model/src/train.py

import os
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from tqdm import tqdm
import random
import ssl
# Disable SSL verification for downloading pretrained weights
ssl._create_default_https_context = ssl._create_unverified_context
from torch.cuda.amp import GradScaler, autocast

from src.config import Config
from src.models import DeepfakeDetector
from src.dataset import DeepfakeDataset

try:
    from safetensors.torch import save_file, load_file
    SAFETENSORS_AVAILABLE = True
except ImportError:
    SAFETENSORS_AVAILABLE = False
    print("Warning: safetensors not installed. Checkpoints will be saved as .pt")

def train():
    # Setup
    Config.setup()
    device = torch.device(Config.DEVICE)
    
    # --- Data Loading with Automatic Split ---
    if Config.TRAIN_DATA_PATH == Config.TEST_DATA_PATH:
        print("Train and Test paths are identical. Performing automatic 80/20 shuffle split...")
        all_paths, all_labels = DeepfakeDataset.scan_directory(Config.TRAIN_DATA_PATH)
        
        if len(all_paths) == 0:
            print(f"No images found in {Config.TRAIN_DATA_PATH}")
            return

        # Combine and shuffle
        combined = list(zip(all_paths, all_labels))
        random.shuffle(combined)
        
        split_idx = int(len(combined) * 0.8)
        train_data = combined[:split_idx]
        val_data = combined[split_idx:]
        
        train_paths, train_labels = zip(*train_data)
        val_paths, val_labels = zip(*val_data)
        
        train_dataset = DeepfakeDataset(file_paths=list(train_paths), labels=list(train_labels), phase='train')
        val_dataset = DeepfakeDataset(file_paths=list(val_paths), labels=list(val_labels), phase='val')
    else:
        # Standard folder-based loading
        train_dataset = DeepfakeDataset(root_dir=Config.TRAIN_DATA_PATH, phase='train')
        val_dataset = DeepfakeDataset(root_dir=Config.TEST_DATA_PATH, phase='val')
    
    # Dataloaders
    train_loader = DataLoader(train_dataset, batch_size=Config.BATCH_SIZE, shuffle=True,
                              num_workers=Config.NUM_WORKERS, 
                              pin_memory=True if device.type=='cuda' else False,
                              persistent_workers=True if Config.NUM_WORKERS > 0 else False)
    val_loader = DataLoader(val_dataset, batch_size=Config.BATCH_SIZE, shuffle=False,
                            num_workers=Config.NUM_WORKERS, 
                            pin_memory=True if device.type=='cuda' else False,
                            persistent_workers=True if Config.NUM_WORKERS > 0 else False)
    
    # Model
    print("Initializing Multi-Branch DeepfakeDetector...")
    model = DeepfakeDetector(pretrained=True).to(device)
    
    # Optimization
    criterion = nn.BCEWithLogitsLoss()
    optimizer = optim.AdamW(model.parameters(), lr=Config.LEARNING_RATE, weight_decay=Config.WEIGHT_DECAY)
    # Optimization
    criterion = nn.BCEWithLogitsLoss()
    optimizer = optim.AdamW(model.parameters(), lr=Config.LEARNING_RATE, weight_decay=Config.WEIGHT_DECAY)
    scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.5)
    
    # Enable AMP only for CUDA (Windows NVIDIA)
    use_amp = (Config.DEVICE == 'cuda')
    scaler = GradScaler() if use_amp else None
    if use_amp:
        print("🚀 Mixed Precision (AMP) Enabled for RTX GPU")
    else:
        print("🐌 Standard Precision (No AMP) for CPU/MPS")
    
    # Resume from checkpoint if exists
    start_epoch = 0
    best_acc = 0.0
    
    # Priority:
    # 1. best_model.safetensors (if we crashed mid-training)
    # 2. patched_model.safetensors (the model we want to improve)
    
    resume_path = os.path.join(Config.CHECKPOINT_DIR, "best_model.safetensors")
    if not os.path.exists(resume_path):
        # Look for latest epoch checkpoint
        import glob
        import re
        checkpoints = glob.glob(os.path.join(Config.CHECKPOINT_DIR, "checkpoint_ep*.safetensors"))
        if checkpoints:
            # Sort by epoch number
            def get_epoch(p):
                match = re.search(r"checkpoint_ep(\d+)", p)
                return int(match.group(1)) if match else 0
            
            latest_ckpt = max(checkpoints, key=get_epoch)
            resume_path = latest_ckpt
            start_epoch = get_epoch(latest_ckpt)
            print(f"🔄 Auto-Resuming from latest epoch: {start_epoch}")
        else:
            resume_path = os.path.join(Config.CHECKPOINT_DIR, "patched_model.safetensors")
    
    if os.path.exists(resume_path):
        print(f"\n🔄 Found existing checkpoint: {resume_path}")
        print("Auto-resuming to FINETUNE this model...")
        
        try:
            if resume_path.endswith(".safetensors") and SAFETENSORS_AVAILABLE:
                state_dict = load_file(resume_path)
            else:
                state_dict = torch.load(resume_path, map_location=device)
            
            # Use strict=False to allow for minor architecture changes or missing keys
            model.load_state_dict(state_dict, strict=False)
            print("✅ Weights loaded. Starting Fine-Tuning.")
        except Exception as e:
            print(f"⚠ Failed to load checkpoint: {e}")
            print("Starting from ImageNet weights.")
    
