Step 5 - ResNet and Keypoints.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e91c0a12",
   "metadata": {},
   "source": [
    "# Basic Setup and Installations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "00f1a469",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use pytorch to build a convolutional neural network\n",
    "import os\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "from torch import optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision.io import read_image\n",
    "from torchvision import transforms\n",
    "from torchvision.datasets import ImageFolder\n",
    "from torchmetrics import Accuracy, Precision, F1Score\n",
    "\n",
    "import itertools\n",
    "# from pyimagesearch import config\n",
    "from imutils import paths\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import shutil\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9eba33d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use GPUs if available\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d02376bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "CURRENT_DATASET_PATH = \"dataset-for-classification\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "214e2d0b",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nose_scaled_x</th>\n",
       "      <th>nose_scaled_y</th>\n",
       "      <th>nose_score</th>\n",
       "      <th>left_eye_scaled_x</th>\n",
       "      <th>left_eye_scaled_y</th>\n",
       "      <th>left_eye_score</th>\n",
       "      <th>right_eye_scaled_x</th>\n",
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       "      <th>right_eye_score</th>\n",
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       "      <th>pose</th>\n",
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       "  </thead>\n",
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       "      <th>./dataset-for-classification/paripurna-navasana/7ef89ad4433e9e5af2218d2c870845a44f994b18a123852e79c585f77bdc1488.png</th>\n",
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       "    <tr>\n",
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       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./dataset-for-classification/upavistha-konasana/105c894c324470bffd7517867d5ac97db8dd8765bd787b0c4b36f1aadf069c1e.png</th>\n",
       "      <td>0.596422</td>\n",
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       "    <tr>\n",
       "      <th>./dataset-for-classification/marjaryasana/8dc6474628fdf057f89bc360f1275070ae7bf5b4b17433c50543e663ee26680a.png</th>\n",
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       "    <tr>\n",
       "      <th>./dataset-for-classification/urdhva-prasarita-eka-padasana/157976074d7a676da7623d971725c399ec46656350936d278bf46eb09a775475.png</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>./dataset-for-classification/baddha-konasana/12ca2a1d6b43266b5bd3c8a4a8229d3ee34067d23bd4ae3c9a9106b034c2401b.png</th>\n",
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      "text/plain": [
       "                                                    nose_scaled_x  \\\n",
       "./dataset-for-classification/paripurna-navasana...       0.558346   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...       0.491987   \n",
       "./dataset-for-classification/upavistha-konasana...       0.596422   \n",
       "./dataset-for-classification/marjaryasana/8dc64...       0.972571   \n",
       "./dataset-for-classification/urdhva-prasarita-e...       0.568889   \n",
       "...                                                           ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...       1.000000   \n",
       "./dataset-for-classification/chaturanga-dandasa...       0.535976   \n",
       "./dataset-for-classification/utthita-parsvakona...       0.142606   \n",
       "./dataset-for-classification/supta-baddha-konas...       0.046092   \n",
       "./dataset-for-classification/baddha-konasana/12...       0.809955   \n",
       "\n",
       "                                                    nose_scaled_y  nose_score  \\\n",
       "./dataset-for-classification/paripurna-navasana...       0.041068    1.949889   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...       0.032258    1.857787   \n",
       "./dataset-for-classification/upavistha-konasana...       0.031532    0.972064   \n",
       "./dataset-for-classification/marjaryasana/8dc64...       0.205837    0.270333   \n",
       "./dataset-for-classification/urdhva-prasarita-e...       0.785776    0.601770   \n",
       "...                                                           ...         ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...       0.787596    0.249642   \n",
       "./dataset-for-classification/chaturanga-dandasa...       0.797297    0.193930   \n",
       "./dataset-for-classification/utthita-parsvakona...       0.252717    0.332290   \n",
       "./dataset-for-classification/supta-baddha-konas...       0.067285    0.932778   \n",
       "./dataset-for-classification/baddha-konasana/12...       0.132428    1.364339   \n",
       "\n",
       "                                                    left_eye_scaled_x  \\\n",
       "./dataset-for-classification/paripurna-navasana...           0.540620   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...           0.540064   \n",
       "./dataset-for-classification/upavistha-konasana...           0.638171   \n",
       "./dataset-for-classification/marjaryasana/8dc64...           0.989714   \n",
       "./dataset-for-classification/urdhva-prasarita-e...           0.724444   \n",
       "...                                                               ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...           0.723926   \n",
       "./dataset-for-classification/chaturanga-dandasa...           0.007342   \n",
       "./dataset-for-classification/utthita-parsvakona...           0.147887   \n",
       "./dataset-for-classification/supta-baddha-konas...           0.025050   \n",
       "./dataset-for-classification/baddha-konasana/12...           0.877828   \n",
       "\n",
       "                                                    left_eye_scaled_y  \\\n",
       "./dataset-for-classification/paripurna-navasana...           0.001027   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...           0.001536   \n",
       "./dataset-for-classification/upavistha-konasana...           0.000000   \n",
       "./dataset-for-classification/marjaryasana/8dc64...           0.168971   \n",
       "./dataset-for-classification/urdhva-prasarita-e...           0.822203   \n",
       "...                                                               ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...           0.751062   \n",
       "./dataset-for-classification/chaturanga-dandasa...           0.483108   \n",
       "./dataset-for-classification/utthita-parsvakona...           0.240489   \n",
       "./dataset-for-classification/supta-baddha-konas...           0.060325   \n",
       "./dataset-for-classification/baddha-konasana/12...           0.086587   \n",
       "\n",
       "                                                    left_eye_score  \\\n",
       "./dataset-for-classification/paripurna-navasana...        1.943505   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...        1.265977   \n",
       "./dataset-for-classification/upavistha-konasana...        1.963699   \n",
       "./dataset-for-classification/marjaryasana/8dc64...        0.216276   \n",
       "./dataset-for-classification/urdhva-prasarita-e...        0.106847   \n",
       "...                                                            ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...        0.069631   \n",
       "./dataset-for-classification/chaturanga-dandasa...        0.220233   \n",
       "./dataset-for-classification/utthita-parsvakona...        0.207598   \n",
       "./dataset-for-classification/supta-baddha-konas...        0.471865   \n",
       "./dataset-for-classification/baddha-konasana/12...        2.059145   \n",
       "\n",
       "                                                    right_eye_scaled_x  \\\n",
