verify uncertainty.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5432c20f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2198497/3617350024.py:7: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.\n",
      "  plt.style.use('seaborn-whitegrid')\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import sys\n",
    "# import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0dfa2f13",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read(path):\n",
    "    testdata = []\n",
    "    index = -1\n",
    "    with open(path) as f:\n",
    "        for line in f:\n",
    "            if \"current step is + \" in line:\n",
    "                index+=1\n",
    "                step_str = line.split(\" + \")[1]\n",
    "                step = int(step_str)\n",
    "                testdata.append([step])\n",
    "            if \"current img is + \" in line:\n",
    "                img_index_str = line.split(\" + \")[1]\n",
    "                img_index = int(img_index_str)\n",
    "                testdata[index].append(img_index)\n",
    "            if \"img loss is + \" in line:\n",
    "                img_loss_str = line.split(\" + \")[1]\n",
    "                img_loss = float(img_loss_str)\n",
    "                testdata[index].append(img_loss)\n",
    "#             if \"loss_depth_un is + \" in line:\n",
    "#                 depth_loss_str = line.split(\" + \")[1]\n",
    "#                 depth_loss = float(depth_loss_str)\n",
    "#                 testdata[index].append(depth_loss)\n",
    "            if \"uncertainty sum is + \" in line:\n",
    "                uncertainty_str = line.split(\" + \")[1]\n",
    "                uncertainty = float(uncertainty_str)\n",
    "                testdata[index].append(uncertainty)\n",
    "            if \"final loss is + \" in line:\n",
    "                final_loss_str = line.split(\" + \")[1]\n",
    "                final_loss = float(final_loss_str)\n",
    "                testdata[index].append(final_loss)\n",
    "            if \"psnr is + \" in line:\n",
    "                psnr_str = line.split(\" + \")[1]\n",
    "                psnr = float(psnr_str)\n",
    "                testdata[index].append(psnr)\n",
    "#             if \"ssim is + \" in line:\n",
    "#                 ssim_str = line.split(\" + \")[1]\n",
    "#                 ssim = float(ssim_str)\n",
    "#                 testdata[index].append(ssim)\n",
    "#             if \"lpips is + \" in line:\n",
    "#                 lpips_str = line.split(\" + \")[1]\n",
    "#                 lpips = float(lpips_str)\n",
    "#                 testdata[index].append(lpips)\n",
    "#     testdata = pd.DataFrame(testdata,columns=['step','img_index','img_loss','uncertainty','depth_loss','final_loss','psnr','ssim','lpips'])\n",
    "    testdata = pd.DataFrame(testdata,columns=['step','img_index','img_loss','uncertainty','final_loss','psnr'])\n",
    "    return testdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "82bdfcd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>img_index</th>\n",
       "      <th>img_loss</th>\n",
       "      <th>uncertainty</th>\n",
       "      <th>final_loss</th>\n",
       "      <th>psnr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2225.000000</td>\n",
       "      <td>2225.000000</td>\n",
       "      <td>2225.000000</td>\n",
       "      <td>2225.000000</td>\n",
       "      <td>2225.000000</td>\n",
       "      <td>2225.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>90000.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>1747.331148</td>\n",
       "      <td>672.774134</td>\n",
       "      <td>6.380517</td>\n",
       "      <td>26.549539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>51392.480485</td>\n",
       "      <td>7.212724</td>\n",
       "      <td>2555.506245</td>\n",
       "      <td>359.216373</td>\n",
       "      <td>0.514614</td>\n",
       "      <td>4.086685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>198.329827</td>\n",
       "      <td>230.992462</td>\n",
       "      <td>5.448143</td>\n",
       "      <td>12.539907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>46000.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>467.034921</td>\n",
       "      <td>374.406036</td>\n",
       "      <td>5.928861</td>\n",
       "      <td>23.131222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>90000.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>927.951804</td>\n",
       "      <td>545.091064</td>\n",
       "      <td>6.303353</td>\n",
       "      <td>27.137157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>134000.000000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>2334.096830</td>\n",
       "      <td>935.650208</td>\n",
       "      <td>6.843845</td>\n",
       "      <td>30.118917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>178000.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>26745.488579</td>\n",
       "      <td>2376.870850</td>\n",
       "      <td>7.778274</td>\n",
       "      <td>33.838531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                step    img_index      img_loss  uncertainty   final_loss  \\\n",
       "count    2225.000000  2225.000000   2225.000000  2225.000000  2225.000000   \n",
       "mean    90000.000000   125.000000   1747.331148   672.774134     6.380517   \n",
       "std     51392.480485     7.212724   2555.506245   359.216373     0.514614   \n",
       "min      2000.000000   113.000000    198.329827   230.992462     5.448143   \n",
       "25%     46000.000000   119.000000    467.034921   374.406036     5.928861   \n",
       "50%     90000.000000   125.000000    927.951804   545.091064     6.303353   \n",
       "75%    134000.000000   131.000000   2334.096830   935.650208     6.843845   \n",
       "max    178000.000000   137.000000  26745.488579  2376.870850     7.778274   \n",
       "\n",
       "              psnr  \n",
       "count  2225.000000  \n",
       "mean     26.549539  \n",
       "std       4.086685  \n",
       "min      12.539907  \n",
       "25%      23.131222  \n",
       "50%      27.137157  \n",
       "75%      30.118917  \n",
       "max      33.838531  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testdata = read('./logs/blender_uncertainty_lego_v1/testdata.txt')\n",
    "testdata.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4b83eb5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw(x,y,xlabel=None,ylabel=None,title=None):\n",
    "    fig = plt.figure(figsize=(10, 6), dpi=100)\n",
    "    ax = plt.axes()\n",
    "    ax.plot(x,y,\"go\")\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d8ee1bca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tt = testdata[:]\n",
    "draw(tt[\"psnr\"],np.log(tt[\"uncertainty\"]/(400*400)),\"PSNR\",\"Uncertainty\",\"Uncertainty-PSNR\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81bc8df4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}