    # Loop
    
    for epoch in range(start_epoch, Config.EPOCHS):
        model.train()
        train_loss = 0.0
        train_correct = 0
        train_total = 0
        
        loop = tqdm(train_loader, desc=f"Epoch {epoch+1}/{Config.EPOCHS}")
        for images, labels in loop:
            images = images.to(device)
            labels = labels.to(device).unsqueeze(1)
            
            optimizer.zero_grad()
            
            if use_amp:
                with autocast():
                    outputs = model(images)
                    loss = criterion(outputs, labels)
                
                scaler.scale(loss).backward()
                scaler.step(optimizer)
                scaler.update()
            else:
                # Standard training for Mac/CPU
                outputs = model(images)
                loss = criterion(outputs, labels)
                loss.backward()
                optimizer.step()
            
            train_loss += loss.item()
            preds = (torch.sigmoid(outputs) > 0.5).float()
            correct = (preds == labels).sum().item()
            train_correct += correct
            train_total += labels.size(0)
            
            loop.set_postfix(loss=loss.item(), acc=correct/labels.size(0))
            
        train_acc = train_correct / train_total if train_total > 0 else 0
        print(f"Epoch {epoch+1} Train Loss: {train_loss/len(train_loader):.4f} Acc: {train_acc:.4f}")
        
        # Save checkpoint after every epoch
        save_checkpoint(model, epoch+1, train_acc, best=False)
        
        # Validation
        if len(val_dataset) > 0:
            val_loss, val_acc = validate(model, val_loader, criterion, device)
            print(f"Epoch {epoch+1} Val Loss: {val_loss:.4f} Acc: {val_acc:.4f}")
            
            # Save best model if validation accuracy improved
            if val_acc > best_acc:
                best_acc = val_acc
                print(f"⭐ New best model! Validation Accuracy: {val_acc:.4f}")
                save_checkpoint(model, epoch+1, val_acc, best=True)
        
        scheduler.step()
    
    print(f"\n🎉 Training Complete!")
    print(f"Best Validation Accuracy: {best_acc:.4f}")

def validate(model, loader, criterion, device):
    model.eval()
    val_loss = 0.0
    correct = 0
    total = 0
    
    with torch.no_grad():
        for images, labels in loader:
            images = images.to(device)
            labels = labels.to(device).unsqueeze(1)
            
            outputs = model(images)
            loss = criterion(outputs, labels)
            
            val_loss += loss.item()
            preds = (torch.sigmoid(outputs) > 0.5).float()
            correct += (preds == labels).sum().item()
            total += labels.size(0)
            
    return val_loss / len(loader), correct / total

def save_checkpoint(model, epoch, acc, best=False):
    state_dict = model.state_dict()
    name = "best_model.safetensors" if best else f"checkpoint_ep{epoch}.safetensors"
    path = os.path.join(Config.CHECKPOINT_DIR, name)
    
    if SAFETENSORS_AVAILABLE:
        try:
            # Try with shared tensors support
            from safetensors.torch import save_model
            save_model(model, path)
            print(f"Saved Checkpoint: {path}")
            
            # 📝 Auto-Log to History
            try:
                from datetime import datetime
                log_path = os.path.join(Config.PROJECT_ROOT, "TRAINING_HISTORY.md")
                timestamp = datetime.now().strftime("%Y-%m-%d | %I:%M %p")
                
                # Create file with header if doesn't exist
                if not os.path.exists(log_path):
                    with open(log_path, "w", encoding="utf-8") as f:
                        f.write("# 📜 Training History Log\n\n")
                        f.write("| Date | Time | Model Name | Dataset | Epochs | Accuracy | Loss | Status |\n")
                        f.write("| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n")
                
                # Append Entry to Summary Log
                with open(log_path, "a", encoding="utf-8") as f:
                    # Format: Date | Time | Name | Dataset | Epoch | Acc | Loss | Status
                    dataset_name = os.path.basename(Config.DATA_DIR)
                    entry = f"| **{timestamp.split(' | ')[0]}** | {timestamp.split(' | ')[1]} | {name} | {dataset_name} | {epoch} | {acc*100:.2f}% | N/A | ✅ Saved |\n"
                    f.write(entry)
                    print(f"📝 Logged to TRAINING_HISTORY.md")

                # 📝 Detailed Lab Notebook Logging
                detail_path = os.path.join(Config.PROJECT_ROOT, "DETAILED_HISTORY.md")
                with open(detail_path, "a", encoding="utf-8") as f:
                    f.write(f"\n## Model: {name} (Epoch {epoch})\n")
                    f.write(f"| Feature | Detail |\n| :--- | :--- |\n")
                    f.write(f"| **Date** | {timestamp} |\n")
                    f.write(f"| **Training Accuracy** | {acc*100:.2f}% |\n")
                    f.write(f"| **Dataset** | {Config.DATA_DIR} |\n")
                    f.write(f"| **Batch Size** | {Config.BATCH_SIZE} |\n")
                    f.write(f"| **Optimizer** | AdamW (lr={Config.LEARNING_RATE}) |\n")
                    f.write(f"| **Device** | {Config.DEVICE.upper()} |\n")
                    f.write("\n---\n")
                    print(f"📘 Detailed log written to DETAILED_HISTORY.md")

            except Exception as e:
                print(f"⚠️ Failed to write log: {e}")

        except Exception as e:
            # Fallback to regular torch save if safetensors fails
            print(f"SafeTensors save failed ({e}), falling back to .pth format")
            torch.save(state_dict, path.replace(".safetensors", ".pth"))
            print(f"Saved Checkpoint (Legacy): {path.replace('.safetensors', '.pth')}")
    else:
        torch.save(state_dict, path.replace(".safetensors", ".pth"))
        print(f"Saved Checkpoint (Legacy): {path}")

if __name__ == "__main__":
    train()