       "./dataset-for-classification/paripurna-navasana...            0.512555   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...            0.448718   \n",
       "./dataset-for-classification/upavistha-konasana...            0.554672   \n",
       "./dataset-for-classification/marjaryasana/8dc64...            0.965714   \n",
       "./dataset-for-classification/urdhva-prasarita-e...            0.760000   \n",
       "...                                                                ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...            0.797546   \n",
       "./dataset-for-classification/chaturanga-dandasa...            0.000000   \n",
       "./dataset-for-classification/utthita-parsvakona...            0.077465   \n",
       "./dataset-for-classification/supta-baddha-konas...            0.011022   \n",
       "./dataset-for-classification/baddha-konasana/12...            0.748869   \n",
       "\n",
       "                                                    right_eye_scaled_y  \\\n",
       "./dataset-for-classification/paripurna-navasana...            0.000000   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...            0.000000   \n",
       "./dataset-for-classification/upavistha-konasana...            0.000000   \n",
       "./dataset-for-classification/marjaryasana/8dc64...            0.168971   \n",
       "./dataset-for-classification/urdhva-prasarita-e...            0.821336   \n",
       "...                                                                ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...            0.751912   \n",
       "./dataset-for-classification/chaturanga-dandasa...            0.479730   \n",
       "./dataset-for-classification/utthita-parsvakona...            0.240489   \n",
       "./dataset-for-classification/supta-baddha-konas...            0.125290   \n",
       "./dataset-for-classification/baddha-konasana/12...            0.084890   \n",
       "\n",
       "                                                    right_eye_score  \\\n",
       "./dataset-for-classification/paripurna-navasana...         2.750488   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...         1.498770   \n",
       "./dataset-for-classification/upavistha-konasana...         4.430774   \n",
       "./dataset-for-classification/marjaryasana/8dc64...         0.173521   \n",
       "./dataset-for-classification/urdhva-prasarita-e...         0.153943   \n",
       "...                                                             ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...         0.249675   \n",
       "./dataset-for-classification/chaturanga-dandasa...         0.024991   \n",
       "./dataset-for-classification/utthita-parsvakona...         0.511607   \n",
       "./dataset-for-classification/supta-baddha-konas...         0.693817   \n",
       "./dataset-for-classification/baddha-konasana/12...         2.218163   \n",
       "\n",
       "                                                    left_ear_scaled_x  ...  \\\n",
       "./dataset-for-classification/paripurna-navasana...           0.304284  ...   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...           0.599359  ...   \n",
       "./dataset-for-classification/upavistha-konasana...           0.681909  ...   \n",
       "./dataset-for-classification/marjaryasana/8dc64...           0.942857  ...   \n",
       "./dataset-for-classification/urdhva-prasarita-e...           0.826667  ...   \n",
       "...                                                               ...  ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...           0.116564  ...   \n",
       "./dataset-for-classification/chaturanga-dandasa...           0.024963  ...   \n",
       "./dataset-for-classification/utthita-parsvakona...           0.061620  ...   \n",
       "./dataset-for-classification/supta-baddha-konas...           0.000000  ...   \n",
       "./dataset-for-classification/baddha-konasana/12...           0.941176  ...   \n",
       "\n",
       "                                                    right_knee_scaled_x  \\\n",
       "./dataset-for-classification/paripurna-navasana...             0.985229   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...             0.067308   \n",
       "./dataset-for-classification/upavistha-konasana...             0.053678   \n",
       "./dataset-for-classification/marjaryasana/8dc64...             0.366857   \n",
       "./dataset-for-classification/urdhva-prasarita-e...             0.000000   \n",
       "...                                                                 ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...             0.564417   \n",
       "./dataset-for-classification/chaturanga-dandasa...             1.000000   \n",
       "./dataset-for-classification/utthita-parsvakona...             0.105634   \n",
       "./dataset-for-classification/supta-baddha-konas...             0.986974   \n",
       "./dataset-for-classification/baddha-konasana/12...             0.000000   \n",
       "\n",
       "                                                    right_knee_scaled_y  \\\n",
       "./dataset-for-classification/paripurna-navasana...             0.465092   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...             0.983103   \n",
       "./dataset-for-classification/upavistha-konasana...             1.000000   \n",
       "./dataset-for-classification/marjaryasana/8dc64...             0.304147   \n",
       "./dataset-for-classification/urdhva-prasarita-e...             0.202082   \n",
       "...                                                                 ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...             0.225149   \n",
       "./dataset-for-classification/chaturanga-dandasa...             0.290541   \n",
       "./dataset-for-classification/utthita-parsvakona...             0.650815   \n",
       "./dataset-for-classification/supta-baddha-konas...             0.000000   \n",
       "./dataset-for-classification/baddha-konasana/12...             0.747029   \n",
       "\n",
       "                                                    right_knee_score  \\\n",
       "./dataset-for-classification/paripurna-navasana...          0.057829   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...          0.143049   \n",
       "./dataset-for-classification/upavistha-konasana...          0.042947   \n",
       "./dataset-for-classification/marjaryasana/8dc64...          0.328321   \n",
       "./dataset-for-classification/urdhva-prasarita-e...          0.167313   \n",
       "...                                                              ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...          0.167277   \n",
       "./dataset-for-classification/chaturanga-dandasa...          0.029234   \n",
       "./dataset-for-classification/utthita-parsvakona...          0.174187   \n",
       "./dataset-for-classification/supta-baddha-konas...          0.242699   \n",
       "./dataset-for-classification/baddha-konasana/12...          0.411823   \n",
       "\n",
       "                                                    left_ankle_scaled_x  \\\n",
       "./dataset-for-classification/paripurna-navasana...             0.995569   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...             0.474359   \n",
       "./dataset-for-classification/upavistha-konasana...             0.675944   \n",
       "./dataset-for-classification/marjaryasana/8dc64...             0.413714   \n",
       "./dataset-for-classification/urdhva-prasarita-e...             0.053333   \n",
       "...                                                                 ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...             0.570552   \n",
       "./dataset-for-classification/chaturanga-dandasa...             0.493392   \n",
       "./dataset-for-classification/utthita-parsvakona...             0.063380   \n",
       "./dataset-for-classification/supta-baddha-konas...             1.000000   \n",
       "./dataset-for-classification/baddha-konasana/12...             0.402715   \n",
       "\n",
       "                                                    left_ankle_scaled_y  \\\n",
