model_training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import os\n",
    "import glob\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mediapipe import solutions\n",
    "from mediapipe.framework.formats import landmark_pb2\n",
    "import numpy as np\n",
    "\n",
    "import mediapipe as mp\n",
    "from mediapipe.tasks import python\n",
    "from mediapipe.tasks.python import vision\n",
    "import glob\n",
    "from tqdm import tqdm\n",
    "from collections import OrderedDict, defaultdict\n",
    "from IPython.display import clear_output\n",
    "import time\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "eating_data=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dir = os.getcwd() + \"/clips_eating/extracted_points/\"\n",
    "dir = \"/home/ns/data_activity/sajay_house/eating_keypoints/clips_eating/extracted_points/\"\n",
    "files = glob.glob(dir + \"*.txt\")\n",
    "for file in files:\n",
    "    file = open(file, 'r')\n",
    "    lines = np.loadtxt(file, dtype=str)\n",
    "    ret=[]\n",
    "    for line in lines:\n",
    "        frame=[]\n",
    "        vals = line.split(',')\n",
    "        for val in vals:\n",
    "            convert = float(val)\n",
    "            frame.append(convert)\n",
    "        ret.append(frame)\n",
    "    r = np.array(ret)\n",
    "    eating_data.append(r)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35, 35)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(eating_data), len(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26, 213, 56.371428571428574)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lengths=[len(data) for data in eating_data]\n",
    "np.min(lengths), np.max(lengths) , np.mean(lengths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_length=30\n",
    "eating_data2=[]\n",
    "for data in eating_data:\n",
    "    if len(data)>=max_length:\n",
    "        eating_data2.append(data[:max_length])\n",
    "\n",
    "len(eating_data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3001, 50)\n",
      "(3201, 50)\n",
      "(3001, 50)\n",
      "(2613, 50)\n",
      "(3001, 50)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dir = os.getcwd() + \"/non_eating_clips/extracted_points/\"\n",
    "dir = \"/home/ns/data_activity/sajay_house/noneating_keypoints/clips_noneating/extracted_points/\"\n",
    "files = glob.glob(dir + \"*.txt\")\n",
    "noneating_data=[]\n",
    "for file in files:\n",
    "    file = open(file, 'r')\n",
    "    lines = np.loadtxt(file, dtype=str)\n",
    "    ret=[]\n",
    "    for line in lines:\n",
    "        frame=[]\n",
    "        vals = line.split(',')\n",
    "        for val in vals:\n",
    "            convert = float(val)\n",
    "            frame.append(convert)\n",
    "        ret.append(frame)\n",
    "    r = np.array(ret)\n",
    "    print(r.shape)\n",
    "    noneating_data.append(r)\n",
    "len(noneating_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noneating_data2=[]\n",
    "for i in range(20):\n",
    "    noneating_data2.append(noneating_data[0][i*max_length: i*max_length+max_length])\n",
    "    noneating_data2.append(noneating_data[1][i*max_length: i*max_length+max_length])\n",
    "\n",
    "len(noneating_data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70, 30, 50), (70,))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=np.concatenate([eating_data2, noneating_data2])\n",
    "y=np.ones(len(X))\n",
    "y[len(eating_data2):]=0\n",
    "\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([70, 30, 50]), torch.Size([70, 1]))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=torch.from_numpy(X).float()\n",
    "y=torch.from_numpy(y).reshape(-1,1)\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([56, 30, 50]),\n",
       " torch.Size([56, 1]),\n",
       " torch.Size([14, 30, 50]),\n",
       " torch.Size([14, 1]))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids=np.arange(X.shape[0])\n",
    "np.random.shuffle(ids)\n",
    "train_ids=ids[:int(len(ids)*0.8)]\n",
    "test_ids=ids[int(len(ids)*0.8):]\n",
    "X_train, y_train=X[train_ids], y[train_ids]\n",
    "X_test, y_test=X[test_ids], y[test_ids]\n",
    "\n",
    "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTM(nn.Module):\n",
