric_value = best_run[f'metrics.{metric}']\n"," run_name = best_run.get('tags.mlflow.runName', 'Unknown')\n","\n"," selected_result = {\n"," 'run_id': run_id,\n"," 'metric_value': metric_value,\n"," 'run_name': run_name,\n"," 'selection_mode': 'automatic'\n"," }\n","\n"," print(f\"🏆 Meilleur modèle identifié:\")\n"," print(f\" • Run Name: {run_name}\")\n"," print(f\" • Run ID: {run_id}\")\n"," print(f\" • {metric}: {metric_value:.4f}\")\n","\n"," except Exception as e:\n"," print(f\"❌ Erreur lors de la recherche du meilleur modèle: {e}\")\n"," return None\n","\n"," # 3. Vérifier si le modèle existe dans le Registry\n"," current_best_metric = -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_metric = current_run.data.metrics.get(metric, -1.0)\n"," print(f\" • Version la plus récente: {latest_version.version}\")\n"," print(f\" • Stage: {latest_version.current_stage}\")\n"," print(f\" • {metric}: {current_best_metric:.4f}\")\n","\n"," except Exception as e:\n"," print(f\"⚠️ Erreur lors de la vérification du Registry: {e}\")\n","\n"," # 4. Logique de comparaison\n"," if specific_run_id:\n"," should_register = True\n"," if selected_result['metric_value'] == -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"," should_register = selected_result['metric_value'] > current_best_metric or force\n","\n"," # 5. Enregistrement\n"," if should_register:\n"," if specific_run_id:\n"," print(f\"\\n🎯 Enregistrement du modèle spécifique...\")\n"," if selected_result['metric_value'] > current_best_metric:\n"," action_msg = f\"Le modèle spécifique ({selected_result['metric_value']:.4f}) est meilleur que l'actuel ({current_best_metric:.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['metric_value'] <= current_best_metric:\n"," print(f\"\\n⚡ FORCE MODE: Enregistrement forcé du modèle\")\n"," print(f\" Nouveau: {selected_result['metric_value']:.4f} vs Actuel: {current_best_metric:.4f}\")\n"," action_msg = \"Enregistrement forcé de la nouvelle version\"\n"," else:\n"," print(f\"\\n🚀 Le nouveau modèle ({selected_result['metric_value']:.4f}) est meilleur que l'actuel ({current_best_metric:.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 ({metric}): {selected_result['metric_value']:.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"," # Chercher l'artefact du modèle\n"," artifacts = client.list_artifacts(selected_result['run_id'])\n"," model_artifact_path = None\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['run_name']} - {metric}: {selected_result['metric_value']:.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"," f\"{metric}\": str(selected_result['metric_value']),\n"," \"model_name\": model_name,\n"," \"source_run_name\": selected_result['run_name'],\n"," \"selection_mode\": selected_result['selection_mode'],\n"," \"selection_metric\": metric,\n"," \"registration_mode\": \"forced\" if force and selected_result['metric_value'] <= current_best_metric 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\" • {metric}: {selected_result['metric_value']:.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"," 'metric_value': selected_result['metric_value'],\n"," 'metric_name': metric,\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","\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_metric:.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":"XZStzg8QSsWX","executionInfo":{"status":"ok","timestamp":1756713668774,"user_tz":-120,"elapsed":25,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":91,"outputs":[]},{"cell_type":"code","execution_count":92,"metadata":{"id":"80mWsWg4Gi7Q","executionInfo":{"status":"ok","timestamp":1756713684227,"user_tz":-120,"elapsed":15047,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"3dcb898f-ccbb-4ace-f8ed-c6753a9ba685"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","============================================================\n","🏆 Enregistrement du MEILLEUR modèle dans le Registry MLflow\n","📊 Métrique de sélection: test_mean_iou\n","🔬 Expérience MLflow: OC Projet 9\n","📦 Nom du modèle Registry: StreetsSegmentationPOC\n","============================================================\n","🔍 Mode automatique - recherche du meilleur modèle...\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:urllib3.connectionpool:Retrying (Retry(total=6, connect=6, read=7, redirect=7, status=7)) after connection broken by 'NameResolutionError(\": Failed to resolve 'mlflow.greg-madman-nas.duckdns.org' ([Errno -2] Name or service not known)\")': /api/2.0/mlflow/experiments/get-by-name?experiment_name=OC+Projet+9\n"]},{"output_type":"stream","name":"stdout","text":["🏆 Meilleur modèle identifié:\n"," • Run Name: SegFormer_b1_pretrained_optimized_prod_2025-08-30_07-25-25\n"," • Run ID: c9835f7094354f48baff73f5d71edfc9\n"," • test_mean_iou: 0.7998\n","\n","📦 Modèle 'StreetsSegmentationPOC' n'existe pas encore dans le Registry\n","\n","🆕 Création du modèle 'StreetsSegmentationPOC' dans le Registry...\n"," Création et enregistrement du premier modèle...\n"," ✅ Modèle 'StreetsSegmentationPOC' créé dans le Registry\n"," 📝 Enregistrement d'une nouvelle version...\n"," 📁 Artefact trouvé: best_model.pth\n"]},{"output_type":"stream","name":"stderr","text":["2025/09/01 08:01:20 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: StreetsSegmentationPOC, version 1\n"]},{"output_type":"stream","name":"stdout","text":[" 🏷️ Ajout des tags de métadonnées...\n"," 🚀 Promotion de la version 1 en Production...\n"]},{"output_type":"stream","name":"stderr","text":["/tmp/ipython-input-1579544897.py:232: FutureWarning: ``mlflow.tracking.client.MlflowClient.transition_model_version_stage`` is deprecated since 2.9.0. Model registry stages will be removed in a future major release. To learn more about the deprecation of model registry stages, see our migration guide here: https://mlflow.org/docs/latest/model-registry.html#migrating-from-stages\n"," client.transition_model_version_stage(\n"]},{"output_type":"stream","name":"stdout","text":["✅ Modèle enregistré avec succès !\n"," • Nom: StreetsSegmentationPOC\n"," • Version: 1\n"," • Stage: Production\n"," • test_mean_iou: 0.7998\n"," • Mode de sélection: automatic\n"]},{"output_type":"execute_result","data":{"text/plain":["{'model_name': 'StreetsSegmentationPOC',\n"," 'version': '1',\n"," 'run_id': 'c9835f7094354f48baff73f5d71edfc9',\n"," 'metric_value': np.float64(0.7997990846633911),\n"," 'metric_name': 'test_mean_iou',\n"," 'selection_mode': 'automatic',\n"," 'action': 'created',\n"," 'tags': {'source_run_id': 'c9835f7094354f48baff73f5d71edfc9',\n"," 'test_mean_iou': '0.7997990846633911',\n"," 'model_name': 'StreetsSegmentationPOC',\n"," 'source_run_name': 'SegFormer_b1_pretrained_optimized_prod_2025-08-30_07-25-25',\n"," 'selection_mode': 'automatic',\n"," 'selection_metric': 'test_mean_iou',\n"," 'registration_mode': 'automatic'}}"]},"metadata":{},"execution_count":92}],"source":["# Enregistrement automatique (seulement si meilleur)\n","register_best_model(experiment_name=\"OC Projet 9\", model_name=\"StreetsSegmentationPOC\")"]},{"cell_type":"markdown","metadata":{"id":"m5gJ0G9SK2Pn"},"source":["## Fonction d'inférence et démonstration"]},{"cell_type":"markdown","source":["### Premier et deuxième modèles"],"metadata":{"id":"mdyfWUukdeRP"}},{"cell_type":"markdown","source":["Les fonctions utiles sont toutes dans les imports en début de notebook"],"metadata":{"id":"IrCVqitDg5sk"}},{"cell_type":"code","execution_count":44,"metadata":{"id":"Az_I9YlrRevb","executionInfo":{"status":"ok","timestamp":1756728500746,"user_tz":-120,"elapsed":6076,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"colab":{"base_uri":"https://localhost:8080/","height":879,"referenced_widgets":["eb4c09d3c7674dc2b613291285e51d46","fb83c899201045a19afd11bed6a62ad3","f5868053ce6f442d98299fd5b758d79f","f5b49e16a16b4a60aa5a6880f7bfcca4","7e1d4569183a4283aad9b2ee29bf0d09","11ff172a785e47e699d3bb21c43e1d35","0b46e852055644a4a335832beb461805","33990a8c58604205833bbfdde2bdb3c9","7afa99f7e6314f86a8520b50df42a4c1","392a38d655c0470383a7c815737231c6","1a26aa41b97b4a359387ae2368dfb237","98c9e4b8aad0491399f6197ad6b268d5","c19186fdfefb4d2290f7ec154c7ee75c","0668b6cfb03e46c5b3d9ff55cc4c52ff","9ac93348a9ba43a89f0394e8fb23c140","4a7b323851ab46ccb4d62adfa8567059","d0227ad17c39481482da7f3aaa60eea3","7eff068527fa4ca38981d52af3a0a18e","ef413c8a0e2d4ba3a0560e4d1f9cb004","ae60ed561b7c4bc68a39b0bddf5609af","9e64aa62722f44ed9404af6dec6c2e99","2282cbfb7578469ba9ac57c4d6633441"]},"outputId":"da7c0e26-476a-4eb2-d2d0-23d61806efa1"},"outputs":[{"output_type":"stream","name":"stdout","text":["🏛️ Récupération du modèle 'StreetsSegmentationPOC' depuis le Registry...