       "./dataset-for-classification/paripurna-navasana...             0.407598   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...             0.955453   \n",
       "./dataset-for-classification/upavistha-konasana...             1.000000   \n",
       "./dataset-for-classification/marjaryasana/8dc64...             0.960061   \n",
       "./dataset-for-classification/urdhva-prasarita-e...             0.884649   \n",
       "...                                                                 ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...             1.000000   \n",
       "./dataset-for-classification/chaturanga-dandasa...             0.956081   \n",
       "./dataset-for-classification/utthita-parsvakona...             0.998641   \n",
       "./dataset-for-classification/supta-baddha-konas...             0.656613   \n",
       "./dataset-for-classification/baddha-konasana/12...             0.920204   \n",
       "\n",
       "                                                    left_ankle_score  \\\n",
       "./dataset-for-classification/paripurna-navasana...          0.091656   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...          0.138958   \n",
       "./dataset-for-classification/upavistha-konasana...          0.078737   \n",
       "./dataset-for-classification/marjaryasana/8dc64...          0.116407   \n",
       "./dataset-for-classification/urdhva-prasarita-e...          0.429937   \n",
       "...                                                              ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...          0.087273   \n",
       "./dataset-for-classification/chaturanga-dandasa...          0.162056   \n",
       "./dataset-for-classification/utthita-parsvakona...          0.279461   \n",
       "./dataset-for-classification/supta-baddha-konas...          0.180462   \n",
       "./dataset-for-classification/baddha-konasana/12...          0.076261   \n",
       "\n",
       "                                                    right_ankle_scaled_x  \\\n",
       "./dataset-for-classification/paripurna-navasana...              1.000000   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...              0.330128   \n",
       "./dataset-for-classification/upavistha-konasana...              0.000000   \n",
       "./dataset-for-classification/marjaryasana/8dc64...              0.000000   \n",
       "./dataset-for-classification/urdhva-prasarita-e...              0.035556   \n",
       "...                                                                  ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...              0.404908   \n",
       "./dataset-for-classification/chaturanga-dandasa...              0.490455   \n",
       "./dataset-for-classification/utthita-parsvakona...              0.058099   \n",
       "./dataset-for-classification/supta-baddha-konas...              0.995992   \n",
       "./dataset-for-classification/baddha-konasana/12...              0.520362   \n",
       "\n",
       "                                                    right_ankle_scaled_y  \\\n",
       "./dataset-for-classification/paripurna-navasana...              0.407598   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...              0.826421   \n",
       "./dataset-for-classification/upavistha-konasana...              0.898649   \n",
       "./dataset-for-classification/marjaryasana/8dc64...              0.906298   \n",
       "./dataset-for-classification/urdhva-prasarita-e...              0.000000   \n",
       "...                                                                  ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...              0.000000   \n",
       "./dataset-for-classification/chaturanga-dandasa...              0.969595   \n",
       "./dataset-for-classification/utthita-parsvakona...              1.000000   \n",
       "./dataset-for-classification/supta-baddha-konas...              0.631090   \n",
       "./dataset-for-classification/baddha-konasana/12...              0.933786   \n",
       "\n",
       "                                                    right_ankle_score  pose  \n",
       "./dataset-for-classification/paripurna-navasana...           0.083489    38  \n",
       "./dataset-for-classification/padmasana/2ba9fd14...           0.221899    37  \n",
       "./dataset-for-classification/upavistha-konasana...           0.047240    60  \n",
       "./dataset-for-classification/marjaryasana/8dc64...           0.208869    33  \n",
       "./dataset-for-classification/urdhva-prasarita-e...           0.297149    63  \n",
       "...                                                               ...   ...  \n",
       "./dataset-for-classification/adho-mukha-vrksasa...           0.144229     2  \n",
       "./dataset-for-classification/chaturanga-dandasa...           0.166776    19  \n",
       "./dataset-for-classification/utthita-parsvakona...           0.481291    70  \n",
       "./dataset-for-classification/supta-baddha-konas...           0.305968    53  \n",
       "./dataset-for-classification/baddha-konasana/12...           0.069572    10  \n",
       "\n",
       "[3313 rows x 52 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build the dataset\n",
    "df = pd.read_csv('training_set.csv', index_col=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "99bd0647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes = df['pose'].unique()\n",
    "len(classes)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d973036d",
   "metadata": {},
   "source": [
    "# Building a Custom Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ea296dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = [\n",
    "    'adho-mukha-svanasana',\n",
    "    'adho-mukha-vriksasana',\n",
    "    'adho-mukha-vrksasana',\n",
    "    'ananda-balasana',\n",
    "    'anjaneyasana',\n",
    "    'ardha-chandrasana',\n",
    "    'ardha-matsyendrasana',\n",
    "    'ardha-pincha-mayurasana',\n",
    "    'ardha-uttanasana',\n",
    "    'astavakrasana',\n",
    "    'baddha-konasana',\n",
    "    'bakasana',\n",
    "    'balasana',\n",
    "    'bharadvajasana-i',\n",
    "    'bhujangasana',\n",
    "    'bhujapidasana',\n",
    "    'bitilasana',\n",
    "    'camatkarasana',\n",
    "    'chakravakasana',\n",
    "    'chaturanga-dandasana',\n",
    "    'dandasana',\n",
    "    'dhanurasana',\n",
    "    'dwi-pada-viparita-dandasana',\n",
    "    'eka-pada-koundinyanasana-i',\n",
    "    'eka-pada-koundinyanasana-ii',\n",
    "    'eka-pada-rajakapotasana',\n",
    "    'eka-pada-rajakapotasana-ii',\n",
    "    'garudasana',\n",
    "    'gomukhasana',\n",
    "    'halasana',\n",
    "    'kapotasana',\n",
    "    'makarasana',\n",
    "    'malasana',\n",
    "    'marjaryasana',\n",
    "    'matsyasana',\n",
    "    'mayurasana',\n",
    "    'natarajasana',\n",
    "    'padmasana',\n",
    "    'paripurna-navasana',\n",
    "    'parivrtta-trikonasana',\n",
    "    'parsva-bakasana',\n",
    "    'pasasana',\n",
    "    'paschimottanasana',\n",
    "    'phalakasana',\n",
    "    'prasarita-padottanasana',\n",
    "    'purvottanasana',\n",
    "    'salabhasana',\n",
    "    'salamba-bhujangasana',\n",
    "    'salamba-sarvangasana',\n",
    "    'salamba-sirsasana',\n",
    "    'savasana',\n",
    "    'setu-bandha-sarvangasana',\n",
    "    'sukhasana',\n",
    "    'supta-baddha-konasana',\n",
    "    'supta-matsyendrasana',\n",
    "    'supta-padangusthasana',\n",
    "    'supta-virasana',\n",
    "    'tadasana',\n",
    "    'tittibhasana',\n",
    "    'tolasana',\n",
    "    'upavistha-konasana',\n",
    "    'urdhva-dhanurasana',\n",
    "    'urdhva-mukha-svanasana',\n",
    "    'urdhva-prasarita-eka-padasana',\n",
    "    'ustrasana',\n",
    "    'utkatasana',\n",
    "    'uttana-shishosana',\n",
    "    'uttanasana',\n",
    "    'utthita-hasta-padangustasana',\n",
    "    'utthita-hasta-padangusthasana',\n",
    "    'utthita-parsvakonasana',\n",
    "    'utthita-trikonasana',\n",
    "    'vajrasana',\n",
    "    'vasisthasana',\n",
    "    'viparita-karani',\n",
    "    'virabhadrasana-i',\n",
    "    'virabhadrasana-ii',\n",
