pytorch_losshist_plotting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4c46310b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ciprian-Florin Ifrim\\AppData\\Local\\Temp\\ipykernel_1336\\1285825539.py:7: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n",
      "  from IPython.core.display import display, HTML\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#-------------------------------------------------------------------------------------JUPYTER NOTEBOOK SETTINGS-------------------------------------------------------------------------------------\n",
    "from IPython.core.display import display, HTML                                    \n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1317a1ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the model checkpoint\n",
    "checkpoint_name = \"checkpoint_epoch_40.pth\"\n",
    "checkpoint_folder = \"FINAL-v2_rcnn_batch-16_epoch-40_full-enchanced-original_augmented-3X\"\n",
    "checkpoint = torch.load(\"pytorch_rcnn_checkpoints/\" + checkpoint_folder + \"/\" + checkpoint_name)\n",
    "\n",
    "# Extract loss histories\n",
    "loss_hist = checkpoint['loss_hist']\n",
    "valid_loss_hist = checkpoint.get('valid_loss_hist', None)  # Returns None if 'valid_loss_hist' does not exist\n",
    "\n",
    "# Define the number of epochs based on the length of loss history\n",
    "num_epochs = len(loss_hist)\n",
    "\n",
    "# Determine the interval at which validation loss is computed\n",
    "n = len(loss_hist) // len(valid_loss_hist)\n",
    "\n",
    "# Repeat each validation loss n times to match the training loss length\n",
    "valid_loss_resampled = [loss for loss in valid_loss_hist for _ in range(n)]\n",
    "\n",
    "# If the lengths are still mismatched, pad with the last validation loss value\n",
    "if len(valid_loss_resampled) < len(loss_hist):\n",
    "    valid_loss_resampled += [valid_loss_hist[-1]] * (len(loss_hist) - len(valid_loss_resampled))\n",
    "\n",
    "# Create a pandas DataFrame for loss history\n",
    "df_loss = pd.DataFrame(data={'Epoch': range(1, num_epochs + 1),\n",
    "                             'Training Loss': loss_hist,\n",
    "                             'Validation Loss': valid_loss_resampled})\n",
    "\n",
    "# Configure Seaborn\n",
    "sns.set_theme()\n",
    "\n",
    "# Plotting the loss using seaborn\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.lineplot(data=df_loss, x='Epoch', y='Training Loss', color='orange', label='Training Loss')\n",
    "\n",
    "# Plot Validation Loss only if it exists in df_loss\n",
    "if 'Validation Loss' in df_loss.columns:\n",
    "    sns.lineplot(data=df_loss, x='Epoch', y='Validation Loss', color='blue', label='Validation Loss')\n",
    "\n",
    "plt.title('Loss over epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the plot\n",
    "plt.savefig(\"training_loss_plots/\" + checkpoint_folder + \".png\", dpi=300)\n",
    "\n",
    "# Show the plot (which also resets the current figure and axes)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch-cude-env",
   "language": "python",
   "name": "pytorch-cude-env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}