\n","📦 Version Registry: 1 (Stage: Production)\n","🔗 Source run détecté: c9835f7094354f48baff73f5d71edfc9\n","➡️ Délégation vers load_model...\n","\n","🔍 Chargement d'un modèle PyTorch depuis MLflow...\n","🎯 Mode run_id spécifique: c9835f7094354f48baff73f5d71edfc9\n","✅ Run trouvé: SegFormer_b1_pretrained_optimized_prod_2025-08-30_07-25-25\n","\n","📋 Paramètres du modèle:\n","🏗️ Architecture: FPN\n","🔧 Encodeur: None\n","📊 Nombre de classes: 8\n","🖼️ Script img size: (768, 768)\n","🔍 Variante détectée: 'SegFormer_b1' → mit_b1\n","⬇️ Téléchargement de best_model.pth...\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading artifacts: 0%| | 0/1 [00:00 Deuxième\")\n","print(f\"⚡ Vitesse: {'Meilleur' if inference_time1 <= inference_time2 else 'Deuxième'} modèle plus rapide\")\n","print(f\"🏆 Trade-off: {'Équilibré' if abs(speedup-1) < 0.1 else 'Précision vs Vitesse'}\")"],"metadata":{"id":"Q7NG2K7i2JjR","executionInfo":{"status":"ok","timestamp":1756728809585,"user_tz":-120,"elapsed":4184,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"517984d0-9037-4c83-9564-48686571bb9f"},"execution_count":48,"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","🎨 Nombre de classes: 8\n","\n","🖼️ Image de test: paris_street.jpg\n","================================================================================\n","\n","🥇 TEST DU MEILLEUR MODÈLE (Run: c9835f7094354f48baff73f5d71edfc9)\n","🔧 Encodeur: mit_b1\n","📐 Taille: (768, 768)\n","=== INFÉRENCE PYTORCH SUR paris_street.jpg ===\n","🔄 Préprocessing de l'image: paris_street.jpg\n","🔮 Exécution de l'inférence PyTorch...\n","✅ Inférence terminée - Shape: (768, 768)\n","⏱️ Temps d'inférence: 1517.6 ms (1.5176 s)\n","💾 Résultat sauvegardé: ./results/paris_street_inference.png\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","📊 Statistiques du masque prédit:\n"," flat: 39878 pixels (6.8%)\n"," human: 74386 pixels (12.6%)\n"," vehicle: 105056 pixels (17.8%)\n"," construction: 223362 pixels (37.9%)\n"," object: 4608 pixels (0.8%)\n"," nature: 39450 pixels (6.7%)\n"," sky: 12340 pixels (2.1%)\n"," void: 90744 pixels (15.4%)\n","================================================================================\n","\n","🥈 TEST DU DEUXIÈME MEILLEUR MODÈLE (Run: 4992e14daff845aabea9f44a56f499f6)\n","🔧 Encodeur: resnet34\n","📐 Taille: (768, 768)\n","=== INFÉRENCE PYTORCH SUR paris_street.jpg ===\n","🔄 Préprocessing de l'image: paris_street.jpg\n","🔮 Exécution de l'inférence PyTorch...\n","✅ Inférence terminée - Shape: (768, 768)\n","⏱️ Temps d'inférence: 896.7 ms (0.8967 s)\n","💾 Résultat sauvegardé: ./results/paris_street_inference.png\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","📊 Statistiques du masque prédit:\n"," flat: 71753 pixels (12.2%)\n"," human: 78837 pixels (13.4%)\n"," vehicle: 137423 pixels (23.3%)\n"," construction: 239263 pixels (40.6%)\n"," object: 5144 pixels (0.9%)\n"," nature: 7150 pixels (1.2%)\n"," sky: 8764 pixels (1.5%)\n"," void: 41490 pixels (7.0%)\n","================================================================================\n","\n","📊 COMPARAISON DES TEMPS D'INFÉRENCE\n","==================================================\n","🥇 Meilleur modèle (mit_b1):\n"," ⏱️ 1517.6 ms (1.5176 s)\n"," 🆔 Run: c9835f7094354f48baff73f5d71edfc9\n","\n","🥈 Deuxième meilleur modèle (resnet34):\n"," ⏱️ 896.7 ms (0.8967 s)\n"," 🆔 Run: 4992e14daff845aabea9f44a56f499f6\n","\n","🚀 Le deuxième modèle est 1.69x plus rapide !\n","📏 Différence absolue: 620.9 ms\n","\n","🎯 RÉSUMÉ DE LA COMPARAISON\n","========================================\n","🖼️ Image testée: paris_street.jpg\n","📊 Précision (supposée): Meilleur > Deuxième\n","⚡ Vitesse: Deuxième modèle plus rapide\n","🏆 Trade-off: Précision vs Vitesse\n"]}]},{"cell_type":"code","source":["# === COMPARAISON DES MODÈLES AVEC MASQUES DE VÉRITÉ ===\n","\n","# 1. Charger la configuration des classes\n","mapping_config = load_cityscapes_config(\"../cityscapes_config.json\")\n","print(f\"🎨 Nombre de classes: {mapping_config['num_classes']}\")\n","\n","# Chemins des données de test avec ground truth\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","\n","print(f\"\\n🖼️ Image de test: {os.path.basename(image_path)}\")\n","print(f\"🎯 Masque de vérité: {os.path.basename(mask_path)}\")\n","print(\"=\" * 80)\n","\n","# 2. Inférence avec le MEILLEUR modèle\n","print(f\"\\n🥇 TEST DU MEILLEUR MODÈLE (Run: {best_model_run_id})\")\n","print(f\"🔧 Encodeur: {best_model_encoder_name}\")\n","print(f\"📐 Taille: {best_model_script_img_size}\")\n","\n","predicted_mask1, inference_time1 = run_inference_and_visualize(\n"," image_path=image_path,\n"," mask_path=mask_path,\n"," model=best_model,\n"," encoder_name=None,\n"," img_size=best_model_script_img_size,\n"," mapping_config=mapping_config,\n"," save_dir=\"./results\"\n",")\n","\n","print(\"=\" * 80)\n","\n","# 3. Inférence avec le DEUXIÈME MEILLEUR modèle\n","print(f\"\\n🥈 TEST DU DEUXIÈME MEILLEUR MODÈLE (Run: {second_best_model_run_id})\")\n","print(f\"🔧 Encodeur: {second_best_model_encoder_name}\")\n","print(f\"📐 Taille: {second_best_model_script_img_size}\")\n","\n","predicted_mask2, inference_time2 = run_inference_and_visualize(\n"," image_path=image_path,\n"," mask_path=mask_path,\n"," model=second_best_model,\n"," encoder_name=None,\n"," img_size=second_best_model_script_img_size,\n"," mapping_config=mapping_config,\n"," save_dir=\"./results\"\n",")\n","\n","print(\"=\" * 80)\n","\n","# 4. COMPARAISON DES PERFORMANCES\n","print(f\"\\n📊 COMPARAISON DES TEMPS D'INFÉRENCE\")\n","print(\"=\" * 50)\n","print(f\"🥇 Meilleur modèle ({best_model_encoder_name}):\")\n","print(f\" ⏱️ {inference_time1*1000:.1f} ms ({inference_time1:.4f} s)\")\n","print(f\" 🆔 Run: {best_model_run_id}\")\n","\n","print(f\"\\n🥈 Deuxième meilleur modèle ({second_best_model_encoder_name}):\")\n","print(f\" ⏱️ {inference_time2*1000:.1f} ms ({inference_time2:.4f} s)\")\n","print(f\" 🆔 Run: {second_best_model_run_id}\")\n","\n","# Calculer la différence de vitesse\n","if inference_time1 < inference_time2:\n"," speedup = inference_time2 / inference_time1\n"," faster_model = \"Meilleur\"\n"," print(f\"\\n🚀 Le meilleur modèle est {speedup:.2f}x plus rapide !\")\n","elif inference_time2 < inference_time1:\n"," speedup = inference_time1 / inference_time2\n"," faster_model = \"Deuxième\"\n"," print(f\"\\n🚀 Le deuxième modèle est {speedup:.2f}x plus rapide !