    "    'virabhadrasana-iii',\n",
    "    'virasana',\n",
    "    'vriksasana',\n",
    "    'vrischikasana',\n",
    "    'vrksasana'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a40e4fba",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nose_scaled_x</th>\n",
       "      <th>nose_scaled_y</th>\n",
       "      <th>nose_score</th>\n",
       "      <th>left_eye_scaled_x</th>\n",
       "      <th>left_eye_scaled_y</th>\n",
       "      <th>left_eye_score</th>\n",
       "      <th>right_eye_scaled_x</th>\n",
       "      <th>right_eye_scaled_y</th>\n",
       "      <th>right_eye_score</th>\n",
       "      <th>left_ear_scaled_x</th>\n",
       "      <th>...</th>\n",
       "      <th>right_knee_scaled_x</th>\n",
       "      <th>right_knee_scaled_y</th>\n",
       "      <th>right_knee_score</th>\n",
       "      <th>left_ankle_scaled_x</th>\n",
       "      <th>left_ankle_scaled_y</th>\n",
       "      <th>left_ankle_score</th>\n",
       "      <th>right_ankle_scaled_x</th>\n",
       "      <th>right_ankle_scaled_y</th>\n",
       "      <th>right_ankle_score</th>\n",
       "      <th>pose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>./dataset-for-classification/paripurna-navasana/7ef89ad4433e9e5af2218d2c870845a44f994b18a123852e79c585f77bdc1488.png</th>\n",
       "      <td>0.558346</td>\n",
       "      <td>0.041068</td>\n",
       "      <td>1.949889</td>\n",
       "      <td>0.540620</td>\n",
       "      <td>0.001027</td>\n",
       "      <td>1.943505</td>\n",
       "      <td>0.512555</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.750488</td>\n",
       "      <td>0.304284</td>\n",
       "      <td>...</td>\n",
       "      <td>0.985229</td>\n",
       "      <td>0.465092</td>\n",
       "      <td>0.057829</td>\n",
       "      <td>0.995569</td>\n",
       "      <td>0.407598</td>\n",
       "      <td>0.091656</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.407598</td>\n",
       "      <td>0.083489</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./dataset-for-classification/padmasana/2ba9fd144da5dee7636bac100b1961875f69e66e0e4007ceba31ea32d40ff0b5.jpeg</th>\n",
       "      <td>0.491987</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>1.857787</td>\n",
       "      <td>0.540064</td>\n",
       "      <td>0.001536</td>\n",
       "      <td>1.265977</td>\n",
       "      <td>0.448718</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.498770</td>\n",
       "      <td>0.599359</td>\n",
       "      <td>...</td>\n",
       "      <td>0.067308</td>\n",
       "      <td>0.983103</td>\n",
       "      <td>0.143049</td>\n",
       "      <td>0.474359</td>\n",
       "      <td>0.955453</td>\n",
       "      <td>0.138958</td>\n",
       "      <td>0.330128</td>\n",
       "      <td>0.826421</td>\n",
       "      <td>0.221899</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./dataset-for-classification/upavistha-konasana/105c894c324470bffd7517867d5ac97db8dd8765bd787b0c4b36f1aadf069c1e.png</th>\n",
       "      <td>0.596422</td>\n",
       "      <td>0.031532</td>\n",
       "      <td>0.972064</td>\n",
       "      <td>0.638171</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.963699</td>\n",
       "      <td>0.554672</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.430774</td>\n",
       "      <td>0.681909</td>\n",
       "      <td>...</td>\n",
       "      <td>0.053678</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.042947</td>\n",
       "      <td>0.675944</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.078737</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.898649</td>\n",
       "      <td>0.047240</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./dataset-for-classification/marjaryasana/8dc6474628fdf057f89bc360f1275070ae7bf5b4b17433c50543e663ee26680a.png</th>\n",
       "      <td>0.972571</td>\n",
       "      <td>0.205837</td>\n",
       "      <td>0.270333</td>\n",
       "      <td>0.989714</td>\n",
       "      <td>0.168971</td>\n",
       "      <td>0.216276</td>\n",
       "      <td>0.965714</td>\n",
       "      <td>0.168971</td>\n",
       "      <td>0.173521</td>\n",
       "      <td>0.942857</td>\n",
       "      <td>...</td>\n",
       "      <td>0.366857</td>\n",
       "      <td>0.304147</td>\n",
       "      <td>0.328321</td>\n",
       "      <td>0.413714</td>\n",
       "      <td>0.960061</td>\n",
       "      <td>0.116407</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.906298</td>\n",
       "      <td>0.208869</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./dataset-for-classification/urdhva-prasarita-eka-padasana/157976074d7a676da7623d971725c399ec46656350936d278bf46eb09a775475.png</th>\n",
       "      <td>0.568889</td>\n",
       "      <td>0.785776</td>\n",
       "      <td>0.601770</td>\n",
       "      <td>0.724444</td>\n",
       "      <td>0.822203</td>\n",
       "      <td>0.106847</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.821336</td>\n",
       "      <td>0.153943</td>\n",
       "      <td>0.826667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.202082</td>\n",
       "      <td>0.167313</td>\n",
       "      <td>0.053333</td>\n",
       "      <td>0.884649</td>\n",
       "      <td>0.429937</td>\n",
       "      <td>0.035556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.297149</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./dataset-for-classification/adho-mukha-vrksasana/e160e25b7e8bea91a6f1d5cf4914a667a5f9b03bc12bbe791fe3ab7d569cfe14.png</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.787596</td>\n",
       "      <td>0.249642</td>\n",
       "      <td>0.723926</td>\n",
       "      <td>0.751062</td>\n",
       "      <td>0.069631</td>\n",
       "      <td>0.797546</td>\n",
       "      <td>0.751912</td>\n",
       "      <td>0.249675</td>\n",
       "      <td>0.116564</td>\n",
       "      <td>...</td>\n",
       "      <td>0.564417</td>\n",
       "      <td>0.225149</td>\n",
       "      <td>0.167277</td>\n",
       "      <td>0.570552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.087273</td>\n",
       "      <td>0.404908</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.144229</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./dataset-for-classification/chaturanga-dandasana/ced03afa1c06fb3cd419e700dbe7357c83ca0b9d28785d240ab5a0e4024633df.png</th>\n",
       "      <td>0.535976</td>\n",
       "      <td>0.797297</td>\n",
       "      <td>0.193930</td>\n",
       "      <td>0.007342</td>\n",
       "      <td>0.483108</td>\n",
       "      <td>0.220233</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.479730</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>0.024963</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.290541</td>\n",
       "      <td>0.029234</td>\n",
       "      <td>0.493392</td>\n",
       "      <td>0.956081</td>\n",
       "      <td>0.162056</td>\n",
       "      <td>0.490455</td>\n",
       "      <td>0.969595</td>\n",
       "      <td>0.166776</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./dataset-for-classification/utthita-parsvakonasana/194d700849c2954d3fa3135214fb5fc5c843836cdeba24a804f880ec477746da.png</th>\n",
       "      <td>0.142606</td>\n",
       "      <td>0.252717</td>\n",
       "      <td>0.332290</td>\n",
       "      <td>0.147887</td>\n",
       "      <td>0.240489</td>\n",
       "      <td>0.207598</td>\n",
       "      <td>0.077465</td>\n",
       "      <td>0.240489</td>\n",
       "      <td>0.511607</td>\n",
       "      <td>0.061620</td>\n",
       "      <td>...</td>\n",
       "      <td>0.105634</td>\n",
       "      <td>0.650815</td>\n",
       "      <td>0.174187</td>\n",
       "      <td>0.063380</td>\n",
       "      <td>0.998641</td>\n",
       "      <td>0.279461</td>\n",
       "      <td>0.058099</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.481291</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./dataset-for-classification/supta-baddha-konasana/e06ef7b69d0c200b15de610cec58147925918eca05ba9c65966901aadcbccef8.png</th>\n",
       "      <td>0.046092</td>\n",
       "      <td>0.067285</td>\n",