    "    \n",
    "    def __init__(self,input_dim,hidden_dim,output_dim,layer_num, seq_len):\n",
    "        super(LSTM,self).__init__()\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.output_dim = output_dim\n",
    "        self.lstm = torch.nn.LSTM(input_dim,hidden_dim,layer_num,batch_first=True)\n",
    "        self.fc = torch.nn.Linear(hidden_dim,output_dim)\n",
    "        self.bn = nn.BatchNorm1d(seq_len)\n",
    "        \n",
    "    def forward(self,inputs):\n",
    "        x = self.bn(inputs)\n",
    "        lstm_out,(hn,cn) = self.lstm(x)\n",
    "        out = self.fc(lstm_out[:,-1,:])\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "n_data_size_train = X_train.shape[0]\n",
    "n_data_size_test = X_test.shape[0]\n",
    "\n",
    "def randomTrainingExampleBatch(batch_size,flag,num=-1):\n",
    "    if flag == 'train':\n",
    "        X = X_train\n",
    "        y = y_train\n",
    "        data_size = n_data_size_train\n",
    "    elif flag == 'test':\n",
    "        X = X_test\n",
    "        y = y_test\n",
    "        data_size = n_data_size_test\n",
    "    if num == -1:\n",
    "        ran_num = random.randint(0,data_size-batch_size)\n",
    "    else:\n",
    "        ran_num = num\n",
    "    pose_sequence_tensor = X[ran_num:(ran_num+batch_size)]\n",
    "    pose_sequence_tensor = pose_sequence_tensor\n",
    "    category_tensor = y[ran_num:ran_num+batch_size,:]\n",
    "    return category_tensor.long(),pose_sequence_tensor\n",
    "\n",
    "n_data_size_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTM(\n",
       "  (lstm): LSTM(50, 128, num_layers=3, batch_first=True)\n",
       "  (fc): Linear(in_features=128, out_features=2, bias=True)\n",
       "  (bn): BatchNorm1d(30, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       ")"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_hidden = 128\n",
    "n_joints = 25*2\n",
    "n_categories = 2\n",
    "n_layer = 3\n",
    "seq_len = 30\n",
    "rnn = LSTM(n_joints,n_hidden,n_categories,n_layer, seq_len)\n",
    "rnn.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "learning_rate = 0.0005\n",
    "optimizer = optim.SGD(rnn.parameters(),lr=learning_rate,momentum=0.9)\n",
    "#scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10000, gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter: 1, loss: 0.687\n",
      "iter: 2, loss: 0.696\n",
      "iter: 3, loss: 0.701\n",
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      "iter: 19755, loss: 0.051\n",
      "iter: 19756, loss: 0.104\n",
      "iter: 19757, loss: 0.092\n",
      "iter: 19758, loss: 0.992\n",
      "iter: 19759, loss: 0.141\n",
      "iter: 19760, loss: 0.124\n",
      "iter: 19761, loss: 0.048\n",
      "iter: 19762, loss: 0.129\n",
      "iter: 19763, loss: 0.047\n",
      "iter: 19764, loss: 0.069\n",
      "iter: 19765, loss: 0.089\n",
      "iter: 19766, loss: 0.465\n",
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      "iter: 19768, loss: 0.085\n",
      "iter: 19769, loss: 0.124\n",
      "iter: 19770, loss: 0.069\n",
      "iter: 19771, loss: 0.125\n",
      "iter: 19772, loss: 0.068\n",
      "iter: 19773, loss: 0.113\n",
      "iter: 19774, loss: 0.081\n",
      "iter: 19775, loss: 0.041\n",
      "iter: 19776, loss: 0.509\n",
      "iter: 19777, loss: 0.503\n",
      "iter: 19778, loss: 0.478\n",
      "iter: 19779, loss: 0.151\n",
      "iter: 19780, loss: 0.510\n",
      "iter: 19781, loss: 0.141\n",
      "iter: 19782, loss: 0.127\n",
      "iter: 19783, loss: 0.142\n",
      "iter: 19784, loss: 0.108\n",
      "iter: 19785, loss: 0.467\n",
      "iter: 19786, loss: 0.111\n",
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      "iter: 19789, loss: 0.117\n",
      "iter: 19790, loss: 0.478\n",
      "iter: 19791, loss: 0.113\n",
      "iter: 19792, loss: 0.042\n",
      "iter: 19793, loss: 0.121\n",
      "iter: 19794, loss: 0.188\n",
      "iter: 19795, loss: 0.476\n",
      "iter: 19796, loss: 0.112\n",
      "iter: 19797, loss: 0.246\n",
      "iter: 19798, loss: 0.105\n",
      "iter: 19799, loss: 0.087\n",
      "iter: 19800, loss: 0.543\n",
      "iter: 19801, loss: 0.088\n",
      "iter: 19802, loss: 0.086\n",
      "iter: 19803, loss: 0.046\n",
      "iter: 19804, loss: 0.033\n",