\")\n","else:\n"," print(f\"\\n⚖️ Les deux modèles ont des temps d'inférence identiques\")\n","\n","# Différence absolue\n","time_diff = abs(inference_time1 - inference_time2)\n","print(f\"📏 Différence absolue: {time_diff*1000:.1f} ms\")\n","\n","# 5. ANALYSE QUALITATIVE (basée sur les visualisations affichées)\n","print(f\"\\n🎯 ANALYSE QUALITATIVE\")\n","print(\"=\" * 30)\n","print(f\"📊 Les visualisations ci-dessus montrent:\")\n","print(f\" • Image originale\")\n","print(f\" • Masque prédit par chaque modèle\")\n","print(f\" • Masque de vérité terrain\")\n","print(f\" • Zones d'erreurs (en rouge)\")\n","print(f\" • Statistiques détaillées par classe\")\n","\n","print(f\"\\n💡 Pour une évaluation quantitative précise,\")\n","print(f\" calculez les métriques IoU, Dice, Accuracy sur le dataset complet.\")\n","\n","# 6. RÉSUMÉ FINAL\n","print(f\"\\n🎯 RÉSUMÉ DE LA COMPARAISON AVEC GROUND TRUTH\")\n","print(\"=\" * 50)\n","print(f\"🖼️ Image testée: {os.path.basename(image_path)}\")\n","print(f\"🎯 Masque de référence: {os.path.basename(mask_path)}\")\n","print(f\"📊 Précision visuelle: Comparez les zones d'erreurs (rouge)\")\n","print(f\"⚡ Vitesse: {'Meilleur' if inference_time1 <= inference_time2 else 'Deuxième'} modèle plus rapide\")\n","print(f\"🏆 Recommandation: Analysez visuellement la qualité des prédictions\")"],"metadata":{"id":"RqYbA_qD52V2","executionInfo":{"status":"ok","timestamp":1756728815025,"user_tz":-120,"elapsed":5437,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"2030c0ec-c4ef-4d7a-e186-4b3dd867aa33"},"execution_count":49,"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","🎨 Nombre de classes: 8\n","\n","🖼️ Image de test: frankfurt_000000_000294_leftImg8bit.png\n","🎯 Masque de vérité: frankfurt_000000_000294_gtFine_labelIds.png\n","================================================================================\n","\n","🥇 TEST DU MEILLEUR MODÈLE (Run: c9835f7094354f48baff73f5d71edfc9)\n","🔧 Encodeur: mit_b1\n","📐 Taille: (768, 768)\n","=== INFÉRENCE PYTORCH SUR frankfurt_000000_000294_leftImg8bit.png ===\n","🔄 Préprocessing de l'image: frankfurt_000000_000294_leftImg8bit.png\n","🔄 Préprocessing du masque: frankfurt_000000_000294_gtFine_labelIds.png\n"," Valeurs uniques avant mapping: [ 1 2 3 4 7 8 11 13 17 20]...\n"," Valeurs uniques après mapping: tensor([0, 1, 2, 3, 4, 5, 6, 7])\n","🔮 Exécution de l'inférence PyTorch...\n","✅ Inférence terminée - Shape: (768, 768)\n","⏱️ Temps d'inférence: 1268.7 ms (1.2687 s)\n","💾 Résultat sauvegardé: ./results/frankfurt_000000_000294_leftImg8bit_inference.png\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","📊 Statistiques du masque prédit:\n"," flat: 224709 pixels (38.1%)\n"," human: 1937 pixels (0.3%)\n"," vehicle: 31553 pixels (5.3%)\n"," construction: 233854 pixels (39.6%)\n"," object: 10470 pixels (1.8%)\n"," nature: 11465 pixels (1.9%)\n"," sky: 10827 pixels (1.8%)\n"," void: 65009 pixels (11.0%)\n","\n","📊 Statistiques du masque de vérité:\n"," flat: 223051 pixels (37.8%)\n"," human: 1915 pixels (0.3%)\n"," vehicle: 32409 pixels (5.5%)\n"," construction: 231660 pixels (39.3%)\n"," object: 10393 pixels (1.8%)\n"," nature: 11844 pixels (2.0%)\n"," sky: 10436 pixels (1.8%)\n"," void: 68116 pixels (11.5%)\n","================================================================================\n","\n","🥈 TEST DU DEUXIÈME MEILLEUR MODÈLE (Run: 4992e14daff845aabea9f44a56f499f6)\n","🔧 Encodeur: resnet34\n","📐 Taille: (768, 768)\n","=== INFÉRENCE PYTORCH SUR frankfurt_000000_000294_leftImg8bit.png ===\n","🔄 Préprocessing de l'image: frankfurt_000000_000294_leftImg8bit.png\n","🔄 Préprocessing du masque: frankfurt_000000_000294_gtFine_labelIds.png\n"," Valeurs uniques avant mapping: [ 1 2 3 4 7 8 11 13 17 20]...\n"," Valeurs uniques après mapping: tensor([0, 1, 2, 3, 4, 5, 6, 7])\n","🔮 Exécution de l'inférence PyTorch...\n","✅ Inférence terminée - Shape: (768, 768)\n","⏱️ Temps d'inférence: 553.8 ms (0.5538 s)\n","💾 Résultat sauvegardé: ./results/frankfurt_000000_000294_leftImg8bit_inference.png\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","📊 Statistiques du masque prédit:\n"," flat: 222442 pixels (37.7%)\n"," human: 2241 pixels (0.4%)\n"," vehicle: 32134 pixels (5.4%)\n"," construction: 231122 pixels (39.2%)\n"," object: 11898 pixels (2.0%)\n"," nature: 12120 pixels (2.1%)\n"," sky: 10426 pixels (1.8%)\n"," void: 67441 pixels (11.4%)\n","\n","📊 Statistiques du masque de vérité:\n"," flat: 223051 pixels (37.8%)\n"," human: 1915 pixels (0.3%)\n"," vehicle: 32409 pixels (5.5%)\n"," construction: 231660 pixels (39.3%)\n"," object: 10393 pixels (1.8%)\n"," nature: 11844 pixels (2.0%)\n"," sky: 10436 pixels (1.8%)\n"," void: 68116 pixels (11.5%)\n","================================================================================\n","\n","📊 COMPARAISON DES TEMPS D'INFÉRENCE\n","==================================================\n","🥇 Meilleur modèle (mit_b1):\n"," ⏱️ 1268.7 ms (1.2687 s)\n"," 🆔 Run: c9835f7094354f48baff73f5d71edfc9\n","\n","🥈 Deuxième meilleur modèle (resnet34):\n"," ⏱️ 553.8 ms (0.5538 s)\n"," 🆔 Run: 4992e14daff845aabea9f44a56f499f6\n","\n","🚀 Le deuxième modèle est 2.29x plus rapide !\n","📏 Différence absolue: 714.9 ms\n","\n","🎯 ANALYSE QUALITATIVE\n","==============================\n","📊 Les visualisations ci-dessus montrent:\n"," • Image originale\n"," • Masque prédit par chaque modèle\n"," • Masque de vérité terrain\n"," • Zones d'erreurs (en rouge)\n"," • Statistiques détaillées par classe\n","\n","💡 Pour une évaluation quantitative précise,\n"," calculez les métriques IoU, Dice, Accuracy sur le dataset complet.\n","\n","🎯 RÉSUMÉ DE LA COMPARAISON AVEC GROUND TRUTH\n","==================================================\n","🖼️ Image testée: frankfurt_000000_000294_leftImg8bit.png\n","🎯 Masque de référence: frankfurt_000000_000294_gtFine_labelIds.png\n","📊 Précision visuelle: Comparez les zones d'erreurs (rouge)\n","⚡ Vitesse: Deuxième modèle plus rapide\n","🏆 Recommandation: Analysez visuellement la qualité des prédictions\n"]}]},{"cell_type":"markdown","source":["### Modèles 1 à 4"],"metadata":{"id":"0YIFIW3YdkOS"}},{"cell_type":"code","source":["# Charger les 4 meilleurs modèles du Registry MLflow\n","model_name = \"StreetsSegmentationPOC\"\n","\n","# Fonction pour charger le modèle par rang\n","def load_model_by_rank(rank):\n"," model, run_id, encoder_name, img_size = load_model(\n"," run_id=None,\n"," experiment_name=\"OC Projet 9\",\n"," metric=\"test_mean_iou\",\n"," top_n=rank\n"," )\n"," return model, run_id, encoder_name, img_size\n","\n","# Charger les 4 modèles\n","models_data = []\n","for i in range(1, 5):\n"," model, run_id, encoder_name, img_size = load_model_by_rank(i)\n"," models_data.append({\n"," 'rank': i,\n"," 'model': model,\n"," 'run_id': run_id,\n"," 'encoder_name': encoder_name,\n"," 'img_size': img_size,\n"," 'input_shape': (*img_size, 3)\n"," })\n"," print(f\"✅ Modèle #{i} chargé du run {run_id} avec encoder '{encoder_name}'\")\n"," print(f\"📐 Input shape: {(*img_size, 3)}\")\n","\n","# Créer dossier de