       "      <td>0.932778</td>\n",
       "      <td>0.025050</td>\n",
       "      <td>0.060325</td>\n",
       "      <td>0.471865</td>\n",
       "      <td>0.011022</td>\n",
       "      <td>0.125290</td>\n",
       "      <td>0.693817</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.986974</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.242699</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.656613</td>\n",
       "      <td>0.180462</td>\n",
       "      <td>0.995992</td>\n",
       "      <td>0.631090</td>\n",
       "      <td>0.305968</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./dataset-for-classification/baddha-konasana/12ca2a1d6b43266b5bd3c8a4a8229d3ee34067d23bd4ae3c9a9106b034c2401b.png</th>\n",
       "      <td>0.809955</td>\n",
       "      <td>0.132428</td>\n",
       "      <td>1.364339</td>\n",
       "      <td>0.877828</td>\n",
       "      <td>0.086587</td>\n",
       "      <td>2.059145</td>\n",
       "      <td>0.748869</td>\n",
       "      <td>0.084890</td>\n",
       "      <td>2.218163</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.747029</td>\n",
       "      <td>0.411823</td>\n",
       "      <td>0.402715</td>\n",
       "      <td>0.920204</td>\n",
       "      <td>0.076261</td>\n",
       "      <td>0.520362</td>\n",
       "      <td>0.933786</td>\n",
       "      <td>0.069572</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3313 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    nose_scaled_x  \\\n",
       "./dataset-for-classification/paripurna-navasana...       0.558346   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...       0.491987   \n",
       "./dataset-for-classification/upavistha-konasana...       0.596422   \n",
       "./dataset-for-classification/marjaryasana/8dc64...       0.972571   \n",
       "./dataset-for-classification/urdhva-prasarita-e...       0.568889   \n",
       "...                                                           ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...       1.000000   \n",
       "./dataset-for-classification/chaturanga-dandasa...       0.535976   \n",
       "./dataset-for-classification/utthita-parsvakona...       0.142606   \n",
       "./dataset-for-classification/supta-baddha-konas...       0.046092   \n",
       "./dataset-for-classification/baddha-konasana/12...       0.809955   \n",
       "\n",
       "                                                    nose_scaled_y  nose_score  \\\n",
       "./dataset-for-classification/paripurna-navasana...       0.041068    1.949889   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...       0.032258    1.857787   \n",
       "./dataset-for-classification/upavistha-konasana...       0.031532    0.972064   \n",
       "./dataset-for-classification/marjaryasana/8dc64...       0.205837    0.270333   \n",
       "./dataset-for-classification/urdhva-prasarita-e...       0.785776    0.601770   \n",
       "...                                                           ...         ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...       0.787596    0.249642   \n",
       "./dataset-for-classification/chaturanga-dandasa...       0.797297    0.193930   \n",
       "./dataset-for-classification/utthita-parsvakona...       0.252717    0.332290   \n",
       "./dataset-for-classification/supta-baddha-konas...       0.067285    0.932778   \n",
       "./dataset-for-classification/baddha-konasana/12...       0.132428    1.364339   \n",
       "\n",
       "                                                    left_eye_scaled_x  \\\n",
       "./dataset-for-classification/paripurna-navasana...           0.540620   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...           0.540064   \n",
       "./dataset-for-classification/upavistha-konasana...           0.638171   \n",
       "./dataset-for-classification/marjaryasana/8dc64...           0.989714   \n",
       "./dataset-for-classification/urdhva-prasarita-e...           0.724444   \n",
       "...                                                               ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...           0.723926   \n",
       "./dataset-for-classification/chaturanga-dandasa...           0.007342   \n",
       "./dataset-for-classification/utthita-parsvakona...           0.147887   \n",
       "./dataset-for-classification/supta-baddha-konas...           0.025050   \n",
       "./dataset-for-classification/baddha-konasana/12...           0.877828   \n",
       "\n",
       "                                                    left_eye_scaled_y  \\\n",
       "./dataset-for-classification/paripurna-navasana...           0.001027   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...           0.001536   \n",
       "./dataset-for-classification/upavistha-konasana...           0.000000   \n",
       "./dataset-for-classification/marjaryasana/8dc64...           0.168971   \n",
       "./dataset-for-classification/urdhva-prasarita-e...           0.822203   \n",
       "...                                                               ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...           0.751062   \n",
       "./dataset-for-classification/chaturanga-dandasa...           0.483108   \n",
       "./dataset-for-classification/utthita-parsvakona...           0.240489   \n",
       "./dataset-for-classification/supta-baddha-konas...           0.060325   \n",
       "./dataset-for-classification/baddha-konasana/12...           0.086587   \n",
       "\n",
       "                                                    left_eye_score  \\\n",
       "./dataset-for-classification/paripurna-navasana...        1.943505   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...        1.265977   \n",
       "./dataset-for-classification/upavistha-konasana...        1.963699   \n",
       "./dataset-for-classification/marjaryasana/8dc64...        0.216276   \n",
       "./dataset-for-classification/urdhva-prasarita-e...        0.106847   \n",
       "...                                                            ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...        0.069631   \n",
       "./dataset-for-classification/chaturanga-dandasa...        0.220233   \n",
       "./dataset-for-classification/utthita-parsvakona...        0.207598   \n",
       "./dataset-for-classification/supta-baddha-konas...        0.471865   \n",
       "./dataset-for-classification/baddha-konasana/12...        2.059145   \n",
       "\n",
       "                                                    right_eye_scaled_x  \\\n",
       "./dataset-for-classification/paripurna-navasana...            0.512555   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...            0.448718   \n",
       "./dataset-for-classification/upavistha-konasana...            0.554672   \n",
       "./dataset-for-classification/marjaryasana/8dc64...            0.965714   \n",
       "./dataset-for-classification/urdhva-prasarita-e...            0.760000   \n",
       "...                                                                ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...            0.797546   \n",
       "./dataset-for-classification/chaturanga-dandasa...            0.000000   \n",
       "./dataset-for-classification/utthita-parsvakona...            0.077465   \n",
       "./dataset-for-classification/supta-baddha-konas...            0.011022   \n",
       "./dataset-for-classification/baddha-konasana/12...            0.748869   \n",
       "\n",
       "                                                    right_eye_scaled_y  \\\n",
       "./dataset-for-classification/paripurna-navasana...            0.000000   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...            0.000000   \n",
       "./dataset-for-classification/upavistha-konasana...            0.000000   \n",
       "./dataset-for-classification/marjaryasana/8dc64...            0.168971   \n",
       "./dataset-for-classification/urdhva-prasarita-e...            0.821336   \n",
       "...                                                                ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...            0.751912   \n",
