""" Loss logging and visualization utilities for SLTUNET training. Automatically saves training loss and generates plots. """ import os import csv from datetime import datetime import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import numpy as np class LossLogger: """Logger for training loss with automatic visualization.""" def __init__(self, output_dir, plot_freq=10): """ Args: output_dir: Directory to save loss logs and plots plot_freq: Frequency to update plots (in steps) """ self.output_dir = output_dir self.plot_freq = plot_freq # CSV file for training loss self.train_loss_file = os.path.join(output_dir, 'train_loss.csv') self.eval_loss_file = os.path.join(output_dir, 'eval_loss.csv') # Initialize CSV files self._init_csv_files() # Cache for plotting self.train_losses = [] self.train_steps = [] self.eval_losses = [] self.eval_steps = [] self.eval_bleus = [] def _init_csv_files(self): """Initialize CSV files with headers.""" # Training loss CSV if not os.path.exists(self.train_loss_file): with open(self.train_loss_file, 'w', newline='') as f: writer = csv.writer(f) writer.writerow(['timestamp', 'step', 'epoch', 'loss', 'gnorm', 'pnorm', 'lr']) # Evaluation loss CSV if not os.path.exists(self.eval_loss_file): with open(self.eval_loss_file, 'w', newline='') as f: writer = csv.writer(f) writer.writerow(['timestamp', 'step', 'eval_loss', 'bleu4', 'bleu1', 'bleu2', 'bleu3', 'otem2', 'utem4']) def log_train_step(self, step, epoch, loss, gnorm, pnorm, lr): """Log training step information.""" timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S') # Append to CSV with open(self.train_loss_file, 'a', newline='') as f: writer = csv.writer(f) writer.writerow([timestamp, step, epoch, loss, gnorm, pnorm, lr]) # Update cache self.train_steps.append(step) self.train_losses.append(loss) # Generate plot if needed if step % self.plot_freq == 0: self.generate_plots() def log_eval_step(self, step, eval_loss, bleu_score, metrics_dict=None): """Log evaluation step information with multiple metrics.""" timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S') # Extract metrics bleu1 = metrics_dict.get('bleu1', 0.0) if metrics_dict else 0.0 bleu2 = metrics_dict.get('bleu2', 0.0) if metrics_dict else 0.0 bleu3 = metrics_dict.get('bleu3', 0.0) if metrics_dict else 0.0 bleu4 = metrics_dict.get('bleu4', bleu_score) if metrics_dict else bleu_score otem2 = metrics_dict.get('otem2', 0.0) if metrics_dict else 0.0 utem4 = metrics_dict.get('utem4', 0.0) if metrics_dict else 0.0 # Append to CSV with open(self.eval_loss_file, 'a', newline='') as f: writer = csv.writer(f) writer.writerow([timestamp, step, eval_loss, bleu4, bleu1, bleu2, bleu3, otem2, utem4]) # Update cache self.eval_steps.append(step) self.eval_losses.append(eval_loss) self.eval_bleus.append(bleu4) # Always generate plot after evaluation self.generate_plots() def load_history(self): """Load historical data from CSV files.""" # Load training loss if os.path.exists(self.train_loss_file): with open(self.train_loss_file, 'r') as f: reader = csv.DictReader(f) for row in reader: self.train_steps.append(int(row['step'])) self.train_losses.append(float(row['loss'])) # Load evaluation loss if os.path.exists(self.eval_loss_file): with open(self.eval_loss_file, 'r') as f: reader = csv.DictReader(f) for row in reader: self.eval_steps.append(int(row['step'])) self.eval_losses.append(float(row['eval_loss'])) self.eval_bleus.append(float(row['bleu_score'])) def generate_plots(self): """Generate loss and BLEU plots.""" if not self.train_steps: return # Create figure with 3 subplots fig = plt.figure(figsize=(14, 10)) gs = fig.add_gridspec(3, 1, hspace=0.3) # Plot 1: Training loss ax1 = fig.add_subplot(gs[0]) if self.train_steps: ax1.plot(self.train_steps, self.train_losses, 'b-', linewidth=1, alpha=0.6, label='Train Loss') # Add moving average if len(self.train_losses) > 20: window = 20 ma = np.convolve(self.train_losses, np.ones(window)/window, mode='valid') ma_steps = self.train_steps[window-1:] ax1.plot(ma_steps, ma, 'r-', linewidth=2, label=f'Moving Avg ({window} steps)') ax1.set_xlabel('Training Step', fontsize=12, fontweight='bold') ax1.set_ylabel('Training Loss', fontsize=12, fontweight='bold') ax1.set_title(f'SLTUNET Training Loss (Current Step: {self.train_steps[-1] if self.train_steps else 0})', fontsize=14, fontweight='bold') ax1.grid(True, alpha=0.3, linestyle='--') ax1.legend(loc='upper right') # Plot 2: Evaluation loss ax2 = fig.add_subplot(gs[1]) if self.eval_steps: ax2.plot(self.eval_steps, self.eval_losses, 'go-', linewidth=2, markersize=6, label='Eval Loss') # Mark best if self.eval_losses: min_loss = min(self.eval_losses) min_idx = self.eval_losses.index(min_loss) ax2.plot(self.eval_steps[min_idx], min_loss, 'r*', markersize=15) ax2.annotate(f'Best: {min_loss:.4f}', xy=(self.eval_steps[min_idx], min_loss), xytext=(10, 10), textcoords='offset points', bbox=dict(boxstyle='round', facecolor='yellow', alpha=0.8), arrowprops=dict(arrowstyle='->', color='red')) ax2.set_xlabel('Training Step', fontsize=12, fontweight='bold') ax2.set_ylabel('Validation Loss', fontsize=12, fontweight='bold') ax2.set_title('Validation Loss', fontsize=14, fontweight='bold') ax2.grid(True, alpha=0.3, linestyle='--') ax2.legend(loc='upper right') # Plot 3: BLEU score ax3 = fig.add_subplot(gs[2]) if self.eval_steps: ax3.plot(self.eval_steps, self.eval_bleus, 'mo-', linewidth=2, markersize=6, label='BLEU Score') # Mark best if self.eval_bleus: max_bleu = max(self.eval_bleus) max_idx = self.eval_bleus.index(max_bleu) ax3.plot(self.eval_steps[max_idx], max_bleu, 'r*', markersize=15) ax3.annotate(f'Best: {max_bleu:.6f}', xy=(self.eval_steps[max_idx], max_bleu), xytext=(10, -20), textcoords='offset points', bbox=dict(boxstyle='round', facecolor='lightgreen', alpha=0.8), arrowprops=dict(arrowstyle='->', color='green')) ax3.set_xlabel('Training Step', fontsize=12, fontweight='bold') ax3.set_ylabel('BLEU Score', fontsize=12, fontweight='bold') ax3.set_title('BLEU Score (Higher is Better)', fontsize=14, fontweight='bold') ax3.grid(True, alpha=0.3, linestyle='--') ax3.legend(loc='lower right') # Add timestamp timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S') fig.text(0.99, 0.01, f'Updated: {timestamp}', ha='right', fontsize=9, style='italic') # Save plot plot_path = os.path.join(self.output_dir, 'training_curves.png') plt.savefig(plot_path, dpi=150, bbox_inches='tight') plt.close() # Also generate a summary text self._generate_summary() def _generate_summary(self): """Generate text summary of training.""" summary_path = os.path.join(self.output_dir, 'training_summary.txt') with open(summary_path, 'w') as f: f.write("=" * 70 + "\n") f.write(" SLTUNET Training Summary\n") f.write(f" Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n") f.write("=" * 70 + "\n\n") if self.train_steps: f.write(f"Training Progress:\n") f.write(f" Current Step: {self.train_steps[-1]}\n") f.write(f" Total Steps: {len(self.train_steps)}\n") f.write(f" Latest Loss: {self.train_losses[-1]:.6f}\n") if len(self.train_losses) > 10: recent_avg = np.mean(self.train_losses[-10:]) f.write(f" Recent Avg Loss: {recent_avg:.6f} (last 10 steps)\n") f.write("\n") if self.eval_steps: f.write(f"Evaluation Results:\n") f.write(f" Total Evaluations: {len(self.eval_steps)}\n") f.write(f" Best Eval Loss: {min(self.eval_losses):.6f} (step {self.eval_steps[self.eval_losses.index(min(self.eval_losses))]})\n") f.write(f" Best BLEU Score: {max(self.eval_bleus):.6f} (step {self.eval_steps[self.eval_bleus.index(max(self.eval_bleus))]})\n") f.write(f" Latest Eval Loss: {self.eval_losses[-1]:.6f}\n") f.write(f" Latest BLEU: {self.eval_bleus[-1]:.6f}\n") f.write("\n") f.write("=" * 70 + "\n") f.write(f"Loss logs saved to:\n") f.write(f" - {self.train_loss_file}\n") f.write(f" - {self.eval_loss_file}\n") f.write("=" * 70 + "\n")