résultats\n","os.makedirs(\"./results\", exist_ok=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["0742a71df452416c9c15a7576c006a7b","80cc5f43cbc241adba3fb8c2debfa63f","cf5ac80a72e1407e97bcc99a59502e42","877850ba45f244a5bf5ef68e4ed836c0","0b262a72d284406f8279f4e3b233e57f","6bad012a4e25480996bb63f5f57be010","1a1b82f3495147128c2116109fe646d4","0837c64fae4d4f4da04a89a777322d89","d991ac78e51444619c55ab958be2743c","49003e2a17f240d1ac06292a19110b80","e3b08f073f124b9e97269e6f52985e37","4600255c6c1a47518c9390510ff74f28","038520d48723437cb5301a9a40d3ef1b","39b5c77afcf548b9bdf8e7bc3e00c140","95c5c71b6a8342b29933a989123c5ad1","b42c03dab4b247268f3ddb71b763e7de","bccec919a94744eca514a2573a9be20f","5634b366ca1540199775d8697148b67a","4482114a91c545429273045bf5991d8a","23b43bdf2dbd4109a60a8499dc0369fb","14c05b67afdd462e8a057dc08d4dcfb0","795206d32fae494a9da26d7485183393","05c9f94cb59d435db35010beeb2f74ca","dc118e8f64db4a7bb86639bc8ecf8051","3c2c77c31f6543d8ba3245334fd77540","9d6e62b10f90426fa3fbe30409f33131","10f21deb730048c59927bc931467c64f","ff7a8ef6d1c943c1bb8980fdf1bd05c5","bd4d9dfb5fa347c3b58b484a126188f0","09d01471dfa6479a97e6e20ef7ae60ba","159139a21c484fc2bf149f5cabbfbcc1","bb64336967f44b67960aa58d8f0eff11","08ea7dc575464cafb800affc3d3f7e88","b447a5e4df33443aa431b1b9a2c36d8f","f22eb347080246f6804643a00859213b","8db0ff2417cb45a8b35952df4a4bd995","44bfa207cd494940906c53ba8518b5ee","b00368895aee4f989fe92b22a40a6a15","deeb3a5d231844abb615c93f992860e7","a8bcaa83cf7b452b855d3da0d638ef8b","78bc62b40f9544258abe8b4460ce86d9","1a1a712c1ba542a6a2660d7b4a89be4b","9fa86934ff484f97a194b5e362ee6976","5b575b9d511c4f35b647e794916ae49d","9deb95856dbd4abfbe2569b8121a7902","391c10ab705c461d82eadd657ecd7946","213e02e24f6c4ad49e469cff7e52d0a8","9005be2accff4ab1833c39ff9aef1816","61786f24fc1e4816bde419d994cb516b","f7f4d591333345e48bdd32d888f95ef5","432cdb1ca3274cd09c979369fab46588","49b19d5186984c998184feff0890cf14","edadd6db43b74b55a34207ec99e6746f","d0054ced081a4ced9cfc18138db06357","890f9c08644a49c98f2fc69256083dc1","e8ee21776d8b4735bdd6696a547e91ee","1eecdb78258d49bb86126d3855ff4880","ea75d25ac79d40c89fbc58e4a53b6a32","dab689f87c3c4c31874719081ff9a5fa","2d1ab1d1b9c142a299928c0edadbd02f","f47fc494f7ee412c9fe2029d303f9a1f","cdf57a9e6b0747c099f86cecada58611","7f5fe418c06f4918be2e6ddc75c34e0c","26f8c9e14bdb49808fb40034bc6dc906","dd7797d34a93485c9bbb33cf67a9ece1","cfd4c558b3e14fd8a2d09089673d0079","51a41124dd8e4becb8c52ac5a3acafd2","5594a269c64c4b109f21804e9dffe53c","e907a311999241a38a60c81cb5c97d23","802d67efe16641199b006c744de9822d","eab2ee6e3f2944259d5c4c0b3058e6b8","965b2fdbb1a444e58e25cdeb19ed7949","d372e433f7d2441f892d2f407ad71aa3","de57bd38481f41dbaf02d3a0f6b3e459","143812a7979b4ac7bce900e090dfb8df","f89fe12544a04ab7af382479a1f31465","4606ebec3d754a1eb9cb7c8be0b0e92c","43d9003ad7e8437b9d08cf49dbc09171","37bb724c48de4be2a546688524671882","c1e3e7be25b1432a9f1300fdadab9345","73ab8ff3480444a9b94e1835bcd01919","02322fa93fd44eac8470b7d7ecfcd37a","c1207504ea8f44f0a2c02758e15ae9f1","b900fba5d63c4b31b1dd2a777776821c","d4dc518e31584c88956477b0f6c8922a","c4915aabf60b4ef3955976e238067013","3e2888e0f7ba4729a7bdc60de65adfd4","b2fe0ae231e44bbd84d27009ab66bd6f"]},"id":"_ZBoPG5hdocB","executionInfo":{"status":"ok","timestamp":1756750090205,"user_tz":-120,"elapsed":31523,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"54b20994-02d6-4daf-f2c9-f5e9ce1b6065"},"execution_count":33,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","🔍 Chargement d'un modèle PyTorch depuis MLflow...\n","🏆 Mode classement: top_1 de 'test_mean_iou' dans 'OC Projet 9'\n","✅ Run sélectionné (position #1):\n"," • Nom: SegFormer_b1_pretrained_optimized_prod_2025-08-30_07-25-25\n"," • Run ID: c9835f7094354f48baff73f5d71edfc9\n"," • test_mean_iou: 0.7998\n","📊 Contexte du classement:\n"," 👑 SegFormer_b1_pretrained_optimi: 0.7998\n","\n","📋 Paramètres du modèle:\n","🏗️ Architecture: FPN\n","🔧 Encodeur: None\n","📊 Nombre de classes: 8\n","🖼️ Script img size: (768, 768)\n","🔍 Variante détectée: 'SegFormer_b1' → mit_b1\n","⬇️ Téléchargement de best_model.pth...\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading artifacts: 0%| | 0/1 [00:00 fast_time:\n"," slowdown = perf_time / fast_time\n"," print(f\" • Coût vitesse pour la précision: {slowdown:.2f}x plus lent\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"output_embedded_package_id":"1vqRqAz6_fUQtD3SLeQkkFDumOPaur6TC"},"id":"SUo5M9-Zdj3c","executionInfo":{"status":"ok","timestamp":1756750115805,"user_tz":-120,"elapsed":20768,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"3dda6747-0e1d-4755-efdd-f68724caf92e"},"execution_count":34,"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}]},{"cell_type":"markdown","source":["# 🏆 Rapport Comparatif - Segmentation Urbaine\n","\n","## 📊 Résumé Exécutif\n","\n","**SegFormer B1** remporte la bataille de la précision tandis que **FPN+ResNet34** domine en termes de vitesse d'inférence. Le choix entre ces deux modèles dépend donc du trade-off précision/vitesse requis pour l'application.\n","\n","## 📈 Métriques Globales\n","\n","| Métrique | SegFormer B1 | FPN+ResNet34 | Avantage |\n","|----------|--------------|--------------|----------|\n","| **Mean IoU** | 0.800 | 0.792 | SegFormer (+0.8%) |\n","| **Accuracy** | 0.927 | 0.925 | SegFormer (+0.2%) |\n","| **Temps d'inférence** | 1393.2 ms | 725.3 ms | **FPN (-47.9%)** |\n","| **Vitesse relative** | 1x | **1.92x plus rapide** | FPN |\n","\n","## 🎯 Performance par Classe (IoU)\n","\n","### ✨ Classes où SegFormer B1 excelle le plus\n","\n","1. **👥 Human** (+1.8%) - **Avantage critique** pour la détection des personnes et cyclistes\n","2. **🚗 Vehicle** (+1.0%) - Segmentation des véhicules\n","3. **🏢 Construction** (+0.9%) - Bâtiments, murs, ponts\n","4. **🌱 Nature** (+1.0%) - Végétation et terrain\n","\n","### ⚖️ Classes équivalentes\n","\n","- **🛣️ Flat** (~0%) - Routes et trottoirs (performance similaire)\n","- **🚦 Object** (+0.6%) - Poteaux et signalisation (légère avantage SegFormer)\n","- **☁️ Sky** (+0.7%) - Détection du ciel\n","- **⚫ Void** (+0.1%) - Zones non-labellisées\n","\n","## Analyse Détaillée\n","\n","### Points Forts du SegFormer B1\n","\n","- **Précision supérieure** sur 7/8 classes\n","- **Architecture Transformer** moderne avec attention globale\n","- **Avantage critique sur les humains** (+1.8%) - essentiel pour la sécurité\n","- **Meilleure généralisation** sur les objets complexes (humains, véhicules)\n","- **Contours plus nets** et moins de bruit dans les prédictions\n","- **Potentiel d'amélioration** avec les variantes B2-B5 (non testées)\n","\n","### Points Forts du FPN+ResNet34\n","\n","- **Vitesse d'inférence exceptionnelle** (2.3x plus rapide)\n","- **Architecture CNN mature** et éprouvée\n","- **Performance très proche** en précision malgré la simplicité\n","- **Efficiency computationnelle** remarquable\n","\n","### 📊 Analyse des Statistiques d'Inférence\n","\n","**Images testées :**\n","1. **🖼️ paris_street.jpg** - Test de généralisation (sans masque de vérité)\n","2. **🎯 frankfurt_000000_000294_leftImg8bit.png** - Test avec ground truth\n","\n","**Cohérence des prédictions** entre les deux modèles observée sur les deux images :\n","- 🏢 Construction : ~39-40% (bâtiments urbains dominants)\n","- 🛣️ Flat : ~37-38% (routes et trottoirs)\n","- 🚗 Vehicle : ~5-17% (variable selon la scène)\n","- ⚫ Void : ~7-15% (zones d'exclusion)\n","\n","## 📋 Recommandations d'Usage\n","\n","### 🎯 Choisir SegFormer B1 si :\n","- La **🔍 précision maximale** est prioritaire\n","- La **🚨 détection d'humains** est critique (sécurité, surveillance)\n","- Le **⏱️ temps d'inférence** n'est pas critique\n","- L'application nécessite une **📐 détection fine** des personnes/véhicules\n","- Les ressources computationnelles sont **💪 suffisantes**\n","- Budget disponible pour **🖥️ matériel d'inférence optimisé**\n","\n","### ⚡ Choisir FPN+ResNet34 si :\n","- La **🚀 vitesse** est critique (applications temps-réel)\n","- Les ressources sont **💰 limitées**\n","- La différence de précision (+0.8%) est **✅ acceptable**\n","- L'application nécessite un **📊 débit élevé** d'images\n","\n","### ⚠️ Limitations et Contexte de l'Étude\n","\n","### 💸 Contraintes Expérimentales\n","\n","- **💰 Budget limité** : Plan mensuel avec crédits restreints pour l'entraînement\n","- **🔍 Modèles SegFormer** : Tests limités au B1, variantes B2-B5 non explorées par contrainte budgétaire\n","- **💻 Temps d'inférence** : Tests réalisés sur **CPU uniquement** (temps non-optimisés)\n","- **🔧 Amélioration CNN** : Potentiel d'optimisation des architectures FPN non exploré\n","\n","### 📊 Protocoles Expérimentaux et Comparaisons\n","\n","**🎯 Performance vs littérature :**\n","Nos résultats SegFormer-B0 (MIoU 73.3% en 512×512, 78.0% en 768×768) dépassent ceux rapportés dans l'article original (71.9% et 73.7% respectivement). Cette différence s'explique par des **protocoles d'évaluation distincts** :\n","\n","- **📄 Article original** : \"Scale the short side to {512, 768}\" sur images complètes Cityscapes (2048×1024) avec sliding window\n","- **🔬 Notre approche** : Images redimensionnées et centrées à résolution fixe (512×512, 768×768)\n","\n","Notre protocole, en se concentrant sur les zones centrales des images, évite potentiellement les régions périphériques plus complexes, expliquant ces performances légèrement supérieures.