      "iter: 19805, loss: 0.052\n",
      "iter: 19806, loss: 0.465\n",
      "iter: 19807, loss: 0.186\n",
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      "iter: 19809, loss: 0.109\n",
      "iter: 19810, loss: 0.111\n",
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      "iter: 19839, loss: 0.522\n",
      "iter: 19840, loss: 0.787\n",
      "iter: 19841, loss: 0.108\n",
      "iter: 19842, loss: 0.029\n",
      "iter: 19843, loss: 0.038\n",
      "iter: 19844, loss: 0.053\n",
      "iter: 19845, loss: 0.098\n",
      "iter: 19846, loss: 0.531\n",
      "iter: 19847, loss: 0.496\n",
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      "iter: 19849, loss: 0.093\n",
      "iter: 19850, loss: 0.504\n",
      "iter: 19851, loss: 0.071\n",
      "iter: 19852, loss: 0.129\n",
      "iter: 19853, loss: 0.090\n",
      "iter: 19854, loss: 0.105\n",
      "iter: 19855, loss: 0.110\n",
      "iter: 19856, loss: 0.156\n",
      "iter: 19857, loss: 0.496\n",
      "iter: 19858, loss: 0.096\n",
      "iter: 19859, loss: 0.037\n",
      "iter: 19860, loss: 0.064\n",
      "iter: 19861, loss: 0.072\n",
      "iter: 19862, loss: 0.287\n",
      "iter: 19863, loss: 0.039\n",
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      "iter: 19871, loss: 1.223\n",
      "iter: 19872, loss: 0.609\n",
      "iter: 19873, loss: 0.270\n",
      "iter: 19874, loss: 0.088\n",
      "iter: 19875, loss: 0.057\n",
      "iter: 19876, loss: 0.109\n",
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      "iter: 19878, loss: 0.122\n",
      "iter: 19879, loss: 0.089\n",
      "iter: 19880, loss: 0.079\n",
      "iter: 19881, loss: 0.124\n",
      "iter: 19882, loss: 0.093\n",
      "iter: 19883, loss: 0.053\n",
      "iter: 19884, loss: 0.063\n",
      "iter: 19885, loss: 0.539\n",
      "iter: 19886, loss: 0.556\n",
      "iter: 19887, loss: 0.088\n",
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      "iter: 19890, loss: 0.028\n",
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      "iter: 19892, loss: 0.528\n",
      "iter: 19893, loss: 0.033\n",
      "iter: 19894, loss: 0.124\n",
      "iter: 19895, loss: 0.127\n",
      "iter: 19896, loss: 0.934\n",
      "iter: 19897, loss: 0.298\n",
      "iter: 19898, loss: 0.044\n",
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      "iter: 19911, loss: 1.096\n",
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      "iter: 19919, loss: 0.029\n",
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      "iter: 19924, loss: 0.530\n",
      "iter: 19925, loss: 1.987\n",
      "iter: 19926, loss: 0.029\n",
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      "iter: 19939, loss: 0.855\n",
      "iter: 19940, loss: 0.151\n",
      "iter: 19941, loss: 0.779\n",
      "iter: 19942, loss: 0.085\n",
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      "iter: 19970, loss: 0.936\n",
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      "iter: 19972, loss: 0.090\n",
      "iter: 19973, loss: 1.025\n",
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      "iter: 19984, loss: 0.888\n",
      "iter: 19985, loss: 1.570\n",
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      "iter: 19987, loss: 0.656\n",
      "iter: 19988, loss: 0.631\n",
      "iter: 19989, loss: 1.432\n",
      "iter: 19990, loss: 1.527\n",
      "iter: 19991, loss: 0.579\n",
      "iter: 19992, loss: 0.427\n",
      "iter: 19993, loss: 0.526\n",
      "iter: 19994, loss: 0.176\n",
      "iter: 19995, loss: 0.390\n",
      "iter: 19996, loss: 0.770\n",
      "iter: 19997, loss: 0.602\n",
      "iter: 19998, loss: 0.084\n",
      "iter: 19999, loss: 1.454\n",
      "iter: 20000, loss: 0.485\n"
     ]
    }
   ],
   "source": [
    "n_iters = 20000\n",
    "print_every = 1000\n",
    "plot_every = 1000\n",
    "batch_size = 4\n",
    "\n",
    "# Keep track of losses for plotting\n",
    "current_loss = 0\n",
    "all_losses = []\n",
    "\n",
    " \n",
    "start = time.time()\n",
    "\n",
    "for iter in range(1, n_iters + 1):\n",
    "   \n",
    "    category_tensor, input_sequence = randomTrainingExampleBatch(batch_size,'train')\n",
    "    input_sequence = input_sequence.to(device)\n",
    "    category_tensor = category_tensor.to(device)\n",
