Fatigue_Life/surrogateAI/utilities/plot_lr.py

import os
import pandas as pd
import matplotlib.pyplot as plt


def plot_training_loss(exp_dir, epoch):

    # Build paths
    log_dir = os.path.join(exp_dir, 'log')
    train_pkl = os.path.join(log_dir, f'temp_train_loss_{epoch}.pkl')
    if not os.path.isfile(train_pkl):
        raise FileNotFoundError(f"Training pickle not found: {train_pkl}")

    # Load data
    df = pd.read_pickle(train_pkl)

    epoch_losses = [float(x) for x in df['train_epoch_losses']]
    # epoch_losses_temp = [float(x) for x in df['train_epoch_losses_temp']]
    # epoch_losses_pos = [float(x) for x in df['train_epoch_losses_pos']]
    # epoch_losses_von = [float(x) for x in df['train_epoch_losses_von']]
    lrs = df['learning_rate']
    epochs = list(range(1, len(epoch_losses) + 1))
    print("min training loss", df['train_min_epoch_loss'])

    # Plot
    fig, ax1 = plt.subplots(figsize=(10,5))
    ax1.plot(epochs, epoch_losses, marker='o', linestyle='-', label='Total Train Loss', color = 'blue')
    # ax1.plot(epochs, epoch_losses_temp, marker='o', linestyle='-', label='Temp Train Loss', color = 'yellow')
    # ax1.plot(epochs, epoch_losses_pos, marker='o', linestyle='-', label='Pos Train Loss', color = 'green')
    # ax1.plot(epochs, epoch_losses_von, marker='o', linestyle='-', label='Von Train Loss', color = 'pink')    
    ax1.set_xlabel('Epoch')
    ax1.set_ylabel('Loss', color='blue')
    ax1.tick_params(axis='y', labelcolor='blue')

    ax2 = ax1.twinx()
    ax2.plot(epochs, lrs, linestyle='--', label='Learning Rate', color='red')
    ax2.set_ylabel('Learning Rate', color='red')
    ax2.tick_params(axis='y', labelcolor='red')

    # Combine legends
    lines, labels = ax1.get_legend_handles_labels()
    lines2, labels2 = ax2.get_legend_handles_labels()
    ax1.legend(lines + lines2, labels + labels2, loc='upper right')

    ax1.set_title(f'Training Loss & Learning Rate (epoch {epoch})')
    ax1.grid(True)

    # Save
    save_dir = os.path.join(exp_dir, 'plots_lr')
    os.makedirs(save_dir, exist_ok=True)
    out_path = os.path.join(save_dir, f'training_loss_epoch_{epoch}.png')
    plt.tight_layout()
    plt.savefig(out_path)
    plt.close(fig)
    print(f"Saved training plot to {out_path}")




def main():
    exp_dir = '/home/rachit/GlassForming/t_dest_output/regDGCNN_seg/train/EXPERIMENT_2/Mon-Jul-14-10-01-31-2025/'
    epoch = 40
    plot_training_loss(exp_dir, epoch)
    
if __name__ == '__main__':
    main()