\n","### 📈 Évolution par rapport aux travaux précédents\n","\n","**🎯 Progrès réalisés :**\n","- **📏 Résolution augmentée** : Passage de 224x224 à 512x512 et 768x768\n","- **🏗️ Architectures modernes** : Introduction des Transformers (SegFormer) vs CNN uniquement\n","- **✅ Bénéfice universel** : L'augmentation de résolution a amélioré tous les modèles testés\n","\n","### 🚧 Limites persistantes\n","\n","**⚙️ Prétraitement générique :**\n","- Normalisation basique (division par 255) pour tous les backbones\n","- Potentiel d'optimisation spécifique à chaque architecture non exploité\n","\n","**📊 Limitations des données :**\n","- **🌍 Généralisation limitée** : Dataset Cityscapes uniquement urbain, conditions météo homogènes\n","- **👥 Classes minoritaires sous-représentées** : Humains et objets nécessitent plus de données d'entraînement\n","- **🎯 Performance sur humains** : IoU perfectible sur la classe la plus critique\n","- **📉 Convergence des performances** : Écarts faibles suggérant une limitation par le dataset\n","\n","**🔧 Contraintes techniques :**\n","- **📐 Tailles fixes** : Images redimensionnées pour l'entraînement, impact sur la généralisation\n","- **⏱️ Performances temps-réel** : Non évaluées sur matériel optimisé\n","\n","### 🚀 Perspectives d'Amélioration\n","\n","**🔬 SegFormer B2-B5** : Versions avec plus de paramètres offrant potentiellement de meilleures performances au prix d'une complexité accrue.\n","\n","**⚙️ Optimisation du preprocessing** : Normalisation spécifique aux backbones.\n","\n","**📊 Data augmentation ciblée** : Utilisation de datasets complémentaires (Mapillary Vistas) pour enrichir les classes minoritaires (humains, objets), potentiellement via IA générative.\n","\n","**🎓 Transfer learning spécialisé** : Backbones ImageNet restent la référence, mais fine-tuning sur données de segmentation urbaine plus diversifiées.\n","\n","**🖥️ Optimisation matérielle** : L'inférence GPU ou sur puces dédiées (Edge AI) réduirait drastiquement les temps de traitement pour les deux architectures.\n","\n","**💰 Trade-offs budgétaires** :\n","- **🏋️ Entraînement** : Modèles complexes nécessitent plus de ressources\n","- **🚀 Déploiement** : Solutions temps-réel requièrent du matériel spécialisé\n","\n","## Conclusion\n","\n","Le choix optimal dépend du contexte applicatif et des contraintes budgétaires. Pour des applications où la détection d'humains est critique (systèmes de sécurité, véhicules autonomes), le gain de +1.8% du SegFormer B1 sur cette classe peut justifier l'investissement. Pour des systèmes de surveillance générale ou d'analyse urbaine, FPN+ResNet34 offre un excellent compromis.\n","\n","**Points clés à retenir :**\n","- **Inférence CPU** : Les temps mesurés ne reflètent pas les performances réelles en déploiement optimisé\n","- **Potentiel SegFormer** : Versions B2-B5 non testées par contrainte budgétaire\n","- **Maturité CNN** : FPN+ResNet34 reste compétitif face aux Transformers modernes\n","\n","**Recommandation stratégique** : FPN+ResNet34 pour le prototypage et les budgets serrés, SegFormer B1+ pour les applications critiques avec budget d'infrastructure adapté."],"metadata":{"id":"639VnZJrP9x2"}},{"cell_type":"code","metadata":{"id":"0qXyX6entBG","executionInfo":{"status":"ok","timestamp":1756730026333,"user_tz":-120,"elapsed":3219,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"source":["!pip freeze > 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-databricks-sdk==0.64.0 -dataproc-spark-connect==0.8.3 -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==0.35.1 -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 -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==2.8.4 -fastapi==0.116.1 -fastcore==1.8.8 -fastdownload==0.0.7 -fastjsonschema==2.21.2 -fastprogress==1.0.3 -fastrlock==0.8.3 -fasttransform==0.0.2 -ffmpy==0.6.1 -filelock==3.19.1 -firebase-admin==6.9.0 -Flask==3.1.2 -flatbuffers==25.2.10 -flax==0.10.6 -folium==0.20.0 -fonttools==4.59.1 -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==2.1 -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-adk==1.12.0 -google-ai-generativelanguage==0.6.15 -google-api-core==2.25.1 -google-api-python-client==2.179.0 -google-auth==2.38.0 -google-auth-httplib2==0.2.0 -google-auth-oauthlib==1.2.2 -google-cloud-aiplatform==1.110.0 -google-cloud-appengine-logging==1.6.2 -google-cloud-audit-log==0.3.2 -google-cloud-bigquery==3.36.0 -google-cloud-bigquery-connection==1.18.3 -google-cloud-bigquery-storage==2.32.0 -google-cloud-bigtable==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-logging==3.12.1 -google-cloud-resource-manager==1.14.2 -google-cloud-secret-manager==2.24.0 -google-cloud-spanner==3.57.0 -google-cloud-speech==2.33.0 -google-cloud-storage==2.19.0 -google-cloud-trace==1.16.2 -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==1.31.0 -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==5.43.1 -gradio_client==1.12.1 -graphene==3.4.3 -graphql-core==3.2.6 -graphql-relay==3.2.0 -graphviz==0.21 -greenlet==3.2.4 -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==4.3.0 -h5netcdf==1.6.4 -h5py==3.14.0 -hdbscan==0.8.40 -hf-xet==1.1.8 -hf_transfer==0.1.9 -highspy==1.11.0 -holidays==0.79 -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 -httpx-sse==0.4.1 -huggingface-hub==0.34.4 -humanize==4.13.0 -hyperframe==6.1.0 -hyperopt==0.2.7 -ibis-framework==9.5.0 -idna==3.10 -imageio==2.37.0 -imageio-ffmpeg==0.6.0 -imagesize==1.4.1 -imbalanced-learn==0.14.0 -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==2025.2.1 -intel-openmp==2025.2.1 -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==4.3.0 -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==4.25.1 -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-hub==0.21.1 -keras-nlp==0.21.1 -keyring==25.6.0 -keyrings.google-artifactregistry-auth==1.1.2 -kiwisolver==1.4.9 -langchain==0.3.27 -langchain-core==0.3.74 -langchain-text-splitters==0.3.9 -langcodes==3.5.0 -langsmith==0.4.16 -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==1.3.0 -Markdown==3.8.2 -markdown-it-py==4.0.0 -MarkupSafe==3.0.2 -matplotlib==3.10.0 -matplotlib-inline==0.1.7 -matplotlib-venn==1.1.2 -mcp==1.13.1 -mdit-py-plugins==0.5.0 -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==3.3.2 -mlflow-skinny==3.3.2 -mlflow-tracing==3.3.2 -mlxtend==0.23.4 -more-itertools==10.7.0 -moviepy==1.0.3 -mpmath==1.3.0 -msgpack==1.1.1 -multidict==6.6.4 -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==2.2.0 -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==12.6.4.1 -nvidia-cuda-cupti-cu12==12.6.80 -nvidia-cuda-nvcc-cu12==12.5.82 -nvidia-cuda-nvrtc-cu12==12.6.77 -nvidia-cuda-runtime-cu12==12.6.77 -nvidia-cudnn-cu12==9.10.2.21 -nvidia-cufft-cu12==11.3.0.4 -nvidia-cufile-cu12==1.11.1.6 -nvidia-curand-cu12==10.3.7.77 -nvidia-cusolver-cu12==11.7.1.2 -nvidia-cusparse-cu12==12.5.4.2 -nvidia-cusparselt-cu12==0.7.1 -nvidia-ml-py==12.575.51 -nvidia-nccl-cu12==2.27.3 -nvidia-nvjitlink-cu12==12.6.85 -nvidia-nvtx-cu12==12.6.77 -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==1.101.0 -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-exporter-gcp-trace==1.9.0 -opentelemetry-resourcedetector-gcp==1.9.0a0 -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==0.11.23 -orjson==3.11.2 -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==0.8.5 -parsy==2.1 -partd==1.4.2 -patsy==1.0.1 -peewee==3.18.2 -peft==0.17.1 -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 -plum-dispatch==2.5.7 -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==0.14.1 -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-settings==2.10.1 -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==1.18.2 -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-cp312-cp312-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==6.9.3 -PyOpenGL==3.1.10 -pyOpenSSL==24.2.1 -pyparsing==3.2.3 -pyperclip==1.9.0 -pyproj==3.7.2 -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==2.32.4 -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==0.27.0 -rpy2==3.5.17 -rsa==4.9.1 -ruff==0.12.10 -s3transfer==0.13.1 -safehttpx==0.1.6 -safetensors==0.6.2 -scikit-image==0.25.2 -scikit-learn==1.6.1 -scipy==1.16.1 -scooby==0.10.1 -scs==3.2.8 -seaborn==0.13.2 -SecretStorage==3.3.3 -segmentation_models_pytorch==0.5.0 -semantic-version==2.10.0 -Send2Trash==1.8.3 -sentence-transformers==5.1.0 -sentencepiece==0.2.1 -sentry-sdk==2.35.0 -setuptools==75.2.0 -shap==0.48.0 -shapely==2.1.1 -shellingham==1.5.4 -simple-parsing==0.1.7 -simplejson==3.20.1 -simsimd==6.5.1 -six==1.17.0 -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==2.0.43 -sqlglot==25.20.2 -sqlparse==0.5.3 -srsly==2.5.1 -sse-starlette==3.0.2 -stanio==0.5.1 -starlette==0.47.3 -statsmodels==0.14.5 -stringzilla==3.12.6 -stumpy==1.13.0 -sympy==1.13.3 -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-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==0.11.0 -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==2.8.0 -torchao==0.10.0 -torchaudio==2.8.0 -torchdata==0.11.0 -torchsummary==1.5.1 -torchtune==0.6.1 -torchvision==0.23.0 -tornado==6.4.2 -tqdm==4.67.1 -traitlets==5.7.1 -traittypes==0.2.1 -transformers==4.55.4 -treelite==4.4.1 -treescope==0.1.10 -triton==3.4.0 -tsfresh==0.21.0 -tweepy==4.16.0 -typeguard==4.4.4 -typer==0.16.1 -types-pytz==2025.2.0.20250809 -types-setuptools==80.9.0.20250822 -typing-inspection==0.4.1 -typing_extensions==4.15.0 -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==0.21.1 -wasabi==1.1.3 -watchdog==6.0.0 -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 -wheel==0.45.1 -widgetsnbextension==3.6.10 -wordcloud==1.9.4 -wrapt==1.17.3 -wurlitzer==3.1.1 -xarray==2025.8.0 -xarray-einstats==0.9.1 -xgboost==3.0.4 -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==0.24.0 diff --git a/start.sh b/start.sh deleted file mode 100644 index 3ebc9b6..0000000 --- a/start.sh +++ /dev/null @@ -1,52 +0,0 @@ -#!/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 deleted file mode 100644 index e69de29..0000000 diff --git a/utils/utils.py b/utils/utils.py deleted file mode 100644 index 17d24bc..0000000 --- a/utils/utils.py +++ /dev/null @@ -1,658 +0,0 @@ -import os -import json -import tempfile -import numpy as np -import torch -import matplotlib.pyplot as plt -import segmentation_models_pytorch as smp -import mlflow -import re -import ast -import time -import random - -# Visualisation -import matplotlib.patches as mpatches -from matplotlib.colors import ListedColormap - -# Preprocessing/Images -import albumentations as A -from albumentations.pytorch import ToTensorV2 - - -def update_colors_to_wcag(mapping_config): - """ - Met à jour les couleurs dans mapping_config pour utiliser les couleurs WCAG accessibles - avec redistribution sémantique intuitive finale - """ - # Redistribution sémantique finale : - # Flat → gris moyen, Human → rouge, Vehicle → violet, Construction → orange, - # Object → teal, Nature → vert, Sky → bleu, Void → gris foncé - wcag_colors = np.array([ - [102, 102, 102], # Flat: gris moyen (routes, trottoirs) - [204, 0, 0], # Human: rouge (piétons, cyclistes) - [102, 51, 153], # Vehicle: violet (véhicules) - [204, 102, 0], # Construction: orange (bâtiments) - [0, 102, 102], # Object: teal (objets, signalétique) - [0, 102, 0], # Nature: vert (végétation) - [0, 102, 204], # Sky: bleu (ciel) - [51, 51, 51] # Void: gris foncé (zones non-définies) - ], dtype=np.uint8) - - # S'assurer qu'on a assez de couleurs pour le nombre de classes - num_classes = mapping_config['num_classes'] - if len(wcag_colors) < num_classes: - print(f"⚠️ Attention: {num_classes} classes mais seulement {len(wcag_colors)} couleurs WCAG disponibles") - # Compléter avec les couleurs originales si nécessaire - original_colors = mapping_config['group_colors'] - while len(wcag_colors) < num_classes: - wcag_colors = np.vstack([wcag_colors, original_colors[len(wcag_colors):len(wcag_colors)+1]]) - - mapping_config['group_colors'] = wcag_colors[:num_classes] - print(f"✅ Couleurs mises à jour vers WCAG pour {num_classes} classes") - return mapping_config - - -def load_cityscapes_config(config_path="../cityscapes_config.json", verbose=True, use_wcag_colors=False): - """ - Charge la configuration Cityscapes depuis un fichier JSON. - - Args: - config_path: Chemin vers le fichier de configuration - verbose: Affichage détaillé - use_wcag_colors: Utiliser les couleurs WCAG accessibles - """ - 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) - - mapping_config = { - "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 - } - - # Appliquer les couleurs WCAG si demandé - if use_wcag_colors: - mapping_config = update_colors_to_wcag(mapping_config) - - 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, mapping_config['group_colors'])): - print(f" Groupe {i}: {name} - Couleur RGB : {color}") - - print(f"\nNombre total de groupes/classes : {num_classes}") - - return mapping_config - - -def map_mask_ids(mask, id_to_group_mapping): - """ - Applique le mapping des IDs Cityscapes vers les groupes - ADAPTÉ PYTORCH : Utilise torch au lieu de tf - """ - if isinstance(mask, np.ndarray): - mask = torch.from_numpy(mask) - - if isinstance(id_to_group_mapping, np.ndarray): - 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 = torch.tensor(id_to_group_mapping, dtype=torch.long) - else: - mapping_tensor = torch.tensor(list(id_to_group_mapping), dtype=torch.long) - - # S'assurer que les valeurs du masque sont dans la plage valide [0, 255] - mask_clipped = torch.clamp(mask, 0, 255) - mapped_mask = mapping_tensor[mask_clipped.long()] - return mapped_mask - -def colorize_mask(mask, group_colors): - """ - Applique la colorisation du masque avec les couleurs définies - INCHANGÉ - Compatible PyTorch (prend numpy en entrée) - """ - if isinstance(mask, torch.Tensor): - mask = mask.numpy() - - 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 get_preprocessing_fn(img_size=(512, 512), encoder=None): - """ - Retourne une fonction qui prétraite à la fois les images et les masques - pour l'inférence, en reproduisant exactement le pipeline CityscapesDataset. - """ - - if encoder is None: - mean, std = [0.0, 0.0, 0.0], [1.0, 1.0, 1.0] - else: - mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225] - - # Pipeline unifié (gérera image ET masque ensemble) - transform = A.Compose([ - A.Resize(height=img_size[0], width=img_size[1]), - A.Normalize(mean=mean, std=std), - ToTensorV2() - ]) - - def preprocess(image_np, mask_np=None): - if mask_np is not None: - # Traitement simultané image + masque - transformed = transform(image=image_np, mask=mask_np) - return transformed['image'], transformed['mask'].long() - else: - # Image seule - transformed = transform(image=image_np) - return transformed['image'], None - - return preprocess - -def apply_random_augmentation(image_np, prob=0.7): - """ - Applique des transformations d'augmentation aléatoires à une image, - similaires à celles utilisées pendant l'entraînement. - - Args: - image_np (np.ndarray): Image numpy array (H, W, 3) - prob (float): Probabilité d'appliquer chaque transformation - - Returns: - tuple: (image_augmentée, liste_des_transformations_appliquées) - """ - transforms_applied = [] - - # Définir les transformations possibles - possible_transforms = [ - { - "name": "Rotation aléatoire", - "transform": A.Rotate(limit=15, p=prob), - "description": "Rotation de -15° à +15°" - }, - { - "name": "Flip horizontal", - "transform": A.HorizontalFlip(p=0.5), - "description": "Miroir horizontal (50% chance)" - }, - { - "name": "Changement de luminosité", - "transform": A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=prob), - "description": "Luminosité et contraste ±20%" - }, - { - "name": "Flou gaussien léger", - "transform": A.GaussianBlur(blur_limit=(1, 3), p=prob*0.3), - "description": "Flou gaussien subtil" - }, - { - "name": "Décalage couleur", - "transform": A.HueSaturationValue(hue_shift_limit=10, sat_shift_limit=15, val_shift_limit=10, p=prob), - "description": "Ajustement teinte/saturation/valeur" - }, - { - "name": "Zoom aléatoire", - "transform": A.RandomScale(scale_limit=0.1, p=prob*0.5), - "description": "Zoom de -10% à +10%" - } - ] - - # Sélectionner aléatoirement quelques transformations - num_transforms = random.randint(1, min(4, len(possible_transforms))) - selected_transforms = random.sample(possible_transforms, num_transforms) - - # Créer le pipeline d'augmentation - augmentation_pipeline = [] - for transform_info in selected_transforms: - augmentation_pipeline.append(transform_info["transform"]) - transforms_applied.append(f"{transform_info['name']}: {transform_info['description']}") - - # Appliquer les transformations - compose = A.Compose(augmentation_pipeline) - - try: - augmented = compose(image=image_np) - augmented_image = augmented['image'] - except Exception as e: - print(f"Erreur lors de l'augmentation: {e}") - # En cas d'erreur, retourner l'image originale - augmented_image = image_np.copy() - transforms_applied = ["Erreur - image originale conservée"] - - return augmented_image, transforms_applied - -# Version simplifiée de preprocess_image_and_mask utilisant la nouvelle get_preprocessing_fn -def preprocess_image_and_mask(image_path, mask_path=None, img_size=(512, 512), - encoder_name=None, id_to_group_mapping=None): - """ - Version simplifiée utilisant get_preprocessing_fn pour la cohérence avec CityscapesDataset - """ - import numpy as np - from PIL import Image - import torch - - print(f"🔄 Préprocessing de l'image: {os.path.basename(image_path)}") - - # Chargement des données - image_pil = Image.open(image_path).convert('RGB') - image_np = np.array(image_pil) - img_display = np.array(image_pil.resize(img_size, Image.BILINEAR)) # Pour affichage - - mask_np = None - if mask_path and os.path.exists(mask_path): - print(f"🔄 Préprocessing du masque: {os.path.basename(mask_path)}") - mask_pil = Image.open(mask_path).convert('L') - mask_np = np.array(mask_pil) - print(f" Valeurs uniques avant mapping: {np.unique(mask_np)[:10]}...") - - # Utiliser notre preprocessing unifié - preprocess_fn = get_preprocessing_fn(img_size=img_size, encoder=encoder_name) - img_preprocessed, mask_preprocessed = preprocess_fn(image_np, mask_np) - - # Appliquer le mapping si fourni ET si on a un masque - if mask_preprocessed is not None and id_to_group_mapping is not None: - mask_preprocessed = map_mask_ids(mask_preprocessed, id_to_group_mapping) - print(f" Valeurs uniques après mapping: {torch.unique(mask_preprocessed)}") - - return img_preprocessed, img_display, mask_preprocessed - -# Version mise à jour de run_inference_and_visualize -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): - """ - Version simplifiée utilisant preprocess_image_and_mask - """ - print(f"=== INFÉRENCE PYTORCH SUR {os.path.basename(image_path)} ===") - - # Utiliser la version simplifiée du preprocessing - img_preprocessed, img_display, mask_gt = preprocess_image_and_mask( - image_path=image_path, - mask_path=mask_path, - img_size=img_size or (512, 512), - encoder_name=encoder_name, - id_to_group_mapping=mapping_config.get('id_to_group') - ) - - print("🔮 Exécution de l'inférence PyTorch...") - model.eval() - device = next(model.parameters()).device - - with torch.no_grad(): - # Ajouter dimension batch - img_batch = img_preprocessed.unsqueeze(0).to(device) # [1, 3, H, W] - - # ⏱️ MESURE DU TEMPS D'INFÉRENCE - start_time = time.time() - predictions = model(img_batch) # [1, num_classes, H, W] - end_time = time.time() - - inference_time = end_time - start_time - - predicted_mask = torch.argmax(predictions[0], dim=0).cpu().numpy() # [H, W] - - print(f"✅ Inférence terminée - Shape: {predicted_mask.shape}") - print(f"⏱️ Temps d'inférence: {inference_time*1000:.1f} ms ({inference_time:.4f} s)") - - # [Le reste du code de visualisation reste identique] - # Visualisation - 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) - 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: - mask_gt_np = mask_gt.numpy() if isinstance(mask_gt, torch.Tensor) else mask_gt - gt_colored = colorize_mask(mask_gt_np, mapping_config['group_colors']) - axes[2].imshow(gt_colored) - axes[2].set_title("Masque de Vérité") - axes[2].axis('off') - - # Visualisation des erreurs - diff_mask = (mask_gt_np != predicted_mask).astype(float) - axes[3].imshow(img_display) - axes[3].imshow(diff_mask, alpha=0.5, cmap='Reds') - axes[3].set_title("Erreurs (en rouge)") - axes[3].axis('off') - - 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}") - - - # Statistiques - 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: - mask_gt_np = mask_gt.numpy() if isinstance(mask_gt, torch.Tensor) else mask_gt - print("\n📊 Statistiques du masque de vérité:") - unique_classes, counts = np.unique(mask_gt_np, return_counts=True) - for class_id, count in zip(unique_classes, counts): - if class_id < len(mapping_config['group_names']): - percentage = (count / mask_gt_np.size) * 100 - print(f" {mapping_config['group_names'][class_id]}: {count} pixels ({percentage:.1f}%)") - - if show_plot: - plt.show() - return predicted_mask, inference_time - else: - return predicted_mask, inference_time, fig - - -def load_model(run_id=None, experiment_name="OC Projet 9", - metric="test_mean_iou", top_n=1): - """ - Charge un modèle PyTorch depuis MLflow avec plusieurs méthodes de sélection - - Args: - run_id (str, optional): Run ID spécifique (prioritaire si fourni) - experiment_name (str): Nom de l'expérience MLflow - metric (str): Métrique pour classer les runs - top_n (int): Position dans le classement (1=meilleur, 2=deuxième, etc.) - - Returns: - tuple: (model, source_run_id, encoder_name, script_img_size) - """ - - print(f"\n🔍 Chargement d'un modèle PyTorch depuis MLflow...") - - client = mlflow.tracking.MlflowClient() - - # 1. Sélection du run selon les paramètres - if run_id: - print(f"🎯 Mode run_id spécifique: {run_id}") - try: - run = mlflow.get_run(run_id) - source_run_id = run_id - print(f"✅ Run trouvé: {run.data.tags.get('mlflow.runName', 'Unknown')}") - except Exception as e: - raise RuntimeError(f"❌ Run {run_id} introuvable: {e}") - - else: - print(f"🏆 Mode classement: top_{top_n} de '{metric}' dans '{experiment_name}'") - try: - # Récupérer l'expérience - experiment = client.get_experiment_by_name(experiment_name) - if experiment is None: - raise RuntimeError(f"❌ Expérience '{experiment_name}' non trouvée") - - # Rechercher les runs avec la métrique, triés par ordre décroissant - filter_string = f"status = 'FINISHED' and metrics.