train_optimized.py

import sys
from pathlib import Path
# Tambahkan parent project ke sys.path sehingga 'src' dapat diimport saat menjalankan skrip langsung
sys.path.append(str(Path(__file__).resolve().parents[1]))

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
import time
import os
from datetime import datetime
import json
import matplotlib.pyplot as plt
import numpy as np
from torch.optim.lr_scheduler import ReduceLROnPlateau, CosineAnnealingLR

# Import modul yang sudah dibuat
from src import config
from src.data_loader import create_dataloaders
from src.model import create_model
from src.engine import train_step, val_step
from src.mixup import MixupTrainer

def setup_optimized_training():
    """

    Setup untuk training yang dioptimalkan untuk mengatasi overfitting.

    """
    print("SETUP TRAINING OPTIMIZED - ANTI OVERFITTING")
    print("="*60)
    
    # Override config untuk training yang lebih optimal
    #config.BATCH_SIZE = 16  # Sedang untuk balance speed vs generalization
    #config.EPOCHS = 30      # Cukup untuk konvergensi
    #config.IMAGE_SIZE = 224  # Resolusi standar
    #config.LEARNING_RATE = 1e-4  # Learning rate yang lebih konservatif
    
    print(f"Konfigurasi Training Optimized:")
    print(f"   - Batch Size: {config.BATCH_SIZE}")
    print(f"   - Epochs: {config.EPOCHS}")
    print(f"   - Image Size: {config.IMAGE_SIZE}x{config.IMAGE_SIZE}")
    print(f"   - Learning Rate: {config.LEARNING_RATE}")
    print(f"   - Device: {config.DEVICE}")
    print(f"   - Model: {config.MODEL_LIST[0] if config.MODEL_LIST else 'None'}")
    
    # Buat direktori untuk hasil
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    experiment_dir = Path("outputs") / f"optimized_training_{timestamp}"
    model_dir = experiment_dir / "models"
    log_dir = experiment_dir / "logs"
    
    experiment_dir.mkdir(parents=True, exist_ok=True)
    model_dir.mkdir(parents=True, exist_ok=True)
    log_dir.mkdir(parents=True, exist_ok=True)
    
    writer = SummaryWriter(log_dir=str(log_dir))
    
    return writer, experiment_dir, model_dir

def train_optimized_model(model_name_key: str, model_name: str, num_classes: int, 

                         train_loader, val_loader, writer, model_dir: Path):
    """

    Training model dengan optimasi anti-overfitting.

    """
    print(f"\nTRAINING MODEL: {model_name_key.upper()}")
    print(f"   Model: {model_name}")
    print(f"   Classes: {num_classes}")
    print("-" * 50)
    
    # Buat model dengan dropout untuk regularization
    model = create_model(model_name, num_classes, pretrained=True, dropout_rate=0.1)
    if model is None:
        print(f"ERROR: Gagal membuat model {model_name}")
        return None
    
    model = model.to(config.DEVICE)
    
    # Setup optimizer dengan weight decay untuk regularization
    loss_fn = nn.CrossEntropyLoss(label_smoothing=0.1)  # Label smoothing untuk mengurangi overfitting
    optimizer = optim.Adam(model.parameters(), lr=config.LEARNING_RATE, weight_decay=5e-4)
    
    # Setup learning rate scheduler
    scheduler = ReduceLROnPlateau(optimizer, mode='max', factor=0.5, patience=3)
    
    # Setup Mixup trainer untuk data augmentation yang lebih kuat
    mixup_trainer = MixupTrainer(model, optimizer, loss_fn, config.DEVICE, alpha=0.2)
    
    # Tracking variables
    train_losses, val_losses = [], []
    train_accs, val_accs = [], []
    best_val_acc = 0.0
    best_epoch = 0
    
    # Early stopping
    patience = 7  # Stop jika tidak ada improvement selama 7 epoch
    epochs_no_improve = 0
    
    print(f"Memulai training {config.EPOCHS} epochs...")
    print(f"   Early Stopping: {patience} epochs patience")
    print(f"   Learning Rate Scheduler: ReduceLROnPlateau")
    print(f"   Weight Decay: 1e-4")
    
    start_time = time.time()
    
    for epoch in range(config.EPOCHS):
        print(f"\nEpoch {epoch+1}/{config.EPOCHS}")
        
        # Training dengan Mixup
        train_loss, train_acc = mixup_trainer.train_step(train_loader)
        
        # Validation
        val_loss, val_acc = val_step(
            model=model, dataloader=val_loader, loss_fn=loss_fn,
            device=config.DEVICE
        )
        
        # Update learning rate scheduler
        scheduler.step(val_acc)
        
        # Simpan metrics
        train_losses.append(train_loss)
        val_losses.append(val_loss)
        train_accs.append(train_acc)
        val_accs.append(val_acc)
        