       "./dataset-for-classification/chaturanga-dandasa...            0.479730   \n",
       "./dataset-for-classification/utthita-parsvakona...            0.240489   \n",
       "./dataset-for-classification/supta-baddha-konas...            0.125290   \n",
       "./dataset-for-classification/baddha-konasana/12...            0.084890   \n",
       "\n",
       "                                                    right_eye_score  \\\n",
       "./dataset-for-classification/paripurna-navasana...         2.750488   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...         1.498770   \n",
       "./dataset-for-classification/upavistha-konasana...         4.430774   \n",
       "./dataset-for-classification/marjaryasana/8dc64...         0.173521   \n",
       "./dataset-for-classification/urdhva-prasarita-e...         0.153943   \n",
       "...                                                             ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...         0.249675   \n",
       "./dataset-for-classification/chaturanga-dandasa...         0.024991   \n",
       "./dataset-for-classification/utthita-parsvakona...         0.511607   \n",
       "./dataset-for-classification/supta-baddha-konas...         0.693817   \n",
       "./dataset-for-classification/baddha-konasana/12...         2.218163   \n",
       "\n",
       "                                                    left_ear_scaled_x  ...  \\\n",
       "./dataset-for-classification/paripurna-navasana...           0.304284  ...   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...           0.599359  ...   \n",
       "./dataset-for-classification/upavistha-konasana...           0.681909  ...   \n",
       "./dataset-for-classification/marjaryasana/8dc64...           0.942857  ...   \n",
       "./dataset-for-classification/urdhva-prasarita-e...           0.826667  ...   \n",
       "...                                                               ...  ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...           0.116564  ...   \n",
       "./dataset-for-classification/chaturanga-dandasa...           0.024963  ...   \n",
       "./dataset-for-classification/utthita-parsvakona...           0.061620  ...   \n",
       "./dataset-for-classification/supta-baddha-konas...           0.000000  ...   \n",
       "./dataset-for-classification/baddha-konasana/12...           0.941176  ...   \n",
       "\n",
       "                                                    right_knee_scaled_x  \\\n",
       "./dataset-for-classification/paripurna-navasana...             0.985229   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...             0.067308   \n",
       "./dataset-for-classification/upavistha-konasana...             0.053678   \n",
       "./dataset-for-classification/marjaryasana/8dc64...             0.366857   \n",
       "./dataset-for-classification/urdhva-prasarita-e...             0.000000   \n",
       "...                                                                 ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...             0.564417   \n",
       "./dataset-for-classification/chaturanga-dandasa...             1.000000   \n",
       "./dataset-for-classification/utthita-parsvakona...             0.105634   \n",
       "./dataset-for-classification/supta-baddha-konas...             0.986974   \n",
       "./dataset-for-classification/baddha-konasana/12...             0.000000   \n",
       "\n",
       "                                                    right_knee_scaled_y  \\\n",
       "./dataset-for-classification/paripurna-navasana...             0.465092   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...             0.983103   \n",
       "./dataset-for-classification/upavistha-konasana...             1.000000   \n",
       "./dataset-for-classification/marjaryasana/8dc64...             0.304147   \n",
       "./dataset-for-classification/urdhva-prasarita-e...             0.202082   \n",
       "...                                                                 ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...             0.225149   \n",
       "./dataset-for-classification/chaturanga-dandasa...             0.290541   \n",
       "./dataset-for-classification/utthita-parsvakona...             0.650815   \n",
       "./dataset-for-classification/supta-baddha-konas...             0.000000   \n",
       "./dataset-for-classification/baddha-konasana/12...             0.747029   \n",
       "\n",
       "                                                    right_knee_score  \\\n",
       "./dataset-for-classification/paripurna-navasana...          0.057829   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...          0.143049   \n",
       "./dataset-for-classification/upavistha-konasana...          0.042947   \n",
       "./dataset-for-classification/marjaryasana/8dc64...          0.328321   \n",
       "./dataset-for-classification/urdhva-prasarita-e...          0.167313   \n",
       "...                                                              ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...          0.167277   \n",
       "./dataset-for-classification/chaturanga-dandasa...          0.029234   \n",
       "./dataset-for-classification/utthita-parsvakona...          0.174187   \n",
       "./dataset-for-classification/supta-baddha-konas...          0.242699   \n",
       "./dataset-for-classification/baddha-konasana/12...          0.411823   \n",
       "\n",
       "                                                    left_ankle_scaled_x  \\\n",
       "./dataset-for-classification/paripurna-navasana...             0.995569   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...             0.474359   \n",
       "./dataset-for-classification/upavistha-konasana...             0.675944   \n",
       "./dataset-for-classification/marjaryasana/8dc64...             0.413714   \n",
       "./dataset-for-classification/urdhva-prasarita-e...             0.053333   \n",
       "...                                                                 ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...             0.570552   \n",
       "./dataset-for-classification/chaturanga-dandasa...             0.493392   \n",
       "./dataset-for-classification/utthita-parsvakona...             0.063380   \n",
       "./dataset-for-classification/supta-baddha-konas...             1.000000   \n",
       "./dataset-for-classification/baddha-konasana/12...             0.402715   \n",
       "\n",
       "                                                    left_ankle_scaled_y  \\\n",
       "./dataset-for-classification/paripurna-navasana...             0.407598   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...             0.955453   \n",
       "./dataset-for-classification/upavistha-konasana...             1.000000   \n",
       "./dataset-for-classification/marjaryasana/8dc64...             0.960061   \n",
       "./dataset-for-classification/urdhva-prasarita-e...             0.884649   \n",
       "...                                                                 ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...             1.000000   \n",
       "./dataset-for-classification/chaturanga-dandasa...             0.956081   \n",
       "./dataset-for-classification/utthita-parsvakona...             0.998641   \n",
       "./dataset-for-classification/supta-baddha-konas...             0.656613   \n",
       "./dataset-for-classification/baddha-konasana/12...             0.920204   \n",
       "\n",
       "                                                    left_ankle_score  \\\n",
       "./dataset-for-classification/paripurna-navasana...          0.091656   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...          0.138958   \n",
       "./dataset-for-classification/upavistha-konasana...          0.078737   \n",
       "./dataset-for-classification/marjaryasana/8dc64...          0.116407   \n",
       "./dataset-for-classification/urdhva-prasarita-e...          0.429937   \n",
       "...                                                              ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...          0.087273   \n",
