SignX/utils/loss_logger.py

"""
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")