    "    category_tensor = torch.squeeze(category_tensor)\n",
    "    \n",
    "    optimizer.zero_grad()\n",
    "    \n",
    "    output = rnn(input_sequence)\n",
    "    loss = criterion(output, category_tensor)\n",
    "    loss.backward()\n",
    "    optimizer.step() \n",
    " \n",
    "    current_loss += loss.item()\n",
    "\n",
    "    print('iter: %d, loss: %.3f' % (iter, loss.item()))\n",
    "    \n",
    "    \n",
    "    # Add current loss avg to list of losses\n",
    "    if iter % plot_every == 0:\n",
    "        all_losses.append(current_loss / plot_every)\n",
    "        current_loss = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f17c06b7250>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(all_losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of 14 test data: 64.286%\n"
     ]
    }
   ],
   "source": [
    "#test on training data\n",
    "n_correct = 0\n",
    "n_total = 0\n",
    "for i in range(n_data_size_test):\n",
    "    category_tensor, input_sequence = randomTrainingExampleBatch(1,'train',i)\n",
    "    input_sequence = input_sequence.to(device)\n",
    "    category_tensor = category_tensor.to(device)\n",
    "    category_tensor = torch.squeeze(category_tensor)\n",
    "    output = rnn(input_sequence)\n",
    "    po=output.argmax(axis=1)\n",
    "    guess=po.item()\n",
    "    category = category_tensor\n",
    "    if category == guess:\n",
    "        n_correct += 1\n",
    "    n_total += 1\n",
    "\n",
    "print('Accuracy of %d test data: %.3f%%' % (n_data_size_test, n_correct / n_total * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of 14 test data: 71.429%\n"
     ]
    }
   ],
   "source": [
    "#test on testing data\n",
    "\n",
    "rnn.eval()\n",
    "\n",
    "n_correct = 0\n",
    "n_total = 0\n",
    "for i in range(n_data_size_test):\n",
    "    category_tensor, input_sequence = randomTrainingExampleBatch(1,'test',i)\n",
    "    input_sequence = input_sequence.to(device)\n",
    "    category_tensor = category_tensor.to(device)\n",
    "    category_tensor = torch.squeeze(category_tensor)\n",
    "    output = rnn(input_sequence)\n",
    "    po=output.argmax(axis=1)\n",
    "    guess=po.item()\n",
    "    category = category_tensor\n",
    "    if category == guess:\n",
    "        n_correct += 1\n",
    "    n_total += 1\n",
    "\n",
    "print('Accuracy of %d test data: %.3f%%' % (n_data_size_test, n_correct / n_total * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# torch.load('model_train_1_dec27.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(rnn.state_dict(), 'model_train_1_dec27.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### quick test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/ns/data_activity/sajay_house/eating_keypoints/clips_eating/extracted_points/Breakfast-Side-view_6.mp4_points.txt'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testdir = \"/home/ns/data_activity/sajay_house/eating_keypoints/clips_eating/extracted_points/\"\n",
    "testfiles = glob.glob(testdir + \"*.txt\")\n",
    "test_fn=testfiles[7]\n",
    "test_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31, 50)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(test_fn, 'r') as file:\n",
    "    lines = np.loadtxt(file, dtype=str)\n",
    "    ret=[]\n",
    "    for line in lines:\n",
    "        frame=[]\n",
    "        vals = line.split(',')\n",
    "        for val in vals:\n",
    "            convert = float(val)\n",
    "            frame.append(convert)\n",
    "        ret.append(frame)\n",
    "    r = np.array(ret)\n",
    "\n",
    "r.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 50)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xt=r[:max_length]\n",
    "Xt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 30, 50])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xt=torch.from_numpy(Xt).float().to(device)\n",
    "Xt=Xt.reshape(1, -1, 50)\n",
    "Xt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1], device='cuda:0')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logits=rnn(Xt)\n",
    "po=logits.argmax(axis=1)\n",
    "po"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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