        # Log ke TensorBoard
        writer.add_scalar(f'{model_name_key}/Train/Loss', train_loss, epoch)
        writer.add_scalar(f'{model_name_key}/Train/Accuracy', train_acc, epoch)
        writer.add_scalar(f'{model_name_key}/Val/Loss', val_loss, epoch)
        writer.add_scalar(f'{model_name_key}/Val/Accuracy', val_acc, epoch)
        writer.add_scalar(f'{model_name_key}/Learning_Rate', optimizer.param_groups[0]['lr'], epoch)
        
        # Cek model terbaik
        if val_acc > best_val_acc:
            best_val_acc = val_acc
            best_epoch = epoch + 1
            epochs_no_improve = 0  # Reset counter
            
            # Simpan model terbaik
            model_path = model_dir / f"{model_name_key}_best.pth"
            torch.save({
                'model_state_dict': model.state_dict(),
                'optimizer_state_dict': optimizer.state_dict(),
                'scheduler_state_dict': scheduler.state_dict(),
                'epoch': epoch + 1,
                'val_accuracy': val_acc,
                'model_name': model_name,
                'num_classes': num_classes
            }, model_path)
            print(f"Model terbaik disimpan: {model_path}")
        else:
            epochs_no_improve += 1
        
        # Progress
        print(f"   Train: Loss={train_loss:.4f}, Acc={train_acc:.4f}")
        print(f"   Val:   Loss={val_loss:.4f}, Acc={val_acc:.4f}")
        print(f"   Best:  {best_val_acc:.4f} (Epoch {best_epoch})")
        print(f"   LR:    {optimizer.param_groups[0]['lr']:.2e}")
        print(f"   No Improve: {epochs_no_improve}/{patience}")
        
        # Early stopping check
        if epochs_no_improve >= patience:
            print(f"\nEarly stopping! Tidak ada kemajuan selama {patience} epoch.")
            print(f"Model terbaik: Epoch {best_epoch} dengan Val Acc: {best_val_acc:.4f}")
            break
    
    end_time = time.time()
    training_time = end_time - start_time
    
    print(f"\nTraining selesai!")
    print(f"   Waktu: {training_time:.1f} detik")
    print(f"   Best Accuracy: {best_val_acc:.4f}")
    print(f"   Epochs trained: {epoch + 1}")
    
    return {
        'model_name': model_name_key,
        'best_val_acc': best_val_acc,
        'best_epoch': best_epoch,
        'final_val_acc': val_acc,
        'training_time': training_time,
        'epochs_trained': epoch + 1,
        'train_losses': train_losses,
        'val_losses': val_losses,
        'train_accs': train_accs,
        'val_accs': val_accs
    }

def main():
    """

    Training optimized untuk mengatasi overfitting.

    """
    print("BATIK VISION - OPTIMIZED TRAINING MODE")
    print("="*60)
    
    # 1. Setup training optimized
    writer, experiment_dir, model_dir = setup_optimized_training()
    
    # 2. Buat data loaders
    print("\nMembuat data loaders...")
    try:
        train_loader, val_loader, class_names = create_dataloaders()
        num_classes = len(class_names)
        print(f"Data siap! {num_classes} kelas ditemukan.")
        print(f"   Kelas: {class_names[:5]}{'...' if len(class_names) > 5 else ''}")
    except Exception as e:
        print(f"ERROR data loader: {e}")
        return
    
    # 3. Model mapping
    model_mapping = {
        "vit": "vit_base_patch16_224",
        "swin_transformer": "swin_base_patch4_window7_224", 
        "convnext_tiny": "convnext_tiny"
    }
    
    # 4. Training
    all_results = []
    
    for model_name_key in config.MODEL_LIST:
        if model_name_key not in model_mapping:
            print(f"WARNING: Model '{model_name_key}' tidak dikenali. Dilewati.")
            continue
            
        model_name = model_mapping[model_name_key]
        
        try:
            result = train_optimized_model(
                model_name_key=model_name_key,
                model_name=model_name,
                num_classes=num_classes,
                train_loader=train_loader,
                val_loader=val_loader,
                writer=writer,
                model_dir=model_dir
            )
            
            if result:
                all_results.append(result)
                
        except Exception as e:
            print(f"ERROR training {model_name_key}: {e}")
            continue
    
    # 5. Ringkasan
    if all_results:
        print(f"\nRINGKASAN HASIL")
        print("="*40)
        
        for result in all_results:
            print(f"{result['model_name']:15} | "
                  f"Best: {result['best_val_acc']:.4f} | "
                  f"Epochs: {result['epochs_trained']} | "
                  f"Time: {result['training_time']:.1f}s")
        
        best_model = max(all_results, key=lambda x: x['best_val_acc'])
        print(f"\nModel terbaik: {best_model['model_name']} "
              f"({best_model['best_val_acc']:.4f})")
    
    writer.close()
    print(f"\nHasil disimpan di: {experiment_dir}")

if __name__ == "__main__":
    main()