       "./dataset-for-classification/chaturanga-dandasa...          0.162056   \n",
       "./dataset-for-classification/utthita-parsvakona...          0.279461   \n",
       "./dataset-for-classification/supta-baddha-konas...          0.180462   \n",
       "./dataset-for-classification/baddha-konasana/12...          0.076261   \n",
       "\n",
       "                                                    right_ankle_scaled_x  \\\n",
       "./dataset-for-classification/paripurna-navasana...              1.000000   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...              0.330128   \n",
       "./dataset-for-classification/upavistha-konasana...              0.000000   \n",
       "./dataset-for-classification/marjaryasana/8dc64...              0.000000   \n",
       "./dataset-for-classification/urdhva-prasarita-e...              0.035556   \n",
       "...                                                                  ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...              0.404908   \n",
       "./dataset-for-classification/chaturanga-dandasa...              0.490455   \n",
       "./dataset-for-classification/utthita-parsvakona...              0.058099   \n",
       "./dataset-for-classification/supta-baddha-konas...              0.995992   \n",
       "./dataset-for-classification/baddha-konasana/12...              0.520362   \n",
       "\n",
       "                                                    right_ankle_scaled_y  \\\n",
       "./dataset-for-classification/paripurna-navasana...              0.407598   \n",
       "./dataset-for-classification/padmasana/2ba9fd14...              0.826421   \n",
       "./dataset-for-classification/upavistha-konasana...              0.898649   \n",
       "./dataset-for-classification/marjaryasana/8dc64...              0.906298   \n",
       "./dataset-for-classification/urdhva-prasarita-e...              0.000000   \n",
       "...                                                                  ...   \n",
       "./dataset-for-classification/adho-mukha-vrksasa...              0.000000   \n",
       "./dataset-for-classification/chaturanga-dandasa...              0.969595   \n",
       "./dataset-for-classification/utthita-parsvakona...              1.000000   \n",
       "./dataset-for-classification/supta-baddha-konas...              0.631090   \n",
       "./dataset-for-classification/baddha-konasana/12...              0.933786   \n",
       "\n",
       "                                                    right_ankle_score  pose  \n",
       "./dataset-for-classification/paripurna-navasana...           0.083489    38  \n",
       "./dataset-for-classification/padmasana/2ba9fd14...           0.221899    37  \n",
       "./dataset-for-classification/upavistha-konasana...           0.047240    60  \n",
       "./dataset-for-classification/marjaryasana/8dc64...           0.208869    33  \n",
       "./dataset-for-classification/urdhva-prasarita-e...           0.297149    63  \n",
       "...                                                               ...   ...  \n",
       "./dataset-for-classification/adho-mukha-vrksasa...           0.144229     2  \n",
       "./dataset-for-classification/chaturanga-dandasa...           0.166776    19  \n",
       "./dataset-for-classification/utthita-parsvakona...           0.481291    70  \n",
       "./dataset-for-classification/supta-baddha-konas...           0.305968    53  \n",
       "./dataset-for-classification/baddha-konasana/12...           0.069572    10  \n",
       "\n",
       "[3313 rows x 52 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('training_set.csv', index_col=0)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "26d8adc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "from torch.utils.data import Dataset\n",
    "from torchvision.transforms import ToTensor, Lambda\n",
    "\n",
    "class CollectionsDataset(Dataset):\n",
    "    def __init__(self, csv_file, root_dir, num_classes, transform=None, target_transform=None):\n",
    "        self.data = pd.read_csv(csv_file, index_col=0)\n",
    "        self.root_dir = root_dir\n",
    "        self.transform = transform\n",
    "        self.num_classes = num_classes\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        img_name = os.path.join(self.data.index[idx])\n",
    "        image = Image.open(img_name).convert('RGB')\n",
    "        # TODO: resizing and whatnot?\n",
    "        pose = self.data.iloc[idx, -1]\n",
    "        dataframe = self.data.iloc[idx, :-1]\n",
    "        feature_tensors = torch.as_tensor(dataframe.values).to(torch.float32)\n",
    "\n",
    "        if self.transform:\n",
    "            image = self.transform(image)\n",
    "\n",
    "        return image, feature_tensors, int(pose)\n",
    "    \n",
    "\n",
    "transformer = torchvision.transforms.Compose([\n",
    "    transforms.Resize(size = (224, 224)),\n",
    "    transforms.ToTensor(),\n",
    "])\n",
    "\n",
    "# create the dataset\n",
    "dataset = CollectionsDataset(csv_file='training_set.csv', root_dir=CURRENT_DATASET_PATH, num_classes=len(classes), transform=transformer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f50bd2fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3313"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ba65cfc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataloader = DataLoader(dataset, batch_size=32, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f26ce83f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch.utils.data.dataloader.DataLoader at 0x7f9105fc80d0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "39be37b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature batch shape: torch.Size([32, 3, 224, 224])\n",
      "Feature batch shape: torch.Size([32, 51])\n",
      "Labels batch shape: 32\n"
     ]
    }
   ],
   "source": [
    "train_features, dataframes, train_labels = next(iter(train_dataloader))\n",
    "\n",
    "print(f\"Feature batch shape: {train_features.size()}\")\n",
    "print(f\"Feature batch shape: {dataframes.size()}\")\n",
    "print(f\"Labels batch shape: {len(train_labels)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "274b9bab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = train_features[0]\n",
    "label = train_labels[0]\n",
    "plt.imshow(img.permute(1, 2, 0))\n",
    "plt.title(classes[label])\n",
    "# label = torch.argmax(label)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "67baf819",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision\n",
    "from torchvision import models\n",
    "import torchvision.models.efficientnet as e\n",
    "import torchvision.models.resnet as r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "71c50ff9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n",
      "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=EfficientNet_B0_Weights.IMAGENET1K_V1`. You can also use `weights=EfficientNet_B0_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "WEIGHTS = r.resnet50(pretrained=True)\n",
    "WEIGHTS2 = e.efficientnet_b0(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d76e76b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "class YogaNet(nn.Module):\n",
    "    def __init__(self, base_model, additional_dims, num_classes=82):\n",
    "        super().__init__()\n",
    "        self.base_model = base_model\n",
    "\n",
    "        for p in self.base_model.parameters():\n",
    "            p.requires_grad = False\n",
    "\n",
    "        self.kpts = nn.Sequential(\n",
    "            nn.Linear(additional_dims, additional_dims*2),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Linear(additional_dims*2, additional_dims)\n",
    "        )\n",
    "        # self.final = nn.Linear(additional_dims, num_classes)\n",
    "        self.base_model.fc = nn.Linear(2048, num_classes)\n",
    "        self.final = nn.Linear(num_classes + additional_dims, num_classes)\n",
    "        for param in self.base_model.fc.parameters():\n",
    "            param.requires_grad = True\n",
    "        for param in self.final.parameters():\n",
    "            param.requires_grad = True\n",