{metric} >= 0" - order_by = f"metrics.{metric} DESC" - - runs_df = mlflow.search_runs( - experiment_ids=[experiment.experiment_id], - filter_string=filter_string, - order_by=[order_by], - max_results=top_n - ) - - if runs_df.empty: - raise RuntimeError(f"❌ Aucun run avec '{metric}' trouvé dans '{experiment_name}'") - - if len(runs_df) < top_n: - raise RuntimeError(f"❌ Seulement {len(runs_df)} runs trouvés, impossible d'obtenir le top_{top_n}") - - # Prendre le run à la position top_n - selected_run = runs_df.iloc[top_n - 1] - source_run_id = selected_run['run_id'] - metric_value = selected_run[f'metrics.{metric}'] - run_name = selected_run.get('tags.mlflow.runName', 'Unknown') - - print(f"✅ Run sélectionné (position #{top_n}):") - print(f" • Nom: {run_name}") - print(f" • Run ID: {source_run_id}") - print(f" • {metric}: {metric_value:.4f}") - - # Afficher le contexte - print(f"📊 Contexte du classement:") - for i, (_, row) in enumerate(runs_df.head(min(5, len(runs_df))).iterrows()): - indicator = "👑" if i == (top_n - 1) else f"{i+1}." - name = row.get('tags.mlflow.runName', 'Unknown')[:30] - value = row[f'metrics.{metric}'] - print(f" {indicator} {name}: {value:.4f}") - - run = mlflow.get_run(source_run_id) - - except Exception as e: - raise RuntimeError(f"❌ Erreur lors de la sélection: {e}") - - # 2-5. [Reste du code identique - extraction params, détection variante, chargement...] - try: - params = run.data.params - - encoder_name = params.get("encoder_name", None) - architecture = params.get("architecture", "FPN") - num_classes = int(params.get("num_classes", 8)) - script_img_size_raw = params.get("script_img_size", None) - run_name = run.data.tags.get('mlflow.runName', 'Unknown') - - # Parser script_img_size - script_img_size = None - if script_img_size_raw: - try: - script_img_size = ast.literal_eval(script_img_size_raw) - if isinstance(script_img_size, (tuple, list)) and len(script_img_size) == 2: - script_img_size = tuple(script_img_size) - except: - script_img_size = None - - print(f"\n📋 Paramètres du modèle:") - print(f"🏗️ Architecture: {architecture}") - print(f"🔧 Encodeur: {encoder_name}") - print(f"📊 Nombre de classes: {num_classes}") - print(f"🖼️ Script img size: {script_img_size}") - - except Exception as e: - raise RuntimeError(f"❌ Erreur lors de la récupération des paramètres: {e}") - - # Détecter la variante SegFormer - def detect_segformer_variant_from_name(run_name, params): - segformer_pattern = r'SegFormer[_-]?b(\d+)' - match = re.search(segformer_pattern, run_name, re.IGNORECASE) - if match: - variant_num = match.group(1) - detected = f"mit_b{variant_num}" - print(f"🔍 Variante détectée: 'SegFormer_b{variant_num}' → {detected}") - return detected - - if encoder_name and encoder_name.startswith("mit_b"): - print(f"🔍 Variante depuis paramètres: {encoder_name}") - return encoder_name - - b_pattern = r'\bb(\d+)\b' - match = re.search(b_pattern, run_name, re.IGNORECASE) - if match: - variant_num = match.group(1) - detected = f"mit_b{variant_num}" - print(f"🔍 Pattern détecté: 'b{variant_num}' → {detected}") - return detected - - print(f"🔍 Aucune variante détectée, utilisation de mit_b0") - return "mit_b0" - - detected_variant = detect_segformer_variant_from_name(run_name, params) - - # Télécharger et charger le modèle - with tempfile.TemporaryDirectory() as tmpdir: - try: - artifacts = client.list_artifacts(source_run_id) - model_artifact_candidates = [a.path for a in artifacts if a.path.endswith('.pth')] - - if not model_artifact_candidates: - raise RuntimeError("❌ Aucun fichier .pth trouvé") - - model_artifact = model_artifact_candidates[0] - print(f"⬇️ Téléchargement de {model_artifact}...") - - local_path = mlflow.artifacts.download_artifacts( - run_id=source_run_id, - artifact_path=model_artifact, - dst_path=tmpdir - ) - - checkpoint = torch.load(local_path, map_location='cpu') - if isinstance(checkpoint, dict) and 'state_dict' in checkpoint: - state_dict = checkpoint['state_dict'] - else: - state_dict = checkpoint - - except Exception as e: - raise RuntimeError(f"❌ Erreur lors du téléchargement: {e}") - - # Créer le modèle - try: - is_transformer = any('patch_embed' in key for key in state_dict.keys()) - - if is_transformer: - print(f"🔧 Création SegFormer ({detected_variant})") - model = smp.Segformer(encoder_name=detected_variant, classes=num_classes) - encoder_name = detected_variant - else: - print("🔧 Création modèle CNN") - if encoder_name is None or encoder_name == "None": - encoder_name = "efficientnet-b0" - - if architecture.lower() == "fpn": - model = smp.FPN(encoder_name=encoder_name, classes=num_classes) - elif architecture.lower() == "unet": - model = smp.Unet(encoder_name=encoder_name, classes=num_classes) - elif architecture.lower() == "deeplabv3plus": - model = smp.DeepLabV3Plus(encoder_name=encoder_name, classes=num_classes) - else: - raise ValueError(f"Architecture {architecture} non supportée") - - print("🔄 Chargement des poids...") - model.load_state_dict(state_dict, strict=True) - model.eval() - print("✅ Modèle chargé avec succès!") - - except Exception as e: - try: - model.load_state_dict(state_dict, strict=False) - model.eval() - print("✅ Chargement partiel réussi!") - except Exception as e2: - raise RuntimeError(f"❌ Erreur lors du chargement: {e2}") - - print(f"🎉 Modèle chargé depuis le run {source_run_id}") - return model, source_run_id, encoder_name, script_img_size - - -def load_best_model_from_registry(model_name): - """ - Charge le meilleur modèle depuis le MLflow Model Registry - (Version refactorisée - wrapper autour de load_model) - - Args: - model_name (str): Nom du modèle dans le Registry - - Returns: - tuple: (model, source_run_id, encoder_name, script_img_size) - """ - print(f"🏛️ Récupération du modèle '{model_name}' depuis le Registry...") - - client = mlflow.tracking.MlflowClient() - - try: - # Récupérer la version en Production - 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)) - print(f"📦 Version Registry: {latest_prod.version} (Stage: {latest_prod.current_stage})") - - # Récupérer le source_run_id depuis le tag du Registry - 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"🔗 Source run détecté: {source_run_id}") - - except Exception as e: - raise RuntimeError(f"❌ Erreur lors de l'accès au Registry: {e}") - - # Déléguer le chargement à load_model avec le run_id trouvé - print("➡️ Délégation vers load_model...") - return load_model(run_id=source_run_id) - - -# Fonctions de convenance -def load_best_model(experiment_name="OC Projet 9", metric="test_mean_iou"): - """Charge le meilleur modèle selon une métrique""" - return load_model(experiment_name=experiment_name, metric=metric, top_n=1) - -def load_second_best_model(experiment_name="OC Projet 9", metric="test_mean_iou"): - """Charge le deuxième meilleur modèle selon une métrique""" - return load_model(experiment_name=experiment_name, metric=metric, top_n=2) - -def load_model_by_run_id(run_id): - """Charge un modèle par run_id spécifique""" - return load_model(run_id=run_id) \ No newline at end of file