    "        \n",
    "    def forward(self, x1, x2):\n",
    "        x1 = F.relu(self.base_model(x1))\n",
    "        x2 = F.relu(self.kpts(x2))\n",
    "        x = torch.cat((x1, x2), dim=1)\n",
    "        output = F.relu(self.final(x))\n",
    "\n",
    "        if self.training is not True:\n",
    "            output = self.softmax_layer(output)\n",
    "\n",
    "        return x1\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "aee77f2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = YogaNet(WEIGHTS, additional_dims=51)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "4f9501fc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Module.state_dict of YogaNet(\n",
       "  (base_model): ResNet(\n",
       "    (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (relu): ReLU(inplace=True)\n",
       "    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    (layer1): Sequential(\n",
       "      (0): Bottleneck(\n",
       "        (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): Bottleneck(\n",
       "        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (2): Bottleneck(\n",
       "        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "    (layer2): Sequential(\n",
       "      (0): Bottleneck(\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): Bottleneck(\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (2): Bottleneck(\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (3): Bottleneck(\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "    (layer3): Sequential(\n",
       "      (0): Bottleneck(\n",
       "        (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (2): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (3): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (4): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (5): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "    (layer4): Sequential(\n",
       "      (0): Bottleneck(\n",
       "        (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "        (downsample): Sequential(\n",
       "          (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "          (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): Bottleneck(\n",
       "        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "      (2): Bottleneck(\n",
       "        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "      )\n",
       "    )\n",
       "    (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "    (fc): Linear(in_features=2048, out_features=82, bias=True)\n",
       "  )\n",
       "  (kpts): Sequential(\n",
       "    (0): Linear(in_features=51, out_features=102, bias=True)\n",
       "    (1): ReLU(inplace=True)\n",
       "    (2): Linear(in_features=102, out_features=51, bias=True)\n",
       "  )\n",
       "  (final): Linear(in_features=133, out_features=82, bias=True)\n",
       ")>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.state_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1e0bb148",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_fn = nn.CrossEntropyLoss().to(device)\n",
    "# change WEIGHTS to model\n",
    "optimizer = optim.Adam(model.parameters(), lr= 0.1)\n",
    "accuracy_fn = Accuracy(task='multiclass', num_classes=len(classes)).to(device)\n",
    "f1 = F1Score(task='multiclass', num_classes=len(classes)).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "7f36603d-90bf-4ad5-9937-9ac6a00f99f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d5b7e15",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.454348087310791 | 0.0\n",
      "1786.0880126953125 | 0.28125\n",
      "1856.5958251953125 | 0.5\n",
      "1927.1036376953125 | 0.65625\n",
      "1997.6114501953125 | 0.875\n",
      "2068.119140625 | 1.03125\n",
      "2138.626953125 | 1.21875\n",
      " Train Loss: 20.8603 | Train Acc: 0.013\n",
      "25.26704978942871 | 0.043870192021131516\n",
      "95.77461242675781 | 0.23137019574642181\n",
      "166.2821807861328 | 0.481370210647583\n",
      "236.7897491455078 | 0.700120210647583\n",
      "307.2971496582031 | 0.950120210647583\n",
      "377.8044738769531 | 1.106370210647583\n",
      "448.3117980957031 | 1.293870210647583\n",
      " Train Loss: 4.6073 | Train Acc: 0.013\n",
      "9.014017105102539 | 0.043991539627313614\n",
      "79.52157592773438 | 0.2627415359020233\n",
      "150.02914428710938 | 0.5127415657043457\n",
      "220.53671264648438 | 0.6377415657043457\n",
      "291.0441589355469 | 0.9189915657043457\n",
      "361.5514831542969 | 1.1064915657043457\n",
      "432.0588073730469 | 1.2314915657043457\n",
      " Train Loss: 4.4510 | Train Acc: 0.013\n",
      "8.857738494873047 | 0.04399270564317703\n",
      "79.36529541015625 | 0.29399269819259644\n",
      "149.87286376953125 | 0.45024269819259644\n",
      "220.38043212890625 | 0.6064926981925964\n",
      "290.88787841796875 | 0.7314926981925964\n",
      "361.39520263671875 | 0.9189926981925964\n",
      "431.90252685546875 | 1.1689927577972412\n",
      " Train Loss: 4.4495 | Train Acc: 0.013\n",
      "8.85623550415039 | 0.013007850386202335\n",
      "79.3637924194336 | 0.23175784945487976\n",
      "149.87135314941406 | 0.41925784945487976\n",
      "220.37892150878906 | 0.7317578792572021\n",
      "290.8863830566406 | 0.9192578792572021\n",
      "361.3937072753906 | 1.0755078792572021\n",
      "431.9010314941406 | 1.2630078792572021\n",
      " Train Loss: 4.4495 | Train Acc: 0.013\n",
      "8.856221199035645 | 0.012745268642902374\n",
      "79.36377716064453 | 0.20024526119232178\n",
      "149.871337890625 | 0.4502452611923218\n",
      "220.37890625 | 0.6377452611923218\n",
      "290.8863525390625 | 0.8564952611923218\n",
      "361.3936767578125 | 1.1064952611923218\n",
      "431.9010009765625 | 1.2627452611923218\n",
      " Train Loss: 4.4495 | Train Acc: 0.013\n",
      "8.856220245361328 | 0.012742743827402592\n",
      "79.36377716064453 | 0.23149274289608002\n",
      "149.871337890625 | 0.4189927577972412\n",
      "220.37890625 | 0.7314927577972412\n",
      "290.8863525390625 | 0.8877427577972412\n",
      "361.3936767578125 | 1.0752427577972412\n",
      "431.9010009765625 | 1.2939927577972412\n",
      " Train Loss: 4.4495 | Train Acc: 0.013\n",
      "8.856220245361328 | 0.012742719613015652\n",
      "79.36377716064453 | 0.20024272799491882\n",
      "149.871337890625 | 0.4814927279949188\n",
      "220.37890625 | 0.7627427577972412\n",
      "290.8863525390625 | 1.0752427577972412\n",
      "361.3936767578125 | 1.2002427577972412\n",
      "431.9010009765625 | 1.2939927577972412\n",
      " Train Loss: 4.4495 | Train Acc: 0.013\n",
      "8.856220245361328 | 0.012742719613015652\n",
      "79.36377716064453 | 0.2627427279949188\n",
      "149.871337890625 | 0.4502427279949188\n",
      "220.37890625 | 0.6689927577972412\n",
      "290.8863525390625 | 0.8564927577972412\n",
      "361.3936767578125 | 1.0439927577972412\n",
      "431.9010009765625 | 1.3252427577972412\n",
      " Train Loss: 4.4495 | Train Acc: 0.013\n",
      "8.856220245361328 | 0.012742719613015652\n",
      "79.36377716064453 | 0.2939927279949188\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 10\n",
    "\n",
    "train_losses, train_acc = 0, 0\n",
    "\n",
    "for epoch in range(EPOCHS):\n",
    "  for batch, (X1, X2, y)in enumerate(train_dataloader):\n",
    "    X1 = X1.to(device)\n",
    "    X2 = X2.to(device)\n",
    "    y = y.to(device)\n",
    "    # should change WEIGHTS to model\n",
    "    model.train()\n",
    "\n",
    "    # should change model to model\n",
    "    y_pred = model(X1, X2)\n",
    "    # print(y_pred[0])\n",
    "    loss = loss_fn(y_pred, y)\n",
    "    train_acc += accuracy_fn(y_pred.argmax(dim=1), y)\n",
    "    train_losses += loss\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    \n",
    "    if batch % 16 == 0:\n",
    "      print(f'{train_losses} | {train_acc}')\n",
    "\n",
    "  train_losses /= len(train_dataloader)\n",
    "  train_acc /= len(train_dataloader)\n",
    "  print(f' Train Loss: {train_losses:.4f} | Train Acc: {train_acc:.3f}')"
   ]
  }
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